esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Through a modular design, ESPNet [41; 42] combines dilated convolutions with a spatial pyramid composed of grouped pointwise convolutions, achieving an inference speed of only 360,000 parameters and approximately 112 FPS. Despite recent advances, current methods still face significant challenges in segmentation accuracy, computational efficiency, and generalization capability: (1) The optical properties of agricultural watersheds in remote sensing images are affected by factors such as water composition, surface reflection, and environmental conditions, often leading to misclassification and reduced segmentation accuracy [43]. (2) Natural water bodies in agricultural watersheds typically have irregular boundaries, fragmented distributions, and large-scale variations, while artificial ones are dense and intertwined. Current models struggle to capture both the semantic information of large-scale water bodies and the geometric details of small ones simultaneously [3]. (3) High-resolution datasets used for water body identification in agricultural watersheds are large in size, require costly annotations, and incur high computational overhead during training [5]. In addition, the scarcity of publicly available datasets restricts the generalization ability of the model. To address the above challenges, this study aims to develop an efficient and generalizable segmentation framework for extracting water bodies in agricultural watershed environments from high-resolution optical remote sensing imagery. The goal is to improve segmentation accuracy while significantly reducing model complexity, enabling its deployment on resource-constrained agricultural devices. The main contributions are as follows:  
* We proposed a learnable Kalman filter module, applied in the feature decoding stage, to enhance segmentation accuracy by stabilizing feature representations and suppressing complex noise through end-to-end optimization. * We proposed a lightweight water body segmentation framework. By reducing the network depth and width, it achieves a favorable trade-off between accuracy and computational efficiency. * Our method demonstrates strong performance on public datasets and effectively generalizes to UAV-captured images via transfer learning.
## 7 Discussion  
Based on the findings of the study, the following implications can be made regarding the appropriate design of the SHG with ferrite magnets:  
* Computer-aided optimization of the SHG design with ferrite magnets, which has a complex three-dimensional structure of the magnetic system, applying widely used methods, such as 3D FEM and multicriteria optimization algorithms, is complicated by very high computational costs [23]. This problem can be solved by applying the computationally efficient Nelder-Mead method and the simplified SHG model with ferrites presented in this article;With an increase in the level of saturation of the magnetic circuit and the magnitude of the MMF of the excitation winding, the resistance to demagnetization of ferrite magnets on the rotor can be increased by increasing their height;
* It is advisable to restrict the flux density in non-laminated sections of the magnetic core made of structural low-carbon steel to a value low enough to ensure a small drop in MMF, despite the fact that magnetic properties of low-carbon steels are not regulated.
# Application of Sensitivity Analysis to Progressive Collapse Resistance of Planar Truss Structures
## 3 Judgment of Critical Elements Based on Sensitivity Analysis  
### Prototype Structure  
Two representative planar truss structures were selected for sensitivity analysis. The initial failure elements of both trusses are marked in Figures 1 and 2 in red, considering the symmetry of structure and load. Figure 1 shows a progressive collapse resistance test truss of a planar Warren truss structure with benchmark significance, conducted by [PERSON] et al. [16]. The geometric dimensions and material properties of the test truss are shown in Table 1.  
A steel roof truss, designed according to the Chinese Standard for Design of Steel Structures (GB50017-2017) [17], taken from a comprehensive warehouse of an engineering project in Tianjin, is shown in Figure 2. All elements are constructed using Q235B steel pipes.
