esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The maximum stress in critical areas A and C are not consistently smaller or greater than the similar ones in the reference model, the variation for the studied model having the interval of (-5 to +5) %. It can be noticed that in two of the studied models, B33 and B38, the maximum stress in all three critical areas is simultaneously smaller than those in the reference model. Model B33 becomes the favoured one, as it is also the second-best solution under the aspect of stiffness increase [16]. In the same context, it can be also remarked that in model B39, the maximum stress in the critical areas is simultaneously greater than in the reference model. B39 is the least favourable model, being at the same time the model with the smallest stiffness increases of all studied \"right tilted\" and 75 mm spaced rib models. In models with intersected ribs, no consistent decrease of the maximum stress in the critical areas can be noted, Figure 25, as a reflection of a possible cumulative positive effect on the increase in stiffness caused by the simultaneous presence of \"left tilted\" and \"right tilted\" ribs. In all models with intersected ribs (B41-B46), the maximum stress in critical area A is greater than in the reference model, and significantly greater than 13% in model B43, while the maximum stress in critical area B is smaller, typically by 1 to 2%, and exceptionally by \(\approx\) 5%. The differences recorded between the maximum stresses in critical area C in relation to the corresponding stress in the reference model are of little significance. The most favourable solution would be model B41, with intersected horizontal and vertical ribs, where the increase of less than +1.2% of the maximum stress in critical area A is well compensated by a decrease of nearly \(-\)2% in critical area C. Model B41 is to be considered the best as it is the model that ensures the largest increase in stiffness (of +3.09%) of all frame models with straight ribs, intersected or not, regardless of their orientation [16]. Figure 17:3 Deviation of maximum stresses in critical areas for frames with right tilted ribs spaced at 75 mm and normal thicknesses (20 mm) of the side walls (model code B33–B40), reported at the reference model B0. Only model B47, with curved ribs, ensures a greater increase in stiffness by +3.64% [16]. In model B47 the maximum stress in critical area A is 5.7% greater than in the reference model, a significantly greater increase than that of less than +1.2% in model B41, but smaller than the maximum stress in critical area B \(-\)4.98% as compared to nearly \(-\)4.26% in model B41.
## 4 Results and Discussion
### Baseline Models  
This study incorporated seven representative baseline algorithms for comparative evaluation: SVM [27], XGBoost [28], LightGBM [29], LSTM [30], TCN [31], FinBERT [32], and StockGCN [33]. SVM is a classic supervised learning algorithm that performs well in high-dimensional, small-sample settings. XGBoost and LightGBM, as gradient boosting tree models, are known for strong feature selection and nonlinear modeling capabilities.  
LSTM is suited for temporal data, capturing long-range dependencies and trends. TCN uses one-dimensional causal convolutions to model sequence structure more efficiently than recurrent architectures. FinBERT, a domain-adapted pretrained language model, can extract fine-grained sentiment signals from policy texts or narratives. StockGCN applies graph convolution to model structural relationships and dynamic interactions across multiple entities. Together, these models span traditional machine learning, deep sequence modeling, semantic feature extraction, and relational representation learning, forming a robust benchmark for evaluating the proposed method's effectiveness.
## 3 Results and Discussion
### Verification of Nt.BbvCI-Assisted TRA  
The feasibility of Nt.BbvCI-assisted TRA was explored by polyacrylamide gel electrophoresis. As observed in Figure 2A, the distinct bands in lanes 1, 2 and 3 correspond to SH-CP, AP and target, respectively. The mixture of SH-CP and AP shows two separate bands (lane 4), indicating that SH-CP and AP are stable when mixed together without any hybridization event. However, once a target is introduced, it hybridizes with SH-CP and AP to form a \"Y\" junction structure; thus, a UV band located closer to the notch is observed (lane 5). There is no significant difference between lanes 4 and 6, which further demonstrates that AP and SH-CP cannot form a duplex, and Nt.BbvCI does not function in the absence of a target. Only in the presence of a target can \"Y\" junction structures and specific recognition sites for Nt.BbvCI be formed. In lane 7, as expected, the brightness of the band at the \"Y\" junction structure location became dim, and a new band near the \"Y\" junction appeared, suggesting a lower molecular weight product forms due to digestion by Nt.BbvCI. Thus, the polyacrylamide gel electrophoresis shows that Nt.BbvCI could effectively degrade the \"Y\" junction structures.  
