esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A unique approach to evaluate the reduction of skin friction drag by riblets was applied to boundary layer profiles measured in wind tunnel experiments. The proposed approach emphasized the turbulent scales based on hot-wire anemometry data obtained at a sampling frequency of 20 kHz in the turbulent boundary layer to evaluate the skin friction drag reduction. Three-dimensional riblet surfaces were fabricated using aviation paint and were applied to a flat-plate model surface. The turbulent statistics, such as the turbulent scales and intensities, in the boundary layer were identified based on the freestream velocity data obtained from the hot-wire anemometry. Those turbulent statistics obtained for the riblet surface were compared to those obtained for a smooth flat plate without riblets. Results indicated that the riblet surface increased the integral scales and decreased the turbulence intensity, which indicated that the turbulent structure became favorable for reducing skin friction drag. The proposed method showed that the current three-dimensional riblet surface reduced skin friction drag by about 2.8% at a chord length of 67% downstream of the model's leading edge and at a freestream velocity of 41.7 m/s (Mach 0.12). This result is consistent with that obtained by the momentum integration method based on the pitot-rake measurement, which provided a reference dataset of the boundary layer profile.  
Keywords:riblet; turbulent boundary layer; hot-wire anemometry; skin friction drag; statistical evaluation +
Footnote †: journal: Applied Physics Letters B
## 4 Redundant Transmission Control Clustering Approach
### Network Data Collection  
The MS has the location information \((X_{m},Y_{m},Z_{m})\) of each RH where \(m\) is the number of regions in the trajectory of the MS. After the start of the network, the MS individually visits each of the DCP, collects data from the RHs and moves toward the next DCP. On the trajectory, the number of DCPs may increase depending upon the depth levels. The RH collects data from all the CHs in a region. The CHs in each region have their unique IDs. The RH is also responsible for splitting up and erasing the similar data sets from the existing data sets. Our algorithm intelligently decides which data is conveyed toward the final destination from the available data sets. Instead of forwarding all the information, only the selected information is conveyed to the MS with higher measures to increase the system efficiency and resources.
# Integrated Model Selection and Scalability in Functional Data Analysis Through Bayesian Learning
## 1 Introduction
### Contributions  
This manuscript proposes a novel _Bayesian framework for functional principal component analysis_ that leverages nonparametric kernel expansions, sparse Bayesian learning for model selection, and efficient variational inference (VI). We abbreviate the proposed method as _BSFDA_ (Bayesian scalable functional data analysis). (The code is available at [[https://github.com/WeenZh/BSFDA](https://github.com/WeenZh/BSFDA)]([https://github.com/WeenZh/BSFDA](https://github.com/WeenZh/BSFDA)), accessed on 21 March 2025). _BSFDA_ addresses critical gaps in existing FDA techniques with irregular sampling, high-dimensional scalability, and the selection of both basis functions and principal components. Specifically, our approach offers the following:  
* **Joint selection of optimum latent factors and sparse basis functions**: This eliminates constraints on parametric representation dimensionality, avoids information loss from discretization, and extends naturally to higher dimensions or non-Euclidean spaces through nonparametric kernel expansion. It further enhances the interpretability by adaptively choosing the model complexity without testing multiple models separately. We achieve these improvements using a Bayesian paradigm that provides robust and accurate posterior estimates while supporting uncertainty quantification.
* **Scalability across domain dimensionality and data size:** The proposed method uses VI for faster computation compared to Markov chain Monte Carlo (MCMC) methods, while still being accurate in terms of the estimation of the intrinsic dimensionality and overall covariance structure. BSFDA reduces the overall computation by partitioning the parameters into smaller update groups and introducing a slack variable to further subdivide the weighting matrix (which is part of the kernel structure) into even smaller parts [18], updating fewer blocks at a time and considering all model options. Introducing a slack variable makes the optimization process more efficient by separating different variable groups. This approach scales well with the data size and works efficiently even with large, complex datasets. We demonstrate this on the 4D global oceanic temperature dataset (ARGO), which consists of 127 million data points spanning across the globe for 27 years, with depths of up to 200 m [37].
