esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Reflection Analysis of AETA Electromagnetic Signals
## 3 Algorithm and Sample Extraction
### Sample Construction  
The AETA signal is highly correlated with time, the quantity of data is large, and the information contained in a single point is insufficient. It is difficult for analysts who directly observe signals or utilize machine learning methods to process data to obtain valuable information, and they are likely to fall into a dilemma that they cannot resolve.  
\[x=\frac{1}{n}\sum_{i=1}^{n}x_{i} \tag{5}\]  
\(n\) represents the total number of AETA electromagnetic data for half an hour.  
We selected the electromagnetic disturbance data from 10 June 2017 to 10 December 2020, and, through the above formula, we obtained the average value of electromagnetic granularity data for 30 min. After that, the sliding window of the feature data processed from the AETA electromagnetic sensor was used as the input sequence, and the label had the same nature as the input with the size of 48, which was used to train the model more accurately, expand the training samples, and seek the abnormal information of the electromagnetic signal. Generally, since there are mostly samples with small magnitudes and too few instances with high magnitudes, it is necessary to consider how to handle class imbalance when performing earthquake prediction or magnitude classification tasks. However, the current study is an anomaly analysis of the AETA signal before the large earthquake, and there is no problem of sample imbalance. The sliding window method is shown in Figure 6, and a total of 58,302 samples were obtained.  
Figure 5: The structure of the autoencoder and LSTM autoencoder. (**a**) Autoencoder model; (**b**) LSTM autoencoder model.
## 3 The Consensus Design
### The Input Design of the Consensus for BESS  
Generally, the control structure of BESS shown in Figure 2 contains the inner current controller, the voltage controller, power calculation and droop controller [33; 34; 35]. However, the inner current controller and the voltage controller respond faster than the droop controller, and they can be ignored reasonably [36; 37]. In other word, the control structure can be simplified by the droop controller expressed by Equation (3) which is used to maintain the stability of frequency and voltage in BESS.  
\[\left[\begin{array}{c}\omega_{i}\\ V_{i}\end{array}\right]=\left[\begin{array}{cccc}1&-1&0&0\\ 0&0&1&-1\end{array}\right]\left[\begin{array}{c}\mathbf{\omega}_{i}^{0}\\ K_{i}^{p}P_{i}\\ V_{i}^{0}\\ K_{i}^{Q}Q_{i}\end{array}\right]=\left[\begin{array}{cccc}1&-1&0&0\\ 0&0&1&-1\end{array}\right]\left[\begin{array}{c}\mathbf{\omega}_{j}^{0}\\ \widetilde{P}_{i}\\ V_{i}^{0}\\ \widetilde{Q}_{i}\end{array}\right] \tag{3}\]  
where \(\mathbf{\omega}_{i}\) and \(V_{i}\) are the frequency and voltage separately, \(\mathbf{\omega}_{i}^{0}\) and \(V_{i}^{0}\) are the nominal frequency and the nominal voltage, \(K_{i}^{p}\) and \(K_{i}^{Q}\) are the droop gains. For simplicity, \(K_{i}^{p}P_{i}\) and \(K_{i}^{p}Q_{i}\) are denoted by \(\widetilde{P}_{i}\) and \(\widetilde{Q}_{i}\) in this paper. Similarly, [23] proposes a simplified model based on a droop controller for a large-scale energy system simulation. So, this paper designs a series of inputs of the simplified model to synchronize all BESSs, i.e., Equations (4) and (5).  
