esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability Analysis of Isolated Roof in Overlapping Goal Based on Strength Reduction
## 2 Strength Reduction Method for Stability Analysis of an Isolated Roof  
### Calculation Process of the Strength Reduction Method  
The basic principle of the strength reduction method is to divide the strength of the rock mass by a certain reduction coefficient to obtain new material parameters and use them for mechanical calculation [18; 19; 20]. Then, the strength of the rock mass is continuously reduced until it is in a critical instability state. Therefore, the reduction factor at critical instability is calculated, which is the safety factor.  
By defining the cohesion of a rock mass as \(c_{0}\), friction as \(\varphi_{0}\), and the reduction coefficient as \(F_{s}\), the shear strength reduction formula is as follows:  
\[c=\frac{c_{0}}{F_{s}} \tag{1}\]  
Figure 3: Parameters indicating the size of the overlapping goaf.  
Figure 2: Collapse processes of the overlapping goaf.  
\[\varphi=\arctan\biggl{(}\frac{\tan\varphi_{0}}{F_{\rm s}}\biggr{)} \tag{2}\]  
With reference to the shear strength reduction method, the formula of tensile strength reduction is proposed:  
\[\sigma_{\rm t}=\frac{\sigma_{0}}{F_{\rm t}} \tag{3}\]  
In Equation (3), \(\sigma_{0}\) is the tensile strength, \(F_{\rm t}\) is the reduction factor, and \(\sigma_{\rm t}\) is the reduced tensile strength.  
It is not difficult to find that the key to the stability analysis of the strength reduction method of the isolated roof is how to accurately calculate the safety factor. This problem can be solved by combining the strength reduction method with numerical simulation. Taking shear strength reduction as an example, the calculation process is as follows:  
1. Establish a model of the overlapping goat, including the upper pillar, the isolated roof, and the lower pillar;
2. Calculate the model by using the initial parameters \(c_{0}\) and \(\varphi_{0}\) of the model until the calculation converges;
3. Continuously reduce the cohesion and friction until critical equilibrium is reached;
4. Using Equations (1) and (2), the safety coefficient \(F_{\rm s}\) of the isolated roof is obtained;
5. Change the relative position of the upper and lower pillars, and calculate the safety factor of the roof by the above steps (1)-(4).  
The above steps are shown in Figure 4.  
From the analysis of the above calculation process, it can be concluded that strength is the main factor affecting the safety factor of the isolated roof. All other conditions being equal, the higher the strength, the greater the safety factor, and the more stable the roof.
# A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range
## 3 Results
### Performance Analysis
#### 3.3.2 Generalization Performance  
To evaluate the generalization capability of the GCSG model across different Logical Range training datasets, we conducted experiments using ten distinct data partitioning schemes with varying random seeds. Each scheme maintained an input window size of  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \multirow{2}{*}{_w_} & \multicolumn{4}{c}{\(\mathbf{R^{2}}\)} & \multicolumn{4}{c}{**MAE**} & \multicolumn{4}{c}{**MSE**} \\ \cline{2-13}  & **CG** & **GCG** & **CSG** & **GCSG** & **CG** & **GCG** & **CGG** & **GCG** & **GCG** & **CGG** & **CGG** & **GCSG** \\ \hline
16 & 0.838 & 0.848 & 0.840 & 0.860 & 4.538 & 4.513 & 4.632 & 4.448 & 35.227 & 34.156 & 35.591 & 33.382 \\
20 & 0.838 & 0.851 & 0.842 & 0.862 & 4.570 & 4.438 & 4.536 & 4.399 & 35.014 & 33.111 & 34.705 & 32.064 \\
24 & 0.834 & 0.846 & 0.842 & 0.859 & 4.590 & 4.441 & 4.595 & 4.461 & 35.470 & 33.693 & 35.332 & 33.152 \\
28 & 0.823 & 0.842 & 0.839 & 0.856 & 4.835 & 4.535 & 4.728 & 4.489 & 38.588 & 35.153 & 36.797 & 34.250 \\
32 & 0.835 & 0.839 & 0.840 & 0.858 & 4.580 & 4.559 & 4.631 & 4.412 & 35.428 & 35.456 & 36.314 & 33.615 \\
36 & 0.831 & 0.843 & 0.839 & 0.856 & 4.625 & 4.503 & 4.671 & 4.468 & 36.231 & 34.448 & 35.815 & 33.804 \\
40 & 0.827 & 0.833 & 0.837 & 0.853 & 4.657 & 4.655 & 4.689 & 4.499 & 37.337 & 37.136 & 37.423 & 34.500 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of \(R^{2}\), MAE, and MSE for different input window sizes (_w_).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \multirow{2}{*}{_d_} & \multicolumn{4}{c}{\(\mathbf{R^{2}}\)} & \multicolumn{4}{c}{**MAE**} & \multicolumn{4}{c}{**MSE**} \\ \cline{2-13}  & **CG** & **GCG** & **CGG** & **GCG** & **CG** & **GCG** & **CGG** & **GCG** & **GCG** & **CGG** & **CGG** & **CGG** \\ \hline
1 & 0.831 & 0.843 & 0.838 & 0.855  
\(w=36\) and prediction steps \(d\) ranging from 1 to 29 with an interval of 2. The experimental results are presented in Figure 7.  
