esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of a 3D Wireless Massively Parallel Computer
## 2 Ball Computer Architecture
### 4D ABC Topology  
A ball in a 4D topology for the ABC consists of eight independent modules (Figure 7). Each'module has two processors and six transceivers. Eight modules are connected to form a cube (with chopped edges).  
The transceivers provide both intra-ball connectivity (between modules inside a ball) and inter-ball connections. This topology has an advantage over a formation of processors in a flat surface or in a matrix fashion in the sense that it leaves enough empty space inside a ball and holes in each side of it to let the cooling liquid flow through.  
A ball (as shown in Figure 7) has 16 processors. They are connected as a {2 \(\times\) 2 \(\times\) 2 \(\times\) 2} 4D hypercube (Figure 8). The network can only grow along the first three axes (\(x\), \(y\) and \(z\)). Such a topology decreases the number of hops compared to the 3D topology (discussed in Section 2.1) for large networks. For the rest of this paper we only focus on the 3D ABC architecture. In the future, more research is needed to measure the performance of the 4D ABC topology.  
Figure 6: The maximum number of hops in Blue Gene/Q compared with 3D wireless hexagonal grids (d is the diameter of each ball).  
Figure 7: A ball in a 4D topology consists of eight modules. Each module (green) has two processors and six transceivers (dark green).
###### Abstract  
Multiscale entropy (MSE) is widely used to analyze heartbeat signals. Even though cardiologists do not use MSE to diagnose heart failure at present, these studies are of importance and have potential clinical applications. In previous studies, MSE discrimination between old congestive heart failure (CHF) and healthy individuals has remained controversial. Few studies have been published on the discrimination between them, using only MSE with machine learning for automatic multidimensional analysis, with reported testing accuracies of less than 86%. In this study, we determined the optimal MSE scales for discrimination by using a low-dimensional exhaustive search along with three classifiers--linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN). In younger people (<55 years), the results showed an accuracy of up to 95.5% with two optimal MSE scales (2D) and up to 97.7% with four optimal MSE scales (4D) in discriminating between young CHF and healthy participants. In older people (\(\geq\)55 years), the discrimination accuracy reached 90.1% using LDA in 2D, SVM in 3D (three optimal MSE scales), and KNN in 5D (five optimal MSE scales). LDA with a 3D exhaustive search also achieved 94.4% accuracy in older people. Therefore, the results indicate that MSE analysis can differentiate between CHF and healthy individuals of any age.  
heart rate variability; multiscale entropy; heart failure; machine learning; low-dimensional exhaustive search; feature selection
## 4 Results
### Denoising Results for Real Data
#### 4.2.3 Quantitative Comparisons with SOTA Methods on Real Datasets  
The objective assessment metrics for different methods on a real dataset are presented in Table 2. It can be seen that for the ex vivo porcine hearts, our method achieves the highest SNR and CNR. Comparing it against the suboptimal method (LPCA), the SNR and CNR are increased by 38% and 13.32%, respectively. However, for the in vivo dataset, BM3D and our method perform much better than the others. BM3D obtains the highest SNR and CNR on the end diastole dataset. However, on both early and end systole datasets, our method generates the best CNR while BM3D achieves the best SNR.
## 2 Materials and Methods
### System Features and Selected Use Cases
#### 2.3.3 Data Collection and Analysis of a New Design  
The data collection is performed during the execution of experiments running specific scenarios (in core component 2).  
Physiological monitoring devices integrated into the VR system provide the input data for an automatic emotional feedback by measuring parameters such as heart rate, blood volume pulse, skin conductivity, muscle contraction (electrical activity), and respiration activity. The physiological signals are analyzed to retrieve performance metrics such as involvement, focus, stress, and arousal (i.e., level of activation of the user). If available, the user gaze is used to quantitatively measure the time spent looking at the road, traffic, and potential obstacles, and the time spent looking at the different cockpit elements while interacting with them (to complete the tasks required during the simulation scenarios). This allows the precise measuring of the reaction and execution time associated to the specific tasks, triggers, and events.  
Analytics of the driving performance are calculated in core component 5 by exploiting the collected physiological data, gaze, logs of interactive activities performed by the user in the cockpit, and related simulation tasks, events, and triggers. These analytics results support the assessment of the emotional impact and overall user experience faced during the different scenarios.  
Results are presented through the web dashboard (core component 6) in a simplified and aggregated way, which enables visualizing and navigating the measured data and calculated metrics, offering detailed insights about user and system performance in terms of interaction frequencies, user engagement, and physiological responses. Thanks to this feedback, designers and engineers can make informed decisions on how to refine the cockpit prototype under evaluation.  
