esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Applications  
### Agriculture  
Crop progress and yield are affected by environmental conditions (e.g., groundwater or soil types) and climatic conditions (e.g., precipitation and temperature ) [(76; 77)]. Those factors vary with time and space. Coarse satellite images, such as MODIS and AVHRR, which can provide daily data were widely used to monitor the crop growth from the sowing to maturing stages and thus estimate the yield [(78; 79; 80; 81)]. Although the short revisit cycle of these images can successfully monitor rapid dynamics in agriculture, the coarse resolution is often inadequate for highly heterogeneous areas such as agricultural landscapes in south China. In recent years, the high spatial and temporal resolution images generated by spatiotemporal data fusion have been explored to improve agricultural studies, such as, crop yield and gross primary productivity (GPP) estimation, crop growth monitoring and management [(82; 83; 84; 85)].  
As the most popular applications in agriculture remote sensing, crop yield, biomass and GPP estimation, can reflect the superiority of spatiotemporal data fusion methods. For example, a study in China blended HJ-1 CCD and MODIS images by the Spatial and Temporal Adaptive Vegetation index Fusion Model (STAVFM) to predict crop biomass. Its result shows that the correlation between winter wheat biomass estimated by STAVFM and actual observations is very high (R\({}^{2}\) reaches 0.876) [(84)]. Similarly, GPP estimated from field observation and from Landsat-MODIS fused images shows a strong agreement in wheat (R\({}^{2}\) = 0.85, \(p\)\(\leq\) 0.01) and sugarcane (R\({}^{2}\) = 0.86, \(p\)\(\leq\) 0.01) in India [(86)]. In addition, timely monitoring of crop growth is important for farming management [(87)]. Synthesized data could be used to provide near real-time estimate of external conditions (e.g., water and soil conditions) and internal attributes (e.g., leaf chlorophyll and protein content) of crops. For instance, soil evapotranspiration (ET), a key factor for crop growth, were estimated from fused ASTER-MODIS images by STARFM and compared with daily ET field observations. The results indicate that the bias and root mean squared error (RMSE) of ET estimated from fused images are smaller than a satellite-based ET product, especially in maize and vegetable sites [(88)].
# A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy
### String Stability
[PERSON] and [PERSON] developed a linear predictor-feedback cooperative adaptive cruise control (LPF-CACC) that is robust to communication delays [10]. Of importance to us is their graphs that show how communication delays affect the region of parameters that guarantee string stability. As they increased the communication delay, the region shrinks, meaning more parameter sets are now string unstable. As with [PERSON] and [PERSON], [PERSON] and [PERSON] also showed that there is a clear tradeoff between allowable communication delay and headway time. This further reinforces the idea that a lower communication delay allows for tighter vehicle platoons. Lastly, [PERSON] et al. performed a similar analysis byperforming a numerical analysis of a CACC vehicle platoon to show how string stability is affected by different parameter changes [11]. One set of graphs showed the string-stable set of control gains for different constant communication delays. The volume of the \"stable body\" as they called it shrinks considerably as the communication delay increases beyond 0.1 s. They later showed how spacing and speed errors propagate rearward through the platoon for different communication delays. In short, the lower the communication delay, the faster the error attenuates as it propagates rearward. When given a communication delay such that the control gains are on the edge of the stable region, the spacing and speed errors remain constant as they propagate rearward.
## 2 Materials and Methods
### Study Area and Data
#### 2.2.2 Data  
We used the 3-h China Meteorological Forcing Data (CMFD), including air temperature, downward shortwave radiation, downward longwave radiation, surface pressure, specific humidity, precipitation, and wind speed [63; 64]. The CMFD data were validated against in situ observations from the China Meteorological Administration. Between 1980 and 2020, the daily air temperature, surface pressure, specific humidity, and precipitation matched observations well, with coefficients of determination (R\({}^{2}\)) of 0.99, 0.96, 0.98, and 0.61, respectively. The annual average precipitation in the CMFD closely matched the observed value (579.6 mm vs. 577.3 mm).  
In the model simulation, vegetation type was set to broadleaf forest, with default vegetation-type-dependent parameters applied. The soil texture within the upper 0-5 m was silt loam. Soil-type-dependent parameters were sourced from the China dataset of soil hydraulic parameters [65], including saturated soil moisture, field capacity, wilting point, residual soil moisture, saturated hydraulic conductivity, saturated matrix potential, and parameters for the soil water retention curve. Vegetation and soil parameters were fine-tuned to better align with the observed soil water content, vertical root fraction, and leaf area index from published work [60].
