esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Magnetic Equivalent Circuit Model for the Corner of Inductor Core
## 4 Results and Discussion
### Flux Density Distribution of the Corner  
By using the local corner MEC model, the MEC model of the square core-type inductor is established, as shown in Figure 9, where the core limb branch and winding leakage flux branch reluctance are \(R_{e}\) and \(R_{limb}\), respectively. The main flux path flows through the limb and is divided into four sub-fluxes into the corner sub-branches. The winding leakage flux of each winding flows  
Figure 10: The average flux density of the corner branch \(R_{c1}\), \(R_{c2}\), and \(R_{c3}\) regions obtained by the finite element method (FEM) compared with the flux density of MEC.  
through the adjacent core limb and winding. These flux paths assure that the flux density \(\mathbf{B}\) of the MEC is correctly distributed with a simple model and high precision.  
The three branches flux density of the corner by the MEC method is obviously different and increases from \(R_{c3}\) to \(R_{c1}\). The corner branch \(R_{c2}\) and the \(R_{c3}\) branch flux density are consistent with the calculated average value of the FEM. Their average relative errors of the currents from 20 A to 1000 A are 0.93% and 4.05%, respectively. The relative error of the \(R_{c1}\) branch is 8.85% because the saturation of the inner corner affects the accuracy of the calculation.  
In summary, the local corner MEC model can effectively illustrate the inner and outer flux density distribution of the corner with high calculation accuracy.
## 2 Methods of Analysis
### Simplest Two-Element Cable Structure
This means that it is the geometric stiffness matrix \(\mathbf{K}_{GN}(\mathbf{N})\equiv N(P)\). It should be noted that for a considered structure, it is possible to obtain a formula for the longitudinal forces \(N(P+S)\) generated jointly by the external load \(P\) and prestress forces \(S\). It can be simply determined by the static equilibrium of the 2 nd node in the actual configuration (Figure 3c). So, the geometric stiffness matrix \(\mathbf{K}_{GN}(\mathbf{N})\equiv N(P)\) can be expressed as  
\[N(P)=\frac{P}{2\sin\alpha}-S;\ \ \ \sin\alpha=\!\frac{q}{\sqrt{l^{2}+q^{2}}}. \tag{24}\]  
The application of non-linear theory takes into account the stiffening of the structure under the influence of an external load. The considered force stabilizes the mechanism. In the absence of initial prestress (\(S=0\) kN), Equation (22) results in the calculation of the displacement  
\[q(S=0)=\sqrt[3]{\frac{P}{EA}}l. \tag{25}\]  
In turn, if the non-linear influence is neglected (\(N(P)=0\)), the solution of Equation (22) results in the calculation of the displacement  
\[q=\frac{Pl}{2S}\, \tag{26}\]  
which increases to infinity in the absence of initial prestress (\(S=0\)). _Step 4--Dynamic stability analysis_  
The boundaries of the stable and unstable regions (Ince-Strutt maps) are determined by solving Equation (13), which takes the following form:  
\[det\bigg{\{}\frac{2 EA}{l} \begin{bmatrix}1&0\\ 0&0\end{bmatrix} +\frac{2S}{l} \begin{bmatrix}1&0\\ 0&1\end{bmatrix} +\left(1\pm\frac{1}{2}\frac{P_{l}}{P}\right)\frac{N(P)}{l} \begin{bmatrix}0&0\\ 0&1\end{bmatrix} -\frac{\theta^{2}}{4}\frac{2\rho Al}{3} \begin{bmatrix}1&0\\ 0&1\end{bmatrix} \bigg{\}}=0 \tag{27}\]  
Solving Equation (27) leads to obtaining two resonance frequencies:  
\[\eta_{1}=\frac{1}{\pi}\sqrt{\frac{3\Big{[}S+\left(1\pm\frac{1}{2}\,v\right)N(P )\Big{]}}{\rho Al^{2}}},\quad\eta_{2}=\frac{1}{\pi}\sqrt{\frac{3(EA+S)}{\rho Al ^{2}}}\enspace. \tag{28}\]  
The first resonant frequency (28)\({}_{1}\) depends on both the initial prestress level \(S\) and the longitudinal force caused by the load \(N(P):\eqref{eq:23}\) or (24). In turn, the second resonant frequency (28)\({}_{2}\) does not depend on both the longitudinal force caused by the load \(N(P)\) and the initial prestress \(S\) (for real structures, the values of prestressing forces \(S\) are much lower than those of the longitudinal stiffness \(S\ll EA\)). This means that only one main unstable region can be identified. This is because the structure is characterized by one infinitesimal mechanism.
