esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Sampling Campaigns  
A monitoring campaign was carried out from 2016 to 2019, collecting aerosol deposit samples from insulators located on high-voltage lines in the whole of Italy (the specification of the scheduled time of the sampling campaign is shown in Supplementary Materials, Table S1). For this purpose, 65 cap-and-pin glass insulator strings (Figure 1) (producer: Sediver S.p.A.) were installed by Terna S.p.A. at a mean height of 16 m above the ground on 150 kV lattice towers uniformly located in Italy as reported in Figure 2. The 65 insulator strings were deliberately unenergized and installed some meters below the lower phase conductor in order to avoid line outages for contamination sampling on the insulator surfaces. Each insulator string was composed of 10 cap-and-pin insulators.  
From a temporal point of view, the monitoring campaign was divided into 8 sampling campaigns. In this work, for each sampling campaign, one insulator per string was used for the collection of the contamination in its glass surfaces. The first sampling campaign started with the second insulator in the string from the top. In the second sampling campaign, the third insulator in the string was used, and so on up to the eighth sampling campaign in which the ninth insulator was used. The first and the last (the tenth) insulators in the strings were deliberately not used in order to avoid side effects in the contamination collection. Each sampling campaign was performed maintaining three months of time lag between two consecutive sampling campaigns. Thus, the monitoring campaign lasted two years with the aim of collecting seasonal effects as well.  
For each campaign, both the ESDD and the NSDD were determined. The results are discussed in Section 3.1 while, in Section 2.2, the ESDD and NSDD methodological determination is presented.  
Figure 1: Cap-and-pin insulators sting: (**a**) a picture; (**b**) its scheme.  
Figure 2: Location of the 65 cap-and-pin insulator strings throughout the peninsula. Colors indicate the three macroregions into which Italy was divided.  
From a spatial point of view, due to the fact that Italy is constituted by a peninsula and islands and that along the peninsula, the Apennines chain divides Italy from north to south into two parts that face two major seas of the Mediterranean, the Adriatic on the eastern side and the Tyrrhenian-Ionic on the southwestern side site, the authors decided to group the 65 insulator strings into three macroregions: MR1 (islands, Sicily and Sardinia), MR2 (mainly the western peninsula), and MR3 (the eastern peninsula), as shown in Figure 1.
###### Abstract  
This work presents a compact four-port MIMO antenna with each radiator consisting of a conventional two-monopole array fed at a single point by a coplanar line and reactively loaded with a stub. The incorporation of a T-stub-loaded tuning technique significantly improves the radiating element's impedance, leading to deeper port coupling, a broader bandwidth, and an increased electrical length. Consequently, the operating frequency is substantially lower compared to a standalone radiator. By implementing this configuration with two monopoles of different lengths fed at the same end, an ultra-wideband effect is achieved. By placing four of these stub-loaded monopole arrays in an axial symmetric configuration, a MIMO antenna array is formed. The proposed MIMO array operates from 2.89 GHz to 12 GHz, exhibiting a _TARC_ of less than \(-10\) dB, an _ECC_ of less than \(0.002\), an average diversity gain of 9.999, and port isolations are within a threshold from \(-18\) dB to \(-50\) dB over the entire bandwidth. The array's footprint is \(32\times 32\) mm\({}^{2}\), equivalent to \(0.083\lambda_{0}^{2}\) at the lower cutoff frequency.  
