esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Sentence Representation Matching in Neural Machine Translation
## 5 Results and Discussions
### Impact Analysis
#### 5.1.4 Performance of Best Matching Layer  
In the French to English task used for the impact analysis, the grid search results are shown in the highlighted table and are drawn in Figure 8. The table shows the most achievable performance for validation and test sets, which are not affected by model selection based on validation performance. The best case in the table is that of the source-side 2 layer, 250 hidden dimension, and Hamming distance. We applied this setting for other translation tasks as an extension because of the large cost of the grid search. From the impact investigation, we found the best settings to use included batch normalization, 2 source-side layers, 250 dimensions of hidden layers, and Hamming distance. With these settings, we performed translation tasks for three language pairs as shown in Table 8. In this table, we evaluated two different performances to show the robust result of the model selection process. One is the best performance in each data set evaluated at every epoch without a model selection process. The other is the best performance in each data set evaluated with the selected best validation model. The expected performance for all metrics and data sets using the matching method improves performance. In a more detailed view, the method improves the translation quality for the English to French, Spanish, and German pairs and the French to English pair. A notable point is that the precision in the training set is improved together when validation and test results are improved in the best validation model. The result implies that the matching method is effective to improve the translation quality. It is more effective in improving the quality from relatively simpler language to more complex language in terms of tense and gender. A probable reason for the improvement is that the probability of generating the target sentences in those pairs is ambiguous in some cases, so the additional information given by the matching method clarifies the tokens to select. The performance improvement in the training and test sets of the best validation models is distinguished compared to the usual regularization methods, which increase the test accuracy by reducing the training accuracy. Figure 7: Layer-wise STD \(\sigma_{L}\) of 2 source-side layers by hidden dimension settings.
## 2 Literature Review and Research Hypotheses
### BIM Technology Effectiveness  
Nowadays, the concept of technological capabilities was interchangeable with other concepts used for the same purpose, such as technological effort [42; 43] or technological ability [43; 44], and it has become a widely accepted term. Previous contributions have developed several industry-level BIM maturity models to help the AEC sector measure the performance of its BIM effectiveness [45] and to be able to make comparisons of maturity models between countries. In the design phase, BIM implementation has made possible the decrease of design errors, which means reducing the cost of the project and the time spent on the design, as well as generating benefits for the designer, the contractor, and the owner [33; 34].  
In the BIM literature, as a high-tech information system, the term \"dimension\" is used to indicate the information-processing capability of this technology [46]. It has been demonstrated that BIM is ahead of CAD, as it is able to turn physical properties into virtual models. In fact, it has been named the 3D BIM construction model. The fourth dimension 4D BIM added on was time in order to carry out study plans. Then, 5D BIM added price as its fifth dimension with the aim to study the cost of projects [47; 48]. Sustainability analysis was the sixth dimension to be added to 6D BIM, allowing energy and sustainable simulation studies to be carried out in projects [49]. Later on, 7D BIM added a seventh dimension in order to include any information or documentation that may be required for systems to be managed in the most suitable way [50]. Finally, 8D BIM was incorporated so as to carry out studies of labor risk prevention in models throughout the design phase [51].  
BIM is a technology that allows the integration of project information. AEC companies that carry out projects with BIM use it for different purposes. On the other hand, effectiveness is the ability to achieve the desired result. Therefore, in this research, the effectiveness of BIM is defined as the ability to properly use BIM by project teams for the objectives set.
# Shunt Damping Vibration Control Technology: A Review
## 1 Introduction
Similar to the electromagnetic transducer, the magnetostrictive transducer consists of a rod of magnetic material and a coil surrounding it. If a high magnetostrictive material, such as Terfenol-D,  
Figure 1: Simplified model of **(a)** piezoelectric shunt damping (PSD) and **(b)** electromagnetic shunt damping (EMSD). is used as a rod, the dynamic strain can be increased by 0.2%. Based on this characteristic, [PERSON] et al. [39] extended the shunt damping method from PSD to that of magnetostrictive transducers, and results implied that the stiffness can be controlled by connecting an external negative inductance circuit. [PERSON] et al. [40,41] used this shunt as an electrically-controlled element to develop a variable stiffness spring based on magnetostrictive transducers. After years of development, shunt damping technology can be classified as passive and active shunts according to the requirement of the external energy. Basically, passive shunts do not need an external energy, and these shunts mainly use the resistor, inductor, capacitor, and diode. However, active shunts require the external energy to drive and implement. In contrast to the traditional vibration control method, shunt damping is implemented using the same transducer. The shunt damping technique has become a remarkable achievement after years of development and research. The fundamentals of shunt damping have been evaluated analytically and experimentally. The objective of the present review is to provide a comprehensive outline of the recent shunts to encompass the breadth of the work accomplished and provide sufficient attention to the critical results of these studies. In the following sections, this review displays a variety of the research investigations in shunt damping technique based on passive and active classification, either PSD or electromagnetic shunt damping (EMSD).
