esa-sceva/satcom-chunk-collection huggingface dataset:
# Factors Affecting the Stability of Loess Landslides: A Review
## 3 Comprehensive Analysis of Loess Landslides
### Stability Tests  
Landslide stability requires the performance of tests. These include both indoor and field tests. However, field tests provide more reliable results for modeling slope stability than values obtained from indoor tests [100]. By studying the dynamic properties under artificial rainfall conditions, the relationship between rainfall infiltration and the stability of loess slopes can be quantified [79]. In the Loess Plateau region, irrigation-induced water infiltration can be shown to lead to failure of loess slope stability by combining water column tests with permeability characterization simulations [74].
# Data Augmentation with Generative Methods for Inherited Retinal Diseases: A Systematic Review
## 1 Introduction
Some data augmentation techniques augment the training dataset by generating new synthetic data from classes with little representation, using generative models, for example, generative adversarial networks (GANs), which are a special type of deep learning [12; 13]. Although these generative techniques are still little explored in the context of IRDs, especially using FAF eye exams [14], they have already been explored in other retinal pathologies. Studies in these areas demonstrate the effectiveness of these approaches in generating synthetic data and using them to train deep learning models [15; 16]. These studies show that using synthetic data to train models on small datasets generally improves their accuracy. Deep generative models (DGMs) have gained significant attention in artificial intelligence due to their ability to approximate high-dimensional complex probability distributions and generate realistic synthetic data. These models--including generative adversarial networks (GANs), variational autoencoders (VAEs), and normalizing flows--have been successfully applied in various domains, such as image synthesis, speech generation, and deepfake technology [17]. Despite these advancements, challenges remain in optimizing their design and training for specific applications [17]. In the medical field, particularly in ophthalmology, DGMs have shown great potential for generating synthetic retinal images. These images can be used to augment small datasets, addressing data scarcity issues, and enhancing deep learning models for detecting inherited retinal diseases (IRDs). Despite promising results in retinal diseases, the evaluation of synthetic data remains a significant challenge. Most medical and GAN-based studies use a combination of metrics: qualitative, which involves visual examination and relies on feedback or description [18; 19] but can be inaccurate because it is dependent on the experience of the evaluator; and quantitative, which evaluates images in terms of visual fidelity, diversity, and generalizability [14]. The purpose of this literature review is to investigate the use of generative models in IRDs to generate synthetic autofluorescence ocular fundus images for these diseases. We intend to identify which techniques and architectures are most used in other retinal diseases, as well as which metrics are used to evaluate the results obtained so that they can be applied to IRDs in the future. Finally, we also aim to review the literature for cases where the use of synthetic data, particularly FAF, in small datasets for training classifiers has improved their efficiency.
# Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models
## 4 Discussion  
### Tested Models  
Using machine learning has innovative potential in any area of science. The basic requirement is that there must be enough data to train and validate the tested models, making a considerable amount of data necessary [25]. Among the models tested, the ANNs stood out for achieving higher means of correct classification and Kappa coefficient, being the most accurate among the evaluated models in identifying soybean cultivars. Using data derived from spectral images, [PERSON] et al. [26] reached an adequate adjustment and generalization capacity using ANNs to predict soybean yield. The modeling used by ANNs can achieve high accuracy, leading to answers to cover several situations [27].  
In some studies, the use of ANN has provided more reliable results than other modeling techniques [28], such as Stepwise Multiple Linear Regression (MLR) and Principal Component Regression (PCR) [29]. The ANNs are also a more accurate alternative in predicting crop yields than traditional regression models [30].  
[PERSON] et al. [31] found high accuracy using ANNs in predicting the earliness of the soybean accesses. [PERSON] et al. [32] found high accuracy in modeling using ANN in predicting several traits of soybeans under different climatic conditions. In eucalyptus, it is widely used to estimate yield since adopting traditional methods is difficult due to the number of independent variables and the complex relationship between them and the dependent variable [33].  
Using ANNs together with spectral bands and/or vegetation indices generates accurate results in providing information about forest inventories with time and labor savings, since they have the ability to learn and present information about non-linear data [34]. These coupled techniques successfully improve accuracy, speed, and reliability in several research lines, as well as to farmers [35].
