esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Preparation and Characterization of Ethanol-Water Extracts of Rose Oil Industry Byproducts
\begin{table}
\begin{tabular}{l c c c} \hline \multirow{2}{*}{**Compound**} & **30\% Extract,** & **70\% Extract,** & **90\% Extract,** \\  & \(\mu\)g/mL & \(\mu\)g/mL & \(\mu\)g/mL \\ \hline Neochlorogenic acid & 148.86 \(\pm\) 2.38 a  & 137.19 \(\pm\) 3.24 b  & 87.22 \(\pm\) 1.99 c  \\ Caffeic acid & 39.37 \(\pm\) 1.24 a  & 37.17 \(\pm\) 1.16 a  & nd *  \\ \(p\)-Cumaric acid & 132.03 \(\pm\) 3.57 a  & 131.78 \(\pm\) 2.68 a  & 28.42 \(\pm\) 2.89 b  \\ Ellagic acid & 92.03 \(\pm\) 3.15 b  & 130.84 \(\pm\) 3.08 a  & 23.98 \(\pm\) 2.84 c  \\ Quercetin & 168.32 \(\pm\) 2.67 b  & 196.54 \(\pm\) 3.04 a  & 129.91 \(\pm\) 2.54 c  \\ Quercetin-3-\(\beta\)-glucose & 721.25 \(\pm\) 3.68 b  & 888.04 \(\pm\) 3.84 a  & 629.64 \(\pm\) 3.79 c  \\ Myricetin & 281.99 \(\pm\) 2.18 b  & 333.63 \(\pm\) 2.68 a  & 151.77 \(\pm\) 1.95 c  \\ Kaempferrol & 16.51 \(\pm\) 1.06 b  & 23.59 \(\pm\) 1.17 a  & 24.05 \(\pm\) 1.35 a  \\ Naringin & 1149.76 \(\pm\) 2.96 a  & 1153.55 \(\pm\) 3.28 a  & 778.10 \(\pm\) 2.86 b  \\ Catechin & 562.78 \(\pm\) 2.79 b  & 968.78 \(\pm\) 3.05 a  & 542.94 \(\pm\) 2.69 c  \\ Epicatechin & 212.56 \(\pm\) 2.17 a  & 165.94 \(\pm\) 1.96 b  & nd \\ \hline \end{tabular} nd *—Not determined; a*: the experiments were performed in triplicate and given as mean \(\pm\) SD. Values in a row with different superscripts differ significantly (\(p<\) 0.05).  
\end{table} 
Table 2: Individual phenolic compounds in the ethanol-water extracts of rose oil industry byproducts. The higher alkanes present in the extracts, n-nonadecane (8.28 \(\pm\) 0.12% TIC) and n-tricosane (4.50 \(\pm\) 0.06% TIC), have a significant contribution as odor fixators leading to stabilization of the perception of other volatile compounds found in the byproducts.
###### Abstract  
With the progress of the eras and the development of science and technology, the requirements of device-to-device (D2D) connectivity increased rapidly. As one important service in future systems, ultra-reliable low-latency communication (URLLC) has attracted attention in many applications, especially in the Internet of Things (IoT), smart cities, and other scenarios due to its characteristics of ultra-low latency and ultra-high reliability. However, in order to achieve the requirement of ultra-low latency, energy consumption often increases significantly. The optimization of energy consumption and the latency of the system in the communication field are often in conflict with each other. In this paper, in order to optimize the energy consumption and the latency jointly under different scenarios, and since the detailed requirements for latency and reliability are diverse in different services, we propose an adaptive UE aggregation (AUA)-based transmission scheme that explores the diversity gain of multiple simultaneous paths to reduce the overall latency of data transmission, wherein multiple paths correspond to multiple coordination nodes. Furthermore, it could provide the feasibility of link adaptation by adjusting the path number according to the real transmission environment. Then, unnecessary energy waste could be avoided. To evaluate the performance, the energy-delay product (EDP) is proposed for the latency and energy comparison. The provided simulation results align with the numerical data. Through the analysis, it can be proven that the proposed scheme can achieve a joint optimization of latency and energy consumption to meet different types of URLLC services.  
