esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multiple Access Interference Characterization and Capture Diversity
It has been shown that Fenton-Wilkinson method accuracy is low for \(\sigma>4\) dB [48] and, for such values of \(\sigma\), the log-normal PDF poorly fits the distribution of the linear combination of log-normal random variables. Nevertheless, the tails distribution of the sum is still well approximated even for larger values of \(\sigma\)[49]. Consequently, the proposed method is expected to exhibit good accuracy when applied to the considered problem, since we are interested in computing the capture probability that is expressed in terms of tail distribution. This aspect is validated in Section 4, where results obtained enforcing the proposed approximation method are compared with a Monte Carlo simulation, which converges to the actual distribution of the weighted sum of log-normal random variables. Finally, we want to remark that the described method is well suited to approximate even randomly weighted sums of log-normal random variables [14]. Then, the proposed rationale can be used to accurately approximate capture probability in scenarios characterized by more general product channels, which include different fading statistics (e.g., Exponential distribution for Rayleigh fading, Non-central Chi-square distribution for Rice fading, Gamma distribution for Nakagami fading, etc.). More precisely, regardless of the fading statistics considered for the single two-way channel, the SIR is approximated by a log-normal random variable with proper parameters. The inclusion of additional random components in Equation (2) would clearly affect the first and second order moment of \(\mathsf{X}_{k}^{(m)}\) and, in turn,the parameters of the approximating log-normal random variable. Nevertheless, this effect can be easily accounted in our framework by varying \(\sigma\).
# A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks
## 4 Cluster Head Election Game  
In this section, we utilize game theory to model the CH election process for clustered WSNs. Our cluster head election game (\(CEG\)) will be played by sensor nodes within a local area of the network for CH election, and we formally define it as \(CEG=\langle N_{G}\rangle\), \(S_{G}\), \(U_{G}\)\(>\), where \(N_{G}\) is the set of players, \(S_{G}=\{S_{i}\}\) is the set of all feasible strategies and \(U_{G}=\{U_{i}\}\) is the set of utility functions. All sensor nodes within a local area act as players and participate in a \(CEG\). Each player can rationally and selfishly choose the strategy based on its own interests. For any player \(i\), its strategies include \"declare myself as CH\" (\(D\)) or \"not declare myself as CH\" (\(ND\)). Since energy is a kind of precious resource in WSNs and any node acting as a CH needs to take the responsibility of relaying data for other normal sensor nodes, each sensor node prefers to abdicate the responsibility to other nodes for energy conservation. However, if none of the sensor nodes take the responsibility of being a CH to serve the other nodes, then network failure occurs and no sensor nodes can enjoy the benefit of data transmission. Therefore, each  
Figure 2: Radio model.  
sensor node has to consider the knowledge or expectations of other sensor nodes when making its own decision. We define the utility function \(U_{i}\) of any node \(i\) when playing the \(CEG\) as follows:  
\[U_{i}=\left\{\begin{array}{ll}v_{i}-e_{i}&ifs_{i}=D\text{ and }\forall v_{i},e_{i} \in R,s.t.v_{i}>e_{i}>0\\ v_{i}&ifs_{i}=ND\text{ and }\exists j\in N_{G},s.t.s_{j}=D\\ 0&s_{j}=ND,\;\forall j\in N_{G}\end{array}\right. \tag{5}\]  
where \(v_{i}\) is the payoff when node \(i\) chooses the strategy \(ND\) while there exists at least another node choosing the strategy \(D\), and \(e_{i}\) is the extra cost when node \(i\) chooses \(D\).
# Methodology for Obtaining High-Quality Speech Corpora
## 3 Components of Speech Corpus Quality Assessment  
To assess quality, the criteria to be followed in the corpus creation process should be defined first. In particular, decisions must be made regarding the type of corpus or sub-corpus that will be created, i.e., whether a mono- or multilingual, reference, monitor, synchronic, or diachronic corpus will be built, for ASR, speech synthesis, linguistic research, etc. If the corpus is created for ASR purposes, then ASR quality can also be used as a corpus quality indicator. Descriptions of corpus types can be found in various books or articles (see [59] for example); thus, they are not discussed here. Moreover, the decision to create a corpus is usually dictated by the need to obtain a specific type of corpus; therefore, the corpus type is characterized at the very beginning of the corpus creation process.  
