esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of PID-Based Algorithms for UGVs
## 4 Experimental Results
### PID Algorithm's Control Performance
During travel, the VFC is also efficient, completing the task in 82.46 s, which is the shortest among all algorithms considered. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **ITAE** & **IAE** & **ISE** & **Mean [m]** & **Standard Deviation [m]** & **Max [m]** & **Navigation Time [s]** \\ \hline ON-OFF & 2815.625 & 118.19 & 46.844 & 0.33 & 0.19 & 0.89 & 103.60 \\ Heading & 1695.307 & 138.11 & 44.1836 & 0.21 & 0.18 & 0.85 & 88.90 \\ CTE & 1541.619 & 131.13 & 46.9071 & 0.18 & 0.14 & 0.65 & 86.45 \\ CTE + H & 1257.305 & 95.303 & 19.6773 & 0.21 & 0.19 & 0.74 & 93.60 \\ Vector Field & 520.9629 & 66.989 & 11.8488 & 0.18 & 0.17 & 0.66 & 82.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation criteria applied to each controller during the experiments. Figure 17: Final result of the trajectory of each PID control algorithm (separately). Figure 18 illustrates the UGV navigating the \(8\times 8\) square path in the actual field where the PID controller algorithms were previously tuned. The figure comprises four distinct images, each capturing the robot positioned at one of the square's corners. These results indicate that the VFC algorithm performs particularly well for applications that require high accuracy in trajectory execution and time efficiency. The potential reason for this superior performance is the ability to continuously optimize control vectors, allowing the UGV (unmanned ground vehicle) to better adapt to planned changes in direction so that it meets and resolves sliding obstructions quickly and accurately. In contrast, the ON-OFF and other algorithms exhibit the highest cumulative error (ITAE = 2815.625, IAE = 118.19, and ISE = 46.844) and the largest position error (Max [m]). = 0.89). This indicates a very basic and inefficient method of control, meaning that they are highly unsuitable for applications requiring more optimized trajectories and higher accuracy. To complement the validity of the robustness of the obtained values, the Pareto-optimal front can be observed in Figures 19-21. These graphs demonstrate how improving one objective impacts others. In our case, the ideal focus is on minimizing each objective, highlighting that the Vector Field controller emerges as the optimal choice, dominating all other controllers. Figure 18: Representation of all paths taken on the terrain during the execution of the \(8\times 8\) m squares: (**a**) initial position; (**b**) first coordinate; (**c**) second coordinate; (**d**) third coordinate.
## 3 Results and Discussion
### Outdoor Experimental Results
#### 3.2.2 Irrigation System Performance  
Under field conditions, irrigation scheduling is commonly conducted based on three main groups: the soil-water balance method, which incorporates environmental parameters, soil water content or potential, and the method that monitors plant water status [8; 24]. In this study, we carried out irrigation based on the soil-water balance equation. With this soil-water balance, an IoT system was enabled for the management of irrigation systems for monitoring throughout the growing period. In particular, the long-range controlling technology LoRaWAN was used to monitor and control precision irrigation systems. Throughout the tomato crop growing season, irrigation was carried out using The Things Network (TTN) platform without any problems. Additionally, this system needed a lower power requirement because a sleep period was set just after the completion of irrigation to minimize power consumption. Further benefits can be achieved by adding a soil moisture sensor in the outdoor environment and a water level sensor that can help the grower to know the amount of water they apply remotely. Overall, the surface drip irrigation system had better performance in terms of distribution uniformity, which was close to 0.88 (Table 2). The subsurface drip irrigation system had poor results due to clogging of the drippers or emitter. However, further field trials is required to confirm the variability of water distribution in the large plots for evaluating irrigation performances.
## 2 Materials and Methods
### Construction and Evaluation of Calibration Models  
Spectral data preprocessing is an important step to address the interference factors such as noise, over-lapping peaks, and baselines affecting the spectra in establishing accurate quantitative analysis models. Firstly, spectral preprocessing was performed to eliminate noise from the raw spectra where the modelled performance was disturbing. Simultaneously, the Kennard-Stone method (sample-set division based on calculation of distances in the x-vector direction) was employed to separate the preprocessed spectral data into a calibration set (70%) and a prediction set (30%) (Table 1). Additionally, variable selection methods helped to fully utilize relevant variables that contributed to predictive performance. Therefore, characteristic intervals were selected using siPLS in the spectral range of 0-2000 cm\({}^{-1}\), and combinations of 2-4 intervals were used as input variables to construct all possible PLS quantitative analysis models. Finally, the lowest RMSECV value obtained using the PLS model was selected as the feature interval. The optimal number of latent variables (LVs) was determined by the results of the best cross-validation (CV). The determination of the accuracy of the prediction results was dependent on the agreement between the PLS model predictions and the reference values. Multiple metrics were used to evaluate the predictive performance of the model, including mean relative error of prediction (MREP), determination coefficient of prediction (R\({}^{2}\)p), root-mean-squared error of prediction (RMSEP), and residual predictive deviation (RPD). Leave-one-out cross-validation (LOO-CV) was employed to assess the stability of the models. All calculations were implemented using MATLAB (2016b). The modeling process is illustrated in Figure 1.
