esa-sceva/satcom-chunk-collection huggingface dataset:
# A State-of-the-Art Review on Robots and Medical Devices Using Smart Fluids and Shape Memory Alloys
## 2 Robots Using ERF and MRF  
### Material Characteristics  
ERFs are a class of colloidal dispersions that exhibit a large reversible change in their rheological behavior when subjected to external electric fields. These changes in rheological behavior are manifested by a dramatic increase in flow resistance, which depends upon the flow regime and the composition of the ERF. The flow resistance can be considered an actuating force controlling dynamic motions, such as vibration and position. In order to achieve flow resistance, ERFs are frequently modeled by a simple Bingham plastic model, in which the field-dependent yield stress is expressed as a controllable resistance. Among the many inherent characteristics of ERFs, the most salient property is the fast response of the actuating force (or torque) caused by application of an external electric field. This important property has triggered numerous application research works on ERF, including automotive shock absorbers, brakes, clutches, and smart structures. In addition, owing to their fast and easy controllability, ERFs are applied to robot systems, where they are embedded into the flexible arms of manipulators. Some other applications of ERF in robotic areas include tactile sensors, and human-friendly soft hands. It is also known that, when subjected to an external magnetic field, MRFs have exactly the same characteristics as ERFs. The rheological properties of MRFs, such as complex moduli, can be tuned or controlled by changing the intensity of the magnetic field associated with appropriate control schemes. Thanks to the robustness and higher material performance of MRFs compared to ERFs in a practical environment, numerous studies on the applications of MRFs are being actively undertaken in many industrial fields such as automotive, aerospace, and civil engineering. MRFs have a much higher field-dependent yield stress than ERFs, and hence, they satisfy the force or torque requirements of many practical systems or devices over a wide temperature range. However, MRF has slower response time compared to ERF. Moreover, the weight of MRF-based devices is much heavier than ERF-based devices because of high density of ferromagnetic metals.
# A Case Study of Floating Offshore Super-Long Steel Pipeline Combing with Field Monitoring
## 4 Analysis of Monitoring Results
### Modal Monitoring Results  
The pipeline modal test adopted the environmental excitation method. The response signals of all the measurement points arranged on the pipeline were collected at once, and then the response signals were analyzed in the frequency spectrum to identify the modal parameters of the pipeline. The spectrum analysis adopted the power spectrum method to identify the system modal parameters by using the self-power spectrum of the pipeline response output and the mutual power spectrum amplitude of the reference point response input. The high-frequency acquisition could make the collected signal closer to the original signal and reduce the influence of industrial noise. As the floating time was as long as 3 h, the modal test of the floating process of pipeline segment 2 was divided into two stages, and the parameter balance was carried out respectively.  
As shown in Figure 12 that A-1, B-1, B-2, C-2, D-1, D-2 are measured in the pipeline modal test. In the first stage of the test, at about 55 min, the time-history curve of acceleration increases significantly, which is the same as the time of strain surge. Since the sailing speed of the additional tugboat was not synchronized with that of other tugboats and the tugboats had been adjusting their positions, the movement of the extra tugboats had a temporary impact on the steel pipeline and the waves and flows near the pipeline. Therefore, there are some big fluctuations in the acceleration time-history curve between 55-93 min, among which the acceleration time-history curve at 62 min and 92 min has a large increase, but the duration is short.  
As shown in Figure 13 that according to the spectrum analysis, from the beginning of the floating movement to the wind rise, the horizontal and vertical first-order vibration frequency is 16.602 Hz, the frequency changes with a small increase to 17.578 Hz after adding a tugboat. At the beginning of floating transportation, the axis of the pipeline was parallel to the direction of the water flow, and the amplitude was the smallest. When the pipeline angle was transferred to 90\({}^{\circ}\), the amplitude increased slightly, and the amplitude  
Figure 12: Data diagram of floating vibration of pipeline segment 2.  
reached the maximum value after the wind rose. Then the tugboat was added, and the amplitude of the pipeline decreased significantly while the frequency increased.  
