esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data  
### Study Area and Hydro-Rainfall Data  
The Ghdat watershed is located in the upstream of the large Tensift basin which drains the High Atlas of Marrakech (Figure 1). Covering an area of 552 km\({}^{2}\), the altitudes are ranging from 693 to 3578 m. The basin, whose average altitude is 1730 m, has a large zone located between 1000 and 2600 m representing 82% of the basin's area. The average slope over the catchment, measured from a 30 m digital terrain model, is 30.7%.  
Figure 1: Geographical location of the Ghdat watershed and hydro-rainfall stations.  
Geologically, the basin consists of two large groups: on the one hand, a rigid Paleozoic and Pre-Cambrian basement upstream, dominated by schists and quartzites, and downstream, soft Permo-Triassic deposits including sandstones, clays, conglomerates and marl-limestones of the Cenozoic age [42; 43; 44]. These formations are generally semi-permeable and favor heavy runoff in the case of intense rain. The hydrometric station of Sidi Rahal located at an altitude of 690 m, controls the discharge of the Ghdat Wadi and also serves as a rain gauge. It receives an average of 359 mm of rainfall per year. Rainfall and discharge data cover a 50-year period from the 1967-1968 to 2016-2017 hydrological years. These data come from gauges installed by the Tensift Hydraulic Basin Agency in Marrakech. This agency installed two other rain gauges upstream of the basin: Adrar Gardouz and Tijdante at altitudes of 2285 m and 1122 m, respectively. This made the Ghdat basin instrumented by three rainfall gauges (Sidi Rahal, Adrar Gardouz and Tijdante) and a hydrometric station (Sidi Rahal) (Table 1).  
The time step of the rainfall and runoff data is 10 min. The data for modeling covers a period from 2011 to 2018. A total of 17 flood events are selected from the hydro-meteorological data. The observed precipitation data at 10-min time step were converted to 30-min to be compared with the satellite data.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 3 Strategies for Scanning the Impact Area
### Comparative Analyses among Scanning Strategies Using Multi-UAV Systems  
In order to determine which strategy provides the shortest scanning time for the area of impact, while simultaneously ensuring a reliable network for exchanging data among UAVs and base station, both proposed strategies will be compared as detailed in Table 1.  
For both of them, the scanning time is proportional to the number of UAVs assigned to the mission. The maximum size for the searched area when the subdivision is not applied is restricted by the range of the node that is leveraged as the hub. When subdivisions are applied, the size of the scanning area can be larger than that in the first scenario, since the information can be routed from a distant aircraft to the base station through a mesh network. Data is sent from one node to another until it reaches its final destination. When the area is not subdivided, the system relies on a dedicated and robust  
Figure 5: Area decomposed into four subareas by using the back-and-forth pattern.  
link that connects the hub to the base station. The remaining UAVs communicate with the hub node through a network configured in a star topology coordinated by the hub. Alternatively, in order to increase the reach, it is possible to establish a mesh network among some of the nodes if they are not able to communicate directly with the hub.  
In the scanning method without subdivision, since the nodes are close to each other, the required time interval for reorganizing the network is shorter when one node fails, where the formation is simply reorganized. Besides, the energy consumption due to signal transmission is reduced in this case, implying the extension of the system's lifespan. When a subdivision is used instead, as the nodes are farther from each other and, if failure happens to occur, the unscanned area must be divided again among the remaining nodes. Due to the greater distance between nodes, more power will be required by the communication system.  
Considering the network behavior for each proposed strategy, as well as the specific characteristics of the addressed application where few nodes are available, a scanning strategy without subdivision was adopted at the first moment. This is due to the limited number of available UAVs embedded with a robust and long-range communication system. Based on the chosen strategy, it is possible to plan a set of experiments and establish metrics in order to measure network parameters, such as the received signal strength indicator (RSSI), latency, transmission rate, packet loss rate, jitter and network overhead, aiming at analyzing the network performance in terms of reliability and capacity of reorganization when adding or removing nodes.
