esa-sceva/satcom-chunk-collection huggingface dataset:
# Saturation-Based Airlight Color Restoration of Hazy Images
## 3 Proposed Dehazing Method
### Extraction of Color Components in Airlight  
Because the entire background airlight \(A(\infty)\) in a hazy image comprises bright pixels, the brightest pixels are generally aligned, and their average is considered to be airlight [1; 2; 3; 4; 5; 6]. However, when there are objects in the image that have white as their inherent color, this method can include information from these objects rather than just the airlight. In particular, in the case of the L channel, which represents the luminance of the image, this method can result in even less reliable airlight extraction results.  
Therefore, the proposed method to prevent such an error shows a way to output airlight by using the method which extracts the dominant color. The most dominant color extraction methods [27; 28; 29] use the K-means algorithm in CIELAB, an RGB color space. In recent research, [PERSON] [29] defined the following hypotheses based on the analysis of color palette datasets:  
1. In most cases, the number of dominant colors is between 3 and 6.
2. High saturation colors are often selected, with the most vivid colors almost always being chosen.
3. The color that occupies the largest area is almost always chosen, regardless of its conspicuousness.
4. When multiple colors are somewhat conspicuous, there is a greater likelihood that other colors will be chosen.
5. Colors that differ significantly from the surrounding environment are more likely to be chosen.  
In this study, clustering is performed by applying that algorithm to most hazy images; the sky area is dominant, and the sky can be differentiated from objects into [PERSON]'s hypotheses 3 and 5. In particular, hypothesis 3 suggests that when the number of clusters k is small, the range may not include large areas, so an appropriate k should be chosen. The results depending on K are described in Section 3.1.2. In addition, Figure 3b,c show the results of the conventional airglow detection method and the proposed method.  
Figure 3a shows the clustering result of the image, and Figure 3b,c show the results from the existing airlight extraction method and proposed method, respectively. In Figure 3, the pink area represents the extracted airlight area.  
In this study, to perform the algorithm, the RGB image is first converted to the LAB color space in Sections 3.1.1-3.1.3 explain the proposed airlight hue extraction method.  
Figure 2: Visualized flowchart of the proposed method.
## 1 Introduction
Due to the high complexity of original problem, we decompose it into two cascaded subcarrier assignment and power allocation problems. While the former is to assign subcarriers for D2D pairs by a heuristic algorithm, the latter can be solved by a power allocation algorithm based on the Lagrangian dual method. Specifically, our work makes the following contributions: (1) We investigate the optimal resource allocation for the case that one D2D pair could reuse more than one subcarrier while guaranteeing the minimum rate requirements of individual CUs; (2) We introduce the system positive gain and the system negative gain which represent the benefit of reusing cellular resources and the penalty for interference exerted on CUs respectively. The network final profit should be guaranteed (i.e., the overall system gain should be positive); (3) We develop a greedy-based algorithm to allocate one or more subcarriers to each D2D pair, as well as an efficient power allocation algorithm. The proposed resource allocation scheme can significantly improve the sum spectrum efficiency of the overall system. The rest of the paper is organized as follows. In Section 2, we describe the system model and problem formulation. In Section 3, we derive and propose the two-stage algorithm to solve the resource allocation problem, and simulation results are presented and discussed in Section 4. Finally, Section 5 concludes the paper.
## 3 Results
### Deployment Site-Gongliao Aquatic Center Cage Aquaculture  
1. Geographic Location: Gongliao District, northeastern Taiwan.
2. Experiment Purpose: To assess the feasibility of cage aquaculture in this region and evaluate the potential application of underwater ROV for smart monitoring.
3. Experiment Content: The self-constructed underwater ROV, equipped with cameras and sensors, conducted underwater monitoring by recording water quality parameters, such as dissolved oxygen and pH levels, as well as the growth status of cultured organisms. The integration of various remote control technologies enabled users to operate the ROV in real-time and acquire data through multiple platforms, including computers and mobile devices.  
