esa-sceva/satcom-chunk-collection huggingface dataset:
# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
## 3 Proposed Methods
### Ablation Study
#### 4.3.2 Impact of Initial Geometry Representation  
The effect of the initial geometry representation was examined by replacing the spherical point cloud, used in the proposed method, with a randomly distributed point cloud. This substitution caused a significant drop in performance, primarily due to the misalignment between image features and the random 3D grid, which resulted in erratic and fragmented reconstructions. The 3D reconstruction process under different geometric initializations is visualized in Figure 7. The first column represents the input 2D image, and the second column shows the results of the initial random point cloud geometry, which lacks meaningful structure and leads to poor-quality outputs. The third column shows results with spherical initial geometry, introducing some structural consistency, but it is still not enough for accurate reconstructions. Finally, the fourth column shows the integration of the spherical prior with attention-based fusion layers, leading to more precise and accurate 3D shapes. These results highlight the importance of combining prior geometric knowledge with attention mechanisms to achieve high-quality single-view 3D reconstructions.  
Figure 5: This Figure illustrates a comparison of average loss per 100 iterations across various attention-based feature fusion strategies, including single-attention fusion, dual-attention fusion, multi-level attention fusion, and direct fusion of initial clouds. Among these strategies, multi-level attention fusion achieves the lowest average loss, highlighting its effectiveness in minimizing reconstruction error. The dotted line represents the trend of the multi-level attention fusion model, indicating a continuous reduction in loss over the iterations. The \(y\)-axis represents the average loss per 100 iterations, while the \(x\)-axis corresponds to the value indices for the first 30 computed values.  
Figure 6: The line graph shows the average loss per 100 iterations for various attention-based feature fusion strategies. Multi-level attention fusion consistently achieves the lowest loss, outperforming dual-attention and single-attention fusion. In contrast, the direct fusion of initial clouds resulted in the highest loss. The \(y\)-axis represents the average loss per 100 iterations, while the \(x\)-axis denotes the value indices for the first 230 computed values.  
The detailed analyses validate the design choices in SS3 DNet-AF and show how the dual attention mechanism and the induction of geometric prior enhance the quality of single-view 3D reconstructions. Furthermore, the model is evaluated on completely unseen data, as illustrated in Figure 8, which presents three sample objects (a car, a sofa, and a plane). The results present the model's ability to generalize effectively and produce accurate 3D reconstructions even for previously unseen inputs.
## 3 Comparison of Steel Temperatures from the Model with Experimental Results  
### Experimental Travelling Fire Test in a Steel Building  
One of the experimental campaigns of the \"TRAFIR\" project aimed at performing three large-scale natural fire tests with no artificial control over of the fire dynamics. The main objectives of this experimental campaign were to understand in which conditions a travelling fire develops, as well as how it behaves and impacts the surrounding structure. This paper considers the test n\({}^{\circ}\)2, for which a travelling fire occurred (details concerning this experiment can be found in ([PERSON] et al. [28]). The test compartment was designed to represent a part of an entire office layout, consisting of steel beams and columns for the main structural frame (protected ones and unprotected ones for data acquisition purpose, see Figure 8a), while hollow-core precast concrete slabs were used for the ceiling. The layout of the structure can be seen on Figure 9; the floor area is 15 m long and 9 m wide between the outer gridlines while the distance from the surface of the finished floor to the ceiling was 2.9 m. Concrete walls were constructed along the two shorter sides of the compartment (i.e., along gridlines 1 and 4). For the two longer sides (i.e., gridlines A and D), downstands were provided to allow for smoke accumulation below the ceiling as well as a concrete block sofit, such that the openings height is 1 m (along both longer sides). Such configuration provided openings with a total area of 30 m\({}^{2}\) that were equally distributed along gridlines A and D. Photographs of the compartment can be seen on Figure 8b,c.  
