esa-sceva/satcom-chunk-collection huggingface dataset:
# GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning
## 3 Preliminaries
### Homomorphic Encryption  
Homomorphic encryption [36] refers to an encryption algorithm that satisfies the homomorphic property of ciphertext operations. In this process, the original data are first encrypted homomorphically and then subjected to preset calculations to obtain a ciphertext result. Afterward, the ciphertext result is homomorphically decrypted, producing a plaintext result that is equivalent to the result obtained by directly performing the same calculations on the original plaintext data. Since the receiver cannot access the sender's original data during the process, homomorphic encryption is commonly used for private data interactions between two or more parties.  
According to the type of computations performed on ciphertext, homomorphic encryption can be categorized into semi-homomorphic encryption and fully homomorphic encryption.  
* Semi-homomorphic encryption: Also known as partially homomorphic encryption, this supports only specific types of computations on ciphertexts, such as addition only, multiplication only, or a finite number of addition and multiplication operations. Taking additive homomorphism as an example, plaintexts \(a\) and \(b\) are encrypted using the encryption algorithm \(Enc\) to obtain their corresponding ciphertexts \(Enc(a)\) and \(Enc(b)\). Performing an addition operation on these ciphertexts yields \(Enc(a)+Enc(b)\), which is equal to the result of encrypting the sum of the two plaintexts, \(Enc(a+b)\). That is, additive homomorphism satisfies \(Enc(a)+Enc(b)=Enc(a+b)\). Common semi-homomorphic encryption schemes include the RSA algorithm, ElGamal algorithm, and Paillier algorithm.
* Fully homomorphic encryption: Unlike semi-homomorphic encryption algorithms that support only partial computations, this enables arbitrary computations on ciphertext data. Common fully homomorphic encryption schemes include the BGV scheme, BFV scheme, CKKS scheme, and others.
## 3 Sequence Admittance Modeling of Grid-Connected Inverter
### Sequence Admittance Modeling of Grid-Connected Inverter Controlled by PI
\right\}\] \[\left[sL_{\text{f}}-G_{\text{del}}(s)K_{\text{PWM}}(0.5F_{\text{ i}}O_{\text{p}}+W_{\text{i}})][s_{\text{p}2}L_{\text{f}}-G_{\text{del}}(s_{ \text{p}2})K_{\text{PWM}}(0.5F_{\text{i}2}O_{\text{p}1}+W_{\text{i}1})]\\ -0.25F_{\text{i}1}O_{\text{p}}F_{\text{i}}O_{\text{p}1}G_{\text{del}}(s)G_{ \text{del}}(s_{\text{p}2})K_{\text{PWM}}^{2}\\ \end{array}\right\}\] \[\left.-0.25F_{\text{i}1}O_{\text{p}}F_{\text{i}}O_{\text{p}1}G_{ \text{del}}(s)G_{\text{del}}(s_{\text{p}2})K_{\text{PWM}}^{2}\right.\]  
The theoretical model of frequency-coupling admittance for the GCI controlled by phase-domain PR control and dq-domain PI control has been derived above, and its accuracy can be verified through frequency sweep simulation. The validated system parameters are shown in Table 1.  
To confirm the accuracy of the model derived, MATLAB/Simulink was used to test the output admittance matrix of two GCIs with different domain current controls, as depicted in Figures 2 and 3. The following conclusions can be made:  
1. [label=(0)]
2. Whether or not the VFL (voltage feedforward link) is considered, the frequency-coupled admittance matrix in different domains aligns with the measured values, confirming the theoretical model's accuracy. 3. Under two different domain current controls, irrespective of VFL, the amplitude values of the diagonal elements \(Y_{11}\) and \(Y_{22}\) are similar to those of the non-diagonal elements \(Y_{12}\) and \(Y_{21}\), highlighting the significance of the frequency coupling effect. 4. According to Figures 2 and 3, when VFL is included, the admittance models of the two GCIs controlled by different domains are very similar. Without VFL, the models are notably different. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline V\({}_{\text{dc}}\) & 700 V & I\({}_{\text{pv}}\) & 14.29 A \\ V\({}_{\text{g}}\) & 220 V & Q\({}_{\text{ref}}\) & 0 Kvar \\ L\({}_{\text{f}}\) & 2 mH & Decoupling coefficient \(K_{\text{d}}\) & 1/350 \\ C\({}_{\text{f}}\) & 20 uF & Voltage feedforward coefficient \(K_{\text{f}}\) & 1/350 \\ R\({}_{\text{f}}\) & 1 \(\Omega\) & PI parameter of PLL & 0.266/10.99 \\ R\({}_{\text{g}}\) & 0.2 \(\Omega\) & Voltage loop PI parameter & 1.72/228.69 \\ L\({}_{\text{g}}\) & 3 mH & Current loop PI parameter & 0.0343/45.714 \\ \(\omega_{\text{v}},\omega_{\text{i}}\) & 2\(\pi\)-4000 & \(K_{\text{PWM}}\) & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of GCI. Figure 3: Frequency-coupled admittance model of GCI considering voltage feedforward. Figure 2: The frequency-coupled admittance model of GCI without considering voltage feedforward.
