esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Calibration of Material Coefficients Within the Digital Models Based on Measured Results
#### Considerations on the Acoustic Limits of the Two Vaulted Halls  
In the plan view, both halls feature long, linear side walls running parallel to the longitudinal axis. Due to the hard brick structure, these walls generate strong lateral early reflections. This volumetric shape is characteristic of certain Baroque churches, which typically have a single nave and a perfectly round vault spanning the entire width, supported by the side walls [36; 37].  
In the section view, the barrel vaults cause a focusing effect, especially because the sound source (e.g., speaker, presenter) is usually positioned at a distance greater than the radius of the vault's curvature. As a result, a more even redistribution of sound energy across the seating area is necessary to prevent flutter echoes and acoustic zoning with unbalanced sound energy [22]. Additionally, speech clarity should be improved, while reverberation time should be reduced: both essential factors for creating optimal listening conditions closer to the criteria of an auditorium [38].  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Materials**} & \multicolumn{3}{c}{**Octave Band Center Frequency—Hz**} \\ \cline{2-7}  & **125 Hz** & **250 Hz** & **500 Hz** & **1 kHz** & **2 kHz** & **4 kHz** \\ \hline Glass—Window & 0.35 & 0.10 & 0.05 & 0.12 & 0.07 & 0.04 \\ Solid Timber & 0.25 & 0.13 & 0.11 & 0.15 & 0.13 & 0.15 \\ Wall Decorations & 0.01 & 0.01 & 0.02 & 0.03 & 0.03 & 0.04 \\ Furniture & 0.28 & 0.14 & 0.19 & 0.18 & 0.16 & 0.3 \\ Desk & 0.18 & 0.08 & 0.11 & 0.15 & 0.20 & 0.25 \\ Floor & 0.04 & 0.04 & 0.05 & 0.06 & 0.03 & 0.03 \\ Plastered Brick Walls & 0.03 & 0.06 & 0.06 & 0.04 & 0.04 & 0.04 \\ Wooden Chairs (AM) & 0.31 & 0.16 & 0.09 & 0.08 & 0.09 & 0.11 \\ Plastic Chairs (SV) & 0.23 & 0.11 & 0.05 & 0.05 & 0.05 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Absorption coefficients used for the calibration process.  
Figure 6: Calibration of digital models based on T20 values.  
It is important to note that measurements were carried out in unoccupied conditions, representing the worst-case scenario. Live venues are typically assumed to be at full audience capacity, which significantly affects acoustic behavior. For this reason, acoustic simulations with the proposed design treatments were performed both with and without an audience, as the audience's absorption plays a crucial role in balancing acoustic parameters.
# A Novel Relay Selection Scheme Based on Q-Learning in Multi-Hop Wireless Networks
## 3 Proposed Relay Selection Scheme
### Q-Learning Theory
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time** & \(Q(s_{1},a_{1})\) & \(\cdots\) & \(Q(s_{m},a_{m})\) & \(\cdots\) & \(Q(s_{N},a_{N})\) \\ \hline
0 & 0 & \(\cdots\) & 0 & \(\cdots\) & 0 \. &. & \(\cdots\) & \(\cdot\) & \(\cdots\) & \(\cdot\) \. &. & \(\cdots\) & \(\cdot\) & \(\cdots\) & \(\cdot\) \. &. & \(\cdots\) & \(\cdot\) & \(\cdots\) & \(\cdot\) \\ t & \(Q_{l}(s_{1},a_{1})\) & \(\cdots\) & \(Q_{l}(s_{m},a_{m})\) & \(\cdots\) & \(Q_{l}(s_{N},a_{N})\) \\ t+1 & \(Q_{l+1}(s_{1},a_{1})\) & \(\cdots\) & \(Q_{l+1}(s_{m},a_{m})\) & \(\cdots\) & \(Q_{l+1}(s_{N},a_{N})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Q-table in Q-learning algorithm. Figure 3: Q-learning agent-environment model. where the \(p\) denotes a random variable at the action \(a_{t}\) in time \(t\) and the \(\varepsilon\) denotes a probability that a random action is chosen. At this point, when learning is completed to a certain extent, epsilon is lowered because it does not need to find another path on purpose. The initial value of epsilon is not determined. This value can be arbitrarily specified in the environment design and is decreased according to the learning process. The process of changing the epsilon value also depends on the learning environment and circumstances. So, it is usually adjusted to a fine value for learning accuracy. In Q-Learning, the epsilon value decreases by the specified number when the goal is reached and the number is applied to the next learning. Ultimately, when the epsilon value approaches zero, learning proceeds in the desired direction. Therefore, at each iteration of the learning process, the agent either chooses random or an action. The selected actions become policy and with repeated actions, the optimal policy \(\pi(s_{t})\) can be found.
