esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The electromagnetic interaction between a circular disk with finite conductivity and finite thickness and a coaxial circular loop of constant current is addressed here. The finite conductivity and thickness of the material disk lead to the adoption of suitable generalized boundary conditions, and the problem is thereby reduced to the solution of two sets of dual integral equations in the Hankel transform domain. Such equations are then solved by expanding the spectral unknowns in Neumann series of Bessel functions. An alternative formulation that is valid for purely conductive screens with no magnetic properties, which is computationally much faster, is proposed as well. The magnetic shielding effectiveness of the structure is studied in detail, pointing out its dependencies and possible critical situations.  
Keywords:dual integral equations; electromagnetic shielding; regularizing Galerkin methods +
Footnote †: journal: _Applied Sciences_
# Equalization Methods for Out-of-Band Nonlinearity Mitigation in Fiber-Optic Communications
## 2 The Time-Varying ISI Model of NLIN
The derivation is identical to the single-polarization case, and the time-varying ISI model is expressed as\[\tilde{s}_{n}=\tilde{a}_{n}+i\sum_{l}\mathbf{R}_{l}^{(n)}\tilde{a}_{n-l}+\tilde{w}_ {n}. \tag{4}\]  
In this case, the ISI coefficients, \(\mathbf{R}_{l}^{(n)}\), are \(2\times 2\) matrices, where the off-diagonal elements describe interactions between the two polarizations.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 10 Weaknesses and Strengths of Different Emerging Computational Intelligence Methods  
Generalized strengths and weaknesses of different artificial intelligence and machine learning-based algorithms are given in Table 4. It may help researchers to select a method for fault detection, classification, and location-based on its strengths, features and complexity levels.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Technique** & **Strength** & **Weakness** \\ \hline \multirow{8}{*}{ANN Technique} & ANN is pretty good in determining the exact fault-type and its implementation is easy. & The training process is quite complex for high-dimension problems. \\ \cline{2-3}  & & A local optimum solution is provided by the gradient-based back-propagation technique for non-linear separable pattern classification problem. \\ \cline{2-3}  & It has a lot of applications in real-life problems. & ANN offers slow convergence in the BP algorithm. \\ \cline{2-3}  & ANN learns and no need for reprogramming. & Convergence is dependent on the selection of the initial value of weight constraints connected to the network. \\ \hline \multirow{8}{*}{PNN Technique} & The learning process is not required. \\ \cline{2-3}  & Determination of initial weights of the network is not needed. & It requires high processing time for large networks. \\ \cline{2-3}  & No correlation of the recalling process and learning process. \\ \cline{2-3}  & Convergence in Bayesian classifier is certain. & Not easy to determine how many layers and neurons are required. \\ \cline{2-3}  & PNN show fast learning time. & Large memory space is required to save the model \\ \hline \multirow{2}{*}{Fuzzy Methods} & Simple ‘if-then’ relation is used to solve uncertainty problems. & No robustness is observed. \\ \cline{2-3}  & & Experts are mandatory in order to determine membership function and fuzzy rules, for large training data. \\ \hline \multirow{4}{*}{ANFIS Technique} & Parameters are tuned properly by the hybrid learning rule. \\ \cline{2-3}  & It offers a faster convergence. & ANFIS is highly complex in computation. \\ \cline{2-3}  & The search space dimension is reduced. \\ \cline{2-3}  & ANFIS is smooth and adaptable \\ \hline \end{tabular}
\end{table}
Table 4: Strengths and weaknesses of various emerging computational intelligence methods.
