esa-sceva/satcom-chunk-collection huggingface dataset:
# Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2 vec and WordNet
###### Abstract  
Words have different meanings (i.e., senses) depending on the context. Disambiguating the correct sense is important and a challenging task for natural language processing. An intuitive way is to select the highest similarity between the context and sense definitions provided by a large lexical database of English, WordNet. In this database, nouns, verbs, adjectives, and adverbs are grouped into sets of cognitive synonyms interlinked through conceptual semantics and lexicon relations. Traditional unsupervised approaches compute similarity by counting overlapping words between the context and sense definitions which must match exactly. Similarity should compute based on how words are related rather than overlapping by representing the context and sense definitions on a vector space model and analyzing distributional semantic relationships among them using latent semantic analysis (LSA). When a corpus of text becomes more massive, LSA consumes much more memory and is not flexible to train a huge corpus of text. A word-embedding approach has an advantage in this issue. Word2 vec is a popular word-embedding approach that represents words on a fix-sized vector space model through either the skip-gram or continuous bag-of-words (CBOW) model. Word2 vec is also effectively capturing semantic and syntactic word similarities from a huge corpus of text better than LSA. Our method used Word2 vec to construct a context sentence vector, and sense definition vectors then give each word sense a score using cosine similarity to compute the similarity between those sentence vectors. The sense definition also expanded with sense relations retrieved from WordNet. If the score is not higher than a specific threshold, the score will be combined with the probability of that sense distribution learned from a large sense-tagged corpus, SEMCOR. The possible answer senses can be obtained from high scores. Our method shows that the result (50.9% or 48.7% without the probability of sense distribution) is higher than the baselines (i.e., original, simplified, adapted and LSA Lesk) and outperforms many unsupervised systems participating in the SENSEVAL-3 English lexical sample task.  
Keywords:natural language processing; word sense disambiguation; word embedding; Word2 vec; WordNet +
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## 2 Methodology
### NLME Model  
NLME models are statistical models used to analyze data that exhibit both fixed and random effects in non-linear relationships [27]. These models find applications in various fields, including pharmacology, growth analysis, and ecology. They capture overall trends using fixed effects while accounting for individual variations with random effects. NLME models are particularly relevant to this study because durability tests are inherently subject to cumulative uncertainties arising from manufacturing processes, measurement techniques, and the operating environment, all of which can influence voltage decay over time.  
A general NLME model for the \(j\)-th response on the \(i\)-th individual test item can be defined as  
\[y_{ij}=f(\beta_{ij},\mathbf{v}_{ij})+e_{ij},\quad i=1,\ldots,M,\quad j=1,\ldots,n_{i} \tag{5}\]  
where \(\beta_{ij}=A_{ij}\theta+B_{ij}b_{i}\)  
where \(y_{ij}\) is the \(j\)-th response for the \(i\)-th individual. Additionally, \(f(\cdot)\) is a non-linear function of \(\mathbf{v}_{ij}\) and, in this study, corresponds to Equation (4). \(\beta_{ij}\) is modeled using a linear mixed-effects model, where \(\theta\) and \(b_{i}\) are vectors of fixed and random effects associated with individual cell \(i\), respectively. Specifically, it includes the parameters \(\phi_{1},\phi_{2},\gamma_{1}\), and \(\gamma_{2}\) from Equation (4). \(\mathbf{v}_{ij}\) is the covariate vector with the temperature \(T\) and measurement time \(t\), whereas \(e_{ij}\) is a normally distributed random error term.  
In this study, the parameter estimation was performed using the \(nlme\) function in R [28]. For simplicity and to avoid overfitting, the random effects \(b_{i}\) were assumed to be independent, implying zero covariance between them. This assumption also contributes to computational efficiency.
# Research on Lazy Theta+
## Route Planning Modeling
### Threat Modeling
#### 3.2.3 Fire Threat  
Fire threats include surface-to-air missiles and anti-aircraft artillery threats [33].  
* Surface-to-air missile threat model The threat range of surface-to-air missiles can be approximated as a drum, with its horizontal section being a sector (Figure 7). Since the missile launch direction is uncertain, the horizontal section can be approximated as a circle. Due to the missile's maneuverability,  
Figure 6: Horizontal section view of the radar’s detection threat range.  
the target within the shortest distance cannot be attacked, and so the cross-section of the missile threat range can be simplified as a ring.  
