Hugo Hadjur
email: hadjur@aivancity.ai

Doreid Ammar
email: ammar@aivancity.ai

Laurent Lefèvre
email: laurent.lefevre@inria.fr

Services Orchestration at the Edge and in the Cloud on Energy-Aware Precision Beekeeping Systems

Keywords: Precision Beekeeping, Internet of Things, Energy Efficiency, Edge-Cloud Computing, Artificial Intelligence

Precision Beekeeping is a subfield of Precision Agriculture. It collects data on colonies of bees using sensors to assist beekeepers and preserve bees. The existing solutions include collecting voluminous data, such as images and sound, from up close or inside a beehive. The collection, transfer, and processing of such data are energy-intensive processes. However, most Precision Beekeeping systems work under a limited energy budget. Thus, such systems must take this constraint into account. This article focuses on the placement of energy-aware Precision Beekeeping services at the edge and in the cloud. We deploy a set of smart beehives in order to conduct experiments and collect real data. Then, we simulate large-scale systems with embedded services to add more realism to the experimental study. Our results show that parameters like the number of smart beehives, the frequency of data collection, and the characteristics of the cloud server impact the placement of these services to enhance energy efficiency.

I. INTRODUCTION

Pollination is essential for the balance of flora and human agriculture. It relies on several actors, mainly insects like butterflies, wasps, flies, and bees. The latter includes different species. Among them, the apis mellifera (western honey bee) is the most common domesticated species.

Precision Beekeeping (PB) [START_REF] Zacepins | Application of information technologies in precision apiculture[END_REF] aims at optimizing the production of honey and preserving bees with data mainly collected thanks to sensors placed around or inside the beehive, making it a connected beehive. In this paper, we will also refer to the term smart beehive when some intelligent service is included in a connected beehive in addition to the collection of data.

In PB, Internet of Things (IoT) is used for gathering, processing and transmitting data and for sending alerts to beekeepers. Developing such systems next to beehives and bees results in several constraints like placing sensors in such ways that intrusion toward bees is minimized. The most important constraint, tackled in this article, is energy consumption and efficiency of PB systems as connected beehive must be autonomous without the availability of a power outlet.

This paper aims at exploring the optimal placement of services inside a smart beehive between the edge devices (the smart beehives) and the cloud servers. It also optimizes the framework of large-scale networks of smart beehives thanks to the simulation of the model of our deployed system.

The paper is organized as follows: Section II focuses on the state of the art of the different tackled subjects. Section III describes the smart beehive system and the collected data. Section IV analyzes the energy of the system without intelligent services. Section V focuses on one intelligent service and the choice between placing it at the edge or in the cloud. Section VI shows a simulation of the architecture of the presented system at large-scale. Finally, Section VII concludes the paper and describes future works.

II. RELATED WORKS

A connected beehive relies on its microcontroller (Raspberry Pi, Arduino), sensors, network, and some energy to power the system. The literature has many approaches and there is not a universal solution in terms of architecture and choice of hardware [START_REF] Hadjur | Toward an intelligent and efficient beehive: A survey of precision beekeeping systems and services[END_REF].

There are different types of data collected like weight [START_REF] Anuar | Iot platform for precision stingless bee farming[END_REF], temperature [START_REF] Giammarini | Beehive lab project -sensorized hive for bee colonies life study[END_REF], bee count [START_REF] Chen | Development of a monitoring system for honeybee activities[END_REF], and image data [START_REF] Bjerge | A computer vision system to monitor the infestation level of varroa destructor in a honeybee colony[END_REF]. In this paper, we focus on services relying on sound data in an energyaware PB system.

Sound & Vibrations: The sound or vibrations is often collected thanks to sensors directly placed inside the hive, either on the inner wooden structure for microphones [START_REF] Cecchi | A preliminary study of sounds emitted by honey bees in a beehive[END_REF] or on frames for accelerometers [START_REF] Ramsey | The prediction of swarming in honeybee colonies using vibrational spectra[END_REF]. In rare cases, such sensors are placed right at the entrance to record takeoffs and landings [START_REF] Kulyukin | Toward audio beehive monitoring: Deep learning vs. standard machine learning in classifying beehive audio samples[END_REF].

Energy: The addition of a sensor is one among many sources of energy consumption. The energy of a smart beehive depends on its microcontroller, whether it has a sleep state, the duration of it being switched on, and the intensity at which the code uses processors. In PB, it is rare that researchers focus on the consumption of energy of their system, but there are occurrences of analyses. The work described in [START_REF] Cunha | A novel non-invasive radar to monitor honey bee colony health[END_REF] mentions the daily consumption of the system (2 watt hours). [START_REF] Murphy | b+wsn: Smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring[END_REF] and [START_REF] Hong | Long-term and extensive monitoring for bee colonies based on internet of things[END_REF] share the current consumption divided by nodes in their system. The authors of [START_REF] Edwards-Murphy | Big brother for bees (3b) -energy neutral platform for remote monitoring of beehive imagery and sound[END_REF] develop the previously cited analysis and present the daily energy budget calculations for each node and for phase (sense, send, sleep). [START_REF] Edwards-Murphy | Development of an heterogeneous wireless sensor network for instrumentation and analysis of beehives[END_REF] opts for a solar-powered battery charge analysis and checks for differences in terms of sampling rates, solar panel orientation, and sampling power. The abnormal case of a power outage is mentioned in [START_REF] Gil-Lebrero | Honey bee colonies remote monitoring system[END_REF], and the autonomy of the system only powered by an unplugged battery is measured (75 hours). The same runtime duration is calculated in [START_REF] Anuar | Iot platform for precision stingless bee farming[END_REF], and this temperature, humidity, and the weight-measuring system can last around 12 days autonomously. A data reduction method is presented in [START_REF] Kridi | Application of wireless sensor networks for beehive monitoring and in-hive thermal patterns detection[END_REF], and raw data transfer versus reduced data is energetically compared. The current drawn over one iteration of measures is displayed in [START_REF] Zacepins | Monitoring system for remote bee colony state detection[END_REF] to spot the consumption of each subtask. The same approach is presented in [START_REF] Hadjur | Analysis of energy consumption in a precision beekeeping system[END_REF], and different variations of parameters within each task are then measured and compared.

III. SYSTEM ARCHITECTURE

This section aims to describe our autonomously running solution for collecting bee-related data capable of monitoring its energy consumption. Energy data is used as a basis for the orchestration of energy-aware services.

Hardware: The system, as shown in Figure 1, relies on two Raspberry Pi, which are single-board mini-computers. Fig. 1: Architecture of the deployed system showing essential hardware, energy node, and network First, the Raspberry Pi 3b+ is the most complete of the two as it includes four USB 2.0 ports, a port for a Raspberry Pi camera, 2.4 gigahertz and 5 gigahertz IEEE 802.11 wireless LAN and Bluetooth connectivity, as well as a quad-core 1.4 gigahertz 64-bit processor and 1 gigabytes of RAM. In comparison, the second one, a Raspberry Pi Zero WH, is a lightweight version of its sister model with one micro USB port, a camera connector, the same Wi-Fi and Bluetooth connectivity, a 1 gigahertz single-core CPU, and 512 megabytes of RAM.

On top of each Raspberry Pi, a Grove Base Hat is installed to wire sensors. For the Raspberry Pi Zero WH, its hat sensors are three identical ±5 amperes DC/AC current sensors. Each of these sensors is linked to an electric wire: the two Raspberry Pi's power supplies and the wire that goes from the solar panel to the battery. For the Raspberry Pi 3b+, a temperature and humidity sensor (SHT31) are used. In addition to hats' sensors, the Raspberry Pi 3b+ collects sound through three USB microphones (range: 20 hertz to 16 kilohertz) and images from a Raspberry Pi camera module 2 The temperature and humidity sensor, the gas sensor, and the microphones are placed inside the beehive on the queen excluder. The camera is placed at one end of the entrance of the beehive and faces the other end to take pictures of the whole bees' takeoff and landing area.

Location: Five smart beehives are currently deployed. Two are located to the South of Paris in Cachan, France, and the others are in Lyon, France. All of them are positioned on a university building's roof and are in an urban area, but there are parks bordering both campuses.

Energy: Each system relies on a solar panel, current converter, and battery for its energy. The solar panel is a monocrystalline 30 watts panel. The battery is a 20 000 mAh power bank. Between the two, the current is converted to 5 volts (the adequate voltage for the battery) thanks to a DC/DC Step-Down 5 volts/3 amperes converter.

IV. ENERGY CONSUMPTION OF THE DEPLOYED SYSTEM

In this section, we aim to calibrate the frequency at which the Raspberry Pi 3b+ wakes up and performs a set of tasks at regular intervals.

Data collection routines: The Raspberry Pi Zero, which records the current, is always switched on. At regular intervals, it sends a signal through GPIO to wake up the other Raspberry Pi, the beehive data recorder. Once the Raspberry Pi 3b+ wakes up, it collects data from all its sensors, including three 10-second audio samples collected at the same time and five 800×600 pixels images spread over five seconds, and it transfers the data through Wi-Fi to a remote data storage cloud server. In addition, at regular intervals, the Raspberry Pi Zero WH transfers the latest consumption data. Sizes of samples and arrangement of steps for the data collection and transfer are chosen thanks to an analysis presented in [START_REF] Hadjur | Analysis of energy consumption in a precision beekeeping system[END_REF]. To complete the data, the weather data from both locations is collected at regular intervals.