Figure 8: Photographs of the experimental campaign carried out by Ulster University in the frame of “TRAFIR” project: (**a**) steel frame with protected and unprotected columns; (**b**) compartment for test n\({}^{\circ}\)2; (**c**) fire at around mid-length of the compartment during test n\({}^{\circ}\)2.  
A fire load defined according to a well-established methodology ([PERSON] et al. [29]) was used, consisting of a uniformly distributed fuel wood with a fire load density of 511 MJ/m\({}^{2}\) arranged in such a way that it would lead to a medium fire propagation (t\({}_{\alpha}\) = 300 s, cnfr EN1991-1-2 [13]). The fuel bed is highlighted on Figure 9; it was provided along the centre of the test compartment, in a rectangular band of 14 m-long stretching from wall to wall along the longer dimension of the compartment (a gap of 0.5 m was kept between the short walls and the edge of the fire load). The width of the fuel bed was 4.2 m and was aligned with the centreline of the compartment. The wood sticks were provided on a steel platform located at 325 mm above the floor level. Ignition was at a point located at mid-width of the fire load, 0.5 m from its edge (i.e., at a distance of 1 m from the back wall). The fire started to grow close to gridline 1 and then travelled from gridline 1 to gridline 4.
## 3 Results and Discussion
### Performance Comparison
#### 3.1.3 Accuracy/Result: Process and Comparison  
To compare the accuracy of the proposed method with Custom Object CNN, we manually classified each \(\mathcal{H}_{i}\) in every image sample as a pore or a fracture after analysing its visual properties (area and shape). This classification was further supported by its geophysical properties from its mother carbonate sample rock. This dataset will be our reference for further comparison. Next, we automatically classify every individual \(\mathcal{H}_{i}\) using the proposed pixel-wise \(k\)-IN and the deep CNN models and compare their results with the above reference dataset. The results, listed in Table 1, demonstrate that the accuracy of the proposed method pixel-wise \(k\)-IN (Figure 5) evidences better performance than the three deep CNN models (Figures 7-9). From Table 1, the accuracy of the proposed method is 85.9%. The error rate shown is due to the few observed variations in the geometric shapes of pores and fractures from the identification logic used in Algorithm 2 (see line nos. 23-24). For example, we manually identified a few micro-pores (\(\mathcal{H}_{i}\)s) after carefully analysing their visual and geophysical properties, classified incorrectly by Algorithm 2. Similarly, we manually identified a few fractures that do not satisfy the criteria shown in line no. 24 of Algorithm 2. Additionally, we showed the variation in the accuracy of the proposed algorithm by varying the model parameters \(\alpha,\ \beta,\ \gamma\) and \(\delta\) in Table 1. This action also shows that the correct combination of parameters for the current dataset is \(\alpha=1.03,\ \beta=0.95,\ \gamma=0.43\) and \(\delta=0.43\).  
In contrast, the error rate of the industry-leading deep CNN models (YOLO and RCNN) was due to the poor learning of different shape-related features and the fewness of training samples. The YOLO model provides accuracy that is based on the intersection of actual bounding boxes and predicted by the feedforward network. Since the predicted bounding box will not match the ground truth (see Figures 7 and 9a), we obtain a ratio by dividing the area of intersection between the two boxes by the area of their union, which is called the intersection of unit. The higher this ratio is, the better the prediction. By equating this prediction with the ground truth, we obtain the accuracy of the model.  
The possible reasons for the better performance of the proposed approach in the presence of the current image samples are elaborated on in the subsequent subsections.
