esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work and System Model  
### Related Work-Wireless Communication Model with Eavesdroppers  
In this section, we review the artificial noise technology and the method of SCO\(-\)AN. Moreover, the effects of imperfect channel estimation are analyzed in detail.  
Figure 1 shows a wireless communication system model with an eavesdropper. In this model, [PERSON] is the transmitter of the message, [PERSON] is the legitimate receiver, and [PERSON] is the eavesdropper. [PERSON] has \(N_{A}\) antennas, [PERSON] has \(N_{B}\) antennas and [PERSON] has \(N_{E}\) antennas. \(\mathbf{H}\) represents the channel state information (CSI) of the legitimate channel ([PERSON] to [PERSON]), while \(\mathbf{G}\) represents the CSI of the eavesdropper channel ([PERSON] to [PERSON]). \(\mathbf{H}_{k}\) and \(\mathbf{G}_{k}\) represent the CSI of \(\mathbf{H}\) and \(\mathbf{G}\) at time \(k\) respectively. The element \(h_{i,j}\) (or \(g_{ij}\)) in \(\mathbf{H}\) (or \(\mathbf{G}\)) is the channel gain coefficient between the i\({}_{th}\) transmitter antenna and the j\({}_{th}\) receiver's (or eavesdropper's) antenna. \(\mathbf{x}_{k}\in\mathbb{C}^{N_{A}}\) represents the signal transmitted by [PERSON] at time \(k\); \(\mathbf{y}_{k}\in\mathbb{C}^{N_{E}}\) represents the signal received by [PERSON] at time \(k\); and \(\mathbf{z}_{k}\in\mathbb{C}^{N_{B}}\) represents the signal received by [PERSON] at time \(k\).  
\[\mathbf{z}_{k}=\mathbf{H}_{k}\mathbf{x}_{k}+\mathbf{n}_{k}, \tag{1}\]  
\[\mathbf{y}_{k}=\mathbf{G}_{k}\mathbf{x}_{k}+\mathbf{e}_{k}, \tag{2}\]  
where \(\mathbf{n}_{k}\) and \(\mathbf{e}_{k}\) are independent and identically distributed (i.i.d) additive Gaussian white noise (AGWN) with the variance of \(\sigma_{n}^{2}\) and \(\sigma_{e}^{2}\) respectively. For the convenience of discussion, we assume that the CSI of \(\mathbf{G}\) and \(\mathbf{H}\) can be obtained by [PERSON] without delay. The maximum transmitting power is assumed to be \(P\), where \(E[\mathbf{x}_{k}^{\dagger}\mathbf{x}_{k}]\leq P\).  
Figure 1: Wireless communication model with eavesdropper.
## 3 Results and Discussion
### Ion Exchange Runs
#### 3.2.2 Kinetics of the Anion Exchange  
Figure 4 shows the kinetics of the anion exchange of As species on CLI-Br, CLI-Cl (Figure 4A), PHI-Br and PHI-Cl (Figure 4B) samples. All the kinetics appear reasonably fast: the equilibrium conditions had already been reached after just 50 min. For each tuff, the HDTMA-Br modified sample is always more effective than the HDTMA-Cl modified one: for PHI-based SMNZ samples, the maximum loading of As for PHI-Br is 4.21 mg g\({}^{-1}\), while for PHI-Cl is 1.20 mg g\({}^{-1}\).  
Figure 3: Effect of pH on the As(V) uptake in CLI-Br and PHI-Br at T = 25 \({}^{\circ}\)C.  
The same thing holds true for the clinoptilolite-based SMNZ samples: the maximum loading value for CLI-Br is 8.14 mg g\({}^{-1}\), while the value for CLI-Cl is 4.52 mg g\({}^{-1}\). These results confirm once again how the surfactant that has Br\({}^{-}\) as a counterion generates a much more complete and compact micellar structure on the surface of the zeolite compared to the surfactant that has Cl\({}^{-}\) as a counterion, which can generate only a patchy bilayer. This results in the greater AEC of both Br-based SMNZs, which is practically equal to the ECEC of the two samples, while the surface AEC of the samples modified with HDTMA-Cl is equal to half of the ECEC.  
