esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Theoretical Analysis of Double-Column Self-Centering Piers with Shear Links
### Model Validation  
In this section, the aforementioned equations to calculate the lateral force-displacement skeleton curve are validated by the FE simulations of the double-column self-centering piers with and without shear links. The FE models are built based on the modeling method presented in Section 2, and the corresponding parameters are listed in Table 1.  
The theoretical and FE results of the double-column self-centering pier with shear links are shown in Figure 15, while the results of the double-column self-centering pier without shear links are shown in Figure 16. The skeleton curve calculated by using the theoretical model consists of four parts. Curves OA and AB refer to the pre-rocking state and rocking state in the positive lateral loading condition, respectively, while curves OC and CD refer to the pre-rocking state and rocking state in the negative lateral loading condition, respectively. The results derived from the theoretical models are in accordance with the results from the FE models. However, during the pre-rocking state, when the lateral displacement approaches the critical point between the pre-rocking and rocking states, the stiffness of the theoretical model is greater than the stiffness of the FE model. This occurs is because the columns are modeled as rigid bodies in the theoretical model. However, the deformation of the column ends will result in decreased stiffness. Further, comparing Figures 15 and 16, it can be seen that the shear links between columns endow the pier with a preferable energy dissipation ability.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameters** & **Pier with Shear Links** & **Pier without Shear Links** \\ \hline \multirow{4}{*}{Geometrical dimensions} & Height of columns, \(h\) & 10.0 m \\  & Cross section of columns, \(A_{\mathrm{col}}\) & 2.0 m \(\times\) 2.0 m \\  & Distance between columns, \(d\) & 3.0 m \\  & Area of steel strands, \(A_{\mathrm{PT}}\) & 12/737.8 mm\({}^{2}\) \\ \hline \multirow{4}{*}{Material properties} & Elastic modulus of concrete, \(E_{\mathrm{c}}\) & \(3.6\times 10^{4}\) MPa \\  & Axial compressive strength of & 38.5 MPa \\  & concrete, \(f_{\mathrm{c}}\) & \(1.95\times 10^{5}\) MPa \\  & Elastic modulus of steel strands, \(E_{\mathrm{PT}}\) & \(1860\) MPa \\ \hline \multirow{2}{*}{Gravitational forces} & Gravity of superstructure, \(W_{\mathrm{ss}}\) & \(3.08\times 10^{4}\) kN \\  & Gravity of columns, \(W_{\mathrm{col}}\) & \multicolumn{2}{c}{l ignored} \\ \hline \multirow{2}{*}{Shear links} & Initial stiffness of shear links, \(k_{\mathrm{SL}}\) & \(1.6\times 10^{4}\) kN/m & / \\  & Yield strength of shear links, \(F_{\mathrm{ySL}}\) & \(2.0\times 10^{4}\) kN & / \\ \hline Other & Initial prestress of steel strands, \(F_{\mathrm{PTO}}\) & \multicolumn{2}{c}{\(1.54\times 10^{4}\) kN} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters in finite element (FE) models.
# Propagation Characteristics of High-Power Vortex Laguerre-Gaussian Laser Beams in Plasma
## 1 Introduction  
The complex interaction of high-power laser with plasma has become a popular research topic due to its various applications in science and technology, including laser-driven inertial confined fusion (ICF) [1], laser-driven accelerators [2] and laser machining [3]. The main research areas have focused on the propagation characteristics of high-power laser beams in plasma [4; 5; 6]. The Drude model of electrical conduction is extensively considered in this research field [7; 8]. [PERSON] et al. [9] have found that the propagation characteristics of an intense laser beam in plasma depend on the power and width of the beam and the ratio of plasma frequency and light wave frequency. [PERSON] et al. [10] have investigated the propagation characteristics of a Gaussian laser beam in unmagnetized cold plasmas, based on the theory of ponderomotive nonlinearity. They also applied the Wentzel-Kramers-Brillouin (WKB) method to study the propagation characteristics of a Gaussian laser beam in cold plasma [11]. However, since the above studies are conducted by only solving the scalar wave equation with complex eikonal function assumption and paraxial approximation, the obtained information about the dynamic laser-plasma interaction is limited. Additionally, the solutions are not accurate enough for tightly focused laser beams whose sizes are in the order of several wavelengths.  
