esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Materials  
The carbon fiber used in the samples was unidirectional AS4 fiber sheet (190 g/m\({}^{2}\) areal weight, 1.79 g/cm\({}^{3}\) fiber density, and 3-K tow size; provided by Hexcel) with a matrix of a resin blend consisting of EPON 862 resin (Miller-Stephenson Chemical Co., Inc., Danbury, CT, USA), Araldite LY 3031 (Huntsman Corp., Basel, Switzerland), Aradur 3032 curing agent (Huntsman Corp., Basel, Switzerland), and PR-24-LD-HHT CNF [27] (Pyrograf Products, provided by Applied Sciences, Inc., Cedarville, OH, USA).  
Through thorough trials, a specific resin-blend ratio was determined with EPON 862 and Araldite LY 3031, mixed at a ratio of 2:1 at room temperature. Aradur 3032 was used as the matrix-curing agent at a ratio of 100:11 (resin blend: curing agent), with curing initiated at 140 \({}^{\circ}\)C.  
In order to help the CNFs to disperse well within the resin matrix, Disperbyk-191 and Disperbyk-192 surfactants (BYK, Wallingford, CT, USA) (1 wt% of each) were mixed into the resin blend when needed [28; 29].
## 2 Principle of Active Vibration Isolation
### FB and FF Control Theory
It can be seen from the dotted gray line that the integral (I) control mode in FFC has a better attenuation effect in the low-frequency domain. Therefore, as shown in the dotted green line, it is necessary to adopt the proportional plus integral (PI) control mode in FFC, rather than P or I mode. It is worth noting that the simulation results show that the FFC has a very limited ability to suppress vibration at the structural resonance frequency. As indicated in the dotted blue curve in the figure, the closed-loop P control was added based on feed-forward PI control, which can effectively suppress the natural frequency of the platform. It can be seen from the pink line and dotted black line in Figure 2 that, with the increase in P, the vibration attenuation effect is better. Not only can the adoption of the closed-loop PI mode regulate the equivalent damping, but it changes the equivalent stiffness of the system. Although the performance of closed-loop PI control is superior to P in the frequency range below 1 Hz, due to the constraints of the ultra-low frequency detection accuracy of sensors, the closed-loop system usually adopts the P control mode. In the absolute velocity feedback control system, although the performance of FBC will be improved as the feedback control gain increases, the system will be unstable at high gains if the influence of the model, the bandwidth of sensors and actuators, the sampling rate, etc., are considered [30; 31]. In addition, under the control of fixed parameters, AVRS may be unstable due to load changes, environmental interference, human operation, and other factors. To ensure stability and improve the performance of AVRS, this study aimed to introduce the DDPG reinforcement learning algorithm to realize the automatic optimization of control parameters through continuous interaction with the working environment.
## 3 Results and Discussion
### Effect of the Presence of Anions  
Several anions such as chloride (Cl\({}^{-}\)), sulfate (SO4\({}^{2-}\)), nitrate (NO3\({}^{-}\)), bicarbonate (HCO3\({}^{-}\)), and phosphate (PO4\({}^{3-}\)) often exist in natural water and wastewater, and they can negatively influence Sb(V) adsorption [9]. The influence of these anions on Sb(V) adsorption was investigated at two different molar concentrations, 1 and 10 mM, as shown in Figure 8. The reason for setting the anion concentration to 1 mM and 10 mM was to simulate natural water and wastewater, respectively [36]. The presence of Cl\({}^{-}\) and NO3\({}^{-}\) negligibly inhibited Sb(V) adsorption because compared to Sb(V), these ions bind less strongly to the adsorbents [9]. However, HCO3\({}^{-}\) and PO4\({}^{3-}\) exhibited negative effects on Sb(V) adsorption, with reductions of 41.6% and 90.6% in the presence of 10 mM HCO3\({}^{-}\) and PO4\({}^{3-}\), respectively. The reduction of Sb(V) adsorption onto iron-based materials by competing anions, especially PO4\({}^{3-}\), has also been reported in other studies [2, 37]. This phenomenon can be explained by the high affinity of PO4\({}^{3-}\) to iron via the formation of inner-sphere complexes [38].
## 3 Application of the Proposed MSPSO Algorithm to the IPTBK Problem  
The proposed MSPSO algorithm was used to analyse the accuracy of the reconstruction attack path for different topology sizes. The basic IPTBK problem is discussed in the following subsections.  
