esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of a Vein Biometric Recognition System on an Ordinary Smartphone
## 4 Implementation and Evaluation of the Recognition System
### Quality Assessment Algorithms  
Since there is not a standardized proposal for quality estimation of vein images, we evaluated three methods employed in the literature and provided by the OpenVein Toolkit [14]: GCF (global contrast factor), Wang17, and HSNR (signal to noise ratio based on the human visual system). GCF [17] computes local contrasts at various spatial frequencies and then, combines them to compute a global contrast. It measures the overall perceived contrast of an image, which indicates the richness of the details observed. Wang17 [18] combines brightness uniformity (computed as the gray mean difference between different parts of the vein image) and clarity (computed by blurring an image with a low-pass filter and comparing the variations between neighboring pixels before and after blurring). HSNR [19] simulates the cognitive function of the human visual system by combining contrast, shifting, effective area, and signal to noise indexes. Contrast index is based on computing the mean square deviation of the gray values of the pixels. Shifting index computes the horizontal and vertical deviation of foreground areas' center of mass with respect to the geometric center of the whole image. Effective area index measures the foreground information with respect to the complete image information. Signal to noise index is computed with the contrast sensitivity weight matrix and the impulse noise distribution matrix of the vein image. The values of these quality metrics range from 0 to 8 for GCF, from 0 to 1 for Wang17, and from 0 to 100 for HSNR. Higher values mean better image quality.
## 5 CSUH Model Iterative Inversion Parameters
### Fujinomori Clay
#### 5.1.2 True Triaxial Test  
The true triaxial test for Fujinomori clay follows the basic procedure of first undergoing isotropic consolidation to \(p\) = 196 kPa, followed by a constant \(p\) (or constant \(\sigma_{\text{m}}\)) shear test at an axial strain rate of 1% per day.  
As shown in Figure 10, the CSUH model predictions agree well with the experimental results. This further demonstrates that, after incorporating the transformed stress method, the CSUH model can quantitatively describe the differences in the frictional behavior of soils under different stress Lode angles (\(\theta\)).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Parameters** & \(M\) & \(\
u\) & \(\kappa\) & \(\lambda\) & \(N\) & \(Z\) & \(\chi\) & \(m\) \\ \hline Values & 1.36 & 0.0 & 0.02 & 0.093 & 1.26 & 1.26 & 0.05 & 5 \\ \hline \end{tabular}
\end{table}
Table 3: CSUH Model Parameters for Fujinomori Clay.  
Figure 9: Comparisons between the constant \(p\) compression test results of the Fujinomori clay and the CSUH predictions [20].  
Figure 10: Comparisons between the true triaxial test measured results under constant \(p\) stress path with different medium principal stress coefficient \(b\) of the Fujinormori clay and the CSUH predictions [21] (dots: measurements; lines: predictions): (**a**) Medium principal stress ratio \(b=0\), equivalent to stress Lode angle \(\theta=0^{\circ}\); (**b**) Medium principal stress ratio \(b=0.268\); (**c**) Medium principal stress ratio \(b=0.5\); (**d**) Medium principal stress ratio \(b=0.732\); (**e**) Medium principal stress ratio \(b=1\).  
As shown in Figure 10, the horizontal axis represents \(\varepsilon_{i}\), where i may denote 1, 2, or 3. The upper part of the vertical axis corresponds to \(\sigma_{1}\) and the ratio \(\sigma_{1}/\sigma_{3}\), while the lower part represents the volumetric stress \(\sigma_{\rm v}\). In Figure 10, lines indicate theoretical calculation results, whereas symbols denote experimental data. Specifically, the black solid square points represent \(\varepsilon_{1}\)-\(\varepsilon_{\rm v}\), the black open square points denote \(\varepsilon_{1}\)-\(\sigma_{1}/\sigma_{3}\), the red open circles indicate \(\varepsilon_{2}\)-\(\sigma_{1}/\sigma_{3}\), and the blue open triangles correspond to \(\varepsilon_{3}\)-\(\sigma_{1}/\sigma_{3}\). The relevant explanations have been added to the figure caption.  
In summary, the predictions for conventional triaxial and true triaxial tests of saturated Fujinomori clay indicate that the CSUH model can not only quantitatively distinguish the differences in dilatancy behavior among clays with different densities but also accurately capture the variations in frictional characteristics under different stress Lode angles.
