esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Various machine learning-based approaches have been made such as predicting the seismic damage to building structures considering soil-structure interaction effects [20], their seismic performance levels [21] or even damage identification [22]. Other studies have dealt with the various dynamic quantities of building structures such as acceleration, and the displacement response quantities trying to manipulate them [23]. This study primarily focuses on utilizing ambient vibration (AV) measurements from a building in conjunction with earthquake (EQ) time histories, which are processed through a neural network (NN) in image format to predict the building's specific earthquake response. The training phase involved the development of 1197 multi-degree-of-freedom (MDOF) 2D shear models, resulting in the creation of a total of 32,319 samples. To assess the NN's performance, various metrics were employed, including the MAPE (mean absolute percentage error), MDA (mean difference amplitude) for time-history comparisons, and magnitude-squared coherence values and phase difference (\(\Delta\varphi\)) for frequency domain comparisons. This proposed model is named MLPER, which stands for machine learning-based prediction of building structures' earthquake response. The remainder of this study is structured as follows: Section 1 presents the structural parameters employed in dataset creation, outlines the assumptions made regarding the MDOF 2D shear models, and provides the list of earthquake recordings used, along with their characteristics. Section 2 offers an overview of the available deep neural network options, highlighting their suitability for addressing the specific problem at hand. It delves into the reasoning behind the rejection of some options and the preference for others in the context of the regression task. It also discusses the underlying principles and the chosen structural parameters of the model. Section 3 examines the format of the training data and discusses decisions made regarding their utilization in the neural network training process. Section 4 provides a detailed description of the MLPER architecture, while Section 5 presents the results using various metrics to assess performance. Section 6 concludes the study, offering remarks on performance and suggesting avenues for future work within the presented network framework.
# An Effective Algorithm for Finding Shortest Paths in Tubular Spaces
## Appendix A  
### Proof of Lemma 2  
By \(S_{X}\), we denote the cross-section of \(\Omega\) that contains \(\mathbf{X}\). Let \(\Omega_{m}\) and \(L_{m}\) be the subspace of \(\Omega\) limited between \(S_{X}\) and \(S\) and its length along the centerline curve, respectively. Under a discrete point of view, \(\Omega_{m}\) can be considered as a series of \((K+1)\) cross-sections perpendicular to the centreline curve: \(S_{X}=S_{0}^{m},\ldots,S_{K}^{m}=S\ (K\in\mathbb{N}^{+})\) with the discrete step \(\Delta l=\frac{1}{K}\). Let \(\sigma_{X}(S)\) be the visible area of the cross-section \(S\) by \(\mathbf{X}\), we then have:  
\[\forall\mathbf{Y}\in\sigma_{X}(S),\forall i\in\{0,\ldots,K\}\Rightarrow\exists \mathbf{a}_{i}=\left(\overline{\mathbf{X}\mathbf{Y}}\cap S_{i}^{m}\right)\
eq\emptyset\]Therefore, \(\mathbf{Y}\) is the perspective projection of \(\mathbf{a}_{i}\) (\(\forall i\in\{0,\ldots,K\}\)) from the view point \(\mathbf{X}\) to the view plane \(S\), hence:  
\[\sigma_{X}(S)\subset\bigcap_{i=0}^{K}P_{X}^{S}(S_{i}^{m})\]  
where \(P_{X}^{S}(S_{i}^{m})\) is the perspective projection of \(S_{i}^{m}\) from the view point \(\mathbf{X}\) to the view plane \(S\). If \(K\rightarrow\infty\), or \(\Delta h\to 0\), then the problem becomes continuous:  
\[\sigma_{X}(S)\subset\bigcap_{i=0}^{\infty}P_{X}^{S}(S_{i}^{m}) \tag{10}\]  
Inversely:  
\[\forall\mathbf{W}\in\bigcap_{i=0}^{\infty}P_{X}^{S}(S_{i}^{m})\Rightarrow\left( \overline{\mathbf{X}\mathbf{W}}\cap S_{i}^{m}\right)=\mathbf{b}_{i}\
eq\emptyset,\forall i \in\mathbb{N} \tag{11}\]  
When \(\Delta h\to 0\), then we obtain:  
\[\overline{\mathbf{X}\mathbf{W}}=\bigcup_{i=0}^{\infty}\mathbf{b}_{i}\subset\Omega\]  
Indeed, if \(\overline{\mathbf{X}\mathbf{W}}\
ot\subset\Omega\), we can always find a value \(\Delta h>0\) in order to have a cross-section \(S_{i}^{m}\) so that \(S_{i}^{m}\cap\overline{\mathbf{X}\mathbf{W}}=\emptyset\) (conflict with (11)). Consequently, \(\mathbf{W}\) can be seen by \(\mathbf{X}\), then we have:  
\[\mathbf{W}\in\sigma_{X}(S)\Rightarrow\bigcap_{i=0}^{\infty}P_{X}^{S}(S_{i}^{m}) \subset\sigma_{X}(S) \tag{12}\]  
From (10) and (12), then:  
\[\sigma_{X}(S)=\bigcap_{i=0}^{\infty}P_{X}^{S}(S_{i}^{o}) \tag{13}\]  
As the cross-section of \(\Omega\) is convex and the convexity is preserved under perspective projection and intersection [41], then \(\sigma_{X}(S)\) is a convex region. (Q.E.D.).
