esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Separation Methods for Characterization of Balsamic Vinegars  
### Gas Chromatography  
For samples containing volatile components, gas chromatography is usually the first method of choice, especially coupled to single- or tandem-mass spectrometry (GC-MS, GC-MS/MS). The history of GC dates to the first half of the twentieth century and its invention is inevitably related to the names of [PERSON] and [PERSON], who constructed the first chromatograph with an inert carrier gas used as a mobile phase [69]. After decades of research, the technique has matured into the routinely used tool for the analysis of gases, liquids, or suitably dissolved solids. It is usually used for the analysis of organic compounds in the range of molecular weights from 2 to 1000 Da.  
The main application area of GC is in the studies of food and edible plants, where a number of groups of compounds are analyzed, including pesticides, endocrine-disrupting chemicals, estrogens, fatty acids, etc. [70]. Balsamic vinegar samples are typically highly complex mixtures containing hundreds of compounds, while most of them are volatile. More than 1500 compounds were detected by GC-MS methods; however, the number of unequivocally identified compounds is usually much lower [38]. Several groups of compounds were quantified using GC, comprising organic acids, esters, alcohols, aldehydes, ketones, furnes, phenols, lactones, odorous compounds, amino acids, and others.  
In the case of the analysis of the profile of volatile organic compounds (VOCs) of balsamic vinegar (Figure 4), GC is most often coupled with MS detection, while the choice of extraction techniques can be diverse (Table 1).
## 4 Trajectory Prediction Model Based on AIS Data and ACDE-SVM
### Results Analysis
#### 4.3.3 Comparison of Prediction Results between ACDE-SVR Model and Neural Network Prediction Model  
Sections 4.3.1 and 4.3.2 respectively verify that wavelet threshold de-noising and ACDE algorithm optimization yield an SVR model with a higher prediction accuracy; thus, the model was used to predict the trajectory sample data. A comparison between predicted results and actual results is shown in Figure 6 and the longitude and latitude prediction errors are shown in Figure 7, where the error value refers to the absolute error, that is, the difference between the predicted value and actual value. It can be seen from Figures 6 and 7 that the results of trajectory prediction using the ACDE-SVR algorithm are basically consistent with the actual values. The magnitude of the longitude and latitude prediction error is \(10^{-5}\) and the maximum absolute values of 20 points longitude and latitude, respectively, are \(3.475414\times 10^{-5\circ}\) (3.86 m) and \(3.957327\times 10^{-5\circ}\) (4.394 m). This error is within the acceptable range and thus the prediction accuracy satisfies the requirements for making collision avoidance decisions during actual maritime navigation.  
To verify the prediction accuracy of the model, the BP neural network, recurrent neural network (RNN) and SVR prediction models were also compared. As the preferred neural network for processing time series data, RNN has good timing prediction performance [45; 46; 47]. Because the initial weights and thresholds of each BP neural network layer are set randomly [12], it has some problems such as a low prediction accuracy and ease of falling into local optima. To solve the influence of initial parameter settings on the BP neural network prediction model, the ACDE optimization algorithm was used to optimize the weights and thresholds of the BP neural network. In summary, the RNN trajectory prediction model and ACDE-BP neural network model were established under the premise  
Figure 6: Comparison between predicted and original data.  
Figure 7: Error values of trajectory prediction model based on ACDE-SVR. (**a**) Longitudinal error values; (**b**) Latitudinal error values.  
of limited data samples and the prediction errors were calculated and compared with the ACDE-SVR prediction results as shown in Table 6. Under the premise of limited sample data, the ACDE-SVR trajectory prediction model has the highest training accuracy and the shortest run time. Therefore, the ACDE-SVR trajectory prediction model proposed in this paper has good prediction results in regard to both prediction accuracy and operation time.
## 2 Literature Review
### LOCOPROL--France  
Another system that is used in France is the technological solution of LOCOPROL [19; 20]--the low-cost satellite-based train location system for signalling and train protection for low-density traffic railway lines. The system was developed by ALSTOM and its partners from Belgium, Germany, and France within a research project focusing on the use of satellite navigation for low-density railway lines. LOCOPROL is used on a single-track line with a 35 km length. Similarly, the main principle is a radio block center (RBC) that collects information about rail vehicle position on the line. The RBC also receives information about the train drive permits. Information about the position is detected by means of GPS, GLONASS, and in the future by Galileo satellite navigation and odometer. Specification of the position at critical points (especially at stations with gridirons or at radio-shadowed areas) is carried out by means of the installed balises. The position locator is integrated directly in the ETCS deck device, using the public operator's network for information transmission. The train integrity is controlled by its engine driver.