The Nt.BbvCI-assisted TRA was further verified by detecting the DPV responses of RuHex at different modified surfaces. Figure 2B shows the DPV curves for modified electrodes at different stages upon TRA. Three peaks with surface-confined RuHex were recorded at approximately -0.27 V, which corresponded to the EC reduction of RuHex. The curve a represents the background current caused by the adsorption of RuHex on the SH-CP-assembled electrode surface via electrostatic interactions between the cationic RuHex and anionic phosphate DNA backbones. The hybridization  
Figure 2: Verification of Nt.BbvCI-assisted catalyzed TRA. **(A)** Polyacrylamide gel electrophoresis: 1. SH-CP; 2. AP; 3. target DNA; 4. SH-CP/AP; 5. SH-CP/AP/target DNA (“Y” junction is observed, which is closer to the notch); 6. SH-CP/ AP/ Nt.BbvCI; 7. SH-CP/AP/target DNA/Nt.BbvCI (most of the “Y” junction is digested by Nt.BbvCI compared with lane 5). The concentrations of SH-CP, AP, target DNA and Nt.BbvCI were 5.0 \(\upmu\)M, 5.0 \(\upmu\)M, 5.0 \(\upmu\)M and 5.0 U, respectively, for the above samples, which were incubated for 2 h before transferring into the gel; **(B)** DPV curves for (a) HT/SH-CP/AuE; (b) Nt.BbvCI/target DNA/AP/HT/SH-CP/AuE; (c) target DNA/AP/HT/SH-CP/AuE, the concentrations are 1.0 \(\upmu\)M AP, 10.0 fM target DNA and 5.0 U Nt.BbvCI, incubation in 10.0 mM Tris-HCl containing 10.0 mM RuHex (pH 7.4) with a pulse amplitude of 50 mV and a pulse width of 0.05 s, respectively.  
between SH-CP, AP and the target DNA causes an increase in current response because of the accumulation of more RuHex on the dsDNA (curve c). A considerable decrease in current response (curve b) was observed after the addition of Nt.BbvCI due to the recycling cleavage at the recognition site, leading to reductive and shorter anionic DNA strands localized at the electrode surface to absorb less cationic RuHex.
## 1 Introduction
Their identified modal parameters for a three-story shear building through the processing of numerically simulated earthquake responses without noise yielded the maximum errors of up to 14% and 70% in frequencies and modal damping ratios, respectively, and the values of the modal assurance criterion (MAC) [28] decreased to 0.4. While output-only approaches offer practical benefits by not requiring input dynamic loadings and saving costs on monitoring input loadings, input-output methods generally yield more accurate modal parameter estimates [29; 30; 31]. Consequently, deterministic subspace identification (DSI) methods and combined deterministic-stochastic subspace identification (CDSSI) methods are much preferred to SSI approaches for processing the earthquake responses of a structure. These two classes of methods were first proposed by [PERSON] et al. [32] and [PERSON] and [PERSON] [33], respectively. To enhance the efficiency of these original subspace approaches, an instrumental variable [34], a generalized Schur algorithm [35], empirical mode decomposition [36; 37], a covariance-driven algorithm [38], and nuclear norm optimization [39] have been incorporated into subspace methods. To expand the application fields of subspace approaches from a time-invariant system to a time-variant system, the moving window technique [40; 41] and recursive update algorithms [42; 43; 41; 44; 45] have been adopted. Capitalizing on the advantages of wavelet transformations, this study presents a novel time-frequency subspace approach by integrating deterministic subspace method with the stationary wavelet packet transform (SWPT). Additionally, a Gaussian weighting function was introduced to collaborate with the moving window technique, ensuring the high-precision identification of modal parameters in time-varying systems. The proposed approach excels in extracting modal parameters mode-by-mode with precision, leveraging signals in various subspaces within the stationary wavelet packet transform. This capability extends to identifying weakly excited modes. Validation was carried out through simulated earthquake responses of three six-story shear buildings, considering noise and incomplete measurements. The shear buildings were modelled as a time-invariant system, a periodically varying system, and a sharply varying system, respectively. Two real-world examples are presented to demonstrate the approach's effectiveness in processing measured responses. The first example concerns the earthquake responses of a two-span cable-stayed 510 m long bridge. The second example concerns the experimental acceleration responses of a five-story steel frame, of which its columns were yielded under a shaking table test with a large excitation input. The identified modal parameters were compared with published results and underwent finite element analysis, affirming the accuracy of the proposed approach.