# Non-Compliance Distalization Appliances Supported by Mini-Implants: A Systematic Review
## 4 Discussion
### Angular and Linear Molar Distalization Movement  
#### 4.1.1 Characteristics of the Appliances Positioned Palatally  
According to the results of the current investigation, it has been shown that linear and angular changes of the maxillary first molars varied from 1.8 mm utilizing a modified pendulum appliance [31] to 7.9 mm using the BAPA [33], while distal molar tipping ranged from 7.34\({}^{\circ}\)[22] to 22.8 with the BAPA. Meanwhile, in the first subgroup including all the palatal devices with TADs, the maxillary first molars distalized from 2.4 mm using a lingualmodified appliance, according to [PERSON] et al., to 5.9 mm using the Dual Force Distalizer appliance according to [PERSON] et al. [17], while distal molar tipping ranged from 0.01\({}^{\circ}\) using the miniscrew-supported distal jet (SDJ) [34] to 11\({}^{\circ}\) using a hyrax screw type distalizer [29]. Our results showed the palatal devices with TADs have lower distal tipping than the Pendulums with TADs [15; 18; 36], and thus are in agreement with the systematic review of [PERSON] et al. and [PERSON] et al. [57; 58].  
Measurements for the distalization of maxillary second molars were inadequate and were available only in 8 out of 28 articles. Second molar distalization with the palatal distalization appliances supported with TADs was evident and ranged from 2 mm using the Beneslider appliance [27] to 4 mm using the MGBM, an intraoral distalization system with palatal-anchored MIs [25]. In addition, the second molar distal tipping ranged between 2.2\({}^{\circ}\) using the Beneslider and 12.62\({}^{\circ}\) using the BAPA [32].
# An Abstractive Summarization Model Based on Joint-Attention Mechanism and a Priori Knowledge
## 4 Experiment
### Evaluation Metrics  
To demonstrate the model's viability and efficacy after receiving the summary it provided, we must assess the model's performance. Both manual and automatic evaluations were used to gauge the abstracts' quality.  
Automatic evaluation methods for text summarization include Rouge [33], BLEU [34], etc., and the commonly used one is Rouge, which is also used as an evaluation metric in this paper. Rouge measures the quality of the model-generated summaries by calculating the degree of overlap of the basic units (n-grams) between the generated summaries and the reference summaries. Rouge typically contains the following metrics.  
1. Rouge-N: Statistics based on the co-occurrence degree of N-gram words. \[Rouge-1=\frac{\sum_{S\in Y}\sum_{1-gram\in S}Count_{match}(1-gram)}{\sum_{S \in Y}\sum_{1-gram\in S}Count(1-gram)}\] (27) \[Rouge-2=\frac{\sum_{S\in Y}\sum_{2-gram\in S}Count_{match}(2-gram)}{\sum_{S \in Y}\sum_{2-gram\in S}Count(2-gram)}\] (28) where \(Y\) is the reference summary and \(S\) is the sentence in the reference summary. The numerator indicates the number of simultaneous \(N-gram\) in the generated summary and the standard summary. The denominator indicates the number of \(N-gram\) occurrences in the reference summary.