## 2 Data Source and Experimental Methodology
### Model Evaluation Method  
This article used the root mean square error function (RMSE) and the R\({}_{2}\) coefficient to measure the prediction accuracy of the model. The R\({}_{2}\) coefficient is a method to measure the correlation between true values and predicted values. The formula is as follows:  
\[R_{2}=1-\frac{\sum_{i=1}^{m}(f(x_{i})-y_{i})^{2}}{\sum_{i=1}^{m}(y_{i}-\overline {y})^{2}} \tag{9}\]  
where: \(f(x_{i})\) represents the predicted value of the i-th sample's the movable fluid percentage, \(y_{i}\) represents the true movable fluid percentage of the i-th sample, and \(\overline{y}\) represents the average value of true movable fluid percentage of all samples.  
RMSE reflects the error between the true movable fluid percentage of the reservoir and the predicted movable fluid percentage. The formula is as follows:  
\[RMSE=\sqrt{\sum_{i=1}^{N}(y_{i}-f(x_{i}))^{2}\times\frac{1}{N}}, \tag{10}\]  
where: \(y_{i}\) is the true movable fluid percentage of the i-th sample, \(f(x_{i})\) is the predicted movable fluid percentage of the i-th sample. N is the total number of samples.
## 1 Introduction  
Patient-specific quality assurance (PSQA) procedures based on measurements are recommended by the American Association of Physicists in Medicine Task Group 218 (AAPM TG218) to ensure that intensity-modulated radiation therapy (IMRT) treatment plans can be delivered as intended [1]. Typically, the treatment plan dose is recalculated on a phantom and then compared to the dose measured using suitable detectors [2]. The most common procedure to compare the planned and measured dose distributions is the \(\gamma\)-index analysis, which quantifies their agreement by considering both the dose and the physical distance [3]. The fraction of points having \(\gamma<1\) is referred to as the gamma passing rate (GPR). The AAPM TG218 recommends, for IMRT plans, the universal action limits of a GPR \(\geq\) 90%, with 3%/2 mm and a 10% dose threshold.  
One major challenge of traditional measurement-based PSQA is the extensive time required to complete the procedures. Additionally, the necessity for replanning and the consequent increase in workload due to PSQA failures may lead to postponing the patient treatment schedules. Consequently, numerous research initiatives have explored the adoption of machine learning (ML) models based on plan complexity indices as virtual QA (VQA) approaches to accurately predict the GPR for IMRT treatments [4, 5]. Among various IMRT techniques, volumetric modulated arc therapy (VMAT) is becoming the most common, where adjustments in gantry speed, the positioning of the multi-leaf collimator, and the dose rate are dynamically modulated for enhanced treatment precision [6, 7].  
Recently, our group trained a tree-based ensemble ML model on a large dataset of over 12,000 VMAT arcs with GPRs measured through electronic portal imaging device (EPID) dosimetry [8]. The model was validated on an internal test set of 1871 VMAT arcs. To this aim, 19 numerical features describing the complexity of an arc were considered to predict the GPR. These complexity parameters quantified the irregularity in a multileaf collimator (MLC) shape, monitor units (MUs), leaves' travel motion and speed, gantry speed and acceleration, dose rate, and field size. All of these factors contribute to the uncertainties related to the ability of the treatment planning system (TPS) to accurately model the dose distribution and the linac's delivery capability, which might compromise the PSQA result [9, 10, 11, 12].  
In this study, we investigated the possibility of developing an automatic re-optimization process for VMAT plans identified at risk of failing the PSQA by our ML model with the aim to improve their deliverability, i.e., reduce the complexity and increase the GPR, while preserving a high dosimetric quality. Our central assumption is that VQA combined with automation could improve the patient's safety and reduce the overall workload. In particular, we considered different treatment regions, comprising the head and neck, mediastum, esophagus, thorax, and prostate, and considered several replanning strategies, including the reduction of the delivered MUs and simplification of the MLC aperture shape during optimization.
