esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of the 3-30-300 Green City Concept: Warsaw Case Study
## 2 Materials and Methods
### Methodological Foundations
From each square/apartment, a visibility buffer for three trees within 20 m was drawn. Additionally, the part of the buffer on the opposite side of the building from the simulated apartment was excluded from the analysis. The operation of the adopted algorithm is illustrated in Figure 1.  
The third issue that was clarified was determining which green areas should be included in the 30% green space requirement. It was assumed that this condition would be considered in two variants. The first variant limits the definition of green areas to spaces with tall vegetation, such as forests and parks. In this variant, only areas with a significant plant height, typically with trees and dense vegetation, would count towards the 30% requirement. This approach emphasizes areas that make a more visible contribution to natural landscapes and provide ecosystems with high biodiversity, shade, and other environmental benefits. The second variant takes a more inclusive approach by considering all green areas, regardless of vegetation type or height. This includes not only forests and parks but also smaller green spaces such us squares, lawns, grassy patches, and other open green spaces. In this case, any designated green space--regardless of vegetation type or density--would be eligible to meet the 30% requirement, providing a broader view of what is considered as green space and potentially increasing accessibility for recreational use. The final condition that was detailed concerned the distance to the nearest park or forest. To ensure a high quality of life for residents, it was proposed to consider only parks and forests larger than 1 hectare. This is based on guidelines from the European Regional Office of the World Health Organization, which suggests that smaller areas do not significantly impact human regeneration and rest [24]. Therefore, these smaller areas were excluded from the analysis. Spatial analyses were employed to carry out the study, including  
* Buffer Analysis to determine the visibility area of three trees;
* Iteration to assess how many trees are within the specified buffer;
* Select by Attributes to identify single-family and multi-family buildings;
* Select by Location to perform analyses for individual districts and determine which buildings meet the second and third conditions. To analyze the impact of each condition on the overall results, the study was conducted separately for each individual condition of the 3-30-300 concept, as well as collectively for all conditions combined. Figure 1: Simulation of dividing a building into apartments and determining the visibility buffer of three trees.
## 4 Discussion
### Synthesis of Outcomes  
The outcomes of the studies examined highlighted that by combining augmented reality and virtual reality technologies with intelligent tutoring systems, effective virtual tutors [83], immersive and engaging environments [78], and realistic simulations that offer hands-on experiences [62; 90; 92] can be created. These systems are flexible and extensible [70; 72] and can use various interfaces [75]. Additionally, these immersive and intelligent systems can identify students' preferences, learning styles, and performance [74; 81; 96], provide predictive and diagnostic solutions [97], consider learners' motivational, cognitive, and psychological states and traits [42; 98], support open educational resources, and increase accessibility [73].  
Additionally, the findings highlighted the ability of this approach to offer interactive, personalized, and adaptive learning experiences [42; 62; 66; 69; 76; 77; 80; 86], support scaffolding, and provide tailored and real-time feedback and assessment [24; 62; 67; 77; 78; 86]. By integrating intelligent tutoring systems into augmented reality and virtual reality environments, students' motivation [42; 77; 79; 80; 88], confidence [77], immersion and engagement [77; 88; 90], as well as enthusiasm and enjoyment [77; 81], can be enhanced. Additionally, students, who learnt through this approach and such systems, demonstrated increased learning outcomes and performance [42; 77; 79; 81; 87; 88; 92] and improved learning gains, knowledge, skills, and understanding [79; 80; 82; 86; 90; 91] compared to those who used traditional methods of learning. Besides the benefits it can yield for students, this combination can also effectively support teachers and enable them to perform better, focus on their students, and be more actively engaged in the educational process [84; 87; 89]. Additionally, these technologies and their combination can be used across educational levels [42], support face-to-face, online, and lifelong learning [76], improve psychomotor, cognitive, affective, and embodied learning [67; 69; 75; 85], and enrich workplace training [91]. However, to reap the benefits of this approach, there are challenges that need to be addressed, such as data quality and availability, data and model transparency, security and privacy issues, ethical and moral considerations, peer interaction and student-teacher interaction and communication, technical challenges, hardware and software limitations, teachers' digital competences, the development of material, design considerations, training programs, and algorithmic bias considerations. It is particularly important to focus on the design of such systems [68; 69; 99], reducing related costs [63; 64], increasing their interoperability, and reducing response time and latency [65].
