esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design Method
### Design Method for Slender Elliptical Tubes in Compression  
The compressive load resistance \(N_{\text{R}}\) of a slender section can be expressed in terms of a reduced effective cross-sectional area \(A_{\text{eff}}\) such that:  
\[N_{\text{R}}=A_{\text{eff}}\sigma_{\text{c}} \tag{7}\]  
The design method proposed by [22] is not reproduced in full here but suffice to say it is a function of slenderness, aspect ratio and tube wall imperfections, and is used to determine a resistance reduction factor \(\rho\) where \(A_{\text{eff}}=\rho A\). Based on the variations in wall thickness shown in Table 7, the magnitude of \(\Delta_{\text{w}}\) is assumed to be \(0.05t\) for all specimens when applying the design method.  
In Figure 17, the design reduction factor \(\rho_{\text{d}}\) is compared with the experimental values \(\rho_{\text{exp}}=N_{\text{u,exp}}/A\sigma_{\text{c}}\) for all elliptical tube specimens, with the reduction factors also shown in Table 8. It can be seen that the elastic buckling curve \(\rho=\overline{\lambda_{\ell}}^{-2}\) provides a general upper bound to the experimental results, indicating that the material properties used to scale the design parameters are appropriate. The value of \(\rho_{\text{exp}}\) for ABS specimen EHS21 falls noticeably below the design curve, which can be attributed to the influence of imperfections during manufacturing. Such high levels of imperfection are particularly deleterious to the load resistance of an elliptical section as slender as EHS21, given that imperfection sensitivity increases with slenderness [22; 23].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material Batch** & **\(\sigma_{\text{c,eff}}\)** \\  & **(N/mm\({}^{2}\))** \\ \hline PLA batch 1 & 67.7 \\ PLA batch 2 & 69.6 \\ ABS & 37.8 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Effective compressive strengths for EHS specimens.  
In Figure 18, values of \(N_{\text{u,exp}}/N_{\text{R}}\) are plotted for all specimens in order to assess the safety and accuracy of the design method. For the PLA specimens, it can be seen that the design method provides safe-sided results without being overly conservative, with accuracy increasing with slenderness. It is noted that the level of accuracy is relatively similar for all aspect ratios. For the ABS specimens, the design predictions are reasonably accurate and safe-sided. As shown in the case of EHS21, reductions in strength due to imperfections can lead to design predictions being overestimated and thus it is imperative that manufacturers pay heed to quality control during fabrication. Overall, the results confirm that, with appropriate rescaling of parameters, the design method proposed by [22] is suitable for use with slender 3D-printed PLA and ABS elliptical tube members in compression. It is envisaged that exercises conducted using design rules for other slender steel cross-sectional geometries could find similar use for a variety of potential 3D-printed profiles.
# Lithium Battery SoC Estimation Based on Improved Iterated Extended Kalman Filter
## 4 Experimental Results and Simulation Analysis
### FUDS Working Condition  
The FUDS condition is a standard chemical condition that is used to test the fuel efficiency and range of an electric vehicle. It is developed based on urban driving data from the U.S. federal government and is primarily used to simulate vehicle performance under urban road driving conditions. The FUDS case simulates changes in different speeds and accelerations during urban driving. It includes typical driving behaviors such as driving at low speeds, parking, accelerating, and decelerating. These modes provide a comprehensive picture of real driving situations in urban traffic. The FUDS scenario takes into account the distribution of stopping time and distance traveled in urban driving. These data can influence the energy efficiency and range of electric vehicles in urban environments. Although FUDS mainly focuses on the simulation of velocity and acceleration, it also considers the influence of partial road slopes and road conditions to improve the realism of the working conditions.  
According to the USABC Electric Vehicle Battery Test Manual [35], the initial SoC of the battery should be reduced from 1 to 0.8 in the test process of the FUDS condition. Therefore, the initial SoC value of the three estimation methods is set to 0.8, and the initial voltage value is 3.8858 V. The current variation under the FUDS condition is shown in Figure 7.  
Figure 6: SoC estimation error for the constant current 1A discharge condition.  
Figure 7: Current variation for FUDS operating conditions.  