# Trajectory Tracking Control Method Based on Adaptive Higher Order Sliding Mode
## 2 Design of Adaptive High-Order Sliding Mode Trajectory Tracking Controller
### Design of the Tracking Controller Based on the Adaptive Super-Twisting Algorithm  
For Equation (8), the sliding surface used in this paper is given by  
\[s=\beta\text{sig}^{\alpha}(x_{1})+\lambda_{1}x_{1}+x_{2} \tag{18}\]  
where \(\alpha\in(1,2)\), \(\text{sig}^{\alpha}(x_{1})=|x_{1}|^{\alpha}\text{sign}(x_{1})\).  
By taking the derivative of Equation (18) with respect to time \(t\), we obtain  
\[\dot{s} =\alpha\beta|x_{1}|^{\alpha-1}\dot{x}_{1}+\lambda_{1}\dot{x}_{1}+ \dot{x}_{2} \tag{19}\] \[=\underbrace{\Big{(}\alpha\beta|x_{1}|^{\alpha-1}+\lambda_{1} \Big{)}(V\sin\theta-V_{n}\sin\theta_{n})-\frac{\xi}{V}\cos\theta+\frac{\xi}{V _{n}}\cos\theta_{n}}_{a}+\underbrace{\frac{\xi}{V_{n}}}_{\dot{b}}u_{y}\]  
It should be noted that from Equation (19), we can see that the relative order of the system is equal to 1, and the above algorithm requires the relative order of the system to be greater than or equal to 2. To solve this problem, we take the derivative of Equation (19):  
\[\ddot{s}=\overline{a}+\overline{b}iu_{y} \tag{20}\]  
where \(\overline{a}\) and \(\overline{b}\) can be calculated using Equation (19), and \(\dot{u}_{y}\) is defined as the new system input; namely, a system with relative order 2 is obtained. Based on the above algorithm, the controller is designed as follows:  
\[\dot{u}_{y}=\frac{1}{\overline{b}}(-\overline{a}+v) \tag{21}\]  
\[\begin{array}{ll}u_{y}&=\int\dot{u}_{y}d\tau\\ &=\int\frac{1}{\overline{b}}(-\overline{a}+v)d\tau\end{array} \tag{22}\]  
where \(v\) is the same as that shown in Equation (12). The controller designed to verify Equation (21) can satisfy the requirement of the sliding mode variable \(s\) approaching 0, so that Equation (21) is substituted into Equation (19). Equation (19) can be transformed into  
\[\ddot{s}=\overline{a}+\overline{b}\dot{u}_{y}=v \tag{23}\]  
If the controller shown in Equation (12) is used, and Equation (12) is substituted into Equation (23), we obtain  
\[\ddot{s}=-k\text{sig}^{0}(s) \tag{24}\]  
Equation (24) shows that when \(s>0\), \(\ddot{s}<0\). At this point, regardless of its value, ultimately \(\dot{s}<0\); when \(s<0\), \(\ddot{s}>0\), regardless of the value of \(\dot{s}\), ultimately \(\dot{s}>0\). Therefore, the above derivation shows that \(\dot{s}\) can approach 0 under any initial conditions, thus ensuring that the desired state approaches 0. Similarly, this conclusion can be obtained by using adaptive Equation (16).
# An Accelerated Slicing Algorithm for Frep Models
## 3 An Accelerated Slicing Algorithm
### Novel Compound Adaptive Criterion  
Application of the common adaptive criteria for slicing of FRep models with the loop operations faces the problem of the overestimation of the intervals [18]. It leads to the processing of almost all the quadtree cells in the space. In this case the adaptive contouring approach becomes the exhaustive enumeration method (Figure 5a). In the accelerated slicing algorithm during the quadtree construction process, the decision of dividing each cell of the quadtree is made based on the novel compound adaptive criterion.  
Figure 4: An accelerated slicing algorithm for an FRep model.  
Let us divide the initial FRep model by the boundary part and the microstructure part consisted of unit cell repetition. The novel compound adaptive criterion consists of the common adaptive criterion for the boundary of the 3D model (interval or affine arithmetic) and the spatial queries, which are applied to the microstructure. It works as an adaptive criterion for each quadtree cell in the following way: if the interrogated quadtree cell contains at least one bounding box of the microstructure unit cell, this criterion returns true; The spatial query (intersection of the bounding boxes) is used for verification. Thus, the novel compound adaptive criterion obtains its value from the common adaptive criteria for the boundary of the 3D model and from the intersection operation with the spatial search. If at least one of these two conditions is true, the quadtree cell is divided for further computations.  
