esa-sceva/satcom-chunk-collection huggingface dataset:
# Fused Gromov-Wasserstein Distance for Structured Objects
## 7 Proofs of the Mathematical Properties
### Proof of Proposition 1--Comparison between FGW, GW and \(W\)  
Proof.: of the Proposition.  
For the two inequalities (9) and (10), let \(\pi\) be an optimal coupling for the Fused Gromov-Wasserstein distance between \(\mu\) and \(\
u\) (assuming its existence for now). Clearly:  
\[d_{FGW,\alpha,p,q}(\mu,\
u) =\Big{(}\int\limits_{(X\times\Omega\times Y\times\Omega)^{2}} \big{(}(1-\alpha)d(a,b)^{q}+\alpha L(x,y,x^{\prime},y^{\prime})^{q}\big{)}^{p} \,d\pi((x,a),(y,b))d\pi((x^{\prime},a^{\prime}),(y^{\prime},b^{\prime}))\big{)} ^{\frac{1}{p}}\] \[\geq\Big{(}\int\limits_{X\times\Omega\times Y\times\Omega}(1- \alpha)^{p}d(a,b)^{pq}\,d\pi((x,a),(y,b))\Big{)}^{\frac{1}{p}}=(1-\alpha)\Big{(} \int\limits_{\Omega\times\Omega}d(a,b)^{pq}dP_{2}\mathbb{4}\pi(a,b)\Big{)}^{ \frac{1}{p}}\]  
Because \(\pi\in\Pi(\mu,\
u)\) the coupling \(P_{2,4}\mathbb{4}\pi\) is in \(\Pi(\mu_{A},\
u_{B})\). So by suboptimality:  
\[d_{FGW,\alpha,p,q}(\mu,\
u)\!\geq\!(1-\alpha)(d_{W,pq}(\mu_{A},\
u_{B}))^{q}\]  
which proves Equation (9). Same reasoning is used for Equation (10).  
For the last inequality (11), let \(\pi\in\Pi(\mu,\
u)\) be any admissible coupling. By suboptimality:  
\[d_{FGW,\alpha,p,1}(\mu,\
u)\leq\Big{(}\int\limits_{(X\times \Omega\times Y\times\Omega)^{2}}\big{(}(1-\alpha)d(a,b)+\alpha|d_{Z}(x,x^{ \prime})-d_{Z}(y,y^{\prime})|\big{)}^{p}d\pi((x,a),(y,b))d\pi((x^{\prime},a^{ \prime}),(y^{\prime},b^{\prime}))\Big{)}^{\frac{1}{p}}\] \[\stackrel{{(*)}}{{\leq}}\Big{(}\int\limits_{(X \times\Omega\times Y\times\Omega)^{2}}\big{(}(1-\alpha)d(a,b)+\alpha d_{Z}(x,y )+\alpha d_{Z}(x^{\prime},y^{\prime})\big{)}^{p}d\pi((x,a),(y,b))d\pi((x^{ \prime},a^{\prime}),(y^{\prime},b^{\prime}))\Big{)}^{\frac{1}{p}}\] \[\stackrel{{(**)}}{{\leq}}2\Big{(}\int\limits_{X \times\Omega\times Y\times\Omega}\big{(}(1-\alpha)d(a,b)+\alpha d_{Z}(x,y) \big{)}^{p}d\pi((x,a),(y,b))\Big{)}^{\frac{1}{p}}\]  
(*) is the triangle inequality of \(d_{Z}\) and (**) Minkowski inequality. Since this inequality is true for any admissible coupling \(\pi\) we can apply it with the optimal coupling for the Wasserstein distance defined in the proposition and the claim follows.
## 1 Introduction  
Unaddressed functional deficits in youth athletes may elevate injury risk and hinder performance development [1, 2, 3]. Long-term development programs therefore prioritizemovement quality, balance, and strength [4, 5, 6, 7]. Early detection of motor skills deficits in youth players is therefore of both preventive and performance importance, guiding individualized training to foster safer long-term athletic development [8]. Despite this, screening practices are often unstructured or rely on isolated tests that fail to capture the multidimensional nature of motor performance [1].  
