esa-sceva/satcom-chunk-collection huggingface dataset:
# C1 DR-Net: Channel-Time Dense Residual Network for Detecting Crew Overboard Behavior
###### Abstract  
The efficient detection of crew overboard behavior has become an important element in enhancing the ability to respond to marine disasters. It remains challenging due to (1) the lack of effective features making feature extraction difficult and recognition accuracy low and (2) the insufficient computing power resulting in the poor real-time performance of existing algorithms. In this paper, we propose a Channel-Time Dense Residual Network (CTDR-Net) for detecting crew overboard behavior, including a Dense Residual Network (DR-Net) and a Channel-Time Attention Mechanism (CTAM). The DR-Net is proposed to extract features, which employs the convolutional splitting method to improve the extraction ability of sparse features and reduce the number of network parameters. The CTAM is used to enhance the expression ability of channel feature information, and can increase the accuracy of behavior detection more effectively. We use the LeakyReLU activation function to improve the nonlinear modeling ability of the network, which can further enhance the network's generalization ability. The experiments show that our method has an accuracy of 96.9%, striking a good balance between accuracy and real-time performance.  
crew overboard; CTDR-Net; DR-Net; CTAM; good balance +
Footnote †: journal: Applied Sciences
# A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data
## 6 Conclusions  
This article shows a novel Bayesian approach using linear regression to characterize neuroimaging data, tested in an animal model of schizophrenia. The proposed DBL-FS+EK model allowed us to efficiently work with neuroimaging data, characterized by a low sample-to-variable ratio. This is achieved by taking advantage of its Bayesian formulation to work in the dual space while learning a voxel importance for feature selection. Furthermore, the use of a specific prior to force sparsity can be combined with expert knowledge to guide the model. The proposed model was analyzed using MRI data from a rodent model of a schizophrenia database and compared to different baselines. The results provided an outstanding classification performance of DBL-FS+EK, improving the accuracy of the second best classifier, SVM, in \(\sim\)6%. Furthermore, looking at the selected voxels and their associated relevance, we can confirm that the proposed model is able to detect biologically relevant areas for the characterization of this disease, as it clearly agrees with known literature.  
Conceptualization, V.G.-[PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; software, [PERSON] and [PERSON]; validation, [PERSON]-[PERSON] and [PERSON]-L.; formal analysis, [PERSON]-[PERSON], [PERSON] and [PERSON]; investigation, [PERSON]-V., [PERSON] and [PERSON]; resources, M.L.S.-M.; data curation, [PERSON]-V. and M.L.S.-M.; writing--review and editing, all; visualization, all; supervision, [PERSON]-V., [PERSON] and [PERSON]; project administration, [PERSON]-[PERSON] and [PERSON]-[PERSON]; funding acquisition, V.G.-V., M.L.S.-M. and M.D. All authors have read and agreed to the published version of the manuscript.  
This paper is part of the project PID2020-115363 RB-I00 funded by MCIN/AEI/10.13039/501100011033. [PERSON]'s work is funded by the Community of Madrid through the \"Excellence of University Teaching Staff\" line of the Multi-year Agreement with UC3M (EPUC3M27), within the framework of the V PRICIT. [PERSON]'s was supported by Ministerio de Ciencia, Innovacion y Universidades, Instituto de Salud Carlos III (project number PI17/01766, and grant number BA21/00030), co-financed by European Regional Development Fund (ERDF), \"A way to make Europe\", CIBER de Salud Mental (project number CB07/09/0031), Delegacion del Gobierno para el Plan Nacional sobre Drogs (project number 2017/085); Fundacion Maptre and Fundacion Alicia Koplowitz. [PERSON]'s work was supported by Ministerio de Ciencia e Innovacion (MCIN) and Instituto de Salud Carlos III (ISCIII) (PT20/00044). The CNIC is supported by the ISCIII, the MCIN and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).  
Not applicable.  
