esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Correlation Analysis  
To discuss the difference between the rank results of these models, a correlation graph is plotted for these models' ranking results in Figure 2.  
It can be seen that compared with other models, the ranking given by the PageRank method differs greatly from the true ranking. Figure 2 shows that the results of PageRank are not similar to the results of other models. This is because the two NBA teams will play multiple times in one season. The PageRank algorithm reduces the results of multiple games to a total goal difference, ignoring the marginal effect of a single game. For example, consider that team \(j\) wins one game with team \(i\) by a score of 121 to 100 and loses a second game by a score of 90 to 91. The two margins of victory would be summed and modeled as a single weighted edge of two points from team \(i\) to team \(j\) in the PageRank model. Consider another example where team \(j\) wins one game with team \(i\) by a score of 110 to 100 and wins a second game by a score of 100 to 90. The two margins of victory would be summed and modeled as a single weighted edge of 20 points from team \(i\) to team \(j\) as well. However, obviously, the strength contrast between the two situations is completely different. The TeamRank model is game-wise, which constructs the graph game-by-game. Hence, compared with the traditional PageRank model, this generalized model improves the prediction ability of the graph model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **2015** & **2016** & **2017** & **2018** & **Average** \\ \hline Baseline & 0.9375 & 0.875 & 0.8125 & 0.8125 & 0.8594 \\ Colley & 0.875 & 0.875 & 0.875 & 0.8125 & 0.8594 \\ Massey & 0.8125 & 0.875 & 0.6875 & 0.8125 & 0.7969 \\ PageRank & 0.8125 & 0.875 & 0.8125 & 0.8125 & 0.8281 \\ Elo & 0.8125 & 0.875 & 0.8125 & 0.8125 & 0.8281 \\ TeamRank & 0.875 & 0.9375 & 0.875 & 0.875 & 0.8906 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Playoff accuracy of the results. Red values indicate the highest playoff accuracy for each season.  
Figure 2: Kendall correlation matrix for different models’ results.  
The problem of ignoring the marginal effect of a single game also exists in the models of [PERSON] and [PERSON], which makes them unable to measure the impact of the opponent's strength following a specific game. Like the TeamRank model, the Elo method considers the influence of the opponent's strength on the team rating. However, the Elo method only focuses on the points, and the update following one game is non-recursive, which makes it impossible to modify the impacts of previous games. Although the predictive ability of [PERSON], [PERSON], and Elo is not as good as the TeamRank model, the high correlations with the results of the baseline model show that the three models are hindsight models, which indicates that they have good backtesting ability.
# Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System
## 4 Performance Evaluation  
### Experimental Platform and Data Set  
In order to test the proposed ACNA algorithm, simulations and evaluations are performed on two data sets collected during the real driving. The experiment is conducted using a self-designed robot system named QJ-Racecar equipped with RTK, GNSS, INS and DDE sensors. Its mechanical structure and sensors are shown in Figure 6. Additionally, the sensor specifications are shown in Table 1 below.  
Figure 6: The mechanical structure and sensors of the QJ-Racecar.
# A Clustering WSN Routing Protocol Based on k-d Tree Algorithm
## 2 Related Work
### Cluster Head Selection  
Cluster-head capabilities depend on the clustering objectives or focus used in their formation, and taking into account the capabilities of the nodes and their effective range. Most of the work on CH node selection has focused on the energy capacity of network nodes [19; 20]. The following node attributes have been differentiating factors among the various clustering and CH node selection schemes.  
Regarding the mobility or stationarity of nodes and CH nodes in WSN hierarchical routing, some proposals adhere to mobile nodes where membership to each node changes dynamically and clusters are required to auto-configure and keep an updated members list with incoming and outgoing nodes [21; 22]. Other proposals favor a stationary approach, in which all nodes tend to group into stable clusters, which allows for simpler network administration and intra/inter-cluster communications [23].  
The literature includes types of nodes of extended hardware capability [24]. Some algorithms include references to advanced nodes, defined as those nodes with more energy [25]. Other approaches focus on network interoperability, with nodes for different types of connectivity [26], and interoperability between nodes of fixed and wireless networks [27].  