The novel compound adaptive criterion optimizes calculation of the FRep defining function. It reduces the number of processed cells as shown in (Figure 5b). More details about the efficiency of the novel compound adaptive criterion are shown in Case study 1.
# Bayesian Estimation of Simultaneous Regression Quantiles Using Hamiltonian Monte Carlo
## 4 Numerical Results  
In this section, we will perform a Bayesian quantile regression procedure to estimate simultaneous regression quantiles using HMC. HMC will be used through the probabilistic programming language STAN [43; 44; 45] program to estimate simultaneous linear quantiles. STAN provides a flexible framework for specifying and fitting a wide range of Bayesian models. Numerous probability densities, matrix operations, and numerical ODE solvers are supported by the expressive language STAN [46].  
Two different models will be considered for the \(\tau\)-th quantile for the model provided by (5):  
1. Univariate linear quantile: \(q_{\tau}(X)=1+2\mathbf{X}\) with \(X\sim\mathcal{U}_{[0,1]}\).
2. Multivariate linear quantile: \(q_{\tau}(X)=1+X_{l}^{\prime}\beta_{\tau}\), with \(\beta_{\tau}\in\mathbb{R}^{d}\), \(d=10\) and either \(X_{l}\sim\mathcal{U}_{[-1,1]}\) or \(\mathbf{X}_{l}\sim\mathcal{N}(0,1)\), \(\forall l\in\{1,\dots,d\}\).  
Our methodology involves the utilization of the state-of-the-art STAN program, leveraging Hamiltonian Monte Carlo for statistical modeling and high-performance statistical computing. This platform has been extensively employed across diverse disciplines, including the social, biological, and physical sciences, engineering, and industry, for tasks such as statistical modeling, data analysis, and prediction. Given the inherent relationship between any two ALD quantiles, we recognize that distinct quantiles vary solely in terms of their intercepts \(\beta_{\tau_{l}}\). Consequently, all quantiles of interest have the same slope (\(\beta_{\tau_{l}}=\dots=\beta_{\tau_{s}}=\beta_{p}\)); then, the unknown vector parameter is \(\beta\). The prior of \(\beta\) is Gaussian, with mean 0 and variance 1. For further details about the STAN code, refer to the Appendix A.
# Averting BER Floor with Iterative Source and Channel Decoding for Layered Steered Space-Time Codes
## 2 Overview of Layered Steered Space-Time Codes  
A three-stage serially concatenated iterative JSCD system is presented deploying the combinational gain technique (V-BLAST, STC and beamforming) for the H.265 video coding standard. The three-stage iterative decoding system employs two iterations between the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Methods and Parameters** & **Video Coding Standard** \\ \hline \multirow{4}{*}{[20]} & Investigated the performance of SBC schemes using DSTS-SP for the two-stage system & H.264 \\ \cline{2-3}  & & Comparison of the regular and irregular error protection schemes using DSTS-SP for the two-stage system & H.264 \\ \hline \multirow{4}{*}{[22]} & Investigated the performance of & \\  & Self-concatenated coding for the two-stage system & H.264 \\ \cline{2-3}  & two-stage system & \\ \hline \multirow{4}{*}{[24]} & Comparison of convergent and & \\  & non-convergent coding schemes for & H.265 \\ \cline{2-3}  & diverse video environments & \\ \hline \multirow{4}{*}{[25]} & Investigated the effects of varying & \\  & \(d_{H,min}\) on the BER and convergence & H.264 \\ \cline{2-3}  & performances for the two-stage system & \\ \hline \multirow{4}{*}{[26]} & Comparison of LDPC and polar codes & \\  & for improvements in 5G networks & Not deployed \\ \hline \multirow{4}{*}{[27]} & Presented a novel parameter for & \\  & distortion measurement and offered a & H.265 \\ \cline{2-3}  & strategy to mitigate distortions & \\ \hline \multirow{4}{*}{[28]} & Systematic network coding and & \\  & physical layer turbo coding for video & H.265 \\ \cline{2-3}  & transmission over satellite channel & \\ \hline \multirow{4}{*}{Ours} & Investigating low complexity SBCs & \\  & with combinational gain technique of & H.265 \\ \cline{1-1}  & LSSTC for the three-stage system & \\ \hline \hline \end{tabular}
\end{table}
Table 1: An overview of major systems for wireless video communication.  
inner SP and intermediate URC decoder and the other iteration between the outer SBSD decoder and the intermediate URC decoder.