\[\left[\begin{array}{c}\mathbf{\omega}_{i}^{0}\\ \widetilde{P}_{i}\\ \widetilde{E}_{i}\\ V_{i}^{0}\\ \widetilde{Q}_{i}\end{array}\right]=\left[\begin{array}{cccc}0&0&0&0&0\\ 0&0&0&0&0\\ 0&\frac{-1}{3600}&0&0&0\\ 0&0&0&0&0\end{array}\right]\left[\begin{array}{c}\mathbf{\omega}_{j}^{0}\\ \widetilde{P}_{i}\\ E_{i}\\ V_{i}^{0}\\ \widetilde{Q}_{i}\end{array}\right]+\left[\begin{array}{c}u_{i}^{\mathbf{\omega} }\\ u_{i}^{p}\\ u_{i}^{E}\\ u_{i}^{U}\\ u_{i}^{Q}\end{array}\right] \tag{4}\]  
\[\left[\begin{array}{c}\mathbf{\omega}_{i}\\ \widetilde{P}_{i}\\ E_{i}\\ V_{i}\\ \widetilde{Q}_{i}\end{array}\right]=\left[\begin{array}{cccc}1&-1&0&0&0\\ 0&1&0&0&0\\ 0&0&1&0&0\\ 0&0&0&1&-1\\ 0&0&0&0&1\end{array}\right]\left[\begin{array}{c}\mathbf{\omega}_{j}^{0}\\ \widetilde{P}_{i}\\ E_{i}\\ V_{i}^{0}\\ \widetilde{Q}_{i}\end{array}\right] \tag{5}\]  
where \(\mathbf{\omega}_{i}\), \(\widetilde{P}_{i}\), \(E_{i}\), \(V_{i}\) and \(\widetilde{Q}_{i}\) are the frequency, active power, energy, voltage and reactive power of BESS respectively, \(u_{i}^{\mathbf{\omega}}\), \(u_{i}^{p}\), \(u_{i}^{E}\), \(u_{i}^{V}\) and \(u_{i}^{Q}\) are five inputs for BESS \(i\), \(\mathbf{\omega}_{i}^{0}\) and \(V_{i}^{0}\) are the nominal frequency and the nominal voltage of BESS \(i\).  
Figure 1: Example for the step-by-step consensus algorithm, L1 and L2 are active leaders, L is a virtual leader.
# Retrieval of Cloud Ice Water Path from FY-3F MWTS and MWHS
## 4 Results
### Spatio-Temporal Evolution Characteristics of IWP Retrieval Products
Moreover, the IWP values retrieved from FY-3F and METOP-C data exhibit similar variation patterns with minimal numerical differences, demonstrating that FY-3F data can be effectively utilized for global IWP retrieval.
## 2 Methodology
### Feature Extraction and Selection  
The openSmile toolkit [37] was used to extract acoustic features from each recording of the C1, C2 and C3 subcorpora. The GeMAPS feature set [38] was selected due to the variety of acoustic and prosodic features contained in this set, which includes frequency related features, energy related features, spectral features and temporal features. The arithmetic mean and the coefficient of variation were calculated on these features. Furthermore, four additional temporal features were added: the silence and sounding percentages, silences per second and the mean silences. The complete description of these features can be found in previous research [39]. In this work, only prosodic features (frequency, energy and temporal) were used because spectral features improve speaker identification, and classifiers can be adapted to each speaker in the classification process. In total, 34 prosodic features were employed (see Appendix A).  
Finally, to rank features by their importance to each C1 speaker, the Caret R package [40] was used. The importance of features was estimated by building an SVM model and ordered using the ROCcurve value of each feature. The Receiver operating characteristic curve (ROC curve) is a graphical representation of the results of a binary classifier system where the discrimination threshold is varied. Some studies recommend using the area under the ROC curve (AUC) in preference to overall accuracy for evaluation of machine learning algorithms, especially when the classes are unbalanced [41]. We also used feature selection before training the classifiers: the features were selected by measuring the information gain of the training set and discarding the ones in which the information gain equals zero (column Feat. in Table 3).
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 5 Methodology
### Hazard Analysis and Risk Evaluation
#### 5.1.3 Fire Hazard Analysis  
This step involves determining fire growth patterns and spread dynamics under post-earthquake conditions. Design fire scenarios are developed based on parametric fire models (e.g., time-temperature curves) or fire load and ventilation-based calculations. Key influencing parameters include the following:  
* Fuel load density;
* Ventilation conditions;
* Thermal properties of interior linings;
* Compartmentation failure due to earthquake damage.  