## 3 Dynamic Traffic Grooming Based on Virtualization-Plane-Aided Optimization
### Dtg-Vpo Algorithm  
In EOSNs, the primary focus of research on dynamic traffic grooming methods is geared toward enhancing the network's overall performance and optimizing resource distribution planning. The key factors influencing the design of these algorithms include wavelength utilization, blocking probability, and hop number, which collectively serve as evaluation metrics for assessing the efficacy of traffic grooming strategies. To settle the problem of tight utilization of optical satellite network resources and high blocking rating, this section proposes a DTG-VPO algorithm. The algorithm is divided into two parts. In the first part, an EOSNV-ETL model and EOSNV-ETL-PE algorithm are designed. In the second part, a DTG-LFA algorithm is proposed. The algorithm flowchart is shown in Figure 7.  
Figure 7: DTG-VPO algorithm.  
* Phase 1: Initialization. In EOSNs, both a physical topology model \(G^{5}\) and a virtual topology \(G^{V}\), are established. The resource matrix within the \(G^{5}\) topology undergoes initialization. While \(G^{5}\) serves as the foundation, \(G^{V}\) is introduced as a virtual network topology that mirrors the node count of \(G^{5}\) but lacks any initial link connections. \(G^{V}\) dynamically updates its structure to reflect service requests' arrival and subsequent allocation. An EOSNV-ETL model is established.
* Phase 2: Request arrival. Determine whether the established node connection in \(G^{V}\) contains the source node \(s_{r}\) and destination node \(d_{r}\) for requests. If the \(s_{r}\) and \(d_{r}\) for requests are included, the \(P_{assess}\) of all feasible paths is calculated in \(G^{V}\) according to the EOSNV-ETL-PE algorithm, and the \(P^{V}\) set is established.
* Phase 3: Structure a collection of available paths for the twin-layer network. The arriving requests are arranged in \(G^{5}\) according to the \(P_{assess}\) value of the alternative path from small to large, and the \(P^{5}\) set is structured. Build the alternative path collection \(P^{V-S}\) with \(P^{5}\) and \(P^{V}\) from Phase 2. The collection adopts the mode of \(P^{V}\) first and \(P^{5}\) last and simultaneously deletes duplicate paths in \(P^{5}\) and \(P^{5}\).
* Phase 4: Find alternative solutions. In the proposed DTG-VPO algorithm, alternative solutions are found through LFA. Traverse time slots one by one on the link resource matrix of the alternative path and calculate the available resource block evaluation index \(F_{E}\). According to the DTG-LFA algorithm in Algorithm 2, we select the resource block with the lowest \(F_{E}\) value for resource allocation.
* Phase 5: Network resource updating. In the established twin-layer network topology, comprising both \(G^{5}\) and \(G^{V}\), the resource matrix within \(G^{5}\) undergoes an update before processing each newly arrived request. Subsequently, the \(G^{V}\) is updated to reflect these changes after wavelength resources are allocated to a particular request on its designated path.
## 1 Introduction
When the ship is connected to the charging station, the energy from the shore-based ESS and the connection from the shore grid for charging the ship's ESS are simultaneously used. In this  
Figure 1: Review of the use of energy for ships in operation and projection for future exploitation (units: EJ/yr, natural gas includes LNG and LPG). case, investment costs are higher. Additionally, there are losses due to the charging and discharging of the terrestrial battery storage [10]. Based on the created daily profile of energy needs, optimization was carried out with the aim of reducing the peak load. In accordance with the defined problem and the subject of the research, a basic and auxiliary scientific hypothesis was set. **Hypothesis 0 (H0):**_In the case of electrification of the ferry fleet, the energy needs exceed the capacities of the shore-based power infrastructure. The profile of daily energy needs is non-periodic and, therefore, not suitable for charging from a shore infrastructure._  
**Hypothesis 1 (H1):**_Proper model parameters selection and adjustment can additionally reduce the required installed power during the optimization process._
## 4 Results and Discussion
### Relationship between K\({}_{\text{f}}\)/K\({}_{m}\) and Lithology  
The K\({}_{\text{f}}\)/K\({}_{\text{m}}\) ratio is not only used as a hydrogeological index for the validation of this study, but also can be used to understand the difference between fractured and intact rock permeability coefficients of different lithologies in the study area. This result is useful for understanding which medium controls the flow of groundwater. Moreover, it may help determine an appropriate groundwater flow model of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
existing geological environment, such as the Dual-Porosity Model, Equivalent Pore Model, or Discrete Fracture Network Model.  
