esa-sceva/satcom-chunk-collection huggingface dataset:
# Natural Rubber/Styrene-Butadiene Rubber Blend Composites Potentially Applied in Damping Bearings
## 3 Result and Discussion
### Compression Properties of NR/SBR-x Composites  
In practical engineering applications, deformation failure is a common failure mode of rubber vibration damping products. Compression set is an important indicator for assessing the deformation resistance of vibration damping bearings. The heat accumulation caused by compression during the service process accelerates the thermo-oxidative aging of the composites, which seriously affects the service life of rubber vibration damping products. Therefore, the compression fatigue temperature rise also has a significant impact on the service life of the vibration damping bearings. The compression set of NR/SBR-x composites is shown in Figure 6, and it can be observed that, as the proportion of SBR increases, the compression set becomes larger in general, which is due to the phenyl group in the SBR. As the presence of phenyl restricts the mobility of the macromolecular chains, it is difficult for the macromolecular chains to return to their original positions after the pressure on the composite is withdrawn, resulting in the poor compression recovery of the composite. The observation of the data shows that when the incorporation ratio of SBR is no higher than 20%, the compression set of the composites is almost unchanged; when the incorporation ratio of SBR is higher than 20%, the compression set of the composites increases significantly. Therefore, the blending ratio of SBR and NR should be determined according to the actual required properties.  
However, it can be observed in Figure 6 that the compression fatigue temperature rising of the composites gradually decreases as the proportion of SBR is increased. The microscopic causes of dynamic heat build-up in rubber composites are mainly the friction between the rubber macromolecule chains, the friction between filler particles and rubber macromolecule chains and the friction between filler particles [44]. The motility of the SBR chains is significantly restricted due to the presence of phenyl groups, and the friction between the macromolecular chains is relatively weak. This is supported by the finding in Figure 4b that the tan \(\delta\) of the composites decreases gradually with increasing SBR content at the same strain. At the same time, the intermolecular force of NR is smaller than that of SBR. During the test process of compression fatigue temperature rising, the friction between the rubber chains increases due to the breaking of the crosslinking network and macromolecule cleavage that tends to occur at high temperature and dynamic compression.  
Figure 6: Values of compression set and compression fatigue temperature rising for NR/SBR-x composites.  
In the application of dynamic products, if the load is large, to achieve strong resistance to compression deformation, it is necessary that a small amount of SBR (\(\leq\)20%) is used; if the frequency is high, to resist fatigue temperature rising, a larger amount of SBR (>20%) can be used to extend service life.
## 5 Conclusions  
In summary, we have demonstrated a 2 \(\upmu\)m InGaAsSb/AlGaAsSb laser diode with a graded composition AlGaAsSb carrier blocking layer(CBL) emitting at 2 \(\upmu\)m by simulation and fabrication. The material database of InGaAsSb/AlGaAsSb LDs is set up to predict the optical and electronical properties of devices. The simulation results indicate that the graded interlayer can reduce the concentration of electrons leakage from 10\({}^{7}\) cm\({}^{-3}\) to near 0, which results in a significant improvement in the radiative recombination rate and leads to a reduction of threshold current density compared to the conventional LDs. All the laser wafers are grown by using a solid source MBE system. The threshold density and slope efficiency of the optimized InGaAsSb/AlGaAsSb LD with CBL is 142 A/cm\({}^{2}\) and 0.158 W/A, which is better than 215 A/cm\({}^{2}\) and 0.122 W/A achieved in un-optimized devices at room temperature. This effect is attributed to an improved carrier injection and confinement that prevents carrier leakage into the high-doped region of the LDs while simultaneously enhancing carrier injection into the active region.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; software, [PERSON] and [PERSON]; validation, [PERSON], [PERSON] and [PERSON]; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON] and [PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON]. This research was funded by Excellent Youth Foundation of Jilin Province Scientific committee: 20170520155 JH, National Natural Science Foundation of China: 61605220.  
Figure 7: CW output power characteristics measured at 300 K for the various InGaAsSb/AlGaAsSb LDs with 90 \(\upmu\)m stripe width (**a**) Without CBL (**b**) With CBL.  