## 2 Materials and Methods
### What Key Success Factors Need to Comply for Reliable CIIoT Support in the Industry?
#### 2.3.2 CIIoT Remote Sensor Systems  
Remote sensor systems (WSNs) have officially demonstrated their helpfulness as of late. Scaled downsize and minimal effort have made sensors a significant wellspring of data gathering. The need to know whenever and anywhere has bolstered the idea that they are joining in IIoT structures [96]. Notwithstanding, data recovery from these sensors remains a test in the substantial-scale topographical arrangement or in-home applications because of range accessibility and obstruction. Besides, assets constrained sensors to produce and transmit crude information rather than expansive and valuable information streams. WSN information can be valuable for little-scale applications, yet it might have issues in the IIoT setting. In this manner, WSNs mix with CRNs and give foundationless and self-composed systems that have great potential for IIoT applications. Cognitive Radio with Actuator Systems (WSANs) performs activities indicated by condition detecting [56; 62; 64; 66; 67; 68; 72; 73; 74; 75]. Their impromptu nature gives the least physical expansion and for the most part, results in a solitary assignment. Joining WSANs in IIoT needs a more comprehensive vision. Transmitting activities to actuators in an opportune and proficient way is a major obstacle. NFC and RFID have generally been appropriate for WSANs; nevertheless, a subjective info-communication viewpoint is additionally researched to improve the IIoT system [97].
## 2 Materials and Methods
### Cephalometric Tracing and Data Handling  
For all the patients, a cephalometric tracing was performed on the collected lateral skull radiographs, with calculation of the variables described in Table 1. A method study was performed on 10 randomly selected radiographs, repeating the measurements after a few days. For each variable, the difference between the first and second measurement was always less than 5% of the natural variation of the variable itself. Data were then analyzed to evaluate the distribution. For each variable, descriptive statistics included the mean and standard deviation at baseline T0 and after maxillary expander removal (T1). Then, to evaluate the relationship between the parameters, due to the smaller sample size, a nonparametric test was used. The Wilcoxon signed rank test was used to evaluate changes overtime. StatPlus for Mac v7 (AnalystSoft Inc., Walnut, CA, USA) software was used for all statistical analysis. For each analysis, \(p\) was set at the 0.05 level.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Skeletal Variables** & \\ \hline SNA (grades) & Angle identified by the points S, N, and A \\ \hline SNB (grades) & Angle identified by the points S, N, and B \\ \hline ANB (grades) & Difference between SNA and SNB angles \\ \hline SN’GOGN (grades) & Angle identified by the intersection of the S–N and GO–GN planes \\ \hline FM’ (grades) & Angle identified by the intersection of the Frankfurt and mandibular planes \\ \hline MM’ (grades) & Angle identified by the intersection of the bispinal and mandibular planes \\ \hline Dental variables & \\ \hline OVB (mm) & Vertical distance between the incisal edge of upper and lower incisor \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cephalometric variables analyzed.  
Figure 1: Maximly expander characterized by the presence of a cast metal framework anchored to one or more deciduous teeth and/or permanent first molars.
## 2 Materials and Methods
### Live/Dead Assay  
To investigate cell cytotoxicity after CDES, a live/dead cell assay kit was used 1 day and 3 days after the treatment. For the live/dead assay, calcein and ethidium homodimer (EthD-1) were used as in the kit information sheet. Calcein is retained within live cells and produces green fluorescence at ex/em 495 nm/515 nm, while EthD-1 enters dead or damaged cells and produces red fluorescence at 495 nm/635 nm as stated in the data sheet of the kit. When ES was run on a 24-well plate, cells were seeded at a density of 3 \(\times\) 10\({}^{4}\) cells per well, and the electrospray voltage was varied (0, 2, 4, and 6 kV). When dye was used for the assay, the live/dead dye was diluted by 5X in PBS. The cells were stained according to the kit instructions and incubated for 20 min at room temperature. The treated cells were observed by fluorescence microscopy (Nikon N-Storm, Nikon, Tokyo, Japan). The cell density in this section was 3 \(\times\) 10\({}^{4}\) because the live/dead assay was part of the optimization process of the assays below. Additionally, considering the existence of the control group and the proliferation rate of HeLa cells, the cell number between day 1 and day 3 was assumed to not make any difference in the results.
