esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Raw Materials, Experimental Methods and Theory Background
### Theory Background of Damage Model and Prediction Model
#### 2.4.2 Multi-Variable Grey Model Represented by GM (1,N)  
The grey system is a widely known mathematical theory in various fields such as economics, engineering, etc., which can be used for cases with partially known information or lacking adequate experimental data [49; 50; 51]. In general, grey system theory is mainly utilized for two aspects, i.e., grey relational degree analysis (GRA) and grey prediction models (GM). GRA is usually applied for measuring the uncertain relationships between factors. Grey prediction models reveal long-term processes for the development of various factors, and it could be used a priori to predict a property's development with less priori data. In this study, a multi-variable grey model, GM (1,N), was adopted and established to predict the freeze-thaw damage of an asphalt mixture.  
Considering that the grey model has \(N\) variables, which is denoted by \(y_{i}\) (\(i=1,2,\ldots,N\)) with \(m\) initial sequences:  
\[y_{i}^{(0)}=\Big{\{}y_{i}^{(0)}(1),y_{i}^{(0)}(2),\ldots,y_{i}^{(0)}(m), \Big{\}},(i=1,2,\ldots,N), \tag{13}\]  
through 1-accumulated generating operation (1-AGO), the 1-AGO sequence of initial sequence can be obtained  
\[y_{i}^{(1)}=\Big{\{}y_{i}^{(1)}(1),y_{i}^{(1)}(2),\ldots,y_{i}^{(1)}(m), \Big{\}},(i=1,2,\ldots,N) \tag{14}\]  
\[y_{i}^{(1)}(t)=\sum_{j=1}^{t}y_{i}^{(0)}(j),(j=1,2,\ldots,m), \tag{15}\]  
the whitening differential equation of the grey model could be determined by  
\[\frac{dy_{1}^{(1)}(k)}{dt}+ay_{1}^{(1)}(k)=\sum_{i=2}^{n}b_{i}y_{i}^{(1)}(k), \tag{16}\]  
then the grey differential equation is written as follows:  
\[y_{1}^{(0)}(k)+az_{1}^{(1)}(k)=\sum_{i=2}^{n}b_{i}y_{i}^{(1)}(k), \tag{17}\]  
\[z_{1}^{(1)}(k)=\frac{y_{1}^{(1)}(k)+y_{1}^{(1)}(k-1)}{2}. \tag{18}\]  
In GM (1,N), the grey parameter \(P_{N}\) can be obtained according to the least square method:  
\[P_{N}=(B^{\mathrm{T}}B)^{-1}B^{\mathrm{T}}Y_{n}, \tag{19}\]  
\[B=\left[\begin{array}{cccc}-z_{1}^{1}(2)&y_{2}^{(1)}(2)&\cdots&y_{n}^{(1)}( 2)\\ -z_{1}^{1}(3)&y_{2}^{(1)}(3)&\cdots&y_{n}^{(1)}(3)\\ \vdots&\vdots&&\vdots\\ -z_{1}^{1}(m)&y_{2}^{(1)}(m)&\cdots&y_{n}^{(1)}(m)\\ \end{array}\right], \tag{20}\]  
\[y_{n}=\left[\begin{array}{cccc}y_{1}^{(0)}(2)&y_{1}^{(0)}(3)&\cdots&y_{1}^{( 0)}(m)\\ \end{array}\right]^{-1}, \tag{21}\]substituting \(P_{N}\) in the Equation (19) into the Equation (16) gives  
\[\hat{y}_{1}^{(1)}(k+1)=\left(y_{1}^{(0)}(1)-\sum_{i=2}^{n-1}\frac{b_{i}y_{i}^{(1 )}(k+1)}{a}\right)e^{-at}+\sum_{i=2}^{n-1}\frac{b_{i}y_{i}^{(1)}(k+1)}{a}, \tag{22}\]  
therefore, the \((k+1)\)-th predictive value can be obtained by an inverse accumulated generating operation.  
\[\hat{y}_{1}^{(0)}(k+1)=\hat{y}_{1}^{(1)}(k+1)-\hat{y}_{1}^{(1)}(k), \tag{23}\]
# Smooth 3D Dubins Curves Based Mobile Data Gathering in Sparse Underwater Sensor Networks
## 3 Smooth 3D Dubins Curves Planning-Based Mobile Data Gathering Algorithm  
It has been mentioned above that mobile AUVs have the ability of passing through all predefined underwater sensors along a smooth trajectory to collect sensory data in the desired mission region. In this section, we detail our Bezier interpolation based 3D Dubins curves construction method for mobile data collection in USNs.