## 3 Results
### Parameter Identification
#### 3.2.3 Reinforced Steel Class  
The steel class is a discrete parameter that affects only amplitude and offset. Table 10 presents the basic association rules.  
Table 10 illustrates that changes in rebar class do not influence the shape attributes for either method. The weak association rules obtained for the By component indicate that \(B_{\text{y}}\) attributes should be excluded from the model. Figures 25 and 26 illustrate example results obtained for samples with varying rebar classes.  
Figures 25 and 26 corroborate the findings presented in Table 6. The identification model for the EC method solely incorporates the A feature. In contrast, the optimal attribute set for the MFL method includes both the aggregated amplitude and offset, while features derived from \(B_{\text{y}}\) are excluded.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{Feature} & \multicolumn{4}{c}{**EC—magnitude**} & \multicolumn{4}{c}{**MFL—\(B_{\text{X}}\) waveform**} \\ \cline{2-9}  & \(A\) & \(d_{x}\) & \(d_{s}\) & \(A\) & \(O\) & \(d_{x}\) & \(d_{s}\) \\ \hline Change & \(\downarrow\) & - & - & \(\uparrow\) & \(\uparrow\) & - & - \\ \hline Confidence (\%) & 69 & 83 & 90 & 94 & 92 & 86 & 89 \\ \hline Support (\%) & 17 & 17 & 17 & 29 & 29 & 29 & 29 \\ \hline \multirow{2}{*}{Feature} & \multicolumn{4}{c}{**MFL—\(B_{\text{Y}}\) waveform**} & \multicolumn{4}{c}{**MFL—\(B_{\text{Z}}\) waveform**} \\ \cline{2-9}  & \(A\) & \(O\) & \(d_{x}\) & \(d_{s}\) & \(A\) & \(O\) & \(d_{x}\) & \(d_{s}\) \\ \hline Change & \(\downarrow\) & \(\downarrow\) & - & - & \(\uparrow\) & \(\uparrow\) & - & - \\ \hline Confidence (\%) & 52 & 57 & 78 & 87 & 99 & 100 & 85 & 91 \\ \hline Support (\%) & 29 & 29 & 29 & 29 & 29 & 29 & 29 & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Basic association rules for assessing changes in steel class (class \(\uparrow\)).  
Figure 25: EC—waveforms obtained for different rebar classes; (**a**) raw data, (**b**) normalized data.  
The results of steel class identification are presented in Figure 27.  
The investigation into steel class confirmed that the magnetic MFL method performs better across a wide measurement range. However, the eddy current method slightly outperforms MFL when the transducer is within its optimal range. Moreover, changes in rebar class affect the EC and MFL methods differently, with stronger effects observed in the EC method.
