esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, for crowd behavior detection, we used a fusion of global and local descriptors, that is, after foreground extraction, we extracted a human crowd contour as a global descriptor and a particles gradient motion (PGM) descriptor, along with geometric and speeded up robust features (SURF) as local descriptors. Using this fusion of global and local descriptors, bat optimization was then applied for optimal descriptors. Finally, by using [PERSON]'s information entropy theory [53], we introduced an improved entropy classifier to detect crowd behavior. Experimental results show that our proposed system performed better compared to existing well-known state-of-the-art methods. The proposed system has huge potential applications, such as crowd density estimation, security, care of the elderly and vulnerable, sports analysis, inspection tasks, military applications, robotic vision, video surveillance, and pedestrian traffic monitoring. The major contributions of this paper can be highlighted as follows:We propose a new particles force model for human silhouettes verification, which is a necessary step for accurate counting and tracking of multiple persons in crowded scenes. 2. We developed a novel particles gradient motion local descriptor and human crowd contour as a global descriptor, while the fusion of global and local features was used for crowd behavior detection. 3. We designed an improved entropy classifier to analyze contextual information and classify crowd behavior in a more efficient manner. 4. We evaluated the performance of our proposed multi-person tracking approach on a publicly available benchmark PETS2009 dataset while crowd behavior detection performance was evaluated on the publicly available benchmark UMN dataset and the proposed method was fully validated for efficacy, surpassing other state-of-the-art methods, including deep learning. The remaining structure of this paper was arranged as follows: Section 2 describes related work. A detailed overview of the proposed model for multi-person tracking and crowd behavior detection is mentioned in Section 3, which includes preprocessing, human silhouettes extraction, the particles force model, multi-person counting, multi-person tracking, global and local features extraction, bat optimization, and an improved entropy classifier. In Section 4, we evaluate the performance of our proposed approach on a publicly available benchmark dataset and give a detailed comparison of our proposed approach with other state-of-the-art methods. Lastly, in Section 5, we sum up the paper and outline future directions.
## 3 Wireless Localization Technology
### Range-Based Techniques
#### 3.1.4 Angle of Arrival (AoA)  
AoA refers to the direction of the signal coming from the device toward the anchor. By leveraging and measuring the delay difference of the arrival at individual antenna array elements, AoA-based methods use the anchor's antenna arrays to determine the angle at which the transmitted signal arrives at the anchor as shown in Figure 5. The received signal angles from two or more anchors are measured and the position of the device is estimated using trigonometry.
# Rheological Characteristics Evaluation of Bitumen Composites Containing Rock Asphalt and Diatomite
## 4 Results and Discussion
### Rotational Viscosity Test  
Table 5 displays the rotational viscosity test for the control (0% RA+0% diatomite) and the modified bitumen of RA and diatomite. With an increase of RA and diatomite dosage, the apparent viscosity increases significantly at the two tested temperatures. Obviously, the RA and diatomite are also beneficial for the high-temperature performance of the bitumen. The apparent viscosity with the same dosage of RA and diatomite increases more at the lower tested temperatures. When the test temperature was 135 \({}^{\circ}\)C, the apparent viscosity of 16% RA+9% diatomite composite-modified asphalt reached 3.78 Pa-S, which exceeded the specification that the apparent viscosity of modified asphalt should not be greater than 3 Pa-S. Therefore, the dosage of diatomite should not exceed 9% when RA is 16% added into the petroleum bitumen by weight. Similarly, the dosage of RA should not exceed 16% when diatomite is 9%.
# Evaluation of Glottal Inverse Filtering Techniques on
## 4 Results
### F0  
Regarding the performance of the inverse filtering techniques for the F0 variable, the general tendency is for RMS, NAQ, and H1H2 for both male and female subsets to present larger errors for higher F0s, with a slight decrease in the higher frequencies for the case of male data for QCP-based methods and IAAF-IOP. This result can be observed from Figure 3 (for a detailed analysis, the reader is referred to the F0 sweep analysis included in Appendix B). This difference between lower and higher frequencies is emphasised in the female case. It can be observed that IAAF-based methods are more sensitive to F0 changes, while QCP approaches are more stable and obtain better results across the whole F0 range, especially for RMS and H1H2 error metrics.  
