esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background
### Controller Area Network (CAN)  
Controller area network (CAN) is the communication protocol of the vehicular network, which enables vehicle ECUs to communicate with each other via CAN messages [31]. The ID field in each CAN message is used to identify which signal is encoded in it, such as vehicle speed, acceleration, engine temperature, etc. In addition, the ID field represents the message transmission priority to address the conflict of multiple ECU nodes being able to send messages on the CAN bus simultaneously. The data field of the CAN frame represents the different payload data according to the CAN ID field. All of the ECUs are connected in a central CAN bus for information exchanges via broadcast communication. Any ECU node can obtain and send the messages on the bus. Although the broadcast communication mechanism of the CAN protocol is suitable for the resource-constrained CAV environment, it is vulnerable to malicious adversaries implementing attacks. In addition, the OBD system connected to the CAN bus is also a commonly attacked target. Considering the characteristics of the in-vehicle network, it is suitable for the anomaly detection system deployment to detect anomaly behaviors by collecting the information of various ECUs on the CAN bus as it would not interfere with the normal communication of the CAN bus.
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 4 Results
### Lake Variability Correlation with GPS Signal  
The maximum and minimum epochs of the gauging data series Chico and O'Higgins in Chile, and Viedma and La Leona in Argentina are coincident with some delays. The influence of lake variability on the GPS signal are shown in Table 5 and Figure 7. The Pearson's correlation coefficients between the seasonal component of the vertical displacement time series and the level variations in the two lakes exceed 67% in GBGR-CS, while VOGH yielded at Chico 48% and at O'Higgins 64%, respectively. For the CHLT station, the correlation of the seasonal component with respect to gauge data does not exceed 22%. The highest correlation was observed at lake Viedma, and Chico achieved the lowest. The long period component of the signal in CHLT is dominated by the linear component and, in the seasonal variation found, the annual signal does not predominate over the interannual ones, as in the other stations.
# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
###### Abstract  
Accurate segmentation of skin lesions is still a challenging task for automatic diagnostic systems because of the significant shape variations and blurred boundaries of the lesions. This paper proposes a multi-scale convolutional neural network, REDAUNet, based on UNet3+ to enhance network performance for practical applications in skin segmentation. First, the network employs a new encoder module composed of four feature extraction layers through two cross-residual (CR) units. This configuration allows the module to extract deep semantic information while avoiding gradient vanishing problems. Subsequently, a lightweight and efficient channel attention (ECA) module is introduced during the encoder's feature extraction stage. The attention module assigns suitable weights to channels through attention learning and effectively captures inter-channel interaction information. Finally, the densely connected atrous spatial pyramid pooling module (DenseASPP) module is inserted between the encoder and decoder paths. This module integrates dense connections and ASPP, as well as multi-scale information fusion, to recognize lesions of varying sizes. The experimental studies in this paper were constructed on two public skin lesion datasets, namely, ISIC-2018 and ISIC-2017. The experimental results show that our model is more accurate in segmenting lesions of different shapes and achieves state-of-the-art performance in segmentation. In comparison to UNet3+, the proposed REDAUNet model shows improvements of 2.01%, 4.33%, and 2.68% in Dice, Spec, and mIoU metrics, respectively. These results suggest that REDAUNet is well-suited for skin lesion segmentation and can be effectively employed in computer-aided systems.  
skin lesions; image segmentation; deep learning; atrous convolution; UNet3+ +
# Fatigue Crack Detection Based on Semantic Segmentation Using DeepLabV3+ for Steel Girder Bridges
## 2 Overview of Semantic Segmentation Model: DeepLabv3+  
In the field of computer vision, deep learning is mainly used for three aspects, namely: image classification, object detection, and semantic segmentation. Image classification predicts labels for an image. Object detection focuses on providing labels and locations for objects in the images. The semantic segmentation technique involves labeling each pixel in the image and hence segmenting the image into several regions with different attributes and categories. Lately, semantic segmentation has been receiving more attention, since it can provide more accurate locations and other information. This paper studies the fatigue crack detection method in steel bridges based on DeepLabv3+ semantic segmentation technology, which can determine the location, length, and shape of the crack.  
DeepLabv3+ adopts an encoding-decoding architecture, as shown in Figure 1. The encoding part designs multiple dilated convolution modules with different dilating rates to obtain semantic information at multiple scales. The features outputted by the encoder are firstly up-sampled to obtain semantic information, which is connected to the feature layer with the same spatial resolution outputted by the low-level network. Bilinear interpolation up-sampling is used to restore the output feature map to the spatial resolution of the input image, leading to semantic segmentation outputs.  
