esa-sceva/satcom-chunk-collection huggingface dataset:
# Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
## 5 Results and Discussion
### Crowd Size Estimation
Both use the mean attenuation values of different virtual sensor networks. When evaluating the crowd size estimation when a vehicle is present, a median error of 3.304 people is observed, along with an MAE of 4.478 people and an RMSE of 6.326. The CDF plot is shown in Figure 8 as an orange dashed line.
## 1 Introduction
Additionally, since the satellite distribution used in the PPP processing change in the sky (as elevation and azimuth), the effect of the terrain conditions on the observations is different, so the position accuracy changes according to used constellations. Although this is not the subject of this study, the PPP technique is also frequently used in real-time (RT) positioning applications. The precise satellite orbit and clock products that are needed at the real-time of measurement for the application of the RT-PPP technique are streamed by IGS and some commercial companies through the internet and/or by L-band communication satellites. As mentioned above, the most notable disadvantage of RT-PPP is that it requires at least 15 min of convergence time for phase ambiguity solution. The convergence time required either for the float or fix phase ambiguity solution in RT-PPP can be shortened by using multi-GNSS data or by adding regional or global ionospheric and tropospheric auxiliary models to the solution. In the second chapter of study, information about multi-constellation GNSS PPP is given. Then, in the materials and method section, the study area and the collected data are introduced. In addition to data, information about the planned study details is given here. In the last chapter of the article, the obtained results are discussed and conclusion section is given.
## 3 Results and Discussion
### Physiological Characterization
#### 3.2.3 Properties of Glucose Tolerance of the Selected Yeasts  
The glucose tolerance of the tested strains was evaluated under glucose concentrations of 150 g/L, 200 g/L, 250 g/L, 300 g/L, and 350 g/L. The results are shown in Figure 2C. As the glucose concentration increased, the growth of all strains was inhibited to some extent. However, _Pichia kudriavzevii_ GAAS-JG-1 exhibited a significantly better growth performance in the high-glucose environments compared to the reference strains. At a glucose concentration of 150 g/L, the OD\({}_{600}\) value of _Pichia kudriavzevii_ GAAS-JG-1 was 2.14 \(\pm\) 0.14, significantly higher than those of all reference strains (OD\({}_{600}\) values ranging  
Figure 2: **(A)** Comparison of pH tolerance between Pichia kudriavzevii GAAS-JG-1 (CGMCC No. 26816) and Other Non-_Saccharomyces_ Yeasts. **(B)** Comparison of Ethanol tolerance between _Pichia kudriavzevii_ GAAS-JG-1 (CGMCC No. 26816) and Other Non-_Saccharomyces_ Yeasts. **(C)** Comparison of glucose tolerance between _Pichia kudriavzevii_ GAAS-JG-1 (CGMCC No. 26816) and Other Non-_Saccharomyces_ Yeasts. **(D)** Comparison of sulfur dioxide tolerance between _Pichia kudriavzevii_ GAAS-JG-1 (CGMCC No. 26816) and Other Non-_Saccharomyces_ Yeasts. **(E)** Comparison of temperature tolerance between _Pichia kudriavzevii_ GAAS-JG-1 (CGMCC No. 26816) and Other Non-_Saccharomyces_ Yeasts.  
from 1.56 \(\pm\) 0.05 to 1.81 \(\pm\) 0.06, \(p\) < 0.05). When the glucose concentration increased to 200 g/L and 250 g/L, _Pichia kudriavzevii_ GAAS-JG-1 maintained a strong growth performance, with OD\({}_{600}\) values of 1.75 \(\pm\) 0.04 and 1.77 \(\pm\) 0.02, respectively, demonstrating significant glucose tolerance (_p_ < 0.05). In contrast, the reference strain CICC 1004 showed weaker growth under the same conditions, with OD\({}_{600}\) values of 1.40 \(\pm\) 0.03 and 1.21 \(\pm\) 0.02, respectively. At extremely high glucose concentrations (350 g/L), _Pichia kudriavzevii_ GAAS-JG-1 continued to exhibit relatively stable growth, achieving an OD\({}_{600}\) value of 1.67 \(\pm\) 0.01, which was comparable to that of CICC 31838 (1.64 \(\pm\) 0.01) and significantly higher than those of CICC 1004 (1.23 \(\pm\) 0.01) and CICC 31399 (1.55 \(\pm\) 0.03). These results suggest that _Pichia kudriavzevii_ GAAS-JG-1 could serve as a valuable strain for enhancing fermentation efficiency under high-sugar conditions. Furthermore, _Pichia kudriavzevii_ GAAS-JG-1's glucose tolerance may be associated with its unique metabolic pathways, osmoregulatory mechanisms, and cell membrane properties, as also reported in previous studies on _P. kudriavzevii_[12; 13].