The types of roles of a node in a hierarchical protocol can be either a data transmission node or a CH node, and each node can change roles between rounds. Some proposals include auxiliary nodesthat can take the role of the CH node in cases where the CH node fails [28]. Other approaches use a fuzzy inference system to improve the adaptability of the selection of CH nodes, finding that the stochastic selection methods can not guarantee the availability of CH nodes [29].  
This article takes into account several aspects that are not approached completely in the cited literature. In particular, this article proposes a hierarchical routing protocol based on the k-d tree algorithm and on a reactive mechanism for the formation of CH nodes, validating its QoS through an experimental approach through simulation. Complementary information can be consulted in [4; 30], also aspects related to the location of nodes in WSN [31], design problems [32] and the extension of possible applications in other areas of knowledge such as robotics [33; 34], social networks [35] and applications that can support QoS [36; 37].
# ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks
## 5 Performance Evaluations
### Simulation Results
#### 5.3.2 Packets Delivery Ratio (PDR) Evaluation  
This series of experiments is carried out with the intention of determining how well the ERCP proposed algorithm performs in comparison to the HMBCR [10], ECRP [11], and EPSO-CEO [24] algorithms in terms of the packet delivery ratio for homogeneous and heterogeneous networks operating at varying traffic rates. Figures 3 and 4 demonstrate the variance in network PDR that occurs with the average traffic rate for networks that are either homogenous or heterogeneous, respectively. The figures show that the proposed ERCP algorithm produces the highest PDR compared to the HUMBCR, ECRP, and EPSO-CEO algorithms for both homogeneous and heterogeneous networks, even upon increasing the average traffic rate in the network. This enhancement is due to two main factors: The first is that the link quality is integrated into the selection decision of the cluster head, preventing the forwarding of data packets from member nodes to their cluster heads via unreliable links. That is to say, the obtained PDR is evidence that the proposed cluster head selection algorithm can improve network reliability through the proposed quality metric. The second factor is that the proposed routing algorithm reliably forwards the data packet by integrating the link quality into the routing decision.  
Figure 1: Influence of increasing average traffic rate on network lifetime for homogenous networks.  
Figure 2: Influence of increasing average traffic rate on network lifetime for heterogeneous networks.  
In contrast, HUMBCR, ECRP, and EPSO-CEO algorithms did not take into account the reliable data transmission, whether from the member nodes to their cluster heads or from the cluster heads to the sink node, which increases the packet loss rate and thus diminishes the network throughput.
# Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix
## 2 Methods
### Object-Oriented Multi-Feature Extraction  
#### 2.2.1 Spectral Features  
The spectrum value is the most direct and basic information of the image; it characterizes the intensity of the target object to electromagnetic wave reflection. In this study, the mean and variance of all pixel spectral features in the optimal segmentation object are selected as the object spectral feature vector.  
Given a remote sensing image \(\mathbf{I}=\{\mathbf{I}_{i}:i=1,\cdots,n\}\), where \(i\) is the pixel index and \(n\) is the number of pixels, we have \(\mathbf{I}_{i}=\{I_{ic}:c=1,\dots,h\}\), where \(c\) is the band index, and \(h\) is the number of bands. Then, the mean and variance characteristics of object \(j\) in \(c\) band can be expressed as:  
\[\mu_{jc}=\frac{1}{\big{|}\Omega_{j}\big{|}}\sum_{i\in\Omega_{j}}I_{ic} \tag{9}\]  
\[\sigma_{jc}^{2}=\frac{1}{\big{|}\Omega_{j}\big{|}}\sum_{i\in\Omega_{j}}\left(I _{ic}-\mu_{jc}\right)^{2} \tag{10}\]  
where \(\big{|}\Omega_{j}\big{|}\) represents the total number of pixels in the object \(j\), and \(i\in\Omega_{j}\) represents the pixels belonging to object \(j\).
