esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review Methodology
### Detailed Exploration of Category Backgrounds
#### 2.1.1 Channel Estimation  
Figure 2 shows a UWA OFDM system that applies ML and DL models [17; 18; 19]. The figure represents a multi-carrier setup but can be adapted to single-carrier systems by removing certain blocks. It is a general diagram and does not include the internal structure of the learning models. Typically, ML or DL models are first trained offline. After that, they are deployed in the live system with online updates or tuning. Common modulation schemes include BPSK (Binary Phase Shift Keying), QPSK (Quadrature Phase Shift Keying), and OFDM (Orthogonal Frequency Division Multiplexing). Performance is typically evaluated using MSE (Mean Squared Error) and BER [20; 21; 22; 23; 24].  
At the transmitter, input data is modulated using QPSK or 16-QAM (Quadrature Amplitude Modulation) [25; 26; 27; 28]. The modulated symbols are split into parallel streams and passed through an inverse fast Fourier transform (IFFT) to produce OFDM signals. A cyclic prefix (CP) is added to reduce inter-symbol interference from multipath effects in UWA environments. The signal is amplified and transmitted through the underwater channel [21; 29; 30; 31]. At the receiver, the signal first passes through a bandpass filter to remove noise. The CP is removed, and the signal undergoes fast Fourier transform (FFT) to return to the frequency domain. ML/DL models such as DNNs and RNNs perform channel estimation by analyzing distortions and adjusting for UWA variations. The estimated channel improves demodulation and supports accurate data recovery. This ML/DL-driven approach enhances signal reliability, reduces BER, and optimizes communication efficiency in challenging underwater environments [32; 33; 34].
# ICARUS--Very Low Power Satellite-Based IoT
## 3 Physical Layer and Doppler Compensation
### ICARUS Channel Properties  
#### 3.3.1 Doppler Effect  
As the ISS travels at a high velocity, the communication links are influenced by the Doppler effect. The Doppler effect is mainly characterized by the well-known Doppler frequency shift which imposes a time-varying carrier frequency offset on the received signal. The first derivative of the Doppler shift is of importance as well. It is denoted as Doppler rate [Hz/s] which describes the change of the frequency offset over time. Figure 12 outlines the main characteristics of the Doppler shift and the Doppler rate along ground track of the ISS.  
Figure 11: 8-PSK approximation of the unit-circle FZC baseband symbols.  
Figure 12: Doppler parameter of ICARUS.
## 4 Theoretical Analysis of the Proposed PCSDF Cooperation Scheme  
### Analysis of Optimal Design-SNR in Proposed Scheme  
For a system with a design-SNR \(\gamma\) and data rate \(R\) in AWGN channels, the polar code can be constructed by the design-SNR [31]. The construction changes with different design-SNR. The optimal design-SNR can produce good BER performance for a fixed data rate \(R\) at a range of SNRs [11].  
For a fixed data rate \(R\), the required SNR \(\gamma\) to fit this data rate \(R\) can be obtained as  
\[\gamma=C^{-1}(R), \tag{24}\]  
where \(C^{-1}(x)\) is the inverse function of \(C(x)\) in (13). The required SNR for a data rate \(R\) is denoted as \(\gamma_{R}\). For a fixed data rate \(R\), \(\gamma_{R}\) is the optimal design-SNR for the construction of the polar codes.  
