esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Digital Twin for HEMS
### Functional Objectives and Modeling Priorities  
The development of a DT for an HEMS requires a strategic approach to modeling that should emphasize both functionality and precision. This section outlines the primary objectives and priorities guiding the creation of the DT model tailored for HEMS applications. The focus is on enhancing user experience and spatial realism while supporting granular data segmentation, and enabling data analytics. By integrating detailed environmental and energy-related data, the DT model can provide users with an immersive and  
Figure 2: Comparison of HEMS with and without Digital Twin, illustrating system architecture, data flows, and differentiated purposes.  
interactive experience while facilitating real-time insights and energy management. Each objective contributes to the overall aim of achieving a 3D model that is technically robust and adaptable to user needs, environmental conditions, and energy optimization goals within different areas of the home. The functional objectives and modeling priorities (Obj) are listed bellow:  
* Obj. 1: Enhance realism and user experience by creating a visually and tactilely accurate 3D model that fosters immersion and ease of navigation within the Digital Twin environment.
* Obj. 2: Support functional segmentation for energy and environmental data by spatially segmenting the 3D model based on material and functional variations, enabling targeted data integration.
* Obj. 3: Capture space-specific energy consumption patterns by enabling the Digital Twin to track and analyze energy use unique to each space (e.g., living room vs. kitchen).
* Obj. 4: Improve environmental monitoring and thermal comfort by using spatial and material segmentation to monitor conditions like temperature and humidity, ensuring each zone meets optimal comfort levels.
* Obj. 5: Facilitate predictive analytics for occupancy and usage patterns, allowing the Digital Twin to predict and adapt to occupancy and behavior trends across different rooms.
* Obj. 6: Enable precision in smart home automation by integrating automation features that adapt to the unique material and spatial characteristics of each room.
# Pipe Crack Recognition Based on Eddy Current NDT and 2D Impedance Characteristics
## 4 Defect Recognition based on a Back Propogation Neural Network  
A neural network is an extensive parallel network composed of adaptive simple units which is capable of simulating the interactive response of a biological nervous system to actual objects. The error back propagation (BP) algorithm represents an outstanding representation of neural networks. Furthermore, it may be considered as the most successful neural network learning algorithm to date [24]. The system assumes the image of the Lissajous curves as the input, whereas the x and y components are the real and imaginary parts of the eddy current signal, and the existence of the cracks as the output. Here, 1 (0) indicates the presence (absence) of the crack. The BP algorithm based on the gradient descent strategy is used, which does not involve the calculation of a second derivative but still has quadratic convergence characteristics, thus allowing for a faster convergence rate and a relatively small number of computations. The input of the BP neural network is the five characteristic parameters of the impedance diagram, and the output is the probability of the defects in the specimen. Since the output is a value between 0 and 1, we choose the Sigmoid function as the activation function. Figure 10: The structure with a spring inside to keep the probe in touch with the specimen surface, ensuring zero lift-off. Figure 9: Impedance diagram of three types of cracks retrieved under different lift-offs: weld crack (**a–d**) body crack (**e–h**) and weld toe crack (**i–l**). There is no definite theorem for the selection of the number of hidden layers and the number of its nodes. In theory, a three-layer BP network with only a single hidden layer may approximate any function [25]. A larger number of hidden layers requires more samples and time for training. Thus, here we use a three-layer BP network with a single hidden layer. Concerning the selection of the number of nodes in the hidden layer, the experimental results show that for a small number of nodes, the network does not have the necessary learning and information processing capacity. On the contrary, a large number of nodes yields significant enhancement in the complexity of the network structure. Moreover, the network falls into a local minimum in the process of learning more easily and exhibits a very slow learning speed. According to the experience, a suitable number of hidden layer nodes is 13-17. After repeated experiments comparing the training error, the minimum training error was achieved for the 15 hidden layer nodes. Table 3 lists the training results. We selected 50 pipeline specimens for the testing and synthesis of the impedance diagrams, with the defects lying in the girth welds and other complex morphologies. The eigenvalues of the impedance diagrams were extracted as the test data using the image processing algorithm and fed to the neural network for identification. The recognition results are listed in Table 4.
