esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
## 1 Introduction
### Need for Detecting Banking Malware  
New malware variants are always being discovered and are becoming more sophisticated in the way that they attack systems [9], and they will continue to increase [10]. Banking malware follows the same trajectory, and, in this category, Zeus and its variants are still the most prevalent and widespread of all the banking malware variants discovered. For example, Figure 1 shows that Zeus and Ramnit were amongst the top ten banking malware variants discovered in Q3 of 2022 [11]. Figure 2 depicts the number of banking malware attacks that were detected during the same time, and an upward trend is clearly visible [11].  
Figure 1: Top ten malware discovered in Q3 of 2022. This is the proportion of distinct users who experienced this malware family relative to the total number of users targeted by financial malware.  
Figure 2: Banking malware attacks detected in Q3 of 2022.  
Banking malware have also diversified their tactics, expanded their capabilities, and, over time, evolved into more sophisticated software tools that leverage several attack vectors to cause financial loss. The fact that threat actors can use Malware-as-a-service (MaaS) providers to target victims has also led to their increased prevalence, and some of these MaaS providers can charge up to USD 4000 per month for their services [12].  
Many strategies exist to detect banking malware, and these include signature-based, anomaly-based, behavior-based, and heuristic-based approaches [13], but these do have limitations [14]. These approaches and limitations are explored further over the next few sections, however, some of these limitations include the following:  
* The need to update signature-based malware systems.
* The inability of these systems to detect newer malware variants.
* The inability to detect malware that uses sophisticated obfuscation techniques.
* The inability to detect zero-day malware.  
This research paper is broken down into the following sections. Firstly, a review is conducted of the Zeus, Zeus Panda, and Ramnit banking malware variants and then a banking malware family tree is proposed. The purpose of this review is to understand the similarities and differences between these various banking malware variants. Next, a literature review of related research is provided, followed by a problem statement. Finally, this paper proposes a machine learning approach for detecting banking malware and also proposes a feature section approach that supports the proposed machine learning approach.
## 5 Summary  
The research shows that GNSS solutions based on broadcast ephemerides (BRDM) and correction services like HAS exhibit a lower performance in terms of convergence time, accuracy, and ZTD estimation, especially when using GPS-only data. The HAS service demonstrated significant improvement when integrated with Galileo, enhancing solution availability and accuracy. In real-time applications, Galileo consistently provides better results than GPS alone, partly because of more frequent updates of broadcast ephemerides. Nevertheless, solutions such as CNES and CODE, offering real-time precise corrections and post-processed precise products, continue to deliver a higher performance for high-precision applications.  
The shortened convergence times observed in this study (1-2 min for CODE and CNES, below 5 min for HAS) were achieved using 1 s interval observations, demonstrating the potential for rapid solution stabilization, which is essential for real-time GNSS applications. While HAS meets its accuracy and convergence time requirements, further development of the service, particularly regarding GPS support, could further enhance its performance.  
The presented results are based on real-time/near real-time simulation conditions under ideal circumstances and compared with the best possible results obtained using postprocessed precise CODE MGEX products. It should also be emphasized that the conditions assumed in the study were ideal, and deviations from them could occur in real-world real-time applications, for example, interruptions in receiving corrections or signal outages.  
**Funding:** This research was funded by the Military University of Technology in Warsaw, Faculty of Civil Engineering and Geodesy, Institute of Geospatial Engineering and Geodesy, research grant for 2025.  
**Data Availability Statement:** The original contributions presented in the study, including the downloaded real-time data (HAS and BRDM), are included in the article and openly available on Zenodo at [[https://zenodo.org/records/15675402](https://zenodo.org/records/15675402)]([https://zenodo.org/records/15675402](https://zenodo.org/records/15675402)) (accessed on 14 June 2025); further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The author thanks the International GNSS Service (IGS) for ensuring observation data and products and the developer of the raPPrid open-source software package.  
