esa-sceva/satcom-chunk-collection huggingface dataset:
# Handover Management in 5G Vehicular Networks
###### Abstract  
Fifth-Generation (5G) vehicular networks support novel services with increased Quality of Service (QoS) requirements. Vehicular users need to be continuously connected to networks that fulfil the constraints of their services. Thus, the implementation of optimal Handover (HO) mechanisms for 5G vehicular architectures is deemed necessary. This work describes a scheme for performing HOs in 5G vehicular networks using the functionalities of the Media-Independent Handover (MIH) and Fast Proxy Mobile IPv6 (FPMIP) standards. The scheme supports both predictive and reactive HO scenarios. A velocity and alternative network monitoring process prepares each vehicle for both HO cases. In the case of predictive HO, each time the satisfaction grade of the vehicular user drops below a predefined threshold, the HO is initiated. On the other hand, in the case of reactive HO, the vehicle loses the connectivity with its serving network and connects to the available network that has obtained the higher ranking from the network selection process. Furthermore, the HO implementation is based on an improved version of the FPMIPv6 protocol. For the evaluation of the described methodology, a 5G vehicular network architecture was simulated. In this architecture, multiple network access technologies coexist, while the experimental results showed that the proposed scheme outperformed existing HO methods.  
5G networks; mobility management; predictive and reactive handover; fuzzy logic +
Footnote †: journal: _Future Internet_
## 2 Data and Methodology  
### Swarm Satellite Mission  
The Swarm mission consists of three identical satellites: Swarm Alpha (Swarm A), Swarm Bravo (Swarm B), and Swarm Charlie (Swarm C). It mostly aims to measure the Earth's magnetic field and its temporal variation with matchless precision [54]. On 22 November 2013, the Swarm mission was launched into an almost polar orbit with an inclination angle of 87.5\({}^{\circ}\) at an initial altitude of about 490 km. From that time, Swarm A and C fly alongside each other at an altitude of about 470 km and with a longitudinal separation distance of about 1.4\({}^{\circ}\) (150 km) [55; 56]. The third spacecraft, Swarm B, swirls the Earth at about 520 km with a higher inclination. To cover the whole 24 h local time, Swarm B requires about 141 days, while Swarm A and C require about 133 days. The load completeness of each spacecraft involves an absolute scalar magnetometer, a vector field magnetometer, an electric field instrument (EFI), an accelerometer, and a group of navigation instruments [20].  
In this investigation, we utilize Swarm Level-2 (IBI) products. One data file of L2-IBI is produced per day and per satellite. However, no L2-IBI data are generated for days when neither the magnetic field data nor Level 1b electron density (\(n_{e}\)) data are existing. The timestamps of the L2-IBI are coincided with those of the corresponding L1b 1 Hz magnetic field data, with a cadence rate of 1 Hz. An updated document describing the IBI product is available on the European Space Agency (ESA) webpage: [[https://www.esa.int/](https://www.esa.int/)]([https://www.esa.int/](https://www.esa.int/)) (10 January 2020). A full description of the data file and explanations regarding the IBI data can be found in the L2-IBI product description file ([[https://earth.esa.int/web/guest/document-library/browse-document-library/-/article/swarm-level-2-ibi-product-description](https://earth.esa.int/web/guest/document-library/browse-document-library/-/article/swarm-level-2-ibi-product-description)]([https://earth.esa.int/web/guest/document-library/browse-document-library/-/article/swarm-level-2-ibi-product-description](https://earth.esa.int/web/guest/document-library/browse-document-library/-/article/swarm-level-2-ibi-product-description)) (20 November 2019)). This product enables ionospheric scientists to easily recognize the equatorial plasma bubbles. The essential section of the L2-IBI product is composed of three parameters: Bubble Index, Bubble Probability, and Bubble Flag.
