esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This article investigates the thixotropy of the structured clay of Zhanjiang and the time-dependent uplift bearing capacity of pile foundations through direct shear tests of the surrounding soil and static uplift load tests on the piles, leading to the following conclusions.  
1. The internal friction angle and cohesion of the Zhanjiang Group structured clay show a significant time effect as the rest time increases after disturbance. The increase is rapid in the initial stage of the test but slows down in the later stage, which is the primary reason for the increase in uplift capacity of the piles in this foundation soil.
2. With the increase in rest time, the uplift bearing capacity of the model pile shows a clear time-dependent effect. When the bearing capacity is insufficient in the early stages of pile driving, the increase in pile foundation bearing capacity due to the time effect can be taken into account when reasonably scheduling the project.
3. The axial force of the model pile decreases rapidly in the lower part of the pile shaft and is nearly zero at the pile tip, indicating that the pullout bearing capacity of the pile is entirely provided by the side friction resistance. The side friction resistance and the ultimate side friction resistance of the pile are mainly distributed in the lower part of the pile shaft, and they show a significant increase with the prolongation of the resting time.
4. A predictive model for the axial pullout ultimate bearing capacity that considers the time-dependence of Zhanjiang Group structured clay has been established. The validity of the predictive model has been verified through model experiments. This provides a reference for predicting the axial pullout bearing capacity of piles in the Zhanjiang Group structured clay foundation in engineering practices.  
Figure 8: Curves of calculated values and measured values of the bearing capacities of pile foundations.  
**Author Contributions:** Conceptualization, Y.Y. and T.B.; funding acquisition, T.B.; investigation, Y.Y., T.B., Y.W., X.H. and Z.Y.; project administration, Y.Y. and T.B.; validation, Y.Y., T.B., Y.W., X.H. and Z.Y.; writing--original draft, Y.Y. and T.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project was financially supported by the National Natural Science Foundation of China (No. 42367020) and Guangxi Natural Science Foundation project (No. 2024 GXNSFAA010450).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Chemical Characterisation of Pinus spp. Bark Polar Extracts
#### 3.2.1 Qualitative Analysis
* Other compounds  
The identification of quinic acid (**1**) (Figure 2) was based on the characteristic UV molecular absorption spectrum, the detection of the [M\(-\)H]\({}^{-}\) at \(m/z\) 191, and the corresponding MS\({}^{2}\) spectrum, which showed a base peak at \(m/z\) 111 and the characteristic product ions at \(m/z\) 173 ([M\(-\)H\(-\)H\({}_{2}\)O]\({}^{-}\)) and \(m/z\) 93 [13]. This compound has already been found in Pycnogenol\({}^{\circledR}\)[13], whereas it was identified for the first time, in this work, in _P. pinaea_ L. bark. After confirmed by authentic standard, compound **2** was assigned as protocatechuric acid (Figure 2). In fact, this phenolic compound had been previously identified in _P. pinaster_ Ait. [10] and _P. pinaster_ Ait. var. _moghrebiana_ barks [61], as well as in commercial pine bark extracts [13; 55]. In contrast, protocatechuric acid was identified for the first time in _P. pinaea_ L. bark, to the best of our knowledge. Compound **15** was identified as quercetin, whose retention time, molecular absorption spectrum, [M\(-\)H]\({}^{-}\) at \(m/z\) 301, and MS\({}^{2}\) spectrum were concordant with those of the authentic standard. This flavonoid has been found in a commercial concentrated extract [56] and hydroethanolic extracts [10; 37], derived from _P. pinaster_ Ait. bark, but it has been reported here for the first time as a _P. pinaea_ L. component.