###### Abstract  
The deployment of the HY-2B/2C/2D satellite constellation marks a significant advancement in China's marine dynamic environmental satellite program, forming a robust three-satellite network. All satellites are equipped with the \"HY2_Receiver\", an indigenous technological achievement. Precise orbit determination using this receiver is critical for monitoring dynamic oceanic parameters such as sea surface wind fields and heights. This study presents a detailed analysis and comparison of the GPS data quality from the HY-2B/2C/2D satellites, emphasizing the impact of phase center variation (PCV) model corrections on orbit accuracy, with a particular focus on high-precision reduced-dynamic orbit determination. The experimental results demonstrate that the GPS data from the satellites exhibit consistent satellite visibility and minimal multipath errors, confirming the reliability and stability of the receivers. Incorporating PCV model corrections significantly enhances orbit accuracy, achieving improvements of approximately 0.3 cm. Compared to DORIS-derived orbits from the Centre National d'Etudes Spatiales (CNES), the GPS-derived reduced-dynamic orbits consistently reach radial accuracies of 1.5 cm and three-dimensional accuracies of 3 cm. Furthermore, validation using Satellite Laser Ranging (SLR) data confirms orbit accuracies better than 3.5 cm, with 3D root mean square (RMS) accuracies exceeding 3 cm in the radial (R), along-track (T), and cross-track (N) directions. Notably, the orbit determination accuracy remains consistent across all satellites within the HY-2B/2C/2D constellation. This comprehensive analysis highlights the consistent and reliable performance of the indigenous \"HY2_Receiver\" in supporting high-precision orbit determination for the HY-2B/2C/2D constellation, demonstrating its capability to meet the rigorous demands of marine dynamic environmental monitoring.  
H - 2B/2C/2D; satellite-borne GPS; reduced-dynamic orbit determination; data quality analysis; antenna phase center change
# Automated Defect Detection Using Threshold Value Classification Based on Thermographic Inspection
## 3 Experimental Configuration
### Experimental Setup of LIT  
The LIT experimental device used in this study consisted of an STS304 specimen, infrared camera, function generator, power amplifier, and halogen lamps, and is shown in Figure 4. The experiments in this study were conducted in an isolated dark room containing the inspector and all equipment. Two 1 kW halogen lamps was used as excitation heat sources, and were controlled by a function generator (Agilent 33210A, Petaling Jaya, Malaysia) and power amplifier. For uniform heat source excitation, the thermal contrast between both ends of the specimen was set not to exceed 0.2 \({}^{\circ}\)C. An SC645 IR camera (un-cooled, 640 \(\times\) 480 pixels, 7.5-13 \(\upmu\)m) model was used to measure the thermal response of the specimen surface [3; 26]. The FOV (field of view) of the lens attached to the IR camera was 25\({}^{\circ}\) (H) \(\times\) 19\({}^{\circ}\) (V) and focal length was 24.6 mm. The distance between the specimen and IR camera was set to 800 mm to match the size of the specimen to the 2D thermal image output to the PC in real time. The thermal image was acquired using FLIR R&D commercial software, and the frame rate of the IR camera for image acquisition was set to 50 Hz.  
\begin{table}
\begin{tabular}{c c} \hline Thermal Conductivity (k) & 16.2 W/m\(\cdot\)K \\ \hline Density & 8000 kg/m\({}^{3}\) \\ \hline Heat Capacity & 500 J/kg\(\cdot\)K \\ \hline Initial Temperature & 23 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 1: The material properties of the STS304 plate.  
Figure 4: Experimental configuration of lock-in infrared thermography system.  
Figure 3: The picture of the specimen used in the study: (**a**) front side coated with black paint, and (**b**) back side with flat-bottomed holes.
# Effect of Poultry and Goat Manures on the Nutrient Content of _Sesammum altatum_ Leaf Vegetables
## 1 Introduction  
_Sesamm altatum_ Thonn., commonly known as winged-seed sesame, is a leafy vegetable that is collected from the wild in KwaZulu-Natal, South Africa [1] and other parts of the globe [2, 3]. It is an erect, year-round, unclaginous herbaceous plant [4]. In Africa and Asia, soft and insect leaves, shoots, pods, and seeds are cooked and eaten fresh or dried and added into soups, sauces, snacks and cakes [3, 4, 5, 6]. _Sesamm altatum_ has excellent amounts of proteins, carbohydrates, dietary fibre, fats, Ca, N, P, K, Mg, and other trace elements such as Cu, Fe, Mn, Na, and Zn, which compare well with _Amaranthus hydridus_[1, 7]. It also contains essential antioxidant which can be linked to it use as an ayurvedic medicine against diseases such as, diarrhoea, intestinal disorders [4], and diabetes [8]. It is also used as an aphrodisiac for human and livestock fertility improvement [4]. Fresh leaves and seeds are valued for their oils and their muclaginous substance that has qualities valued in hair shampoo, cloth washers, and hair conditioner [3].  