In Figure 7, \(\overline{AB}\) is the highest limit of the strike area and the maximum height is \({h_{m}}^{\max}\), \(\overline{DC}\) is the lowest limit of the strike area and the lowest height is \({h_{m}}^{\min}\), and \(\overline{BC}\) is the farthest limit of the strike area and the farthest distance is \({R_{m}}^{\max}\). \(\overline{AED}\) is the closest limit of the strike area and the closest distance is \({R_{m}}^{\min}\).  
The aircraft's flying altitude is typically lower than the striking height of the surface-to-air missile. Therefore, the influence of the height on the threat is ignored, with the corresponding overall missile threat probability being formulated as:  
\[P_{m}=\begin{cases}0&d_{m}\leq R_{m}{}^{\min}\\ \frac{4\left({R_{m}}^{\max}-{d_{m}}\right)\left({d_{m}}-{R_{m}}^{\min}\right) ^{2}}{\left({R_{m}}^{\max}-{R_{m}}^{\min}\right)^{2}}&{R_{m}}^{\min}\leq d_{m }\leq{R_{m}}^{\max}\\ 0&d_{m}\geq{R_{m}}^{\max}\end{cases} \tag{15}\]  
where \(d_{m}\) is the slant distance between the aircraft and the launch point of the missile, and \({R_{m}}^{\min}\) and \({R_{m}}^{\max}\) are the shortest and the farthest distances the missile can strike, respectively.  
* Anti-aircraft gun threat model  
The threat range of the anti-aircraft gun is approximately a sphere, with its horizontal section depicted in Figure 8.  
The center of the sphere is the deployment position of the anti-aircraft gun, and the radius is the effective range. The threat probability of the anti-aircraft gun can be simplified to:  
\[P_{a}=\begin{cases}1&d_{a}<R_{a}\\ \frac{1}{d_{a}}&{\rm R}_{a}\leq d_{a}\leq{R_{a}}^{\max}\\ 0&d_{a}>{R_{a}}^{\max}\end{cases} \tag{16}\]  
Figure 8: Horizontal section of anti-aircraft gun threat range.  
Figure 7: Horizontal section of surface-to-air missile threat range.  
where \(d_{a}\) is the slant distance from the aircraft to the anti-aircraft gun, \(R_{a}\textsuperscript{max}\) is the farthest strike distance of the anti-aircraft gun, and \(R_{a}\) is the effective range.
# Dose Calculation Algorithms for External Radiation Therapy: An Overview for Practitioners
## 3 Dose Calculation Algorithms for Electron Beams  
An electron beam crossing a medium undergoes two main processes: angular deflections and energy loss. Angular deflections may occur during both elastic and inelastic interactions. Energy loss is typically due to bremsstrahlung at high electron energy, whereas low-energy electrons mainly lose their energy through atomic ionization or excitation. The energy loss process is quantified by the mass stopping power, which includes the terms associated with any kind of inelastic interaction.  
As in photon beams, a number of computational algorithms have been applied to electron beams, including the PB model and the MC method [36].  
The core of the PB model for electrons is described in [37; 38; 39]. The multiple scattering of electrons determines the spatial distribution of the absorbed energy of high-energy electron beams. Typically, the transversal dose spread of electrons is smaller than that of a photon beam, owing to the decreased range of electrons [40; 41].  
MC-based electron-beam algorithms represent a subset of those introduced for high-energy photon-beam calculations. Of note, the indications for using MC algorithms against PB approaches are more stringent for electron beams than for photons for essentially two main reasons. First, electron PB models fail to provide sufficient accuracy in sharp inhomogeneities to an even higher degree than for photon beams [42; 43]. Second, for electron beams, MC methods are fast enough to be considered for routine treatment planning since electrons directly ionize the medium and a lower number of simulations are needed to contain the statistical uncertainties.  
Finally, the LBTE approach is another viable option that is directly implemented based on the same equations described in Section 1.4 [44].
