esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 3 Materials and Methods
### Data Preprocessing
#### 3.2.1 Tokenization  
In natural language processing (NLP), tokenization--the process of breaking text into smaller components--is a crucial first step. Depending on the method, tokens may represent characters, subwords, or words. Among the various tokenization techniques, WordPiece and SentencePiece have become particularly popular for their adaptability to multiple languages and applications [57].  
WordPiece, developed for the BERT architecture, is a subword tokenization method [58; 59]. It constructs a vocabulary from the characters in the text and identifies frequently co-occurring token pairs to merge into new tokens. This iterative process continues until either no additional pairs can be merged or a pre-defined vocabulary size is achieved, maximizing the likelihood of representing the training data efficiently.  
SentencePiece, developed by Google, is another subword tokenization tool. Unlike WordPiece, it applies a data-driven, language-independent strategy by treating the input as a series of characters. SentencePiece supports Byte Pair Encoding (BPE) and Unigram Language Models to produce subword tokens suitable for various NLP applications [57].  
Table 1 summarizes the tokenization methods employed by the pretrained transformer models used in this study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Tokenizer** \\ \hline AraBERT & SentencePiece \\ AraELECTRA & WordPiece \\ ARBERT & WordPiece \\ [PERSON] & WordPiece \\ Arabic-BERT & SentencePiece \\ CAMeLBERT & WordPiece \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tokenization methods for various pretrained transformer models.  
Figure 5: Meter-wise distribution for the training, validation, and test sets.  
For the proposed study, we employed pretrained tokenizers alongside the corresponding models. The code snippet for invoking the tokenizer is shown in Figure 6. The maximum sequence length was set to 60 and padding was enabled.
## 4 WSN Clustering
### Clustering Models Hierarchies  
Clustering simplifies network administration by lowering energy consumption, increasing scalability, and improving load balance, resilience, and data aggregation. Nodes are used to organize clusters. The CH node collects data from member nodes (MN) and adds it to the BS directly or through an intermediary CH. Instead of broadcasting data from the entire cluster sensing nodes, CH communicates only collected data to reduce the number of packets transmitted and also the amount of power used by a network. The data from a CH node is displayed at the BS and made available to end users. The location of the BS can vary; it can be deployed in the field or outside of the network area [56].  
Most of the time, BS is located outside of the sensor nodes at a significant distance from them. A gateway (CH) connects the sensor node to the base station (BS). A multi-level clustering architecture can be used by more than one BS in a network [60]. Many studies have been conducted to improve energy efficiency using various clustering approaches by addressing the challenges of efficient cluster creation, CH selection and re-selection, equitable load distribution, and cluster restructuring. However, only limited techniques were being discussed.
# Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System
## 3 Proposed Sensors
### Inter-Satellite Ranging  
Utilizing inter-satellite communication links for ranging has grown increasingly popular in recent years given the rise of mega-constellations, a climbing demand for ground stations, and a growing desire for autonomous satellite operations in the face of threats. ISL ranging was treated early for improving performances to the GPS [54], and could similarly be considered for an LCNS. The ISL range limit is 4500 to 45,000 km [55], and the link separation will not exceed this range in the analysis of Section 4.  
Managing ISLs with multiple satellites has been proposed by numerous authors for an LCNS [18; 19; 56; 57]. Each follows the same ranging signal model structure, typically measuring a distance between neighbouring satellites \(\rho\),  
\[\rho=|\mathbf{r}_{i}-\mathbf{r}_{j}|, \tag{21}\]  
where \(\mathbf{r}_{i}\) and \(\mathbf{r}_{j}\) is the position vector of some satellite indexed by \(i\) or \(j\). The management of the ranging set by multiple neighbouring satellites is described in Section 4.2.  
Similarly, the Doppler signal may be measured to deliver a satellite velocity estimate. This would require the relative velocity \(v\) of the satellite \(j\), leading to the following model,  
\[\dot{\rho}=|\mathbf{v}_{i}-\mathbf{v}_{j}|. \tag{22}\]  
Given that the velocity information is unknown from any other sensor treated in this work, the solution would be highly valuable.  