# Performance Characteristics of an Orthopter-Type Vertical Axis Wind Turbine in Shear Flows
## 3 Numerical Approach
### Torque and Power Coefficients  
As mentioned in sub-Section 2.4, this study considers only the aerodynamic torque generated by the blades as the rotor torque of the O-VAWT. Since each of the blade axes was connected with the main shaft by a chain via sprockets, the aerodynamic torque on each of the blades about each of the blade axes was transmitted through the chain and contributed to the torque about the main shaft. Therefore, the rotor torque generated by a blade at an azimuthal angle \(\varphi\) is expressed as:  
\[T_{B}\left(\varphi\right)= T_{B\_rev}\left(\varphi\right)+T_{B\_rot}\left(\varphi\right), \tag{19}\]  
where \(T_{B\_rev}\left(\varphi\right)\) is the conventional blade torque that is calculated by multiplying the rotor radius and the component of the aerodynamic force on the blade at \(\varphi\) in the rotor-revolution direction; and \(T_{B\_rot}\left(\varphi\right)\) is the torque generated by the component of the aerodynamic force on the blade at \(\varphi\) in the blade-rotation direction about the blade axis. Hereafter, we call \(T_{B}\left(\varphi\right)\) the \"blade torque,\" \(T_{B\_rev}\left(\varphi\right)\) the \"rotor-revolution torque\" and \(T_{B\_rot}\left(\varphi\right)\) the \"blade-rotation torque.\" The blade torque coefficient (\(C_{TB}\)), the rotor-revolution component (\(C_{TB\_rev}\)) and the blade-rotation component (\(C_{TB\_rot}\)) are defined as:  
\[C_{TB}\left(\varphi\right)= \frac{T_{B}\left(\varphi\right)}{0.5\rho AU_{0}^{2}R}, \tag{20}\]  
\[C_{TB\_rev}\left(\varphi\right)= \frac{T_{B\_rev}\left(\varphi\right)}{0.5\rho AU_{0}^{2}R}, \tag{21}\]  
and  
\[C_{TB\_rot}\left(\varphi\right)= \frac{T_{B\_rot}\left(\varphi\right)}{0.5\rho AU_{0}^{2}R}. \tag{22}\]  
It should be noted that \(C_{TB}\), \(C_{TB\_rev}\) and \(C_{TB\_rot}\) are coefficients of one blade.  
The CFD simulations were conducted for eight revolutions of the rotor. Using the data of the last two rotor revolutions, the torque coefficient \(C_{T}\) was computed by the following formula:  
\[C_{T}=\frac{n}{N_{e}-N_{s}}\sum_{N=N_{s}+1}^{N_{e}}\int\limits_{0}^{2\pi}C_{TB }\,d\varphi. \tag{23}\]  
Here, \(n\) (\(=3\)) is the number of the blades; \(N_{s}\) (\(=6\)) is the number of the rotor revolutions before starting the computation of \(C_{T}\); \(N_{e}\) (\(=8\)) is the number of the rotor revolutions before finishing the computation of \(C_{T}\). The power coefficient of \(C_{p}\) was computed by Equation (6).
# Improving a Rapid Alignment Method of Tomography Projections by a Parallel Approach
## 1 Introduction  
_Soft X-ray cryo-nano tomography_ is an effective imaging tool that allows one to analyse biological samples in their native environment, providing ultrastructural three-dimensional information of cells [1]; indeed, the high resolution of this technique, typically in the tens of nanometres [2], is paired not only to a profitable contrast behaviour in a specific range of energy, _the water window_, but also permits imaging in the hydrated state without staining, as the sample preparation requires only vitrification of water via a cryo-fixation process [3]. Phase contrast techniques [4] are especially used in the hard-X-ray regime, as the contrast due to phase shift is typically three orders of magnitude larger than the corresponding absorption [4].  
