esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
If any individual's objective or constraint deviation from historical records exceeds the threshold, significant renewable energy output variation is confirmed: \(\max\left(\frac{dP_{\text{wind}}}{dt},\frac{dP_{\text{pv}}}{dt},\frac{dP_{ \text{load}}}{dt}\right)>\theta_{\text{threshold}}\), where \(\theta_{threshold}\) is the preset rate-of-change threshold correlated with renewable output standard deviations: \(\theta_{\text{threshold}}=k\cdot\max\left(\sigma_{\text{wind}},\sigma_{\text{pv }}\right)\); (4) Based on renewable energy output state variations, evaluate the applicability of two strategies within the proposed adaptive overcurrent protection method: DNSGA-II-A (random solution injection strategy) and DNSGA-II-B (mutated solution strategy). A rapid state transition is defined by \(\frac{dP_{\text{wind}}}{dt}>2\sigma_{\text{wind}}\). If renewable power fluctuates drastically or grid topology changes rapidly, apply DNSGA-II-A; if renewable states evolve gradually, apply DNSGA-II-B. \[\mathrm{I_{set}^{new}}=\mathrm{I_{set}^{current}}\cdot U(1-\alpha,1+\alpha) \tag{1}\]1 Flowchart of adaptive overcurrent protection method calculation. (5) Perform non-dominated sorting, crowding distance calculation, and elitist preservation on the extended population R to generate the new parent population. A composite objective function F is proposed to quantify multiple conflicting optimization objectives into a comparable metric. The optimization objectives for this distribution network overcurrent protection are defined as follows: minimizing the operation time \(T_{\text{action}}\), maximizing selectivity S, minimizing the sensitivity coefficient \(K_{\text{sens}}\), and minimizing the misoperation rate E. Compute F and select the individual with the smallest F value as the current optimal solution. (6) Terminate iterations if the maximum iteration count (set to 100 generations) is reached or the Pareto front change rate remains below 1% for 5 consecutive generations. Validate optimized protection parameters against constraints: sensitivity \(K_{\text{sens}}>1.5\), selectivity \(t_{\text{upper}}>t_{\text{lower}}+\Delta t\), and misoperation rate \(E<1\%\). Output qualified parameters (overcurrent protection thresholds Iset, time delay \(t_{\text{delay}}\)) to protection devices to ensure real-time adaptation to short-circuit current variations caused by renewable fluctuations, thereby enhancing protection reliability, selectivity, and speed.
# Research on Fracture Mechanism and Stability of Slope with Tensile Cracks
## 2 Formation Mechanism and Value of Vertical Crack  
### Cracking Mechanism  
It is widely known that under the action of external forces, the slope will gradually lose stability, and the original mechanical balance will be destroyed, resulting in a new coordinated deformation. The shear outlet of the clay slope is generally located at the toe of slope. Due to the concentration of shear stress, a large plastic deformation occurs, and its deformation degree is greater than the top of slope. Because of the cumulative effect of deformation, a certain nonuniform settlement will occur near the slope top, thus forming a crack region. In fact, the soil at the top of slope does not have significant tensile strength and thus cannot withstand tension. Therefore, the soil at the top of the slope can usually be simplified as a material that cannot bear the tensile stress.  
Rankine earth pressure theory is a calculation method of earth pressure based on the limit equilibrium conditions of soil by studying the stress state in elastic half space [9]. For total stresses, the Rankine active earth pressure beneath a horizontal ground surface, the stress field, is only generated by the weight of soil in infinite half-space, as shown in Figure 1. The vertical stress at any position can be expressed as \(\sigma_{x}=\gamma h\), and it is the maximum principal stress \(\sigma_{1}\). Similarly, the horizontal stress can be expressed as \(\sigma_{x}\), and it is the minimum principal stress \(\sigma_{3}\). Based on the condition of static equilibrium, mathematically,  
\[\left\{\begin{array}{l}\sigma_{1}=\sigma_{2}=\gamma z\\ \sigma_{3}=\sigma_{x}=\sigma_{1}\tan^{2}\left(45^{\circ}-\frac{\varphi}{2} \right)-2c\tan\left(45^{\circ}-\frac{\varphi}{2}\right)\end{array}\right. \tag{1}\]  
where \(\sigma_{1}\) and \(\sigma_{3}\) are the maximum and minimum principal stresses, respectively; \(\sigma_{x}\) and \(\sigma_{x}\) are the vertical and horizontal stresses, respectively; \(\gamma\) is soil weight, \(z\) is the distance to the surface of ground, \(\varphi\) is internal friction angle of soil.  