## 3 Proposed Method
### Feature Extraction Module
Firstly, these blocks effectively capture long-range dependencies and complex contextual relationships in the data on a layer-by-layer basis. Each Self-Attention Block is capable of addressing varying levels of feature representation, enabling the gradual extraction of more abstract and high-level features. Secondly, each Self-Attention Block is designed to emphasize the correlations among different segments within the sequence. By stacking multiple Self-Attention Blocks, the model can integrate this relevance and contextual information, thereby enhancing its representational capacity regarding the data. Thirdly, stacking multiple Self-Attention Blocks enables the model to address noise and variability more effectively, thereby improving its generalization across diverse data patterns. Figure 2: Architecture of the feature extraction module: (**a**) PFEM. (**b**) MFEM. Modality-specific features from the PAN and MS images are extracted independently using multiple consecutive SABs. In the feature extraction process for the PAN image, we introduce an additional patch merging layer. This layer is used for combining patches, which enables the transformation of features from size \(2H\times 2W\times C\) to match the size \(H\times W\times C\) of the MS features for subsequent processing. After extracting these modality-specific features, they are input into the subsequent cross-modality feature extraction module. Further processing occurs within the CMFFM, which is detailed in the following section.
## 5 Limitations and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Malicious** & **Benign** & **Model** & **Detection Results (\%)** \\ \hline MalBehavD-V1 [13] & 1285 & 1285 &
\begin{tabular}{c} API-MalDetect [13] \\ DawnGNN [2] \\ Proposed Framework \\ \end{tabular} & Accuracy: 98.0 \\ \hline \multirow{3}{*}{APIMDS [49]} & \multirow{3}{*}{23,080} & \multirow{3}{*}{300} & MINES [2] & Micro-F1: 97.03, Macro-F1: 93.70 \\  & & & DawnGNN [2] & Accuracy: 99.75 \\  & & & Proposed Framework & Accuracy: **95.41** \\ \hline PEMalware (Ours) & 1000 & 1000 & Proposed Framework & Accuracy: **99.61** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of detection results across different datasets and models. while DL approaches incur the highest computational cost due to their layered architecture, high parameter counts, and GPU dependency. However, the overhead is often justified by the superior performance and generalization capabilities of DL-based frameworks. As demonstrated in Section 4.7, the model achieves 99.61% accuracy and extremely low error rates, significantly outperforming conventional baselines. Like most DL-based malware detection systems, the proposed framework may face challenges when encountering novel or evolving malware that exhibits behavior patterns not present in the training data. Although the integration of graph-based and sequential learning components (Node2 Vec, GAT, and CNN-GRU) enhances the model's ability to generalize, it remains inherently limited by the scope and diversity of the training set. To address this, periodic retraining with newly collected and labeled malware samples can improve the framework's adaptability. Additionally, more advanced solutions such as automated model updating via AutoML techniques may enhance responsiveness to evolving threats [20]. However, such techniques could not be used in this study due to their high computational costs and intensive resource requirements, which are beyond the scope of the current study. It is planned to focus on this issue in the future work.
# Analytical Model for the Duty Cycle in Solar-Based EH-WSN for Environmental Monitoring
## 5 Simulations
### Basic Validation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Value** \\ \hline \(BW\) & 802.15.4 Bandwidth & 250 Kbps \\ \(L_{pkt}\) & Length of data packets & 41 Bytes \\ \(T_{pkt}\) & Duration of data packets & \(\frac{L_{txt}}{2\text{W}}\) \\ \(L_{ack}\) & Length of acknowledgement packets & 17 Bytes \\ \(T_{ack}\) & Duration of acknowledgement packets & \(\frac{L_{txt}}{2\text{W}}\) \\ \(T_{CCA}\) & CCA interval & 0.4 ms \\ \(W_{ack}\) & Waiting time for ACK & 1 ms \\ \(T_{c}\) & Transmission cycle & 2.712 ms \\ \(T_{rnd}\) & Round time (reporting period) & 30 s \\ \(DC\) & Duty cycle & 33\%, 