# Challenges Associated with Implementing 5G in Manufacturing
## 1 Introduction
### Ultra-Reliable and Low-Latency Communications (URLLC)  
URLLC can address critical needs of communications where bandwidth is not quite as crucial as speed, e.g., an end-to-end latency of 1 ms or less. The design of a low-latency and high-reliability service involves several components, including, incredibly fast data turnaround, efficient control and data resource sharing, grant-free based uplink transmission, and advanced channel coding schemes. Uplink grant-free structures guarantee a reduction in user equipment (UE) latency transmission by avoiding the middle-man process of acquiring a dedicated scheduling grant. These services are supported by the 5G New Radio (NR) standard. URLLC should be the tier that addresses the autonomous vehicle category, where decision time for a reaction to a possible accident needs to be almost non-existent. URLLC makes 5G a possible competing solution with satellite, for Global Positioning System (GPS) geolocation services [4; 5].
## 2 The Biosynthetic Pathway of Astaxanthin in _X. dendrohous_
Two enzymatic activities convert \(\beta\)-carotene into astaxanthin through several biosynthetic intermediates: a ketolase, which incorporates two 4-keto groups in the molecule of \(\beta\)-carotene, and a hydroxylase, which introduces two 3-hydroxy groups. In _X. dendrohous_, both activities are included in a single enzyme (astaxanthin synthetase; CrtS), belonging to the cytochrome P450 family and encoded by the _crtS_ gene, which sequentially catalyses 4-ketolation of \(\beta\)-carotene followed by 3-hydroxylation [25; 26]. In contrast, two independent genes have been described in other astaxanthin producing microorganisms. A cytochrome P450 reductase, encoded by the \(crtR\) gene, has been shown to have an auxiliary role to CrtS in _X. dendrohous_, providing the necessary electrons for substrate oxygenation [59]. A mutant of _X. dendrohous_ lacking the \(crtR\) gene accumulates \(\beta\)-carotene and is unable to synthesise astaxanthin, demonstrating that CrtR is essential for the synthesis of astaxanthin [59]. In _Saccharomyces cerevisiae_ strains expressing _X. dendrohous_ carotenogenic genes, astaxanthin production was only achieved when CrtS was co-expressed with CrtR [60]. The existence of a monocyclic pathway diverging from the dicyclic pathway at neurosporene and proceeding through \(\beta\)-zeacarotene, 3,4-didehydrolycopene, torulene, 3-hydroxy-3\({}^{\prime}\)\(A^{\prime}\)-didehydro-\(\beta\), \(\psi\)-carotene-4-one (HDCO) to the end product 3,3\({}^{\prime}\)-dihydroxy-\(\beta\), \(\psi\)-carotene-4,4\({}^{\prime}\)-dione (DCD) (Figure 1) was also proposed [42].
## 5 Result
### Results on CWRU Dataset
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **F1 Score** & **Param** & **Energy (mJ)** \\ \hline VGG(A) [39] & 100 \(\pm\) 0 & 1 & 137.32 M & 239.67 \\ SEW-Resnet18 [26] & 87.46 \(\pm\) 9.82 & 0.8400 & 3.85 M & 6.56 \\ SpikeVGG(A) [39] & 99.76 \(\pm\) 0.17 & 0.9970 & 137.32 M & 16.94 \\ SpikeCAN (Ours) & 99.86 \(\pm\) 0.16 & 0.9983 & 1.45 M & 3.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison in CWRU four-class classification using five-fold cross-validation. Figure 4: Confusion matrices illustrating the classification performance on CWRU. The classification performance of SpikeCAN on the more challenging 10-class classification scenario from the CWRU dataset was further evaluated through a five-fold cross-validation, with detailed results summarized in Table 4. Since there are currently no available SNN-based studies specifically reporting results for this 10-class CWRU setup, we compared SpikeCAN's performance against state-of-the-art ANN-based approaches [45; 46]. In this comparison, SpikeCAN achieved an accuracy approximately 0.7% and 1.54% lower than FaultNet and CNN-LSTM-GRU methods, respectively. Despite this marginal performance gap, SpikeCAN demonstrates competitive accuracy while significantly reducing energy consumption compared to conventional ANN architectures. This result clearly highlights the robust classification capabilities of our spike-driven model alongside its substantial advantage in energy efficiency. Additionally, the confusion matrix corresponding to the experiments is illustrated in Figure 5, providing further insight into SpikeCAN's classification behavior. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Accuracy (\%)** & **F1 Score** \\ \hline CNN-LSTM-GRU [45] & 99.34 & - \\ FaultNet [46] & 98.50 \(\pm\) 0.23 & 0.9806 \\ SpikeCAN & 97.80 \(\pm\) 1.94 & 0.9674 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison in CWRU 10-class classification. Figure 5: Confusion matrices illustrating the classification performance on the CWRU dataset (50 samples per class).