## 6 Conclusions  
An improved cloud-TODIM method for the RRS selection of TSF failure is proposed in this paper. First, a cloud model was employed to convert evaluation information and compute the cloud-based evaluation results to illustrate uncertainty, fuzziness, and randomness. Furthermore, an extended TODIM method was proposed based on the cloud model. Finally, an example was employed to verify the proposed method. The comparative analysis proved the high accuracy, obvious superiority, and rationality of the proposed method. The main conclusions of this paper are as follows:  
1. The cloud model was applied to quantify the linguistic evaluation criteria of RRSs, which solves the problem of the fuzziness, uncertainty, and randomness of qualitative contents. Additionally, a comprehensive weighted cloud was used to integrate the evaluation information of the expert group.
2. An improved cloud-TODIM method was proposed and used for ranking the order of RRSs, which takes full account of the psychological behavior of DMs.
3. A TSF was applied as a case study to verify the proposed method. The superiority of the extended cloud-TODIM method was confirmed by comparison with another method. The obvious advantages of the proposed method in this paper were further presented; particularly, this method considers the psychological behavior of DMs under risk.  
The main contribution of this article is to propose an improved multi-attribute decision-making method aimed at solving the problems of parameter sensitivity and excessive decision object parameters in traditional TODIM methods. It is noteworthy that this study still has some shortcomings that warrant further research. First, with increasing attention to risk reduction, more and more evaluation criteria should be considered and analyzed, whereas only four criteria are selected and sub-criteria are ignored in this paper. Moreover, the weight of the evaluation criteria is determined by the AHP method, which may be relatively subjective. In further research, a more rational method can be used to compute the weight of evaluation criteria.  
**Author Contributions:** Methodology, writing, and review, Y.Z.; data and analysis, C.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of Fujian Province under grant number 202305112 and the Chinese National Key Research and Development Program under grant number 2017 YFC0804605.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest.
## 2 Modeling and Implementation
### Mathematical Modeling
Therefore, to make the evaluation more efficient, we propose to exploit the information provided by the triangle and define additional auxiliary points \(\mathbf{a}_{j}^{k},j\in\{1,2,3\}\) on the midpoints of the edges. Since the vectors are already defined, these points can be easily computed by setting \(\lambda=0.5\). With the help of the auxiliary points, new vectors can now be generated and are used to determine further intersection points according to the same scheme. Hence, the number of intersection points increases since for each triangle; in total, nine vectors are evaluated, while the resolution of the surface remains unchanged. In this context, Figure 2 qualitatively shows our approach for a random triangle \(k\in\mathbf{K}\) intersecting a plane. In addition, Figure 3 illustrates common types of offset planes with the corresponding values for the plane parameters. Figure 2: Determination of intersection points for a given triangle \(k\in\mathbf{K}\).
## 2 Overview of SWARM IoT Constellation
### Global Applications  
The message transfer system we describe can be adapted for global use, depending on specific goals and geographic requirements. Given the extensive network of SWARM satellites operating in low Earth orbit, this innovation is particularly relevant for northern regions such as Canada, Greenland, and the Arctic. For example, in these remote and harsh environments, SWARM devices could be employed for automated sample processing and data collection, eliminating the need for scientists to gather data under challenging weather conditions manually. These devices can seamlessly connect with sample collectors, such as thermocouples for temperature measurement, and transmit data without requiring physical presence.  
The most significant advantage of this system in the Arctic is that it can be placed in the coldest areas (a SWARM device can handle \(-40^{\circ}\)C to \(+60^{\circ}\)C [27]). These devices are designed for long-term use, supported by backup batteries and solar panels. They utilize real-time RF background noise readings to optimize signal strength. In open areas with minimal obstructions above the antenna, the signal remains robust, ensuring efficient data transmission. For comparison, in previous experiments described in Section 4 of this paper, devices tested under sub-optimal conditions--such as in urban environments with obstructions like trees--still managed to transmit messages within 10 to 20 min, demonstrating reliable connectivity even without Internet access.  
