esa-sceva/satcom-chunk-collection huggingface dataset:
## Design and Evaluation of a Wireless Sensor Network Based Aircraft Strength Testing System  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 2, [PERSON] 3, [PERSON] 1 and [PERSON] 1**  
The Aeronautic Key Laboratory for Smart Materials and Structures, Nanjing University of Aeronautics and Astronautics, 29# Yu Dao Street, Nanjing, China, 210016; E-Mails: [EMAIL_ADDRESS] (J.W.); hjf641 [EMAIL_ADDRESS] (Z.W.); [EMAIL_ADDRESS] (Y.W.)  
Jinangsu Teachers University of Technology, 1801# Zhongwu Road, Changzhou, China, 213001; E-Mail: [EMAIL_ADDRESS] (G.Z.)  
Nanjing University of Information Science and Technology, 219# Ningliu Road, Nanjing, China, 210044; E-Mail: [EMAIL_ADDRESS] (S.J.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-25-84893460; Fax: +86-25-84892294  
Received: 31 March 2009; in revised form: 9 April 2009 / Accepted: 13 April 2009 /  
Published: 3 June 2009  
The verification of aerospace structures, including full-scale fatigue and static test programs, is essential for structure strength design and evaluation. However, the current overall ground strength testing systems employ a large number of wires for communication among sensors and data acquisition facilities. The centralized data processing makes test programs lack efficiency and intelligence. Wireless sensor network (WSN) technology might be expected to address the limitations of cable-based aeronautical ground testing systems. This paper presents a wireless sensor network based aircraft strength testing (AST) system design and its evaluation on a real aircraft specimen. In this paper, a miniature, high-precision, and shock-proof wireless sensor node is designed for multi-channel strain gauge signal conditioning and monitoring. A cluster-star network topology protocol and application layer interface are designed in detail. To verify the functionality of the designed wireless sensor network for strength testing capability, a multi-point WSN based AST system is developed for static testing of a real aircraft undercarriage. Based on the designed wireless sensor nodes, the wireless sensor network is deployed to gather, process, and transmit strain gauge signals and monitor results under different static test loads. This paper shows the efficiency of the wireless sensor network based AST system, compared to a conventional AST system.  
keywords: aircraft strength testing; wireless sensor network; cluster-star topology protocol; undercarriage static test +
Footnote †: journal: Journal of the Acoustical Society of America
## 3 Results
### Experiment 1: Evaluation of SMF Model
#### 3.2.2 Experimental Results  
In this experiment, the experimental medium was potting soil made from mushroom wood chips and coconut fibers. The total size of the experimental plot was 120 cm \(\times\) 90 cm and the height was 6 cm. The sensors were placed at VPA#4. The area of each VPA was a 39 cm \(\times\) 28 cm rectangle. The watering volume was 15 mL each time the SMF performed the irrigation task. The moisture forecasting in this experiment was executed seven consecutive times, and each forecasting result was compared with the observed soil moisture values. The objective was to verify the error of the SMF model.  
Figure 7(a)-c shows the forecasting errors when the SMF applied the 2, 3, and 4 h irrigation intervals. In the case of the 2 h irrigation interval, the error on the moisture level was no more than 7. The 3 h irrigation interval was relatively similar, with an error of no more than 8. The maximum error was in the 4 h irrigation interval, at 11.8, with an average forecasting error of less than 9.9 in this interval. These results show that the forecasting error of the SMF model was no more than 12 moisture levels out of 1024 possible levels.  
Figure 7: The forecasting errors of different order polynomial functions. The 1 st-order to 5 th-order polynomial regression function with different irrigation intervals are evaluated. The forecasting error is calculated by moisture level, which is ranged from 0 to 1023. (**a**) the results of the sensors placed at the 2 cm depth layer of soil. (**b**) the results of the sensors at the 3 cm depth layer of soil. (**c**) the results of the sensors at the 4 cm depth layer of soil.
