esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis and Results
### Impact Zone of Accidents  
The status of traffic accidents in South Korea was examined to analyze and compare the extracted low-speed sections and unstable traffic flow segments. Based on data from April 2018, 18 traffic accidents occurred between the peak traffic hours of 9 AM and 11 AM, 1 of which was fatal, as depicted by the GIS data in Figure 10.  
To analyze the impact of accidents on traffic flow, actual accident cases were examined using data from a traffic accident analysis system. This system provided detailed information, including the location and time of a vehicle-to-vehicle collision that occurred between the Yongin IC and the Maseong Tunnel on Expressway 50, as shown in Figure 11. Since the traffic accident analysis system records only the timing of accidents, DTG data from 10:00 AM to 10:55 AM were analyzed to capture the traffic flow patterns and speed variations during the incident time frame.  
Table 3 presents a time series analysis of traffic flow characteristics in 10 min intervals, classified by spatial segmentation for a segment of Expressway 50. The spectral color scheme in each figure visually represents the mean speed in kilometers per hour (kph) for each homogeneous segment, indicating segment-specific speed values. As observed in Table 3, congestion began at the Yongin IC sorting section at 10:15 AM, followed by congestion near the accident site at 10:35 AM, which led to the development of an accident impact zone extending upstream by 10:45 AM. Following the accident, an unstable flow was observed in the rearward segment, transitioning into low-speed segments that progressively lengthened and moved upstream.  
This analysis suggests that the accident may have resulted from a rear-end collision due to the sudden deceleration caused by congestion near the tunnel at 10:25 AM. Detailed  
Figure 10: Status of traffic accidents.  
traffic flow speed data thus enable not only an estimation of the accident's impact zone and timing but also an analysis of how the impact zone expanded over time and of the evolving characteristics of traffic flow in response to the incident.  
Understanding the accident impact zone is essential for identifying risks of secondary accidents, as noted in the introduction. The effect of a traffic accident on the rearward traffic flow can prompt abrupt behaviors, such as sudden braking or lane changes, which can increase the likelihood of secondary collisions. The accident impact zone is influenced by pre-accident segment speeds and affects subsequent speed changes both downstream and upstream from the site, potentially leading to temporary increases in upstream speeds as drivers respond to disruptions.  
Insights from analyzing these patterns in accident impact zones can significantly aid in preventing secondary accidents by providing advance information to following vehicles through various communication channels, including systems for autonomous vehicles. This proactive approach enhances road safety by enabling drivers and autonomous systems to anticipate and react to upcoming disruptions in real time.  
Figure 11: Accident point on Expressway 50.
# Class Retrieval of Detected Adversarial Attacks
## 4 Results  
### Mnist  
To validate our hypotheses, detailed experiments were conducted using the MNIST and other datasets, as we will see in the next paragraphs. MNIST is a commonly investigated dataset with ten classes containing 70 thousand images of handwritten digits with a 28 \(\times\) 28 resolution. We investigated five different adversarial attacks (PGD, MPGD, Deepfool, TPGD and PGDDLR) to create a matrix of success rate with another two counter attacks (PGD and MPGD). In each case, we attempted to retrieve the class of 1000 successfully attacked samples, which means 10,000 experiments altogether were conducted. The maximum distortion of the adversarial attack was set to 0.2. The number of iterations, \(NbIter\), is 10 for the adversarial attacks insuring a successful attack, but we set it to 3 for the counter adversarial attack, illustrating the fast convergence to the original class.  
AlexNet architecture was used throughout our experiments, providing a good baseline network where the average accuracy on clean samples is 96% (the original 28 \(\times\) 28 images were rescaled to 224 \(\times\) 224, ensuring the required input size). Figure 5 demonstrates the high accuracy of the class retrieval algorithm investigating the usage of two counter attacks against the adversarial samples of five different attacks. On average, 72% of the  
Figure 4: A flowchart explaining our class retrieval method starting from the input image until the output label where the \(argmin\) process returns the index of the minimum loss, \(MaxY\) is the number of classes and \(AdvY\) is the adversarial label.  
attacked samples were correctly recovered, predicting their original class and averting misclassification.
# Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks
## 1 Introduction
We confirm that among the three types of PV-NoN models, the one based on the B-NoN model, which reproduces the complementary interdependence of brain functional networks, achieves high availability and communication performance while preventing interference among the VNs. We also investigate a method for designing reliable network structures in the PV-NoN model. To this end, we configure the deployment of _network influencers_, which are the network components whose fluctuations have the largest influence on the entire network [14; 15; 16], from the perspective of inter/intranetwork _assortativity_. Assortativity is a network metric for evaluating the correlation of node centrality, i.e., node influence [17]. Evaluation results show that configuring the assortativity within each VN and among the VNs can improve NoN availability and communication performance. Obtaining guidelines on influencer design with the PV-NoN model contributes directly to controlling the performance of network slicing under unpredictable environmental changes, thereby leading to the design of highly reliable interdependent network architectures in future IoT scenarios. Our results also suggest the potential of the NoN model to be applied to other interconnected network systems wherein there is mutual internetwork dependence.
## 3 Lightweight Cryptography for Smart Objects
### Symmetric-Key LWC Algorithms
#### 3.1.2 Scalable Encryption Algorithm (SEA)  
The Scalable Encryption Algorithm (SEA) is targeted for small embedded applications [27]. The design considers a context with very limited processing resources and throughput requirements. Another design principle of SEA is flexibility: the plaintext size \(n\), key size \(n\), and processor (or word) size \(b\) are design parameters, with the only constraint that \(n\) is a multiple of \(6b\); for this reason, the algorithm is denoted as SEA\({}_{n,b}\). The motivation of this flexibility is the observation that many encryption algorithms perform differently depending on the platform, e.g., 8-bit or 32-bit processors. SEA\({}_{n,b}\) is designed to be generic and adaptable to different security levels (by varying the key size) and target hardware. A great advantage of SEA\({}_{n,b}\) is the \"on-the-fly\" key derivation. The main disadvantage is that SEA\({}_{n,b}\) trades space for time and this may not be negligible on devices with limited computational power.
## 3 Experimental Results and Analysis
### Simulated Signal Experiments
#### 3.1.2 Poor Satellite Geometry

Table 9: Performance comparison of different optimization algorithms with poor geometry. Figure 8a shows the convergence curves of each algorithm with the number of iterations. As the number of local optima rises due to the deterioration of the peak states of the optimization problem, all algorithms require more iterations to converge. The values of the local optima and the global optimum become closer, increasing the likelihood that algorithms will get trapped in local optima. Without a suitable strategy, it is challenging for the population to escape these local optima and find the global optimum. Figure 8b shows the position error curves for different algorithms. Due to the more challenging optimization problem posed by poor geometry, the fitness values decrease while the corresponding positional errors increase. Many algorithms appear to converge successfully in the fitness function plots, but in reality, they only fall into local optima with large positional errors. The ADBO algorithm, equipped with the improved strategies, not only converges quickly but also effectively escapes local optima. Consequently, it successfully converges to the correct position, achieving the least positioning error.