**Conflicts of Interest:** The author declares no conflicts of interest.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 4 OFDMA in Wi-Fi 6
### Related Work
#### 4.3.6 Synchronization  
OFDMA-based multiple access for IEEE 802.11 ax (OMAX) [92] resolves the synchronization problem. The stations sense the whole channel rather than RUs to avoid inter ference and the backoff decreases by the number of RUs. Upon backoff completion, each station randomly chooses one of the RUs. Moreover, it enhances the RTS/CTS mechanism by embedding the scheduling in CTS to reduce overheads in the network. Furthermore, [93] introduced a symbol-timing synchronization scheme for uplink multi-user scenarios to enhance the synchronization quality. This symbol timing depends on the legacy and HE 802.11 preambles to provide synchronous frame arrival in uplink multi-user mode.
## 2 Case Report
### Design and 3D Printing of Surgical Guide  
An intraoral scan (TRIOS; 3 Shape) was acquired to aid in fabricating a surgical guide (Figure 3a). Digital Imaging and Communications in Medicine (DICOM) files were converted to Standard Tessellation Language (STL) format and then superimposed with STL files acquired from the intraoral scan using coDiagnostiX 9.7 software (Dental Wings INC, Montreal, Canada). Then using the design software (3 Shape Premium Dental System; 3 Shape), the level of the cementoenamel junction was marked (Figure 3b) to guide the gingivectomy incision line, followed by a second line 3 mm apical to the cementoenamel junction line to guide the bone resection. The virtual design of the surgical guide was performed accordingly.  
Data was transferred to a three-dimensional printer (Formlabs II; Formlabs Inc., Somerville, MD, USA). The printer used utilizes stereolithography (SLA) technology with Laser Spot size of 140 microns and layer thickness of 25-300 microns. The guide was printed (Figure 4) and then checked to ensure it fits in the mouth of the patient prior to starting the surgery.  
Figure 3: (**a**) Intraoral scan of the maxillary jaw; (**b**) After superimposition of the CBCT and the intraoral scan, the cementoenamel junction and future bone level marked.  
Figure 2: Distance from cementoenamel junction to facial bone crest as measured on cone beam computed tomography (CBCT).
# TRAL: A Tag-Aware Recommendation Algorithm Based on Attention Learning
## 5 Experiments
### Performance Comparison  
Experimental results of eight recommendation models on two public datasets are presented in Table 3, where we show the best results in boldface and best baseline results in underline. Additionally, we calculate the improvement (imp.) of the proposed model compared to the best baseline. It is clear that the proposed TRAL consistently surpasses other approaches in all evaluation metrics. For example, the performance of TRAL achieves 5.9% and 2.6% improvement over the best baseline in the light of HR@10 and NDCG@10 on Delicious.  
It is worth noting that CCF performs worst in most metrics among these baselines, which adequately reveals that the clustering method is insufficient to capture accurate abstract representation for users or items compared to deep learning strategies. In addition, the performance of DSPR, TRSDL, and TRAL are significantly better than ACF. The reason is that ACF only employs the autoencoder to capture low dimensional feature representations of users by constantly optimizing the reconstruction error. In contrast, other deep learning models directly optimize the correlation between users and items so as to extract more accurate feature representation. Furthermore, we can see that NCF behaves marginally better than ACF, but it is obviously worse than other deep learning recommendation methods with the help of tagging information. This convincingly proves the important role of tags as auxiliary information in improving the recommendation performance. The performance of BPR-T and STEM is obviously better than that of NCF, but worse than the proposed TRAL, which indicates that the deep learning model should be well designed for current problems to bring competitive results.  
To summarize, the main reasons why the proposed model is superior to other baselines are as follows: (1) constructing the deep architecture to capture effective tag-based features; (2) exploiting the attention mechanism to distinguish the importance of different tag-based features adaptively; and (3) combining the deep and the wide component to learn the high-order and low-order interactions between user and item latent features.
