esa-sceva/satcom-chunk-collection huggingface dataset:
# Groundwater Storage Changes in China from Satellite Gravity: An Overview
## 2 Data and Methodology
### Data
#### 2.2.3. Ground Well Observations  
Groundwater level observations were further collected and analyzed to validate GWS change estimates from GRACE and HMs. For the NCP, monthly water levels from 64 phreatic monitoring wells and 66 confined monitoring wells were obtained from the groundwater yearbooks published by the China Institute of Geological Environment Monitoring (CIGEM) over the period 2005-2013 [74]. Another observation network in the NCP deployed by the MWR contains more than 1000 monitoring wells. However, the original level observations from the network were inaccessible. Fortunately, basin-scale mean level changes over the NCP based on the network were available and used to estimate the temporal variability of GWS changes in the NCP from 2002 to 2014.  
For the Song-Liao Basin, water level observations from 151 wells were collected from the CIGEM and the Hydrological Bureau of Inner Mongolia (HBIM) from 2005-2013. It should be noted that, considering the uneven distribution of wells, water levels were averaged over a 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) mesh where each cell of the mesh was assigned the mean value of water levels within it [20]. Then, mean water level change over the study region was calculated as the area-weighted average of values in all cells of the mesh within the region. For the Tarim Basin, however, water level observations are limited to 6 wells; and half of them contain significant gaps from 2005-2013. Therefore, we fail to estimate GWS changes over the Tarim Basin based on ground well observations.  
To estimate GWS changes from ground well observations, level changes should be multiplied by specific yields. For the NCP, the specific yield map based on pumping tests from [PERSON] et al. [75] was used, with an average of 0.06 over the NCP. For the Song-Liao Basin, a mean specific yield value of 0.09 was used according to pumping tests by [PERSON] et al. [76].
## 2 State of the Art
### Benefits of Blockchain for Data Management in IoV  
Blockchain complements the IoV by offering a decentralized and secure system for real-time operational data management. In port terminals, blockchain can ensure the transparency and integrity of data related to loading times, economic transactions, and vehicular routes [8].  
Smart contracts, based on blockchain, make it possible to automate administrative processes such as the collection of fees and the allocation of storage spaces, reducing costs and operational errors [17]. However, the scalability of blockchain solutions and the initial cost of implementation remain significant challenges to their adoption in port environments [19].  
The integration of IoV and blockchain into Ro-Ro terminals offers a promising solution to address today's operational challenges. While IoV provides real-time communication and dynamic vehicular traffic management, blockchain ensures that the data generated is immutable and auditable [13]. This synergy allows for greater transparency and trust between the actors involved, improving coordination and optimizing operation times [20]. However, joint implementation requires overcoming technical and economic barriers to ensure its viability in complex environments such as ports.  
The introduction of IoV and blockchain in Ro-Ro terminals will not only impact internal port operations but also have significant implications for the broader supply chain. For these technologies to be effective, logistics providers, shipping companies, and customs authorities must integrate them into their workflows. Without such coordination, the full benefits of automation and real-time data exchange may not be realized, limiting efficiency gains.
## 4 Verification and Analysis
### Accuracy Analysis in Selecting Different Vehicle Types at Different Periods  
The peak hours of test road are 07:00-09:00 and 17:00-19:00. The average speed of vehicles is about 25 km/h during peak time and 50 km/h during idle time. In order to verify the detection accuracy of different vehicle types at different periods, this paper selects six time periods to verify the accuracy of the traditional single magnetism-based and the proposed two-sensor data fusion vehicle detection method.  
Generally, the length of family car is generally less than 5 m [21] and the most of the vehicles run on the urban roads are family cars. For the sake of statistical simplification, a big vehicle refers to a vehicle with the length greater than 5 m, such as buses, trucks, etc. And a small vehicle refers to a vehicle with the length less than 5 m, such as family cars.  
From Table 1, we can find that both the traditional single magnetism-based detection method and our proposed method have higher detection accuracy in the idle time than that in the peak time of the road. Simultaneously, the traditional single magnetism-based method obviously has multiple-detection problems in the detection of large vehicles during the peak period. The main reason is that the average speed of vehicle is slow during peak time or the stopping position of vehicle is not standard when the vehicles waiting for the traffic light, the blind zone problem causes the same vehicle to be detected multiple times by the detector with the traditional single magnetism-based algorithm.  
