esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 3 Agriculture-Based Energy-Efficient Schemes in Literature
### Agriculture-Based Energy-Harvesting Techniques
#### 3.2.6 Microbial Fuel Cell Energy
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Energy Harvesting Techniques** & **Reference** & **Average** & **Wireless** & **Harvesting Energy/Power Series** & **Sensors/Actuates** & **Applications** & **Limitations** \\ \hline \multirow{4}{*}{**WWT**} & Inductive coupling & [121]/2016 & Zigbee & 2.4 W & Vibration, pressure soil moisture, and temperature & Agriculture fields & Strong coupled magnetic resonance are required \\ \cline{2-9}  & Magnetic encounter coupling & [137]/2015 & N/A & 1315] & Agricultural environments sensors/water processing system & Agriculture areas & Exhausting the UAV battery \\ \cline{2-9}  & Electromagnetic wave & [122]/2016 & Zigbee & N/A & Temperature, Strain, humidity, and displacement & Agriculture fields & Harvested energy is inadequate to replenish an ad hoc network with multi-hop \\ \cline{2-9}  & Wind turbine & [123]/2014 & Zigbee & 70-100 mW & Ambient temperature, rain kill, and soil moisture / irrigation system & Vineyard & Wind power is inefficient when the wind \\ \hline \multirow{4}{*}{**Vibration Energy**} & Piezotictic currents & [127]/2010 & Zigbee (CC2420 and CC200) & 20\(\mu\)W & Ambient vibration sensor & Agricultural machinery & Transmission errors due to interferences from similar neighboring WSN and blind-party system \\ \cline{2-9}  & Piezotic currents & [138]/2016 & IEEE 82.51.54 & 14\% & MEMS inertial & Agricultural machinery & N/A \\ \cline{2-9}  & & [139]/2011 & IEEE 802.15.4 & 724\(\mu\)W20.2q & Vibration sensor & Agricultural machinery & Duty-cycle of the end device must be modified according to the total power collected by the piezoelectric-converse \\ \hline \multirow{2}{*}{**Thermal Energy**} & Thermolectrical elements & [140]/2012 & Zigbee (CC2530 and embedded in HallOWEIn platform) & N/A & Temperature and soil moisture/ irrigation control system & Precision migration & Harvested energy is comparatively low based on thermoelectric element \\ \hline \multirow{2}{*}{**Water flow Energy**} & \multirow{2}{*}{[125]/2008} & Zigbee & 16–19 mW & Soil moisture, air temperature, relative humidity, soil temperature, and solar radiation/ irrigation control system & Precision migration & The amount of energy harvested is not enough alone to supply the Zigbee water node \\ \cline{2-9}  & & & & & & & \\ \hline \multirow{2}{*}{**Microbial Fuel Cell Energy**} & \multirow{2}{*}{[125]/2016} & LoRa & 296\(\mu\)W & Capacitive photosimeter/ irrigation system & Precision agriculture & The amount of microbial fed cell power is not enough to power the LoRa wireless protocol and microcontroller directly. \\ \cline{2-9}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._
## 4 Results and Discussions
### SEUT Versus PAUT Methods
#### 4.1.2 Sensitivity Comparison and Defect Detection  
Figure 8 shows the signals associated with the defect (artificially drilled holes) reflections by SEUT and PAUT methods. The depth of the hole can be determined based on the obtained velocity values, as in Equations (1) and (2). Both SEUT and PAUT techniques provided results that are very close to what was obtained by real time x-ray imaging (i.e., 11.175 mm) for validation. It was observed that both SEUT and PAUT techniques can detect a 0.8 mm diameter hole as the minimum size and sensitivity limit; however, PAUT method provides approximately 15% higher SNR for the defect signal. We believe that in PAUT, lower SNR and better signal characteristics, such as higher focusing energy, could assist in detecting smaller-sized defect sizes, and this needs further experimental evaluation.  
\[\text{Depth}(\text{SEUT})=\frac{\text{Time}\times\text{Velocity}}{2}=\frac{7.1 2\times 3.07}{2}=10.9\text{ mm} \tag{1}\]  
\[\text{Depth}(\text{PAUT})=\frac{\text{Time}\times\text{Velocity}}{2}=\frac{7. 05\times 3.23}{2}=11.4\text{ mm} \tag{2}\]  
In Figure 8, PAUT has a clearer and more easily detectable reflection from the defect (reflector), as well as a better detectable back wall reflection. However, when looking at SEUT signal, due to less smoothness in signal from one transducer element, it is more difficult to identify these reflection locations. In addition, as can be seen from the data in Table 2, the gain value is a very important factor. However in some cases the SNR in SEUT looks to be higher, but it was obtained with higher gain value. This happened when a small decrease in the gain value, less than the values in Table 2, did not provide a good detectable signal.  