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## 5 Experiments
### Experimental Results  
Experiments were conducted using the CWRU, MFPT, and PU datasets, and the detailed performance metrics for each model under various noise conditions--including noiseless original data, Gaussian noise, electrical interference noise, harmonic noise, and random shock noise--are summarized in Tables 2-4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Noise Type** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{8}{*}{No noise} & LSTM & 0.993 & 0.9966 & 0.9939 & 0.9952 \\ \cline{2-5}  & GRU & 0.992 & 0.9962 & 0.9934 & 0.9948 \\ \cline{2-5}  & FCN & 0.993 & 0.9966 & 0.994 & 0.9953 \\ \cline{2-5}  & ResNet & 0.9919 & 0.9958 & 0.9932 & 0.9945 \\ \cline{2-5}  & InceptionTime & 0.9923 & 0.9971 & 0.9932 & 0.9952 \\ \cline{2-5}  & XceptionTime & 0.9939 & 0.9977 & 0.9951 & 0.9964 \\ \cline{2-5}  & XCM & 0.9928 & 0.9967 & 0.9939 & 0.9953 \\ \cline{2-5}  & LSTMAttention & 0.9943 & 0.9975 & 0.9948 & 0.9962 \\ \cline{2-5}  & GRUAttention & 0.9943 & 0.9971 & 0.9948 & 0.996 \\ \cline{2-5}  & **TSSequencer** & **0.9951** & **0.9975** & **0.9961** & **0.9968** \\ \cline{2-5}  & PatchTST & 0.9932 & 0.9971 & 0.994 & 0.9956 \\ \cline{2-5}  & MultiPatchTST & 0.9942 & 0.9973 & 0.9954 & 0.9963 \\ \hline \multirow{8}{*}{Gaussian noise} & LSTM & 0.9809 & 0.9857 & 0.9842 & 0.9846 \\ \cline{2-5}  & GRU & 0.9801 & 0.9856 & 0.9846 & 0.9848 \\ \cline{2-5}  & FCN & 0.9786 & 0.9848 & 0.9828 & 0.9832 \\ \cline{2-5}  & ResNet & 0.9786 & 0.9826 & 0.9833 & 0.9826 \\ \cline{2-5}  & InceptionTime & 0.9767 & 0.9819 & 0.9828 & 0.9821 \\ \cline{2-5}  & XceptionTime & 0.9782 & 0.9826 & 0.9842 & 0.9828 \\ \cline{2-5}  & XCM & 0.9797 & 0.9832 & 0.9854 & 0.9841 \\ \cline{2-5}  & LSTMAttention & 0.9787 & 0.9827 & 0.9833 & 0.9826 \\ \cline{2-5}  & GRUAttention & 0.981 & 0.9854 & 0.9867 & 0.9858 \\ \cline{2-5}  & TSSequencer & 0.9768 & 0.9839 & 0.9819 & 0.9826 \\ \hline \multirow{2}{*}{PatchTST} & 0.9795 & 0.9847 & 0.9834 & 0.984 \\ \cline{2-5}  & **MultiPatchTST** & **0.9832** & **0.9863** & **0.9889** & **0.9875** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results on CWRU dataset with mixed noise.
## 2 Multi-Scenario Generation Based on Wind Power Forecasting Errors
### Multi-Scenario Generation of Wind Power Output  
Let the forecasted wind power output for each time interval on Day 30 be denoted as \(\hat{p}_{30}(t)\), \(t=1,2,\ldots,144\), with a data sampling interval of 10 min. To generate candidate scenarios, \(S\) samples are first randomly drawn from the joint distribution constructed using the wind power data from the first 29 days, resulting in a standardized forecasting error matrix. The mathematical expression is given by the following:  
\[U_{s}=\begin{bmatrix}u^{1}(1)&u^{1}(2)&\ldots&u^{1}(t)\\ u^{2}(1)&u^{2}(2)&\ldots&u^{2}(t)\\ \vdots&\vdots&\ddots&\vdots\\ u^{s}(1)&u^{s}(2)&\ldots&u^{s}(t)\end{bmatrix} \tag{9}\]  
where \(u^{s}(t)\in[0,1]\) represents the standardized forecasting error at time interval \(t\) in the \(s\)-th candidate scenario. By applying the inverse of the empirical cumulative distribution function \(\hat{F}_{t}(e)\) for each time interval, the standardized sampled values can be transformed into actual forecasting errors:  
\[e^{s}(t)=\hat{F}_{t}^{-1}(u^{s}(t)) \tag{10}\]By adding the inverse-transformed actual forecasting errors to the forecasted wind power values of each time interval on Day 30, the wind power output scenarios for Day 30 can be constructed as follows:  
\[p_{30}^{s}(t)=\hat{p}_{30}(t)+e^{s}(t) \tag{11}\]  
Directly using all candidate scenarios may lead to severe data redundancy, making it difficult for the scheduling model to accurately capture the characteristics of wind power uncertainty, and substantially increasing the computational complexity of the optimization problem. Therefore, k-means clustering is employed to reduce the dimensionality of the S candidate scenarios by grouping them into \(K\) clusters, from which representative wind power output scenarios are extracted [27]. This reduction not only preserves the statistical structure of the original scenario set but also significantly improves the computational efficiency of the subsequent optimization process. Let \(n_{k}\) denote the number of candidate scenarios in the \(k\)-th cluster. The weight assigned to the representative scenario of this cluster is defined as follows:  
\[w_{k}=\frac{n_{k}}{k} \tag{12}\]  
Figure 3 illustrates the overall process of the proposed approach, from data acquisition and error modeling to scenario generation, reduction, and scheduling optimization. It outlines the key steps involved in constructing the wind/storage day-ahead scheduling model.  
