esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map
## 1 Introduction
However, these algorithms rely on many natural images for training the network, heavily depend on the number of input images and are computationally complex and time-consuming. Although many researchers have proposed numerous low-light image enhancement algorithms, several problems still need to be addressed. For instance, some methods may not be able to completely extract contents at very dark regions, some may pose the risk of excessive enhancement, and some may blur detail information. In this paper, we propose a retinex theory-based algorithm to enhance images well and solve the above problems. The proposed method enhances the bright and dark areas to varying degrees and comprises three parts: (1) estimation of illumination component and reflection component from raw image; (2) blending two input images obtained by BEF and IACE to enhance the illumination component by Gaussian Laplacian pyramid; and (3) deriving the final enhanced image based on retinex theory. The general process of the proposed model is shown in Figure 1, and the main innovations are marked in red font and explained in detail in Section 3.2. Our main contributions can be summarized as follows. 1. We designed a neighborhood-weighted illuminance map estimation algorithm by considering the correlation among pixels. The estimated illuminance map carries important information about objects' structure in the visual scene. 2. We designed two functions to acquire input images for fusion, including a brightness enhancement function (BEF) and an improved adaptive contrast enhancement (IACE). The BEF can not only highlight the contents within dark areas but also retain the details within bright areas, and the IACE can enhance contrast while avoiding noise amplification. 3. We found that significant details exist in regions with large gradients, and large variances are rich in information. Hence, we designed a detail weight for the fusion procedure by considering the gradient and variance of local regions. 4. Compared with previous algorithms, the proposed method has several advantages. Firstly, the brightness of the illumination map is differentially enhanced (i.e., the light and dark areas of the image are enhanced to different degrees), so that not only is the content of the dark areas displayed, but also the details of the bright areas are not blurred. Then, the high-frequency component in contrast enhancement tends to amplify the noise, and in this paper, the image is enhanced in the form of a piecewise function to reduce noise. Finally, the weight based on gradient and variance enriches the image details. The rest of this paper is organized as follows. Section 2 discusses the relevant work. The proposed method is detailed in Section 3. The performance of the proposed method is evaluated both quantitatively and qualitatively in Section 4. Section 5 highlights some limitations of the proposed work and future research directions. Section 6 concludes the paper.
# GIS-Based Solar Radiation Mapping, Site Evaluation, and Potential Assessment: A Review
## 2 Solar Radiation Mapping Using GISs  
### Overview  
Solar radiation is the most important consideration when installing PV or CSP plants. Therefore, it is necessary to identify areas where solar radiation is abundant and to predict the spatial and temporal distribution of solar radiation for effective solar resource utilization. GISs are very useful in spatial and temporal analyses of solar resources while implementing location-specific technologies.  
Solar radiation analysis can be performed for points such as stations as well as for many pixels over a large area. It may also be conducted for specific administrative districts. In addition to spatial mapping of solar radiation, analysis and visualization over time are performed. The spatial and temporal considerations are summarized in Table 1 based on the reviewed literature.  
The solar radiation analysis methods using GISs can be classified according to the type of data employed. If solar radiation data are obtained at certain stations, it is useful to estimate the solar radiation through interpolation in the areas in which it is not measured. In addition, there have been some studies on solar radiation prediction at stations that acquire other weather information but do not obtain solar radiation information. If there are no data for stations at which observations are made, solar radiation models can be employed considering various geographical and terrain information. These methods are focused on predicting or mapping solar radiation. On the other hand, there have been many studies on analysis or simply visualization of the information about the solar resources in the area using the existing solar radiation map as a database (DB). The methods and types of data used for solar radiation analysis are summarized in Table 1 based on the reviewed literature.