## 4 Joint Extraction on Tunnel Faces Based on Image Segmentation Neural Network Models
### Joint Extraction of Tunnel Face Based on Mask R-CNN Deep Learning Architecture
#### 4.3.3 Analysis of Mask R-CNN Convolutional Neural Network for Tunnel Face Joint Extraction  
In this experiment, the dataset is the same as in Section 4.1.3. The 8580 sample images of size 512 \(\times\) 512 are divided into training and test sets in an 80% to 20% ratio. The preprocessed dataset is input into the Mask R-CNN convolutional neural network for training. The initial learning rate is set to 0.001, and the maximum number of iterations (max epochs) is set to 200. The classification loss (\(loss_{cls}\)), localization loss (\(loss_{bbox}\)), segmentation loss (\(loss_{mask}\)), and total loss are calculated, as shown in Equation (11).  
\[loss=loss_{cls}+loss_{bbox}+loss_{mask} \tag{11}\]  
When the loss function reaches its minimum value and stabilizes, the model converges. The changes in the loss functions are shown in Figure 29.  
As shown in Figure 29, the Mask R-CNN achieved stable loss values at epoch 35, with localization loss lower than classification and segmentation losses. The trained Mask R-CNN convolutional neural network is then used to test the test set. The comparison of the five groups of predicted images with their corresponding labeled images, as in Section 4.1.3, is shown in Figure 30.  
Figure 28: Bilinear interpolation effect.  
Figure 29: Changes in loss values.  
As shown in Figure 30, after classification, bounding box selection, and mask calculation, the Mask R-CNN network prediction results achieve good joint segmentation effects compared to the annotated results of the original images. Additionally, comparing the prediction results of the U-Net network shows that the joint segmentation effect is not affected by the proportion of the segmentation target in the image. Both the overall and local details are accurately segmented.
# Numerical Assessment of the Seismic Vulnerability of Bridges within the Italian Road Network
## 5 Discussion
### Fragility Curves
As stated earlier, this study is oriented toward a large-scale assessment of existing bridges. While matching ground motions to a Conditional Mean Spectrum is less conservative, the uncertainties related to existing bridges are quite high, and given that a code-based spectrum is essentially an envelope of spectral acceleration values at all periods, matching ground motions to a Uniform Hazard Spectrum allows for obtaining a certain degree of conservatism in the assessment. In the literature, examples of large-scale assessments can be found. The research of [PERSON] et al. [50] has its focus on the seismic vulnerability evaluation of the Italian bridge network and is here recalled since the approach that was used is essentially equal to the one employed in the present study (with the difference that natural records have been used in the present study). The proposed methodology, part of a framework for the automatic assessment of the Italian bridge network, employed artificial records to perform the NLTHAs needed to develop the fragility curves. It has been reported that collapse fragilities are more conservative if artificial accelerograms are used and that the comparison with natural records shows that the differences, which are non-negligible, are accepted for the purposes of the large-scale vulnerability assessment. A recent study by [PERSON] et al. [41] addresses the selection of ground motions for seismic fragility analyses. The research highlights the importance of ground motion selection in the construction of fragility curves using NLTHAs. The proposed methodology is almost the same as the one presented in this work, with only slight differences. Eight code-based spectra are defined following the indications reported in NTC2018 and EC8. The \(T_{r}\) of the elastic spectra goes from 50 to 10,000 years. This ensures that the selected ground motions represent a broad range of IM levels, gradually increasing from low to high levels. Two sets of 125 natural accelerograms are then selected, for stiff and soft soil conditions, matching the code-based spectra introduced earlier. The ground motions are then employed to perform NLTHAs on a benchmark structural model, analyzing the results to check if the selected sets of natural records are able to cover all the damage levels generally adopted in seismic risk analyses. The results of the study demonstrate that the selected sets are able to populate all the considered damage levels, providing reliable estimates of fragility parameters. The study is oriented, as the present work is, to a large-scale assessment.
# New Compact Wearable Metamaterials Circular Patch Antennas for IoT, Medical and 5G Applications
## 8 Conclusions  
Passive and active circular patch wearable antennas may be used in receiving or transmitting WBAN, 5G, IoT and medical systems. In transmitters, an HPA power amplifier is connected to the antenna. In receiving channels, a low noise amplifier (LNA) is connected to the antenna.  
Wideband passive and active circular patch antennas may be employed in wideband wearable 5G communication systems for commercial and medical applications. Passive and active printed antennas bandwidth, efficiency and gain and noise figure are presented and summarized in this paper. The directivity and gain of the antennas with split-ring resonators is higher by 2.5 to 3 dB than the antennas without CSRR. The resonant frequency of the antennas without CSRR is higher by 6% to 9% than the antennas with CSRR.  