# Model-Guided Manufacturing of Transducer Arrays Based on Single-Fibre Piezocomposites
## 3 Results
### Assembly Analysis  
Table 1 summarises the four EMI properties introduced in Section 2.5 at the four assembly stages. The column next to the measured average and standard deviation shows the modelled responses. The initial state represents data from blank PZT fibre discs. The next rows correspond to data after the PCB and the matching layers are attached. The final dataset was measured after applying the backing material and the housing.  
Figure 11: Measured and modelled EMI after attaching: the PCB (**a**); and the matching layers (**b**). For modelling the PCB attachment, \(C_{\text{par}}\) was increased. The matching layers are added to the model by setting the measured thickness of the respective transmission lines.  
Figure 12: Final transducer EMI with at first only increased backing impedance \(Z_{\text{B}}\) (**a**). The magnitude offset is compensated by increasing \(C_{\text{par}}\). The induced additional resonance can be suppressed by increasing the attenuation coefficient \(\alpha_{2}\) (**b**).  
Comparing the properties before and after the assembly, the series resonance \(f_{\rm s}\) slightly rises. \(C_{\rm p}\) more than doubles with a significant rise in standard deviation. \(Z_{\rm min}\) falls by 17%, whereby the highest deviation was measured after attaching the matching layer. The most significant change is the phase angle \(\varphi_{\rm t}\). It drops by more than 60\({}^{\circ}\) and shows less standard deviation after the assembly, compared to the initial state.  
Stepwise, attaching the PCB to the PZT fibre discs does not affect the resonating behaviour, as \(f_{\rm s}\) remains constant. It only adds a parallel capacitance of in average 2.07 pF. The increase in standard deviation of \(C_{\rm p}\) is caused by the routing of the PCB. Adding the matching layer in the next step induces only small variations of the measured parameters, but shifts \(f_{\rm s}\) already to the final value. Casting the backing finally causes an additional rise in \(C_{\rm p}\) and a strong drop of \(\varphi_{\rm t}\).
## 3 Performance of IWP with Steel Anchor Box
### FE Analysis Results and Performance Comparison
#### 3.1.4 Stress State at the Steel-Concrete Interface
From the above comparisons, it can be observed that the connectors of the internal and exposed steel anchor boxes experience a relatively uneven force distribution across the columns. In contrast, the connectors of the IWP detail mainly experience vertical shear, and  
Figure 23: Distribution of stud force on the side plate of exposed type anchorage. Figure 24: Distribution of perfobond connector force on the end plate of IWP-type anchorage. the force distribution across the columns is more uniform, with the maximum force not exceeding 1.14 times the average force. This indicates that the steel-concrete interface in the IWP detail transfers force in a more uniform and smooth manner. Table 6 shows the proportion involved in vertical force transfer at the steel-concrete interface for the three types of steel anchor boxes and the corresponding proportions they bear. For the internal type, the entire vertical force transferred to the pylon wall is borne by the end plate; for the exposed type, the entire vertical force is borne by the side plates; for the IWP type, the vertical force transferred to the pylon wall is approximately 70%, 25%, and 5% borne by the perforated side plates, end plate, and surface plate, respectively. Compared to the internal and exposed types, where a single component transfers the entire load, the IWP type transfers the vertical force through the combined effort of the side plates, end plate, and surface plate, offering higher reliability in force transfer.
