esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Parameter Settings  
Based on previous research in [23; 24; 47; 64], the best population size for each algorithm has been defined. However, due to the difference of function between software effort estimation methods, we conducted ten experiments with \(p=10\), \(20\),..., \(100\) to find the optimum \(p\) for each algorithm based on the Friedman Mean Rank (FMR).  
Figure 5 illustrates the experimental results for the COCOMO estimation method. The behavior of \(p\) is quite similar for CPSO, MUCPSO, and GA. The larger the \(p\), the better the rank. The optimum value is reached using \(p=100\) for the three algorithms. Meanwhile, \(p\) gives a different effect for SPSO and UCPSO that achieves the optimum value through \(p=60\) and \(70\), respectively.  
Figure 4: Parameter tuning for COCOMO estimation model.  
Figure 3: Parameter tuning for Agile estimation model.  
Next, Figure 6 illustrates the experiment results for the UCP estimation method. The behavior of \(p\) is similar for SPSO, CPSO, and UCPSO. The larger the \(p\), the better the rank. The optimum value is reached on \(p=100\) for the three algorithms. Meanwhile, the behavior of \(p\) provides a different effect for MUCPSO and GA that achieves the optimum value via \(p=10\) and \(90\), respectively.  
Figure 5: Friedman mean rank calculated using 10 different population sizes \(p\) for each optimization algorithms in COCOMO estimation method.  
Figure 6: Friedman mean rank calculated using 10 different population sizes \(p\) for each optimization algorithms in UCP estimation model.  
Finally, Figure 7 illustrates the experimental results for the Agile estimation method. The behavior of \(p\) is similar for SPSO, CPSO, and GA. The larger the \(p\), the better the rank. The optimum value is reached using \(p=100\) for the three algorithms. Meanwhile, the behavior of \(p\) also provides similar effect for UCPSO and MUCPSO that achieves the optimum value through \(p=10\).  
The parameter settings for MUCPSO and other benchmark algorithms are listed in Table 4. Parameters \(c_{1}\) and \(c_{2}\) are set to integer two since it, on average, sets the weights for \"social\" and \"cognition\" to 1 [14,64]. The stochastic parameters \(r_{1}\) and \(r_{1}\) are two random functions in the range [0,1] [64]. The parameters are introduced in SPSO, and UCPSO, while CPSO and MUCPSO use the chaotic map function as their stochastic parameters.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 3 State of the Art in Linear Computation Coding
### Multidimensional Functions  
Lossless linear computation coding for products of structured matrices with unknown real or complex vectors is widely spread in the literature. The most known example is probably the fast Fourier transform [31]. Computation coding for linear functions with unstructured matrices is much less investigated. Similar to [29], existing work [32; 33; 34; 35] is based on subexpression sharing. These algorithms achieve significant savings, but their complexity scales unfavorably with matrix size. This makes them infeasible for large matrices. Lower bounds on the number of additions required for subexpression sharing are proven in [36].  
The algorithm proposed in this work can be seen as the happy marriage between two algorithms proposed earlier: The coordinate rotation digital computer (CORDIC) algorithm [37] and the mailman algorithm [38]. Both of them have their advantages and shortcomings. Our proposed method keeps the advantages and avoids the shortcomings. We start with discussing the latter one.
