esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Generation Model Effect Analysis
### Control of the Heterogeneity and Partition  
The new algorithm characterizes the heterogeneity of the pore-throat network through partition control. The area shown in Figure 14a is divided into three regular regions, with each region using different constraint parameters, including porosity, coordination number, pore radius, and throat radius. Figure 14b shows the generated pore-throat network model. In the upper part of the image, the pores are smaller, and the mineral particles are finer; in the lower part of the image, the pores are larger, and the mineral particles are coarser. The image shows a relatively uniform gradient from top to bottom, reflecting the macro-heterogeneity of the pore-throat network model.  
Figure 13: Results of the pore-throat network generation under different porosities. (**a**) Porosity of 10%; (**b**) porosity of 15%; (**c**) porosity of 20%; (**d**) porosity of 30%.  
The new algorithm automatically recognizes the image features directly, so it has the same good processing ability for irregular regions. Figure 15 shows a self-defined river morphology. According to the provided picture, the new algorithm generates the corresponding pore-throat network model by regions and controllably, in which the channel region has better porosity.  
The new algorithm supports the generation of pore-throat network models in irregular regions in order to connect with macro-geological maps so as to support the modeling work of cross-scale numerical simulation in the future. Figure 16a is the real sedimentary facies diagram. Different sedimentary facies usually have different microscopic pore throat structures, which control the range of oil-water flow. Traditional microscopic models cannot describe this scale and heterogeneity. By extracting its regions, the new algorithm can set different constraint parameters according to the porosity and permeability differences of  
Figure 14: Micro pore-throat model of the heterogeneous reservoir. (**a**) Different colors represent partitions with different permeability. The darker the color, the greater the permeability. (**b**) The heterogeneous microscopic pore-throat network model generated according to (**a**).  
Figure 15: Heterogeneous pore-throat network model for customized river morphology. (**a**) Schematic diagram of an irregular fluvial facies. The blue part represents an ancient river channel, with a permeability different from that of the surrounding area. (**b**) The heterogeneous microscopic pore-throat network model generated according to (**a**).  
sedimentary facies and then generate a microscopic pore-throat network model that is in line with the complex and heterogeneous characteristics at the macro level (Figure 16b). This makes it possible to study both macroscopic heterogeneity and microscopic pore scale simultaneously.
###### Abstract  
Tracking and grasping a moving target is currently a challenging topic in the field of robotics. The current visual servo grasping method is still inadequate, as the real-time performance and robustness of target tracking both need to be improved. A target tracking method is proposed based on improved geometric particle filtering (IGPF). Following the geometric particle filtering (GPF) tracking framework, affine groups are proposed as state particles. Resampling is improved by incorporating an improved conventional Gaussian resampling algorithm. It addresses the problem of particle diversity loss and improves tracking performance. Additionally, the OTB2015 dataset and typical evaluation indicators in target tracking are adopted. Comparative experiments are performed using PF, GPF and the proposed IGPF algorithm. A dynamic target tracking and grasping method for the robot is proposed. It combines an improved Gaussian resampling particle filter algorithm based on affine groups and the positional visual servo control of the robot. Finally, the robot conducts simulation and experiments on capturing dynamic targets in the simulation environment and actual environment. It verifies the effectiveness of the method proposed in this paper.  
dynamic target grasping; improved geometric particle filtering; affine groups; robot position servo grasping; visual servo control; robotics +
Footnote †: journal: Applied Sciences
## 4 Accuracy Analysis of LSTM Network Models for Predicting Buildings' Settlement
### Analysis of the Effect of Multi-Point Co-Prediction on the Accuracy of Model  
Since there are usually multiple buildings around the pit and multiple measurement points are set up for one building, the buildings will also affect each other, thus aggravating the settlement of each building. Therefore, in addition to the static and dynamic parameters mentioned above that affect the accuracy of the prediction model, the joint prediction of different monitoring points will also have an impact on the model prediction accuracy. Now, the settlement monitoring values of monitoring points F147-148 of I, F168-169 of J, and F1 of K are taken as inputs, and the settlement prediction values of monitoring point F1 are taken as outputs, and the proposed LSTM model is used for the joint prediction of settlement at multiple points of the buildings, and the obtained monitoring values and prediction values are shown in Figure 10.  
