esa-sceva/satcom-chunk-collection huggingface dataset:
# A Note on the Soft Group Category
## 4 Properties of the Soft Group Category
Theorem 3.1: _Let \((F_{1},A_{1})_{G_{1}}\) and \((F_{2},A_{2})_{G_{2}}\) be any two objects in \(S\)Gp, and let \((U,A_{1}\times A_{2})_{G_{1}\times G_{2}}=(F_{1},A_{1})_{G_{1}}\hat{\times}F_{ 2},A_{2})_{G_{2}}\) be the soft product of \((F_{1},A_{1})_{G_{1}}\) and \((F_{2},A_{2})_{G_{2}}\). Then, the product of \((F_{1},A_{1})_{G_{1}}\) and \((F_{2},A_{2})_{G_{2}}\) in \(S\)Gp is no other than \(((U,A_{1}\times A_{2})_{G_{1}\times G_{2}},(p_{i},\Pi_{i}))\), where for \(i=1,2,\ \Pi_{i}:A_{1}\times A_{2}\to A_{i}\) is the \(i\)-th projection function on the parameter sets and \(p_{i}:G_{1}\times G_{2}\to G_{i}\) is the \(i\)-th projection homomorphism on groups._  
Proof. : Assume that \((H,B)_{K}\) is an object and each \((g_{i},q_{i}):(H,B)_{K}\to(F_{i},A_{i})_{G_{i}},i=1,2\) is a morphism in \(S\)Gp. To prove the uniqueness of the product of \((F_{1},A_{1})_{G_{1}}\) and \((F_{2},A_{2})_{G_{2}}\), we need to show that there is a unique soft group homomorphism \((\gamma,\theta):(H,B)_{K}\to(U,A_{1}\times A_{2})_{G_{1}\times G_{2}}\), where we define \(\theta=(q_{1},q_{2})\) and \(\gamma=(g_{1},g_{2})\). For this reason, we will verify that the diagram below is commutative:  
For any element \(b\) of \(B\), we have  
\[\tikzfig{p(c)=a_{1}\times A_{2}\xrightarrow{U}P(G_{1}\times G_{2})}\]0 \[\tikzfig{p(c)=a_{1}\times A_{2}\xrightarrow{U}P(G_{1}\times G_{2})}\]1 \[\tikzfig{p(c)=a_{1}\times A_{2}\xrightarrow{U}P(G_{1}\times G_{2})}\]2 \[\tikzfig{p(c)=a_{1}\times A_{2}\xrightarrow{U}P(G_{1}\times G_{2})}\]3 \[\tikzfig{p(c)=a_{1}\times A_{2}\xrightarrow{U}P(G_{1}\times G_{2})}\]4 \[\tikzfig{p(c)=a_{1}\times A_{2}\xrightarrow{U}P(G_{1}\times G_{2})}\]5  
so \((\gamma,\theta)\) is a soft group homomorphism. We illustrated the morphisms in the diagram below for the purpose of a better explanation of the subject. Now, we show that for each \(i=1,2\) the realtion \((p_{i},\Pi)\circ(\gamma,\theta)=(g_{i},q_{i})\). For any \(k\in K\), we have  
\[\tikzfig{p(c)=a_{1}\times A_{2}\xrightarrow{U}P(G_{1}\times G_{2})}\]6  
and so \(p_{i}\circ\gamma=g_{i}\). Similarly, we can prove the relation \(\Pi_{i}\circ\theta=q_{i}\). Finally, let \((\gamma^{\prime},\theta^{\prime}):(H,B)_{K}\rightarrow(U,A_{1}\times A_{2})_{G_{1} \times G_{2}}\) be another morphism satisfying the condition \((p_{i},\Pi)\circ(\gamma^{\prime},\theta^{\prime})=(g_{i},q_{i})\). Since  
\[\tikzfig{p(c)=a_{1}\times A_{2}\xrightarrow{U}P(G_{1}\times G_{2})}\]7  
for each \(k\in K\), then we have \(pi\circ\gamma^{\prime}=pi\circ\gamma\). We conclude that \(\gamma^{\prime}=\gamma\) due to the fact that each \(p_{i},\;\;i=1,2\) is a group monomorphism. In a similar manner, one can see \(\theta^{\prime}=\theta\). Consequently, we obtain \((\gamma^{\prime},\theta^{\prime})=(\gamma,\theta)\), which means \((\gamma,\theta)\) is unique. Thus, we complete the proof.
# Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
## 3 Proposed Approach
### First Phase
#### 3.1.2 Pre-Processing  
Our approach uses structured data. For ML/DL, data must be numeric in order to use it as input to the 1D-CNN network. The two used datasets are heavily imbalanced. Normalisation plays a key role in the training phase of a DL network. The following are the pre-processing steps we adopted in this work to handle the previously mentioned issues:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **\# of Samples** & **\# +ive Sam** & **\# –ive Sam** & **\%+ive vs. \%\(-\)ive** & **O-F** & **N-F** \\ \hline
**TCCPD** & 7043 & 1869 & 5174 & 26.58 vs. 73.42\% & 19 & 28 \\ \hline
**CCFDD** & 285,299 & 492 & 284,807 & 0.172 vs. 99.83\% & 30 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the two datasets. Sample, positive, negative, original features, and new features are represented by Sam, +ive, \(-\)ive, O-F, and N-F, respectively.  
1. Convert categorical data to numerical data: Where categorical features have more than two values, we create an individual feature for each categorical value, with Yes (1) or No (0) values. The features that were numerical are normalised between 0 and 1 by zero mean and unit variance technique. In TCCPD, there are four features with categorical data. When converted, this results in nine additional features bringing our total dataset features to be 28 (Table 1).
2. We then apply the SMOTE [54] technique to up-sample the minority class (both datasets) in order to balance the data. SMOTE generates synthetic nearest neighbours for training instances in the minority class in the training set only.
3. Where numeric features ranges are wider than two-choice binary values e.g., monthly charges, the features are normalised feature-wise in the range of 0 to 1.
# A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting
## 2 Related Work
### Machine Learning Method  
[PERSON] et al. [15] proposed a novel short-term load forecasting framework for the tree-based model LightGBM algorithm to improve forecasting accuracy in the STLF field. Firstly, the proposed framework used time series clustering and an early stochastic classification algorithm to mine deeply hidden information from the historical load characteristics to create several new input features for the LightGBM model training. The dimension of input features is crucial to improve the forecasting accuracy of data-driven models. Then, authors adopted a hybrid multistep forecasting method and combined the advantages of the single-step and recursive multistep methods. This work indicates that the forecasting performance of the LightGBM model can be significantly improved by using this novel framework. [PERSON] et al. [16] proposed a novel machine learning model called ISSA-SVM for mid-long-term load forecasting based on support vector machine (SVM). An improved sparrow search algorithm (ISSA) was adopted in this model to solve the problem of hyperparameter selection of the SVM model. The experiment results indicated that the ISSA-SVM can effectively improve the prediction accuracy compared with the original SVM, BP neural network, multiple linear regression, etc. However, the SVM-based model has memory problems when the dataset is huge and lacks the ability to excavate the internal feature of time series. Compared with SVM, NODE extrapolates hidden state through ODE solver and does not store any intermediate quantities of the forward pass. Therefore, NODE has constant memory cost. In addition, NODE can learn the law of internal evolution equation to capture internal feature of time series. These two advantages address the existing problems of the SVM-based model.
# Dynamic Analysis of an Offshore Knuckle-Boom Crane Under Different Load Applications Laws
## 1 Introduction
This methodology utilizes screw transformations and bond graph techniques to integrate crane dynamics with hydraulic cylinder mass balancing, corroborated by finite-element analysis conducted with ANSYS\({}^{\copyright}\). All work on this crane focused on variations in the selected models or methodology used, with the goal of reducing calculation time or improving result accuracy, which was occasionally supported by experimental data. The research studies do not look into the impact of load condition variables, such as the duration of load application, which could be critical to the crane's safety. The objective of this research is to examine the dynamic behavior of an offshore knuckle-boom crane, focusing on finding a correlation between the time of load application and the crane's dynamic behavior. This research study implies modal and dynamic analysis through FEM on a simplified model adopted to reduce the computational time. Since various positions of the crane can be studied, here the two most dangerous configurations are analyzed, which are critical for the structure itself. It must be highlighted that complex phenomena, such as the effect of blast loading on the properties of the material [26] or the damage of the ground due to the operation [27], are left out. The relevance of this work stems from the potential applications that become feasible upon a comprehensive understanding of its dynamic behavior. Moreover, since both the resonance frequenciesand the consequences given by the time of load's application (e.g., the accelerations) depend on the stiffness of the crane, this result can be implemented for other cranes with a similar dynamic behavior and natural frequencies. In particular, once this aspect is fully elucidated (considering also other load conditions such as the wind loads [28] or the earthquake vibrations [29; 30]), a range of materials can be suitably employed with the aim of lightweighting the structure as already done by the authors [31] and in the literature [32; 33]. The ensuing portions of the research article are structured as follows: Section 2 specifies the materials and model utilized, as well as the configurations analyzed. Section 3 delineates the outcomes obtained from the dynamic analysis, which are further examined in Section 4. Section 5 eventually provides a summary and assessment of the findings.