# Practical Application of Mesh Opportunistic Networks
## 2 Theoretical Background
### OppNets and Broadcasters  
To date, too little attention has been paid to ad hoc electronic communications and opportunistic networks (or OppNets) in professional settings, outside of security services [25]. Broadcast work in crowded environments, saturated with terminals, a lot of noise, at large events or in places with a complicated orography, places important demands on efficient electronic communications. During the coverage of large events, such as demonstrations, events in stadiums, circuits, outdoor sports, concerts, fairs, etc., coordinating intercommunications is decisive. Recently, this problem has been aggravated by live mobile journalism (MoJo) broadcasts through dedicated Wi-Fi and data networks, especially when connected multi-camera or mobile streaming techniques are employed [26]. For audiovisual retransmissions of such events, it is best to free up the network as much as possible and not to use the entire data bandwidth, directing or sending data to servers for team communications or coordination. In addition, these are often very noisy environments that reach saturation due to the high density of people connected or the difficulties in obtaining a connection that allows for broadcast-quality streaming. Indeed, it is possible that communication is impeded by the inhibition of telephone frequencies due to passive security issues, and/or the presence of authorities [27]. Hence, an electronic communication system through mesh OppNets is presented here as a commercial and professional communications alternative in the field of broadcasting, in particular for mass events such as those described above.  
This study aimed to examine a collaborative mesh network without a mobile data plan, Wi-Fi, or satellite devices in different contexts: in one-to-one communications, in group communications, and in a professional communication workflow. As such, an experiment was designed and carried out in which the subjects pretended to be professional journalists from a communications company that were required to cover a cultural event in a coordinated manner, communicating and coordinating their activity through this opportunistic network model. To our knowledge, no previous experimental study has used a mesh network for professional journalism as we have done here.
## 3 Results  
### Model Parameters Variation  
We optimized the key parameters of the LUE, LUE-SW and the Farquhar model (Table 2). For the selected 21 sites, the values of \(\varepsilon_{max}\) ranged from 0.53 to 1.72 gC\(\cdot\)MJ\({}^{-1}\) in LUE. The highest \(\varepsilon_{max}\) was exhibited in the boreal forest of deciduous broadleaf species. Considering the limitation of soil moisture, the optimized \(\varepsilon_{max}\) in LUE-SW increased by 0.08 gC\(\cdot\)MJ\({}^{-1}\) on average. However, the statistical differences between the optimized parameters in each simulation (LUE or LUE-SW) and the default values (LUE\({}_{\mathrm{def}}\)) were not significant (\(p>0.05\)) according to the [PERSON]'s least significant difference test. The two-leaf Farquhar model calculated photosynthesis of sunlit and shaded leaves separately, but used the same values of the leaf maximum carboxylation rate at 25\({}^{\circ}\)C constrained by leaf nitrogen for both leaves (Equation (A3)). The average parameter was 37.16 \(\mathrm{\SIUnitSymbolMicro mol\cdot}\mathrm{m}^{-2}\cdot\mathrm{s}^{-1}\) with a standard deviation of \(\pm\)9.48 \(\mathrm{\SIUnitSymbolMicro mol\cdot}\mathrm{m}^{-2}\cdot\mathrm{s}^{-1}\) for 21 sites. The absolute value of the standard deviation accounted for 25.51% of the average, which was less than the percentage of the absolute standard deviation in LUE (31.80% for \(\varepsilon_{max}\)) and was comparable with that in LUE-SW (25.56% for \(\varepsilon_{max}\)). These comparisons indicate that the key parameters in the LUE-SW and the Farquhar model were robust based on site-specific optimization.  
We adopted the one-way analysis of variance (ANOVA) to determine whether there were differences among site-specific parameters in LUE, LUE-SW and the Farquhar model according to biome types or climate zones. As presented in Table 2, the differences were significant (\(p<0.05\)) among nine plant functional types (_i.e._, biome types + climate zones) for all three simulations, but were not significant among biome types or climate zones, except for the biome-based category in the Farquhar model. These results suggest that it is necessary to specify parameters according to PFTs in both the MODIS algorithm and the Farquhar model to reduce errors introduced by parameter classification in regional simulation.
