esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Jet Morphology  
The morphology (length and diameter) and head and tail velocities of the SCJ formed from the two materials at identical time points were calculated through numerical simulations and the theoretical model, respectively. The results are compared with those obtained from the X-ray experiment.  
As shown in Table 2, the profiles of the OFHC-Cu and Mo jets predicted by the theoretical model at the given time are consistent with the numerical simulations and experimental data. The head and slug velocities of the jet formed from each material predicted by the theoretical model differed by less than 5% and 12% from the experimental data, respectively. Overall, the theoretical calculation model was effective in yielding accurate predictions for the tensile motion of the jet.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{J-H-2 [27]} & \(\rho\) & \(D\) & \(P_{Cl}\) & \(A\) & \(B\) & \multirow{2}{*}{\(R_{1}\)} & \multirow{2}{*}{\(R_{2}\)} & \multirow{2}{*}{\(\omega\)} & \(E_{0}\) & \multirow{2}{*}{\(V_{0}\)} \\  & (g/cm\({}^{3}\)) & (m/s) & (GPa) & (GPa) & (GPa) & & & & & (GPa) \\  & 1.71 & 8212 & 29.6 & 854.5 & 20.493 & 4.6 & 1.35 & 0.25 & 10.0 & 1.0 \\ \hline \multirow{4}{*}{OFHC-Cu [28]} & \(\rho\) & \(G\) & \(Tm\) & \(A\) & \(B\) & \multirow{2}{*}{\(n\)} & \multirow{2}{*}{\(C\)} & \multirow{2}{*}{\(m\)} & \(C_{0}\) & \multirow{2}{*}{\(S_{1}\)} \\  & (g/cm\({}^{3}\)) & (GPa) & (K) & (GPa) & (GPa) & & & & & (m/s) \\ \cline{1-1} \cline{5-10}  & 8.96 & 47.7 & 1356 & 0.09 & 0.292 & 0.31 & 0.02 & 1.09 & 3940 & 1.49 \\ \cline{1-1} \cline{5-10}  & \(S_{2}\) & \(S_{3}\) & \(\gamma_{0}\) & \(E\) & \(V_{0}\) & \(C_{p}\) & & & & \\ \cline{1-1} \cline{5-10}  & 0.6 & 0 & 1.99 & 0 & 1.0 & 383 & & & & \\ \hline \multirow{4}{*}{Mo [29; 30]} & \(\rho\) & \(G\) & \(Tm\) & \(A\) & \(B\) & \multirow{2}{*}{\(n\)} & \multirow{2}{*}{\(C\)} & \multirow{2}{*}{\(m\)} & \(C_{0}\) & \multirow{2}{*}{\(S_{1}\)} \\  & (g/cm\({}^{3}\)) & (GPa) & (K) & (GPa) & (GPa) & & & & & (m/s) \\ \cline{1-1} \cline{5-10}  & 10.03 & 131 & 3660 & 0.3 & 0.32 & 0.3 & 0.07 & 1 & 5124 & 1.232 \\ \cline{1-1} \cline{5-10}  & \(S_{2}\) & \(S_{3}\) & \(\gamma_{0}\) & \(E\) & \(V_{0}\) & \(C_{p}\) & & & & \\ \cline{1-1} \cline{5-10}  & 0 & 0 & 1.52 & 0 & 1.0 & 243 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main material parameters of the numerical model.  
Figure 5: Computational simulation model.
## 5 Realistic Modeling of Resource Constraints  
To ensure the viability and applicability of our game-theoretic framework for FL, we integrate realistic resource constraints directly into the client decision-making process. Our system captures heterogeneity in hardware capabilities, battery availability, communication bandwidth, and privacy sensitivity. These parameters influence each client's utility function and are critical in shaping participation strategies across repeated FL rounds. The battery and privacy parameters used in this study were derived from real-world device specifications and prior empirical studies to ensure that the simulation reflects realistic operating conditions. Battery capacities and power consumption profiles were obtained from manufacturer datasheets and hardware performance reports for devices such as the ESP32 microcontroller [25], the Jetson Nano embedded platform [32], and common android smartphones like the Google Pixel 6 [33]. The battery consumption per training round was aligned with values reported in recent edge AI benchmarks and FL performance analyses [26]. Privacy sensitivity levels for IoT and edge devices were modeled based on foundational work in privacy attacks on FL systems [28, 29], which highlight the higher vulnerability of low-end devices to inference risks. These references collectively support the realism and credibility of the simulation model adopted in RBPS.
