esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Design of Space Camera Enclosure Based on Bionics
## 2 Biomimetic Structural Design
### Similarity Evaluation  
Similarity evaluation is a necessary step in biomimetic structural design and an effective means of determining whether the selected biological characteristics and the designed structure are reasonable. However, this similarity is generally not directly expressed by precise mathematical formulas, and researchers commonly use the similarity theory in fuzzy mathematics for calculation [21; 22].  
Generally, the similarity is denoted as \(Q\) and expressed as follows:  
\[Q=\sum_{i=1}^{n}\left(\beta_{1}q(m_{1})+\beta_{2}q(m_{2})+\ldots+\beta_{n}q(m_{ n})\right)=\sum_{i=1}^{n}\beta_{i}q(\mu_{i}) \tag{1}\]  
Among them, \(Q\) represents similarity, \(0<Q<1\);  
\(\beta_{i}\) represents the weight coefficient. \(0<\beta_{i}<1\), and \(\sum\limits_{i=1}^{n}\beta_{i}=1\);  
\(q(\mu_{i})\) represents the similarity degree of similar elements.  
Figure 2: Leaf structure of _Victoria lindl_.  
It is also necessary to determine the evaluation factor set \(U=\{\mu_{1},\mu_{2}\ldots\mu_{N}\}\), among them, \(\mu_{a}\in U,a=(1,2\ldots N)\). \(\mu_{ab}\) represents the relative importance of \(\mu_{a}\) compared to \(\mu_{b}\); its value is referenced from the literature. Based on the analysis of the actual adaptability of the design-stiffened plate structure, this article determines the evaluation factor set \(U=\{Function,Structure,Load,Constraint\}\). The judgment matrix \(P\) can be expressed as:  
\[P=\begin{bmatrix}\mu_{11}&\mu_{12}&\ldots&\mu_{1b}&\ldots&\mu_{1N}\\ \mu_{21}&\mu_{22}&\ldots&\mu_{2b}&\ldots&\mu_{2N}\\ \cdots&\ldots&\ldots&\ldots&\ldots&\ldots\\ \mu_{a1}&\mu_{a2}&\ldots&\mu_{ab}&\ldots&\mu_{aN}\\ \ldots&\ldots&\ldots&\ldots&\ldots&\ldots\\ \mu_{N1}&\mu_{N2}&\ldots&\mu_{Nb}&\ldots&\mu_{NN}\end{bmatrix}=\begin{bmatrix} 1&2&2&2\\ \frac{1}{2}&1&\frac{3}{3}&1\\ \frac{1}{2}&3&1&3\\ \frac{1}{2}&1&\frac{1}{3}&1\end{bmatrix} \tag{2}\]  
Among them, \(\mu_{ab}>0,\mu_{aa}=1,\mu_{ab}={\mu_{ba}}^{-1},a=1,2,\ldots N\), \(b=1,2,\ldots N\) and Vector \(\beta=(\beta_{1},\beta_{2}\ldots\beta_{N})^{T}\) are the eigenvectors of matrix \(P\). Obtain \(\lambda_{max}=4.155\).  
Due to the subjective nature of the selected judgment matrix scale values in the above calculation process, it is necessary to verify the consistency of the judgment matrix \(P\) for a more objective evaluation. Researchers generally use the calculation of Consistency Ratio (C.R.) to evaluate the credibility of the judgment matrix. When \(C.R.<0.1\), the judgment matrix is considered acceptable [23; 24]. After calculation, the above plan \(C.R.=0.037<0.1\), indicating that the judgment matrix \(P\) is reliable.
# Colored Petri Net Modelling and Evaluation of Drone Inspection Methods for Distribution Networks
## 3 Materials and Methods
### Design of the Model
#### 3.1.3 Preflight Procedures  
The general operation process includes checklist execution and battery charging as preflight procedures. The checklist is a procedure regulated by the UAV manufacturer to be carried out before each flight. It is necessary to make sure that all systems are working properly and that no unexpected events will occur during the flight. Depending on the communication system used, drone configuration, payload and other components, the procedure may vary significantly.  
However, to simulate this process, we only need to know the time it will take to follow such a checklist for a particular drone.  
In addition to the checklist, charging the drone's batteries is a mandatory procedure to prepare the drone for flight. Almost all drones used for monitoring are equipped with an electric power train. This is primarily due to the convenience and safety of using such a propulsion system. The drone with such a propulsion system uses battery charging for refutelling.  
Drones use high-current lithium batteries. The battery is a highly stressed element and needs frequent inspection and testing. For this reason, the batteries in drones are not built-in and must be removed each time they are charged. Three mainly used types of batteries include:  
* Li-ion--lithium-ion batteries used for small machines, high capacity with minimum weight.
