esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Analysis
### Motor Rotary Velocity Regulation Performance Analysis  
#### 5.1.1 Based on PID Algorithm  
When the controlled object is known, there are three methods of PID parameter tuning: Ziegler-Nichols method, ISTE optimal setting method, and experience based method. This manuscript adopts the empirical method to adjust the PID control parameters. For example, when the target rotary velocity is 3000 rpm, \(\mathrm{K_{P}}=16\), \(\mathrm{K_{i}}=1\), \(\mathrm{K_{d}}=0.2\). Figure 15 shows the changes in the motor rotary velocity with the different target rotary velocities when the PID algorithm was applied to the motor rotary velocity control system. As can be seen from Figure 15, the rise time, peak time, and regulation time were long in the process of rotary  
Figure 12: Membership functions of input and output variables. (**a**) Membership function of accelerator pedal opening; (**b**) membership function of change rate of accelerator pedal opening; (**c**) membership function of initial oil pressure change.  
Figure 13: Fuzzy control rule surface of CL2 initial oil pressure change.  
velocity regulation. Thus, the dynamic response effect was poor. The specific law was that, with an increase in the target rotary velocity, the rise time and peak time of the motor rotary velocity regulation remained basically unchanged, while the regulation time and maximum overshoot both gradually increased with an approximately linear growth trend.
###### Abstract  
Different tasks and conditions in gait call for different stiffness of prosthetic foot devices. The following work presents a case study on design modifications of a prosthetic foot, aimed at variable stiffness of the device. The objective is a proof-of-concept, achieved by simulating the modifications using finite element modeling. Design changes include the addition of a controlled damping element, connected both in parallel and series to a system of springs. The aim is to change the stiffness of the device under dynamic loading, by applying a high damping constant, approaching force coupling for the given boundary conditions. The dynamic modelling simulates mechanical test methods used to measure load response in full roll-over of prosthetic feet. Activation of the element during loading of the foot justifies the damped effect. As damping is in contrast to the main design objectives of energy return in prosthetic feet, it is considered important to quantify the dissipated energy in such an element. Our design case shows that the introduction of a damping element, with a high damping constant, can increase the overall rotational stiffness of the device by 50%. Given a large enough damping coefficient, the energy dissipation in the active element is about 20% of maximum strain energy.  
finite element analysis (FEA); dynamic modelling; dampers; mechanical design; stiffness; biomechanics; prosthetics +
Footnote †: journal: Applied  
_Article_  
Use of Dynamic FEA for Design Modification and Energy Analysis of a Variable Stiffness Prosthetic Foot
# Structure-Aware Trail Bundling for Large DTI Datasets
## 3 Method
### DTI Bundling
#### 3.2.3 Tract Reseeding  
Anisotropic bundling (Section 3.2.2) cannot _fully_ prevent the appearance of gaps in fiber manifolds. Indeed, if we set the anisotropic threshold \(\alpha_{0}\) too high, then gaps will be prevented, since only the highest anisotropy fiber fragments, in linear anisotropy regions, will be bundled. However, this causes too little bundling, thus does not decrease occlusion sufficiently. Conversely, of we set \(\alpha_{0}\) too low, then occlusion is strongly reduced, but bundling may also occur in planar anisotropy regions, creating the aforementioned gaps. Tuning \(\alpha_{0}\) to strike the right balance between occlusion reduction and fiber manifold preservation is delicate. Moreover, such manifolds may not be fully captured by the original trail set \(T\), unless a very high seed density is used, which only increases occlusion.  
We solve this problem by _adaptive reseeding_, performed during the bundling process, as follows. After each bundling iteration, we create (seed) new trails in regions of high planar anisotropy \(c_{p}\) and low linear anisotropy \(c_{l}\), where  
\[c_{p}=\frac{2(\lambda_{2}-\lambda_{3})}{\lambda_{1}+\lambda_{2}+\lambda_{3}} \tag{7}\]  
and  
\[c_{l}=\frac{\lambda_{1}-\lambda_{2}}{\lambda_{1}+\lambda_{2}+\lambda_{3}}. \tag{8}\]  
Similar to [PERSON] et al. (2018), we use the constraints \(c_{p}\geq 0.25\) and \(c_{l}\leq 0.2\) to find the seeding region, and trace fibers, randomly seeded at voxels in these regions, until they exit them, i.e., not meet the above-mentioned constraints. Fiber tracking is done using Euler integration along the major eigenvector of the DTI tensor, and trilinear interpolation. Figure 6 shows the effect of reseeding of a relatively small 15 K trail set. Reseeding adds a total of 65 K trails (marked in red) to the original 15 K ones (marked in gray).
