esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 4 Results
### Papers Descriptions
#### 4.1.3 Lstm  
Twenty-seven articles that used LSTM technology for stock and Forex prediction are briefly described below. Table 4 shows the author, variables, dataset, and model of the papers mentioned. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference No. ** & **Author** & **Dataset** & **Variables** & **Model** \\ \hline
[32] & [PERSON], [PERSON]. & \ \begin{tabular}{c} Shares MSCI United Kingdom \\ \end{tabular}  & \begin{tabular}{c} Close price \\ Open price, high price, low price, \\ close price, adjusted close price, \\ volume, volatility, Williams \(\%\)R, \\ and RSI \\ \end{tabular}  & LSTM \\
[33] & [PERSON] & \begin{tabular}{c} Skp 500 \\ \end{tabular}  & \ \begin{tabular}{c} Open price, high price, low price, \\ close price, adjusted close price, \\ volume, volatility, Williams \(\%\)R, \\ and RSI \\ \end{tabular}  & LSTM \\  & & Microsoft (MSFT), PO logistics & \begin{tabular}{c} Open price, high price, low price, \\ close price, AD, AD, ADK, EMA, KAMA, \\ MA, MACD, RSI, SAR, AMA, etc. \\ and finance tweet sentiment \\ \end{tabular}  & Attention-based LSTM \\
[34] & [PERSON]. & \begin{tabular}{c} Attention-based LSTM \\ \end{tabular}  \\  & &[PERSON] proposed an LSTM model and compared it with the ANN model, Support Vector Regression (SVR) model, and RF model. The results showed that the LSTM model performed better in the prediction of the closing prices of iShares MSCI United Kingdom than the other models mentioned in paper [32]. [PERSON] proposed an LSTM model; his result showed an improvement using LeakyReLU. The Mean square error (MSE) was below 0.1, whilst compared with the ReLU function, the MSE was well above 0.2. He examined the effect of the Relative Strength Index (RSI), Williams %R, and volatility on the loss of the model. It was shown that the model's loss was reduced by using only RSI, which contributed to the performance of the model [33]. [PERSON] proposed an attention-based LSTM model that performs better than the regular LSTM. It was found that finance tweets that were posted from market closure to market open the next day had more predictive power on the next day's stock movement. The weighted sentiment on the max follower on StockTwits also performed much better than other methods [34]. [PERSON] proposed an LSTM model combined with crude oil price, gold price, and moving average, which performed much better than the LSTM model without them and the SVM model. It showed that the crude oil and gold prices had some impact on stock price prediction [35]. [PERSON] proposed an LSTM model that outperformed the LR and SVR models. He also compared the different activation functions with different optimizers and concluded that the tanh activation with the Adam algorithm performs best with an accuracy of 98.49% [36].
## 4 Discussion
From our analysis, we can infer that, during geomagnetic storm periods, there is a strong likelihood of simultaneous irregularity manifestation in both the bottomside and topside ionosphere. These appear as SF in the bottomside and steep Ne gradients in the topside, as detected in Swarm data. Contributing factors to these disturbances include increased thermospheric density, enhanced prompt penetration electric fields (PPEFs) [31], and potentially strong sub-auroral polarization streams (SAPs) at auroral latitudes [31].
# UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging
###### Abstract  
With the development of our society, unmanned aerial vehicles (UAVs) appear more frequently in people's daily lives, which could become a threat to public security and privacy, especially at night. At the same time, laser active imaging is an important detection method for night vision. In this paper, we implement a UAV detection model for our laser active imaging system based on deep learning and a simulated dataset that we constructed. Firstly, the model is pre-trained on the largest available dataset. Then, it is transferred to a simulated dataset to learn about the UAV features. Finally, the trained model is tested on real laser active imaging data. The experimental results show that the performance of the proposed method is greatly improved compared to the model not trained on the simulated dataset, which verifies the transferability of features learned from the simulated data, the effectiveness of the proposed simulation method, and the feasibility of our solution for UAV detection in the laser active imaging domain. Furthermore, a comparative experiment with the previous method is carried out. The results show that our model can achieve high-precision, real-time detection at 104.1 frames per second (FPS).  
