esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 3 Experiment and Results
### Result
#### 3.2.2 Training Time Comparison  
Table 1 shows the training time of the three models in the Vaihingen data set. From the table, it can be seen that SwinUnet has the shortest training time, SETRnet follows, and TransUnet has the longest training time. In each epoch, SwinUnet's time is 36.79 s and 79.75 s faster compared with SETRnet and TransUnet, respectively.  
Figure 12: Comparison of transformer’s segmentation results in the Potsdam data set. The red boxes are where the differences between the three network segmentations are large.
## 3 Mid-Infrared Fe: ZnSe and Cr: ZnSe Solid-State Lasers
Spectroscopic and material properties of the Cr: ZnSe and Fe: ZnSe crystals are shown in Table 7.  
It can be seen from Table 7 that the absorption cross section and emission cross section of Fe\({}^{2+}\): ZnSe are larger than that of Cr\({}^{2+}\): ZnSe. While the Cr: ZnSe crystal exhibits excellent room temperature fluorescence properties, both of them have a wide tuning range and high quantum efficiency, which have attracted more and more attention in the field of mid-infrared wave band research. The research and development status of Cr: ZnSe and Fe: ZnSe lasers are shown in Table 8.  
Compared with Cr: ZnSe laser, the single energy or the average power is higher for the Fe: ZnSe laser. However, for the Fe: ZnSe crystal, the temperature is the key factor affecting its fluorescence lifetime. High-power Fe: ZnSe laser can be realized at low temperatures. As temperature rises, the fluorescence lifetime of Fe: ZnSe crystal decreases, which makes it difficult to achieve a high-power, mid-infrared laser. Future research can focus on the external cooling method of the laser to ensure that it maintains good mid-infrared laser output performance at room temperature. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Crystal** & **Cr:ZnSe** & **Fe:ZnSe** \\ \hline Symmetry of crystal & Cubic system & Cubic system \\ Size (mm\({}^{3}\)) & 40 \(\times\) 40 \(\times\) 50 & 40 \(\times\) 40 \(\times\) 50 \\ Launch range (\(\upmu\)m) & 1.9–3.3 & 3.4–5.2 \\ Gain bandwidth (nm) & 500 & 500 \\ Peak absorption cross section (\(\times 10^{-20}\) cm\({}^{2}\)) & 87 & 97 \\ Peak absorption wavelength (\(\upmu\)m) & 1.78 & 3 (300 K) \\ Peak emission cross section (\(\times 10^{-20}\) cm\({}^{2}\)) & 90 & 140 \\ Peak emission wavelength (\(\upmu\)m) & 2.45 & 4.140 \\ Emission bandwidth (nm) & 0.9 & 1.1 \\ Fluorescence lifetime (300 k, \(\upmu\)s) & 8 & 0.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Parameters of Cr: ZnSe and Fe: ZnSe crystals. Figure 10: Absorption and emission spectrum of Cr: ZnSe crystal. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Crystal** & **Year** & **Research** & **Crystal** & **Pump Source** & **Mid-Infrared Output** & **References** \\  & & **Establishment** & **Parameter** & & & **Parameter** & **Reference** \\ \hline  & & & & & Temperature 300 k (0.38 \(\upmu\)s) & \\  & & & & & Output wavelength 4.3 \(\upmu\)m & \\  & & & & & Average power 0.3 mW & \\
## 3 Results
### Refractivity Differences between COSMIC and Radiosonde Data  
According to Equation (1), radiosonde refractivity is calculated based on the radiosonde temperature, pressure and humidity profiles, and the results are compared with the COSMIC  
Figure 6: The COSMIC vapor pressure biases compared to radiosonde observations in China. The red dots are positive biases, and the blue dots are negative biases. The number of radiosonde stations for each bias class is listed in parentheses.  
Figure 7: The vapor pressure RMSEs between COSMIC and radiosonde data in China. The number of radiosonde stations for each RMSE class is listed in parentheses.  
refractivity. The vertically averaged COSMIC refractivity biases compared to radiosonde observations in China are presented in Figure 8. The refractivity biases are mainly positive; however, some negative biases occur in northern China. The positive refractivity biases are small in northern and south-eastern China and large in the remainder of China. Among the 120 stations, 65 stations (54%) have biases with a magnitudeof less than 0.2 N, and 36 stations (30%) with a magnitude of between 0.2 N and 0.4 N. The refractivity bias varies from \(-\)0.29 N to 0.63 N, with an average value of 0.17 N. A negative correlation is found between the absolute refractivity bias and station latitude, with a correlation coefficient of \(-\)0.3846 at a significance level of greater than 99%. This indicates that station latitude is one of impact factors on the refractivity bias between COSMIC and radiosonde.  