Day-Ahead Planning and Scheduling Model for Wind/Storage Systems Based on Multi-Scenario Generation and CVaR
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 2 Materials and Methods
### Performance Metrics
#### 2.4.5 Average Energy Consumption  
According to [31], MANET's energy consumption can be calculated using Equation (5). Energy consumption calculation for each node is done by subtracting the initial value (\(i\)) of energy at each node with the remaining energy (\(r\)); then, the value is divided by the total number of nodes (\(N\)).  
\[\text{Average Energy Consumption}=\frac{\text{i}-\text{r}}{\text{N}}. \tag{5}\]
## 1 Introduction
studied the parallel implementation scheme of Kirchhoff pre-stack depth migration for large-scale 3D seismic data based on dynamic programming travel time calculation and MPI + OpenMp hybrid programming mode and realized efficient and high-precision migration imaging [22]. [PERSON] et al. used the difference method to solve the eikonal equation for Kirchhoff migration and pointed out that the method of solving the eikonal equation to calculate the travel time (without interpolation) is more advantageous than the traditional method of ray tracing [23]. [PERSON] and [PERSON] (2025) proposed a stochastic wavelet method with exponential auto-correlation to generate artificial accelerograms, optimizing time-frequency trade-offs for seismic engineering [24]. The Kirchhoff integral method is the most widely used pre-stack depth migration method in actual production. Its main advantage is that it has the ability to process complex velocity fields relatively efficiently, and its computational efficiency is high. However, its reliance on Sensitivity to velocity model errors, especially laterally; high computational cost for wide-aperture and 3D surveys; and its neglect of finite-frequency and elastic effects are disadvantages. In contrast, pre-stack depth migration based on wave equation micro-decomposition leverages wave equation solutions to improve the imaging of steep dips and multi-scale structures but requires careful frequency sampling to avoid aliasing, a larger memory footprint for storing wavefields, and more complex implementation and parameter tuning, which restricts its real-time applicability [25; 26; 27]. At present, in areas with simple geological structures, seismic data imaging is mainly based on post-stack time migration technology. For the areas where underground structures are developed, the above methods often cause the fault position to swing, the back and syncline to return poorly, and so on. The structure of seismic interpretation has a displacement swing with the structure exposed by mining, and the size of the structure is very different, which seriously affects the fine interpretation of seismic data and is not conducive to guiding the safe mining of coal mines. Therefore, in this paper, the Kirchhoff integral pre-stack depth migration imaging method based on a model constraint is proposed. Firstly, the synthetic model is used to verify the accuracy of the method. Secondly, the model-constrained pre-stack migration technology is used to process and interpret the original seismic data in the complex structural area. The position and direction of the fault are accurately divided; the problem of the low imaging resolution of faults, steeply dipping strata, and other structures is solved; and the problem of multiple solutions of data interpretation is focused on. This technology not only has certain theoretical significance but also has great practical value.
## 2 Materials and Methods  
### Site Description  
Our study was carried out in an alpine wetland ecosystem of the National Wetland Natural Reserve of Zoige, located at the northeast edge of the Qinghai-Tibetan Plateau (33\({}^{\circ}\)55\({}^{\prime}\)42\({}^{\prime\prime}\)N, 102\({}^{\circ}\)52\({}^{\prime}\)44\({}^{\prime\prime}\)E). The alpine wetlands of this region cover an area of 6180 km\({}^{2}\), which is 31.5% of the whole plateau of Zoige. The site has an average altitude of 3430 m above sea level (Figure 1). This region belongs to the cold Qinghai-Tibetan climatic zone. The climate of this region is characterized by a long period of frost (October to April) and a relatively short growing season (May to September). The mean annual air temperature in 2008 was 1.5 \({}^{\circ}\)C lower than the long-term (1990-2009) mean (1.6 \({}^{\circ}\)C); whereas the respective value in 2009 was 2.6 \({}^{\circ}\)C. The annual precipitation (rainfall and snowfall) in 2008 and 2009 was 465 and 596 mm, respectively, which is lower than the long-term mean (623 mm). There are two peaks of rainfall, in May and August. The air pressure is low, about 668.8 hPa, due to its high altitude.  
