esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 2 Principles of Grounding Systems
### Key Parameters Influencing Grounding Performance
#### 2.3.3 System Voltage Level and Fault Current Magnitude  
The voltage level and fault current magnitude significantly influence the design of grounding systems. Higher-voltage systems, such as 10 kV and 35 kV, necessitate grounding systems designed to manage higher fault currents. The fault current \(I_{fault}\) during a short circuit can be calculated using [PERSON]'s law:  
\[I_{fault}=\frac{V_{system}}{Z_{total}} \tag{6}\]  
where \(V_{system}\) is the system voltage, \(Z_{total}\) is the total impedance of the fault path, including the impedance of the grounding system.  
In high-fault-current systems, grounding electrodes must withstand large currents without damage. Proper design mitigates ground potential rise (GPR) to ensure safety, equipment protection, and system stability. Effective grounding requires careful planning of soil properties, electrode materials, and fault levels, adhering to international standards for optimal performance at various voltages (400 V, 10 kV, 35 kV).
# Fast and High Precise Spatial Documentation of Traffic Accident Site Using Only Low-Cost RPAS
## 2 Materials and Methods  
### Current State  
Current methods that traffic police in the Czech Republic use involve two main stages. The first includes in situ measuring (using a surveying wheel and steel tapeline, predominantly) and sketch creating (Figure 1a). The accuracy of the measurements is on the order of decimeters. The second step involves creating a plan in the specialized software (in the office) based on a sketch created on a traffic accident site. This plan is in the local coordinate system, with the possibility of measurement (Figure 1b). The amount of time spent creating sketches and plans depends highly on the traffic accident type, including the number of involved vehicles. In the case of the traffic accident depicted in Figure 1, it took approx. one hour to create a sketch and another two hours to create a plan at the office. Sometimes Google Maps, with a combination of initial measurement points, is used to create a plan from a sketch. Traffic police typically use tourist GNSS to obtain the absolute coordinates of the center of the traffic accident site. Using this central point, they can approximately place the plan into the absolute coordinates system, but they do all measurements in the local coordinates system. The measurements are related to the initial measurement point (e.g., a lamp drawn in a technical map); therefore, the absolute position accuracy of the traffic accident site is not important for our investigation [11]. The whole process is quite time consuming, considering this example does not represent a serious accident that involves more vehicles, skids, debris, injured persons, casualties, etc. It could be quite unfeasible to measure and draw every important distance using a handmade sketch. Although traffic officers take many images of the traffic accident site using a digital single lens reflex (DSLR) camera, the resulting pictures are not to scale. Some evidence might be useless in situ and therefore officers do not present it in a plan. Consequently, officers might miss it in further investigations. Moreover, there are gentle differences between measurements, sketches, and plans that different traffic officers create. It is highly dependent on experiences, dexterity, abilities, etc.  
Figure 1: Sketch created on traffic accident site (**a**). Plan in the local coordinate system (**b**) [11].
# Quantum PSO-Based Optimization of Secured IRS-Assisted Wireless-Powered IoT Networks
## 2 System Model and Problem Formulation
### Information Transmission
Hence, the problem can be formulated mathematically as follows:  
\[\text{P1}\quad\max_{\mathbf{\Phi}_{0},\mathbf{\Phi}_{1},t_{0},\{t_{k}\}} \quad\sum_{k=1}^{K}[R_{k}-R_{E}]^{+}\text{subject to} \tag{10a}\] \[[R_{k}-R_{E}]^{+}\geq\bar{R}\quad k=1,2,\ldots,K\] (10b) \[[\mathbf{\Phi}_{0}]_{n,n}=1;\quad[\mathbf{\Phi}_{1}]_{n,n}=1,\quad n=1,2, \ldots,N\] (10c) \[\sum_{j=0}^{K+1}t_{j}\geq 1\] (10d) \[0\leq t_{j}\leq 1,\quad j=0,1,\ldots,K+1 \tag{10e}\]  
where \(R_{k}\) is the achievable throughput for user k, as calculated with (7), while \(R_{E}\) is the achievable rate for the eavesdropper, as calculated with (9). Constraint (10b) ensures that the secrecy throughput for each user k is maintained above a predefined threshold, R. Constraint (10c) ensures that each diagonal element of the IRS phase-shift matrices \(\mathbf{\Phi}_{0}\) and \(\mathbf{\Phi}_{1}\) has a unit modulus. The IRS is assumed to have perfect phase shifts, meaning that each reflecting element must have a phase shift represented by a complex exponential with a magnitude of 1. Constraint (10d) ensures that the total time allocated across all users in the downlink and uplink phases does not exceed 1 in total, while (10e) restricts the time allocation for each user \(j\). Since optimization problem P1 is inherently non-convex because of the complex interdependence of variables, it is challenging to solve directly. Therefore, we consider an alternating optimization approach that integrates quantum particle swarm optimization with the bisection method to solve the problem.
