esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Increasing \(S\) or Multiple Cameras  
In cases where \(r_{o}<1\) (Case 3), a practical solution to increase \(\overset{\rightharpoonup}{v}_{max}\) is to raise the camera mounting height, which, in effect, shall increase \(S\). However, if raising the camera mounting height is inappropriate as doing so shall also decrease object details, the use of multiple cameras can be a viable solution.  
Figure 11 illustrates the effect of increasing the effective \(S\) or using multiple cameras on the calculated values of \(\overset{\rightharpoonup}{v}_{max}\) for the three levels of infencing speeds (2.4, 12.2, and 22 _fps_) simulated at \(S=0.5\) m. The results showed that treatments with missed detections exceeded the allowable \(\overset{\rightharpoonup}{v}_{max}\). For treatments 4 and 7, the allowable travel velocity was only 1.2 m/s using a single camera module, which was less than the simulated \(\overset{\rightharpoonup}{v}\) of 1.3 and 2.5 m/s, respectively.  
Calculating \(n_{vis}\) using Equation (6) for treatments 4 and 7 showed that 2 and 3 vision modules, respectively, were required to prevent missed detections. Thus, using two vision modules for treatment 4 prevented missed detections, as shown in Figure 12. The 6 th, 20 th, and 34 th frames captured by the second camera detected the plants undetected by the first camera.  
As predicted, a two-vision-module configuration for treatment 7 was insufficient in preventing missed detections since the simulated \(\overset{\rightharpoonup}{v}\) of 2.5 m/s of the vision system was still higher than the increased \(\overset{\rightharpoonup}{v}_{max}\). As illustrated in Figure 12, without a third camera, the two-camera configuration would still result in an undetected hill on the 16 th frame.  
Based on these simulated results, the problem of missed detection due to the slow inferencing speed of embedded systems could be potentially solved by using multiple, adjacent, non-overlapping, and colinear cameras along the traversed row when raising the height of the camera was unwanted.  
Figure 11: Theoretical maximum travel velocity to prevent missed detections with 1, 2, and 3 cameras at 2.4, 12.2, and 22.0 _fps_.
## 3 Results
### Focusing on Damage Onset and Propagation  
In Figure 4, the DIC principal (E1) strains maps visualize the surface cracks formed on the TRC skin under bending. The cracks were identified by tracking the zones of relatively high displacement at both crack fonts, which were therefore of high strain concentration (color ruler added in Figure 4 to indicate the strain magnitude) compared to the rest of the panel, where the strain remained negligible (marked with purple color). The principal strains E1 were selected since they highlight the dominant damage phenomena at the panel  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Camera Resolution (Pixels)** & **2564 \(\times\) 2048** & **Strain Window Size (Pixels)** & **9** \\ \hline Spatial sensitivity (mm/pixels) & 0.15 & Capturing software & VIC-Snap \\ Subset/Step (pixels) & 21/7 & Post-procession & VIC-3D \\ \hline \hline \end{tabular}
\end{table}
Table 3: DIC setup details.  
surface and were obtained as the critical combination of maximum normal strains along the length and the width of the panel.  
Figure 2: AE hits analysis: load and absolute energy plots in time for (**a**) Reference (REF) and (**b**) Internal Interfacial Debonding (IID) panels; (**c**) cumulative plot of the hit population for both test cases. Incremental loading step of 0.5 kN/m\({}^{2}\).  
Several micro-cracks formed as early as at 0.5 kN/m\({}^{2}\) (E1 strain measured not negligible, marked in circle in Figure 4) and extensively propagated, leading to the formation of the first macro-crack at 1.5 kN/m\({}^{2}\) in the IID case. This means that early high AE energy values are sourced from a combination of debonding and cracking. It is speculated that interfacial debonding leads to crack formation or that cracking leads to TRC internal debonding. However, since these two phenomena were overlapping, this new information cannot clarify the sequence of events.  
On the other hand, and as expected, a series of cracks nucleated for the REF panel only after the AE energy jumped at 3.5 kN/m\({}^{2}\); at that moment, several zones of strain E1 concentrations were marked throughout the panel's bottom side. As the loading increased, more cracks form and the cracks' density increased, as proven by the extended zones of high E1 strain concentrations. At the end of the monitoring period (at 5 kN/m\({}^{2}\)), it is believed that crack saturation was reached, and beyond that point only the textiles resisted further loading. By observing the cracks' distribution and density at that load level (bottom images in Figure 4), it was concluded that potential early debonding appears to have no impact on the cracking pattern.