The selected type of corpus determines the construction procedure and, thus, the criteria this corpus should meet. Following the corpus parameters listed in the previous subsection, the corpus quality assessment should be based on the following factors:  
* Reference corpus or monitor corpus according to the criteria specific for these types of corpora;
* Diachronic corpus or synchronic corpus according to the criteria specific for such corpora;
* Language: general corpus or specialized corpus according to the criteria for such corpora;
* Speakers: the number of speakers and specification of speakers (as many details about the speakers as possible should be documented);
* Text specification, namely the vocabulary in the corpus (the number of words, nonwords, and text types);
* Corpus for speech synthesis: including all phonemes and sequences that capture transitions between phonemes in a target language (diphones, i.e., transitions between two phonemes; triphones, i.e., sequences of three phonemes) and that are used in text-to-speech (TTS) synthesis systems;
* Balance, where all planned domains are taken into account, and the corpus can also be gender-balanced;
* Corpus size (in hours);
* The number of languages in multilingual corpora;
* Usability based on the users' needs, where the intuitiveness of design, efficiency of use, ease of learning, and user satisfaction can be assessed via user experience (UX) tests, as can errors made by users while performing tests. The corpus should be accessible for non-technical users;
* Database quality, namely the quality of the database management system (e.g., see [60]) and the data in the database (see [23]);
* Annotation quality, namely accuracy and consistency [61];
* The technical quality of the recording, including the equipment and setup used (studio or outside of a studio, mobile platforms) and the parameters of the audio files (bit depth and sampling rate);
* Noise in the recordings, namely the signal-to-noise ratio (SNR).  
This paper is mainly focused on creating speech corpora that contain audio files enriched with annotations. Audiovisual and text-only corpora are not discussed here.
## 4 Stochastic Traffic Model  
### Framework of the Stochastic Traffic Model  
In cases of fatigue evaluation, it is crucial to determine the stress spectra. With advanced monitoring systems, the stress spectra can be now directly established from measurements [31]. However, due to the limitations of budget and feasibility, the monitoring system could not yet be extensively applied [32]. Further, the spectra are not available at the design stage since it is not transferable for all the bridges. Alternatively, finite element (FE) analysis can be employed to derive the stress spectra in combination with the traffic model. As a result, the accuracy of the spectra largely depends on the rationality of the applied traffic model. In most of the codes and specifications, the vehicles are represented by the deterministic truck model. Several coefficients are usually applied to account for the randomness in traffic [20; 21; 22; 23; 24]. As a matter of fact, this method deviates from the random nature of vehicle loads, which may lead to inaccurate results [31].  
Thus, a stochastic traffic model is proposed and applied in this study. The framework of the model is established by considering two primary elements: (1) the probabilistic model of vehicle properties; (2) the lateral distribution of vehicles. Since the stress response in RD joints are extremely sensitive to the configuration of the vehicle model [1; 33], it is modelled in detail with the parameters including the axle number, axle spacing, axle weight, axle track and footprint. In the assumption, the lateral distribution is determined after both the occupancy rate and in-lane position. Table 2 summarizes the parameters defined in the framework, along with a brief description.
###### Abstract  
This study investigates the impact of material parameters such as yield strength (\(S_{y}\)), Young's modulus (\(E\)), and tangent modulus (\(T\)) on the safety factor (\(SF\)) of autofrettaged cylinders under 400 MPa working pressure, considering the three scenarios: no machining, internal machining, and external machining. Finite element (FE) simulations were conducted based on the Taguchi experimental design and converted into signal-to-noise (\(\mathsf{S/N}\)) ratios to determine the optimal settings. ANOVA was utilized to evaluate the significance and percentage contributions of each factor. The analysis indicated that \(S_{y}\) is the most influential parameter on \(SF\), contributing approximately 98.20% across all scenarios, including no machining, internal machining, and external machining. The contributions of \(E\) and \(T\) were minimal, but \(T\) had a slightly greater effect than \(E\). The analytical validation of the FE model showed good agreement, with a maximum deviation of 4.37% for no machining, 4.75% for internal machining, and 5.20% for external machining. Regression analysis further confirmed the high prediction capability of the model, validated using AISI 4340 steel. The study concludes that internal machining results in higher residual stress loss compared to external machining. Overall, the analytical method tends to provide lower \(SF\) values than the numerical method, highlighting its conservative nature.  
