esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
All statistical analyses were performed using SPSS (version 25.0; SPSS, Inc., Chicago, IL, USA). The results were expressed as means with standard deviations (\(\pm\)SD) and 95% confidence intervals. Statistical significance was set at \(p<\) 0.05. The normality of data distribution was examined using the Shapiro-Wilk tests, [PERSON]'s test of sphericity was conducted to test for the homogeneity of data and if it did not comply (\(p<\) 0.05), the Greenhouse-Geisser adjustment value was used. The percentage of differences in muscle activity imbalance between each muscle group (Q; H; G) within treatment group was analyzed by a three-way (muscle \(\times\) pre_post \(\times\) time; 3 \(\times\) 2 \(\times\) 5) ANOVA with repeated measures. Similarly, the differences between the treatment and control group were analyzed with a three-way (muscle \(\times\) group \(\times\) time; 3 \(\times\) 2 \(\times\) 6) ANOVA with repeated measures. Effect sizes for main effects and interaction were estimated by calculating the partial eta squared (\(\eta^{2}\)). In the event of a significant main effect, post-hoc comparisons were conducted using the Bonferroni test.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Muscle Group** & **Description** \\ \hline
**Quadriceps** & In a sitting position with a knee and hip joint angle flexed at 90\({}^{\circ}\) with hands kept across the chest. Participants placed their feet on the pad and performed a maximum bilateral voluntary isometric contraction. Participant started the contraction via a visual cue. During the maximum isometric effort, a chair with a non-movable back supported the trunk. The contraction was held for 3 s. \\ \hline
**Hamstrings** & In a prone position with knees flexed to 45\({}^{\circ}\). Manual resistance was provided by the two instructors at the distal portion of the leg towards extension, while the third instructor stabilized the hip and the pelvis. The participants then performed a maximum bilateral voluntary isometric contraction for 3 s. \\ \hline
**Gluteal** & In a prone position, with the knee flexed to 90\({}^{\circ}\). Manual resistance was provided by the two instructors in the downward direction through the ventral foot and distal posterior thigh while the pelvis was stabilized by the third instructor. The participants then performed a maximum bilateral voluntary isometric contraction by lifting their thighs and leg off the table as hard as possible. The contraction was held for 3 s. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of maximal voluntary isometric contraction tests for each muscle group.
## 4 Applications of Multimodal Learning for Fault Detection Diagnosis
### Applications in Automotive Systems
#### 4.3.3 Vehicle Engine Fault Detection  
Engine fault detection represents another crucial application area. Modern vehicles are outfitted with a range of sensors such as oxygen, pressure, and temperature sensors to monitor engine performance. These devices produce data regarding combustion, fuel efficiency, and emissions.  
Through data analysis, machine learning models can identify issues such as misfires, clogged injectors, or emission control failures [76]. Algorithms such as DT, SVM, RF, and DNNs are used to classify fault types and aid maintenance decisions [77].  
These models facilitate predictive maintenance scheduling, reducing breakdowns and ensuring vehicle reliability.
# Sport Performance Analysis with a Focus on Racket Sports: A Review
## 3 Results
### SR2: A Systematic Review of Publications on Racket Sports
#### 3.2.1 Classification by Racket Sport and Methodology
For racquet sports, a significant opportunity has been identified to apply machine learning, decision trees, and neural networks (particularly for tennis and squash, where no reviewed literature has used this method) for performance analysis (especially for tennis and squash). Looking at Figure 3 it can be seen that the neural network technique is showing some promising patterns. Greater participation of software technologies, such as automatic in-game movement recognition and evaluation and in-game wearable devices for badminton, table tennis, and squash, should be considered in future research efforts. Research hypothesis R3-not confirmed. Source Title Categorization  
The specific source title is assigned to publications covered by the Web of Science Core Collection and SCOPUS databases. For the sixty-five selected English-language publications, thirty-nine distinct sources have been identified. Eight of the title sources have multiple publications. The first twenty sources are listed in Table 4.  
Surprisingly, all nine publications published in the \"International Journal of Sports Science and Coaching\" were about tennis. Publications from the second most countable source, the \"International Journal of Performance Analysis In Sport\", cover all five sports. Figure 4 displays a bar graph for the selected publications, displaying the total number of publications, the average number of citations per publication, and the corresponding average number of pages per publication. The graph displays the results according to the type of source. The first section displays the frequency of documents for various research areas. The second section focuses on the average number of citations, while the third section displays the average number of pages per article. The International Journal of Sports Science and Coaching received the fewest citations. German Journal of Exercise And Sport Research publications have the fewest pages on average. **Summary:** Source title categorization showed the expected results with the International Journal of Sports Science and Coaching as a top source followed by the International Journal of Performance Analysis In Sport. Looking at the source analysis with more than two publications, the minimum is 24 average number of citations, and the maximum is 44 average number of citations. The number of pages varies from seven to thirteen. Those numbers could be a good indicator for future work in terms of the quality of the sources and the ideal length of the publications.
