esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Marker Technique to Enhance User Interactions in a Marker-Based AR System
## 3 Virtual Marker Technique
### Why Virtual Markers  
In marker-based AR systems, markers are usually relatively independent. Moreover, markers are predefined by the system creator in advance, and users can only use these predefined markers to complete the construction of certain specified content. Therefore, such systems usually lack flexibility and do not allow users to create freely. However, unless the operation performed by the user is predictable, it is often difficult to accurately provide a large number of physical markers. Therefore, we propose a virtual marker technique to solve these problems. Virtual markers provide two main advantages. The first advantage is that they allow users to use simple template markers to customize content, including function names, variable names, and numbers. This can solve the inconvenience of providing users with a large number of physical markers. The second advantage is that virtual markers can be used to store combined information. That is, the content of the virtual marker can be changed in real time. Users can continuously process and merge content to achieve more complex functions.
# Semantic Distributed Data for Vehicular Networks Using the Inter-Planetary File System
## 3 IPFS (Inter-Planetary File System)
### Quic  
Since version 0.5 (April 2020), IPFS has had support for using QUIC instead of Transmission Control Protocol (TCP) connections. QUIC is a transport-layer network protocol originally designed by Google, part of Chromium open-source code, and published as an Internet-Draft by the Internet Engineering Task Force (IETF) [37]. The QUIC protocol has been recently selected to replace TCP as the default HTTP (Hypertext Transfer Protocol) transport-layer protocol [38]. QUIC uses standard UDP datagrams and it is fully compatible with current IPv4 and IPv6 networks and equipment. Besides, the usage of UDP allows applications such as IPFS to implement QUIC without requiring an update of the underlying operating system. This is a summary of the advantages of QUIC over TCP connections:  
* Built-in security. QUIC connections provide TLS (Transport Layer Security) 1.3 authentication and encryption by default. QUIC is more efficient than TCP plus TLS. For example, a short HTTP request using TCP plus TLS requires two handshakes, for a total of eight messages. The same request with QUIC requires one handshake for a total of 5 messages.
* Improved multiplexing support. Applications can use a single QUIC connection to efficiently transmit multiple streams of data. QUIC keeps independent tracking of every stream, and when one frame is lost, only that frame is retransmitted and only one of the streams is affected. In general, QUIC has better loss recovery and congestion control than TCP.
* Connection migration. Every QUIC connections has an unique identifier. This makes possible to seamlessly continue an ongoing connection in the event of a network handover. This feature is especially useful in mobile networks (e.g., vehicular networks).
* Multipath. Although not yet supported, future QUIC implementations will also include the option to exchange data over multiple networks using a single connection [39]. This feature would increase the performance of IPFS in vehicular networks, as discussed in the experimental section of this paper.
# A Spatial Compounding Method for Non-Delayed Sequential Beamforming
###### Abstract  
We present a new spatial compounding method to improve the contrast of ultrasonic images for non-delayed sequential beamforming (NDSB). Sequential beamforming adopts more than one beamformer to reconstruct B-mode images which has the advantage of simple front-end electronics and fast data transfer rate. Via field pattern analysis, we propose a compounding method where two more sub-images can be reconstructed along with the NDSB sub-image. These sub-images can be seen as being produced with different transmit origins; thus, their summation enhances image contrast. Image quality was analyzed in terms of spatial resolution, contrast ratio (CR), and contrast-to-noise ratio (CNR). The proposed compounding method improves the lateral resolution up to 41%. In vitro results confirm a 13.0-dB CR and 4.0-dB CNR improvement. In vivo results reveal 10.9-dB and 6.0-dB improvement in CR and CNR for cross-section jugular vein and 8.0-dB and 4.5-dB improvement in CR and CNR for the longitudinal-section carotid artery.  