## 5 Experimental Setup
### Significant Findings
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithms** & \multicolumn{2}{c}{**DoS Attack**} & \multicolumn{2}{c}{**Brute-Force Attack**} & \multicolumn{2}{c}{**Malformed Attack**} \\ \hline - & **Precision** & **Recall** & **Precision** & **Recall** & **Precision** & **Recall** \\ \hline NB & 89.51\% & 87.27\% & 93.95\% & 93.53\% & 94.79\% & 95.18\% \\ DT & 96.03\% & 95.93\% & 99.80\% & 99.80\% & 99.66\% & 99.67\% \\ NN & 96.01\% & 95.91\% & 99.46\% & 99.46\% & 99.47\% & 99.48\% \\ RF & 96.03\% & 95.93\% & 99.80\% & 99.80\% & 99.66\% & 99.67\% \\ GB & 96.51\% & 95.51\% & 99.30\% & 99.31\% & 97.40\% & 97.33\% \\ LR & 89.62\% & 87.34\% & 93.28\% & 93.25\% & 95.88\% & 95.70\% \\ MLP & 94.88\% & 94.79\% & 99.74\% & 99.74\% & 97.57\% & 97.51\% \\ DNN & 95.92\% & 95.83\% & 99.41\% & 99.42\% & 94.38 \% & 76.59\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Precision and Recall of DoS, BF, Malformed anomalies attacks—Without FS. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithms** & \multicolumn{2}{c}{**DoS Attack**} & \multicolumn{2}{c}{**Brute-Force Attack**} & \multicolumn{2}{c}{**Malformed Attack**} \\ \hline - & **\({}^{*}\) DA** & **\({}^{*}\) FS** & **DA** & **FS** & **DA** & **FS** \\ \hline NB & 87.27\% & 87.27\% & **93.53**\% & 92.71\% & 95.19\% & 95.19\% \\ DT & 95.94\% & 95.94\% & 99.81\% & 99.81\% & 99.67\% & 99.67\% \\ NN & 94.50\% & **9
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 1 Introduction
The feature fusion module fuses the multiscale features to generate the reconstructed image. Numerous experiments on the MPDD [2] and VisA [3] datasets have demonstrated that the proposed end-to-end anomaly detection method has excellent performance. The main contributions of this study are summarized as follows:  
1. We introduce a novel unsupervised anomaly detection method based on the noise-to-norm paradigm. 2. We propose a residual attention module that can be embedded in the encoder and decoder to achieve high-quality reconstruction of noisy images. 3. Our method achieves state-of-the-art (SOTA) performance on the MPDD dataset.
# The Impact of Large Language Models on Programming Education and Student Learning Outcomes
## 3 Materials and Methods  
Thirty-two second-year undergraduate students participated in our study designed to explore the impact of informal LLM usage on learning outcomes in programming education. The selection of second-year undergraduate students as participants in this study was carefully considered and aligned with the research objectives. They were chosen based on their foundational knowledge in web development using HTML, CSS, and vanilla JavaScript, acquired through coursework undertaken in the first year of their undergraduate studies. Despite possessing this foundational understanding, they had not been formally introduced to React, a prominent JavaScript library widely utilized for building dynamic user interfaces [17]. This made them ideal candidates for examining the efficacy of utilizing LLMs in facilitating the learning of new technology.  
This experiment, conducted over a period of ten weeks, was organized into distinct phases, as depicted in Figure 1.  
Given the increasing prevalence of LLMs in the programming domain, we provided students with the freedom to employ any LLM tool they deemed suitable for completing their programming assignments in the initial phase. By adopting this approach, we sought to mimic authentic programming environments where developers frequently leverage such tools to enhance their productivity and problem-solving capabilities.  
To assess the influence of LLM utilization on students' learning experiences, we implemented a controlled phase where the usage of LLMs was prohibited. This phase was designed to isolate the effects of LLMs on learning outcomes by eliminating their presence during specific learning tasks. By comparing the performance and perceptions of students across the unrestricted and controlled phases, we aimed to observe the extent to which LLMs contribute to educational effectiveness.
