esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study aimed to perform a three-dimensional assessment of the effects of Kinesio taping (KT) on mandibular condylar volume during Twin Block (TB) appliance therapy in individuals with skeletal Class II malocalclusions. Thirty children (16 female, 14 male) aged between 10 and 15 years (12.80 \(\pm\) 1.08) who were scheduled for TB therapy were randomly assigned to two groups of 15 patients each. One group (Group 1) received KT on the temporomandibular joint (TMJ) area with TB therapy while the other group (Group 2) only received the TB. KT was applied in cycles of 3 days, followed by 1 day off for 3 months. All patients underwent computed tomography (CT) scanning before KT and TB therapy (T0) and 3 months later (T1). Changes in right and left condylar volumes were measured (cm\({}^{3}\)) using a GE Advantage Workstation (General Electric Medical Systems, USA) and compared between the two groups using the Mann-Whitney U test and Wilcoxon signed-rank test. The KT group showed a significant differences in condylar volumes for both right (mean 0.13; \(p\) = 0.015; [0.04: 0.22]) and left condyle (0.30; \(p\) = 0.001; [0.18: 0.42]) from baseline (T0) to 3 months (T1). The comparisons between Group 1 and Group 2 revealed no statistically significant difference in initial and final condylar volumes for both the right (0.13; \(p\) > 0.05; [\(-\)0.16: 0.09]) and the left condyle (0.30; \(p\) > 0.05; [\(-\)0.04: 0.42]). Kinesio taping, which demonstrated a relative and slight increase on condylar volume, may provide clinical benefits such as a reduction in the duration of functional appliance use, decreased risk of relapse, and effective correction of overjet.  
Kinesio taping; twin block; skeletal class II malocclusion; condylar volume +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
To address the need for replacing traditional machine detection methods with deep learning, this paper proposes a PCB defect detection algorithm based on YOLOv8n to solve the issues of missed and false detections caused by numerous tiny objects and complex background textures in PCBs, as well as the difficulty of embedding large-sized models into portable devices. Initially, the SCConv, a spatial and channel reconstruction convolution, is integrated into the C2f structure of the backbone network. This enhancement reduces redundant computations, thereby improving the model's learning efficiency. Subsequently, an adaptive feature selection module is incorporated to enhance the network's capability in recognizing micro-target defects on PCBs. Furthermore, the original Decoupled Head is replaced with a Lightweight Shared Convolution Head. This modification aims to streamline computational complexity and enhance detection accuracy. Finally, the WIoU loss function is introduced to refine evaluation metrics and bolster generalization capabilities. The proposed algorithm achieves a detection accuracy of 98.6% on an image-enhanced PCB dataset, outperforming other algorithms. Moreover, the network model size is optimized to 4.1 M, and the detection speed indicator FPS is 144.1, aligning it more closely with the requirements of industrial inspection.  
J.A.: Conceptualization, methodology, validation, writing--original draft; Z.S.: Resources, conceptualization, writing--review and editing, project administration. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are openly available in HRI Lab PKU-Market-PCB series data set at Open Laboratory of Intelligent Robots, Peking University ([[https://pku.edu.cn/](https://pku.edu.cn/)]([https://pku.edu.cn/](https://pku.edu.cn/))).  
On behalf of all authors, the corresponding author states that there are no conflicts of interest.
