esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Polymerization Shrinkage Evaluation  
Polymerization shrinkage kinetics are demonstrated in Figure 7, and the ultimate linear strain values are listed in Table 2. Shrinkage during the photocuring process of a dental composite resin is attributed to the conversion of van der Waals spaces to shorter covalent bonds [62] that contribute monomers like TEGDMA [63] into the polymer network. As a result, internal contraction stresses are created, leading to deformation in the surrounding tooth structure [64]. The real-time strain measurements (Figure 7) showed a steep augmentation at the early stage of 0.17-1.29 min for the total of nanocomposites, yielding a profile which is very close to that described by [PERSON] et al. [65]. The calculated values of the ultimate strain recorded for the majority of the experimental nanocomposite resins are in accordance with those reported by [PERSON] et al. for Bis-GMA/TEGDMA dental composites incorporating SiO\({}_{2}\) particles and fibers, namely in the range of 3.9-2.5% [66]. [PERSON] et al. also referred to 1.76-5.07% shrinkage regarding 17 commercially available dental composite resins [67]. Similar values have been also recorded for dental composites reinforced with clay nanoparticles [68]. Composite resins filled with the MPS-modified silica nanoparticles showed the lowest setting contraction (2.87%), whilst the addition of silanized particles with MPS blends increased the strain values. Even higher curing shrinkage was found for the nanocomposites containing the pure ACPS (3.35%) and its corresponding blend with OTMS. The highest setting contraction was finally observed when composites were loaded with non-treated silica and OTMS silanized nanofillers (3.68% and 3.65% respectively). The inorganic filler percentage, the molecular weight of involved monomers, and the degree of conversion are some parameters capable of affecting the shrinkage [62]. Herein, the overall tendency of the strain data almost followed the trend of degree of conversion, as polymerization shrinkage is directly related to the number of the reacted double bonds. It is worthy to point out that the enhancement with all the silane blends resulted in linear strain data within the values recorded for nanocomposites with the relative individual silanes.  
Figure 6: Degree of conversion (DC %) versus curing time for the experimental dental nanocomposite resins reinforced with 60 wt% of different types of silica nanoparticles.
###### Abstract  
Ground Penetrating Radar (GPR) is a non-destructive geophysical technique utilizing electromagnetic pulses to detect subsurface material properties. The analysis of regions of interest (ROIs) in GPR images often entails the identification of hyperbolic reflection regions of underground targets through accurate segmentation, a crucial preprocessing step. Currently, this represents a research gap. In the hyperbolic reflection region, manual segmentation not only demands professional expertise but is also time-consuming and error-prone. Automatic segmentation can aid in accurately determining the location and depth of the reflection region, thereby enhancing data interpretation and analysis. This study presents a deep residual Convolutional Neural Network (Res-CNN) that integrates skip connections within an encoder-decoder framework for ROI-binarized hyperbolic segmentation. The proposed framework includes designed downsampling and upsampling modules that facilitate feature computation sharing between these two modules through skip connections within network blocks. In the evaluation of both simple and complex models, our method attained PSNR, SSIM, and FSIM values of 57.1894, 0.9933, and 0.9336, and 58.4759, 0.9958, and 0.9677, respectively. Compared to traditional segmentation methods, the proposed approach demonstrated clearer segmentation results, enabling intelligent and effective identification of the ROI region containing abnormal hyperbolic reflection waves in GPR images.  
