esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Establishment of the Active-Passive Single Pile Calculation Model
### Establishment of The Flexural Differential Equation of the Pile  
In order to study the internal force and deformation characteristics of an active-passive single pile adjacent to a surcharge load, we must first establish the pile's flexural differential equation. This paper mainly analyzes the characteristics of the horizontal internal forces and the deformation of the pile shaft, so the axial force on the pile shaft is assumed to be constant during the calculations. Consider the pile element with length \(dz\) at depth \(z\) below the ground, as shown in Figure 2. Axial force (\(N\)), shear force (\(Q\)), and bending moment (\(M\)) act on the upper end of the element. The axial force (\(N\)), shear force (\(Q+dQ\)), and bending moment (\(M+dM\)) act on the lower end of the element. The pile side has soil resistance given by \(P=b_{0}\cdot c_{z}\cdot y\) and additional load \(q(z)\).  
The force balance analysis of the pile body micro-element is carried out. Then the flexural differential equation of the pile can be given by  
\[EI\cdot\frac{d^{4}y}{dz^{4}}+N\cdot\frac{d^{2}y}{dz^{2}}+b_{0}\cdot c_{z}\cdot y -q(z)=0 \tag{1}\]  
where \(EI\) is the bending stiffness of the pile (MPa\(\cdot\)m\({}^{4}\)), \(N\) is the axial force acting on the pile (kN), \(c_{z}\) is the horizontal resistance coefficient of foundation soil (MN/m\({}^{4}\)), \(c_{z}=p/y\), \(y\) is the pile horizontal displacement (m), \(z\) is the depth of the calculated point (m), \(q(z)\) is the additional line load concentration (kN/m). \(b_{0}\) is the calculated width of the pile (m), determined according to the relevant specifications [21].  
Figure 1: Calculation model diagram of a single pile using our coordinate system. (**a**) Elevation; (**b**) plane.  
Figure 2: Schematic diagram for the stress analysis of a foundation pile.
# Clustering-Based Segmented Regression for Particulate Matter Sensor Calibration
## 3 Results  
### Experiment Settings  
#### 3.1.1 System and Data  
To justify the proposed method, we conduct evaluation with testing data from a practical deployed air pollution sensing system in Anji County, an area near the Taihu Lake, beside Huzhou City in Zhejiang Province as shown in Figure 1. The air pollution sensing system design is shown in Figure 7. Both sensory data and reference data are sampled continuously and saved in the database. Calibration algorithm is implemented on raw in schedule and only calibrated data can be utilized in applications and presented on user interface.  
Sensory data are generated by PMS5003S laser particulate matter sensor. It can output PM\({}_{2.5}\) and PM\({}_{10}\) concentration measurement (\(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)) at one-second intervals together with temperature (\(\mathrm{\SIUnitSymbolCelsius}\)) and relative humidity (\(\mathrm{\SIUnitSymbolMicro g}\)). The reference data for calibration are provided at one-hour intervals from official standard atmospheric environment monitoring station, including PM\({}_{2.5}\) and PM\({}_{10}\) concentration. For in-field calibration testing, the sensor selected is deployed nearby the monitoring station within 30 m range to satisfy the co-location condition.  
We prepare three groups of data and each group contains sensory data and reference data in the same three-month period including July-September 2021, October-December 2021 and January-March 2022. To justify the adaptability of the method, the three groups are sampled, respectively, in three different seasons with distinct temperature and humidity conditions, as well as different initial measurement error levels.  
When the model calibrates the data set, it first uses the clustering algorithm to cluster the data set, and then divides each cluster into a training set and a test set according to a certain ratio and calculates. The division ratio of the training set is discussed in Section 3.2.5, and it is found that the division ratio of the training set does not have a significant impact on the calibration results.
