esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results
### Trajectory Tracking in the Presence of Disturbance
#### 5.3.2 Trajectory Tracking via Attitude Stabilization  
As in the previous subsection, Figures 17 and 18 illustrate the trajectory tracking of a circular trajectory, where the system is in a disturbed environment.  
Figure 16: Quadcopter tracking of a circular trajectory in 3D space (solid red line) performed using the RMHC and GAHC approaches in the presence of disturbances.  
Figure 15: Quadcopter position trajectory tracking performed using the RMHC and GAHC approaches in the presence of disturbances.  
These results show that the TTRM-PID and TTGA-PID controllers give the best responses, when compared to TTRM-PD and TTGA-PD in the presence of disturbances. In conclusion, the use of PD controllers is more suitable for this strategy of controller only in the absence of disturbances.
## 8 Discussions  
In this study, a new type of IM for MS-AS sequences, i.e., \(IM_{2}\)\({}^{\mathrm{MS-AS}}\) = \(IM^{\mathrm{AS}}\)/\(IM^{\mathrm{MS}}\), is verified to be an desirable IM to predict MS-AS damage potential due to its high correlation with \(DI_{2}\)\({}^{\mathrm{MS-AS}}\) = \(DI^{\mathrm{AS/MS}}\)/\(DI^{\mathrm{MS}}\). Based on this observation, reliable log-linear relationships can be established between \(IM_{2}\)\({}^{\mathrm{MS-AS}}\) and \(DI_{2}\)\({}^{\mathrm{MS-AS}}\) to predict the structural damage due to MS-AS sequences. Take the cases of \(T\) = 0.4 s and \(T\) = 3.0 s as examples; both periods are selected according to Equation (2) in the ranges of \(T\) \(\leq T_{c}\) and \(T\) \(T_{c}\) 6.0 s, respectively. According to the results of Sections 5 and 6, \(I_{A,2}\)\({}^{\mathrm{MS-AS}}\) and \(v_{r_{8},2}\)\({}^{\mathrm{MS-AS}}\) are proven to be the optimal IMs, respectively. The linear regressions between \(I_{A,2}\)\({}^{\mathrm{MS-AS}}\) and \(DI_{2}\)\({}^{\mathrm{MS-AS}}\) and between \(v_{r_{8},2}\)\({}^{\mathrm{MS-AS}}\) and \(DI_{2}\)\({}^{\mathrm{MS-AS}}\) are also provided at log-log axis in Figure 17.  
It is clear that clear linear relationships have been developed in these two figures. Based on the developed log-linear relationships, once the IMs of the mainshock and the following aftershock are determined, the damage ratio \(DI_{1}\)\({}^{\mathrm{MS-AS}}\) can be predicted directly. If the initial damage of structures due to mainshock (i.e., \(DI^{\mathrm{MS}}\)) has been estimated, the aftershock-induced damage increment (i.e., \(DI^{\mathrm{AS/MS}}\)) and the accumulative damages (i.e., \(DI_{1}\)\({}^{\mathrm{MS-AS}}\)) due to the MS-AS sequence can be further calculated. Besides, a reliable log-linear relationship between \(IM_{2}\)\({}^{\mathrm{MS-AS}}\) and \(DI_{2}\)\({}^{\mathrm{MS-AS}}\) is also useful for fragility assessment under MS-AS sequences since it commonly assumes a log-linear relationship between earthquake IMs and engineering demand parameters (EDPs). To sum up, there are three folds of applications of the developed log-linear relationship between \(IM_{2}\)\({}^{\mathrm{MS-AS}}\) and \(DI_{2}\)\({}^{\mathrm{MS-AS}}\), as: (1) to predict  
Figure 16: Regressed step-wise equation of standard deviation of \(\rho\) values, \(\sigma_{\rho}\).  
Figure 15: Regressed step-wise equation of median \(\rho\) values, \(\rho_{50\%}\).  
\(DI_{2}\)MS-AS given \(IM_{2}\)MS-AS; (2) to predict \(DI_{1}\)MS-AS and \(DI\)IASIMS given \(IM_{2}\)MS-AS and \(DI\)IMS; and (3) to represent the relationship between IM and EDP for fragility assessment under MS-AS sequences.
