esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Spatial Time Domain Reflectometry
### Spatial Time Domain Reflectometry Forward Modeling  
Based on the above assumptions and ideal sensor installation conditions, the inverse technique on two-way TDR tracing to extract \(C\) and \(G\) profiles can be executed. According to the electrical circuit simplified in Figure 3a, the forward modeling telegraph equation can predict the TDR trace along a flat ribbon cable. Therefore, the finite difference method is applied to numerically solve these equations with boundary conditions exactly matching the physical sensor design to predict the TDR trace [38; 40; 41; 42]. To simplify the original telegraph equations for solving, [PERSON] transformed two first-order governing partial differential equations (PDE) into a single PDE in second-order as Equation (4):  
\[\left[LC(x)\frac{\partial^{2}}{\partial t^{2}}+LG(x)\frac{\partial}{\partial t }+\frac{\partial L/\partial x}{L}\cdot\frac{\partial}{\partial x}-\frac{ \partial^{2}}{\partial x^{2}}\right]U_{i}(x,t)=0 \tag{4}\]  
Figure 2: (**a**) Flat ribbon cable used for spatial TDR measurements; (**b**) equivalent circuit of an infinitesimal section of cable sensor [18]; (**c**) capacitance model of the flat ribbon cable [17].  
where \(L\) is inductance, assumed to be constant because of the second assumption; \(C\) and \(G\) are, separately, capacitance and conductance in functions of cable length \(x\) because of the third assumption; \(U\) is the voltage predicted for plotting the TDR trace (\(i\) = 1,2 represents first and second TDR tracing); the resistance \(R\) is assumed to be zero because of the first assumption depicted in Figure 3a. The appropriate initial and boundary conditions for solving Equation (4) to replicate the TDR trace have been discussed in [PERSON]'s forward modeling work in detail [42]. Due to the requirement of two unknown input state variables (\(C\) and \(G\) profiles), two-way TDR tracing has to be implemented using a TDR device combined with a multiplexer, as described in Figure 3b [18,19]. The TDR device should send voltage steps through one channel of the multiplexer and then should be immediately switched to the second channel to collect the TDR waveform from the other terminal. Thus, through solving Equation (4) twice to match the two TDR waveforms collected from both terminals, the two unknown input profiles can be determined. Moreover, the forward modeling domain is constrained within two sensor terminals corresponding to two inflection points along a single TDR waveform [17].
## 4 EHA Power Matching
### Power Matching Comparative Analysis
In consideration of ensuring that the motor meets the power requirements of the load at each point, the motor must have a certain margin so that under fixed \(k_{h}\) conditions, the power curve of the motor completely includes that of the load. When using ALS-EHA, \(k_{h}\) is a variable. As long as the maximum output power of the motor meets the requirements of the maximum power of the load, under different load conditions, \(k_{h}\) can be adjusted to make the motor work at a suitable speed and torque to meet the power requirements of the load at every point. In other words, the motor power curves at different operating points (different \(k_{h}\) values) are different, but the areas covered by the power curves are identical. Under low-speed and heavy-load conditions, the load power requirements can be met by appropriately reducing \(k_{h}\); otherwise rising \(k_{h}\) for high-speed and light-load conditions. Considering the copper loss of the motor, the motor power of ALS-EHA as follows:  
\[P_{M}^{\prime}=P_{a}+P_{Ml}=F_{l}v_{a}+\left(\frac{F_{l}}{K_{h}}k_{h}\right)^ {2}R_{m} \tag{32}\]  
Figure 14: Matching actuator power requirement with motor by constant \(k_{h}\). Figure 15: Matching actuator power requirement with motor by variable \(k_{h}\). where,\(P^{\prime}_{M}\),\(P_{Ml}\) represent the input power and copper loss of the motor, respectively; \(P_{a}\) is load power. the power differential with EHA-FPVM is derived as shown below. \[\Delta P^{\prime}_{M}=\Big{(}\frac{F_{l}}{K_{l}}\Big{)}^{2}\Big{(}k_{\text{max} }^{2}-k_{h}^{2}\Big{)}R_{m} \tag{33}\]  
where \(\Delta P^{\prime}_{M}\) is the power difference. In ALS-EHA, a reasonable match between the motor output power and the load power demand of the actuator can be achieved maximum reduction of \(100\Big{(}1-\left(k_{\text{limit}}/k_{\text{max}}\right)^{2}\Big{)}\) percent over EHA-FPVM. ALS-EHA enables to choose the smaller motor and possess the greater power density, compared with the traditional EHA-FPVM.
# Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques
## 3 Methodology
### Multivariate Adaptive Regression Splines (MARS)
#### 3.2.2 Backward Phase  
In order to make a model with higher generalization capacity, the backward phase prunes the model. The backward deletion phase starts its work with the largest model (based on number of MARS equations) obtained at the end of the forward model and, in each step, deletes the least important basis function (according to general cross validation, GCV criterion) that has a minimum share in residual sum of squares. By deletion of each basis function, a new model is estimated that may be a nominee for the final model. The forward phase adds terms in pairs. However, in the backward phase, distinctively, one side of the pair is deleted; therefore, there will be no pair in the final model. The performance of the new model is assessed considering the GCV criterion. The backward phase deletes terms one by one; indeed, it deletes the least effective term in each step to reach the best sub-model. MARS selects the optimal model with the minimum GCV value. The raw residual sum of squares (RSS) of the training set is unsuitable for model comparison because when the MARS terms are deleted, RSS normally increases. In other words, if RSS is used for model comparison, the backward phase will always choose the largest model (based on the number of MARS equations). Assume that GCVk belongs to the \(k\)-th model in the deletion phase and is defined by the following relation [84]:  
\[GCV_{K}=\frac{1}{n}\sum_{i=1}^{n}{(y_{i}-f_{k}(x_{i}))^{2}/(1-C(k)/n)^{2}} \tag{8}\]  
where \(f_{k}\) is the estimated model in the \(k\)-th step of the backward deletion phase, and \(C(k)\) is the number of effective parameters in the model that is equal to the number of the model's terms plus the number of parameters used for selection of the best knot locations, in other words:  
\(C(k)=\) the number of the model's terms (BF) at the \(k\)-th step + \(\lambda\) (the number of the model's terms (BF) at the \(k\)-th step -1)/2, where \(\lambda\) is a smoothing parameter (penalty per knot (GCV)) and is practically selected between 2 and 4. Additionally, BF is basic function. It should be noted that the clause \"(the number of the model's terms (BF) at the \(k\)-th step -1)/2\" indicates the number of hinged functions' knots h existing in the model [84].
# Strain Transfer Analysis of a Clamped Fiber Bragg Grating Sensor
## 4 Theoretical Analysis Results
### Main Factors Affecting the Strain Transfer Rate  
#### 4.2.1 Effect of the Gauge Ratio  
Based on the previous theoretical analysis, the gauge ratio is an important factor that can affect the strain transfer rate. FBG strain transfer rate vs. gauge ratio curve is shown in Figure 5, based on the default parameters as shown in Table 1. The gauge ratio is a value of the gauge length divided by the interlayer length. It can be seen from Figure 5 that the maximum and average strain transfer rates increase with the reduction of the gauge ratio. In order to obtain the accurate strain using a clamped FBG sensor, the gauge ratio should be reduced appropriately in the process of sensor fabrication.
