esa-sceva/satcom-chunk-collection huggingface dataset:
# Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems
It is projected that by applying data-driven models to satellite data, modern agriculture will become a real-time artificial intelligence system, achieving high efficiency in optimizing inputs and increasing yields [17]. Towards offering precision farming services to rice growers, Ecodevelopment S.A. entered the field in 2016, specializing in fertilization applications. The company has been monitoring and consulting about 1500 ha of rice (shared between 15 different farms) throughout Greece all these years, providing vertical precision farming support to the growers (from crop heterogeneity detection, to interpretation, decision-making, and applications). The company is steadily receiving scientific and technical backup by the Soil and Water Resources Institute, Hellenic Agricultural Organization--Demeter (see [PERSON] et al., (2019) [18]). In this direction, the main aim of this research was to set up a new methodology for predicting topdressing nitrogen demands in rice cultivation from soil and satellite data. Soil data are necessary to provide basic information on soil type and behavior, while satellite data can provide the required near real-time information on rice growth and possible particularities. The amount, type, and distribution of the available dataset allowed the use of machine learning in this attempt. According to our knowledge, machine learning has not been applied with satellite-based remote sensing data for predicting nitrogen topdressing demands in rice up until now. More specific objectives were to:  
* Build a Machine Learning System (MLS) for rice yield prediction to select the optimum nitrogen fertilization scheme. * Develop a Computer Vision algorithm to map inundated rice fields, which, in turn, allows determination of rice growth stage. Getting this important information from satellite data is faster and more secure than querying all the growers of a large rice cultivation area.
# Aerial Coverage Analysis of Cellular Systems at LTE and mmWave Frequencies Using 3D City Models
## 2 System Model
### Scenarios
#### 2.2.1 Load Types  
Two different load cases were considered. The first case is the operation of base stations in full-load. This is the worst case scenario for UAVs, as indicated by [PERSON] et al. (2016), because all neighboring cells have data to transmit in downlink and thus cause inter-cell interference to the receiver, especially if this receiver is a UAV at a high altitude. In the simulations, all base stations were made to transmit simultaneously on all sectors with their respective power \(P_{tx}\) (see Table 2).  
The other case is operation in partial load. In partial load operation, active ground users are distributed randomly in the city with a certain density, \(\rho\), using a Poisson Point Process. The sectors that contain an active user, according to the assignment map, are turned on and are behaving as possible interferers. Not every sector is on and this results in an increase in SINR. In the partial load case, interference power can be calculated as follows:  
Figure 3: Cell assignment at different altitudes in a three-sector scenario. Each unique color corresponds to a unique sector. A pixel with a specific color indicates to which sector a user at this specific location will be assigned. A black pixel indicates a building.  
\[I_{a}=\sum_{i\
eq a}^{A}P_{rx,i}\mathcal{P}(A_{i},\rho), \tag{9}\]  
where \(\mathcal{P}(A_{i},\rho)\) is the probability that a base station is transmitting on a certain sector depending on the ground surface area of that sector \(A_{i}\) and the active user density \(\rho\), with \(P_{rx,i}\) being the power that is received when the sector is transmitting. Several simulation runs were performed with new user locations each run. Values for \(\rho\) and the number of iterations can be found in Table 1.  
Following the partial load case, we tried to reduce the interference power by increasing the number of sectors per base station to create narrower sectors and thus smaller sector areas. This can be achieved by deploying phased antenna arrays, which is an easy way to reduce the azimuth beamwidth of an antenna radiation pattern. A reduced ground area of a sector reduces the probability that the base station will transmit in this sector. In our simulations, when increasing the number of sectors, all antenna parameters are scaled proportionally, as explained in Section 2.1.2 (parameters can be found in Table 2). Note that a high number of sectors (45) results in a very small 3 dB beamwidth (4.33\({}^{\circ}\)); we assume this is possible using phased antenna arrays at mmWave frequency. Figure 3(a),b shows example scenarios where the number of sectors per base station is increased to 24 and 45, respectively.
# Prediction of Friction Coefficient Based on 3D Texture Characteristics of Road Surfaces
## 2 Measurement Tools and Experimental Methods
### Static Road Scanner  
Several studies have demonstrated that 3D texture analysis yields more accurate results than 2D profiles. Evaluating 2D texture parameters from profiles often falls short in describing particle shape and distribution, which are also crucial for determining tire-road interactions. One challenge in establishing a reliable relationship between texture parameters and coefficient of friction is the resolution limitations of 3D surface analyzers. Low-resolution scanners fail to capture essential microtexture surface properties crucial for determining friction levels. To address these issues and gather information on road surface texture, a newly developed device, the Static Road Scanner (SRS), was utilized in this study.  
