esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Dispersal of Natural and Biomimetic Maple Samaras
## 3 Experimental Methods  
### Samara Morphology  
There are several aspects of the maple seed that give it its autorotation abilities, such as a thick leading edge compared to the wing, and a dense seed that optimizes the center of mass for autorotation. To capture this morphology of the maple seed, we used a sectioning method to obtain a linear mass density along the long axis of the seed. Although each maple seed is unique in its shape, mass, and wing style, a common characteristic obtained from the linear density is the spanwise position of the center of mass. To approximate the linear density and thus the center of mass location, each of the 30 Norway maples used in dynamical testing were sliced into segments ranging from 2 to 7 mm (see Figure 2a), and each segment weighed using a 1 microgram precision scale. Using this mass and length data, the center of mass was calculated for each samara. As this measurement requires destruction of the samara, this analysis was done after both the laboratory (still air) and field experiments.  
Figure 1: For both _Acer platanoides_ (Norway maple) and _Acer saccharinum_ (silver maple), we show the natural samara (seed) and an artificial, 3D-printed version.  
Figure 2: **(a)** A Norway maple seed (_Acer platanoides_) is sliced into sections and weighed in order to estimate the spanwise linear mass density, that is, mass per unit spanwise length, **(b)**, shown on a relative scale for 30 natural Norway maple seeds, as well as an average (thick line). With the spanwise length denoted as \(L\), the center of mass was found to be located slightly more than a quarter of the way from the heavy (seed) end, more precisely, at 28.5 \(\pm\) 4.0% \(L\).
## 3 Analysis of Bibliographic Results on Road Network Pricing
### Geographical Distribution of Research Activity  
In this analysis, a bibliographic coupling was undertaken to identify top-ranked geographic areas and their levels of collaboration in terms of joint author publications (Figure 5). The U.S. was ranked highest, followed by China, England, Sweden, Canada, and Australia. The link strength and distance between any two countries represents the volume of articles that are co-authored by researchers from these countries. The U.S. studies have mainly explored implications of congestion pricing, policy, public acceptability, and legislative requirements [6; 22; 47; 48].  
In China, where the rate of urbanisation soared during the past decade, road pricing research was given priority in recent times as a strategy to decrease congestion and road-based emissions [49; 50]. Studies undertaken in China explored public acceptance and legislation [51]; time varying on-street parking pricing revenue [52]; and optimal road pricing charges [53].  
In the UK, studies were conducted around different aspects of dynamic pricing. The research evaluated impacts of London's congestion charging and how to extend it [19]. The scheme, implemented in 2003, was found to generate revenue of PS122m in the year 2005/2006, including PS100m that was used to improve public transport [3]. Other countries included also had significant publications around dynamic pricing, including Singapore [54], Sweden [55], Australia [7], Germany [56], and the Netherlands [20]. Studies  
Figure 4: Historical overview of different pricing schemes around the world.  
on road network pricing in developing countries were non-existent, which suggests these concepts have not played a significant role in academic and government thinking to date. Opportunities exist in future research to target studies on impacts of road pricing in emerging economies and how government policies and community acceptance can benefit from lessons learned elsewhere. This analysis is directly related to answering both **Q-3** and **Q-4**.
# One-Step Enhancer: Deblurring and Denoising of OCT Images
## 2 Related Work
### GAN-Based Deblurring  
[PERSON] et al. proposed a method for unsupervised deblurring via disentangled representations with a single image [21]. To properly encode blur information into the deblurring framework, the model disentangles the content and blur characteristics from blurred images.
## 2 Proposed Methods
### The Proposed CNN Model  
The schematic diagram of the CNN used in this work is shown in Figure 6. The proposed CNN is a variant of Vgg11. The number of parameters of Vgg11 is up to 128 M. Such a large number of parameters led to slow training speed and also caused over-fitting. For this reason, we made modifications to Vgg11 to make it suitable for the task of gear end face defect detection.  
