esa-sceva/satcom-chunk-collection huggingface dataset:
# The Construction of a Digital Agricultural GIS Application Suite
###### Abstract  
With the increasing expansion and deepening of GIS applications across diverse industries, the limitations of industry-specific GIS application systems in terms of development efficiency, flexibility, and customization have become increasingly apparent. This paper employes the concept of application suites and proposes a design approach for tailored GIS application suites in digital agriculture, considering its specific application requirements. Additionally, it outlines an implementation method based on low-code development and microservice technologies. A GIS application system for digital agriculture was developed to conduct experimental validation. The results indicate that the GIS application suite developed in this study demonstrates readily deployable characteristics, granular assembly capabilities, and ease of scalability, facilitating the rapid development of customized GIS applications for digital agriculture. This approach enhances both development efficiency and flexibility while meeting the customization needs inherent to such applications.  
digital agriculture; application suite; GIS; web components; custom development +
Footnote †: journal: applied sciences
# Numerically Optimized Fourier Transform-Based Beamforming Accelerated by Neural Networks
## 3 Algorithms for Phase Distribution Optimization
### Evaluating Optimization Methods  
In this section, we discuss how optimization algorithms could be implemented in order to decrease the sidelobe levels in a beamforming system equipped with the reflecting PMS introduced previously. For this purpose, several optimization algorithms have been employed to conduct simulations and control the phase distribution over the MS to diminish the sidelobe levels. By decreasing sidelobe levels, the gain of the main beams should increase due to the law of energy conservation. Optimization algorithms such as Nelder-Mead, PSO, as well as other methods to a find a minimum of (possibly constrained) nonlinear multivariable function (e.g., patternsearch, fmincon from MATLAB) have been tested.  
We have discovered that straightforward algorithms such as Nelder-Mead could not converge to a stable value of the cost function, and the generated results oscillated around the initial values. In contrast, the PSO algorithm produced better results when starting optimization process with the initial phase distribution obtained by the Fourier transform method. We observed that in most cases this algorithm gradually converges to a stable minimal value of the cost function.  
We have observed that optimization using a pattern search algorithm patternsearch also converged, but rather slowly. Finally, an optimization method that searches for a minimum of a constrained nonlinear multivariable function, fmincon, was selected as producing the most reliable results. By default, this optimization method uses an interior-point algorithm to reduce the cost function. The employed algorithm converges to the final result faster than other tested algorithms when starting from the initial phase distributions obtained from the Fourier transform. Moreover, this method has the ability to be combined with other optimization algorithms to obtain even faster convergence. Some results illustrating the convergence rate and the achievable minimal cost function value are presented in Figure 7.
###### Abstract  
This paper presents a supervised reinforcement learning (SRL)-based framework for longitudinal vehicle dynamics control of cooperative adaptive cruise control (CACC) system. A supervisor network trained by real driving data is incorporated into the actor-critic reinforcement learning approach. In the SRL training process, the actor and critic network are updated under the guidance of the supervisor and the gain scheduler. As a result, the training success rate is improved, and the driver characteristics can be learned by the actor to achieve a human-like CACC controller. The SRL-based control policy is compared with a linear controller in typical driving situations through simulation, and the control policies trained by drivers with different driving styles are compared using a real driving cycle. Furthermore, the proposed control strategy is demonstrated by a real vehicle-following experiment with different time headways. The simulation and experimental results not only validate the effectiveness and adaptability of the SRL-based CACC system, but also show that it can provide natural following performance like human driving.  
Keywords:cooperative adaptive cruise control; driving behavior; longitudinal dynamics control; supervised reinforcement learning; vehicle following control +
Footnote †: journal: Applied
# Optimization of Design Parameters Using SQP for an Agricultural Pipe Extraction Device
## 2 Materials and Methods
### Optimization Model Construction  
Table 2 shows the initial values of the design variables and ranges of each parameter set, reflecting ergonomic factors. The objective function for design variable optimization is expressed as Equation (7) by substituting Equations (1) and (2). In order to compare the size of the operating force for any \(P\), the ratio of the vertical force (\(P\)) and the operating force (\(F_{r}\)) was expressed as \(R\). This is a non-dimensional and is represented by Equation (8).  
Equation (9) limits the maximum value of the width of the equipment as a constraint, and Equation (10) represents the range of the overall height of the device. Equation (11) is a constraint on the height of the scaffolding of the device defined in the constraints section.  