Figure 8: SoC prediction results for FUDS operating conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Work Condition** & **Algorithm** & **Maximum Error** & **Average Error** \\ \hline \multirow{4}{*}{Constant current 1 A discharge} & EKF & 4.1334 & 1.5419 \\  & IEKF & 2.2845 & 0.6852 \\  & LM-IEKF & 1.4212 & 0.5786 \\  & EKF & 4.5482 & 1.2942 \\ \multirow{2}{*}{FUDS} & IEKF & 4.5326 & 1.1113 \\  & LM-IEKF & 1.9756 & 0.4714 \\  & EKF & 2.3198 & 0.7016 \\ \multirow{2}{*}{BJDST} & IEKF & 2.7437 & 0.7915 \\  & LM-IEKF & 1.9032 & 0.5619 \\ \hline \hline \end{tabular}
\end{table}
Table 3: SoC estimation results of different algorithms under each operating condition.  
Figure 9: SoC estimation error for FUDS condition.
# Performance Modeling and Analysis of LTE/Wi-Fi Coexistence
## 5 Conclusions and Future Work  
Performance modeling and the analysis of integration/coexistence between LTE and Wi-Fi technologies in the downlink is proposed in this research. Via MOSEL-2 simulation, the numerical solution of the model is developed, and the model is solved mathematically to validate the numerical results obtained by MOSEL-2. The provided analysis and results confirm that, unlike other recommended models, the proposed coexistence model improves the overall cell performance by balancing the load to maximize cell capacity; however, it sometimes degrades LTE performance. Therefore, the main objective of this research is achieved by providing new service deployment strategies between LTE and Wi-Fi to achieve the maximum capacity on the network, while maintaining better QoS for the end-user. As per the team effort and experience in developing and solving the suggested model, numerically by the simulation and analytically by the mathematical solution, this new methodology can be used as a new tool that has an important commercial value for mobile operators. Therefore, the proper utilization of the above methodology and associated tools can help in proposing new deployment and integration scenarios to accomplish better network performance by maximizing the capacity of the network as well as achieving a better QoS for the end-user. The limitation of this approach is the possibility of including the mobility model and investigating the solution of the model in different propagation environments to be more realistic. Moreover, the possibility of comparing the results to similar scenarios as a future work can be beneficial. Furthermore, considering a multi-cell scenario, taking into consideration the handovers between different cells, is an excellent suggestion for future work. Finally, the integration between 5G and Wi-Fi technologies is another possible extension of the current work.  
[PERSON] and S.A. were involved in the process of preparing the manuscript; conceptualization; methodology; software; validation; formal analysis; investigation; resources; writing--original draft preparation; review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 6 Results
### Performance of Learning Methods by Reinforcement without Sensor Fusion  
As a justification for the use of the DDQN method, before carrying out its implementation, a simpler alternative was tested, that is, a more elementary reinforcement learning algorithm, such as DQN. Initially, only one perception sensor, the RGB-D camera, was used. Thus, in the initial tests, there was no implementation of sensor fusion. Figure 7 presents the result of implementing the DQN through the Rewards Obtained \(\times\) Training Episodes graph.  
It is possible to observe that the growth rate of the moving average is relatively low, reaching a maximum average of \(\approx\)8. This indicates that with the DQN method and exclusive use of the RGB-D visual sensor, the AMR learns few efficient actions regarding the proposed mission. In addition, it is possible to observe high volitivity in the gross rewards obtained per episode (gray line of the graph), an indication that the stability of the method is weak.  
Figure 8 presents the result of implementing the DDQN through the graph of Rewards Obtained \(\times\) Training Episodes.  
As it can be seen in Figures 7 and 8, DDQN tends to provide higher values of rewards than the DQN algorithm. The method also presents greater stability and reached an average  
Figure 8: Rewards through the DDQN Network.  
Figure 7: Rewards through the DQN Network.  
of \(\approx\)10. The difference between the computational cost of DQN and DDQN is practically imperceptible. These were indicative for the selection of the DDQN in the work in question.  
