esa-sceva/satcom-chunk-collection huggingface dataset:
# Additional Criteria for Playground Impact Attenuating Sand
## 4 Sand Degradation Analysis
### Fineness Modulus  
A key limitation of the coefficient of uniformity is that it fails to provide any indication of a sand's approximate particle size--it only indicates whether particles are of similar or different sizes. As an example, the three gradings indicated in Figure 10 would yield the same coefficient of uniformity--but they are notably different in terms of particle size.  
Therefore, a second indicator has been employed to mathematically indicate the fineness or coarseness of particle size--the fineness modulus.  
[PERSON] and [PERSON] [38] describe this as the sum of the cumulative percentages of material retained on the sieves of the standard series, divided by 100. It must be appreciated that the standard series consists of sieves with aperture size twice that of the preceding sieve--for this project 0.075, 0.15, 0.3, 0.6, 1.18 and 2.36 mm. This modulus is used to indicate the approximate particle size of a sand sample and thereby addresses the failure of the coefficient of uniformity to do so.
## 4 Conclusions  
In terms of energy and resource efficiency; control; and cost-effectiveness, LVDC technology outperforms LVAC technology. Disadvantages of LVDC technology compared with LVAC technology include the complexity of voltage regulation and grid connection as well as the use of traditional protection components. In this study, renewable energies were integrated into DC and AC distribution systems to improve the performance of these distributions. With the advent of transformers and the advancement of power electronics, such as the voltage source converter and the rectifier converter, it is now possible to use HVDC in addition to LVDC/LVAC distribution. For this purpose, we modeled and analyzed an LVAC, LVDC, and HVDC power distribution system with an electrical grid. Furthermore, transmission and distribution lines and system protection were used to highlight the dependability of this multizone architecture. Several significant components which must be taken into consideration, including DERs, power electronic devices, and ESSs, were modeled and implemented using MATLAB/Simulink. A hybrid electrical grid with a distribution network test for LVDC, HVDC, and LVAC was used in this study. Based on the connection of the modeled distribution test system to the electrical grid, we performed steady-state and transient-state simulations, including the connection of renewable energy sources (PV and wind) as a DER, and analyzed the features in these states. In this novel architecture, we concluded that the LVAC distribution THD was equal to 1.01% and more acceptable compared with other existing works. Additionally, the voltage drops of the LVDC distribution system were roughly 0.234% while LVAC voltage drops were approximately 1.268%, which illustrates the LVDC's performance. In light of the obtained simulation results, we highlight the high efficiency and performance of this novel architecture compared with LVAC and LVDC distribution only, even in healthy and OCF cases, as well as compared with both combined. However, this innovative multizone architecture requires additional power electronics and transformers, which makes it more complicated. Finally, the suggested work can be expanded with regard to future directions toward the optimal placement of renewable energy sources in this novel architecture.  
Figure 26: Fault variables.  
**Author Contributions:** Conceptualization, M.A.Z., B.D., Z.A. and H.H.A.; methodology, M.A.Z. and B.D.; software, M.A.Z., B.D. and Z.A.; validation, M.A.Z., B.D., Z.A. and H.H.A.; formal analysis, M.A.Z., B.D. and H.H.A.; investigation, M.A.Z., B.D., Z.A. and H.H.H.A.; writing--original draft preparation, M.A.Z. and B.D.; supervision, Z.A. and H.H.A.; project administration, Z.A. and H.H.A.; funding acquisition, Z.A. and H.H.H.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Lightweight Insulator and Defect Detection Method Based on Improved YOLOv8
###### Abstract  
Insulator and defect detection is a critical technology for the automated inspection of transmission and distribution lines within smart grids. However, the development of a lightweight, real-time detection platform suitable for deployment on drones faces significant challenges. These include the high complexity of existing algorithms, limited availability of UAV images, and persistent issues with false positives and missed detections. To address this issue, this paper proposed a lightweight drone-based insulator defect detection method (LDIDD) that integrates data augmentation and attention mechanisms based on YOLOv8. Firstly, to address the limitations of the existing insulator dataset, data augmentation techniques are developed to enhance the diversity and quantity of samples in the dataset. Secondly, to address the issue of the network model's complexity hindering its application on UAV equipment, depthwise separable convolution is incorporated for lightweight enhancement within the YOLOv8 algorithm framework. Thirdly, a convolutional block attention mechanism is integrated into the feature extraction module to enhance the detection of small insulator targets in aerial images. The experimental results show that the improved network reduces the computational volume by 46.6% and the mAP stably maintains at 98.3% compared to YOLOv8, which enables the implementation of a lightweight insulator defect network suitable for the UAV equipment side without affecting the detection performance.  
insulator detection; YOLOv8; data augmentation; lightweight network; attention mechanism +
Footnote †: journal: Applied sciences
# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 4 Design of the Outdoor Positioning System  
To obtain an accurate position of the RC car, our proposed positioning system makes use of different data sources: the UWB ranging system, Odometry and IMU. This data fusion makes the positioning more robust. Figure 2 shows the architecture of the developed positioning system.  
