esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
## 3 Methodology
### Ship-Handling Simulation for Turning Circle Observing  
The turning circle was observed using the trajectory formed by the turning maneuver of OS, and the trajectory data was obtained by performing a ship maneuvering simulation experiment. Figure 4 is a snapshot of a simulation scene.  
The ship maneuvering simulation experiment was conducted using a ship maneuvering simulator built by Kongsberg digital AS (K-sim Polaris ship's S-bridge simulator) [34]. The simulator uses a ship maneuvering device (i.e., steering wheel for changing the rudder angle and engine telegraph for changing engine power), and provides a human-machine interaction logging system between the user and hull movement. Recorded data can be obtained in an Excel file format containing ship maneuvering history, where the OS trajectory data are given in time series of geographic latitude/longitude with a sampling rate of 0.5 s.  
The vessel type used in the experiment is a cargo vessel (171.8 m long and 23.17 m wide). The measurement of the OS trajectory was conducted by turning maneuver of one turn (360\({}^{\circ}\)) to the starboard of OS for each combination of seven rudder angles and six vessel speeds. The seven rudder angles are 5\({}^{\circ}\), 10\({}^{\circ}\), 15\({}^{\circ}\), 20\({}^{\circ}\), 25\({}^{\circ}\), 30\({}^{\circ}\), and 35\({}^{\circ}\), and the six vessel speeds are 5, 10, 15, 20, 25, 30 knots.  
The methodology of the research is summarized as shown in Figure 5 below.  
Figure 4: Scene of ship maneuvering simulation for observing the turning circle.  
Figure 3: Four scenarios designed for evaluating the estimated performance of critical latency.
# Multi-Task Learning for Medical Image Inpainting Based on Organ Boundary Awareness
## 2 Related Works
### Inpainting in Medical Field
#### 2.2.2 Learning-Based Approach  
These days, medical image inpainting has been studied extensively with deep learning models [1; 24; 25; 3; 24]. GAN is used to incorporate two patch-based discriminator networks with style and perceptual losses for the inpainting of missing information in positron emission tomography-magnetic resonance imaging (PET-MRI) [1]. A generative framework is proposed to handle the inpainting of arbitrary-shaped regions without a prior localization of the regions of interest [3]. Several improvements are made to deep learning models and are reported with better performance than conventional methods. However, these methods do not use the inherent structure of information in the medical images, resulting in blurry images and often lacking detail. The authors in [7] proposed a method using structural information which is represented by the edges of the image. The network decouples image repair into two separate stages: edge connection and contrast completion. The first stage is to predict the edges inside the missing region. The result edge map is used for inpainting. Even though the use of edges succeeded in improving the performance, it does not provide deeper knowledge of organ structures in the body, resulting in still poor quality of restoration. Recently, a deep neural network for medical inpainting has been proposed in [26]. This framework generates 3D images from sparsely sampled 2D images. They employed an inpainting deep neural network based on a U-net-like structure and DenseNet sub-blocks. However, because of ignoring boundary information in training, their method meets the problem of boundary artifacts. Additionally, since [26] was trained and tested on a dataset that is not publicly available, it is hard to compare performance with this study.  
In this paper, we propose a multi-task learning model based on auxiliary tasks of edge reconstruction, and organ boundary prediction with the main task of CT image inpainting. The proposed method is more consistent and effective for image inpainting through simultaneous training of the prediction of edges and organ boundaries.
## 3 Automatic Retransmission Algorithm Strategy Based on Relay Listening Decoding  
A cooperative automatic retransmission request policy algorithm based on listening nodes was designed. The best relay node was selected to compete through channel parameters, the maximum-minimum law is used to select the listener node to send the retransmission signal, and the listener node and the relay node determine the retransmission signal through competition. The signal transmission and retransmission process for cooperative communication designed is shown in Table 2. The evaluation metrics were calculated by subroutine calls. The algorithm symbol S denotes the source node, d deletes the destination node, \(\text{r}_{i}\) denotes the \(i\)-th best relay node, and \(\text{L}_{i}\) denotes the \(i\)-th best listener node. The detailed steps of the algorithm design were designed as shown in Table 2.
