esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimum System for Diagnosing Power Quality in Electrical Microgrids
## 4 Results
### Performance of the SDMD  
The experimental setup involving the SDMD consisted of a test bench with a grid simulator, which provided well-calibrated sinusoidal voltages and currents to a purely resistive load across a range of stepped RMS amplitudes. The measurements from the SDMD were compared with those from a Circutor(r) measurement device. Both measurement devices were evaluated against reference values established by the grid emulator, with their deviations from these values expressed as percentages for both voltage and current.  
Figure 9 provides the comparative performance of the proposed SDMD and Circutor(r) in measuring voltage and current, against a set of reference values from the grid emulator.  
Figure 8: Mounting plate developed with the SDMD and the SBC.  
In Figure 9a, which addresses voltage measurements, the SDMD exhibits performance comparable to the Circutor(r) for a reference line voltage from 90 V to 210 V. Beyond 210 V and up to the highest reference line voltage considered, the SDMD shows slightly higher deviations.  
In contrast, Figure 9b, which focuses on current measurements, indicates higher deviations for the SDMD across the entire range when compared to the Circutor(r).  
The SDMD shows a maximum deviation of 0.7527% in voltage measurements and 0.7364% in current measurements. These deviations correspond to absolute offsets of 1.6950 V and 0.0181 A, respectively.
# Research on Interference Mechanism of 25 Hz Phase Sensitive Track Equipment from Unbalanced Current
## 2 Cause Analysis of Abnormal Red Ribbon
### Influence of Unbalance Current Intrusion on Cloke Transformer  
As shown in Figure 3, there is an insulation joint in the rail of the 25 Hz track circuit. In order to ensure the normal flow of traction current at the rail and the matching of signal transmission, we need to install a choke transformer and connect its adjacent neutral points.  
However, due to the invasion of the unbalanced current, the magnetic flux induced by the two-rail traction current flowing through the primary side of the choke transformer no longer offsets each other. The interference current generated on the secondary side invades the signal side, which affects the voltage on the signal side and causes misoperation. The following analyzes unbalanced current intrusion into the receiving end circuit choke transformer.
## 6 Future Developments  
In the field of atmospheric water use, one of the most important topics is the quality of the harvested liquid. While this aspect was often overlooked in the past, recent studies have begun to address it, as highlighted by [52]. Currently, most comparisons among different atmospheric water capturing technologies are primarily centred on production metrics--such as daily or hourly water yield, production efficiency, and the operational range of the system. These factors, though important, often neglect the crucial aspect of water quality, which plays a significant role in determining the practical usability of the harvested water. The chemical composition, the presence of contaminants, and the biological safety of the water are key determinants of whether it can be used for drinking, irrigation, or industrial purposes [16].  
The review of the existing indicators and indices carried out in the current paper highlights the absence of evaluation tools focused on water quality. Nevertheless, it is essential to consider such a characteristic as a critical factor in the overall assessment. This expands the scope beyond energy metrics alone, acknowledging that the utility of the harvested water depends not just on the volume produced but also on its chemical and biological suitability for various applications.  
To this end, developing a robust method for comparing different AWG systems based on both the quality of water produced and the corresponding energy consumption will be of utmost importance. Such a methodology would enable a more holistic evaluation, integrating both energy and quality parameters into a unified framework, thus addressing the multidisciplinary nature of atmospheric water harvesting.  
To address this gap, a new method and accompanying metric to evaluate the \"quality efficiency\" of AWG systems will be developed by the authors. This approach will not only quantify the water produced but will also account for the energy expenditure required to ensure its quality. By doing so, it will provide a more accurate representation of the system's overall performance, guiding future decision-making processes and technological advancements in the field of atmospheric water harvesting.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 4 Imaging Fusion Algorithms
### Arithmetic Combination  
The fastest fusion method is the arithmetic combination. Simple weighted fusion, also known as 'Weighted Averaging', is of the most simple and straightforward methods in arithmetic combination. The principle of the weighted average image fusion algorithm is to take pixel values of the original images directly and perform weighted averaging to obtain the pixel value of the fused image. Similarly, in the Simple Maximum/Minimum Method, the resultant fused image is obtained by selecting the maximum/minimum intensity of corresponding pixels from the input images. Arithmetic combination has the advantages of easy implementation and fast calculation speed. However, detailed information within the image cannot be captured, image contrast is reduced, and the edge of the image is altered with this technique, resulting in unsatisfactory fusion effects in most applications. Furthermore, due to this method requiring strict registration in advance, the fusion effect of noisy image is no ideal.