# Effect of Pole Head Profile on Torque Performance of a Disc-Type Multipole Magnetorheological Brake
## 6 Conclusions  
This research paper reported the electromagnetic simulation outcomes on compact multipole dual disc magnetorheological brakes by varying pole head profiles. From the analysis, it can be concluded that the maximum shear stress of the fluid is in the controllable active state for the hexagonal pole head and benefits from the increased magnetic field strength compared to other pole head configurations. The magnetic flux flow for all the pole head profiles provides good correlation with the torque generated. The magnetorheological fluid effective area of a hexagonal shape form as a loop of magnetic flux from one pole to the adjacent pole and a higher rate of flux flow can travel in all directions of a pole. Torque enhancement was achieved at 28.5 mm radial dimensions that create a greater active area of the fluid. The fixed dispersive flux condition affects the resistance of the coil based on parameters such as greater axial width of the coil and a greater number of turns, resulting in equally distributed flux flow and higher braking performance at optimum coil saturation. Six different pole head geometries (pentagon, square, hexagonal, circle, ellipse and trapezoid) were analyzed, wherein the hexagon profile resulted in a high shear force value, and the square profile had the lowest output. An increment of 3.88% for braking torque performance was obtained using the hexagonal pole head profile and the braking torque was significantly enhanced.  
Conceptualization, Y.S.; Software, M.B.; Investigation, M.B. and Y.S.; Resources, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by Ministry of Science and Technology, Taiwan (project no. MOST 109-2221-E-027-048).  
Figure 11: Output braking torque of varied pole head geometries.
## 2 Existing Work  
### Autonomous Ship Navigation System  
Autonomous Ship Navigation System refers to an integrated framework of sensors, control algorithms, and navigation technologies enabling ships to operate and navigate safely and efficiently without human intervention. [PERSON] et al. (2018) investigated the design, modeling, and implementation challenges of a Guidance, Navigation, and Control (GNC) architecture for an autonomous ship navigation system in harbor conditions. They developed a mathematical model validated with field-test data and implemented a line-of-sight guidance system using LiDAR for obstacle avoidance, with their GNC architecture tested in both simulation and field scenarios. [PERSON] et al. (2018) developed algorithms for multiple target detection and tracking using sensor fusion for the autonomous navigation and collision avoidance system of the USV Aragon. By integrating radar, LiDAR, and cameras, and applying automatic ship-detection algorithms, they achieved persistent and reliable target tracking and designed collision avoidance maneuvers in compliance with the International Regulations for Preventing Collisions at Sea (COLREGs) ([PERSON] et al., 2018), with validation through field experiments. [PERSON] et al. (2018) conducted sea trials for an Autonomous Surface Vehicle (ASV) equipped with a Model Predictive Control (MPC)-based collision avoidance system in the North Sea, to verify compliance with the COLREGs. The trials demonstrated that the MPC approach effectively finds safe solutions in challenging scenarios, often meeting the expectations of experienced mariners, indicating the higher-than-expected technical maturity of autonomous vessels. [PERSON] et al. (2018) developed autonomous navigation capabilities for small cruise boats by converting a cruise boat into an ASV with various sensors and actuators. They designed and implemented navigation, object-detection, path-planning, and control algorithms, and they validated the system's performance through field experiments in a canal and surrounding waters.
# Dynamic Packet Duplication for Industrial URLLC
## 4 Tests
### KPIs Recollection  
The first part of the test consists in obtaining a dataset from URLLC devices--that is, a collection of KPIs in order to train the ML part. To do this, a simulation was performed, where an URLLC device moves from the entire scenario and recollects different KPIs. This movement along with the scenario setup is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Channel and propagation loss model & 3 GPP 38.901 \\ System bandwidth & 20 MHz \\ Center frequency & 3.7 GHz \\ Numerology & 2 \\ Scenario & InF-DH \\ Transmission direction & Downlink \\ Modulation & Adaptive \\ Scheduler & Round-Robin \\ UE height & 1.5 m \\ gNB height & 8 m \\ Transmission power & 23 dBm \\ Xn interface delay & 100 us \\ MAC to PHY delay & 2 slots \\ Transport block decode latency & 100 us \\ HARQ feedback delay & 1 slot \\ HARQ retransmission attempts & 1 \\ Packet size & 64 bytes \\ Packet interval & 10 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main configuration parameters.  
Upon a packet reception at UE, it knows the SINR received if the packet was not decoded successfully and the modulation index used in the transmission. These KPIs along with the latency measured are the inputs to train the ML model. Based on these samples, it is possible to train the model when there are sufficient samples.  
