esa-sceva/satcom-chunk-collection huggingface dataset:
# Video Captioning Based on Channel Soft Attention and Semantic Reconstruct
## 5 Conclusions  
Existing convolutional RNNs for video feature extraction have exploited the video's global temporal structure for fully connected features, but not include the global temporal structure of each channel, and the semantic information is not fully used. In this paper, a video captioning model based on channel soft attention and semantic reconstructor is proposed. Specifically, the soft attention is combined with channel attention, to compute every weighted channel for each time step based on the global feature maps. A semantic reconstructor is introduced to make semantic information fully used. Compared with the state-of-the-art methods on two popular benchmarks MSVD dataset and MSR-VTT dataset, the results of our experiment have shown that our methods achieve competitive or even superior performance. In the future work, we will explore more effective approaches such as reinforcement learning to optimize the sentence-level metrics for more interpretable sentences.  
**Author Contributions:** Abstract, introduction, related works, conclusions, and the revising of the entire manuscript, Z.L.; algorithms, experiments, and results, Y.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is partially supported by The National Natural Science Foundation of China (NSFC) under grant No. 61572306, The National Key Research and Development Program of China (NO. 2017 YFB0701600) and The Science and technology committee of Shanghai Municipality under grant No. 19511121002.  
**Data Availability Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**User** & \(MSVD_{pred}\) & \(MSVD_{gt}\) & \(MSVD_{rate}\) & \(MSR-VTT_{pred}\) & \(MSR-VTT_{gt}\) & \(MSR-VTT_{rate}\) \\ \hline No.1 & 9 & 11 & 45\% & 7 & 13 & 35\% \\ No.2 & 9 & 11 & 45\% & 6 & 14 & 30\% \\ No.3 & 7 & 13 & 35\% & 8 & 12 & 40\% \\ No.4 & 10 & 10 & 50\% & 7 & 13 & 35\% \\ No.5 & 9 & 11 & 45\% & 9 & 11 & 45\% \\ No.6 & 8 & 12 & 40\% & 7 & 13 & 35\% \\ No.7 & 11 & 9 & 55\% & 9 & 11 & 45\% \\ No.8 & 6 & 14 & 55\% & 7 & 13 & 35\% \\ No.9 & 9 & 11 & 45\% & 5 & 15 & 25\% \\ No.10 & 8 & 12 & 40\% & 7 & 13 & 35\% \\ \hline Avg & 8.6 & 11.4 & 43\% & 7.2 & 12.8 & 36\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Human judgment of generated sentences on testing subsets of MSVD and MSR-VTT.  
**Acknowledgments:** The authors would like to thank reviewers for their fruitful feedback and comments that have helped them improve the quality of this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
### Sources and Bioavailability of Silicon from Foods
\begin{table}
\begin{tabular}{c c} \hline
**Product Group** & **Silicon Content (mg/100 g or mg/100 mL)** \\ \hline
**Cereal and Related Products (Highest Concentration of Silicon in Grain Husk, Especially in Oat Bran)** \\ \hline Breakfast cereals & 7.79 \\ Cornflakes & 2.42 \\ Biscuits & 1.56 \\ Other wheat cakes & 2.78 \\ White rice & 1.24 \\ Brown rice & 2.07 \\ Pasta & 0.60 \\ Bran & 10.17 \\ Wheat bread & 1.69 \\ Whole-grain bread & 2.25 \\ Brown bread & 4.47 \\ Croissant & 1.67 \\ \hline Fruit & \\ Banana & 5.44 \\ Orange & 0.32 \\ Strawberries & 0.12 \\ Mango & 2.00 \\ Raisins & 8.25 \\ Dried dates & 16.61 \\ Nuts and seeds & 0.78 \\ \hline Vegetables & \\ Carrots (peeled, raw) & 0.29 \\ Potatoes (peeled, cooked) & 0.34 \\ Iceberg lettuce & 0.41 \\ Chickpeas & 0.76 \\ Green beans (cooked) & 2.44 \\ \hline Drinks & \\ Beer & 2.19 \\ Wine & 1.24 \\ Black tea & 0.86 \\ Mineral water & 0.69 \\ \hline \end{tabular}
\end{table} 
Table 1: Silicon content in food products [27; 34]. The richest source of silicon in the plant world is horsetail (25% of its dry weight is silica), which has been used for thousands of years as a herbal remedy, and current scientific evidence supports most of its potential benefits [37]. Horsetail has multiple potential health benefits, including improved bone, skin, hair, and nail health. It is most commonly consumed in the form of herbal teas, capsules, and tinctures. Despite being a widely recognized and used herb and supplement, horsetail is not approved by the Food and Drug Administration (FDA) as with most herbal supplements [38].
# Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements
## 2 RBF-Kriging for borehole Data Interpolation
### Variation Function Determination Using RBF  
In the process of calculating the interpolation weights, the variation function often depends on experience. For the commonly used variation functions such as the linear model, Gaussian model, exponential model, etc., it was shown in practical applications that the relative error is relatively large, the spatial characteristics of the existing sampling points may not be well described by these models, and the integrity of the direction and distance information in the data space cannot be guaranteed [14].  
The RBF network has its origin in performing an exact interpolation of a set of data points in a multidimensional space. The RBF network is a universal approximator and it is a popular alternative to the multilayer perception (MLP) neural network, since it has a simpler structure and a much faster training process. It is widely used for classification and function approximation. The RBF network consists of three layers: input layer, hidden layer, and output layer. The input layer is connected to the hidden layer by radial basis functions, and the hidden layer to output layer is connected through a simple weight connection. The Gaussian radial function output from the hidden layer is linearly weighted to generate output. As a result, the RBF network can approximate any function with any required precision. This property has great potential to learn the variation function for Kriging interpolation. Furthermore, the RBF variation function can alleviate the long-distance point interference due to its simple three-layers structure. Finally, normalization is carried out to normalize the distance vector h to keep the distance h and the variation function values \(\gamma\) in the same level of magnitude.  
Because of the relationship between the distance and variation function, the variation function must be greater or equal to 0. While using the RBF network to fit the variation function, the value of variation may be less than 0, but the probability is almost 0 as the result of the efficient sample chose at part 1.2. If the value of variation function that is fitted by RBF is less than 0, then we will configure the value as 0, because the distance of two points is too long and the effect of two points at the distance is negligible.
# Numerical Test of Several Controllers for Underactuated Underwater Vehicles
## 1 Introduction
This test is based on the assumption that the selection of controller parameters should be intuitive (manual or otherwise by trial and error) to avoid the use of additional methods. That is why only such algorithms were chosen for which the intuitive selection of parameters in the source work turned out to be sufficient and there was no mention of the use of an additional selection method or an optimization method. Moreover, a concept of a testing algorithm which gives an insight into the vehicle dynamics is considered. In the limited research, it is interesting to find a potential application of some selected control algorithms suitable for trajectory tracking of underactuated underwater vehicles moving horizontally. Inspiration for considering such a problem is the emerging need to use an appropriate control algorithm that guarantees acceptable tracking results for a desired trajectory. When the model of the underwater vehicle is known and our task is to use the controller to track the desired trajectory one can do two ways: develop our own control scheme or adapt one or more of the control algorithms known from the literature. This work is devoted to this second issue only. For this purpose, a comparison among the control algorithms developed in [4; 17; 18], and [11] is shown. The first strategy [4] serves for control of an underactuated hovercraft and it is here used as tester for further investigation. The others are intended originally for control of marine vehicles. All algorithms are applied to the control of two selected vehicles treated as underactuated. The other three strategies, i.e., considered in [11; 17; 18], are various kinds of the sliding mode control. The proposed approach is particularly important before undertaking research on the usefulness of control algorithms already at the simulation verification stage. At this stage, it is necessary to decide whether it is worth carrying out an experiment on a real object using the selected controller. The originality of the proposed work can be stated as follows:  
1. the use of a simple test of algorithms which give an insight into the dynamics of the vehicle and control performance;
2. to verify in simulation the selected control algorithms appropriate for underactuated underwater vehicles in which gain coefficients are chosen without any additional optimization methods and to show their performance;
3. to compare the simulation results obtained for the linear and sine trajectory and for two models of the vehicle. This work is organized in the following way. In Section 2, the equations of motion used here are given. In Section 3, the selected tracking control algorithms are described shortly. In Section 4, simulations are presented for two different underwater vehicles to demonstrate the effectiveness of each control scheme. Finally, conclusions are made in Section 5.
# Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network
## 3 Proposed Method
### Time GAN Generation of Data
#### 3.3.1 Fundamentals of Time GAN  
In December 2019, [PERSON] et al. introduced a novel Time GAN framework that captured not only the allocation of characteristics for each moment in a time series of a variable [30], but also the potentially complicated interplay of the variables over time. Time GAN is made up of four network components: the embedding network, the recovery network, the generator network, and the discriminator network. The embedding and recovery networks are members of the automatic encoding component, while the sequence generator and discriminator are members of the adversarial component. The adversarial and automatic encoding components are jointly trained so that Time GAN can learn encoding features, generate representations, and perform cross-time iterations all at the same time. Figure 9 depicts the Time GAN training structure framework.  
In the figure, \(\theta_{e}\), \(\theta_{r}\), \(\theta_{g}\), and \(\theta_{d}\) denote the parameters of the embedding, recovery, generator, and discriminator networks, respectively, \(L_{R}\) denotes the reconstruction loss function of the output results of the embedding and recovery networks, \(L_{U}\) denotes the  
Figure 9: Training structure framework of Time GAN.  
unsupervised loss function of the output results of the generator and discriminator, and \(L_{S}\) denotes the supervised loss of the output results of the embedding and generator networks.  
\(\theta_{e}\) and \(\theta_{r}\) are trained based on the reconstructed loss function and supervised loss, as shown in Equation (4).  
\[\underset{\theta_{e},\theta_{r}}{\text{min}}(\lambda L_{S}+L_{R}) \tag{4}\]  
where \(\lambda\) is the hyperparameter that balances the two loss functions of \(L_{R}\) and \(L_{S}\), and the embedding process is enabled by \(L_{S}\) not only to lower the dimensionality of the adversarial learning space but to allow the generator network to learn time series features from real data.  
In the next step, the generator and discriminator networks are trained adversarially, as shown in Equation (5).  
\[\underset{\theta_{g}}{\text{min}}(\eta L_{S}+\underset{\theta_{d}}{\text{max }}L_{U}) \tag{5}\]  
where \(\eta\) is the hyperparameter that balances the two losses of \(L_{U}\) and \(L_{S}\), and the above equation minimizes the supervised loss of the generator in addition to the unsupervised game in classification accuracy. In summary, it is through this combined training approach that Time GAN achieves encoding, generation, and iteration.
## 3 Proposed Network Model and the Problem Formulation
### Deployment and Cluster Formation  
The deployment of the entire heterogeneous network is based on the placement of the different nodes with a different energy distribution for the remote, advanced and sink nodes [31]. However, the sink node is associated with the mobilizer to enable the movement when required. Based on the above-mentioned assumptions, the initial position of the mobile sink node is set at the middle of the sensor field. The cluster head is selected from the set of advanced nodes only.  
The entire sensor field is represented as \(\mathrm{G}e<\mathrm{N},\mathrm{E}>\) with the N number of nodes and the E number of links that is further divided into the number of sectors, and the CHs decide how many sectors there should be. Depending on its geographic information, each node joins a single cluster. The angle of each sector is given as Equation (1):  
\[\varnothing=\frac{2\pi}{N_{CHs}} \tag{1}\]  
where \(N_{CHs}\) represents the CH count. Then, the node taking part in the cluster is represented using Equation (2):  
\[CH_{id}=\left\{\begin{array}{ll}\frac{tan^{-1}\left(\frac{y}{y}\right)}{ \varnothing}if\ x\geq 0\ and\ y\geq 0\\ \frac{tan^{-1}\left(\frac{y}{y}\right)+\pi}{\varnothing}\ \ \ \ \ \ if\ x<0\\ \frac{tan^{-1}\left(\frac{y}{y}\right)+2\pi}{\varnothing}if\ x\geq 0\ and\ y<0\end{array}\right. \tag{2}\]  
where \(x\) denotes the position of the sensor in the coordinate axis x, and \(y\) denotes the position of the sensor in the coordinate axis \(y\) with the node positioning as \((x,y)\).
