esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of OMMT on the Properties of Vehicle Damping Carbon Black-Natural Rubber Composites
## 3 Results and Discussion
### Dynamic Mechanical Performance
It can be seen from Figure 5 that the rubber composites with 2 phr OMMT had a lowest storage modulus, and the storage modulus was found increase gradually with increasing of OMMT amount. Therefore, a small content of OMMT can improve the dispersibility of filler. In order to analyze the effect of it, \(\Delta\)G' (G'0-G'\(\infty\)) was presented with the variation of OMMT content. It was found that the \(\Delta\)G' with 2 phr OMMT is the lowest and more amounts of OMMT made it gradually stronger, which was similar to the results of three consecutive strain scans on it. Since the \(\Delta\)G' is analyzed under large strain conditions, the filler network can be completely broken, and its accuracy should be higher than that obtained by the RPA8000 three-strain scan. Based on the results of two strain scans, the order of the filler network strength was obtained as \(8>6>0>4>2\) phr. As showed in Figure 6, the \(T_{\rm g}\) of the OMMT/CB/NR composites slightly shifts toward the high temperature direction for the addition amount of 2, 4, and 6 phr OMMT. The slight increase of \(T_{\rm g}\) is due to the fact that the rubber macromolecular segment is restricted by the sheet structure of OMMT and the exercise capacity is thus reduced. In the case of the 8 phr content, the agglomeration was produced and \(T_{\rm g}\) was found decrease slightly, which is consistent with the analysis results of RPA. The peak value of Tan\(\delta\) increased after OMMT was added, which was because the density of cross-linking within the vulcanized rubber increased. In the glass transition zone, the rubber was deformed by the external force and the flaky filler OMMT was oriented subsequently, which would generate friction with the rubber. This will transform the mechanical energy generated by the vibration into the heat energy loss, and then improve the damping of the material. As a result, the damping value of the material increased and the damping temperature range (Tan\(\delta\geq 0.3\)) was widened (Figure 7). Finally, the damping performance was increased. Figure 5: (a) \(\rm G\)’-strain and (b) \(\Delta\)G’ of CB/NR composites with different contents of OMMT. Figure 6: The loss factor-temperature curves of the CB/NR composites with different contents of OMMT. (Note: (b) is a local enlarged view of (a).)
## 3 Current Developments
### Data Quality Analysis  
The quality assessment of geographical information follows a predefined set of quality measures and criteria. A variety of publications are available that are related to the definition of these characteristics [74, 75]. In 2002, the International Organization for Standardization (known as ISO) released a standard that defines the quality attributes of geodata in ISO 19113 [76] (principles for describing the quality of geographic data) and ISO 19114 [77] (framework for procedures for determining and evaluating quality). According to ISO 19113:2002, the following five parameters define the quality of geodata: completeness, logical consistency, positional accuracy, temporal accuracy, and thematic accuracy. By the end of 2013, both ISO standards (19114 and 19113) have been aggregated to one single standard: ISO 19157 [78] (geographic information data quality). In the next sections, we will present several OSM research projects dedicated to one or multiple of the aforementioned spatial data quality parameters.
###### Abstract  
In societies with aging populations, the active participation of elders in all aspects of societal life is crucial. Cultural heritage provides a rich vehicle for engaging elders, stimulating both cognitive and affective responses, and keeping human brains active. This study focused on gathering requirements for creating content for virtual cultural experiences. Specifically, participants were shown various images and a film, and their self-reported thoughts and emotions were collected. The images were chosen by experts for the specific emotions they could trigger. The same images were analyzed with sentiment analysis software and finally given to the elders to express their emotions. The results of sentiment analysis, the analysis of experts, and the data from the participants were compared, showing differences between the perceived emotions. Regarding the historical film, the participants discussed the main emotions they experienced. The results were analyzed to extract guidelines for the content creation of VR applications.  
virtual reality; elders; content requirements +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-00000-0000-00000-00000-0000-00000-00000-0000-00000-00000-00000-00000-00000-00000-00000-000000-00000-000000  
many cognitive benefits, learning is widely acknowledged as a part of cultural experiences [7]. Cultural heritage can also trigger creativity [8]. In addition, cultural heritage can evoke strong emotions, such as joy, pride, and inspiration, contributing to overall emotional wellbeing [9].  
