esa-sceva/satcom-chunk-collection huggingface dataset:
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 3 Training Data Selection
### Clutter Normalization
Figure 16: Data containing a ship target: (**a**) range-Doppler image before clutter normalization, (**b**) average Doppler spectrum estimated without using pre-detection for removing the target from (**a,c**) normalized image after using the average Doppler spectrum from (**b,d**) target free estimation of average power spectrum after using pre-detection algorithm, (**e**) normalized image after using (**d**). From Table 1, it is clear that the proper estimation of an average Doppler spectrum to perform normalization over Doppler maintains the target SCNR. However, if the high peaks are not removed beforehand using pre-detection, then after normalization the target SCNR is severely degraded (\(\approx\)10 dB in this case).
# Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning
## 2 The Improved Model
### Quadratic Polynomial Model  
According to the performance of the satellite clock, the systematic part of clock offset can be described by polynomial models of a different order. Taking GPS satellite as an example, a linear polynomial is usually applied, as the difference between predictions using first-order or second- order polynomial is only 0.1 ns over three hours [19]. Since the stability of the GPS clock is higher than that of BDS, the quadratic polynomial model is commonly adopted as a basic model for BDS [12; 28], which is presented as,  
\[clk(t_{i})=a_{0}+a_{1}(t_{i}-t_{0})+a_{2}(t_{i}-t_{0})^{2}+\varepsilon_{i} \tag{1}\]  
where \(a_{0}\) is the clock bias correction coefficient, \(a_{1}\) is the clock drift correction coefficient, and \(a_{2}\) is clock drift rate correction coefficient. \(t_{0}\) is the reference epoch of clock offset series. \(t_{i}\) and \(clk(t_{i})\) denote the i-th epoch and its corresponding clock offset. \(\varepsilon_{i}\) is the noise.
# New Step Size Control Algorithm for Semi-Implicit Composition ODE Solvers
## 1 Introduction  
One of the promising areas in numerical integration methods is composition and splitting methods. These solvers are known for their ability to preserve geometric properties of the basic method [1, 2] and construct higher-order schemes for solving problems described by Hamiltonian ordinary and partial differential equations [3, 4]. [PERSON] reported some coefficients for constructing higher-order symplectic integrators by composition in [5]. [PERSON] developed this approach further in [6], providing a generalized formula for so-called fractal composition methods. In [7], [PERSON] and [PERSON] developed an alternative technique for finding order conditions of composition schemes to improve the overall solver's performance. In [8], the authors show that semi-implicit basic methods allow one to efficiently solve non-Hamiltonian systems via composition schemes, and that the computational efficiency of semi-implicit and semi-explicit composition solvers can be close to the efficiency of the broadly used explicit Runge-Kutta methods.  
It is known that adaptive step size control algorithms greatly increase the efficiency of ODE solvers due to the possibility to achieve better accuracy with fewer calculation steps. Applying variable-step integration techniques to composition methods is one of the key problems in the field. The main reason that prevents researchers from using the variable-step control in composition schemes is the lack of computationally cheap local truncation error estimators. Known approaches based on parallel solutions obtained via different methods are barely applicable due to significant overheads.  
Recently, [PERSON] et al. showed the possibility to construct embedded error estimators for splitting and composition schemes [9] and proved the efficiency of the proposed approach when simulating Hamiltonian systems.  
In this paper, we present a novel step size control algorithm for composition solvers that are based on simultaneous usage of semi-explicit and semi-implicit basic methods in a single composition scheme. The proposed approach can be used with an arbitrary composition scheme and requires only a single additional evaluation of the right-hand side (RHS) function per the method's stage, which makes it computationally efficient. In our study, we compare the developed adaptive step size technique with several known methods of step control using a representative set of test problems.  
The rest of the paper is as follows. In Section 2, semi-implicit CD and semi-explicit midpoint methods are considered, and a new error estimator based on the difference between their solutions is proposed. In Section 3, several composition solvers with different error estimators are tested on three nonlinear test problems. Performance and step size behavior plots are used to evaluate the efficiency of the studied step control algorithms.