To obtain a reconstruction from a series of 2D projections, tomography relies on a corpus of a priori information which constitutes an image formation model [5]. In its simplest form, the rotation axis is fixed in the 3D space, and its 2D projection lays in the central column (or row) of the detector, which is also fixed and locked to the source; a pure rotation is what describes the relative motion of the sample with respect to the detector.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 2 WSN Management
### Maintenance Management  
Maintenance management as part of network management would enable higher QoS and an extended network lifetime. Faults in WSN occur frequently due to interruptions in connectivity, energy shortages, changes in the environmental conditions and other network events expected and unexpected. The network therefore, should be configured, managed and maintained in a manner that allows for self-healing and fault tolerance resulting in a robust characteristic enabling the WSN to provide useful information even when some nodes fall out of the network. [PERSON] and [PERSON] [51] have presented various fault mitigation techniques through management of the network topology.  
An important aspect of maintenance management and fault mitigation is network monitoring required to collect information on the state of the network (e.g., node energy levels, bandwidth and link states). Thus, interfaces and tools have been developed to provide a visual representation of the network state such as Mote-view [52], SNAMP [53], spy-glass [54], tinyviz [55], nanomon [56] among others. [PERSON] et al. [31] mention that systems such as the mote-view and others that have a central management scheme with all analysis data being sent collectively to a central server have the disadvantage of being passive and lack flexibility in configuration in an event of fault occurrence. This can be seen as a leverage for SDN in solving this lack of flexibility, an aspect that becomes visible as management of WSNs based on SDN is explored in this paper. An Energy-efficient Passive MOnitoringSysTem (EPMOSt) has been proposed taking into account the need for energy efficiency in WSNs [57]. Similar to WSNManagement [33] EPMOSt uses an SNMP agent to provide monitoring information with reduced energy consumption. This energy efficiency ensures longer network monitoring lifetime.
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 5 Transition Modes Process Monitoring Method
### Artificial Intelligence Method  
#### 5.3.1 Neural Network Method  
[PERSON] et al. [81] proposed a neuron-clustering method; the online data of the transition mode were mapped into a track by self-organization mapping (SOM) and transformed into a series of neuron clusters, which represented the characteristics of the operating state. The monitoring of the transition mode was realized by comparing it with the known characteristic sequence of the transition mode in the knowledge base. This method realized the visualization of the operating state in the transition mode with rapid online application speed. [PERSON] et al. [82] projected the source and target domains into the sub-feature space through an adaptive layer. They also introduced a convolutional neural network (CNN) into a joint domain adaptive model to reduce the maximum mean difference (MMD) and to improve the feature distribution difference of the adaptive layer as well as the migration ability. Additionally, CNN can be divided into the sharing layer and the full connection layer (adaptive layer). [PERSON] et al. [83] proposed a method based on a deep belief network (DBN) optimized by multi-order fractional Fourier transform (FRTF) and a sparrow search algorithm (SSA). Firstly, the FRTF, based on curve feature segmentation, was used to extract the fault characteristic frequency of the system vibration signal. Then the SSA was used to iteratively optimize each weight parameter of DBN the maximum number of times, which was used as the initial weight of the model, train the SSA-DBN model together with the fault characteristics, and apply it to fault monitoring.
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 4 Algorithm Design
### Received Signal-Strength Factor  
Link stability depends not only on link duration, but also on signal strength. The received signal power is primarily affected by path loss and shadow fading. Path loss is caused by the dissipation of the signal and the effects of the propagation channel. Shadow fading is caused by obstacles between the transmitter and receiver, where these obstacles lead to signal power attenuation through phenomena such as absorption and reflection.  
