esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
## 1 Introduction  
Digital image-capture devices may capture images with uneven illumination or low light, resulting in issues such as excessive enhancement in bright areas, insufficient brightness in dark areas and insufficient detail resolution. These problems significantly impact the usability of the images captured [1]. Therefore, it becomes essential to apply image enhancement techniques to improve brightness or contrast.  
Currently, image enhancement algorithms remain a thriving area of research, employing various approaches like spatial domain, Retinex theory, frequency domain, etc. These algorithms mainly focus on denoising or enhancing brightness and contrast to make low-light images appear brighter and more natural [2, 3], such as histogram equalization and grayscale transformation, which can unify the histogram to achieve higher contrast. However, these methods tend to over-enhance certain areas, making them less ideal. [PERSON] et al. [4] introduced an adaptive mapping function for image enhancement, showing promising results in enhancing the dynamic range of light intensity. [PERSON] et al. [5] proposed a method that optimizes the combination of a homomorphic filter and mapping curve, which strikes a balance between improving image contrast and preserving naturalness. [PERSON] al. [6] presented an enhancement method for a single low-light image at night, obtaining the initial transmission value of the brightness channel and correcting it using the darkness channel. [PERSON] et al. [7] developed a method using depth estimation and deep convolutional neural networks (CNN) for solving underwater images in low-light conditions, along with an improved spectral correction method for image color restoration. [PERSON] et al. [8] proposed an adaptive least squares method to address video surveillance problems under poor and rapidly changing lighting conditions. [PERSON] et al. [9] introduced a non-uniform image enhancement algorithm combining a filtering method and an \"S-curve\" function, applying it in a coal mining environment. Additionally, swarm intelligence algorithms [10] and deep learning methods [11; 12] have been extensively employed in image enhancement, yielding favorable outcomes. However, deep learning-based algorithms often necessitate a large amount of training data, making them challenging to apply in specific environments.  
While the above studies made strides in adaptive image enhancement, both traditional algorithms and deep learning-based methods still have room for improvement. Challenges persist, such as the over-enhancement or under-enhancement in certain image areas, low image quality after processing, and the presence of noise. To address these shortcomings, this paper introduces an image block enhancement method based on the Retinex theory. Experimental results demonstrate the effectiveness of the proposed method in enhancing illumination and improving image details.
## 2 System Model  
### Orbital Geometry  
This model consists of a LEO satellite that passes over a cloud and multiple ground receivers equipped with electronically steerable phased array antennas [14]. The overpass time is determined by the orbital parameters of the satellite and the minimum elevation angle of the ground receivers. The satellite has a direct spot beam to the ground receivers, and the receivers use their antennas' steering capability to track the transit of the satellite. The SNRs at the ground receivers are estimated for the purpose of measuring the path-integrated attenuation.  
During a single overpass, the signal link between a satellite and a ground receiver moves along a curved surface, thereby the attenuation caused by water droplets on this surface can be detected by the SNR estimation at the receiver. If we consider the scenario where the satellite passes directly over the receiver, the curved surface can be regarded as flat because the curvature is purely induced by the slight Earth rotation during the overpass and can be ignored. When multiple receivers are placed along the ground track of the satellite, the moving signal links will cover most of the vertical plane [6], and thereby offer the possibility of retrieving the vertical attenuation field. An illustration of the orbital geometry and the targeted attenuation field is shown in Figure 1. Depending on the satellite system, the surface coverage of a single beam could vary from 2800 km\({}^{2}\) to 72,000 km\({}^{2}\)[2]. An example of the \(-3\) dB footprint contour is shown by the black dashed lines in Figure 1. Each satellite can achieve a data-rate above 20 Gbps [2], which means that it can easily serve 10,000 user terminals (assuming 2 Mbps bandwidth each) simultaneously within a beam.  
The proposed method can be easily extended to 3D attenuation field retrieval with additional receivers placed in a two-dimensional array on the ground. Previous studies have shown that 3D  
Figure 1: An illustration of the satellite, the ground receivers and the retrieval of the attenuation field. The blue dotted line shows the trajectory of the satellite, the black dashed lines show the \(-3\) dB footprint contour of the satellite signals, the red solid line is the ground track and also where the receivers are placed.  
tomographic attenuation field reconstruction can be achieved through the aggregation of a series of reconstructed vertical planes [6]. This paper will only focus on the retrieval of one vertical cloud attenuation field.