In recent decades, the finite-difference time-domain (FDTD) method has become a popular numerical technique in directly solving Maxwell's curl equations [12]. The FDTD method also has well-developed techniques for accurately simulating the Drude model [13; 14; 15] and numerically generating any types of laser beams based on the total-field/scattered-field (TF/SF) technique [16].  
Therefore, the current study evaluates the FDTD method in investigating the propagation characteristics and relevant phenomena of high-power Laguerre-Gaussian laser beams in plasma.
## 4 Experiments and Results
### Image Preprocessing  
Significant variations in resolution and contrast across the photos in the OIA-ODIR dataset occurred as different pieces of equipment were not used to capture them simultaneously. Other issues included uneven illumination and lens blurring. Since the model was prone to overfitting and the fundus images had little data, this paper aimed to improve the images before submitting them to the network for training.  
Since most of the photos in the dataset were rectangular and the input model was square, as shown in Figure 7a, directly scaling the cropped extra black region would result in the distortion issue depicted in Figure 7c. Thus, as illustrated in Figure 7b, we first searched for non-black pixels in the four directions before cropping the image to create a square that only included the bottom of the eye. This was used to scale and crop the image to the dimensions needed by the network. Next, random flipping of the image's horizontal  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Epochs & 30 \\ Batch size & 16 \\ Optimizer & SGD \\ Learning rate & 0.0007 \\ Momentum parameter & 0.9 \\ Weight decay parameter & \(1\times 10^{-6}\) \\ Learning rate scheduler & StepLR \\ Scheduler step size & 5 \\ Learning rate decay factor & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter settings in training.  
Figure 6: Fundus images of different categories randomly selected from the OIA-ODIR [19] dataset containing normal images (N) and six diseases, such as diabetic retinopathy (D), glaucoma (G), cataract (C), AMD (A), myopia (M), and hypertension (H), as well as other diseases (O).  
and vertical axes increased its diversity. The overfitting problem was resolved by adjusting the image's brightness, contrast, saturation, and hue. Finally, the image was normalized using the mean and standard deviation to quicken the model's convergence.
# A Systematic Analysis of Real-World Energy Blockchain Initiatives
## 2 Background
### Challenges and Motivation  
There are many potential technical and market challenges that must be considered to advance the development and adoption of real-world energy blockchains. Some of the more prominent and obvious challenges are summarised below.  
* **Regulation** will be needed to support the introduction of co-operatives and private individuals across the energy value chain [16], given new types of market participants (e.g., peer-to-peer prosumers) cannot realistically adhere to the same regulations as large-scale energy providers [17].
* **Scalability** concerns relating to different aspects of energy blockchains must be addressed to enable large-scale rollouts on public infrastructure [4]. A consequence of poor scalability may manifest unreliable real-time transaction performance, and unsustainable blockchains that consume more energy than the transactive value of underlying energy trades [14]. These economically imbalanced blockchains are somewhat synonymous with the proof-of-work consensus model, where computationally intensive cryptographic problems must be solved to commit transactions. Additionally, other scalability concerns pertaining to memory and compute limitations may arise when advanced models (e.g., machine learning) and ancillary operating data are embedded within the blockchain [2]. Therefore, engineers must carefully consider which assets belong inside and outside the blockchain to promote scalability [3; 5].
* **Integration** methods for (a) blockchain-to-grid and (b) blockchain-to-blockchain must be addressed to ensure real-time, reliable and trusted information flows between market participants and stakeholders. Interfaces with a physical grid infrastructure should be considered important when financially accounting for two-way electricity flows, dynamic tariffs and market models [13], while integration and interoperability between disparate energy blockchains may be needed, where energy providers and grid operators support different architectures and topologies [4].
* **Market** policies and logic are needed to ensure the trust and security characteristics of blockchain technology are not circumvented by ambiguous transactional data, or inaccurate business logic. Such market challenges may include the development of formal methods to identify, calculate and settle energy losses between different combinations of market participants [5], and the introduction of formal and trusted methods to manage the temporal lifecycle of smart contracts as terms and conditions change [2].