**Acknowledgments:** The content is supported by Key Laboratory of Space Object and Debris Observation, Purple Mountain Observatory, Chinese Academy of Science.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
This study describes the technical-systemic and conceptual-informative interoperability tests for the integration of a Historic Building Information Modeling (HBIM) model in a 3D Geographic Information System (GIS) environment aimed to provide complete and useful documentation for multiscale analyses on cultural heritage particularly exposed to risks. The case study of the San Lorenzo Church in Norcia (Italy) has been chosen given the urgent need to update the existing documentation for its protection and conservation issues, due to the extensive damage suffered after the series of earthquakes that occurred in central Italy starting from summer 2016. Different tests to evaluate two levels of conceptual interoperability (technical and semantic) when importing the HBIM model into a GIS environment were performed, whether with commercial software or with open source ones (ArcGIS Pro and QGIS, respectively). A data integration platform (Feature Manipulation Engine, FME) has been used for converting the IFC (Industry Foundation Classes) data format into the GML (Geography Markup Language) format, in order to obtain a unique and unified model and vocabulary for the 3D GIS project, structured with different levels of detail, according to CityGML standard. Finally, as HBIM-GIS integration is considered, the loss of geometric and informative data has been taken into account and evaluated.  
HBIM; GIS; IFC; CityGML; FME; conceptual interoperability; cultural heritage; multiscale analyses; earthquake +
Footnote †: journal: Applied Sciences
## 6 Results  
This section discusses results obtained from each experimental evaluation of each modulation technique.  
Figure 9 shows that as the distance and ambient light intensity increases the SNR value decreases. Further experiments were conducted by keeping the ambient light intensity between 50-60 lux as it provided better SNR. Stress pattern of alternate 0 and 1 is used for finding BER.
# Blockchain Applications in Education: A Systematic Literature Review
## 4 Discussion
### Research Question 2: What Are the Features of Blockchain That Could Benefit Education?
#### 4.2.9 Uniqueness  
Every transaction stored in a blockchain is unique and traceable. This feature prevents the possibility of double spending or double owning of a token or asset.  
As discussed in Section 4.2.6, academic information reflects personal, professional or institutional skills, reputation or knowledge, and these properties cannot be spent or transferred, so uniqueness for these applications is not relevant.  
On the contrary, in the educational sector, uniqueness is necessary for token-related initiatives when they are equivalent to currency or economic incentives [15; 28; 36].
## 2 Materials and Methods
### DNA Extraction and Amplicon Sequencing  
The total genome DNA from the samples was extracted using the CTAB/SDS method. The DNA concentration and purity were monitored on 1% agarose gels. The V4 region of the bacterial 165 rRNA gene was amplified with the universal primers 515F (5'-GTGCCAGCMGCGCGTAA-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'). The PCR mixture (30 \(\upmu\)L) consisted of 15 \(\upmu\)L of the Phususon(r) High-Fidelity PCR Master Mix (New England Biolabs), 0.2-\(\upmu\)M primers, and 10 ng of template DNA. The amplification cycles were as follows: 98 \({}^{\circ}\)C for 1 min; 30 cycles (98 \({}^{\circ}\)C for 10 s, 50 \({}^{\circ}\)C for 30 s, and 72 \({}^{\circ}\)C for 60 s); and then a final extension at 72 \({}^{\circ}\)C for 5 min. PCR products were mixed with the same volume of 1\(\times\) loading buffer (contained SYB green) and operated electrophoresis on 2% agarose gel for detection. Samples with bright main strips between 400-450 bp were chosen for further experiments. PCR products were mixed in equidensity ratios and then purified with a Qiagen Gel Extraction Kit (Qiagen, Hilden, Germany). Sequencing libraries were generated using a TruSeq(r) DNA PCR-Free Sample Preparation Kit (Illumina, San Diego, CA, USA) following the manufacturer's recommendations, and index codes were added. The library quality was assessed on a Qubit@ 2.0 Fluorometer (Thermo Scientific, Waltham, MA, USA) and the Agilent Bioanalyzer 2100 system. At last, the library was sequenced on an IlluminaHiSeq2500 platform, and 250-bp paired-end reads were generated.
# Policy-Based Composition and Embedding of Extended Virtual Networks and SFCs for IIoT
## 5 Results
### Evaluation of the Chain Embedding Using a Random Topology
#### 5.3.2 Comparison to LightChain  
A simplified version of LightChain from [6] is implemented to compare our proactive chain embedding algorithm to another algorithm. LightChain algorithm tries to allocate the FG on the shortest path between two end-points. If the chosen shortest path is consumed, then the algorithm calculates another shortest path and the remaining VNFs are allocated. LightChain approach does not allow for placing multiple VNFs on a single SNo. Our algorithm can place multiple VNFs that might belong to different SFCs on a single substrate node as long as enough capacity is available. If the selected path cannot be used, then another shortest path is tried. The proactive behavior of the algorithm reduces the probability of path rejection.