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 2 Theoretical Background
### Photon Energy and Quantum Efficiency  
The energy E of a photon is inversely proportional to its wavelength [1; 2; 3]:  
\[\text{E}\;=\frac{\text{hc}}{\lambda} \tag{9}\]  
where  
\(\text{h}\;=\;6.626\times 10^{-34}\;\text{Joule-hertz}^{-1}\) is Planck's constant;  
\(\text{c}\;=\;3.0\times 10^{8}\;\text{m/s}\) is the speed of light;  
\(\lambda\) is the wavelength in meters.  
Shorter wavelengths (e.g., 405 nm) carry more photon energy than longer wavelengths (e.g., 650 nm). While this higher energy can enhance photoelectric conversion efficiency in some applications, it also increases atmospheric scattering and thermal load on optical components, potentially reducing system stability.  
The quantum efficiency (QE) of a photodetector--defined as the ratio of generated charge carriers to incident photons--varies with wavelength. Silicon-based detectors, which are the most widely used in visible-light applications, exhibit peak QE in the red-to-near-infrared range (approximately 650-900 nm) [1; 21; 3; 21]. This makes red light particularly well-suited for systems that leverage silicon photocells for both signal detection and energy harvesting.  
While QE declines at shorter wavelengths, it remains sufficiently high across the entire visible spectrum to enable effective operation with green and violet light. The widespread adoption of silicon photodetectors is further supported by their low cost, low noise, high availability, and ease of integration with standard electronics. As a result, they are considered the default choice for VLC and short-range FSO applications, especially in experimental or cost-sensitive environments.
# Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
## 5 Discussion
Such a configuration would still be viable for an elevated data capture frequency in the slave node and the mid-air data extraction with the UAV. Another possible scenario is when the buoys are fixed to the seafloor and their location is known. Under these circumstances there are two possible ways of extracting the data from the buoys, using the UAV from a remote location to collect the data or with a master base station. The first scenario would be exactly the same as when the buoys are drifting, except that the location is already known and a more precise flight pattern can be planned. If only a master base station were to be used, the LoRa RF configurations of the nodes could be set for long-range transmissions and with high data capture frequency, with a functional behavior similar to that of current buoy implementations. The main advantage of this operation mode is the possibility of transmitting data across several kilometers, and that the base station is only formed by a master node connected to a laptop, allowing the station to be static or mobile. Although this system completely fulfills the requirements of the SIMMA project, there are several improvements that can be made to create a more robust and functional WSN. In addition to the already mentioned enhancements made in the post-field analysis section, the inclusion of energy harvesting technologies, as proposed by [PERSON] et al. (2017), is one of the most desirable improvements, as it would allow the buoys to monitor areas for an almost indefinite time. Also, the addition of other types of sensors and backup wireless communications, such as GSM, would grant the system with important ecological data and new ways to find marooned devices. Regarding the use of the UAV, it would be a significant improvement if the aircraft carried additional sensors, such as infrared or multispectral cameras, granting additional information that could be correlated with the data extracted from the buoys. This could be feasible with the same aircraft design, given that the UAV can carry additional payload, other than the master node, yet this was not contemplated within this article. Moreover, further integrating the UAV with the LoRa master node would be of great use, as information provided by the master node could automatically trigger different behaviors on the UAV, such as a change in flight pattern or an emergency return-to-home.