## 4 Case Study  
### Data Description  
This section utilizes the SDSADT data from a specific missile tank to verify the effectiveness and feasibility of the SDRD method. The tank is the cooling device for a missile seeker, which has the characteristics of high-reliability and long-life. It contains a certain amount of from, which will continuously leak due to the influence of temperature during storage in the warehouse. When the leakage amount reaches the failure threshold, the cooling effect of the tank makes it difficult to meet the requirements of the missile seeker, at which point the tank is judged to have failed. Under normal stress levels, the leakage process of freon is very slow. High temperatures can accelerate the leakage of freon; therefore, it is necessary to carry out temperature stress ADT to evaluate its storage life. Reference [22] assessed the storage life of the tank based on the cumulative damage theory and provided detailed test plans and test data. This article selects the same test data as in Reference [22] to verify the effectiveness and feasibility of the proposed method. The specific details of the SDSADT for the specific missile tank are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Test Parameter** & **Specific Details** \\ \hline
1 & Sample size & \(n=6\) \\
2 & The number of stress levels & \(k=3\) \\
3 & Stress levels/K 1  & \(S_{3}=333\), \(S_{2}=323\), \(S_{1}=318\) \\
4 & Normal stress level/K 1  & \(S_{0}=298\) \\
5 & Detection number & \(m_{3}=15\), \(m_{2}=15\), \(m_{1}=23\) \\
6 & Stress transition moment/h & \(t_{1}=1258\), \(t_{2}=2672\), \(t_{3}=4802\) \\
7 & Failure threshold/g 2  & \(D_{f}=1.2\) \\
8 & Test period/h & About 72 \\
9 & Index storage life/h 3  & 43,800 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specific details of the SDSADT for a specific missile tank.  
Figure 2: The flowchart for parameter estimation and life assessment based on the MC method.  
In the test, the freon leakage amount of the missile tank is selected as its performance parameter. When the leakage amount exceeds the set value of 1.2 g, the tank is considered to have failed. The missile tank has an indicator storage life of 5 years under normal stress levels, which equates to 43,800 h. The degradation curve of the missile tank under the SDSADT is shown in Figure 3. From the figure, it is evident that the degradation paths of the six products in the SDSADT exhibit significant randomness. Therefore, it is meaningful to introduce the Gamma distribution to characterize the randomness of the product degradation paths in this article.
## 4 Modeling and Assessment  
### Index Normalization and Correlation Analysis  
In order to model driver behavior in different environments, it is necessary to further normalize the quantified values obtained through different methods. By using the method of min-max normalization, we can ensure comparability of the quantified values obtained in different environments, thereby facilitating better behavior modeling and analysis. The three environmental data indicators are normalized using the min-max normalization technique, resulting in values within the range [0, 1]. The transformation function is as follows:  
\[A^{*}=\frac{A-min}{max-min} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Feature Description** & **Unit** \\ \hline \(v_{max}\) & maximum speed & m/s \\ \(v_{avg}\) & speed average & m/s \\ \(\theta_{max}\) & throttle maximum angle & \({}^{\circ}\) \\ \(\theta_{avg}\) & throttle average angle & \({}^{\circ}\) \\ \(\delta_{max}\) & braking maximum angle & \({}^{\circ}\) \\ \(\delta_{avg}\) & braking average angle & \({}^{\circ}\) \\ \(\eta\) & the proportion of braking time & 1 \\ \(\sigma\) & steering wheel angle range & \({}^{\circ}\) \\ t & driving time & s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Driver Data Feature Set.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Field of View Obstruction** & \(NCC(p,d)\) \\ \hline existence & 1.566 \\ nonexistence & 0.254 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Visual Obstruction Quantification Result.  
where \(max\) represents the data with the largest median value of the environmental index, \(min\) represents the data with the smallest value in the environmental index, \(A\) represents the quantitative values of each environmental indicator, \(A^{*}\) represents the normalized results of each indicator.  
To validate the correlation between different environmental indicators and driver behavior, this study conducted a correlation analysis between driver data and environmental indicators. In the correlation analysis, this paper collected a dataset of driver features and compared it with three quantified environmental indicators.  
Table 7 presents the Pearson correlation coefficients between selected driver behavioral features and environmental quantified indicators. All of these coefficients exceed 0.925, indicating that the chosen indicators are accurate and that there exists a strong correlation between driver behavior and the environment. This conclusion also confirms the validity of our model based on assumptions. Based on this conclusion, we conducted a driver's environmental perception and behavior model.
