esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
_Streptomyces_ represent a significant source of bioactive natural products (NPs), accounting for 39% of all microbial metabolites [1]. NPs isolated from _Streptomyces_ are diverse and include antibiotic, anticancer, antioxidant, and other bioactivities [2; 3; 4]. Since extreme environments form severe habitats, they may give rise to unique metabolic pathways in microorganisms. For instance, the desert isolates _Streptomyces sp._ SAJ15 produces aryl polyene, terpenoid, and macrolide compounds, and the relative abundance of different biosynthetic gene clusters (BGCs) in arid _Streptomyces_ differed from the non-arid counterparts [5]. Therefore, it is necessary to search in extreme environments for potential microorganisms as a source of bioactive molecules [6]. In fact, there has been extensive research focus on extreme environments such as deep marine, desert, cryosphere, volcanic, strong acids, alkalis, and polluted water and soil. For example, [PERSON] and others [7] isolated a new benzoxazole antibiotic from the marine _Streptomyces sp._ NTK 937. [PERSON] et al. [8] purified chaxapeptide, a novel lasso peptide with significant in vitro inhibitory activity against human lung cancer cells, which was isolated from _Streptomyces leeuvenhoekii_ strain C58 collected from the Atacama Desert. [PERSON] et al. [9] cultured an Antarctic soil-derived _Aspergillus ochraccopetaliformis_ strain and isolated of new polyketides, ochracepones with antiviral activity against influenza viruses.  
Desert habitats are challenging for microorganisms, due to the scarcity of water, high UV radiation, extreme temperatures, high salinity, and the presence of inorganic oxidants [10]. Life in extreme habitats requires the presence of unique metabolites that allow microorganisms to survive. For example, _Streptomyces aaidamensis_ S10\({}^{\text{T}}\), isolated from a sand sample collected in Qaidam Basin, Qinghai Province, China, has been verified to exhibit anti-Methicillin-resistant Staphylococcus accureus (anti-MRSA) activity [11]. Given the particular origin of _S. qaidamensis_, we hypothesize that _S. qaidamensis_ can produce NPs with antioxidant activities to ensure its ecological niche.
# Development of a Path Tracker Based on a 4 WS Vehicle for Low-Speed Automated Driving Systems
## 4 Evaluation
### Simulation  
#### 4.1.1 Simulation Environment  
A simulation was conducted to verify the basic functionality of the 4 WS path tracker proposed in this paper. Therefore, a figure-eight course in Figure 6, which is commonly used in path-tracker simulations, was generated using MATLAB R2022b's Driving ScenarioDesigner. The course can provide valuable insight into some important characteristics of a path tracker. This course was chosen to illustrate the steady-state characteristics of the path tracker while executing a constant, nonzero curvature path. This course also includes a point with discontinuous curvature where vehicles transition from one circle to another. The response of the path tracker to this discontinuity provides insight into its robustness [11].  
Figure 7 shows the simulation architecture, which was implemented using MATLAB R2022b (developed by MathWorks). The plant model of the vehicle for the simulation used the 4 WS-based kinematic vehicle model described in \"4 WS-Based Kinematic Vehicle Model\" in Section 2.  
The simulation was conducted at a driving speed of 21.6 [KPH], considering the international standardfor Low-Speed Automated Driving (LSAD), which defines the maximum speed for low-speed autonomous driving as 8.89 [m/s] (approximately 32 [KPH]) [3].
# Kinematic In Situ Self-Calibration of a Backpack-Based Multi-Beam LiDAR System
## 4 Experiment Description and Result Analysis
### Analysis of Kinematic Self-Calibration Results
The moderate (not significantly strong) correlation values were, also, found between \(Z_{o}\) and \(\omega\), \(Z_{o}\) and \(\phi\), and \(Y_{o}\) and \(\kappa\). They were marked in bold in the tables. This correlation between \(Z_{o}\) and the orientation parameters was likely due to the small number of points with respect to the horizontal plane (floor and ceiling), and, moreover, the amount of tilting angle was not sufficient for perfectly de-coupling between the translation and orientation parameters. Nevertheless, it should be noted that the estimated parameters did not have strong correlations and were derived reliably.
