esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Architecture
In each trainer, the dataset is trained using a different algorithm generating the respective models (6), which are then sent to the collector. The collector replaces the models in the classifiers with the new generated models (7) and compares the models to send to the IDS of companies the one with the best accuracy and precision (8). The master's internal procedures for classification, voting and dataset generation are depicted in Figure 6. The master sends the multiple logs received from the companies to the classifiers (2) to be categorized by different algorithms. The machine learning models classify the logs based on the training, and the respective classification of each model is sent to the majority voting system (3), that will output these logs classified by the majority. Then, the winning logs are sent (4) to a data assembler, where a classified dataset with recent logs is generated. After generating the dataset, the master requests a new training of the machine learning models (5) with the generated dataset. The models are then sent to the collector and comparator (6); it replaces models in the classifiers (7) and deploys the best models (8) in the IDS of the companies. In particular, the training, selection and deployment stages comprehend three phases, depicted in Figure 7. In phase 1, the new classified dataset is trained by different algorithms  
Figure 5: Master internal procedures. in the trainers to generate new machine learning models. In phase 2, the new models are deployed in the classifiers. The new machine learning models are used in the voting system that classifies the most recent records received from the companies as shown previously in Figure 6. Finally, in phase 3, the model with better accuracy and precision is sent to the IDSs of companies to increase their performance in detecting attacks. The proposed architecture assumes the regular training of different machine learning models, which is a time- and resource-consuming task. In order to be implemented, scalable testbeds and parallel processing tools can be used. Figure 6: Master internal procedures for classification, voting and dataset generation.
## 2 Related Works
### Negative Sampling Methods  
During model training, a negative triplet needs to be generated for each positive triplet. KGE models establish the loss function by calculating the scoring functions of the two triplets, so high-quality negative triplets give the model a better generalization ability. Therefore, some works have explored different negative sampling methods to obtain high-quality negative triplets for the link prediction.  
The existing negative sampling is divided into two main categories: fixed distribution sampling and dynamic distribution sampling [38]. The previous sampling works, such as the TransE model, use a uniform random sampling method to generate negative samples,  
Figure 2: Example of complex relationships. The left side is the 1-to-N relationship type, the right side is the N-to-1 relationship type.  
which uses any entity in the entity set to randomly replace the head entity or tail entity in the triplet with equal probability. However, this method is overly simple, and the space of the sampling is fixed and large. The possibility of getting high-quality negative samples is small, which will lead to an inability to update the entity vectors and relation vectors effectively when training the KGE models. In addition, there would be a greater possibility of getting a false negative triplet, i.e., a generated negative triplet that happens to be a positive triplet present in the knowledge graph, which is not conducive to model training. The TansH model uses the Bernoulli sampling method, which aims at reducing false negative triplets by replacing the head entity or tail entity of the triplets with different probabilities. However, the sampling method still samples from a fixed space.  
Inspired by the generative adversarial network (GAN) [39], some works have attempted to generate high-quality negative triplets using an adversarial training framework. IGAN [40] and Kbgan [41] both introduce GAN for negative sampling in KGE; when GAN is applied to the negative sampling, it not only generates high-quality negative triplets but also dynamically adapts to the new distributions through model training. However, GAN has instability and is prone to large deviations during experiments. To solve this problem, IGAN and Kbgan are trained by adding many parameters, which raises the cost of model training and increases the difficulty of training. NSCaching [38] has fewer parameters than both IGAN and Kbgan. It stores negative triplets directly by setting the cache and dynamically updates the cache during the training process to contain more high-quality negative triplets. These negative sampling methods mentioned above, although helping to improve the performance of KGE, do not take into account the different mapping properties between entities when negative sampling.
# DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation
## 3 Domain Analysis and Definition of Conceptual Model
### Conceptual Model  
In order to achieve the prediction of waterway depth, we have designed, evaluated, and verified the proposed DDTree model. First, the AIS data must go through preprocessing and abnormal data detection and other methods to remove redundant and abnormal data (see Figure 2). There are many errors, omissions, and noises in AIS data. The use of erroneous data for calculation or analysis may lead to a wrong conclusion. Therefore, it is critical to preprocess the data and delete the obvious noise information, such as latitude 89\({}^{\circ}\), negative longitude, and the data whose MMSI is not nine digits. At the same time, some redundant information should also be deleted; for example, AIS dynamic information has much duplicate data. Then, the AIS data and waterway depth data are combined to build a deep neural network dataset. After training datasets and verification datasets are divided, the network structure is adjusted and the depth data are predicted by the combination of a neural network and a decision tree. Finally, the prediction performance, accuracy, is evaluated.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**MMSI** & **Longitude/(\({}^{\circ}\))** & **Latitude/(\({}^{\circ}\))** & **SOG/(\(u\) Mile/h)** & **COG/(\({}^{\circ}\))** & **UTC** \\ \hline
413769173 & 114.3079 & 30.58732 & 6.0 & 33.8 & 2016/4/23 1:08 \\
413769173 & 114.3102 & 30.59013 & 6.0 & 31.6 & 2016/4/23 1:10 \\
413769173 & 114.3113 & 30.59168 & 6.1 & 34.0 & 2016/4/23 1:11 \\
413769173 & 114.3124 & 30.59305 & 6.1 & 32.0 & 2016/4/23 1:12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fragment of automatic identification system (AIS) data: Maritime Mobile Communication Service Identification (MMSI) code is a unique identifier for ships. SOG is the speed of the ship to the ground, COG is the heading of the ship to the ground, and UTC is the current time.  
Figure 1: Waterway depth data (ground truth) from: (**a**) electronic chart display and information system (ECDIS) depth data; (**b**) berth map of depth.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
###### Abstract  
Remote pulse rate measurement from facial video has gained particular attention over the last few years. Research exhibits significant advancements and demonstrates that common video cameras correspond to reliable devices that can be employed to measure a large set of biomedical parameters without any contact with the subject. A new framework for measuring and mapping pulse rate from video is presented in this pilot study. The method, which relies on convolutional 3D networks, is fully automatic and does not require any special image preprocessing. In addition, the network ensures concurrent mapping by producing a prediction for each local group of pixels. A particular training procedure that employs only synthetic data is proposed. Preliminary results demonstrate that this convolutional 3D network can effectively extract pulse rate from video without the need for any processing of frames. The trained model was compared with other state-of-the-art methods on public data. Results exhibit significant agreement between estimated and ground-truth measurements: the root mean square error computed from pulse rate values assessed with the convolutional 3D network is equal to 8.64 ppm, which is superior to 10 ppm for the other state-of-the-art methods. The robustness of the method to natural motion and increases in performance correspond to the two main avenues that will be considered in future works.  
