esa-sceva/satcom-chunk-collection huggingface dataset:
# An Open-Source Algorithm for 3D ROck Slope Kinematic Analysis (ROKA)
## 4 Discussion
Figure 21: Lower hemisphere projection of the mean planes representing the slope used to perform the traditional Kinematic Analyses (KAs) and contour per 1% area of the poles indicating the slope’s local orientations in each point of the point clouds considered by ROKA algorithm. (**a**) Case study A and (**b**) case study B.  
KA considers only the discontinuity set K2 as critical for planar failure (see Table 2), the ROKA algorithm considers critical also the sets K2, K3, K4, and K5 (see Table 4). Another substantial advantage of the ROKA method consists of evaluating the intersection dimension between the slope and a critical discontinuity. In this way, it is possible to filter the results by discarding the smallest fractures and visualizing and classifying each critical discontinuity for every portion of the slope (Figures 22 and 23). This can be an important advantage for the stability analysis of complex or very large natural outcrops, where the localization of the critical discontinuities and critical portion of the slope can be crucial. ROKA can highlight where to focus possible detailed field investigations, as the definition of the discontinuity parameters that the remote sensing techniques could not totally or partially assess (e.g., roughness, aperture, seepage, infilling, wall strength). Big discontinuity datasets, such as those acquired by the automatic discontinuity planes detection algorithms (e.g., [46; 47]), can be positively processed by ROKA. As shown in [23], automatic discontinuity planes detection algorithms require a difficult and time-expensive validation process that could be done manually or automatically. The manual validation is a time-consuming process that increases the time needed for the automatic operation. ROKA permits simplifying the validation process with the possibility to detect and visualize, even in a very large dataset, only the discontinuities that could activate an MOF and filter them by their critical value. Figure 22: (**a**) Identification of the critical parts of the slope for every specific MOF—the box identifies the sector presented in figures b and c—(**b**) visualization of the discontinuities that could activate the detected MOF and (**c**) their inspection and validation. In the figure, the ROKA algorithm results for planar failure MOF for case A are reported.
# Observer-Based Rejection of Cogging Torque Disturbance for Permanent Magnet Motors
## 1 Introduction  
High-precision motor control is one of the long-standing and critical issues in both academia and industry because they are used in many control applications [1]. Especially cogging, which arises from the interaction of the rotor magnets with the steel teeth on the stator, causes torque ripples and thus speed ripples in permanent magnet motor systems [2; 3; 4; 5; 6]. This may not be acceptable in some applications--in particular for low speed regulation. Thus it is not surprising that there have been a tremendous amount of studies for eliminating the cogging disturbance over the course of several decades [7; 8; 9; 10; 11; 12].  
More recently, under an assumption that the cogging torque can be expressed by a state-space harmonic representation, an observer using feedback motor velocity has been proposed to obtain more accurate speed regulation [7; 8; 10]. In the research, from the point of view of disturbance rejection, there exists a plant input disturbance equivalent to the cogging torque. Especially, it can be combined with the preinstalled PI controller, which guarantees the stability of the closed-loop system but is not appropriate for the rejection of the cogging disturbance. However, the observer requires a redesign of the gain when the motor velocity changes because the system matrix of the observer includes the feedback motor speed. Therefore, it is a disadvantage to apply to the actual systems in which the rotation speed varies.  
In this paper, unlike the observer in [10], we propose a control method with a state observer independent of the motor velocity. For this, several coordinate transformations for observable canonical form are used in order to make the motor speed an observer input. Then, we show that the gain of the proposed state observer is independent of the feedback motor speed. Simulation and experimentalresults for an actual brushless DC motor verify the effectiveness of the proposed method more than the previous study of [10].  
The remainder of this paper is as follows. In Section 2, we present the problem statement. Section 3 provides an observer design method and stability analysis. Then, in Section 4, the proposed control method is evaluated in terms of an actual permanent magnet motor. Finally, conclusions are laid out Section 5.