# A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution
## 4 Information Life-Cycle Management Solution  
Figure 4 shows a schema of the information life cycle exemplified within this work and implemented at database level in PostgreSQL. New information enters the database workflow in the creation phase. Along with the actual object to be added to the database, several meta-data attributes need to be defined. Among these are the source of the information item and its value. The use of data entry forms for user driven information creation can enforce these additional meta-data attributes. Once new information is created, it is integrated with already existing information. In the case of geospatial information, it is checked if geoinformation already exists in the database at the same location of the newly created item. A spatial overlap between new and old items forces their values to be compared, and the information item with the lower value is replaced with the higher value item. The overall values of the database are then updated accordingly, and the information is ready for usage for further analysis and interpretation by the user. Several customized spatio-temporal query functions and views are implemented in order to improve information accessibility during the usage phase of the life-cycle model. The database model supports transaction logging and record archival. Once information is modified or disposed, its previous state can be archived and the database transaction can be logged. In case the modification refers to an update with information with higher value (e.g., better information became available), the archiving mechanism allows previous database states to be retrieved or recovered at any given transaction time. In case of the modification or disposal  
referring to real-world changes, this mechanism allows the full history of an object from its creation (e.g., construction of a building), through modifications (e.g., retrofitting of a building) to its disposal (e.g., destruction of a building) to be archived. The different parts of the database workflow are described in greater detail in the following paragraphs.  
The content of the database can be released and versioned at any time in order to distribute and share it in a decentralized way. Information to be released can be the whole database or subsets of it in the form of summary views. Once information is released, it enters a new workflow. For releases and distributed versioning, we currently use Git [42] and GeoGig [10]. A possible release workflow is schematized in Figure 4 and is further discussed in Section 5.
## 4 Results
### Validation and Application  
Due to a lack of ground measurement data, the assessed flooded extent and affected elements have not been quantitatively verified. Disaster information included in the OCHA report issued in mid-August was used for our qualitative validation analysis. For the estimated flooded extent, 13 provinces and states were affected, and satellite remote sensing results show that Naypytiaw was not included in the list of affected areas in the report and was estimated as only slightly affected. Ayeyarwady was estimated as the most severely impacted area, as stated in the report. The number of villages estimated to be affected was slightly less than the reported 138. The estimated flooded arable land area differs considerably from the reported amount for inundated arable land. The estimated area was 1.6 times that of the reported number. Time differences between remote sensing and reporting,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Province/State** & \begin{tabular}{c} **Flooded** \\ **Extent (km)** \\ \end{tabular} & \begin{tabular}{c} **Affected Township** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Affected Village** \\ **Number** \\ \end{tabular} & \
\begin{tabular}{c} **Flooded Arable** \\ **Land (1000 ha)** \\ \end{tabular} &
\begin{tabular}{c} **Flooded Arable** \\ **Land Proportion (\%)** \\ \end{tabular} \\ \hline Ayeyarwady & 3980.84 & 14 & 488 & 312.1 & 78.4 \\ Bago & 1861.44 & 18 & 485 & 129.7 & 69.7 \\ Chin & 2.56 & 3 & 0 & 8.0 & 31.3 \\ Kachin & 775.25 & 3 & 59 & 60.6 & 78.2 \\ Kayin & 679.93 & 3 & 79 & 48.3 & 71.0 \\ Magway & 1017.51 & 18 & 201 & 56.2 & 55.2 \\ Mandalay & 676.85 & 14 & 35 & 39.8 & 58.8 \\ Mon & 22.08 & 4 & 2 & 2.2 & 99.6 \\ Naypytiaw & 34.62 & 2 & 1 & 1.4 & 40.4 \\ Rakhine & 207.24 & 2 & 9 & 6.2 & 29.9 \\ Sagaing & 2124.31 & 16 & 304 & 175.2 & 82.5 \\ Shan & 325.16 & 8 & 38 & 18.0 & 55.4 \\ Yangon & 627.08 & 3 & 46 & 52.0 & 82.9 \\ Sum & 12,334.9 & 108 & 1747 & 909.7 & 73.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Flood Impacted Townships, Villages, and Arable Land.  
