esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
## 4 QoS-Linked Privileged Content Caching
### Flow Table and PIT Management  
The purpose of the Flow Table is to maintain a record of equivalence classes of the PIT entries that are currently active through the given QoS node. Table 3 provides the format of the Flow Table attributes. The Flow ID and equivalence class ID are extracted from the content name hierarchy, where the granularity of QoS is implemented. Eviction score is a function that generates a value that assists with evaluating the candidate content for PIT eviction in cases of PIT saturation. The novelty of eviction-score assignment is the ability to arbitrate the priority between the same level of QoS privilege. Though the IETF documents [14; 15; 16] abstractly order the privileges into (prompt, reliable), prompt, reliable, and regular services, respectively, eviction score acts as an additional tool to handle inter-prompt, inter-reliable PIT entries which are not addressed by the said IETF publication.  
The ECNCT [15] name component for QoS is embedded in the name hierarchy at the granularity level of QoS enforcement and identified using the naming convention \"name--Equivalence Class ID\". The application searches for the name--Equivalence Class ID string pattern in the content name and extracts the Flow ID and equivalence class ID to populate the Flow Table. Each content name is associated with the eviction score value, as per Equation (2).  
\[EvictionScore=\frac{TTL}{r_{c}} \tag{2}\]  
where TTL represents the time To live for the given PIT entry, and \(r_{c}\) represents the number of request interfaces (indicates the number of consumers waiting to be satisfied) associated with the given PIT entry. A higher eviction score represents a higher probability of PIT eviction for a given equivalence class. The PIT eviction strategy selects an entry with a higher eviction score to evict among the same equivalence class, when there is a requirement for more memory after the time-expired entries are removed from the PIT. The eviction score mechanism acts as an arbitrator between contesting QoS-privileged interests in a resource-constrained PIT. The steps to evict PIT entries by referring to the Flow Table and using the eviction score are provided in Algorithm 1.  
Algorithm 1 first searches for TTL-expired PIT entries and evicts them, followed by the eviction of PIT entries of regular content in descending order of eviction score. Further memory requirements are satisfied by evicting PIT interest entries of reliable-equivalence-class in descending order of eviction score, followed by prompt-equivalence-class PIT entries in descending order of eviction score. The content names of the (prompt, reliable) equivalence class, indicating both prompt and reliable privileges, are evicted only on TTL expiry.
## 4 MSE Analysis of the Proposed Algorithm
### Discussion on \(Q\)  
We introduce the iteration operation into the frequency estimation and decrease the final estimation error by iteratively updating the estimate of the frequency offset and the DTFT samples used in interpolation. To choose the number of iteration, we make a simulation about the performance of the proposed algorithm versus iteration number \(Q\) at different \(\delta\) when \(SNR=0\) dB and \(N=512\). The RMSE/CRLB is shown in Figure 5. From Figure 5, it can be seen that the RMSE/CRLB decreases obviously after the second iteration, when \(|\delta|=0.5\) or \(|\delta|=0.3\). However, When \(Q>2\), the RMSE/CRLB does not decrease as Q increases. Thus, considering the accuracy improvement and the calculation complexity, we choose \(Q=2\) as the iteration number for simulation in Section 5. This iteration number is also consistent with the research in [24].  
Figure 4: The RMSE/CRLB of the proposed estimator versus \(\delta\) at different values of \(M\) when \(SNR=0\) dB and \(N=512\).  
Figure 5: The RMSE/CRLB of the proposed estimator versus \(Q\) at different values of \(\delta\) when \(SNR=0\) dB and \(N=512\).
# An Overview of CubeSat Missions and Applications
## 2 The Success of CubeSats and Its Enabling Factors  
As presented in Figure 4, in recent years, there has been a significant rise in the number of CubeSats in orbit around the Earth, a trend that is forecasted to continue for decades to come. This increase is largely attributed to several enabling factors, including the reduction of launch costs, the emergence of rideshare launch missions, the miniaturization and commercialization of space-grade electronic components, the existence of a large number of commercial CubeSat manufacturers, the standardization of structural and electronic components, and the involvement and support provided by space agencies through financial, programmatic, and technical assistance. These enabling factors are examined in detail in the following section.