The SRS was developed at the University of Zilina and is shown in Figure 3. To enhance data accuracy and minimize blind spots, the SRS uses dual red lasers with 15 \(\upmu\)m resolution,  
Figure 1: The TWO—Traction Watcher One and the metal sheet for the determination of the point of beginning. (1—Traction Watcher One; 2—Metal sheet; 3—Steel grid).  
Figure 2: An example of results measured by TWO (friction coefficient \(\mu\)) with the influence of metal sheet on obtained values. (Different line colors represent individual passes of the TWO device).  
scanning in opposite directions. The device can measure an area of \(120\times 100\) mm per scan. The system is generator-powered and computer-operated, enabling immediate computation of texture parameters post-scan. For subsequent processing and analysis, surface coordinates in the format X, Y, Z were utilized.  
Ensuring precise localization of all measurements was crucial to investigate the relationship between friction coefficients and texture parameters. A steel grid was created to achieve this with its corners marked on the pavement. The SRS device was positioned over this grid to determine the scanning locations. Each scan's position was then marked on the pavement. Figure 3 illustrates these marked locations on the pavement of the measurement section. This meticulous process was essential for maintaining accuracy and consistency in the measurements, creating reliable data for the study.
# Development of Patient Specific Conformal 3D-Printed Devices for Dose Verification in Radiotherapy
## 3 Results and Discussion
### Physical Characterization of 3D Printed Objects  
The most important factors to consider for 3D printing are the used material and the printing parameters (e.g., infill pattern, infill percentage, etc.) since they influence the properties of the printed object such as density and X-ray attenuation properties. Both PLA (polylactic acid) and ABS (acrylonitrile butadiene styrene) are common printing materials and were previously reported as tissue-equivalent [29]; however, based on our previous experience on 3D printing, PLA was used in this paper since, as opposed to ABS, it experiences no warping and deformation during printing due to its lower melting temperature (130-180 \({}^{\circ}\)C) [23; 24; 30; 31].  
The influence of the material's infill ratio on 3D-printed structures was assessed comparing HUs derived from computed tomography DICOM images of the patient and 3D-printed objects (Figure 4). It was found that HU value (\(-\)32) measured in the patient-specific 3D-printed PLA phantom (at 90% infill ratio) was in the HU range characteristic for soft tissues in the facial region of interest; however, both PLA boluses printed at 100% infill ratio indicated significantly higher HU value of 469. It should be noted that the selection of different infill densities for 3D phantom and boluses was based on our previous study [23] where the impact of infill ratio on homogeneity of dose distribution in 3D-printed object was investigated. In this study, the performed 2D Gafchromic film dosimetry showed that when using 90% infill density for 3D printing, \"patterned\" dose distribution was observed beneath relatively thin printed objects (3D bolus) resulting in additional uncertainties in the measured doses [23]. This may particularly compensate for the lack of soft tissue equivalency in the bolus, which was printed using 100% infill ratio in order to avoid inhomogeneous dose distribution beneath the bolus. In the case of a massive object (3D phantom), the influence of the \"patterning\" effect was significantly reduced; thus enabling printing with the lower infill ratio (90%) and achieving density of the printed object comparable with a density of soft tissue.  
The geometrical accuracy and reproducibility of the printed object depend on the errors introduced during each step of the 3D-printing process from image segmentation and subsequent modifications of the segmented model to printing and post-processing. Our patient-specific 3D-printed phantom had good geometrical accuracy. This was obtained by determining the Dice similarity coefficient, which measures the overlap between the CT images of the patient and phantom (Figure 5) [26,27]. The calculated DSC value was 0.957 and other similarity coefficients indicated good agreement between the external shape of the 3D-printed phantom and patient (Table 2).
## 5 Results and Discussion
### The Performance of the SIDE Variant
#### 5.3.1 Comparative Results of SIDE with the Most Promising DE Variants  
As with the rest of the DE variants studied, 30 independent runs were performed with the SIDE. For this, the parameters of the SIDE remained the same as those of the other DE variants studied. Table 15 shows the results obtained for each case study. The information contained in this table includes the same columns as Tables 4-7. Shaded cells indicate results where the SIDE performed equal to or better than all other DE variants based on the information previously reported in Tables 4-7. The main finding of this table is that the SIDE had an outstanding performance in half of the performance indicators. For the rest of the indicators, the SIDE showed a competitive performance. The best mechanisms synthesized by the SIDE and their developed trajectories are depicted in Figure 6 for all cases studies. The corresponding mechanism parameters can be consulted in the next Section 5.3.2.