# Real-Time Method and Implementation of Head-Wave Extraction for Ultrasonic Imaging While Drilling
###### Abstract
These algorithms are based on post-measurement processing methods in the surface computer but offer limited real-time guidance while drilling. Recently, downhole digital signal processing has emerged as a promising solution because it can overcome the disadvantages of the poor stability and low accuracy of the analog circuit method but also provide real-time guidance while drilling [22; 23; 24]. After the UILWD echo signal is digitized in the downhole, head-wave information data can be extracted according to the difference between the useful signal and ambient noise. Then, the extracted result needs to be simply uploaded to the surface. This method reduces the amount of uploaded data and also ensures the real-time surface observation of borehole conditions. Thus, it is beneficial to the integration of drilling and logging operations. The amount of computation for head-wave extraction is large, thereby necessitating a certain amount of logic resources and a high processing speed. FPGA has rich logic gate resources and supports hardware parallel operation. It can also meet the requirements set by the algorithm and ensure the real-time performance of downhole data processing. [PERSON] et al. developed a two-branch hybrid framework for LWD sonic logging to predict hydrate-bearing sediments based on existing logging data. One branch based on the seismic rock physics model is used to generate the background elastic wave velocity profile, and the other deep learning branch is used to compensate for the residual between the actual observations and the branch output of the physical model. The hybrid model improves the prediction accuracy of the physical process model. The method can be extended to apply appropriate inputs and physical models to predict other types of logging or reservoir parameters [25; 26]. It provides a new reference for the feature extraction of UILWD data. The paper concerns the formulation of an algorithm for signal analysis in the time domain. The subject of the paper is the issues of identifying specific features of the recorded pulse signal, taking into account interference and low signal quality. On the basis of previous studies, an improved energy ratio head-wave arrival extraction algorithm, which is based on the weighting coefficient and characteristic function, and an amplitude detection method based on peak-to-peak values are proposed herein. An echo reception pre-processing analog circuit and a digital signal processing circuit based on FPGA are designed to realize a pipeline algorithm that can measure the arrival time and amplitude extraction of head waves. Software simulations, laboratory tests, and field experiments related to this method are also conducted to validate the performance and efficiency of the method.
# Body Height Trajectories in Pediatric Competitive Athletes from 46 Different Sport Types
###### Abstract  
There is some concern that competitive sports in children and adolescents might hinder their growth. This study's purpose was to examine height changes in pediatric competitive athletes from 46 different sports. Clinical data from athletes aged <18 years that underwent annual preparticipation examinations at a single sports medicine center were extracted from computerized medical records. Height was transformed into standard deviation scores (SDS). Generalized estimating equations were used to analyze height SDS changes over time in the total cohort and in subgroups of age, sex and several sport types. Data on 2276 athletes were available (71.3% males, age: 12.3 \(\pm\) 2.6 years), of which 688 had repeated measurements. The median duration between examinations was 1.9 years (interquartile range: 1.0-2.9, maximum: 9.3 years). Height SDS neither significantly changed throughout follow up in the total cohort (\(-\)0.01 per year, 95%CI = \(-\)0.48-0.03, \(p\) = 0.62) nor in subgroups of age and sex. However, height SDS was significantly reduced by \(-\)0.12 to \(-\)0.23 per year in athletes engaged in endurance sports (i.e., swimming, cycling and triathlon) but not in gymnastics, tennis, basketball or football. We conclude that competitive sports in youth are generally not associated with significant changes in body height relative to age. However, this might occur in endurance sports, possibly due to low energy availability.  
Keywords:adolescents; children; growth; sport; stature +
Footnote †: journal: Medical Image and Vision
## 3 Results and Discussion
### Evaluation of SFE-CO\({}_{2}\) of Hops at 24-45 MPa Pressure
#### 3.2.3 Sfe-Co2 Optimization by the Desirability Function  
Based on the response surface plots and the predictive equations that describe the model, the SFE-CO2 optimization was upgraded to obtain _Ella_ hop extract combining a  
Figure 2: Pareto charts (\(p\) = 0.05) for the main effects of SFE-CO2 pressure (P), temperature (T) and time (\(\tau\)) and interactions thereof on the _Ella_ hop: (**a**) SFE-CO2 extract yield (g/100 g HP); (**b**) oxygen radical scavenging capacity (TEACORAC, mg TE/g HP).  