Globally, SWARM Technologies is already proving its value. For instance, the Department of Conservation in New Zealand uses IoT hardware and software solutions from SWARM to monitor critical communication equipment in remote locations [36]. Similarly, in Australia, SWARM's satellite-enabled technology is employed for water monitoring in the outback, addressing water scarcity issues [28]. These examples underscore the versatility and effectiveness of SWARM Technologies in diverse and challenging environments.  
Figure 6: Figure shows a chart that has two urban locations with high population (Winnipeg, Manitoba, and Regina, Saskatoon), one lightly populated urban area (Iqaluit, Nunavut), and three remote locations with low to no population (Wager Bay, NU; Princess Catherine, NU; Summit Camp, Greenland.
# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## Appendix A  
The following tables report the detailed results of two approaches across various features: chroma, mel-spectrogram, MFCC, pitch, RMS, spectral bandwidth, spectral centroid, spectral contrast, spectral rolloff, and zero crossing rate for different meeting records.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Meeting ID** & **FAR (KL)** & **MDR (KL)** & **FAR (BIC)** & **MDR (BIC)** \\ \hline EN2002a & 0.9935 & 0.0080 & 0.5173 & 0.5106 \\ EN2002b & 0.9910 & 0.0114 & 0.5049 & 0.5114 \\ EN2002c & 0.9913 & 0.0046 & 0.5082 & 0.5034 \\ EN2002d & 0.9926 & 0.0041 & 0.4973 & 0.5035 \\ ES2004a & 0.9927 & 0.0090 & 0.4939 & 0.5157 \\ ES2004b & 0.9932 & 0.0067 & 0.5016 & 0.4944 \\ ES2004c & 0.9930 & 0.0086 & 0.5089 & 0.5388 \\ ES2004d & 0.9935 & 0.0066 & 0.4828 & 0.4926 \\ IS1009a & 0.9968 & 0.0105 & 0.5032 & 0.5000 \\ IS1009b & 0.9934 & 0.0057 & 0.5111 & 0.5200 \\ IS1009c & 0.9882 & 0.0056 & 0.5000 & 0.4958 \\ IS1009d & 0.9960 & 0.0023 & 0.4973 & 0.5012 \\ TS3003a & 0.9921 & 0.0030 & 0.4991 & 0.5046 \\ TS3003b & 0.9929 & 0.0019 & 0.5235 & 0.5374 \\ TS3003c & 0.9908 & 0.0020 & 0.5073 & 0.5556 \\ TS3003d & 0.9930 & 0.0093 & 0.4979 & 0.5206 \\ \hline \end{tabular}
\end{table}
Table 2: Speaker change detection results of KL-GMM and BIC-GMM for mel-spectrogram.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Meeting ID** & **FAR (KL)** & **MDR (KL)** & **FAR (BIC)** & **MDR (BIC)** \\ \hline EN2002a & 0.5144 & 0.5146 & 0.4697 & 0.4788 \\ EN2002b & 0.5352 & 0.5190 & 0.4820 & 0.4544 \\ EN2002c & 0.5140 & 0.5  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Meeting ID** & **FAR (KL)** & **MDR (KL)** & **FAR (BIC)** & **MDR (BIC)** \\ \hline EN2002a & 0.6429 & 0.3820 & 0.4935 & 0.4867 \\ EN2002b & 0.6336 & 0.3878 & 0.4877 & 0.4658 \\ EN2002c & 0.6249 & 0.3913 & 0.5034 & 0.4966 \\ EN2002d & 0.5957 & 0.3817 & 0.5054 & 0.4620 \\ ES2004a & 0.6320 & 0.3453 & 0.4782 & 0.4619 \\ ES2004b & 0.6128 & 0.3893 & 0.4984 & 0.4474 \\ ES2004c & 0.6247 & 0.3405 & 0.4943 & 0.4741 \\ ES2004d & 0.6277 & 0.3570 & 0.4867 & 0.4810 \\ IS1009a & 0.4858 & 0.4368 & 0.4842 & 0.4211 \\ IS1009b & 0.5250 & 0.4600 & 0.5099 & 0.5229 \\ IS1009c & 0.5111 & 0.4594 & 0.4910 & 0.4650 \\ IS1009d & 0.5351 & 0.4895 & 0.4934 & 0.4660 \\ TS3003a & 0.5633 & 0.4134 & 0.4956 & 0.4620 \\ TS3003b & 0.6352 & 0.2821 & 0.4979 & 0.4683 \\ TS3003c & 0.6261 & 0.3475 & 0.4850 & 0.4485 \\ TS3003d & 0.6412 & 0.3267 & 0.4968 & 0.4914 \\ \hline \end{tabular}
\end{table}