When the attenuation is particularly strong, the signal becomes blocked. The impact of shadow fading is undoubtedly particularly pronounced in urban areas, where the presence of various buildings poses a significant challenge. Especially at intersections, there are significant changes in the structure of the vehicle movement, thereby leading to signal attenuation around building corners. The received signal strength comes from neighbor beacons.  
The received signal strength factor can be calculated as follows:  
\[RSSF(l)=\begin{cases}RSSI(v)-RSSI_{min}&RSSI(v)>RSSI_{min}\text{ {and}}\\ \overline{RSSI_{max}-RSSI_{min}}^{2},&RSSI(v)<RSSI_{max}\\ 1,&RSSI(v)>RSSI_{max}\\ 0,&RSSI(v)<RSSI_{min}\\ \end{cases}\quad. \tag{9}\]  
In Equation (9), \(RSSI_{max}\) is the maximum-received signal-strength threshold, and \(RSSI_{min}\) is the minimum-received signal-strength threshold. When the signal strength received by the neighbor's beacon is greater than \(RSSI_{max}\), it indicates that the working environment of the vehicle network has little interference, and a stable link can be established. Otherwise, it indicates that there is some interference between the two nodes. Here, a linear model is used to calculate the strength factor. When the value is less than \(RSSI_{min}\), this object is not considered.
## 3 Experimental and Analysis
### Ablation Study  
#### 3.4.1 Ablation Study on the Replacement Positions of the CGC3 Module  
To validate the impact of different replacement positions of the CGC3 module on model performance, an ablation study was conducted using YOLOv5s as the reference baseline. The results are shown in Table 3. YOLOv5s + backbone and YOLOv5s + neck represent the replacement of the C3 module in the backbone network and the neck network, respectively. Compared with the baseline model, the mAP increased by 1.1% and 0.8%, the number of parameters decreased by 1.14 M and 0.95 M, and the computational load decreased by 3.4 G and 2.0 G, respectively. In contrast, YOLOv5s + all, which replaced the C3 module throughout the entire network, achieved a final mAP of 97.1%. The parameters and computational load were only 70.1% and 66.9% of the baseline model, respectively, making it more conducive to achieving model lightweighting.
## 3 Results and Discussion
### GC-MS/MS Analysis of Volatile Compounds in Lamb Patties
However, they did not find the presence of (E,E)-2,4-decadienal as a key odorant. We also found similar compounds in our cooked lamb patty samples, but there was no presence of hexanal; however, hexanol was present in our samples. The reason might be differences in cooking conditions and temperature requirements. In addition, the animal finishing system has also influence on lamb flavour. Ref. [42] reported that meat from lambs finished on pastures--without a concentrate supplement--had very low amounts of unsaturated aldehydes derived from lipids, ketones and Strecker aldehydes. The reason could be the protective effect of natural antioxidants in the diet. These results are consistent with TBARS values suggesting the development of lipid oxidation during cooking. Since we did not measure TBARS values, further investigation is therefore needed on this point. Volatile compound content in lamb increases during storage time [42; 43], but the meat from the present study was not stored or aged. That could explain the lack of aldehydes such as hexanal. Therefore the cooked meat in the current study could be characterised as lamb meat of low characteristic cooked lamb odour and flavour. Branched-chain fatty acids (BCFAs) are not the only factor that influences juiciness, tenderness and flavour; other chemical compounds generated also effect consumer acceptance. Moreover, the overall sheep meat flavour and odour will be affected by other factors such as the ultimate pH of meat. It has also been reported that goat and mutton-like odours contain branched-chain fatty acid (BCFA), 4-methylnonanoic acid (MNA), 4-ethylocanoic acid (EOA) and 4-methyloctanoic acid (MOA), along with octanoic acid, and thus during the cooking process, these compounds contribute to mutton odour. However, Ref. [44] suggested that reducing the effect of 4-methyloctanoic and 4-ethyloctanoic acid on the odour of grilled lamb will result in an improvement of the consumer acceptance of cooked meat. We found that 2-amino-6-methylbenzoic acid was present in our samples which might contribute to cooked lamb aroma. In a study, Ref. [8] observed, a strong relationship between the intensity of the roasted flavour or cooked flavour of meat, amount of free amino acids, carnosine, pyrazines, hexanol and cooking temperature.
