esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Custom Insoles on Muscle Activity in Diabetic Individuals with Neuropathy
## 1 Introduction
Offloading is one of the crucial aspects of the treatment aiming to redistribute pressure away from the ulcer site [18], to reduce further tissue trauma, and to facilitate the wound healing process [17; 18; 19]. It can be achieved through casting, footwear, surgical interventions [20], shoe modifications, orthotic walkers, and other offloading techniques [21; 22]. In particular, [PERSON] et al. demonstrated that forefoot plantar pressure can be reduced by removing plugs and adding arch support to foam-based insoles in PN individuals [20]. With this said, the most common outcome measures to determine the neuromuscular and biomechanical effects of foot orthosis or insoles are lower limb kinematics, kinetics, plantar pressure, and surface electromyography (sEMG) [23; 24; 25], of which plantar pressure is the most commonly used [26; 27]. On the side of these more common measures, finite element modelling [28; 29] and ergonomic evaluations [30] have been applied to plan and test the effects of different insoles, not only on diabetic foot individuals, but also on healthy subjects. In the latter case, comfort assessment questionnaires were adopted to complement the electromyographic and plantar pressure measures [30]. In this scenario, to the best of the authors' knowledge, no previous studies investigated the effect of custom insoles on PN individuals by combining dynamic plantar pressure with surface electromyography during gait. Based on this evidence, the aim of this study was to investigate whether the biomechanical effects of Custom Insole (CI), measured in terms of dynamic plantar pressure and ground reactions forces, are associated with the neuromuscular effects, in terms of sEMG, in subjects at risk of diabetic foot.
## 3 Calculation
### Calculation of the Interpolation Parameters  
The interpolation parameters were calculated based on the adopted theoretical variograms. The length and direction of semi-major and semi-minor axes in the ellipse, number of sectors, partial sills, interpolation points in sectors, and maximum and minimum neighbors were analyzed in each set. Standardized root mean square (RMS) and the average standard deviation of prediction (\(\sigma\)) were the main selection criteria. The relationships between changes in the semi-major and semi-minor axes vs. RMS and \(\sigma\) are presented in Figures 5 and 6, respectively. The values of RMS and \(\sigma\) were closest to 1 when the semi-major axis and the semi-minor axis were increased six-fold. The influence of the number of lags on the values of the direction, partial sill, predicted value, RMS, and \(\sigma\) for dataset A is presented in Figure 7. The selection of four sectors in all datasets supported the calculation of the remaining parameters.  
The value of RMS was stabilized with a multiplier higher than 10 (Figure 6). The direction changed in a non-continuous manner with an increase in the number of lags.  
The values of the nugget effect, anisotropy, direction, and partial sill in each dataset are presented in Table 3.  
The values in Table 3 confirm the anisotropy and the nugget effect. Datasets A and E were the most anisotropic, which points to the presence of factors that strongly influenced the vertical movements. Minor nugget effects were observed in datasets A, B, C, and E (proximal points with different values of vertical movements were rarely encountered), which could be attributed to the combined adjustment of data. The nugget effect was greater in dataset D, which could be explained by the presence of GNSS stations where vertical movements differed considerably from those in other stations for geological and technical reasons. In datasets merged using co-kriging, anisotropy was observed only when vertical movements were based on leveling data only. Datasets were isotropic in the remaining two cases. The nugget effect was nearly four times greater in dataset BD than in dataset DB. Semivariance values were highest in the dispersed datasets A and E.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multicolumn{2}{c}{**Nugget Efect**} & \multirow{2}{*}{**Anisotropy**} & \multirow{2}{*}{**Direction**} & \multicolumn{2}{c}{**Partial Sill**} \\ \cline{2-2} \cline{5-7}  & **[1][0]** & **[1][1]** & & & **[1][0]** & **[0][1]** & **[1][1]** \\ \hline A & & 0.01 & 2.44 & 10.01 & & 0.39 \\ \hline B & & 0.05 & 1.83 & 1
## 4 Numerical Results and Discussion
### Dependence of the Magnetization in CBCO on the Dopant Concentration at the Ca Site  
It is worth mentioning that ion substitution can also occur at the Ca sites within CBCO, which is a topic that we will briefly address. The ionic radius of the Ca\({}^{2+}\) ion is 1.14 A, and when doped with ions such as Y\({}^{3+}\) (\(r\) = 1.04 A) or Sr\({}^{2+}\) (\(r\) = 1.18 A), different strain effects emerge. Specifically, when Y is substituted, resulting in a smaller ionic radius than that of Ca, a compressive strain (where \(J_{d}>J\)) ensues, whereas with Sr doping, which possesses a larger radius than Ca, a tensile strain (where \(J_{d}<J\)) arises. Consequently, within our model, Y doping at the Ca site is anticipated to enhance magnetization, \(M\), while Sr doping is expected to reduce \(M\), aligning with experimental observations by [PERSON] et al. [25] and [PERSON] et al. [26], respectively. These predictions are illustrated in Figure 7, curves 1 and 2. Additionally, it is noteworthy that the extent of magnetization modification resulting from Ca site substitution is comparatively smaller than that observed with Co site substitution.  
