esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential of Liquid-Crystal Materials for Millimeter-Wave Application
## 2 Measurement of Complex Refractive Indices in Millimeter Wave
### Principle of Measurement  
Figure 1 shows an MMW propagation model on the CPW fabricated using an Si substrate. The incident MMW from the contact probe on the left-hand side propagates to the right, and is reflected at the right-side edge of the CPW. When a pulsed light (905 nm) from the laser diode (LD) is irradiated at a part of the CPW, a temporal electrical short is created, which works as a reflector. Then, a part of the incident MMW is reflected by the optical short. However, the reflectance of the optical short is very small in this stage; thus, a large amount of MMW transmits and is then reflected at the right-side edge. In this case, interference between the reflected MMWs from the optical short and that reflected from the right-side edge is observed. If we move the irradiation position laterally, the length of the propagation path difference between them can be changed. The intensity of the interference changes periodically according to the waveguide's wavelength, which is influenced by the refractive index of the substrate and upper air. If we introduce an LC material into the CPW, the wavelength in the waveguide is modulated by the refractive index of the LC material. Furthermore, if we focus  
on the intensity peak, it gradually decreases with the increasing propagation length of the reflected MMW, which is related to the loss of the substrate and the upper introduced material. Here, a bare CPW fabricated using an Si substrate was used for the experiment to check the validity of this method. In this case, the refractive index and loss of the Si substrate could be evaluated, because the loss is negligible for the upper air, while its refractive index is unity.  
Figure 2 shows a schematic diagram of our measurement system. A Gunn diode oscillator (40 GHz) was used as the MMW source and it was fed to the CPW by the contact probe through an isolator, attenuator, and circulator in turn. The reflected MMW from the CPW was guided to a diode detector through the isolator, and a temporal change in the interference intensity was observed using an oscilloscope. The synchronized intensity change referring to the LD pulse train was monitored using a lock-in amplifier.
## 3 Results  
### Investigation of Induction Period in Supersaturated Drug-Polymer Systems  
The crystallization tendency of drug molecules in solution can be assessed by analyzing their nucleation induction times [40]. Figure 3 illustrates a comparison of the nucleation induction time measurements for RTV, both with and without a polymer. Remarkably, RTV remained at a high concentration in the buffer for up to 30 min, indicating a slow crystallization rate, thus classifying it as a class III compound according to [PERSON]'s classification. The addition of polymers significantly extended the induction time of RTV from 12 h (without a polymer) to approximately 64 h when both PVP K30 and Eudragit L100 were present. This considerable increase is likely due to the strong intermolecular interactions between RTV and the polymers, effectively inhibiting RTV crystal nucleation in the supersaturated solution. The induction times reported in Figure 3 were calculated using high-performance liquid chromatography (HPLC) to monitor the concentration of RTV over time. This method allows for the precise tracking of RTV's concentration and the onset of crystallization. The significant increase in induction time with the addition of polymers highlights their critical role in stabilizing amorphous drug formulations, improving the solubility and stability of poorly water-soluble drugs in supersaturated solutions.  
The data highlight how the presence of polymers significantly influences the crystallization process of RTV. Without the polymer additives, RTV begins to crystallize relatively quickly, with an induction time of around 12 h. This suggests that, under these conditions, RTV has a moderate tendency to form crystals. However, the addition of PVP K30 and Eudragit L100 extends the nucleation induction time to approximately 64 h, indicating a substantial delay in the crystallization process. This extension is likely due to the ability of these polymers to interact with RTV, thereby stabilizing the drug molecules in the solution and disrupting the crystal formation process. Such interactions may form complexes or create a more stabilized environment that hinders the nucleation of crystals, allowing RTV to remain in a supersaturated state for a prolonged period [41]. This finding underscores the importance of polymer selection in pharmaceutical formulations, where controlling the crystallization rate can be crucial for optimizing drug stability and performance.
# Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review
## 4 Discussion
### Ethical and Clinical Considerations  
Smartwatch-based mental health monitoring raises significant ethical and clinical challenges. While these devices support early detection through continuous biometric tracking, they also collect highly sensitive data--such as stress levels, sleep patterns, and heart rate variability--that require careful handling.  
A central ethical concern involves data privacy and security. Without strong safeguards, personal health information may be exposed to misuse by insurers, employers, or unauthorized third parties. Developers must implement secure data storage, anonymization procedures, including secure storage, anonymization protocols, and compliance with privacy regulations such as the General Data Protection Regulation (GDPR [60]) and HIPAA.  
From a clinical standpoint, smartwatch data offers opportunities for remote assessment and early intervention, particularly in underserved populations. However, these benefits depend on rigorous validation. Without clinical trials or regulatory approval (e.g., CE marking or FDA clearance), such devices cannot replace standardized diagnostic methods or inform medical decisions reliably.  
Another concern is how users interpret feedback from these devices. Without clinical guidance, users may misread outputs or rely too heavily on them for self-diagnosis. Developer must ensure that the information presented is clear, contextualized, and supports user autonomy rather than inducing anxiety or false reassurance.  
Ultimately, the ethical integration of smartwatch technologies into mental health care depends not only on technical accuracy but also on responsible design, clinical collaboration, and transparency in communication.
## 3 Results
### Principal Component Analysis and Heatmap Analysis  
To obtain more accurate and intuitive classifications of the volatile components present, PCA was performed for the volatile compounds in the fruits of the 24 apricot cultivars. The PCA diagram clearly reflects the clustering and distribution of different apricot cultivars (Figure 3a), and the principal components explain the changes in the volatile compounds in apricot fruits. Principal component analysis revealed three principal components (PCs), and PC1, PC2, and PC3 accounted for 29.18%, 16.66%, and 12.45% of the total variables, respectively. The cumulative variance contribution rate reached 58.29%, and the distribution of samples in each group was relatively concentrated, indicating good repeatability of samples within groups. Evident separation was found among the 24 tested apricot cultivars. The results indicated that the volatile compounds differed among the cultivars. MTYLK and KMT were distant from those of the other 22 apricot cultivars; these results indicated that the volatile compound contents of the MTYLK and KMT fruits were considerably different from those of the fruits of the other cultivars.  
Figure 3: (**a**) Principal component analysis of the volatile compounds in the fruits of 24 apricot cultivars and (**b**) clustering heatmaps of the volatile compounds in the fruits of 24 apricot cultivars. In this figure, the cultivar name is shown in the horizontal direction, the volatile compound information is presented in the vertical direction, the group is under the group column, and the different colors correspond to different values obtained after the standardization treatment of different relative contents (red represents high content, and green represents low content).  
To directly show subtle differences in the contents and components of the mature fruits of the 24 apricot cultivars, a cluster heatmap of 429 volatile aroma compounds was generated (Figure 3b). Lower contents of the volatile aroma compounds correlate to a greener color, whereas higher contents correlate to a redder color; these results reflect differences between the different cultivars and the contents of the volatile compounds.
## 1 Introduction
### Principles of the Finite Volume Method  
The finite volume method (FVM) divides the computational domain into a series of non-overlapping control volume elements. By integrating the differential equations to be solved within each control volume, a set of discrete equations is derived. A key feature of the discrete equations obtained by FVM is that the integral conservation of dependent variables must be satisfied for any set of control volumes [7; 27]. In contrast, the finite difference method (FDM) only satisfies integral conservation when the grid is extremely fine. FVM can ensure integral conservation even with coarse grids, allowing for more flexible grid division [28; 29; 30; 31; 32; 33]. In FVM, staggered grid discretization (Yee grid) is commonly used [27].  
