esa-sceva/satcom-chunk-collection huggingface dataset:
# Precedence Table Construction Algorithm for CFGs Regardless of Being OPGs
## 4 Approach
### Problem Analysis  
#### 4.2.1 Absolute Operator Grammar  
Given the rightmost derivation series of the form:  
\[S\stackrel{{+}}{{\Longrightarrow}}\gamma_{1}Ax_{1} \Longrightarrow\gamma_{1}\omega_{1}x_{1}\text{, where }A\longrightarrow\omega_{1}\text{ and }\omega_{1}=\gamma_{2}B\gamma_{3}. \tag{1}\]  
It is noted that \(\omega_{1}\) is a handle according to the previous definition in Section 2.7.  
The right-sentential form of (1) can be rewritten as follows:  
\[\gamma_{1}\omega_{1}x_{1}=\gamma_{1}\gamma_{2}B\gamma_{3}x_{1} \stackrel{{+}}{{\Longrightarrow}}\gamma_{1}\gamma_{2}Bx_{2}x_{1} \Longrightarrow\gamma_{1}\gamma_{2}\omega_{2}x_{2}x_{1}\text{, where }B \longrightarrow\omega_{2}. \tag{2}\]  
From the last sentential form in (2), it follows that \(\omega_{2}\) is a handle according to the definition in Section 2.7.  
Because of the above, the analysis will focus on the productions born of \(A\), which have at least one non-terminal \(B\) on the right, as seen in (1). The productions in \(A\) will be traversed through grammar to find the relations of precedence of non-terminal \(B\) located on the right side.  
Within the context of \(\omega_{1}\), having to \(B\longrightarrow\omega_{2}\), and \(\omega_{2}\) is a handle, the following forms can be obtained from its definition in (1):  
(a) \(\omega_{1}=\gamma_{4}aB\), it follows that \(a\lessdot\mathcal{L}(B)\). Here \(\gamma_{2}=\gamma_{4}a\) and \(\gamma_{3}=\varepsilon\).  
(b) \(\omega_{1}=Bb\gamma_{5}\), it follows that \(\mathcal{R}(B)\geqslant b\). Here \(\gamma_{2}=\varepsilon\) and \(\gamma_{3}=b\gamma_{5}\).  
(c) \(\omega_{1}=\gamma_{4}aBb\gamma_{5}\), it follows that \(a\lessdot\mathcal{L}(B)\) and \(\mathcal{R}(B)\geqslant b\). Here \(\gamma_{2}=\gamma_{4}a\) and \(\gamma_{3}=b\gamma_{5}\).  
For the non-terminal start symbol, \(S\), an initial dummy production is defined \(E\longrightarrow\$\$\) and rule (c) is applied.  
For a production \(A\longrightarrow\gamma_{1}aXb\gamma_{2}\), \(X\in N\cup\{\varepsilon\}\), it follows that \(a\doteq b\).
# Three-Dimensional Imaging via Time-Correlated Single-Photon Counting
###### Abstract  
Three-dimensional (3D) imaging under the condition of weak light and low signal-to-noise ratio is a challenging task. In this paper, a 3D imaging scheme based on time-correlated single-photon counting technology is proposed and demonstrated. The 3D imaging scheme, which is composed of a pulsed laser, a scanning mirror, single-photon detectors, and a time-correlated single-photon counting module, employs time-correlated single-photon counting technology for 3D LiDAR (Light Detection and Ranging). Aided by the range-gated technology, experiments show that the proposed scheme can image the object when the signal-to-noise ratio is decreased to \(-13\) dB and improve the structural similarity index of imaging results by 10 times. Then we prove the proposed scheme can image the object in three dimensions with a lateral imaging resolution of \(512\times 512\) and an axial resolution of \(4.2\) mm in \(6.7\) s. At last, a high-resolution 3D reconstruction of an object is also achieved by using the photometric stereo algorithm.  
