esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Framework
### Characterization of the Foam  
The phase decoupling frequency is defined as the frequency for which the inertial effects of the solid phase are equal, in magnitude, to the viscous effects of the fluid part. If one considers a foam, excited by an acoustic load at a frequency much higher than the phase decoupling one, with a stiffness (or a weight) much greater than the one of the air, the material frame can be considered as rigid and motionless [42]. Therefore, the porous medium with motionless skeleton can be replaced by the homogeneous fluid layer without modifying the reflected field in the external medium [43]. Two classes of equivalent fluid models (that is expressions of complex density \(\rho\) and bulk modulus \(K\) as functions of the frequency and of the pore shape) can be listed [42]:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Foam Parameter** & **Interval** & **Model** \\ \hline Airflow resistivity, \(\sigma\) [kg/sm\({}^{3}\)] & [1000, 87,000] & DBM, JCA \\ \hline Unit cell dimension, \(d\) [m] & [0.01, 0.0950] & DBM, JCA \\ \hline Transmission Loss (TL) peak frequency, \(f_{peak}\) [Hz] & [6276.346, 15,560] & DBM \\ \hline Porosity, \(\phi\) & [0.956, 0.99] & JCA \\ \hline Tortuosity, \(\alpha_{\infty}\) & [1, 3] & JCA \\ \hline Viscous characteristic length, \(\lambda_{\mathrm{p}}\) [1/m] & [0.01, 0.294] & JCA \\ \hline Thermal characteristic length, \(\lambda_{th}\) [1/m] & [0.1, 1] & JCA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Intervals of foam characteristics.  
* Empirical models, which generally require the knowledge of a small number of parameters (or information); even if they are based on approximating assumptions, they are still very popular and very used;
* Semi-phenomenological models, which have been formulated for more complex pore morphologies; for this class of models, only the asymptotic behaviors are known, while a behavior between these asymptotes is assumed without being mathematically proven.  
In this work, two models are considered: DBM, which is an empirical model, then JCA, which is a semi-phenomenological one.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 4 4G and NSA-Based 5G V2X Services
### Band Combinations Support for 4G & 5G Band  
The frequency bands of the access network for 4G V2X service from the technical report (_TR 36.787 Rel-15_) play a vital role in completing V2X operations. Other band combinations are required for not only concurrent operations over 4G interfaces but concurrent operations over 4G & 5G interfaces i.e., 5G/4G Uu bands/band combinations and 5G/4G PC5 band as reported in the technical document (_TR 37.875 Rel-17_). Apart from other extensions reported in the technical document, the following three scenarios for concurrent operations can be considered when a single band is selected i.e., 4G-Uu band and 5G-PC5 band; 5G-Uu band and 5G-PC5 band; and 5G-Uu and 4G-PC5 band. Other band combinations and the given scenarios provide enhancements for fulfilling the growth of the 4G & 5G V2X ecosystem. In this connection, the available RAN aspects from technical report (_TR 37.985 Rel-16_) are important for expanding and supporting the access network part for V2X services.
## 6 Comparisons between Static Responses of the Numerical Models and Full-Scale Measurement Data  
Using the FE model established for the Huangsha Harbor Bridge, with and without model updating, the static responses of the bridge under gravity and all constructional effects are calculated. They are presented in Table 3 and compared with the data measured on the location after Huangsha Harbor Bridge is built and the results calculated using a baseline FE model updated using the traditional method (see Appendix A). As can be seen in Table 3, compared with the results calculated from the initial FE model, the along-bridge altitudes calculated using the FE model updated using the new method are much closer to the full-scale measurement data. In Table 3, most relative errors between the full-scale measurement results and the data from the FE model updated using the new method are no greater than 10%; however, they are generally greater than 10% between the full-scale measurement results and the data from the initial FE model. In addition, the RMS error is 32.74% for the model updated using the new method, and it is 41.07% for the initial model. These all suggest the effectiveness of the proposed model updating approach. With regard to the model updated using the traditional method, it can be found in Table 3 that its accuracy is not much better than the initial model without model updating. Similar to the initial model, most relative errors between the full-scale measurement results and the data from the FE model updated using the traditional method are greater than 10%, and the RMS error is 38.48% for the model updated using the traditional method. These demonstrate the superiority of the proposed model updating approach utilizing data from the construction monitoring to the traditional method using structural responses of the built structure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Along-** \\ **Bridge** \\ **Position (m)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Full-Scale** \\ **Altitude (m)** \\ \end{tabular} } & \multicolumn{2}{c}{**Initial Model without Model**} & \multicolumn{2}{c}{**Model Updated Using the Proposed Method**} & \multicolumn{2}{c}{**Model Updated Using the Traditional Method**} \\ \cline{3-8}  & & **Updating** & & & & \\ \cline{
## 2 Materials and Methods  
### Simulation Model and TMFBG Structures  
The structural parameters of the TMFBG mainly included the grating length (\(L\)), grating tilt angle (\(\theta\)), grating period (\(\Lambda\)), cladding diameter (\(d\)), and refractive index modulation depth. The schematic is illustrated in Figure 1a. By changing the structural parameters of the TMFBG, the transmission spectrum could be changed, in addition to its regularity. The variation in the transmission spectrum mainly refers to the central wavelength, bandwidth, and transmittance.  
