esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Network Effect Before and After Improvement  
A QP value of 32 was selected for comparison of the pre-improved CycleGAN and the improved CycleGAN network in restoring the distortion of the individual YUV video sequences mentioned in Section 3.1. The resulting PSNR values are presented below.  
As illustrated in Table 2, the PSNR values of the enhanced CycleGAN network exhibit a notable improvement of an average of 5.0944% for the same coded QP values across the tested video sequences, which were initially encoded and decoded using the unimproved and enhanced CycleGAN networks, respectively. To assess the restoration performance, we utilized the LPIPS value as an evaluation metric, resulting in the data presented in Table 3.  
As illustrated in Table 3, the LPIPS values of the network for video restoration exhibited a mean reduction of an average of 8.2353% following the implementation of enhancements. This observation suggests that the upgraded network displays a discernible advantage in terms of human visual perception.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration Names** & **Specific Environment** \\ \hline Operating System & Windows 10 \\ CPU & i7-14900 kf \\ GPU & RTX3090 \(\times\) 2 \\ RAM Size & 32 GB \\ Memory Size & 24 GB \(\times\) 2 \\ Deep Learning Frameworks & Pytorch1.12 \\ Cuda Version & Cuda11.4 \\ Batch Size & 32 \\ Num Works & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental configurations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Video Sequences** & **Pre-Improved** & **Improved** \\ \hline Akiyo & 29.4268 & 30.9709 \\ \hline Bus & 32.9634 & 34.6446 \\ \hline Bridge & 27.9703 & 29.3993 \\ \hline Foreman & 33.0887 & 34.7747 \\ \hline Flower & 31.3318 & 32.9228 \\ \hline Container & 30.0143 & 31.5415 \\ \hline Tempete & 26.9252 & 28.2482 \\ \hline Basketball & 29.9703 & 31.5014 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of PSNR values before and after improvement.
## 3 Results  
### The Data Used in This Work  
The mesoscale air-sea interaction characteristics above four typical sea areas including the Gulf Stream (GS) [33; 34; 35], Kuroshio Extension (KE) [36; 37; 38], Brazil Malvinas Confluence (BMC) [39; 40; 41], and Agulhas Return Current (ARC) [42; 43; 44] have been widely studied due to the stronger air-sea coupling in these four seas. The locations of these four areas are shown in Figure 4. These four sea areas are selected as typical regions for air-sea coupling research in this study. This study focuses on examining the coupling coefficients between an _SSTA_ and _SCA_, as well as those between an _SATDA_ and _SCA_.  
The wind speed data acquired by the QSCAT (Quick Scatterometer) from January 2002 to June 2009 are utilized in this work. The QSCAT satellite provides the equivalent neutral wind speed at a height of 10 m above the sea surface [45; 46]. The data are available at various temporal resolutions, including daily data, three-day-average data, weekly average data, and monthly average data. The spatial resolution of the wind speed is 0.25\({}^{\circ}\) by 0.25\({}^{\circ}\) along the longitude and latitude directions.  
The microwave scattering coefficient data used in this work are sourced from the second-level product of the QSCAT. Unlike the wind speed data, the spatial resolution of the scattering coefficient is 1\({}^{\circ}\) latitude \(\times\) 1\({}^{\circ}\) longitude, and the temporal resolution is daily data. In this work, the daily scattering coefficient data are averaged to obtain the monthly scattering coefficient data, which can be used to study the coupling between the microwave scattering coefficient and \(SST/SATD\).  
The air temperature data are sourced from the European Centre for Medium-Range Weather Forecasts (ECMWF). The spatial resolution of the air temperature data utilized in this study is 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) along the longitude and latitude directions. In terms of temporal  
Figure 4: The four typical sea areas studied in this paper. The map illustrates the sea–air temperature difference anomaly in August 2015.  
resolution, the ECMWF can provide hourly updated data, which can be further processed to derive daily and weekly average data. Furthermore, the ECMWF directly offers monthly average data, which can be downloaded as required.  
The _SST_ data used in this work are derived from the AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) data [47] and the ECMWF. The _SST_ data employed in this study have a spatial resolution of 0.25\({}^{\circ}\) latitude \(\times\) 0.25\({}^{\circ}\) longitude. The temporal resolutions include daily, three-day-average, weekly average, and monthly average data. The temporal and spatial resolution of the _SST_ data provided by the ECMWF are the same as that of the air temperature data.
