esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Analysis
### SEU (SEMNU) Immunity Comparisons  
The tolerance of an SEU can be evaluated by measuring the critical charge (Qc), and the values for each cell can be found in Table 1. This evaluation typically employs a method that artificially induces a double exponential SEU waveform on the node using a current source and a capacitor [4; 5]. For the current source, a negative pulse is applied to the OFF NMOS and a positive pulse to the OFF PMOS. The theoretical formula for this current source is represented by  
\[I(t)=I_{0}\left(e^{\frac{-t}{\tau_{a}}}-e^{\frac{-t}{\tau_{B}}}\right) \tag{1}\]  
\[I_{0}=Q/\left(\tau_{a}-\tau_{B}\right) \tag{2}\]  
I\({}_{0}\) approximates the peak current, and Q is the total applied charge. Parameters \(\tau_{a}\) and \(\tau_{B}\) are time constant parameters for forming the double exponential, set to 200 ps and 50 ps, respectively, for this paper [4; 5]. Critical charge signifies the minimum amount of charge needed for SEU to toggle the stored value of the cell at the most sensitive node and is the total charge applied at the moment when Q and QB are inverted [21]. To evaluate this point, I\({}_{0}\) is gradually increased, and the corresponding total charge at the I\({}_{0}\) value where Q and QB toggle is compared. The simulations for SEU and SEMNU at the structurally recoverable node pair (Q-QB) of RSLP16T and its fundamental operation are presented in Figure 9. As discussed in Section 2, for the initial state Q = 1, the SEU waveforms for the nodes Q, QB, and S0 are as shown in Figure 10, confirming that they withstand significantly larger charges than other comparison cells under full SEU conditions. The simulations were carried out by sequentially applying current pulses corresponding from 20 fC to 100 fC to each node, with the previously mentioned up-pulse and down-pulse time constants set at 50 ps and 200 ps, respectively. Furthermore, it can be observed that the proposed cell can recover from an SEMNU induced at the storage node pair Q-QB, even at levels above 100 fC. Additionally, it can be observed in both Figures 9 and 10 that the recovery waveforms for nodes storing '0' experience a longer delay in their restoration. As previously explained, this delay occurs because the restoration of the nodes storing '0' takes place after the recovery of the nodes storing '1'.  
The area of each cell was compared through the designed layouts, considering the features presented in each paper, and the comparison results are shown in Table 2. The proposed cell has a larger area compared to the comparison cells, as it possesses the highest number of transistors.  
\begin{table}
\begin{tabular}{c c} \hline
**Cell** & **Effective Qc (fC)** \\ \hline WE-QUATRO [12] & 16.46 \\ QUCCE12T [9] & 16.76 \\ RHD12T [14] & 23.52 \\ RSP14T [15] & 24.61 \\ RIPD14T [16] & 42.58 \\ RSLP16T & >100 \\ \hline \end{tabular}
\end{table}
Table 1: Comparisons of critical charge (\(Q_{C}\)).  
Figure 10: Soft-error recovery when an SEU affects (**a**) node Q, (**b**) node QB, (**c**) node S0 individually, and (**d**) node pair (Q–QB) simultaneously.  
Figure 9: Simulation results of the proposed RSLP16T cell.
# Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
## 5 Conclusions  
To address the impact of low illumination environments on vision tracking algorithms for coal mine drilling robots. An effective tracker for mobile robot applications, LLCT, was proposed for long-term VOT in low-illumination environments. The trick of fast DSST was used to calculate the image correlation instead of the kernel convolution in LCT, and a projection matrix was incorporated into the traditional DCF filter to reduce the dimensionality of the extracted sample features. An effective method was proposed for the detection of target illumination, which allows an accurate estimation of the brightness around the target. An image enhancement module was added to achieve tracking in low-illumination environments and proposed a fast image enhancement method, which can run at a frame rate of around 60 FPS at 720P resolution. The experimental results demonstrated that the LLCT had good robustness and excellent performance on the OTB-2013 benchmark and UAV123 benchmark. Finally, a low-light vision tracking system based on the ROS operating system was designed and successfully applied to a coal mine drilling robot. In low illumination image sequences, the proposed tracker improves performance by more than 200% over the baseline tracker and by more than 50% over its contemporaries. A field experiment was conducted with an underground drilling robot in a coal mine. The results revealed that the proposed LLCT was superior to other methods for target tracking in low-illumination environments, and it can track the target trajectory correctly.  
In the future, we aim to develop an autonomous detection and tracking system based on our current work to achieve visual auto-following between the drilling robot and the drill pipe transporting robot. In addition, the processing speed for large images requires optimization, and the running speed of the image enhancement module must be improved.  