## 5 Conclusions  
Muti-methods (FE-SEM, N\({}_{2}\)/CO\({}_{2}\) isothermal adsorption, porosity, permeability, and rock mechanics parameters) have been utilized to characterize the pore structure and petrophysical properties of marine shale and continental shale. The following are the main conclusions from our research:  
1. The mesopores of marine shale and continental shale are the main pore spaces. Marine shale is mainly composed of organic matter pores, including micropores and mesopores (2-30 nm), while continental shale is mainly composed of inorganic pores, including mesopores (30-50 nm) and macropores. In addition, the contribution rate of organic pores in marine shale to the total pore volume is much higher than that of continental shale.
2. Except for TOC content, the thermal evolution degree also controls the pore structure in shale samples, particularly for the micropores structure, and organic matter maturity also affects rock density. On the other hand, the mineral composition and fabric present more influence on the macropore volume.
3. The bedding plane structure caused by the lithological stratification is the main reason for the difference in petrophysical properties of marine and continental shale. Continental shale is a typical transversely isotropic, while marine shale can be regarded as a homogeneous rock, leading to low permeability and high strength.  
Figure 12: The cylinder marine shale, which shows homogeneity in centimeter scale (**a**); the fracture pattern of marine shale, where the crack propagates along the principal stress direction until failure (**b**); the cylinder continental shale, showing obvious lithological stratification (**c**); the fracture pattern of continental shale, where the crack propagates along the principal stress direction and activates the bedding, then shows continued growth until failure (**d**). The red line represents the principal stress cracks and the green line represents the activating bedding cracks.  
* Bedding structure is the main reason for the crack propagation difference. Since the matrix strength of shale is much greater than the strength of bedding planes, continental shale tends to crack and fail along the weak surface of the bedding during the axial compression, showing a lower peak strength, while the cracks in the marine shale tend to propagate along with the principal stress.  
Conceptualization, investigation, methodology, visualization, writing--original draft preparation, D.W.; writing--review and editing, validation, supervision, X.L.; Supervision, G.L.; Experiment, software, T.M.; Experiment, visualization, B.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (Grant No. 42090023), National Key R&D Program of China (Grant No. 2020 YFA0710500), and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB10030304, XDB10030301).  
Not applicable.  
The authors declare that they have no conflict of interest.
## 1 Introduction  
Recently, the State Grid Cooperation Company of China, which is a state-owned company, decided to construct new grids based on Smart Grid technology across China. Power-line communication (PLC) over the low-voltage (LV) distribution networks has become one of the potential technologies to communicate information between end users and power providers, and thus, recent research in China has focused on PLC [1].  
At present, research on PLC focuses primarily on the aspect such as input impedance and attenuation characteristics of the channel [2], channel noise characteristics [3,4], channel models [5,6], impedance matching and coupling circuit design [7], power line noise suppression technology [8,9], the application of orthogonal frequency division multiplexing (OFDM) in PLC, frequency hopping modulation and demodulation techniques [10]. An effective method to improve the reliability of PLC systems from network level is through the establishment of network routing or relay techniques [11,12]. An artificial cobweb routing algorithm for routing the tree-type physical topology of a low-voltage distribution network is analysed in this paper based on the artificial cobweb routing of the bus-type data communications layer of a low-voltage distribution network proposed in [13]. The artificial cobweb routing algorithm is further improved. To provide a theoretical foundation for further research, the network parameters after networking are accurately calculated. The simulation results demonstrate the accuracy of the theoretical calculation. The results indicate that the artificial cobweb routing algorithm is effective for guaranteeing QoS and reliability in power line communication.
# Real-Time Stereo Visual Odometry Based on an Improved KLT Method
## 2 Method Overview  
Figure 1 depicts the workflow of the proposed SVO which can be outlined in the following steps:  
1. Searching for a series of SURF key points in the first stereo frame and computing their normalized descriptors with 64 dimensions.
2. With the epipolar constraint, stereo matching is performed using the Euclidean distance between the SURF descriptors.
3. A subset of the matched features is selected by means of bucketing to ensure the features are uniformly distributed over the image plane.  
