esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## 2 Materials and Methods
### Machine Learning and Fuzzy Logic Models for Soil Temperature Estimation
#### 2.2.3 Multilayer Perceptron (MLP)  
In this study, an MLP neural network with a feedforward structure and backpropagation learning algorithm was employed for modeling purposes. The MLP model comprises an input layer, one or more hidden layers, and an output layer. Although an MLP can include multiple hidden layers, theoretical studies have demonstrated that a single hidden layer is sufficient for approximating complex nonlinear functions [48; 49]. Therefore, a one-hidden-layer MLP was used. The network architecture and the number of neurons in the hidden layer were determined through a trial-and-error approach. The Levenberg-Marquardt (LM) algorithm was selectively employed for training, as it is known for effective learning performance. MLP networks are widely preferred due to their strong capability for arbitrary input-output mapping. A detailed explanation about MLP neural networks can be found in the study of [50].
## 3 Comparing SESO and SERA with Other Topology Optimization Methods
### Comparing Topology Optimization Algorithms without a Mesh-Independency Filter  
The problem above is analyzed again using the same mesh, but now without using the mesh independence filter. Table 2 lists the parameters and solutions obtained. It is highlighted that SESO and ESO are similar in their structural design and the final values of the objective function. This is not surprising, as the evolutionary procedure for these methods uses the same removal heuristic. However, SESO is a bidirectional method; that is, it has a \"soft-kill\" removal. It allows adding elements during the evolutionary process, and ESO is unidirectional because it has its most radical \"hard-kill\" removal. The SERA and SIMP methods have a density as the design variables. These methods showed a higher concentration of zones with checkerboard patterns and optimal settings quite different from those presented by ESO and SESO methods. As the SIMP has a penalty factor \((p=3)\) and the central element is not filtered, the sensitivity of the stiffness of this element increases proportionally to the triple of the square of its density, justifying this concentration, i.e., the sensitivity is given by Equation (14). Replacing the value of \((p=3)\) in this equation results in the proportionality factor of \(3\overline{x}_{i}^{2}\), where \(x_{i}\) is the density of the element.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Parameters** & \begin{tabular}{c} **Number of** \\ **Iterations/Costs** \\ \end{tabular} & \begin{tabular}{c} **Optimal Settings/Compliance** \\ \end{tabular} \\ \hline ESO & \begin{tabular}{c} \(RR=0.02\) \\ \(ER=0.02\) \\ \(r_{min}=1.5\) \\ \end{tabular} & \begin{tabular}{c} \(iter=200\) \\ \(time=815.65\,s\) \\ \end{tabular} \\ \hline SERA & \begin{tabular}{c} \(PR=0.02\) \\ \(SR=1.02\) \\ \(B=0.007\) \\ \(r_{min}=1.5\) \\ \end{tabular} & \begin{tabular}{c} \(PR=0.02\) \\ \(iter=200\) \\ \(time=775.51\,s\) \\ \end{tabular} \\ \hline SESO & \begin{tabular}{c} \(RR=0.02\) \\ \(ER=0.02\) \\ \(r_{min}=1.5\) \\ \end{tabular} & \begin{tabular}{c} \(iter=200\) \\ \(time=887.90\,s\) \\ \end{tabular} \\ \hline SIMP & \
\begin{tabular}{c} \(p=3\) \\ \(MOVE=0.02\) \\ \(r_{min}=1.5\) \\ \end{tabular} &
\begin{tabular}{c} \(iter=200\) \\ \(time=853.08\,s\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparing Topology Optimization algorithms with a mesh-independency filter.  
In a refined mesh, the number of finite elements within the radius increases, providing greater control over the region. Thus, it is possible to smooth out large variations in the objective function (compliance); that is, the peaks in the objective function can be controlled by the filter. In addition, the filter has the ability to control topology complexity. The absence of the filter allows the appearance of checkerboard formations (regions where compliance is high).