Methodology, S.Y.; software, M.L.; validation, S.Y., M.L. and C.T.; formal analysis, Y.L.; writing--original draft preparation, S.Y.; writing--review and editing, S.Y.; project administration and funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key Research and Development Program of China under Grant (No. 2018 YFC0808000); in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions of China (PAPD); in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No.19 KJB460014).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Discussion
### Environmental Influences--Water Cover  
An essential aspect in the interpretation of SAR images from tidal areas is the varying presence of water. Apart from the tidal cycle, the water regime is influenced by external conditions affecting tidal water level and, to a lesser extent, also residual water remaining on the flats. Therefore, knowledge of weather and environmental conditions at the time of recording or in the time before may be essential for image analysis. Time of exposure, wind speed and direction, as well as spring/neap tides affect the water coverage in the area but they may also influence the roughness of water and sediment surfaces or sediment moisture. Hence, the same area may appear partly different in SAR acquisitions taken at different times. In general, the flats are better drained after low tide compared to the time of falling tide, even at the same gauge level. Such effects should be taken into account as well as knowledge about general processes and phenomena occurring in tidal areas.  
As an example, the appearance of tidal channels, creeks, and gullies reproduced in SAR images is heavily dependent on water level as soon as the water reaches the tideway's edges or goes beyond. Therefore, changes in water level due to weather conditions or even wind-drift may have an effect especially on the gently rising slip-off slopes in contrast to the steep edges of the eroding banks, whose positions will not be markedly affected even for larger variations of the water levels. Thus, to observe and compare the courses of channels and gullies, they should on the one hand be imaged close to maximum drained stage, and on the other hand the steep eroding banks should be used as markers. The same applies when determining migration rates for bedforms in the upper island flats, whose slopes have been found to be slightly asymmetrical [19]. Conversely, in the case of multi-temporal acquisitions, the magnitude of changes in the water level lines would indicate the slope inclination.  
On the whole, the SAR data used in this study not only image the channel network and drainage system in tidal areas but they also provide a valuable source of insight into surface morphology of tidal flats mapped due to accumulation of residual water on the exposed flats. Such are the distribution of depressed areas indicated by frequent water coverage or troughs marking bedforms of the sediment surface.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 7 Location-Based Techniques for Enhancing PHY Layer Security
### Signal Analysis and Anomaly Detection  
Signal analysis and anomaly detection techniques can be applied to identifying FBSs by exploring the wireless communication signals exchanged between mobile devices and base stations. These methodologies leverage the distinctive characteristics and behaviors of legitimate base stations to differentiate them from malicious entities by exploiting signal strength clustering [153], or higher-order noise statistics [154]. [PERSON] et al. [155], suggested a method of Received Signal Strength (RSS)-based LTE base station localization with only a single mobile receiver when the path-loss exponent parameter is unknown. RF fingerprinting involves analyzing the unique radio frequency signatures emitted by legitimate base stations to create a reference database. By comparing the received signals with the reference fingerprints, anomalies indicative of FBSs can be detected. The usability of this technique for such a purpose has been studied in [156; 157; 158]. Utilizing the PSS as a signal strength metric to distinguish between genuine and false eNodeBs was introduced in [108].
# Near-Optimal Weather Routing by Using Improved A* Algorithm
## 4 Case Study
### Result and Discussion
Thus, the 16-way search method combined with adaptive grids yields more-economical paths than other methods in terms of ETA, distance, and the number of searched nodes. The 16-way search method combined with adaptive grids reduces the ETA by approximately 1.65% and the number of searched nodes by 4.79%. From the viewpoint of economical operations, although the estimated ETA is not much reduced, the target ship can theoretically save as much as $30,000 under the assumption that the target ship consumes 1500 gallons of diesel per hour and the cost of diesel is $2.8 per gallon. In addition, it is observed that the adaptive grids reduce the number of search nodes to approximately 4.79%, facilitating a more effective ship-routing process. The number of search nodes is also decreased by approximately 27.38%, which means that the optimal path with a smoother shape has been derived as opposed to the one without the adaptive grid, enabling reduction of the difference between the original path and the smoothed optimal path. When the obtained optimal routes using the four methods are compared with the AIS trajectory, they appear to be more continuous than the real AIS trajectory. Accordingly, this continuous ship route reduces the travel distance by approximately 1000 km for all cases when compared with the AIS trajectory, which also results in decreasing the ETA cost. Thus, the 16-way search combined with adaptive grids ensures economical ship operation along with the efficiency of the ship route optimization. When it is used in real operation, the navigator can easily obtain guidelines for economical ship operation, which reduces ship operating costs. Even though the improved A* algorithm utilized data operated in the past to compare with actual routes operated, it can be still used for real-time data. In the algorithm, the SOG used for ETA prediction can periodically update the SOG prediction results based on weather data collected every 6, 12 or 24 h independently of ship operation. The results of this calculated SOG prediction can be uploaded in an indexing manner with low memory requirements from the improved A* algorithm, so the optimum route finding can be updated periodically within about two seconds of the actual voyage.