10\% \\ \(T_{l}\) & DUTY_ON_TIME & 5 ms \\ \(DAR\) & DELAY_AFTER_RECEIVE & 100 ms \\ \(B\) & Battery & 2500 mAh \\ \(V\) & Voltage & 3 V \\ \(l_{off}\) & Current draw in Power Off & \(0.02\cdot 10^{-6}\) A \\ \(l_{down}\) & Current draw in Power Down & \(20\cdot 10^{-6}\) A \\ \(l_{idle}\) & Current draw in Idle & \(0.426\cdot 10^{-3}\) A \\ \(l_{rx}\) & Current draw in TX (at 0 dbM) & 0.0174 A \\ \(l_{rx}\) & Current draw in RX & 0.0188 A \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters used in the validation of the analytical model. in the source code to the value 100 ms and that, in general, its real duration tended to be slightly larger than 100 ms. For this reason, we evaluate the scenario using two different values of \(DAR\), namely 100 ms and 125 ms. The results are shown in Figure 7 on the left, where the energy consumed by node A to transmit a packet is represented in terms of the number of tries (\(k\)). As it could be expected, energy consumption grows linearly when \(k\) increases. We also noticed that the deviation between the results provided by Avrora and by the model was approximately 6% in the worst case (for \(DAR=125\) ms), which is an acceptable value. In the second experiment, we evaluated the energy consumed by node B to receive and forward the packet from A. In this case, we varied the level of asynchrony between node B and its parent node while keeping the time difference between nodes A and B fixed. The results are shown in Figure 7 on the right, where again a linear trend is observed. Note that the results in Figure 7 on the right are slightly larger than the figure on the left, a fact that is explained by the small extra energy required by node B to receive the packet from node A. Again, the maximum deviation between Avrora and the analytical model is below 6%.
## 3 Results
### The Influence of Commercial Function
#### 3.3.2 The Influence on Sound Environment  
Figure 9b shows the relationship between crowd density and A-weight sound pressure level: \(R^{2}\) was 0.638 in the Di zone; 0.527 in the Sp zone; and 0.621 in the Pe zone (\(p<0.001\)). In these three zones, the level of sound pressure takes a linear trend of continuous increase as the crowd density increases. The level of sound pressure indicates significantly different ranges when the crowd density ranges from 0 to 0.4 persons/m\({}^{2}\), with three commercial functions. In the Di zone, the sound pressure level ranged from 68 to 77 dBA, while the ranges were 70-75 dBA and 71-77 dBA in the Sp and the Pe zone, respectively. When the crowd density is constant, such as a crowd density of 0.1 persons/m\({}^{2}\), the sound pressure level in the Pe zone was higher than that in the Di zone (2.9 dBA) and Sp zone (1.0 dBA). The sound pressure level in the Pe zone was higher than that in the Di zone (2.8 dBA) and the Sp zone (1.7 dBA) when the crowd density was 0.3 persons/m\({}^{2}\). When the crowd density ranged from 0.1 to 0.3 persons/m\({}^{2}\), the level of sound pressure in the Di zone and Pe zone increased more quickly than that in the Sp zone. Considering that the number of talkers in the three zones was not significantly different (\(p>0.1\)), the reason may be that the voices of persons in the Di zone and Pe zone increased when the crowd density increased. In the Di zone, when the crowd density increased, the sound pressure level of speech increased, and the sound of hawking was masked by speech and other sounds. To attract more customers, the volume of the hawking produced by sellers may be enhanced. In the Pe zone, instrumental music was unusual in commercial pedestrian streets, which may arouse users' curiosity, and the conversation produced by users may increase. However, in the Sp zone, background music was common in commercial pedestrian streets so users pay little attention to background music.  
Figure 9c shows the relationship between crowd density and loudness, where it is seen that \(R^{2}\) is 0.644 in the Di zone, 0.490 in the Sp zone, and 0.621 in the Pe zone (\(p<0.001\)). In these three zones, the loudness has a linear trend of continuous increase as the crowd density increases. The loudness in the Pe zone is considerably higher than that in the Di zone (1.83 sone) and the Sp zone (1.12 sone), when the crowd density ranges from 0 to 0.4 persons/m\({}^{2}\).