esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Autoencoder Model for Repairing Defect Areas
### Autoencoder with Latent Vector Clustering  
The Autoencoder model with a defective area repair capability is shown in Figure 11. Three modules will be integrated, namely the encoder module, decoder module, and latent vector clustering module. The encoder and decoder also follow the structure mentioned  
Figure 10: Structure of Deconv2d module.  
in the previous section, and the clustering module establishes the capability to repair the defective area.  
Before using the clustering module to start training the model, the clustering module needs to initialize the cluster centers with the K-means clustering algorithm. First, the feature map (16 \(\times\) 16 \(\times\) 10) is segmented into 256 latent vectors with dimension 1 \(\times\) 10. However, the dimension of the latent vector is not necessarily limited to the size of 1 \(\times\) 1 \(\times\) 10. The dimension of the latent vector depends on the texture complexity of the input image. In this paper, we randomly select 100,000 feature vectors to initialize the K-means clustering algorithm, and the number of clusters is preset to 40 clusters.
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 4 Analysis and Discussion
### Localization
#### 4.1.1 Degraded GPS Reception  
We simulated degraded GPS reception by adding zero mean Gaussian noise with a standard deviation of 5 m to each observed GPS coordinate. The incremental update to the state vector and the current estimate of the state converged after 15 LM iterations. Figure 7 shows six snapshots during optimization. The global orientation parameter converged after the 6 th iteration. Figure 8 shows the converged path after global alignment and local refinement with a root mean squared error (RMSE) of less than 0.1 m compared to the ground truth position track. This suggests that, as long as GPS errors are Gaussian distributed, we can expect accurate position tracking.  
We also tested robustness to non-Gaussian distributed noise in GPS readings. We randomly selected 5% of the coordinates from the GPS track and added uniformly distributed noise with a range of 0 to 200 m. Figure 9 shows the converged path after global alignment and local refinement with non-Gaussian distributed noise in GPS coordinates plotted over the ground truth path (RMSE \(<\) 0.1 m). According to this result, the algorithm is insensitive to GPS coordinate outliers.  
Figure 6: GPS position track denoted by the black solid line. Optimized position track denoted by the red solid line. VO track rotated by the global orientation parameter denoted by the blue dashed line.  
Figure 7: Global alignment with degraded GPS reception. Each subfigure shows a snapshot during optimization.
## 5 Economic Evaluation
### Changes in Investment Cost According to Solar Power Generation Equipment  
The price of photovoltaic power generation devices was investigated in order to understand the energy increase and decrease effect on the investment of active elements. First, we selected the photovoltaic module among the photovoltaic power generation devices of the National Public Procurement Service. Next, we derived the standard unit price of the investment cost by converting the photovoltaic power generation device composed of the inverter and the connection panel to the unit price by capacity. As a result of examining the two manufacturers, the price of photovoltaic power generation equipment is similar to the unit price per kW. Like the passive element, the active element shows a very high correlation between investment cost and energy increase (Figure 9 and Table 11). The coefficients of determination of the product of manufacturer S and the product of manufacturer B were 0.9185 and 0.9925, respectively.  
Figure 8: Coefficient of determination between window cost and energy demand variation.  
Figure 9: Coefficient of determination between PV power plant cost and energy generation variation.
# Investigation of Vocal Fatigue Using a Dose-Based Vocal Loading Task
## 4 Experimental Protocol Design and Data Acquisition
### Data Analysis  
The mean values and standard deviations of the normalized SAVRa and the original CAPE-V rating scores were calculated. The SAVRa scores were first normalized for each participant, and then the normalized scores were clustered for statistical analysis. The scores in the SAVRa and CAPE-V ratings are inversely related to the voice quality, i.e., a higher score implies lower voice quality and vice-versa. This rule applies to all dimension ratings of the SAVRa and CAPE-V. For correlation analysis, all voice quality data were assumed to obey a Gaussian distribution. A rigorous validation of this assumption would require a larger data set, which was beyond the scope of the present study. A Pearson correlation analysis was done between each pairs of dimensions of the SAVRa rating scores and the CAPE-V rating scores.  