## 6 Conclusions  
By conducting two blasting experiments, we compared the stemming effects of the developed STF-based stemming material and those of commonly used sand stemming material. The conclusions drawn from the blasting experiments are as follows:  
1. The measured pressure was 2.80 MPa for the STF-based stemming case and 5.84 MPa for the sand stemming case based on the direct dynamic pressure measurement at the top of the blast hole. The lower the measured pressure value, the lower the loss of explosive pressure in the blast hole owing to stemming transfer. In addition, the explosive gas pressure action time in the STF stemming case was 5 ms longer than the sand-stemming case. The longer the duration of the explosive pressure, the greater the energy that can be used to fracture the rock;
2. The measured pressure behavior in the blast hole correlated with the resistance characteristics of the stemming material. It is judged that the STF-based stemming material can effectively resist the blasting pressure and maintain a relatively high pressure in the blast hole for a longer duration compared to the conventional sand stemming material;
3. The hydraulic pressure measurement system was developed to measure blasting pressure in a blast hole. This was done by improving the method of measuring the blasting pressure by drilling a dummy hole, which was mainly performed for the explosion pressure measurements. The pressure behavior depending on the voltage of pressure meter with cerabar (PMC) was set through the sensor calibration performed before the blasting experiment, the test method to evaluate the pressure constraint capacity of the stemming material was presented, and the validity of the pressure measurement system in the blasting hole was verified;
4. As a full-scale bench blasting experiment, a rock pile fragmentation analysis was performed after blasting, and it was confirmed that the average fragment size was reduced by an approximate minimum of 25% to a maximum of 60%, when using STF-based stemming material. This suggests that the secondary crushing work in aggregate quarry mines can be reduced.  
Conceptualization, Y.J.; Investigation, C.S.; Methodology, Y.K.; Supervision, S.C.; Writing--original draft, Y.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from the project \"Development of new stemming material and blasting method using shear thickening fluid by high shock-wave reaction\", funded by the Korea Institute of Civil Engineering and Building Technology (KICT).  
Not applicable.  
The authors declare no conflict of interest regarding the publication of this paper.
# Internet of Things for Sensing: A Case Study in the Healthcare System
###### Abstract  
Medical healthcare is one of the fascinating applications using Internet of Things (IoTs). The pervasive smart environment in IoTs has the potential to monitor various human activities by deploying smart devices. In our pilot study, we look at narcolepsy, a disorder in which individuals lose the ability to regulate their sleep-wake cycle. An imbalance in the brain chemical called orexin makes the sleep pattern irregular. This sleep disorder in patients suffering from narcolepsy results in them experience irrepressible sleep episodes while performing daily routine activities. This study presents a novel method for detecting sleep attacks or sleepiness due to immune system attacks and affecting daily activities measured using the S-band sensing technique. The S-Band sensing technique is channel sensing based on frequency spectrum sensing using the orthogonal frequency division multiplexing transmission at a 2 to 4 GHz frequency range leveraging amplitude and calibrated phase information of different frequencies obtained using wireless devices such as card, and omni-directional antenna. Each human behavior induces a unique channel information (CI) signature contained in amplitude and phase information. By linearly transforming raw phase measurements into calibrated phase information, we ascertain phase coherence. Classification and validation of various human activities such as walking, sitting on a chair, push-ups, and narcolepsy sleep episodes are done using support vector machine, K-nearest neighbor, and random forest algorithms. The measurement and evaluation were carried out several times with classification values of accuracy, precision, recall, specificity, Kappa, and F-measure of more than 90% that were achieved when delineating sleep attacks.  
Internet of Things; S-band sensing; smart devices +
Footnote †: journal: Applied Sciences
### \(H_{2}\)S and COS Concentration
#### 3.3.3 Variation of Steam to Carbon Ratio \(n_{\text{SC}}\)  
Figure 14 shows the H\({}_{2}\)S and COS concentrations for different steam to carbon ratios \(n_{\text{SC}}\). The measured H\({}_{2}\)S concentrations increased with \(n_{\text{SC}}\). The lowest S/C ratio tested was 0.6 mol mol\({}^{-1}\) which lowered the H\({}_{2}\)S concentration to 1340 \(\times\) 10\({}^{-6}\) m\({}^{3}\) m\({}^{-3}\). A similar trend was found by [34]. This follows well the trend of the equilibrium where the H\({}_{2}\)S concentrations are increasing with \(n_{\text{SC}}\) following [PERSON]'s principle: With higher \(n_{\text{SC}}\), more steam is introduced into the gasifier, driving Equation (5) to the left towards less H\({}_{2}\)S capture.  