# A Two-Level Rolling Optimization Model for Real-time Adaptive Signal Control
## 2 Traffic Signal Timing Optimization Algorithm  
The signal timing optimization algorithm optimizes signal phase durations based on a dynamic traffic flow prediction model. The optimization method consists of two levels of optimization in this paper. At the upper layer, a dynamic programming (DP) is applied to each barrier group, with each barrier group between two barriers defined as a phase group. Based on the above definition, a standard National Electrical Manufacturers Association (NEMA) ring barrier controller structure is shown in Figure 1. The figure illustrates a phase sequence with left-turn movements leading the opposing through movements on both the major and minor streets. The diagram shows phases 1 and 5 ending at different times. The subsequent phase (phases 2 and 6 respectively) may begin once the previous phase has used its time. Once the barrier is crossed, phases 3 and 7 operate followed by phases 4 and 8. The cycle ends with the completion of phases 4 and 8. The calculation of the performance function of the upper level is passed to the lower level. The lower level (individual phase) optimization is formulated as an integer linear programming problem. In this study, the objective is to minimize the total vehicle delay and the sequence of barrier groups is assumed to be fixed. Time is discretized to 1 sec intervals, and the optimization is performed on a predetermined planning horizon, e.g., 80 s. Therefore, the problem is to find an optimal length for each phase to minimize vehicle delay. In addition, some constraints should be considered, such as the minimal and maximal green time in each phase. The two-level optimization model will be discussed in the next section.
## 5 Conclusions  
In this paper, the integration of 6G technologies presents a transformative opportunity through which to revolutionize the energy sector through smart grids in terms of energizing the key enablers of smart cities. This paper has demonstrated the potential of 6G wireless communication in enabling time-critical smart grid applications with massive connectivity and enhanced security by improved KPIs and novel technologies of next-generation intelligent wireless communication.  
Nevertheless, numerous significant challenges are associated with 6G-enabled smart grid network implementation. These challenges encompass limitations in THz and mmWave communication, energy consumption optimization, network management, 3D network coverage, ultra-high capacity backhaul links, channel estimation, physical layer security, and interference management. Addressing these obstacles necessitates interdisciplinary research, collaboration, and innovative solutions. In addition, future research directions have been outlined in this paper to overcome these challenges and to harness the full potential of 6G in smart grid communication networks. By effectively addressing these issues, we can unlock the capabilities of 6G, as well as aim to create intelligent, interconnected, and resilient smart grid systems.  
In conclusion, continuous research and innovation are crucial to fully harness the transformative power of 6G technologies for smart grid applications. With concerted efforts and advancements in these areas, we can propel the energy sector of smart cities toward a more sustainable, efficient, and intelligent future.  
Conceptualization, A.A.A. and W.S.; visualization, A.A.A.; writing--original draft preparation, A.A.A., J.H., V.-H.B., and W.S.; writing--review and editing, A.A.A., J.H., V.-H.B., and W.S.; supervision, A.A.A. and W.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Not applicable.  
Conflicts of Interest: The authors declare no conflict of interest.
# Miniaturized Arrow-Shaped Flexible Filter-Embedded Antenna for Industrial and Medical Applications
## 3 Experimental Results
### |S11|  
Figure 9a exhibits the comparison of simulated and measured |S11| results of proposed filtenna. It is noticed that the proposed arrow-shaped filtenna obtained am ISM band at 2.45 GHz ranging from 2.276 to 2.75 GHz, demonstrating the strong correlation between simulated and measured results. The results obtained by the proposed design ascertain their suitability as a potential candidate for future portable electronic devices operating at the ISM band. Figure 9b contains the antenna prototype under test for S-parameter measurements using a PNA series network analyzer having model number E8362B by Agilent Technologies (Santa Clara, CA, USA). Moreover, Figure 9c contains the antenna for far-field measurements inside the anechoic chamber.
# Laser Applications in Metal Orthodontic Bracket Debonding: A Systematic Review
## 3 Results  
### Study Selection  
The study selection process according to the PRISMA checklist is reported in Figure 2.  
