esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Calibration and Validation of Simulations  
Our study involves comparing two potential renovation approaches and examining the impact of different typhoon conditions on roof wind pressures. While simulation results can typically be validated through wind tunnel experiments or field measurements, when both methods are impractical, validation work was completed prior to formal simulation. To verify the accuracy of the simulation for ancestral hall architecture, wind tunnel test results of a gabled-roof low-rise building with ewes from the Tokyo Polytechnic University (TPU) Aerodynamics Database were utilized. TPU conducted a series of wind tunnel tests on various geometric models and provided open-source software for data comparison and validation [29].  
Type O was selected from the TPU database as the validation case, representing low-sloped roof buildings with proportions similar to the hall volume in ancestral architecture and lacking significant ewes. The tested building model, depicted in Figure 7, has dimensions of 24 m in length (D), 16 m in width (B), and 12 m in height, with a roof slope of 26.7\({}^{\circ}\). The height-to-length ratio is 1:2, consistent with the proportions of the hall in ancestral architecture. The wind speed at a height of 10 m is 23.4 m per second, with an average wind speed profile exponent of 0.20. Steady-state simulations were conducted using the SST k-\(\omega\) turbulence model to obtain the distribution of mean wind pressure coefficients on the building surfaces, which were subsequently compared with the TPU wind tunnel experimental results. As shown in Figure 7, there is a high degree of consistency between the CFD simulation results and the TPU wind tunnel experimental results. Therefore, the CFD method employed demonstrates reliability and suitability for the preliminary analysis of the wind pressure distribution on building surfaces.  
Before validating the simulation of the ancestral hall building, a grid resolution sensitivity analysis was conducted on the TPU building model to determine the optimal grid resolution for subsequent simulations. The effects of three grid sizes (0.1 m, 0.25 m, and 0.4 m) on the calculated roof wind pressure coefficients were compared. The results indicated that the deviation between the 0.25 m grid and the TPU results was small, with high computational efficiency. Thus, a grid size of 0.25 m was utilized for subsequent analysis. The turbulence model and computational settings for the validation analysis remained consistent with the previous settings, and the wind direction angles were set in accordance with the TPU database. The experiments showed that the wind pressure distribution calculated using the SST k-\(\omega\) turbulence model was acceptable, with reasonable agreement between the numerical simulation and wind tunnel test results. This consistency  
Figure 7: Comparison of CFD calculation results with TPU database: (**a**) geometric model; (**b**) TPU database; (**c**) CFD calculation results.  
indicates that the method is suitable for simulating the surface wind pressures of ancestral hall buildings.
## 2 Theoretical Frameworks
### Ahp  
The Analytical Hierarchy Process (AHP) is a method that is used to assign weights to criteria in a multi-criteria decision-making process [11]. This method is used to rank a set of alternatives or to select the best in a set of alternatives. The ranking or selection is performed with respect to an overall goal, which is broken down into a set of criteria. In summary, the method aims at establishing weights to be associated with each criterion in defining the overall goal [12]. The AHP is structured in a hierarchy consisting of a minimum of three levels, i.e., the top level, the second level, and the lowest level. The top, second, and lowest levels are represented by the goal of the decision-making problem, the criteria, and the alternatives, respectively. The AHP consists of four main steps in its analysis. The first step involves identifying the goal, criteria, and alternatives. In the second step, the weights of the alternatives are determined based on pairwise comparisons. In the third step, the criteria are ranked using the alternative weights. Step four is a criteria-alternative performance scores analysis [13]. The pairwise comparison method compares the relative importance of each attribute in relation to the other attributes. The importance of each attribute is dependent on the responses of the decision makers, which are considered to be devised based on their experience and the information about the attributes that is available. The decision makers make the judgements, which are represented by numbers on a fundamental scale. One of the earliest scales of comparison was introduced [14] in the 1980s with the introduction of the standard scale of 1-9, where an integer value of 1 indicates equal importance, 9 indicates extreme importance, and reciprocals are considered to indicate a degree of less importance; that is, 1/9 indicates very low importance [14]. The AHP is a reliable method and has been applied to a variety of industries and in a variety of decision-making problems, including integrated resource planning, environmental impact assessment, climate change negotiations, etc. [12].