## 4 Discussion
### Impacts  
This study also aimed to identify impact intensity zones during competition for male U14 basketball players, providing specific and practical information for coaching staff and sport scientists. Such data support the individualization of training loads based on player characteristics and contextual team dynamics [5]. Impact zones offer valuable insight into the frequency and intensity of physical contact during gameplay (e.g., rebounds, screens, positional contests) [3]. While some studies have reported no significant positional differences in impact or jump variables during official matches [6], most existing research has focused on senior male [5; 6; 11; 55] or female players [3; 5].  
For the U14 male category, five impact zones were defined: <133.45, 133.45-158.75, 158.76-181.45, 181.46-206.59, and >206.59 contacts per minute (cont./min). The highest proportion of impacts occurred in Zone 3 (158.76-181.45 cont./min), accounting for 36.54% of total impacts. Additionally, 24.69% of impacts were recorded in Zone 4 (181.46-206.59 cont./min), and 9.07% exceeded 206.59 cont./min (Zone 5), with a cluster center of 221.46 cont./min--indicating an extremely high level of physical contact. This elevated impact frequency may be attributed to tactical strategies such as full-court pressing and a moderate level of technical execution, which increases turnovers and contested possessions.  
In comparison, senior players have been reported to operate within different impact zones: <78.00, 83.22-119.60, 120.53-143.60, 143.81-169.14, and 169.43-223.47 cont./min [11].  
While senior athletes can reach high-impact levels (Zone 5: 169.43-223.47 cont./min), this zone accounts for only 14.01% of total impacts. Positional averages for senior players show point guards and centers typically perform at 143 cont./min--corresponding to 23.01% of U14 players' Zone 2--while forwards average 162 cont./min, aligning with 36.54% of U14 players' Zone 3. These comparisons highlight both the physical demands placed on U14 athletes and the influence of tactical and technical factors on impact frequency.  
These findings underscore the physical nature of U14 basketball and highlight the importance of preparing young athletes for the contact demands of the sport. The high proportion of impacts in Zones 3 to 5 suggests that training programs should include components that develop physical resilience, body control, and contact readiness to support performance and reduce injury risk.
# Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas
## 1 Introduction
### Paper Contributions  
The research introduces this unique combination of features into the specialized literature for AQ studies as a novel element. This statement is based on a search in the Web of Science platform that identifies all works containing the terms related to the features proposed in this research TS = ((\"air quality\" OR \"AQ\" OR \"AQI\") AND \"CO\" AND \"NO\({}_{2}\)\" AND \"SO\({}_{2}\)\" AND \"PM\({}_{2.5}\)\" AND \"temperature\" AND \"humidity\" AND \"wind speed\" AND \"traffic\" AND (\"machine learning\" OR \"ML\")). The query returned zero results in the specialized literature, meaning no other research has addressed the issue of _AQI_ using the same input elements for an ML model.  
The contributions of the study are mentioned below:  
* Implementation and integration of six customized ML algorithms, six pre-implemented ML models using ML.NET, and seven customized DL algorithms designed to address the specific characteristics of AQ data;
* Comparative analysis of ML versus DL methodologies, including the identification of the best approach based on performance metrics using an innovative parameter proposed by the authors;
* Integration of a second model for window timeframe forecasting integrated into a user-friendly application, including a prediction model and a forecasting model;* Proposing a decision-making framework based on the implemented models, using the Azure Communication Service for data-driven actions in AQ management.  
The paper consists of five sections. Section 2 describes importing data for the analyzed area (Bronx, New York) utilized in developing the AQ monitoring model. This section also explains the implementation and integration of the customized ML and DL algorithms explicitly tailored for AQ predictions, including a novel framework for real-time monitoring based on the customized models and relevant meteorological data. A case study is presented to detail the application of the proposed models in predicting AQ parameters under various environmental conditions. A comprehensive description of the real-time AQ monitoring prototype, which utilizes an IoT infrastructure to enhance community engagement, is provided in this section. This section also proposes an AI-based decision-making framework for AQ management. Section 3 presents the testing phase of the proposed AQ model and integrates the results into a new real-time monitoring application. The section includes examples of the proposed methodology for evaluating AQ predictions. Section 4 outlines the discussions, limitations of the study, and possible directions for future research in AQ monitoring and predictive analytics. Section 5 provides the conclusions of the paper.
