esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Hardware Accelerator Design
### Submodule Design  
#### 4.2.1 Filter Module  
The filter modules include two kinds, which realize Gaussian filtering and maximum value filtering, respectively.  
The Imfilter_0, Imfilter_1, and Imfilter_2 modules can realize Gaussian filtering; these three modules are designed for different levels of precision, and their internal logic structure is basically the same, as shown in Figure 7.  
The Imfilter module consists of three main parts: The first part is a row and column counter that counts the row and column pixels of the input image data and indicates the current element position for the calculation. The second part is used to generate a 3 \(\times\) 3 window, consisting of two RAM-based shift registers with the depth of the RAM being the number of columns of the input image and the output of which is a 3 \(\times\) 3-sized filter window for calculating the value of the current element, containing nine windows of data. The third part implements the actual filtering function and contains mainly multiplication and addition logic, which first multiplies the input nine window data corresponding to the values of the convolution kernel and then adds the nine multiplication results. This part uses a pipelined design, where the result of the multiplication and the intermediate result of the addition are saved in registers to form a three-stage pipeline. The convolution kernel can be parameterized as required, and by configuring a specific convolution kernel, this module can also implement mean value filtering.  
The Imfilter_0 module is designed for input image data with no extra precision, and the input data bit width is eight. The Imfilter_1 module and Imfilter_2 module are designed for input image data with 1\(\times\) fixed and 2\(\times\) fixed precision, respectively, and the input data contain extra fixed bits and 2\(\times\) fixed decimal places.  
The Ordfilt2 module can achieve maximum filtering; its internal structure of the row and column counter and window generator part is the same as the Imfilter module, and the difference is that the filtering function part of the multiply-add logic is replaced by two levels of comparison logic to achieve the maximum filtering function.
## 3 Numerical Simulation  
Different from the forgetting factor adopted by other algorithms, the proposed algorithm brings a moving window with fixed input and output data size. To demonstrate the effectiveness of the proposed method for seismically excited structures, an 8-story shear-type frame is numerically simulated with a seismic excitation of the El Centro Earthquake (18 May 1940) in the north-south direction. The mass of each story is lumped at the center and assumed to be 10 tons. The stiffness at each story is supposed to be 1200 kN/m. The damping coefficients are calculated via the modal damping and the damping ratio is assumed to be 2% for each mode. Accordingly, the 8 modal frequencies from 1.02 Hz to 10.84 Hz are listed in Table 1. The measurement is assigned to be the 8 acceleration responses of the frame and an additional measurement is the ground acceleration. Although the frame is simulated using a 1 kHz sampling rate, the one in measurement can be varied. All the simulation is done using the software, MATLAB, issued by MathWorks.
## 5 Results of the Mathematical Model
The figure shows the dependence of the power factor \(C_{p}\) on the total speed ratio (TSR) for wind turbines with different blade designs: with and without plates. The curve demonstrates that for all designs, \(C_{p}\) increases with the TSR to a value of about 4, after which it decreases. Models with plates show higher \(C_{p}\) values at all stages of operation compared to designs without plates, which indicates their improved aerodynamic characteristics. The experimental data (circles) are in good agreement with the numerical calculations (squares), confirming the reliability of the simulation. Small deviations are observed in the range of low TSR values, which may be due to experimental errors. The maximum \(C_{p}\) value is achieved at TSR = 4, which corresponds to the optimal operating mode of the wind turbine. The results confirm that the use of plates increases the efficiency of the turbine, especially in the range of optimal TSR values.
# Enhancing Density Prediction of Agricultural Land Soil through Void Area Curve Analysis
## 2 Materials and Methods
### Image Thresholding  
Image thresholding is one of the image segmentation methods. The image thresholding method divides an image into two regions based on a threshold value. The threshold value is an image intensity value between 0 and 255. After thresholding, if the intensity value of the image is greater than the threshold value, it is replaced with 255 (max value). If it is less than the threshold value, it is replaced with 0 (min value) [40]. Therefore, a threshold image is a binary image that has only two values of 255 (white) and 0 (black) for each pixel intensity [41]. It is important to determine the appropriate threshold value to achieve the desired result because the threshold value determines the outcome of the image thresholding. Image thresholding can be classified as simple thresholding or automatic thresholding. In simple thresholding, the threshold value is determined manually. Alternatively, automatic thresholding is a method of determining the threshold value according to preset algorithms.  
