esa-sceva/satcom-chunk-collection huggingface dataset:
# Generating Scenery Images with Larger Variety According to User Descriptions
###### Abstract
Images of bird and flower images are synthesized based on human-written texts using a character-level text encoder and class-conditional GAN. It is the first end-to-end differentiable architecture from the character level to pixel level, which introduces a manifold interpolation regularizer for the GAN generator that significantly improves the quality of generated samples. In [27], a semantic compositional network is proposed for image captioning. The network uses a Long Short-Term Memory (LSTM) network and extends each weight matrix of the LSTM to an ensemble of tag-dependent weight matrices. In earlier research, the network architecture did not include training word embeddings. To address the research topic of the relationship between natural language and images, [28] proposed a network to extract visual descriptions that can represent images. To generate high-resolution photo-realistic images from text descriptions, it is not feasible to simply add more upsampling layers in the network. Therefore, StackGAN [29] used two stages of generative networks to first generate a low-resolution image conditioned on the text description, and then refine the results to produce a high-resolution image. Although some good results have been achieved, these studies are only conditioned on global sentence encoding when generating images. Without important fine-grained information at the word level, this degrades the quality of generated images when generating complex scenes. AttnGAN [30] improved the work in [29] by adding embeddings of both word-level and sentence-level descriptions and built an attention model to compute a region-context vector for each word. The work in [32] combined the method of reinforcement learning in the framework to give higher rewards to novel generated images with unseen features and to give lower rewards to repetitive and similar images. This work focuses on outdoor scenery image generation. To give the generated images more variety, the proposed system improves the architecture of AttnGAN [30] and adds an imagination mechanism in the framework to increase the diversity of the generated scenes. In this way, the variety of the generated images can be effectively increased. The rest of the paper is organized as follows. Section 2 elaborates the details of the proposed framework. Section 3 shows and discusses the experimental results. Finally, conclusions are made in Section 4.
# Current Applications of Ultrasound in Fruit and Vegetables Osmotic Dehydration Processes
## 5 Properties of the Product Treated with US before or during OD
### Functional Properties of the Product Treated with US before or during OD  
Fruits and vegetables are a good source of micronutrients and non-nutrient functional compounds, such as polyphenols, carotenoids, vitamins, minerals as potassium, calcium and magnesium and dietary fiber, which are known to have a benefit effect on the human health [116]. However, the application of US in combination with OD and/or drying could have an influence on functional compounds retention. Table 2 summarizes some of the recent findings regarding the effect of US on vitamin C, polyphenol content and other bioactive compounds, as well as antioxidant activity of different fruit and vegetables subjected to OD and/or drying process. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Product** & **Treatment Applied** & **Functional Properties after OD** & **Functional Properties after Drying** \\ \hline \multirow{6}{*}{Cranberries} & \multirow{6}{*}{UOD (21 kHz, 180 W, 30 and 60 min)} & Both pretreatments promoted a decrease in vitamin C, which was higher in BL+UOD samples. & \multirow{6}{*}{-} \\  & UOD (21 kHz, 180 W, 30 and 60 min) & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{6}{*}{[19]} & BL (90 \({}^{\circ}\)C for 5 min) + US (21 kHz, 200 min) + OD (72 h, 40 \({}^{\circ}\)C) & Combined treatment resulted in a better preservation of anthocyanins and similar retention of TPC, TF and vitamin C if compared to untreated samples. & \multirow{6}{*}{-} \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \multirow{6}{*}{[16]} & OD (40 \({}^{\circ}\)C, 2 h) & & Better preservation of total carotenoids was observed in UOD treated samples and dried at low temperature (55 \({}^{\circ}\)C). \\ \cline{1-1}  & UOD (35 kHz, 130 W, 200 mbar, 15 min) & & & \\ \cline{1-1}  & UOD (35 kHz, 130 W, 200 mbar, 15 min) & & & \\ \cline{1-1}  & & & \\ \hline \multirow{6}{*}{Garlic [48]} & US (40 kHz, 600 W, 40 min, water) & & All pretreatments increased the TPC, TFC, antioxidant activity and allicin content, following the trend: UVOD \textgreater{} UOD \textgreater{} US \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \end{tabular}
\end{table} 
Table 2: The functional properties of products osmo-dehydrated with the assistance of US.
