esa-sceva/satcom-chunk-collection huggingface dataset:
# Bridge Displacements Monitoring Method Based on Pixel Sequence
## 4 Experimental Results and Analysis
### Experimental Data Collection
#### 4.1.2 LVDT Data Collection  
To authenticate the precision of the algorithm introduced in this paper, the data from Linear Variable Differential Transformer (LVDT) sensors offer the actual extent of deformation of the bridge structure. Below are the time-history displacement curves for both static and dynamic load conditions (Figure 12). Curves labeled 1 and 7 depict displacement at the near and far ends, respectively, at the one-eighth points. Curves 2 and 6 represent the displacement curves at the near and far ends, respectively, at the one-quarter points. Curves 3 and 5 represent the displacement curves at the 3/8 and 5/8 points, respectively. Curve 4 represents the measured displacement curve at the mid-span.
## 3 Results and Discussion
### The Determination of Mineralogical Composition
Experimental XRD patterns were recorded at room temperature using a Rigaku SmartLab multipurpose diffractometer, using Cu K\(\alpha_{1}\) radiation (\(\lambda\) = 1.54056 A) and equipped with a 9 kW rotating anode. SmartLab Guidance (v.2) software was used to acquire the experimental data. The sample was ground to obtain a fine homogeneous powder using an agate pestle and mortar and mounted in a sample holder. The measurement was performed in the 5-90\({}^{\circ}\) range in steps of 0.01\({}^{\circ}\). The powder X-ray pattern is presented in Figure 7 and highlights the presence of four minerals. Silicon oxide (found in the form of quartz, SiO\({}_{2}\)) is the dominant mineral [52], being highlighted by the intense peak that appears at 20 = 26.6\({}^{\circ}\) and a series of smaller peaks that appear at higher diffraction angles. Another two present phases are muscovite and illite, which act as cohesion agents of the muscovite crystals. Both are identified by the specific diffraction peak at 20 = 8.80 [53] and other peaks seen at 17.5\({}^{\circ}\) and 26.8\({}^{\circ}\). Kaolinite (Al\({}_{2}\)(Si\({}_{2}\)O\({}_{5}\))(OH)\({}_{4}\)) represents another constituent phase [54] whose presence is given by the slightly broader diffraction peaks compared with the rest of the phases and appears at 12.21\({}^{\circ}\), 21.0\({}^{\circ}\), and 24.8\({}^{\circ}\).
# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
## 2 Related Works and Background Information
### Background on Machine Learning Algorithms
#### 2.5.1 Support Vector Machine (SVM)  
SVM finds ready application in pattern classification and regression by finding a separating hyperplane which distinguishes two classes (such as emergency and no emergency) with a finite margin. The separating hyperplane is derived or computed, based on euclidean distance measure during the training phase of the SVM model. A recent alternative approach to this is the use of distance metric learning. This approach is aimed at solving the challenge of possible misclassification from euclidean distance. The distance metric learning reduces the distance between similar instances [43].
# Underwater Target Recognition Based on Multi-Decision
## 5 Numerical Results
### Multi-Step Decision LOFAR Line Spectrum Enhancement Algorithm Validity Test
#### 5.4.1 CNN Network Offline Training Process  
According to the frame structure of underwater target recognition in Figure 1, the specific settings and steps can be divided into:  
1. Read the high-resonance component signals in sequence, then windowing and framing the signal. We choose Hanning window (Hanning), and the window size is 2048 (i.e., FFT points are 2048). The overlap between frames is 75%.
2. The signal of each frame is normalized and decentralized. The power of the signal is uniform in time and the average value of the sample is 0. It means the data are  
Figure 10: LOFAR spectrum after line spectrum enhancement.  
Figure 9: LOFAR spectrum of the original signal.  
limited to a certain range, which can eliminate singular sample data. At the same time, it can also avoid the saturation of neurons and accelerate the convergence rate of the network.