# Biological Properties of Sandalwood Oil and Microbial Synthesis of Its Major Sesquietrepenoids
## 5 Metabolic Engineering Strategies to Increase the Yield of Santalene and Santalol in S. cerevisiae
### Engineering of FPP Branch Point  
Overexpressing key MVA pathway genes is expected to expand the pool of FPP and enhance the flow of FPP into other pathways, such as the synthesis of sterols from squalane as a precursor, which is formed through condensation of FPP molecules under squalane synthases, encoded by ERG9 genes (Figure 4) [96]. Due to yeast cells' elevated sterol requirements, ergosterol biosynthesis exhibits high activity levels, leading to the predominant consumption of most FPP [98; 99]. Consequently, manipulating the FPP branch point represents a crucial approach to guiding the flux toward \(\alpha\)-santalene production while reducing the excess FPP directed toward squalane generation. Several gene promoters have depressed the squalene synthase expression, redirecting FPP flux toward sesquiterpene synthesis. Substituting the strong promotor ERG9 with a weak one, such as MET3 and CTR3, has efficiently inhibited ergosterol biosynthesis [100; 101; 100]. The glucose-induced promoter, PHXT1, is predominantly activated at elevated glucose levels in _S. cerevisiae_. It has been observed to exhibit greater efficacy in suppressing the expression of ERG9 in yeast, thus leading to an increase in \(\alpha\)-santalene production [88; 89]. Also, recent research achieved a high titer of santalene and santalol by depressing ERG9 gene expression in _S. cerevisiae_[12]; it was also deduced that downregulating ERG9 enhanced target product production while decreasing by-product output, with PHXT1 being more effective than PERG1 [67]. The Farnesol biosynthesis pathway from the FPP branch involves two phosphatase genes, LPP1 and DPP1, which are lipid phosphate phosphatase codes [102]. Down-regulation of DPP1 led to a significant improvement in santalene synthesis and suppression of farnesol biosynthesis in _S. cerevisiae_[88; 89]. The high yield of other terpene products like patchoul and _trans_-nerolidol was also achieved through manipulating the FPP branch point [70; 103].  
Figure 4: Schematic diagram showing FPP branch point. To increase the FPP pool towards santalene synthesis, ERG9, the encoding squalene synthase, is downregulated by replacing its promotor with PHXT1, and the lipid phosphate phosphatases LPP1 and DPP1, enhancing the FOH formation, are deleted. FPP: farnesyl diphosphate; FOH: farnesol; SanSyn: santalene synthases.
## 3 Methods and Modeling
### Traffic Regression Models  
Next, the Weibo dataset that contains traffic incidents or adverse weather messages, as well as the three typical feature sets of time, location, and adverse weather were built. Specifically, we regard the heat value of each traffic incident as the forecasting label. To find the relevance between adverse weather and traffic incidents in social media, and make an estimation of city-level traffic incident heat for the same period in the same city, we vectored all the features and used a regression model to achieve that goal.  
The regression model determines a function and corresponding parameters that can accurately forecast future city-level traffic incident heat. To ensure ideal regression performance, we selected an appropriate model by comparing and evaluating the models of a Gradient-Boosting Regression Tree (GBRT) [52], Random Forest Regression (RFR) [53], Support Vector Regression (SVR) [54] and Linear Regression (LR) [55]. We implemented the above regression work by calling APIs from packages in Scikit-Learn [56; 57]. To benefit from the strength of deep learning technologies, we also selected Stacked Auto-Encoder (SAE) [14] and gcForest [58] as deep neural network and deep random forest to improve the precision of regression.  
Note that, facing the real-time city-level traffic incidents forecasting problem in this paper, we only employed social media data for traffic regression because the messages from social media are more timely than the messages from news media. Although the messages from both news media and social media can be involved in the regression model for better accuracy, the timeliness of forecasting model could be reduced. Moreover, additional research about the combination of messages from news media and social media still needs to be performed, such as processing with different text length, calculation of cross-platform incidents' heat, weight setting of multi-source messages, etc.
## 4 Experiments
### Image Preprocessing Experiment  
When using the CLAHE method for image preprocessing with a contrast limit threshold set at 2 and a grid size of 8, the preprocessed image is shown in Figure 8. Compared to the original image, the enhanced image has a significant improvement in contrast, with more apparent edge details of the rail spikes. Moreover, the histogram distribution of the enhanced image is more balanced, avoiding the problem of information loss due to concentrated grayscale values.  
In quantitative analysis, the intensity of illumination can be represented by the mean gray level of an image. Meanwhile, image information entropy is a key indicator for measuring the richness of elements in an image. For sample images with illumination changes, the greater the information entropy, the more gray levels there are in the image  
Figure 8: Image enhancement results: (**a**) the original image; (**b**) the histogram of the original image; (**c**) the enhanced image; (**d**) the histogram of the enhanced image.  
