esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Optimized Raman anti-Stokes Light-Based Demodulation Method  
As the output power of the pulse laser may not be stable, a calibration zone should be set first. A fiber front end with a length of approximately 100 m was chosen, and a thermometer was adopted to measure the temperature value for reference. Thus, the calibration data \(P_{a}(L_{c},T_{c})\) can be described as Formula (11).  
\[P_{a}(L_{c},T_{c})=P_{0}K_{a}v_{a}^{4}R_{a}(T_{c})\exp[-(\alpha_{R}+\alpha_{a} )L_{c})] \tag{11}\]  
where \(L_{c}\) is a fixed position, for example, 80 m from the fiber front end, and \(T_{c}\) is the temperature value in real measurement. Moreover, by using the fiber attenuation coefficient measured from the OTDR trace, the calibrated data can be obtained and saved as a reference, which is described in Formula (12).  
\[P_{a}(L,T_{c})=P_{0}K_{a}v_{a}^{4}R_{a}(T_{c})\exp[-(\alpha_{R}+\alpha_{a})L)] \tag{12}\]  
However, in the next measurement, the Raman anti-Stokes signal obtained is marked as \(P_{a}^{R}(L,T)\). In the calibration zone, the temperature \(T_{c}\) may change to be \(T_{1}\), and the output power of the pulse laser may change by a factor \(R\), so the corresponding Raman anti-Stokes signal at the calibrated position \(L_{c}\) can be expressed by Formula (13).  
\[P_{a}^{R}(L_{c},T_{1})=RP_{0}K_{a}v_{a}^{4}R_{a}(T_{1})\exp[-(\alpha_{R}+ \alpha_{a})L_{c})] \tag{13}\]  
Moreover, by Formulas (11) and (12), the factor \(R\) can be computed by expressions (14) and (15).  
\[\frac{P_{a}(L_{c},T_{c})}{P_{a}^{R}(L_{c},T_{1})}=\frac{1}{R}\frac{R_{a}(T_{c })}{R_{a}(T_{1})}=\frac{1}{R}\cdot\frac{\exp(h\Delta v/k_{B}T_{1})-1}{\exp(h \Delta v/k_{B}T_{c})-1} \tag{14}\]  
\[R=\frac{\exp(h\Delta v/k_{B}T_{1})-1}{\exp(h\Delta v/k_{B}T_{c})-1}\cdot\frac{ P_{a}^{R}(L_{c},T_{1})}{P_{a}(L_{c},T_{c})} \tag{15}\]  
In Formulas (3) and (12), the temperature \(T\) at each position \(L\) along the FUT can be obtained. This process is shown in expressions (15) and (16).  
\[\frac{P_{a}^{R}(L,T)}{P_{a}(L,T_{c})}=R\frac{R_{a}(T)}{R_{a}(T_{c})}=R\cdot \frac{\exp(h\Delta v/k_{B}T_{c})-1}{\exp(h\Delta v/k_{B}T)-1} \tag{16}\]  
\[\frac{1}{T}=\frac{K_{B}}{h\Delta v}\ln\{[R\cdot(\exp(h\Delta v/k_{B}T_{c})-1) ]\cdot\frac{P_{a}(L,T_{c})}{P_{a}^{R}(L,T)}+1\} \tag{17}\]From expression (16), it can be seen that the new scheme completely avoids the complex issue of walk-off effect correction in traditional double-channel-based methods. What is more, it saves a data channel, which has benefits for the integration of hybrid distributed optical fiber sensing systems.
## 1 Introduction
The specific algorithmic process will be described later. In comparison to existing methods, the proposed algorithm possesses the following characteristics:  
* This paper proposes a new partitioning strategy, which is different from existing clustering algorithms that use only representative dimensions for partitioning, and our partitioning strategy is based on all dimensions of the data. It utilizes a non-parametric density measure to calculate the local density peaks of all dimensions in the dataset and takes each peak as the division benchmark for the first stage of subcluster division. In the second phase, the final subcluster is obtained by intersecting the results of each dimension. This partitioning strategy can accurately identify clusters with density differences, avoiding the problem that traditional density-based clustering algorithms and their variants incorrectly partition sparse clusters into dense clusters. Furthermore, the proposed partitioning strategy is effective in identifying clusters with irregular shapes and uneven densities, enabling the adaptive partitioning of subclusters. * This paper proposes a new similarity metric for merging subclusters from the perspective of structural similarity. Specifically, a new similarity metric is defined for evaluating the degree of interaction of shared nearest neighbors among the subclusters to be merged and iteratively merging the subclusters with the maximum similarity until the desired number of clusters is achieved. This merging strategy effectively addresses the issues of ambiguous boundaries and the high overlap between subclusters, enhancing the accuracy of clustering performance. * The CPDD-ID algorithm was tested on 20 benchmark datasets, and the comparison results with the existing clustering algorithms show that the proposed algorithm achieves good performance in discovering the underlying data structures in all cases. The rest of the paper is organized as follows: Section 2 reviews the related work. Section 3 describes the algorithm proposed in this paper in detail. Section 4 compares the proposed algorithm with six advanced clustering algorithms and presents the experimental results. Section 5 summarizes the whole paper.