## 6 Conclusions and Future Developments
The recording of prolonged students' VR sessions using QSA layout could be limited by image size: we already addressed this issue employing paging techniques [27]. We previously used the encoding model also to capture ergonomics, like neck strain while wearing HMDs (see [27]) tracking comfort levels over time for multiple immersive sessions in a single QSA. Consumer 6 degrees of freedom (DOF) headsets allow to track areas of a few meters: session volumes could be also attached to physical spaces to capture and/or assess students' spatial performances. For instance a 3 \(\times\) 3 m physical tracked area matching a virtual session volume with the same extents, would result in small quantization errors (\(\pm\) 5 mm, using lossless PNG with bit-depth = 8) to capture spatial attributes. We foresee advantages in capturing physical motions inside the tracked area and its usage during the session, for instance local HMD motions (e.g., assessment of real walking techniques), positional VR controllers or tracked hands (see for instance Oculus Quest HMD). Since the framework deals with images, QSAs and saliency tables can be also employed in machine learning (ML) approaches as training data to classify user performances or recognize interaction patterns for learning assessment. The QSA layout also offers a simple and coherent way to reduce original dataset resolution (see temporal compression in Section 3.2), creating progressive approximations of the record. The framework and the encoding model proven to be suitable for online WebVR/XR sessions: we foresee also fruitful integration with MOOCs providing VR training [14; 41] offering online tools for teachers/instructors to assess students spatial learning and easily detect interaction patterns. Conceptualization, investigation, visualization and software, B.F.; Writing-review and editing, L.C. All authors have read and agreed to the published version of the manuscript. Authors would like to thank [PERSON]. [PERSON] (CNR ISFC) for providing portions of old 3D models used for Keys2 Rome project ([http://keys2 rome.eu/](http://keys2 rome.eu/)); the Hallwyl Museum and their 3D models freely available (CC BY-SA 4.0) on SketchFab; all the volunteers involved in online WebVR/XR experiments and all CNR ISFC people involved in VR installation for \"TourismA 2018\" event. The authors declare no conflict of interest.
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 5 Experimental Setup and Results
### Results of Previous Work on Malevis Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Method** & **File** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Accuracy (\%)** \\ \hline [PERSON] et al. (2021) [6] & Random Forest & Bytes & 93.10 & 93.10 & 93.20 & 93.14 \\ \hline [PERSON] et al. (2021) [6] & SMO (RBF) & Bytes & 94.80 & 94.70 & 94.70 & 94.65 \\ \hline [PERSON] et al. (2021) [6] & SVM (Linear) & Bytes & 92.80 & 92.90 & 92.80 & 92.91 \\ \hline [PERSON] et al. (2021) [6] & XGBoost & Bytes & 91.90 & 91.60 & 91.70 & 91.63 \\ \hline [PERSON] and [PERSON] (2022) [39] & CNN (Proposed system without augmentation by using RGB image) & & 95.80 & 95.40 & 95.82 & 98.89 \\ \hline [PERSON] and [PERSON] (2022) [39] & CycleGAN + CNN (Proposed system with augmentation by using RGB image) & Bytes & 97.39 & 96.87 & 97.08 & 99.60 \\ \hline [PERSON] et al. (2024) [40] & MobileNet fine-tuning (FT) mode & Bytes & 96.09 & 96.02 & 96.04 & 96.04 \\ \hline Proposed system &
\begin{tabular}{l} Hybrid CNN Model \\ with Fine-Turing: \\ EfficientNetB0 \& \\ DenseNet121 (Overall \\ Average Result) \\ \end{tabular} & Asm & 97.45 & 99.48 & 98.5 & 98.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Recent research on MaleVis dataset. racy of 98.45% on the benchmark dataset as shown in Table 14. This performance surpasses previous methods, including the 96.04% accuracy reported by [PERSON] et al. [40] using MobileNet fine-tuning and the 93.10% accuracy achieved by [PERSON] et al. [6] with Random Forest. The improvement can be attributed to our novel assembly based visualization approach, which effectively captures semantic relationships in malware code through opcode sequences, outperforming traditional byte-level representations. The hybrid architecture, combining EfficientNetB0 and DenseNet121, leverages the strengths of both networks--EfficientNet's efficient scaling and DenseNet's feature reuse--resulting in more robust feature extraction. This is reflected in the balanced precision (97.45%) and recall (99.48%), indicating strong detection capability with minimal false positives or negatives. Our method achieves comparable accuracy without the computational overhead of intensive data augmentation techniques used in prior work. While models like [PERSON] and [PERSON]'s [39] CycleGAN + CNN achieve marginally better performance (99.60%), they require substantially more resources. This makes our assembly-to-image approach more practical for real-world deployment. However, performance degrades slightly (~94%) for rare malware families (<50 samples), suggesting opportunities to incorporate few-shot learning techniques in future work to maintain efficiency while improving the detection of uncommon variants.