## 3 Temperature-Aware Routing Protocols
### TSHR (Thermal-Aware Shortest Hop Routing)  
TSHR, or Thermal-aware Shortest Hop Routing [25], is an improvement of HPR. It is almost similar to HPR in that it has two phases--a setup and routing phase--and makes use of a threshold. The difference is that in TSHR, there are two kinds of thresholds introduced: a fixed threshold and dynamic threshold. A fixed threshold is a threshold value that is applied for all nodes, while a dynamic threshold is a threshold value that is specific to each node and set based on the temperature of the node and its neighbors. The dynamic threshold is used to mark a node as a hotspot. If the next hop node temperature exceeds this dynamic threshold, the sending node will look for another coolest node, which has not been visited by the packet. On the other hand, the fixed threshold is used every time a node is going to transmit a packet. It compares the next hop node temperature to the fixed threshold, and if it exceeds the threshold, the packet will be buffered until the next hop node temperature is below the threshold. The fixed threshold implies the maximum node temperature allowed in the network. Another difference is that no packets are dropped in TSHR. If the hop counts exceed the maximum hop threshold, then the packet is forwarded using the shortest hop algorithm.
# Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network
## 2 Materials and Methods
### Finite Element Modelling and Analysis
#### 2.1.1 Modeling of the Spar Platform  
The installation of a spar platform at a depth of 1018 m was selected for this study. The spar platform is mainly made up of three parts: spar hulls, a riser, and mooring lines. The geotechnical and mechanical characteristics of the mooring system are listed in Table 1. Table 2 also shows the hydrodynamic characteristics and properties of the marine environment.  
In the vertical guiding position, the spar hull was depicted as a rigid beam with a mooring line securing it to the bottom. The riser's reaction point was attached to the riser. Six springs were used to connect the hard spar platform to the flexible mooring lines (three for translations such as surge, heave, and sway and three for rotations such as pitch, roll, and yaw). The translation spring had a relatively high individual stiffness, but the rotation spring had a relatively low stiffness, approximating a hinge connection.  
The relevant mooring line was designed to cover all sorts of significant non-linearity, including low deformation and variable deformation non-linearity. The mooring line was developed using the hybrid beam element. It has been called a hybrid system since the situation uses two shape functions to preserve the catenary shape of every mooring line: one to simulate elastic behavior and the other to simulate axial tension. For simplicity of convergence, other elements such as linear or nonlinear truss elements can be explored in addition to the hybrid beam element.  
For modeling purposes, the seabed was treated as a rigid plate. The mooring line was in contact with the seafloor but did not penetrate it. This interface was modeled as a frictionless surface-to-surface contact. For contact interaction, the surfaces around the mooring lines and the seabed surfaces were chosen. For the final pieces, the integrated spar hulls and mooring lines were simulated in the ABAQUS code, as illustrated in Figure 1.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Description** & **Coefficient** & **Value** & \\ \hline \multirow{3}{*}{Spar} & Drag & 0.6 \\  & Inertia & 2.0 \\  & Added mass & 1.0 \\ \hline \multirow{3}{*}{Mooring line} & Drag & 1.0 \\  & Inertia & 2.2 \\ \cline{1-1}  & Added mass & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hydrodynamic Properties for spar platform.  
Figure 1: Two-dimensional finite element model of the spar platform in ABAQUS.