# A Cooperative Transmission Scheme in Radio Frequency Energy-Harvesting WBANs
## 4 Cooperation Scheme
### Initialization Phase
\begin{bmatrix}c_{11}&c_{12}&\ldots&c_{1n}\\ &\ldots&&\\ c_{m1}&c_{m2}&\ldots&c_{mn}\end{bmatrix} \]  
\[M_{2}= \begin{bmatrix}a_{1}&a_{2}&\ldots&a_{n}\end{bmatrix} \]  
\[M_{3}= \begin{bmatrix}b_{1}&b_{2}&\ldots&b_{m}\end{bmatrix} \]  
\[M_{4}= \begin{bmatrix}l_{1}&l_{2}&\ldots&l_{m}\end{bmatrix} \]Values \(a_{i}(i\in[1,n])\) and \(b_{j}(j\in[1,m])\) denote the probabilities that packets can be received by the coordinator correctly; these are uploaded by common node \(s_{i}\) or relay node \(r_{j}\). The coordinator updates these values per superframe according to whether the packets of the corresponding nodes are received correctly. Value \(l_{i}\) denotes the maximum data length that can be relayed by relay \(r_{i}\), which is calculated as  
\[l_{i}=R\frac{E_{i}-E_{i}^{*}}{P_{i}^{*}+p_{r}}, \tag{10}\]  
where \(p_{r}\) denotes the power used when receiving signals. Value \(c_{ij}\) denotes the probability that packets can be received by relay \(r_{i}\) correctly when common node \(c_{j}\) broadcasts packets. During the initialization phase, each relay cyclically monitors the timeslots used by common nodes according to the guidance of the coordinator and obtains the initial values of \(c_{ij}\). Relay node \(r_{i}\) packs related values \(l_{i}\) and \(c_{ij}\) into its packets, and the coordinator updates the corresponding data after receiving the packets.
# Low-Thrust Transfer to Quasi-Synchronous Martian Elliptic Orbit via Nonlinear Feedback Control
###### Abstract  
This study considers the problem of injecting a spacecraft into an elliptic, repeating-ground-track orbit about Mars, starting from a 4-sol highly elliptical orbit, which is a typical Martian capture orbit, entered at the end of the interplanetary transfer. The final operational orbit has apoares corresponding to the maximum (or minimum) latitude, and nine nodal periods are flown in 5 Martian nodal days. The orbit at hand is proven to guarantee coverage properties similar to the Molinya orbit about Earth; therefore, it is especially suitable for satellites that form constellations. Low-thrust nonlinear orbit control is proposed as an affordable and effective option for orbit injection, capable of attaining significant propellant reduction if compared to alternative strategies based on chemical propulsion. This work introduces a new, saturated feedback law for the low-thrust direction and magnitude that is capable of driving the spacecraft of interest toward the operational orbit. Remarkable stability properties are proven to hold using the Lyapunov stability theory. Because no reference path is to be identified a priori, this technique represents a viable autonomous guidance strategy, even in the case of temporary unavailability of the low-thrust propulsion system or in the presence of widely dispersed initial conditions and errors on estimating orbit perturbations. Monte Carlo simulations prove that the feedback guidance strategy at hand is effective and accurate for injecting a spacecraft into the desired, repeating-ground-track operational orbit without requiring any reference transfer path.  
low-thrust orbit transfers; Mars missions; nonlinear orbit control; Martian constellations 20232002310670[[https://doi.org/10.3390/aerospace10080670](https://doi.org/10.3390/aerospace10080670)]([https://doi.org/10.3390/aerospace10080670](https://doi.org/10.3390/aerospace10080670))
# Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm
###### Abstract  
Positioning is an essential element in most Internet of Things (IoT) applications. Global Positioning System (GPS) chips have high cost and power consumption, making it unsuitable for long-range (LoRa) and low-power IoT devices. Alternatively, low-power wide-area (LPWA) signals can be used for simultaneous positioning and communication. We summarize previous studies related to LoRa signal-based positioning systems, including those addressing proximity, a path loss model, time difference of arrival (TDoA), and fingerprint positioning methods. We propose a LoRa signal-based positioning method that uses a fingerprint algorithm instead of a received signal strength indicator (RSSI) proximity or TDoA method. The main objective of this study was to evaluate the accuracy and usability of the fingerprint algorithm for large areas in the real world. We estimated the locations using probabilistic means based on three different algorithms that use interpolated fingerprint RSSI maps. The average accuracy of the three proposed algorithms in our experiments was 28.8 m. Our method also reduced the battery consumption significantly compared with that of existing GPS-based positioning methods.  
long-range (LoRa); low-power wide-area (LPWA); fingerprint algorithm; positioning; localization; Internet of Things (IoT) +
Footnote †: journal: Computer Vision and Image Processing  
1
## 5 Results and Discussion
### Action Recognition Performance

Table 1: Description of the different labels used in Figure 8.  