## 3 Regulatory Mechanisms of Volatile Terpenoids in Abiotic Stress Responses
### Temperature Stress
When heat stress occurs, increased fluidity of thylakoid membranes reduces the efficiency of photosynthesis. Isoprene enhances the hydrophobicity of membranes, thereby stabilizing the thylakoid membranes and enabling plants to exhibit improved thermotolerance [172; 173]. Isoprene prevents alterations in the PSII microenvironment [174], ensuring a more stable and uniform distribution of light-harvesting complexes while maintaining thylakoid membrane rigidity under elevated temperatures to support photosynthetic efficiency. Second, isoprene acts as an antioxidant, protecting plants from oxidative stress by quenching ROS [130; 134], thereby enhancing resistance to abiotic stressors. Additionally, some studies suggest that isoprene mitigates heat stress by improving photosynthetic stability. Temperature regulates terpenoid metabolism not only during plant growth but also significantly impacts postharvest fruit physiology. In strawberry (_Fragaria \(\times\) ananassa_), cold treatment (4 \({}^{\circ}\)C), ambient storage (25 \({}^{\circ}\)C), and heat treatment (37 \({}^{\circ}\)C) differentially regulate aroma biosynthesis, with _FaNES_ and other volatile synthesis genes reaching peak expression under heat stress. Both cold and heat treatments decrease geraniol proportions relative to ambient conditions, while HSFs enhance aroma compound production in response to thermal fluctuations [175], indicating temperature-dependent differential regulation of metabolic pathways via HSFs. During postharvest ripening, trans-nerolidol levels increase, with dark incubation at 15 \({}^{\circ}\)C facilitating its accumulation via _FaNES_1 activation, whereas \(\beta\)-linalool remains unaffected by light or temperature changes. Notably, cultivar-specific temperature responses show contrasting trends: after 9 days of cold storage, _'Akihime'_ exhibits higher terpenoid content than under room temperature storage, whereas 'Sweet Charlie' accumulates fewer terpenoids at 15 \({}^{\circ}\)C than at 25 \({}^{\circ}\)C [176]. In summary, temperature plays a complex and multifaceted role in regulating plant volatile terpenoid emissions, with substantial interspecies differences in thermal responsiveness. With escalating global warming, plant physiological states are undergoing significant alterations, highlighting the urgent need to investigate temperature-driven modulation of terpenoid metabolism during key developmental stages as a research priority.
# Performance Evaluation of the New Compound-Carrier-Modulated Signal for Future Navigation Signals
## 4 Evaluation and Analysis of NSCC with Representative Parameters
### Multipath Error Envelop  
Simulations were conducted to compare the multipath (MP) mitigation performance between the NSCC and other legacy satellite navigation signals. The simulation conditions are set as correlator spacing \(d\) = 0.1 chip, MDR \(a\) = 0.1. Figure 10a-d show the multipath error envelopes with 2, 4, 10, 20 MHz pre-filtering bandwidths, respectively.  
According to the Nyquist Sampling Theorem, we computed the envelopes of a signal only when the pre-filtering bandwidth is larger than the main lobes bandwidth. For the 2 MHz pre-filtering bandwidth, the MSK presents better multipath mitigation capability than C/A, but worse than the other modulation schemes investigated. As the pre-filtering bandwidth increases, the MPEEs of the legacy signals and the NSCC decrease. The greater bandwidth allows more high-frequency component in the EML discriminator, and the detailed component is demonstrated to improve the multipath performance. At the 20 MHz bandwidth, the NSCC possesses the smallest multipath envelope which is less than 1 m, and its MPEE decreases fastest and is even invisible when the multipath delay is larger than 25 m. Note that the MPEEs of the NSCC have more ripples compared with P code due to the more number of side-lobes of the NSCC ACF.  
Figure 11 shows the details about the multi-path error (MPE) for NSCC(19) with different MDRs and correlator spacings. The multipath errors are larger for the greater MDRs. This is understandable as greater multipath power will no doubt cause additional multipath power contribution. The multipath error decreases, as the correlator spacing becomes narrow. The impact of the correlator spacing is more distinct for larger geometric delay.  
Figure 10: Multipath error envelops of pre-filtering bandwidth of 2 MHz (**a**), 4 MHz (**b**), 10 MHz (**c**), 20 MHz (**d**), a = 0.1, d = 0.1 chip.  
Figure 12 shows the details for multipath-induced error and average multipath error for NSCC modulation. The MPE for NSCCs increases slightly as the chip rate grows, while AMPEs for the NSCCs appear similar within the short geometric delays such as \(<\)10 m, and are diverging visibly above 10 m path delay. According to the overall tendency of AMPE curves, the NSCCs with lower chip rate present better multipath performance which coincides with the MPE.  
The MPE and AMPE for the two baselines also diverge evidently. This indicates that a large proportion of the high-frequency components is a prominent factor to improving multipath mitigation capability within a limited bandwidth.
