esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Description  
### Market Segmentation and DP Strategy  
When providing cargo transportation services to customers, container liner companies should guarantee that cargo is transported securely and completely between the departure and destination ports. Moreover, liner companies are supposed to provide bespoke services owing to the customer's different preferences, including but not limited to transportation rates, transportation time, and the supply and service capabilities of empty containers.  
Some cargo has a short shelf life, and the requirements for transportation time are strict, such as fresh meat, seafood, eggs, vegetables, fruits, flowers, and dairy products. These products are called \"time-sensitive\" (TS) cargo. To ensure the quality of TS cargo, lines are expected to transport containers laden with TS cargo to the destination port as soon as possible. The shorter the transportation time, the better the quality; hence, the shipper can obtain higher revenue. Therefore, shippers are agreeable to remit higher rates for TS cargo to shorten the waiting time for transportation. This prompts liner companies to provide the fastest transportation services for TS cargo at higher rates. In order to enhance the satisfaction of shippers, liner companies ought to subdivide the market and provide specialized services for the segmentation market. The market is divided into the TS cargo market and the ordinary cargo market in this paper.  
The DP strategy proposed by [16; 17] is adopted, since the capacity of container liners is limited. It refers to dividing the cargo into different grades according to the freight paid by the shippers. When the ship capacity of the present voyage is insufficient, some low-grade shipments will be delayed until the next voyage. According to the sensitivity of cargo to time, container cargo is divided into TS cargo and ordinary cargo. The TS cargo arriving at the port on time will be serviced quickly and transported on time. For ordinary cargo, when the remaining capacity of the liner is sufficient, it will be transported on the current voyage. In contrast, if the remaining capacity of the liner is inadequate or the cargo is no-show (delayed arrival at the port, that is, for various reasons, failing to appear at the designated port before the scheduled time that agreed with the liner company), it will be postponed to the next voyage. Through this strategy, liner companies could formulate proper slot allocation schemes to augment capacity utilization of liners and increase their revenue. The essence of the DP strategy is that the liner company allocates the limited slots' resources as much as possible to customers who are willing to pay higher freight for reliable and fast transportation services, so as to achieve the goal of maximizing revenue and utilizing slot resources.
# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
## 3 Challenges of RRM in Cognitive HTS Systems
### Robustness to CSI  
When conducting RRM in the cognitive HTS system, a critical issue is the acquisition of the CSI at the satellite/gateway. However, the availability of perfect CSI is a major challenge in practical implementation. Remarkably, the round-trip delay from a geostationary satellite corresponds to about 500 ms. Due to the long round-trip time and time-varying channel conditions in satellite communications, any degradation of CSI (quantization errors, outdated information, or transmission errors) would compromise the performance of interference mitigation, and thus deteriorate the performance of cognitive HTS systems. Consequently, it makes much sense to design a robust RRM scheme that is insensitive to channel estimation impairments.
# Automatic Failure Modes and Effects Analysis of an Electronic Fuel Injection Model
## 4 Case study: Electronic Fuel Injection System  
### Electronic Fuel Injection System  
#### 4.1.1 Overview of the Electronic Fuel Injection System  
The electronic fuel injection (EFI) system is a system that calculates the amount of fuel injected into the engine. The EFI system may be described using the sensor, engine control unit (ECU), and engine, as shown in Figure 8. The sensor sub-system measures the engine state and transmits the measurement result to the ECU subsystem. The ECU subsystem calculates the amount of air and fuel using the sensor information. The engine subsystem generates the power of a vehicle through four-stroke cycles using fuel and air.  
The sensor subsystem consists of four sensors: (1) The throttle position sensor (TPS) measures a throttle valve angle. (2) The crankshaft position sensor (CPS) measures the rotational speed per second of the crankshaft of the engine unit. (3) The exhaust gas oxygen sensor (EGO) measures the oxygen concentration in the exhaust gas. (4) The manifold absolute pressure sensor (MAP) measures the internal pressure of the intake manifold. The measured sensor data are connected to the data bus and transmitted to the ECU subsystem.  
