esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussions
### Model Interpretability and Explainability
#### 4.2.1 Attention Heatmap  
Attention Heatmap is a method that visualizes the attention mechanism of Transformer-based models, showing which words the model focuses on when making predictions [1]. The attention mechanism generates a weight matrix to determine the importance of each word in the context of the sentence. Words with higher attention weights are considered more relevant to the classification task, while words with lower weights contribute less. Using Seaborn, these attention weights are visualized as heatmaps, where darker colors indicate higher attention, and lighter colors indicate lower attention.  
Figure 8 presents the attention heatmap visualization for a positive sentence and shows that the model strongly focuses on the words \"kusursuz\" and \"uyumu\", which are the primary indicators of the positive sentiment in the sentence. In contrast, context-providing words such as \"evin\" and \"dekorasyonunda\" receive lower attention. This pattern suggests that the model effectively prioritizes meaningful words while correctly processing the contextual structure.  
Figure 7: Testing the Electra model with positive, negative, and neutral domain-specific samples.  
Figure 9 illustrates the attention heatmap for a negative sentence. The figure reveals that the model assigns high attention weights to the words \"bozulan\" and \"fiyasko\", both of which convey strong negative sentiment. On the other hand, words such as \"aldiktan\" and \"sonra\", which provide temporal context but do not directly indicate sentiment, receive significantly lower attention. This distribution indicates that the model correctly identifies keywords that define the negative sentiment, ensuring a precise classification.  
Figure 10 depicts the attention heatmap for a neutral sentence. The figure demonstrates that the model primarily focuses on the phrases \"belirtilen tarih\" and \"teslim edildi\", as these words determine the neutral nature of the sentence. Meanwhile, lower emotional intensity words like \"aralignda\" and \"adresime\" receive less attention, aligning with the expected distribution for a neutral statement. This suggests that the model successfully applies attention weights to classify neutral expressions accurately.
# Capacity of Heterogeneous Mobile Wireless Networks with \(D\)-Delay Transmission Strategy
## 8 Conclusions  
In this paper we propose a new heterogeneous mobile network architecture, which is composed of normal nodes and more powerful helping nodes. Four types of mobile network model are investigated: two-dimensional i.i.d. fast mobility model, two-dimensional i.i.d. slow mobility model, three-dimensional i.i.d. fast mobility model and three-dimensional i.i.d. slow mobility model. First we provide a brief description of our network model. We analyze the throughput capacity of the four types of network model intuitively by virtual channel systems. Then we obtain the network capacity by strict mathematical proof respectively. We find that the throughput capacity is \(O\left(\sqrt{\frac{D}{n}}\right)+\Theta\left(\frac{\sqrt{m}W_{2}}{n}\right)\) in two-dimensional i.i.d. fast mobility model, \(O\left(\sqrt[3]{\frac{D}{n}}\right)+\Theta\left(\frac{\sqrt{m}W_{2}}{n}\right)\) in two-dimensional i.i.d. slow mobility model, \(O\left(\sqrt[3]{\frac{D}{n^{2}}}\right)+\Theta\left(\frac{\sqrt{m^{2}}W_{2}}{ n}\right)\) in three-dimensional i.i.d. fast mobility model and \(O\left(\sqrt[3]{\frac{D}{n^{2}}}\right)+\Theta\left(\frac{\sqrt[3]{m^{2}}W_{ 2}}{n}\right)\) in three-dimensional i.i.d. slow mobility model. Finally we analyze the asymptotic expressions we obtained. We consider two extreme cases: \(D\) is not limited and \(D\) is limited, where \(D\) is the delay constraint. We find that if \(D\) is not limited, the capacity contributed by _ad hoc_ transmission mode can achieve constant order, while the price is that the delay will go to infinity. If \(D\) is limited, the capacity contributed by _ad hoc_ transmission mode will decrease to zero. Hence there is a positive correlation between the throughput capacity and \(D\). We also find that the number of helping nodes in the network is an important factor on the network capacity greatly. If the number of helping nodes is large enough, most of packets can be transmitted by helping transmission mode. In this condition the network capacity can achieve constant order without a long time delay. If the number of helping nodes is small, most of packets have to be transmitted by helping transmission mode, and the time delay can not be ensured.  
