esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Single-crystal quartz and vitreous silica were evaluated for use as reference materials for MW and THz measurements of complex permittivity. A secondary aim of this work was to produce a set of accurate data in order to clarify the large variations in the data reported in the literature.  
Single-crystal \(Z\)-cut quartz was confirmed as a highly reproducible material suitable for use as a reference. Ordinary and extraordinary refractive indices and absorption coefficients were measured between 0.2 and 6 THz. The optical phonon resonance in the ordinary ray spectrum was determined to be at 3.855 \(\pm\) 0.005 THz, with the absorption maximum at 10.7 \(\pm\) 0.1 cm\({}^{-1}\) and the FWHM of 145 \(\pm\) 5 GHz. The results were compared with the published literature. The ordinary refractive index at 36 GHz was found to be equal within measurement uncertainty to that at 1 THz, indicating a flattening of dispersion at frequencies below 1 THz.  
Silica glass samples from four different batches were examined and had their refractive indices and absorption coefficients measured between 0.2 and 4.5 THz. Variability among samples was found to be sufficiently significant to render silica glass unsuitable as a reference material. The results were compared with the published literature. As in quartz, the refractive index at 36 and 144 GHz was found to be equal within measurement uncertainty to that at 1 THz, indicating a flattening of dispersion at frequencies below 1 THz. In order to examine in more detail variations in absorption behavior, the value of \(\alpha(\
u)/\
u^{2}\) was plotted vs. frequency, revealing large differences in the Boson peak profiles. By studying multiple glass samples, it was possible for the first time to observe the negative relationships between refractive index and Boson peak frequency, and between Boson peak height and its frequency.  
Conceptualization, M.N. and A.G.; methodology, M.N. and A.G.; formal analysis, M.N. and A.G.; investigation, M.N. and A.G.; writing--original draft preparation, M.N.; writing--review and editing, M.N. and A.G. All authors have read and agreed to the published version of the manuscript. This work was supported by the project TEMMT, grant number 18 SIB09, under the EMPIR H2020 program of the European Union, and by the National Measurement System Programs Unit of the UK Department for Business, Innovation, and Skills. Not applicable. Not applicable. Not applicable. Not applicable.  
The authors declare no conflict of interest.
# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 3 Methodology
### Active Learning Query Strategy
#### 3.2.5 Querying Informative and Representative Examples (QUIRE)  
QUIRE combines the informative and representative strategies, taking a min-max view of active learning and providing a systematic way to measure and combine informativeness and representativeness. QUIRE measures both the informativeness and representativeness of an instance; specifically, the informativeness of an instance \(x\) is measured using its prediction uncertainty based on the labeled data, while the representativeness of \(x\) is measured by its prediction uncertainty based on the unlabeled data [41].
## 5 Discussion and Future Directions
Furthermore, assistive robots are also used combined with ambient sensors for activity recognition and monitoring. The connectivity is extremely relevant as it is responsible to provide data transmission and sharing among the sensing devices and the rest of the equipment in the framework. The majority of healthcare systems are based in wireless communications technologies [138]. The Bluetooth and Zigbee protocols are usually used for device-to-device communication, as well as Wi-Fi and mobile networks technologies, such as 3G/4G, have been used recurrently to provide Internet access [139]. The access to monitored data is provided using several approaches. On the one hand, the majority of the healthcare systems provides mobile access to data collected using a web interface. On the other hand, some healthcare systems do not provide remote data access. However, there is a clear tendency to develop systems that can be ubiquitously accessed anywhere, anytime [140; 141]. The results of the research on healthcare is promising as it provides real-time patient monitoring solutions as well as it often incorporates off-the-shelf mobile devices as sensing and processing units. It also offers activity and anomaly behavior recognition, real-time notifications, and alerts to the medical team and caregivers and a relevant acceptation rate by the users. Particularly, the IAQ monitoring solutions can provide a relevant dataset of the occupant's environmental quality and be correlated with their health status and living environment to support medical diagnostics. Moreover, real-time monitoring data can be used to generate alerts to advise the occupants to act in a useful time to promote health and well-being. Therefore, there is a relevant need to design and develop cost-effective IAQ solutions based on open-source technologies for ELEs and occupational health. However, there are also several limitations, such as energy consumption, validation and calibration in real scenarios, modularity and scalability, and more effective and ubiquitous notifications to act in useful time for ELE and well-being [142; 143].