## 4 Results and Discussion
Figure 13 illustrates that the isosteric heat of adsorption, calculated using the Clausius-Clapeyron and Tooth equations, exhibited a consistent trend, as follows: decreasing as CO\({}_{2}\) loading increased. Initially, CO\({}_{2}\) molecules preferentially entered smaller pores during adsorption, resulting in a stronger gas-adsorbent interaction and, consequently, higher isosteric heat at lower loading. As loading increased, the interaction weakened due to pore saturation, causing a decline in the isosteric heat of adsorption. The values of the isosteric heat of adsorption are lower than 50 kJ/mol, indicating the physical nature of CO\({}_{2}\) adsorption over DT0 samples. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Temperature [\({}^{\circ}\)C]**} \\ \hline  & **0** & **10** & **20** & **30** \\ \hline DT0 & & & & \\ q\({}_{mT}\) & 326 & 167 & 115 & 44 \\ b\({}_{T}\) & 0.0016 & 0.0015 & 0.0012 & 0.0016 \\ n\({}_{T}\) & 0.1915 & 0.2228 & 0.2474 & 0.3104 \\ HYBRID & 3.94 \(\times\) 10\({}^{-05}\) & 3.76 \(\times\) 10\({}^{-05}\) & 1.45 \(\times\) 10\({}^{-05}\) & 5.34 \(\times\) 10\({}^{-06}\) \\ DTO\_1 & & & & \\ q\({}_{mT}\) & 364 & 244 & 125 & 56 \\ b\({}_{T}\) & 0.0015 & 0.0011 & 0.0011 & 0.0013 \\ n\({}_{T}\) & 0.1884 & 0.2112 & 0.2468 & 0.2996 \\ HYBRID & 3.97 \(\times\) 10\({}^{-05}\) & 2.55 \(\times\) 10\({}^{-05}\) & 1.70 \(\times\) 10\({}^{-05}\) & 1.64 \(\times\) 10\({}^{-05}\) \\ DT0\_2 & & & & \\ q\({}_{mT}\) & 306 & 201 & 129 & 49 \\ b\({}_{T}\) & 0.0017 & 0.0013 & 0.0011 & 0.0015 \\ n\({}_{T}\) & 0.1932 & 0.2179 & 0.2429 & 0.3059 \\ HYBRID & 1.14 \(\times\) 10\({}^{-04}\) & 5.40 \(\times\) 10\({}^{-05}\) & 2.40 \(\times\) 10\({}^{-05}\) & 1.39 \(\times\) 10\({}^{-05}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The parameters of the Toth equation at different temperatures and HYBRID values for CO\({}_{2}\) adsorption over activated carbons before and after cold nitrogen plasma modification. Figure 12: Adsorption isosteres of activated carbons for different adsorption values. Figure 13: Variation of isosteric heat of adsorption with respect to surface loading.
## 3 Results and Discussion
### Comparison of Flow Field Predictions with Different Turbulence Models  
It is known that the structure of a VAWT causes more wake-blade interactions than a HAWT. Based on this idea, this study aimed to reveal the effect of different turbulence models on the wake predictions for a VAWT.  
Contours distributions were taken from XFlow CFD software involving velocities and turbulence intensities at TSR1 and azimuthal angle, \(\theta\) of 77\({}^{\circ}\), as shown in Figures 8 and 9.  
The velocity distributions on the cutting plane are demonstrated in Figure 8. It shows that the boundary layer of the WALE and Spalart-Allmaras model gives a similar separation flow from the blade's surface. In contrast, the Smagorinsky turbulence model exhibits earlier flow separated from the blade surface. Figure 9 gives better results that enable us to understand changing viscosity effect on VAWT. Turbulence intensity provides to observe better wake predictions, the wake is separated from the trailing edge of the blade earlier, and LES-WALE model contours have a more intense wake structure than the other.  
Figure 8: Velocity contours around VAWT in different turbulence models for TSR 1 and \(\theta=\) 77\({}^{\circ}\).  
For TSR 1.2 and \(\theta=90^{\circ}\) velocities contours are found the higher values at RANS-Spalart-Allmaras turbulence model as shown in Figure 10. LES models show that WALE and Smagorinsky demonstrated more turbulence density around the blade, like each other, as shown in Figure 11.  
As for TSR 1.6 and \(\theta=180^{\circ}\), RANS and LES modes reveal similar wake structures, and the higher rotor speed can be visualized to demonstrate wake predictions using vorticity structures. The RANS model, Spalart-Allmaras, agrees with the LES-WALE model at a higher rotor speed. LES-Smagorinsky showed that the wake is cutting from the flow separation of the trailing edge earlier, as shown in Figures 12 and 13.  
Figure 9: Turbulence Intensity (%) around VAWT in different turbulence models for TSR 1 and \(\theta=77^{\circ}\).  
Figure 11: Cont.  
Figure 10: Velocity contours around VAWT in different turbulence models for TSR 1.2 and \(\theta\) = 90\({}^{\circ}\).  
Figure 11: Turbulence Intensity (%) around VAWT in different turbulence models for TSR 1.2 and \(\theta=90^{\circ}\).  
Figure 12: Velocity contours around VAWT in different turbulence models for TSR 1.6 and \(\theta=180^{\circ}\).
## 2 Materials and Methods
### Data Acquisition and Feature Extraction  
The workflow consisted of three stages: data acquisition, task segmentation, and feature computation.  
Data acquisition: All nine tasks were performed on a Wacom Bamboo Slate A4 smartphone. Using the Wacom Ink SDK [35], we recorded the planar coordinates \((x,y)\) (mean sampling rate: 200 Hz; range: 180-200 Hz), stylus pressure (1-1024), 5 ms timestamps, and event phases (Begin/Move/End).  