The work is supported by NUDT Science Foundation (No. ZDYYJCYJ20140701) and National Natural Science Foundation (No. 61401476 and No. 61201166). [PERSON] proposed the model and conceived the rough idea; [PERSON] and [PERSON] reviewed the manuscript; [PERSON] derived the capacity of the network model and wrote the manuscript.  
The authors declare no conflict of interest.
###### Abstract  
The Mw 7.8 Gorkha Earthquake of 25 April 2015 triggered over 25,000 landslides across central Nepal, with 4775 events concentrated in Gorkha District alone. Despite substantial advances in landslide susceptibility mapping, existing studies often overlook the compound role of post-seismic rainfall and lack robust spatial validation. To address this gap, we validated an ensemble machine learning framework for co-seismic landslide susceptibility modeling by integrating seismic, geomorphological, hydrological, and anthropogenic variables, including cumulative post-seismic rainfall. Using a balanced dataset of 4775 landslide and non-landslide instances, we evaluated the performance of Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) models through spatial cross-validation, SHapley Additive exPlanations (SHAP) explainability, and ablation analysis. The RF model outperformed all others, achieving an accuracy of 87.9% and a Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) value of 0.94, while XGBoost closely followed (AUC = 0.93). Ensemble models collectively classified over 95% of observed landslides into High and Very High susceptibility zones, demonstrating strong spatial reliability. SHAP analysis identified elevation, proximity to fault, peak ground acceleration (PGA), slope, and rainfall as dominant predictors. Notably, the inclusion of post-seismic rainfall substantially improved recall and F1 scores in ablation experiments. Spatial cross-validation revealed the superior generalizability of ensemble models under heterogeneous terrain conditions. The findings underscore the value of integrating post-seismic hydmeteorological factors and spatial validation into susceptibility assessments. We recommend adopting ensemble models, particularly RF, for operational hazard mapping in earthquake-prone mountainous regions. Future research should explore the integration of dynamic rainfall thresholds and physics-informed frameworks to enhance early warning systems and climate resilience.  
c 202515847  
1 Graduate School of Science and Engineering, Ehime University, Matsuyama 790-8577, Japan;  
[EMAIL_ADDRESS]  
2 Department of Environmental Design, Faculty of Collaborative Regional Innovation, Ehime University,  
Matsuyama 790-8577, Japan  
25 July 202515847
# Software Engineering Techniques for Building Sustainable Cities with Electric Vehicles
## 2 Literature Review
### Benefits and Challenges of Electric Vehicles  
The surge in popularity of electric vehicles has been a focal point in academic circles due to EVs' potential to mitigate greenhouse gas emissions, improve air quality, and bolster energy security [23; 24]. Studies show that EVs can drastically reduce greenhouse gas emissions, with potential reductions of up to 90% when compared to gasoline-powered vehicles [25; 26].  
However, it is critical to acknowledge the environmental impact of the production and disposal of batteries used in EVs. The manufacturing process requires significant energy, and the disposal of batteries can lead to toxic waste and environmental pollution [27; 28]. Despite these considerations, the use of renewable energy sources for charging EV batteries can help mitigate these environmental impacts [8].  
Moreover, EVs have the potential to promote energy security by relying on domestically-produced renewable energy sources rather than foreign oil imports, thereby reducing a country's dependence on foreign oil and increasing its energy independence [29; 30]. The literature also discusses the cost and convenience benefits of EVs. Although the initial cost of purchasing an EV may be higher, the overall cost of ownership may be considerably lower due to reduced fuel costs and maintenance requirements [31].  
While the benefits of EVs are well-documented, research also points to challenges associated with their widespread adoption, such as the need for more extensive charging infrastructure and improvements in battery technology [32].
