esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Results in Terms of \(Edr\)  
FEMA 356 and ATC-40 are initially adopted with both the SD and SSD models for the hinges of the roof and to vary the hysteretic variable \(\beta_{\textit{-HYS}}\). The control node is set in the middle of the roof and its transversal displacement is plotted under the seven accelerograms. When the nonlinear static analyses (pushover) are performed, the values of the target control displacement changes in relation to the SD or SSD models, and it could be different in FEMA 356 and ATC-40. Table 2 shows the target displacement values for the different hysteretic variables.  
The Time History method (TH) is applied to the case study considering the control node's target displacements obtained by FEMA 356 and ATC-40. Seven spectrum-compatible accelerograms, selected by Rexel software v3.5 [35], simulating the L'Aquila earthquake [58], were considered. The main parameters characterizing the elastic spectrum of the considered site, according to the Italian design code [59], are the peak ground acceleration PGA = 0.30 g, the return period \(T_{R}\) = 475 years, the horizontal amplification factor \(F_{o}\) = 2.36, the vertical amplification factor \(F_{v}\) = 1.63, and the acceleration-velocity transition period \(T^{\ast}_{c}\) = 0.347 s.  
The analyses are performed by varying the hysteretic variable \(\beta_{\textit{-HYS}}\) between 0 and 2.1. The Time History of the control node displacement is considered to select the time window of the reference cycle according to the procedure described in Section 3.2. Once the cycle is selected, the hysteresis loops of the inelastic hinges of the roof can be extracted, and  
\begin{table}
\begin{tabular}{c c c c} \hline \(\beta_{\textit{-HYS}}\) & **Method** & \
\begin{tabular}{c} **SD—Target** \\ **Displacement [m]** \\ \end{tabular} &
\begin{tabular}{c} **SSD—Target** \\ **Displacement [m]** \\ \end{tabular} \\ \hline
0.3 & PO-FEMA/PO-ATC & 0.038/0.055 & 0.041/0.057 \\
0.6 & PO-FEMA/PO-ATC & 0.028/0.051 & 0.030/0.056 \\
0.9 & PO-FEMA/PO-ATC & 0.028/0.044 & 0.031/0.050 \\
1.2 & PO-FEMA/PO-ATC & 0.027/0.045 & 0.030/0.050 \\
1.5 & PO-FEMA/PO-ATC & 0.037/0.054 & 0.039/0.057 \\
1.8 & PO-FEMA/PO-ATC & 0.036/0.051 & 0.035/0.050 \\
2.1 & PO-FEMA/PO-ATC & 0.021/0.039 & 0.021/0.039 \\ \hline \end{tabular}
\end{table}
Table 2: Target displacements of the control node by PO in FEMA 356 and ATC-40.  
the corresponding dissipated energy is calculated. Moreover, the bilinear behavior of the walls is detected as well, and the strain energy is computed and used for the evaluation of the EDR. This procedure is performed using both the SD and SSD models for comparison.  
The EDR values obtained with the TH method by using the FEMA and ATC displacements of the reference point are plotted in Figures 8a and 8b, respectively. When the TH method is used with the equivalent FEM (Figure 9a), hysteretic loops are detected starting from the cyclic responses of the inelastic hinges of the roof (Figure 9b), while elastic-plastic bilinear cycles are identified for the walls (Figure 9c). In Figure 9a-c, for the sake of brevity, the cycles and the extraction of sub-cycles
## 4 Data Quality Control in TasMAN Using Fuzzy Rules
### Sensor Q4 QC Inputs
#### 4.1.1 Time since Last Calibration  
The output of a sensor drifts over time with a given rate. These drifts are generally related to the quality of a sensor and, hence, sensors require regular calibration. If a sensor is not calibrated according to the schedule outlined by the manufacturer, confidence in data obtained from the sensor is reduced.
## 4 Models of Implementing Vaccinations in European Pharmacies
### France  
In 2017, a pilot project to increase the vaccination coverage rate was launched in France. It included nearly 3 thousand pharmacies located in four regions of France. A requirement for participation in the programme was the completion of special training sessions by pharmacists organized by the French Pharmaceutical Society (the project included over 5000 pharmacists). During the six months of the pilot project (September 2017-March 2018), 159,139 people were vaccinated against flu [35], which resulted in implementing vaccinations in pharmacies in the whole of France.  