Figure 2a displays the activity of the Raspberry Pi 3b+ of one beehive together with the in-hive temperature and humidity, and the meteorological data at the same moment. At this moment, the colony of bees was yet to be introduced inside the beehive, hence the abnormally low inside temperature. The data is shown over one full week to emphasize energy consumption dynamics. In this graph, the gray background shows time periods after sunset, whereas the white background shows daytime periods. This graph also shows moments when the system is not running due to the lack of light at night. We suppose that the low luminosity takes the solar panel's output voltage to uncontrolled values, thus affecting the batteries and the electronics. Figure 2b highlights the intervals at which the Raspberry Pi 3b+ wakes up. Its energy consumption is shown alongside the in-hive temperature and humidity, and the meteorological data. The spikes of consumption correspond to the moments between turning on and shutting down the Raspberry Pi 3b+. In this graph, the frequency of the wake-up GPIO signal is set to 10 minutes. Comparing wake-up frequencies: We focus on the frequency at which the Raspberry Pi 3b+'s data collection routine is performed. We choose to monitor the energy of the Raspberry Pi 3b+ at different wake-up frequencies: every 5, 10, 15, 30, 60, and 120 minutes. For example, Figure 2b shows the frequency of 10 minutes, where two consecutive wake-up calls from the Raspberry Pi Zero are spaced by 10 minutes. The frequency parameter's values are realistic and could be implemented in the system, depending on the chosen beehive-related services. For example, for a service tracking the temperature of the beehive, collecting data every 60 or 120 minutes suffices. On the other hand, in a period of collection of large datasets, collecting data every 5 minutes becomes reasonable. The experiment is designed so that each setting is active for at least nine hours in a row in order to collect substantial energy data. This duration is chosen because it corresponds to daylight time, where our energy node is more stable and able to provide constant power to the electronics. Doing so, the collected energy is a sum of the active power (when the Raspberry Pi boots, performs tasks, and shuts down) and the sleep power of the sleep state, which consumes nonnull resources: the cost of being able to receive wake-up calls.

The data collected during this experiment covers 319 routines of the system. All routines are identical. They follow the steps presented at the beginning of this section. It is then possible to compute average costs for one routine. On average, the Raspberry Pi 3b+ is turned on, performs its tasks, and shuts down in 1 minute and 29 seconds, with an average power of 2.14 watts. This gives an average energy cost of 190.1 joules from the boot to the shutdown. The standard deviation for the lengths of routines is 3.5 seconds. This relatively high variability can be explained by the variance of the duration of the data transfer correlated to the unstable network throughput. The standard deviation for the average power of routines is 0.009 watts, which shows stability. The average consumed power for on/off cycles at different wake-up frequencies is presented in Figure 3. At the highest frequency, when the system wakes up every 5 minutes, the average power values reach their maximum: 1.19 watts on average. When the duration between two consecutive wake-ups increases, the average power decreases and converges toward a value close to 0.62 watts, which is the consumption of the Raspberry Pi 3b+ in a sleep state. This shows that the sleep state is significant when considering long-term consumption. Fig. 3: Average power used for several durations between two consecutive wake-ups of the smart beehive's system V. ORCHESTRATION OF SMART SERVICES As observed in 2a, there are moments where the edge is not able to fully capture and process the data because of limited available energy. Since collecting data has to be performed at the edge, gaining flexibility in terms of costs can be achieved during the processing step of the data thanks to a cloud server.

In this section, we consider adding smart services on top of data collection and transfer systems. We investigate which option is more energy efficient between performing the services' tasks at the edge or in the cloud. We consider two main scenarios:

• Edge scenario: the edge system wakes up, collects data, executes the service's tasks at the edge, optionally stores the data locally, sends results directly to the user (the beekeeper's phone, for example), and shuts down. This scenario does not require a cloud server. • Edge+cloud scenario: the edge system wakes up, collects data, sends it to a remote cloud server, and shuts down. Meanwhile, a cloud server receives the data and performs the service's tasks. The unfolding of these steps can be found in Figure 4. In the previous section, we introduced a routine that collects and transfers the collected data to a remote cloud server, so the energy consumption data collected in that section will compute the costs of the edge device in this scenario.

Fig. 4: Task diagram for one cycle of data collection, processing, and transfer for edge+cloud scenario Our selected cloud server has an Intel i7-8700K processor and an Nvidia RTX2070 GPU. This allows train the deep learning models considered in this paper in few minutes. Compared to the average bare-metal cloud server, this is a less energy-intensive option. Our selected edge device is the previously introduced Raspberry Pi 3b+, capable of performing numerous tasks: pollen detection, counting bees, and swarm prediction, among others. Most of these tasks include AI models. In this paper, we focus on the energy consumed by classification models for the presence of the queen inside a beehive, thanks to sound data. In practice, in addition to the previously introduced data collection routine, an audio sample is used to predict the presence of the queen bee. For this paper's connected beehive architecture, the prediction is performed thanks to the sample from the microphone placed in the middle part of the queen excluder grid. The selected task of queen detection has already been studied with deep learning (convolutional neural network, CNN) and classical machine learning (support vector machine, SVM) methods [START_REF] Nolasco | To bee or not to bee: Investigating machine learning approaches for beehive sound recognition[END_REF]. The latter is an adapted choice because it allows acceptable performances for reasonably small datasets (less than tens of thousands of samples). We choose to follow the same methodology by selecting one classical machine learning option and one deep learning option.

For the training of both models, 1647 audio samples labeled with the presence of the queen are used. The selected training features are mel-scaled spectrogram features computed from 10-second audio recordings of bees sampled at 22050 hertz. For the spectrogram function, the length of the fast-Fourier transform window is 2048, the number of audio samples between adjacent short-time Fourier transform columns is 512, and the number of generated Mel-bands is 128. Vector features are passed as it is for the training phase of the SVM model, whereas they are converted into images for the CNN model. The chosen architecture for the deep learning model is ResNet18. It is trained on a dedicated cloud server, through 4 epochs, with a learning rate of 0.001. Although pre-trained, thanks to regular real-world pictures, the model is able to achieve state-of-the-art performances when trained with spectrogram images. For the classical machine learning alternative, the SVM classifier is set with a radial basis function kernel, a regularization parameter of 20, and a kernel coefficient of 10 -5 . The training phase of CNN models has a significant energy cost, but it is a less frequent task than the use of the trained models. Therefore, in this paper, we only focus on the energy consumption of the usage of the trained models.

These models and parameters reproduced state-of-the-art performances for the queen detection task. Figure 5 shows the measured energy for the Raspberry Pi 3b+ to perform predictions and the accuracy of the trained models as functions of images' length. The 100 by 100 pixels resolution is the smallest that shows converged results, with a classification accuracy of 99%. Larger images as input of the CNN model show similar performances, but the energy cost when executing the model on the Raspberry Pi is greater than 100 by 100 pixels images. This cost increases as a quadratic function of the number of pixels in the images, which is the time complexity of the use of the trained AI model. Therefore, using 100 by 100 pixels images for the CNN model is the optimal choice when considering the accuracy and the energy cost of the model's execution at the edge. Thanks to this focus on the energy consumption of one service, the upcoming paragraphs will generalize the results on complete edge and edge+cloud scenarios that include this service. We observe the edge+cloud scenario in real conditions. First, the Raspberry Pi 3b+ and the cloud server are in sleep and idle mode, respectively. The edge device is then turned on and starts collecting data. Then, the edge transfers the collected data to the cloud, and the edge starts shutting down as the server executes the service's tasks. Finally, the server switches to idle mode. The energy consumption graph of the Raspberry Pi during this routine shows a spike during the data transfer step. This step is more energy-intensive than the data collection because the network components have a larger energy cost than the sensors.

We then focus on energy consumption measures performed during both scenarios and for both queen detection AI models. Each of the two models (SVM and CNN) is tested in the edge and edge+cloud scenarios. Thanks to the measures of the energy consumption of the edge and the cloud for the four options, we aim to select the best scenario for each service. Table I and Table II show the duration and the energy consumption for all cases when 5-minute cycles are considered. For each scenario, we show the collected time and energy consumption step by step, in chronological order. Since the queen detection model's execution in the cloud takes less time than the shutdown of the Raspberry Pi 3b+, the shutdown is divided into two lines in the edge+cloud scenario, and their sum adds up to the 9.9 seconds shown in edge scenarios. The choice of the model does not generate a significant difference: only 1.2 joules of difference (0.3% of the edge's total cost) for the energy cost of the Raspberry Pi 3b+ in the edge scenarios and 61.7 joules (0.4% of the server's total cost) for the energy cost of the cloud server in the edge+cloud scenarios. In the edge+cloud scenarios, the service is not executed at the edge, so the energy cost of the edge is not influenced by the choice of the service. Then, the global energy cost of the IoT system is more significant for a edge+cloud scenario than an edge scenario because of the presence of a server that must be turned on and available at all times to receive data. However, this cost allows the edge to perform fewer tasks, hence a smaller energy cost for the edge+cloud scenario at the edge: there is a reduction of 12.1% and 12.4% of consumed energy for the SVM and CNN model, respectively. This can be useful in cases where the edge's energy must be saved. For example, when the energy available in the cloud is not a limiting factor and the energy produced by the solar panel at the edge is limited. A large-scale simulation with many smart beehives matched with one server could demonstrate the viability of the edge+cloud scenario. Currently, five smart beehives are deployed and equipped with the system described in this paper. It is probable that, most likely, PB researchers and even beekeepers who want to equip their hives require embedding such a system at a larger scale or in a whole apiary. We can also suppose an organization of several beekeepers putting their hardware in one unique network of edge and cloud computing.