###### Abstract  
This study describes the impact of surfactant molecular weights (PEG 2000 and PEG 4000) on the photocatalytic activity of TiO\({}_{2}\) films, deposited via dip-coating from a PEG-stabilized suspension and silver-functionalized photo-fixation of Ag\({}^{+}\) under UV illumination. The photocatalytic activity of pure and Ag/TiO\({}_{2}\) films is assessed in the aqueous-phase degradation of Malachie green and Methylene blue in distilled and tap water under UV and visible illumination. The results indicate a positive effect of both the higher-molecular-weight non-ionic surfactant and Ag-functionalized yield higher photocatalytic efficiency. Notably, films photo-fixed with \(10^{-2}\) M Ag\({}^{+}\) show the highest degradation percentages in all experimental conditions. A direct correlation between the concentration of Ag\({}^{+}\) ions and the enhancement of the photocatalytic activity is revealed: pure TiO\({}_{2}\) < Ag, \(10^{-4}\)/TiO\({}_{2}\) < Ag, \(10^{-3}\)/TiO\({}_{2}\) < Ag, \(10^{-2}\)/TiO\({}_{2}\). Flame atomic absorption spectrometry is used to study the Ag\({}^{+}\) leeching from the Ag/TiO\({}_{2}\) films. The structural properties of the nanostructures are investigated through scanning electron microscopy, Brunauer-Emmett-Teller analysis, energy-dispersive X-ray spectroscopy, and X-ray diffraction. Additionally, after three cycles of operation, Ag, \(10^{-2}\)/TiO\({}_{2}\) (PEG 4000) films can maintain their photocatalytic activity, suggesting a potential application in the treatment of dye wastewater.  
Ag/TiO\({}_{2}\) powder films; non-ionic surfactant; organic dyes; tap water +
Footnote †: journal: Applied Sciences
## 2 Methodology
### Self-Organizing Feature Maps in Image Segmentation and Classification  
SOFMs are characterized by their outstanding properties in clustering (classification), data visualization, probability density estimation, etc. Kohonen artificial neural networks are one of the most widely used SOFMs [42].  
Kohonen artificial neural networks (KNN) map a high-dimensional input space/layer (\(x_{i}\) for \(i\) = 1, 2,..., \(n\), where \(n\) is the number of inputs) onto an output space/layer (\(N_{j}\) = 1, 2,..., \(m\), where \(m\) is the number of neurons) with fewer or an equal number of dimensions. KNN contains only a single layer of neurons--the Kohonen layer. The number of neurons \(n\), which is the network's parameter, is optional and usually equals the number of target classes. The inputs to the network are interconnected with all neurons in the Kohonen layer. This means that every neuron has information from all the inputs. Synaptic weights leading to each neuron (designated as a weight vector, \(w_{\bar{y}}\), of the neuron) can be understood as coordinates, which indicate the location of the neuron in the array (Figure 3). Neurons are mostly organized in a regular two-dimensional grid/matrix or one-dimensional row/vector array.  
Figure 3: The structure of a Kohonen network.  
Figure 2: The conceptual design of the diagnostic system.  
There are lateral links (local connections) between the neurons in the Kohonen layer (dashed line in Figure 3), which means that any particular neuron affects other neurons in its neighborhood.  
In order to segment an IRT image, a layer that has \(1\times m\) neurons (Figure 4) is proposed. Using this type of array, it is possible to segment thermal infrared images into \(m\) different segments, which can be labeled as different areas of the temperature field (cold, hot, etc.). Thus, if a higher number of neurons is used, the classification will be more detailed.  
In the most simplified way, the self-organization process (Kohonen learning) consists of four main steps: initialization, competition, cooperation, and adaptation [55]:  
* Initialization: Random values are set for all neuron weights. The input vector from the input space is selected.
* Competition: From the inputs, the discriminant function (Equation (1), below) of a particular neuron is computed. A Kohonen unit computes the Euclidean distance between an input \(x_{i}\) and its weight vector \(w_{ij}\). The winning neuron is the one with the smallest value of the discriminant function.