2. Rouge-L: refers to the longest common subsequence (LCS) in the generated abstracts and reference abstracts. \[Rouge-L=\frac{(1+\beta^{2})R_{LCS}P_{LCS}}{R_{LCS}+\beta^{2}P_{LCS}}\] (29) \[R_{LCS}=\frac{LCS(X,Y)}{n}\] (30) \[P_{LCS}=\frac{LCS(X,Y)}{m}\] (31) \[\frac{1}{Rouge-L}=\frac{1}{(1+\beta^{2})P_{LCS}}+\frac{\beta^{2}}{(1+\beta^{ 2})R_{LCS}}\] (32) where \(R_{LCS}\) denotes the recall rate in Equation (30), \(P_{LCS}\) denotes the precision rate in Equation (31), \(LCS(X,Y)\) denotes the length of the longest common subsequence between the generated summary and the reference summary, \(n\) is the length of the reference summary, and \(\mathrm{m}\) is the length of the generated summary. In general, \(\beta\) is set to a large value, so \(Rouge-L\) usually considers only \(R_{LCS}\) (recall) in this case. As shown in Equation (32), if \(\beta\) is large, the inclusion of the first half of Equation \(P_{LCS}\) can be neglected.  
When performing a manual evaluation, we primarily look at readability and whether or not the text retains important details from the original (generalization). We manually select 100 examples from the CNN/DailyMail dataset and give the generated summaries and reference summaries generated by the different models to the evaluators. The five evaluators scored the abstracts of the different models based on the above two aspects, from 1 to 5, with higher scores representing the more the resulting abstracts met the two evaluation metrics. Finally, the average value is taken as the final score of the manual evaluation of the model.  
The model is evaluated comprehensively using both automatic and manual evaluations. to ensure the quality of the model-generated abstracts.
## 1 Introduction
### SLR Framework  
Figure 1 shows the framework of this SLR. We selected studies from four databases: IEEE, Springer, Elsevier, and Google Scholar. The inclusion and exclusion criteria are explained in Section 2. The selected research was grouped into three areas. These are channel estimation, adaptive modulation, and modulation recognition. Section 3 discusses the ML and DL methods for channel estimation. It shows how these models predict signal distortions and improve decoding accuracy. Section 4 focuses on adaptive modulation. It explains how models adjust transmission settings to match underwater conditions. Section 5 examines modulation recognition. It highlights how ML models classify signals in noisy environments with high accuracy. Section 6 outlines key challenges and suggests future directions. Section 7 gives answers to the research questions. Section 8 describes search and scope limitations. Section 9 presents the conclusion.  
Figure 1: Overview of the SLR: from the article selection to comparative analysis.
# Hybrid Particle Swarm Optimization for High-Dimensional Latin Hypercube Design Problem
## 3 Proposed Method
### Boundary Processing Scheme  
In the PSO algorithm, the particles exceeding the boundary during the movement process will significantly be increased [50; 51]. An appropriate boundary processing scheme will improve the optimization capabilities of PSO when optimizing high-dimensional problems. This article solves the boundary problem by using the boundary reflection processing scheme. A schematic diagram of the boundary reflection scheme is shown in Figure 6.
## 2 Data and Methods
### Multi-Parameter Data and Processing
 \tag{1}\]  
\[\text{IQR}=\text{Q}_{3}-\text{Q}_{1},\text{ x}_{\text{UB}}=\text{Q}_{2}+1.4 \text{IQR},\text{ x}_{\text{LB}}=\text{Q}_{2}-1.4\text{IQR}, \tag{2}\]  
Seismic-related disturbances in TEC data can be identified by dividing the daily TEC values into overlapping time windows, calculating the IQR, and detecting deviations beyond a threshold, with Q\({}_{2}\) (the median) as a reference. x\({}_{\text{UB}}\) and x\({}_{\text{LB}}\) are the lower and upper bounds, respectively. The ESA satellite mission of 3 Swarm (A, B, and C) low Earth orbit quasi-polar satellites was launched on 22 November 2013. Swarm B is at 520 km and A and C are at approximately 470 km; they are composed of specialized instruments, including a Langmuir probe, electric field instrument, absolute scalar magnetometer, and vector field magnetometer [51; 52]. Ne and Te data from Swarm satellites A and C were downloaded from the IDP-IRR Level-2 daily Entire Mission data product for nearly three months. The Swarm Ne and Te data were analyzed using the revisit trajectory method. Trajectory data from three months before and five days after each earthquake were examined. Satellites A and C traveled through the epicenter at certain day and night times; for example, for the earthquake on 26 October 2023, the day and night revisit times were around 11 and 19 UTC, respectively, with an 8-day cycle. The data were categorized into two 2D groups and eight clusters based on access time, and background values from daytime and nighttime data were extracted for Ne and Te based on access time. When \(\Delta\)Ne and \(\Delta\)Te values exceeded specified thresholds, as indicated by descending and ascending orbital-revisit plots, individual thresholds were calculated for Ne and Te during both day and night anomalies. Anomalies were defined as absolute values of \(\Delta\)Ne and \(\Delta\)Te that exceeded 2\(\sigma\) (standard deviation of 2) [53]. The correlation between these aspects was analyzed to identify common patterns in the occurrence of anomalies. An ionosonde radar system is also incorporated that probes the ionosphere by broadcasting radio signals at frequencies ranging from 0.1 to 30 MHz and receiving reflected signals from various ionospheric areas where the plasma frequency matches the radar frequency. In this work, the data utilized were gathered from the DID GIRO high-frequency pulse sounding system. This experimental configuration is focused on monitoring critical frequency (foF2) and F2-layer height (hnF2) at a specific geographic location approximately over the epicenters (latitude: 34\({}^{\circ}\)N, longitude: 62\({}^{\circ}\)E), for selected timescales (Figure S5). To assure accuracy, the data were calibrated using International Reference Ionosphere (IRI) models, which efficiently eliminated biases and reduced various types of noise. The system records, displays, and transmits vertical ionospheric data in the form of digital ionograms, accompanied by their associated parameters.