4. The three-dimensional (3-D) coordinates of the selected features are computed using triangulation.
5. The two-dimensional (2-D) features are tracked between the left images in frames \(k-1\) and \(k\) using the AKT.
6. The SKM is performed by combining the standard KLT method [16] with the epipolar constraint. Then, the 3-D coordinates of the matched features are computed through triangulation.
7. The perspective-3-point (P3P) algorithm [18] is carried out in a random sample consensus (RANSAC) framework [19] to estimate the ego-motion from the 3-D-to-2-D correspondences.
8. The maximum likelihood estimator (MLE) [9; 10] is applied to produce a robust ego-motion estimation.
9. The features inherited from Step 5 between the left images in frames \(k\) and \(k+1\) are continuously tracked. If the number of new tracked features is smaller than a predefined threshold \(N\), repeat from Step 1. Otherwise, the features are inherited successfully and repeat from Step 6. The threshold is set to 30 to ensure both computational accuracy and efficiency. This process is called FI.
10. After Step 8, for a turning maneuver, the drift error is reduced via a VCM scheme. This scheme consists of a veer frame detection process and a veer frame matching process. If this is the first time through the corner or the intersection, a veer frame update will collect the current frame as the unique keyframe of this corner. If not, the motion between the current veer frame and the first veer frame of this corner is estimated and the drift error is corrected. This novel scheme will be described in Section 3.
## 4 Numerical Analysis
### Numerical Analysis of Governing Equations for Carbon Fiber-Heated Pavement  
4.1.1 Heat Transfer Process Governing Equations  
Carbon fiber heat generation is based on the principle that electrons in carbon fiber heating wires undergo energy level jumps upon activation, resulting in tiny vibrations and heat from carbon atoms. This heat is then released as far-infrared thermal radiation. Assuming a constant heating power of 20 W/m and given the speed of this operation, it is reasonable that the temperature of the carbon fiber heating filaments at different locations under the pavement remains consistent. Equation (1) was used to determine the transient heat transfer process of the carbon fiber-heated pavement. This was due to the fact that the boundary conditions change during the actual testing process, thus making the numerical model more realistic:  
\[Q=\lambda\Delta TS/L \tag{1}\]  
where \(\lambda\) is the thermal conductivity, \(\Delta T\) is the temperature difference, \(S\) is the area, and \(L\) is the thickness. This equation is based on the definition in the Cartesian coordinate system that each successive point must satisfy energy conservation.  
Because the surface of the model in the test is in direct contact with the air, the surface's boundary conditions must meet Equation (2):  
\[Q=\varepsilon\sigma\Big{(}T_{amb}^{4}-T_{2}^{4}\Big{)} \tag{2}\]  
where \(Q\) is the pavement model's ambient thermal radiation, \(\varepsilon\) is the surface emissivity (a fixed value of 0.93 for this research), \(\sigma\) is the Stefan-Boltzmann constant, \(T_{amb}\) is the ambient temperature, and \(T_{2}\) is the pavement surface temperature.  
Because the model's surface is in direct contact with the air during the test, there will be convective heat exchange, which must meet Equation (3).  
\[q=h(T_{amb}-T_{2}) \tag{3}\]  
where \(q\) is the heat flow density between the concrete pavement's surface and the air, and \(h\) is the convective heat transfer coefficient between the concrete pavement's surface and the air (a fixed value of 2.3 W/(m\({}^{2}\cdot\)K) for this study).  
Because the goal of this study was to investigate heat transport within the pavement, the boundary conditions around the pavement and at the bottom were thermally insulated. Furthermore, because the model testing was conducted in a constant-temperature laboratory without the influence of sun radiation, the numerical model did not take solar radiation into account. That is, the ambient temperature was the same as the pavement's surface temperature.