## 3 Results and Discussion
### Method Validation
#### 3.2.2 Linearity, LOD and LOQ  
HPLC-PDA linearity results for berberine, palmitate, geniposide and paeoniflorin showed excellent linearity (\(r^{2}\geq\) 0.9994) in the concentration range 2.50-25.00 \(\mu\)g/mL for berberine and palmatine and 5.00-50.00 \(\mu\)g/mL of geniposide and paeoniflorin. The LOD and LOQ values of the four compounds ranged from 0.131-0.296 \(\mu\)g/mL and 0.398-0.898 \(\mu\)g/mL, respectively (Table 1).  
Figure 4: Photodiode array (PDA) detector spectra of the 4 reference standards (**A–D**) and of a HST extract (**A’**–**D’**). (**A**), berberine; (**B**), palmatine; (**C**), geniposide; (**D**), paeoniflorin.  
Figure 3: High-performance liquid chromatography (HPLC) chromatograms of standard mixtures (**A**) and a Haedoksamul-tang (HST) sample (**B**). (1), berberine; (2), palmatine; (3), geniposide; (4), paeoniflorin.
## 3 Results
### FT-IR Spectral Characterization of EPSs  
The FT-IR spectrum of V-EPS and T-EPS are presented in Figure 4. Both spectra were typical for polysaccharides with the characteristic signals at 3290.51 (3313.80) and 2927.72 (2926.56) cm\({}^{-1}\), arising from \(\
u\)(OH) and \(\
u\)(CH and CH\({}_{2}\)) vibrations, respectively [34].  
The presence of proteins in the samples was confirmed by the signal at 1542.14 cm\({}^{-1}\) assigned for \(\delta\)(N-H) and \(\
u\)(C-N) of amide II structure. Interestingly, the band at 1637.69 (1637.91) cm\({}^{-1}\) can be related to the absorbed water in the EPSs; however, it should be also linked to the \(\
u\)(C-O) of amide I structure vibrations in detected proteins [35]. Furthermore, the absorption bands at 1605.39 and 1413.57 (1413.28) cm\({}^{-1}\) could be attributed to \(\
u_{\mathrm{as}}\)(COO-) and \(\
u_{\mathrm{s}}\)(COO-) vibrations of the ionized form of the carboxylic group of uric acids present in EPSs [19]. However, the signal at 1413-1414 cm\({}^{-1}\) might be a result of \(\delta_{\mathrm{s}}\)(-CH\({}_{2}\)) and \(\delta_{\mathrm{s}}\)(-CH\({}_{3}\)) of proteins because of the low uronic acid content in the samples [36]. The signal at 1366.91 (1365.05) cm\({}^{-1}\), which generally is associated with the presence of C-H bond oscillation in methyl groups and CH\({}_{2}\) groups, can originate from C-H vibrations in \(\beta\)-glucose units [36]. It was suggested that the signal at 1245.71 (1244.30) cm\({}^{-1}\) was a result of amide III (\(\delta\)(N-H) and \(\
u\)(C-N)) in proteins in EPSs [34; 35]. The peak at 1032.54 (1031.39) cm\({}^{-1}\) arose from \(\
u\)(CC)(CO) interactions in the monosaccharide rings [19]. The FT-IR spectra revealed the existence of both \(\alpha\)- (826.05 cm\({}^{-1}\)) and \(\beta\)-anomer configurations (895.31(48) cm\({}^{-1}\)) in the monosaccharide units in the EPSs [37]. Two EPSs were rich in mannose and glucose (Table 1), and therefore, the absorption bands at 1031-1033 and 895 cm\({}^{-1}\) should be linked to the presence of \(\beta\)-glucan [38]. On the other hand, bands at 919 and 797 cm\({}^{-1}\) might be associated with mannans in EPS studied [39; 40]. It can be hypothesized that V-EPS and T-EPS contained (non-\(O\)-acetylated) hetero-mannans, \(\beta\)-glucans, or a mixture of them.
## 4 Experiments and Analysis
### Optimal Token Fill Rate Detection
When the token fill rate is 400 tokens/s (the highest), the total task average latency is the third highest; when the token fill rate is 25 tokens/s (the lowest), the average latency is the highest. The token fill rate corresponding to the lowest average latency is selected as the optimal rate for complex task scenarios, which is determined to be 50 tokens/s. At a concurrency level ranging from 100 to 200, the average latency at this optimal rate is 1672 ms. Under the current computing environment, the optimal token bucket rate for simple tasks is set at 200 tokens/s, the optimal token bucket rate for general tasks is set at 100 tokens/s, and the optimal token bucket rate for complex tasks is set at 50 tokens/s. It can be illustrated that it is difficult to adapt to various task requests by setting a fixed token bucket rate.