* Li-Po--lithium polymer batteries used for drones with a high payload. They have the highest current output.
* Li-PoFe4--lithium iron phosphate batteries used for extreme weather conditions such as subzero temperatures.  
High C-rate batteries are usually used. The C-rate indicates the measure of a battery's charge and discharges in relation to its capacity. In other words, 1C-rate means that a battery can be discharged in 1 h, 0.5C-rate in two hours, and 6C-rate in 10 min [33]. It is the same with battery charging. However, a higher C-rate has a negative impact on battery life. Therefore, we will assume that the batteries are charged at 1C-rate, i.e., they will be fully charged in one hour.  
A charging time of one hour can be considered reasonable because even if the drone is charged faster, it has to include mandatory procedures such as removing and installing the battery.  
Most manufacturers attach graphical illustrations to help with such work on the drone. These materials are called flow charts and are used to help with work or personnel training. If the person operating the drone follows all the guidelines, we can predict in advance how long a particular operation will take. According to the manufacturer's instructions, all the actions described in the model under development can be specified as the time intervals required to perform each operation.
## 3 Data analysis and BP Neural Network Model Building
### Input and Output Variables Processing  
For the model in this article, the input layer contains four variables (i.e., particle volume fraction of 13.2-16.0 mm, 9.5-13.2 mm, 4.75-9.5 mm and 2.36-4.75 mm) for Gradation-13 and five variables for Gradation-16, and output layer includes the only dependent variable, porosity, for both gradation types.  
The first and foremost step in BP neural network modeling is to process the original input data. Because the recorded data are relatively close to each other in the whole range of 0.30-0.45, it is necessary to standardize the original data and transfer variables into the range of [1; 2; 1] according to Equation (3). Meanwhile, the original gradation should also be processed through Equation (3). This procedure can achieve the best performance for modeling.  
\[y_{0}=\frac{(y_{max}-y_{min})\times(x_{0}-x_{min})}{(x_{max}-x_{min})}+y_{min} \tag{3}\]  
where \(y_{max}\) and \(y_{min}\) represent 1 and \(-1\) respectively in this article, while \(x_{max}\) and \(x_{min}\) refer to the maximum and minimum value of recorded porosity and \(x_{0}\) refers to original porosity, while \(y_{0}\) refers to standardized porosity.  
Through the above equation, the procedure for standardizing the input variables includes these three parts: (1) finding maximum and minimum values in the recorded porosity as \(x_{max}\) and \(x_{min}\); (2) calculating each standardized porosity (\(y_{0}\)) value through Equation (3); (3) recording standardized porosity (\(y_{0}\)) values.  
The next step is to divide the available data set into two groups, one for training and another for testing the developed model. Meanwhile, the testing set will not be used in the model training process. It should be noted that grouping is done randomly. In this study, 200 particle compositions and corresponding porosity for both Gradation-13 and Gradation-16 were randomly divided into training and testing groups.  
Finally, the output results still need to be anti-standardized in a similar way by following Equation (4):  
\[x_{1}=\frac{(x_{max}-x_{min})\times(y_{1}-y_{min})}{y_{max}-y_{min}}+x_{min} \tag{4}\]  
where, \(x_{max}\) and \(x_{min}\) refer to the maximum and minimum value of recorded porosity and \(x_{1}\) refers to prediction porosity, while \(y_{1}\) refers to standardized prediction porosity.  
The standardized and anti-standardized can be completed automatically by function \"mapminmax\" in MATLAB only if the original data is inputted.