Figure 8: The confusion matrix of the proposed methodology for the UCF Sports dataset. of key frame extraction, as key frame extraction not only reduced the computational cost of extracting skeleton features, since only 10-20% of the frames in a video have to be processed, but it also improved the action recognition performance by almost 4%. Moreover, it was also helpful to increase the frame rate from 18 to 24 frames per second (FPS) (i.e., the proposed framework can process video input at a higher frame rate of 24 FPS for action, emotion and face recognition in real-time). Extraction of skeleton features from the key frames alone reduced the size of the training vectors by leaving out redundant and potentially irrelevant features, which resulted in better generalization performance and reduced training time for the DDNet. Table 3 presents the comparison of different methods for action recognition on the self-collected HNH dataset. The proposed key frame-based skeleton feature estimation and action recognition network (KFSENet) achieved an accuracy of 98.9%. The other two methods were different from the proposed method in only one respect: the human skeleton features for those two methods were extracted from all the frames in the video, whereas for the proposed KFSENet, the human skeleton features were extracted only from the key frames. Thus, key frame extraction had a significant contribution toward the comparatively better accuracy of the proposed method. Moreover, the results in Table 3 also demonstrate that HRNet yielded higher accuracy than OpenPose; however, Openpose could achieve a relatively higher frame rate compared with HRNet.
## 2 Materials and Methods
### System Performance Evaluation
#### 2.4.1 Stability Evaluation of Visible and Thermal Image Acquisition  
To quantitatively evaluate the stability of visible light images and thermal infrared images, this paper innovatively proposes the Difference Structural Similarity Index Measure (DSSIM) based on the structural similarity index to eliminate the influence of illumination brightness differences in different images [28; 29; 30]. In the actual calculation process, DSSIM replaces the brightness \(\mu_{y}\) of image \(y\) with the brightness value \(\mu_{x}\) of the reference image \(x\), so that only the differences in contrast and structure are considered during the calculation, and the brightness difference between the images is not considered. DSSIM was calculated from  
\[DSSIM(x,y)=\frac{\left(2\mu_{x}^{2}+c_{1}\right)\left(2\sigma_{xy}+c_{2} \right)}{\left(\mu_{x}^{2}+\mu_{x}^{2}+c_{1}\right)\left(\sigma_{x}^{2}+\sigma _{y}^{2}+c_{2}\right)}=\frac{\left(2\sigma_{xy}+c_{2}\right)}{\left(\sigma_{x} ^{2}+\sigma_{y}^{2}+c_{2}\right)} \tag{1}\]  
where \(\sigma_{x}\) and \(\sigma_{y}\) are the variances of image \(x\) and image \(y\), respectively, representing the estimate of contrast; \(\sigma_{xy}\) is the covariance between image \(x\) and image \(y\), reflecting the structural similarity between the two images; \(c_{2}\) is a stability constant used to avoid a zero denominator. It is usually set as \(c_{2}=\left(K_{2}\times L\right)^{2}\), where \(L\) is the dynamic range (usually 255), and \(K_{2}\) is a small constant (usually 0.03). The closer the value of DSSIM is to 1, the smaller the difference between the images.  
In addition, the maximum error and the mean absolute error of the acquired data are also used to comprehensively measure the stability of the data acquired by the system developed in this paper.