In this study, image thresholding was used to distinguish between the soil solid and the void. Three thresholding methods were used to threshold the soil images: simple thresholding, the Otsu method, and the Triangle method. The Otsu method and the Triangle method are representative automatic thresholding methods. The Otsu method is a famous automatic thresholding method that returns a single threshold value determined by minimizing the intraclass intensity variance, or equivalently, by maximizing the inter-class variance [42]. The Triangle method is an algorithm for finding the end of an image histogram and it determines the threshold value by normalizing the height and dynamic range of the histogram [43].
## 2 Materials and Methods
### Data
In addition, because the LANDSAT temporal resolution is quite low, we obtain MODIS MCD43A4 BRDF-corrected nadir surface reflectance [75] at daily time steps (based on a weighted window of 16 days of measurements) at 500 m for the FLUX sites, allowing for temporal analysis of the retrieval performance. MODIS data was available for the entire study period for the FLUX sites.
## 3 EV Battery Chargers and Their Architectures
### Based on the Charger Installation
#### 3.2.2 Off-Board Charger  
Off-board charging technology, a type of EV charging technology, enables electric automobiles to be charged remotely from the vehicle. The technology that is widely used in public charging stations allows EVs to be charged while parked in a public area. Off-board charging technology allows EVs to be charged inside of private residences when parked in a garage or driveway. Off-board charging technology frequently uses an AC or DC power supply, depending on the sort. Off-board charging systems are frequently more efficient than on-board charging systems, despite being able to power multiple vehicles at once and enabling shorter charging times [63].  
Limitations of Off-Board Chargers:
* Limited Range: The length of the power cord determines how far an off-board charger can travel. This may make it challenging to charge vehicles in locations far from a power source.
* Cost: Because off-board chargers need specialized hardware and installation, they can be pricey.
* Limited Availability: Off-board chargers may not always be accessible in all areas, and, in some places, the infrastructure required to use them may not be present.
* Installation: Setting up an off-board charger can be time- and labor-intensive.
* Risk of Overcharging: There is a chance that the off-board charger will overcharge the battery, which could result in damage.
* Safety: Compared to on-board chargers, off-board chargers present a greater risk of shock or electrocution.  
A comparative analysis of on-board chargers and off-board chargers is shown in Table 7. This table shows a critical analysis between both types of chargers and helps to select the best charger for EVs.
## 3 Results and Discussion
### Soil Texture Effect on Runoff Generation  
Runoff generation was higher in loamy sand than in sandy soil in the CP and DP tillage treatments (Figure 6). The heavy rainfall events recorded at experimental sites could have promoted more surface sealing of the soil in loamy sand, which has more silt content. The sealing of soil pores in the surface layer slows the infiltration of rainwater, and this  
Figure 4: Pairwise comparison of the effects of different tillage methods on runoff generation across soil types and experimental sites in Insiza and Gwanda districts, Zimbabwe.  
Figure 5: Rainfall–runoff relationships in each tillage treatment across sites and seasons in Gwanda and Insiza districts, Zimbabwe.  
in turn promotes the generation of more overland flow in loamy sand than sandy soil. On the contrary, runoff was higher in sandy soil than in loamy sand in the planting basin treatment. Loamy sand had 12%, 11%, and 7% more runoff than the sandy soil under CP, DP, and Ripper, across sites, respectively. In the planting basin treatment, sandy soil had 3.4% more runoff than loamy sand across sites. Runoff generation at any given slope of land varies with soil texture [51]. [PERSON] and [PERSON]'s [51] study showed that clayey soil generated more runoff (1.49 mm) than loam soil (1.03 mm) at the same slope and plot size. The runoff amount increased with an increase in rainfall amount regardless of the soil texture (Figure 7). The results in Figure 6 are consistent with the findings of [51], where more surface runoff was generated in clayey than light-textured soil in the conventionally ploughed treatments. However, the runoff was similar in loamy sand and sandy soils in the minimum tillage treatments. This can be attributed to better infiltration in minimum-tillage systems arising from improved soil structure, which occurs through increased porosity and reduced bulk density [9; 52].  
