esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
The mechanical model for tunnel excavation used in this study was as follows: considering a tunnel of circular cross-section with a radius of R and of infinite length, a tunnel at infinite depth in an isotropic homogeneous rock mass was excavated. The tunnel was subjected to a uniform hydrostatic stress (\(\sigma_{\text{o}}\)). The uniform pressure p\({}_{0}\) of support structures was applied to the tunnel wall surface. The field stress can be considered to be axisymmetric and all stress components at a point in the coordinate system are shown in Figure 5.  
Replacing strain modulus E in Equation (1) with the new form of E in Equation (25), the obtained formula is as follows:  
\[L^{*}t^{*(1-\alpha)}=\frac{4\Big{\{}\Big{[}0.02+\frac{1-D/2}{1+e^{(60+15D- CSI)/11)}}\Big{]}}{3\gamma H}\frac{(1-\alpha)}{\delta}u_{l}^{*}}{ \tag{26}\]  
In fact, in relation to tunnel excavation, it is possible to determine tunnel wall surface displacement using sensors. Therefore, the authors suggest considering the values of allowable displacement for tunnel walls as a criterion for estimating tunnel stability (rock mass stability) during excavation. Values of allowable displacement for different types of rock, such as those proposed in SniP-II-94-80 in 1980, are presented in Table 3.  
Figure 5: Mechanical model for tunnel excavation, adopted in this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Stability Category y**} & \multirow{3}{*}{
\begin{tabular}{c} **Rock Stability** \\ **Assessment** \\ \end{tabular} } & \multicolumn{3}{c}{**Maximum Displacement u (mm)**} \\ \cline{3-5}  & & \multicolumn{2}{c}{**Sedimentary Rocks**} & \multicolumn{2}{c}{**Igeneous Rocks**} & \multicolumn{2}{c}{**Rock Salts**} \\  & & **(Sandstone, Silstone,** & **(Granite, Diorite,** & **(Rock Salt, Sylvinite,** \\  & & **Coal, etc.)** & **Porphyrite, etc.)** & **Karnalite, etc.)** \\ \hline I & Stable & 50 & 20 & 200 \\ II & Moderate stable & 50–200 & 20–100 & 200–300 \\ III & Very unstable & 200–500 & 100–200 & 300–500 \\ IV & Highly unstable & \textgreater{}500 & \textgreater{}200 & \textgreater{}500 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Stability of rock categories regarding maximum rock material displacements (u).
## 5 High Voltage Direct Current  
The HVDC is a technology for transmitting electrical power in a safe and efficient way across large distances. It is suitable for specific applications such as interconnections, bulk power transmission, long-distance transmission via cable or overhead line, grid enforcement, multiterminal networks, and grid access for offshore applications. The first experimental trial for transmitting power in DC was developed in 1882, when Miesbach and Munich, in Germany, were connected through a 50 km long link with 2 kV DC lines [44].
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 3 Methodology
### Regression Strategy and Optimization  
The goal of this framework is to estimate forest carbon stock (Mg/ha) for each inventory plot based on input multispectral image patches. The model is trained to minimize the discrepancy between predicted and observed carbon values.
# Optimal Configuration and Path Planning for UAV Swarms Using a Novel Localization Approach
###### Abstract  
In localization estimation systems, it is well known that the sensor-emitter geometry can seriously impact the accuracy of the location estimate. In this paper, time-difference-of-arrival (TDOA) localization is applied to locate the emitter using unmanned aerial vehicle (UAV) swarms equipped with TDOA-based sensors. Different from existing studies where the variance of measurement noises is assumed to be independent and changeless, we consider a more realistic model where the variance is sensor-emitter distance-dependent. First, the measurements model and variance model based on signal-to-noise ratio (SNR) are considered. Then the Cramer-Rao low bound (CRLB) is calculated and the optimal configuration is analyzed via the distance rule and angle rule. The sensor management problem of optimizing UAVs trajectories is studied by generating a sequence of waypoints based on CRLB. Simulation results show that path optimization enhances the localization accuracy and stability.  