## 3 Feature Extraction
### Time Domain Feature Extraction
#### 3.2.5 Zero Crossing (ZC)  
Zero crossing (ZC) is a metric of frequency information in the time domain of EMG signals. This feature can to some extent reflect the jitter and oscillation of the signal during a specific action segment. It also indicates the number of times the amplitude of the EMG signal crosses the zero level. To avoid capturing low-voltage fluctuations or background noise, a threshold condition is added. The specific calculation process is as follows:  
\[\begin{split} ZC=&\frac{1}{N}\sum_{i=1}^{N-1}f_{1}(x _{i+1}*x_{i})\bigcap f_{2}(x_{i+1}-x_{i})\\ & f_{1}(x)=\begin{cases}1,&ifx<Th\\ 0,&otherwise\end{cases}\\ & f_{2}(x)=\begin{cases}1,&if|x|>Th\\ 0,&otherwise\end{cases}\end{split} \tag{7}\]where \(f_{1}(\dots)\) indicates whether \(x_{i+1}\) and \(x_{i}\) have opposite signs. Only when they are opposite and below the threshold can it be counted as 1. \(f_{2}(\dots)\) indicates that \(x_{i+1}\) and \(x_{i}\) are not the same point, and there is a distance between them that surpasses the threshold, only then can it be counted as 1. In the end, the two functions are intersected to count the number of zero crossings.
## 2 Foundations on RISs
### RIS Hardware Architecture  
A RIS is a planar array with electromagnetic properties designed to control the signal propagation. A RIS is also considered a metasurface because it is composed of a metamaterial, which is digitally programmable [32]. Figure 1 shows the basic hardware architecture of an RIS; on the left, we can see the metasurface, which is a planar array of units called reflective elements or meta-atoms [33]. The RIS elements (i.e., geometrical shape, size, dimension, orientation, and arrangement) modify the electromagnetic (EM) wave response. Changing the meta-atom reflection coefficient allows dynamic wireless channels in real time, making this technology suitable for wireless communication applications.  
Figure 1 shows the structure of a reflective element. Each element contains an embedded PIN diode, which switches between the two states (on and off). The PIN diode controls the biasing DC voltage of each element. The equivalent circuit with two states has a phase-shift difference of \(\pi\) radians between them [32]. Therefore, it is possible to obtain different discrete phase shifts by setting different voltages through the RIS elements.  
Figure 1: RIS hardware architecture, detailing reflecting elements and equivalent circuits.  
Furthermore, the amplitude of each reflected signal can be controlled by setting different values of resistors in each element [34].  
Figure 1 also shows the three layers structure of the RIS hardware. The outer layer comprises an array of metallic patches (reflective elements) printed on a dielectric substrate that directly interacts with an incident signal. The next layer, behind the metallic patches, consists of a copper plate. It is used to avoid a leakage in the energy of the signal. Finally, the inner layer is a control circuit board whose main task is to adjust the amplitude or phase shift of the incident signal in each element. Typical electronic devices that can be RIS controllers are field-programmable gate arrays (FPGAs) or microcontrollers.
# Evolution of Flow and Streaming in Exponential Variable Cross-Section Resonators
###### Abstract  
A gas-kinetic scheme (GKS) based on an unstructured grid is applied to simulate the evolution of the fluid motions in exponential variable cross-section resonators. The effects of the acoustic field intensity on the oscillatory pressure, velocity, temperature, and flow streaming structure were investigated numerically, and the model was validated. The results demonstrate that the geometry and driving strength are the main factors affecting the final performance of the system. For the quasi-linear and moderate non-linear cases in optimum exponential tube, the periodic generation, evolution, and shedding of vortices in flow fields are associated with the storage and release of energy, which is the transmission mode of the third type of direct current (DC) flow, and its driving mechanism is attributed to the asymmetrical pressure and temperature. Meanwhile, some new physical characteristics were also discovered for the highly non-linear case, e.g., the disorder and unsteadiness of the flow direction accomplished with turbulent flow streaming structures. The secondary flow is manifested as multiscale, irregular and unsteady vortices throughout the tube. The smallest increment of pressure and velocity amplitude occurs concurrently with the biggest increment of temperature amplitude. These evidences suggest that there is an optimal driving strength, even for a good configuration tube, with which the maximum efficiency can be obtained.  