Figure 6: Runoff amounts generated in different tillage treatments under sandy and loamy sand soils across sites and seasons in Gwanda and Insiza districts, Zimbabwe. Means with the same letter are not significantly different (5%). The crossed blue square in each box plot represents the treatment mean.  
Figure 7: Rainfall–runoff relationships in each tillage treatment under sandy and loamy sand soils across sites and seasons in Gwanda and Insiza districts, Zimbabwe.
## 4 Antibacterial Nanotheranostics
### Theranostic Nanoparticles (NPs) and Nanofibers  
Nobel metal NPs, such as silver (Ag), gold, and platinum, have long been used as therapeutic antibacterial agents [102; 112]. Gold NPs (GNPs) could specifically be tied with therapeutic drug delivery and diagnostic applications because of their unique properties (e.g., facile preparation, surface functionalities, tunable core size, photothermal, and surface plasmon-related optoelectronic properties, inertness, and biocompatibility) [112]. Semiconducting NPs (defined as quantum dots, QDs), such as zinc sulfide, cadmium sulfide, and zinc oxide, have become vital tools in the diagnostic bioimaging and therapeutic drug delivery because of their size- and shape-dependent optoelectronic properties and high surface area to volume ratios [112; 146]. Mesoporous silica NPs (MSNs) provide massive thrust to the area of antibacterial drug delivery, loading up to 10-fold more drug than non-porous silica NPs [74]. The available tuning of the core size, surface functionalities, and structural features of MSNs (e.g., high surface areas and tunable nanometer-scale pore sizes) make them a groundbreaking system that optimizes multifunctional therapeutic controlled drug release and diagnostic imaging modalities [102; 147]. MSNs could palpably deliver the antibiotic cargo in a targeted manner, achieving the required antibiotic concentration at the infected tissue and reducing the drawbacks of systemic antibiotics [102; 148].
# Predicting the Performance of Solar Power Generation Using Deep Learning Methods
## 5 Conclusions  
The main purpose of this research is to explore the prediction and evaluation methods of solar power generation. Therefore, in this work, we conducted intensive experiments using deep learning models to effectively predict power generation from our experimental results. It has been confirmed through the prediction of experiment A-1, that only using the monitoring system data on-site installed in the solar power plant cannot effectively establish an accurate prediction model. The actual applications still need to collect other possibly related feature data, and it may make the solar power prediction model gradually improved to optimization through different model experiments.  
Since it only uses the related feature values and the data set of a single inverter which are collected in the above experiment (total 3948 training data) for training, the performance indicator predicted by the model can only be increased to \(r^{2}\) = 0.890179. So once again, the concept of a Generative adversarial network (GAN) is extended to multiple inverters (8 sets of training data total 31584), then aggregated and reshaped the dataset by the temperature of the inverter and the feature variables. Finally, experiment B-5 obtains the optimal performance indicator of \(r^{2}\) = 0.934156 by using the Bidirectional-GRU model. These feature variables used in the optimal prediction results will increase the prediction effect of power generation. The most likely reason is the correlation between the greenhouse effect and solar thermal radiation. Therefore, if it is necessary to further strengthen the prediction accuracy in the future, it is recommended to collect the relevant feature variables of the solar irradiance or the greenhouse to obtain better results. Currently, there are many new and improved neural networks that can be applied to the study. In future work, the methods using a hybrid deep learning model can be considered into the system to continuously improve the prediction performance of solar power generation.  
