esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
### Numerical Results in the IEEE 69-Bus System  
For this test feeder, the same combination of loads of the IEEE 33-bus system are considered, which implies that, for the benchmark case (i.e., without STATCOMs installed), the annual operative costs of the grid is 151,705.9839 USD/year. Once the optimization methodology is used to determine the optimal site and size of the STATCOMs in the IEEE 69-bus system, the annual operative cost of the network is reduced to 115,714.0404 USD/year, which corresponds to annual energy loss costs of 102,241.5784 USD/year and an inversion in STATCOMs of 13,472.4620 USD/year. These results imply that the reduction of the annual operative costs of the network after installing the STATCOMs is about 23.72%. The nodes where the STATCOMs are located correspond to 11 and 61, with nominal rates of 0.1579 and 0.9017 MVAr, respectively.
## 5 Discussion
### The Influence of Adding Polarization Measurements
In contrast, the differences among the coarse mode aerosol types affected by polarization are large. For instance, when the scale height is 1 km, the uncertainty reduction might change by as much as 49%, but the H retrieval uncertainty will only be reduced by 1-5% (1-8%) for C-BHM(C-BNM). It can be seen that the polarization enhancement effect is very limited for the above two coarse mode aerosol types with a moderate particle radius and weak absorption in the UV band.
## 4 Testbed Development
### Testbed Architecture
#### 4.2.2 Encapsulation of the Uu Protocol Stack  
The transmission of NR via Starlink employing RFSimulator entails two relevant considerations. Firstly, traffic encapsulation is part of Starlink's proprietary technology. Although this issue should not be present in real transparent architectures, it is not a major concern in the backhaul and midhaul, since 3 GPP does not define the technology to be adopted at lower layers. However, it is of greater importance in the NR architecture, as unknown layers are being added to the protocol stack of the Uu interface. Secondly, RFSimulator's encapsulation of the NR signal in TCP/IP appends even more layers to the protocol stack. This encapsulation also implies the sampling of the NR signal for the subsequent transmission in TCP packets, which results in the disruption of the inherent continuity of radio signals.
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 4 Evaluation
### Simulation Results
#### 4.4.5 Evaluation for the Dual Connectivity Combined with CRE and ABS  
Both the dual connectivity and the e-ICIC can improve the performance of the UE at the edge of BSs. For this reason, we propose a comprehensive scheme to explore the feasibility of combining the dual connectivity with CRE and ABS, and evaluate whether it can effectively improve the QoS bandwidth requirements of edge UEs via a scenario 5 simulation. Since the comprehensive scheme is based on the connection mode, cell selection, resource allocation, e-ICIC, and CRE bias, we compared its performance with individual schemes, including SC, DC, non-ABS and Static ABS, and Q-learning dynamic ABS.  
In this scenario, we focused on the dual connectivity mode. Notably, the DC mode with CRE and ABS did not yield better system QoS satisfaction, but it was useful to increase the QoS satisfaction of edge UEs. Figure 14 shows the QoS satisfaction of different combinations of schemes of different aspects where the labels RE, RE VoIP, RE Video on the x-axis denote the UEs in the CRE region, the UEs of type GBR in the CRE region, and the UEs of the type MBR in the CRE region respectively. UEs in CRE region are referred to as cell edge UEs. We can observe from Figure 14 that by integrating the mechanisms of dual connectivity, CRE, and ABS, the QoS satisfaction ratio of cell edge UEs can be improved by 10.76% as compared to the traditional approach.  
Figure 12: Satisfaction ratio of the different enhanced inter-cell interference coordination (e-ICIC) schemes in the overload situation of the Macro BS.  
Figure 13: Satisfaction ratio of the different e-ICIC schemes when UEs are evenly distributed.