## 3 Case Study  
### Station Selection  
In this study, the typical conventional railway station illustrated in [62] is selected as the research object. The proposed method is applied to evaluate the importance of nodes in the station yard. The station's signal layout diagram is shown in Figure 2.  
The interlocking tables of this station include train routes, extended routes, shunting routes, combined train routes, and others. Considering the scale of the station and the article's length constraints, this study models and analyzes only the first three types of tables, without affecting the validity of the proposed method. A portion of the interlocking table is shown in Table 1. The table systematically classifies train routes, extended routes, and shunting routes within a railway station yard, providing detailed information on route type, direction, start, end, and the corresponding track sections involved. This classification facilitates a comprehensive understanding of train movements, route interdependencies, and operational complexity in the station. This station includes 175 train routes, 18 extended routes, and 204 shunting routes, involving a total of 67 track sections.  
Figure 2: Signaling layout of the typical conventional railway station. It consists of a set of railway tracks and signaling equipment, such as track sections, switches, signals, insulation joints, and others.
## 3 Results
### Absolute and Relative Errors for All Wavelength  
While using simulated values to subtract the measured fluorescence, the absolute error between the simulations without FQE inversion and the measured fluorescence can be calculated from 660 nm to 800 nm. An example of averaged WT (high-chlorophyll-content) and MG (low-chlorophyll-content) fluorescence leaves is shown in Figure 7. In the average absolute and relative error charts, the simulated fluorescence was always overestimated at approximately 687 nm for each condition. Therefore, the first peak in the simulated fluorescence curve was higher than that in actual conditions. As the RE between the simulated and measured fluorescence was over 100%, it could be observed that the model did not perform adequately during the first peak. The other portion of the differential percentage was less than 50%, especially above 700 nm. In Figure 7, the average RE (normalized by the measured fluorescence) drops after the FQE is instituted. Thus, it appears that the outcomes using the FQE inversion code were more accurate than those simulated without the code. The simulated minus measured percentage differential decreases from 150% to 20% in its first peak in the upward fluorescence curve. The second peak of the fluorescence curve also decreased to 20%, further indicating the improvement to the model made with the addition of the code.  
Figure 7: The average absolute error (AE) and relative error (RE) between the simulations and measurements, with/without the FQE inversion code of the upward fluorescence for all wavelengths of (**a**) WT, high-chlorophyll-content and (**b**) MG, low-chlorophyll-content leaves.
## 2 Target Structure and Numerical Modeling  
### Target Structure  
To analyze the performances of the major components of a cable-stayed bridge under a blast load, we selected the target bridge as shown in Figure 2; it is a cable-stayed bridge featuring three spans, with a main span length of 470 m and a side span length of 200 m. It is a composite superstructure formed of precast reinforced concrete (RC) slabs and steel girders (edge girder, center stringer, and crossbeam). The deck is 310 mm thick near the pylon and 260 mm thick in other sections, and it is made of 68 standard segments (length (L) = 12.3 m). In addition, the bridge features 144 cables of varying areas and lengths and two RC pylons (PY1 and PY2), which contain hollow sections. To evaluate each component of the cable-stayed bridge, a single 260-mm thick segment at the center of the span was selected from the superstructure. We selected the following cables for analysis: Cable no. 37 (area (A) = 0.012450 m\({}^{2}\), L = 252 m), which is the longest cable, has the largest cross-sectional area, and extends to the mid-point of the superstructure; Cable no. 53 (A = 0.005185 m\({}^{2}\), L = 81 m), which has the smallest cross-sectional area; and Cable no. 68 (A = 0.009385 m\({}^{2}\), L = 206 m), which is attached to the side span, where the largest stress is applied. We selected PY2 as the pylon; its base segment (CB1 in the figure) is 2.37 m taller than that of PY1.  
Tables 1 and 2 summarize the specifications of the steel girders and rebars comprising each component; these values were taken from [23].  
Figure 2: Target bridge components.