## 3 Methods
### The Choice of the Five Sets of the Hugoniot Parameters  
Whereas the parameters \(c\), \(b\), and \(a\) for the med-\(P\) regime are available for all substances, and, therefore, \(d\) and \(f\) for the high-\(P\) regime can be calculated for all substances from Equation (4) with \(n=U_{p}^{**}/U_{p}^{\text{max}}\), where \(U_{p}^{\text{max}}=\sqrt{c/a}\) and \(U_{p}^{**}\) satisfies Equation (5), the parameters \(C\) and \(B\) for the low-\(P\) regime must be specified (and then \(A=a-(B-b)^{2}/\big{(}4(c-C)\big{)}\)[22]). Here, we use the original version of the analytic Hugoniot model [22], which describes the entire low-\(P\) regime in terms of a single \((C,B,A)\) parameter set, regardless of whether the actual low-\(P\) portion of the Hugoniot is multi-segment (two or more linear segments forming a continuous broken line, just as for the lanthanides), or multi-linear (two or more quasi-parallel segments, which is the case of, e.g., zirconium [29]), or a combination of both. In our case of the four lanthanides, it is logical to describe the low-\(P\) regime in terms of the corresponding parameters of the upper segment of the two-segment portion in order to match the upper segment directly with the med-\(P\) regime. These values come from [8] for Tb and Tm, and from [7] for Lu. Since no \((C,B)\) data exist on Pm, we obtain both values from smooth interpolation of the corresponding values for other members across the entire lanthanide series based on the results of [8; 20]. For Ac, for which no \((C,B)\) data exist either, assuming that it is a single-phase material (in fact, Ac is fcc, and there is no evidence for a phase transition to any other solid phase [18]), two parameters can be calculated as [26; 27]\(C=\sqrt{B_{s}(\rho_{0})/\rho_{0}}\) and \(B=(B_{s}^{\prime}(\rho_{0})+1)/4\), with bulk modulus \(B_{s}(\rho_{0})\) and its \(P\)-derivative \(\mathcal{B}_{s}^{\prime}(\rho_{0})\) coming from the corresponding EOS. The best fit to the ab initio data on the EOS of Ac of Ref. [30] of the third-order Birch-Murnaghan form results in \(\mathcal{B}_{s}(\rho_{0})=23.7\pm 3.7\) GPa and \(\mathcal{B}_{s}^{\prime}(\rho_{0})=3.9\pm 0.3\); therefore, with \(\rho_{0}=10.05\,\text{g}/\text{cm}^{3}\), \(C=\sqrt{23.7}/10.05=1.535\) km/s, and \(B=(3.9+1)/4=1.225\). Table 1 contains all the five complete sets of the Hugoniot parameters.
## 3 Results and Discussion
### Sgs Cases  
#### 3.2.1 Sgs Cases: Model  
Building on our previous study [14], an idealized cosine function is employed to simulate the subglottic stenosis (SGS) in the subglottal tract (Figure 6a). To generate the SGS, we first rotate the entire tract counterclockwise by \(22^{\circ}\) around the z-axis, aligning the  
Figure 5: **(a)** Location of the probe 5 cm outside of the mouth; **(b)** the phase-averaged acoustic pressure waveform at this probe over one vibration cycle in the baseline case; **(c)** the pressure Fourier transform spectra, highlighted with calculated formants using the transfer function method, experimental study [33], and LES study [43].  
subglottal tract with the y-axis, as shown in Figure 6b. We then apply Equation (8) to all tract nodes within the SGS range:  
\[\begin{split} x_{s}&=x_{1}-x_{1}\times\frac{a}{2} \times\left(1-\cos\bigl{(}2\pi\frac{V_{c}}{L}\bigr{)}\right)\\ z_{s}&=z_{1}-z_{1}\times\frac{a}{2}\times\left(1- \cos\bigl{(}2\pi\frac{V_{c}}{L}\bigr{)}\right)\\ y_{s}&=y_{1}\end{split} \tag{8}\]  
where \((x_{1},y_{1},z_{1})\) are the original coordinates of the undeformed tract node. \(y_{c}\) is the distance between the lower bound of the SGS and \(y_{1}\). \(L\) is the SGS length, and \(a\) is the percentage reduction of the hydraulic diameter of the tract cross-section due to the SGS. \((x_{s},y_{s},z_{s})\) are the new coordinates of the node after applying SGS (Figure 6b). Lastly, we rotate the whole tract back clockwise by \(22^{\circ}\) to its original orientation. The resulting shape is shown in Figure 6a.  