Because the tensile stress of top soil is very small, it can be considered that \(\sigma_{3}=0\). Thus, the crack depth can be expressed as,  
\[\delta=\frac{2c}{\gamma\sqrt{K_{a}}} \tag{2}\]  
Figure 1: Stress distribution of soil.  
where, \(\delta\) is crack depth, \(c\) is cohesion of soil, \(K_{a}=\tan\left(45^{\circ}-\frac{\varphi}{2}\right)\).
# Monolithically Integrated THz Detectors Based on High-Electron-Mobility Transistors
## 3 Design  
We fabricated the THz detectors using our in-house GaN HEMT MMIC process, with the epitaxial layers of the heterostructure also grown internally. The epitaxial structure comprises an Al\({}_{0.28}\)GaN barrier layer with a thickness of 10 nm on a SiC substrate. A schematic representation of the MMIC stack is shown in Figure 3. The process incorporates two metallization layers, enabling the formation of metal-insulator-metal (MIM) capacitors. Silicon nitride, with a thickness of 200 nm, serves as the dielectric material. While the process also supports thin-film resistors, these were not utilized in the presented designs. The epitaxial layer structure and the specifics of the in-house MMIC process are described in detail in [21,22].  
Figure 3: (**Left**): Stack of the used MMIC process. (**Right**): Schematic representation of the transistor with RF short circuit between the gate and the drain terminal.  
Figure 2: (**a**) High-resolution drain current measurement; (**b**) Differential output conductance; (**c**) Differential output conductance differentiated according to the drain voltage; (**d**) Differential output conductance differentiated according to the gate voltage.
## 5 Comparison between Direct Scanning Calorimeter, Step Scanning Calorimeter and Fluxmetric Methods  
This section compares the results between the three characterization methods to obtain the enthalpy curve h(T) for RT58 and PEG6000. The characterization methods rely on two different pieces of equipment, a calorimeter for the DSC and STEP methods, and a specific apparatus for the FM method, using two very different masses of sample, being ten milligrams for the DSC and STEP methods, and between two hundred and three hundred grams for the FM method.  
Figure 11 shows the results for RT58. A very good consistency was observed between the three curves, despite the differences in method and apparatus. In particular, the curves on heating and cooling are nearly congruent, confirming that this PCM showed very little supercooling.  
For PEG6000, the results visible in Figure 12 show differences both in heating and cooling. During the heating process, there was a noticeable difference in the onset of melting, in the range of 55 \({}^{\circ}\)C to 60 \({}^{\circ}\)C. The repeatability of the determinations on small samples (DSC, STEP) suggested an effect related to heat transfers within the macroscopic sample. The total mass enthalpy changed during melting (which is, by abuse of language, called latent heat of melting, which is defined only for a pure substance), however, this was consistent for all methods.  
During cooling, a difference of nearly 5 \({}^{\circ}\)C on the supercooling rupture temperature between the FM method and the DSC and STEP methods, was observed. This can be explained by the difference in volume of the two samples; it is indeed well known [54; 55] that the degree of supercooling increases with the reduction of the size of the sample considered. As the PCM mass was roughly 50,000 times greater for the FM method than for DSC and STEP methods, the observed behavior was therefore consistent. For a fictive latent heat storage with 100 kg of PCM, the mass ratio between the system and the sample characterized would be close to 400 for the FM method and around 20 million for the DSC and STEPS methods. Therefore, the FM method enables to get closer to the thermal behavior of real scale storage of DHW.  