## 2 Materials and Methods
### Case of Study: City of Valencia  
The Valencian region mobility plan offers a detailed survey of mobility within three sub-regions: Castellon, Valencia, and Alicante. The sub-region of Valencia has the largest mobility structure of the three, with almost five million daily journeys. We therefore focused on the city of Valencia as the mobility epicenter of the Valencia sub-region.  
Valencia has implemented advanced methodologies to calculate transportation emissions. The methodology based on Vehicle Kilometers Traveled (VKT) and the use of the COPERT emissions calculator allows for estimating greenhouse gas emissions from daily travel data and journey patterns [9; 13].  
To provide a broader context, it is useful to compare Valencia's approaches and results with those of other cities. For example, Madrid and Barcelona (Spain), like Valencia, have used VKT-based methods to assess transportation emissions. These cities have implemented traffic restriction policies in high pollution areas and promoted the use of public transportation and bicycles [14].  
Valencia's main pollutant is road traffic, and therefore, like other cities, it is in the process of implementing low-emission zones [15]. The study of Valencia and its comparison with other cities highlights the importance of addressing urban mobility from a holistic and data-driven perspective. Advanced methodologies, such as those used in Valencia, provide valuable tools for understanding and mitigating transportation emissions. Adopting similar approaches and learning from international best practices can help develop effective strategies to tackle environmental challenges in global cities.  
According to the Statistics National Institute, Valencia city had just over 800,000 inhabitants in 2023. Nevertheless, the Valencian metropolitan area has more than 1.7 million inhabitants [15]. Each day, in the metropolitan area of Valencia, almost 40% of daily trips are by private vehicle compared to just over 20% by public transport [15]. In addition, Valencia city council and Joint Research Centre (European Commission) have signed a Collaboration Agreement to use Valencia as a City Lab under the framework of the Community of Practiceon CITIES [16], to identify new approaches in the areas of urban indicators and sustainable development goals. Thus, Valencia City is chosen as our case study.  
The city of Valencia is administratively divided into 88 neighborhoods (Figure 1). Thus, the methodology developed aims to obtain neighborhood resolution.
# A UML Activity Flow Graph-Based Regression Testing Approach
## 2 Our Testing Approach
### Associating UML Sequence Diagrams and Activity Diagrams
#### 2.2.2 Formal Definition of Activity Diagram  
An activity diagram is a graphical representation of a process, where nodes are connected by edges to show the flow of control and information. This type of diagram is commonly used in software modeling and can be associated with various modeling elements such as Use Cases, Classes, Interfaces, and Collaborations. Tokens, which represent control or information values, move along the edges from the source node to the sink nodes based on actions and conditions. Activity diagrams consist of two types of modeling elements: activity nodes and activity edges.  
Activity nodes are classified into three types: action nodes, control nodes, and object nodes. Action nodes take input data and control tokens, create new tokens, and transmit them to output activity edges when they are ready. Control nodes route tokens through the graph and have components for decision-making, splitting or merging the flow for parallel processing, and so on. Object nodes give and take data tokens and can also act as buffers, collecting tokens while waiting to go downstream.  
Figure 1: Activity Diagram and Sequence Diagram Association.  
Activity edges are classified into two types: control flow edges and object flow edges. Control flow edges depict the flow of control throughout the activity, while object flow edges depict the movement of items during the activity. This article focuses on the data and control flow of activity diagrams, both of which are critical for test generation.  
