esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Location Estimation  
We describe location estimation based on the projected beacon points. As shown in Figure 3(a), the projected first-heard beacon point is denoted by \(F\) and the projected last-heard beacon point is denoted by \(L\). The projected point of the beacon received just before the first-heard beacon point is defined as the projected prior-heard beacon point \(F^{\prime}\) (e.g., in Figure 2(b), the projected point of \(B_{0}\)), and the projected point of the beacon after the last-heard beacon point is defined as the projected post-heard beacon point \(L^{\prime}\) (e.g., in Figure 2(b), the projected point of \(B_{7}\)).  
Figure 2: System environment and beacon point selection and projection for LDB. (**a**) System environment with an AUV that has a directional transceiver; (**b**) Selection and projection of beacon points from among the received beacons.  
Four circles are then drawn with radius \(r_{b}\) centered at the four points \(F^{\prime}\), \(F\), \(L\), and \(L^{\prime}\). Because the sensor node should be located outside the circles centered at \(F^{\prime}\) and \(L^{\prime}\) and inside the circles centered at \(F\) and \(L\), the intersection areas \(ABCD\) and \(A^{\prime}B^{\prime}C^{\prime}D^{\prime}\) represent possible locations of the sensor node. One of the two points, \(\tilde{S}\) and \(\tilde{S^{\prime}}\), within the two areas will be estimated as the location of the sensor node. Here, \(\tilde{S}\) is the midpoint of \(B\) and \(D\) and \(\tilde{S^{\prime}}\) is the midpoint of \(B^{\prime}\) and \(D^{\prime}\). When just one projected first-heard beacon point and one projected last-heard beacon point are used, two possible node locations are found. The choice between the two points \(\tilde{S}\) and \(\tilde{S^{\prime}}\) can be made after an additional two beacon points are obtained, as shown in Figure 3(b). In LDB, the first two beacon points are used to compute two possible points for the location of a sensor node based on geometric constraints and the next two beacon points are used for the choice between the two possible points.
## 2 Materials and Methods
### Homology Modeling  
Homology models of both the active and the inactive state were built using the MOE Homology Model Tool. Only aligned residues were overridden. C/N-terminus outgap modeling was disabled, while automatic detection of disulfide bonds was enabled. The maximum number of main chain models was set to 10 and side chain sampling was set to 1 at 300 K. Differently from the intermediates, the final state was ionized at physiological pH (7.4 units) and subjected to medium minimization in order to moderately relieve the steric strain, according to the AMBER12:EHT force field. The final output model was checked, inspecting the wellness of its assigned biophysical parameters and scores, along with good superposition with the reference structure, transmembrane domain side chain rotamers and exposure, dissociation state of charged residues and tautomeric state of the histidine residues. For inactive state model of the hA\({}_{3}\) AR, the following template _PDB_ structures were adopted and then subjected to the comparison (see Section 3): 3 PWH, 5 NM2 (A\({}_{2a}\)) and 5 UEN (A\({}_{1}\)). For the active state model 2 YDO (A\({}_{2A}\)-based) and 6D9H (A\({}_{1}\)-based) PDB templates were used. All generated homology models passed with all major biophysical checkpoints, basing on the MOE tool. For the adenosine bound hA\({}_{1}\) AR crystal structure (PDB code: 6D9H), two homology models were carried out by disabling or enabling the _Induced Fit_ option, respectively. This option permits the side chains of the amino acids defining the ligand binding pocket to adapt their conformational position around the ligand during the construction of the homology model.
# Assessing the Techno-Economic Feasibility of Bamboo Residue-Derived Hard Carbon
## 3 Results
### Operating Cost  
Figure 4 shows the operating costs of hard carbon production across the ten provinces, along with the detailed breakdown for the Fujian case. Fujian, with the largest production scale, exhibits the highest annual OPEX at approximately CNY 3.97 billion, followed by Guangxi at CNY 3.08 billion. Provinces with smaller bamboo residue availability, such as Anhui and Yunnan, require substantially lower operating costs, generally below CNY 800 million.  
Utilities represent the dominant share of the operating costs, particularly due to the high electricity consumption required for the pyrolysis and graphitization processes. In the case of Fujian, utilities account for nearly 60% of the total OPEX, followed by sulfuric acid input (21%), bamboo residue procurement (14%), and labor (1%) (Figure 4, right). Other components such as maintenance, local tax, and insurance together constitute approximately 4% of the total.  