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### GANs in Histological Image
#### 5.4.2 Stain Normalization  
Stain normalization has emerged as one of the most critical and frequently addressed applications of generative GANs in digital pathology. To overcome this challenge, numerous studies have adopted GAN-based approaches that aim to harmonize the visual characteristics of histological slides while preserving the morphological integrity of the tissues [67; 68; 97]. Among the various architectures, CycleGAN has become one of the most commonly used models due to its ability to perform unpaired image-to-image translation, a crucial advantage when paired samples across different staining domains are unavailable. For instance, [PERSON] et al. [68] applied CycleGAN to normalize slides from diverse datasets, achieving strong visual fidelity as indicated by Frechet Inception Distance (FID) scores of 15.32 on MoNuSeg and 6.80 on head and neck cancer datasets. Other notable implementations of CycleGAN include its use by [PERSON] et al. [67], who reported an AJI of 0.6570 and PQ of 0.625 in downstream nucleus segmentation, as well as [PERSON] et al. [97], who explored its utility in unsupervised stain adaptation. Additionally, [PERSON] and [PERSON] [75] pioneered early stain normalization with GANs, achieving Inception Scores (IS) of 0.700 for MITOSIS, 0.950 for COLON, and 0.260 for OVARY datasets using adversarial training on an NVIDIA Titan X GPU. Beyond CycleGAN, more advanced architectures have demonstrated improvements in both chromatic coherence and structural fidelity. [PERSON] et al. [70] employed Pix2 Pix GAN (P2P-GAN) and DSTGAN across datasets such as TUPAC, MITOS, and MICCAI, attaining high performance with SSIM > 0.97 and PSNR values exceeding 34. Similarly, Restore-GAN introduced by [PERSON] et al. (2023) [89] reached SSIM = 0.949 and PSNR = 30.678, validating the efficacy of more customized GAN variants for stain normalization tasks.
## 4 Experiment and Results
### Experiment Results
The difference between the experiment using the algorithm itself and the experiment using the Dual Segmented Reward Model is summarized as follows:  
First, the reward generation pattern is different at the beginning of the experiment. For experiments on the algorithm itself, little reward occurred until the reward value increased. However, for the experiment to which the proposed reward model was applied, a small reward was continuously generated from the beginning of the experiment. Figure 18 shows the difference. Second, there is also a big difference in the time when the reward value starts to increase. For the experiment using the algorithm itself, the starting point of the increase in the reward value was not constant and there were many differences between the experiments. However, as a result of applying and testing the proposed reward model, the reward value started to increase consistently at a certain time. Figure 19 shows the changes. Figure 17: Comparison of Experimental Results by Test Case. Figure 18: Comparison of Improvement #1: (**a**) DQN Test; (**b**) DQN + Dual Segmented Reward Model; (**c**) DDQN Test; (**d**) DDQN + Dual Segmented Reward Model. Third, there is a difference in the pattern of increase in reward value. For the experiment using the algorithm itself, the increase trend of the reward value was not constant and the reward value did not increase or decrease intermittently. However, when the proposed reward model was applied and tested, the pattern of increase in reward value was stable and consistent, and there was no decrease in the increased reward value. It can be confirmed in Figure 12:0  
Given these improvements, we judged that it was helpful in improving the sparse reward problem. In the experiment using the proposed reward model, there was no case of learning failure that was intermittently observed in the experiment using the algorithm itself.
# Data Embedding in SHVC Video Using Threshold-Controlled Block Splitting
## 2 Related Work
To embed data into a scalable coded video, researchers often exploit the multi-layer coding structure defined in the scalable video coding standards. Specifically, a scalable coded video consists of a base layer (BL) and multiple enhancement layers (ELs). [PERSON] exploits quantization parameters and MVs in all layers of an SNR scalable coded MPEG video to hide data [28]. [PERSON] et al. identify highly textured blocks in H.264/SVC video based on a predefined complexity model. They restrict data embedding to these identified blocks so that the error caused by coefficient manipulation is less noticeable [25]. Taking a similar approach, [PERSON] et al. restrict data embedding to regions with high texture and high contrast in H.264/SVC video [29]. Both [PERSON] et al. and [PERSON] et al. embed data by manipulating the AC coefficients. On the other hand, [PERSON] et al. associate the data bit with the intra prediction mode in SHVC video when the selected mode disagrees with the payload bit [30]. The proposed method carries additional payload in multiple layers while suppressing video quality degradation by using a self-cancelling method, but it is only applicable to intra-coded blocks. Recently, [PERSON] et al. embeded data into AC coefficients chosen by using a key-control selection algorithm in H.264/SVC [31]. While some data embedding methods have been put forward by researchers for scalable coded video, most methods are designed for an older generation of scalable video coding standard, namely H.264/SVC. This situation further motivated us to design a data embedding method for SHVC video.