Analysis of the datasets partitioned with different random seeds in the Logical Range revealed that the GCSG model can maintain consistent load forecasting performance. Figure 7a demonstrates stable \(R^{2}\) values around 0.85, while Figure 7b shows MAE values consistently near 4.4. Similarly, Figure 7c confirms the stabilization of the MSE at around 35. Compared to other models, GCSG exhibited superior stability across all three metrics without significant fluctuations, demonstrating its robust generalization capability across different data partitions.
## 2 Problem Statement and Methodology  
The objective of this research is to enable a quadcopter UAV engaged in search and rescue operations to acquire the capability of classifying objects in a wreckage of collapsed building. For this to be realized, a companion computer onboard the quadcopter has to be well trained with plausible indoor objects. For the first phase of this work, we randomly picked plausible indoor and surrounding objects, such as windows, doors, walls, columns, pipes, poles, tables, fans, nets, and trees.  
For training, we considered supervised deep learning of the convolutional neural network approach. The architecture of this CNN is problem specific. Therefore, we designed a custom CNN architecture specific to the aforementioned problem. Often, network training requires big input data. There are no big data for the stated scenario. In cases when the available input data are scarce, a common method to enhance the number input data is to augment the available data through various methods. [PERSON] et al. [37] conducted an extensive survey on augmentation methods of input image data. In our case, we used mission specific augmentation methods.  
A mission specific augmented input data generation approach is pertinent to the quadcopter UAV engaged in search and rescue operations, in a collapsed building. Under such an operation, the quadcopter has to enter the collapsed building through any hole available and avoid collision with obstacles as it searches for targets. The quadcopter will possibly encounters environments with variable brightness as well as laid or inverted objects. While the quadcopter is taking images of the scene, the images can be blurred due to the vibration of the quadcopter. If images are taken during the rolling of the quadcopter, objects in the images might appear rotated. If images are taken at a glance, objects in the images appear sheared. These possibilities are taken into consideration during image data augmentation. In designing the neural network model, data cleaning and code development took most of the time and computing resources. Data training frameworks and code development are carried out with Python libraries. Image preparation and manipulation is performed with the Image Processing Toolbox(tm) from MathWorks(r). Units' dropout is randomly implemented in a ratio of 0.1 to 0.4 on the fully connected (dense) layer of applied layers.
## 3 Design and Calculation Results of Vertical Shaft Multi-Rope Friction
### Dynamic Analysis and Calculation of the Hoisting System
#### 3.4.3 Elongation of Hoisting Wire Rope  
The elongation of the steel wire rope at the connection between container and rope at different stages in different rope selection schemes is shown in Figures 16-18.  
As can be seen from Figures 16a, 17a and 18a, in the heavy-load lifting phase, the elongation of the hoisting ropes at the heavy-load end presents a downward trend, and the variation ranges are 0.1 mm-0.8 mm, 0.07 mm-0.86 mm, and 0.05 mm-1.68 mm, respectively. The reason is that with the lifting of heavy-load, the overhang height and dynamic tension of the hoisting rope at the heavy-load end decrease continuously, and the elongation of the rope decreases when the elastic modulus and cross-sectional area of rope are unchanged. On the contrary, the elongation of the hoisting rope at the light-load end continues to increase, and their variation ranges are 0.05 mm-0.5 mm, 0.06 mm-0.55 mm, 0.02 mm-1.04 mm, respectively.  