From Table 1, we can know that the detection accuracy of our proposed two-sensors data fusion detection method has relatively higher precision than the traditional single magnetism-based method in the large vehicle detection process, the detection accuracy of ours is up to 95.4% while traditional singlemagnetism-based method is only 83.4%. However, the detection accuracy of ours is similar to the traditional method in the small vehicle detection process. Therefore, above analysis shows that our proposed method can significantly improve the large vehicles detection accuracy, effectively solve the problem of multiple-detection of those vehicles running at low speeds.  
From Table 1, we can also find that whether large or small vehicles, the detection accuracy of the inductance loop vehicle detector is higher than the vehicle detectors with the traditional single magnetism-based detection method and our proposed method. The main reason is that the sensing size of inductive loop vehicle detector is large, resulting in the blind zone is smaller than the vehicle detectors with the traditional single magnetism-based detection method and our proposed method.
# Optimized Analytical-Numerical Procedure for Ultrasonic Sludge Treatment for Agricultural Use
## 5 Temperature-Humidity Coupled Model for Sludge Treatment
### On Moisture Reduction  
To calculate the reduction of moisture \(W(\mathbf{x},t)\) in a sludge from \(T(\mathbf{x},t)\), we use a model based on the Balance Equation (5). \(W(\mathbf{x},t)\), representing the water content per unit volume, varies with the evaporative flux, \(J_{e}(\mathbf{x},t)\), described as:  
\[\frac{\partial W(\mathbf{x},t)}{\partial t}=-\
abla\cdot J_{e}(\mathbf{x},t), \tag{6}\]  
where \(J_{e}(J_{e}(\mathbf{x},t)\) is a function of \(T(\mathbf{x},t)\) and vapor pressure, and is written as:  
\[J_{e}(\mathbf{x},t)=k_{e}(T(\mathbf{x},t))\big{(}p_{v}(T(\mathbf{x},t))-p_{v, \text{amb}}\big{)}, \tag{7}\]  
with \(k_{e}(T(\mathbf{x},t))\) temperature-dependent evaporation coefficient, \(p_{v}(T(\mathbf{x},t))\) saturated vapor pressure and \(p_{v,\text{amb}}\) ambient vapor pressure. The source \(Q_{m}(\mathbf{x},t)\), associated with water evaporation, is related to the latent heat \(\lambda\) and the rate of moisture reduction:  
\[Q_{m}(\mathbf{x},t)=\lambda\frac{\partial W(\mathbf{x},t)}{\partial t}. \tag{8}\]  
The overall moisture reduction is calculated by integrating the residual content \(W(\mathbf{x},t)\) over time and space, compared with the initial content \(W_{0}(\mathbf{x})\):  
\[\Delta W=\int_{\Omega}\big{(}W_{0}(\mathbf{x})-W(\mathbf{x},t)\big{)}\,d \mathbf{x}. \tag{9}\]  
Then, substituting into the (5) the (8), we obtain:  
\[\rho C_{p}\frac{\partial T(\mathbf{x},t)}{\partial t}+\rho C_{p} \mathbf{u}(\mathbf{x},t)\cdot\
abla T(\mathbf{x},t)+\
abla\cdot(-k\
abla T( \mathbf{x},t))\] \[=Q(\mathbf{x},t)+\lambda\frac{\partial W(\mathbf{x},t)}{\partial t}. \tag{10}\]
## 2 Materials and Methods
### Durability Testing
#### 2.5.3 Absorption Capacity of SAPs in NaCl Solution by Means of the Filtration Test  
The filtration test was performed as described in [23]. In this method, excess test liquid was added to dry SAP particles in a beaker, so that the particles could swell freely. The mass of dry SAP particles and the mass of added test liquid were noted. The beaker was then sealed to prevent carbonation of the testing liquid and evaporation of the liquid, and storedout of direct sunlight to avoid UV exposure. After predetermined time intervals, the mixture was filtered, and the mass of the filtered liquid was recorded. For each measurement time, a set of three beakers of \"SAPs + solution\" was used. The absorption capacity of the SAPs was then defined as the amount of solution [g] absorbed per gram of SAP. The same NaCl solution (with 35 g/L concentration) used in the chloride penetration test was used.