## 3 Methodology
### Experimental Design
#### 3.3.3. Satellite SSM Assimilation  
The assimilation of remotely sensed SSM products derived from S1 is constrained by the acquisition configuration of S1. For D1 and R1, SSM products are available during both the 2016-2017 and 2017-2018 seasons with a revisit time of 6 days as both S1A and S1B satellites were used. The assimilation window is therefore set to 6-day for both fields during both seasons. For the flood irrigated fields F2 and F3, observations are available every 12-day since only S1A was into orbit during the 2015-2016 season. As already stated, D2 was discarded from the analysis because of the very specific growing condition of this field.
###### Abstract  
This study was designed to evaluate the influence of maturity status in the inter- and intra-session reliability of curvilinear sprinting (CS) and compare the reliability of the half-CS trials with the complete CS trials. Forty-two youth basketball players from an elite academy (13.1 \(\pm\) 1.7 years; 166.7 \(\pm\) 16.2 cm; 57.2 \(\pm\) 17.0 kg) performed two sessions of three CS trials each on both right and left sides with seven days of separation between sessions. The predicted peak height velocity (PHV) was used to establish players' maturity status (pre-PHY, \(n\) = 14; mid-PHY, \(n\) = 14; post-PHY = 13). Mid- and post-PHY groups showed a high relative (interclass correlation coefficient [ICC] \(\geq\) 0.75) and absolute (coefficient of variation [CV]! 5%) reliability inter- and intra-session, and pre-PHY showed high relative and absolute reliability in the left trials and in the CS right trial, but moderate (ICC = 0.73) relative reliability in the half-CS right side. Based on these findings, it is recommended that practitioners consider players' maturity status to ensure accurate and reliable assessments of CS performance.  
curvilinear sprint; maturity status; peak height velocity; basketball; assessment Article
## 1 Introduction
It is also reported that many volatile terpenes resulting from TPSs of TPS-a, b, and g subfamilies can protect plants by either conferring antiscenosis to kill insects or attracting their natural enemies [21; 24; 35; 36]. However, the anti-GMO or some consumers may be prudent with transgenic soybeans expressing exogenous genes that confer terpene-based biopesticides. Herein, it is important to characterize soybean _TPS_ genes functional in soybean protection from pests. Research on volatile soybean terpenes has been conducted for decades. For example, [PERSON] et al. discovered the monoterpene \(\alpha\)-pinene in wild soybeans in 1984 [37]. It is also found that (_E_)-\(\beta\)-ocimene and lanalool might have anti-insect effects in insect-resistant soybean cultivars, but the underlying _GmTPS_ genes remain to be uncovered [38]. In addition, (_E_,_E_)-\(\alpha\)-farresene is among the compounds with the greatest variation between damaged and undamaged soybean plants [11]. The advent of fast-developing omics sequencing tools has significantly accelerated modern studies such as gene characterization. It is reported that the soybean TPS family consists of more than 20 members in the soybean genome [39], of which _GmTPS5_ and _GmTPS18_ (_GmNES_) were reported to encode geraniol synthase and nerol synthase, respectively, to defend against cotton leaf worms (_Spodoptera lituna_) in transgenic tobaccos [39; 40]. In addition, _GmTPS21_ (_GmAFS_) was reported to encode \(\alpha\)-farresene synthase functioning in defense against nematodes and insects [24]. The _S. litura_, which is reported to infest 87 species of host plants with economic importance, is a major defoliator causing significant yield loss mainly by damaging the foliage [41]. In the present study, we re-mined the soybean _TPS_ family and checked _TPS_ genes responding to _S. litura_, methyl jasmonate (MeJA), and mechanical wounding treatments. Based on the genome-wide analysis of the _GmTPS_ gene family, _GmTPS3_ (later renamed as _GmOCS_) was subjected to further detailed analyses such as subcellular localization analysis, in vitro enzymatic assay, overexpression assays in soybean and tobacco,and dual-choice assay, demonstrating that _GmOCS_ is an excellent candidate in further anti-insect soybean modification through controlling the biosynthesis of (_E_)-\(\beta\)-ocimene. Our findings provide valuable insights into understanding the mechanisms underlying defense responses of soybean against pests and also shed light on an environmentally friendly strategy for soybean pest control.
