esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study: Practical Implementation of DT for HEMS
### Evaluation of Key Performance Metrics
A comparative analysis with traditional HEMS systems highlights the transformative potential of integrating Digital Twin technology. The comparison evaluates improvements in real-time data synchronization, predictive capabilities, and overall system scalability, demonstrating how the proposed framework addresses limitations in conventional systems. This dual approach ensures a comprehensive assessment of the framework's performance and its practical implications for users and HEMS. As a result, the importance of the standardized DT model, where users still enjoy the full benefits of a DT-enabled HEMS, in a scenario involving 50 new users is considered. Comparing the suggested framework to traditional methods of both IoT and DT integration, the results are summarized in Table 5. The proposed framework demonstrates significant efficiency and scalability advantages. By utilizing a single, standardized DT model and plug-and-play IoT devices, the framework reduces the time and resource burdens typically associated with traditional systems. Specifically, based on the results, a 94% reduction in overall deployment time was achieved, minimizing the effort required to set up IoT systems and build the DT. Moreover,  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  
the use of a single standardized DT model resulted in a 98% reduction in memory usage compared to the traditional approach, which involves creating individual DT models for each household. With plug-and-play capabilities, the framework reduced IoT setup time per user by 50%, providing a more streamlined and efficient process. The non-intrusive nature of the proposed framework resulted in 75% fewer installations requiring structural modifications, ensuring easier adoption and implementation in diverse home environments. These results clearly demonstrate the superiority of the proposed framework in terms of time, scalability, and user convenience while maintaining the high-quality experience and energy management capabilities enabled by Digital Twin integration. In addition to these operational efficiencies, the cost analysis highlights the economic benefits of the proposed framework. In terms of cost for a 50-household deployment, the proposed framework is significantly more economical, with an estimated total cost of approximately EUR 9050 (EUR 180 hardware per household + negligible setup costs and EUR 1-2 for the standardized model). In contrast, traditional systems require an estimated EUR 15,000-EUR 20,000 (EUR 300-EUR 400 per household due to higher hardware, setup, and custom modeling expenses). This translates to a cost reduction of 40-55% for the proposed framework, making it a highly cost-effective solution for large-scale adoption in smart home technologies.
## 2 Modeling the VLC Networks: Architecture, System Model and Scenarios
### Multi-Carrier Modulations
#### 2.1.2 Receiver
In this sense, with the aim of providing both wide FoV and optical gain simultaneously, angle diversity receivers (ADRs) have been proposed in several works such as [61; 75]. ADRs are composed of several PDs following a geometrical pattern, so that each PD is categorized by an azimuthal and elevation angle, i.e., each PD \(p\) of user \(k\) provides an orientation vector denoted by \(\hat{\mathbf{n}}^{[k,p]}\). Therefore, the PDs of an ADR provide distinct incidence angles for the same optical AP. In [76], the pyramidal and hemispherical arrangements of PDs are analyzed for MIMO channels. For illustrative purposes, Figure 8 shows an ADR following a four faces pyramidal arrangement, where only PDs 1 and 2 are visible, while PDs 3 and 4 are hidden. This way, allocating several PDs around the six faces of a hand-held device and considering the natural movements of the hand, i.e., roll, pitch and yaw, an alternative ADRis proposed in [77]. A variable receiving orientation angle (vROA) photodetector composed of a gyroscope and two servo-motors that modify the azimuthal and elevation angles is proposed in [78] to optimize the orientation of each user, which is denoted by \(\hat{\mathbf{n}}^{[k]}\) for user \(k\). The architecture of the vROA photodetector is depicted in Figure 9. As demonstrated in [76], the use of ADRs improves the performance in terms of sum-rate and BER in MIMO systems. In this sense, the concepts of ADR and vROA photodetector provide a new degree of freedom in the design and the signal processing algorithms [29; 79; 80; 81]. Notice that ADRs improve the visibility of the users in VLC systems and, combined with novel transmission schemes, may be an effective approach for overcoming the LoS-link blocking effects, even without much signal processing. Figure 8: Pyramidal arrangement for an ADR comprising 4 PDs. Figure 9: Variable receiving orientation angle photodetector.
# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
## 6 Experiment
### Experiment Setting
#### 6.1.2 Experiment Setting  
In order to evaluate the performance of the proposed heuristic algorithm based on the symmetric recurrent neural network in each test case, we set up 5 planning scenarios. The number of observation tasks in each planning scene is 100-500, and the step size is 100. The target is randomly selected from the urban dataset of STK. There are 10 test cases for each planning scenario.
## 2 Materials and Methods  
### Study Area Description  
The Jhelum River Basin (JRB) is one of the most crucial sub-basins (SBs) of the Upper Indus River Basin (UIB), located within the longitude of 73\({}^{\circ}\)-75.62\({}^{\circ}\)E and latitude of 33\({}^{\circ}\)-35\({}^{\circ}\)N, covers the western mountainous region of Himalayas (Figure 1). The entire basin has large spatio-temporal climate variability with an annual average precipitation of 835.83 mm and an annual mean temperature of 13.5 \({}^{\circ}\)C, with increasing elevation from south to north decreasing the temperature of the basin. For instance, Narran (situated northern part of the basin) and Mangla (situated southern portion) experience the coldest and hottest areas, having annual mean temperatures of 7.16 \({}^{\circ}\)C and 26.54 \({}^{\circ}\)C, respectively. Generally, the climate of the JRB is influenced by two central rainfall systems, south Asian summer monsoon (atmospheric circulation) and winter westerlies circulations (anticyclonic circulation). Furthermore, JRB is the main eastern tributary of the Indus River, that flows toward the south slopes of the Himalayan and Pir Panjal, which drainage 33,468 km\({}^{2}\) of area, and finally dammed up through Mangla reservoir, which is the second largest storage reservoir of Pakistan and seventh in the world, having 7.29 km\({}^{3}\) of gross storage capacity [29]. This dammed water is primarily utilized for two primary purposes, (i) for irrigation, which helps to irrigate 60,000 km\({}^{2}\) of land, and (ii) used for the generation of electricity. Its entire installed capacity is 1000 MW, which is 6% of the country's total installed capacity [30].  
The area above the Mangla reservoir is called Mangla catchment; about 14.97% area of the basin is located above 6000 m above sea level (a.s.l.) (Figure 1b, Table 1). Thus, in the JRB catchment, snow and glacier melt plays a significant role in runoff in early summer (April-mid-June), while amasses in the winter owing to westerlies influences, whereas, during the summer season (end of June to August), monsoon precipitation is dominated [31]. Also, JRB is a straddling river basin distributed between Pakistan and India. About 56% (18,965 km\({}^{2}\)) of the area is in the territory of India, while 46% (14,965 km\({}^{2}\)) is in Pakistan.
# Optimization of CART Models Using Metaheuristics for Predicting Peach Firmness
## 4 Methods
### Artificial Neural Networks  
Artificial neural networks (ANNs) are machine learning models used for classification and numerical prediction. The strength behind ANNs is their ability to capture complicated relationships among features in the dataset [34]. The multilayer feedforward network, which is a fully connected network with a one-way flow of information and no cycles, is the most widely applicable ANN architecture [34]. The input layer of the multilayer feedforward network consists of nodes that accept the predictor values. The successive layers of nodes receive weighted input from the previous layers, process the information, and output the information to the next layer. The output layer is the last layer of the ANN, and the layers between the input and output layers are called hidden layers [34].  
To learn relationships among features in the dataset, the ANN uses the backpropagation algorithm [28]. Before the learning process is started, the weights of the ANN are initialized to random values. The basic working of the backpropagation algorithm is that it iterates through many epochs (cycles) of forward and backward phase processes until a stopping criterion is reached [28]. In the forward phase, the neurons are activated in sequence from the input layer to the output layer, applying each neuron's activation function along the way. When the final layer is reached, an output signal (prediction of the outcome feature) is produced [28]. In the backward phase, the ANN's output signal produced during the forward phase is compared to the true outcome value in the training data. The difference between these two signals makes the error, which is propagated backwards in the network to modify the connection weights between neurons and reduce future errors. The gradient descent technique is used to adjust the weight's values. The reason that it is important to have a differentiable neuron's activation function is that the gradient descent calculates its derivative to identify the gradient in the direction of each of the incoming weights [28]. The gradient identifies how steeply the error will be reduced or increased for a change in weight value. The backpropagation algorithm will attempt to find the weights that result in the greatest error reduction by an amount known as the learning rate [28]. Over time, the total error will be reduced, and the ANN will learn how to accurately predict the outcome. Figure 3 shows the ANN model used in this research for peach firmness prediction.