The measured COS concentration stayed on the same level throughout the \(n_{\text{SC}}\) variation, whereas the equilibrium predicts a very small increase. Since steam is not present in Equation (6), \(n_{\text{SC}}\) has no direct effect on COS capture. However through the water gas shift reaction, steam addition also produces more CO\({}_{2}\) influencing the COS capture.  
Additionally, the equilibrium for 750 \({}^{\circ}\)C is shown since sulfur capture is maximized at this temperature. It can be seen that, at operation conditions of 750 \({}^{\circ}\)C and low \(n_{\text{SC}}\), very low concentrations are achievable according to the equilibrium.  
In literature [34; 43], similar trends of H\({}_{2}\)S concentrations over \(n_{\text{SC}}\) in the presence of CaO were also reported in experiments and equilibrium calculations.
## 5 Results and Discussion
### Load-Settlement Analysis--Numerical Result  
Figures 9a-c and 10a-c depict the load-settlement graphs of VESC and HRSC reinforced columns for three varying diameters (50, 75, and 100 mm) obtained after numerical analysis. For OSCs, the load improvement was 13.49% compared to the unreinforced case. In VESCs, for \(D\) = 50 mm, when \(Lr\) = 0.25 L, the improvement was 11.88% compared to OSCs. Similarly, for \(Lr\) = 0.5 L, 0.75 L, and L, compared to OSCs, the improvement was found to be 16.22, 19.74, and 22.98%, respectively. For \(D\) = 75 mm, the load capacity improvement for \(Lr\) = 0.25 L, 0.5 L, 0.75 L, and L was calculated as 13.42, 21.53, 23.39, and 29.51%. Likewise, for \(D\) = 100 mm, the improvement was 12.5, 16, 19.12, and 24.62% for \(Lr\) = 0.25 L, 0.5 L, 0.75 L, and L, respectively, when compared to the OSC results. A comparison with the numerical data obtained by Plaxis 3D was also performed to validate the experimental results.  
Figure 9: Numerical analysis result of load–settlement variation of VESC for various \(Lr\) on single stone columns having diameters of (a) 50 mm, (b) 75 mm, and (c) 100 mm.  
A similar analysis was conducted for various HRSC cases and compared with the OSC results. For \(D\) = 50 mm, when \(Lr\) = 0.5 L from the centre of the column to the end, 0.5 L from the column head to the centre, and L throughout the column length, the load improvement was found to be 3.81, 13.10, and 17.65%, respectively. Similarly, the improvements for \(D\) = 75 mm were 13.42, 20.86, and 25.86%. For \(D\) = 100 mm, the load improvement was calculated as 15.03, 16, and 20.97%. The trend was very similar to that obtained from the experimental results. The slow settlement of stone columns acquired with loads is another interesting finding regarding the FE results. This can be explained by the fact that the geotextile-clay and aggregate-geotextile interfaces were rough, resulting in higher interface stiffness than in the more flexible interfaces used in model testing. Therefore, settlement was observed during loading in the context of finite element (FE) analysis due to the direct transmission of hoop stresses to the base of the column of stone upon contact with the simulated interface of higher stiffness.  
Figure 10: Numerical analysis result of load–settlement variation of HRSC for various arrangements on single stone columns with diameters of (**a**) 50 mm, (**b**) 75 mm, and (**c**) 100 mm.
## 6 Challenges and Future Research Directions
### Challenges
#### 6.1.5 Energy Harvesting Efficiency  
The efficiency of energy harvesting in SWIPT-enabled systems is another area that requires attention. In practical deployments, the amount of energy that can be harvested is often limited by various factors, including the distance between the transmitter and the receiver, the quality of the channel, and the level of interference. Enhancing energy harvesting efficiency without compromising the information decoding process is a significant challenge that needs to be addressed.