## 2 Background
It has the important advantage of being available today at relatively high temporal resolution from many different satellites. Our project mainly uses this type of remote sensing data to build a \"good enough, right now\" emissions-monitoring system that does not need to wait for future satellites. Since multi-spectral imagery cannot directly measure CO\({}_{2}\) concentrations, we developed a set of proxies, or activity measurements, that are directly tied to emissions, such as visible water vapor plumes from power plant cooling towers. Our approach is similar to countries that employ proxy and activity measurements to estimate land use, land-use change, and forestry (LULUCF) emissions for different land cover types [27] and the Tier 3 methods to collect activity data to estimate fuel consumption and CO\({}_{2}\) emissions in the United Nations Framework Convention on Climate Change Resource Guide for Preparing National Inventories applied to individual power plants [28]. Prior work on this task includes a proof of concept by the Carbon Tracker Initiative to estimate emissions of coal plants in the EU, US, and China using Planet Labs' satellite images [29]. Other efforts have been made to estimate power plant emissions using Sentinel-2 imagery and a multitask deep learning approach [30; 31; 32]. However, these studies were based on a heterogeneous dataset of European plants that include a variety of cooling types with inconsistent signals. Our own prior work expands to additional satellite constellations and power plant technologies but was focused on models to predict the plant operational status (on/off and capacity factor) at a single point in time [33; 34]. This article extends on our previous work by developing an ensemble model to aggregate data from across all satellite imagery sources and predict generation information for rolling 30-day intervals to produce annual CO\({}_{2}\) emissions estimates. We also validated our models on globally distributed plants from outside our training set.
# Test Trials and Analysis of Pod-Shattering Characteristics of Harvested Rapeseed Silique
## 3 Results and Analysis
### Swing Impact Test Results and Analysis
#### 3.1.4 Influence of Silique Growth Position on Silique Shattering  
In order to analyze the shattering influence of the different growth positions of rapeseed siliques on the plant, with Ningza 1818 rapeseed varieties as the research object and the rapeseed silique petal bonding surface as the collision force surface, the collision impact speed was 3.5 m\(\cdot\)s\({}^{-1}\), and the collision impact test of the middle of the silique was carried out at the top, middle, and bottom of the main rapeseed branch. The test results are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Impact** & \multicolumn{2}{c}{**Ningza 1818**} & \multicolumn{2}{c}{**Zhenyou 8**} \\ \cline{2-5}
**Velocity/m\(\cdot\)s\({}^{-1}\)** & **Right Ahead** & **Bonding Surface** & **Right Ahead** & **Bonding Surface** \\ \hline
1.5 & 0\% & 5\% & 0\% & 0\% \\
2.5 & 10\% & 30\% & 5\% & 15\% \\
3.5 & 15\% & 50\% & 5\% & 40\% \\
4.5 & 30\% & 65\% & 15\% & 60\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Impact velocity and stress on silique shattering rate test data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & \begin{tabular}{c} **Moisture Content of** \\ **Silique/\%** \\ \end{tabular} & \
\begin{tabular}{c} **Moisture Content of** \\ **Rapeseed/\%** \\ \end{tabular} &
\begin{tabular}{c} **Silique Shattering** \\ **Rate/\%** \\ \end{tabular} \\ \hline
1 & 20.61 & 23.85 & 25 \\
2 & 15.83 & 17.29 & 45 \\
3 & 9.24 & 8.06 & 75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Silique shattering rate experimental data on moisture content.  