# Utilizing Multilevel Modeling to Measure Neighborhood Dynamics and Their Impact on House Prices
## 3 Methods and Results
### Multilevel Random Effects Model  
Multilevel modeling is proposed for this research as it can model micro-relations (at house level) and macro-relations (at neighborhood level), simultaneously. As described previously, we use a three level hierarchical model, which can be written as follows:  
_Level 1:_  
\[\begin{array}{ll}\text{y\_salesamount}=&\text{f}_{0}+\text{f}_{1}(\text{houseage })+\text{f}_{2}(\text{tla})+\text{f}_{3}(\text{basebsmt})+\text{f}_{4}(\text{ garagesqft})\\ &+\text{f}_{5}(\text{bedroom2})+\text{f}_{6}(\text{fullbath})+\text{f}_{7}( \text{lotsize})+\text{f}_{8}(\text{cond})\\ &+\text{f}_{9}(\text{traffic})+\text{f}_{10}(\text{year2013})+\text{f}_{11}( \text{year2014})\\ &+\text{f}_{12}(\text{year2015})+\text{f}_{13}(\text{year2016})+\text{f}_{14}( \text{census\_var})+\varepsilon\end{array}\]  
Figure 4: Distribution patterns of variables: poverty, race heterogeneity, foreclosure score, and walk score.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Minimum** & **Maximum** & **Mean** & **Std. Deviation** \\ \hline bachelor & 0 & 0.800 & 0.27 & 0.180 \\ unemployme & 1.2 & 43.7 & 9.55 & 6.696 \\ fc\_score & 0.855 & 14.8728 & 7.49 & 3.289 \\ walkscore & 0 & 91 & 41.61 & 17.731 \\ rural & 0 & 1 & 0.14 & 0.348 \\ perform & 51.4 & 92.6 & 70.02 & 11.74 \\ per\_pail & 7187 & 14,234 & 9536.50 & 758.892 \\ p\_expend\_c & 61.07 & 76.6 & 67.2284 & 2.281 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont.__Level 2:_  
\[\begin{array}{ll}\beta_{14}=\mathrm{f}_{14,0}+\mathrm{f}_{14,1}&(\mathrm{ poverty})+\mathrm{f}_{14,1}(\mathrm{raceheter})+\mathrm{f}_{14,3}(\mathrm{bachelor})\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad+ \mathrm{f}_{14,4}(\mathrm{unemployme})+\mathrm{f}_{14,5}(\mathrm{fc\_score})+ \mathrm{f}_{14,6}(\mathrm{walkscore2})\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad+ \mathrm{f}_{14,7}(\mathrm{walkscore3})+\mathrm{f}_{14,8}(\mathrm{walkscore4})+ \mathrm{f}_{14,9}(\mathrm{walkscore5})\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad+ \mathrm{f}_{14,10}(\mathrm{school\_dist})+\mathrm{\varepsilon}_{14}\end{array}\]  
_Level 3:_  
\[\begin{array}{ll}\beta_{14,10}=\mathrm{f}_{14,10,0}+&\mathrm{f}_{14,10,1}( \mathrm{perform})+\mathrm{f}_{14,10,2}(\mathrm{per\_pupil1})\\ &\qquad\qquad\
## 5 The Proposed AFB-GPSR Strategy
### Fuzzy Logic Composition  
In an adaptive beaconing strategy, a fuzzy logic system (FLS) is used to control and predict the new beacon interval time, i.e., (Optimal BIT) by enforcing a set of rules on three multiple variables (NS, OHND and LR) as per the concept presented in Figure 5. The model is written in C++ combined with OMNeT++ to compute the imprecise data of the three input parameters.  
Figure 3: Routing table information in AFB-GPSR protocol.  
Figure 4: The flowchart of the proposed AFB-GPSR mechanism.  
The fuzzy input parameters are described as follows:  
* Node speed (NS): The mobility of the nodes causes the link to become unstable over time, with the increased mobility speed leading to an increase in link failure. This entails more traceability of the neighbor nodes [29; 57].
* One-hop neighbors' density (OHND): The density of neighbors is determined by setting a counter for one-hop nodes that travel in the same direction as the source node, which is calculated as per (3). And the difference between the current source node angle and the neighbor angle is less than 45 degrees to be considered in the counter, where the neighbor recognized as per (3), \[\text{NN}=(|\text{Sa}-\text{Na}|\text{+}180/\pi)<45\] (3) where NN is defined as the neighbor node Sa is source angle, and Na is Neighbor angle.
* Link reliability (LR): Link reliability has a significant impact on the network's performance in terms of the link quality metric ETX [41; 58]. The establishment of communication links between nodes is influenced by changes in the network topology, and these communication links may fail due to link failures. Hence, link reliability is described as per Equation (4). \[\text{Link Reliability}=\text{Total Received Beacons}/\text{Total Sent Beacons}\] (4)
## 4 Discussion  
### Abandoned Farmland Data Extraction  
The high spatial resolution of GF-6 images can effectively improve the accuracy of the results, but it is difficult to construct a continuous and widely covered time series dataset due to the low temporal resolution and the influence of weather. To address this problem, this study fused GF-6 and Landsat 8 data based on STARFM to construct the NDVI time series dataset and then extracted abandoned farmland data based on the differences in the NDVI cycle changes between natural vegetation and planted crops. The overall accuracy and Kappa coefficient of the extraction results were significantly improved compared with those obtained by using GF-6 data or Landsat 8 data alone. In addition, for the time series analysis based on the SVM, the overall accuracy and Kappa coefficient using the fused dataset were also improved, which proved that the fused dataset could, indeed, improve the accuracy of the abandoned cropland data extraction results. The overall accuracy and Kappa coefficient of the extraction results of this study are also higher than those obtained from the SVM-based analysis based on the fused dataset. Using the SVM for analysis also enables the extraction of abandoned farmland data, but it is easier to misidentify cultivated land as abandoned land, resulting in the expansion of the scope of the abandoned land. Compared with the results obtained by studies using SVM, the results of this study are able to better distinguish between abandoned land and cultivated land, with higher precision and consistency.
