esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Discussion
### Comparison of Test Results
#### 4.4.2 Comparison with Algorithms Related to Biological Characteristics  
Figure 12 shows the comparison between the IYEPDNN algorithm and the PPI+ biometric-related basic protein recognition algorithms in DIP datasets. Figure 13 is the comparison in the GAVIN dataset. Based on PPI, the identification accuracy of essential proteins is improved by introducing biometric features. Among the first 61 (1%) candidate essential proteins in Figure 12, the minimum number of recognitions is 36, which is higher than the maximum number of recognitions (32) in Figure 10. In Figure 13, among the first 19 (1%), the minimum number of recognitions is 13, which is close to the maximum number of recognitions (14) in Figure 11. However, with the increasing number of candidate essential proteins, the recognition accuracy of basic proteins improves, but the effect is not obvious  
Figure 11: Comparison of PPI network topology related algorithms in GAVIN dataset.  
Figure 10: Comparison of PPI network topology related algorithms in DIP dataset.  
at the later stage. At 1273 (25%) candidate essential proteins in the DIP dataset, the number of LAC essential proteins identified is 552 in Figure 10, the minimum identified essential proteins is 493 in Figure 12, and the maximum identified essential proteins is 669. In the GAVIN dataset of 464 (25%) candidate essential proteins, the number of basic proteins recognized is basically above 220 in Figure 11, and the number of LAC basic proteins recognized reaches 254. Instead, it exceeds the number of basic protein identifications of PPI+ biometric in Figure 13. In the DIP dataset, the IYEPDNN algorithm is lower than DEP-MSB (45) and OGN (44) in the first 61 (1%) candidate essential proteins and far higher than the PPI+ biometric-related basic protein recognition algorithm in other links. The number of essential proteins correctly identified by the IYEPDNN algorithm is 1.67 times higher than the highest number of essential proteins identified by the PPI+ biometric correlation algorithm in 25% of DIP datasets. It reaches 1.72 times at 25% of candidate essential proteins in the GAVIN dataset.
## 3 Results  
Associations of MS with Anthropometric Factors, Blood Parameters, Urinary Parameters, and Spirometric Factors  
Shown in Tables 3 and 4 are the associations of MS with the anthropometric and spirometric factors, and blood and urinary parameters in Korean men and women. Overall, 29.2% (N = 4085) of the 13,978 participants exhibited MS. The prevalence of MS was higher in women than in men (30.2% and 28.0%, respectively). Figure 2: Sample selection procedure for association analysis. In men, among all factors, triglycerides (TG) (\(p\) < 0.0001, OR = 2.719, 95% CI [2.350-3.146]) presented the strongest association with MS during crude analysis. Additionally, TG (adjusted \(p\) < 0.0001, adjusted OR = 2.711 [2.328-3.158]) exhibited the strongest association after adjusting for age, BMI, alcohol consumption, smoking, income, recognized stress rate, and education level. Among anthropometric factors, WHtR demonstrated the strongest association with MS in crude (\(p\) < 0.0001, OR = 2.164 [2.006-2.334]) and adjusted (adjusted \(p\) < 0.0001, adjusted OR = 2.069 [1.922-2.228]) analyses. In the second, WC had the next strongest association in the crude (\(p\) < 0.0001, OR = 2.164 [2.006-2.334]) and adjusted (adjusted \(p\) < 0.0001, adjusted OR = 1.468 [1.287-1.674]) analyses. Among blood parameters, TG (adjusted \(p\) < 0.0001, adjusted OR = 2.711 [2.328-3.158]) was the strongest negative indicator of MS in both crude and adjusted analyses. GLU (\(p\) < 0.0001, OR = 2.348 [2.048-2.691]) was associated with MS in crude analysis. However, HDL (adjusted \(p\) < 0.0001, adjusted OR = 0.384 [0.342-0.431]) showed a negative association with MS in the adjusted analysis. Among urinary parameters, uric acid pH (UPH) showed the highest negative association with MS in crude (\(p\) < 0.0001, OR = 0.823 [0.767-0.883]) and adjusted (adjusted \(p\) < 0.0001, adjusted OR = 0.852 [0.791-0.918]) analyses. Among spirometric factors, forced expiratory volume in 6s (FEV6) was negatively associated with MS in crude (\(p\) < 0.0001, OR = 0.733 [0.685-0.784]) and adjusted (adjusted \(p\) < 0.0001, adjusted OR = 0.822 [0.749-0.903]) analyses, followed by forced vital capacity (FVC) in both crude (\(p\) < 0.0001, OR = 0.755 [0.706-0.807]) and adjusted (adjusted \(p\) < 0.0001, adjusted OR = 0.839 [0.769-0.915]) analyses.