## 4 Applications of Microextraction Techniques to the Determination of Organic Micropolultants
### Pharmaceuticals
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**Compounds**} & **Matrix** & \begin{tabular}{c} **Extraction** \\ **Technique** \\ \end{tabular} & **Optimal Times** & **Handling** & \begin{tabular}{c} **Reeovery** \\ **Accuracy (\%)** \\ \end{tabular} & \begin{tabular}{c} **LOD** \\ **(ng-L\({}^{+}\))** \\ \end{tabular} & **Ref.** \\ \hline Estrogens (estrone, 179-estradiol, estriol, estriol, ethyl serial stratol, & Wastewater, river & IT-SPME & 20 draw/eject cycle & Lower handling & \\ diethylstitested) & water & & 30 min & 48 samples/day & 86.1–106.8 & 2.7–11.7 & [85] \\ \hline Sulfonamide antibiotics (sulfanguanidine, sulf sulfenamide, sulfethazine, sulfethazine, sulfethrazine, sulfethrazine, sulfethrazine, sulfethrazine, sulfethrazine) & Wastewater & SPME & \begin{tabular}{c} Extraction: 20 min \\ Desorption: 30 min \\ \end{tabular} & Easy to use & 29-229 & 9000-55300 & [88] \\ sulfamethicovine, sulfashazine & & & & & & \\ \hline Antibiotics (sulfamethazine, sulfisooxazole, sulfamethicovine, sulfpyridine, trimethoprim, roclithromycin, erthyromycin, cirthromycin) & Wastewater & SPME & \begin{tabular}{c} Extraction: 30 min \\ Desorption: 10 min \\ \end{tabular} & Easy to use & – & 2.8–410.0 & [89] \\ \hline Analise and anti-inflammatory, antidepressant, antibiotics, lipid regulator, 9-bakers, durictics, antibiotics, antiepileptic, antipsychotic & Wastewater & dSPME & \begin{tabular}{c} Extraction: 30 min \\ Desorption: 10 min \\ \end{tabular} & \begin{tabular}{c} Minimizes laborious \\ and complicated \\ sample preparation \\ procedures \\ \end{tabular} & 89.2–109.7 & \begin{tabular}{c} 5.0–50.0 \\ (LOQ) \\ \end{tabular} & [90] \\ \hline Pharmaceuticals (carbanazepine) & Wastewater & TIME & - & 96 well-plate & – & [91] \\ \hline Fluoroquinolones (enoxoxen, efficacious, ciprofloxacin, surfloxacin, nonfloxacin, wastewater) & IT-SPME & 20 draw/eject cycles & Lower handling 48 & 81.8–98 & 7.0–29.0 & [92] \\ \hline Non-steroidal anti-inflammatory drugs (acetaminophen, ibuprofen, naproxen, fenoproft, flukipprofen, toxopoefen, ketoprofen, merfaemunic acid, flufenamic acid, diclofenac, tofennamic acid, evapororin, phenylbutazone, indomethacin, acemcstein) & IT-SPME & 20 draw/eject cycles & Lower handling 48 samples/day & 80.4–100.4 & 5.0-65.0 & [93] \\ \hline Pharmaceuticals (parenacetamol, naproxen, diclofenac, caffeine, antityping, propranolol, carbamazepine) & Wastewater & SBSE & \
\begin{tabular}{c} Extraction: 240 min \\ Desorption: 20 min \\ \end{tabular} & Practical & 10–92 & 10.0–50.0 & [94] \\ \hline Pharmaceuticals (parenacetamol, caffeine, antityping, propranolol, carbanazepine, ibuprofen, diclofenac) & Wastewater & SBSE &
\begin{tabular}{c} Extraction: 240 min \\ Desorption: 15 min \\ \end{tabular} & Practical & 9–110 & 10–50 & [95] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Microextraction techniques to determine hormones and pharmaceuticals in environmental water samples by liquid chromatography-tandem mass spectrometry.
## Appendix B Proof of Theorem 2  
Given two different clusters \(C_{k}\) and \(C_{l}\). The number of UAVs in \(C_{k}\) that belongs to group \(g_{t}\) is denoted as \(m_{t,k}\). \(\mathcal{P}\) is a subset of \(C_{k}\cap g_{t}\). Suppose that \(m_{t,l}+|\mathcal{P}|>m_{t,k}\) (i.e., \(C_{l}\cup\mathcal{P}\) has more UAVs belongs to \(g_{t}\) than \(C_{k}\)). We also assume that both \(C_{l}\cup\mathcal{P}\) and \(C_{k}\backslash\mathcal{P}\) satisfy the cluster limits. We need to prove that the total intra-cluster compound correlation will increase after transferring \(\mathcal{P}\) from \(C_{k}\) to \(C_{l}\).  