## 3 Results
### Sensor Thin Layer Analysis  
SEM, EDX and Raman analysis were performed on the surface of the chosen MOS sensors to understand if the different responses obtained depended on some different physic-chemical property of the MOS layers (i.e., layer thickness, layer surface roughness, metal oxide composing the thin layer).  
Figure 3 demonstrates the SEM/EDX analysis and Raman spectroscopy of produced sensors. It is clearly seen that the surface of sample (a) has a very rough surface. EDX analysis shows the presence of W, O and C in the film. The layer of WO\({}_{3}\) consist of spherical nanoparticles with an average diameter about 100-150 nm. In order to study the phase composition of the produced sensors we used Raman spectroscopy. The Raman spectrum of the sample (a) indicates at least six modes at 93, 141, 277, 336, 724, 813 cm\({}^{-1}\) which correspond to W-W bending, O-W-O bending and O-W-O stretching of monoclinic crystalline WO\({}_{3}\) phases, respectively [30; 31].  
The sample (b) is presented in the Figure 3b. It can be seen that the surface of the produced sensors is composed mostly of Sn and O. The surface is quite smooth; however, the highly magnified SEM images demonstrate that it consisted of micrometer crystalline blocks, which may be associated with a polycrystalline structure of the film. Raman spectroscopy confirmed this assumption. One may observe at least two weak peaks at 612 and 747 cm\({}^{-1}\) corresponding to TO and LO Eu modes of SnO\({}_{2}\), respectively [32; 33].
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 4 Proposed Scheme for Linear Computation Coding
### Codebook Design
#### 4.4.2 Two-Sparse Codebook  
We choose the alphabet \(\mathcal{S}\subset\{0,\pm 2^{0},\pm 2^{1},\pm 2^{2},\dots\}\) as a subset of the signed positive powers of two augmented by zero. Then, we find \(K\) vectors of lengths \(N\) such that no pair of vectors is collinear and each vector has zero norm equal to either \(1\) or \(2\). For sufficiently large sizes of the subset, those vectors always exist. These vectors are the columns of the codebook matrix. The ordering is irrelevant. It turns out to be useful to restrict the magnitude of the elements of \(\mathcal{S}\) to the minimum that is required to avoid collinear pairs.