The range and appropriate position covariance should be transferred between satellites. This transfer is illustrated in Figure 9 and should be communicated through the link. \(P\) denotes the covariance of the position \(\mathbf{r}\) and velocity \(\mathbf{v}\) estimates, and \(t\) is the time information of the transmitted position and range.  
Figure 8: Crater estimation performance alongside average number of visible craters, at varying orbital altitudes, with the ELFO altitude range highlighted.  
Figure 9: Inter-satellite link information transfer between satellites \(i\) and \(j\). The \(t\) indicates the transfer of time information.  
The ranging performance of the inter-satellite links is conservatively set to 6 m and 1 mm/s. This is close to the higher bound of those parameters used in the literature [56; 57]. It should be noted that this technique only provides a relative position and velocity between spacecraft, and is dependent on other techniques to provide a sufficient absolute accuracy. It cannot be used independently but can enhance performance. Performance analysis of this technique is deferred to Section 4.
# A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion
## 3 Deep Learning-Based TKGC Methods
### GAN-Based Methods  
A graph attention network (GAN) [66] is an improved model of GCN, which applies an attention mechanism to impose different weights on nodes in the neighborhood in the aggregation step. Formally, the feature vector of each node in the \(k\)-th layer is computed as:  
\[h_{v}^{(k)}=\sigma(\sum_{u\in N(v)\cup\{v\}}\alpha_{vu}Wh_{u}^{(k-1)}), \tag{8}\]  
in which  
\[\alpha_{vu}=softmax(e_{vu})=\frac{exp(e_{vu})}{\sum_{j\in N(v)}exp(e_{vj})},e_ {vu}=a(Wh_{v}^{(k-1)},Wh_{u}^{(k-1)}), \tag{9}\]  
where \(W\) is a weight matrix; \(\alpha_{vu}\) represents the weight value of node \(u\) for node \(v\); \(\sigma\) indicates the activate function, e.g., ReLU; \(e_{vu}\) indicates the importance of node \(u\)'s feature to node \(v\); and \(a()\) represents the attention mechanism and is a single-layer feed-forward neural network.  
[PERSON] et al. [45] divide the attribute information of entities into inherent attributes and temporal attributes and propose IAGAT for TKGC. IAGAT utilizes GAN to learn the inherent embeddings of entities and relations under the inherent attributes. In addition, a rotation in complex space is used to obtain temporal-evolving embeddings of entities and relations, which are then combined with intrinsic embeddings. Finally, a distance-based scoring function is used to calculate the plausibility of the facts.  
DEGAT [46] is an end-to-end encoder-decoder model. It uses GAN as an encoder to obtain entity features by aggregating features of neighboring entities, and it obtains relation features by capturing multi-hop information in the neighborhood of any given entity. The encoder does not take into account temporal information. In the decoder, a diachronic embedding function is introduced to perceive the dynamic features of entities and learn the changing features of entities over time. Furthermore, a novel model DE-ConvKB is proposed, which combines DE [58] and ConvKB [25] to calculate the plausibility of facts.  
T-GAE [47] simultaneously applies GAN and LSTM to the TKGC task. In essence, the model is an encoder-decoder architecture, where the encoder consists of LSTM and GAN. Specifically, LSTM encodes both timestamps and relations into token sequences to produce relation embeddings with temporal information. GAN considers neighborhood aggregators to learn the features of entities. As for the decoder, the scoring function of ConvKB, a KGC method, is selected to calculate the plausibility of the facts. T-GAE is trained in a step-by-step training paradigm, which means that the encoder (LSTM and GAT) and the decoder (ConvKB) are trained independently.  
Methods in this category have similar characteristics to the GNN-based and GCN-based methods. Since GAN introduces an attention mechanism based on GCN, which enables it to characterize the different importance of different neighbor entities in the aggregation process, in theory, the GAN-based methods can better model the heterogeneity of TKGs.
