esa-sceva/satcom-chunk-collection huggingface dataset:
# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
## 2 Related Works
### Fixed Mobility  
A rendezvous-based data collection approach is proposed in [35] to select the optimal fixed path due to the delay limitation in WSNs with a mobile Sink. The mobile Sink visits exact locations called rendezvous points, according to the pre-computed schedule to collect data. The rendezvous points buffer and aggregate data originated from the source nodes through multi-hop relay and transfer to Sink on arrival. However, this method is only suitable for uniform distributed networks and the multi-hop relay mode will easily cause imbalances in energy consumption.  
[PERSON] _et al._ [36] proposes another type of mobile sensor networks with a path-fixed Sink supporting multi-hop communication. A speed control algorithm of the mobile Sink is suggested to improve the energy performance and a Shortest Path Tree (SPT) is used to choose the cluster heads and route data, which may cause imbalance in traffic and energy dissipation.  
[PERSON] and [PERSON] proposed a Minimum Wiener index Spanning Tree (MWST for) as the routing topology network. Sensing data is collected by multiple mobile nodes [37] and the shortest path could also be found that effectively reduces energy consumption. Nevertheless, due to the multi-hop data transmission, nodes near the mobile Sinks run out of energy quickly.
## 6 Comparison
### Security Comparison  
The security comparison is shown in Table 1. We compare our proposed scheme with the schemes BDOP-PEKS [5] and VSEF [33], and [PERSON]'s scheme [16]. BDOP-PEKS is the classical public key searchable encryption scheme, and most of the public key searchable encryptions are based on the improvement of this scheme. However, this scheme has weak security, requires a secure channel transmission trapdoor, cannot resist keyword guessing attacks, and does not consider the verification of the retrieval results. VSEF and [PERSON] et al. studied verifiable search, but they only considered whether the cloud server returned the correct set of files, and only verified the file IDs without considering the size of each file, i.e., only the correctness of the search results can be verified, and the integrity of the search results cannot be verified. Our solution integrates the security, document set, and validation of each file size in the document set. In addition, the BDOP-PEKS, VSEF, and [PERSON] et al. schemes all rely on bilinear mapping operations, which cannot resist quantum attacks. In contrast, our VR-PEKS scheme is constructed based on the fully homomorphic encryption BFV, which is based on the RLWE hard problem and is able to resist quantum attacks [39].
# Evaluation of the Urban Weather Generator on the City of Toulouse (France)
###### Abstract
To our knowledge, this model has been evaluated on nine cities: Toulouse (France) and Basel (Switzerland) (2019), Singapore (2020), Boston (USA) (2021), Rome (Italy) and Barcelona (Spain) (2021), Mendoza (Argentina) and Campinas (Brazil) (2021), and Abu Dhabi (United Arab Emirates) (2021), with slightly different versions since UWG is still under development and continuously updated (2021). The main conclusions made in these papers are (i) that UWG performances are satisfactory with an RMSE of about 1 \({}^{\circ}\)C and (ii) that UWG overestimates urban air temperatures during daytime in winter and underestimates nighttime air temperatures in summer. In this paper, we utilize air temperature predictions to calculate the urban heat island (UHI). To mitigate the extensive deployment of local networks, leveraging simulation models is particularly attractive for estimating air temperatures across various cities and configurations. However, due to the intricacies of the urban environment and the multitude of interactions within it, simulating air temperatures presents a complex challenge with no singular solution; instead, a range of techniques is available. While a prior evaluation was conducted for the city of Toulouse (2019), it primarily focused on the experimental periods (BUBBLE and CAPITOUL) and did not specifically address the evaluation of days characterized by a high urban heat island effect--a focus that our paper aims to address. In addition, remote sensing data are used to extract the necessary surface parameters required by UWG. The analysis is conducted on the most urbanized areas in the city of Toulouse (namely Carmes). The paper is organized as follows: In Section 2, we present the city of Toulouse, its local weather stations network, the UWG model, and finally our evaluation approach of UWG. In Section 3, we analyze the impact of the choice of the rural station on air temperature simulation, present the sensitivity analysis, and study the performances of UWG using residual analysis. We end up with a discussion of our results in light of other studies of UWG before providing some conclusions and perspectives.