bandwidth enhancement; MIMO antenna; reactive load; monopole +
Footnote †: journal: Applied sciences
## 1 Introduction
[16] utilized a machine learning approach to estimate the system reliability of a cable-stayed bridge considering cable damage due to fatigue and corrosion effects. Deep learning is a recently developed ANN approach that is widely used in the fields of graphics and image recognition [17]. A deep belief network (DBN) is a type of deep learning method that is commonly used for probability training [18]. DBN is feasible for approximating the structural mechanical behavior, which is the basis for subsequent reliability evaluation. Compared to traditional AI-based approaches, DBN has the following advantages [19, 20, 21]. First, it utilizes hidden layers which are more efficient compared to traditional multilayer perceptions, such as the back-propagation (BP) neural networks, and thus the training process is time-saving. Second, DBN has a deep architecture like a mammal brain, which is able to capture the depth nonlinearity of a structural complex system, while SVM is a shallow architecture benefiting from the nonlinear kernel function. Finally, DBN is more robust in the classification of various data with large-scale parameters. The second critical problem is how to search for the failure sequence of a system efficiently. In this regard, the most commonly used approach is the \(\beta\)-bound approach that utilizes the reliability index range to determine the components that are likely to fail. [PERSON] and [PERSON] [22] presented a branch-and-bound method together with the finite element (FE) method to update the limit state function. [PERSON] et al. [23] developed a matrix-based system reliability method to replace the failure tree of complex structures. [PERSON] et al. [24] proposed an adaptive SVR approach combined with the advanced \(\beta\)-bound approach to develop the fault tree of a pre-stressed concrete cable-stayed bridge. This study proposes a novel and intelligent approach for system reliability evaluation of cable-supported bridges under stochastic traffic load by utilizing DBNs. Mathematical models for the system reliability of cable-supported bridges were derived taking into consideration structural nonlinearities and high-order statically indeterminate characteristics. A computational framework was developed to exhibit the procedures for system reliability evaluation using DBNs. In a case study, a prototype suspension bridge was selected to investigate the system reliability under stochastic traffic loading considering site-specific traffic monitoring data. Numerical results indicated the feasibility of utilizing DBN as a meta-model to estimate the structural failure probability. The dominant failure mode of the suspension bridge was determined, and the effect of cable degradation due to fatigue-corrosion damage on the system reliability of the suspension bridge was investigated.
# Differences Evaluation of Pavement Roughness Distribution Based on Light Detection and Ranging Data
## 3 Calculation of the Pavement Roughness Based on 3D LiDAR Data
### Correlation Test of the Calculated Results  
To validate the effectiveness of the proposed method for calculating the pavement roughness using the LiDAR data, a correlation test was conducted to compare the calculated results with those obtained using the standard test method. The standard test method, as prescribed in the \"Field Test Methods of Highway Subgrade and Pavement\" (JTG 3450-2019) [28], employs precise leveling instruments or total stations to measure the elevation of the road surface. Therefore, the total station was used to measure the road elevation of the test roads, and the IRI was obtained as the reference value. According to the requirements of the specifications [28], 15 test road sections with lengths of no less than 500 m were chosen for the correlation test. In each test road section, two longitudinal profiles located at distances of 0.5 m and 1.5 m from the left edge of the road were extracted as the survey  
angles in the three directions between the total station coordinate system and the point cloud coordinate system.  
After obtaining the reference value and calculated results of the IRI, the correlation test method in Appendix C of the specification [28] was used for validation. The calculation equations are presented in Formulas (10)-(13):  
\[Y=AX+B \tag{10}\]  
\[A=\frac{n\sum xy-\sum x\Sigma y}{n\sum x^{2}-\left(\sum x\right)^{2}} \tag{11}\]  
\[B=\overline{y}-A\overline{x} \tag{12}\]  
\[R=\frac{\overline{xy}-\overline{xy}}{\sqrt{\left(\overline{x^{2}}-\overline{ x}^{2}\right)\left(\overline{y^{2}}-\overline{y}^{2}\right)}} \tag{13}\]  
where \(Y\) represents the calculated results using the point cloud data, denoted as \(IRI_{calculated}\), \(X\) corresponds to the reference value of the IRI obtained from the total station measurements, denoted as \(IRI_{reference}\), \(A\) is the slope derived from the linear regression formula, \(B\) signifies the corresponding intercept, and \(R\) denotes the correlation coefficient.  