laser active imaging; object detection; transfer learning +
Footnote †: journal: Applied sciences
# Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction
## 2 Materials and Methods
### Building a Fusion Model
#### 2.3.1 Dynamic Prediction and Incremental Training Mechanism
This is achieved by adding a cross-constraint term to the overall loss function:  
\[L_{total}=L_{pred}+\lambda_{cross}{\sum_{i<j}}\Big{|}\big{|}F_{i}-F_{j}\big{|} \big{|}_{2}^{2} \tag{13}\]  
where \(L_{\text{pred}}\) is the primary prediction loss, \(\lambda_{\text{cross}}\) is a regularization parameter controlling the strength of the cross-constraint, and the sum \(\sum_{i<j}\big{|}\big{|}F_{i}-F_{j}\big{|}\big{|}_{2}^{2}\) computes the pairwise Euclidean distance between feature vectors from different submodels, encouraging them to maintain a consistent representation of the input data. The overall training process integrates the mechanisms described above into a unified framework. The dynamic prediction mechanism continuously evaluates prediction uncertainty, triggering local retraining if needed. The adaptive learning rate adjustment and time-decay sample weighting strategies ensure that the incremental training phase effectively incorporates the most recent data. Simultaneously, multistrategy fusion with cross-constraints aligns feature representations across submodels, leading to a more robust final prediction. The advantage lies in synthesizing the diverse perspectives of each submodel on the data. Through weighted combination, the model can flexibly adapt to different prediction scenarios. This integrated approach ensures that the model continuously learns from new data while maintaining consistency across multiple submodels. The combination of these innovations results in a robust, adaptive, and high-precision yield prediction system tailored for the dynamic environment of RFFE chip production testing.
## 2 Materials and Methods
### Dataset and Preprocessing
#### 2.2.1 Proposed Approaches  
Five deep learning architectures were evaluated: AlexNet, GoogLeNet, VGG16, ResNet50, and ViT. For transfer learning, pretrained weights from ImageNet, which is a 100-category object classification dataset, were used during training. As CNN models have been extensively used in previous studies, this study focuses primarily on describing the ViT.  
The AlexNet architecture consists of eight layers, including five convolutional layers and three fully connected layers [30]. The network primarily comprises convolutional activation function layers, pooling layers, local response normalization, and fully connected layers.  
GoogLeNet is a deep neural network model based on the inception module to address challenges such as overfitting, computational complexity, and gradient vanishing that arise as the network depth and width increase. The inception module enhances the receptive field by combining various convolutional kernels and overlapping outputs, thereby improving robustness. Multiple versions of GoogLeNet have been developed (e.g., V1-V4) [31]. For this study, the V1 version was selected to facilitate comparison with other models.  
ResNets are renowned for their ability to improve accuracy with deeper layers, making them a powerful choice for computer vision tasks and recognition applications. The ResNet architecture employs residual learning to address the degradation problem associated with deep networks. Among the available versions, ResNet50 was utilized in this study for its balance between depth and computational efficiency [32].  
The VGG model, developed by a group at Oxford University, is a renowned algorithm for large-scale image recognition [33]. Its performance improves as the network depth increases. The network uses only 3 \(\times\) 3 convolutional kernels (with a maximum pooling size of 2 \(\times\) 2). In this study, the VGG16 model was employed, comprising 13 convolutional layers, five pooling layers, three fully connected layers, and a softmax activation function for classification.  
The ViT has been evaluated as an effective classification model by directly processing image patch sequences through a transformer architecture. Its relatively simple structure and excellent performance and scalability render it highly suitable for computer vision applications (Figure 3).  
Figure 3: ViT framework.