## 2 Infrastructure, Instrumentation and Calibration Methods  
This section presents the infrastructure and instrumentation of the Permanent Facility for Altimetry Calibration in west Crete, Greece. The principle of operations for each calibration method (i.e., transponder, sea-surface, crossover analysis) employed along with the parametrization of each is also briefly described.
# Water Temperature and Salinity Measurement Using Frequency Comb
## 3 Materials and Methods
### Salinity Measurement Principle  
Based on the Quan-Fry formula mentioned above, with the Equation (11), the salinity can be expressed as:  
\[S=\frac{\lambda^{3}(n_{w1}-n_{0}-n_{4}T^{2})-2\lambda^{2}(n_{5}+n_{7}T)-3\lambda n _{8}-4n_{9}}{\lambda^{3}(n_{1}+n_{2}+n_{3}T^{2})+2\lambda^{2}n_{6}} \tag{13}\]  
When the water temperature is relatively constant, with the Equations (7) and (13), the group refractive index \(n_{w1}\) and salinity value \(S\) can be converted to each other.
# Frequency-Based Adaptive Interpolation Filter in Intra Prediction
###### Abstract  
This paper proposes a method to improve the fractional interpolation of reference samples in the Versatile Video Coding (VVC) intra prediction. The proposed method uses additional interpolation filters which use more integer-positioned reference samples for prediction according to the frequency information of the reference samples. In VVC, a 4-tap Discrete Cosine Transform-based interpolation filter (DCT-IF) and 4-tap Smoothing interpolation filter (SIF) are alternatively performed on the block size and block directional prediction mode for reference sample interpolation. This paper uses four alternative interpolation filters such as 8-tap/4-tap DCT-IFs, and 4-tap/8-tap SIFs and an interpolation filter selection method using a high-frequency ratio calculated from one-dimensional (1D) transform of the reference samples are proposed. The proposed frequency-based Adaptive Filter allows to achieve the overall Bjentegaard Delta (BD) rate gains of \(-0.16\%\), \(-0.13\%\), and \(-0.09\%\) for Y, Cb, and Cr components, respectively, compared with VVC.  
discrete cosine transform (DCT); interpolation filter; intra prediction; versatile video coding (VVC)/H.266
# Filtering Organized 3D Point Clouds for Bin Picking Applications
## 1 Introduction
For the organized 3D points clouds acquired with the line-of-sight system, such groups contain points that float in space disconnected from other data points, obtained from the surfaces of real, physical objects. The organized 3D point cloud is a set of points \(\mathbf{P}_{n}=(x_{n},y_{n},z_{n})\), \(n=1,\ldots,N\), such that \(n=row\times N_{col}+col;row=0,\ldots,N_{row}-1,col=0,\ldots,N_{col}-1\). Thus, \(N=N_{row}N_{col}\) and each Cartesian coordinate of a set point can be represented as a matrix of size \(N_{row}\times N_{col}\), for example, \(\mathbf{Z}=z(row,col)\). Most 3D machine vision systems have an option to output the data in an organized point cloud format (unorganized point clouds still provide a set of 3D points \(\mathbf{P}_{n}\), but their components do not form a regular 2D matrix that corresponds to the pixels on the sensor plane). The organized 3D point cloud format enables the use of 2D image processing techniques (many of which are based on the concept of pixel connectivity), while voxelization of the unorganized 3D point cloud is a more complex and time-consuming process. The proposed filtering method was tested on six datasets, obtained by scanning six bins filled with different parts with a line-of-sight vision system. Recorded 3D point clouds were processed with the generic statistical outlier removal (SOR) algorithm and the proposed procedure. SOR could filter only a small portion of outliers: a majority of the correctly identified outliers relevant for bin-picking were identified by the new procedure. Figure 3: Example of a 3D point cloud of a bin filled with parts: (**a**) red dots are outliers above the top edge of a bin (marked in blue); (**b**) zoomed-in subregion after outliers in (**a**) were removed—red dots show unremoved outliers.