In the cage tests conducted at the Gongliao Aquatic Center, various remote control technologies demonstrated excellent adaptability. The research team utilized different control platforms--computers, tablets, and smartphones--to operate the underwater ROV, transmitting control commands to the site through 4G mobile communication networks. Figure 8 demonstrates the smartphone-based operations in cage net aquaculture, showing the practical implementation of mobile control capabilities. Test results indicated that in near-shore environments, the signal coverage of the 4G mobile communication network was robust, enabling smooth real-time image transmission. Additionally, sensor data--including temperature, dissolved oxygen, and pH levels--transmitted via the MQTT protocol could be reliably returned to the database, allowing the remote monitoring system to display and analyze changes in the cage environment in real-time.  
Figure 8: Smartphone-based operations in cage net aquaculture at the Gongliao Aquatic Center.  
Figure 7: Smart device-controlled ROV monitoring. (**a**) Smartphone-controlled laboratory test at National Taiwan Ocean University. (**b**) Smartwatch interface during offshore testing at Keelung Harbor.  
Water quality monitoring data indicated that the ROV camera could effectively display the condition of the cage structure. It was particularly adept at detecting sediment accumulation at the bottom of the cage and identifying potential damage areas on the net surface. This technology offers comprehensive monitoring capabilities that are not attainable through traditional manual inspections.
## 4 Algorithm Design
### Conflict Resolution Mechanisms for Limited Charging Resources
#### 4.1.2 Dynamic Rescheduling Conflict Mitigation  
Based on Theorem 1, we conduct conflict detection on path charging tasks accessing the same CI, identifying potential conflicts due to capacity constraints. Through recursive rescheduling, the charging task order is dynamically adjusted to ensure a globally feasible and conflict-free charging sequence.  
Assume that the current available capacity of the CI is 1. Define the set of electric logistics vehicles \(V=\{v_{1},v_{2},\ldots,v_{n}\}\), sorted by their initial arrival times at CI (i.e., \(ES_{v_{i}}\leq ES_{v_{i+1}}\)). The charging sequence \(\sigma\) stores all feasible charging tasks in the actual charging order. \(F(v_{i})\) represents the set of vehicles charged after \(v_{i}\) in \(\sigma\). A conflict pair \(v_{j}\prec v_{i}\) indicates a charging conflict between \(v_{j}\) and \(v_{i}\), as defined in Theorem 1. The conflict resolution strategy moves the charging task of \(v_{j}\) ahead of \(v_{i}\). The dynamic rescheduling mechanism follows the rules below.  
Step 1. Initialization. Initialize the scheduling sequence \(\sigma=\varnothing\). Then, subsequently add vehicle charging tasks from \(V\) to \(\sigma\).  
Step 2. Step-by-step insertion and recalculation. For the current vehicle \(v_{k}\in V\), detect whether it conflicts with all vehicles in \(\sigma\) based on Theorem 1. If a conflict exists, insert \(v_{k}\) before the first conflicting vehicle \(v_{j}\), denoted as \(v_{k}\prec v_{j}\). If no conflict exists, append \(v_{k}\) to the end of \(\sigma\) according to the FIFO principle. After adjusting the task order, dynamically recalculate the actual start time \(S_{k}\) and finish time \(F_{k}\) for \(v_{k}\) in \(\sigma\) as the following formulas:  
\[\begin{cases}S_{k}=\max\Bigl{(}ES_{k},F_{v_{k}p_{k}}\Bigr{)}\\ \quad F_{k}=S_{k}+T_{k}\end{cases} \tag{36}\]  
Here, \(v_{k}p_{k}\) represents the immediate predecessor of \(v_{k}\) in the charging schedule, which may result from conflict resolution (\(v_{k}p_{k}\prec v_{k}\)) or the originally conflict-free \(\sigma\).  
Step 3. Conflict resolution. If any vehicle \(v_{i}\in\sigma\) satisfies \(S_{i}>LS_{i}\), remove \(v_{i}\) from \(\sigma\) and recursively regenerate the schedule for the remaining vehicles.  
Step 4. Termination condition. The dynamic rescheduling mechanism terminates when all charging tasks are processed and no conflicts remain in \(\sigma\).