# Kinematic Modelling and Motion Analysis of a Humanoid Torso Mechanism
## 3 Results
### Workspace of the Proposed Torso Mechanism  
Whereas the original motion planning was based on a regression between calibration points, the new analytical kinematic model enables a full characterization of the reachable workspace of the robot by defining all the reachable points of the mechanism. By using the forward kinematics in Equation (3), the reachable workspace of the mechanism was evaluated, given the size and motion parameters of the torso prototype in Figure 2, which are reported in Table 2. Once defined the motion limits on angle and direction of bending, as per Table 2, and by assuming a negligible compression of the backbone, the minimum and maximum tendon lengths were computed through Equation (4). The reachable workspace was defined as the geometrical locus of all the points that can be reached by the upper endpoint of the backbone's centreline, and it was computed in MATLAB R2021a as plotted in Figure 5. As expected, for a negligible compression, the operational workspace shape resembles a convex surface.
# A Super-Resolution Reconstruction Driven Helmet Detection Workflow
## 4 Results
### Performance of the Proposed Helmet Detection Method  
We show the detection results produced by different end-to-end workflows in Table 3. Each workflow employs the same YOLOv5 architecture but different input. The Interpolation+YOLOv5 workflow is exactly the same as with normal YOLOv5 since it uses pure interpolation to resize the input images to the standard resolution. The DRN+YOLOv5 workflow is also super-resolution reconstruction driven detection, which consists of the DRN model and YOLOv5. The proposed SR module+YOLOv5 indicates the workflow described in this paper. From Table 3, the combination of the proposed SR module and YOLOv5 achieves the best precision, which means that 88.4% predicted helmets are real helmets. Compared with the other two results, it has the fewest false predictions. However, since precision is normally in contradiction with recall, the recall of the proposed SR module+YOLOv5 lags a little behind that of the DRN+YOLOv5. The AP of the proposed SR module+YOLOv5 is the highest, which indicates it has the best overall performance. It can be seen that the input images produced by the SR module will improve all metrics due to the improvement in image quality. The proposed workflow surpasses the original DRN+YOLOv5 workflow on precision and AP.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Interpolation**} & \multirow{2}{*}{**DRN-S**} & \multirow{2}{*}{**Our Method**} \\ \cline{2-2} \cline{4-5}  & **Nearest Neighbor** & & & & \\ \hline PSNR & 23.716 & 25.277 & 25.343 & 27.964 & 27.991 \\ \hline SSIM & 0.737 & 0.782 & 0.784 & 0.850 & 0.850 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results achieved by different methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Interpolation+YOLOv5 DRN+YOLOv5** & **Proposed SR Module+YOLOv5** \\ \hline
**Precision** & 0.853 & 0.878 & **0.884** \\
**Recall** & 0.632 & **0.716** & 0.715 \\
**AP (\%)** & 0.435 & 0.500 & **0.501** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detection performance of different workflows.  
Figure 4: The comparison of the images produced by different SR method. From left to right, columns (**a**,**c**) indicate the complete images, columns (**b**,**d**) indicate the zoom-in regions defined by the blue bounding boxes. From the first row to the last row: original high-resolution image, images achieved via nearest neighbor interpolation, images achieved via bilinear interpolation, images achieved via bicubic interpolation, images achieved via DRN, images achieved via the proposed SR module.
## 6 Applications and Simulations
### Data Storage and Retrieval
#### 6.2.3 Networks with Multiple Internal Holes  
For networks with multiple internal holes, we can use a similar strategy as described in Section 5. First, we segment the network and apply VHM to map each CCELL into a spherical shell. Next, we modulus the mapped bits of a datum with \(m\) (the number of CCELLs of the network) to get \(i\) and assign this datum to CCELL \(i\). Then, following the same procedure as discussed above, this datum is mapped into a point position within a spherical shell. According to the mapped position, the datum is routed by following our proposed greedy routing scheme in Section 5 to the target location and stored by one of the vertices of the tetrahedron containing this position. It is worth mentioning that the radius, \(r^{\prime}_{i}\), of the inner sphere of each CCELL \(i\) in VHM needs to be broadcast to all the nodes, while the radius of the outer spheres (which is one) is well known by all nodes.