# Time-Sensitive Networking Mechanism Aided by Multilevel Cyclic Queues in LEO Satellite Networks
## 4 Time-Sensitive Networking Technique Aided by Multilevel Cyclic Queues
### Gating-Based TSN Scheduling Mechanism  
We first introduce the CQF mechanism and its drawbacks in LEO networks. The CQF uses eight queues to meet different service requirements, as shown in Figure 2. \(Q_{0}\) and \(Q_{2}\) use best-effort forwarding to serve regular Ethernet traffic; \(Q_{1}\) queues background traffic with the lowest priority. \(Q_{5}\), \(Q_{4}\), and \(Q_{3}\) maintain the reservation bandwidth for non-time-sensitive traffic, including audio, video, and a bandwidth guaranteeing service called AVB traffic. \(Q_{5}\) and \(Q_{4}\) buffer delay-constrained audio and video traffic, respectively, and thus have higher scheduling priority than that of \(Q_{3}\). Highest-priority queue \(Q_{7}\) and second-highest-priority queue \(Q_{6}\) are used for TSFs, and only these two queues require gating control mechanisms. As shown in Figure 2, CQF divides time into time slots, of which each allows for one queue to receive traffic (e.g., \(Q_{6}\)) and the other queue to only send traffic (e.g., \(Q_{7}\)). The next time slot opens another queue to accept traffic (\(Q_{7}\)), and queue \(Q_{6}\) only sends traffic. Since different TSFs may have different sending periods, the incoming queue control requires a different gating logic. The output port of CQF switching is manageable, i.e., the user can manage the configuration of the priority level, inbound gating, outbound gating, queue measurement, and shaping logic.  
The existing CQF mechanism forwards time-sensitive traffic separately from regular Ethernet traffic, and the SDN controller can manage the gating control list in real time in terrestrial networks. However, in the large-scale space of an LEO satellite network, it is difficult for the controller to real-time adjust TSFs in a short period. As a result, it is challenging to adapt CQF to burst traffic, and it causes packet loss. In addition, time-sensitive services possess different delay characteristics and have different demands on the forwarding mechanism. However, it is difficult for existing CQFs to differentiate delay demands to forwarding.
# Path Planning for Laser Cladding Robot on Artificial Joint Surface Based on Topology Reconstruction
## 6 Conclusions  
In this paper, the laser cladding path planning for artificial joints was mainly investigated. In order to improve the efficiency of laser cladding on the surface of artificial joints, a path planning method for a laser cladding robot on an artificial joint surface based on topology reconstruction and a novel processing mode were designed. The main conclusions are as follows:  
Figure 16: Changes of robot pose angles (\(\alpha\), \(\beta\), \(\gamma\)) during simulation process.  
Figure 15: Different positions and poses of robot during simulation process.  
1. Different from traditional laser cladding mode, the presented mode of the laser beam fixation during workpiece movement met the requirements of the laser cladding process of the artificial joints and ensured the safety of the operator and equipment.
2. The path planning method based on the topological relations greatly reduced the amount of intersection calculation and directly obtained the sequential and simplified cladding path, which can be translated to the robot program with a small scale.
3. The planned path on the surface of a 46 mm artificial acetabular cup model was carried out by the simulation experiment. The results show that the robot accurately drove the artificial acetabular cup to move along the planned cladding path without mechanical collisions, trajectory deviations and so on.  
Many facets of path planning remain for future investigation. We will still focus on the efficiency and quality of the path planning method. Moreover, we will combine laser cladding technology to investigate the quality of the coatings processed by applying the path planning method.  
Project administration, D.L.; software, Y.L.; supervision, T.C.; writing--original draft and editing, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China, China (Grant 51775559), the Open Sharing Fund for Large-Scale Instruments and Equipment of Central South University, China (Grant CSUZC201919), and the Fund of the State Key Laboratory of High Performance Complex Manufacturing (Grant ZZY)KT2019-06).  
The authors would like to thank the editors and reviewers for their helpful suggestions and comments, which indeed improved the manuscript.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Hybrid Sensing Platform for IoT-Based Precision Agriculture  
As discussed before, the HSP is composed of aerial and ground-based nodes. Aerial nodes are mounted on quadcopter UAVs that are used to periodically collect crop and related environmental data. The environmental data in this research refer to temperature, humidity, wind speed, wind gust, rainfall, and solar radiation. Ground-based nodes include a weather station to monitor related environmental conditions and also sensors for observing specific plants on the ground, which we refer to as target plants. The design and implementation of the ground-based nodes are discussed in Section 3.1.1, aerial nodes are presented in Section 3.1.2, and the cloud node is described in Section 3.1.3.