## 3 Experiments
### Experiments on AIRS Dataset
#### 3.4.3 Comparison with Other Filter Pruning Approaches  
We compared our approach with several state-of-the-art methods to show the effectiveness of the importance weights as a pruning criterion in deep CNNs. Table 4 presents the results obtained on the AIRS dataset. We can see that our filter pruning approach led to a stronger reduction of the FLOPs and network parameters while only moderately changing the accuracy and loss. We observed that, when pruning TeranusNet, a random  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Network** & **Model** & **Val. Loss** & **Val. Acc. (\%)** & **Val. Acc. (\%)** & **FLOPs** & **FLOPs** \(\downarrow\) (\%) & **\# Param.** & **\# Param. \(\downarrow\) (\%)** \\ \hline \multirow{8}{*}{\(\text{TernausNet}\)} & Baseline & 0.0174 & 99.32 & - & 32.3 B & - & 22.9 M & - \\ \cline{2-8}  & Proposed & 0.0190 & 99.27 & 0.05 & 5.06 B & 84.34 & 3.78 M & 83.50 \\  & (Random) & 0.0190 & 99.24 & 0.07 & 7.31 B & 77.37 & 4.27 M & 81.36 \\ \cline{2-8}  & Proposed (He) & 0.0181 & 99.27 & 0.05 & 7.80 B & 75.84 & 5.64 M & 75.42 \\ \cline{2-8}  & Proposed & 0.0178 & 99.29 & 0.03 & 8.08 B & 74.96 & 5.90 M & 74.27 \\ \cline{2-8}  & Proposed & 0.0184 & 99.27 & 0.04 & 7.80 B & 75.84 & 5.64 M & 75.42 \\ \hline \multirow{8}{*}{U-Net} & Baseline & 0.0169 & 99.34 & - & 54.6 B & - & 31.0 M & - \\ \cline{2-8}  & Proposed & 0.0178 & 99.28 & 0.06 & 8.95 B & 83.63 & 5.02 M & 83.84 \\ \cline{1-1} \cline{2-8}  & Proposed & 0.0179 & 99.28 & 0.06 & 10.1 B & 81.46 & 5.97 M & 80.75 \\ \cline{1-1} \cline{2-8}  & Proposed & 0.0170 & 99.31 & 0.02 & 13.0 B & 76.13 & 7.50 M & 75.82 \\ \cline{1-1} \cline{2-8}  & Proposed & 0.0173 & 99.31 & 0.03 & 13.4 B & 75.41 & 7.78 M & 74.92 \\ \cline{1-1} \cline{2-8}  & Proposed & 0.0170 & 99.32 & 0.02 & 13.8 B & 74.68 & 7.61 M & 75.47 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results on AIRS dataset. “Baseline” represents the normal training results, i.e., without pruning. “\(\downarrow\)” represents the reduction rate.  
filter pruning method showed an almost similar accuracy drop as that of the proposed approach with random initialization. However, our approach is superior because random filter pruning reduced the FLOPs by 74.73%, which is 8.9% less than what was achieved using our approach. One interesting observation was that the \(l_{1}\)-norm failed again, delivering much inferior results compared with the baseline approach or the results of the competitors. Despite strongly reducing the FLOPs, only the drop in accuracy reflected the degradation in network performance induced by the pruned filters. Similarly, our approach also strongly reduced the FLOPs when pruning U-Net with a random initialization strategy. Moreover, our approach, after removing more than 74% of the network parameters, achieved better accuracy than the state-of-the-art methods.