According to the proof of Theorem 1, we have \(f^{g}(C_{l}\cup\mathcal{P})=f^{g}(C_{l})+f^{g}(\mathcal{P})+2\sum_{i\in C_{l} }\sum_{j\in\mathcal{P}|(i,j)\in\mathcal{E}}f^{g}_{ij}\) and \(f^{g}(C_{k})=f^{g}(C_{k}\backslash\mathcal{P})+f^{g}(\mathcal{P})+2\sum_{i\in C _{k}\backslash\mathcal{P}}\sum_{j\in\mathcal{P}|(i,j)\in F}f^{g}_{ij}\). Then we can deduce that \(f^{g}(C_{l}\cup\mathcal{P})+f^{g}(C_{k}\backslash\mathcal{P})-f^{g}(C_{l})-f^{ g}(C_{k})=2\sum_{i\in C_{l}}\sum_{j\in\mathcal{P}|(i,j)\in F}f^{g}_{ij}-2\sum_{i\in C _{k}\backslash\mathcal{P}}\sum_{j\in\mathcal{P}|(i,j)\in\mathcal{E}}f^{g}_{ij} =2m_{t,l}|\mathcal{P}|-2(m_{t,k}-|\mathcal{P}|)|\mathcal{P}|\). Since \(m_{t,l}+|\mathcal{P}|>m_{t,k}\), then \(f^{g}(C_{l}\cup\mathcal{P})+f^{g}(C_{k}\backslash\mathcal{P})-f^{g}(C_{l})-f^{ g}(C_{k})>0\).  
For the communication link correlation, we also have \(f^{c}(C_{l}\cup\mathcal{P})+f^{c}(C_{k}\backslash\mathcal{P})-f^{c}(C_{l})-f^{ c}(C_{k})=2\sum_{i\in C_{l}}\sum_{j\in\mathcal{P}|(i,j)\in F}f^{c}_{ij}-2\sum_{i\in C _{k}\backslash\mathcal{P}}\sum_{j\in\mathcal{P}|(i,j)\in F}\). If \(\sum_{i\in C_{l}}\sum_{j\in\mathcal{P}|(i,j)\in F}f^{c}_{ij}\geq\sum_{i\in C _{k}\backslash\mathcal{P}}\sum_{j\in\mathcal{P}|(i,j)\in F}f^{c}_{ij}\), then \(f(C_{l}\cup\mathcal{P})+f(C_{k}\backslash\mathcal{P})-f(C_{l})-f(C_{k})\) must \(>0\). When \(\sum_{i\in C_{l}}\sum_{j\in\mathcal{P}|(i,j)\in F}f^{c}_{ij}<\sum_{i\in C_{k} \backslash\mathcal{P}}\sum_{j\in\mathcal{P}|(i,j)\in F}f^{c}_{ij}\), we can set a lower weight for the communication link correlation, which can make \(f(C_{l}\cup\mathcal{P})+f(C_{k}\backslash\mathcal{P})-f(C_{l})-f(C_{k})>0\).  
Then we obtain that when transferring UAVs \(\mathcal{P}\) to the cluster with more UAVs belonging to the same group as \(\mathcal{P}\), the total intra-cluster compound correlation will be higher.
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 3 The Proposed e-RLRP Algorithm
### Reinforcement Learning Used in Routing Protocol  
The reward propagation with Acknowledgment message, the reward generation and the estimation values are presented.
# PDASTSGAT: An STSGAT-Based Multipath Data Scheduling Algorithm
## 5 Conclusions  
This study integrates graph neural networks with the MPTCP protocol and conducts simulations to implement it. A novel multipath scheduling algorithm, namely PDASTSGAT, is proposed, leveraging the graph neural network algorithm. The algorithm focuses on augmenting the throughput of multipath transmission while ensuring fairness among links. Compared to MPTCP, MPTCP-PDASTSGAT incorporates a packet scheduler called PDASTSGAT based on a graph convolutional neural network algorithm, which utilizes the spatiotemporal characteristics of links to distribute and send packets to the optimal subflow path within the link. It optimizes the spatial structural relationships to facilitate the selection of the optimal subflow path. Simulation results demonstrate that under asymmetric path conditions, MPTCP-PDASTSGAT outperforms other traditional methods in terms of transmission performance. However, it is worth noting that PDASTSGAT has certain limitations. For instance, its performance may be influenced by specific network topologies or traffic patterns, and further investigation is needed to assess its effectiveness in such scenarios. Despite these limitations, PDASTSGAT has shown promising results in extracting spatiotemporal feature directions. These findings will contribute to the improvement and expansion of MPTCP data scheduling in the future.  
Conceptualization, S.X., C.W. and A.Z.; Methodology, S.X.; Software, S.X.; Validation, C.W.; Formal analysis, S.X.; Investigation, J.H.; Data curation, J.H.; Writing--original draft, S.X.; Writing--review & editing, C.W. and A.Z.; Visualization, J.H.; Supervision, A.Z.; Project administration, A.Z.; Funding acquisition, C.W. and J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Fund of the Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications, under grant no. BDIC-2023-B-002.  
Data Availability StatementData available on request from the authors.  
Author [PERSON] was employed by the company Hangzhou Mergex Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.