Virtual reality (VR) is a great medium to explore cultural heritage since it can make heritage and knowledge accessible to more people. Technology can also allow the personalization of experiences to match people's needs and interests [10], increasing motivation to participate in cultural experiences [11].  
Regarding the elderly, research is still scarce regarding VR cultural experiences for this age group. In addition, most studies focus on accessibility, immersion, and presence issues (e.g., [12]) and/or visitor satisfaction and benefits [13]. Very few studies, if any, focus on VR cultural content. Usually, content is designed before user testing, and content creation seems to be an intuitive process. Thus, the present work aims to gather content requirements for VR cultural experiences for the elderly.
## 5 Results and Discussion
### Results from Consideration Propensity Model
#### 5.1.3 Bias and Practical Implications of Disregarding Heterogeneity in Consideration  
In this subsection, the bias due to ignoring heterogeneity is investigated by comparing the signs, magnitude, and significance of key explanatory factors in the segmented models with the corresponding unsegmented model. The unsegmented model is unable to detect the significance of some key variables and also underestimates the influence of specific factors. Specifically, access distance to a bus stop in the MPM segment, the ease of purchasing the ticket in the HPM segment, and the effect of gender among young respondents showed spurious insignificance in the unsegmented model. The magnitude of the level of service variables related to the perception of security and reliability in trains was also underestimated. Therefore, the failure to account for these differences can lead to biased estimates and ineffective policy interventions.  
Figure 7: Probability of train consideration vs. access time for different segments.  
The consequences of these differences in terms of the overall probability of consideration for different levels of access time and the presence of a station within 1.5 km are depicted in Figure 7. It is seen that due to differences in intercepts, the access time has a different effect across the three segments. The unsegmented model underestimates the consideration of LPM and MPM segments and overestimates it for the HPM segment. The observed variation in consideration propensities implies that different access and first-last-mile solutions may be suitable for different segments.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 5 Enhancing the EPR Effect
### Enhancing the EPR Effect by Targeting the Tumor microenvironment (TME)
#### 5.1.3 Hyaluronic Acid (HA)-Based Nanodrugs  
Hyaluronic acid (HA)-based nanodrugs have also been extensively explored for their ability to enhance drug accumulation in tumors through the EPR effect. HA is a natural polysaccharide that binds to CD44 receptors, which are overexpressed in many cancer cells. This binding facilitates the targeted delivery of therapeutic agents to the TME. For example, a thermoresponse self-assembled system has been developed to deliver both a matrix metalloproteinase (MMP) inhibitor and paclitaxel conjugated with HA. This dual-targeting approach effectively addresses both the TME and tumor cells, demonstrating significant potential for inhibiting tumor growth and metastasis [171].
## 4 Calculation
### Moment-Curvature Relation Calculation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CMOD** & **CTOD** & \(\kappa\) & \(\delta_{\text{standard}}\) & \(\delta_{\text{calculated}}\) & **F** \\
**[mm]** & **[mm]** & **[1/m]** & **[mm]** & **[mm]** & **[mm]** & **[kN]** \\ \hline
0. & 0 & 2.27 \(\times\) 10\({}^{-3}\) & 0.04 & 0.04 & 12.31 \\
2.11 \(\times\) 10\({}^{-2}\) & 1.76 \(\times\) 10\({}^{-2}\) & 0.0036 & 0.06 & 0.05 & 14.14 \\
0.5 & 0.42 & 0.0301 & 0.47 & 0.47 & 6.54 \\
1.5 & 1.25 & 0.0860 & 1.32 & 1.35 & 7.43 \\
2.5 & 2.08 & 0.1416 & 2.17 & 2.22 & 8.42 \\
3.5 & 2.92 & 0.1971 & 3.02 & 3.08 & 9.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the calculated values (\(\delta_{\text{calculated}}\)), based on the back-calculated tensile strength values, and displacement values defined as per EN14651 standard [19] (\(\delta_{\text{standard}}\)), corresponding to specific CMOD values (with corresponding CTOD values, calculated curvature values (\(\kappa\)) of the notched cross-section, and load values (F)). Figure 12: Calculated load-displacement relation (the dotted line presents the results based on tensile strength values from expressions of the _fib Model Code for Concrete Structures 2010_[17]). CMOD values as the crack width values. The actual CMOD value is larger than the crack width (CTOD) value. Consequently, for the load stage denoted by CMOD = 2.5 mm in Table 8 for the M-\(\kappa\) (fib) model, the displacements correspond to about 20% larger CMOD values. If this is considered, the actual displacements of the M-\(\kappa\) (fib) calculation at the actual CMOD values of 0.5 mm and 2.5 mm are equal to 0.480 mm and 2.220 mm, which are much closer to the \(\delta_{\text{standard}}\) values.