spatial compounding; sequential beamforming; focused imaging; synthetic aperture; beamforming; B-mode +
Footnote †: journal: Applied  
1
## 3 Influence of Physical Properties of NS on Communication, Navigation, and TT&C Integration
### Atmospheric Loss in NS
#### 3.3.4 Influence of NS Plasma Sheath on Electromagnetic Wave
From the simulation results in Figure 8a, it can be seen that as the signal frequency increases, the attenuation coefficient \(k\) and the phase shift coefficient \(m\) also gradually increase, and the influence of the attenuation coefficient of the plasma sheath is greater than that of the phase shift coefficient, playing a dominant role. From the electron density distribution of the spatially inhomogeneous plasma in Figure 8b, as the vertical distance from the measurement position to the surface of the vehicle increases, both the Gaussian distribution and the Epstein distribution, the electron density gradually increases to a maximum value and then gradually decreases. For the NS, under the surfaces of vehicles in the lower and middle altitudes of 20-48 km, using the Epstein distribution for the electron density of the plasma sheath is closer to the real situation, while for the surfaces of vehicles at altitudes above 48-100 km, using the Gaussian distribution is more appropriate [82].
# Comparison of Volatile Profiles of Meads and Related Uniflorl Honeys: Traceability Markers
## 2 Results and Discussion
### Headspace Profiles of the Investigated Mead and the Corresponding Honey Samples
Interestingly, the not boiled meads contained notably more aliphatic esters than the boiled ones, which greatly affects the final aroma. A particularly high difference in ester percentage between the not boiled and the boiled honeys was related to the content of ethyl decanoate and ethyl dodecanoate in the not boiled (up to 23.91% and 13.43%, respectively) and in the boiled (up to 4.26% and 4.84%, respectively). Aliphatic esters are responsible for fruity odors [14]. Ethyl decanoate and ethyl dodecanoate are described, respectively, as sweet, fatty, nut-like, with a winey-cognac odor and oily, fatty, floral, with fatty fruity taste [13; 15]. The percentage of pyran and furan derivatives were in general reduced in the meads compared to the corresponding honey, except for furan derivatives in the honeydew mead. The reduction of furan may be related to polymerization into melanoidins and their precipitation.
## 2 Numerical Approximation of RLFI  
This section presents the GBFA method adapted for approximating the RLFI. We shall briefly begin with some background on the classical Gegenbauer polynomials, as they form the foundation for the GBFA method developed in this study. The classical Gegenbauer polynomials \(G_{n}^{\lambda}(x)\), defined for \(x\in\mathbf{\Omega}_{-1,1}\) and \(\lambda>-1/2\), are orthogonal with respect to the weight function \(w^{\lambda}(x)=(1-x^{2})^{\lambda-1/2}\). They satisfy the three-term recurrence relation:  
\[(n+2\kappa)G_{n+1}^{\lambda}(x)=2(n+\lambda)xG_{n}^{\lambda}(x)-nG_{n-1}^{ \lambda}(x), \tag{1}\]  
with \(G_{0}^{\lambda}(x)=1\) and \(G_{1}^{\lambda}(x)=x\). Their orthogonality is given by  
\[\mathcal{I}_{-1,1}^{(t)}\left(w^{\lambda}G_{n}^{\lambda}G_{m}^{\lambda}\right) =h_{n}^{\lambda}\,\delta_{m,n}, \tag{2}\]  
where  
\[h_{n}^{\lambda}=\frac{2^{1-2\lambda}\,\pi\,\Gamma(n+2\lambda)}{n!\,(n+\lambda )\,\Gamma^{2}(\lambda)}. \tag{3}\]  
The SG polynomials \(\hat{G}_{n}^{\lambda}(t)\), used in this study, are obtained via the transformation \(x=2t-1\) for \(t\in\mathbf{\Omega}_{1}\), inheriting analogous properties adjusted for the shifted domain. In particular, they satisfy the three-term recurrence relation:  