In order to accurately assess the quality of semantic segmentation results, evaluation indicators [9], such as category accuracy, intersection union ratio (IoU), and mean intersection union ratio (MIoU), are often employed. For readers' convenience, the latter two are described herein. The IoU of crack pixels can be written as follows:  
\[IoU_{i}=\frac{TP_{i}}{TP_{i}+FP_{i}+FP_{j}} \tag{1}\]  
where \(TP_{i}\) is the number of pixels for true crack prediction, and \(FP_{j}\) is the pixel number for false background (non-crack) prediction. MIoU is defined as the average of the IoU of two categories, namely:  
\[MIoU=\frac{Iou_{i}+Iou_{j}}{2} \tag{2}\]
## 3 Results
### Oxidative Stress Parameters  
The effect of cyprodinil on the GSH/GSSG ratio in the A-375 and DLD-1 cell lines is shown in Figure 2. Glutathione belongs to the group of very important low-molecular-weight antioxidants, which are crucial for maintaining oxidative balance in the cell. In the A-375 cell line, at a concentration of 0.05 uM, cyprodinil increased the GSH/GSSG ratio after 24 h of incubation by almost 13%, while 0.2 uM cyprodinil concentration increased the tested parameter compared to the control by about 37%. In the DLD-1 cell line, treatment with 0.025 uM cyprodinil caused an increase in the GSH/GSSG ratio of about 28% compared to the control. Treatment with 0.2 uM cyprodinil increased the GSH level compared to untreated cells by about 6%. Based on the obtained results, we conclude that cyprodinil increases GSH/GSSG levels depending on the analyzed concentration and on the type of cancer.  
In maintaining redox homeostasis at the cellular level, the key role is played by the enzymes, which comprise the first line of antioxidant defense. These enzymes are GPx, catalase and SOD. In the A-375 cell line, a significant increase in catalase activity under the influence of cyprodinil at a concentration of 0.05 uM was observed after 24 h of incubation (Figure 2). On the other hand, at a concentration of 0.2 uM, catalase activity was almost at the same level as compared to untreated control cells. At a concentration of 0.025 uM, cyprodinil in DLD-1 cells increased catalase activity by approximately 30%. The opposite results were observed in the case of SOD (Figure 2). SOD activity was the lowest under the influence of 0.2 uM cyprodinil after 24 h of incubation. Our results showed that glutathione peroxidase activity was at similar level as in the case of catalase stimulated by cyprodinil.  
Figure 1: Cytotoxicity of cyprodinil in A-375 and DLD-1 cell lines. Relative viability of A-375 (**A**) and DLD-1 (**B**) cells exposed to different cyprodinil concentrations. Relative number of dead A-375 (**C**) and DLD-1 (**D**) cells exposed to selected cyprodinil concentrations. Each value on the graph is the mean of three independent experiments, and error bars show the standard deviation (SD). \({}^{*}p<0.05\) represent significant effects between treatments and controls followed by a Dunnett’s test.
## 4 Discussion
### Formation of the Two-Phase Region
#### 4.8.3 Interface Displacement and Capillary Liquid Velocities  
The interface displacement velocity between the two-phase and saturated zones has been approximately calculated by dividing the derivative of \(V_{dt}(t)\) by \(Ae\). The results are shown in Figure 17.  
This method of calculation is a rough approximation. The average velocity of liquid displacement by the capillaries in the saturated region of the porous medium must be calculated with the following equation:  
Figure 16: Saturation of the porous media corresponding to the state of dryout prior to the replenishment.  
Figure 17: Interface displacement velocity between the two-phase and saturated zones.  
\[\overline{v}=-\frac{k_{r}k^{\prime}}{\overline{\mu}}\bigg{[}\frac{\partial}{ \partial z}\{p_{c}-\rho g\beta(T_{si}-T_{sc})\}\bigg{]} \tag{18}\]  
where \(p_{c}\) is the capillary pressure in the interface between the two-phase and saturated zones, and \(T_{si}\) and \(T_{sc}\) are the liquid temperatures in the interface and the upper end of the porous medium, respectively. The capillary pressure is calculated by using Equation (8) and its gradient as:  
\[\partial(p_{c})/\partial z=\frac{\sigma cos\theta\sqrt{\phi/\overline{k}}J(S) }{\tau h_{lst}} \tag{19}\]  
where \(\tau\) is the capillary tortuosity and \(h_{lst}\) is the distance to the interface from the free surface of the liquid in the tray. It is possible to relate the capillary pressure in the two-phase zone with the global saturation measured as propose by [PERSON] [38; 39]. The form proposed by [PERSON] [18], to fit the data given by [PERSON] on sands, is:  
\[J=\frac{\left(s_{ef}^{-1}-1\right)^{0.175}}{\sqrt{5}},\ s_{ef}=\frac{s-s_{r}}{ 1-s_{r}} \tag{20}\]  
By knowing the value of \(S_{ef}\) for the porous media at any time \(t\), the pressure gradient is calculated by means of Equation (19) and the average velocity of the liquid displaced to the tray by using Equation (18), but only if [PERSON]'s law and the Boussinesq approximation are accomplished.  