Figure 8: Flooded Residential Area Mapping Product.  
errors of information processing and analysis, and insufficient exposure information updating shaped the estimated deviation.  
More than 20 emergency mapping products and estimation reports of the 2015 Myanmar flood have been produced from a reference map based on pre-flood data to estimate the information on flooded areas drawn from multiple sources. These products have been available to the Myanmar government for emergency management support and have been recognized for their efficient service.
# A Model-Driven Approach for Solving the Software Component Allocation Problem
## 3 Related Work
[30] introduce AQOSA (automated quality-driven optimization of software architecture) toolkit for optimizing component-based architecture designs. In AQOSA, the system architectures are modeled in a supported software architecture description language such as AADL. AQOSA uses a simulation engine to evaluate architecture alternatives. Three objectives are considered: processor utilization, cost, and data flow latency. AQOSA implements several evolutionary multi-objective optimization algorithms to find approximations to the Pareto optimal sets. The optimization process starts with some initial input architectures. Then the tool enters into a metaheuristic loop in which it keeps generating alternative architecture models by applying genetic operations such as crossover and mutation. In [31], the authors describe a model-driven approach to specify domain-specific languages and tools for optimizing architecture variants. The specifications are based on the use of the profile mechanism for extending UML. The authors demonstrate the applicability of their approach on an example from the communication system domain. The approach utilizes a model-to-text generator to transform specified models into executable code, which an optimization framework can execute. Since the approach uses UML's profile extension mechanism, it can optimize the architecture of systems modeled in UML. A model-driven approach for specifying allocation problems and finding feasible allocations is proposed and validated by [PERSON] and [PERSON] [32]. To specify feasibility constraints, the authors define a domain-specific language (DSL), named allocation specification language (ASL), and an Eclipse-based tool that can be used for specifying allocation constraints. The allocation problem is formulated as a 0-1 Integer Linear Program (ILP) and solved by an ILP solver. A model specified in ASL is transformed into a form that the ILP-solver can solve. The approach does not find optimal allocations; instead, it is limited to defining allocation constraints and finding feasible ones. Different types of constraints can be specified, such as timing, priority, and deadlines.
## 4 Discussion
### Body-Slip and Buttock Pressure Changes during Wheelchair Reclining
[PERSON] and [PERSON] showed that when the wheelchair was reclined, pelvic rotation occurred and the buttock center of pressure moved backward. The authors reported that when the wheelchair was laid down, the center of pressure shifted behind the ischial tuberosity and the weight shifted to the lumbar region [18]. This is similar to the results of our study, in which the pressure on the ischial tuberosity area decreased and the pressure in the sacral area increased when reclining was performed in our study. In general, previous studies that analyzed changes in the buttock pressure related to wheelchair reclining have mainly focused on the relief of buttock pressure according to changes in the tilt and reclining angle of the wheelchair. They attempted to determine the optimal wheelchair angle by analyzing the changes in pressure. [PERSON] and [PERSON] also reported a 10% decrease in pressure around the ischial tuberosity for every 10\({}^{\circ}\) backrest angle tilted; however, this was not statistically significant [19]. In a similar study conducted on patients with SCI, IPP decreased by 27% and 47%, respectively, when the wheelchair was tilted posteriorly by 35\({}^{\circ}\) and 65\({}^{\circ}\)[16], and another study reported a 5% reduction in pressure when the wheelchair was tilted from 0\({}^{\circ}\) to 25\({}^{\circ}\)[20]. [PERSON] et al. reported that the maximum buttock pressure decreased by approximately 40% at a tilt angle of 45\({}^{\circ}\) and a recline angle of 120\({}^{\circ}\)[21], and another author reported a 28% reduction in vertical force when the backrest was reclined at 58\({}^{\circ}\)[7; 22]. However, [PERSON] [4] reported that IPP decreased by 14% at a wheelchair tilt angle of 20\({}^{\circ}\), and IPP increased by 12% at a 120\({}^{\circ}\) reclining angle. The author said that adjusting the tilt angle of the wheelchair is more effective in reducing IPP than the reclining angle; this is contrary to our study's finding, which showed a tendency for IPP to decrease during reclining [4]. Considering that the experimental conditions of our study and those of [PERSON] [4] were different, it is difficult to directly compare or interpret the two studies' results.