3. First, perform Fourier transform on each frame signal. Second, take the logarithmic amplitude spectrum of the transformed spectrum and arrange it in the time domain. Last, take 64 points on the time axis as a sample, which means obtaining a size of \(1024*64\) LOFAR spectrum sample. The sampling frequency of audio is 52,734 Hz, and the duration of each sample is about 0.62 s. The numbers of training and testing sets of various samples are shown in Table 6. The ID in the table is the label of the audio in the ShipsEar database. The corresponding type of ship for each segment can be obtained according to the ID. The type of ship corresponding to audio is used as a label for supervised learning of deep neural networks.
4. The sample obtained in step (3) is treated with LOFAR spectrum enhancement. The specific sample processing process and calculation process are in Section 3.3. Then the LOFAR spectrum with enhanced line spectrum characteristics is obtained. The LOFAR spectrum is a two-dimensional matrix, which can be regarded as a single-channel image. After that as shown in Figure 1, the data enhanced by the multi-step decision LOFAR spectrum is input into the CNN network for subsequent identification.
## 3 Results and Discussion
### Saccharomyces as Inhibiting Species  
Table 3 reported the inhibitory spectrum of tested _Saccharomyces_ strains with respect to all the studied yeast and bacterial strains.  
In this study, the inhibitory activity of _S. cerevisiae_ strains was detected on both culture medium and wine agar. With regard to the first substrate, _S. cerevisiae_ superlievito alocligens inhibited all the bacterial strains, with the exception of _O. oeni_ strains and _L. plantarum_ T1. These findings confirm the potential of selected _S. cerevisiae_ strains against spoilage LAB in wine [72]. In particular, it was proven the existence of a _S. cerevisiae_ peptidic fraction, with a molecular weight lower than 10 kDa, that inhibits the growth of _L. hilgardii_[63]. This antagonism could find application also in the reduction of biogenic amines produced by specific _L. hilgardii_ strains in wine [72, 73, 74]. The trends against _L. plantarum_ strains, but not counter to _O. oeni_ strains, could contribute to explaining the impact of inoculation of the _S. cerevisiae_ selected strain on the spontaneous malolactic consortium [30]. This is an aspect of particular interest if we consider the rising attention delve a new species of interest in malolactic control [75]. In general, the evidence confirms the continuous interest in the study of yeast-bacteria compatibility in winemaking [28; 69; 76].  
In this study, it has been detected inhibitory activity of _S. cerevisiae_ T2 toward two other strains of _S. cerevisiae_: superlievito alocligens (on wine agar) and on the autochthonous strain I6 (on culture medium and wine agar). This second inhibition represents the only case in which the same inhibition occurred on more than one substrate by means of interactions plate assay in the present study. In the last years, intra-specific inhibitions between different strains of _S. cerevisiae_ have been reported, also with contrasting results in terms of prevalence among wild and commercial _S. cerevisiae_ strains [77; 78; 79].
## 3 Network Models and Related Definitions
### Network Models
#### 3.2.2 Network Connectivity Rate  
Network connectivity rate \(C_{n}\) is defined as the ratio of \(n_{c}\) to \(n\), where \(n_{c}\) denotes the number of nodes that can communicate with the sink node through sing-hop or multi-hop communication, and \(n\) is the total number of nodes in the monitored underwater environment space, therefore, \(C_{n}\) can be calculated as follows:  
\[C_{n}=\frac{n_{c}}{n} \tag{7}\]  
If \(C_{n}\) is equal 1, the network achieves full connectivity, namely, all nodes can communicate with the sink node through single-hop or multi-hop communication.