This section aims at putting the currently deployed systems and their cloud servers on a large scale. A simulation method is proposed to determine whether a large network of smart beehives is energetically viable.

A. Description of the simulation model

The simulation model has three main components: the client, the server, and the allocator. In the practical case of this paper, one client refers to one smart beehive and one server refers to one cloud server. However, this method can encapsulate any IoT device linked to a server.

• Client: its tasks are to acquire and optionally process and transfer data. It is initialized thanks to the power consumption in the sleep state, a series of actions (active state) and their respective time and power consumption, and the time between two consecutive wake-ups. • Server: its tasks are to receive data from clients and process them. It also has a series of actions, time, and power consumption. It supports a maximum amount of clients allowed in parallel. Each server allows their clients to start communication at specific times so that every client within a group has to start their communication with the server at the same time as the other clients of the group, all synchronized in time thanks to a specific hardware (GPS, for example). We will refer to these specific time windows as time slots. One server can allow multiple time slots. The shorter the time window for the server's tasks, the greater the number of time slots. For example, in a 5-minute cycle, given a data transfer and a model execution's duration of 1 minute, a server can allow 5-time slots. • Allocator: its main task is to allocate several clients to servers. It takes a list of clients, creates servers based on their features and the features of the server type, allocates every client to one server, and links them to a wake-up time slot. Currently, it has one filling policy: filling a server with clients by filling one slot up to its maximum after another. Simulation will be performed in theoretical perfect situation as well as realistic cases involving losses. The results will be described in the following paragraphs.

B. Results in an ideal theoretical situation

Figure 6 shows the simulation results for the number of servers required and the energy consumed per client and in total, respectively. This simulation recreates the previously introduced edge+cloud scenario and edge scenario for a network of clients and servers with clients ranging from 10 to 400. The tasks' duration and power are initialized thanks to the measures described in Section IV and Section V, considering that one client is a smart beehive. The simulation also supposes a client's wake-up frequency of 5 minutes and a server that can allow a maximum of 10 clients per time slot. Since the cost per client increases exponentially when considering less than 10 clients, the lowest number of clients is set at this value for readability reasons. Figure 6 shows that the energy consumption per client of edge devices (shown in red) is equal to one client's energy consumption, 322 joules. It is due to the to 116 + 322 = 438 joules. This can be described as the theoretical best cost per client. Also, it is 16% greater than the overall cost in the edge scenario. However, it is fair to compare the worth of the energy produced at the edge by the solar panel and the energy available in the cloud. It is more likely that cloud servers are powered by nationally produced electricity, so this part is not (or at least less) under pressure.

On the other hand, the energy produced at the edge, thanks to the solar panel and stored inside the battery, is less reliable, and the amount of energy available is much lower than in the cloud. Therefore, one joule of energy used at the edge is not equivalent to one joule of energy used on the cloud in terms of availability and resilience. One could argue that the 16% increase of energy in the edge+cloud scenario does not make this scenario less efficient than the edge scenario. This difference between the two scenarios decreases as the number of clients allowed in parallel per time slot increases. 7 focuses on the simulation data for the end-to-end total energy for both scenarios between 100 and 2000 clients and highlights the differences when the maximum clients allowed in parallel changes. This range for the number of clients is different from the simulation results shown previously because increasing the number of clients allowed in parallel increases the total number of clients allowed per server. Figure 7a shows the same setting as Figure 6: 10 clients at maximum in parallel. Figure 7b shows the simulation results when this parameter takes 35 as values. In those three figures, the blue area corresponds to settings where the average cost of one client is lower in the edge scenario than in the edge+cloud scenario. The green area corresponds to parameters making the edge+cloud scenario more energy-efficient. We have observed that 26 clients are the tipping point when the edge+cloud scenario can become more energy efficient when used efficiently. Figure 7b shows that when 35 clients are allowed in parallel, 406 clients are needed to make the edge+cloud scenario more energy-efficient than the edge scenario. With these settings, the maximum difference in favor of the edge+cloud scenario is 12.5 joules at 630 clients, just before the need for an additional server when the first server is fully used. This graph also shows that from 803 clients, the edge+cloud scenario is more energyefficient than the edge scenario and remains this way for any higher number of clients considered. This is because the more servers needed, the fuller they are on average because they are all full but one.

C. Results with losses for clients and cloud servers

In this paragraph, the following parameters are added to depict real-life losses and costs:

• Loss scenario A: A penalty when a server's time slot starts saturating with its number of clients. The limit at which the penalty starts is set at 5 clients below the maximum allowed per slot. Each additional client penalizes the whole energy slots by 10%. we use a random Gaussian distribution (mean: 10% of the total number of clients; standard deviation: 2) to draw the number of lost clients. The above-mentioned values for the loss parameters are chosen thanks to the understanding gained thanks to the data collection period. Figure 8 shows the simulation results of the energy consumed per client when losses are added to the models. Figure 8a shows that the first considered loss increases the energy consumed by the server. In this setting, the cost of the server converges towards 186 joules (to be compared with the 116 joules, which represent the lowest server cost for no loss model). Figure 8b highlights that extra transfer duration causes the number of time slots per 5 minutes to decrease, thus decreasing the maximum number of clients per server. More servers are needed for an equivalent number of clients compared to the no-loss scenario (for 350 clients: 4 servers when duration penalty is applied versus 2 servers in the no-loss case). Since there are fewer clients per server, the cost of servers per client increases. In this case, its minimal value is 212 joules. In Figure 8c, we display the random Gaussian number of clients lost alongside the energy consumption metrics. The x-axis displays the initial number of clients, so the energy consumed appears to be less than the default case without any loss. However, this is a case where lost clients cause other losses: all the services and the data would be missing. Figure 8d generalized the three Finally, Figure 9 shows the comparison of the two scenarios with all three types of loss considered, and diplays more realistic simulated energy consumption values. This set of parameters shows that the limit of clients per time slot of 35 becomes of little bit worse than its equivalent without loss, but still has some intervals where the edge+cloud scenario is more energy-efficient than the edge scenario. Thanks to this simulation, it is possible to scale a network of clients and servers, given costs and penalties of energy consumption or time, which depend on the number of clients. For example, it is safe to assign three servers when the number of clients is between 1600 and 1750, and the edge+cloud scenario will be more energy-efficient than the edge scenario.

VII. CONCLUSION

We present in this article an advancement in the orchestration of services in IoT systems at the edge and in the cloud with the use of direct continuous measures and large-scale simulation. We develop an end-to-end energy-aware Precision Beekeeping edge system and introduce a client-server energy simulation model. We analyze the consumption costs of our system and their variation when the frequency of the main routine varies. The system and the model show that the design of systems using one edge should aim towards removing the use of a cloud server which requires much more energy than the edge. In a case with many clients, adding a cloud server is more energy-efficient than the edge-only counterpart when the cloud server is used close to its maximal capacity. Future work will refine our simulation model, add new scenarios and refine the numerical estimations of losses. We will also investigate the use of machine learning and deep learning models to improve the simulation model and build connected beehives' intelligence to tune its parameters and choose between a set of scenarios.

Fig. 2 :

 2 Fig. 2: (a) The in-hive temperature and humidity, the external temperature and humidity, and the averaged Raspberry Pi 3b+ consumption during one week of Spring 2022 in Cachan (France). (b) The in-hive temperature and humidity, the external temperature and humidity, and the Raspberry Pi 3b+ consumption showing consecutive wake-ups during 13 hours of one day in Summer 2022 in Cachan (France).

Fig. 5 :

 5 Fig. 5: Energy of CNN model execution and its accuracy on the test set as a function of training set's images' size.

Fig. 6 :

 6 Fig. 6: Simulation of energy consumption for all entities in a client-server scenario with no loss. Number of clients allowed in parallel in time slots: 10.

  (a) Number of clients allowed in parallel in time slots: 10. (b) Number of clients allowed in parallel in time slots: 35.

Fig. 7 :

 7 Fig. 7: Comparison of end-to-end energy per client for the two scenarios with different server settings

Figure

  Figure7focuses on the simulation data for the end-to-end total energy for both scenarios between 100 and 2000 clients and highlights the differences when the maximum clients allowed in parallel changes. This range for the number of clients is different from the simulation results shown previously because increasing the number of clients allowed in parallel increases the total number of clients allowed per server. Figure7ashows the same setting as Figure6: 10 clients at maximum in parallel. Figure7bshows the simulation results when this parameter takes 35 as values. In those three figures, the blue area corresponds to settings where the average cost of one

•

  Loss scenario B: A time penalty of 1.5 extra second per client for clients' data transfer time. Since the model considers that clients of the one-time slot are synchronized, they send their data simultaneously. • Loss scenario C: A loss of clients at every wake-up time:

  (a) Loss scenario A (b) Loss scenario B (c) Loss scenario C (d) All three types of losses combined.