Figure 6: Temperature dependence of the specific heat \(C_{p}\) of CBCO for different Fe dopant concentrations \(x\): (1) \(x\) = 0, (2) 0.1.  
Figure 7: Dependence of the magnetization on the dopant concentration \(x\) of CBCO for different doping ions at the Ca site: (1) Y, (2) Sr.
## 4 System Model
### BER in Concatenated ASTBC and BCH on Node j  
In this section the closed-form expression of BER of ASTBC using BCH coding is derived. The probability of error on Node j using ASTBC and BCH can be written as  
\[P_{ej}=\int_{0}^{+\infty}~{}P_{BCH}.P_{ASTBC}.d\gamma \tag{15}\]where \(P_{BCH}\) is given in expression (4). The probability of error in ASTBC (\(P_{ASTBC}\)) can be written as  
\[P_{ASTBC}=\frac{1}{2}-\frac{1}{2}.(1+\frac{1}{\overline{\gamma}})^{-1/2}\]  
Plugging above expression and expression (4) in expression (15) yields  
\[P_{ej}=\int_{0}^{+\infty}Q\sqrt{2.R_{c}.\gamma}.(\frac{1}{2}-\frac{1}{2}.(1+ \frac{1}{\overline{\gamma}})^{-1/2})d\gamma \tag{16}\]  
Using the substitution used in previous section where  
\[Q\sqrt{2.x}=Q(x)\]  
in expression (16) can be written as  
\[P_{ej}=\int_{0}^{+\infty}Q(2.R_{c}.\gamma).(\frac{1}{2}-\frac{1}{2}.(1+\frac{ 1}{\overline{\gamma}})^{-1/2})d\gamma \tag{17}\]  
Converting Q function into the error function yields  
\[P_{ej} =\int_{0}^{+\infty}\frac{1}{2}.erfc(\frac{2.R_{c}.\gamma}{\sqrt{ 2}}).(\frac{1}{2}-\frac{1}{2}.(1+\frac{1}{\overline{\gamma}})^{-1/2})d\gamma\] \[=\int_{0}^{+\infty}\frac{1}{4}.erfc(\frac{2.R_{c}.\gamma}{\sqrt{ 2}})-\int_{0}^{+\infty}(\frac{1}{4}erfc(\frac{2.R_{c}.\gamma}{\sqrt{2}}). \tag{18}\] \[(1+\frac{1}{\overline{\gamma}})^{-1/2})d\gamma\]  
By using the relation described in expression (10), i.e.,  
\[\int_{0}^{+\infty}\frac{1}{2}.erfc(2.R_{c}.\gamma/\sqrt{2})=\frac{1}{2}.\frac {1}{\sqrt{pi}.R_{c}.2/\sqrt{2}}\]  
expression (18) can be written as  
\[Pej=\frac{1}{4}.\frac{1}{\sqrt{pi}.R_{c}.2/\sqrt{2}}-\frac{1}{4}.\frac{1}{ \sqrt{pi}.R_{c}.2/\sqrt{2}}.(1+\frac{1}{\overline{\gamma}})^{-0.5} \tag{19}\]  
Expression (19) gives us a new tractable closed-form expression of BCH in ASTBC.
