esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation
### Truth Table Generation for Object Distance Measurement with YOLO Segmentation and LiDAR Clustering  
The KITTI object detection benchmark dataset comprises annotation object coordinates, and the objective of this part was to generate a truth table for comparison with our method's distance measurements. YOLO object detection was employed to acquire object segmentation masks. The coordinates of these segmentation masks were compared with the KITTI object annotation. Afterward, the matched annotations were used to verify whether the LiDAR points were inside the segmentation mask of the detected object. If a LiDAR point was located within the mask, the (x, y, z) coordinates were stored. The collected data comprised LiDAR points associated with things, along with extraneous noise and irrelevant LiDAR information. Clustering was employed to eradicate this noise. Figure 19 illustrates that the clustering comprises 15 types.  
After that, the clustering was focused on objects within a range of 0 to 2.5 m. The nearest cluster was located, and the minimum value was retrieved to ascertain the object's distance. Ultimately, the truth table was generated for several item categories, encompassing pedestrians, bicycles, cars, buses, and trucks. The occlusion levels were categorized into four classifications, respectively, 0, 1, 2, and 3.  
Table 6 presents the distribution of objects in the truth table categorized into four difficulty levels: Easy (fully visible objects), Moderate (partially occluded objects), Hard (highly occluded or difficult-to-see objects), and Unknown (objects with uncertain visibility or occlusion status). This classification helps in analyzing object detection performance under varying visibility conditions.  
Figure 18: Refined bounding box data, after filtering out irrelevant objects (illustrated with red arrows), show only the relevant detections in the image.
## 2 Challenges and Routing in 6G Networks
### Challenges of Routing
#### 2.1.6 Low Ability to Adapt to Dynamic Networks  
As discussed in detail in the previous sections, 6G networks contain many complex systems, techniques, approaches, and goals. It is not possible for existing traditional methods and systems to efficiently serve such diverse networks. In order to produce optimal and efficient outputs, maximize network performance, achieve the predicted goals, and respond quickly to changes in the network, existing mobility management and routing techniques need to be developed to produce a quick reaction. It is impossible to achieve quick reactions using manual techniques. For this reason, strategies involving machine learning and deep learning come to the fore, as the existing routing protocols cannot meet the needs of 6G networks. Existing routing protocols should be redesigned intelligently in order to provide network optimization in 6G and adapt routing protocols to rapid changes. One of the main goals of satellite networks is the simplification of the routing process. The routing step is a key point of communication technology. Optimizing the routing process is a process that increases the end-to-end efficiency of communication. As mentioned earlier, existing routing techniques have difficulties in meeting the needs of 5G and beyond. Therefore, it is not possible for us to achieve full efficiency in satellite networks by using existing routing protocols that we cannot integrate into ground networks at full efficiency. The dynamics of satellite networks differ significantly from those of ground networks [63; 64; 65]. In addition, we will be talking about and even trying to implement the incorporation of 6G networks into satellite networks in the future. For this reason, there is already a need for more viable, efficient, and new architectures and approaches. Routing problems in satellite networks can be summarized by the following items:  
* LEO and MEO satellite networks have a dynamic structure due to the fact that they are mobile relative to the ground;
* Their coverage area constantly changes due to their movement;
* Satellite networks have high latency, high power, and high bandwidth;
* Transmission losses are greater in satellite networks.
# Visualization of a Multidimensional Point Cloud as a 3D Swarm of Avatars
## 5 Discussion
### Advantages and Utility of Spatial Avatars  
Compared to conventional dimensionality reduction methods, the spatial avatar approach provides direct, user-controlled interpretability by allowing the explicit mapping of intuitive features to avatar traits. It supports immediate visual recognition of unusual or exemplary cases and leverages innate human pattern recognition skills. Technical variables projected onto spatial coordinates reveal underlying structures, clusters, or outliers, while avatars simultaneously communicate key user-relevant attributes. Interactive mapping and dynamic exploration enable users to iteratively test hypotheses, adjust variable assignments, and visualize the impact on the data structure.  
Empirical case studies (see Section 4) show that spatial avatars reveal patterns and support interpretations that are less accessible or even hidden in standard visualization techniques such as PCA. Sensitivity analyses demonstrate how variable assignment impacts interpretability and cluster formation, underscoring the method's flexibility and value across diverse analytical contexts.