## 5 Discussion
Previous research has demonstrated that machine translation struggles when dealing with morphologically rich languages, particularly those with heavy inflection. The abundance of inflected word forms can result in data sparsity, inaccurate estimations, and difficulty accurately translating pronouns and handling negation expressions [17]. As well as translating from one language to another, translation requires faithfully reproducing the subtleties, structure, and meaning of the original text, which can be difficult. When surface-level linguistic patterns are altered or lost during translation, the accuracy of the translation can be affected. In Japanese, for example, the verb is usually placed at the end of the sentence, whereas in English, it is placed before the verb. When translating from English to Japanese or vice versa, it may be necessary to restructure the statement significantly in order to maintain its meaning. It is also possible for non-native English speakers to have difficulty using the articles \"a\" and \"the\" in English, and translating articles can be problematic as well. As a result, a literal translation might not accurately convey the intended meaning in such circumstances.
# Commercially Biochar Applied for Tartrazine Removal from Aqueous Solutions
## 4 Conclusions  
This study examines the effectiveness of commercial biochar in eliminating tartrazine from aqueous solutions. The ideal parameters for extracting tartrazine from aqueous solutions were determined by this study to be pH 2, 30 \({}^{\circ}\)C working temperature, 0.9 g L\({}^{-1}\) adsorbent dosage, 18 min contact duration, and 1 mg L\({}^{-1}\) beginning dye concentration.  
The equilibrium data for adsorption of tartrazine on the biochar were evaluated using Langmuir, Freundlich, and Temkin isotherms. Of the three isotherms used, the Langmuir isotherm was most suited to explain the process of adsorption; the maximal monolayer uptake capacity was 3.28 mg g\({}^{-1}\). The uptake kinetics demonstrate that the tartrazine adsorption onto biochar was well-fitted by the pseudo-second-order model.  
Biochar can be reused after regeneration because its adsorption capacity does not decrease significantly after four adsorption-desorption cycles.  
According to the obtained results and taking into account the fact that commercial biochar is an efficient and cost-effective material which can be employed for practical engineering applications on an industrial scale, this can be used to remove the tartrazine from the aqueous solutions.  
Conceptualization, M.-L.S. and A.S.; investigation, M.B., S.P., A.S., I.L. and O.O.; writing--original draft preparation, M.B., S.P., A.S. and I.L.; writing--review and editing, M.-L.S. and A.S.; visualization, M.-L.S. and A.S.; supervision, M.-L.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported through the Core Program within the National Research Development and Innovation Plan 2022-2027, carried out with the support of MCID, project no. 27N/03.01.2023, component project code PN 23 24 01 03.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank [PERSON] (National Institute for Research and Development of Isotopic and Molecular Technologies, Cluj-Napoca, Romania) for morphological examination of the commercial biochar by TEM.  
Author [PERSON] is employed by the company VERNICOLOR Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Modern Physical Layer
### Modulation
#### 2.2.3 Special Modulation Schemes  
Different advanced modulations are being used in order to try to better utilize the available bandwidth. In direct-sequence spread-spectrum (DSSS) modulation, the original signal is spread over a wider spectrum in a ratio of \(L=\frac{W}{B}\) (B is the original signal bandwidth, and W is the spread signal bandwidth). The original signal is multiplied by a pseudo-noise (PN) code with length L. At the receiver side, the received signal is de-spread using the same spreading code before demodulation [36]. Multi-user communication may be supported by assigning each user with a unique spreading sequence with good autocorrelation and cross-correlation properties that can resist interference from multiple users [37]. Moreover, fading effects are also corrected if a RAKEreceiver is employed. Nevertheless, the main drawback of this technique is the bandwidth efficiency, which is lower than 0.5 bit/s/Hz.  
Long delay spread can be reduced by using multiple-carrier modulations [38,39]. A certain number of closely-spaced orthogonal sub-carrier signals are used to carry data on several parallel data streams or channels, using a conventional modulation scheme. Thus, each sub-carrier can be considered as a flat-fading sub-channel that is easy to equalize by multiplying it by a single complex value. The most well-known example is orthogonal frequency-division multiplexing (OFDM).  
