esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation and Measurement Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameter** & **Physical Size (mm)** \\ \hline \(g\) & Gap between feed line and ground plane & 0.4 \\ \(r\) & Radius of wide circular slot & 17 \\ \(l_{1}\) & Width of a single-arm ILSS & 2 \\ \(l_{2}\) & Thickness of a single-arm ILSS & 1 \\ \(l_{3}\) & Length of a single-arm ILSS & 13 \\ \(p\) & Position of a single-arm ILSS & 30 \\ \(s\) & Spacing between the bottom of & 1 \\  & rectangular Patch and ground plane & 1 \\ \(h\) & Thickness of the substrate & 1.6 \\ \(t\) & Thickness of the copper layer & 0.003 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)Figure 11: Simulated and measured radiation pattern at 2.45 GHz: (**a**) \(xz\)-plane and (**b**) \(yz\)-plane. Figure 12: Simulated and measured radiation pattern at 5.5 GHz: (**a**) \(xz\)-plane and (**b**) \(yz\)-plane. Figure 10: Simulated and measured \(1\)S\({}_{11}\)\(\mid\) of the proposed antenna with a single-arm and two-arm ILSSs. Furthermore, the performance of the proposed multi-band antenna was compared with existing omnidirectional and bidirectional antennas, as listed in Table 2. In [18], the bidirectional pattern with the slightly lower gain was achieved at the lower and middle bands, contrasting with the higher frequency band that more gain was yielded. In [5, 9], the antenna achieved higher gain but lower frequency and large size. In [1, 6, 14], the antennas were smaller than the proposed antenna but achieved lower antenna gain. In [2], the antenna had narrower bandwidth and lower antenna gain. In [13], the antennas achieved wide bandwidth but suffered from bulkiness. For antennas operable in the three-frequency band, the proposed single-fed bidirectional antenna efficiently achieves high bidirectional antenna gain with compact size. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**References**} & **Fractional** & & & & **Overall Antenna** & \\  & **Bandwidth of** & **Pattern** & **Peak Gain (dBi)** & **Application** & **Dimension (mm)** & **Structure** \\  & \(\left.\left.
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
## 5 Feasibility and Safety Analysis
### Feasibility of No Key Negotiation  
Message transmission security can be ensured even without the key agreement process by using the NFEM. Messages can be decrypted by recipients by using the state value of the vehicle as the key. The state value is the most distinctive property of the moving vehicle, as vehicles have different state values at different times and in different positions. These state values are divided into two types: the dominant state value and the implicit state. The dominant state values include the vehicle color,  
Figure 4: Decryption authentication.  
brand and other known properties. The implicit state values cannot be known by others, including the vehicle's own internal attributes and status. This can ensure that the state value can be used as a key. If one vehicle wants to encrypt a message using the other vehicle's state value, they must first obtain the hashed state values of that car from the trusted cloud. One vehicle can get its own historical state value from the trusted cloud and choose one as the decryption key according to the timestamp of the received message. Therefore, before communications, two vehicles can also use a key to encrypt and decrypt without the key negotiation.
## 3 Case Analysis and Discussion
### Driving Behavior Risk Measurements' Threshold Results  
Through the comparison of the threshold results of the driving behavior risk metrics, the division degree of the sample data of the indicator by the interquartile difference method was analyzed (Table 4).  
Table 4 shows that the interquartile difference method could extract the respective thresholds according to different data to judge the distribution of abnormal data better. The proportion of dangerous driving behaviors that corresponded to different indicators varied. The boxplot of the interquartile difference method is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator** & **Threshold** & **Percentage of Dangerous Driving Behaviors** \\ \hline \(R_{1}\) & 2.560 & 3.35\% \\ \hline \(R_{2}\) & 2.160 & 6.12\% \\ \hline \(R_{3}\) & 0.324 & 6.26\% \\ \hline \(R_{4}\) & 1.470 & 0.90\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Threshold results of the risk measure indicators.  
Figure 6: Frequency distribution of the driving behavior risk measurements.  
Figure 7: Boxplot graph of the risk measure indexes.  