The active and passive antennas presented in this paper are compact, low-cost, wideband passive and active antennas for receiving and transmitting wearable IoT, medical and 5G communication systems.  
Wearable body area networks seem to be a significant option for healthcare organizations, hospitals and patients. Wearable technology provides a convenient service that may improve the long-term health and physiological response of patients. Wearable systems support the development of personal health-care networks with online immediate response to treat and improve patients' health.  
The active transmitting circular patch output power is around 18 dBm. A summary and comparison of the electrical characteristics of the wearable antennas with and without CSRRs are presented in this paper.  
Figure 26: Global Medical monitoring health system with WBAN and BAN Systems.  
This paper presents wideband active and passive circular patch antennas with high efficiency for commercial and medical applications. The active circular patch antennas bandwidth is around 25% for a reflection coefficient lower than \(-10\) dB. The antenna bandwidth and gain were improved by optimizing the active antenna matching network. The active receiving circular patch antenna gain is around 13.5 dB. The wideband active and passive circular patch antennas may be designed as dual polarized and circular polarized antennas.  
In future work circular polarized circular patch metamaterial antennas with high efficiency for medical, IoT and 5G communication systems will be presented. Moreover, in future work fractal circular patch antennas, with and without CSRRs, with high efficiency for medical, IoT and 5G communication systems will be presented.  
This research received no external funding.  
The author declares no conflict of interest.
## 2 Materials and Methods
### Data Collection  
GPS data collars were fitted to sheep to evaluate if camp scale variability in vegetation composition and condition affects grazing patterns of small stock. In situ vegetation and soil surveys were carried out across the entire study area to assist with the interpretation of the remotely sensed data. Spectral indices were calculated from the Sentinel-2 imagery to make rangeland scale observations of vegetation conditions with the aim of better understanding the spatial relationships between soil depth, vegetation composition and sheep movement patterns. The following subsections provide more detail about these data collection activities.
# Students' Acceptance and Tracking of a New Container-Based Virtual Laboratory
## 3 Methods
### Proposed Hypotheses  
This study aims to analyze the theoretical factors/indicators/variables that influence students in their learning process. To perform this analysis, a Structural Equation Modeling (SEM) is proposed.  
Figure 1: Network Structure.  
These models are a general multivariate analysis technique, which hypothesizes and tests causal relationships among factors with a linear equation system [41; 42]. In addition to this, we have studied the results related to the students' interaction from several resources of the virtual platform (evaluation items, contents and forums).  
Causal models can involve either manifest variables, latent variables, or both. A manifest variable is a specific statement of the opinion survey, whereas a latent variable is a factor or indicator that groups a set of statements (in particular, 3 to 4 statements, in our case). The behavior of a latent variable can only be computed through their influences over manifest variables. In particular, a SEM model is visually represented as a directed graph where nodes are factors and directed edges (arrows) label the correlation among them. Our proposed model is shown in Figure 2.  
The factors/indicators (or latent variables) defined in this work are based on the UTAUT model defined in [40]. These factors are the following ones:  
* Perceived Usefulness (PU). Usefulness perceived by the student when using the laboratories based on containers.
* Estimated Effort (EE). Ease of use (or effort) perceived by the student when using the laboratories based on containers.
* Attitude (A). Students' resistance of using the proposed technology, and the benefits of using it for the practical activities.
* Social Influence (SI). Students' mind perceived from colleagues and lecturers about the practical experience with the laboratories.
* Ease of Access (EA). Perceived availability about educative resources by students.
* Intention of Use (IU). Possibility of using this type of technology for other experiences in the future.  
Therefore, next hypotheses are defined for our SEM model:  
* H1. The PU factor using the CVL technology will positively influence the A factor.
* H2. The EE factor using the CVL technology will positively influence the A factor.
* H3. The A factor using the CVL technology will positively influence the IU factor.
* H4. The SI factor using the CVL technology will positively influence the IU factor.
* H5. The EA factor using the CVL technology will positively influence the IU factor.  
Figure 2: UTAUT structural model and hypotheses (H1, H2, H3, H4, H5).