# A Quantum-Secure Cryptographic Algorithm Integrating Fractals and Prime Numbers
## 2 Construction of the Fractal Sub-Key  
The algorithm randomly chooses between six different fractal sets listed below:  
1. A Cantor set;
2. A Sierpinski set;
3. A Mandelbrot set;
4. A Peano set;
5. A Barnsley set;
6. A Vicsek set.  
The first four fractals were already used in the previous work [1], while the last two are new additions. The addition of new fractals compared to the previous version aims to increase the randomization of the fractal component of the algorithm; moreover, while the choice of the fractal to use was human-driven in the previous solution, in this solution, the choice is made in a random and rolling mode.  
In five of the six fractals listed above, the procedure for creating the number is using Iterated Function Systems (IFSs). An IFS fractal is a fractal generated by iterating a certain number of affine transformations. As is well known, an affine transformation in a plane is a biunivocal application that takes a point P of coordinates (x,y) and returns a point P\({}^{\prime}\) of coordinates (X,Y) according to the following relation:  
\[\begin{cases}X=ax+by+e\\ Y=cx+dy+f\end{cases}\]  
In an equivalent way, in the matrix representation, we can write  
\[\begin{pmatrix}x\\ y\end{pmatrix}=\begin{pmatrix}a&b\\ c&d\end{pmatrix}\begin{pmatrix}x\\ y\end{pmatrix}+\begin{pmatrix}e\\ f\end{pmatrix}\]  
where  
\[det\begin{pmatrix}a&b\\ c&a\end{pmatrix}\
eq 0\]  
An affine transformation changes the position of a point in the plane. In this way, we can, for example, start from a geometrical figure and apply an affine transformation to every vertex of the figure, thus obtaining a translation, rotation, or distortion of the shape. In the case of the Mandelbrot set, we use a specific formula to generate points in the set. Let us show how the sub-key is created for every fractal.
# Object/Scene Recognition Based on a Directional Pixel Voting Descriptor
## 2 Related Works
### State of the Art for Object Recognition
The proposed method was validated by classifying 18 real objects with data from an off-the-shelf tactile sensor, achieving a recognition rate of 91.33% with a dictionary size of 50 clusters using only 15 touches. Additionally, in [28], the authors have compared the performance of the Shi-Tomasi corner detector with SIFT and SURF feature descriptors and evaluated the performance of Shi-Tomasi in combination with SIFT and SURF feature descriptors. In both cases, relatively high accuracy in terms of detection was achieved, motivating us to continue this trend with an edge-based approach which increases the robustness of traditional approaches (SIFT/SURF/ORB). Finally, hybrid approaches that combine deep learning with traditional feature-based methods have gained traction, aiming to maintain the strengths of both paradigms. For instance, deep learning models can be augmented with features extracted using SIFT or SURF, providing complementary information and improving overall recognition accuracy. For example, in [29], a technique was proposed using deep convolutional neural network (DCNN) and scale invariant features transform (SIFT). First, an improved saliency method is implemented, and the features points are extracted. Then, DCNN features are extracted from two deep CNN models like VGG and AlexNet. The proposed method is evaluated on three public datasets including Caltech101, Barkley 3D, and Pascal 3D and reached classification accuracy of 93.8%, 99%, and 88.6%--it demonstrated high performance compared to previous works.
# An Evaluation of the Security of Bare Machine Computing (BMC) Systems against Cybersecurity Attacks
## 3 Bare Machine Computing (BMC) Systems
### Compilation Process  
This section illustrates compilation and the BMC development process. It shows that developing BMC applications is simple and independent of other development environments, which change often. It also shows that the BMC application suite is a single monolithic executable with one address space for a given set of applications. All the batch files used are shown in Figure 7.  
Figure 6: Memory map.  
Currently, the development process in the BMC uses batch files and takes complete control by using a particular version of Visual Studio (VS 64-bit) as it changes often. These batch files allow us to conduct a compilation process independent of any version of the OS release. At present, we use a Visual Studio/bin directory to compile and link our bare code. The development environment does not use/lib and/include files. A bootasm has a boot code, and entry.asm has an entry code for the 64-bit application suite. The asmb.bat file is used to generate these binary files, which are written in the NASM assembler. The mk.bat file is used to create a bootable USB, which has boot, entry, and test64.exe (application suite). The PEfmt tool is written for BMC to properly organize code and data segments as needed for the Microsoft PE format.  
