esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### X-ray Diffraction (XRD) Analysis  
Figure 3 presents the XRD patterns of the pure PEG, GNPs and PEG/GNPs composites. The low intensity diffraction peak at 26.23\({}^{\circ}\) is the main peak of GNPs, which reveals the random stacking of a few of graphene sheets and represents the crystallization of GNPs [23]. The two sharp diffraction peaks of the PEG appeared at 19.06\({}^{\circ}\) and 23.17\({}^{\circ}\), which indicates a polymer with high crystallinity. After GNPs are uniformly dispersed into the PEG matrix, the typical diffraction peaks of PEG can still be observed and the peak positions do not change. However, the characteristic peak of the GNPs is not shown in the PEG/GNPs composites, which can be ascribed to the fact that the GNPs loading is extremely low compared to that of PEG. One the other hand, the peak intensities of PEG slightly changed after the incorporation of GNPs, especially for the peak at 23.17\({}^{\circ}\). For the composites, the intensities of peaks at 23.17\({}^{\circ}\) are relatively lower than that of pristine PEG when the content of GNPs varies from 0.5% to 1.5%. The possible reason is that the strong interaction between GNPs and PEG can restrict the mobility of PEG molecular chains, resulting in the decrease in crystallization of PEG. Therefore, from the XRD results, it can be confirmed that the PEG/GNPs composites can still possess well-maintained crystallization behaviors, and there is no significant effect of the GNPs on the crystal structure of the pure PEG. Moreover, the XRD results can be seen as an indication that no chemical reaction between GNPs and PEG occur.  
Figure 2: Fourier transform infrared (FT-IR) spectra of GNPs, pure PEG and PEG/GNPs composites with different contents of GNPs.
## 3 Experimental Setup
### THz 2 \(\times\) 2 MIMO Wireless Link and THz Receiver Modules  
Figure 3 shows the THz 2 \(\times\) 2 MIMO wireless transmission link and lens position. The two parallel THz-wave signals from the AIPMs are transmitted over a 3-m 2 \(\times\) 2 MIMO wireless transmission link. In order to focus the wireless THz-wave  
Figure 3: Experimental setup of photonics\(-\)aided THz wireless transmission system over 20-km SSMF and 3-m wireless distance with detailed DSP blocks at Tx- and Rx-side, including **(a)** Tx-side DSP block, **(b)** the optical spectra of the optical signal with tunable optical LO after optical coupler (0.03 nm resolution). Insets: **(c)** 3-m 2 \(\times\) 2 MIMO wireless transmission link. **(d)** Lens position at THz receiver side. **(e)** Rx-side DSP block with three proposed J\(-\)DBN optional schemes.  
are deployed to maximize the received THz-wave signal power and are manually aligned. Lenses 1, 2, 5 and lenses 3, 4, 6 are aligned with the X-polarization and Y-polarization wireless link, respectively. Lenses 1-4 are identical, each having a 20-cm focal length and 10-cm diameter. The smaller lenses 5 and 6 have a 10-cm focal length and 5-cm diameter, which are used for THz-waves' high-accuracy alignment to horn antennas (HA). For the X-polarization (Y-polarization) THz wireless link, the longitudinal separation distance between the AIPM and lens 1 (lens 3), lens 2 (lens 4), and lens 5 (lens 6) and the receiver HA are 0.2 m, 3 m, and 5 cm, respectively. The lateral separation between the two AIPMs and two HA pairs is 25 cm. In order to avoid multi-path fading from reflections on the optical table, the conversion modules are placed at the height of 20 cm. Photos of the 3-m wireless transmission link and lens position are shown in Insets Figure 2(c) and Figure 2(d), respectively.  
At the THz-wave receiver end, THz-wave wireless signals are received with two parallel THz-band HAs with 26-dBi gain. Electronic LO sources drive two identical THz receivers to implement analog down-conversion for X- and Y-polarization THz-wave wireless signals. Each consists of a mixer, a \(\times\)12 frequency multiplier chain, and an amplifier, and operates at 500 GHz. Then, the down-converted X- and Y-polarization intermediate-frequency (IF) signal at 20 GHz is boosted by two cascaded electrical low-noise amplifiers (LNAs) with a 3-dB bandwidth of 47 GHz and captured by a digital oscilloscope with a 128-Gsa/s sampling rate.