## 4 Methods for Improved Reliability and Robustness
### Automatic Correction
#### 4.2.3 Data Acquisition  
Methods trying to mitigate data acquisition problems to improve the robustness of CMR segmentation models have mostly focused on improving the image quality at the image reconstruction phase. Among these, [PERSON] et al. [100] propose two different methods to segment the heart directly from the k-space of dynamic MRI data, bypassing middle reconstruction stages. The first method relies on an end-to-end synthesis network that exploits the spatiotemporal redundancy of the input to generate the segmentations directly from the input k-space. The second method is conceived for heavily undersampled and aliased images, where there may be a loss of geometrical information and the first approach fails. It uses an autoencoder and a predictor network. The autoencoder is trained to encode and decode segmentations. The predictor learns to map undersampled images to latent encodings. The predicted encodings are used by the autoencoder to decode the corresponding segmentation maps. [PERSON] et al. [101] propose a method that takes as input the undersampled k-space data from CMR scans to solve the reconstruction and segmentation problems simultaneously. The reconstruction is derived from the fast iterative shrinkage-thresholding algorithm (FISTA), while the segmentation is based on a U-Net architecture. Combining the two modules into a joint single-step, the reconstructed image becomes a set of differentiable parameters for the segmentation module itself, allowing the two to mutually benefit from each other through backpropagation. Finally, [PERSON] et al. [102] propose to detect, correct and segment CMR images with motion artifacts, integrating reconstruction and segmentation in a unique framework, which combines a spatiotemporal 2D+time CNN for artifact detection, a convolutional recurrent neural network for reconstruction and a classical U-net for segmentation. The full framework is trained by incorporating terms from all three subnetworks into an overall loss function.
## 4 Discussion
Furthermore, mechanical properties of structures fabricated using AM processes such as SLS, FDM, and 3 DB are generally anisotropic [19; 39; 40; 41]. This directional dependency arises, in part, due to poor inter-strand and inter-layer adhesion that occurs as a result of layer-wise processing. At the inter-layer interface, the degree of macromolecular entanglement during recrystallization of the polymer melt is lower, resulting in decreased mechanical strength [42]. The mechanical properties are also affected by the strand lay pattern [19; 42]. In the case of FDM and 3 DB, the lay pattern is dictated by the strand lay orientation and inter-strand spacing. The lay pattern can affect the mechanical properties due to the differences in effective interfacial area and macromolecular (re)crystallization at the interface between layers. The degree of macromolecular polymer chain re-entanglement within a strand is generally higher than at the inter-layer interface. [PERSON] et al. have demonstrated how process parameters such as lay orientation, inter-strand spacing, and extrusion temperature affect the tensile strength of FDM-printed ABS structures [19]. [PERSON] and [PERSON] have documented the differences in mechanical properties of SLS-printed PCL structures in response to changes in parameters such as lay orientation, inter-strand spacing, and laser power and speed [22]. [PERSON] et al. have reported on the differences in compression strength of extrusion-plotted PCL scaffolds in response to changes in the strand lay orientation and inter-strand spacing [43]. Similar anisotropic relationships were observed in the tensile properties of bioplotted PCL in Phase-2. The specimens of 0\({}^{\circ}\) configuration exhibited higher values for all three tensile measures. In these specimens, the tensile load was borne by the cross-sections of individual strands, which were aligned along the direction of loading. On the other hand, in 90\({}^{\circ}\) configuration specimens, the tensile load was borne by interfacial area of adjacent strands, which were perpendicular to direction of loading. The relative weakness in this interfacial area can be attributed to the nature of the 3 DB process wherein molten strands are deposited adjacent to strands that have already solidified completely or partially. Such differences in strength resulting from variations in strand lay orientation can be utilized to additively manufacture scaffolds for tissues that require anisotropy. The 44% reduction in elastic modulus of porous specimen compared to bulk 0\({}^{\circ}\) configuration in bioplotted PCL was a similar in trend as reported in SLS-printed PCL (40%) [22].
## 3 The Virtual-Force-Based Guidance Law
### The Virtual Forces for Straight-Line Following  
Inspired by the spring mass system, a virtual spring force (\(F_{vl}\)) and a virtual drag force (\(F_{vl}\)) are designed to govern a UAV to track a reference line. \(F_{vl}\) points to the reference point, which is the nearest point on the reference straight line to the UAV. The direction of \(F_{vl}\) is opposite to the relative velocity \(v_{VP}\), which is the velocity of the UAV relative to the moving reference point as shown in Figure 2.  
