esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Automatic building extraction based on high-resolution aerial imagery is an important challenge with a wide range of practical applications. One of the mainstream methods for extracting buildings from high-resolution images is deep learning because of its excellent deep feature extraction capability. However, existing models suffer from the problems of hollow interiors of some buildings and blurred boundaries. Furthermore, the increase in remote sensing image resolution has also led to rough segmentation results. To address these issues, we propose a generative adversarial segmentation network (ASGASN) for pixel-level extraction of buildings. The segmentation network of this framework adopts an asymmetric encoder-decoder structure. It captures and aggregates multiscale contextual information using the ASPP module and improves the classification and localization accuracy of the network using the global convolutional block. The discriminator network is an adversarial network that correctly discriminates the output of the generator and ground truth maps and computes multiscale \(L_{1}\) loss by fusing multiscale feature mappings. The segmentation network and the discriminator network are trained alternately on the WHU building dataset and the China typical cities building dataset. Experimental results show that the proposed ASGASN can accurately identify different types of buildings and achieve pixel-level high accuracy extraction of buildings. Additionally, compared to available deep learning models, ASGASN also achieved the highest accuracy performance (89.4% and 83.6% IoU on these two datasets, respectively).  
high-resolution aerial images; generative adversarial network; deep learning; WHU building dataset; China typical cities building dataset; semantic segmentation +
Footnote †: journal: Applied sciences  
Article  
An End-to-End Atrous Spatial Pyramid Pooling and Skip-Connections Generative Adversarial Segmentation Network for Building Extraction from High-Resolution Aerial Images [PERSON] et al. (2022), [PERSON] et al. (2022), [PERSON] et al. (2022), [PERSON] and [PERSON] (2022) and [PERSON] (2022)
## 5 Conclusions  
To investigate the evolution mechanisms of crack fields in coal under mining-induced stress, uniaxial cyclic loading and unloading experiments were conducted. These experiments included various loading stress levels to examine the compaction stage, linear elastic stage, initial crack propagation stage, accelerated crack propagation stage, and post-peak stage. Five CT scans were performed on coal samples: one prior to loading and one after each loading-unloading cycle. These tests were complemented by SEM and EDS analyses. The main conclusions are as follows:  
1. As the number of loading-unloading cycles increased, the coal samples exhibited an initial decrease in volume, followed by a subsequent increase. This volume expansion was associated with the rapid propagation of CT-scale cracks.
2. During the linear elastic stage, microscopic cracks continued to develop but remained below the CT detection limit of 10\({}^{6}\)\(\,\mathrm{\SIUnitSymbolMicro m}^{3}\) in this study. As the applied stress increased, the size of cracks grew, leading to their interconnection and coalescence.
3. Hard mineral particles, such as iron ore, played a significant role in impeding crack propagation. These particles employed mechanisms such as deflection toughening and blunting toughening, which enhanced crack propagation resistance and hindered crack expansion.  
Conceptualization, J.X.; methodology, R.Z.; software, D.X.; validation, D.X., Y.L. and X.L.; formal analysis, R.Z.; investigation, J.X.; resources, J.X.; data curation, D.X.; writing--original draft preparation, J.X.; writing--review and editing, R.Z.; visualization, Y.L.; supervision, X.L.; project administration, R.Z.; funding acquisition, J.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (Grant number: 2024 YFC3015805, 2022 YFC3004703).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We would like to thank the Advanced Analysis & Computation Center of China University of Mining and Technology for their support in providing experimental facilities.  
Author [PERSON] was employed by the company Shandong Guoxing Smartech Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
XRM X-ray Microscopy SEM Scanning Electron Microscopy EDS Energy Dispersive Spectroscopy CT Computed Tomography
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 2 Literature Review
### AI-Driven Emotion Recognition Models and Fine-Tuning Approaches  
The adoption of deep learning techniques has significantly enhanced EEG-based emotion recognition by automating feature extraction and hierarchical pattern learning [25]. Studies have successfully implemented CNNs, long short-term memory (LSTM) networks, and hybrid deep learning architectures to enhance EEG-based emotion classification [26]. Additionally, transformer-based models, originally developed for natural language processing (NLP), have been investigated for EEG analysis [27].  
Fine-tuning pre-trained deep learning models has emerged as an effective method for improving EEG-based classification by adapting models to specific datasets or environments [8]. This technique enables models to retain general knowledge while refining them for domain-specific tasks. Research has shown that fine-tuned CNNs and RNNs significantly enhance EEG classification accuracy [28], and transfer learning techniques, in which models are initially trained on one EEG dataset and then fine-tuned using another, have shown promising results [29].  