ground-penetrating radar; ROI; anomaly separation and extraction; residual convolutional deep neural network
# Weighting of Multi-GNSS Observations in Real-Time Precise Point Positioning
## 3 Results
### Convergence Time  
Figures 8 and 9 show the convergence time to the level of 0.01 m in both test periods for horizontal and vertical coordinate components, respectively. Additionally, mean convergence times for both test periods are listed in Table 7. Again, the GREC_L50 solution provides the worst results, with the convergence time exceeding 18 h for the horizontal component and some sessions with the vertical component not converging below the level of 0.01 m at all. Other multi-GNSS weighting schemes result in a significantly shortened convergence time for the majority of stations and days both in the horizontal and vertical component. The convergence time of the horizontal component is reduced by 30%, 24%, and 39% for GREC_L10, GREC_GxN, and GREC_GxS, respectively, when compared to the GPS-only solution. The convergence time of the vertical component is reduced by 37%, 26%, 47% for GREC_L10, GREC_GxN, GREC_GxS, respectively. In the GREC_N&S solution, the convergence time elongates by a factor of 2 and 3 for horizontal and vertical component, respectively.  
One can see again that the GREC_GxS weighting scheme leads to the best results, with a mean convergence time to 0.01 m level of about 2 and 3 h for the horizontal and vertical component, respectively. When compared to the GPS-only solution, this is an improvement of 39% for the horizontal components and 47% for the vertical component. The obtained convergence times are still too long for real-time PPP application or the anticipated error level of 0.01m is too strict. On the other hand, there are some fields, such as tsunami detection systems or precision agriculture, for which convergence time is not an issue.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **G\_L10** & **GREC\_L10** & **GREC\_L50** & **GREC\_GxN** & **GREC\_GxS** & **GREC\_N\&S** \\ \hline North [mm] & 4.2 & 6.5 & 9.2 & 5.7 & 3.9 & 6.5 \\ East [mm] & 8.4 & 14.0 & 28.8 & 15.6 & 8.0 & 22.5 \\ Up [mm] & 11.0 & 16.1 & 27.9 & 13.6 & 10.1 & 19.6 \\
3D [mm] & 15.2 & 22.8 & 42.4 & 22.2 & 14.2 & 31.5 \\ \hline \end{tabular}
\end{table}
Table 6: Mean coordinate repeatability for all stations during both test periods.  
Figure 7: Three-dimensional (3D) coordinate repeatability obtained in static PPP using different weighting schemes.
## 5 Probability of Data Error in IEEE 802.15.4
### Chip Error Rate to Data Symbol Error Rate
#### 5.1.1 Coherent Detection  
In the case of coherent detection, we can calculate the Hamming distance of each chip sequence. In particular, the recurrences of the Hamming distances of the data symbols is given in Table 3.  
Using these recurrences, the symbol error probability can be bounded as follows:  
\[P_{d}\leq\sum_{i=1}^{N}r_{i}P(h_{i},P_{c})=2P(12,P_{c})+2P(14,P_{c})+3P(16,P_{c}) +2P(18,P_{c})+6P(20,P_{c}) \tag{38}\]  
where \(P(h_{i},P_{c})\) is taken from Equation (37) and \(h_{i}\) and \(r_{i}\) are given in Table 3. \(i\) is the index for columns, and \(N=5\) is the number of columns in the table.  
Note that, for a fixed value of \(P_{c}\), the computational complexity of the previous upper bound is \(O(1)\).
# Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
## 5 Evaluation
### Impact of the Maximum Waiting Time  
The delay function specified in equation 6 governs the time nodes must wait for issuing a data packet to transmit their sensed information. This delay function has three parameters: \(p_{1}\), \(p_{2}\) and \(p_{3}\).  
\(p_{1}\) corresponds to TMAX, the maximum waiting time for a node to send a new packet with its measurements. In this section, we make an analysis of this parameter which must be adjusted according to the lifetime of the gathered information. Specifically, we have studied the impact of TMAX parameter with the following values 2, 5, 7, 10, 20, 40, and 50 s.  
Figure 14 presents the control overhead of both strategies with and without compression. We have obtained results for CS with different values of TMAX (2, 5, 10, 20, and 40 s), and we have compared it with the strategy without compression with a value of TMAX of 20 s.  
A first conclusion that we can deduce from this graph is that CS outperforms the no-compression option when the TMAX value is higher than 20 s. Actually, a TMAX value of 10 s produces a similar performance to the no-compression strategy. This can be considered, therefore, a threshold value in this scenario.  
