esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Shape Design of Direct-Drive Permanent Magnet Generator for 1 kW-Class Wind Turbines
## 3 Electromagnetic Analysis
### Equivalent Circuit  
To estimate the electrical power output of the AFPM generator, the equivalent circuit and voltage equation must be employed [24]. The circuit and equation include electrical  
Figure 6: Mesh generation of two-dimensional finite element model.  
Figure 7: Flux linkage and electromotive force for \(\mathrm{A}\), \(\mathrm{B}\) and \(\mathrm{C}\) phases under no-load condition by lapse of time: (**a**) two-dimensional finite element model; (**b**) three-dimensional finite element model.  
parameters, such as the phase internal resistance and synchronous inductance. The internal resistance \(\mathrm{R_{ph}}\) of each phase can be determined using Equation (4), which accounts for the number of turns per phase \(\mathrm{N_{ph}}\), the average length of a turn \(\mathrm{I_{w}}\), the cross-sectional area of the wire for the winding \(\mathrm{A_{c}}\) and the electrical conductivity \(\mathrm{\rho_{c}}\) of the winding material at a given temperature (for copper, \(\mathrm{\rho_{c}}\approx 57\times 10^{6}\) S/m at 20 \({}^{\circ}\)C) [25].  
\[\mathrm{R_{ph}}=\mathrm{N_{ph}}\mathrm{\rho_{c}}\frac{\mathrm{I_{w}}}{\mathrm{ A_{c}}} \tag{4}\]  
The synchronous inductance \(\mathrm{L_{ph}}\) for each phase can be computed by calculating the linkage flux generated by the induced current in the winding coil [25]. When using NdFe-based permanent magnets, the synchronous inductances of the A, B and C phases are equal, since the relative recoil permeability becomes 1.0 [26]. The following equation represents the synchronous inductance:  
\[\mathrm{L_{ph}}=\mathrm{m}\mathrm{\mu_{0}}\frac{1}{\pi}\bigg{(}\frac{\mathrm{ N_{ph}}\mathrm{k_{wn}}}{\mathrm{p}}\bigg{)}^{2}\frac{\big{(}(\mathrm{D_{i}}+2 1)^{2}-\mathrm{D_{i}^{2}}\big{)}}{4\mathrm{g}} \tag{5}\]  
where m is the number of winding phases, \(\mathrm{\mu_{0}}\) is the permeability of free space (\(0.04\pi\times 10^{-6}\) H/m), \(\mathrm{N_{ph}}\) is the number of turns per phase for winding, \(\mathrm{k_{wn}}\) is the winding factor, \(\mathrm{p}\) is the total number of pole pairs, 1 is the slot length of stator, \(\mathrm{D_{i}}\) is the inner diameter and \(\mathrm{g}\) is the air gap.  
To consider the load condition, Figure 8 illustrates the three-phase equivalent circuit diagram. A Y-connection was employed for the three-phase winding coils, and the load resistance was represented by Load-R, which corresponds to the maximum output power [5].
## 3 Experimental Results Analysis
### Effect of Loading Rate and Inclination Angle on Macroscopic Failure Characteristics of Coal Samples
Figure 14: _AF–RA_ relation curve at different inclination angles at a loading rate of 10 mm/min: (**a**) 0\({}^{\circ}\); (**b**) 5\({}^{\circ}\); (**c**) 10\({}^{\circ}\); (**d**) 15\({}^{\circ}\); (**e**) 20\({}^{\circ}\); (**f**) 25\({}^{\circ}\). In summary, when the loading rate is 10 mm/min, the number of tensile and shear cracks in coal samples decreases significantly. However, the proportion of tensile and shear cracks at 10 mm/min is close to that at 1 mm/min. Within the inclination angle of 0\({}^{\circ}\)-10\({}^{\circ}\), the proportion of tensile crack is more than 50% and higher than that of shear crack; the tensile splitting failure is the main failure mode of coal samples. Within the inclination angles of 15\({}^{\circ}\)-25\({}^{\circ}\), the proportion of shear cracks is more than 50% and higher than that of tensile cracks, and shear failure is the main failure mode. Therefore, shear stress components are conducive to the initiation and propagation of shear cracks and accelerate the instability failure of coal samples. It should be noted that there are different cracking modes under low loading rate and high loading rate at the inclination angle of 10\({}^{\circ}\). It indicates that the cracking mode under this condition is more complex and is greatly affected by external load conditions. During the loading process, tensile crack or shear crack may lead to the failure of the sample, which mainly depends on the degree of the sample heterogeneity. It is also possible that the high loading rate is beneficial to the propagation of tensile cracks and the initiation of shear cracks, which leads to the shear failure of coal samples at the loading rate of 1 mm/min and tensile failure of 10 mm/min. This law is consistent with the macroscopic failure mode of coal samples, indicating the accuracy of the experimental results.