# An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
## 2 GNSS Satellite Constellation Determination
### Optimization Algorithms
#### 2.3.4 Particle Swarm Optimization (PSO)  
PSO is an algorithm that simulates bird/fish foraging behavior, and it was developed by [PERSON] and [PERSON] (1995) [48]. It is a population-based algorithm in which a number of particles are generated to find the optimal solution in the search space. By sharing information on the discovered possible solutions (quality and location in the search space), particles can converge towards optimal solutions, as shown in Figure 4. PSO is a flexible [45] and simple algorithm and it can be easily implemented compared to other optimization algorithms [45; 49]. However, in a high-dimensional search space, PSO slowly converges toward the optimal solution, and the quality of its results decreases [50; 51].
# Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models
## 3 Experimental Set-Up
### Experimental Data  
In the first stage of the experimental process, two datasets of video sequences commonly used in pedestrian detection were selected: CDnet2014 [29] and CASIA Gait dataset [30; 31]. CDnet2014 contains six recording categories: baseline, dynamic background, camera jitter, shadow, intermittent object motion, and thermal. CASIA Gait is a database used to investigate the walking patterns of various pedestrians.  
The CDnet2014 and CASIA Gait datasets contain scenes where videotaped subjects appear. They present three critical problems: angle of vision, clothing, and changes in conditions when walking; thus, their use is appropriate for the objectives of this study because they involve situations where there are underlying problems and allow the evaluation of the effectiveness of the proposed model. In this sense, only scenes with situations where pedestrians present problems with background variations were selected.  
Three video sequences with pedestrians were chosen from the CDnet2014 database: office, PETS2006, and sofa. The first two video recordings belong to the baseline category, and the third belongs to the intermittent object motion category (see Figure 1). The three selected video sequences (Figure 1), contain changes, mainly lighting and background, were recorded a speed of 0.17 frames per second, and are 360 \(\times\) 240 pixels in the cases of office scenes and sofa, whereas the PETS2006 scene has a resolution of 720 \(\times\) 576 pixels. The CASIA Gait dataset contains video sequences where encoded videos are stored (320 \(\times\) 240 pixels). They have information on 124 pedestrians walking in different directions. For experimentation, four scenes of pedestrians walking in four directions (0\({}^{\circ}\), 36\({}^{\circ}\), 54\({}^{\circ}\), and 90\({}^{\circ}\)) were selected (see Figure 2).  
It is essential to notice that our approach converts color frames from the selected video sequences into grayscale images to perform a unique solution to process color frames. Thus, it allows the processing of not only color frames but also frames from closed circuit television video (CCTV) or video surveillance systems.
## 4 Interference Characterization
### Packet Error Rate  
_Packet error ratio (PER)_ is the number of incorrectly received data packets divided by the total number of received packets. A packet is declared incorrect if at least one bit is erroneous. When offset quadrature phase shift keying (OQPSK) (Adopted by the PHY in IEEE 802.15.4 standard at 2.4 GHz [39].) is utilized as the modulation scheme, the average bit error rate (BER) and PER for BSN \(i\) can be expressed as,  
\[P_{BER}=\frac{1}{2}erfc\left(\sqrt{2E_{b}/N_{0}}\right)=\int\limits_{t_{min}}^{ t_{max}}\int\limits_{\gamma_{min}}^{\gamma_{max}}\frac{1}{2}erfc\left(\sqrt{P_{ SINRi}(t)}\right)\cdot P(\gamma)d\gamma dt \tag{3}\]  
\[P_{PER}=\sum\limits_{a=1}^{m+k-m/2}C_{m+k}^{a+m/2}\cdot P_{BER}{}^{(a+m/2)} \cdot(1-P_{PER}){}^{(m+k-(a+m/2))} \tag{4}\]  
where \(E_{b}/N_{0}\) is SINR per bit, \(erfc\left(\cdot\right)\) is the complementary error function [40], \(m\) is the number of information bit, \(k\) is the number of additional coding bit, and \(C_{m+k}^{a+m/2}\) is the set of all (\(a+m/2\)) combinations out of a set (\(m+k\)), \(t_{min}\) and \(t_{max}\) are the lower and upper integral limits of time factor, \(\gamma_{min}\) and \(\gamma_{max}\) are the lower and upper integral limits of shadow fading factor [41]. Equations (3) and (4) can be applied to other modulation schemes as well with little modification [42].  