In the proposed model, 7 convolutional layers, 4 pooling layers, 3 fully connected layers, and 2 dropout layers were used. The model structure and parameters are shown in Table 3. We retained the depth of Vgg11 but reduced the number of channels per convolution core. With the continuous convolution and pooling of the network, the size of the feature graph kept decreasing. However, in order to obtain more comprehensive information, it was necessary to increase the number of convolutional kernels (channels), so as to effectively combine the previously learned features. Therefore, the number of channels in the convolutional kernels was set to 64-128-256-384, which reduced the number of model parameters while obtaining effective feature combination. The convolutional layer was divided into 4 blocks to learn the input features. Each block consisted of a convolution, activation, and maximum pooling order. The convolutional filter of each layer was of size 3 \(\times\) 3, and the stride was fixed at 1 pixel. These settings allowed the network to efficiently extract image features. For the choice of activation function, we adopted LeakyReLU, which, as a variant of ReLU, solved the case where the gradient is 0 in the case of negative input and avoids the gradient disappearing. This was followed by two fully connected layers (Dense 1, Dense 2) of 256 channels and a dropout layer with a retention rate of 0.2. These settings reduced the risk of network overfitting. Finally, we connected a 5-channel fully connected layer and the Softmax activation function.  
Figure 6: Schematic diagram of proposed model.
# Deepwater 3D Measurements with a Novel Sensor System
## 2 Materials and Methods
### Underwater 3D Sensor System
#### 2.1.3 Graphical User Interface  
A key factor in the effective use of this sensor for measuring applications is the user interface developed as part of the project. This software provides visualization for the ROV pilot as well as the interface for the operator of the sensor system.  
The software takes over the parameterization of the sensors to quickly adapt the exposure time and light sensitivity to the environmental conditions in the water. Furthermore, it helps to detect incorrect recordings in certain areas of the image and directly displays the 3D reconstruction result for quality control. In addition, the user interface allows the operator to control the data stream recording as well as trigger individual recordings, e.g., as part of the calibration process or 3D measurement. The ROV pilot shows the color image stream with a short latency in a separate software application. The color image can be adapted to the ambient conditions during the operation using various contrast improvement options.  
To support navigation, the measuring distance is also displayed. The visualization of 3D data, which is more difficult to interpret than color images, is not necessary for the ROV pilot. To visualize the complete 3D model, a browser-based solution using Potrees [31] was developed. This includes user management, organization in scan projects, and the automated generation of point cloud previews.  
Two examples of GUI are shown in Figure 3.  
Figure 3: Two examples of the graphical user interface of the monitoring software: a scenic view with different lighting and contrast levels, a histogram equalization sketch (**above**), and a 3D point of view with latency display (**below**); blue boxes are added for explanation.  
To enable remote access and 3D data observation without special 3D software tools, web technology was chosen. This means that anyone interested could observe and analyze data independently. In addition, it enabled data transmission via a cellular network or a satellite connection between the measurement location and an office location. This promoted parallel data analysis and could reduce the amount of personnel on the ship during the measurement process.
###### Abstract  
Photoplethysmography (PPG) has been extensively employed to acquire some physiological parameters such as heart rate, oxygen saturation, and blood pressure. However, PPG signals are frequently corrupted by motion artifacts and baseline wandering, especially for the reflective PPG sensor. Several different algorithms have been studied for determining the signal quality of PPG by the characteristic parameters of its waveform and the rule-based methods. The levels of signal quality usually were defined by the manual operations. Thus, whether the good PPG waveforms are enough to increase the accuracy of the measurement is still a subjective issue. The aim of this study is to use a fuzzy neural network to determine the signal quality indexes (SQI) of PPG pulses measured by the impedance cardiography. To test the algorithm performance, the beat-to-beat stroke volumes (SV) were measured with our device and the medis(r) CS 2000, synchronously. A total of 1466 pulses from 10 subjects were used to validate our algorithm in which the SQIs of 1007 pulses were high, those of 71 pulses were in the middle, and those of 388 pulses were low. The total error of SV measurement was \(-18\pm 22.0\) mL. The performances of the classification were that the sensitivity and specificity for the 1007 pulses with the high SQIs were 0.81 and 0.90, and the error of SV measurement was \(6.4\pm 12.8\) mL. The sensitivity and specificity for the 388 pulses with the low SQIs were 0.84 and 0.93, while the error of SV measurement was \(30.4\pm 3.6\) mL. The results show that the proposed algorithm could be helpful in choosing good-quality PPG pulses to increase the accuracy of SV measurement in the impedance plethysmography.  