Figure 7 illustrates that the values of each indicator basically conformed to the normal distribution, and most of the data were within the interquartile difference range. Only a small part was outside the boundary range, which represented the dangerous driving behavior. For example, for \(R_{1}\), the threshold of the interquartile difference was 2.560, which means that the data outside the upper boundary of 2.560 were abnormal data, that is, the vehicle appeared to perform serpentine driving, which accounted for 3.35% of the total data.
# Fast Coherent Video Style Transfer via Flow Errors Reduction
## 5 Experiments
### Quantitative Evaluation
#### 5.2.3 Quantitative Evaluation in Literatures  
The stylized perceptual strokes/patterns in the results of the different methods are clearly distinct even for one particular style, which may make the IQA comparison unfair. Thus, we here give a detailed comparisons of the stability errors which are invariant to diverse strokes/patterns. Table 2 lists the stability errors created by the three different approaches in the MPI Sintel dataset [66] and [PERSON] 2017 dataset [68]. Our approach takes per-frame stylized results produced by per-frame processing methods as inputs and obtains the stable outputs; thus, we give two comparisons in Table 2. There are five different scenes chosen from each dataset and combined with two representative styles. It is noticed that, for all testing videos, our method significantly reduces the stability errors compared to per-frame processing methods [10; 13].  
Table 3 lists the stability errors and SIFID metrics of state-of-the-art (SOTA) methods and our approach. We verified our method and six other representative coherent video style transfer approaches on 20 different scenes from the MPI Sintel dataset [66]. All  
Figure 18: Ablation study on image quality assessment of Alley_2 scene from MPI Sintel dataset [66]. _A lower score indicates better visual image quality_. Note that adding multi-scale scheme (magenta line) causes image quality loss (higher score) compared to naive method (blue line), while adding incremental mask and multi-frame fusion (red line and green line) contributes to achieve lower scores than naive method (blue line).  
the testing videos used the \(1024\times 436\) resolution, and the groundtruth optical flow with corresponding masks are all provided from MPI Sintel dataset [66]. The stability errors are re-calculated. As the dataset only provides the forward direction of optical flow, the image \(w^{t}\) in equation (12) warps the per-frame stylized result \(f_{s}^{t+1}\) at time \(t+1\) back to \(t\). Our method may achieve higher average stability errors than [PERSON] et al. [36], but we achieved the lowest SIFID distance, which indicates that our transferred results are closer to the style reference image in terms of faithful texture patterns and colour preservation. Our method also achieves both lower average stability errors and SIFID scores than more recent SOTA methods such as [PERSON] et al. [35], [PERSON] et al. [62], [PERSON] et al. [37] and [PERSON] et al. [38], which is consistent with the qualitative comparisons shown in Figures 14 and 15.
## Appendix B  
Coordinate systems  
The main coordinate system that used in a present work is a system \(xCy(z)\)--_geocentric_ non-inertial, uniformly _rotating_, basic in terms of describing the SC motion (see Figure 1). The main plane of this system coincides with the ecliptic, the main direction--\(Cx\) axis--always aims to the Sun. Its rotation is synchronized with the Sun mean motion (the corresponding angular velocity vector lies on \(z\) axis).  
\(\zeta C\eta(\zeta)\) coordinate system (see Figure 1)--_geocentric_ non-inertial, _non-rotating_ with the fixed main axis in space. The main plane of this system is also coinciding with ecliptic, main direction--\(C\zeta\)--coincides with the Sun position at the initial moment of time.  
\(\zeta_{M}C\eta_{M}(\zeta)\) coordinate system (see Figure 19)--selencentric non-inertial. Its main plane and main direction are coinciding with the same ones of the system \(\zeta C\eta(\zeta)\).  
\(x_{M}C\eta_{M}(z_{M})\)--_selencentric rotating_ coordinate system (see Figure 20); its main plane is the Moon orbital plane (in our case also coincides with ecliptic); main direction axis--\(Cx_{M}\)--always coincides with the instant Moon-Earth direction, thus the rotation of this system is synchronized with the Earth-Moon orbital motion.  