autofrettage; residual stress; Taguchi analysis; analysis of variance (ANOVA); finite element method (FEM) +
Footnote †: journal: Applied Sciences  
Article
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 6 Mathematical Models
### Receiver Signal Strength  
With the path loss calculated, the link budget calculation, shown in Figure 6 is used to determine the _RSL_, which measures the arriving signal strength at the receiver in decibels relative to a milliwatt (dBm). _RSL_ is essential for evaluating the quality and reliability of a communication link, ensuring the signal is strong enough for accurate interpretation by the receiver [29; 30].  
\[RSL=P_{TX}+G_{Tx}-L_{Tx}+G_{Rx}-L_{Rx}-L_{FSPL} \tag{2}\]
# From Iris Image to Embedded Code: System of Methods
## 7 Determining the Threshold  
So, if the registration template \(T\) and the presentation template \(T^{\prime}\) are at Hamming distance (32) below the threshold \(\theta\), then the encrypted message \(M\) is recovered with high confidence; otherwise, the probability to recover it is extremely low (order of random guess). Thus, the threshold \(\theta\) separates \"genuine\" and \"intruder\" templates \(T^{\prime}\) with respect to \(T\).  
It is necessary to determine the value of the threshold \(p\), which will be used for separating \"genuines\" and \"impostors\". With this value redundant code \(\Phi_{p}\) and decoder \(\Psi_{p}\) will be devised, capable to restore message for \"genuine\" template and making it impossible for \"impostor\" template.  
The following publicly available databases were used for the experiments: CASIA-4-Thousand [32], BATH [33], ICE subset of NDIRIS [34], UBIRIS-1 [35].  
Table 5 gives a list of databases used with the obtained thresholds.  
For each database the following numbers are given:  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Database**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{1}{c}{\(\theta\) **at**} & \multicolumn{1}{c}{**FAR at**} & \multicolumn{1}{c}{**FRR at**} \\  & **Eyes** & **Images** & **FAR\(=\mathbf{10^{-4}}\)** & \(\theta=\mathbf{0.35}\), \(\times\mathbf{10^{-4}}\)** & \(\mathbf{\times 10^{-2}}\) \\ \hline BATH & 1600 & 31988 & 0.402 & 0.03 & 4.46 \\ CASIA & 2000 & 20000 & 0.351 & 0.97 & 6.71 \\ ICE & 242 & 2953 & 0.395 & 0.011 & 7.13 \\ UBIRIS & 240 & 1207 & 0.401 & 0.001 & 5.18 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Database characteristics and thresholds.  
* The number of individual eyes. It does not match the number of participating persons as some persons in the database supplied images from both eyes.
* The number of eye images. Each eye produced from one to several hundred images, depending on the database collection scenario.
* Value of the threshold \(\theta\), at which the false accept rate (FAR) is \(10^{-4}\). This value is the probability of a random guess of a four-digit pin code. It is used for reference in developing biometric recognition systems so that they would have the same or less probability of being defeated as pin-code-based systems.
* False accept rate at \(\theta=0.35\).
* False reject rate at \(\theta=0.35\).  
Since \(FAR(\theta)\) is a monotonous growing function, we select a minimal \(\theta(FAR=10^{-4})\) from the fourth column of the table. It is \(\theta=0.351\) for the CASIA database. Other databases have even smaller \(FAR\) with this value of \(\theta\).  
So, the value of \(\theta=0.35\) is the tolerated error probability \(p\) for constructing the ECC. Table 5 shows the values of false accept and false reject rates for this threshold. The maximum false reject rate does not exceed 8%.
# Evaluation of BDS/GPS Multi-Frequency RTK Positioning Performance under Different Baseline Lengths
## 3 Data Collection and Processing Strategies  
In this paper, five groups of IGS/MEGX stations with different baseline lengths which can receive BDS-3 multi-frequency observations were selected, using data from DOY215 (3 August 2020) to DOY221 (9 August 2020) for a total of 7 days with a sampling interval of 30 s and a total of 2880 epochs per day. The distribution of the stations is shown in Figure 1 (Since METG and MET3 are too far away to be displayed on the map, they are inserted into the top left corner of the map). Receivers and antenna types of these stations are specified in Table 1.  