high yield (>26 g/100 g HP) and TEACORAC (>360 mg TE/g HP) under the lowest possible P and shortest \(\tau\). For this task the Design-Expert software suggested 37-38 MPa, up to 43 \({}^{\circ}\)C, 80-85 min. For example, 80 min extraction at 37 MPa and 43 \({}^{\circ}\)C yielded 26.3 g/100 g HP of greenish-brown extract with the TEACORAC of 1481 mg TE/g E, equivalent to 390 mg TE/g HP (Table 1). Good agreement between the experimental and the predicted values under deduced optimal conditions additionally confirmed the suggested model's validity for both response factors (Supplementary Materials, Table S2). Generally, maximum extract yields from Ella hops at 10-37 MPa were higher than those previously reported under the various experimental conditions; e.g., for _Hallertau Mittelfriuh_ pellets it was 7 g/100 g at 20 MPa/55 \({}^{\circ}\)C/180 min [10]; for _Nugget_ variety and five Chilean hop ecotype pellets it was 3-13 g/100 g at 20 MPa/40 \({}^{\circ}\)C/150 min [33]; for several unspecified _H. Inpuulus_ samples it was 2-9 g/100 g at 30-35 MPa/250-300 min [11; 12].  
Comparing the 10, 12.5, 15 and 37 MPa results (Table 1), an up to \(\sim\)3-fold increase in SFE-CO\({}_{2}\) extract yield was obtained in \(\sim\)4-fold shorter \(\tau\) (80 versus 300 min) when optimized P of 37 MPa was applied. Similarly, the extract with high TEACORAC (1481 mg TE/g E) was produced at 37 MPa, remarkably reducing the \(\tau\) of supercritical CO\({}_{2}\)-soluble antioxidant constituents' recovery from hop pellets and augmenting its content by up to 334% as compared to 10-15 MPa treatments (Table 1).
# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
## 4 Data Gathering with the Help of Multi-Sink
### Redundancy Reduction on Data Gathering
#### 4.2.1 Active Node Selection in Each DGA  
It is known that, energy consumption on sensing for node \(S_{i}\) (defined as \(e_{S}(S_{i})\)) per unit time is related to the length of its sensing radius \(R_{S}(S_{i})\)[42]. That is,  
\[e_{s}(S_{i})=\lambda_{1}R_{s}(S_{i})^{\mu} \tag{6}\]  
\(\lambda_{1}\) and \(\mu\) are the adjustable parameters while the residual energy of \(S_{i}\) is defined as \(E_{r}(S_{i})\). To prolong network lifetime, we have,  
\[e_{s}(S_{i})\times T=\lambda_{2}E_{r}(S_{i}) \tag{7}\]  
Figure 6: Leader nodes selection in each DGA.  
Figure 7: Distributed sleep scheduling strategy in each DGA.  
\(T\) is the expected value of network lifetime and \(\lambda_{2}\) is the adjustable parameter whose value is between 0 and 1. Thus, with the decrease of the residual energy, length of sensing radius of \(S_{i}\) should also be reduced to conserve its energy. Thus, according to Formulas (6) and (7) we get,  
\[R_{s}(S_{i})=\left(\frac{\lambda_{2}E_{r}(S_{i})}{\lambda_{1}T}\right)^{\frac{1 }{p}} \tag{8}\]  
For the convenience of calculation, the network is divided into a number of virtual grids, whose length and width are \(m\) and \(l\) respectively, as shown in Figure 8. It is assumed that \(m\) and \(l\) could be divisible by \(M\) and \(L\). \(g_{j}\) is defined as the sensing area coverage degree of grid \(j\), whose initial value is 0.  
At first, all the leaders in each DGA are selected as the active nodes and the length of their sensing radiuses could be calculated by Formula (8). Thus, the value of each \(g_{j}\) now is shown in Figure 8.  