# Hybrid ANN-Based and Text Similarity Method for Automatic Short-Answer Grading in Polish
## 3 Text Similarity Methods
### Revised Split Algorithm [12]  
The revised algorithm introduced significant improvements, including the incorporation of the Jaccard index to measure similarity and new rules to handle differences in answer lengths [51]:  
\[J=\frac{|X\;\cap\;Y|}{|X\;\cup\;Y|}\,5.0 \tag{1}\]  
In (1), \(X\) is the set of words from the model answer, and \(Y\) is the set of words from the examined answer.  
1. Length adjustment: If the teacher's answer was shorter, the Jaccard index was used as before. For longer student answers, a new rule ensured all words in the teacher's answer were checked for inclusion, with proportional scoring based on word matches, Table 1 shows the differences in the length for both teacher and student answers.  
Error handling: Spelling mistakes were addressed using Levenshtein distance, improving recognition accuracy.  
1. Final scoring: The maximum score from both similarity measures (length adjustment and error handling) was selected, as shown in Listing 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Question 1:Do zapisu znaków alfanumycznych w komputerach PC stosuje sig** & \\ \hline
**Teacher Answers** & **Student #1 Answer** & **J** & **Student \#2 Answer** & **J** \\ \hline kod ASCII & & 3.33 & 2.5 \\ ASCII & & 1.66 & 5 \\ znaki ASCII & tablice kodow ASCII & 1.66 & 2.5 \\ ASCI & & 0 & 0 \\ \hline
**Question 2: Dyski twarde zaliczamy do pamiçi** & & & \\ \hline
**Teacher Answers** & **Student \#1 Answer** & **J** & **Student \#2 Answer** & **J** \\ \hline magnetycznych & & 2.5 & 0 \\ trwalych & elektryczny & 0 & trwalych & 5 \\ zewnetranych & magnetyczny & 0 & 0 \\ masowych & & 0 & 0 \\ \hline
**Question 3: Pojecie “hardware” okresla:** & & & \\ \hline
**Teacher Answers** & **Student \#1 Answer** & **J** & **Student \#2 Answer** & **J** \\ \hline wszystkie elementy & & 0.5 & 0 \\ materialne komputera & Sprzet ktory sklada sie & 0.55 & sprzet & 0 \\ komputera & podzespoly komputera & 0.71 & & 5 \\ sprzet komputera & & 1.42 & & 2.5 \\ \hline
**Question 4: Budowa i zasada zialania plyly DVD jest najbardziej zblizona** & & & \\ \hline
**Teacher Answers** & **Student \#1 Answer** & **J** & **Student \#2 Answer** & **J** \\ \hline Ptyty CD & & 3.33 & 2.5 \\ CD & 1.66 & & 5 \\ Dysku CD & & 1.25 & & 2.5 \\ Kražka CD & & 1.25 & & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example shows the differences in the length for both teacher and student answers.
## 4 Discussion
It is strongly recommended to balance plastic stability with organic removal, while considering safety and ease of use [23]. The experimental conditions used in preparative digestion techniques, particularly the choice of reagent and temperature, could lead to losses, damage, and degradation of microplastics [31]. Changes in the size of microplastics due to digestion should be carefully assessed, especially when investigating their adverse effects on the human body. Experimental evidence suggests that the transport of microplastics into human body systems largely depends on their size [32; 33; 34]. Studies on marine model organisms have indicated that the size of microplastics plays a crucial role in their impact on biological organisms. Smaller particles are more likely to exhibit toxic effects on organisms [35]. Therefore, the stability and resistance of microplastics to digestive treatments are crucial for accurately assessing their adverse effects on the human body. Regarding the negative effects of digestion solutions on microplastics, acidic approaches, particularly the use of high-concentration nitric acid (i.e., 65%, 69%), are not suitable for microplastic analysis. The scientific literature has reported aggressive effects of high-concentration nitric acid on polymers [36]. Experimental evidence has shown that polystyrene microplastics can fuse when treated with HNO\({}_{3}\) (65%). Furthermore, small microplastics (i.e., 10-30 \(\upmu\)m) may melt together [37], a finding confirmed by our research. Alkaline digestion performed with KOH 5 M at 50 \({}^{\circ}\)C for 48 h showed a slight reduction in the diameter of PS-MPs. No discoloration was recorded (Figure 4). Among alkaline solutions, sodium hydroxide (NaOH) has been found to impact microplastics such as nylon, polyvinylchloride (PVC), polyethylene (PE), and PS. It has been suggested that both KOH and NaOH could affect the shape and size of polymers such  