## 2 Endocannabinoid System (ECS)
### ECS--Cannabinoid Receptors
#### 2.1.1 ECS--CB1 Receptors  
CB1 receptors exhibit a high degree of conservation across vertebrate species and are also discovered in certain invertebrates. In humans, they are encoded by the _CNR1_ gene. Initially cloned from rats, a human CB1 receptor cDNA was subsequently identified. Interestingly, the human CB1 receptor displays one less amino acid in its N-terminus compared to other mammalian species (472 amino acids versus 473 amino acids). The similarities between the rat and human receptors are significant, boasting 93% identity at the nucleic acid level and 97% at the amino acid level. Similarly, the mouse and rat clones share 95% nucleic acid identity (100% amino acid identity), and the mouse and human clones exhibit 90% nucleic acid identity (97% amino acid identity) [8]. It is worth noting that the CB1 receptor stands out as the most abundant G protein-coupled receptor (GPCR) within the mammalian brain [9]. CBRs are found in the spinal cord (where they participate in pain signaling modulation [10]), as well as in peripheral sensory neurons and the autonomic nervous system [11]. In the GIT, cannabinoid type 1 (CB1) receptors are located in neurons of the enteric nervous system (mainly in the myenteric plexus), epithelial cells, and sensory endings of both vagal and spinal neurons. In the ENS circuit, CB1 receptors are located on the excitatory motoneurons, ascending interneurons, and intrinsic primary afferent neurons (IPANs). Activation of CB1 receptors has been demonstrated to regulate multiple functions in the GI tract, encompassing gastric secretion, gastric emptying, and intestinal motility [12; 13]. CBRs are primarily located presynaptically and exert their main influence through negative feedback on the synaptic neurotransmission of other neurotransmitters,such as acetylcholine (ACh), glutamate, and GABA [14]. Apart from the nervous system, mild expression can be observed in the liver, spleen, adipose tissue, eye, testes, uterus, and even embryonic cells [15].
## 2 Theory  
### Lossy Helmholtz Equation Approach  
We consider the following plane wave propagation model with the air absorption effect described in ISO 9613-1 [37] as  
\[p(x,t)=p_{\rm i}\mathrm{e}^{-ax}, \tag{1}\]  
where the pure-tone sound pressure at time \(t\) and at distance \(x\) is represented by \(p(x,t)\). In this model, a plane wave \(p_{\rm i}\) exponentially attenuates against the propagation distance according to the pure-tone sound attenuation coefficient \(\alpha\) [Np], which describes the attenuation per unit length attributable to the classical absorption and molecular relaxation absorption. Note that Np represents a unit called _neper_, which has a relation 1 [Np] = 20/ln(10) [dB]. Plane wave \(p_{\rm i}\) takes the form of  
\[p_{\rm i}=\mathrm{e}^{-\mathrm{j}(k_{0}x-\omega t)}, \tag{2}\]  
with the wave number of lossless air \(k_{0}=\omega/c_{0}\), where \(\omega\) is the angular frequency, and where \(c_{0}\) is the speed of sound. With the plane wave form, Equation (1) can be expressed as  
\[p(x,t)=\mathrm{e}^{-\mathrm{j}\tilde{k}\mathrm{r}}\mathrm{e}^{\mathrm{j}\omega t}, \tag{3}\]  
where \(\tilde{k}\) denotes the complex wavenumber given as \(\tilde{k}=k_{0}-\mathrm{j}\alpha\). Based on this plane wave propagation model with \(\tilde{k}\), the air absorption effect is incorporated into the acoustic simulation in the frequency domain as the lossy Helmholtz equation approach, which solves the following equation as  
\[(\
abla^{2}+\tilde{k}^{2})\hat{p}(\mathbf{r},\omega)=-\mathrm{j}\omega\rho_{0} \hat{q}(\omega)\delta(\mathbf{r}-\mathbf{r}_{\rm s}), \tag{4}\]  
where \(\hat{p}(\mathbf{r},\omega)\) stands for the complex-valued sound pressure at the position \(\mathbf{r}\) in the Cartesian coordinate system, \(\rho_{0}\) represents the air density, \(\hat{q}(\omega)\) signifies the volume source strength density, and \(\delta\) expresses the delta function. The position vector at a source point is represented by \(\mathbf{r}_{\rm s}\).
