esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
On the one hand, in reference [15], we have carried out dynamic field tests under 120 high-speed vehicle running tests at speeds of 5-360 km/h and analyzed the governing factors of dynamic responses such as train speed, train running direction, vehicle axle load, and adjacent load. On the other hand, a 3D vertical coupling dynamic simulation method based on the D'Alembert's principle of energy weak variation and the Lagrange format were verified by the field test [16]. Further, a tunnel-culvert-tunnel transition section was established, which explored a more economical filling to replace the graded gravel +5% of the transition section. However, our above analysis in [16] simulated the action of a train by moving load but did not include the vehicle vibration that is critical to our current study. To conclude, the existing numerical model has the following shortcomings: (1) The simplified model does not consider the vibration of subgrade or train, leading to some limitations for application; (2) The train-track-subgrade system of the transition section will generate a large number of system degrees of freedom for the complexity of the system, resulting in the low efficiency in analyzing. To overcome the above shortcomings, we first established the train-track coupling model based on the coupling theory; then the generated results from the coupled train-track model were used as a boundary condition of the other track-subgrade coupling model that is based on D'Alembert's principle. In doing so, it helped reduce the number of total freedoms of the coupling system and improved the efficiency when analyzing the dynamic responses of the track and subgrade. Furthermore, the influence of different influencing factors on the dynamic response of the bridge-embankment transition section system was studied deeply. The paper is organized as follows: The general situation of the construction site of the transition section is described in Section 2. The numerical analysis model method of the transition section of the bridge-embankment is then described. After that, the numerical model considering the interaction and vibration of vehicles, track and subgrade are established and verified in Section 3. In Section 4, the change characteristics of vehicles, track and subgrade in the transition section along the longitudinal direction and the depth direction are analyzed in order to study the influence factors of the dynamic response of the bridge-embankment transition section. All the above analysis leads to the conclusions in Section 5.
## 2 Materials and Methods
### Life Cycle Assessment (LCA) Analysis of WSWSCO\({}_{2}\) Extract  
A cradle-to-gate LCA analysis of the WSWSCO\({}_{2}\) extract was performed according to the PEF methodology (PEF Environmental Footprint 3.1). The analysis focused mostly on the production process of WSWSCO\({}_{2}\) extract, from the upcycled raw material to the final product. The data quality of the analysis is guaranteed since all the information needed was collected directly from the companies involved: Rigoni di Asiago provided data for the cultivation/production phase, and Societa Agricola Moldoi provided the foreground data (energy and water consumption in the production process). Secondary data about transport (considering distance and way of transport) were taken from the database Ecoinvent v3.10. The elaboration of data was performed through SimaPro v9.6 software. The obtained data were elaborated according to the Product Environmental Footprint Category Rules Guidance (PEFCR v6.3--May 2018), and the environmentalimpact of the extract were split into 16 environmental indicators. The most relevant impact category is climate change, as known as global warming potential, which alone contributes 21% to the overall environmental impact for the raw material.  
In Table 1 are listed all the environmental impact indicators, their units of measure, and their contribution to the overall impact.  
\begin{table}
\begin{tabular}{c c c c} \hline  
\begin{tabular}{l} kg CO\({}_{2}\) eq: kilograms of carbon dioxide equivalent; kg CFC-11 eq: kilogram of Trichlorfluoromethane equivalent; CTUh: Comparative Toxic Unit for humans; kBq U235 eq: kilogram Becquerel of Uranium 235 equivalent; kg NMVOC eq: kilogram of non-methane volatile organic compounds equivalent; mol H+ eq: equivalent of mole of H+; mol N eq: equivalent of moles of nitrogen; kg P eq: equivalent of kilograms of phosphorus; kg N eq: equivalent of kilograms of nitrogen; m\({}^{3}\) world eq: equivalent amount of water used; kg Sb eq: equivalent of kilograms of antimony; MJ: Mega Joule; pt point.  
\end{table}
Table 1: Environmental impacts, their units of measure, and their contribution to the overall impact according to Annex A—Product Environmental Footprint Category Rules ((PEFCR) Guidance.