Common field tests include the Functional Movement Screen (FMS), which assess movement quality, and the Y-Balance Test (YBT), which quantifies dynamic balance [6, 9]. Both have been used to flag \"at-risk\" athletes [1, 10, 11]. Hand-held dynamometry is also gaining support for strength screening [12, 13]. Yet, evidence linking a low FMS score (\(\leq\)14) to future injury is inconsistent [1, 14]. A meta-analysis by [PERSON] et al. found no significant association between low FMS and injury risk in roughly half of the studied cohorts [14] Similarly, the proposed YBT cut-off points have shown limited predictive value for injury in meta-analyses [11]. This raises concern that using single cut-off values may oversimplify an athlete's complex movement profile. More critically, binary cut-offs mask the complexity of athletes' movement patterns--an individual may show a strength deficit but still score \"normal\" overall, leading to missed opportunities for targeted intervention [15].  
Integrative systems such as Move2 Perform attempt to combine FMS, YBT, injury history, and demographics into a single risk score [2, 16], but are often proprietary and yield non-specific outputs with broad labels (e.g., \"moderate risk\"). Such labels do not indicate which domain of motor skills should be trained. For practical decision making, coaches and clinicians need tools that are both comprehensive and interpretable--capable of efficiently identifying which functional domains to target [17].  
Machine learning (ML) offers promise, but most models remain opaque or impractical for daily use in real-world settings [18, 19]. High-complexity ML can identify patterns (predict injury risk, improve performance, and capture talents) [20, 21, 22] but lacks interpretability and clinical relevance [18]. In youth sport, practical, transparent models are needed.  
Our objectives were two-fold: (1) to develop an interpretable machine learning model that classifies youth athletes into one of four modifiable functional profiles based on common field tests (FMS, YBT, and hand-held dynamometry) and (2) to create an open-access digital tool that generates athlete-specific reports with visual explanations and targeted training suggestions. This proof-of-concept system aims to bridge the gap between complex analytics and everyday use in sport development settings.
# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 3 Methodology
### Point Association and Normal Computation  
We calculated the nearest point in the reference cloud \(R\) to each point in the query cloud \(M\). A brute-force search necessitates \(O(n^{2})\) distance calculations in total, which can be expensive when dealing with large point clouds. We used the reference cloud to build a KD tree to overcome this problem. Then, we performed a distance search using the KD-tree with \(O(n\,\log n)\) distance calculations to find all nearest neighbors.  
The KD-tree associated with the reference point cloud \(R\) was then used to calculate the normals. A common approach is to select a subset of points within a predefined radius around each reference point. The eigenvalue decomposition of the covariance matrix of the subset then yields the principal components. The component with the smallest eigenvalue corresponds to the normal of the approximated plane in that point (cf., e.g., [37]). Finally, we minimized the normal distances between all associated points in the optimization process. It is worth mentioning that this method is fully automatic and is not limited to flat surfaces. Cylinders and other arbitrarily shaped artificial objects can also be locally approximated by planes (see Figure 3) and thus used in our approach.  
Figure 2: Workflow of the proposed methodology.
## 4 Experimental Analysis and Verification  
### Experimental Analysis  
This section analyses the charging curves on a large-scale battery pack consisting of 238 series-connected battery cells shown in Figure 3 using the battery anomaly detection method proposed in Section 3.  
Step 1: RPCA is used to perform extreme denoising on the data, completely removing noise and outliers, as shown in Figure 6. The average curve (the black curves) is used as the consistency baseline for the normal battery cell charging state for a cell consistency assessment.  
Step 2: The singular values \(\sigma_{2}\sim\sigma_{5}\) are preserved in the singular value matrix \(\mathbf{\Sigma}\), other singular values are set to 0, and then the data curve is obtained, composed of an abnormal battery cell voltage and a small amount of noise components through RPCA decomposition, as shown in Figure 7.  
Step 3: The \(A.D_{i}\) values of the curves shown in Figure 7 are calculated using Equation (14) in Section 3. Subsequently, the \(A.D_{i}\) values of battery cells within the \(3\sigma\) anomaly threshold are conducted to screen, and the serial numbers of the anomaly battery cells are obtained, as shown in Figure 8.  
Figure 8: Anomaly detection results of battery cells.  
Figure 6: Charging curves of battery cells after extreme denoising.  
Figure 7: The charging curves of battery cells composed of outliers and a small amount of noise.  
As shown in Figure 8, during the charging process, the characteristics of battery cells numbered 3, 21, 33, 53, 58, and 108 exhibit inconsistently with the consistency baseline. The results indicate that they are obviously anomaly battery cells and that the anomaly detection method is reliable.
