esa-sceva/satcom-chunk-collection huggingface dataset:
# Stress Analysis and Spalling Failure Simulation on Surrounding Rock of Deep Arch Tunnel
## 4 Discussion
### Stress Distribution Analysis of Surrounding Rock under Different Working Conditions
If the lateral pressure coefficients of the two specimens are equal and the effect of \(\sigma_{\rm X}\) is out of consideration, the maximum tangential stress under the conditions of \(f/B=1/3\) is smaller than that under the conditions of \(f/B=1/4\). This indicates that the maximum tangential stress can be increased by reducing the arch height of the three-centered arch hole. Figure 17: Stress at the boundary of tunnels with different arch heights. Figure 18: Tangential stress distribution diagram of surrounding rock of arch tunnels. From the above analysis, it can be concluded that the initial failure stresses of the arch-shaped holes are 0.39-0.48 times the related UCS. The hole contained in specimen A12-42.2-27.1 tested here is consistent with that in the literature [50], which is also a D-shaped hole. The stress precondition for the initial spalling failure of the D-shaped hole is that \(\sigma_{\rm Zi}/\sigma_{\rm c}\approx 0.35\)-0.44 under different stress states at the same burial depth. This is consistent with our results. It has also been stated that the \(\sigma_{\rm Zi}/\sigma_{\rm c}\approx 0.175\)-0.22 (half of the above test results) can be used for a rough evaluation of tunnel spalling failure. [PERSON] et al. [70] evaluated the stability of brittle rock tunnels in South African gold mines using the ratio of the maximum far-field stress (\(\sigma_{1}\)) to UCS (\(\sigma_{\rm c}\)). This also pointed out that spalling occurred when \(\sigma_{1}/\sigma_{\rm c}>0.2\). In our study, the \(\sigma_{\rm Zi}/\sigma_{\rm c}\) at the initial failure of tunnels with different arch heights is in the range of 0.195-0.24, with further confirms the findings of [PERSON] et al. In addition, the initial failure in this work all occurred at the arch foot, where the tangential stress was the maximum. When the lateral pressure coefficient is in the range of 0.38-0.50, which is 3.2-3.5 times the UCS. It has been reported that the ratio of the maximum tangential stress to UCS was 1.27 when the sidewall of a 50 mm diameter circular hole started to fail and that the tangential stress of the hole surrounding rock reached UCS when the failure occurred [71]. Our test results (\(\sigma_{\rm max}/\sigma_{\rm c}\approx 3.2\)-3.5) and the published data indicate that \(\sigma_{\rm 0 max}/\sigma_{\rm c}>1\). The reason for this is that there is a significant difference in the distribution of tangential stress between circular and arch holes. That is, the maximum tangential stress of a circular hole on both sidewalls is smaller than that of an arch hole at the arch foot. In addition, this may also be attributed to the strength size effect and structural effect of rocks [4].
## 2 PFC Converters Power Components Design Analysis and Selection
### Switching Elements Selection  
#### 2.3.1 Power Mosfet Switches Selection  
The power Mosfet switches in the PFC boost converters are selected based on the maximum peak current with considering the value of the on-state drain-source resistance (Rdson) to reduce the conduction losses.  
In the conventional CCM PFC topology, the rms current (IQb) and the maximum peak current stress (IQb_peak) in the switch Qb can be calculated as given in [7; 40].  
\[I_{\text{Qb}}=\frac{P_{o}}{V_{in\_min}}\sqrt{1-\frac{8\sqrt{2}V_{in\_min}}{3\pi V _{o}}} \tag{21}\]  
\[I_{\text{Qb\_peak}}=\sqrt{2}\times I_{\text{Qb}} \tag{22}\]In case of the interleaved CCM PFC topology, the maximum peak current of the switches Q1, Q2 is the half in case of the conventional CCM PFC converter as given  
\[\text{I}_{\text{Q1\_peak}}=\text{I}_{\text{Q2\_peak}}=\frac{\text{I}_{\text{Qb \_peak}}}{2} \tag{23}\]  
The conduction losses (PQ Loss cond) of the switches can be calculated at the switch rms current and using the value of the drain-source resistance of a MOSFET switch \(\text{Rds}_{\text{on}}\) at 100 C as given  
\[\text{P}_{\text{Q Loss\_cond}}=\text{I}_{\text{Qb}}^{2}\times\text{Rds}_{\text {on}} \tag{24}\]  
The switching losses (PQ Loss switch) are calculated using the rise time (t\({}_{\text{r}}\)) and fall time (t\({}_{\text{f}}\)) of the Mosfet gate and the capacitance losses (Coss) for the chosen switch.  