## 5 Attacks and Countermeasures
### Attacking MANETs
#### 5.3.3 Sybil Attacks on MANETs  
The susceptibility of MANETs to Sybil attacks is comparable to the susceptibility of P2P systems. MANETs are distributed networks and, therefore, in most scenarios no centralized admission control exists. Due to this, a single node can generate multiple virtual identities in a network. Like in P2P systems, malicious nodes may generate multiple virtual identities to increase the impact of other attacks, to evade trust-based countermeasures, or to claim an increased amount of resources.
## 3 Theory and Methods
### Convolutional LSTM Networks
Latitude and Longitude: Sort the data by latitude and longitude. These are data subsets prepared for distinct geographical regions. Height: The data are sorted by elevation (height above sea level) and temporal factors Date: Sort the data based on date values. Subsets of data are created for each unique date in the collection. Time: Sort the data based on time values. Data subsets are created for each time of day. Season: Divide dates into seasons (e.g., winter, spring, summer, and  
\[i_{t}=\sigma(W_{i}\times[h_{t-1},x_{t}]+b_{i}) \tag{7}\]9 Distribution of the input values on different days. autumn) and create data subsets for each. Distance: The distance between measurements is measured and recorded; the data are grouped based on the distance values. Subsets of data are created for various distance intervals, and the model is trained to learn the spatial and temporal structure. Figure 6 depicts an overview of RMSDC.
## 4 Dataset and Experimental Setup  
To evaluate the performance and robustness of the proposed Edge-FLGuard framework, we conducted a series of experiments using both publicly available datasets and synthetically generated attack scenarios. Detailed simulation parameters for these attack scenarios are provided in Appendix A. This section presents the dataset sources, preprocessing steps, attack scenarios, evaluation metrics, and experimental environment.
## 1 Introduction
There are multiple attempts to build such autonomic systems, e.g., [6,7], and different design approaches such as externalization and internalization [8]. According to IBM, the evolutionary path to autonomic computing is represented by five levels [9], starting from basic, through managed, predictive, adaptive and finally to autonomic. Such approaches as software agents [10], active networks [11] and policy-based systems [12] have been developed to automate the management tasks to achieve higher response times with less management cost. Attempts have been made to solve the communication complexity problem by using be solved by built-in mechanisms to let systems manage their own communications [13]. The self-management capability aims to solve the rapidly growing complexity of computing system management and enable dynamic future growth of the system. There are four properties that enable autonomic capabilities in a system: self-configuration, self-optimization, self-healing and self-protection [14]. Self-Configuration is the ability of the system to perform configurations according to pre-defined high level policies and seamlessly adapt to change caused by automatic configurations. Self-Optimization is the ability of the system to continuously monitor and control resources to improve performance and efficiency. Self-Healing is the ability of the system to automatically detect, diagnose and repair faults. Self-Protection is the ability of the system to pro-actively identify and protect itself from malicious attacks or cascading failures that are not corrected by self-healing measures. An autonomic system should satisfy these properties through a reactive or a proactive behavior. A reactive autonomic system tries to detect problems or meaningful events and then finds an appropriate action or solution after the problem has been already detected. A proactive autonomic system [15] uses preventive measures to maintain, improve or optimize the system performance. Those measures are based on the analysis of the current state, anticipated events and the predicted system reaction to them. Thus, we review here the technologies to evaluate their capabilities to solve the grand challenge in the context of M2M service networks. The approaches for M2M service networks are first discussed in Chapter 2 and the taxonomy for the review is defined. Then, the related M2M standards and technologies for each category are reviewed as follows: Chapter 3 M2M information and services, Chapter 4 M2M communication and Chapter 5 M2M security. After the review, a discussion is provided in Chapter 6, and finally concluding remarks are provided in Chapter 7.
# Application of Blockchain Technology in Emergency Management Systems: A Bibliometric Analysis
## 5 Discussion
### Limitations  
Although this study provides a comprehensive bibliometric analysis of blockchain technology applications in emergency management systems, it is important to acknowledge certain limitations.  
First, the scope of data resources was restricted to the WoSCC. Although the WoSCC is a widely respected and comprehensive database, limiting the data source to a single platform may have excluded relevant publications indexed in other databases such as Scopus, IEEE Xplore, or Google Scholar.  