## 3 Results of Experiments and Their Discussion
### MBN Measurements
Figures 12 and 13 illustrate that the maximum degree of MBN asymmetry expressed in \(\Delta\)MBN or/and \(\Delta\)\(PP\) can be found for \(\epsilon=\) 5-15%, followed by a progressive decrease. The extent of the plastic straining for \(\epsilon=\) 5-15% is limited to those grains having suitable crystallographic orientation, grains shape, and misorientation angle, and the grains' average misorientation alterations are quite limited and a marked non-homogeneity in dislocation density among the grains is considered. As soon as \(\epsilon\) exceeds 15%, dislocation straining in the plastically strained grains is more or less fully consumed, dislocation motion is trans  
ferred into grains of less suitable crystallographic orientation (unaffected by the previous tension), and the MBN asymmetry tends to vanish due to the decreasing degree in the grain straining non-homogeneity. Expressed in other words, the higher degree of MBN asymmetry can be found when the character of plastic deformation (with respect of the neighbouring grains) is only local, and the average degree of plastic straining is low. This statement proves that the evolution of the specific aspects obtained from the XRD and EBSD techniques when the region of \(\varepsilon\) = 5-15% strongly coincides with:  
* medium or nearly no alterations of the crystallite size and micro-deformation (see Figure 1),
* nearly untouched grain size distribution (no valuable grains elongation) and the average grain size (see Figures 4 and 5),
* and finally, the less-affected grains misorientation (see Figure 7). Finally, the influence of magnetising voltage and the corresponding \(\mathrm{d}H/\mathrm{d}\tau\) should be explained. Figure 13b,c and Figure 15 depict that the degree of MBN asymmetry attains a maximum near the magnetising voltage 4V. The MBN asymmetry tends to vanish at lower voltages since the weaker magnetic field is not capable of unpinning DWs in either the heavily strained or the unaffected grains. For this reason, the MBN signal is quite weak and the MBN asymmetry is lost. The medium voltages (4-5 V) provide a strong contrast between the easily unpinned DWs in non-strained grains and DWs strongly pinned due to high dislocation density in the heavily strained grains (discussed above). Finally, the high voltages and stronger \(\mathrm{d}H/\mathrm{d}\tau\) tend to unpin the more homogenous DWs across the whole matrix. Therefore, the degree of MBN asymmetry drops down along with the magnetising voltage; however, it does not vanish entirely (see Figure 15).
# Supramolecular Materials as Solid-Phase Microextraction Coatings in Environmental Analysis
## 4 Covalent Organic Frameworks (COFs)
### COFs as SPME Coatings for Environmental Applications
#### 4.3.1 Extraction of Polycyclic Aromatic Hydrocarbons and Nitroaromatic Compounds
The functionalization with either the biochar or COF nanospheres proved to be useful in increasing the BET surface from 10.07 m\({}^{2}\)/g to 15.97 m\({}^{2}\)/g and 32.75 m\({}^{2}\)/g, respectively. The extraction capability of the different materials was tested for the extraction of PAHs, estrogens, and bisphenols, resulting in the selection of TiO\({}_{2}\)NARs-CFs, biochar nanosphere- TiO\({}_{2}\)NARs-CFs, and COF nanosphere-TiO\({}_{2}\)NARs-CFs as the best coatings  
Figure 7: Schematic representation of ISV-HS-SPME extraction using hybrid MOF/COF coating, reprinted from [110]. for the extraction of the different classes of compounds. The IT-SPME-LC-UV method was validated obtaining LODs in the 5-10, 1-5, and 5-100 ng/L ranges for PAHs, estrogens, and bisphenols, respectively, and RSDs always lower than 15%. Hydrogen-bonded organic frameworks (HOFs) are other emerging SPME coatings consisting of organic or metal-organic architectural units connected by hydrogen bonds. They are gradually developing into an attractive new type of porous crystalline material for the determination of nitroaromatic compounds. To increase the extraction selectivity toward this class of analytes, [PERSON] and coworkers developed an SPME coating based on the self-assembly of melamine and 1,3,6,8-tetra(4-carboxylphenyl) pyrene to obtain a MA/PFC-1 HOF [132]. The use of melamine promoted the formation of hierarchical micropores with dimensions in the 5-20.0 A range, a surface area of 1550(\(\pm\)26) m\({}^{2}\)/g, and strong hydrogen bonds between the constituting units. The HOF coating significantly improved the extraction performance of five model nitroaromatic compounds via polar, electrostatic, hydrogen bonding, \(\pi\)-\(\pi\) stacking, and C-H\(\cdots\pi\) interactions between the analytes and the adsorbent material. The HS-SPME-GC-MS method was validated, obtaining LOQs in the 4.3-20.8 ng/L range and RSDs \(\leq\) 9%, allowing for the detection of \(p\)-nitrochlorobenzene and \(p\)-nitrotoluene and p-nitrochlorobenzene in water samples at the sub-\(\upmu\)g/L level.
