esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of an Anthropomorphic Prosthetic Hand with Underactuated Mechanism
## 3 Analysis of The Finger Module
### FEM Analysis  
In the design of an underactuated hand mechanism using gears and linkages, the components of the hand are designed in small size. Particularly, the thicknesses of frames composing the four-bar linkages in the suggested model were designed to be thin. Thus, verification of the configuration of the design components in the desired conditions should be performed. In this section, finite element method (FEM) analysis of the finger module was performed to investigate the stress distribution and deflection. Above all, the mechanical parts such as shafts and bearings and the other components were set to stainless steel, steel, and aluminum 6061, respectively. Then, the analysis condition was given such that external force was applied along the \(y\)- and \(x\)-coordinate axes at the fingertip, respectively, as shown in Figure 6. The analysis configuration of the finger module is the shape after finishing self-adaptive grasping because the magnitude of external force applied to the finger module is the largest in this configuration. The magnitude of the applied external force is 9 N, which is the magnitude implying a safety factor of 3.  
Figure 6 shows the results of the FEM analysis. Figure 6a,b represents the stress distribution and deflection, respectively, in each condition. In analyses of the stress distribution in each case, the maximum stresses are produced at the linkage frame of the four-bar in the proximal phalanx; these are represented by red arrows in Figure 6a, and the maximum stresses in each case are \(54.4\times 10^{6}\) N/m\({}^{2}\) and \(157\times 10^{6}\) N/m\({}^{2}\), respectively. Because the linkage part producing the maximum stress is fabricatedusing steel and the tensile strength of steel is 620 MPa, the prosthetic hand is designed for safety and can be used with adequate rigidity in the desired conditions.  
Furthermore, in Figure 6b, which is the results of the deflection analysis, the maximum deflections in the two conditions are 0.055 mm and 0.151 mm, respectively. Thus, the developed prosthetic hand can perform the desired motions suitably.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Technique** & **No. of Input Variables** & **No. of Datasets** \\ \hline [PERSON] et al. [70] & ANFIS & 2 & 44 \\ [PERSON] et al. [68] & ANN & 4 & 182 \\ [PERSON] et al. [1] & ANN & 2 & 130 \\ [PERSON] [31] & ANN, FIS & 2 & 162 \\ [PERSON] et al. [69] & FIS & 2 & 33 \\ [PERSON] et al. [72] & SVM & 2 & 32 \\ [PERSON] et al. [71] & SVM, ANN & 2 & 37 \\ [PERSON] et al. [73] & FIS & 6 & 120 \\ [PERSON] et al. [74] & ANN & 3 & 20 \\ [PERSON] et al. [4] & PSO–ANN & 9 & 44 \\ [PERSON] et al. [29] & ICA–ANN & 7 & 95 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the previous studies on peak particle velocity (PPV). ANFIS—adaptive neuro-fuzzy inference system; ANN—artificial neural network; FIS—fuzzy inference system; SVM—support vector machine; PSO—particle swarm optimization; ICA—imperialism competitive algorithm; CART—classification and regression trees; GEP—gene expression programming. Despite the vast use of soft computing and ML techniques to predict PPV, a very limited number of studies are available that investigated the use of decision trees to predict PPV [68; 73]. To this end, this study develops three decision tree-based models to find the best modeling approach. These models are classification and regression trees (CART), chi-squared automatic interaction detection (CHAID), and random forest (RF). To conduct a more comprehensive comparison, two other ML techniques that showed a desirable performance in the previous studies on PPV prediction were added to this study. This paper continues with brief explanations of the methods used and the case study. Then, the comparison results are presented. Finally, this paper ends by presenting some conclusions, limitations, and suggestions for future studies.