Fig. 8 :

 8 Fig. 8: Simulation of energy consumption in a client-server scenario with several types of loss. The number of clients allowed in parallel in time slots: 10.

Fig. 9 :

 9 Fig. 9: Comparison of end-to-end energy per client for the two scenarios with different server settings and with loss. Number of clients allowed in parallel in time slots: 35.

TABLE I :

 I Tasks of edge with time and energy consumed per 5-minute cycle for queen detection edge scenarios VI. SIMULATION AT LARGE SCALE

TABLE II :

 II Tasks of edge and cloud with time and energy consumed per 5-minute cycle for queen detection edge+cloud scenarios


G M Vanacore¹

L F Zagonel²

N Barrett²

Surface enhanced covalency and Madelung potentials in Nb doped SrTiO3 (100), (110) and (111) single crystals

) and (111) SrTiO3 surfaces after annealing in UHV at 630 °C. Deconvolution of the core level spectra (O 1s, Sr 3d and Ti 2p) distinguishes bulk and surface components, which are interpreted in terms of surface enhanced covalency (SEC). By comparing the experimentally measured binding energies with theoretical calculations developed in the framework of the Localized-Hole Point-Ion Model, we quantitatively determine the effective electron occupancy at bulk and surface Sr and Ti sites. Our results confirm the essentially ionic character of Sr-O bond and the partially covalent character of Ti-O bond in bulk STO. The cation Ti and Sr electron occupation is greater for all the three surfaces than in the bulk. Surface covalency shifts the Madelung potential at the surface by ΔEM. ΔEM is a minimum for the (111) surface, and increases through (100), attaining a maximum for (110). The angle-resolved valence band spectra and the work function values also confirm this trend. The results are consistent with d-d charge fluctuations dominating at the surface, whereas metalligand charge transfers are more energetically favorable in the bulk.

Introduction

The Madelung potential reflects the cohesive strength of a crystal and as such is one of the most fundamental properties of solids. Its magnitude is determined by both charge and distance, and in ionic solids it will be influenced by both the effective valency and the crystal structure. Fascinating new material properties like metallicity at the interface of insulating oxides [1][2][3], have renewed interest in the Madelung potential. Very recently Wadati et al. [4] studied the changes of the Madelung potential in strained La0.6Sr0.4MnO3 by monitoring the core level binding energy shifts. They measured the Madelung potential as a function of strain, but also showed evidence of the influence of covalency in the Mn-O bonds. In bulk single crystals the strain can be considered negligible which should allow one to identify the contribution of covalency to the Madelung potential.

The electrostatic contribution to the surface energy of a slab cut along a polar direction diverges [5], making such surfaces (or interfaces) inherently unstable. Relaxation, reconstruction and charge redistribution may all take place to compensate the surface dipole. In principle this should also change the Madelung potential at the surface with respect to that in the bulk solid [6]. Observing such changes using photoelectron spectroscopy requires measurement of both surface and bulk core level emissions. In the present article we focus on the influence of the surface enhanced covalency on the Madelung potential for (100), (110) and(111) SrTiO3 (STO) surfaces using angle-resolved photoemission.

A degree of covalency in formally ionic compounds, such as the model perovskite oxide SrTiO3, is well known. Piskunov et al. [7], using ab initio calculations determined the valence density charge inside the bulk and found that the Sr-O bond is typically ionic, while the Ti-O bond has a partially covalent character. Bocquet et al. [8] using a cluster model, evaluated the effective occupancy of Ti 3d states for different Tioxides in which the Ti formal valence is 4+ (d 0 ). For the STO, they predicted an electron occupancy of 1.1 (d 1.1 ), and thus an effective valence of 2.9+. Courths et al. [9], using a point-ion model, estimated the effective charge on the Ti ion to be about 2.5+. Ab initio cluster calculations performed by Sousa and Illas [10] confirmed this picture also for titanium dioxide, finding the Ti-O bond to have only 55% ionic character and an effective charge of 2.3+. Moreover, different authors [9,[11][12][13] have suggested that the degree of covalency in Ti-O and Sr-O bonds might change at the surface due to the reduced coordination of each cation.

SrTiO3 (STO) belongs to the family of ABO3 perovskite-type oxides (see Fig. 1). At room temperature the lattice parameter, a0, is 3.91 Å [14]. Each Ti atom shows an octahedral coordination, while each Sr atom has a cuboctahedral coordination. Along the [100] direction the lattice can be built up by alternatively stacking apolar SrO and TiO2 planes, and thus the (100) surface can have either a SrO or a TiO2 terminating plane. The [110] and [111] directions are also comprised of alternate, stoichiometric polar planes, which are SrTiO 4+ and O 4-for the [110], and SrO 4-and Ti 4+ for the [111]. However, given their polar nature these surfaces will relax or reconstruct, modifying the surface electronic structure. Indeed, recent structural studies of the (111) [15] and (110) [16] polar surfaces have demonstrated the presence of complex surface reconstructions, providing that the UHV annealing temperature is sufficiently high to allow atomic diffusion.

In photoelectron spectroscopy the binding energy, EB, of a core electron can be written as:

E B = E 0 + ε sym -E M ; ð1Þ
in which E0 is the eigenvalue of the spherically symmetric part of the Hamiltonian operator for each ion or free-atom ionization potential, εsym is the correction term to the spherical eigenvalues which accounts for the cubic symmetry of the crystal, and EM is the electrostatic Madelung potential, which describes the inter-atomic interactions. The chemical specificity of each site is included in E0. εsym contains the effects of the splitting of d and p orbitals on the core levels. The Madelung potential term in the above equation therefore contains information on the specific crystalline structure of the surface with respect to the bulk and possible changes in bonding covalency at the surface provided of course that the surface core level binding energy can be distinguished from the bulk emission. Knowledge of the chemical potential, which can also be measured using photoemission, will then allow correlation of core level shifts and the Madelung potential. The appearance of covalency in formally ionic bonding should also influence the electronic structure. It is therefore interesting to relate changes in the Madelung potential with the insulating nature of the transition metal oxide. Many studies of the STO electronic band structure have been carried out, both theoretically [7,[17][18][19][20][21][22][23] and experimentally [24,25]. The indirect energy band gap is 3.2 eV, and the direct energy band gap is 3.75 eV, as measured by ellipsometry [23]. Zaanen et al. [26] investigated the origin of band gaps and the character of the valence and conduction electron states in 3d transition metal (TM) compounds. They described the physics in terms of: (i) the on-site d-d Coulomb repulsion energy U, the energy required for charge fluctuations of the type 𝑑 𝑖 𝑛 𝑑 𝑗 𝑛 ↔ 𝑑 𝑖 𝑛-1 𝑑 𝑗 𝑛+1 ; (ii) the charge transfer energy Δ, which describes charge fluctuations of the type 𝑑 𝑖 𝑛 ↔ 𝑑_{𝑖}^{𝑛 + 1} L, where L denotes a hole in anion valence band (ligand p states); and (iii) the ligand p-metal d hybridization energy T. The late TM compounds mainly fall in the charge transfer regime (Δb U) and the band gap is proportional to U [27], while the early TM compounds (including the STO) were originally classified in the Mott-Hubbard regime (U b Δ) and the band gap is proportional to U [26]. Several authors have reclassified the STO as a charge transfer insulator with a high p-d hybridization energy, which will result in strong covalency [8,28,29].

After briefly describing the sample preparation procedure and the experimental details, the core level (O 1s, Ti 2p and Sr 3d) and valence band photoemission spectra are presented. Deconvolution and fitting of the core level spectra are performed in order to distinguish between bulk and surface components. Comparing the experimen-tally measured binding energies with theoretical calculations devel-oped in the framework of the Localized-Hole Point-Ion Model, we quantify the effective electron occupancy at bulk and surface Sr and Ti sites. The results are confirmed by the valence band spectra and the quantitative determination of the work function for the three surfaces. Finally, we discuss possible charge fluctuations mechanism at the surface with respect to the bulk.

Experiment

Three commercial SrTiO3 single crystals (100), (110), and (111), doped with Nb atoms (0.5 wt.%) (SurfaceNet GmbH) were finely ground with diamond paste (smallest grain size: 0.5 μm) and chemomechanical polishing, removing 20 μm of the material. Seyton like slurries, containing colloidal SiO2, KOH or NaOH and some Tensides (pH of the freshly prepared slurry was about 12) were used for the matter. Final surface roughness was 0.2-0.4 nm, as measured by scanning tunnelling microscopy. In order to clean the surface, in situ ion etching, oxygen plasma, and annealing have been tested, during which the Sr, Ti, O, and C concentrations were monitored by Auger Electron Spectroscopy (AES). In the case of Ar + ion etching, oxygen was preferentially sputtered leading to the formation of surface vacancies and the increase in surface roughness made carbon removal difficult. Our oxygen plasma efficiently removed the carbon, but altered continuously the Ti and Sr stoichiometries. Annealing at 630 °C was the most effective cleaning procedure, both for carbon removal and for maintaining the oxygen stoichiometry (see Fig. 2a). Preliminary XPS analysis showed no evidence of NbxOy formation after the annealing process excluding the appearance of Nb surface segregation phenomena, and negligible surface carbon contamination as shown in Fig. 2b.