## 1 Introduction
Some scholars have also studied the effect of cyclic dry-wet conditions on sandstone by visualizing and quantifying the distribution of microstructure in rock through 3D reconstruction and mixed image segmentation of CT images [19; 20] and surface topography scanning tests [21]. [PERSON] et al. [22] analyzed the relationship between pore size changes and porosity through NMR tests and studied the evolution of microscopic pore structure of rocks and soils after different dry-wet cycles. Many scholars have studied the damage characteristics of rocks caused by dry-wet cycle by various monitoring methods. [PERSON] et al. [23] comprehensively carried out uniaxial compression tests, splitting tests, and full-section CT scanning tests to analyze the meso-damage evolution law of sandstone. [PERSON] et al. [24] discussed the strength degradation and microstructure changes of sandstone under dry-wet cycles through uniaxial compression, triaxial compression and scanning electron microscopy tests. [PERSON] et al. [25], [PERSON] et al. [26], and [PERSON] et al. [27] studied the microstructural changes of sandstone under dry-wet cycles by uniaxial compressive strength test, SEM, and backscattered electron image (BSE). [PERSON] et al. [28], [PERSON] et al. [29], and [PERSON] et al. [30] comprehensively used SEM, NMR, CT cross section scanning, and 3D reconstruction experiments to analyze the microstructure evolution of sandstone under dry-wet cycles and revealed the deterioration characteristics and mechanism of rock mass in the fluctuating zone of the Three Gorges reservoir area. Based on mechanical tests and microscopic monitoring methods, the above studies studied the failure mechanism during unloading and the influence of dry-wet cycles on the microscopic structure of rock. At the same time, under the action of excavation unloading, the deterioration characteristics of rock mass will directly affect the deformation and stability of reservoir slopes. It is necessary to study the deterioration mechanism of excavated slopes under water-rock interaction [31]. Therefore, based on the above research results, this paper superimposes and considers the occurrence environment characteristics of the reservoir slope undergoing excavation unloading and long-term periodic storage and discharge in the reservoir area, takes the unloaded samples of different degrees obtained from the staged unloading test as the research object, and adopts the rapid cycle of \"drying-saturation-drying\" in the laboratory to approximately simulate the long-term repeated fluctuation of the water level in the reservoir area. By means of NMR, the pore evolution characteristics of rocks under dry-wet cycles are analyzed from a mesoscopic perspective. The research results can provide theoretical reference for the structural deterioration and deformation control of rock mass on reservoir slopes.
## 5 Our Proposed Scheme
### Advertisement Packet Message (ADV PKT)  
Generally, for clusters formation a threshold number \(T(N_{i})\) is calculated based on the probability function as given in Equation (10). If the randomly generated \(R(N_{i})\) number between 0 and 1 is less than \(T(N_{i})\) then the node \((N_{i})\) will be selected as CH from the set \(G\) of nodes that have not been elected as CH in the previous \(1/\text{p}\) rounds. The first round of LEACH-PRO is similar to that of LEACH but next rounds will follow the working principle of LEACH-PRO, as illustrated in Figure 3. ADV PKT is a control packet with a size of 200 bits as set in our simulation, that contains the current energy level of the CH node, the distance separating it from the BS and its cluster Id denoted by \(E_{CH}\), \(D_{CH}\) and \(C_{id}\) respectively as shown in Figure 5.  
\[T(N_{i})=\begin{cases}\frac{p\times E_{CM}\times D_{CM}}{E_{AVG}\times D_{AVG }\times(1-p\times(r\text{ mod }\frac{1}{p}))}&\text{if }n\in G\\ 0&\text{\emph{Otherwise}}\end{cases} \tag{10}\]  
Figure 4: Border sensor nodes distribution in a sensing field.  
where \(E_{AVG}\) is the Average Energy of System, \(E_{CM}\) refers to the Current Energy of CM, \(D_{CM}\) is the Distance separating a CM from the BS, \(r\) represents the Number of Rounds, \(1/p\) denotes the Number of Nodes in each Cluster, \(D_{AVG}\) is the Average Distance to the BS, and \(G\) represents the set of nodes that have not been selected as CH in 1/p rounds.