## 2 Experimental Section
### Characterization of Treated Fabric
#### 2.3.4 Stain Recovery and Stain Resistance Tests  
Stain tests were performed on untreated control and treated fabric samples and compared to results for the commercial reference samples. Standardized measurements followed AATCC test method 130, referred to as the oily stain release method, and AATCC test method 118, referred to as the oil repellency test. In AATCC test method 118, 50 \(\mu\)L drops of different grades of hydrocarbon oil specified in the protocol are applied to the fabric material and scored on a scale from 1 to 8. Higher values indicate the point at which ever lighter aliphatic oils penetrate the fabric in a period of 30 s or less and thus greater oil repellency. In AATCC test method 130, the fabric is placed on a blotting paper and 5 drops corn oil of 40 \(\mu\)L each are deposited in the center. A blotting paper is laid on top followed by a 5 lb weight for 60 s. The fabric is washed in the specified manner at 41 \({}^{\circ}\)C and evaluated according to the test protocol. To further assess stain resistance and recovery, we examined the effects of common staining materials like oil, mustard and ketchup by reflectance measurements of the fabric samples. Stain resistance measurements were performed after wiping with tissue paper while stain recovery evaluation was done after washing. Reflectance was determined by using an Ultrascan colorimeter (Hunter Lab) at a wavelength of 440 nm. Percentage of stain resistance and percentage of stain recovery were calculated using the formulas  
\[\%\,\text{of Stain Resistance}\,=\frac{\text{Reflectance of stain on treated fabric after wiping}}{\text{Reflectance of untreated fabric}}\,\text{x}\,100\]  
\[\%\,\text{of Stain Recovery}=\frac{\text{Reflectance after one wash - Reflectance after staining}}{\text{Reflectance before staining - Reflectance after staining}}\,\text{x}\,100\]
# General Periodic Cruise Guidance Optimization for Hypersonic Vehicles
## 3 Augmented States Based Periodic Cruise Optimization  
Due to the mathematical form constraint of periodic cruising trajectory, GPM cannot be directly applied. To solve this problem, we formulate the periodic functional constraint as a path constraint by treating the characteristic parameters as augmented states. Then, we augment the original system with additional path constraints embodying the periodic property.  
The periodic altitude can be in the following form:  
\[h=h_{a}\cos\omega r+h_{b}, \tag{10}\]  
where \(h_{a}\), \(h_{b}\), and \(\omega\) are the characteristic parameters to be optimized. Considering the first sub-Equation (2):  
\[\frac{dh}{dr}=\tan\gamma\bigg{(}1+\frac{h}{\mathrm{R_{e}}}\bigg{)}, \tag{11}\]  
As \(h\ll\mathrm{R_{e}}\) up to more than two orders of magnitudes, we can approximate Equation (11) as:  
\[\frac{dh}{dr}=\tan\gamma, \tag{12}\]  
Combining Equations (10) and (12) yields:  
\[\gamma=-\tan^{-1}(h_{a}\omega\sin\omega r), \tag{13}\]  
This can provide a practical guidance law for a hypersonic vehicle to achieve periodic cruise; otherwise, it is difficult to implement accurate tracking of the altitude, as shown in Equation (10), due to the slow altitude response [37; 38; 39; 40]. According to Equation (10), the cycle range is  
\[r_{c}=2\pi/\omega; \tag{14}\]  
To sum up, three characteristic parameters describe a periodic solution, which should be optimized. We specify these constant parameters as augmented states as  
\[\left\{\begin{array}{l}\frac{dh_{a}}{dr}=0\\ \frac{dh_{b}}{dr}=0\\ \frac{du_{a}}{dr}=0\end{array}\right., \tag{15}\]  
Combining the original four states, we obtain seven-state augmented dynamics. Subsequently, we perform the optimization for this system. We may include Equation (10) as a path constraint as  
\[\left|h-(h_{a}\cos\omega r+h_{b})\right|<h_{\varepsilon}, \tag{16}\]  
where \(h_{\varepsilon}\) is an acceptable tolerance for numerical computation. In this way, the path constraints can be naturally embedded in the GPOPS.
# Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning
## 4 Control Design
### Neural Network Modeling
By leveraging memory cells, our model accurately predicts sea states without requiring external wave measurement devices. Experimental results show that our LSTM network achieved an R\({}^{2}\) score of 98.4% and a deployment accuracy of 94%, demonstrating its effectiveness in real-time sea state identification.
# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 5 Conclusions  
Mandible segmentation is a challenging task due to the presence of metal artifacts, the low contrast of condyles and the large variation in mandibles among patients. CT modality is the most frequently used imaging technique for OMFS. To date, mandible segmentation methods proposed in the literature have achieved promising results in CT scans. Before the machine learning era, the atlas-based segmentation method was the most common method and performed well. With the advancement of technology, deep learning-based segmentation methods have increased substantially. Deep learning-based methods perform better and much faster than traditional methods, although they require more data and higher computing resources in training. There is still room for improvement since the datasets are limited and cannot fully represent the general patient population in clinic. Furthermore, mandible segmentation in CBCT and MRI is valuable for further exploration due to their low-dose radiation or no radiation. Therefore, mandible segmentation is still an open research area to be improved.  
Conceptualization, all authors; methodology, B.Q. and J.G.; validation, B.Q. and J.G.; formal analysis, B.Q.; writing--original draft preparation, B.Q., H.v.d.W. and J.G.; writing--review and editing, all authors; visualization, B.Q.; supervision, all authors; project administration, J.K., M.J.H.W. and P.M.A.v.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All relevant data are included in the study.  
The author is supported by a joint Ph.D. fellowship from China Scholarship Council.  
The authors declare no conflict of interest.
# Robust CNN Compression Framework for Security-Sensitive Embedded Systems
## 4 Experiments
### The Convergence Behavior
#### 4.2.2 Filter Pruning  
We also traced both original accuracy and adversarial accuracy of AP and APD with the filter pruning scheme in the compression rate of \(\times 1.5\), \(\times 2\), and \(\times 2.5\). The results are described at Figure 2. APD improved the overall convergence behavior of the filter pruning. With LeNet, APD reduced the drop of the original accuracy on the first epoch about 5%. With VGG16, the improvement in the first epoch was more significant. For instance, in the compression rate of \(\times 1.5\), APD reduced the drop of the original accuracy on the first epoch by about 20%. Mitigating the drop of original accuracy in the first epoch led to an improvement in the overall convergence behavior. For instance, in the compression rate of \(\times 1.5\) with LeNet, APD required 49 epochs for the average value of the original accuracy and the adversarial accuracy to reach 96.63% (the maximum average value achieved by AP), whereas AP required 86 epochs to achieve that. In the compression rate \(\times 1.5\) with VGG16, APD required 33 epochs for the average value of both accuracies to reach 54.46% (the maximum average value achieved by AP), whereas AP required 59 epochs to achieve that. With ResNet18, APD also reduced the drop of original accuracy in the initial stage of pruning but the amount of improvement decreased in the high compression rate.
## 2 Materials and Methods
### Influence of Temperature on Parameters
#### 3.1.2 Influence of Time  
The duration of the extraction process plays a vital role in affecting different parameters of tomato pomace. Extending the extraction time usually facilitates a more comprehensive extraction of target compounds from the pomace matrix, resulting in increased yields of bioactive substances like antioxidants, phenolic compounds, and other valuable components [9]. The highest lycopene content was observed at 6 h of extraction, with a content of 516.46 mg/100 g. The influence of time was not significant for lycopene, TPC, and TFC; only DPPH activity was influenced significantly by the time.
# Control for Ship Course-Keeping Using Optimized Support Vector Machines
## 3 Controller Design and Stability Analysis  
Usually, the problem of ship course-keeping control can be viewed as a tracking control issue. Without loss of generality, two general assumptions with respect to the continuous cascaded system described by (5) can be given as \([A1]\). The desired system output signal \(\psi_{d}\) is differentiable and the cascaded system is controllable. \([A2]\). The external disturbance \(w(t)\) is bounded with \(|w(t)|\leq\overline{w}\), \(\forall t\geq 0\), \(\exists\overline{w}>0\).
