esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multilevel Adaptive Path-Planning Model in Off-Road Environments
## 4 Performance Evaluation of Improved ACA
### Parameter Optimization
As Figure 6b shows, with the increase in \(\gamma\), the average number of iterations declined linearly, but the decline was not as sharp as that in Figure 5b. With the average traveling time and average number of iterations considered, the trafficability heuristic factor was set at 3; that is, \(\gamma=3\). (2) Pheromone volatility coefficient  
The major improvement in the ACA in this study was the pheromone concentration of routes; therefore, the value of the pheromone volatility coefficient is discussed as follows. Eleven simulated experiments were designed for the value range of the coefficient, and the other parameters remained the same as in the default setting. The experimental method was the same as above, and the results are shown in Figure 7.  
As Figure 7a shows, with the increase in \(\rho\), the average traveling time on different maps showed no tangible changes. In a conventional ACA, \(\rho\) is a key factor that affects the algorithm's performance. Figure 7a proves that our improved algorithm was more robust than the conventional one. As Figure 7b shows, the average number of iterations decreased as \(\rho\) increased, and when \(\rho>0.4\), the decline slowed. Prior experience shows that the value of \(\rho\) should not be extremely large or small. Therefore, the pheromone volatility coefficient was set at 0.4; that is, \(\rho=0.4\). Figure 7: Influence of \(\rho\) on the algorithm’s performance. (**a**) The change trend of maneuvering time with \(\rho\). (**b**) The change trend of iterations time with \(\rho\). (3) Number of ants  
The value of \(m\) was determined by simulation experiments. The average traveling time and average number of iterations were calculated with the number of ants set at 5, 10, 25, 50, 100, 150, and 200. Figure 8 shows the experiment results. As Figure 8a shows, when \(m<50\), the average traveling time on different maps significantly dropped with the increase in \(m\); on map C, the algorithm reported an error at a small value of \(m\). This was because map C had a high barrier rate and complex environments, making it difficult for the ants to find a route if the colony is too small. As \(m\) continued to grow, the average traveling time became stable. As Figure 8b shows, the number of iterations slightly declined at a small colony (when \(m<25\)) but grew quickly as \(m\) grew, and the algorithm showed poor convergence performance. Therefore, the number of ants was set at 50 in the present work; that is, \(m=50\). (4) Pheromone intensity  
The influence of pheromone intensity on the algorithm's performance relies on the configuration of the heuristic factor and the pheromone volatility factor; therefore, after the optimal values of these two parameters were determined via simulations, the value of \(Q\) was set at 10 (\(Q=10\)) to keep the value of \(Q\) aligned with the pheromone concentration.
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 2 Materials and Methods
### Evaluation Criteria
#### 2.4.1 Statistical Methods  
The first line of evaluation typically involves statistical comparisons between the real and synthetic datasets. These methods aim to assess whether the synthetic data are drawn from a similar probability distribution to the original data. One commonly used metric is the Kullback-Leibler (KL) divergence [31], which measures the divergence of one probability distribution from another. However, since KL is asymmetric and sensitive to zero probabilities [32], many researchers prefer the Jensen-Shannon divergence (JSD) [12; 33; 34], a symmetric and more stable variant that quantifies similarity between real and generated distributions, as follows:  
\[\text{JSD}(P||Q)=\left(\frac{1}{2}\;D_{KL}(P||R)+D_{KL}(Q||R)\right), \tag{4}\]  
where \(R=\frac{P+Q}{2}\) and JSD are non-negative or 0 when both distributions, the real and the synthetic, are identical.  
Another popular metric is the Wasserstein distance, particularly relevant in the context of Wasserstein GANs, as it evaluates the \"cost\" of transforming the synthetic distribution into the real one, as follows:  
\[WassersteinDistance(u,v)=\underset{\pi\in\Gamma(u,v)}{\inf}\int_{R\times R}| x-y|\;\mathrm{d}\pi(x,y), \tag{5}\]  
where \(\Gamma(u,v)\) is the set of (probability) distributions on \(R\times R\), whose marginals are \(u\) and \(v\) on the first and second factors, respectively. For a given value of \(x\), \(u(x)\) results in the probability of \(u\) at position \(x\) and the same for \(v(x)\).  