# Extending Partial Domain Adaptation Algorithms to the Open-Set Setting
## 7 Conclusions  
We have extended the popular ANN algorithms used in the DA and PDA settings to work on the OSDA setting. We showed how to utilize heuristics for transfer relevance in order to obtain algorithms for the constrained latent distribution alignment problem and cut-off thresholds for identifying subsets of the target dataset that can be reliably labeled. In particular, we introduced the DIWAN algorithm, which uses an adapted popular heuristic from the literature. Extensive experimentation illustrated the benefits of our method in the OSDA setting. This methodology can be very effective for improving the applicability of machine learning models to real-world data. It could be used for image processing that is generated in an online manner in uncontrolled environments to detect objects or events of interest. Examples include social media content, assisted living, surveillance data, and traffic monitoring. The proposed methodology can also be extended in the future to utilize other weighting methods for target domain classes.  
Conceptualization, G.P.; methodology, G.P., E.S. and S.J.P.; validation, G.P.; writing--original draft preparation, G.P. and E.S.; writing--review and editing, G.P., E.S. and S.J.P. All authors have read and agreed to the published version of the manuscript.  
This research was co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE-INNOVATE (project code: T1 EDK-02070).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **Threshold** & **TTR** & **TR** & **
\begin{tabular}{c} **\#Target** \\ **Instances** \\ \end{tabular} & **Accuracy** \\ \hline \multirow{4}{*}{**Obstruction**} & 0 & 100.00 & 47.10 & 4142 & 36.12 \\  & 0.5 & 100.00 & 62.53 & 3120 & 47.95 \\  & 1 & 97.54 & 80.13 & 2375 & 62.95 \\  & 1.5 & 32.39 & 99.22 & 637 & 98.43 \\ \hline \multirow{4}{*}{**Rescaling**} & 0 & 100.00 & 58.42 & 4455 & 37.15 \\  & 1 & 53.51 & 78.39 & 1777 & 67.36 \\  & 1.25 & 32.85 & 98.16 & 871 & 92.66 \\  & 1.5 & 20.86 & 100.00 & 543 & 100.00 \\ \hline \multirow{4}{*}{**Rotation**} & 0 & 100.00 & 42.96 & 3247 & 42.22 \\  & 1 & 55.84 & 59.19 & 1316 & 38.98 \\  & 1.25 & 21.57 & 86.24 & 349 & 50.43 \\  & 1.5 & 5.96 & 98.96 & 97 & 35.16 \\ \hline \multirow{4}{*}{**Displacement**} & 0 & 100.00 & 43.37 & 6002 & 49.92 \\  & 1 & 61.93 & 65.90 & 2446 & 60.95 \\ \cline{1-1}  & 1.25 & 30.43 & 92.63 & 855 & 84.21 \\ \cline{1-1}  & 1.5 & 7.99 & 96.74 & 216 & 91.16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: TTR denotes the percentage of total transfer-relevant instances captured by the threshold. TR denotes the percentage of TRIs in the dataset after the threshold is applied.
## 1 Introduction
Real-time DLS measurements moreover are challenging since a flow is imposed on the dispersion in laboratory and pilot-scale reactors, e.g., through stirring or pumping. Accurate particle sizing using DLS usually necessitates a resting fluid in the measured sample [12]. In actively mixed fluids, the diffusion is overlaid by a turbulent convection which prohibits diffusion measurements. Here, we demonstrate a set-up based on modulated 3D cross-correlation DLS, which is capable of separating the scattering information resulting from diffusional and translational motion of particles, and consequently allows flow cell coupled measurements on flowing nanoparticle dispersions [13]. In this setup, we pump a particle dispersion from the reactor through an analysis loop comprising a flow cell, and back into the reactor. We successfully validated the set-up presented in this article through monitoring of the size of spherical silica (SiO\({}_{2}\)) nanoparticles during Stober synthesis starting from the molecular precursor tetraethyl orthosilicate (TEOS). Conventional off-line DLS on non-moving liquid dispersions using a state of art DLS instrument, and transmission electron microscopy (TEM) are used to validate the particle size information obtained with our newly developed flow cell coupled DLS instrument.