Keywords:time-correlated single-photon counting technique; range-gated technique; three-dimensional imaging +
Footnote †: journal: Applied Soft. 2020, 10, 1930; doi:10.3390/app10061930
# Structural Features Guiding the Design of Liquid-Crystalline Elastomeric Fluorescent Force Sensors
## 1 Introduction
It is also interesting to stress at this point that mechanochemical reactions can also be performed in polymeric matrixes containing carefully chosen mechanophores with an extremely high spatial precision, permitting 3D nanoprinting and the production of nanodevices and nanosensors for measuring mechanical stress in situ [32]. It should be also mentioned that non-polymeric materials, mainly crystalline solids, have also been proved to change their luminescence upon grinding, smearing, shearing, etc., due to drastic modifications of their crystalline structures [33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51]. However, in many of these instances, the initial state of the system must be recovered either by recrystallization or by exposing the solid to vapors of a suitable solvent. This fact increases considerably the response time of the material and compromises its reversibility, thereby limiting its potential applicability. Elastic materials with the ability to detect small deformations, forces or pressures are of great importance within a wide variety of areas, ranging from information transmission technology to biomedicine. In this scenario, mechanofluorescent LSCEs emerge as valuable candidates. Thus, gaining insight into the mechanisms underlying mechanofluorescence in LSCEs is essential to pave the way to a new generation of mechanoluminescent elastomeric materials with novel functionalities and enhanced performances. In this context, a careful design of the carbazole fluorophores at the molecular level and a proper engineering of the intermolecular interactions established with the surrounding mesogenic molecules is fundamental to improve the efficiency of the resulting force sensor. Our research group has been involved during recent years in the development of carbazole-containing LSCEs for efficient optical force sensing. In particular, we have explored the impact of not only several features concerning the carbazole fluorophores but also the presence of a long range orientational and positional order in the LSCE on the sensing abilities of the system. Therefore, it is the main aim of this review to present the reader the main progresses achieved on the way to efficient optical force sensors based on fluorescent liquid single crystal elastomers. Specifically, five main features have been investigated so far, which are illustrated schematically in Scheme 1 and summarized below:  
* Fluorophore content. * Presence of long range orientational order in the host matrix. * Length of the flexible spacer connecting the carbazole fluorophores to the polymer backbone. * Anchoring mode (side-on or end-on) of the fluorogenic moieties. * Type of mesophase exhibited by the elastomeric material.
# Auditory Device Voice Activity Detection Based on Statistical Likelihood-Ratio Order Statistics
## 4 Proposed VAD Based on LR Order Statistics
### VAD Based on LR Order Statistics  
First, the log LR sets \(\log\Lambda_{0}(\ell),\log\Lambda_{1}(\ell),\ldots,\ \log\Lambda_{K-1}(\ell)\) are arranged in descending order of magnitude as  
\[\log\Lambda_{\{0\}}(\ell)\geq\log\Lambda_{\{1\}}(\ell)\geq\cdots\geq\log \Lambda_{\{K-1\}}(\ell) \tag{16}\]  
where the subscript \(\{k\}\) is the new index of the log LR after ordering. We then denote the elements of the new log LR set as \(\Psi_{k}(\ell)\), such that \(\Psi_{k}(\ell)\equiv\log\Lambda_{\{k\}}(\ell)\). Thus, the left side in Equation (14) can be expressed as \(\sum\limits_{k^{(1)}}\log\Lambda_{k}(\ell)+\sum\limits_{k^{(2)}}\log\Lambda_ {k}(\ell)=\sum\limits_{k=0}^{\widetilde{K}^{\prime}-1}\Psi_{k}(\ell)\). Finally, using the LR order statistics, the LRT rule becomes  
\[\frac{1}{\widetilde{K}^{\prime}}\sum\limits_{k=0}^{\widetilde{K}^{\prime}-1} \Psi_{k}(\ell)\ \geq\ \ \begin{array}{lcl}\eta_{0}&:&H_{1}\\ <&\eta_{0}&:&H_{0}\end{array} \tag{17}\]  
From Equation (17), the problem of separately estimating \(\sum\limits_{k^{(1)}}\log\Lambda_{k}(\ell)+\sum\limits_{k^{(2)}}\log\Lambda_ {k}(\ell)\) from the observed noisy speech is focused on the estimation of \(\widetilde{K}^{\prime}\), which is a tuning parameter used to control the FRP robustness. When \(\widetilde{K}^{\prime}=\widetilde{K}\), the proposed VAD in Equation (14) equals the conventional VAD in Equation (11).
# High-Resolution Pixel LED Headlamps: Functional Requirement Analysis and Research Progress
## 2 Functional Requirement Analysis
### Fusion with Sensors for High-Resolution Headlamps  
At present, existing automatic driving environment perception modules install multiple sensors in various parts of the car and do not have integrity. With the increase in the number of sensors, redundant information input about values and methods, and high-resolution LED pixels, the combination of headlamps and commonly used devices represents a future development trend, and it is also a direction currently being discussed by manufacturers and research institutions.  