# Recent Development of High-Energy Short-Pulse Lasers with Cryogenically Cooled Yb:YAG
## 2 Changes in Crystal Properties with Temperature
### Spectral Properties
To summarize, the changes in various properties of Yb:YAG crystals at low temperatures reduce the decrease in beam quality caused by thermal effect, decrease the difficulty of heat conduction, decrease the pumping threshold, increase the pumping efficiency, increase the energy storage capacity, and increase the laser gain, which makes Yb:YAG crystals at low temperatures very suitable as gain media for high-power solid-state lasers.
## 3 Energy-Optimal Control Problem
### Optimization Process  
The optimization process is depicted in Figure 2. The input of the system comprises the initial variable values of model \(X\) and the constraints of the walking gait, which consists of physical system constraints and the initial value of parameter constraints. The optimal walking gait is obtained as the output of the system. The continuous hip joint torque is acquired by employing cubic spline interpolation to approximate the torque variables at the grid points. Subsequently, the gait solver is employed to compute the walking gait. The equations of motion are integrated and applied to the push-off and heel-strike phases, extending until the end of the swing phase (\(t=t_{step}\)). During each step, the \(COT\) and constraint functions are computed based on the smoothed objective and constraint functions calculator. The optimization problem is effectively tackled by employing the SQP, a sequential quadratic programming package. The variables vary within the constraints until the energy consumption is minimal. Then, the energy-optimal gait is achieved. The aforementioned process was implemented in this study using custom-coded MATLAB software. (Version 7.10.0).  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Number** & **Constraint Condition** \\ \hline
1 & During the swing phase, the angle between the legs is within \([0,\pi]\). \\
2 & The swing angle between the single leg and the ground normal is within \([0,\frac{\pi}{2}]\). \\
3 & The swing angular velocity of both legs does not exceed \(u_{\text{max}}\). \\
4 & Hip torque does not exceed \(\tau_{\text{max}}\). \\
5 & The push-off phase is within \([0,\,p_{\text{max}}]\). \\
6 & Periodic times are 0. \\  & In order to ensure a periodic walking gait, it is necessary to ensure that the initial condition \(q(t=0)\) at the initiation of the walking cycle is congruent with the subsequent step. \\
8 & Upon the contact of the swing leg with the ground, both legs exhibit symmetry relative to the ground normal. \\
9 & All mass points of the model are above the surface of the ground. \\  & To ensure that the swing leg descends and makes contact with the ground, the \\
10 & y-axis velocity of the swing leg should be negative (less than 0) prior to the \\  & heel-strike moment. \\
11 & The normal force \(u_{\text{N}}\) exerted by the stance foot should be greater than zero. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Constraints on walking gait.  
Figure 2: Dynamic optimization control system with full-range energy efficiency.
## 3 UAV Deployment and Trajectory
### A Mobile UAV
#### 3.2.2 UAV BS/RS  
A mobile UAV as UAV BS or RS could not be the proper choice in terms of energy efficiency. To this end, there have been little studies on mobile UAV BS or RS. Ref. [67] introduces a simple circular UAV trajectory and maximizes the energy efficiency of UAV by jointly optimizing UAV's flight radius and speed. In [68], the coexistence of UAV and device-to-device (D2D) communication networks is considered for the downlink transmission. The minimum number of stop points of UAV to completely cover the area is derived using the disk covering problem and the outage probability of D2D devices. Ref. [69] solves the throughput maximization problem in mobile relaying systems by optimizing the transmit powers, the relay trajectory and the information-causality constraint at the relay. It demonstrates that the throughput-optimal power allocations at source and relay node follow a staircase water filling structure over time. In addition, the throughput is further improved by optimizing the UAV's trajectory by successive convex optimization when power allocations are given. Ref. [70] studies UAV-enabled mobile relaying systems to assist the information transmission from a ground source to a ground destination when a direct link between them is blocked. The spectrum efficiency and energy efficiency are maximized by jointly optimizing the time allocations for UAV's relaying along with its flying speed and trajectory.