# Study on Bridge Displacement Monitoring Algorithms Based on Multi-Targets Tracking
## 4 Evaluation
### Accuracy Experiment  
As shown in Figure 3, we used Photoshop to make the picture to simulate actual scenes. We first move the target boards in the graph by certain amounts of displacement, which is shown in Table 1. The program calculates the target boards' displacement values of two pictures before and after the displacement. The results of the operation are shown in Figure 4 (The first column represents the \(X\) coordinate of the displacement, the second column represents the \(Y\) coordinate of the displacement, and each row represents the target board number), reflecting that the accuracy of the algorithm can reach a sub-pixel level.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target Board Number** & **Initial Position (Pixel)** & **Shifted Position (Pixel)** & **Displacement (Pixel)** \\ \hline
1 & (110, 339) & (117, 346) & (7, 7) \\
2 & (169, 306) & (189, 306) & (7, 0) \\
3 & (286, 262) & (286, 269) & (0, 7) \\
4 & (383, 216) & (383, 209) & (0, 7) \\
5 & (484, 169) & (491, 169) & (7, 0) \\
6 & (575, 121) & (568, 128) & (7, 7) \\
7 & (680, 81) & (680, 81) & (0, 0) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Certain displacement values of movement (the leftmost target board is No. 1).  
Figure 3: Image that simulates real environments (seven target boards).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Size** \\ \hline Basler ace classic & acA2040-90 uc \\ Sensor & CMV4000 \\ Resolution (H\(\times\)V pixels) & 2048\(\times\)2048 \\ Frame rate (fps) & 90 \\ Mono/color & c \\ Bit depth & 8/12 \\ Interface & USB 3.0 \\ Pixel size (\(\mu\)m\({}^{2}\)) & 5.5\(\times\)5.5 \\ Sensor size (mm\({}^{2}\)) & 11.26\(\times\)11.26 \\ Optical size & 1” \\ \hline \hline \end{tabular}
\end{table}
Table 4: The parameters of the CCD camera.  
Figure 13: The CCD camera equipment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Theoretical Displacement (mm)** & **Monitoring Displacement (Left) (mm)** & **Monitoring Displacement (Right) (mm)** \\ \hline (0, 0) & (1.23, 0.02) & (0.08, 0.05) \\ (38.05, 191.35) & (39.78, 191.01) & (39.07, 190.89) \\ (146.45, 353.55) & (141.5, 354
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 4 Proposed Scheme-I (Key Recovery System Using Signcryption)
### KRA Fault Recovery Phase  
In this phase, if some KRAs fail to operate properly, the selected KRA or KRAs will instead perform key recovery as shown in Figure 7.  
* **Step 1:**\(Dev_{A}\) refers to the total number of KRAs \(n\) and the number of KRAs required for key recovery as \(mr\).
* **Step 2:**\(Dev_{A}\) calculates the number of KRAs \(t\) required to distribute \(TT_{i}\). \[t=n-mr\] (20)
* **Step 3:**\(Dev_{A}\) selects a KRA or KRAs to replace the failed \(KRA_{i}\) as follows: \[j=i-mr\] (21) \[KRA_{i}\rightarrow\left\{\begin{matrix}KRA_{i+1},KRA_{i+2},\ldots,KRA_{i+t} \ (i\leqq mr)\\ KRA_{i+1},\ldots,KRA_{n},KRA_{1},\ldots,KRA_{j}\ (i>mr\ and\ i\
eq n)\\ KRA_{1},KRA_{2},\ldots,KRA_{t}\ (i=n)\end{matrix}\right\}\] (22)
* **Step 4:**\(Dev_{A}\) distributes \(TT_{i}\) to selected KRA or KRAs.