The simulation model of the TMFBG was established utilizing OptiFDTD 8.0 software, as shown in Figure 1b. The length of the entire grating part was set to 20 \(\upmu\)m. The main parameters of the model included the following: the refractive index of the fiber core was 1.46; the diameter of the fiber core was 8 \(\upmu\)m; the refractive index of the cladding was 1.45;  
Figure 1: Schematic of TMFBG structure: (**a**) detailed parameters and (**b**) FDTD simulation model.  
the microfiber diameter was 9 \(\upmu\)m; the ambient refractive index was 1; and the working wavelength was centered at 1.55 \(\upmu\)m.
## 6 Discussion  
The purpose of this paper was to answer the two main scientific questions presented in the introduction. How can industrial protocol classification be achieved? What publicly available datasets can currently be used specifically to classify these protocols? Classification, recognition, and identification of protocols are closely related techniques that use the same methods. The most commonly used methods for traffic classification and protocol recognition have been presented and compared. Each method has its specific use and depends on the purpose for which it is to be used. Among the state-of-the-art methods are machine learning algorithms and especially neural networks. These algorithms allow for fast traffic classification and protocol recognition and provide high-quality metrics. However, these algorithms are limited in terms of input data. An analysis of the state of the art revealed that the majority of research is in the IT domain. Similarly, research is not targeted at encrypted versions of protocols.  
The available datasets often do not achieve the qualities needed for good and accurate classification, such as the number of records, the diversity of records, or the number of logs in the dataset. Currently, the number of datasets from IT environments exceeds the number of datasets. Although it is possible to use some IT protocols in OT systems from the point of view of the convergence of IT and OT networks, it is not advisable to rely on this fact alone. OT networks require specific protocols and requirements that are not as strict in IT networks. Based on the analysis of publicly available datasets, key requirements for future research were identified. Namely, the creation of a representative dataset containing industrial protocols using real industrial devices. Currently, no suitable dataset has been found for protocol recognition research in OT. It is the creation of such a dataset that would enable follow-up research and the comparison of different methods from the ML and NN domains.
# Common Data Model and Database System Development for the Korea Biobank Network
## 1 Introduction  
As the number of studies using human biospecimens increases worldwide, the importance of clinical information linked to human materials is increasing [1, 2, 3]. The common data model (CDM) is an internationally popular approach, and many joint studies are being conducted to standardize data structures and terms to enable joint research [4, 5, 6]. As data structure and format standardization progress in research using biospecimens [7], an environment for the more active use of clinical information has been created, and various models using bioresources have become the basis for expansion and development [8].  
In Korea, the NBK (National Biobank of Korea) developed the HuBIS_Sam (Human Biobank Information System_Sample), which is a historical management system for all processes involving the collection, storage, and distribution of human biospecimens. The HuBIS_Sam was developed in 2008, and by 2020, it had been used by 60 human biobanks throughout Korea. Among these biobanks, 17 human material banks have formed a network called the Korea Biobank Network (KBN) under the Korea Centers for Disease Control and Prevention. Biobanks use Excel files or other clinical information management systems to generate and manage clinical information linked to samples. This information is then distributed to researchers [9]. There is no integrated management system that can manage the collected clinical information and systematically distribute it [10, 11, 12].
# Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review
## 4 Discussion
### Limitations  
This systematic review has several limitations that should be acknowledged. First, the studies included in the analysis vary widely in their methodologies, populations, and sensor configurations. This heterogeneity limits the ability to generalize findings or directly compare algorithmic performance across studies.  
Second, many studies relied on subjective self-reports to define ground truth for stress or mental health states. While such labels are common, they introduce variability and potential bias, especially when no physiological benchmarks are used alongside them.  
Third, the majority of included research was conducted in short-term or laboratory-controlled settings. As a result, the long-term feasibility and reliability of smartwatch-based stress detection in real-world conditions remain underexplored.  
Fourth, few studies explicitly addressed issues related to algorithm interpretability or transparency. Most models are treated as black boxes, which could hinder their acceptance in clinical or public health contexts.  
Finally, publication bias and the exclusion of non-English or unpublished data may have resulted in missing relevant studies, especially from underrepresented populations or regions.
###### Abstract  
Hand detection and tracking are key components in many computer vision applications, including hand pose estimation and gesture recognition for human-computer interaction systems, virtual reality, and augmented reality. Despite their importance, reliable hand detection in cluttered scenes remains a challenge. This study explores the use of deep learning techniques for fast and robust hand detection and tracking. A novel algorithm is proposed by combining the Kernelized Correlation Filter (KCF) tracker with the Single-Shot Detection (SSD) method. This integration enables the detection and tracking of hands in challenging environments, such as cluttered backgrounds and occlusions. The SSD algorithm helps reinitialize the KCF tracker when it fails or encounters drift issues due to sudden changes in hand gestures or fast movements. Testing in challenging scenes showed that the proposed tracker achieved a tracking rate of over 90% and a speed of 17 frames per second (FPS). Comparison with the KCF tracker on 17 video sequences revealed an average improvement of 13.31% in tracking detection rate (TRDR) and 27.04% in object detection error (OTE). Additional comparison with MediaPipe hand tracker on 10 hand gesture videos taken from the Intelligent Biometric Group Hand Tracking (IBGHT) dataset showed that the proposed method outperformed the MediaPipe hand tracker in terms of overall TRDR and tracking speed. The results demonstrate the promising potential of the proposed method for long-sequence tracking stability, reducing drift issues, and improving tracking performance during occlusions.  
Keywords:human-computer interaction; hand detection; hand tracking; single-shot detection; kernelized correlation filter +
Footnote †: journal:
# Sc/SiC/Al Multilayer Optimization for Li K Spectroscopy
## 2 Experimental Details
These two measurements allowed for the extraction of the quantity T\({}_{\rm R}\)(E) given by the ratio between the diode and monitor currents, once the dark current was subtracted. The I\({}_{0}\) scan and relative dark measurement of the current of the diode positioned in correspondence with the incoming beam after removing the sample, and in parallel with the monitor current, are both functions of energy. Similarly, these two measurements allowed for extracting the quantity T\({}_{0}\)(E) given by the ratio between the diode and monitor currents, once the dark current was subtracted. The ratio between T\({}_{\text{R}}\)(E) and T\({}_{0}\)(E) gave the experimental reflectivity. For the analysis of the reflectivity curves, we used software for modeling the optical properties of multilayer films: the commercial Leptos(r) package and IMD program [24]. The calculation of the optical functions (reflection and transmission, etc.) of multilayer films is based on the application of Fresnel equations. Fits to the GIXR data allowed us to determine the multilayer period, layer thicknesses, and average values of the interfacial roughness/diffusion. A simulation of the multilayer reflectivity was performed using the same software (IMD) that can be used for modeling in the X-ray range the optical properties, reflectance, transmittance, electric field, and intensities, etc., of multilayer films. Multiple layers can be specified in the software to create a multilayer structure with the desired thickness and roughness. The roughness is defined at each surface/interface to take into account the reduction in reflectance due to imperfections. Detailed information on the use of IMD and Leptos is found in [24; 25]. The calculations use the optical constants database by the Center for X-ray Optics (CXRO) [26] in conjunction with a homemade Python script established using the trial and error method for the optimization part with pre-imposed constraints. The calculations are performed either at a fixed photon energy as a function of the glancing angle or at a fixed angle as a function of the photon energy.