## 2 Related Research  
### Network Topology Construction  
Network topology metrics measure network resilience from a static perspective, reflecting the connectivity of transportation networks under disruptions. Scholars have proposed various methods to construct and optimize road network topology models. For instance, some studies utilize complex network theory to analyze the robustness and efficiency of urban road networks [21]. For example, [PERSON] [22] revealed the crucial impact of road network structural characteristics on traffic flow by studying the evolution of the road network in Changchun. Additionally, graph theory-based methods are widely applied to the dynamic topology generation and optimization of road networks [23]. Machine learning techniques, particularly deep learning, have also been introduced into road network topology modeling to enhance the accuracy and efficiency of traffic flow prediction.  
Despite significant advancements in using complex network theory and deep learning techniques for road network topology modeling [24], most research has focused on graph theory-based single-layer topology micro-models. These model abstract road network intersections as nodes and road segments as edges. However, when simulating large-scale sparse road networks, their computational efficiency is low, failing to meet the timeliness and real-time requirements of emergency rescue. Additionally, most applications of road network topology models are limited to static or pre-defined scenarios, lacking the capability to respond to real-time changes in road network conditions under sudden disaster events. Consequently, they cannot dynamically represent the traffic state and emergencies of sparse road networks.
# Piston Error Measurement for Segmented Telescopes with an Artificial Neural Network
###### Abstract  
A piston error detection method is proposed based on the broadband intensity distribution on the image plane using a back-propagation (BP) artificial neural network. By setting a mask with a sparse circular clear multi-subaperture configuration in the exit pupil plane of a segmented telescope to fragment the pupil, the relation between the piston error of segments and amplitude of the modulation transfer function (MTF) sidelobes is strictly derived according to the Fourier optics principle. Then the BP artificial neural network is utilized to establish the mapping relation between them, where the amplitudes of the MTF sidelobes directly calculated from theoretical relationship and the introduced piston errors are used as inputs and outputs respectively to train the network. With the well trained-network, the piston errors are measured to a good precision using one in-focused broadband image without defocus division as input, and the capture range achieving the coherence length of the broadband light is available. Adequate simulations demonstrate the effectiveness and accuracy of the proposed method; the results show that the trained network has high measurement accuracy, wide detection range, quite good noise immunity and generalization ability. This method provides a feasible and easily implemented way to measure piston error and can simultaneously detect the multiple piston errors of the entire aperture of the segmented telescope.  
piston error detection; segmented telescope; BP artificial neural network; modulation transfer function +
Footnote †: journal: Sensors
# 3D DNA Nanostructures: The Nanoscale Architect
## 5 Applications
### DNA Nanomechanics
#### 5.3.2 Biosensing  
Rather than amplifying low concentration signals, DNA nanomechanical devices may provide an alternative, physical indicator for an analyte. Binding events permanently alter the physical configuration of the DNA nanostructure which serves to transduce the chemical input into a physical signal that can be readout with less sensitive microscopy techniques. One such device is a scissor-like DNA origami nanostructure with receptor strands on both sides of the resemblant blade edge. An analyte triggers hybridization between these two receptor strands and causes the scissors to close, and this new configuration can then be characterized by AFM [173]. Tetrahedron DNA nanostructures have been used to detect intracellular microRNA in a very characteristic demonstration of the core concepts of a nanomechanical DNA device. A FRET pair was placed at two vertices of the tetrahedron, while a ssDNA region spanned the edges as a probe. The length of the probe sequence was shorter than the analyte sequence, such that when the microRNA analyte was bound, it would also displace a bit more of the ssDNA endpoints that were hybridized into the edges of the triangular face. This would lengthen the spanning duplex between the edges and consequently increase the spacing between the FRET pair, providing an optical readout of the analyte being detected [172] (Figure 8B). [PERSON] et al. demonstrated a extension and contraction of a chain of 2D DNA origami rectangles, where adjacent structures were linked by two points, a fixed hinge and a duplex latch. Undoing latches caused the chain to unravel and lengthen, and this change could then be imaged by physically characterizing the structure [174].