This cost structure indicates that the hard carbon industry is energy-intensive and feedstock-sensitive. The H\({}_{2}\)SO\({}_{4}\) consumption cost, while relatively high, remains consistent across provinces due to fixed stoichiometric requirements. Labor costs are minor but reflect regional salary differences, while maintenance and tax costs scale proportionally with FCI, thus remaining relatively stable across locations.  
Notably, despite similar bamboo availability, provinces like (Guangdong vs. Jiangxi) and (Zhejiang vs. Hunan) show different OPEX patterns--largely due to differences in electricity pricing and slightly from the feedstock acquisition cost (due to differences in transportation distance, see Table 4). This demonstrates the importance of regional electricity price policies and supply chain optimization for reducing costs in future hard carbon deployment.  
Figure 4: Operating cost for ten provinces (**left**); breakdown operating cost for Fujian case (**right**); corresponding numerical results are presented in Table 3.
# Laser Vibration Characteristics of Marble Specimens and Failure Criterion
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Phase-Stage**} & \multirow{2}{*}{**Profile**} & **Dominant Frequency** & **Maximum Amplitude** & **Second Dominant Frequency** & **Second Maximum Amplitude** & **Wide Frequency** & **Maximum Amplitude** \\  & & **(Hz)** & **(mm/s)** & **(Hz)** & **(mm/s)** & **(Hz)** & **(mm/s)** \\ \hline \multirow{3}{*}{(a)-(f)} & Unimodal & \multirow{3}{*}{27.5} & \multirow{3}{*}{0.01325} & \multirow{3}{*}{/} & \multirow{3}{*}{/} & \multirow{3}{*}{/} & \multirow{3}{*}{} \\  & low & frequency & & & & & \\ \hline \multirow{3}{*}{(b)-(f)} & Bimodal low & \multirow{3}{*}{10} & \multirow{3}{*}{0.00801} & \multirow{3}{*}{27.5} & \multirow{3}{*}{0.00656} & \multirow{3}{*}{} \\  & frequency & & & & & \\ \hline \multirow{3}{*}{(c)-(f)} & Bimodal low & \multirow{3}{*}{7.5} & \multirow{3}{*}{0.01616} & \multirow{3}{*}{30} & \multirow{3}{*}{0.01679} & \multirow{3}{*}{} \\  & frequency & & & & & \\ \hline \multirow{3}{*}{(d)-(II)} & Wide & \multirow{3}{*}{/} & \multirow{3}{*}{/} & \multirow{3}{*}{/} & \multirow{3}{*}{/} & \multirow{3}{*}{30–1070} & \multirow{3}{*}{0.00932} \\  & frequency & & & & & & \\ \hline \multirow{3}{*}{(e)-(II)} & Bimodal low & \multirow{3}{*}{10} & \multirow{3}{*}{0.0075} & \multirow{3}{*}{30} & \multirow{3}{*}{0.00695} & \multirow{3}{*}{} \\  & frequency & & & & & \\ \hline \multirow{3}{*}{(f)-(III)} & Unimodal & \multirow{3}{*}{7.5} & \multirow{3}{*}{0.04553} & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & low & frequency & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of frequency spectra in each phase in the Brazilian test. Consequently, the appearance of wide frequency spectra could be regarded as an early warning of the failure of marble specimens. Furthermore, relatively high velocity amplitudes, i.e., 3.92 mm/s under uniaxial compression stress and 0.35 mm/s under indirect tensile stress, were observed, which could have an alarming effect for wide frequency signals. Compared with AE monitoring, it is especially important that the LDV technology is remote, non-contacting, and much earlier in warning, which is significant for forecasting geological disasters.
## 5 Conclusions  
In this paper, a local moment-driven robust reversible data-hiding (LM-RRDH) using pairwise embedding has been proposed. The proposed LM-RRDH scheme was a spatial domain based RRDH scheme which could provide the security to the hidden message against unintentional modifications. The proposed LM-RRDH first decomposed the image into LSB and MSB plane and embedded the secret data into the MSB image. For an efficient embedding, the proposed scheme divided the image into sub-blocks and segregated the pixels of each block to form highly correlated groups. Next, the secret data could be embedded into the pixels of each group by selecting an embedding strategy adaptively. As a result, the proposed LM-RRDH could limit the distortion to the human visual system  
Figure 6: PSNR vs. EC evaluation for the test images [28; 29; 30].  
while providing a decent embedding capacity. The experimental results have shown that the proposed LM-RRDH could provide better embedding capacity, while keeping a good PSNR, than the existing spatial-domain-based RRDH schemes. Further, the proposed LM-RRDH scheme provided the same level of robustness as the SBDE and TLE schemes. However, it can be vulnerable to various attacks when modifying higher significant bits. In future studies, a new scheme will be suggested to improve the embedding capacity while maintaining the same degree of robustness additionally.  