Conceptualization, C.-H.L.; Formal analysis, H.-C.Y.; Investigation, H.-C.Y. and G.-B.Y.; Methodology, C.-H.L. and G.-B.Y.; Software, H.-C.Y. and G.-B.Y.; Supervision, C.-H.L.; Validation, H.-C.Y.; Visualization, G.-B.Y.; Writing--original draft, C.-H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# Development of a Predictive Tool for the Parametric Analysis of a Turbofan Engine
## 2 Materials and Methods
### Deep Learning  
An artificial neural network that has many layers of neurons connecting the input and the output is known as a multilayer perceptron (MLP). MLPs are often referred to as feedforward neural networks. Data transmission in one direction from the input to the output layer is known as feedforward. The output of each neuron is normally connected to the output of every neuron in the following layer. The hidden layers are found between the input and output layers. MLPs are suitable for supervised learning and were hence used for this problem. They are extremely adaptable and may be implemented to learn the relationship between variables and their responses. Since the dataset for the parametric analysis is tabularized with the input and output variables, MLPs can be easily trained to identify the mapping between them and predict their trends.  
A deep neural network was developed in the Python programming language using Keras. The holdout technique is another type of cross-validation. The dataset was divided into two parts: the training set and the testing set. The key purpose behind separating the dataset into a validation set was to prevent our model from overfitting. Training data comprises 70% of the total data and 30% is used for the test data. The network's hyperparameters include two hidden layers: the Adam optimizer and the ReLU activation function. The mean square error (MSE) cost function was minimized to be used as the evaluation metric. This is summarized in Table 2.
## 4 Conceptual Framework: DDPLM
### Machine Learning Techniques Utilized in DDPLM  
The Data-Driven Personalized Learning Model (DDPLM) integrates multiple machine learning techniques, each playing a crucial role in enhancing language acquisition through real-time analysis. The model uses a combination of supervised, unsupervised, and reinforcement learning algorithms, creating a system that constantly adapts to the learner's progress and needs.  
Supervised learning operates by predicting outcomes based on labeled data, using historical patterns to determine what learning materials or exercises will most benefit a particular learner. This allows the system to anticipate struggles or successes before they manifest. For instance, task completion rates, quiz performance, and interaction patterns are all used to predict where a learner may face difficulty. The system adjusts its approach accordingly, guiding the learner toward areas needing more attention.  
In contrast, unsupervised learning serves a different purpose. Without pre-labeled data, clustering algorithms search for hidden structures within the learner data. Groups of learners that exhibit similar learning behaviors are identified, allowing the system to generate learning paths that cater to each group's unique trajectory. These algorithms detect learning patterns that are not immediately obvious, which helps the system refine its content delivery. The creation of these clusters is essential to making real-time decisions about which resources, exercises, or interactions will be most effective for each group.  
Reinforcement learning, a key component of DDPLM, actively responds to learner feedback by optimizing future content based on past interactions. The system learns from every input, adjusting the timing and intensity of challenges presented to the learner. Feedback becomes a central element in shaping the learning experience. When a learner encounters a challenge or succeeds in a task, the system immediately recalibrates, offering progressively tailored content. This dynamic process keeps the learner engaged, neither overwhelming nor under-challenging them, ensuring an optimal flow of information.  
The DDPLM also incorporates deep learning elements, particularly for processing complex linguistic inputs. Natural language processing (NLP) tools embedded within the model handle vast amounts of text data, improving the system's ability to interpret learner responses, flag errors, and suggest corrections that align with both linguistic rules and the learner's current proficiency level. These tools are particularly helpful in spoken and written language exercises, where subtle nuances in grammar or syntax might be missed by simpler algorithms.  