Additionally, first- and second-order statistics, such as mean and standard deviation (STD), are used to compare load magnitudes and variability among datasets. A simple distance-based metric like root mean square error (RMSE) can also provide insight into the point-wise deviation between real and synthetic time series when aligned across corresponding time steps. Together, these metrics result in a baseline indication of whether the GAN has learned to mimic the distributional structure of the original data.  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{N}(x_{i}-\hat{x_{i}})^{2}}{N}}. \tag{6}\]
## 3 Materials and Methods
### Reliability Indicators
This function is determined by Equation (3):  
\[F(t)=1-e^{(-\frac{t^{\alpha}}{\beta})} \tag{3}\]  
The probability density function for the correct operation time until the first damage is determined as a derivative of the unreliability function after time. In this simulation, that function was determined on the basis of Formula (4):  
\[f(t)=\frac{\alpha}{\beta^{\alpha}}\cdot t^{\alpha-1}\cdot e^{(-\frac{t}{\beta })^{\alpha}} \tag{4}\]  
where  
\(t\)--object correct operation time;  
\(\alpha\)--the shape parameter;  
\(\beta\)--scale parameter. By solving the system of Equation (5), the scale parameter \(\beta\) and shape parameter \(\alpha\) are determined, which are necessary for the determination of the diagrams of the functions mentioned above. The values of the various parameters are summarised in Table 3.  
\[\left\{ \begin{aligned} \alpha\cdot\sum_{i=1}^{\frac{\alpha}{2}} lnx_{i}-\frac{n}{2}\cdot ln\beta=\sum_{i=1}^{\frac{\alpha}{2}}ln\left(ln\frac{1}{1- h(x_{i})}\right)\\ \alpha\cdot\sum_{i=\frac{n}{2}}^{n}lnx_{i}-\left(n-\frac{n}{2} \right)\cdot ln\beta=\sum_{i=\frac{n}{2}}^{n}ln\left(ln\frac{1}{1-h(x_{i})} \right)\end{aligned} \right. \tag{5}\]  
where  
\[h(x_{i})=\frac{i-0.5}{n}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Vehicle with a Damaged Suspension System** & **The Car Moving Correctly** \\ \hline \(\alpha\) & 1.751502413 & 1.755654928 \\ \hline \(\beta\) & 281,954,166.4 & 245,000,509.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The value of the shape and scale parameters. The expected correct operation time \(E(t)\)[28] is one of the major reliability indicators. In this article, the value of the operation time median was determined graphically, following the previous collation of the cumulative distribution function \(F(t)\) and reliability function \(R(t)\). The indicator may be determined analytically using Formula (6) [29]. \[E(t)=\int_{0}^{\infty}t\cdot F(t)dt=\int_{0}^{\infty}R(t)dt \tag{6}\]
_Int. J. Mol. Sci._**2015**, _16_, 9078-9096; doi:10.3390/ijms16059078  
International Journal of  
**Molecular Sciences**  
**ISSN 1422-0067**  
www.mdpi.com/journal/ijms  
_Article_  
Novel Tertiary Amino Containing Blinding Composite Membranes via Raft Polymerization and Their Preliminary CO\({}_{2}\) Permeation Performance  
[PERSON] 1,\({}^{\dagger}\), [PERSON] 2,\({}^{\ddagger}\), [PERSON] 2,\({}^{\dagger}\) and [PERSON] 2,\({}^{\star}\)  
\({}^{\dagger}\): These authors contributed equally to this work.  
\({}^{\star}\): Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-571-8832-0711; Fax: +86-571-8649-6357.  