Moreover, clinoptilolite-based SMNZs can always remove more As than phillipsite-based ones. This could be justified by the ECEC of the former that is greater due to a higher amount of zeolite in the sample: the mineralogical analysis showed that while the clinoptilitic tuff has a zeolite content of 79%, the phillipsitic tuff reaches a maximum of 58% (even if the analcime contribution to the exchange is very low). However, both the modified samples were not able, in any of the experiments, to exploit the totality of their AEC, as reported in Table 2, in which the AEC, for practical purposes, is reported as mg g\({}^{-1}\) of exchangeable arsenic.  
Figure 4: As exchange kinetics for PHI-based (**A**) CLI-based (**B**) Surface modified natural zeolites (SMNZs).  
This evidence is once again justified by the steric hindrance and shape complexity of the As anions compared to the \"simple\" Br- and Cl-. Such complexity forces a rearrangement of the micelle (either bilayer or patchy bilayer) to allow the entrance of the As species into its structure. This micellar rearrangement prevents the complete exploitation of its anionic exchange sites, because, as already proven [32], the micellar structure is not perfectly flexible and has limited capacity to accommodate complex anions.  
Kinetic exchange data were modeled with the pseudo-first and pseudo-second order equations (see Section 2.4.1). The results are shown in Figure 5 and Table 3.  
From the characteristic parameters of the models, as well as from the trend of the model curves reported in Figure 5, it is clear how the kinetics of anion exchange are closer to a pseudo-second order trend, as also confirmed by the obtained values of R\({}^{2}\).
# Efficient Wheel-Rail Stick-Slip Numerical Modeling for Railway Traction Vehicles
## 2 Materials and Methods
### Event-Driven Algorithm  
Integration methods for non-smooth dynamical systems fall into two main categories: time-stepping and event-driven schemes [35]. Time-stepping methods are popular due to their reliability and ability to handle systems with numerous contact points. While event-driven schemes offer greater precision and efficiency in computation costs, they also enable the calculation of constraint forces. However, these advantages come with limitations on the sequence of events and the number of contact points that can be handled [35]. Fortunately, the stick-slip model under consideration adheres to these restrictions.  
The event-driven approach divides the simulation timeframe into discrete, non-overlapping subintervals. Within each subinterval, the system operates in a specific mode, either stick or slip, between the contacting bodies. If there is no change in the contact phases, the system can be described by a set of ordinary differential equations. When a transition occurs between contact phases (e.g., from sticking to relative motion or vice versa), the corresponding friction law, and potentially its value, also changes. A crucial challenge in simulating constrained systems is accurately detecting these phase transition moments, such as the switch from slip to stick and vice versa.
## 5 Conclusions  
This paper investigated the ADP-based robust control problem of a quadrotor UAV trajectory tracking control system subject to external disturbances and parameter uncertainties. Firstly, the error subsystems were obtained by preprocessing the quadrotor UAV system using the feedforward technique. Subsequently, two FTNNOBs were designed to estimate external disturbances and parameter uncertainties quickly and accurately (observation errors converged within 0.5 s with an accuracy of the order of \(10^{-4}\)). Then, the AC NNs approximated the optimal value functions and the optimal control policies by designing two new weight update laws. Meanwhile, the designed weight update laws not only avoided utilizing the PE condition, but also improved the adaptive ability of the AC neural network. Finally, the ADPFFNs' control scheme with high accuracy tracking performance (tracking accuracy of the order of \(10^{-5}\)) and low control cost (a total control cost of position subsystem savings of 5.5%; a total control cost of attitude subsystem savings of 38.9%) was proposed by combining the FTNNOBs and the approximate results of the AC NNs. Through Lyapunov stability analysis, the proposed control scheme can guarantee the closed-loop tracking control systems to be UUB stable. In the future, we will construct some experiments to demonstrate the practicability of the developed control scheme and perform some research on a multi-UAV system. Meanwhile, we will further improve the adopted model to enhance its accuracy and applicability to ensure that it better reflects the actual situation.  
Conceptualization, S.Y.; methodology, S.Y.; software, S.Y.; validation, S.Y.; formal analysis, H.L. and H.Z.; investigation, H.L.; resources, H.L.; data curation, S.Y. and H.M.; writing--original draft preparation, S.Y. and F.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Natural Science Foundation of China (Grant Number 62073212).  
Not applicable.  