# Performance Evaluation of an Imaging Radiation Portal Monitor System
## 2 Materials and Methods
### Detection System
The PSD algorithm for distinguishing gamma-ray interactions from those of fast neutrons in the scintillators is based on the standard charge comparison method, illustrated in Figure 2. Energy information for each trace is extracted using a moving window de-convolution algorithm and the timing of the detector signals is extracted using a digital constant fraction discrimination (CFD) algorithm. The triggerless data acquisition system provides information on individual gamma-ray and neutron energies, timing, and the detector that registered the interaction. Standard RPM observables such as single-gamma and neutron detection rates as well as coincidence rates for gamma-gamma, neutron-neutron, and gamma-neutron events can be derived in real time from the data stream. Typical coincidence time windows are 0-100 ns for gamma-gamma and neutron-neutron events, and 10-100 ns for gamma-neutron events, and can easily be adjusted in the software. Figure 1: Schematic illustration of the RPM prototype system. In the current configuration, the detector assemblies cover approximately a 100 cm vertical range while the horizontal distance between their front faces is 100 cm as specified in the ANSI N42.35-2016 RPM industry standard. Figure 2: Pulse-shape discrimination plot for one of the eight detector cells in the RPM. The PSD parameter (vertical axis) is the ratio of signal tail charge integral and total charge integral, while the full integrals of the PMT signals are plotted on the horizontal axis. The signals saturate at around 7 MeVee. The number of counts are indicated by the color scale shown on the right side of the plot. Gamma rays and neutrons were selected by choosing events within the regions indicated by the red and black lines, respectively.
# Effect of Working Atmospheres on the Detection of Diacetyl by Resistive SnO2 Sensor
## 3 Results and Discussion
### Gas Sensor Measurements
#### 3.2.6 Response and Recovery Time in Alcoholic Diacetyl Solution  
Response and recovery times of diacetyl in alcoholic solution under a CO\({}_{2}\) atmosphere are analyzed, being important to understand the performance of the sensor.  
Figure 6a shows response/recovery times of the SnO\({}_{2}\) sensor in different diacetyl concentrations. Their analysis shows that for all the evaluated diacetyl concentrations, recovery time is higher than response time. Response and recovery time values decrease with an exponential law upon increasing the concentration; response time is almost halved, from 3.2 min to 1.7 min with a diacetyl increment from 0.4 mg/L to 3.2 mg/L. Analogously, the recovery time decreases from 6.6 min to 2.2 min in the same working conditions. According to literature data [42], basic effects of surface covering kinetics and diffusion may be the cause of a dependence on concentration change. Moreover, since the semiconductor can be considered as an RC filter with a time constant \(\tau\), electronic effects may have influence too. Due to the increasing of diacetyl concentration, the resistance decreases and thus also the time constant.  
Figure 5: Sensor response in CO\({}_{2}\) atmospheres at different diacetyl concentrations in (**a**) aqueous solution and (**b**) alcoholic solution. Error bars are calculated on three runs.  
Indeed, the sensor resistance as a function of time during the exposure to the analyte and during the recovery can be described through the following equations [42]:  
\[\mathrm{R(t)}=\mathrm{R_{max}}-\left(\mathrm{R_{max}}-\mathrm{R_{0}}\right)\exp \left[\frac{-\mathrm{t}}{\tau_{\mathrm{resp}}}\right], \tag{1}\]  
\[\mathrm{R(t)}=\mathrm{R_{0}}+\left(\mathrm{R_{max}}-\mathrm{R_{0}}\right)\exp \left[\frac{-\mathrm{t}}{\tau_{\mathrm{rec}}}\right], \tag{2}\]  
where R(t) is the resistance after the analyte exposure, \(\mathrm{R_{0}}\) is a constant, \(\mathrm{R_{max}}\) is the saturated resistance, t is the time, and \(\mathrm{\tau_{resp}}\) and \(\mathrm{\tau_{rec}}\) are the response and recovery time constants, respectively. Figure 6b shows the plot of time constants as a function of diacetyl concentration. The curves show that the time constants decreased as diacetyl concentration increased. According to response and recovery time analysis, the range of \(\mathrm{\tau}\) values recorded at low diacetyl concentration is much wider than that recorded at high diacetyl concentration, where \(\mathrm{\tau}\) is less than 50 s.  
The parameter \(\mathrm{\tau}\) is therefore considered a more suitable parameter to characterize sensors response and recovery times.