## 3 Results
### Simulation-Based ART Training Data Generation
A similar offset behavior between the simulations and the measurements was found for all of the parts, which clearly depends on the eigenmode. This indicates that an easy multiplicative adaption via mode-specific correction factors is suitable. Table 2 shows the correction factors (measured data divided by simulated data), which were averaged from the reference parts depending on the eigenmode and finally applied to the synthetic training data. Although the five reference parts show similar deviations between the simulation and the measurement data, the deviations are not exactly the same. For analyzing the quality of the correction factor method, one reference part was excluded and the correction factors were averaged again from the remaining parts. Then, the factors were applied to the simulated eigenfrequencies of the excluded part. Finally, the adapted simulation data of the excluded part were evaluated again versus its measured frequencies, which led to small residuals. Figure 9 shows this for the five reference parts, which are indicated with the letters A to E. The data transformation from the simulation to the measurement world is not perfect, but the remaining differences of the adapted simulation data to the measured values are, in nearly all of the cases, in terms of amounts smaller than 0.1%, indicating a good performance of the correction factor method. To cover an imperfect data adaption by the correcting factors, three scenarios for the synthetic ART training data were subsequently considered. The synthetic training data were used directly for the ART training procedure. Additionally, uniformly distributed random noise was applied to each frequency value of the synthetic training data, either in the relative range from \(-0.1\%\) to \(+0.1\%\), or from \(-0.3\%\) to \(+0.3\%\), respectively. This was executed to represent the uncertainty of the simulation data adaptation to the measurement world, in an order of magnitude as it was observed or in an exaggerated way. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mode** & **Correction** & **Mode** & **Correction** & **Mode** & **Correction** \\
**no.** & **Factor** & **no.** & **Factor** & **no.** & **Factor** \\ \hline
1 & 1.0272 & 7 & 1.0203 & 13 & 1.0202 \\
2 & 1.0073 & 8 & 1.0089 & 14 & 1.0122 \\
3 & 1.0238 & 9 & 1.0148 & 15 & 1.0199 \\
4 & 1.0256 & 10 & 1.0183 & 16 & 1.0098 \\
5 & 1.0196 & 11 & 1.0175 & 17 & 1.0178 \\
6 & 1.0210 & 12 & 1.0120 & 18 & 1.0125 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correction factors for adapting FEM eigenfrequencies to ART data. Figure 9: Relative deviations of initially simulated and then with correction factors adapted eigenfrequencies to measured eigenfrequencies (the diagrams show the adaption for in each case one reference part, that means for part A, B, C, D, or E, based on factors derived from the other parts).
## 3 Results and Discussion
### Validation of the Method for the Quantitative Analysis of DPD Concentration  
As the goal of this study was to quantitatively determine the DPD concentrations in various environmental samples, the DPD concentrations were controlled in a wide range of 0.3125-10.00 uM, equivalent to 41.30-1322 ng/mL in the constant excess concentration of DAN for the reaction in Figure 1. Subsequently, each reaction mixture was analyzed by HPLC to monitor each peak of the AI-2 derivative corresponding to each DPD concentration. The chromatogram was obtained using a UV detector at 268 nm (Figure 3a).  
The plot of the peak area versus the DPD concentration showed good linearity (r\({}^{2}\) = 0.999), proving that this analytical method is precise enough to be applied to the quantitative measurement of DPD (Figure 3b). The limit of detection (LOD) for the HPLC-UV method developed in this study was determined to be 0.25 uM (33 ng/mL), using the linear-regression model [28]. This value is higher than those of the other methods listed in Table 1; however, the cost of equipment for other methods is very high (LC-MS/MS, GC-MS) and their reproducibility is poor (AI-2 bioassay). Although the reported LOD for HPLC-FLD is lower, in practice, there was no significant difference between the two methods. In addition, there was no particular inconvenience when using HPLC-UV for the detection of trace amounts of DPD in environmental samples, such as activated sludge in wastewater, as long as such a sample is concentrated, for instance by liquid-liquid extraction. This is discussed further in the following section.  
Figure 2: (**a**) HPLC chromatogram of the reaction mixture of DPD and DAN with a UV detector at 225 nm, and (**b**) UV spectrum for the AI-2 derivative.