Therefore, annealing at 630 °C for 1 h 30 min in UHV (the pressure was always below 1 • 10 -7 Pa) was adopted before every measurement sequence. The moderate temperature minimizes the oxygen vacancy creation and atomic diffusion responsible for previously observed surface reconstructions. Attaining the correct surface stoichiometry whilst minimizing the oxygen vacancies is essential for the comprehension of the intrinsic effects of the surface on the bonding covalency between different atoms. The effectiveness of our treatment is confirmed by the LEED patterns (see Section 3.1), where sharp, bright spots appear, and by UPS Valence Band spectra (see Section 3.3), where the absence of intra-gap surface states indicates a negligible surface carbon contamination and a low concentration of oxygen vacancies [30]. A gold foil was placed in contact with the STO samples in order to provide an energy reference in both XPS and UPS experiments.

Angle-resolved XPS spectra have been acquired using a monochromatic X-Ray source, and a 125 mm hemispherical analyzer (both Omicron Nanotechnology). The source provides Al Kα (1486.7 eV) radiation with a spot size of 0.75 mm, and a line-width of 250 meV. The electron energy analyzer was operated with an energy pass of 25 eV,entrance slit 1 mm, exit slit 5 mm, and angular acceptance of ±8° (in order to suppress photoelectron diffraction effects). The sample manipulator allowed both polar and azimuthal rotations. XPS spectra of O 1s, Ti 2p and Sr 3d core levels have been acquired for STO (100), (110) and (111), at photoelectron detection angles of ϑ=0∘ (normal detection), and ϑ=60∘(grazing detection). The latter gives a twofold increase in surface sensitivity. The bulk Au 4f7/2 peak (at 83.9 eV) was used as the energy reference for the core level spectra. Carbon contamination has been checked by monitoring the C 1s core level peak (at 284.7 eV). A worst case estimation of C atomic concentration on the sample surface has been obtained using a discrete-layer model [31], in which the C is assumed to be only at the surface. This gives an atomic carbon concentration between 1-2% of a monolayer after the UHV annealing at 630 °C. The angle-resolved UPS experiments have been performed using a standard He I discharge lamp (21.2 eV), and a 65 mm hemispherical analyzer (both Omicron Nanotechnology). The photon beam had a spot size of about 2 mm in diameter and a line-width of about 1 meV. The electron analyzer was operated with pass energy of 5 eV, entrance slit 1 mm, and angular acceptance of ±1°, giving an energy resolution of ~ 36 meV. The analyzer is placed inside the UHV chamber, and is able to rotate around the sample, allowing polar rotation of detection direction without moving the sample. The base pressure in UHV chamber during all measurements was between 1 and 5 × 10 -8 Pa. UPS spectra have been acquired for (100), ( 110) and (111) surfaces at detection angles of ϑ =0 ° (normal detection) and ϑ =66 °(grazing detection).

Results

Low energy electron diffraction (LEED)

The LEED measurements have been obtained with a standard experimental setup. Fig. 3 shows the raw diffraction patterns (inverted images) for (100), ( 110) and (111) surfaces, measured at primary beam energy around 100 eV sensitive to the first few atomic layers (the inelastic mean free path (IMFP) of 100 eV electrons in STO is ~ 5 Å [32]). The spots are small and sharp reflecting long range order and high coherence length. The patterns are consistent with a (1 × 1) termination plane for all the three surfaces. Slight charging is observable in the lower part of Fig. 3a for the (100) surface. The absence of higher order reconstructions is consistent with the fact that they are only observed at higher annealing temperature, however we cannot exclude some relaxation particularly for (110) and ( 111) surfaces [15]. 110) and ( 111) surfaces measured at normal and grazing detection. The spectra have been normalized to the maximum intensity. The fitting procedure was performed using a non-linear Gaussian/Lorentzian line shape. A Shirley algorithm [33] has been used for background subtraction in the case of O 1s and Sr 3d spectra, while in the case of Ti 2p an inelastic energy-loss background from experimentally measured electron energy-loss spectra (EELS) (see below) was used to remove extrinsic loss features. Table 1 contains the principal peak parameters of the different components in each spectrum. They are in substantial agreement with data reported in literature for the STO [34][35][36][37][38].

Angle-resolved XPS

For the Sr 3d spectra, fitting attempts (not shown) using a single doublet yielded unphysical angle-dependent mixing parameters, full width half maximum (FWHM) and branching ratios. A more physically consistent deconvolution of all Sr 3d spectra can be obtained using two overlapping doublets separated by 0.8 ± 0.1 eV for the (100) surface and 0.7 ± 0.1 eV for the (110) and (111) surfaces, with a constant spin-orbit splitting (S.O.S.) equal to 1.8 ± 0.1 eV, a FWHM of 0.9 ± 0.1 eV and a branching ratio of 1.5. The S.O.S value obtained from our fitting is very close to the values reported by several authors [35,36]. The FWHM of each doublet within a given spectrum was the same, since the core hole lifetime should not change significantly between surface and bulk. A further confirmation of the reliability of our deconvolution was the ability to obtain good fits to the spectra at different detection angles simply by changing the relative area between the high binding energy (HBE) doublet (Doublet 1) and the low binding energy (LBE) doublet (Doublet 0). These results suggest that the Sr atoms in STO crystals are in two distinct chemical or structural environments. The relative intensity of the HBE doublet (Doublet 1) increases for the surface sensitive spectrum (ϑ =60 °), whereas that of the LBE doublet (Doublet 0) does not (Table 2). Therefore, the Doublet 0 can be related to the photoelectron signal from bulk Sr atoms, while the Doublet 1 can be related to surface Sr atoms.

For O 1s, all fitting attempts with less than three peaks led to inconsistent results. Good fits are obtained for the O 1s spectra using three overlapping almost-Gaussian peaks. The HBE peaks, Peak 1 and Peak 2, are shifted with respect to the LBE peak, Peak 0 (at ~ 530.1 ± 0.1 eV), by 0.7 ± 0.1 eV and 2.1 ± 0.1 eV for the (100), 1.0 ± 0.1 eV and 2.1 ± 0.1 eV for the (110), and 0.9 ± 0.1 eV and 2.1 ± 0.1 eV for the (110) surfaces, respectively. Table 3 shows how the peak intensities change as function of the detection angle: the HBE peaks increase their intensity when the spectrum is acquired in more surface sensitive conditions, while the LBE peak is almost unaffected. Thus, we consider the HBE peaks as a surface signal, while the LBE peak is related to bulk O atoms.

Moreover, the relative intensities of the two surface peaks show no correlation as function of the detection angle, suggesting that both Peak 1 and Peak 2 represent O atoms in distinct surface chemical environments. Emission with a binding energy of Peak 2 (~ 532.1 eV) is frequently reported in literature [36][37][38] as being characteristic of carbonate compounds or chemisorbed H2O, OH -or CO2 groups on the sample surface. Due to the low level of carbon contamination on the sample surface (see Section 2) we believe the Peak 2 to originate mainly from chemisorbed H2O or OH -. On the other hand, the Peak 1, whose intensity variation as function of detection angle is Fig. 5. O 1s core level spectra measured for the (100), ( 110) and ( 111) surfaces, at normal and grazing detection (60°). ffiffiffi B B Fig. 6. Ti 2p core level spectra measured for the (100), ( 110) and ( 111) surfaces, at normal and grazing detection (60°).

qualitatively quite similar to that of the Sr surface doublet (see Tables 2 and3), can be associated to O surface atoms in the terminating layer, as recently reported by some of us for polycrystalline STO [38].

For Ti 2p spectra, the fitting can only be correctly performed after the removal of the inelastic scattering part present in the XPS spectrum. We have measured EELS spectra for the three surfaces (see Fig. 7), and extracted the inelastic energy-loss backgrounds for the experimental photoemission spectra. The EELS spectra were measured at electron kinetic energies matching those of the main photoemission peak (~ 1024 eV), with an analyser band pass set to 10 eV in order to obtain a FWHM of the elastic peak of about 1 eV, matching the FWHM of the 2p3/2 component in the XPS spectra.

The STO(100) EELS spectrum compares well with data in the literature [23,39]. Below 10 eV the main loss structure is attributed to interband transitions between the O 2p valence electrons to Ti 3d conduction band states. The intensity centred on ~ 14 eV is associated with O 2p resonances [39], whereas Van Benthem et al. [23] identify O 2p →Sr 4d t2g and eg transitions. The broad loss peak at 30 eV is interpreted as being made up of interband transitions involving O 2s →Ti 3d and Sr 4d levels and the bulk plasmon, whose estimated position from the electron density is ~ 27 eV. The corresponding surface plasmon (ωS = ωB = p 2) may well be the resonance around 20 eV, which is more intense for the (111) surface. The no-loss photoemission spectra were obtained by subtracting a loss background obtained from the EELS data at each point in the XPS spectrum proportional to the ratio between the EELS elastic peak and the photoemission intensity (following a procedure similar to that used in Table 1 Binding energies of the bulk components and surface core level shifts (ΔEB = E surf -E bulk ) [8]). Thanks to this procedure we were able to remove from the XPS spectra the loss feature at ~ 6.5 eV from the main peak.