# Quantum-Secure Coherent Optical Networking for Advanced Infrastructures in Industry 4.0
## 4 Vulnerability Analysis
### Digital Signal Processing (DSP) Algorithm Exploitation  
This study next employed a case analysis on the DSP blocks responsible for adaptive equalization, chromatic dispersion compensation, and polarization tracking. Documenting the block diagrams and comparing them to known exploit scenarios [33] suggested that adaptive algorithms could be misled by minuscule, precisely timed phase adjustments. An iterative approach tested repeated injections across different convergence intervals, confirming that malicious actors could embed covert channels into the adapted signal without alerting standard intrusion detection systems [34]. This threat intensifies in post-quantum contexts because an adversary able to break the encryption layer might remain undetected by manipulating the DSP layer at the same time, reinforcing conclusions about quantum-augmented hacking [13].  
Coherent optical networks are based on modern DSP algorithms, such as adaptive equalization, chromatic dispersion compensation, and polarization tracking. Figure 5 illustrates the DSP chain, showing two particularly vulnerable components: the adaptive  
Figure 4: Coherent optical module architecture based on OIF-400 ZR.  
equalizer and phase estimation blocks. Attackers can introduce carefully timed modifications to interfere with adaptive equalizer convergence during convergence periods. The phase estimation block is also vulnerable to attacks by means of carefully crafted phase alterations that can be introduced to create covert channels while appearing as normal signal variations. These vulnerabilities are especially critical because they capitalize on the inherently adaptive nature of coherent detection. For instance, an adversary may inject extremely small phase modifications during the time that the equalizer is converging and steer the adaptation process toward a compromised state that permits covert channels of communication. In performing the carrier phase recovery in the phase estimation block, attackers can corrupt the process to embed hidden data while preserving the overall normal operation. Such a sophisticated attack necessitates advanced optical signal analyzers and custom modification equipment capable of precisely controlling both the time and the amplitude of injected signal alterations.
## 4 Simulation Results
### Case Study 4: Maximum-Power-Output-Varying Condition  
In this study case, the focus is on testing the performance of the proposed method when the maximum power output points of RGs and RHDs are changed. In this paper, we let EOA4, EOA6 and HOA3 represent a photovoltaic, a wind generator and a solar water heater, respectively. Their maximum power output profiles are shown in Figure 8a. In this case, the scheduling cycle is set to 20 s, i.e., EOA4, EOA6 and HOA3 measure and reset their corresponding maximum power output point every 20 s. The initial values can be obtained by Equation (13). Then, the proposed method is used to find the optimal operating point during each scheduling cycle. In Figure 8b, it can be observed that, within each scheduling cycle, all of the electrical power incremental costs converge to a common value and all of the heat power incremental costs converge to another common value. That implies the optimization objective is achieved. It is worth noting that the electrical (or heat) incremental cost decreases as the total electrical (or heat) power outputs of EOA4 and EOA6 (or HOA3) increasing. That is because EOA4, EOA6 and HOA3 are renewable energy resources, which have lower cost compared with fuel energy resources. Moreover, Figure 8c shows that both the estimated electrical and heat power mismatches can also converge to zeros within each scheduling cycle, which indicates that the total electrical and heat supply-demand balance constraints are satisfied. In addition, the electrical  
Figure 7: Case study 3, plug and play test: (**a**) estimated electrical and heat incremental cost; (**b**) estimated electrical and heat power mismatch; and (**c**) electrical and heat power outputs.  
and heat power outputs are shown in Figure 8d. It can be seen that, within each scheduling cycle, each agent can find its corresponding operating point, which is limited in its feasible operating region. Based on the discussion above, it can be concluded that, by using the proposed method, the system can respond automatically to converge to new solution as the changes of the maximum power outputs of EOA4, EOA6 and HOA3. Therefore, this case study clearly shows that the proposed method can automatically adjust to meet the maximum-power-output-varying condition.  
Figure 8: Case study 4, maximum power output varying: (**a**) maximum power output; (**b**) estimated electrical and heat incremental cost; (**c**) estimated electrical and heat power mismatch; and (**d**) electrical and heat power outputs.