## 1 Introduction
Consequently, applying the fixed sampling rate method proposed in the previous research may degrade the performance of sign language recognition models in mobile environments. This paper proposes an improved dynamic sampling rate adjustment method for mobile environments based on the technique suggested in previous research. Our method dynamically adjusts the sampling rate to detect the start and end points of sign language utterances based on the number of frames extracted in real time during sign language utterances in mobile environments. By applying this dynamic sampling rate adjustment method, we can enhance the accuracy of sign language recognition models. To evaluate the recognition accuracy of the proposed dynamic sampling rate adjustment method, we compared its performance to that of a fixed sampling rate method and a no-sampling baseline across various frame rates: 5, 10, 15, 20, and 25 fps. The experimental results indicated that the proposed method consistently outperformed the fixed sampling rate method, particularly at lower frame rates of 5 and 10 fps, demonstrating its effectiveness in computationally constrained environments. Even at higher frame rates, the dynamic method maintained superior accuracy. Overall, it improved top-5 accuracy by up to 83.64% and top-1 accuracy by up to 66.54%, underscoring its ability to enhance both the accuracy and robustness of sign language recognition systems. The remainder of this paper is organized as follows. Section 2 reviews related research. Section 3 provides a detailed explanation of the proposed dynamic sampling rate adjustment method. Section 4 describes the performance evaluation environment and presents the results of the performance evaluation in detail. Finally, Section 5 concludes the paper with a discussion of the conclusions and future work.
## 3 Numerical Simulation  
### Numerical Methods  
Three-dimensional steady numerical simulation and flow analysis were carried out using the commercial code EURANUS (European Aerodynamic Numerical Solver). This code is an integral part of the software NUMECA FINE. The one-equation Spalart-Allmaras turbulence model was used for the calculations. The Reynolds-averaged Navier-Stokes equation method is still the most widely used approach in industrial CFD due to the computational cost and the design schedule. The spatial treatment of the RANS equations was performed using [PERSON]'s cell-centered explicit finite volume. Time integration was performed using a four-step Runge-Kutta configuration. To speed up convergence to steady-state, local time stepping, residual smoothing, and multi-grid techniques were all applied. In the computation, the fluid is air modeled as an ideal gas. At the inlet boundary, uniform standard total pressure (101,325 Pa) and total temperature (288.15 K) with axial flow direction were imposed. At the subsonic outlet under the peak efficiency condition, the average static pressure was applied (259,000 Pa). At solid boundaries, i.e., hub, blade, and casing surfaces, adiabatic non-slipping conditions  
Figure 1: Schematic diagram of the 2.5-stage highly loaded compressor.  
Figure 2: Simplified schematic diagram of the design process of the highly loaded compressor.  
were imposed. A single blade passage was used for the calculations, using periodic boundaries to account for the number of blade passages. A mixing plane was chosen for the rotor-stator interface. The maximum number of iteration steps was set to 1000. The convergence global root mean square residual was set to \(1\times 10^{-6}\) to ensure the correct simulation results, the difference between the inlet and outlet calculation mass flow rate was less than 0.01%. All the numerical simulations were started at the choked condition and then marched towards near stall condition with a gradual increase in the average outlet pressure. The predicted near stall point was judged to be the last stable condition before the oscillation in the flow parameters and numerical divergence with increasing iteration numbers. To quantitatively analyze the compressor stability, the stall margin is defined as follows [23]:  
\[\text{SM }= \left(\frac{\pi_{ts}*m_{d}}{\pi_{td}*m_{s}}-1\right)\times 100\%\]  
where \(\pi_{td}\), \(m_{d}\), \(\pi_{ts}\), and \(m_{s}\) correspond to total pressure ratio at the design point, mass flow rate at the design point, total pressure ratio at near stall point and mass flow at near stall point, respectively.
# A Novel Measurement Standard for Surface Roughness on Involute Gears
## 2 Methods for the Analysis of Surface Roughness on Involutes
### Form Removal  
The developed optimisation algorithm combines the transformations between all coordinate systems described in Section 2.2. As a result, when applied to a Cartesian data set of profile measurement data (such as in Figure 2b) and the base circle radius \(r_{\text{b}}\) of the ideal involute is provided, the algorithm directly provides the primary profile as the deviation of the measured coordinates from the ideal involute in reference to the unwound arc length \(s\) (such as in Figure 2c). This _fitted_ profile also provides absolute information about the position of the measured data on the involute and, therefore, enables the reference of the profile to the gear dimensions. Note that even an incomplete form removal provides an adequate primary profile for the roughness evaluation as remaining form contributions will also be removed by removing the waviness profile, as described in Section 2.4.  
A different approach to remove the form from the measured data is to by directly applying a smoothing spline to the measured coordinates. This effectively projects the profile onto a _chord_ of the ideal involute, along which the profilometer has registered the measured coordinates (see the dashed green axis in Figure 2a). The obtained primary profile is (slightly) compressed in comparison to the fitted primary profile. This can be compensated by stretching the primary profile laterally, if (e.g., by comparing with the fitted profile) the correct lengths of the measured profile segment are known. This _stretched_ profile still contains a deviation in the position of the coordinates, as linear stretching does not account for the real distribution of measured points along the shape of the involute.
# Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
## 4 Discussion
### Limitations and Future Work  
While this study proposes a promising horse activity classification technology based on wearable hardware devices and has achieved some success, certain limitations warrant future work.  