## Abstract
In this paper, an approach based on a virtual observer is proposed for discrete-time LPV systems subject to sensor faults. The proposed LPV model is used to simplify the complexity of the nonlinear system for the fault-tolerant predictive control (FTPC) and, at the same time, to maintain high accuracy for the controlling procedure. The proposed virtual observer is similar in theory to the observer presented in [16; 17] but incorporated with the MPC approach. As a result, an extended state-space LPV model is used to construct a virtual observer, then the formulated virtual observer is used to propose a real observer. Knowing that the real observer is based on the parameters of the virtual observer, both proposed observers will be used to formulate the fault-tolerant predictive control strategy. The proposed methodology has the ability to reduce the influences of faults in the dynamics of the system and ensure the robust stability of the closed-loop system. In addition, sufficient stability conditions are proposed, in terms of linear matrix inequality (LMI) constraints, to ensure the robust stability of the overall closed-loop system containing system states, and the estimation of error dynamics. Moreover, the gains of both observers and the fault-tolerant controller are computed by solving the linear matrix inequalities (LMIs). This paper is structured as follows: the formulation of the LPV as well as fault representations are presented in Section 2. Section 3 presents both virtual and real observers to estimate the states and sensor faults at the same time. Based on the LPV model, an augmented robust observer fault-tolerant predictive control strategy is presented in Section 4. The simulation results are illustrated in Section 5. Finally, Section 6 concludes the paper. Notation. The superscripts \(A^{T}\) and \(A^{-1}\) are used to denote the transposition and inverse of matrix \(A\), respectively. The symbols \(F_{r}\) describe the controller gains matrices while the terms \(L_{r}\) represent the observer gain matrices. \(\mathbb{R}^{n}\) denotes the \(n\)th dimensional Euclidean space. \(0_{n}\) and \(I_{n}\) are the zero matrix and the identity matrix, respectively. In addition, the symbol \(\|p\|\) represents the Euclidean norm of vector \(p\). The term \(RMS(\cdot)\) represents the root mean square function. The symbol \({}^{*}\) is used to describe the symmetric part in equations. The term \(Co\{\cdot\}\) denotes a convex hull, where its element \"\(\cdot\)\" refers to the vertex of the polyhedron. In addition, the matrices \(M_{r}\), \(N_{r}\), \(X_{r}\), \(Y_{r}\), \(Z_{r}\), and \(H_{r}\) are symmetric positive definite matrices. The expression \(S\geq 0\) (or \(S>0\)) denotes that the matrix \(S\) is positive semi-definite (or positive definite, respectively). Finally, the symbols \(Q_{0},~{}R_{0}\) are the weighting matrices.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 2 Overview of the UWA OFDM Communication System
### Deep Learning UIWA OFDM Communication System Methods  
Recent research shows that DNN-based methods for channel estimation can directly learn channel characteristics from received signals, outperforming traditional methods and simplifying system design by eliminating the need of channel estimation and equalization steps. During training, the received signal, containing both amplitude and phase information, serves as the input to the DNN, while the transmitted data are used as labels. The DNN learns to adjust its parameters by comparing predicted and actual transmitted data, allowing it to directly predict the transmitted data from the received signal without the need for explicit channel estimation, equalization, and demodulation as shown in Figure 2. Figure 3 clearly illustrates the difference between deep learning and TL. In TL, knowledge gained from one task can be transferred to a related task, thereby improving learning efficiency and performance. In contrast, traditional deep learning typically requires a separate learning process for each task, with its own dataset.
## 2 Method
### Optimization Objectives
#### 2.2.3 Life-Cycle Carbon Emission  
The term 'life-cycle carbon emission' refers to the total amount of carbon emissions, including the embodied carbon emissions generated during the manufacturing and shipping of retrofit materials, as well as those from total building energy consumption during operational phases, serving as a comprehensive measure of environmental impact. A carbon emission factor (CEF), derived from the national building carbon calculation standard, was used to quantify both the embodied carbon emissions and energy-related emissions of building materials [52]. The objective function is described as follows:  
\[LCCE=\sum_{j=1}^{I}(N_{j}\cdot f_{j}+n\cdot[E_{H}\cdot\mu_{coal}\cdot f_{coal }+(E_{\text{C}}+E_{L})\cdot f_{elec}] \tag{8}\]  
where j denotes the material type for renovation; Nj represents the specific material quantity; fj is the CEF for the particular material; fcoal denotes the CEF for coal, which is 2.27 in this study; and flec represents the CEF for electricity, which is 1.15 in this study.
## 3 System Configuration
### Multi-Reflection Chamber  
The spots of the traditional optical chamber are roughly distributed in a circular shape on the spherical mirror, as shown in Figure 5a. When the distance between the mirrors is extremely close (i.e., the physical size is small), the optical path length cannot easily satisfy the detection requirements. To improve the detection sensitivity, the proposed gas chamber uses three mirrors with a mirror diameter of 60 mm. There are two semi-circular mirrors on the front and a circular mirror on the back. The mirror surface is coated with a silver film by a vacuum coating process. The effective reflection spectrum ranges from 450 nm to 20 \(\upmu\)m and the reflectance is greater than 95%. The optical path length can be changed by manually adjusting the incident optical path. As shown in Figure 5b, the reflected spots on the mirror surface of the gas chamber are distributed in 13 rows, which increases the utilization of the mirror surface.  
Since the mirror is made of fused silica (JGS1) material, and the main body of the gas chamber is made of 316 steel. Therefore, the material is easy to obtain, and the cost is relatively low compared with the products of the same market. The chamber adopts a ferrule connector/ asperity polishing  
Figure 4: **(a)** Curve of the laser’s emitting peak wavelength versus the driving current at 20 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 30 \({}^{\circ}\)C. **(b)** Curve of the laser’s emitting peak wavelength during four hours of continuous operation.  