According to the results of \(\mathrm{K_{f}}/\mathrm{K_{m}}\) in Section 4.1, this study further explored the range of \(\mathrm{K_{f}}/\mathrm{K_{m}}\) ratio distribution between sedimentary and metamorphic lithologies in Central and Southern Taiwan, as shown in Figure 11. The log(\(\mathrm{K_{f}}/\mathrm{K_{m}}\)) ratios for 88 data samples are between 0 and 7, and about 60% of the log(\(\mathrm{K_{f}}/\mathrm{K_{m}}\)) ratios are between 3 and 5. Figure 11 also shows the respective proportions of sedimentary and metamorphic rock types within the same ratio class. The percentage of each rock type in each ratio class shows that about 76% of the metamorphic rock types have log(\(\mathrm{K_{f}}/\mathrm{K_{m}}\)) ratios between 4 and 6, while the sedimentary rocks have a relatively small percentage of 24%, and the remaining ones are mostly between 1 and 3. In other words, the metamorphic rocks in Southern and Central Taiwan have a higher chance of having high permeability ratios than the sedimentary rocks, and fractures dominate the groundwater flow.
## 4 Experimental Results  
### Dataset  
To address the need for standardized, high-quality datasets in cross-platform remote sensing (RS) segmentation, we introduce four widely used benchmarks--SWINSEG [38], SWIMSEG [38], TCDD [39], and HRC_WHU [40]--each designed to tackle specific challenges in single-modal RGB segmentation.  
The SWINSEG dataset contains 1128 daytime and nighttime images of the sky/cloud masses, along with the corresponding binary ground truth maps. The images in the SWINSEG dataset are taken from two early sky/cloud image segmentation datasets from Nanyang Technological University in Singapore--SWINSEG and SWINSEG. All the images were taken in Singapore during a 12-month period from January to December 2016 using the calibrated ground-based full-sky imager WAHRSIS. The ground truth annotation was accomplished through the collaboration of experts from the Singapore Meteorological and Geophysical Bureau.  
The TCDD (Tianjin Dual Platform Dataset) dataset was released by Tianjin University in 2023 and consists of 320 pairs of image sets, covering 80 geographical areas in Tianjin, China. Each image set includes drone images captured by the DJIatricatric 300 RTK camera and satellite images from the High-Resolution Satellite 6 mission (China's High-Resolution Earth Observation System). The dataset covers urban, suburban, and rural areas, ensuring the diversity of land-cover types. It is often used to evaluate the ability of models to adapt to the differences in spatial resolution, perspective, and feature granularity between drone and satellite data.  
The HRC_WHU dataset contains 1776 virtual images along with their corresponding depth maps and camera parameters. These images were captured by a five-angle tilt camera installed on a drone and cover an area of approximately 6.7 \(\times\) 2.2 square kilometers in Meitan County, Guizhou Province, China. The dataset was processed and edited using software and manual methods, creating a complete 3D city scene and simulating the imaging process of a single-lens camera, generating a virtual aerial image with a resolution of 5376 \(\times\) 5376 pixels. This dataset is typically used to verify fine segmentation and boundary preservation.