time-difference-of-arrival (TDOA); Cramer-Rao low bound (CRLB); optimal configuration; UAV swarms; path optimization Article
## 5 Analysis of the Hybrid Propagation Channel Model
### Effect of Scattering Angles on the Proposed 3 DFPHDCM
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scattering Angle (\({}^{\circ}\))** & **RMSE (SP)** & **MAE (SP)** & **Running Time (s)** \\ \hline
3 & 54.4215 & 39.3436 & 93.022 \\
4 & 46.0455 & 28.7651 & 93.917 \\
5 & 4.1778 & 3.4927 & 94.56 \\
6 & 32.7552 & 25.6358 & 95.009 \\
7 & 41.8929 & 40.2752 & 95.218 \\
8 & 43.2574 & 41.9560 & 95.657 \\
9 & 51.0809 & 50.0220 & 95.46 \\
10 & 51.3264 & 50.1893 & 96.974 \\
11 & 50.5802 & 48.8019 & 97.507 \\
12 & 54.1937 & 52.8255 & 98.980 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMSE, MAE, and runtime at different scattering angles when the number of scattered rays is 9. Figure 8: The trend of path loss with distance for different scattering angles when the number of scattered rays is 9.
# Distributed Uniform Streaming Framework:
## 6 Platform Transparency Using Dust: A Connected Driving Use Case  
The experiments in Section 5 focusses on the distributed resource distribution using the DUST Coordinators. In this section, we describe a connected driving use case we developed using the DUST-Core. We chose to discuss this use case to highlight the advantages and design considerations when using the DUST-Core without the DUST Coordinators to make the platform transparent to the application components.  
In the future, cars will use communication technologies to share useful information with other cars. For example, when a car detects an obstacle on the highway, we need to share this information with other vehicles to improve the safety on the road. [PERSON] et al. (2020) describe the Smart Highway project in which we are developing a testbed for Vehicle-to-everything (V2X) communication. We do this by installing communication equipment on the highway and by creating an Onboard Unit (OBU). The OBU is capable of sensor processing and can use communication equipment within the vehicle. In this use case, we will focus on the development of the OBU.  
A big requirement when we were developing the OBU was that the system needs to be very flexible because it will be used within a Smart Highway testbed. This means that the software for the OBU needs to be able to handle different kind of sensor hardware, actuator hardware, communication hardware, and processing hardware. This large flexibility in hardware forms a challenge to develop the standardized software which is required for the testbed. In the next subsections, we will describe how we used the DUST-Core to create a platform transparent software architecture to create a flexible testbed.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
## 4 Maximum Likelihood and CLEAN RELAX Estimators
### Sub-Optimal Estimator: CLEAN-RELAX  
In order to deal with the CMLE heavy computational load, alternative estimators can be used. In this paper, a relax version of the well-known CLEAN algorithm was implemented [40]. This estimator assumes that the two signals are decoupled. The method first estimates the parameters \((\widehat{\tau}_{0},\widehat{b_{0}},\widehat{\rho_{0}},\widehat{\phi_{0}})\) of the strongest signal by using the cross-correlation function, it subtracts this estimated signal from the measured input, and then estimates the secondary signal parameters \((\widehat{\tau}_{1},\widehat{b_{1}},\widehat{\rho_{1}},\widehat{\phi_{1}})\). These two estimation steps are iterated until the cost function, which is the resulting likelihood, is stabilized. The method used in this contribution only looks for two signals. In GNSS, the same algorithm is named Multipath Estimating Delay Lock Loop (MEDLL) and is typically used to mitigate multipath [8]. The CRE can be seen as a sub-optimal estimator because it works only under certain assumptions. Indeed, considering that the two signals can be decoupled stands only when \(\Delta\tau\) is large enough compared to the sharpness of the cross-correlation function. This estimator is not expected to perform well when the two signals are very close in time. On the other hand, when the signals are clearly parted, the CRE asymptotically behaves as the CMLE. See Section 5 for simulated results on the CRE limit.