## 3 Carbon-Aware Training Strategies
### Algorithms Comparison and Application  
The implementation of the algorithms is quite simple, but despite this, the results are very interesting. First, the strategies were compared by taking the historical data from 2020 for different regions and using two sets of values for the time window, specifically the following:  
* \(\{6,12,18,24\}\) h for Flexible Start.
* \(\{25\%,50\%,75\%,100\%\}\) for Pause & Resume.  
These values are used to calculate the time window size: calling \(t\) the duration of the workload, the first set explains what values need to be added to \(t\) to obtain the time window; the second set explains how much larger the time window will be compared with \(t\).  
Regarding emissions reduction, the strategies are remarkably efficient in complementary cases.
* Flexible Start. This strategy has been shown to be particularly efficient for short-duration workloads, such as in the case of DenseNet, where the best percentage of emissions reduction reaches 80% in the West US region. The optimal time window was found to be 24 h. In contrast, for jobs longer than one day, the reductions are less significant. For instance, applying this strategy to a 6.1B parameter transformer led to reductions of less than 2%. This may be explained by the fact that shorter jobs are less affected by the variability of marginal emissions during the time window. Therefore, this strategy is applicable to workloads that need to be run regularly but have some flexibility regarding their start times.
* Pause & Resume. This strategy yields considerable reductions in regions with wide variability of marginal emissions throughout the day and for workloads longer than a day. When applied to DenseNet, it brought a small reduction of less than 10%. Conversely, when applied to the 6.1B parameter transformer, the reduction reaches almost 30%.  
The authors also conducted an analysis by applying the same time window to the strategies: either an increase of 24 h or an increase of 100% (Table 1).  
It should be kept in mind that using historical data leads to the best results, so the emission reductions turn out to be lower bounds for a realistic scenario. WattTime also provides forecast data. Considering that Flexible Start does not extend too much over time, these forecasts can be used to achieve realistic results. Regarding Pause & Resume, it is preferable to stop the training whenever emissions exceed a certain threshold and restart when they return to lower levels. The threshold would be set in such a way as to arbitrarily increase the time window. In any case, as of today, even 24 h forecasts are accurate, so the application through their use would yield reliable results.
## 3 Implementation of Vehicular VLC System
### Transmitter and Receiver  
For VLC, LEDs function as the light source, providing a wide viewing angle that facilitates easier alignment between the transmitter and receiver. In this system, the PHOTRON PP625-8L615-STAR LED is utilized, which operates within the 615-645 nm wavelength range and delivers 1 W of output power, accompanied by a lens that provides a 125-degree viewing angle. As illustrated in Figure 3a, serial communication through the Arduino Mega's pin transmits binary signals of 0 V and 5 V, controlling the LED via an  
Figure 2: Detailed descriptions for data exchange between two Arduino Mega boards in the proposed vehicular VLC system.  
MPS2222A transistor. To prevent overvoltage, two LEDs are connected in series, ensuring stable current flow. For signal detection, a Broadcom AFBR-S4K11C0125B avalanche PD is employed, as shown in Figure 2(b). With a rise time of 110 ps, the PD generates a signal between 0 and 0.6 V. To amplify the signal to the receiver's 2 V threshold, an LM2904N amplifier is used. A reference voltage of 400 mV is set using a voltage divider. If the PD's output exceeds this reference voltage, the amplifier outputs 0 V; otherwise, it outputs 5 V. When two PDs are connected in series, they produce up to 1.2 V, which is digitized by a transistor, with 5 V representing binary 1 and 0 V representing binary 0 at the RX pin.
# Traffic Signal Time Optimization Based on Deep Q-Network
## 2 Related Works
In addition, in [11], the vector reflects only the position and speed of the vehicles, whereas in [12] it contains additional information regarding the current light configuration. By contrast, the approach in [14] considers the surrounding intersections and includes information about the lane where the vehicle exits the intersection in the vector. The use of a 2N-dimensional feature vector has the advantage of accurately representing the current environment. However, it can increase the complexity of the data as the structure of the intersection changes. In the present paper, a TSC technique is proposed that considers the situation of the surrounding intersections. The proposed technique adjusts the signal time to maximize the capacity and minimize wasted green light time. In addition, it aims to prevent selfish signal distribution by setting the order of signal assignment. Moreover, a new technique is proposed that does not modify the MDP even if the intersection structure has changed.