## 2 A Brief Comment on Geometrophysical Modeling
### Relevance of Geometrophysical Modeling/Examples  
Understanding geometrophysical modeling is essential for anyone involved in the planning, design, or regulation of highways. It provides a scientific basis for making decisions affecting the safety and functionality of road systems, ensuring that these infrastructures can accommodate the current and future transportation needs effectively. Here there are some examples. [PERSON] (2016) uses spatial satellite remote sensing data integrated with geographic information system (GIS) models to optimize highway design in [19]. It includes factors like land use, geology, and environmental impacts to select the best route corridor for minimizing costs and conforming to environmental requirements. [PERSON] et al. (2009) modeled highway systems using a modified geometrical network model, integrating paths instead of edges for optimal parameter configurations, and comparing models from different countries in [20]. In another example, [PERSON] et al. (2020) explore the integration of building information modeling (BIM) with procedural modeling tools for road infrastructure, focusing on geometric and physical aspects. The use of BIM for complex road design is highlighted, enhancing data interoperability and management of geometric properties critical for transportation infrastructure in smart urban environments [21]. [PERSON] et al. (2023) introduce a framework intended to enhance geometric information extraction and digital modeling from LiDAR data, targeting road scenarios. This approach addresses the challenge of handling unorganized point clouds and complex environments, demonstrating high accuracy in semantic segmentation and geometric precision, which is crucial for practical applications in road engineering [22]. [PERSON] et al. (2023) present a method for three-dimensional fine modeling of in-service roads using vehicle laser scanning technology. It focuses on multi-level reverse modeling based on point cloud data, offering a structured approach to reduce data redundancy and enhance model accuracy. The method supports various levels of detail (LOD), allowing for tailored model fineness and application-specific accuracy, which proves effective in managing complex road infrastructures [23]. [PERSON] et al. (2023) present an innovative approach for simultaneous multi-curve highway reconstruction from mobile laser scanning data, leveraging deep reinforcement learning with the proximal policy optimization algorithm. This method allows for the effective geometric modeling of complex highway curves, demonstrating superior performance in capturing accurate road configurations compared to the previous techniques [24]. These papers provide a range of instances related to geometrophysical modeling applied to the context of roads and highway engineering, highlighting advancements such as GIS integration, 3D geometric modeling, and network optimization in transportation planning.
# Analysis and Identification of Nonlinear Acoustic Damping in Miniature Loudspeakers
## 4 Experimental Validation and Discussion  
Experimental validations were conducted to verify the effectiveness of the identification algorithm proposed in Section 3 for nonlinear acoustic damping. A miniature loudspeaker with dimensions of 12 \(\times\) 16 \(\times\) 2.8 mm was selected as the experimental object. The opening area of the orifice on the front cover was selected as the control variable. Three front covers with different opening areas were fabricated and mounted in turn on the same miniature loudspeaker. Nonlinear acoustic damping and other nonlinearities were identified. The geometric parameters of the orifices on the front covers are listed in Table 1.
## 3 The Time Source Techniques
### Low Earth Orbit  
Low Earth Orbit (LEO) satellites offer an alternative option for timing applications, providing unique advantages due to their proximity to Earth.
# Reliability and Risk Centered Maintenance: A Novel Method for Supporting Maintenance Management
## 4 Case Study
### RRCM Application Discussion  
The demonstration of the RRCM method in this case study contributed to highlighting its distinct features and evidence of how it expands upon the capabilities of individual RCM and RBM methods. In essence, the proposed RRCM method integrates the key aspects from both methods into a singular and innovative methodology that supports maintenance management to economically determine maintenance plans while considering reliability and risk. Consequently, the outcomes derived from this RRCM application, as presented in Tables 2, 7 and 8, could not have been achieved through the implementation of either RCM or RBM separately. For better comprehension, Table 9 presents a comparison of the applications of traditional RCM [13] and RBM [14] methods, as demonstrated by case studies from the literature review, with the proposed RRCM. All the applications were analyzed considering aspects such as item study, risk estimation and evaluation, maintenance planning, and periodic review, as presented in Table 1.  
While the traditional RCM method shares similarities with the proposed RRCM in terms of conducting item studies, it lacks the inclusion of activities for risk estimation and evaluation, which are incorporated for the selection of appropriate failure management policy for each failure mode in RRCM. It is worth mentioning that some recent applications of RCM may include a risk estimation and evaluation activity that derives a metric for failure mode prioritization [35; 36]. However, it is not usually considered for the selection of the failure management policies, concentrating on the use of the failure consequences for that purpose. Accordingly, the proposed FMRL classification of RRCM, which addresses risk estimation and evaluation and drives the determination of the appropriate failure management policies and, consequently, the maintenance tasks, offers a more comprehensive perspective compared to the FCC classification of RCM [13]. The quantification of hazards and the probabilistic assessment to classify the risk associated with the failure mode is typically exclusive to the RBM method [39; 40; 41; 42; 43]. Thus, compared to traditional RCM, RRCM provides a risk assessment that is considered for the selection of appropriate failure management policies. Additionally, the proposed RRCM method advances the field by providing enhanced support during the maintenance planning decision-making, offering more detailed decision diagrams and guidelines to verify the applicability, technical feasibility, and effectiveness of a potential maintenance task.