The Yee grid is a widely adopted grid division method in electromagnetic field simulations and represents a finite-volume extension of tensor-product Cartesian grids with variable grid spacing. In this grid, the spatial sampling points of electric and magnetic field components are staggered such that each electric field component is surrounded by four magnetic field components, and each magnetic field component is surrounded by four electric field components. This arrangement makes the discretization of electromagnetic fields more consistent with physical laws, thus being widely applied in electromagnetic  
Figure 1: Hydraulic fracturing monitoring by Borehole-to-Surface electromagnetic imaging.  
field simulations. The Yee grid facilitates divergence calculations and better simulates characteristics such as electromagnetic wave propagation and scattering [27]. Extensive research has been conducted on its applications in finite difference methods, and further optimization can enable efficient computations in borehole-to-surface electromagnetic imaging scenarios [33; 34; 35].
# Effects of _Gynura bicolor_ on Glycemic Control and Antioxidant Ability in Predicates
## 2 Materials and Methods
### Total Polyphenol and Total Anthocyanin Analysis of GB Extracts  
Total polyphenol contents were determined by the colorimetric method using Folin-Ciocalteau reagent [21]. Briefly, the extracts, blank or standard (galic acid), were added to 2 mL of 2% Na\({}_{2}\)CO\({}_{3}\). After 2 min, 100 \(\mu\)L of 50% Folin-Ciocalteau reagent was added. The mixture was left at room temperature in the absence of light for 30 min. The absorbance of the colored product was measured at 750 nm. The standard curve of gallic acid was used for calculating the polyphenol contents.  
Total anthocyanins were determined by using the pH differential method [22]. Before analysis, two dilutions of the sample were prepared, one for pH 1.0 using potassium hydroxide buffer (0.025 M, KOH) and the other for pH 4.5 using sodium acetate buffer (0.4 M, 54.4 g CH\({}_{3}\)COONa). Samples were diluted 100 times and waited 15 min. The absorbance was measured at 520 and 700 nm. The concentration (mg/L) of each anthocyanin was calculated according to the following formula and expressed as cyanidin-3-glucose equivalent (CGE):  
\[\text{Total anthocyanins}=\frac{\left(\text{A}\times\text{MW}\times\text{DF}\times 1 00\right)}{\varepsilon\times\text{L}}\]  
where A is the absorbance =(A\({}_{520\text{nm}}-\text{A}_{700\text{nm}}\))pH1.0 - (A\({}_{520\text{nm}}-\text{A}_{700\text{nm}}\))pH4.5, MW is the molecular weight (g/mol) = 449.2 g/mol for cyanidin-3-glucose, DF is the dilution factor = 100, and \(\varepsilon\) is the extinction coefficient = 26,900, where L (path length in cm) was 1.
## 2 Technical Backgrounds
### Security Considerations and Requirements
#### 2.4.4 Independence of Authentication Authority  
The security architecture of 5G encompasses the service area and enhances security level through additional authentication between services and devices [5]. The enhanced authentication process for users through the concept of secondary authentication allows network and service providers to perform mutual authentication separately [25]. It can be considered that the mutual authentication authority that the network provider has in the existing network is also granted to the service provider, which requires the user to authenticate the data network additionally. That is, the service provider is independently granted the authority of mutual authentication for the user device, and this independent procedure cannot be omitted for the availability of the network. However, this new mutual authentication process puts an additional load on the overall network system, which puts security and availability in a trade-off relationship. Therefore, the security architecture for 5G should be designed to maintain the maximum independence of network providers and service providers' authority without sacrificing availability.
## 1 Introduction  
Magnetometry is one of the most common tools used in geophysical research for prospecting and archaeology due to the rapidity of data collection and the general applicability of the technique to shallow research [1].  