# DPMF: Decentralized Probabilistic Matrix Factorization for Privacy-Preserving Recommendation
## 3 The Proposed Model
### Decentralized PMF  
Inspired by [18], in this study, we devise an efficient and privacy-preserving method to train the model. Specifically, the user latent factor matrix \(U\) and the offset matrix \(Q\) are kept on users' end devices and are updated locally using each user's private data. The item rating latent factor matrix \(V\) and the item selecting latent factor matrix \(Z\) are learned collaboratively by users. Since \(V\) and \(Z\) are stored on each user's hand, we use \(V_{j}^{i}\) and \(Z_{j}^{i}\) to denote the local version of \(V_{j}\) and \(Z_{j}\) stored on user \(i\)'s end device. To prevent malicious inference, \(V_{j}^{i}\), \(Z_{j}^{i}\) are decomposed into personal parts \(vq_{j}^{i}\), \(zq_{j}^{i}\) that are stored at user \(i\)'s end and public parts \(vp_{j}\), \(zp_{j}\) that users collaboratively learn:  
\[V_{j}^{i}=vp_{j}+vq_{j}^{i} \tag{20}\]  
\[Z_{j}^{i}=zp_{j}+zq_{j}^{i} \tag{21}\]As shown in Figure 3, in real-world scenarios, \(vp_{j}\) and \(zp_{j}\) are the public latent factor vector that all users collaboratively learn, thus at every moment there is a copy in each user's hand. That is to say, what user \(i\) actually holds are \(vp_{j}^{i}\) and \(zp_{k}^{i}\):  
\[V_{j}^{i} =vp_{j}^{i}+vq_{j}^{i} \tag{22}\] \[Z_{j}^{i} =zp_{j}^{i}+zq_{j}^{i} \tag{23}\]  
Another question for implementing training is determining how users decide which neighbors they should communicate with. Normally, there is a large number of users in a decentralized learning model, so it is impractical to let all users communicate with each other for two reasons: (1) the communication cost is heavy, (2) fewer users are online simultaneously. Thus, we let users only communicate with a small group of other users, which we call neighbors in this study. According to [3], different neighbors have a different impact on learning a global model with user \(i\), therefore we need to add a weight for each of user \(i\)'s neighbor \(j\). To solve the above problems, we define a user adjacency matrix \(E\) with each of its element \(E_{ij}\) denotes the relationship between user \(i\) and user \(j\). Normally user \(i\) and user \(j\) having a close connection leads to a large \(E_{ij}\). Furthermore, we obtain \(d\) closest users for user \(i\) (\(\mathcal{N}^{d}(i)\)) by sorting all \(E_{ij},j\in 1,2,...,n\) and select the largest \(d\) ones. For the sake of simplicity, we also call \(\mathcal{N}^{d}(i)\) as user \(i\)'s neighbors in this study. Once user \(i\) has finished one round of its local training, it will send its public gradients to its neighbors to help them learn a global model. The overall process is summarized in Algorithm 2. Figure 3: Users share public gradients with its neighbors while keeping personal gradients at their end.
# Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry
## 2 Principles of Industry 4.0 from Food Perspectives and Meat 4.0 Concept
### Food Industry 4.0 Enablers
#### 2.2.6 Blockchain  
The meat supply chain has not been very efficient and has always been a cause of concern when it comes to environmental sustainability [48]. JBS, the largest meat producer, has been accused of deforestation of Amazonian forests for livestock grazing as well as passing off these cattle as legitimate [49]. These issues triggered JBS to implement blockchain technology to ensure traceability of its livestock and meat [50], since blockchain consists of distributed, decentralized, digital ledgers supported by a network of multiple computers. Thus, blockchain technology has the ability to provide market regulators and consumers with increased levels of transparency and confidence in food quality and safety. Several applications of blockchain technology in the meat sector have resulted in raising consumer confidence through tracking technologies, tackling food fraud, non-tampering of data, secure information storage and providing greater levels of trust in meat supply chains [51].
