esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The pull-back method for determining dynamic tensile strength assumes one-dimensional stress wave propagation and material homogeneity. This study validates these assumptions for unidirectional reinforced concrete (UDRC) through experiments and numerical simulations. Split Hopkinson pressure bar tests were conducted on plain concrete, plain UDRC, and deformed UDRC specimens containing a central 6 mm steel bar. Ultra-high-speed digital image correlation at 500,000 fps enabled precise local strain rate measurements (3 s\({}^{-1}\) to 55 s\({}^{-1}\)) at fracture locations. Finite element simulations revealed that while reinforcement induces localized multi-axial stresses near the steel-concrete interface, the bulk concrete maintains predominantly uniaxial stress conditions. Experimental results showed less than 1 % variation in pull-back velocity between specimen types. Statistical analysis confirmed a unified strain rate-strength relationship: \(\sigma_{\text{spall}}=4.1+4.7\log_{10}(\ell)\) MPa, independent of reinforcement configuration (ANCOVA: \(p=0.2182\) for interaction term). The dynamic tensile strength is governed by concrete matrix properties rather than reinforcement type. These findings are the first to experimentally and numerically validate the pull-back method's applicability to UDRC systems, establishing that dynamic tensile failure is matrix-dominated and enabling simplified one-dimensional analysis for reinforced concrete under impact.  
dynamic tensile strength; spalling; pull-back method; reinforced concrete; digital image correlation; strain rate effects Article
# Data Access Based on a Guide Map of the Underwater Wireless Sensor Network
## 3 System Model and Problem Description
### System Model
After that, the routing and specific data requests will be sent to the storage node. At the same time, the notification of query result \"_ready to receive data_\" and data transmission routing will be returned to the user. 5. To transmit the specific application data. After the user receives the notification \"_ready to receive data_\", the data is transmitted according to the data transmission routing, which is completed together with the sensor node and storage node. For the basic ideas of DAGM, it cannot only carry the storage and query real time data with less query time and energy consumption as possible, but also support data discovery, large volume data aggregation and a lot of data requests from one user for UWSNs. The data storage, metadata diffusion, data query and data transmission of DAGM are as shown in Figure 1.  
In Figure 1, \(n\times n\) sensor nodes are distributed in the square area where the length of a side is \(L\). Each node occupies a square lattice and the length of lattice is \(g\). The relationship between nodes distance \(R\), communication radius \(r_{b}\) and detection radius \(d_{b}\) is \(r_{b}>2d_{b}\), \(r_{b}>\sqrt{2}R\), and two neighbor nodes in the network can communicate through a single hop. We built the coordinate system for the sensor node-deployed plane; the node in the lower left corner is the origin, and its coordinate is \((X_{0},\,Y_{0})\). According to the deployment of sensor nodes, each node corresponds to a square lattice, and each square lattice has an ID number \(G(G_{X},\,G_{Y})\). Then, we can obtain the formula \(G_{X}=\lfloor\frac{X-X_{0}}{g}\rfloor\), \(G_{Y}=\lfloor\frac{y-y_{0}}{g}\rfloor\). Assuming that each node has a global information table _Tab_ (_ID_, \(C\), \(G\), _Adj_) of the network, _ID_ represents the node identification number; \(C\) represents the node coordinate; \(G\) represents the node lattice ID number; and _Adj_ represents the neighbor nodes. Because the sensor node has a limited communication radius, it must know which nodes are neighbor nodes and can communicate with it in a single hop. At the initialization of the network, each sensor node will broadcast its own _ID_ in a single hop, and obtain the node _ID_ that other nodes published. Thus, each node can know the neighbor nodes they can communicate with. In addition, in the global information table, the _Adj_ of the corresponding record is set to 1. After that, the node sends a message to the neighbor nodes in every certain period, and informs them of the update condition (nodes join in or get out) in the global information table they stored. After receiving the update message from neighbor nodes, the local global information table in the nodes is updated. In each node, if the message from a neighbor node was not received in a predetermined period, that means this neighbor node is invalid, and the corresponding record is deleted in the local global information table.