## 4 Performance Evaluation and Obtained Results
### Obtained Results, Analysis and Discussion
#### 4.3.3 Network Lifetime Performance Evaluation Results  
It was also crucial to assess the contribution of the proposed approach towards enhancing the network's lifetime. To assess the network lifetime performance, the number of simulation runs  
Figure 5: Packets transmission success rate versus network scalability.  
have been performed to compute the number of remaining active nodes as the rounds of packet transmissions is increased to evaluate at what rate the network dies when the proposed N-MANC approach is used as compared to the N-LEACH, N-MANNC and the N-AFHSS. The obtained results are shown in Figure 6.  
It can be observed in Figure 6 that the number of remaining active nodes reduces as the number of rounds of packet transmissions increases. Since most of the NB-IoT applications work based on periodic packet transmissions, the rounds of packet transmissions often correspond to the number of packet transmission periods. For a typical smart water metering application with a daily water consumption packet transmission, the maximum round of packet transmissions considered for our simulations (3960) corresponds to a total of almost 11 years of operation for a yearly number of days of roughly 365 days; considering that the NB-IoT node is in deep sleep mode for the rest of the day where it consumes almost no power.  
It can also be noticed in Figure 6 that the number of remaining active NB-IoT nodes when the proposed N-MANC approach is used remains always higher as compared to when the N-MANNC, the N-LEACH, and the N-AFHSS are used respectively. it is also important to mention that with the use of the N-AFHSS, the network dies much faster as compared to the others (N-LEACH, N-MANNC, and N-MANC) respectively. This demonstrates that the energy consumption reduction of the proposed mixed approach (N-MANC) as depicted in Figure 4 coupled with its enhanced network reliability as shown in Figure 5; considerably contributes towards significantly extending the NB-IoT network lifetime.  
The results in Figure 6 are further confirmed by the results of the evaluation of the average remaining energy per node of the proposed N-MANC approach as compared to the N-MANNC, N-LEACH, and the N-AFHSS; as shown in Figure 7.  
Figure 6: Network lifetime: remaining nodes versus round of transmission packets.  
The results in Figure 7 clearly show that the same way the number of remaining active nodes in Figure 6 as more rounds of packets are transmitted, the same way the average remaining energy per node also reduces. The results in Figure 7 also show that the N-MANC approach maintains the highest average remaining energy per node for all the considered rounds of packet transmissions as compared to the N-MANNC, the N-LEACH, and the N-AFHSS respectively.
## 3 Analysis of Torque
### Influence of Temperature on MR Torque  
The rheological properties of MRF, such as shear stress and viscosity, decrease significantly under high-temperature conditions, and the performance decays significantly. To describe the relationship between MRF temperature and torque, an expression for the relationship between temperature and MRF shear yield stress was established.  
Figure 13 shows the relationship between the MR torque of the curved disc-type MR drive and the temperature according to Equation (1) when the current was 3.0 A and the temperature increased from the ambient temperature of 20 \({}^{\circ}\)C to 100 \({}^{\circ}\)C. As can be seen from Figure 13, the MR shear torque of the drive follows an approximately linear and decreasing trend as the temperature increases, and the maximum torque decreases from 82.627 N\(\cdot\)m to 55.021 N\(\cdot\)m (a decrease of 33.41%).
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 4 Experiments and Results Analysis
### Experimental Procedure
#### 4.1.4 Fog Removal Training  
Four GridNet-based dehazing models were trained using the synthetic foggy images generated by each method:  
* ModelS : Trained on images with simple fog;
* ModelU: Trained on images with GridNet-based fog;
* ModelDA: Trained on images with depth-aware fog;
* ModelDC: Trained on images with dark channel fog.  
Each model was trained for 30 epochs using the same GridNet architecture, with a loss function combining L1 and perceptual losses (using VGG-16 features). The training process mirrored that of the Fog Generator. The goal was to assess how the quality of synthetic fog influences dehazing performance.