## 3 Evaluation of Experimental Results
### EDX Analyses: The Degree of Dilution  
The hardfacing welding process is accompanied by penetration of the component elements of the substrate material into the first layer of deposited metal. Therefore, the dilution of the deposited layer occurs due to mixing with the melted substrate, affecting its functional properties to some extent. It is worth noting that dilution increases with the increase in linear energy during welding. The measurement of the degree of dilution was  
Figure 7: Optical micrograph image (\(\times 1000\)) of the work-hardened layer.  
conducted through chemical composition analysis using energy-dispersive X-ray analysis (EDX) (see Figure 8). The calculation relationship is as follows:  
\[\text{Gd}=(\text{Fe}_{\text{wm}}-\text{Fe}_{\text{fm}})/(\text{Fe}_{\text{bm}}+ \text{Fe}_{\text{fm}})\times 100=(81.02-75.10)/(65.27+75.10)\times 100=4.2\%\]  
where  
Gd is the degree of dilution;  
Fe\({}_{\text{wm}}\) is the iron content in the deposited layer, mass %;  
Fe\({}_{\text{fm}}\) is the iron content in the filler material, mass %;  
Fe\({}_{\text{bm}}\) is the iron content in the base material, mass %.  
The low value of the dilution degree provides an efficient control of the deposited metal properties, in correlation with the intended purpose. Conducting the welding process in pulsed current offers the possibility of unlimited adjustment of the technological regime parameters, thus limiting the heat input introduced into the base material.
# Precise Measurement of Hyperfine Structure of Cesium 7S\({}_{1/2}\) Excited State
## 3 Results
### Electromagnetically Induced Spectroscopy
The 4-4-4 and 4-3-4 peaks sit on the blue side originating from the hyperfine level of \(|\mathcal{7}S_{1/2}(F^{\prime\prime}=4)\rangle\), whereas 4-4-3 and 4-3-3 EIA peaks are on the red side coming from the contributions of hyperfine level of \(|\mathcal{7}S_{1/2}(F^{\prime\prime}=3)\rangle\). The positions of EIA peaks were attained by Lorentz fittings to the EIA signals, as displayed with solid lines in Figure 3. From the separation between 4-4-4 and 4-4-3 (4-3-4 and 4-3-3) peaks, we attained the hyperfine splitting of \(7S_{1/2}\) of 2184.18 \(\pm\) 0.26 MHz with 2\(\sigma\) error of five independent measurements. Similar analyses of the spectra in Figure 3a,c yielded the hyperfine splittings of 2183.16 \(\pm\) 0.35 MHz (Figure 3a) and 2183.51 \(\pm\) 0.08 MHz (Figure 3c), respectively. The average of three cases yielded the hyperfine splitting 2183.61 \(\pm\) 0.50 MHz of excited \(|\mathcal{7}S_{1/2}\rangle\) state. To investigate the line shape distortion and further the energy shift caused by quantum interference [16; 17], as shown at the bottom of Figure 3a-c, we present the residuals, \(R\), of the Lorentzian fits and spectra. The residuals demonstrate the small deviation of 4-5-4 peak in Figure 3a and the 4-4-4 peak in Figure 3b, but a larger deviation of 4-4-4 and 4-3-4 peaks in Figure 3c. We fitted the spectrum in Figure 3c with the Fano-Vogit function to analyze the line shape distortion induced by the quantum interference. The corresponding residuals of the Fano-Vogit fits and spectrum are labeled with \(R_{FV}\) at the bottom of Figure 3c. The estimated shift due to the quantum interference is \(\lesssim\)100 kHz.
## 4 Experiments and Analysis
### Experiment Parameters  
To obtain the TKG (temporal knowledge graph) embeddings, the TComplEx embedding method was used, with the embedding dimension set to 1024. The question text information is generated using the ALBERT pre-trained language model,the specific version we are using is albert-base-v2, which can be downloaded and used from Hugging Face at the following URL: [[https://huggingface.co/](https://huggingface.co/)]([https://huggingface.co/](https://huggingface.co/)) (accessed on 5 March 2025). The number of encoder layers was set to two, with four heads per layer. The sentence embedding dimension was set to 768, the dropout rate in the forward pass was 0.3, and the learning rate was set to 0.0002. A two-layer convolutional network was used for feature processing, with both convolution layers having a kernel size of 3, a stride of 1, and padding size set to 1. The model was trained for 10 epochs, with the final parameters determined by the best validation performance. We have rented a cloud server, all experiments were conducted on a server equipped with a 24 GB GPU (RTX 3090) and 43 GB of memory. The RTX 3090 is manufactured by NVIDIA. NVIDIA is headquartered in Santa Clara, CA, USA.