###### Abstract  
Post-activation performance enhancement (PAPE) has been proposed to improve strength, power, or speed following a conditioning contraction, yet, few studies have investigated its impact on postural performance. This study aimed to examine the effects of an acute bout of plyometric exercise with varying recovery intervals on unilateral countermovement jump (CMJ) performance and postural sway in female volleyball players. Twenty-four national-level female athletes (age: 20.83 \(\pm\) 2.93 years; height: 1.72 \(\pm\) 0.18 m; body mass: 61.21 \(\pm\) 11.77 kg; and body mass index: 20.60 \(\pm\) 2.67 kg/m\({}^{2}\)) participated in the study. They conducted unilateral CMJ and postural sway assessments on both dominant and non-dominant legs under baseline conditions, as well as four recovery intervals (2 min: R2, 4 min: R4, 6 min, R6, and 8 min: R8). These assessments were performed following a 30 s repetitive CMJ (RCMJ-30) serving as an acute plyometric conditioning activity. A force plate was used to capture force-time- and center of pressure-derived metrics. Maximum and mean jump heights during the RCMJ-30 test demonstrated moderate-to-good interday reliability (intraclass correlation coefficient = 0.62-0.83). Significant differences in unilateral CMJ and postural sway metrics were observed across conditions following the plyometric stimulus. R8 was the most effective recovery interval for improving both jump height and flight time in both legs (\(p<0.01\)). The greatest enhancement in unilateral postural performance was found in the R6 condition (\(p<0.05\)), with more pronounced effects in the non-dominant leg. These findings support the effectiveness of the RCMJ-30 as a PAPE protocol for enhancing both unilateral CMJ performance and postural control. Practitioners should adjust recovery intervals based on specific performance goals to maximize PAPE benefits.  
b +
Footnote †: journal: Applied Sciences  
_Article_
## 5 Conclusions  
In this paper, the problems of low contrast between defects and the background, the large scale difference, and insufficient training samples in the detection of rail surface defects are studied. To address these problems, the YOLOv5s method was improved and the following research was carried out. Firstly, in view of the problem of low contrast between defects and the background as well as insufficient training samples, the rail surface defect image dataset was augmented with flip transformation, random cropping, brightness transformation, and a generative adversarial network. Then, the CDConv was added into the backbone network to reduce the number of network parameters and the number of calculations, while improving detection speed and accuracy. Targeting the problem of large scale differences in defects, the Swin Transformer module was combined with the Backbone and Neck end to improve the C3 module of the original network. Furthermore, the GAM was introduced in PANet to form a new detection head, SGPH, to expand the global dimension interaction across the spatial channel dimension. It enables the network to obtain better global information and rich context information, while improving the ability of the model to capture different dimensions of information. Finally, the Soft-SloUNMS loss function replaced the original CIoU loss function, which accelerated the model convergence speed and achieved different levels of bounding box regression accuracy. The experimental results show that the mAP of our improved YOLOv5s rail surface defect detection method reached 96.9% with a detection time of 29.2 ms. This meets the requirements for accurate, real-time rail surface defect detection. Future research will study rail surface defect detection in complex environments and improve on the lightweight nature of the network model while maintaining detection accuracy.  
Conceptualization, L.C. and C.L.; formal analysis, L.C. and H.L.; investigation, L.C. and H.L.; methodology, L.C.; project administration, H.L.; validation, C.L.; writing--original draft, L.C. and H.L.; writing--review and editing, L.C. and H.L. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (Grant No. 62262021).  
The sample image data of rail surface defects used in this study are available from the website: [[http://icn.bjtu.edu.cn/Visint/resources/RSDDs.aspx](http://icn.bjtu.edu.cn/Visint/resources/RSDDs.aspx)]([http://icn.bjtu.edu.cn/Visint/resources/RSDDs.aspx](http://icn.bjtu.edu.cn/Visint/resources/RSDDs.aspx)), accessed on 1 December 2017.  
The authors declare no conflict of interest.