# Preclinical Performance of Customized Polycaprolactone-Based Hybrid Bone grafts
## 4 Discussion
To evaluate cell viability, it was decided to use the MTT test, which is a safe, reliable and widely accepted method in this field of research. However, this assay has a limitation when it comes to performing cell viability counting based on absorbance, because the MTT reagent tends to be introduced in the initial stages, during which the cell is entering the apoptosis phase. This can lead to false positive results, as metabolic activity can be falsely observed due to cells in the apoptosis phase being detected as living cells. For this reason, the assay was used alongside Hoechst 33342, a complementary technique because it allows for the identification of cells that have nuclei in an apoptotic state [25]. The results of the apoptosis assay showed that there were no significant differences between the materials in terms of the number of dead cells in each sample, presenting values that were very similar to each other and a value of \(p\) = 0.8371; this is well above \(p\) < 0.05, indicating that the numbers of live and dead cells for each material were very similar. According to [PERSON] et al., polycaprolactone is a polymer with a porous internal structure that allows for the penetration of cells from surrounding tissues such as periodontal ligament cells, thus increasing the capacity for biocompatibility and favoring bone regeneration. In addition, a study has shown that PCL combined with tricalcium beta phosphate (\(\beta\)-TCP) presented better mechanical and biological results, as it favored the induction of osteogenic cells, achieving a higher degree of mineralization [26]. This study analyzed the biocompatibility of PCL in its pure form or combined with other materials, and no significant differences were found between the different concentrations. Based on these results and after testing the biocompatibility of the three materials, future research can be carried out to evaluate the individual osteoinductive capacity of each of these materials to later use them in the field of guided bone regeneration. Regarding the limitations of this study, this in vitro test does not consider the evaluation of mechanical properties, as well as other factors such as the observation of cell adhesion to materials using SEM. A long-term biological study would also be necessary to analyze factors such as osteogenic differentiation. The results of this in vitro study demonstrate that pure or combined PCL with strontium or nanohydroxyapatite are biocompatible materials that should be considered for use, since in addition to not producing toxic effects on the cells of the periodontal ligament, they seem to enhance osteogenic effects favoring bone regeneration. Once its biocompatibility has been proven, this emerging material should be analyzed in detail in future research to fully understand its regenerative potential, guaranteeing safety in treatments and efficacy in clinical practice.
# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
## 4 Production Performance of Single Well Pair
### Sensitivity Analysis of Inter-Well Connectivity  
In the above analysis, it has been found that applying pressure stimulation leads to a noticeable response in inter-well connectivity. This section further explores the effect of the pressure impulse duration and amplitude on inter-well connectivity. In addition, the effect of high permeable channels is also examined. Due to the complex geological conditions of the reservoir, a high permeable channel plays a crucial role in determining the breakthrough time of the water cone and the development of ineffective circulation channels. The detailed setting and parameter ranges of the sensitivity studies are shown in Table 3 below, including the permeability of the permeable channel (scenario 1), the width of the permeable channel (scenario 2), injection pressure duration (scenario 3), and injection pressure differential (scenario 4).
## 2 System and Failure Mode Analysis
### Failure Mode Analysis  
The damping force build-up is related to the pressure generation mechanism of the CDC damper inner components. The damper consists of a piston rod and the end of the piston that separates the rebound chamber and the compression chamber, an inner cylinder that guides the piston's reciprocating motion, a body valve and rod guide that is mounted on the end of the cylinder, and a rebound chamber that is fitted on the outside of the inner cylinder. An intermediate cylinder forms a flow path through which oil from the rebound chamber and compression chamber can flow to the variable valve, a storage compartment guide that is inserted into the intermediate cylinder and serves to prevent gas in the storage compartment from entering the variable valve. The valve adapter and valve plate connect to the variable valve, and these structures are installed; the base shell that forms the storage compartment and the oil seal is installed and inserted into the piston rod so that the rod guide and inner cylinder are fixed to the base shell. In summary, damping force is generated by the physical movement of the damper and the control current generated by the vehicle's behavior.  