## 6 The Evaluation of Unmatched Receiving of ACE-BOC
### Performance Evaluation
#### Tracking Performance under Interference  
The anti-interference rate \(n\) proposed in Equation (24) can be used to evaluate the performance of code tracking under interference, that larger value of \(n\) means the code tracking is more vulnerable  
Figure 3: Equivalent Gabor bandwidth of ACE-BOC, where the central frequency of receiving of Matched receiving and IMR is at \(f_{0}\), and the central frequency of BLR is at \(f_{0}-f_{sc}\). The IMR and BLR receives the component \(s_{\text{LQ}}\), whose power ratio among the useful transmitting power is 3/8.  
to such interference. The anti-interference rate for ACE-BOC(15,10,[1,1,3,3]) is shown in Figure 4, with the receiving strategies of IMR and BLR for the lower sideband, Q-channel signal component \(s_{\text{LQ}}\), under three kinds of interference. The narrowband interference has a bandwidth of 10 kHz and a center frequency 1 MHz apart from the main lobe center of \(s_{\text{LQ}}\), and the wideband interference has double-side band width of 5 MHz. Matched spectrum interference has the spectrum The interference configuration is listed in Table 2. As an advantage of the decoupling of the anti-interference rate, there is no need to assume the interference strength, integration duration, loop bandwidth, etc.  
As can be seen in Figure 4, firstly, in general the anti-interference rate decreases as the receiving bandwidth increases, indicating that the interference resistance ability is improved with larger bandwidth. Secondly, for both IMR and BLR of ACE-BOC, narrowband interference is much serious than the bandlimited and matched spectrum interference, under the given conditions, implying that in a challenging environment, narrowband interference resistance techniques should be the primary consideration to improve the overall positioning performance.  
In terms of the receiving configuration, evaluation results show that IMR achieves better anti-interference performance at most bandwidths. However, when the receiving bandwidth is about 36 MHz, IMR shows no advantage over BLR under narrowband interference and bandlimited white interference. With the proposed evaluation criterion, it is able to provide the inherent performance of signal, and support various unmatched receiving.
## 3 Results  
### Comparative Analysis of Theoretical and Experimental Capacitance for MT and MNT Sensors  
In the experiment, a UNI-T UT603 LCR digital capacitance and inductance meter (UNI-T, Shenzhen, China) with a tolerance of 1% was used to determine the capacitance in pF of each of the proposed sensors, considering the conditions and type of waste material.  
Figure 4 shows a capacitance value \(C^{\prime}_{MT}=21\ pF\) delivered by the LCR for the implemented prototype of the MT sensor, when it is free of residues, accompanied by the proposed diagram. From Equation (4), we proceed to calculate the capacitance value \((C_{MT})\) according to the sensor dimensions that were considered at the discretion of the researchers for its construction (\(a\) = 2.1 cm, \(b\) = 2.3 cm, \(c\) = 3 cm, and \(d\) = 0.2 cm).  
\[C_{MT}=\pi\varepsilon_{o}\varepsilon_{r}\bigg{[}\frac{ab}{b-a}\bigg{]}=17.458\ pF \tag{6}\]  
\[e_{abs}=C^{\prime}_{MT}-C_{MT}=3.542\times 10^{-12} \tag{7}\]  
\[e_{r}=\frac{C^{\prime}_{MT}-C_{MT}}{C_{MT}}=0.203 \tag{8}\]  
Equations (6)-(8) present the result of this process accompanied by the absolute error (\(e_{abs}\)) and the relative error (\(e_{r}\)) [24] in contrast to the result obtained experimentally, where it can be seen that the theoretical value and the one obtained experimentally are very similar to each other, with reduced error levels taking into account that the capacitance values are of the order of \(10^{-12}(pF)\), an aspect that is very common in commercial capacitors, which establish a tolerance value given by the manufacturer and considering that the relative permittivity values of the materials used for the construction of the sensors may vary a little compared to those that were theoretically adopted.  
The proposed diagram and the capacitance value of \(C^{\prime}_{MNT}=28\ pF\) for the implemented MNT sensor prototype, when free of residues, are presented in Figure 5. Using  
Figure 4: Capacitance value with prototype sensor traditional model (MT) without residuals for reading.  
Equation (5), the capacitance value (\(C_{MNT}\)) is determined based on the sensor's dimensions, which were selected by the researchers during its construction (\(a\) = 5 cm, \(b\) = 0.9 cm, \(c\) = 1.5 cm and \(d\) = 0.1 cm).  
\[C_{MNT}=\frac{e_{o}e_{r}A}{d}=24.5\ pF \tag{9}\]  
\[e_{abs}=C_{MNT}^{\prime}-C_{MNT}=3.49\times 10^{-12} \tag{10}\]  
\[e_{r}=\frac{C_{MNT}^{\prime}-C_{MNT}}{C_{MNT}}=0.143 \tag{11}\]  
The results of this process are presented in Equations (9)-(11), together with the absolute error (\(e_{abs}\)) and the relative error (\(e_{r}\)) when compared to the results obtained experimentally. It can be observed that the theoretical and experimental values are very similar, as in the previous case, showing low levels of absolute and relative error. From this, it can be concluded that the mathematical expressions proposed for each type of sensor are consistent with the experimental results, which allows establishing a valuable reference point for future research in which it is planned to use any of the proposed models or make modifications regarding their design or dimensions.
