esa-sceva/satcom-chunk-collection huggingface dataset:
# Vision-Based Distance Measurement in Advanced Driving Assistance Systems
## 2 Related Work
In this study, we used instance segmentation to build a bridge between the depth map estimation and the distance measurement of a specific object. Instance segmentation is based on object detection and semantic segmentation, providing different labels for separate instances of objects belonging to the same class. As a relatively flexible model for instance segmentation, Mask R-CNN inherits the basic framework of Faster R-CNN and adds an object mask prediction branch [26]. As Mask R-CNN is easy to transfer to other tasks, is superior to most methods of instance segmentation, and only increases the computational load slightly as compared to Faster R-CNN, this paper employs Mask R-CNN to segment the target from the background for target distance estimation. One of the earliest applications of instance segmentation for distance estimation was performed by [PERSON] et al. They proposed a method that combines instance segmentation and a projection geometry model for distance estimation [27]. In the latest work of [PERSON] et al., they obtained the vehicle attitude angle using an angle regression model and a segmentation algorithm, and then estimated the distance to the vehicle ahead by constructing an \"area-distance\" geometric model [4]. In this paper, we combine the results of depth map estimation, which provide the depth information of each pixel, with the results of instance segmentation, which provide the classification information of each pixel, to estimate the absolute distances to different participants on the road, e.g., cars, vans, trucks, and pedestrians.
## 7 Simulation and Results
### Signature Based Routing  
In this section, we compare the Signature Based Routing with the hotline assisted routing and all wireless routing. The simulation setup is same as described earlier. Multiple sources send data to a sink. However, for simplicity, in Figure 22 we show the sources of a single PAN. In Figure 22, we show the node Id (please note that the node ids are the same as in our experiment, only the nodes of one PAN are shown here) against Average end-to-end Delay. Each source sent 100 packets to the sink.  
As we have already explained, SBR rectifies the latency problem for hotline assisted routing. This makes communication reliable with less number of hops. As shown in Figure 22, the value of SBR is either close to all wireless routing or hotline assisted routing. In other words, we can say that for every communication SBR chooses the best of either all wireless routing or hotline assisted routing.  
Figure 21: Average End-to-End Delay _versus_ number of Nodes.  
Figure 22: Comparison of SBR with hotline assisted routing and all wireless routing.  
We can see from Figure 22 that SBR is almost the same as hotline assisted routing for all cases except two (_i.e.,_ node 3 and node 30). For these two exceptions, SBR adopted the all wireless path as this path has fewer hops. Fewer hops mean more reliability so SBR helps to not only decrease end-to-end delay but also to increase the reliability.  
From these simulation results, we conclude that the proposed hotline assisted approach provides much better reliability as compared to the all wireless approach. Therefore, hotline assisted routing can serve as an effective and viable transmission alternative for mission critical applications. Moreover, SBR solves the delay problems of hotline approach making it more robust and reliable.
## 3 Results and Analysis of Results
### (Non) Use of Helmets and Reflective Vests
#### 3.2.3 Summary Regarding Helmets and Reflective Vests  
From the obtained results, it can be concluded that there are statistically significant differences between the non-use of the equipment and wearing a helmet only (\(t=5.489\); \(p=0.019\)), as well as between the non-use of the equipment and wearing both a helmet and a reflective vest (\(t=4.165\); \(p=0.042\)). These results apply to all speed testing and all vehicle categories. The results showed that if the cyclist used equipment of any type, the vehicles overtook him at a shorter lateral distance, while in situations when the cyclist did not use the equipment, the vehicles overtook him at a greater distance.
