esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Verification  
Figure 4a shows the simulation of sensitivity analysis, which includes the logarithmic scaling of the pressure drop of dust-loaded elliptical fibers for the different grid partitions. It is found that the curve of \(192\times 96\times 96\) is similar with that of \(128\times 64\times 64\) (the error is less than 2%), indicating that the mesh used in this study was adequate. Then, we simulated the Brownian-diffusion-dominated collection processes under different flow rates and particle diameters [37], where particle size is comparatively small. The simulations take into account the Brownian random diffusion, the van der Waals forces, and the drag forces. The fluid velocity was set as 0.2126 m/s for 1.0 L/min, and the diameter of the fiber was set as 65 \(\upmu\)m. The particles can randomly deposit after colliding and contacting with the woven fiber. As shown in Figure 4b, the simulation results well correspond with the results of [PERSON] et al. [37], proving the reliability of the simulations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case** & \begin{tabular}{c} **Packing** \\ **Density** \\ \end{tabular} & \begin{tabular}{c} **Aspect** \\ **Ratio** \\ \end{tabular} & \(d_{\text{P}}\) **(\(\upmu\)m)** & \(u_{\text{in}}\) **(m/s)** & \
\begin{tabular}{c} **Perimeter** \\ **(\(\upmu\)m)** \\ \end{tabular} &
\begin{tabular}{c} **Equivalent** \\ **Diameter (\(\upmu\)m)** \\ \end{tabular} \\ \hline
1 & 5\% & 2 & 0.2 & 0.1 & 133.8 & 22.8 \\
2 & 5\% & 2 & 0.4 & 0.1 & 133.8 & 22.8 \\
3 & 5\% & 2 & 1 & 0.1 & 133.8 & 22.8 \\
4 & 5\% & 2 & 2 & 0.1 & 133.8 & 22.8 \\
5 & 5\% & 1 & 0.4 & 0.1 & 71.7 & 22.8 \\
6 & 5\% & 3 & 0.4 & 0.1 & 177.0 & 22.8 \\
7 & 2.5\% & 2 & 0.4 & 0.1 & 94.6 & 16.1 \\
8 & 7.5\% & 2 & 0.4 & 0.1 & 163.8 & 28.0 \\
9 & 10\% & 2 & 0.4 & 0.1 & 189.2 & 32.3 \\
10 & 5\% & 2 & 0.4 & 0.2 & 133.8 & 22.8 \\
11 & 5\% & 2 & 0.4 & 0.3 & 133.8 & 22.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the simulation cases.  
Figure 3: (a) Mesh and (b–d) fluid field together with streamlines around the clean woven fibers of Case 2.
## 4 Discussion
The evaluation suggests that the FM-PI speed controller performs better in terms of discrete-level speed transitions. The FM-PI speed controller reached the desired speed with the minimum rise time and settling time without undershooting. The speed response tests indicate that the FM-PI speed controller displays robust speed control with better precision and accuracy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Controller Type** & **Rise Time (s)** & **Settling Time (s)** & **Overshoot (\%)** \\ \hline FOPID & \(\approx\) 30 & 11 & 0.505 \\ \hline KW-WOA-PID & 0.38 & 0.51 & 3.56 \\ \hline SVR-PI & 0.367 & 0.408 & 0.0 \\ \hline FM-PI & 0.26 & 0.24 & 12.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Speed response comparison for a speed of 40 km/h. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sr. No** & **Author** & **HP Rating (Induction** & **Motor** & **Efficiency** \\  & **Machine**) & **Operation** & **Efficiency** \\ \hline
1 & [PERSON], et al. [19] & 50 & Unloaded & 92\% \\ \hline
2 & [PERSON], [PERSON], & 50 & Unloaded & 94.37\% \\  & and [PERSON] [15] & & & \\ \hline
3 & [PERSON] and & & & \\  & [PERSON] [20] & 50 & Unloaded & 93\% \\ \hline
4 & [PERSON], [PERSON] [PERSON], & 50 & Unloaded & 92.6\% \\  & and [PERSON] [26] & & & \\ \hline
5 & [PERSON], et al. [27] & 50 & Loaded & 75\% \\ \hline
6 & Proposed Model & 50 & Loaded & 78.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Efficiency comparison of IM with the unloaded model at the same ratings.