# Bi-Objective Function Optimization for Welding Robot Parameters to Improve Manipulability
## 5 Simulation Results
### Comparison of Initial and Optimized Parameters
Linear velocity has an overall negative correlation with manipulability Obj.1 and the overall design variables. The shorter the length of the design variables, the more favorable the linear velocity manipulability. Angular velocity manipulability Obj.2 has an overall positive correlation with the total design variables, as opposed to linear velocity manipulability. The longer the length of the design variable, the more favorable it is for angular velocity manipulability. There is a negative correlation between the objective functions because two objective functions with conflicting correlations between the design variables must be improved simultaneously. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\mathbf{L_{1}}\) & \(\mathbf{L_{2}}\) & \(\mathbf{L_{3}}\) & \(\mathbf{L_{4}}\) & \(\mathbf{L_{5}}\) & \(\mathbf{L_{6}}\) \\ \hline Initial Parameters (m) & 0.309 & 0.295 & 0.432 & 0.278 & 0 & 0 \\ \hline \multirow{4}{*}{Optimized Parameters (m)} & 0.26 & 0.349 & 0.549 & 0.317 & 0.145 & \(-\)0.015 \\ \cline{2-7}  & 0.259 & 0.34 & 0.548 & 0.314 & 0.145 & \(-\)0.01 \\ \cline{1-1} \cline{2-7}  & 0.259 & 0.34 & 0.548 & 0.314 & 0.146 & \(-\)0.009 \\ \cline{1-1} \cline{2-7}  & 0.257 & 0.354 & 0.549 & 0.316 & 0.148 & \(-\)0.016 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Optimized design variables. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Manipulability** & **Measurement** & \multicolumn{2}{c}{**Comparison Item**} & \multicolumn{1}{c}{**W566/H300**} & \multicolumn{1}{c}{**W880/H400**} & \multicolumn{1}{c}{**W1036/H600**} \\ \hline \multirow{6}{*}{Linear Manipulability} & \multirow{2}{*}{\(u_{1}(A)\)} & Initial & Mean & 4.1298 & 4.6458 & 5.0344 \\ \cline{3-6}  & & & Std & 0.5039 & 0.4975 & 0.6741 \\ \cline{3-6}  & & Optimum & Mean & 4.1162 & 4.4927 & 4.7170 \\ \cline{3-6}  & & & Std & 0.4550 & 0.3732 & 0.4204 \\ \cline{2-6}  & & & Mean & 0.1359 & 0.1475 & 0.1519 \\ \cline{3-6}  & & & Std & 0.0124 & 0.0180 & 0.0188 \\ \cline{3-6}  & & Optimum & Mean & 0.2392 & 0.2548 & 0.2628 \\ \cline{3-6}  & & Std & 0.0216 & 0.0300 & 0.0314 \\ \hline \multirow{6}{*}{Angular Manipulability} & \multirow{2}{*}{\(u_{1}(A)\)} & Initial & Mean & 1.7415 & 1.7917 & 1.7518 \\ \cline{3-6}  & & Std & 0.2898 & 0.3366 & 0.3822 \\ \cline{3-6}  & & Optimum & Mean & 1.6204 & 1.5936 & 1.5679 \\ \cline{3-6}  & & Std & 0.2095 & 0.2599 & 0.3620 \\ \cline{3-6}  & & Initial & Mean & 2.4347 & 2.3968 & 2.4286 \\ \cline{3-6}  & & Std & 0.2061 & 0.2384 & 0.2655 \\ \cline{3-6}  & & Optimum & Mean & 2.5119 & 2.5306 & 2.5498 \\ \cline{3-6}  & & Std & 0.1500 & 0.1834 & 0.2494 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison with initial and optimum variables. Figure 15: Linear and angular velocity manipulability evaluation results by work object size.
## 3 Proposal of Ground Station Direction Detection Method Using Color Markers
### Color Marker Detection Algorithm
#### 3.1.1. Blob Coordinate Detection  
The blob detection process binarizes a specified color gamut in an acquired image and calculates the center of gravity of the detected blobs. In binarizing an image, a histogram of luminance is flattened to improve contrast by influencing backlighting and reflections.  