## 3 The Proposed Method
### Watermark Embedding  
The first step is to do a wavelet analysis until an intermediate level \(I\) is reached, after applying one wavelet decomposition of the original mesh. The watermarking primitive is the wavelet coefficient in the level \(I\). The watermark bits are inserted by modifying the norm wavelet coefficients associated with the sorted vertices according to a predefined order. This order is defined as the Euclidean distance between the vertices and the mesh center. Firstly, the vertices are sorted in the descending order according to their distance to the mesh gravity. Next, the salient points in the level \(I\) are extracted by calculating the mesh saliency using [PERSON] et al.'s methods [PERSON] et al. (2018). Afterwards, the wavelet coefficients corresponding to the salient vertices in the level \(I\) and their Euclidean norms are calculated. To define a salient point, a threshold \(T_{r}\) is defined in order to choose a candidate wavelet coefficient to be altered in the embedding process. The parameter \(T_{r}\) is here chosen as 70% maximum values of the saliency vector, which has been adopted for all 3D objects. Thus, the wavelet coefficients corresponding to the salient points are quantized using a QIM scheme. Finally, the watermarked 3D mesh is reconstructed starting from the modified wavelet coefficients norms using Equation (8) [PERSON] et al. (2018).  
\[V^{\prime}(x^{\prime},y^{\prime},z^{\prime})=\frac{\left\|W_{C} \right\|}{\left\|W_{C}\right\|}V(x,y,z) \tag{8}\]  
where \(\left\|W_{C}\right\|\) and \(\left\|W_{C}\right\|\) refers to the modified wavelet coefficient after embedding and the wavelet coefficients calculated before the watermark embedding, respectively. \(V^{\prime}(x^{\prime},y^{\prime},z^{\prime})\) is the new vertex coordinate of the watermarked mesh, and \(V(x,y,z)\) is the vertex coordinate of the original mesh. Figure 4 sketches the watermark insertion process, which is described in detail in Algorithm 1.  
```
Require: Original 3D mesh, Watermark, key_1, key_2. Ensure: Watermarked 3D mesh.  1. Do wavelet analysis until an intermediate level \(I\) is reached.  2. Extract salient points of the 3D mesh in scale \(I\) using mesh saliency.  3. Find out the wavelet coefficients corresponding to the salient vertices according to the threshold \(T_{r}\) and calculate their norms.  4. Quantify the norms of wavelet coefficients by the QIM scheme using Equation (4).  5. Reconstruct the mesh starting from the modified wavelet coefficients norms to obtain the watermarked 3D mesh.
```  
**Algorithm 1**Watermark embedding.
# Service-Driven Dynamic Beam Hopping with Resource Allocation for LEO Satellites
## 4 Simulation Results and Analysis  
In this section, we demonstrate the performance of the algorithm applied in this paper through simulations in the dynamic traffic scenarios of LEO beam hopping satellites.
## 3 Factors Influencing U-WSN Design
### Architectures of U-WSN
#### 3.1.4 UAV-GateWay Link (U-GW)  
Similarly to U-SP, the connection between UAV and gateway used to collect information. Thus, their communication range are at the same level. The U-GW link use medium range protocols such as IEEE 802.16p (WiMAX) for real-time data delivery and LTE/LTE-A for transmitting large data (e.g., video) in real-time monitoring.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 2 Related Work
### ETSI NFV Reference Architectural Framework  
The NFV framework, shown in Figure 2, makes it possible to decouple applications from the underlying physical infrastructure, thus transforming network functions previously implemented on bespoke hardware into VNFs.  
Figure 2: ETSI NFV reference architectural framework.  
A VNF runs as a software appliance over an NFV Infrastructure (NFVI) [30]; it may have one or multiple internal VNF components (VNFCs), each running in a virtual machine, and can be chained with other VNFs and/or Physical Network Functions to realize a network service (NS).  
A major element in NFV is the Management and Orchestration (MANO) block, composed of the Virtualized Infrastructure Manager (VIM), the VNF Manager (VNFM), and the NFV Orchestrator (NFVO). It orchestrates the allocation of resources required by NSs and VNFs, and manages their lifecycle and the NFVI.  
To describe the on-boarding process of ME apps into an ME system, we will make extensive use of the concept of the VNF Package [33; 34]. A VNF Package contains all the necessary files and meta-data descriptors required to validate and instantiate a VNF, i.e.:  
* The VNF Descriptor (VNFD);
* The software images needed to run the VNF;
* The Manifest file that provides package integrity and authenticity;
* (Optional) additional files to manage the VNF.  