Figure 3: Distributions and median of error measures of RMS, NAQ, H1H2, HRF, and ST between the estimated glottal flow and the ground-truth signals for each GIF method evaluated as boxplots with whiskers from 5% to 95%, grouped by F0 range. Columns shows results for the male and female subsets, respectively.  
On the contrary, the evaluation through HRF presents better results for high frequencies for both male and female speech data. For the ST error, IOP-IAIF and GFM-IAIF are the best performing methods for lower frequencies in the male subset, whilst the ST-QCP method presents lower values in the male high F0 range. In the case of the female subset, the QCP-based approaches are better, obtaining the best results with the original version of the algorithm.  
In this case, as shown in Table 14, of the 150 pair-to-pair Wilcoxon statistical analyses conducted, 94% in the case of male data and 98.6% in the case of female data are significant.
# Simulation Tool for Winter Navigation Decision Support in the Baltic Sea
## 7 Model Functionalities
### Convoys  
Icebreakers often try to combine trips for multiple vessels requiring assistance so that the waiting time is reduced overall. When a single icebreaker assists more than one vessel, the arrangement is known as a convoy. A convoy is different from a towing arrangement, where a vessel is physically connected to the icebreaker using mechanical links. In convoys, the icebreaker leads the way for a group of vessels to follow. There are multiple factors in deciding how many vessels can be part of a convoy. The convoy travels at the speed of the slowest vessel; hence, the icebreaker needs to ensure that the minimum speed of the convoy is greater than the threshold. If the speed of a convoy is less than \"x\" knots (default is 6), then the slowest vehicle is left behind. In practice, the slowest vessel may be taken up for towing by the icebreaker and the rest of the vessels may follow in a convoy. However, this case is not considered in the current model. The current model considers conventional convoys, with one icebreaker clearing a path for one or more merchant vessels. There are other convoys possible where a group of vessels follows a towed vessel or a wide vessel convoy with two icebreakers involved. These are not considered in the present model. While forming a convoy, the travel paths of the vessels are also considered. An icebreaker leading a convoy does not escort each individual vessel to its destination ports.  
Figure 6: Routing logic.  
Safe waypoints are identified where convoys can be dismantled and reformed if required with different vessels. It is possible that due to convoy formations one or more vessels end up taking a longer route to their destinations. However, keeping in mind the overall waiting time in the system, the longer route with convoys ends up being more efficient.
## 2 Materials and Methods
### Meta-Classification Using Machine Learning Algorithms
#### 2.4.3 K-Nearest Neighbors (KNN)  
K-Nearest Neighbors is a machine learning method for classification and regression in pattern recognition [5]. It operates in real-time by classifying incoming data based on the nearest reference data point in the feature space [51]. It assigns labels by considering characteristics shared with nearby data points, distinguishing test and training data organized into tuples. Each tuple represents a multidimensional point in space, forming a reference set. To determine their classification, the KNN involves comparing uncertain tuples with their nearest neighbors. This process is influenced by the closest K-value neighbors.  
In the KNN algorithm [52], distance metrics such as Euclidean, Mahalanobis, and Minkowski Distance assess the similarity or dissimilarity between data points. This study focuses on the Euclidean Distance (ED) formula Equation (1), which measures the dissimilarity between data points in multidimensional coordinates (\(x_{i_{k}}\) and \(x_{j_{k}}\)) within the dataset \(p\). This ED calculation, denoted as \(d(i,j)\), is pivotal in KNN-based decision-making processes, impacting various machine learning and pattern recognition applications.  
\[d(i,j)=\sqrt{\sum_{k=1}^{p}(X_{i_{k}}-X_{j_{k}})^{2}} \tag{11}\]  
Determining the optimal \(k\) value involves systematic experimentation, starting with \(k=1\), and iteratively testing to identify the optimal value of \(k\) that yields the best results. Alternatively, the meta-learner automates \(k\) value selection based on predefined criteria to maximize result accuracy by considering relationships between data points and their neighbors.