# Using Neural Networks for Bicycle Route Planning
## 3 Materials and Methods
### Modelling Bicycle Path Using the Multicriteria Analysis
#### 3.1.1 Points of Interest  
The road network of the Croatian town Imotski was used for the route selection model. Since cycling in this area is mainly tourism-oriented, leisure was the model's most important objective. Therefore, the aim was to create a bicycle route that will link points of interest in the Imotski area. Among all points of interest in Imotski area, nine were chosen: Blue and Red Lake (the most favoured tourist attraction, water-filled massive sinkholes), Dva oka (lake with the shape of two eyes), Prolosko blato (Prolozac lake), [PERSON]'s statute (Croatian writer), Perinusa (mill on the Vrljika river), Panoramic view on Prolozac lake, Topana (Imotski fortress), Zelena katedrala (Green cathedral).
# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
## 2 Materials and Methods
### Experiments under Acoustic Degradation Scenarios
#### 2.4.2 Dynamic Range Compression  
We apply dynamic range compression with a threshold of \(-\)50 dB and a ratio value of 5:1. The compressor acts with an attack time of 0.05 s, and a release time of 0.2 s.
# A Framework to Quantify the Quality of Source Code Obfuscation
## 2 Background  
### Source Code Obfuscation  
Source code is critical for delivering software functionality. Malicious reverse engineering can lead to intellectual property violations by allowing attackers to extract sensitive information from the source code. To mitigate this risk, various obfuscation techniques have been developed. Based on their application scope and methodology, these techniques are generally categorized into layout [34; 35], data [36; 37; 38; 39; 40; 41], and control flow obfuscation [42; 43; 44].
## 2 Preliminaries  
### Coordinate Systems  
The asteroid (433) Eros is a highly elongated asteroid, so it has a highly irregular gravity field. In our study, the gravity truncated at the fourth order and degree is chosen as an example. As a conceptual study, only two coordinate systems are involved--the asteroid-centered inertial frame and the asteroid's body-fixed frame. Assuming that Eros is uniformly rotating in space, the transformation between the two coordinate systems is simply a rotation matrix along the asteroid's rotational axis.
# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 3 AF Arrhythmia Detection Methods
Established HRV analysis practice covers:  
Figure 2: Automatic AF detection methodologies. * Statistical features of temporal nature, such as minimum RR intervals (minRR), maximum RR interval (maxRR), mean RR (\(<\)RR\(>\)), RR standard deviation (SDRR), root mean square of successive \(\Delta\)RR differences (RMSSD) and standard deviation of successive differences (SDSD) working as derivative approximations, pNN20 and pNN50 ratios characterizing the amount of heartbeat intervals greater than a given value (20 or 50 milliseconds, respectively) with respect to all the intervals within the processed window. * Nonlinear features, such as those derived by the elliptic geometry of the recurrence plot depicting RR\({}_{i}\)-RR\({}_{i+1}\) consecutive interval relationship (known as _Poincare_ recurrence plot or Lorenz plot depending on the research discipline). * Spectral (frequency) features of the tachogram, characterizing power spectral densities, and different spectral band ratios obtained by spectral analysis methods such as Fourier transform approximations, Lomb-Scargle estimates or wavelet transforms, among others. * Geometric features, derived by histogram shape analysis, such as triangular indexes and interpolation. Other features applied to RR or directly to the ECG waveforms have often been used, such as the study of turning points, the application of different definitions of sample entropy (SampEn), Shannon entropy (SHE), and coefficient of sample entropy (COSEn), more elaborate statistics such as ANOVA analysis, statistical tests and coefficient of variation (CV) taking mean and standard deviation ratios, or different approximations to parameter derivatives. Moreover, it is well-known that the QRS complex is the least sensitive trait to muscle artefacts and/or other noises [21]. Unlike sinus rhythm, which is characterized by quite similar RR durations, AF has an irregular RR interval variability. Many researchers have developed different methods to detect AF by RR time series. Among the different AF detection methods centered on RR features, automatic techniques follow univariate or multivariate approaches.