\[\text{P}_{\text{Q Loss\_swit}}=\text{F}_{\text{sw}}\Big{[}0.5\times\text{V}_ {\text{o}}\times\text{I}_{\text{in\_pk}}\times(\text{t}_{\text{r}}+\text{t}_{ \text{f}})+0.5\times\text{C}_{\text{oss}}\times\text{V}_{\text{o}}^{2}\Big{]} \tag{25}\]  
In case of the interleaved PFC, 2 switches were used to implement the two channels boost converter, but the cost of the switching elements is still lower than the conventional topology because low current rating switches can be used. Based on the current and voltage ratings for both topologies and considering the value of the Rdson, the IPZ60R040C7 switch is used for the conventional, and 2 switches of IPW60R099P6 were used for the interleaved PFC boost converters in this work.
# Genetic Multi-Objective Optimization of Sensor Placement for SHM of Composite Structures
## 1 Introduction
The authors also showed comparison of the multi-objective approaches with a single-objective approach and sequential-based sensor-placement strategies, indicating robustness of the sensor networks selected using the multi-objective approach for damage detection of civil construction. Another case study with multi-objective optimization of sensor placement on a composite rotor blade is presented in [17]. The objective function is based on the minimalization of the sensor number and, simultaneously, the minimalization of the error between the reconstructed real mode and the mode based on the information from the candidate sensor networks. Another study [18], presents multi-objective optimization with the combination of maximization of the nominal performance that could be defined using the mean and maximum of MAC, the conditional number, or the effective independence and minimalization of the data uncertainty. Many of the above-mentioned multi-objective-optimization problems use different algorithms; most of them are dominance-based methods based on popular NSGA-II [17; 18; 19; 20]. This algorithm is often used as long as no more than three objectives are taken into account [21; 22]. There are also decomposition-based methods which decompose multi-objective problems into a set of single-objective problems. For instance, [PERSON] et al. [23] presented a bi-optimization objective function based on the accuracy of the strain-map reconstruction based on the optimal-sensor-placement network. They used the concept of minimalization of type-I and type-II errors of the SHM system assuming that these errors can be reduced by minimizing the mean absolute error between the real system and estimated strain map response and by minimalizing the maximum difference between them. It should be noted that the use of multi-objective optimization is an important direction in developing OSP strategies in SHM systems. In this study, the SHM system is based on the classification-of-information system, which allows to estimate the sensitivity and specificity of damage recognition. These characteristic features, together with the number of used sensors, are taken as individual objectives in the multi-objective optimization of sensor placement. The outcomes are presented as Pareto solutions which, linked to design variables, allow the user to obtain required performance and costs of an SHM system. This is an ongoing study within a series of previously performed studies [7; 8] with a general focus on development of a customizable SHM system with a trade-off between performance and cost effectiveness.
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 2 Related Work
### Encryption and Others  
Encryption techniques can defend against various attacks principally in public key cryptography. However, secure key storage, encryption, and decryption operations may bring more storage, computing, and communication overhead. [PERSON] et al. (2019) developed an authentication scheme to ensure secure time synchronization in heterogeneous wireless sensor networks. [PERSON] et al. (2018) utilized pairing-based cryptography by computing the shared secret keys to secure the synchronization processes, with low communication and storage costs.  
Due to the diversity of time synchronization methods, other security defense techniques have also been proposed. [PERSON] et al. (2019) proposed an extension to the precision time protocol (PTP) to defend against attacks on the grand master clock, network, transparent clock, and slave clocks. The extension deploys a redundant clock as the network time reference to detect the synchronization of slave clocks. As a consequence, a feedback closed loop security-aware PTP is achieved. [PERSON] et al. (2019) provided redundant ways and multiple time sources for each node to synchronize with the time source. Each node computes the source clock offsets with all its neighbors and chooses the median as the source clock offset. The developed methods can be resilient to message missing and distortion caused by the malicious nodes, and each node with \(l\) normal neighbors can tolerate up to \(l-1\) malicious nodes.  