The shattering rates of siliques at the top, middle, and bottom of the main rapeseed branches were 50%, 40%, and 25%, respectively. The shattering rates of siliques at the top and middle of the branch were close to each other but differed significantly from those at the bottom of the branch. Under the same external force, the shattering rate of the silique from the top to the bottom of the main rapeseed branch gradually decreased. The reason is that during the rapeseed maturity period, the maturity degree of the silique at the top, middle, and bottom of the same plant is not consistent, and siliques with low maturity are strong and are not easy to shatter, while the moisture content of rapeseed siliques with relatively high maturity is on the low side, the brittleness is more obvious when subjected to the same collision impact force, and plastic deformation leads to silique shattering.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 4 Proposed Protocol
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline x, y & Entities: patient (P), health center (H), doctor (D), cloud server (C). \\ \hline \(ID_{\text{x}}/TID_{\text{x}}\) & Real identity of entity x/ Temporary identity of entity x. \\ \hline \(k\) & Random numbers generated in the registration phase. \\ \hline \(R_{k}\) & k random number generated. \\ \hline MACxy & Message Authentication Code generated from entity x to entity y. \\ \hline \(R_{x}\) & Random number generated by entity x. \\ \hline \(R_{\text{C}y}\) & Random number generated by the cloud and sent to entity y. \\ \hline \(T_{x}\) & Timestamp generated by entity x. \\ \hline \(K_{xy}\) & Session key generated by entities x and y. \\ \hline \(C_{xy}\) & Validator of the session key generated by x and y. \\ \hline \(E_{Kxy}/D_{Kxy}\) & Encryption/Decryption operation that used the session key generated by x and y. \\ \hline \(IMSI_{x}\) & International Mobile Subscriber Identity of device x \\ \hline \(h_{1}\) & Temporary identity generation hash function. \\ \hline \(h_{2}\) & MAC generation hash function. \\ \hline \(h_{3}\) & Session key generation hash function. \\ \hline \(h_{4}\) & Session key verifier generation hash function. \\ \hline \(\bullet\) & Secure channel. \\ \hline \(\bullet\) & Insecure channel. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Notations used in the protocol. Figure 1: The architecture of the proposed scheme.
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 4 Experiments and Results  
The performance evaluation of our proposed AVS-SMASK algorithm has been carried out with simulated data and recorded data. Five commonly used performance measurement metrics--SNR, the signal-to-interference ratio (SIR), the signal-to-interference plus noise ratio (SINR), log spectral division (LSD), and the perceptual evaluation of speech quality (PESQ)--have been adopted. The definitions are given as follows for presentation completeness.  
1. Signal-to-Noise Ratio (SNR): \[SNR=10\log\left(\|s(t)\|^{2}/\|n(t)\|^{2}\right)\] (44)
2. Signal-to-Interference Ratio (SIR) \[SIR=10\log\left(\|s(t)\|^{2}/\|s_{i}(t)\|^{2}\right)\] (45)
3. Signal-to-Interference plus Noise Ratio (SINR): \[SINR=10\log\left(\|s(t)\|^{2}/\|x(t)-s(t)\|^{2}\right)\] (46) where \(s(t)\) is the target speech, \(n(t)\) is the additive noise, \(s_{i}(t)\) is the \(i\)th interference, and \(x(t)=s(t)+s_{i}(t)+n(t)\) is the received signal of the \(o\)-sensor. The metrics are calculated by averaging over frames to get more accurate measurement [22].
4. Log Spectral Deviation (LSD), which is used to measure the speech distortion [22]: \[LSD=\|\ln\left(\psi_{ss}(f)/\psi_{yy}(f)\right)\|\] (47) where \(\psi_{ss}(f)\) is the power spectral density (PSD) of the target speech, and \(\psi_{yy}(f)\) is the PSD of the enhanced speech. It is clear that smaller LSD values indicate less speech distortion.
5. Perceptual Evaluation of Speech Quality (PESQ). To evaluate the perceptual enhancement performance of the speech enhancement algorithms, the ITU-PESQ software [23] is utilized.  
In this study, the performance comparison is carried out with the comparison algorithm AVS-FMV [17] under the same conditions. We do not take other SE methods into account since they use different transducers for signal acquisition. One set of waveform examples that is used in our experiments is shown in Figure 5, where \(s(t)\) is the target speech, \(s_{i}(t)\) is the \(i\)-th interference speech, \(n(t)\) is the additive noise, and \(y(t)\) is the enhanced speech.  
Figure 5: Waveform examples: \(s(t)\) is the target speech, \(s_{i}(t)\) is the interference speech, \(n(t)\) is the additive noise, and \(y(t)\) is the enhanced speech signal.
# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 3 Proposed Congestion Control Prediction Approach
### Modeling and Mathematical Formulation
#### 3.1.2 Enhanced Congestion Avoidance  
The establishment of Congestion Avoidance (C.A.) enhancement occurs when either the cwnd value or the amount of transmitted data exceeds the threshold. Thus, the congestion avoidance will enhance cwnd by 1 \(\times\) MTU per round-trip time (RTT) when cwnd \(>\) ssthresh. The sender has either the transmitted data which is greater than the outstanding amount to the transport address or the cwnd value. The congestion avoidance mechanism works simultaneously with slow-start since cwnd is always compared with ssthresh. When the cwnd is less than or equal to ssthresh, the slow-start will be established; otherwise, the congestion avoidance phase is predominant. However, the interaction among these phases occurs especially when cwnd routes are varied in order to be occupied by particular phases. The PMTU is measured by the PMTU of the destination for each one multiplied by MTU per RTT, when cwnd \(>\) ssthresh [54; 55; 56; 57; 58].  