## 2 Three-Dimensional Forward Modeling Algorithm  
### Three-Dimensional Forward Modeling of the Resistivity Method  
We used the finite difference method to realize the three-dimensional forward simulation of the resistivity method. According to forward modeling of the resistivity method derived by [PERSON] et al. [17], the partial differential equation satisfied by the point-source resistivity method is as follows:  
\[\
abla\cdot[\sigma(x,y,z)\
abla\varphi(x,y,z)]=-j\delta(x-x_{0})\delta(y-y_{0}) \delta(z-z_{0}), \tag{1}\]  
where \(\sigma\) is the electrical conductivity of rock ore (S\(\cdot\)m\({}^{-1}\)), \(\varphi\) is the potential array (V), \(j\) is the electric current density (A\(\cdot\)m\({}^{-2}\)) and \(\delta\) is a Dirac function, which specifies the position of the supply point.  
The finite difference method is used to discretize (1), forming large sparse linear equations [18], whose matrix form can be expressed as follows:  
\[A\varphi=b, \tag{2}\]  
where \(A\) is the coefficient matrix and \(b\) is a vector that contains information with respect to the field source.  
In this study, the biconjugate gradient stabilized method (BICGSTAB) based on incomplete LU decomposition was introduced to solve the equation to obtain the potential [19]. After the potential was obtained, the apparent resistivity was calculated according to (3):  
\[\left\{\begin{array}{l}U=\alpha\varphi\\ \rho_{a}=K\frac{\Delta U}{T}\end{array}\right., \tag{3}\]  
where \(\alpha\) is the vector that is designed to select the potential at the receiver among all grid node potentials; \(\Delta U\) is the potential difference between the receiving electrodes (V); \(I\) is the electric current (A); and \(K\) is the geometric factor.
## 3 Results
### Cell-Free DNA (cfDNA) Quantity and Library Yields in Clinical Specimens
#### 3.2.2 Molecular and Read Coverage of Sequenced Libraries  
In all sequenced samples it was possible to proceed with NGS data analysis to validate both library quality and variant call.  
Library performance was in particular evaluated by two TSS parameters such as MedReadCov and MedMolCov, i.e., the median number of reads across target regions and original DNA molecules identified (tagged), respectively.  
We found that the increasing cfDNA input (ng) used for test leads to higher rates of MedMolCov (r\({}_{\text{s}}\) = 0.85; \(p<\) 0.0001) and consequently to a more confident number of DNA molecules investigated for mutational search (Figure 4a).  
Taking into account all the 92 sequenced libraries, it emerged that 51/92 achieved a MedMolCov >4000, with a median value of MedReadCov across the samples of 37,405 (range 18,252-88,731), whereas 27/92 samples obtained a MedMolCov between 2500-4000 with a median value of MedReadCov of 24,801 (range 10,116-45,861). Of the remaining 14 samples, 11 showed a MedMolCov between 1500-2500 and only 3 samples <1500, with a median MedReadCov of 24,170 and 25,826, respectively (Figure 4b).  
Since the assay was designed to reach as optimal yield a MedReadCov >25,000 and a MedMolCov >2500 in order to obtain reliable sequencing results and reach a LoD of 0.1%, we showed that in our setting, a complete fulfillment of both parameters was found in 56/92 samples (60.9%). Separately, 81/92 (88%) libraries reached outperforming values of MedMolCov >2500 whereas 63/92 (68.5%) achieved values of MedReadCov >25,000.  
Figure 3: Assessment of the 35 amplicons of the OLcfA among the 92 samples tested. Mean total read coverage was investigated across all clinical samples tested (n = 92) and reported on y axis.  
An overall moderate positive correlation was revealed between the values of MedMolCov and MedReadCov (r\({}_{\rm s}\) = 0.57, \(p\) < 0.0001) (Figure 4c), thus confirming an optimal NGS workflow performance involving both library generation and amplification steps.  
In regard to LoD values of the assay, i.e., the lowest variant allelic frequency that could be reliably detected, in our 92 plasma samples we obtained a median LoD of 0.10%, (range 0.02-0.40%), and as expected, these data negatively correlated with cfDNA input amount (r\({}_{\rm s}\) = -0.41, \(p\) < 0.0001) (Figure 4d). Notably, it was observed that 37/92 samples (40.22%) reached at least 0.1% LoD and among these, 16 (43.24%) initially presented <20 ng of cfDNA input for NGS library preparation.
