esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The GSAT (Grouped Self-Attention Transformer) is a cutting-edge architecture tailored for hyperspectral image classification that effectively tackles the challenges of diverse input sizes and feature dispersion. With the integration of a Grouped Pixel Embedding Module, GSAT successfully captures a comprehensive set of both global and local features, mitigating the issue of feature over-scattering that is common in traditional global self-attention mechanisms. This module facilitates the integration of spatial-spectral features across both broad and localized scales. In addition, the GSAT architecture incorporates a SSF (Selective Self-Focus) Module, which is crucial for targeting the most relevant tokens, thereby minimizing redundancy and amplifying the extraction of multi-scale feature details. This selective attention strategy not only strengthens the model's ability to represent local features but also significantly boosts classification accuracy. Through extensive experimental validation on six diverse hyperspectral image datasets, the GSAT has demonstrated consistent and robust performance, marking a significant leap forward in the field of hyperspectral image classification. It presents itself as a promising approach capable of addressing the complex requirements of this field, ensuring the effective application of hyperspectral sensors for a wide range of environmental monitoring purposes. While the GSAT does introduce a computational burden, ongoing research efforts are focused on optimizing the architecture to maintain or even improve its classification performance and precision while reducing computational costs. This is a critical step towards the practical implementation of the GSAT algorithm in real-world remote sensing scenarios, where computational efficiency is paramount for widespread adoption.  
Conceptualization, S.S., X.L., X.F. and Q.L.; methodology, S.S., X.L., X.F. and Q.L.; software, S.S., X.L., X.F. and Q.L.; validation, S.S., X.L., X.F. and Q.L.; formal analysis, S.S., X.L., X.F. and Q.L.; investigation, S.S., X.L., X.F. and Q.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
The manuscript employs the following abbreviations:  
HSI Hyperspectral image  
GSAT Group-Sensitive Selective Attention Transformer  
PGM Pixel Group Mapping  
SSF Sensitivity Selection Framework  
SF SpectralFormal  
GIS Geographic information systems  
SVMs Support vector machines  
RNNs Recurrent neural networks  
MSI Multispectral Instrument  
ViT Vision Transformer  
CNNs Convolutional neural networks  
VNIR Visible and near-infrared  
SWIR Shortwave infrared bands  
ROI Region of interest  
NCALM NSF-funded Center for Airborne Laser Mapping  
Aviris Airborne Visual Infrared Imaging Spectrometer  
HSCSE Half sequence grouping spectral embedding  
MCA Multihead Channel Attention  
OA Overall accuracy  
SAM Spatial Attention Module  
AA Average accuracy
## 2 Background and Related Studies
### Hybrid Consensus Mechanism
Availability Requirements: needs to be available at all times with redundant systems to ensure availability in case of a failure. Safety: safety requirements to be able prevent hazards by detecting unusual behavior and triggering alarms and safety measures. Multiple attacks have occurred in the past using different attack vectors using malware payload to take control of the ICS system. This includes but is not limited to the following ICS cyber attacks: 1. Stuxnet Malware [50; 51; 52; 53; 54; 55]. Multiple attacks on the Ukraine Power Grid in 2015 and 2016 [56; 57]. 2. Ransomware attacks by NotPetya [18]. TRITON attack framework targeting the safety instrumented system [58; 59; 60]. By analyzing the requirements and the risk level of traditional ICS environments, we can see that the data these systems transfer and receive within these environments are considered to be very sensitive. Any data exposure to unauthorized parties can have a major impact on organizations and potentially nations as a whole. Implementing IIoT may involve exposing some of these sensitive systems directly to the cloud. There have been multiple incidents that involve compromising IoT devices in order to use them to launch DDoS attacks [61; 62; 63] or to breach data to Command-Control (C2) servers [64; 65; 66; 67]. By performing appropriate threat modeling of a 150 private Ethereum blockchain solution, our paper examined the following research questions:  
1. What are the threats that IIoT will face when blockchains are utilized in their environments? 2. How can blockchain transparency impact the exposure of IIoT environments to external threats? 3. What are the implications of compromising blockchain nodes within IIoT environments?