# On Transparency and Accountability of Smart Assistants in Smart Cities
## 5 Results  
With the purpose to demonstrate the importance of the role of transparency in the design of smart assistants and find its impact on their accountability and privacy protection of smart city citizens, this research was conducted to find answers to the following questions.  
1. _Is GA transparent in its risk communication and implementation?_
2. _How does the transparency of GA affect its accountability?_
3. _How does the transparency of GA affect the privacy protection of its users?_  
In this section, we report the answers to these questions in light of the findings of our research.  
_Question 1: Is GA transparent in its risk communication and implementation?_  
_Answer:_ Looking at the findings of our diagnostic analysis of the GA app for verifying its description-to-permissions fidelity and description-to-API-usage fidelity, it is discovered that GA does not conform to both these tests. Neither do its permission requirements correspond to its description shown to its users in the Google Play store nor does its API usage reflects its functions discovered in its description. Table 4 shows the dangerous permissions in Alexa, Cortana, Dragon, and Lyra Virtual Assistant. It is easy to map their demands for dangerous permissions with their functions given in Table 1. This is the transparency that is expected from permission use in Android apps. However, in the case of GA, its permission demands cannot be mapped with its functions.  
_Question 2: How does the transparency of GA affect its accountability?_  
_Answer:_ Transparency of a system provides explainability, which is particularly important for its validation and, therefore, accountability [4]. The lack of transparency makes the assessment of the harmful effects of a system difficult [32]. As mentioned in the Introduction, there are many state-of-the-art risk evaluation models that use an app's description, its permissions, and API usage to assess its risks. However, as the results of our analysis show, a lack of transparency in the design and implementation of GA leads to an absence of explainability, which makes many risk evaluation models ineffective and affects its validation. This form of design can severely affect the check and balance system in smart cities.  
_Question 3: How does the transparency of GA affect the privacy protection of its users?_  
_Answer:_ Our two user studies verified that the nonconformance of description-to-permissions fidelity could affect its potential users from knowing its risks. The first user study confirmed a gap between the expected and actual permission requirements of GA, and the results of the second user study suggested that users did not know the sensitive nature of this particular permission. These results suggest that Google Assistant does not convey its risks to its potential users transparently, which can make its risk evaluation difficult for its potential users.
## Materials and Methods
### Optimal Feature Selection and Discriminant Analysis  
The collected hyperspectral imaging data (hypercube) consists of over 100 contiguous waveband images [11]. In this study, SFS with classifiers was applied to the calibration set to select the optimal wavelengths for building a discriminative model to classify sound and diseased rice seeds. The first step begins with an empty set, and all the variables that have not yet been selected are considered for selection, and their impact on the evaluation score are recorded. At the end of the step, the variables resulting in the best score are included in the set. Then a new step begins, and the remaining variables are considered. This is repeated until a prespecified number of variables has been included [21; 22; 30]. The optimal wavelengths were selected by performing SFS on the calibration set and repeating until the prespecified number of 10 wavelengths was obtained. An independent validation set was used separately to determine the final generalization performance. The accuracy of the four different classification methods using the SFS selected optimal band pairs was evaluated. The aim of this study was to develop a classification model based on the optimal wavelengths to discriminate sound rice samples from diseased ones. Thus, an SVM-based multivariate classification model and discriminant analysis were considered.  
The SVM finds the best hyperplane, known as the decision boundary, in feature dimensional space. The method determines the optimal hyperplane for group separation by the largest margin between groups [26]. In this paper, SVM and SVM with Gaussian radial basis function (RBF) were performed. The SVM finds the linear decision boundary in feature space. To find the non-linear decision boundary in feature space, the SVM with RBF finds the decision boundary in a higher dimensional feature space by using mapping methods. The values of cost function (c) and gamma (\(\gamma\)), which are parameters for building the SVM model, were chosen by a grid search method that scans for optimal parameters for a given model by building a model on all possible parameter combinations.  