The interaction between seismic damage and fire spread is crucial, as breached fire barriers, compromised fireproofing systems, and damaged suppression mechanisms significantly alter conventional fire behavior.
## 3 Results and Discussion
The relationship between resistance and structural properties for a sample is worth investigating, especially at measurements of 290 K, which is close to room temperature. Figure 5a shows that the resistance was about 7, 1.3, 9.9, and 25.6 M\(\Omega\) when the sample was sulfurized at 800, 850, 900, and 950 \({}^{\circ}\)C, respectively. The above SEM and XRD results suggest that the sample sulfurized at 800 \({}^{\circ}\)C had an obvious amorphous structure, which led to its slightly higher resistance in comparison with the sample sulfurized at 850 \({}^{\circ}\)C. However, as increasing the sulfurizing temperature improved the crystal quality of the sample, the porous feature would be a critical reason for increasing the resistance of the Mo\({}_{\mathrm{x}}\)W\({}_{1-\mathrm{x}}\)S\({}_{2}\) thin film. The highest resistance value of 25.6 M\(\Omega\) could be attributed to the severe interface separation between the Mo\({}_{\mathrm{x}}\)W\({}_{1-\mathrm{x}}\)S\({}_{2}\) thin film and the substrate due to large stress when the sample was sulfurized at 950 \({}^{\circ}\)C. The cross-section SEM image reveals these sheet-like Mo\({}_{\mathrm{x}}\)W\({}_{1-\mathrm{x}}\)S\({}_{2}\) thin films were partially detached. We also calculated the TCR of each sample from Figure 5a using Equation (1) [11,12]:  
\[\mathrm{TCR}=\frac{1}{R_{0}}\frac{dR}{dT} \tag{1}\]  
where \(R_{0}\) is the resistance of each sample at 290 K. Figure 5b displays the TCR values of the samples measured at different temperatures. In comparison with the TCR values (0.29% to 0.48%) for metallic the MoW alloy obtained by [PERSON] et al. [23], the Mo\({}_{\mathrm{x}}\)W\({}_{1-\mathrm{x}}\)S\({}_{2}\) thin film sulfurized at 950 \({}^{\circ}\)C had a TCR value of \(-1.633\%/\mathrm{K}\). [PERSON] et al. reported that any degradation in crystal quality would lower the TCR of a semiconductor [24]. Moreover, a well-crystallized structure can result in a high TCR for a VO\({}_{2}\) thin film [25]. So far, no relevant study reports the correlations between crystal quality and TCR for the Mo\({}_{\mathrm{x}}\)W\({}_{1-\mathrm{x}}\)Scompound. Therefore, our results show that a well-crystallized Mo\({}_{\text{x}}\)W\({}_{1-\text{x}}\)S\({}_{2}\) thin film has a high TCR, which is beneficial for its application in thermal sensors.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 4 Challenges of Smart Grid Communication
### Security
Investigations later identified the source to be manipulated smart meter power readings [134]. In 2013-2014 an attack affected more than 1000 energy companies in 84 countries including Germany, France, Italy, Spain, Poland, and the US [135]. In December 2015, Ukraine experienced a cyber attack on three regional power distribution companies, leaving people in the dark for over six hours. Over two months after the attack, control centers were not fully operational. The attack was distributed via spear-phishing email, targeting IT staff and systems administrators in companies responsible for power distribution. By opening an attachment in an email, malicious firmware were uploaded SCADA-network. The intruders gained access to substation control centers via Virtual Private Networks (VPNs) and was able to send commands to disable Uninterruptible Power Supply (UPS) systems, and open breakers in substations. The blackout affected around 225,000 customers, and manual operations were required to turn the power back on [114; 133]. In 2016 Ukrainian power distribution was once again attacked, parts of the city of Kyiv lost power for an hour. The malware enabled control of circuit breakers to the attackers. In 2020, the European Network of Transmission System Operations for Electricity experienced an attack on its office network. The attack did however not infect any of the systems responsible for controlling the power grid [135; 136].