###### Abstract  
Proton range monitoring and verification is important to enhance the effectiveness of treatment by ensuring that the correct dose is delivered to the correct location. Upon proton irradiation, different positron emitting radioisotopes are produced by the inelastic nuclear interactions of protons with the target elements. Recently, it was reported that the \({}^{16}\)O(p,2p2n)\({}^{15}\)N reaction has a relatively low threshold energy, and it could be potentially used for proton range verification. In the present work, we have proposed an analysis scheme (i.e., algorithm) for the extraction and three-dimensional visualization of positron emitting radioisotopes. The proposed step-by-step analysis scheme was tested using our own experimentally obtained dynamic data from a positron emission mammography (PEM) system (our developed PEMGRAPH system). The experimental irradiation was performed using an azimuthally varying field (AVF) cyclotron with a 80 MeV monoenergetic pencil-like beam. The 3D visualization showed promising results for proton-induced radioisotope distribution. The proposed scheme and developed tools would be useful for the extraction and 3D visualization of positron emitting radioisotopes and in turn for proton range monitoring and verification.  
proton therapy; positron emitters; PEM; spectral analysis; proton range monitoring +
Footnote †: journal: Physics Letters B  
Article  
An Analysis Scheme for 3D Visualization of Positron Emitting Radioisotopes Using Positron Emission Mammography System
## 3 Results and Discussion
### Ligand Concentration Effect  
One of the key factors affecting the efficiency of extraction is the ligand-chelate concentration. The effect of amount of the chelating ligand DLNL on the recovery of lead ion was examined. Different concentrations of the chelating reagent in the range of \(1.0\times 10^{-4}\) to \(5.0\times 10^{-5}\) M were used. The results showed that the maximum recovery of lead ions was obtained at \(2.0\times 10^{-5}\) M DLNL concentration, as shown in Figure 3. At higher DLNL concentrations, the recovery percentage decreases. This could be explained by the excessive chelating agent being co-extracted into the phase rich with the surfactant, thus decreasing the efficiency of extraction of lead ions.  
Figure 3: Effect of ligand concentration on CPE efficiency. Conditions: 40 \(\mu\)g/L Pb(II); pH 6; Triton X-114 0.4% (p/\(\
u\)).  
Figure 2: Effect of pH on cloud point extraction (CPE) efficiency. Conditions: 40 \(\mu\)g/L Pb(II); Triton X-114, 0.4% (v/v); DLNL \(2\times 10^{-5}\) M.
## 4 Evaluation
### Data Collection for Activity Recognition Based Evaluation  
We have selected the broad domain of aerobic like physical exercises for our evaluation since it is an interesting and relevant application area while at the same time being well suited for our approach (see Section 2). Specifically, we selected a set of 10 activities we refer to as the \"Drill dataset\" and that we have already used in our preliminary work described in [14]. Those are part of a light cardio workout taken from a popular YouTube fitness channel [40]. The recorded exercises can be seen in Figure 2. Altogether we have collected 12 YouTube videos of people doing those exercises. The full list of videos used can be seen in Table 1. In the videos individual exercises are performed sequentially and overall they contain about 24 min of usable footage of the 10 activities. In addition to the YouTube material we have recorded data with volunteers at our lab. To this end we have equipped them with IMUs (XSENSE) fixed to their lower legs and wrists (see Figure 3 for the sensor placement) and asked them to imitate the exercises in the video. We have recorded the subjects on video with a camera perspective similar to the online videos to ensure that we have not only real sensor data but can also generate simulated data for the subjects using our regression model. Overall we had 28 subjects performing the 10 exercises. Of those we have 17 users who performed a single session only, which are considered the training set. The test set consists of the 11 users who performed more than one session (5 performing 2 sessions, 4 doing 3 and 2 performing 4 for a total of 30 sessions). This gives us a diverse training set, a large number of testing sessions and the ability to test on the most difficult scenario: the training and testing sets being different users. For an overview of the datasets used and the length and number of users in each one, see Table 2.