# Outage Performance of SWIPT-D2D-Based Hybrid Satellite-Terrestrial Networks
###### Abstract  
This paper investigates the outage performance of simultaneous wireless information and power transfer (SWIPT)-assisted device-to-device (D2D)-based hybrid satellite-terrestrial networks (HSTNs). In the considered system, an energy-constrained terrestrial user terminal (UT) harvests energy from the radio frequency (RF) signal of a terrestrial amplify-and-forward (AF) relay and utilizes the harvested energy to cooperate with the shadowed terrestrial Internet of Things (IoT) devices in a D2D communication. Both power splitting (PS)-based and time switching (TS)-based SWIPT-D2D schemes are adopted by the energy-constrained UT to obtain sustainable energy for transmitting information to the shadowed IoT device. Considering shadowed Rician fading for satellite-terrestrial links and Nakagami-\(m\) fading for terrestrial links, we analyze the system performance by deriving the closed-form expressions for the outage probability (OP) of both the UT and the IoT device. Our theoretical analyses are validated via Monte Carlo simulations.  
Keywords:simultaneous wireless information and power transfer; internet of things; hybrid satellite-terrestrial networks; device-to-device; outage probability +
Footnote †: journal: Sensors
## 3 Case Study
### Prediction Results and Analysis  
#### 3.4.1 TVF-EMD-LSSVM Model Prediction  
According to the forecasting process of the proposed model in Section 2.3, TVF-EMD is used to decompose the experimental data A into 10 subsequences, as shown in Figure 4.  
By constructing training and test sets for each subsequence, the LSSVM model is built to predict them. The dimension parameter was determined by minimizing the root mean square error of the output value in the training part [20]. Moreover, the optimal penalty coefficient and kernel function  
Figure 4: The pictures of TVF-EMD decomposition results (A).  
parameters of each subsequence were determined by the optimization function. Finally, the traffic volume prediction value was obtained by accumulating the forecasting results of the subsequences.
## 4 Performance Analysis
### Cluster Reformation  
Cluster Merging/Splitting  
When merging two nearby clusters according to the procedures in Section, the energy consumption for determining the candidate cluster and initializing the merge process yields  
\[E_{m}^{(ini)}=1\cdot E_{P}+E_{T}^{(ini)}+E_{R}^{(ini)}, \tag{36}\]  
where  
\[E_{T}^{(ini)}\simeq E[N_{hop}^{(chi,ch_{j})}]\cdot E_{T} \tag{37}\]\[E_{R}^{(ini)}\simeq E[N_{hop}^{(ch_{i},ch_{j})}]\cdot E[N_{i}]\cdot E_{R}. \tag{38}\]  
Note that \(E[N_{hop}^{(ch_{i},ch_{j})}]\) represents the average number of hops between the two clusterheads involved in the merge operation, \(E[N_{i}]\) is the average number of neighboring sensors, and \(E_{P}\) is for determining the cluster ID of the merged cluster. Finally, a local flooding is applied within a cluster such that the cluster ID information can be unified in the merged cluster. Therefore, when combining two neighboring clusters \(C_{i}\) and \(C_{j}\) to form a new merged cluster and given that the merged cluster ID is \(i\), the operation of updating the cluster ID requires in cluster \(C_{j}\)  
\[E_{m}^{(id)}=(1+|N_{C_{j}}|)\cdot E_{P}+(1+H_{j}^{(2)})\cdot E_{T}+(\sum_{k\in H _{j}^{(2)}}N_{j}+N_{j})\cdot E_{R}, \tag{39}\]  
where \(N_{C_{j}}\) is the number of cluster members of cluster \(C_{j}\).  
For the split procedure, the energy consumption analysis in Section 5.1 can be applied since the split cluster may be regarded as a small network and the SAMCA algorithm may be used to spread the energy usage over the splitted cluster.