Figure 5: **(a)** Mirror spots of the conventional optical chamber distributed in a circular shape. **(b)** Mirror spots of a multi-reflection chamber distributed in 13 rows. **(c)** Photo of the multi-reflection chamber.  
connector (FC/APC) fiber coupling interface, the light is coupled into the gas chamber by the fiber, and the docking is convenient. The chamber's actual structure is shown in Figure 5c, and the optical path length is set to 80 m with 250 reflections by adjusting the incident optical path.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 2 Related Work
### MAC Security Issues on WSNs  
The state of the art proposes different approaches to secure communication in WSNs [20], and it shows that the cryptography is one of the common solutions for providing security.  
Some works present an overview on the security issues over WSNs showing attacks, vulnerabilities and potential countermeasures [21; 22]. They show that, generally, the security aspects in WSNs try to resolve issues due to four main concepts: confidentiality, integrity, authenticity, and availability [23]. Clearly, for the nature of WSNs, the nodes inside these networks have to provide more effort than in the other networks for their characteristics of elaboration, memory, and storage capacity [21]. Moreover, a deep analysis of the main protocols is needed for being able to quantify the effort to deal with security at the different protocol layers [22].  
A deep study on MAC protocols concerning security attacks is in [20]. In this paper, the authors have provided an analysis on different MAC protocols discussing their security issues. They, on the basis of the detailed review, indicate the advantages and disadvantages of the studied MAC protocols along with countermeasures for the security issues.  
Sensor nodes under attack might effect the accuracy and integrity of information by misrepresenting sensed data in the process of aggregation in order to send information towards the sink node. In [24], in order to deal with these security concerns, the authors provide the study on a novel approach for WSNs MAC safe data aggregation based on homomorphic propriety and called HMSDA. This approach has been shown to offer acceptable data confidentiality and integrity protection in a WSN.  
A novel cryptography approach called aggregating secure data-separate MAC (SDA-SM) is shown in [25]. The proposed mechanism is able to separate the protected aggregate data and the message authentication codes into two distinct messages and, further, transmit these messages in a new random time-slot as indicated into the TDMA scheduling. The authors show that SDA-SM reaches its goal respecting a lesser computation time in the communication overheads than previous approaches. Furthermore, SDA-SM is proved to maintain the correct security proprieties of aggregate data, avoiding energy waste to prolong the lifetime of the network.  
The proposal of a modified version of the Z-MAC protocol with the introduction of the elliptic-curve encryption (ECC) technique is provided in [26]. The authors analyse the MAC protocol able to transfer data in a safe manner. This new protocol is proved capable of offering low contention, high throughput, reduced latency, low power consumption, and increased efficiency.
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 4 Thermal Analysis
### Parameters of Working Condition in Thermal Analysis
#### 4.2.2 Condition II, Low Temperature Condition  
* Solar constant is at its valley, 1322 W/m\({}^{2}\) (at June solstice)
* MLI made of polyimide, at the beginning of its life, \(\alpha\)s/\(\varepsilon\) = 0.36/0.69;
* At the beginning of the life of the radiation region coatings, \(\alpha\)s/\(\varepsilon\) = 0.17/0.87;
* All instruments onboard are working at their minimum power consumption;
* Active thermal control is working well to meet the design requirement.
# Blind Deconvolution with Scale Ambiguity
## 4 Discussion and Experimental Results
### Discussion
#### 4.1.3 Parameter Analysis  
The proposed model involves two main parameters, \(\zeta\) and \(\lambda\). The parameter \(\mathbf{\zeta}\) is used in the final non-blind deblurring step to recover the final deblurred images with the estimated kernels. For the selection of \(\zeta\), we follow the existing non-blind deblurring algorithm [42]. The parameter \(\lambda\) is a regularization weight parameter. We evaluate the effects of the parameter \(\lambda\) on deblurred results on the dataset [19] with the average kernel similarity metric and average PSNR value. Figure 6 shows that the selection of \(\lambda\) does not have very great influences on the quality of the deblurred results. Overall,  
Figure 4: Evaluation for deblurring methods with different \(p\) values on a synthetic dataset which contains 800 images. In terms of the three evaluation criterions, \(p=2\) appears to be a good choice.  
Figure 5: Convergence property of the proposed algorithm with \(p=2\) in terms of two commonly used evaluation criterions.  
the proposed algorithm achieves the best performance when \(\lambda=0.09\). Based on these statistical evaluations, the regularization weight parameter \(\lambda\) is empirically set to be 0.09 in all experiments.