## 3 Results
### Relationship Between Ecological Factors and Biomass of Halophytes Under Salt Stress Conditions
#### 3.4.2 Redundancy Analysis of Different Ecological Factors with Halophytes Biomass  
In RDA ordination diagrams, ecological factors are generally represented by hollow arrows. The length of the arrow connecting the lines represents the magnitude of correlation of an ecological factor with the analyzed indicator. Redundancy analysis of the ecological factors with the biomass of haplotypes showed that 71.53% and 12.17% were explained by the first and second sorting axes, respectively, i.e., the total explanation of the two sorting axes was 83.7%. It indicates that the first two axes can effectively reflect the relationship between ecological factors and halophyte biomasses, and the ordering is good (Figure 11a). As can be seen from the figure, the longer connecting lines for salt concentration, plant height, malondialdehydehyde, chlorophyll a, intercellular carbon dioxide concentration, stem diameter, chlorophyll b, atmospheric temperature, net photosynthetic rate, stomatal conductance, and relative humidity indicate a greater correlation between these ecological factors and the biomass of halophytes. The degree of interpretation of the biomass of halophytes by different ecological factors is shown in Figure 11b. From the figure, it can be seen that salt concentration, plant height, malondialdehydehyde, chlorophyll a, intercellular carbon dioxide concentration, stem diameter, chlorophyll b, atmospheric temperature, net photosynthetic rate, stomatal conductance, and relative humidity were more highly interpreted compared with the other ecological factors, and all of them could significantly affect the biomass of the halophytes (\(p<0.05\)).  
Figure 10: Effect of different salt concentration treatments on biomass of halophytes. The error bar above the graph represents the standard error.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Halophyte Species** & **Fitting Formula** & \(\mathbf{R^{2}}\) & **ANOVA Test** \\ \hline _Haloxylon_ & Y = \(-9.0108\)x + 62.415 & 0.663 & \(p<0.05\) \\ _Tamarix chinensis_ & Y = \(-6.431\)x + 39.663 & 0.708 & \(p<0.05\) \\ _Phragmites australis_ & Y = \(-3.5003\)x + 20.407 & 0.597 & \(p<0.05\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Equations for fitting salt concentration treatments to halophyte biomasses.