# Allosteric Activation of GDP-Bound Ras Isoforms by Bisphenol Derivative Plasticisers
## 4 Materials and Methods
### G-LISA Ras Activation Assay  
G-LISA Ras activation assay was performed as described before [30]. Briefly, HEK293T cells were seeded in 10-cm dishes followed by overnight incubation and afterwards serum-starved for 16 h. Cells were then treated with individual bisphenols (BPAF [20 \(\upmu\)M] and BPNH\({}_{2}\) [33 \(\upmu\)M]) or DMSO (control, Ctrl) in a time-dependent manner (1, 2, 4 h), subsequently lysed and the protein concentration was determined using Precision RedTM Advanced Protein Assay Reagent. Quantitative analysis of active Ras was performed with a G-LISA Ras activation assay (Cytoskeleton, Inc., Denver CO, USA) using 1 mg/mL of the sample. The signal intensity was determined by measuring absorbance at 490 nm using a microplate spectrophotometer. Additionally, these lysates were subjected to western blot analysis in order to determine the activation of ERK and AKT signalling. To test for statistical significance, a two-tailed Student's \(t\)-test was applied using Prism version 5.0 (GraphPad Software, La Jolla, CA, USA).
## 2 Models and Problem Formulation  
In order to clarify our analysis, we firstly propose a directional antenna model and an interference model in Section 2.1. Then Section 2.2 gives the definitions for a link set, a valid assignment as well as the node density, and presents the problem formation for the upper bound on the number of channels. In Section 2.3, we define the conflict graph.
## 2 Materials and Methods
### Numerical Modeling Evaluation Based on Geodetic Measurement  
The accuracy of the FEM modelling of subsidence can be assessed on the basis of various error measures [5; 70]:  
* Mean error: \[\text{ME}=\frac{\sum\bigl{(}\text{s}_{\text{obs}}^{\text{i}}-\text{s}_{\text{ FEM}}^{\text{i}}\bigr{)}}{n}\] (6) where: \(\text{s}_{\text{obs}}^{\text{i}}\)--observed subsidence of i-th observation point, \(\text{s}_{\text{FEM}}^{\text{i}}\)--FEM simulated subsidence of i-th observation point, and \(n\)--number of observation points.
* Mean absolute error: \[\text{MAE}=\frac{\sum(|\text{s}_{\text{obs}}^{\text{i}}-\text{s}_{\text{FEM}}^ {\text{i}}|)}{n}\] (7)
* Root mean squared error: \[\text{RMSE}=\sqrt{\frac{\sum\left(\text{s}_{\text{obs}}^{\text{i}}-\text{s}_{ \text{FEM}}^{\text{i}}\right)^{2}}{n}}\] (8)
* Percent error for maximum subsidence: \[\text{\mu}_{\text{s}_{\text{max}}}=\frac{\text{s}_{\text{max}}^{\text{obs}}- \text{s}_{\text{max}}^{\text{FEM}}}{\text{s}_{\text{max}}^{\text{obs}}}\cdot 1 00\%\] (9) where: \(\text{s}_{\text{max}}^{\text{obs}}\)--maximum value of observed subsidence; \(\text{s}_{\text{max}}^{\text{mod}}\)--maximum value of FEM simulated subsidence.  
The accuracy measures expressed by the Formulas (6)-(8) determine the average error sFEM of a single point from the entire profile. These measures are very susceptible to the occurrence of systematic errors caused by the presence of the operating rim (horizontal shift of the subsidence trough profile). Successive measures expressed by Formula (9) indicate the percentage error of the modeled subsidence. They describe the relative errors, i.e., in relation to the maximum subsidence values s\({}_{\text{max}}^{\text{obs}}\).