# Lightweight CoAP-Based Bootstrapping Service for the Internet of Things
## 3 The Bootstrapping Service: CoAP-EAP
### Additional Considerations
#### 3.6.2 Discovering the Controller  
One aspect that has not been discussed until now is how the Smart Object discovers the Controller Or, in other words, how the Smart Object knows which entity supports the bootstrapping service in a particular security domain. Actually, this process lies outside the bootstrapping service, since there are already mechanisms for that process in CoAP. For example, the CoRE Resource Directory [86] provides the framework to discover services.  
In particular, it describes an entity called _Resource Directory_, which can register the bootstrapping service (_rt = \"bootstrapping\"_). The access to this information should be public and not protected since it will be the first service to be used for Smart Objects that want to join the security domain. The Resource Discover will return a \"coap\" URI schema (e.g., \(coap://[IP-Controller]/boot\)) for our bootstrapping service, meaning the initial exchanges goes to the unprotected CoAP port.
# Designing Advanced Multistatic Imaging Systems with Optimal 2D Sparse Arrays
## 2 Multistatic Architecture
### Point Spread Function  
The PSF plays a crucial role in radar systems, as it defines how electromagnetic waves interact with point-like targets, guiding the detection and localization of objects. It characterizes the spatial distribution of radar energy received from a point scatterer and directly impacts the system's resolution. A smaller PSF signifies higher radar resolution, enabling the distinction of closely spaced targets. Various factors, including radar frequency, antenna characteristics, and signal processing methods, influence the PSF, thereby affecting the accuracy of target localization. Thus, a comprehensive understanding of the PSF is indispensable for optimizing radar systems utilized in surveillance, tracking, and remote sensing applications.  
According to the reciprocity theorem, the total PSF of the system when focused at point \(p_{i}\) can be computed from the PSF of the transmitting and receiving arrays as follows [28]:  
\[PSF_{tx}(p_{i})=\sum_{l,m}e^{-j_{R}|t_{m}-p_{i}|} \tag{1}\]  
\[PSF_{rx}(p_{i})=\sum_{l,m}e^{-j_{R}|t_{m}-p_{i}|} \tag{2}\]  
\[PSF_{total}(p_{i})=PSF_{tx}(p_{i})\times PSF_{rx}(p_{i}) \tag{3}\]  
Here, multiple frequencies \(f_{l}\) are employed in an ultra wideband (UWB) radar configuration, and \(\kappa_{l}=2\pi\frac{f_{l}}{c}\) represents the wavenumber at the l-th frequency.
# Utilizing Multi-Dimensional MmWave MIMO Channel Features for Location Verification
## 5 Numerical Results
### Impact of SNR on the Verification Performance  
We plot in Figure 2 ROC (receiver operating characteristic, ROC) variations under the settings of (\(N_{r}=128\), \(L=20\), \(k_{\phi}=2\) dB, \(k_{\theta}=2\) dB, \(k_{\alpha}=2\) dB) at different SNRs. From Figure 2, we can see that the authentication performance (\(P_{f}\) and \(P_{d}\)) under SNR = 5 dB outperforms that of the others and the performance for SNR = \(-5\) dB is poorest. This indicates that the SNRs have severe impacts on the authentication performance. One can improve the authentication performance by setting a higher SNR. However, the SNR level usually has a limit due to the practical conditions. In many practical environments, \(P_{f}<0.1\) and \(P_{d}>0.9\) are usually a required constraint pair to satisfy the requirements of a practical authentication system. As shown in Figure 2, the authentication performance (\(P_{f}\) and \(P_{d}\)) for SNR = 0 dB and SNR = 5 dB can satisfy the practical requirements and the performance at SNR = 5 dB is best. From Figure 2, we also can see that when \(P_{f}\) is fixed, \(P_{d}\) increases when SNR increases. This means that a higher SNR can lead to a better authentication performance.  
Figure 2: ROC curves under different SNRs with the settings of (\(N_{r}=128\), \(L=20\), \(k_{\phi}=2\) dB, \(k_{\theta}=2\) dB, \(k_{\theta}=2\) dB).
