esa-sceva/satcom-chunk-collection huggingface dataset:
# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
###### Abstract  
This paper proposes a novel Sea Drift Trajectory Prediction method based on the Quantum Convolutional Long Short-Term Memory (QCNN-LSTM) model. Accurately predicting sea drift trajectories is a challenging task, as they are influenced by various complex factors, such as ocean currents, wind speed, and wave morphology. Therefore, in a complex marine environment, there is a need for more applicable and computationally advanced prediction methods. Our approach combines quantized convolutional neural networks with Long Short-Term Memory networks, utilizing two different input types of prediction to enhance the network's applicability. By incorporating quantization techniques, we improve the computational power and accuracy of the trajectory prediction. We evaluate our method using sea drift datasets and AUV drift trajectory datasets, comparing it with other commonly used traditional methods. The experimental results demonstrate significant improvements in accuracy and robustness achieved by our proposed Quantum Convolutional Long Short-Term Memory model. Regardless of the input mode employed, the accuracy consistently surpasses 98%. In conclusion, our research provides a new approach for sea drift trajectory prediction, enhancing prediction accuracy and providing valuable insights for marine environmental management and related decision-making. Future research can further explore and optimize this model to have a greater impact on marine prediction and applications.  
Long Short-Term Memory Network; quantum convolution; trajectory prediction 2023  
[PERSON], [PERSON], [PERSON], M.M.; [PERSON] Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model. _Appl. Sci._**2023**, _13_, 9969. [[https://doi.org/10.3390/app13279969](https://doi.org/10.3390/app13279969)]([https://doi.org/10.3390/app13279969](https://doi.org/10.3390/app13279969))
## 2 Natural Polyphenols Directly Inhibiting Transmembrane Glucose Transport
### Green Tea Extracts with Inhibitory Effects on Glucose Uptake and Output  
Catechins, including epicatechin gallate (ECG) and epigallocatechin gallate (EGCG), are major active green tea polyphenols with remarkable inhibitory activity against GLUT1 in cancer cells (Table 1) [17; 18]. ECG and EGCG inhibit GLUT1 activity through direct binding to the transporter [31, 32]. [PERSON] et al. reported that green tea catechins could significantly reduce the enzymatic efficiency of GLUT1 in human choriocarcinoma BeWo cells [55]. Using three-dimensional topological analysis, [PERSON] et al. discovered a putative binding site of the green tea catechins on the intracellular side of the transmembrane protein GLUT1 [31]. This binding site consists of amino acids Arg126-Thr30-Asn288 and Arg126-Thr30-Asn29 that are close to each other in the three-dimensional structure of GLUT1. A more recent study further revealed that the binding region of the three green tea catechins has a hydrophobic cavity consisting of residues Ile164, Val165, Ile168, Phe291, Phe379 and Glu380 [56]. The binding of green tea polyphenols to GLUT1 could alter its substrate recognition in either a competitive or non-competitive manner. For the inward transport, the association of ECG, EGCG and quercetin (Figure 2) with the extracellular side of GLUT1 could competitively block glucose binding to GLUT1; thus, these catechins are competitive inhibitors of glucose uptake [56, 57, 58]. In contrast, for the outward transport of glucose, the green tea polyphenols quercetin was demonstrated to exert non-competitive inhibition through attaching to GLUT1, overlapping with the binding cavity of cytochalasin B (CB), another GLUT1 inhibitor [56]. Kinetic analysis also showed that green tea polyphenols decreased both the Km and Vmax values of GLUT1 [55], implicating that its modulation was unlikely through simple competitive or non-competitive inhibition. Figure 1: Metabolic pathways active in cancer cells are directly inhibited by naturally occurring compounds.
