esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Model Training and Evaluation
The mispredicted landslide pixels were also mainly located in elevation 174.78-358.94 m (76.4%), the lithology of sedimentary carbonate rocks (73.6%), the yellowish red soil on claystone and metamorphic rocks (87.5%), distance to fault >700 m (76.4%), and distance to river >200 m (79.2%). Distribution of the mispredicted landslide pixels in the classes in the other factors was more even. Regarding the mispredicted non-landslide pixels, they were mainly located in the distance to river >200 m (79.3%), the dense forest land (69.0%), and the yellowish red soil on claystone and metamorphic \begin{table}
\begin{tabular}{c l c} \hline \hline
**No.** & **Removing Factor** & **Classification Accuracy-CLA (\%)** \\ \hline
1 & Slope & 91.74 \\
2 & Aspect & 92.31 \\
3 & Elevation & 92.49 \\
4 & Land cover & 93.60 \\
5 & Soil type & 93.59 \\
6 & Lithology & 91.97 \\
7 & Distance to fault & 92.83 \\
8 & Distance to river & 93.35 \\
9 & Distance to Fault and Soil type & 91.69 \\
10 & Elevation, Land cover, Distance to fault and Soil type & 89.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Contribution of the landslide predisposing factors to the BE-LMT model. Figure 7: Confusion matrices and performance measures of the three landslide models using the training dataset: (**a**) the BE-LMTree model; (**b**) the LMTree model; and (**c**) the SVMC model. The global prediction capability of the BE-LMTree model is summarized and presented using the ROC curve and AUC (Figure 10). It can be seen that AUC is 0.834, indicating that the prediction capability of the proposed model is 83.4%, which is a high prediction capability.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 3 Test Cases  
Five different test cases were analyzed in this work to be representative of a wider range of conditions, considering the main features of the involved areas/rivers, as well as the events dynamic and damage. In addition, reference information was available for all these events, making them suitable for our analysis. The main information on the investigated events is reported in Table 2, where we also indicate the information of the S1 image processed to produce the results. It is clear that more data (before, during, and after the event) could have been used to better describe the dynamics of the events in the multi-temporal domain; however, this kind of analysis is beyond the scope of this paper.  
A brief description of each event, the localization of which is shown in Figure 1, is presented below.  
Figure 1: Localization of the study area. Flooded areas, as detected by CEMS, are shown in blue.
## 3 Numerical Results
### Compared Transient Responses of w(a/2,b/2) and \(\sigma_{x}\)  
Transient of w(\(a/2\),\(b/2\)) response vs. \(t\) and \(\sigma_{x}\) response vs. \(t\) for \(t\) = 0.001 s-0.025 s are obtained under the TSDT with \(c_{1}\) = 0.925925/mm\({}^{2}\), \(L/h^{*}\) = 5, and \(k_{c}c(t)\) = 0, \(\omega_{11}\) = 0.008380/s, \(\gamma\) = 15.707963/s, \(\theta_{\rm L}=30^{\circ}\), and \(60^{\circ}\), respectively. Also, it used \(h_{1}\) = \(h_{2}\) = 0.55 mm, \(h_{3}\) = 0.1 mm, \(R_{n}\) = 1, advanced \(k_{\alpha}\) = -3.535402, \(T\) = 600 K, and \(\overline{T}_{1}\) = 100 K. Figure 5 shows compared transient \(w(a/2\),\(b/2)\) for \(\theta_{\rm L}=30^{\circ}\) and \(60^{\circ}\). The w(\(a/2\),\(b/2\)) transient values are in greater oscillations under \(\theta_{\rm L}=60^{\circ}\) are presented. The compared \(\sigma_{x}\) on \(z=0.5h^{*}\) for \(\theta_{\rm L}=30^{\circ}\) and \(60^{\circ}\) are also presented. The transient \(\sigma_{x}\) values are in greater oscillations under \(\theta_{\rm L}=60^{\circ}\). The compared transient w(\(a/2\),\(b/2\)) vs. \(t\) and \(\sigma_{x}\) vs. \(t\) on \(z=0.5h^{*}\), \(\theta_{\rm L}=60^{\circ}\) are also obtained for \(k_{c}c(t)\) = \(10^{9}\) and \(k_{c}c(t)\) = 0, respectively, in Figure 6. Thus, the transient oscillating values can be reduced into smaller under adjusted control value with \(k_{c}c(t)\) = \(10^{9}\) of control law.  
Figure 5: _Cont._Figure 5: Advanced transient responses of \(L/h^{*}=5\) for: (**a**) w(\(a/2\),\(b/2\))(mm) vs. \(t\)(s); (**b**) \(\sigma_{x}\)(GPa) vs. \(t\)(s).