[PERSON] et al. proposed that, due to the complex road driving of vehicles, when the sensor is driving on rugged mountain roads, there will be a cut-off line to move up and down and increase the computing time [27]. In order to release the full potential of high-resolution lighting, precise alignment and stability of light distribution are essential elements. Through the intelligent sensor fusion algorithm used to obtain a detailed vehicle environment and direction dynamic information, and using high-pixel headlamps to enhance the user experience by accurately guiding light, the duration of scene creation and the shape of high-resolution headlamps can be enhanced, compensated by prediction algorithms and eliminating high-frequency light oscillations caused by road interference.  
[PERSON] et al. proposed that, although more mature technologies such as radar, ultrasound, and camera technologies can be used in various fields of autonomous driving, in order to meet the National Highway Traffic Safety Administration (NHTSA)'s relevant road regulations, in addition to advanced driver assistance system (ADAS) [28], spectral sensing is also particularly important and has good performance in 3D and in long-distance range. Photonics is currently the only technology that can meet the requirements.  
[PERSON] et al. discussed the challenges and the benefits of integrating digital lighting and sensor modules [29]. The overall goal is to integrate sensors for autonomous driving into the headlight system. Traditional information acquisition is only used to provide freedom for some special applications. The fusion of spatial information sensor data ignoring the optimization of lighting fusion is mainly guided through the perspective of perception rather than deeper recursive guidance with perception functions, ignoring the reliability of the installation location and the feasibility of dealing with bad weather. The author proposes to integrate a single sensor in the headlamps and gradually develop some of the basic capabilities of current vehicles regarding the peripheral photonic system.  
As the driving environment becomes increasingly complex, autonomous driving requires an increasing number of sensors, which, together with computing and communication capabilities, are gradually increasing. The combination of high-resolution LED pixel headlamps and sensors can reduce computing time, eliminate light shocks caused by road infections, and save costs, which is a trend in the current research.
## 5 System Design
### Public Cloud IoT System  
This work employs game theory to deploy its security scheme based on global trust and the selection of CIN. To accomplish this, a simplified dual controller design, namely GTC and CSC, is used in the development of an agreement system. The following is an explanation of how these two controllers work:  
* Global Trust Controller (GTC): This controller system is responsible for managing all the updates for legitimate IoT nodes. The process is carried out over the public cloud system, making the operation in adherence to the agreement system as illustrated in Figure 3. A reliable source is provided to all nodes to perform the validation for the obtained controlled message.
* The classification of dependable parameters, as shown in Figure 3, demonstrates that the entire validation process carried out by GTC is done using the address of the mobile IoT node and a list of all connected nodes maintained in TRM. To access the legitimate IoT node, obtain its global trust, and update its value, an explicit set of functions is built. The first function, 'index legitimate IoT node,' is in charge of indexing the legitimate mobile IoT node while also determining whether the target node is present in the list of node addresses in TRM. In the case of a new mobile IoT node, it configures the address of the mobile IoT node with the address of the destination mobile IoT node, along with initialising the default value of global trust. This information is then added to the list. The second function, 'obtain global trust', is responsible for validating the global trust score that finally returns the score of global trust stored in the public cloud system. The new value of the global trust is configured using this function's agreement system, which is the prime operation of the third function, 'update global trust score'.
* Core node Selection Controller (CSC): This controller aggregates all the possible strategies of both the players, i.e., CIN and RIN, along with the constructs of the core structure that retain information about the RIN, CIN and local information of mobile IoT nodes. As shown in Figure 4, the core structure associated with the CIN maintains information about their respective identity and residual channel capacity. At the same time, the RIN holds information about the address of TRM and payoff. Furthermore, the dependent entities of CSC will consist of an object of CIN, as well as its respective address, as shown in Figure 4. The first function, 'define CIN,' is used to self-declare a mobile IoT node as a CIN in the public cloud. This function generates a CIN object by extracting the global trust score from GTC, while the second function, 'obtain CIN,' returns a list of all of the defined CIN. The third function, 'Strategy,' is used to build RIN.  
Figure 2: Format of newly formulated control message: (a) format for \(C_{msg1}\); (b) format for \(C_{msg2}\).