Figure 9 presents the refractivity RMSEs between COSMIC and radiosonde data in China. The refractivity RMSE is generally larger in eastern China than in western China. Additionally, the refractivity RMSE varies from 1.37 N to 6.12 N. There are 47 stations (39%) with an RMSE of less than 3 N, and 65 stations (54%) with an RMSE of between 3 N and 5 N. On average, the refractivity RMSE between COSMIC and radiosonde data in China is 3.42 N. Like the vapor pressure RMSE, a negative correlation exists between the refractivity RMSE and station altitude, and the correlation coefficient is \(-\)0.6102 at a significance level of greater than 99%. Note that the refractivity RMSE also has a negative correlation with station latitude, with a correlation coefficient of \(-\)0.4505 at a significance level of greater than 99%. These results imply that both station altitude and latitude can impact the refractivity RMSE between COSMIC and radiosonde in a certain way.  
Figure 8: The COSMIC refractivity biases compared to radiosonde observations in China. The red dots are positive biases, and the blue dots are negative biases. The number of radiosonde stations for each bias class is listed in parentheses.
# A Chaotic Secure Communication System Design Based on Iterative Learning Control Theory
## 2 The Canonical ILC System  
Let us consider the next first-order nonlinear system in the \(i\)-th iteration [8]:  
\[\dot{x}_{i}=\theta(t)\xi(x_{i},t)+u_{i},\quad t\in[0,T],\quad x(0)=x_{0}, \tag{1}\]  
where \(\xi(x_{i},t)(=\xi_{i}\) for simplicity) is a known nonlinear function which can be locally Lipschitz, \(\theta(t)\in C[0,T]\) is the _unknown_ time-varying parameter, and \(T\) represents the iteration (or operation) time.  
Given the reference trajectory supplied by:  
\[\dot{x}_{r}=f(x_{r},r,t), \tag{2}\]  
where \(f_{r}=f(x_{r},r,t)\) is a known smooth-function and \(r\) is a reference input signal which produces a bounded response \(x_{r}(t)\) over the time interval \([0,T]\), the _ILC objective_ is to find a sequence of appropriate control inputs \(u_{i}\) for \(t\in[0,T]\) such that the system state \(x_{i}\) tracks (as well as possible) the reference trajectory \(x_{r}\) as \(i\rightarrow\infty\)[8].  
A solution to the ILC objective is as follows [8]:  
\[u_{i} = k\epsilon_{i}+f_{r}-\hat{\theta}_{i}(t)\xi_{i}, \tag{3}\] \[\hat{\theta}_{i}(t) = \text{proj}(\hat{\theta}_{i-1}(t))-\xi_{i}e_{i},\quad\hat{\theta} _{-1}(t)=0, \tag{4}\]  
where \(e_{i}=e_{i}(t)=x_{r}(t)-x_{i}(t)\),  
\[\text{proj}(x)=\begin{cases}x,&\mid x\mid\leq\theta^{*}\\ \text{sign}(x)\theta^{*},&\text{otherwise}\end{cases}; \tag{5}\]  
and \(\theta^{*}\) is the _parameter projection bound_, assumed sufficiently large. According to [8], this ILC--even under random and bounded initial conditions \(e_{i}(0)\) (if \(e_{i}(0)=0\) implies _ideal_ resetting condition.)--assures the ILC control objective. Finally, the _learning_ parameter \(\hat{\theta}_{i}(t)\) gives an estimation of the time-varying parameter \(\theta(t)\) over each iteration of the repetitive process.
## 4 Numerical Simulation
### Numerical Results
#### 4.2.2 Different LRs  
Figure 12b illustrates the number of cracks within the specimen under different LRs. To be specific, the development and number of internal cracks vary during uniaxial compression damage of the specimens with the same HDR at different LRs. As the LR increases, the number of cracks formed gradually rises, especially in the dynamic loading phase, where the number of cracks formed by specimen damage is significantly higher than that observed in static and quasi-dynamic loading phrases. Especially at a LR of 50 mm\(\cdot\)min\({}^{-1}\), the number of damaged fissures increases dramatically to 5931.  