The height of plants is about 50-80 cm. The dominant vegetation at the dry hummocks is composed of _Kobresia tibetica_, _Cremanthodium pleurocaule_, and _Potentilla_, accounting for approximately 45% of the site area. _Carex muliensis_ (CM) and _Eleecharis valleculosa_ (EV) are the two predominant plant species; they are scattered in hollow areas and cover 90% of the remaining aquatic surface. The size of CM and EV hollows is about 5 m\({}^{2}\) on average, with 3 to 7 cm standing water. The size of dry hummocks (DH) is about 0.8 m\({}^{2}\) and their height is from 20 to 50 cm. At regional scale, hollows account for a small part of the total area even in the growing season. In the non-growing season, both CM and EV sites were covered with ice. The total aboveground and belowground biomass is 166-184 and 2104-2246 g\(\cdot\)m\({}^{-2}\), respectively. The low temperature and large diurnal temperature variation inhibit the decomposition of plant litter and soil organic matter and stimulate the deposition of peat [5]. The depth of peat in the Zoige alpine wetland ranges from 0.3 to 8.8 m. In all sites, the greatest root density was found in the 0 to 20 cm soil depth. Below that layer there is a peat layer ranging from 50 to 80 cm in thickness. In the past several decades, artificial drainage for cultivation and peat mining greatly modified the water table, the ratio of hollows to hummocks, and the vegetation composition, leading to a potential change of GPP.
## 5 Discussion
### Implications for Clinical Practice  
The introduction of the EYEFUL system and its development protocol into clinical settings could improve the way functional capacities are assessed, moving beyond subjective observations and self-reports to a more accurate and unbiased evaluation. The system's ability to provide detailed metrics when performing ADLs can help healthcare professionals diagnose functional limitations, plan personalized care, and track the progression of conditions over time. In addition, the use of this technology can increase the accuracy of evaluations of dependency status, thereby improving resource allocation and support for these individuals.
# Boosting the Learning for Ranking Patterns
## 4 AHP-Based Learning Approach for Ranking Patterns
### Comparing a Single Measure to a User Ranking  
Let \(S=\langle P_{1},\ldots,P_{n}\rangle\) be a user ranking of patterns. Let \(\mathcal{M}=\{M_{1},\ldots,M_{m}\}\) be a set of interestingness measures for patterns. To evaluate how well a particular measure performs in terms of overall ranking accuracy compared to a user ranking, we use Kendall's W concordance coefficient [33]. For a measure \(M_{i}\) and a user ranking \(S\), we define \(K_{i}(S)\in[0,1]\) as the Kendall's W coefficient between the ranking obtained using \(M_{i}\) and the user ranking \(S\), where a value of \(1\) indicates perfect concordance and a value of \(0\) indicates no concordance.  
\[K_{i}(S)=\tfrac{3\alpha}{|S|^{3}-|S|}\,\quad\alpha=\sum_{\ell=1}^{|S|}(R_{ \ell}-\bar{R})^{2}, \tag{5}\]  
where \(R_{\ell}=rank_{M_{i}}(P_{\ell})+rank_{u}(P_{\ell})\) with \(P_{\ell}\in S\), and \(\bar{R}=\tfrac{1}{|S|}\sum\limits_{\ell=1}^{|S|}R_{\ell}\).
## 2 Wireless Communication Technologies in Harsh Underground Mining Operations
### Harsh Underground Environment
These operational influences can include the following [12; 13]:  
* Limited LOS (as discussed)
* Waveguide effect at certain frequencies
* Ionized air
* Heat, humidity and their variances along mining sections
* Varying gas concentrations and hazardous gases, fumes, exhaust
* Electromagnetic noise from equipment and infrastructure  
The above-mentioned harsh environmental factors can significantly limit the signal transmission reliability. For the operation of underground wireless networks, coverage needs to be ensured. For this, it is crucial to balance between maximizing network characteristics (signal strength, range and other parameters) and minimizing cost and effort [17]. Simulations can be helpful to predict wireless network characteristics in under-ground environments; however, simulation accuracy is limited due to the high level of environment complexity. The authors in [18] show how measuring environmental underground parameters is a challenging and time-consuming task and [19] emphasize the amount and diversity of necessary simulation data needed. Research in this area is ongoing, with projects such as the NexGen SIMS involving the authors and their institute. This project aims to prepare and analyse different communication options in mining environments. However, further research is needed to fully understand the behaviour and potential of these options in the unique and often unknown [20] data situations encountered in mining operations.