As shown in our previous study [14], vertical location of the SGS yields negligible effects on phonation. Therefore, the SGS location in current study, defined as the distance between the superior surface of the vocal folds and the narrowest section of the SGS is kept at 3.13 cm, which is the average value observed in 92 SGS patients [46; 47]. Additionally, the most common SGS length was reported to be 1.0 cm [46], and this value is used as the length L in our model. To study the effect of SGS severity, we simulated five SGS severities: 0% (baseline case), 50%, 75%, 90%, and 96%, where the severity level corresponds to the percentage of area reduction due to SGS. These area reductions are applied by adjusting the value of a (as defined in Equation (8)) to 0, 0.2929, 0.5, 0.6838, and 0.8, respectively.
## 6 Transceiver Architectures' Classification and Basic Concepts
### 2D-Based Architectures
#### 6.2.1 DP-IM/DD DP-IM or the Stokes Vector Receiver  
The DP-IM/DD DP-IM or Stokes vector receiver exploits two DoFs to enhance the system performance, as shown in Figure 11a. Furthermore, with the DP-IM scheme, two RF signals can be encoded on the intensities of orthogonal polarizations of a single laser at the transmitter [14; 33; 34]. Furthermore, two potential receiver structures for the DP-IM based transmitter are the direct detection receiver (Figure 11a(i)) and Stokes vector receiver (Figure 11a(ii)). Based on the former implementation, a DSP-free receiver that employs a polarization beam splitter (PBS) and two PDs can be employed to detect the DP-IM signal. However, the implementation demands optical polarization tracking to avoid polarization wandering. On the other hand, as expatiated in Section 8, based on the latter structure, polarization demultiplexing can be achieved with the aid of a Stokes vector receiver with the outputs feeding four ADCs that are followed by a MIMO DSP unit. The employed MIMO filter bank in the Stokes vector receiver accomplishes Stokes space-based polarization derotation. It also facilitates residual ISI mitigation [14; 23; 34].  
Figure 11: The 2D transceiver architectures based on (**a**) the DP-IM/DD DP-IM or SVR with (i) direct detection DP-IM and (ii) SVR and (**b**) PolMux \(IQ+C\)/SVR. \(\Phi\): \(90^{\circ}\) polarization rotation. SVR: Stokes vector receiver (adapted from [12; 13; 34; 33; 37]).
## 4 Discussion  
In this study, we observed significant physiological responses to both the Sprint (5 km) and Super (13 km) obstacle course races (OCR). The decrease in leg strength was more pronounced in the Sprint race ([PERSON]'s d = \(-\)0.56, \(p\) < 0.01) compared to the Super race ([PERSON]'s d = \(-\)0.54, \(p\) = 0.01), suggesting that shorter, more intense efforts may lead to a more acute reduction in muscular power. This aligns with the anaerobic demands of the Sprint race, which require high-intensity exertion over a shorter duration, thus leading to rapid muscle fatigue. Similarly, the decline in urine pH was significant in both distances but slightly more pronounced in the Sprint ([PERSON]'s d = \(-\)0.70, \(p\) = 0.03) compared to the Super race ([PERSON]'s d = \(-\)0.67, \(p\) = 0.01). This indicates a greater accumulation of metabolic byproducts, such as hydrogen ions, during the shorter race, further emphasizing the intense anaerobic activity. The increase in blood lactate was substantial in both races, with a large effect size observed for both the Sprint ([PERSON]'s d = 34.15, \(p\) < 0.01) and Super ([PERSON]'s d = 21.09, \(p\) < 0.01). However, lactate accumulation was higher in the Sprint race, likely due to the more significant reliance on anaerobic energy pathways. This higher lactate concentration suggests that athletes in shorter races need greater lactate tolerance to sustain high performance over short bursts of activity.  
Perceived exertion also increased significantly post-race, with similar effect sizes in both the Sprint ([PERSON]'s d = 0.10, \(p\) < 0.01) and Super ([PERSON]'s d = 0.10, \(p\) < 0.01) distances. This reflects the overall physical demands of the races, though it is worth noting that while the effect size for perceived exertion is similar, athletes in the longer Super race may have experienced more cumulative fatigue due to the extended duration of exertion. The comparison of these physiological responses between the Sprint and Super races underscores the differing demands of short versus long OCR events. Shorter races appear to elicit a greater reliance on anaerobic metabolism, leading to faster declines in strength and higher levels of lactate accumulation. In contrast, the Super race, while still physically demanding, places more emphasis on endurance and sustained effort, with a less pronounced immediate impact on certain physiological markers, such as leg strength and urine pH. These findings highlight the need for tailored training programs that address both anaerobic capacity for short-distance races and endurance for longer courses.
