esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Time Constant of PV Roof Tiles Working under Different Conditions
[17] proposed and experimentally revised a photo-electro-thermal model (PET model) for PV modules, based on which they confirmed the generated electrical energy dependence on PV cell temperature. In [18], the authors studied the temperature of PV cells working under different environmental conditions, considering such thermal processes as convection and radiation. They proposed new values of the coefficients of convective heat transfer based on a literature review and their own studies for different conditions. The obtained results were confirmed by other scientific papers [19; 20; 21]. [PERSON] [22] presents a mathematical model of a thin-film PV panel developed with the use of a PV cell single-diode electric model and temperature relationships, based on their own measurement data. Heat exchange between PV roof tiles, the roof structure and air can occur in three physically different ways: convection, radiation and conduction. With regard to the roof construction and arrangement of PV roof tiles on the roof, heat exchange by conduction and convection have the greatest share, while radiation processes can be neglected [23]. Based on a literature review and our own studies it can be concluded that the temperature of PV cells has a significant impact on PV conversion efficiency and the quantity of generated electrical energy,which is particularly evident in the case of PV cells integrated with the building (BIPV items) [24; 25; 26]. The photo-electro-thermal (PET) and resistance-capacity (RC) models of PV cells available in the literature are described and verified for traditional PV panels, but there is no confirmed validity test of these models for cells built in PV roof tiles integrated with the roof. Therefore, we attempted to determine the thermal time constant of PV cells integrated with PV roofs glued to the roof structure (based on the RC model) and to determine the maximum temperatures that they can achieve in different working conditions (using the PET model). In the RC and PET models, we considered the parameters of the roof structure (thermal resistance and thermal capacity of the board) to which the solar roof was glued and considered the temperature changes of the PV roof tiles integrated into the roof and working in various environmental conditions. By knowing the warming and cooling rates of PV cells--which are affected by heat processes in the system--one can identify energy losses related to temperature changes in the cells, which helps in the more precise estimation of the possible electrical energy yield.
## 1 Introduction
### Background on Previous Literature Assessments, including Findings, Recommendations, and Limits
The launch of new (or the improvement in existing) satellite imagery archive platforms such as the Sentinel-Hub ([[https://www.sentinel-hub.com/](https://www.sentinel-hub.com/)]([https://www.sentinel-hub.com/](https://www.sentinel-hub.com/)), accessed on 12 April 2023) or USGS ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), accessed on 13 April 2023) ones. However, despite the substantial positive impact of these studies in the improvement in this line of research, some limitations and open questions remain. In the case investigated in this paper, i.e., damage to cultural heritage caused by both human and natural-induced hazards, these limitations include  
1. A lack of a clear reconstruction of the trends in the study of hazards and resulting damage to cultural heritage encompassing both scientific research and Grey literature. 2. A lack of a concise understanding of the RS methods and tools that can be used for each type of hazard. This last limitation is crucial to providing end users, such as national authorities, international organizations (e.g., UNESCO), and private consultants, with a suite of well-defined methods and tools to be used to efficiently document and monitor each type of hazard. This paper aspires to contribute to bridging these gaps through a tailor-made literature assessment regarding more than 20 years of research in the field. Building on the three main necessities that emerged from the previous assessment, as well as on the current limitation, in the present paper, we analyze the current trends in the study and the assessment of damages to cultural heritage in Europe caused by both human-induced damage and natural hazards using RS and EO techniques. In particular, our research provides a fresh and ample look at the long-term evolution of both the indexed scientific and the Grey literature regarding hazards and damage to archaeological sites, monuments, and cultural landscapes conducted by Europe-based researchers and heritage professionals from 2000 until today using RS technologies and techniques. To do so, and following previous recommendations [7; 11], we developed a multidisciplinary collaboration to investigate the current trends and suggest improvements in the processes of documenting, safeguarding, and monitoring the cultural heritage and cultural landscapes under threat. To address this scope, the group that worked on this research was intentionally formed to bring together some of the main (although not exhaustive) perspectives and expertise, i.e., concrete requirements of the heritage professionals that are the final end-users of RS and EO technologies; knowledge of these technologies and their use in the field; methodologies and practice for implementation to support the tasks of the cultural heritage management cycle.
# Autonomous Wireless Sensor Networks in
## Materials and Methods  
### The PentaSense Network  
The ground sensors form a WSN with a smart gateway, surrounded by modular, multi-sensor nodes in a multi-hop mesh. The complete system is divided into three semi-independent parts, the PentaSense WSN, the communication module and the SDSS (spatial decision support system) which will aggregate WSN sensor and meta-data with EO on a remote server. Each multi-sensor node communicates the collected data to the central sink node or the gateway. The gateway is equipped with a two-way communication system integrating a satellite module and terrestrial communication. In addition, finally, the SDSS is designed to store the data in a local database, and to analyze and display the information in an easy to interpret way for the user through a web application. Overview of the system architecture is shown in Figure 1.  