# A Review of Unmanned Aerial Vehicle Based Antenna and Propagation Measurements
###### Abstract  
This paper presents a comprehensive survey of state-of-the-art UAV-based antennas and propagation measurements. Unmanned aerial vehicles (UAVs) have emerged as powerful tools for in situ electromagnetic field assessments due to their flexibility, cost-effectiveness, and ability to operate in challenging environments. This paper highlights various UAV applications, from testing large-scale antenna arrays, such as those used in the square kilometer array (SKA), to evaluating channel models for 5G/6G networks. Additionally, the review discusses technical challenges, such as positioning accuracy and antenna alignment, and it provides insights into the latest advancements in portable measurement systems and antenna designs tailored for UAV use. During the UAV-based antenna measurements, key contributors to the relatively small inaccuracies of around 0.5 to 1 dB are identified. In addition to factors such as GPS positioning errors and UAV vibrations, ground reflections can significantly contribute to inaccuracies, leading to variations in the measured radiation patterns of the antenna. By minimizing ground reflections during UAV-based antenna measurements, errors in key measured antenna parameters, such as HPBW, realized gain, and the front-to-back ratio, can be effectively mitigated. To understand the source of propagation losses in a UAV to ground link, simulations were conducted in CST. These simulations identified scattering effects caused by surrounding buildings. Additionally, by simulating a UAV with a horn antenna, potential sources of electromagnetic coupling between the antenna and the UAV body were detected. The survey concludes by identifying key areas for future research and emphasizing the potential of UAVs to revolutionize antenna and propagation measurement practices to avoid the inaccuracies of the antenna parameters measured by the UAV.  
Keywords:absorbers; broadcasting systems; knife edge diffraction (KED); parabolic reflector; path loss; propagation measurements; square kilometer array (SKA); UAV-based measurements; unmanned aerial vehicles (UAVs) +
Footnote †: journal: Sensors
## 4 Simulation and Experimental Results
### Network Lifetime
On the other hand, alive sensor nodes can be identified as green circles, while cluster heads are yellow circles, and dead nodes are simulated as black circles. The links that communicate intracluster nodes can be observed as cyan lines, while communication links from cluster heads to the base station are represented as magenta dashed lines. The connection between independent nodes and the base station is represented as blue lines. Figure 3a-d illustrate the optimal cluster network configuration in rounds 5, 100, 270, and 400, respectively.
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## Materials
### Montpellier Dataset  
Irrigation dates over three plots in Montpellier are registered for the year 2017. The three plots includes one maize plot denoted by (P1), one soya plot denoted by (P2) and one sorghum plot denoted by (P3) (Figure 1). Table 1 summarizes the frequency and period of irrigation for each studied plot. In general, irrigation over Montpellier takes place between May and October of each year corresponding to the dry summer season. The sprinkler irrigation technique is generally used. It is good to mention that not only the frequency of irrigation is available but also the exact date of irrigation for these plots. Therefore, these three plots were first used to analyze the effect of irrigation events on the backscattered SAR signal in order to build the suitable method capable of describing irrigation events at the plot.