# A Survey of Machine Learning Methods for Time Series Prediction
## 1 Introduction  
A Time Series Prediction is the process of predicting a future value based on historical sequential observations. Accurate predictions based on time series data play a crucial role in a wide range of domains where forecasting future values is essential for strategic planning, resource management, and decision making. The applications of time series prediction span numerous fields, including electricity consumption forecasting, environmental quality assessments (e.g., air and water quality), meteorological predictions (e.g., rainfall, solar radiation, and wind patterns), medical diagnostics (e.g., forecasting COVID-19 case trends and pneumonia incidences), traffic flow prediction, and financial domains like sales forecasting and stock market analysis.  
In recent years, models based on Machine Learning (ML) have demonstrated the most success in time series forecasting and are able to generalize well to unseen data, unlike models based solely on probability and statistics. Specifically, Tree-Based Machine Learning (TBML) and Deep Learning (DL) have emerged as the most prominent approaches, excelling in scenarios where complex, nonlinear dependencies exist within the data. Their ability to generalize to unseen data makes them highly applicable to real-world problems with diverse and dynamic characteristics.  
While numerous studies have examined these techniques within specific domains and several survey papers [1; 2] have analyzed various approaches to time series prediction across domains, the existing literature reviews face a significant limitation. Current survey papers struggle to draw meaningful comparisons between models because they analyze independent studies, each utilizing different implementations and datasets. This heterogeneity in experimental setups prevents direct model comparisons and obscures true performance differences. This paper addresses this gap by exclusively reviewing studies that compare both TBML methods and DL approaches within the same experimental framework. By focusing on research where both methodologies are implemented and evaluated by the same researchers using identical datasets, this survey enables more robust conclusions about the relative strengths and weaknesses of these modeling approaches.  
The remainder of this paper is organized as follows: Section 2 outlines the methodology employed in this survey. Section 3 reviews TBML architectures, while Section 4 examines DL architectures. Section 5 presents experimental results and discusses findings, and Section 6 highlights recent time series prediction competitions. Section 7 provides an overview of the most important takeaways of this paper, and Section 8 provides the direction for future research.
# Accurate Estimation of Air Pollution in Outdoor Routes for Citizens and Decision Making
## 5 Results
### Comparison with Related Work  
In Table 5, we present a comprehensive comparison between our proposal and the current state-of-the-art with a similar approach. Specifically, we conducted a detailed analysis of various alternative approaches mainly discussed in Section 2. For each alternative, we examined its advantages and disadvantages (pros and cons), accompanied by relevant comments and insights.  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & SO\({}_{2}\) & O\({}_{3}\) & NO\({}_{2}\) & PM10 & PM2.5 \\ \hline \(\mathbf{SO}_{2}\) & \(1.00\) & \(0.18\) & \(-0.38\) & \(-0.01\) & \(-0.25\) \\ \(\mathbf{O}_{3}\) & \(-\) & \(1.00\) & \(-0.70\) & \(-0.31\) & \(-0.63\) \\ \(\mathbf{NO}_{2}\) & \(-\) & \(-\) & \(1.00\) & \(0.755\) & \(0.85\) \\ \(\mathbf{PM10}\) & \(-\) & \(-\) & \(-\) & \(1.00\) & \(0.76\) \\ \hline \end{tabular}
\end{table}
Table 4: Correlation between AQ pollutants and their concentrations  
Figure 10: Behaviour of the different pollutants (19 July 2023, Wednesday, at 10:00 a.m.).  
Finally, as we can see compared to the related work, our proposal provides detailed and accurate air pollution estimation along a specified or given route, taking into account the user's profile (or medical history) and the WHO AQ Guideline.
# Point Cloud Repair Method via Convex Set Theory
## 3 Results and Discussion
### Quantitative Analysis  
It is very important to objectively evaluate the geometric differences of point clouds. Similar to [5], we used GPSNR and NSHD as quantitative evaluation indicators of the experiment. Tables 1 and 2 show the quantitative comparison results between our method and related work. Experimental data show that our method achieved the best performance  
Figure 8: Irregular-hole repair effect.  