ICRS (J2000.0) is used for problem (29) (see Figure 31).  
[PERSON] On Optimal Two-Impulse Earth-Moon Transfers in a Four-Body Model. _Celest. Mech. Dyn. Astron._**2013**, _117_, 279-313. [CrossRef]
* _[PERSON] et al. (2020)_ [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Geometrical Tools for the Systematic Design of Low-Energy Transfers in the Earth-Moon-Sun System. _Adv. Astron. Sci._**2020**, _175_, 5233-5250.
* _[PERSON] et al. (2016)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Extended Tisserand-Poincare Graph and Multiple Lunar Swingby Design with Sun Perturbation. In Proceedings of the International Conference on Astrodynamics Tools and Techniques, Darmstadt, Germany, 14-17 March 2016; p. 10.
* [PERSON] (1997) [PERSON] _Analytical Mechanics/Boston Studies in the Philosophy of Science_; Springer: Berlin, Germany, 1997; Volume 191, pp. 283-291. [CrossRef]
* [PERSON] (2001) [PERSON]; [PERSON] [PERSON] Completely derandomized self-adaptation in evolution strategies. _Evol. Comput._**2001**, \(9\), 159-195. [CrossRef] [PubMed]
* _[PERSON] et al. (1987)_ [PERSON]; [PERSON]; [PERSON] _Solving Ordinary Differential Equations, I. Non-Stiff Problems_; Spinger: Berlin, Germany, 1987.
* [PERSON] (1962) [PERSON] The Evolution of Orbits of Artificial Satellites of Planets Under The Action of Gravitational Perturbations of External Bodies/Planet. _Planet. Space Sci._**1962**, \(9\), 719-759. [CrossRef]  
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## 4 Experimental Validation
### Experimental Setup
#### 4.1.4 System Configuration  
We use TensorFlow 1.15 and MPICH 3.1.4 to implement all algorithms including SHAT on Ubuntu 18.04 OS. We evaluate SHAT on the cluster with four machines, where each machine has two NVIDIA RTX 2080 Ti GPUs and an Intel i7-9700k CPU with 64 GB memory. All machines are interconnected by 10 Gbps Ethernet (Mellanox ConnectX-4 Lx).
## 2 The TPUMSTSR Under RLV Model
### Description of CS Dispatch Method  
Each individual CS consists of multiple thermal power units and renewable energy generation facilities of various types. By coupling geographically proximate renewable energy sources and thermal power units through PCC, the system is linked to the main power grid, enabling simultaneous power supply to both local loads and the larger grid. Externally, the CS is treated as an integrated entity, allowing dispatch authorities to manage it as a whole, thereby improving operational efficiency. Internally, the power sources within the CS can be coordinated locally without considering the network topology. The dispatch method is illustrated in Figure 1.  
In a similar concept on the source side, the unit combination is mainly determined by the grid dispatching department to calculate the grid flow and determine the coordination strategy for different power sources connected to the grid. Multi-power bundling mainly transmits thermal power and renewable energy power between different regions through high-voltage AC and DC transmission lines but does not emphasize the geographical relationship between power sources. Virtual power plants emphasize the coordination of distributed resources such as energy storage, controllable loads, and electric vehicles on the demand side--rather than the power generation side--to achieve flexible regulation.  
Figure 1: CS dispatch method.  
Compared with the above categories, the CS coordinates the output of internal power sources based on the dispatch requirements from the main grid's scheduling department, achieving economic operation and enhancing the integration of renewable energy. The CS does not need to consider complex power flows or coordination between power operators in different regions. The large power grid dispatch department does not need to conduct detailed modeling of the power supply within the CS, which simplifies the dispatch difficulty of the dispatch department. At the same time, qualified power sources do not need to make a large amount of additional investment and can directly use existing power grid resources to aggregate into a CS. The thermal power units in the CS can more effectively participate in DPR.  
However, since multiple heterogeneous energy sources collectively function as a unified entity within the CS, addressing the intermittency and uncertainty of high-penetration renewable generation necessitates technical modifications to internal thermal power units. Within the current operational framework of CSs, the primary focus is on enhancing the flexibility and response speed of thermal power output regulation.