## 2 Theoretical Background
### Waveguides with Circular Outer Conductors and Eccentric Circular Inner Conductors
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Truncation Number \(N\)**} & \multicolumn{2}{c}{**Computed Cut-Off Wavenumber of**} & \multicolumn{1}{c}{**Time**} \\  & \multicolumn{2}{c}{**the Lowest Symmetric Mode**} & \multicolumn{1}{c}{**(Seconds)**} \\ \hline
64 & 3.472157345180335 \(-\)\(i\)\(\cdot\) 0.00000009781369 & 1.796 \\ \hline
128 & 3.472247785036224 \(-\)\(i\)\(\cdot\) 0.00000000152295 & 3.121 \\ \hline
256 & 3.472259067569722 \(-\)\(i\)\(\cdot\) 0.000000000002378 & 10.515 \\ \hline
512 & 3.472260477172100 \(-\)\(i\)\(\cdot\) 0.00000000000037 & 10.515 \\ \hline
1024 & 3.472260653349862 \(-\)\(i\)\(\cdot\) 0.000000000000001 & 207.292 \\ \hline
2048 & 3.472260675371377 \(-\)\(i\)\(\cdot\) 0.000000000000000 & 1340.219 \\ \hline
4096 & 3.472260678124045 \(-\)\(i\)\(\cdot\) 0.0000000000000000 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Convergence of the approximations to the wavenumber \(\gamma_{c01}\) of the lowest even mode \(TM_{c01}\) as a function of truncation number. Figure 6: Magnified view of the spectral dependence \(cond\ A_{N}(ka)\), \(N=128\) (\(b/a=d/a=0.25\)), in the neighbourhood of resonance points: (**a**) \(\left(ka\right)_{res}=3.47226\); (**b**) \(\left(ka\right)_{res}=4.26396\); (c) \(\left(ka\right)_{res}=6.75274\); (**d**) \(\left(ka\right)_{res}=7.72429\). again confirmation that Aitken extrapolation is an effective tool in achieving high accuracy for modest truncation numbers.
## 6 Experimental Evaluation
### Methodology
#### 6.1.5 Parameter Setting  
_Exact Mathematical Programming._ The default set of parameters of CPLEX was used to find exact solutions, except for the timeout parameter, which was set to 7200 seconds. Variant #2 with \(\alpha=0\) is equivalent to variant #1, where headways are fixed and equal to \(F^{i}\). However, problem formulations are not equal nor are the proposed methods to solve them. Variant #2 implies significantly more variables than variant #1, so it is worth checking that algorithms are also able to rapidly find solutions when \(\alpha=0\). Such instances were solved within a few seconds, so the timeout limit only applies for \(\alpha=0.3\). It was verified, though, that the performance of the more general variant #2 is quite good for very low values of \(\alpha\), comparable to the performance of the much simpler version #1. Runtimes degrade for higher values of \(\alpha\), and in fact, the best solutions found for \(\alpha=0.3\) were not proven to be optimal according to the defined tolerance for the MIP solver (0.01%). There is room to explore changes in parameters in order to improve performance, which is appointed as a future line of work. _Evolutionary algorithm._ Since EAs are non-deterministic, parameter configuration analysis is mandatory to find the proper combination of parameter values to compute the best results. Studied parameters included population size (\(ps\)), recombination probability (\(p_{R}\)), and mutation probability (\(p_{M}\)). Experiments were performed on five small problem instances with different features to avoid bias. Candidate values considered for each parameter were \(ps\in\{15,25,50\}\), \(p_{R}\in\{0.5,0.75,0.9\}\), and \(p_{M}\in\{0.001,0.01,0.1\}\). A summary of the results obtained in the analysis of the population size is presented in Table 2. Results of median and best fitness values are reported for each population size. The Friedman rank test is applied to analyze the results distributiResults in Table 2 demonstrate that the best results were computed using a population size of 25 individuals. Using a larger population size turned to be counterproductive for the EA, since suboptimal solutions dominate quickly and results do not improve in the long term. Table 3 reports the best fitness values and the Friedman ranks of the nine configurations (C1,..., C9) considered in the analysis of the recombination and mutation probabilities. The \(p\)-value of the Friedman rank was below \(10^{-3}\), thus assuring statistical significance of the results. In turn, the boxplots in Figure 6 graphically compare the median, best, worst, Q1, and Q3 values obtained for each studied configuration in the problem instance, which is representative of the results computed for other instances too. According to the reported metrics, the best results were obtained with configuration C2, i.e., setting \(PS=25\), \(p_{R}=0.5\), and \(p_{M}=0.005\). Configuration C2 systematically obtained the best Friedman ranking in all but one of the studied problem instances.