### What Will Be Objectives of This Work  
In this study, we introduced 3D waveform inversion for mine microseismic event location and applied a multi-scale grid search algorithm based on global optimization into source location problem in a complex 3D local region (abbreviated as MS-GSWI location). Our main contributions and innovations are shown as follows:  
1. The SEM was used to effectively generate high-accurate 3D Green's function in a mine region with complex structures, which overcomes the difficulties of modeling multi-path effects and focusing and defocusing effects in ray tracing methods.
2. A 3D Green's function database in the mine was efficiently generated by the reciprocity theorem and served as multi-scale grid-search strategy, which largely improves the computational efficiency of 3D GSWI location.
3. Both the data-driven SVD-averaged fractional derivative based wavelet function (DD-SVD-FD wavelet) obtained by windowed specified seismic phases and traditional fractional-order Gaussian wavelet can be used for estimating the source-time function to generate synthetic waveformsfor waveform inversion well. By comparing them, waveforms synthesized by the DD-SVD-FD wavelet have a higher accuracy and more reasonable physical explanation.
4. Synthetic test results show that the accuracy of velocity model is important for waveform inversion-based location, and the average location error of eight blasting events is only 15.0 m. The location error is smaller than the 3D ray tracing-based location method (average location error 26.2 m) under the same 3D velocity model.  
The rest of this research is arranged as follows: Section 2 introduces main theories including synthetic waveform modeling based on the reciprocity theorem, estimation of source-time function with DD-SVD-FD wavelet, waveform modeling considerations, and multi-scale grid search strategy. In Section 3, dependence of GSWI location results to the accuracy of velocity model is tested by using velocity models with different smoothing degrees. Section 4 further tests effectiveness of the 3D proposed MS-GSWI location method by eight blasting events with known locations and compares it with previous published location results. In Section 5, the critical issues of 3D Green's function database modeling, estimation of source-time function, and effects of 3D velocity model on location accuracy are discussed. Section 6 makes summaries and puts forward prospects for future work.
## 4 Conclusions  
The concentrations of atmospheric PAHs and OH-PAHs showed strong correlations, with a seasonal increase in winter and a decrease in summer. Additionally, the health risk of each substance was evaluated in terms of estrogenic and antiestrogenic activities, with a focus on endocrine disruption. The current results indicated that the health risks of endocrine disruption caused by atmospheric OH-PAHs are higher in the winter than in the summer. Among the OH-PAHs, OH-chrysenes and OH-benz[\(a\)]anthracene had particularly high health risks. This is the first study to focus on the contribution of atmospheric OH-PAHs to endocrine disruption in humans. Further studies are needed for more target substances such as OH-benzo[\(c\)]phenanthrene, which show strong antiestrogenic activity, so as to evaluate the uptake factors and the effects of environmental conditions on secondary formation.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app12199469/s1](https://www.mdpi.com/article/10.3390/app12199469/s1)]([https://www.mdpi.com/article/10.3390/app12199469/s1](https://www.mdpi.com/article/10.3390/app12199469/s1)): Figure S1: box plot of seasonal atmospheric concentrations of PAHs in Kanazawa; Figure S2: box plot of seasonal atmospheric concentrations of OH-PAHs in Kanazawa; Figure S3: PCA score plot in which the first two PCs for atmospheric PAHs; Figure S4: PCA score plot in which the first two PCs for atmospheric OH-PAHs; Figure S5: Diagnostic ratios calculated for atmospheric PAHs; Table S1: Estrogenic and antiestrogenic activities of OH-PAHs from a yeast two-hybrid assay conducted by [PERSON] et al., (2007); Table S2: Binding affinities of PAHs and OH-PAHs to HER conducted by [PERSON] et al., (2007). Reference [30] is cited in the supplementary materials.  