As can be seen from Figure 10, the change trends of predicted values and monitoring values are basically the same. The maximum relative error between the predicted and monitored values of single-point settlement of the building is 19.96% (the predicted value is \(-\)3.59 mm, and the monitored value is \(-\)2.99 mm), the prediction accuracy is more than 80%, and the evaluation indexes RMSE, MAE, and \(R^{2}\) are 0.55, 0.42, and 0.81, respectively. The maximum relative error between the predicted and monitored values of the coordinated prediction of multi-point settlement is 11.79%, the prediction accuracy is more than 88%, and the network evaluation indexes RMSE, MAE, and \(R^{2}\) are 0.51, 0.37, and 0.89, respectively. The maximum relative error between the predicted value and the monitored value is 11.79%, the accuracy of the prediction is more than 88%, and the network evaluation indexes are 0.51, 0.37, and 0.89 for RMSE, MAE, and \(R^{2}\), respectively, Compared with the single-point settlement prediction, the accuracy of the multi-point settlement cooperative prediction is increased by 8%, and the evaluation indexes are increased by 7%, 12%, and 10%, respectively, which means that the prediction of the building settlement using the constructed LSTM model is more accurate by taking into consideration the interaction effect of the buildings.
###### Abstract  
The main purpose of this study is to perform shaking table tests to validate the inelastic seismic analysis method applicable to pressure-retaining metal components in nuclear power plants (NPPs). To do this, the test mockup was designed and fabricated to be able to describe the hot leg surge line nozzle with a piping system, which is known to be one of the seismically fragile components in nuclear steam supply systems (NSSS). The used input motions are the displacement time histories corresponding to the design floor response spectrum at an elevation of 136 ft in the instructive building in NPPs. Two earthquake levels are used in this study. One is the design-basis safe shutdown earthquake level (SSE, PGA = 0.3 g) and the other is the beyond-design-basis earthquake level (BDBE, PGA = 0.6 g), which is linearly scaled from the SSE level. To measure the inelastic strain responses, five strain gauges were attached at the expected critical locations in the target nozzle, and three accelerometers were installed at the shaking table and piping system to measure the dynamic responses. From the results of the shaking table tests, it was found that the plastic strain response at the target nozzle and the acceleration response at the piping system were not amplified by as much as two times the input earthquake level because the plastic behavior in the piping system significantly contributed to energy dissipation during the seismic events. To simulate the test results, elastoplastic seismic analyses with the well-known Chaboche kinematic hardening model and the Voce isotropic hardening model for Type 316 stainless steel were carried out, and the results of the principal strain and the acceleration responses were compared with the test results. From the comparison, it was found that the inelastic seismic analysis method can give very reasonable results when the earthquake level is large enough to invoke plastic behavior in nuclear metal components.  
Keywords:inelastic seismic analysis; shaking table test; nuclear metal component; design-basis earthquake; beyond-design-basis earthquake; plastic strain; strain gauge; Chaboche kinematic hardening model; Voce isotropic hardening model; strain gauge rosettes; principal strain +
Footnote †: journal: Applied Sciences
## 4 Routing Using SPWO Algorithm
### Fisher Score-Based on Feature Selection  
The input Information \(Z_{u}\) is acquired from the dataset and is fed to select the features for expanding the effectiveness of the arrangement in the network worldview. Here, the determination of elements is finished by utilizing the Fisher score. In an image processing system, the selection of a unique set of features from medical data is more important, as features of the data help to increase the efficiency of the proposed model to generate more robust and accurate classification results. The Fisher score [12] is employed to find a set of features among the total number of textures available in the data. The high dimensional data may not be suitable for generating effective results, hence the selection of features is performed to reduce the dimension of the data which in turn helps with obtaining a robust classification performance [38]. The Fisher score in the computing features is given in Equations (39) and (40):  
\[FS(f^{q})=\frac{\sum_{\ell=1}^{\alpha}\delta_{\ell}\Big{(}\chi_{i}^{q}-\chi^{ q}\Big{)}^{2}}{\left(\sigma^{q}\right)^{2}} \tag{39}\]  
\[(\sigma^{q})^{2}=\sum_{\ell=1}^{\alpha}\delta_{\ell}\Big{(}\sigma_{\ell}^{q} \Big{)}^{2}, \tag{40}\]  
where \(X_{l}^{q}\) denotes the mean of the \(q\)th feature in class, \(\sigma_{q}\) implies the standard deviation of the data set corresponding to the \(q\)th feature, \(FS(f^{q})\) implies the Fisher score of the \(q\)th feature, and \(\delta_{l}\) indicates the size of the \(l\)th class.  