# Bounding Volume Hierarchy-Assisted Fast SAR Image Simulation Based on Spatial Segmentation
## 5 Conclusions  
We propose a BVH-assisted fast SAR image simulation method based on spatial segmentation and build a simulation architecture to realize fast SAR image simulation of distributed complex targets, including the simulation of EM wave transmission and the calculation of a multiple backscatter field The method utilizes the BVH algorithm to assist the EM wave transmission simulation and then adopts the ray tracing method for backward multiple scattered field calculation. Then, an efficient CUDA kernel function is designed based on the spatial segmentation theory and the echo matrix cell method, regarding each cell as a subfield of the total backscatter field. The corresponding two-dimensional envelope window of each cell is used as a constraint, effectively reducing the workload of the single-thread execution task. Finally, based on the CUDA platform,  
Figure 14: Simulation results of the Quest Kodiak 100. (**a**) Quest Kodiak 100. (**b**) Comparison of simulated and real SAR images of the Quest Kodiak 100.  
the target backscatter field is quickly obtained by traversing the positions of all cells of the echo matrix in parallel, and the simulated echo is processed by the RD imaging algorithm to obtain the target SAR simulated image. The test results show that the method proposed in this paper can effectively improve the simulation efficiency under the premise of ensuring the fidelity of the simulated SAR image. With the rapid development of the hardware acceleration platform, the following research will be carried out based on a high-performance parallel hardware platform and integrated spatial iterative segmentation, which will further improve the practical value of SAR image simulation technology.  
Conceptualization, [PERSON] and G.J.; methodology, [PERSON]; software, K.W.; validation, K.W.; formal analysis, K.W. and G.J.; investigation, K.W.; resources, [PERSON]; data curation, K.W. and G.J.; writing--original draft preparation, K.W.; writing--review and editing, K.W. and X.X.; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, G.J. and Q.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 42201492 and the 2022 Transportation Research Funding Plan of Henan under Grant 2022-3-2.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors would like to express their sincere thanks to the members of the editorial team and the anonymous reviewers for their guidance and valuable comments on this article.  
Author [PERSON] was employed by the company Henan Jingwei Beidou Navigation Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Proposal Architecture and Its Deployment
### Detailed Architecture Description  
Our architecture integrates MQTT with the TOR network to provide enhanced privacy and anonymity. The key components of our system are:  
* MQTT Clients: IoT devices or applications that publish or subscribe to MQTT topics.
* TOR Network: Provides anonymity by routing traffic through multiple relays.
* MQTT Broker: Handles message routing between publishers and subscribers, now accessible as a TOR hidden service.
* VLANs: Used to segment and secure local network traffic before it enters the TOR network. The communication flow in our architecture is as follows:
* MQTT clients connect to the TOR network using a TOR proxy.
* The MQTT broker is set up as a TOR hidden service.
* MQTT messages are encrypted and routed through the TOR network.
* The MQTT broker receives messages via its hidden service address.
* Messages are then distributed to appropriate subscribers, maintaining end-to-end encryption and anonymity.  
The following architecture, which is based on free software, is integrated with the necessary parts and protocols according to the proposal.  
TOR allows one to deploy onion services, accessible only through TOR, combining HTTPS security with the TOR browser's privacy advantages. They offer numerous privacy and security benefits, shielding IP addresses and preventing impersonation. Traffic to onion services is encrypted from client to host, akin to robust SSL/HTTPS protection. Additionally, they bypass firewall restrictions by punching through NAT, eliminating the need for open ports. The protocol for onion services employs the TOR network to facilitate client introduction to the service and establish rendezvous points, ensuring anonymity and security. This involves the service setting up introduction points, publishing descriptors, and clients verifying onion address signatures. Finally, clients establish rendezvous points, allowing end-to-end encrypted communication between client and service, with the entire connection routed through a series of relays for location hiding.
## 3 Results and Discussion
### Four-Point Bending Fatigue Test
#### 3.3.3 Fatigue Equation Considering Failure Probability
As shown in Table 11, the determination coefficients \(R^{2}\) under the failure probability of CNFPUC were above 0.90, indicating that the linear relation of the \(N_{f}\) double-logarithm equation of equivalent fatigue life under the two parameter Weibull distribution was basically valid. The equivalent fatigue life of CNFPUC under failure probability \(N_{f}\) decreased with the increase of the stress level and temperature. Based on the above analysis, a \(P\)-\(S\)-\(N\) fatigue equation considering the failure probability can be obtained by taking the corresponding coefficient in Table 11 into account. After the survival rate is determined according to the reliability requirements in engineering practice, the fatigue life of the CNFPUC bending fatigue with different survival rates at the corresponding ambient temperature and the stress level can be generally predicted.
# Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction
###### Abstract  
Intermittency of electrical power in developing countries, as well as some European countries such as Turkey, can be eluded by taking advantage of solar energy. Correct prediction of solar radiation constitutes a very important step to take advantage of PV solar panels. We propose an experimental study to predict the amount of solar radiation using a classical artificial neural network (ANN) and deep learning methods. PV panel and solar radiation data were collected at Duzce University in Turkey. Moreover, we included meteorological data collected from the Meteorological Ministry of Turkey in Duzce. Data were collected on a daily basis with a 5-min interval. Data were cleaned and preprocessed to train long-short-term memory (LSTM) and ANN models to predict the solar radiation amount of one day ahead. Models were evaluated using coefficient of determination (R\({}^{2}\)), mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean biased error (MBE). LSTM outperformed ANN with R\({}^{2}\), MSE, RMSE, MAE, and MBE of 0.93, 0.008, 0.089, 0.17, and 0.09, respectively. Moreover, we compared our results with two similar studies in the literature. The proposed study paves the way for utilizing renewable energy by leveraging the usage of PV panels.  
renewable energy; solar energy; artificial neural network; deep learning; LSTM; radiation prediction +
Footnote †: journal:  
Article
# Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments
## 5 Experiments and Results
### Results and Discussion
#### 5.3.2 Experiments in Noisy Environments
These results demonstrated the effectiveness of the attention mechanism in replay detection. The experimental results also demonstrated that 100 was a good choice for the prefixed frame length. The detection methods that used the 100 frame segments performed better than with 200 or 300 frames in all the three SNR environments and for the AB-LSTM algorithm. The experiments also demonstrated that bagging using different lengths of inputs performed better than those using simple averaging. In addition, with the decrease of SNR, the EERs of discrimination also decreased, which indicated that the intensity of the noise affected the accuracy of the replay detection. As can be seen from Tables 4-7 and Figure 8, EERs did not show a significant decreasing trend with the increase in SNR. The reason for this phenomenon may be that the additional noises caused more serious interference with the original speech signal. However, we found that EERs obtained by AB-LSTM in this study were significantly better than the CQCC-GMM baseline method and traditional  
Figure 8: The average EER values of CQCC-GMM, traditional LSTM, and AB-LSTMs at different SNR levels. LSTM in noisy environments. The algorithm proposed in this paper deserves further study in replay attack detection.
# Performance Testing and Evaluation of Drum-Type Stem-Separation Device for Pepper Harvester
## 2 Materials and Methods  
### Clili Pepper Harvester  
The pepper harvester picked peppers from the stem by rotating the open helix of the picking parts in the opposite direction while the pepper harvester was moving. The harvested peppers and debris were placed on a conveyor, run through a stem-separation device that separates the peppers from stems, and then stored in the final collection tank after removing debris with an air-wash. The mid-size pepper harvester was composed of a travelling part, a stem-separation part, a debris-sorting part, and a collecting part. The size of length, width, and height of the pepper harvester was 4380 mm, 1850 mm, and 1780 mm, respectively, and its weight was 2020 kg. The pepper harvester used in this study is shown in Figure 1, and the detailed specifications are listed in Table 1.  
The drum-type stem-separation device was located at the upper part of a conveyor, and it was driven by deceleration and acceleration according to the gear ratio in a belt power transmission method connected to the engine; the compositions of the 3-shaft drum and the 4-shaft drum are shown in Figure 2. Peppers and stems were separated by the friction of the brush as the stem-attached peppers passed between the drum, with a number of cylindrical brush teeth attached in the circumferential direction and the drum rotating in the opposite directions. According to the order of conveyor transfer, the first drum was drum shaft 1(A), and it was followed in order by shaft 2(B), shaft 3(C), and shaft 4(D).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Data** \\ \hline Power (kW) & 22.5 \\ Weight (kg) & 2020 \\ Standard working speed (m/s) & 0.1–0.3 \\ Rated revolution per minute (rpm) & 2600 \\ Rotational speed of picking part helix (rpm) & 650 \\ Separating method & Drum type and air fan \\ Collecting tank (kg) & 220 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the mid-size chili pepper harvester.  