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 3 Overview of Network Protocols
### Application Layer
#### 3.1.3 Data Distribution Service  
DDS protocol is a communication standard designed for messaging and data-centric exchange in distributed systems, and the protocol dynamically discovers a new node within a network architecture such as Vehicular Ad Hoc Network (VANET) and Mobile Ad Hoc Network (MANET) [55]. DDS uses the publish-subscribe architecture, allowing publishers to disseminate data to multiple subscribers asynchronously and in a decoupled manner. DDS is characterised by its support for real-time and high-performance communication, achieved using a decentralised architecture with a focus on data-centric principles. In the context of V2I applications, supporting high data rates and low latency requirements, enabling a single node to subscribe and receive information from multiple sources bi-directionally, facilitating effective communication. In addition, DDS is a highly functional middleware that is suitable for a multitude of V2I communication that supports efficient distributed communication. This protocol's inherent features, such as its reliable communication, scalability, interoperability, and flexible configuration of various QoS settings, make it a suitable option for V2I applications. Refer to Figure 5 for DDS architecture.
# The Impact of Cloud Versus Local Infrastructure on Automatic IoT-Driven Hydroponic Systems
## 1 Introduction
Eliminating soil from the plant growing process reduces the risk of soil-borne diseases and pests. The absence of these leads to a decrease in the need to use chemical pesticides [1; 2]. Unlike traditional agriculture, which is seasonal, hydroponics is not dependent on seasonal changes. Hydroponics is a form of controlled and imposed environment agriculture, which allows for a constant supply of products to retail chains [16; 17]. Such agriculture is advantageous in regions where climate and agricultural seasons do not favor continuous delivery. Food production in controlled environments, such as greenhouses or indoor farms, is beneficial for urban areas, where demand is high and supply is limited [1; 2]. Hydroponics replaces soil usage with water. Since water is a valuable resource, hydroponics recirculates nutrient solutions within a closed system, reducing water consumption [1; 2]. Additionally, recycling water and nutrients leads to a reduction in waste [15]. Studies [1; 6] have explored automation and remote monitoring technologies to align with modern technological standards. Monitoring environmental conditions, nutrient delivery, and system maintenance are elements studied by automatic systems dedicated to hydroponics.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### Thermal Analysis  
In the space environment, the main method of heat transfer is via radiation. When a satellite is in orbit around a planetary body, there are three external heat sources that need to be taken into consideration: the direct solar radiation, the albedo, and the thermal emittance from the celestial body. Additionally, the internal heat dissipation of the satellite has to be accounted for.
# Hybrid Wavelet-CNN Fault Diagnosis Method for Ships' Power Systems
## 4 Research Methodology
### Discrete Wavelet Analysis
Moreover, we decided to focus on the results of the reconstructed signals from the detail coefficients of Level 5 (d5) since higher levels add unnecessary complexity, as depicted in Figure 5. Diagnosis using DWT was successful since the harmonic frequencies of the fault were captured. The MATLAB Wavelet Toolbox was used for the implementation of DWT analysis.
## 3 Materials and Methods
### Data Preprocessing  
#### 3.1.1 Tunnel Slicing Along the Tunnel Axis  
Slicing the tunnel along its longitudinal axis represents a critical preprocessing step for tunnel point cloud data. This segmentation facilitates localized analysis and ensures higher accuracy in subsequent processing tasks. The slicing process begins by identifying the longitudinal axis of the tunnel through principal component analysis (PCA), which extracts the principal axis from the 3D point cloud data to define the slicing direction and maintain geometric alignment [31]. Once the principal axis is established, the point cloud is divided into slices of uniform thickness. Let the point cloud coordinates be \(P(x_{i},y_{i},z_{i})_{i=1,\ 2,\...,\ n^{\prime}}\) and the slice thickness denoted as \(w\). With the tunnel axis aligned with the \(Y\)-axis, the number of slices \(nslice\) is calculated using Equation (1).  
\[nslice=floor((y_{\max}-y_{\min})/w) \tag{1}\]  
where \(floor(\cdot)\) denotes the ceiling function. The coordinates of the point cloud within each slice are stored as \(p_{slice}^{k}(k=1,\ 2,\...,\ nslice)\), enabling spatially segmented analysis. Figure 2 illustrates the slicing process for the tunnel point cloud.
## 4 Experiments
### Authentication Results
The experimental results are summarized in Table 4. They show the probability of the true authentication capability of the proposed method compared to the methods: wavelet-based, SVD-based features and the corresponding semi-supervised method. Here, for the wavelet-based method, \(\psi=0.02\) and \(\xi=0\); and for SVD-based method, \(\psi=0.005\) and \(\xi=0\). The column of similar image represents the true authentication capability of the judgment of a similar image, which indicates the robustness of the algorithm. The column of tampering image represents the true authentication capability of tampering image, which indicates the discrimination of the algorithm. Higher values mean better robustness and differentiation. Only our approach selected adaptive thresholds, as other approaches choose a fixed threshold that balances robustness and discrimination.