# Design of Tracking, Telemetry, Command (TT&C) and Data Transmission Integrated Signal in TDD Mode
## 4 Performance Analysis of the TT&C Signal and Data Transmission Signal
### The Design of the Simulation Scenario  
The design of the simulation scenario comes from the satellite toolkit (STK). For intersatellite links, we set the maximum distance and relative velocity between different nodes as 783 km and 602 km/h, respectively. From [31], The relationship between satellite's transmitting signal power and receiving power is shown in (11).  
\[P_{R}=P_{T}+G_{T}+G_{R}+20\lg\left(\frac{\lambda}{4\pi d}\right)-L_{A} \tag{11}\]  
Here, \(P_{R}\) and \(P_{T}\) is the received signal power and transmitted signal power. \(G_{R}\) and \(G_{T}\) is the antenna gain at receiver and transmitter. \(\lambda\) is the wavelength. \(d\) represents the distance between the receiver and transmitter. \(L_{A}\) is the atmospheric loss, which can be ignored for intersatellite links.  
We set the satellite's transmitting power \(P_{T}\) is approximately 10 W, and the satellite's antenna gain \(G_{T}\) is less than 37.1 dB. The receiver antenna gain \(G_{R}\) is 0. The Ka band is used for the intersatellite links [6; 7; 44]. The carrier-to-noise ratio (CNR) of the receiving end is higher than 55 dB-Hz. The trajectory in this process is obtained by the STK simulation, as shown in Figure 4a. The orbit parameters of satellites are shown in Table 1. Figure 4b) shows the distance between the visible satellites in adjacent orbital planes. The value changes following a cosine curve in the range of [482.293 km, 783.774 km].  
\begin{table}
\begin{tabular}{c c} \hline Satellite number & 3364 \\ Orbit inclination & 51\({}^{\circ}\) \\ Orbit altitude & 508 km \\ Orbital plane & 58 \\ The satellites in each plane & 58 \\ Number & 3364 \\ \hline \end{tabular}
\end{table}
Table 1: The orbit parameters of satellites.  
Figure 4: Simulation scenario design. (**a**) Satellite orbit. (**b**) The distance between visible satellites (24 h). (**c**) Aircraft scenario.  
The inter-aircraft link scenario is shown in Figure 4c. The range between different nodes are less than 40 km, and the trajectory includes uniform motion, acceleration motion and S-curve motion. However, the carrier-to-noise ratio between UAVs is higher than the satellites, and the result is better than the satellite scene. Therefore, we only give the simulation results of the satellite scene. And the simulation results of aircraft scenes in some environments are given in Appendix A.
###### Abstract  
This study presents surface roughness modeling for machined parts based on cutting parameters (spindle speed, cutting depth, and feed rate) and machining vibration in the end milling process. Prediction models were developed using multiple regression analysis and an artificial neural network (ANN) modeling approach. To reduce the effect of chatter, machining tests were conducted under varying cutting parameters as defined in the stable regions of the milling tool. The surface roughness and machining vibration level are modeled with nonlinear quadratic forms based on the cutting parameters and their interactions through multiple regression analysis methods, respectively. Analysis of variance was employed to determine the significance of cutting parameters on surface roughness. The results show that the combined effects of spindle speed and cutting depth significantly influence surface roughness. The comparison between the prediction performance of the multiple regression and neural network-based models reveal that the ANN models achieve higher prediction accuracy for all training data with R = 0.96 and root mean square error (RMSE) = 3.0% compared with regression models with R = 0.82 and RMSE = 7.57%. Independent machining tests were conducted to validate the predictive models; the results conclude that the ANN model based on cutting parameters with machining vibration has a higher average prediction accuracy (93.14%) than those of models with three cutting parameters. Finally, the feasibility of the predictive model as the base to develop an online surface roughness recognition system has been successfully demonstrated based on contour surface milling test. This study reveals that the predictive models derived on the cutting conditions with consideration of machining stability can ensure the prediction accuracy for application in milling process.  