Figure 2: The PRISMA protocol.  
The initial search of the electronic databases yielded 453 records. After removing 256 duplicates, 197 unique records were available for abstract screening. In this phase, 179 articles were excluded based on their abstracts: 96 studies were not related to the debonding of orthodontic brackets; in 48 studies, the authors used brackets made of materials other than metal; in 18 studies, the authors did not use lasers for debonding; 11 articles were reviews; 3 articles were written in other languages than English, and in the remaining 3 publications, no in vitro or in vivo research was conducted. Eighteen articles were left for full-text review. Of these, ten articles were excluded: seven studies were related to removing adhesive remnant with lasers but debonding was performed in the traditional way, and three authors did not conduct laser debonding. As a result, eight articles were selected for both qualitative and quantitative analyses.
# Advances on Water Quality Detection by UV-Vis Spectroscopy
## 3 Theoretical Basis of UV-Vis Spectrum Data Processing  
The accuracy of the UV-Vis method used to detect water quality parameters is highly dependent on the established mathematical model. Therefore, the selection of an appropriate mathematical model to improve the prediction accuracy has become the focus of water quality parameter study in UV-Vis detection. The UV spectrum data processing is divided into four steps: spectrum data preprocessing (Section 3.1), characteristic wavelength extraction (Section 3.2), water quality parameter model establishment (Section 3.3), and model performance evaluation (Section 3.4). In recent years, researchers have improved and innovated based on various algorithms in the field of UV-Vis spectrum detection of water quality parameters and established a high-precision prediction model between water quality spectrum data and water quality parameters. These studies have significantly contributed to rapid, accurate detection of water quality parameters [14].
# Identification of Ratholes in Desert Steppe Based on UAV Hyperspectral Remote Sensing
## 2 Materials and Methods  
### Overview of the Study Area  
The experimental area is located in the Gegen Tara grassland (41.78\({}^{\circ}\) N, 111.88\({}^{\circ}\) E, 1440 m above sea level) in Shiziwangqi, Ulanqab City, Inner Mongolia Autonomous Region (Figure 1). The area is a typical desert steppe environment, with a mid-temperate continental monsoon climate, annual average temperature of 1-6 \({}^{\circ}\)C, minimum temperature reaching \(-\)39 \({}^{\circ}\)C, maximum temperature around 36 \({}^{\circ}\)C, annual rainfall around 300 mm, and soil water content of 10.99-13.78%. The soil type is predominantly light chestnut calcium soil with a low vegetation cover and an overall sparse, low performance, mainly dominated by short-flowered needlegrass, cold artemisinin, awnless cryptomeria, and amelina [15; 16]. The area is a natural source of highland gerbil plague, and common pests include Meriones unguiculatus, Rhombomys opimus, Spermophilus dauricus, Meriones meridianus pallas, Ochotona curzoniae, Myospalax psilurus, Myospalax aspalax, Lasiopodomys brandtii, and 16 other species [17]. Affected by climatic conditions, rathole openings are relatively small and hidden. Their typical shape is shown in Figure 2. Grassland pest rats start to breed in spring and reach their peak numbers in the summer and autumn; then, the temperature drops, and the mortality rate of pest rats increases until their number reaches the minimum in the following year, with the cycle starting over again. According to surveys, the number of long-clawed gerbils, which are the main host of the plague, has been increasing year by year, and the risk factor for grassland destruction has increased.  
Figure 1: Overview map of the test area.
# Developments, Trends, and Challenges in Optimization of Ship Energy Systems
## 5 Further Research Needs and Challenges in Optimization of Ship Energy Systems
### Optimization with Uncertainty Considerations  
The objective function and the model of the system, both technical and economic, include a multitude of parameters. Many of those may have values not known with certainty. For example, uncertainty may exist regarding the future operation of the ship and, consequently, the load profile,the cost of fuel, the weather conditions, etc. Therefore, it is not sufficient to solve the optimization problem for one set of conditions, i.e., for one set of parameter values only. There is need of at least a parametric study or sensitivity analysis with respect to crucial parameters, which is performed relatively easily, i.e., the optimization problem is solved for several values of the parameters and the effect on the optimal solution is recorded. This is the procedure followed in most of the articles in Table 1.  