The VNF Descriptor is a template that defines the necessary resources to run the VNF, and its behavior in lifecycle management. It specifies connectivity, interfaces, and performance requirements to establish the Virtual Links (VLs) between VNFC instances, or between a VNF instance and the endpoints to other VNFs.  
To manage NSs, the NFV MANO relies on the NS Descriptor (NSD). The NSD references the descriptors of all the components of an NS, including the VL Descriptors and the VNF forwarding graph, which defines the topology of the NS [35].
# Comparing the Performance of Robust Controllers for Vibration Suppression in Long Rotor Systems
## 4 Mixed-Sensitivity \(H_{\infty}\) Control
The singular values of the sensitivity matrix at low frequencies are required for the assessment of step-response transients, while the high frequency values give information on noise attenuation. However, for a significant disturbance rejection at a steady state, the sensitivity weighting function could be represented by the following:  
\[w_{i}(s)=\gamma_{i}\frac{s+M_{i}}{s+1}\;\;M_{i}\gg 1 \tag{36}\]  
The weighting function \(w^{\prime}_{i}(s)\) should have small gains at a low frequency, bounding the sensitivity matrix so that  
\[w^{\prime}_{i}(s)=\alpha\frac{s+\varepsilon}{s+1}\;\;\varepsilon\ll 1 \tag{37}\]  
The forms in (37) and (36) are also recommended by [PERSON] in [19]. In order to achieve a lower control effort, \(w^{\prime}_{i}(s)\) should approach unity. The functions \(w_{i}(s)^{-1}\) and \(w^{\prime}_{i}(s)^{-1}\) represent upper bounds for the sensitivity and control effort, respectively. Similarly, both the upper and lower bounds of the sensitivity matrix should be considered to evaluate the effectiveness of the controller. This requires the determination of the maximum and minimum singular values of the sensitivity matrix in (25). The ratio of the output to disturbance, according to [13], is limited to the following:  
\[\underline{\sigma}(\mathbf{S})<\frac{\|\boldsymbol{y}(t)\|_{2}}{\|\boldsymbol {d}(t)\|_{2}}<\overline{\sigma}(\mathbf{S}) \tag{38}\]  
The symbols \(\overline{\sigma}\) and \(\underline{\sigma}\) denote the maximum and minimum singular values of \(\mathbf{S}\) over the entire frequency range. A controller exhibits robustness to model perturbations if the lower and upper bound in (38) are close together. A criterion for the closeness of \(\overline{\sigma}\) and \(\underline{\sigma}\) can be defined by the condition number, which is as follows:  
\[\kappa=\frac{\overline{\sigma}(\mathbf{S})}{\underline{\sigma}(\mathbf{S})} \tag{39}\]  
When the condition number \(\kappa\) is large, the plant is _ill conditioned_, which means that the sensitivity variations as a result of model perturbations are significant [17]. Therefore, attempts to make \(\kappa\approx 1\) are important, as is minimising \(\overline{\sigma}\). This statement is not always true [18]. We will show, for example, that by using a PI controller to achieve disturbance rejection, \(\kappa\) decreases, but this has an adverse effect on the transient responses. A further condition number, which could be assigned for a particular frequency range, can be defined as follows:  
\[\kappa(\mathbf{S}(j\omega))=\frac{\overline{\sigma}(\mathbf{S}(j\omega))}{ \underline{\sigma}(\mathbf{S}(j\omega))} \tag{40}\]  
Assuming that the controller has produced satisfactory transient and steady-state simulation results, if \(\kappa\) is decreased, the system exhibits robustness to model perturbations, thereby enhancing performance.
