esa-sceva/satcom-chunk-collection huggingface dataset:
# Derating of Electrical Contacts with Varying Surface Roughness
## 4 Conclusions and Summary
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Current [A]** & 1 & 3 & 6 & 10 & 12.8 & 16 & 20 \\ \hline
**Corrected current [A]** & 0.8 & 2.4 & 4.8 & 8 & 10.24 & 12.8 & 16 \\ \hline Sample 1 [\({}^{\circ}\)C] & 23.29 & 24.38 & 26.36 & 29.50 & 35.99 & 45.94 & 59.04 \\ \hline Sample 2 [\({}^{\circ}\)C] & 23.74 & 24.72 & 27.04 & 30.03 & 34.37 & 45.31 & 60.35 \\ \hline Sample 3 [\({}^{\circ}\)C] & 23.85 & 24.96 & 26.01 & 29.81 & 34.44 & 45.13 & 59.74 \\ \hline
**Average [\({}^{\circ}\)C]** & 23.63 & 24.67 & 26.47 & 29.78 & 34.93 & 45.46 & 59.71 \\ \hline
**Temp. increase [\({}^{\circ}\)C]** & 0.06 & 1.14 & 2.76 & 5.70 & 11.18 & 21.15 & 35.13 \\ \hline
**Max. amb. Temp. [\({}^{\circ}\)C]** & 99.94 & 98.86 & 97.24 & 94.30 & 88.82 & 78.85 & 64.87 \\ \hline \end{tabular}
\end{table} 
Table 6: Current and ambient temperature of R\({}_{\mathrm{z}}\)2 values testing. efficient current conduction. Contacts exhibiting lower surface roughness demonstrate superior suitability for high-load operational conditions, while those with increased roughness are more appropriate for moderate current applications where thermal constraints are less stringent. In practical terms, this necessitates careful thermal analysis and the implementation of effective cooling strategies, especially for contacts with a surface roughness of R\({}_{\rm z}\) = 10 \(\mu\)m.  
The novel contribution of this work lies in the quantitative assessment of the thermal and electrical implications of contact surface roughness on derating behavior, thereby providing a more rigorous basis for engineering design decisions. Smoother contact surfaces offer enhanced thermal stability and are thus more compatible with high-current, high-reliability environments. Conversely, rougher surfaces exhibit increased susceptibility to thermal stress, limiting their applicability unless accompanied by robust thermal management strategies. This research advocates for the elevation of surface roughness from a mere manufacturing artifact to a fundamental design parameter. In systems where thermal reliability and energy efficiency are paramount, the surface finish should be integrated into the early stages of design to ensure compliance with contemporary performance standards. The results reaffirm the necessity of stringent surface quality control to meet the evolving demands of modern electrical systems. Conceptualization, A.K. and V.K. ; Formal analysis, V.K. ; Methodology, A.K. and V.K. ; Supervision, A.K. ; Writing--original draft, V.K. ; Writing--review and editing, A.K. and V.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflict of interest.
## 5 Noise Robustness and Excitation Adaptability
### Robustness Analysis
Figure 21 shows the first four order \(CS(b,s)\) of scenario I under an impulsive load when \(\gamma=10\%\), Figure 22 shows the \(\overline{CS}(b,s)\) and \(SSSM_{DI}\) after the averaging operation. The results of the impulsive load are basically consistent with those of white noise, which indicates the applicability of the proposed method for approximate uniform spectral excitation. Figure 21: \(CS(b,s)\) of scenario I under impulsive load when \(\gamma=10\%\). Figure 22: Damage identification results of scenario I under impulsive load when \(\gamma=10\%\): (a) \(\overline{CS}(b,s)\); (b) Damage index \(SSSM_{DI}\).
# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 2 Materials and Methods
### Classifier Algorithms
#### 2.4.1 Support Vector Machine  
The support vector machine (SVM, also known as a support vector network) has been widely used in the context of remote sensing classification applications. The classifier tries to determine a  
Figure 2: Soil moisture map over the Kairouan Plain, derived from the combined use of Sentinel-1 and Sentinal-2 data, with the upper maps representing 01/12/2017 (in the left) and 20/09/2017 (in the right).  
hyperplane (a decision boundary) that can provide optimal separation between two sets of data, while maximizing the margin between these two sets. The margin is then defined by the minimum distance between a training sample and the decision boundary (the distance between the two samples, which are closest to the hyperplane).  