adaptive UE aggregation; URLLC; IIoT, single-path; multi-path; multi-connectivity; energy-delay product Article
## 3 Results and Discussion
### Fungal Isolation and Identification
_Epicocum nigrum_ is an endophytic fungus whose role in the biodeterioration of cultural heritage is already known, with previous reports identifying it as a contaminant of paper-based documents and soapstone states [70; 71; 72; 73], with its biodeteriorative potential lying in its proteolytic, lipolytic and cellulolytic activities, as well as its mineralization capabilities in calcium carbonate and weddellite [51; 73]. As was the case with the _Epicocum_ genus, both _Trichoderma_ and _Wallemia_ genera were represented by only one species each. _Trichoderma longibratiatum_ was isolated from both wooden and leather components of ANT.Moc.490, and has previously reported proteolytic and cellulolytic activities, including in a cultural heritage context [51; 74; 75; 76]. _Wallemia sebi_ was found in two of the five samples, ANT.Moc.344 and ANT.Moc.490 (Wood). It is a xerophilic and halophilic fungus common in indoor environments, and has also been detected in air samples from Portuguese archives and 18 th-century leather binding of manuscripts [77; 78]. However, no biodeterioration phenomena have been associated with this species. On the contrary, it has been proposed as a potential bioremediation tool in desalinating stone monuments [79]. All this considered, the presence of most of these fungi constitutes a serious threat to the MCUC collections. The majority of fungal species identified have one or more potential avenues for degrading and deteriorating not only the sampled artifacts, but all objects that constitute this museum's repository. In the context of the sampled artifacts, the presence of fungi capable of producing fibrinolytic, proteolytic and lignolytic enzymes, alongside organic acids, is of particular concern due to the materials that make up these objects. Furthermore, some of the isolates can also pose a health concern for both museum staff and visitors. Finally, this study constitutes, to the best of our knowledge, the first record of both _Penicillium phoenicium_ and _Talromyces coalescens_ being isolated from museum artifacts, with further studies evaluating their biodeteriorative potential being essential for a better understanding of their impact on cultural heritage.
# An Automatic Partition Time-Varying Markov Model for Reliability Evaluation
## 2 Materials and Methods
### The Automatic Partition Time-Varying Markov Model for Reliability Evaluation  
The reliability analysis method based on the automatic partition time-varying Markov process mainly includes five steps: degradation feature extraction, state number decision, training data preparation, time-varying Markov process modeling, and reliability assessment. The automatic partition method is used to automatically divide the component degradation sample data into multiple stages, to avoid the artificial and time-consuming selection of the number of Markov process states. In the data processing procedure, in order to deal with the different degradation processes of bearings, the slope-based dynamic time warping method is used to effectively regularize the collected samples, so as to get more accurate bearing Markov model. On the basic Markov process, a time-varying transition probability matrix is proposed to describe the changing trend of the device's own characteristics with time, which provides a necessary means for more accurate assessment of device state reliability and life prediction. The functions for reliability and remaining useful life are provided at the last step.  
The basic steps are provided as follows and shown in Figure 1.  
(1) Extract feature on the full-life-running vibration data.  
(2) The automatic partition method is used to decide the state number of Markov model, corresponding to the degradation state of device.  
(3) Prepare training data. The slope-based dynamic time warping method is used for sample time warping to complete the sample data processing in this step.  
(4) Establish the time-varying Markov model. The model parameters are re-estimated using the conditional probability formula for calculating the initial forward probability, backward probability, and state sequence.  
(5) Reliability and remaining useful life calculation and prediction. Determine the current state and obtain the sojourn time in the current state, \(i\). Then, calculate it in the remaining states \(i+1,i+2,\ldots,N\). Calculate the reliability and remaining useful life of the equipment with current state, \(i\), and sojourn time, \(st_{t}^{i}\).
# Alignment Method of an Axis Based on Camera Calibration in a Rotating Optical Measurement System
###### Abstract  
The alignment problem of a rotating optical measurement system composed of a charge-coupled device (CCD) camera and a turntable is discussed. The motion trajectory model of the optical center (or projection center in the computer vision) of a camera rotating with the rotating device is established. A method based on camera calibration with a two-dimensional target is proposed to calculate the positions of the optical center when the camera is rotated by the turntable. An auxiliary coordinate system is introduced to adjust the external parameter matrix of the camera to map the optical centers on a special fictitious plane. The center of the turntable and the distance between the optical center and the rotation center can be accurately calculated by the least square planar circle fitting method. Lastly, the coordinates of the rotation center and the optical centers are used to provide guidance for the installation of a camera in a rotation measurement system. Simulations and experiments verify the feasibility of the proposed method.  