## 3 Methods
### Classical SVEP Detection Methods
Figure 6: Schematic illustration of using classical methods for SSVEP detection. Figure 7: Schematic illustration of using dedicated methods (CCA, sMP) for SSVEP detection. Feature extraction was performed for each channel separately. The use of three EEG signal channels triples the number of features. For case 1, there are 120 features, and for case 2, there are 48 features in total. To ensure that the classifier is correctly trained using standard machine learning techniques, only the most useful features should be utilized, necessitating a feature selection stage. Various methods are employed to select the best features, with filter and wrapper approaches being the most common [43]. A typical filter method that is widely used is the \(t\)-test, which assumes a normal distribution of features. We implemented the absolute value two-sample \(t\)-test with a pooled variance estimate [44]. However, the \(t\)-test selection is typically designed for two groups, and in our case, there are four classes (5, 6, 7, and 8 Hz). Consequently, we adopted a strategy of selecting the best features for one class in contrast to all other combined classes. This approach allowed us to select the most distinctive features for the groups: 5 Hz versus (6, 7, 8 Hz), 6 Hz versus (5, 7, 8 Hz), 7 Hz versus (5, 6, 8 Hz), and 8 Hz versus (5, 6, 7 Hz). Subsequently, a subset of 14 features was chosen, which yielded the best classification performance for the training set. The number 14 was determined experimentally. Unfortunately, feature selection methods do not always yield the best results because they do not consider the interdependencies between features [45]. A method that accounts for these types of dependencies is sequential forward selection (SFS) [46]. This method operates by selecting an initial feature, assessing the classification accuracy, and then incrementally adding the feature that most improves classification. For feature selection using SFS, the LDA and QDA classifiers were employed [47]. During the experiments, it was observed that the optimal number of features for achieving the highest classification accuracy was 25 for both LDA and QDA methods. All experiments were conducted using MATLAB R2021a software on a computer equipped with an Intel Core i7-9800X processor, 128 GB of RAM, and an NVIDIA GeForce RTX 2080 Ti graphics card. The time required to execute various algorithms on the applied dataset, considering the established training parameters, varied significantly. Table 2 illustrates the time required to create the augmentation set, train the different classification methods, conduct feature selection, and train the CNN. However, it is important to note that both the CNN and MLP algorithms used a GPU for their calculations.
## 3 Results
### Seasonal Variation in Soil Respiration and Sap Flow  
Figure 5 shows diurnal variation in \(\mathrm{R_{s}}\) and sap flow in three seasons. Sap flow was very low on rainy days, for instance, on 5 and 6 July (Figure 5b). The daily maximum rate of sap flow remained high; the daily maximum \(\mathrm{R_{s}}\) decreased from 14 to 26 July. Therefore, only \(\mathrm{R_{s}}\) and sap flow data on sunny days were analyzed. Daily maximum \(\mathrm{R_{s}}\) had a significant positive relationship with daily maximum sap flow (Figure 6). The mean daily maximum sap flow on sunny days was 32.2 \(\pm\) 2.3 cm\({}^{3}\) m\({}^{-2}\) s\({}^{-1}\) (\(n\) = 19) in spring, 42.3 \(\pm\) 5.5 cm\({}^{3}\) m\({}^{-2}\) s\({}^{-1}\) (\(n\) = 39) in summer, and 26.4 \(\pm\) 9.0 cm\({}^{3}\) m\({}^{-2}\) s\({}^{-1}\) (\(n\) = 15) in autumn, and these seasonal differences were significant (\(p\) < 0.01, Holm-Bonferroni method). Thus, daily maximum sap flow was lower in autumn than in spring, but Rs was higher in autumn than in spring at the same soil temperature. Sap flow and VPD had a significant positive relationship in each season, and VPD tended to be higher in spring than in autumn (Figure 7). Soil water content was higher in spring than in autumn (Figure 3); therefore, the contribution of \(\mathrm{R_{a}}\) was likely higher in spring than in autumn.  
Figure 1: Seasonal variation in soil respiration and environmental factors during (**a**) spring, (**b**) summer, and (**c**) autumn 2018. SR_1 and SR_2, R\({}_{\text{s}}\) in two chambers of R\({}_{\text{s}}\); ST, soil temperature at a depth of 5 cm; SWC, soil water content at a depth of 10 cm.  
Figure 4: Relationship between hourly mean soil respiration rate (chamber 1) and soil temperature at a depth of 5 cm on sunny days in (**a**) spring (28 April to 3 May), (**b**) summer (14 to 18 July), and (**c**) autumn (5 days from 3 to 22 October).  
Figure 3: Relationship between daily mean soil respiration rate and daily mean soil water content (SWC) at a depth of 10 cm in the three seasons in 2018. Closed black circles, open gray triangles, and closed gray circles were data in spring, summer, and autumn.  
Figure 2: Relationship between soil respiration rate and soil temperature at a depth of 5 cm in the two chambers. Closed black circles, spring; open gray triangles, summer; closed gray circles, autumn.  