Fifteen acoustic features were calculated from the microphone and the NSA data using Matlab (2018a). The symbols and a description of the features are shown in Table 2. Features commonly used in previous vocal fatigue studies were selected. The original microphone and NSA signals were segmented into uniform frames of duration 100 ms (20-40 vocal cycles). Frames that were too short (20-30 ms) were insufficient for extracting effective jitter and shimmer, and frames that were too long (>500 ms) could not satisfactorily resolve \(f\)0 variations. The unvoiced frames were detected and removed using the zero-cross rate method [43].  
The procedure of multivariate analysis in the present study was that a trend was searched in SAVRa scores at first to build a baseline of identifying vocal fatigue. A similar trend in the CAPE-V scores was then searched to verify the consistency between self-administered and SLP-rated methods. Finally, this trend was searched in the fifteen acoustic features to demonstrate some specific features have the potential of indicating vocal fatigue.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbols** & **Explanation** & **Source** \\ \hline \(f\)0 & fundamental frequency & microphone \\ SPL & sound pressure level & microphone \\ duty ratio & voicing percentage in recording time & microphone \\ CPP\_MIC & cepstral peak prominence & microphone \\ jitter\_MIC & pitch perturbation & microphone \\ shimmer\_MIC & loudness perturbation & microphone \\ CPP\_NSA & cepstral peak prominence & NSA \\ jitter\_NSA & pitch perturbation & NSA \\ shimmer\_NSA & loudness perturbation & NSA \\ TILT\_MIC & spectral tilt & microphone \\ HRF\_MIC & harmonic richness factor & microphone \\ H1H2\_MIC & different between H1 and H2 magnitudes & microphone \\ TILT\_NSA & spectral tilt & NSA \\ HRF\_NSA & harmonic richness factor & NSA \\ H1H2\_NSA & different between H1 and H2 magnitudes & NSA \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of features extracted from the microphone and NSA signals.
## 1 Introduction  
Real-time communications between vehicles through vehicular ad hoc networks (VANETs) and intelligent transport systems has been proposed as a possible solution to the increasing number of traffic accidents that has concerned public health officials in recent years [1]. In these systems, key information, such as vehicle speed, acceleration, and position, is periodically transmitted in a two-way manner, in a process known as beaconing, allowing nearing vehicles to be well aware of each other [2].  
Vehicle-to-vehicle (V2V) communications is not, however, without problems at intersections [3, 4]. In scenarios with too many transmitting vehicles, the channel can get overloaded, and consequently, packets can be lost [5]. One could mitigate this by lowering the rate at which packets are transmitted, but this creates another set of problems, such as widening the discrepancy between the real and estimated position of near cars [6]. Furthermore, shadowing caused by buildings and other objects that obstruct the line of sight (LOS) communication significantly reduces the transmission range, especially at urban intersections [7; 8].  
The previous problems can be alleviated through beamforming, that is directing the radiation beams in relevant directions, where vehicles can be located [9; 10]. In this work, we used beamforming by generating radiation patterns that can overcome obstacles at urban intersections, increasing the range of communication in this type of scenario.  
Some research works that have dealt with the presented problems examined the use of roadside units (RSUs) as relay nodes for the communication between the cars [5; 8]. However, this solution is too costly, making it impossible to deploy at a large scale. Furthermore, most of the literature regarding RSUs is concerned with the optimization of coverage and cost [11; 12], neglecting the minimization of a key aspect in road safety applications: communication delay. Even in works where the position of the RSUs is optimized to decrease the communication delay [13; 14], results are not satisfactory regarding road safety applications in dense scenarios.  
Unlike RSUs, beamforming does not use relay nodes that can get overloaded in dense scenarios, and thus can provide a communication link with less delay. The main concern in beamforming is finding an algorithm that can calculate the optimal antenna excitation pattern that generates the beam shapes that best adjust to the car's surroundings.
