esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
Recent explorations into topic modeling have showcased its utility across various English corpora, affirming the effectiveness of methodologies such as NMF and LDA in distilling topics from extensive document collections [4; 7]. The emergence of Bidirectional Encoder Representations from Transformers (BERT), a transformer model introduced by [PERSON] et al. [11], marks a significant advancement, broadening the scope of NLP tasks, including topic modeling.  
A study by [PERSON], [PERSON], and [PERSON] [12] explored the effectiveness of LDA, NMF, and BERTopic algorithms in identifying themes related to system thinking. Their research demonstrated the potential of these methods in extracting coherent and relevant topics from complex data sets. Additionally, it contributes significantly to the field by comparing traditional and advanced topic modeling techniques, underscoring practical implications for enhancing thematic analysis across various disciplines.  
In 2022, [PERSON] introduced BERTopic [9], which uses a mix of document analysis and a special approach to identify topics better than previous methods. Through testing with English news articles and comparing it to LDA, NMF, CTM, and Top2 Vec, their study proved BERTopic's ability to create clear topics.  
While these studies primarily focus on English, their insights are valuable for similar investigations in other languages, such as Arabic.  
The introduction of AraBERT by [PERSON], [PERSON], and [PERSON] [13] has significantly advanced Arabic NLP, demonstrating its superior performance in tasks such as sentiment analysis and named entity recognition. However, the application of AraBERT in topic modeling has yet to be fully explored, indicating a promising area for further research.  
[PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [14] examined the efficacy of topic modeling algorithms, specifically LDA and Dirichlet Multinomial Mixture (DMM), in processing Arabic texts. Their study focused on the impacts of various Arabic stemming processes on the efficiency of these algorithms. Through a comparative analysis of root-based, stem-based, and statistical approaches to stemming and evaluating the performance of models on documents processed with four different stemmers, their research shed light on the significant influence that stemming can have on the accuracy and coherence of the topics generated. Among the stemmers evaluated, the Farasa stemmer was highlighted for its effectiveness, which is ascribed to its advanced approach to navigating the morphological complexity of the Arabic language.  
Despite individual investigations into LDA, NMF, and BERTopic for Arabic NLP tasks, there is a noticeable lack of comparative analyses focused on these methods within the context of Arabic topic modeling. The following section highlights recent comparative studies in this area.
# A Review on Reconfigurable Liquid Dielectric Antennas
###### Abstract  
The advancements in wireless communication impose a growing range of demands on the antennas performance, requiring multiple functionalities to be present in a single device. To satisfy these different application needs within a limited space, reconfigurable antennas are often used which are able to switch between a number of states, providing multiple functions using a single antenna. Electronic switching components, such as PIN diodes, radio-frequency micromechanical systems (RF-MEMS), and varactors, are typically used to achieve antenna reconfiguration. However, some of these approaches have certain limitations, such as narrow bandwidth, complex biasing circuitry, and high activation voltages. In recent years, an alternative approach using liquid dielectric materials for antenna reconfiguration has drawn significant attention. The intrinsic conformability of liquid dielectric materials allows us to realize antennas with desired reconfigurations with different physical constraints while maintaining high radiation efficiency. The purpose of this review is to summarize different approaches proposed in the literature for the liquid dielectric reconfigurable antennas. It facilitates the understanding of the advantages and limitations of this technology, and it helps to draw general design principals for the development of reconfigurable antennas in this category.  