The output of the analysis can be accessed on a dashboard through a timeline directly associated to the video recording of the simulation (more details in Section 3.3).
# AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
## 4 Experimental Results and Discussion
### Nodule Detection  
To detect the nodule in the input CT images, we used different state-of-the-art deep learning detector models, such as R-CNN [37], Fast R-CNN [38], original Faster R-CNN [39] and Optimized Faster R-CNN. The aforementioned detection models were trained and tested on the LIDC-IDRI and LUNA16 datasets. To train the above models, we used the data splits as discussed in Section 4.1. We used all default parameters for training the R-CNN [37], Fast-RCNN [38], and original Faster R-CNN [39] models based on their original papers. We fine-tuned the parameters of the original Faster R-CNN to find the best parameters to achieve the highest performance, and named it Optimized Faster R-CNN. The best combination for this model was a learning rate of 0.001, step size of 70,000, gamma of 0.1, and a dropout ratio of 0.5. The model was trained by the pre-trained ResNet50 model to extract the features with a batch size of 64. We finally compared the average precision (AP) of the detection as shown in Table 2 to select the best detection model among the tested models. The Optimized Faster R-CNN model yielded the best results, with the highest AP on both datasets. In turn, R-CNN, Fast R-CNN, original Faster R-CNN models did not properly detect all nodules in the input CT images. Therefore, we have selected the Optimized Faster R-CNN model to detect nodules in CT images. Some examples of lung nodule detection using Optimized Faster R-CNN are shown in Figure 6. As shown, the Optimized Faster R-CNN model is able to detect the nodule regions, even for small nodules.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Datasets** & **Models** & **AP(\%)** \\ \hline \multirow{4}{*}{LIDC-IDRI} & Optimized Faster R-CNN & **91.44** \\  & Original Faster R-CNN & 85.45 \\  & Fast R-CNN & 79.41 \\  & R-CNN & 75.48 \\ \hline \multirow{4}{*}{LUNA16} & Optimized Faster R-CNN & **92.67** \\  & Original Faster R-CNN & 89.31 \\ \cline{1-1}  & Fast R-CNN & 82.32 \\ \cline{1-1}  & R-CNN & 78.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average precision (AP) comparison of the four detection models (bold represents the best performance).  
Figure 6: Examples of lung nodule detection using Optimized Faster R-CNN; (**a**) Detection results from the LIDC-IDRI dataset; (**b**) Detection results from the LUNA16 dataset.
# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 5 Traffic Flow Models
### Microscopic Models
#### 5.2.4 Psychophysical-Psychological Models  
Psychophysical-psychological models, also known as action point models, focus on how drivers perceive and react to changes in their environment, particularly the behavior of the vehicle ahead. These models are grounded in the concept introduced by [PERSON] [185], who suggested that drivers detect the approach of a leader vehicle primarily through changes in the apparent size of the vehicle and by perceiving relative velocity.  
This foundational idea was further developed and refined by several researchers, including [PERSON] [163], [PERSON] [186], [PERSON] and [PERSON] [187], [PERSON] et al. [188], and [PERSON] and [PERSON] [178]. Each of these studies contributed to the understanding of how drivers use visual cues to make decisions about speed and the following distance, emphasizing the importance of psychological factors in driving behaviors.  
One of the key advantages of psychophysical-psychological models, as highlighted by [PERSON] et al. (2010), is their ability to differentiate driving behaviors based on headways. At large headways, the driving behavior is generally uninfluenced by the presence of other vehicles, as the distance is sufficient for the driver to operate independently. Conversely, at small headways, the driving behavior becomes more reactive, but only when the changes in relative velocity and headway are significant enough to be perceived by the driver. This perception-driven approach adds a layer of realism to traffic modeling by accounting for the thresholds at which drivers begin to react to their surroundings.  
Once the influence of a leading vehicle is detected, these models often transition to incorporate elements from safe-distance or stimulus-response models. This integration allows for a more comprehensive representation of driving behaviors, as it acknowledges that driver reactions are not only a function of the immediate physical environment but also of the psychological processes that determine when and how these reactions occur.  
In summary, psychophysical-psychological models provide a nuanced understanding of driver behaviors by emphasizing the role of perception in determining when a driver will react to changes in the environment. The development of these models by various researchers illustrates a progression towards integrating psychological factors with traditional car-following models, resulting in a more complete and realistic depiction of traffic dynamics. This approach highlights the importance of considering both the physical and mental processes that govern driving behaviors, offering valuable insights for improving traffic flow models and safety interventions.