# Fractional Order Processing of Satellite Images
## 3 Results
### Overall Performance with Varying Parameters  
To analyse the overall performance of detectors, the mean Jaccard of the best results for each edge detection operator (for all images in the data set) was computed. The results are shown in Table 6 and Figure 25.  
Figure 24: Analysis of performance comparison between grey-scale and color based detectors.
## 2 Communication Framework Description
### Use Case--The Vision  
Like in the aim of different standardization processes, the WBAN system can be used as a continuous health monitor. Therefore, in a concrete example, a patient with hypertension and a high risk for cardiovascular diseases could wear one of these systems to continuously track different vital signs, alerting them and their healthcare providers when anomalies are detected through the use of AI classifiers or specially trained networks. Sensors of different vendors can communicate with each other in a ring topology and exchange information securely. Because information traffic will be distributed through the different sensors through the ring topology, wearing many different sensors will hardly overload the master node, which could be a smartwatch or mobile phone. through the workload distribution, less energy is needed by each sensor and master node. Through an HBC effortless new sensors can be connected securely if distributed at the patient skin. Grounded on the same protocol a doctor can have access to continuously monitored data of the sensors, whereas the present patient is just touching an HBC-enabled monitor device. Securely, data can be transmitted wirelessly to a hospital server, established through biometric authentication with the help of the WBAN sensors and/or combined with the HBC key exchange.
## 1 Introduction
Recognizing the important differences in the fundamental sources that contribute to the innovation and competitive advantages between developed and developing country industries, this study adopts the ABCD model [4]. It stresses the behavior and attitude variables of firms for creating competitive advantages given the limited resources and core competences compared to the leading firms. The ABCD model was first developed to analyze the strategy for Korea's economic success [4], but later has been widely applied at various levels including nation [12,13], industry [14], and firm [15,16]. Specifically, this paper adopts the ABCD model to analyze the relative competitiveness of the Indian and Korean film industries. The model is composed of four competitiveness factors: agility, benchmarking, convergence, and dedication. To operationalize this conceptual model, this study conducts an empirical test by using the natural language processing (NLP) method, which is a novel approach in this kind of research, particularly in the film industry. As a methodological tool for empirical research, text mining was adopted. This tool has proven, over the past decade, its effectiveness in generating knowledge from large amounts of text data [17]. Text mining is widely used by many researchers in various fields specially to measure concepts that were previously very difficult to measure. The empirical analysis of this paper shows that competitiveness factors of Korean and Indian film industries are different from the US filmmakers' success formula which is well supported by the conventional theories of IBV and RBV. Nevertheless, Korean and Indian film industries show different but complementary aspects of strengths with regard to the competitiveness elements of the ABCD model. These findings therefore provide the opportunity for partnership between the two countries to utilize each other's strengths while overcoming their weaknesses. This paper's main contribution to strategic manage ment and competitiveness literature lies in operationalizing the ABCD model, by using the novel method of Natural Language Processing for data collection and competitiveness measurement. It also points out the significance of the alternative way of creating advantages given the limited technology and human resources, as well as an underdeveloped market system. The empirical study of Korean and Indian film industries thus provides a good example and practical implications for developing indigenous industries in other emerging economies by using the ABCD model. The rest of this paper is organized as follows: in the next section, we review the existing studies on the competitiveness of Indian and Korean film industries and point out the research gaps between existing studies and this research. The ABCD model and hypotheses based on the model are presented in Section 3. Methods of the empirical test are shown in Section 4. The empirical results and discussions are illustrated in Section 5 and Section 6. For the Conclusions section, we summarize the contributions and implications of our approach.