Figure 6 indicates the correlation test results, with a correlation coefficient of R\({}^{2}\) = 0.9690 (>0.95). This high correlation coefficient demonstrates a strong relationship between the IRI results calculated using the point cloud data and those obtained from the total station measurements. Therefore, the method proposed in this study for calculating road roughness using point cloud data is reliable.
# Implementation of MS Circle Map in Digital Image Encryption
## 1 Introduction
A nonlinear relationship between flux and memristor charge was introduced into the 4D hyperchaotic Liu system, combining chaotic dynamics with DNA sequence operations and SHA-256 for encryption [20]. An image encryption algorithm was developed by integrating 2D compressive sensing with a 6D hyperchaotic system-based index sequence, enabling the encryption of two different color images simultaneously [21]. To address performance limitations, an image encryption technique using compressive sensing (CS) was proposed [22]. The wavelet packet transform (WPT) matrix was generated from the Xorshift algorithm, with confusion performed using 2D Cat-Throp mapping, followed by sparse coefficient matrix processing and hybrid bidirectional diffusion. The encrypted data were then embedded into a carrier image selected from a publicly accessible database, utilizing HT embedding in a newly introduced YCbCr orthogonal color space [22]. More recently, a novel 2D Log-Logistic-Sine Chaotic Map (2D-LLSCM) was introduced. By combining a chaotic matrix with a nonlinear log-polar transformation, this approach achieved enhanced image scrambling and diffusion, further improving encryption security [23]. In this study, we propose a new chaotic function called MS Circle Map and develop a novel digital image encryption and decryption algorithm that integrates the chaotic dynamics of the MS Map and Circle Map. Additionally, we implement a software prototype for encryption and decryption on a Windows operating system, demonstrating high resistance to brute-force, statistical, and differential attacks. The rest of this paper is organized as follows: Section 2 describes the proposed MS Circle Map-based image encryption and decryption algorithm, along with its mathematical foundations and software implementation. Section 3 presents the experimental setup, simulation results, and performance evaluation. Finally, Section 4 provides the conclusion and discusses potential future research directions. The key contribution of this study lies in the development of the MS Circle Map as a new chaotic function for keystream generation, ensuring strong randomness and security. Based on this function, we propose an image encryption and decryption algorithm designed to enhance security and computational efficiency. Furthermore, we implement a software prototype on the Windows operating system to demonstrate the practicality of the proposed method, showcasing its resistance to brute force, statistical, and differential attacks. These contributions reinforce the significance of chaos-based encryption in securing digital images against modern cryptographic threats.
# Impact of Sentence Representation Matching in Neural Machine Translation
## 5 Results and Discussions
### Impact Analysis
#### 5.1.4 Performance of Best Matching Layer
\begin{table}