Data Availability Statement: The dataset generated and analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
# An Adaptive Fast Incremental Smoothing Approach to INS/GPS/VO Factor Graph Inference
## 5 Conclusions  
This paper introduces an adaptive fast integrated navigation algorithm for INS/GPS/VO, employing a factor graph framework. The study assesses the algorithm's efficacy in managing dynamic changes in sensor effectiveness, handling sensor measurements with asynchronous and delayed presence, and optimizing the factor graph. The simulation results demonstrate that when the effectiveness of the sensor changes, it only needs to suppress or add its factor nodes into the factor graph model to achieve seamless switching of the integrated navigation system working mode, which makes the system highly robust. For asynchronous and delayed sensors, the factor graph algorithm directly defines received sensor measurements as factor nodes linked to the respective variable nodes in the factor graph. This approach eliminates the need to synchronize measurements from different sensors to the same time, thereby mitigating the introduction of additional human error. In comparison to traditional incremental smoothing factor graph optimization algorithms, the AFIS algorithm proposed in this paper significantly enhances optimization speed while maintaining comparable accuracy. The excellent scalability and flexibility of the factor graph model confers substantial advantages in addressing multi-sensor integrated navigation challenges. Consequently, the INS/GPS/VO integrated navigation algorithm, based on the factor graph, holds considerable reference value for diverse multi-sensor integrated navigation systems in various application environments.  
Conceptualization, Z.T.; methodology, Z.T.; software, Z.T. and S.Y.; validation, Z.T. and S.Y.; formal analysis, Z.T. and S.Y.; investigation, Z.T. and S.Y.; resources, Z.T., Y.C. and S.Y.; data curation, Z.T.; writing--original draft, Z.T.; writing--review and editing, Z.T., Y.C. and S.Y.; visualization, Z.T.; supervision, Y.C.; project administration, Y.C.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the National Key Laboratory on Blind Signal Processing, grant number 61424131903, and the National Natural Science Foundation of China, grant number U20B2067.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 3 Prediction and Future of EVs in the UK
### Accuracy of Models and Their Use  
In order to compare the above three models, the UK's actual and predicted EV sales forecasted by each model for the period 2015-2024 are compared in Figure 10. As seen in Figure 10, despite their methodological differences, the three models show similar predictions over 2015-2024 primarily due to three factors: (1) the strong upward trend in EV sales dominates the data, making it easier for all models to fit; (2) key drivers like charger density and time have high explanatory power across all models; and (3) the models were trained and evaluated on the same input features over a limited historical window. Divergences become more apparent in longer-term forecasts, where the structural assumptions of each model play a larger role.  
Furthermore, to evaluate how well each model fits the historical EV sales data from 2015 to 2024, we compute three standard metrics: Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R\({}^{2}\)). These metrics are calculated by comparing each model's predicted values to the actual observed EV sales  
Figure 10: Actual vs. predicted EV sales forecast in the UK (2015–2024) used for model comparison.  
over the same period, using the following equations. The resulting values are shown in Table 3, enabling direct comparison of model performance.  
\[\text{MAPE}=\frac{100\%}{n}\sum_{t=1}^{n}\ \left|\frac{y_{t}-\hat{y}_{t}}{y_{t}}\right| \tag{7}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{t=1}^{n}\ \left(y_{t}-\hat{y}_{t}\right)^{2}} \tag{8}\]  
\[\text{R}^{2}=1-\frac{\sum_{t=1}^{n}\left(y_{t}-\hat{y}_{t}\right)^{2}}{\sum_{t =1}^{n}\left(y_{t}-\overline{y}_{t}\right)^{2}} \tag{9}\]  
where \(y_{t}\) is the actual value, \(\hat{y}_{t}\) is the predicted value, \(\overline{y}_{t}\) is the mean of the actual values, and \(n\) is the number of time points.  
In addition, the near-perfect R\({}^{2}\) values reported in Table 3 also reflect the structure of the historical EV sales data, which is linked to the strong and monotonic upward trend from 2015 to 2024. This pattern is easily captured by all three models, especially given the dominance of explanatory features such as Year and Charger Density Index. Since the models were evaluated over a short, and hence relatively noise-free, time window, their fit to the observed data is naturally high. Moreover, the use of flexible models like XGBoost, which are capable of modeling complex nonlinearities, can further inflate R\({}^{2}\) unless regularized or tested on unseen data.