Keywords:photoplethysmography; signal quality index (SQI); impedance cardiography (ICG); stroke volume (SV); self-constructing neural fuzzy inference network (SoNFIN) +
Footnote †: journal: Applied Sciences
# SiGe-Surrounded Bitline Structure for Enhancing 3D NAND Flash Erase Speed
###### Abstract  
Three-dimensional NAND Flash has adopted the cell-over-peripheral (COP) structure to increase storage density. Unlike the conventional structure, the COP structure cannot directly increase the channel potential via substrate bias during the erase operation. Therefore, the gate-induced drain leakage (GIDL) erase method, which utilizes band-to-band tunneling (BTBT) to raise the channel potential, is employed. However, compared to bulk erase, the BTBT-based erase method requires a longer time to generate holes in the channel, leading to erase speed degradation. To address this issue, we propose a structure which enhances the erase speed by surrounding the bitline (BL) PAD with SiGe. In the case of a SiGe thickness (\(\text{t}_{\text{SiGe}}\)) of 13 nm, the lower bandgap of SiGe increases the BTBT generation rate, boosting the channel potential rise at the end of the erase voltage ramp-up by 861% compared to the Si-only structure, while limiting the reduction in read on-current to within 4%. We modeled the voltage and electric field across the SiGe layer, as well as BTBT generation rate and GIDL current in the SiGe layer, by varying \(\text{t}_{\text{SiGe}}\), Ge composition ratio (SiGex), and the voltage difference between \(\text{V}_{\text{BL}}\) and \(\text{V}_{\text{GIDL}\_\_\_\_\_\_\_\_}\).  
3D NAND Flash; BTBT; COP structure; erase speed; GIDL erase; SiGe; TCAD simulation +
Footnote †: journal: Applied Sciences
# Design and Analysis of a Novel Floating Docking Mechanism for On-Orbit Refueling
## 4 Analysis and Discussion of Simulations and Experiments
### Kinematic Analysis of the Docking Mechanism  
A kinematic simulation and experiment on the tolerance range of the docking mechanism are performed in this section to verify R1 and R2.  
First, a RecurDyn simulation was performed. The 3D model is directly imported from the model reported in Figure 3, and the kinematic diagram of the docking mechanism is shown in Figure 9. The probe is fixed, and the servo motor drives the drogue with a five-DOF adjustment to dock with the probe. By changing the initial state (the linear and angular misalignments between the probe and the drogue), the tolerance range of the docking mechanism can be ascertained by simulation.  
An experiment was then performed to validate the simulation results of the tolerance range. As shown in Figure 19, the experimental system consists of a docking mechanism (including an active part and a passive part), foundation support, an adjustment device with five DOFs (except in the docking direction), a six-component force sensor used for the dynamics experiment in the next section, and a control box. The five-DOF adjustment device, whose accuracy is 0.01 mm and 0.01\({}^{\circ}\), is decoupled so that it can adjust for each DOF alone. Through the adjustment device, the experiment can recreate the working conditions of the simulation to validate the results of the tolerance range of the docking mechanism.  
Using this design, the tolerance range of the docking mechanism was estimated. To validate the theoretical range, working conditions must be chosen around the boundaries of the theoretical range. In addition, by considering the central symmetry of the passive alignment subassembly, a quarter of the working conditions are sufficient to validate the range. Thus, nine types of working conditions were tested in the simulations and experiments, as shown in Figure 20. In presenting the results, the horizontal and vertical axes represent two linear and three angular deviations, respectively. For example, point (\(-2\), \(-1\)) reveals that the initial deviations between the drogue and the probe are (\(-2\) mm, \(-2\) mm, \(-1^{\circ}\), \(-1^{\circ}\), \(-1^{\circ}\)); in the simulations and experiments reproducing these deviations, docking is successful. The kinematic simulations and experiments verified the validity of the estimated tolerance range (\(\pm 4\) mm on two linear directions and \(\pm 2.5^{\circ}\) on three angular directions).  
Figure 18: Experiment for axial stiffness testing of the elliptical-helical pipe with bias loading.