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
### Organisation of Paper  
This paper is structured as follows: Section 1 introduces the concept of HSI, highlighting one of the major challenges of HSI which is Curse of Dimensionality and how semantic segmentation helps in HSI analysis. Section 2 discusses the existing state of the art techniques for HSI dimension reduction and segmentation. Section 3 gives a deeper and mathematical understanding of the techniques proposed in this research paper. Section 4 outlines our Proposed Technique, elucidating the fusion of CCA and SVM within the context of per-pixel prediction i.e., semantic segmentation. Section 5 presents our experimental setup, including the datasets used and performance metrics employed. The results and discussion are provided as well, offering insights into the effectiveness of our approach. Finally, Section 6 encapsulates our conclusions and outlines avenues for future research.
# Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data
## 4 Experiments
### Comparisons with Other Algorithms
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} \(RTIME_{\lambda}\) \\ **(Degree/h)** \\ \end{tabular} & \
\begin{tabular}{c} \(RTIME_{\varphi}\) \\ **(Degree/h)** \\ \end{tabular} &
\begin{tabular}{c} \(RTIME_{Dis}\) \\ **(km/h)** \\ \end{tabular} \\ \hline interpolation extrapolation [11] & 3.2547 & 3.7510 & 340.0171 \\ fitting extrapolation & 0.2777 & 0.2139 & 36.4040 \\ grey prediction [9] & 0.2625 & 0.2084 & 25.1501 \\ autoregressive prediction [22] & 0.2744 & 0.2615 & 41.98 \\ BP neural network [16] & 0.305 & 0.127 & 32.513 \\ LSTM neural network [8] & 1.018 & 0.536 & 119.306 \\ the algorithm presented in this paper & 0.157 & 0.118 & 20.610 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison with other algorithms. Figure 6: Visualization of trajectory predictions. The horizontal axis is longitude, and the vertical axis is latitude. In addition, the blue curve is the real trajectory, and the orange curve is the prediction trajectory. extrapolation can give better prediction accuracy. The grey and autoregressive prediction methods based on uniform sampling data have strict requirements for time series data. The experiments found that much of the trajectory sequence data cannot satisfy the requirements, so the method is not ideally applicable. Only the data satisfying the requirements are presented in Table 3. From the perspective of model complexity, interpolation, fitting, grey, and autoregressive models are simple and require fewer samples for prediction. However, since these models can only be used for short-term prediction, it needs to retrain a new model based on the data at previous moments; thus, these models have poor reusability. However, although the training process based on the historical data of this method is complicated, once the model is completed, it can be used repeatedly without the need to retrain and make predictions at any time. In order to verify the rationality of multi-dimensional trajectory feature extraction in the input features of this paper, the comparison experiments of inputting only position features and of inputting multi-dimensional trajectory features are carried out based on the BP network model and CNN model. The experimental results are shown in Table 4.  
As seen in Table 4, feature amplification by extracting multi-dimensional trajectory features can greatly improve prediction accuracy. Furthermore, the network model in this paper performs better than the BP network model.
# Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach
## 4 Results and Discussion
### The Results of Road Cluster and Outlier Analysis of Polk County  
The study uses cluster and outlier analysis (the local Moran's I, as discussed in Section 2.3.3) from geo-processing tools in ArcGIS [44; 45], by taking the following input parameters, as shown in Table 3, to calculate local Moran's I index, _z-score_, and _p_-value for each linear road to obtain road clusters and outliers across Polk County.  
All roads should have at least one neighbor [32] according to the best practice guidelines of cluster and outlier analysis. In this research, a total of 27,491 roads were selected in the input feature class since we find 115 roads in OpenStreetMap roads of Polk County have no neighbor when we construct the SWMR of Polk County.  