It is important to point out that this system is a two dimensional model, meaning that it only calculates the \(x\) and \(y\) position of the RC car. As it can be seen in Figure 2, the developed positioning system is based on an EKF. On the one hand, the prediction step of the EKF is applied each time new data from the RC car sensors are received. On the other hand, each time a new UWB ranging estimate arrives into the system, the correction step of the EKF is applied for correcting the last position of the RC car computed by the prediction step.  
Figure 1: RC car as testbed.  
To initialise the positioning system it is necessary to know the initial position of the RC car. The accuracy of the initial position is significantly important as it affects the accuracy of the positioning system during the initial steps. In the tests carried out in this work, the initial position of the RC car corresponds with the one provided by its RTK-SN positioning system based on GNSS signals. This RTK positioning system provides a two-dimensional position of the car with an error lower than 5 cm under good conditions for the GNSS signals [19].  
The main purpose of the overall solution is to provide a high rate accurate position of a vehicle based on UWB ranging estimates combined with inertial and odometry data from the vehicle sensors. This data combination is done through two different processes:  
* Updating the RC car position. This is carried out in the prediction step of the EKF. This step is applied each time new odometry and inertial data of the RC car on-board sensors arrive into the system, resulting in a new position of the RC car. Note that the data of the RC car on-board sensors are generated at a data rate of 100 Hz, indicating that every 10 ms the prediction step of the EKF is applied.
* Correcting the RC car position. This is accomplished in the correction step of the EKF. This step is applied each time a new UWB ranging estimate arrives into the system, correcting the last position of the RC car computed in the prediction step. The main idea when correcting periodically the position computed by the prediction step is to bound its positioning error over time. Note that the UWB ranging estimates are generated at a data rate of 10 Hz, indicating that every 100 ms the correction step of the EKF is applied.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
###### Abstract  
With the rapid development of satellite remote sensing technology, carbon-cycle research, as a key focus of global climate change, has also been widely developed in terms of carbon source/sink-research methods. The internationally recognized \"top-down\" approach, which is based on satellite observations, is an important means to verify greenhouse gas-emission inventories. This article reviews the principles, categories, and development of satellite detection payloads for greenhouse gases and introduces inversion algorithms and datasets for satellite remote sensing of XCO\({}_{2}\). It emphasizes inversion methods based on machine learning and assimilation algorithms. Additionally, it presents the technology and achievements of carbon-assimilation systems used to estimate carbon fluxes. Finally, the article summarizes and prospects the future development of carbon-assimilation inversion to improve the accuracy of estimating and monitoring Earth's carbon-cycle processes.  
satellite remote sensing; inversion algorithms; XCO\({}_{2}\); carbon assimilation  
11 July 2024  
131 August 2024  
Accepted: 4 September 2024  
Published: 12 September 2024
# Influence of Preconditioning and Tunnel Support on Strain Burst Potential
## 2 Assessment of Strain Burst Hazard  
### Strain Burst Charts  
The strain burst failure process is driven by stress increases around the excavation in such a manner that the stress reaches the rock mass strength [35]. Therefore, it is possible to estimate the strain burst hazard by assessing the stresses around the excavation and comparing them with the brittle rock mass strength. Based on this premise, and considering field observation of brittle rock failure, [PERSON] et al. [20] proposed an empirical relation to estimate the depth of spalling failure. In this relation, the depth of failure, _Rf_, is normalized to the tunnel diameter, \(a\), and is plotted as a function of the ratio between the maximum wall stress, \(\sigma_{max}\) (\(\sigma_{max}=3\sigma_{1}-\sigma_{3}\), where \(\sigma 1\) and \(\sigma 3\) are the total major and minor principal stresses, respectively), and the uniaxial compressive strength (UCS) of intact rock. [PERSON] et al. [21] completed the relation through a review of available literature [36; 37; 38; 39; 40; 41; 42], where twenty-three cases of spalling failure have been studied (reported in [21]). Figure 1 presents the measured depths of failure given by the literature.  
A more rigorous chart has been proposed by [PERSON] [3] based on numerical analysis using the damage initiation and spalling limit (DISL) approach [8], detailed below. The failure depth is predicted as a function of maximum wall stress, the crack initiation stress (CI) and the in situ stress ratio, K (Figure 2). CI is equal to 0.5 UCS if no data exist.