Under these conditions, an SVM is a binary classifier, as it is generally used to separate data points into one or the other of two distinct classes. As the data may not always be linearly separable, the training data can in some cases contain more than two classes. To extend the SVM to a multiclass classification algorithm, by breaking it down into a predefined series of binary problems [40; 41], two main strategies have been developed which are: One-against-one [42] and One-against-all [43].  
In cases where the data are not linearly separable, a non-linear transformation is applied to the input data, using a kernel function. Various kernel functions have been proposed, of which the most commonly used are the Gaussian and radial basis function (RBF) kernels. The regularization parameter C for the kernel functions must be tuned. In the present study, a Gaussian radial basis function (SVM-RBF) was chosen.  
The major strength of SVMs is their kernel function: through the selection of an appropriate kernel function, any complex problem can be solved. The main disadvantage of the SVM classification technique is that it has a high cost in terms of computing time compared to other classification algorithms such as RF classifier [44; 45]. It is also difficult to set the critical parameters, for which cross-validation optimization can be needed [36].
# Review on Mechanisms and Kinetics for Supercritical Water Oxidation Processes
## 2 Fundamental Processes
### Reaction Pathways
#### 2.1.2 N-Containing Compound  
The degradation of nitrogenous wastes can produce stubborn intermediates, and the resulting acids and/or salts can lead to corrosion and salt deposition in the reactor, so the treatment of nitrogenous wastes in SCWO is relatively difficult [36]. The products of N-containing species include N\({}_{2}\), nitrate, ammonia, nitrite, and NOx, depending on the state and position of N atoms in organics [37], reaction conditions [38], and the presence or absence of catalysts [39] or auxiliary fuels [40; 41]. In the new study, ammonia can be effectively removed through ion exchange with zeolite in intensified SCWO process and ammonia removal was up to 97% when ion exchange was applied to SCWO as a subsequent step [42]. Nitrate is mainly obtained from the compounds containing nitro-group and diazonium, while ammonia is primarily produced from amino-group and N-heterocyclic compounds [37], and N\({}_{2}\) is formed through the interaction of amino and nitro group [43]. [PERSON] [44] found that all forms of nitrogen, such as ammonia, nitrate, nitrite and organic nitrogen, could be converted into N\({}_{2}\) or N\({}_{2}\)O rather than NO\({}_{x}\) under appropriate supercritical water conditions, and that N\({}_{2}\)O could be further removed by adding catalysts or raising reaction temperature. Additionally, [PERSON] et al. studied the synergistic denitrification mechanism in SCWO processes and found the intermolecular synergistic denitrification between nitro and amino/N-heterocyclic compounds had weaker denitrification efficiency than the intramolecular synergistic denitrification [45]. Figure 2 shows the degradation path of the compound containing N, and C\({}_{m}\)N\({}_{o}\)H\({}_{n}\)O\({}_{r}\) can be considered either as an initial reactant or as an unstable intermediate.
## 2 Computational Model  
### Differential Equations for the Control of Water and Gas  
According to the principle of continuum mechanics [15], soil can be considered as a continuum composed of three independent phases, including solid, liquid, and gas phases. If the soil was saturated, the voids would be completely occupied by water; if the soil was dry, the voids would be full of air and water, or the soil would be partially saturated.  