Find \(\alpha,L_{1},L_{2},L_{3},\) and \(k\)  
To minimize  
\[F_{r}(\alpha,L_{1},L_{2},L_{3},k)=\frac{P\cdot L_{2}\cdot\sin(\alpha+\cos^{-1} \left(\frac{L_{2}\cos a-k}{L_{1}}\right))}{(L_{2}+L_{3})\sin(\cos^{-1}\left( \frac{L_{2}\cos a-k}{L_{1}}\right))-L_{2}\cdot\sin(\alpha+\cos^{-1}\left(\frac {L_{2}\cos a-k}{L_{1}}\right))\cdot\cos\alpha} \tag{7}\]  
Subject to  
\[R=\frac{F_{r}}{P} \tag{8}\]  
\[(L_{2}+L_{3})\cdot\cos\alpha\leq 300 \tag{9}\]  
\[L_{1}\cdot\sin(\cos^{-1}(\frac{L_{2}\cos\alpha-k}{L_{1}}))+L_{2}\cdot\sin \alpha+k\leq 300 \tag{10}\]  
\[150\leq L_{1}\cdot\sin(\cos^{-1}(\frac{L_{2}\cos\alpha-k}{L_{1}}))-L_{3}\cdot \sin\alpha\leq 200 \tag{11}\]  
The optimization process of this study is briefly shown in Figure 7. The initial design variables were determined in the model before optimization, and the objective function was derived based on the statics theory. In order to increase user convenience, the overall length and height of the footrest were set as the constraints. After setting the objective function, constraints, and initial design variables, the optimization process of the SQP algorithm was performed using the FMINCON module in MATLAB (Version R2022a) [16].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Design Variable** & **min** & **max** & **Initial** \\ \hline Angle between the extension line of Link3 and Link2 (\(\alpha\)) [\({}^{\circ}\)] & 0 & 60 & 21.37 \\ \hline Length of Link1 (\(L_{1}\)) [mm] & 30 & 500 & 218.19 \\ \hline Length of Link2 from Point B to Point C (\(L_{2}\)) [mm] & 30 & 500 & 119.39 \\ \hline Length of Link2 from Point B to Point D \(L_{3}\) [mm] & 30 & 500 & 203.21 \\ \hline Horizontal distance between Points A and C (\(k\)) [mm] & 0 & 50 & 27.84 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial values and range of the design variables.
# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
## 4 Conclusions  
This study proposed a two-phase pansharpening technique to improve the spatial resolution of Worldview-3 satellite images with various spatial and spectral resolutions. Specifically, when applying the panchromatic images was difficult, selected and synthesized band schemes were used to produce optimal panchromatic images; then, sharpened images were generated by applying the GSA and GS2 techniques, which rely on CS- and MRA-based algorithms, respectively. The sharpening results were compared with the results of existing pansharpening techniques. Through both quantitative and qualitative evaluation, among the proposed techniques of this study, and the sharpening performance using the selected band scheme and GS2 was found to be the best. This study shows that sharpened images can be generated that minimize the spatial and spectral distortion of satellite images containing various spatial and spectral resolutions. Thus, the proposed techniques are expected to be useful in land cover mapping, change detection, and monitoring with satellite images that contain various spatial and spectral resolutions. Furthermore, sharpened images can be produced utilizing the proposed techniques with other satellite images that are similar to those of Worldview-3.  
[PERSON] and [PERSON] designed the proposed algorithm, implemented the experiments, and wrote the manuscript. [PERSON] and [PERSON] supported the experiments. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Nuclear Safety Research Program through the Korea Foundation Of Nuclear Safety (KoFONS) using the financial resource granted by the Nuclear Safety and Security Commission (NSSC) of the Republic of Korea (No. 1905009).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Site 1**} & \multicolumn{2}{c}{**Site 2**} \\ \cline{2-5}  & **8 Bands** & **16 Bands** & **8 Bands** & **16 Bands** \\ \hline
**Blue Roof** & 100\% & 100 & 99.90\% & 100\% \\ \hline
**Green Roof** & 84.21\% & 93.49 & 99.85\% & 90.73\% \\ \hline
**Black Roof** & 96.35\% & 96.59 & 87.67\% & 96.88\% \\ \hline
**Road** & 77.05\% & 95.72 & 92.96\% & 92.57\% \\ \hline
**Vegetation** & 100\% & 100 & 82.07\% & 85.83\% \\ \hline
**Bare Soil** & 93.11\% & 93.08 & 99.01\% & 96.66\% \\ \hline
**Water Bodies** & 98.59\% & 94.75 & 99.17\% & 98.37\% \\ \hline
**Cultivation Facility** & 67.53\% & 89.26 & 64.08\% & 76.42\% \\ \hline
**Overall Accuracy** & 88.56\% & 95.29 & 88.89\% & 91.73\% \\ \hline
**Kappa Accuracy** & 0.8692 & 0.9461 & 0.8729 & 0.9055 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the quantitative accuracy evaluation.