# On the Local and String Stability Analysis of Traffic Collision Risk
## 5 Simulation Verification
### Consistency Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Disturbance** & **Model** & **Traffic** & **Traffic** & **Risk (Local)** & **Risk (String)** \\ \hline
0.3 & IDM & 98.6\% & 91.9\% & 98.4\% & 90.9\% \\  & OVM & 98.7\% & 89.6\% & 97.6\% & 89.7\% \\  & ACC & 99.8\% & 98.9\% & 98.5\% & 95.9\% \\ \hline
0.8 & IDM & 98.1\% & 89.2\% & 97.9\% & 88.1\% \\  & OVM & 98.3\% & 88.2\% & 96.5\% & 86.6\% \\  & ACC & 98.5\% & 97.9\% & 98.3\% & 95.8\% \\ \hline
1.5 & IDM & 96.9\% & 88.9\% & 96.1\% & 87.0\% \\  & OVM & 95.8\% & 87.1\% & 95.6\% & 84.2\% \\  & ACC & 98.3\% & 97.6\% & 97.2\% & 94.8\% \\ \hline
3 & IDM & 95.5\% & 86.8\% & 95.3\% & 85.6\% \\  & OVM & 95.6\% & 81.6\% & 95.1\% & 80.7\% \\  & ACC & 98.0\% & 96.4\% & 96.9\% & 93.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Consistency between theoretical derivation and simulation results.
## 4 The Proposed DRLEA Approach  
In this section, we present three algorithms that have been used as benchmarks and the proposed DRLEA algorithms for reducing the number of low-throughput users by dynamic antenna control. In the current paper, we address the case of a single HAPS movement. Our objective is to control only the HAPS in question (which has supposedly moved) to reduce the number of low-throughput users in the area it was serving before it moved. This means that no overlap or exchange of areas with surrounding HAPS will occur.  
Therefore, we can simplify the system model as follows. We consider \(M-1\) fixed-position HAPS around the HAPS in question that is moved due to wind pressure. Our target is to optimize the antenna parameters of this HAPS to reduce the number of users with low throughput. No handovers between the different HAPS is accounted for, and no new users are introduced to the coverage area of the HAPS in question.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 3 Data Collection II: Stationary Target Reflection
### Binary Target Detection: Experimental Results
#### 3.5.1 Target Echo  
We take measurements of the target echo from the locations \((R,\theta)=\) (3.35 m, 0\({}^{\circ}\)), (6.70 m, 0\({}^{\circ}\)) and show the results for the latter in Figure 7. The captured target echo (solid) matches well with the copy of the transmitted waveform (dashed), demonstrating that the experimental fast-time RCS behavior agrees well with [49]. Also included in Figure 7 is the interference plus noise sample data, providing a visual representation of the neg-pos-neg test in Section 3.4.  
Figure 7: Captured data from the neg-pos-neg test at \((R,\theta)=\) (6.70 m, 0\({}^{\circ}\)) of an FDA radar system with operational parameters \(f_{0}=\) 2 GHz, \(\Delta f=\) 12 kHz, and \(N=3\).
## 3 Results
### Force Characteristics Results of Initial Support
#### 3.2.3 The Influence of Soil Poisson's Ratio on the Forces Experienced by the Initial Support  
The Poisson ratio of the soil is one of the parameters used to evaluate the performance of the soil. In this study, different Poisson's ratios were selected, with values of 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, and 0.45, to investigate their influence on the initial support stress.  
The analysis of the variation in internal force values shown in Table 5 and Figure 6c indicates that the maximum bending moment of the section was linearly and negatively related to the soil's Poisson's ratio, decreasing as Poisson's ratio increased. The variation in the maximum axial force value fluctuated less than that of the maximum bending moment value, initially decreasing and then increasing with an increase in Poisson's ratio. The minimum value was reached at a Poisson ratio of 0.3, indicating that the soil layer with a Poisson ratio of 0.3 had the least impact on the maximum axial force value of the initial support.  
Figure 6: Initial support stress variation curve. (**a**) influence curve of tunnel burial depth on initial support stress; (**b**) influence curve of soil bulk density on initial support stress; (**c**) influence curve of soil Poisson’s ratio on initial support stress; and (**d**) influence curve of initial support thickness on initial support stress.  