Once the ML model was trained, the latency predictor is used by the MN. That is, based on the actual conditions, such as SINR, modulation index and HARQ feedback of the previous packet, the estimator predicts the E2E latency. Since URLLC transmission intervals are very short, we assume that the channel conditions are the same between the current packet and the previous (since \(\Delta T>T_{coh}\), \(T_{coh}\approx\frac{\lambda/2}{p}\approx 20\) ms is the coherence time during which channel conditions are stable, where \(\lambda\) is the wavelength and \(v\) is the speed), which is why HARQ feedback from the previous packet was selected as an input.  
Based on the output of the estimator, the MN decides whether to duplicate or not the actual packet taking into account a latency threshold. In this case, the latency threshold was set at 2 ms.
## 4 MAC Protocol for Scheduling Concurrent Transmissions  
In this section, we describe an extension of the location-assisted MAC protocol presented in our prior work [15], for scheduling concurrent transmissions under log-normal shadowing channels. The key elements of the proposed protocol include identifying an exposed node, validating, and scheduling a concurrent transmission. For completeness, we present the MAC protocol briefly in this section and refer interested readers to [15] for more details. The core module of the extension, the validation of concurrent transmissions, is based on the analysis presented in the previous section. We also show how to estimate the channel parameters when they are not known _a priori_.  
Figure 2: The impact of channel parameters on the success probability \(P_{succ}\) with \(T_{SIR}=10\) and \(\beta=4\). (a) \(P_{succ}\) vs. \(r\) when \(d=20\) m. (b) \(P_{succ}\) vs. \(d\) when \(r=35.6\) m.
## Probabilistic Upscaling of Material Failure Using Random Field Models - A Preliminary Investigation  
**[PERSON] and [PERSON]** *  
Department of Civil, Environmental, and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07307 USA; E-mails: [EMAIL_ADDRESS]; [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed.  
_Received: 31 December 2008; in revised form: 2 March 2009 / Accepted: 22 April 2009 / Published: 30 April 2009_  
**Abstract:** Complexity of failure is reflected from sensitivity of strength to small defects and wide scatter of macroscopic behaviors. In engineering practices, spatial information of materials at fine scales can only be partially measurable. Random field (RF) models are important to address the uncertainty in spatial distribution. To transform a RF of micro-cracks into failure probability at full structural-scale crossing a number of length scales, the operator representing physics laws need be implemented in a multiscale framework, and to be realized in a stochastic setting. Multiscale stochastic modeling of materials is emerging as a new methodology at this research frontier, which provides a new multiscale thinking by upscaling fine-scale RFs. In this study, a preliminary framework of probabilistic upscaling is presented for bottom-up hierarchical modeling of failure propagation across micro-meso-macro scales. In the micro-to-meso process, the strength of stochastic representative volume element (SRVE) is probabilistically assessed by using a lattice model. A mixed Weibull-Gaussian distribution is proposed to characterize the statistical strength of SRVE, which can be used as input for the subsequent meso-to-macro upscaling process using smeared crack finite element analysis.  
**Keywords:** Random field, probabilistic upscaling, hierarchical multi-scale, stochastic representative volume element
# Incorporating Grey Total Influence into Tolerance Rough Sets for Classification Problems
###### Abstract  
Tolerance-rough-set-based classifiers (TRSCs) are known to operate effectively on real-valued attributes for classification problems. This involves creating a tolerance relation that is defined by a distance function to estimate proximity between any pair of patterns. To improve the classification performance of the TRSC, distance may not be an appropriate means of estimating similarity. As certain relations hold among the patterns, it is interesting to consider similarity from the perspective of these relations. Thus, this study uses grey relational analysis to identify direct influences by generating a total influence matrix to verify the interdependence among patterns. In particular, to maintain the balance between a direct and a total influence matrix, an aggregated influence matrix is proposed to form the basis for the proposed grey-total-influence-based tolerance rough set (GTI-TRS) for pattern classification. A real-valued genetic algorithm is designed to generate the grey tolerance class of a pattern to yield high classification accuracy. The results of experiments showed that the classification accuracy obtained by the proposed method was comparable to those obtained by other rough-set-based methods.  