# Method for Detecting Underwater Microbubbles Using Dual-Mode Fusion of Laser Polarization
## 2 Dual-Mode Fusion Detection Mechanism
### Dual-Mode Fusion Discrimination
This causes the GF value to increase continuously. In addition, as Figure 11 illustrates, an increase in bubble size has a greater effect on the image's grayscale fluctuation than an increase in bubble number density. This is due to the increased significance of the turbulence effect following the bubble size increase, which causes the overall image's light intensity to become more uneven and unstable, leading to sharp variations in the grayscale value and more noticeable variations in the GF value. In order to verify the environmental adaptability, detection accuracy, and real-time performance of the detection mechanism, and provide a potential application scheme for subsequent engineering, we tried to carry out field verification of the detection method in a lake environment. In the lake environment, the detection equipment is integrated in a watertight box, placed at a water depth of 0.5 m, in the form of a UUV mount, and slowly brought closer to the propeller cavitation bubble area to verify the detection mechanism in a real natural environment [25]. The external field design and original capture results are shown in Figure 12, and the recognition and extraction of bubble features are shown in Figure 13.  
Figure 13 characterizes the results of the continuous dynamic detection of the proposed method in the outfield lake environment. As can be seen from Figure 13, with the increase in time, that is, the closer the detection device is to the propeller, the number of bubbles increases significantly. At the same time, with the gradual distance, the number of large bubbles and its proportion in the detected bubble group also increased significantly, which is consistent with the distribution law of cavitation bubbles produced by the propeller. At the same time, compared with the existing dual-mode detection methods [26], although the dual-mode fusion method improves the bubble detection capability, it is difficult to quan  
Figure 12: Schematic diagram of the field experiment. titatively extract and analyze the characteristics of bubble size and quantity distribution. This method can accurately perceive and identify the changes in bubble size and density distribution at the second time scale. The scheme has high practical value and engineering application prospects.
## 2 Materials and Methods
### Database  
The database is the BraTS 2017-2020 database [14] used in BraTS Challenges in recent years. This database is considered the gold standard for the task of multi-modal brain tumor segmentation. According to our knowledge, this is the only publicly available database for researchers in the field. The dataset was expanded yearly from 35 training images to 369--the number used at the 2020 BraTS competition. The images of resolution \(240\times 240\times 155\) pixels were manually annotated by 4 experts, i.e., radiologists following a very strict annotation protocol described in [4]. An annotation was accepted if 50% of the experts agreed on the corresponding label. During the annotation, a hierarchical majority vote was considered to include prior knowledge about the structure of the tumor and the ranking of the labels. Image datasets that can be used for training automatic systems have to provide not only the original record but also a very accurately annotated gold-standard segmentation label. The images are also aligned, registered to the same template, and consider the same resolution of voxel/mm\({}^{3}\). All these steps were conducted according to a standardized protocol described in [4]. Only this type of image dataset can be applied for supervised learning techniques, such as deep CNN-based segmentation. In the absence of this annotation, the acquired medical image data are useless. This is the main reason why there is only the above-mentioned database available for research purposes. The total number of images were collected by 19 different clinical research institutions worldwide participating in an acquisition and annotation project [48]. The BraTS contains 3D MRI brain scans with tumoral and healthy brain tissues. The resolution of the images is \(240\times 240\times 155\) pixels with a sample rate of 1 mm\({}^{3}\)/voxel. The images are co-registered to the same template, interpolated to the mentioned resolution, and skull-stripped. The images are multi-modality images. The 4 modalities are T1 (native), T2 Gd (post-contrast weighted using a gadolinium contrast agent), T2-weighted, and T2-FLAIR (Fluid-Attenuated Inversion Recovery) images, along with the ground truth annotation image from multiple experts. The total number of images have been acquired since 2012 and available for research in TCIA (The Cancer Imaging Archive). The image dataset consists  
Figure 4: Adapted DeepLabv3+ [47].  