###### Abstract  
Soil thermal conductivity in the near-phase-transition zone is a key parameter affecting the thermal stability of permafrost engineering and its catastrophic thermal processes. Therefore, accurately determining the soil thermal conductivity in this specific temperature zone has important theoretical and engineering significance. In the present work, a method for testing the thermal conductivity of fine sandy soil in the near-phase-transition zone was proposed by measuring thermal conductivity with the transient plane heat source method and determining the volumetric specific heat capacity by weighing unfrozen water contents. The unfrozen water content of sand specimens in the near-phase-transition zone was tested, and a corresponding empirical fitting formula was established. Finally, based on the testing results, temperature variation trends and parameter influence laws of thermal conductivity in the near-phase-transition zone were analyzed, and thermal conductivity prediction models based on multiple regression (MR) and a radial basis function neural network (RBFNN) were also established. The results show the following: (1) The average error of the proposed test method in this work and the reference steady-state heat flow method is only 7.25%, which validates the reliability of the proposed test method. (2) The variation in unfrozen water contents in fine sandy soil in the range of 0\(\ldots\)3 \({}^{\circ}\)C accounts for over 80% of the variation in the entire negative temperature range. The unfrozen water content and thermal conductivity curves exhibit a similar trend, and the near-phase-transition zone can be divided into a drastic phase transition zone and a stable phase transition zone. (3) Increases in the thermal conductivity of fine sandy soil mainly occur the drastic phase transition zone, where these increases account for about 60% of the total increase in thermal conductivity in the entire negative temperature region. With the increase in density and total water content, the rate of increase in thermal conductivity in the drastic phase transition zone gradually decreases. (4) The \(R^{2}\), \(MAE\), and \(R\)SME of the RBFNN model in the drastic phase transition zone are 0.991, 0.011, and 0.021, respectively, which are better than those of the MR prediction model.  
thermal conductivity; fine sandy soil; near-phase-transition zone; unfrozen water content; radial basis function neural network +
Footnote †: journal: Applied sciences
# Recent Progress on Underwater Wireless Communication Methods and Applications
## 4 Future Trends
### Technological Innovation and System Integration  
Future underwater communication systems will transition from reliance on single-technology solutions to multi-modal collaborative networks that synergize acoustic, optical, and RF technologies. For instance, hybrid acoustic-optical systems can leverage the long-range capabilities of acoustic communication for command transmission in deep-sea environments while exploiting optical channels for high-bandwidth data streaming. RF communication, though limited by high attenuation in seawater, remains critical for short-range, high-speed data exchange in freshwater or shallow turbid zones. Recent breakthroughs, such as OFDM-SPM modulation, have doubled acoustic data rates by combining DPSK with subcarrier power modulation, highlighting the potential of signal processing innovations. Emerging technologies like quantum communication are poised to redefine underwater security paradigms. Quantum key distribution (QKD) using blue-green wavelengths could enable ultra-secure links resistant to eavesdropping. While turbulence and scattering pose challenges, advancements in single-photon detectors and adaptive optics may soon extend quantum communication ranges to 50-100 m. Hardware advancements will further accelerate progress. Wide-bandgap semiconductors like GaN-based laser diodes enhance optical transmission efficiency, while bio-inspired systems like BECS-II achieve 20 kbps data rates over 20 m using dual-frequency electric field communication, ideal for low-power robotic swarms. Innovations such as TENGs demonstrate self-powered signal transmission via Maxwell's displacement currents, enabling energy-autonomous sensor networks in complex environments.  
As cybersecurity concerns grow in critical maritime infrastructures, QKD has emerged as a potential enabler for unbreakable underwater communication links. The use of blue-green laser wavelengths makes QKD physically viable in low-scattering water columns. Experimental setups have demonstrated secure key exchange at distances up to 20-50 m in clear water using coherent phase-based or polarization-based protocols.  
However, practical deployment is still hindered by environmental challenges such as turbulence-induced decoherence, limited photon budgets, and alignment sensitivity.  
In the near term, stationary or semi-fixed QKD nodes in harbors, research stations, or underwater sensor grids may become feasible, especially for high-security but low-throughput applications. The integration of adaptive optics, single-photon avalanche diodes, and quantum-compatible underwater transceivers will be key enablers.  
Ultimately, widespread adoption of QKD in mobile underwater networks depends on both hardware miniaturization and protocol adaptation for unstable and lossy aquatic quantum channels--an exciting but long-term research endeavor.
## 2 Theoretical Background  
### Nonparametric Kernel Density Estimation  
Kernel density estimation is a nonparametric method for estimating the probability density function. \(X_{1},X_{2},\cdots,X_{n}\) are \(n\) sample points of an independent and homogeneous distribution, and their probability density is \(f\); its kernel density estimation can be calculated by the following equation:  
\[\hat{f}_{ker}(x)=\frac{1}{n}\sum_{i=1}^{n}K_{h}(x-X_{i})=\frac{1}{nh}\sum_{i=1 }^{n}K\bigg{(}\frac{x-X_{i}}{h}\bigg{)} \tag{1}\]  
where \(\hat{f}_{ker}(x)\) is the estimated probability density function, and \(K\Big{(}\frac{x-X_{i}}{h}\Big{)}\) is the \(i\)th kernel function and satisfies \(\int K\Big{(}\frac{x-X_{i}}{h}\Big{)}dt=1\) to ensure the true density estimation. \(h\) is the smoothing parameter (bandwidth) of the kernel function.
