esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Future Work and Limitations  
1. Coal mine roadway rock control is an important proposition, especially at depth. Due to the difficulty of on-site experimental comparisons, simulation is generally used as a means to analyze the effects of process parameters (including anchor support, top-cutting, bottom-cutting, drilling pressure relief, etc.) on the control of roadway perimeter rock in terms of indicators. When changing the parameters, it often results in stress redistribution, leading to the phenomenon of perimeter rock control conflict. The machine learning objective function fitting-multi-objective optimization provides a reasonable solution for conflict resolution, which can be extended to different tunnel types and different processes.
2. The finite element simulation has some limitations and does not take into account the resulting geological phenomena such as cracking, dilatancy, creep, etc. This study did not focus on an in-depth study of the effects that differences in rock and bulk fill material properties (including water content, rock anisotropy, etc.) can have on the stress-relief groove. We plan to simulate these heterogeneities with more sophisticated models and test their specific effects on the performance of the stress-relief groove in future work.
3. In the field of Artificial Intelligence in mining engineering, it is important to increase the interpretability and transparency of models. The concept of Explainable Artificial Intelligence (XAI) enhances the explanatory power of our models [35; 36]. XAI models can help to explain the decision-making process of complex models, which is crucial to increase the trust of stakeholders and the transparency of the models in real engineering applications. Among other things, real-world engineering data and feedback are very important to further validate and improve our models [37; 38].
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Similarity and Distance Measure  
The similarity measure determines the similarity between diverse terms, such as words, sentences, documents or concepts. The goal of determining similarity measures between two terms is to determine the degree of relevance by matching the conceptually similar terms but not necessarily lexicographically similar terms [138].  
Generally, the similarity measure is a significant and essential component of any clustering technique. This is because it makes it easier to measure two things, group the most similar elements and entities together and determine the shortest distance between them [139; 140]. In other words, distance and similarity have an inverse relationship, so they are used interchangeably. In general, similarity/distance measures are computed using the vector representations of data items.  
Document similarity is vital in text processing [141]. It calculates the degree to which two text objects may be identical. Nonetheless, the similarity and distance measures are used as a retrieval module in information retrieval. Similarity measurements include cosine, Jaccard and inner products; distance measures include Euclidean distance and KL divergence [142]. An analysis of the literature studies shows that several similarity metrics have been developed. However, none of the similarity metrics appears to be the most effective for any research [143].
## 3 Artificial Neural Network (ANN)
### Multiple Layer Perceptron Regression  
We applied multiple layer perceptron regression (MLP Regression) algorithm (Figure 4). The number of hidden layers is assigned as 1. The number of hidden units for each layer is changed as a parameter during experimentation.  
At each layer, we first compute the total input \(z\) to each unit, which is a weighted sum of the outputs of the units in the layer below. For the first hidden layer, the input \(z_{i}\) for the ith hidden unit,  
\[z_{i}=\sum_{j=1}^{d+1}w_{ij}^{1}x_{j} \tag{9}\]  
where \(w_{ij}^{1}\) is the connection weight from the jth input unit to the ith hidden unit, and d is the number of input units. For the output unit, the input is computed as:  
\[z_{o}=\sum_{j=1}^{m+1}w_{j}^{2}\;h_{j} \tag{10}\]  
where, \(w_{j}^{2}\) is the connection weight from the jth hidden unit of the hidden layer to the output unit, \(h_{j}\) is the output of the jth hidden unit of the hidden layer, and m is the number of hidden units of the hidden layer. For the input layer and hidden layers, a unit with output 1 is added to include a bias term.  
Figure 4: Schematic diagram of MLP regression.  
Then a non-linear function \(f^{h}(.)\) is applied to \(z_{i}\) to get the output of the hidden unit. The activation function used is the sigmoid function, i.e.,  
\[f^{h}(z_{i})=\frac{1}{1+e^{-z_{i}}} \tag{11}\]  
The activation function for the output unit is a linear function, i.e.,  
\[f^{o}(z_{o})=z_{o} \tag{12}\]  
Output \(f^{o}(z_{o})\) is the predicted output \(\text{F}(\textbf{x}^{d})\) of the current input feature vector \(\textbf{x}^{d}\).  