## 5 Steady State Solutions
As a result of linearization and the reality of the fixed points of (25), we get\[\begin{array}{ll}\frac{d\mu_{11}}{d\tau}&=\frac{1}{2}(f_{1}\theta_{11}\cos\theta_ {10}-c_{1}a_{11}),\\ a_{10}\frac{d\theta_{11}}{d\tau}&=a_{11}[\sigma_{1}-\alpha z_{r}-\frac{(1+w_{1} ^{2})^{2}}{2(1-w_{1}^{2})}r^{2}\beta^{2}]-\frac{3(1-w_{1}^{2})}{4(1-4w_{1}^{2} )}w_{1}^{2}a_{20}(a_{20}a_{11}+2a_{10}a_{21})\\ &+\frac{1}{2}f_{1}\theta_{11}\sin\theta_{10},\\ \frac{d\mu_{11}}{d\tau}&=\frac{1}{2}(\frac{m_{0}}{m})\theta_{21}\cos\theta_{20 }-c_{2}a_{21}),\\ a_{20}\frac{d\theta_{21}}{d\tau}&=a_{21}\Big{\{}\sigma_{2}-w_{1}(\alpha z_{r} ^{2}-r^{2}\beta-\frac{r^{2}\beta^{2}}{4(1-w_{1}^{2})}w_{2}^{2}}\big{[}(14-3r^{ 2})\,w_{2}^{2}-2(4-w_{2}^{2})\,w_{1}^{4})\\ &+(8+3r^{2}w_{2}^{2})\,w_{1}^{2}])\Big{\}}-\frac{(1-w_{1}^{2})w_{1}}{8(1-4w_{1 }^{2})}[12a_{10}(a_{10}a_{21}+2a_{20}a_{11})\\ &-3(1+8w_{1}^{2})a_{20}^{2}a_{21}]-\frac{m_{0}^{2}}{2w_{1}}a_{21}\sin\theta_{ 20},\\ \frac{d\mu_{11}}{d\tau}&=-\frac{1}{2}[\frac{(1-w_{1}^{2})w_{1}^{2}}{(1-4w_{1} ^{2})w_{2}}a_{20}^{2}(a_{20}a_{31}\cos\theta_{30}+3a_{21}\sin\theta_{30})+c_{ 3}a_{31}],\\ a_{20}a_{30}\frac{d\theta_{31}}{d\tau}&=(a_{20}a_{31}+a_{21}a_{30})\bigg{\{} \sigma_{3}+3\,w_{1}(\alpha z_{r}^{2}-r^{2}\beta-\frac{r^{2}\beta^{2}}{4(1-w_{1 }^{2})w_{2}^{2}}\big{[}(14-3r^{2})\,w_{2}^{2}\] \[-2(4-w_{2}^{2})\,w_{1}^{4}+(8+3r^{2}w_{2}^{2})\,w_{1}^{2}])+\frac{1}{2w_ {1}}[\beta(w_{1}^{2}-w_{2}^{2})+\frac{\beta^{2}}{4w_{2}^{2}}(4r^{2}w_{2}^{4}\] \[+4\,w_{2}^{2}(w_{1}^{2}-w_{2}^{2})+(w_{1}^{2}-w_{2}^{2})^{2})]\Big{\}} +\frac{3(1-w_{1}^{2})}{8(1-4w_{1}^{2})}w_{1}[12a_{10}(a_{10}a_{20}a_{31}\] \[+a_{10}a_{30}a_{21}+2a_{20}a_{30}a_{11})-(1+8w_{1}^{2})(a_{20}^{2}a_{ 31}+3a_{20}^{2}a_{30}a_{21})]+\frac{w_{1}^{2}}{4w_{2}}\] \[\times(a_{30}^{2}a_{31}+3a_{20}^{2}a_{30}a_{21})\,-\,\frac{3m_{0} }{2w_{1}}(a_{31}\cos\theta_{20}-a_{30}a_{21}\sin\theta_{20})\] \[-\frac{(1-w_{1}^{2})w_{1}^{2}}{2(1-4w_{1}^{2})w_{2}}a_{20}^{2}(4a_ {21}\cos\theta_{30}-a_{20}\theta_{31}\sin\theta_{30}).\]  
Since \(a_{j1}\) and \(\theta_{11}\)\((j=1,2,3)\) are perturbed functions of amplitudes and phases of the aforementioned linear system. Then, the linear function \(k_{s}e^{\lambda\tau}\)\((s=1,2,3,4,5,6)\) of the exponential form can be used to express about their solutions, where \(k_{s}\) and \(\lambda\) are constants and the perturbation's eigenvalue, respectively. The real parts of the roots of the next characteristic equation of (29) should be negative if the steady state solutions are asymptotically stable [33,34]. \[\lambda^{6}+\Gamma_{1}\lambda^{5}+\Gamma_{2}\lambda^{4}+\Gamma_{3}\lambda^{3}+ \Gamma_{4}\lambda^{2}+\Gamma_{5}\lambda+\Gamma_{6}=0, \tag{30}\]  
where \(\Gamma_{s}\)\((s=1,2,\ldots,6)\) are functions of \(a_{j0},\theta_{j0}\), and \(c_{j}\) (see Appendix B). The required and sufficient requirements of stability for certain solutions at steady state can be expressed as follows  
\[
# Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling
## 1 Introduction
We introduce a new connection structure called Concat_BiFPN, which performs weighted fusion and the concatenation of feature maps to achieve more precise feature fusion. This approach offers unique advantages for detecting small, irregular steel defects. The new model demonstrates stable, high-precision performance when detecting surface defects in steel materials.
