esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards the Feasibility of Long Range Wireless Power Transfer over an Ocean Surface
## 3 Results  
During the experimental work, the experimental setup as shown in Figure 2 was used to conduct the following experiments:  
1. Basic functionality test.
2. An experiment testing for the feasibility of wireless power delivery to the receiving end when a tap water was used.
3. An experiment testing other materials as medium 2.
4. An experiment testing for wireless transmission of power under different salinity levels.
5. Transmission efficiency vs. distance test under different antenna heights. This section reports the results of these experiments.
## 1 Introduction
The current study results in original mathematical descriptions of the type of drill string systems and the corresponding vibration analysis through numerical simulation methods and experimental validation. The most important expected outcomes of this work include a fuller knowledge of dynamic phenomena occurring in force-excited drill string systems with an account of the energy source properties. This may require advanced and precise equipment to measure and diagnose vibration parameters using an orbit model, frequency spectrum, and wavelet synchrosqueezing techniques. The research conclusions of this work offer theoretical guidance for detecting subterranean structures and preventing failures in rotating dynamic systems. The results will also provide a reference for controlling drilling parameters in a fluid medium. The remainder of this paper is organized as follows: in Section 2, theory on the modeling of the drill string system with a coupled axial/lateral/torsional displacement is established in detail, and the review and the principles of nonlinear frictional drill string-casing contact under the influence of full torque applied on the drilling system are expressed. In Section 3, the mathematical model of the inviscid fluid is established and coupled with the vertical rotor equation to generate the governing equations of the fluid-rotor system. In Section 4, the practical implementation is described, and the impulse extraction algorithm is presented. In Section 5, the performance of the method in terms of orbit model, frequency spectrum, denoise robustness, and energy concentration is verified by the drill string fault simulation signal. Real signal analysis is also extracted based on the proposed method applied to complex drill string fault cases, such as variable speed conditions and multipoint defects. Finally, the conclusion is obtained in Section 6.
## 4 Data Collection Platform: MarSense  
MarSense is a software platform specially developed for smartphone sensor data collection. In previous studies, sensor data were mostly collected by wearing special equipment, which has a great impact on users' daily life and is not convenient for more users to participate in data collection. With the large-scale popularization and rapid development of smartphones, smartphones are generally equipped with a variety of physical sensors. Therefore, using smartphones to collect data can reduce the interference in users' daily life,since users can complete the data collection by some simple interaction, which reduces the cost of data collection and the impact on users' life. Therefore, more participants will participate in the data collection experiment, which is conducive to involving more users and improving the performance of the model in real-life scenarios. The overall architecture of the MarSense platform is shown in Figure 5.  
MarSense is mainly composed of two parts:  
1. **Android client**. Android client is the core of the whole platform, which is to collect sensor data and labels. Data collection is automatically completed by the built-in sensor of the mobile phone, while label collection will be completed by interacting with the users according to the experimental setting.
2. **Server and database**. The main function of the server is to provide the experimental data with storage, retrieval, and download services. In the MarSense client, the experimental data will be stored in a file in the form of a log. At an appropriate time, the log file will be uploaded to the Object Storage Service (OSS) of the server through the Hyper Text Transfer Protocol (HTTP). These log files are always stored in OSS. At the beginning of data pre-processing, the log data generated by the experiment will be retrieved and downloaded from OSS through corresponding tools, and these data will be used for filtering, segmentation, and transformation, and finally assembled into an experimental dataset for model training.  
After the model training, we use the built-in smartphone sensors to collect the data and input the data into the model for activity recognition and finally display the results of the activity recognition on the smartphone. The mobile phone is only responsible for data collection, data uploading, and result display. The Android client calls the built-in sensors of the system for data collection and short-term storage. When the data volume reaches the length of the window, it will be uploaded to the server. The server inputs the data into the model and obtains the classification results of activity recognition. The result will be sent and displayed to the Android client to realize real-time recognition. The flowchart of activity recognition using MarSense is shown in Figure 6.