## 2 Proposed Simulation Framework  
During the design cycle of COVID-19 temporary hospitals, the analysis objects in different design phases may present building information with different levels of detail (LODs). Specifically, in the initial phase, the analysis object is the conceptual design, which only contains low-LOD information relating to building configurations and sizes; in the transition phase, the analysis object is the temporary hospital with detailed design drawings or a building information model (BIM); and in the final phase, the analysis object is the overall built environment including the temporary hospital and surrounding buildings. It is worth noting that while detailed design drawings of the temporary hospital are available in the later design phase, it is challenging for engineers to collect the detailed drawings or BIMs of all surrounding buildings in a limited period given that the surrounding buildings may be constructed by different companies and may also include old buildings that may lack the requisite detailed drawings. The proposed high-efficiency simulation framework (see Figure 1) adapts to the building information with different LODs during various design phases of the temporary hospital.  
The simulation framework is composed of three modules: (1) high-efficiency modeling of the temporary hospital, (2) cloud computing-based CFD computation, and (3) monitoring and visualization of the harmful air. In Module 1, the geometric model of the temporary hospital building is firstly constructed, after which detailed information of other objects (i.e., computational domain, air outlets, wind profile in the urban boundary layer (UBL), monitoring points and slices for recording the concentration of harmful air) that are required for the CFD simulation, is supplemented to build a complete FDS model. To handle building information with various LODs during the three design phases mentioned above, three high-efficiency modeling methods are proposed correspondingly and have been integrated into the specially-developed FDSCGenerator program. In Module 2, the simulations and analyses of cases with various wind profiles and design schemes are completed based on cloud computing technology. In Module 3, the simulation results are visualized to help engineers evaluate design schemes in an improved manner.
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 6 Experimental Results and Comparison
### Comparison and Evaluation  
The main contributions of this study include the proposal of a comprehensive framework that improves both the prediction and anomaly detection performance of traditional methods. For predictions, we propose the BIC to improve the fitting degree of the ARIMA model and mitigate the over- or underfitting problems caused by fixed parameters. This approach not only improves the prediction accuracy of the model but also reduces the training cost of the ARIMA process. This new design approach combines different models to overcome their individual limitations. For anomaly detection, we developed an independent anomaly detection process. The advantages of this method are as follows. First, only a small amount of data is used to train the model and achieve a high detection accuracy. This approach reduces the training cost and provides useful guidelines for the application of machine learning technology. Second, this method overcomes the main issue of general methods of regression model-based anomaly detection, which over rely on the outputs of prediction models, by proposing a rational and reference-based detection process. To assess these contributions, a comparison is performed from two perspectives: (1) for prediction, a comparison of the original ARIMA model and the BIC-improved ARIMA model is made; (2) for anomaly detection, a comparison of a general SVM with a fixed detection threshold and the FOA-SVM approach with a variable detection threshold is made. The test data used in the comparisons are 5-day raw power plant data, and the training data used are the benign data introduced in Section 5.1 benign data training.
# Fast Type-II Hartley Transform Algorithms for Short-Length Input Sequences
## 5 Discussion of Computational Complexity  
Analyzing the obtained results, we note that for \(N=4\), the proposed algorithm did not differ from the existing algorithms by the number of multiplications. If \(N=8\), the number of multiplications may be reduced with the proposed solution by \(33\%\) compared to [PERSON]'s algorithm [24] and [PERSON]'s algorithms [26; 27]. Compared to the prime factor algorithm [23], the number of multiplications for \(N=8\) was not reduced. The number of additions for \(N=8\) was reduced by \(7\)-\(46\%\) compared to [PERSON]'s algorithm [24], the prime factor algorithm [23], and the radix-\(2\) algorithms [24; 25]. Compared to [PERSON]'s algorithms [26; 27], the number of additions for \(N=4\) was not reduced and the number of additions for \(N=8\) was increased by \(8\%\). For \(N=4\), the number of additions was reduced by \(40\%\) and \(67\%\) compared to the algorithms from [23; 24], respectively.  
The constructive difference between the proposed DHT-II algorithms and the existing algorithms is that the critical path in the signal flow graph of any of the presented algorithms contains only one multiplication. If there is more than one multiplication on the critical path of the algorithm, then the multiplication of two \(n\)-bit operands results in a \(2n\)-bit product. Repeated multiplication requires more manipulation of the operands and, therefore, requires more time and effort compared to multiplication alone. In fixed-point devices, this may result in overflow-underflow processing. To maintain accuracy, double access to memory is required both when writing and when reading. Using floating point arithmetic requires exponent alignment, mantissa addition, etc. Thus, the presence of more than one multiplication on the critical path for the known algorithms is a noticeable drawback.  