remote photoplethysmography; convolutional neural networks; pulse rate +
Footnote †: journal: Applied Sciences
## 5 Discussion
Also, it is important to note that there were instances where the model failed to segment images or produced low-quality results. These failures can occur at the dataset level, where all models struggled to segment certain datasets, as seen with the SIJ dataset. Additionally, failures can be specific to a particular model; for example, the regression model produced significantly lower-quality segmentation for the Knee dataset compared to other models. These issues could be attributed to factors such as dataset complexity, the number of samples, and the models' capabilities. In conclusion, this study explored the effects of multiple annotations and dropout rates on uncertainty segmentation using three deep learning architectures across four datasets. We assessed the influence of multiple annotations on segmentation models using public datasets and analyzed the impact of dropout rates on MC models. Additionally, we examined uncertainty quantification across different body parts and image types. The study evaluated the performance of three widely used deep learning architectures in an unbiased setting, utilizing various metrics to quantify the performance of uncertainty estimation methods, including a gray-level generalization of the Dice coefficient introduced in this work. The main conclusion of this paper is that the choice of uncertainty estimation method, whether MC dropout or ensembles, and the specific MC dropout rate, does not significantly impact performance. Similarly, the study design--whether using multiple or single annotations--appears to have little effect on the accuracy of uncertainty segmentation. Based on our findings, we recommend that simpler experimental designs, such as using a single annotation per image and either MC dropout or ensembles, can be used interchangeably for assessing segmentation uncertainty. Conceptualization, N.J. and Z.T. ; methodology, N.J.; software, N.J., M.S. and Z.T. ; validation, N.J. and Z.T. ; formal analysis, N.J.; investigation, N.J.; data curation, W.W., I.K., M.R., K.K., P.M. and J.J.; writing--original draft preparation, N.J.; writing--review and editing, M.S., W.W., I.K., M.R., K.K., P.M., J.J. and Z.T. ; visualization, N.J.; supervision, Z.T. All authors have read and agreed to the published version of the manuscript. This research was funded by National Science Center, Poland, grant number is AR-TIQ/0004/2021. Not applicable  
Data Availability Statement: Data is contained within the article. We gratefully acknowledge the funding support by the program \"Excellence Initiative--Research University\" for the AGH University in Krakow and the ARTIQ project: UMO-2021/01/2/ST6/00004 and ARTIQ/0004/2021. The funding source was not involved in planning, conducting the research, or preparing the article. We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2023/016441.
## 5 Conclusions  
In this paper we propose a case study on the applicability of NNs in hyper-constrained devices. We identify a relevant image classification task, which mirrors as faithfully as possible many real world scenarios where AI is required. We then consider a set of NN architectures--ranging from well-established heavy and general models, to custom-made small and specific models--and embed such NNs in a microcontroller. The chosen hardware is specifically selected to be as constrained as possible, mirroring the focus on costs saving that characterise the design process of low-end appliances.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Model** & **Accuracy** & **Average Accuracy** \\ \hline \multirow{4}{*}{SOTA-based} & \(1\times\) MobileNet\({}_{4}\) & 83\% & \multirow{4}{*}{79.75\%} \\  & \(0.5\times\) MobileNet\({}_{4}\) & 76\% & \\  & \(1\times\) ResNet\({}_{3}\) & 74\% & \\  & \(0.5\times\) ResNet\({}_{3}\) & 86\% & \\ \hline \multirow{4}{*}{Custom} & \(1\times\) SimpleNet & 75\% & \multirow{4}{*}{66.5\%} \\  & \(1\times\) XSimpleNet & 67\% & \\ \cline{1-1}  & \(0.5\times\) XSimpleNet & 73\% & \\ \cline{1-1}  & \(1\times\) Light-XSimpleNet & 50\% & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Best performing models where selected and tested over online images, not belonging to the test set.  
The extracted results show that state-of-the-art NNs can not be deployed successfully on such constrained devices. However, SOTA-based and custom-made NNs can be embedded effectively on the microcontroller at hand, showing acceptable accuracy. While being mostly accurate, such models are still characterised by long inference times, which often do not cope well with real-time applications. On the other hand, those NNs suit well scenarios where a one-off prediction is required. The complexity and power-hungriness of NNs represent strong limitations of AI for low-end embedded hardware. Therefore, a research effort is required in the field of NNs simplification in order to enable the deployment of AI-based solutions on hyper-constrained devices.  
Conceptualization, A.A.; Methodology, A.A.; Software, A.A.; Supervision, A.O.; Writing--original draft, A.A.; Writing--review & editing, A.A. All authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by the H2020 Project \"StairwAI\" (G.A. 101017142) and by Electrolux Professional (Italy).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Definition and Design of the System's Components
#### 2.2.1 LED Driver
Consequently, the bandwidth of the IR HSDL-4250 LED and its LED driver, based on 2 equalization and 2 amplification stages, exceeded 20 MHz, as demonstrated in Figure 10. This effectively complies with the 10 MHz requirement of the IR channel. Figure 8: Simulated frequency response of the proposed LED driver based on two amplification and two equalization stages.