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Forecasts Combination
#### 2.3.1 Quantile Regression Averaging  
Simple average methods of forecast combination from individual point forecasts use equal weight and have proven to be a viable means of improving forecast accuracy [37]. However, the inability of the interval forecasts resulting from point forecasts of simple average combined models to ensure a nominal coverage rate requires the application of unequal weights in the forecast combination [19]. The estimation of interval forecasts follow a complex process and involves applying weights based on the quantiles [39]. Quantile regression (QR) thus processes and applies quantile-based weights to individual point forecasts from a number of forecasting models to give interval forecasts with nominal coverage rate that can be used to ascertain the uncertainties in the combined forecasts as well as in the individual forecasting models. To combine point forecasts from individual models, we present the use of QRA methods to generate interval forecasts for the forecasting process. Along with the combined interval forecasts, we also generate interval forecasts from individual forecast models by using QRA to assess the uncertainties in each constituent model comparable to that of the combined model. The QR problem can be expressed as Equation (7), as given by [PERSON] and [PERSON] [19]:  
\[Q_{y}(q|X_{i})=X_{i}\beta_{q}, \tag{7}\]  
where \(Q_{y}(q|.)\) stands as the conditional \(q\)th quantile of the actual wind speed \((y_{i})\), \(X_{i}\) are the independent variables and \(\beta_{q}\) is a vector of quantile \(q\) parameters estimated by minimising a loss function for a \(q\)th quantile using Equation (8).  
\[\min_{\beta_{q}}\big{\{}\sum_{(ny_{i}\geq X_{i}\beta_{q})}q|y_{i}-X_{i}\beta_{ q}|+\sum_{(ny_{i}<X_{i}\beta_{q})}(1-q)|y_{i}-X_{i}\beta_{q}|\big{\}}=\min_{ \beta_{q}}\big{\{}\sum_{i}(q-I_{y_{i}<X_{i}\beta_{q}})(y_{i}-X_{i}\beta_{q}) \big{\}}, \tag{8}\]  
where \(y_{i}\) is the actual wind speed and \(X_{i}=[1,\hat{y}_{1},\cdots\hat{y}_{k},i]\) is the vector of point forecasts from \(K\) individual forecasting models and \(\mathbf{I}\) is an indicator function.
# Secure and Efficient Federated Gradient Boosting Decision Trees
## 6 Experiments
### Experimental Results
#### 6.2.1 Performance Impact of the Number of Participants  
The core idea of federation learning is to enable multiple participants to jointly train a model to improve prediction accuracy. Therefore, to investigate the effectiveness of federation learning, we set up different numbers of participants in this experiment, while fixing the amount of data for each participant, and evaluated the prediction performance for each case.  
The results of the experiment are shown in Table 6 and Figure 4. Each participant owns 10% of all the data. We adjusted the number of participants from 2 to 10. The results show that the performance of all models increases when the number of participants increases. Moreover, in all cases, all the indexes of SeFB are better than Independent, TFL and eFL-Boost.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set** & **Data** & **Features** & **Positive Data Ratio** \\ \hline Credit 1 [44] & 284,805 & 30 & 0.2\% \\ Credit 2 [45] & 120,269 & 10 & 7\% \\ Adult [46] & 32,651 & 14 & 24\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiment on the dataset. The task in the following dataset is binary classification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Credit 1** & **Credit 2** & **Adult** \\ \hline Non & 21.8\% & 16.5\% & 18.6\% \\ Independent & 25.7\% & 19.3\% & 21.3\% \\ TFL & 27.7\% & 21.4\% & 23.5\% \\ eFL-Boost & 24.6\% & 18.2\% & 20.7\% \\ SeFB & 22.4\% & 17.3\% & 19.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test errors of different scheme.  
Figure 4 shows the test errors with different numbers of participants, and the horizontal coordinate indicates the number of participants. The results show that the upper limit of SeFB generalization error may increase with the increase in the number of participants. However, in most cases, the test error of SeFB is lower than the minimum error of Individual and eFL-Boost. The test error of TFL changes sharply with the increase in the number of participants, while SeFB is much more stable.