In dynamic and hostile environments, node mobility and malicious nodes are unavoidable and have a serious effect on the averaged-based consensus time synchronization protocols. [PERSON] et al. (2019) proposed a neighbor-aware time synchronization protocol (NTP) to overcome the limitations of gradient time synchronization protocol (GTSP). NTSP first adopts a marking technique to classify the neighbor's status for each node as synchronized, new, or unsynchronized. Secondly, depending on the number of synchronized neighbors, each node calculates the clock compensations including only synchronized neighbors or whole neighbors. The classification and calculating rules render NTSP more robust.  
However, most countermeasures for various attacks seldom consider Sybil attacks. Additionally, due to the inherent vulnerability of topology, centralized time synchronization methods are more vulnerable to disruption under Sybil attacks. The child nodes take their parent nodes as their time sources, and then the branch of the parent nodes will be disabled when the parent nodes are attacked. Although encryption and authentication techniques are effective ([PERSON] et al., 2019; [PERSON] et al., 2019), additional computing and communication overhead cannot be popularized for the design of time synchronization protocol in resource-constrained IWSNs. Different from existing secure synchronization methods, our method automatically filters the adverse impact of attackers in utilizing the public neighbor broadcasting mechanism.
# A Review of Vehicle to Vehicle Communication Protocols for VANETs in the Urban Environment
## 4 Simulation and Analysis
### Results and Discussion
#### 4.2.2 End-to-End Delay  
Figure 11 illustrated that, with an increase in traffic density, the end-to-end delay decreases. This is because an increase in traffic density increases the probability of packets being routed instead of being apprehended in suspension buffer. TFOR and E-GyTARD achieve much lower end-to-end delay than DGSR. This is due to the fact that DGSR first computes the sequence of junctions statically before transmitting a packet, without considering the traffic density, which causes delay.  
Figure 11: End-to-end delay vs. the number of nodes (@5 packets/s).  
Figure 10: Packet delivery ratio vs. the number of nodes (@5 packets/s).
# Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
## 5 Conclusions  
In this study, we employed large quantities of Landsat-8 and Sentinel-1a/b data to map ice motion pole-to-pole in Greenland and Antarctica. We compared and contrasted data quality between these sensors at retrieving ice motion. We implemented a system which processes the data automatically, calibrates them automatically given prior maps of ice motion, and which mosaics them automatically. We combined the results from image pairs with different time separation and hence difference noise levels, weighting the results with data noise, to create merged products from SAR and optical sensors that cover the entire ice sheets on an annual basis. We have demonstrated that the quality of the Landsat-8 data is now comparable to that achievable with SARs so that the merging of data products (spatially as well as for time series) is not only possible but desirable. We expect the resulting output products of ice motion to be of considerable interest to study glacier dynamics, ice discharge, the contribution of glaciers and ice sheets to sea level rise, and to improve numerical models used to project the evolution of these ice sheets in a warmer climate.  
Figure 10: Composite mosaic of surface ice speed generated from ERS-1&-2/ESA, RADARSAT-1&-2/CSA, ENVISAT/ESA, ALOS/JAXA, TerraSAR-X/DLR, Sentinel-1/ESA and Landsat-8/USGS acquired between 1992 and 2016 in Greenland. The ice speed is coded on a logarithmic scale overlaid on MOG background mosaic [33].  
**Acknowledgments:** We thank the space agencies (NASA, ESA, CSA, JAXA, ASI, DLR) which made this work possible by making satellite observations of the polar regions free access to our research group and the entire scientific community. We also thank the PSTG for coordinating data acquisitions in the polar regions since the 2007-2008 International Polar Year. This work was performed at UC Irvine and at Caltech's Jet Propulsion Laboratory under contracts with the Cryosphere Program and the MEASURES Program of the National Aeronautics and Space Administration (NASA). Annual velocity maps as well as the integrated multi sensor velocity map will be made available at NSIDC ([[http://nsidc.org/data/measures](http://nsidc.org/data/measures)]([http://nsidc.org/data/measures](http://nsidc.org/data/measures))).  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] wrote the paper and supervised the project. [PERSON] and [PERSON] processed the data. [PERSON], [PERSON] and [PERSON] designed the processing chain. All authors discussed the results and commented on the manuscript at all stages.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Analysis of the Onsite Monitoring Data
### Surface Settlement Monitoring  
Figure 5 shows the settlement curves of the monitoring points on both sides of the shaft during the sinking process. The disturbance caused by construction increases the effective stress in the soil, resulting in greater settlement. Within the monitored cross section, the settlement of the shaft exhibited a groove-shaped pattern, with the settlement magnitude increasing closer to the shaft center.  