## 2 Wavelet Transform Algorithm Transmittance  
As described in [16; 17; 18], many data processing algorithms can be represented in matrix form. In denoting the successive input quantities of the algorithm as \(x(i)\) and the output quantities as \(X(j)\), their relationships can be expressed in the following form :  
\[\begin{bmatrix}X(0)\\ X(1)\\ \vdots\\ X(M-1)\end{bmatrix}=\begin{bmatrix}a_{0,0}&a_{0,1}&\cdots&a_{0,N-1}\\ a_{1,0}&\ddots&&a_{1,N-1}\\ \vdots&&\ddots&\vdots\\ a_{M-1,0}&\cdots&\cdots&a_{M-1,N-1}\end{bmatrix}\begin{bmatrix}x(0)\\ x(1)\\ \vdots\\ x(N-1)\end{bmatrix}, \tag{1}\]  
where \(N\) is the number of input quantities, \(M\) is the number of output quantities, and the symbol \(a_{i,j}\) denotes the coefficients of the transformation matrix \(\mathbf{A}\) of the algorithm, obtained according to the method described in [18] or determined analytically in accordance with the assumptions of the analyzed algorithm, as in [21]. The method discussed was previously utilized in papers [16; 22] that described the transfer of random errors using discrete wavelet transform algorithms.  
When analyzing how the discussed algorithm transfers errors of a deterministic nature, the transmittance of this algorithm should be taken into account. This algorithm will act as a filter and thus modify the spectrum of the processed signal. This work assumed that the transmittance of the algorithm is ideal, which means that the algorithm does not introduce any deterministic, own errors, but only transfers the errors present in the signal (in case the actual transmittance of the algorithm differs from the ideal one, an additional component of the self-error signal should be considered, as proposed in work [19] in the attached example). Additionally, the rounding errors introduced by the algorithm during the multiplication and addition operations are taken into account, as presented later in this paper. In general, the analysis discussed should be carried out individually for each output quantity of the algorithm. However, as described in [16; 22], the values for successive rows of the transformation matrix related to the same level of decomposition are only shifted toward each other, and therefore, their impact on the transmission of the error signal remains constant. Therefore, an analysis can be performed for each level of signal decomposition and not for each output quantity separately, as demonstrated in this paper.
###### Abstract  
It is rather challenging to collect ocean wave energy at high efficiency because of its ultra-low frequencies and variable amplitudes. Triboelectric Nanogenerator (TENG) technology is more suitable for harvesting low-frequency than electromagnetic power generation technology. In this work, we designed a built-in cylindrical Triboelectric Nanogenerator (C-TENG) installed inside the ocean buoy (BUOY-41). The hydrodynamic properties of the C-TENG are consistent with the ocean buoy, which are calculated by CFD software (Star-CCM+). The Energy Capture Properties of the C-TENG are established by the finite element software (COMSOL). The C-TENG has high power density (30 mW/m\({}^{2}\)) and can meet the power demand of the ocean buoy (10 mW). The implementation of the present work is of great academic value and practical significance for the development of efficient marine renewable energy conversion technology, enhancement of marine equipment energy replenishment, enrichment of hydrodynamic theories and revealing of the complex mechanisms.  