It is worth mentioning that despite the improvements regarding the DDQN method, the exclusive use of the RGB-D sensor was not enough to complete the proposed mission. Perception by the RGB-D camera results in states sufficient enough to navigate without collision; this is due to the level of scene depth provided by the sensor. However, it is not sufficient enough to accurately represent the points needed to pass through each workstation accurately. Figure 9 shows this method deficiency. It is possible to observe that the trajectory (blue line) of the AMR passes through station 1 and 2; however, when passing through station 3, the AMR deviates from the route.
# An Eccentricity Based Data Routing Protocol with Uniform Node Distribution in 3D WSN
## 3 Proposed EDR Protocol  
### Network Model  
The proposed protocol considers the following network model of an area coverage. A sink can be located anywhere within the communication range from the network field. Sensor nodes \(n\) are dispersed randomly and uniformly in the unit cube of volume \(V\) according to the \(X\), \(Y\) and \(Z\) coordinates. To simplify our generalized idea, a 3D WSN data routing model has been designed based on a few rational assumptions as follows. The space is divided into 3D subspaces which are ordered sequentially in different levels \(l_{i}\), \(i\) = {1,2,3,... \(Jh\)} according to the location of sink in the network field. All the nodes and the sink are stationary after deployment. The nodes are homogeneous and each node has a unique identity (\(id\)). Unlike the nodes, the sink has no energy constraint. The nodes know about their positions' coordinates (\(x\),\(y\),\(z\)) as well as being familiar with the coordinates of the edges of the subspaces. Several sensor node localization methods have been proposed based on global positioning system (GPS), received signal strength (RSS) in 3D WSNs [20; 21]. Here, a Cartesian coordinate system is used to represent the position of a node that can be obtained from a GPS module. The GPS module calculates the position of each node and it will be used at the time of node deployment. After that, it will be switched off to save energy. During network operation phase, different types of messages are used in Table 1.
## 4 Lunar Antenna Types
### Planar Antennas
#### 4.1.3 All-Metal Circular Patch Antenna  
The Rashid rover employs a dual-antenna system for its communication needs. The first antenna, a sleeve dipole antenna, is exclusively dedicated to primary communication between the rover and the lander, which will be demonstrated in the succeeding section. In the event of a primary communication failure, the secondary communication subsystem comes into play using a circular stacked patch antenna. This secondary communication serves multiple purposes: it not only acts as a backup for primary communication but also fulfills a crucial mission objective, which is to establish direct communication with Earth during the second lunar day. Additionally, it serves as an experimental payload to evaluate the capabilities of an innovative and power-efficient lunar-to-Earth communication system. The primary component of the secondary antenna is an all-metal circular patch as shown in Figure 8, designed to operate at the uplink frequency of 2101.2 MHz and the downlink frequency of 2266 MHz, with a gain of 6 dBi [18]. This choice of antenna was due to its high gain and multi-band operation. Tables 3-5 compare the three reported planar lunar antennas in terms of application, performance, and simulation parameters.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value (mm)** \\ \hline w\({}_{\text{sub}}\) and 1 & 100 \\ \hline W & 60.8 \\ \hline l\({}_{\text{slot}}\) & 40 \\ \hline w\({}_{\text{slot}}\) & 25 \\ \hline D & 14 \\ \hline l\({}_{\text{throat}}\) & 4.27 \\ \hline r\({}_{\text{stub}}\) & 15 \\ \hline l\({}_{1}\) & 1.7 \\ \hline \end{tabular}
\end{table}
Table 2: QESA structural parameters.  
Figure 7: Mimo array layout [50].
# A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing
## 5 Results
### Data Processing and Analytic Systems
#### 5.2.2 Hadoop Batch Processing Innovations  
In the Hadoop batch processing system, the following enhancements were applied to optimize performance in a batch-oriented context (refer to Algorithm A6):  
* Batch limiting for efficient processing: the system processes a set number of records (10,000) to optimize batch sizes for improved performance in a traditional Hadoop setting.
* Artificial delay for simulated batch processing: a controlled delay simulates the time needed to gather and process large data batches, replicating real-world batch intervals in a manufacturing environment.
* System metrics' tracking pre- and post-processing: CPU and memory usage are monitored before and after processing to assess resource utilization throughout the batch processing cycle.
* Throughput and latency calculation: processing time and throughput are recorded to evaluate the batch system's efficiency.