## 3 System Model
### Service Coverage Model
#### 3.3.1 UAV Service Coverage  
Each UAV \(m\in\mathcal{M}\) maintains a circular service coverage area with radius \(R_{m}^{cov}\), within which IoTDs can establish communication links for task offloading. At time slot \(t\), UAV \(m\) operates at position \(\mathbf{L}_{m}(t)=(q_{m}^{X}(t),q_{m}^{Y}(t),q_{m}^{Z})\), where \((q_{m}^{X}(t),q_{m}^{Y}(t))\) denote the horizontal coordinates, and \(q_{m}^{Z}\) represents the fixed flight altitude. For IoTD \(k\) located at \(\mathbf{L}_{k}=(q_{k}^{X},q_{k}^{Y},0)\) to successfully offload tasks to UAV \(m\), the following coverage constraint must be satisfied:  
\[d_{k,m}(t)=\sqrt{(q_{m}^{X}(t)-q_{k}^{X}(t))^{2}+(q_{m}^{Y}(t)-q_{k}^{Y}(t))^{2 }}\leq R_{m}^{cov}. \tag{3}\]  
This constraint ensures that only IoTDs within the UAV's coverage can access its computational resources, directly impacting the feasibility of offloading decisions.
## 3 Laboratory Study
### Testing
#### 3.3.5 Thermogravimetric Analysis (TGA) Test  
Thermogravimetric Analysis (TGA) is one of the methods to study the thermal properties of materials. The sample weight change is measured at a given temperature and time while the temperature is rising under a constant heating rate. The sample could lose weight due to the decomposition and debonding of the structural components or gain weight due to an instant gas reaction. Thermograms of TG and derivatives thermogravimetric (DTG) provide valuable information. DTG curves are the first differential of TG curves, which is an indicator of the degradation rate at a given temperature. Different interpretations and multifunctional analyses can be made from this information to investigate thermal characteristics of the material. The thermal and oxidative stability, durability of material against temperature, determining the multi-component material according to degradation stages, kinetic reaction, amount of volatile component, and moisture, are the most crucial parameters that could be identified. In this laboratory study, the TGA test was conducted with the STA8000 PerkinElmer's device under air atmosphere for Kraft lignin powder and bitumen samples. The testing temperature range was considered from room temperature to 1000 \({}^{\circ}\)C, with a heating rate of 10 \({}^{\circ}\)C/min and a gas flow of 50 mL/min.
## 6 Glycerol as Solvent and Reductant for the Formation of Metal Nanoparticles  
During the last decade there has been extensive research into the synthesis of metal nanoparticles by the so-called polyol process, where a polyalcohol, generally coupled with a base, acts not only as a solvent but also as a mild reducing agent for the reduction of metal salt precursors [63]. In this context, glycerol has been successfully employed in the preparation of a variety of Ag [64; 65; 66; 67; 68; 69; 70; 71], Tl [64], Pd [67; 72], Pt [67; 73; 74], Ru [67], Au [75], Pt-Ru [74], Pt-Pd [76; 77; 78] and Co-Se [79] nanoparticles with various morphologies. Similarly, uniform Mn(OH)\({}_{2}\) and MnCO\({}_{3}\) nanocrystals with diameters of _ca._ 20 and 60 nm, respectively, and VO\({}_{2}\)(B) nanoflakes could be obtained from KMnO\({}_{4}\)[80] and V\({}_{2}\)O\({}_{5}\)[81] using glycerol as solvent and reductant.  
Quite recently, diameter-controlled (50-200 nm) Ag nanowires were also fabricated from AgNO\({}_{3}\) in glycerol under non-stirred microwave irradiation conditions [82]. Sodium dodecyl sulphate (SDS) was used to prevent the growth of the diameter, which could be finely controlled by changing the amounts of AgNO\({}_{3}\), glycerol and SDS.  
We must note that in comparison with more classical polyols, such as ethylene glycol or propylene glycol, the use glycerol as solvent and reducing agent for metal nanoparticles formation is clearly more convenient because of its lower cost and toxicity.