## 2 Related Work
### Ctgan  
The CTGAN architecture was developed based on the GAN (Figure 1) and modified the TGAN regarding the loss function and normalization method for continuous data. CTGAN introduces the Wasserstein GAN (WGAN) [23] into the loss function (Equation (1)). During learning in the discriminator, the WGAN measures the convergence of the distance between distributions using the Wasserstein distance rather than the Jensen-Shannon divergence in the vanilla GAN. The loss function was optimized using adaptive moment estimation (Adam). The Wasserstein loss function is expressed as  
\[\mathcal{L}(D,G)=E_{G(z)\sim p_{g}}[D(G(z))]-E_{x\sim p_{g}}[D(x)] \tag{1}\]  
The loss function is represented as \(\mathcal{L}(\cdot)\). \(E[\cdot]\) represents the expected loss. \(D\) is the discriminator function. \(G\) is the generator function. Neural networks implement both functions. \(z\) is the noise derived from the standard normal distribution (\(N\)(0,1)). \(G(z)\) represents the synthetic data generated by the generator function. \(p_{r}\) and \(p_{g}\) represent the real and generated data distribution. \(x\) represents real data. This modification decreased the mode-dropping phenomenon observed in vanilla GAN [8]. Moreover, CTGAN implements the normalization method with the variational Gaussian mixture model (VGMM) [24] rather than min-max normalization using the Gaussian mixture model (GMM) [24] used in TGAN. Unlike the min-max normalization, VGMM can generate continuous data from normalized complex distributions. This approach is referred to as mode-specific normalization.
# Analytical Design of Fractional-Order PI Controller for Parallel Cascade Control Systems
## 2 Materials and Methods
### Analytical Design of FOPI Controller for PCCS Combining with Smith Predictor  
The general parallel cascade control system is shown in Figure 2, where \(G_{p_{1}}(s)\) and \(G_{p_{2}}(s)\) are the transfer functions of the primary and secondary processes, respectively. \(G_{c_{1}}(s)\) and \(G_{c_{2}}(s)\) are the primary and secondary controllers; \(G_{d}(s)\) is used to stabilize the primary control loop in case the transfer function \(G_{p_{1}}(s)\) is kind of an unstable or integrating process. \(G_{d_{1}}(s)\) and \(G_{d_{2}}(s)\) denote the transfer functions of disturbances affecting the outputs. \(G_{p}(s)\) denotes the equivalent closed-loop transfer function for servo response of the secondary loop (from the input \(r^{\prime}{}_{2}\) to the output \(y_{1}\)), and it can be calculated as follows:  
\[G_{p}(s)=\frac{G_{p_{1}}(s)}{1+G_{c_{2}}(s)\Big{(}G_{p_{2}}(s)-\widetilde{G}_{ p_{2}}(s)\Big{)}+G_{d}G_{p_{1}}(s)} \tag{13}\]  
Assuming that \(G_{p_{2}}(s)=\widetilde{G}_{p_{2}}(s)\) (perfect model), Equation (13) becomes:  
\[G_{p}(s)=\frac{G_{p_{1}}(s)}{1+G_{d}G_{p_{1}}(s)}=G_{pm}(s)e^{-\theta_{p}s} \tag{14}\]  
where \(\theta_{p}\) is the delay time at the primary output, and normally, it equals the delay time of the primary transfer function (\(G_{p_{1}}\)); \(G_{pm}\) is the delay-free part of \(G_{p}\).  
In this work, the secondary process model is assumed as a well-known first order plus time delay process (FOPTD), as in Equation (15). The primary process model is investigated by one of the following transfer functions, as in Equations (16)-(18)  
\[G_{p_{2}}(s)=\frac{K_{z}e^{-\theta_{2}s}}{\tau_{2}s+1} \tag{15}\]  
Figure 2: The proposed parallel cascade control system.  
\[G_{p_{1}}(s)=\frac{K_{1}e^{-\theta_{1}s}}{\tau_{1}s+1} \tag{16}\]  
\[G_{p_{1}}(s)=\frac{K_{1}e^{-\theta_{1}s}}{\tau_{1}s-1} \tag{17}\]  
\[G_{p_{1}}(s)=\frac{K_{1}e^{-\theta_{1}s}}{s(\tau_{1}s+1)} \tag{18}\]  
where Equation (17) represents the unstable first order plus time delay (UFOPTD) and Equation (18) is the second order integral plus time delay (SOIPTD).