## 3 Simulation Results
Figure 12: Simulated results of the C\({}_{\rm S}\) compensator’s compensation linearity: (**a**) Ideal and simulated results across the 0–160 pF range, and (**b**) their differences. Figure 13: Monte Carlo standard deviation simulation results for (**a**) Cs-compensator’s source charge (Q\({}_{\rm SC}\)) and (**b**) sink charge (Q\({}_{\rm SK}\)) scaled by mean value of (**a**). Figure 14 shows the layout of the sensing front-end designed for 20 dot-matrix sensor electrodes, comprising 10 front-end blocks and 5 SAR ADCs. The C\({}_{\text{S}}\) compensator is positioned at the top of the layout, with the adjacent area allocated for general analog blocks, such as the LDO, BGR, and OSC, which will be shared by the display driver IC. The total front-end area is double the 1.017 mm\({}^{2}\) for 40 dot-matrix sensors, CH\({}_{\text{Dot}}\)[40:1]. Table 1 summarizes the front-end performance compared to previous TDDI circuit designs. Compared to previous works, the proposed IC operates with a single supply voltage of 3.3 V, allowing the PMIC to power the controller IC using a single voltage rail. This design enhances both implementation and area efficiency, as it requires only a single bypass capacitor on the 3.3 V supply pin, thereby minimizing the PCB footprint in compact smartphone designs. It also achieves a high scan rate of 3.125 kHz for 40 TSP electrode sensing, which is advantageous in TDDI designs, as TSP and DDI operate sequentially in a time-division manner. The 40 TSP electrodes correspond to a display size of 1.91 inches, assuming a channel pitch of 4.5 mm, making it suitable for wearable device panels. With the aid of a channel-dependent C\({}_{\text{S}}\)-compensator featuring independent fine and coarse tuning, the system can accommodate various TSP shapes that exhibit location-dependent C\({}_{\text{S}}\) variations. Although a fair comparison with previous studies presenting measurement results is not feasible, the proposed design demonstrates superior area efficiency with 0.05 mm\({}^{2}\) AFE area per sensor electrode and enhanced noise immunity using an analog Gaussian filter in the DT S/H circuit.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 3 Road Extraction Based on Different Data Sources
### Road Extraction Based on High-Spatial Resolution Images  
Extracting road information from high-resolution images requires clarification of the road features, including the radiation features, geometric features, topological features and texture features [71]. First, information on the various elements such as features, textures and edges are extracted from the image by analysing the road information. Then, the extracted image information is comprehensively analysed, selected and reorganised and combined with the road features. Finally, it is fused with the structural relationship, model and road-related rules of the road elements to identify the road.
## 5 Conclusions  
We have proposed a two-stage approach for reconstructing an HDR image from a single LDR image-based histogram learning. We introduce a cumulative histogram learning technique based on deep neural networks for dynamic range expansion. The proposed approach predicts HDR intensity distributions from LDR histograms and reallocates image pixels through histogram matching. HDR images are then reconstructed using reinforcement learning and pixel-level rewards for local consistency adjustments. The experiments are conducted on HDR-Real and HDR-EYE datasets. The quantitative evaluations on  
Figure 11: The visual comparison of the proposed approach with the previous methods. (**a**) Input image, (**b**) HDRCNN, (**c**) DrTMO, (**d**) ExpandNet, (**e**) SingleHDR, and (**f**) the proposed approach.  
Figure 10: The visual comparison of the proposed approach with the previous methods. (**a**) Input image, (**b**) HDRCNN, (**c**) DrTMO, (**d**) ExpandNet, (**e**) SingleHDR, and (**f**) the proposed approach.  
HDR-VDP-2, PSNR, and SSIM demonstrate the effectiveness of the proposed approach compared to the previous methods. In addition, the qualitative evaluations show that the proposed approach can maintain a certain quality for reconstructing comparable HDR images. Hence, we can obtain favorable HDR images.  
In the proposed approach, we use a network architecture with only fully connected layers for histogram learning. This architecture needs a large number of parameters and takes a lot of memory. We will develop a more suitable network architecture for experiments and test the proposed approach on different datasets [24]. Moreover, the proposed approach processes the three color channels separately in the RGB color space and then merges them into one color image. However, it may cause chromatic aberration in the resulting images. We will perform joint learning on all channels of images for improvement.  
Methodology, Y.-R.L. and H.-Y.L.; supervision, H.-Y.L., W.-C.L. and C.-C.C.; writing--original draft, Y.-R.L. and H.-Y.L.; writing--review and editing, W.-C.L. and C.-C.C. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the National Science and Technology Council of Taiwan for financially supporting this research under contract no. NSTC 113-2221-E-027-092-.  
Not applicable.  