While the flow of the liquid phase is driven by gravity and the water pressure gradient, it could also be affected by the pore characteristics of the soil, moisture conditions, and the viscosity of the water phase. According to the law of conservation of mass, the liquid flow equation could be expressed as follows [16]:  
\[\frac{\partial(gS_{r})}{\partial t}+\
abla\cdot\left[-\frac{k_{rw}k}{\mu_{w}}( \
abla p_{w}+\rho_{w}g)\right]-\frac{Q_{w}}{\rho_{w}}=0 \tag{1}\]where \(\varphi\) is the porosity, \(S_{r}\) is the degree of saturation, \(\mu_{w}\) is the viscosity coefficient of the water phase (Pa-s), \(k_{rw}\) is the relative permeability coefficient of the water phase, \(k\) is the intrinsic permeability of soil (m\({}^{2}\)), \(p_{w}\) is the pressure of the water phase (Pa), \(Q_{w}\) is the source of the water phase (kg/m\({}^{3}\cdot\)s), \(\rho_{w}\) is the density of the water phase (kg/m\({}^{3}\)), and \(g\) is the gravitational acceleration (9.8 m/s\({}^{2}\)).  
The movement of gas in soil is driven by the air pressure gradient. It is also affected by the pore characteristics of the soil, moisture condition, and the viscosity of the gas phase. Based on the law of conservation of mass, the gas flow governing the differential equation could be written as follows [17]:  
\[\frac{\partial[(1-S_{r})\varphi]}{\partial t}+\
abla\cdot\left[-\frac{k_{rg}k} {\mu_{g}}\big{(}\
abla p_{g}+\rho_{g}g\big{)}\right]-\frac{Q_{g}}{\rho_{g}}=0 \tag{2}\]  
where \(\varphi\) is the porosity, \(S_{r}\) is the degree of saturation, \(\mu_{g}\) is the viscosity coefficient of the gas phase (Pa-s), \(k_{rg}\) is the relative permeability coefficient of the gas phase, \(k\) is the intrinsic permeability of soil (m\({}^{2}\)), \(p_{g}\) is the pressure of the gas phase (Pa), \(Q_{g}\) is the source of the gas phase (kg/(m\({}^{3}\cdot\)s)), \(\rho_{g}\) is the density of the gas phase (kg/m\({}^{3}\)), and \(g\) is the gravitational acceleration (9.8 m/s\({}^{2}\)).
## 4 Data Analysis
### SEFI Classification  
The logical bitmap that is shown in Figure 9 enables another type of analysis that was possible with the proposed instrumentation. It uses the timestamped bit errors to plot a logical bitmap representation. This information is displayed in real-time on the host computer based on the latest log files, allowing the operators to easily be aware of the DUT behavior and follow the experiment progression. In this figure, an example of the reported errors is shown during distinct SEL events. We selected this run because the three main observed SEFIs show the following behavior: 1. lines in the edges of the bitmap; 2. lines errors in central regions; and 3. sparse error clusters, i.e., forming lines, in four distinct regions. This result supports the analysis of the ECC effectiveness in correcting the SEFIs since, despite being a robust hardening strategy, it has limitations with error accumulation. With access to the memory's physical bitmap, it would be possible to further investigate, classify, and correlate error types with the observed error location and current behavior. It  
Figure 8: Logical bitmap of the memory address space with the identified bit upsets during a March C- (1) run with a LET of 48.5 MeV\(\cdot\)cm\({}^{2}\)/mg. The \(x\) and \(y\) axis represent addresses in an arrangement based on the logical memory organization with arrays, rows, and columns, as specified on the datasheet [12]. For this bitmap, bit upsets during and outside SEL events were shown in distinct grayscale tones and color hues, respectively.  
is worth noting that for this memory device, the observed SEFIs are intuitively observed as block errors and correlated with SELs, but for other systems, the observed SEFIs can be difficult to classify or distinguish their source. Thus, as proposed, with precise and synchronized current monitoring, it is possible to identify current signatures for each SEFI type, supporting their classification.
# A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra
## 3 Results and Discussion
### Data Preprocessing and Analysis  
When Raman spectroscopy was used for sample analyze, signal noise and fluorescence background were important factors which affect the accuracy of the analysis severely. The noise of the instrument, external environment, etc. leads to signal noise. In this study, the Savitzky-Golay (S-G) 5-point smoothing method ([PERSON] and [PERSON], 1980) was used to remove the spectral noise. This method was proposed by [PERSON] and [PERSON] and was widely used in data stream smoothing and noise reduction. And the method was a filtering method based on local polynomial least squares fitting in the time domain. The biggest advantage of the method was that it could ensure the shape and width of the signal while filtering out the noise.  