The data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
## 3 Proposed Algorithm
### Basic operation of the algorithm
#### 3.2.2 Route maintenance and restoration  
Route maintenance and restoration are important factors in MANETs. In this subsection, the proposed route maintenance and restoration schemes are discussed.  
Figure 3 illustrates the route maintenance method between the source node and the destination node. The black nodes are the main route nodes, the gray nodes are one-hop distance nodes from the main route node, and white nodes are two hops or more from the main route nodes. For instance, if a white node moves to a one-hop distance from the main nodes, it will then change its status to a gray node. Under the same approach, if a gray node moves away from the main route node by more than two hops, it will change its status to a white node. The difference between gray and white nodes is whether or not the node receives a beacon message from the one of the main route nodes.  
In Figure 3, node 1 transmits the information of the location of the destination node to its one-hop neighbor node 8. This information is broadcast with a beacon message every second to its one-hop neighbor nodes by setting the TTL of beacon message to 1. Similarly, nodes 8, 9, and 4 broadcast beacon messages to their neighbors that have the location information and address of the destination node. Next, the one-hop-distant neighbor nodes from the main route nodes write the routing information on each routing table. The purpose of beaconing is to find the location of the destination immediately when route failure occurs along the main route nodes. This procedure is used as an effective type of local repair.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**DestID**} & **Direction to** & **Next** & \multirow{2}{*}{**Distance**} & **HopCnt** & **SeqNo** & **Lifetime** & **State** \\  & **Destination** & **Node** & & & & & \\ \hline Node 14 & 1 & Node 17 & 220 & 4 & 341 & 10 & VALID \\ Node 46 & 3 & Node 54 & 160 & 5 & 268 & 0 & INVALID \\ Node 75 & 4 & Node 32 & 130 & 5 & 342 & 5 & VALID \\ \hline \hline \end{tabular}
\end{table}
Table 1: Local routing table.  
Figure 3: Route maintenance.  
Regarding the local repair procedure of an AODV, it broadcasts an RREQ message to find the destination node set as TTL field = 2 when a node finds a link failure. If no destination node is found, it then sends an RERR message back to all active nodes that are dependent on the broken link. When every node receives the RERR message, those nodes delete the route of the broken link. Particularly for a source that receives such an RERR message, it can restart the route discovery procedure; a stale route is expired according to a timer-based technique in what is known as an AODV local repair procedure.  
In the present scheme, when an upstream node detects a link failure, the node sends an RREQ message toward only the direction of the destination. These procedures contribute toward reducing the routing message overhead. Figure 4 shows a flow chart of the proposed local repair procedure.
## 4 Simulation Analysis and Measurement Verification
### Transmission Loss Analysis  
When the BTS works, its performance is influenced by the transmission medium. The uplink transmission loss variations are studied in both free space and water debris. Based on the analysis results shown in Figures 7-9, an electrical conductivity of \(\sigma=0.1\) S/m is selected for transmission loss analysis in water debris. In the experiment, the number of turns is \(n=1\), the network analyzer port resistances are \(R_{1}=R_{4}=50\)\(\Omega\), the antenna equivalent resistances are \(R_{2}=R_{3}=0.1\)\(\Omega\), and the cavity thickness \(h_{0}\) is 5 mm.  
Figure 10 shows the S21 parameter corresponding to vertical movement along the z-axis. As the perpendicular distance between the OAU and the balise increases from 220 mm to 460 mm, S21 gradually increases, indicating greater transmission loss. The transmission attenuation in water debris is greater than that in free space, and the calculated results are largely consistent with the experimental results. The data points below the graph indicate the deviation between the measured results and calculated results at various heights in free space, with a maximum deviation of 0.34 dB. The values above show the deviation in water debris, with a maximum of 0.21 dB.  
In Figure 11, it can be observed that the S21 parameter of the uplink FSK signal in the BTS gradually increases as the relative horizontal distance grows, indicating greater signal attenuation. The attenuation rate of the S21 parameter in the Y-axis direction is faster than that in the X-axis direction, owing to the fact that changes along the Y-axis lead to greater variations in the relatively overlapping area between antennas. The calculated results are generally consistent with the experimental findings. In free space, the maximum attenuation deviation along the X-axis is 0.38 dB, while along the Y-axis, it is 0.49 dB. In water debris, the maximum attenuation deviation along the X-axis is 0.35 dB, and along the Y-axis, it is 0.39 dB. The S21 parameter's maximum relative deviation closely aligns with the inductance's relative error trend, demonstrating a strong correlation between these key metrics. Given the operational range of the balise, the model's results remain valid for our purposes.  