Figure 7: Experimental sample classification: (**a**) normal category; (**b**) water stain category; (**c**) rust category; (**d**) oil stain category.  
and the stronger the contrast of the image. The primary purpose of image preprocessing is to reduce the interference of illumination changes on subsequent positioning, so there is no need to distinguish the surface of the rail spikes. Therefore, the experiment directly uses all sample samples. Table 1 shows the information entropy of some sample images with different illumination intensities after enhancement, and Table 2 shows the mean information entropy of all sample images after enhancement.  
By analyzing Tables 1 and 2, it can be found that in sample images with different lighting intensities, the image entropy after enhancement is higher than that of the original images. This indicates that the enhanced images have a significant improvement in the richness of detail information, and the overall contrast has also been optimized. As a result, the images can maintain stable and accurate information expression under different lighting conditions, providing a higher-quality image data basis for subsequent rail spike positioning, thereby improving the accuracy of positioning.
## 3 CAN Communication  
Controller area network (CAN) is a serial communication bus primarily used for vehicle component communication defined by the ISO [9]. CAN is a multi-master bus that allows microcontrollers and devices to communicate with each other in applications without a host computer or central processing unit (CPU). It was originally developed by the German BOSCH Corporation in the early 1980s for the automotive industry to replace the complex wiring harness with a two-wire bus.  
The CAN bus is a broadcast-based protocol where every individual node listens to the bus and it is a job for the CAN physical layer to filter messages according to their identifiers [10]. Each CAN node contains a CAN transceiver and microcontroller with a CAN controller. The CAN transceiver receives the data stream and converts it to a form that the CAN controller can use. On the other hand, the CAN transceiver sends the data coming from the CAN controller after converting it to the CAN bus level. The CAN bus has two transmission lines: CAN-Low and CAN-High [11].  
The latest version of CAN, CAN 2.0, was announced in 1991 with two parts: CAN 2.0A and CAN 2.0B. The former has an 11-bit identifier while the latter uses a 29-bit identifier. CAN 2.0 has bitrates up to 1 Mbit/s with a message frame having data field up to eight bytes as shown in Figure 1[12].  
SGF-064-1 uses the CAN bus for communication. When an EV wants to get charged from an EVSE, it has to establish CAN communication first. With the CAN bus, the EV and EVSE can exchange their information for the charging process; for example, at the charging period, the EV keeps sending the state of charge (SOC) to let the EVSE know what the current percentage of charge is in the EV battery. The communication in SGF-064-1 is bidirectional, so that each side has its own identifiers. The EVSE sends messages with identifier = 0x631 for data and identifier = 0x630 for environment information, while the EV uses identifiers 0x639 and 0x638, respectively, for the same purpose. Like the CAN bus, the data field of the Korean standard includes one message and this message is divided into signals. The length of each signal varies according to its message [13].
## 3 Experiment Results and Analyses
### Average Strain Rate of Sample  
The variation in the mean strain rate of the prefabricated fractured rock samples with the impact pressure is illustrated in Figure 8.  
In Figure 8, the mean strain rate of prefabricated fractured rock specimens increases with increasing impact pressure and the growth rate of the average strain rate slows down. At 0.3 MPa impact pressure, the mean strain rate is 53.45 s\({}^{-1}\). When the shock pressure increases to 0.4 MPa, the average strain rate is 101.9 s\({}^{-1}\) and has increased by 90.6%.  
The prefabricated fractured sandstone samples exhibit a quadratic relationship between the mean strain rate and the change in impact pressure, with a high correlation. The fitting relationship is illustrated in Formula (1).  
\[\dot{\epsilon}=-194.08+1.06\times 10^{3}\;p-800p^{2}\quad(R^{2}=0.9946) \tag{1}\]  
where \(\dot{\epsilon}\) is the average strain rate of the prefabricated fractured rock samples; \(p\) is the impact pressure.
## 5 Simulation Studies  
To verify the performance of the proposed technique, numerical simulations were performed on a UVMS with a torpedo-type AUV and a 3-DOF underwater manipulator [15] shown in Figure 1. The AUV is driven by five thrusters in total and its thruster configuration is shown in Figure 5. The thruster configuration matrix \(\mathbf{B}_{v}\) and its pseudo inverse \(\mathbf{B}_{v}^{+}\) are shown in the Appendix A. The parameters for the AUV and manipulator are given in the Appendix A and Tables 3 and 4. From Table 4, it can be seen that the whole system is neutrally buoyant, while the manipulator's links have negative buoyancy. Thus, the UVMS used for numerical simulations in this paper can approximate to the real system.