# UWB-Based Human-Following System with Obstacle and Crevasse Avoidance for Polar-Exploration Robots
## 6 Simulation and Experiment Results
### Experimental Results of Applying KAREX in a Real-World Environment  
In this section, the performance of the proposed UWB-based tag position estimation and collision-avoidance explorer-following system is verified by applying it to the actual KAREX robot, rather than a simulated environment. Figure 13 shows the results of applying the proposed UWB-based human-following algorithm, capable of avoiding obstacles, to the real polar-exploration robot KAREX.  
Figure 13 illustrates the robot avoiding an obstacle and following a person carrying a UWB tag after the person passes by a randomly placed obstacle. As shown in Figure 13a, when the robot detects an obstacle while following the person, it follows a path to avoid the obstacle, as seen in Figure 13b,c, rather than moving in the direction of the person. After avoiding the obstacle, the robot resumes following the person's LoS, as shown in Figure 13d. Figure 14 demonstrates the performance of the system in avoiding existing infrastructure and following the person.  
Figure 14 shows the movement path of KAREX as it avoids collisions with curbs on a road while following a user carrying a UWB tag. As depicted in Figure 14a, the robot  
Figure 14: Route of robot and target during simulation (**a–f**).  
Figure 13: Route of robot and target during simulation (**a–d**).  
maintains the LoS with the user while moving. When the user passes a curb, causing the robot to face a potential collision with the curb as shown in Figure 14b, the robot avoids the curb while continuing to follow the user, as seen in Figure 14b-d.  
As shown in Figure 15a, the obstacle information obtained through LiDAR is marked as obstacles on the local map in Figure 15b. Using the wave-front method, a pre-path is generated, and then the final local path is created by connecting the points with a straight line. As seen in Figure 15b, the proposed algorithm can continuously generate paths that allow the robot to avoid collisions while following the user, even without maintaining the LoS.
## 4 Composite Optimization
### Calculation of Optimal Parameter Set  
The optimal set was derived after a minimum of 200 generations and a stagnation period of 50 generations. To mitigate the computational burden associated with determining the optimal design parameter combination, specific constraints--such as enforcing symmetry in the composite material lamination--were imposed to reduce processing time. The optimization outcome is deemed to be in close proximity to the Pareto optimal point, as further attempts to reduce the structure's mass resulted in unfeasible designs. Alternative designs generated and evaluated by the algorithm are depicted as points in Figure 16, where the x-axis represents the design ID and the y-axis the corresponding computed mass. Due to the predominantly discrete nature of the design variables, a stepwise reduction in the structure's mass was anticipated and observed, as shown in Figure 16, reflecting the incremental addition or removal of laminae in the composite material stacking. Finally, the initial composite characteristics alongside the optimization results are presented in Table 9 and Figure 17.  
Figure 16: Output of the genetic algorithm objective function.  
The analysis of the O.A. results indicates a mass reduction of approximately 12.36 kg (41.66%) from the initial lamination of \(\left[0^{\mathrm{WV}}_{9}/0^{\mathrm{UD}}_{3}/0^{\mathrm{WV}}_{7}/0^{\mathrm{UD}}_ {3}/0^{\mathrm{WV}}_{7}/0^{\mathrm{UD}}_{5}/0^{\mathrm{WV}}_{7}/0^{\mathrm{CORE}} /0^{\mathrm{WV}}_{7}/0^{\mathrm{CORE}}/0^{\mathrm{WV}}_{7}\right.\)/0\({}^{\mathrm{UD}}_{3}/0^{\mathrm{WV}}_{7}/0^{\mathrm{UD}}_{3}/0^{ \mathrm{WV}}_{9}\)] while maintaining developed stresses in the monocoque structure at suitably low levels. However, despite significant mass savings, a disproportionately greater decrease in torsional rigidity was observed. This reduction is attributed to the removal of laminae from the composite, which diminished the stiffness matrix components. Notably, torsional rigidity measurements were recorded at a horizontal distance of 937.6 mm from the plane defined by the fixation points.
## 2 Three-dimensional Object Detection Techniques
### Direct Point Cloud Processing  
In direct point cloud processing, the point information (such as intensity, 3D coordinate values, color, etc.) of point clouds are directly processed by neural networks to extract features and predict 3D object boundaries. The network has to handle higher dimension data compared to the 2D plane projection-based method. A visual representation is shown in Figure 2. Object segmentation can be performed directly on a point cloud using a neural network [29; 30]. In these research works, authors performed semantic segmentation using the features of points by applying max pooling for the overall skeleton of the object. They further improved this process by applying this technique in different layers and detected fine patterns as well. But this process is not demonstrated for instance level segmentation. Instance segmentation can be required when each object needs to be identified individually. Semantic segmentation can locate the same class of all objects with common category names, such as cars, people, bicycles, etc., whereas instance segmentation is capable of distinguishing one car from the others. This is often essential in object tracking or surveillance.  