## 4 Results and Discussion
### Tensile Strength  
The results of the splitting test are shown in Figure 9a-c for water-to-binder ratios of 0.44, 0.49, and 0.62, respectively. The improvement trend of tensile strength is the same as the compressive strength. The highest tensile strength (4.9 MPa) is about 10.6% more than the control samples belonging to the samples with water-to-binder ratio of 0.44 and replacement of 1% nano-MgO after 56 days of curing. Maximum improvement in the tensile strength for a water-to-binder ratio of 0.49 is obtained by replacing 1.5% nano-MgO after 56 days of curing; the tensile strength is 4.3 MPa, which is 3.3% more than the control samples. As discussed in the previous section, the expanding effect of nano-MgO causes excessive inner pressure after the completion of hydration, which leads to the creation of micro-cracks in higher percentages of nano-MgO usage. The other result of Figure 9 is that the optimum amount of nano-MgO differs by variation of the water-to-binder ratio. For example, the optimum amount of nano-MgO for a water-to-binder ratio of 0.44 and 0.49 is 1% and 1.5%, respectively. While for a water-to-binder ratio of 0.62, the optimum amount is 0.5%, and the reason can be because of the high viscosity of the fresh concrete mixture due to high special surface of nano-MgO. The viscosity becomes extensively high at higher water-to-binder ratios, which can be the reason for the non-uniform dispersion of nano-MgO, which decreases the compressive and tensile strength.  
Figure 8: Compressive strength vs. age of concrete for water-to-binder ratios of (**a**) 0.44, (**b**) 0.49, and (**c**) 0.62.  
Figure 9: _Cont._
## 6 Conclusions  
In this study, a combined testing method of the transmission wave method in the pile and the dynamic penetration test was used to test the effect of base post-grouting on the base soil layer. The proposed combined testing method can effectively estimate the effect of base post-grouting on the base.  
1. The influencing range of base post-grouting was firstly evaluated by the transmitted wave method in the pile. The depth of the pile base reinforcement was 2 m for No. 1 pile, and 1 m for No. 2 pile, which was consistent with the grouting amount.
2. The strength of the soil before and after grouting under the same pile base was measured by the dynamic penetration method. The elastic modulus of the base soil after post-grouting was about twice that of the base soil before grouting.
3. The properties of the base soil increased with the grouting amount. The bearing capacity of the base soil layer increased from 35.5% to 41.7%, as the grouting amount increased from 250 kg to 500 kg.  
Conceptualization, H.H. and J.Z.; data curation, H.H.; formal analysis, H.H., Q.J. and F.Y.; funding acquisition, J.Z.; investigation, H.H. and J.M.; methodology, H.H., J.Z. and X.G.; project administration, J.Z. and X.G.; resources, J.Z. and X.G.; software, H.H.; supervision, H.H.; validation, H.H.; wriating--original draft, H.H.; writing--review and editing, Q.J., F.Y., J.G. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (52108350 and 52078457).  
Not applicable.  
The authors declare no conflict of interest.
# Hybrid Dual-Polarization Synthetic Aperture Radar
## Appendix B Quad-pol Reflections  
The significance of the unsupervised classification attaining a result better than its quad-pol derived competition (Section 4.2) has an implication that goes beyond the cited agricultural application. It is proof by example that _conventional decompositions of quad-pol data are sub-optimum_.  
A quad-pol SAR collects twice as much data per pixel than does a compact-pol system, which, being a dual-pol radar (hence inferior, as quad-pol advocates have asserted), has access only to the backscattered field, not the complete polarization signature of the scene. It would be tempting to presume that twice as much data collected should imply that classifications using those data should be twice as good. One takes for granted that access to more data about the observed scene should lead to much better polarimetric information retrieval. Evidently, that is not the case. Why, then, are the classification results from the two architectures so comparable?  
There are three possible reasons: (_i_) the extra data contain no additional information; (_ii_) the usual quad-pol decomposition techniques do not take the extra data into account; or (_iii_) the extra data are accounted for, but they have very little influence on the classification.  