Figure 6: Effect of visualization on point cloud collected by Stanford [38] and our method.  
Figure 7: Repair effect of multiple holes.  
in all categories. Specifically, Table 1 shows the quantitative comparison values of the normalized symmetric Hausdorff distance (NHSD), which is a normalized metric based on the unilateral Hausdorff distance. The lower the NHSD is, the smaller the difference between A and B. The experimental data show that our method achieved the lowest NHSD on each point-cloud model. On average, our method was 23.67 lower than the second method, with a relative decrease of 59.2%. SpareNet, PF-Net, and SnowflakeNet changed the 3D position in the source point cloud and generated redundant filling points in the nonhole area, so there was a large difference in the NHSD value between them and the ground truth. On the monkey, monster, and pipe surface models, the output of the SpareNet, PF-Net, and SnowflakeNet methods severely distorted the geometric shape of the point cloud (see Figure 6), so the values of NHSD of the SpareNet and SnowflakeNet methods were larger on these three types of point clouds. These results show that our method could repair point clouds with high stability and quality.  
Table 2 shows the GPSNR performance comparison values of our and other methods. GPSNR is based on the optimized peak signal-to-noise ratio (PSNR) to measure the error between Point Clouds A and B. The higher the GPSNR is, the smaller the difference between A and B. The experimental data show that our method achieved the highest GPSNR on each point-cloud model with an average of 48.47 DB, which was 44.9% higher than that of a suboptimal method. The experimental results show that the point cloud repaired by our method had high fidelity.
## 3 Numerical Model for Short-Wall Spacing Strip Filling  
### Numerical Model and Simulation Procedure for Mining and Filling  
Due to its ability to perform a nonlinear large deformation analysis of continuous media and its relatively simple data and image processing, FLAC3D has been widely used and widely recognized in the field of geotechnical engineering. Based on the geological conditions of the CT30101 working face in Mahuangliang coal mine [20] and the short-wall interval strip filling process, without considering the influence of coal seam dip angle, a two-dimensional calculation model was established using FLAC3D, as shown in Figure 2. The model size is 350 m \(\times\) 234 m, a 100 m long coal pillar model is retained on both sides of the working face with fixed horizontal and vertical constraints at the bottom, and the horizontal displacement is limited on both sides. The top is a free boundary.  
According to the assumptions, the filling strip's width and spacing are denoted by '\(a\)' and '\(b\)', respectively. The coal seam mining and filling simulation process is segmented into four phases, as depicted in Figure 3. The specific process unfolds as follows: in the initial phase, a coal pillar with width '\(a\)' is mined and backfilled, with the remaining coal pillar having a width of '\(a+2b\)' on the left side. During the second phase, a coal pillar with width '\(b\)' is extracted from the right side of the '\(a+2b\)'-wide coal pillar, leaving behind a coal pillar with a width of '\(a+b\)'. In the third phase, a coal pillar with a width of '\(a\)' is mined and backfilled from the right side of the '\(a+b\)'-wide coal pillar, leaving a coal pillar with a width of '\(b\)'. The final phase entails mining the remaining coal pillar. Following this pattern, the simulation encompasses a total of six filling strips and six mining strips.  
Figure 1: Two modes of mining with strip-filling: (**a**) long-wall strip filling; (**b**) short-wall strip filling.
## 4 Results and Discussions
### Physicochemical Properties
#### 4.1.2 Porosity  
Porosity, one of the key physical properties of rocks, significantly influences their strength and deformation. It is defined as the volume of voids and pores within a rock. The porosity of rocks is affected by factors such as the size, shape, and mineral composition of the grains, with clay minerals having the most notable impact [17]. As porosity increases, rock strength tends to decrease. Additionally, porosity is often inversely related to specific gravity and directly related to water absorption. In the studied samples, porosity values ranged from 0.27% to 1.32% (see Figure 10 and Table 2). The porosity was determined using the saturation method, and calculations were made using Formula (2). Similarly, our study reported low porosity, which aligns with the findings where porosity values ranged from 0.31% to 0.41%. Both studies show that low porosity contributes to higher rock strength, making the materials suitable for construction use. Rock strength decreases as porosity increases. Therefore, we can infer that the low porosity values in these samples contribute to their higher strength and suitability as construction materials.  
\[\text{Porosity}=\left(\frac{w_{1}-w_{3}}{w_{1}-w_{2}}\right)\times 100 \tag{2}\]  
where  
\(w_{1}\) = weight in the air.  