# Two-Layer Network Caching for Different Service Requirements
## 3 Model
### Caching Problems
#### 3.3.2 Case 2: Variable-Size Items  
In this case study, we remove the hypothesis of equal-size items. This model can be useful when the number of items that can be cached is relatively small and the specific size of each can significantly affect the hit ratio. Given the different sizes of the items, in order to consider it in the optimal use of the available cache memory, we introduce the concepts of _density of popularity_. It is defined as the ratio \(d(m,i)=\frac{P_{MZ,i}(m)}{size(m)}\), \(\forall i\in[1,W]\). In what follows \(\mathbf{D}\) denotes the \(\left[W\times C\right]\) matrix of the elements \(d(m,i)\). The Problem 3 changes as follows:  
**Problem 4**. : \[\max_{g_{j,j}} P_{hit,K2}\] (14) \[st\] \[\sum_{m=1}^{C}g_{j,m}size(m)\leq K_{2,j}\forall j\in[1,\left|K_{2 }\right|]\] \[g_{j,m} \in\{0,1\}.\]  
Note that, in this formulation, the caching problem is combined with the _zero-one knapsack problem_, which is known to be NP-complete. _Cost_ of items, _value_ of items, and available _budget_ attributes of the knapsack problem correspond to _item size_, _item popularity_, and _cache size_ of the caching problem. For the knapsack problem an arbitrarily good polynomial time algorithm, based on dynamic programming exists [33], providing a metric \(M=(1-e)M_{opt}\). However, due to the submodularity of the optimization function in Problem 4, it is necessary to use a different approach. Also in this case it is very easy to show that the inequality constraints in (14) can be mapped into a uniform matroid. More details how matroid constraints combine with the knapsack problem can be found in [34]. Hence, also in this case the problem can be faced through a heuristic greedy algorithm which selects items according to their density of popularity. It is listed in Code 2. As for the analysis of performance and complexity, the use of content size does not alter the analysis shown for Algorithm1. Therefore, the computational cost of Algorithm1 is equal to that of Algorithm2 (see Code 2).
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Inter-Cell Interference Mitigation Techniques
#### 4.3.3 Coordinated Multipoint  
Introduced in Rel-11 [188], CoMP allows JP or dynamic point selection (DPS) across a cluster of BSs. In UL CoMP, signals from a UE are jointly received by multiple cells, improving reception at the cost of increased backhaul complexity. TDD systems benefit further from channel reciprocity, reducing feedback overhead.
# Broadband Millimeter-Wave Front-End Module Design Considerations in FD-SOI CMOS vs. GaN HEMTs
## 3 Simulation and Measurement Results
### Broadband FR2-Band PA Design  
#### 3.2.1 Measurement and Simulation of the Differential Cascade/Stacked Si PA  
The design of the differential cascode/stacked Si PA is described in [11], with the measurement and post-layout PEX EM simulation results summarized in Figure 4. The measurement data appear to match up recently well with the PEX EM simulations for this PA. In measurement, the CMOS PA reaches \(P_{OUT,SAT}\)\(\sim\)14-15 dBm across its BW from 24 to 39 GHz with \(\sim\)14 dB gain.
# A Survey of Big Data Archives in Time-Domain Astronomy
## 3 Time-Domain Astronomy  
Time-domain astronomy focuses on studying systems that change over time. In recent years, large-scale surveys of the sky have made it more important to study this topic because it is possible to detect changes that were previously too small to detect [21]. Time-domain astronomy is a branch of astronomy and astrophysics that explores lifetime evolution and changes in a wide variety of cosmic objects, particularly when these changes occur on short cosmic time scales (hours or days to a year) [22]. Novae, supernovae, gamma-ray bursts, active galactic nuclei, binary stars, and pulsars are the specific objects of interest. These are appropriately referred to as transients because the electromagnetic signature radiated by an event such as an explosion is transient. It briefly appears as a flash in the sky before fading away gradually. By capturing these electromagnetic signatures, astronomers can learn about cosmic objects and the physical processes that govern their evolution [23].  
Sequences of observations or data points grouped chronologically are called \"time series\" [24]. It is not typical for researchers in fields such as meteorology, electroencephalography, and financial markets to use time series in their work. Many characteristics of time series data can be observed, such as their non-isolated generation, their temporal variation, and the presence of a trend or cyclic components. Multiple goals can be achieved by studying time series data, such as gaining insight into the mechanism that generates the data or predicting future outcomes and behaviors [25].  