During Stage 1, after the shaft sank to a depth of 12.4 m, the soil layer transitioned from silty soil to mucky clay, resulting in a notable increase in settlement magnitude at the monitoring points. During the first construction pause, owing to the high compressibility, low permeability, and rheological characteristics of Shanghai soft soils, settlement continued, with the maximum settlement increment at monitoring points on both sides of the shaft reaching approximately 5.9 mm. At a location farther from the shaft, the monitoring  
Figure 4: VSM sinking time history diagram.  
Figure 5: Settlement development curve of the surface monitoring section of the shaft.  
point DB-1-7 rebounded during the pause, which was attributed to horizontal compression caused by stress redistribution in the distant soil. The rebound magnitude was 1.7 mm.  
In Stage 2, owing to the low permeability of the deep soft soil, the dissipation of pore water pressure required a prolonged period, resulting in slow consolidation deformation. Monitoring point DB-1-7 continued to rebound, with an additional magnitude of 0.2 mm. During the second construction pause, the maximum settlement increment was approximately 8.0 mm. In the subsequent pit bottom construction and slurry replacement stages, the settlement changes were minimal and essentially remained stable.  
The cumulative surface settlement variations at the five monitoring points of DB-2 are shown in Figure 6. After the shaft construction (including slurry replacement for the sidewalls) was completed, the maximum settlement recorded at the monitoring points reached 37.23 mm. During the first construction pause (January 30 to February 8) and the second pause (February 12 to 17), soil settlement exhibited continuous growth, with settlement increments of 5.9 mm and 8.0 mm, respectively. This behavior is attributed to the rheological properties of soft soils in Shanghai, which increase the time-dependent effects of excavation-induced disturbances, resulting in continuous settlement under unchanged stress conditions [26]. Soft clay in the excavation area exhibits pronounced creep behavior over time [27; 28; 29; 30].  
The two pause periods lasted 10 days and 6 days, contributing to 15.85% and 21.49%, respectively, of the total settlement. A comparison of the first 6 days of the first pause with the entire second pause revealed that the increase in settlement during the second pause was 3.25 times greater than that during the first pause. This finding indicates that, with the increase in excavation depth, construction stagnation leads to greater surface deformation.
## 4 Results and Discussion
### Response Optimization with NSGA-II  
Since _Ra_ and \(T\) are related, both responses were taken into account to optimize the process. Tool-tip temperature affects tool wear, which eventually influences the surface quality. Therefore, the purpose is to minimize both responses. The NSGA-II algorithm was employed to achieve this goal because it is well-established for multi-objective optimization purposes [53; 54; 55] based on evolutionary principles. It is designed to find a set of Pareto-optimal solutions where improving one objective cannot be achieved without degrading another. NSGA-II works by evolving a population of solutions through selection, crossover, and mutation, just like a typical genetic algorithm. Key features include fast, non-dominated sorting to rank solutions based on Pareto dominance. Crowding distance to maintain diversity by preferring solutions in less crowded areas. And elitism, where the best solutions are selected and advanced to the next generation to ensure convergence.  
NSGA-II is widely used in engineering design, including machining optimization, where trade-offs between multiple objectives like surface roughness and tool temperature are crucial. For this study, both the population and the maximum number of generations were set to 100. Additionally, stopping criteria were not used. Figure 10 depicts the plots derived from the optimization process. Specifically, Figure 10a illustrates the Pareto fronts of solutions for the dry cutting of reinforced PETG, whereas Figure 10b depict the different combinations that generated the specific front of solutions, suggesting that optimal surface roughness and temperatures are generated at cutting speeds between 100 m/min and 200 m/min, combined with low feeds below 0.6 mm/rev and depth-of-cuts close to 0.5 mm.  