# Easy Fabrication of Ultrafiltration Membrane via Polyethersulfone-Fumed Silica
## 3 Results and Discussion
### Protein Separation Performance Analysis  
The level of protein adsorption on the membrane surface significantly impacts the measurement of fouling resistance ability. When protein adsorption is lower, the membrane exhibits greater resistance to fouling. This study investigated the impact of incorporating low-concentration fumed silica on polyethersulfone membranes for its selectivity and indicated the potential reduction in pore size that could occur with the introduction of fumed silica. The rejection rates for the aqueous solutions of proteins, including bovine serum albumin (66.5 kDa), pepsin (40 kDa), and lysozyme (14.3 kDa), were examined. Membranes selectively allow proteins of specific sizes to pass through. Smaller proteins can diffuse through larger pores, while larger proteins are retained. Figure 4 illustrates the protein rejection behavior of PES/FS membranes as a function of concentration. The selectivity of all membranes toward BSA was all above 90%, indicating a tight ultrafiltration membrane even without the addition of fumed silica. The FS-0.0 membrane has a rejection rate of 92%, while the other blends of PES/FS membranes reject an average of 95% BSA, indicating a close value to the reference. This implies that the pore size of a pure polyethersulfone membrane is sufficient to retain materials with molecular weights equal to or larger than BSA molecules, without the need to incorporate fumed silica into the dope solution.  
Examining membrane rejection for smaller proteins, the PES/FS membranes outperformed the pure PES membrane. Specifically, for pepsin with a molecular weight of 40 kDa, the pure PES membrane exhibited a rejection rate of 76%, while the rejection rate for lysozyme was only 36%. Lysozyme is approximately 4.6 times smaller than BSA, and the rejection rate of the pure PES membrane already decreased by 56%. Conversely, the pepsin rejection rate for all PES/FS membranes is consistently above 83%, with FS-0.2 exhibiting the highest rejection rate of 87%, or an 11% improvement compared to the pure PES membrane. Moving on to the lysozyme, the smallest protein tested in this study, FS-0.2, also demonstrated the highest rejection rate of 55%. Interestingly, the trend of rejection is similar for both pepsin and lysozyme: it initially increases with the addition of fumed  
Figure 3: The pure water flux of PES membranes of varying fumed silica concentrations.  
silica up to 0.2 wt.%, but then decreases afterward. Figure 4 illustrates that PES membranes containing low concentrations of fumed silica perform well with molecules having a weight larger or at least close to 40 kDa. Maintaining the proper concentration of fumed silica was beneficial for salt rejection in desalination field; however, high FS content also led to membrane fouling [35]. The underlying mechanism involves a significant reduction in electrostatic repulsion between the fumed silica particles and the membrane surface, yielding stronger adhesion forces within the membrane regions [36].
# Modeling and Design of a Novel 5-DOF AC-DC Hybrid Magnetic Bearing
###### Abstract  
This paper investigates a novel integrated AC-DC hybrid magnetic bearing (HMB) to reduce the volume, weight, manufacturing, and operation cost of magnetic suspension motors. Two radial MBs and an axial MB are integrated with the proposed HMB. The five-degree-of-freedom (5-DOF) suspension is realized in one unit. Two axially polarized permanent magnets provide the radial and axial bias fluxes. First, the HMB structure and the suspension mechanism are introduced. Second, based on the method of equivalent magnetic circuits, magnetic circuits are calculated. The mathematical models of suspension force are discussed. Third, the main parameters of the 5-DOF AC-DC HMB are given. The 3D finite element method (FEM) is adopted to analyze the proposed system's electromagnetic characteristics, and the suspension mechanism of the 5-DOF is verified. The radial suspension forces versus the radial control current, the axial suspension forces versus the axial control current, the relationship between the axial suspension force and the axial control current with the X direction offset, and the relationship between the radial suspension force in the X direction and the axial control current with the Z direction offset are calculated. Based on the research results, it is shown that the HMB structure is compact and reasonable, and the mathematical models and suspension mechanism are correct.  