Through this research on the proposed DHT-II algorithms, the following shortcomings of the traditional algorithms were determined. The number of additions and multiplications for the algorithms described in the literature, in general, is greater compared to the proposed algorithms. In addition, traditional algorithms often limit the length of the input data sequence to powers of two or three. However, the application of the proposed algorithms may be limited by the following. First, the structural approach is more suitable for constructing fast algorithms for short data sequences. For long data sequences, the structure of the transformation matrices is more difficult to identify. Second, the efficiency of synthesizing fast algorithms based on the structural approach depends on the structural properties of the transformation matrices, namely, how efficiently these matrices can be transformed so that their block structure corresponds to the matrix patterns formulated in [5].
# Research on Intelligent Optimization of Wellbore Trajectory in Complex Formation
## 4 Algorithm Design Process  
As a swarm intelligence optimization algorithm, MOPSO exhibits significant differences from traditional multi-objective optimization methods such as NSGA-II and TOPSIS  
way, the particles in the sub-population can balance the diversity and convergence by tracking their respective neighborhood optimal solutions, which effectively avoids the problem that the whole population loses diversity due to prematurely falling into the local optimum in the global mode. For individuals in vulnerable groups, although they have good diversity, their convergence is weak. In order to accelerate the convergence process of these individuals, we introduced an elite learning strategy to achieve this goal by optimizing the velocity update formula of particles in vulnerable groups.  
This paper proposes an improved MOPSO algorithm and presents the optimization workflow in Figure 6. Figure 6 outlines the optimization workflow of the improved MOPSO algorithm, which includes the following key steps:  
**Step 1:** Uses Latin hypercube sampling (LHS) to generate the initial population, improving solution diversity.  
**Step 2:** Evaluates the fitness of particles, considering factors such as wellbore trajectory smoothness, drilling cost, and operational constraints.  
**Step 3:** Clusters the population into Elite Group, Standard Group, and Inferior Group based on fitness values to enhance search efficiency.  
**Step 4:** Applies Gaussian mutation to individuals in the Elite Group to enhance local search capability and prevent premature convergence.  
**Step 5:** Utilizes K-means clustering to ensure balanced population distribution and further improve optimization effectiveness.  
**Step 6:** Terminates the algorithm and outputs the optimal solution once the maximum iteration count or convergence criteria are met.  
By introducing a grouping strategy and elite learning strategy into the MOPSO algorithm, the performance and stability of the algorithm are effectively improved. In the  
Figure 6: (**a**) Flowchart of the original MOPSO algorithm. (**b**) Flowchart of the improved MOPSO algorithm.  
initialization phase, the empty matrix of elite group, ordinary group and inferior group is predefined, and three sub-groups are dynamically updated according to the fitness value in each iteration, so as to realize the hierarchical management of particle swarm. The elite learning strategy enhances the diversity of particles and avoids the algorithm falling into the local optimum by adding Gaussian noise to the elite particles. In the main cycle, the elite learning operation is dynamically called to continuously optimize the elite group. These improvements not only improve the ability of the algorithm to solve multi-objective optimization problems, but also improve the convergence efficiency and result quality, and provide stronger technical support for solving complex optimization problems.
## 3 Methodology  
### Preliminary  
The multi-sensor spatial-temporal graph is defined as \(G=(V,E,A)\), and the input signals with \(T\) time slices can be represented as \(X=\left\{X^{(t-T+1)},X^{(t-T+2)},\cdots,X^{(t)}\right\}\in\mathbb{R}^{T\times N \times C}\). The task of the proposed STAGCN is to learn mapping between \(G\) and the tool wear \(y^{t}\) based on the input \(X\). Figure 1 illustrates the overall framework of the STAGCN proposed in this paper. The STAGCN consists of an embedding layer, multiple stacked spatial-temporal layers, and two fully connected layers as the output layer. Each spatial-temporal layer is composed of AGCN for adaptively capturing spatial dependencies, Bi-Mamba for capturing temporal dependencies, and MTLA for effective feature fusion. The details of these components are introduced in the subsequent sections.
## 2 Materials and Methods
### The Probe  
Frequency Domain Reflectometry (FDR) was used to determine moisture content in the soil. The proposed FDR probe measures the capacitance based on the change in frequency of reflected radio waves at the resonance frequency of 433 MHz. The probe primarily comprises a pair of circular plate capacitors, with the soil acting as the dielectric. The sensor capacitance is proportional to the polarization between the measured dielectric constant and the medium.  
The probe was designed to accommodate a wide range of dielectric permittivity from 2 to 15, allowing it to measure soil moisture and the electromagnetic wave propagation properties at a target depth. This adaptability ensures that the probe can be used in various soil conditions. It operates in a frequency domain within the range of 433 MHz. A key advantage of the Frequency Domain Reflectometry approach is that it allows for the detection of changes in salt concentration, which significantly reduce the real part of the dielectric constant (\(\epsilon^{\prime}\)), compared to traditional Time Domain Reflectometry (TDR), which measures apparent permittivity [17; 18].  