# High-Reliability Wireless Pressure Measurement System Based on FEC Algorithm
## 4 Key Control Logic Design  
### Overall Logic Design  
The overall logic design process is illustrated in Figure 7. The monitoring and acquisition module includes optocoupler trigger control and analog-to-digital conversion acquisition, primarily completing the collection of analog signals through the control of analog-to-digital conversion sampling logic, while also receiving digital signals as trigger commands; the readback storage module mainly functions to store the collected data and FPGA logic code; the data transmission module is composed of wireless communication control and gigabit Ethernet control, realizing various data communication functions.
## 2 Validation, Evidence Based on Response Processes
### Challenges and Contradictory Findings  
RT analysis is recommended in assessment design as it enhances and evaluates numerical models by reducing subjectivity [47; 48]. For instance, RT analysis has been applied to examine test-takers' engagement and motivation, particularly in detecting rapid guessing [49]. Group comparisons often focus on final answers and responses, overlooking differences in RPs across groups. However, RT alone does not explain why users spent a certain amount of time on tasks. Therefore, for validation purposes, it is advisable to supplement RT data with other process data [50]. [PERSON] et al. emphasized that while RP data can vary, only relevant data linked to the intended constructs are crucial [51]. Not all processes reflect the targeted construct, and typically only some are used in scoring.
###### Abstract  
The objective of this study was to evaluate the antimicrobial effect of direct cold atmospheric plasma (CAPP) treatment for pre-harvest application using four different crop species: _Hordeum valgare_ L. (barley), _Tritcium aestivum_ L. (wheat), _Brassica napus_ L. (rapeseed) and _Lupinus angustifolius_ L. (lupine). The model bacterium _Bacillus atrophaeus_ served as a proxy for spore-forming plant pathogens on the seed surface. After semi-dry inoculation of spores onto the seeds, treatment with two different plasma sources, a volume-dielectric barrier discharge and a corona discharge, and different exposure times was carried out. Subsequently, recovery of viable spores from the seeds' surfaces was performed. Moreover, seed viability was determined based on maximum germination, as well as water contact angle as a measure for seed surface hydrophilicity. Direct CAPP treatment was efficient in reducing viable spores of _B. atrophaeus_ with no significant differences between the plasma sources, reaching a mean inactivation of 1 log\({}_{10}\) CFU/mL across all treatment times and crops species. Maximum germination of seeds was not negatively affected under any treatment condition. Seed hydrophilicity was increased for both plasma sources tested. Overall, this study provides valuable information on the efficiency of direct CAPP treatment of seeds with the purpose of seed hygienation with the premise of unaltered seed vitality and evaluates the potential application in comparison with previous investigated CAPP methods.  
decontamination; seeds; germination; cold atmospheric plasma; water contact angle +
Footnote †: journal: Physics Letters B
###### Abstract  
In this study, we aimed to prepare and characterise hydrogel formulations using cellulose nanocrystals (CNCs), alginate (Alg), and polyethylene glycol diacrylate (PEGDA). The CNC/Alg/PEGDA formulations were formed using a double network crosslinking approach. Firstly, CNC was extracted from oil palm trunk, and the size and morphology of the CNCs were characterised using TEM analysis. Secondly, different formulations were prepared using CNCs, Alg, and PEGDA. The mixtures were crosslinked with Ca2+ ions and manually extruded using a syringe before being subjected to UV irradiation at 365 nm. The shear-thinning properties of the formulations were tested prior to any crosslinking, while the determination of storage and loss modulus was conducted post extrusion after the Ca2+ ion crosslink using a rheometer. For the analysis of swelling behaviour, the constructs treated with UV were immersed in PBS solution (pH 7.4) for 48 h. The morphology of the UV crosslinked construct was analysed using SEM imaging. The extracted CNC exhibited rod-like structures with an average diameter and length of around 7 \(\pm\) 2.4 and 113 \(\pm\) 20.7 nm, respectively. Almost all CNC/Alg/PEGDA formulations (pre-gel formulation) displayed shear-thinning behaviour with the power-law index \(\eta\)! 1, and the behaviour was more prominent in the 1% [\(w/v\)] Alg formulations. The CNC/Alg/PEGDA with 2.5% and 4% [\(w/v\)] Alg displayed a storage modulus dominance over loss modulus (G' > G\") which suggests good shape fidelity. After the hydrogel constructs were subjected to UV treatment at 365 nm, only the F8 construct [4% CNC: 4% Alg: 40% PEGDA] demonstrated tough and flexible characteristics that possibly mimic the native articular cartilage property due to a similar water content percentage (79.5%). In addition, the small swelling ratio of 4.877 might contribute to a minimal change of the 3D construct's geometry. The hydrogel revealed a rough and wavy surface, and the pore size ranged from 3 to 20 \(\upmu\)m. Overall, the presence of CNCs in the double network hydrogel demonstrated importance and showed positive effects towards the fabrication of a potentially ideal 3D bioprinted scaffold.  