Another satisfying conclusion we obtain from these graphs is that the advantages of using CS are more notable as vehicle density increases. In low-density scenarios, vehicles are less likely to find a neighboring node soon to forward the data to the destination. For this reason, their delay timer expires very often despite having a TMAX value of near one minute. However, as the density increases, the probability of finding a neighbor increases too. It does it in such a way that even a difference of a couple of seconds in TMAX causes a notable difference in performance, as we can see in the graph.  
Figure 13: Overhead of compression strategy vs no-compression strategy in msgs for TMAX = 20 s.  
This also occurs when measuring the carried sensed values. Figure 15 again presents a rising trend as the TMAX values increases for every density. This trend reaches its maximum in highly dense scenarios where the maximum number of data is reached with a value of TMAX of 20 s, e.g., with density 30. We can figure out that there is a critical gap during the 10 first seconds, where the number of data presents more variate results.  
Figure 14: TMAX impact onto the overhead without accumulation.  
Figure 15: TMAX impact onto the carried data by nodes.  
Regarding the differences between using an accumulation strategy, given that the TMAX value increases, there is not a significant variation between using CS with or without accumulation. That is, it influences both strategies equally.
# Design of a Binocular Pupil and Gaze Point Detection System Utilizing High Definition Images
###### Abstract
Recently, eye tracking technologies have revealed options useful for pupillometry. To have correct results in pupillometry, a high frequency and high resolution image-based eye tracking system is required. Nowadays, commercially available eye gaze tracking devices have monocular or binocular head movement-free applications and achieve up to 1000 fps (frames per second) frequencies [31; 32]. Low resolution (up to 640 \(\times\) 480) and low frequency (up to 30 Hz) cameras are selected to decrease the cost of design. [PERSON] et al. [33] proposed a monocular eye gaze tracking system which works at 15.7 Hz with 1600 \(\times\) 1200 pixel images for users wearing glasses. Their study is based on the detection of user's glasses and minimizing reflections on the surface of the glasses to facilitate pupil region detection by utilizing an illuminator controller device. [PERSON] et al. [11] presented a monocular head mounted eye gaze tracking system which works at 27 Hz with 640 \(\times\) 480 pixel images. Their method is aimed at reducing points seen by the user. This is inconvenient for the user during calibration stage as it generates virtual calibration points. Also, [PERSON] et al. [34] proposed a new method for gaze estimation. They diminished the additional corneal reflections caused by external light using band pass filter located on front of the camera. Their system is a binocular remote eye tracker that operates at 20 Hz with 1600 \(\times\) 1200 pixel images. In this study, we presented a remote eye gaze tracking and record system that has the ability to process full high definition (full HD) images. The system frequency is 40 Hz for full HD images. This corresponds to 280 Hz for 640 \(\times\) 480 size images. Outputs of our system are pupil sizes, pupil center positions, glint center positions and estimated gaze points. With these outputs, it is possible to interpret eye gaze and pupil information. The accuracy of our system is to less than one degree, which is a good level for single camera-based binocular systems. We developed this system by employing the LabVIEW program which is inherently parallel and has less time-consuming image processing algorithms. The remainder of this paper is organized as follows: Section 2 describes the details of the proposed method. Section 3 explains the experimental setup and results. Finally, the conclusions are presented in Section 4.
# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
## 5 Experiment and Analysis  
In order to verify the availability of our proposed solution, a virtual multi-data center environment was set up based on the OpenStack cloud computing framework. The main data center was composed of three Linux virtual machines. All of the three machines were developed with the SolrCloud environment, responsible for metadata index and retrieval. One of them was developed with OODT system framework, responsible for data ingestion and thumbnail archiving. The distributed sub-center was composed of eight Linux virtual machines, corresponding to eight satellite data centers. Each machine was mounted with a one-terabyte (TB) cloud drive so as to provide image storage space. In addition, all the machines in the main and sub centers were configured with 4 gigabytes (GBs) of RAM and 2 virtual processor cores. The framework of the virtual multi-data center environment is shown in Figure 8.