reconfigurable antennas; frequency tuning; radiation pattern; liquid dielectric; water +
Footnote †: journal: materials  
_Article_  
A Review on Reconfigurable Liquid Dielectric Antennas
###### Abstract
[12] addresses the problem of the relative orientation of the object and the primary field direction, another layer of complexity is set by possible variation of the distance between the object and the primary field source (lift-off, marked with a red arrow in Figure 1). In this paper, we identify and discuss issues related to variable lift-off and suggest possible solutions to this problem based on the combination of the different magnetometer functionalities. The complexity of material identification introduced by the variable distance between the primary field source and object is not only limited to the change in field strength. In more general terms, it amounts to a problem of how the amplitude and spatial distribution of the primary field impact the outcome of the measurement. We are going to identify different aspects of this issue in the first part of the paper and point out practical implications. The second part of the paper explores three possible solutions, that address the issue of variable lift-off, (1) monitoring of the object's signature phase, (2) frequency dependence of the angular scans, and (3) implementation of more than one source of the primary rf field. The first concept is based on the immunity of the signal phase to lift-off change, while others rely on the evaluation of the lift-off value from the measurement's output. Our aim is not to provide specific measurement procedures but to point out the observables that contain the essential information for object composition identification in complex test scenarios, i.e., ones that include variable relative orientation between object and the sensor and variable lift-off. In most cases discussed in this paper, we concentrate on 'local measurement', which we define as an inductive measurement performed at a single location, e.g., the centre of the object of interest. The signal readout at a single position could be performed relatively quickly, between tens of milliseconds and a couple of seconds, which allows on-line operation of the detection system (e.g., integrated into a continuous process such as a conveyor belt). As shown in ref. [12], the information regarding object composition could be also acquired by recording 2D (XY) inductive amplitude and phase images. This would be relevant to off-line inspection triggered by the positive result of an on-line test.
# State of the Art of Telecommunication Systems in Isolated and Constrained Areas
## 3 Comparison of the Solutions
### Criteria for Comparing Solutions
#### 3.1.1 The Power Supply of the Telecommunication System and the Autonomy of the System
To implement a telecommunication system in an isolated environment of the UHF type, for example, the powers involved in terms of system consumption (electronic cards) on the transmitter side as on the receiver side are very low. As an indication, the amplifier of a low-speed UHF transmitter using the ZigBee standard [64] requires only 1 W to get ranges of around 40 km with XBee XTEND modules. In the same vein, an Internet of Things system collecting sensor data on-site to transfer it in LoRa modulation [65] to the gateway works at 25 MW [66] in transmit power to reach transmitter-gateway distances of 22 km [67]. We chose the PVs to provide renewable energy in an isolated environment to power a telecommunication system alone [68]. To simplify installation, reduce costs, and mostly avoid ice and snow accumulation on panels, the PVs are often placed vertically, as shown in Figure 11.  
Admittedly, the orientation is not optimal regarding the sun, but it avoids the deposit of frost or the accumulation of snow [69], which drastically reduces the PV return. Telecommunication towers, optical fiber, and flying telecommunication systems allow the use of photovoltaic panels to meet the telecommunication system's needs in electrical energy. For telecommunication towers and exit points or transmitters (relays) for optical fiber, photovoltaic panels are, most of the time, installed vertically. This limits the buildup of frost and prevents snow buildup. However, this solution is not optimal since the photovoltaic panel is not permanently oriented to face the sun. The balloons overcome this drawback: the Stratobus by Thales moves inside a ring that allows it to face the sun permanently. On the other hand, drones of conventional dimensions do not have photovoltaic panels since it is their onboard lithium-ion battery that supplies electrical energy. This is very limiting when you want to deploy a fleet of drones to ensure significant radio coverage. The flight autonomy due to the batteries is, on average, three hours, which practically condemns the use of drones for this type of mission.
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 2 TCS Design and Requirements  
### Definition of Satellite Coordinate System  
The whole satellite structure system adopts a frame structure design using lightweight aluminum alloy and weighs about 22 kg. The in-orbit status dimensions are 600 mm \(\times\) 920 mm \(\times\) 450 mm and the coordinate system is shown in Figure 1. The origin of the satellite coordinate system O is located in the geometric center of the bottom surface of the +X panel. Axis OZ lies in the bottom surface of the +X panel and it's parallel to the optical axis of the satellite camera. Axis OX is vertical to the bottom surface of the +X panel and axis OY is determined by the Cartesian right-handed coordinate system. The OZY surface overlaps with the docking surface of the satellite and the launch vehicle and the explosive view of the satellite components is shown in Figure 2.  
Figure 1: Luojia 1-01 satellite coordinate system.  
It's clearly seen that the camera is mounted on the \(-\)X panel via a camera holder. The separation device is connected to the +X panel. There are two solar panels on the \(\pm\)Y direction which are stowed on the surface of the satellite frame during the launch phase and shall be deployed by 90\({}^{\circ}\) when the satellite is in-orbit. There is a third solar panel stowed to the surface of the satellite frame in the direction of \(-\)Z. Inside the cabin of the satellite, there are on-board data handling system (OBDH), navigation system, battery, reaction wheel I, reaction wheel II and III, etc. Outside the cabin of the satellite, there are star tracker A, star tracker B, MEMS gyroscope, magnetometer, digital sun sensor, etc.