## 2 Overview of the Proposed Neural Networks and Theoretical Background
### Gated Recurrent Unit (GRU)  
The GRU model was developed to give solutions for the long-term dependency problem. A GRU network is created by incorporating a gating mechanism into a basic RNN, enabling it to regulate the flow of information within the neural network. This enhancement allows a GRU network to efficiently capture large step dependencies in time series data and address issues like gradient vanishing or exploding gradients during backpropagation. It employs two gates, the reset gate r, to decide from the previously stored data how successfully the new inputs should be combined, and the update gate z, to set the quantity of information from the past to be let through. In a GRU, the previous hidden state \(H_{k}-1\) and the current input \(x_{k}\) do not directly affect the current hidden state \(H_{k}\) as in the standard RNN. Figure 2 illustrates the general structure of the GRU [63; 64].  
The GRU activation is a linear combination of the previous state \(H_{k-1}\) and a candidate state \(\hat{\text{H}}_{k}\):  
\[\text{H}_{k}=(1-z_{k})*\text{H}_{k-1}+\text{z}_{k}*\hat{\text{H}}_{k} \tag{4}\]  
where \(z_{k}\) is called the update gate, which is activated by a sigmoid function:  
\[\text{z}_{k}=\sigma(\text{W}_{x}x_{k}+\text{W}_{\text{z}}\cdot\text{H}_{k-1}+b _{\text{z}}) \tag{5}\]  
The GRU candidate state is calculated as follows:  
\[\hat{\text{H}}_{k}=\tanh(\text{W}_{\text{b}}x_{k}+\text{W}_{\text{b}}\cdot( \text{r}_{k}*\text{H}_{k-1})+b_{\text{b}}) \tag{6}\]  
Figure 2: Structure of the gated recurrent unit (GRU).  
where \(r_{k}\) is the reset gate activated by a sigmoid function:  
\[\mathrm{r}_{k}=\sigma(\mathrm{W}_{\mathrm{r}}x_{k}+\mathrm{W}_{\mathrm{r}}\cdot \mathrm{H}_{k-1}+b_{r}) \tag{7}\]
## 4 Discussion
### Study Limitations  
It is acknowledged that results should be tempered by the limited analysis of data from a single individual. Given the unique nature of the recording setup, the fact that vocal fold collision characteristics were captured in the intraglottal pressure signal was encouraging. Introducing a probe into the glottis has the potential to disrupt the natural oscillatory mechanisms of the vocal folds. Figure 3 shows the size of the tip of the ISP probe in relation to the size of the glottis and surrounding vocal fold tissue; anterior and posterior glottal gaps may have been introduced due to the presence of the ISP probe. However, despite the introduction of the ISP probe, the study participant was still able to sustain a vowel during the endoscopic procedure and produce relatively stable phonation. Future work is needed to study the effects of varying degrees of loudness levels, pitch conditions, and glottal configurations leading to breathy, modal, and pressed phonation.  
Previous work with bench-top physical models of the vocal folds have suggested that caution should be used when interpreting waveform signatures of the intraglottal pressure sensor signal [12]. There could be some uncertainty with respect to whether the intraglottal sensor waveform in Figure 4 was actually from the vocal fold coming into contact with the sensor, or whether the sensor was slightly superior to the strike zone and actually sensing acoustic pressures. In addition, the pressure sensors exhibited signal drifts in the DC level that were also observed in previous studies [8; 9]. We believe this drift is primarily caused by thermal energy fluctuations in the glottis due to internal body temperature and the use of a bright xenon light source necessary for high-speed videoendoscopic imaging. Future work could address thermal energy changes by pre-heating the tip of the ISP probe in hot water to match the internal temperature of the glottis.
# Metal Surface Defect Detection Using Modified YOLO
## 3 The Proposed Method  
### Network Architecture  
YOLOv3 divides the input image into grids of different sizes by adding feature layers of different scales to improve the detection ability of targets of different sizes. However, there are a large number of small-sized targets for defects on the metal surface, and YOLOv3 is not sensitive to small target detection, and it is easy to cause missed detection. Therefore, targeted improvements to YOLOv3 are needed to improve the detection accuracy of small defect targets.  
Based on the YOLOv3 network structure, this paper incorporates a new scale feature layer to improve the detection of small defect targets. The improved network model is shown in Figure 2, and concat is a tensor splicing operation to expand the tensor dimension. The res-\(n\) represents a residual block containing n residual units, each consisting of two DBL units and a shortcut link. Among them, DBL consists of the convolution layer, the Batch Normalization layer, and the Leaky ReLu activation function.  