\[(n+2\lambda)\hat{G}_{n+1}^{\lambda}(t)=2(n+\lambda)(2t-1)\hat{G}_{n}^{\lambda} (t)-n\hat{G}_{n-1}^{\lambda}(t), \tag{4}\]  
with initial conditions \(\hat{G}_{0}^{(\lambda)}(t)=1\) and \(\hat{G}_{1}^{(\lambda)}(t)=2t-1\). They are directly related to Jacobi polynomials by the following identity:  
\[\hat{G}_{n}^{(\lambda)}(t)=\frac{n!\,\Gamma\!\left(\lambda+\frac{1}{2}\right)} {\Gamma\!\left(n+\lambda+\frac{1}{2}\right)}J_{n}^{\left(\lambda-\frac{1}{2},\lambda-\frac{1}{2}\right)}(2t-1),\quad t\in\mathbf{\Omega}_{1},\quad n\in \mathbb{Z}_{0}^{+}, \tag{5}\]  
where \(J_{n}^{\left(\gamma_{1},\gamma_{2}\right)}(t)\) is the \(n\)th-degree Jacobi polynomial with parameters \(\gamma_{1:2}>-1\)[32] (Equation (A1)). One may also directly evaluate the SG polynomial of any degree \(n\) using the identity  
\[\hat{G}_{n}^{(\lambda)}(t)=\sum_{m=0}^{\lfloor n/2\rfloor}\frac{(-1)^{m}n!\, \Gamma(2\lambda)\Gamma(n-m+\lambda)}{n!(n-2m)!\,\Gamma(\lambda)\Gamma(n+2 \lambda)}(4t-2)^{n-2m}; \tag{6}\]  
cf. [33] (Equation (D.2)). For a comprehensive treatment of their properties and quadrature rules, see [34; 35; 36; 37]. Let \(\alpha\in\mathbf{\Omega}_{1}^{\circ}\), \(f\in L^{2}(\mathbf{\Omega}_{1})\), and \(\hat{G}_{n}^{\lambda}=\left\{\hat{t}_{n,0:n}^{\lambda}\right\}\).
## 5 Conclusions  
1. Anisotropy significantly impacts the tensile strength of layered shale. Both the BST and DTT demonstrated that tensile strength increases with the loading angle (\(\beta\)). At \(\beta=90\)\({}^{\circ}\), the maximum tensile strengths for BST and DTT are 10.45 MPa and 9.60 MPa, respectively. Conversely, at \(\beta=0\)\({}^{\circ}\), the minimum tensile strengths for BST and DTT are 6.89 MPa and 4.06 MPa, respectively. The anisotropy ratios for BST and DTT are 1.52 and 2.36, respectively, indicating the significant impact of the loading angle on tensile strength.
2. The accumulated AE energy and ringing counts for DTT and BST specimens exhibit distinct trends under different loading angles. For DTT specimens, the accumulated AE energy and ringing counts are generally higher than those of BST specimens. Most DTT specimens display varying degrees of progressive failure characteristics. In contrast, BST specimens typically do not generate significant AE energy or ringing counts during the loading process, with a sharp increase observed only during the final failure stage.
3. Anisotropy also significantly affects the failure modes of DTT and BST specimens. For DTT specimens, the primary failure mode is tensile-shear mixed failure, with pure tensile failure across the bedding plane observed only at loading angles of \(\beta=60\)\({}^{\circ}\) and \(90\)\({}^{\circ}\). Similarly, BST specimens predominantly exhibit tensile-shear mixed failure, except for tensile failure along the bedding plane at \(\beta=0\)\({}^{\circ}\) and tensile failure across the bedding plane at \(\beta=90\)\({}^{\circ}\).  
Figure 13: Typical failure modes of DTT specimen of layered Longmaxi shale. where the letter relates to (A) Tensile failure within the rock matrix, (B) Slipping failure along the bedding plane, (C) Tensile–slipping mixed failure, and (D) More complex tensile–shear failure.  
* The difference between direct and indirect tensile strength is influenced by rock anisotropy, the testing method, and specimen geometry. The results obtained from the L-P criterion, SS criterion, and CPA criterion show good agreement with the measured results of both direct and indirect tensile strength. Furthermore, based on the statistical analysis in this study, estimating the indirect tensile strength as 1.11 times the direct tensile strength provides a reliable approximation for most cases.  