## 3 Methodology
### Input Parameters  
The proposed study classifies these influencing factors of the ground settlement surrounding a foundation pit into six categories: excavation time, supporting structures, weather conditions, construction stages, soil types, and previous settlement monitoring data, as shown in Table 1.  
Figure 1: A typical BPNN.  
The first category is excavation time, indicating the time effect on ground settlement. Generally, ground settlements will continuously increase overtime during the construction of a foundation pit because excavation works will lead to the stress release of the nearby soil and change the earth pressure on supporting structures [3]. The second category includes types, stiffness, and depths of supporting structures, and this study considers five types of supporting structures that are widely used in eastern China. The third category includes nine time-dependent influence factors, indicating different construction stages of a cut-and-cover method. The fourth category is the weather condition, which may affect the level of underground water and the property of soils. The fifth category is the soil effect, represented by types of soils above the bottom of a foundation pit, given that soils in this zone directly affect earth pressure on supporting structures. Specifically, this study uses soil types, instead of specific soil parameters, such as soil weight, internal friction angle, and soil cohesion, to represent the soil effect because a foundation pit may cross a number of soil layers, whose soil parameter values are different and cannot be represented by unified values. Moreover, the proposed study considers five types of soil, which are commonly found in the Yangtze River Delta region, China. The last category includes a set of previous settlement monitoring data, which provides insight into the future ground settlement in a direct way. Finally, the values of these input parameters are shown and explained in the last column of Table 1.
## 4 Changing the Cultivation Conditions for Optimizing the Production of Bioactive Compounds
### Lipids  
#### 4.2.1 Temperature  
The effects of temperature on growth and on lipid and fatty acid metabolism have been studied on several microalgae. Indeed, in natural conditions, the temperature is one of the main environmental factors that can influence the biology of microalgae, and thus, influencing the carbon metabolism.  
In microalgae _Pavlova lutheri_, it has been shown that the decrease of growth temperature (from 25 to 15 \({}^{\circ}\)C) has the consequence to increase the polar lipids such the betaine lipids [163]. In these conditions, the levels of sulfoquinovosyldiacylglycerol and phosphatidylglycerol were increased while the MGDG and TAG levels were decreased. Concerning the omega-3 fatty acids, the EPA and DHA levels were also increased. These results are considered as an adaptative response to temperature with a modulation of omega-3 fatty acids incorporated into lipids involved in the membrane building.  
In the diatom _Phaeodactylum tricornutum_, the decrease of growth temperature (from 25 to 10 \({}^{\circ}\)C) showed that the representative fatty acids in the total fatty acids decreased proportionally by about 30% in palmitic acid and 20% in palmtideic acid but increased about 85% in EPA [258]. Other species such as the Prymnesiophyte _Isochrysis galbana_, when cultured at temperatures from 15 to 30 \({}^{\circ}\)C showed a global lipid accumulation, with a slight decrease of the neutral lipids, by comparison with other polar lipids. The glycosyllglyceride levels were increased while no change was observed concerning the phospholipid classes. At 15 \({}^{\circ}\)C, as observed for other strain, higher levels of omega-3 fatty acids were obtained (\(\alpha\)-linolenate and DHA) while the monounsaturated and saturated fatty acid levels were decreased [259]. Concerning the omega-3 fatty acid levels (EPA and DHA), they were lower at the highest temperatures [260]. The diatom _Chaetoceros_ sp., a tropical Australian microalga, was studied at different growth temperatures, from 25 to 35 \({}^{\circ}\)C. The highest lipid percentage was obtained when the cells were cultured at 25 \({}^{\circ}\)C. Authors showed that the highest omega-3 fatty acid levels were slightly lower at the highest temperatures [260,261]. The same observations were made concerning _Phaeodactylum tricornutum_ in which the saturated, monounsaturated but also the omega-6 fatty acids were increased with temperature, while the EPA level was decreased [262]. Lipids produced at varing temperatures can have varing biological activities such as antibacterial and cytotoxic activitiesas reported by [PERSON] _et al._[263] for the fatty acids from _Gloeocapsa_ sp.