## 4 Computation Unit Design for the Decoder  
### System Architecture  
The architecture of our FPGA-based LDPC decoder is presented in Figure 5. It consists of several components, including CNU, VNU, shift-register-based memory, overlap controller, and hard decision module. The system utilizes a partial parallel architecture with a degree of parallelism of \(2\times 57=114\) for the CNU and \(16\times 57=912\) for the VNU.  
The received soft message is quantified and truncated to 2 bits, and then stored in the shift-register based memory, which will be discussed in detail in Section 5.1. With the help of Modified 2-bit MSA, the memory efficiency is improved, and up to two thirds of memory resources can be saved.  
After CNU operation, a c2v information consisting of 3 bits is generated. These 3 bits are the product of all symbol bits calculated by the XOR tree, as well as the minimum and the subminimum value of all amplitude bits. It is worth noting that in our decoder, the computation of the minimum and the subminimum values is achieved through logical operations, rather than comparators, which leads to a smaller computational delay. Furthermore, since all gate circuits are dual-ported, we further simplify the logical expression, thereby simplifying the circuit and reducing resource overhead. A detailed explanation of CNU will be provided in Section 4.2.  
Once enough rows have been iterated, the overlap controller sends the synchronization signal to VNU, and the v2c result is derived after the conversion and addition. The specific operation in VNU will be introduced in Section 4.3. Both c2v and v2c messages are stored in the shift-register-based memory as well.  
Hard decisions begin when the number of iterations reaches the preset value. Using the proposed QC-LDPC matrix optimized for overlap, the overlap controller allows VNU to operate when three groups of CNU have been updated, like Figure 6 shows, and satisfied the data dependency at the same time, which accelerates decoding and triples the throughput.
# Multilevel Hierarchical Bayesian Modeling of Cross-National Factors in Vehicle Sales
## 4 Discussion and Conclusions  
### Discussion  
The WAIC (Watanabe-Akaike Information Criterion) is a model criterion based on the log-likelihood function. Its estimation process consists of employing the ELPD (Expected Log Predictive Density) for a new data point calculation and then applying a correction for over-fitting [7]. A ranking plot shows the ELPD values for each model. The model with the best predictive precision is believed to have the highest ELPD value.  
In Figure 20, we noticed that, during graph generation, the models were sorted. The better-ranked ones (with a higher ELPD) were higher. The WAIC plot only confirms the effectiveness of the fourth model. It has the highest ELPD, indicating that it has the best predictive accuracy. The remaining models took comparable values. Besides the points themselves, the plot also contains error bars and ELPD difference symbols.  
The second criterion under our consideration was LOO-CV (Leave-One-Out Cross-Validation). It is based on the mean of predicted errors across all points in the dataset. For a single data point, this error was calculated as follows (iteratively for each dataset point) [7]:  
1. The point is skipped;
2. The model is fitted again, without the omitted point, to the rest of the data;
3. The predicted error is calculated for the missing point.  
According to Figure 21, due to the highest ELPD, the fourth model has the best predictive accuracy again. The second and third models have fairly close ELPD values. The remaining model with number one has significantly low efficiency. The error bars indicate the range within which the ELPD difference is likely to fall, reflecting the uncertainty in this comparison. The consistency in ranking between both criteria can further affirm a model's performance.  
The results obtained for the fourth model prove the effectiveness of using a unique alpha distribution for each of the samples. It allows for more flexibility by introducing more parameters and, therefore, more probability distributions. Originally, we also wanted to conduct a similar procedure only for the beta distribution based on the second model, but due to the problematic learning of the model, we abandoned this approach.  
Comparing our results with other existing models in the literature is challenging due to the absence of models that cover the same factors analysed in this article. This unique approach distinguishes our work and highlights the novelty of our research. Our study was intended to fill this gap and pave the way for future development.  