Figure 1: 3D design (**a**) and picture (**b**) of the prototype pepper harvester.  
Figure 3 shows the detailed dimensions of the stem-separation rotating drum and brush teeth. The distances between the centers of rotating drum A and the final drum (C or D) were 248 mm and 429 mm for 3-shaft and 4-shaft, respectively. The distance between the two drums was set to 5 mm so that the teeth brushes did not interfere with each other during rotation.
## 5 Parametric Study of the PMA Unit Cell
### Effect of Changing the Octagonal Ring Width  
Moreover, the effect of changing the octagonal width on absorption is presented in Figure 10a, where both resonance frequencies shift to higher as octagonal width increases. As octagonal width increases, the equivalent inductance increases, causing the lower resonance frequencies swings to higher and upper resonance remain unchanged.
## 3 Results and Discussion
### Hourly Daylighting Quantity
#### 3.2.2 Women's Prayer Hall Analysis  
Located on the mosque's first floor, the women's prayer area features a distinctive daylighting profile, significantly influenced by its architectural design. In the multi-lateral lighting scenario, the effect of the eastern exposure is evident. Illuminance near the external glazing reaches about 1470 lux during the Dhuhr prayer, creating a brightly lit environment. Moving 2 m from the interior glazing, illuminance moderates to around 790 lux in summer solstice and 563 lux in winter, displaying the diffused impact of zenithal light from the west. The area's uniformity index, with the highest value of 0.86, suggests a potential for even light distribution. The clerestory design, combining direct and diffused light, creates a nuanced illuminance gradient. Central areas register below 1000 lux, while the edges are brighter. This design maintains consistent light distribution, as indicated by a uniformity index above 0.6, except during winter solstice at Dhuhr, suggesting effective design implementation. Skylights offer a different lighting approach, with central illuminance often exceeding 2000 lux near the interior glazing. Areas near the eastern facade experience lower levels, below 923 lux in the morning. However, the uniformity index in these areas does not meet the minimum requirement, especially during Asr prayer, indicating uneven light distribution, which aligns with [PERSON] et al. in their findings that the design of a square skylight lets an excessive light into the various levels [59]. Integrating horizontal and vertical shading devices produces a complex range of illuminance, from 200 lux to 1000 lux. Notably, during Asr prayer, the Worst-Case Performance Index (WPI) drops below 200 lux. Nevertheless, this hybrid approach generally enhances uniformity index values, though WPI often exceeds recommended levels, potentially causing visual discomfort as shown in Figure 9. Overall, the women's prayer space in the mosque achieves varied illuminance levels in different scenarios. The uniformity index is a crucial indicator of the balance between brightness and even light distribution. The interplay of direct and diffused light in spaces like this prayer hall underscores the importance of architectural choices in enhancing user experience and comfort.
## 3 Neutral Formats for Data Exchange  
Neutral formats (often interchangeable terms such as standards, couplers, converters, norms, or translators are used) of data exchange ensure communication between systems of a similar class, such as CAx.  
These systems are often built on the basis of various representation schemes. The best-known translators are DXF, IGES, and STEP [5; 34; 49].
## 3 Materials and Methods
### Image Pre-Processing
#### 3.4.2 Image Resizing and Normalization  
To standardize the dataset and optimize model performance, all images were resized to \(244\times 244\) pixels. This resolution was chosen to balance computational efficiency and feature preservation. Resizing ensures that images maintain a uniform scale, preventing size discrepancies from affecting the feature extraction process.  
Additionally, pixel intensity values were normalized to the range [0, 1] to improve the stability of deep learning models. Normalization is essential for CNN-based models, as it allows faster convergence during training and reduces sensitivity to illumination variations.  