The engine control unit (ECU) subsystem calculates the amount of fuel by performing three processes from the input sensor subsystem data: First, the sensor data filter process checks whether there is any abnormality in the data received from the data bus. Second, the control logic process sets the air ratio using the verified sensor data and current vehicle state information. Third, the fuel rate calculator process calculates the amount of air intake using vehicle condition information and throttle position data, and calculates the amount of fuel based on the set air ratio.  
The engine subsystem consists of eight devices: (1) The fuel injector is a device that injects fuel into the intake manifold according to the fuel measured by the ECU. (2) The throttle body is a device that controls the amount of air using the throttle valve. (3) The intake manifold is a pipe that supplies the cylinder with a gas mixture that consists of air and fuel. (4) The intake valve sends the gas mixture from the intake manifold to the cylinder. (5) The cylinder is a space in which four strokes (intake, compression, explosion, and exhaust) of the engine are performed. (6) A crank converts energy obtained from the four strokes of an engine into kinetic energy. (7) The exhaust valve then exhausts gas burned in the cylinder to the exhaust manifold. (8) The exhaust manifold is a pipe that sends the discharged exhaust gas to the exhaust pipe.
# Automatic Estimation of Dynamic Lever Arms for a Position and Orientation System
## Appendix C  
Referring to Table 2, the initial state \(\mathbf{x}_{0}\), initial covariance matrix \(\mathbf{P}_{0}\), and system noise variance matrix \(\mathbf{Q}_{k}\) needed by the state equation of the reduced-order filter are set as:  
\[\mathbf{x}_{0}=\mathbf{0}_{21\times 1}, \tag{14}\]  
\[\mathbf{P}_{0}=\text{diag}\Bigg{(}\begin{array}{cccc}[0.01^{\circ};&0.01^{ \circ};&0.1\text{ m/s};&0.1\text{ m/s};&0.1\text{ m/s};&5\text{ m};&5\text{ m};&0.01^{\circ}/\text{h};&0.01^{\circ}/\text{h};&0.01^{\circ}/ \text{h};\\ 30\text{ \mu g};&30\text{ \mu g};&30\text{ \mu g};&0.5\text{ m};&0.5\text{ m};&2\text{ m};&2\text{ m};&2\text{ m}]\times 3\end{array}\Bigg{)}^{2}, \tag{15}\]  
\[\mathbf{Q}_{k}=\text{diag}\Big{(}[5\text{ \mu g}/\sqrt{\text{Hz}};&5\text{ \mu g}/\sqrt{\text{Hz}};&5\text{ \mu g}/\sqrt{\text{Hz}};&0.001^{\circ}/\sqrt{\text{h}};&0.001^{\circ}/\sqrt{ \text{h}};&0.001^{\circ}/\sqrt{\text{h}};&\mathbf{0}_{15\times 1}\Big{]}\times 3 \Big{)}^{2}t_{s}, \tag{16}\]  
where \(t_{s}\) denotes the state propagation cycle, set as 0.01 s and equal to the SINS solution cycle. The units given above are for easy interpretation, and should be changed to \(SI\) in practical application.  