Figure 11: Comparison of the results obtained by the three methods for RT58.  
The FM method showed a rise in temperature at the moment of rupture: locally, i.e., at the location of the metastability rupture, the material reached its equilibrium state characterized by the \"real\" h(T) curve, i.e., at heating. This phenomenon was totally masked by the calorimeter because of the small volume of the sample and the control device which had the function of regulating the temperature. For the fluxmeter device, as the parietal exchanges are limited, a slight rise in temperature was observed. It is noteworthy that the qualifier \"real\" attributed to the results in heating allowed support of the fact that there was no hysteresis phenomenon related to the function h(T), which is a state quantity: the curves in cooling resulting both from the supercooling and from the internal transfers in the sample.
# The Concept of Segmented Wind Turbine Blades: A Review
## 5 Blade Segmentation Strategy
### Segmenting to Obtain a Variable Rotor Loading  
Control strategies such as varying the blade pitch or the rotor speed are used to produce the maximum amount of energy while limiting the load to the turbine's rated power. Additionally, various strategies are used to reduce the extreme and fatigue loads on the rotor. Reducing the loads on the rotor can affect the loads on other components such as the bearings, gearbox and generator and could reduce the \(COE\). Such strategies include cyclic pitch, individual pitch control and aeroelastic tailoring [74]. Alternative strategies using the relative displacement of different blade segments are possible. One such approach uses telescopic blades. In that case, one segment is retracted into the other to vary the swept area of the rotor [75; 76; 77; 78]. This allows the turbine to produce more power at low wind conditions while avoiding the extreme loading at high wind speeds. However, this requires a mechanism to perform the retraction that has to transmit all of the loads from the outboard segment to the inboard segment. Alternatively, various active'smart' control strategies are under development [74]. These use distributed sensors and actuators along the blades. The actuators include trailing edge flaps. [PERSON] [79] demonstrated this concept on a turbine with 13 m-long blades. The average flap-wise blade root moment decreased by 14% along with 20% of the amplitude of the 1 Ploads. [PERSON] [80; 81] tested trailing edge flaps on a turbine with 9-m blades. An average load reduction of 14% was reported.
# Remote Sensing and Machine Learning for Safer Railways: A Review
## 4 Discussion
### Methodological Patterns  
This section examines the various models and algorithms documented in the reviewed literature, with a focus on their practical implications and limitations in real-world railway inspection scenarios. The effectiveness of each model, using CNNs, YOLO, or GANs, depended on its applicability in different railway monitoring conditions. Hence, they differed in specific strengths, such as high accuracy or real-time processing capabilities, and limitations, such as high computational demands or sensitivity to data quality. The authors noted several commonalities among the methods. Noteworthy machine vision models included versions of YOLO, Faster R-CNN, Mask R-CNN, ResNet, VGGNet, and others. Most of the reviewed studies utilized image datasets comprising hundreds or thousands of images for training and testing the deep learning models. Some studies incorporated video footage converted into images, with image resolutions varying from 256 \(\times\) 256 pixels to 4096 \(\times\) 2160 pixels.  
Some studies discussed the effectiveness of transfer learning in enhancing accuracy by utilizing pre-trained models like ImageNet [52]. Moreover, models underwent fine-tuning to set parameters such as learning rate, number of epochs, batch size, dropout rate, momentum, decay rate, and regularization techniques. Mean Average Precision (mAP) and accuracy emerged as the most common evaluation metrics, with some studies incorporating Intersection over Union (IoU) and F1-scores to assess performance. It is noteworthy that most models achieved high accuracy levels, surpassing 90%. Another notable trend in the reviewed methods was that both non-ANN and ANN-based methods required pre-processing steps, such as noise removal, contrast enhancement, and converting RGB to greyscale. These studies used statistical methods like edge detection, Haar transform, SVM, kNN, random forest, and frequency-resolved optical gating (FROG) to identify defects based on real-world image datasets. Some of the studies compared multiple methods.