# A Room Monitoring System Using Deep Learning and Perspective Correction Techniques
## 5 Experimental Results
### Summary  
In our experiments, the MobileNet-SSD and YOLO v3 schemes exhibited great performance in simple situations. However, these two schemes showed unsatisfactory performance results and many false-negative detections in complicated situations such as crowds of people, small-sized people, and the variety of activities in the room. The Mask R-CNN scheme had better detection in small-sized people, but with many false-negative detections. The proposed Mask R-CNN + Tiles scheme achieved excellent performance, especially for small-sized people far from the camera. By applying the tiles scheme, small-sized people were detected more efficiently. Furthermore, two algorithms were proposed to merge the bounding boxes in the overlapping areas. In the room monitoring system, it is a common situation where the objects in the upper part of the image look small owing to their distance from the camera.
## 3 Results and Discussion
### Accumulation of Sinigrin  
Together with the analysis of the volatiles profile, the accumulation of GSLs in these materials of wall rocket was also evaluated. Sinigrin was identified as the main glucosinolate in the leaves of wall rocket and was, therefore, the target compound quantified in our HPLC analysis. The identification of sinigrin as the main GSL is in agreement with the previous description of the species [17; 18]. This aliphatic GSL is also of relevance in other Brassicaceae crops such as kale, horseradish or mustard [50; 51; 52], while its occurrence in the commercial rocket crops has not been described so far [8; 17; 53; 54]. The average value for the total of populations was 7.76 mg 100 g\({}^{-1}\) FW, while differences among them reached up to 13-fold. DER102 was the population with the lowest content (1.35 mg 100 g\({}^{-1}\) FW), while DER064 showed the highest value (18.18 mg 100 g\({}^{-1}\) FW) (Table 5). Nevertheless, and despite the broad range found among populations, the contents were significantly lower than reported by other authors, between 8.4 and 13.4 mg g\({}^{-1}\) DW [17; 18]. There are several factors that can affect the accumulation of glucosinolates in plant tissues including the growing conditions [55], and this may explain in part the differences found among the current study and previous reports. Thus, modifying culture practices may increase the levels of sinigrin as main GSL of wall rocket. In fact, we found that some populations used in the present study showed up to 15-fold increase when they were tested under field conditions [8]. Therefore, our results suggest that the conditions used in the present work could be adapted to increase the accumulation of sinigrin. Together with sinigrin, the identification of other ITCs in the volatile profile could indicate the co-occurrence of other minor GSLs in wall rocket, although it could not be detected in our chromatograms. Therefore, we considered clarifying this issue by performing an additional analysis with an equipment that could provide greater accuracy. Thus, two additional samples, which were grown as described in [PERSON] et al. [10] to increase levels of GSLs, were analyzed by the external service Analysis of Quality in Plants from the Mision Biologica de Galicia-Consejo Superior de Investigaciones Cientificas (MBG-CSIC) with ultra-high performance liquid chromatography (UHPLC). A total of six GSLs were identified in this analysis: glucoiberin, sinigrin, glucotropaeolin, glucobrassicin, neoglucobrassicin, and a sixth compound that may correspond to gluconasturtin/methoxyglucobrassicin -the peak could not be clearly identified at the low levels in which it was found, and may correspond to gluconasturtin, methoxyglucobrassicin or a mixture of both- (Figure 1). Glucotropaeolin and gluconasturtin derive in benzyl ITC and phenylethyl ITC,
# Covert Communication for Integrated Satellite-Terrestrial Relay Networks with Cooperative Jamming
### Motivation and Contributions  
Recently, the security of wireless communications in the ISTRN has gradually gained attention from scholars. In this article, we select the covert communication technique to solve the above problem, which is more direct and effective. Nevertheless, the existing related works are mostly based on terrestrial networks or use an individually deployed jammer. This motivated us to explore the covert performance of the ISTRN with relay-assisted cooperative jamming. Notably, the main features and contributions of this article are summarized as follows:  
1. Covert communication in the ISTRN with relay-assisted cooperative jamming is studied for the first time, where one full-duplex terrestrial relay is utilized to support communications and acts as a jammer to confuse eavesdroppers.