Conceptualization, Y.V.S. and S.K.; methodology, Y.V.S. and S.K.S.; visualization, S.K.; writing--Y.V.S. and K.-H.J.; writing--review and editing, Y.V.S., S.K.S., S.K. and K.-H.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Brain Pool program funded by the Ministry of Science and ICT through the National Research Foundation of Korea (2019H1D3A1A01101687, 2021H1D3A2A01099390) and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1 IIA3049788).  
Not applicable.  
The data can be made available on request to any of the authors.  
The authors declare no conflict of interest.
## 3 Experimental Setup and Results
### Target Tracking  
We tested the integrated wireless Doppler radar system to track a target moving within the field of view of the radar transceiver. We checked whether the radar can track the changes in range and velocity dynamically. In a specific experiment, we started to track the target starting at a distance of 2.3 m from the receiver. The velocity of the train was 0.74 m/s, _i.e._, the max velocity corresponding to dial position D = 100. We captured four snapshots while the train was moving at the given velocity toward the radar mode.  
Figure 7: Range and velocity intensity plots for target tracking experiment: (**a**) Capture 1; (**b**) Capture 2; (**c**) Capture 3; (**d**) Capture 4.  
The results are illustrated on the range-velocity intensity plots in Figure 7. Based on the Doppler signatures, the range and velocity has been estimated, see Table 5. Except for the first capture, the train is moving toward the radar at a velocity close to the expected constant value (0.75 m/s). The range is in general decreases at a constant rate, except for the first capture. The velocity in capture 1 is lower than the other snapshots. This discrepancy is attributed to the fact that during the first capture the train was moving at the curved section of the track, and had lower speed. These preliminary results of tracking are encouraging and further elaborate tracking experiments are underway.
## 1 Introduction
The islanding detected by the selected harmonic distortion (SHD) was calculated by the KF after the different changes in the power system. This changes was detected by a residual signal. An unintentional islanding detection method based on the combination of the main criterion of switch status and the auxiliary criterion based on the no-current and difference-voltage criteria was proposed for \"hand-in-hand\" DC distribution network in [14]. However, the power supply reliability of the DC distribution network must be considered. A novel islanding detection approach based on the relationship between the angular frequency and the derivative of the equivalent resistance observed from the small-scale synchronous generators in microgrids, including small-scale synchronous generators, was proposed in [15]. The derivative of the equivalent resistance observed from the small-scale synchronous generators approaches to zero when the islanding occurs in small-scale synchronous generator side. In [16], we proposed an active islanding detection method by injecting perturbation signal to make the power imbalance and voltage fluctuation exceed the thresholds faster and easier in the islanding condition. This method can rapidly and efficiently detect the islanding condition. However, the cancellation problem when using this method in multi-PV operation has not been considered. Many converters are connected in parallel because of their capacity limitation in a large-scale solar system. Therefore, the cancellation issue can occur in a multi-PV operation. On basis of the literature, no research has been conducted on the cancellation issue in multi-DG operation when using active islanding detection method. In this study, the cancellation issue when using the active Islanding Detection Method (IDM) in [16] is analyzed. Moreover, the solution improves the islanding detection method in [16] to eliminate the injected signal cancellation is proposed and verified by Matlab/Simulink. The system in this paper is the DC grid connected-photovoltaic system. The rest of this paper is organized as follows: The system description and cancellation problem are presented in Section 2. The simulation results of the cancellation problem is analyzed in Section 3. The solution for this problem is shown in Section 4. The conclusions are presented in Section 5.
## 3 Results and Discussion
### PL Spectroscopy  
Using the 532 nm wavelength laser as a light source, the PL spectra of the HTTP-treated diamonds were measured in a liquid nitrogen environment. The PL spectra of the five samples obtained are shown in Figure 6.  