Figure 7: Gain (dB) value for back wall reflection detection in different sample thickness for attenuation characteristics evaluation using SEUT and PAUT techniques.
# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 5 Experimental Tests
### Discussion
In this paper, we have employed a simple MLP-based indoor positioning system to analyze the directional antennas' effect on the positioning performance. However, it is worthy to note that to overcome the system limitations, extra information from other data sources (e.g., accelerometers or other sensors, such as infrared) or building geometry, could be integrated in the post-processing layer engine [32], in order to refine the raw estimation obtained from the classifier layer, although at the expense of increasing system complexity and computational cost. To this aim, several algorithms and techniques have already been largely studied [21; 22; 23; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35] that could be combined with our system for future studies. To finalize, another point that is interesting to remark on before concluding this section is that similar conclusions are expected if another classifier, such as KNN, ML or SVM, is employed in the location engine, instead of the MLP. The authors obtained similar results to the ones presented in Table 2 and Figure 13 employing the KNN classifier; however, for the sake of space, and to focus on the principal concern of this paper, we have presented the results for an MLP, which is a well-known classifier that has been demonstrated to be stable and advantageous compared to others in terms of time response (which is quite important to run the prototype during the tests).
# Research on a Partial Discharge Expert System for the Diagnosis of Damaged Transformation Equipment
## 2 Materials and Methods
### Hardware Architecture
#### 2.1.4 Technical Architecture of High-Speed Data-Acquisition Device System  
The analog-to-digital converter (ADC) in the system used for capturing the raw discharge waveform of PD employs an FPGA as the controller for the ADC to achieve a higher sampling rate, meeting the requirements for transmission speed.  
The FPGA circuit functions designed in this study, as depicted in Figure 5, include a controller. The design adopts a parallel approach to allow data transmission between the microcontroller unit (MCU) and the controller. The parallel port connection lines include chip select (CS), output enable (OE), write enable (WE), address valid (ADV), clock (CLK), and address data (AD) lines. Additionally, there is a digital clock manager (DCM) used for configuring and controlling the sampling rate of the analog-to-digital converter, as well as a memory interface generator (MIG) for controlling the write and read operations of the DDR3 memory. Finally, if there are differences in the data width or transmission rates during data transmission, a buffer must be added in the middle of the transmission.
###### Abstract  
This article addresses the design and real-time implementation of an expert model predictive controller (Expert MPC) for the control of the brackish and seawater desalination process in a pilot-scale reverse osmosis (RO) plant. This pilot-scale plant is used in order to obtain the optimal operation conditions of the RO desalination process through the implementation of different control strategies, as well as in the training of operators in the new control and management technologies. A dynamical mathematical model of this plant has been developed based on the available field data and system identification procedures. Predictions of the obtained model were in good agreement with the available field data. The designed Expert MPC is distinguished by having a plant identification block and an expert system. The expert system, using a rule-based approach and the evolution of the plant variables, can modify the plant identification block, the plant prediction model, and/or the optimizer in order to improve the performance, robustness and operational safety of the overall control system. The real-time comparison results of the designed Expert MPC and a well-designed model predictive controller (MPC) show that the proposed Expert MPC has a significantly better performance and, therefore, higher accuracy and robustness.  
water scarcity; expert model predictive controller; brackish and seawater desalination; pilot-scale reverse osmosis plant; model identification Article
# An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems
## 4 Proposed Trust Model
### Indirect Reputation
#### 4.2.3 Model Certainty  
The witness's certainty also counts for trust estimation in MASs. It is stressed that uncertainty caused by the lack of certainty and complete information is imperative when generating BPAs. Thus, certainty is independent of evidence (BPAs). From our perspective, certainty comes from two perspectives.  
* First, certainty decreases as the extent of conflict increases in the evidence (we manage certainty by the ratio of positive and negative observations, namely, trust and distrust). Therefore, evidence certainty decreases as the ratio of positive and negative interactions increases.
* Second, certainty decreases as uncertain information increases. That is to say, evidence certainty increases if \(m(T,nT)\) decreases.  
At the same time, we wish the value of certainty to fall in the \([0,1]\) range. Due to the listed reasons, the entropy of the Dempster-Shafer theory of evidence is an ideal tool for uncertainty measurement.  
Many definitions of entropy have been proposed for the space of the Dempster-Shafer theory of evidence, for instance, Deng entropy [57] and [PERSON]'s definition [56]. In this paper, we adopt the entropy definition presented by [PERSON] and [PERSON] [60]. The main reasons are that five of the six listed properties are satisfied, which could be regarded as near-perfect in some perspectives. Moreover, the entropy given by [PERSON] and [PERSON] is designed from two components. The first component is designed to measure conflicts in the BPAs, and the second is used to measure non-specificity in BPAs, which fits for the main reasons of uncertainty in this paper. As explained, we believe that uncertainty yields from the conflict of positive and negative interaction, and a lack of information could also result in uncertainty. For the frame of discernment defined in this paper, i.e., \(\Omega=\{T,nT\}\), we have \(m(T)=x\), \(m(nT)=y\) and \(m(T,nT)=1-x-y\), which is under the conditions of \(0\leq x\leq 1\), \(0\leq y\leq 1\), and \(0\leq 1-x-y\leq 1\). The relation between the entropy [60] is presented in Figure 3.  