## 2 Assimilation Method and Experiment Configuration
### Component of the Assimilation System  
The POD-4 DEnVar assimilation and forecast system mainly consists of three components: Ensemble forecast, assimilation and forecast module, as is shown in Figure 1. The nature of POD-4 DEnVar method is constructing flow-dependent background error covariance by making four-dimension ensemble forecast. Thus, the ensemble forecast plays an important role in the implementation of POD-4 DenVar, which is essential in improving the assimilation impact. In order to make the ensemble member reveal the real atmosphere, as well as decrease the computational costs, the historical samples are employed to generate the four-dimension ensemble samples [6]. The ensemble forecast is developed with the Weather Research and Forecast (WRF) model. Due to the different temporal and spatial resolution of satellite and radar observations, satellite data is assimilated in the outer domain (D1), and radar data is assimilated in the inner domain (D2). Therefore, nested ensemble samples should be generated by the ensemble forecast.  
The second part of the system will assimilate satellite and radar observations separately. According to the POD-4 DenVar method, the Ops matrix will be decomposed in D1 and D2. Then the MPs and Ops matrix are transformed by the decomposed eigenvector. The optimal solution can be obtained separately for satellite and radar assimilation. Finally, a deterministic forecast will be made by the WRF model, with the boundary condition updated by satellite and radar assimilation analysis. In the subsequent sections, key points of the main components are described in detail.
# GAN-TL: Generative Adversarial Networks with Transfer Learning for MRI Reconstruction
## 2 Method and Material  
The formulation of the multi-channel image reconstruction problem for parallel imaging is as follows:  
\[w=PFRI+m \tag{1}\]  
where \(I\) is the image we intend to solve, \(P\) is the under-sampling mask, \(w\) is the collected k-space measurements, \(m\) is the noise, \(F\) is the Fourier transform and \(R\) is the coil sensitivity maps.  
By incorporating past knowledge, CS-MRI constricts the solution space in order to solve the inverse problem of Equation (1). Furthermore, the optimization problem can be stated as:  
\[I=min_{i}\frac{1}{2}\left\|PFR_{i}-Y\right\|\frac{2}{2}+\lambda S(i) \tag{2}\]  
where the prior regularization term is denoted by \(S(i)\), and the first term reflects data fidelity in the k-space domain, which ensures that the reconstruction results are consistent with the original under-sampled k-space data. Term \(\lambda\) is a balance parameter that establishes the trade-off between the data fidelity term and the prior knowledge. In a specific sparsity transform domain, \(S(i)\) is often an L0 or L1 norm.  
Typically, an iterative strategy is necessary to tackle the above optimization issue. The regularization term \(S(i)\), which is based on CNN, can now be used to denote, i.e.,  
\[I=min_{i}\frac{1}{2}\left\|PFR_{i}-Y\right\|\frac{2}{2}+\lambda\|I-fCNN(I_{u} |\theta)\right\|\frac{2}{2} \tag{3}\]  
Utilizing the training dataset, the model's parameters can be tuned, and the output of \(CNN\) is \(fCNN(I_{u}|\theta)\) with the parameters \(\theta\). \(I_{u}=F_{u}^{H}\,w\), where H stands for the Hermitian transpose operation, and also refers to the ZF images that were reconstructed from under-sampled k-space data. Recently, MRI reconstruction has also incorporated conditional GAN.  
A GAN has a discriminator D and a generator G. Both the discriminator and the generator need to be trained. The generator G can be taught, through training, to predict the distribution of the genuine data that are provided and to produce data that will deceive the discriminator D. Distinguishing between the output of the generator G and the actual data is the discriminator D's goal. Then, after training, the generator can be used independently to generate new samples that are comparable to the original ones.  