During the whole process of floating transportation, on-site real-time monitoring and dynamic construction reduced the impact of abrupt weather on the vibration of the pipeline, and the monitoring results show that there is no resonance phenomenon with excessive amplitude. All these further verify the rationality of the dynamic construction scheme based on the timely feedback of monitoring data.
# Smooth Bend Structures Using Hybrid Compensation for Common-Mode Noise Reduction
## 3 Proposed Hybrid Compensation  
As has already been established, whether in the case of compensation capacitance [5] or inductance [6], the compensation circuits are placed on the unbalanced bend section. Our proposed hybrid compensation design applies a 150-degree smooth bend structure and uses compensation capacitance and inductance.  
There are four types of applied hybrid compensation for 150-degree bend structures, including different combinations of capacitance and inductance. These are named the C-C-C type, L-L-L type, C-L-C type, and L-C-L type, and are shown in Figure 9. All performances were compared with a 150-degree smooth bend structure without compensation in frequency and time domains. The physical size of the compensated capacitance was W1 \(\times\) W1, where W1 = 1.5 mm, and the compensated inductance dimensions were W2 = 0.25 mm, S2 = 0.16 mm, and L2 = 0.94 mm.  
Figure 8: Sdd21 simulated results for the full-wave and equivalent circuit models.  
Figure 7: Scd21 simulated results for the full-wave and equivalent circuit models.  
We first observe Scd21 in the frequency domain analysis from DC to 6 GHz. Figure 10 shows the simulated results for C-C-C, L-L-L, C-L-C, and L-C-L types, all with a 150-degree smooth bend. Compared with other compensation types, we found that the 150-degree smooth-bend structure with the L-C-L type of compensation in the black line had the best performance on Scd21, which was below \(-17.3\) dB.  
Maintaining the differential signal integrity is equally important. We observe Sdd21 in the frequency domain analysis from DC to 6 GHz. Figure 11 shows the 150-degree smooth bend structure with L-C-L compensation in the black line, which maintained the best signal integrity of the differential transmission. The differential insertion loss (S  
Figure 10: The simulated results of differential-to common-mode conversion loss (Scd21).  
Figure 9: Hybrid compensation types, C-C-C, L-L-L, C-L-C, and L-C-L.  
Figure 12 shows the simulated result of the common-mode noise in the time domain, including C-C-C, L-L-L, L-C-L, and C-L-C types, and a 150-degree smooth bend. The port definition is provided in Figure 6. The even-mode impedance was set to 85 Ohms. The input was set by the pseudo-random binary sequence (PRBS) with an amplitude of \(\pm\)0.5 in voltage, a data throughput of 5 Gbps, and a rise time of 40 ps.  
For ease of comparison, all the values of common-mode noise are set out in Table 3, showing an improved rate compared to the 150-degree smooth bend structure without compensation. Figure 12 and Table 3 show that when the L-C-L compensation type was applied on a 150-degree bend structure, it had the smallest value, reducing common-mode noise by 48.9%.
## 4 Theoretical Optimization of Converter Inductor
### Thermal Considerations  
Another dimensioning factor of magnetic devices is the thermal behaviour of the component during operation. It is important to check that even if the design optimization tends to minimize the losses in the components, still the produced heat does not induce an operating temperature above limits. For this purpose, the Coilcraft(r) datasheet has been analysed further in order to extract an approximation of the thermal resistance of each inductor series. The link between DC current magnitude, the DC resistance of the component and component temperature increase is used to estimate the thermal resistance of each component. Figure 24 below presents the evolution of the DC losses with respect to the value of the inductor for a temperature rise of 20 \({}^{\circ}\)C and 40 \({}^{\circ}\)C based on datasheets. The curves are flat, validating that the total amount of allowed losses per inductor shape (per inductor series) is constant. From these plots, one can derive the equivalent thermal resistance using Equation (20) below easily. These thermal resistances are listed in Table 5.  
\[R_{th}=\frac{\Delta T}{(I^{2}\times R_{dc})} \tag{20}\]  
It is interesting to note that the inductor series with the largest dimensions seems to have a higher thermal resistance than the two others. This is somewhat strange and we were not able to provide a justification for this.  