The problem of attack path reconstruction can be expressed as a directed graph as \(G=(X,E)=(x_{i},e_{i})\), where \(X\) represents a set of nodes, \(x_{i}=\{x_{i1},\ldots,x_{id},\ldots,x_{iD}\}\), \(x_{s}\) is a set of source nodes (i.e., attack sources), \(x_{d}\) is a set of sink nodes (i.e., victims) and E denotes the edge \(e_{ij}\) of the graph from node \(x_{i}\) to node \(x_{j}\) in \(D\)-dimensional space.
## 4 Results and Validation
### Results of B-W Model  
In this section, the B-W model's ability to predict cyclic plasticity is presented. The parameter values were obtained using the entire first cyclic loading, tested at \(R_{\varepsilon}\approx-1\); then, cycle II was validated, and vice versa. After this, the stress-controlled test was also used to obtain the values of B-W parameters, and cycle I and cycle II were employed for validation. In the case of the stress-controlled test, considering what is stated in Section 3.1, only a few initial cycles (from the second to the fifth) were considered. The ability of the B-W model to predict the stabilized cycles of strain-controlled tests is shown in Figures 17-19, whereas in Figure 20, the stress-controlled test and its corresponding modeled trend is presented.  
Figure 16: Best-fit curve and experimental data that can predict the trend in the elastic limit.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\sigma_{0}\) **(MPa)** & \(Q\) **(MPa)** & \(b\) \\ \hline
3 backstress & 385 & \(-69\) & 44.5 \\
4 backstress & 309 & \(-69\) & 44.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Numerical values of the parameters of Voce isotropic hardening model.  
Figure 15: Cycle II transient used to calculate the parameters of the Voce model by exploiting the stress values at the inversion points.  
Figure 19: Comparisons between experimental and modeled stabilized cycles of strain-controlled tests using B-W model: (**a**) cycle I, used for validation, (**b**) cycle II, used for validation. The stress-controlled test was involved in calibration.  
Figure 17: Comparisons between the experimental and modeled stabilized cycles of strain-controlled tests using B-W model: (**a**) cyclic loading I, used for calibration, (**b**) cycle II, used for validation.  
Figure 18: Comparisons between experimental and modeled stabilized cycles of strain-controlled tests using B-W model: (**a**) cycle I, used for validation, (**b**) cyclic loading II, used for calibration.  
The numerical values of the B-W model parameters obtained for the three different cases are summarized in Table 6.
# A Congestion Control Strategy for Power Scale-Free Communication Network
## 3 Congestion Control Strategy for the Power Scale-Free Network
### Allocation of Resources to Communication Nodes  
Because the delivery capacity and cache capacity of the power communication nodes are limited and the scale-free characteristic is shown in the power communication network topology, if the nodes' resources are evenly distributed, this will cause the hub nodes to be overloaded. Therefore, it is important that the limited resources are allocated reasonably to improve the traffic efficiency of the whole network. In order to ensure that the nodes' resources can be effectively utilized, the nodes' delivery capacity and cache capacity are allocated based on the node betweenness, and the following parameters are defined.  
* \(\rm B_{i}\) is the betweenness of power communication node \(\rm i\), and it is denoted by the number of the shortest paths through node \(\rm i\).
* \(\rm C_{i}\) is the delivery capacity of power communication node \(\rm i\), and it is denoted by the maximum number of packets \(\rm C_{i}\) that node \(\rm i\) can deliver at each time step.
* \(\rm L_{i}\) is the maximum number of packets that power communication node \(\rm i\) can cache, and when the cache capacity is full, the incoming packets will be discarded.
* \(\rm n_{i}\) is the queue length of node \(\rm i\), and it is denoted the load of power communication node \(\rm i\).  
The node betweenness is the fixed value after the network topology is determined, so the allocation of nodes' resources is no longer changed. The delivery capacity \(\rm C_{i}\) and cache capacity \(\rm L_{i}\) of the power communication nodes can be denoted as Equation (3).  
\[\left\{\begin{array}{ll}\rm C_{i}&=\ \frac{B_{i}}{\sum_{j=1}^{N}B_{j}}C\\ \rm L_{i}&=\ \frac{B_{i}}{\sum_{j=1}^{N}B_{j}}L\end{array}\right.\left(i\ =\ 1,\ 2 \cdots,\ \rm N\right) \tag{3}\]  
where \(\sum_{j=1}^{N}B_{j}\) is the sum of the node betweenness, and the total delivery capacity and cache capacity resources are denoted as \(\rm C\) and \(\rm L\), respectively. For the specific power communication network, \(\rm C\) and \(\rm L\) are deterministic values. In the allocation strategy, to ensure that the communication nodes' deliver capacity and cache capacity are integers greater than zero, the calculation results are rounded up to the nearest integer.