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 2 Polypyrrole-Based Electrodes
### 3D Nanostructures
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Nanostructure** & **Recepior** & **Analyte** & **Mechanism** & **LOD 1** & **Regenes** & **Working** & **Linearity** & **Cycle** & **Sensitivity** & **Reference** \\ \hline CPPyNP2 & hPTHR & hTPHR3 & FET4 & 48 mM & \(<\)10 s & 25 °C & 48 mM-480 pM & 2 weeks & [79] \\ \hline Pt,CrPyNP & - & Dopamine & FET & 100 mM & \(<\)10 s & 25 °C & 01 pM-1 nM & 4 weeks & [80] \\ \hline Ti-CrPyNP & \({}^{\star}\)Embedded & Cadaveire & FET & 100 mM & \(<\)5 s & 25 °C & 01 RM-100 mM & 5 weeks & [81] \\  & mandisk & & & & & & & & \\ \hline CPPyNT & H232838 & PTC7 PROP* & FET & 1 MM 10 mM & \(<\)5 s & 25 °C & 1 MM-1 mM & 1 week & [84] \\ \hline CTPN & 175-estradiol & & & & & & & & \\ binding aptamer & 175-estradiol & & & & & & & & \\ \hline Pt,CrPyNP & - & & & & & & & & \\ \hline Pt,CrPyNP & - & & & & & & & & \\ \hline Pt,CrPyNP & CT-deltaDNA & 2-aminoaminoamino & 0.01 ppm 0.1 ppm & \(<\)0.8 & 25 °C & 00.01\(-\)20 ppm 0.1\(-\)30 ppm & - & [87] \\ \hline Pt,CrPyNP & \({}^{\star}\)162 & CDo12 & Cholesterol & Ampereometric & 0.11 mM & - & 25 °C & 01.1 mM-19 mM & 30 days & [88] \\ \hline Pt,CrPyNA & hTP30 Co12 & H2O3 & Glucose & Ampereometric & 10 mM mM & \(<\)10 s & 25 °C & 10 mM-10 mM 1.5 mM & - & [89] \\ \hline
30 DPNP & FAF413 & Cortisol & FET & 100 mM & \(<\)5 s & 25°C & 100 AM-10 mM & 30 days & [80] \\ \hline
30 CPPy & FDC*-binding & & & & & & & & \\ plate-based & aptamer & & & & & & & & \\ \hline
30 DPy film & & & & & & & & \\ \hline Urush-like & & & & & & & & \\ \hline CrPyNP & \({}^{\star}\)18 & & & & & & & & \\ \hline \hline \end{tabular}  
* Limit of detection, \({}^{\star}\) carboxylated polypyrrole nanoparticle, \({}^{\star}\)3 human parathyroid hormone, \({}^{\star}\) field effect transistor, \({}^{\star}\)5 olfactory receptor, \({}^{\star}\)6 bitter taste receptor, \({}^{\star}\) phenylthocarbamide, \({}^{\star}\)8 proportionallypolyprople-polypvinyl sulphonate, \({}^{\star}\) double stranded calf thymus DNA, \({}^{\star}\)10 polypyrrole-dodecyl benne sulphate, \({}^{\star}\) cholesterol oxidase, \({}^{\star}\)19 horseradish peroxidase, \({}^{\star}\)19 glucose oxidase, \({}^{\star}\)19 cortisol antibody, \({}^{\star}\) serum length is surface antigen, \({}^{\star}\) platelet-derived growth factor, \({}^{\star}\)19 bisphenol A.  
\end{table} 
Table 1: Comparative sensing performance and limit of detection of polypyrrole nanostructure-based biosensors.
## 5 System-Level Performance Evaluation
### Performance Evaluation Setup
#### 5.1.1 Short-Range Wireless Communication Radio Characteristics  
The second component influencing the system-level performance is the specific off-the-shelf radio transceivers used to transfer data. In the context of this evaluation, we considered the Shimmer2R platform [25] comprising a prominent commercial solution offering highly efficient Bluetooth Class 2.1 as well as IEEE 802.15.4 interfaces.  
_Bluetooth Interface Characteristics:_  
The specific platform uses the RN-42 Bluetooth radio chip exhibiting the following voltage and current demands [26].  
Voltage supply = 3.3 V  
Typical Current draw during transmission = 45 mA  
Additionally, the respective node is programmed using TinyOS operating system so additional delay could be added as expected to be the case in any off-the-shelf WSN sensor.  
_IEEE 802.15.4 Interface Characteristics:_  
The specific platform uses the CC2420 IEEE 802.15.4 radio chip exhibiting the following voltage/current demands [14].  