\begin{table}
\begin{tabular}{c} \hline \hline Procedures of the improved PCSDF cooperation scheme. \\ \hline Step (1). First, The source node broadcasts messages to the destination and all relays. Then they estimate \(h_{sd}\) and \(h_{sr_{i}}\), respectively, to obtain the gain of link S-D \(\beta_{sd}\) and the capacity of the relay channel \(C_{sr_{i}}\). \\ Step (2). Compare the capacity of the relay channel \(C_{sr_{i}}\) with the information transmit rate \(V\). if \(C_{sr_{i}}\geq V\), the relay \(R_{i}\) is selected into the candidate relay set \(\Omega_{k}\). Then, goto step (3). \\ Step (3). If \(\Omega_{k}\) is empty, the source transmits messages to the destination. Otherwise, the optimally selected relay \(R_{opt}=\underset{R_{i}\in\Omega_{k}}{\arg\max}\{\left|h_{r,d}\right|\}\) are chosen from the candidate relay set \(\Omega_{k}\). Here, \(R_{opt}\) can be a simple relay or a compound one composed of several relays. And the latter is jointly combined by several relays for the same channel coefficient with the maximum ratio combination (MRC) criterion. Subsequently, the cooperative capacity of the optimal relay \(\beta_{r_{opt}}\) is obtained. Then, goto step (4). \\ Step (4). Compare the relationship between \(\beta_{r_{opt}}\) and \(\beta_{sd}\). If \(\beta_{r_{opt}}/\beta_{sd}>\theta\), the optimal relay forwards source messages. Otherwise, the optimal relay keeps silent in the whole process. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Procedures of the proposed SDF cooperation scheme.  
Figure 4: Block diagram of the PCSDF cooperation scheme.
## 3 Mobile Node Design
### Structure Decomposition  
Figure 2 shows the structure of the proposed mobile sensor node in an exploded view. The whole architecture includes a sensor module, a radio module, a mainboard, a driver board and a chassis. The radio module is connected with the mainboard by an expansion connector. It works in the 2.4 GHz frequency band and establishes wireless communication channels with other nodes in range. The mainboard is the brain of this smart device. It is responsible for most of the data processing tasks inside the node and manages the wireless communication links to and from neighboring nodes. The chassis is the base platform custom designed for building our mobile sensor nodes. It has two separate driving wheels and one universal wheel for supporting the node body and flexible steering. A rechargeable NiMH battery pack is attached to the bottom of the chassis for power supply. A co-processor is added to the mainboard for better performance on motor control and motion parameters detection. The sensor module is an optional module that varies from application to application. For example, in environment monitoring applications, the most commonly used sensors are temperature, humidity, light, atmospheric pressure and so on. In our design, we add two IR sensors to the front side of the mobile node for obstacle avoidance and add three light sensors to the mainboard for ambient lighting detection.  
A prototype of the mobile sensor node we designed and implemented by following the proposed architecture is shown in Figure 3. The mobile node, which we call _RacemoteZ_, provides a novel robotic platform for adding controlled mobility to wireless sensor networks or other distributed measurement and control systems. The size of RacemoteZ is 105 mm\(\times\)90 mm\(\times\)80 mm. This tiny mobile node includes all the modules described above. Those modules are connected together via extended interfaces and they form a sandwich-like structure. By using this kind of structure, it is easy  
Figure 2: Exploded view of the proposed mobile node structure.  
to assemble and disassemble the node. The extended interfaces make it possible to add additional modules to the node when a system upgrade is needed at a future date.
## 3 Maximum Power Point Tracking Algorithms  
The maximum power point tracking (MPPT) system is an essential component in solar photovoltaic (PV) power systems, designed to maximize energy extraction by continuously regulating the operational point of the PV power system. Its primary function is to guarantee that the system consistently operates at its optimal power point. Various MPPT techniques have been developed to improve PV system efficiency, with the choice of an appropriate method depending on factors like accuracy requirements, computational capabilities, and environmental circumstances [19]. In this research, the Cuckoo Search Optimizer (CSO) and Particle Swarm Optimization (PSO) are applied, which are highly effective techniques for maximizing the power harvested from the PV energy systems due to their adaptability and robustness. CSO, inspired by the brood parasitism behavior of cuckoo birds, excels in global search capabilities, making it particularly effective in navigating complex, multi-modal P-V curves under partial shading circumstances. PSO, inspired by the social behaviors of swarms, combines simplicity and fast convergence, efficiently tracking the MPP even under dynamic irradiance changes. Both methods significantly improve energy extraction by addressing limitations of conventional techniques such as slow convergence or susceptibility to local maxima, thereby improving the efficiency and reliability of PV power systems. Their complementary strengths make them invaluable for advancing renewable energy technologies. More detailed information for both CSO and PSO is provided in the following sections.