## 2 Recent Works  
[PERSON] [36], in 2010, in his paper has described function of IceCube installed at South Pole. As per his observation IceCube is the largest Neutrino observatory currently in operations. Located at the geographical South Pole, the detector modules are deployed up to 2,450 m deep into the Antarctic ice. A combination of intelligent sensor modules and a farm of industry standard servers are used to operate the detector and reduce the data to accommodate the limited connectivity from the South Pole to the northern hemisphere. He has given a detailed description of the technical implementation of the sensor modules, data acquisition system and filtering farm used in the IceCube experiment.  
Figure 1: A wireless sensor network structure.  
[PERSON], _et al_, in March 2010, considered a hybrid wireless sensor network with static and mobile nodes. Static sensors monitor the environment and report events occurring in the sensing field. They scheduled the mobile sensors' traveling paths in an energy-balanced way so that their overall lifetime could be maximized and they shown that it has been a NP-complete problem. They proposed a centralized and a distributed heuristics to schedule mobile sensors' traveling paths. Their heuristics allowed arbitrary numbers of mobile sensors and event locations in each round and had an energy-balanced concept in mind. The centralized heuristic tries to minimize mobile sensors' moving energy while keeping their energy consumption balanced [37].  
[PERSON], _et al._, presented a reliable and lightweight routing protocol for wireless sensor networks in their paper. They claimed more than 90% savings in number of transmissions compared to the message flooding scheme when the same route was used to transmit data messages. This saving increased exponentially as the number of transmissions increased over a same route. The protocol occupied only 16% of total available RAM and 12% of total program memory in MICAz platform which maked it very lightweight to implement in wireless sensor networks [38].  
[PERSON] and [PERSON], 2007, proposed [39] a new probabilistic coverage protocol (denoted by PCP) that considered probabilistic sensing models. PCP was fairly general and used with different sensing models. In particular, PCP required the computation of a single parameter from the adopted sensing model, while everything else remained same. They showed how this parameter could be derived in general, and the calculations for two example sensing models: (i) the probabilistic exponential sensing model, and (ii) the commonly-used deterministic disk sensing model. They compared their protocol with two existing protocols and claimed for the better performance as they proposed.
## 3 Experimental Temperature Graphs  
In the Q-Thermie 1 borehole, a thermal response test (TRT) was conducted with a power input of 3485 W, while in the Q-Thermie 2 borehole, temperature was recorded at a depth of 22.62 m over a period of three months. This power value was selected to ensure a clear and stable thermal signal at the second borehole (Q-Thermie-Uniovi 2) without inducing excessive temperature gradients in the surrounding ground. The preliminary simulations confirmed that 3485 W provided a measurable thermal response after several weeks while keeping the fluid temperatures within operational limits. Moreover, this value is consistent with the typical power levels used in TRT experiments for boreholes of comparable depth, ensuring the practical relevance of the experiment and its suitability for model validation. The two boreholes are separated by a distance, \(r\), of 7.3 m. The thermal properties of the ground at this depth are as follows: thermal conductivity, \(\lambda\), was estimated to be 1.835 W/m\(\cdot\)K, and thermal diffusivity, \(\alpha\), was 8.52 \(\times\) 10\({}^{-7}\) m\({}^{2}\)/s [20].  
With the objective of studying the behavior of temperature over time, the data are provided in Table 1. These values correspond to the ones indicated above.  