# Simulation of Aerosol Evolution within Background Pollution for Nucleated Vehicle Exhaust via TEMOM
## 2 Governing Equation
### Coagulation Kernel  
As summarized in the review [25], the vehicle nucleated particles are primarily between 32 nm and 90 nm. Thus, the collision kernel \(\beta\) in the present study is limited in the free molecular regime in the present study [21]:  
\[\beta(v,u)=B_{1}\left(\frac{1}{v}+\frac{1}{u}\right)^{1/2}(v^{1/3}+u^{1/3})^{2} \tag{9}\]  
in which  
\[B_{1}=\left(\frac{3\pi}{4}\right)^{1/6}\left(\frac{6k_{b}T}{\rho_{p}}\right)^{ 1/2} \tag{10}\]  
where \(k_{b}\) is Boltzmann constant; and \(\rho_{p}\) is particles density. The \(k\)th moment of particle size distribution is defined as follows:  
\[M_{k}(t)=\int\limits_{0}^{\infty}v^{k}n(v,t)\mathrm{d}v \tag{11}\]  
If only the first three moments are considered, the coagulation kernel moment equation is obtained by multiplying Equation (8) by \(v^{k}\) and then integrating it from 0 to \(\infty\)[24]:  
\[\begin{split}\left(\frac{\partial m_{0}}{\partial t}\right)_{ \text{coa}}&=\frac{\sqrt{2}B_{2}(65m_{2}^{2}m_{0}^{23/6}-1210m_{ 2}m_{1}^{2}m_{0}^{17/6}-9223m_{1}^{4}m_{0}^{11/6})}{5184m_{1}^{23/6}}\\ \left(\frac{\partial m_{1}}{\partial t}\right)_{\text{coa}}& =0\\ \left(\frac{\partial m_{2}}{\partial t}\right)_{\text{coa}}& =\frac{\sqrt{2}B_{2}(701m_{2}^{2}m_{0}^{11/6}-4210m_{2}m_{1}^{2}m _{0}^{5/6}-6859m_{1}^{4}m_{0}^{-1/6})}{2592m_{1}^{11/6}}\end{split} \tag{12}\]  
TEMOM is a general method for solving the PBE. The closure of the moment equations is approached by the Taylor-series expansion technique. This is why this method has no prior requirement for particle size distribution. As the study [24] shows, 3-order TEMOM is preferable when precision and efficiency are simultaneously considered in which the first three order moments are calculated. In other words, the most important indexes for describing aerosol, including particle number density, particle mass, and geometric standard deviation, are obtained.
# Efficient Machine Learning-Based Prediction of Solar Irradiance Using Multi-Site Data
## 2 Proposed Solution
### Exploratory Data Analysis
Other significant factors included two lagged versions of the accumulated irradiance and three-to-five lagged versions of GHI. For the ULL site, the important features were limited to the lagged version of GHI up to 30 min in the past, and to a lesser extent, the second lagged versions up to 1 h earlier. Other values were mostly not significant. This pattern extended to the HSU site, where only two features were crucial: the lagged version of GHI up to 30 min and the lagged version of the zenith angle up to 30 min. These observations indicate a common consensus across all sites, highlighting that the most important feature for prediction is the first lagged version of GHI. All these results can be seen in Figure 8.  
Figure 6: Shift impact on \(R^{2}\) for different forecasting horizons: (**a**) 30 min, (**b**) 1 h, and (**c**) 2 h.  
Figure 7: Shift impact on \(RMSE\) for different forecasting horizons: (**a**) 30 min, (**b**) 1 h, and (**c**) 2 h.