## 5 Airborne Measurements
### Unmanned Aircraft Systems  
Unmanned aircraft have been used for military purposes already for a century [93], but instrumented UASs have become popular in meteorological research only during the last few decades. There exists a wide range of different types and sizes of UASs, their weights ranging from a few hundred grams to several thousands of kilograms, operating altitudes from a few tens of meters to 20 km and operating distances from a few kilometers to several thousands of kilometers [93]. In meteorological research, the typical UASs comprise relatively small aircraft and rotorcrafts, which are either ground-controlled or semi-autonomous (e.g., [94; 95]), but also massive UAS, such as the Global Hawk, has been applied [96].  
The wind speed measurements on board an unmanned aircraft can be performed based on instrumentation carried by the aircraft (typically a five-hole probe) or indirectly from the attitude and position records based on the inertial measurement unit (IMU) and GPS, respectively [95]. Yet, wind gusts have not been observed based on unmanned aircraft, but probably, a similar method as for manned research aircraft [38] could be applied. However, due to the generally lower flight velocities (ground speed) of small and medium-size UASs (up to about 30 ms\({}^{-1}\)) compared to manned research aircraft, the measured wind signals are also affected by temporal changes in the wind velocity. Moreover, the speed and the size of the aircraft may pose limitations to the wind velocity measurement range. [PERSON] et al. (2012) report that their small unmanned meteorological observer (SUMO) provides wind measurements comparable to radio soundings or tethered balloons, but is not suitable for extreme wind conditions [94]. The success of UAS measurements in strong wind conditions depends on the size of the aircraft, and therefore, most UASs are not best suited for extreme gust conditions when wind gust speeds exceed the UAS operational speeds. The largest UASs, however, have potential to provide wind gust observations in conditions too dangerous for manned aircraft.  
Unmanned rotorcrafts, small remotely-piloted copters with multiple rotors, can also be used to measure wind. However, also they are suitable to be used in light and moderate winds, not in extreme gust conditions. The wind can be measured directly by the instrument carried by the copter, like by a sonic anemometer, or indirectly from the attitude of the copter [97].
## 3 Results  
### Numerical Model  
The model was calibrated at the beginning in steady state and the general statistics are reported in Table 1. The model results are shown in Figure 11a where the residual values of hydraulic head are also indicated. Figure 11b shows the scatterplot of the observed vs. simulated heads. Calibration was performed using the piezometric levels of the November 2015 survey assuming that they could be representative of the average piezometric distribution in the area since no other useful data were available. The automatic calibration process with Pilot Point technique (PEST, [44]) was applied to the hydraulic conductivity parameter and resulted in a hydraulic conductivity distribution for each layer.  
The hydrogeological study and the model runs demonstrated that the reason for the 2014 flooding was mainly due to the concurrence of three causes: (1) the hydrogeological structure of the area recognized as a stagnation zone, (2) the groundwater rising--which has been happening for several years in this area and generally in Lombardy [43; 45]--and (3) the abnormal quantity of rainfall that occurred in 2014 (100-years return period).  
Once the model was calibrated, the attention was focused on the area where flooding occurred in 2014 to assess the feasibility of groundwater level control through pumping wells. The optimization process result shows that 8 wells are necessary to achieve the goal, with pumping rates varying between 3 and 6 L/s for a total of about 40 L/s in the area (Figure 12). The action of pumping wells close to the areas that mainly experienced the flooding problem could thus be an effective system to keep groundwater levels under control and to avoid the repetition of such a phenomenon.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Statistical Indicators** & **Value** \\ \hline Target Number & 28 \\ Observation Range (m) & 68.39 \\ Minimum Residual (m) & \(-1.09\) \\ Maximum Residual (m) & 2.27 \\ Residual Mean [M] (m) & \(-0.06\) \\ Root Mean Square error [RMS] (m) & 0.63 \\ Mean Absolute error [MA] (m) & 0.38 \\ Mean Absolute error/Range (calibration fit) & 0.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: General statistics resulting from the model.  