\(w_{2}\) = weight in water.  
\(w_{3}\) = oven-dried weight.  
Figure 10: Porosity results for the Inzari Formation rock samples. This graph presents the range of porosity values obtained from the tested specimens. Lower porosity values indicate a denser rock, which is typically associated with higher mechanical strength, making the rocks more suitable for construction applications.
# ChatGPT in Education: A Systematic Review on Opportunities, Challenges, and Future Directions
## 4 Opportunities of ChatGPT in Education
### Personalized Learning and Adaptive Education  
ChatGPT can enhance learning through boosting productivity and engagement by providing personalized training and automated grading. In education, personalization is the process of adjusting curriculum, pedagogy, and learning settings to each student's needs and goals. This has historically been a resource-intensive procedure that is frequently constrained by practical and logistical issues. However, these restrictions have lessened in the new era of individualized learning ushered in by AI technologies like ChatGPT [26].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Year** & **Methods** & **Dataset** & **Findings** & **Limitations** \\ \hline \multirow{4}{*}{[39]} & \multirow{4}{*}{2024} & \multirow{4}{*}{Qualitative study} & Views of three established professors in South Africa & \multicolumn{1}{c}{Professors welcome the use of AI technologies like ChatGPT and emphasize the importance of teaching students how to engage with such tools. The responsibility lies with lecturers and universities to create an environment conducive to integrating these technologies into teaching and learning, especially in assessment.} & \multicolumn{1}{c}{Limited to the views of three professors in South Africa; may not represent a broader perspective.} \\ \cline{3-5}  & & & & Chatbots were mainly used on web platforms, primarily teaching computer science, language, and general education, with some used for engineering and mathematics. Most chatbots acted as teaching agents, with more than a third as peer agents. More than a quarter of chatbots employed personalized learning approaches. Chatbots evaluations show improved learning and satisfaction. & \multicolumn{1}{c}{} & \\ \cline{3-5}  & \multirow{4}{*}{2023} & \multirow{4}{*}{Systematic review} & 36 papers on chatbot-learner interaction design & \multicolumn{1}{c}{} & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._Because of its capacity to process and produce language-based content, ChatGPT is a perfect tool for developing personalized educational resources and experiences. This could be achieved by utilizing the natural language processing skills of the model to comprehend the student's learning needs and tailor the instruction accordingly. Using ChatGPT to create practice questions specifically tailored to the student is one method to accomplish this [27]. ChatGPT enables personalized learning by adapting to individual student needs, offering customized feedback, and providing tailored study materials [32]. AI-driven tutoring can support students at different learning paces, ensuring a more inclusive and effective learning environment [33].
## 3 Construction of the Model
### Level 5 Evaluation: Communication Performance
#### 3.5.1 Communication Distance  
The relationship between communication distance and receiver sensitivity decrease is as follows:  
\[\frac{d^{\prime}}{d}=10^{\frac{-\Delta S}{20}} \tag{26}\]  
Above, \(d^{\prime}\) is the communication distance with interference, and \(d\) is the communication distance without interference.  
The communication distance reduction \(\Delta d\) can be given by the following Equation (27).  
\[\Delta d=\frac{d-d^{\prime}}{d}(\%) \tag{27}\]
## 5 Result
### Time Series Analysis of Pyroclastic Deposit  
Pyroclastic deposits area are constantly changing after an eruption, and therefore, time series analysis is needed to compare the area of pyroclastic deposits in each year. Time series analysis of pyroclastic deposit land classification with Landsat imagery data is used to calculate the area of pyroclastic deposits. We calculate the area of the pyroclastic deposits using ArcMap. The data of the land classification map with a multilayer format are used to calculate pixels in each class [34], because the concern of this research is the pyroclastic deposit, so we only calculate red pixels, which means pyroclastic deposit on the land classification map. The Landsat image has a resolution of 30 \(\times\) 30 m, so we just multiply the number of pixels by the resolution of the Landsat image to get the area of pyroclastic deposits.  