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 3 Overall Architecture
### Original Network: LLFlow  
LLFlow proposes a low-light image enhancement model based on normalizing flow, which consists of two key components. First, the encoder \(g\) takes the low-light image \(x_{l}\) as input and generates an illumination-invariant color map \(g(x_{l})\), which can be interpreted as a reflectance map inspired by Retinex theory. Second, LLFlow employs an invertible network to map normally exposed images to latent codes \(z\), thereby learning the distribution of normally exposed images under low-light conditions. During training, the method maximizes the log-likelihood of the well-exposed image \(x_{h}\) based on the change-of-variables theorem. Additionally, LLFlow utilizes a stochastic selection mechanism to determine the mean of the Gaussian-distributed latent variable \(z\) from either the color map \(C(x_{h})\) of a reference image or the color map \(g(x_{l})\) extracted from the low-light image by a conditional encoder. During inference, \(z\) can be randomly sampled from \(N(g(x_{l}),1)\), allowing for the generation of diverse normally exposed images based on the learned conditional distribution \(f_{\text{flow}}(x|x_{l})\). Compared to existing pixel-wise reconstruction loss methods based on deterministic processes, LLFlow employs the negative log-likelihood (NLL) loss for normalizing flow training and conditions it on low-light images or features. This approach naturally captures the structural information of images and measures visual distances on the image manifold. Consequently, LLFlow can more accurately model the complex conditional distribution of normally exposed images, thereby improving the quality of low-light image enhancement, including more balanced exposure, effective noise and artifact suppression, and richer color representation.
## 2 Material and Methods
### Analysis of Volatile Components  
The volatile components of each sample were analyzed using GC-MS (Agilent 7890A & 5975C, Agilent Technologies, Palo Alto, CA, USA). These experiments were performed using the headspace analysis method and 50/30 \(\upmu\)m, divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS)-coated solid-phase microextraction (SPME) fibers (Supelco Inc., Bellefonte, PA, USA). A 2 g sample was placed in a vial, sealed with an aluminum cap, and equilibrated at 60 \({}^{\circ}\)C for 20 min. The SPME fiber was then exposed to the inside of the vial for 25 min. The SPME fiber was adsorbed and analyzed using a system equipped with an HP-5 MS column (30 m \(\times\) 0.25 mm, id: 0.25 um film thickness, Supelco, Inc.). The injector temperature was 220 \({}^{\circ}\)C, the flow rate was 1.0 mL min\({}^{-1}\), the carrier gas was helium, and the analysis was performed under splitless analysis conditions. The oven temperature was maintained at 40 \({}^{\circ}\)C for 5 min, then increased to 200 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C min\({}^{-1}\). Volatile compounds isolated from the total ion chromatogram (TIC) were identified by reference to a mass spectrum library. For quantitative analysis, the peak area of each volatile component was determined and converted to \(\upmu\)g/kg after comparison with the internal standard (C15:0, pentadecane). The separated volatile substances were also analyzed using an olfactory detection port (ODP-III, Gerstel, Inc., Linthicum, MD, USA) that was equipped on the GC-MS. The intensity of the perceived odor was scored from 1 to 4, with a higher number indicating a stronger odor. The intensity and duration of the fragrance were also recorded.
## 3 The RIS-Aided UAV Secure Transmission  
Based on PLS, the maximum secure transmission rate over \(N\) time slots can be expressed as  
\[R_{s}[n]=[R_{u}[n]-R_{e}[n]],\quad\forall n\in N \tag{8}\]  
To optimize the maximum secure transmission rate, we formulate the problem as  
\[(\textbf{P1}):\max_{\mathbf{w}[n],\mathbf{\phi}[n]\mathbf{s}_{u}[n]} \frac{1}{N}\sum_{n=1}^{N}R_{s}[n]\] (9) s.t. \[\mathbf{s}_{u}[1]=\mathbf{s}_{S},\] \[\mathbf{s}_{u}[N]=\mathbf{s}_{E},\] \[||\mathbf{s}_{u}[n]-\mathbf{s}_{u}[n-1]||^{2}\leq V\delta,\] \[|\phi_{n}|^{2}\leq=\textbf{1},\forall n=1,\ldots,N. \tag{10}\] \[||\mathbf{w}[n]|\leq I.\]  
In P1, the first three constraints are associated with the starting, ending and flight velocity of UAV; the last but one constraint is associated with the phase shift; and the last constraint is associated with the power allocation. It can be observed that the objective function and the last constraint are non-convex, which means that they are difficult to solve. In the following, we transform P1 into a convex problem and propose an iterative algorithm to solve it.  