wave energy; hydrodynamic properties; energy capture properties +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Description of Network Functionality and Network Contingency Quantification
For the sake of simplicity, both Base Transceiver Station (BTS) and wireless linkage W1 are not accounted for in this depiction, premised on the assumption of their backed-up status, implying they are devoid of failure. For this demonstration, LV power supply and Distributed Energy Resources (DER) are excluded. Unlike the studies in references [26] that address dependencies within a singular system, this paper faces a challenge when applying the same methodology of constructing an AG to a connected test network treated as two individual systems. Directly combining two AGs is not feasible in this context. Each system has an element from the other, creating a dependency that results in a feedback loop within the graph. By definition, AG cannot have such loops, making the optimization problem unmanageable. To address this, the entire graph is segmented into four subgraphs, each symbolizing a critical subfunction of the complete system. The system's reliable operation is fundamentally determined by the consistent and accurate functioning of its four subfunctions. To illuminate the interdependencies in the comprehensive test network, it is discerned that the system achieves its intended performance when each of the following subfunctions is effectively met:  
* Function (i): Ensuring that the HV level supplies the MV level, as signified by transformer T1 in the system. * Function (ii): Guaranteeing a consistent voltage to the MV busbar prior to the Distribution Transformers, as well as ensuring the supply to the critical POD, denoted as u1 in the network. * Function (iii): Establishing and safeguarding the data communication link between the SCADA operation center and the RTU which oversees the LBS operations in the distribution network. This is represented by subsystem S2 in the system's configuration. * Function (iv): Ensuring a stable power supply to the BTS from the MV level. This is facilitated by subsystem S3 through transformers DT2 or DT3 and via cables C2 or C3 in the system architecture. Figure 6: AG of the communication network part of the simplified test network. Taking into account these outlined functionalities, an AG for the comprehensive test network can be conceptualized. It is worth noting that specific elements within this graph might be replicated, such as element DT2. The resultant AG is graphically presented in Figure 7 and forms the foundational basis for the maintenance optimization techniques which is elaborated upon in Section 3.4.
## 4 Discussion
The spatiotemporal variations in the gravity field can reflect the movements of major tectonic faults in the monitored area [2], and deep tectonic activities are also capable of altering the surface gravity field [85]. This enables the extraction and analysis of anomalous gravity field signals to evaluate the seismic risk of a region. Because the inversion of crustal apparent density can reflect deep material migration and medium flow, the comprehensive analysis of the apparent density variation trend and the tectonic activity background can help in the identification of possible anomaly signals before an earthquake, which is essential for seismic risk assessment and early earthquake warnings. Conceptualization, X.K. and D.L. ; methodology, X.K. and D.L. ; software, X.K., A.Y. and R.C. ; validation, X.K., A.Y., and R.C. ; formal analysis, X.K. ; investigation, X.K. and R.C. ; resources, X.K., A.Y., R.C. and L.C. ; data curation, X.K., D.L., A.Y., J.L. and R.C. ; writing--original draft preparation, X.K. and D.L. ; writing--review and editing, X.K. and D.L. ; visualization, X.K. and R.C. ; supervision, D.L., A.Y., J.L., L.C., and R.L. ; project administration, D.L., X.K., A.Y., and J.L. ; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript. This work was jointly supported by the Xinjiang Uygur Autonomous Region Key R&D Project (2020B03006-2), Spark Program of Earthquake Science and Technology (XH22007 YA), Natural Science Foundation of the Xinjiang Uygur Autonomous Region (2022D01A106, 2023D01A105), National Natural Science Foundation of China (42274014), Intelligence Introduction Project of the State Administration of Foreign Experts Affairs (G2022045013L), the Third Xinjiang Scientific Expedition Program (2022 zjkk1305), and Science and Technology Innovation Team Program of Xinjiang Seismological Bureau (XJDZCXTD2020-1). Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the unavailability of ground gravity data to the public. **Acknowledgments:** We thank all the teachers who participated in the North Tianshan mobile gravity observation for their hard work; the Management Department of Gravity Observation Technology of China Earthquake Administration for providing the relevant basic data; the developers of the GEOIST open source Python package ([[https://cea2020.gitee.io/geoistdoc/](https://cea2020.gitee.io/geoistdoc/)]([https://cea2020.gitee.io/geoistdoc/](https://cea2020.gitee.io/geoistdoc/)) (accessed on 1 January 2024)) and GMTSAR software ([[https://github.com/gmtsar/gmtsar](https://github.com/gmtsar/gmtsar)]([https://github.com/gmtsar/gmtsar](https://github.com/gmtsar/gmtsar)) (accessed on 1 January 2024)), which provided support in this paper for the model testing and inversion. We also thank the reviewers for their constructive comments and suggestions. The authors declare no conflicts of interest.