Organic manures are organic residues of animal and plant by-products [9, 10]. Small-scale farmers usually rely on organic manures due to high costs and less accessibility of inorganic manures [11, 12, 13], which includes animal manures, compost, and green manures [14]. Organic manures improve plant root rizzoster conditions which enhance theassimilation of nutrients from the soil [15], as well as the nutrient content of the plant itself [12; 16].  
Applications of 2.5-6 t ha\({}^{-1}\) poultry manure cause an increase in the Ca, Mg [17], ash, fats, N, P, and protein contents in _S. indicum_ leaves [18]. The ash, moisture content, proteins, carbohydrates, fibre, fat, N, P, and K in _C. olitorious_ leaves increase when poultry manure is applied at 1 and 2 t ha\({}^{-1}\)[12]. The increase in the ash, fat, and protein content was also noted in _Amaranthus caudatus_ and _A. cruentus_ when poultry manure was applied [19]. Further, there is an increase in proximate and mineral contents with the application of 0.03 t ha\({}^{-1}\) of poultry manure on _A. cruentus_, _A. hydridus_, _A. deflexus_, and _A. spinosus_[20]. Goat manure applied at rates ranging from 2.5-13 t ha\({}^{-1}\) resulted in an increase in Ca, Mg, N, P, and K content in _Capsicum annuum_ leaves [21]. Application of 8 t ha\({}^{-1}\) goat manure increases Ca, Cu, Fe, K, Mg, Mn, Na, P, and Zn content in _Cleome gynandra_ shoots [22].  
The nutrient content of _S. alatum_ is only known for plants collected from the wild [1]. _Sesamum alatum_ is one of the wild-collected leafy vegetables with no agronomic studies on its improvements under different soil fertilities using animal manure. To improve nutrient richness in _S. alatum_, a soil with necessary nutrients that will facilitate production and maintain or improve the crop nutrient content is a prerequisite. Therefore, the objective of this study was to examine the effect of poultry and goat manures on the nutrient content of _S. alatum_.
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 3 Performance of HWLNets
### Network Capacity and QoS  
In wireless networks, capacity is a measure of the maximum data rate of the network with regard to bit error ratio. In HWLNets, the network capacity can be increased by increasing the available bandwidth. This can be achieved using wider optical channels in the VLC component and higher frequency bands in the RF component. HWLNets can improve network performance by taking excess traffic or providing certain users high data rates for bandwidth-consuming applications such as 4K video streaming. This especially helps large and dense WiFi networks. On the other hand, WiFi can provide stable connections to areas that cannot have decent LiFi coverage [32]. This type of network has been extensively researched in the last couple of years, focusing on load balancing which will be elaborated on in Section 5.  
However, increasing the bandwidth can also lead to higher signal attenuation and interferences, affecting the quality of service (QoS). Packet throughput, loss ratio, latency, and jitter, unlike capacity (part of the physical network layer), are measured on the network layer where support for applications with diverse QoS requirements is necessary. To ensure both network capacity and QoS, HWLNets can implement several techniques. One technique is to use multiple-input multiple-output (MIMO) [35] technology, which uses multiple antennas to increase the network capacity and improve the signal quality. Another technique uses adaptive modulation and coding [36], which adjusts the modulation scheme and coding rate in real-time based on channel conditions. This can help ensure that the system uses the most efficient modulation technique for the current channel conditions, which can improve both network capacity and QoS.  
HWLNets can also use advanced transmission protocols, such as hybrid automatic repeat request (HARQ), to improve the QoS of the network [37]. HARQ is a retransmission-based protocol that retransmits only the lost data packets, reducing the number of retransmitted packets and the associated latency.  