## 4 Test Results and Discussion
### Test Result Evaluation Method  
In the tests, corresponding rules are established to quantify the responses provided by the RAG system. The scoring ranges from 0 to 10, with 0 indicating an incorrect response and 10 indicating a fully correct response. Three different evaluation methods are developed for the three types of questions.  
Type I: Directly compare the numeric values extracted from the generated response with those in the standard answer. A perfect match earns 10 points, while any discrepancy results in 0 points.  
Type II: Use similarity calculation methods to assign a quantitative score based on how closely the response aligns with the standard answer.  
Type III: This involves a two-step process. (1) Extract and compare numeric values, contributing 50% to the final score. (2) Calculate the similarity between the response and the standard answer, accounting for the other 50% of the score.
# Aluminum-Loaded Bifunctional Resins for Efficient Fluoride Removal from Aqueous Solutions
## 3 Results and Discussion
### Effect of Initial pH on Fluoride Adsorption  
Figure 3 illustrates the fluoride removal performance of Al-S957 across a pH range of 3 to 11. At pH 3, fluoride predominantly exists as HF that hinders the removal of fluoride [26]. Above pH 4, the fluoride adsorption capacity decreases gradually with increasing pH and declines sharply at pH 11. This decline is primarily attributed to the competition between OH\({}^{-}\) and fluoride for adsorption sites [31]. Overall, Al-S957 has been shown to have a decent fluoride removal behavior throughout a wide pH range, which aligns with the typical pH of fluoride-contaminated water, indicating its potential for practical applications [32; 33].  
As shown in Figure 3, the equilibrium pH after adsorption was lower than the initial value, primarily due to the hydrolysis of aluminum on Al-S957 [8]. The isoelectric point of Al-S957 was determined (Figure S1), and the pH\({}_{\text{pzc}}\) value was 2.85, indicating that the surface of Al-S957 was negatively charged over the tested pH range. However, the adsorption performance did not significantly decrease with pH exceeding pH\({}_{\text{pzc}}\), suggesting that electrostatic interactions are not the fundamental mechanism of fluoride removal by Al-S957. This conclusion is further supported by subsequent XPS characterization. In addition, the high selectivity of S957 observed in the coexisting anion experiments indicates that the adsorption mechanism involves specific ligand exchange rather than non-specific electrostatic attraction.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Adsorbents**} & **BET Surface Area** & **Average Pore Diameter (nm)** & **Total Pore Volume (cm\({}^{3}\)/g)** \\ \hline S957 & 31.38 & 32.66 & 0.25 \\ \hline Al-S957 & 40.53 & 26.39 & 0.23 \\ \hline \end{tabular}
\end{table}
Table 1: Physical properties of S957 and Al-S957.  
Figure 3: Effect of pH on fluoride adsorption and the equilibrium pH value.
# Research on the Influence of Loading Frequency on the Dynamic Response of Concrete Sleopers
## 3 Dynamic Responses of Sleepers at Different Loading Frequencies
### Influence of Loading Frequency on the Contact Force between Particles in the Sleeper  
The discrete element model of the sleeper is composed of fine particles according to a certain arrangement law and combined by contact bonding. Under the action of force, the load transmitted from the rail to the sleeper is borne by the particles in the sleeper, and the transmission of the load is also transmitted through the contact force between the particles in the sleeper. To explore the effect of loading frequency on the contact force of sleeper particles, the variation of the maximum contact force between particles in the sleeper with the loading frequency under 5 cycles of loading was simulated and recorded, as shown in Figure 9.  
Figure 10 shows that after cyclic loading at four loading frequencies of 5 Hz, 10 Hz, 20 Hz, and 50 Hz, the maximum value of the contact force between particles in the sleeper increased with increasing loading frequency. This shows that under the same loading times, the greater the load frequency applied to the rail, the greater the contact force between the particles in the sleeper.  
Figure 10: Relationship between the maximum contact force of the particles and the loading frequency.  
Figure 9: Vertical displacement cloud diagram of sleepers corresponding to different loading frequencies. (**a**) 5 Hz. (**b**) 10 Hz. (**c**) 20 Hz. (**d**) 50 Hz.