# Construction Progress and Aviation Flight Test of BDSBAS
## 4 BDSBAS Aviation Flight Test
### Flight Test-III
By comparison, it can be seen that for the 8334 th epoch in Figure 24 where the number of the available augmented GPS satellites decreases to five, it is completely normal in Figure 25, and the number of the available augmented GPS satellites has even exceeded nine. This indicates that the abnormality of the receiver's does not come from BDSBAS but from other environmental factors. For example, the electromagnetic interference from other airborne equipment (e.g., very high frequency (VHF), high frequency (HF), distance measuring equipment (DME), transponder (XPDR), satellite (SAT), etc.). It also suggests that in the design, manufacturing, and aviation navigation applications of SBAS receivers, special attention must be paid to strengthening the detection and protection of the aviation electromagnetic interference.
## 2 Materials and Methods
### Pilot Operational Application  
A pilot period was run in operational production mode from January to August 2022 during which company reports were generated monthly in step with the forest cover loss analyst reports being updated with the latest Sentinel-2 change detection images. These reports incorporate farm location data obtained from UK coffee importers with the aim of detecting and reporting on any forest cover loss happening in the vicinity of the farms as part of their environmental due diligence. The data supplied by the coffee importer were in the form of point locations, recorded by handheld GPS devices. Polygons for the farm extent were not available as the importers had not collected these data.
# Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator
## 2 Background
### Emulated and Virtualized Systems  
Network emulation refers to the use of software to replicate a network environment and nodes using specific files, such as Docker containers, that utilize the resources of a server's host system. On the other hand, network virtualization involves the utilization of virtual devices to replace physical devices in a production network [11]. Two variations of packaged computing environments are commonly used: _containers_ and _Virtual Machines (VMs)_.  
Containers are isolated sets of one or more processes that have limited access to specified resources. They leverage copy-on-write layered file systems and version control, enabling continuous integration and delivery in software development. The SEED Internet Emulator utilizes Docker integration, making it an efficient network-emulation platform. Containers generally outperform VMs in terms of performance and scalability, making them well-suited for application deployment. However, in multi-tenant scenarios, where performance interference may occur, VMs offer enhanced security and root privilege, making them preferable for handling business-critical data [12].
# A Safety Prediction System for Lunar Orbit Rendezvous and Docking Mission
## 3 Human-Machine Collaborative Self-Judgment Framework for Safety of RVD
### Safety Judgment Knowledge Base
#### 3.3.1 The Classification of Safety Levels  
The spacecraft dynamics model is used to generate the simulated trajectory sequence and the deviation of the rendezvous and docking result. The deviation of the rendezvous and docking result includes three displacement deviations and three attitude angle deviations. Assuming that these six random variables obey the normal distribution of different parameters, the six deviations are set to four levels according to the standard deviation of their distribution. We obtain 1500 sets of simulated rendezvous and docking deviation results by using the spacecraft dynamics model to simulate the rendezvous and docking  
Figure 3: The feature points that are detected and matched by using feature clustering when the chaser approaches the target (The picture is mosaic-processed).  
The safety level of the one deviation of the rendezvous and docking result is calculated by the following equation:  
\[l=\left\{\begin{array}{ll}1&\mu-\sigma\leq e\leq\mu+\sigma\\ 2&\mu-2\sigma\leq e<\mu-\sigma,\mu+\sigma<e\leq\mu+2\sigma\\ 3&\mu-3\sigma\leq e<\mu-2\sigma,\mu+2\sigma<e\leq\mu+3\sigma\\ 4&\textit{others}\end{array}\right. \tag{1}\]  
where \(e\) is the deviation, \(\mu\) denotes the mean and \(\sigma\) denotes the standard deviation. The final safety level of the rendezvous and docking is obtained by taking the maximum value of safety levels for the 6 DOF deviations, as follows:  
\[L=\max\left(l_{e_{x}},l_{e_{y}},l_{e_{z}},l_{e_{\varnothing}},l_{e_{\varnothing} },l_{e_{\varphi}}\right). \tag{2}\]  
where \(l_{e_{x}}\), \(l_{e_{y}}\), \(l_{e_{z}}\), \(l_{e_{\varnothing}}\), \(l_{e_{\varnothing}}\) and \(l_{e_{\varphi}}\) denote the safety level for x, y and z, and \(e_{\varnothing}\), \(e_{\theta}\) and \(e_{\varphi}\) deviations, respectively.