Not new data were created or analyzed in this study.  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### Discussion
#### 4.2.2 Laboratory Test Samples of Shaping I Hydropower CSGRD
Insight into Mortar Margin vs. Compressive Strength Development  
From Table 23, the relationship between mortar margin and compressive strength is inverse, as indicated by the negative Pearson correlation coefficients at all curing ages. The details are provided as follows:  
1. 14 Days: The Pearson correlation coefficient is \(-0.544\), with a \(p\)-value of 1.96 \(\times\) 10\({}^{-2}\). This moderate negative correlation indicates that a higher mortar margin is associated with lower compressive strength at this early stage, and the statistically significant \(p\)-value suggests that this relationship is reliable. * 28 Days: The correlation strengthens to \(-\)0.628, with a \(p\)-value of 5.3 \(\times\) 10\({}^{-3}\), implying a more pronounced negative impact of mortar margin on compressive strength as the CSGR continues to cure. The stronger correlation and lower \(p\)-value emphasize the significance of this negative relationship. * 90 Days: The correlation weakens slightly to \(-\)0.565, with a \(p\)-value of 1.46 \(\times\) 10\({}^{-2}\). Although the negative relationship persists, the slight reduction in correlation strength may indicate that other factors begin to mitigate the impact of mortar margin on compressive strength. * 180 Days: The correlation returns to \(-\)0.628, with a \(p\)-value of 5.3 \(\times\) 10\({}^{-3}\), similar to the 28-day data. This suggests that the negative impact of mortar margin on compressive strength is sustained over time. * Remarks on Mortar Margin and Compressive Strength Development  
The consistent negative correlations and significant \(p\)-values across all curing periods in Table 23 suggest that mortar margin detrimentally affects compressive strength [4]. This effect is particularly strong at 28 days and is sustained through to 180 days. The data imply that a higher mortar margin could lead to weaker CSGR, and controlling this factor is crucial for achieving desired strength outcomes [14]. Existing research works suggested that the sand ratio should be modified to improve the workability margin in such cases [15,16]. SL 678-14 also recommends a sand ratio between 18 and 35% [14]. Overall Remarks on both Paste and Mortar Margins and Compressive Strength Development  
From Table 23, the analysis of paste and mortar margins reveals critical insights into their roles in CSGR performance. The paste margin consistently shows a positive correlation with compressive strength development, highlighting its importance in achieving higher strength, especially as the CSGR cures. Conversely, the mortar margin exhibits a negative correlation, which relates well with the findings of USACE on HCRCC, suggesting that higher mortar content may weaken the CSGR, particularly in the long term, and therefore there was the need to regulate and control the sand ratio and fine content to achieve the design requirement of the project [16,17].
###### Abstract  
This study investigates the possibility of inducing damage diagnosis capabilities in carbon fiber reinforced polymer composite slabs using custom-built integrated sensors and conventional, affordable equipment. The concept utilizes magnetoelastic strips integrated via 3D printing procedures in composite slabs. Under external mechanical loading, the strip magnetization changes due to the magnetoelastic phenomenon. Accordingly, electrical signals may be passively induced in conventional reception coil circuits placed at a distance from the slab. Since these signals quantify the vibrating slab's response, which is affected by the slab's structural integrity, damage may be detected when specific signal characteristics change. Two main issues are examined, namely the ability of receiving meaningful (with respect to noise) electrical signals from the built-in strips despite their contact-less passive reception, and the potential of diagnosing damage using such signals. Hence, slabs of various sizes and levels of structural damage (notches) have been vibrated at different frequencies and amplitudes. Treating the experimental data from integrated strips by applying the proposed processing framework allows for calculating eigenfrequencies sensitive to occurring damage (and its severity), as verified by finite element models of the vibrating slabs. Accordingly, damage may be detected and evaluated via the currently proposed experimental testing and analysis framework.  
damage detection; damage assessment; smart sensor; magnetoelastic strip; 3D printing; additive manufacturing +
Footnote †: journal: Applied Physics Letters
## 2 Theoretical Discussion
### Investigation into the Applicability of Korean Dynamic Stability Evaluation Criteria  
This study demonstrates the application of the track performance evaluation method currently practiced in Korea in the form of the Korean Railway Code (KR-C 14030), a standard adopted from the old and new American Railway Engineering and Maintenance-of-Way Association (AREMA) and parts of the Union of Railways Code (UIC). Evaluation is conducted on 10 different tracks, 5 of them having different ballasted track structures, and 5 of them having different concrete track types (comprised of different track components). The subsequent sections outline the evaluation criteria described in this code; these criteria are applied to all types of high speed tracks, both ballasted and concrete. The parameters do not include instructions on how to consider differences of track properties and components; only included are the methods on how to calculate the performance levels of each parameter. The investigation into the result comparison of various types of track structures should be able to contribute towards our understanding of the applicability of today's dynamic stability evaluation methods and criteria.