esa-sceva/satcom-chunk-collection huggingface dataset:
# Compression Challenges in Large Scale Partial Differential Equation Solvers
## 4 Communication in Distributed Systems
### Error Metrics  
An important aspect of compressor design is the norm in which to measure compression errors. While in some applications pointwise error bounds are important and the \(L^{\infty}\)-norm is appropriate, other applications have different requirements. For example, if the inexact parallel-in-time method sketched above is applied to parabolic equations, spatially high-frequency error components are quickly damped out. There, the appropriate measure of error is the \(H^{-1}\)-norm.  
Nearly optimal compression factors for given \(H^{-1}\)-distortion can be achieved in TCUG by replacing the hierarchical basis transform with a wavelet transform, which can efficiently be realized by lifting [67] on unstructured mesh hierarchies. Level-dependent quantization can be used for near-optimal compression factors matching a prescribed reconstruction error in \(H^{s}\). Rigorous theoretical norm-equivalence results are available for \(|s|<3/2\) with a rather sophisticated construction [68]. A simpler finite element wavelet construction yields norm equivalences for \(-0.114<s<3/2\)[69], but in numerical practice it works perfectly well also for \(s=-1\). A potential further improvement could be achieved by using rate-distortion theory for allocating quantization levels, as has been done for compression of quality scores in genomic sequencing data [70].  
Figure 6 shows the quantization errors for the 2D test function \(f(x)=\sin(12(x_{0}-0.5)(x_{1}-0.5))\) on a uniform mesh of 16,641 nodes, with a grid hierarchy of seven levels. Using a wavelet transform almost doubles the compression factor here, while keeping the same \(H^{-1}\) error bound as the hierarchical basis transform [22].  
A closely related aspect is the order of quantization and transform. In the considerations above, a transform-then-quantize approach has been assumed. An alternative is the quantize-then-transform sequence, which then employs an integer transform. It allows guaranteeing strict pointwise reconstruction error bounds directly, and is therefore closely linked to \(L^{\infty}\) error concepts. In contrast, quantization errors of several hierarchical basis or wavelet coefficients affect a single point, i.e., a single nodal basis coefficient. The drawback of quantize-then-transform is that the quantization step shifts energy from low-frequency levels to high-frequency levels, leading to less efficient decorrelation if error bounds in Sobolev spaces are important. A brief discussion and numerical comparison of the two approaches for some test functions can be found in [21].  
Figure 5: Error vs. compression factor: A priori estimates for transform coding of finite element functions with hierarchical basis transform, cf. [21].
# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
## 4 Smart Mobility Services and Applications
### Autonomous Vehicles
#### 4.4.1 Levels of Autonomous Driving  
The Society of Automotive Engineers [85] defined six levels of vehicle automation which were adopted by the U.S. Department of Transportation. The levels range from 0 (fully manual) to 5 (fully automated), as described in Figure 7.  
Level 0 (Fully Manual--No Driving Automation): most vehicles today are at this level where they are fully controlled by the driver. There may be some driver support systems, such as an emergency braking system and some type of warning to the driver, but that is not considered automation.  
Level 1 (Driver Assistance): this level is characterized by the existence of some type of autonomous action on the part of the vehicle, such as accelerating or braking (cruise control), but driver intervention is still required in most situations.  
Level 2 (Partial Driver Automation): this level is essentially characterized by the existence of Autonomous Advanced Driving Assistants (ADAS) that control the acceleration and braking performed by the vehicle, in limited scenarios. The level of automation is still low as the driver must maintain full attention as they may have to take care of driving at any time.  
Level 3 (Conditional Driving Automation): level 3 represents a substantial technological advance since the vehicle must be able to control the environment around it, such as its speed based on the movement of the car in front. Even so, the driver must remain alert in case the vehicle is unable to perform some tasks.  
Level 4 (High Driving Automation): the main feature introduced at level 4 is the ability of the vehicle to be able to take over almost all driving independently and to intervene in case something goes wrong or there is a failure. In most cases, driver intervention is not necessary, but the driver can still take care of the car, if they wish.  
Level 5 (Full Driving Automation): level 5 represents full vehicle automation where there is no need to have a steering wheel or pedals in the car. The vehicle will be able to go anywhere and make all the necessary decisions.