The coordinates of the center of gravity of each blob after binarization can be calculated as follows. Assuming that the coordinates of white pixels in the image are (\(x\), \(y\)) and the pixel value is I(\(x\), \(y\)), the area A of a blob is expressed by Equation (1).  
\[\mathrm{A}=\sum_{\alpha}\sum_{y}I(x,y) \tag{1}\]  
Furthermore, the coordinates of the center of gravity of blob \(\left(x_{g},y_{g}\right)\) are given by Equations (2) and (3).  
\[x_{g}=\frac{1}{\mathrm{A}}\sum_{\alpha}\sum_{y}x\cdot I(x,y) \tag{2}\]  
\[y_{g}=\frac{1}{\mathrm{A}}\sum_{\alpha}\sum_{y}y\cdot I(x,y) \tag{3}\]  
This coordinate is called blob coordinate \(B_{n}\left(x_{gn},y_{gn}\right)\).
# 3D-CNN-Based Fused Feature Maps with LSTM Applied to Action Recognition
## 4 Experiments
### Results and Comparison Analysis
#### 4.3.1 Effect of Different Feature Fusion Techniques  
In this section, we analyze the effect of different early fusion methods such as element-wise sum, concatenation, element-wise max and linear weighted fusion on our proposed framework. We show the recognition accuracy for UCF Sports dataset and also each split of UCF 101 dataset, and the average recognition accuracy over the three splits. The results are reported in Table 2. We observe that linear weighted fusion enhances the recognition accuracy of our approach by a fair margin, compared to other fusion methods. This enhancement may be due to the fact that the linear weighted fusion method efficiently fuses spatial and motion features. Therefore, we choose the linear weighted fusion method as our fusion scheme to fuse spatio-temporal features.  
Figure 7: Sample frames from UCF101 (**first row**), HDMB51 (**second row**) and UCF Sports (**third row**).
## 3 SIF Retrieval Methods
### O\({}_{2}\) Transmittance Compensation on FLD and SFM
Note that Equation (11) indicates that it is mathematically equivalent to minimizing the spectral difference between the modelled and measured radiance at TOC or at sensor level, given that the O\({}_{2}\) transmittance correction is included in the formulation. The first order O\({}_{2}\) compensation techniques detailed here are addressed for measurements acquired at a few meters distance from TOC surface level. These techniques cannot be extrapolated to airborne or satellite level, where the atmospheric path radiance (\(L_{0}\)) and spherical albedo (\(S\)) must be included in the atmospheric correction scheme. Furthermore, whereas O\({}_{2}\) absorption affecting proximal sensing is still dominated by O\({}_{2}\) gas concentration (Figure 2), as the atmospheric path increases, aerosols and molecular scattering also play an important role in this spectral region and thus must be carefully compensated.
# Joint Cell Association and User Scheduling in Carrier Aggregated Heterogeneous Networks
## 3 Joint Cell Association Scheme for Multi-Flow Carrier Aggregation
### Multi-Flow Problem Reformulation  
In this part, for each user it is assumed that they could be associated with multiple BSs on different carriers at one time, which indicates that the constraint on \(\psi_{k,m}^{n}\in\{0,1\}\) has been relaxed to \(\psi_{k,m}^{n}\in[0,1]\). As long as \(\psi_{k,m}^{n}>0\), user \(m\) will be fractionally scheduled on carrier band \(n\) by BS \(k\), with regard to which, a single flow problem is relaxed to a multi flow problem formulation.  
In order to achieve better performance on traffic load balancing across different layers of the HetNets, logarithmic utility is considered instead of the linear utility in (8). With such considerations, fairness among users are better covered by the concaveness of the logarithm, which also helps to balance the traffic between different tiers.  