Conceptualization, T.M.; methodology, A.M.F.; investigation, A.M.F., T.M. and H.W.; writing--original draft preparation, A.M.F. and H.W.; writing--review and editing, T.M.; visualization, A.M.F. and H.W.; supervision, T.M.; funding acquisition, T.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Sichuan Province, grant number 2024 NSFSC0023.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 4 Seismic Risk Assessment
### Nonlinear Time History Analysis (NTHA) Results  
The response in terms of the median value of the interstorey drift ratios (MIDR) and peak floor accelerations (MPFA), along the building height, in both principal directions, is of particular interest, with a view to loss assessment. For the sake of brevity, only the results for the intensity measure (IM) level 6, which corresponds to an AvgSa (\(0.1\leq\) T\({}_{1}\) [s] \(\leq\) 2) of 0.28 g, are provided in Figures 7 and 8. A reduction in the MIDR (Figure 7a,b) when increasing the stiffness (also strength) of the masonry infill is highlighted. This behavior corresponds to a stiffening of the structure, as already confirmed by the results of the eigenvalue and pushover analyses. More specifically, for the IM-level 6, going from weak to strong masonry infills, the reduction of the MIDR values is in the range of [38-79%] and [22-76%] for infilled and partially infilled structural configurations, respectively. Moreover, in the case of openings, a mean increment of the MIDR values of almost 50% and 92% is observed for weak and strong masonry infills, respectively, with respect to those obtained for the infilled structural configuration.  
Figure 6: Normalized pushover curves for the case study school building: completely infilled configuration, (**a**) X and (**b**) Y direction, and partially infilled configuration, (**c**) X and (**d**) Y direction.  
The higher values noticed for the MIDR at the first story were due to the structural characteristics of the buildings: the stiffness of this floor is quite lower than that below due to a reduction of the column cross sections along the building height.  
On the other hand, looking at MPFA (Figure 8a,b), along the building height, the higher the stiffening provided by the masonry infills, the higher the recorded floor accelerations. This trend should not be generalized for every design/assessment condition, as it depends on several aspects, such as the shear capacity and deficiencies of the columns or premature shear failure due to frame-infill interaction [41; 42]. In this case-study building, no premature shear failures were observed. Moreover, for the IM-level 6, going from weak to strong masonry infills, the increment of the MPFA values is in the range of [60-92%] and [87-117%] for infilled and partially infilled structural configurations, respectively. It is also worth mentioning that, in the case of openings, a mean reduction of the MPFA values of almost 29% and 17% was noticed for weak and strong masonry infills, respectively, with respect to those obtained for the fully infilled structural configuration.
# Dual-Frequency Signal Enhancement Method of Moving Target Echoes for GNSS-S Radar
## 5 Conclusions  
GNSS-S radar uses GNSS signals as the illumination source, which has significant advantages and broad application prospects. However, due to the fact that GNSS signals are mainly used for positioning and timing and are not specifically designed for target detection, their signal power is extremely weak, and their processing gain is easily affected by Doppler frequency shift. When used for detecting moving targets, it is necessary to eliminate the influence of residual Doppler frequency in order to effectively improve signal gain. This paper makes full use of the two-frequency signals transmitted by the GNSS satellite and proposes a dual-frequency GNSS-S echo signal processing method. By analyzing the relationship between the amplitude and phase of two-frequency echo signals, a new ideal differential signal is constructed by combining the additive amplitude and differential phase of the dual-frequency signal. The amplitude of the signal is enhanced, and the phase is consistent, which can be used for long-term coherent accumulation to improve the echo SNR. The effectiveness of this method is verified by simulation and field experiments. It can significantly improve the processing gain of coherent accumulation and successfully realize the detection of high-speed moving cars by GNSS-S radar.  
[PERSON] implemented the methods and conceived and designed the experiments; [PERSON] supervised the research; [PERSON] put forward valuable suggestions; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed part of the data; [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. It was carried out by the State Key Laboratory of Space-Earth Integrated Information Technology with self-financing.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to intellectual property protection.  
The authors declare no conflict of interest.