In order to solve P1, we propose an iterative algorithm. First, we optimally allocate the beamforming power. Then, the RIS reflect phase shift on the UAV is optimized. Finally, the trajectory optimization is solved. An alternating optimization (AO) algorithm is shown in Algorithm 1.  
```
Initialize the coordinates of UAV, GU and BS;  Initialize RIS array response vector;  Generate channel parameters;  Generate pathloss parameters; Repeat until convergence fortime\(t=1,2,\ldots,100\)do  Calculate beamforming vector \(\mathbf{w}_{DA}[time]\) by (16);  Calculate the azimuth of signal transmission according to \(s_{u}[time-1]\);  Calculate the RIS array response vector: forIteration times\(con=1,2,\ldots,n\)do  Calculate the Riemannian gradient of \(f(\varphi)\);  Search tangent vector of current direction \(\mathcal{T}(d)\);  Taking tangent vector into the complex circle manifold; if\(\|\mathbf{\Phi}[\mathbf{n}]-\mathbf{\Phi}[\mathbf{n}-1]\|\ \to 0\)  Update \(\mathbf{\Phi}[time]\); break;  end for  Calculate the UAV coordinate: forIteration times\(con=1,2,\ldots,n\)do  Perform a first-order Taylor spread on (19);  Calculate the \(\mathbf{s}_{u}[n]\) in (10) with fixed \(\mathbf{\Phi}[time]\) and \(\mathbf{w}[time]\); if\(\|\mathbf{s}_{u}[\mathbf{n}]-\mathbf{s}_{u}[\mathbf{n}-1]\|\ \to 0\)  Update \(\mathbf{s}_{u}[time]\) break;  end for  end for
```  
**Algorithm 1** The AO Algorithm for the Power Allocation and Phase Shift and Trajectory Design
## 1 Introduction
[39] studied the effect of gender on the fundamental diagram using the data obtained from a single-file walking experiment conducted in Palestine. The results indicated that the gender effect on pedestrian fundamental relations was insignificant. Later, using the same empirical data, [PERSON] et al. [40] compared the speed-density diagrams for exclusive males, exclusive females, and mixed groups. This comparison revealed that because of social customs, male and female pedestrians tend to walk slowly when they are in a mixed-gender group. The outcomes of this study further indicated that factors, such as age and culture, could affect the movement of pedestrians. The outcomes of the study by [PERSON] et al. [41] also highlighted that there was no significant effect of gender on fundamental diagrams. The recent study by [PERSON] et al. [42] also examined the age and gender influences on pedestrian fundamental diagrams. The outcomes of this study explained that even though age could influence the fundamental diagrams, gender has no influence. [PERSON] et al. [43] investigated the time-delayed speed correlation between consecutive leading and following pedestrians in a single-file crowd. They used the data collected from a single-file experiment conducted with German students in the 5 th and 11 th grades (11-12 years old and 17-18 years old, respectively). Their results indicated that the characteristic delay time depends only on the crowd density. However, demographics (age and gender) of the leading, and following pedestrians did not show any significant influence on it. It can be noted that almost all these studies have focused on macroscopic characteristics, i.e., mainly related to fundamental diagrams of pedestrian dynamics. The influence of gender on microscopic behaviors has not been comprehensively studied so far. Microscopic walking dynamics are the underlying mechanics of microscopic pedestrian simulation models. This study aims to explore the effect of gender on microscopic walking behaviorswhen people walk in a mixed crowd. The data were obtained from a walking experiment on single-file maneuvers conducted under controlled conditions. The paper is organized as follows: The next section will discuss the methods that include a description of the data and microscopic variables. This is followed by the results of the study. Finally, a discussion and conclusions are presented.