As can be seen from Equation (4), multiple factors affect the PER performance, including neighboring BSN number, traffic load, and transmission power. The effects of those factors are investigated in the case study.
## 3 Results and Discussion
### Comparisons with Other Adsorbents  
As shown in Table 2, a comparison of the maximum MB adsorption capacity of CPP200 with those of other previously reported adsorbents was carried out. Obviously, the CPP-based adsorbents were prepared under relatively mild and non-toxic environmentally friendly conditions. In addition, CPP200 demonstrated relatively excellent adsorption selectivity in addition to its high adsorption capacity.
# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 3 Recognition Model
### Network Structure
#### 3.2.2 Attention Mechanism  
The SE module incorporated in the network adopts an attention mechanism, whose main purpose is to automatically obtain the importance of each feature channel in the convolution process through continuous learning. Then, the weights of each channel are learned based on the importance level, which can increase the influence of the effective channel and suppress the channel features with a lesser effect. The one-dimensional SE model is shown in Figure 4, where the input data are \(\mathbf{X}=(\mathbf{u}_{1},\mathbf{u}_{2},\cdots,\mathbf{u}_{C^{\prime}})\), and the features extracted after the convolution operation are \(\mathbf{U}=(\mathbf{u}_{1},\mathbf{u}_{2},\cdots,\mathbf{u}_{C})\). \(C\) and \(C^{\prime}\) represent the channel dimensions. \(L\) and \(L^{\prime}\) represent the feature dimensions of each channel. The module is divided into three steps. Firstly, a squeeze operation \(F_{sq}(\cdot)\) is performed to compress the feature dimension of each channel to 1. Then, an excitation operation \(F_{ex}(\cdot,\mathbf{W})\) is performed, leading to a normalized weight between 0 and 1. Finally, the normalized weight is use to weight the features of each channel by the \(F_{scale}(\cdot,\cdot)\) operation.  
Figure 4: One-dimensional SE model.
## 3 Results and Discussion
### Response Surface Optimization ANOVA for the Ultrasound-Assisted Enzymatic Extraction of SDF
#### 3.2.3 Two-Factor Interaction Analysis  
The projection of the response surface graph in the horizontal direction is a contour line. The elliptical contour line indicates a highly significant interaction between these two factors, while circular contour indicates no significant interaction. As can be seen from Figure 2, the response surface of factor (B) and factor (C) was the steepest, and the contour was elliptical, indicating their highly significant interaction effect. The response surface of factor (A) and factor (B) was the second steepest, indicating their significant interaction effect. The slopes of the response surface of factor (A) and factor (C) were the smallest, and their interaction effect was not significant.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Sum of** & \multirow{2}{*}{**df**} & **Mean** & \multirow{2}{*}{**F-Value**} & \multirow{2}{*}{**_p-Value_**} & \multirow{2}{*}{**Significance**} \\  & **Squares** & & & & & \\ \hline Model & 1186.2 & 9 & 131.8 & 543.51 & \textless{}0.0001 & ** \\ A & 188.18 & 1 & 188.18 & 776 & \textless{}0.0001 & ** \\ B & 89.11 & 1 & 89.11 & 367.47 & \textless{}0.0001 & ** \\ C & 32.4 & 1 & 32.4 & 133.61 & \textless{}0.0001 & ** \\ AB & 2.4 & 1 & 2.4 & 9.91 & 0.0162 & * \\ AC & 1.1 & 1 & 1.1 & 4.55 & 0.0704 & Not significant \\ BC & 4.84 & 1 & 4.84 & 19.96 & 0.0029 & ** \\ A\({}^{2}\) & 183.41 & 1 & 183.41 & 756.33 & \textless{}0.0001 & ** \\ B\({}^{2}\) & 316.87 & 1 & 316.87 & 1306.66 & \textless{}0.0001 & ** \\ C\({}^{2}\) & 277.96 & 1 & 277.96 & 1146.23 & \textless{}0.0001 & ** \\ Residual & 1.7 & 7 & 0.2425 & & & \\ Lack of Fit & 0.5575 & 3 & 0.1858 & 0.652 & 0.6222 & Not significant \\ Pure Error & 1.14 & 4 & 0.285 & & & \\ Cor Total & 1187.9 & 16 & & & & \\ \hline \hline \end{tabular} Note: * in the figure represents significant differences between the data (* \(p\) < 0.05, ** \(p\) < 0.01). A. enzyme addition amount; B. material–liquid ratio; C. reaction time.  