# Moving Deep Learning to the Edge
## 1 Introduction
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **Cloud Computing** & **Edge Computing** \\ \hline Computing Power & Cloud servers are powerful computing machines, & Edge servers and devices are computationally less powerful, but shared by many users \\ \hline Latency & Services run faster in cloud servers, but the access to the servers is done & The proximity of edge servers and devices to the source of data improves the latency between service request and answer; it allows real-time services \\ \hline Bandwidth & Since all processing is done at the cloud, all data need to be transmitted to the cloud servers; therefore, it requires high communication bandwidth & Since some or all of the processing is done at the edge, close to the data source, it requires less communication bandwidth \\ \hline Cost & Cloud usage has a cost and requires a large costly bandwidth to & Local processing with edge servers \\  & transfer data. Both increase with the addition of new data sources & and devices reduces communication costs \\ \hline Security & Transferring sensitive data to the cloud using public & Edge computing reduces data-sensitive transfer, which increases security and keeps data private \\  & shared communication links reduces security and privacy & which increases security and keeps data private \\ \hline Services & Cloud services are offered at the application level to & Distributed and local computing allows services at \\  & be generic enough to satisfy more customers & task level directed to specific needs of a user or customer \\ \hline Management & Central services are easier to manage; from the point of view of & Edge services are harder to manage since \\  & the user, it offers a free management service & they are distributed and closer to the user \\ \hline Energy & Computing platforms of cloud centers are not & Edge and end devices are energy-constrained; \\  & energy-constrained; the main goal is performance & performance is traded of with energy \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of cloud computing versus edge computing. The paper is organized as follows. Section 2 introduces the methodology employed to do the review. Section 3 describes the fundamentals and the state-of-the-art on deep learning. Section 4 describes the methods and architectures being used to execute deep learning inference at the edge. Section 5 describes the methods and architectures proposed to train deep neural networks at the edge. Section 6 discusses the challenges and open issues about the deployment of deep learning at the edge. Section 7 concludes the paper.
###### Abstract  
The aim of this study was to assess the validity and intra-session reliability of the MyJump 2 smartphone application for evaluating countermovement jump (CMJ) and squat jump (SJ) performance in youth soccer players using the Chronojump contact mat system as a reference. Twenty-one male soccer players from an elite team (U14-U19) were randomly selected to participate in countermovement jump (CMJ) and squat jump (SJ) assessments, with jump heights recorded simultaneously by both systems. Validity was examined through Bland-Altman analysis, Pearson and Spearman correlations and linear regression. Reliability was assessed using intraclass correlation coefficients (ICC), standard error of measurement (SEM) and Bland-Altman test-retest analysis. Results revealed minor systematic overestimation by MyJump 2 (+0.943 cm for CMJ; +1.042 cm for SJ). Strong correlations were found between MyJump 2 and Chronojump (r = 0.972 for CMJ; r = 0.955 for SJ), while ICCs exceeded 0.97 for both jump types, indicating excellent reliability. These findings support MyJump 2 as a valid and reliable alternative for vertical jump testing in adolescent and junior soccer players as an accessible and suitable tool for field-based performance monitoring regarding vertical jumping ability.  
MyJump 2; vertical jump; countermovement jump (CMJ); squat jump (SJ); validity; reliability; athletes; Chronojump; football; performance analysis--monitoring; sports technology 2