# Empowering Tourism Accessibility: A Digital Revolution in Pieria, Greece
## 2 Materials and Methods
### Data Collection and Auditing Tool  
The core of this initiative relies on the comprehensive collection of data on accessibility infrastructure and the use of the Seek & Go auditing tool [27; 28]. This tool is essential for capturing and evaluating the status of the accessibility infrastructure in the Pieria region, Greece. The data collection process was designed to provide detailed, reliable information that users can rely on when planning their trips. To achieve this, audits of pedestrian routes and points of interest (POIs) were conducted, ensuring that critical elements affecting accessibility are evaluated, including ramps, crossings, sidewalk widths, blind routes, various obstacles (broken tiles, trees, low-hanging tree branches, cafe chairs and tables, signage posts, etc.).  
The Seek & Go tool is used to assess both horizontal and vertical routing of POIs, including entryway accessibility, the availability of accessible WCs, and accessible elevators, along with other crucial infrastructure. The audit ensures that the data collected are as comprehensive as possible, allowing users to make informed decisions about their travel routes or visits to sites and buildings. The tool also provides feedback on the usability of the infrastructure, accounting for potential barriers that could impede mobility for disabled or elderly individuals. Figure 2 illustrates the interface of the Seek & Go auditing tool, which was crucial in standardizing data collection and ensuring consistency across different areas of Pieria.  
Figure 2: The Seek & Go auditing tool.
###### Abstract  
The density-based spatial clustering of applications with noise (DBSCAN) is able to cluster arbitrarily structured datasets. However, the clustering result of this algorithm is exceptionally sensitive to the neighborhood radius (Eps) and noise points, and it is hard to obtain the best result quickly and accurately with it. To address this issue, a parameter-adaptive DBSCAN clustering algorithm based on the Sparrow Search Algorithm (SSA), referred to as SSA-DBSCAN, is proposed. This method leverages the local fast search ability of SSA, using the optimal number of clusters and the silhouette coefficient of the dataset as the objective functions to iteratively optimize and select the two input parameters of DBSCAN. This avoids the adverse impact of manually inputting parameters, enabling adaptive clustering with DBSCAN. Experiments on typical synthetic datasets, UCI (University of California, Irvine) real-world datasets, and image segmentation tasks have validated the effectiveness of the SSA-DBSCAN algorithm. Comparative analysis with DBSCAN and other related optimization algorithms demonstrates the clustering performance of SSA-DBSCAN.  
Keywords:DBSCAN algorithm; adaptive parameter; sparrow optimization algorithm; data mining +
Footnote †: journal:
## 2 System Architecture and Components
### Energy Storage System Model
#### Priority Determination Algorithm  
The priority determination rules are calculated based on different decision factors including both vehicle and user preference. The main parameters of priority rules are arrival time, departure time, SOC, trip distance and battery capacity. After calculating the EV priority, the aggregator assigns these priorities to EVs for charging, as given in Equation (10) where \(Pr_{stay}\), \(Pr_{SOC}\), \(Pr_{trip}\) and \(Pr_{batt}\) are stay time, state of charge, trip distance and battery capacity, respectively. After EVs have been prioritized, the highest priority EVs are allocated first for charging (timeslots with low electricity cost).  
\[Pr=Pr_{Stay}+Pr_{SOC}+Pr_{Trip}+Pr_{Batt} \tag{10}\]  
We defined three different priority levels as high, medium and low, as shown in Table 1. Regarding the EV stay time, low priority is given if the EV stay time is greater than 6 h while high priority is considered if the EV stay time is less than 3 h. With respect to the initial SOC of an EV battery, we define a high priority for SOC as less than 20%, medium as 20-80% and low priority as a SOC greater than 80%. We also consider trip distance; low priority for a trip is less than 10 km, medium 10-60 km and high priority greater than 60 km. Two priority levels for EV battery capacity are defined; medium and low. Low priority is assigned to a battery capacity less than 30 kWh while medium priority is greater than 30 kWh. As a result, the charging service will be allocated to EVs with higher priorities withoutviolating the grid's operational constraints. Algorithm 1 explains the priority determination algorithm in detail.  