For the input, we used an auto encoder to transform the input feature vector to a new feature vector using unsupervised learning. The autoencoder is a 2-layer perceptron with one hidden layer. The autoencoder learns effective representation of the input data from the provided unlabeled training data, and it tries to generate output so that it is equal to input. The used cost function is:  
\[\text{J}(\textbf{w})=\frac{1}{n}\sum_{i=1}^{n}\frac{1}{2}h\Big{(}\textbf{w}, \textbf{x}^{i}\Big{)}-\textbf{x}^{i2}+\frac{\alpha}{2}\{\sum_{i=1}^{d}\sum_{j =1}^{m}\left(w_{ji}^{1}\right)^{2}+\sum_{i=1}^{m}\sum_{j=1}^{d}\left(w_{ji}^{2} \right)^{2}\} \tag{13}\]  
where \(m\) is the number of hidden units, \(h\big{(}\textbf{w},\textbf{x}^{i}\big{)}\) is the output of autoencoder for \(\textbf{x}^{i}\), and \(\textbf{w}\) is the weight vector of the encoder and decoder. The first term of \(\text{J}(\textbf{w})\) is an average sum-of-squares error term. The second term is a regularization term (also called a weight decay term) to decrease the magnitude of the weights. The \(\alpha\) is called the weight decay parameter to control the relative importance between the two terms. After learning, the output value of the hidden layer \(\textbf{h}=[h_{1},\dots,h_{m}]^{T}\) becomes a transformed vector. This transformed vector will be used as a new input vector for MLP regression for each data.
## 3 Energy Efficient Routing
### Cross Layering Optimization
#### 3.3.2 Cross-layering with the MAC Layer  
Cross-layering with the MAC layer improves (1) EOLSR reactivity to topology changes by an early detection of broken links and (2) routes stability by the choice of good quality links. Indeed, the MAC layer provides the network layer with the received messages and their corresponding signal power. Using this information, EOLSR is able to select links with good quality and reject links with poor quality. Moreover, as soon as the received signal quality falls under a specific threshold, the corresponding link is immediately considered broken, instead of waiting for the expiration timer.
## 5 Results and Analysis
### Limitations and Future Directions  
While the proposed EIRL framework demonstrates promising results, several limitations warrant discussion. First, the reliance on epilepsy-adjacent EEG data, though methodologically compatible with motor imagery paradigms, necessitates validation on dedicated MI datasets to confirm generalizability across physiological states. Second, the discrete 4-directional action space, while simplifying EEG-robot mapping, restricts applica  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Path** & **Manhattan Distance (D)** & **Risk Level 2** & **Risk Level 1** \\ \hline \(A^{*}\) & path 1 & 14 & 0 & 2 \\ \hline \multirow{2}{*}{Q-learning} & path 1 & 14 & 0 & 6 \\  & path 1 & 14 & 1 & 5 \\ \hline \multirow{2}{*}{**EIRL**} & path 1 & 14 & 1 & 6 \\  & path 2–5 & 14 & 0 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the effect and risk of path planning.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Average Risk Value of the Algorithm** \\ \hline \(A^{*}\) & 4 \\ Q-learning & 13 \\
**EIRL** & **6.4** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of average risk values of different path planning algorithms.  
bility to continuous control tasks. Future work will integrate hierarchical RL architectures to bridge this gap. Third, our participant pool's homogeneity (healthy adults aged 22-28) limits insights into neurodiverse populations. Collaborative trials with clinical cohorts (e.g., stroke survivors) are planned to address this. Lastly, scalability to complex environments (e.g., 3D dynamic terrains) remains an open challenge, motivating research into multimodal biosignal fusion and adaptive resolution grids. These limitations, while non-trivial, define clear pathways for advancing EEG-driven autonomy in real-world applications.
## 3 The \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) Filter  
### Definition  
The \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter proposed by [PERSON] [31] is a second-order fixed-gain PVM tracker. It can be considered to be an extension of the \(\alpha\)-\(\beta\) filter, which uses position measurements only. The \(\alpha\)-\(\beta\)-\(\eta\)-\(\theta\) filter iterates prediction and smoothing (update) processes. The prediction process is conducted under the assumption that the target's velocity is constant over the sampling interval and yields a position prediction:  
\[x_{\mathrm{p},k} =x_{\mathrm{s},k-1}+Tv_{\mathrm{s},k-1}, \tag{1}\] \[v_{\mathrm{p},k} =v_{\mathrm{s},k-1}, \tag{2}\]  
where \(x_{\mathrm{s},k}\) is the smoothed target position at time \(kT\), \(T\) is the sampling interval, \(x_{\mathrm{p},k}\) is the predicted position, \(v_{\mathrm{s},k}\) is the smoothed velocity and \(v_{\mathrm{p},k}\) is the predicted velocity. The smoothing process is defined as in [31]:  
\[x_{\mathrm{s},k} =x_{\mathrm{p},k}+\alpha(x_{\mathrm{o},k}-x_{\mathrm{p},k})+T\eta (v_{\mathrm{o},k}-v_{\mathrm{p},k}), \tag{3}\] \[v_{\mathrm{s},k} =v_{\mathrm{p},k}+(\beta/T)(x_{\mathrm{o},k}-x_{\mathrm{p},k})+ \theta(v_{\mathrm{o},k}-v_{\mathrm{p},k}), \tag{4}\]  
where \(x_{\mathrm{o},k}\) is the measured position, \(v_{\mathrm{o},k}\) the measured velocity and \(\alpha\), \(\beta\), \(\eta\) and \(\theta\) are fixed filter gains that we must design.