Figure 1 represents the exponential integral function for the data shown in Table 1. From the figure, it can be observed that as time progresses, the temperature exhibits a gradual increase; however, for initial time values, the function remains virtually constant.  
Figure 2 illustrates the temperature evolution at a depth of 22.62 m in the Q-Thermie 2 borehole during the thermal heating process in the Q-Thermie 1 borehole. This thermal behavior reflects the propagation of heat generated in Q-Thermie 1 into the surrounding ground, as indicated by the increase in temperature inside the Q-Thermie 2 borehole over the course of the test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{T_{0}(^{\circ}C)}\) & \(\boldsymbol{q(W/m)}\) & \(\boldsymbol{r(m)}\) & \(\boldsymbol{\alpha(m^{2}/s)}\) & \(\boldsymbol{\lambda(W/m\cdot K)}\) \\ \hline
16.17 & 72.60 & 7.3 & \(8.52\times 10^{-7}\) & 1.835 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data for analyzing the interference between geothermal boreholes.
# Prediction of Structural Damage Trends Based on the Integration of LSTM and SVR
## 5 Conclusions  
This paper studied the prediction of structural damage trends based on the integration of the LSTM and SVR methods. The main conclusions were determined as follows:  
1. VMD effectively avoids the modal aliasing phenomenon of EMD and can accurately extract the structural damage feature information.
2. According to numerous experiments, it is found that, when 0.5 was used as the weight coefficient, the \(R^{2}\) of the integration model reached the highest value of 0.9975, the prediction result of the integration method was closest to the actual value, and the prediction accuracy was the highest.
3. Through many experiments, in the integrated method validation experiment, the LSTM-SVR based integrated method has the highest prediction accuracy for smaller prediction samples compared to the other two single methods, and the coefficient of determination is 12% higher than the LSTM model.
4. In the prediction of actual engineering vibration data, among the RMSE, MAPE, SDAPE, \(R^{2}\), and RWI indicators, the integrated method has a smaller prediction error value for the engineering data, higher accuracy, more robustness, and better prediction performance compared to the LSTM network model, especially the average absolute percentage error. Compared to the LSTM model, the integrated method has improved the numerical value by an order of magnitude. It is of great significance to predict the damage trend of structures when there is less engineering vibration data in the future.  
This finding is of great significance to the future prediction of structural damage trends when the amount of available engineering vibration data is low.  
This research was funded by the Key Research and Development Program of Shaanxi Province grant number [2021 GY-098].  
**Data Availability Statement:** Some data and models generated during the study are available online at: [[http://cive.sea.wustl.edu/wusceel/ASCE.shm/experimim_phase_2.htm](http://cive.sea.wustl.edu/wusceel/ASCE.shm/experimim_phase_2.htm)]([http://cive.sea.wustl.edu/wusceel/ASCE.shm/experimim_phase_2.htm](http://cive.sea.wustl.edu/wusceel/ASCE.shm/experimim_phase_2.htm)) (accessed on 6 June 2023).  
**Conflicts of Interest:** The author declares no conflict of interest.
## 4 Applications of WSN in Urban Areas
### Water Pipeline Monitoring
* The studies offer innovative, cost-effective solutions for water pipeline monitoring, addressing challenges like leak detection, network efficiency, and sensor functionality, while tackling issues such as water loss, pipeline corrosion, and water quality maintenance. * These methodologies use different network topologies and sensor types to improve leak detection accuracy, optimize energy use, and enhance water pipeline monitoring systems. Advanced sensors and machine learning algorithms enable timely leak detection and prevention, aiding water conservation and infrastructure maintenance. * The Star Network Topology is ideal for centralized monitoring, providing easy implementation and efficient data collection. However, it can be less energy-efficient and may not minimize delays. * The Peer-to-Peer (P2P) Network Topology is efficient for decentralized monitoring, optimizing power consumption, and enhancing network reliability and lifetime, which is ideal for robust and sustainable applications. * Decentralized P2P architectures with advanced sensors enhance pipeline monitoring, improving reliability, efficiency, and sustainability. They support effective water resource management and reduce environmental impact.