\end{table}
Table 3: Variance analysis of response surface results.
# Wave Propagation in Laminated Cylinders with Internal Fluid and Residual Stress
## 1 Introduction
The method is applied using a 2D formulation for axisymmetric structures such as that described in [25]. Compared to the 1D formulation, applied for example in [26], here only an arbitrarily small segment is assumed as the unit cell instead of an entire axial segment of the structure. This results in significant advantages compared to the 1D formulation: the dispersion curves can be evaluated for a specified circumferential mode order separately without requiring any further analysis; helical wave propagation can be also studied; due to the small segment analysed, the use of solid elements instead of shell elements is not an issue--computational cost is very small; by avoiding the use of shell elements, numerical issues related to the use of rotational DOFs are removed while, at the same time, the through-thickness dynamics up to high frequency can be predicted accurately. In this paper, the WFE method is developed and applied to model wave propagation in cylindrical structures with internal fluid and including pre-stress effects due to internal pressure. In this method a very small segment of the system is discretised using 3D solid elements, through the thickness, and 3D fluid elements. In our numerical examples, we use the FE software ANSYS, although any FE package could be used. Thus, each layer is discretised using one or more solid elements to accurately represent the stress/strain behaviour of the structure. It is shown that the inclusion of pre-stress in the structure is also straightforward. In particular, it is shown that pre-stress conditions can be applied with very little effort using FE software by evaluating the FE static stiffness matrix of the loaded 2D small segment of the structure using the same FE model of the unloaded structure. Residual stresses in the structure are included in the model by evaluating the FE pre-stress stiffness matrix of the loaded segment, which is then added to the segment's original stiffness matrix. The paper is organised as follows. In the next section the formulation of the method for the coupled motion of a cylindrical structure and internal acoustic fluid is briefly presented. The resulting eigenvalue problem is formulated such that the real-valued frequency is given and the complex-valued wavenumbers (eigenvalues) and wavemodes (eigenvectors) are found. In section 3 numerical examples are presented. The first example concerns a thin-walled steel pipe filled with water, for which results are validated with respect to results obtained using an analytical formulation. The effect of increasing pipe thickness is also studied. The second example shows the application of the method to the more complicated example of a laminated sandwich cylinder containing air. The third example deals with a helical reinforced hard rubber pipe with internal pressure, this being an example of a laminated cylinder used for high-pressure applications.
## 2 Related Works
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Technique(s) Used** & **Dataset(s) Used** & **Metrics** & **Key Limitations** & **Planned Improvements/Advancements** \\ \hline \multirow{4}{*}{[16]} & SMOTE + Extra Trees + ELM & \multirow{2}{*}{UNSW-NB15} & 98.43\%, strong ROC AUC & High FNR for minority classes, needs labeled data & Introduce unsupervised and hybrid approaches to reduce labeling needs \\  & GA + SMOTE-Tomek + ELM + SVM & IoT\_ToN, UNSW-NB15 & 99\% (IoT\_ToN), 86\% (UNSW-NB15) & Supervised only, high computational load & Adopt scalable and semi-supervised methods with fewer dependencies \\  & ELM + SMOTE + one-hot encoding & NSL-KDD, Distilled Kitsune & \multirow{2}{*}{F1: 0.9541, 0.9555} & Weak in nonlinear environments, lacks adaptability & Use deep models with nonlinear handling and less preprocessing overhead \\  & PySpark + ML (XGB, RF, etc.) & IoT-23 & \multirow{2}{*}{IoT-23} & 98.89\% (XGB), RF prediction time: 0.0311 s & Dataset-dependent, scalability issues & Incorporate dataset-agnostic feature selection and real-time streaming \\  & Custom UAV dataset & Accuracy: 99.59\%, Precision: 98.76\%, Recall: 98.77\% & No real-time testing, dataset specific & Extend model testing across standard, diverse datasets for generalization \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of recent ELM-based intrusion detection techniques in existing studies.