## 4 Experiments and Results
### Analysis of Classification Accuracy and Efficiency Results  
The analysis of the classification accuracy and efficiency results provides valuable insights into the performance of the plastic waste classification system. The accuracy was measured by comparing the algorithm's predictions with the ground truth labels, while the efficiency was assessed in terms of the algorithm's processing speed and real-time capabilities.  
The obtained classification accuracy (Figure 12b) of 91.7 mAP demonstrates the system's ability to accurately identify and classify plastic waste samples. The algorithm's high accuracy ensures minimal misclassifications, contributing to the overall efficiency and effectiveness of the waste sorting process. In terms of efficiency, the learning algorithm demonstrated real-time performance, keeping up with the conveyor belt's speed of 2 m per s. The algorithm's fast processing capabilities enable timely classification decisions, ensuring efficient waste-sorting operations.  
Figure 13 provides a graphical representation of the performance metrics achieved by the trained YOLO model. In the Precision graph (Figure 13a), one can see value of 1 at a confidence level of 0.84. In the Recall graph (Figure 13b), there is a 0.98 value recorded at a confidence level of 0. The Precision-Recall curve (Figure 13c) shows a mAP (mean Average Precision) score of 91.7 at a confidence level of 0.5. In the F1-score graph (Figure 13d), one can see 0.87 value at a confidence level of 0.519. As for Figure 14, it presents the confusion matrix generated by the trained YOLO model, which categorizes objects into PET and background across the entire dataset. One can observe the recognition rates as True Positive (TP): 91%, False Negative (FN): 100%, False Positive (FP): 9%, and True Negative (TN): 0%.  
Figure 13: Proposed System metrics: (**a**) Precision. (**b**) Recall. (**c**) Precision–Recall. (**d**) F-1 score.  
The combination of high classification accuracy and real-time efficiency indicates the system's potential for practical implementation in industrial settings. The accurate and efficient classification of plastic waste aids in optimizing recycling processes, reducing contamination, and promoting sustainable waste management practices.  
The experimental results and analysis highlight the success of the developed plastic waste classification system in accurately and efficiently classifying PET and PET-G samples. The high classification accuracy and real-time performance pave the way for the system's integration into industrial waste sorting operations, contributing to a more sustainable and environmentally conscious approach to plastic waste management.
## 4 Experiments and Results
### Error Analysis  
In order to improve the performance of the proposed method, zero failure cases were analyzed and these error cases were classified into the following categories, which can be used as references to further improve the performance of the model in the long run.  
Incomplete knowledge: (\(\approx\)35%) There is no corresponding knowledge base in the given knowledge, resulting in a mismatch between the question and knowledge. This is common in knowledge-based datasets, which reduces the accuracy of the model.  
Unbalanced information: (\(\approx\)25%) It is difficult to match the question-and-answer pairs because of information imbalance. Some answers in the datasets are short and simple, such as \"Of course\" or \"In the west\" in knowledge-based QA. Some questions and answers are too long to understand. Therefore, the model cannot determine the right answer.  
Wrong spelling or labeling: (\(\approx\)20%) Some questions have two answers, but only one is labeled correct. The model cannot understand because of misspellings. These are common in datasets. This indicates that the accuracy of the knowledge and datasets construction is essential.  
Other issues: (\(\approx\)20%) There are still some issues t that have not appeared and been analyzed, such as the equipment setup, training process, and noise issues.
## 4 Discussion  
The simulation code, validated in [33; 34; 35; 36], is able to predict the behavior of an EDFA under ionizing radiation, when introducing the measured RIA values into the simulation code itself. Then, the simulated estimate of the gain degradation can be compared with the measured ones, as presented in Figure 9a,b, for the dose rate 0.28 Gy/s and 0.093 Gy/s, respectively.  