## 4 Conclusions  
_Beta vulgaris_ L. extract was used as a reducing and capping agent, as it is eco-friendly and can be easily scaled up for the phytogenic synthesis of metallic silver/bimetallic nanoparticles. These nanoparticles were characterized using a variety of techniques, including UV-Vis spectroscopy, FTIR, TEM, and zeta potential analyses, to investigate their optical properties, functional groups, surface morphology, size, shape, and aggregation of nanoparticles. The antioxidant, antibacterial, and cytotoxic activities of _Beta vulgaris_ extract as well as nanoparticles were assessed using a variety of in vitro assays. The metallic/bimetallic nanoparticles revealed reduced phytochemical constituents compared with the plant extract, thus reducing their antioxidant capacity. This performance is attributed to the participation of the phenolic hydroxyl groups in the bio-reduction of metal ions in the solution. The metallic/bimetallic nanoparticles displayed improved antibacterial  
Figure 9: Comparison of the percentages of cell viability at different concentrations.  
activity against the variety of bacterial species; this is attributed to the large surface area and aggregation factors of the nanoparticles. In addition, the in vitro cytotoxic assessment of metallic/bimetallic nanoparticles revealed strong cytotoxicity against the HePG2 and MCF7 cell lines, with decreased cell viability percentages, by increasing the sample dose. The phytochemical analyses indicated the significance of phenolic, flavonoid, and tannin contents for developing biological activities with targeted therapeutic effects. In conclusion, the biosynthesis of nanoparticles, characterization of nanoparticles, biological assessments, and phytochemical analyses are all critical aspects of the development of safe and effective nanoparticles for biomedical applications.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app131810110/sl](https://www.mdpi.com/article/10.3390/app131810110/sl)]([https://www.mdpi.com/article/10.3390/app131810110/sl](https://www.mdpi.com/article/10.3390/app131810110/sl)): Tables S1-S5 and Figures S1 and S2.  
Conceptualization, K.M.E., M.S.E.-H. and W.I.A.S.; data curation, K.M.E. and A.A.G.; formal analysis, K.M.E., A.A.G., F.O.A.-O. and Y.A.H.; funding acquisition, F.O.A.-O.; investigation, K.M.E. and A.A.G.; methodology, K.M.E., A.A.G. and F.O.A.-O.; resources, F.O.A.-O. and Y.A.H.; software, F.O.A.-O. and Y.A.H.; supervision, W.I.A.S.; validation, K.M.E., M.S.E.-H., Y.A.H. and W.I.A.S.; visualization, M.S.E.-H.; writing--review and editing, K.M.E. and W.I.A.S. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Researchers Supporting Project, number RSP2023R114, King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
All data generated or analyzed during this study are included in this published article and the Supplementary File Materials.  
The authors declare no conflict of interest.
## 2 Related Work
At the same time, the harsh electromagnetic interference will obscure the distinct features of the constellation and spectrum diagrams, leading to a deterioration in the performance of the AMR scheme which takes this as the input to the network, especially in the case of low SNR. Most of the above-mentioned studies [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29] only consider Gaussian white noise and overlook the effect of channel fading on the received signal. Moreover, the complexity of the deep learning models used is high, making them less applicable in scenarios with limited computational resources. Deep learning methods generally have drawbacks such as a high demand for the number of labeled samples and stringent requirements on computational and storage resources. As a result, the recognition performance of the aforementioned deep learning-based methods is limited under non-Gaussian noise and small sample number conditions. In order to tackle the aforementioned issues, we replicated some of the methods mentioned in the article and our proposed scheme, which conducted experiments on the model with our data. Considering that the current deep learning methods mainly deal with two-dimensional data structures, we selected image classification DNNs, which have better effects for experiments, and the results show that the proposed model performed well.