## 4 Results
### Statistical Significance  
To rigorously validate the model's performance superiority, two-tailed independent samples \(t\)-tests were conducted, comparing CLIP--BCA--Gated against 24 baseline models (including unimodal, static fusion, and contrastive learning architectures) on the Crisis-MMD test set. The analysis focused on classification accuracy, a core metric for crisis response systems.  
For each baseline, we computed the mean accuracy across five independent training-testing splits (to account for data randomness). Results showed that CLIP--BCA--Gated achieved a mean accuracy of 91.77 \(\pm\) 0.11%, with statistically significant improvements over all baselines (\(p<0.001\)). The effect size, measured by Cohen's d, reached 1.23--a large effect size per conventional social science benchmarks. This indicates that the model's accuracy gain (e.g., 1.86-8.74% improvement over static fusion and unimodal baselines) is not only statistically robust but also operationally meaningful: in real-world crisis response, such gains could reduce misclassification of critical events (e.g., delayed disaster alerts) by over 15% relative to legacy systems.  
In essence, the combination of a negligible \(p\)-value (\(p<0.001\)) and large effect size validates that CLIP--BCA--Gated's performance is not an artifact of random chance. Instead, it reflects a genuine improvement in modeling multimodal crisis data--one that can be trusted to inform high-stakes humanitarian decision-making.
# Improving Low-Frequency Panel Absorbers with Two-Dimensional Acoustic Black Hole
## 2 Numerical Modeling
### Material Properties  
The first paper on the ABH effect by [PERSON] [10] already showed that a maximum reduction in the thickness must be aimed for. Considering this, 3 mm thick High-Pressure Laminate (HPL) is selected as a panel material for the study. It is known from previous studies [6; 7] that this thickness without modification has low performance as a DMA front panel. This configuration thickness makes it possible to produce an ABH with a residual thickness \(h_{\text{res}}=0.3\) mm using a CNC machine for furniture construction. This corresponds to a factor of \(\frac{h_{\text{res}}}{h_{0}}=\frac{1}{10}\), which enables the ABH to significantly influence the  
Figure 1: Distributed Mode Absorber (DMA) geometry as it is modeled in the Finite Element Analyses Software. Blue represents the front panel, green represents the back cavity.  
wave propagation on the panel. The material properties used in the numerical modeling are presented in Table 1.  
For modeling the air inside the back cavity, the standard material _air_ was chosen from the ANSYS library. Its properties are shown in Table 2.
# Monitoring Perishable Commodities Using Cellular IoT:
## 3 Results and Discussion
### Autonomy
During Deep Sleep, the datalogger consumed 379.87 \(\mu\)A.  
Figure 11: Current consumption of the datalogger during its activity with communication. Figure 12: Current consumption of the datalogger (**a**) during its activity without communication; (**b**) during an accelerometer event. Using the value of the average current and the value of the lithium battery capacity (\(Q_{bat}\)) that would be used, which is 6000 mAh, the autonomy of the system can be calculated as shown in Equation (11), obtaining an autonomy \(t_{A}\) of about one month. Moreover, considering the \(V_{bat}\) battery voltage of 3.7 V, a consumption of about 29.26 mW of power (\(P\)) and about 21.07 Wh of energy (\(E\)) in one month was determined, values obtained according to the equations shown in (12) and (13), respectively,  
\[t_{A}=\frac{Q_{bat}}{\overline{I}}=\frac{6000\;\text{mAh}}{7.91\;\text{mA}}=31.61\;\text{days} \tag{11}\]  
\[P=V_{bat}\overline{I}=3.7\;\text{V}\times 7.91\;\text{mA}=29.26\;\text{mW} \tag{12}\]  
\[E=P\cdot time=V_{bat}\overline{I}\cdot time=3.7\;\text{V}\times 7.91\;\text{mA} \times 1\;\text{month}=21.07\;\text{Wh} \tag{13}\]
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 3 Experiments and Results
### Data Collection
#### 3.1.2 Connecting Equipment and Sensors  
In the experiment, a high-impact acceleration sensor with the model number M350D02 (PCB Piezotronics, Depew, NY, USA) was used. The main parameters of the sensor are shown in Table 5.  