Voltage supply = 3.3 V  
Typical Current draw during transmission (max) = 17.4 mA  
Additionally, the respective node is programmed using TinyOS operating system so additional delay could be added as expected to be the case in any off-the-shelf WSN sensor.
# Collision-Free Motion Planning of a Six-Link Manipulator Used in a Citrus Picking Robot
## 4 Experiments and Results
### Application in EC63M Manipulator  
In this section, APF and IAPF algorithms were used to carry out path planning for the EC63M manipulator. The experimental parameters were shown in Table 6, in which the initial configuration was \(\mathbf{\Theta}_{i}\), the target configuration was \(\mathbf{\Theta}_{g}\), and the obstacle coordinate was \(\mathbf{P}_{0i}\), the obstacle radii were all 120 mm. The simulation results are shown in Table 7. The joint angle curves and the distance curves between each ETS line segment of the EC63M manipulator and the obstacles are shown in Figure 13.  
The simulation results show that IAPF is better than APF. The joint angles' curves of IAPF are smoother and the change rate is almost the same. The joint angles' curves of the APF algorithm change more obviously, and the change rate is quite different. The joint angle \(\theta_{3}\) begins to converge after a reverse process, while the joint angle \(\theta_{2}\) shows a wave peak. The convergence speed of APF is very slow. Compared with IAPF, it directly reaches the target configuration at the 238 th time, but APF converges at the 549 th time. Compared with APF, IAPF reduces the operation time by 54.89% and the total joint error by 45.41% in terms of running speed and accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & \multicolumn{5}{c}{**Parameter**} & \multicolumn{5}{c}{**Parameter**} \\ \hline \(\Theta_{i}=\left[\begin{array}{cccccc}0&0&0&0&0&0\\ \end{array}\right]\) & \(\Theta_{g}=\left[\begin{array}{cccccc}\frac{\pi}{3}&-\frac{\pi}{3}&\frac{ \pi}{3}&0&0&0\\ \end{array}\right]\) & \(\mathbf{P}_{01}=\left[\begin{array}{cccccc}-400&100&300\\ \end{array}\right]\) \\ \(\mathbf{P}_{02}=\left[\begin{array}{cccccc}-400&100&400\\ \end{array}\right]\) & \(\mathbf{P}_{03}=\left[\begin{array}{cccccc}400&100&450\\ \end{array}\right]\) & \(\mathbf{P}_{04}=\left[\begin{array}{cccccc}400&100&500\\ \end{array}\right]\) \\ \(\mathbf{P}_{05}=\left[\begin{array}{cccccc}400&100&550\\ \end{array}\right]\) & \(\mathbf{P}_{06}=\left[\begin{array}{cccccc}400&100&300\\ \end{array}\right]\) & \(\mathbf{P}_{07}=\left[\begin{array}{cccccc}400&100&500\\ \end{array}\right]\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: The concrete parameters used in experiment Section 4.4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Iterations** & **Runtime/s** & **Joint Error\({}^{\circ}\)** & **Total Error\({}^{\circ}\)** \\ \hline APF & 549 & 2.35 & \(\left[-0.03,0.1,-0.30,0.30,-1.79,0.00\right]\) & 1.85 \\ IAPF & 238 & 1.06 & \(\left[-0.39,0.8,-0.39,0.02,0.00,0.00\right]\) & 1.01 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The path planning results of IAPF and APF applied to EC63M.  
Figure 12: Simulation curve of LMT. (**a**) Distance curve to goal; (**b**) Distance curve to the obstacle; (**c**) Attractive force curve.; (**d**) Repulsive force curve.
## 3 Results and Discussion
### Volatile Compound Profiles in Cheeses  
The volatile composition of all cheeses was relatively quantified, and the results are presented in Table 3. In total, the analysis of VOCs revealed the presence of seventy-eight VOCs in the cheese samples, belonging to different chemical families: twenty-two esters, eighteen alcohols, thirteen ketones, ten acids, five aldehydes, one terpene and nine other compounds belonging to different chemical families (furan, pyrazine and heterocycle). To illustrate the differences among chemical families among cheeses, Figure 1 was drawn to represent the average percentage of the classes of volatile compounds found in the cheeses, classified in function of the cow's breed provenance of milk and of cheese categories. First, it was observed that the volatile composition was very similar between OB_M and Salers_M cheeses, with alcohol contents between 45.4% and 48.2%, followed by ketone contents between 26.9% and 30.9%, acid contents 16.4% for the both and ester contents between 6.5% and 8.5%. However, sixteen volatile compounds presented statistical differences (\(p<0.05\)) according to the provenance of cow's breed milk (Table 3). Among them, only six did not present significant interactions with cheese category: butanoic acid, n-decanoic acid, 1-penten-3-ol, 3-methyl butanal, benzaldehyde and nonanal (Table 3).