1. Hardware resource limitations:  
Due to hardware limitations, including restricted memory and processing capabilities, it was not feasible to incorporate additional features in algorithm design and experimentation.
2. Sample limitations:  
The experimental samples in this study primarily cover specific types of horses, specific environmental conditions, and states with specific training. Therefore, the generalizability of the results to other scenarios may be limited at present for this experiment.
3. Algorithm simplification:  
To adapt to the constraints of hardware devices, this study simplified machine learning algorithms to some extent. While this enhances the ability to deploy on resource-limited devices, it may also impact classification accuracy.
4. Activity level evaluation model accuracy:  
Despite achieving some successes in experiments, the accuracy of recognition during high activity motion of horses is lower than in slow and medium activities.  
Future research should focus on developing more intelligent adaptive algorithms to meet the demands of complex behavioral patterns. This involves integrating other sensory data and adopting advanced machine learning methods to construct more accurate models [31; 32], requiring ongoing improvements in chip technology and algorithm optimization [33].
## 2 Coherent Burst-Mode Receivers for PDM-QPSK and PDM-16 QAM Modulated Payloads
### Digital Coherent Burst-Mode Receiver Experimental Characterization  
The experimental setup for the characterization of the coherent burst-mode receiver is shown in Figure 4a. The output of an external cavity laser (ECL) was split and modulated using two dual-parallel Mach-Zehnder modulators (DPMZM). These modulators were driven with four independent bit-streams at 10 Gb/s each to independently modulate every PDM-QPSK tributary component. Channels 2 and 4 included electrical phase delays and polarization X contained an optical delay line for precise aligning of the tributary components of the optical signal. After modulation, polarizations X and Y were combined in a polarization beam combiner (PBC). The PDM-QPSK optical signal was amplified using an erbium-doped fiber amplifier (EDFA), and, finally, and an optical attenuator was used to set the desired optical power before the optical receiver. The optical receiver consisted of an ECL free-running laser used as local oscillator (LO), a polarization-diverse optical hybrid plus balanced photodetectors, and a digital sampling oscilloscope used as a digitizer, having an electrical bandwidth of 16 GHz. The four electrical signals after the balanced photodiodes were digitized at 50 GS/s.  
Figure 4b depicts a diagram of the generated optical packets used for the characterization of the burst-mode receiver. Each packet consisted of a header of 1096 symbols followed by a payload of 15,288 symbols. The pattern used on each payload binary tributary consisted of the first 15,288 bits of a pseudo-random binary sequence (PRBS) sequence of order 20 and the accumulated PRBS decorrelation between different tributaries was 262,144 bits. In the experimental setup of Figure 4a, no packet carver such as an acousto-optical modulator (AOM) or SOA was used. Instead, to simulate optical packets with a desired header length, the oscilloscope was triggered at the beginning of the pattern and then optical packets were digitally chopped with knowledge of the timing. The optical packet header consisted of two PDM tributaries, each repeating the constellation pattern shown in Figure 2b,c. This constellation has the property that the transitions to nullify optical power are maximized, which is beneficial for timing recovery. Furthermore, sufficient points in the Stokes space are generated for having an accurate estimation of the SOP.
# Adaptive Threshold-Aided K-Best Sphere Decoding for Large MIMO Systems
## 2 Background  
### MIMO System Description  
In a MIMO system with \(N_{t}\) transmit and \(M_{r}\) receive antennas, the received signal vector \(\tilde{\mathbf{y}}=[\tilde{y}_{1},\tilde{y}_{2},...,\tilde{y}_{N_{r}}]^{T}\) is given by  
\[\tilde{\mathbf{y}}=\tilde{\mathbf{H}}\tilde{\mathbf{s}}_{t}+\tilde{\mathbf{n }}, \tag{1}\]  
where \(\tilde{\mathbf{s}}_{t}=[\tilde{s}_{t,1},\tilde{s}_{t,2},...,\tilde{s}_{t,N_{t} }]^{T}\) is the transmitted signal vector with each component independently drawn from a complex constellation, such as quadrature amplitude modulation (QAM), and \(\tilde{\mathbf{H}}\) denotes an \(N_{r}\times N_{t}\) channel matrix, where \(\tilde{h}_{i,j}\) is the complex channel gain between the \(j\)th transmit antenna and \(i\)th receive antenna. Furthermore, \(\tilde{\mathbf{n}}=\left[\tilde{n}_{1},\tilde{n}_{2},\ldots\tilde{n}_{N_{r}} \right]^{T}\) is a noise vector consisting of independent and identically distributed additive white Gaussian noise (AWGN) samples with zero mean and unit variance. We assume that the channel matrix is perfectly known at the receiver.  