Figure 2: The improved YOLOv3 model.  
As is shown in the red box, the shallow output of the second residual block is merged with the deep output of the network after 2 times of upsampling in Darknet-53. Furthermore, then a new feature layer is formed through a convolutional layer with a convolution kernel size of \(1\times 1\), making the network more capable of extracting features. The size of the newly added feature layer is 1/4 the size of the input image, and the input image is divided into smaller 4 \(\times\) 4 grids, that is, the number of grids is 104 \(\times\) 104. A smaller grid makes the network more sensitive to small targets. The shallow features are merged with the deeper features output by Darknet-53 to generate a feature layer that is conducive to the detection of small targets. It not only inherits the deep features but also makes full use of the shallow features of the network to enhance the model's ability to extract small target features, reduce the probability of missed small defect targets and improve detection accuracy.  
The number of anchor boxes for each feature layer in the network is still 3, after adding a feature layer, the total number is increased from 9 to 12 to strengthen the detection density. The superposition of the number of feature layers can divide the size levels of defect targets, and enhance the network's comprehensive detection capabilities for different sizes of targets.
# Underwater Sea Cucumber Identification Based on Improved YOLOv5
## 3 Sea Cucumber Identification Network
### YOLOv5 Network Model  
The YOLOv5 network model is shown in Figure 5 and includes components such as Backbone, Neck, and Head. In the Backbone network of YOLOv5, the CSPDarknet53 structure is used as the basic network, and the main role of the Backbone network is to extract the basic characteristics of the input information [15,16]. The Neck network is located between the Head network and the Backbone network, and its main function is to further enhance the diversity of features to be extracted and the robustness of the network. The Head network function is the result of output detecting the target. The YOLOv5s model had three levels of direct, corresponding to three sets of initialized Anchor values. The Head network receives three feature maps of different scales, and then performs grid prediction on these three feature maps and uses convolution operation to obtain feature output [17]; and the detection was carried out by mainly identifying the three dimensions of large target, medium target, and small target [18].  
Compared with the previous generation YOLOv4, the First Step was to add the Focus structure to prevent information loss when downsampling. The Focus module is a slice of the image before it enters the backbone. The specific operation is to get a value for every other pixel in a picture, similar to proximity downsampling, so that four pictures are obtained, and the four pictures are complementary, but no information is lost. In this way, the horizontal and vertical information is concentrated in the channel space, and the input channel is expanded by 4 times, that is, the stitched picture becomes 12 channels compared to the original RGB three-channel mode [19,20]. Finally, the resulting new image is convoluted to obtain a double downsampled feature map without information loss.  
Figure 5: YOLOv5 network model: Image (**a**) is the CBL module, the CBL module consists of a Conv+BN+Leakyrelu activation function, Conv is convolution; image (**b**) Resunit borrowing from  
the residual structure of the Resnet network, the network can be built deeper; image (**c**) CSP1_n, borrowing from the CSPNet network structure, which is composed of CBL module, Resunint module, Conv and Concate, and image (**d**) CSP2_n, borrowing from the CSPNet network structure, which consists of Conv and \(n\) Resunint modules [21], Concat; image (**e**) is the Focus structure, which first Concats multiple slice results, and then feeds them into CBL module; image (**f**) uses the maximum pooling method of \(1\times 1\), \(5\times 5\), \(9\times 9\), \(13\times 13\) to perform multi-scale fusion [22].  
Taking YOLOv5s as an example, the original 640 \(\times\) 640 \(\times\) 3 image input Focus structure, using a slice operation, first becomes a feature map of 320 \(\times\) 320 \(\times\) 12, and then after a convolution operation, it finally becomes a feature map of 320 \(\times\) 320 \(\times\) 32 [23]. The slicing operation is shown in Figure 6.
## 3 Results
### Crop Classification
#### 3.2.2 Optimal Combination of Polarimetric Observables  
Figure 10 shows the RF overall accuracies for the optimal combination of polarimetric observables produced by a forward feature selection procedure using all multitemporal RADARSAT-2 images. A specific combination of eight groups (ND, FD, Ra, Pha, Ro, CP, RVI and Pauli) of polarimetric observables achieved the optimal crop classification accuracies in this study, with an OA of 93.75%. The corresponding optimal classification map is shown in Figure 11, which has better clustering patterns and less scattered errors than the maps in Figure 10. In addition, the usual combination of all features improved classification results in comparison with that using a single type of features alone. However, there were some combinations that produced better accuracies when using only two groups. This suggests that a reduced set of well-selected polarimetric features may generate better classification accuracies than using all features.