Figure 1: Distribution of the selected IGS/MGEX station.  
The stations were selected to form baselines of different lengths to be solved, and the specific information is shown in Table 2. Data processing of the screened stations was performed using the Net_Diff [17] software developed at the GNSS Analysis Center of Shanghai Astronomical Observatory ([[http://center.shao.ac.cn/shao_gnss_ac/](http://center.shao.ac.cn/shao_gnss_ac/)]([http://center.shao.ac.cn/shao_gnss_ac/](http://center.shao.ac.cn/shao_gnss_ac/)) (accessed on \(11\) March 2022)), the elevation fixed weight was selected by the weight ratio strategy, and the mean value of the three-day precise point positioning (PPP) solution was used as the true value. The satellite visibility (the number of satellites, position dilution of precision (PDOP)), ratio (the ratio of the residual quadratic of the next best integer solution to the residual quadratic of the best solution), positioning accuracy (root mean square (RMS)), and time to first fix (TTFF) of the ambiguities are analyzed. The specific data processing strategies are shown in Table 3.
# Semi-Supervised GNSS Scintillations Detection Based on DeepInfomax
## 2 The Need for Unsupervised Compression  
As depicted in Figure 1, one possible semi-supervised classification scheme is composed of two main blocks: an unsupervised feature extractor and a supervised classifier that is trained using as input the features. Notice that for some features the label correspondence is missing. The attentive reader might wonder why the first portion of the deep learning based scheme, the unsupervised encoding part, depicted in Figure 1, is necessary at all and a simple training of a classifier on the labeled portion of the dataset is not sufficient. The reason is that, as explained in the introduction (Section 1), in this work, we improved on two weak points of the previous decision tree based classification algorithms: being able to consider a collection in time of the features and to reduce the number of labeled training points. Generally speaking, in machine learning, these two requirements are in conflict: when increasing the cardinality of the inputs, as a rule of thumb, to maintain a good level of performance the number of training points must be increased. This is typically solved when dealing with high-dimensional inputs either by performing some form of manual feature engineering and reducing the dimensionality of the input or by increasing the amount of labeled data by acquiring larger labeled datasets. The approach of semi-supervised deep learning is instead different. Given a large dataset in terms of number of samples, with a large input cardinality, with a small number of labeled datapoints, an automatic, unsupervised, end-to-end feature extraction (we use interchangeably the term feature extraction or compression.) is performed on the whole dataset. A classifier is then trained using as inputs the small feature vectors thus resolving the problem of having few labeled datapoints, having the input be a small cardinality, a small number of labeled samples is sufficient to maintain the required level of performance. It will be shown in Section 5 in this work we consider a compression ratio of 32:1. In this work, we decided to use an information theoretic based feature extraction scheme [21].
# Simulative Investigation of the Radar Cross Section of Wind Turbines
## 5 Conclusions  
In this paper, a parameter study of a wind-turbine components, rotating and nonrotating, was presented with the aim of deriving strategies to minimize wind-turbine influence on radar systems for civic protection, which can be employed with currently existing installations.  
Unfortunately, given the complexity of the interaction of radar waves with the rotor blades, a \"general\" model of a wind-turbine blade cannot be derived from the presented studies. Further research, as proposed below, has the potential to improve the situation for coming generations of wind turbines.  
An exchange of operational data between radar installations and wind parks could be a simple way to coordinate operations so that negative mutual influence can be reduced. However, this means additional investment with the risk of increased downtime or lower yield, which is not very desirable from an economic point of view.  
From a wind-turbine manufacturer's point of view, preference should be given to rounded-off shapes for the nacelle. This will distribute the reflected radar beam over a wider angle and thereby reduce the disturbance a single monostatic radar installation will experience. For the most sensitive frequency bands (depending on the wind turbine site), using radar-absorbing material should be considered for all of the wind turbine's parts.  