## 2 Multi-Winner Voting
### Voter Fairness in Ranking-Based Voting
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(q_{\text{H}}\)**-PSC** & \(q_{\text{D}}\)**-PSC** & **Weak \(q_{\text{H}}\)-PSC** & **Weak \(q_{\text{D}}\)-PSC** & **Complexity** \\ \hline \(k\)-Borda & N [17] & N [17] & N [17] & N [17] & P (trivial) \\ Bloc & N [17] & N [17] & N [17] & N [17] & P (trivial) \\ SNTV & N [17] & N [17] & N [17] & N [17] & P (trivial) \\ CC & N [17] & N [17] & N [17] & N [17] & NP-complete [20] \\ Monroe & N [17] & N [17] & N [17] & N [17] & NP-complete [21] \\ H-STV & Y [16] & Y [16] & Y [16] & P (trivial) \\ D-STV & Y [16] & Y [16] & Y [16] & P (trivial) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A summary of the PSC properties satisfied by several important multi-winner voting rules and the complexity of computing a winning committee with respect to these rules. In the table, “N” means that the rule in the corresponding row does not satisfy the property in the corresponding column, and “Y” means that the rule satisfies the property. Observing that weak \(q\)-PSC is a too strong property for many rules to satisfy, [PERSON] et al. [17] studied three weak versions, namely, solid coalitions, consensus committee, and unanimity. They showed that each of SNTV, Bloc, \(k\)-Borda, CC, and Monroe fails at least one of these weak versions, and these results imply the ones for these rules in the table.
## 4 Modeling and Results
### Results
#### 4.3.5 Statistical Significance of Performance Improvements  
The statistical significance of our accuracy improvements compared to those of previous state-of-the-art methods was evaluated using binomial tests. Because our methodology involves fine-tuning pretrained models, precluding variance estimation through repeated runs from random initializations, the binomial test serves as a suitable alternative. This test allows for comparing the final accuracy scores based on the respective test set sizes to determine if observed differences are statistically significant [37].  
Performing the binomial test for the model trained with Algorithm 2 in the task of recognizing 150 kanji resulted in an extremely low \(p\)-value (approximately \(2\cdot 10^{-37}\)). This value falls well below typical significance thresholds (e.g., \(\alpha=0.001\)), strongly indicating the statistical significance of the achieved results.  
Additionally, we calculated approximate 95% confidence intervals for our accuracy measurements using the Agresti-Coull method [38], which is well suited for proportions, like accuracy.  
The non-overlapping confidence intervals shown in Table 8 further support that our improvements are statistically significant, confirming that our cross-language transfer-learning approach provides meaningful enhancements in kanji recognition accuracy.
# Formation Control and Distributed Goal Assignment for Multi-Agent Non-Holonomic Systems
## 3 Stability of the System  
In this section stability analysis of the closed-loop system is presented. When the \(i\)-th robot is out of the collision regions of the other robots (APF takes the value zero) the analysis given in [4] is actual and will not be repeated here. Further the analysis for the situation in which the \(i\)-th robot is in the collision region of other robot is presented. For further analysis a new variable is introduced: \(\theta_{iE}=Atan2(-E_{iy},-E_{ix})\) (\(Atan2(\bullet,\bullet)\) is a version of the \(Atan(\bullet)\) function covering all four quarters of the Euclidean plane)--auxiliary orientation variable. Figure 3: Control system. **Proposition 1**. : _The system (1) with controls (16) is stable if the desired trajectories fulfil the condition \(\theta_{iE}\
otin\left\langle\frac{\pi}{2}\pm\theta_{EA}\pm\pi d\right\rangle\) (\(d=0,\pm 1,\pm 2,...\)), where \(\theta_{EA}\) is a small constant._  
As stated in [7], if \(\theta_{iE}\in\left\langle\frac{\pi}{2}\pm\theta_{EA}\pm\pi d\right\rangle\) (the combination of obstacle position and reference trajectory drive the robot into the neighbourhood of a singular configuration where the condition in Proposition 1 does not hold) one solution is to add perturbation to the desired signal. The system can also leave the neighbourhood of the singularity easily since the robot can reorient itself in place if the condition is not satisfied. This requires a special procedure to be implemented, which will not be discussed here. Proof. : Consider the following Lyapunov-like function:  
\[V=\sum_{i=1}^{N}\left[\frac{1}{2}(e_{ix}^{2}+e_{iy}^{2}+e_{i\theta}^{2})+\sum_ {j=1,j\
eq i}^{N}V_{aij}\right]. \tag{20}\]  
When the robot is outside of the collision avoidance region, i.e., \(l_{ij}\geq R_{j}\), the system is equivalent to the one presented in [4] (robot moving in a free space) and stability analysis presented in this paper still holds.
## 2 Mobility Models in FANETs
### Random Mobility Models  
The most popular models used in network research are randomized mobility models. They depict a group of movable nodes with independent actions that can be easily implemented with several models. However, these types of mobility models were unable to properly replicate the actual behavior of the UAV due to the abrupt changes in the UAV's speed and direction, as well as considering only two-dimension movement.