The conditional GAN loss was therefore applied to the reconstruction of MRI images, which is:  
\[min_{\theta_{c}}max_{\theta_{e}}\ \mathbb{f}_{b}GAN(\theta_{c},\ \theta_{e})=E_{I_{t-p} _{train}(I_{t})}\left[\log D_{\theta_{e}}(I_{t})\right]\ E_{I_{t-p}(I_{t})}\ \left[\log D_{\theta_{e}}(G_{\theta_{e}}(I_{u}))\right] \tag{4}\]  
where \(I_{t}\) is the fully sampled ground truth, and \(I_{u}\) is the equivalent reconstructed image produced by the generator. \(I_{u}\) is the ZF image that serves as the generator's input.
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 4 Experimental Results and Discussion
### Visualization Results  
To further validate the effectiveness of STFEformer, we visualized the prediction results for the three datasets and made a comparison between the predicted and target values. The results are shown in Figure 8. It is evident that traffic flow exhibits periodicity, and the prediction results are the same as the target values, demonstrating the model's success in accurately predicting overall traffic flow trends and capturing these characteristics. Additionally, traffic flow displays distinct short-term features (such as during extreme weather, holidays, or unexpected events) in a certain period of time, and our model also successfully fits these features. Even during periods of dramatic traffic fluctuations, our model accurately captures these changes and closely aligns with actual values. Achieving consistent high performance across different datasets further substantiates the robustness and reliability of our model.  
Figure 9 displays the partially adaptive similarity matrices obtained from training on three datasets. In the heat maps of the adaptive similarity matrices, the strength of the similarities between node pairs is markedly evident. Through iteratively training the matrices obtained from the fusion embedding layer across three datasets, similarity matrices that represent the spatial correlations between nodes can be obtained. Figure 9 illustrates the similarity matrices obtained through training for all datasets, revealing a clear aggregation pattern among node pairs with stronger similarities. This observation is consistent with the fact that the similarity matrices obtained from the fusion embedding layer are not able to capture the similarity matrices.  
Figure 8: The visualization results for prediction. (**a**) PeMS04. (**b**) PeMS07. (**c**) PeMS08.  
underscores that STFEformer can effectively symbolize the spatial relationships between different node pairs via the similarity matrices. Therefore, it reiterates the efficacy of the fusion embedding layer in capturing spatial characteristics.
# Stratosphere: A Fast Virtual Circuit Switch Layer for Data Center Network
## 3 Prototype Design
### Principles of Stratospheric
#### 3.1.1 Virtual Circuit Switching  
In the stratospheric, the links are divided into multiple time slots to form virtual channels, which are assigned different priorities, so applications of different priority are mapped to different virtual channels. Under the configuration of the controller, packets from multiple input ports could be switched to different virtual channels in the same or different output ports.  
VCS adopts time-division multiplexing to schedule multiple virtual channels on the same physical link, and each virtual channel occupies the same amount of the time slot. The first time slot is named the first virtual channel, and the second time slot is named the second virtual channel, and so on. For the specification of IETF DiffServ, the link is also divided into eight virtual channels, and the bandwidth of each virtual channel is 1/8 of the link rate, which supports dynamic allocation based on the QoS of applications. VCS is configured by the controller, and the input port receives packets from different virtual channels, which are then switched to different virtual channels in different output ports based on forwarding rules.  
Tor switches in adjacent racks are connected through virtual circuit channels, which enable Tor switches in multiple racks to communicate direct bypass aggregation switches. Packets could be forwarded by VCS to ToR switches in adjacent racks and then transmitted through their uplink, which enables multiple Tor switches to share the same uplink. The links between the VCS modules in different Tor switches are always connected, and there is no link re-setup for the whole data center network.
## 2 Materials and Methods
### Materials
#### 2.1.2 Biopolymers  
This study utilized three biopolymers as binders: xanthan gum (XG), locust bean gum (LBG), and guar gum (GG). The xanthan gum used in this experiment was produced by Erdos Zhongxuan Biochemical Co., Ltd. in Hohhot, Inner Mongolia Autonomous Region, China, the locust bean gum was produced by Wanbang Chemical Technology Co., Ltd. in Shangqiu, Henan Province, China, and the guar gum was produced by Tongsheng Biological Technology Co., Ltd. in Hangzhou, Zhejiang Province, China.  