Academic Editor: [PERSON]  
_Received: 9 February 2015 / Accepted: 10 April 2015 / Published: 23 April 2015_  
**Abstract:** Facile synthesis of poly (_N_,_N_-dimethylaminoethyl methacrylate) (PDMAEMA) star polymers on the basis of the prepolymer chains, PDMAEMA as the macro chain transfer agent and divinyl benzene (DVB) as the cross-linking reagent by reversible addition-fragmentation chain transfer (RAFT) polymerization was described. The RAFT polymerizations of DMAEMA at 70 \({}^{\circ}\)C using four RAFT agents with different R and Z group were investigated. The RAFT agents used in these polymerizations were dibenzyl trithiocarbonate (DBTTC), s-1-dodecyl-s-(\(\alpha\),\(\alpha\)'-dimethyl-\(\alpha\)-acetic acid) trithiocarbonate (MTTCD), s,s'-bis (2-hydroxyethyl-2'-dimethylacrylate) trithiocarbonate (BDATC) and s-(2-cyanoprop-2-yl)-s-dodecyltrithiocarbonate (CPTCD). The results indicated that the structure of the end-group of RAFT agents had significant effects on the ability to control polymerization. Compared with the above-mentioned RAFT agents, CPTCD provides better control over the molecular weight and molecular weight distribution. The polydispersity index (PDI) was determined to be within the scope of 1.26 to 1.36. The yields, molecular weight, and distribution of the star polymers can be tuned by changing the molar ratio of DVB/PDMAEMA-CPTCD. The chemical composition and structure of the linear and star polymers were characterized by GPC, FTIR, \({}^{1}\)H NMR, XRD analysis. For the pure Chitosanan membrane, a great improvement was observed for both CO\({}_{2}\) permeation rate and ideal selectivity of the blending composite membrane upon increasing the content of SPDMAEMA-8. At a feed gas pressure of 37.5 cmHg and 30 \({}^{\circ}\)C, the blinding composite membrane (Cs: SPDMAEMA-8 = 4:4) has a CO\({}_{2}\) permeation rate of 8.54 \(\times\) 10\({}^{-4}\) cm\({}^{3}\) (STP) cm\({}^{-2}\)-s\({}^{-1}\)-cm\(\cdot\)Hg\({}^{-1}\) and a N\({}_{2}\) permeation rate of 6.76 \(\times\) 10\({}^{-5}\) cm\({}^{3}\) (STP) cm\({}^{-2}\)-s\({}^{-1}\)-cm\(\cdot\)Hg\({}^{-1}\), and an ideal CO\({}_{2}\)/N\({}_{2}\) selectivity of 35.2.  
**Keywords:** reversible addition fragmentation chain transfer (RAFT) polymerization; divinyl benzene; star polymer; CO\({}_{2}\) facilitated carrier
## 4 Results and Discussion
### Navigation Using Aruco Markers
#### 4.2.1 First Test Case  
After learning how to use the prototype, they have tested it several times by selecting a start point and destination. The prototype assists them in moving from the starting point to the destination using navigation feedback. During the process, the authors have discovered some problems: 1. Sometimes PVI failed to understand the feedbacks and as a result, the feedback needed to be improved. 2. PVI can hardly detect markers because they were placed higher than the view of the camera. So, installing markers at a lower position is necessary. 3. PVI move their hands rapidly during navigation which causes images to be captured with occlusion. 4. PVI cannot detect markers because they are moving their hands a lot and tags move out of the smartphone's camera view. 5. PVI take shorter steps compared to blindfolded participants, so the authors should calculate the number of steps based on PVI rather than the blindfolded individuals. 6. PVI occasionally create situations that cannot be managed by our prototype. For example, if their next point is node 7 and they go in the wrong direction leading to another point. The prototype should check whether this point is node 6 or not. If it is node 6, the prototype should continue navigating, because node 6 in the graph is the next point to destination after the node. However, if it was another point, the prototype should ask the PVI to go back and search for node 7 again.
## 3 Results and Discussion
### Seismic Resilience Assessment
#### 3.3.2 Development of the Resilience Index  
Figure 13a,b show the resilience index (\(Re\))'s evolution for the studied tunnel under the different \(PGA\) and \(PGV\) values, respectively. Naturally, it is clearly demonstrated that the seismic resilience index of tunnels decreases gradually when the \(PGA\) or \(PGV\) increases. More specifically, as the \(PGA\) is ranged between 0.0 g and 1.0 g, the resilience of tunnels is found to remain at a level between 0.87 and 0.99. It is more significant in the case of \(PGV\), for it is indicated that the resilience of tunnels maintains a high level between 0.95 and 0.99 when the \(PGV\) is ranged between 0.0 m/s and 1.0 m/s. From the above analysis, it is concluded that the examined tunnel is generally good in terms of seismic resilience. Nevertheless, the critical roles of adequate seismic design of the tunnel structure and rapid recovery measures at the post-earthquake event in case of severe seismic intensities have been demonstrated.  
Figure 12: Functionality recovery function of the examined tunnel in terms of (**a**) \(PGA\) and (**b**) \(PGV\) at free-field ground surface.  