## 3 Proposed Solution
### Stage 2  
Algorithm 2 utilizes the results obtained from Algorithm 1 and implements K-means clustering predicated on SNR to categorize the vehicles into distinct clusters. Subsequent to the clustering process, the peripheral vehicles within each cluster are discerned as those situated at the greatest distance from the cluster centroids. These peripheral vehicles are subsequently assigned the responsibility of relaying information to proximate vehicles within a designated distance threshold. The algorithm culminates by delineating the positions of the vehicles, identifying the peripheral vehicles, and establishing the communication pathways to facilitate the visualization of the network and the flow of information.  
```
1:Step 1: Input data from Algorithm 1.
2:Step 1: Calculate SNR based on distance.
3:Step 2: Apply K-means clustering on vehicles.
4:Step 3: Identify edge vehicles in each cluster by finding the vehicle farthest from the cluster centroid.
5:Step 4: Transmit information from edge vehicles to nearby vehicles within a defined distance threshold.
6: Identify vehicle positions, clusters, edge vehicles, and transmission lines.
```  
**Algorithm 2** SNR-based information spreading using K-means clustering
# ATLAS: Latest Advancements and First Observations
## 4 Discussion  
### Calibration  
Figure 10 shows not only the offset of the main beam but also the shape of the main beam and sidelobes. The sidelobes of the antenna are not symmetric and require realignment. This is due to it being an old antenna that was moved and underwent several upgrades and repairs over the years. A 3D point cloud was recently obtained using LIDAR, showing that the feed and the secondary reflector are aligned, but some of the panels of the main reflector are misaligned, compromising the parabolic shape. This issue is currently being addressed.
## 1 Introduction
### Machine Learning in Workplace Safety  
Machine learning (ML) is a subset of artificial intelligence (AI) that involves developing algorithms [50; 51] and models that render computers able to learn and make predictions or decisions without being explicitly programmed. It leverages statistical techniques to enable systems to improve their performance in a specific task over time as they are exposed to more data. Machine learning plays a crucial role in analyzing and interpreting complex data to improve safety practices, identify potential risks, and contribute to accident prevention. Machine learning algorithms can analyze historical data on workplace incidents, near misses, and identify patterns and correlations [17; 52].  
This enables the prediction of potential safety risks, allowing organizations to take preventive measures to avoid accidents and improve overall safety. ML models can be taught to identify abnormal patterns in data, such as equipment malfunctions, deviations from standard operating procedures, or unusual worker behavior. Anomaly detection helps in the timely identification of potential safety issues, enabling early intervention before accidents occur [53; 54]. ML models can provide a dynamic real-time assessment of risk levels, enabling businesses to prioritize safety measures and allocate resources effectively. Additionally, they analyze human behavior in the workplace to identify unsafe practices or deviations from safety protocols. By recognizing patterns in behavioral data, organizations can proactively address unsafe actions and implement targeted safety interventions. Theycan also be used to direct the correct use of Personal Protective Equipment (PPE). Cameras and sensors can detect whether employees are wearing the required PPE, ensuring that safety guidelines are being followed.  
Using modern enhanced models, ML can assist in managing worker fatigue and optimizing shift schedules. By analyzing data on working hours, breaks, and sleep patterns, these models can provide recommendations for scheduling practices that minimize fatigue-related risks and enhance overall worker well-being. Machine learning facilitates the development of customized safety solutions designed to meet the specific needs and challenges of different industries and workplaces. By adapting models to the unique characteristics of a given environment, organizations can implement more effective and targeted safety measures [10; 55].  
Machine learning continues to revolutionize the approach to workplace safety by harnessing the power of data analysis, pattern recognition, and predictive modeling. Its adaptability, scalability, and ability to handle diverse data sources make it a valuable tool for organizations and businesses aiming to create and maintain safe work environments.