Xanthan gum (XG) is a natural polysaccharide produced by the fermentation of _Xanthomonas campestris_, a bacterium from wild rapeseed. It is typically produced through  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Natural Moisture** & **Plastic Limit (\%)** & **Liquid Limit (\%)** & **Plasticity Index** & **Maximum Dry Density (g/cm3)** & **Optimum Moisture Content (\%)** \\ \hline
7.209 & 23.14 & 53.16 & 30.02 & 1.728 & 18 \\ \hline \end{tabular}
\end{table}
Table 1: Basic physical parameters of the soil samples.  
Figure 1: Test soil samples: (**a**) GRS; (**b**) compaction curve; (**c**) gradation curve.  
a fermentation process that converts corn starch or other starch-containing raw materials into xanthan gum. The XG, shown as a white powder in Figure 2a, readily dissolves in water at room temperature. It is a polysaccharide substance produced by the fermentation of Xanthomonas bacteria and is primarily composed of D-glucose, D-mannose, and D-glucuronic acid in a 2:2:1 ratio, forming a high-molecular-weight polysaccharide compound (see Figure 2b).  
Locust bean gum (LBG), also known as carob gum, is a natural polysaccharide primarily derived from the seeds of the carob tree. The LBG appears as a white powder (see Figure 3a). It partially dissolves in water at room temperature but dissolves completely at 80 \({}^{\circ}\)C. LBG consists of mannose and galactose in a 4:1 ratio, forming a galactomannan structure. The mannose units form the main chain, with a galactose molecule attached to every fourth mannose unit (see Figure 3b).  
Guar gum (GG) is a natural polysaccharide extracted from guar bean seeds, also appearing as a white powder (see Figure 4a), and it is easily soluble in cold water. It consists of mannose and galactose in a 2:1 ratio, forming a galactomannan structure. Mannose forms the main chain, with a galactose molecule attached to every two mannose units (see Figure 4b). The properties of biopolymers are shown in Table 2.  
Figure 4: (**a**) GG powder; (**b**) structural formula of GG.  
Figure 3: (**a**) LBG powder; (**b**) structural formula of LBG.  
Figure 2: (**a**) XG powder; (**b**) structural formula of XG.
## 5 Experimental Results and Discussions  
This section presents experimental results that validate the watermarking method described in Section 4. The evaluation follows the dual scenario structure introduced earlier: applying watermarking to JPEGs within forensic disk images and to stand-alone images from public datasets.  
The tests presented here address the three core requirements of forensic watermarking, capacity, imperceptibility, and robustness, with each result linked to specific components of the embedding and extraction process. These results illustrate how the adaptive DCT-DWT domain technique performs in realistic forensic settings.  
A watermarking system is judged by how well it preserves visual quality and withstand distortions, accidental or deliberate. The results in this section provide a detailed assessment of capacity, imperceptibility, and resilience in multiple attack scenarios.  
The experiments were conducted on two types of image source: JPEGs extracted from disk images and images from publicly available datasets (COCO2017 for coloured images and BOSSBase for greyscale images).
## 3 Mathematical Formulation
### Second Stage: Joint Restoration Scheme
#### 3.2.2 DG Constraints  
DGs are used via microgrids as the primary source of energy during restoration to reduce the load shedding. Equations (22)-(24) model the DG's active, reactive, and apparent power limits.  
\[0\leq P_{g,t}^{DG}\leq P_{g}^{DG,max} \tag{22}\]  
\[-Q_{g}^{DC,max}\leq Q_{g,t}^{DG}\leq Q_{g}^{DG,max} \tag{23}\]  
\[\left\|P_{g,t}^{DG}\ \ Q_{g,t}^{DG}\right\|\leq S_{g}^{DG,max} \tag{24}\]
# Interaural Level Difference Optimization of Binaural Ambisonic Rendering
## 3 Objective Evaluation
### Spectral Difference  
To assess the effect of AIO on spectral reproduction of Ambisonic signals, a perceptual FFT-based spectral difference (PSD) model was used that takes into account various features of human auditory perception [43]. The PSD model weights input signals using ISO 226 equal loudness contours [44] to account for the frequency-varying sensitivity of human hearing, with a cone scale to account for the loudness-varying sensitivity of human hearing, and ERB weightings to address how the linearly spaced samples of an FFT do not fairly represent the approximately logarithmic sensitivity of the inner ear. For each measurement location, PSD was taken as the absolute mean of the left and right calculations:  
\[\mathrm{PSD}=|\frac{\mathrm{PSD}_{left}+\mathrm{PSD}_{right}}{2}| \tag{17}\]  
PSD between \(\mathbf{H}\) and \(\mathbf{\hat{H}}\) was calculated for all measurement locations \(\{\varrho_{1},\varrho_{2},...,\varrho_{16,020}\}\), for Ambisonic orders \(\{M=1,M=2,...,M=5\}\), both without and with AIO. Figure 8 shows PSD values for Ambisonic orders \(\{M=1,M=2,...,M=5\}\) over all locations on the left hemisphere, both without (top) and with (bottom) AIO. A small improvement in most measurement locations can be observed. Binaural Ambisonic rendering is known to produce differences in spectral reproduction accuracy between front and side, something which is illustrated in Figure 8 (especially for \(M=2\) and \(M=3\), Figure 8b,c, respectively). While the implementation of AIO does reduce this to some extent, it is still evident.  