\begin{tabular}{|c|c|c c c c c c|} \hline \hline \multirow{2}{*}{**structure**} & \multirow{2}{*}{**capacity**} & \multirow{2}{*}{**distance**} & \multicolumn{2}{c|}{**valid**} & \multicolumn{2}{c|}{**test1**} & \multicolumn{2}{c|}{**test2**} \\ \cline{3-8}  & & & prec & BLEU & prec & BLEU & prec & BLEU \\ \hline s1 & 50 & cosine & 19.97 & 28.15 & 19.44 & 24.47 & 16.19 & 26.79 \\ t1 & 50 & cosine & 17.84 & 22.00 & 16.57 & 17.12 & 13.66 & 16.50 \\ s2 & 10 & cosine & 19.98 & 28.09 & 19.29 & 24.03 & 16.16 & 24.57 \\ s2 & 50 & cosine & 19.88 & 28.16 & 19.29 & 24.55 & 16.12 & 24.57 \\ s2 & 100 & cosine & 19.96 & 28.22 & 19.34 & 24.45 & 16.09 & 24.63 \\ s2 & 250 & cosine & 19.98 & 28.22 & 19.10 & 24.06 & 16.02 & 24.24 \\ s2 & 500 & cosine & 19.74 & 27.92 & 19.01 & 24.13 & 15.68 & 24.39 \\ t2 & 10 & cosine & 17.28 & 22.00 & 16.66 & 17.73 & 13.07 \\ t2 & 50 & cosine & 17.38 & 21.65 & 19.55 & 16.49 & 13.29 & 16.60 \\ t2 & 100 & cosine & 17.40 & 21.14 & 16.26 & 16.03 & 13.42 & 16.38 \\ t2 & 250 & cosine & 19.61 & 27.64 & 18.55 & 23.40 & 15.38 & 23.81 \\ t2 & 500 & cosine & 18.80 & 26.37 & 17.65 & 25.91 & 14.88 & 22.13 \\ b1 & 10 & cosine & 19.86 & 28.09 & 19.29 & 24.36 & 16.04 & 24.57 \\ b1 & 50 & cosine & 19.95 & 28.00 & 19.32 & 24.20 & 16.26 & 24.55 \\ b1 & 100 & cosine & 19.79 & 27.93 & 18.71 & 24.28 & 15.03 & 24.38 \\ b1 & 250 & cosine & 19.35 & 27.61 & 18.24 & 23.86 & 15.26 & 23.83 \\ b1 & 500 & cosine & 19.37 & 27.63 & 18.25 & 23.57 & 15.39 & 23.63 \\ s2 & 10 & Hamming & 20.16 & 28.31 & 19.65 & 24.56 & 16.22 & 24.37 \\ s2 & 50 & Hamming & 20.12 & 28.21 & 19.66 & 24.26 & 16.10 & 24.60 \\ s2 & 100 & Hamming & 19.80 & 28.16 & 19.32 & 24.51 & 15.99 & 24.58 \\ s2 & 250 & Hamming & 20.00 & 28.81 & 19.62 & 24.53 & 16.04 & 24.47 \\ s2 & 500 & Hamming & 19.80 & 28.31 & 18.63 & 23.95 & 16.07 & 24.63 \\ s2 & 10 & Euclidean & 18.57 & 26.93 & 18.06 & 23.12 & 14.94 & 23.36 \\ s2 & 50 & Euclidean & 18.61 & 27.05 & 18.46 & 23.28 & 12.77 & 23.51 \\ s2 & 100 & Euclidean & 18.46 & 26.46 & 17.92 & 23.20 & 14.77 & 23.00 \\ s2 & 250 & Euclidean & 18.20 & 24.10 & 18.71 & 20.58 & 17.22 & 20.35 \\ s2 & 500 & Euclidean & 19.69 & 27.46 & 19.01 & 23.51 & 15.80 & 23.66 \\ \hline  & & & & & & & & \\ \end{tabular}
\end{table} 
Table 8: Performance changes after sentence representation matching for various language pairs (\(\delta\): performance of models using the matching cost subtracted by original cost). Figure 8: Grid search results of best achievable performance for French to English translation (red: maximum, blue: minimum, white: mean, s1: source-side 1 layer, s2: source-side 2 layers, t1: target-side 1 layer, t2: target-side 2 layers, b1: both-sides 1 layer).
## 3 Results and Discussion
### Mechanical Strength  
The mechanical properties of the PLA-based electrospun textiles were evaluated through tensile strength analysis, as illustrated in Figure 9. The stress-strain curves revealed three distinct phases in the mechanical behavior of the nanofiber garments. Initially, a linear elastic region with a steep slope was observed, followed by a gradual increase in stress levels. The final phase was characterized by rapid failure, resulting in a significant reduction in stress. Samples M1 and M3 exhibited comparable ultimate tensile strengths of  
Figure 8: TGA results of the prepared nanofibrous electrospun cloth: (M1) PLA-Ag-E, (M2) PLA-Ag-S, (M3) PLA-PEG-Ag-E, and (M4) PLA-PEG-Ag-S.  