The design of the communication framework considers aspects like end-to-end reliability, expected network lifetime and existing infrastructure, etc. A power-independent WSN designs that provides low-power, low-cost and reduced data rate wireless transmissions either in mesh networks is evaluated.
## 3 Related Work  
The early studies in the field of pain assessment extracted the features from frames using the active appearance model (AAM) and used a support vector machine (SVM) clas  
Figure 2: ResNet50 architecture where each color represents a block of layers with the same kernel size.  
sifier [28; 29] to create automated pain assessment systems. [PERSON] et al. [30] later compared SVM in their proposed framework for pain along with three other classifiers (decision tree (DT), random forest (RF), and 2 nearest neighbors (2 NN) based on Euclidean distance). Their framework detects the face from a frame, horizontally halves the detected face, and uses the halves as the two inputs of the model; it employs the shape information using a pyramid histogram of oriented gradients (PHOG) and the appearance information using a pyramid local binary pattern (PLBP) to obtain a unique representation of the face. However, more recent pain assessments utilize neural networks. [PERSON] et al. [31] utilized the recurrent convolutional neural network (RCNN) to introduce a real-time regression framework for automatic pain intensity estimation, while [PERSON] et al. [32] employed the advantage of combining CNNs with long short-term memory (LSTM) in their model. Some researchers even used dual models that consisted of a fusion structure of CNNs, such as [PERSON] and [PERSON], who used two shallow neural networks--spatial appearance network (SANET) and shape descriptor network (SDNET)--in one model [9], and multiple neural networks in another [33].  
Inspired by [PERSON] et al. [30], our proposed model used two inputs based on the detected face parts to mimic the PSPI code; however, instead of using both halves of the face we used the full face as one input and the upper face for the other input. We employed neural networks for automatic feature extraction from frames, just as [31] and [32] did. However, instead of RCNN or a combination of CNNs and LSTM, we employed the VGG16, InceptionV3, ResNet50, and ResNeXt50 each with a shallow CNN replacing the classifier layer.  
The higher accuracy obtained by [9] and [33] encouraged us to adopt a fusion structure of CNNs.  
We compared our model's performance to the aforementioned models [9; 30; 32; 33] that used the same dataset (UNBC-McMaster shoulder pain expression archive), as well as the measurement strategy (the k-fold cross validation accuracy), as ours. [PERSON] and [PERSON]'s state-of-the-art model [34], which employed Kaze algorithm to extract features from the detected face, obtains a fisher vector and sends it to an SVM classifier. It also uses the same testing dataset and metric measurements. Therefore, we compared our results against it. The resulting optimal model is then embedded in our FEAPAS system.
## 2 Related Work
[12] implemented an efficient hybridization of the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) to determine and solve the problem regarding inverse kinematics on arbitrary joint chains. The approach was applicable, since it ensures high accuracy and success rates of real-time full-pose objective solutions. [PERSON] et al. created a multi-objective fitness function--Hybrid Genetic Swarm Algorithm (HGSA)--based on natural evolution across diverse and constantly changing environments. Moreover, they used simultaneous local extrema exploitation to obtain more precise solutions because dead ends could be solved using simple heuristics. [PERSON] et al. demonstrated that their solution is comparable to the Orocos KDL and TRAC-IK methods in terms of success rate and computation time. Among all the methods above, the solution of [PERSON] et al. is the most powerful and efficient. Unfortunately, many of the reviewed swarm intelligence robotic manipulator inverse kinematics solutions only deal with end effector position rather than orientation. In 2022, [PERSON], [PERSON] and [PERSON] [13] demonstrated the convergence of a complete end-effector pose using PSO. The algorithm's precision is demonstrated using the Baxter Research Robot, which has two seven-joint arms, but the method can be applied to any general serial robotic manipulator. However, [PERSON] et al. only concentrated on getting a complete solution of IK and benchmarked execution time, which is critical in real-time applications. Particle swarm optimization applies to solving many other problems in robotics. For instance, based on the actuator power consumption, [PERSON] [14] proposes an optimization problem for the 3-degrees-of-freedom RRR fully planar parallel manipulator (3-RRR). The optimization seeks to determine the optimal link and platform masses in order to reduce the electrical energy consumed by the actuators while adhering to kinematic, geometric and dynamic constraints by using PSO. Among the traditional solution techniques for robot manipulator inverse kinematics, the Inverse Kinematics (IK) algorithms in the open-source Orocos Kinematics and Dynamics Library (KDL) are considered the most popular generic IK solvers worldwide. In an effort to find a better IK solver for generic manipulator chains, [PERSON] and [PERSON] [15] performed quantitative comparisons, using several different humanoid robots, between an improved implementation of the KDL inverse Jacobian algorithm and various sequential quadratic programming IK algorithms. Another powerful inverse kinematics solver, IKFast, is also available within [PERSON]'s Open Robotics Automation Virtual Environment (OpenRAVE) motion planning software [16].