# Opportunities and Challenges for Error Correction Scheme for Wireless Body Area Network--A Survey
## 1 Introduction
The reliability of a network can be measured in various units, such as Bit Error Rate (BER), Packet Error Rate (PER), Packet Acceptance Rate (PAR), Received Signal Strength (RSS), throughput efficiency, Mean Absolute Error (MAE), and residual BER. We make two major contributions in this paper. First, we conducted an extensive review on available literature on ECS on WBAN. Second, we also put forward future research challenges and opportunities on design and implementation of ECS on WBAN. The remainder of the paper is organized as follows. In Section 2, we outline the various types of ECS that are available in the literature that are used in communication systems. Section 3 summaries IEEE 802.15.6 standard for WBAN, which is followed by channel and network models in Section 4. In Section 5, we review papers on different aspects. Section 6 presents future research issues, including unsolved challenges in WBAN. Finally, we present concluding remarks of the paper in Section 7. All of the acronyms used in this paper are presented in Table 1 in the Appendix Section.
## 2 Materials and Methods
### Data Analysis
#### 2.4.3 Modeling Based on Effective Wavelengths  
Partial least squares regression (PLSR), multiple linear regression (MLR), and artificial neural network (ANN) models were employed to determine the optimal model for predicting bell pepper quality parameters based on effective wavelengths. As previously discussed, the coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to deviation (RPD) (Equations (4)-(6)) were utilized to evaluate model performance. Artificial neural networks (ANNs) are computational models inspired by the human brain's information processing capabilities. ANNs learn from data rather than explicit programming, identifying patterns and relationships within the data. A network comprises numerous interconnected processing elements (PEs) or artificial neurons, organized in layers and associated with weights. The network's strength lies in its interconnected structure. Each PE receives weighted inputs, processes them through a transfer function, and generates an output. The network's behavior is defined by the transfer functions of its neurons, learning rules, and architecture. Weights are adjustable parameters, making ANNs parametric systems. The weighted sum of inputs constitutes the neuron's activation, which is transformed into an output by the transfer function. This function introduces nonlinearity to the network. During training, connections between units are optimized to minimize prediction errors and achieve desired accuracy. Once trained and validated, the network can predict outputs based on new input data.  
A multilayer perceptron (MLP) artificial neural network, based on the backpropagation algorithm, was employed for this study. The network architecture comprised an input layer with a number of neurons equal to the effective wavelengths, a single-neuron output layer representing the qualitative property, and one or two hidden layers consisting of 10 neurons each. Data were partitioned into training, validation, and testing sets using a 70%-15%-15% split. As a supervised machine learning algorithm, the MLP was implemented using MATLAB's nstart toolbox. The Levenberg-Marquardt algorithm was selected to optimize network weights due to its efficiency in minimizing error [48]. Data analysis was conducted using The Unscrambler X 10.4 and MATLAB 2022a.
# Compact Design and Impact Ionization: Utilizing Small-Sized Thyristors in a 4-Stage Marx Generator
## 2 Materials and Methods
### Capacitive Probe
The same signal was measured simultaneously using both the capacitive probe and the coaxial cable setup. Figure 12 presents a comparison of the two measurement methods at the input (G-B SiC MOSFET out, Figure 4), with the attenuator measurement represented by the blue line (\(U_{att}\)), and the capacitive probe measurement represented by the yellow line (\(U_{cp}\)). The biasing voltage for the SiC MOSFETs is set at 1.3 kV during operation. A lower voltage would not ensure sufficiently fast switching, while a higher voltage risks damaging the MOSFETs. A linearity test is pending to verify the accuracy of the system within the lower voltage range permissible by the reference measurement system, ensuring that performance scales appropriately across the intended operational voltage spectrum. Future work should include a comprehensive linearity check, extending to an anticipated voltage of 10 kV to validate both the system's voltage linearity and the associated electric field stresses on components. This assessment will confirm the safe operational limits of the FR4 dielectric material's electric field strength, as well as examine its dielectric  
Figure 11: Capacitive probe setup on the back side of the PCB. Figure 12: Comparison of the capacitive probe (yellow) and attenuator (blue) measurements at the input of the Marx generator (G-B SiC MOSFET out, Figure 4). strength and lifetime under repetitive 10 kV pulsing, ensuring measurement accuracy and durability of the system over prolonged use. In summary, our exploration and implementation of capacitive probes for high-voltage measurements, embedded within the PCB, have enabled us to achieve sub-nanosecond measurements while safeguarding our measuring equipment.
# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
## 2 Materials and Methods
### Fuzzy Modeling Approach  
#### 2.1.1 Description of Fuzzy Logic  
In traditional logic, the degree of truth can be represented by either the values of 1 (true) or 0 (false), but this has limitations because some elements' membership is unclear, thereby rendering traditional methods incapable of handling complex environmental problems that have some kind of vagueness in them. In a crisp set, an element is either a member of the set or not, but also crisp elements can belong to more than one set, for example, height measurements. Therefore, fuzzy logic comes in to cater to fuzziness in solving real-life problems. In fuzzy logic, the degree of truth ranges between 0 and 1, both inclusive. Fuzzy sets allow elements to be partially in a set.  
Fuzzy logic helps to compute linguistic variables, that is, variables whose values are not numbers but words or sentences in natural or artificial languages as proposed by Dr. [PERSON] of the University of California in the 1960s [20]. According to [PERSON] [21], fuzzy logic can efficiently handle soft computing complex issues. Its techniques have been widely applied in all aspects of today's society, such as industrial manufacturing, diagnosis, automation control, academic education and forecasting. A linguistic variable is a collection of five things represented as \(<\)T(x), U, G, M\(>\) where x is a variable name T(x) is a set of terms; U is universe of discourse; G is set of syntax rules; M is a set of semantic rules.  
Fuzzy logic works well in designing non-linear complex control solutions with multiple parameters because of the following [22]:  
* Fuzzy logic has the ability to describe systems in terms of a combination of numeric and linguistic means.
* Fuzzy logic measures the certainty or uncertainty of the membership of an element of the set.
* Fuzzy algorithms are often robust in the sense that they are not very sensitive to changing environments and erroneous or forgotten rules.  
In the other words, the fuzzy logic method shows the satisfactory value of air pollutants in a continuous value between 0 and 1. Fuzzy logic uses if-then implication reference rules with suitable linguistic description rules. A fuzzy rule is written as **if**_situation_, **then**_conclusion_[23]. In this case, the situation is also called rule premise or antecedent. The conclusion part is called consequence or conclusion, that is, IF the \"antecedent\" is satisfied, THEN the \"consequent\" is inferred.  
Therefore, the designed rules are inferred, according to the fuzzy inference knowledge base to generate a generic, fuzzy based algorithm. Then, the model designed as the output represents the fuzzy function to predict the Air Quality Index for Kampala city.
## 2 Methodology
### Petrographic, Mineral Chemistry, and Geochemical Techniques  
The samples collected in the first field trip were processed in thin sections for petrography and mineral chemistry analysis and powdered for X-ray diffraction (XRD) and X-ray fluorescence (XRF) analysis (Figure 3).  
The petrography included conventional petrographic techniques by transmitted and reflected light polarizing microscopy, complemented with scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), which also provided a valuable semi-quantitative analysis of the mineral chemistry using a Hitachi S3700N interfaced with a Quanta EDS microanalysis system. The Quanta system was equipped with a Bruker AXS XFlash Silicon Drift Detector (129 eV Spectral Resolution at FWHM/MnK\(\alpha\)). Standardless PB/ZAF (self-calibrating EDS spectrum procedure) quantitative elemental analysis was performed using Bruker Espirit software. The operating conditions for EDS analysis were 20 kV accelerating voltage, 10 mm working distance, and 120 \(\mu\)A emission current. The detection limits for major elements (>Na) were on the order of 0.1 wt %.  