## 4 Numerical Analysis for Verification
### Coupled Strain Separation  
The coupled strain time history data are collected by the long-gauge FBG sensors for four load cases. The coupled strains were then decoupled to obtain vertical bending strains for the tunnel unit via the proposed method. The results are shown in Figure 6.  
Figure 6 illustrates the separated vertical bending strain along the tunnel in case C3. Focusing on point 1 on the section as the research object, Figure 6a,b shows the distribution of measured strain and vertical bending strain, respectively. The vertical  
Figure 5: Load conditions: (**a**) case C1; (**b**) case C2; (**c**) case C3; (**d**) case C4.  
Figure 6: Strain separated from coupled strain in case C3: (**a**) monitored strain; (**b**) vertical bending strain.  
bending strain is noticeably smaller in comparison to the measured strain. Moreover, the vertical strain distribution estimated from the measured strain is in close accordance with the actual values.  
To further evaluate the accuracy of the estimated vertical bending strain, the separated strain was compared to the actual strain and the relative error (RE) was calculated. The results are shown in Figure 7.  
Figure 7a-d shows the REs of separated strains in all four cases. Analyzing the RE values for the separated strain responses involved determining the maximum absolute value of RE (MAVRE):  
\[\text{MAVRE}=\text{max}(|\text{RE}|) \tag{16}\]  
In all four cases, the MAVRE of the estimated vertical bending strain predominantly occurred on the unit near the support. Most did not exceed 0.5%. As shown in Figure 7a-d, the difference between the maximum and minimum values was generally maintained within a range of 0.3%. This implies that the proposed method ensures a high level of accuracy in calculating the vertical bending strain for all tunnel units, irrespective of the load conditions.
## 3 Numerical Simulation of CSAMT  
Numerical simulation research not only enhances people's understanding and knowledge about the exploration methods but also assists in selecting the correct exploration approach and setting appropriate field acquisition parameters. Therefore, we employed a three-dimensional CSAMT forward modeling program based on the coupled finite element-infinite element method to conduct a numerical simulation on the geophysical model of the Jinding lead-zinc mine. Three-dimensional CSAMT forward modeling using the coupled finite element-infinite element method is an efficient numerical simulation technique for electromagnetic fields, which combines the advantages of the finite element method in handling complex geological structures and boundary conditions with the characteristics of the infinite element method in simulating infinite far-field attenuation. This method achieves rapid and high-precision forward modeling, reduces the computation domain and the number of nodes, accelerates the computation, and is of great significance to the development of exploration geophysics [20,21].
## 2 Material Properties  
### GeSbTe Alloys  
Germanium-antimony-tellurium (GST) alloys are a type of phase change material from the general group of chalcogenide glasses. Within the GST alloy system - the most widely used memory materials for phase-change applications - are the GeTe-Sb\({}_{2}\)Te\({}_{3}\) pseudobinary compounds. This can be viewed as a mixture between the two binary compounds GeTe and Sb\({}_{2}\)Te\({}_{3}\). The time lapse images from HTOMPT (high-throughput optical mapping of phase transition) of the nonisothermal crystallization measurements are used to extract the transition temperature of different compositions and shown in Figure 1. Compounds on this pseudobinary line have different behaviors due to the variation of material composition. Moving along the pseudobinary line, from Sb\({}_{2}\)Te\({}_{3}\) to GeTe, the melting point, glass transition temperature, data retention, and activation energy increase, while the crystallization speed decreases [18].  
Figure 1: The transition temperature as a function of composition. The black lines represent the position of the know GeTe-Sb\({}_{2}\)Te\({}_{3}\) pseudobinary line as well as the observed valley of lowest crystallization along the Sb\({}_{2}\)Te\({}_{3}\)-Ge\({}_{2}\)Te\({}_{3}\) compositions. Adapted with permission from Reference [18]. ©2017 American Chemical Society.  