## 2 Equations of Saturated Soils
### **u-w-p** Formulation  
The **u-w-p** formulation is a complete equation of saturated porous media. It consists of the basic governing equations, shown as follows:  
(1) Mass conservation equation of the pore fluid:  
\[-\
abla^{T}\mathbf{w}=\frac{1}{Q_{b}}p+\alpha\
abla^{T}\mathbf{u} \tag{1}\]  
(2) Dynamic equilibrium equations:  
\[\
abla\mathbf{\sigma}=\rho\ddot{\mathbf{u}}+\rho_{f}\ddot{\mathbf{w}} \tag{2}\]  
\[-\
abla p=n\rho_{f}\ddot{\mathbf{u}}+\rho_{f}\ddot{\mathbf{w}}+k_{f}^{-1}\dot{\mathbf{w}} \tag{3}\]  
(3) Effective stress principle:  
\[\mathbf{\sigma}=\mathbf{\sigma}^{\prime}-\alpha\mathbf{m}p \tag{4}\]  
where the definitions of the symbols used in Equations (1)-(4) are listed in Table 1. Equations (1)-(4) consist of the basic governing equations of saturated porous media.  
Equations (1) and (4) are substituted into Equations (2) and (3) and the **u-w-p** formulation is expressed by displacement and the pore pressure variables can be written as follows:  
\[(\lambda+G)\
abla\
abla\cdot\mathbf{u}+G\
abla^{2}\mathbf{u}-\alpha\
abla p-\rho\ddot{ \mathbf{u}}-\rho_{f}\ddot{\mathbf{w}}=\mathbf{0} \tag{5}\]  
\[\alpha\
abla^{T}\dot{\mathbf{u}}-\
abla^{T}\overline{k}\
abla p+\frac{1}{Q_{b}} \dot{p}-\rho_{f}\overline{k}\
abla^{T}\ddot{\mathbf{u}}-\frac{\rho_{f}}{n}\overline {k}\
abla^{T}\ddot{\mathbf{w}}=\mathbf{0} \tag{6}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Definitions** \\ \hline \(\mathbf{u}\) & Displacement of soil skeleton \\ \(\mathbf{w}\) & Displacement of pore fluid with respect to the soil skeleton \\ \(p\) & Pore pressure \\ \(\rho\) & Density of the solid–fluid mixture \\ \(\rho_{f}\) & Density of the fluid \\ \(n\) & Porosity \\ \(\mathbf{b}\) & Body force acceleration \\ \(k\) & Permeability coefficient of pore fluid \\ \(\overline{k}\) & Dynamic permeability coefficient \\ \(\sigma\) & Total stress of the two-phase media \\ \(\sigma^{{}^{\prime}}\) & Effective stress of the soil skeleton \\ \(\mathbf{\sigma}^{{}^{\prime}}\) & Effective stress of the soil skeleton \\ \(\rm{K_{T}=E/[3(1-2v)]=\lambda+2G/3}\); where, \(\rm{K_{T}}\), \(\rm{K_{S}}\) and \\ \(\rm{K_{f}}\) are the bulk modulus of soil skeleton, soil \\ \(\rm{particles}\) and pore fluid respectively. \\ \(\rm{Q_{b}}\) & Compressibility coefficient of fluid \\ \(\rm{in}\) & For the material of soil, soil particles are nearly incompressible relative to the soil skeleton, thus \\ \(\rm{they}\) have the relations of \(\rm{K_{S}>K_{D}}\), \(\alpha\approx 1\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definitions of symbols.