The standard equation of congestion avoidance is written as:  
\[\Delta\textit{cwnd}\ CA\ =\ (1\ \times\textit{PMTU}\ \textit{per}\ RTT), \tag{3}\]  
where the proposed equation of congestion avoidance is expressed as:  
\[\Delta\textit{cwnd}\ CA\ =\ (1\ \times\textit{PMTU}\ \textit{per}\ RTT)-B,\ \textit{where}\ B\ \
eq\ 0\ \textit{and}\ B\ =\ \frac{\textit{ cwnd}}{I}. \tag{4}\]  
The proposed congestion avoidance equation subtracts B based on the value of I and the selected mechanism discussed previously. Congestion avoidance reduction to this amount can accelerate the congestion control process instead of slowing it down, as in the standard congestion avoidance equation. Furthermore, this decrease in congestion avoidance and the increase in slow-start can refine and optimize the congestion control of SCTP [56; 59; 60]. Thus, as explained previously, the proposed congestion avoidance equation should be converted into a non-homogeneous equation after undergoing the total average change.
## 4 Results and Discussion
### Wave Structure across the Plate Thickness  
As noted above, the wave modes' characteristics can be very different for the same waveguide and frequency range. For this reason, wave structures for the S and A modes are generated with the SBFEM method from the studied models, and are compared with the analytical solution for validation. This information is helpful for the selection of possible points of dispersion curves, leading to a valid test. The wave structures are generated, with semi-analytical methods using isoparametric and spectral elements, from the eigenvectors of Equation (30). These wave structures are compared with those generated analytically using Equations (37) to (40). Figure 11 shows the good agreement of the results, calculated with low computational cost. Figure 11 shows the in-plane (uz) and out-of-plane (uy) displacement for S0 mode at frequency f = 400 kHz. The results in Figure 11 were generated analytically, those in Figure 11b semi-analytically using isoparametric elements, and those in Figure 11c using spectral elements.  
Figure 11: The wave structure of 50 mode at 500 kHz. (**a**) Analytical solution. (**b**) SBFEM isoparametric elements. (**c**) SBFEM spectral elements.  
Figure 10: Interpolation order required for convergence with plate thicknesses up to 100 mm.
In fact, little attention has been given to evaluate the vast variety of kinematic parameters used in current robot-assisted rehabilitation research, particularly to the suitability of the above parameters to significantly capture the condition and the changes in the patient's upper limb functions [16]. Although these few parameters (volume, ROMs, and weight support) are not sufficient to allow anyone to capture clinical status in stroke and effectively personalize interventions for patients, also in relation to the heterogeneity of stroke severity and manifestations, they are the only parameters that the therapist could set in the device for tailoring the robotic intervention. The recent Italian Consensus Conference on Robotics in Neurorehabilitation claimed that in most studies, the information on the technical aspects of the device was not exhaustive for explaining the complexity of the performed movements and poor attention was deserved to the level of robotic assistance, types of the movements performed, and in general to the training modalities [17]. This consensus conference also reported that a lot of studies were focused on chronic stages of stroke, implicitly suggesting that neuroplasticity induced by robotic interventions may occur also in this phase [17]. On the other hand, studies about robotic training and conventional rehabilitation could be affected by the so-called \"effectiveness paradox\" [17; 18; 19]. This paradox highlights the discrepancy between the clinical approach, claiming that therapy should be personalized to be more effective, and the scientific approach which needs standardized therapeutic protocols to prove the efficacy of a treatment. Because robots for neurorehabilitation are equipped with sensors for warranting their adaptability (without which they should be classified as simple electromechanical devices), the use of the measured parameters could help obtain objective information about the progression of therapy and, hence, of the recovery of a patient's abilities [19]. In this study, we aimed to analyze the interconnection between the robotic parameters (working volume, weight support), the clinical status of patients (assessed by different clinical scales) and the changes that occurred during rehabilitation in a wide sample of patients with chronic stroke treated with one of the most used and most advanced robotic devices for upper limb rehabilitation, the Armeo(r) Power. This study was focused on patients with chronic stroke because their clinical conditions are more stable than in the subacute phase, so the robotic intervention could be less influenced by possible effects of spontaneous recovery, rehabilitative and pharmacological interventions, clinical complications, and other factors more frequent in the subacute phase.