## 3 Results
### Shot Noise  
For many spectroscopy applications, noise is expressed as a ratio of instrument radiance to the expected variation at that radiance (a signal to noise ratio). However, for SIF retrievals, the most important relationship is between the noise and the expected magnitude of SIF, since SIF does not reliably co-vary with radiance. Therefore, noise results are most usefully expressed as absolute values of noise (Figure 9). The highly conserved relationship (R\({}^{2}\) = 0.997) of the expected standard deviation of the noise with radiance was found to be  
\[\sigma=365.99\text{-}R^{0.5295} \tag{6}\]  
where \(\sigma\) is the expected standard deviation in mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\) and \(R\) is the radiance of the FIREFLY signal, also in mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\).  
Results from the controlled acquisitions of thousands of observations of four targets (white, gray, black, and spinach) (Figure 10), as described in Section 2.6, exhibited higher magnitude variation in the radiance of FIREFLY bands than would be expected based on the characterization of noise with the integrating sphere (Figure 11). The residuals between the expected coefficients of variation and the observed coefficients of variation were generally negatively associated with radiance (Figure 12).  
Figure 9: (**a**) The relationship (gray line) between FIREFLY radiance and standard deviation based on observations made with the integrating sphere and ASD (black x); (**b**) the relationship (gray line) between the signal to noise ratio (SNR) and radiance, based on observations made with the integrating sphere and ASD (black x); (**c**) relationship (gray line) between coefficient of variation and radiance based on sphere observations (black x); and (**d**) an example FIREFLY vegetation spectra (gray line) with an example SIF signal (green dashed line, not drawn to scale) and the ratio of SIF to the expected noise based on the radiance values from the example vegetation spectra (black line).  
In other words, the variation observed agreed more closely with the expected variation from noise as the radiance increased.  
Figure 10: The radiance of FIREFLY spatial pixel 133, band 1272 (735.003 nm) over thousands of consecutive samples (**a–d**) of four targets, with an example of a single spectrum (**e–h**) from each panel, taken from spatial pixel 133.  
Figure 11: The coefficient of variation of FIREFLY radiance in spatial pixel 133, all bands, over thousands of consecutive samples (black dots) of four targets; white (**a**), gray (**b**), spinach (**c**) and black (**d**); compared to the expected coefficient of variation based on the noise characterization with the integrating sphere and ASD (red dashed line).  
Figure 12: (**a**) The coefficient of variation of FIREFLY radiance in spatial pixel 133, all bands, all targets over thousands of consecutive samples (black dots), compared to the expected coefficient of variation based on the noise characterization with the integrating sphere (red dashed line). (**b**) The residual of the coefficient of variation (observed–expected) (black dots) based on the samples in (**a**).
# Extraction and Synthesis of Silicon Nanoparticles (SiNPs) from Sugarcane Bagsase Ash: A Mini-Review
###### Abstract  
This current study reviews the utilization of the traditional extraction methods and latest findings in extraction of silica from agricultural wastes, in particular, sugarcane bagasse, using inorganic acids to produce nano-silicon. The three key processes discussed in detail include electrochemical, ball milling, and sol-gel processes. The sugarcane bagasse has been identified as the cheapest source of producing silica from the potential raw material for the preparation of nano-silicon. The acid-base extraction and precipitation methodology involves the use of bases like sodium hydroxide (NaOH) and potassium hydroxide (KOH), and acids such as hydrofluoric acid (HF), sulphuric acid (H\({}_{2}\)SO\({}_{4}\)), nitric acid (HNO\({}_{3}\)), and hydrochloric acid (HCI) for the treatment of the ash. Sugarcane bagasse has notably emerged as an excellent and sustainable source of both tailored silica particles and bioenergy production. The ability to manipulate the engineered silica particles at the nano-level from sugarcane bagasse-based silica is explained in detail. Silica is a significant raw material with various industrial applications, with much research underway to extract it efficiently from industrial agro-waste, such as sugarcane bagasse. The production of highly pure silicon nanoparticles from sugarcane bagasse ash will serve as an important synthetic route in lowering the manufacturing costs and providing a low-cost polycrystalline silicon semiconductor for niche application in thin film solar technology.  