Some applications such as 4K video streaming and 3D holographic require high data rates and others such as remote surgery or automated guidance for vehicles (AGV) need low latency [38]. With the growing number of user equipment requiring more bandwidth from WiFi APs and thus increasing the average latency and jitter, one of the possible improvements that would be cost-effective and solve the demand for high packet rate would be implementation of LiFi APs in hybrid software-controlled networks [39].  
User experience is another requirement that is significant to any network. This measures the average user satisfaction with a given service and thus determines the need for a specific type of network. [PERSON]'s fairness index, used for evaluating the fairness of resource allocation among traffic flows, can be used to establish a network suitable to a vast number of users with different application requirements. Fairness schemes in HWLNets are also discussed in Section 5.
## 4 Validation and Comparison
### Comparison of the Numerical Predictions with Experimental Data
It is observed that, as the number of simulations increases, the average elastic moduli and the relevant bounds gradually approach to the final results and present so small variations after 12 simulations that it can be considered as being converged for the examples under investigation. Hence, 15 repeated simulations are performed for each porosity of a certain porous medium, which leads to the fact that the effective isotropic moduli predicted numerically in Figure 13 vary in a small range near the relevant mean value. It is observed that both the numerical predictions and the relevant experimental data of all three porous materials under investigation have the same variation as the porosity \(f\) increases, and most of the experimental data points are in the range predicted by our computational method. Particularly, the maximum relative error of the numerical predictions and the relevant test data of all instances is no larger than 6%. The validity of our computational method is thus verified. Note that only the effective moduli of the porous media tested are employed here for validating our computational strategy because the detailed information about the real tomography microstructure of porous media is rarely provided. More investigations will be carried out on comparing the statistical data of the numerically generated porous microstructrues and the real tomography ones and on verifying the fidelity of the generated porous models in our future works.
# Privacy-Aware Cloud Architecture for Collaborative Use of Patients' Health Information
## 3 Proposed Architecture
### Architecture Fundamental Aspects
#### 3.1.1 Structuring Patients Information  
A fundamental aspect of the proposed cloud architectural design is the accommodation of patients' health information under four main categories, which were identified in the case study findings [58]. These categories are: Information that is constantly required in every patient's visit (All_V); information that is required in patients' emergency visits (Em_V); information that is required in out-patients' clinical visits (OutP_V); and information required for research purposes (R). This paper focuses on information categories that are used for medical treatment purposes; therefore, the (R) category is explained in other work.  
The main goal of structuring patients' health information is two-fold: first, to limit the exposure of information in instances when it is not needed. Second, limiting the exposure of information leads to better means of privacy protection that patients desire for their health information. The proposed system design stores patients' information in three groups referred to as documents. Each document has identifying tags and contains files.  
Each file has the name of the patient, the name of the document, and a sub-tag used by the application system to identify and locate it. The system's identifying tags are used to technically facilitate access to documents and do not indicate their content.  
For example, and for simplification purposes, the tags used for the documents are 1, 2, and 3. All patients registered in the proposed system have their information organized into doc-1, doc-2, and doc-3. In the practical implementation of the proposed system, the information stored in each document is subject to change according to the medical treatment's changing needs. The information categories comprise information contained in different documents, therefore, accessing a category of information is a result of accessing one document or more. For example, when a user has the right to access information about a patient in an emergency setting (Em_V), doc-1 and doc-2 are released to the user, while a combination of doc-1, doc-2, and doc-3 are released for users who have access to all information related to patients' health (OutP_V). Table 2 illustrates the information categories and their comprising documents.