According to the specifications of CDGPS, the position measurement covariance matrix of the reduced-order filter is set as:  
\[R_{k}=\text{diag}([0.01;\;0.01;\;0.015]\text{m})^{2} \tag{17}\]
## 4 Data Analysis and Results
### Structural Model Test
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Construct**}} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Std. D.**} & \multirow{2}{*}{**Factor Loading**} & \multirow{2}{*}{**Lambda Stand.**} & \multicolumn{2}{c}{**Composite**} & \multirow{2}{*}{**AVE**} & \multirow{2}{*}{**Cronbach’s \(\alpha\)**} \\  & & & & & & & & \\ \hline \multirow{3}{*}{ENJ} & EN1 & 4.26 & 0.950 & 0.942 & 0.923 & & & \\  & EN2 & 4.26 & 0.954 & 0.960 & 0.953 & 0.912 & 0.777 & 0.925 \\  & EN3 & 4.13 & 0.980 & 0.796 & 0.756 & & & \\ \hline \multirow{3}{*}{FAN} & FA1 & 2.77 & 1.30 & 0.826 & 0.832 & & & \\  & FA2 & 2.18 & 1.32 & 0.868 & 0.854 & 0.885 & 0.720 & 0.883 \\  & FA3 & 2.75 & 1.32 & 0.842 & 0.860 & & & \\ \hline \multirow{3}{*}{ESC} & ES1 & 3.05 & 1.33 & 0.829 & 0.841 & & & \\  & ES2 & 2.50 & 1.33 & 0.869 & 0.870 & & & \\  & ES3 & 2.70 & 1.32 & 0.898 & 0.897 & 0.918 & 0.736 & 0.914 \\  & ES4 & 2.82 & 1.32 & 0.816 & 0.821 & & & \\ \hline \multirow{3}{*}{SINT} & SIN1 & 2.27 & 1.36 & 0.866 & 0.865 & & & \\  & SIN2 & 2.59 & 1.38 & 0.931 & 0.931 & 0.956 & 0.843 & 0.955 \\  & SIN3 & 2.42 & 1.36 & 0.952 & 0.955 & 0.955 & & \\  & SIN4 & 2.36 & 1.32 & 0.919 & 0.92 & & & \\ \hline \multirow{3}{*}{SPRE} & SOP1 & 2.78 & 1.32 & 0.827 & 0.815 & & & \\  & SOP2 & 2.35 & 1.32 & 0.810 & 0.813 & 0.877 & 0.705 & 0.874 \\  & SOP3 & 2.95 & 1.36 & 0.869 & 0.888 & & & \\ \hline \multirow{3}{*}{ACH} & ACH1 & 3.47 & 1.34 & 0.815 & 0.809 & & & \\  & ACH2 & 3.02 & 1.4 & 0.910 & 0.933 & & & \\  & ACH3 & 2.96 & 1.39 & 0.876 & 0.904 & & & \\  & ACH4 & 3.20 & 1.34 & 0.840 & 0.807 & & & \\ \hline \multirow{3}{*}{SELFP} & SEP1 & 2.58 & 1.31 & 0.948 & 0.954 & & & \\  & SEP2 & 2.66 & 1.32 & 0.957 & 0.951 & 0.935 & 0.827 & 0.928 \\  & SEP3 & 2.73 & 1.36 & 0.803 & 0.817 & & & \\ \hline \multirow{3}{*}{CI} & CI1 & 3.49 & 1.23 & 0.864 & 0.837 & & & \\  & CI2 & 3.96 & 1.13 & 0.864 & 0.893 & 0.856 & 0.749 & 0.853 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measurement scales. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **ENJ** & **FAN** & **ESC** & **SINT** & **SPRE** & **ACH** & **SELFP** & **CI** \\ \hline \hline Enjoyment (ENJ) & 0.88 & & & & & & \\ Fantasy (FAN) & 0.245 & 0.94 & & & & & \\ Escapism (ESC) & 0.357 & 0.397 & 0.96 & & & & \\ Social Interaction (SINT) & 0.183 & 0.300 & 0.339 & 0.98 & & & \\ Social Presence (SPRE) & 0.334 & 0.417 & 0.466 & 0.616 & 0.93 & & \\ Achievement (ACH) & 0.383 & 0.321 & 0.446 & 0.307 & 0.433 & 0.96 & \\ Self-Presentation (SELFP) & 0.185 & 0.444 & 0.337 & 0.479 & 0.666 & 0.321 & 0.96 & \\ Continuance Intention (CI) & 0.526 & 0.215 & 0.338 & 0.277 & 0.386 & 0.352 & 0.229 & 0.92 1  \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Discriminant validity.