The magnitude of the virtual spring force and the virtual drag force are  
\[F_{vl}=k_{v}d \tag{6}\]  
\[F_{vl}=c_{v}v_{VP} \tag{7}\]  
where \(k_{v}\) is the constant of the virtual spring, \(c_{v}\) is the coefficient of the virtual drag, and the cross-track error \(d\) is the distance between the UAV and \(P_{r}\). In decomposing \(F_{vl}\) and \(F_{vl}\) along \(VP_{r}\) and the normal direction of \(VP_{r}\), the motion of the UAV can be decomposed in the two directions.  
In the \(VP_{r}\) direction, there are two forces. One is the virtual spring force \(F_{vl}\), and the other is the component of the virtual drag force in the \(VP_{r}\) direction, \(F_{vlm}=c_{v}v_{m}=c\dot{d}\). Then, the dynamics of the cross-track error \(d\) are  
\[\ddot{d}+c_{v}d+k_{v}d=0 \tag{8}\]  
This is same as the dynamics of the mass spring system (3). The conclusions of the mass spring system are also valid here: if \(c_{v}\geq 2\sqrt{k_{v}}\), the cross-track error \(d\) converges to 0 without overshoot or oscillation.  
In the normal direction of \(VP_{r}\), the initial velocity of the UAV relative to the moving reference point is \(v_{n}(0)=v_{n0}\), the resultant force is the component of the virtual drag force in this direction, \(F_{vln}=c_{v}v_{n}\). The motion of the UAV in this direction is given by  
\[\begin{array}{l}\dot{v}_{n}(t)=-c_{v}v_{n}(t)\\ v_{n}(0)=v_{n0}\end{array} \tag{9}\]  
The analytical solution of (9) is  
\[v_{n}(t)=v_{n0}e^{-c_{v}t} \tag{10}\]  
Figure 2: Diagram of the virtual spring force and the virtual drag force for straight-line following.  
For the drag coefficient \(c_{v}>0\), the relative velocity \(v_{n}\) exponentially converges to 0.  
Using the two virtual forces, both \(d\) and \(v_{n}\) converge to 0, which means that the UAV converges to the reference point and follows the reference line with the reference velocity \(v_{r}\).
## 1 Introduction
### Empirical and Statistical Modeling Approach
[22] attempted to enhance the existing empirical models through the application of a differential evaluation (DE) optimization algorithm to a non-linear model created using dimensional analysis (DA). Data comprising input parameters \(B\), \(S\), \(l_{s}\), \(l_{lh}\), \(d\), \(q\), specific drilling (\(D_{sp}\)), and charge weight (\(Q\)) were collected for 300 blasts conducted using ANFO and detonating cord initiation at the Sungun Copper Mine, Iran. The MCS-based sensitivity analysis showed that \(q\) and \(l_{s}\) were the most significant influencing variables. The authors recommended applying the principles of physics and trajectory motion considering the properties of flying fragments to improve prediction capabilities.
## 3 Machinery
### Variables, Signatures, Alphabets, Terms and Trees  
We start with signatures and term algebras.  
* Let \(\mathcal{S}\) be a set of sorts. Sorts are names or placeholders for abstract data types.
* We assume the existence of countably infinite sets of variables \(V_{\alpha}\) for every sort \(\alpha\) of \(\mathcal{S}\). The union of all \(V_{\alpha}\) is denoted by \(V\).
* We assume a finite \(\mathcal{S}\)-sorted signature \(F\). An \(\mathcal{S}\)-sorted signature is a set of function symbols together with a sort declaration \(\alpha_{1}\times\ldots\times\alpha_{n}\rightarrow\alpha\) for every \(f\) of \(F\). Here \(\alpha_{1},\ldots,\alpha_{n},\alpha\in\mathcal{S}\) and \(n\) is called the arity of \(f\). Function symbols of arity 0 are called constant symbols.  
* A signature \(F\) may include an alphabet \(\mathcal{A}\), which is a designated subset of the nullary function symbols (constant symbols) in \(F\). Where the distinction matters, the function symbols in \(\mathcal{A}\) are called _characters_, and the other function symbols are called _operators_.
* \(T(F,V)\) denotes the set of well-typed terms over \(F\) and \(V\). It is the union of the sets \(T_{\alpha}(F,V)\) for \(\alpha\) in \(\mathcal{S}\) that are inductively defined as follows: 1. each variable of \(V_{\alpha}\) is a term of \(T_{\alpha}(F,V)\), 2. if \(f\) is a function symbol of \(F\) with sort declaration \(\alpha_{1}\times\ldots\times\alpha_{n}\rightarrow\alpha\) and \(t_{i}\in T_{\alpha_{i}}(F,V)\) for all \(1\leq i\leq n\), then \(f(t_{1},\ldots,t_{n})\) is in \(T_{\alpha}(F,V)\). \(T(F,V)\) is called the term algebra over \(F\) and \(V\). The set of variables appearing in a term \(t\) is denoted by \(var(t)\), and its sort by \(sort(t)\) such that \(sort(t)=\alpha\) when \(t\in T_{\alpha}(F,V)\).