## 3 Method
### Auto Encoder  
CTDI adopts an Auto Encoder for feature extraction with similar settings to the related works [12; 13; 17; 18], as depicted in Figure 2. The encoder transforms the original feature X into the extracted feature Y. The decoder transforms the extracted feature back to output feature Z. Y is the Auto Encoder feature extracted from X, and the size of Y is the same as X.  
The encoder expressed in Equation (11) transforms X to Y with the weight W, bias \(b_{f}\), and activation function \(f\) in the encoder.  
\[\mathrm{Y}\ =\ f\ \left(\mathrm{W}\ \mathrm{X}\ +b_{f}\right) \tag{11}\]  
The decoder expressed in Equation (12) transforms Y to Z with the weight V, bias \(b_{g}\), and activation function \(f\) in the decoder layer. The activation function is Relu, which is the common choice of the related works [12; 13].  
\[\mathrm{Z}\ =\ f\ \left(\mathrm{V}\ \mathrm{Y}\ +b_{g}\right) \tag{12}\]  
Auto Encoder trains the hidden representation to minimize the differences between X and Z by the loss function, which consists of Mean Square Error (MSE), L2 regularization, and Kullback-Leibler divergence and is similar to the related works [12; 17; 18]. MSE,expressed in Equation (13) is the mean square error between X and Z, where D is the size of the dataset.  
\[\text{MSE}\ (\text{X},\text{Z})\ =\ \frac{1}{\text{D}}\sum\
olimits_{i\ =\ 1}^{\text{D}}\left(\text{X}_{i}-\text{Z}_{i}\right)\ ^{2} \tag{13}\]  
L2 regularization expressed in Equation (14) can avoid overfitting between the hidden layers by reducing their weights, where H is the number of neurons in the hidden layers and F is the number of features in the dataset.  
\[\Omega_{\text{L2}}\ =\ \frac{1}{2}\sum\
olimits_{i\ =\ 1}^{\text{H}}\sum \
olimits_{j\ =\ 1}^{\text{D}}\sum\
olimits_{k\ =\ 1}^{\text{F}}\left(\text{W}_{ji}\right)^{2} \tag{14}\]  
Kullback-Leibler (KL) divergence expressed in Equation (15) calculates the sparsity regularization to prevent overfitting of the hidden neurons by reducing them. The variable \(\rho\) is the sparsity parameter, as it is near 0 to reduce neuron values. KL divergence calculates the difference between the sparsity parameter and average activation, \(\rho_{i}\), to minimize their differences.  
\[\begin{array}{c}\Omega_{\text{sparsity}}\ =\ \sum\
olimits_{i\ =\ 1}^{\text{H}}\ \text{KL}\ (\rho\ ||\ \rho_{i})\\ =\sum\
olimits_{i\ =\ 1}^{\text{H}}\left(\rho\ log\frac{\rho}{\rho_{i}}+(1-\rho) log\frac{1-\rho}{1-\rho_{i}}\right)\end{array} \tag{15}\]  
The final loss Function (16) summarizes MSE, L2 regularization, and sparsity regularization, and the minimum error is set to \(10^{-4}\).  
\[Error\ =\ \text{MSE}\ (\text{X},\text{Z})+\ \Omega_{\text{L2}}+\Omega_{\text{sparsity}} \tag{16}\]
# Near Infrared Illumination Optimization for Vein Detection: Hardware and Software Approaches
## 2 Methodology
### Vein Image Processing and Morphological Process Enhancement
#### 2.3.5 Contrast Enhance  
Since the Hessian algorithm generated a low-contrast image, an increment of contrast is needed. This time, the clipping limit was 10. The clipping limit is determined by averaging the median and mean intensity values. This was to make the vein more apparent and distinct as the higher the clipping limit, the higher the contrast of the image. However, if the clipping limit is too high, the probability that the feature becomes obscure is high.
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 4 Experiments, Results, and Ablation Study
### Complexity  
In this section, we provide a quantitative analysis of our proposed method in terms of model parameters, computational complexity, and inference time. The detailed experimental results are summarized in Table 6. Compared to the baseline method PointMBF [24], our approach exhibited an increase of 25.5% and 14.9% in the number of model parameters and computational cost, along with an extension of 8.0% in inference time.