Currently, French pharmacists are authorized to administer flu vaccinations, and the law determines the staff qualifications and technical requirements for pharmacies. Pharmacists can administer flu vaccinations to adults, excluding patients with a history of allergic reactions. Patients can buy vaccinations in the pharmacy or receive it if they are entitled to free of charge vaccinations, and decide where the vaccination should be conducted.
# Improved Analytic Expansions in Hybrid A-Star Path Planning for Non-Holonomic Robots
## 3 An Improved Method
### Fine Tunings  
Observing Figure 5, the improved hybrid A-star paths in the forward search phase are still unrealistic since they deviate substantially from the true shortest paths, or have many turning points. In Figure 6, we illustrate the smallest continuous motions in discrete domain with two different motion primitives, or arcs, defined as the smallest entities of the path [20]. Each arrow from its tail to head depicts the robot's movement from a current pose to a possible successor's pose. Due to different steering angles, the headings of the successors are different in the same motion primitive. Moreover, the headings of the successors also differ by the influence of motion primitives. Compared to Figure 6a with the same steering angle, the headings of successors in Figure 6b are smaller.  
After tuning the motion primitives without any exhaustive, we found a value of 1.5 for motion primitive to give good results. The resulted routes for grid map A and map B are shown in Figure 7a,b. The number of turning points in these figures is dropped from 5 to 2 points and from 6 to 5 points compared to those in Figure 5a,b, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Maps**} & \multicolumn{4}{c}{**Original RS Path**} & \multicolumn{4}{c}{**Improved RS Path**} \\ \cline{2-9}  & **Curvature** & **Cost** & **Length (m)** & **Time (s)** & **Curvature** & **Cost** & **Length** & **Time** \\ \hline Map A & 0.23 & 15.27 & 34.07 & 0.022 & 0.15 & 8.18 & 34.05 & 0.1 \\ Map B & 0.23 & 5.85 & 19.83 & 0.015 & 0.1 & 5.52 & 20.25 & 0.139 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of original and improved methods.  
Figure 6: Smallest continuous motions in discrete domain. (**a**) Long motion primitive. (**b**) Short motion primitive.
## 2 Materials and Methods
### Learning Adaptive-Compliance Control
#### 2.2.2 Multimodal Policy Architecture  
The control policy was represented using neural networks, as shown in Figure 3. The policy input was the robot state. The robot state included the proprioception information of the manipulator and haptic information. Proprioception included the pose error between the current robot's end-effector position and predicted target pose \(\mathbf{x}_{e}\), end-effector velocity \(\dot{\mathbf{x}}\), desired insertion force \(F_{g}\), and actions taken in the previous time step \(\mathbf{a}_{t-1}\). Proprioception feedback was encoded with a neural network with 2 fully connected layers with an activation function RELU to produce a 32-dimensional feature vector. For force-torque feedback, we considered the last 12 readings from the six-axis F/T sensor, filtered using a low-pass filter, as a 12 \(\times\) 6 time series:  
\[[F_{ext}^{0},\,\ldots,\,F_{ext}^{12}],\quad\text{where}\quad F_{ext}^{i}=[F_{ x},F_{y},F_{z},M_{x},M_{y},M_{z}] \tag{1}\]  
The F/T time series was fed to a temporal convolutional network (TCN) [39] to produce another 32-dimensional feature vector. The feature vectors from proprioception and haptic information were concatenated to obtain a 64-dimensional feature vector, and then fed to two fully connected layers to predict the next action.  
The policy outputs actions for a parallel position-force controller. The policy produces two types of actions, \(\mathbf{a}\doteq[\mathbf{a}_{x},\mathbf{a}_{p}]\), where \(\mathbf{a}_{x}=[\mathbf{p},\phi]\) are position/orientation subgoals, and \(\mathbf{a}_{p}\) are parameters of the parallel controller. The specific parameters controlled by \(\mathbf{a}_{p}\) are described in Section 2.2.3.
## 4 Results and Discussion
### Risk of Bias in the Studies  
The 120 papers included underwent a thorough process to ensure effective evaluation without bias. The findings of the studies that were part of this SLR and the QA that went along with them are illustrated in Table 9 and Figure 6, which indicates that all 120 research projects met the QA requirements, indicating that they were accepted and qualified for additional examination. Figure 6 illustrates that 4.65% of studies evaluated using the QA checklist obtained 75 percent. Of the studies, 23.15% obtained an 83 percent QA score, 50.38% of the evaluated studies obtained a 92 percent QA score, and 21.70% of the evaluated studies obtained a 100 percent QA score.