# Progress in Construction Robot Path-Planning Algorithms: Review
## 2 Environmental Modeling and Path Quality Assessment Standards
### Environmental Modeling  
The core of environmental modeling lies in the systematic organization and application of the known environment. A well-designed environmental model can significantly aid mobile robots in pathfinding. Typically, there are four main methods for constructing environmental models: geometric models (GM), topological maps (TM), grid cell models (GCM), and metric representations (MR) [16; 17; 18; 19; 20].
## 5 Summary and Analysis of Some Simplified Estimation Schemes
### Remarks  
Some comments on the above estimators are as follows:  
* These estimators are obtained from approximations made to a structure motivated by DAML estimation of the APOE.
* The estimator in Equation (10) is convenient and with acceptable complexity as compared with Equation (9). Since it works only for very small frequency-offset values and reasonably high SNRs, it is a CFO and SNR limited estimator. Correspondingly, a severe degradation to the system packet error performance in the detection process will be introduced, which is not suitable for our purposes.
* Equation (12) achieves an estimate with quite reasonable accuracy. It does so by four preconditions, i.e., smart geometric division to the observation space, accurate subspace locating criterion, equivalent avenue for the calculation of \(\angle Y\), and advisable approximation \(\tan^{-1}\left(x\right)\approx x\). One arctan operation is required for a DAML estimator given in Equation (9). However, this computationally intensive operation is completely avoided in Equation (12). Surprisingly, such a complexity reduction results in almost no frequency-offset invariance degradation in the detection process, as will be inferred in Section 7.
* The canonical approximation \(\tan^{-1}\left(x\right)\approx x\) involved in Equation (12) is much more accurate than the atypical approximation \(\tan^{-1}\left(x\right)\approx 0\) involved in Equation (13). This implies that, as such inaccuracy frequently makes the measurement in Equation (13), either a large overestimation or a large underestimation of the CFOE, whereas no such inappropriate estimation is likely to occur in Equation (12). Actually, Equation (12) allows a real-time adjustment with additional one division and one addition operations, which, however, can achieve almost 1.5 dB gains at PER of \(1\times 10^{-3}\)[10].  
In a word, Equation (12) makes a detector with such a reasonable balance between complexity of the postcompensation step and performance of the detection step that it may be more attractive for WSNs. However, a division operation is still required as indicated in Equation (12). Clearly, division is an energy intensive and costly operation [16], and should be avoided as much as possible in WSNs. In the following, we are concerned with further simplication to wipe off this complex nonlinear operation in the first processing, while without unaccepted performance degradation in the second processing.
## 2 Theoretical Background
### Reliability Function  
The reliability functions, \(R(t)\) for continuous variables and \(R(k)\) for discrete variables, of a component are given by the following formula [20; 21]:  
\[\begin{array}{ll}R(t)=P(T>t)=1-F(t),&R(k)=P(T>k)=1-F(k),\\ R(t)=\int_{t}^{\infty}f(t)dt,&R(k)=\sum_{i=k+1}^{\infty}f(ki)\end{array} \tag{2}\]  
Here, \(F(t)\) indicates the cumulative distribution function (CDF), and \(f(t)\) is the probability density function (PDF) and \(Pr(T>t)\) yield the probability of a non-failure over time [20; 21].