# A Novel Data-Aided Frame Synchronization Method Based on Hough Transform for Optical Communications
## 2 Fundamental of AC-Based Frame Synchronization Algorithm  
Information is usually transmitted using frames as basic unit in the optical transmission systems. Figure 1a gives a schematic diagram of frame sequence with synchronization word inserted centrally. It can be shown that an intact frame, the length of which is \(L\), contains two parts, fixed frame synchronization word (S) and varying payload (D). The length of them are \(M\) and \(N\), respectively, and \(L=M+N\). In the data received by receiver, the synchronization word is periodic emergence, while the payload is random. Through locating the synchronization word, frame synchronization is attained, and the payload can be extracted further for subsequent operations.  
In cooperative communications, S is known to the receiver and synchronization is achieved through finding the optimal delay that maximizes the cross-correlation of received sequence and S. The high SNR approximation of this method is given in [8]:  
\[\hat{\tau}=\underset{\tau_{m}}{\mathrm{argmax}}\frac{1}{M}\Bigg{|}\sum_{i=0}^{M-1 }r_{\tau_{m}}^{i}s_{i}^{i}\Bigg{|}^{2} \tag{1}\]  
where \(s_{i}\) is the \(i\)th element of S and \(r_{\tau_{m}}^{i}\) is the \(i\)th element of received sequence in the sliding window, starting from \(\tau_{m}\), as shown in Figure 1b.  
However, in the context of non-cooperative communications, S is unknown to the receiver. Hence, the cross-correlation in Equation (1) is not computable. An alternative option using auto-correlation is given in [24]:  
\[\left(\hat{\tau},\hat{L}\right)=\underset{\tau_{m},\,l}{\mathrm{argmax}}\frac {1}{l}\Bigg{|}\sum_{i=0}^{l-1}r_{\tau_{m}}^{i}\left(r_{\tau_{m}+l}^{i}\right) ^{*}\Bigg{|}^{2} \tag{2}\]  
where \(r_{\tau_{m}}^{i}\) and \(r_{\tau_{m}+l}^{i}\) are the \(i\)th element of received sequence in the sliding window starting from \(\tau_{m}\) and \(\tau_{m}+l\), respectively, \(l\) is the length of sliding window and \(\hat{L}\) is an estimate of the frame length.  
Specifically, a sampling data storage matrix is built in the AC-based synchronization algorithm, and \(r_{\tau_{m}}^{i}r_{\tau_{m}+l}^{i}\) is sequentially filled into the matrix. Since the probability distributions of 0 and 1 in S and D are different, the synchronization word can be detected through calculating the probability distributions of 0 and 1 at each position of the sliding window, with \(\tau_{m}\) and the \(l\) constant.
# Numerical Simulation of Flapping Airfoil Aerodynamic Characteristics
## 2 Control Equations, Numerical Methods, and Model Validation
### Model Validation  
For accuracy verification, the butterfly wing model is established and the variation curve of wing lift \(L\) with time \(T\) is simulated for \(Re=1000\). After comparing with Senda's [27] simulation results, it is found that the comparison error is small, with a maximum error of only 4.7%. The comparison between the lift curve obtained from simulation experiments in this article and the results obtained from Senda's numerical research is shown in Figure 3. It is detected that the lift coefficient curve calculated in this article is consistent with the changing trend calculated by Senda, and the values are relatively close.  
To optimize computational speed while ensuring accuracy, a mesh sensitivity check is conducted for two distinct cases: the parallel flapping-airfoil configuration and the 45\({}^{\circ}\) trajectory condition. Three sets of grids with varying resolutions are evaluated: Grid 1: Minimum grid size of 1 \(\times\) 10\({}^{-5}\). Grid 2: Minimum grid size of 5 \(\times\) 10\({}^{-6}\) (with refined resolution around the flapping airfoils compared to Grid 1). Grid 3: Minimum grid size of 5 \(\times\) 10\({}^{-7}\). The lift coefficients obtained from the three grids under both conditions are presented in Figures 4 and 5. For the parallel flapping airfoil configuration, the relative error between Grid 2 and Grid 3 is found to be 1.7%, while for the 45\({}^{\circ}\) trajectory condition, the error is 2.1%. In contrast, Grid 1 exhibits significantly larger deviations when compared to both Grid 2 and Grid 3.  