###### Abstract  
In the neighborhood area network (NAN), the advanced metering infrastructure (AMI) enables a bidirectional connection between the smart meter (SM) and the data concentrator (DC). Sensors, such as smart meter nodes or environmental sensor nodes, play a crucial role in measuring and transmitting data to central units for advanced monitoring, management, and analysis of energy consumption. Wired and wireless communication technologies are used to implement the AMI-NAN. This paper delves into a novel approach for optimizing the choice of communication medium, air for radio frequency (RF) or power lines for power line communication (PLC), between the SM and DC in the context of the AMI-NAN. The authors methodically select the specific technologies, RF and NB-PLC (narrowband power line communication), and meticulously characterize their attributes. Then, a comparative analysis spanning rural, urban, and industrial settings is conducted to evaluate the proposed method. The overall reliability performance of the AMI-NAN system requires a packet error rate (PER) lower than 10%. To this end, an efficient approach is introduced to assess and enhance the reliability of NB-PLC and RF for AMI-NAN applications. Simulation results demonstrate that wireless communication is the optimal choice for the rural scenario, especially for a signal-to-noise ratio (SNR) lower than 25 dB. However, in urban environments characterized by higher SNR values and moderately dense networks, NB-PLC gains prominence. In denser networks, it outperforms wireless communication, exhibiting a remarkable 10 dB gain for a bit error rate (BER) of 10\({}^{-3}\). Moreover, in industrial zones characterized by intricate network topologies and non-linear loads, the power line channel emerges as the optimal choice for data transmission.  
power line communication; RF mesh; urban area; industrial area; rural area; electric grids; sensing and measurement; BER +
Footnote †: journal: Sensors  
0023
# GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization
## 4 Simulation Analysis
\begin{table}
\begin{tabular}{c c} \hline \hline
**Compression Acquisition Algorithm** & \(\mu(\mathbf{\Theta})\) \\ \hline GP & 0.44 \\ ELAD & 0.34 \\ CG & 0.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparison of mutual coherence (\(M=128\), \(N=256\)). Figure 7 compares the detection probability of several algorithms with different code lengths when the SNR is \(-15\) dB. We can see that with the increase of the code length, the detection probability of the CG acquisition algorithm is always higher than the other acquisition algorithms. The figure validates the theoretical result in Section 3.4: the im  
Figure 5: The algorithm acquisition results. (**a**) GP. (**b**) ELAD. (**c**) CG. Figure 6: The detection probability of several algorithms with different SNRs. (**a**) \(M\)/\(N=1/2\), \(N=1024\). (**b**) \(M\)/\(N=3/4\), \(N=1024\). (**c**) \(M\)/\(N=3/4\), \(N=512\). (**d**) \(M\)/\(N=3/4\), \(N=256\). provement of the column incoherence makes the noise power closer to Welch bound, which is beneficial to improving the performance of the compression acquisition algorithm. Figure 8 shows the detection probability of the proposed algorithm with different compression ratios. We can find that when the SNR is \(-15\) dB, the detection probability of \(M/N=3/4\), \(1/4\), and \(1/8\) are \(99.1\%\), \(71.4\%\), and \(21.9\%\), respectively. With the increase of \(M/N\), the noise power increases by a factor proportional to \(N/M\), which affects the detection probability of the proposed algorithm. Hence, it is necessary to choose an appropriate compression ratio in practical engineering. Figure 9 compares the detection probability of several algorithms with different compression ratios when the SNR is \(-15\) dB. With the decrease of \(M/N\), the detection probability of the compression acquisition significantly decreases. As we can see, the compression measurement will cause SNR loss, but the detection probability of CG is always higher than the others. As discussed in Section 3.4, the proposed algorithm reduces noise variance to \(\sigma_{n}^{2}\frac{N}{M}\mathbf{I}_{M}\), which improves the detection probability of the compression acquisition. Figure 8: The detection probability of the proposed algorithm with different compression ratios. Figure 7: The detection probability of several algorithms with different code lengths.