## 1 Introduction
In [14], four feature selection algorithms were utilized to reduce the sweeping frequencies of the reflected signals before taking them as the dataset to train the surface crack classification model. In [15], two ML models supported measuring the thickness of thin oxide scale using the terahertz time-domain signals as the input feature. In this paper, the material thickness classification models were established by taking the new training dataset extracted from the initial scattering parameters of free space measurement simulations including five input features in the frequency domain: relative permittivity, loss tangent, real and imaginary parts of S21, and frequency. We employed many support vector machine (SVM) kernels and their optimal pair values to construct the SVM model by comparing their results. We also designed and evaluated many deep neural network (DNN) architectures utilizing the same input dataset for the most suitable model. The classification abilities of these SVM and DNN models were then taken into consideration with a conventional method for comparison. Figure 1a illustrates the traditional method in measuring material thickness by approximating back from the S-parameters and the permittivity. The small dataset and the low accuracy of the approximation process may lead to unstable results. By adopting this proposed method, once the ML models are established with enough dataset, they support a reduction in time spent on a large amount of calculations as well as improve the estimation accuracy, which can bring about stable results. The methodology of this paper is described in Figure 1b. Firstly, the scattering parameters are attained from the simulations before processing through time-domain gating and de-embedding to obtain the exact S-Parameters of the material under test (MUT). The dielectric constants of the MUT are then extracted by the National Institute of Standards and Technology (NIST) iterative method. Thereafter, by incorporating five inputs including frequency, relative permittivity, loss tangent, and real and imaginary parts of S21 into the training phase, the thickness classification models are established. These five inputs extracted from the finite simulated free-space measurements were selected due to their correlations with the material thickness, as illustrated in Section 2. The paper is organized as follows. After the introduction, Section 2 presents the theory of the dielectric constants extraction algorithm and the method of collecting data for training, the results obtained for numerous sample thicknesses, and the input feature determination. Section 3 details the thickness classification results using the deep neural network (DNN) compared to those which are obtained by the support vector machine (SVM). In Section 4, conclusions are drawn.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 2 Underwater Communication Technology Overview
### Acoustic Communication  
As mentioned, RF transmission and optical transmissions have narrow propagation ranges. The first is rigorously affected by heavy attenuation, leading to a minor propagation distance, while the latter depends on the turbidity of water. So, acoustic communication is another technology which may be applied to greater distances, and is presently the leading technology for wireless communication underwater. The waveform's propagation speed depends on the medium's EM or mechanical properties. EM waves can propagate through air at a speed near to that of light in a vacuum, i.e., about 4 to 5 orders of magnitude greater than the propagation speed of acoustic waves in fluids. This levies tremendous checks on the complete transmission process by means of acoustic waves. Certainly, in acoustic-based communications, the parameters for the propagation speed play a very significant role. Underwater, the model for sound speed is calculated up to 1000 m.  
Figure 1: General underwater architecture and communication technologies.
## 3 Results and Discussion

Table 2: Univariate data analysis of image data: ID indicates the feature name, in columns half-red and red the median (5 th–95 th) percentile is reported for the two maturity stages, \(p\) is the \(p\)-value of the factor maturity stage obtained by LME, FC is the fold change calculated as the ratio between the median of red and the median of half-red, AUC is the area under the ROC curve and CI 95% is the confidence interval of AUC at the level of 95%. Figure 5: Correlations between ATR-FTIR and chemical data: heatmap; a different colour code was used to represent the clusters discovered by cluster analysis; \(\rho\) is the Pearson correlation coefficient; in the bottom, the average IR spectrum is reported. The relationships between image data and the chemical attributes (TA, TP, AA and TSS) were explored by heatmap (Figure 7). As in the case of ATR-FTIR data, two clusters were discovered for the chemical data. Specifically, TA, TP and AA behave similarly being positively correlated to the colour parameters, while TSS showed an opposite trend, being negatively correlated to the colour parameters. On the other hand, the colour parameters were grouped into two clusters, one formed by Chroma and a* and the other including b*, L* and Hue-angle. Only TA resulted to be significantly correlated to the image data that mirrored its trend. The development of fruit colour during ripening is considered a maturity index and when its intensity increases, the ripening attributes also improve. As the fruit colour development enhances, the TSS content increases proportionately. On the contrary, the trend of the fruit acidity decreases with the enhancement of the colour of fruit during ripening [30]. This result suggests that the prediction of TA by the colour parameters obtained by IA of the strawberry represent a valid method to rapidly assess the ripening stage. Figure 6: PCA model of the image data: circles represent the observations (technical replicates), while triangles indicate the medians of the technical replicates of the same sample; green is used for observations belonging to the half-red group and red for those of the red group. Figure 7: Correlations between image data and chemical data: heatmap; a different colour code was used to represent the clusters discovered by cluster analysis; \(\rho\) is the Pearson correlation coefficient.