rotation measurement system; camera calibration; camera and turntable alignment Article
## 4 Simulations and Experimental Results
### Case 1: Collision Avoidance Experiment of Four Ships under Normal Navigation  
In the first scenario, all ships are assumed to adopt a cooperative approach and try to complete their ship CA decisions under the guidance of COLREGs. The time cross-section of the whole CA process is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ship** & **Ship 1** & **Ship 2** & **Ship 3** & **Ship 4** \\ \hline Initial Position (nm) & [0, \(-\)5.00] & [3.83, 3.21] & [3.85, \(-\)2.22] & [\(-\)2.55, 2.55] \\ Initial Speed (kn) & 18 & 18 & 16 & 13 \\ Initial Course (deg) & 0 & 230 & 300 & 135 \\ Decision Cycle Set (s) & 3 & 4 & 5 & 5 \\ Detection Range Set (nm) & 5 & 5 & 5 & 5 \\ Shortest Initial DCPA (m) & 0 & 0 & 0 & 0 \\ Shortest Initial TCPA (s) & 1250 & 1250 & 1250 & 1250 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial multi-ship encounter situation.  
Figure 9: Multi-ship encounter situation setting.  
It can be seen from Figure 10 that in the subfigure t = 500 s, all ships have completed the communication of the ship CA's intention and formed the behavioral trend of ship 1 turning left, ship 2 keeping going, and ship 3 and ship 4 turning right to different degrees. Here, ship 1 is a stand-on ship relative to ships 3 and 4. According to COLREGs, ship 3 and ship 4 should turn right and pass by the stern of ship 1. While ship 1 turns left, it will contribute more to form the scenario that ship 3 and ship 4 pass by its stern. In subfigure t = 1000 s, all four ships continue their sailing behavior from the previous stage. However, due to the specificity of the relative positions of ship 1 and ship 4, ship 1 passes from the stern of ship 4. This behavior is in conflict with the spirit of COLREGs but is obviously more suitable for the current scenario. In the subfigure t = 1500 s, all ships have completed  
Figure 10: Collision avoidance trajectory of four ships in case 1.  
ship CA and started to gradually resume the original heading of their own ships. The time of broadcasting is also illustrated in Figure 10 (subfigures t = 100 s and t = 500 s).  
In the end, all ships successfully completed ship CA, especially ship 2, after communicating with other ships, without taking any measures to keep the original navigation under the double decision pressure of heading on and crossing. In this case, the four ships achieve a behavior similar to a \"centralized\" CA decision through CA communication, which can achieve better than traditional distributed CA decisions in terms of economy and safety.
## 4 Application Example
### Comparison of Calculation Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Section Position**} & \multirow{2}{*}{
\begin{tabular}{c} **Finite Element** \\ **Results (m)** \\ \end{tabular} } & \multicolumn{2}{c}{**Without Considering the Stiffness of the**} & \multicolumn{2}{c}{**Considering Stiffness of Main Tower**} \\ \cline{3-6}  & & **Computed Value (m)** & **Error** & **Computed Value (m)** & **Error** \\ \hline Mid-span section of & \multirow{2}{*}{0.038} & \multirow{2}{*}{0.057} & \multirow{2}{*}{50.00\%} & \multirow{2}{*}{0.035} & \multirow{2}{*}{\(-\)7.89\%} \\ the left span & & & & & \\ Mid-span section of & \multirow{2}{*}{\(-\)0.194} & \multirow{2}{*}{\(-\)0.361} & \multirow{2}{*}{86.08\%} & \multirow{2}{*}{\(-\)0.189} & \multirow{2}{*}{\(-\)2.58\%} \\ the left main span & & & & & \\ Mid-span section of & \multirow{2}{*}{0.065} & \multirow{2}{*}{0.104} & \multirow{2}{*}{60.00\%} & \multirow{2}{*}{0.070} & \multirow{2}{*}{7.69\%} \\ Mid-span section of & & & & & \\ \multicolumn{1}{c}{} & \multirow{2}{*}{0.007} & \multirow{2}{*}{0.019} & \multirow{2}{*}{\(-\)23.59\%} & \multirow{2}{*}{0.006} & \multirow{2}{*}{\(-\)14.29\%} \\ the right span & & & & & \\ Mid-span of the right & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\ \multicolumn{1}{c}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\ main span & & & & & \\ \multicolumn{1}{c}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\ main span & & & & & \\ Support of right & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\ lower & & & & & \\ Mid-span of the right & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\ span & & & & & \\ span & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The deformation of control section of stiffening beam under live load in the middle section of left main span. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Section Position**} & \multirow{2}{*}{ \begin{tabular}{c} **Finite Element** \\ **Results (KN-m)** \\ \end{tabular}
###### Abstract  
Time-frequency analysis is usually used to reveal the appearance of different frequency components varying with time, in signals, of which time-frequency spectrogram is an important visual tool to display the information. The Mesh Surface Generation (MSG) algorithm is widely used in three-dimensional (3D) modeling. Removing hidden lines from the mesh plot is an essential process that produces explicit depth information. In this paper, a fast and effective method has been proposed for a time-frequency Spectrogram Mesh Surface Generation (SMSG) display, especially, based on the painter's algorithm. In addition, most portable fault diagnosis devices have little function to generate a 3D spectrogram, which generally needs a general computer to realize the complex time-frequency analysis algorithms and a 3D display. However, general computer is not portable and then not suitable for field test. Hence, the proposed SMSG algorithm is applied to an embedded fault diagnosis device, which is light, low-cost, and real-time. The experimental results show that this approach can realize a high degree of accuracy and save considerable time.  