To depict the brightness change of an object over time in time-domain astronomy, light curves [26] are commonly used to represent data collected by telescopes (for a visual representation). Based on the variability features of light curves, astronomical objects can be categorized into several groups (quasars, long-period variables, and eclipsing binaries) and consequently can be researched in-depth. There are a variety of approaches for classifying data into groups based on light-curve data, the most common of which is the use of machine learning algorithms to extract features from the light-curve data and then arrange the features into categories. Variability classes can be characterized and differentiated using these light-curve traits. There are a wide variety of features, including simple statistical properties, such as the mean or standard deviation to more complicated time series properties, such as autocorrelation function. Machine learning and other algorithms can use these properties to distinguish between different types of light curves [25].
# Dynamics Analysis of a Double-Plunger-Type Turbine Overspeed Protection Mechanism
## 2 Device Operation Principle  
The basic structure of the double-plunger type is shown in Figure 1.  
The entire device is placed in a radial through hole in the main shaft, and the device can only move along the radial direction of the main shaft. The set bushings and adjustable bushings are attached to the main shaft with threads. In the initial state, the outer spring is in a compressed state, the outer flyweight is pressed by the outer spring on the lower cock, and the displacement is limited. By adjusting the depth of the adjustable bushing screwed into the main shaft, the overall height of the plungers can be adjusted. Compared with the traditional single-plunger type overspeed protector [8], the outer plunger is a cavity, and the inner plunger, inner spring, and spring base can be placed in the cavity of the outer plunger. The back cover and the outer plunger are integrally connected by threads, and the inner spring is in a compressed state at this time, which can limit the movement of the inner plunger.  
Figure 2 shows the whole system in the complete state in (a), the main shaft cut state in (b), and the outer plunger cut state in (c).  
Figure 1: Structure of double-plunger type overspeed protection mechanism.  
As shown in Figure 3, since the center of mass of the inner plunger does not coincide with the center of rotation of the spindle when the main shaft starts to rotate, the inner plunger is subjected to centrifugal force. When the rotational speed reaches a critical value, the centrifugal force of the inner plunger overcomes the spring force to extend outward, which causes the eccentricity of the overall center of mass of the whole device to further increase. Subsequently, the outer plunger overcomes the spring force to fly out, and the extended plunger hits the lever mechanism to create subsequent actions, which then finally leads to the main shaft stopping.  
Figure 2: (a) Orthographic projection; (b) half-section view; (c) complete cut view.
## 6 Experimental Results and Discussion
### Discussions  
Aiming to check whether or not there is a statistical difference between the best results obtained using LPQ, Opening Vectors, Closing Vectors, and the best late fusion result, we performed the Friedman statistical test.  
The Friedman test was made using the accuracies obtained by the Late Fusion (\(LPQ_{7}\)-SVM, BinAC-5-NN and BinSC-Reg), BinAC-SVM, BinAO-SVM and \(LPQ_{7}\)-SVM classifiers. The accuracies were computed over each folder, as described in the beginning of Section 6. The test presented a \(p\)-value of 0.0299; considering \(\alpha\) = 0.05, we can conclude that the performance of the classifiers are not all equivalent to each other.  
Furthermore, the selected classifiers were ranked according to their accuracies, as can be seen in Table 7. As a result, the superior performance of the Late Fusion technique is attested.  
In respect to **RQ3**, we can conclude that classifiers built with LPQ and GBD presented a good level of complementarity to each other. As a consequence of this complementarity, the late fusion obtained the best overall results reported in this work.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Individual Results (\%)** & **Combination Results (\%)** \\ \hline \(LPQ_{7}\)–SVM & 91.67 & \\ BinAC–5-NN & 95.83 & **99.16** \\ BinSC–Reg & 92.50 & \\ \hline \(LPQ_{7}\)–SVM & 91.67 & \\ AC–SVM & 95.00 & **99.16** \\ BinAC–5-NN & 95.83 & \\ \hline \(LPQ_{7}\)–SVM & 91.67 & \\ AC–SVM & 95.00 & 98.33 \\ BinSC–SVM & 82.50 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracies obtained with late fusion combinations.