## 2 Baseline UDS-Based SSI
### Performance of Pitch Estimation and V/Uv Decisions
The experimental results are summarized in Table 1. Although the performance of UDS-based pitch estimation is not as high as the speech-based pitch estimation (typically, \(\text{GPE}_{\text{rate}}<20\%\) for clean speech signal [42]), the superiority of the UDS signal in terms of pitch estimation is clearly found for all metrics. Such results suggest that UDS provides more useful information for pitch estimation. The relationship between the accuracy of the pitch estimation and the quality improvements gained by HE was not analyzed quantitatively. Nevertheless, a high degree of accuracy for the UDS-based pitch estimation would be helpful in improving the quality of the reproduced speech signals when the HE technique is adopted. Similar trends were observed in the V/UV decisions, as shown in Table 2. For the voiced frames, there was no remarkable difference in overall accuracy among the three modalities. However, the results clearly showed that more reliable detection of the unvoiced frames can be achieved by the UDS signal. The V/UV information is an important parameter that determines whether to apply the harmonic enhancement technique to a given speech frame. In other words, an incorrect decision in the unvoiced regions potentially leads to an unnecessary harmonic emphasis on many unvoiced regions, which degrades the overall quality of the reproduced speech signals. The results indicate the artifacts caused by inadequate harmonic emphasis can be reduced to some extent, by using the UDS-based V/UV decisions.
# Extended Geometry and Probability Model for GNSS+ Constellation Performance Evaluation
## 4 Experimental Results and Analyses
### Bds-3 Vs Gps
For the correlation level between station height and receiver clock difference, as shown in Figure 4o,p, BDS-3 in augmentation area (60\({}^{\circ}\) S-60\({}^{\circ}\) N and 50\({}^{\circ}\) E-170\({}^{\circ}\) E) is slightly higher than GPS in the same area. This may be because IGSO and GEO fly slowly and will persist over a user receiver for a longer time. Table 3 shows the average, minimal, and maximal visible satellite number and DOPs on global and regional scales. In the regional augmentation area, for BDS-3, 13.2 satellites can be seen on average, 2.7 and 2.2 more than the BDS-3 and GPS global average, respectively. Similarly, BDS-3 DOPs in the regional augmentation area is more favorable than BDS-3 and GPS on global scales. Because of fewer operational satellites of BDS-3 than GPS, on a global scale, visible satellites number and DOPs are slightly worse than GPS.
## 3 Results and Discussion
### Physicochemical and Chemical Characterization of Juices
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Cultivar**} & \multirow{3}{*}{**Stabilization** **Treatment**} & \multirow{3}{*}{**pH \(\pm\) SD**} & \multicolumn{2}{c}{**Total**} & \multirow{3}{*}{**Soluble**} & \multirow{3}{*}{**Colorimetric Parameters**} \\  & & & & **Titratable** & & **Soluble** & \\  & & & **Acidity** & & **Solids** & & \\ \hline \multirow{3}{*}{Golden} & NT & 3.7 \(\pm\) 0.1 & 4.6 \(\pm\) 0.7 & 12.2 \(\pm\) 0.4 & 49.9 \(\pm\) 0.7 & 0.1 \(\pm\) 0.3 & 33.7 \(\pm\) 1.5 \\  & TT & 3.2 \(\pm\) 0.0 & 4.5 \(\pm\) 0.2 & 12.1 \(\pm\) 0.2 & 59.4 \(\pm\) 0.4 & \(-\)7.0 \(\pm\) 0.0 & 43.0 \(\pm\) 0.9 \\ \cline{1-1}  & HPP & 3.4 \(\pm\) 0.0 & 5.3 \(\pm\) 0.2 & 11.8 \(\pm\) 0.9 & 51.2 \(\pm\) 0.2 & \(-\)2.9 \(\pm\) 0.2 & 39.1 \(\pm\) 0.7 \\ \hline \multirow{3}{*}{Pinova} & NT & 3.2 \(\pm\) 0.1 & 4.4 \(\pm\) 1.2 & 12.1 \(\pm\) 0.2 & 47.2 \(\pm\) 0.6 & 2.8 \(\pm\) 0.2 & 36.7 \(\pm\) 1.2 \\ \cline{1-1}  & TT & 3.3 \(\pm\) 0.0 & 4.8 \(\pm\) 0.8 & 12.7 \(\pm\) 0.4 & 44.6 \(\pm\) 0.6 & 2.2 \(\pm\) 0.4 & 34.3 \(\pm\) 1.1 \\ \cline{1-1}  & HPP & 3.3 \(\pm\) 0.0 & 5.2 \(\pm\) 0.4 & 12.5 \(\pm\) 0.1 & 41.1 \(\pm\) 0.5 & 3.4 \(\pm\) 0.6 & 36.9 \(\pm\) 1.1 \\ \hline \multirow{3}{*}{Red} & NT & 3.7 \(\pm\) 0.2 & 2.7 \(\pm\) 0.4 & 12.6 \(\pm\) 0.4 & 38.9 \(\pm\) 1.2 & 11.1 \(\pm\) 1.2 & 26.8 \(\pm\) 0.9 \\ \cline{1-1}  & TT & 3.7 \(\pm\) 0.0 & 2.7 \(\pm\) 0.1 & 12.2 \(\pm\) 0.5 & 40.9 \(\pm\) 0.8 & 4.4 \(\pm\) 0.4 & 34.9 \(\pm\) 0.8 \\ \cline{1-1}  & HPP & 3.7 \(\pm\) 0.0 & 3.0 \(\pm\) 0.0 & 12.3 \(\pm\) 0.2 & 37.6 \(\pm\) 1.0 & 6.3 \(\pm\) 0.9 & 32.0 \(\pm\) 2.6 \\ \hline \multirow{3}{*}{Cultivar (CV) Stabilization Treatment (ST) CV * ST & 0.000 & 0.000 & 0.091 & 0.000 & 0.000 & 0.000 \\ \cline{1-1}  & 0.017 & 0.070 & 0.841 & 0.011 & 0.003 & 0.000 \\ \cline{1-1}  & 0.001 & 0.891 & 0.322 & 0.042 & 0.166 & 0.001 \\ \hline \hline \end{tabular} Two way ANOVA, Tukey’s test (\(p<0.05\)). NT—non-treated juices; TT—thermal-treated juices; HPP—high-pressure processed juices;  
\({}^{1}\)—lightness, A*—Red/Green Value, B*—Blue/Vellow Value. °Rx is the symbol used for Brix degree.  
\end{table} 
Table 1: Influence of cultivar and stabilization treatment on physicochemical parameters of apple juices.
# A Hierarchical Decision Fusion Diagnosis Method for Rolling Bearings
## 3 Case Study
### Diagnostic Results of Rolling Bearings
#### 3.3.5 Fault Diagnosis Results under Variable Load  
All the above diagnosis results are based on zero load (HP0) vibration data fault diagnosis. In order to verify the adaptability of the proposed method to load change, the fault data under three constant load conditions, which are \(1\) horsepower (HP1), 2 horsepower (HP2) and 3 horsepower (HP3), respectively, were input into BPNNs based on zero load data training. Decision fusion diagnosis was used to diagnose rolling bearings.  
From Table 7, it can be seen that when the diagnostic condition was extended to other loads, the fault diagnosis in each layer could still reach a higher diagnostic rate, except for the fault degree diagnosis of the ball. Especially for fault detection, inner race fault degree estimation and outer race fault degree estimation, the diagnosis rate did not change too much after changing the working condition to a nonzero load. The proposed method has good adaptability to load change. In addition, the diagnostic rate of wavelet packet reconstruction signal is higher than that of raw vibration signal under the same working condition, which indicates the necessity of wavelet packet reconstruction of the original vibration signal.
# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 5 Use Cases
### Use Case 2: Root Cause Analysis for Service Problems
#### 5.2.1 Trace Cycles-Based Analysis  
In the first mode, it is based on the test cycles. This means, in all the analyzed 2742 test cycles, finding out how many trace cycles have these problem events and trying to look for the root cause. To that end, we use Python capabilities to achieve data-frames slicing with the Pandas library to select the desired trace cycles for both system and user views as defined previously [10]. Table 8 presents the results for the two points of view.  