# Machine Learning Methods in Weather and Climate Applications: A Survey
## 6 Medium-to-Long-Term Climate Prediction  
Medium-to-long-term climate predictions are usually measured in decadal quarters. In the domain of medium-to-long-term climate forecasting, the focal point extends beyond immediate meteorological events to embrace broader, macroscopic elements such as long-term climate change trends, average temperature fluctuations, and mean precipitation levels. This orientation is critical for a wide array of sectors, spanning from environmental policy planning to infrastructure development and agricultural projections. Over time, the forecasting methodologies have experienced significant advancements, evolving from conventional climate models to cutting-edge, computational methods such as Probabilistic Deep Learning for Climate Forecasting (CGF), Machine Learning for Model Down-scaling (DeepESD), and Machine Learning for Result Bias Correction (CycleGAN).
# Learn and Tell: Learning Priors for Image Caption Generation
## 4 Experiments
### Ablative Analysis  
We conducted ablative studies to explore three kinds of impacts for our proposed method, including (1) the effectiveness of PBM priors and POS priors on the decoder, (2) the trade-off factor \(\lambda\) between the priors and content weights defined in Equation (16). All of the results were obtained using XE-loss and trained under the same training strategy.  
**The effectiveness of PBM and POS priors on the decoder.** We tried to explore the effectiveness of the POS priors on various decoder network structures, i.e., LSTM [6], Up_Down [42], and transformer [7] and to compare these approaches with our proposed PANN. From the results shown in Table 2, we can see that the decoder with PANN achieved higher scores and better results compared to the normal attention module. In particular, our approach outperformed its competitors' overall metrics, which can be seen in the last line of Table 2. As we introduced previously, our proposed PANN tries to incorporate the PBM and POS priors into the visual feature extraction process, and it could bring more parameters for the final model and further affect the inference speed and memory allocation in the test phrase. We performed our test on a desktop computer, which was equipped with a 1080 Ti GPU card and a 16G memory card. Wedid not perform these experiments on a server, because there were many other tasks that were running, which could affect the accuracy of the results. There were 5000 test samples in the test set, and the batch size was set to 100; therefore there were 50 iterations in the test phase. From Table 3, we can see that our proposed PANN resulted in an additional 1.77 million parameters for each attention module, and the speed was relatively slower (1.64 to 2.9 s) compared to the three \"base\" models.  
**The effect on the trade-off factor.**\(\lambda\) We also studied the effect of \(\lambda\) on final performance, i.e., the trade-off factor between two types of priors and the content weights for caption generation, which is defined in subsection 3.4. The result is shown in the Figure 9, in which the value of \(\lambda\) was chosen from the from 0.1 to 0.5 and with a separation distance of 0.05. From the result, we can see that the \(\lambda\) value actually affects the final performance of our model, and it is also hard to get such a value for \(\lambda\) with which all metrics could be optimized to maximum score. We took BLEU-4 as our bias, and set the \(\lambda\) to 0.2 in our experiments, in which we could obtain the BLEU-4 score 37.9; corresponding results are shown in Table 1.  
Figure 8: POS distribution over four POS tags when generating the above caption, which illustrates that the word generation is highly affected by the previously obtained POS priors.
# Conditional GAN-Based Two-Stage ISP Tuning Method: A Reconstruction-Enhancement Proxy Framework
## 3 Methods
### Proxy Training and Tuning Scheme  
In this section, we will briefly discuss the proxy training and tuning scheme. As mentioned above, the two-stage proxy training uses the method of staged and joint training. Due to the difference between the reconstruction stage and the enhancement stage in the ISP pipeline, the two stages of proxy training use different learning rate (LR), number of epochs, size of batch, etc. The specific training environment will be discussed in the Section 4. Firstly, the two networks of the reconstruction stage and the enhancement stage need to be trained separately. For the Re-stage, the loss function is \(\mathcal{L}_{Re}\), as mentioned above. \(I_{Re}\) is obtained by turning off the En module of target ISP, and \(I_{RAW}\) and \(I_{Re}\) as \(I_{GT}\) are used as the input and output of the network. \(D_{Re}\) is used to provide \(\mathcal{L}_{patchGAN}\), while the output of \(G_{Re}\) is used to calculate \(\mathcal{L}_{1}\) and \(\mathcal{L}_{VGG}\). At the same time, the En-stage also needs to conduct training synchronously, using \(\mathcal{L}_{En}\) as the loss function, obtaining \(I_{En}\) through the complete target ISP, using \(I_{Re}\) and \(I_{En}\) as the input and output of the network, and using \(D_{En}\) for the \(\mathcal{L}_{globalGAN}\), and the output of \(G_{En}\) as \(I_{GT}\) is used to calculate \(\mathcal{L}_{1}\) and \(\mathcal{L}_{color}\).  