# Review of Professionally Applied Fluorides for Preventing Dental Caries in Children and Adolescents
## 3 Results and Discussions
### Discussion  
Fluoride topical products contribute to the prevention of dental caries to varying degrees depending on the pharmaceutical form used. Mouthrinses and foams seem to be least effective and are more frequently and easily related to acute overdosage. Fluoride gels can be associated with clinical precautions and over ingestion symptoms, leaving fluoride varnishes as the optimal solution for topical fluoride treatments due to its high fluoride concentration and long-time wearing off the teeth.  
Professionally applied topical fluoride usage must be correlated with the patient's age, caries risk, systemic input of fluoride, and, also, with the fluoride concentration of home-use toothpaste. There is a difference regarding the age threshold for the use of fluoride gels, with the ADA and EAPD setting it at 6 years, while Australia's ARCPOH recommends gel applications after the age of 10. There is a common point of view regarding the use of professionally applied fluorides in high caries risk patients. The total amount of fluoride ingested (systemic and during toothpushing) is important for avoiding dental fluorosis, especially in small children who swallow higher percentages of the used toothpaste.  
Future research regarding the prevention of dental caries is oriented towards the improvement of antimicrobial properties of restorative materials, combining this well-known inorganic, monoatomic anion with other antimicrobial substances (e.g., chlorhexidine, quaternary ammonium compounds), development of chemical modifications enhancing drug release, and surface modification for an antimicrobial and biofilm modulation effect. A comparative study on the costs of dental decay prevention using professionallyapplied fluorides and/or community water fluoridation in various countries should also be performed.  
The limitations of this study are related to the extension of the literature search, as well as related to characteristics of RCTs, with some of them presenting unclear randomization methodology, a reduced number of subjects, or a short follow-up period.
# Performance Evaluation of China's First Ocean Dynamic Environment Satellite Constellation
## 3 Results
### Sea Surface Wind Speed (SSWS) Verification
#### 3.4.1 Comparison of SSWS with the Buoy Data  
In this study, the same spatio-temporal matching method as the SWH verification was used to match 842, 1053, and 494 points, respectively. The mean deviation of HY-2B, HY-2C, and HY-2D satellites' wind speed data was \(-\)0.05 m/s, 0.95 m/s, and \(-\)0.52 m/s, respectively, with root-mean-squared errors of 1.48 m/s, 1.85 m/s, and 1.45 m/s, and standard deviation of 1.48 m/s, 1.59 m/s, and 1.35 m/s, respectively. Therefore, there was found to be significant agreement between the SSWS of HY-2B/C/D satellite network and the buoy data. Based on the above data, it was determined that the wind speed data of  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**SWH** & **N** & **RMSE (m)** & **STD (m)** & **R** & **Mean SWH Div (m)** \\ \hline HY-2B/NDBC & 354 & 0.23 & 0.23 & 0.99 & 0 \\ HY-2C/NDBC & 389 & 0.25 & 0.25 & 0.98 & \(-\)0.02 \\ HY-2D/NDBC & 197 & 0.30 & 0.26 & 0.99 & 0.15 \\ HY-2B/Jason-3 & 1052 & 0.17 & 0.17 & 0.99 & 0 \\ HY-2C/HY-2B & 1077 & 0.15 & 0.14 & 0.99 & \(-\)0.06 \\ HY-2D/HY-2B & 758 & 0.30 & 0.25 & 0.99 & 0.17 \\ \hline \end{tabular}
\end{table}
Table 3: HY-2B/C/D satellite SWH data verification.  
the HY-2B and HY-2C satellites were of high quality. However, the HY-2D satellite had matched the least number of NDBC data. The densest matching was concentrated in the wind speed range of 3 to 15 m/s. The number of SSWS matches beyond 15 m/s was low.  
The NDBC buoy data used in this paper have been calibrated. Anemometer heights on NDBC buoys vary according to buoy type. Anemometers on 3 m discus and 6 m NOMAD buoys are located approximately 5 m above the waterline. Anemometers on 10 and 12 m buoys are located 10 m above the waterline. Anemometer heights at CMAN stations vary widely, depending on site structure and elevation above sea level. NDBC adjusts wind speeds to conform to the universally accepted reference standard of 10 m. This is conducted to provide marine forecasters and data modelers a means to directly compare buoy observations with ship observations. This is one of the reasons why there is a discrepancy between the altimeter wind speed data and the NDBC buoy data.