Keywords:sugarcane; silica; SiNPs; silicon; green technology +
Footnote †: journal:
## 2 Summary of the Special Issue Contents
### Study Case-Comparison between CFD and Experimental Results Using Three Different Software  
In the aerospace industry, computational fluid dynamics (CFD) methodologies are researched for advancing aerodynamics studies on aircrafts. The results obtained by these methodologies are compared among themselves, and with experimental wind tunnel tests and flight tests results. The laminar, turbulent, and transition flow results are analyzed using these numerical methodologies.  
In this study case [2], two turbulence models--the shear stress transport (SST) model and the Spalart-Allmaras (SA) model--were implemented in the UNS3D in-house code at the Texas A&M University, and their results were compared with those of FUN3D and CFL3D codes developed by NASA. The UNS3D code has two versions: UNS3D-SEQ (sequential version) and UNS3D-PAR (parallel version). In addition, these numerical results were compared with experimental results from the literature. The methodologies were applied on four different configurations: a flat plate, an airfoil near-wake, a backward-facing step, and a turbine cascade, also called the eleventh standard configuration.  
Regarding the comparison of the results, the solutions' residuals were very small, more precisely, less than \(10^{-11}\). The SST model predicted, better than the SA model, the turbulent fluctuations and skin friction coefficients in comparison with experimental data, while the SA model predicted better than the SST model, as the flow went away from the backward-facing step. In fact, most of the results obtained using the SST model fitted the experimental data better than the SA model, while the main disadvantage of the SST model resided in its computational execution time, that was higher than the SA model execution time, with its values between 4-38%.
## 4 The Impact of Thinning on Fruit Quality
Fruit width has been observed to be positively correlated with cuticular cracking, but contrary to what has been maintained in literature, the relationship between the concentration of soluble sugars or firmness and the incidence of cracking is low and debatable. It seems that the decisive factors, in addition to the genetic propensity of the cultivar, are crop load and fruit size [110]. As discussed above, flower bud thinning can regulate the fruit-to-leaf area ratio on trees grown on dwarf rootstocks. This ratio influences the yield, fruit quality, and vigour of trees, but it has no effect on the whole canopy net CO\({}_{2}\) exchange rate (NCER canopy), which indicates that it does not affect the intensity of photosynthesis. According to the results obtained at Michigan State University, NCER canopy and net assimilation are more strongly determined by weather and sun altitude. Trees thinned to 20 fruit/m\({}^{2}\) of leaf area had yield reduced by 68%, but higher fruit weight (+25%), higher firmness (+25%), higher soluble solids (+20%), and fruit diameter (+14%) compared to unthinned (control) trees (84 fruit/m\({}^{2}\)). In the same study, the flower-to-leaf ratio did not affect the subsequent induction of flower buds, although the fruit-to-leaf area ratio influenced the growth in the second part of summer [38]. Studies assessing the effects of thinning with ATS on the fruit quality of three self-sterile or partially self-sterile cultivars ('Blaze Star', 'Samba', 'Techlovan') were conducted in Germany. Thinning efficiency was not dependent on the ATS concentration used. The treatments had no effect on fruit weight and anthocyanin content, but resulted in an increase in total soluble solids and pH value of the juice [22]. Thinning tests were also carried out with limestone sulphur and the fungicide Netzschwefel Stulln (in which the active substance is soluble sulphur in the form of micro granules), but no visible results were achieved. The only positive outcome of the application was lower flower infestation by _Monilitia laxa_[15,16].