embedded device; time-frequency spectrogram; mesh surface; hidden surface removal; painter's algorithm +
Footnote †: journal: Applied Sciences
## 5 Elastic Scaling  
In elastic scaling, the provisioned resources (\(PR\)) must be in the range between the Upper Bound (\(UB\)) and Lower Bound (\(LB\)), as Figure 4 depicts. In Google data centers, scaling is measured by the number of transitions between the Bayesian states in Figure 9. The number of Edges \(E\) in the Bayesian graph is 17. Six of these edges transition to the fail state, which are considered in the case of emergency operation. The remaining 11 transitions are for scaling operations. The metric of optimal scaling is the minimum number of transitions that gives the right provisioning resources \(PR\) in the fit-provisioning range \(FP=UB\geq PR>LB\). The scaling action in Figure 7 is found by updating the configuration of the machine list to match the workload demands as closely as possible. Google and Alibaba trace machine tables are used to find the number of participating machines (\(MCount\)) in scaling action and the values of scaling that will be near the demand values, making the scale factor (\(SF=\frac{PR}{RD}\)) closest to 1. It is found that for Google, the number of participating machines gradually increases with respect to time. On the other hand, for Alibaba, the machines list initially starts with a high volume of machines, and later the number of machines participating decreases. A machine may go to a multiple transitions operation (\(TO=\sum E\)). The increase in provisioning resources in transition states \(A\), \(B\), \(C\), \(D\) and \(E\) may return back to their own state and make the configuration more costly. The best transition happens in one direction only.  
Scaling time (\(ST\)) is the time to find the best machine list (\(MListTime\)) with the least service operation time (\(OP\)) (time to configure machines to handle the job). In both traces, these values are not released, but we assume the operation time is fixed for all types of tasks or instances, and it can be configured in parallel. This means the scaling time can be found by adding the time slot of finding the machine's list of scaling and the service operation time (i.e., \(ST=MListTime+OP\)), assuming all machines are configured to serve in parallel. Based on the aforementioned characteristics for elastic scaling, we propose six metrics to evaluate the scaling: (1) scaling upper boundary violation \(UB\), (2) scaling lower boundary violation \(LB\), (3) scaling time \(ST\), (4) number of transition operations participating in scaling \(TO\), (5) number of machines participating \(MCount\) and (6) scaling factor \(SF\).
## 3 Pull-Out Test Results
### Load-Slip Characteristic of Pull-Out Specimens  
Given the different embedded depths and the existence (or lack) of perforated rebars, the pull-out specimens with different constructional details showed diverse mechanical characteristics in the form of load-slip curves. For PA specimens, as shown in Figure 4a, the load-slip curve consisted of the pseudo-elastic stage, the elastic-plastic stage, and the yield strengthening stage [47]. The three specimens had similar load-displacement relationships. Furthermore, the three specimens started to yield at approximately 750 kN, and the ultimate capacities of PA1, PA2, and PA3 were about 1150 kN, 1100 kN, and 1150 kN, respectively. Figure 4b provides the load-slip curves of the PB specimens. From this graph, we can see that the displacement was small (about 2 mm) at the end of loading. As shown in Figure 4c, the load-slip relationship in this figure can be compared with the relationship in Figure 4a,b, which shows that the slip increased almost linearly with the load for PC specimens. The steel plate was pulled out quickly when the load was above 540 kN. Consequently, the ultimate capacity for PC specimens was about 540 kN, and the corresponding slip was about 1.5 mm.  