After connecting the data recorder to the sensor, it was installed on the high-impact testing platform, as shown in Figure 2(a). Acceleration curves were then collected, as shown in Figure 2(b).
## 2 Materials
### Data  
Sentinel series satellites are the important branches of the Copernicus Programme satellite constellation conducted by the European Space Agency (ESA) and an essential part of Global Monitoring for Environment and Security (GMES). Sentinel-1 satellite constellation is the first of this specific program, and it carries C-band synthetic-aperture radar (SAR) instrument with a temporal resolution of 6 days [44]. The Sentinel-2A/B satellites equipped with Multispectral Instrument (MSI) are capable of acquiring high spatial resolution (up  
Figure 1: Geographical locations of the Pearl River Basin (Guangdong) and study area (annual median images of sentinel-2A (2020) after cloud removal).  
to 10 m) optical images in 13 spectral bands every 5 days [45]. The ESA and European Commission's policies makes Sentinel's data easily accessible [46]. More detailed technical specifications of both Sentinel-1 SAR and Sentinel-2A MSI data are available in previous studies [47; 48] and will not be repeated here.  
The standard Level-1 (ground range detected, GRD) data product of Sentinel-1 in Interferometric Wide Swath (IW) mode and the calibrated Bottom of Atmosphere (BOA) products of Sentinel-2 used in this study were offered by GEE. Considering the data availability, this study was only focused on 2020. One arc-minute global relief model of the Earth's surface that integrates land topography and ocean bathymetry (ETOPO1) dataset was also collected to extract ocean bathymetry [49]. The Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) that comprises World Vector Shorelines (WVS) [50] was applied to extract shorelines of the study area. All these datasets were re-projected to the uniform coordinate reference system (WGS_1984_UTM_Zone_50N) and resampled to a same ground spatial resolution of 10 \(\times\) 10 m.
# Extraction of Catastrophe Boundary and Evolution of Expressway Traffic Flow State
## 2 Analysis of Catastrophe Characteristics of Traffic Flow
### Analysis of Catastrophe Characteristics of Traffic Flow
In addition, comparing the relative error values of speed and traffic volume of each section, it can be seen that the relative error in the middle section was the smallest. Moreover, the relative error measured with speed as the index was smaller. Thus, it is more accurate to describe the evolution law of traffic flow with speed as the index in the cusp catastrophe theory model. Characteristic indicators such as traffic volume, time occupancy rate, and average speed in the middle section of the traffic flow in the weaving section were selected. The relationship between them is shown in Figure 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Detector Number** & **Measured Speed (km/h)** & **Simulated Speed (km/h)** & **Relative Error (\%)** & **Measured Speed (km/h)** & **Simulated Speed (km/h)** & **Relative Error (\%)** \\ \hline
1 & 69.31 & 71.13 & 2.63 & 1386 & 1296 & 6.49 \\
2 & 70.58 & 71.52 & 1.33 & 1601 & 1546 & 3.44 \\
3 & 68.14 & 69.96 & 2.67 & 1518 & 1403 & 7.58 \\ Average value & 69.34 & 70.87 & 2.21 & 1502 & 1415 & 5.83 \\ \hline \end{tabular}
\end{table} 
Table 1: Verification results of simulation model in free flow state. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Detector Number** & **Measured Speed (km/h)** & **Simulated Speed (km/h)** & **Relative Error (\%)** & **Measured Speed (km/h)** & **Simulated Speed (km/h)** & **Relative Error (\%)** \\ \hline
1 & 25.94 & 26.78 & 3.24 & 2625 & 2473 & 5.79 \\
2 & 20.13 & 20.72 & 2.02 & 2268 & 2197 & 3.13 \\
3 & 18.63 & 19.16 & 2.84 & 2016 & 1876 & 6.94 \\ Average value & 21.63 & 22.22 & 2.70 & 2303 & 2182 & 5.29 \\ \hline \end{tabular}
\end{table} 
Table 2: Verification results of simulation model in congested flow state. During the evolution of traffic flow, the state of traffic flow exhibited the following characteristics:  
1. The distribution of traffic flow parameters in free flow and congested flow was relatively concentrated, and the speed change between the two states was discontinuous. The state of traffic flow indicated a catastrophe;
2. The speed interval between free flow and congestion flow, i.e., the catastrophe interval, had few data points. In this interval, the traffic flow state changed effortlessly, highlighting the unreachability of the catastrophe interval;
3. Under the same traffic volume, there were two different corresponding speeds, and the traffic flow states corresponding to these two speeds were different. There were two steady states of traffic flow, showing a dual modality. Through the research and analysis of the distribution law of traffic parameters, it can be seen that the traffic flow had prominent catastrophe characteristics, which is highly consistent with the cusp catastrophe theory. Therefore, the cusp catastrophe theory could accurately describe the evolution law of traffic flow.