Ti 2p spectra can be well represented by a dominant doublet at 459.1 ± 0.1 eV with S.O.S. of 5.8 eV, and by a low intensity structure on low binding energy (LBE) side of the dominant doublet at ~ -2.0 eV for the (100) and (111) surfaces, and -1.9 eV for the (110). The dominant doublet line shape and S.O.S value obtained from the fitting are closely similar to those reported in the literature [8,39]. The dominant doublet is usually associated to Ti ions with a formal valence 4+ (bulk STO), while the structure on LBE side is believed to be the 2p3/2 peak of the doublet associated to Ti ions with a reduced charge state [40]. This last peak may therefore have two origins: emission from reduced charge state Ti at the surface, and, based on electronegativity arguments, emission from Ti ions oxygen bridged to Nb dopants in the bulk. Table 4 shows the intensity variations as function of the detection angle for the 2p3/2 peak of the dominant doublet (Peak 0) and for the shoulder at LBE side (Peak 1). Looking closely, Peak 1 is enhanced for surface sensitive grazing angle detection for both (100) and (110) surfaces, whereas it is attenuated for the (111) surface. Thus, for (100) and (110) surfaces Peak 1 could simply be considered to have a surface origin, associated with surface Ti ions in a reduced charge state. The (111) surface also has a significant Peak 1, however, its behaviour as a function of the detection angle and the Sr 3d spectra suggest a bulk origin, and a Sr rather than Ti rich surface termination. Given the doping level, 1% of Ti sites should contain substitutional Nb. The intensity ratio of the two components at normal detection is about 0.03.

A complete fit to the Ti 2p spectrum is shown in Fig. 6 for the (100) surface at normal detection. The FWHM for the spin-orbit components of the dominant doublet are respectively ~ 1 eV and ~ 1.75 eV, with a branching ratio of 2.13. The difference in the FWHM could be due to for O 1s, Sr 3d and Ti 2p. The uncertainty is shown in brackets. All values are expressed in eV. For the case of Ti 2p3/2 at (111) surface (indicated by the symbol *) ΔEB does not represent a surface shift, but is due to Ti-O-Nb coordination in the bulk (see text).

Table 2

Intensity of peak components in Sr 3d spectra as a function of the detection angle. AreaPeak AreaPeak Coster-Kronig decays of the core hole in the 2p1/2 level, as reported by Zaanen and Sawatzky [41]. Three additional peaks are present in Ti 2p spectrum at 2.0 ± 0.1 eV (S1), 4.2 ± 0.1 eV (S3) and 7.9 ± 0.1 eV (S2) on the high binding energy side of the 2p3/2 peak (Peak 0), representing satellite structures due to intrinsic loss processes. The peaks S1 and S2 are similar to those reported by Oku et al. [42], while the peak S3 is reported here for the first time. The physical origin of the latter is unknown and still being studied.

AreaDouble AreaDouble t0ðϑ t0ðϑ

= = 60 • Þ 0 • Þ AreaDouble AreaDouble t1ðϑ t1ðϑ = = 60 • Þ 0 • Þ Sr 3d STO(100) 1,
0ðϑ 0ðϑ = 60 • Þ = 0 • Þ AreaPeak AreaPeak 1ðϑ = 60 • Þ 1ðϑ = 0Þ
For the case of (100) surface the proportion of SrO-terminated and TiO2-terminated regions can be estimated from the relative surface/ bulk core level intensities: Table 4 Intensity of peak components in Ti 2p spectra as a function of the detection angle. 8b). They are practically constant for the three samples, suggesting that the Fermi level is pinned by the bulk Nb doping, just below the conduction band. Thus, the chemical potential μ for all three samples is the same. This will be important in correlating the core level shifts with variations in the Madelung potential. The valence band widths, W, obtained from linear fitting the upper and lower valence band edges (see inset in Fig. 8a) were 5.60, 5.50 and 5.67 eV for the (100), ( 110) and (111) surfaces, respectively (see 110) and (111) faces, while those of the surface sensitive spectra measured along the Ti-O bond have been corrected in order to take in account the contribution of the bulk emission coming from Ti ions bridged with Nb dopants. This yields 88% SrO-termination and 12% TiO2-termination, in good agreement with a recent photoelectron diffraction study [43], where a comparison between experimental XPD patterns and multiple scattering calculations gave 90% of SrO-termination and 10% of TiO2termination.

AreaPeak AreaPeak

0ðϑ 0ðϑ = = 60 • Þ 0 • Þ AreaPeak AreaPeak 1ðϑ 1ðϑ = = 60 • Þ 0 • Þ Ti 2p STO(
I surf = I STD % SrO = Sr Sr I surf = I STD + I surf = I STD I surf = I STD % TiO 2 = Ti Ti : I surf = I STD + I surf = I STD

Angle-resolved UPS

Fig. 8a shows the raw data of the normally detected spectra, after background subtraction [44]. The principal structures appearing in the spectra originate from the valence band (VB) region between 3 eV and 9 eV, which corresponds primarily to O 2p non-bonding and bonding states [22]. The experimental VB at normal emission for STO(100) is compared with the Density of States (DOS) calculated by Chambers Fig. 7. Electron energy-loss spectra measured at 1024 eV on the STO(100), ( 110) and (111). direction are 5.64 and 5.70 for the (100) and ( 110) faces (see Table 5).

The valence band width for STO(100) agrees well with previous results [44]. Along the Sr-O bond direction it is reasonable that a lower coordination reduces the hybridization, giving a narrower valence band. The increase of the bandwidth along the Ti-O bond direction could be due to an enhancement of the p-d hybridization between cations and anions, and thus higher covalency at surface. Again, this suggests a more complex behaviour than simple band narrowing due to overall reduced surface coordination and is developed in the discussion section. The energy band gap region does not show significant surface states (see Fig. 8b), confirming a low

Binding energy calculations

Here we present the basic concepts underlying the O, Ti and Sr core level binding energy calculations. We denote n the electron occupancy of Ti 3d states, m the electron occupancy of Sr 5s states, and with 6k the electron occupancy of O 2p states, which is obviously anticorrelated with those of Sr and Ti. The subscripts b and s indicate bulk and surface sites. First, we calculate the binding energies for Ti 2p3/2 and Sr 3d 5/2 core levels as a function of the valence electron occupancy concentration of oxygen vacancies [30,46] induced by our annealing process and low surface carbon.

The sample polarization with a potential of -6 V allows to clearly distinguish the photoemission cut-off, determined using a complementary error function fit to the threshold region. In this way, we could evaluate the spectrum width, ΔE, defined as the difference between the Fermi Level and the photoemission cut-off, and by using the formula, ΔE =ℏω -Φ, the work function, Φ, for each surface (see Table 5). The value of Φ varies with the crystallographic orientation of the surface (Φ (111) b Φ (100) b Φ (110) ), known as work function anisotropy [47]. In a recent theoretical study of the work function anisotropy of an oxide surface (CrO2), Attema et al. [48] have suggested a model based on surface stability/valency, atom density and the electronegativity of the surface atoms. The surfaces with the most electropositive element should have the lowest work function. For the (111) surface the lower work function points to a Sr-O rich surface termination. Finally, the pinning of the Fermi level is just below the STO conduction band providing further evidence that our STO surfaces are free from significant band bending, like the fractured surfaces studied by Kohiki for bulk and surface sites. Comparison between the calculated and the experimentally measured binding energies allows deduction of nb, mb, ns and ms.

The calculations have been performed in the framework of the Localized-Hole Point-Ion (LHPI) model [50], modified to account for the symmetry of the specific site, where the binding energy of a core electron is given by the Eq. ( 1) (see Section 1). The free-atom ionization potential, E0, and the correction term for cubic symmetry, εsym, have been calculated using Cowan's program [START_REF] Cowan | The Theory of Atomic Structure and Spectra[END_REF], and the specific coordination symmetry is included following Butler's point group notation [START_REF] Butler | Point Group Symmetry and Applications-Methods and Tables[END_REF]. Oh symmetry is considered for Ti and Sr bulk sites, with a crystal field splitting for Ti 3d states 10 Dq set to 2.0 eV [START_REF] Higuci | [END_REF]. For the case of Sr, the crystal field splitting is considered to be about 15% less than for Ti sites [54], and relativistic corrections to the binding energies have been included in the calculations. A first approximation to cation screening is included through a one electron transfer from the ligands.

The Madelung potential, EM, has been calculated using Tosi's method [START_REF] Tosi | Solid State Physics[END_REF]. Charge neutrality for the unit cell in the bulk leads to the following expression for the bulk potential:

Discussion

Origin of the surface core level shifts

in which E bulk (0, i) is the bulk Madelung potential at sites i (Ti, Sr) with all ions in their ideal oxidation states (nb = 0 and mb = nb/2 = 0).

To first order, the bulk-surface Madelung shift can be derived as:

From LEED patterns, surface relaxation is small and there is no significant long range reconstruction. However, redistribution of electronic charge is indeed likely, particularly for the polar directions. The surface core level shift of titanium, strontium and oxygen are therefore interpreted with the help of a model of surface enhanced covalency (SEC) [11,49]. Within this framework, the charge distribution will change the Madelung potential. In the absence of changes in the chemical potential, the surface core level shifts can be directly related to the value of the Madelung potential at the surface. In the surface plane each cation (Sr and Ti) is coordinated with a lower number of nearest-neighbours anions (O) with respect to the bulk. Since oxygen is more electronegative than strontium and titanium, this induces an increase of the valence electronic charge on each cation site. The effective charge state is thus reduced for both cations in which is the bulk-surface shift of the Madelung potential when nb =ns = 0, E surf (ns, i) is the Madelung potential at the surface of the solid, and E plane (0, i) is the contribution to the Madelung energy of the 2D surface plane in the ideal oxidation state. The LHPI model allows explicit inclusion of bulk and surface covalency. The first term in Eq. ( 3) is zero-order and represents the variation of the Madelung potential due to the different surface geometry and the reduced coordination in the terminating plane with respect to the bulk (this is the equivalent of the term ΔVM in eqn. 1 of Wadati et al. [4]); the second term is first order and describes how the Madelung potential can vary as a function of the effective charge state of the surface ions, with .