However, fine-tuned models often inherit biases from the original datasets, making them less adaptable to real-time, dynamic environments [30]. Since most models are trained on static datasets, they do not account for continuous emotional variations in real-world conditions [31]. This study addresses these limitations by fine-tuning a deep learning model for real-time EEG processing, ensuring adaptability in architectural settings where emotional responses fluctuate dynamically [32].  
In addition, with regard to classification techniques, several studies have highlighted the high potential applicability of the diverse approaches presented in An overview of machine learning classification techniques [33], A study on classification techniques in data mining [34], Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm [35], and Revealing the nature of cardiovascular disease using DERGA, a novel data ensemble refinement greedy algorithm [36] for EEG-based emotion recognition. In particular, advanced ensemble methods such as DERGA (data ensemble refinement greedy algorithm) have been employed in complex biomedical data analyses [37], suggesting that they could serve as promising alternatives for developing real-time EEG emotion recognition models and improving classification accuracy.
# Past, Present and Future Monitoring at the Vallcebre Landslide (Eastern Pyrenees, Spain)
## 2 Monitoring Methods Used in Valleebre and Sample Results
### Terrestrial Photogrammetry  
The first monitoring network established on the landslide was based on terrestrial (or \"close-range\") photogrammetry. A total of seven campaigns were performed at the landslide foot between 1987 and 1992, covering only a small area of about 100 \(\times\) 50 m (Figure 4). Stereopairs were taken with a Wild P32 metric camera (Figure 4). Each campaign included three photograms that produced two photogrammetric models.  
The results of each survey were the change in the coordinates of the main points (displacements). The precision ranges between 1 cm in well-defined points (pointing targets for instance) and 10 cm (rock blocks, trees and so on). Maps with contour lines were also produced (Figure 5). The interpretation of this kind of maps is not simple: the variations of the terrain surface are caused by the landslide movement, but also by the soil erosion during rainfalls and the eroding effect of the Valleebre Torrent at the base of the toe. Although several blocks exhibit displacement of up to 8 m, the variation of the contour lines is very moderate as a whole (Figure 5).  
Figure 4: **Left: example of a 6 \(\times\) 8 cm glass plate photogram of landslide toe taken from the Valleebre Torrent. Right: Wild P32 camera for terrestrial photogrammetry over a theodolite, which was used for the landslide toe monitoring between 1987 and 1992.**  
At that time, the use of terrestrial photogrammetry in landslides was not straightforward, because of the difficulty of creating a proper setup with an adequate view over the hill slope. Moreover, specialized and costly equipment had to be available for a precise stereocompilation. Although out of the scope of this paper, it is worth to say that the photogrammetry processing has evolved in the last decade towards automation, ease of use and low cost, thanks to the so-called Structure from Motion (SfM) concept [11; 12; 13]. In parallel, the use of drones (or UAS) as aerial photogrammetry platforms permits to overcome the point-of-view issues during acquisition [12; 13; 14; 15; 16; 17] In this way, photogrammetry is once again a powerful landslide monitoring technique, providing information that is almost continuous in space, but discontinuous in time.
# IWF-TextRank Keyword Extraction Algorithm Modelling
## 5 Conclusions  
This study presents a new keyword extraction algorithm, IWF-TextRank, which combines multi-feature weighting, BP neural network optimisation, and the CBOW model from Word2 Vec to achieve enhanced performance in domain-specific datasets like traffic accident reports. The main contributions of this work are as follows:  
* A multi-feature weighting mechanism incorporating word frequency, lexicality, and word length, enabling more accurate identification of core keywords;
* BP neural network optimisation for automated weight distribution, reducing subjectivity and improving stability and precision in keyword extraction;
* Integration of the CBOW model, enhancing the model's semantic understanding and enabling deeper contextual associations for more accurate keyword identification.  
These contributions result in significant improvements in keyword extraction accuracy and robustness within traffic accident analysis, providing a useful framework for similar applications.  
Future work could involve expanding the dataset to other regions and years to validate the robustness of IWF-TextRank, combining it with more advanced deep learning models such as Transformers for complex text analysis, and exploring its potential in other specialised fields (e.g., medical or legal documents). Further research could also investigate automated parameter adjustments to improve adaptability across diverse datasets.  
Conceptualisation, L.Z. and W.W.; methodology, W.W.; software, L.Z.; validation, L.Z., W.W. and Y.W.; formal analysis, J.M.; investigation, Y.W.; resources, J.M.; data curation, W.W.; writing--original draft preparation, W.W.; writing--review and editing, L.Z.; visualisation, W.W.; supervision, J.M.; project administration, L.Z.; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the support of the participating organisation and its personnel who provided various effective assistance. The authors also sincerely thank the financial support provided by the abovementioned mechanism. This research was funded by the Postgraduate Research and Practice Innovation Program of Jiangsu Province (SJCX20_1117, SJCX21_1420 and KYCX21_2999), the Construction System Project of Jiangsu Province (2020 ZD14 and 2018 ZD258), Suzhou Social Science Fund (Y2020 LX017 and Y2020 LX025), and the Philosophy and Social Science Projects of Universities in Jiangsu Province (2018 SJA1348 and 2023 SJYB1420).  