Figure 20: Model comparison using WAIC.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 1 Introduction
### Context  
The research presented in this paper stems from the ASTRI Mini-Array project. The ASTRI (_Astrofisica con Specchi a Tecnologia Replicante Italiana_) [3] Mini-Array is an international project led by the Italian National Institute for Astrophysics (INAF) to build and operate an array of nine 4 m class IACTs at the Observatorio del Teide (Tenerife, Spain). The project's scientific agenda focuses on investigating the origin of cosmic rays, the extragalactic background light (EBL) and other fundamental physics topics, and time-domain and multi-messenger astrophysics at the TeV and multi-TeV energy scales. An equally significant aspect of the research encompasses the examination of gamma-ray bursts and multi-messenger transients in the very high-energy (VHE) domain. Additionally, the ASTRI Mini-Array will conduct stellar intensity interferometry studies.  
The ASTRI Mini-Array is designed to be sensitive to energies in the 1-200 TeV energy range, and to achieve an angular resolution of \(\sim\)3', and an energy resolution of \(\sim\)10% above about 10 TeV.  
Astrophysical observations in this energy range can offer valuable insights into high-energy particle sources, such as the so-called PeVatrons recently seen by LHAASO up to 1.4 PeV [4].  
This performance would enable the extraction of crucial morphological and spectral data, which is essential for distinguishing between various proposed acceleration mechanisms. For further information on the ASTRI Mini-Array Core Science program, please refer to the dedicated paper by the ASTRI collaboration [5].
# Sulfuric Acid Resistance of CNT-Cementitious Composites
## 2 Experimental Programs  
### Materials and Mixture Proportions  
Ordinary Portland cement (OPC, Type I KS L 5201 [52]), powder-type single-walled (SW) and multi-walled (MW) CNTs, and standard sand (KS L ISO 679 [53]) were employed to fabricate CNT cementitious composites. Tables 1 and 2 show the chemical and physical properties of OPC and physical properties of CNTs, respectively. Figure 1 shows powder-type SWCNT and MWCNT, and their schematics. The particle size distribution of standard sand is summarized in Table 3. The mixture proportions of CNT mortar are provided in Table 4 in detail. The amount of CNT in cementitious composites was determined based on previous studies ([PERSON] et al., 2020 [37]; [PERSON] et al., 2020 [38]). The poly carboxylate-based high-performance water reducing agent (KS F 2560 [54], Chemical Admixtures) was added in cementitious composites, as summarized in Table 1. To improve workability and uniform dispersion of SWCNT in the composites, the mixing time was increased by an additional 30 s. The specimens were fabricated, and tests were conducted in accordance with KS L ISO 679 [53].  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{3}{c}{**Chemical Properties (\%)**} & \multicolumn{3}{c}{**Physical Properties**} \\
**SiO2** & **Al2O3** & **Fe2O3** & **CaO** & **MgO** & **Density(g/cm\({}^{3}\))** & **Fineness(cm\({}^{2}\)/g)** \\ \hline
22.23 & 5.21 & 3.38 & 64.58 & 2.3 & 3.15 & 3300 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical and physical properties of ordinary Portland cement (OPC).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**W/C**} & \multicolumn{3}{c}{**Weight (kg/m3)**} & \multicolumn{3}{c}{**Admixture (\%)**} \\  & & **Cement** & **Water** & **Sand * & **CNT** & **MWCNT** & **SWCNT** \\ \hline CNT 0 & & & & 1530 & 0 & 0 & 0 \\ CNT 0.5 & & & 1530 & 2.55 & 0 & 2 \\ CNT 1.0 & & & 1530 & 5.10 & 2 & 6 \\ CNT 2.0 & & & 1530 & 10.2 & 4 & 14 \\ \hline \hline \end{tabular}  
* Standard sand (KS L ISO 679 [53]).  
\end{table}
Table 4: Mixture proportion of cement mortar.  
Figure 1: Picture of powder-type carbon nanotubes: (**a**) single-walled carbon nanotubes (SWCNTs); (**b**) multi-walled carbon nanotubes (MWCNTs); (**c**) schematics of SWCNTs; (**d**) Schematics of MWCNTs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **MWCNT** & **SWCNT** \\ \hline Diameter (nm) & 5\(\sim\)100 & 1.2–3.0 \\ Length (\(\mu\)m) & 10 & 10 \\ Tension (GPa) & \(<\)50 & \(\sim\)45 \\ Electrical resistance (\(\Omega\cdot\)m\({}^{2}\)) & 5.1 \(\times\) 10\({}^{-6}\) & 10 \(\times\) 10\({}^{-4}\) \\ Thermal conductivity (W/m\(\cdot\)K) & Max. 3000 & Max. 6000 \\ Specific surface area (m\({}^{2}\)/g) & 130–160 & 700–900 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical properties of carbon nanotubes (CNTs).
# Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach
## 5 Experiments  
Various scenarios were verified to determine the success rate of the trained detection model under different conditions. The model was tested on drone images and videos as well as various backgrounds. The testing scenarios were as follows:  
* drone in a static image;  
Figure 16: Progress of the model’s training concerning the overall aspects of reliable and accurate object detection.  
Figure 15: Progress of the training in drone detection using TensorFlow.  
* drone in the sky;
* multiple drones in one image;
* drone with another flying object in the sky.
# Hyperspectral Imaging for Minced Meat Classification Using Nonlinear Deep Features
## 2 Material and Methods
### Hyperspectral Imaging System (HSI)  
The hyperspectral camera used in this study is Specim FX-10. A push-broom (line scanning) system, which is considered as a standard way of collecting lab-oriented spatial-spectral information. HSI camera is equipped to capture an electromagnetic spectrum of visible and near-infrared range (400 nm \(\times\) 1000 nm). The system includes a conveyor belt with a translational try of 21 \(\times\) 40 cm, which moves with the speed of 25 mm/s at an exposure time of 16 ms and frame rate of 60 Hz. The sensor is also coupled with a lens of 1.4/8 mm and three halogen lamps of 75 Watts for each light. The system was also connected to a laptop via serial port to generate three raw extension files containing information of minced meat, (ceramic) white reference tile, and a dark reference cube.  
Figure 1: The entire process of sample preparation; (**a**) represents chuck meat; and (**b**) represents the minced meat.
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
## 2 Damage Identification Method Based on Additional Virtual Mass and Damage Sparsity
### Damage Identification Method Based on Sparsity
#### 2.2.2 OMP Method  
When the constraint term is the \(l_{0}\) norm, it represents the number of nonzero elements of \(\Delta\mathbf{\mu}\). The traditional greedy iteration-OMP method is used to solve this function. The advantages are that it does not need to estimate the regularization coefficient \(\lambda\) value, and it can approach the real sparse solution of the original model satisfactorily.  
The OMP method identifies damaged substructures by picking the sensitivity column vector having the most significant correlation with the frequency residual. The structure is divided into \(n\) substructures. The \(s\)th iteration is used as an example; \(\mathbf{C}_{s-1}\) is the matrix composed of the sensitivity column vectors filtered out in the previous \(s-1\) step, \(\mathbf{C}_{s-1}^{+}\) is its pseudo-inverse matrix, and the frequency residual is expressed as \(\mathbf{\varepsilon}_{s}=\Delta\mathbf{\omega}-\mathbf{C}_{s-1}\big{(}\mathbf{C}_{s-1}^{+} \Delta\mathbf{\omega}\big{)}\).  
By calculating the correlation coefficient of \(\mathbf{\varepsilon}_{s}\) with each column vector of the remaining sensitivity matrix \(\widetilde{\mathbf{R}}_{s-1}=\Big{[}\mathbf{r}_{1},\dots\dots\mathbf{r}_{n-(s-1)}\Big{]}\), the sensitivity column vector \(\mathbf{r}_{j}\) corresponding to the largest correlation coefficient \(A_{j}\) is filtered out:  
\[A_{i}=\frac{\mathbf{\varepsilon}_{s}^{\intercal}\mathbf{r}_{i}}{\left\|\mathbf{r}_{i} \right\|_{2}} \tag{13}\]  
where \(\mathbf{r}_{i}\) is the \(i\)th column vector of \(\widetilde{\mathbf{R}}_{s-1}\). The \(s\)th iteration-chosen sensitivity matrix \(\mathbf{C}_{s}\) and the remaining matrix \(\widetilde{\mathbf{R}}_{s}\) are expressed as follows:  
\[\begin{split}\mathbf{C}_{s}=\mathbf{C}_{s-1}\cup\mathbf{r}_{j}\\ \widetilde{\mathbf{R}}_{s}=\Big{[}\mathbf{r}_{1},\dots,\mathbf{r}_{j-1},\bm {r}_{j+1},\dots,\mathbf{r}_{n-(s-1)}\Big{]}\end{split} \tag{14}\]  
The sparsity \(K\) of the damage-factor variation \(\Delta\mathbf{\mu}\) is estimated through experience to determine iteration steps of this algorithm, and the damage-factor variation with \(n\)-\(K\) nonzero elements \(\Delta\widetilde{\mathbf{\mu}}=\mathbf{C}_{K}^{+}\Delta\mathbf{\omega}\) is determined.