Figure 11: (a) Model simulation result and observed-simulated values—zoom on Grandate, (b) observed/simulated scatterplot before and after the calibration procedure.
## 2 Related Work  
### OCR Tools  
There are various excellent OCR tools and technologies available on the market today [6; 7; 8; 12; 13] with key distinctions between cloud- or on-premise-based as well as paid or open source. The focus of this research study was not aimed at comparing OCR tools but rather the workflow after the OCR had digitalized information. Many studies mention and address the need to run steps after OCR in order to get better results [9; 14]. That is because small fonts or formatted text (like italic text) [9] as well as bad document quality [12] often contribute to characters being misread by the OCR tool. One way that this can be overcome is by document pre-processing techniques. As [PERSON] et al. [15] state, \"current OCR...do not work well for documents with text printed against shaded or textured backgrounds\", and interest around different ways to pre-process these documents is growing [1; 5; 8].  
An alternative approach to avoid steps after the OCR step is by training the OCR tools on samples of input characters to improve the reading accuracy [12; 16]. This attempts to overcome cases where the characters appear different than normal characters--for example, when a new font is introduced, when the OCR characters to be read are handwritten, or when the language to be read contains different alphabetical characters. For this study, the OCR tool picked does not yet provide this functionality and the font is normal printed text, so this was not deemed necessary.  
Although previous research studies combine a multi-step approach to enhance the OCR output [9; 17] none of the approaches presented offered an automated workflow with minimal manual intervention which could be implemented in the industry to produce matched key-value pairs automatically. Many of the workflows stopped at the point of suggesting possible candidates for the human to analyze and select.
# Solar Power System Assessments Using ANN and Hybrid Boost Converter Based MPPT Algorithm
## 5 Conclusions  
The increase in electric load is recorded during last decade. Solar plants are the fruitful solution to minimize pressure on the main power grids. In this paper, load management is discussed using an ANN based MPPT and hybrid boost converter. Furthermore, to maintain output power smart transformer is installed before load distribution. The ANN based MPPT controller with hybrid converter have fast tracking and less fluctuations. The mechanism of ANN based is explained with mathematical background. The traditional and proposed HBC are compared theoretically and the features of HBC are highlighted. The results of proposed ANN and HBC based MPPT are evaluated in terms of power and time for using traditional boost converter with ANN and proposed HBC with ANN. The results show the superiority of solar power system with hybrid converter to improve the efficiency of solar power system. In this work, the performance and productivity of the solar power system are strengthened by utilizing the ANN and HBC based MPPT. However, in future the power generation can be further improved by using advance algorithms and machine learning methodologies, such as the flower pollination algorithm (PFA), optical swarm optimization (PSO), convolution neural network (CNN), and coyote optimization algorithm (COA). In addition, for forecasting the solar power system condition the hybrid based ANN and adaptive neural-fuzzy inference system (ANFIS) using hybrid whale optimization and pattern search (HWO-PS) algorithm can be evaluated.  
Conceptualization, I.H., F.A. ([PERSON]), and A.A.; methodology, F.A. ([PERSON]), and I.H.; software, F.A. ([PERSON]), and A.M., F.A. ([PERSON]); validation, F.M., F.A. ([PERSON]) and N.A.; formal analysis, F.M., F.A. ([PERSON]), A.M., F.A. ([PERSON]); investigation, A.M., F.A. ([PERSON]), A.A. and N.A. data analysis, F.A. ([PERSON]), I.H.; writing--original draft preparation, I.H.; writing--review and editing, W.K., F.A. ([PERSON]); and A.A.; visualization, F.M., F.A. ([PERSON]); supervision, F.A. ([PERSON]), A.A.; project administration, F.A. ([PERSON]); funding acquisition, W.K., N.U., F.R.A. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the support from Taif University Researchers Supporting Project Number(TURSP-2020/331), Taif University, Taif, Saudi Arabia.  