## 1 Introduction
[16] performed experiments on double-layered fiber-reinforced concrete (FRC) beams and conducted numerical simulations while considering material nonlinearities with ANSYS software. [PERSON] et al. [17] introduced a localized composite structure by combining steel and steel fiber concrete (LCSSFRC), which was evaluated through four-point bending tests. This study assessed the effects of various parameters, such as steel fiber volume fraction, specimen length, and the thickness of the concrete protective layers on both the upper and lower flanges of the compressed steel sections. Additionally, [PERSON], [PERSON] et al. [18] explored the bending performance of deep steel fiber-reinforced concrete beams with varying depths of fiber layers. The results revealed that both load and deflection decreased with reduced fiber layer depth. Incorporating steel fibers into the bottom quarter of the tensile zone achieved equivalent stiffness, ductility, and load-carrying capacity as full cross-sectional fiber integration, saving 20% of the costs. The preceding analysis indicates that the current research on the local inclusion of steel fibers is predominantly focused on reinforced concrete beams and roadbed pavements. With regard to RC arch bridges, the current research is largely concentrated on the ultimate bearing capacity [19; 20; 21], corrosion [22] and reinforcement [23; 24]. However, the mechanical behavior of RC arches following a local incorporation of steel fibers is rarely reported. In particular, the issue of how to introduce steel fibers according to the damaged area of the RC arch remains to be further studied. The objective of this paper is to investigate the bearing performance and damage evolution of an LSFRC arch under static load. Firstly, the application of steel fiber-reinforced concrete in the CDP model is described, and the static properties of SFRC units and LSFRCbeams are compared and analyzed. Subsequently, the RC arch model was constructed using ABAQUS finite element software, and the steel fiber inclusion area in the LSFRC arch was identified through damage identification. The bearing capacity and damage evolution trend of the reinforced concrete arch, steel fiber-reinforced concrete arch, and low-strength fiber-reinforced concrete arch were compared and analyzed. Finally, the impact of varying steel fiber volume ratios and cross-section steel fiber heights on the LSFRC arch was demonstrated.
# Features of Structure and Absorption in the Jet-Launching Region of M87
## 3 Results  
We show the resulting VLBI images in Figures 1 and 2 as contour plots overlapping with pseudo-color images of the same frequency respectively. We list the parameters of the images (e.g., synthesized beam size, peak flux density and root-mean-square noise \(\sigma\)) in Table 1. Some structural features in the jet are apparently seen, e.g., on the 22 GHz image, the jet shrinks locally between 1-2 mas from the center of the image, and the northern limb is interrupted between 5 and 7 mas from the center of the image; on the 43 GHz image, the jet bends southward between 1-2 mas from the center of the image; on the 86 GHz image, the northern limb looks rather ragged, while the shape of its southern counterpart is far better defined; emission at the northeast of the most luminous region which is probably the hint of the counter jet, is seen on images at all three frequencies.  
Figure 1: 22 and 43 GHz contour plots overlapping with pseudo-color images of the same frequency respectively, with the synthesized beam shown at the top-left corner of each panel. Panel (**a**) shows the image of M87 at 22 GHz with a synthesized beam of 0.93 \(\times\) 0.45 mas at a position angle of 12.6\({}^{\circ}\); contours in this image are from (\(-\)1, 1, 2, 4, 8...)\(\times\) 0.86 mJy beam\({}^{-1}\). Panel (**b**) shows the image of M87 at 43 GHz with a synthesized beam of 0.56 \(\times\) 0.23 mas at a position angle of 16.7\({}^{\circ}\); contours in this image are from (\(-\)1, 1, 2, 4, 8...) \(\times\) 0.54 mJy beam\({}^{-1}\).  
For the further analysis, we fit the most luminous region on the images as an elliptical Gaussian with procedure MODELFIT in DIFMAP, and refer it as \"core\" hereafter. We list the fitting results of the parameters in Table 2 with their uncertainty estimated as in [13]. We also estimate the brightness temperature of the core as in [14]. The brightness temperature of the core is estimated to be at the magnitude of \(10^{10}\) K for all three frequencies. This is the same as given in [9; 10], which is nearly one order of magnitude lower than the equipartition brightness temperature.