Fluorescence background interference was the most important factor in Raman signal analysis especially for organic or biological samples, which made signal of the target analyte submerged. And accurate and effective removing fluorescence background was very important in Raman analysis. The principle of adaptive iterative reweighted penalty least squares (airPLS) was to control the fidelityand roughness of the fitting curve by weighting coefficients so as to obtain an ideal fitting curve in this study [20; 21]. This method was time-saving and flexible. The weight of the overall variance between the baseline of the fit and the original signal was changed through iteration. The overall variance weight was acquired from the difference between the baseline and the original signal. The first derivative and second derivative methods could eliminate the interference from the baseline and other backgrounds effectively and improve the resolution and sensitivity. But the method may lead to an increase in the signal-noise ratio. The standard normal variable transformation could eliminate the Raman spectrum noise. It could not remove the fluorescence background interference due to the power change of the laser light source and the attenuation of the light intensity. Baseline calibration deducted the fluorescence background effectively and preserved the original spectral information and eliminated the effects of the instruments. Two baseline calibration methods, the polynomial fitting method of 8 times and the signal minimum maxima, were used in the study. Adaptive scaling was a common calibration method.
# LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches
## 3 Materials and Methods
### LIMUNet Algorithm Design  
In the context of daily wearable smartwatches, this paper proposes the LIMUNet lightweight algorithm for user behavior recognition. The specific structure of LIMUNet is shown in Figure 2. The different color lines in the input block represent the changes in acceleration, gyroscope, and magnetometer in different directions. The LIMUNet algorithm includes a data preprocessing module, a conventional convolution module, a LIMUNet lightweight feature extraction module, and finally a classification module.  
In order to further lightweight the activity recognition model based on the LIMUNet network architecture, this paper also proposes LIMUNet-Tiny, which reduces the model parameter count by an additional 75% and the computational complexity by an additional 80% after removing the LIMUNet module 1 from LIMUNet.  
The core of the LIMUNet network is the LIMUNet module, which consists of two main parts: the lightweight convolution module and the dual attention mechanism module. The lightweight convolution module reduces the parameter count and computational complexity of the convolutional neural network through lightweight design, without causing a significant decline in activity recognition accuracy. The dual attention mechanism module significantly enhances the feature extraction capability of the neural network through a specially designed attention mechanism for IMU data, without noticeably increasing model complexity, thus achieving better activity recognition performance than non-lightweight models. The following subsections of this section provide a detailed description and analysis of the specific module designs in LIMUNet.
# Deep Learning-Based Efficient Analysis for Encrypted Traffic
## 3 System Framework of Encrypted Traffic Identification
### Data Cleaning for Encrypted Traffic  
Data collected in real-world network environments often include packets that are irrelevant to a particular application. If there is an excessive presence of such irrelevant packets within a particular traffic type, and their statistical properties significantly deviate from those of normal packets, it may hinder the convergence of the deep learning model and hinder the accurate identification of the corresponding traffic type. After analyzing the pcap files in the data set using the Wireshark tool, it was found that the collected data consisted of packets from ARP, DNS, NBNS, and LLMNR protocols. These protocols are used in networked environments to establish routing and resolve domain names and physical addresses. However, they do not provide information pertaining to specific application packet characteristics. Therefore, filtering operations should be performed during the traffic cleaning procedure. The traffic associated with the TCP protocol also encompasses packets for the initiation and termination of TCP connections. These data packets can enable the normal establishment and termination of the link and complete congestion control on the link. The integrity of the payload is checked but it does not carry information related to specific applications, which is not helpful for the deep learning model to extract traffic characteristics. After filtering, the size of the dataset can be reduced without losing information related to the recognition task. It can speed up the convergence rate of the model loss function during the training process. The algorithm for the encrypted traffic cleaning step is shown in Algorithm 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Traffic Type** & **Application or Protocol** \\ \hline Email (VPN-Email) & SMIPS, POP3S, IMAPS \\ Chat (VPN-Chat) & ICQ, AIM, \\ Streaming (VPN-Streaming) & Vimeo, YouTube \\ File Transfer (VPN-File Transfer) & Skype, FTPS, SFTP \\ VoIP (VPN-VoIP) & Facebook, Skype, Hangouts \\ P2P (VPN-P2P) & uTorrent, Transmission \\ \hline \hline \end{tabular} \
\begin{tabular}{c} Global Header \\ \hline Packet Record 1 \\ \hline Packet Record 2 \\ \hline Packet Record 3 \\ \hline \hline \end{tabular}
\begin{tabular}{c} Packet Header \\ \hline Packet Data \\ \hline \end{tabular}
\end{table}
Table 1: ISCXVPN2016 dataset traffic content [18].  