Not applicable  
Data Availability Statement: Data will be available as per request.  
The authors declare no conflict of interest.
## 2 Background  
Polar codes are linear block codes denoted by \(\mathcal{P}(N,K)\), with code length \(N=2^{n}\), \(K\) information bits and code rate \(R=K/N\). Polar codes use the phenomenon of channel polarization [1] to divide bit-channels into reliable and noisy channels. Information bits are sent over reliable channels specified in the information set \(\mathcal{I}\). The frozen set \(\mathcal{F}=\mathcal{I}^{\mathcal{C}}\) of length \(N-K\) specifies the unreliable channels, which are used to include redundancy. Bit channels correspond to the rows of the generator-matrix \(\mathbf{G}_{N}=\mathbf{G}_{2}^{\otimes n}\). Here, the polarization kernel \(\mathbf{G}_{2}=\left[\begin{smallmatrix}1&0\\ 1&1\end{smallmatrix}\right]\) and \(\otimes n\) denotes the \(n\)-th Kronecker product. The bits of an input vector \(\mathbf{u}\) of length \(N\) at the positions of \(\mathcal{F}\), called frozen bits, are set to zero. The codeword \(\mathbf{x}\) is calculated from \(\mathbf{u}\) by  
\[\mathbf{x}=\mathbf{u}\mathbf{G}_{N}. \tag{1}\]
# Plant Disease Detection Using Deep Convolutional Neural Network
## 3 Materials and Methods
### Dataset Preparation and Preprocessing
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No** & **Plant Name** & **Class Names** \\ \hline
30 & & Healthy \\
31 & Paddy & Brown Spot \\
32 & & Hisp \\
33 & & Leaf Blast \\ \hline
34 & Peach & Healthy \\
35 & & Bacterial Spot \\ \hline
36 & Pepper & Healthy \\
37 & & Bacterial Spot \\ \hline
38 & & Healthy \\
39 & Potato & Early Blight \\
40 & & Late Blight \\ \hline
41 & Strawberry & Healthy \\
42 & & Leaf Scorch \\ \hline
43 & & Healthy \\
44 & & Leaf Blight \\
45 & & Red Leaf Spot \\
46 & & Red Scab \\ \hline
47 & & Healthy \\
48 & & Bacterial Spot \\
49 & & Early Blight \\
50 & & Late Blight \\
51 & Tomato & Leaf Mold \\
52 & & Leaf Spot \\
53 & & Spider Mite \\
54 & & Target Spot \\
55 & & Mosaic Virus \\
56 & & Yellow Leaf Curl Virus \\ \hline
57 & & Healthy \\
58 & Wheat & Leaf Rust \\ \hline
59 & no-leaves & no-leaves \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._different input images to generate an augmented output image, such as the content image and the style reference image. At first, the content image contains the essential features to be added to the output image. Second, the style reference image contains style patterns to apply to the output image. To generate the output image, the NST augmentation model applies the style features of the style image to the content image. The NST augmentation technique creates 17,500 augmented images in the dataset. Finally, The BIM, DCGAN and NST techniques were created for the augmented images to balance the data counts in each class of the dataset. The proposed dataset is named the PlantDisease59 dataset. These augmentation techniques increased the number of images in the dataset from 61,459 to 147,500 images. Additionally, the size of individual classes increased to 2500 images in each. Leaf images on the PlantDisease59 dataset were captured in the face-up direction. Figure 1 shows the sample augmented images generated by the BIM, DCGAN and NST techniques. The first two images in Figure 1 were created using BIM techniques. The third and fourth leaf images in Figure 1 were created using the DCGAN augmentation technique. The last two sample images in Figure 1 were generated using the NST technique. The random selection technique was used to select the images for training, validation and testing from the PlantDisease59 dataset. Table 3 illustrates the number of images in the training, validation and testing dataset. The design and development of the Proposed DCNN model for plant leaf disease detection using the hyperparameter tuning techniques and the PlantDisease59 dataset are discussed in the following subsection.