## 4 Conclusions  
Using STXM and two orthogonal linearly polarized X-rays, we performed carbon chemical structure mapping of the cross and longitudinal sections of PAN- and pitch-based CFs, both with similar elastic modulus. The results showed that the distribution of the \(\pi\)-orbital-oriented domains in the CFs varied considerably, depending on the raw material. In contrast, no difference in the distribution of the \(\sigma\)-orbital orientations between the PAN- and pitch-based CFs could be observed. With respect to the PAN-based CFs, the graphene sheet structure was distributed along the fiber axis direction, and the cross section consisted of randomly distributed domains with sizes less than approximately 50 nm, as reported in a previous study [19]. By contrast, the pitch-based CFs were composed of aggregates of graphite domains with \(\pi\)-orbital-oriented domains in the cross sections ranging from \(\sim\)100 nm to 1 \(\upmu\)m. It was confirmed that these domains exhibited a striped shape with a width of 200-300 nm along the CF axis (longitudinal section). The size and distribution of the \(\pi\)-orbital-oriented domains, in addition to the crystallite sizes, may be the determining factors of the mechanical properties of the CFs.  
The use of STXM can provide information on the chemical structure of various molecules. In particular, STXM will be indispensable in clarifying and proposing design guidelines for other highly functional materials, when chemical structures, such as \(\pi\)-orbital-oriented domains, are closely related with macroscopic properties.  
Figure 4: XRD patterns of PAN- and pitch-based CFs. The bar denotes the XRD peak position of the (002) plane of the graphite in a previous study [22].  
**Author Contributions:** Conceptualization, T.H., M.U., Y.A., R.M., and N.N.; methodology, Y.T. and M.K.; software, Y.T.; validation, T.H., Y.T., and M.K.; data curation, T.H. and Y.T.; writing--original draft preparation, T.H. and M.K.; writing--review and editing, T.H., Y.T., M.U., Y.A., and R.M.; visualization, T.H.; supervision, M.K.; project administration, R.M. and M.K.; funding acquisition, R.M. and M.K. All authors read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** We are grateful to [PERSON] and [PERSON] (Nippon Steel Corporation) for discussions in this study. We would like to thank [PERSON], [PERSON], [PERSON], and [PERSON] (Nippon Steel Technology Co. Ltd.) for useful discussions and support on the manufacturing process of CFs. The technical advice and support received from [PERSON] at BL-13A (KEK) is also gratefully acknowledged. The experiment was conducted with the approval of the Photon Factory Program Advisory Committee (proposal nos. 2015C206 and 2019C202).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Cross-Layer and Energy-Aware AODV Routing Protocol for Flying Ad-Hoc Networks
## 2 FANET Topology  
In the area of UAV, the primary topologies are the star, multi-star, and mesh. Other UAV topologies are mobility, energy, Inter-UAV distance, noise, link quality, and path availability. To transfer the message from the UAVs to the Ground Station (GS), as shown in Figure 1. Many mechanisms and practices are introduced to create the finest design for UAV communication. Many inventions are done for the betterment of FANET including routing optimization technology. FANET instantiates links in the form of direct, indirect, group, and multiple groups. The link form or model is often employed based on the FANET applications [8; 13; 14; 15]. The architecture of the FANET network in Figure 1 shows various types of connections. The FANET cluster head (CH) has a direct connection between the UAV cluster members and indirect connections with the other clusters (VANET and MANET). The overall network has multiple group connections.  
Figure 1: The general architecture of FANETs.  
However, due to its higher mobility, FANET topology changes more frequently than MANET and VANET topology. When a UAV route fails, the FANET in which it was concerned also fails, resulting in a FANET topology update. Links outages are the most disturbance factor affecting the FANET performance. Due to the changes in the UAV schedules and fluctuations in FANET node position, link quality rapidly deteriorates, resulting in link failures and topology updates [1; 2; 3; 4; 5]. These issues require routing protocols with sophisticated characteristics to improve FANET systems.