## 3 Results and Discussion
### Minerals in Carrot Powder (CP) and Pumpkin Powder (PP)
#### 3.1.1 Macroelements  
Figure 2 shows the macroelements content of vegetable powders. The macronutrient content in CP was Na \(>\) Ca \(>\) K \(>\) Mg, while in PP, it was Na \(>\) K \(>\) Ca \(>\) Mg. [PERSON] et al. [42], investigating the mineral profile of carrot flour, found the highest K content in orange, red, and black carrot flours. [PERSON] et al. [43] identified higher contents of Ca (880 \(\pm\) 3.0 mg/100 g vs. 301.88 \(\pm\) 4.95 mg/100 g), Na (615.0 \(\pm\) 4.0 mg/100 g vs.  
530.83 \(\pm\) 9.07 mg/100 g), K (532.7 \(\pm\) 0.2 mg/100 g vs. 208.01 \(\pm\) 5.78), and Mg (171.7 \(\pm\) 0.2 mg/100 g vs. 108.29 \(\pm\) 3.23 mg/100 g) in comparison to our study. The differences in mineral content may be due to the carrot variety included as study material, the soil profile on which the carrots were grown (soil type, soil mineral content), and environmental factors [44, 45]. The content of Ca and Mg in PP was 179.08 \(\pm\) 3.59 mg/100 g and 90.21 \(\pm\) 0.02 mg/100 g and was similar to the values of these elements in 'Gomez' pumpkin determined by [PERSON] et al. [46] (119.08 \(\pm\) 0.17 and 111.11 \(\pm\) 0.19 mg/100 g dm). These authors also report that the content of Ca and Mg varies in pumpkin depending on the variety and can range from 92.12 \(\pm\) 0.19 mg/100 g in Hokkaido pumpkin to 264.89 \(\pm\) 0.59 mg/100 g dm in Yellow Melon pumpkin (Ca) and 97.97 \(\pm\) 2.18 mg/100 g dm in Blue Kui pumpkin to 135.35 \(\pm\) 0.08 mg/100 g dm in Bamboo pumpkin (Mg) [46]. In the case of Na, the determined content (775.94 \(\pm\) 10.52 mg/100 g) was more than twice as low as determined by [PERSON] et al. [47]. The same authors indicate a higher Mg content in PP (146 mg/100 g) compared to our study. In contrast, [PERSON] et al. [48] indicate a similar Mg content (94.5 mg/100 g) as in our study. Comparing the two powders, PP contained higher Na and K quantities, while CP contained Ca (_p_ < 0.05). According to the literature, the Ca content of PP is significantly lower than that of CP [49, 50].
## 4 Simulation Results
### Fiber Distribution  
In the following sub-section, the fiber distribution extension is discussed. For this use case, it was assumed that the quantum signal was not detected in the OGS site, where it is captured by the telescope, but it was further transmitted via a low-loss SMF. To investigate whether the remaining link budget allows for fiber transmission, additional loss was added to the modeling, which was attributed to the fiber distribution segment. Figure 12 depicts the calculated SKR over a single pass for (a) a 1 m telescope receiver, (b) a 2.3 m telescope receiver equipped with SNSPDs, and (c) a 1 m telescope receiver with SPAD detection. The space-based source iQBER was once again set to 1.5%, while the number of transmitted bits was set to \(N_{trans}=10^{10}\). For the fiber coupling, tip-tilt correction was considered. As expected, larger apertures yield higher link budgets, enabling the transmission of collected single photons over larger fiber distances, without the need for a trusted node in the OGS position. It was evident that, as shown in Figure 12b, key rates of up to 1 kbps can be achieved when the satellite reaches the maximum elevation angles, with an additional 20 dB penalty attributed to the fiber segment (i.e., up to about 100 km of fiber distribution assuming an ideal fiber segment with no connection or bending losses). In general, for the three different assumptions of Figure 12, namely (a), (b), and (c), the key rates dropped to zero if the additional fiber loss exceeds 20 dB, 30 dB, and 7 dB, respectively. It is crucial to target a minimum value for the average required SKR on Bob's side in order to determine the end-to-end link budget.