# Automatic Point-Cloud Registration for Quality Control in Building Works
## 3 Results and Discussion
### Quality Control
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Element** & **BIM Width (mm)** & **As-Built Width (mm)** \\ \hline \multirow{5}{*}{Study Case 1} & Pillar 1 & 500 & 500 \\  & Pillar 2 & 450 & 450 \\  & Pillar 3 & 350 & 351 \\  & Pillar 5 & 450 & 449 \\  & Pillar 6 & 450 & 450 \\  & Pillar 7 & 350 & 348 \\  & Pillar 8 & 600 & 600 \\ \hline \multirow{5}{*}{Study Case 2} & Pillar 1 & 600 & 597 \\  & Pillar 2 & 450 & 452 \\  & Pillar 3 & 450 & 451 \\  & Pillar 4 & 450 & 448 \\  & Pillar 5 & 450 & 448 \\  & Pillar 6 & 400 & 398 \\  & Pillar 7 & 500 & 498 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the control measurements performed in the different pillars, seven for the first study case and seven for the second study case. Provided that the pillars are the elements more influenced by the results of the registration, and given the results obtained for them, the automatic registration methodology proposed presents adequate quality and precision for its application in quality control of construction works: the methodology allows for the measurement of the pillars with an error below 10 mm and the performance of any additional measurement from the point cloud.
# A Study of Colormaps in Network Visualization
## 3 User Study
### Experimental Design
#### 3.1.3. Datasets  
Four different networks were used for the study: one for the training session and the other three for the actual user study. All the networks have different numbers of nodes and edges.  
* _Karate_: this dataset, which has 34 nodes and 78 links, is the well-known and popularly used Zachary karate club network [38]. It was used for training;
* _Lesmis_: this network, which has 77 nodes and 254 links, is constructed based on co-occurrences of characters in _Les Miserables_[39];
* _Football_: this is a network of an American football game from 2000 [40] with 115 nodes and 613 links;
* _Jazz_: this is a collaboration network between jazz musicians [41] with 198 nodes and 2742 links.  
All networks were laid out with the _sfdp_ layout algorithm [42]. There are numerous layout algorithms for network visualization [43]. An experiment with multiple layouts and colormaps can reveal interesting interactions between layouts and colormaps. However, adding one more factor (layout) in our experiment design, with possibly two to four levels, significantly increases the number of tasks for the participants which will lead to fatigue in the user study. As the main focus of this study is the effect of different colormaps in the context of network visualization, we did not use other layout methods in our experiment.
# Experimental and Theoretical Study on Operation Characteristics of an Oscillating Heat Pipe
## 4 Results and Discussion
### Thermal Resistance Analysis  
Figure 5 shows the temperature variation in the evaporation and condenser sections and the thermal resistance of the OHP under different heat inputs. The thermal resistance clearly indicates two different periods as highlighted by A and B, which are associated with the heating periods in the OHP. In the first period, the thermal resistance presented a large amplitude and low frequency as the heat is added into the evaporation section of the OHP. This showed that the OHP is in an unstable situation. As the heat was added to the evaporation section, the whole evaporator wall was heated up, and the working fluid received thermal energy via radial conduction. The fluid vaporized to produce the vapor volume expansion. The heat was transferred to the condenser section via axial conduction. In the condenser section, the heat was removed via radial conduction, and the vapor was condensed leading to the vapor volume contraction in the condenser section. The expansion and contraction of the vapor volume generated the large amplitude and low-frequency thermal resistance oscillation in the OHP. The vapor volume variation acted as a spring in the system, generating oscillating motion accompanied by heat and mass transfer. As the heat was continuously added to the evaporation section wall, the temperatures in both the evaporator and condenser sections were becoming stable. Then, the heat transfer rates from the wall to the liquid in the evaporation section and from the liquid to the wall in the condenser section were becoming stable. This stable heat transfer rate lowered the  
Figure 5: Variation of temperatures and thermal resistances of the OHP under heating power between 75 W and 100 W.  
oscillation of the thermal resistance. Then, the system entered a stable period during which the oscillation of the thermal resistance exhibited a moderate amplitude and high frequency. The average thermal resistance had stabilized.  
Figure 6 shows the thermal resistance of OHP under different heating powers. Data were collected after the system was running for at least twenty minutes to ensure the system was stable. The thermal resistance was found to decrease almost linearly as the heating input increased from 25 to 75 W. However, as the heating input increased from 75 to 100 W, the thermal resistance did not show a significant change. This indicated that the proposed OHP system provided the highest heat transmission efficiency and entered the optimum operation condition when the heating input is 75 W. A further increase in heating power will not substantially enhance the heat transfer. This finding is consistent with those presented in the literature [15].