Different kinds of magnetometers can be found [1-3]; fluxgate magnetometers can be used to measure any component of the geomagnetic field and currently, depending on the sensor alignment, most of them achieve a sensitivity of 0.1 nT and can measure the field continuously. Proton-precession magnetometers measure the Earth's total field; their advantage over fluxgate magnetometers is that sensor alignment is not critical. However, the measurements with this tool cannot be continuous, as the proton-precession magnetometer requires several seconds due to the polarizing and relaxing time needed by the protons, and currently most of them achieve a sensitivity of 0.1 nT. Proton-precession magnetometers with Overhauser-effect (such as the GSM-19 instrument used in this work) can achieve two measurements per second with 0.2 nT absolute accuracy over its full temperature range. Alkalis-vapor magnetometers, usually used for airborne gradiometers, measure the Earth's total field. The orientation of this instrument is not critical as they have a high sensitivity, on the order of 0.01 nT.  
The majority of magnetometer surveys require high-density data and accurate positioning. The most common procedure is to divide the survey area (which is usually small) into data grids. This procedure requires the physical placement of these grids in the field, which increases the work time.  
If precise data location could be collected simultaneously with magnetometer data collection, the additional time required for grid implementation could be avoided because no grid materializations should be needed. The most appropriate tool to carry that out is GPS. The use of GPS for navigation and location is a common theme in many prospecting applications, for example as described in [5].  
Currently, it is easy to find magnetometers with GPS of different precisions and prices [4]. Stand alone options can obtain accuracies of approximately five meters and stand alone can combine WAAS/EGNOS augmentations, achieving precisions of better than one meter, [6]. The best precision (better than 0.1 m) can be obtained with a DGPS-RTK system, but this system requires another GPS device working in the area, and this implies both an increase in time needed and a significant larger budget. Recently, real-time kinematic (RTK) technology has been used in magnetic surveys, [7,8]. Following this procedure, the integration of magnetic data with GPS using a VRS (Virtual Reference Station) instead of RTK technology is presented. VRS technology requires less equipment, time dedication and information than RTK, because differential corrections are sent by the GNSS reference network control centre using the Internet. That means that no master station located on known coordinates is needed near the prospection area. In terms of accuracy, RTK and VRS offer the same results [9].
## 7 Simulation Results and Discussion
### Analysis of Proposed Scheme Results against Existing State of the Art
#### 7.2.4 Energy Consumption  
The depletion of node battery is directly proportional to distance between transmitter node and receiver node. The energy utilization is presented in Figure 11 which clearly states that GRMC-SM outperforms rest of the compared schemes in the plot. Initially, the energy is 2 J at 100 nodes while as the density increases it goes down gradually to less than 0.5 J at 500 node number. The reason of this continuous fall down is that nodes start finding plenty of neighbors within the communication vicinity. As we said in the start of that discussion, energy consumption is directly related to distance, thus, when nodes find neighbors in the transmission range quite often and mobile sonobuoys continuously patrolling the acoustic environment than energy is significantly reduced by deploying more number of nodes. The pattern of energy dissipation in GDGOR-IA is the same, however, because of the consideration of interference, it needs to choose next hop with utmost care.  
Figure 10: PDR plots.  
While, GDGOR-IA has more energy dissipation at node 100 however, it reduces as the node density increases but still has more energy than AUV-CH. In GEDAR and GDGOR-IA, energy consumption is mainly due to the depth adjustment for recovery purpose. At the beginning, fraction of void node is high in sparse network as shown in Figure 8. Hence, more energy consumption occurs due to large displacement of nodes on average to recover communication voids. The trend of energy consumption follows the same behaviour for GEDAR and GDGOR-IA when node number is below 250.  
The AUV-CH and EnOR show moderate energy consumption from beginning till the node density 500. While, GEDAR has high energy consumption initially, but, it reduces suddenly after the node density increases from 150. The EnOR has minimum energy consumption 1.25 J when number of nodes are 500. Whereas, AUV-CH has slightly higher energy dissipation than GDGOR-IA as clearly depicted in Figure 11. Whereas, the dissipation of node battery is high in RE-PBR due to consistent rotation of relay node which helps in balancing energy, however, the involvement of more hops results in high energy consumption as compared to proposed schemes.