## 4 Results and Discussion
### Classification of Articles by Paper Orientation
#### 4.1.2 Design Innovation (Experimental) Paper
Moreover, different age groups ranging from infants [132, 169, 143], children [103, 126, 128, 129, 130, 140, 183, 184, 210, 185, 126, 127, 128, 129, 130, 140, 183, 184, 211, 212, 213], adolescents [59, 65, 70, 135, 139], and elderly [105, 214] in different experiments were considered as study samples. Some studies were conducted on a single gender, such as those investigating women's emotions during motherhood and parental status [133, 164, 134] and a study of men [215]. Some studies targeted a specific type of subject, such as healthy people or patients, to investigate and observe the differences in emotions between two different groups, such as control and healthy groups in [107, 76, 135], groups of women and men in [177, 46, 97], young adults versus older adults in [87, 216], or children versus adults in [126, 143]. **EEG correlates of emotion (signals):** Numerous research studies have examined neural correlates of emotion in humans. Frontal EEG asymmetry, event-related desynchronization/synchronization, event-related potentials, and steady-state visually evoked potentials have been found to be associated with emotional states. We found that the majority of the 130 articles used event-related potentials, whereas 48 articles used Frontal EEG asymmetry in their analysis, six articles used event-related desynchronization/synchronization, and four articles used steady-state visually evoked potentials. **Emotion types and numbers:** Human feelings and emotions can be quantified for various emotional states. However, only certain types of emotions can be recognized using EEG. Moreover, finding key emotional states to be recognized is mandatory; for example, six emotions were detected in [122, 122, 106, 102, 42], whereas in [108], a real-time EEG signal to classify happy and unhappy emotions was proposed, and in [113], a fear evaluation system was proposed. In our review, we found that most of the articles aim to detect unpleasant, pleasant, and neutral emotions, such as in [105, 217],
## 4 Experiments
### Effectiveness of SSL
#### 4.2.2 Comparison with Random Initialization  
We performed experiments on three representative backbone networks and found that using SSL for network initialization yielded significant improvements compared to random initialization. We needed only a very small number of GPU hours for initialization to obtain much better results than those achieved with random initialization. The consistent performance gains obtained on the three backbone networks indicate that our proposed framework is effective for different network designs, reflecting the robustness of our approach to some extent.
# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 5 Experiments and Results
### Time Domain Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Feature/Algorithm** & **PSO** & **GWO** & **FFA** & **CS** & **BAT** \\ \hline \multirow{5}{*}{\(HFD\)} & Best & Accuracy & 97.9 & 90.7 & 66.7 & 54.9 & **99** \\ \cline{3-8}  & & No. channels & 8 & 3 & 1 & 1 & 6 \\ \cline{3-8}  & & No. channels & 81.5 & 67.4 & 45.1 & 43.2 & **92.8** \\ \cline{3-8}  & & No. channels & 12 & 6 & 3 & 2 & 13 \\ \cline{3-8}  & & Mean & Accuracy & 94.4 & 76.2 & 56.7 & 50.6 & **97.5** \\ \cline{3-8}  & & No. channels & 9 & 4 & 2 & 2 & 10 \\ \hline \multirow{5}{*}{HjAc} & Best & Accuracy & 97.1 & 88.4 & 68.8 & 55.1 & **97.7** \\ \cline{3-8}  & & No. channels & 5 & 3 & 1 & 1 & 4 \\ \cline{3-8}  & & Accuracy & **85.3** & 70.4 & 41.7 & 42.1 & 77.9 \\ \cline{3-8}  & & No. channels & 9 & 5 & 3 & 2 & 14 \\ \cline{3-8}  & & Mean & Accuracy & **94.4** & 82.1 & 60.7 & 48.7 & 90.8 \\ \cline{3-8}  & & No. channels & 8 & 4 & 3 & 2 & 8 \\ \hline \multirow{5}{*}{HjComp} & Best & Accuracy & 91.2 & 78.9 & 58.8 & 48.7 & **93.8** \\ \cline{3-8}  & & No. channels & 7 & 3 & 2 & 1 & 5 \\ \cline{3-8}  & & No. channels & **84.7** & 58.1 & 47.4 & 40.9 & 74.7 \\ \cline{3-8}  & & No. channels & 15 & 6 & 3 & 2 & 13 \\ \cline{3-8}  & & Accuracy & **88.6** & 69.9 & 53.9 & 44.2 & 88.2 \\ \cline{3-8}  & & No. channels & 9 & 4 & 3 & 1 & 9 \\ \hline \multirow{5}{*}{HjMo} & Best & Accuracy & 96.5 & 89.3 & 67.9 & 53.5 & **97.9** \\ \cline{3-8}  & & No. channels & 6 & 2 & 2 & 1 & 5 \\ \cline{3-8}  & & No. channels & 86.4 & 51.8 & 53.9 & 43.8 & **88.6** \\ \cline{3-8}  & & No. channels & 11 & 6 & 3 & 2 & 12 \\ \cline{3-8}  & & Mean & Accuracy & 92.3 & 80.6 & 61.2 & 47.9 & **95.4** \\ \cline{3-8}  & & No. channels & 9 & 4 & 3 & 1 & 9 \\ \hline \multirow{5}{*}{\(Hur\)} & Best & Accuracy & **72.8** & 57.9 & 47.2 & 44.1 & 70.1 \\ \cline{3-8}  & & No. channels & 4 & 2 & 1 & 1 & 5 \\ \cline{3-8}  & & Accuracy & **56.6** & 44.1 & 41.5 & 41.3 & 53.9 \\ \cline{3-8}  & & No. channels & 10 & 6 & 3 & 2 & 11 \\ \cline{3-8}  & & Accuracy & **65.2** & 51.7 & 44.7 & 42.6 & 64.9 \\ \cline{3-8}  & & No. channels & 8 & 4 & 3 & 2 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The performance of meta_Heur_istic algorithms with several feature extraction approaches for motor/imaging EEG dataset.
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## 1 Introduction  
The Internet of Things (IoT) is the consequence of the seamless integration of devices between wireless communications and diverse technologies. The devices can perceive their surrounding environment and gather data from it for processing and decision making [1]. The smart city is one of the substantial domains of IoT. A smart city is composed of innumerable services and applications that aim to increase the quality of life and services to residents [2; 3; 4]. However, the devices must communicate via the network layer in the IoT architecture. This layer utilizes various standards, protocols, and techniques, such as Routing Protocol for Low Power and Lossy Networks (RPL), protocols to smoothen the secure transfer of data packets between devices. The RPL is intended to facilitate the functionalities of numerous link-layer protocols. These layers may be lossy or consumed with strongly constrained devices. The RPL can have alternative routes and adapt with the network conditions when there is no access to the default routes.
## 3 Related Technologies
### Node Remote Reprogramming Mechanisms
#### Protocols for Update Dissemination  
The proposed protocols for software update dissemination are mainly based on data dissemination protocols, such as RMTS [46] and direct diffusion [47]. Software dissemination protocols operate in three steps: (i) advertisement of available software, (ii) source selection and (iii) reliable download to the destination.  
Deluge is generally accepted as the state of the art for code dissemination in wireless sensor networks, and has been included in the latest TinyOS distributions [48]. Deluge is a reliable data dissemination protocol for disseminating data objects from one or more source nodes to many other nodes over a multihop wireless network. Data objects are represented as a set of fixed size pages to enable incremental updates and to allow spatial multiplexing.  
Code dissemination protocols can be used to compromise LoWPAN networks. For example, an attacker may attempt to modify or replace the authentic code image introducing malicious code into the network nodes. Code dissemination protocols can also be used to perform denial-of-service attacks, where the malicious node injects bogus code and force network nodes to verify and forward them leading to exhausting their battery power. Several recent protocols, based on Deluge, were proposed to address secure code dissemination. For example, Sluice integrates cryptographic digital signatures and hash functions to provide authentication for code dissemination. Like Deluge, Sluice also splits code images into fixed size pages. The hash code of each page is included in the previous page. The hash code of the first page is signed and included in the packet signature. This approach solves the authenticity attacks, but it does not address denial-of-service attacks. Since a node can only perform authentication when a complete page is received, many packets must be processed before the authenticity can be verified. Seltge [49,50] inherits the efficiency and robustness properties from Deluge providing authentication mechanisms for code dissemination and protection for DoS attacks against signature packets, code dissemination packets and maintenance packets. Seltge uses public key mechanisms based on elliptic curve cryptography.