artificial neural network; cutting parameters; machining vibration; regression analysis; surface roughness Article
# Laboratory Measurements of Zeta Potential in Fractured
## 3 Results and Discussion
### Effect of Rock Type and Mineralogy
All in all, our experimental results demonstrated that all zeta potentials of Lewisian gneiss, saturated with low- to high-salinity aqueous solutions consisting of a single salt or a mixture of different salts, were unique. The main constituent minerals of this rock type are quartz, feldspar and mica, so the electrochemical response to ionic species in tested solutions is expected to reflect the individual contribution of each mineral. Since there are no reported zeta potential measurements of feldspars and micas in contact with various aqueous solutions at equilibrium conditions, we hypothesize that the electric surface charge on these complex minerals is controlled by multi-valent PDIs, similar to the mechanism of the surface charge development on calcite surface (e.g., [58]). Therefore, the resulting surface charge and the corresponding zeta potential of gneiss-hosted feldspars in our experiments caused the zeta potential to become less negative compared with pure quartz systems. Micas in contact with aqueous solutions ([55; 56]) were reported to produce more negative zeta potential compared with feldspars ([54; 57]). At the same time, the negative zeta potentials measured with micas were smaller in magnitude compared with negative values on silica surface, consistent with a published study of silica and mica minerals in contact with \(10^{-3}\) M KCl solution [56]. Moreover, previously published studies also reported a shift in the zeta potentials towards more positive values with solutions containing divalent cations relative to NaCl/KCl measurements for all types of minerals, with a more pronounced shift reported with micas [59] and feldspars (comparing the results obtained with albite in contact with \(10^{-3}\) M NaCl in [PERSON] et al. [60] vs. \(10^{-3}\) M CaCl\({}_{2}\) in [PERSON] et al. [61]) compared with pure quartz (e.g., [62]). Hence, the zeta potential in our experiments responded to changes in chemical composition in a fashion similar to that of calcite/mica/feldspar and became more positive with divalent cations, while the electric charge on the gneiss-hosted quartz surface sites was mostly controlled by pH, thus, remaining insensitive to changes in the electrolyte composition. Moreover, the uniqueness of the gneiss zeta potentials was demonstrated by the impact of varying ionic strength, which appeared to be less pronounced than that obtained with either carbonates or pure sandstones (\(>\)95% quartz). Both the impact of composition and ionic strength on the zeta potential in gneiss samples will be discussed in more detail in the subsequent section.
## 5 Conclusions  
In this paper, two NMF-based feature extraction methods are proposed for weakly supervised SED. Inspired by the NMF applications in the acoustic signal processing systems capable of analyzing the frequency characteristics of the acoustic signals, the proposed methods were designed to extract features from heterogeneous database for weakly supervised SED. To generate the frequency basis matrix, the class-wise data matrices were composed from strongly- and weakly-annotated data. The class-wise frequency basis matrices were estimated using the NMF algorithm with the KLD, and then cascaded to compose the whole frequency basis matrix. In the iterative feature extraction method, the temporal basis matrix was calculated via iterations of the NMF equations using the whole frequency basis matrix. Moreover, we developed a non-iterative feature extraction method using a least-squares solution of the NMF problem. The classifier was constructed based on the mean-teacher model for the proposed features and enhanced by the proposed event-wise post-processing method.  
To evaluate the proposed data-dependent feature extraction methods, simulations of weakly supervised SED were performed using the DCASE 2020 Task 4 database. The proposed methods were compared with the conventional features, e.g., Mel-spectrogram, log-Mel spectrum, gammatonegram, and CQT. Although the proposed features did not outperform other features, they yielded results comparable to those of the Mel-spectrogram and gammatonegram, which are state-of-the-art features. Moreover, they demonstrated 3-5% better F1-score performance than the log-Mel-spectrum and CQT.  