# Fractional Frequency Reuse Optimal SINR Threshold Selection Using NIR and ISODATA
## 3 Methodology  
The proposed dynamic FFR technique calculates the optimal SINR threshold using NIR and ISOATA techniques. The frequency partitioning factor, \(\beta_{FR}\), is set proportionately to the number of subscribers in the inner region that is based on the optimal threshold. The simulations are carried out after every 50 transmission time intervals (TTIs) to continuously update the threshold value. The results are analyzed based on edge user equipment (UE) throughput and fairness. Table 1 shows the variables used in simulations to obtain the results. The performance of the two mentioned approaches is also compared with cases where the SINR threshold is set at 5 dB and \(\beta_{FR}\) of 0.5. For each FFR configuration, UEs ranging from 10 per cell to 50 per cell are simulated. The MATLAB-based Vienna LTE-A Downlink System Level Simulator v2_0_Q3_2018 created by Institute of Telecommunication at Technische Universitat Wien-Vienna Austria, was used to perform the simulations [18]. For every cell, the number of user equipment per cell was continuously raised from 10 to 50 and their histograms plotted.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline UE speed & 5 Km/h \\ Number of cells & 21 \\ UEs per cell & 10, 20, 30, 40, 50 \\ Antenna pattern & TS 36.942 \\ Transmit power & 40 W \\ Feedback & AMC: CQI, MIMO: Rl \& PMI \\ Shadow fading & None \\ Minimum-coupling loss & 70 dB \\ Noise spectral density & \(-174\ dBm/Hz\) \\ Simulation length & 50 sub frames (TTIs) \\ Receiver model & Zero forcing \\ Inter-eNodeB distance & 500 \\ Transmission bandwidth & 20 MHz (100 resource blocks) \\ Antennas (NTX \(\times\) NRX) & \(4\times 2\) \\ Channel model & TU \\ Pathloss model & TS 36.942—Urban area, 70 dB MCL \\ Scheduling algorithm & Proportional fair \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used for the LTE FFR simulations.
# Toward SDM-Based Submarine Optical Networks: A Review of Their Evolution and Upcoming Trends
## 2 Submarine Optical Networks Basics and Demonstrations Technologies on Submarine Cable Systems
### Important Milestones at Submarine Systems Evolution  
Table 1 shows a time-based taxonomy of all important milestones achieved in the submarine networks' history. For each milestone, we report the release year, the cable system, the key technology, the total capacity achieved (and/or the channels deployed), and finally the span length. \begin{table}
\begin{tabular}{c c c c c} \hline
**Year** & **Cable System** & **Key Technology** & **Capacity/Channels** & **Length** \\ \hline
1956–1978 & **TAT-1** & First transatlantic * telephone cable, **electronic repeaters, hotline** & **36** (initial), **48** final telephone channels & **1942** \(\mathrm{km}\) \\ \hline
1978–1994 & **TAT-7** & Employs **coaxial cable technology** & **4000** (initial), **10,500** final telephone channels & **8910** \(\mathrm{km}\) \\ \hline
1988–2002 & **TAT-8** & First * fiber-optic transatlantic cable** & **295.6** Mbit/s traffic or **40,000** phone circuits & **6705** \(\mathrm{km}\) \\ \hline
1991 & **TAT-9** & First system to **switch traffic on demand** between five landing points & **560** Mbit/s traffic or **80,000** voice circuits & **9305** \(\mathrm{km}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Important milestones in submarine networks history [1; 2; 3; 4; 5]. Figure 4: Google cloud platform submarine network [3]. Figure 3: Submarine worldwide cable map [2].
# ISAR Imaging Analysis of Complex Aerial Targets Based on Deep Learning
## 2 ISAR Model
### ISAR Signal Model  
Inverse synthetic aperture radar (ISAR) is a method for acquiring two-dimensional images of targets using signal processing technology, which is widely employed in target detection and recognition, as illustrated in Figure 1. The ISAR imaging results for a target can be obtained by analyzing the reflection cross-section data with respect to frequency and orientation. Specifically, the longitudinal distance (down-range) refers to the distance along the axis that is parallel to the incident direction, and achieving longitudinal distance imaging necessitates the use of broadband scattering field data. The cross-range, on the other hand, is the axis that is perpendicular to the longitudinal distance, which can be simulated by processing the scattered field data obtained from various angles.  
For the scattering field data of the target in a small frequency band and a small azimuth angle, that is, if \(\cos\phi\cong 1,\sin\phi\cong\phi,k\cong k\) is satisfied, the corresponding ISAR imaging can be obtained by using two-dimensional inverse Fourier transform (IFT). The RCS scattering field of the target is defined as \(E^{s}(k,\phi)\), and then \(ISAR(x,y)\) is as follows:  
\[ISAR(x,y)=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}[E^{s}(k,\phi)]e^{j2 \pi(\frac{2f}{c})\cdot x}\cdot e^{j2\pi(\frac{k_{c}\phi}{\pi})\cdot y}d(\frac{2 f}{c})d(\frac{k_{c}\phi}{\pi}) \tag{5}\]  
where \(x\), \(y\), and \(\phi\) are the space coordinates; \(f\) is the frequency; \(k=2\pi f/c\) is the wave number; and \(k_{c}\) is the wave number corresponding to the center frequency.