flow streaming; exponential-shaped resonator; non-linearity; gas-kinetic BGK scheme +
Footnote †: journal: Applied Physics Letters
The study found that in most cases, the transition-doped CeO\({}_{2}\) produced higher amounts of CO in ten cycles than pure CeO\({}_{2}\). Rhodium-doped ceria [29] has been successfully employed for long-term methane generation, simultaneously splitting CO\({}_{2}\) and H\({}_{2}\)O. The findings suggest that Ce\({}_{0.99}\)Rh\({}_{0.01}\)O\({}_{2\cdot 8}\) materials possess an enhanced oxygen storage capacity and exhibit activity towards methane formation. The existing body of research suggests that including various metal cations (such as transition, rare earth, and noble) into the structure of ceria leads to alterations in the thermal, chemical, kinetic, and physical properties. Most research efforts have been dedicated to doping transition metal cations in the ceria crystal structure. The outcomes have been less than promising, except for cases involving Zr and Hf. Consequently, the decision was made to redirect our focus toward utilizing noble metals as dopants for ceria to facilitate the solar fuel production cycle. To date, only Rh noble metal has been doped in CeO\({}_{2}\) for the simultaneous splitting of CO\({}_{2}\) and H\({}_{2}\)O into hydrocarbon through solar power [29]. In this study, we have used a more abundant and low-cost precious metal, silver (Ag\({}^{1+}\)), as a dopant in the ceria structure [1% Ag-doped ceria redox materials (ACRM): Ce\({}_{0.99}\)Ag\({}_{0.01}\)O\({}_{2\cdot 8}\)]for the thermochemical redox cycle, which splits CO2 into CO and O2. We prepared the ACRM using a co-precipitation method and tested it for the CDS by performing multiple thermochemical cycles using a TGA set-up. The typical schematic of the ACRM redox CDS cycle is presented in Figure 1.
## 3 Development of Wireless Laser Displacement Monitoring Equipment
### The Principle of Laser Ranging Measurement
#### 3.1.1 Phase Laser Ranging  
Figure 1 shows that the phase ranging method continuously modulates the amplitude of the emitted laser, usually using sine wave modulation. Since the delay between the laser round-trip ranging system and the target will produce a phase change, the phase of the delay between the receiving and transmitting optical signals is measured. Since the frequency of the modulating signal is known, the laser beam can calculate flight time and finally calculate the distance to the target according to the formula [26].  
The distance \(D\) satisfies Formula (3):  
\[D=\frac{ct}{2} \tag{3}\]  
\[t=\frac{\varphi}{\omega}=\frac{2\pi N+\Delta\varphi}{2\pi f}(N+\frac{\Delta \varphi}{2\pi})\frac{1}{f} \tag{4}\]  
Put Formula (4) into Formula (3) to obtain Formula (5):  
\[D=\frac{\lambda c}{2}(N+\frac{\Delta\varphi}{2\pi}) \tag{5}\]  
where \(c\) is the speed of light in the air;\(f\) and \(\lambda\) are the frequency and wavelength of the modulated signal, respectively; \(\Delta\varphi\) and \(N\) are the phase differences caused by the marking-modulated optical signal flying back and forth between the ranging system and the target to be measured, respectively; \(N\) is the integer part of one phase difference; and \(\Delta\varphi\) is the mantissa of the unsatisfied integer period.  
The half wavelength of the modulation signal is \(\frac{\lambda}{2}\) defined as the measuring ruler; then, the phase laser ranging can be expressed as using a measuring range as the \(\frac{\lambda}{2}\) ruler to measure the distance. Formulas (4) and (5) can be obtained by a certain method, but \(N\) is difficult to obtain by a simple method, so the distance \(D\) obtained by Formula (5) actually has multiple results.  