* \(T(F)\) denotes the set of ground terms of \(T(F,V)\), where \(var(t)=\emptyset\).
* A _substitution_\(\sigma\) maps a set of variables to a set of terms, preserving sorts. The application of a substitution \(\sigma\) to a term \(t\) replaces all variables in \(t\) for which \(\sigma\) is defined by their images. The result is denoted \(t\sigma\).
* _Terms and trees_: We adopt a dual view on terms as trees. A term can be seen as a finite rooted ordered tree, the leaves of which are labeled with constant symbols or variables. The internal nodes are labeled with function symbols of arity greater than or equal to \(1\), such that the outdegree of an internal node equals the arity of its label. Note that alphabet characters, being nullary function symbols, can only occur at the leaves of a tree.
* A _position_ within a term can be represented as the sequence of positive integers that describe the path from the root to that position (the path is given in [PERSON]'s notation). We denote by \(t|_{p}\) the subterm of \(t\) at position \(p\) and \(t[u]_{p}\) the replacement of \(t|_{p}\) in \(t\) by \(u\).
# Semi-CNN Architecture for Effective Spatio-Temporal Learning in Action Recognition
## 1 Introduction  
Action recognition via monocular video has valuable applications in surveillance, healthcare, sports science and entertainment. Deep learning methods such as convolutional neural network (CNN) [1] have demonstrated superior learning capabilities and potential in discovering underlying features when given a large number of training examples.  
An action in video sequences can be characterized by its spatial and temporal features across consecutive frames. Spatial features provide contextual information and visual appearance of the content, while temporal features define the motion dynamics that happens in the range of the video frames. The task of action recognition is to effectively learn discriminative and robust spatio-temporal representations from video sequences for identifying different action classes. However, network performance often degrades when dealing with high variations of realistic and complex videos, due to major challenges such as occlusion, camera viewpoints, background clutter and variations in the subjects and motion involved.  
Supervised learning with CNNs has been studied and exploited to perform action recognition, where representations in the spatial and temporal dimensions can be encoded in separate streams or simultaneously. Spatial features are extracted directly from RGB frames using 2D CNNs, whiletemporal features are represented by pre-computed hand-crafted features such as optical flow or motion trajectory, or a stack of consecutive frames. Direct learning of spatio-temporal features from video frames can be implemented using 3D CNNs, which share a similar structure as 2D CNNs, but replace all the 2D convolution kernels with 3D ones.  
This paper exploits the architecture of 2D and 3D CNNs and introduces an efficient fusion approach that combines the spatial layers in 2D CNN and spatio-temporal layers in 3D CNN. We utilize pre-trained models on ImageNet to initialize our 2D convolution layers and perform fine-tuning on the 1D and 3D convolution layers. Our empirical results demonstrate that segregation and fusion of convolution layers in the spatial and temporal spaces outperforms its 3D model of the same depth, when evaluated on the action recognition dataset UCF-101 [2].
## 3 Results
### Oil Quality and Aroma Characteristics Evaluation  
#### 3.5.1 Lipid Composition in Rapeseed Oil  
The main components such as TAG, DAG, MAG, and FFA in the rapeseed oils before and after enzymatic deacidification were compared. As shown in Figure 5a, the high-acid rapeseed oil consisted of 90.0% of TAG, 1.2% of DAG, 0 MAG, and 8.8% of FFA. After the enzymatic esterification catalyzed CALB@MCM-41-C\({}_{8}\), both TAG and FFA were sharply reduced to 55.0% and 0.5%, respectively. Here, FFA reduction was associated with its esterification reaction with glycerol, while the decrease in TAG was mainly due to the glycerolysis reaction with glycerol and simultaneous TAG hydrolysis to some extent [64; 65]. Such reactions could also promote the generation of DAG and MAG, which jumped to 40.2% and 4.3%, respectively. Both DAG and MAG are functional structural lipids that are  
Figure 4: Effects of (**a**) temperature, (**b**) substrate molar ratio of FFA to glycerol, (**c**) adding an amount of CALB@MCM-41-C\({}_{8}\), and (**d**) the addition of 4 Å molecular sieve on the enzymatic deacidification of rapeseed oils. Reactions were carried out in conditions with high-acid rapeseed oil (10 g), glycerol (0.02–0.1 mol), CALB@MCM-41-C\({}_{8}\) (0.01–0.3 g), and 4 Å molecular sieve (0–0.8 g).  