## 3 Methodology
### Inversion of the HVSR Curves  
The inversion of the HVSR curves allows for the retrieval of the shear-wave velocity values for the sedimentary layers, along with the bedrock depths of our study area.  
The velocity profiles are calculated using the neighborhood algorithm [44]. As there is no unique solution for the inversion problem, several velocity models are calculated. For each model, the calculated ellipticity of the Rayleigh wave fundamental mode is compared to the HVSR curve and a misfit value is given [45]. The ellipticity curves and the corresponding velocity models with the lower misfit values are considered.  
As for all the inversion processes, the nonuniqueness of the solution is a very delicate matter to deal with, and hundreds of thousands of velocity models can result from one simple HVSR curve. For that reason, some soil parameters are required to better guide and constrain the process. The main parameter is the number of the sedimentary layers and the range of thicknesses for each layer. Geological or lithological cross-sections are usually good enough for providing these two parameters. Some geotechnical parameters are also required for each layer, either as fixed values or as intervals. These parameters are the Vp, Vs, density, and Poisson's ratio. The latter is set between 0.2 and 0.5 (universal values for soils). For good and reliable results, it is necessary to have good data coverage in the study area, as well as boreholes and geophysical prospections (e.g., the seismic refraction experiment).
# B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
## 4 Materials and Experimental Setup
### Evaluation Metrics  
As an evaluation metric for the B-line classification task, we used the harmonic mean of precision and recall expressed as the \(F1\) score in percentages.  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall}\times 100. \tag{2}\]  
Intersection Over Union (IoU) of the predicted and ground truth temporal labels was used as the temporal attention mechanism error metric.  
The Pearson correlation coefficient [32] was applied as a metric for the B-line spatial localization task. The Pearson correlation coefficient, \(r\), is a measure of the linear correlation between two datasets which are B-line region annotation (shown in Figure 5) and generated attention map (shown in Figure 6). This measure was calculated as follows:  
\[r=\frac{\sum_{i=1}^{n}(x_{i}-\bar{x})(y_{i}-\bar{y})}{\sqrt{\sum_{i=1}^{n}(x_{ i}-\bar{x})^{2}}\sqrt{\sum_{i=1}^{n}(y_{i}-\bar{y})^{2}}}. \tag{3}\]  
This equation shows how much the two datasets, \(x=[x_{1}x_{2}...x_{n}]\) and \(y=[y_{1}y_{2}...y_{n}]\), vary together compared to how much they vary separately. The magnitude of the correlation coefficient indicates the strength of the correlation and the sign indicates whether the correlation is positive or negative.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Resolution** & **Frame Rate** & **Video Length** & **No Patient** & **Annotation** \\ \hline
640 * 480 & 30 & About 5 h in total (avg 5 min per patient) & 60 & Frame-level \& line annotation \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of our LUS video dataset properties.
## 4 Conclusions  
In this paper, the XFEM is introduced to solve the axisymmetric electrostatic field problem. Three models with bubbles in liquid nitrogen are used to prove the reliability and accuracy of XFEM.  
The errors between XFEM and CFEM, or between XFEM and analytic solution, are small. The maximum relative error is only 0.22%. Increasing complexity of models will not lead to larger errors. The maximum relative error is 0.29% of four bubbles and is 2.34% of moving bubble.  
Compared with CFEM, XFEM needs fewer nodes and elements. XFEM can reduce computing resources in half for the same example. It verified that XFEM can accurately solve the discontinuous problem with lower calculation space than CFEM.  
XFEM can effectively and accurately describe the electric potential distribution of liquid nitrogen with bubbles, which provides a basis for the insulation design of HTS power equipment.  