## 4 Analysis of the Initial Interface Cracking
### Width of the BLC-C Composite Wall  
The width of the BLC-C composite wall was altered to determine its effect on the horizontal displacement at the initial interface cracking; the other factors were maintained as constants. As depicted in Figure 12, when the width of the BLC-C composite wall decreased from 2390 mm to 1930, 1730, 1530, 1130, and 730 mm, the horizontal displacement at the initial interface cracking grew from 1.25 mm to 1.35, 1.43, 1.58, 1.74, and 1.84 mm, respectively. With an increase in the width of the BLC-C composite wall, the horizontal displacement at the initial cracking of the interface dropped and the appearance of interface cracks occurred earlier.
## 3 Rectifier Design
It can be seen that the proposed rectifier circuit covers the required frequency bands in Figure 16. At 2.45 GHz, the simulated rectifier efficiency is 72% and the measured result is 66%. At 5.8 GHz, the simulated rectifier efficiency is 65% and the measured result is 62%. The measured efficiency is lower at both low frequency and upper frequency bands, which might be caused by the difference in diode parameters between simulation and fabrication. When the load resistance is 2 K\(\Omega\), the measured conversion efficiency variation with the input power level at 2.45 GHz and 5.8 GHz frequency is shown in Figure 17. The efficiency can be maintained above 50% when the input power level is 0-15 dBm (2.45 GHz) and 5-13 dBm (5.8 GHz). Figure 18 shows the measured conversion efficiency varying with load resistance at 2.45 GHz and 5.8 GHz. When the load resistance is 1-8 K\(\Omega\) (2.45 GHz) and 0.8-2.2 K\(\Omega\) (5.8 GHz), the efficiency can be maintained above 50%. The range for 5.8 GHz is very small, which is caused by larger diode loss at higher frequency. Figure 16: Simulated and measured conversion efficiency of the proposed dual-band rectifier. Figure 14.0 Measured conversion efficiency (Input power level is 10 dBm).
## 3 Regularization Method
### Existence of Solutions to Optimization Problems  
In the following theorem, we establish the existence of a solution to the minimization problem. Let \(M=(0,1)\times[0,T]\) and  
\[\Phi_{\lambda}(\alpha,\sigma,\hat{V})=||(\alpha,\sigma)||_{*}^{2}+\lambda\sum_ {i=1}^{N}\sum_{j=1}^{M_{i}}[V(\tau_{0},r_{0},S_{0},T_{i},K_{ij},\alpha,\sigma( \tau))-\hat{V}_{ij}]^{2}\]  
and we define \(\phi_{\lambda,\hat{V}}(\alpha,\sigma):(\alpha,\sigma)\mapsto\Phi_{\lambda}( \alpha,\sigma,\hat{V})\), where \((\alpha,\sigma)\in L^{2}(M)\).  
**Theorem 1**.: _Fix \(\lambda\), \(\hat{V}\) and define \(q=\inf_{(\alpha,\sigma)\in L^{2}(M)}\phi_{\lambda,\hat{V}}(\alpha,\sigma)\), assume that for any \(z\in(q,+\infty)\), the set \(Q_{\lambda,\hat{V}}(z)=\phi_{\lambda,\hat{V}}^{-1}(\alpha,\sigma)\subset L^{2} (M)\) is bounded and \(\phi_{\lambda,\hat{V}}(\alpha,\sigma)\) is weakly lower semicontinuous. Then, the minimization problem (9) has a solution belonging to \(Q_{\lambda,\hat{V}}(z)\)._  
Proof.: Because \(z>q\), then there must be a minimization sequence \((\alpha_{n},\sigma_{n})\). \(L^{2}(M)\) is a Hilbert space, that is, it is a reflexive Banach space, so we can deduce that any bounded sequence in \(L^{2}(M)\) has a weakly convergent subsequence. Therefore, \((\alpha_{n},\sigma_{n})\) contains subsequence \((\alpha_{n_{k}},\sigma_{n_{k}})\), which weakly converges to \((\tilde{\alpha},\tilde{\sigma})\). And because of the weakly lower