# Shared Graph Neural Network for Channel Decoding
## 1 Introduction
The performance evaluation and complexity analysis are shown in Section 4, which systematically analyzes the performance and complexity of the SGNN and GNN-based channel decoding algorithms. Section 5 discusses the issues of the paper and future research directions. Section 6 concludes the paper.
## 2 Related Work
In addition, they used the ant colony concept to mimic the pheromone-based technique ants use to find a given path and communicate the information to colony members. Experiment results showed the efficiency of their approach in adapting to network changes over time. Self-adaptive routing in multi-hop sensor network was presented by [PERSON] _et al._[32]. A study of an effective method that uses time series analysis to forecast the occurrence of errors and faults was presented. The work considered an autonomic routing service through adaptation to avoid areas where failure or errors are expected. Simulation results showed the benefit of their approach. The authors in [38, 39] compared the performance of three routing protocols, namely, the Multi-Parent Hierarchical (MPH), AODV, and DSR protocols. The protocols are evaluated both when exposed to different \"jamming\" attacks and without attacks, and considering diverse locations of the jammer. Simulation results show that MPH routing has greater immunity for tolerating attacks than DSR and AODV. This is because MPH reduces and encapsulates the network segment when subjected to a network attack. In addition, the self-configuring features of MPH yield higher resiliency and better routing performance compared with AODV and DSR protocols. [PERSON] _et al._[39] proposed metrics for efficient energy consumption in wireless sensor networks. They compared the performance of three routing protocols, namely MPH, AODV, DSR, and Zigbee Tree Routing (ZTR). Simulation results demonstrated the impacts of communication metrics on performance, throughput, reliability, and energy consumption. They showed that MPH achieved 19.3% reduction of network packet retransmissions, 26.9% reduction in routing overhead, and 41.2% increase in protocol recovery from topology failure compared with AODV routing. In addition, their approach achieved 15.9% decrease in energy consumption compared with AODV, 13.7% compared with DSR, and 5% compared with ZTR protocols. Apart from the few related works mentioned above and in the literature in this paper, we propose an autonomous self-aware and adaptive fault-tolerant routing technique for wireless sensor networks. We integrate the autonomic self-aware and adaptive mechanism for route formation and repair in the presence of transient and permanent node failure rates in order to achieve high routing performance, energy efficiency and resiliency against errors and failure of sensor nodes, and adapt to simultaneous network topology changes.
# Botnet Detection Technology Based on DNS
## 3 Botnet Detection Technology
### DGA-Based Botnet Detection
intended to show how heuristics applied to large-scale proxy logs, considering a typical phase in the life cycle of botnets, such as the search for C and C servers through algorithmically-generated domains, may provide effective and extremely rapid results. [PERSON] et al. proposed a DGA detecting procedure from DNS query data [44]. The way it works is to identify the client IP address with an unusual distribution of second-level string length in the domain that they query. [PERSON] et al. propose a technology for detecting randomly-generated irreversible domains [45]. Their theoretical basis is that most of the random DGA-generated domains that a bot queries would result in NXDomain responses and that bots from the same botnet (generated by the same DGA algorithm) would produce similar NXDomain traffic. They use a combination method of clustering and classification algorithms. The clustering algorithm groups domains based on the similarity in the structure of domain names as the same as the groups of machines that queried these domains. The classification algorithm is designed to assign the generated clusters to models of known DGAs. If a clusters cannot be assigned to a known model, this indicates a new DGA variant or family model is to be generated. Their work concentrated on detecting DGAs, one component of financial botnets. Recently, [PERSON] et al. proposed a DGA-based botnet-detecting scheme. This scheme is designated as DBod, which relies on an analysis of the behavior of the query of the DNS traffic [32]. The scheme makes use of the fact that hosts infected by the same DGA-based malware query the same sets of domains in the domain list. Most of these queries fail because a very finite number of the domains are, in fact, associated with an active C and C server. Although a surplus of present studies on identifying DGA-generated domains in DNS traffic, coping with such threats still depends on detecting the DNS behavior of every single device. However, in large-scale networks with complicated DNS infrastructure features, we still cannot find the method, or sufficient resources, to exhaustively investigate each part of the network, which is necessary to identify each device infected in a short time. Therefore, there is a great interest to evaluate the species distribution of DGA-based bots inside the networks and to prioritize the remediation efforts [46]. The authors presented a tool, namely BOTMETER, to evaluate the amounts of DGA-based bot species over large networks using only DNS traffic observable at upstream DNS servers.