Figure 4d and Table 2 compare the load-slip curves and major experimental characteristics of the pull-out specimens. There was a significant difference between the three series. Making a comparison of PA and PB specimens, we can see that the yield and ultimate load were increased by 10% and 21% on average, respectively, with the greater embedded depth. Further, the average slip corresponding to the ultimate load was increased from 2.05 mm to 10.41 mm (increased by 408%). Hence, a greater embedded depth enhanced the bearing capacity and deformability. Turning now to the experimental evidence in a comparison of PA and PC specimens, the average ultimate load was reduced from 1133.33 kN to 540 kN without the perforated rebars, and the slip was reduced by 86.4%. Therefore, these results indicate that the presence of perforated rebars had a great influence on bearing capacity and deformation.  
Figure 3: Failure modes of pull-out specimens. (**a**) the crack development of PA specimens; (**b**) the failure mode of the perforated rebars in PA specimens; (**c**) the crack development of PB specimens; (**d**) the failure mode of the perforated rebars in PB specimens; (**e**) the crack development of PB specimens; (**f**) the junction between the steel plate and top of the concrete in PC specimens.
# High-Performance QC-LDPC Code Co-Processing Approach and VLSI Architecture for Wi-Fi 6
###### Abstract  
The QC-LDPC code, with its excellent error correction performance and hardware friendliness, has been identified as one of the channel encoding schemes by Wi-Fi 6. Shorting, puncturing, or repeating operations are needed to ensure that user data can be sent with integer symbols and complete rate matching. Due to the uncertainty of the user data size, the modulation's selectivity, and the difference in the number of spatial streams, the receiver must deal with more than 10\({}^{6}\) situations. At the same time, other computationally intensive tasks occupy the time slot budget of the receiver. Typical are demodulation and decoding. Hence, the receiver needs to quickly reverse the demodulated data process. This paper first proposes a co-processing method and VLSI architecture compatible with all code lengths, code rates, and processing parameters. The co-processor separates field and block splicing, simplifying the control logic. There is no throughput rate bottleneck, and the maximum delay is less than 1 us.  
QC-LDPC; Wi-Fi 6; co-processing 2023
# A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks
## 5 Experiments and Results
### The Experimental Results
#### 5.2.2 Ablation Experiments
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CSSR (\%)** & **Autoencoder** & **CTT** & **Hybrid** \\ \hline
1.41 & 81.03 & 78.41 & **83.26** \\
1.69 & **84.74** & 81.69 & 84.39 \\
1.97 & **87.16** & 84.69 & 86.40 \\
2.25 & **92.63** & 87.49 & 89.92 \\
2.53 & 94.25 & 93.82 & **96.20** \\
2.81 & **98.03** & 96.62 & 97.05 \\
5.62 & **99.90** & **99.90** & 99.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The recall of solutions on various CSSR. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Hits@1 (\%)** & **Hits@10 (\%)** & **Hits@50 (\%)** & **MRR (\%)** & **Time (s)** \\ \hline DomainEA-f & 58.39 & 95.73 & 97.66 & 71.14 & 49.47 \\
**DomainEA** & **64.66** & **98.07** & **98.91** & **79.02** & **6.43** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Entity alignment results with a change of filtering module. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\#Layers** & **Hits@1 (\%)** & **Hits@10 (\%)** & **Hits@50 (\%)** & **MRR (\%)** \\ \hline
1 & 53.88 & 93.98 & 97.74 & 71.91 \\
2 & 64.32 & 97.24 & 98.16 & 77.72 \\
3 & **64.66** & **98.07** & 98.91 & **79.02** \\
4 & 64.41 & 97.91 & **98.93** & 78.86 \\
5 & 62.32 & 97.15 & 98.33 & 76.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Entity alignment results with various numbers of hidden layers of the SA. other performance metrics. However, for the metric Hits@n, for the same score, the smaller the value of n was the more superior the method. Moreover, when the number of layers of the SA exceeded three, the performance of DomainEA declined. Overall, these findings suggest that increasing the SA layer can capture the higher-order entity features, but the model will incur an overfitting problem as the number of SA layers increases. It is important to select the appropriate number of hidden layers of the SA for the specific application.