## 4 Methodologies
### Stream-Cluster Feature Tree Algorithm  
In the tests in the study, the problem of \"separate clustering of similar data\" was encountered due to the parameter dependence of the traditional BIRCH algorithm and the goal of creating a balanced tree. To eliminate this problem, a new stream mining algorithm is proposed in this study, retaining the \(CF\) features of the BIRCH algorithm.  
This algorithm internally uses the \(k\)-Means++ algorithm to place the data in a binary tree with a top-down hierarchy, as in the DIANA (divisive analysis clustering algorithm). The number of clusters in the \(k\)-Means++ algorithm was assumed to be constant (\(k=2\)). In this way, we aimed to obtain a binary balanced tree.  
The algorithm was designed such that each child had 2 entity constants. Initially, all data was clustered with 2-Means++ to form the root, which creates the structure to be included in the entity to contain the CF capsule data.  
The S-CF Tree data structure is illustrated in Figure 2 below.  
To create the SC-F Tree data structure, job postings, as big data, should first be divided into two Entities using \(k\)-Means++. After the \(k\) cluster centers and \(k\) CF nodes obtained are saved in the Entities, other sub-branches are created recursively. In this way, the algorithm completes the SC-F tree. The algorithm can terminate in two ways:  
* N \(\leq\) p, where N is the number of elements in the entity, and p is the threshold parameter. If N \(\leq\) p is encountered in an entity, this entity is named as a leaf, and the algorithm does not continue for this branch;
* The optimal SSE is discovered by using \(k\)-Means++ for all instances. According to the number of clusters in the optimal pattern, the maximum level on the tree is determined; this number becomes the threshold of recursive calling for a branch.  
The S-CF Tree algorithm flow is illustrated in Figure 3 below.  
Figure 3: Locating the new centroid in an SC-F Tree for two clusters in each entity.  
Figure 2: S-CF Tree Data Structure.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 6 Conclusions  
As satellite constellations proliferate, SatGuard offers a critical toolkit for safeguarding global communication infrastructures against increasingly sophisticated cyber threats. The framework's AI-driven automation and nonlinear risk prioritization enable commercial satellite operators to streamline security audits by reducing manual inspection cycles, thereby accelerating vulnerability identification and remediation. For instance, the semi-automated penetration testing workflow demonstrated in this study achieved a 73.3% success rate with minimal human intervention, suggesting that large-scale operators (e.g., Starlink or Kuiper) could reduce audit timeframes by over 40% while maintaining rigorous security standards. Furthermore, the integration of dynamic risk scoring--tailored to satellite-specific architectures--enhances the efficiency of triaging high-impact vulnerabilities. By bridging AI scalability with domain-specific threat modeling, SatGuard empowers operators to fortify next-generation constellations against both legacy and emergent attack vectors, ensuring resilient and adaptive cybersecurity postures in an era of rapid space infrastructure expansion.  
Conceptualization, J.X.; Investigation, R.D.; Writing--original draft, J.X.; Writing--review & editing, B.W., R.D., Z.Z. and B.Z.; Supervision, B.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 62472437.  
We confirm that this experiment is based on large language models and no new dataset was established. However, to enhance the reproducibility of the experiment, the model types, versions, and all prompt words used have been publicly disclosed in the main text.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} LLM & Large Language Model \\ LEO & Low Earth Orbit \\ GNSS & Global Navigation Satellite Systems \\ LEO SCS & Low-Earth-Orbit Satellite Communication Systems \\ IDS & Intrusion Detection System \\ AI & Artificial Intelligence \\ RIS & Reconfigurable Intelligent Surface \\ SS & Spread Spectrum \\ CCI & Co-channel Interference \\ VAPT & Vulnerability Assessment and Penetration Testing \\ AWS & Amazon Web Services \\ GCP & Google Cloud Platform \\ CVSS & Common Vulnerability Scoring System \\ IT & Information Technology \\ VSAT & Very-Small-Aperture Terminal \\ GS & Ground Station \\ NCC & Network Control Center \\ COTS & Commercial Off-The-Shelf \\ ISL & Inter-Satellite Link \\ API & Application Programming Interface \\ IoT & Internet of Things \\ CLI & Command-Line Interface \\ \end{tabular}