esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 2 Agriculture 5.0: Revolutionizing Agriculture with Cutting-Edge Technologies
### The Ultra-High Requirements of Agriculture 5.0  
In addition to financial constraints, the latency, security, and reliability of the supporting communication network are critical success factors for implementing effective cloud-based AI control mechanisms in autonomous robotic systems. Furthermore, the need to reduce energy consumption while also dealing with the increased cost of agricultural inputs such as fertilizers, herbicides, and insecticides necessitates technological solutions that either do not currently exist or are in their early stages of development and are not ready for large-scale adoption [18]. Following are some examples of precision agriculture use cases that require novel technology solutions to be applied efficiently and on a wide scale.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Protocol Operation
#### 3.7.3 Negotiation-Based  
These protocols are based on a negotiation process between neighboring nodes. This process is performed by naming the data using high-level data descriptors before sending it to ensure that there is no duplicate information sent throughout the network, which helps to reduce energy consumption. Researchers in [105] propose the sensor protocols for information via negotiation protocol (SPIN) on which several other protocols were based as described in Table 9:  
The SPIN-based routing protocols use resource-adaptive algorithms and data negotiation.
# Present and Future Marine Microwave Satellite Missions in China
## 5 Development Trends of Marine Microwave Remote Sensing Satellites in China  
### Development Trends of Marine Dynamic Environment Satellites in China  
In the future, China will continue to strengthen the monitoring of marine dynamic environments and provide monitoring information regarding sea surface temperatures, waves, sea surface heights, and other factors used in the forecasting of global climate changes and marine disasters. China has planned two batches of marine dynamic environment satellite constellations. The first constellation, which includes the HY-2B, HY-2C, and HY-2D satellites, was established on 19 May 2021. The satellites of this constellation will be replaced by the HY-2E, HY-2F, and HY-2G satellites at the end of their respective lifespans in order the establish China's second constellation. The two constellations will continuously monitor the global marine dynamic environment information in a comprehensive way [16; 17].  
In addition to building marine dynamic environment satellite constellations based on existing mature technologies, China is also exploring new microwave remote sensing payloads for marine dynamic environments. These include wide-swath interferometric radar altimeters, dual-frequency full-polarized microwave scatterometers, and full-polarized microwave radiometers. As follow-on satellites of the HY-2 series satellites, the main purpose is to improve the sea surface height values obtained via sub-satellite point measurementsto wide-swath measurements [84]. The maximum wind speed measurement capacity is increased from 24 m/s to 50 m/s, and the sea surface temperature measurement accuracy can reach higher than 0.5 \({}^{\circ}\)C based on the existing observational accuracy [84].  
Furthermore, China is exploring new technology for observations of sea surface salinity levels and surface flow fields, using microwave remote sensing technology [85].  
All China's ocean satellite data are free for users all over the world (The data download address is [[https://osdds.nsoas.org.cn/](https://osdds.nsoas.org.cn/)]([https://osdds.nsoas.org.cn/](https://osdds.nsoas.org.cn/))). To facilitate the use of users related to global marine science, the data product format design of China ocean satellite is basically the same as that of ocean observation satellites of other countries. Therefore, China's ocean satellites can form a larger virtual constellation with the ocean satellites of other countries to serve global climate change studies, disaster prevention and control, etc.