## 2 Materials and Methods
### Thermodynamic Parameters and Raman Activity Calculations  
Phonon frequencies were obtained using the diagonalisation of dynamical matrices computed using linear response techniques, known also as density functional perturbation theory (DFPT). DFPT is the primary ab initio technique utilised for phonon computations. This method is different from the direct method, as DFPT calculates the change in the Hamiltonian due to a specific perturbation in charge density or wavefunction, rather than actually displacing atoms as in a direct method. The q-point separation parameter, denoting the mean distance between Monkhorst-Pack mesh q-points utilised in real space dynamical matrix computations, was established at 0.05 A\({}^{-1}\). The convergence criterion for the force constants during a phonon properties calculation was established at 1 \(\times\) 10\({}^{-5}\) eV/A\({}^{2}\). This model was utilised to compute band structure, density of states (DOS), phonon spectrum, and phonon density of states (phonon DOS) features. For the dispersion calculations the distance between q-vectors along the reciprocal space trajectory was established at 0.015 A\({}^{-1}\). The DOS calculations employed a 3 \(\times\) 3 \(\times\) 3 Monkhorst-Pack k-points grid, yielding a q-vector separation of 0.04 A\({}^{-1}\).  
The outcomes of a phonon spectra calculation were utilised to ascertain, within the quasi-harmonic approximation, zero-point vibrational energy (Ezp), entropy (S), Gibbs free energy (G), and enthalpy (H) as functions of temperature, employing Equations (1)-(4) formulated by [PERSON] et al. [33]. In the equation, \(\omega\) represents phonon frequency, F(\(\omega\)) indicates the vibrational density of states for a phonon spectrum, Etot symbolises the total electronic energy at absolute zero, k is [PERSON]'s constant, and \(\hbar\) is [PERSON]'s constant.  
\[E_{zp}\ =\ \frac{1}{2}\int F(\omega)\hbar\omega d\omega \tag{1}\]  
\[S(T)\ =\ k\Bigg{\{}\int\frac{\frac{\hbar\omega}{kT}}{\exp\left(\frac{\hbar \omega}{kT}\right)-1}F(\omega)d\omega-\int F(\omega)\text{ln}\left[1-\exp \left(-\frac{\hbar\omega}{kT}\right)\right]d\omega\Bigg{\}} \tag{2}\]  
\[G(T)\ =\ E_{tot}+E_{zp}+kT\int F(\omega)\text{ln}\left[1-\exp\left(-\frac{\hbar \omega}{kT}\right)\right]d\omega+pV \tag{3}\]  
\[H\ =\ G+TS \tag{4}\]  
Activities for Raman vibrations were calculated with the required convergence for the second-order response to the electric field set as 1.0 \(\times\) 10\({}^{-5}\) A\({}^{3}\) and with the finite displacement method has been used to calculate Raman intensities. CASTEP analysis was used to construct Raman-mode intensities for a given temperature (298 K) and laser wavelength (532 nm) from calculated activities. No imaginary frequencies for both Phase I and Phase II were observed at 20.1 GPa.
## 2 Materials and Methods
### Multiple Linear Regression (MLR)  
Multiple linear regression models the relationship between input features and the target variable (\(C_{c}\)) by assuming a linear relationship. The general formula for a multivariate linear regression model is as follows:  
\[C_{c}=\beta_{0}+\left(\sum\
olimits_{i=1}^{n}\beta_{i}X_{i}+\epsilon\right) \tag{6}\]  
where \(C_{c}\): the compression index, \(X_{i}\): the input features (e.g., \(e_{0}\), the LL, the PI), \(\beta_{0}\), \(\beta_{i}\): coefficients determined via least squares, and \(\epsilon\): the error term  
The model's coefficients were calculated by minimizing the residual sum of squares (RSS):  
\[\text{RSS}=\sum\
olimits_{i=1}^{N}\left(C_{c,i}-\hat{C}_{c,i}\right)^{2} \tag{7}\]  
where \(C_{c,i}\): the actual compression index, and \(\hat{C}_{c,i}\): the predicted compression index.  
Multiple linear regression (MLR) is often used as a baseline model to explore the simplest relationships in the data. Its performance is evaluated using metrics like the coefficient of determination (\(R^{2}\)), the mean squared error (MSE), and the mean absolute error (MAE). \(R^{2}\) indicates how much of the variation in the dependent variable is explained by the independent variables, with values closer to 1 showing a better fit.  