# Fault Diagnosis of HV Cable Metal Sheath Grounding System Based on LSTM
## 5 Fault Diagnosis
\[\left\{ \begin{array}{l}f_{t}=\sigma\Big{(}W_{f}[h_{t-1},x_{t}]+b_{f}\Big{)}\\ i_{t}=\sigma(W_{i}[h_{t-1},x_{t}]+b_{i})\\ \widetilde{c}_{t}=\tanh(W_{c}[h_{t-1},x_{t}]+b_{c})\\ c_{t}=f_{t}*c_{t-1}+i_{t}*\widetilde{c}_{t}\\ o_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o})\\ h_{t}=o_{t}*\tanh(c_{t})\end{array} \right. \tag{27}\]  
where \(x_{t}\) is the input value, \(h_{t}\) is the hidden layer state, and \(c_{t}\) indicates memory cells. \(W_{f}\), \(W_{i}\), \(W_{c}\), and \(W_{o}\) are the corresponding weights for each gate in each cell, and \(b_{f}\), \(b_{i}\), \(b_{c}\), and \(b_{o}\) are the corresponding biases. 1. Model application stage. The test set is input to the training saved model for fault diagnosis identification. The implicit units of the LSTM were set to 100, the output nodes were 18, the learning rate was set to 0.001, the maximum number of training rounds was 100, and the GPU was selected as the execution environment of the network. The training accuracy and loss values of the long and short-term memory neural network algorithm are shown in  
Figure 12: LSTM model diagnostic framework diagram. Figure 13: Basic unit structure of LSTM. Figure 14. The confusion matrix of the classification results of the different algorithms are shown in Figure 15.  
As shown in Figure 14, the test set has a total of 7182 data identified with 100% accuracy. Comparing the diagnosis results of DT, DA, NBC, KNN, and SVM, as shown in Table 5.  
As can be seen from Table 5, the accuracy of DT, DA, NBC, KNN, and SVM are 87.37%, 81.64%, 87.65%, 96.88%, and 97.37%, respectively. Compared with traditional machine learning algorithms, the accuracy of the algorithm proposed in this paper is higher, and the accuracy reaches 100% in 18
# Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems
## 5 Modelling
### Model Configurations  
The implemented models took input from the measured soil properties, the cultural practices (variety and broadcasting units), the growth stage, the LNC, and the yield. Growth stage and cultural practices can affect modelling stability in operational remote sensing solutions, and thus it was found necessary to be included in model building [41]. However, other equally important parameters, such as weather conditions, water regime, soil microbial activity, soil compaction, sun irradiance, etc. [42], were not available.  
Eighteen soil properties were available from the laboratorial analyses in numerical format. Two varieties were cultivated in the pilot farm during the study period: Ronaldo and Bonnet; the former variety was denoted as VARIETY_1 and the latter as VARIETY_2 in the input list of features. The growth stage was denoted as DATE and was recorded as number of days after seeding. Six growth stages can be recognized in rice [2]: early vegetative stage following sowing, mid-to-late vegetative stage (mid-tilering), early reproductive stage (panicle initiation to booting), mid-to-late reproductive stage (heading to flowering), ripening stage and harvest stage. Normally, the N topressing application in Greece occurs at the early reproductive stage and more specifically around the booting stage. The LNC was recorded numerically and the yield as well.  
In addition to the single parameters, though, a combination of LNC with growth stage (in terms of a multiplication: LNC*DATE) was also tested. The reason is that LNC alone does not consider the relativity of rice growth, which was represented by the introduced combination.  
Prior to model running, the dataset was checked using multicollinearity diagnostics with the Mctest package in R [43], in order to detect collinearity in the dataset among regressors on the yield variable. All the variables which remained after multicollinearity were scaled and power transformed using the StandardScaler of the Scikit-Learn library for feeding the algorithms [44].  
The dataset was randomly separated into training and test parts consisting of 70% (183 samples) and 30% (91 samples) of the total data, respectively. The algorithms were trained either including the growth stage (denoted as DATE) or without, by selecting optimal parameters in every case. The XGBoost and Random Forest models were configured both for runs with the inclusion of the DATE and LNC*DATE parameters and without them. The algorithms were written in Python programming language [45]; the optimized hyperparameters for all models are presented in Table 4.