## 2 Methodology
### Aircraft Main Geometry and Moment of Inertia Data  
The main objective of this article is to present a dynamic stability monitoring method based on the approach of oscillatory motion and eigenvalue motion modes for the twin-jet transport aircraft response to sudden plunging motions. The data of main geometry and moments of inertia are presented in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Twin-Jet** \\ \hline Takeoff gross weight (kg) & 180,168 \\ Wing reference area, 5 (m\({}^{2}\)) & 361.300 \\ Mean chord length, (\(\overline{r}\)m) & 6.005 \\ Wing span, b (m) & 60.289 \\ Moment of inertia—\(x\)-axis, \(I_{mx}\) (kg-m\({}^{2}\)) & 12,815,012 \\ Moment of inertia—\(y\)-axis, \(I_{yy}\) (kg-m\({}^{2}\)) & 12,511,197 \\ Moment of inertia—\(z\)-axis, \(I_{zz}\) (kg-m\({}^{2}\)) & 53,650,986 \\ Moment of inertia—\(y\)-axis, \(I_{yy}\) (kg-m\({}^{2}\)) & 0.0 \\ Moment of inertia—\(x\)-axes, \(I_{zz}\) (kg-m\({}^{2}\)) & 0.0 \\ Moment of inertia—\(x\)-axes, \(I_{zz}\) (kg-m\({}^{2}\)) & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main geometry and moment of inertia data for the twin-jet transport aircraft.
# A Novel Error Correction Approach to Improve Standard Point Positioning of Integrated BDS/GPS
## 4 Experimental Analysis
### Integrated BDS/GPS Point Positioning Results
#### 4.2.2 Dynamic Positioning Results  
The dynamic positioning results are shown in Figure 9, and the dynamic positioning errors are presented in Table 2, from which it can be found that the 3D error of dynamic positioning is slightly higher than that of static positioning.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Positioning** & \multicolumn{2}{c}{**Root Mean Square Error (m)**} & \multicolumn{2}{c}{**3D Position**} \\
**Methods** & **X** & **Y** & **Z** & **Error (m)** \\ \hline WLS method & 1.347 & 2.945 & 1.322 & 3.498 \\ Kalman filter & 1.379 & 2.897 & 1.123 & 3.406 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Positioning errors of the two methods.  
Figure 8: Result of standard point positioning on the three axes: (**a**) positioning error of weighted least square method; and (**b**) positioning error of Kalman filter.  
Figure 7: The PDOP and the number of visible satellite during the experimental period.
## 3 Measuring Course Effectiveness  
### Post-Course Feedback  
Quantitative metrics data were collected from courses held from 2013 to 2017, when the course was funded by the NOAA Joint Polar Satellite System (JPSS) program as a means to increase the awareness and usage of ocean color satellite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) ocean color instrument. Participants were asked about their familiarity with VIIRS before the course and if they planned to use it in the future. The results of the survey clearly demonstrated an increased awareness of VIIRS data by class participants and an intent to use the data in the future. Prior to taking the course, only 19% of respondents were aware of the existence of VIIRS data and less than 2% had used the data. After having the opportunity to learn about and use VIIRS data in the course, most respondents indicated that they either planned to use (80%) or might use (16%) VIIRS data in their future work. Those not planning to use VIIRS data, or who were unsure, primarily cited the lack of a long timeseries for VIIRS data (the first VIIRS sensor launched in 2012) or that the VIIRS data did not cover their time period of interest.
# Key Issues on Integrating 5G into Industrial Systems
## 4 Modbus Protocol
### Introduction to Modbus
#### 4.1.2 Modbus Communication Stack  
The Modbus communication stack is shown in Figure 3[46], where the bottom two layers are physical layers, the hardware of which is specified by IEEE. The first column on the right side defines the protocol on the Modbus Ethernet link, where IP belongs to the network layer and TCP corresponds to the transport layer. TCP-based Modbus is mainly used to process information from Modbus TCP/IP packet headers and then transmit it upwards to the Modbus application layer [47]. The second column from the right side of the figure defines the protocol on the Modbus serial link. The physical layer of serial transmission is RS-485 or RS-232, which defines the specifications of signal transmission. The Modbus serial link protocol directly defines different packet types and standards on RS-485 or RS-232 for communication on the serial link. Modbus+/HDL and others represent Modbus protocols on different physical layers. The Modbus protocol only specifies the format of data packets, so there is no restriction on what must be carried on RS485 or Ethernet. Any device that can communicate can be used to apply the Modbus protocol.