## 2 Background and Review of Related Works
### Review of Related Works
#### 2.5.2 Review of Survey on Security Issues of ICVs  
[PERSON] et al. [31] gave a comprehensive survey on the impending cybersecurity for ICVs detailing the challenges and constraints of attack mitigation, three common approaches to IDS in the vehicle network as well as three emerging technologies to solve the network security problems of ICVs. The identified IDS approaches are (1) feature-based detection, (2) information theory and statistical analysis detection, and (3) detection based on machine learning. Although the authors highlighted the need for a robust machine learning-based IDS, they did not clarify what constitutes a robust ML. The XAI in the view of this paper is a promising and robust solution for IDSs for ICVs.  
Mitigating the vulnerabilities and threats to ICVs at the early stage of vehicle development has been acknowledged by automakers to be a veritable means of securing ICVs with minimal cost. [PERSON] et al. in [94] presented a comprehensive survey on threat analysis and risk assessment (TARA) for connected vehicles. They [94] concluded among other issues that the large amount of data collected by ICVs can bring many possibilities for TARA as it could guarantee the accuracy of threat model training. This data-driven TARA process is a new research direction and it will be promising to see the possibility of incorporating TARA and XAI to secure ICVs.  
[PERSON] et al. [95] provided existing defense mechanisms against attacks on ICVs while preferring research directions. The defense mechanisms listed corroborated with other researchers and included cryptography, software vulnerability detection, malware detection, and network security. The paper recommended the use of deep learning over existing ML. However, the issue of computational complexity, huge data traffic, and latency will necessitate the need for deep learning on edge computing. In all, no position was taken on the important role of XAI.  
Following the same pattern as other reviewed works, [PERSON] and [PERSON] [96] exhaustively discussed all detection approaches for smart vehicles where they proposed high-performance intrusion, anomaly, and attack detection methods for all communication dimensions of smart vehicles, particularly external and internal network vehicles. Their statistical study of the detection evaluation criteria further reaffirms the position of this paper that XAI is yet to be studied and explored by most authors. One argument in favor of this is the issue of how to manage the practical implementation of XAI while handling the challenge of computational complexity.  
In conclusion, [PERSON] et al. [97] noted that the application of AI algorithms for inv-vehicle communication has become a new research hotspot. They suggested improving the quality of the data set, reduction in the interference caused by emergencies, improving and optimizing the AI algorithm, and improving the ride comfort of the road.
# An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning
## 6 Conclusions  
In this paper, we propose an intelligent DOA estimation error calibration method based on transfer learning, which learns error knowledge from a small number of samples. We generate a large number of ideal simulation signal samples to train the model constructed based on CNN. The CNN model learns the mapping between input signals and their azimuths. Then, we use a small number of actual signal samples collected in the actual environment to fine tune the model. During the fine-tuning process, the model learns the knowledge of various error factors. Through experimental comparison and analysis, this method can effectively improve the DOA estimation accuracy in practical applications. It is of great significance for intelligent DOA estimation from an ideal simulation to practical engineering application. Despite the preliminary results in this paper, however, fine-tuning is a relatively simple approach of transfer learning. In future work, we will investigate more reasonable transfer learning approaches to make the model better learn the error knowledge in the actual environment.  
Conceptualization, M.Z. and W.Z.; methodology, M.Z. and C.W.; software, C.W. and W.Z.; validation, M.Z. and Y.S.; formal analysis, M.Z.; investigation, M.Z. and C.W.; resources, W.Z.; data curation, W.Z.; writing--original draft preparation M.Z. and C.W.; writing--review and editing M.Z. and W.Z.; visualization, Y.S.; supervision, M.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 61971473.  
Not applicable.  
This work is supported by the National Natural Science Foundation of China 61971473 and Anhui Provincial Natural Science Foundation 1908085 QF291.  
The authors declare no conflict of interest.