Figure 4: PS-MPs treated with KOH 5 M at 50 \({}^{\circ}\)C for 48 h and standard ones. Images were obtained using Capture 2.0 software. as cellulose acetate (CA) and polyethylene terephthalate (PET) [38]. However, experimental evidence has shown that a 10% KOH aqueous solution at 60 \({}^{\circ}\)C did not alter the size of PET microfibers [39]. Regarding PS, the current understanding of it is conflicting. A study by [PERSON] et al. suggested that KOH concentrations higher than 10% (2 M) changed the color of PS microplastics [38], while [PERSON] et al. (2021) [40] found no such impacts [40]. Additionally, a study by [PERSON] et al. [10] reported that KOH treatment (60 \({}^{\circ}\)C for 24 h) had a minor effect on PS fluorescent microbeads, resulting in a slight reduction in fluorescence. However, no investigation into size modifications was conducted [10].
## 6 Challenges and Future Research Directions  
This article has shown that several ML and DL methods have been successfully applied to UWA systems for channel estimation, adaptive modulation, and modulation recognition. Although these methods have demonstrated promising performance, several open challenges and future research directions remain. Based on the studies referenced from [9; 20; 23; 27; 28; 29; 30; 31; 33; 34; 36; 38; 40; 45; 46; 47; 53; 55; 59; 61], the following key challenges and future research directions have been identified.
# Multi-Objective Pareto Optimization of Tensile Membrane Architecture for Energy Harvesting
## 3 Methodology
### Simulation Procedures  
Multi-objective Pareto-optimization was performed with the objectives of maximizing the material stiffness and electric generation and determining the optimal stability and generated amount of electricity. The optimization was performed in the following order. First, the area where the TMA was to be built was selected; each region had different wind patterns, according to the average wind speed and wind direction. Second, the geometry of the TMA was defined using three parameters: the stiffness of the material, shape, and orientation. Third, the energy generated from the TMA when the wind blew for a certain period of time was calculated. Finally, a multiobjective Pareto-optimization algorithm was implemented with the objectives of power generation and material stiffness. The details of each step are presented in the following sections.
## 1 Introduction
### Motivation and Contributions  
Through the analysis of existing IoV authentication protocols, we identified certain deficiencies in several of the existing authentication protocols, such as high computational costs, a lack of multi-factor secrecy and no known session key secrecy, and also found that they may suffer from ID/password guessing attacks, RSU captured attacks and so on. Recently, [PERSON] et al. [5] proposed a conditional privacy protection and desynchronization-resistant authentication scheme for IoV. However, we found that [PERSON] et al.'s [5] scheme suffers from several attacks. To solve these problems, we proposed a secure and efficient V2I authentication scheme for IoV. The contributions of this article are as follows:  
* Firstly, we point out that [PERSON] et al.'s protocol cannot resist identity guessing attacks, impersonation attacks and no session key secrecy when the attacker obtains data stored in SC through side-channel attacks, as well as that it is vulnerable to RSU captured attacks and lacks perfect forward secrecy.
* Secondly, we propose a lightweight V2I authentication protocol based on PUF and the Fuzzy Extractor algorithm for the IoV. In particular, we used PUF to resist RSU captured attacks, designed a three-factor secrecy strategy to resist side-channel attacks, and also adopted a conditional privacy-preserving strategy to achieve anonymity and malicious user tracking.
* Our proposed protocol is provably secure under the random oracle model. In comparison to various related protocols, our protocol demonstrates resilience against a range of known attacks and offers lower computational and communication costs.  