5.9 and 5.6 MPa, respectively, due to their similar material compositions. However, their elongation percentages differed significantly, with M1 reaching 120% and M3 only 40%. This disparity can be attributed to the post-electrospinning incorporation of AgNPs in M3 through solution casting, which introduced brittleness to the material. Samples M2 and M4, containing hydrophilic PEG-PPG-PEG components, demonstrated lower tensile strengths of 4.8 and 2.3 MPa, respectively. Their elongation percentages were 30% for M2 and 39% for M4. The post-electrospinning treatment applied to M4 resulted in reduced tensile strength compared to M2. The observed decrease in the mechanical properties of the post-treated samples (M3 and M4) correlates with their diminished thermal characteristics. This correlation may be explained by the lower crystallinity of these samples, as evidenced by the XRD analysis. The post-electrospinning treatment likely disrupted the molecular alignment and crystalline structure of the fibers, leading to reduced mechanical and thermal performance. These findings emphasize the need to balance the incorporation of AgNP functional additives with preserving mechanical properties in electrospun textiles. Future research should aim to optimize post-electrospinning treatments to reduce adverse effects on the mechanical and thermal properties while maintaining functionality.
## 3 Recording of Atmospheric Conditions Using a Weather Monitoring Station to Predict RSSI Value
### Recording of Atmospheric Conditions Using Unmanned Aerial Vehicle (ULAV)
The main disadvantages or limitations of our proposed system are the congestion of the system and its computational complexity. If we use more data, the prediction calculation will take much longer than if we use less data. Therefore, it is desirable that only the data necessary for RSSI parameter prediction be selected to select the input amount of data. As a possible solution, it would be possible to use only data from a given period (for example, a day) and not to use data recorded by a weather station from a period of one week, two weeks, a month, etc. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Latitude** & **Longitude** & **Temperature** & **Humidity** &
\begin{tabular}{c} **Particles** \\ **in the Air** \\ **(\(\upmu\)g/m\({}^{3}\))** \\ \end{tabular} & **Icon** \\ \hline
49.1476795377 & 19.2358069399 & 10 & 30 & 129 & 165 \\
49.1480705071 & 19.2363364994 & 7 & 37 & 482 & 175 \\
49.1485925283 & 19.2357429895 & 6 & 35 & 551 & 175 \\
49.1478324859 & 19.2351676248 & 5 & 38 & 506 & 175 \\
49.1478180217 & 19.2342893291 & 4 & 39 & 535 & 175 \\
49.1487775524 & 19.2348703706 & 4 & 43 & 432 & 175 \\
49.1473503714 & 19.2348536109 & 3 & 44 & 432 & 175 \\
49.1489112933 & 19.2352287229 & 2 & 48 & 375 & 175 \\
49.1469179250 & 19.2359150177 & 2 & 49 & 424 & 175 \\
49.1464293509 & 19.2348498906 & 2 & 52 & 420 & 175 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sample data suitable for analysis in a graphical interface. Figure 7: Map of the mapped area in the TUKE locality with recorded values.