## 3 Results  
### Raman Spectroscopy Analysis  
As a non-destructive detection method, Raman spectroscopy can fully display the biological molecular information present in a substance, such as proteins, lipids, and carbohydrates [38]. In this experiment, the spectral data in the 1289 to 4000 cm\({}^{-1}\) waveband was studied. In order to better observe the differences in the spectral curves, reference materials were consulted, and the difference in the spectral curves was plotted. In Figure 8, it can be seen that the differences in spectral images of different microorganisms are mainly reflected in the positions and heights of the peaks. For example, at 1300 cm\({}^{-1}\), the CH2 twisted vibration of lipid molecules can be observed, and at 1542 cm\({}^{-1}\), the stretching vibration of the indole ring of tryptophan molecules can be found. The amide I band vibration of protein molecules can be observed at 1660 cm\({}^{-1}\), and there are significant differences in the peak intensities of the four pathogens at this position. The peak at 2715 cm\({}^{-1}\) may be caused by the stretching vibration of C-H in lipid molecules, while the interval between 2800 and 3000 cm\({}^{-1}\) is believed to contain multiple possible molecular vibrations. The main peak positions and bands, as well as the corresponding molecular vibrations, are displayed in Table 1.  
Figure 8: Spectral differences of four pathogenic bacteria. The main peak positions in the figure have been marked, and some peaks with significant differences are marked with gray icons.
# The Response of NDVI to Climate Change in the Lowest and Hottest Basin in China
## 2 Materials and Methods
### Data Sources
#### 2.2.1 NDVI Data  
This study utilized the MOD13A3 dataset from the MODIS Terra satellite (2001-2020), which provides NDVI products with a temporal resolution of 30-days and spatial resolution of 1 km. The data preprocessing workflow included the following: (1) clipping and mosaicking of the study area using the Python 3.9 GDAL library; (2) coordinate system transformation to WGS84 via ArcGIS 10.8; (3) removal of outliers caused by cloud contamination, snow cover, and sensor noise using the Savitzky-Golay (S-G) filtering algorithm combined with the quality assessment (QA) band; and (4) generation of seasonal and interannual NDVI datasets through Maximum Value Composites (MVC).  
Figure 1: Situations of the study area: (**a**) digital elevation model (DEM); (**b**) land use types; (**c**) precipitation (P); and (**d**) temperature (T). Note: The base map is made from the standard map of the Ministry of Natural Resources, with the review number GS (2025) 1508, and no changes have been made to the boundary of the base map. The same applies below.
## 3 Governing Equations  
### Magnetic Field FEM Formulation  
The electromagnetic field model is obtained with the consideration of assumptions that the conduction current is assumed to be dominant and therefore the displacement current is neglected. It is based on [PERSON]'s equations and on the concept of magnetic vector potential (MVP) in a quasi-stationary state, defined as follows [39; 40; 41]:  
\[\overrightarrow{\
abla}\times\left(\frac{1}{\mu}\left(\overrightarrow{\
abla} \times\overrightarrow{A}\right)\right)+j\omega\sigma\overrightarrow{A}= \overrightarrow{J}_{S} \tag{3}\]  
\(\overrightarrow{A}\)--the magnetic vector potential, \(\overrightarrow{J}_{S}\)--the source current density, \(\mu\)--the magnetic permeability, \(\sigma\)--the electrical conductivity, and \(\omega\)--the angular frequency.  
In the heat-affected zone, the relative permeability and the electrical conductivity are noted with indices (1) and given as \(\mu_{1}=\left[\mu_{a}\dots\dots\dots\mu_{T_{HAZ}}\right]\) and \(\sigma_{1}=\left[\sigma_{a}\dots\dots\dots\sigma_{T_{HAZ}}\right]\). \(T_{HAZ}\) denotes the total number of triangular elements obtained from the finite element meshing in the heat-affected zone. After the mesh creation step, the electrical conductivity and the relative permeability are distributed in accordance with the number of elements contained in the heat-affected zone area, exploiting the generation of pseudo-random numbers to reproduce the distribution of these physical properties on the basis of the experimental measurement. Then, the obtained algorithms are combined with the finite element code [25].  