hybrid magnetic bearing; electric machines; five degrees of freedom; magnetic circuits; magnetic suspension +
Footnote †: journal: Applied Sciences
## 1 Introduction
Slope deformation prediction was calculated using the grey model by [PERSON] et al. [12] and [PERSON] et al. [13]. In the mining environment, multivariate singular spectrum analysis was used by [PERSON] et al. [14] for accurate gate road support deformation forecasting. [PERSON] et al. [15] proposed mining roadway displacement forecasting using the support vector machine theory. [PERSON] et al. [16] used a grey algebraic curve model for the prediction of the roof fall. The aim of this paper is to develop an accurate grey forecasting algorithm for coal mines where large deformations of support constructions occur in a relatively short period of time. These deformations are the result of high underground pressure which occurs around the underground roadways, and the main factors for the occurrence of high underground pressure are bad geological conditions and poor physic-mechanical properties of the surrounding rock mass, as well as the presence of the clay which swells when in contact with water, adding additional pressure to support construction. In underground coal mines in the Republic of Serbia, the most used support for underground roadways is steel arch supports, followed by steel circular and wooden supports. Because of this, in our paper, we will focus on the deformations forecast in the case of the application of steel arch supports in underground roadways. This paper has four sections. In the section Materials and Methods, the forecasting model of displacements of the steel arch support based on the grey system (1,1) and stochastic theory is described. A novel approach is used that considers the displacements of the markers along the steel arch support that better describes the real situation (an example was given for marker M4 and its movement along the \(x\) coordinate in both directions). Using grey-stochastic simulation and the autoregressive process, the configuration of the steel arch support was described and forecasted based on the observed data. For error estimation of the model due to the nature of the problem we described, besides the usual MAPE error approach, which deals with displacements of the marker along the \(x\) and \(y\) axes as two separated time series and does not consider the fitted position of the marker, we introduced the novel approach of model accuracy dealing with the analysis of the closeness between fitted and observed position of the marker. The calculation process is shown in the section Numerical Example to represent the possibilities of this model. All steps of the calculation in the section Numerical Example are discussed, and the results,efficiency, and the area of implementation of the model are represented. Results show that the model is capable, and it can be used, to solve real-time problems concerning the forecasting deformations of the steel arch support in underground coal mines.
## 4 Advanced Machine Health Diagnosis Framework
### Spectral Feature Extraction  
From the transformed signals, key features are extracted, such as amplitude values at specific frequency bands, sideband patterns, and statistical descriptors. These features form the diagnostic basis for identifying anomalies and feeding machine learning models with structured, highly informative data.
# Herbicide Bioassay Using a Multi-Well Plate and Plant Spectral Image Analysis
## 3 Results
### Changes in RGB Spectral Responses to Herbicides  
As visual symptoms became evident, the mNDI and ExG values experienced a significant reduction (Figure 3). mNDI and ExG exhibited comparable trends to visual symptoms, with a gradual decrease over time. Paraquat (PSI) showed the fastest and greatest reduction in mNDI and ExG values, followed by tiefenacil and glufosinate. The mNDI and ExG values of paraquat dropped to 53.9% and 73.5% compared to the untreated control, respectively, at 6 HAT. This reduction aligned with the rapid herbicidal symptom of paraquat (PSI) demonstrated in the RGB images (Figure 2A). Crabgrass plants exhibited near-fatal conditions at 24 HAT with paraquat (PSI) and 48 HAT with tiefenacil (PPO). Glufosinate (GS) and glyphosate (EPSPS) started to show color changes at 24 HAT and 72 HAT, respectively, coinciding with the decreases in mNDI and ExG values. Isoxaflutole (HPPD) and penoxsulam (ALS) showed minimal herbicidal activity based on visual observations, and no significant changes were observed in mNDI and ExG after their treatment until 120 HAT. These differential changes in mNDI and ExG after herbicide treatment indicate that mNDI  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Spectral Parameter**} & \multicolumn{1}{c}{**Source of Variation**} & \multicolumn{1}{c}{**F Value**} & \multicolumn{1}{c}{**\(p\) Value**} & \multicolumn{1}{c}{**\(p\) Value**} \\  & \(\mathrm{F_{v}/F_{m}}\) & \begin{tabular}{c} Herbicide \\ Time \\ \end{tabular} & \
\begin{tabular}{c} 8354.295 \\ 1894.120 \\ 341.633 \\ \end{tabular} &
\begin{tabular}{c} \textless{}0.0001 \\ \textless{}0.  
and ExG values can be used to diagnose herbicidal activity and modes of action by a plant image analysis.