* **Step 5:** If \(KRA_{i}\) fail to operate properly, the selected KRA or KRAs obtain \(c_{i},r_{i}\) and \(s_{i}\) of failed KRA using the distributed \(TT_{i}\) and \(SGN\). \[TT_{i}\oplus SGN =(Tc_{i}\parallel\Upsilon_{i}\parallel Ts_{i})\oplus SGN\] (23) \[=(c_{i},r_{i},s_{i})\]  
Figure 7: KRA fault recovery phase of proposed scheme-I.
# Covert Surveillance Video Transmission with QAM Constellations Modulation
## 2 Transmission Scheme and Hypothesis Test  
### Transmission Scheme  
Hierarchical modulations are initially proposed to provide different classes of data for users under different wireless signal reception conditions [37; 38]. In Figure 1a,b, 16\(-\)QAM and 64\(-\)QAM square constellations are provided. In the 16\(-\)QAM square constellation, the distance of each constellation point from the boundary of its decision region for the two most significant bits (MSBs) is either \"1\" or \"3\" [15]. The 16\(-\)QAM coordinates sequence is given as  
\[\mathbf{d}_{16-\mathrm{QAM}}=\begin{bmatrix}-3&-1&1&3\end{bmatrix}, \tag{1}\]  
and the coordinates of the constellation points of the in-phase (I) and quadrature-phase (Q) bits are selected from that sequence. In conventional coding schemes, constellation points are selected with equal probability [13]. In this work, we optimize the modulation probability of the constellation points. Furthermore, we can provide the general form of the coordinate sequence for QAM as  
\[\mathbf{d}=\begin{bmatrix}-N+1&-N+3&\cdots&N-3&N-1\end{bmatrix} \tag{2}\]  
and the value of the \(i\)-th symbol is given by  
\[\mathbf{d}[i]=-N+2i-1, \tag{3}\]  
where \(N\) is the number of in-phase (I) and quadrature-phase (Q) coordinate value sets of constellation points. Specifically, when employing 16\(-\)QAM, the parameter \(N\) is 4; meanwhile, when utilizing 64\(-\)QAM, the parameter \(N\) is 8. Commonly recognized signal modulation techniques encompass four distinct QAM constellations: 4\(-\)QAM (\(N=2\)), 16\(-\)QAM (\(N=4\)), 64\(-\)QAM (\(N=8\)), and 256\(-\)QAM (\(N=16\)).  
Assuming that the transmission probability of the constellation point in row \(j\) and column \(k\) is \(p_{j,k}\), the probability density function at the receiver can be expressed as  
\[P_{b}=\sum_{j=1}^{N}\sum_{k=1}^{N}p_{j,k}\cdot q_{j,k,b}(x,y), \tag{4}\]  
where  
\[q_{j,k,b}(x,y)=\frac{1}{2\pi\sigma_{b}^{2}}\Bigg{[}\exp\bigg{(}-\frac{(x+g_{b} \mathbf{d}[j]\delta)^{2}}{2\sigma_{b}^{2}}-\frac{(y+g_{b}\mathbf{d}[k]\beta)^ {2}}{2\sigma_{b}^{2}}\bigg{)}\Bigg{]}, \tag{5}\]  
and \(g_{b}\) denotes the channel gain coefficient, \(\sigma_{b}\) is the one-sided noise spectral density of the additive white Gaussian noise [15], and \(\beta\) denotes the amplitude gain of the transmitted symbols.  
When the encoded surveillance videos are transmitted through AWGN channels, they are susceptible to being corrupted by Gaussian noise. In Figure 2, the blue constellation points represent the original constellation, while the red constellation points depict the constellation points that are corrupted by noise.  
Assuming a surveillance video is transmitted with \(n\) symbols, the transmission rate can be measured with mutual information, which is given by  
\[R_{b}=\mathbb{I}(Z_{b}^{n};X^{n}), \tag{6}\]  
where \(Z_{b}\) denotes the symbols received by the receiver and \(X\) denotes the symbols sent by the transmitter.