**Definition 2** (Activity Diagram).: _An activity diagram is defined as a 6-tuple,_  
\[A_{D}=(A_{s},T_{c},C_{g},F_{r},a_{S},a_{E})\]  
_where_  
1. \(A_{s}=a_{s1},\)__\(a_{s2},\cdots,a_{sm}\)_, represents a set of activity states_
2. \(T_{c}=t_{c1},\)__\(t_{c2},\cdots,\)__\(t_{cn}\) _represents a set of completion transitions_
3. \(C_{g}=c_{g1},\)__\(c_{g2},\cdots,\)__\(c_{gn}\) _denotes a set of guard conditions_
4. \(C_{i}\) _corresponds to the transition_ \(t_{i}\)__
5. \(F=(a_{i},\)__\(t_{j},\)__\(c_{gk})\) _or (_\(t_{j},\)__\(c_{gk},\)__\(a_{j}\)__\(|\)__\(t_{j}\)_\(\in\)__\(T_{c},\)__\(a_{i}\)__\(\in\)__\(A_{s},\)__\(c_{gk}\)__\(\in\)__\(C_{g}\) _represents the flow relationship between transitions and activity states_
6. \(a_{S}\)__\(\in\)__\(A_{s}\) _is the starting activity state_
7. \(a_{E}\)__\(A_{s}\) _is the ending activity state_
8. \((a_{S},\)__\(t), (\(t,\)__\(a_{S}),\)__\((a_{E},t)\)__\(F\) _for only one transition_ \(t\)_._  
In more detail, \(A_{s}\) is a finite set of activity states, and \(T_{c}\) is a finite set of completion transitions. \(C_{g}\) is a set of guard conditions where \(C_{i}\) corresponds to transition \(t_{i}\) in \(T_{c}\). \(F\) is a set of flow relationships between transitions and activity states, where an element in \(F\) can be either \((a_{i},\)__\(t_{j},\)__\(c_{gk})\) or \((t_{j},\)__\(c_{gk},\)__\(a_{i})\) depending on the direction of flow.
# Microstructure, Durability and Mechanical Properties of
## 3 Results  
### Mercury Intrusion Porosimetry  
In relation to the mercury intrusion porosimetry technique, the results of total porosity are depicted in Figure 1. As can be observed, at 28 hardening days the lowest values of this parameter corresponded to the REF and S series. At the same age, the mortars made with ternary binders SL and SF showed slightly higher total porosities than those noted for the REF and S mortars. On the other hand, the greatest total porosities at 28 days were observed for binary mortars L and F and for the ternary binder FL. Scarce changes were observed in total porosity values between 28 and 250 days for the majority of mortars studied, although a slight reduction in this parameter was observed for the F mortars.  
Regarding the pore size distributions, they are represented in Figure 2. At 28 hardening days, the percentages of pores with diameters lower than 100 nm (pore ranges \(<\)10 nm and 10-100 nm) were relatively similar for all studied mortars. However, at that age, binary and ternary binders with fly ash and ground granulated blast furnace slag generally presented higher relative volume of pores with sizes in the range \(<\) 10 nm, especially for the SF series. At 250 days, an increase of those finer pores for all the studied mortars was observed, showing a higher pore refinement of the F and FL series. Finally, the less refined pore network corresponded to the L mortars along the time period studied.  
Figure 1: Total porosity results noted for the analyzed binders.
## 3 Numerical Experiments
### Multi-Frequency Sinusoidal Load  
Next we aim to test the proposed algorithm for identifying a periodic load. We redefine the excitation force as \(F(t)=0.5\sin(40\pi t)+1.5\sin(60\pi t)\). All the remaining parameters of the model are kept unchanged. However, we measure the beam response at node A which is indicated in Figure 4. Unlike the previous case here we collect the beam response in a point that is different from the excitation point. We use the proposed algorithm to reconstruct the dynamic load in time steps. Again we consider clean and polluted data as an input for the four different cases considered in the previous test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **No Noise** & \begin{tabular}{c} **Gaussian White Noise** \\ **Noise** \\ \end{tabular} & \
\begin{tabular}{c} **5\% Modal** \\ **Error** \\ \end{tabular} &
\begin{tabular}{c} **5\% Modal Error** \\ **and Gaussian Noise** \\ \end{tabular} \\ \hline PREM & 2.97\% & 2.78\% & 9.44\% & 9.49\% \\ ACM & 0.9816 & 0.8026 & 0.8778 & 0.7087 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation table of continuous multiple impact load identification results.  
Figure 6: The identification results of continuous multiple impact loads.  