2. Based on the above, the closed-form expression of the PDE and the effective covert rate are deduced to quantify the system's covert performance.
3. Moreover, a power optimization algorithm is designed to explore how to enhance the effective covert rate under covertess constraints.
4. Subsequently, numerical simulation results are presented to observe the influence of relevant parameters on the covert performance. Finally, the observation results are analyzed and summed up in detail.
# Time-Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks
## 2 Method
### Generator Network Structure
To enhance the feature learning capability of the network on noisy data, we propose a residual shrinkage structure that incorporates the soft-thresholding function into the residual network. The soft-thresholding function is widely used in signal-denoising algorithms and shrinks the input data toward zero, using the following formula:  
\[y=\left\{ \begin{array}{l}x-\tau,x>\tau\\ 0\quad,-\tau\leq x\leq\tau\\ x+\tau,x<-\tau\end{array} \right. \tag{2}\]  
According to Equation (2), the derivative of the soft-thresholding function is either zero or one, which is beneficial for preventing vanishing or exploding gradients. As illustrated in Figure 3, the Residual Shrinkage Building Unit (RSBU) [28] adaptively learns the coefficient \(\alpha\) via a subnetwork and constrains the value of \(\alpha\) within the range \((0,1)\) using the \(\sigma\) activation function. The absolute value of the input feature map is then multiplied by \(\alpha\) to obtain an adaptive threshold. As shown in Figure 3, RSBU performs \(1\times 1\) convolution on the absolute value of the input feature \(x\) to extract effective features and output a vector \(\alpha\) ranging from (0,1) through the Sigmoid function. Then, \(x\) is multiplied by \(\alpha\) to obtain the threshold \(\tau\). \(x\) is compared with \(\tau\), and the output feature \(y\) is determined according to Equation (2). We adopt an identity mapping to directly add the output of the previous layer to \(y\) to obtain the final output feature, ensuring that the network can fully utilize the features of the previous layer.
# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 3 Results
### Precipitation Identification Model
In the area where the GPM IMERG product observed precipitation, the FY-4B/AGRI operational precipitation product did not observe precipitation, especially in the coastal area. Figure 6: Hour-by-hour results of precipitation identification model on testing dataset (**a**) FAR, (**b**) POD, (**c**) CSI and (**d**) ETS. Figure 7: Precipitation identification results from 0200 UTC to 0230 UTC on 18 August 2022: (**a**) GPM IMERG product, (**b**) FY-4B/AGRI operational precipitation product and (**c**) RF model. The retrieval results for the ocean and land are compared and analysed, respectively, as shown in Figure 9. The results show that the ocean has a better ability to identify precipitation than the land, and the ocean has a lower FAR score and higher POD, CSI and ETS scores. Figure 8: Precipitation identification results of 1500 UTC\(\sim\)1530 UTC on 9 August 2022: (**a**) GPM IMERG precipitation product, (**b**) FY-4B/AGRI operational precipitation product and (**c**) RF model inversion results. Figure 9: Evaluation indicators of ocean and land, (**a**) FAR, (**b**) POD, (**c**) CSI and (**d**) ETS. The land is subdivided according to different land cover types, and the precipitation identification results are evaluated, respectively, as shown in Figure 10. The results show that there is no significant difference in the evaluation indicators of the underlying surfaces of different land cover types, indicating the universal applicability of the model. In the day model, water bodies perform better. In the night model, woodland performs better, with a lower FAR score and higher POD, CSI and ETS scores.