As a general recommendation regarding regulatory procedures, it seems advantageous that turbine blades are subjected to an RCS analysis in a type-approval process. This analysis can be performed by numerical simulation. A numerical method which includes the effects of multiple reflections and penetrations into the object should be chosen when dealing with nonmetallic/CRP objects, e.g., SBR/GORL. Different polarizations need to be considered. For metallic objects, using the Physical Optics (PO) method is usually sufficient.
# Improved FraSegNet-Based Rock Nodule Identification Method and Application
## 3 Evaluation Methods
### Barton-Bandis Parameter Mapping
#### 3.4.2 Conversion from Rock Parameters to Rock Mass Parameters  
The link between rock density and rock mass density includes porosity:  
\[\rm\rho_{rock\ mass}=\rm\rho_{rock}(1-\varphi) \tag{21}\]  
where  
\(\rm\rho_{rock}\): Rock density;  
\(\rm\varphi\): Porosity.  
According to the Hoek-Brown model, the link between rock mass cohesion (\(\rm c_{rock\ mass}\)) and intact rock cohesion (\(\rm c_{rock}\)) can be found by transforming strength parameters:  
\[\rm c_{rock\ mass}=\rm c_{rock}\cdot exp\ (-\frac{GSI-100}{28-14D}) \tag{22}\]  
where  
D: Disturbance parameter, this article takes 0.9.  
GSI: The Geological Strength Index is used to assess the quality of the rock mass.  
In the Hoek-Brown model, the friction angle (\(\theta\)) of the intact rock is connected to the strength parameter (\(\rm m_{h}\)) and the integrity parameter (S). The relationship is shown below:  
\[\rm\tan(\theta_{rock\ mass})=\rm\tan(\theta_{rock})\cdot(s\cdot exp\ (-\frac{GSI-100}{9-3D})) \tag{23}\]The link between the rock's elastic modulus (E\({}_{\text{rock}}\)) and the rock mass's elastic modulus (E\({}_{\text{rock mass}}\)) in the Hoek-Brown model is shown below:  
\[\text{E}_{\text{rock mass}}=\text{E}_{\text{rock}}\cdot(0.02+\frac{1-\text{D}}{ 1+\text{exp}\,(\frac{60+15\text{D-GS}}{11})}) \tag{24}\]  
The link between the rock's Poisson's ratio (\(\text{r}_{\text{rock}}\)) and the rock mass's Poisson's ratio (\(\text{r}_{\text{rock mass}}\)) in the Hoek-Brown model is shown below:  
\[\text{r}_{\text{rock mass}}=\text{r}_{\text{rock}}+\text{k} \tag{25}\]  
where  
k: Empirical parameter is related to the rock type and joint characteristics.  
The shear modulus (G\({}_{\text{rock}}\)) can be calculated using the elastic modulus (E\({}_{\text{rock}}\)) and Poisson's ratio (\(\text{r}_{\text{rock}}\)) with this formula:  
\[\text{G}_{\text{rock mass}}=\frac{\text{E}_{\text{rock mass}}}{2(1+\text{r}_{ \text{rock mass}})} \tag{26}\]  
The bulk modulus (K\({}_{\text{rock}}\)) can be calculated from the elastic modulus (E\({}_{\text{rock}}\)) and Poisson's ratio (\(\text{r}_{\text{rock}}\)) using this formula:  
\[\text{K}_{\text{rock mass}}=\frac{\text{E}_{\text{rock mass}}}{3(1-2\text{r}_{ \text{rock mass}})} \tag{27}\]  
The rock tensile strength (T\({}_{\text{rock}}\)) is linked to the Hoek-Brown parameters and can be shown as follows:  
\[\text{T}_{\text{rock}}=-\frac{\sigma_{\text{ci}}}{\text{m}_{\text{b}}}\cdot( \frac{\text{GSI}}{100})^{\text{a}} \tag{28}\]  
where  
\(\sigma_{\text{c}}\): Compressive strength of the intact rock.  
Using the nodal parameters from the optimized FraSegNet algorithm, rock mass parameters were found using the Barton-Bandis model. These were compared with the results from indoor tests using the Hoek-Brown model to check the accuracy of the extracted nodal features. This comparison helps confirm the FraSegNet algorithm's ability to capture key geological parameters.