A phase laser ranging system \(D\) with a modulated ruler measures \(\frac{\lambda}{2}\) the distance of length. If the distance \(D\) is between \(N\) times \(\frac{\lambda}{2}\) and \(N+1\) times \(\frac{\lambda}{2}\) the ruler measures \(N\) times, and then there is an unsatisfactory part d of \(\frac{\lambda}{2}\) the distance to be measured \(D=\frac{N\lambda}{2}+\text{d}\). However, due to continuous laser modulation, the system can only distin  
Figure 1: Schematic diagram of phase ranging.  
guish the unsatisfactory part of \(\frac{\lambda}{2}\), and it is difficult to distinguish the integer times of the measurement. Therefore, only when the measurement distance does not exceed the length of the measuring ruler, that is, \(N=0\), does the distance measurement result have a unique solution.  
Phase laser ranging is usually suitable for the measurement of short and medium distances, and the measurement accuracy can reach the millimeter and micron levels. It is also one of the methods with the highest ranging accuracy at present.
## 4 Case Study
### Discussion
#### 4.3.4 Verification of Value Structure  
One of the limitations of this study itself was the small number of participants to derive data for network analysis. It is possible that this sample had a biased experience with ten participants. Of course, statistical analyses which usually have conservative assumptions were not applied, and it is not a critical problem. Nonetheless, additional experiments were carried out to supplement this. This study tried to evaluate the value structure and the reliability of the main experiment through further experiments.  
Although the performance of the value structure models revealed that they would be reliable, some considerations seem to be required in this study. First, value dimensions, which were used in the post hoc analysis, should be assumed to be defined correctly, even if an essentialistic interpretation of any definition on a certain construct could distort the meaning [16]. Second, the coefficients of models should be interpreted carefully and empirically. For example, a value dimension desire to use turned out to be positively related to fatigue. For clarification over a certain construct, additional work using tools such as structural equation modeling would be needed.  
Of course, there could be various methods to formulate a value structure. For example, noncompensatory equations such as conjunctive and disjunctive models [40] or nonlinear equations such as the S-shaped value model [41] should be verified. Validation of the equations extracted from the network structure will be carried out in future work. The focus of this study is to confirm the possibility of building hierarchical value structures.
## 3 Effect of Fixed-Altitude Heterogeneous LEO Constellations on CIT
### Results of CIT
After adding observations from 15 LEO satellites, the number of voxels increases to 13,333, an increase of only 468 voxels. As more LEO satellites are added (30/60/96/192), the number of voxels increases by 455, 613, 551, and 506, respectively. These increases are relatively small compared to the total number of voxels in this interval (20,448), so the IED results do not show significant improvements in this altitude range after adding the LEO satellites. On the other hand, above 300 km, especially in the 300-800 km altitude range, the increase in the number of rays passing through the voxels is more significant after adding the LEO satellite observations. With 96 LEO satellites, the number of voxels in the 300-500 km altitude interval reaches 20,363, which is 99.58% of the total number of voxels (20,448) in this range. Similarly, the number of voxels in the 500-800 km interval reaches 30,620, or 99.83% of the total (30,672) in this range. Therefore, the IED results improve notably in this altitude range, which explains the unstable behavior observed above the altitude of each constellation in Figures 8 and 9.
# Bioremediation Methods for the Recovery of Lead-Contaminated Soils: A Review
## 3 Phytoremediation
### In Field Phytoremediation  
Phytoremediation is an in-situ restoration process, so the application of this technique in the field by planting suitable species has gained importance. [PERSON] et al. [44] led an in-the-field field experiment on a farm in Quzhou, China. The soil was polluted by heavy metals owing to a previous intensive use of agricultural chemicals, fertilizers, and irrigation with contaminated canal water, and the total concentration of Pb was \(123.66\pm 0.84\) mg/kg. The authors compared the phytoextraction efficiency of eighty different types of _Brassica juncea L_. (Indian mustard), the oldest cultivated crop in this area, by sowing and growing the plants in the contaminated soil. The plants were harvested at 90 days maturity, dried, and then digested with an HNO\({}_{3}\): HClO\({}_{4}\) mixture. The total content of heavy metals in plant samples were determined using an inductively coupled plasma mass spectrometer (ICP-MS). In general, although the average uptake in shoots was higher than roots, TF greatly varied among the 80 Indian mustard cultivars, of which 57 genotypes exhibited TF \(>\) 1 for Pb. In particular, genotypes IM-24 and IM-32 accumulated more Pb, proving to be the most efficient varieties for in-the-field phytoremediation among the studied genotypes.  
A summary of the considered phytoremediation studies is reported in Table 2.