Figure 2: PCAP file structure.
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 3 Results and Discussion
### Applications and Implications
#### 3.3.3 Policy Formulation and Regulatory Decision-Making  
The findings of this study can also inform policy formulation and regulatory decision-making. Policies that prioritize the reduction of pollutants contributing to high BOD and turbidity levels can be enacted. Further, the clear demonstration of the impact of residential sewage and stormwater runoff on water quality could motivate strict regulations on waste disposal and land use in residential areas [62; 63; 64].
## 2 Thermal Evaluation of Overhead Electrical Lines
### Dynamic Line Rating
#### 2.2.2 Direct Dynamic Line Rating  
The direct method of DLR is based on directly measuring the properties of the electrical line, such as conductor temperature, mechanical stress on the line, and conductor sag. Line evaluation is typically calculated using additional data from a weather monitoring system, whereas there are already numerous approaches discussed for estimating the DLR of overhead transmission lines [11].
# An Overview of Non-Isolated Hybrid Switched-Capacitor Step-Up DC-DC Converters
## 4 The Charge Interchange between Capacitors
### Energy Analysis in the Resonant Charge Interchange
The initial energy is equal to the final energy in a resonant switched-capacitor circuit. That does not mean the energy conversion can be made with 100% of the efficiency as there are still losses in the transistors, switching and conduction losses, as well as in all components of the converter, but the discharging losses that we analyzed for the pure switched capacitors in the former section are not present in resonant switched capacitors. This result is also because we opened the switch (see Figure 19 or Figure 22) when the current has a zero crossing after the first half sine cycle. This may be challenging since the tolerance of the components may produce small changes in the resonant frequency from one converter to another one. Some solutions have been proposed to overcome this challenge, such as the use of diodes or thyristors to ensure the current will stop at the zero crossing [40,44,45].
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 1 Upper-level model
### Constraints on the smoothness of signal timing.  
This research adopts a rolling horizon scheme [39]. It is able to continuously collect and update real-time traffic conditions during the optimization process, reducing errors caused by vehicle uncertainties.  
In the process of rolling-horizon optimization, the signal timing optimization is repeated in every \(\tau_{\omega}\). To mitigate the adverse effects of abrupt changes in signal timing updates on traffic, a portion of the timing plan from the previous optimization is retained during each optimization. Specifically, the first optimization occurs at time \(t_{0}\), which is followed by a second optimization at \(t_{0}+\tau_{\omega}\). During the second optimization, a safety interval \(\tau_{a}\) is introduced to preserve the green phases planned before \(t_{0}+\tau_{\omega}+\tau_{n}\). To accommodate this, the model incorporates constraints:  
\[u_{k}^{1}=\max_{i}\Bigl{\{}\left.u^{\prime}_{k}\right|\Bigr{|}u^{\prime 1}_{k} \leq t_{0}+\tau_{\omega}+\tau_{n}\Bigr{\}},\forall keK \tag{15}\]  
\[o_{k}^{1}=\max_{i}\Bigl{\{}\left.o^{\prime}_{k}\right|\Bigr{|}o^{\prime 1}_{k} \leq t_{0}+\tau_{\omega}+\tau_{n}\Bigr{\}},\forall keK \tag{16}\]  
where \(u^{\prime}_{k}^{1}\) represents the signal timing solutions from the previous optimization.