In Figure 4b, the pressure produced in each valve creates a damping force determined by the flow rate resulting from the movement of the piston in the damper. The total of these pressure-producing elements constitutes the damping force. Figure 5 details the process by which alterations in vehicle behavior are assessed, the target damping force is computed, solenoid valve control is executed, pressure is produced within the damper, and ultimately vehicle behavior is modified.  
Figure 3: Working principle of CDC system point of damping force variations.  
When analyzing the cases in which damping force is not generated in Figures 4 and 5, there are cases where the pressure generation ability is reduced due to damper oil leakage and cases where the pressure generation of the valve responsible for tension and compression is poor. Cases where the pressure generation of the valve is defective include cases where internal foreign substances affect the valve flow path and cases where the cavitation  
Figure 4: CDC damper’s internal hydraulic components and flow: (**a**) CDC damper’s assembly and flow; (**b**) fluid flow and control current; and (**c**) damper assembly (variable valve integration).  
Figure 5: CDC system’s damping force creation flow diagram (in detail).  
of the hydraulic oil occurs, and damping force is not generated well. If the damping force control amount and the stroke, which generates a lot of damping kinetic energy, increase, this can be seen as a potential cause for performance deterioration.
## 3 Fuzzy-BLS Model for Slope Risk Assessment
### Fuzzy-BLS Risk Assessment Model Structure
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Proportion**} & \multirow{2}{*}{\
\begin{tabular}{c} **Training** \\ **Time/s** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Training** \\ **Accuracy** \\ \end{tabular} } & \multicolumn{5}{c}{**Testing Accuracy**} \\ \cline{4-9}  & & & **Blanket** & **Attention** & **Warning** & **Alert Level** & **Alarm Level** \\ \hline
6:4 & 5.0207 & 68.75\% & 78.75\% & 85\% & 71.67\% & 74.17\% & 84.17\% \\
7:3 & 6.7221 & 76.67\% & 83.61\% & 90\% & 81.11\% & 74.44\% & 88.89\% \\
8:2 & 7.4879 & 88.13\% & 92.08\% & 91.67\% & 88.33\% & 93.33\% & 95\% \\
9:1 & 10.2057 & 82.22\% & 93.33\% & 96.67\% & 86.67\% & 93.33\% & 96.67\% \\
19:1 & 12.0384 & 82.46\% & 93.33\% & 100\% & 80\% & 93.33\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effect of model training with different sample ratios. Figure 5: Accuracy of models with a different number of fuzzy subsystems. The data analysis in the table shows that the Fuzzy-BLS model is significantly superior to other models in the risk assessment of slope landslides in hydropower station reservoir areas. This superiority mainly stems from the problem of periodic differences in the collection of key indicator data for slope landslide assessment, which may affect the accurate understanding of the input parameters by other risk assessment models. This leads to the inability of the model to fully take into account the temporal nature of the data, which, in turn, makes it difficult to learn the effective features of the input data. Owing to the introduction of the fuzzy subsystem module, Fuzzy-BLS can automatically extract the nonlinear features and hidden patterns in the data; deal with nonlinearity, uncertainty, and ambiguity; and improve the flexibility of the model. Thus, it performs well with the problem of slope risk level assessment.
# Predicting Pedestrian Crashes in Texas' Intersections and Midblock Segments
## 3 Geometry Estimation
### Crash Location and Classification  
After the geometry estimation, pedestrian crashes are mapped to intersections or midblock segments. Two criteria are considered in determining whether a crash is \"midblock.\" First, the original police report contains a field that indicates whether the officer categorizes the crash as happening at an intersection. Unfortunately, it is sometimes unclear as to whether that intersection is the center \"box\" area where public streets typically cross, a right turn yield link, or a roundabout. Second, the geographic crash location analysis against a map may show that the crash occurs far enough away from an intersection that it may be considered a midblock crash.  