Applying the vectors weighting functions \(\alpha_{i}\), the weighted Galerkin residual equation is obtained from Equation (1), as set out below:  
\[\iint\limits_{\Omega}\alpha_{i}\left(\overrightarrow{\
abla}\times\left( \frac{1}{\mu}\left(\overrightarrow{\
abla}\times\overrightarrow{A}\right) \right)+j\omega\sigma\overrightarrow{A}\right)d\Omega=\iint\limits_{\Omega} \alpha_{i}\overset{\rightarrow}{J}_{S}d\Omega \tag{4}\]  
After applying discretization and assembly, the following algebraic system is derived:  
\[([K]+j\omega[M])[A]=[F] \tag{5}\]  
\[K_{ij}=\iint\limits_{\Omega}\frac{1}{\mu}\overrightarrow{\
abla}\alpha_{i} \overset{\rightarrow}{\
abla}\alpha_{j}\:d\Omega \tag{6}\]  
\[M_{ij}=\iint\limits_{\Omega}\sigma\:\alpha_{i}\:\alpha_{j}\:d\Omega \tag{7}\]  
\[F_{i}=\iint\limits_{\Omega}\alpha_{i}\overset{\rightarrow}{J}_{S}d\Omega \tag{8}\]  
where: \([K]\), \([M]\), \([A]\), and \([F]\) are the stiffness matrix, dynamic matrix, unknowns vector, and source vector, respectively.
# GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks
## 4 Proposed System Overview and Models
### System Models
#### 4.2.3 Attack Information Model  
The \(BS\) can determine the expected event reports generated by the \(CH\) for a query-response session. Upon receiving legitimate reports at the \(CH\), the counter for such reports is incremented by one at the \(BS\). Here, no extra cost for the messages or energy is needed at the sensors.  
Similarly, fabricated event reports are filtered in the path or at the \(BS\). If a fabricated report was dropped on the path, the \(BS\) can know after a predefined was time elapsed. In a second case, the fabricated event report will finally be dropped at the \(BS\) if it fails to be detected by en-route filtering nodes.  
Figure 8: First order radio model.  
Figure 7: Sensor field.  
Therefore, the \(BS\) knows the total of fabricated and legitimate reports by exploiting respective counter information without using extra energy or messages on the sensor nodes. Using this information at counters, the current value of \(FTR\) can be calculated. The computational cost at the \(BS\) can be justified because of a sufficient amount of resources.  
For \(m\) events in the WSN, the current \(FTR\) can be calculated by Equation (10) as  
\[FTR=\frac{\sum\limits_{i=1}^{m}(1\times F_{i})}{\sum\limits_{i=1}^{m}\big{\{}1 \times F_{i}+1\times L_{i}\big{\}}}, \tag{10}\]  
where \(F_{i}\in[0,1]\), \(L_{i}\) depicts legitimate reports, and \(F_{i}\) indicates the fabricated, defined as follows:  
\[F_{i}=\begin{cases}1\text{ if the }i_{in}\text{report is false,}\\ 0\text{ if the }i_{in}\text{report is valid.}\end{cases} \tag{11}\]  
In Equation (11), \(i\) represents the total event count from 1 to \(m\).
###### Abstract  
The recent tailings storage facility (TSF) dam failures recorded around the world have concerned society in general, forcing the mining industry to improve its operating standards, invest greater economic resources, and implement the best available technologies (BATs) to control TSFs for safety purposes and avoid spills, accidents, and collapses. In this context, and as the era of digitalization and Industry 4.0 continues, monitoring technologies based on sensors have become increasingly common in the mining industry. This article studies the state of the art of implementing sensor technologies to monitor structural health and safety management issues in TSFs, highlighting advances and experiences through a review of the scientific literature on the topic. The methodology applied in this article adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and utilizes scientific maps for data visualization. To do so, three steps were implemented: (i) a quantitative bibliometric analysis, (ii) a qualitative systematic review of the literature, and (iii) a mixed review to integrate the findings from (i) and (ii). As a result, this article presents the main advances, gaps, and future trends regarding the main characteristics of the sensor technologies applied to monitor TSF structural health and safety management in the era of digitalization. According to the results, the existing research predominantly investigates certain TSF sensor technologies, such as wireless real-time monitoring, remote sensors (RS), unmanned aerial vehicles (UAVs), unmanned survey vessels (USVs), artificial intelligence (AI), cloud computing (CC), and Internet of Things (IoT) approaches, among others. These technologies stand out for their potential to improve the safety management monitoring of mine tailings, which is particularly significant in the context of climate change-related hazards, and to reduce the risk of TSF failures. They are recognized as emerging smart mining solutions with reliable, simple, scalable, secure, and competitive characteristics.  