# RD-SLAM: Real-Time Dense SLAM Using Gaussian Splatting
## 4 Results
### Quantitative Evaluation
#### 4.2.2 Rendering Performance  
Tables 4 and 5 show the rendering performance of our approach on real-world datasets. RD-SLAM achieves competitive results on the average of all sequences from TUM-RGBD and ScanNet datasets. Our method achieves better environment reconstruction in challenging environments where depth images are severely missing by implementing the dual keyframe selection strategy that fully adds and optimizes 3D Gaussians. Notably, RD-SLAM system runs at 10 FPS, more than 60 times faster than SplaTAM. Figure 5 shows the rendering results of the system.  
Table 6 shows the results of comparing the rendering performance of RD-SLAM with other neural SLAM methods on the Replica dataset. Our method is the best performer in most evaluated sequences, and many are second best. RD-SLAM outperforms the second ranked method, Point-SLAM, by 2.96 dB in PSNR. This excellent rendering and real-time performance allow RD-SLAM to be more easily applied to autonomous driving, virtual reality, AR, and other fields.  
The visualization results in Figure 6 show that RD-SLAM can generate higher quality and more realistic images than previous SOTA methods, avoiding ghosting and local reconstruction errors. From the rendering results, Point-SLAM is too smooth to reconstruct fine details, and SplaTAM produces more apparent holes in some areas, as shown in the second, fourth, and sixth rows of Figure 6.
## 5 Results
### Analysis of the Automatic and Manual Point Classification
These values confirm the algorithm's ability to reliably classify buildings with minimal errors (FP = 228,719, FN = 182,048). Trimble Realworks achieved very high precision for ground point classification (0.997), indicating minimal points were incorrectly labeled as ground (FP = 13,813). However, its sensitivity was lower (0.905), leading to a higher number of unclassified ground points (FN = 569,521). The resulting F-score (0.949) is still high but falls behind Leica Cyclone 3 DR's performance. For high vegetation, Trimble Realworks delivered weaker results, with low precision (0.554) and very low sensitivity (0.367). This indicates a substantial portion of vegetation points were misclassified (FP = 1,362,413, FN = 2,911,669), resulting in a poor F-score of 0.441. For buildings, Trimble Realworks achieved a precision of 0.952 and a sensitivity of 0.924, leading to an F-score of 0.938. While these results are solid, they fall short of Leica Cyclone 3 DR's performance due to a higher number of misclassifications (FP = 1,931,690, FN = 3,161,156). Overall, Leica Cyclone 3 DR provides a better balance between precision and sensitivity across the classification classes of ground, vegetation, and buildings. Trimble Realworks excels in precision for ground points, but its lower sensitivity and weaker performance in vegetation and building classification place it at a disadvantage. These differences highlight the importance of selecting the appropriate tool for point cloud classification based on the specific requirements and focus of the analysis.
## 4 The Performance Parameters and Application Scenarios of 6G Networks  
In the transition from 5G to 6G technology, it is important to acknowledge that the International Telecommunication Union Radiocommunication Sector (ITU-R) had previously established eight principal performance metrics for International Mobile Telecommunications 2020 (IMT-2020) [22]. These metrics are as follows: 1. peak data rate, 2. user-experienced data rate, 3. latency, 4. mobility, 5. connectivity density, 6. energy efficiency, 7. peak spectral efficiency, and 8. area traffic capacity. Nevertheless, the swift evolution of mobile communication ecosystems calls for a reassessment. Although the initial eight performance metrics are still relevant, their benchmarks require revision to align with recent technological progress and the introduction of novel use cases. Furthermore, the advent of 6G introduces the need for new performance metrics that address location tracking, detection capabilities, security measures, and computational intelligence.  
The objective of this paper is to put forth a set of more thorough and logically justified KPIs for 6G networks, establishing their reference values through extensive research and analysis. We present a total of 17 proposed KPIs for 6G networks, which will be elaborated upon in the subsequent discussion.
## 3 Development History of Navigation Signal Authentication Technology  
Satellite navigation signal authentication technology has undergone three stages of development: concept, technical research, technical trials, and on-orbit testing.