Conceptualization, K.H. and H.N.; methodology, M.H. and L.Z.; validation, K.H.; formal analysis, M.H.; investigation, L.Z. and N.T.; resources, K.H. and H.N.; writing--original draft preparation, M.H.; writing--review and editing, K.H., L.Z., N.T. and H.N.; supervision, K.H.; project administration, K.H. and H.N.; funding acquisition, K.H. and H.N. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by Scientific Research (No. 17H06283) from Japan Society for the Promotion Science, the Environment Research, and the Environment Research and Technology Development Fund (Project Nos. 5-1951) of the Ministry of the Environment, Japan.  
Not applicable.  
We thank [PERSON] and staff of Food Industry Department, Niigata Agro-Food University for the analysis of OH-PAHs. We thank [PERSON], Faculty of Life and Environmental Sciences, Shimane University for review and editing. We thank [PERSON], from Edanz ([[https://jp.edanz.com/ac](https://jp.edanz.com/ac)]([https://jp.edanz.com/ac](https://jp.edanz.com/ac)): accessed on 22 August 2022) for editing a draft of this manuscript. We thank the Kanazawa City Environment Division Environmental Policy Department for arranging the PM\({}_{2.5}\) sampling site.  
The authors declare no conflict of interest.
## 4 Case Study and Results
### Experimental Setup
#### 4.1.3 Simulation Protocol  
For each bus line, a full delivery sequence was simulated, where the UAV performs a delivery at each stop along the line. The drone takes off from the roof of the bus, delivers a parcel near the stop, and rejoins the same or following bus.  
Simulations were executed for the following conditions:  
* **Mass variation**: Payloads from 1 kg to 10 kg were tested in 1 kg increments.
* **Scenarios**: All four mission scenarios (S1 to S4) were applied to each line and each payload configuration.
* **Bus lines**: Five representative lines of the Belfort network were used, with total road distances ranging from 6.78 km to 19.01 km (see Table 4).  
Each simulation outputs the total energy consumed by the UAV for all deliveries along the bus line. The results are analyzed in Section 4.2.
###### Abstract  
Driver fatigue has become one of the major causes of traffic accidents, and is a complicated physiological process. However, there is no effective method to detect driving fatigue. Electroencephalography (EEG) signals are complex, unstable, and non-linear; non-linear analysis methods, such as entropy, maybe more appropriate. This study evaluates a combined entropy-based processing method of EEG data to detect driver fatigue. In this paper, 12 subjects were selected to take part in an experiment, obeying driving training in a virtual environment under the instruction of the operator. Four types of enthrones (spectrum entropy, approximate entropy, sample entropy and fuzzy entropy) were used to extract features for the purpose of driver fatigue detection. Electrode selection process and a support vector machine (SVM) classification algorithm were also proposed. The average recognition accuracy was 98.75%. Retrospective analysis of the EEG showed that the extracted features from electrodes T5, TP7, TP8 and FP1 may yield better performance. SVM classification algorithm using radial basis function as kernel function obtained better results. A combined entropy-based method demonstrates good classification performance for studying driver fatigue detection.  