With these thermal characteristics, it is possible to verify the thermal behaviour of the magnetic component under the stress caused by the converter action. Under optimal configuration, for the 50 W converter, each inductor's power loss and thermal rising are presented in Table 6. These temperatures being under the manufacturer's limits, there is no reason to dismiss one of these technologies during the optimization process.  
Figure 24: Maximum losses allowed in the inductor for various inductor values and for two temperature rises. Data extracted from datasheets [15; 16; 17].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Series** & **MSS1210** & **MSS1260** & **MSS6060** \\ \hline Median of the thermal resistance \(R_{th}(^{\circ}\)C/W) at +20\({}^{\circ}\) & 75 & 79 & 26 \\ \hline Median of the thermal resistance \(R_{th}(^{\circ}\)C/W) at +40\({}^{\circ}\) & 80 & 83 & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Median thermal resistance of copper to the core in MSS1210, MSS1260 and XGL6060 series.  
To conclude this third part, the inductor model was successfully used to select the best inductor value, together with the converter switching frequency in order to minimize the overall losses. It was shown that, in the particular design and optimization case study considered here, the selected inductor and switching frequency may lead to significant current ripple magnitude in the inductors. This conclusion needs now to be validated with experiments.
# Performance Optimal PI controller Tuning Based on Integrating Plus Time Delay Models
## 7 Concluding Remarks  
The discussion and concluding remarks are itemised as follows.  
* The method in [1; 2] is further developed with more optimal MP tuning parameters as well as tuning for some special case integrating systems.
* Two optimal settings for the MP parameter are presented in Section 4. These are optimal in the sense that they minimise the main performance objective \(V_{M}\) on two different aspects. Interestingly, one of the MP parameters may (arguably) be deduced from approximating the time delay with a (2, 1) Pade approximation in Section 3.3.
* In the case of a small or zero time delay \(\tau=0\), we propose a variant in which the MTDE \(d\tau_{\text{max}}\) is the tuning parameter.
* Note that for an IPTD model, the SIMC tuned PI controllers are seen far from optimal, i.e., PO (or (almost) equivalently, Algorithm 1 with the MP parameter setting as \(\bar{c}=2.5\)). See Section 4.
* The presented method (and variants of this) is successfully demonstrated and compared to the SIMC and PO PI controllers on numerous motivated process model examples in Section 5.
* Note that, for the higher order process models in Examples 4 and 5, we use the PRC model reduction technique, which is generally easier to apply than the half-rule technique proposed in [12]. The half-rule technique is not compatible with handling complex poles.
* Some surprisingly optimal results are documented for Example 6, where a tuning method based on varying the gain velocity, \(k=\zeta R_{1}\), (\(R_{1}\), is the ZN unit reaction rate), i.e., the tuning parameter is \(\zeta\). Note that setting the RTDE \(\delta=\bar{c}\) (i.e., an ad hoc choice) equal a constant is advisable.
* Note that the results in Section 5 are based on the original (possible) higher order models. The approximated IPTD models are only used for the PI controller design.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\zeta\)**-Alg. 1** & **PO PI** \\ \hline \(K_{p}\) & \(-1.70\) & \(-1.70\) \\ \(T_{i}\) & 14.82 & 14.90 \\ IAE\({}_{yy}\) & 5.88 & 5.88 \\ IAE\({}_{eu}\) & 8.69 & 8.72 \\ \(J\) & 1.00 & 1.00 \\ TV & 5.06 & 5.06 \\ GM & 11.85 & 11.84 \\ PM & 44.15 & 44.20 \\ DM & 2.74 & 2.74 \\ \(M_{s}\) & 1.59 & 1.59 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Example 6. The corresponding controller parameter, indices and margins are given for prescribed robustness \(M_{s}=1.59\). \(\zeta\)-PRC + Algorithm 1.
# Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
## 3 Deep-Learning-Based Framework
### Methodology Framework
#### 3.2.2 Deep-Learning Models and Training  
Given the nature of a disaster scene, it bears two attributes simultaneously: the hazard type and the damage level. Two classification schemes can be designed. First, one can multiply the two sets of class labels, attempting to create a learning model that predicts nine different classes. The problem at hand can be achieved by using a single model outputting nine class labels. The second one is to predict hazard-type and damage-level, separately, by two independent models. In this effort, to achieve straightforward performance evaluation and to reveal the insights about what aspect of the underlying disaster-scene mechanics is identifiable by a computer model, the second scheme is adopted. Accordingly, each of the two disaster-scene attributes is learned separately from the data using a standalone model.  
As reviewed and reasoned previously, the generic bounding-box-based object-detection models, Faster R-CNN and SSD, are adopted in this work as the baseline architectures. First, given the flexibility of choosing a user-defined CNN feature extraction in Faster R-CNN, two CNN-based extractors are evaluated, which are the ZF (Zeiler and Fergus) CNN as used in the original Faster R-CNN model in [32], and the ResNet-50, a popular and high-performance extractor proposed by [40]. Second, given the computational gain of using the SSD architecture, a modified SSD model with the ResNet-50 as the feature extractor is developed for comparison with the Faster R-CNN counterpart.  
With this treatment and the consideration of two different deep-learning architectures and two CNN-based feature extractors, three different deep-learning models for the two different predictive tasks are designed, trained, and tested in this work, as defined in Table 1. In Table 1, the symbol \(M_{HT}\) means to learn hazard-type: \(M_{HT}(FRC,ZF)\) using Faster R-CNN with a ZF extractor, \(M_{HT}(FRC,RN)\) with ResNet50, and \(M_{HT}(SSD,RN)\) using SSD with ResNet50. The model with the symbol \(M_{DL}\) outputs the damage level. Similarly, \(M_{DL}(FRC,ZF)\) refers to the Faster R-CNN model with ZF, \(M_{DL}(FRC,RN)\) means Faster R-CNN with ResNet50 and \(M_{DL}(SSD,RN)\) applies the SSD network. Table 1 summarizes the achieved trained models and their goals of prediction. With this attribute separation, Table 1 simplifies the model outputs: for both types of models, Class 1, 2, and 3 correspond to either the three hazard types or the three damage levels, respectively. Therefore, besides the modification as presented in the following, all the models are adapted to output three class labels.
## 4 Model Parameters and Simulation Scheme
### Seismic Simulation Observation Model  
Based on the actual 3D seismic acquisition parameters of the Yexian salt mine area, a study observation model was designed, as shown in Figure 11. The model has a length of 8000 m and a depth of 2000 m. It employs the method of middle blasting with two-sided reception, where the detection point moves along with the blasting points. The track spacing is set at 20 m, with an array length of 1000 m and a blasting spacing of 20 m. The blasting commences at 500 m and concludes at 7500 m, totaling 351 blasts. For the seismic wavelets' polarity and spectrum, a positive polarity Rayleigh wave with a main frequency of 45 Hz is selected.
# Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model
###### Abstract
[30] adopted the random gradient lifting method to classify and predict 254 data from rock bursts and assesses the model accuracy using the Kappa method. [PERSON] et al. [31] constructed a data-driven model for rock bursts with regard to convolutional neural networks and deep learning and calculated the rock burst probability for each corresponding level. [PERSON] et al. [32] used the LightGBM algorithm and correlation coefficient heat map to screen the characteristic variables of rock bursts and used the random forest classification model to classify and predict the rock burst risk level at the \"t + 1\" moment. [PERSON] et al. [33] embraced the genetic algorithms to optimize the parameters of a support vector machine (SVM) and established a GA-SVM model to predict the rock burst using microseismic monitoring data. Although these algorithms offer certain advantages and have achieved certain results in the early warning research of rock bursts, multiple problems still need to be addressed, such as the slow model learning speed, the ability to easily fall into local optimization, the poor anti-interference ability, and the inaccurate early warning results. In accordance with the insufficient results of the aforementioned methods, the model proposed in this paper has been verified in the risk rank prediction of rock bursts in the Yanshitai Coal Mine. To be more specific, the innovation points of the model predominantly included the use of the principal component analysis method to reduce the dimension of the 10 main factors affecting the occurrence of rock bursts, eliminating the influences of superimposed information and simplifying the network structure of the model. Meanwhile, the PSO algorithm was used to optimize the input weights and the thresholds of the hidden layer of the limit vector machine, which overcame the disadvantage of the randomness of the input weights and the thresholds of the hidden layer of the ELM. In contrast to the other methods, the prediction accuracy of the model has been tremendously enhanced, providing several advantages such as a good prediction performance, fast learning speed, strong generalization ability, and applicable robustness, and is of great significance in engineering practice. Hence, this research firstly describes the basic principles and methods of the PCA-PSO-ELM model. Then, it explains how this model was applied to the risk rank prediction of rock bursts in the Yanshitai Coal Mine, and compares the accuracy to the other models. Lastly, it summarizes some conclusions concerning the existing research and the planned future work.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries
### Beyond IoT Technologies  
Aside from IoT, numerous major technologies enhance smart grid security by increasing flexibility, enabling real-time processing, and providing adaptive defense mechanisms. Their functions, characteristics, and contributions to the security of smart grid environments are presented below (see Table 7).  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Type** & **Description** & **Characteristics** & **Link to Smart Grid** & **Use Cases** & **Advantages** & **Limitations** & **Challenges** & **Mitigation** \\ \hline Edge Computing & Local data processing at or near smart grid endpoints & Low latency, decentralized, real-time response & Enhances local anomaly detection or contact systems & Fault isolation at real-time load reduces reliance on central systems & Reduced latency, suchations, real-time load savings, improved resilience & Limited processing power, distributed complexity & Data synchronization, firmware attacks & Secure boot, local authentication, OTA updates \\ \hline Fog Computing & Intermediate data processing between edge cloud & Distributed, aggregation, near-real-time & Distributed, aggregation, regional responses & Supports collaborative detection and correlated responses & Regional anomaly aggregation, and computation, predictive load analytics & Balances speed and computation, architecture & Latency higher than edge, still dependent on network health & Resource management & Encrypted channels, rate-based access controls \\ \hline SDN & Programmable network control control, dynamic hardware & Centralized control, dynamic configuration, flexible routing & Quick re-routing in case of attacks, centralized monitoring & DDoS containment, centralized assignment & Fine-grained control, better visibility & Fine-grained control, better visibility & Controller security, full legacy & Redundant controllers, secure APIs \\ \hline NFV & Virtualization of network services & Hardware feedback, flexible firewalls or cloud-native & On-demand deployment of cloud-aware security services & Dynamic firewall placement, remote security services & Cost-effective, flexible security functions & Performance can vary under load & Resource contention, function chaining complexity & Quality of Service (QoS) policies, function isolation \\ \hline Digital Twin & Real-time virtual model of physical grid components & Real-time monitoring, predictive simulation & Allows testing of attack scenarios and impact analysis & Predictive maintenance, training simulations & Resquiring continuous data monitoring, fault prediction & Requires continuous data flow, high setup & Model accuracy, synchronization, complexity & Regular validation, AI-assisted modeling \\ \hline \hline \end{tabular}
\end{table}
Table 7: Beyond IoT technologies for smart grids.