A detailed description of the final geometries is summarized in Tables 2 and 3, showing summary statistics of infrastructure characteristics as well as pedestrian crash counts based on the crash type and map matching process.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{5}{c}{**Intersections**} & \multicolumn{5}{c}{**Roadway Segments (1 mi Uniform)**} \\ \cline{2-9}  & **Mean** & **S.D.** & **Min.** & **Max.** & **Mean** & **S.D.** & **Min.** & **Max.** \\ \hline \#Pedestrian crashes per year & 0.020 & 0.215 & 0 & 31 & 0.062 & 0.502 & 0.0 & 37.0 \\ \hline Signalized intersection indicator & 0.021 & 0.143 & 0 & 1 & & & & \\ \hline Number of approaches & 3.177 & 0.672 & 0 & 5 & & & & \\ \hline Intersections crossed & & & & & 2.781 & 2.693 & 0 & 25 \\ \hline Walking density [Walk-miles traveled (WMT) per sq. mile] & 325 & 453 & 0 & 15,339 & 244 & 398 & 0 & 15,339 \\ \hline Daily vehicle-miles traveled (DVMT) & 2458 & 8900 & 0 & 432,194 & 1448 & 6935 & 0 & 432,194 \\ \hline Speed limit (miles/hour) & 56.970 & 6.460 & 10 & 75 & 59 & 5 & 10 & 75 \\ \hline Number of lanes & 2.229 & 0.713 & 1 & 8 & 2.068 & 0.401 & 1 & 12 \\ \hline Lane width (ft) & 10.494 & 2.113 & 0 & 48 & 9.915 & 1.439 & 0 & 48 \\ \hline Median width (ft) & 0.364 & 6.771 & 0 & 519 & 1.341 & 10.614 & 0 & 710 \\ \hline Design: one-way road indicator & 0.009 & 0.096 & 0 & 1 & 0.011 & 0.102 & 0 & 1 \\ \hline Annual average daily traffic (AADT) per lane & 953 & 1852 & 1 & 142,733 & 527 & 1615 & 1 & 100,335 \\ \hline Percentage of trucks & 4.803 & 5.328 & 0 & 93 & 4.914 & 5.964 & 0 & 93 \\ \hline Functional class: local & 0.677 & 0.467 & 0 & 1 & 0.814 & 0.389 & 0 & 1 \\ \hline Functional class: collector & 0.178 & 0.382 & 0 & 1 & 0.110 & 0.313 & 0 & 1 \\ \hline Functional class: arterial & 0.145 & 0.352 & 0 & 1 & 0.075 & 0.
# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 4 Future Research Challenges
### Processing Data with Unknown Types  
In the process of data collection, it is very likely to encounter data of new formats and new types, which is an open-set recognition problem [70].  
If the semantic meaning of the data is not well recognized, the automated data collection process cannot be completed. The future data processing direction will be mainly semantic ontology recognition technology based on ontology theory. At the same time, the machine learning method based on the deep learning framework is used to identify new types of data, and the results of learning are evaluated with certain manual judgment to give feedback. It is also possible to integrate various sorted material data to form a knowledge graph [71], which is convenient for knowledge reasoning and the discovery of new knowledge.
# Research on Grouting Pressure of Earth-Rock Dams Reinforced by Polymer Based on Discrete Elements
[10] constructed a three-dimensional numerical model of polymer grouting grounded in reaction kinetics theory, simulating grouting processes within complex cracks and examining the impact of crack medium parameters on polymer diffusion. [PERSON] et al. [11] devised a simulation approach for the grouting process, integrating fluid-solid coupling considerations, and assessed the grouting performance at a hydropower station. Nonetheless, scant research exists on numerical simulation studies of PFA-reinforced rockfill materials technology, particularly those based on discrete elements. Therefore, the rockfill zone at the crest of an earth-rock dam demands particular attention, yet the existing seismic reinforcement measures exhibit notable shortcomings. The PFA-reinforced rockfill materials technology presents a promising application prospect. Owing to the concealed nature of grouting, this study employs discrete element numerical simulation to delve into the reinforcement mechanisms of polymer-grouted materials in rockfill, aiming to provide an in-depth analysis of their efficacy. The numerical model of PFA-reinforced rockfill materials was developed using the matrix discrete element software MatDEM, developed independently by Nanjing University. Initially, circular particles were utilized to simulate the gravitational deposition of rockfill particles, followed by the establishment of the fundamental discrete element accumulation model post-compaction. Subsequently, the grouting process was simulated by excavating grouting holes and employing the fluid-solid coupling method to establish the model. This research simulated the dynamic process of polymer grouting in rockfill bodies and analyzed the slurry diffusion patterns and the effects caused by different grouting pressures. The research findings provide valuable references for the practical application and optimization of construction techniques of PFA-reinforced rockfill materials technology, possessing certain engineering significance.