# A Hybrid Key Generator Model Based on Multiscale Prime Sieve and Quantum-Inspired Approaches
## 11 Conclusions  
This work exploits a fundamental principle of quantum mechanics, wave function collapse, to generate an intrinsically random encryption subkey. This is particularly useful for quantum cryptography applications and can also be further developed to create quantum-resistant encryption systems. Future development could improve this idea by incorporating the BB84 protocol or quantum entanglement, which would further enhance the security of key distribution. This implementation provides a good basis for exploring quantum-based cryptographic applications. Next, a second subkey was generated using a special multiscale algorithm for prime number generation. Last but not least, in the context of the JDL paradigm with its new implementation presented here, a specific key generator was developed so that the two subkey generators could be combined to produce an ultra-secure quantum-primary hybrid algorithm for key generation. In addition, post-quantum cryptography (PQC), such as lattice-based algorithms, including Kyber and Dilithium, also provides resistance to quantum attacks.  
Thanks to the conceptual innovation introduced by MuReQua and DeSSE, the updated version of the algorithm will incorporate not only the first two aspects described above (fragmentation and obfuscation), but also the remaining two, thus becoming fully crypto-agile. This evolution will enable the use of post-quantum cryptographic schemes, such as Kyber and Dilithium, in place of the previous NIST tests mentioned, as the resulting system will be fully post-quantum and not simply hybrid and inspired by quantum mechanics.  
Conceptualisation, G.I. and E.B.; writing--review and editing, G.I. and E.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Initial Data Processing: Avoiding Model Bias
### From Packets to Images
It is expected that a neural network can take advantage of image acceleration hardware architectures to process the 2D and 3D images in parallel. Moreover, in these cases it must be considered that an artificial dimension is created in the packets, where the information is originally sequential, and a row of pixels does not have to be related to the one immediately above or below it, as will be discussed in Section 5. In the work by [PERSON] et al. [28], they presented a comparative analysis of 1D, 2D, and 3D convolutional neural networks (CNNs) for traffic packet classification. The results showed that the 1D-CNN achieves the highest precision and F1-score, which is consistent with previous findings. Interestingly, the 3D-CNN closely matches the performance of the 1D-CNN and even surpasses that of the 2D-CNN, despite the potential increase in complexity and spatial information introduced by the 3D images. Furthermore, that paper provides insight into the resource usage, speed, and bandwidth of the three cases. It is observed that, as expected, the addition of dimensions to the convolutional input layer leads to improved speed performance of the classifier. Regarding the size of the packet, it could be considered that a larger image size will result in better accuracy results, due to the increase in information about each packet that is provided to the neural network. In our preliminary results, we observed that increasing the image size from \(28~{}\times~{}28\) pixels to \(32~{}\times~{}32\) pixels significantly improves neural network performance in packet recognition tasks, with accuracy increasing from 56% to 66%. However, it is important to note that further increases in image size beyond \(32~{}\times~{}32\) pixels may not necessarily lead to performance improvements. For example, we  
Figure 2: Protocol headers. found that 38 \(\times\) 38 pixel images produced the same 66% accuracy. In this work, a size of 1024 pixels was used, i.e., square images of 32 \(\times\) 32 pixels, so that we did not exceed the typical MTU of 1500 bytes. We empirically chose this size because smaller images yield worse performance, while larger ones do not exhibit any noticeable improvement.
## 2 Design  
A schematic representation of the pump-probe experiment is shown in Figure 1a. The optical design of the nanoparticles is such that they strongly absorb at one wavelength of light (\(\lambda_{pump}\), Figure 1b), making them efficient thermoelastic generators of elastic waves and have a strongly modulated scattering cross-section at a second wavelength (\(\lambda_{probe}\)) when the device vibrates (Figure 1c). Consequently, elastic waves are generated when the particle is illuminated with a short (ps) pulse of light at \(\lambda_{pump}\) and illumination with a second pulse of light at \(\lambda_{probe}\) detects the changes in the reflected/transmitted light intensity produced by the (instantaneous) change in size (caused by mechanical vibration). Section 2.1 contains a detailed optical model that provides the means to choose the most suitable material and size of the particle to match the surrounding media and probe laser wavelength, \(\lambda_{probe}\).  