## 3 Results
### Structural Characterization
#### 3.2.2 TGA/TDA Analysis  
Thermal stability is an important factor in polymer research. TGA/TDA analysis allows to set the presence of structural units through their degradation pattern. More specifically, TGA and DTA were performed to characterize the thermal stability of \(\beta\)-CD and crosslinkers inside the polymers. Figure 2 shows the TGA and DTA curves of \(\beta\)-CD, PEDGE, EGCD, PEGCD1 and PEGCD2.  
Figure 1: FTIR spectra of (**a**) \(\beta\)-CD, (**b**) EDGE or PEDGE, (**c**) PEGCD1, (**d**) EGCD and (**e**) PEGCD2.  
Figure 2: Thermogravimetric analysis (TGA) and differential thermal analysis (DTA) curves for (**a**) \(\beta\)-CD, (**b**) PEDGE, (**c**) EGCD, (**d**) PEGCD1 and (**e**) PEGCD2.  
Figure 2b shows that PEDGE thermal decomposition started at about 125 \({}^{\circ}\)C and was completely combusted at about 500 \({}^{\circ}\)C. According to the literature [64], \(\beta\)-CD thermal decomposition is initiated by the formation of the residue char between 250 and 350 \({}^{\circ}\)C, afterwards following with a slow thermal degradation of the char (280-800 \({}^{\circ}\)C). In our case, the same behavior was observed for \(\beta\)-CD. The initial weight loss observed for EGCD, PEGCD1 and PEGCD2 represents the loss of residual water, which was more important for PEGCD2 (15%) than for EGCD (8.6%) and PEGCD1 (2.8%). Then two weight loss steps can be distinguished, associated with three exothermic peaks. The first step between 200 and 400 \({}^{\circ}\)C--91% for EDGE, 85% for PEGCD1 and 77.5% for PEGCD2--accounted for most of the weight and was associated with the formation of the residue of the \(\beta\)-CD/EGCD or PEGCD polymers. The last step represents the slow charring of the polymer residue along with the degradation of the char. As mentioned by [PERSON] [65], the results confirmed that the aliphatic and flexible crosslinker containing polymers are less stable toward thermal action.
# Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
## 2 System Description  
In this section, brief descriptions of DVB-T2, DVB-C2, and DVB-S2 standards as well as the proposed DVB-X2 receiver are given.
# Performance of Narrow Band Wide Area Networks with Gateway Diversity
## 4 Numerical Results
Figure 10: PER versus distance to the GW in suburban terrain (1 GW, 1 EN, and 44 bytes payload). The coverage area results highly depend on the traffic load on the network as determined by the number of ENs, GWs, and packet rates. The CDF of the distance to the nearest GW is presented in Figure 12 and calculated across all the iterations for an area size of \(1200^{2}\) square meters. The TP-99 distance, d, refers to the case where 99% of the iterations have a distance less than d meters to the nearest GW. This is when the CDF curve shows a value of 0.99 at distance d. This metric can be used to refer to 99% of the cases across all the possible cases for sufficiently large iterations. For 50 ENs served by a single GW, the TP-99 distance to the GW is 1.3 km. The TP-99 distance to the nearest GW drops to 790 m if there are at least five GWs serving the same area size. This improves the link budget significantly and results in a higher PSR in both the UL and the DL, as presented in Table 3.  