classification problems; tolerance rough set; grey relational analysis; genetic algorithm +
Footnote †: journal: Applied Sciences
## 3 Results
### Spme-Gc-Ms Results
As is known, the autoxidation of unsaturated fatty acids causes the formation of hydroperoxides as primary products that decompose to several secondary products as mainly saturated and unsaturated aldehydes (e.g., pentanal, hexanal, (_E_)-2-heptenal, nonanal, and (_E_,_E_)-2,_A_-heptaional); the aldehydes may be then oxidized to organic acids (e.g., formic and propanoic acid) [48]. Hexanal and (_E_)-2-hexenal are very important molecules for the positive sensory attributes of fresh olive oils produced by the LOX pathway, but during storage these compounds, hexanal in particular, can also be formed by the cleavage of the same precursors (13-hydroperoxylinoleic acid 13-hydroperoxylinelonic) through autoxidation [49]. Table 2 shows the concentrations, during storage, of each marker reported in Figure 7. The aim was to investigate which markers can predict when an EVOO sample is at risk of being sensorily downgraded to VOO for rancid defect during storage. The richness of the \"freshness\" markers is clear, especially at time zero (T0), in samples that maintain their classification as EVOO throughout the 12-month storage period. Considering all samples and ANOVA results, a clear time trend was observed: (_Z_)-3-hexenyl acetate was detected exclusively in freshly produced samples (T0), while (_Z_)-3-hexenal showed a significant decrease over the storage period (Table 2). Additionally, 4,8-dimethyl-1,7-nonadiene was not detected in samples stored for 12 months (T12). Considering the seven markers associated with lipid oxidation, it is important to note the absence of pentanal, nonanal and (_E_)-2_A_-heptaional in T0 oils, and the significant increase in hexanal during storage. Furthermore, (_E_)-2-heptenal, propanoic acid, and formic acid were only present in samples that had been stored for one year. The only sample classified as VOO showed a  
Figure 6: Loading plot obtained from a PCA developed using SPME-GC–MS data. Variables highlighted in red are important to the clustering of stored samples, while those in green are relevant to fresh samples. characteristic evolution of volatile markers over time. This sample was distinguished by the disappearance of two fruity markers, (\(Z\))-3-hexenyl acetate and 4,8-dimethyl-1,7-nonadiene (as for the others samples stored for one year), and by the highest content of (\(E\))-2-heptenal that reached a concentration (0.103 mg/kg) corresponding to 20 times its odor threshold (0.005 mg/kg). This compound is recognized as one of the main contributors to the rancid defect [28]. The downgrading can be attributed to a combination of factors, including the lower fruity intensity of this sample. The synergistic effect between the reduced fruity markers and the occurrence of rancid defect contributed to its classification as a VOO.
# Optimal Energy Integration and Off-Design Analysis of an Amine-Based Natural Gas Sweetening Unit
###### Abstract  
The present paper focuses on the efficiency enhancement of the energy-intensive natural gas (NG) sweetening process in the context of upstream natural gas production. A bi-level heat integration scheme is proposed including direct recycling of available high-temperature waste heat and harnessing the excess low-temperature waste heat in an optimized organic Rankine cycle (ORC) for power production. The energy performance of the whole model was studied under a range of possible reservoir conditions. A particle swarm optimization (PSO) algorithm was adopted to simultaneously optimize the parameters of the heat recovery network as well as the ORC cycle parameters. Finally, in order to account for the impact of perturbations of the heat source and sink, an off-design performance analysis was conducted using real-time data from an industrial plant. The proposed integration methodology was found to be effective across most of the reservoir conditions covered in this study. At optimal integration, a reduction of 40% up to 100% in heating requirements of the amine process was reported, as well as a net electricity production of 30% up to 190% of the electrical demand of the background process. The use of propane (R290) as a working fluid resulted in the highest energy output, whereas higher carbon number fluids allowed a better energy/working pressure trade-off. The off-design analysis allowed for the quantification of the impact of operational fluctuations of the background process on integration performance. Energy savings resulting from direct heat integration were found to range from 68% up to 103% of the expected design value, whereas the ORC net energy output respective to the use of R290, R600a, and R601a was found to range from 60% to 132%, 47% to 142%, and 52% to 135%.  
carbon capture; decarbonization of natural gas industry; process integration; particle swarm optimization; organic Rankine cycle