# An LLC Converter with Capacitive Insulation
## 5 Conclusions
\begin{tabular}{l l} \(Q_{1}\), \(Q_{2}\) & Main switches \\ \(Q_{3}\), \(Q_{4}\) & SR switches \\ \(C_{o}\) & Output capacitor \\ \(T_{1}\) & Main transformer \\ \(N_{p}\) & Primary winding of \(T_{1}\) \\ \(N_{S1}\), \(N_{S2}\) & Secondary windings of \(T_{1}\) \\ \(n\) & Turn ration of \(T_{1}\) \\ \(L_{r}\) & Resonance inductor \\ \(L_{lk}\) & Leakage inductance of \(N_{p}\) of \(T_{1}\) \\ \(L_{r-cat}\) & Additional resonance inductor \\ \(C_{r}\) & Resonant capacitor \\ \(C_{r1}\) & High-side resonant capacitor \\ \(C_{r2}\) & Low-side resonant capacitor \\ \(D_{mxx}\) & Maxima duty cycle \\ \(B_{mxx}\) & Maxima flux density of \(T_{1}\) \\ \(f_{p}\) & Resonant frequency of \(L_{r}\), \(L_{m}\), and \(C_{r}\) \\ \(f_{s}\) & Resonant frequency of \(L_{r}\) and \(C_{r}\) \\ \(f_{sw}\) & Switching frequency (Hz) \\ \(Q_{FL}\), \(Q_{LL}\) & Quality factor of full load and light load, respectively \\ \(R_{o}\) & Equivalent resistance of output load \\ \(R_{ac-FL}\), \(R_{ac-LL}\) & Reflected load resistance of output load at full load and light load, respectively \\ \(\omega\) & Switching frequency (rad/sec) \\ \(\omega_{p}\) & Resonance frequency of parallel resonant converter \\ \(\omega_{s}\) & Resonance frequency of series resonant converter \\ \(M_{FL}\), \(M_{LL}\) & Voltage gain of the output and input at full load and light load, respectively \\ \(A_{e}\) & Effective core area \\ \(v_{Cr}\) & Voltage cross \(C_{r}\) \\ \(v_{Cr({\rm min})}\) & Minima voltage of \(C_{r}\) \\ \(v_{Cr({\rm max})}\) & Maxima voltage of \(C_{r}\) \\ \(i_{Cr1({\rm pk})}\) & Peak current of \(C_{r1}\) \\ \(v_{Cr1}\) & Voltage cross \(C_{r1}\) \\ \(v_{Cr1({\rm min})}\) & Minima voltage of \(C_{r1}\) \\ \(v_{Cr1({\rm max})}\) & Maxima voltage of \(C_{r2}\) \\ \(v_{Cr2}\) & Maxima required turns of \(N_{p}\) \\ \(i_{Np}({\rm rms})\) & RMS current of \(N_{p}\) \\ \(i_{N1({\rm rms})}\), \(i_{N2({\rm rms})}\) & RMS current of \(N_{s1}\) and \(N_{s2}\), respectively \\ \(B_{mxx-Lm}\) & Maxima flux density of \(L_{m}\) \\ \(i_{Lm({\rm pk})}\) & Peak current of \(L_{m}\) \\ \(DCR_{Np}\) & \(DCR_{Np}\) resistance of \(N_{p}\) \\ \(DCR_{N1}\), \(DCR_{N2}\) & \(DCR_{Np}\) resistance of \(N_{s1}\)_and_\(N_{s2}\) \\ \(A_{e({\rm T1})}\) & Effective core area of \(T_{1}\) \\ \(V_{e({\rm T1})}\) & Effective core volume of \(T_{1}\) \\ \(P_{cmp-Np}\) & Copper loss of \(N_{p}\) \\ \(P_{cmp-N_{S}}\) & Copper loss of \(N_{s}\) \\ \(P_{curc-T1}\) & Core loss of \(T_{1}\) \\ \(P_{CV(T1)}\) & Unit core loss of \(T_{1}\) \\ \(P_{T1}\) & Total loss of \(T_{1}\) \\ \(i_{Lr({\rm pk})}\) & Peak current of \(L_{r}\) \\ \(N_{Lr-cat}\) & Winding turns of \(L_{r-cat}\) \\ \(L_{r-cat}\) & Additional resonant inductor \\ \(A_{e(L_{r-cat})}\) & Effective core area of \(L_{r-cat}\) \\ \(B_{mxx-Lr}\) & Maxima flux density of \(L_{r-cat}\) \\ \end{tabular}
## 3 Results and Discussion
Figure 8 presents the SEM images of tubular AAO anodized at 40 V. In Figure 8a, a barrier layer having an ordered compact structure was observed at the bottom of the AAO. Figure 8b shows the nano-pores after partially removing the barrier layer. In Figure 8c, the entire barrier layer has been removed, and the bottom view image reveals the nano-pore structure. In Figure 8d, the average pore dimension is about 60 nm and the pore density is about 10\({}^{8}\)-10\({}^{9}\) pore/cm\({}^{2}\) in the AAO structure. A complete tubular AAO process takes a long time, and the whole process includes (a) stress relief of Al tube (1 h); (b) mechanical grinding on the outer surface of the Al tube (1 h); (c) electrolytic polishing (10 min); (d) 1 st anodization (1 h); (e) removal of 1 st anodization film (50 min); (f) 2 nd anodization (5 h); (g) removal of Al substrate (15 min); (h) removal of barrier layer (1 h); and, (i) pore widening (45 min). A complete tubular AAO process includes nine steps and takes around 10 h, at least. In order to increase the fabrication efficiency of tubular AAO a batch tubular AAO making is necessary. Figure 9 showed a batch tubular AAO process, including (a) a specified length of raw Al tubes is used; (b) a batch of mirror-like Al tubes surface formation after electrolytic polishing; (c) a batch of AAO production on the Al tubes through anodization; and, (d) a batch of tubular AAO formation after removing the retaining Al substrates. In our future work, high purity aluminum, such as 99.999% Al  
Figure 7: The schematic diagram of transmembrane pressure test on the tubular AAO; (**a**) tubular AAO; (**b**) exploded view of gas connector with tubular AAO; (**c**) gas connector with tubular AAO; and, (**d**) image of gas connector with tubular AAO.
# Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement
###### Abstract  
The Global Navigation Satellite System (GNSS), Internet of Things (IoT) and cloud computing technologies enable high-precision positioning with flexible data communication, making real-time/near-real-time monitoring more economical and efficient. In this study, a multi-sensor GNSS-IoT system was developed for measuring precise water surface elevation (WSE). The system, which includes ultrasonic and accelerometer sensors, was deployed on a floating platform in Googong reservoir, Australia, over a four-month period in 2024. WSE data derived from the system were compared against independent reference measurements from the reservoir operator, achieving an accuracy of 7 mm for 6 h averaged solutions and 28 mm for epoch-by-epoch solutions. The results demonstrate the system's potential for remote, autonomous WSE monitoring and its suitability for validating satellite Earth observation data, particularly from the Surface Water and Ocean Topography (SWOT) mission. Despite environmental challenges such as moderate gaze conditions, the system maintained robust performance, with over 90% of solutions meeting quality assurance standards. This study highlights the advantages of combining the GNSS with IoT technologies and multiple sensors for cost-effective, long-term WSE monitoring in remote and dynamic environments. Future work will focus on optimizing accuracy and expanding applications to diverse aquatic settings.  
hydrology; low-cost GNSS; water surface elevation; satellite validation; SWOT 202525356
## 3 Materials and Methods
### Method of Data Collection  
The raw rainfall, cloud and atmospheric gases dataset, and other parameters used to conduct the detailed prognostic evaluation of total radio wave propagation attenuation losses in this study, were collected entirely from the Nigerian Meteorological Agency (NIMET). It is the leading Nigerian government agency equipped with the appropriate facilities and charged with the responsibility to robustly monitor and collect data on all areas of meteorology for policy formulation, planning, guidance, and decision making. The agency also prepares, projects, and provides weather (and climate) information needed for aircraft safety and related operations.  
Specifically, the 12-year atmospheric datasets from 2008 to 2020 contain monthly rainfall depth, rainy days, minimum temperature, average temperature, maximum temperature, average snowfall, average pressure, wind speed, and average temperature and humidity. Figures 3 and 4 show the mean yearly rainfall depths and the humidity percentage values for the study location.  
Figure 2: Map of the study location showing the geographical coordinates.  
The agency has workstations over the country. The average precipitation data in depth is collected yearly using an electronic data logger such as the tipping-bucket rain gauge and weather radar, which measure the rainfall millimetres and other atmospheric variables over twenty hours. Remarkably, the tipping-bucket is a specialized automatic mechanical device used by this agency to measure the amount of rainfall in increments of approximately 0.25 mm, on one tip and at a detached point with a collection pair and funnel, which is usually positioned at an uncluttered geographical point. With the aid of a connecting wireless transceiver, the rainfall depth can be recorded at each 'tip'. The tipping bucket rain gauge is designed to measure the depth of the rainfall in terms of precipitated water height accumulated by the gauge per given time. Once an equivalent of about 0.25 mm of rainfall has been collected employing the bucket at the pivot level, it is emptied (i.e., tipped off).
## 3 Analysis of Monitoring Results  
### Monitoring Point Layout  
The layout of the surface settlement monitoring points at the construction site is shown in Figure 1. Along the jacking direction of the tunnel, 10 longitudinal settlement monitoring points, labelled B10 to B1, are arranged. A transverse monitoring section D0 is set up at B8, which includes settlement monitoring points D1-D6. The launch line is located at a distance of 17.5 m from the transverse monitoring section.
## 4 Results and Discussion
### Discussion  
This paper treats the issue of conveyor belt deviation as a problem of straight-line detection in specific scenarios. By leveraging the unique characteristics of the monitoring scene and the object being monitored, a method is proposed to represent the conveyor belt edge lines using the diagonal features of the prediction boxes from the object detection network. Additionally, a quantitative determination method for the conveyor belt deviation status is introduced. End-to-end conveyor belt edge detection is achieved using the Yolov5 network on a custom dataset.  