As is shown, the uncertainty is in the range of \([0,2*log2|\Omega|]\). Thus, we define the certainty of the information \(m_{kj}\),  
\[Cer(m_{kj}^{d})=\frac{2-H(m_{kj}^{d})}{2} \tag{16}\]For example, a BPA is given as \((1,0,0)\) or \((0,1,0)\), then the witness is one hundred percent confident in the evidence. However, the witness is one hundred percent uncertain towards its judgments if the evidence is \((0,0,1)\).
# Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response +
## 3 Proposed Methodology
### Solution Architecture
#### 3.1.1 Detection Phase  
Figure 1 shows the workflow architecture of the detection phase. SDN offers centrally managed programming for network functionality via software applications that use APIs. The SDN application layer facilitates network integration with proprietary open-source network applications and automation tools. We leverage this layer for the integration of our machine learning-based solution with SDN. The detection phase is also connected to the controller to obtain the visibility of the entire SDN topology. The detection phase is intended to receive incoming packets (Packet-Ins) from any SDN environment, sample them, and check for timing probe activity. It anticipates the timing of the attack, then attempts using an algorithm trained on a historical dataset. Along with each received packet, it extracts information about the source of each packet. This is achieved by obtaining flow tables from all switches connected to the SDN topology. Subsequently, this information is used to locate the adversary's source. The output of this phase depicts the timing of the attack-probing activity and accurately displays its origin. A network administrator can use this output to perform further actions based on a source machine. In the case of the proposed solution, this output was used in the subsequent response phase. Such responses can be denied, permitted, or monitored. The innovative method of integrating SDN and machine learning makes this approach an efficient method for detecting timing probes and determining the source of attacks.  
The operational phase of machine learning-based detection occurs in real time and has potential implications for the network performance. To mitigate this impact, the detection phase operates on a dedicated virtual server, thereby ensuring that the SDN environment remains unencumbered. Our design decision enables us to retain the advantages of centralised control while mitigating the performance disadvantages. As a result, the incorporation of SDN and machine learning in our solution proves to be an effective approach for identifying timing probes and determining the origin of attacks, while maintaining an acceptable network performance.
# Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing
## 2 Methods
### Linear Learning Rate Decay  
The most essential hyperparameters when training a convolutional neural network are the initial learning rate, the number of training epochs, the learning rate schedule, and the regularization method (L2, dropout). Most neural networks are trained with the Stochastic Gradient Descent (SGD) algorithm, which updates the network's weights \(W\) with:  
\[W+=\alpha\cdot gradient \tag{1}\]  
where \(\alpha\) is the learning rate, which parameter determines the size of the gradient step. Keeping the learning rate constant during network training might be a good choice in some situations, but more often decreasing the learning rate over time is more advantageous.  
When training CNNs, we are trying to find global minima, local minima, or just an area of the loss function with sufficiently low values. If we have a constant but large learning rate, it will not be possible to reach the desired loss function values. On the contrary, if we decrease our learning rate, our CNNs will be able to descend into more optimal areas of the loss function [38]. In a part of our experiments, we use a linear learning rate decay schedule, which decays our learning rate to zero at the end of the last training epoch, as shown in Figure 5. The learning rate \(\alpha\) in every training epoch is given with:  
\[\alpha=\alpha_{1}\cdot(1-\frac{E}{E_{max}}) \tag{2}\]  
where \(\alpha_{1}\) is the initial learning rate, \(E\) is the number of the current epoch, and \(E_{max}\) is the maximum number of epochs.  
All of the CNNs used in our experiments for fine-tuning were originally trained on ImageNet with learning rate schedules: ResNet50 and DenseNet121 with step-based learning rate schedule and Inception V3 and Xception with exponential learning rate schedule.  
Figure 5: Linear learning rate decay applied to Convolutional Neural Network (CNN) training of 100 epochs.