In general, there are four types of road cluster and outlier in the road cluster and outlier shapefile, as discussed in Section 2.2.3: high-high cluster (cotype is HH), low-low cluster (cotype is LL), high-low outlier (cotype is HL), and low-high outlier (cotype is HL). The high-high cluster, high-low outlier, and low-high outlier are colored in red, black, and blue, respectively. The results, as shown in Figure 10 using map visualization, clearly demonstrate the road clusters and outliers.  
Figure 10: Distribution of road clusters and outliers of Polk County.  
\begin{table}
\begin{tabular}{c c} \hline
**Input Parameter** & **Input Value** \\ \hline Input Feature Class & road\_polk \\ Input Field & crash\_count \\ Output Feature Class & c:\(\backslash\)data\(\backslash\)myproject\(\cdot\)gdb\(\backslash\)road\_clustersoutliers \\ Conceptualization of Spatial Relationships & get\_spatial\_weights\_from\_file \\ Standardization & none \\ Distance Band or Threshold Distance & none \\ Weights Matrix File & c:\(\backslash\)data\(\backslash\)swrm\(\cdot\)gwt \\ Apply False Discovery Rate Correction & no\_fdr \\ \hline \end{tabular}
\end{table}
Table 3: Input Parameters of cluster and outlier analysis (Anselin local Moran’s I).  
High-high road cluster indicates that there is a positive autocorrelation. The roads in this cluster all have high crashes and the neighbor roads also have high-frequency crashes. That means, High-high road cluster is the RCHC that we should identify from the 27,606 roads of Polk County.  
The RCHC of Polk County, as shown in Figure 10, centered along with I 35, I235, US69, I80, and US 6, can be discovered is relevant to hazardous roads detection. There are 738 roads in the RCHC of Polk County, which account for 2.67% of all roads and 24, 652 crashes occurred in RCHC, accounting for 59.07% of all crashes. That means 59.07% crashes occurred
# Visual Saliency Detection Using a Rule-Based Aggregation Approach
## 3 Experimental Results
### Evaluation  
To calculate the performance of our proposal, we compared RSD against seven state-of-the-art approaches: Minimum Barrier Salient (MBS) [40], Minimum Directional Contrast (MDC) [25], Saliency Filters (SF) [23], Histogram-Contrast and Region-Contrast (HC, RC) [22], Frequency-Tuned (FT) [15], and Context-Aware (CA) [24]. In addition, we considered for comparison purposes a learning-based aggregation model such as Pixel-Wise (PW) [28]. For a fair comparison, the PW model learns from the same feature descriptors proposed for our RSD approach.  
In Figure 5, we present samples of the binary map obtained by our RSD and the saliency maps from the methods used for comparison purposes. In this figure, we can observe that the saliency maps obtained by individual and aggregation methods are given in a grayscale image. Conversely, the saliency map obtained from our proposed system is given as a binary map.  
Figure 5: Saliency maps of individual and aggregation methods. The binary outcome obtained from our approach is presented: (**a**) Input image; (**b**) Ground truth; (**c**) FT; (**d**) CA; (**e**) RC; (**f**) HC; (**g**) SF; (**h**) MBS; (**i**) MDC; (**j**) PW; and (**k**) RSD.  
Considering that there is not an optimal threshold to use with each individual method, our evaluation approach was twofold. In our first experiment, we used one of the most common methods to automatically obtain the best threshold and to evaluate all the methods under the same specification. Specifically, we utilized the adaptive threshold [15], which is one of the most popular methods used for image saliency and it is simple to calculate. The adaptive threshold is defined in Equation (16).  
\[T_{a}=\frac{2}{W\times H}\sum_{x=1}^{W}\sum_{y=1}^{H}S(x,y), \tag{16}\]  
where \(W\) and \(H\) are the width and height of the saliency map image, respectively, and \(S(x,y)\) is the saliency level of the outcome inspected. In our second experiment, to evaluate all the methods under their best specific conditions, we obtained the binary result of the saliency maps with all possible thresholds in the range of \([0,255]\). In both experiments, we adopted a cross-validation method to estimate the performance of our proposed approach with five folds.