# A Compact Printed Monopole Antenna for WiMAX/WLAN and UWB Applications
## 1 Introduction
The M element primarily produced the 2.45-GHz band, and the F element produced the 5.80-GHz band. The ground plate consisted of rectangular patches. The miniaturization of the antenna design reduced the internal space required for the instrument. In addition, the antenna was simple in structure, easy to manufacture, and realized ultra-wideband high-frequency characteristics, thereby  
Figure 1: Cable-free self-positioning seismic exploration system architecture. improving its ability to receive signals. This antenna proved to be suitable for the WLAN/WiMAX (5.15-5.35 GHz) high-frequency band. This article presents the detailed geometric configuration of the antenna and discusses some of its parameters, including the size of the radiation patch and ground patch. A comparison of this antenna with several antennas proposed in recent years for WLAN/WiMAX applications is presented in Table 1. Compared with the other antennas, it is apparent that the proposed antenna is very small and provides a very wide bandwidth at high frequencies, completely covering the WLAN frequency band. Although the relative bandwidth of an antenna in the high-frequency band outlined in a previous study [3] does not differ greatly from that of the design proposed in this paper, the current design has the advantages of a simple and compact structure, small volume, and easy manufacture for small-volume high-frequency antenna applications. The second part of the article covers the design of the antenna, the third part presents an analysis of the current distribution and parameters, and the fourth part presents the simulation and test results. Concluding remarks are provided in the fifth part.
## 5 The PMOD Scheduler  
The PMOD scheduler aims at dividing the overall tuple transmission into a set of communication steps, without re-positioning the tasks, so that the following characteristics are achieved.  
1. Each node receives tuples from only one other node. In other words, each node's tasks receive tuples previously processed by the tasks of only one other node. The communicating tasks are defined by the application's topology.
2. Load balancing is achieved.
3. The overall communication schedule is simple and fast, as it has to be implemented during runtime.  
In the big data literature, the latency of communication between two nodes is generally defined by their index difference. For two nodes \(n_{i}\) and \(n_{j}\), the communication latency \(\ell\) increases as the difference \(|i-j|\) becomes larger. In our example, if the tasks from node 5 need to send tuples to the tasks of node 0 or vice versa, we have the maximum possible latency of \(\ell=|5-0|\) or \(\ell=|0-5|=5\) time units. In our context, we use the term \"time units\" as a unit that measures the inter-node communication latencies. To organize the overall communication in a pipeline fashion, we first need to transform the class table (CT) into a table that defines the communication steps between different nodes. This is described in the following subsection, along with the theoretical approach of the communication cost.
## 4 Results and Analysis of Three-Dimensional Forward Modeling
### Single-Anomaly Model
#### 4.2.3 Individual Anomalies at Different Locations  
This study also investigated models where the anomalous body was positioned at different locations. Specifically, the anomaly was placed near the grounded wire (Figure 10a), close to the survey line (Figure 10b), and outside the survey line (Figure 10c), with all other parameters consistent with those in Figure 6c. Figure 11 presents the forward modeling results for these scenarios. The anomaly near the transmitter showed a minimal response, making it more challenging to detect. In contrast, anomalies near the inner or outer areas of the survey line yielded larger and distinctly opposite responses. According to electromagnetic field diffusion theory, if the anomaly is near the source, the primary field arriving at the anomaly is stronger, inducing a stronger secondary field. However, since the anomaly was farther from the receivers, the received response was weaker due to attenuation. Conversely, when the anomaly was closer to the receivers, the primary field was weaker, but the secondary field underwent less attenuation, resulting in a stronger response. Additionally, when the anomalies were located on the outer side of the survey line, the electric field and first-order differential were opposite to those on the inner side, due to variations in the spatial distribution of the electric field.