## 3 Bayesian Updating with Detection Information
### Updating of the Chloride Diffusion Model  
Assuming that the structure is tested after \(t_{0}\) years of use, experiments and analyses have been conducted on the distribution and diffusion of chloride ions in concrete structures. In general, components with different degrees of corrosion were selected to sample drilled powder concrete at different depths. In order to make the data representative and facilitate statistical analysis, no fewer than three sampling points were selected for each sample (with the same component and elevation), with depths of 0-10 mm, 10-20 mm, 20-40 mm, 40-60 mm, and 60-80 mm, respectively. The chloride ion content in the concrete powder samples was measured using the potential titration method. After calculation, the Cl\({}^{-}\) concentration of concrete at different depths could be obtained. Regression fitting based on [PERSON]'s second law of Cl\({}^{-}\) concentration was performed at different depths to calculate surface chloride ion concentration \(C_{s}\), chloride ion diffusion coefficient \(D_{Cl}\), and model uncertainty coefficient \(\epsilon_{1}\).  
Scholars from different countries have determined the values of surface chloride ion concentration based on their respective national survey data. [PERSON] et al. [27], based on the inspection data of concrete structures on the coasts of Victoria and Tasmania in Australia, determined that the distribution of surface chloride ion concentration in the splash zone was described by a log-normal distribution, with a mean of 7.35 kg/m\({}^{3}\) and a coefficient of variation of 0.7. [PERSON] et al. [28], based on the inspection data of 36 coastal bridges in Norway, considered that the \(C_{s}\) mean value was 0.51% (of the concrete mass) at 0 m to 3 m above sea level, with a standard deviation of 0.23% (of the concrete mass). The European DuraCrete method [29] considered that the average value of \(C_{s}\) was related to environmental conditions, the water-cement ratio of the concrete, and the type of cementitious materials, which could be represented as \(C_{s}=A_{c}\cdot(W/B)\), where \(W/B\) is the water-binder ratio, and for ordinary Portland cement in the splash zone, \(A_{c}\) was 7.76% (of the cementitious material mass). In the American Life-365 [20] calculation program, the instantaneous value in the tidal splash zone \(C_{s}\) was set to 0.8% (of the concrete mass). [PERSON] et al. [30] measured the statistical values of surface chloride ion concentration in concrete structures of Chinese docks and considered that they followed a log-normal distribution, with a mean of 0.27% (of the concrete mass) and a standard deviation of 0.04% (of the concrete mass). [PERSON] and [PERSON] [27; 28] also suggested describing the distribution of \(C_{s}\) using a log-normal distribution.
## 5 Wind Fatigue Tests
### Test Equipment and Installation  
The test equipment used included a test chamber, wind pressure supply devices, and pressure measurement and control devices. The plan view size of the test chamber was 3070 mm \(\times\) 2130 mm. The testing instrument was a KFJC-006 flow wind resistance test machine, which belongs to Chess Lab in Zhuhai, China. The experimental setup is shown in Figure 10.  
Figure 10: Experimental setup: (**a**) purlin installation; (**b**) roof panel installation; (**c**) lifting; (**d**) installed apparatus in the test chamber.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Load Mode** & **Purlin Distance (mm)** & **600** & **800** & **1000** \\ \hline Typhoon Chantu & 0.009 & 0.167 & 0.643 \\ \hline NELCS & 0.076 & 0.504 & 1.193 \\ \hline LHL & 0.269 & 0.706 & 1.972 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical results of roof fatigue damage.  
The specimen YX35-760 self-drilling screw roof claddings were installed in the test chamber. The roof claddings were galvanized profiled steel panels. The material parameters are shown in Table 2. ST5.5 \(\times\) 65 hexagon self-drilling screws with washers were used as the fasteners. The purlin is C150 \(\times\) 65 \(\times\) 13 \(\times\) 2.0. The purlin distances were set as 600, 800, and 1000 mm, in order to consider the influence of purlin distance. The layouts of the roof test specimens and the screw arrangement are shown in Figure 11.