The MSE measures the average squared difference between the observed and predicted values, with lower values indicating greater accuracy. The MAE, on the other hand, calculates the average absolute difference between the observed and predicted values, providing a straightforward measure of prediction errors. The Mean Absolute Percentage Error (MAPE) measures the accuracy of a predictive model by calculating the average of the absolute percentage differences between the actual values and the predicted values. The Root Mean Square Error (RMSE) measures the square root of the average of the squared differences between the actual and predicted values. Together, these metrics offer a comprehensive understanding of the model's accuracy and reliability.  
\[R^{2}=1-\frac{\sum\left(y_{i}-\hat{y_{i}}\right)^{2}}{\sum\left(y_{i}-\bar{y} \right)^{2}} \tag{8}\]  
\[MSE=\frac{1}{n}{\sum\
olimits_{i=1}^{n}\left(y_{i}-\hat{y_{i}}\right)^
## 4 Discussion
Because ion release generally increases with time, it could be used to predict the long-term antibacterial effect of GaMBN-containing resins as orthodontic treatment is a long-term treatment. Therefore, long-term ion release tests of GaMBN-containing resins could be used to develop a guideline for clinical applications [20]. The micro-CT results demonstrated that the remineralization length increased with the GaMBN concentration. The amounts of Ca\({}^{2+}\) and PO\({}_{4}\)[3]- released by GaMBN5 were consistent with the amounts required for remineralization during the 14 days of the pH cycle [8; 9; 10; 11; 19]. The greatest advantage of MBN is its pore structure, which makes it a functional material that can be used to transport various genes and drugs. Non-ionic surfactants or cationic surfactants, such as CTAB, can be used to control the pore size of MBN. In this study, CTAB was used in order to produce MBN with high mesopore volume (PV) and surface area(SA) because PV and SA have been reported to provide sufficient space for transporting small chemicals, such as genes or drugs [21; 22]. Furthermore, the size of the mesopores is influenced by the hydrolysis of alkyl silicate and can be controlled by adjusting the amount of TEOS [23]. Based on the results of this study, MBN is an efficient anti-demineralizing agent that should be considered for future research aiming to transport Ga or other antibacterial nanomaterials using mesopores of various sizes. This study of GaMBN-containing resins demonstrated that these resins exhibit the physical and biological properties required for safe clinical use, as well as the ability to prevent demineralization. Future studies should focus on in vivo and clinical research.
# Hybrid Chirp Signal Design for Improved Long-Range (LoRa) Communications
## 1 Introduction
Thus, the novelty and contributions of this paper can be summarized as follows:  
* A joint up- and down-chirp-based modulation scheme which is more resilient against noise/interference is proposed;
* A hybrid demodulation scheme to enhance demodulation performance is proposed;
* The proposed techniques are validated through simulations for different scenarios. The remainder of the paper is organized as follows. In Section 2, a brief literature review on LoRa performance enhancement is described. In Section 3, we briefly present the modulation and demodulation strategies for traditional LoRa communication. The proposed and enhanced modulation schemes are presented in Section 4. The performance of the proposed schemes is presented and compared in Section 5. Finally, we conclude in Section 6.
# Application of Internet of Things in a Kitchen Fire Prevention System
## 2 Literature Review
### Kitchen Fire
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Item** & **Time** & **2013** & **2014** & **2015** & **2016** & **2017** & **2018** & **Total** \\ \hline Total & 1451 & 1417 & 1704 & 1856 & 30,464 & 27,922 & 64,814 \\ \hline Arson & 210 & 213 & 268 & 278 & 323 & 285 & 1577 \\ \hline Suicide & 19 & 27 & 21 & 22 & 72 & 64 & 225 \\ \hline Lights and candles & 11 & 6 & 18 & 16 & 67 & 51 & 169 \\ \hline Cooking & 63 & 69 & 72 & 125 & 3659 & 3591 & 7579 \\ \hline God and ancestor worship and tomb sweeping & 42 & 43 & 45 & 31 & 1936 & 1604 & 3701 \\ \hline Cigarette & 135 & 146 & 147 & 169 & 1461 & 1530 & 3588 \\ \hline Electricity & 508 & 451 & 582 & 608 & 3433 & 2972 & 8554 \\ \hline Mechanical equipment & 41 & 30 & 29 & 40 & 469 & 365 & 974 \\ \hline Playing with fire & 12 & 12 & 13 & 16 & 57 & 39 & 149 \\ \hline Fires started for warmth & 5 & 3 & 4 & 4 & 28 & 24 & 68 \\ \hline Construction accidents & 35 & 42 & 38 & 51 & 272 & 283 & 721 \\ \hline Spontaneous combustion of flammable products & 7 & 9 & 8 & 10 & 32 & 26 & 92 \\ \hline Gas leak or explosion & 26 & 16 & 28 & 25 & 98 & 88 & 281 \\ \hline Chemicals & 11 & 8 & 5 & 10 & 24 & 19 & 77 \\ \hline Burning of firecrackers & 15 & 19 & 27 & 22 & 181 & 229 & 493 \\ \hline Traffic accidents & 15 & 6 & 18 & 23 & 121 & 91 & 274 \\ \hline Natural disasters & 1 & 3 & 2 & 2 & 19 & 7 & 34 \\ \hline Unattended small sources of fire & - & - & - & 142 & 5810 & 6353 & 12,305 \\ \hline Unknown causes & 17 & 25 & 17 & 23 & 48 & 32 & 162 \\ \hline Other & 278 & 289 & 362 & 239 & 12,354 & 10,269 & 23,791 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of fire incidents and fire losses in Taiwan from 2013 to 2018.