Figure 13: Resilience index \(Re\) of the examined tunnel in terms of (**a**) \(PGA\) and (**b**) \(PGV\) at the free-field ground surface.
## 3 Results and Discussion  
### Simulation Design of Ion Imprinted Polymer  
Screening for suitable functional binding ligands remains a key factor in the design of the template binding ligand model. Therefore, some functional binding ligands that may have strong affinity to uranyl ions were selected in this study. Based on computational chemistry methods, several functional binding ligands such as 1H-pyrazolo[3_A_-B] pyridine-3-carboxylic acid (1H-PPCA), 2-hydroxypyridine (2-HP), 3-hydroxypyridine (3-HP), salicaldehyde acridine (H\({}_{2}\)SA), 2,6-dihydroxypyridine (DHP), pyridine-2,5-dicarboxylic acid (H\({}_{2}\)Pdc), and nicotinic acid (3-PCA) were screened. The structures of 1H-PPCA [44], 3-PCA [45], H\({}_{2}\)SA [46], and H\({}_{2}\)Pdc [47] were chosen as binding ligands because of their strong interaction with uranyl ions; 2-HP, 3-HP, and D-HP may have some affinity with uranyl ions because they contain pyridine and hydroxyl structures [48]. As presented in Figure 2, the structure of individual molecules was first optimized by density functional methods (Figure S1), and then the configuration of the complexes was optimized structurally after binding of functional binding ligands to template ions. The binding energies of the optimized template functional binding ligands (1H-PPCA, 2-HP, 3-HP, H\({}_{2}\)SA, DHP, H\({}_{2}\)Pdc, 3-PCA) complexes were computed, as seen in Table 1. Despite the strong interaction of the 1H-PPCA and 3-PCA structures with UO\({}_{2}\)\({}^{2+}\)[49; 50], there is still a considerable spatial potential barrier with the template ion, leading to the low binding energy. In summary, the system composed of pyridine-2,5-dicarboxylic acid +UO\({}_{2}\)\({}^{2+}\) was the best complex system for the experiment, as seen in Figure 2. Since the simple structure of pyridine-2,5-dicarboxylic acid is more readily associated with UO\({}_{2}\)\({}^{2+}\) due to its lower spatial potential resistance when it binds to the template. Additionally, the structure formed is relatively stable. Therefore, the binding capacity of pyridine-2,5-dicarboxylic acid +UO\({}_{2}\)\({}^{2+}\) is also maximized.
# Neural-Network-Based Synchronization Acquisition with Hankelization Preprocessing
## 1 Introduction
By minimizing noise and extracting dominant signal features, the proposed method improves synchronization signal detection accuracy, establishing its potential as an effective tool for modern wireless communication systems. The remainder of this paper is organized as follows: Section 2 presents the system model and problem formulation. In Section 3, the Hankelization-based preprocessing and NN design are presented, along with an analysis of the effectiveness of the proposed method through the low-rank property of the Hankel matrix. In Section 4, we present the simulation results, covering experimental configurations and performance evaluations against baseline approaches. Finally, Section 5 presents the concluding remarks.
###### Abstract  
Chirp transform spectrometer (CTS) has become a powerful tool widely used in spectral analysis nowadays. In this paper, a novel single-channel structure of Chirp transform spectrometer for high-resolution spectrum detection is developed. By adding an additional signal source, a mixer, a power divider, and a combiner, one channel of the classical CTS is replaced and the matching problem between the two channels is avoided. The circuit principle and characteristics of the novel single-channel structure are presented. Two simulation models with ideal devices and nonideal devices for the classical two-channel CTS structure and the improved single-channel structure are built to verify the effectiveness of the novel structure. Simulation results show that the time distributions of the obtained compression pulses resulting from the improved structure are the same as that from the classical two-channel CTS. The obtained amplitudes and spectral resolution are almost the same in both structures with ideal devices. The introduced nonideal devices mainly influence the amplitudes of the output pulses. In addition, an experiment with hardware implementation is verified on real chains. The influence of nonideal devices is measured and analyzed. Compared to the classical two-channel structure of CTS, the modified one-channel arrangement avoids the matching problem between different channels and saves devices.  