In 1979, [PERSON] proposed a theory explaining the effect of the number of bonds per atom (coordination number) in chalcogenide alloys [19]. When the average coordination number for a material is between 2 and 3 (preferably 2.45), the ability of this material to form an amorphous state is high. By applying this theory into the phase-changing material GST, based on the assumption that the average coordination number can be calculated from the maximum number of bonds for the atoms, the Sb\({}_{2}\)Te\({}_{3}\) easily tends to form an amorphous state due to the coordination number being around 2.4, while is close to the ideal value. GeTe is less apt to form into an amorphous state because the coordination number is 3, which is relatively away from the ideal value [20]. Therefore, as a mixture of GeTe and Sb\({}_{2}\)Te\({}_{3}\), the Ge-Sb-Te material becomes less apt to form an amorphous state as the proportion of GeTe increases, because of more GeTe content in the material, the greater difference between the average coordination number to the ideal number. In GeTe and GeTe-rich compounds, as indicated in the top region of the pseudobinary line, the number of excess vacancies move the Fermi energy to the region of extended states, which results in a metallic behavior. For the vacancy-rich GST compounds in the middle part of the line, the Fermi level lies in the region of localized states and the system exhibits insulating behavior. At the bottom region of the line, it becomes increasingly difficult to obtain a cubic phase experimentally. It has been found that Sb\({}_{2}\)Te\({}_{3}\)-rich compounds exhibit stable hexagonal structures with very elongated primitive cells [21].
# Joint Communication-Motion Planning in Networked Robotic Systems
## 2 Conventional Relay-Assisted Robot System
### Performance Assessment: Orient
#### 2.2.1 Communication Performance Assessment  
As discussed in [14], the realistic channel model (RCM)-based connectivity quality metric (CQM) may better capture the communication performance of the NRSs in realistic environments. Hence, we adopt the packet error ratio (PER) as the metric for quantifying the communication performance.  
Next, we quantify the relationship between the communication energy and PER. According to [15], then, the instantaneous per-hop PER between node \(i\) and \(j\) is given by [15].  
\[\text{PER}_{n}\big{(}\gamma_{ij}\big{)}\approx\bigg{\{}\begin{matrix}1,&\text{ if }0<\gamma_{ij}<\gamma_{pn}\\ a_{n}\exp\big{(}-g_{n}\gamma_{ij}\big{)},&\text{ if }&\gamma_{ij}\geq\gamma_{pn} \end{matrix} \tag{1}\]  
where \(\gamma_{ij}\) is the received signal-to-noise ratio (SNR) at node \(j\) received from node \(i\), \(\gamma_{pn}\), \(a_{n}\) and \(g_{n}\) are relative to the transmission mode index \(n\), which represents the specific modulation and channel coding rate as shown in Table II of [15]. We may select a mode from a set of six modes for different scenarios. As the channel is Rayleigh-faded, the resulted per-hop PER is  
\[\begin{array}{ll}\text{PER}_{n}\Big{(}\overline{\gamma}_{ij}\Big{)}&=\int_{ 0}^{+\infty}\text{PER}_{n}\big{(}\gamma_{ij}\big{)}\frac{1}{\gamma_{ij}}\exp \Big{(}-\frac{\gamma_{ij}}{\overline{\gamma}_{ij}}\Big{)}d\gamma\\ &=1-\exp\Big{(}-\frac{\gamma_{pn}}{\overline{\gamma}_{ij}}\Big{)}+\frac{a_{n} }{g_{n}\overline{\gamma}_{ij}+1}\exp\big{(}-g_{n}\gamma_{pn}\big{)}\exp\Big{(} -\frac{\gamma_{pn}}{\overline{\gamma}_{ij}}\Big{)}\end{array} \tag{2}\]  
where \(\overline{\gamma}\) is the average received SNR. As the channel coefficients over the two hops are mutually independent and the relay is decode-and-forward, we can obtain the end-to-end PER of the relay-assisted channel.  
\[\begin{array}{ll}&\text{PER}_{n\!f}^{62\!e}\\ &=\ 1-[1-\text{PER}_{n}(\overline{\gamma}_{sr})][1-\text{PER}_{n}(\overline{ \gamma}_{rd})]\\ &=\ 1-\Big{[}\exp\Big{(}-\frac{\gamma_{pn}}{\overline{\gamma}_{sr}}\Big{)}- \frac{a_{n}}{\overline{\gamma}_{sr}\overline{a}_{n}+1}\exp\big{(}-g_{n}\gamma _{pn}\big{)}\exp\Big{(}-\frac{\gamma_{pn}}{\overline{\gamma}_{sr}}\Big{)} \Big{]}\\ &\times\ \ \Big{[}\exp\Big{(}-\frac{\gamma_{pn}}{\overline{\gamma}_{rd}}\Big{)}- \frac{a_{n}}{\overline{\gamma}_{nr}\overline{a}_{nr}\overline{a}_{nr}\overline {a}_{nr}\overline{a}_{nr}\overline{a}_{nr}\overline{a}_{nr}\overline{a}_{nr} \overline{a}_{nr}\overline{a}_{nr}\overline{a}_{nr}\  
allocation. Hence, we opt for the asymptotic expression of Equation (3) by omitting the \(o\left(\frac{1}{\gamma}\right)\) components.