The surface covalency enters into the and anions, while the bonding charge and the ligand-metal hybridization energy (T) become larger. In this sense, surface Sr-O and Ti-O bonds have a more covalent character than in the bulk. The reduction of the effective charge state of the surface ions induces two competitive effects on the core level binding energy positions: (i) on one hand, the modification of the screening reduces the nuclear Coulomb attraction experienced by the core electrons in the case of Sr and Ti, while it is enhanced for O ions; and (ii) on the other hand, the crystalline potential generated by each ion is effectively reduced, and thus the core electrons are more strongly attracted by their own nucleus. Depending on the relative strength of these effects, positive (increase of binding energy) or negative (decrease of binding energy) surface shifts can appear for Sr and Ti, whereas only a positive shift is expected for the O core levels, as observed (see Table 1). model through the effective electronic occupation at the surface, ns, thus modifying the Madelung potential.

The symmetry of the crystal field at lattice sites at and near the surface is lower than in the interior of the crystal, and this leads to additional splitting of d and p orbitals. However, we found that the bulk-surface variation of the binding energies induced by this term is always much less than the variation induced by the Madelung energy, and thus in all the calculations we always used the correction term εsym obtained for a bulk cubic symmetry. In the single crystals there is no strain contribution to the Madelung potential. The UPS measurements strongly suggest a constant chemical potential, thus Δμ in Wadati et al. [4] can be set to zero. Like these authors, we also neglect the contribution of the extra-atomic screening. This is probably et al. [39]. reasonable for the purposes of a comparative study of the surface core level shifts of the three crystal orientations.

Work function VB width (eV) VB maximum (eV) (eV) θ =0° θ = 66° θ = 66° θ =0 θ = 66° θ = 66° (Sr-O) (Ti-O) (Sr-O) (Ti-O) (100

Bulk and surface Ti and Sr occupancies

Fig. 9a shows the binding energy of Ti 2p3/2 calculated for a bulk site as a function of the valence electron occupancy, n. Thus, 4n is the effective valence charge ground state. By comparing these values with the experimentally measured 2p3/2 binding energy (459.1 eV), nb is estimated to be ~ 1.5 (Ti 2.5+ ). Fig. 9c shows the Sr 3d5/2 binding energy calculated for the case of a bulk site as function of the valence occupation, m (2m is the charge state). The experimentally measured 3d5/2 binding energy is 133.5 eV, giving the effective ground state bulk occupancy m ~ 0.1 (Sr 1.9+ ). These results confirm and m (111) b m (100) b m (110) suggest that the surface covalency is a b s s s the essentially ionic character of Sr-O bond, and the partially covalent character of Ti-O bond in STO bulk, and agree with the values found in literature [11][12][13]. Photoemission spectra should be more correctly interpreted in terms of the Quasi-particle (QP) spectral function including all correlation and exchange effects, whereas the LHPI model is a ground state model. However, the use of Al Kα radiation probably limits the effect of correlation and exchange. Fig. 9b andd show, respectively, the binding energy of Ti 2p3/2 and Sr 3d5/2 calculated for Ti and Sr surface sites as a function of their valence electron occupancy ns and ms. Comparison between the calculations and the experimentally measured Ti 2p3/2 and Sr 3d5/2 surface shifted binding energies, reported in Table 1 and reproduced in Fig. 9b andd, allows determination of ns and ms for the three STO surfaces investigated. In the Table 6, the effective bulk and surface electron occupancies are summarized both for Ti and Sr sites.

The uncertainty in these values is around ±0.05, which is the range of variation for the electron occupancies within the uncertainty of the binding energies (±0.1 eV as determined by the best fits to the core level spectra and shown by the shaded zones in the Fig. 9b andd. Thus, the electron occupation at surface cation sites is greater than at the bulk sites for all three surface orientations, consistent with the picture of covalency enhancement of the cation-anion bonds (Ti-O and Sr-O) in the surface plane. The relative increase in covalency of the Ti-O bond at the surface with respect to the bulk is three times bigger than that of the Sr-O bond, indicating a much stronger tendency for the O 2p orbitals to hybridize with the Ti 3d rather than with the Sr 5s. This is in qualitative agreement with the conclusions of Ghosez et al. [START_REF] Ghosez | [END_REF] on dynamical atomic charge behaviour in the case of small ionic displacements. Furthermore, the relations n (100) b n (110) minimum for the (111) surface, increasing both for Ti and Sr sites, for the (100) and (110) surfaces, attaining a maximum for the latter case. The results are in good agreement with several previous theoretical works. For the (100) surface, Ellialtioglu and Wolfram [11] found an increase of the Ti 3d electron occupancy from nb = 1.1 in the bulk to ns = 1.55 in the surface (Δn = nsnb = 0.45). For the (110) surface, Bottin et al. [57] used Density Functional Theory (DFT) calculations within the local density approximation (LDA), obtaining Δn = 0.44 for Ti sites and Δm = 0.18 for Sr sites in the SrTiO termination plane. Recently, Eglitis and Vanderblit [13] using Hartree-Fock and DFT calculations found an increase of the bonding charge of Ti-O bonds for both (100) and (110) surfaces with respect to the bulk, predicting a higher covalency in the (110) with respect to the (100) surface.

The key point in the calculations is represented by the computational methods, and the validity of the hypotheses used to determinate each term present in Eq. ( 1). The latter is commonly used in photoemission to represent the binding energy of a localized electron in a crystal. To calculate the free-atom ionization potential we used the Cowan program, which performs atomic multiplet calculations of electronic states in the proper crystal field symmetry using a self-consistent approach. Lindgren [58] has studied various methods for free-atom electron binding energy calculations, such as Self-Consistent-Field (SCF), all-order perturbative, Green's function and DFT methods, showing that the obtained values have a relative dispersion of only a few percent.

The Madelung energy has been obtained using the Tosi's method within the framework of the point-ion model, whose main hypothesis is the approximation of the ion's charge density by a point charge. This will be reasonable if the electron distribution of each ion is spherically symmetric, which is a good approximation in the case of Sr ions because of the s-character of the partially filled orbitals. The most general case involves the inclusion of higher order moments of the electronic charge density, and thus the Madelung energy can be obtained from a Taylor expansion. The value calculated within the framework of the point-ion model represents the first order term (monopole). Birkholz [59] demonstrated that higher order moments have to be included in the calculations only for systems where the point symmetry causes the occurrence of crystal electric field at ion's positions, and thus inducing a local net polarization. For the centro-symmetric Oh structure such fields cannot occur [59] and only the sum of the point charge potentials gives a non-zero result. Thus for STO, higher order moments are forbidden from symmetry, and the value obtained within the point-ion model is a good approximation to the Madelung energy. We conclude that our results and the relative discussion are sufficiently robust both with respect to the theoretical methods used for the binding energies calculation and to the theoretical framework within they are computed.

For the (100) surface, LEED patterns and surface shifts in O 1s and Sr 3d XPS peaks are consistent with a majority SrO-termination. However, the presence of a Ti 2p shifted peak (i.e. surface peak) suggests the existence of a small proportion of TiO2-termination (as shown in Section 3.2). The Ti and Sr valence electron occupancy increases at the surface plane with respect to the bulk: Ti 2+ (Δn = 0.5) and Sr 1.74+ (Δm = 0.16) are the effective charge states on the (100) surface. The existence of surface rumpling should be integrated into this picture, since it would modify the bond hybridization. The (111) surface is predominantly charge compensated SrO 4-terminated, as shown by the fact that only Sr 3d and O 1s peaks show a surface component. The Sr valence electron occupancy increases at the surface plane respect to the bulk: Sr 1.81+ (Δm = 0.09) is the effective charge state at the (111) surface. Thus, more covalency appears in the Sr-O bond at the surface compared to the bulk where it is more ionic (Sr 1.9+ ). At the (110) surface the Ti and Sr valence electron occupancy increases with respect to the bulk: Ti 1.8+ (Δn = 0.7) and Sr 1.69+ (Δm = 0.21) are the effective charge states in the (110) surface. These results confirm the validity of the SEC picture, and show that the Ti-O and Sr-O bonds on the (110) plane are characterized by higher covalency with respect to the (100) and ( 111) surfaces. The simple model used to quantify the surface enhanced covalency demonstrates the link between covalency and the Madelung potential.

Surface and bulk charge fluctuations

The measured valence band widths compare well with the UPS results of Chambers et al. [45] and their self-consistent quasi-particle calculations. As in [45], a small but significant photoelectron tail extends about 1 eV into the gap region. This is as yet unexplained. We note that Chambers et al. [45] do not observe such a broadening in XPS valence band spectra. One explanation may be due to the finite k perpendicular resolution of the photoelectron final state which can significantly broaden the measured spectral function at UPS energies [60].