Keywords:electroencephalography (EEG); driver fatigue; entropy; support vector machine (SVM) +
Footnote †: journal: Applied Sciences
# Fast and Improved Real-Time Vehicle Anti-Tracking System
## 4 Conclusions  
The experimental results obtained show that our system of tracking presents the best results by using the Adaboost algorithm and SURF algorithm to detect a possible following vehicle. The proposed anti-tracking system has three mains parts: preprocessing, detection, and recognition. In the detection work, the proposed method was based on the AdaBoost algorithm with three cascades of classifiers (front view, left profile, and right profile) to detect vehicles in the video sequences. This algorithm, combined with good input image processing, is fast and robust in its ability to detect vehicles of different shapes and different brightnesses. The combination of three cascades allows us to obtain a higher rate of good detections. The SURF algorithm was used to detect previously detected vehicles in a new frame. An improvement can be made by enriching the base of images of different positions and rotations to cause several cascades of different poses--but that was not enough. We needed to find a system robust and accurate enough for our objective, so we improved the algorithm with a precise positive dataset, a feedback system, and a new segmentation method that gave us excellent results. Finally, the anti-tracking system determines which vehicle is a possible follower by running the recognition system every 30 results. The vehicle that is repeatedly recognized by the system is labeled as a potential pursuer. Further improvement to this project to get even more impressive results would be to associate a license plate recognition system to our tracking system to remove the vehicle color complexity.  
O.O.: conceptualization, data analysis, investigation, writing, and experiments; C.T.: supervising, reviewing, and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Tshwane University of Technology.  
The authors declare no conflict of interest.
# Efficient QC-LDPC Encoder for 5G New Radio
## 4 Proposed 5G NR QC-LDPC Encoder Design
### Proposed QC-LDPC Encoding Algorithm
As soon as \(c_{i,j}s_{j}\) and \(c_{i,k_{b}+j}p_{a_{j}}\) have been obtained, they can be used to determine the value of the corresponding parity bits in the second parity portion \(p_{c}\). This step can be performed in a single clock cycle. Hence, all the \(p_{c}\) parity bits can be acquired in \((m_{b}-g)\) clock cycles. The encoded codeword is then a combination of the original message \(s\) and the two calculated parity portions \(p_{a}\) and \(p_{c}\).
# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 3 Survey Methodology  
We used a variety of sources to search for journal articles and conference papers. These sources include the following databases and digital libraries: IEEE Xplore, ACM Digital Library, Springer, Elsevier, ScienceDirect, ResearchGate, Google Scholar, Scopus, MDPI online database, and PubMed. The distribution of papers read from different sources can be seen in Figure 1. The main keywords used for our searches were medical, image, and semi-supervised. Other searches included the addition of the terms \"analysis\" and \"classification.\" Additionally, for each category, the keywords related to that category were added to the search; consistency, adversarial, graph, GANs. Results were filtered for articles published between the years 2019 and 2022, and sorting was conducted based on relevance and number of citations when possible.  
The exclusion and inclusion of research articles were decided first by a preliminary abstract analysis, then by a full review of the article. The research papers which focused only on medical image segmentation and did not contain a dedicated section on classification were excluded. The inclusion criteria for our research were as follows:  
* The study must have a primary focus on semi-supervised learning.
* The study must include a clear report on the performance of the classification model.
* The study must present an in-depth description of the model architecture.  
The exclusion criteria of our research were as follows:  
* The study is not peer-reviewed or indexed in a reputable database.
* The study does not propose any significant addition or change to previously existing deep learning models.
* The study presents vague descriptions of the experimentation and classification results. For the popular benchmark datasets X-ray14 and ISIC2018, the models which did not achieve results comparable to existing state-of-the-art models were excluded.  
As the latest survey on this topic covered papers published in 2018 and prior [46], our paper presents research published between 2019 and 2022. Additionally, while [46] offered a general overview of unsupervised and semi-supervised methods in medical image analysis, this paper is specifically about semi-supervised learning for classification tasks and provides a more in-depth description of each of the models presented.