widely used in food and healthcare areas [66; 67]. Hence, enzymatic deacidification using glycerol as an acyl donor can not only effectively reduce the acid value of rapeseed oil but also simultaneously produce functional lipids to improve the oil quality economically and functionally.  
Moreover, the antioxidant stability of deacidified rapeseed oil was compared with commercial refined rapeseed oil and high-acid rapeseed oil, based on the corresponding oxidation induction time and peroxide values (Figure 5b). Compared to the high-acid rapeseed oil, whose oxidation induction time was 4.25 h, the deacidified rapeseed oil and commercial refined oil extended the oxidation induction time to 7.35 h and 10.51 h, respectively. Namely, the deacidification to remove FFA is an essential strategy to improve the oil antioxidant stability [68]. This can be confirmed by the peroxide values, which were commercialized oil, high-acid oil, and deacidified oil for 0.07 g/100 g, 0.3 g/100 g, and 0.16 g/100 g, respectively. Since the peroxide values of both commercial oil and deacidified oil were below the standard level of 0.25 g/100 g for edible oil, it is concluded that enzymatic deacidification using CALB@MCM-41-C\({}_{8}\) is applicable to improve the antioxidant stability of rapeseed oil based on the Chinese GB/T 1536-2021 standard [69].
# Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm
## 4 Performance Analysis
### Statistical Performance  
This experiment analyzes the statistical performance of the proposed algorithm, which is evaluated using the root mean square error (RMSE), which is defined as  
\[\text{RMSE}=\sqrt{\frac{1}{moto*K}\sum_{t=1}^{moto}\sum_{k=1}^{K}\left(\hat{\theta }_{k,t}-\theta_{k}\right)^{2}} \tag{18}\]  
where _moto_ denotes the number of Monte Carlo iterations, \(\hat{\theta}_{k,t}\) is the estimated angle of the \(k\)-th target in the \(t\)-th Monte Carlo experiment, and \(\theta_{k}\) is the true angle of the \(k\)-th target. It is assumed that there are two wideband signals in the far-field space coming from angles of \(-9.56^{\circ}+\zeta\) and \(15.72^{\circ}+\zeta\), respectively. \(\zeta\) takes a random value in the range of \([-0.5,\ 0.5]\). The number of frequency-domain snapshots is fixed at 1024. The SNR increases from 0 dB to 15 dB in steps of 1 dB, and 500 Monte Carlo experiments are carried out for each SNR value. The resulting RMSE curve of the angle estimation versus the SNR is shown in Figure 8a. In addition, the SNR is fixed at 6 dB. The number of frequency-domain snapshots increases from 10 to 1050 in steps of 50, and 500 Monte Carlo experiments are performed for each number of frequency-domain snapshots. The resulting RMSE curve of the angle estimation versus the number of frequency-domain snapshots is shown in Figure 8b.  
In Figure 8a, it can be seen that the RSS-MUSIC method can obtain better estimation performance when the angular interval is larger. RSSMUSIC has a smaller RMSE at a low SNR, but its statistical performance no longer improves as the SNR increases. The RMSE of the L1-SVD method is smaller than that of RSSMUSIC. The CWSVR method relies more on the training data and has a certain tendency of overfitting. When the SNR is greater than 3 dB, the RMSE of the proposed algorithm is lower than those of other methods. Furthermore, it achieves notable advantages in high-SNR scenarios with superior estimation accuracy. Additionally, minor fluctuations can be observed in the RMSE curve, particularly in the SNR range from 14 dB to 16 dB. This is attributed to the SNR mismatch between the training and testing data. From Figure 8b, it can be seen that the RMSE of the proposed algorithm decreases with the increase in the number of frequency-domain snapshots. When the number of frequency-domain snapshots exceeds 350, the proposed algorithm achieves the lowest RMSE compared to the other algorithms under the same conditions, demonstrating its superior performance.  
Figure 8: The estimation performance of two signals: (**a**) varying SNR; (**b**) varying number of frequency-domain snapshots.