As stated by [PERSON], at high strain rates, plastic strains may not develop sufficiently before the next level of stress increases, leading to hardening of the rock material and thus higher strength [55]. As can be seen from Figure 13, under static loading conditions (0.1\(\sim\)0.5 mm\(\cdot\)min\({}^{-1}\)), due to the slow LR, the original cracks within the specimen to  
Figure 11: Crack extension in uniaxial compression of the specimens with different HDRs.  
Figure 12: The pattern of variation in the number of internal cracks formed when specimens are damaged: (**a**) different HDRs; (**b**) different LRs.  
continue converging under the axial load and develop into multiple large cracks. In the quasi-dynamic loading phase (1-5 mm\(\cdot\)min\({}^{-1}\)), the LR is larger than that of the static loading phase, and the specimen has been compressed and damaged before the internal fissures come to be \"aware\", so that the primary cracks inside the specimen do not fully integrate. At this point, tensile cracks occur mainly between the spheres and the specimen and form a shear damage pattern along the diagonal direction. This is the same as the numerical simulation results for different HDRs. In the dynamic loading phase, cracking is in the form of a \"large X\" type of damage development. Especially at the LR of 50 mm\(\cdot\)min\({}^{-1}\), the number of cracks within the specimen increases significantly, resulting in the formation of multiple X-damage patterns.
# Bayesian Survival Analysis Model for Girth Weld Failure Prediction
## Survival Prediction Model
### Kaplan-Meier Survival Analysis Function  
The survival analysis is based on data obtained from experiments or surveys and analyzes and infer the survival time of materials and objects. This method has been used in medicine, biology, economics, statistics, and other disciplines [22; 23]. In this paper, we took a closer look at the patterns of girth weld failure and leaks in 64 girth-welding accidents and created a Bayesian model that predicts the probability of a girth weld maintaining its integrity in the future. The life of a girth weld is the time from its construction to its failure, due for example to cracking; girth-weld failure represents its \"death\". After determining the life of a girth weld, a non-parametric Kaplan-Meier survival-method [24] is used for analysis. The life of a girth welds is described using parameters: \"\(d\)\" represents the number of \"deaths\" of the sample at a certain time \"\(t\)\", and \"\(n\)\" represents the number of \"survivals\" of the sample at a certain time. The survival function indicates the probability of surviving the time \"\(t\)\", as shown in Equation (1):  
\[S(t)=\prod\left(\frac{n-d}{n}\right) \tag{1}\]  
Hazard function is another basic function in survival analysis that measures the probability of a sample's \"death\". The hazard function \(h(t)\) is calculated as:  
\[h(t)=\frac{d}{dt}[-lnP(t)] \tag{2}\]  
The survival curve example in Figure 2a shows that the probability of failure of girth welds increases sharply in the first seven years and is relatively constant between 10 and 40 years; the failure rate increases after about 40 years. This is consistent with the three stages of the Bathtub curve for pipeline accidents, namely, the early \"infant mortality\" failure, the \"constant\" failure, and the \"wear-out\" failure due to pipeline aging.  
According to accident records, the girth-weld with the longest life failed in its 63 rd year. Therefore, this article can analyze only the 63 rd year using the Kaplan-Meier method. There is only one variable allowed in the Kaplan-Meier method, and it cannot predict the future development of an event.  