# Experimental Study of Steel Reinforced Concrete (SRC) Joints
## 3 Experimental Program
### Bending Moment Strength Prediction of SRC Joints.  
In the pre-break phase the section was plasticized (Figure 10).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{**Typology**} & \multirow{2}{*}{\begin{tabular}{c} **Beam** \\ **Section** **(mm)** \\ \end{tabular} } & \multirow{2}{*}{**Lower Bars**} & \multirow{2}{*}{**Cross-Section**} & \multirow{2}{*}{\begin{tabular}{c} **Distance** \\ **between** \\ **Supports** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Inertia** \\ **Cross-Section** \\ **\(\times\) 10\({}^{4}\) (mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Steel** \\ **Weight** \\ **(kg/m)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Theoretical** \\ **Maximum** \\ **Moment** \\ **MpLd (8N-m)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Maximum** \\ **Load (8N)** \\ **MpLd (8N-m)** \\ \end{tabular} } \\ \hline P03 & RC & 300 \(\times\) 250 & 40\(\pm\)12 & - & 3.30 m & - & - & 50.96 & 67.90 \\ P04 & SRC & 300 \(\times\) 250 & 40\(\pm\)12 & HEB-100 & 3.30 m & 449.5 & 20.40 & 95.20 & 126.93 \\ P05 & RC & 300 \(\times\) 250 & 20\(\pm\)16 \(\pm\) 2 Using the strength superposition method, Equation (5), the bending moment strength of the SRC beam-column joint Mpl,Rd is:  
\[\text{Mpl,Rd}=\text{Wpa fy/ya + Wpc 0,85 fck/yc + Wps fsk/ys} \tag{5}\]  
where Wpa, Wpc and Wps indicate the plastic module of steel section, concrete and reinforcing bars.
# Non-Probabilistic Reliability Analysis of Slopes Based on a Multidimensional Parallelepiped Model
## 4 Example Analysis  
### Basic Information  
A single-layer homogeneous slope proposed in the literature [28] was studied, and the spatial variability of soil was not considered. The cross-sectional dimensions of the slope are shown in Figure 5. The weight \(\gamma\) of the layer of this slope is 20 \(\mathrm{kN/m^{3}}\), the modulus of elasticity \(E\) is 100 MPa, and the Poisson's ratio \(\mu\) is 0.3. No water table or external water was considered. Thus, it is appropriate to use the total stress method. This study used the Bishop method in the limit equilibrium method as the basis for calculating the slope safety factor. In the non-probabilistic reliability analysis of slopes, we can get two kinds of slip surfaces. One is the slip surface corresponding to the mean value of geotechnical parameters, which is only related to the mean value of geotechnical parameters. The other is the minimum safety factor slip surface. In the non-probabilistic reliability analysis, the parameters are as interval variables, there will be a lot of slip surfaces. We take the slip surface with the minimum safety factor, this kind of slip surface will change with the parameter range of intervals. The cohesion \(c\) and the internal friction angle \(\varphi\) are uncertain variables, and the test data of shear strength parameters are shown in Table 1.  
Figure 4: Analysis process of the proposed non-probabilistic reliability analysis method for the slope.  
Figure 5: The slope calculation cross-section.
###### Abstract  
According to the WHO, overweight and obesity, defined as abnormal or excessive fat accumulation, are a major risk factor for many diseases. The bioelectrical impedance analysis (BIA) is a commonly used method of assessing body composition in clinical practice and medical research. When the BIA reveals abnormalities, the recommended therapeutic procedure is to modify the diet and implement physical activity. One method that can reinforce or support physical activity is whole-body vibration (WBV). Vibrating devices with stochastic resonance (SR) generate vibrations of variable amplitude and frequency. For people with unhealthy body composition who cannot undertake physical activity for various reasons, procedures with stochastic resonance seems to be a good solution. The aim of this study was to evaluate the impact of stochastic resonance whole-body vibration (SR-WBV) on the body composition of women. Measured BC parameters included fat mass (FM, kg), percent body fat (PBF, %), visceral fat area (VFA, cm\({}^{2}\)), soft lean mass (SLM, kg), fat-free mass (FFM, kg), skeletal muscle mass (SMM, kg), body cell mass (BCM, kg), protein (kg), minerals (kg), bone mineral contents (BMC, kg), intracellular water (IV, I), extracellular water (EW, I), total body water (TBW, I), extracellular water/total body water (EW/TBW). The study involved 240 healthy young women with normal body composition (BC) and low or moderate physical activity levels. Two groups were randomly formed from among all participants: the V group included 134 women participating in 12 SR-WBV procedure sessions over 6 weeks; the C group included 106 women not participating in SR-WBV procedure sessions over 6 weeks. The stochastic procedure consisted of 12 sessions over 6 weeks. One session lasted 15 min, consisting of nine active series of vibrations lasting 45 s each with 40 s breaks between series. The vibration frequency was 2-8 Hz and the amplitude ranged 0.5-3.5 mm. While observing the effect of SR-WBV vibrations on body composition in the group of women, no statistically significant changes were found. Hence, we conclude that the stochastic resonance vibration procedure cannot be recommended as a way to modify body composition in healthy young women characterized by normal body composition and low or moderate physical activity levels.  