As can be seen from Figures 16b, 17b and 18b, in the heavy-load lowering phase, the elongation of the hoisting ropes at the heavy-load end presents an upward trend, and the variation ranges are 0.09 mm-0.8 mm, 0.1 mm-0.76 mm, and 0.18 mm-1.53 mm, respectively. The reason is that with the lowering of heavy-load, the overhang height and dynamic tension of the hoisting rope at the heavy-load end increase continuously, and the elongation of the rope increases when the elastic modulus and cross-sectional area of rope are unchanged. On the contrary, the elongation of the hoisting rope at the light-load end  
Figure 16: Elongation of round strand wire rope at different stages: (**a**) heavy-load lifting phase, (**b**) heavy-load lowering phase.  
continues to decrease, and the variation ranges are 0.03 mm-0.52 mm, 0.04 mm-0.54 mm, 0.07 mm-0.98 mm, respectively.  
In addition, it is found that the elongation of the compacted strand wire rope is much larger than the other two kinds of steel wire rope, because its elastic modulus is smaller and it deforms easily.
# A Trajectory Optimisation-Based Incremental Learning Strategy for Learning from Demonstration
## 2 Review of Related Work  
### Trajectory Generation  
Trajectory generation is an important function to support robotic applications. [PERSON] et al. [3] developed an LfD-enabled trajectory generation algorithm that combines Gaussian noise with a task-parameterised Gaussian mixture model (TP-GMM). This algorithm facilitates the creation of the generation trajectory by augmenting the original demonstration. [PERSON] et al. [21] proposed a method based on multi-region cost learning. By dividing a demonstration (trajectory) into various sub-regions and establishing cost functions for each sub-region, this method can fine-tune and preserve the geometric features of the demonstration more accurately to optimise the generated trajectory. [PERSON] et al. [11] designed a generative adversarial imitation learning (GAIL)-based algorithm to optimise generated trajectories. In this algorithm, reinforcement learning and generative adversarial networks were combined to allow for more diversity in trajectory generation.  
In order to achieve the local adjustment capability and meet the precision requirement of trajectory generation, [PERSON] et al. [12] proposed a trajectory generation method that combines a B-spline algorithm with a pruning algorithm. The pruning algorithm was used to create a trajectory, and B-spline was applied to smoothen the trajectory. However, the control points of the B-spline algorithm cannot accurately mimic the features of the created trajectory. To enhance the similarity of the generated trajectory to the original demonstration and enable local adjustment, sampling-based trajectory generation algorithms are highly promising choices, attributed to their simplicity, flexibility, and computational efficiency [13]. [PERSON] et al. [13] and [PERSON] et al. [22] proposed a trajectory generation method based on PRM. PRM can generate trajectories through the Dijkstra algorithm and sampling points. The distribution of sampling points can affect both the algorithm's efficiency and the quality of the generated trajectory. It is important to design a suitable sampling strategy.  
However, several limitations persist when applying these approaches in manufacturing processes: (1) due to the lack of efficient trajectory segmentation methods, the generation of trajectories can only rely on the entire original demonstration, which may not be economical enough when trajectory adjustments are needed for just one sub-region. (2) Mimicking the characteristics of the demonstration proves challenging, and the shape of generated trajectories is hardly controlled because there is a lack of research on efficient sampling strategy and similarity measurement between the original demonstration and extended demonstration.
# Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
## 2 Overview of Flexible Wearable Heart Rate Monitoring Systems
### Definition of Flexible Wearable Heart Rate Monitoring Systems
Third, system maintenance and functional expansion are more flexible [54]: it supports independent replacement and repair of modules, and new functions can be quickly implemented through expansion modules, greatly reducing development and maintenance costs. From a technical perspective, the operation of flexible wearable heart rate monitoring systems mainly relies on PPG and ECG technologies. As shown in Figure 2a,b, PPG sends out light with specific wavelengths onto the skin tissue. By using the way that blood absorbs and reflects light, it changes the changes in blood volume caused by heartbeats into electrical signals. This makes it possible to calculate the heart rate accurately. On the contrary, ECG technology measures the differences in electrical potential on the body's surface. These differences are caused by the heart's electrical activity, and through this, an electrocardiogram is obtained. This enables people to analyze the heart rate and the electrophysiological condition of the heart. Compared with PPG, ECG can provide more detailed information about the heart's electrical activity. Also, it is more sensitive when it comes to detecting heart diseases such as arrhythmias. In practical applications, these two technologies can play roles in different scenarios. PPG technology enables long-term low-power heart rate monitoring to meet the needs of daily continuous monitoring, while ECG technology plays a key role when a more accurate diagnosis of heart problems is required. However, whether the research is based on ECG technology or PPG technology, its core essentially revolves around the theme of non-invasive monitoring [56]. It can be foreseen that in the future, researchers will be able to develop flexible wearable heart rate monitoring systems with better performance and greater portability. Figure 2: (**a**) Principle and waveform diagram of PPG heart rate monitoring. (**b**) Principle of 12-lead ECG heart rate monitoring. (**c**) The circuit of a flexible hybrid electronic system for heart rate monitoring fabricated by multi-layer screen printing [55]; this image is licensed under CC BY 4.0, copyright 2024, Wiley Online Library. In summary, the flexible wearable heart rate monitoring system realizes the complete process from heart rate signal acquisition, preprocessing, and computational analysis to signal output through modular design. The modular design significantly enhances the wearing comfort of the system and makes system maintenance and functional expansion more flexible. Meanwhile, the technical principles adopted by the system, namely PPG and ECG, provide diverse and reliable technical support for heart rate monitoring and cardiac health assessment.