[PERSON] et al. illustrated another point cloud-based network called PointRCNN where points are segmented as foreground and background in the first stage to generate 3D region proposals. Then, coordinates of the proposed regions are identified for performing object detection. However, this network showed poor performance on pedestrian detection compared to vehicles detection. The sparse nature of the point cloud from a small-sized object (here a pedestrian) may be a reason for the lower precision rate [31].  
[PERSON] et al. utilized an interesting geometric concept, Hough voting, to identify the core of the 3D object. From the point cloud of the surface of the object, the center can be located using Hough voting. They have successfully demonstrated 3D object detection by locating household items such as a sofa, chair, table, etc. in the SUN RGB-D and ScanNet datasets. But the applicability of this method in long-range detection such as on UAV images is uncertain based on the information of this research [32].  
Some researchers have rasterized the information of point clouds, which are called voxels, and then the feature of every voxel is encoded [33; 34; 35; 36]. An improved method of this network is introduced by [PERSON] and [PERSON] (2018). Instead of handcrafted feature encoding, they have incorporated a region proposal network for detection. They have detected different vehicles and pedestrians in the KITTI dataset, proving this method to be suitable in autonomous vehicles. Interestingly pedestrians detection required close distance LiDAR, compared to vehicles detection, It suggests that small objects such as pedestrians and cyclists generate too sparse point cloud to detect from long distance [37].
# Plant Exosomal Vesicles: Perspective Information Nanocarriers in Biomedicine
## 4 Biochemical Characterization of Nanovesicles
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Plant Species** & **Name of Protein** & **Protein Function** & **References** \\ \hline \hline \begin{tabular}{c} Thale c (_Arabidopsis thaliana_) \\ \end{tabular} & \begin{tabular}{c} ABC transporter G \\ family member 36 \\ (PEN3) \\ \end{tabular} & \begin{tabular}{c} Defense responses, \\ transmembrane transport \\ \end{tabular} & [35] \\ \hline \hline \begin{tabular}{c} Thale c (_A. thaliana_) \\ Kenya violet (_Craterostigma_ \\ _plantagineum_) \\ orange (_C. litmus sinensis_) \\ lemon (_C. litmus_) \\ orange (_C. aurantium_) \\ \end{tabular} & Tetraspanin-8 (TET8) & \begin{tabular}{c} Signaling pathway, defense \\ response to bacterium \\ \end{tabular} & [35; 43; 44; 45] \\ \hline \hline \begin{tabular}{c} Thale c (_A. thaliana_), Kenya \\ violet (_C. plantagineum_), orange \\ (_Citmus sinensis_) \\ lemon (_C. litmus_) \\ grapefruit (_C. paradise_), bitter \\ orange (_C. aurantium_) \\ \end{tabular} & \begin{tabular}{c} Annexin D1 and D5 \\ (ANN1, ANN5) \\ \end{tabular} & \begin{tabular}{c} Phospholipids and calcium \\ ions transmembrane \\ transport \\ \end{tabular} & [35; 43; 45] \\ \hline \hline \begin{tabular}{c} Thale c (_A. thaliana_) \\ \end{tabular} & \
\begin{tabular}{c} RPMI-interacting \\ protein 4 (RIN4) \\ \end{tabular} &
\begin{tabular}{c} Defense responses, \\ regulation of plant-type \\ hypersensitive response \\ \end{tabular} & [35] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Protein components of plant nanovesicles. Moreover, the plant nanovesicles contain a significant amount of genetic material, represented mainly by small RNA patterns (Table 2). MicroRNAs (miRNAs) are of particular interest as important epigenetic regulators of cellular processes that can specifically suppress the work of genes by degradation of target mRNAs [18,46]. The miRNAs are frequently found in the extracellular fluids of plants in both freeform and protein-miRNA complexes due to the passive diffusion or active secretion [47]. The free miRNA molecules are unstable and susceptible to destructive effects, while their packaging inside the plant nanovesicles allows them to avoid these consequences and even to be transported to the external environment. Currently, several hundred representatives of the exosomal miRNAs from various plant species are known. However, the majority of these miRNAs were probably coppurified with the exosomes associated with the RNA-binding proteins, and only a limited fraction were factually encapsulated inside the vesicles [48]. Thus, in _Arabidopsis_, only seven miRNAs, namely: miR187c, miR167a, miR168a, miR168b, miR169a, miR172e, and miR8175, were primarily accumulated in the nanovesicles, while the other 55 miRNAs were located outside [48]. This observation means that, as in the case of proteins, the content of plant exosomal miRNAs (and probably from other sources) could also be greatly
## 6 Microbiome, Dysbiosis and Osteoporosis  
In recent years, a few more pieces to the puzzle of the association between OP and oral health have been added from studies on the microbiome and its relationship to bone metabolism: after the completion of the mapping of the human genome to sequence and identification of all human genes [49], research has begun to focus on mapping the human microbiome and the related microbiota [50]. By definition, the human microbiota is the collection of all the microorganisms, including eukaryotes, archaea, bacteria and viruses, living in association with the human body in specific ecological niches, while the term \"microbiome\" refers to all of their genetic material [51; 52; 53].  