## 2 Study Area and Datasets  
The Qingjiang River basin is located in the south-central region of China, specifically in southwestern Hubei Province (108\({}^{\circ}\)35\({}^{\prime}\)-111\({}^{\circ}\)35\({}^{\prime}\) E, 22\({}^{\circ}\)33\({}^{\prime}\)-30\({}^{\circ}\)50\({}^{\prime}\) N), as illustrated in Figure 1. It is a tributary of the upper Yangtze River, with a stream length of approximately 423 km. It drains a total area nearing 16,700 km\({}^{2}\), which is predominantly characterized by mountains and hills, accounting for 80% of its land cover. The elevation in the west is higher than that in the east. The basin experiences an average annual temperature of approximately 14 \({}^{\circ}\)C, and receives an average annual precipitation of approximately 1400 mm. The climatic influence of the humid subtropical monsoon is evident, with nearly 45% of the annual precipitation being received during the summer months (June-August). The rainstorms in summer are responsible for the basin's seasonally variable flow regime, which contribute to around 40% of the annual streamflow. It is noteworthy to mention that human-induced activities, particularly dam constructions, affect the downstream regions of this basin. To reduce such influences and focus on more natural flow regimes, this study is centered on the upper Qingjiang River basin, which is relatively less affected by anthropogenic interventions. The outlet of this section is the Shuibuya hydropower station.  
Table 1 presents a comprehensive list of datasets used in this study, including observed meteorological data and four EPFs collected during the summer periods of 2014-2018, as well as observed hydrological data collected during the summer periods of 2014-2017. The observed meteorological data, including air temperature, precipitation, and potential evaporation, were collated from 175 automatic meteorological stations distributed across the upper Qingjiang River basin. The basin-averaged values were derived through arithmetic mean calculations using these observations. The observed precipitation was used to assess and post-process the EPFs as a benchmark, as well as to calibrate the hydrological model together with the other observed meteorological data. The EPFs, used for precipitation and streamflow forecasts, were obtained from four renowned NWP centers, i.e., the CMA,  
Figure 1: The Qingjiang river basin.  
ECMWF, JMA, and NCEP. The ensemble numbers for these EPFs are 15, 50, 26, and 20, respectively. The values of the EPFs collected every six hours were aggregated with daily metrics. All the gridded EPFs were then translated to basin-averaged values using the Thiessen polygon approach. Consistent with previous findings that precipitation forecasts lose their skill beyond seven days, this study incorporates forecasts only up to one week. Lastly, the observed hydrological data obtained from the Shuibuya hydropower station was used for hydrological model calibration and validation. Based on these datasets, this study post-processed EPFs from the summers of 2014-2018 and conducted hydrological modeling from the summers of 2014-2017.
## 3 Results
### Spatial Autocorrelation: Global Moran's I Statistic  
Initially, the level of statistical clustering of the different indicators was analyzed to evaluate the degree of spatial randomness of their numerical distribution. To evaluate this behavior we resorted to Global Moran's I spatial autocorrelation statistic [29] and the city of Lorca was discretized spatially into 53 areas of homogeneous conditions to simplify the analysis of data with a greater number of elements.  
To avoid possible bias in the results because of the design of homogenous areas, these divisions essentially consider the administrative and historical structure of the city's neighborhoods, but other design criteria have also been introduced to assure homogeneity. The segmentation of the city was carried out by selecting areas with a significant number of buildings and following a criterion of 80/80/80 percentage (all areas with at least 80% of its buildings built in the same time frame of 20 years, 80% of soil classified in the same category according to previous work [17], and 80% of its surface under similar orographic conditions). The results for each of the indicators can be seen in Table 1.  