It can be seen from Figure 6 that except for the weak emission peak of the sample R-YG-1 at 552 nm, no other samples show emission peaks here. The luminescence peak at 552 nm is the intrinsic peak of the diamond, but because the fluorescence of the diamond sample is too strong, it will weaken or even disappear completely. In addition, the five samples showed luminescence peaks at 637 nm and 575 nm, but the relative intensities of the two peaks were different in different color samples: the yellow (R-Y-1, R-Y-2), yellow-green (R-YG-1), and brown samples (R-B-1) had a stronger emission peak at 637 nm than at 575 nm, while the purple-red sample (R-P-1) showed the opposite phenomenon.  
Figure 5: Micro-infrared absorption spectra of samples.  
The peak at 575 nm corresponds to the (N-V)\({}^{0}\) center, and the peak at 637 nm corresponds to the (N-V)\({}^{-}\) center. According to the study of [PERSON] et al. [14], when the temperature of the HTHP treatment is higher than 1960 \({}^{\circ}\)C, the aggregate A in the diamond may decompose to produce isolated nitrogen, and isolated nitrogen may combine with a hole to form an uncharged (N-V)\({}^{0}\) center. However, the (N-V)\({}^{0}\) center is very likely to capture an electron during HTHP treatment to form a negatively charged (N-V)\({}^{-}\) center. According to most studies, the peak strength of HTHP-treated diamonds at 637 nm is stronger than that at 575 nm, mainly because more aggregates of nitrogen are decomposed under HTHP treatment, resulting in a higher concentration of the (N-V)\({}^{-}\) center [26]. The reason for the opposite phenomenon in the purple-red samples needs further experimental investigation. In the photoluminescence spectrum test of diamond samples processed by the US Lotus Colors Diamond Company, [PERSON] and [PERSON] [27] also found that the (N-V)\({}^{0}\) center and (N-V)\({}^{-}\) center intensity of purple diamonds did not have an obvious uniform rule, but most of the (N-V)\({}^{-}\) center intensity was stronger. Whether there is an obvious relationship between this phenomenon and color remains to be further confirmed.
## 3 Results and Discussion
### Comparison of GC-MS and GC-IMS  
Both GC-MS and GC-IMS were able to distinguish the flavor of samples at different processing stages, although GC-IMS showed the difference of flavor between samples more intuitively. As shown in Figure 6, the qualitative results of volatile compounds revealed certain differences between the two technologies. Seventy-five volatile compounds were identified by GC-MS, while 79 were detected by GC-IMS, even though only 55 were ultimately identified. Compared with GC-MS, the number of volatile compounds identified by GC-IMS was lower, which was consistent with previous reports. This might have been related to the limitation of IMS database and the inability to characterize volatile compounds [34; 35]. The numbers of hydrocarbons, aldehydes and terpenes identified by GC-MS were much higher than those identified by GC-IMS, while the number of esters identified by GC-IMS was much higher than that identified by GC-IMS. Additionally, five acids and three phenolic compounds were identified by GC-IMS which were not identified by GC-MS. The contents of acids and phenols in chicken were very low. GC-MS was not sensitive to low level volatile compounds, which were difficult to detect, while GC-IMS was highly sensitive to trace volatile compounds [17; 36]. Esters identified by GC-IMS were mostly with branched chains, while acids were low-grade fatty acids and fatty acids with branched chains. These compounds had relatively low boiling points, and GC-IMS was more sensitive to high-volatile compounds than GC-MS [37; 38]. Note that dimers of five compounds were identified by GC-IMS, depending on the analyte concentration, their proton affinity or drift tube temperature [12; 39]. Therefore, the combined use of GC-MS with GC-IMS made up for their limitations and provided more comprehensive odor profiles of the samples. This study also found that GC-MS was more sensitive to hydrocarbons, aldehydes and terpenes, while GC-IMS was more sensitive to trace compounds such as esters, acids and phenolics. The combination of GC-MS and GC-IMS was shown to be able to provide a comprehensive flavor profile for Daokou braised chicken during processing.  
Figure 6: Comparison of the number of volatile flavor compounds detected by GC-MS and GC-IMS.
# A Hybrid Mapping Method with Position and Stiffness for Manipulator Teleoperation
## 4 Stiffness Mapping Method for Compliant Teleoperation
### Stiffness Identification System at the Human Arm Endpoint
#### 4.3.1 Feature Extraction of sEMG Signals  
The feature extraction of sEMG signals is an important item in the stiffness identification of human arm. In this paper, the following seven features of sEMG signals are extracted. The \(x_{i}\) is the \(i\)th channel value of sEMG signals, \(N\) is the window length of time domain, SVD represents the singular value decomposition, and WPT represents the wavelet packet transform.  