Task segmentation: Each stroke inherits a task label provided by the recording interface. The results for the five drawing tasks (SE, CL, CU, CO, and PE) were recorded using legacy firmware that stored only stroke-level summaries; hence, the higher-order kinematic features could not be derived.  
Feature computation: Extraction was implemented in Python 3.8 and produced the following domains:  
* E-SDMT (9 features): From the response timestamps (esdmt_timestamps), we computed the interval timing statistics (mean, SD, median, IQR, min/max), acceleration patterns (first-/second-half ratio, linear slope), and variability (coefficient of variation);
* The P-SDMT and the HTP test (31 features each): From full pen streams (pressureList, timeList, inputPhase, xList, yList), we derived time features (total/air/paper time and ratios), length features (air/paper/total length and ratios), stroke statistics (count, length statistics), pressure features (mean, SD, extremes), kinematic features (velocity, acceleration, pauses), and coordination features (the pressure-velocity correlation);
* Legacy drawing tasks (13 features each): From the stroke summaries only, we computed time, length, stroke, and pressure metrics, plus efficiency ratios.  
Figure 1: Examples of the nine cognitive and drawing assessment tasks used in this study. From left to right, (**A**) the P-SDMT, (**B**) free sentence-writing, (Chinese text: “I love singing and dancing”) (**C**) pentagon-copying, (**D**) the Trail-Making Test, (MoCA Beijing version uses Chinese sequential characters, equivalent to A-B-C-D in English version) (**E**) cube-copying, (**F**) clock drawing, (**G**) house drawing, (**H**) tree drawing, and (**I**) person drawing.  
Finally, we appended three demographic covariates (gender, age, education_years) and nine clinical/scale scores (MMSE, COWAT, HAMA, HAMD, MADRS, PASAT, SDMT-D, P-SDMT total, disease label), yielding a total of 210 predictors.The MoCA-Beijing score was excluded from the predictor set to prevent label leakage, as it defined the CN/CI outcome.  
The complete variable list: A spreadsheet containing all 210 predictor names and descriptions is provided as Supplementary File S1 (Complete_Feature_List.xlsx).  
Beyond standard timing and spatial statistics, we incorporated several higher-order metrics--such as the pressure-velocity correlation, the high-pressure/high-speed ratio, the acceleration change count, and detailed pause measures--that are less commonly applied in IDD handwriting analyses [36]. Such features capture the fine-grained motor-cognitive coupling and enhance the sensitivity to early impairments.
## 2 Materials and Methods  
### Monte Carlo Simulation: Geant4-DNA  
Monte Carlo simulations are based on the statistical Monte Carlo method, which is the statistical process of predicting probability of radiation transport using random sampling. It is a stochastic method that uses random numbers and probability to solve complicated physical and mathematical problems that cannot be computed by hand, such as multi-dimensional integrals [28; 29]. The results of Monte Carlo simulations increase in confidence when there are many random numbers. Depending on the computer's central processing unit, the simulation may take some time to process.  
The pathway of one individual radiation particle is known as radiation transport, and every interaction the particle undergoes is tracked from its interactions with tissue, water, etc. The simulation of radiation transport is important when computing dosimetry quantities [30]. One simulation of an individual particle's pathway is also known as a history. The more histories or simulations there are, the more accurate the results and the less variance there is. Each calculation is made in each voxel. After the code runs, the deposited energy values in each voxel can be used to calculate dosimetric quantities or strand breaks. The number of histories used in this simulation is 10,000,000.  
The Monte Carlo code used was Geant4 (GEometry ANd Tracking), which is a free Monte Carlo simulation toolkit. It is used for simulations of particles interacting with matter. It is used in different areas of physics such as high-energy physics, nuclear physics, and medical physics [31]. Geant4 is accessed through downloading source code files from the Geant4 Download section on their website using the terminal and instructions in the user guide documentation. The programming language used is C++, and an integrated development environment (IDE), such as Xcode on MacBooks, is used to create and edit the codes. To simulate the DNA and gold nanoparticles, an extension, Geant4-DNA, was used [32]. The files that were downloaded contain examples of different physics applications. There are basic examples containing models with simple detector geometry and shapes. There is an advanced section for more specific and difficult simulations. The last folder is the extended folder containing the medical physics examples.