## 3 System Model
### Network and Security Assumptions  
Vehicles are already registered with the Home TA, and the vehicle and the Home TA can establish a secure communication link. In practice, this can be achieved via PKI and secure network technologies such as VPN, TLS/SSL, and HTTPS. Moreover, new vehicles entering the V2X network area have a way of identifying V2X network details, which can be achieved by broadcasting network information from RSU, especially RSUs in the edge of the V2X coverage area. An example can be from the tall plaza of a highway for motorway entries and entry locations of a city.  
The V2X network provides a way to communicate with the Home TA for new vehicles. Fifth generation-enabled vehicles can directly connect to the Home TA via the 5G network, and Wi-Fi-only vehicles will use a RSU to connect to the Home TA.  
It is assumed that each vehicle carries a unique identity UID, which is based on Physical Unclonable Functions (PUFs) [35; 36] that are unique and tamper-resistant during their lifetime.  
The work of [PERSON] used the random oracle model [38] to validate the cryptographic scheme, and the security of the scheme is based on the computational Diffie-Hellman (CDH) assumption [39]. That relies on the problem of computing the discrete logarithm in cyclic groups. Consider a cyclic group \(\mathbb{G}\) of order q The CDH assumption states that, given \(g,g^{a},g^{b}\) for a randomly chosen generator g and random \(a,\ b\ \in\{0,\ldots,\ q-1\}\), it is computationally intractable to compute the value \(g^{ab}\). The CDH assumes this cannot  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline T & This is the threshold value users need to supply based on specific security requirements. \\ \(p\) & This should be a large prime number (\(p>100\)). \\ q & This is a factor of \(p\)-1 which satisfies \(p\)-1 is divisible by \(q\). \\  & This is a generator for a group \(\mathbb{G}\), if the group of elements \(\{\ g^{0},g^{1},g^{2},\ldots\}\) \\ g & is precisely the group \(\mathbb{G}\); that is, every element \(h\in\mathbb{G}\) can be expressed as \(h=g^{l}\) for some i, and conversely, for every i, \(g^{i}\in\mathbb{G}\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Global cryptographic parameters.  
be solved quickly with current computational power. However, if computing the discrete logarithm (base g) in \(\mathbb{G}\) is straightforward (such as availability of a supercomputer or quantum computer), the CDH problem can also be solved effortlessly.
###### Abstract  
IMUs (inertial measurement units) and cameras are widely utilized and combined to autonomously measure the motion states of mobile robots. This paper presents a loosely coupled algorithm for autonomous localization, the ICEKF (IMU-aided camera extended Kalman filter), for the weighted data fusion of the IMU and visual measurement. The algorithm fuses motion information on the velocity layer, thereby mitigating the excessive accumulation of IMU errors caused by direct subtraction on the positional layer after quadratic integration. Furthermore, by incorporating a weighting mechanism, the algorithm allows for a flexible adjustment of the emphasis placed on IMU data versus visual information, which augments the robustness and adaptability of autonomous motion estimation for robots. The simulation and dataset experiments demonstrate that the ICEKF can provide reliable estimates for robot motion trajectories.  
loosely coupled visual-inertial measurement; visual inertial odometry; robot autonomous localization; data fusion 2020 IEEEexample:BSTcontrol  
10 December 202414 January 202514 January 202517  
satellite system), LIDAR (light detection and ranging), etc. [9; 10]. Especially for application on the multiagent [11], loosely coupled methods are naturally capable of processing simultaneous multi-source information. Although the estimation accuracy is relatively low owing to the lack of an optimization process, which tight-coupled methods [12; 13] usually perform, the integration of multiple measurement data into a loosely coupled framework is inevitable to address the challenges of environmental uncertainty during practical utilization. Therefore, a loosely coupled data fusion approach that is capable of adjusting the weights of the measurement results from individual sensors or agents is necessary to achieve a balanced output, considering how much the information source can be trusted.