The pyroclastic deposits on Mt. Sinabung increased after the eruption in 2010. Since Mt. Sinabung has been highly active since 2010, the pyroclastic deposit area changes every year. We can see the change of the pyroclastic deposit of Mt. Sinabung each year based on Table 1. Before the eruption, pyroclastic deposits were calculated as 0.3807 km\({}^{2}\) in 2007. The area of pyroclastic deposits increased in 2011 due to the eruption in 2010 to 1.5066 km\({}^{2}\). The increase of pyroclastic precipitate in 2011 was not too big because the eruption that occurred in 2010 was a small eruption. Then, in 2013-2017, the second eruption occurred, and in 2016, the pyroclastic deposit area due to the second eruption of Mount Sinabung in the last ten years increased very significantly to 20.5911 km\({}^{2}\); this can be seen in Figure 7. The current pyroclastic deposit area of Mount Sinabung is estimated at 18.4572 km\({}^{2}\).  
Mount Merapi erupted in 2006; thus, the pyroclastic deposit of Mount Merapi was still high in 2007, as it was calculated to be 16.443 km\({}^{2}\) in Table 2. In 2010, Merapi erupted with VEI (Volcanic Explosivity  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mount Sinabung No.**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Pyroclastic Deposit Area**} \\ \cline{3-4}  & & **Pixel** & **km\({}^{2}\)** \\ \hline
1 & 2007 & 423 & 0.3807 \\
2 & 2011 & 1674 & 1.5066 \\
3 & 2016 & 22,879 & 20.5911 \\
4 & 2017 & 20,508 & 18.4572 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time series of pyroclastic flow deposits.  
Figure 7: Time series of pyroclastic flow deposits for Mount Sinabung.  
Index) 4. Therefore, much volcanic material was ejected and caused damage. The pyroclastic deposits caused by the eruption in 2010 were very big, increasing two-fold from previous eruptions (Figure 8). We can calculate the pyroclastic deposit of by the 2010 eruption by 2011 data; it is 38.2536 km\({}^{2}\). After the big eruption of 2010, the pyroclastic deposits area was decreased, calculated for Landsat 2014 as 20.5911 km\({}^{2}\). The current pyroclastic deposit area of Mount Sinabung is estimated at 18.4572 km\({}^{2}\).
# On the Role of Seismic Damage Tolerance on Costs and Life Cycle of CLT Buildings
## 4 Discussion of Results
### Construction Costs and Life Cycle Assessment
* Traditional CLT buildings (Group 1-A1) characterized by severe seismic damage in the connecting zones imply higher environmental impact related to the phases of 'use' and 'end of life' due to demolition and disposal, whereas for those based on low damage (Group 1-A2 and Group 2), reduced or null damage is expected, also under high-intensity earthquakes (red circles). * The solutions of Groups 1-A2 and Group G2 have a limited impact (blue circles) on the phases of 'use' and 'end of life' due to limited (or null) damage of the connections, thus limiting the demolition and recycling of material. * The solution of Group G2 has an impact (which can be also significant) on the phases of 'use' and 'end of life' due to the high amount of non-structural components which can be potentially damaged in the case of earthquakes (black circle). As a matter of fact, damage-tolerant solutions represented by Group 1-A2 and Group G2 are both evidently more effective in terms of damage control and sustainability than that of Group 1-A1. Those of Group 1-A2 have the further advantage of reducing the number of non-structural components and then costs related to their eventual damage. Note that both in the cases of Group 1-A2 and Group G2, low-damage non-structural components can also be enclosed in the structure, but with the disadvantages of additional costs related to the high level of prefabrication and specialized technology required by such systems.