## 4 Results and Discussion
#### Forecasting Results Based on Data Set 2: Algerian PV Array Datasets  
Now, the effectiveness of the eight methods will be tested based on power output data collected from the 9 MWp PV plant in Algeria (Data Set 2). In this experiment, the above-trained models will be evaluated using the testing solar PV power output collected from 20 October to 31 December 2018. The measured test set together with model forecasts are charted in Figure 9. Similar conclusions are also valid for these datasets. One major reason is that RNN-based models have a strong capability to describe time dependents data and can better model the complicated relationship between historical and future PV power output data than other methods. The RNN-based models and the VAE model again confirm the superior forecasting performance of PV power output as shown by the scatter plots in  
sequence \((x_{1},\dots,x_{k})\) and an output variable \(x_{k}\) and we train RNN-based models to learn the mapping function from the input to the output. After that, these trained models are used to forecast new data. Even if the VAE model is trained in an unsupervised way, it can provide comparable forecasting performance to those obtained by the supervised RNN-based models. Accordingly, the VAE-based forecasting approach is a more flexible and powerful tool to be used in real-time PV power forecasting.  
Overall, the NRMSE (%) quantifies the quality of power forecasting between the measured and forecasted PV power output time-series data, where the larger value indicates a better prediction performance. A visual display of the NRMSE (%) derived with the eight considered deep learning methods based on the testing datasets from the two PV systems is displayed in Figure 10. The first dataset is with a one-minute resolution and the second dataset is with fifteen minutes resolution. The VAE model achieves better PV power flow forecast performance compared to the RBM and SAE models and RNN-based models. Furthermore, the results show that VAE models are efficient in capturing the linear and nonlinear features in PV power data with different time resolutions.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **R2** & **RMSE** & **MAE** & **EV** \\ \hline LSTM & 0.992 & 246.781 & 107.799 & 0.992 \\ GRU & 0.991 & 250.004 & 117.622 & 0.992 \\ RNN & 0.992 & 241.170 & 125.960 & 0.992 \\ BILSTM & 0.991 & 259.884 & 117.579 & 0.991 \\ ConvLSTM & 0.692 & 1500.070 & 846.750 & 0.692 \\ RBM & 0.977 & 407.238 & 170.568 & 0.977 \\ SAE & 0.983 & 349.066 & 124.772 & 0.983 \\ SVM & 0.924 & 770.996 & 699.837 & 0.954 \\ LG & 0.899 & 886.665 & 837.721 & 0.926 \\ VAE & 0.995 & 199.645 & 99.838 & 0.995 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Forecasting performance of the eight methods using the test set of the second dataset.  
Figure 9: Scatter graphs of PV power forecasting and measurements using the eight models: (**a**) LSTM, (**b**) GRU, (**c**) RNN, (**d**) BiLSTM, (**e**) ConvLSTM, (**f**) RBM, (**g**) SAE, and (**h**) VAE.