# A Random Subcarrier-Selection Method Based on Index Modulation for Secure Transmission
## 2 System Model
### The Proposed Random Subcarrier-Selection Method Based on Index Modulation
\tag{14}\]  
Next, a subcarrier was selected from every row of \(I_{sub}\), respectively. For every row, \(p\) bits were used to select the subcarrier, where  
\[p=log_{2}\frac{N_{s}}{N_{T}}=m_{1}-m_{2}. \tag{15}\]  
Figure 4 depicts the flow chart of the proposed scheme, RSCS-IM. As shown in Figure 4, firstly, all the indices from the orthogonal subcarrier set in (9) were divided into the matrix in (14). Next, \(p\times N_{T}\) bits were generated and each \(p\)-bit of binary data was converted to a decimal, according to the data bits generated by the source, where \(p=log_{2}\frac{N_{s}}{N_{T}}\). Finally, one index from every row of matrix (14) was picked in turn using these \(N_{T}\) decimal data as columns and we obtained the \(N_{T}\) desired indices. So, we could see that the proposed scheme avoided the repeated block interleaving operation and guaranteed the randomness of the selected subcarriers. Moreover, the indices of the activated subcarriers could transmit extra data bits, mitigating the waste of spectrum. Hence, the whole block diagram of FDA OFDM-IM system is described in Figure 5, where \(m=(p+q)N_{T},p=m_{1}-m_{2},q=log_{2}M\) and \(M\) is the order of modulation. Figure 4: Flow chart of RSCS-IM. As shown in Figure 5, a total of \(m\) information bits entered the bit splitter. These \(m\) bits were split into \(2N_{T}\) groups. For the first \(N_{T}\) groups, each group contained \(p\) bits for index-selection while the other \(N_{T}\) groups each contained \(q\) bits for mapping. After that, the signal was conveyed by the FDA OFDM-IM system in the far-field scenario and [PERSON] received the signal \(s(t)\) in (10). Just as depicted above, taking down-conversion and sampling for \(s(t)\) in turn, we could obtain \(s(n)\) in (12). Next, an \(N_{s}\)-point FFT was obtained for \(s(n)\). Then, in order to detect the activated subcarriers, we adopted the scheme of greedy detection and determined the location of maximum energy for every row in (14) as the index of the activated subcarrier. Finally, the index demodulation and QAM demodulation were obtained, respectively, and the original data could be restored. As for the eavesdroppers, assuming that they were consistent with the demodulation method used by the legitimate receiver, from (13) we could see that there was a phase offset between the received data and original data. So they could not restore the original data correctly. In summary, the proposed scheme could guarantee the randomness of the selected subcarriers because of the randomness of the information bits. Moreover, the indices of the activated subcarriers could transmit extra data bits, improving the SE. In Section 3, we will analyse the performance of RSCS-IM and PSS Plus RP in detail.
# Identifying Influential Nodes of Complex Networks Based on Trust-Value
## 1 Introduction
For example, the node deletion method cannot distinguish the influence of nodes that make the network no longer connected after deletion [25]. Most of the above algorithms are based on the topology of the network, and do not consider the attribute information between nodes, where the structures of networks have certain limitations in expressing nodes' information. The interaction between nodes in networks may also affect the importance of the nodes. For example, in a social network, if there are more commonalities between two individuals, the more similar they are, the more they trust each other, and the more information they may exchange. In addition, if an individual has correlation with more individuals in the network, then transmitting information to the individual will facilitate the rapid spread of information throughout the network. [PERSON] proposed an algorithm based on LeaderRank and nodes' similarity but only considered the number of nodes pointed in and pointed out [26]. Inspired by the PageRank algorithm and considering the attribute information between nodes, we constructed a new algorithm for identification of influential nodes, trust-PageRank (TPR), by combining the trust-value and PageRank algorithm. The algorithm considers the topology characteristics of networks and the interaction behavior between nodes to avoid one-sidedness of the evaluation. We introduce the trust-PageRank algorithm in detail in Section 2. In Section 3, the TPR, DC, BC, PageRank, and HITS algorithms are applied to the specific real network and compared. The results show that the TPR algorithm can reasonably and effectively identify the influential nodes of complex networks and can effectively distinguish the nodes with similar influence. Section 4 concludes our work as well as its prospects.