* Cooperation: The winning neuron influences its adjacent neurons (i
## 3 Results and Discussion
### Removal of Basic Blue Properties
#### 3.2.5 Effect of WB Modification  
As reported in previous paragraph, the higher uptake of BB-41 was achieved at higher pH values of the BB-41 solution. In this part, the WB materials were treated with a NaOH solution prior to the removal tests and the BB-41 solution was used at its natural value (without adjustment). The removal of the WB improved after the NaOH treatment, especially for Jed-WB, with a maximum of 16 mg/g. However, it was still lower than the Med-WB (38 mg/g) (Figure 8). The improvement could be related to the improvement of the S\({}_{\text{BET}}\) values, however, there was slight variation of these values after the NaOH treatment, as presented in Table 2.  
Figure 8: Effect of (**a**,**b**) base treatment of (**a**,**b**) Med-WB and Jed-WB, respectively, on the removed amount of BB-41 dye.  
Figure 7: Effect of the BB-41 pH solution on the removal percentage of (**a**) Med-WB and (**b**) Jed-WB materials.  
The increase of removal efficiency was related to the surface properties. At a lower pH the surface of the silica becomes positively charged, whilst at a higher pH it becomes negatively charged [70]. In our case, the increase of the number of negatively charged sites on the WB materials when treated with NaOH solution enhanced the electrostatic attraction between the BB-41 cations (positively charged) and the negatively charged surface of the WB materials [71].  
At a lower pH, the adsorption of the cationic dye should be less because of the lower dissociation of the dye molecules, as well as the positively-charged surface [70]. At a higher pH, both the dye and the surface will be dissociated, which results in a higher adsorption through increased cation exchange capabilities.
## 4 Design of the Sliding-Mode Controller
### Reachability Test  
Since the transversality value (22) is always negative, the reachability conditions reported in (14) must be analyzed. Then, replacing the derivative of \(\psi_{x}\), given in (21), into the first reachability condition of (14) leads to Expression (23), which is simplified as given in (24). Such an expression provides the maximum limit \(SR_{p}\) (positive value) of the reference derivative able to guarantee stability.  
\[\lim_{\psi\to 0^{-}}\left.\frac{d\psi}{dt}\right|_{u=0}=k_{1} \cdot\frac{i_{pv}}{C_{pv}}-k_{1}\cdot\frac{dv_{mptpt}}{dt}+\frac{dip_{pv}}{dt} +\frac{k_{2}\cdot v_{o}}{n\cdot L_{m}+L_{k}/n}>0 \tag{23}\] \[\frac{dv_{mptpt}}{dt}<\frac{i_{pv}}{C_{pv}}+\frac{1}{k_{1}}\cdot \left(\frac{dip_{pv}}{dt}+\frac{k_{2}\cdot v_{o}}{n\cdot L_{m}+L_{k}/n}\right)= SR_{p} \tag{24}\]  
Similarly, replacing the derivative of \(\psi_{x}\) into the second reachability condition produces the inequality given in (25), which is simplified by considering the relation between \(i_{m}\) and \(i_{pv}\) given in (8) to obtain the minimum limit \(SR_{n}\) (negative value) of the reference derivative able to guarantee stability, given in (26).  
\[\lim_{\psi\to 0^{+}}\left.\frac{d\psi}{dt}\right|_{u=1}=k_{1} \cdot\frac{i_{pv}-i_{m}}{C_{pv}}-k_{1}\cdot\frac{dv_{mptpt}}{dt}+\frac{dip_{pv }}{dt}-\frac{k_{2}\cdot v_{pv}}{L_{m}}<0 \tag{25}\] \[\frac{dv_{mptpt}}{dt}>-\frac{i_{pv}\cdot(1-d)}{C_{pv}\cdot d}+ \frac{1}{k_{1}}\cdot\left(\frac{dip_{pv}}{dt}-\frac{k_{2}\cdot v_{pv}}{L_{m}} \right)=SR_{n} \tag{26}\]  
Therefore, the derivative of the MPPT reference (\(v_{mptpt}\)) must be restricted to the limits reported in (24) and (26); otherwise, the SMC will not fulfill the reachability conditions. This design condition will be addressed in Section 5.2.