# Structural Optimization of MicroMED Dust Analyzer
## 1 Introduction
External Envelope; B. Inlet/Outlet; C. Optical Head; D. Pump Group; E. Laser Group; F. Optical Bench. single-mode nominal optical power of 150 mW and a lasing wavelength of 830 \(\pm\) 5 nm) and finally returned to the Martian environment. The scattered light produced by the interaction between the particles and the laser is conveyed by a parabolic mirror towards a photodiode detector, responsible for the particle size measurements. MicroMED functional components and subassemblies are mounted on the OB and shielded from the harsh Martian land by the External Envelope (EE) [20; 21; 22]. The activities described in the following aim to complete the redesign work aiming to obtain two different optimal OB design proposals with an improved performance. The feasibility design was successfully carried out by the author in [23], but the numerical investigation has been extended, focusing on the evaluation of the thermo-elastic behaviour of the proposed solutions in the worst-case temperature. Moreover, the experimental assessment of the modal behaviour of a mockup of one of the two optimal solutions is presented, aiming to validate the presented methodology and related results. The added value of this research work lies in the innovative approach adopted for the OB improvement. The latter is presented in Section 2 along with the FEA procedure involved within the parts' numerical assessment, whose results are summarized in Section 3. The achievements consist of optimal design proposals to improve the current--suboptimal--material arrangement within the OB. Starting from the achieved design proposals, as a further improvement of the research activity a mockup of the lightened OB was manufactured and tested to prove the effectiveness of the TO design. Finally, the experimental validation campaign is presented in Section 4, comparing the outcomes with the numerical ones and completing the OB optimization activity.
# Reconfigurable Antennas: Switching Techniques--A Survey
## 2 Electrical Reconfiguration
### PIN Diodes  
The electrical parameters of the diode equivalent model with forward and reverse biases in the ON and OFF states are illustrated in Figure 1. PIN diodes are widely used as the switching components in different wireless systems. For reconfigurable antennas, an additional dimension for the RF and the direct current (DC) blocks is required and should be considered in the design of the antenna [33]. In Reference [34], a frequency reconfigurable MIMO antenna is presented for laptop applications. The schematic and the performance of the antenna are represented in Figure 2. As can be observed, its configuration contains a pair of planar inverted-F antenna (PIFA) elements and a T-shaped DC line feed structure. Two PIN diodes are embedded in conjunction with the proximity-coupled feeding. For different states of the employed diodes, the operation frequency of the antenna can be tuned to cover LTE 17/13 and LTE 20/7 bands including 704-787, 791-862 and 2500-2690 MHz.  
The varactor is nonlinear with a low dynamic range. It also requires a complex bias circuitry. However, compared with other active elements such as a PIN diode or MEMS, it has a small current flow and continuous tuning characteristics.
# Thermal Threat Monitoring Using Thermal Image Analysis and Convolutional Neural Networks
###### Abstract  
Monitoring of the vital signs or environment of disabled people is currently very popular because it increases their safety, improves their quality of life and facilitates remote care. The article proposes a system for automatic protection against burns based on the detection of thermal threats intended for blind or visually impaired people. Deep learning methods and CNNs were used to analyze images recorded by mobile thermal cameras. The proposed algorithm analyses thermal images covering the field of view of a user for the presence of objects with high or very high temperatures. If the user's hand appears in such an area, the procedure warning about the possibility of burns is activated and the algorithm generates an alarm. To achieve this effect, the thermal images were analyzed using the 15-layered convolutional neural network proposed in the article. The proposed solution provided the efficiency of detecting threat situations of over 99% for a set of more than 21,000 images. Tests were carried out for various network configurations, architecture and both the accuracy and precision of hand detection was 99.5%, whereas sensitivity reached 99.7%. The effectiveness of burn risk detection was 99.7%--a hot object--and the hand appeared simultaneously in the image. The presented method allows for quick, effective and automatic warning against thermal threats. The optimization of the model structure allows for its use with mobile devices such as smartphones and mobile thermal imaging cameras.  
Keywords:thernvision cameras; thermal images; image segmentation; image analysis; hand detection; deep learning; blind and visually impaired people +
Footnote †: journal: Applied