stochastic resonance; whole body vibration; BC; BIA Article
### QoE Assessment Model Based on Optimal Mathematical Modeling Method  
The steps involved in this method are as follows: Firstly, a number of parameters that characterize the influencing factors are chosen; secondly, the functional model is set up; and finally, the coefficients of the model are determined to obtain optimum prediction performance. The QoE assessment model that is devised using this method usually adopts an exponential or logarithmic form.  
Reference [5] is one of the earliest works that incorporated building a QoE model for HAS applications. This model quantifies QoE as a linear model based on factors such as initial delay, re-buffering frequency and duration. The exponential model can be used to map a relationship between the influencing factors and the MOS; whereas, in reference [6], the re-buffering frequency and duration are also considered. eMOS [7] considers re-buffering time, count and the video coding bitrates as major influencing factors and establishes an exponential model between the three influencing factors and the MOS, separately. The three models are linearly combined to obtain the final QoE prediction model. Reference [8] studies the effects of the three different influencing factors, initial delay, re-buffering time and video quality switching on QoE, and establishes an exponential model for each of the three influencing factors and the MOS. These are linearly combined to obtain the QoE prediction model. Reference [9] focuses on the impact of interruption in the video playback process on QoE of the streaming media services and establishes an exponential model between the MOS and the duration of interruption. Reference [10] also studied the effect of switching of the quality of a video QoE, and concluded that the switching frequency between different Video Quality Levels (VQL) interferes with the user's attention. It also affects a user's QoE, and by combining the exponential and logarithmic model the effect of video quality switching on the user's QoE can be quantified. It is observed that when the frequency is greater than 1/14 per second, the value of MOS drops significantly. Reference [11] uses a linear combination of multiple exponential models to establish a relationship between QoE and video compression, initial delay and re-buffering time, separately.  
Hence, it can be summarized that only few influencing factors can be considered in this model owing to limitations in parameters that are taken into consideration. Thus, this method is unable to establish a relationship between the complex influencing factors and QoE; the accuracy of this model is typically low. These QoE models usually cannot effectively guide the client's bitrate selection in dynamic adaptive streaming services.
# Multi-Classifier Pipeline for Olive Groves Detection
## 3 Results  
### Classification Pipeline Resulting Maps  
From the three experimented pipelines, we have obtained various classification maps that are presented in Figure 5.  
The baseline pipeline classified the input images into 5 classes: Urban, Forest, Nature, Grassland and Olive, while the two other ones assigned the pixels into Forest, Impervious, Olive and Non-olive classes. We observed significant visual changes between the map resulting from the one classification process and the ones resulting from the multi-classifier pipeline. We identified a significant decrease in the coverage of the olive class (represented in dark green on all land cover maps) in the multi-classifier pipeline's map. This can be confirmed particularly in the West, Northwest, and North of the area of interest.  
Figure 5: Classification maps from (**a**) the baseline pipeline; (**b**) the multi-classifier pipeline using Multi-Layer Perceptron (MLP); (**c**) the multi-classifier pipeline using One-class Support Vector Machine (OCSVM).  
The differences in olive detection can be observed on the subtraction maps presented in Figure 6.  