# A Network Analysis-Based Approach for As-Built BIM Generation and Inspection
## 3 Methodology
### Practical Structure Analysis and Comparison  
This step involves analysing the practical implications of the network analysis and comparison results, identifying structural features and generating model modification advice. The global analysis of BIM networks considers overall parameters and community detection outcomes, providing insights into the overall structure and connectivity of the building. Node matching evaluates the accuracy of corresponding relationships between nodes in the two networks, ensuring that the as-built model accurately reflects the design.  
By comparing the as-designed and as-built BIM models, deviations in structural elements such as walls, beams, and columns are identified, highlighting their impact on structural performance and compliance with design specifications. The analysis results are used to provide recommendations for necessary corrections and updates to the BIM model, ensuring it accurately represents the built environment. Implementing these modifications enhances structural reliability, reduces costs by minimising rework, and improves overall project outcomes by facilitating better planning and decision-making throughout the project lifecycle.
# Seismic Data Denoising Based on Wavelet Transform and the Residual Neural Network
## 2 Methods and Evaluation Index
### Denoising Performance Evaluation Index  
SNR is a popular metric for gauging the effectiveness of seismic data processing. In this paper, the signal-to-noise ratio is applied to evaluate the noise rejection ability of DWT-Resnet. The equation is as follows:  
\[\text{SNR}=10\log_{10}\left\{\frac{\left\|z\right\|_{F}^{2}}{\left\|z- \widetilde{z}\right\|_{F}^{2}}\right\} \tag{6}\]  
where \(z\) is the original seismic data used as labels and \(\widetilde{z}\) is the denoised seismic data.
## 3 Results
### Microsurfacing Test Results
#### 3.3.3 Cohesion Test Results
According to the visual assessment defined by EN 12274-4, the 5 and 10 min samples were considered disintegrated, the 15 and 30 min samples were classified as cracked, and the 60 to 120 min samples were considered solid. These results confirmed the quick-set class of this microsurfacing mixture. After analyzing each microsurfacing mixture separately, Figure 16 shows the evolution of the cohesion values with the curing time for the two selected compositions. This graph demonstrates how the mixtures developed strength with time. It can be noted that both mixtures showed similar patterns, even though cohesion increased more rapidly and for higher values in the case of Emulsion 1. According to the ASTM [33] classification, the pattern of cohesion-time curves was a quick-set, quick-traffic system for Emulsion 1 and a quick-set, slow-traffic system for Emulsion 2. These results could be related to the binder cohesion values presented previously (1.00 J/cm\({}^{2}\) and 0.46 J/cm\({}^{2}\) for bitumen recovered from Emulsion 1 and 2, respectively). Thus, the bitumen cohesion could be used as an indicator of the shear strength of the microsurfacing mixture. Nevertheless,  
Figure 14: Variation of cohesion values according to the material added to the mixture produced with the Emulsion 2. Figure 15: Samples’ final appearance after the cohesion test for the selected mixture produced with Emulsion 2.  
the ultimate cohesion values observed after 120 min were similar for both emulsions and higher than 20 kgf cm, showing that the mix design method proposed in the present work could adequately select the compositions for different types of emulsions.
## 3 Network Background
### Sixth-Generation Cellular Networks
#### 3.2.2 Reliable Low-Latency Mobile Broadband  
Multi-sensory XR applications will be a key feature of 6G, requiring high bandwidth and ultra-low latency to prevent performance issues [9]. Because XR devices are wearable, user movement can trigger frequent handovers and service migrations, adding system uncertainties. These applications demand significant computing power and real-time responsiveness, with network congestion potentially causing latency violations. To ensure seamless performance, the 6G cellular network must dynamically detect changes and efficiently reallocate resources.