## 3 Current Developments  
In 2007, early questions were raised about the usefulness of VGI in science [66]. While a number of publications highlighted that the credibility and reliability of VGI could be questionable [48, 67], more recently, researchers focused on the actual quality analyses of the VGI datasets [19, 21, 68]. These first research contributions were followed by studies about trust [69, 70], contributor behavior [23, 71] and gender distributions in VGI projects [72, 73]. The goal of the following three sections is to give a comprehensive and detailed overview about VGI research progress in recent years with the focus on OSM.
## 3 Methodology
The GAN used can be described as follows. Let \(G:N\to S\) represent the mapping performed by the GAN, with \(N:\mathcal{N}(0,1)\) an input generated from a random-intensity pixels distribution and \(S\) being an output image. According to the original definition of GAN [31], the GAN plays a zero-sum game, where the generator is trained to make more and more realistic images and the discriminator is trained to differentiate between synthetic and real input images. Both the generator and the discriminator are Convolutional Neural Networks (CNN), in order to enable the generation and recognition of images. The classification task studied in this paper \(C:I\to L\) is a binary task: the target is to determine if an input image shows a patient with a diagnosis 'pneumonia' or a patient without one, 'healthy'. To assess the accuracy of the classification \(C\), we use a direct accuracy measure. If the classification can be expressed as \(a(i)=l\) with \(a\in\mathbb{A}\) and \(i\in I\) and \(l\in L\), then the accuracy measure for a single Algorithm is shown Equation (1). \[acc(a)=\frac{\sum_{h=1}^{n}a(i_{h})==l_{h}}{n} \tag{1}\]  
where \(l_{h}\in L\) is the ground truth for the input image \(i_{h}\) and \(n\) is the number of samples in the dataset under evaluation. This accuracy is the ratio of correctly classified images over the total number of images under evaluation. When evaluating the Algorithm selector approach, the Equation (1) is modified to take into account the Algorithm selection process. First, let \(a^{h}\in\mathbb{A}\) be the Algorithm selected by the Algorithm selection for the input sample \(i_{h}\in I\). Second, let the Algorithm selection be described by \(s(i_{h},\mathbb{A})=a^{h}\). Finally, let the accuracy for the Algorithm selector can be written as in Equation (2). \[acc(s)=\frac{\sum_{h=1}^{n}a^{h}(i_{h})==l_{h}}{n} \tag{2}\]  
Note that the mapping \(S:\mathbb{I}_{S}\rightarrow\mathbb{L}_{S}\) is purposefully abstracted in order to allow the evaluation of the Algorithm selector using the image classification accuracy measure. This means that we do not directly measure the Algorithm selector accuracy with respect to the labels \(\mathbb{L}_{S}\) but rather we measure the final classification accuracy over the set of image labels \(L\) by implicitly learning the labels \(\mathbb{L}_{S}\) maximizing the classification accuracy. In general, we will refer to accuracy from Equations (1) and (2) by \(\mathcal{A}\). We use different Algorithms on the same dataset to obtain different Algorithm bias instead of a different data bias. Each Algorithm is trained according to its specific training requirements. In most of the cases, the classification Algorithms are CNN based.
# A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data
## 4 Examples
### Validation with E-SAR P-Band Data  
#### 4.2.1 Study Area and Data Sets  
The proposed TSVD-based method was also applied to E-SAR P-band PolInSAR data, which were collected under the framework of the BioSAR 2008 campaign by the German Aerospace Center, Munich, Germany. The test site is a forest area within the Krycklan River catchment in Northern Sweden, and is mainly covered by mixed boreal forest with heights ranging from 0 to 35 m. The topography elevation is between 150 to 380 m above mean sea level (AMSL). The baseline PolInSAR data were acquired in the repeat-pass configuration. Moreover, as part of the BioSAR2008 campaign, a light detection and ranging (LiDAR) measurement was also obtained by the Swedish Defense Research Agency (FOI). The derived forest height is regarded as the reference in the following analysis.  
In this experiment, the forest heights were extracted from single-baseline data. The temporal and spatial baselines were 70 min and 32 m, respectively. The vertical wavenumber ranged from 0.051 to 0.181.The Pauli-basis RGB composite intensity image for the test site is shown in Figure 5.  
Figure 4: Root-mean-square errors (RMSEs) of each forest stand.  
Figure 3: (**a**) forest height inversion result of the three-stage method; and (**b**) forest height inversion result of the truncated singular value decomposition (TSVD)-based inversion method.