Methodology, S.L. and X.M.; project administration, N.D.; supervision, W.X. and F.J.; writing--review and editing, S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under Grant 52077161, 52007141, and 51707142.  
Not applicable. There is no data published on the Internet, and there is no relevant link.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **XFEM** & **CFEM** & **XFEM** & **CFEM** \\  & **Node** & **Node** & **Element** & **Element** \\  & **Number** & **Number** & **Number** & **Number** \\ \hline Bubble & 312 & 402 & 552 & 727 \\ Bubbles & 312 & 680 & 552 & 1265 \\ Moving bubble\(-1\) & 312 & 408 & 552 & 738 \\ Moving bubble\(-2\) & 312 & 434 & 552 & 784 \\ Moving bubble\(-3\) & 312 & 453 & 552 & 818 \\ Moving bubble\(-4\) & 312 & 520 & 552 & 944 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerical cost of CFEM and XFEM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Model**} & **Maximum Relative Error (\%)** & **Average Relative Error (\%)** \\ \hline Moving bubble\(-1\) & 2.34\% & 0.06\% \\ Moving bubble\(-2\) & 0.59\% & 0.05\% \\ Moving bubble\(-3\) & 1.87\% & 0.02\% \\ Moving bubble\(-4\) & 0.54\% & 0.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Electric potentials on nodes by using CFEM and XFEM.
# Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review
## 3 Training Algorithms of Photonic Neural Networks for Equalizing Communication Signals
The typical training time is a few tens of seconds and is mainly limited by oscilloscope-computer data exchange. Specifically, a current generator transmits an array of pre-set currents for each experimental data acquisition, with time-constant amplitude, to the photonic neural network to drive the neural network weights. Subsequently, a triggering signal is sent to the oscilloscope to acquire the resulting periodic trace at the end-of-line receiver [81]. The output trace is then aligned through the cross-correlation method with the digital target sequence obtained as a periodic version of the digital input sequence. Each aligned output trace serves as an input to the loss function that estimates the transmission quality, which is then provided to the PSO algorithm for the photonic neural network training [81]. The PSO iterates these steps until error-free or until the max iteration number is reached. All the input traces provided to the photonic neural network during the training are unique since the various traces are all corrupted by noise. As a result of the training, optimal currents found are used in the testing phase, where new traces are acquired and processed.
## 5 Conclusions  
In this paper, a novel embedded helix dielectric rod antenna is presented for high gain radiation with CP and low side lobe level. Different from the conventional dielectric rod antenna, the innovative design consisting of three main parts contributes to gain enhancement and SLL suppression significantly. The gain of 2.7 \(\lambda\) long tapered helix with tapered dielectric cover gives the improvement of 3 dBi for the same length. The 3 \(\lambda\) printed loops embedded with a solid dielectric rod are introduced to improve the directive gain of the structure 6.58 dB further. A 2 \(\lambda\) long uniform solid dielectric rod adds almost 2 dB directive gain; this part also provides design feasibility for the required gain. Additionally, the presented antenna has a simple structure, compact size, low cost, and highest radiation efficiency. It is useful for satellite communication, commercial wireless LAN with high resolution, and IoT Applications.  
Conceptualization, M.N., Y.X., G.G. and Q.H.A.; methodology, M.N., Y.X., A.B.S., G.G. and Q.H.A.; software, M.N., A.B.S., Y.X., Q.Z. and G.G.; validation, Q.H.A., M.U.R. and Y.X.; formal analysis, A.B.S., G.G., Q.H.A. and M.U.R.; resources, M.N., A.B.S., Y.X., Q.H.A. and M.U.R.; writing--original draft preparation, M.N. and Y.X.; writing--review and editing, Q.H.A., A.B.S., G.G., M.U.R. and Q.Z.; visualization, M.N., G.G. and Q.Z.; supervision, Q.Z., Q.H.A., M.U.R. and Y.X. All authors have read and agreed to the published version of the manuscript.  