Keywords:mine tailings; safety; risks; sensors; remote sensing; real-time monitoring; wireless; Internet of Things; Industry 4.0
## 3 Bridge Inspection  
Bridges are vital for daily transport and communication across cities and land masses. Regular and close inspections are necessary to ensure their safety, integrity, and service-rendering ability. Bridge inspections ensure the detection of faults/kinks in the preliminary stages and directed maintenance can be planned and executed. Bridge inspections in Europe typically incorporate several types of inspections. The first is a routine or regular visual examination; the other is a significant inspection due to particular events or concerns. There is also a special inspection that involves the microscopic examination of specific components or the conduct of a loading test for the formulation of a diagnosis [73]. Irrespective of the type of inspection carried out, all of them lead to holistic conclusions about the health of the bridge and its functional capability. Alongside this, it also ensures maintenance strategies and solutions for risk mitigation.  
NDT techniques enable timely management and maintenance of bridges under a bridge management system without causing damage themselves [74]. Various NDT techniques are now readily available, ensuring high-intensity inspections to conduct structural monitoring and detailed inspections [75]. One such technique at the network level is the Interferometric Synthetic Aperture Radar (InSAR), which is self-sufficient for continuous bridge monitoring. Its major characteristics include the microscopic detection of surface changes with good precision [76]. As a constant monitoring system, it surpasses conventional inspection methods. It might, however, overlook some upcoming deterioration changes or changes in dormant periods (the time lapse between two scheduled periodic inspections) [77].  
Among the technologies that can be applied during the inspection phase are LiDAR (light detection and ranging) and GPR (ground penetrating radar). Both effectively work at the surface and sub-surface levels to give a grounded assessment of bridge conditions [78]. They both provide baseline data related to bridge condition and reference points for future inspections to enable the assessment of the deterioration rate. The only shortcoming is differentiating gathered data from data integration and visualisation aspects. This shortcoming can be mitigated if the bridge management system functions as a unified unit combining high-resolution point clouds provided by LiDAR and sub-surface information made available by GPR. These data, with visual and structural inspection information, provide a comprehensive overview of the bridge condition, thereby enhancing the accuracy of condition assessment and facilitating precise decision-making for the maintenance of the bridge.
# Signature Split Method for a PQC-DSA Compliant with V2V Communication Standards
## 3 Proposed Simulation  
### Selection of Crypto Codes  
The purpose of this study is to apply PQC-DSA to V2 Verifier and, at the same time, satisfy the correctness of the WAVE protocol, which was not achieved by Crystals-Dilithium in a previous study [7]. The target PQC-DSAs in this study were initially Crystals-Dilithium,  
Figure 1: Overview of V2 Verifier.  
Figure 2: (**left**) Structure of “run_remote” and “run_local” and (**right**) components of payload.  
Falcon, and SPHINCS+, as these are the target algorithms chosen by the NIST for standardization after [5] was published. However, the signature size of SPHINCS+ [11] is in the order of tens of kilobytes, which is not suitable for simulation considering the latency of the specification of V2V protocol and the capacity of SRAM in embedded devices used as the OBU. Therefore, we chose the Crystals-Dilithium and Falcon as the target algorithms. We investigated state-of-the-art optimization implementations in OBUs, which are widely used in autonomous driving environments. Among various optimization studies, we selected Crystals-Dilithium [2] and Falcon [12] implementations performed on the ARMv8-based OBU.
## 3 Case Study
### Prediction Results
#### 3.4.2 Warning Predictor for SMA  
Based on the prediction results, we proposed a warning predictor integrating ML algorithms and DL algorithms for SMA parameters. Once all algorithms predict that the SMA will deviate significantly from DTA in the future drilling cycle, the predictor can send a warning message in advance to provide auxiliary support for the machine operators to adjust the tunneling parameters. However, the prediction performance of various algorithms for SMA parameters was uneven, and the calculation results were also different. Accordingly, when the prediction results of the algorithms are inconsistent, the following warning criteria are used to judge the SMA situation:  
* The prediction results of the two best-performing algorithms shall prevail.