## 3 Method and Experiment
### Material Selection
After the normalization of each curve is completed, considering multiple factors, in order to reduce the influence of individual differences, the five curve values are combined together. One value is used to represent the unconformity structure, and a new logging curve-unconformity structure identification factor \(Q\) is proposed. The principle is that different weights are given to the normalization results of each curve, multiplied by the corresponding values, and finally added together. The result is the unconformity identification factor \(Q\), as shown in Equation (2):  
\[Q=a_{0}\cdot\Delta AC+a_{1}\cdot\Delta DEN+a_{2}\cdot\Delta SP+a_{3}\cdot \Delta RT+a_{4}\cdot\Delta GR \tag{2}\]  
In order to reduce the interference of human factors, the choice of weight is calculated using a formula. Firstly, the amplitude difference of the change rate of each parameter curve is calculated as shown in Equation (3):  
\[\Delta Y_{i}=(|Y_{i-1}-Y_{i}|+|Y_{i+1}-Y|)/Y_{i} \tag{3}\]  
In the formula, \(Y\) is the logging curve value (\(AC\), \(DEN\), \(SP\), \(RT\), \(GR\)), \(i\) is the depth of a certain point, while \(i+1\) and \(i-1\) are the logging values of the depth points adjacent to \(Y_{i}\) in the curve. Because the numerical range of each curve is different, the difference between the partial amplitude difference results is too large. The calculated change rate curve is normalized, and the processed result, \(Z\), is obtained from Equation (4):  
\[Z_{i}=(\Delta Y_{i}-\Delta Y_{\min})/(\Delta Y_{\max}-\Delta Y_{\min}) \tag{4}\]  
The results of the five curves are accumulated, and the proportion of each curve in the sum is the weight of the curve in the logging response characteristics. The higher the weight, the more obvious the logging curve changes in the process of unconformity from the upper layer to the middle layer and from the middle layer to the lower layer, as shown in Equation (5). \[a_{i}=Z_{i}/(Z_{GR}+Z_{SP}+Z_{AC}+Z_{DEN}+Z_{RT}) \tag{5}\]  
After calculation, the weight of each curve is obtained by averaging a in the whole well area: \(a_{GR}\) = 0.25, \(a_{SP}\) = 0.15, \(a_{DEN}\) = 0.15, \(a_{AC}\) = 0.2, \(a_{RT}\) = 0.25. Therefore, the calculation formula of the unconformity structure identification factor in the study area is Equation (6):  
\[Q=0.25\times\Delta GR+0.15\times\Delta SP+0.15\times\Delta DEN+0.2\times \Delta AC+0.25\times\Delta RT \tag{6}\]
# A Reinforcement Learning Approach to Speech Coding
## 9 \(L\) Step Lookahead Cost Function  
The approximate Cost-to-Go, \(\sum{I_{k}}=\sum[s(k)-x(k)]_{w}^{2}\), is the sum of perceptually shaped squared errors in the reconstruction, which can be evaluated only at some finite search depth in order not to incur too high a delay that would prevent real time speech coding. In general, however, it is desirable to search as far into the future as possible for the best control sequence although the length of the utterances to be coded can be 40,000 samples easily. For the 4-2 control tree at a search depth of only \(L=10\), a full search of the tree to depth \(L=10\) would explore \(4^{5}\) times \(2^{5}\) or 32,768 paths through the control tree. Even this is clearly impossible, particularly considering that all of the parameter adaptation algorithms would need to be performed over each path.  
As a consequence, we adopt the \(M\) algorithm from channel coding that extends only the \(M\) best paths to depth \(L\) at each time instant [33]. Out of these paths, we find the best path and release only the first control value in the best path, and then the \(M\) best paths starting from this first step are extended to depth \(L\) again and the process is repeated. This single control symbol release rule is not optimal but it simplifies the implementation. Further, if the number of control symbols released is too close to the full search depth,  
Figure 7: Control tree gain adaptation for 2-level tree segments.  
\(L=10\) here, then the samples near the end have not had the benefit of sufficient lookahead. A variable symbol release rule may perform better but these have not been explored as yet. A readable development of the \(M\) algorithm is available in [PERSON] and [PERSON] ([33], pp. 300-306).  
However, constraining the number of paths (to \(M\)) extended after each control value achieving the minimum depth \(L=10\) search of the control tree may cause important paths not to be explored. So, it is important to try and keep the dual control pursuing both exploration and exploitation in other ways.
# Translation-Based Embeddings with Octonion for Knowledge Graph Completion
## 4 Experiments and Discussion
### Euclidean or Poincare?  
To further test the effects of hyperbolic space and Euclidean space in link prediction, we design an ablation experiment. In this experiment, we attempt to evaluate the MRR and Hits@10 of the TransO and PTransO models on the WN18 RR dataset under the dimension of \(d\in\{32,200,400,496\}\), and calculate the relative difference value between TransO and PTransO.  