Figure 1: (**a**) schematic representation of the experiment, showing the spherical nanoparticle, the pump and probe laser beams and the vibrations; (**b**) the absorption cross-section for a 150 nm gold nanoparticle computed [3] with the fixed pump wavelength (\(\lambda_{pump}=415\) nm) marked; (**c**) backscattering cross-section (blue) and the optical sensitivity (orange) of a variable sized gold nanoparticle (0–550 nm) illuminated with a fixed probe laser wavelength (\(\lambda_{probe}=780\) nm) in water. The first optimal size for acting as a detector is shown (\(\sim\)160 nm); here, the backscattering cross-section is very sensitive to the size and, as the particle vibrates, the backscatter is modulated, additional maxima in the sensitivity are shown, but these are for increasingly large particles.  
In our experiment, the pump laser wavelength was dictated by the tuning range of the lasers and the need to operate without damage or significant absorption in the surrounding media. A wavelength of 415 nm was chosen because it is strongly absorbed by all the different sizes of gold nanoparticles used but not by the surrounding media (air and water), and this region is away from the plasmon edge so it is not affected by small changes in the particle size, ensuring that the generation efficiency is the same for all of the particle sizes used in this paper.  
The probe laser wavelength was chosen in the near infrared (NIR) region (700-850 nm) because of the low absorption (Figure 1b) and high sensitivity for 150-200 nm particle sizes (Figure 2). This region shows high signal-to-noise ratio for backscattered light unlike the plasmon edge where the DC is nearly 0 (Figure 2a-d).
# FANet: Improving 3D Object Detection with Position Adaptation
## 4 Experimental Results and Analysis
### Dataset  
Currently, the KITTI [32] dataset, Waymo [33] dataset, and nuScenes dataset are commonly used for 3D object detection. The KITTI dataset is one of the most important test sets in the field of auto-driving. It plays an important role in 3D object detection.  
Therefore, this paper chose the KITTI 3D point cloud dataset as the training and test set for the experiment.  
The KITTI dataset contains 7481 training samples and 7518 test samples. The dataset consists of four parts: Calib, Image, Label, and Velodyne. Calib is the camera calibration file; Image is a two-dimensional image data file; and Label is a data label file (there is no label in the test set). There are three types of objects labeled by the data: car, cyclist, and pedestrian. Velodyne is a point cloud file. For efficiency reasons, Velodyne scans are stored as floating-point binary files, with each point stored in its (x, y, z) coordinates and additional reflectance value R.  
This network model uses, in order, 80% of the training sample data to train the model and the remaining 20% for validation [3].
## 3 Results
### Cluster Analysis (CA), Two-Way Cluster Analysis (TWCA) and TWINSPAN  
Cluster analysis (CA), using PC-ORD, grouped the vegetation into three plant communities, as indicated in the dendrograms under the impact of various environmental gradients (Figure 2). TWCA displays the detailed distribution patterns of 162 plant species recorded at 23 sampling sites (stands). Based on influenced environmental gradients, all of these stands were clustered into three distinct groups (plant communities) (Figure 3). According to TWINSPAN classification, a total of three communities (_Cedrus-Isodon-Cynodon_, _Cedrus-Cynodon_-Dryopteris_, and _Sambucus-Cedrus-Desmodium_) were established by clustering 162 reported plants species in 23 stands under the strong influence of environmental variables in pure Deodar forests (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Plant Species**} & \multicolumn{3}{c}{**Plant Communities**} \\ \cline{2-4}  & _Cedrus–Isodon–Cynodon_ & _Cedrus–Cynodon–Dryopteris_ & _Sambucus–Cedrus–Desmodium_ \\ \hline _Silbyun mariantum_ ([PERSON]) [PERSON]. & 0.00 & 0.61 & 0.00 \\ \hline _Silene vulgaris_ (Moench) [PERSON] & 0.00 & 0.00 & 0.62 \\ \hline _Smilax glaucophyllla_ [PERSON] & 0.00 & 0.00 & 0.20 \\ \hline _Sonchus asper_ (L.) [PERSON] & 0.00 & 0.95 & 0.49 \\ \hline _Sorghum halepense_ (L.) [PERSON]. & 3.37 & 1.91 & 1.17 \\ \hline _Sorburia tomentosa_ ([PERSON].) [PERSON] & 0.00 & 1.00 & 3.62 \\ \hline _Spiraea affinis_ [PERSON] & 0.00 & 0.00 & 0.23 \\ \hline _Spiraea vaccinifolia_ [PERSON]. [PERSON] & 0.00 & 0.00 & 0.02 \\ \hline _Sporobolus diandrus_ ([PERSON].) [PERSON]. & 0.00 & 0.00 & 0.70 \\ \hline _Stellaria