To check the valence band data, we have measured band structure along two high symmetry directions (ΓR and ΓX). The results are shown in Fig. 10. The principal bands agree well with the selfconsistent GW calculations by Kotani et al. [61], with the exception of the supposed O 2p bands dispersing along ΓR between 4.5 and 8 eV. This may be linked to increased p-d hybridization; a greater d character in these bands may well induce strong cross-section effects [62]. The top of the VBM at the R point, combined with the expected conduction band offset at the Γ point, given the Nb doping level, return an energy gap of 3.25 ± 0.10 eV, in excellent agreement with optical absorption measurements for the indirect band gap.

In light of the measured VB data, we can discuss the charge fluctuation trends predicted by Bocquet et al. [8], in particular to determine how the charge fluctuation mechanism evolves going from the bulk to the surface. Bocquet and co-workers classified the STO bulk in the charge transfer regime (Δ=4.0 eVb U = 4.5 eV), and noted that Δ rapidly increases (+2 eV for each added 1 d-electron), while U slowly decreases (-0.5 eV for each added 1 d-electron), as a function of the electron occupancy of Ti 3d states. We found that the increase of the electron occupancy, Δn, is 0.5 for the (100) surface and 0.7 for the (110) surface. In both cases, Δ becomes greater than U and thus the two surfaces tend more towards the Mott-Hubbard regime. Therefore, while into the bulk the metal d-ligand p charge transfers are energetically become more important. Indeed the higher covalent character will stiffen the bonding making ligand-cation charge transfer more difficult.

Conclusion

We have investigated the bulk and surface covalency of Sr-O and Ti-O bonds in SrTiO3(100), ( 110) and (111). We provide quantitative confirmation of the essentially ionic character of Sr-O bond, and the partially covalent character of Ti-O bond in STO bulk: the effective ground state bulk occupancy for Ti ions has been evaluated to be nb ~ 1.5 (Ti 2.5+ ), and for Sr ions to be mb ~ 0.1 (Sr 1.9+ ). The electron occupation at surface cation sites is evaluated for all the three surfaces to be greater than that at bulk sites. This increase is consistent with the picture of an enhancement of the covalency of cation-anion bonds (Ti-O and Sr-O) at the surface. The relations n (100) =2 b n (110) = 2.2 surface covalency is a minimum for the (111) surface, and increases for both Ti-O and Sr-O bonds, for the (100) and (110) surfaces, attaining a maximum in the latter case. Bond covalency is also studied in relation to measured valence band widths. Using the variations of Δ and U as a function of the Ti 3d states occupancy we suggest that on the (100) and (110) surfaces charge fluctuations between two neighbouring Ti ions are more important, while in the bulk the metal d-ligand p charge transfers are more energetically favourable. Our results also underline that correlations between small surface structural changes (relaxation or rumpling) and covalency should be more systematically measured by experiment, providing valuable comparison with first principles calculations such as those presented in [13]. Quasi-particle spectral function appears necessary in order to fully understand the valence band spectra and some careful investigation of the reasons behind the smearing of the valence band structure at low photon energies should be done.

3 Fig. 1 .

 31 Fig. 1. Unit cell for SrTiO3 cubic lattice.

Fig. 2 .

 2 Fig. 2. (a) Main panel: Auger wide spectrum after annealing at 630 °C; inset: Auger intensities Sr, Ti, O, and C as a function of annealing time at 630 °C. (b) XPS Sr 3p spectra and survey spectrum (inset) after annealing at 630 °C showing the absence of carbon contamination.

Figs. 4 ,

 4 Figs.4, 5 and 6 show the raw data and the corresponding peak deconvolution of the Sr 3d, O 1s and Ti 2p core level spectra for the (100), (110) and (111) surfaces measured at normal and grazing detection. The spectra have been normalized to the maximum intensity. The fitting procedure was performed using a non-linear Gaussian/Lorentzian line shape. A Shirley algorithm[33] has been used for background subtraction in the case of O 1s and Sr 3d spectra, while in the case of Ti 2p an inelastic energy-loss background from experimentally measured electron energy-loss spectra (EELS) (see below) was used to remove extrinsic loss features. Table1contains the principal peak parameters of the different components in each spectrum. They are in substantial agreement with data reported in literature for the STO[34][35][36][37][38].For the Sr 3d spectra, fitting attempts (not shown) using a single doublet yielded unphysical angle-dependent mixing parameters, full width half maximum (FWHM) and branching ratios. A more physically consistent deconvolution of all Sr 3d spectra can be obtained using two overlapping doublets separated by 0.8 ± 0.1 eV for the (100) surface and 0.7 ± 0.1 eV for the (110) and (111) surfaces, with a constant spin-orbit splitting (S.O.S.) equal to 1.8 ± 0.1 eV, a FWHM of 0.9 ± 0.1 eV and a branching ratio of 1.5. The S.O.S value obtained from our fitting is very close to the values reported by

Fig. 3 .

 3 Fig. 3. Experimental LEED patterns (inverted images) measured on: (a) (100) surface, (b) (110) surface, and (c) (111) surface using a primary energy of ~ 100 eV.

Fig. 4 .

 4 Fig.4. Sr 3d core level spectra measured for the (100), (110) and (111) surfaces, at normal and grazing detection (60°).

  of the valence band. The valence band widths obtained from the surface sensitive UPS spectra measured along the Sr-O bond direction The standard values, I STD , for Ti and Sr are approximated by the bulk intensities: I STD ≈ I bulk , and I STD ≈ I bulk . The surface intensities are 5.52, 5.42 and 5.54 eV for the (100), (

Fig. 8 .

 8 Fig. 8. (a) UPS spectra measured at normal detection for the three surfaces; the experimental VB for STO(100) is compared with the raw (dotted curve) and the properly broadened with FWHM of 70 meV (thin solid line) and 1 eV (thick solid line) scGW DOS [45]; the inset shows the linear fitting of the band edges for the determination of the VB width. (b) Close ups of the valence band maximum (VBM) region for the spectra in (a), indicating the values of VBM as determined by a linear fitting of the upper edge.

Fig. 9 .

 9 Fig. 9. Theoretical calculations of the binding energies as a function of the valence electron occupancy for: (a) Ti 2p3/2 bulk site (as comparison the experimental values for Ti 2p3/2 binding energies in TiO2 and TiO are showed; the effective d-electron population for Ti ions in TiO2 and TiO is taken from [8]); (b) Sr 3d5/2 bulk site; (c) Ti 2p3/2 surface sites; (d) Sr 3d5/2 surface sites.

Fig. 10 .

 10 Fig. 10. (a) UPS raw spectra measured along [001] direction on a (110) surface giving the dispersion along the ΓX line; (b) UPS raw spectra measured along [1 11] direction on a (110) surface giving the dispersion along ΓR line; (c) Colour coded second derivative experimental valence band mapping along Γ-X and Γ-R lines in the bulk Brillouin zone and the same using Gaussian multipeak fits (black dots). The solid black curves represent the theoretical band structure calculated by Kotani et al. [61] using a scGW approach. Inset (c): schematic of the high symmetry directions of the Brillouin zone of a cubic lattice.

  the (100) and (110) surfaces charge fluctuations between two neighbouring Ti ions of the type d n d n ↔ d n -1 d n +1

on Surface Science 604 (2010) 1674-1683-doi: 10.1016/j.susc.2010.06.012, G.M. Vanacore et al

		Ti	Ti	Sr	Sr
		Bulk	(100)		(110)	(111)
		ΕB	ΔΕB		ΔΕB	ΔΕB
	O 1s	530 (0.1)	+0.7 (0.1)	+1.0 (0.1)	+0.9 (0.1)
			+2.1 (0.1)		02	1,55
						Sr 3d STO(110)	0,9	1,4
						Sr 3d STO(111)	0,93	1,29

+2.1 (0.1) +2.1 (0.1) Sr 3d5/2 133.5 (0.1) +0.8 (0.1) +0.7 (0.1) +0.7 (0.1) Ti 2p3/2 459.1 (0.1) -2.0 (0.1) -1.9 (0.1) -2.0 (0.1)* Published

Table 3

 3 Intensity of peak components in O 1s spectra as a function of the detection angle.

Table 5 )

 5 . These values are reasonable since one would expect a greater overlap of the O 2p orbitals for denser surfaces, leading to broadening

	Sr	Sr	Ti	Ti	Sr	Sr	Ti	Ti

Table 5

 5

	STO work function, valence band width and valence band maximum for (100), (110)
	and (111) surfaces.

Table 6

 6 Effective state of charge and effective bulk and surface electron occupancies for Ti and Sr sites (the uncertainty is around 0.05).

		Ti		Sr
		Q	n(Δn)	Q	m(Δm)
	Bulk	+2.5	1.5	+1.9	0.1
	(111)	-	-	+1.81	0.19(0.09)
	(100)	+2	2(0.5)	+1.74	0.26(0.16)
	(110)	+1.8	2.2(0.7)	+1.69	0.31(0.21)

Acknowledgements

The authors would like to thank O. Renault, J. Leroy and B. Delomez for technical assistance, R. Gusmeroli for the precious aid with the Cowan's program, N. Vast and A. Tagliaferri for fruitful discussions. This work was financially supported by the European commission under contract Nr. NMP3-CT-2005-013862 (INCEMS) and by the French National Research Agency (ANR) through the "Recherche Technologique de Base" Program.