This work is supported in parts by Engineering and Physical Sciences Research Council (Grant no. EP/X525716/1).  
Not applicable.  
The authors declare no conflict of interest.
# A Novel OFDM-Based Time Domain Quadrature GSM for Visible Light Communication System
###### Abstract  
In order to improve the spectral efficiency (SE) as well as the receiver performance of band-limited visible light communications (VLCs), two orthogonal frequency division multiplexing (OFDM)-based quadrature generalized multiple-input multiple-output (QG-MIMO) transmission schemes, including time domain (TD) quadrature generalized spatial modulation (TD-QGSM) and TD quadrature generalized spatial multiplexing (TD-QGSMP), are proposed in this paper. Firstly, the constellation symbols in the frequency domain are split into in-phase and quadrature components to perform the OFDM modulation separately. Then, the corresponding time domain signal is spatially mapped on different light emitting diodes (LEDs) for achieving the diversity or multiplexing. In addition, we also propose an illegal vector correction (IVC)-based orthogonal matching pursuit (OMP) detection algorithm to deal with the error propagation and noise amplification effect, where a novel correction criterion is involved for assisting the index vectors estimation and thus for improving the demodulation performance. The simulation results demonstrate that the SE can be significantly improved by the proposed schemes as compared with the existing OFDM-based generalized MIMO schemes, with the TD-QGSM increasing by at least 56.5% and the TD-QGSMP increasing by at least 72.3%. Moreover, the bit error rate (BER) performance can be further improved when applying the proposed IVC-OMP detection method, which outperforms the traditional maximum-likelihood and maximum ratio combining (ML-MRC) detection by at least 62.5%.  
Keywords:visible light communication; spectral efficiency; orthogonal frequency division multiplexing; orthogonal matching pursuit +
Footnote †: journal: Electronics Electronics
## 2 Numerical Models
### Turbulence Model  
In a previous work [26] we compared the results obtained using LES (Large Eddy Simulation) and RANS (Reynolds Averaged Navier Stokes equations) models for a scenario similar to that considered here, showing the advantage of using LES. Therefore, all the results of the present work were obtained using LES to simulate turbulence. In FDS, the Deardorff eddy viscosity subgrid-scale (SGS) model [27] (default choice in version 6.1.2 of the code) was used in most of the simulations. In this model, the turbulent viscosity is expressed as \(\mu_{t}=\rho\text{C}_{\text{P}}\Delta\sqrt{k_{\text{sgs}}}\), where \(\text{C}_{\text{P}}=0.1\), the filter width \(\Delta=(\Delta x\,\Delta y\,\Delta z)^{1/3}\) and \(k_{\text{sgs}}\) is the subgrid-scale kinetic energy [18; 19]. The turbulent Schmidt and Prandtl numbers are set equal to the default value of 0.5, although the effects of using other values will also be investigated. The influence of different SGS models on the numerical results was assessed by using, as an alternative, the Smagorinsky model with different values of the model constant \(C_{s}\)[18; 19]. In FLUENT, we used a LES model with the default options (Smagorinsky-Lilly SGS model, with \(C_{s}~{}=~{}0.1\)).
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 7 Evaluation
### RQ2: Information Completeness  
This research question asks how fully ErrorExplainer fills each error context. For a normalized error record, we expect five fields \(\{\tau,\bar{\epsilon},e,\phi,r\}\). We define the completeness of a contract \(C\) as the mean fraction of filled fields:  
\[\mathcal{I}(C)=\frac{1}{\left\|\mathbb{E}_{C}\right\|}\sum_{e\in\mathbb{E}_{C} }\frac{\mathtt{filled}(e)}{\mathcal{T}},\]  
where \(\mathcal{T}\) is the total number of fields (i.e., \(\mathcal{T}=5\)) and \(\mathtt{filled}(e)\) counts non-empty fields in error \(e\).  