For the highest dose rate, in Figure 9a, it can be highlighted that the simulated curve that best adheres to the experimental one is the one that considers the RIA values obtained with the highest pump time percentage (97% of the time slot). Therefore, we can denote that the higher the pump time percentage is, the smaller the deviation between the simulated and experimental curves and the better the software prediction will be. We have a maximum error of 20% in the case of the 85% injected pump. In this case, the deviation between the experimental and simulated curves increases with the dose, since the RIA at both  
Figure 8: Gain degradation as a function of the dose, for two dose rates: 0.28 Gy/s (blue dots) and 0.0093 Gy/s (red dots), at 25 \({}^{\circ}\)C.  
Figure 9: Evolution of measured–simulated gain degradation as the dose varies, for two dose rates: (a) 0.28 Gy/s; (b) 0.093 Gy/s.  
wavelengths of interest increases faster with the dose than in the 97% pump injection configuration in Figure 7.  
In conclusion, a larger time percentage of the pump injection implies a smaller overestimation of the RIA and a result closer to the one occurring in the EDFA.  
Figure 9b compares the experimental curve at a dose rate of 0.093 Gy/s and the simulated ones with a pump percentage of 97 and 99%. Both simulated curves agree with the measured one, within their error bars; however, by increasing the pump injection from 97 to 99%, the agreement slightly improves. In the closest possible situation to the real one, that of the blue curve in Figure 9b obtained by injecting the pump 99% of the time, the EDFA performance can be predicted with an error of less than 2%, since the overestimation of the RIA is strongly minimized. For the case of the red curve, with a pump injection of 97%, there is a maximum error of 5%, found at the maximum dose absorbed by the EDFA, which is 3 kGy. However, a pump percentage of 99% cannot be applied for each dose rate, because in cases of very high dose rates, in which the time slot is short, there might not be the possibility of recording a minimum number of acquisitions.  
We probably have a pump signal that remains in the 4.3 m of fiber sufficient to have a positive effect of the PB linked to the pump on the RIA.
# Positioning Information Privacy in Intelligent Transportation Systems:
## 1 Introduction
Potential misuse of such sensitive data by LSP while user blindly agrees to the terms of use or even unauthorized tracking of the vehicle may occur when relying on the ITS positioning techniques. Note, that such situations appear even today and many security-related questions of said networks are still open [31]. For example, U.S.-based LocationSmart company was leaking the sensitive information about the positions of the smartphones operating under various cellular operators [32]. A similar case was reported earlier the same month by The New York Times, the service initially used to monitor calls from inmates and allowing to find almost any cell phone in the U.S. in seconds was misused [33]. Note, it is possible to uniquely identify the node based on just four hours of the monitoring while having access to the position-related data provider in 95% cases [34] based on said digital activity 'footprints' [35]. From the V2X positioning perspective, researchers have proposed a considerable number of privacy-preserving protocols for data exchange between the vehicle and the surrounding nodes [36]. Most of these protocols imply authentication of the vehicle to the trusted anchor nodes [37]. However,protocols without mutual authentication may be vulnerable to a wide range of attacks that should be carefully taken into consideration during the system development phase. The main contributions of this paper can be listed as:  
1. an overview of existing privacy-related V2X solutions for infrastructure-based ITS systems;
2. a modified solution for data privacy enhancement based on well-known protocol;
3. a discussion of possible cybersecurity attacks on mentioned systems;
4. an overview of present standardization and General Data Protection Regulation (GDPR) related activities. The rest of the paper is organized as follows. First, we survey the existing solutions for vehicular localization in Section 2. Next, we provide an overview of the protocols that utilize additional data from the environment/user to assist in locating the vehicle in Section 3. Further, we propose a simple extension to said protocols in Sections 3 and 4. Next, we elaborate on potential attacks on proposed architecture in Section 5. The last section provides future perspectives concerning standardization aspects and concludes the paper.