The first candidate reason must be false. The second reason is likely to be at least partially true. The third reason is certainly true. For example, it is known that the data from a quad-pol SAR are necessary and sufficient to derive the polarimetric signature of the scene [36], which cannot be done with the data collected by a CP SAR. However, decomposition schemes in popular use do not include derivation of a scene's polarization signature.  
One must conclude that most quad-pol decomposition results derive primarily only from polarimetric constituents conveyed by the field backscattered by the polarization actually transmitted. If so, then one should expect that there should be far more similarity between HCP and QP polarimetric measurements than is generally presumed, at least as seen through comparisons of their respective classification accuracies. Stated another way, given the state of the art, HCP classifications should be comparable to results derived from QP data, an expectation which is borne out by most well-done studies.
# Blockchain-Based Loyalty Management System
## 4 Discussion
### System Benefits  
The designed system architecture offers countless benefits for loyalty program ecosystems; it helps to reshape the traditional approach for a more modern, intuitive, and secure model.  
The main benefits of the system are the following:  
* The platform fully embraces decentralization and leverages the power of the ERC-1155 token to efficiently manage tokens and reduce the number of contracts required.
* The system provides near real-time transaction speed, allowing users to redeem their rewards faster without having to wait too long;
* Due to the behavior of smart contracts, third-party tools and intermediaries are no longer necessary;
* The system helps to reduce costs. As intermediaries and third-parties are abolished, retailers can save money. Furthermore, the infrastructure needed to manage the loyalty program is transferred to the blockchain;
* Blockchain offers a secure infrastructure;
* A single digital wallet can be used to store all retailers' tokens, eliminating the problem of managing rewards from multiple brands. It also gives complete control to the user;
* The platform accommodates all distinct levels of user knowledge, allowing more experienced users to use their wallet providers, and inexperienced people to have a smoother first contact with it;
* The platform is capable of managing several types of loyalty mechanisms, increasing the diversity of loyalty programs. In addition, the platform welcomes any retailer of any business size. This makes it easier for new retailers to join and customers with more options to choose from;
* It provides a marketplace with all available loyalty programs. On the one hand, the marketplace helps retailers gain greater exposure to attract more customers. On the other hand, customers have a better access point to all available loyalty programs, meaning they are able to discover new businesses;
* The system takes advantage of the fact that smartphones are increasingly present in people's lives, thus enhancing the overall experience.
## 4 Methodology and Setup
### Evaluation Methodology
being tested, and conduct the experiments using RF coaxial cables while each board is enclosed in an individual Faraday cage. Second, when both transmitter and interferer devices use the same modulation, it is possible that the receiver synchronizes to the header transmitted by the interferer. To avoid this, the interferer device uses continuous transmit mode and starts transmitting 10 ms before the transmitter device starts sending data packets.
# Plant Exosomal Vesicles: Perspective Information Nanocarriers in Biomedicine
## 4 Biochemical Characterization of Nanovesicles  
The composition of nanovesicles varies depending on the source of their isolation. However, all of them contain specific proteins, messenger RNAs (mRNAs), microRNAs (miRNAs), and other small non-coding RNAs, as well as lipids and low-molecular-weight metabolites. Some established proteins and miRNAs frequently found in the exosomes are given in Tables 1 and 2. The nanovesicles contain two main groups of lipids: phospholipids and glycolipids, which comprise their membrane and are necessary for proper formation, release, and targeted delivery. Many nanovesicles contain phosphatidic acid that modulates membrane fission and fusion [35]. The plant nanovesicles are also enriched in transmembrane proteins, such as tetraspannins (TETs), which are an integral part of the membrane and involved in the movement and recognition of the nanovesicles. TETs are analogues of animal protein markers of exosomes (CD9, CD63, CD81) [36]. In addition, the nanovesicles contain the plant-specific secretory syntaxin called penetration 1 (PEN1 = SYP121). The plant-specific protein PEN1, an important player in plant cell wall biogenesis and modification, is required for fungal pathogen resistance [37; 38]. PEN1, together with the GTPase Rab, forms complexes in the exosomes that stimulate membrane fusion and regulate their transport [39]. The vesicles often contain proteins associated with the plant defense system, namely chitinases, peroxididases, \(\beta\)-glucosidaseases, and others [40]. Interestingly, a negative regulator of the plant immunity (the RPM1-interacting protein RIN4) which inhibits the development of the cell response towards the pathogen-associated molecular patterns (PAMP) has been also found in the nanovesicles [41]. Apart from the proteins listed above, the exosomes may contain a large number of unclassified proteins that have probably a random origin. Thus, more than 200 proteins were found in the nanovesicles from the model plant _Arabidopsis thaliana_, but most of them were present in trace amounts [35]. It is evident that the plant species, tissue, organ, physiological state, and other factors could influence both protein composition and high the variabilities of the published data (Table 1). In addition, the method used to isolate the nanovesicles influences their contents greatly. It is known that the highly purified fractions of the exosomes from the human placenta contain a relatively small amount of the main proteins, therefore other reported samples have rather impurities associated with their preparation [42]. Considering the fact that the study of plant exosomes is still at the initial stage, the number of proteins found in them can be greatly exaggerated.