The soil volumetric water content could be measured using the sensor capacitance and the corresponding relationship with soil moisture. FDR, with its faster response time compared to TDR, ensured efficient soil moisture measurement. The design of the probe required careful selection of the capacitor's form, size, and the distance between the rods, to optimize performance.
## 4 Experiment and Discussion
### Evaluating Indicator  
In this study, the model performance is evaluated by prediction accuracy, and we compare the predicted values to the real data in the test set and calculate the prediction error. The three prediction error evaluation metrics used in this study are as follows:  
Mean Absolute Error (MAE):  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|p-\hat{p}| \tag{17}\]  
Mean Square Error (MSE):  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(p-\hat{p})^{2} \tag{18}\]  
Root Mean Square Error (RMSE):  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(p-\hat{p})^{2}} \tag{19}\]  
where \(p\) is the true value, \(\hat{p}\) is the predicted value, and n is the sample size. MAE is the mean absolute deviation between the predicted value and the true value, which ignores the direction of the error and focuses only on the magnitude of the error [43]. MSE is a common measure of time series forecasting performance. Similar to MAE, it also measures the absolute error in the forecast, which is squared, making MSE more sensitive to outliers but also more expressive of the distribution of the error [44]. RMSE is identical to the raw data in terms of magnitude and is therefore easier to visualize and understand. Similar to MSE, RMSE gives higher weight to large errors [45]. This study uses RMSE as the loss function for model training.
# Global Path Planning for Differential Drive Mobile Robots Based on Improved BSGA* Algorithm
## 3 Improved BSGA* Algorithm
### Improved Heuristic Functions
On the 30 \(\times\) 30 grid map, compared with the A* algorithm and GA* algorithm, the calculation time of BSGA* is reduced by 80.94% and 69.73% and the number of search nodes is reduced by 64.80% and 49.64%, respectively. The A* algorithm makes several turns in the path after bypassing the obstacles, and the total turning angle rises rapidly, while the total turning angle of the BSGA* algorithm and GA* algorithm is relatively more stable. Although the BSGA* algorithm has obvious advantages over the GA* and A* algorithms in search efficiency, the path length will be slightly longer and the advantages in total turning angle are not obvious.
## 2 Literature Review  
### Assessment Frameworks for Human Activity and Ecological Environment  
Establishing a scientifically sound framework for assessing human activity and the ecological environment is a prerequisite for achieving coordinated human-environment development [6; 7; 8]. From the human activity perspective, current human activity assessment systems mainly rely on composite indicators linked to anthropogenic pressure. The ecological footprint index measures human demand by converting it into biologically productive land area [9]. While it emphasizes sustainability, it overlooks spatial heterogeneity and regional development variation. The environmental disturbance index quantifies human impacts based on pollution [10]. Life cycle assessment evaluates resource consumption and environmental impacts across activity stages and is suitable for product- or project-level analysis, but it is less applicable at large spatial scales [11]. The urban expansion index measures construction land growth but overlooks pressures from population and economic activity. Landscape pattern indices capture spatial configuration changes but ignore underlying human drivers. Although valuable, these methods are limited for comprehensive assessments at the urban agglomeration scale. In contrast, the human footprint index integrates land use, population density, nighttime lights, and transport networks, offering high spatial resolution and temporal comparability [12]. It more accurately reflects surface-level human activity intensity and is widely used in macro-scale studies of regional development and human-environment interactions. It is well-suited for systematically assessing HF at the urban agglomeration scale in this study.  
Regarding the ecological environment dimension, current approaches to measuring EQ can be grouped into three main types. The first approach is based on ecological vulnerability, focusing on how ecosystems respond to natural or human disturbances and their potential risks [13]. It is helpful for vulnerability zoning and risk warnings but cannot fully capture dynamic ecosystem conditions. The second uses the InVEST model to quantify ecosystem services like water purification, carbon storage, and biodiversity as proxies for ecological quality [14]. While theoretically robust and highly modular, this method relies heavily on input data, parameters, and geographic context, limiting regional applicability. The third approach uses the Remote Sensing-based Ecological Index (RSEI), which integrates indicators like biological abundance, vegetation cover, and pollution load to reflect ecosystem conditions [15]. The first two methods are helpful for fine-scale assessments and localized modeling but are constrained by limited spatial coverage, incomplete time series, and inconsistent indicators. In contrast, RSEI requires no manual input and offers high automation, operability, and adaptability. It is particularly effective for large-scale, multi-temporal monitoring and macro-level ecosystem policy analysis. Each method serves a distinct purpose, and the choice should depend on the study area's characteristics and research goals.