## 5 Experiment and Result
### Implementation Details and Training  
In this study, Cochleogram and Mel Spectrogram which is generated from the utterances is used as an input for the CNN architecture used to our experiment. For Cochleogram and Mel Spectrogram generation, a 30 ms hamming windows with the overlapping size of 15 ms are used for the 128 filters and 2048-point FFT. Finally, Cochleogram and Mel Spectrogram of size 1088 \(\times\) 288 (frequency \(\times\) time) is generated for each of the utterances and used as an input to each of the models. Since the aim of this study is to analyze the robustness of the Cochleogram and Mel Spectrogram features, none of the audio preprocessing such as voice activity detection or silence removal is applied. None of the normalization and data augmentation is applied to Cochleogram and Mel Spectrogram during training the model.  
The implementation of this study is conducted by using TensorFlow deep learning frameworks written in Python, which can be executed on the graphics processing unit (GPU). Our experiment is conducted on the NVIDIA TITAN Xp GPU. The experiment is conducted by using the CNN architectures such as: basic 2 DCNN, VGG-16, ResNet-50, ECAPA-TDNN and TitaNet architectures which are discussed on the Tables 1-3 and in the Figures 3 and 5, respectively. For evaluating the performance of the Cochleogram and Mel Spectrogram, separate models are trained for speaker identification and verification on each of the CNN architectures. Separate models are trained for Cochleogram and Mel Spectrogram features during identification and verification. For evaluating the performance of Cochleogram and Mel Spectrogram features at different levels of SNR (\(-5\) dB to 20 dB) and at different types of noises (i.e., Babble, Street and Restaurant noises), a single model is trained for each Cochleogram and Mel Spectrogram features. For evaluating the speaker identification and verification performance of Cochleogram and Mel Spectrogram features without additive noises, a separate model is trained for Cochleogram and Mel Spectrogram features. During conducting our experiment, publicly available python codes are used by customizing into appropriate forms.  
During both speaker identification and verification, the models are trained for 20 epochs with a minibatch size of 32. For each of the epochs, the training pairs are re-shuffled. Each of the models used RMSprop optimizer with the minimum learning rate 0.0001 and categorical cross-entropy used as the loss function. The training is performed using the Softmax function. The weights in the models were initialized randomly at the start of the training process, and progressively updated throughout the process. The validation set of the dataset is used for hyper-parameter tuning and early stopping. The speaker identification performance is measured by using accuracy metrics for training, validation and test split of the dataset. Verification performance is measured by using Equal Error Rate (EER) for test split of the dataset.
# A Pulse Shaping Based Optical Transmission System of 128 QAM for DWDM with N \(\times\) 904 Gbps
## 2 Pulse Shaping
The functional model of the optical filter can be described by:  
\[H(f)=\alpha\cdot\exp\left[-\ln\Bigl{(}\sqrt{2}\Bigr{)}\left(\frac{f-f_{c}}{B /2}\right)^{2N}\right] \tag{5}\]  
where \(\alpha\) is insertion loss, \(f_{c}\) is center frequency, \(B\) is bandwidth, and \(N\) is filtering order. From Figure 1, it can be seen that only the bandwidth of the central carrier-wave is kept. The more modulated photonic carrier-waves can be transmitted over the limited bandwidth of a certain specific wavelengths; the more available wavelength channels can be realized in a DWDM transmission system.
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
## 6 Discussion  
The proposed algorithm reached kilometer- and meter-per-second-level accuracy in state estimation using a combined EKF-SLFN approach and the CHAOS-7 geomagnetic field model to simulate magnetometer measurements. The results obtained in this work, particularly in the performance of the EKF, were compared with the study conducted in [25], where a magnetometer-based EKF was applied to real flight data from four satellites with different altitudes and inclinations. This approach was justified by the discrepancy between the magnetic field models used in this work, which makes it closer to real data-based methods compared to the majority of cases where the onboard model and the measurement model are identical. Based on Table 6, it can be observed that the accuracy of the EKF in this work, where magnetometer data were simulated using the CHAOS-7 model, is comparable to that achieved using real measurements. While this does not necessarily imply that the algorithm can be directly transferred to real data with the same level of accuracy, it highlights the validity of the CHAOS-7 model in simulating realistic magnetic field measurements.