## 4 Results and Discussion
### Comparison of SOE Estimation at Different Temperatures  
Temperature can also significantly influence battery performance by altering the rate of electrochemical reactions within the battery and modifying key parameters such as internal resistance and depth of discharge [29]. To verify the performance of the proposed model across various temperatures, data from the dataset at 15 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 35 \({}^{\circ}\)C are utilized to test and estimate SOE. Figure 5 shows the results at 25 \({}^{\circ}\)C. To avoid redundancy, only the results from the remaining two temperatures are presented in this part.  
Table 3 illustrates the three evaluation criteria of SOE estimation results at various temperatures. At a temperature of 15 \({}^{\circ}\)C, the RMSE and MAE decreased from 1.85% and 1.58% to 0.89% and 0.71% without mechanical force and with mechanical force, respectively, and the maximum absolute error significantly reduced from 7.32% to 2.10%. Similarly, the estimation results with force at a temperature of 35 \({}^{\circ}\)C showed RMSE of 0.76% and MAE of 0.69%. The maximum absolute error was reduced from 7.25% to 2.58%. After combining the results at three temperatures and introducing the force signal, the RMSE was found to be less than 0.9%, the MAE less than 0.8%, and the MAXE less than 3%. By comparing the results in Sections 4.1 and 4.2, it was found that after introducing mechanical force as an input feature, the average improvements in MARE, MAXE, and MAE were 67.06%, 66.38%, and 66.46%, respectively.  
Figure 8: SOE estimation results under different operating conditions: (**a**) SOE curves under FUDS; (**b**) SOE errors under FUDS; (**c**) SOE curves under US06; (**d**) SOE errors under US06.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Data** & **Test Data** & **RMSE\%** & **MAXE\%** & **MAE\%** \\ \hline DST, BJDIST & US06 (15 \({}^{\circ}\)C) & 1.85 & 7.32 & 1.58 \\ (without force) & US06 (35 \({}^{\circ}\)C) & 2.65 & 7.25 & 1.84 \\ \hline DST, BJDIST & US06 (15 \({}^{\circ}\)C) & 0.89 & 2.10 & 0.71 \\ (with force) & US06 (35 \({}^{\circ}\)C) & 0.76 & 2.58 & 0.69 \\ \hline \hline \end{tabular}
\end{table}
Table 3: SOE estimation results at different temperatures.  
Furthermore, the proposed model demonstrates excellent capability in capturing the complex nonlinear mapping relationships among current, voltage, temperature, mechanical force, and SOE, while maintaining high accuracy of SOE estimation and robustness across different temperatures. As shown in Figure 9, the estimation results with force are much better than those without force.