Table 6: Speaker change detection results of KL-GMM and BIC-GMM for spectral bandwidth.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Meeting ID** & **FAR (KL)** & **MDR (KL)** & **FAR (BIC)** & **MDR (BIC)** \\ \hline EN2002a & 0.7799 & 0.1976 & 0.5159 & 0.5186 \\ EN2002b & 0.7713 & 0.2053 & 0.4738 & 0.4354 \\ EN2002c & 0.7686 & 0.2151 & 0.4860 & 0.4886 \\ EN2002d & 0.7588 & 0.2282 & 0.4
# Resonant Power Supply for Plasma Cleaning Based on Fuzzy Logic Power Tracking
## 2 Plasma Cleaning Power Supply Design
The control panel will send these instructions to the main control chip, and then execute the corresponding control instructions. This paper will analyze and design the resonant power supply system for plasma cleaning according to the system architecture mentioned above, and its main indicators are shown in Table 1.  
Figure 2 is the cross section of the resonant power supply plasma torch for plasma cleaning. By applying positive and negative pulses of high frequency and high voltage to the plasma torch, the working gas is continuously decomposed, and a filamentary plasma column is generated between the electrodes, which is blown away from the plasma torch under the action of gas pressure, thus generating plasma [8]. Plasma torch load is imposed by the working gas, nitrogen, argon, or compressed air. Nitrogen and argon gas on the material surface modification effect is better, but they are expensive, not suitable for mass production, and generally used in places that have requirements of specific requirements. Compressed air has a low cost and only needs a compressor to be achieved; therefore, it has been widely used in industry [9]. No matter what kind of gas is used, the gun head is equivalent to the capacitive load, so an equivalent \begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Indicators** & **Performance Parameters** & **Parameters Unit** \\ \hline Input voltage range & 198–242 & V \\ Input current range & 2.8–5 & A \\ Input voltage frequency & 50/60 & Hz \\ Output voltage range & –3.2–3.2 & kV \\ Output power & 500–1000 & W \\ Work efficiency & \(\geq\)90 & \% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance index of resonant power supply system for plasma cleaning. Figure 1: Structure of resonant power supply system for plasma cleaning. capacitance can be used to replace it in the circuit analysis, and the main circuit diagram of the system can be expressed, as shown in Figure 3. Wherein, LPLK is the primary leakage inductance of the transformer, LSLK is the secondary leakage inductance of the transformer, Lg is the inductance of plasma torch, R is the line resistance, and Cg is the equivalent capacitance of the nozzle air gap. The main control chip of the resonant power supply for plasma cleaning is STM32F103.
## 4 Results
### Correlation between Ground Deformation and Factors
#### 4.2.2 Analysis of Influencing Factors to Ground Deformation  
The surrounding environmental factors have certain impacts on the ground deformation [63; 64; 65; 66; 67]. Reasons for the happening of ground deformation in the southern Erhai region were analyzed, and the prediction of the future deformation was carried out accordingly. The cumulative monthly deformation data of the severe deformation area in the southern Erhai region were calculated, and the correlations between the cumulative temperature, cumulative precipitation, urban building area, and water level were tested to obtain the fitting equation, and their linear correlation coefficients (R\({}^{2}\)) were shown in Figure 10. The R\({}^{2}\) between cumulative deformation and four factors were 0.764, 0.746, 0.431, and 0.113, respectively.