Figure 9: Error distribution for Models I (**a**) and II (**b**).
# The Future of the Human-Machine Interface (HMI) in Society 5.0
## 5 Human-Centric Manufacturing (HCM)
### Human Digital Twins  
A human digital twin is a virtual representation of a real human being that is created using digital data. It is a model that replicates the physiological, biological, and behavioral  
Figure 9: Comparison between biological vision system and cyber-physical vision system.  
characteristics of an individual, allowing for simulations and predictions of their responses to different stimuli, situations, or environments. Here are some of the key characteristics of a human digital twin:  
Real-time data: A human digital twin relies on real-time data from sensors, wearable devices, and other sources to accurately represent the current state of the individual. This data can include physiological measures such as heart rate, blood pressure, and temperature, as well as behavioral measures such as movement, speech patterns, and facial expressions [52];  
Multi-dimensional representation: a human digital twin represents a person in a multi-dimensional manner, incorporating aspects such as their physical attributes, cognitive processes, emotions, and social behaviors [53];  
Personalization: A human digital twin is personalized to the individual it represents, capturing their unique physical and behavioral characteristics. This allows for more accurate simulations and predictions of their responses to different stimuli and situations [25];  
Machine Learning and AI: A human digital twin is often developed using Machine Learning and Artificial Intelligence algorithms that can learn from the data collected from the individual over time. This allows the twin to adapt and evolve as the individual's characteristics change [54];  
Simulation and prediction: A human digital twin can be used to simulate and predict the individual's response to different stimuli and situations, such as exposure to a new drug or a change in environment. This can be used to optimize treatments or interventions, as well as improve the individual's overall health and wellbeing [27].  
Digital twins that reproduce not only the outer aspects of a human being, such as physical and physiological characteristics, but also inner qualities such as personality, sensibilities, thoughts, and skills pave the way for the reproduction of social aspects in digital space, such as human behavior and communication. Thus, a human digital twin is a powerful tool for understanding and predicting human behavior and responses and has a wide range of applications in fields such as healthcare, sports science, and social science research (Figure 10).
## 5 Case Study
### Results  
From Figures 14 and 15, we note that the blasting damage funnel is basically formed about three milliseconds after detonating.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Density (kg/m3)** & \
\begin{tabular}{c} **Dynamic Elasticity** \\ **Modulus (GPa)** \\ \end{tabular} & **Poisson’s Ratio** & **Yield Stress (MPa)** &
\begin{tabular}{c} **Tangent Modulus** \\ **(GPa)** \\ \end{tabular} \\ \hline
2680 & 50 & 0.2 & 100 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main physical and mechanical parameters of surrounding rock.  
Figure 13: Geometry model schematic diagram with eight straight slotting bores.  
Figure 14: Blasting damage evolution law of surrounding rock at the bottom of blasting bore (Plane \(z=-2.0\) m). (**a**) \(t=0.1\) ms. (**b**) \(t=0.5\) ms. (**c**) \(t=1.5\) ms. (**d**) \(t=3.0\) ms.  
Figure 15: Blasting damage evolution law of surrounding rock along the longitudinal direction of tunnel (Plane \(y=0\) m). (**a**) \(t=0.1\) ms. (**b**) \(t=0.5\) ms. (**c**) \(t=1.5\) ms. (**d**) \(t=3.0\) ms.  
From Figure 14, we note that blasting damage can be divided into three stages in the process of slot-blasting as follows: at the initial stage, the damage to surrounding rock generates around every blasting bore (Figure 14a); at the second stage, the damage zone of surrounding rock adjacent blasting bores is connected (Figure 14b,c); at the third stage, the surrounding rock damage spreads continuously to the periphery along with the blasting bores until a complete damage zone is generated (Figure 14d).  
The blasting stress wave reaches the non-reflective boundary about one millisecond after detonating. Rock damage is caused by stress waves reflected from the non-reflective boundary generated at about one-half millisecond after detonating. With the appearance of the reflective stress wave, the damage develops around the original damage zone.  
The calculation results are used to guide the slotting-blasting of the subway tunnel. It is proved by practice that the numerical simulation results can basically reflect the effect of tunnel blasting.