Figure 5: Flowchart of main algorithm.  
```
0:V--Set of charging requests from \(EV\)s Info--information of arrival of \(EV\)s (\(SoC_{i}^{init},\;i^{stay}_{i},d_{i}^{trip},\;B_{i}^{cp}\)) Output: P--Set of priority lists for charging \(EV\)s
1. \(Pr_{i}^{SoC}=0,\;Pr_{i}^{stay}=0,\;Pr_{i}^{trip}=0,\;Pr_{i}=0\);
2. for\(i\gets 1\) to \(|V|\)do
3. if\(SoC_{i}^{init}\leq 20\)then
4. \(Pr_{i}^{SoC}=3\);
5. else if\(20<SoC_{i}^{init}<80\)then
6. \(Pr_{i}^{SoC}=2\);
7. else if\(80\leq SoC_{i}^{init}\)then
8. \(Pr_{i}^{SoC}=1\);
9. end if
10. if\(i^{stay}_{i}\leq 3\)then
11. \(Pr_{i}^{stay}=3\);
12. else if\(3<t^{stuy}_{i}<6\)then
13. \(Pr_{i}^{stay}=2\);
14. else if\(6\leq t^{stuy}_{i}\)then
15. \(Pr_{i}^{stay}=1\);
16. end if
17. if\(d_{i}^{trip}\leq 10\)then
18. \(Pr_{i}^{trip}=1\);
19. else if\(10<d_{i}^{trip}<40\)then
20. \(Pr_{i}^{trip}=2\);
21. else if\(40\leq d_{i}^{trip}\)then
22. \(Pr_{i}^{trip}=3\);
23. end if
24. if\(B_{i}^{cp}<10\)then
25. \(Pr_{i}^{bat}=1\);
26. else if\(30\leq B_{i}^{cp}\)then
27. \(Pr_{i}^{bat}=2\);
28. end if
29. \(Pr_{i}=Pr_{i}^{SoC}+Pr_{i}^{stay}+Pr_{i}^{trip}+Pr_{i}^{bat}\);
30. \(\Pi\)insert (\(Pr_{i}\));
31. end for
```  
**Algorithm 1** Priority Determination Algorithm
## 4 Simulation Results and Discussions
### Performance Comparison
Figure 13: BER of the proposed SS-ELM scheme with \(\delta\) as parameter and the LS algorithm for system configuration 2.
# Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification
## 2 Materials and Methods
### Deep CNN  
Deep learning, in particular Convolutional Neural Networks (CNN), is a validated image representation and classification technique for the analysis of biomedical images and applications. In recent years, the scientific community has produced many encouraging works that report new and more recent state-of-the-art performance on quite challenging problems in this domain [14; 15; 16]. The main reason behind this stream of work is probably because the effective task-dependent image features can be directly or intrinsically learned through the hierarchy of convolutional kernels inside CNN. Most deep learning methods use neural network architectures, which is why deep learning models are often referred to as deep neural networks. The term \"deep\" usually refers to the number of layers hidden in the neural network. Traditional neural networks contain only 1-2 hidden layers, while deep networks can contain up to 150. Deep learning models are trained using large labeled data sets and neural network architectures that learn features directly from data without having to manually extract them.  
One of the most common types of neural networks is known as a convoluted neural network (CNN or ConvNet). A CNN conveys the characteristics learned with the input data and uses the convolutional layers in 2D, which make this architecture suitable for 2D data processing, such as images.  
CNNs eliminate the need for manual feature extraction [17; 18; 19], so the user does not have to identify features used for image classification. In fact, it is possible to use the power of the pre-trained networks, without investing time and effort in training, to implement the extraction phase of the characteristics. Feature extraction can be the fastest way to use in-depth learning. The operation of CNN is based on the extraction of the features directly from the images. The automatic extraction of the features allows the high precision of the deep learning models intended for artificial vision activities, such as the classification of objects.  
In this work, it was decided to perform fluorescence intensity classification by analyzing the whole image without applying a segmentation to the cells. As it is known that the problem of finding the best set of discriminating features for a given classification problem is very complex, we have decided, in line with recent scientific trends, to use pre-trained CNNs to extract features.