## 4 Proposed Method
### Performing the Track-Touring Process
#### 4.2.2 Track-Transition Method (TTM)  
The track-transition method (TTM) is composed of three steps: (1) randomly select the original track from the pool of the tracks that were not selected by the TTM as the black box; (2) randomly generate a new track; (3) find the average value of the track for each track, denote this number as lower boundary (_LB_), and denote the track to which this number belongs as _TLB_; (4) find the minimal number of values in the tracks of the maximal value of the track that is not a member of track _TLB_, denote this number as the upper boundary (_UB_), and let the track to which the _UB_ belongs be _TUB_; (5) generate transition rates (_TRs_) for every element of the track; (6) transit the original track to a new track by using Equation (18), while the value in track \(i\) in position \(h\) of the new track (\(VT_{ih}^{N}\)) is constructed. A track that is neither _TLB_ nor _TUB_ is called an unplaced track (_UT_).  
\[VT_{h}^{N}=\ \left\{\begin{array}{l l}VT_{h}^{TLB}&\quad if\ TR\leq\ LB\\ VT_{h}^{TUT}&\quad if\ LB<TR\leq UB\\ VT_{h}^{TUB}&\quad if\ TR>\ UB\end{array}\right. \tag{18}\]  
where \(VT_{h}^{*}\) is the type of track, and * can be a _TLB_, _UP_, or _TUB_ track. For example, if we have Tracks 1 and 2, and a random track as shown in Figure 0(a), then the average value in the positions of Track 1, Track 2, and TR is 0.34, 0.46, and 0.51, respectively, as shown in Figure 0(b). Therefore, _LB_ = 0.34, and _TLB_ is Track 1. The maximal numbers of _VT_ of Track 2 and the random track are 0.83 and 0.97; therefore, _UP_ = 0.83 and _TUB_ = Track 2. As a result, Equation (18) was modified as shown in Equation (19), and the result of the transition is shown in Figure 0(b). The result of the new track is shown in Figure 0(c).  
\[VT_{h}^{N}=\ \left\{\begin{array}{l l}VT_{h}^{1}&\quad if\quad TR\leq\ 0.34\\ VT_{h}^{RT}&\quad if\quad 0.34<TR\leq 0.83\\ VT_{h}^{2}&\quad if\quad\ TR>0.83\end{array}\right. \tag{19}\]  
After the track was generated, the decoding method shown in Section 4.1.1 was performed to find the answer for the proposed problem.
# Application of Machine Learning Techniques to
## 3 Methodology
### Selection of Forcing Scenarios  
Four climate forcing scenarios are commonly used in climate research for climate simulation studies on both long term and short term scales. There are four common Representative Concentration Pathways (RCPs)--RCP 2.6 is a mitigation scenario; RCP 4.5 and RCP 6.0 are scenarios of medium stabilization and RCP 8.5 is a high baseline emissions scenario [75]. We did not include RCP 2.6 for the ensemble of climate models, as it is not considered a realistic or practical scenario necessary to promote adaptation planning. These rest of the three scenarios represent the complete extent of radiative forcing. In this study, we used the RCP 4.5 and RCP 8.5. The same framework applies to other forcing scenarios [22].  
Extraction of Selected GCMs Data for RCP 4.5 and 8.5  
The daily precipitation data for the selected GCM in the specific climate zone for the projected period from 2006-2099, as well as forcing scenarios RCP 4.5 and 8.5, were sampled for all seasons at every grid station.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 4 Intrusion Detection and Prevention Systems
### Machine-Learning-Based Techniques  
Machine learning (ML) algorithms have been successfully applied to AIDS to improve their effectiveness and efficiency [33]. The ability to process large datasets makes machine learning a suitable technique for analyzing network traffic. Several algorithms have been successfully applied for intrusion detection, including decision trees, neural networks [34; 35], nearest neighbors [35], and genetic ones [36]. Although deep learning has made considerable progress in the application of IDS, there is a potential problem of slow detection due to a large amount of traffic and the increased dimensionality of the data. [PERSON] et al. [37] studied the impact of training sample size, feature selection techniques, and hyper-parameter optimization techniques on detection performance.  
ML solutions can generally be classified into supervised learning or unsupervised learning. Supervised algorithms operate with labeled, classified data and can identify the relationship between different variables within the data. Supervised learning typically involves a training phase and a testing phase.  