# Method to Measure Light Spot Position in Beacon-Less Inter-Satellite Communication System
## 2 The Beacon-Less Is-OWC System Description
### Description of the ATP Subsystem  
Unfortunately, the ATP subsystem uses part of the received light as the beacon light, so that some of the reflected light will be leaked into the QD. Furthermore, after long distance transmitting, the intensity of the received light is far less than that of the reflected light. Therefore, two light spots will appear on QD's target surface together, which means that the photocurrent output from QD contain two signals, one corresponding to the weaker beacon light and another corresponding to the stronger reflected light. We need to extract the amplitude of the weaker signal from the stronger signal to measure the beacon light spot position. In the acquisition and targeting step, the digital signal has not been loaded into the signal light, so we can consider using cosine signals of different frequencies to  
Figure 1: Structure of the beacon-less Is-OWC system. FSM: Fast Steering Mirror.  
modulate the intensity of the two light beams, so as to separate the corresponding photocurrent in the spectrum.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 1 Introduction  
Pulse Position Modulation (PPM) is a well-established technique in optical and wireless communication [1], encoding information through the temporal position of pulses. PPM is traditionally classified into carrier-based and carrierless types. Carrier-based methods, while effective, depend on reference signals, which add to system complexity. Carrierless PPM avoids reference signals but often requires elaborate demodulation systems such as phase-locked loops (PLLs) [2], time-to-voltage converters, or signal reconstruction circuits.  
In this study, we propose a novel carrierless PPM modulation scheme based on the derivative of the input signal, a voltage-controlled oscillator (VCO), and a monostable multivibrator. This architecture eliminates the need for carrier generation or reconstruction, enabling a simple and compact implementation. Using Fourier analysis, we demonstratethat carrierless PPM inherently encodes the derivative of the message signal in its lowest frequency component. This allows signal recovery using basic analog operations, such as integration and low-pass filtering, without requiring complex recovery circuits.  
The approach contributes to both the theoretical understanding and practical realization of simplified modulation systems by offering a direct mapping from the input signal to carrierless PPM using minimal analog components. The remainder of this paper outlines the theoretical basis, proposed circuit design, and simulation-based validation of the method.
# Optical Hyperspectral Image Cryptosystem Based on Affine Transform and Fractional Fourier Transform
## 3 Numerical Simulations
### Known Plaintext Attack and Chosen Plaintext Attack Experiments  
Furthermore, the known plaintext attack and chosen plaintext attack [23; 24] are also considered to demonstrate the robustness of the proposed algorithm. Firstly, an encryption model is designed to complete the attack experiment as follows:  
\[E(u,v)=\wp^{\alpha}\{I(x,y)\exp[i\cdot\phi_{1}(x,y)]\}\exp[i\cdot\phi_{2}(x^{ \prime},y^{\prime})], \tag{7}\]  
where the phase functions \(\phi_{1}(x,y)\) and \(\phi_{2}(x^{\prime},y^{\prime})\) represent two random phase masks. The output function \(E(u,v)\) denotes the encrypted image by using the proposed cryptosystem. In the attack experiments, the phase retrieval algorithm is used in known plaintext attack while the impulse function is employed in chosen plaintext attack.  
Only the single band encryption process of the intact encryption scheme is tested because of the huge amount of calculations in the attack experiment. Two test images, \"Peppers\" and \"Cameraman\", having 128 \(\times\) 128 pixels, are considered in the plaintext attack experiment. Firstly, two test images and their encrypted versions are shown in Figure 8a-e, respectively. Before the attack operation, assume that the original data \"Peppers\" and its encrypted pattern are obtained by the illegal user. Subsequently, the known plaintext attack and chosen plaintext attack are employed successively for the decrypted data of \"Cameraman\". The attack results are illustrated in Figure 8c-f, which are random patterns.
# Novel Faulted-Section Location Method for Active Distribution Networks of New-Type Power Systems
## 4 Genetic Tabu Algorithm
### Genetic Tabu Algorithm
#### 4.5.2 Genetic Tabu Algorithm Implementation Process  
The flowchart of fault location using the genetic tabu algorithm is shown in Figure 6.  
The implementation process of the genetic tabu algorithm is as follows:  
1. Read the original data such as network node parameters, protection action and equipment status information, and set the population size, and the crossover and mutation rate, and encode according to the designed rules;
2. Generate the initial population according to the greedy algorithm;
3. According to the fitness function, the fitness of individuals in the population is calculated, respectively. Check whether the genetic algorithm termination condition is met. If the condition is not met, go to Step 4; otherwise, go to Step 8;
4. The individuals are cross-calculated according to the crossover rate;
5. Calculate the mutation of individuals according to the mutation rate;
6. Natural selection is carried out to generate new populations;
7. Record \(N\) individuals with the highest fitness to the tabu table according to the contempt criterion, and go back to Step 3;
8. Stop the operation and output the positioning result.
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 5 Evaluation  
The performance of all methods simulated in the experiment are measured in terms of total average of localization error, closeness value, communication cost, computation cost, and energy consumption. Both communication and computation costs depend on the nodes mobility model, where in our case, modified random waypoint has been used to simulate the environment. The modified random waypoint model has been extensively used in WSN and has been proven experimentally by many researchers such as in References [4; 15; 16; 17; 18].  