# A Multi-Robot Formation Platform based on an Indoor Global Positioning System
## 4 Experiments of Leader-Follower Formation Control
### The Experiment of Trajectory Tracking of Leader-Follower Formation under Deterministic Environment
#### 4.1.1 Experiment of Triangle Formation Switching  
We use the platform to perform a formation switching from the triangle formation (\(l\) = 0.60 m) to another triangle formation (\(l\) = 0.30 m) while tracking a straight line. The initial pose of leader robot \(L\) is \((3.26\ \mathrm{m},1.25\ \mathrm{m},\pi/2)\), the linear velocity is \(\
u=0.05(\mathrm{m}/\mathrm{s})\) and angular velocity is \(\omega=0(\mathrm{rad}/\mathrm{s})\). The initial pose of follower robots \(F_{1}\) and \(F_{2}\) are \((2.84\ \mathrm{m},0.61\ \mathrm{m},\pi/2)\) and \((3.88\ \mathrm{m},0.56\ \mathrm{m},\pi/2)\). In addition, initial velocity of the three robots are 0. The velocity constraints of all the robots are set as \(\
u_{Max}=0.3(\mathrm{m}/\mathrm{s})\) and \(\omega_{Max}=\pi/6(\mathrm{rad}/\mathrm{s})\).  
The experimental process in Figure 8 shows that the leader robot leads the two follower robots \(F_{1}\) and \(F_{2}\), forming and maintaining a desired triangle formation. The formation begins to switch into another triangle formation at T = 43 s, and after 25 s a new desired formation is formed autonomously. From Figure 9a,b, we can see at T = 43 s, the distance and angular errors suddenly increased because of the formation switching, but during T = 43-50 s, under the autonomous control of the controller, the follower robots calculated their new control inputs to form the new desired formation, at the same time the distance error was decreasing rapidly. At last, the three robots formed the new triangle formation autonomously. In summary, the leader-follower formation based on our platform can implement the application and verification of the theories and methods in the multi-robot formation. The experiment video of the triangle formation switching can be found at Supplementary Materials [[https://youtu.be/7](https://youtu.be/7) WtsZoNVp5A]([https://youtu.be/7](https://youtu.be/7) WtsZoNVp5A).  
Figure 7: The real scenario of leader–follower formation experiments.
## 3 Artificial Intelligence Techniques for 6G Networks
### Modulation Recognition  
Different modulation techniques are used in a communication system to achieve efficient and effective data transmission by modifying the delivered signal in light of the growing amount of data traffic in smart cities. In this situation, the goal of modulation recognition is to distinguish the signals' modulation information amid loud interference. Applications including cognitive radio, interference detection, spectrum monitoring, threat assessment, and signal recognition benefit from the assistance of modulation recognition in signal demodulation and decoding. For modulation recognition in smart city applications, the traditional statistical pattern recognition and decision theory-based approaches become computationally complex and time-consuming [19].
# Compositional Analysis of Chalcopyrite Using Calibration-Free Laser-Induced Breakdown Spectroscopy
## 3 Results
### Quantitative Analysis
#### 3.2.6 Calibration-Free Analysis
Before analysis of multi-elemental chalcopyrite mineral, the energy calibration of the equipment was carried out using standard material JSX-3200. The XRF analysis showed the presence of five compositional elements in the investigated sample viz. Cu, Fe, Si, Se, and Ag, with weight percentages of 58.1%, 35.4%, 5.7%, 0.7%, and 0.1% respectively. To compare quantitative results of CF-LIBS results with those obtained using XRF, the concentration of sulfur in XRF analysis was ignored. Results obtained with CF-LIBS and XRF are presented in Figure 6.
## 3 Results and Discussion
### Impact of PEF Technology on Tocopherols  
Tocopherols play an important role in the prevention of the oxidation of mono- and polyunsaturated fatty acids (PUFAs) [34], which helps in the stability of products during shelf life. The \(\alpha\)-tocopherol homologue is the most common in many edible oils, and in extra virgin olive oil, it is the major tocopherol. The consumption of tocopherols has potential health benefits, like the prevention of cardiovascular diseases, adjuvants in cancer treatment, and the prevention of diabetes and obesity [35].  