## 2 RS Systems
### Active
#### 2.2.5 Gravimeter  
The mass distribution in the Earth, including the Terra-firma and fluid Earth (e.g., ocean), determines the Earth's gravity field [55]. Changes in the Earth's gravity field are mostly caused by air and water redistributions. The most important RS system for gravimetric studies was the Gravity Recovery and Climate Experiment (GRACE) satellite mission, launched in March 2002. GRACE mission included twin satellites flying 220 km apart and mapped the gravity field of the Earth by accurate measurements of the distance changes between two satellites [56]. Various oceanographic applications, including sea-level rise [57], ocean circulations [58], estimating ocean bottom pressure [59], ice sheet loss monitoring [60], and glacier mass balance [61], have been investigated using the GRACE satellites. Despite the remarkable advantages of GRACE satellites, several limitations restrict their applications. For instance, raw data from the GRACE mission is just the distance between two satellites, and scientists have to determine the cause of the distance change in any changes in the mass below them, from OSC to atmospheric circulations [62]. Moreover, the smallest area that can be studied with GRACE data is 200,000 km\({}^{2}\), which limits many smaller-scale applications [56]. Above all, the gravity field solutions from GRACE are mostly in monthly intervals, restricting short-term change detections [62]. The GRACE mission was operational until 2017, and the GRACE-FO, a follow-up mission with multiple enhancements from GRACE, was launched in 2018 to provide continuous satellite gravimetry data.
## 1 Introduction
The \(\delta\) is the loss angle of the material. It is also written in the form of loss tangent as in Equation (2) [5]:  
\[tan\delta=\frac{\varepsilon_{r}^{\prime\prime}}{\varepsilon_{r}^{\prime}}, \tag{2}\]  
which is the ratio of loss energy and stored energy in the material due to external electric fields [6]. In addition, the electrical conductivity \((\sigma)\) can be calculated as in Equation (3) [5]:  
\[\sigma=2\pi f\varepsilon_{0}\varepsilon_{r}^{\prime\prime}, \tag{3}\]  
where \(\varepsilon_{0}\) is the permittivity of free space and \(f\) is the electric frequency (Hz). The phase constant \((\beta)\) can be determined as in Equation (4) [5]:  
\[\beta=\frac{2\pi f}{c_{0}}\sqrt{\varepsilon_{r}^{\prime}} \tag{4}\]  
where \(c_{0}\) is the speed of light. In this paper, the complex relative permittivity, the electrical conductivity, and the phase constant of honey are characterized at the constant temperature (25 \({}^{\circ}\)C) over the frequency range of 0.5-4.0 GHz with different water contents using the open-ended transmission line technique. The proposed sensor is newly designed from the modification of a SMA-connector. The benefits of this technique are its compact design, ease of design, ability to be used in a wide range of frequencies, simple measuring process and non-destructive test, relatively high measurement accuracy and low-cost, all of which make this technique suitable for measuring the complex relative permittivity, electrical conductivity, and phase constant of honey. The obtained values are useful in the analysis of the relationship between water adulteration in honey, complex relative permittivity, electrical conductivity, and phase constant of honey. Knowledge of the properties of honey is an important step in the development of microwave sensors to determine the quality of honey for agricultural and food industries. Section 2 describes the materials and methods. In addition, it explains the experimental setup and the validation of the proposed sensor. Section 3 demonstrates the experimental results and discussions. Finally, the conclusions are presented in Section 4.