## 2 Experimental Setup  
Figure 1 displays a schematic of the test system. The motor mainly includes a hydrogen peroxide catalyst bed, a HTPB-based grain, a pre-combustion and a post-combustion chamber, and a Laval nozzle. The oxidizer feeding system is composed of a gas tank with high pressure, a hydrogen peroxide tank, a pressure regulator, hand valves, solenoid valves, pressure transducers, a mass flowmeter, a variable area cavitating venturi, and other components. A programmable logic controller (PLC) is employed to control the valves during the operation of the test. MultiscaleVoxel-1000 is used for the CT scans. A CMF025M/1700 flowmeter with a percentage error of 0.1% is adopted to measure the oxidizer mass flow rate. A CYB-601S thrust sensor is utilized to measure the engine thrust, and the measurement error is 0.1%. An electronic scale with an error of 0.1 g is utilized to weigh the mass of the motor before and after the experiment. A three-jaw caliper is adopted to measure the nozzle throat diameter before and after the experiment, and the measurement error is 0.01 mm. Table 1 shows the solid fuel formulas and nozzle material of the test motor. Table 2 lists the main design parameters of the test motor.  
Based on a rocket propulsion analysis (RPA), the theoretical vacuum impulse and density impulse for the four grains of aluminum particles with mass fractions of 38%, 48%, 58%, and 65%, respectively, are calculated and analyzed. The combustion chamber pressure is 2 MPa and the expansion ratio of the nozzle is 3. Figure 2 shows the calculated results of the theoretical vacuum impulse and density impulse. The specific impulse of the propellant combination of HTPB-based grains with aluminum particles and 95% hydrogen peroxide is significantly higher than that of the propellant combination of HTPB-based grains without aluminum particles or 95% hydrogen peroxide with the region of the oxygen-to-fuel ratio of the nozzle. The results of the theoretical vacuum impulse and density impulse are shown in Figure 2.  
being from 1 to 4. The specific impulse increases slightly with the increase in the mass fraction of the aluminum particles at lower oxygen-to-fuel ratios, while it decreases with the increase in the mass fraction of the aluminum particles at higher oxygen-to-fuel ratios. Apparently, the addition of aluminum particles reduces the optimal oxygen-to-fuel ratio of the propellant combination, resulting in a miniaturized oxidizer tank. As can be seen in Figure 2b, the increase in the mass fraction of the aluminum particles in the grain has a significant effect on the increase in the density-specific impulse in the region of an oxygen-to-fuel ratio from 1 to 4.
## 2 System Model
### LEO Satellite Mega-Constellation Network Mobility Calculation Model
#### 2.2.2. ISL Geometry Relationship of Iridium Constellation  
Communication between any satellite nodes in the Iridium constellation exists in two ISL geometry relationships: two communicating satellite nodes on the same orbit (case 1) and two communicating satellite nodes on adjacent orbital planes (case 2) [41].  
Case 1: ISLs between adjacent satellite nodes on the same orbital plane (see Figure 4).  
In Figure 4, \(r_{e}\) is the radius of the Earth, \(r_{s}\) is the orbital height, \(\theta\) is the angle between adjacent satellite nodes, and \(\varphi\) is the angle between \(OS_{0-0}\) and \(S_{0-0}S_{0-1}\), and then the distance of satellite node \(S_{0-0}\) and node \(S_{0-1}\) is:  
\[d_{00-01}=\frac{\alpha\times\sin\theta}{\sin\varphi}, \tag{5}\]  
Case 2: ISLs between adjacent satellite nodes on adjacent orbital planes (see Figure 5).  
Figure 4: ISLs between two satellite nodes on the same orbit.  
Figure 5: ISLs have the closest communication distance between adjacent orbital planes.  
The distance between \(S_{0-0}\) and \(S_{1-0}\) is:  
\[d_{00-10}=\frac{\alpha\times\sin\theta/2}{\sin\varphi}, \tag{6}\]