We compute this metric for every smart contract in both the original and mutated versions (670 smart contracts in total). The global average completeness is 0.952 (i.e., 95.2%).  
Figure 5 presents per-version completeness statistics. Completeness remains consistently high, ranging from 0.9 to 1.0 for all compiler releases newer than 0.4.24. The dip in 0.4.24 is caused by unconditional throw statements and blank revert, where two fields of normalized error records are skipped by ErrorExplainer.  
Figure 6 shows the relationship between completeness and lines of code. The correlation is mild but positive (+0.19). Larger smart contracts tend to include more modifiers, library calls, and descriptive error messages, all of which let ErrorExplainer fill more fields.  
Higher completeness means that ErrorExplainer can identify more necessary information to describe the error. However, this also means that the developers fundamentally provide more precise transaction-reverting statements (e.g., suppressing unconditional revert statements and empty error messages). This tendency persists as more enhanced features for error definition are introduced.  
Figure 5: Information completeness by Solidity version. Completeness is fraction of fields that ErrorExplainer successfully filled. ErrorExplainer sustains \(\geq\) 90% completeness for all compiler versions newer than 0.4.24.
## 1 Introduction
Computing the dense grids requires significant computational resources, making it impractical for real-world applications. Transforming into sparse grids may result in the loss of certain 3D features. In contrast, point-based methods directly operate on the 3D point clouds, and can balance the semantic segmentation effect and computational cost. Recently, scholars have proposed many methods for semantic segmentation of scenes, such as RandLA-Net [5]. Due to the enormous scale of outdoor scenes, extremely uneven distribution of data samples, and high requirements for clarity of object boundaries, existing methods were not specifically designed for outdoor environments and thus cannot perform well in such environments [6]. Training deep learning network models for semantic segmentation typically utilizes supervised learning, which requires a large number of outdoor sites and cloud semantic segmentation data sets. Currently, there are no publicly available datasets specifically designed for outdoor environments that we can use directly. The problems we have encountered are summarized as follows: the mainstream semantic segmentation methods do not exhibit high accuracy, there is inadequate extraction of mine boundaries' features, and small-scale data features are also inadequately extracted. To solve the above problems, we designed LDE-Net, a semantic segmentation network of 3D point clouds in outdoor environments for driverless unmanned vehicles. As shown in Figure 1, the LDE-Net consists of an encoder-decoder framework, local-global feature extraction modules, and a local feature aggregation classifier. The LDE-Net utilizes the local-global information extraction module, effectively resolving the issue of disconnected neighborhoods among 3D point clouds in outdoor environments. The local aggregation classifier can consider neighborhood information at the final classification stage to ensure more accurate classification in outdoor environments. The rest of this article is organized as follows: Section 2 reviews related work that has been carried out; Section 3 provides a detailed introduction to the LDE-Net; Section 4 presents experimental results to demonstrate the advantages of the network; and Section 5 draws conclusions.
# Performance Assessment of the Medium Frequency R-Mode Baltic Testbed at Sea near Rostock
## 2 The MF R-Mode System
Due to the fact that the transmitters and receiver are not calibrated, the current implementation of the receiver solves the ambiguity by using a calibration process. The mechanism is based on the knowledge of the accurate location provided by the GNSS receiver at the time of calibration. A 30 s averaging time window allows one to calibrate the measurements and solve the ambiguities for all the available signals [14]. Afterwards, the tracking is in charge of computing the accumulated phase measurements. With the calibrated measurements, the receiver can estimate the latitude and longitude in the WGS-84 coordinate frame and the receiver time offset by using an iterative least squares algorithm. Due to the signal propagation, the transmitter-receiver distance is modelled by the [PERSON]'s formula [17], which gives the distance between two points on the WGS-84 ellipsoid, assuming that the height of the two points is zero. Further details on the least squares algorithm are given in [14].