The LDA usually builds up the model which minimizes the within-group variance while maximizing the between-group variance [24; 25; 26]. QDA is close to LDA except that a covariance matrix must be estimated for each group. In this case, the decision boundary between groups is non-linear (i.e., quadratic). However, if the training data set does not follow the Gaussian distribution, the LDA and QDA would lead to erroneous results since these methods are based on the concept of Bayes' theorem [24]. In this investigation, the SVM and discriminant analysis were used for classifying the diseased and sound rice groups. To enhance the generalization and prevent over-fitting, all of the methods were coupled with a 10-fold cross-validation method. All image correction, spectral extraction, preprocessing and modeling were performed using programs developed in MATLAB (MathWorks, Natick, MA, USA). Figure 2 details the procedure used in the data processing.
# Application of Direct Shear Test to Analysis of the Rate of Soil Improvement with Polyester Fibres
###### Abstract  
When improving soil shear strength using various materials, determination of the improvement rate is a key issue and can be carried out using a direct shear test (DST). However, many standards for DST require only three specimens in the test and do not deal with test result uncertainty. In this study, shear strength parameters of clay of intermediate plasticity (CI) and sandy clays (CSI, CS2) improved with the addition of polyester fibres of 70 mm in length in amounts of 0.5%, 1.0%, and 1.5% of dry soil mass were obtained using DST with a shear box of size 0.3 m \(\times\) 0.3 m \(\times\) 0.08 m. The results show that using fibres provides significant improvement and the number of tested specimens (three or four) in DST has a significant impact on the obtained values of shear strength parameters. It is not recommended to carry out DST with only three specimens. The analysis of uncertainty shows that covariance between correlated input quantities (normal stresses and shear stresses) has a negligible influence on result uncertainty. The worst-case estimated uncertainties are very high and should not be applied. Analysis of the state of the fibre surface before and after shearing using scanning electron microscopy (SEM) shows that suitable fibre scratch resistance may be the reason for the large improvement.  
direct shear test; number of specimens; uncertainty of shear strength parameters; covariances; worst-case estimated uncertainties; fibre scratch resistance +
Footnote †: journal: Applied Sciences  
Article  
Application of Direct Shear Test to Analysis of the Rate of Soil Improvement with Polyester Fibres
# Historic Built Environment Assessment and Management by Deep Learning Techniques: A Scoping Review
## 3 Results
### Post-Disaster Damage at District Scale
#### 3.3.3 Multi-Label Classification  
[PERSON] and [PERSON] conducted further work [57] in order to identify damaged buildings from remote sensing images and predict the building's damage level after a disaster event using a multi-label approach. Unlike the previous method, the proposed model removed the dependence on available building footprint geometries. It assumed that only an estimate of the location and damage level of the damaged building is available to train the model, assuming a scenario where data are limited. In this work, an end-to-end CNN based on ResNet50 architecture was developed to learn deep feature matching from pre- and post-disaster images. Given a pair of images, the goal was to generate a two-dimensional predictive patch in a regression-based approach, where each cell in the patch contains a value representing the predicted level of damage. Tests conducted on large-scale satellite imagery and aerial photographs (Hurricane Irma dataset) demonstrated the model's effectiveness in identifying damaged buildings and predicting damage levels (No damage/affected/minor damage/major damage/destroyed), with lower Mean Square Error (MSE) and Mean Absolute Error (MAE) values than other configurations. Instead, [PERSON] et al. [58] presented a novel two-step solution for automatic building damage detection using satellite imagery, addressing the challenges of imbalanced data distribution. Two separately trained prediction models were developed: one for building location, which identifies building features from the image background through binary classification, and one for damage level classification (No Damage, Minor Damage, and Major Damage). By incorporating the disaster context, an additional feature (e.g., distance between buildings) was introduced as a model input function when concatenated with the 2D images for the classification model. To address the highly imbalanced problem in damage classification, an incremental learning strategy was proposed, which exploited a large number of training data subsets with imposed normality in their data distribution to continuously train and update the classification model. An experimental study tested the proposed approach on an open-source satellite dataset on three catastrophic events, including earthquakes, floods, and tsunamis. The location model achieved a test accuracy of 97.29% and an IoU of 53.78%. In contrast, the proposed incremental learning strategy for highly imbalanced class distributions, tested on buildings extracted from satellite images of the \"Mexico-earthquake\" disaster event, achieved an accuracy score of 99.55% and a weighted average F1-score of 99.53%.