## 3 Background
### Matrix Factorization  
The Matrix Factorization method, as stated in Ref. [48], utilizes the utility matrix to identify latent features that represent users and items. If we are given a rating matrix  
Figure 1: The Transformer encoder architecture.  
of shape (\(n\times m\)), which presents the ratings of n users on m items, the aim of Matrix Factorization is to decompose this matrix into two thin matrices, \(P\) and \(Q\), both with a shape of \(n\times f\) and \(m\times f\), respectively (shown in Equation (5)). The value of \(f\) indicates the number of important latent factors contained in the matrices. Figure 2 depicts the decomposition process of matrix \(R\) into matrices \(P\) and \(Q\).  
\[R\approx P\cdot Q^{T} \tag{5}\]  
The representation of the formula used to predict the rating \(r\) for a pair consisting of user \(u\) and item \(i\) is expressed as Equation (6), where the preference of users for items is denoted by the product of the user vector \(p_{u}\) and the transpose of the item vector \(q_{i}\), both of which have \(f\) dimensions.  
\[\hat{r}_{u,i}=p_{u}\cdot q_{i}^{T} \tag{6}\]
## 4 Experimental Results
### Performance and validation criteria for WP-SVM  
Because ML algorithms have a tradeoff between the classification accuracy on training data and the generalization accuracy on novel data, and because FP occurrences are much more frequent than those of TP1 and TP2, we calculated four performance measurements: the confusion rate (_Mis_Err),_ the True Positive, the True Negative, and the False Positive Ratios. These can be derived from the entries s\({}_{\text{ij}}\) of the confusion matrix _CM:_  
\[CM=\begin{bmatrix}s_{11}&s_{12}&s_{13}\\ s_{21}&s_{22}&s_{23}\\ s_{31}&s_{32}&s_{33}\end{bmatrix}\]  
Index \(i\) represents the correct class and index \(j\) the predicted class. Thus, s\({}_{\text{ij}}\) represents the number of data belonging to class \(i\) that _WP-SVM_ classified as belonging to class \(j\). The True Positive Ratio _(TPR),_ also known as sensitivity, reflects how sensitive _WP-SVM_ is in detecting polyps, whereas the True Negative Ratio _(TNR),_ also referred to as specificity, represents how accurately the classifier identifies false positives. The False Positive Ratio _(FPR)_ is simply the complement of _TNR,_ and _Mis_Err_ is the overall misclassification rate.  
Figure 3: Effect of Shape Index in Differentiating Polyps.
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## 6 Field Data Analysis  
GPS L1 C/A, L2C (M + L) and L5 (I + Q) code, carrier and C/N\({}_{0}\) observables were collected (every one second) in multipath environments to evaluate the performance of the monitoring techniques discussed before. The ephemeris data was also collected to extract satellite positions and clock corrections required for position solutions. First, the CMC-based monitoring metrics and C/N\({}_{0}\) measurements are monitored for multipath error correction and a stochastic weighting model, respectively. For detection, exclusion and de-weighting purposes, the performance of the C/N\({}_{0}\), DC/N\({}_{0}\) and GF-based detection metrics are first evaluated and detection results are then used to exclude or de-weight the measurements affected. Finally, the combination of the CMC-based error correction and detection/exclusion or detection/de-weighting techniques are investigated in a C/N\({}_{0}\)-based stochastic model.