# Embedding Security Awareness in IoT Systems: A Framework for Providing Change Impact Insights
## 1 Introduction
Therefore, systematically assigning _IF_ values based on both functional significance and system interdependence is crucial for enhancing resilience, strengthening security, and improving autonomous decision-making in uncertain, dynamic environments. To address this challenge, we propose a procedural approach for deriving the _IF_ of processes within IoT systems. We model the system's interconnections as a directed call graph [23; 24], which captures structural relationships between processes by representing their invocation dependencies. To assess the criticality of each process, we adopt a weighted PageRank centrality algorithm [25], incorporating both degree centrality [26; 27] and PageRank centrality [28]. We develop a procedural method to compute the _IF_ of each process while considering the multi-layer architecture of the IoT system. Building upon our prior work [6], we extend the security profile to incorporate _IF_ values and generate interpretation reports that include both the affected functionalities and their corresponding importance. These enriched interpretation reports serve as the foundation for security awareness, enabling intelligent, prioritized, and context-sensitive responses to evolving threats. However, designing a complete mitigation strategy in response to evolving threats is beyond the scope of this paper. The key contributions of this paper are twofold:  
* We develop a procedural approach to assign an _Importance Factor (IF)_ to each system component based on its significance to the system's overall operation. * We enhance the security profile by incorporating _IF_ values and extend the _security awareness_ framework to generate enriched interpretations that provide insights into the affected components, their functionalities, and their operational significance. To evaluate the effectiveness of our approach, we implemented a testbed based on an IoT-enabled smart irrigation system and deployed the developed component. The experimental results demonstrate the component's effectiveness in extracting information from XAI outputs and generating interpretations that align with the system's security profile, thereby validating the proposed methodology.
## 4 Discussion
### Limitations and Potential Improvement  
The 3-PG model was used to estimate DBH, H, and ACS of eucalyptus based on forest age data, meteorological data, and site conditions in the study area, and obtained a high simulation accuracy. The model can not only simulate the normal growing forest, but also estimate the growth state of the forest under different management measures such as thinning. Through thinning management, forests can achieve the goal of adjusting stand density, changing stand structure, and reducing competition among individual tree species, thus, changing the normal growth of trees. The simulation results at the SP scale show that the model captured well the thinning effects on forest growth. Considering thinning information can improve the simulation accuracy, which is consistent with the research results of [PERSON] et al. (2010). However, the response of NPP to thinning measures has not been well-explored, and positive, negative, and neutral impacts coexist in different studies ([PERSON] et al., 2011; [PERSON] et al., 2012; [PERSON] et al., 2013). This should be better considered in future simulations. Landsat time-series data-based forest age data fails to monitor management such as thinning in eucalyptus plantations due to its coarse resolution in spatial and temporal data. This may reduce the accuracy of model simulation. Therefore, these subtle changes in forest dynamics should be better characterized through spectral mixture analysis or the use of higher spatial-temporal resolution data (such as Sentinel, Gaofen).
# Inter-Satellite Link Prediction with Supervised Learning: An Application in Polar Orbits
###### Abstract
Due to the Earth's rotation, polar LEO satellites pass over all latitudes, entirely covering the Earth's surface and providing global coverage for remote sensing and Earth observation missions. As in previous works, an encounter is defined as a close approach between two satellites assuming the Simplified General Perturbation 4 (SGP4) orbital model. SGP4 is a well-known orbital model that considers the effect of Earth's non-sphericity and atmospheric drag [17; 18]. However, in contrast to previous publications, these encounters are better defined based on two distance thresholds: an upper-bound threshold beyond which satellites are too far apart to communicate, and a lower-bound threshold below which satellite-to-satellite communication is always feasible. This new definition enables the solution to accommodate a range of user-defined threshold distances, making it suitable for various antenna characteristics. In particular, this work contributes to the state of the art by (1) offering an alternative solution based on machine learning to forecast encounters between polar LEO satellites; (2) implementing a method to easily generate a synthetic dataset of satellite encounters given a range of orbital elements; (3) providing a trained model that obtains good predictions given any pair of orbital elements in the range of polar LEO for any distance threshold within a predefined range; (4) evaluating the SL model with realistic data from real polar LEO Celestrak satellites; and (5) analyzing the results for two different datasets and three different scenarios, extracting important conclusions  
The remainder of this article is structured as follows. Section 2 defines the problem statement, introducing the structure of the datasets used for training, validation, and testing. Section 3 introduces polar orbits and presents their characteristics using real data from Celestrak [19]. Section 4 details the SL model architecture used to fit the input and output data. Section 5 presents an exhaustive analysis of the model performance on two different datasets--one containing synthetic data and another one with realistic satellite orbits from Celestrak. Finally, Section 6 concludes the paper with a recap of the main discoveries and offers insights for further exploration within this field.