## 4 Road Sensing
### Detecting and Monitoring Moving Vehicles
#### 4.1.1 Sensors  
There are several factors to consider before the selection of a proper sensor. These are, among others, the functionality it may provide, the power consumption incurred, the detection range and the need for special encapsulation. The possible applications have been outlined in the previous paragraphs and they determine, to a great extent, the particular sensor employed. The power consumption depends on the selected sensor and, more importantly, on the purpose given to it since it establishes the sampling rate. The range of detection, apart from setting the distance from which vehicles can be detected, also reveals whether a single sensor can perform detections in multiple lanes. However, multi-lane sensing with long range sensors complicates the election of detection thresholds. Conversely, sensors with shorter detection ranges have the advantage of being able to effectively distinguish between traffic in different lanes. The sensitivity to external factors such as temperature, rain or wind may in turn affect the performance of sensors negatively, providing inaccurate measurements which may require filtering or a constant adjustment of the detection criteria. Finally, the use of some sensors introduces additional encapsulation requirements depending on whether the sensor needs to be placed in a part of the road where vehicles may pass, requiring extra physical resistance, or whether it needs to improve its detection capabilities (e.g., use of Fresnel lenses with infrared sensors), _etc._
## 3 Model Development
### Dynamic Mechanistic Model
#### 3.2.2 Water Flow in the Cathode and Anode  
The water flow model is used to describe the water mass inside the flow channel quantitatively and ultimately to obtain the dynamic characteristic of the relative humidity and the gas partial pressure. To simplify the calculations, some assumptions are made:  
1. Gas behaves as an ideal gas and conforms to the ideal gas law.
2. The gas temperature inside the flow channel equals the stack temperature.
3. The temperature, pressure, and humidity inside the flow channel and at the exit are equal.
4. When the relative humidity of the gas is above 100%, the vapor condenses to liquid form but does not leave the stack. The liquid water evaporates into the gas or accumulates in the flow channel.
5. The flow channel volume is constant.  
According to the mass conservation law, the following expression is valid for the water mass in the cathode flow channel [38]:  
\[\frac{dm_{w,ca}}{dt}=W_{v,ca,in}-W_{v,ca,out}-W_{l,ca,out}+W_{w,m}+n\frac{IM_{H_{ 2}\text{O}}}{4F} \tag{17}\]  
where the water mass passing through the membrane \(W_{w,m}\) (kg/s) is derived from the membrane hydration model. \(W_{v,ca,in}\) is the water mass entering the cathode, \(W_{v,ca,out}\) is the water mass leaving the cathode, and \(F\) is the Faraday constant. The maximum vapor mass \(m_{v,\text{max},ca}\) (kg/s) that can be accommodated on the cathode side is determined from the saturated vapor pressure:  
\[m_{v,\text{max},ca}=\frac{p_{sat}V_{ca}}{R_{v}T_{st}} \tag{18}\]  
When liquid water is generated in the cathode, the vapor mass is maintained at the maximum vapor mass \(m_{v,\text{max},ca}\) (kg). Further, the cathode relative humidity \(\phi_{ca}\) is expressed as  
\[\phi_{ca}=\frac{p_{v,ca}}{p_{sat}(T_{st})} \tag{19}\]  
where vapor partial pressure \(p_{v,ca}\) (bar) is given by the ideal gas law:  
\[p_{v,ca}=\frac{m_{v,ca}R_{v}T_{st}}{V_{ca}} \tag{20}\]  
The cathode inlet gas consists of compressed air and gaseous water from the humidified water tank, and the cathode inlet air partial pressure \(p_{a,ca,in}\) (bar) is given by the ideal gas law:  
\[p_{v,ca,in}=\phi_{ca,in}p_{sat}(T_{ca,in}) \tag{21}\]  
\[p_{a,ca,in}=p_{ca,in}-p_{v,ca,in} \tag{22}\]  
where \(T_{ca,in}\) is the cathode inlet temperature and \(p_{v,ca,in}\) is the cathode inlet vapor partial pressure.  
The relative humidity and inlet hydrogen partial pressure on the anode side are calculated similarly to the cathode side, and the following relationship exists for the water mass in the anode flow channel:  
\[\frac{dm_{w,an}}{dt}=W_{v,an,in}-W_{v,an,out}-W_{l,an,out}-W_{v,m} \tag{23}\]  
where \(W_{v,an,in}\) is the water mass entering the anode and \(W_{v,an,out}\) is the water mass leaving the anode.