These preprocessing steps collectively improve the clarity and consistency of the dataset, facilitating more accurate feature extraction and classification.
# FMS Scheduling under Availability Constraint with Supervisor Based on Timed Petri Nets
###### Abstract  
This paper proposes an optimal solution to large-scale Flexible Manufacturing System (FMS) scheduling problems under availability constraints based on Timed Petri Nets (TPNs). First a decomposition method of TPNs is proposed, then a mathematical model is derived based on their properties. The mathematical model is built to determine the optimal firing sequence of TPN transitions to minimize the total manufacturing time. The resulting firing sequence of TPN transitions is used to generate the manufacturing system supervisor operated by TPN and digital controllers. Several numerical examples and comparative studies are provided in this paper in order to prove the new approach's efficiency.  
scheduling problem; timed Petri net; discrete event system; supervisor synthesis; mathematical model; genetic algorithm Article
# Directed Network Disassembly Method Based on Non-Backtracking Matrix
## 4 Method
### Algorithm Complexity  
The time complexity of the approximate feature vector is equal to the number of iterations \(\tau(m)\) multiplied by the product of matrix \(H\) and vector \(v\), namely \(O\big{(}\tau(m)m^{2}\big{)}\), where \(m\) is the number of network edges.  
The complexity of performing a bisection for the entire network is \(O\big{(}m^{2}\tau(m)\big{)}\). The complexity of performing another bisection on the two modules with an approximate size of \(m/2\) after the division is \(2\cdot O\Big{(}\big{(}\frac{m}{2}\big{)}^{2}\tau(m)\Big{)}\). The complexity of another bisection for the four modules with an approximate size of \(m/4\) after division is \(4\cdot O\Big{(}\big{(}\frac{m}{4}\big{)}^{2}\tau(m)\Big{)}\). Until \(O(GSC)=1\), the complexity of another bisection for \(m/2=2^{\log_{2}(m)-1}\) modules with an approximate scale of 2 after division is \(2^{\log_{2}(m)-1}O\Big{(}\big{(}\frac{m}{2^{\log_{2}(m)-1}}\big{)}^{2}\tau(m )\Big{)}\). The total time complexity is as follows:  
\[\begin{split}& O\Big{(}m^{2}\tau(m)\Big{)}+2\cdot O\bigg{(} \big{(}\frac{m}{2}\big{)}^{2}\tau(m)\Big{)}+4\cdot O\bigg{(}\Big{(}\frac{m}{4} \big{)}^{2}\tau(m)\Big{)}\\ &+\ldots+2^{\log_{2}(m)-1}O\bigg{(}\bigg{(}\frac{m}{2^{\log_{2}( m)-1}}\bigg{)}^{2}\tau(m)\bigg{)}\\ &=\sum_{i=0}^{\log_{2}(m)-1}2^{i}O\bigg{(}\Big{(}\frac{m}{2^{i}} \Big{)}^{2}\tau(m)\bigg{)}=O\Big{(}m^{2}\tau(m)\Big{)}\left(\sum_{i=0}^{\log_{ 2}(m)-1}1\right)^{2}\\ &=O\Big{(}m^{2}\tau(m)\log^{2}(m)\Big{)}\end{split} \tag{7}\]  
The computational complexity of the dismantling recursive algorithm is \(O\Big{(}m^{2}\tau(m)\log^{2}(m)\Big{)}\). For a sparse network, \(\tau(m)=\log(m)^{1+\varepsilon}\) at moment \(\varepsilon>0\) and there is an upper bound \(1/\log\Big{(}\big{|}\frac{\varepsilon}{A_{3}}\big{|}\big{)}\)[14]; therefore, a better dismantling effect can be obtained. The computational complexity is \(O\big{(}m^{2}\log(m)^{3+\varepsilon}\big{)}\) at this moment.  
In the algorithm, the space required for each non-backtracking matrix is \(O\big{(}m^{2}\big{)}\), the recursive depth is \(O(\log(m))\), and the required space complexity is \(O\big{(}m^{2}\log(m)\big{)}\), where \(m\) is the number of network edges.