# Recent Development of Heavy Atom-Free Triplet Photosensitizers for Photodynamic Therapy
## 7 Concluding Remarks and Outlook  
In summary, the recent development of the small organic molecule triplet photosensitive (PSs) for photodynamic therapy (PDT) is introduced in this review article. We focused on the heavy atom-free triplet PSs because these organic compounds have the advantages of low cost of preparation and low dark toxicity. These features are beneficial for the application of these compounds as PDT reagents. The triplet PSs we introduced include those based on spin-orbit charge transfer ISC (SOCT-ISC), twisted \(\pi\)-conjugation framework-induced ISC, radical-enhanced ISC, and thionated carbonyl-induced ISC. The ISC mechanisms and molecular structure design rationales are discussed. We briefly introduced the study of the electron spin selectivity of the ISC of the triplet PSs with pulsed laser-excited time-resolved electron paramagnetic resonance (TREPR) spectroscopy. Moreover, the hydrophobicity is a disadvantage of these triplet PSs and it still needs to be addressed for clinical translation. The rigid molecular structures of triplet PSs can cause the issue of poor water solubility when applied in PDT and bioimaging. By attaching targeting groups to PSs, water-soluble and nonaggregating PSs can be developed and by introducing nano drug delivery systems, tumor imaging and therapeutic efficacy can be improved. This information is useful for the future design of new efficient triplet PSs suitable for PDT application.  
Literature survey and writing, X.X., K.Y., M.I. and J.Z.; supervision and writing, J.Z. All authors have read and agreed to the published version of the manuscript.  
J.Z. thanks the NSFC (U2001222), the State Key Laboratory of Fine Chemicals, the Fundamental Research Funds for the Central Universities (DUT22 LAB610) and the Department of Education of the Xinjiang Uyghur Autonomous Region for financial support.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 22: (**a**) TREPR spectra of the anthryl-Bodipy dyads and (**b**) observation of the three triplet states of \({}^{3}\)CT, \({}^{3}\)An and \({}^{3}\)BDP simultaneously for dyad **12**. IBDP refers to 2,6-diiodoBodipy; BDP refers to Bodipy. Reproduced with permission from reference [38], copyright 2019, American Chemical Society.
## 6 Conclusions  
In this paper, we propose the GDRL-SFCR method, which realizes end-to-end routing decision by jointly optimizing transmission delay and network load balancing while satisfying SFC constraints in low-orbit mega constellation satellite networks. The method models the routing process as an MDP and uses a graph neural network to transform node features and topological information in the satellite network into feature embeddings. It uses reinforcement learning to make routing decisions based on historical experience and the current network state, and it uses PPO to train agents to learn optimal routing policies. Extensive simulation results show that our proposed GDRL-SFCR algorithm outperforms other routing methods regarding end-to-end path delay, average network load, and service access success rate performance. Considering a real mega-constellation network scenario such as Starlink, the GNN module of the GDRL-SFCR method can be used to parse satellite orbit parameters and link status in real time, predict the dynamic connection windows of different satellites, and guide the DRL agent to plan low-latency paths in advance. Consid  
Figure 8: Average running time of different data flows for all algorithms. The red curve is the result based on the reinforcement learning algorithm (DQR), the green curve is the result of the traditional graph theory algorithm (SFC-APS), the magenta curve is the result of the algorithm proposed in this paper (DQN-LRR), and the blue curve is the result of the algorithm proposed in this paper (GDRL-SFCR). FN represents the total number of functional nodes in the end-to-end path SFC constraint.  
ering the computational and scalability challenges of the proposed method, it is possible to consider adding federated learning and lightweight model deployment strategies in the future to significantly reduce computational and communication overhead while ensuring routing in real-time. In addition, multi-agent joint learning is a promising approach to solving the routing decision problem of mega-constellation networks. In future work, we will consider using multi-agent reinforcement learning to study smarter routing algorithms.  
Methodology, Y.C. and L.W.; software, Y.C., D.C. and Z.L.; validation, Y.C., D.C. and Z.L.; writing--original draft preparation, Y.C.; writing--review and editing, L.W., Y.Z. and H.C.; supervision, Y.C., H.C., L.W., D.C., Z.L., Y.Z. and J.S.; funding acquisition, Y.Z. and H.C. All authors have read and agreed to the published version of the manuscript  
This work was supported by the National Key Research and Development Program of China (no. 2020 YFB1808004), in part by the Jiangsu Key Research and Development Program (no. BE2021013-2).  
Not applicable.  
The data are contained within the article.  
The authors declare no conflicts of interest.