## 2 Materials and Methods
### LC-ESI(\(-\))-MS/MS Analysis  
The identification of phenolic compounds was performed with a Thermo Finnigan Surveyor HPLC System (Thermo Fisher Scientific, Waltham, MA, USA) coupled with an LCQ Fleet Ion Trap Mass Spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Separation of the standard compounds was carried out at a column and autosampler temperature of 25 \({}^{\circ}\)C with a Kromasil (Nouryon AB, Inorganic specialties, 402 58 Goteborg, Sweden) C18 Hypersil Gold C18 column (length: 100.0 mm, internal diameter: 2.1 mm, particle size: 3.5 \(\upmu\)m). The mobile phase consisted of acetonitrile and 0.1% acetic acid (Solvent A) and water and 0.2% acetic acid (Solvent B).  
Phenolic standards (concentration = 10 \(\upmu\)g/mL) were eluted and separated according to the following gradient elution program: 0-1 min at flow rate 0.300 mL/min with 10% A, 1.1-8 min at flow rate 0.300 mL/min with 20% to 30% A, 8.1-9 min at flow rate 0.350 mL/min with 50% A, 9.1-10.2 min at flow rate 0.350 mL/min with 65% to 100% A, 14.1-15 min at flow rate 0.350 mL/min with 100% A and, finally, recondition of the column from 15.1-18 min at flow rate 0.300 mL/min with 10% A. The injection volume was set at 10 \(\upmu\)L. Four milligrams of the dried extracts was diluted at 1000 \(\upmu\)L of methanol:water 70:30 \(v/v\) (injection solvent). Samples were filtered through Mini-UniPrep 0.45 \(\upmu\)m, Nylon Filter Media with Polypropylene (GE Healthcare Companies, Chicago, IL, USA).  
The electrospray ionization (ESI) MS/MS analysis was conducted in negative ion mode after the optimization of the following conditions: spray voltage = 3.94 kV, capillary voltage = \(-\)48.49 V, capillary temperature = 300 \({}^{\circ}\)C, sheath gas flow rate = 4 a.u., and sweep gas flow rate = 20 a.u. Full-scan MS was performed from m/z 80 to 700. Multiple reaction monitoring (MRM) mode was applied for the MS/MS transitions. The processing of the data was performed by using Xcalibur software (version 2.1, Thermo Scientific, Waltham, MA, USA).  
The annotation of the phenolic compounds in herbal infusions was based on the LC-MS/MS in-house library of the 40 phenolic standards and on published data. The latter was performed by recording and comparing the retention times and MS/MS data of the peaks at mass tolerance of 5 ppm.
## 1 Introduction
### Contributions  
In order to address the highlighted research gap in Section 1.2, the contributions of this article are summarized as follows:  
* The first contribution is the effective utilization of BECs for cryptographic computations through the innovative application of the NAF algorithm. It ensures robust security and computational efficiency. The utilization of the NAF technique for point multiplication on BECs successfully fills the research gap concerning scalability and adaptability. The NAF algorithm presents a versatile method that may be applied to different curve sizes and levels of security. Our architecture is adaptable and scalable, as evidenced by achieved results, showing strong performance over a range of curve parameters.
* The second contribution is the optimization of the Arithmetic Logic Unit (ALU) within the hardware design, maximizing ALU efficiency with minimal additional hardware. Moreover, the incorporation of a radix-4 multiplier eliminates resource-intensive divisions and substantially boosts computational speed while reducing hardware complexity. The trade-off between resource efficiency and speed is addressed by the integration of a radix-4 multiplier and the optimization of the Arithmetic Logic Unit (ALU). Our design minimizes the use of hardware resources while carefully balancing processing speed. Rapid cryptographic operations are facilitated by the radix-4 multiplier, which is renowned for its speed benefits, and the optimized ALU, which guarantees efficient operations. Our approach achieves an optimal equilibrium with great progress, especially useful for devices with limited resources.
* Finally, to perform cryptographic operations with precision, our design integrates a Finite State Machine (FSM). This FSM is designed for an optimal control flow, minimizing latency, and managing system resources, thus optimizing overall performance.  
To summarize, the proposed framework presents a substantial advancement in ECC, improving both security and computational efficiency. Our design provides a reliable ECC solution through the creative application of the NAF algorithm for PM on BEC. The NAF method streamlines processes and speeds up computations by lowering the amount of nonzero digits. Lowering the possibility of side-channel attacks improvessecurity. Additionally, our design utilizes 2 \(\times\) 1 multiplexers to optimize the ALU. While the area is slightly increased, throughput is significantly increased. The integration of NAF and ALU optimization ensures not only better security measures but also improved computational speed.