## 4 Result and Discussion
### Pressure Acting on a Silo Hotspot Detector  
If a combustible object such as coal is stored inside the silo, an unexpected fire may occur, and this can lead to considerable damage; therefore, it is necessary to monitor the temperature inside the silo with the silo hotspot detector. However, if the silo hotspot detector is destroyed by the pressure and load generated when the material inside the silo discharges, another accident can occur. Therefore, it is necessary to measure the load generated in the silo hotspot detector under material discharge. In this study, the generated load was measured through experiments, obtained through an analytical solution, and confirmed through numerical simulation.  
Figure 13 shows the pressure acting on the detector because of the material discharge. The pressure profile in Figure 13 is the result of the analysis at 1, 3, and 5 s, the time after which all particles start flowing. Although it did not coincide with the pressure acting on the wall, the pressure pattern acting according to the section was the same. In the vertical wall section, the pressure increased with depth, and in the hopper section, it gradually decreased toward the outlet. As shown in Figure 13, the difference between the equation result and the FEM remained in the entire interval. In addition, there was no transition section unlike the silo wall, and it can be confirmed that it was different from the pressure applied to the silo wall.  
Figure 12: Silo internal stress distribution (stress unit is Pa) (**a**), comparison of silo wall pressure (Analytic vs. FEM) (**b**).  
In Figure 15, the tensile force generated in the silo hotspot detector for storage capacities of 83.8%, 71.7%, 62.2%, and 48.8% were compared with the theoretical formula, experimental values, and numerical analysis results. Figure 15 indicates that the numerical analysis and theoretical results are similar. In addition, it can be confirmed that the numerical analysis results and experimental results generally coincide; however, there are sections with differences. The experimental results at the storage capacity of 85% are different from the numerical analysis results, similar to the theoretical results. Since this was the value at the start of the experiment, the results of the static state would have been measured, which caused the above difference.  
Based on the above results, it can stated that the numerical analysis method used in this paper is suitable for silo discharge process analysis and can be used to perform structural safety evaluation of silo hotspot detectors.
## 2 Materials and Methods
### Reaction-Based Modeling and Stochastic Simulation Algorithm
In this work, the PE problem for the inference of \(\mathbf{c}\) is tackled by assuming that the sets \(\mathcal{S}\) and \(\mathcal{R}\), and the vector \(\mathbf{x}(0)\) that denotes the molecular amounts of the species present in the RBM at time \(t=0\) are known. We also assume that experimental data (e.g., the amounts of a subset of molecular species measured at some time points \(\mathbf{t}=\{t_{1},\ldots,t_{K}\}\)) are available. The PE consists of identifying the vector \(\mathbf{c}\) of stochastic constants that allow for obtaining simulated dynamics that resembles the behavior of the system as observed in the experimental data. Formally, the PE problem can be re-stated as the minimization of the following fitness function:  
\[f(\mathbf{c})=\sum_{k=1}^{K}\sqrt{\sum_{n=1}^{N}(X_{n}^{\mathbf{c}}(t_{k})-O_{n }(t_{k}))^{2}}, \tag{2}\]  
where \(X_{n}^{\mathbf{c}}(t_{k})\) is the simulated amount of species \(S_{n}\) at time \(t_{k}\), obtained with the putative parameterization \(\mathbf{c}\) and \(O_{n}(t_{k})\) is the experimental (target) amount of \(S_{n}\) measured at time \(t_{k}\). Note that, due to the stochasticity of SSA simulations, two or more evaluations of the fitness of the same parameterization \(\mathbf{c}\), considering the same initial state \(\mathbf{x}(0)\), generally lead to different values. In Figure 1, we show the result of 100 independent SSA simulations of the model of Michaelis-Menten (MM) enzyme kinetics [20], which will be used as a test case in this paper. The MM model consists of the following reactions:  
* \(R_{1}:S+E\xrightarrow{c_{1}=10^{-1}}ES\);
* \(R_{2}:ES\xrightarrow{c_{2}=10^{-1}}S+E\);
* \(R_{3}:ES\xrightarrow{c_{3}=1}E+P\). Figure 1 highlights that different SSA runs of the MM model--executed using the stochastic constants associated with the reactions, and the initial condition given in Table 1--generate simulation outcomes that are quantitatively different from each other (due to stochastic noise) but in agreement from a qualitative point of view, as denoted by the distributions of the final amount of the three species \(S\) (substrate), \(E\) (enzyme), and \(P\) (product). Figure 1: Example of stochastic simulation: 100 independent SSA runs of the MM model—all starting from the same initial state (\(S=200,E=100,ES=0\), and \(P=0\)) and using the same parameterization—lead to quantitatively different trajectories and a final distributions of the chemical species. The noise due to stochastic simulations affects the fitness evaluations and thus hampers the identification of the global optimum of the PE problem: any hill climbing or gradient-based approach would be misled by this issue and generally be unable to converge to the global optimum. This is the reason why computational intelligence meta-heuristics should be definitely preferred in this context [19]. In this paper, we exploit the swarm intelligence method named FST-PSO [14] that is briefly described in the next section.