These results demonstrate that Grid 2 achieves an optimal balance between computational efficiency and accuracy, despite its lower node count. Consequently, Grid 2 is selected for all subsequent numerical simulations of the flapping-airfoil flow field in this study.  
Figure 4: Mesh sensitivity check (parallel layout).  
Figure 3: Comparison of lift coefficient results.
# Spindle Error Movements and Their Measurement
## 5 Publications Related to the Measurement of Spindle Error Movements
### Measurement Methods with Optical Sensors
#### 5.1.4 Measurement with Diode and PSD Matrix  
Contrary to the traditional measurement set (Figure 11), a measurement performed with a laser diode and Position Sensitive Detector (PSD) matrix was proposed [29]. The idea of the measurement is presented in Figure 27. The tests of the used sensor are described in [30].  
Figure 26: Photograph of the optical system used for research [28].  
Figure 25: Principle of measurement: (**a**) initial state and (**b**) shift in the X axis [28].  
The PSD matrix determines the position of the light signal from the laser diode, and after A/D processing, the signal is sent to the computer. The authors of the idea shown in the Figure 28 state that the diode casing is made of aluminium alloy; that, together with the battery, weigh less than 200 g; and that its length is not more than 10 cm with a diameter of 4 cm.  
Due to its low weight, the device can be mounted in high-speed spindles. The properties of the PSD matrix allow the measurements of the following:  
* incorrect spindle movement,
* spindle rotational speed, and
* angular position of the spindle. All these measurements are performed at the same time during the test.  
Comparative tests were carried out using capacitive sensors. They proved that the idea of measuring spindle error movement with a laser diode and a position sensor (PSD) can be applied. The advantage is the lightness of the device mounted in the spindle, easy assembly, no expensive elements (e.g., ball standard), and only one sensor. The system can be used up to 300,000 rpm. The Figure 29 shows prototype device with a laser diode and two Position Sensitive Detector (PSD) sensors.  
Figure 28: Exploded view of a holder with a laser diode [29].  
Figure 27: System for measuring high-speed spindles [29].  
Previous studies of a similar system but with two position sensors are described in [30]. The light signal from the laser diode mounted in the spindle was split into two streams. One PSD sensor measured the components of error movement in the X and Y axes, and the other measured angular tilt. Both sensors are fixed to the machine table: one in the XY plane and the other in the perpendicular to it. The laser beam coming from the diode mounted in the spindle is divided into two, perpendicular to each other (Figure 29). During spindle rotation, error movements change the position of the laser beams, and these changes are read by PSD sensors. The value measured by the sensors (total error) is converted by the software into two radial errors and one angular error. The study presents the method to measure the individual components of the error movement. The research confirmed theoretical considerations, and the authors believe that the proposed solution can be used to measure the spindle error movements.
# A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2
###### Abstract  
Traffic sign detection is an important task in traffic sign recognition systems. Chinese traffic signs have their unique features compared with traffic signs of other countries. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, we present a Chinese traffic sign detection algorithm based on a deep convolutional network. To achieve real-time Chinese traffic sign detection, we propose an end-to-end convolutional network inspired by YOLOv2. In view of the characteristics of traffic signs, we take the multiple 1 \(\times\) 1 convolutional layers in intermediate layers of the network and decrease the convolutional layers in top layers to reduce the computational complexity. For effectively detecting small traffic signs, we divide the input images into dense grids to obtain finer feature maps. Moreover, we expand the Chinese traffic sign dataset (CTSD) and improve the marker information, which is available online. All experimental results evaluated according to our expanded CTSD and German Traffic Sign Detection Benchmark (GTSDB) indicate that the proposed method is the faster and more robust. The fastest detection speed achieved was 0.017 s per image.  
Keywords:object detection; CNNs; YOLOv2; Chinese traffic sign; CTSD; GTSDB +
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