spectrum measurement; pulse compression; chirp transform spectrometer; surface acoustic wave filters Article
# Wind Pressure Coefficient on a Multi-Storey Building with External Shading Louvers
Some research focused on local values along specific lines to investigate the impacts of different factors on wind pressure coefficient [42; 44]. This method provides comparisons between different cases intuitively, but local values along lines are also not sufficient for calculating airflow rate, as natural ventilation occurs on opening areas instead of lines. For these reasons, the average wind pressure coefficient for each room surface is more feasible for evaluating natural ventilation capacity in multi-storey buildings [17]. Some studies focused on wind pressure distribution on surfaces of buildings with shading louvers. [PERSON] et al. [44] experimentally measured airflow fields around a sealed cubic building and investigated wind pressure by simulation. The results show that rotation angle and louver width had an influence on airflow fields, and the fluctuation of wind pressure coefficient was primarily on the windward surface and roof. [PERSON] et al. [45] numerically investigated distribution of wind pressure coefficient on a building with shutter louvers. The solution was significantly dependent on wind direction. However, these studies focused on the shaded buildings mainly treat one building facade as a whole surface, rather than considering building surfaces as multi-storey zones. Some parameters, like the range of wind pressure coefficient in a specific room surface, were not investigated in shaded buildings. So these studies are not sufficient for the ventilation design of shaded buildings. In the present study, wind pressure coefficient on building surfaces of a cube building were investigated numerically under various shaded conditions. A reference case is established to perform validation based on velocity data and wind pressure coefficient data from the previous study. The airflow pattern of approaching flow is investigated, and the impact of various rotation angles of single-sided shading lovers, on local wind pressure coefficient, is analyzed. Then, the average wind pressure coefficient is evaluated in different rows and floors. Afterwards, several conclusions of the present study are provided. The results will be helpful to natural ventilation design of multi-storey buildings with external shading louvers.
## 2 Materials and Methods
### A Web Application for Audio Tampering Detection and Crowdsourcing  
The main goal of the web application is to combine the effectiveness of state-of-the-art signal processing, machine learning advances and human perception for computer-assisted audio authentication. The application:  
1. Implements state-of-the-art analysis options. An ensemble of algorithms is incorporated, addressing multiple audio tampering strategies. Such strategies may include encoding detection, recording conditions, background noise clustering, and others.
2. Follows a modular approach. The algorithms that are provided in the initial implementation are available as individual modules. This allows the existing algorithms to be upgraded in the future, as well as the extension of the initially provided toolbox.
3. Supports human-centered decision-making. As was explained, it is within the rationale of the MaThe solutions to promote computer-assisted decision making. The algorithmic implementations provide intuitive visualizations aiming at assisting the user in content authentication, taking also into consideration the user's personal experience and perception, as well as the context of the asset under investigation.
4. Is publicly available. As was explained, the web framework aims to address a wide public. An important prerequisite for this is that it is freely available for anyone to use and contribute.  
5. Requires no audio or technical expertise. The design principles prioritize ease-of-use, following a typical workflow. A more experienced user with a technical and signal processing background, may get better insight and understanding of the produced visualizations. However, the detection of outliers or suspicious points in a file timeline is self-explanatory and does not require a deep understanding of the algorithms and mechanisms.
6. Promotes crowdsourcing. Users and teams can become involved and contribute to the project in several ways to further advance the field of audio tampering detection. They can submit files, annotated as tampered or not tampered, with a brief justification. Users can also randomly browse files from the dataset, analyze them, and mark them as tampered or not tampered. Finally, as this is an open-source project following a modular architecture, researchers and teams are encouraged to contribute with code and extensions.  
The main functionality of the MAthE AudioVisual Authentication framework is shown in Figure 1. Users can submit files for analysis and investigation, or contribute by annotating existing files concerning their authenticity. Once a file is submitted, the application returns analysis results, and the user can decide if they want to submit the file to the database along with an annotation (tampered or not tampered), submit the file to the database without annotation, or not submit anything to the database. Contributing users can access submitted files, annotated or not, examine the analysis results, and provide annotation (tampered or not tampered), following a crowdsourcing methodology.
###### Abstract  
Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to cloud computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in mobile edge computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among cloud or mobile edge computing (MEC) server or local IoT devices to achieve better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%.  
cost minimization model; energy consumption; Internet of things; mobile edge computing; scheduling algorithm +
Footnote †: journal: Sensors