## 3. Results and Discussion  
### Life Cycle CO2 Emission per Communication Volume  
Table 5 shows the results of CO2 emissions for each process. The results for listening and/or watching music and video streaming were displayed by classifying ICT equipment and type of streaming. The result revealed that when listening and/or watching music streaming from PCs had the biggest CO2 emission out of all the processes. In contrast, video streaming from smartphones had the least CO2 emission out of all the processes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Item** & **Unit** & **Source** \\ \hline \multirow{2}{*}{Data center} & CO2 emission intensity & \multirow{2}{*}{341} & t-CO2/million \\  & for disposal & & JPY/facility \\ \multirow{2}{*}{Servers} & CO2 emission intensity & \multirow{2}{*}{\(1.03\times 10^{-7}\)} & kg-CO2/kg/unit \\  & for disposal & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Utilized data for dispose process.  
\begin{table}
\begin{tabular}{l l c} \hline \hline \multicolumn{1}{c}{**Process**} & **Sub Process** & **CO2 Emission** \\ \hline \multicolumn{1}{c}{Construction and manufacture} & Construction of data center and manufacture of servers & \(4.09\times 10^{-6}\) \\ \hline \multirow{8}{*}{Usage} & Data center & \(5.48\times 10^{-6}\) \\  & Communication network & \(5.35\times 10^{-5}\) \\  & Listening and/or watching streaming by users & \\  & -PCs: music screaming/subscription and free & \(4.49\times 10^{-4}\) \\ \multirow{2}{*}{Usage} & -PCs: video streaming/subscription & \(1.70\times 10^{-5}\) \\  & -PCs: video streaming/free & \(3.23\times 10^{-5}\) \\ \multirow{2}{*}{} & -Smartphones: music screaming/subscription and free & \(2.98\times 10^{-6}\) \\  & -Smartphones: video streaming/subscription & \(1.13\times 10^{-7}\) \\ \multirow{2}{*}{} & -Smartphones: video streaming/free & \(2.15\times 10^{-7}\) \\ \hline Dispose & Dispose of data center and servers & \(7.68\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: CO2 emission of each process.  
Table 6 shows the results of life cycle CO\({}_{2}\) emissions. Each life cycle, CO\({}_{2}\) emissions were calculated by adding the CO\({}_{2}\) emissions of each piece of ICT equipment and type of streaming and other CO\({}_{2}\) emission results. Comparison between ICT equipment showed that CO\({}_{2}\) emissions from listening to music through online streaming from PCs was approximately 4.1 times higher than that from smartphones. Although CO\({}_{2}\) emissions from PCs were also high for video streaming, the difference was only 1.1 to 1.2 times. This was because of the significantly large communication volume (see Table 3) of videos compared to music. Thus, CO\({}_{2}\) emissions derived from watching videos through online streaming was relatively small compared to 1 MB of communication volume.  
In this study, the median power consumption of PCs was considered. When the power consumptions of 25 and 74 percentiles were considered, CO\({}_{2}\) emissions for music streaming varied from 23% to 66%. For video streaming, the variation was from 3% to 9% and 6% to 16% for subscribed and free users, respectively.
# Accurate Modeling, Operation Laws and Commutation Timing Matching for Concrete Pumping Systems
## 3 Results and Discussion
### Operation law of the CPS
#### 3.1.2 Operation law of swing mechanism  
The displacement of the swing valve and the change curve of the pressure of the control chamber in the pumping cycle are shown in Figure 11. The pressure of the two control chambers XA1 and XB1 of the swing valve changes alternately, with high pressure on one side and low pressure on the other side, and the swing valve is driven to change direction by the pressure difference in the control chamber, with a spool displacement of 40 mm and a changeover time of about 80 ms.  
Figure 12 shows the curves of the stroke of the swing cylinder and the pressure of the left and right buffer chambers. The stroke of the swing cylinder is 197 mm and the commutation time is about 210 ms, of which the buffer time is 60 ms, accounting for 30.9% of the total time. By reducing the buffer time, the reversing time of the swing cylinder can be reduced. When the swing cylinder swings left and right, the pressure of the left and right buffer cavities changes alternately. When the swing is in place, the pressure in the high-pressure chamber is about 190 bar and the pressure in the low-pressure chamber is about 5 bar. At the beginning and end of the swing, there is a pressure shock with a peak of 274 bar. Reducing the buffer time can shorten the changeover time of the swing cylinder.  