## 4 Discussion
### Study Limitations and Future Prospects  
This study utilizes NDVI, temperature, precipitation, and nighttime light data to systematically analyze vegetation dynamics and their driving factors in the Aksu region. However, there are certain limitations that future research could build upon to expand this analysis.  
First, although MODIS NDVI and other remote sensing data provide valuable large-scale, long-term monitoring, they are relatively low in spatial resolution and can be influenced by atmospheric interference and sensor calibration issues. Integrating higher-resolution remote sensing data, such as Landsat, could offer more detailed spatial insights in future studies. Additionally, this study employs nighttime light data as an indicator of human activity intensity, which effectively captures urbanization trends. However, nighttime light data alone cannot fully reflect finer land-use changes. Incorporating detailed land-use datasets would allow for a more comprehensive analysis of human impacts.  
In terms of climate factors, temperature and precipitation are critical variables affecting vegetation dynamics, but other factors, such as soil moisture, wind, and evapotranspiration, may also play significant roles. These climate factors may impact vegetation types differently, and future studies that incorporate a wider range of climate variables could deepen our understanding of vegetation response mechanisms.  
Moreover, this study primarily uses correlation and trend analysis to reveal relationships between NDVI and climate and human activity. While this approach effectively captures general trends, multivariate methods such as multiple regression or machine learning algorithms could reveal more complex interdependencies. These advanced methods, however, require higher-dimensional data and refined model tuning. With advances in data availability and analytical techniques, future research could employ these methods to explore the complex drivers of vegetation dynamics in greater depth. Furthermore, comparative studies in areas with similar urbanization and climate impacts could enhance the generalizability of the findings.  
Lastly, due to the remote location of Aksu, field data collection poses practical challenges. This study's conclusions are primarily based on analyses of remote sensing and meteorological data. Future research that integrates field observations and high-resolution imagery from multiple sources would provide a more robust foundation for ecological management and sustainable development in the region.
# Computer Vision and Image Processing Approaches for Corrosion Detection
## 3 Corrosion Prediction Model
### Deterministic Model  
The deterministic model involves the examination of the relationships between the parameters or variables of corroded materials through field experiments conducted with the aid of images and videos. This prediction model is easier to develop compared with the other models. Vast expertise or knowledge in corrosion is not crucial for the development of the deterministic model. Nevertheless, this model cannot extract the realistic nature of corrosion in the environment. Thus, the developed prediction model could be inaccurate in terms of the extrapolated results [33].  
Examples of deterministic models for corrosion detection using computer vision and image processing approaches can be found in [34; 35]. The structural health monitoring (SHM) and digital twin models were used in [34] to increase the durability of infrastructural assets. Corrosion progression can be regularly revalidated in terms of the structure's reliability. The results showed that this method can improve the behavioral prediction of metallic structures. Moreover, the texture descriptors with cellular automata [35] were used when the training data were not sufficient. This method counterbalanced the formulation with controlled deterministic chaos. According to the results, the proposed method outperformed the other approaches during the real-world detection process. Other deterministic models in the literature can be explored in [36; 37; 38]. The comparisons of different corrosion prediction models are illustrated in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction Model** & & **Description** & & **Application/Method** \\ \hline \multirow{3}{*}{Knowledge-Based} & \(\bullet\) & AI is used to predict corrosion & \(\bullet\) & Automated seven inspection [15; 16]. \\  & & progression from images & \(\bullet\) & Combination of machine learning \\  & & and videos. & and CCTV [20; 21; 22]. \\ \hline \multirow{6}{*}{Probabilistic} & \(\bullet\) & This model is used when historical & \(\bullet\) & Gamma distribution [23]. \\  & \(\bullet\) & Requires expertise and an in-depth & \(\bullet\) & Gamma process and copulas of \\ \cline{1-1}  & & knowledge of physics and & \(\bullet\) & Spatio-temporal aspects [24]. \\ \cline{1-1}  & & mathematics to develop & \(\bullet\) & Probabilistic model and finite \\ \cline{1-1}  & & sophisticated mathematical models. & \(\bullet\) & Monte Carlo finite elements [26]. \\ \cline{1-1}  & & Quite difficult to develop. & \(\bullet\) & \(\bullet\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of different corrosion prediction models.