Another interesting approach to avoid multipath spread is the use of multiple transmitters and receivers to take advantage of spatial diversity. In this technique, multiple copies of the same information are sent through different independently fading channels, which increase the probability of correct reception. On the other hand, transmitting multiple independent streams of information through spatial channels may increase the total available data rate. The resulting system is labeled as a multiple-input-multiple-output (MIMO) system [40,41].
## 1 Introduction
First, in Section 2, we explain the stereo-vision-based nighttime V2V positioning process, which utilizes the stereo triangulation approach for estimating the world coordinates of the front vehicle using the pixel coordinates of its taillights. Second, three proposed methods for taillight detection, stereo taillight matching, and taillight pairing are respectively described in Section 3. Section 4 demonstrates our experiments in the real traffic scenario and evaluates the performances of the proposed methods on the captured images. Finally, we present the conclusions in Section 5.
# Algorithms for Finding Shortest Paths in Networks with Vertex Transfer Penalties
## 3 Shortest Paths with Vertex Transfer Penalties
Similarly, \(E^{+}(v)=\{(v,u,i):(v,u,i)\in E\}\) is the set of edges of which the starting point is \(v\) and \(C^{+}(v)\) is the set of colours that leave \(v\)._  
Finally, we also need to define a set of _transfers_\(T\). A transfer occurs when we arrive at a vertex \(v\) on an edge of colour \(i\) and leave \(v\) on an edge of colour \(j\). Hence \(T=\{(v,i,j):v\in V\wedge i\in C^{-}(v)\wedge j\in C^{+}(v)\}\). The penalty cost of a transfer is denoted by \(t(v,i,j)\) and it is assumed that if \(i=j\), then \(t(v,i,j)=0\). Edge-coloured graphs like these have many practical uses. As noted, an obvious example is with public transport networks where additional costs (such as financial or time) can be incurred when switching between different lines. Another example is with the multimodal shortest path problem where we are in interested in transporting goods efficiently between two locations using a combination of different travel modes (sea, train, road, etc. ), and where transfer penalties represent the cost of moving the goods from one mode to another [18]. Constraints stemming from real-world road networks can also be defined using this model. For example:  
**Intersection delays. **: Often vehicles will need to wait at an intersection due to crossing traffic and pedestrians. Such delays can be modelled using an appropriate transfer penalty at the vertex. **Kersbide routing. **: Vehicles will often need to arrive at a location from a particular direction (e.g., if a road contains a central reservation and crossing is not permitted). In this case, the shortest path problem will be constrained so that we must arrive at the destination vertex on a particular subset of edge colours. Note that this can result in shortest paths that contain repeated vertices (cycles); for example, in Figure 2 the shortest \(v_{1}\)-\(v_{4}\)-path that also arrives on a red edge is \((v_{1},v_{4},v_{5},v_{4})\). **Initial headway. **: Similarly to the previous point, vehicles may also need to leave a location in a particular direction (e.g., if they previously approached from a particular direction and turning is not possible). In this case, the shortest path should be specified as having to leave this vertex on a particular edge colour. **Illegal routes and turns. **: On occasion, large vehicles will not be permitted to drive on particular roads or make particular turns at an intersection. In these cases we can simply change the corresponding edge weights and transfer penalties to infinity. Note that this might also result in routes containing repeated vertices.
## 2 Materials and Methods
### Data Preparation  
The OHTS was conducted at 22 different sites with various fundus cameras, which resulted in differences in image qualities. The collected images were stereophotographs, capturing the retina from two slightly different angles. Many images contained large black borders, with approximately one-third displaying both stereophotographs side-by-side on the same image slide (simultaneous stereophotographs). To ensure uniformity in the model inputs, we developed a Python algorithm to crop out black borders; in cases where both stereophotographs were displayed on the same image slide, only the right image was retained.  
The number of longitudinal images (per image per visit) per patient varied, ranging from 3 to 15, with most patients having 10 longitudinal images per eye from 10 different longitudinal visits. Our model input size was standardized to 15 images to ensure that no images were discarded. For patients with fewer than 15 images, we included empty placeholders to maintain consistency for the model input. These empty patches were defined by zero images (completely black images). Each image was also labeled with a corresponding ground truth for the absence or presence of POAG as had been determined by the clinical endpoint criteria per study protocol. The empty patches were given a label based on the most recent image's label.  