## 2 Materials and Methods
### PSCDR: A Deep Contrastive Regression Method for Point Cloud Data
#### 2.2.1 Problem Definition  
The point cloud biomass prediction task can be defined as follows: Given a point cloud set \(\mathrm{S}\in\mathrm{P}^{\mathrm{D}}\), \(\mathrm{S}=\left\{\mathrm{P}_{\mathrm{i}}|\mathrm{i}=1,2,\ldots,\mathrm{n}\right\}\), containing \(\mathrm{n}\) unordered points, where each point \(\mathrm{P}_{\mathrm{i}}\) is represented as a vector \((\mathrm{x}_{\mathrm{i}},\mathrm{y}_{\mathrm{i}},\mathrm{z}_{\mathrm{i}})\) in three-dimensional space, and \(\mathrm{P}_{\mathrm{i}}\in\mathrm{P}^{\mathrm{\Delta}}\). Now, given a set containing \(\mathrm{N}\) point cloud sets \(\mathrm{S}\) and their corresponding labels \(\mathrm{l}\), the set is denoted as \(\left\{(\mathrm{S}_{\mathrm{i}},\mathrm{l}_{\mathrm{i}})\right\}_{\mathrm{i =0}}^{\mathrm{N}}\), where \(\mathrm{l}_{\mathrm{i}}\in\mathrm{P}^{\mathrm{D}^{\prime}}\). Here, a feature encoder \(\Pi(\cdot)\) was designed to encode the point cloud set \(\mathrm{S}\), along with a regression prediction function \(\mathrm{P}(\cdot)\). These two components together formed the regression prediction model, where the predicted label \(\mathrm{\hat{l}}_{\mathrm{i}}=\mathrm{P}(\Pi(\mathrm{S}_{\mathrm{i}}))\). The objective was to minimize the L1-distance between the model's predicted values and the true labels \(\mathrm{l}_{\mathrm{i}}\).
## 3 Results
### Threshold Stress Intensity Factor  
For test specimens produced from the drawn rod, the starting bending moment range was set at \(\Delta M=20.61\) Nm, with a medium bending moment value of \(M_{\rm m}=12.6\) Nm, and ending with \(\Delta M=2.58\) Nm, with \(M_{\rm m}=1.58\) Nm. The measured crack growth was 1.774 mm. The threshold stress intensity factor was measured to be \(\Delta K_{\rm th}=3.84\) MPam\({}^{0.5}\). For the SLM-produced test specimen, the starting bending moment range was \(\Delta M=13.79\) Nm, with \(M_{\rm m}=8.43\) Nm, while the ending was \(\Delta M=3.85\) Nm, with \(M_{\rm m}=2.35\) Nm, with a crack growth of 0.872 mm. The threshold stress intensity factor was measured to be \(\Delta K_{\rm th}=4.53\) MPam\({}^{0.5}\). The curves representing the first part of a typical crack growth curve for Ti-6 Al-7 Nb are drawn and presented in Figure 11.  
The second part of the crack growth curve is defined by Equation (4), as mentioned in Section 3. Using experimental data, the values \(C\) and \(m\) are determined, and the relations can be written as follows:  
\[\frac{da}{dN}=8.555\cdot 10^{-14}\ \Delta K^{5,077}\ \frac{m}{cycle} \tag{6}\]  
for the test specimen produced from a drawn rod, with R\({}^{2}=0.9629\), and  
\[\frac{da}{dN}=7.199\cdot 10^{-12}\ \Delta K^{4.6}\ \frac{m}{cycle} \tag{7}\]  
for the SLM-produced test specimen, with R\({}^{2}=0.8948\).  
This study reveals manufacturing-driven disparities in Paris' law parameters. The lower factor \(C=8.555\times 10^{-14}\) of the alloy produced by drawing and its higher exponent \(m=5.077\) reflect its homogeneous microstructure, which resists early crack propagation but becomes highly sensitive to stress intensity increases. In contrast, the SLM-produced alloy exhibits a \(C\) value that is two orders of magnitude greater due to a known porosity issue, accelerating low-\(\Delta K\) growth, while its reduced exponent means that cracks are less sensitive to increases in \(\Delta K\) compared to the drawn alloy. This is consistent with observed \(R\)-curve behavior in additively manufactured metals, where internal defects can paradoxically increase fracture toughness at high \(\Delta K\) values by approximately 15-20% [26]. Regime-specific analyses show higher growth rates across a large range of \(\Delta K\) values (0.1-30 MPam\({}^{0.5}\)) for the SLM-produced alloy, with 250\(\times\) faster propagation at \(\Delta K\) = 0.1 MPam\({}^{0.5}\) and 16\(\times\) faster rates at \(\Delta K\) = 30 MPam\({}^{0.5}\), though the drawn rod's steeper curve indicates greater high-stress vulnerability. These findings show that the drawn rod suits high-cycle fatigue applications requiring threshold performance, while SLM's geometric flexibility benefits complex parts (e.g., biomedical implants).