# The Application of Augmented Reality in the Automotive Industry: A Systematic Literature Review
## 2 Research Methodology  
### Paper Selection  
To find relevant literature sources, five well-known online research databases related to technology were used: ACM digital library, IEEE Xplore, ScienceDirect, Scopus and ISI Web of Science,  
Figure 1: Adapted Milgram’s reality-virtuality continuum for the automotive industry.  
Two consecutive searches were carried out in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) [19]. PRISMA is the most common reporting guideline for systematic reviews, helping authors to improve the reporting of reviews and meta-analysis. The primary search occurred on 15 April 2019 and the second one on 28 January 2020. AR papers were selected and approved based on the following criteria:  
* search terms: \"augmented reality\" (\"AR\") AND \"automotive\" (\"automotive industry\");
* publication year: no limit was imposed;
* publication language: only English;
* publication type: only research and review articles published in journals.  
The search focused only on works that rely specifically on AR solutions in the automotive industry. The papers published in another language than English and other forms of publication (e.g., articles published in conference proceedings, book chapters, newspaper articles, etc.) were not included. The second consideration was taken into account because journal papers are subject to review and thus have a higher degree of research relevance. Figure 2 shows the PRISMA flow chart and the search results.  
The search produced a total of 1128 results extracted from the aforementioned library databases. After removing the duplicate papers (56 papers), 1072 publications were analyzed in order to identify the papers that study the application of AR in automotive industry. After the analysis, 112 papers resulted, which were carefully reviewed to meet the selection criteria. Three papers could not be accessed as full text and they were rejected. A final total of 55 articles were selected. Only a few exceptions were made regarding 4 conference papers, which were preserved because they have a high degree of visibility due their presentation at a premier AR conference (e.g., ISMAR) and their subject being of high interest for the present study.  
Figure 2(a) presents a chart with the number of papers according to the database where they were found. The highest number of articles that matched the search criteria were found in Scopus (31.91%), while from ACM only one article (1.1%) was kept after PRISMA analysis. From these articles, some of  
Figure 2: PRISMA flow chart and search results.  
them were duplicates: 52.73% of them were found in a single database, 25.45% in two databases and 21.82% in three databases (Figure 3b).
# Sub-Diffraction Focusing Using Metamaterial-Based Terahertz Super-Oscillatory Lens
## 3 Results and Discussion
Although our lens is designed for 0.1 THz, a typical oscillator does not emit a single frequency, but rather a certain range of frequencies. Thus, the effect of different incident frequencies was also investigated. Figure 9a,b show the intensity distribution of 0.097 THz and 0.103 THz beams, respectively, after they pass through the lens, as calculated by the finite element method. The distance between focal spots is 7.5 mm in Figure 9a and 8.35 mm in Figure 9b. Figure 9c,d show the experimental intensity distribution of 0.097 THz and 0.103 THz beams, and they agree with the calculation results of Figure 9a,b. Figure 9e shows the frequency dependence of the interval between focal points. The black line represents the calculated results using the Talbot principle while the red squares show the calculated results by the finite element method. The green circle shows the experimental results. This graph shows good agreement between calculations by the finite element method, experimental results, and calculations by the Talbot principle. This characteristic makes it possible to use the SOL for focusing incident THz beams with different frequencies and consequently performing measurements with variable focal lengths.
## 7 Experimental Results  
An experimental setup has been built to evaluate both the performance and behavior of the different lookup algorithms and the communication architecture for control purposes. To achieve this objective, a server with a hypervisor (VMware ESXi) (VMware Inc, Palo Alto, CA, USA) has been used to run 25 virtual machines. Each VM runs Ubuntu 14.04. The VMs are connected to the LAN through a virtual switch limited to 100 Mbps.  