## 5 Conclusions
\right.\] \[\overline{E}_{TM}(x,y,z)= \frac{32}{a\ b\ c\ d\ y}\sum\limits_{m,n,p}\frac{1}{k_{m,n,p}^{2} -g_{TE}^{2}}\sin(k_{x}\Delta_{x})\sin(k_{z}\Delta_{z})\left\{M_{eqx}^{i} \frac{k_{y}}{k_{x}\ k_{z}}\sin(k_{x}x_{H})\cos(k_{y}y_{H})\cos(k_{z}z_{H}) \right.\] \[\left.-j\omega\mu\frac{k_{z}^{2}}{k_{m,n,p}^{2}}\sin(k_{y}y_{H}) \left[\frac{J_{eqx}^{i}\sin(k_{x}x_{H})\cos(k_{z}z_{H})}{k_{x}\ k_{z}}\right.\right.\] \[\left.\left.+\frac{J_{eqz}^{i}\cos(k_{x}x_{H})\sin(k_{z}z_{H})}{k_ {x}^{i}}\right]\right\}\left\{ \begin{array}{ll}-\frac{k_{x}k_{z}}{k_{x}^{i} }\cos(k_{x}x)\sin(k_{y}y)\sin(k_{z}z)\hat{x}&\\ -\frac{k_{y}k_{z}^{2}}{k_{x}^{2}}\sin(k_{x}x)\cos(k_{y}y)\sin(k_{z}z)\hat{y}&,\\ \sin(k_{x}x)\sin(k_{y}y)\cos(k_{z}z)\hat{z}&\end{array} \right.\] \[\text{Where }\beta_{TE,TM}^{2} =\beta^{2}[1-1+j]\frac{\omega_{m,m,p}}{\omega Q_{m,n,p}^{TE,TM} }\delta_{k}=\left\{ \begin{array}{ll}1,&k\
eq 0\\ 2,&k=0\end{array} \right.,\ k_{x}=\frac{m\pi}{a},k_{y}=\frac{n\pi}{b},k_{x}= \frac{p\pi}{c}.\]  
\(\overline{f}_{m,n,p}\) and \(\Xi_{m,n,p}^{TE,TM}\) are irrotational and TE, TM divergenceless electric eigenvectors respectively, whereas \(\overline{h}_{m,n,p}^{TE,TM}\) are the TE, TM divergenceless magnetic eigenvectors. \(k_{m,n,p}\), \(\overline{C}_{m,n,p}^{TE,TM}\) and \(\omega_{m,m,p}\) are the corresponding eigenvalues, quality factors, and resonant angular frequencies, respectively, and \(\beta^{2}=\omega^{2}\mu\epsilon\).
## 3 Materials and Methods
### The EduDCM Framework
#### 3.1.4 Optimized Semantic Disambiguation for Multilingual Adaptability  
EduDCM incorporates an optimized semantic disambiguation strategy that leverages multilingual lexical resources and advanced embedding techniques to ensure robust annotation across diverse linguistic contexts. When the cosine similarity between the embedding of an unannotated utterance (\(\vec{u}\)) and the mean embedding of sampled examples (\(\vec{e}_{j}\)) from a category falls below the threshold \(\theta\), additional steps are employed to refine the annotation process. To address cases where semantic alignment fails due to insufficient contextual information, a contextual embedding optimization technique is applied. This approach recalculates the embedding of the target utterance by incorporating surrounding utterances in the dialogue. The optimized embedding is defined as  
\[\vec{u}_{\text{context}}=\alpha\cdot\vec{u}+\frac{1}{n}\sum_{i=1}^{n}\beta_{i }\cdot\vec{u}_{i} \tag{3}\]  
where \(\vec{u}\) is the embedding of the target utterance, \(\vec{u}_{i}\) represents the embedding of the \(i\)-th contextual utterance, \(\alpha\) is the weight for the target utterance, and \(\beta_{i}\) is the weight for each contextual utterance. The weights \(\alpha\) and \(\beta_{i}\) are dynamically adjusted based on the semantic similarity of the contextual utterances to the knowledge base examples. This ensures that relevant context contributes more significantly to the optimized embedding. If the recalculated similarity score \(\text{Sim}(\vec{u}_{\text{context}},\vec{e})\) still fails to exceed the threshold \(\theta\), the system employs a threshold adjustment mechanism:  
\[\theta^{\prime}=\theta-\Delta\theta \tag{4}\]  
where \(\Delta\theta\) is a small step size determined experimentally to balance recall and precision. By iteratively lowering the threshold, the system expands the search space to include more candidate categories without compromising overall accuracy.  
In addition to embedding-based refinement, EduDCM integrates multilingual WordNet to support cross-lingual semantic mapping. WordNet's hierarchical structure is used to identify synonyms and related terms across languages, facilitating annotation in multilingual datasets. For example, an utterance labeled as \"feedback\" in English can be aligned with its equivalent term in another language through shared semantic paths in WordNet. If all refinement attempts fail, the utterance is flagged for manual review, ensuring that even ambiguous cases are handled effectively.