## 1 Introduction
### Clinically Available Remote Microphone Systems  
These systems consist of a transmitter/microphone worn by the speaker and a receiver worn by the listener [7]. Research has shown that RM systems significantly improve speech recognition in noisy environments for individuals with hearing impairments and typical hearing. [PERSON] [8] reported that the Phonak Roger RM system enhanced speech recognition performance in persons with moderate-to-severe hearing loss from 45% to 61% compared to using hearing aids (HAs) or cochlear implants alone. In 2024, [PERSON] et al. [9] reported that RM systems benefited adults with TH. The observed improvements were 21.37% and 9.87% when using the Phonak Roger Select and Roger Pen, respectively, compared to no RM system in 10 participants with TH, aged 20 to 63 years [9]. [PERSON] and colleagues [10] found RM benefits for children aged 6 to 12 years. They reported improvements in the sentence recognition scores by an average of 3.03, 21.40, 21.61, and 49.95% when tested in SNRs of +12, +5, 0, and \(-\)10 dB, respectively. They also reported improvements in visual and auditory attention with RM use.  
Although listeners with TH can potentially benefit from using RM systems designed for persons with hearing loss [9], the adoption of such devices is limited due to their high cost. For example, [PERSON] et al. [11] reported that none of the participants (n = 27) with TH expressed a preference for purchasing a pair of mild-gain HAs priced at approximately USD 3250, despite the potential benefits for their hearing difficulties. Additionally, the higher expenses associated with RM systems and the inconvenience of carrying multiple devices may deter individuals from using RM systems in daily communication. Furthermore, the social stigma often associated with using hearing devices can be a concern for some people. [PERSON] et al. [12] reported that persons with hearing impairment might be perceived as \"less intelligent\" or labelled as \"deaf and dumb\", causing feelings of shame and embarrassment. These negative emotional feelings of self-imposed stigma may lead to social isolation and affect decision-making regarding the initial acceptance and pursuit of potential treatment [12]. Therefore, it is essential to find more affordable and approachable alternatives to increase the acceptance and use of hearing devices for addressing hearing difficulties.
# Real-Time Prediction of Fuel Consumption Based on Digital Map API
## 3 Methodology
### Fuel Consumption Rate in Different Speed Intervals and Engine Displacement Intervals  
After the construction of VSP distribution and fuel consumption model at different speed intervals, the fuel consumption rate in each speed interval is calculated according to Equation (8).  
\[\mathrm{F_{k}}=\sum_{i=-20}^{20}\mathrm{FC_{i}}\times\mathrm{Bin_{i}}, \tag{8}\]  
where \(\mathrm{F_{k}}\) is the rate of fuel consumption in the speed interval of k in the unit of mL/s, \(\mathrm{FC_{i}}\) is the rate of fuel consumption in the VSP interval of i in the unit of mL/s, and \(\mathrm{Bin_{i}}\) is the distribution of i-th VSP interval in the kth speed interval.  
After calculation, the fuel consumption rates in each speed interval and engine displacement intervals are recorded, as shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Engine Displacement Interval** & **Speed Interval (km/h)** & \(a\) & \(b\) & \(R^{2}\) \\ \hline \multirow{6}{*}{(0,1.6)} & \(\leq\)20 & 0.81 & 4.82 & 0.95 \\ \cline{2-5}  & 20–40 & 0.72 & 4.97 & 0.97 \\ \cline{2-5}  & 40–60 & 0.59 & 5.46 & 0.97 \\ \cline{2-5}  & 60+ & 0.71 & 5.77 & 0.98 \\ \hline \multirow{6}{*}{(1.6,2.5)} & \(\leq\)20 & 1.62 & 3.21 & 0.99 \\ \cline{2-5}  & 20–40 & 1.40 & 3.87 & 0.99 \\ \cline{2-5}  & 40–60 & 1.22 & 4.20 & 0.98 \\ \cline{2-5}  & 60+ & 0.92 & 7.47 & 0.98 \\ \hline \multirow{6}{*}{(2.5,4)} & \(\leq\)20 & 1.54 & 8.78 & 0.92 \\ \cline{2-5}  & 20–40 & 1.63 & 4.53 & 0.99 \\ \cline{2-5}  & 40–60 & 1.41 & 4.67 & 0.99 \\ \cline{2-5}  & 60+ & 1.11 & 7.22 & 0.98 \\ \hline \multirow{6}{*}{\(\geq\)4} & \(\leq\)20 & 1.56 & 9.29 & 0.82 \\ \cline{2-5}  & 20–40 & 2.10 & 7.86 & 0.98 \\ \cline{1-1} \cline{2-5}  & 40–60 & 1.95 & 6.30 & 0.97 \\ \cline{1-1} \cline{2-5}  & 60+ & 1.62 & 8.21 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The model parameters.