The resources of each VM have been limited to 512 MB of RAM and 200 MHz of CPU. This configuration agrees with the one of the embedded systems that we have initially chosen to migrate from virtual to physical machines, so the experimental results that are presented are realistic and valuable to preliminarily validate the proposed solutions. The physical platform that we will finally use is based on the UDOO X86 (UDOO, Burlington, MA, USA), whose computational features are higher than the initially pre-defined for the virtual machines (2 GB of RAM and CPU working at 2 GHz). Therefore, a superior performance can be expected by means of this platform.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Definition** \\ \hline P\({}_{\text{PVn}}\) & Power supplied by the photovoltaics (PV) arrays \\ P\({}_{\text{PV}}\) & PV power generated by the DC MG \\ P\({}_{\text{DCLoad}}\) & Overall power consumed by the DC loads \\ P\({}_{\text{Grid}}\) & Power injected from the HY MG to the grid \\ P\({}_{\text{ILC\_AC}}\) & Power injected from the DC bus to the AC bus by the InterLinked \\ P\({}_{\text{ILC\_DC}}\) & Converter (ILC), measured at the AC side of the ILC \\ P\({}_{\text{ILC\_DC}}\) & Power injected from the DC bus to the AC bus by the ILC, measured at the DC side of the ILC \\ P\({}_{\text{ESS}}\) & Battery bank charge power seen from the DC bus \\ P\({}_{\text{ACLoad}}\) & Overall power consumed by the AC loads \\ P\({}_{\text{AC\_DGs}}\) & Power supplied by the AC DGs \\ V\({}_{\text{Grid}}\) & RMS value of the grid voltage \\ V\({}_{\text{DC}}\) & DC bus voltage \\ \hline \hline \end{tabular}
\end{table}
Table 2: Nomenclature.  
Figure 11: Conceptual scheme of the experimental hybrid microgrid with eight nodes.
# A Wideband Power Amplifier in 65 nm CMOS Covering 25.8 GHz-36.9 GHz by Staggering Tuned MCRs
## 4 Results and Discussion
In Figure 28b, the power amplifier achieves a flat \(P_{sat}\) of 17.08-17.87 dBm, a flat \(OP_{1 dB}\) of 13.22-13.88 dBm, and a \(PAE_{max}\) of 20.72-24.7%, between 26 GHz and 36 GHz. These results indicate that the broadband characteristics of the designed power amplifier are relatively stable. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **TCAS-II 2021 [38]** & **TCAS-II 2021 [23]** & **MWCL 2022 [29]** & **ISSCC2017 [39]** & **This Work** \\ \hline Technology & 130 nm SiGe & 28 nm CMOS & 28 nm FD-SOI & 40 nm CMOS & 65 nm CMOS \\ Frequency (GHz) & 35 & 24/26/28/30 & 30 & 27 & 31/32 \\ Peak gain (dB) & 25.3 & 21.2 & 19 & 22.4 & 25.87 [EMAIL_ADDRESS] GHz \\
3 dB S21 BW (GHz) & 11 & 21.8–30 & 26–39.8 & 26–32 & 25.8-36.9 \\ \(P_{sat}\) (dBm) & 22.8 & 19.7/20.3/20/20 & 17.1 & 15.1 & 17.84/17.81 \\ \(1\) dB \(P_{sat}\) (dBm) & 7 & N/A & 12 & N/A & 10 \\ \(OP_{1 dB}\) (dBm) & 22.6 & 18.2/18.2/17.8/17.2 & 16.1 & 13.7 & 14.09/14.04 \\ \(1\) dB \(OP_{1 dB}\) BW (GHz) & N/A & N/A & 13 & 7 & 10 \\ \(PAE_{max}\) (\%) & 27 & 34.5/33.1/30/30.3 & 25.3 & 33.7 & 24.37\% \\ VDD (V) & 3.3 & 2.2 & N/A & 1.1 & 1.1 \\ Area (mm\({}^{2}\)) & 0.48 & 0.189 & 0.162 & 0.225 & 1.326 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance comparison with the state of the art. Figure 24: **(a)** The layout of the designed wideband power amplifier and (**b**) the post-simulated S-parameters of the designed power amplifier. Figure 25: **(a)** The simulated S12 of the designed power amplifier and (**b**) Pout, PAE, and gain of the designed power amplifier at 28 GHz, 31 GHz, and 32 GHz, respectively.