.2.4 The Influence of the Initial Support Thickness on the Forces Experienced by the Initial Support  
In the mining method, the structural stiffness of the initial support ring was directly affected by the thickness of the initial support. The internal forces of the initial support at various thicknesses were obtained in the simulation by adjusting the thickness of the initial support in the tunnel model to 0.25, 0.27, 0.29, 0.30, 0.31, 0.33, and 0.35 m. The maximum bending moment and maximum axial force of the initial support are positively correlated with thickness and increase with thickness, as shown in Table 6 and Figure 6d. However, there is a discrepancy in the slope of the bending moment and axial force curves as the thickness increases. This indicates that the maximum bending moment of the starting section is influenced by its thickness; the maximum bending moment changes more significantly than the maximum axial force; and the maximum bending moment changes abruptly.
## 4 Results of the Real-Time Atmospheric Turbulence Measurement
### Real-Time Atmospheric Turbulence Measurement  
Subsequently, we employed a measurement system to quantify the atmospheric turbulence at the NVST site. The GLAO system operates at a high frame rate of 2100 Hz and constantly listens to data requests from the measurement system. The measurement system requests data at a fixed rate of 200 Hz, which triggers subsequent computational tasks once per second. If each measurement can be completed within one second, the system can continuously output one result per second in real time.  
In terms of actual computation time, the system accomplishes all calculations within 0.6 s and displays the results on the host computer interface, effectively satisfying the envisaged real-time requirements.  
The measurement results are shown in Figure 10.  
During the observation process, the FOV of the observed solar activity area exceeded 1\({}^{\prime\prime}\). The average atmospheric coherence length is approximately 10.8 cm. During the observation process, we systematically adjusted the open- and closed-loop states to collect validation data. The results obtained in the closed-loop state are almost identical to those in the open-loop state. The results show that the measurement system can measure atmospheric turbulence under both open- and closed-loop conditions, even in natural atmospheric turbulence conditions.  
To compare the S-DIMM+ method, we saved the original data during the real-time measurement process. After collecting the data, we measured them using S-DIMM+. The S-DIMM+ measurement results are shown in Figure 10b.  
We have conducted a statistical analysis of atmospheric turbulence during this period to better compare the measurement results of the two methods. Figure 11 and Table 3 show the statistical results.  
Figure 10: Observation results on 28 May 2023. (**a**) the measurement results. The top graph indicates the variation of the atmospheric coherence length, the bottom chart shows the atmospheric coherence length of different heights, and the middle figure shows the system’s status, where green means the open loop and yellow means the closed loop. (**b**) The measurement results of S-DIMM+ for the same data and parameters.  
The results show that the statistical results obtained via the two methods are relatively similar, with most turbulence located below 2000 m. Approximately 86% of the real-time measurement results obtained by the measurement system are located below 2000 m, with only a small portion of turbulence occurring in the upper layers. The results of the S-DIMM+ method are relatively scattered. Due to the sun's irradiation, the ground temperature rises rapidly, resulting in very strong atmospheric turbulence near the ground. It is reasonable for daytime turbulence to be concentrated near the ground.  
Statistical analysis of the results of both the SLODAR and S-DIMM+ methods reveals that approximately 80% of the turbulence is located below an altitude of 2000 m; only a small amount occurs at a greater height.
# Strategies for Enhancing Extracellular Electron Transfer in Environmental Biotechnology: A Review
## 2 Mechanism of Extracellular Electron Transport in Microorganisms  
### Direct Electron Transfer (DET)  
Microorganisms can carry out EET in different ways as depicted in Figure 1. For DET, electrons can move from the bacteria to electrodes independent of external mediators. This method necessitates the proximity of microorganisms to the outer electron recipient and entails the transfer of electrons via outer membrane proteins known as c-type cytochromes [23; 24]. The limited distance of electron transfer in DET is known to be a contributing factor to the improved EET process. The transfer of electrons through the cytochrome pathway has been extensively studied in _Geobacter sulfuruduchens_. This has been achieved through the use of mutant strains, wherein the gene responsible for encoding cytochrome c proteins has been either overexpressed or deleted [25].  