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 4 Improved Genetic Algorithm to Solve TT Flow Routing Problem
### Working Pattern of GATTRP  
We added a TT routing program based on GATTRP into the central control server of the smart factory TSN to optimize the TT routing. When a new TT subnetwork sent a registration message to apply to join the current TSN, the transmission continued normally while the GATTRP process was created to perform offline route planning. The newly joined subnetwork was treated as a node in \(V\) and the delay cost between it and other nodes was calculated to generate the new \(\{d_{ij}\}\). The updated \(G(V,E)\) and \(\{d_{ij}\}\) were input to the GATTRP process to calculate \(m\) new routes. Starting from the next time period, the central control server traversed the entire TT network with \(m\) new paths to complete the registration of the new subnetwork.
## 4 Case Study
### Corrosion Impact on Fragility Curves  
In the analysis of structural degradation due to corrosion, a dataset that was yielded in the previous steps was utilized. This dataset provides detailed degradation rates at specified time intervals up to 120 years, categorized by varying levels of corrosion: minor, moderate, and intensive. To estimate degradation rates at unrecorded time points, interpolation methods were utilized. The fragility parameters are computed using the method described in Section 2. Baseline fragility curves, derived from the HAZAS database, are adjusted to account for corrosion effects using the deterioration patterns established in our earlier analysis. This adjustment modifies both the median capacity and dispersion parameters of the fragility curves over time, as per Equations (21) and (22), allowing for a time-dependent assessment of seismic vulnerability that incorporates corrosion impacts.  
This approach facilitates dynamically adjusting the fragility curves over time, reflecting the true progression of corrosion and its impact on seismic vulnerability. Figure 9 shows a snapshot of adjusted seismic fragility curves for a one-story RC moment frame building at different points in its lifecycle, considering an intensive level of corrosion. The baseline  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Damage State**} & \(\theta_{i}\) & \(\beta_{i}\) & **Damage Ratio** \\ \hline DS1 & Slight & 0.12 & 0.64 & 0.05 \\ DS2 & Moderate & 0.15 & 0.64 & 0.11 \\ DS3 & Extensive & 0.27 & 0.64 & 0.55 \\ DS4 & Complete & 0.45 & 0.64 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Baseline fragility curve parameters and damage ratio.  
To provide a more comprehensive evaluation of corrosion's impact on seismic vulnerability, fragility curves for different damage states (DS1 to DS4) under various corrosion intensities are compared across different time intervals (50, 75, 100, and 120 years). The results, as shown in the Figure 10, highlight the progressive shift in the probability of exceeding specific damage states due to corrosion over time. These curves clearly illustrate how the seismic risk increases with the severity of corrosion, offering valuable insights into the long-term structural performance under varying environmental conditions.
# Structure-Function Coupling Reveals Seizure Onset Connectivity Patterns
###### Abstract
Many prior works favoured the combination of stereo EEG with dMRI [6; 9; 24; 25], or only explored normal (non-ictal) awake EEG data with dMRI [26]. Several methods for electrical source localisation, which utilise a range of forward and inverse solutions, have been proposed and evaluated [27; 28; 29]. Thus the current study is distinguished from those prior works for the following reasons. We aimed to understand whether a patient-specific, structure-function coupling pattern could be observed without requiring manual digitisation of electrodes or applying one of the several forward and inverse solutions. We sought to apply our existing model [30], which maps cortex regions to individual electrodes, to a larger cohort. We specifically examined the seizure onset period (regardless of wakefulness state). Lastly, we aimed to validate the feasibility of our model as a clinically translatable method to leverage the potential of dMRI, with the view of elevating it to the established state currently held by structural MRI (i.e., T1) [31]. The dMRI component of our tool was designed to be deployed on a clinician's computer, allowing straightforward data processing from new patients (with ethics approval). The contribution of this work is twofold: a. We extend the application of our spatial mapping model to a new patient cohort, highlighting consistent between-patient variance in region to electrode mapping, and b. We add to the growing body of research showing that connectivity data derived from structural MRI may augment scalp EEG observations for certain patients; acting as an additional tool during the diagnosis stage.