# Prediction of Harvest Time of Apple Trees: An RNN-Based Approach
###### 1 Introduction
We assumed as basic hypotheses, which guided the development of this work, that the apple harvest date can be estimated through ML methods using a multivariate approach based on historical data of phenology and climatic parameters related to temperature, and also that multivariate models based on thermal summation can improve the accuracy of predictions in days, better explaining the relationship between temperature variables and the amount of heat needed for the plant to complete its cycle. The model contributes with a methodology that makes the predictive  
Figure 1: Apple phenologial stages. capacity more effective, enabling the prospection of future scenarios. The development of this work contributes to the fruit growing area, making it possible to anticipate information about the harvest date, improving the accuracy of predictions. This information can be helpful to the fruit grower to perform planned activities, avoiding unnecessary costs with treatments, handling, and management as well as reflecting the quality of agricultural production. It can be used as an essential tool to support decision making, generating financial savings for the fruit grower. This work is organized as follows. Section 2 presents an analysis of the works related to the theme proposed in this article. Section 3 presents the model for predicting the harvest date. Section 3.3 presents the evaluation methodology, details about prototype, input data and parameters, evaluation metrics, and evaluation scenarios. Then, in Section 4, the results obtained with the experiments carried out are presented. Section 5 includes a discussion of the results obtained. Finally, Section 6 features the conclusion.
## 3 Verification of Noise Reduction Method by Combined Use of Improved Wavelet and EMD
### Denoising Analysis of Defect Signal  
#### 3.4.1 Noise Reduction of Joint Electromagnetic Signal  
The conveyor belt joint electromagnetic signal containing the noise is shown in Figure 8a, the noise includes 10 dB Gaussian white noise and non-stationary noise with frequency and amplitude modulations, the noise function is shown in Equation (11). Figure 8b is the normalized spectrum diagram of joint electromagnetic signal with noise, it can be seen that the noise function has the same frequency band with the signal before denoising. The denoised result by the improved threshold  
Figure 7: Joint and break of steel cord conveyor belt and responding electromagnetic signal: (**a**) Internal structure of conveyor belt joint; (**b**) Electromagnetic signal at joint; (**c**) Steel wire rope break of belt; (**d**) Electromagnetic signal of wire rope break defect.  
wavelet is shown in Figure 8c, while the denoised result by combined use of improved wavelet and EMD is shown in Figure 8d.  
\[f_{noise}=(0.2+0.2\cdot\sin(2\cdot pi\cdot 3\cdot t))\cdot\cos(2\cdot pi\cdot 5 \cdot t+0.2\cdot\sin(2\cdot pi\cdot 6\cdot t))+0.2\cdot\sin(2\cdot pi\cdot 2 \cdot t) \tag{11}\]  
Based on the experimental investigation, the wavelet basis function is selected as dB8, the threshold determination strategy is Birge-Massart, and the wavelet decomposition level is six. The eigenvalues of \(11\) IMF components are \(\lambda_{i}\), the ratio K\({}_{i}\) and \({}_{i}\) is shown in Table 1.  
According to Table 1, \(\Delta_{6}\) is smaller than 1.0. Thus, the selected IMF components are IMF\({}_{1}\) to IMF\({}_{7}\). The reconstruction result of seven IMF components is shown in Figure 8d, and two denoising methods are compared, as shown in Table 2. According to Table 2 and Figure 8c,d, the proposed  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**IMF Component** & **Eigenvalue**\(\lambda_{i}\) & K\({}_{i}=\frac{\lambda_{i}}{\lambda_{i+1}}\) & \(\Delta_{i}=\frac{\text{K}_{i}}{\text{K}_{i+1}}\) \\ \hline IMF\({}_{1}\) & 29.62 & 1.68 & 1.37 \\ IMF\({}_{2  
method obtained by combined use of the improved wavelet and EMD has the better noise reduction effect for the non-stationary strong-noise joint electromagnetic signal and the SNR is bigger.
# A Systematic Experimental and Computational Analysis of Commercially Available Aliphatic Polyesters
## 3 Results
The satisfactory agreement between the model results and experimental data confirms that the slowing effect provided by HA can be safely lumped into \(k_{d}\) without sacrificing the reliability of the model results. Kinetic constant of PDLG 5004/HA compound is equal to \(6.73\times 10^{-4}\) cm\({}^{6}\) mol\({}^{-2}\) s\({}^{-1}\), about 50% of the value related to the pure polymer sample. On the other hand, the kinetic constant of RG505/HA compound is equal to \(1.01\times 10^{-3}\) cm\({}^{6}\) mol\({}^{-2}\) s\({}^{-1}\), only 20% lower than the value of the pure polymer sample.