## 3 Energy from Renewable Sources Scheduling Approach
### Smart Home Energy Scheduling  
Finding the lowest-cost schedule to process a series of orders, indicated with \(I\), using a set of dissimilar parallel controlled or uncontrolled home appliances, denoted with \(M\), was the approach used to solving the scheduling of home appliances problem.  
The processing of the order \(i\in I\) must start just after scheduled release \(r_{i}\), and finish no later than the due date \(d_{i}\). Every one of the appliances can process order \(i\).  
The order \(i\in I\) on appliance \(m\in M\) processing costs and times, in relative terms, are \(C_{im}\) and \(P_{im}\). As a result, the goals may be formulated as follows: to reduce the processing costs of all of the orders, the important decisions in this scheduling problem are the assignment of orders to appliances, the order in which orders are placed on each appliance, and the start time for all orders.  
Given a set of electrical tasks to be distributed throughout N\({}_{\text{TIME}}\) time samples (e.g., the appliance begins), the energy scheduling problem [28] can be represented in this approach (see Equation (4)) as the minimization of a cost function:  
\[\min_{w,\ L_{c}}\sum_{k=1}^{N_{\text{last}}}\sum_{i=1}^{N_{\text{time}}}w_{k}( i)L_{k}(i)C_{k}(i) \tag{4}\]  
where \(w_{k}(i)\) expresses whether or not a task is running at time; \(C_{k}(i)\) is the energy cost at time \(i\); \(L_{k}(i)\) is the power consumed by the task in time interval \(i\).  
With regard to the total power used at each time and the lack of interruptions for each activity, the following restrictions (Equations (5)-(7)) are specifically placed on this minimization problem [29; 30]:  
\[\forall\ \tilde{i}\rightarrow\sum_{k}w_{k}(\tilde{i}).L_{k}(\tilde{i})\leq P_{ max} \tag{5}\]  
\[\forall\ \overline{k}\to w_{\overline{k}}(i)=1,\ \forall\ i:\ T^{start}<i<T^{end} \tag{6}\]  
\[C_{\overline{k}}(\tilde{i})=\begin{cases}R(\overline{i})\ if\sum_{k\
eq \overline{k}}w_{k}(\tilde{i}).L_{k}(\tilde{i})>P_{PV}\\ \ \ \ \ \ \ \ 0\ if\sum_{k\
eq\overline{k}}w_{k}(\tilde{i}).L_{k}(\tilde{i}) \leq P_{PV}\end{cases} \tag{7}\]  
According to a predetermined policy, the shiftable tasks can set \(w_{k}(i)\) to \(1\).  
We consider Equation (8) one per shiftable load \(k\):  
\[J(i)=P_{PV}^{*}(i)-L\hat{T}_{TOT}(i) \tag{8}\]  
where \(P_{PV}^{*}(i)\) is the PV production at time \(i\); \(L\hat{T}_{TOT}^{*}(i)\) is the averaged consumption profile for the considered day of the week; \(L\hat{T}_{TOT}(i)\) is the computed averaging of the last 10 days, excluding the power consumed by the shiftable loads.  
Then, as Equation (9) indicates, we must determine when to begin the task in order to minimize costs:  
\[\min[J(j)_{i}-L_{k}(j)]C_{k}(j)unde\ j=i,\ldots,i+T^{op} \tag{9}\]  
where \(L_{k}(j)\) is the power consumed by the load at relative time \(j\); \(C_{k}(j)\) is the energy cost; \(T^{op}\) is the total cycle time of the load.  
Our experimental strategy was based on this algorithm, which schedules smart home users to best optimize their usage of energy from renewable sources against that from the national energy grid.
## 2 Preliminaries and Related Works
### Link Correlation Reasoning and Measurement
If there are large batches of packets to transmit, we may require long-term link correlation (the historical data plane link correlation is more meaningful); on the other hand, if we would like to transmit only one or a small number of packets, short-term link correlation (derived from RSSI and noise readings) is preferred.