We can confirm that the area of Olive pixels changing to Non-olive pixels, which are represented in orange color, is emphasized in the area where we observed the significant decrease of olive coverage. Furthermore, we also observed that there are more pixels that turned into Non-olive class than into Olive class as orange coverage is broader than the green one. The change of these areas was calculated and results are presented in Table 3. The quantification of area in orange reached approximately 62.5 km\({}^{2}\) and 42.9 km\({}^{2}\) for the baseline-MLP and the baseline-OCSVM respectively. On the other hand, the area that changed from Non-olive to Olive in both subtractions is about 16 km\({}^{2}\).  
\begin{table}
\begin{tabular}{c c c} \hline
**Raster Subtraction** & **Change Category** & **Area [km\({}^{2}\)]** \\ \hline \multirow{3}{*}{Baseline–MLP} & Non-olive to Olive & 16.8 \\  & Olive to Non-olive & 62.5 \\  & No change & 192.9 \\ \hline \multirow{3}{*}{Baseline–OCSVM} & Non-olive to Olive & 16.3 \\  & Olive to Non-olive & 42.9 \\  & No change & 212.9 \\ \hline \end{tabular}
\end{table}
Table 3: The total area in km\({}^{2}\) based on each change category.  
Figure 6: Raster subtraction of classification maps between: (**a**) the baseline pipeline and the multi-classifier pipeline using MLP; (**b**) the baseline pipeline and the multi-classifier pipeline using OCSVM. X\(\mid\)Y indicates class X in the baseline pipeline was classified as class Y in the multi-classifier pipeline.
# Multi-UAVs Communication-Aware Cooperative Target Tracking
## 4 Distributed Motion Control Algorithm  
After target state estimation and fusion based on consistency spread information filtering, each UAV can form a consistent target view; that is the target information matrix.  
In this paper, the receding horizon method [2; 24; 25; 26] was used to make maneuver decision for multi-UAVs target tracking problem. We set the length of horizon \(L=3\), that means, \"look forward 3 steps, take one step\".  
The method of finite step prediction of target state is shown in Equation (51):  
\[X_{i,t}^{pred}[k+L|k]=\left[\begin{array}{c}X_{i,t}^{pred}[k+1|k]\\ X_{i,t}^{pred}[k+2|k]\\ \ldots\ldots\\ X_{i,t}^{pred}[k+L|k]\end{array}\right]=\left[\begin{array}{c}\hat{\alpha}_{t}^ {-}[k+1|k]\\ A\cdot\hat{\alpha}_{t}^{-}[k+1|k]\\ \ldots\ldots\\ A^{L-1}\cdot\hat{\alpha}_{t}^{-}[k+1|k]\end{array}\right] \tag{51}\]  
where \(X_{i,t}^{pred}[k+L|k]\) is the \(L\)-step prediction of the target state at time \(k\). A is the state transition matrix, in this paper, it's \(F_{cv}\).  
The estimation error corresponding to Equation (51) is written as:  
\[\varepsilon[k+L|k]=X[k+L]-X_{i,t}^{pred}[k+L|k]. \tag{52}\]  
Thus, the covariance of the target prediction in the entire time range \(L\) is obtained:  
\[\sigma_{i}[k+L|k]=\left[\begin{array}{c}\varepsilon^{2}[k+1|k]\\ \varepsilon^{2}[k+2|k]\\ \ldots\ldots\\ \varepsilon^{2}[k+L|k]\end{array}\right]=\left[\begin{array}{c}\left(Y_{i} [k+1]^{-}\right)^{-1}+R\\ A\left(Y_{i}[k+1]^{-}\right)^{-1}A^{T}+Q+R\\ \ldots\ldots\\ A^{L-1}\left(Y_{i}[k+1]^{-}\right)^{-1}\left(A^{L-1}\right)^{T}+Q+R\end{array} \right]. \tag{53}\]  
Then, we get the optimization function as:  
\[u_{i}^{*}=arc\mbox{max}\rho(Y_{i}(X_{i,t}^{pred}[k+L|k],u) \tag{54}\]  
where \(\rho(\cdot)\) is the scalar function of the matrix; in this paper we used the determinant as scalar function. \(X_{i,t}^{pred}[k+L|k]\) represents the prediction of target state after \(L\) steps for \(i\)-th UAV and \(Y_{i}\) the information matrix of \(i\)-th UAV.