Figure 10: S21 parameter variation curve versus vertical displacement.
## 4 Method
### EIT Inverse Model  
The process of reconstructing the EIT image involves the solution of an inverse problem. Its objective is to form an image representing the distribution of resistivity/conductivity within the sensitive field W of the medium. This reconstruction is accomplished by utilizing the measured values of boundary voltage in the sensitive field. Resistance tomography is the established method for EIT and serves as the fundamental imaging technology. In electrical impedance imaging, the primary goal of inverse problem-solving is to extract the model parameters from observational data.  
To convert the FEM grid data into graph data, we aim to employ GCNM instead of traditional CNNs.  
\[F(\sigma)=\frac{1}{2}\|\Lambda(\sigma)-\mathbf{V}_{\text{meas}}\|^{2}+Reg(\sigma) \tag{7}\]  
where \(\mathbf{V}_{\text{meas}}=\left(V_{1}^{(1)},\ldots,V_{L}^{(1)},\ldots,V_{1}^{( M)},\ldots,V_{L}^{(M)}\right)^{T}\) in \(\mathcal{R}^{ML}\) denotes a vector of measured voltages at each \(L\) electrode in a \(k\)-linearly independent current pattern, and then, the vector \(\Lambda=\left(\begin{array}{cccc}\Lambda_{1}^{(1)}(\sigma),&\ldots,&\Lambda_{ L}^{(1)}(\sigma),&\ldots,\Lambda_{1}^{(M)}(\sigma),\ldots,&\Lambda_{L}^{(M)}( \sigma)\end{array}\right)^{T}\) in \(\mathcal{R}^{ML}\) represents the simulated voltages obtained from \(L\) electrodes. These voltages are generated by applying the same \(K\)-current pattern with the conductivity \(\sigma\). The \(Reg(\sigma)\) denotes potential regularization factors.  
The best constant conductivity was chosen in the experiment to match the data for the first assumption \(\sigma_{0}\). Then, Equation (6) may be changed to  
\[F(\sigma_{0}+\phi\sigma)=\frac{1}{2}\|\Lambda(\sigma+\phi\sigma)-\mathbf{V}_{ \text{meas}}\|^{2}+Reg(\sigma+\phi\sigma) \tag{8}\]  
By solving (7) iteratively, the following update equation is  
\[\sigma_{k+1}=\sigma_{k}+\phi\sigma_{k} \tag{9}\]  
Given an estimate of \(\sigma_{k}\), this process is iterated until a satisfactory solution is found.
# Reversed Lempel-Ziv Factorization with Suffix Trees +
## 3 Reversed LZ Factorization
### Factorization Algorithm  
In the following, we describe our factorization algorithm working with ST. This algorithm performs traversals on paths connecting leaves with the root, during which it marks certain nodes. One kind of these marked nodes are phrase leaves: A phrase leaf is a leaf whose suffix number is the starting position of a factor. We say that a phrase leaf \(\lambda\) corresponds to a factor \(F\) if the suffix number of \(\ell\) is the starting position of \(F\). We call all other leaves non-phrase leaves. Another kind are witnesses, a notion borrowed from [21] (Section 3): Witnesses are nodes that create a connection between referencing factors and their referred positions. We formally define them as follows: given \(\lambda\) is the phrase leaf corresponding to a referencing factor \(F\), the witness \(w\) of \(F\) is the LCA of \(\lambda\) and a leaf with suffix number \(2n-j\) (with \(j\in[1\mathinner{.}n-1]\)) such that \(T[j-\text{str\_depth}(w)+1\mathinner{.}j]^{\text{R}}\) is the longest substring in \(T[  
Consequently, the referred position of a factor \(F_{x}=T[i\,..\,i+\ell-1]\) is the smallest text position \(j\) in \(T\) with \(j\leq i-1\) and one of the two equivalent conditions hold:  
* \(T[j-\ell+1\,..\,j]^{\mathsf{R}}=T[i\,..\,i+\ell-1]\); or
* \(R[i\,..]\) and \(R[2n-j\,..]\) have the longest common prefix of length \(\ell\).