The remaining part of this article is as follows. Section 2 presents a summary of the related work. Section 3 provides the preliminaries, and Section 4 reviews [PERSON] et al.'s scheme. Section 5 analyzes the security of [PERSON] et al.'s scheme. In Section 6, the proposed scheme is introduced. Section 7 presents formal and information security analysis of the proposed scheme. In Section 8, we present security and performance comparisons with related protocols. Finally, the study is concluded in Section 9.
# Metrics for Evaluating Synthetic Time-Series Data of Battery
## 3 Proposed Evaluation Method
### Rate of Change in Correlation Coefficient of Linear Regression  
Linear regression, an analysis technique that models the linear correlation between two variables from the given data, describes the relationship between variables by finding the correlation coefficient that expresses the tendency of the given data.  
In this study, the quality of the data is evaluated by comparing the correlation coefficients of the linear regressions of the original dataset and the dataset to which the composite data is added. The more similar the linear characteristics of the data, the more similar the correlation coefficient between these datasets.  
The reliability of the correlation coefficient is ensured through the determination coefficient [20], and the correlation coefficients of the original dataset and synthetic dataset including original data are calculated. The calculated correlation coefficient is applied to Equation (4) to calculate the rate of change (ROC) in the correlation coefficient. The quality of the data is evaluated using the rate of change in the correlation coefficient.  
\[Rate\,Of\,Change(\%)=\frac{|r_{ori}-r_{syn}|}{r_{ori}} \tag{4}\]  
where \(r_{ori}\) and \(r_{syn}\) represent the correlation coefficients of the original dataset and synthetic dataset including original data, respectively. When the linear characteristics of the original and composite datasets are similar, the rate of change of the correlation coefficient has a value close to 0. When the linear characteristics of the original and composite datasets differ, the rate of change of the correlation coefficient has a large value.
# Retrieval-Augmented Generation (RAG) Chatbots for Education: A Survey of Applications
## 3 Results
### Thematic Scope  
As RAG chatbots are used in various fields of education, for the purpose of this study, we have arranged them into the following four types:  
1. Institutional-domain knowledge--chatbots focused on the knowledge about a given institution, including its policies and procedures, regardless of its field(s) of education.
2. Area-specific--chatbots designed to support education in a particular area of education, including the following: 1. Biomedical; 2. Computer science; 3. Culture; 4. Education; 5. Finance; 6. Health sciences; 7. Language; 8. Law; 9. Math.
3. Various--chatbots designed to support education in more than one field.
4. Any--chatbots that may be used in any field of education.  
Figure 3 shows the number of chatbots reported in the analyzed papers that belong to specific types and areas according to the classification presented above.  
As shown in Figure 3, the most commonly reported were chatbots providing access to the institutional-domain knowledge, with 11 publications [25; 26; 27; 28; 29; 35; 36; 37; 38; 46; 49]. The majority of them concern the organization and policies of universities [25; 27; 28; 29; 46; 49].  
Among the chatbots providing area-specific knowledge, the most popular domain of knowledge is health sciences, with nine papers [14; 15; 24; 42; 45; 50; 52; 57; 58].  
Closely following is computer science, with six publications. In this group, four chatbots support the learning of specific subjects: databases [21], data science and artificial intelligence [18], and programming [17; 31], whereas the remaining two [44; 48] provide access to area-specific knowledge sourced from the literature.  
Next were law, with three publications [41; 43; 55]; education, with two publications [32; 39]; and the special case of chatbots providing knowledge from various areas of science [40; 51].  
The remaining areas were addressed by only one paper: biomedical [56], finance [59], language [13], culture [35], and math [22].  
Lastly, there are 10 publications [16; 19; 20; 23; 30; 33; 34; 47; 53; 54] that were assigned to the \"Any\" type. Some of them, however, mention targeting a specific level of education, most often higher education [23; 47; 54], with one paper that focused particularly on secondary-school education [33].