Figure 5: Temporal variation in soil respiration (chamber 1) and two sap flows in (**a**) spring, (**b**) summer, and (**c**) autumn in 2018.  
Figure 6: Relationship between daily maximum sap flow (Max_sap) and (**a**) daily maximum soil respiration rate and (**b**) daily mean soil temperature at a depth of 5 cm on sunny days only in three seasons. The daily maximum sap flow and soil respiration were calculated as the average values of two samples on the same day.
###### Abstract  
The enormous computational power and time required for simulating the complex phonation process preclude the effective clinical use of computational larynx models. The aim of this study was to evaluate the potential of a numerical larynx model, considering the computational time and resources required. Using Large Eddy Simulations (LES) in a 3D numerical larynx model with prescribed motion of vocal folds, the complicated fluid-structure interaction problem in phonation was reduced to a pure flow simulation with moving boundaries. The simulated laryngeal flow field is in good agreement with the experimental results obtained from authors' synthetic larynx model. By systematically decreasing the spatial and temporal resolutions of the numerical model and optimizing the computational resources of the simulations, the elapsed simulation time was reduced by 90% to less than 70 h for 10 oscillation cycles of the vocal folds. The proposed computational larynx model with reduced mesh resolution is still able to capture the essential laryngeal flow characteristics and produce results with sufficiently good accuracy in a significant shorter time-to-solution. The reduction in computational time achieved is a promising step towards the clinical application of these computational larynx models in the near future.  
numerical larynx model; clinical potential; glottal aerodynamics; computational cost
# Microwave NDT of Smart Composite Structures with Embedded Antennas
## 2 Detection Concept and Simulation
Figure 2: Magnitude of the reflection coefficient of the simulated patch antenna in pre- and post-damage infriction. It can be seen that the patterns are similar. However, the damaged pattern directivity is reduced in the broadside relative to the healthy state while maximizing at 400 in both directions suggesting the main beam was split. The antenna radiation pattern shows that the antenna is operational in both states, albeit with less gain due to greater losses. The total efficiency of both antenna states is reported to be 93% and 84%, respectively. The linear 3-D radiation pattern shown in Figure 4a,b and the electric field distribution along the patch antenna depicted in Figure 4c,d further corroborate the fact that the damaged antenna is still radiating. In Figure 4c, it can be observed that the electric field density from the patch is high, indicating a healthy antenna, and is in line with expectations. For the damaged patch antenna however, the cut on the transformer creates a radiating stub as seen in Figure 4d with a relatively denser electric field when compared to the patch. While the electric field intensity is lower around the patch, it can still be deemed radiating. This explains the antenna's resonance at a higher frequency, as presented earlier in Figure 2. Consequently, the post-damage functionality of the antenna is established based on these observations. Figure 4: The 3-D linear radiation pattern for (**a**) healthy and (**b**) damaged antenna and electric field distributions of the (**c**) healthy and (**d**) damaged patch antenna. Figure 3: Simulated gain radiation patterns of healthy and damaged patch antenna in the (**a**) E-plane and (**b**) H-plane. After confirming the operability of the embedded antenna, simulations were performed to analyze the sensitivity and performance of the imaging probes. To this end, the UHF probe and a K-band rectangular aperture were modeled in CST microwave studio. The CLAS model shown in Figure 1 was used as structure under test (SUT). A picture of the simulation setup to assess the probe response is depicted in Figure 5. A 0.25 mm wide slit was made onto the patch creating a healthy and damaged version of the CLAS, as before. The reflection coefficient of the probes was measured for both healthy and damaged cases and the change in magnitude of the reflection coefficient (linear) was recorded for both probes and is tabulated in Table 1. It is evident that both probes detected a change in the reflection coefficient when placed over the damaged patch covered under the GFRP sheet. The UHF probe rendered a relatively higher change (more than 2.5 times) when compared to the rectangular aperture probe. Additionally, the probe is also polarization-independent and can detect slits/cuts regardless of their orientation on the embedded antenna. Based on the aforementioned observations, it is evident from the simulation that the UHF probe is relatively more sensitive and better suited for such applications.