Therefore, under equal resource allocation assumption among all of the users, the joint cell association problem is reformulated as  
\[\begin{array}{ll}\underset{\boldsymbol{\Psi},\boldsymbol{L}}{\max}&\sum \limits_{n=1}^{N}\sum\limits_{k=1}^{K}\sum\limits_{m=1}^{M}\psi_{k,m}^{n}log( \frac{C_{k,m}^{n}}{L_{k}^{n}})\\ \text{s.t.}&\sum\limits_{k=1}^{K}\sum\limits_{n=1}^{N}\psi_{k,m}^{n}=1,\ \ \forall m,\\ &0\leq L_{k}^{n}=\sum\limits_{m=1}^{M}\psi_{k,m}^{n}\leq M,\ \ \forall k,n,\\ &0\leq\psi_{k,m}^{n}\leq 1,\ \ \forall k,n,m,\end{array} \tag{9}\]  
where \(\boldsymbol{\Psi}=(\psi_{k,m}^{n})_{k\in\mathcal{K},m\in\mathcal{M}}^{n\in \mathcal{N}}\) and \(L=(L_{k}^{n})_{k\in\mathcal{K}}^{n\in\mathcal{N}}\).
# Multipath Error Fusion Modeling Methods for Multi-GNSS
## 2 Materials and Methods
### Sidereal Filtering for Multi-GNSS Multipath Correction
The orbit repeat periods of the GEO and IGSO satellites of BDS was 1 day, and the time shift was set as 246 s, which is the average statistical results of such satellites. The orbit repeat period of MEO satellites was 7 days, and the time shift was set as 1700 s, which is the average statistical results of such BDS2 and BDS3 satellites. These were also consistent with the results calculated by [PERSON] et al. using [PERSON]'s third law and broadcast ephemeris for different constellation types of BDS2 [24]. There are few analyses related to the SF period of the Galileo in related research at present. Based on the above statistical method of GPS and BDS satellites, the orbit repeat period of Galileo satellites was 10 days, and the time shift of Galileo satellites was set as 2424 s, which is the average statistical results. According to the above analysis results of each GNSS, we further extended the SF method of the observation domain in Galileo, so as to realize the SF modeling of multi-GNSS. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Constellation Type** & \(n\) & \(k\) \\ \hline GPS & MEO & 1 & 2 \\ BDS & GEO & 1 & 1 \\  & IGSO & 1 & 1 \\  & MEO & 7 & 13 \\ Galileo & MEO & 10 & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Values of \(n\) and \(k\) for the different constellations in each system.
# Shear Resistance Assessment of the Y-Type Perfobond Rib Shear Connector under Repeated Loadings
###### Abstract  
The steel-concrete composite structures consist of two different material parts, which are connected with reliable shear connectors to enable the combined action of the steel and concrete members. The shear connectors may experience either one-directional repeated cyclic loadings or fully reversed cyclic loadings depending on the structural functions and acting loadings. It is essential for structural engineers to estimate the residual shear strength of the shear connectors after action of repeated loads. The characteristics of deteriorating shear capacities of Y-type perfobond rib shear connectors under repeated loads were investigated to estimate the energy dissipating capacity as well as the residual shear strength after repeated loads. To perform the repeated load experiments four different intensities of repeated loads were selected based on the monotonic push-out tests which were performed with 15 specimens with five different design variables. The selected load levels range from 35% to 65% of the representative ultimate shear strength under the monotonic load. In total, 12 specimens were tested under five different repeated load types which were applied to observe the energy dissipating characteristics under various load intensities. It was found that the dissipated energy per cycle becomes stable and converges with the increasing number of cycles. A design formula to estimate the residual shear strength after the repeated loads was proposed, which is based on the residual shear strength factor and the nominal ultimate shear strength of the fresh Y-type perfobond rib shear connectors. The design residual shear strength was computed from the number of repeated loads and the energy dissipation amount per cycle. The reduction factor for the design residual shear strength was also proposed considering the target reliability level. The various reduction factors for the design residual shear strength were derived based on the probabilistic characteristics of the residual shear strength as well as the energy dissipation due to repeated loads.  
steel-concrete composite structure; Y-type perfobond rib shear connector; repeated loads; energy dissipation; residual shear strength; reduction factor; target reliability; probabilistic characteristic +
Footnote †: journal: Applied Sciences