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 5 General Challenges and Open Issues
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline
**Ref.** & **C or D** & **E or P** & **2D or 3D** & **Af or Ab** & **Rf or Rb** & **Sy or As** & **Sa or M or H** & **A or Si** & **Ss or Ms** \\ \hline [57] & & & & & & & & & & & & & & & \\
[58] & & & & & & & & & & & & & & & \\
[59] & & & & & & & & & & & & & & & \\
[60] & & & & & & & & & & & & & & & \\
[61] & & & & & & & & & & & & & & & \\
[62] & & & & & & & & & & & & & & & \\
[64] & & & & & & & & & & & & & & & \\
[65] & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._  
**Node deployment and node mobility**: Unlike the node deployment in the WSNs, in the underwater environment, it is more challenging and costly. If deploying the node in the deep sea environment, the researcher may face more challenges. In addition, the node is mobile with the current and other activities [74]. Considering that the speed of the current varies by time and unpredictable, the estimation of a moving node in the localization process may obtain some errors. **Security and privacy**: Many papers do not take the two aspects into account during the design of the positioning algorithms. However, there is no doubt that they are crucial in UWSNs. In [75], the authors mainly focus on the security attacks on the underwater localization and countermeasures, as well as privacy issues in the underwater localization and countermeasures. A node must reveal certain information to be localized, which may lead to privacy vulnerabilities. Both in the location-related information collection step and the location estimation step the location privacy is discussed, and some techniques to preserve privacy in underwater localization are presented in [75]. The attacks contain DoS attacks, range-based measurement attacks, and range-free measurement attacks, advertising false information and non-cooperation. These techniques for securing underwater localization, which are misbehavior detection, robust location computation, and location verification, will be put forward.
## 1 Introduction
Similar algorithms with slight modifications will be used for ECG authentication in this paper. The first modification was on the filter dimension, i.e., 2D-CNN to 1D-CNN. The second modification was on the structure of CNN layers. In Xception and PydMobileNet, DSC was applied to single layers while, in our algorithm, DSC was implemented for the group of three layers, as has been explained for a cardiac arrhythmia classifier [14]. A further modification was conducted for ECG authentication, in which a shortcut residual is applied to the three-layer DSC. Furthermore, for ECG authentication, none of the convolutional layers is similar to the ones in Xception and PydMobileNet. Each convolutional layer is an ordinary 1D convolution, not a DSC. As discussed earlier, the common problems in ECG authentication include time-consuming signal processing and feature extraction. Another problem is accuracy; it is difficult to obtain 100% accuracy, which is required for authentication purposes. Therefore, in this paper, a fast and efficient algorithm will be proposed to overcome these problems using a residual depthwise separable convolution neural network. The training process required high computational resources, which can be alleviated using GPU. However, the testing process required low computational resources; a CPU alone is sufficient to be used during the authentication process.
# Security and Independence of Process Safety and Control Systems in the Petroleum Industry
## 5 Measures to Resist Cyberattacks
### OPC UA PubSub as an Approach to Computer Diodes  
Publish-Subscribe (PubSub) is described in OPC UA part 14 [32]. The model can be illustrated as in Figure 2; one or more entities that publish data and one or more entities that subscribe to data. This is done through a middleware that can be implemented in different ways. Essentially, all the devices can be \"normal\" OPC UA devices that communicate in the usual way, but one explicit option is to use a UDP datagram without a receipt. In this case, it will be possible to place a \"unidirectional gateway\" (i.e., data diode) between the manufacturer and the consumer (see Figure 3).  
PubSub claims to support signing or signing and encryption (although references to a shared key between publisher and subscriber would indicate that what is involved is a Message Authentication Code rather than a digital signature). Individual data items are grouped into a \"DataSetMessage\", and several of these can again be grouped into a \"NetworkMessage\", which eventually makes up the payload in a transport protocol message (in our case, UDP). If there are several subscribers, one can use UDP multicast addresses; however, for our purposes, it is assumed that UDP unicast addresses will suffice.  
The data field in \"DataSetMessage\" consists of the information specified in Table 1. The most important ones here are an ID that identifies the sender, a sequence number, and a timestamp.  
Figure 2: OPC UA PubSub model.  