The richest and most studied human niche is the gut environment, which accounts for 95% of all microorganisms present on and in the human body, but other environments have also been identified, such as nose, eyes, stomach, genitalia, skin and mouth [54].  
The findings of differences in oral bacterial composition in unhealthy states, also known as oral dysbiosis, have increasingly opened up new scenarios in the study of the mechanisms underlying OP.  
The first research on the human oral microbiome was published in 2010 [55], and, among the over 600 species identified with cultural and non-cultural techniques, 169 seem to be indigenous of the oral cavity (the so-called \"core oral microbiome\"), arranged, in physiological conditions, in quantity and  
Figure 1: The bidirectional interrelationships between osteoporosis (OP) and periodontitis (PD). They share a series of common risk factors. The bone mineral density (BMD) loss associated with OP may be responsible for increasing the alveolar bone resorption in PD, and, conversely, the local inflammation peculiar of PD may activate systemic osteoclastic cytokines responsible for further BMD decrease. In this scenario, the role of gut and oral dysbiosis may enhance and/ or trigger these events, leading towards a worsening of both pathologies and the healthy state of the affected subjects. BMD, Bone Mineral Density.  
quality in three different ecological niches/intraoral habitats, classified as: Group 1, buccal mucosa, keratinized gingiva, and hard palate; Group 2, saliva, tongue, tonsils, and throat (back wall of orophrx); and Group 3, sub- and supra-gingival plaque [56; 57].
# Large Language Models' Trustworthiness in the Light of the EU AI Act--A Systematic Mapping Study
## 5 Results and Discussion
### Results of RQ4  
**Research Question:** What types of research contributions are primarily presented in studies on LLM trustworthiness?  
**Objective:** To classify the nature of **research contributions,** such as frameworks, methodologies, tools, or empirical studies, made by researchers in this area.  
Analysis and Results  
Figure 19 illustrates the distribution of the types of research contributions in studies on LLM trustworthiness. The analysis reveals the following key insights:  
* **Solution Proposal:** The largest share of research types falls under solution proposals, with 1517 papers (42.3%). This indicates a strong focus on proposing new solutions and approaches for enhancing the trustworthiness of LLMs.
* **Evaluation Research:** Research on evaluation constitutes 808 papers (22.5%), highlighting a significant interest in assessing and evaluating the effectiveness of LLMs in ensuring their trustworthiness.
* **Validation Research:** Research focused on validation account for 648 papers (18.1%), emphasizing the importance of validating the methods, models, and solutions used to evaluate LLM trustworthiness.
* **Conceptual Proposal:** A total of 397 papers (11.1%) were categorized under conceptual proposals, suggesting that there is ongoing work to define new concepts and frameworks related to LLM trustworthiness.
* **Opinion Paper:** There were 127 papers (3.5%) related to opinion papers, indicating a smaller focus on providing subjective viewpoints or discussions related to LLM trustworthiness.
* **Experience Paper:** Experience papers constituted 91 papers (2.5%), reflecting a relatively smaller body of work focused on sharing practical experiences related to LLM trustworthiness.  
Figure 18: LLM trustworthiness distribution in 2024 (up to June).  
These findings demonstrate that the majority of research in this area is focused on solution proposals and evaluation, with a notable amount of validation research. While conceptual, opinion, and experience papers are valuable, they make up a relatively smaller proportion of the overall research types.