As an example, the velocity of the liquid has been calculated through the saturated layer of the porous medium B+DS at time 5220 s, obtaining a velocity of 0.03 mm\(\cdot\)s\({}^{-1}\). The same order of magnitude obtained from the experimental results is 0.02 mm\(\cdot\)s\({}^{-1}\).
# A Study on the Effect of Gear Hobbing Process Parameters on the Residual Stress of the Tooth Root
## 3 Experimental Process and Method of Gear Hobbing
### Test Scheme  
According to the analysis of the kinematic relationship of hobbing machining in Section 2, the machining parameters (hob speed, axial feed speed, and radial cutting depth) were only changed during the experiment to explore the law of the influence of hobbing machining technology on the residual stress of the tooth root. Orthogonal test is a commonly used test optimization analysis method. The advantages of orthogonal experimental design include small experimental size and comprehensive experimental results, but the multi-level study on a single factor is not intuitive and comprehensive. Single-factor experimental design can compensate for the shortcomings of orthogonal experimental design, as the effect of different levels on a factor can be visually observed [24]. Therefore, for this experiment, both the single-factor experimental design and the orthogonal experimental design were used, with three factors and four levels in each case. The orthogonal test scheme is shown in Table 4, and the single-factor test scheme is shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Number** & \begin{tabular}{c} **Hob Speed/** \\ **(r-min\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Axial Feed Velocity/** \\ **(mm/min)** \\ \end{tabular} &
\begin{tabular}{c} **Radial Cutting Depth/** \\ **(mm)** \\ \end{tabular} \\ \hline
1 & & 2 & 0.84375 \\
2 & 450 & 2.75 & 1.125 \\
3 & & 3.5 & 1.6875 \\
4 & & 4 & 3.375 \\ \hline
5 & & 2 & 1.125 \\
6 & & 2.75 & 0.84375 \\
7 & & 3.5 & 3.375 \\
8 & & 4 & 1.6875 \\ \hline
9 & & 2 & 1.6875 \\
10 & & 2.75 & 3.375 \\
11 & & 3.5 & 0.84375 \\
12 & & 4 & 1.125 \\ \hline
13 & & 2 & 3.375 \\
14 & & 2.75 & 1.6875 \\
15 & & 3.5 & 1.125 \\
16 & & 4 & 0.84375 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Orthogonal experiment scheme.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hob Parameter** & \multicolumn{2}{c}{**Gear Parameter**} \\ \hline The normal module & 1.5 & Module & 1.5 \\ Normal pressure angle & \(20^{\circ}\) & Pressure angle & \(20^{\circ}\) \\ Number of heads & 1 & Number of teeth & 59 \\ Angle of thread rise & \(3^{\circ}3^{\prime}\) & Spiral angle & \(0^{\circ}\) \\ Outside diameter & 32 mm & Tooth width & 28 \\ Length & 50 mm & & \\ Direction of thread & dextrorotation & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic parameters of hob and gear.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**C** & **Si** & **Mn** & **S** & **P** & **Cr** & **Ni** & **Cu** & **Ti** \\ \hline
0.17–0.23 & 0.17–0.37 & 0.80–1.10 & \(\leq\)0.035 & \(\leq\)0.035 & 1.00–1.30 & \(\leq\)0.030 & \(\leq\)0.030 & 0.04–0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tempering chemical component of 20 CrMnTi for test (mass score, %).