## 2 Related Research  
### AI Cyberthreats  
This section describes the need for cybersecurity of AI by examining forgeries or errors in AI learning data. First of all, accidents that have occurred in AI learning data are as follows [1].  
AI chatbots' malicious learning: In 2016, Microsoft presented AI chatting robot Tay, but the learning was closed in 16 h because of intentional messages of racial and sexual discrimination included in the learning. Microsoft-made AI chat robot Zo also showed similar problems despite precautions having been taken [2].  
Adversarial patch-based attacks: Google Research Group announced the Adversarial Patch, which can make the image recognition AI algorithm malfunction. Adversarial patch, a patch with round-shape abstract images, caused malfunction in the AI algorithm, which recognized an image when the printed patch was stuck to an object [3].  
Error in pre-qualifying the recidivism rate in Brown County of Florida, USA: The COMPAS (Correctional Offender Management Profiling for Alternative Sanctions)'s prediction of recidivism for 7000 arrests over the two years of 2013 and 2014 was racially biased, with a 44.9% chance that African Americans would not repeat crimes, about twice as much as 23.5% of whites. It was also incorrectly predicted that white people, repeat offenders, had a 47.7% chance of not re-offending, while African Americans had a 28% chance of not re-offending [4].  
Forgery of medical records through deep learning: The AI research group of Ben-Gurion University of the Negev in Israel conducted an experiment where malware generated through deep learning technology was used to manipulate patients' 3D scan images, deceiving all of three doctors. At that time, the problem that 3D CT (Computed Tomography) images, X-ray, and MRI were distributed without a security system was raised [5].  
Deepfake using AI: In late 2017, fake videos appeared on the U.S. social news website Reddit through Deepfake, which changes faces using a swapping algorithm, and it was difficult to winnow the truth from falsehood. At that time, it was claimed that responses to the leakage of AI learning data from AI technology platforms should be prepared [6].  
The above cases revealed that AI processing myriad data is vulnerable to inappropriate or incomplete data, and when malignant data are applied, incorrect and wrong outputs can be derived regardless of the AI functions and performance. There is a security risk that an attacker may exploit AI based on its efficiency and dissemination by using data or carrying out attacks disturbing the AIsystem. In other words, when data forgery occurs due to cyberattacks, the effect of AI learning and learning concepts can be damaged, so data control is required before providing AI learning data.
## 3 Application of Emerging Technologies in Seeding Selection for Large Yellow Croaker
### Molecular Breeding Technology
This technology can predict the genomic breeding value (GEBV) of individuals based on whole-genome markers, improving genetic evaluation accuracy while shortening the breeding cycle. [PERSON] et al. [65] estimated the genetic parameters of nine quantitative traits using SNP data, showing that there was a high genetic correlation between some morphological traits. Molecular-assisted breeding has progressed rapidly. A \begin{table}
\begin{tabular}{c c c c} \hline
**Technical Approach** & **Core Principles** & **Characteristics** & **Limitations** \\ \hline
**Marker-assisted selection**[54] & Selection based on genotype–phenotype association & Effective for traits that are hard to measure or have low heritability (e.g., disease resistance). & Limited to traits controlled by major genes or a few QTLs. \\
**Genomic selection** & Uses genome-wide dense markers for phenotype prediction & Does not require QTL detection; it provides higher prediction accuracy. & Requires close-related reference populations; model-dependent performance. \\
**Genome Editing**[57] & Precise and stable modification of genomic sequences & Enables targeted improvement of complex traits; shortens breeding cycles. & Few functional targets for complex traits; subject to regulatory approval. \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._GWAS based on ddRAD sequencing in 220 individuals identified 13 SNPs across 8 linkage groups associated with growth and body-shape traits. Additionally, genomic selection for hypoxia tolerance and marker-assisted selection (MAS) leveraging SNP and SSR markers have been successfully implemented [66]. Although Genome Editing (GE) technology has not yet been applied in large yellow croaker, it has been used for precise trait improvement in various fish species (Figure 3) [57; 58; 67; 68]. Genomic data can be used for early screening of high-quality seedlings and, combined with environmental factors, can optimize breeding strategies to improve selection accuracy [69]. However, genomic breeding technology still relies on professional support and equipment investment. Due to technical barriers and cost limitations, the application of this technology in aquaculture breeding remains at an early stage, and further technological optimization and resource integration are urgently needed to achieve its widespread application at the industrial level.