# Development of a CFD-Based Wind Turbine Rotor Optimization Tool in Considering Wake Effects
###### Abstract  
In the present study, a computational fluid dynamic (CFD)-based blade optimization algorithm is introduced for designing single or multiple wind turbine rotors. It is shown that the CFD methods provide more detailed aerodynamics features during the design process. Because high computational cost limits the conventional CFD applications in particular for rotor optimization purposes, in the current paper, a CFD-based 2D Actuator Disc (AD) model is used to represent turbulent flows over wind turbine rotors. With the ideal case of axisymmetric flows, the simulation time is significantly reduced with the 2D method. The design variables are the shape parameters comprising the chord, twist, and relative thickness of the wind turbine rotor blades as well as the rotational speed. Due to the wake effects, the optimized blade shapes are different for the upstream and downstream turbines. The comparative aerodynamic performance is analyzed between the original and optimized reference wind turbine rotor. The results show that the present numerical optimization algorithm for multiple turbines is efficient and more advanced than conventional methods. The current method achieves the same accuracy as 3D CFD simulations, and the computational efficiency is not significantly higher than the Blade Element Momentum (BEM) theory. The paper shows that CFD for rotor design is possible using a high-performance single personal computer with multiple cores.  
wind turbine blade optimization; computational fluid dynamic; actuator disc; wake effect; Non-dominated Sorting Genetic Algorithm (NSGA-II) +
Footnote †: journal: Applied Sciences
# Comparing EEG-Based Epilepsy Diagnosis Using Neural Networks and Wavelet Transform
## 3 Methods and Materials
### Evaluation  
To assess the effectiveness of the proposed method for diagnosing epilepsy, we use common performance measures such as accuracy (AC), sensitivity (SN), specificity (SP), and precision (P). In Equations (8)-(11) below, TP refers to true positives (correctly diagnosed epilepsy), TN refers to true negatives (correctly diagnosed non-epilepsy), FN refers to false negatives (incorrectly diagnosed as non-epilepsy), and FP refers to false positives (incorrectly diagnosed as epilepsy).  
\[\text{AC}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FN}+\text{FP}} \tag{8}\]  
\[\text{SN}=\frac{\text{TP}}{\text{FN}+\text{TP}} \tag{9}\]  
\[\text{SP}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{10}\]  
\[\text{P}=\frac{\text{TP}}{\text{FP}+\text{TP}} \tag{11}\]
# MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
## 4 Results  
### Experimental Environment and Configuration  
During the experiment, in addition to the proposed model, we also compared four segmentation models commonly used in remote sensing: UNet, UNet++, PSPNet, and DeepLabV3+. PSPNet and DeepLabV3+ use Resnet50 as the feature extraction module in their encoder parts to achieve better segmentation results. Throughout the experimental process, we trained the models using the training set of each dataset. Within a fixed number of iterations, we identified the best MioU index through the validation set to save the model parameters. Ultimately, we tested the trained models on the test set and compared the evaluation metrics across different models. Moreover, during the training process, each image had a 70% probability of undergoing horizontal and vertical flipping and random 90\({}^{\circ}\) rotations to perform data augmentation on the training set. No additional processing was conducted for testing and validation.  
We use the PyTorch framework to implement our model. The version number of PyTorch is 1.13.1. The initial learning rate is 0.0001, and the models are trained within 100 epochs. The learning rate drops to 0.00002 after 50 cycles. The optimizer we use is Adam, and the loss function is cross-entropy loss. In addition, all programs use a consistent random seed number 707.  
In terms of hardware, we use a single CPU (12 th Gen Intel(R) Core(TM) i5-12400F) and two GPUs (GeForce RTX 3060 12 G) to accelerate training. Our memory is 32 G, and the hard drive capacity is 1.5 T.