###### Abstract  
MANET (Mobile Ad hoc Networks) functionality is determined by routing protocols' ability to adjust to atypical changes in information and communication technologies, topological systems, and connection status. Due to interference, node migration, the growth of several pathways, security, and propagation loss, MANET network configurations are dynamic. The proactive routing protocol enhances the message flow utilized in the neighborhood discovery process by using the multipoint relays (MPR) approach. In order to increase the protocol's effectiveness and efficiency while maintaining the OLSR protocol's reliability, the research presented in this paper proposed an improved OCI-OLSR (Optimized Control Interval-Optimized Link State Routing) that focuses on better control interval management, an advanced MPR selection process, reducing neighbor hold time as well as decreasing flooding. The suggested proposed protocol was examined using the NS3 simulator, and it was compared to the standard OLSR version and AODV(Ad-hoc On-Demand Routing) routing protocol. According to the analysis's findings, the suggested system has a lot of promise in terms of a variety of performance metrics under diverse conditions. Overall, the article makes the case that the OCI-OLSR protocol may enhance the performance of the regular OLSR protocol in wireless ad hoc networks by addressing a number of the protocol's flaws.  
Optimized Control Interval OLSR; OLSR Routing; MANETs; Network Infrastructure; Enhanced OLSR Routing Protocol
# Dynamic Routings in Satellite Networks: An Overview
###### Abstract  
The Satellite network is an important part of the global network. However, the complex architecture, changeable constellation topology, and frequent inter-satellite connection switching problems bring great challenges to the routing designs of satellite networks, making the study of the routing methods in satellite networks a research hotspot. Therefore, this paper investigates the latest existing routing works to tackle the dynamic routing problems in satellite networks. The architecture and development of satellite networks are presented and analyzed first. Afterward, dynamic routing problems in satellite networks are analyzed in detail based on the time-varying network topology. According to the latest works, the advanced satellite network routing schemes, including single-layer and multi-layer dynamic routing are introduced and analyzed. In addition, the merits, shortcomings, and applications of these schemes are analyzed and summarized. Finally, potential technologies and future directions are discussed.  
single-layer satellite networks; multi-layer satellite networks; dynamic routings; overview +
Footnote †: journal: 2022, 22, 4552. [[https://doi.org/10.3390/s22124552](https://doi.org/10.3390/s22124552)]([https://doi.org/10.3390/s22124552](https://doi.org/10.3390/s22124552))
###### Abstract  
Temperature monitoring is the main indicator to ensure the safe operation of the coal mine explosion-proof box. In this study, a three-way flameproof cable box was considered for coal mines. A three-dimensional transient temperature field simulation model was constructed for magnetic-thermal coupling and subsequently used to conduct temperature field simulation for the failure of the flameproof cable box for coal mines. An experimental platform was developed to test the temperature characteristics of failure flameproof cable boxes for coal mines and obtain temperature curves at various failure currents. Using the experimental results of temperature characteristics and temperature field simulation outcomes, we analyzed the time responses and spatial distribution characteristics of the temperature rise of flameproof cable boxes for coal mines. The error between simulation and experimental results does not exceed 1 K, and the upper limit of time to ensure safe operation under different failure modes is determined. Thus, the acquired temperature distribution characteristics in a failure mode can be used as a reference for the design, inspection, and status warning of flameproof cable boxes for coal mines.  
coal mine; flameproof cable box; failure operating mode; magnetic-thermal coupling; temperature characteristics
## 3 Analysis and Results  
### Data Processing  
The DTG data provided by the Korea Transportation Safety Authority, as shown in Table 3, were utilized. The data were selected based on the time period with the highest traffic volume. Data from the peak hours for freight trucks on weekdays, between 9 a.m. and 10 a.m., were used. According to the Korea Transport Institute (2019), Tuesday had the highest freight truck traffic volume, with an average travel time of 339.5 min and a travel distance of 165.1 km.  
The location data from DTG (Digital Tachograph) are provided in GPS latitude and longitude coordinates, which need to be converted into a plane rectangular coordinate system for practical use. The geographic coordinate system used was WGS84, and this was converted to the Central Origin (N38, E127) TM (Transverse Mercator) coordinate system.