From the results, we observe that the problems occur rarely with only 2.2% (58 traces) of the dataset. In these cases, we observe that the Web and download problems are detected a little more than the two problems of \"radio provisioning\" and \"Servers problem\". In particular, in 36 test cycles, the download service does not work well while the video service does. Contrary, from the user's point of view, 23 test cycles have the video service working well while the web browsing service is working less well. This is due to the model used to define the status (system view), which is based on the physical bitrate, while the calculation of the user MOS of the web service is based on the launch time [23; 24].  
Now, let us analyze the root causes of these problems. When we explore the six traces of the provisioning problem, we remark that the radio indicators (RSRP, RSRQ, etc.) are very poor during all of the test cycles. This is why all services are not available. Concerning the web and download problems, we notice that the radio indicators are good and then we assume that the servers are not well responding to their load.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Datatype** & **Description** \\ \hline Radio provisioning & All considered services not works. \\ \hline Web problem & Video is working but web failed \\ \hline Download problem & Video is working but download failed. \\ \hline Servers problem & Video is working but both web and download failed. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Service problem events list.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Problem According To** & **Problem Label** & \(\sharp\)**Measures** \\ \hline \multirow{4}{*}{System view} & Radio provisioning & 6 test cycles. \\  & Web problem & 14 test cycles. \\  & Download problem & 36 test cycles. \\  & Servers problem & 2 test cycles. \\ \hline \multirow{4}{*}{User view} & Radio provisioning & 8 test cycles.. \\  & Web problem & 23 test cycles. \\ \cline{1-1}  & Download problem & 13 test cycles. \\ \cline{1-1}  & Servers problem & 6 test cycles. \\ \hline \hline \end{tabular}
\end{table}
Table 8: Statistics of service problem events in the context of trace cycles mode.
# A Performance Prediction Method Utilizing Time-Dependent Subsystem Transfers between Family Systems
## 5 Performance Prediction for Cluster Systems
### Predicting Changes in Family Systems  
By continuously updating the inter-family transition matrix in real time and taking into account the current family system dynamics of the subsystem, it becomes feasible to ascertain the probability of any alterations in ownership within the family system of said subsystem. Moreover, this facilitates prognostication regarding the total number of subsystems that are likely to undergo a transition towards a failed state within their respective family systems, thereby enabling anticipation of the remaining lifespan of the cluster system. When the current time is \(t_{i}\), the number of subsystems belonging to the five family systems is \(ST_{1}^{t_{i}}\), \(ST_{2}^{t_{i}}\), \(ST_{3}^{t_{i}}\), \(ST_{4}^{t_{i}}\), and \(ST_{5}^{t_{i}}\), and the transition matrix between family systems is \(T_{i_{i-1}\to i_{t}}\). Therefore, at time \(t_{i+1}\), the number of subsystems belonging to family system \(ST_{1}^{t_{i+1}}\) is calculated using the following process.  
\[\begin{array}{rl}ST_{1}^{t_{i+1}}&=ST_{1}^{t_{i}}\times\frac{{}^{1}S_{1}^{T_ {1}}}{S_{1}^{t_{i-1}}}+ST_{2}^{t_{i}}\times\frac{{}^{1}S_{1}^{T_{2}^{t_{i}}}}{ {S_{1}^{t_{i-1}}}}\\ &+ST_{3}^{t_{i}}\times\frac{{}^{1}S_{1}^{T_{3}^{t_{i}}}}{{S_{1}^{t_{i-1}}}}+ ST_{4}^{t_{i}}\times\frac{{}^{1}S_{1}^{T_{4}}}{S_{1}^{T_{1}}}\\ &+ST_{5}^{t_{i}}\times\frac{{}^{1}S_{1}^{T_{5}}}{S_{1}^{T_{5}}}\\ \end{array} \tag{12}\]  
\(ST_{2}^{t_{i+1}}\), \(ST_{2}^{t_{i+1}}\), \(ST_{2}^{t_{i+1}}\), and \(ST_{2}^{t_{i+1}}\) can be obtained similarly. This calculation process is recorded as:  
\[S_{t_{i}}\otimes T_{t_{i-1}\to t_{i}} \tag{13}\]  
where \(S_{t_{i}}=\left[ST_{1}^{t_{i}},ST_{2}^{t_{i}},ST_{3}^{t_{i}},ST_{4}^{t_{i}},ST_ {5}^{t_{i}}\right]\).