* Logging of aggregate results and performance metrics: key performance metrics and aggregate results are logged, creating a performance record for further analysis and optimization.  
These improvements make both systems more efficient and adaptable for CM applications. The analysis (see Table 2) highlights the low-latency and real-time capabilities of Spark Streaming, which offers distinct advantages over traditional Hadoop batch processing. Although Hadoop is effective for large-scale data processing, it introduces higher latency and lower throughput, making Spark Streaming preferable for scenarios that require immediate data insights.
# A Survey of Machine Learning-Based System Performance Optimization Techniques
## 2 Background
### Machine Learning Based Index Structures
#### 3.1.1 Learned B-Tree  
B-Tree index model predicts a position of look-up key within a sorted set of keys. Original B-Tree guarantees that found the key is the first key or higher than the look-up key. In addition, B-Tree determines whether the look-up key is in the page through binary search. In this paper [7], a regression-based range index is proposed.  
For the Learned B-Tree design, the Recursive Model Index (RMI) [7] was designed to increase accuracy and reduce complexity rather than a single CDF model. Because RMI trains only for each range of data, such as B-Tree, RMI can easily build with a simple model. In addition, this paper supports B-Tree. If learning data distribution does not fit for prediction, they use B-Tree.  
In Figure 2, the upper-level model receives the key as input and predicts the next-level model until it is located at the lowest level. Upon reaching the lowest level model, the model predicts the location of the queried key and finds the queried key between min_err and max_err of the predicted location.  
Compared with the original B-Tree under various conditions, the learning index model achieved much less memory consumption and faster look-up speed than B-Tree.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Research** & **Model** & **Description** \\ \hline Learned Cache  Replacement  Policy [15] & LSTM, SVM & It proposed an attention-based LSTM for offline prediction and SVM for online prediction. Although there are differences in the architecture of the two models, both models work with the same input. This model classifies whether the input is cache-friendly or not. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Learned B-Tree [7]: The top-level model receives a key as an input and it predicts the final position between min_err and max_err.
## 2 Results and Discussion
### Determination of Aroma Profile via Sensory Attribute  
The typical aroma profiles generated from each sample were surveyed: odorless, flower, grass (tree), apple, soy sauce, gasoline, rotten, ammonia, orange, rubber, fishy, alcohol, banana, excreta, burnt, sour, milk (butter), vinegar, and others. As shown in Figure 5, panels selected grass (tree) (23.8%) and soy sauce (23.8%) smells as the major aromas for the sheep placenta, whereas grass (tree) (33.3%) and odorless (14.3%) smells were selected as the major aromas for the placenta fermented with _B. deamine_ kh3. Soy sauce (16.7%), tree (14.3%), and rotten (14.3%) smells were the major aromas of sheep placenta fermented with _B. bruxellensis_. Interestingly, odorless (+14.3%), grass (tree) (+9.5%), rubber (+7.1%), and burnt (+7.1%) smells increased, whereas the score of soy sauce (\(-\)19.0%) drastically decreased for the sheep placenta fermented with _B. deamine_ kh3, compared with the normal sheep placenta. This indicates that fermentation of sheep placenta with _B. deamine_ kh3 could improve its flavor preference by changing its major aromas.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & **N** & **Mean** & \(\mathbf{Z}\) & \(\mathbf{H}\) & \(\mathbf{p}\)**Value** & **Rank** \\ \hline SP & 42 & 1.98 & \(-\)0.22 & & & 2 \\ SP-BD & 42 & 2.19 & 1.74 & 4.04 & 0.133 & 1 \\ SP-BB & 42 & 1.83 & \(-\)1.52 & & & 3 \\ \hline \hline \end{tabular}  
* QDA was evaluated using 42 panels, and the ranking test was performed by selecting the worst, normal, and best samples by marking numbers 1, 2, and 3, respectively. SP, sheep placenta; SP-BD, sheep placenta fermented with B. deamine kh3; SP-BB, sheep placenta fermented with Student’s \(t\)-test between SP and each group.  
\end{table}
Table 3: Quantitative descriptive sensory analysis using the ranking scale method.