###### Abstract  
Machine learning (ML) is the technology that allows a computer system to learn from the environment, through re-iterative processes, and improve itself from experience. Recently, machine learning has gained massive attention across numerous fields, and is making it easy to model data extremely well, without the importance of using strong assumptions about the modeled system. The rise of machine learning has proven to better describe data as a result of providing both engineering solutions and an important benchmark. Therefore, in this current research work, we applied three different machine learning algorithms, which were, the Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Network (ANN) to predict kyphosis disease based on a biomedical data. At the initial stage of the experiments, we performed 5- and 10-Fold Cross-Validation using Logistic Regression as a baseline model to compare with our ML models without performing grid search. We then evaluated the models and compared their performances based on 5- and 10-Fold Cross-Validation after running grid search algorithms on the ML models. Among the Support Vector Machines, we experimented with the three kernels (Linear, Radial Basis Function (RBF), Polynomial). We observed overall accuracies of the models between 79%-85%, and 77%-86% based on the 5- and 10-Fold Cross-Validation, after running grid search respectively. Based on the 5- and 10-Fold Cross-Validation as evaluation metrics, the RF, SVM-RBF, and ANN models achieved accuracies more than 80%. The RF, SVM-RBF and ANN models outperformed the baseline model based on the 10-Fold Cross-Validation with grid search. Overall, in terms of accuracies, the ANN model outperformed all the other ML models, achieving 85.19% and 86.42% based on the 5- and 10-Fold Cross-Validation. We proposed that RF, SVM-RBF and ANN models should be used to detect and predict kyphosis disease after a patient had undergone surgery or operation. We suggest that machine learning should be adopted and used as an essential and critical tool across the maximum spectrum of answering biomedical questions.  
artificial intelligence; machine learning; random forest; support vector machine; artificial neural network; kernels; biomedical data; kyphosis +
Footnote †: journal: Applied Soft. 2019, 9, 3322; doi:10.3390/app9163322
## 2 Materials and Methods
### Physical-Layer Security Analysis: Colluding Eavesdroppers
#### 2.1.3 Asymptotic Secrecy Outage Probability as \(\tilde{\tau_{B}}\rightarrow\infty\)  
In this section, we assess the impact of having very reliable conditions on the main link in terms of the average received SNR over the security. That is, the asymptotic \(OP_{sec}^{L}\) as \(\tilde{\tau_{B}}\rightarrow\infty\) is evaluated. The secrecy outage probability in (14) can be rewritten as  
\[OP_{sec}^{L} =\sum_{v_{1}=0}^{\infty}\sum_{v_{2}=0}^{\infty}\cdots\sum_{v_{N} =0}^{\infty}\sum_{r_{1}=0}^{\infty}\sum_{r_{2}=0}^{\infty}\cdots\sum_{r_{ \kappa_{\kappa}}=0}^{\infty}\frac{c_{a}c_{d}^{-\mu_{1}-v_{1}}}{2\pi i}\] \[\times\int_{C}\frac{\prod_{j=1}^{N}\Gamma[\mu_{j}+v_{j}-s] \Gamma[s]\prod_{j=1}^{n_{e}}\Gamma[\mu_{ej}K_{j}+r_{j}+s]\Gamma[s]}{\Gamma[1 +s]}M^{s}ds, \tag{18}\]  
where \(c_{d}=\frac{2^{C_{h}}\tilde{\tau_{E}}K\prod_{i=1}^{N}\Gamma[X_{i}^{2}]\mu_{i}( 1+\kappa_{i})}{\prod_{j=1}^{n_{e}}\mu_{ej}K_{j}(1+\kappa_{ej})}\) and \(M=\frac{c_{d}}{\gamma_{B}}\). Similar to the previous section, using the residue method [33], the asymptotic secrecy outage probability can be finally given by  
\[OP_{sec,B}^{L} \approx\sum_{v_{1}=0}^{\infty}\sum_{v_{2}=0}^{\infty}\cdots\sum_{ v_{N}=0}^{\infty}\sum_{r_{1}=0}^{\infty}\sum_{r_{2}=0}^{\infty}\cdots\sum_{r_{ \kappa_{\kappa}}=0}^{\infty}c_{a}c_{d}^{-\mu_{1}-v_{1}}\prod_{j=1,j\
eq I}^{N }\Gamma[\mu_{j}+v_{j}-\mu_{I}-v_{I}]\}\] \[\times\prod_{j=1}^{n_{e}}\Gamma[\mu_{ej}K_{j}+r_{j}+\mu_{I}+v_{ I}]M^{\mu_{I}+v_{I}}, \tag{19}\]  
where \(\mu_{I}+v_{I}=\min\mu_{j}+v_{j}\), for \(j=1,2,\cdots,N\), which represents the minimum pole at which the residue method is evaluated.