# Capacity and Delay Analysis for Large Social-Aware Mobile Ad Hoc Wireless Networks
## 6 Network Delay of Our Scheduling Scheme
Using the same analytical method, we can get a same result in source super-cell. The source node have an expected number of data given by \(\overline{L}_{s}=\frac{\rho(1-\lambda_{i})}{1-\rho}\), where \(\rho=\frac{\lambda_{i}}{\mu}\). In relay super-cell and the destination super-cell, both the input and the output are Bernoulli stream of rate \(\lambda_{i}\) because of a reversible queue. Data from this output process is transmitted to relay node. Because the total number of relay nodes in each super-cell is \(nm-1\), data delivered to a relay node with probability \(\frac{\
u 1}{u}\) equally. Hence, each relay node independently receives data with probability \(\lambda_{r}=\frac{\lambda_{r}\Gamma 1}{\mu(nm-1)}\). Because the total number of relay nodes in each super-cell is \(nm-1\), the relay node is scheduled for data transmission to the destination node with probability \(\frac{\rho}{u}\). Hence, each relay node independently sends data with probability \(\mu_{r}=\frac{\
u 2}{nm-1}\). Similarly, receiving and sending data is mutually exclusive events in cache queue of each relay node, which can be seen as a continuous time queue with input rate \(\lambda_{r}\) and service rate \(\mu_{r}\). Figure 4: Queue in network for \(\delta\leq 3/2\). This holds for all the relay super-cell, the expected number of data of the relay node is \(\overline{L}_{r}=\frac{\rho}{1-\rho}\), where \(\rho=\frac{\lambda_{r}}{\mu}\). Data is delivered along \(\mathbb{SD}\) lines super-cells. To reach the destination node the average number of super-cells passing through by each source node is \(\frac{E\{\mathcal{L}(S,D)\}}{\sqrt{m}}\). Finally, summing the delay in all super-cell, we can get the total network delay  
\[D(n) = \frac{\sum\overline{L}}{\lambda_{i}} \tag{26}\] \[= \frac{\overline{L_{s}}+\sum_{i=1}^{h}\overline{L_{r}}}{\lambda_{ i}}\] \[= \frac{1-\lambda_{i}}{\mu-\lambda_{i}}+\frac{E\{\mathcal{L}(S,D)\} }{\sqrt{m}}(nm-1)\frac{\overline{L}_{r}}{\lambda_{i}}.\]  
Substituting \(E\{\mathcal{L}(S,D)\}\), we get the network delay in Theorem 3. Theorem 3: _Based on the multi-hop relay and slots allocation scheme, the network delay \(D(n)\) in large social-aware mobile ad hoc wireless networks can be expressed as_  
\[D(n)=\left\{ \begin{array}{ll}\Theta\left(n\sqrt{m}\right)&0\leq\delta<1\\ \Theta\left(\frac{n}{\log n}\sqrt{m}\right)&\delta=1\\ \Theta\left(n^{2-\delta}\sqrt{m}\right)&1<\delta<3/2\\ \Theta\left(n^{1/2}\log n\sqrt{m}\right)&\delta=3/2\\ \Theta\left(nm\right)&\delta>3/2.\end{array} \right.\]
# Siberia: A Self-Sovereign Identity and Multi-Factor Authentication Framework for Industrial Access
## 5 Conclusions  
SIBERIA demonstrates a novel and effective approach to identity management in industrial environments, enhancing both security and user autonomy [5,33]. By integrating an SSI framework with advanced biometric technologies, it addresses critical vulnerabilities found in conventional centralized authentication systems [4].  
The SIBERIA architecture demonstrates a successful fusion of three key technologies: a decentralized identity model based on SSI principles, multi-factor authentication through SASV, and CA through behavioral biometrics. The use of an SSI framework, aligned with European standards such as EBSI and GDPR, empowers users by giving them direct control over their digital credentials, which are stored securely in a personal digital wallet [6].  
A significant contribution of this work is the enhancement of biometric security and privacy. The SASV module not only provides robust protection against sophisticated spoofing and deepfake attacks but also guarantees the confidentiality of sensitive voiceprint data by using HE. This allows verification computations to be performed directly on encrypted data, ensuring that raw biometric templates are never exposed. Furthermore, the behavioral biometrics module offers a non-intrusive layer of continuous security by monitoring user interaction patterns post-authentication, enabling the detection of potential session hijacking in real-time.  
Moreover, this work demonstrates the validity of the integrated behavioral biometric model, whose effectiveness lies in a robust structural design based on a dual-head architecture. This configuration, which separates and processes user behavior signals from mouse and keyboard, enables the precise and complementary capture of temporal interaction patterns. This structure not only facilitates the efficient reconstruction of the original signals but also enables a robust characterization of user behavior, an essential feature for a reliable CA system. Its integration within SIBERIA enhances the detection of behavioral deviations accurately and non-intrusively, consolidating its applicability in industrial environments with high security demands.  
Ultimately, SIBERIA proves that it is feasible to build a secure, scalable, and user-centric identity management system for critical industrial services. It establishes a strong balance between high-level security and user autonomy, mitigating risks such as credential theft and impersonation while ensuring regulatory compliance.
# FPGA Implementation of a Higher SFDR Upper DDFS Based on Non-Uniform Piecewise Linear Approximation
## 4 Experimental Result  
This section describes the experimental setup of our proposed DDFS and the experimental results. The DDFS is implemented using the Verilog hardware description language and deployed to the Xilinx AXU15 EG development board after simulation and synthesis by the Vivado tool. The input bit width N is uniformly set to 12, and the output data is read using ila IP cores, followed by MATLAB for spectrum analysis. The performance of the single-core DDFS is shown first, and then the experimental results of multi-core DDFS is introduced.