## 3 Discussion
### Biological Activities
#### 3.2.1 Cytotoxic Activity  
Artichoke leaf extracts are characterized by the presence of numerous bioactive compounds, particularly polyphenols, which appear very promising in the prevention and management of specific cancer types. Recent studies conducted both in vitro and in vivo conditions demonstrated the ability of artichoke extracts from different plant parts to inhibit the angiogenesis and/or the proliferation of some cancer cell lines [25]. For instance, it was demonstrated that artichoke leaf extracts were able to induce a dose-dependent reduction in cell viability and inhibition of the growth of the human leukemia cell line K562 [53], HepG2 cells in hepatocellular carcinoma [31], HT-29 and HCT116 cell lines in colon cancer [54], and MCF7 and MDA cell lines in breast cancer [55; 56]. In the present study, the potential cytotoxic activity of artichoke leaf extracts was performed by analyzing the cell viability on human cancer cell lines (SH-SYSY, MCF7 and MDA) and on normal human cell lines (MCF10A) after treatment carried out in a dose- and time-dependent manner. The results highlighted that all extracts studied showed antiproliferative activity and cytotoxic effects on all the cancer cell lines considered, while no effect was found on normal cell lines, indicating selectivity toward human cancer cells.  
The observed antiproliferative and cytotoxic effects of artichoke leaf extracts are suggested to be attributed to the induction of cell cycle arrest and apoptosis, evidenced by increased caspase activity and DNA fragmentation [31], disruption of the mitochondrial membrane potential and activation of caspase-dependent apoptotic pathways [55; 56], and potentially by the modulation of crucial signaling pathways involved in cell survival and proliferation [54]. In addition, the ability to modulate several signaling pathways involved in cancer progression was demonstrated, as reported for artichoke leaf extracts that were found to downregulate the expression of genes associated with inflammation and cell proliferation in a colon cancer model [57].
# Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest
###### Abstract  
Windthrow (i.e., trees broken and uprooted by wind) is a major natural disturbance in Amazon forests. Images from medium-resolution optical satellites combined with extensive field data have allowed researchers to assess patterns of windthrow tree-mortality and to monitor forest recovery over decades of succession in different regions. Although satellites with high spatial-resolution have become available in the last decade, they have not yet been employed for the quantification of windthrow tree-mortality. Here, we address how increasing the spatial resolution of satellites affects plot-to-landscape estimates of windthrow tree-mortality. We combined forest inventory data with Landsat 8 (30 m pixel), Sentinel 2 (10 m), and WorldView 2 (2 m) imagery over an old-growth forest in the Central Amazon that was disturbed by a single windthrow event in November 2015. Remote sensing estimates of windthrow tree-mortality were produced from Spectral Mixture Analysis and evaluated with forest inventory data (i.e., ground true) by using Generalized Linear Models. Field measured windthrow tree-mortality (3 transects and 30 subplots) crossing the entire disturbance gradient was 26.9 \(\pm\) 11.1% (mean \(\pm\) 95% CI). Although the three satellites produced reliable and statistically similar estimates (from 26.5% to 30.3%, \(p\)\(<\) 0.001), Landsat 8 had the most accurate results and efficiently captured field-observed variations in windthrow tree-mortality across the entire gradient of disturbance (Sentinel 2 and WorldView 2 produced the second and third best results, respectively). As expected, mean-associated uncertainties decreased systematically with increasing spatial resolution (i.e., from Landsat 8 to Sentinel 2 and WorldView 2). However, the overall quality of model fits showed the opposite pattern. We suggest that this reflects the influence of a relatively minor disturbance, such as defoliation and crown damage, and the fast growth of natural regeneration, which were not measured in the field nor can be captured by coarser resolution imagery. Our results validate the reliability of Landsat imagery for assessing plot-to-landscape patterns of windthrow tree-mortality in dense and heterogeneous tropical forests. Satellites with high spatial resolution can improve estimates of windthrow severity by allowing the quantification of crown damage and mortality of lower canopy and understory trees. However, this requires the validation of remote sensing metrics using field data at compatible scales.  
blophysics; crown damage; forest inventory; extreme wind gusts; natural disturbances; spatial resolution; Spectral Mixture Analysis 2023  
1
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 3 Materials and Methods
### HYBRID Model  
The HYBRID model integrates the estimation process by assigning weights to RSSI, AOA, and EOA. First, the probability density function (PDF) values are computed using the mean and standard deviation of the training data for RSSI, AOA, and EOA. Using Equations (12)-(14), the  
Figure 12: Elevation angle (EOA) observed at the target position. Used to analyze deviations from training data in 3D localization environments.  