Similarly to object detection in other scenarios, this method eliminates the need for the threshold setting processes inherent in traditional image processing methods. For instance, traditional image processing involves steps such as Canny edge detection and Hough line detection, which require setting numerous thresholds, such as grayscale transformation thresholds, edge detection gradient thresholds, Hough transform accumulator thresholds, minimum line segment length parameters, and gap parameters. These thresholds need to be readjusted for different monitoring scenarios or conveyor belt system scenarios. Therefore, the method proposed in this paper is comparatively simpler and more straightforward and the algorithm possesses stronger adaptability. Moreover, thanks to the end-to-end detection approach, the proposed method significantly enhances the detection speed. Compared to methods based on conveyor belt edge segmentation or conveyor belt region segmentation,  
Figure 12: Influence of rain or snow noise on conveyor belt edge detection: (**a**) image with rain or snow noise, (**b**) detection result of DHT, (**c**) detection result of the method proposed in this paper.  
the object detection-based monitoring method greatly reduces the difficulty and intensity of dataset creation.  
However, the method proposed in this paper still faces some issues, such as ensuring that the diagonal vertices of the prediction boxes always fall on the edge of the conveyor belt. This study used Grad-CAM technology to explore the effectiveness of different categories' attention mechanisms in improving the detection accuracy and identifying the feature locations utilized by the current network predictions. This investigation revealed feature locations that may affect the detection results, providing a reference to ensure the detection accuracy and reliability in future work. This is also one of the main differences between the work described in this paper and our previous versions. We are attempting to find a method that always maintains a high degree of coupling. We believe that the ideal feature distribution should have all utilized features located within the prediction box, with greater weight at the relevant diagonal vertex positions.  
In future work, we will attempt to use the detection approach of the CornerNet network, representing the conveyor belt edge by directly predicting the vertex coordinates of the left and right edges.
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 3 Techniques for Explainable AI
### Model-Based Interpretation Techniques
#### 3.1.4 Random Forests (RFs)  
[PERSON] [33] was the first to extend the DT algorithm and introduce RFs, which build a large number of decision trees using bootstrapped samples of the data and random subsets of the features. The final prediction is made by aggregating the predictions of all the trees in the forest. Because the structure of an RF is more complex than the structure of a DT, they can be more challenging to understand and interpret. Moreover, as the complexity of relationships among the variables increases, the number of trees required to achieve good accuracy also increases and with more trees, it becomes challenging to identify important variables and evaluate their contribution to the predictions.  
RFs have several advantages over DTs, including reduced over-fitting, improved accuracy, and the ability to handle high-dimensional data. RFs, like DTs, also provide local interpretability by explaining the model's predictions for individual instances. To improve the interpretability of tree-based models for complex tabular datasets, [PERSON] et al. [28] proposed computing the SHAP interaction values for trees. These SHAP interaction values capture not only individual predictions, but also the interaction effects among all pairs of those predictions.