## Materials and methods  
### Hardware  
All audio data for this investigation were captured by BeePi, a multi-sensor EBM system we designed and built in 2014 [13]. A fundamental objective of the BeePi design is reproducibility: other researchers and citizen scientists should be able to replicate our results at minimum costs and time commitments. Each BeePi monitor consists of exclusively off-the-shelf components: a Raspberry Pi computer, a miniature camera, a microphone splitter, four microphones connected to a splitter, a solar panel, a temperature sensor, a battery and a hardware clock. We currently use Raspberry Pi 3 model B v1.2 computers, Pi T-Cobblers, breadboards, waterproof DS18B20 digital temperature sensors, and Pi cameras. Each BeePi unit is equipped with four Neewer 3.5 mm mini lapel microphones that are placed either above the landing pad or embedded in beehive walls. These microphones have a frequency range of 15-20 KHz, an omnidirectional polarity, and a signal-to-noise ratio greater than 63 dB. There are two versions of BeePi: solar and regular. In the solar version, a solar panel is placed either on top of or next to a beehive. For solar harvesting, we use Rengoy 50 watts 12 Volts monocrystalline solar panels, Renogy 10 Amp PWM solar charge controllers, and Renogy 10 ft 10 AWG solar adaptor kits. The regular version operates either on the grid or on a rechargeable battery. For power storage, we use two types of rechargeable batteries: the UPG 12 V 12 Ah F2 lead acid AGM deep cycle battery and the Anker Astro E7 26,800 mAh battery. All hardware components, except for solar panels, are placed in a Langstroth super. Interested readers are referred to [36] for BeePi hardware design descriptions, pictures, and assembly videos.  
We have been iteratively improving the BeePi hardware and software since 2014. BeePi monitors have so far had five field deployments. The first deployment was in Logan, UT, USA (September 2014) when a BeePi monitor was placed into an empty hive and ran exclusively on solar power for two weeks. The second deployment was in Garland, UT, USA (December 2014-January 2015) when a BeePi monitor was placed in a hive with overwintering honey bees and successfully operated for nine out of the fourteen days of deployment exclusively on solar power and captured about 200 MB of data. The third deployment was in North Logan, UT, USA (April 2016-November 2016) where four BeePi monitors were placed into four beehives at two small apiaries and collected 20 GB of data. The fourth deployment was in both Logan and North Logan, UT, USA (April 2017-September 2017) when four BeePi units again placed into four beehives at two small apiaries to collect 220 GB of audio, video, and temperature data [15]. The fifth deployment started in Logan, UT, USA in May 2018 with four BeePi monitors placed in four new beehives freshly initiated with Carniolan honeybee colonies. As of July 2018, we collected 66.04 GB of data.
## 4 Numerical Simulation of Inclined Shaft Lining
### Numerical Calculation Model
#### 4.1.1 Basic Assumptions  
To facilitate the analysis, the following fundamental assumptions are adopted for the model:  
(1) The shaft lining is a homogeneous, continuous, isotropic medium with small deformations.  
To ensure safety, the shaft lining must always remain in an elastic state during service. Furthermore, strength verification based on elastic analysis results generally provides a conservative safety margin. In fact, concrete is a multiphase heterogeneous material.  
(2) The self-weight of the shaft lining is negligible during burial.  
For deeply-buried inclined shaft lining, the in-situ earth pressure and pore water pressure constitute the primary loads, where neglecting the self-weight of the shaft lining does not introduce significant errors.  
(3) Plane analysis follows the generalized plane strain model.  
Extensive research findings demonstrate that both the inclined shaft lining and surrounding rock exist in a state of plane strain. The plane strain model will fail when axial deformation is present and cannot simulate end effects.  
(4) Three-dimensional analysis follows the generalized plane stress model.  
The three-dimensional model analysis, when simplified using a plane stress model, is not applicable to cases with significant axial loads and neglects end effects at the shaft lining boundaries.