## 2 Materials and Methods
### Machine Learning Regression  
For analyzing the interactions between dependent and independent variables, multivariable regression analysis (MVRA) has been frequently used in research works related to geosciences, especially blasting operations [5; 50; 52]. However, this approach assumes a linear relationship between variables, which may not yield accurate results in this domain. On the other hand, alternative methods such as polynomial regression can model the complex interactions by incorporating higher-order terms but can lead to overfitting if the degree of the polynomial is too high. Therefore, for more accurate modeling of the complex, intricate relationships, non-linear multivariable regression (NLMR) approaches must be considered. During a comprehensive review of the literature related to modeling of complex non-linear problems in geotechnical and mining engineering, it was discovered that machine learning facilitates fast and accurate predictive analysis as compared to the empirical and numerical modeling approaches. The research works related to the application of machine learning regression for prediction of flyrock have already been summarized in Table 1, and a careful assessment of the literature revealed that many researchers have utilized ANNs as a predictive tool for estimating flyrock distance. However, it is important to note that ANNs are highly sensitive to hyperparameter selection and tend to build complex networks that can overfit smaller datasets (similar to the data used in this study) with higher dimensionality [53; 54]. The importance of exploring a variety of machine learning models, ranging from simple white box models to more advanced alternatives, has been demonstrated in recent studies [55; 56; 57]. This approach enables the assessment of the necessity of complex models and facilitates the development of highly accurate and efficient models.  
Hence, with the aim to develop the best machine learning model that can ensure robust modeling and generate precise predictions for \(\forall y\), a similar approach was adopted by conducting a preliminary evaluation of relatively simple regression models, which included Ridge Regression (RR), Lasso Regression (LR), Elastic Net Regression (ENR), Decision Trees Regression (DTR) and K-nearest neighbors (KNN) regression along with advanced black box regression models such as Support Vector Regression (SVR), Random Forest Regression (RFR), Adaptive Boosting Regression (ABR), Extreme Gradient Boosting Regression (XGBR), and Extremely Randomized Trees Regression (ERTR). The fundamental principles of the models are provided in the ensuing sub-sections.
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 4 Proposed System Overview  
The system that we propose is based on the traditional anonymous download architecture and also incorporates opportunistic networking. Fine-grained targeted adverts are dispatched from broadcasting stations and then are propagated across the nodes (users) of a social network. Our system improves on previous attempts, as it eliminates the need for trust among users, reduces memory and bandwidth overhead and also addresses the issue of security against impersonation and fake-ad injection attacks.  
Our system is designed to be highly versatile in terms of ad-targeting and opportunistic routing. The targeting algorithm, which is the mechanism that determines the user's advertising needs, can operate independently of the rest of the system for as long as it produces an output of a standard format. The opportunistic routing algorithm that we use in this paper follows a probabilistic approach, which is inspired by PRoPHET [17]. However, our model is also compatible with a wide range of other opportunistic networking schemes.