## 4 Results
### Accuracy Assessment of Simulated GPP
In general, the PODEn4 DVar method performed slightly better with \(r\) = 0.923 and \(RMSD\) = 32.676 gC/m\({}^{2}\)/month than LPJ-DGVM (\(r\) = 0.840, \(RMSD\) = 45.410 gC/m\({}^{2}\)/month) and LPJ-EnKF (\(r\) = 0.887, \(RMSD\) = 38.531 gC/m\({}^{2}\)/month) schemes (Figure 6). It can be concluded that PODEn4 DVar had the best advantage for improving model estimations when integrated in dynamic vegetation models. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**r**} & \multicolumn{3}{c}{**RMSD (gC/m\({}^{2}\)/Month)**} & \multicolumn{3}{c}{**RMSDr (\%)**} \\ \cline{3-10}  & & \(GPP_{L}\) & \(GPP_{LE}\) & \(GPP_{LP}\) & \(GPP_{L}\) & \(GPP_{LE}\) & \(GPP_{LP}\) & \(GPP_{L}\) & \(GPP_{LE}\) & \(GPP_{LP}\) \\ \hline \multirow{3}{*}{Din} & 2003 & - & 0.677 & **0.869** & 21.610 & 18.933 & **12.323** & 16.7 & 16.0 & **9.7** \\  & 2004 & 0.677 & 0.791 & **0.873** & 32.740 & 26.829 & **22.303** & 26.0 & 21.3 & **17.5** \\  & 2005 & 0.600 & 0.778 & **0.898** & 31.032 & 24.056 & **17.289** & 27.5 & 21.3 & **15.1** \\ \hline \multirow{3}{*}{Qia} & 2003 & 0.902 & 0.948 & **0.965** & 38.613 & 31.383 & **17.763** & 31.6 & 25.2 & **12.2** \\  & 2004 & 0.863 & 0.922 & **0.949** & 38.027 & 29.908 & **24.400** & 28.0 & 20.6 & **16.2** \\  & 2005 & 0.918 & 0.920 & **0.956** & 32.228 & 26.974 & **19.791** & 27.7 & 21.4 & **14.3** \\ \hline \multirow{3}{*}{Cha} & 2003 & 0.896 & 0.919 & **0.954** & 56.923 & 50.171 & **35.961** & 55.9 & 47.9 & **31.5** \\  & 2004 & 0.879 & 0.922 & **0.942** & 58.590 & 46.209 & **43.410** & 54.3 & 39.5 & **33.7** \\  & 2005 & 0.921 & 0.935 & **0.941** & 56.983 & 47.964 & **39.624** & 67.9 & 52.7 & **35.0** \\ \hline \multirow{3}{*}{Dan} & 2004 & 0.870 & 0.877 & **0.894** & 40.668 & 41.343 & **32.211** & 87.4 & 87.0 & **75.3** \\  & 2005 & 0.958 & 0.958 & **0.965** & 50.924 & 41.976 & **37.071** & 103.4 & 91.9 & **88.0** \\ \hline \multirow{3}{*}{HaM} & 2003 & 0.895 & 0.919 & **0.932** & 38.294 & 34.898 & **31.873** & 85.0 & 77.6 & **70.5** \\  & 2004 & 0.756 & 0.864 & **0.895** & 57.797 & 44.210 & **38.521** & 121.5 & 95.4 & **75.0** \\ \hline \multirow{3}{*}{Cng} & 2007 & 0.846 & 0.819 & **0.872** & 46.552 & 36.920 & **30.618** & 69.9 & 68.2 & **57.8** \\  & 2008 & 0.773 & 0.815 & **0.856** & 50.716 & **45.762** & 47.182 & 75.1 & 70.3 & **59.8** \\ \cline{1-1}  & 2009 & 0.795 & **0.843** & 0.838 & 41.552 & **37.908** & 38.517 & 69.4 & **62.9** & 65.4 \\ \cline{1-1}  & 2010 & 0.741 & 0.769 & **0.839** & 56.017 & 49.835 & **39.929** & 74.9 & 70.0 & **61.3** \\ \hline \hline \end{tabular} Bold numbers represent the optimal results among \(GPP_{L}\), \(GPP_{LE}\) and \(GPP_{LP}\).  
\end{table} 
Table 2: Accuracy of model-estimated gross primary productivity (GPP) from three models by withholding site-year.
## 4 System and Threat Model
### Security Model  
We assume that the risk of data interception by malicious parties during user-server communications. To counteract this, we developed a strategy centered around the computational Diffie-Hellman (CDH) problem. Our belief is that the CDH conundrum is computationally arduous, with the chance of any probabilistic polynomial-time attacker (PPTA) deciphering it being practically negligible [40].
# Topp-Leone Modified Weibull Model: Theory and Applications to Medical and Engineering Data
###### Abstract  
In this article, a four parameter lifetime model called the Topp-Leone modified Weibull distribution is proposed. The suggested distribution can be considered as an alternative to Kumaraswamy Weibull, generalized modified Weibull, extend odd Weibull Lomax, Weibull-Lomax, Marshall-Olkin alpha power extended Weibull and exponentiated generalized alpha power exponential distributions, etc. The suggested model includes the Topp-Leone Weibull, Topp-Leone Linear failure rate, Topp-Leone exponential and Topp-Leone Rayleigh distributions as a special case. Several characteristics of the new suggested model including quantile function, moments, moment generating function, central moments, mean, variance, coefficient of skewness, coefficient of kurtosis, incomplete moments, the mean residual life and the mean inactive time are derived. The probability density function of the Topp-Leone modified Weibull distribution can be right skewed and uni-modal shaped but, the hazard rate function may be decreasing, increasing, J-shaped, U-shaped and bathtub on its parameters. Three different methods of estimation as; maximum likelihood, maximum product spacing and Bayesian methods are used to estimate the model parameters. For illustrative reasons, applications of the Topp-Leone modified Weibull model to four real data sets related to medical and engineering sciences are provided and contrasted with the fit reached by several other well-known distributions.  
Topp-Leone distribution; modified Weibull distribution; moments; moment generating function; maximum likelihood approach; maximum product spacing approach; Bayesian approach Article Topp-Leone Modified Weibull Model: Theory and Applications to Medical and Engineering Data  
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