## 5 Challenges and Future Directions
### Robustness  
GNN-based solutions need to maintain high performance even when faced with perturbations in the graph structure or feature information. Adversarial attacks pose a particular concern, where small, intentionally harmful changes are made to the graph (such as adding, removing edges, or altering node features), or simply dealing with noisy or incomplete data. Within an NDT system, this attack can be performed by exploiting the vulnerabilities in the communication channels between the network and the digital replica. For instance, if data collection involves transmitting data over a network, attackers could attempt to intercept and modify the data packets, altering network statistics, topology information, or node attributes. Adversarial attacks significantly impact the integrity and reliability of GraphNDT systems and can lead to incorrect predictions or interpretations by GNN models. For example, in a semi-supervised node classification task, adversarial modifications can make a GNN misclassify the label of a node. The authors in [139] highlighted this problem, where they demonstrated that most GNNs are highly vulnerable to adversarial attacks.  
In addition to securing the communication channels, enhancing the robustness of GNN models against adversarial attacks is an intrinsic solution. This can be achieved through the development of robust GNN architectures and the application of adversarial training techniques. For robust GNN architectures, some research papers have designed new GNN architectures that are inherently more robust against adversarial attacks. For example, _RGCN (Robust Graph Convolutional Network)_ incorporates a smoothing mechanism to reduce the effect of adversarial perturbations [127]. In the same direction, a _GNN-Guard_ against adversarial attacks on GNN was proposed in [126]. The main idea is to monitor the information flow in a GNN during message passing and to control the influence of nodes that have irregular behavior. To achieve this, significant modification is involved in the way GNN aggregates and propagates information. This includes introducing guarded aggregation to limit the influence of suspicious nodes and guarded influence computation to attenuate the impact of adversarial nodes. On the other hand, adversarial training methods involve integrating adversarial examples into the training process. By training the model on both original and adversarial data, GNN models learn to improve their performance in the presence of adversarial perturbations [128]. To further mitigate the effects of some types of adversarial attacks, adding regularization terms to the loss function of the model can help improve the model's robustness by discouraging complex models that could overfit the perturbed data.
## 2 Results and Discussion
### Method Calibration and Figures of Merit  
Calibration standards of IBMP were prepared at 6 levels (0.5-100 ng/kg) in a _Vitis vinifera_ (cv. Barbera) homogenate with no detectable concentration of IBMP as previously determined by HS-SPME-GC-MS. An isotopically standard (d\({}_{3}\)-IBMP) was added at 100 ng/kg to all standards. The use of isotopically matched internal standards to increase precision and accuracy in SPME extraction approaches, including SPMESH extraction, is well documented in the literature [22; 23; 24]. A representative HRMS selected ion chronogram (SIC) for IBMP and d\({}_{3}\)-IBMP at 18 ng/kg and 100 ng/kg, respectively, in Barbera grape homogenate is show in Figure 3.  
Figure 3: DART-Orbitrap-MS/MS selected ion chronograms for IBMP (_m/z_ 167.1427) _m/z_ 124.0632) at 18 ng/kg and d\({}_{3}\)-IBMP at 100 ng/kg (_m/z_ 170.1351) _m/z_ 128.0896) in _Vitis vinifera_ (cv. Barbera) grape homogenate with no detectable IBMP as previously determined by HS-SPME-GCMS. Wells 1 and 3 are analytical replicates spiked with IBMP at 18 ng/kg and d\({}_{3}\)-IBMP at 100 ng/kg and wells 2 and 4 are blank wells.  
Linearity, limits of detection, recovery, and repeatability for IBMP analyses using SPMESH-DART-HRMS/MS were compared with SPME-GC-MS for similar calibration samples, and to previous SPMESH-DART-HRMS/MS work in Table 1. The optimized SPMESH-DART-HRMS/MS approach in the current work yielded an LOD of <0.5 ng/kg as compared to 4 ng/kg in a previous study [13]. Improvements in precision were reduced from 32% RSD in previous work to 5% RSD in our current approach. Recovery for DART-HRMS/MS at 5 ng/kg was acceptable (110%). The figures of merit for SPMESH-DART-HRMS/MS were also comparable to those achieved with the standard SPME-GC-MS approach (Table 1). Improvements in the limit of detection and repeatability are a result of improvements in extraction apparatus, sample preparation, DART source parameters, and MS/MS parameters.