# Realistic Simulation for Body Area and Body-To-Body Networks
## 2 Related Works, Simulators and Theirs Limits
### Classical Wireless Network Simulators  
There are a number of network simulators that can be found in the literature. This includes NS-2 [21], NS-3 [22], OPNET [23], OMNeT++ [24], WSNet [25], _etc._  
The NS-3 simulator is an open-source and discrete-event network simulator, which was first developed in 2006 [22]. The recent version NS-3.24.1 was released on 15 September 2015 [26]. NS-3 is not an extension of NS-2. Both simulators are written in C++, but NS-3 is a new simulator, which does not support the NS-2 APIs. Concerning the BAN studies, using NS-2 and NS-3, there are a few studies, such as [27; 28]; however, these studies are based on unrealistic BAN channel models, which impact the interference evaluation. OPNET is another widely-used general wireless network simulator [23]. The latest version of the OPNET modeler was released in November 2015 [29]. BAN-specific coexistence is studied in [30; 31]; however, the simulator does not consider the peculiarities of BAN propagation. OMNeT++ is an extensible, modular, component-based simulator in which a number of libraries and frameworks are available. The most relevant frameworks for BAN are MiXiM [14] and Castalia [13], as explained in Section 2.2.2. MiXiM is an OMNeT++ modeling framework for mobile and fixed wireless networks. MiXiM concentrates mainly on the physical (PHY) layers of the protocol stack and offers detailed models of radio wave propagation, interference estimation, radio transceiver power consumption [32] and mobility models. In addition, the integration of various mobility models (_i.e._, MoBAN [17]) makes it a more realistic option to simulate low layers (_i.e._, PHY and MAC). Finally, WSNet [25] is another event-based packet-oriented network simulator, which is used for node and environment simulation. The simulated nodes are built with a hardware component, a software component or the behavior/resource of the node. One of the key advantages of using the WSNet simulator is that there is an associated node platform simulator, WSIM [33], which allows simulating different components of the sensor nodes. Regarding BAN-specific works, traffic-aware dynamic MAC for BAN is implemented in WSNet; further, convergence behavior is heuristically modeled and simulated in WSNet [34; 35]. However, the protocol is evaluated under unrealistic channel and mobility models. Please note, similar studies in the future can be evaluated more realistically after having all of the modules presented in this work. Mo recently, a physical simulator, PyLayers [9], has been interfaced in real time with WSNet. This open source project addresses indoor radio propagation and provides a site-specific ultra-wide band-oriented simulator for BANs. This new framework will be discussed in Section 2.2.2.
## 3 Results
### Comparison between Final Model and Models without Sentinel-1 and DEM  
Our final RF model with Sentinel-1, Sentinel-2, and the DEM produced classifications with the highest Overall Accuracy and Kappa Coefficient, when compared to models without Sentinel-1 and the DEM (Table 6). Likewise, it generally outperformed the other models on most occasions at the habitat class level (Table 6). The alternative model that achieved the closest performance to our final model was the one associating Sentinel-1 and Sentinel-2 predictors, while the poorest performance was delivered by the model including only Sentinel-2 predictors (Table 6).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Stats**} & \multicolumn{2}{c}{**Final Model**} & **S2 Model** & **S2 + S1 Model** & **S2 + DEM Model** \\  & **S2 + S1 + DEM** & **14 Predictors** & **14 Predictors** & **16 Predictors** \\ \hline \multicolumn{2}{c}{**Overall Accuracy**} & **81.2** & 77.3 & 80.3 & 78.4 \\ \hline \multicolumn{2}{c}{Kappa} & **71.9** & 66.1 & 70.6 & 67.5 \\ \hline \multirow{2}{*}{Rocks} & User’s Accuracy & 69.3 & **92.9** & 75.2 & 88.2 \\  & Producer’s Accuracy & **74.2** & 61.7 & 68.8 & 64.1 \\ \hline \multirow{2}{*}{Shell beds} & User’s Accuracy & 90.7 & 59.9 & **91.7** & 72.4 \\  & Producer’s Accuracy & **58.5** & 48.1 & 54.7 & 53.0 \\ \hline \multirow{2}{*}{Macroalgae} & User’s Accuracy & **84.2** & 75.7 & 82.7 & 79.3 \\  & Producer’s Accuracy & 72.3 & 77.5 & 72.0 & **78.7** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of model performance between the model associating Sentinel-1, Sentinel-2, and DEM and three alternative models: using Sentinel-2 alone (S2 model); model using Sentinel-2 and Sentinel-1 (S2 + S1 model); and a model including Sentinel-2 and the digital elevation model. All alternative models were computed using the same workflow applied to obtain the best model. Performance statistics were calculated from independent validation with the training dataset and are based on unfiltered outputs. FCB: fiddler crab burrow areas. Numbers in bold signal the highest score.  