# Metaheuristic with Cooperative Processes for the University Course Timetabling Problem
## 4 Computational Results
### Landscape and Efficacy of SACP
For small03, SACP obtained the optimum in 82 of 100 executions in the first iteration where processes cooperation is not yet applied, and for the remaining 18 executions, only a second iteration of SACP was required to obtain the global optimum. For small04, SACP obtained the optimum in 91 of 100 executions in its first iteration where processes cooperation is not yet applied, and for the remaining 9 executions, only a third iteration of SACP was required to obtain the global optimum. For the other small instances, SACP only required the execution of a single iteration to obtain the optimal solution; naturally the first execution of SACP does not apply processes cooperation. In the case of SAP, the global optimum is also obtained in all small instances, according to the mean, mode, median and standard deviation. The time is similar and does not reach more than 10 s in both cases (SACP vs. SAP). Figure 8: Processes with cooperation SACP and without cooperation SAP, in function of d-Hamming with 48 processes. Results of 30 tests. (**a**) Instance large01. (**b**) Instance large02. In the case of medium and large instances, SACP is always better than SAP in obtaining the best solution. The best mean and median value is for SACP; in the case of mode, SAP is best only in medium02. In the standard deviation, SAP is best in all except medium02. The execution time is a little longer for SACP because cooperation between processes is applied in this algorithm, which is a little more time consuming, and in SAP there is no cooperation between the processes. Figure 9: Best solution obtained by SACP with 48 processes with the soft constraints. (**a**) Instance large01. (**b**) Instance large02. Table 5 presents a comparison of the best results obtained with SACP and other algorithms present in the literature. In the case of small problems, the best and worst result is presented in each algorithm. As can be seen for small01, in 100 executions, SACP always finds the global optimum (0-0) and, for example, in 100 executions, GA (0-19) finds the global optimum but not always, so the worst value found is 19. The value presented in the table in parentheses is the average rank and, as it can be seen, SACP always achieves first place in all the revised instances. In some cases, some algorithms presented in the literature do not find a solution for large instances, this is represented with * (ACO, GA and SA), which does not happen in SACP because it always finds solutions for any instance.
## 2 Concept Formulation
### Consensus Protocol  
Definition 1: Let \(\{x_{i}\in R,t\in\mathcal{T},i=1,...,N\}\) be \(N\) processes, where \(\mathcal{T}\) is time index set, and in the time continue situation \(\mathcal{T}=[0,\infty)\), in discrete time situation \(\mathcal{T}=\{t_{0},t_{1},...,t_{j},...\}\), where \(t_{j}(j=0,1,...)\) is monotone increasing of discrete time. If,  
\[\lim_{\mathcal{T}\
i t\rightarrow\infty}|x_{i}(t)-x_{j}(t)|=0 \tag{3}\]  
where \(i,j=1,...,N\), then the dynamic network \((\mathcal{G},\mathbf{x})\) is weak consensus.  
Definition 2: Let \(\{x_{i}\in R,t\in\mathcal{T},i=1,...,N\}\) be \(N\) processes, if there exists \(x^{*}\in R\), lead  
\[\lim_{\mathcal{T}\
i t\rightarrow\infty}x_{i}(t)=x^{*} \tag{4}\]  
where \(i=1,...,N\), then the dynamic network \((\mathcal{G},\mathbf{x})\) is strong consensus.  
Definition 3: Let \(h:R^{N}\to R\) be a function with \(N\) variables, and \(\{x_{i}\in R,t\in\mathcal{T},i=1,...,N\}\) are \(N\) processes, if for arbitrary \(x_{i}(0)(i=1,...,N)\), there will be  
\[\lim_{\mathcal{T}\
i t\rightarrow\infty}x_{i}(t)=h(x_{1}(0),x_{2}(0),...,x_{ N}(0)) \tag{5}\]  
where \(i=1,...,N\), then the dynamic network \((\mathcal{G},\mathbf{x})\) is \(h\)-consensus. \(h(x_{1}(0),x_{2}(0),...,x_{N}(0))\) is called the group decision value--especially, when \(h(x_{1}(0),x_{2}(0),...,x_{N}(0))=\frac{1}{N}\sum\limits_{j=1}^{N}x_{j}(0)\), is called as average consensus.  
If the dynamic network \((\mathcal{G},x)\) is weak consensus (strong consensus, \(h\)-consensus), and \(\
u\) is called a weak consensus (strong consensus, \(h\)-consensus) protocol.