# Investigation of a Precise Control Scheme for Rice Quality
## 1 Introduction
[15] investigated the impact of different fluidization regimes and temperatures in a fluidized bed dryer on rice drying time and quality characteristics. They developed an artificial neural network-based model using response surface methodology (RSM) to relate input and output variables. The RSM model outperformed the neural network in terms of prediction accuracy, enabling the optimization of the drying process based on this model. Previous studies have offered guidance and recommendations for rice drying operations. However, it should be noted that an optimal process may not necessarily provide a high reference value due to the unique characteristics of rice drying when compared to fruit and vegetable drying. Variations in rice variety and water content at harvest can also result in significant differences in tempering ratios among grain dryers with varying structural designs [9; 11]. Therefore, it is imperative to select a process plan based on the specific situation under consideration. In this study, a representative of the available thin-layer drying equipment was utilized for conducting hot air-drying experiments that mirrored actual drying processes. The effects of various parameters, such as drying temperature, relative humidity, initial moisture content, air velocity, and tempering ratio, were analyzed with respect to their impact on both milling quality and drying time. The experiments were designed using the Design-Expert v8.0.6.1 software (Stat-Ease Inc., Minneapolis, MI, USA) with RSM to investigate potential interactions among the parameters and construct regression models. Additionally, process reference diagrams were generated based on these models for drying time, ACP, and HRY. The novelty of these charts lies in their ability to demonstrate potential variations in grain quality throughout the drying process. By consulting these charts, users can ascertain the grain quality index at a specific time and location during drying without requiring actual quality testing. Additionally, presetting the quality index parameters enables obtaining corresponding operating parameters for the dryer, thereby providing guidance for effective grain drying operations and ensuring optimal drying outcomes. These charts can be utilized for predicting drying quality and searching for optimal processing conditions, thus providing a valuable resource for industrial-scale operations.
## 4 Radiative Transfer in Coupled Atmosphere-Water (Including Snow/ice) Systems
### Radiative Transfer Equation--Unpolarized Radiation
#### 4.1.1 Isolation of Azimuth Dependence  
The azimuth dependence in Equation (112) may be isolated by expanding the scattering phase function in _Legendre polynomials_, \(P_{\ell}(\cos\Theta)\), and making use of the addition theorem for spherical harmonics [14]  
\[p(\cos\Theta)\ =\ p(u^{\prime},\phi^{\prime};u,\phi)=\sum_{m=0}^{2N-1}(2-\delta _{0,m})p^{m}(u^{\prime},u)\,\cos m(\phi^{\prime}-\phi) \tag{115}\]  
where \(\delta_{0,m}\) is the Kronecker delta, i.e., \(\delta_{0,m}=1\) for \(m=0\) and \(\delta_{0,m}=0\) for \(m\
eq 0\), and  
\[p^{m}(u^{\prime},u)=\sum_{\ell=m}^{2N-1}(2l+1)\chi_{\ell}\Lambda_{\ell}^{m}(u^ {\prime})\Lambda_{\ell}^{m}(u). \tag{116}\]  
Here  
\[\chi_{\ell}=\frac{1}{2}\int_{-1}^{1}P_{\ell}(\cos\Theta)p(\cos\Theta)d(\cos\Theta) \tag{117}\]  
is an expansion coefficient and \(\Lambda_{\ell}^{m}(u)\) is given by  
\[\Lambda_{\ell}^{m}(u)\equiv\sqrt{\frac{(\ell-m)!}{(\ell+m)!}}P_{\ell}^{m}(u) \tag{118}\]  
where \(P_{\ell}^{m}(u)\) is an associated Legendre polynomial of order \(m\). Expanding the radiance in a similar way,  
\[I(\tau,u,\phi)=\sum_{m=0}^{2N-1}I^{m}(\tau,u)\,\cos m(\phi-\phi_{0}) \tag{119}\]  
where \(\phi_{0}\) is the azimuth angle of the incident light, one finds that each Fourier component satisfies the following RTE (see [14] for details)  
\[\mu\frac{dI^{m}(\tau,u)}{d\tau}\ =\ I^{m}(\tau,u)-\frac{\sigma(\tau)}{2}\int_{ -1}^{1}p^{m}(\tau,u^{\prime},u)\,\,I^{m}(\tau,u)d\mu-S^{*m}(\tau,u) \tag{120}\]  
where \(m=0,1,2,\ldots,2N-1\) and \(p^{m}(\mu^{\prime},\mu)\) is given by Equation (116).