## 2 Materials and Methods
### Post-Processed Kinematic (PPK)  
Post-processed kinematic (PPK) positioning is a relative positioning technique that is commonly employed in UAV-based photogrammetric projects. It makes use of the baseline solution post-field measurements. The PPK positioning technique commonly utilizes at least two GNSS receivers. In this configuration, the reference receiver, commonly called the base station, remains stationary on the ground, while the rover serves as the mobile receiver. To provide precise positional information, the PPK data are processed utilizing a reference station after the data have been collected. This processing method enhances the accuracy and reliability of the results obtained. Additionally, during the post-processing phase, precise ephemeris data for GNSS satellites are accessible, which often results in a more accurate solution. The PPK technique is applicable when the RINEX data are recorded during an active flight and are synchronized with data obtained from local receivers or the Continuously Operating Reference Station (CORS). Specifically, utilizing CORS data enables the execution of automated flight missions without the requirement of a base receiver or a connection to a network service for obtaining NRTK corrections [53].
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 3 System Model and Problem Formulation
### Network Model  
The relay satellite network considered in this paper consists of four parts: a set of user satellites denoted as \(i\in U=\{1,2,\ldots,M\}\) that are moving on their low orbits, geosynchronous relay satellites \(j\in R=\{1,2,\ldots,N\}\), ground stations \(G=\{1,2,\ldots,L\}\) and data management center (DMC). Consider a typical time-slotted RSN that operates over \(T\) time slots presented as \(t\in\mathcal{T}=\{0,1,2,\ldots,T\}\), and the interval of the slot is \(\tau\). As shown  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(U\) & Set of user satellites \\ \(R\) & Set of relay satellites \\ \(T\) & Set of time slots \\ \(\mathcal{D}_{i}(t)\) & Data queue of user satellite \(i\) in slot \(t\) \\ \(\delta_{ij}(t)\) & Connection state of ISL in slot \(t\) \\ \(\delta_{ij}(t)\) & ISL capacity in slot \(t\) \\ \(\rho_{i}(t)\) & Data acquisition rate of user satellite \(i\) in slot \(t\) \\ \(\gamma_{i}(t)\) & Inter-satellite transfer rate of user satellite \(i\) in slot \(t\) \\ \(\rho_{i}(t)\) & Energy Harvesting rate of user satellite \(i\) in slot \(t\) \\ \(E_{i}^{c}(t)\) & Energy consumption at user satellite \(i\) in slot \(t\) \\ \(E_{i}^{h}(t)\) & Harvested energy at user satellite \(i\) in slot \(t\) \\ \(E_{i}(t)\) & Energy queue of user satellite \(i\) in slot \(t\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations description.  
in Figure 1, the user satellite collects data from the earth or space environment and stored it in the buffer. The relay satellite provides forwarding service for user satellites with intermittent ISLs and then downloads the received data to ground stations via continuous SGLs (i.e., DMC) directly. Here, we set a binary variable \(\delta_{ij}(t)\) to indicate whether user satellite \(i\) is connected to relay satellite \(j\) or not at time slot \(t\). When \(\delta_{ij}(t)=1\), the user satellite transmits spatial data to the relay satellite, otherwise, \(\delta_{ij}(t)=0\).  
In particular, each relay satellite employs \(K\) antennas, which can establish ISLs with up to \(K\) user satellites simultaneously. Additionally, a user satellite can only connect to one relay satellite due to the limited antenna. For example, when \(K=1\), if the \(i\)-th user satellite establishes a connection with the \(j\)-th relay satellite at time slot \(t\), then the relay satellite \(j\) cannot provide forwarding services for other user satellites at that slot. We have the following formulas:  
\[\sum_{j=1}^{N}\delta_{ij}(t)\leq 1,\qquad\forall i\in U. \tag{1}\]  
\[\sum_{i=1}^{M}\delta_{ij}(t)\leq K,\qquad\forall j\in R. \tag{2}\]