Not applicable.  
The data are available on request to the corresponding author.  
Thanks are extended to the relevant departments for providing the 2020 traffic accident data so that the research data in this paper is true and accurate.  
The authors declare no conflicts of interest.
## 1 Introduction
The international standard ISO 2631-1997 has three orientations and positions, as shown in Table 1. Considering these reference standards, using the weighted root-mean-square acceleration is recommended when considering ride comfort [13; 14]. Figure 1: Schematic representation of energy flow in regenerative braking systems. Figure 2: Comparison of regenerative braking and discharging process. This paper proposes a comfort regenerative braking system (CRBS) to control the regenerative braking force. The novelty of this research is that the regenerative power is generated within the comfort range specified by ISO 2631-199, but the traditional braking method lacks a definite control strategy during braking. In addition, the proposed model predicts braking and speed limits by using backpropagation to ensure driving comfort. The acceleration and deceleration are reduced at the start, slow, and stop of vehicle motion by using numerical analysis with a tangent function as a speed change graph. CarSim and MATLAB/Simulink were used to validate the developed backpropagated algorithm. The generated data were input into the neural network algorithm.
## 4 Hydration Model for Cement-Quartz Binary Blends  
### Calibration of Hydration Model Parameters  
A three-parameter equation (TPE) for hydration heat is shown as follows [40]:  
\[\text{Q(t)}=\text{Q0}\times\text{exp}(-\text{b}\times(\text{t}^{\text{c}})) \tag{3}\]  
where Q(t) is the test results for hydration heat, Q0 is the hydration heat of 1 g of fully hydrated cement, b is the reaction rate parameter, and c is the shape parameter.  
Given the test results for the hydration heat normalized by cement mass (shown in Figure 3d), the parameters of the hydration model can be determined (shown in Table 5) as follows.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Qu0** & **Qu7.5** & **Qu15** \\ \hline Combined water (wt\%) & 19.55 & 18.24 & 16.88 \\ Portlandite (wt\%) & 18.48 & 17.30 & 15.55 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Combined water and portlandite in Qu0, Qu7.5, and Qu15 over 28 days.  
Figure 10: Relationship between combined water and strength.  
b0, b7.5, and b15 are the reaction rate parameters, b, for the control, 7.5% quartz, and 15% quartz specimens, respectively. The steps for parameter regression are as follows: First, based on the experimental results of 7.5% quartz powder, the values of the parameters Q0, b7.5, and c are obtained. Then, keeping the values of Q0 and c unchanged, the value of b0 is obtained based on the experimental results without quartz powder. In the same way, based on the experimental results of 15% quartz powder, the value of b15 is obtained.  
In this way, the values of the five parameters can be obtained, and these values can describe the curve of the cumulative heat of hydration of the three mix ratios with time.  
For these three specimens, the values of Q0 and c are the same, and only the values of b are different. This means that quartz can accelerate the hydration of cement, but it does not change the essence of cement hydration.  
A comparison of the test and analysis results is shown in Figure 11. The analysis results generally agree with the test results. At about 10 h, the test results are slightly higher than the analysis results. This is because the three-parameter equation model does not consider the hydration heat from the initial contact period.
_applied sciences_  
_Article_  
**Gradiometry Processing Techniques for Large-Scale of**  
**Aeromagnetic Data for Structural and Mining Implications: The Case Study of Bou Azzer Inlier, Central Anti-Atlas, Morocco**  
**[PERSON] 1,*\(\copyright\),**  
[PERSON] 1, [PERSON] 2,*, [PERSON] 1, [PERSON] 3,4,*\(\copyright\)  
and [PERSON] 5  
1 Geosciences Laboratory, Department of Geology, Faculty of Sciences, Ibn Tofail University, BP 133, Kenitra 14000, Morocco; [EMAIL_ADDRESS] (S.C.); [EMAIL_ADDRESS] (Z.B.)  
2 Resources Valorization, Environment and Sustainable Development Research Team (RVESD), Department of Mines, Mines School of Rabat, Av Hadi Ahmed Cherkaoui, BP 753, Rabat 10090, Morocco  
3 Research Team of Geology of the Mining and Energetics Resources, Faculty of Sciences and Technology, Hassan First University of Settat, BP 577, Settat 26000, Morocco  
4 Laboratory of Intelligent Systems Georesources and Renewable Energies, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, BP 2202, Fees 3000, Morocco  
5 Ministry of Energy Transmission and Sustainable Development, Directorate of Geology, BP 6208, Rabat 10090, Morocco; a2 [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] (A.S.); [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (A.M.)  