(1) Modified absolute mean type1 (MAV1).  
\[\begin{array}{l}MAV1=\frac{1}{N}\sum\limits_{i=1}^{N}w_{i}|x_{i}|\\ w_{i}=\left\{\begin{array}{cc}1,&0.25N\leq i\leq 0.75N\\ 0.5,&\text{other}\end{array}\right.\end{array} \tag{21}\]  
(2) Zero crossing point (ZC).  
\[\begin{array}{l}ZC=\sum\limits_{i=1}^{N-1}\left[f(x_{i}x_{i+1})\cap\left|x_{ i}-x_{i+1}\right|\geq\text{threshold}\right]\\ f(x)=\left\{\begin{array}{cc}1,&x\geq\text{threshold}\\ 0,&\text{others}\end{array}\right.\end{array} \tag{22}\]  
(3) Symbol change slope (SSC).  
\[\begin{array}{l}SSC=\sum\limits_{i=2}^{N-1}\left[f[(x_{i}-x_{i-1})(x_{i}-x_{ i+1})]\right]\\ f(x)=\left\{\begin{array}{cc}1,x\geq\text{threshold}\\ 0,&\text{others}\end{array}\right.\end{array} \tag{23}\]  
(4) Waveform length (WL).  
\[WL=\sum\limits_{i=1}^{N-1}\left|x_{i+1}-x_{i}\right| \tag{24}\]  
(5) TD4 (MAV+ZC+SSC+WL), is the combination of MAV, ZC, SSC, and WL.  
(6) Singular Value Decomposition of Wavelet Coefficient (WTSVD).  
\[WTSVD=SVD(WT(x))\]  
(7) Singular Value Decomposition of Wavelet Packet Coefficient (WPTSVD).  
\[WPTSVD=SVD(WPT(x))\]  
Figure 6: Experimental system of stiffness identification.
# Improvement of Electro-Optical Properties of PSLC Devices by Silver Nanowire Doping
## 1 Introduction
used inorganic nanoparticles to improve the view angle of PDLC [26; 27]. [PERSON] et al. found the droplet size in PDLC decreases with the increase of Ag nanoparticle concentration, thus reducing the response time [28]. [PERSON] et al. doped single-walled carbon nanotubes (SWNTs) in reverse PDLC. They found that the driving voltage was reduced [29]. [PERSON] et al. doped nano-graphite in PDLC devices and reduced the driving voltage [30]. [PERSON] et al. reported the surface plasmon excitation of doped Au nanoparticles improves the transmittance and driving voltage of PDLC [31]. [PERSON] et al. used a silver nanowire-graphene oxide hybrid electrode to prepare a flexible polymer dispersed liquid crystal display, which makes the electro-optical performance of PDLC superior [32]. Nanoparticles are also doped in polymer stabilized cholesteric liquid crystal (PSCLC) to improve device performance. [PERSON] et al. doped Fe\({}_{\text{x}}\)Ni\({}_{\text{y}}\) nanoparticles into the PSCLC film, which extends the viewing angle of PSCLC and improves the frequency response [33]. Inspired by these works, and to improve the electro-optical properties of PSLC devices, this study doped Ag nanowires in PSLC devices. The effects of Ag nanowires concentrations on the electro-optical properties of PSLC were studied, including driving voltage, response time and frequency modulation.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 3 Evolution of V2X Technologies
### Evolution of Cellular V2X
#### 3.2.2 Comparison between Cellular and Non-Cellular V2X Technologies  
In a V2V scenario, one of the key challenges to realize ultra-reliable connectivity is the high Doppler shift. The authors in [33] showed that a high Doppler shift causes a high packet error rate, even at a high SNR in case of 802.11 bd due to the outdated use of channel estimations combined with deep fades. The results showed that NR V2X can better handle the Doppler shifts, and as a result, it can outperform IEEE 802.11 bd based on reliability [33]. The authors tried to enhance 802.11 bd efficiency by introducing midambles under high Doppler changes. Moreover, the authors showed that the DCM and range extension mode further improves 802.11 bd reliability. While the features in 802.11 bd, such as midambles, the extended range preamble, and DCM showed increased reliability; it cannot outperform NR-V2X. The factors behind this are better channel estimations due to high density in the DMRS, lower code rates and SC-FDMA in NR V2X.  
The salient features described above make NR V2X more reliable, efficient and flexible compared to other technologies. A comparative summary, including all the common features of 802.11p, 802.11 bd, LTE V2X, and NR V2X are given in Table 5[34; 35].