## 2 Mission Concept
### Platform Selection  
After studying the possible options for the satellite platform, it was found that the minimum configuration that satisfies the proposed mission needs is the 3U CubeSat configuration. The 3U configuration has an equivalent size of three 1U CubeSats, as illustrated in Figure 3. The standard size of a 3U CubeSat is 34 cm \(\times\) 10 cm \(\times\) 10 cm. The main driving factor behind this decision is the volume that each platform provides to in-house all of the required subsystems. The small Field of View (FoV) of the Argus 2000 spectrometer dictates accurate pointing and hence a sophisticated ADCS, that would occupy at least 0.8 U. The payloads would require an estimated 1U, which led to the decision of adopting a 3U platform for MeznSat.
## 6 Conclusions  
This article has proposed a generalized algorithm, based on equivalent circuits, for evaluating the SEs of electronic equipment enclosures. We first reviewed and systematized most of the existing analytical models that use equivalent circuits in calculating the SE. After generalizing these models, we developed the SE evaluation algorithm and described it in the form of flowcharts. Next, using six different enclosures as examples, the algorithm was validated by two measurement methods and numerical analysis based on FEM. The validation results showed that the proposed algorithm allows one to calculate the SE more than \(10^{6}\) times faster than FEM. At the same time, it has an acceptable accuracy, since the average value of the absolute error modulus in all considered cases did not exceed 5.6 dB. The last part of this article presented a prototype of our software for analyzing shielding enclosures of electronic equipment, which we plan to develop further.  
In conclusion, we prefer to note that, despite the good accuracy and low computation time, the algorithm proposed in this article has some disadvantages. The main one is its low variability due to the use of analytical models that cannot describe any arbitrary enclosure structure. For example, the algorithm cannot be used to analyze nested enclosures, unshielded cables penetrating the shield, enclosures with uneven or curved walls, etc. In contrast, almost any enclosure can be analyzed using numerical methods, although, as mentioned in the introduction, this requires significant computational costs. We believe that a new tool for a fast SE evaluation in the future could be a machine learning model. For this reason, some of our future research is expected to be devoted to developing and analyzing such a model. Another important disadvantage of the algorithm is that the enclosure is assumed to be perfectly conductive, i.e., penetration directly through the walls is ignored. This means that the algorithm is not well suited to the analysis of shields exposed to low-frequency and static \(H\)-fields.  
Conceptualization and methodology, M.E.K.; validation, A.A.I. and A.V.D.; software, A.A.I. and A.A.K.; writing--original draft preparation, A.A.I.; writing--review and editing, S.P.K. and T.R.G. All authors have read and agreed to the published version of the manuscript.  
The review and algorithm development were financially supported by the Ministry of Science and Higher Education of the Russian Federation (Project FEWM-2022-0001). The measurement and software development were carried out at the expense of Russian Science Foundation grant 19-79-10162.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the funding organization policy.  
The authors declare no conflict of interest.
# Urban Congestion Avoidance Methodology Based on Vehicular Traffic Thresholding
## 2 Related Work
### Large Scale Urban Traffic Congestion Simulation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Solution** & **Simulator** & **Road Network** & **Area Size** & \begin{tabular}{c} **Number of** \\ **Simulated** \\ **Vehicles** \\ \end{tabular} & \begin{tabular}{c} **Maximum of** \\ **Concurrent** \\ **Vehicles** \\ \end{tabular} \\ \hline VNS [15] & DIVERT & Porto City & N/A & 130 K & 15 K \\ \hline Elbery [16; 17] & INTEGRATION & Los Angeles & 133 km\({}^{2}\) & 563 K+ & 30 K \\ \hline Farag [18] & INTEGRATION & Los Angeles & 133 km\({}^{2}\) & 145 K & 30 K \\ \hline FOXS [45] & SUMO & Ottawa, Cologne & 8 km\({}^{2}\) and 4 km\({}^{2}\) & 2.2 K and 14 K & N/A \\ \hline SOPHIA [46] & SUMO & Cologne & approx. 25 km\({}^{2}\) & 46 K & N/A \\ \hline Re-RouTE [47] & SUMO & Los Angeles, Paris & 25 km\({}^{2}\) & 1.25 K to 6.25 K & N/A \\ \hline ATOM [48] & SUMO & Baltimore & approx. 1 km\({}^{2}\) & 7.2 K & N/A \\ \hline Stan [8; 19] & OsmAnd & \
\begin{tabular}{c} New York, \\ Cluj-Napoca \\ \end{tabular} & N/A & 10 K and 20 K & N/A \\ \hline CAVTTM & SUMO &
\begin{tabular}{c} Barcelona, \\ Bucharest, New \\ York, Tokyo \\ \end{tabular} & 65 km\({}^{2}\) & 50 K+ and 100 K+ & 49 K \\ \hline \hline \end{tabular}  
* Peak number of concurrent vehicles running on the roads simultaneously. N/A: not applicable.  