After finding the Fisher score of individual features, top-ranked features with the highest scores are selected and are represented as those used to perform the classification task.
# MAC Address Anonymization for Crowd Counting
## 1 Introduction  
Many an event organizer deals with crowd monitoring and management [1]. Recently, works from different teams proposed crowd counting systems using WiFi signals [2; 3; 4]. These works describe counting systems detecting special control packets of the WiFi protocol: probe requests (PRs). Such packets are periodically transmitted by WiFi user terminals to detect nearby access points. Therefore, a PR-based counting system eludes the need for user cooperation and the need for an active WiFi connection from terminals to access points within range.  
Typically, several WiFi sensors are deployed over the monitored area to detect PRs and then extract and anonymize their media access control (MAC) addresses. Sensors then timestamp anonymized PRs and transmit them to a central server processing them jointly. The number of distinct PRs acquired during a time frame of \(T\) seconds (with \(T=60\) s in this paper) implicitly provides a _rate of PR transmission_, which is proportional (on average) to the number of attendees (as shown experimentally in [3] and theoretically in [5]). The proportionality between what is measured (the rate of PR transmission) and what is interesting to event organizers (the number of attendees) is referred to as the _extrapolation factor_ in our previous works and is determined experimentally.  
Only in circumstances where occupation varies significantly in 60 s is our system less accurate (because it averages probe requests over a time frame of one minute, thereby smoothing any occupancy change occurring over a one-minute time frame). However, both indoor and outdoor measurements in [3; 5] indicate it does not seem common for events or buildings hosting at least a few hundreds of individuals, probably because they enter and leave monitored areas at different times and also because entry and exit points have limited flow capacity.
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 1 Introduction
### Contributions and Research Gaps  
This study addresses the gap in existing literature by presenting an integrated HPC-powered pipeline that combines Apache Spark for distributed data preprocessing, MPI for parallel execution, and TensorFlow with CUDA for GPU-accelerated deep learning. Thisunified framework enables large-scale data processing while maintaining computational efficiency, allowing frequent model retraining without excessive time or resource constraints.  
Furthermore, this study builds upon prior research that presented a comprehensive forecasting framework, prioritizing data quality improvement steps (clustering Analysis of Reservoir Dynamics, outlier removal, imputation of anomalous readings) before applying predictive models [16]. [PERSON] [10] also applied clustering-based approaches for detecting anomalies in water demand datasets, while [PERSON] and [PERSON] [17] explored efficient missing data imputation strategies for smart water systems. The present study integrates these preprocessing techniques into the HPC pipeline to ensure high-quality input data for forecasting models.  
This research makes the following significant contributions:  
* Introduction of an integrated HPC framework that combines Spark, MPI, and GPU acceleration to enhance water demand forecasting efficiency at scale;
* Comparative evaluation of an advanced deep learning model (f-LSTM) versus a traditional ensemble learning model (Random Forest) to analyze trade-offs in accuracy, scalability, and computational efficiency;
* Optimization of computational resources, supported by empirical analysis of parallel execution logs, demonstrating the feasibility of real-time water demand forecasting.  
By demonstrating significant improvements in both processing speed and predictive accuracy, this study contributes a scalable and efficient solution for urban water management, bridging the gap between big data analytics and high-performance computing.