# SVDC: Synchronous Update with the Division and Combination of SRv6 Policy
## 3 Policy-Dividing and Combination Mechanism in SUDC
### SRv6 Policy Combination  
The SRv6 policy combination module aims to combine policy distribution SID blocks to generate complete SRv6 policies. This function is executed at the data plane, and the policy target SRv6 nodes execute this function. Each relevant SRv6 node combines the received policy distribution SID blocks in ascending order of sequence number to generate a complete SRv6 policy. The destination Internet Protocol version 6 (IPv6) address matched by the combination-complete policy is the last address of the SID list. Then, the node writes the combination-complete SRv6 policy to the SRv6 node pipeline.  
We continue to illustrate the process of the SRv6 policy combination using the example given in the SRv6 policy-dividing module.  
After SRv6 policy pushing, node a receives two policy distribution SID blocks, which are shown in the first and second lines of Table 2. Node a combines the received SID blocks in ascending order of sequence number to generate the complete SRv6 policy, 1->2->3->6->7->9->5->4->8. The destination IPv6 address matched by the combination-complete policy is the IPv6 address of node 9. Then, node a writes this policy to its SRv6 pipeline.  
After SRv6 policy pushing, node b receives one policy distribution SID block, which is shown in the first line of Table 2. Node b receives one policy distribution SID block, so the complete SRv6 policy is 1->2->3->6->7. The destination IPv6 address matched by the combination-complete policy is the IPv6 address of node 7. Then, node b writes this policy to its SRv6 pipeline.  
After SRv6 policy pushing, node c receives two policy distribution SID blocks, which are shown in the second and third lines of Table 2. Node c combines the received SID blocks in ascending order of sequence number to generate the complete SRv6 policy, 9->5->4->8->3->2. The destination IPv6 address matched by the combination-complete policy is the IPv6 address of node 2. Then, node c writes this policy to its SRv6 pipeline.
## 3 The Proposed Coverage Flight Path Planning Algorithm
### Modelling of the UAV Operating Environment  
After getting the coverage mission specification, the proposed algorithm models the UAV operating environment. Environment modelling usually refers to the classification of free spaces \((Q_{free})\) and obstacles regions \((Q_{obstacles})\). \(Q_{obstacles}\) refers to the parts of the AOI \(Q\) where the UAV cannot fly due to the presence of obstacles. In contrast, \(Q_{free}\) refers to those parts of \(Q\) where UAV can operate without collision with obstacles. The obstacles' regions are represented with the geometrical shapes (i.e., squares, cubes, circles, and cylinder, etc.). In this work, we model the UAV operating environment from the raw environment containing the elevation data about the urban environment buildings and apartments, etc. with a set of 3D convex obstacles by calculating the convex hull. Each obstacle has random width, length and altitudes depending upon the real environment geometry. Each obstacle has eight vertices and six faces. The obstacle vertices \(u\) in the modelled environment can be represented with three co-ordinates values, \(u=(x,y,z)\). All eight vertices of the \(i\)th obstacle along with their numerical values can be mathematically expressed as following matrix.  
\[O_{i}=\begin{bmatrix}x_{min}&y_{min}&z_{min};x_{min}&y_{min}&z_{max}\\ x_{min}&y_{max}&z_{min};x_{min}&y_{max}&z_{max}\\ x_{max}&y_{min}&z_{min};x_{max}&y_{min}&z_{max}\\ x_{max}&y_{max}&z_{min};x_{max}&y_{max}&z_{max}\\ \end{bmatrix}=\begin{bmatrix}874&909&0;874&909&252\\ 874&1008&0;874&1008&252\\ 954&909&0;954&909&252\\ 954&1008&0;954&1008&252\\ \end{bmatrix}.\]  
The source location is represented with a 3D point \(p\), where \(p=(x_{s},y_{s},z_{s})\) and destination location is represented with a point \(q\), where \(q=(x_{t},y_{t},z_{t})\). In some coverage mission, the source \(p\) and destination \(q\) are the same because the UAV returns to the starting location after completing the mission. The objective of the proposed algorithm is to find a coverage path \(\zeta\) that guarantees the perfect coverage of \(Q\). The path \(\zeta\) consists of nodes set which can be defined as, \(\zeta\in Q_{free}\) and \(\zeta\cap Q_{obstacles}=\emptyset\).