\end{table} 
Table 1: Large scale urban traffic congestion simulation solutions. More specific traffic modelling approaches are described in [8; 19]. In these cases, the traffic state is defined during vehicle route computation and stored using range query data structures. The vehicle simulation was performed by adapting the OsmAnd navigation application to compute vehicular routes by considering the current state of the traffic. The current state of the traffic is obtained by querying the vehicular density on a road segment at a specific moment. The range query data structure used for storage, query and update of vehicular traffic information is segment tree for the work in [19] and K-ary tree for the solution in [8]. The segment tree lacks scalability due to query and update operations performance, the K-ary tree, especially the K-ary entry point (KEP) tree, scales efficiently and without any bottlenecks due to high performing query and update operations. As is shown in [8], the KEP tree data structure performs better than any other scalable traffic modelling solutions. The number of simulated vehicles in [19] is 10 K, while the number of simulated vehicles in [8] is 20 K. The road network size used for the simulation and the maximum number of concurrent vehicles are not specified in [19] nor in [8].
# Highlight Removal Emphasizing Detail Restoration
## 4 Results
### Runtime  
Runtime is an important criterion for determining whether an algorithm can process in real-time. This paper selected highlight images at different resolutions to detect the runtime of the algorithm at different resolutions. All the methods mentioned in this paper were run on the same computer using MATLAB (2021b), with a computer configuration of i7-11800H, GTX 2060, and 16 GB of memory (Intel, Santa Clara, CA, USA).  
Table 8 represents the runtime of the images. The table uses images with three different pixel resolutions for runtime comparison: 256 \(\times\) 256, 512 \(\times\) 512, and 1280 \(\times\) 720. The table shows the runtime and final average runtime for each corresponding resolution, measured in seconds. * indicates that the image cannot be processed at the current resolution.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Decorations** & **[PERSON] [20]** & **[PERSON] [31]** & **[PERSON] [10]** & **[PERSON] [14]** & **[PERSON] [3]** & **[PERSON] [32]** & **Ours** \\ \hline MSE/\(10^{-2}\) & 5.12 & 30.34 & **3.05** & 47.93 & 285.01 & 3.42 & 3.34 \\ \hline PSNR & 21.08 & 13.76 & 24.21 & 12.27 & 13.61 & 22.99 & **24.35** \\ \hline SSIM & 0.93 & 0.78 & 0.90 & 0.67 & 0.76 & 0.92 & **0.94** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean performance metrics of images in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**[PERSON] [20]**} & \multicolumn{2}{c}{**[PERSON] [31]**} & \multicolumn{2}{c}{**[PERSON] [10]**} & \multicolumn{2}{c}{**[PERSON] [14]**} & \multicolumn{2}{c}{**[PERSON] [3]**} & \multicolumn{2}{c}{**[PERSON] [32]**} & \multicolumn{2}{c}{**Ours**} \\ \cline{2-13}  & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline AVG & 18.86 & 0.59 & 14.86 & 0.31 & 28.64 & 0.83 & 7.43 & 0.41 & 16.33 & 0.41 & 28.77 & **0.93** & **28.83** & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance metrics on the real welding seam dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**[PERSON] [20]**} & \multicolumn{2}{c}{**[PERSON] [31]**} & \multicolumn{2}{c}{**[PERSON] [10]**} & \multicolumn{2}{c}{**[PERSON] [14]**} & \multicolumn{2}{c}{**[PERSON] [3]**} & \multicolumn{2}{c}{**[PERSON] [32]**} & \multicolumn{2}{c}{**Ours**} \\ \cline{2-13}  & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline AVG & 20.57 & 0.65 & 10.86 & 0.40 & 24.55 & 0.94 & 7.06 & 0.45 & 14.73 & 0.61 & 25.96 & 0.92 & **29.44** & **0.95** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance metrics on the SHIQ dataset.  