cellulose nanocrystals; alginate; polyethylene glycol diacrylate; biomaterials; bioprinting 4.0/. +
Footnote †: journal: Applied Sciences
###### Abstract  
The fast advancement of unmanned aerial vehicle (UAV) technology has facilitated its use across a wide range of scenarios. Due to the high mobility and flexibility of drones, the images they capture often exhibit significant scale variations and severe object occlusions, leading to a high density of small objects. However, the existing object detection algorithms struggle with detecting small objects effectively in cross-scale detection scenarios. To overcome these difficulties, we introduce a new object detection model, RPS-YOLO, based on the YOLOv8 architecture. Unlike the existing methods that rely on traditional feature pyramids, our approach introduces a recursive feature pyramid (RFP) structure. This structure performs two rounds of feature extraction, and we reduce one downsampling step in the first round to enhance attention to small objects during cross-scale detection. Additionally, we design a novel attention mechanism that improves feature representation and mitigates feature degradation during convolution by capturing spatial- and channel-specific details. Another key innovation is the proposed Localization IOU (LIOU) loss function for bounding box regression, which accelerates the regression process by incorporating angular constraints. Experiments conducted on the VisDrone-DET2021 and UAVDT datasets show that RPS-YOLO surpasses YOLOv8s, with an mAP50 improvement of 8.2% and 3.4%, respectively. Our approach demonstrates that incorporating recursive feature extraction and exploiting detailed information for multi-scale detection significantly improves detection performance, particularly for small objects in UAV images.  
YOLOv8; small target detection; unmanned aerial vehicle; attention mechanisms +
Footnote †: journal: Applied sciences
# Dielectric Elastomer Actuator for Soft Robotics Applications and Challenges
## 8 Optical Application with Tunablility
### All-Polymeric Lens  
As an alternative, all-polymeric lens methods using DEAs have been suggested instead of the liquid-filled elastomeric lens. A thin film active-lens, which consists of a convex polydimethylsiloxane (PDMS) lens with a diameter of 1 mm, a DE membrane coated with compliant electrode using silver-nanowires, and an acrylic frame, was proposed [137]. The active-lens shows optical transmittance as high as 90%, and can change its focal point as high as 18.4% under 4 kV at 1 Hz. By adopting an all-polymer lens structure, the active lens can solve the gravity effect problem and enhanced mechanical robustness. However, the focal length change is relatively small compared to the fluid-filled elastomeric lenses. To extend the tunable range, a lens structure operated by a combination of DE membrane deformation and electro-static attraction force, was suggested [138]. The combined active lens module has two active lenses, with a convex and a concave structure, both with a diameter of 5 mm. Each active lens consists of a DE membrane and silver nanowire electrodes, as shown in Figure 14b. With the benefits of a hybrid driving force, each active-lens can be controlled bi-directionally and shows a wide operating frequency range of 0.1-100 Hz. Furthermore, when using both DE actuation and electro-static force, the amplitude of translational displacement of the active-lens increases 230%, as compared to the case of only relying on DE actuation. This increased displacement enables a wide variable focusing range.  
Also, inspired by the human eye, an expansion-tunable lens module using disk-type DEA is presented by the same group [139]. A plano-convex PDMS lens, with a diameter of 3 mm, is connected to disk-type DEA pairs, and is efficiently stretched under an applied voltage. The tunable lens shows a 65.7% change in focal length at 4.6 kV. In addition to the all-polymeric lenses, a DEA can also be adapted in a tunable binary phase Fresnel lens [140]. The tunable Fresnel lens (TFL) is composed of a thin DE membrane (Elastosil P7670, thickness:200 \(\upmu\)m) with binary-phase microstructure with a diameter of 10 mm, and annular compliant electrodes made of silver nanowires. As an electric voltage is applied across the DE membrane, a compression in thickness induces in-plane deformation of the binary-phase Fresnel zone plate, and thereby, the focal length decreases. The change of the focal length is reported as 17.7% with a driving voltage at 4 kV. Since the all-polymer lens type employs fluid, they are mechanically stable, compact, and exhibit a fast response. However, due to a limitation of the DEA's maximum output force, making a large aperture tunable lens system using an all-polymer lens is still a challenge.