# Unsupervised Transformer-Based Anomaly Detection in ECG Signals
## 3 Materials and Methods
### ECG Time Series Data
#### 3.1.2 The MIT-BIH Arrhythmia Database  
The MIT-BIH Arrhythmia database is a clinical database [31] that includes two-channel ECG recordings of 48 patients at the Beth Israel Hospital (BIH) Arrhythmia Laboratory. Each recording was half an hour long. Modified limb lead II signals were chosen for this study. Following the advice of the Association for the Advancement of Medical Instrumentation (AAMI) [33] five categories were used to categorize the chosen heartbeats as follows: normal beat (N), supraventricular ectopic (S), ventricular ectopic (V), fusion beat (F), and unknown beat (Q). According to the AAMI suggestions, we chose 44 of the 48 recordings for our experiment and removed four recordings (102, 104, 107, and 217) since they were of poor quality.  
Preprocessing  
Signal preprocessing is an essential step, especially when dealing with physiological data, such as the ECG, taking into account all potential sources of noise, such as motion artifacts and power line interference, that can impair the performance of any ensuing models. Our preprocessing stage contains two steps. First, we applied filter to eliminate noise and then we extracted the heartbeat. We have tried different filters such as, Butterworth  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Normal** & **Anomalous** & **Total** \\ \hline Train data & 2335 & 0 & 2335 \\ Validation data & 292 & 0 & 292 \\ Test data & 266 & 234 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of each set number of sequences on the ECG5000 dataset.  
Figure 1: Time series nature of the signals of different types of heartbeats on ECG5000.  
bandpass filter, bandpass filter and one median filter, we found that two median filters perform better. The following preprocessing procedures were used in this work:  
1. Median Filter: We used a median filter with a 200-ms sliding window. Then, using a 600-ms window, we applied a second median filter. The baseline of the raw signals was contained in the second filter's output. The second filter output was subtracted from the unprocessed ECG data to eliminate the baseline wander (see Figure 2). This step enhanced the baseline correction and eliminated some artifacts [34].
2. Heartbeat Extraction: This entails picking a neighborhood around each beat. This interval was estimated using R-peak annotation with \(\pm\)50 ms before and after the beat.  
After heartbeat extraction, the total number of normal sequences was 72,722, while the total number of anomalous sequences was 10,579. Since the number of normal sequences was large compared with anomalous sequences, we used a sample of 18,824 sequences of them. The data were divided into 80% training data and 20% testing data. The training data that was split into training data and validation data contained only N beats. Table 2 summarizes the number of sequences in each dataset.
## 2 Methodology
### Total Energy Consumption Across All Households  
To estimate the hourly total consumption in kWh for a number \(C\) of households related to each end use, the following procedure was followed:  
1. Construction of the probability matrix: For each end use, a matrix was created where * The rows correspond to the 24 h of the day (\(h=1,2,\ldots,24\)). * The columns represent the households (\(i=1,2,\ldots,C\)).  
Each cell \(P_{h,i}\) of the matrix contains the probability that the end use is activated at hour \(h\) in house \(i\).
2. Sum of probabilities for each hour: For each hour of the day, the sum of probabilities across all households was calculated. The sum of probabilities for hour \(h\) is given by \[P_{sum}(h)=\sum_{i=1}^{c}P_{h,i}\] (12)
3. Determination of total daily consumption: The total daily consumption for a single household is known from the TSO data. This value, denoted as \(E_{tot\_house}\), is multiplied by \(C\) to obtain the total daily consumption for all the households, assuming that the consumption is approximately the same across all households: \[E_{loc\_C}=C\cdot E_{tot\_house}\] (13) This value represents the households' total consumption on a specific day (e.g., January 1 st).