Even though more demanding, uncertainties can be taken into consideration during the optimization procedure. There are two approaches for optimization under uncertainty [95]: stochastic programming and robust optimization. The subject with related methodology is presented thoroughly in [95; 96; 97; 98; 99; 100]. Examples with applications on ship energy systems are as follows:  
In No. 15 [49], the uncertainty due to the intermittent PV generation caused by the irradiation and the swinging of the ship is taken into consideration with an interval optimization method in order to determine the optimal size of the battery unit and the load of the diesel generator.  
In No. 24 [58], a load power prediction model based on multiresolution analysis of wavelet neural network (MRA-WNN) is established to predict the electric load of the ship under load uncertainty. The model is then used for optimization of load allocation among the sources of electric energy.  
In No. 36 [69], the uncertainty of the hourly global irradiation and its effect on the power output of the PV system are taken into account by the probability density function in a two-stage stochastic mixed integer nonlinear programming method in order to determine the optimal size of the hybrid electric power system of an oil tanker.  
The complete SDO optimization taking uncertainties into consideration remains to be developed and applied.
# Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
## 5 Experiments
### Performance Comparison  
We employed Accuracy, AUC, and Precision metrics to assess the performance of EPEM, as presented in Table 2. Our EPEM performs best on three datasets, outperforming the current best models. While predicting unobserved links of distinct types, other comparative methods typically extract shared features across all link types, disregarding the adverse effects of weakening the representation bias caused by type-priori distinctions among various types of links.  
The Support Vector Machine (SVM) serves as a traditional machine learning method, often employed for binary classification. However, it frequently struggles to sufficiently capture diverse link representations, leading to suboptimal outcomes. Meanwhile, PME and SLiCE are embedding models, tailored to derive link features and build models for analyzing unobserved link likelihoods. However, these models do not capitalize on the type-priori knowledge acquired from observed links, potentially causing observed link features to unduly impact the representation of unobserved links.  
The SEAL employs graph neural networks to create network graphs and learns from local sub-graphs of links. While this retains rich link-related features, the inability to share such features across different link types could constrain its capacity to learn unobserved link characteristics. Similarly, the Heterogeneous Adversarial Network (HAN) leverages the generative adversarial concept to combine different link feature representations. However, when applied to our datasets, the HAN method lacks precise mining of the effect of each type-priori feature representation on unobserved links.  
Heterogeneous Network Embedding (HHNE) presents a unified model that addresses the challenge of embedding learning in heterogeneous networks. Although it can handle large-scale networks, it cannot directly predict unobserved links with different link feature representations. Type-Aware Link Prediction (TALP) models the influence of type and fusion information on user node alignment from local and global viewpoints. However, its type-priori knowledge extraction is not as robust as EPEM's. EPEM enhances unobserved link prediction by effectively incorporating features from diverse observed link types, yielding optimal results across the datasets.
## 2 Literature Review
### Accident History of Bauxite Carriers  
At present, bauxite is not formally listed in Group A (cargo that may liquefy) by the International Maritime Organization (IMO). The international maritime community still has doubts about its liquefaction characteristics. Depending on the particular circumstances of any given shipment, it would appear that bauxite may come with the risk of liquefaction and shift during shipping, which can cause a vessel to capsize at a moment's notice. Fortunately, up until 2 July 2013, none of the incidents resulted in losses to vessels or crew members, according to data from the North P&I Club. However, on 2 January 2015, M.V. Bulk Jupiter with 46,400 tons of bauxite capsized and sank in strong winds and swells off the coast of Vietnam, killing 18 crew members. The disaster of Bulk Jupiter, a Handy bauxite carrier, aroused a series of responses related to risks of the bauxite shipping process.  