## 3 Experimental Analysis
### Biases in Carrier Phase Geometry-Free and Ionosphere-Free Combination
After the single differencing between two stations, there are still some variations of about 1.0 cm  
Figure 10: GFIF combination (blue) and elevation (red) of BDS satellites at stations (**a**) GMSD and (**b**,**c**) CUT0. remaining in the GFIF time series. This could be explained by the high-order ionosphere and residuals of receiver-end hardware delays. After an examination of Figure 10a,b, it is found that the phase shifts for the periodicities for the three GEO satellites are station-independent. Although the pattern of GFIF combination for each satellite may be different from station to station, the sequence in which the GFIF combinations get their crests or troughs is shared by different stations, _i.e._, C04, C01, and C03. This order is consistent with how they are positioned above the equator from east to west. The longitudes for the GEO satellites C04, C01, and C03 are \(160.0^{\circ}\) E, \(140.0^{\circ}\) E, and \(110.5^{\circ}\) E [1; 22; 23]. Each satellite reaches the trough of its GFIF combination at around 1:00 P.M. in local time. These results support the above discussion that the variation of GFIF is satellite-dependent. Further investigation was conducted to confirm the above. Using observables of ten days, cross-correlation is performed on the GFIF combinations' time series of different GEO satellites at the stations GMSD and CUT0. As shown in Figure 12, the maximum correlations between the time series of C01 and C04 could reach 0.8. Similar results can be obtained for C03 and C04. The high correlations mean that the patterns of GFIF combinations of the three GEO satellites are almost the same. Furthermore, the peak values of correlation occur when the lag equals to the local time difference between the two satellites. Simply speaking, the local time here indicates the hour angle or, in other words, the relative geometry between the sun and a certain GEO satellite, the latter staying at an almost-fixed longitude. Therefore, the phase shift between GFIF combination time series of each two GEO satellites depends on their longitude difference, suggesting that the GFIF combination of a certain GEO satellite varies according to its relative geometry with the sun. For example, it is likely that the exposure to the sun affects the transmitting hardware at the satellite. Although it is not that explicit or direct, it can provide a possible explanation to the variation in GFIF combinations of BDS GEO satellites. It is also found that the GFIF time series of C02/C05 show patterns quite different from those of C01/C03/C04 and the correlations between C01/C03/C04 and C02/C05 are low. This may be due to the difference in the satellite manufacturing. The satellites C01/C03/C04 were launched in 2010 and 2011, while the satellites C02/C05 were launched in 2012 [1; 23]. Figure 11: GFIF combinations at station F783 (**top row**), station GMSD (**middle row**) and differencing result between the two stations (**bottom row**) for different BDS satellites, the red curve showing the satellite elevation.
# Disposal and Recycle Economic Assessment for Aircraft and Engine End of Life Solution Evaluation
## 5 Aircraft Disposal and Recycle Economic Indicator  
### Definition of Disposal and Recycle Economic Indicator \(I_{\text{D\&R}}\)  
For aircraft that are no longer in service, the airline will consider the trade-off between direct resale and disassemble & dismantle (scenarios I, II, and III). At the same time, the EoS Company will decide whether it will conduct the aircraft disposal and recycle processes (scenarios II and III). In order to provide a measure for the economic performance of the D&R process, a general economic indicator (\(I_{\text{D\&R}}\)) is proposed (see Equation (14)). We consider the ratio between the values valorized versus the cost spent through the D&R process as \(I_{\text{D\&R}}\). It is assumed that the decommissioned aircraft with/without D&R process remains positive in value, i.e., the salvage value and residual value are positive. Once the aircraft is parked, it incurs a D&R cost; therefore, the D&R cost is also assumed to be positive. Note that all values and costs should be evaluated from the same fiscal year (\(FY\)).  
\[I_{\text{D\&R}}=\frac{V_{\text{s}alvage}-V_{\text{residual}}}{C_{\text{D \&R}}}=\frac{V_{\text{s}ulorization}}{C_{\text{D\&R}}}, \tag{14}\]  
When \(I_{\text{D\&R}}>1\), we have \(V_{\text{s}alvage}-V_{\text{residual}}>C_{\text{D\&R}}\), i.e., \(V_{\text{a}orization}>C_{\text{D\&R}}\). The salvage value is therefore larger than the residual value, i.e., the valorization value is larger than the D&R cost. It indicates that the value valorized via the D&R process is able to cover the cost incurred. Therefore, the corresponding D&R solution should be selected, and the larger the positive \(I_{\text{D\&R}}\), the better the solution is.  
Other situations all lead to unfavorable solutions: Where if \(I_{\text{D\&R}}<0\), the salvage value is smaller than the residual value, and the D&R process would introduce a negative effect. When \(I_{\text{D\&R}}=0\), salvage value equals the residual value, and the D&R process introduces zero effect. When \(0<I_{\text{D\&R}}\)\(<1\), the salvage value is larger than the residual value, which means that the valorization value is less than the D&R cost, where, the anticipated valorization recovers part of the aircraft residual value. Hence, the process cannot fully harvest the residual value of the aircraft. When \(I_{\text{D\&R}}=1\), the salvage value is larger than the residual value, and the valorization value is equal to the D&R cost, where the D&R solution is at the critical point.