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 6 Future Directions
### Advanced Security and Trust Mechanisms  
Securing AI workflows on constrained devices calls for continuous innovation in cryptography, secure enclaves, and tamper-evident hardware. One emerging trend is the exploration of homomorphic encryption, which allows computations to be performed on encrypted data without exposing raw information [12]. Although computationally heavy for large-scale networks, scaled-down variants of homomorphic encryption might become more feasible as specialized hardware accelerators evolve. Another area of interest is secure multiparty computation (MPC), which enables multiple parties to jointly compute functions over their data while keeping inputs private [24]. Applied to federated learning, MPC can further mitigate privacy risks when aggregating updates across untrusted devices. Ensuring that these security measures remain lightweight enough for microcontrollers remains a central challenge [23].
## 3 Results
### Cell Membrane Damage Due to LED Treatments  
High-intensity light pulses emitted from LEDs can induce cell membrane damage in the bacterial cells. Therefore, a PI exclusion assay was performed in low-a\({}_{\mathrm{w}}\)\(S\). Typhimuriumium in this study. Since the preparation of the low-a\({}_{\mathrm{w}}\)\(S\). Typhimuriumium involved a total drying period of 5-6 days followed by an equilibration period of 7 days, the PI exclusion analysis was performed after the drying and equilibration steps during the preparation of the dry bacteria. In addition, the cell membrane damage in the overnight culture of \(S\). Typhimuriumium was performed, which showed damage in 38.9% of the cells (Figure 2). The drying step (air-drying and silica gel drying) produced a higher (\(p\) = 0.0008) percentage of damaged cells labelled with PI. However, the percentage of cell population labelled with PI was reduced (\(p\) = 0.0391) after equilibration (Figure 2). This could be due to the lysis of dead cells as the flow cytometer measures only intact cells and does not detect the lysed cells.  
The PI exclusion assay of the LED-treated low-a\({}_{\mathrm{w}}\)\(S\). Typhimurium did not show additional damage to the cell membrane (Figure 2). Since a majority of the bacterial cells already suffered damage after drying and equilibration, the PI exclusion assay could not detect any significant cellular damage after the LED treatments. There was no effect of treatment time (or dose) on the cell membrane damage in \(S\). Typhimuriumium cells due to the LED treatments. Moreover, there was no effect of varying the wavelength of the light pulses on the cellular membrane damage in bacterial cells treated by 365 and 395 nm LEDs (Figure 2). Overall, the PI exclusion study was not suitable to understand the effects of the 365 and 395 nm LED treatments on the cell membrane of the low-a\({}_{\mathrm{w}}\)\(S\). Typhimurium due to long drying and equilibration periods involved in the preparation of dry bacteria in this study. In further experiments, the low-a\({}_{\mathrm{w}}\)\(S\). Typhimurium prepared with shorter drying and equilibration periods were used to minimize the cell death by the exclusion of oxygen and faster equilibration [24].  
Figure 2: Effect of vacuum drying and equilibration to 0.75 a\({}_{\mathrm{w}}\) and 365 and 395 nm LED treatments on the cell membrane of the \(S\). Typhimuriumium ATCC13311 cells by PI (propidium iodide) exclusion assay. Here the Y-axis in the graph represents the percentage of PI-permeable cells of the total population when analyzed using a flow cytometer. Treatments with the 365 and 395 nm light pulses of low-a\({}_{\mathrm{w}}\)\(S\). Typhimurium cells were performed for 10 and 60 min, corresponding to treatment doses of 28.9 and 188.1 J/cm\({}^{2}\) for 365 nm and 138.8 and 834.4 J/cm\({}^{2}\) for 395 nm, respectively, at 60% power level and at 4 cm from the LED heads. Results are represented as means \(\pm\) standard deviation of three independent replicates. Values with different letters differ significantly (\(p\) < 0.05).