When the training of \(G_{Re}\) and \(G_{En}\) converges, the joint fine-tuning of \(G_{Re}\) and \(G_{En}\) will be conducted. We use \(I_{RAW}\), \(I_{Re}\) and \(I_{En}\) as the input and \(I_{GT}\) of the two-stage proxy, respectively, obtain a joint two-stage proxy through alternate training, and use the equal combination of \(\mathcal{L}_{Re}\) and \(\mathcal{L}_{En}\) without \(\mathcal{L}_{GAN}\) as the loss function. \(G_{En}\) accepts the gradient from the \(\mathcal{L}_{Re}\) part, while \(G_{Re}\) accepts the gradient from the complete combined loss function [14].  
In the tuning stage, the training parameters of the trained two-stage proxy will be fixed, and \(\mathcal{L}_{1}\) is used as the loss function, which will be back-propagated to the ISP parameters of the proxy. The tuning details can be seen in the Sections 4.4 and 4.5 below.
# Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism
## 4 Experiment and Analysis  
### Dataset  
In this paper, we use three widely used datasets: the MovieLens ([[https://grouplens.org/datasets/movielens](https://grouplens.org/datasets/movielens)]([https://grouplens.org/datasets/movielens](https://grouplens.org/datasets/movielens)), accessed on 27 November 2023) movie dataset, the LastFM ([[https://www.last.fm](https://www.last.fm)]([https://www.last.fm](https://www.last.fm)), accessed on 27 November 2023) music dataset, and the Yelp ([[https://www.yelp.com/dataset](https://www.yelp.com/dataset)]([https://www.yelp.com/dataset](https://www.yelp.com/dataset)), accessed on 27 November 2023) business dataset. The MovieLens dataset contains information about users, movies, and users' ratings of movies. The LastFM dataset contains information about users, artists, and the number of times users listen to the artists' songs. The Yelp dataset contains information about the properties of companies and users' ratings of them. The details of the dataset are shown in Table 1. Also, the last column of Table 1 is the meta-path used for the experiment, and a maximum of four short meta-path steps are chosen to avoid introducing noise.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Relations (A–B)** & **\#A** & **\#B** & **\#A–B** & **Meta-Paths** \\ \hline \multirow{4}{*}{MovieLens} & User–Movie & 943 & 1682 & 100,000 & UMUM \\  & User–User & 943 & 943 & 47,150 & UMGM \\  & Movie–Movie & 1682 & 1682 & 82,789 & UUUM \\  & Movie–Genre & 1682 & 18 & 2861 & UMMM \\ \hline \multirow{4}{*}{LastFM} & User–Artist & 1892 & 17,632 & 92,834 & UATA \\  & User–User & 1892 & 1892 & 18,802 & UAUA \\  & Artist–Artist & 17,632 & 17,632 & 153,399 & UUUA \\  & Artist–Tag & 17,632 & 11,945 & 184,941 & UUA \\ \hline \multirow{4}{*}{Yelp} & User–Business & 16,239 & 14,284 & 198,397 & UBUB \\  & User–User & 16,239 & 16,239 & 158,590 & UUB \\  & Business–City (Ci) & 14,267 & 47 & 14,267 & UBCiB \\ \cline{1-1}  & Business–Category (Ca) & 14,181 & 511 & 40,009 & UBCaB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical information of the two datasets.