# A Bimodal EMG/FMG System Using Machine Learning Techniques for Gesture Recognition Optimization
## 3 Results
### Classifiers Comparison  
A total of thirty-three classifiers were trained, and the assessment of the performance of each classification model was made using accuracy (or \"effectiveness\") as the main metric. However, additional metrics--the F-score and the area under the ROC curve (ROC-AUC)--were also introduced to obtain a more complete picture of the performance of the models.  
The F-score combines accuracy and sensitivity (or recall) into a single metric that evaluates the accuracy and robustness of the model. ROC-AUC, on the other hand, measures the model's ability to distinguish between classes.  
Figure 6: (**a**) Dataset for each gesture. (**b**) Distribution of the number of features extracted per muscle and per sensor type. (**c**) 4 features are extracted from FMG and EMG signals simultaneously (green), 8 features from EMG signal (red), and 2 features from FMG signal (black).  
Table 3 summarizes the results obtained. Initially, using only the effectiveness, twelve models were selected, and with the additional metrics, the five that can be highlighted were confirmed: Quadratic SVM, Cubic SVM, Fine KNN, Weighted KNN, and Wide Neural Network.
## 2 Related Work
### Dual Connectivity  
For the study of DC applications, although there is no study that combines the DC and network slicing technology, some literatures have used DC technology to meet different service requirements [22; 23; 24; 25]. In [22], a user-centric resource scheduling management mechanism with different quality of service (QoS) requirements for DC heterogeneous networks was investigated, and a general framework was proposed to maximize system energy efficiency. In order to meet the ultra-reliable and low-latency requirements of URLLC services, the author in [23] introduces DC and proposes a new admission mechanism to control the number of active users in dual-connection mode. In order to provide users with seamless video streaming services, a video quality-aware traffic offloading system is proposed in [24], and the authors use fountain codes to achieve DC enhancement. [PERSON] et al. [25] proposed a raptor code-based dual connectivity (RCDC) scheme to solve the out-of-order packet arrival problem and reduce delivery delay. DC also plays an important role in mobility management and network handover [26; 27; 28]. In order to solve the problem of the seamless connection of aircrafts, the authors introduced the DC technique in [26] and used a two-dimensional genetic algorithm to obtain the optimal switching scheme. [PERSON] et al. [27] have investigated a DC-assisted active mobility management scheme to provide mobile users with real-time services more effectively, with minimum data rate requirements. Since DC has a lower amount of handover interruption, the authors propose a DC-assisted mobility management algorithm in [28] to efficiently perform handover between 4G and 5G radio access technology (RAT). In other aspects, the authors in [29] considered a two-tier over-the-air heterogeneous network with decoupled access and investigated the DC characteristics of users at the edge of the network. [PERSON] et al. [30] introduced a dual-connectivity-assisted offloading scheme for edge computing, and used a deep learning-based intelligent offloading scheme to fully utilize the computing power at the edge. In wireless network systems using dual connectivity, interference tends to be more complex. To solve the channel interference problem within the system, a nonlinear self-interference canceller using neural networks was designed and a low-complexity training scheme was proposed in [31].
# Communication Technologies in Emergency Situations
## 4 Emergency Communications Technologies--A Systematic Literature Review
### Satellite Communications Technologies  
Finally, satellite communications, although already mentioned in some of the previous references ([33; 45]), are also one of the most important technologies related to CIRS communications identified in our systematic review.  
In particular, research in [52] is based on the background of space-based big data and satellite internet, combined with low-orbit satellite constellations and a complex network communication environment. The application and development of the grid inter-satellite connection and calculation algorithm proposed in this study is not only applicable to remote sensing constellations, but it is also applicable to other constellations that require interconnection, such as communication and navigation. The algorithm, thus, can be used in emergency communications, disaster warning, and maritime rescue, and can contribute to the next generation of satellite internet and \"satellite-ground\" integrated networks.  
In [57], LEO Satellite is proposed as the backhaul node for connecting the UAVs to the core network, as LEO satellite networks also have short transmission delay, large bandwidth, and small path loss characteristics.