# Lp-Norm Inversion of Gravity Data Using Adaptive Differential Evolution
## 3 Lp-Norm Gravity Inversion Based on Adaptive Differential Evolution
### Inversion Method  
The objective function of Lp-norm gravity inversion can be expressed as:  
\[\begin{array}{l}\text{min}\Phi(\mathbf{m})=\Phi_{d}(\mathbf{m})+\lambda\Phi_{m}(\bm {m}),\\ s.t.\ m_{a}\leq\mathbf{m}\leq\mathbf{m}_{b}\end{array} \tag{20}\]  
In which \(\lambda\) denotes the regularization factor, \(\mathbf{m}_{a},\mathbf{m}_{b}\) are the search range of the parameters, while there are M parameters need to be inverted, then the upper and lower bounds of the model parameters are constrained as \(\mathbf{m}_{a}=(m_{a,1},\ m_{a,2},\cdots,m_{a,D})^{T}\), \(\mathbf{m}_{b}=(m_{b,1},\ m_{b,2},\cdots,m_{b,D})^{T}\). \(\Phi_{d}(\mathbf{m})\) is the misfit function of gravity data, which is defined as:  
\[\Phi_{d}(\mathbf{m})=\frac{\left\|\mathbf{W}_{d}\left(\mathbf{d}^{obs}-G(\mathbf{m})\right) \right\|_{2}^{2}}{\left\|\mathbf{W}_{d}\mathbf{d}^{obs}\right\|_{2}^{2}} \tag{21}\]  
where \(\mathbf{W}_{d}\) is the weight to the observed data, and determined by the followed equation [43; 44]:  
\[W_{d,i}=\frac{1}{\left|d_{i}^{obs}\right|+0.5\left(d_{max}^{obs}-d_{min}^{obs} \right)},\ i\in[1,N] \tag{22}\]  
In which, \(d_{max}^{obs}\) and \(d_{min}^{obs}\) denote the maximum and minimum data among the observed data, respectively. In Equation (21), the normalization process is carried out, and the purpose is to weaken the influence of the observation data error on the misfit function [45]. \(\Phi_{m}(\mathbf{m})\) is regularization term of Lp \((1\leq p<\infty)\), and expressed as:  
\[\Phi_{m}(\mathbf{m})=\sum W_{m,}|m_{i}-m_{0,i}|^{p} \tag{23}\]\(W_{m,i}\) is the weight coefficient of parameter \(m_{i}\), and it is the empirical information of the model, such as depth weighting value (see [2]). The notation \(m_{0,i}\) denotes the reference model or 0 model. While the model distribution is known, \(m_{0}\) can be used to strengthen the constraints on the inversion. Combining (21) and (23), the objective function of Lp-norm gravity inversion can be written as:  
\[\begin{array}{c}\min\ \Phi(\mathbf{m})=\frac{\|W_{d}\big{(}\mathbf{a}^{\text{obs} -\ G(\mathbf{m})}\big{)}\|^{2}_{2}}{\|W_{d}\mathbf{a}^{\text{obs}}\|^{2}_{2}}+\lambda \sum W_{m,i}|m_{i}-m_{0,i}|^{p},\\ s.t.\mathbf{m}_{a}\leq\mathbf{m}\leq\mathbf{m}_{b}\end{array} \tag{24}\]
## 4. Discussion
### Machine Learning
(2024) [122] developed an enhanced flood model that uses the K-means to determine uncertain parameters in the SWMM model, avoiding the tedious and complex parameter calibration process, and improving modeling efficiency and simulation performance. Deep reinforcement learning (DRL) can dynamically predict the probability and potential impact range of floods based on real-time updated meteorological and hydrological data. Trained DRL agents directly provide control strategies with faster computation speed. Unlike supervised learning, DRL does not require a large amount of monitoring data during training [123]. DRL has been applied in various aspects of flood risk assessment under climate change, such as storm surge flood simulations [124], real-time control of rainwater systems in coastal cities [125], and socio-economic system modeling in regions experiencing sea level rise [126]. This provides an efficient framework for accurately planning adaptation strategies for flood risks in the context of climate change. However, current literature research reveals limited research on the application of DRL in multi-factor driven compound floods. Although algorithms such as RF, SVM, and CNNs have shown promising applications, the recent trend is to apply ensemble and hybrid models. On one hand, they can compare and analyze the performance of different machine learning methods, and on the other hand, they can improve the overall performance and accuracy of the model [127]. For instance, [PERSON] and [PERSON] (2020) [128] evaluated the flood risk caused by climate change in the coastal areas of South Korea in 2030, 2050, and 2080 using three different algorithms: KNN, RF and SVM. The results showed that the KNN model had the highest reliability and accuracy. Hybrid ML technology has demonstrated high prediction accuracy in flood forecasting, typically exhibiting higher computational power than traditional ML models [129]. Unlike traditional ML methods for predicting storm surge water levels at a single site or specific time, [PERSON] et al. (2023) [130] effectively captured the temporal and spatial characteristics of storm surge water levels in Guangdong Province, China, using the ConvLSTM model. This integrated model can accurately predict water level changes in scenarios lacking terrain and boundary conditions. Similarly, [PERSON] et al. (2023) [131] provided reliable estimates of storm surge peaks using a model combining CNN and LSTM. In theory, ensemble algorithms are more effective than single algorithms, but this effectiveness needs to be judged based on specific circumstances [132].
# Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks
## 4 Simulation Results
### Scenario 2:
#### 4.2.2 Results and Analysis
For EEDOR-VA, the forwarding candidate priority and holding time reduces the total energy consumption through minimizing the redundant transmissions, and the hop count discovery process maximizes the PDR by guaranteeing that the packet is delivered successfully. It is important to note that the simulation results in both scenarios presented above show that our proposed EEDOR-VA outperforms DBR and PCR in terms of energy usage and PDR.
## 2 Related Works
According to the statements mentioned above, we introduce an adaptive optimized core distribution adaptive optimization topology reconfiguration algorithm (ACTR), which employs a 2D REmesh structure for physical topology reconfiguration [34; 35]. Distinct from previous algorithms, our algorithm emphasizes the distribution of faulty cores and introduces an adaptive core distribution optimization algorithm. By calculating the Euclidean distance between faulty cores, we derive a core distribution factor within the sliding window to pinpoint the location of faulty cores. This distance serves as a quantitative measure to assess whether the distribution of faulty cores is concentrated or dispersed. Additionally, bidirectional search traversal order is determined by setting a threshold value, followed by the application of a sliding window algorithm to enhance algorithmic efficiency. The search order is expanded to include not only clockwise sliding along the \(x\)-axis but also counterclockwise sliding along the \(y\)-axis. Compared to the original algorithm, our algorithm has demonstrated improvements in performance metrics such as successful reconfiguration rate, average recovery time, and core reuse rate. The contributions of this paper are summarized as follows:  
1. We propose an adaptive topology reconfiguration algorithm based on the faulty core distribution mechanism, which effectively improves the success rate of topology reconfiguration;
2. We propose a search algorithm based on a sliding window after determining the distribution location of the faulty core, which effectively improves the efficiency of the search process and the recovery time of faulty systems.
## 6 Calculation of Bonding Strength and Critical Lapping Length
### Critical Lapping Length Considering Grouting Defect  
It is stipulated that the lapping length when the tensile failure of the steel bar and the bonding slip failure of the grouting occur simultaneously is the critical lapping length of the steel bar, and the steel bar reaches the ultimate tensile strength \(f_{\text{u}}\). According to the balance of the force, the formula for calculating the critical lapping length can be obtained as follows:  
\[l_{\text{crx}}=\frac{df_{\text{u}}}{4\overline{\tau}_{\text{ux}}}, \tag{3}\]  
Figure 26: Fitted and experimental values of the ultimate bonding strength.  
where \(l_{\text{crx}}\) is the critical lapping length considering the grouting defects and \(\overline{\tau}_{\text{ux}}\) is the average ultimate bonding strength considering the grouting defects.  
The following can be obtained by combining Equations (2) and (3):  
\[l_{\text{crx}}=\left[\frac{f_{\text{u}}}{0.95\Big{(}6.27-0.08\frac{D}{d}+31.56 \frac{t}{D}\Big{)}f_{\text{ts}}\cdot\omega}-11\right]d. \tag{4}\]  
By inserting the test data of the ultimate tensile strength of the steel bar from this test and [PERSON]'s test [25] into Equation (4), the values of the critical lapping length \(l_{\text{crx}}\) of the specimen considering the grouting defects were calculated and compared with the values of the critical lapping length \(l_{\text{crx}}\) of the specimen when it was damaged during testing; the results are shown in Table 6. As the grouting defects were considered, the error or variance of the equation was decreased and the calculated values were in good agreement with the experimental results.  
As shown in Table 3, the residual deformation of the specimens of group G with a lapping length of \(12.5d\) under the anti-deflection condition met the specification requirements. The critical lapping length calculated by Equation (4) in this paper was \(13d\), indicating that the specimens designed according to the calculation formula of the critical lapping length in this paper can also meet the specification requirements of the residual deformation of specimens under high-stress repeated tension and compression loading.