## 3 Establishment of Adaptive Identification Model for Flexible Air-Breathing Hypersonic Vehicle
### Online Updating for DNN
\begin{array}{l}\dot{V}=-\frac{1}{2}e^{T}Qe+e^{T}P\Big{(}B\widetilde{W}\Phi( \hat{x})+B\widetilde{\beta}\Psi(\hat{x})u\Big{)}+\sum\limits_{i=1}^{11}\frac{1 }{\eta_{i}}\widetilde{W}_{i}\dot{\widetilde{W}}_{i}^{T}+\sum\limits_{i=1}^{11} \frac{1}{\lambda_{i}}\widetilde{\beta}_{i}\dot{\widetilde{\beta}}_{i}^{T}\\ =-\frac{1}{2}e^{T}Qe+e^{T}PB\widetilde{W}\Phi(\hat{x})+\sum\limits_{i=1}^{11} \frac{1}{\eta_{i}}\widetilde{W}_{i}\dot{\widetilde{W}}^{T}+e^{T}PB\widetilde{ \beta}\Psi(\hat{x})u+\sum\limits_{i=1}^{11}\frac{1}{\lambda_{i}}\widetilde{ \beta}_{i}\dot{\widetilde{\beta}}_{i}^{T}\\ =-\frac{1}{2}e^{T}Qe+e^{T}PB\begin{bmatrix}\widetilde{W}_{1}\\ \widetilde{W}_{2}\\ \vdots\\ \widetilde{W}_{11}\end{bmatrix}\Phi(\hat{x})+\sum\limits_{i=1}^{11}\frac{1}{ \eta_{i}}\widetilde{W}_{i}\dot{\widetilde{W}}^{T}+e^{T}PB\begin{bmatrix} \widetilde{\beta}_{1}\\ \widetilde{\beta}_{2}\\ \vdots\\ \widetilde{\beta}_{11}\end{bmatrix}\Psi(\hat{x})u+\sum\limits_{i=1}^{11}\frac{1 }{\lambda_{i}}\widetilde{\beta}_{i}\dot{\widetilde{\beta}}_{i}^{T}\\ =-\frac{1}{2}e^{T}Qe+\sum\limits_{i=1}^{11}e_{i}p_{i}b_{i}\widetilde{W}_{i} \Phi(\hat{x})+\sum\limits_{i=1}^{11}\frac{1}{\eta_{i}}\widetilde{W}_{i}\dot{ \widetilde{W}}_{i}^{T}+\sum\limits_{i=1}^{11}e_{i}p_{i}b_{i}\widetilde{\beta}_ {i}\Psi(\hat{x})u+\sum\limits_{i=1}^{11}\frac{1}{\lambda_{i}}\widetilde{\beta}_ {i}\dot{\widetilde{\beta}}_{i}^{T}\end{array} , \tag{14}\]  
\[ \begin{array}{l}\mbox{where }e=\begin{bmatrix}e_{1}\\ e_{2}\\ \vdots\\ e_{11}\end{bmatrix},P=\begin{bmatrix}p_{1}&0&\cdots&0\\ 0&p_{2}&&\vdots\\ \vdots&&\ddots&0\\ 0&\cdots&0&p_{11}\end{bmatrix}.\end{array} \]  
If \(\sum\limits_{i=1}^{11}e_{i}p_{i}b_{i}\widetilde{W}_{i}\Phi(\hat{x})+\sum\limits_ {i=1}^{11}\frac{1}{\eta_{i}}\widetilde{W}_{i}\dot{\widetilde{W}}_{i}^{T}=0\), i.e.,  
\[\dot{\widetilde{W}}_{i}^{T}=-\eta_{i}e_{i}p_{i}b_{i}\Phi(\hat{x}),i=1,2,\cdots,11 \tag{15}\]and \(\sum\limits_{i=1}^{11}e_{i}p_{i}b_{i}\widetilde{\beta}_{i}\Psi(\hat{x})u+\sum \limits_{i=1}^{11}\frac{1}{\lambda_{i}}\widetilde{\beta}_{i}\dot{\widetilde{ \beta}}_{i}^{\top}=0\), i.e.,  
\[\dot{\widetilde{\beta}}_{i}^{\top}=-\lambda_{i}e_{i}p_{i}b_{i}\Psi(\hat{x})u,i= 1,2,\cdots,11. \tag{16}\]  
\(\beta\) is a \(11\times 11\) diagonal matrix,  
\[\dot{\widetilde{\beta}}_{ii}=-\lambda_{i}e_{i}p_{i}b_{i}\Psi_{i}(\hat{x})u,i= 1,2,\cdots,11, \tag{17}\]  
where \(\Psi_{i}(\hat{x})\) is the \(i\)th row vector of matrix \(\Psi(\hat{x})\). Then,  
\[\dot{V}=-\frac{1}{2}e^{T}Qe\leq 0. \tag{18}\]  
The above equation illustrates that \(\dot{V}\) is negative semidefinite and the identification error is convergent. Using [PERSON]'s lemma [38; 39], it can be seen that as \(t\rightarrow\infty\) and \(e\to 0\).