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 5 Results and Discussion
### Limitations and Future Works  
This study has several limitations that need to be addressed:  
The relationship between workload and human physiological characteristics varies significantly between individuals, yet the model categorizes them into just four classes. Expanding the dataset with more participants would improve the spectral clustering algorithm, allowing the model to detect more-precise physiological patterns and refine its predictive accuracy. Additionally, conducting experiments with different aircraft and simulators under various flight conditions can help collect diverse data and enhance the model's generalizability across different flying environments.  
This study did not consider the potential impact of environmental factors such as temperature, humidity, and noise on pilot workload as these variables were controlled at consistent levels throughout the experiment. Future work will include further investigation into environmental factors on workload to enhance the applicability of the model.  
Moreover, the experiments in this study were conducted using flight simulation devices. Although these advanced devices are officially certified, they cannot fully replicate all the detailed behaviors of real flight. For example, real aircraft maneuvers involve movements of rolling and yawing, which causes the gravitational force and the pressure exerted on the pilot to not be aligned. This may affect the measurement of pressure distribution.
## 1 Introduction
This leads to the underutilization of computing resources, with part of the computing power being allocated to features that are irrelevant to the improvement of model accuracy [39]. From the perspective of modeling approaches for feature extraction at different layers, a new real-time crack segmentation model (RIIAnet) is proposed in this paper, which deploys limited computing resources in different types of feature extractors, and also avoids the information redundancy problem of convolution in deep extraction. Our contributions are as follows:  
* The traditional involution operator is improved to solve the problem that the verification set is difficult to converge when training in a small batch size. * Based on the feature extraction characteristics of asymmetric convolution for cracks, the asymmetric convolution enhancement module (ACE) is designed in this paper to solve the problem of excessive extraction of non-features after asymmetric convolution parallel connection. * Based on the characteristics of convolution and involution operators for crack extraction, the residual expanded involution module (REI) is designed, and its effective position in the model is located in this paper to alleviate the problem of information redundancy caused by convolution in the deep layers. * A lightweight crack segmentation model called RIIAnet is designed, which can run in real time with high accuracy on high-resolution images. The remainder of this article presents the organization as follows. Section 2 details the proposed network model and design modules. Section 3 first describes the generation of the dataset, and introduces the parameter settings and evaluation indicators; second, it analyzes the extraction characteristics of convolution, involution, and asymmetric convolution for crack features, and then determines the proportion of REI module in the overall model through experiments and the efficiency of the ACE module is reflected by the ablation study. Finally, we give the qualitative and quantitative analysis results of the proposed model and the alternative models, and display prediction results intuitively. In Section 4, we discuss the reasons why the existing involution operator fails to converge in the test set, the efficiency of the REI module, and the role of the ACE module in the overall model. The last part makes a systematic summary of the whole.
# Effect of Buildings on the Radiation Characteristics of MF Broadcast Antennas
## 3 Results
### Radiation Patterns of the Array  
The indicative radiation patterns of the array radiating at 1008 kHz in the presence of one or two buildings (including the corresponding curves in free space) are plotted herein. The patterns in Figure 9a,b refer to the \(\varphi\)-plane (at a specific value of \(\theta\), i.e., \(\theta=90^{\circ}\)) and the \(\theta\)-plane (for \(\varphi=90^{\circ}\)), respectively. The length \(L\) and the width \(W\) of the building were constant (given in the inset), whereas three different values of \(H\) were considered. The  
Figure 8: \(\theta\)-plane of the radiation patterns of the monopole in free space (black, solid curve) and in the presence of two buildings \(30\times 10\times H\) m\({}^{3}\). Three different heights \(H\) are considered (dashed and dotted curves, as indicated at the upper right corner). The distances between the antenna and each building are (**a**) \(R=100\) m \(\approx 0.5\lambda\) and (**b**) \(R=200\) m \(\approx\lambda\).  
Figure 7: The same as Figure 6, except for the building size (\(L=10\) m \(\approx 0.05\lambda\) and \(W=60\) m \(\approx 0.3\lambda\)). (**a**) \(\varphi\)-plane and (**b**) \(\theta\)-plane.  
distance between the antenna and the building (\(R=100\) m \(\approx 0.34\lambda\)) indicates that the latter was in the reactive near-field region of the antenna.