The petrographic analysis was also complemented by detailed identification of crystalline phases through XRD analysis using a Bruker AXS D8 Discover diffractometer with a Cu-K\(\alpha\) source, operating at 40 kV and 40 mA with a Lynxeye one-dimensional detector. Scans were performed from 3 to 75\({}^{\circ}\) 2\(\theta\), with a 0.05 20 step and 1 s/step measuring time by point. Diffract-Eva Bruker software with PDF-2 database (Powder Diffraction File by the International Centre for Diffraction Data) was utilized to interpret all XRD patterns.  
The whole rock geochemistry analysis was conducted by XRF, which allows for the quantification of major oxides (SiO\({}_{2}\), TiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), Na\({}_{2}\)O, K\({}_{2}\)O, CaO, MgO, MnO, FeO, and P\({}_{2}\)O\({}_{5}\)), sulfur, and some minor elements (Rb, Sr, Y, Zr, Nb, Th, Cr, Co, Ni, Cu, Zn, Ga, As, Pb, Sn, V, U, and CI). Analyses were performed with an Energy-Dispersive X-Ray Spectrometer S2 Puma (Bruker) using a methodology similar to that adopted by Georgiou [29]. A description of the standard reference materials (SRM) utilized in the calibration method can be found elsewhere [30]. Samples were fused on a Claisse LeNeo heating chamber using a flux (Li-tetraborate) to prepare fused beads (ratio sample/flux = 1/10). The software utilized for acquisition and data processing was Spectra Elements 2.0, which reported the final oxide/element concentrations and the instrumental statistical error associated with the measurement.
# Reinforcement Learning Decision-Making for Autonomous Vehicles Based on Semantic Segmentation
## 3 Experimental Procedure and Results  
### Decision-Control Framework for Autonomous Vehicles  
To achieve accurate and stable decision-control for autonomous vehicles, this paper proposed a decision-control framework, as shown in Figure 4. First, key elements and road outlines were extracted from real road scenes to generate road maps, which were then imported into the simulation platform as scene models. The semantic segmentation neural network was trained using real road image datasets, achieving good segmentation results. The pre-trained semantic segmentation model loaded its weights through CARLA's Python API and connected with the simulation platform. Cameras were installed on the ego vehicle, and the RGB images captured by the cameras were identified and predicted by the pre-trained semantic segmentation model, extracting different types of scene elements from the images and completing label classification. Road visual perception data were simplified to extract drivable areas and reduce environmental complexity. The output of the semantic segmentation model (single-channel semantic label map) was downsampled and then fed into the neural network of the reinforcement learning (RL) agent for state embedding. Subsequently, measurement data were obtained via radar and the Global Navigation Satellite System (GNSS) in CARLA, including vehicle speed, distances to other vehicles, lane markings, collision information, and obstacle positions. These data constituted the state space input for the agent model. In the proposed framework, speed and collision data were combined as part of the state representation and fused with the features extracted from the semantic label map. The fusion process was as follows: a convolutional neural network (CNN) was used to extract semantic segmentation features from the single-channel semantic label map. The CNN output was a flattened feature vector representing the spatial and semantic context of the environment. Numerical features, such as speed, collision data, and distance to obstacles, were preprocessed and normalized into low-dimensional vectors. In the fully connected (FC) layer of the reinforcement learning (RL) agent's neural network, the semantic feature vector and the numerical feature vector were concatenated, ensuring that both types of features were jointly considered in the decision-making process.  
The agent received state space information and determined decisions in the action space, such as steering and speed control, which were then executed and presented in the CARLA environment. Reward values were generated through the reward mechanism and fed back to the agent to update future decisions. Finally, the decision feedback from the agent in the simulation environment guided the driving decisions of the autonomous vehicle on real roads. According to the framework design, the training was set as a closed-loop system, enabling the agent to learn from the environment and improve its driving strategy in the test scenarios.