Table 4 shows the concentration of \(\alpha\)-tocopherol, \(\beta\)-tocopherols, \(\gamma\)-tocopherol, \(\sigma\)-tocopherols, and total tocopherols in control and PEF olive oils. No significant differences (\(p>0.05\)) were observed.  
[PERSON] et al. (2023) [25] analyzed \"Galega Vulgar\" extra virgin olive oils from seven Portuguese locations obtained by the traditional extraction process and concluded that the total tocopherol content ranged between 299.9 mg/kg and 394 mg/kg, which is in line with the values obtained in the present study. Puertolas & Maran  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Control** & **PEF** \\ \hline \(\alpha\)-toc & 313 \(\pm\) 6.1 \({}^{\text{a}}\) & 289 \(\pm\) 15.0 \({}^{\text{a}}\) \\ \(\beta\)-toc & 4 \(\pm\) 0.7 \({}^{\text{a}}\) & 3 \(\pm\) 0.6 \({}^{\text{a}}\) \\ \(\gamma\)-toc & 9 \(\pm\) 1.6 \({}^{\text{a}}\) & 8 \(\pm\) 0.2 \({}^{\text{a}}\) \\ \(\sigma\)-toc & 0 \(\pm\) 0.0 \({}^{\text{a}}\) & 0 \(\pm\) 0.0 \({}^{\text{a}}\) \\ Total & 326 \(\pm\) 8.3 \({}^{\text{a}}\) & 300 \(\pm\) 15.8 \({}^{\text{a}}\) \\ \hline \hline \end{tabular} Values are means \(\pm\) standard deviation (SD). \({}^{\text{a}}\) Different superscript letters on the same row indicate a significant difference at \(p\leq 0.05\).  
\end{table}
Table 4: Tocopherol content (mg/kg) in control and PEF olive oils was obtained.  
quantified total tocopherols in the traditional extraction process with the _Arroniz_ variety, resulting in a concentration of 192 mg/kg. With the application of PEF, the concentration increased to 220 mg/kg, with the \(\alpha\)-tocopherol isomer being the most abundant, with a significant increase upon application of PEF. However, in another study, [PERSON] et al. (2022) [9] did not find any differences between PEF and control EVOOs respecting this isomer. In line with the various previous studies and the different trends observed, the variability of this compound in EVOO depends on genetic and agronomic conditions and not on the process of extraction [36].
# A Practical and Efficient Node Blind SignCryption Scheme for the IoT Device Network
## 3 The Proposed Scheme
### Offline-Signing Phase  
* When \(DS_{i}\) has received (\(\gamma\), \(\hat{n}\), \(z\), \(\hat{z}\)) from the \(BS\), \(DS_{i}\) also computes the following messages if the checking of \(z\) is valid, where \(\hat{z}=H_{1}(z)\mod\hat{n}\). \(DS_{i}\) selects a random number \(r\in Z_{n}^{*}\) and computes the following: \[\begin{split} C_{1}&=E_{pk_{R}}(r)\\ C_{2}&=H_{1}(r)\oplus m\\ C_{3}&=H_{1}(C_{1},C_{2},r,\hat{z},m)\end{split}\] (1)
* After computing the above equations, \(DS_{i}\) also allows \(\beta^{2}/\alpha^{2}\) as \(\tau\) and performs the following: \[\begin{split} C_{1}^{\prime}&=C_{1}*\tau^{2}*\gamma\\ C_{2}^{\prime}&=C_{2}*\gamma\\ C_{3}^{\prime}&=C_{3}*\gamma\\ h&=H_{1}(C_{1}^{\prime},C_{2}^{\prime},C_{3}^{ \prime})\end{split}\] (2)
* From the above equations, we know that \(DS_{i}\) blinds the sensor data and computes a cipher-text (\(C_{1}^{\prime},C_{2}^{\prime},C_{3}^{\prime})\). Then, \(DS_{i}\) forwards (\(C_{1}^{\prime},C_{2}^{\prime},C_{3}^{\prime},h,z,\hat{z}\)) to \(BS\). When \(BS\) hasreceived these messages from \(DS_{i}^{\prime}\), it verifies above them with \(z\), checks the \(h\) from \((C_{1}^{\prime},C_{2}^{\prime},C_{3}^{\prime})\), and enters the online-signing phase.