The asmb.bat file is used to compile MASM assembly code that provides direct hardware interfaces. There is a cpp.bat file in every directory that has C/C++ code. The cpp.bat file shown below is for the main directory in the application suite. Similarly, there is a separate cpp.bat file in each directory, where there is C/C++ code. The object files from each directory are used together to run a link process. The in.bat file creates an application suite executable named test64.exe. This description of batch files refers to creating a multicore web server using the BMC paradigm. This total control of compiling, linking, and creating a static executable prevents other malware from being linked with this application suite at run time.
# Study on the Properties and Fatigue Characteristics of Glass Fiber Composites Due to Porosity
## 3 Materials and Methods
### Numerical Models
#### 3.2.1 Failure Initiation  
In this paper, the Hashin-Rotem [28] failure criterion was utilized as the criterion for progressive damage initiation. The Hashin-Rotem failure criterion provides insights into the failure modes of composite materials and is represented by Equations (1)-(4) below.  
1. Fiber tensile failure:  
\[\mathcal{F}_{A}(\sigma)=\frac{\sigma_{11}^{2}}{X_{1}^{2}}+\frac{\sigma_{12}^{ 2}}{S^{2}}\;if\;\sigma_{11}\geq 0,\;0\;otherwise \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Value** & **Units** \\ \hline Young’s modulus & 4000 & MPa \\ Poisson’s ration & 0.33 & \\ Tensile Strength & 50.419 & MPa \\ Compressive strength & 54.737 & MPa \\ Shear strength & 46.807 & MPa \\ \hline \hline \end{tabular}
\end{table}
Table 1: Matrix properties.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Value** & **Units** \\ \hline Axial Young’s modulus & 60,128 & MPa \\ In-plane Young’s modulus & 64,663 & MPa \\ Transverse shear modulus & 4942.2 & MPa \\ In-plane Poisson’s ration & 0.13933 & \\ Transverse Poisson’s ration & 0.17416 & \\ Tensile Strength & 1161.7 & MPa \\ Compressive strength & 441.15 & MPa \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fiber properties.  
2. Fiber compressive failure:  
\[\mathcal{F}_{B}(\sigma)=-\frac{\sigma_{11}}{X_{c}}\;if\;\sigma_{11}<0,\;0\; otherwise \tag{2}\]  
3. Matrix tensile failure:  
\[\mathcal{F}_{C}(\sigma)=\frac{\sigma_{22}^{2}}{Y_{I}^{2}}+\frac{\sigma_{12}^{2 }}{S^{2}}\;if\;\sigma_{22}\geq 0,\;0\;otherwise \tag{3}\]  
4. Matrix compressive failure:  
\[\mathcal{F}_{D}(\sigma)=\frac{\sigma_{22}^{2}}{4S_{I}^{2}}+\frac{\sigma_{12}^ {2}}{S^{2}}+\left[\left(\frac{Y_{c}}{2S_{I}}\right)^{2}-1\right]\frac{\sigma_ {22}}{Y_{c}}\;if\;\sigma_{22}<0,\;0\;otherwise \tag{4}\]  
In the above formula, the breakage occurs when the breakage index (\(\mathcal{F}_{A}\), \(\mathcal{F}_{B}\), \(\mathcal{F}_{C}\), \(\mathcal{F}_{D}\)) reaches 1.
## 4 Results
### Features for a Calibration Customer  
In the studied cases, the validation of the data format was performed when the calibration provider uploaded the DCC to the DCC platform. For this reason, one key benefit of the platform is that it ensures the interoperability between the platform users as all the data are validated when imported to the platform. Using the signing service, the instrument owner can validate the digital signatures to the received DCCs to ensure that the received data are authorized by the expected organization and that the document has not been manipulated.  
After the data integrity of the document is validated, the calibration data need to be imported to the calibration customer's CMS. For this purpose, an API was developed to enable integration between the DCC platform and receiving CMS. In the receiving CMS, a parser was used to convert the calibration data within the DCC to the data format used in the CMS. This allowed the imported calibration results to be reviewed and approved in the system using existing features to return the instrument for production use.  
Alternatively, the human-readable format of the DCCs also allowed the data to be imported manually, similarly to how it is performed with paper or PDF certificates. However, this would also entail data integrity issues caused by the human interaction, making the approach far from ideal in comparison to the automated process.