For the deep learning method, because it could only handle images up to 512 \(\times\) 512 pixel, the processing time at the corresponding resolution is the training time of the model. The final average processing time is the processing time per image after the model is trained.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 4 Discussion
### Balanced Integration of Complementary Forecasting Strengths  
The evaluation radar charts reveal that regions exhibit complementary strengths across our three primary indicators. While Jiangsu demonstrates exceptional performance in grid impact, Shanghai excels in managing forecasting complexity, and Anhui shows particular strengths in winter conditions. Our weighted fusion methodology effectively combines these complementary capabilities, creating an integrated forecast that leverages the specialized knowledge embedded in each provincial system.  
This balanced integration approach is particularly valuable for regional grid operations that must coordinate across provinces with heterogeneous characteristics. Rather than imposing a single forecasting methodology across all regions--which would fail to account for local specialization--our approach preserves the autonomy of provincial forecasting systems while implementing a systematic framework for optimal integration. This preserves institutional knowledge while addressing the multi-source heterogeneity challenge that has limited previous integration efforts.
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## Abbreviations  
\begin{tabular}{l l} A2C & Advantage Actor-Critic \\ A3C & Asynchronous Advantage Actor-Critic \\ ANN & Artificial Neural Network \\ CN & Core Network \\ CNN & Convolutional Neural Network \\ DDQN & Double Deep Q-Network \\ DL & Deep Learning \\ DNN & Deep Neural Network \\ DQN & Deep Q-Network \\ DRL & Deep Reinforcement Learning \\ E2E & End-to-End \\ eMBB & enhanced Mobile Broadband \\ ERLLC & Extremely Reliable and Low Latency Communications \\ FNN & Feed-forward Neural Network \\ GAN & Generative Adversarial Network \\ GAN-DDQN & Strategy Generative Adversarial Network Powered DDQN \\ GD & Gradient Descent \\ IoT & Internet of Things \\ HetNet & Heterogeneous Network \\ KPI & Key Performance Indicator \\ LSTM & Long Short-Term Memory \\ MBRLLC & Mobile Broadband Reliable Low Latency Communications \\ MDP & Markov Decision Process \\ MILP & Mixed Integer Linear Programming \\ MIoT & Massive Internet Of Things \\ ML & Machine Learning \\ mMTC & massive Machine Type Communication \\ mURLLC & massive ultraReliable Low Latency Communications \\ NFV & Network Functions Virtualization \\ NN & Neural Network \\ NSL & Network Slicing \\ NSLR & Network Slice Request \\ PPO & Proximal Policy Optimization \\ QoS & Quality of Service \\ QoE & Quality of Experience \\ RAN & Radio Access Network \\ ReLU & Rectified Linear Unit \\ RL & Reinforcement Learning \\ RNN & Recurrent Neural Network \\ SARSA & State-Action-Reward-State-Action \\ SDN & Software-Defined Networking \\ sEMBB & strengthened Enhanced Mobile Broadband \\ SFC & Service Function Chain \\ SGD & Stochastic Gradient Descent \\ SLA & Service Level Agreement \\ TD3 & Twin Delayed Deep Deterministic Policy Gradient \\ TN & Transport Network \\ UAV & Unmanned Aerial Vehicle \\ umMTC & ultra Massive Machine-Type Communications \\ \end{tabular}