# A Novel Self-Forming Virtual Sub-Nets Based Cross-Layer MAC Protocol for Multihop Tactical Network
###### Abstract  
A tactical network mainly consists of software-defined radios (SDRs) integrated with programmable and reconfigurable features that provide the addition and customization of different waveforms for different scenarios, e.g., situational awareness, video, or voice transmission. The network, which is mission-critical, congested, and delay-sensitive, operates in infrastructure-less terrains with self-forming and self-healing capabilities. It demands reliability and the need to survive by seamlessly maintaining continuous network connectivity during mobility and link failures. SDR platforms transfer large amounts of data that must be processed with low latency transmissions. The state-of-the-art solutions lack the capability to provide high data throughput and incorporate overhead in route discovery and resource distribution that is not appropriate for resource-constrained mission-critical networks. A cross-layer design exploits existing resources to react to environment changes efficiently, enable reliability, and escalate network throughput. A solution that integrates SDR benefits and cross-layer optimization can perform all the mentioned operations efficiently. In tactical networks, SDR's maximum usable bandwidth can be utilized by exploiting radios' autonomous behavior. This paper presents a novel virtual sub-nets based cross-layer medium access control (VSCL-MAC) protocol for self-forming multihop tactical radio networks. It is a MAC-centric design with cross-layer optimization that enables dynamic routing and autonomous time-slot scheduling in a multichannel network environment among SDRs. The cross-layer coupling uses link-layer information from the hybrid of time division multiple access and frequency division multiple access (TDMA/FDMA) MAC to proactively enable distributed intelligent routing at the network layer. The virtual sub-nets based distributed algorithm exploits spectrum resources and provides call setup with persistently available \(k\)-hop route information and simultaneous collision-free transmission of voice and data. The experimental results over extensive simulations show significant performance improvements in terms of minimum control overhead, processing time in relay nodes, a substantial increase in network throughput, and lower data latency (up to 76.98%) compared to conventional time-slotted MAC protocols. The design is useful for mission-critical, time-sensitive networks and exploits multihop simultaneous communication in a distributed manner.  
cross-layer design; mission-critical networks; multihop networks; software-defined radios; tactical communication; virtual sub-nets +
Footnote †: journal: Applied Sciences
## 3 Results
### Correction Factor
\begin{table}
\begin{tabular}{c c c} \hline \(\beta\) **(SEZ)** & **Regression Equation** & \(\mathbf{R^{2}}\) \\ \hline \(40^{\circ}\) (A) & CF = 8.0863 \(\times\)\(10^{-9}\)\(\beta^{3}\) + 4.1615 \(\times\)\(10^{-6}\)\(\beta^{2}\) + 3.9379 \(\times\)\(10^{-5}\)\(\beta\) + 0.9998 & 1 \\ \(45^{\circ}\) (B) & CF = 1.1413 \(\times\)\(10^{-8}\)\(\beta^{3}\) + 7.0911 \(\times\)\(10^{-6}\)\(\beta^{2}\) + 7.0532 \(\times\)\(10^{-5}\)\(\beta\) + 0.9997 & 1 \\ \(50^{\circ}\) (C) & CF = 2.0347 \(\times\)\(10^{-8}\)\(\beta^{3}\) + 6.4319 \(\times\)\(10^{-6}\)\(\beta^{2}\) + 9.3106 \(\times\)\(10^{-5}\)\(\beta\) + 0.9996 & 1 \\ \(40^{\circ}\),45\({}^{\circ}\),50\({}^{\circ}\) (A,B,C) & CF = 1.1802 \(\times\)\(10^{-8}\)\(\beta^{3}\) + 5.9782 \(\times\)\(10^{-6}\)\(\beta^{2}\) + 6.4260 \(\times\)\(10^{-5}\)\(\beta\) + 0.9997 & 1 \\ \hline \end{tabular}
\end{table} 
Table 4: Regression equations for the best-fit curves to the CF–\(\beta\) data points averaged over all respective sites in the period 2005–2016, together with their R\({}^{2}\) values, for SEZ-A, SEZ-B, SEZ-C, and all SEZs. Figure 12: Variation of the correction factor, CF, as function of the ground-albedo ratio, \(\rho_{\rm r}\), for various tilt angles, \(\beta\), for a flat plane tracking the Sun in Saudi Arabia, averaged over the period 2005–2016. The blue lines correspond to CF = \(\rho_{\rm r}\) = 1. Notice that the datum point (1,1) is the site of Arar (#24), as expected. The black dotted lines are the best fits to the CFs of all sites having the same tilt angle. Figure 13: _Cont._  
Figure 13: Variation of the correction factor, CF, as function of the tilt angle, \(\beta\), for an inclined surface in (**a**) SEZ-A, (**b**) SEZ-B, (**c**) SEZ-C, and (**d**) all SEZs, averaged over the period 2005–2016. The black solid lines are the mean CF values, while the red and blue ones correspond to the mean \(+1\sigma\), and mean \(-1\sigma\), respectively. The green dotted lines are the best fits to the mean curves and can hardly be distinguished as they coincide with the mean curves.