# HYDRUS-1D Simulation of Nitrogen Dynamics in Rainfed Sweet Corn Production
## 3 Results and Discussion
### Nitrogen Concentrations in the Soil Profile  
Figure 6 presents the comparison between simulated and observed concentrations of NO3\({}^{-}\)-N in soil profile within the root zone (0-60 cm) during two seasons of the sweet corn. The maximum simulated NO3\({}^{-}\)-N concentrations in the root zone during the first and second seasons of sweet corn were recorded as 26 mg/L and 20.2 mg/L, respectively. Whereas the maximum observed NO3\({}^{-}\)-N concentrations for the first season and second season of field crop in root zone were 24.2 mg/L and 18.4 mg/L. The maximum concentrations were observed in topsoil (20 cm). The difference in maximum value between two seasons is regular rainfall events that occurred during the second season. The leaching rainwater carried the dissolved NO3\({}^{-}\)-N to deeper soil depth quicker than the first season where the resident time NO3\({}^{-}\)-N was higher. This reflects in high NO3\({}^{-}\)-N concentrations at 60 cm depth in the second season. Relatively longer NO3\({}^{-}\)-N resident time in topsoil is supported by the fact that concentrations at 20 cm tend to increase with time till the 61 st day of growth while in the second season it increased till 51 st day. The fertilizer application timing during the growth period was the same for both seasons. As the initial input value was defined as N concentration in a chain reaction, the minimum NO3\({}^{-}\)-N concentration value for both seasons was 0 mg/L. The simulated and observed NO3\({}^{-}\)-N concentrations in soil profile below the root zone (60-140 cm) are also presented in Figure 6. The NO3\({}^{-}\)-N concentration at 140 cm depth is negligible during the first season as compared to the second season. Figure 7 presents the NH4\({}^{+}\)-N concentration within the root zone. The results imply  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Season** & **Solute** & **R\({}^{2}\)** & **RMSE (mg.L\({}^{-1}\))** \\ \hline \multirow{2}{*}{1} & NO3\({}^{-}\)-N & 0.99 & 0.62 \\  & NH4\({}^{+}\)-N & 0.97 & 0.26 \\  & NO3\({}^{-}\)-N & 0.99 & 0.46 \\  & NH4\({}^{+}\)-N & 0.95 & 0.28 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical analysis of the comparison between observed and simulated N contents.  
that NH\({}_{4}\)+-N remained within the root zone. No concentrations of NH\({}_{4}\)+-N have been recorded below the root zone. Overall both NO\({}_{3}\)-N and NH\({}_{4}\)+-N concentrations were higher in shallower depths (root zone) due to direct contact with fertiliser applied surface.  
Figure 6: Comparison of simulated (Sim.) and observed (Obs.) NO\({}_{3}\)–N concentrations during the (a) first season and (b) second season.  
Figure 7: Comparison of simulated (Sim.) and observed (Obs.) NH\({}_{4}\)+-N concentrations during the (a) first season and (b) second season.
## 3 Problem Formulation
### Transmission Performance Optimization  
The optimization goal of the IRS-assisted ISAC system is to maximize the data rate while satisfying the sensing performance requirement. Then, the objective of data transmission optimization can be formulated as follows:  
\[\max_{\mathbf{W},\mathbf{\Phi}}\Psi_{1}=\sum_{k=1}^{K}\log_{2}(1+\rho_{c,k}), \tag{20}\]  
subject to  
\[\text{Tr}[\mathbf{R_{X}}]\leq P^{\text{max}}, \tag{21}\]  
\[\mathbf{R_{X}}\succeq 0, \tag{22}\]  
\[\rho_{r,k}\geq\rho^{\text{thr}},\forall k\in\mathcal{K}, \tag{23}\]  
where \(\rho^{\text{thr}}\) is a threshold value for the radar SINR. Constraint (21) depicts the transmit power limit for deploying the ISAC. Constraint (23) ensures the sensing performance while optimizing the communication performance.