# Comparative Analysis of Local Differential Privacy Schemes in Healthcare Datasets
## 5 Discussion
### Real-World Applicability of the Proposed Method  
The proposed LDP scheme based on BRR holds significant potential for revolutionizing data analysis in the healthcare industry while ensuring the utmost protection of patient privacy. LDP techniques enable healthcare organizations to conduct data analysis, such as epidemiological studies and treatment outcome analysis, without compromising patient privacy. Additionally, LDP provides a secure framework for data sharing, allowing collaboration on initiatives like population health management and clinical trials while safeguarding patient information. Moreover, LDP facilitates personalized medicine by analyzing patient-specific data while preserving confidentiality. Integration of LDP into healthcare analytics and machine learning models enables predictive analysis and proactive decision-making without exposing individual patient data. Furthermore, LDP enhances the privacy and security of telemedicine and remote patient monitoring systems, ensuring patient confidentiality in virtual healthcare settings. Overall, LDP has the potential to transform healthcare data analysis while maintaining patient privacy and confidentiality.  
Unfortunately, direct comparison with the [PERSON] et al. (2016) study is not feasible, despite both methods applying LDP to medical data. Their primary objective lies in enhancing accuracy in machine learning models, while our focus centers on estimating the joint probability distribution with minimal error.
###### Abstract  
Background: The rate of force development (RFD) is a measure of explosive strength, commonly evaluated within the same repetition as peak force/torque, by instructing the participants to produce \"fast\" and \"hard\" contraction. Previous studies have indicated that attempting to achieve the maximal RFD and maximal force/torque within the same contraction may not be optimal. Methods: This study assessed the differences between explosive sustained (\"fast-and-hard\") and ballistic (pulse-like) contractions on the RFD during isometric ankle and knee extensions in young, recreationally active participants (n = 33; age: 23.4 \(\pm\) 5.6 years). The participants performed both contraction types for isometric ankle and knee extension tasks. The RFD was calculated for time intervals 50, 100, 150 and 200 ms from the contraction onset and also in between these time points (50-100, 100-150 and 150-200 ms). Findings: The results of this study showed a higher RFD in the ballistic contractions in later time intervals (100-150 and 150-200 ms) compared to that of the explosive sustained contractions (effect size (Hedge's g) = 0.51-0.73). The differences between explosive sustained and ballistics contractions were larger and occurred for more outcome variables in females than males. Peak RFD was also larger in ballistic contractions, both for females (\(p\) = 0.010; Hedge's g = 0.54) and males (\(p\) = 0.003; Hedge's g = 0.78). The intra-session reliability of the RFD was similar for both contraction types, increasing from moderate to excellent with longer time intervals. Conclusion: Our results suggest that ballistic contractions should be used when the assessment of the RFD is the primary goal. When the aim is to assess both the RFD and peak force, it is suggested to use the combination of separate explosive sustained contractions and ballistic contractions in the protocol.  
p 2022
# Determination of Intrinsic Sodium in Grass-Fed Bonsmara (_Bos taurus indiacus_) Meat
###### Abstract  
There is a view that grass-fed beef has human health benefits coupled with better quality attributes. Yet, organic sodium in grass-fed beef remains elusive and overlooked in meat processing. This study aims to determine intrinsic sodium and saline mineral in grass-fed meat derived from Bonsmara (_B. indicius_) cattle that have been feeding on grass pea (_Lathyrus sativus_) for 18-20 months. A total of 70 (N = 70) samples from topside, silverside, thin flank, 80/20, and 60/40 trimmings were collected from seven carcasses. The samples were digested and tested using a validated ICP-OES. The results showed that the range of intrinsic sodium (mg/100 g) in grass-fed beef is 45-83, whereas in topsides = 49-74, silverides = 54-67, thin flanks = 62-83, 80/20 trimmings = 58-70 and 60/40 trimmings are 45-64. The results suggest that there is no significant difference of intrinsic sodium in similar cuts (\(p>0.005\)), while it was significantly different (\(p<0.0001\)) when all samples were considered. There were contrasting correlations of inherent grass-fed beef saline minerals (K, Mg, Ca and Cu) and intrinsic sodium concentrations in different meat cuts. Therefore, intrinsic sodium in raw grass-fed beef is higher than previously estimated. Consequently, intrinsic sodium must be considered in processed meat formulation concepts for the sake of consumer health.  
intrinsic sodium; grass-fed; beef; topside; silverside; flank; trimmings; processed meat; Bonsmara Article