If we analyze the numerical results in detail, we can see that in theory none of the indicators have a significant a priori spatial autocorrelation level. The statistic values are generally low, as is the \(z\)-score value, and the value of p does not exceed the 1% significance level in any case. Only the values related to the soil (0.71 for \(I_{SAC}\) and 0.63 for \(I_{SF}\)) present a tendency toward clustering, with a certain level of spatial autocorrelation being reasonable due to the more or less grouped distribution of terrain layers and layers with homogeneous geotechnical properties. However, it can be stated that in theory, at this initial stage these indicators present an independent spatial distribution not subject to the effect of other indicators. In particular, it is of special interest to observe the moderately low distribution of the indicator of the intensity of damage to buildings, which in principle suggests a statistically random distribution of this indicator at the spatial level.
## 4 Results and Discussion  
### Prediction and Analysis of Land Subsidence  
After applying the SSA-LSTM neural network to the training and testing samples of monitoring point 1, the output of the network was obtained. Using de-normalization and comparison with the actual values, the prediction residuals were calculated. As shown in Table 5, the model outputs the predicted settlement values, actual values, and their prediction residuals for the training samples. Based on Table 5 and Equations (10)-(12), the MAE, MSE, and RMSE for the LSTM neural network training samples were calculated to be 0.3650, 0.1456, and 0.3816, respectively. The corresponding values for the SSA-LSTM neural network were 0.0216, 0.0008, and 0.0278, respectively. By comparing the evaluation metrics of the two models, it is evident that the SSA-LSTM has significantly improved in terms of prediction accuracy, resulting in more stable predictions. Some of the model's output for test sample settlement predictions, actual values, and prediction residuals are presented in Table 6.  
Based on Table 6 and Equations (10)-(12), the MAE, MSE, and RMSE values of the LSTM neural network on the test samples were 0.4020, 0.1693, and 0.4115, respectively. In contrast, the MAE, MSE, and RMSE values of the test samples from the SSA-optimized LSTM neural network were 0.0184, 0.0004, and 0.0207, respectively. Comparing the prediction evaluation indicators of the two models on the test samples, it was evident that the SSA-LSTM model notably surpassed the original LSTM model regarding forecast precision and robustness. Therefore, based on these evaluation results, the SSA-LSTM model is more suitable as a prediction model for land subsidence.  
When the sample data were input into other commonly used models, as shown in Figure 8, the residual values of the SSA-LSTM model were distributed around 0 with minimal fluctuations. The residual values of the other three models had shown larger fluctuations, indicating poor prediction accuracy. The SSA-LSTM model had the highest and most consistent prediction accuracy, further demonstrating its suitability for predicting ground settlement.
## 3 Data and Preprocessing
Therefore, it is essential for subsequent classification models to be sensitive enough to capture these minor shifts in data characteristics, which could enhance fault detection performance. At the full-dataset level, we extracted statistical features from each sample sequence, including mean, range, and standard deviation for each of the seven measurable variables (excluding the non-comparable timestamp). This process compressed the original sample dimension from (28,389, 256, 7) to (28,389, 21). Subsequently, sample labels were incorporated into the correlation analysis, and a Pearson correlation heatmap was generated to illustrate global feature-label relationships, as shown in Figure 3.  
Figure 1: Feature correlation heatmaps: normal vs. faulty sample. Figure 2: Feature correlation heatmaps: All normal samples vs. all faulty samples. The results indicate that many features in the battery operation data exhibit varying degrees of correlation with the fault label. For instance, as shown in Figure 4, the mean values of maximum and minimum cell temperatures in faulty samples are generally higher than those in normal samples, potentially reflecting risks of thermal runaway or overheating. To confirm the statistical significance of these differences, independent two-sample t-tests and Mann-Whitney U tests were performed on the per-sample mean values, yielding \(p\)-values less than 0.001, thus indicating a highly significant difference. Overall, the correlation analysis demonstrates that battery faults can lead to subtle changes in intra-sample feature relationships, and, at the population level, all measured features provide useful discriminative information for fault detection. Therefore, in subsequent model development, we retain all original features without dimensionality reduction  
Figure 4: Max/Min cell temperature box plots: normal vs. faulty samples. Figure 3: Global feature correlation heatmap (all samples). or feature selection, enabling the deep learning model to capture spatial coupling and weak abnormal signals among multivariate time-series data.