## 3 Methodology
### Validation Framework
#### 3.5.2 Height Classification Framework  
The validation methodology implements a systematic classification approach using five height categories  
\[Height\;Categories=\left\{\begin{array}{l}Category\;1:\;\;\;\;\;\;\;\;\;\;\;\;\; \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;  
It is important to note the temporal difference between the end of the PSInSAR observation period (November 2020) and the drone survey (March 2024). While VUG is a dynamic process, this validation primarily aims to assess the accuracy of the PSInSAR-derived height estimates for structures that were largely completed by late 2020 and were presumed stable in height during the intervening ~3.5 years. To confirm this stability, we visually inspected historical Google Earth imagery (available for the 2018-2023 period) for the specific validation area, verifying that the vast majority of buildings measured by the drone in 2024 were already present and appeared structurally complete by the end of our PSInSAR observation window. Therefore, the drone data provide a high-accuracy ground truth snapshot, against which the final state captured by the PSInSAR analysis (primarily the 2020 results, or specific years like 2018 for comparison with GHSL) can be evaluated. This allows for the assessment of systematic biases (e.g., under/overestimation) and overall accuracy for completed buildings. Potential minor height changes or significant construction/demolition occurring solely between late 2020 and early 2024 for a small subset of surveyed buildings would represent a limitation of this specific temporal comparison.
## 3 Results
### Training Models Using Physico-Chemical Features
Moreover, more advanced approaches that integrate biological knowledge into the machine learning algorithm for performing feature selection or for selecting groups of features are used in different recent tools. Such an approach was adopted by different tools such as SVM RCE, SVM-RCE-R [87; 88; 89], maTE [90], CogNet [91], miRcorrNet [92], miRModuleNet [93], and Integrating Gene Ontology-Based Grouping and Ranking [94]. Recently, these tools and their competitors were reviewed in [95]. In this study, for each tested machine learning algorithm, we have recorded the scores assigned to each feature during the MCCV (100 iteration) procedure. Since we obtain higher performance metrics using RF classifier, we have utilized the feature scores of this model throughout the rest of the paper. When we analyze the feature scores (shown in Figures 4 and 5), we observe that Net Charge, Isoelectric Point, Disordered Conformation Propensity, Normalized Hydrophobicity, and Normalized Hydrophobic Moment are more crucial features than others for both Gram-negative and Gram-positive datasets. Figure 4: Feature ranking according to their importances in classification using RF model in Gram-negative dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **Specificity** & **Precision** & \begin{tabular}{c} **Area Under** \\ **Curve** \\ \end{tabular} & \
\begin{tabular}{c} **F1** \\ \end{tabular} &
\begin{tabular}{c} **Balanced** \\ **Acc.** \\ \end{tabular} \\ \hline AdaBoost & 0.84 \(\pm\) 0.06 & 0.85 \(\pm\) 0.08 & 0.83 \(\pm\) 0.14 & 0.83 \(\pm\) 0.10 & 0.86 \(\pm\) 0.06 & 0.83 \(\pm\) 0.05 & 0.84 \(\pm\) 0.11 \\ Decision Tree & 0.77 \(\pm\) 0.07 & 0.77 \(\pm\) 0.10 & 0.77 \(\pm\) 0.16 & 0.769 \(\pm\) 0.09 & 0.77 \(\pm\) 0.06 & 0.76 \(\pm\) 0.05 & 0.77 \(\pm\) 0.13 \\ LogitBoost & 0.83 \(\pm\) 0.06 & 0.84 \(\pm\) 0.09 & 0.82 \(\pm\) 0.15 & 0.83 \(\pm\) 0.10 & 0.87 \(\pm\) 0.05 & 0.83 \(\pm\) 0.05 & 0.83 \(\pm\) 0.12 \\ RF & **0.87 \(\pm\) 0.04** & **0.87 \(\pm\) 0.07** & **0.87 \(\pm\) 0.08** & **0.87 \(\pm\) 0.07** & **0.90 \(\pm\) 0.04** & **0.87 \(\pm\) 0.04** & **0.87 \(\pm\) 0.07** \\ SVM & 0.77 \(\pm\) 0.07 & 0.85 \(\pm\) 0.11 & 0.71 \(\pm\) 0.19 & 0.75 \(\pm\) 0.12 & 0.81 \(\pm\) 0.06 & 0.78 \(\pm\) 0.05 & 0.78 \(\pm\) 0.15 \\ SVM + kNN & 0.76 \(\pm\) 0.08 & 0.81 \(\pm\) 0.11 & 0.72 \(\pm\) 0.21 & 0.76 \(\pm\) 0.13 & 0.80 \(\pm\) 0.07 & 0.77 \(\pm\) 0.05 & 0.76 \(\pm\) 0.16 \\ LogitBoost + kNN & 0.77 \(\pm\) 0.07 & 0.85 \(\pm\) 0.11 & 0.71 \(\pm\) 0.19 & 0.75 \(\pm\) 0.12 & 0.81 \(\pm\) 0.06 & 0.78 \(\pm\) 0.05 & 0.78 \(\pm\) 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different models according to different performance metrics for Gram-positive dataset, using physico-chemical features.