###### Abstract  
RF-MEMS, i.e., Micro-Electro-Mechanical Systems (MEMS) for Radio Frequency (RF) passive components, exhibit interesting characteristics for the upcoming 5G and Internet of Things (IoT) scenarios, in which reconfigurable broadband and frequency-agile devices, like high-order switching units, tunable filters, multi-state attenuators, and phase shifters will be necessary to enable mm-Wave services, small cells, and advanced beamforming. In particular, satellite communication systems providing high-speed Internet connectivity utilize the K and Ka bands, which offer larger bandwidth compared to lower frequencies. This paper focuses on two design concepts of multi-state phase shifter designed and manufactured in RF-MEMS technology. The networks feature 4 switchable stages (16 states) and are developed for the K and Ka bands. The proposed phase shifters are realized in a surface micromachining RF-MEMS technology and the experimentally measured parameters are compared with Finite Element Method (FEM) multi-physical electromechanical and RF simulations. The simulated phase shifts at both the operating bands fit well the measured value, despite the measured losses (S21) are larger than 5-7 dB if compared to simulations. However, such a non-ideality has a technological motivation that is explained in the paper and that will be fixed in the manufacturing of future devices.  
RF-MEMS; MEMS; RF possives; phase shifters; multi-state passive networks; 5G; Internet of Things (IoT); wideband operability +
Footnote †: journal: Sensors
# Biomimetic Liquid-Repellent Surfaces by Ultrafast Laser Processing
## 1 Introduction
### Laser-Irradiated Structures
#### 2.2.2 Columnar Microstructures
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Process Parameters** & **Influence on Morphology of Columnar Structures** & **Material** & **References** \\ \hline \multirow{8}{*}{Number of laser pulses} & Columns start to develop after irradiation with a defined number of laser pulses. & Silicon (Si) & [31] \\ \cline{2-4}  & Average column height and distance between neighboring columns grow with increasing number of laser pulses. & Titanium (Tl) & [16; 28; 32; 33] \\ \cline{2-4}  & After exceeding a certain number of laser pulses, the column morphology does not change any more. & & Nickel (Ni) & [34] \\ \hline \multirow{8}{*}{Laser fluence} & There are low and upper fluence thresholds for the formation of columnar structures. & Steel & [28; 35; 36] \\ \cline{2-4}  & At laser fluences below the low threshold fluence, column structures cannot be generated independent of the applied number of laser pulses. & Aluminum (AI) & [28] \\ \cline{2-4}  & At fluences higher than the upper threshold fluence, column structures can either be destroyed or different structure type starts to develop. & Titanium (Tl) & [30] \\ \cline{2-4}  & Average column height and distance between neighboring columns grow with increasing fluence. & & Nickel (Ni) & [34] \\ \hline \multirow{8}{*}{Polarization} & Linear polarization leads to elliptical shape of the column bases. & Steel & [18; 28; 38] \\ \cline{2-4}  & Circular polarization leads to circular shape of the column bases. & Silicon (Si) & [37] \\ \cline{2-4}  & Processing with laser pulses at shorter wavelengths leads to reduction of average column height and distance between neighboring columns. & Steel & [40] \\ \cline{2-4}  & Processing with nanosecond laser pulses leads to the generation of columnar structures with larger column height and average distance between neighboring columns compared to processing with ultrafast laser pulses. & Silicon (Si) & [41; 42; 43] \\ \cline{2-4}  & Column structures generated by nanosecond pulses are much smoother than those generated by ultrafast lasers. & & Nickel (Ni) & [39] \\ \hline \multirow{8}{*}{Wavelength} & Laser processing of silicon in SF\({}_{6}\) or Cl\({}_{2}\) atmosphere leads to the formation of sharp columnar structures; processing of silicon in SF\({}_{6}\) leads to higher density of columns. & Silicon (Si) & [37; 44; 45] \\ \cline{1-1} \cline{2-4}  & Laser processing of different materials in liquids leads to much smaller column height and average distance between neighboring columns compared to processing in gasses or air. & Titanium (Tl) & [28; 32; 46] \\ \cline{1-1} \cline{2-4}  & Increasing of gas pressure leads to decreasing of column height and average distance between neighboring columns in laser processing of metals. & Steel & [28; 47] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Influence of process parameters on morphology of columnar microstructures.