Figure 7(a)-d show the algorithm output compared to the exact excitation. We also show in Table 2 the corresponding errors in the four considered cases. As can be expected the best approximation is achieved in the case where clean data is used i.e., Figure 7(a) where the PREM and the ACM errors are about 0.38% and 0.9741, respectively. This relatively small error was achieved using only six mode shapes in the approximation. The 5% Gaussian white noise introduced into the measured response has a small impact on both types of errors. However, a 5% error in the modal properties cause an order of magnitude increase in the PREM error or even two orders of magnitude when added to the Gaussian white noise. But it should be also noted that despite the significant increase in the error when identifying the peak load but the global error i.e., ACM remains reasonably small and the identified wave pattern matches with high accuracy the actual sinusoidal load.
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.3 Result on the arXiv-10 Dataset  
Accordingly, RQ-OSPTrans is compared with the baseline models on the arXiv-10 dataset, which has text lengths similar to those of the THUCNews dataset. The results are shown in Table 5. Here, the tokenizer's expansion length is set to 512. On the non-pre-trained models, RQ-OSPTrans exhibits a significant performance improvement of 5.0% compared with the self-learning Transformer framework Protoformer [45] on this task. In Transformer pre-trained models, BERT's bidirectional attention mechanism shows a significant advantage on this task, indicating its excellent capability to learn long-term dependency semantic relationships within sequences of length 512. Nevertheless, our RQ-OSPTrans still achieves a performance improvement of 1.0% on the test set. A possible explanation is that the language expression in the arXiv-10 dataset is more rigorous compared with that in THUCNews, where simpler models may already sufficiently learn semantic features. By contrast, RQ-OSPTrans exhibits sensitivity to the learned sequence length. We will conduct an ablation study on sequence length in Section 4.5.
# Characterization of a COTS-Based RF Receiver for Cubesat Applications +
## 5 Test Results
### Measure of the DC Power Absorption  
In this subsection, the measure of the receiver DC power absorption is reported. The test setup is the same as shown in Figure 9. As shown in the figure, the absorbed current is equal to 0.98 A on the 5 V output channel, and 30 mA on the 3.3 V one. Therefore, the total DC power absorption is about 5 W. However, the absorbed power also considers the presence of the PLL evaluation board, which is implemented inside a linear voltage regulator to generate the 3.3 V voltage, starting from the 5 V input voltage. This 3.3 V voltage is not used to power supply the receiver, and is only used for the PLL normal operation and other minor components. Therefore, the current absorption, due to this 3.3 V  
Figure 10: Measured input dynamic range of the receiver board for from -115 dBm to -70 dBm with a 2.5 dB step.  
voltage (which has been estimated equal to 84 mA reading the datasheet), must not be considered in the total DC power absorption of the receiver.  
The DC power dissipated (\(\mathrm{P_{D}}\)) by the voltage regulator was estimated as equal to 142.8 mW using Equation (1)  
\[\mathrm{PD}\cong\mathrm{(VIN-VOUT)}^{*}\mathrm{\leavevmode\
obreak\ IOUT}, \tag{1}\]  
where \(\mathrm{V_{IN}}\) is the regulator input voltage, and \(\mathrm{V_{OUT}}\) and \(\mathrm{I_{OUT}}\) are the regulator output voltage and current, respectively. Therefore, the real DC power absorbed by the receiver has been calculated by subtracting to 5 W the power dissipated by the PLL evaluation board (142.8 mW), obtaining a total DC power absorption of about 4.86 W. This value is higher than the system requirement (3.5 W).
# A Graph Neural Network Node Classification Application Model with Enhanced Node Association
## 3 Model Analysis
### ENode-GAT Model
#### 3.1.2 Feature Extraction Layer  
In this study, the feature extraction layer primarily employs the TF-IDF [33] formula to determine the association between each feature word. Equation (1) demonstrates the IDF formula.  
\[IDF(x)=log\frac{N+1}{N_{(x)}+1}+1 \tag{1}\]  
In the equation above, \(N\) represents the total number of documents in the corpus, and \(N_{(x)}\) represents the number of documents containing the corresponding computed words. Adding 1 to the numerator denominator prevents the numerator denominator from becoming 0 and influencing the calculation.  
After calculating the word frequency and IDF value of each word, the TF-IDF value between each word is calculated using Equation (2). This allows the node text in the original graph structure to be converted into a sparse matrix of TF-IDF, which is subsequently used to represent the feature information of the nodes.  
\[TF-IDF(x)=TF(x)\times IDF(x) \tag{2}\]  
In the equation above, \(TF\) represents the frequency of words and \(IDF\) represents the inverse document frequency.