## 6 Further Discussions  
Based on the above critical analysis of related work, we observe that there is still room for improvements in the existing routing solutions to achieve reliable path-finding in LLNs. More specifically, we have made the following observations.  
Firstly, none of the existing solutions designed for LLNs, i.e., RPL solutions, consider the most reliability-effecting factors, particularly node energy level and link quality during a path selection process using a path selection method that can make a global optimum decision based on multiple reliability-effecting factors in real time in adaptation to the network topology, link quality, and energy level changes. Considering these features during path-finding in LLNs may result in achieving reliable paths with high PDRs and extended network lifetime.  
Secondly, existing routing solutions designed for LLNs assume that the main causes of reliability issues are non-malicious factors such as link quality and energy levels. None of the existing routing solutions explicitly addresses the issue of maximising PDR, considering the most reliability-effecting factors mentioned above, with the presence of malicious nodes. Detecting malicious behaviours, such as packet dropping, in LLNs is challenging using standard link quality metrics. Malicious nodes can appear legitimate when joining a network but engage in malicious activities during the packet forwarding stage, impacting packet delivery reliability.  
Thirdly, even though there are multiple security solutions in the literature that can successfully measure the trustworthiness of participating nodes and isolate malicious nodes from communicating with other nodes in the network, they only consider the reliability issue caused by malicious nodes. To the best of our knowledge, none of the existing security solutions address (i) the reliability issues caused by multiple reliability-effecting factors, particularly energy levels, or (ii) how to make a global optimum decision in real time with adaptation to the network changes.
# Reliability of a New Portable Dynamometer for Assessing Hip and Lower Limb Strength
## 1 Introduction  
Measurement of athletes' hip and lower limb muscle strength is a common practice in amateur and professional sports. Indeed, numerous research studies have demonstrated the importance of muscular strength in athletic performance (for review, see [PERSON] et al. [1]) and an association between insufficient hip and lower limb muscle strength and an increased muscle injury risk in field-based sports (e.g., soccer, rugby, ice hockey, American football, etc.) [2; 3; 4; 5; 6]. Aside from performance optimization and injury prevention in sports, assessment of hip and lower limb muscle strength is also relevant for prevention and treatment of injuries in older individuals [7].  
Three main methods for measuring hip and lower limb muscle strength are being used in sports and clinical settings. Isokinetic dynamometry is currently considered as the gold standard for assessing human muscle strength; however, its use is often limited to a laboratory setting as these devices are expensive and not portable. Manual muscle testing is practical and a device-independent method; however, its reliability is questionable and depends to a large extent on the experience of the examiner [8]. Finally, hand-held dynamometry is a valid, reliable, portable, and more accessible strength assessment method compared with isokinetic dynamometry [9]. However, studies have shown that the validity and reliability of hand-held dynamometric measurements depend on external stabilization because measurement characteristics often rely on an examiner's strength [10; 11]. In addition, there is considerable variability in test procedures, with substantial effects of body position (supine, prone, side-lying, standing, sitting), stabilization methods (manuallyvs. non-elastic straps), and dynamometer placement (short vs. long lever arm) being reported [12].  
Therefore, new, externally fixed (independent of examiner's strength), portable, easy to use, and accessible dynamometers with standardized test protocols are needed. One externally fixed dynamometer called GroinBar (later renamed ForceFrame) has recently been introduced and evaluated [13; 14; 15]; however, this device does not allow the measurement of lower body muscles. To this end, a new portable dynamometer called MuscleBoard(r) was recently developed to assess the bilateral and unilateral strength of the hip muscles in all planes, as well as knee flexors, knee extensors, and plantar flexors. The purpose of the study was to verify the intra- and inter-session reliability of this novel strength testing device. We hypothesized that this new dynamometer system would produce good to excellent intra- and inter-session reliability results with low within-individual variation.
# An Under-Sampling Array Signal Processing Method Based on Improved Hadamard Matrix
## 1 Introduction
Therefore, this paper proposed a new deterministic measurement matrix based on a partial Hadamard matrix and used it to sample and compress the received array signal to obtain target-bearing information through the relevant adaptive reconstruction algorithm. The method can enhance the column-to-column non-correlation in the measurement matrix and improve its processing speed and reconstruction estimation performance. The performance was verified by simulation and real data on the lake.