# Hybrid Clustering Algorithm Based on Improved Density Peak Clustering
###### Abstract  
In the era of big data, unsupervised learning algorithms such as clustering are particularly prominent. In recent years, there have been significant advancements in clustering algorithm research. The Clustering by Density Peaks algorithm is known as Clustering by Fast Search and Find of Density Peaks (density peak clustering). This clustering algorithm, proposed in _Science_ in 2014, automatically finds cluster centers. It is simple, efficient, does not require iterative computation, and is suitable for large-scale and high-dimensional data. However, DPC and most of its refinements have several drawbacks. The method primarily considers the overall structure of the data, often resulting in the oversight of many clusters. The choice of truncation distance affects the calculation of local density values, and varying dataset sizes may necessitate different computational methods, impacting the quality of clustering results. In addition, the initial assignment of labels can cause a 'chain reaction', i.e., if one data point is incorrectly labeled, it may lead to more subsequent data points being incorrectly labeled. In this paper, we propose an improved density peak clustering method, DPC-MS, which uses the mean-shift algorithm to find local density extremes, making the accuracy of the algorithm independent of the parameter dc. After finding the local density extreme points, the allocation strategy of the DPC algorithm is employed to assign the remaining points to appropriate local density extreme points, forming the final clusters. The robustness of this method in handling uncertain dataset sizes adds some application value, and several experiments were conducted on synthetic and real datasets to evaluate the performance of the proposed method. The results show that the proposed method outperforms some of the more recent methods in most cases.  
density peak clustering; mean-shift clustering; data mining +
Footnote †: journal:  
Article
## 6 Application II: Fast Filtering of Time-Vertex Signals
### Time-Vertex Filtering
As a two-dimensional extension of spectral filtering, the time-vertex filtering decomposes the input signal into \(NT\) orthogonal components, where each component corresponds to an outer product of a graph Laplacian eigenvector and a DFT basis function. Then, the components are amplified or attenuated by the corresponding scalars \(h(\lambda_{G},\lambda_{R})\). Finally, the scaled components are added together to obtain the filtered signal.
### Experimental Results
#### 2.3.1 Plates Subjected to a Single FSP Impact
It should be noted that an angle of 0\({}^{\circ}\) corresponds to a point placed horizontally at the right side of the impact hole and increases counterclockwise. For both configurations, the maximum out-of-plane displacement is recorded for points placed on the closest circle to the impact center. An analysis of the HSC images of the ballistic event highlights that the FSP effect is directly related to its caliber. This finding is confirmed by analyzing the in-plane strain fields corresponding to configurations 1 and 2. For example, the major principal strains of the second test of both configurations are represented in Figure 9. It is found that 12.7 mm size FSPs induce larger strains. In Figure 10, the major principal strains of points situated on the same three circles (as previously described) are analyzed and presented for the second test of configuration 2. The results suggest that the orientation of the FSP front surface at the moment of impact affects the magnitude of the major principal strain near the hole. Figure 8: The out-of-plane-displacement of points placed on a circle of a given radius (15 mm, 30 mm or 45 mm) from the impact center as a function of their orientation angle (the 0\({}^{\circ}\) angle corresponds to a point placed horizontally at the right side of the impact hole and increases counterclockwise) for the three tests of configurations 1 and 2 and shows the FSP anisotropic effect on plate deformation. Two main conclusions are extracted from the analysis of the DIC results of both configurations. The first one reveals that plugging is the main failure mechanism in the plates. The second uncovers that the same strain orientation pattern appears in all specimens regardless of the FSP's size or position on the target. However, the strain amplitude is influenced by the FSP caliber.
## 4 Discussion
This study only considered axial cracks under internal pressure, necessitating further research to extend the analysis to circumferential cracks and multi-axial loading conditions. Moreover, the assumption of a uniform adhesive layer thickness may not hold in practical applications due to manufacturing imperfections, making it essential to evaluate the impact of thickness variations on the results. Additionally, as this study was conducted under static loading conditions, future investigations should explore the effects of fatigue loading and environmental degradation on the performance of composite patch reinforcements to provide a more comprehensive and realistic assessment.