The DET process is not exhibited by all microorganisms; hence, there has been a focus on investigating an alternative mechanism that is comparable but less comprehended, referred to as microbial nanowires. This mechanism has been occasionally referred to as a variant of DET. Nanowires are cellular membrane protrusions that bear a resemblance to pili. Due to their relatively elevated conductivity, it is postulated that they facilitate the conduction of electrons. Consequently, this confers an advantage to microorganisms by enabling them to inhabit a location that is distanced from the metal oxide or electrode. Additionally, this could facilitate inter-species electron transfer within a biofilm [23].
# Integrated Microwave Photonics for Wideband Signal Processing
## 6 Integrated MWP for Inline Photonic Signal Processing and Full Platform Integration
Several other platforms such as InP and group III-V materials have demonstrated the capability to support light generation, amplification, modulation, and detection, which are inherent properties that can be exploited to fill in the missing gap in silicon based integrated photonic circuits [83]. For example, reconfigurable photonic integrated signal processors based on the InP-InGaAsP material system have been reported in [8; 84]. In [8], an example of a MWP filter with a reconfigurable RF response is demonstrated. It includes a laser, an optical single-sideband (SSB) modulator, a tunable optical filter, and a photodiode, where the tunable optical filter is implemented based on a ring-assisted Mach Zehnder interferometer.
## 2 Related Work
### The Cold Start Problem  
Trust and reputation models typically perform poorly when dealing with newcomer service providers. This challenge, known as the cold start problem, arises because service requesters have difficulty assessing the trustworthiness of newcomer service providers with whom they have no previous interactions but are now connected to. Stereotyping, first introduced by [PERSON] et al. in [11], operates on the idea that agents with similar observable characteristics are likely to behave similarly. This approach helps improve trust assessments for newcomers or agents with no prior interaction history. More recently, the cold start problem has been addressed using machine learning, where trust models predict a newcomer's trustworthiness [12]. Additionally, roles within virtual organizations and social relationships among entities can serve as sources of trust information [5], particularly when direct or indirect trust data are unavailable. Another approach involves assigning an initial trust value until the agent can gather direct experience. This value may represent the average performance or be inferred from interactions with other agents [4].  
The CA algorithm applies this principle to mitigate the cold start problem. When a service provider receives a request to complete a specific task for a particular service requester for the first time, it establishes a connection and assigns an initial weight based on the average of existing weights for that task (from interactions with other service requesters). In essence, the CA algorithm allows the trustee to leverage prior experience from performing the same task for other trustors. If no prior knowledge is available, the algorithm sets the initial weight to a predetermined value, representing a baseline performance level.
# Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters
## 4 Conclusions  
In this study, a novel method based on the Hessian matrix was presented for sEMG spatio-temporal image enhancement. The proposed algorithm was also compared with some of the available digital line-like structure enhancement algorithms using experimental and simulated signals. We proposed multi-scale-based filter enhancement that detects and models the MUAP pattern in spatio-temporal EMG images. Results on various noisy signals showed the precision of the proposed filter for detection and enhancement of the MUAP pattern. The proposed multi-scale filter first modeled the MUAP propagation as a Gaussian line-like structure, and then enhanced it, using the Hessian eigenvalues. The proposed multi-scale filter enhanced both the bright and dark portions of the MUAP propagation structure in the sEMG images, as opposed to the other methods, which can only enhance the bright region. The results for noisy signals at a very low SNR show that the proposed filter is the best method for enhancing the EMG and suppressing the noise.  
Conceptualization, K.U.; methodology, K.U.; software, K.U.; validation, R.R. and K.K.; formal analysis, K.K.; investigation, K.U.; resources, K.U.; data curation, K.U. and K.K.; writing--original draft preparation, K.U. and M.A. ([PERSON]); writing--review and editing, M.A. ([PERSON]); visualization, M.A. ([PERSON]); supervision, T.-S.C.; project administration, T.-S.C. and M.A. ([PERSON]); funding acquisition, M.A. ([PERSON]) and T.-S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education under grant 2019R1F1A1058548 and grant 2020R1G1A103221.  