# CvAMoS--Event Abstraction Using Contextual Information
## 4 Approach
### Overview  
The approach is divided into two phases, the first consists of the identification and verification of the motifs, while the second step consists of the replacement of the subtraces containing the motifs in the original event log (i.e., the abstraction). An overview of the approach is illustrated in Figure 1. To obtain the motifs, we first need to construct a set of candidate motifs, and then verify them. The construction of the set of candidate motifs is a crucial part of the approach, as the verified motifs will be a subset of this set. For the construction of the set of candidate motifs, different lines of thought were considered. The first involves the construction of all possible sequences of activities (i.e., \(\mathcal{A}*\), cf. Definition 1), but this would be unfeasible, especially since most of the candidates would be completely irrelevant (being very different from any trace on the event log). The second one (i.e., the one implemented in vAMoS) considers a two-step approach: it firstly identifies small sub-traces that compose the \"alphabet\", and then combines the elements of the alphabet in order to populate the set of candidate motifs. To some extent, the approach followed in vAMoS is powerful, as it allows us to construct a larger set of candidate motifs that is populated also by non-observed sequences. This means that the algorithm is more tolerant of the variability as there are more and different candidates to evaluate. Furthermore, the way the set of motifs is constructed, and the fact that the approach also considers sequences that never appeared in the log, causes an explosion in the generation of the candidate motifs. To overcome this problem, we opted to focus only on observed sequences. In CvAMoS, the set of candidates is based only on observed sequences, while the variability is still guaranteed by the distance function. The verification of the candidate motifs verifies whether a given motif is observed in a minimum number of traces, up to a certain dissimilarity. The similarity checking verifies both the motif and the corresponding set of attributes. Hence, a list of verified motifs is returned. The second step consists of the filtering of the verified motifs in the event log, in order to obtain the abstracted event log. As indicated in Figure 1, the second step checks the verified motifs in the log, and filters them by replacing the instance in the trace with the activity label. In the rest of the section, we provide a detailed description of all the components of the approach, along with a formal definition for each of them. In particular, the two steps of the proposed approach are explained in detail.
## 2 Materials and Methods
### Data Collection and Analysis Procedures  
This study employed a cross-sectional observational design to compare jump height measurements obtained from Chronojump and MyJump during countermovement jump (CMJ) and squat jump (SJ) assessments. All data collection took place in a controlled laboratory environment, ensuring consistent testing conditions. The testing session was conducted between 09:00 and 11:00 on a stable and level indoor surface, with consistent artificial lighting and an ambient temperature maintained at 21 \(\pm\) 2 \({}^{\circ}\)C and \(\sim\)40% humidity.  
Upon arrival at the laboratory, participants underwent anthropometric measurements. A standardized 10 min warm-up protocol was implemented, beginning with 6 min of cycling on a Monark 839 ergometer (Varberg, Sweden) at a steady intensity of 100 Watts. This was followed by approximately 4 min of dynamic exercises.  
Participants were instructed on the proper execution of the CMJ and SJ tests. Given that prior jump testing experience was an inclusion criterion, all athletes were familiar with the testing protocol. Participants performed the jumps wearing their own athletic footwear, as no standardized shoes were imposed. Additionally, a familiarization session was conducted prior to testing to ensure participants were comfortable with the specific measurement tools (Chronojump and Mylump 2). During the session, both tools were used simultaneously to record jump performance, ensuring consistency across the measurements. The CMJ test required participants to stand with their hands on their hips, perform a rapid countermovement and then jump as high as possible. The SJ test followed a similar setup, but participants started from a static squat position without a countermovement.  
Each participant completed three maximal effort jumps per test (CMJ and SJ), with a 2 min rest interval between attempts to minimize the effects of fatigue, in accordance with previous research [7; 21]. The highest jump from each test was selected for statistical analysis. The order of testing was fixed, with all participants performing the countermovement jump (CMJ) followed by the squat jump (SJ).  
Jump heights were recorded simultaneously using Chronojump and Mylump to enable a direct comparison between the two systems. For intra-rater reliability assessment, the jump videos were analyzed on the day of measurement and reanalyzed using the Mylump app seven days later by the same researcher [27], who was blinded to the original data during reanalysis to assess consistency across repeated measurements and minimize potential bias.