Supervised learning-based intrusion detection system methods detect intrusions utilizing labeled training data. In supervised learning, data are organized in pairs, mapping a host data source or a network and a connected output's value (i.e., label) that should be in normal modes, intrusion, or precise. Functional features are fitted to the model in the training stage, which discovers the data relationship. Then, the supervised learning algorithms utilize the selected features in training data and find the inherent relationships between the input data and the labeled outcome. These learning algorithms are referred to as classifiers, and they perform the classification task in the detection system. With supervised learning, the labeled outcomes are prearranged and utilized to teach the algorithm how to achieve the necessary outcomes for unobserved data types. In contrast, labels are not specified in unsupervised learning models. Unsupervised learning outlines machine learning techniques that aim to obtain attractive information from input data lacking output labels. The positions of the input data points are processed as an accidental feature set, and a combined density model is then fitted to the dataset. In unsupervised learning models, the data are clustered into a range of output types through procedural learning. In IDS, the unsupervised learning method employs machinery to detect intrusions by applying unlabeled data to train the model. See Figure 2 for cluster classification: intrusion and normal clusters.
# Research on the Influence of Rectifying Orifice Plate on the Airflow Uniformity of Exhaust Hood
## 2 Materials and Methods
### Control Equations and Boundary Conditions  
For the numerical simulation of the 3D model of the exhaust hood, a double precision 3D solver is used in order to improve the calculation accuracy. We employ the conventional \(k\)-\(\varepsilon\) double equation model, as it provides a respectable level of accuracy in engineering simulation computations [21]. In our simulation, while the momentum transfer is considered in the model, the thermal conductivity is neglected, so the energy equation is set to be closed. In order to fully calculate the flow field characteristics, the convergence standard is set to \(10^{-6}\). The boundary at the outlet of the exhaust hood is set as a velocity inlet with a velocity of 13 m/s in the positive \(z\)-axis. The hydraulic diameter is the exhaust hood outlet diameter of 0.2 m. According to the empirical formula, the turbulence intensity can be calculated using the following formula:  
\[I=0.16(Re_{D_{H}})^{-1/8}, \tag{3}\]  
where \(Re_{D_{H}}\) is the Reynolds number of the exhaust hood outlet.  
\[Re_{D_{H}}=\frac{\rho vl}{V}, \tag{4}\]  
where  
\(\rho\)--Air density, kg/m\({}^{3}\) (1.225 kg/m\({}^{3}\)).  
\(v\)--Wind velocity, m/s (13 m/s).  
\(l\)--Hydraulic diameter, m (0.2 m).  
\(V\)--Dynamic viscosity, Pa-s (1.7894 \(\times\) 10\({}^{-5}\) Pa-s).  
The combined calculation of Equations (3) and (4) gives a turbulence intensity of 3.53%. The boundaries surrounding the computational domain are set as outflow to simulate an open laboratory space, while the top and bottom are set as walls to simulate the floor and roof of the laboratory. The model solving parameters and boundary conditions are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Settings** \\ \hline Viscous Model & \(k\,\varepsilon\) \\ Energy & Off \\ \hline \hline \end{tabular}
\end{table}
Table 1: Solving parameters and boundary conditions.
## 4 Results and Analysis
### Evaluation of Hybrid Encryption Algorithm Strength
#### 4.1.4 Differential Cryptanalysis
On the contrary, as observations shift to those that compare distinct ciphertext pairs, it becomes apparent that the density plots for the ciphertext differences were spread wider than their counterparts, rendering such plots clearly to be in the wider range of values. The broad range of ciphertext differences from \(-100\) to \(300\) indicates that the encryption algorithm must be performing notably well. This effective diffusion of differences in the plaintext is entirely guaranteed to enhance security by reducing any patterns or correlations in the ciphertext.
## 2 Proposed Localization System
### Offline Phase
#### 2.1.2 Offline AP Filtering and Training  
In order to refine the collected fingerprint data, AP filtering was carried out. This process is essential for removing noisy and redundant data, enhancing system reliability, and reducing computational workload. The proposed method involves a new AP filtering algorithm that utilizes WiFi sensing probability and an absolute RSS threshold value. This method is demonstrated (in Section 3.2) to be more effective than traditional filtering methods such as MaxMean and Stable AP selection (used in [20; 21], respectively). The MaxMean criterion selects APs based on the average RSS, while Stable AP methods consider the variance of RSS values over time.  
Following the AP filtering, the training process commences with an Adaboost algorithm. Similar AP filtering and Adaboost algorithms are also employed in the online phase, which is detailed in the next subsection.