4. Application of the ratio of consumption for each end use: Each end use has its ratio of consumption based on the month and the type of use. This value, denoted as %(\(M\)), represents the share of energy allocated to that specific end use in month \(M\). Therefore, the specific daily consumption for that end use is given by \[E_{end\_use}=E_{tot\_C}\%(M)\] (14)
5. Distribution of consumption for each hour: To obtain the hourly \(E(h)\) related to each end use, the following calculation was used: \[E(h)=\frac{P_{sum}(h)}{\sum_{h=1}^{24}P_{sum}(h)}\cdot E_{end\_use}\] (15) where * \(P_{\text{sum}}(h)\) is the sum of the probabilities for hour \(h\) across all households. * \(\sum_{h=1}^{24}P_{sum}(h)\) represents the total sum of probabilities over all hours and all households. * \(E_{end\_use}\) is the total daily consumption for the specific end-use calculated earlier.
6. Results: By repeating this calculation for all hours (\(h\) = 1, 2, \(\ldots\), 24), the kWh consumed for each hour of the day for the given end use is obtained. To calculate the total consumption for each hour considering all end uses, the energy consumption for each end use \(u\) at the same hour \(h\) is summed. Therefore, the total hourly consumption \(E_{total}(h)\) at the \(h\)-th hour of the day is given by \[E_{total}(h)=\sum_{n\in U}E_{u}(h)\] (16) where * \(E_{total}(h)\) is the total consumption for all households at hour h, considering all end uses. * \(E_{u}(h)\) is the hourly consumption for end use \(u\) at hour h. * \(U\) is the set of all end uses (e.g., heating, lighting, hot water, etc.).
# Investigating the Physical Mechanisms of Quicksand Using a Custom-Designed Experimental Apparatus
## 2 Experimental Materials and Methods
### Experimental Apparatus
#### 2.2.2 Composition of the Testing Apparatus and the Operating Method  
A custom-designed hydrodynamic seepage testing system was developed for this investigation, comprising three core components: a water injection unit, a permeameter, and pressure monitoring modules (Figure 2). The water injection unit integrates a heater and a water pump to regulate water temperature and flow rate. The heater maintains water temperature within a preset range throughout the experiment (operational range: 5-50 \({}^{\circ}\)C), while the pump adjusts flow via a reflux pipeline and valve system, ensuring stable injection velocities that meet hydrodynamic seepage requirements. As the core component, the permeameter houses the specimen and injects water upward through its basal inlet, simulating subsurface seepage processes to investigate soil permeability (flow direction indicated by arrows in Figure 2). The LZB-WB glass rotameter monitors real-time flow rates (25-250 mL/min range) to maintain target hydrodynamic velocities. SCYG 314 micro-pressure transducers (accuracy: \(\pm\)0.1% FS) are installed at multiple specimen elevations to track pressure variations during seepage, elucidating physical state transitions during quicksand evolution. All operational parameters are recorded and monitored in real time by the data acquisition system, ensuring data integrity for subsequent analysis.  
The operating method of the equipment is as follows: After preparing the quartz sand specimen and placing it in the permeameter chamber, the water injection system is connected, and the inlet valve is opened to allow water to flow into the chamber. The flow rate is adjusted using the control valve and rotameter. The outlet valve is then closed to ensure unidirectional flow through the specimen. During the experiment, pore water pressure is continuously monitored at three vertical positions (bottom, middle, and top) using pressure sensors. The data are logged in real-time to track pressure variations and observe the transition to quicksand behavior under varying water velocities. Once the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Specimen** & \multicolumn{6}{c}{**Percentage Content by Particle Size/\%**} \\ \cline{2-10}
**Number** & **0.08–0.09** & **0.09–0.14** & **0.14–0.21** & **0.21–0.43** & **0.43–0.71** & **0.71–1.18** & **1.18–2.00** & **2.00–4.00** & **4.00–8.00** \\ \hline
1\# & 0 & 0 & 0 & 0 & 8 & 14 & 28 & 35 & 15 \\
2\# & 0 & 0 & 0 & 4 & 11 & 20 & 30 & 25 & 10 \\
3\# & 0 & 0 & 3 & 11 & 16 & 25 & 25 & 15 & 5 \\
4\# & 0 & 3 & 7 & 20 & 20 & 25 & 15 & 8 & 3 \\
5\# & 3 & 8 & 14 & 25 & 20 & 15 & 10 & 5 & 2 \\ \hline \end{tabular}
\end{table}
Table 1: Percentage of grain size of each group of quartz sand samples.  
experiment reaches the desired conditions or duration, both the inlet and outlet valves are closed, and the effluent is collected for measurement.