# Challenges Associated with Implementing 5G in Manufacturing
## 7 Security in the Wireless Factory of the Future  
One of the initial concerns that companies have about deploying a 5G network to a site that will have connectivity to thousands of devices is security, because 5G connected devices will dramatically expand the potential network-attack surface, Figure 6. A smart factory's system will include countless pieces of equipment and devices that are connected to a single organisational network. Vulnerilities in any one of those devices can create a gap in the network that could open the system up to security threats. From a factory viewpoint, OT systems have predominantly been isolated from external networks  
Figure 5: Private 5G infrastructure supporting Industry 4.0.  
for many decades. However, due to the introduction of TCP/IP and other network communication standards within OT environments, the OT elements have become susceptible to cyber-attacks in recent years [52, 53].  
As factories of the future transition increasingly towards wireless infrastructures, both currently available and due for release, they will have to build a comprehensive cybersecurity plan that addresses the threats resulting from IT/OT convergence, such as:  
* Denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks on production systems.
* Man-in-the-middle attack for production data theft due to gaps in the IT structure of the newly adapted manufacturing processes.
* Anomalies based on human behaviour and errors.
* Critical system access to third-party contractors.
* Legacy devices with limited or no security services such as programmable logic controllers (PLCs), remote terminal units (RTUs), supervisory control and data acquisition (SCADA) servers, integrated with fully accessible and network-enabled IoT devices.
* Lack of adequate visibility and cyber-resilient systems. Traditionally OT systems have achieved security by obscurity and this is no longer a guarantee of protection with the advent of wireless connectivity and massive machine-type communication (mMTC).  
Historically, successful transitions into new technologies have always been focused on end goal results with security rarely at the forefront. While 5G has incorporated security into its design a logical approach for smart manufacturing should be to develop a more robust suite of security protocols at the outset [54]. This will allow for an easier adoption by manufacturers and for a more robust end user system that would allow for multiple wireless technologies operating in parallel across a production floor. As giving a level of trustworthiness in the system to meet regulatory requirements will be an issue when systems are deployed in regulated manufacturing environments, a model that demonstrates trustworthiness and reliability will need to be honed for the manufacturing sector.  
Figure 6: The 5G threat landscape.
## 1 Introduction
Studies investigating the antiviral activity of betulin confirmed its action against various common viruses presented in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Virus** & **References** \\ \hline Herpes simplex virus type I (HSV-1-also known as HHV-1) & [23; 24; 25] \\ Herpes simplex virus type II (HSV-2; HHV-2) & [24] \\ Influenza FPV (H7N1) & [23] \\ Enteric cytopathic human orphan virus 6 (ECHO 6) & [23] \\ Epstein–Barr virus (EBV) & [26] \\ Human cytomegalovirustivs (HCMV) & [27] \\ Semlli Forest Virus (SFV) & [28] \\ Vesicular stomatitis virus (VSV) & [29] \\ Encephalomyxeditis virus (EMCV) & [29] \\ Cytopathogenic bovine orphan virus (ECBO also known as BEV) & [30] \\ SARS CoV-2 M\({}^{\text{pro}}\) & [31] \\ Dengue virus (DENV-NS5 RdRp) & [32] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Antiviral properties of betulin. Figure 1: Structure of betulin and betulinic acid. The betulin oxidation product, betulinic acid, was modified by introducing a dimethyl-succinyl group into the C3 position, resulting in a derivative that, under the name bevirimat, qualified for phase II clinical trials as an anti-HIV-1 agent with a new mechanism of action, the so-called virus maturation inhibitor [33]. Compounds containing a phosphorus atom, including phosphonates, possesses antiviral and antimicrobial activity. Among them, there is an important antiviral drug called cidofovir, an anti-HSV-1 and HSV-2 agent. Other phosphorus-containing antivirals are adefovir and pradefovir, which are used in the treatment of hepatitis B virus (HBV); tenofovir, used against HIV-1, 2, and HBV; as well as foscarnet, which is active against herpes