## 3 Analysis of Stochastic Natural Frequency
### Laminated Cylindrical Shell  
Laminated cylindrical shell structures are widely used in airspace, aerospace and energy engineering fields, with applications such as the dual launch system (SYLDA) and interstage skirt structure (ISS) of Ariane 5 launcher [33]. Especially, circular cut-outs are made on the laminated cylindrical shell of the SYLDA. In this section, stochastic natural frequency of a laminated cylindrical shell with circular cut-outs is investigated. The geometry of the laminated cylindrical shell is shown in Figure 5a, which was based on a scaled model of the SYLDA of Ariane 5 launcher where the shell diameter is equal to the shell length and the cut-out diameter is around 0.1-0.2 times of shell length. The laminate was set as eight-layered E-glass fibre/epoxy material, and the ply orientation was \((\pm \theta)_{4}\). A ply with an orientation at 0 indicates that fibre is aligned in the cylinder length direction. Ply thickness was selected as 0.45 mm. Both ends of the laminated cylindrical shell were clamped. FE modeling was employed to obtain its first order natural frequency, as shown in Figure 5. The laminate was modeled by four-node linear-elastic shell element (Shell181 in Ansys 14.5), and a mesh size of \(5\times 5\) mm was found to be fine enough to provide a convergent solution. FE solution on the first order vibration mode of \((\pm 45)_{4}\) laminate cylindrical shell is shown in Figure 5b.  
Similar to the analysis of laminate plates in last section, a combination of RSM and Monte-Carlo simulation is adopted to obtain the statistics of the first order natural frequency, by considering'micro-scale uncertainty', 'independent meso-scale uncertainty' and 'correlated meso-scale uncertainty'. The derived standard deviation of the first order natural frequency is shown in Figure 6. It is seen that the difference on the standard deviation between the three difference uncertainty consideration was fairly small for \(\theta\) at 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\) and 60\({}^{\circ}\). However, for the situation of \(\theta=90^{\circ}\), the standard  
Figure 4: Linear correlation coefficient between first order natural frequency of laminate plate and meso-scale random variables.  
deviation derived from 'independent meso-scale uncertainty' was 10.4% larger than that of 'correlated meso-scale uncertainty' or'micro-scale uncertainty'. This difference agrees with that of laminate plates shown in Table 4, which again demonstrates that ignoring the statistical correlation between meso-scale uncertainty probably leads to overestimation of the standard deviation of structure natural frequency.
## 2 Object Detection
### Regression-Based Object Detectors (One-Stage Detectors)  
#### 2.4.1 You Only Look Once (YOLO)  
[PERSON] et al. [40] implemented YOLO architecture, as shown in Figure 10. YOLO is the strongest, fastest, and simplest object detection algorithm used in real-time object detection. YOLO runs at 155 fps achieved \(mAP=52.7\%\), and its improved version runs at 45 fps achieved \(mAP=63.4\%\) on the VOC-2007 dataset. YOLO designers completely replaced the previous object detection model's proposed detection plus verification.  
All previous object detection algorithms use regions to localize objects within the image, but the YOLO approach is entirely different; the entire image is applied to a single CNN. YOLO network splits the entire image into regions, and for each region, it predicts bounding boxes and class probabilities. The main drawbacks of the YOLO object detector are: detection of small objects in an image, and localization accuracy dropping off when compared to two-stage detectors. YOLOv2, YOLOv3, and SSD [41] detectors paid much attention to YOLO drawbacks. To the basic YOLO detector, [PERSON] [40] later made improvements and implemented YOLOv2 and YOLOv3 [42; 43] which have achieved better detection accuracy without scarifying detection speed.  
Figure 9: Mask R-CNN architecture [38].