## 3 Results and Discussion
### Model Comparison  
Ref. [2] clearly defines the aim: \"This paper aims to investigate the generalization ability of the proposed ML models and the accuracy of the proposed models for different lead times\". Figure 3 collects the models to be compared: all models (with one exception) are linear or linear in parameters (curvilinear), or easily linearizable by logarithmic transformation. That is, the normal equations can be solved easily in closed form. For example, the estimated parameters of Equation (7) in Figure 3 by maximum likelihood (\(\hat{A}\) and \(\hat{B}\)) can be calculated without any minimum search (using the notations of Equation (7) in Figure 3):  
\[\hat{A}=\frac{n\sum_{i}^{m}x_{i}R_{i}-\sum_{i}^{n}x_{i}\sum_{i}^{m}R_{j}}{n \sum_{i}^{m}x_{i}^{2}-\left(\sum_{i}^{n}x_{i}\right)^{2}} \tag{1}\]  
And  
\[\hat{B}=b\bar{\pi}-\overline{R} \tag{2}\]  
What is the need to apply search algorithms when solutions are readily available? Why is it necessary to fit the models by global optimization algorithms (which are developed for complicated problems, implicit dependent variables, and many hundreds or even thousands of parameters and variables)? Similarly, all parameters of other models (except the logistic one) can be derived easily by solving the normal equations (eventually after logarithmic transformation). Logistic regression can also be solved by iteration easily without the usage of global optimization algorithms. In other words, the principle of parsimony was neglected or overlooked without any justification.
## 4 Results: The Usage of CARIMO
### Usage Data Logging for CARIMO
#### 4.2.4 Selection of the Logging Component(s) and Data Source(s)  
Since there is a variety of logging tools available (see Section 3.2.1), we decided not to go for a time-consuming proprietary development. We selected a logging component that met the following system and privacy requirements: (i) supporting mobile application logging, enabling (ii) the research team to get access to the raw usage data for an independent usage data analysis, and (iii) self-hosting the data since third-party hosting did not fulfill data privacy requirements.  
According to [PERSON] and [PERSON] [40] and our own investigations, only two tools (Piwik and Google Analytics 360) were able to support mobile application usage logging at the time of decision in 2016. Piwik (today known as Matomo, InnoCraft, 150 Willis St, Wellington, New Zealand) was preferred to Google Analytics 360 as its use was free of charge and allowed self-hosting with access to raw usage data. For the server, we used Piwik version 3.0.1. On the client level (CARIMO tablet), we started data logging with Piwik SDK for Android 0.0.3. In month four, a new version of CARIMO including additional games and bug fixes was released. This release was also used for a client update of Piwik to version 2.0.0.  
For the fitness program of CARIMO, Piwik usage measurement alone was not sufficient for two reasons. The operating system of the activity tracker (Tizen) was not supported by Piwik. Thus, the use of the activity tracker (e.g., recording of steps and activities) could not be tracked by Piwik. We accessed this information indirectly via the CARIMO system database where these data were stored. Second, the duration and the number of completed exercises on the tablet were difficult to measure and extract. As the determination of a completed exercise involved considering many exceptions (e.g., skipping orpausing of exercises), the recorded time per exercise stored in the CARIMO system database, excluding, for example, skipped exercises and pauses, was used to measure the number of exercises performed.  
As we were interested in comparing the take-up of CARIMO and its features between two countries (Austria and Italy), we imported this information from the CARIMO system database, where the place of residence of each user was stored for support reasons. As different data sources (Piwik and the system's database) were used, we implemented a UUID in each data source, which allowed for merging the data into one data set.
## 5 Evaluation
### Network Evaluation  
In this section we evaluate the network connectivity aspects, including virtual routing and the creation of communication channels. We have implemented the routing of reachable nodes \(r_{i}\), unreachable nodes \(u_{i}\) and the establishment of transmission channels for unreachable nodes in JAVA based on the standard Kademlia algorithm based on the method described above. To facilitate the management of the nodes, we create a thread for each node in the main program, which does not communicate with the main process except to guide behaviour and statistics, but transmits packets through a virtual switch in the same way as the real network.