The authors declare no conflict of interest.  
Figure 9: Accuracy of the MUAP enhancing filters for 12 sets of simulated signals, where each consisted of 15 channel single differential EMG signals against various noise levels.
# Study on the Effect of Vibrating Process on the Compactness of Slipform Concrete
## 3 Analysis of Experimental Results
### Effect of Vibration Parameters on the Distribution of Different Particle Size Aggregates  
This study separated the different color aggregates, macroscopic pores, and slurry precisely using Image-Pro Plus software. In addition, it obtained the distribution of all the aggregates, different color particle sizes, and macroscopic pores (Figure 6).  
The aggregated size distribution values were calculated for different particle sizes of aggregates at different vibration frequencies of 30 s, 60 s, and 90 s using Equation (1). In addition, the distribution coefficients of different particle sizes of aggregates at different vibration frequencies were plotted (Figure 7).  
The results (Figure 7) showed that with the increase in the vibration duration, the distribution coefficient values of different-sized aggregates increased. Therefore, the degree of uniformity of the aggregates became worse. The aggregated distribution coefficient of slipform concrete increased with an increase in vibration frequency for the same bounding duration. In addition, the degree of homogeneity of the different aggregate sizes under the same vibration liquefaction conditions varied significantly. The larger the aggregated size was, the larger and less uniform the aggregated distribution coefficient value became, and the faster the value increased with the increase of the vibration liquefaction effect.  
Therefore, when using slipform paving concrete construction, the maximum particle size of the aggregate used should be reduced to maintain the concrete's quality, improve the uniformity of the aggregate in the slipform paving process, and enhance the quality of slipform concrete paving construction.  
Figure 5: Compressive strength of slipform concrete under different vibrating parameters.
## 5 Conclusions  
This paper conducts preliminary comparative studies between Moon-based platforms and halo orbits around the L1 point in the Earth-Moon system, with a specific focus on Earth observation. We carried out numerical analyses using Ephemeris data to elucidate the Earth observation characteristics of halo orbits, encompassing variations in the nadir point, distribution of viewing angles, and spatiotemporal ground coverage. The findings of this study suggest that halo motion around the L1 point does indeed have a role in modestly expanding the range of nadir point latitudes and introducing higher and more intricate rates of variation in nadir point latitudes. In conclusion, the geometric characteristics of halo orbits for Earth observation closely resemble those of Moon-based platforms. However, considering the distinct advantages of Moon-based platforms, such as their vast lunar surface with remarkable stability and nearlyternal operational lifespans, Moon-based platforms remain the optimal choice for Earth observation in the cislunar space. Nonetheless, L1, along with other Lagrange points, still holds promise as an extension of the Moon-based platform, contributing to the formation of a comprehensive globally-coverage network for Earth observation in cislunar space. This network would be characterized by its ability to conduct hemisphere-scale observations and offer a unique observational perspective. This study has contributed to a broader understanding of the future of Moon-based Earth observation, encompassing not only the lunar surface and orbit but also the Lagrange points within the entire cislunar space.  
Conceptualization, R.D., H.G. and G.L.; Methodology, R.D. and G.L.; Validation, R.D. and G.L.; Visualization, R.D.; Writing--original draft, R.D.; Writing--review & editing, R.D., H.G. and G.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals under Grant [number CBAS2022 IRP02], the National Natural Science Foundation of China under Grant [number 41590852 and number 42101413] and the National Key Research and Development Program of China under Grant [number 2020 YFE0202100].  
Publicly available datasets were analyzed in this study. This data can be found here: [[[ftp://ssd.jpl.nasa.gov/pub/eph/planets/](ftp://ssd.jpl.nasa.gov/pub/eph/planets/)]([ftp://ssd.jpl.nasa.gov/pub/eph/planets/](ftp://ssd.jpl.nasa.gov/pub/eph/planets/))], accessed on 10 January 2021.  
The authors declare no conflict of interest.