We categorized patients into three subgroups based on their diagnosis method by the Endpoint Committee: the \"Disc\" group diagnosed by only CFPs for structural POAG, the \"VF\" group diagnosed by only VF tests for functional POAG, and the \"Both\" group diagnosed by both CFPs and VF tests for structural and functional POAG, respectively. Our cohort was randomly split into training (80%), validation (10%), and testing sets (10%) at the participant level to ensure that the training and test sets were independent.
# Easy Fabrication of Ultrafiltration Membrane via Polyethersulfone-Fumed Silica
## 1 Introduction
They used a hybrid combination of fumed silica, polydimethylsiloxane, and polyurethane to develop an innovative, layered coating that demonstrated superhydrophobicity, robust abrasion resistance, and preferable air permeability [18]. According to their hypothesis, the extensive surface area and branched structure of fumed silica can be utilized to develop PDMS-PU and FS grafted coatings with improved functionalities. In their study, [PERSON] et al. explored the permeation behavior of thin films composed of fumed silica-loaded polyvinyl alcohol (PVA) coated onto a tubular porous ceramic support. Using a solution-diffusion-based model that they developed, they revealed that the flux increased along with temperature and the loading of fumed silica, and decreased with the thickness of the membrane [19]. In the gas separation field, [PERSON] and [PERSON] [20] investigated the modification of fumed silica nanoparticles with amine groups to develop a nanocomposite membrane. Their findings revealed that adding 15% of amine-functionalized fumed silica nanoparticles into the casting solution significantly improved the membrane's CO\({}_{2}\) permeability. This study investigates the effects of incorporating a low concentration of fumed silica as a blend additive into a polyethersulfone casting solution for the fabrication of an ultrafiltration membrane. The goal was to enhance the membrane's selectivity properties while maintaining its permeability at ultrafiltration level. The membrane was prepared by the wet phase inversion technique. The effects of fumed silica at low concentrations on the surface morphology, pore parameters, water flux, and membrane hydrophilicity were investigated. The membrane's morphology was visually examined using scanning electron microscopy (SEM). To assess membrane performance and selectivity, the fabricated PES membranes were challenged with an aqueous solution containing proteins of varying molecular weights and the percentage of proteins retained by the membrane was calculated.
# Landslide Susceptibility Assessment by Using Convolutional Neural Network
## 3 Materials and Methods  
### CNN-Based Model  
CNNs is a deep-learning-based feed-forward neural network that is applied to the analysis of visual imagery [42]. They are neural networks that use convolution instead of general matrix multiplication in at least one of their layers, which consists of an input and an output layer, as well as multiple hidden layers [42]. The basic architecture of the applied CNN algorithm is presented in Figure 6. CNN's hidden layers typically consist of convolutional layer series that convolve with multiplication or other dot product [43]. The activation function is usually the RELU layer and is, subsequently, followed by additional convolutions, such as pooling layers, fully connected layers, and normalization layers, referred to as hidden layers because their inputs and outputs are masked by the activation function and final convolution [43]. When the evaluation reaches the end of each stage and desires to reduce the sampling, the spatial information storage capacity decreases. So, in order to keep this information, the pooling functions are utilized. The final convolution, on the other hand, frequently involves backpropagation in order to achieve a more precise weighting of the end-product. This path helps to achieve high precision in feature extraction form in visual imagery [44].  
A look at the development of machine-learning models and their ability to adapt to GIS has led various researchers to use a variety of prediction models to assess landslides [45; 46; 47].  
Figure 5: The water conditional triggering factors: (**a**) hydrological gradient, (**b**) drainage pattern, and (**c**) flow gradient.  
Figure 6: The basic CNN model architecture [43; 45].  
Among these, the susceptibility models can be classified into fuzzy-based, shallow learning, deep learning, hybrid, and/or metaheuristic algorithms. Each of these approaches has its own advantages and disadvantages, such as integration, the possibility of considering large data, implementation with data containing high uncertainty, and so on. In the meantime, deep learning methods have received more attention due to providing more accurate and reliable results. CNN net is capable of extracting varied information from satellite images, which is useful in susceptibility zonations for landslides [48].