## 5 Optimal Dispatch Model for DN  
### Objective of the Optimal Dispatch Model  
The objective of the DN dispatch model is to minimize the operation cost \(C^{\rm DN}\), including the cost of purchased electricity \(C^{\rm DN}_{b}\), the penalty cost for curtailment for PV and WT \(C^{\rm DN}_{f}\), and the revenue from the sale of electricity \(C^{\rm DN}_{s}\), as shown in the following:  
\[\min C^{\rm DN}=C^{\rm DN}_{b}+C^{\rm DN}_{t}-C^{\rm DN}_{s} \tag{33}\]  
\[C^{\rm DN}_{b}=\sum_{t}\sum_{i\in N^{\rm D}}P^{\rm SG}_{i,t}c^{\rm E,1}_{t} \tag{34}\]  
\[C^{\rm DN}_{t}=\sum_{t}\sum_{i\in N^{\rm D}}\left[y^{\rm PV}_{t}\left(\bar{P} ^{\rm PV}_{i,t}-P^{\rm PV}_{i,t}\right)+y^{\rm WT}_{t}\left(\bar{P}^{\rm WT}_{ i,t}-P^{\rm WT}_{i,t}\right)\right] \tag{35}\]  
\[C^{\rm DN}_{s}=\sum_{t}\sum_{i\in N^{\rm D}}c^{\rm E,2}_{t,t}(P^{\rm D-H}_{i,t }+P^{\rm D-E}_{i,t}) \tag{36}\]  
where \(P^{\rm SG}_{i,t}\) is the active power transmitted from the superior grid to the DN node \(i\) at period \(t\); \(c^{\rm E,1}_{t}\) is the price of electricity sold by the superior grid at period \(t\); \(\bar{P}^{\rm PV}_{i,t}\) and \(P^{\rm PV}_{i,t}\) are the predicted and actual output of PV at DN node \(i\) at period \(t\), respectively; \(\tilde{P}_{i,t}^{\text{WT}}\) and \(P_{i,t}^{\text{WT}}\) are the predicted and actual output of WT at DN node \(i\) at period \(t\), respectively; \(y_{i}^{\text{PV}}\) and \(y_{i}^{\text{WT}}\) are the penalty cost coefficients of curtailed power for PV and WT, respectively; \(P_{i,t}^{\text{D-H}}\) is the active power transmitted from DN node \(i\) to DHN at period \(t\); \(P_{i,t}^{\text{D-E}}\) is the active power transmitted from DN node \(i\) to BESS at period \(t\).
## 6 Performance Evaluation
### Real-Time Evaluation  
To evaluate the end-to-end data delivery delay, 29 pairs of sensors and actuators were considered in the network. These pairs are chosen in such a way that the total hop distances of the sensor to the gateway and of the gateway to the actuator are spread in different hop levels. In Figure 19 a sample of an end-to-end connection is shown between a sensor node (37) and an actuator node (45) based on WirelessHART and D-MSR network, respectively.  
We evaluate the average end-to-end data delivery delay and the average number of hops that the received packets need to travel to reach their destinations through the 29 connections. The results are shown in Figure 20 for both D-MSR and WirelessHART. In this figure, we classified connections into five categories based on the total hop distance of sensor to actuator via the gateway. We forward the traffic (periodic sensor data) from sensors towards actuators, by employing the constant bit rate (CBR)  
Figure 18: The network topology from animation tool of NS-2 simulator (nam).  
\begin{table}
\begin{tabular}{l l l l}
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \multirow{2}{*}{Number of nodes} & Gateway, two access points, & \multirow{2}{*}{Radio range} & \multirow{2}{*}{15 meters} \\  & 53 field devices & & \\ \hline Simulation area & 100 \(\times\)100 m\({}^{2}\) & Frequency Band and channel & 2.4 GHz, 11–26 channels \\ \hline Placement & Regular distribution & Sensor traffic rate & 1 per 2 seconds \\ \hline Data rate & 250 kb/s & Application traffic model & CBR \\ \hline \end{tabular}
\end{table}
Table 3: NS-2 simulation parameters.  
traffic model in NS-2 for all end-to-end connections. The requested publishing period of the sensor data for all 29 connections is set to two seconds. Subsequently, communication resources are reserved to exchange sensor data messages between the sensors and actuators/gateway based on that period.  
The end-to-end delay in D-MSR is close to that of WirelessHART, which implies that D-MSR achieves similar results in addressing the real-time requirements during an operational phase. In those connections, in which the periodic sensor data packets have to travel more hops to reach their destination, more end-to-end delay is expected.