## 3 Tests of the Developed PD Monitoring System on Power Transformers
### Test of the System on 31.5 MVA Power Transformer
Figure 34 illustrates the value of the average number of the AE, UHF, and HF pulses between particular oil temperature levels (low, moderate, and high), which indicate a strong causal relationship between these variables. Figures obtained for the sensors ISO-002-ADW/UHF1 and ISO-002-ADW/UHF2 show that the higher the oil temperature level was, the higher was the number of the registered AE and UHF pulses. The exception is the high-frequency current transformer ISO-002-HF sensor, for which the highest average number of HF pulses was registered for the average oil temperature level. To investigate which compared groups are statistically significantly different from one another, the Gamesa-Howell post hoc test, which is recommended in the situation when the assumption of homogeneity of variance in the compared groups was violated. The post hoc comparisons revealed statistically significant differences (\(p<0.0005\)) between all groups (oil temperature levels) except the groups \"moderate\" and \"high\" temperature for the UHF pulses registered with the sensor ISO-002-ADW/UHF-2 (Table 7). The highest differences in the number of PD pulses were observed between groups \"low\" and \"high\" temperature. As it was mentioned before, the voltage value variability was low. Therefore, while interpreting the obtained results it may be concluded that in the monitored power transformer, the oil temperature growth is a significant factor inducing partial discharges. This hypothesis is in line with the research results, which showed that the temperature growth in combination with an increased moisture content of the oil-paper insulation system may induce the activity growth of partial discharges [107; 108; 109; 110]. Additionally, the presence of water molecules in oil as a result of the moisture migration process, as well as the effect of gas bubbles exuding from cellulose insulation, may significantly influence the intensification of the PD phenomenon [15; 111; 112; 113; 114; 115; 116].
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 4 Experimental Results and Analysis
### Anti-Recompression and Anti-Requantization Test  
#### 4.3.1 Anti-Recompression Test  
Recompression and requantization are the two main attacks on the video, and experiments on both are conducted separately in this paper. In the anti-recompression experiment, the experimental video is recompressed with equal quantization values (the original quantization parameter is 20). The paper uses the normalized correlation coefficient NC (normalized correlation) and the false bit rate BER as evaluation metrics, and when the NC value is greater than 0.8, the correlation between the two watermarked images is higher and the algorithm is more robust [23]. The NC is calculated as follows:  
\[NC=\sum_{i=0}^{P-1}\sum_{j=0}^{Q-1}w(i,j)\times w^{\prime}(i,j)/\sum_{i=0}^{P- 1}\sum_{j=0}^{Q-1}(w(i,j))^{2}\times\sum_{i=0}^{P-1}\sum_{j=0}^{Q-1}(w^{\prime }(i,j))^{2} \tag{13}\]  
where \(w\) is the original watermark, \(w^{\prime}\) is the extracted watermark, \(P\) and \(Q\) are the size of the watermark.  
Figure 6 shows the NC and BER values of the watermark extracted from the experimental video after the first, second and third recompressions, respectively. It can be seen that the NC values of the watermarks extracted from the experimental videos after three recompressions are above 0.98 and the BER are below 0.016, which indicates that the algorithm has a strong resistance to recompression.  
Figure 6: BER and NC values under recompression attack.
## 1 Introduction
Taking the flat panel as the research object, the effect of the signal arrival time difference and the Lamb wave velocity on the locating error was analyzed in the triangle method [45]. It is found that the location error of the threshold method is large and unstable. Moreover, according to the previous study, the Lamb wave signal is very complicated in the stiffened panel in which the stiffener is high, and the signals received by different sensors differ greatly, which further increases the difficulty of locating, especially the impact point location near the apex of the triangle formed by the sensors. The above research has the following problems: the lack of research on the propagation law of the Lamb wave signal in the stiffened panel; the test piece is the aluminum or composite panel or with small stiffeners panel, the influence of the stiffeners is not obvious; there is no large-scale impact locating; locating accuracy and stability need to be improved. In order to solve the above problems, this paper proposes an adaptive energy compensation threshold filtering (AECTF) method, which can realize large-scale, fast and accurate locating of the impact source on a high-stiffened panel. The AECTF method is based on three-dimensional finite element simulation to analyze the influence of high stiffeners on Lamb waves. The filter frequency band is selected according to the simulation results. The energy compensation algorithm is used to determine the threshold. In the polar coordinate system, the hyperbolic method is used to locate the impact source on a 22 mm fan-ring shaped high-stiffened panel of the spacecraft's sealed bulkhead (the height of the stiffeners is 4.8 mm and the thickness is 4.8 mm in the bulkhead of the International Space Station). After experimental verification, it can effectively locate impact points.