Other than that, we investigate the maximum number of samples, maximum velocity of each nodes, degree of irregularity, and node density in the performances of IACS, MSL* and LCC. However, it should be noted that the performances of all methods are measured only at the normal nodes without including the seed nodes, which is similar to prior studies conducted in MSL* and LCC.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 5 Experimental Results
### Real-World Outdoor Experiments
#### 5.2.1 Costmap Selection of Landing Sites  
The experimental process (Figure 11) began with a predefined UAV trajectory, and sensors were used to capture point clouds and images. The algorithm integrated area information and autonomously identified potential landing sites from the point cloud data by calculating the costmaps. The experimental results demonstrated that the terrain analysis algorithm developed in this study could successfully identify several potential safe landing sites. However, in certain cases, despite meeting the terrain requirements of the initial experiment, the selected landing site was near areas unsuitable for landing, such as the swimming pool area shown in Figure 11e(iii). This limitation primarily stemmed from using point cloud data alone to evaluate the characteristics of a specific area. Consequently, the region recognition landing approach proposed in this study became necessary.
## 2 Materials and Methods  
### Materials  
Styrene (St), 99% (Duksan pure chemical, Seoul, Republic of Korea) and n-butylacrylate (nBA), 99% (Samchun pure chemical co., Ltd., Gyeongg-do, Republic of Korea) were used as monomers without further purification. Benzoyl peroxide (BP), 97% (Alfa Aesar, Ward Hill, MA, USA) and divinylbenzene (DVB), 80% (Merck, Hohenbrunn, Germany) were used as the initiator and a crosslinking agent, respectively. Polyvinyl alcohol (PVA; 87-89% hydrolyzed, high-molecular-weight; Alfa Aesar, Ward Hill, MA, USA) was used as the stabilizer. Sodium n-dodecyl sulfate (SDS; International Laboratory, South San Francisco, CA, USA) was used as the dispersant (as suspension stabilizer). Ethanol, 99% (Fisher Chemical, Fair Lawn, NJ, USA) was used as the solvent, and distilled water was used as the dispersion medium.  
The carbon nanotubes (CNTs) used in the current work were multi-walled CNTs (MWCNTs) with lengths of 0.5-12 um and an average diameter of 3.0-30 nm. Four types of CNT were investigated: (1) long and (2) short carboxylated, (3) aminated, and (4) boron-incorporated. The CNTs were used as received (purity >90%; Grafen Chemical Industries, Ankara, Turkey), and their properties are listed in Table 1.  
Two types of CCAs were obtained from KMT Co. Ltd., Republic of Korea, and their properties are listed in Table 2. The CCAs were chosen from the group comprising benzyl-tributyl-ammonium 4-hydroxy-naphthalene-1-sulfonate (MEP-51) and aluminum salicate (MEC-88).
## 3 Results and Analysis
### Second Data Preprocessing for Reduction of Elements  
Through the first data preprocessing stage, 16 variables from the initial dataset were reduced to seven important ones. However, because there were up to 13 elements under each variable, numerous elements remained, which made it difficult to interpret the construction accident data and identify trends. Therefore, the second data preprocessing step was performed, to reduce the number of elements while preserving the data characteristics as much as possible; in this step, the elements showing similar trends in the type of work, injured part, and assailing materials variables (which each contained more than ten elements) were standardized and reduced down to 5-6 elements. Among the ML methods, the severity was predicted for eight cases using the SVM and the DT-based ensemble method which were reported to be more suitable for the analysis of construction accident data [8], and this was performed to find the minimum number of elements that maintains the prediction accuracy. In previous studies, the injury types (bruise, ligament injury, fracture, etc.) were included to predict severity [1; 8; 37]. However, since most severity levels are determined based on the injury type, there is a very large correlation between the two. In this study, it was confirmed that the prediction including variable of the injury type has high accuracy and low bias as in previous studies. However, since this study aims to extract and optimize the main variables through a simple method and to confirm the correlation between the variables through the alluvial flow diagram, variables of the injury type that significantly weaken the contribution of other variables were excluded. Therefore, because the variable having the greatest relationship with the severity is excluded, the predictive performance may be lower compared to previous studies. Table 4 shows the severity prediction results for eight cases, obtained using ensemble- and SVM-based methods. The nested cross validation (CV) was applied for the purpose of minimizing the bias of the prediction result due to overfitting in training and verification of ML. The SVM predicted severity with high accuracy prior to the nested CV, though it showed low accuracy afterward. These results are thought to be a more generalized result by the nested CV.