**Abstract: Due to its unique geographic and tectonic location, the Bou Azzer inlier has drawn increased interest in mining studies. The inlier's basement structure remains subject to investigation meanwhile faults and igneous rocks affect the local geology. In order to comprehend the Bou Azzer inlier's structure, we use aeromagnetic data. The edge enhancement method described in this work is based on the gradiometry tensor analysis (GTA) of aeromagnetic data, which yields estimated magnetic tensors, rotational invariants, horizontal invariants, computed strike lines, and Eigensystems. This study's primary objective is to use GTA to define structural boundaries in complicated geological and tectonic environments. The vertical and horizontal positions of the geological border's limits have been determined via analysis of the acquired answers. The borders of the anomalous sources are marked in space by the lowest eigenvalue. According to the research, the inlier demonstrates potential for further mineralization with regard to its complicated structure, which is mostly dominated by WNW-ESE, ENE-WSW, NE-SW and E-W trending lineaments with varying depths between 3.45 and 9.06 km. Certainly, the derived structural scheme has enabled the identification of various formations that may be favorable for the circulation of mineralizing fluids, facilitating the concentration of economically valuable mineral deposits, similar to existing metal reserves in the examined area.**  
geophysics; gradient tensor analysis; rotational invariants; horizontal invariants; eigensystems
## Appendix A Proof of Monotonicity  
The noise \(\sigma_{k}^{2}\) in (17a) is a given variable, and \(\sigma_{k}^{2}>0\), \(\left\|v_{k}^{H}\mathbf{\Phi}_{k}\right\|^{2}>0\). Based on the above, we can give a simplified equation  
\[f_{1}(x)=\log_{2}\biggl{(}1+\frac{x}{x+a}\biggr{)}+\log_{2}\Bigl{(}1+\frac{x}{a }\biggr{)}\]  
where \(x\) is a variable greater than \(0\) and \(a\) is a constant greater than \(0\). Let us define the following:  
\[f_{11}(x) =\log_{2}\biggl{(}1+\frac{x}{x+a}\biggr{)}\] \[f_{12}(x) =\log_{2}\Bigl{(}1+\frac{x}{a}\Bigr{)}\]  
where it clear that \(f_{1}(x)=f_{11}(x)+f_{12}(x)\), for \(f_{12}(x)=\log_{2}\bigl{(}1+\frac{x}{a}\bigr{)}\). Since \(a>0\), \(\frac{x}{a}\) is strictly increasing in \(x\). The function \(\log_{2}(1+t)\) is strictly increasing in \(t\). Therefore, \(f_{12}(x)\) is strictly increasing in \(x\). So, function \(f_{1}(x)\) maintains monotonicity. We have come to the conclusion that for variable \(\left\|v_{k}^{H}\mathbf{\Phi}_{k}\right\|^{2}\), problem (17a) is a monotonically increasing function.
## 1 Introduction
Building upon cutting-edge advancements in change detection, we propose a Differential-Perception-Enhanced Attention Network based on Multi-Scale Feature Aggregation (MDNet), which deeply integrates convolutional neural networks (CNNs), multi-scale feature aggregation, differential perception enhancement mechanisms, attention mechanisms, and Transformer architecture. We construct a novel CNN-Transformer hybrid framework: First, the EMF is used to extract multi-scale features of bi-temporal images, while the CASAM is utilized to progressively fuse the semantic features of adjacent layers in the backbone. Then, the Transformer and attention mechanisms are jointly employed to capture long-range dependencies. Simultaneously, the DPEM constructs a spatio-temporal feature difference mapping space to enhance change region features. The key contributions of this work include the following:(i) MDNet, a novel CNN-Transformer hybrid framework that effectively combines the complementary strengths of both architectures while integrating multi-scale feature extraction, differential enhancement, and attention mechanisms. (ii) A Multi-Scale Feature Extraction Module (EMF) is designed. Through the collaborative design of the channel attention mechanism and the deformable convolutional feature pyramid, it enables the extraction of multi-scale spatial structure features across resolution levels of bi-temporal images. A Cross-Scale Adjacent Semantic Aggregation Module (CASAM) is introduced to perform progressive fusion of semantic features of adjacent levels, strengthening the structural representation of ground objects and semantic consistency modeling. (iii) A Differential-Perception-Enhanced Module (DPEM) is designed to construct a feature difference mapping space in the spatio-temporal dimension, systematically enhancing the joint spectral-structural representation ability of change regions and achieving a significant improvement in the completeness of multi-dimensional feature representation in complex scenarios. The rest of this paper is organized as follows. Section 2 introduces the related work. Section 3 presents detailed information on the method proposed by us. Experimental results and analysis are provided in Section 4. Conclusions and future work are presented in Section 5.