## 5 Testing, Results, and Analysis  
### Expert Panel  
To carry out the evaluation of the universally accessible housing (UAH) case study, a group of five experts participated in testing sessions. The objective was to assess both the simulation, and the tool used to implement it, as well as the parameters related to the core focus of this research. To ensure a representative sample of the key topics involved, the expert group included professionals from university education, care and disability, UX/UI development, and virtual reality (see Table 3).  
Figure 11: Key functionalities of the VR experience. (**a**) Interactive menu with restart, exit, and save options, (**b**) wheelchair movement system with realistic physics, (**c**) door interaction with programmed opening animations, (**d**) light switch interaction for turning lights on/off, (**e**) instructional widgets for user guidance; (**f**) save point system for resetting to safe locations.  
The expert evaluation was conducted through individual testing sessions with each participant, ensuring that each experience remained unique and free from external influence. Experts interacted independently with the simulation, allowing for unbiased assessment of performance and usability. During these sessions, they explored the virtual environment while the system recorded key evaluation parameters, such as time spent in specific zones and the number of collisions per area. This individualized approach provided consistent and objective data for analyzing the effectiveness of the VR-based assessment tool.
# Benefits of Advanced Air Mobility for Society and Environment: A Case Study of Ohio
## 4 Benefit Factors
### BF-3: Cost and Delivery Time Savings in Package Delivery by Drones  
#### 4.3.1 Cost Savings for Logistics Companies  
The drone delivery system would help a company to save money and increase its operational efficiency [45]. Organizations would be able to focus their present human resources more toward creative, inventive endeavors that can help them to extend their services even more with drone-delivery. The employees will have more time to concentrate on the company's day-to-day operations while also assuring that consumers receive a greater quality product. This technology would allow people to spend more time and money on ideas that might one day transform the world, rather than performing repetitive operations that lower productivity levels unnecessarily [46]. The global drone package delivery market is projected to grow from $343.30 million in 2019 to $39,013 million in 2030 at a 53.8% Compound Annual Growth Rate (CAGR) [47]. Using Equation (9), the value of global drone package delivery market in year \(i\) (\(V_{i}\)) was calculated (unit in $ million), relative to \(V_{2019}\), which denotes the value of the global drone package delivery market (unit in $ million) in 2019:  
\[V_{i}=V_{2019}\times(1+CAGR)^{t}\qquad i\in\{2022,2023,...,2032(year)\};t\in\{ 3,4,...,13\}. \tag{9}\]  
In 2019, North America achieved $237.70 million in the drone package delivery industry, and 89% share of this market value was captured by the US [47]. Moreover, it is estimated that the percentage will be increasing in the future. Thus, the market size of the US for drone package delivery \(U_{2019}\) is $211.553 million. The value of the US drone package delivery market relative to the value of the global drone package delivery market (r) is:  
\[r^{\prime}=\frac{U_{2019}}{V_{2019}}=\frac{211.553}{343.303}=0.6162. \tag{10}\]  
Using \(r^{\prime}\), we calculated \(U_{i}\), which is the size of the drone package delivery market in the US in year \(i\):  
\[U_{i}=r^{\prime}\times V_{i}\qquad i\in\{2022,2023,...,2032(year)\}. \tag{11}\]  
Amazon has already been testing Prime Air delivery drones for several years [48]. In the United States, Amazon ships over 2.5 billion packages every year [49], and, by 2022, it will be around 6.5 billion parcels [50]. Amazon says that 87% of its parcels weigh less than five pounds, making them ideal for the type of drone delivery that the FAA has approved [45]. Thus, the number of packages that drones per year can potentially ship by 2022 in the USA (_a_) is 5.59 billion (0.86 \(\times\) 6.5 billion).
# A Heuristic Algorithm for Locating Line-to-Line Faults in Photovoltaic Systems
## 3 Proposed Method  
This section presents a heuristic line-to-line fault location algorithm for identifying line-to-line faults within PV arrays. The proposed algorithm is designed to locate faults in PV arrays where line-to-line faults have already occurred. Fault locations are detected based on voltage measurements of module groups, combined with empirical rules derived from observation to identify the likely faulted module group, thereby reducing troubleshooting time.