UDP makes no guarantees about timeliness, delivery receipt, order, or duplicate protection. When using a data diode, it will not be possible to request the retransmission of missing packets; however, it is possible to send multiple copies of the same package. This must then be configured if there is a communication channel with too much packet loss. The sequence number then makes it possible to discard the duplicate packets [32]. If one uses UDP, consumers must probably be adapted so that they can work even if some packages do not arrive.
# Research on Slope Stability Based on Bayesian Gaussian Mixture Model and Random Reduction Method
## 3 Comprehensive Safety Factor
### Calculation of Safety Factor  
The constructed Bayesian Gaussian Mixture Model successfully classifies slopes under different reduction coefficients into stable and unstable categories. For data in the unstable state, if no other unstable state data exists where the reduction coefficients of cohesion and internal friction angle are both smaller than those of this dataset, the dataset is defined as critical slope data.  
To address potential deviations caused by insufficient precision in the reduction step size, further processing is applied to the critical slope data: data points with dissipated energy exceeding 20% of the average value are excluded. The critical slope data refined according to this rule are detailed in Table 6.  
Taking the slip surface of the critical slope in Group 1 as a case study, the initial slope is labeled as Group 0. Seven elements were randomly and uniformly sampled, as illustrated in Figure 11. The stress parameters (\(\sigma_{1}\), \(\sigma_{3}\)) of the selected elements under both initial and critical state (Group 1) were extracted using FLAC3D software. The average shear strength of the selected elements was calculated as follows:  
Shear strength on the potential slip surface of the initial slope: The average angle (\(\alpha_{0}=60.02\)) between the \(\sigma_{1}\) action surface and the failure surface of the selected elements under the initial state was calculated using Equation (28). Substituting \(\alpha_{0}\) into Equation (26) \(\alpha_{0}\) and combining it with Equation (22), the average shear strength of the selected elements on the potential slip surface was determined as \(\tau_{0}=193235.64 Pa\).  
Shear strength on the potential slip surface of critical slope 1: The average angle (\(\alpha_{1}=56.86^{\circ}\)) between the failure surface \(\sigma_{1}\) and the action surface of the selected elements was calculated using Equation (28). Substituting \(\alpha_{1}\) into Equation (26) and combining it with Equation (22), the average shear strength of the selected elements on the slip surface of the critical slope was determined as \(\tau_{1}=141124.40 Pa\).  
Similarly, the shear strengths for all critical slopes were calculated and are listed in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Group No.** & \(\boldsymbol{c}\)**-Reduction** & \(\boldsymbol{\varphi}\)**-Reduction** & **Dissipated Energy** \\ \hline
1 & 1.43 & 1.32 & 3890 \\
2 & 1.372 & 1.366 & 3958.9 \\
3 & 1.36 & 1.376 & 3968.9 \\
4 & 1.37 & 1.368 & 3974.3 \\
5 & 1.33 & 1.4 & 4037.5 \\
6 & 1.38 & 1.36 & 4045.5 \\
7 & 1.378 & 1.362 & 4077 \\
8 & 1.368 & 1.37 & 4107 \\
9 & 1.366 & 1.372 & 4145.2 \\
10 & 1.376 & 1.364 & 4262.3 \\
11 & 1.364 & 1.374 & 4387.7 \\ \hline \end{tabular}
\end{table}
Table 6: Critical slope data.  
Figure 11: Displacement diagram of critical slope No. 1.  
From this, it can be determined that \(\tau=193.24 kPa\) and \(\tau^{\prime}=144.43 kPa\), resulting in a final safety factor of 1.338.
# Roll Control of Morphing Aircraft with Synthetic Jet Actuators at a High Angle of Attack
## 4 Simulation Results and Discussion
### Roll Controlled by the Morphing Span  
In this simulation, the structural change of large-scale morphing is taken as an aerodynamic forcing input determined by the feedback control algorithm, which corresponds to the second approach mentioned. To maintain the roll motion of aircraft by the morphing span only, the input due to SJA is set as \(\delta_{\text{jet}}=\delta_{\text{jet}}=\text{Constant}\) to guarantee the aerodynamic characteristic at a high AoA. The control input of the morphing span with a saturation constraint is depictured in Figure 9. It indicates that the peak value of the input has reached the maximum, with the corresponding compensation by the RBF neural network shown in Figure 10. Figure 11 gives the tracking responses of the angle and speed, with the target angle signal \(x_{d}=\text{sin}t\). The roll motion is maintained by deploying one side of the wing while keeping the other side alternately stationary. One can see from Figure 11 that although the control input for the morphing span reaches saturation during the roll motion, the RBF neural network can compensate well, and it takes less than \(5\) s when the roll angle \(\gamma\) matches the reference signal well.  
Figure 8: Different methods for flight control of morphing aircraft. (**a**) Independent shape and flight control and (**b**) integrated shape and flight control.