## 1 Introduction  
The incidence of lower limb fractures has been estimated to reach about 300,000 cases per year in the United States alone. As known, this type of fracture mainly results from a high-energy impact [1] or a fall from height [2], and is evenly distributed among age groups.  
Fracture fixation follows well-established procedures finalised to immobilise the respective position of bone fragments [3]. The classical approach, called'static fixation', bypasses completely the fracture site through alternative support structures, which can be placed externally (e.g., the classical cast) or internally (plates [4], nails [5], etc.). Dynamic fixation [6] is gaining consensus among orthopaedics, as an alternative technique where only torsional and flexural loads are bypassed by the bone synthesis device; by contrast, axial loads, produced by body weight or muscle contraction, are left to the contacting bone fragments [7]. This technique has very specific indications (sufficiently stable fractures); nonetheless, it deserves greater consideration due to its benefits which can be identified in a faster rehabilitation as well as an improved new-bone quality [8]. Axial loads were proved to stimulate the cell proliferation for the periosteal callus formation in the early phase and to accelerate the bone remodelling in the healing phase [9; 10; 11]. Acting as stable fixation devices at the beginning of the healing process, dynamizable synthesis devices can be converted into dynamic fixation devices at a later step [12]. That said, it is noteworthy that in the current case, dealing with a plate implanted through a percutaneous approach, the dynamization was designed to be performed through a magnetic actuator in order to avoid the need for a surgical operation and to minimize patient discomfort.  
Being medical devices, fracture synthesis devices need to be certified in Europe according to the respective Medical Device Regulation (EU) 2017/745 [13]. As a consequence, the whole design and manufacturing process was performed accordingly. This study considers how the design has some peculiarities, compared to static fracture synthesis plates: the plate is actually made of two parts sliding with respect to each other and all the respective interactions must be studied in relation to design specifications, such as the maximum source energy demand and device limited size. It is also shown that the uncertainty of some important mechanical parameters must be included in the design, for example the friction value of materials to be posed in strict contact, and the pre-load of the spring to be used for discharging the pin.  
In addition, in order to evaluate the structural mechanical response in a realistic environment, experimental tests were performed, as well as a numerical stress analysis in order to define a reference geometry complying with osteosynthesis device regulation. Finally, first prototypes were designed and realised according to an approved company process flow in accordance with ISO 13485:2016 [14].
# Reduction of Phase Shifters in Planar Phased Arrays Using Novel Random Subarray Techniques
###### Abstract  
Reducing the number of phase shifters by grouping antenna elements into subarrays has been extensively studied for decades. The number of phase shifters directly affects the cost, complexity, and power consumption of the system. A novel method for the design of phased planar antenna arrays is presented in this work in order to perform a reduction of up to 70% in the number of phase shifters used by the array, while maintaining the desired radiation characteristics. This method consists of creating fusions of subarrays to generate random sequences that form the best feeding network configuration for planar phased arrays. The obtained solution allows scanning the mainlobe at \(\theta=-40^{\circ}\) elevation with a range of scanning of \([-75^{\circ}<\phi<75^{\circ}]\) in the azimuth plane, while maintaining a side lobe level below \(-10\) dB and achieving a reduction of 62% in the number of phase shifters. It is shown that each solution is created based on search criteria, which influence the morphology of the array in terms of subarray size and orientation. The proposed methodology shows great flexibility for creating new phased antenna array designs that meet the requirements of specific applications in a short period of time.  
beamforming; planar array; phase shifter; randomized algorithm; subarray