As can be seen from Figure 8, the MRR and Hits@10 of both the TransO and PTransO models show an upward trend, and the effects of PTransO are always better than those of TransO. However, with the increase in embedded dimension, the relative difference value between these two models decreases.  
The above experimental results show that hyperbolic Poincare space has more advantages for hierarchical data modeling than Euclidean space under low-dimensional embeddings. When the embedded dimension increases, the advant  
Figure 8: MRR and Hits@10 of the link prediction experiment of TransO and PTransO models on the WN18 RR dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Relation Name** & **RotatE** & **QuatE** & **TransO** & **PTransO** \\ \hline hypernym & 0.148 & 0.173 & 0.206 & **0.207** \\ derivationally\_related\_form & 0.947 & **0.953** & 0.941 & 0.950 \\ instance\_hypernym & 0.318 & 0.364 & 0.397 & **0.405** \\ also\_see & 0.585 & 0.629 & **0.656** & 0.636 \\ member\_meronym & 0.232 & 0.232 & 0.258 & **0.261** \\ synset\_domain\_topic\_of & 0.341 & **0.468** & 0.427 & 0.420 \\ has\_part & 0.184 & 0.233 & 0.239 & **0.246** \\ member\_of\_domain\_usage & 0.318 & **0.441** & 0.400 & 0.373 \\ member\_of\_domain\_region & 0.200 & 0.193 & **0.397** & 0.368 \\ verb\_group & **0.943** & 0.924 & 0.874 & 0.888 \\ similar\_to & **1.000** & **1.000** & **1.000** & **1.000** \\ \hline \hline \end{tabular}
\end{table}
Table 5: MRRs on each relation of WN18 RR dataset.  
is no longer obvious because Euclidean space also gradually has the capability to model complex data.  
We can infer that when the embedded dimension increases further, the experimental effect of TransO will be equal to or even surpass that of PTransO. Therefore, hyperbolic Poincare space is more suitable for use in the case of low-dimensional embeddings, which can not only save computing time and storage space consumption, but also retains rich hierarchical information in the data. Under low-dimensional embeddings, hyperbolic space has incomparable advantages over Euclidean space.
## 2 Materials and Methods
### Determination of VOCs by HS-SPME-GC-MS  
Five milliliters of rose beverage specimens were measured and transferred into 20 mL hermetic headspace vials. Using precision microinjection equipment, 6 \(\upmu\)L of internal reference standard (Octane, 20 \(\upmu\)g/mL) was administered. The automated AOC-6000 autosampler executed the HS-SPME protocol, wherein a specialized fiber assembly (50/30 \(\upmu\)DVB/CAR on PDMS Extraction Head) was positioned in the headspace compartment. The system maintained continuous agitation (250 rpm) under thermostatic control at 70 \({}^{\circ}\)C for 30 min. Upon completion of extraction, the fiber matrix was promptly transferred to the GC-MS injection port for thermal desorption (250 \({}^{\circ}\)C, 4 min), followed by subsequent chromatographic analysis.  
Chromotographic separation of volatile organic compounds (VOCs) was performed using an Agilent HP-INNOWAX capillary column (60 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness). The injector temperature was maintained at 250 \({}^{\circ}\)C, and ultra-high purity helium was used as the carrier gas at a flow rate of 1 mL/min. The oven temperature program was as follows: initial hold at 40 \({}^{\circ}\)C for 4 min, followed by a ramp to 140 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min with a 5-min hold. Subsequent ramps were to 150 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min (1 min hold), 197 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min (1 min hold), 205 \({}^{\circ}\)C at 1 \({}^{\circ}\)C/min, and finally to 240 \({}^{\circ}\)C at 7 \({}^{\circ}\)C/min with a 10-min hold.  
Mass spectrometric detection was performed using electron ionization (EI) at 70 eV. The ion source and detector temperatures were set at 230 \({}^{\circ}\)C, and the quadrupole temperature was maintained at 150 \({}^{\circ}\)C. Full-scan acquisition was performed from \(m/z\) 40 to 400 with a dwell time of 250 ms per mass channel.