of 293 high-grade tumor images (HGG), also called glioblastoma (TCGA-GBM) [49], and 76 low-grade tumor images (TCGA-LGG) [50] included in the TCGA (The Cancer Genome Atlas). The goal of the segmentations that use this database is to identify 5 types of classes or in other words 3 types of tumors (Figure 5). The 5 classes are background (class 0) brain (class 1), edema (class 3), necrosis and non-enhanced tumor (class 2/4 joined), and enhanced tumor (class 5).
## 3 Results
### Content and Predictive Validity  
There were positive \"moderate\" within-measure correlations (r = 0.42-0.62; \(p<0.05\)) between the acceleration and steady-state phases. The shared variance of the measures ranged from 17.9 to 37.8% (Table 4). Two \"stepwise\" multiple regression analyses were run to predict the maximal skating velocity from the spatiotemporal variables obtained in the acceleration and steady-state phases. The first regression significantly predicted the maximal skating velocity, F\({}_{(5,26)}\) = 8.34, \(p<0.001\), adj. R\({}^{2}\) = 0.62, with step frequency being the only significant predictor (\(p<0.001\)). The second regression model also significantly predicted the maximal skating velocity, F\({}_{(5,26)}\) = 13.60, \(p<0.001\), adj. R\({}^{2}\) = 0.67. Only the glide length and step frequency added significantly to the prediction (\(p<0.001\)). The glide time was excluded from both models due to the high multicollinearity (variance inflation factor > 5). Table 5 shows the regression coefficients and standard errors. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Kinematic Variables** & **r (Cl95\%)** & **\% of Common Variance** \\ \hline Velocity (m/s) & 0.62 * (0.34–0.79) & 37.8 \\ Glide time (ms) & 0.46 * (0.14–0.70) & 21.4 \\ Double support time (ms) & 0.42 * (0.09–0.67) & 17.9 \\ Glide length (m) & 0.52 * (0.21–0.74) & 27.4 \\ Double support length (m) & 0.44 * (0.11–0.69) & 19.5 \\ Step length (m) & 0.46 * (0.13–0.70) & 21.2 \\ Step frequency (steps/s) & 0.59 * (0.30–0.78) & 34.3 \\ \hline \hline \end{tabular} Legend: r = Pearson’s correlation coefficient; Cl95% = 95% confidence interval of r; * = significant correlation, \(p\leq 0.05\).  
\end{table} 
Table 4: Correlation between the kinematic variables in the acceleration and steady-state phases.
# PPO-Based Joint Optimization for UAV-Assisted Edge Computing Networks
## 6 Conclusions  
In this work, we designed a UAV-assisted edge computing network application scenario for minimizing the average delay. By analyzing this scenario, we established the optimization problem for joint UE-ECS matching and deployment of three UAV hovering positions. However, the joint optimization problem involves mixed integer nonlinear programming, which poses a great challenge to the solution. To address the joint optimization, we established a PPO-based joint optimization algorithm based on the PPO framework and implemented it for problem solving. Simulation analysis verified the convergence and outstanding performance of the proposed scheme. This study concentrated on edge computing networks deployed by a single UAV. Expanded deployment of multiple UAVs will enhance network flexibility, making it a valuable research element. Furthermore, future research will investigate UAV-assisted edge computing networks that consider multidimensional characteristics, such as energy consumption and delay, in a comprehensive manner.  
Z.L.: Theoretical analysis, numerical simulation, writing--original draft preparation. Q.Z.: System model, theory, review, and proofreading. Y.S.: System model, editing, and proofreading. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Young Elite Scientists Sponsorship Program by CAST (2022 QNRC001), the CAST funding program (2022 LX-312-Y-GZ-15), and Open Fund of Key Laboratory of Flight Techniques and Flight Safety, CAAC (No. FZ2022 KF16).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Author [PERSON] was employed by the company Space Star Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.