###### Abstract  
In this study, the effect of replacing 5 or 10% of wheat flour with lyophilized kale (_Brassica oleracea_ L. var. _sabellica_) on the rheology of dough and bread characteristics (physical and textural properties, sensory acceptability, staling tendency) was evaluated. The farinographic analysis showed an increase in the development time, index of tolerance to mixing, and water absorption. The share of lyophilized kale in the dough affected changes in its rheological properties, e.g., increased the values of storage and loss moduli with a decrease in the value of the phase shift angle (tan \(\delta\)) from 0.36 to 0.31 at 1 rad/s. A significant decrease in the values of instantaneous and viscoelastic compliance was also observed, and an increase in the value of zero shear viscosity. The incorporation of lyophilized kale into the dough caused a noticeable decrease in bread volume by about 10%, and porosity, by about 8%, despite the lack of statistical significance. Statistically significant changes were found in pore size and the presence of large pores > 5 mm\({}^{2}\) in the crumb, while pores density increased. The enrichment of bread with lyophilized kale influenced a decrease in the brightness of the crumb from 73.7 to 49.5 while increasing the proportion of yellow and green color as a result of a considerable increase in the content of chlorophyll pigments and carotenoids. Bread enriched with lyophilized kale had lower acceptability than the control bread. The enrichment of the bread with powdered kale also caused changes in the texture of the crumb, e.g., the hardness on the first day of the study was 2.14 N in the control bread, while in the bread with 10% kale content it was 6.46 N. In addition, the enriched bread showed a decrease in springiness, cohesiveness, and resilience.  
Keywords:wheat bread; kale powder; dough rheology; bread quality; texture +
Footnote †: journal: Medical Physics
# Domain Knowledge Graph Question Answering Based on Semantic Analysis and Data Augmentation
## 3 The Construction of Household Registration Domain Knowledge Graph
### Knowledge Representation  
In a KG, the triplet is a commonly used representation, denoted as \(G=(E,R,F)\), where \(E~{}=~{}\{e_{1},e_{2},\cdots e_{m}\}\) represents the entity set containing m distinct entities, \(R~{}=~{}\{r_{1},r_{2},\cdots r_{n}\}\) represents the relation set containing n distinct relationships, and \(F=\big{\{}\big{(}e_{a},r_{b},e_{c}\big{)},\cdots,\big{(}e_{i},r_{i},e_{k}\big{)}\big{\}}\) represents the set of fact triplets. In knowledge extracted from household registration business text data, many entities have multiple relationships that cannot be effectively represented using conventional triplet forms. Currently, there are two primary methods for representing multiple relationships: one involves adding the multiple relationships as edge attributes, as exemplified by the knowledge base ConceptNe [34]. However, this method yields redundant relationship data, reducing the efficiency of data retrieval and querying. The second method involves using compound value types (CVT) nodes to represent multiple relationships, as exemplified by the knowledge base Freebase [23]. However, this method requires data to be stored as a graph data structure.  
Considering efficiency in querying and the redundancy in relationship data, we used CVT nodes to represent multiple relationships and convert the fact triplets under different circumstances into tuples represented by CVT nodes. CVT nodes are a node type in the Freebase knowledge base that is used to collect multiple attributes of an event and model complex relationships more accurately between entity nodes. We used the CVT nodes to represent household registration events that involve different constraint conditions and complex relationships, as illustrated in Figure 3.  
After completing entity relation extraction and deduplication operations, the extracted triplets were reorganized using CVT nodes. The statistics of the quantity and categories of the entity relationships extracted from the document are shown in Table 1. This paper utilized the NEO4J graph database to store multi-tuple data. NEO4J provides Cypher statements to import and query graph data, which is a descriptive graph query language with simple syntax and powerful functions. In this paper, the entity relationships in the Guidelines and the corresponding question corpus were imported into the NEO4J database using the Cypher CREATE statement.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Data Sources** & **Entity Count** & **Entity Category Count** & **Relationship Count** & **Relationship Category Count** \\ \hline Guidelines & 622 & 8 & 1512 & 9 \\ Question corpus & 1770 & 12 & 2564 & 13 \\ Total & 2392 & 20 & 4067 & 22 \\ \hline \end{tabular}
\end{table}
Table 1: Entity relationship data statistics.