Also, since only the cracked, failed girth-weld samples are considered in the modeling analysis, if data related to functional samples are combined with those of the failed girth weld, for example, the planar defect found by in-line inspection is defined as failure, then the failure probability and survival function of the failed girth weld will change.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 2 Domain Adaptation Techniques
### Overview of Domain Adaptation Techniques
#### 2.1.1 Scholarly Landscape Visualization  
In this section, we present a visual representation of the scholarly landscape pertaining to our research focus on domain adaptation in computer vision. To provide a comprehensive understanding of the evolution and interconnectivity of research in this domain, we have utilized Connected Papers, a powerful tool for exploring and visualizing academic papers and their relationships. We have conducted searches for three key topics: \"Domain adaptation for image segmentation\", \"Domain adaptation for object identification\", and \"Domain adaptation for object classification\". The graphs presented below in Figures 3-5 and Tables 2-6 offer insights into the seminal works that laid the foundation for these topics (\"prior papers\") and the subsequent research that has built upon these foundations (\"derivative papers\"). These visualizations serve as a valuable reference to contextualize our own contributions within the broader academic landscape of domain adaptation in computer vision. Figure 3: Source—relaxed domain adaptation for image segmentation [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. Figure 2: Google Trends—region-wise interest in domain adaptation, computer vision, and robotic vision (percentage of searches is represented by color intensity). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **Title** & **Last Author** & **Year** & **Citations** & **Graph Citations** \\ \hline
[30] & CyCADA: Cycle-Consistent Adversarial & & & & \\ Domain Adaptation & [PERSON] & 2017 & 2445 & 29 \\ \hline
[31] & Adversarial Discriminative Domain & & & & \\ Adaptation & [PERSON] & 2017 & 3784 & 25 \\ \hline
[32] & Deep Residual Learning for Image & & & & \\ Recognition & [PERSON] & 2015 & 141,511 & 23 \\ \hline
[33] & DeepLab: Semantic Image Segmentation & & & & \\ with Deep Convolutional Nets, Atrous & & & & \\ Convolution, and Fully Connected CRFs & & & & \\ \hline
[34] & The SYNTHIA Dataset: A Large Collection & & & \\ of Synthetic Images for Semantic & & & & \\ Segmentation of Urban Scenes & & & & \\ \hline
[35] & Adam: A Method for Stochastic & & & \\ Optimization & [PERSON] & 2014 & 124,700 & 18 \\ \hline
[36] & Playing for Data: Ground Truth from & & & \\ Computer Games & & & & \\ \hline
[37] & The Cityscapes Dataset for Semantic Urban & & & \\ Scene Understanding & & & & \\ \hline
[38] & Learning Transferable Features with Deep & & & \\ Adaptation Networks & & & & \\ \hline
[39] & Unpaired Image-to-Image Translation Using & & & \\ Cycle-Consistent Adversarial Networks & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prior works on domain adaptation for image segmentation.
# Lifting Activity Assessment Using Kinematic Features and Neural Networks
###### Abstract  
Work-related low-back disorders (WLBDs) can be caused by manual lifting tasks. Wearable devices used to monitor these tasks can be one possible way to assess the main risk factors for WLBDs. This study aims at analyzing the sensitivity of kinematic data to the risk level changes, and to define an instrument-based tool for risk classification by using kinematic data and artificial neural networks (ANNs). Twenty workers performed lifting tasks, designed by following the rules of the revised NIOSH lifting equation, with an increasing lifting index (LI). From the acquired kinematic data, we computed smoothness parameters together with kinetic, potential and mechanical energy. We used ANNs for mapping different set of features on LI levels to obtain an automatic risk estimation during these tasks. The results show that most of the calculated kinematic indexes are significantly affected by changes in LI and that all the lifting condition pairs can be correctly distinguished. Furthermore, using specific set of features, different topologies of ANNs can lead to a reliable classification of the biomechanical risk related to lifting tasks. In particular, the training sets and numbers of neurons in each hidden layer influence the ANNs performance, which is instead independent from the numbers of hidden layers. Reliable biomechanical risk estimation can be obtained by using training sets combining body and load kinematic features.  
work-related low-back disorders; biomechanical risk; kinematic; artificial neural networks +
Footnote †: journal: Applied Sciences
# Uncovering Vegetation Changes in the Urban-Rural Interface through Semi-Automatic Methods
## 2 Materials and Methods
### Built-Up Areas and the Urban-Rural Interface  
Regarding forest planning against fires, the _Direcao Geral do Territorio_ (DGT) from Lisbon (Portugal) in partnership with the _Instituto da Conservacao da Natureza e das Florestas_ (ICNF) sited in Lisbon (Portugal) developed the mapping of the built-up areas and the urban-rural interface 2018 [36]. This cartography represents the perimeter of the built-up areas classified according to the proximity of the surrounding tree cover, which can be classified into two categories, with fuel and without fuel. The urban-rural interface can belong to three classifications: (i) direct, if it is directly connected to the fuel material; (ii) indirect if there is a distance between 1 and 500 metres from the interface for the fuel material; and (iii) null if the fuel material is located more than 500 m away. The production of this mapping was based on the official Portugal land use and land cover 2018 map (COS) made by DGT and Database of Classic Residential Buildings 2011-2019 made by Statistics Portugal placed in Lisbon (Portugal) (_Instituto Nacional de Estatistica_--INE).
# Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism
## 2 Related Works
### Detection Based on Vehicle Behavior Characteristics  
The second fatigue monitoring method is to measure the behavior of the vehicle. This method is mainly to monitor the changes of the data during the driving of the vehicle, such as speed, steering wheel rotation angle and lane departure detection, brake and accelerator pressure changes, etc., to indirectly evaluate the driver's fatigue through these data.  
[PERSON] et al. [6] calculated the approximate entropy (ApEn) based on the steering wheel angle (SWA) obtained in real time by the steering rod sensor of the car, used the given deviation for adaptive linear fitting and finally evaluated the driver's fatigue state according to the binary decision classifier. Only 78% accuracy was achieved in the car experiment. In recent years, based on the hybrid characteristics, the method of using the driver's physiological characteristics and vehicle behavior to monitor driving fatigue has become popular. To improve the robustness of fatigue detection, [PERSON] et al. [7] proposed an adaptive dynamic recognition model that combines multiple information (blink frequency, closed eye time, vehicle turning angle deviation, etc.) and uses dynamic basic probability assignment (BPA) to realize the dynamic allocation of the weight of each information source. Experimental results show that information fusion can indeed improve the accuracy of fatigue detection, but there is still a certain gap with the real-world application standards.  
This type of method is non-contact, but this method is greatly affected by driving conditions, driving experience, vehicle type, etc., and it takes a lot of time to collect and analyze the behavior of the driver and it is difficult to detect slight fatigue.
# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 4 Experiments
### Ablation Study  
To evaluate the effectiveness of the edge detection extension module proposed in this paper for enhancing the integrity of the target area, we conducted an ablation experiment. We analyzed and compared the differences in the enhanced images obtained before and after the integration of the edge detection extension module. The results of the experiment are presented in Figure 13.  
In addition, this paper conducts more detailed ablation study on whether to use RetinexFormer preprocessed images for target region extraction. The specific experimental design is as follows: (1) target region extraction using original images without edge detection extension module; (2) target region extraction using RetinexFormer preprocessed images without edge detection extension module; (3) target region extraction using RetinexFormer processed images with edge detection extension module. Some visual comparison is illustrated in Figure 14. Please zoom in to compare more details.  
The experimental results demonstrate that the edge detection extension module, coupled with the preprocessing of RetinexFormer, effectively recovers missing portions of the target region and enhances the integrity of the satellite target area.  
Figure 14: The results of ablation study. Please zoom in to compare more details.  
Figure 13: Experimental comparison of using edge detection extension and not.
## 2 _Article_  
### Dynamical Jumping Real-Time Fault-Tolerant Routing Protocol for Wireless Sensor Networks  
**[PERSON] 1, [PERSON] 1, [PERSON] 1,*, [PERSON] 1,2*, [PERSON] 1 and [PERSON] 1**  
1 School of Software, Dalian University of Technology, Dalian 116620, China;  
E-Mails: wgwdut(d)dlut.edu.cn (G.W.); linchix(gmail.com (C.L.);  
fofospeak(d)live.cn (H.Z.); liubing-051(d)live.cn (B.L.)  
2 School of Electronics & Information, Dalian University of Technology, Dalian 116021, China  
* Author to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (F.X.);  
yaolin(d)dlut.edu.cn (L.Y.); Tel.: +86-411-87571521; Fax: +86-411-87571567.  
_Received: 26 January 2010; in revised form: 3 March 2010 / Accepted: 10 March 2010 / Published: 23 March 2010_  
**Abstract:** In time-critical wireless sensor network (WSN) applications, a high degree of reliability is commonly required. A dynamical jumping real-time fault-tolerant routing protocol (DMRF) is proposed in this paper. Each node utilizes the remaining transmission time of the data packets and the state of the forwarding candidate node set to dynamically choose the next hop. Once node failure, network congestion or void region occurs, the transmission mode will switch to jumping transmission mode, which can reduce the transmission time delay, guaranteeing the data packets to be sent to the destination node within the specified time limit. By using feedback mechanism, each node dynamically adjusts the jumping probabilities to increase the ratio of successful transmission. Simulation results show that DMRF can not only efficiently reduce the effects of failure nodes, congestion and void region, but also yield higher ratio of successful transmission, smaller transmission delay and reduced number of control packets.  
**Keywords:** real-time; fault-tolerance; wireless sensor networks; routing protocol