A single value of PSD for all locations on the sphere, \(\overline{\mathrm{PSD}}\), was obtained as the solid-angle weighted sum of all PSD values by  
\[\overline{\mathrm{PSD}}=\sum_{\varrho=1}^{16020}\Omega_{\varrho}\mathrm{PSD}_{\varrho} \tag{18}\]  
The \(\overline{\mathrm{PSD}}\) values for Ambisonic orders \(\{M=1,M=2,...,M=5\}\), without and with AIO, are presented in Figure 9. Here AIO is shown to produce an overall improvement in PSD for all tested orders of Ambisonics.  
Figure 8: _Cont._  
Figure 7: Median values of \(\Delta\)ILD between HRIRs and binaural Ambisonic rendering (Ambisonic orders \(\{M=1,M=2,...,M=5\}\)) for five frequency bands over all directions on the sphere, with 25% and 75% percentile bars.
# FAS-Res2 net: An Improved Res2 net-Based Script Identification Method for Natural Scenes
## 4 Experiments
### Experimental Analysis  
To verify the effectiveness of our method, we further predicted the identification accuracy of each language in each dataset. The prediction results are shown in Figure 9, and a confusion matrix was generated for each dataset prediction, as shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **SIW-13** & **CVSI-2015** \\ \hline Our & 94.7 & 96.0 \\ DisCNN [21] & 88.0 & 94.0 \\ CNN + LSTM [32] & 92.0 & 94.0 \\ SRS + LBP + KNN [33] & - & 94.0 \\ Conv.feature + NBNN [34] & - & 95.0 \\ CNN + BoVW [35] & 92.0 & 97.0 \\ CNN + LSTM, VGG16 [31] & - & 88.3 \\
2-stage CNN [36] & - & 97.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison accuracy (%) on two public script datasets.  
Figure 9: Classification performance on two public datasets. (**a**) SIW-13, (**b**) CVSI-2015.  
As shown in Figure 9a, it shows that the identification performance of Tibetan in the SIW-13 dataset is the best, reaching an identification rate of 99.2%. Greek had the lowest accuracy rate of 81.8%. From the confusion matrix in Figure 10a, it can be seen that 42 Greek images are misclassified as Russian. The reason for this is a language similarity issue, and that there are shared similar characters. English has a relatively low accuracy rate and is mainly misidentified as Russian and Thai. Most other languages have an identification rate of 94% or above, and our overall identification rate on this dataset is 94.7%.  
To illustrate the accuracy of the proposed method on the CVSI-2015 dataset, Figure 9b shows the identification accuracy for each category and Figure 10b shows the corresponding confusion matrix. Since Arabic and Gujarati have their own independent and special character forms, the identification rate has reached 100%. Except for Hindi and Kannada,  
Figure 10: Confusion matrix on two publicly available datasets. The x-coordinates and y-coordinates represent the real label and the predicted category, respectively, and the darker the color, the higher the proportion of correct predictions. (**a**) SIW-13, (**b**) CVSI-2015.  
the identification rate of other languages has reached over 96%. Except for Kannada, none of the scripts had more than seven misclassifications. Most of the misclassifications of Kannada were identified as Telugu because they all have relatively curved font textures, and a small part were identified as Oriya and Bengali. We achieved an overall identification rate of 96.0% on this dataset.