## 3 Numerical Validations of 3D Embedded Flaws Simulations
### Shear Waves  
#### 3.2.1 Immersion Pulse Echo Mode: S45\({}^{\circ}\) Waves--Various Incidences on the Flaw  
Figure 6a describes the first validation case in the pulse echo mode involving defects of various heights, a fixed 0.5 mm extension and of an arbitrary tilt angle \(\alpha\). A 12.7 mm diameter immersed transducer positioned 20 mm above the component generates transversal 45\({}^{\circ}\) waves at 5 MHz.  
The probe is not very divergent, and the flaw is in the area of the maximal field amplitude (33-40 mm depths). The flaw extension is small (0.5 mm extent), which will enable us to study the 3D effects of the flaw size in configurations unfavourable to analytical models such as PTD.  
In Figure 7, the 3D FEM and PTD models predict the flaw echo amplitude versus the tilt angle \(\alpha\) for three different flaw heights: 5, 1 and 0.5 mm (ka \(\sim\) 2.4 both in height and extension in the latter case). An improvement consisting in a smoothing of PTD coefficients near critical angles has been devised and described in [26]: it is referred to as \"PTD3D critical smoothing\". The results referred to as \"PTD3D\" were obtained without this treatment, using a GTD code based on previous works [56]. Its response exhibits peaks around critical angles.  
For specular reflection, PTD generally leads to a relatively good approximation and to simulated results close to that of FEM even for small heights. Nonetheless, for the smallest height (0.5 mm), the PTD response versus the tilt angle exhibits two local maxima, contrary to FEM. This suggests that some phenomena are better predicted by the numerical model than by PTD, possibly due to interferences between the waves reflected from the flaw surface and those diffracted from the contour.  
The difference between PTD and FEM can be significant away from the specular direction, especially beyond the critical angle. This is due to head waves not being accounted  
Figure 6: (a) pulse echo configuration with the SV45\({}^{\circ}\) wave at 5 MHz of a rectangular defect of varying height (5 mm here—red segment), 0.5 mm extent and various tilts; (b) \(\alpha=-45^{\circ}\), specular reflection configuration; (c) \(\alpha=0^{\circ}\), classical configuration of a vertical flaw.  
Figure 7: A planar component containing an embedded rectangular flaw of 0.5 mm extent and of height: (a) 5, (b) 1 and (c) 0.5 mm. Comparison of FEM and PTD (standard or with smoothing around the critical angle) models.  
for in the PTD model presented here. Consequently, the 3D extension of the flaw can be assumed to be a source of inaccuracies for the PTD model, possibly due to quantitative errors in the PTD prediction of corner diffractions.  
PTD differs most significantly from the FEM reference--for specular reflection when the flaw height or extent is small--near and above the critical angle for S waves (head waves) and small heights.
# Rapidly Deployable IoT Architecture with Data Security: Implementation and Experimental Evaluation
###### Abstract  
Internet of Things (IoT) has brought about a new horizon in the field of pervasive computing and integration of heterogeneous objects connected to the network. The broad nature of its applications requires a modular architecture that can be rapidly deployed. Alongside the increasing significance of data security, much research has focused on simulation-based encryption algorithms. Currently, there is a gap in the literature on identifying the effect of encryption algorithms on timing and energy consumption in IoT applications. This research addresses this gap by presenting the design, implementation, and practical evaluation of a rapidly deployable IoT architecture with embedded data security. Utilizing open-source off-the-shelf components and widely accepted encryption algorithms, this research presents a comparative study of Advanced Encryption Standards (AES) with and without hardware accelerators and an eXtended Tiny Encryption Algorithm (XTEA) to analyze the performance in memory, energy, and execution time. Experimental results from implementation in multiple IoT applications has shown that utilizing the AES algorithm with a hardware accelerator utilizes the least amount of energy and is ideal where timing is a major constraint, whereas the XTEA algorithm is ideal for resource constrained microcontrollers. Additionally, software implementation of AES on 8-bit PIC architecture required 6.36x more program memory than XTEA.  
Keywords:block ciphers; edge computing; IoT security; modular architecture; AES; XTEA +
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