# Shear Wave Velocity Estimation Based on Deep-Q Network
## 5 Numerical Experiments
### Discussion  
Since the ground truth information is not available in many real scenarios, Table 9 expresses the performance comparison based on the metrics of misfit and running times and concludes the analysis in Sections 5.2 and 5.3.  
Table 9 demonstrates that the proposed framework performs a faster and lower misfit inversion (highlighted with bold fonts) compared to other methods. Furthermore, Figures 5 and 6 illustrate that the proposed framework provides the inversion results with relatively lower uncertainties than other methods.  
As mentioned in Section 4.2, Action 0 explores the parameter space more roughly since it compresses the search bounds in a relatively fast way. On the other hand, Action 1 is more suitable for finely exploring one local area in the parameter space. To understand the learned search strategy of the DQN framework, Figure 9 exhibits how the actions are executed by the DQN framework in an independent inversion for Case 1, where the blue star, the orange dot, and the black curve refer to Action 0, Action 1, and the relative misfit as a function of iteration numbers, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **GA** & **ASSA** & **DE** & **DQN Framework** \\ \hline \(H\) & 6.74\% & 2.74\% & 1.85\% & **1.22\%** \\ \(Vs_{t}\) & 1.48\% & 0.58\% & 0.40\% & **0.32\%** \\ \(Vs_{b}\) & 5.24\% & 2.18\% & 1.50\% & **0.89\%** \\ Misfit (m/s) & 4.24 & 1.92 & 1.37 & **1.24** \\ Running time (s) & 176.92 & 340.60 & 468.13 & **53.78** \\ \hline \end{tabular}
\end{table}
Table 8: Performance analysis for Case 2.  
Figure 8: The estimated shear wave velocity profiles with the ground truth in Case 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **GA** & & **ASSA** & & **DE** & **DQN Framework** \\ \hline  & **Case 1** & **Case 2** & **Case 1** & **Case 2** & **Case 1** & **Case 2** & **Case 1** & **Case 2** \\ \hline Misfit (m/s) & 17.60 & 4.24 & 12.46 & 1.92 & 15.72 & 1.37 & **6.03** & **1.24** \\ Running time (s) & 181.03 & 176.92 & 876.29 & 340.60 & 371.01 & 468.13 & **112.70** & **53.78** \\ \hline \end{tabular}
\end{table}
Table 9: Overall performance analysis.  
As shown in Figure 9, the agent executes Action 0 at the early stage of iterations and mainly executes Action 1 after that. This pattern can be interpreted as the DQN framework executing Action 0 at the early iterations to locate the rough area of the solution in the parameter space and executing a finer exploration (i.e., Action 1) after that to determine the final solution.  
Note that we do not need to set a hard threshold for the agent to change the action from Action 0 to Action 1 because each independent inversion is initialized randomly. Furthermore, the relative misfit can exceed 100% at the first several iterations since the normalization factor \(E_{norm}\) is defined by a random initialization.
## 7 Challenges and Future Directions
### Interoperability  
A variety of sensors featuring various sensing methods or communication spans are found in a heterogeneous network. Researchers should undertake the required analysis compared to isomorphic situations because it is feasible that heterogeneity influences the problem formulation. In a network, there will be devices from multiple vendors, which need to ensure communication among those devices. Moreover, a system needs to establish connectivity with another network of different functions which can not be ensured without interoperability. Security and privacy are the major concerns while data traverse from one network to another. The data structure should be uniform to process the data and make decision.