The synergy of these machine learning techniques ensures that the DDPLM remains highly responsive, adapting in real time to the evolving needs of learners. Through continuous feedback loops, predictive modeling, and real-time adjustments, the model evolves with each interaction, crafting a personalized and efficient learning experience.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 3 NP Properties and Characterization Methods
### Physical Properties and Their Characterization
#### 3.1.1 Transmission Electron Microscopy (TEM)  
TEM is essential for capturing high-resolution images of MNPs. It provides comprehensive information regarding their dimensions, form, and interior composition. This technique is crucial for observing the inorganic core and any organic shell present on the surface of the nanoparticles, offering crucial insights into their microstructure or ultrastructure. In contrast to other methods of size assessment, it accurately measures the true radius of samples. TEM, which requires desiccation before measurement, gives information about magnetic nanoparticles in their dry state. However, a significant limitation of TEM is its restriction to small sample sizes and the considerable time required for measurements. Furthermore, as operator bias can impact the selection of photos, researchers are recommended to refrain from introducing subjectivity. To obtain a statistically precise measurement of NPs' diameters, it is necessary to collect many images at an appropriate level of magnification. This will ensure that there is sufficient contrast between the particles and the background, while also allowing for the inclusion of multiple particles (a minimum of 300 in order to obtain adequate statistical data) in each image. The photos can thereafter be examined with a software application like ImageJ [94].  
High-resolution transmission electron microscopy-electron energy loss spectroscopy (HRTEM-EELS) provides a comprehensive investigation of MNPs' composition. It is highly valuable for determining the elemental and electrical structures, including the characteristics of multifunctional NPs such as core-shell Fe\({}_{3}\)O\({}_{4}\)/Zn\({}_{x}\)Co\({}_{1}\)-xFe\({}_{2}\)O\({}_{4}\)[95].
## 5 Results and Analysis
### Ensemble-2 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scoring** & **Averaged** & **Averaged** & **Averaged** & **Averaged** & **Averaged** & **Averaged** & **Cost** \\
**Functions** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-Measure** & **AUC** & **Cost** \\ \hline DT Only & 79.5 \(\pm\) 3.44\% & 70.21 \(\pm\) 2.94\% & 99.01 \(\pm\) 1.28\% & 27.32 \(\pm\) 7.05\% & 81.78 \(\pm\) 2.27\% & 0.937 \(\pm\) 0.056 & 123.29 \\ RF Only & 96.01 \(\pm\) 2.64\% & 95.30 \(\pm\) 2.71\% & 98.78 \(\pm\) 1.91\% & 90.42 \(\pm\) 5.92\% & 96.98 \(\pm\) 2.25\% & 0.995 \(\pm\) 0.006 & 197.05 \\ GBT Only & 85.24 \(\pm\) 3.88\% & 84.71 \(\pm\) 4.58\% & 98.34 \(\pm\) 2.14\% & 63.22 \(\pm\) 13.18\% & 90.25 \(\pm\) 3.01\% & 0.941 \(\pm\) 0.05 & 150.25 \\ DT-RF & 93.78 \(\pm\) 3.04\% & 95.29 \(\pm\) 4.90\% & 95.31 \(\pm\) 6.50\% & 91.41 \(\pm\) 9.00\% & 95.04 \(\pm\) 2.57\% & 0.99 \(\pm\) 0.02 & 143.69 \\ DT-GBT & 91.40 \(\pm\) 2.27\% & 91.89 \(\pm\) 3.01\% & 96.01 \(\pm\) 4.36\% & 83.79 \(\pm\) 5.61\% & 93.59 \(\pm\) 1.89\% & 0.95 \(\pm\) 0.02 & 162.54 \\ GBT-RF & 93.39 \(\pm\) 2.70\% & 91.53 \(\pm\) 4.07\% & 99.19 \(\pm\) 1.47\% & 83.80 \(\pm\) 8.19\% & 95.04 \(\pm\) 1.96\% & 1.00 \(\pm\) 0.01 & 172.05 \\
**Ensemble-1** & **96.26 \(\pm\) 2.93\%** & **95.73 \(\pm\) 3.81\%** & **98.79 \(\pm\) 1.64\%** & **91.94 \(\pm\) 7.56\%** & **97.14 \(\pm\) 2.21\%** & **1.00 \(\pm\) 0.01** & **95.05** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Multiple combinations of scoring functions and their respective results.