## 3 Methodology
### Automatic Dialect Identification  
The Klaam library offers a pre-trained model for speech audio classification and is an open-source framework created especially for Arabic Natural Language Processing (NLP) tasks. A machine learning architecture that has already been trained on a sizable speech information set is known as a pre-trained model. It permits the model to learn generalizable features and patterns, including phonetic information, linguistic subtleties, and acoustic characteristics. By removing the need for in-depth training from the ground up, this pre-training procedure drastically cuts down on the time and resources needed for development. Dialect, speaker, emotion, and language identification are just a few of the speech classification tasks that can be accomplished using the speech classification model in the Klaam library. Using this pre-trained model helps add quick and efficient speech classification capabilities to the Arabic NLP applications. Datasets such as MGB-3, ADI-5, Common Voice, and the Arabic Speech Corpus are used to build a pre-trained Arabic dialect speech classification model: ADI-5 contains a wide variety of regional dialects (Egyptian, Levantine, Gulf, North African, and Modern Standard Arabic) sourced from Al Jazeera TV;  
Figure 2: Research flow diagram.  
MGB-3 is a sizable corpus of Egyptian Arabic speech gathered from YouTube; the Common Voice dataset, which is accessible on Hugging Face, is a multilingual resource with a significant amount of Arabic speech data; the Arabic Speech Corpus offers useful aligned speech and transcriptions for training and assessing speech recognition and classification models. These datasets make it possible to develop a high-quality pre-trained model for Arabic dialect classification with suitable data preprocessing, feature extraction methods, and strong model architectures. The basic architectural block diagram using the speech classification pre-trained method is shown in Figure 3.  
Of the five classes obtained, we selected two for region-wise classification of Arabic dialects. The problem is binary, since the two classes chosen are Gulf Arabic (GLF) and Modern Arabic (MSA).
# Intelligent Beam-Hopping-Based Grant-Free Random Access in Secure IoT-Oriented Satellite Networks
## 3 Beam-Hopping-Based Grant-Free Random Access  
In this section, we propose an intelligent new access framework for the LEO satellite Internet of Things. In response to the abovementioned discussion, to the best of our knowledge, large-scale IoT access does not use signaling interactions to increase the access success rate, which can cause signaling storms. Furthermore, in the satellite-to-terrestrial scenario, the conflict between agile beam switching frequency, resource scheduling period, and service latency requirements needs to be resolved. Therefore, we designed a direct frequency-hopping sequence according to different preambles in the time slot of the beam-hopping pattern for data transmission. In addition, transmission constraints were imposed by the spatio-temporal distribution of different devices, and the probability of user activation was iteratively generated. It also better classified time-sensitive services and sparse data transmission services, improved the overall access success rate under this framework, and reduced transmission delay.
# Unsupervised Pre-Training for Voice Activation
###### Abstract  
The problem of voice activation is to find a pre-defined word in the audio stream. Solutions such as keyword spotter \"Ok, Google\" for Android devices or keyword spotter \"Alexa\" for Amazon devices use tens of thousands to millions of keyword examples in training. In this paper, we explore the possibility of using pre-trained audio features to build voice activation with a small number of keyword examples. The contribution of this article consists of two parts. First, we investigate the dependence of the quality of the voice activation system on the number of examples in training for English and Russian and show that the use of pre-trained audio features, such as wav2 vec, increases the accuracy of the system by up to 10% if only seven examples are available for each keyword during  
authors of [21] used millions. This presents a question of how to build a high-quality voice activation system in cases when limited training data are available. This can be useful for the following reasons:  
* customizing a product by using user-defined keywords, e.g., for personal voice assistants,
* creating voice activation for low-resource languages such as Lithuanian, Latvian, and others.  
In this work, we propose to use unsupervised pre-training for building voice activation systems with limited training data. We use the wav2 vec method [22] and show that it can improve the quality of the resulting system if there are less than 20 samples per keyword for several datasets, namely Google Speech Commands [14] and our private Russian dataset, even though the wav2 vec model was trained only on English recordings. We verify this statement on a new Lithuanian dataset [23], which we collected and present in this work.