Then, other nodes in the same DGA execute the following judgment one by one according to the value of their residual energy \(E_{r}(S_{i})\) (from high to low). For each grid \(j\) that in the area of the circle whose center and radius are \(S_{i}\) and \(R_{S}(S_{i})\), if  
\[\left(x_{j}\pm\frac{m}{2}-x\left(S_{i}\right)\right)^{2}+\left(y_{j}\pm\frac{ l}{2}-y\left(S_{i}\right)\right)^{2}\leq R_{S}\left(S_{i}\right)^{2} \tag{9}\]  
Then, \(g_{j}=g_{j}+1\). \(x_{j}\) and \(y_{j}\) are the horizontal and vertical coordinates of the center of grid \(j\), while \(\left(x(S_{i}),y(S_{i})\right)\) is the coordinate of \(S_{i}\). As for all the grids in circle \(S_{i}\), if the value of \(g_{j}\) are all greater than 1, it is considered that the sensing area of \(S_{i}\) has been completely covered by other active nodes. Thus, \(S_{i}\) goes into sleeping mode and in its sensing area, \(g_{j}=g_{j}-1\). Otherwise, \(S_{i}\) becomes the active node.
# Non-Local Means Hole Repair Algorithm Based on Adaptive Block
## 2 Depth Image Inpainting Algorithm
### Image Preprocessing  
Image preprocessing is a critical stage in in-depth image repair, serving to enhance image quality and prepare the data for subsequent repair algorithms. In this section, we delve into the significance of image preprocessing, its objectives, and the specific operations employed to align grayscale and depth images.  
Depth images captured using cameras often encounter voids caused by surface reflections, occlusions, and other factors, particularly in the interior and boundary regions of objects. Image preprocessing plays a crucial role in improving image quality, enhancing visualization effects, reducing noise interference, extracting meaningful features, and adapting to specific processing requirements, which provides a solid foundation for subsequent image processing and analysis tasks.  
In the image preprocessing stage, this paper employs the following operations: First, a Gaussian filter is applied to the color image to reduce noise. Then, the color image is converted to a grayscale image. Subsequently, a perspective transformation matrix is used to align the grayscale image with the depth image. Finally, aligned depth and grayscale images are obtained, and the depth image is overlaid onto the grayscale one, as shown in Figure 1c. It can be observed that the aligned depth image and grayscale image exhibit similar structures [22]. To enhance the accuracy and quality of depth image hole repair, the similarity in structure between the grayscale and depth images can be leveraged to predict and repair the holes present in the depth image.  
Figure 1: A schematic representation of the structural similarity between grayscale images and depth images.
# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 3 State-of-the-Art Privacy Measures
### Key Findings
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Privacy Approach** & **Means of Privacy Protection** & **Effects of the Measures** \\ \hline General Privacy Measures & The three relational algebra operators—selection, projection, and aggregation—can be applied to base data. & Entire data items or certain attributes can be concealed, and the base data can be condensed. \\ \multirow{3}{*}{Location Privacy} & Fake locations can be used, and spatial cloaking, path confusion, and temporal cloaking can be applied. & Individual locations or entire trajectories as well as their temporal correlations can be concealed. \\ \multirow{3}{*}{Privacy-Preserving Time-Series Data} & The data can either be compressed to reduce details or they can be amplified by fake data. & Only temporal progressions can be observed but no details on single data points. \\ \multirow{3}{*}{Voice Privacy} & The VDA can be jammed, data are preprocessed locally, and the recordings are filtered. & A VDA cannot spy on its users, and the information shared with the VDA backend is minimized. \\ \multirow{3}{*}{Image Privacy} & Blanking, scrambling, or blurring can be used to mask certain areas of an image. & Objects on an image can be obfuscated in a fine-grained manner based on their privacy sensitivity. \\ \multirow{3}{*}{Pattern-Based Privacy} & Data items can be added, removed, altered, or reordered. & Private patterns in terms of data sequences can be concealed. \\ \multirow{3}{*}{Differential Privacy} & In statistical calculations, noise ensures \(e\)-differential privacy. & No knowledge about single individuals is disclosed to third parties. \\ \multirow{3}{*}{Federated Learning} & Data processing is primarily performed locally by data producers. & Data processors only gain insight into highly aggregated knowledge. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the Key Lessons Learned from the Review of the Privacy Techniques. It is therefore sufficient to provide data processors with means to express their privacy requirements and to verify whether they are respected. So, in this context, federated learning is rather an exception that can be used if highly sensitive data are involved. Now, the question arises as to what extent these state-of-the-art privacy measures are suitable for the deployment in the application scenarios discussed in Section 2. In the following section, we therefore map the strengths and weaknesses of these approaches against the opportunities offered by and privacy threats posed by the application scenarios in order to answer this question.