media_ (L.) [PERSON]. & 0.00 & 0.00 & 0.12 \\ \hline _Tagetes minuta_ L. & 0.00 & 4.33 & 0.77 \\ \hline _Taraxacum officinal_ aggr. F.H. [PERSON]. & 0.00 & 1.52 & 0.00 \\ \hline _Thalictrum peduncultum_ [PERSON]. & 0.00 & 0.00 & 0.03 \\ \hline _Torilis japonica_ ([PERSON].) DC. & 0.00 & 0.00 & 0.04 \\ \hline _Trachypersermum amii_ (L.) [PERSON] & 0.00 & 0.00 & 0.01 \\ \hline _Trifolium repens_ L. & 3.26 & 3.60 & 0.00 \\ \hline _Vicia sativa_ L. & 0.00 & 0.00 & 0.04 \\ \hline _Vincetoxicicum petrense_ ([PERSON] and Lace) Rech. f. & 0.00 & 0.00 & 0.04 \\ \hline _Viola odorata_ L. & 0.00 & 0.00 & 0.29 \\ \hline _Viola serpens_ Wall. Ex Ging & 0.00 & 0.00 & 0.36 \\ \hline _Vilex negundo_ L. & 0.00 & 0.00 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
# GNSS Jamming Observed on Sounding Rocket Flights from Northern Scandinavia +
## 3 Rough Estimation of the Jammer Location
The minimum elevation angle \(\varepsilon\) is set to zero so that the cone of the minimum elevation mask degrades to a plane that is tangential to the Earth's ellipsoid at the jammer location \(\mathbf{r}_{j}\). Besides the jammer location \(\mathbf{r}_{j}\), the jammer location error covariance \(\mathbf{P}_{j}=E\left[\left(\mathbf{r}_{j}-E\left[\mathbf{r}_{j}\right]\right)\left(\mathbf{r }_{j}-E\left[\mathbf{r}_{j}\right]\right)^{T}\right]\) is estimated. It considers an uncertainty of the minimum elevation angle \(\varepsilon\) of 0.5 deg (1\(\sigma\)) and uncertainties of the start and end points of the jamming signals of 1000 m (1\(\sigma\)) in horizontal direction and 2000 m (1\(\sigma\)) in vertical direction. The covariance \(\mathbf{P}_{j}\) is calculated from Monte-Carlo runs using 10,000 randomly chosen, Gaussian distributed elevation angle and position errors. Figures 5 and 6 illustrate the result of the estimation of the rough location of the jammer from two viewing directions. The 95% uncertainty ellipse is computed from the covariance matrix \(\mathbf{P}_{j}\). \[a\]1 Illustration of the estimated location of the jammer, the 95% uncertainty ellipse and the trajectories of the SOAR and MAIUS\(-\)2 sounding rockets. The transparent red surface represents the minimum elevation mask of the jamming signal. Since the elevation angle was chosen as zero, it is a plane that is tangent to the Earth’s ellipsoid at the jammer location \(\mathbf{r}_{j}\).
# An Approach to Analyze Vulnerability of Information Flow in Software Architecture
## 2 Related Work
### Architecture Analysis Based on Graph Theory  
Graphs are a fundamental construct in complex systems research, and the use of graph theoretic algorithms and metrics to extract useful information from a problem is a primary method of analysis for complex systems [37]. A graph-based representation is used to provide a particular perspective on a problem. Generally, graph-theoretic representations of a problem emphasize aspects that are either structural such the connection of components or processes in the problem, or that depend on structural properties such as the robustness of a network of components. Graph representations are particularly useful in understanding systems involving multiple interacting entities. Such complex systems lend themselves to a graph-theoretic representation and the rich body of network flow methods that can represent and analyze the component structures and patterns of interactions at both local and global levels [38; 39].  
Architecture analysis methods based on graph theory have been widely adopted by researchers form systems engineering and software engineering area. In systems engineering area, graph theory provides a structural perspective for systems, with a focus on structural properties such as robustness, resilience, and security [40; 41; 42]. In software engineering area, researchers usually utilize methods based on graph theory to model the interactions between web services or classes [43; 44; 45; 46; 47]. Graph-based modeling can be classified into two categories: The first category focuses on topological structure, modeling the static interdependent structure of components, such as classes and services. The second category focuses on the dynamic interaction, modeling the data flow, interactions between services, invocation relationship between classes in software architecture. To improve the modeling capability of these graph-based models, researchers usually extend the nodes and edges in the graph to satisfy the needs of their respective research. This paper will be based on the second category of graph-based models.