## 2 Methodology and Site Description
### Sea Water Elevation Prediction
Sea water elevation assessment as a consequence of simultaneous effects for those three mechanisms is assessed as follows:  
* From observed tidally induced oscillations, the harmonic parameters (amplitude, period and phase) are initially determined based on the Least Square Method application;
* After all harmonic parameters have been determined, tidal-induced sea level oscillation is determined by using Equation (1);
* From observed barometric pressure data, the change in sea level induced by the drop/rise of the barometric pressure is determined from Equation (3);
* Deep water wave parameters are determined depending on incident direction and wind velocity and duration parameters;
* For relevant incident directions, numerical simulation of the wave transform has been performed by incorporating shoreline reflection coefficients determined on site;* The study area has been divided into ten zones fundamentally different with regard to reflection features;
* For each zone wave, parameters have been determined within the zone close to the shoreline (4 m away from the shoreline);
* Final determination of the sea water elevation by incorporating three abovementioned mechanisms is done by: \[h_{sea}=h_{t}+h_{at}+h_{S(d=4\text{m})}\] (5) which offers an easy-to-implement way to assess absolute sea level, where \(h_{t}\) represents the tidally induced component, \(h_{at}\) represents the barometric pressure-induced component and \(h_{S(d=4\text{m})}\) stands for the significant wind-generated height as found 4 m offshore. The value of \(h_{sea}\) is expressed relative to HVRS71 datum [33].
###### Abstract  
Resonance frequency analysis (RFA) is the most extended method for measuring implant stability. The implant stability quotient (ISQ) is the measure obtained by different RFA devices; however, inter- and intra-rater reliability and agreement of these instruments remain unknown. Thirty implants were placed in three different pig mandibles. ISQ was measured parallel and perpendicular (lingual) to the peg axis with Osstel(r) Beacon, Penguin(r) and MegaISQ(r) by two different investigators and furthermore, one performed a test-retest. Intraclass correlation coefficient was calculated to assess the intra- and inter-rater reliability. Pearson correlation coefficient was used to assess the agreement. Intraclass correlation coefficients ranged from 0.20 to 0.65 for the Osstel(r) Beacon; 0.57 to 0.86 for the Penguin(r); and \(-0.01\) to 0.60 for the MegaISQ(r). The highest ISQ values were obtained using Penguin(r) (66.3) in a parallel measurement; the lowest, using the MegaISQ(r) (60.1) in a parallel measurement. The highest correlation values with the other devices were obtained by MegaISQ(r) in a parallel measurement. Osstel(r) Beacon and MegaISQ(r) showed lower reliability than Penguin(r). Osstel(r) had good agreement for measuring ISQ both in parallel and perpendicular, and MegaISQ(r) had the best agreement for measuring ISQ in parallel.  
resonance frequency analysis; implant stability quotient; reliability; agreement 2021
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Universal Feature Extraction from Dual- and Compact Polarimetric SAR Data
#### 2.4.1 Elements in Measurement Vector \(\overset{\rightarrow}{K}\)  
The elements of the measurement vector \(\overset{\rightarrow}{K}\) of dual and compact polarimetric SAR modes can be derived from Equations (18)-(20):  
\[\overset{\rightarrow}{K}=\left(\begin{array}{cc}E_{H}&E_{V}\end{array} \right)^{T} \tag{22}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Transmit Receive** & **H** & **V** & **H and V (Incoherently)** & **H and V (Coherently)** & **R and L (Coherently)** \\ \hline H & Single & Single & Alternating & Dual Pol & — \\ V & Single & Single & Alternating & Dual Pol & — \\ H and V & — & — & Alternating & — & — \\
45\({}^{\circ}\) & — & — & — & \(\pi/4\) Compact & \\ R/L & — & — & — & \(\pi/2\) Compact (Hybrid) & Dual-circular (DCP) \\ \hline \hline \end{tabular} Note: Blank means that at the present stage, there is not an operational SAR system with such a transmit/receive combination. R and L stand for right and left circular polarization, respectively.  
\end{table}
Table 2: Different polarimetric SAR modes.  
where \(T\) stands for the transpose.  
Since for the sea surface, usually \(S_{HV}^{2}\) is much smaller compared with the backscatter of co-polarized channels, \(E_{V}^{2}\) represents close physical meaning to \(S_{VV}^{2}\). It is selected as one of the features in classification experiments based on compact polarimetric SAR modes.
## 3 Available Techniques to Reduce the Bandwidth of the 360-Degree Video
### Comparison between Techniques
The proposed scheme ML also improves video quality, improves bitrate and predicts viewpoint in real-time which as is also effectively reduces bandwidth consumption while minimizing performance degradation. Some of the tiling and viewport-adaptive methods also use some algorithms such as Artificial Neural Network [34], Heuristic algorithm [38] and adaptive algorithm [12] to optimize tile selection and predict the users' viewpoint.