Figure 13: Comparison of EOA distributions between the training and target data. Larger discrepancies than RSSI and AOA suggest potential modeling limitations under multipath conditions.  
data are converted into dimensionless values. In these equations, \(R_{X}\) represents the received data at the candidate Rx position, and \(FP\) denotes the fingerprint database.  
\[\text{prob}_{\text{rssi}}=\frac{1}{\sqrt{2\pi}\sigma}\exp\left(-\frac{\left(\text {rssi}_{\text{tgt}}-\mu_{rssi}\right)^{2}}{2\sigma_{rssi}^{2}}\right) \tag{12}\]  
Here,  
\[\mu_{rssi}=\text{rssi}_{\text{tng}}\]  
Similarly, the probabilities for AOA and EOA are defined as follows:  
\[\text{prob}_{\text{aoa}}=\frac{1}{\sqrt{2\pi}\sigma}\exp\left(-\frac{\left( \text{aoa}_{\text{tgt}}-\mu_{aoa}\right)^{2}}{2\sigma_{aoa}^{2}}\right) \tag{13}\]  
\[\text{prob}_{\text{eoa}}=\frac{1}{\sqrt{2\pi}\sigma}\exp\left(-\frac{\left( \text{ea}_{\text{tgt}}-\mu_{oaa}\right)^{2}}{2\sigma_{eaa}^{2}}\right) \tag{14}\]  
where  
\[\mu_{\text{aoa}}=\text{aoa}_{\text{tng}},\quad\mu_{\text{eoa}}=\text{eoa}_{ \text{tng}}\]  
\[\sigma_{\text{aoa}}=\text{mean}\left(\text{FP}_{\text{rx}}^{\text{Training}} \text{\bf{e}}_{\text{aoa}}\right)\]  
\[\sigma_{\text{eoa}}=\text{mean}\left(\text{FP}_{\text{rx}}^{\text{Training}} \text{\bf{e}}_{\text{eoa}}\right)\]  
For AOA and EOA, weight coefficients are determined through normalization using the standard deviation of the training data via linear interpolation. For RSSI, its weight coefficient is computed by subtracting the sum of AOA and EOA weights from a total weight value of 3. Using these weights, the probability is calculated as in Equation (15). By performing pattern matching to find the location with the highest probability, the source location is estimated.  
\[\text{prob}_{\text{hybrid}}=w_{\text{rssi}}\cdot\text{prob}_{\text{rssi}}+w_{ \text{aoa}}\cdot\text{prob}_{\text{aoa}}+w_{\text{eoa}}\cdot\text{prob}_{ \text{eoa}} \tag{15}\]
# Global GNSS-RO Electron Density in the Lower Ionosphere
## 3 Results  
### Diurnal Variations  
The \(D/E\)-region \(N_{e}\) is a strong function of \(\chi\) as the lower ionosphere is largely influenced by the Ly-\(\alpha\) ionization of mesospheric species such as NO. To characterize the \(N_{e}\) morphology, we averaged all the COSMIC-1 RO retrievals from 2006-2020 to generate a monthly climatology for each 2-hourly local time, \(4^{\circ}\) latitude, and 2 km altitude bin. As shown in Figure 8 for four selected months and four selected altitudes, the new \(N_{e}\) data reveal several interesting features and variations in the lower ionosphere. A complete set of monthly climatology can be found in Figure S1.  
Although the \(D/E\)-region \(N_{e}\) shows a clear \(\chi\)-dependence, it exhibits strong latitudinal variations for the same \(\chi\). For example, at almost at all altitudes there is an enhanced daytime peak at the summer midlatitude during the solstice months (e.g., June and December). This peak occurs at a late morning hour slightly before noon. For the months near equinox (e.g., March and September), a dip at the magnetic equator is evident in the daytime \(N_{e}\), especially at 100 and 120 km. This low \(N_{e}\) band, known as the \"fountain effect\" in the \(F\)-region, is likely an imprint of an upward plasma draft induced by the equatorial electrojet (EEJ). As expected for an eastward electric field build-up from EEJ, the \(\mathbf{E}\times\mathbf{B}\) force could create an upward plasma drift [48]. Because of the strong vertical gradient in the \(D\)/\(E\)-region \(N_{e}\), this vertical drift would manifest itself as a low \(N_{e}\) value compared to those at nearby latitudes without the drift.  
Another noticeable feature is the asymmetry of \(N_{e}\) distribution about the noon. The \(D\)/\(E\)-region \(N_{e}\) climatology clearly shows that \(N_{e}\) arises slightly late in the morning with a lagged decay into the evening. The \(D\)-region \(N_{e}\) asymmetry about noon was also reported in ground-based observations [49]. In all monthly climatologies, the extended \(N_{e}\) enhancement from evening to midnight is observed. During the months near the equinox, such a prolonged \(N_{e}\) enhancement in the tropics is punctuated by a short decrease around LST = 19 h.