virus and HIV [34; 35; 36]. In a previously published paper, the results of studies on the anti-HIV-1 activity of bevirimat analogues containing a phosphorus substituent at the C30 and C29 positions were described [37]. As a continuation of our previous research, we determined the activity of phosphonate derivatives of betulin and betulinic acid against clinically important viruses from the _Picornaviridae_ (BEV, ECBO; cytopathogenic bovine orphan virus), _Rhabdoviridae_ (VSV; vesicular stomatitis virus) and _Herpesviridae_ (HHV-1; herpes simplex virus type I) and _Adenoviridae_ (HAdv-5; human adenovirus type-5) families. By conducting structure-activity studies, an important aspect of the research was also to determine the influence of the phosphonate group and its position relative to the double bond in the isopropyl substituent on the effect of the obtained derivatives. In addition, using in silico methods, an analysis of ADMET (absorption, distribution, metabolism, excretion, and toxicity) parameters and an assessment of the drug similarity of the studied structures were carried out. Moreover, for the compound that showed activity against the HAdv-5 virus, the quantum chemical properties were calculated and molecular docking was performed.
# Transfer Learning for Operator Selection: A Reinforcement Learning Approach
## 4 Experimental Results
### Experimental Settings  
The experimental study reported in this article is conducted to demonstrate that transfer learning helps improve the efficiency of swarm intelligence algorithms in solving combinatorial optimisation problems. For this purpose, three algorithms have been set up: (i) RLABC taken from [4] is the baseline algorithm that solves the problems with ABC embedded with a Q learning-based adaptive operators selection scheme, (ii) RLABC-T extends RLABC with a static transfer learning that switches off the gain experience in upcoming runs, and (iii) RLABC-TC keeps online learning while solving new problems and executes new runs. This means \(\delta=0\) for RLABC-T, while \(\delta>0\) for RLABC-TC.  
The parametric settings for all algorithms have been taken from previous works [3; 4] rolling over the fine-tuned set of parameters accordingly. The configuration applied to all three algorithms includes the following settings: \(\gamma\) is 0.3, the window size \(W\) is 25 iterations, _reward_ is chosen as _extreme_ (\(f_{i,t}\)), \(\epsilon\) is 0.1, and \(\alpha\) is 0.5. The termination criteria is used as the maximum number of iterations, which is determined as the problem size. For the algorithm parameters, the population size is 20 and maximum trial number is 100.
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 1 Introduction
### State-of-the-Art Issues
#### 1.1.2 Overcoming the Locking Phenomena
The hexahedral elements corresponding to three-dimensional elasticity, equipped with independent transverse and longitudinal approximations of the higher order, and assigned for plate and shell analysis, are proposed in [31] and applied in \(hp\)-adaptive version in [32], for example. The recent applications of the higher-order models and approximations to locking removal are presented in the works [33; 34]. These proposals are not consistent with the hierarchical approach. Let us conclude the above survey of the methods of overcoming the locking phenomena in the context of needs of the \(hpq\)-adaptive method for complex structures analysis. Firstly, it should be noticed that only low-order longitudinal approximations and first-order plate and shell models are possible in the cases of the mixed and hybrid methods, the uniform or selective reduced integration, and the consistent field method. In the case of the discrete Kirchhoff constraints, only shear locking can be removed effectively. Additionally, this method of removal leads to large variability of elements. In the case of the assumed strains approach, the claimed generalization of the method for high-order in-plane approximations has not been proved in practice. For the reduced integration and assumed stress method, there are problems with their application to triangular or prismatic elements. In the case of the discrete Kirchhoff constraint and consistent field methods of removal, one deals with different number and location of translational and rotational degrees of freedom. Note that the higher-order elements are free from all these defects. Due to our earlier choices concerning the applied hierarchical models and \(hpq\)-approximations, the displacement formulation of such elements becomes our obvious choice.