* When the two best-performing algorithms fail to reach the same standard, the votes of all predicted results shall prevail.
* When it still cannot be determined by voting, the algorithms are given different weights according to the prediction index EVS, and the judgment remade.  
The specific prediction process is shown in Figure 9. This paper does not consider the KNN algorithm with poor prediction performance, so the SMA situation can be judged by the first two criteria.  
The following is an example of an actual VDST deviation (exceeding \(\pm\) 70) during the shield tunneling process, which illustrates the basic idea of the warning scheme. As shown in Figure 10, VDST gradually increased from the 130 th drilling cycle, which indicates that the shield tail migrated downward in the vertical direction, and EPB started to move away from the DTA. In the 139 th drilling cycle, the LSTM and GRU algorithms successfully predicted the VDST deviation based on the data of the previous five drilling cycles (Figure 10b). Then the machine operators took some corrective measures, after which the VDST returned to the reasonable range successfully, and the warning was lifted in the 149 th drilling cycle (Figure 10c).  
In conclusion, when the SMA is about to deviate significantly, the predictor can warn in time and provide the machine operators with preparation time to adjust the tunneling parameters. Compared with the manual control of the SMA, AI algorithms have almost no delay and can provide real-time future SMA information. The proposed SMA warning predictor can provide a basis for future correction research and a reference for similar projects. Using the basic AI algorithms, this paper constructed the warning predictor by backtracking a certain length of historical data. When the computing power and the database are further improved and enriched, more historical data will be able to be used for predicting a longer-distance SMA situation of EPB.  
Figure 10: Prediction results of VDST by warning predictor: (**a**) a case of actual VDST deviation; (**b**) warning start; (**c**) Warning end.  
Figure 9: Process of warning predictor for SMA.
# Internet of Things and Its Applications: A Comprehensive Survey
## 5 IoT Applications
### Healthcare Applications
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Focus Area** & **Application** & **Protocol** & **Device** \\ \hline
[91] & Disease management system to & A guide for IoT & & Independent \\  & improve reliability & healthcare service & - & hand-held device \\  & & providers & & and smartphones \\ \hline
[92] & Healthcare monitoring for & Battery energy & & \\  & chronic diseases like depression & efficiency approach & & \\  & and diabetes & using a machine & - & Wearable devices \\  & & learning technique & & \\ \hline
[93] & Healthcare monitoring system & & & \\  & which uses low-cost sensors and & & & \\  & ensures a lower energy & & & \\  & consumption & & & \\ \hline
[94] & Mobile medical home monitoring & & & \\  & system to improve the rapidity of & & & \\  & factor measurements and ensure & & & \\  & a low energy consumption & & & \\ \hline
[95] & Adaptive security management & Adaptive security & & \\  & based on metrics to enhance & & management & - & Boy sensors \\  & security & & & \\ \hline
[96] & Synthesis method for e-health to & A new structure for & & In connection with \\  & ensure high availability & & e-health & & the patient’s body \\ \hline
[97] & IEEE 802.15.4 transceiver with a & & & \\  & low error rate and a higher & & & \\  & probability & & & \\ \hline
[98] & An efficient protocol to counter & Algorithm and & Multi-tier device-based & \\  & PUEA attacks & structure protocol & authentication protocol & - \\ \hline
[99] & Biotelemetry application to & & & \\  & ensure lower costs and energy & & - & Wearable antennas \\  & consumption & & & \\ \hline
[100] & Energy-efficient routing protocol & & & \\  & to ensure a lower energy & Implementation and & & \\  & consumption & algorithm & & \\ \hline
[101] & Super-resolution algorithm for & & & Multi-kernel SVR \\  & healthcare images with slower & & & learning-based \\  & response time and cost & & & image \\ \hline
[102] & Healthcare monitoring system & A new algorithm for & & \\  & with lower delay rate and & healthcare & NB-IoT & - \\  & time response & monitoring system & & \\ \hline
[103] & Human factor evaluation in & & It promotes data & & EPR system in \\  & information exchange in the & & & hospital emergency \\  & healthcare environment & healthcare providers & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: IoT healthcare applications.