# Acceleration of a Production-Level Unstructured Grid Finite Volume CFD Code on GPU
## 2 Unstructured Grid Finite Volume Method (FVM) CFD Solver
### Data Racing Due to Irregular Memory Access Pattern  
The computations on unstructured grids are loops over a set of grid entities. If a loop over a set only writes the data defined on that set, then the loop can run in parallel. However, if there is indirect access to data, there may be situations where iterating from multiple entities simultaneously updates the same memory location value, leading to data race issues. This kind of loop with indirect memory access is common in CFD applications based on unstructured grids, such as the convective flux evaluation (Algorithm 1).  
```
0: Pointers from face to cell \(f2 cl\), \(f2 cr\). Flow variables \(q\).
0: Right hand side residuals \(RHS\)
1:for\(face\) = 0 to \(nFaces\)do
2:\(c_{0}\) = \(f2 cl[face]\)
3:\(c_{1}\) = \(f2 cr[face]\)
4:\(F_{ij}\) = InvicidFlux(\(q[c_{0}]\), \(q[c_{1}]\),...)
5:\(RHS[c_{0}]\) = \(F_{ij}\)
6:\(RHS[c_{1}]\) += \(F_{ij}\)
7:endfor
```  
**Algorithm 1** Calculation of convection flux based on face-based loop  
In Algorithm 1, the face-based loop follows a _gather-scatter_ memory access pattern. Firstly, obtain the flow variables from a face's two adjacent cells (line 2 and line 3). Then calculate the fluxes by calling the inviscid flux-computing kernel (line 4). Finally, update \(RHS\) in cells using the computed fluxes (line 5 and line 6). Because one cell has multiple faces, simultaneous updates variables on it from different faces may lead tio data races. Take Figure 1 as an example. \(f_{0}\), \(f_{1}\), and \(f_{2}\) belong to different threads, after completing flux calculation separately, simultaneously updating the value of \(c_{0}\) will lead to data racing.
# Resilient Formation Reconfiguration for Leader-Follower Multi-UAVs
## 1 Introduction
Until now, BRB has been applied to many fields successfully, including fault diagnosis [21; 22; 23; 24], performance assessment [25], and risk analysis [26]. Therefore, it is reasonable to introduce the BRB technique into the leader election link of the formation reconfiguration strategy for leader disengagement. Motivated by the above observations and analyses, the research on formation reconfiguration for leader-follower multi-UAVs triggered by changes to the swarm scale are carried out in this article. In addition, a knowledge-based performance assessment model for UAVs is established for electing a new leader. The main contributions of this article can be summarized as:  
(1) Focused on three scenarios of the formation reconfiguration problem for leader-follower multi-UAVs with a changeable swarm scale--namely, leader disengagement, follower detachment, and new member addition--three reconfiguration strategies are developed, which include most of the existing studies [27; 28] on fixed swarm scales as special cases. (2) With respect to the communication topology adjustment link of the formation reconfiguration for leader-follower multi-UAVs with a dynamic swarm scale, a node-number-based strategy is proposed with the aim of minimizing the frequency of connection changes and making the formation reconfiguration process more efficient and resilient, especially in the case of large swarm scales. (3) Targeting the leader election link of the formation reconfiguration strategy for leader disengagement, a hierarchical-BRB-based performance assessment model is established with two kinematic pivotal performance metrics and two non-kinematic pivotal performance metrics as the inputs and the final performance assessment score as the output. The rest of the article is arranged as follows. Related works are discussed in Section 2. In Section 3, the kinematic models of the leader-follower multi-UAVs and the main objectives of the article are clarified. In Section 4, the construction of the hierarchical BRB performance assessment model for UAVs is detailed. The formation control protocol and three formation reconfiguration strategies are outlined in Section 5, and their effectiveness is demonstrated in Section 6. The article is concluded in Section 7, which also proposes some directions for future studies.
## 3 Materials and Methods
### Hand-Crafted Feature Extraction  
To enhance robustness and interpretability, hand-crafted features were used alongside CNN-based deep features. These methods capture low-level image characteristics that may be overlooked by deep models, particularly fine-grained textural and color variations caused by pest infestation.  
Three techniques were employed: Gabor filters for texture, Gray Level Co-occurrence Matrix (GLCM) for spatial relationships, and Hue-Saturation-Value (HSV) color space for color variation--each contributing critical cues for identifying pest-induced damage.
# Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients
###### Abstract  
Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.  
clustering technique; fuzzy clustering; fuzzy C-means clustering; fuzzification coefficient; objective function; performance indices; clustering efficiency; machine learning +
Footnote †: journal:  
_Article_
## 4 Task Allocation Based on CEAGA in IUWSNs  
To address the task allocation problem in IUWSNs, this paper devises the optimization scheme of the CEAGA. Through literature such as [10; 11; 13; 15], it can be found that genetic algorithms have solved many similar problems. This article utilizes an improved sine chaotic operator to initialize the population, expanding the solution space and accelerating the global search ability of the CEAGA. At the same time, elite operators are used to select the best individual in the population, which speeds up the rate of convergence of the algorithm. Finally, adaptive operators are used to adaptively adjust the crossover and mutation probability of the genetic algorithm, so that the algorithm can avoid falling into local optimization. The advantages of chaos operator, elite operator and adaptive operator are combined by the GA. By employing this scheme, the CEAGA effectively addresses the task allocation problem while effectively circumventing the search for local optima. Figure 2 is the flow chart of the CEAGA.  
1. The objective function is set, the population is initialized by chaos algorithm, and the parameters are set.
2. Select superior individuals as parents to feed into the stochastic operation.
3. Apply crossover and mutation between the picked individuals and the rest of the population.
4. Check and evaluate all the result function values of each individual.
5. Let new individuals compete to build an advantage. Continue operations 2 through 5 until the evolution process satisfies the termination criteria. The remaining paragraphs describe a general discussion of the CEAGA.
# Topology and Parametric Optimization-Based Design Processes for Lightweight Structures
## 4 Results
### L-Bracket  
The second case study in this paper is a simple L-Bracket, as shown in Figure 14. The optimization procedure of the L-Bracket is identical to the Hollow Plate's case. The same material, mesh type, and element size were also used here. Concerning the boundary conditions, the L-Bracket is fixed on the top, and a vertical force F = 250 N is applied on its right side. The used factors in the size/shape parametric optimizations, as well as their allowable value range, are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Description** & **Initial Value (mm)** & **Range (Step) (mm)** \\ \hline t1 & thickness 1 & 20 & 10–30 (5) \\ \hline l1 & length 1 & 100 & 50–150 (5) \\ \hline t2 & thickness 2 & 20 & 10–30 (5) \\ \hline l2 & length 2 & 100 & 50–150 (5) \\ \hline w & width & 30 & 10–50 (5) \\ \hline r & Radius of fillet & 5 & 1–19 (1) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The used design parameters (factors) in the optimization of the L-Bracket, their description, initial value, allowable range, and value increment (step) in parentheses.  
Figure 14: **(a)** The initial design of the L-Bracket, as well as the finite element model, (**b**) the FEA of the L-Bracket (\(\sigma_{\text{max}}\) = 19.7 MPa), and (**c**) the design space for the TO.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Parameter** & **Initial** & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** & **(7)** & **(8)** & **(9)** & **(10)** \\ \hline Mass (g) & 1103.5 & 450.7 & 448.1 & 285.9 & 101.5 & 66.6 & 65.6 & 55.5 & 45.0 & 39.8 & 34.7 \\ \hline Max Stress (MPa) & 34.7 & 120.1 & 60.2 & 109.4 & 55.7 & 94.8 & 106.5 & 115.4 & 75.2 & 115.4 & 114.6 \\ \hline Optimization time (min) & - & 5.5 & 20.1 & 70.4 & 75.9 & 76.9 & 86.8 & 124.7 & 275.5 & 285.5 & 323.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of all design processes of the Hollow Plate optimization.  
In addition, the following geometrical rule was defined here:  
\[0<r<(l1-t1+l2-t2)/2 \tag{21}\]  
Figure 15 illustrates the design solutions from all the implemented design processes presented in a \(3\times 4\) matrix. The lines represent the three workflows and the columns the optimization levels. In this way, the reader can easily track the design processes and their solutions. For example, the element 2,2 of the matrix is the design solution that resulted by the PO_TO_NR design process. In addition, the element 1,1 is the initial design of the L-Bracket. Finally, the element 3,1 is the optimized design by the PO within the simultaneous PO and TO workflow.  
Table 5 contains the results of the mass, maximum stress, and optimization time in the ten applied design processes. It seems, also in this case, that the mass is decreasing while the optimization time is increasing as we are going from design process (1) to (10).  
Figure 15: The design solutions of the L-Bracket.