## 3 Polyphenols as Potential Agents in TMD
### Molecular Mechanisms of Polyphenols Targeting Inflammation
#### 3.1.3 Epigallocatechin3-Gallate  
EGCG is a catechin, belonging to the polyphenol family and is the most abundant catechin in green tea [86]. The health-promoting activity of EGCG has been widely described, including the protective role in cardiovascular and metabolic diseases, and anti-cancer effect [86, 87]. Accumulating evidence has indicated EGCG as an anti-inflammatory agent in joints for the prevention of OA inflammation, by reducing synovial hyperplasia, cartilage degradation, and bone resorption [88]. The anti-inflammatory effect of EGCG has been widely demonstrated by _in vitro_ and _in vivo_ data, indicating that EGCG can regulate the expression of cytokines, chemokines, MMPs, and ROS production, NO, COX-2, and PGE2 promoting the reduced expression of pain mediators [89]. For example, it has been demonstrated that EGCG (50 mg/kg) significantly reduces the level of proinflammatory cytokines such as TNF-\(\upalpha\) and IL-17, reducing the perisynoish inflammation involved in cartilage-bone destruction in an experimental rat model of arthritis [88, 90]. The role of EGCG in the suppression of inflammatory processes is mediated by the interaction with MAPK, AP-1, and JNK activation, whose inhibition leads to a reduced pro-inflammatory cytokine-induced stimulus in chondrocytes [91]. The protective effect of human chondrocytes is also indicated by the decreased production of the TNF-\(\upalpha\), MMP-1, and MMP-13 by EGCG, mediated by the inhibition of NF-\(\upkappa\)B activation, promoting the repair of degraded cartilage matrix [92].  
Analyzing the mechanisms underlying the anti-inflammatory role of EGCG, it has been shown that it (100 \(\upmu\)M) inhibited the production of NO induced by IL-1\(\upbeta\) by interfering with the activation of NF-\(\upkappa\)B in human chondrocytes [93]. Moreover, [PERSON] et al. have reported the ability of EGCG (50 \(\upmu\)M) to inhibit the expression of inflammatory mediators, namely COX-2, PGE2, and IL-8, whose production was upregulated by IL-1\(\upbeta\) in human synovial fibroblasts [94].  
Another mechanism by which EGCG could suppress the inflammatory response is the modulation of microRNAs (miRNAs) expression [95], whose alteration is involved in cartilage pathophysiology and OA progression [95, 96]. In support of this view, it has been reported that EGCG upregulates the expression of microRNA hsa-miR-199a-3p in IL-1\(\beta\)-stimulated human OA chondrocytes through the inhibition of COX-2 mRNA/protein expression and PGE2 production [97]. A similar study has also demonstrated the ability of EGCG to upregulate the expression of hsa-miRNA-140-3p in human OA chondrocytes, which is a regulator of aggrecarse-2 (or ADAMTS5) expression, involved in the degradation of the cartilage matrix [95]. Finally, EGCG is also able to increase the redox-regulated transcription factor Nrf2, suppressing the inflammatory mediator production [88].