## 3 Results and Discussions
### Electrochemical Analysis
One possible mechanism refers to an inhibitor adsorbed on metal with a positively charged surface (Figure 5a). In this case, the inhibitor molecules are not only physisorbed (represented by a dotted line) but also chemisorbed, owing to the coulombic forces between the negatively charged oxygen groups of the inhibitor and the positively charged metallic surface (represented by a solid line) [39]. It is possible that inhibitor molecules may first be physically adsorbed and then form electron bonds, ultimately resulting in chemisorption. In the case of EEA4 NB, the mixed-type adsorption (physisorption + chemisorption) is in accordance with the negative \(\Delta G^{\circ}_{\mathit{ads}}\) value and a high value of \(K_{\mathit{ads}}\), indicating spontaneous adsorption and the formation of a stable layer on the metal surface through a combination of physical and chemical adsorption. In an environment where chloride ions are present, another mechanism can be taken into account. The chloride ions are adsorbed on the surface of carbon and favor the adsorption of inhibitors through NH\({}_{2}\)\({}^{+}\) (Figure 5b, left). These adsorbed chloride ions further act as a passage that provides conditions for the chemical adsorption of the NH\({}_{2}\)\({}^{+}\) group and/or the O\({}^{-}\) group (Figure 5b, right) [40]. The presence of chloride ions allows the formation of bridges between the inhibitor molecules and the metallic surface. In this manner, the adsorption of organic inhibitors is facilitated. This is supported by EDX images showing the presence of Cl\({}^{-}\) on the surface of the electrode (Figure 4c). Quantum chemical parameters such as energy gap, energy of the highest occupied molecular orbital, energy of the lowest unoccupied molecular orbital, ionization potential, electron affinity, polarizability, hardness, global softness, and electronegativity were calculated to investigate which mechanism is more likely in the corrosive protection offered by EEA4 NB to carbon steel in saline. Figure 5: A schematic representation of the inhibiting mechanism proposed for EEA4 NB on the carbon steel surface (**a**) without Cl\({}^{-}\) adsorption and (**b**) with Cl\({}^{-}\) adsorption on Fe surface.
# Coordinated Dispatch of Multi-Energy Microgrids and Distribution Network with a Flexible Structure
###### Abstract  
With the increasing scale of multi-energy microgrids (MGs) and complicated operation modes, the coordinated operation of microgrids and the distribution network (DN) has posed great challenges. In this paper, a bi-level optimal coordinated dispatch framework of the DN and multi-energy MGs based on CCHP (combined cooling, heating, and power) is proposed. The first level studies the optimal operation of the DN with power interaction on tie lines between MGs considering the coupling relationship and constraints of the equipment and network. The network reconfiguration with limited control actions is considered to increase the flexibility of the topology and further improve the working state. For the second level, MGs receive orders from the DN and determine the optimal strategies of multi-energy devices to achieve optimized operation under the condition of satisfying the different types of load and requirement for the DN. To solve the optimal dispatch problem of both the DN and the multi-energy MGs considering the DN reconfiguration, a method combining particle swarm optimization algorithm (PSO) with mixed-integer linear programming (MILP) is proposed. Cases studied in an IEEE33-node DN with renewable power sources and grid-connected MGs validate that the proposed method is very effective in reducing the power loss and voltage offset of the DN while ensuring the benefits of the MGs.  
microgrids; combined cooling; heating and power; distribution network reconfiguration; coordinated dispatch; limited control actions +
Footnote †: journal: Applied Physics Letters B
# The POSEIDON Ocean Observing System: Technological Development and Challenges
## 2 The POSEIDON System
### System Operations  
The in situ operations of the system platforms can be divided into four discrete activities:  
* Assembly and Deployment;
* Operational status, remote monitoring and field validation;
* Recovery;
* Delayed mode data retrieval, maintenance, calibration, replacement of sensors, technical upgrades.  
These successive activities constitute the operational cycle of the POSEIDON field platforms and define the actions that need to be performed taking into consideration the platform-specific characteristics--each platform requires different protocol during the system operations (Figure 4).