Figure 7: Mean \(\pm\) SE reflectance, backscatter, and index values of the 14 best predictors for each of the seven habitat types classified in the intertidal flats of the Bijagös Archipelago.
## 3 Optimization Methods
### Differential Evolution Algorithm
#### 3.2.2 Crossover  
After the mutation, crossover operation is applied to each pair of the target vector \(\textbf{X}_{i}^{t}\) and its corresponding mutant vector \(\textbf{V}_{i}^{t}\) to generate a trial vector (offspring) \(\textbf{U}_{i}^{t}=[\textbf{u}_{i,1}^{t},\,\textbf{u}_{i,2}^{t},\ldots,\, \textbf{u}_{i,D}^{t}]\). Common crossover methods include single, multi-point crossover,exponential crossover, and uniform crossover. Uniform (binomial) crossover is generally used, and the formula is as follows:  
\[u_{i,j}^{t}=\left\{\begin{array}{ll}V_{i,j}^{t}&\text{if }rand_{i,j}(0,\ 1)\leq C_{r} \text{ or }j=j_{rand}\\ x_{i,j}^{t}&\text{otherwise},\end{array}\right.\ \ j=1,\ 2,\ \ldots,\ D \tag{25}\]  
where \(j\) represents the dimension corresponding to the individual, \(Cr\) is the user-specified crossover rate within the range [0, 1], U\({}_{i}^{t}\) is the trial vector generated after crossover, and \(rand_{i,j}(0,1)\) is a uniform random number in [0, 1] and ensures that the trail vector U\({}_{i}^{t}\) obtains at least one component from the mutant vector \(\mathbf{V}_{i}^{t}\).
# Analyzing Port State Control Data to Explore Future Improvements to GMDSS Training
## 3 Results
### Binary Regression Analysis Results Were Applied to Databases of Table 4
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**AGE**} & \multicolumn{2}{c}{**Total Database**} & \multicolumn{2}{c}{**HRSC2 Subset**} & \multicolumn{2}{c}{**HRSC3 Subset**} \\ \cline{2-7}  & **N of Ships** & **\%** & **N of Ships** & **\%** & **N of Ships** & **\%** \\ \hline \multirow{2}{*}{\(<\)10 years} & 4512 & 19.0\% & 106 & 5.4\% & 429 & 10.0\% \\
10–20 & 6052 & 25.5\% & 319 & 16.1\% & 779 & 18.2\% \\
20–30 & 7195 & 30.3\% & 688 & 34.8\% & 1363 & 31.9\% \\
30–40 & 4655 & 19.6\% & 666 & 33.7\% & 1323 & 30.9\% \\ \textgreater{}40 years & 1311 & 5.5\% & 200 & 10.1\% & 382 & 8.9\% \\ \hline \multirow{2}{*}{**PORT**} & \multicolumn{2}{c}{**Total Database**} & \multicolumn{2}{c}{**HRSC2 Subset**} & \multicolumn{2}{c}{**HRSC3 Subset**} \\ \cline{2-7}  & **N of Ships** & **\%** & **N of Ships** & **\%** & **N of Ships** & **\%** \\ \hline Mediterranean & 11,318 & 47.7\% & 1433 & 72.4\% & 2495 & 58.3\% \\ Gratlantic Ports & 12,406 & 52.3\% & 546 & 27.6\% & 1781 & 41.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Crosstabulation of ship ages and port location using the database of inspections. Table 11 results. The chi-square goodness of fit tests are statistically significant for subsets HRSC2 and HRSC3: \(\chi^{2}(3)=1165\) and \(\chi^{2}(3)=632\) of country flag distribution with \(p<0.001\). Also, chi-square goodness of fit tests are statistically significant for subsets HRSC2 and HRSC3, \(\chi^{2}(3)=4304\) and \(\chi^{2}(3)=6994\) for ship type distribution, with \(p<0.001\). Table 12 results. The chi-square goodness of fit tests are statistically significant for subsets HRSC2 and HRSC3: \(\chi^{2}(4)=645\) and \(\chi^{2}(4)=550\) of ship-age distribution with \(p<0.001\). The chi-square goodness of fit tests is statistically significant for subset HRSC2 \(\chi(1)=23.1\) port location distribution with \(p<0.001\). However, for subset HRSC3: \(\chi^{2}(1)=4\) with a \(p\)-value = 0.0453, which is significant at \(p<0.05\). Therefore, the null hypothesis can be rejected and conclude that there are statistically significant differences between the data distribution (proportions) of flags and ship types in both subsets with respect to the total of the record.