# Characterizing Perception Deep Learning Algorithms and Applications for Vehicular Edge Computing
## 1 Introduction
Consequently, deploying a memory-intensive machine learning model on a resource-constrained edge device can lead to performance issues, such as slower execution times or system crashes. In scenarios where multiple tasks are simultaneously scheduled, only four tasks may be completed despite each having sufficient resources. Therefore, optimizing memory usage is essential for enhancing the performance and reliability of machine learning systems, reducing hardware costs, and improving overall workflow efficiency. Understanding how memory utilization affects workloads at various levels is critical for diverse deployment scenarios. Our analysis indicates that memory can be effectively generalized through characterization functions to model and predict workload behavior across different machine learning methods and platforms. By conducting a thorough analysis, we can extract the performance characteristics for each layer within a perception network. We monitor the behavior of individual layers during the inference process under varying constraints and conditions. This information is vital for understanding model performance and for optimizing its architecture and parameters. However, monitoring the behavior of individual layers can be complex, particularly with intricate models that contain many layers. Our research reveals that convolutional layers, along with routing, shortcut, and ReLU activation layers, are particularly sensitive to factors such as memory availability. For instance, under constrained memory conditions, some layers may experience significant increases in computation time, with convolutional layers seeing over a 2849% rise, routing layers over 1053%, ReLU layers over 1173.34%, and shortcut layers over 271%. These findings are instrumental in identifying memory bottlenecks in perception networks and developing dynamic memory allocation strategies to enhance performance. Our main contributions include the following:  
* Characterizing and modeling the impact of memory on machine learning inference workloads for edge devices. * Conducting deep layer analysis to identify specific layers affected by memory contention and over-subsidization. * Generalizing the edge components that influence machine learning inference workloads and outlining challenges for future research. The remainder of this paper is structured as follows: Section 2 discusses the background and motivation. Section 3 details the profiling setup and variables. Section 4 presents and analyzes the results. Section 5 elaborates on the findings, and Section 6 concludes the paper.
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 3 Background
### Random Forest  
The random forest (RF) algorithm was introduced by [PERSON] in 2001 [46]. It is a learning method used for classification and regression that operates by building multiple decision trees during the training and production of the final prediction.  
The name came from random decision forests that were first proposed by [PERSON] [47]. The method combines [PERSON]'s idea of bagging and the random selection of features, independently introduced by [PERSON] [47] and [PERSON] and [PERSON] [48], to build a collection of decision trees with a controlled variation.  
The trees used in RF are based on binary partitioning trees. These trees partition the predictor space using a sequence of binary divisions on individual variables. The root node of a tree comprises the entire predictor space. The nodes that are not split are called terminals and form the final partition of this space. Therefore, each non-terminal node is divided into two descending nodes, according to the value of the predictor variables. A division is determined by a division point or a continuous predictor variable. When the points for the predictor are less than the split point, the decision is made to go to the left, and the remainder goes to the right [49].
## 3 Results and Discussion
### Transportation of MSN in Cucumber Plants  
There are many ways to study the absorption and conduction of pesticides in plants. [PERSON] et al. (2016) labeled pyraoxystrobin with radioisotope \({}^{14}\)C; the radiation intensity of various parts of plants can be observed using autoradiography, and then the distribution and route of pesticides in plants can be judged, but the method is costly and radioactive. [PERSON] et al. (2018) used a non-destructive method to study the absorption and conduction of pesticides in plants, which would allow monitoring over a much longer period without degrading the plants, and the data are more reliable. In this study, the fluorescence labeling method was used to study the conduction process of nanoparticles in plants. This method is simple to operate and is low cost, and its fluorescence properties can be used to visualize the transport behavior of pesticides. Meanwhile, residue analysis was used to determine the difference in content and the distribution of pesticides in plants.  