IMO requested that the global bauxite industry undertake research into the behavior and characteristics of bauxite cargoes during ocean transportation. From 14 to 18 September 2015, the second meeting of the Subcommittee on Cargo and Container Transport (CCC) of the Maritime Safety Committee approved CCC.1/Circ.2 to remind people of the potential risks of bauxite in maritime transport [33]. Considering that Handy carriers do not have a special structural design for cargoes that may liquefy, IMO recommends that the captain, on the basis of experience and relevant certifications, may refuse to carry the cargo if the carrying of such cargo may fail to ensure the absolute safety of the voyage; if the captain decides to carry it, necessary measures must be taken to ensure the safety of the vessel.  
In recent years, the Global Bauxite Working Group (GBWG) designated by the IMO carried out a series of research studies on bauxite properties. From 11 to 15 September 2017, the fourth meeting of the CCC Subcommittee adopted CCC.1/Circ.2/Rev.1 in the draft amendment to classify certain bauxite as cargo that may liquefy, submitting it to the Maritime Safety Committee for consideration [34]. The 2019 amendments of the International Maritime Solid Bulk Cargoes (IMSBC) code was adopted by the 101 st session of the Maritime Safety Committee. The new individual schedule for bauxite fines as a Group A cargo is expected to be implemented on 1 January 2021.
# Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things
## 7 Experiments and Performance Evaluation
### Attack Resistance  
To evaluate the effectiveness of the proposed model, we examined three distinct variables, as shown in Figure 12. The first variable is the anticipated value, which signifies the recommended trust level of a node assuming no unreliable recommendations in the network. This ideal scenario assumes a high trust score (0.9) for normal nodes and a low trust score (0.25) for malicious nodes. The second variable is the computed recommended trust level using the defensive recommendation evaluation process proposed in this paper. The third parameter is the average recommended trust level without employing any validation on the received recommendations.  
As depicted in Figure 12, during bad-mouthing attacks, the trustworthiness of normal nodes declines as the number of malicious nodes increases. Similarly, in ballot-stuffing attacks, shown in Figure 12, the trust value of malicious nodes increases with their numbers, but the changes within our proposed method remain manageable. This is because, without a filtering mechanism, each recommendation node is assigned the same weight, leading evaluating nodes to accept all propagated recommendation values indiscriminately. Consequently, as the proportion of malicious nodes increases, the dissemination of false information in the recommendation sequence escalates, significantly impacting the final trust value. In contrast, the recommendation evaluation method discussed in this  
Figure 11: Evaluation of indirect trust obtained over time (solid line = average, shaded area = range from min to max across all nodes).  
Figure 12: Evaluation of average indirect trust values obtained for nodes under attack.  
paper adjusts recommendation trust values based on various attributes after filtering the recommendation received.
## 2 Materials and Methodology
### Principle and Mechanism of the TEG Module
This study's goal is to transform waste heat from hot water pipes through which solar-heated water flows into useful power, as shown in Figure 2. An electric voltage can be obtained to charge a battery using a TEG panel when a temperature differential is applied to its surfaces. An inverter can convert the battery's DC voltage into AC to power low-power devices such as CCTV recording devices or internet routers in homes. During the night, the TEG can supply electricity, since the water is usually stored in tanks for the purpose of using it in the evening. Certainly, this will lower household electricity bills.
## 2 Data and Methods
### Data
#### 2.1.2 Built-Up  
The Global Human Settlement (GHS) database distributed by the European Commission (EC) provides built-up grid data and contains a multi-temporal information layer on built-up presence as derived from Landsat image collections (GLS1975, GLS1990, GLS2000, and ad hoc Landsat 8 collection 2013/2014). Through data-interpolation processing, this database provides variables of Built-Up for the years 1990, 2000, and 2010.