# A Multi-Agent Deep Reinforcement Learning System for Government Interoperability
## 4 System Design
### Adaptation to Complex Inter-Departmental Interactions and Performance Benchmarks  
Leveraging distributed computing, the framework integrates cloud computing for scalable resource allocation, distributed ledger technology (DLT) for secure data transactions, and a microservices architecture for operational flexibility. In macro-party organization, cloud platforms dynamically adjust resources, centralize data management, and enhance coordination among security, health, and coordination teams. For agricultural wastewater use, cloud-based analytics optimize irrigation and enable remote monitoring of wastewater treatment. In financial audits, cloud computing centralizes financial data, automates reporting, and ensures transparency.  
Inter-departmental collaboration is critical to system efficiency. A unified command center in macro-party organization ensures seamless coordination, while standardized communication protocols enhance clarity. In agricultural wastewater use, a cross-departmental task force and shared data repositories facilitate oversight and compliance. For financial audits, an inter-agency audit committee and regular briefings ensure transparency, data integrity, and financial accountability.  
Performance benchmarks ensure measurable success. Macro-party organization is evaluated based on operational efficiency, coordination, and risk management effectiveness. Agricultural wastewater use is assessed through water usage optimization, environmental compliance, and inter-agency collaboration. Financial audits focus on data accuracy, audit timeliness, and accountability. By integrating advanced computing, structured collaboration, and precise benchmarks, the framework enhances scalability, decision-making, and governance, ensuring a more efficient, transparent, and adaptive municipal system.
## 3 Methodology
### Analysis of Water/Energy-Limited ET Regimes  
In this study, 11 images from Landsat 8 at Level 1T during 2014 have been used in the HidroMORE simulation. Firstly, we have obtained reflectances at the top of the atmosphere (TOA). They have been converted to bottom of the atmosphere (BOA) reflectances through a relative atmospheric correction and the NDVI has been obtained and cloud-masked at a spatial resolution of 30 m. Later, the NDVI maps from Landsat together with other datasets, such as a land cover map at the same spatial resolution, climatic data and soil data, have been used as input for HidroMORE. The climatic data has been obtained from the REMEDHUS weather stations. The soil database came from the Sistema de Informacion Agroclimatica para el Regadio (SiAR) service of the Spanish Ministry of Agriculture, Food and Environment [53].  
A linear relationship between the basal crop coefficient \(K_{cb}\) and NDVI has been computed. This NDVI-\(K_{cb}\) approach has been applied in the HidroMORE simulation as follows:  
\[K_{cb}=1.36\cdot NDVI-0.03 \tag{4}\]  
The water balance has been calculated for each day and cell, and results at each REMEDHUS station location have been extracted. Runoff has not been considered in this simulation, owing that in this area, the topographic features and the soil properties make the infiltration the predominant process. HidroMORE considers the root-zone soil layer and calculates SM and \(ET_{a}\), but our interest lies in the comparison between ET and the SM at the surface layer. Thus, the SM at root-zone from HidroMORE has not been used.  
Since the SM-LST coupling/decoupling periods are an indicator of a different ET regime (water/energy-limited, respectively), the behavior of the ET has been studied over the year 2014, comparing the mean potential evapotranspiration (\(ET_{0}\)) of the four weather stations from the REMEDHUS network with the actual evapotranspiration (\(ET_{a}\)) obtained from the HidroMORE hydrological model. The correlation between the \(ET_{a}\) from HidroMORE and SM from REMEDHUS has also been analyzed for the different ET regime periods during 2014.  
The flowchart in Figure 2 graphically displays the data and methodology approach followed in this study.  
Figure 2: Flowchart that graphically displays the data and methodology approach.
## 4 Discussion
### Limitations of the Study  
The interpretation of this study's findings must consider several limitations. Firstly, due to the cross-sectional design, it was not possible to establish causal relationships, and the findings only offer a momentary glimpse of the observed connections. Conducting longitudinal studies would be more suitable for gaining deeper insights into how cervical proprioception, stability, and mediating factors change over time. Secondly, relying on self-reported pain and kinesiophobia measures introduces the possibility of response bias and subjectivity. To improve the results' accuracy, incorporating objective measures of pain and psychological factors would be advisable. Thirdly, since the study focused on a specific age group and a population with moderate to severe CS, the generalizability of the findings to broader demographics or individuals with milder conditions may be limited. Encompassing a more diverse range of participants could provide a more comprehensive understanding of the relationship between proprioception, stability, and associated factors. Lastly, although the study explored the mediating effects of pain and kinesiophobia, it did not investigate other potentially influential variables. Therefore, future research should examine these unexplored variables to gain a more comprehensive understanding of the complex interplay in this context.