Second, limiting the type of article meant that this paper focuses solely on peer-reviewed research articles and reviews within the Science Citation Index and Social Sciences Citation Index. This criterion excluded other potentially valuable sources such as conference proceedings, books, and gray literature, which could provide additional insights into emerging trends and practical implementations.  
Third, the effect of language introduces a bias towards English-language publications. By excluding studies published in other languages such as Chinese, Spanish, or French, theanalysis may have overlooked significant regional contributions and diverse perspectives on blockchain-based solutions in emergency management.  
Finally, there are limitations involving the research software. Although this study used CiteSpace 6.4.R1, which is a well-established and specialized tool for bibliometric analysis, the obtained results can vary depending on the chosen parameters, algorithms, and visualization techniques. Alternative software or analytical approaches may yield different but equally valid interpretations.
## 1 Introduction
However, the high computational costs and time-consuming nature make CFD unsuitable for the time-sensitive demands of carrier-based aircraft/UAVs during landings (e.g., the dynamic monitoring of airwake along the glide path and the corresponding real-time handling). With the rapid development of artificial intelligence technology in recent years, neural network technology has increasingly become an effective means of prediction, regression, and classification, providing new possibilities for solving this problem [33; 34; 35]. Data-driven artificial intelligence methods, such as backpropagation (BP) neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), etc., show strong predictive abilities when combined with different optimization strategies due to their powerful nonlinear modeling capabilities [36; 37]. To address the trade-off between high spatial resolution and high temporal resolution in predicting ship airwake, this study explores an integrated prediction method combining CFD numerical simulations, BP neural networks, and DWL. A novel prediction framework is proposed that achieves both high spatial resolution and high temporal resolution prediction for the 3D ship airwake along the glide path. The paper is structured as follows: Section 2 focuses on the CFD simulation of the 3D ship airwake, Section 3 presents the BP neural network prediction for the 3D airwake along the glide path, and Sections 4 and 5 provide the discussion and conclusions.
## 3 Normalization Algorithms
### Pseudo-Invariant Point Feature-Based Normalization
#### 3.2.1 Pseudo-Invariant Point Features with the Single Band (Point-Single)  
The invariant features with single\(-\)band regression is:  
\[Y_{i}=aX_{i}+b \tag{4}\]  
where \(X_{i}\) and \(Y_{i}\) are the \(i\)th band of subject and reference image data. \(a\) and \(b\) are the normalization coefficient and bias, respectively.  
Fifty spectra from each surface type in Landsat-8 OLI images (reference) and Sentinel-2A images were selected. Band data were used to calculate the least square fitting equation and calibrate the radiation data from Sentinel-2A at the reference image's scale (Figure 4). These analyses indicate that the fitting coefficients are less than 1, meaning that all band reflectance from Sentinel-2A are higher than the reference of Landsat-8 OLI data. Among the spectra from eight surface types, all except for man-made, artificial surfaces are clustered. The complexity of artificial pixels, their geometrical location, and spatial resolution increases the difficulty of accurately matching these point features from two different sensor images.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 4 Proposed Protocol
### Password Update Phase  
Vehicle users can update their passwords as often as desired, as follows:  
**Step 1:**: After entering \(ID_{i}\) and \(PSW_{i}\) in the smart card, \(V_{i}\) sends a password change request.
**Step 2:**: The smart card computes \(RPSW_{i}=h(ID_{i}\parallel PSW_{i}\parallel s_{0})\) and \(M_{i}=h(h(ID_{i})\oplus PSW_{i})mods_{0})\) and then checks whether or not \(M_{i}^{\prime}=M_{i}\) matches the stored \(M_{i}\) to verify the authenticity of \(M_{i}^{\prime}\). If the verification is confirmed, the smart card inputs \(ID_{i}\) and \(PSW_{i}\).
**Step 3:**: First, the new password, \(PSW_{i}^{new}\), must be entered. Then, the smart card generates \(2^{4}\leq s_{0}^{new}\leq 2^{8}\) and calculates \(RPSW_{i}^{new}=h(ID_{i}\parallel PSW_{i}^{new}\parallel s_{0}^{new})\) and \(M_{i}^{new}=h(h(ID_{i})\oplus PSW_{i}^{new})mods_{0}^{new})\). Finally, the smart card replaces \((N_{i}^{*},M_{i},s_{0})\) with \((N_{i}^{new},M_{i}^{new},s_{0}^{new})\).