## 3 Problem Formulation
It can still happen that some nodes activate their radios unnecessarily, since the communication rate required by the application can be much bigger than the check interval. (The check interval is typically in the order of milliseconds, while the communication rate can be in the order of seconds and even minutes.) Under perfect synchronization, the sender and the receiver wake up at the same time and thus avoid that BoX-MAC retransmits packets (because the ACK will be sent immediately). However, under realistic network conditions, packets travel along multi-hop paths towards the destination and accumulate delays at each hop. These delays are due to several reasons: (1) channel contention due to high traffic loads and small check intervals, which causes BoX-MAC to retransmit a large number of packets; (2) node processing due to packet forwarding and sensing activities; (3) protocol control packets that increase network load; (4) packet transmission times which may not match the check intervals of receivers. **Figure 3.** On the left, the routing protocol selects the path to connect two remote nodes \(i_{1}\) and \(i_{5}\) through the nodes \(i_{2}\), \(i_{3}\), and \(i_{4}\). On the right, nodes experience communication delays which are propagated down along the path from the source to the destination node. Figure 3 shows, on the left hand side, a simple scenario where \(i_{1}\) sends data to a remote receiver \(i_{5}\). The arrows show the path between the two nodes as computed by CTP. The right hand side shows the delays incurred as data propagates from \(i_{1}\) to \(i_{5}\), and how these are accumulated during the multi-hop transmission. In Figure 4 we present the behavior of BoX-MAC protocol, where nodes can incur communications delays. As explained in Section 2.3.1, a BoX-MAC node keeps transmitting a packet until it receives an acknowledgment packet from the receiver. Let \(\gamma\) be the delay introduced by node \(i_{k}\) in the path (we define more precisely this delay in Section 4). In the figure, during the check interval \(c\), node \(i_{1}\) introduces a delay before sending the packet and propagates it to the next hop in the path \(i_{2}\), which cannot receive the packet correctly. When \(i_{2}\) forwards the packet to the next hop \(i_{3}\) (note it is common that this happens in the next check interval), it introduces a new delay. Since \(i_{3}\) cannot receive the packet at the check interval \(c+1\) because the accumulated delay spans its time of channel sampling, \(i_{2}\) continuously retransmits the packet during the sleep time of \(i_{3}\). When \(i_{3}\) awakes in the next check interval \(c+2\), it receives the packet and sends an acknowledgment packet to \(i_{2}\), which stops the retransmission. Thus, the reception of the packets could be shifted to the next rounds of communication. To deal with these communication delays the check intervals need to be adjusted in each hop in order to match the time to turn the radio on in the sender and the receiver.
# Performance Analysis in Heterogeneous Networks with Spatiotemporal Traffic and Scheduling
## 5 Conclusions  
In this letter, the downlink packet throughput of a _K_-tier PPP distributed heterogeneous network with spatiotemporal traffic was analyzed. The dynamic traffic involves both spatial and temporal randomness, which respectively reside in PPP distributed user locations and Bernoulli packet arrivals. To balance traffic among tiers, bias association was employed. For traffic management, we used RR and RS protocols. For spectrum reusing, we assumed all tiers use the same spectrum for more realistic modelling. Interference interactions of the proposed spatiotemporal heterogeneous network were accurately captured. As a result of linking the transmission success probability to the active probability, we obtained accurate expressions of mean throughput of the network under universal frequency. The parameters of our analysis provide guidance for designing spatiotemporal models in heterogeneous networks.  
For example, based on the analysis, we can see that the proper design optimal deviation factor can maximize the average packet throughput of the network under various packet arrival rates. Conversely, according to our analysis results, the BS density can also be designed to ensure the network reaches a certain average packet throughput level under a given user density.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
The work is supported by Natural Science Foundation of China (61571234, 61631020), Innovation project for postgraduates of Jiangsu province (KYCX19_0887), Youth Foundation of Anhui Polytechnic University (2014 YQ40) and General research project of Anhui higher education promotion plan (Grant TSKJ2015B18, KZ00215Q21), the High-level Introduction of Talent Scientific Research Start-up Fund of Jiangsu Police Institute (2019).  
The authors declare no conflict of interest.