Conceptualization, S.L., S.P., and Y.J.; methodology, S.L., validation, S.L., M.K., and S.S.; and data curation, M.K. and S.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (N0. 2017-0-00050, Development of Human Enhancement Technology for auditory and muscle support.  
Not applicable.  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://project.inria.fr/desed/dcase-challenge/dcase-2020-task-4/](https://project.inria.fr/desed/dcase-challenge/dcase-2020-task-4/)]([https://project.inria.fr/desed/dcase-challenge/dcase-2020-task-4/](https://project.inria.fr/desed/dcase-challenge/dcase-2020-task-4/)).  
The authors declare no conflict of interest.
## 5 Conclusions  
The SecPri-BGMPOP strategy aims, security, and privacy protection have been the subjects of distinct research endeavours in the past. The purpose of this essay was to provide big data analytics with security while maintaining privacy. The Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, it is enlarged by employing a piece of the magnifying bit string to generate a safe key; pinpointing-based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid Particle Swarm and Grey Wolf Optimisation (HPSGWO). Our proposed method is currently kept in a cloud environment, allowing analytics users to utilise it without risking their privacy or security. We compared the technique based on Boost Graph Convolutional Network Clustering to the current FCM approach and created Boost Graph Convolutional Network Clustering to show that it has delivered superior results. Examining memory usage and execution time is part of measuring performance. Our SecPri-BGMPOP approach achieves an efficiency of 58.130 s with an information loss of 0.024%. The encryption and decryption times are 0.0086 s and 0.315 s, respectively. Confirming the experiment's results, it was found that the SecPri-BGMPOP strategy significantly cuts down on memory usage and computation time. We intend to present additional real-world uses for the SecPri-BGMPOP scheme in the future and contrast it with other encryption techniques. In order to accelerate the encryption and decryption processes even more, we also aim to improve the SecPri-BGMPOP scheme.  
M.K. Conceptualization; J.P.A. initial draft of manuscript; H.-S.G. Experimental design and Methodology; B.P.K.: Validation and Analyses; J.S. Review; W.-C.L. Editing., Software. All authors have read and agreed to the published version of the manuscript.  
Department of Electrical Engineering, Ming Chi University of Technology, Taiwan.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare that they have no potential conflicts.
## 4 Experiments
### Data Collection and Tracking  
The experimental procedure involved recording videos of drosophilas' flight from two orthogonal views, utilizing high-speed cameras. This dual-view setup allowed for comprehensive coverage of the flight trajectories, capturing the complex and dynamic movements of the drosophilas within the cubic container. The recorded videos served as the primary data source for our tracking system, which applied advanced computer vision and machine learning techniques, including Mask-RCNN, for detection and segmentation and a self-attention-enhanced particle filter for dynamic state estimation.  
To enhance the performance of our Mask-RCNN model, initially pre-trained on the COCO dataset [15], we selected a random subset of 100 frames from the recorded videos for manual annotation. Each frame was meticulously annotated to identify between 150 and 200 drosophila targets along with their corresponding masks, creating a custom dataset tailored to our specific tracking needs. This annotated dataset was used not only for fine-tuning the Mask-RCNN model through transfer learning but also as the ground truth for validating the detection results. Importantly, 10% to 15% of the drosophilas in each frame were partially occluded. The dataset was partitioned into 70% for training, 20% for validation, and 10% for testing, ensuring the rigorous assessment and optimization of the model's performance.  
The combination of backlit illumination, precise camera calibration, and high-frame-rate recording was instrumental in achieving high-quality video data. These data were subsequently processed to extract accurate 3D trajectories of the drosophilas, demonstrating the effectiveness of our tracking methodology in capturing the intricate details of their flight patterns. This comprehensive approach, from detailed annotation to targeted training of the detection model, significantly enhanced the model's capability to accurately recognize and segment drosophilas even in challenging scenarios characterized by partial occlusions and rapid movements.