# Sudden Event Prediction Based on Event Knowledge Graph
###### Abstract  
Event prediction is a knowledge inference problem that predicts the consequences or effects of an event based on existing information. Early work on event prediction typically modeled the event context to predict what would happen next. Moreover, the predicted outcome was often singular. These studies had difficulty coping with both the problems of predicting sudden events in unknown contexts and predicting outcomes consisting of multiple events. To address these two problems better, we present the heterogeneous graph event prediction model (HGEP), which is based on an event knowledge graph. To cope with the situation of missing contexts, we propose a representation learning method based on the heterogeneous graph transformer. We generate event scenario representations from arguments of the initial event and other related concurrent events for the event prediction. We improve the prediction ability of the HGEP model through prior knowledge provided by scenario models in the event knowledge graph. To obtain multiple prediction outcomes, we design a scoring function to calculate the score of the occurrence probability of each event class. The event classes with scores higher than a priori values are adopted as prediction outcomes. In this paper, we create an event knowledge graph in the domain of transportation for an event prediction experiment. The experimental results show that the HGEP model can effectively make predictions with event scenario representations and has a more accurate matching rate and higher precision than the baseline model.  
event knowledge graph; event prediction; event scenario representation; heterogeneous graph neural network +
Footnote †: journal: Applied Sciences  
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1195[[https://doi.org/10.3390/app12211195](https://doi.org/10.3390/app12211195)]([https://doi.org/10.3390/app12211195](https://doi.org/10.3390/app12211195))
# Fate of Soil Carbon Transported by Erosional Processes
## 4 Soil Erosion and the Global Carbon Budget
### Emissions from Tillage-Induced Soil Erosion  
Tillage-induced soil displacement downslope is a significant process on cultivated steep lands. Soil disturbance by plow tillage (PT) or its lack under notill (NT) can have variable effects on the magnitude of SOC removal by erosional processes and pedological processes involved in gaseous emissions. In the Kwa Zulu-Natal Province of South Africa, [PERSON] et al. [31] evaluated the effects of NT vs. PT on losses of SOC from soil. [PERSON] and colleagues observed that soil under NT had greater SOC density than that under PT(17.70 vs. 13.19 kg/m\({}^{3}\)), lower gaseous emissions by 4.4% (10.40 vs. 10.88 g CO\({}_{2}\)-C/m\({}^{2}\)) but reduced the release of CO\({}_{2}\) from eroded sediments (0.185 vs. 0.778 g CO\({}_{2}\)C/m\({}^{2}\)) representing 76.3% decline. In the case of PT, cumulative emissions over a 141-day period were 19% greater in sediments (0.048 g CO\({}_{2}\)C/g C) compared to soils (0.04 g CO\({}_{2}\)C/g C). In the case of NT, emissions were 33% lower in sediments (0.024 g CO\({}_{2}\)C/g C) compared to soils (0.032 g CO\({}_{2}\)C/g C). The authors' hypothesized these trends to a high aggregate stability and better physical protection of SOC within stable aggregates. Conservation agriculture (CA), based on NT with residue mulding and cover cropping, is widely reported to sequester SOC [32]. A modeling study by [PERSON] et al. [33] showed higher losses of SOC under PT than NT through enhanced CO\({}_{2}\) emissions and increased losses through intensified erosion. [PERSON] and colleagues suggested that tillage effects on SOC losses through soil erosion must also be accounted for in the GCB. The loss of SOC from eroded soil is also determined by the sediment size distribution and its MRT [34].  
Plow tillage is also a source of dust. In some cases, tillage-induced emissions of dust are more than that by wind. [PERSON] et al. [35] observed that tillage-induced emissions are determined by the soil moisture content at the time of tillage. [PERSON] and colleagues found that the threshold level of soil water content for fine dust emissions of soil was 2% to 5% for sandy soil, 5% to 10% for silty soils, \(\sim\)30% for clayey soils, and 25% to 45% for organic soils.