Figure 13 shows the pressure and flow curves for the swing pump and accumulator. When the swing cylinder is reversed, the pressure of the swing pump drops rapidly and the accumulator is rapidly discharged with a flow rate of 680 L/min, much higher than the flow rate of the swing pump. At the end of the reversal, the swing pump delivers the maximum flow, which is about 55.8 L/min, and the outlet pressure gradually rises, filling the accumulator with liquid. When the pressure rises to 190 bar, charging stops. At this point, the pressure of the oscillating pump is maintained at 190 bar and the flow rate is only 4.7 L/min to replenish the internal leakage of the swing unit. As can be seen, the accumulator provides the main power for the rapid oscillation of the oscillating cylinder, while the main role of the swing pump is to fill the accumulator with liquid.  
Figure 11: Dynamic characteristic curves of the swing valve: (**a**) main cylinder displacement; (**b**) and (**c**) swing valve control pressure; and (**d**) swing valve displacement.  
Therefore, optimizing the parameters of the accumulator can increase the reversal speed of the swinging cylinder.  
Figure 12: Dynamic curves of swing cylinder: (**a**) left cavity pressure, (**b**) right cavity pressure and (**c**) swing cylinder displacement.  
Figure 13: Pressure and flow curves of swing pump and accumulator: (**a**) swing cylinder displacement; (**b**,**c**) swing pump and accumulator pressure; and (**d**,**e**) swing pump and accumulator flow.
# Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
## 4 Methodology
### Computational Complexity Analysis  
#### 4.5.1 Time Complexity  
The **Edge Attention Mechanism** has two main computational steps. First, the attention coefficient calculation for each edge \((u,v)\) involves concatenating node features and passing them through a linear layer, leading to a time complexity of \(O(|E|\cdot d_{n})\). \(|E|\) is the number of edges and \(d_{n}\) is the node feature. Then, the message aggregation process for each node \(v\) involves integrating neighboring edge features, resulting in a complexity of \(O(|E|\cdot(d_{n}+d_{e})\cdot h)\). The \(h\) is the hidden layer dimension.  
The **Hierarchical Detection Framework** consists of two stages. Coarse-grained classification employs a Softmax function, with a complexity \(O(|E|\cdot C_{coarse})\). Fine-grained classification is performed only on positive samples from the coarse-grained stage, leading to a complexity of \(O(|E_{attack}|\cdot C_{fine})\).  
The **Adversarial Training Module** utilizes projected gradient descent (PGD) for generating adversarial perturbations. Each perturbation generation requires \(T\) iterations of gradient calculations, resulting in a time complexity of \(O(T\cdot|E|\cdot d_{e})\).
# The TORC2-Dependent Signaling Network in the Yeast _Saccharomyces cerevisiae_
## 4 Structure, Function, and Regulation of Ypk1
### Ypk1 Activity Is Not Controlled by Sterol  
In marked contrast to its activation upon sphingolipid depletion [22; 28], TORC2 does not appear to serve as a sensor of sterol stress, based on several observations. TORC2 is not activated when sterol synthesis is blocked by treatment of cells with lovastatin [139], a potent inhibitor of HMG-CoA reductase, a key enzyme in the mevalonate pathway, that blocks isoprenoid and sterol synthesis in yeast [140; 141; 142]. In a similar regard, on the basis of a proteome-wide lipidomics screen, it was reported that Ypk1 binds ergosterol and that Ypk1 activity in vitro is stimulated by ergosterol [143], suggesting that Ypk1 itself might be a direct sterol sensor. However, subsequent work documented that, in the cell, absence of ergosterol does not detectably diminish either basal or myriocin-induced Ypk1 activity [22].
# Recognition of Earthquake Surface Ruptures Using Deep Learning
## 2 Data  
### Study Area  
An MS 7.4 earthquake occurred in Maduo County, Guoluo Tibetan Autonomous Prefecture, Qinghai Province, on 22 May 2021. This earthquake ruptured along the southern branch of the known East Kunlun Fault Zone and the Jiangcuo fault [24].  
The coseismic surface ruptures were an oblique combination of tension, tension-shear, shear, compression, bulge, extrusion ridges, and goose-shaped tension fissures (Figure 1). These ruptures were distributed in mountainous areas, grasslands, swamps, alluvial fans, and several dunes at an altitude of 4200-4600 m (Figure 2).