## 2 Influence of tRNA Core Modification on tRNA Structure and Stability
### Modifications in the tRNA Core and Their Impact on tRNA Structure and Dynamics
coli_, namely TruB introducing Y55 and TrmA introducing m\({}^{5}\)U54 [81; 82]. The chaperone activity of both of these enzymes comes from their ability to partially unfold and open the tRNA elbow region by disrupting tertiary interactions. It is thus likely that other modification enzymes possessing the capacity to unfold the tRNA elbow region would also act as tRNA-specific chaperones independently of their catalytic activity [83; 84]. For instance, eukaryotic fission yeast Trm1 has been recently reported to have a chaperone-like activity similar to prokaryotic TruB and TrmA [85]. Trm1 enhances tRNA functionality in vivo, even in the absence of catalytic activity, and promotes RNA strand annealing and dissociation, as previously reported for other RNA chaperones [85; 86]. It is worth mentioning that the RNA chaperone La, implicated in tRNA 3\({}^{\prime}\)-end protection and pre-tRNA folding, shares some functional redundancy with tRNA modification enzymes displaying chaperone activity, such as Trm1 and Pus4 [85; 87; 88]. The La chaperone does not discriminate between misfolded and properly folded tRNAs (as was also observed for the modification enzyme TruB [81; 89]), which creates potential challenges for tRNA chaperones to bind and refold all defective substrates.
## 4 Conclusions  
This study investigates the effect of print orientation angles on the fracture toughness and the critical energy release rate of durable resin specimens produced via low-force stereolithography. Fracture toughness tests were conducted on samples printed at 0, 45, and 90 degrees according to ASTM D5045-99 standards.  
The results indicate that print orientation has a minimal impact on the fracture toughness and critical energy release rate of the resin. While slight variations in maximum load and stiffness were noted, these differences are not significant enough to affect the overall performance of the material.  
This finding suggests that factors such as process efficiency and design geometry can be prioritized over print orientation without compromising mechanical integrity. This flexibility is advantageous for optimizing production and adapting designs to project-specific needs, potentially improving operational efficiency and reducing costs.  
Moreover, this flexibility is highly beneficial across various industries, such as automotive, aerospace, healthcare, and consumer goods, where customized designs, minimal material waste, and mechanical reliability are crucial. The ability to produce complex geometries quickly while maintaining structural integrity accelerates prototyping, improves product durability, and reduces overall costs. Additionally, these results have implications beyond biomedical applications, as they may be relevant for other industries requiring durable materials, such as the automotive and aerospace sectors. The study also paves the way for further innovation in biocompatible materials, encouraging exploration of new manufacturing techniques and material combinations to enhance printed component properties.  
In conclusion, this study makes a significant contribution to the understanding of print orientation effects and paves the way for future research into the effects of different materials and printing parameters on the performance and application of 3D-printed components in diverse fields.  
Figure 5: Testing the fracture patterns of specimens with (**a**) orientations at 0 degrees; (**b**) orientations at 45 degrees; and (**c**) orientations at 90 degrees. Photo credit (each): authors.
# Noise Reduction Method of Nanopore Based on Wavelet and Kalman Filter
## 2 The wavelet Method
### Method Classification
Then, the corresponding extreme values can be found within the wavelet function at different scales. This property can also be used to remove the noise to retain the useful signal. However, the modal maxima of the signal mutation points become larger as the function scale becomes larger, while the modal maxima of the noise mutation points become smaller as the function scale becomes larger. Because of this difference, the noise can be easily removed, and the effective signal retained.
## 4 Marker Tracking
### Mean-Variance Adaptive Kalman Filter  
In this paper, the system state vector includes the position, velocity and acceleration of the marker and is represented as  
\[X_{t}=\left[\begin{array}{c}x(t)\\ \dot{x}(t)\\ \ddot{x}(t)\end{array}\right] \tag{11}\]  
The state equation can be expressed by  
\[\left[\begin{array}{c}\dot{x}(t)\\ \ddot{x}(t)\\  
\[P_{k|k-1}=\Phi P_{k-1|k-1}\Phi^{\rm T}+Q_{k} \tag{18}\]  
where the \(\vec{a}\) is taken as a step of acceleration prediction.  
The equations of filter gain, state update and covariance update are summarized as follows:  
\[K_{k}=P_{k|k-1}H^{\rm T}\Big{(}HP_{k|k-1}H^{\rm T}+R_{k}\Big{)}^{-1} \tag{19}\]  
\[\hat{X}_{k|k}=\hat{X}_{k|k-1}+K_{k}\Big{(}Z_{k}-H\hat{X}_{k|k-1}\Big{)} \tag{20}\]  
\[P_{k|k}=P_{k|k-1}-K_{k}HP_{k|k-1} \tag{21}\]  
where the \(R_{k}\) stands for the covariance matrix of observation noise.