Carbon quantum dots-MSN have a strong fluorescence signal, which makes translocation visible under LSM. Figure 6 shows the LSM image of a cucumber plant. It was observed that all treated groups (M15, M100, and M200) had a strong fluorescent signal and a blank sample without any treatment of nanoparticles had no interference with the fluorescent signal. This finding was line with the fact that MSN could penetrate the cell wall of arabidopsis thaliana into the root maturation area ([PERSON] et al., 2018). From the LSM images of stem and leaf, fluorescent signals with patches of carbon quantum dots-MSN of different particle sizes were also observed. This indicated that carbon quantum dots-MSN with particle sizes of 15, 100, and 200 nm could enter the cucumber plant cell wall and translocate upward to other parts, such as the stem, petiole, and leaves, through transpiration, indicating that MSN would be used as the carrier of pesticide in plants.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **k/min\({}^{-\mathbf{n}}\)** & **n** & **R\({}^{2}\)** \\ \hline Pyr & 1.40235 \(\pm\) 0.099 & 0.56322 \(\pm\) 0.013 & 0.98953 \\ Pyr@M15 & 1.94883 \(\pm\) 0.174 & 0.59100 \(\pm\) 0.016 & 0.98659 \\ Pyr@M100 & 4.11986 \(\pm\) 0.331 & 0.46659 \(\pm\) 0.015 & 0.98049 \\ Pyr@M200 & 2.60443 \(\pm\) 0.181 & 0.52989 \(\pm\) 0.013 & 0.98871 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Kinetic parameters obtained from the fitting of the release data using the Ritger–Peppas equation.  
Figure 5: The release curves of Pyr (**a**) and fitting plots using the Ritger–Peppas equation (**b**). Error bars correspond to standard errors of triplicate measurements.
# An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors
## 3 Experimental Design and Evaluation Criteria
### Acceleration Decomposition Based on EEMD  
The EEMD algorithm can decompose the original measured acceleration data into several IMF components and an RES residual sequence, where the IMF components can reflect the frequency domain characteristics of the original acceleration signal with time, and the EEMD decomposition signal does not need to be predetermined or forced to give the basis function in advance but relies on the signal itself to be decomposed adaptively. Therefore, the feature information of the original signal can be extracted effectively. The EEMD decomposition results of the three datasets are shown in Figures 7-9. In each picture, the \(x\)-axis represents the sampling points, the \(y\)-axis represents the EEMD decomposition  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **No. of Samples** & **Mean** & **Standard Deviation** & **Minimum** & **Maximum** \\ \hline White & 15,360 & 0.0673 & 0.5269 & \(-2.6461\) & 2.3082 \\ El-Centro & 15,360 & 0.0441 & 0.1509 & \(-1.2667\) & 1.0134 \\ RG (artificial wave) & 15,360 & \(-0.0260\) & 0.2602 & \(-1.7171\) & 1.5201 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical indicators for the three acceleration datasets.  
Figure 5: The layout of the sensors.  
values, and the IMF components are listed in the order of frequency extraction from high to low.  
Figure 6: Data input and output of the proposed model.  
Figure 7: Decomposition of the white noise dataset.  
From Figures 7-9, it can be found that the first few IMF high-frequency components reflected the random characteristics of the original signal data, and the changing pattern was not significant, while the middle IMF components saw fluctuations in the characteristics of the original signal, reflecting more characteristics of the original data; the latter few IMF low-frequency components reflected more the change process of the signal data, and their values were relative to the original signal, which was three orders of magnitude smaller (1000 times). The final residual component reflected the overall trend of the acceleration data.
# A High-Utility Differentially Private Mechanism for Space Information Networks
## 2 Background Knowledge  
### Space Information Networks  
The coined SINs paradigm is an integrated high-speed self-organizing hyper network consisting of terrestrial ground-based information networks, e.g., Internet, mobile communication networks, sensor networks, etc., and space-and deep space-based entities within outer space, e.g., satellites, robotic spacecraft, crewed vehicles, rovers, landers, sensors, etc., to provide maximum network capacity [23; 24]. The Space Information Network consists of the space-based backbone network, space-based access network, and ground-based node network. Its structure is shown in Figure 1a. The space-based backbone network consists of GEO (Geosynchronous Earth Orbit) satellites and their laser links. The space-based backbone network is responsible for remote transmission, control, and data exchange. The space-based access network consists of air floating platforms and LEO (Low Earth Orbit) satellites. The space-based access network provides access for many communication systems' entities including specialized sub-networks, entities in near space, air-based entities, ground-based entities, sea-based entities and so on. The ground-based node network comprises ground stations (gateways), internet, and mobile telecommunication networks.  
The complete Space Information Network is made up of GEO satellites, MEO (Medium Earth Orbit), and LEO satellites, as shown in Figure 1b. GEO satellites make up the majority of the space-based backbone network because they are required for broadcasting events. LEO and MEO satellites serve as the space-based access networks since they are required  
Figure 1: Networking structure of Space Information Networks. (**a**) is architecture of space information network. (**b**) is topological structure of space information network.  
for real-time services. SpaceX's project Starlink provides high-speed Internet service via LEO communication satellites. In this paper, experiments are considered on 1665 satellites of Starlink in a two-line element set, which CelesTrak collects.