Figure 11: PER versus distance to each GW for a single EN in suburban terrain/with GW diversity. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**SF**} & \multirow{2}{*}{**\# of ENs**} & \multirow{2}{*}{**\# of GWs**} & \multirow{2}{*}{**Avg. PSR**} & \multirow{2}{*}{**Avg. PSR**} & \multicolumn{2}{c}{**TP-99 PSR**} & \multicolumn{2}{c}{**TP-99 PSR**} & \multirow{2}{*}{**Simulated**} & \multirow{2}{*}{**95\% Confidence**} \\  & & & & & & \(\geq\) & & **Area Size** & **Interval on Avg. ** \\ \cline{3-8}  & & & **UL [\%]** & **DL [\%]** & **UL [\%]** & **DL [\%]** & **[km\({}^{2}\)]** & **PSR [\%]** \\ \hline
11 &
###### Abstract  
The productivity ratio is a vital metric for assessing the efficiency of perforated completions. Accurate and rapid prediction of this ratio is essential for optimizing the perforation design. In this study, we propose a novel approach that combines three-dimensional finite element numerical simulation and machine learning techniques to predict the productivity ratio of perforated wells. Initially, we obtain the productivity ratio of perforated wells under various perforation parameters using three-dimensional finite element numerical simulation. This generates a sample set for machine learning. Subsequently, we employ the least squares support vector machine (LSSVM) algorithm to establish a prediction model for the productivity ratio of perforated wells. To optimize the parameters of the LSSVM algorithm, we utilize the particle swarm optimization (PSO) algorithm. We compare our proposed PSO-LSSVM model with that established based on other parameter optimization methods and machine learning algorithms, such as Grid search-LSSVM, PSO-ANN, and PSO-RF. Our results demonstrate that the PSO-LSSVM model exhibits rapid convergence, high prediction accuracy, and strong generalization ability in predicting the productivity ratio of perforated wells. This research provides a valuable reference and guidance for optimizing perforation design. Additionally, it offers new insights into predicting the productivity of complex completions.  
finite element numerical simulation; least squares support vector machine; particle swarm optimization; productivity ratio; perforated wells
## 3 System Reliability Evaluation Method That Considers Multiple Failure Criteria
### System Reliability Evaluation That Considers Multiple Criteria  
After calculating the weight of each index using the AHP and EW methods, the subjective and objective weights of each index were multiplied and normalized to obtain the comprehensive weight \(\mathbf{C}=(c_{1};c_{2};\ldots;c_{j})\), as shown in Equation (23):  
\[c_{j}=(w_{j}u_{j})/\sum\limits_{k=1}^{n}w_{k}u_{k},j=1,2,\ldots,n. \tag{23}\]  
It can be seen from Equation (23) that it not only considers the knowledge and experience of peer experts to scale the indexes, but also distinguishes the quality of the index data, overcomes the shortcomings of the single-weighting method, and makes the evaluation result more reasonable.  
The system reliability under each failure criterion can be obtained based on copulas. According to the analytic hierarchy model shown in Figure 5, the system reliability obtained by considering multiple failure criteria is a weighted summation of the system failure probability under each failure criterion, as shown in Equation (24):  
\[p_{f}=\sum\limits_{j=1}^{n}p_{f}c_{j},j=1,2,\ldots,n. \tag{24}\]where \(p_{f}\) is the system failure probability obtained by considering multiple failure criteria. \(p_{f_{j}}\) is the system failure probability under the \(j\)th failure criterion.
## 3 Results and Discussion  
### Statistical Analysis of Developed Model  
An effort has been made to examine the suitability of the developed model using ANOVA. The second-order model for all output responses the ANOVA has shown in Table 4. For free gossypol, \"Pred R2\" of 0.8977 is in reasonable agreement with the \"Adj R2\" of 0.9625. For total gossypol, \"Pred R2\" of 0.95 is in reasonable agreement with the \"Adj R2\" of 0.9816. \"Adeq precision\" measures the signal to noise ratio greater than 4 is desirable. The developed models and approaches were found reliable and effective because the R2 value was found as 0.98 and 0.99 for the model of free gossypol and total gossypol, respectively (Table 4). The predicted values were compared with the corresponding experimental values are illustrated in Figures 3 and 4.  
The second-order model for summarized ANOVA has shown in Table 5 for output responses. The values of \"Prob. \(>\) F\" less than 0.0500 indicate that the models are significant, which is desirable and it indicates that the terms in the model have a significant effect on the output responses. In the present case, A, B, C, D, A2, B2 are significant model terms. The values greater than 0.1 indicate the model terms are not significant. These insignificant model terms (not counting those required to support hierarchy) can be removed and may result in an improved model. For both the models, the comparison of the residual error to the pure error obtained in a lack of fit test has been found insignificant. The lack of fit is insignificant that is desirable.