Complex system model (1) can be transformed to its equivalent real signal model \(\mathbf{y}=\mathbf{Hs}_{t}+\mathbf{n}\), i.e.,  
\[\left[\begin{array}{c}\Re(\hat{\mathbf{y}})\\ \Im(\hat{\mathbf{y}})\end{array}\right]=\left[\begin{array}{cc}\Re(\hat{ \mathbf{H}})&-\Im(\hat{\mathbf{H}})\\ \Im(\hat{\mathbf{H}})&\Re(\hat{\mathbf{H}})\end{array}\right]\left[\begin{array} []{c}\Re(\hat{\mathbf{s}}_{t})\\ \Im(\hat{\mathbf{s}}_{t})\end{array}\right]+\left[\begin{array}{c}\Re(\hat{ \mathbf{n}})\\ \Im(\hat{\mathbf{n}})\end{array}\right]. \tag{2}\]  
In (2), the dimension of the matrices and vectors doubles compared to (1), i.e., \(\mathbf{s}_{t}\) contains \(N=2N_{t}\) elements, and the \((M\times 1)\)-received signal vector \(\mathbf{y}\), \((M\times N)\)-channel matrix \(\mathbf{H}\), and \((M\times 1)\)-noise signal vector \(\mathbf{n}\) also double their dimensions, such that \(M=2N_{r}\).  
Based on signal model (2), the optimal solution \(\hat{\mathbf{s}}_{\text{ML}}\) can be determined as  
\[\hat{\mathbf{s}}_{\text{ML}}=\arg\min_{\mathbf{s}\in\Omega^{N}}\|\mathbf{y}- \mathbf{Hs}\|^{2}, \tag{3}\]  
where \(\|\mathbf{y}-\mathbf{Hs}\|^{2}\) is the ML metric value for \(\mathbf{s}\), and \(\Omega\) denotes the set of real entries in the constellation, e.g., \(\Omega=\left\{-3,-1,1,3\right\}\) in the case of 16-QAM.
# Study of the Precipitation Process in Aging Steel Pipeline Weldments by Thermoelectric Power Means
## 3 Experimental Method  
In this present study, the two weldments of different steel pipelines used in the oil industry, which are namely API X60 and X65, were studied. These micro-alloyed steel pipelines were manufactured by Productora Mexicana de Tuberia (PMT), which have a diameter of 106.68 cm (X60) and 60.96 cm (X65) with a wall thickness of 1.42 cm (X60) and 1.27 cm (X65). The welding process used was Submerged Arc Welding (SAW) in two-passes. The chemical composition of these steels is shown in Table 1. The two materials were subjected to different heat treatments. Firstly, the materials were normalized by holding at 1200 \({}^{\circ}\)C for 1 h, which was followed by water cooling/quenching. After this, the two micro-alloyed steels were aged at 300 \({}^{\circ}\)C for different aging times that ranged from 1 min to 30 h for the X60 weldment and from 1 min to 45 h for the X65 weldment in order to promote the formation of precipitates.  
The characterization of the weldment was conducted through Optical Microscopy (OM) and Scanning Electron Microscopy (SEM). The specimens used in the characterization by the microscopy techniques were etched using 2% Nital and 50%, respectively. The characterization of the inclusions was evaluated by SEM using energy dispersive X-ray spectroscopy (EDS) microanalysis. Hardness measurements were carried out using a Rockwell scale in the metal base (BM), heat affected zone (HAZ) and the weld bead (WB).  
The thermoelectric (TEP) measurements were performed using a calibrated ATS-6044T Alloy Thermo-Sorter. This instrument is a thermoelectric alloy tester that is used in NDT, which provides relative readings with arbitrary units. Therefore, this must first be calibrated by materials of known absolute thermoelectric powers [6]. The _hot tip thermoelectric instrument_ settles the temperature difference by the means of a dual tipped reference probe. One tip is at room temperature (25 \({}^{\circ}\)C) and the other is heated to a specific temperature (50 \({}^{\circ}\)C). In our case, an annealing copper hot tip (standard probe) was used in order to measure the thermoelectric power of the weldments of X60 and X65 micro-alloyed steel artificially aged samples. The dual-tipped probe was placed on the three main zones (BM, HAZ and WB) of the weldments, before an electric circuit was completed to generate a signal. This signal was then processed to obtain a peak-to-peak reading, which is displayed on the microvolts digital display. The reading is representative of the crystalline microstructure changes in the material.
## 3 Stability Analysis Based on Lyapunov Exponential Method  
### Lyapunov Exponent Calculation Process  
The Lyapunov exponent is an important quantitative indicator for measuring the dynamics of a system. It characterizes the average exponential rate of convergence or divergence between adjacent orbits in phase space. There are generally two ways to calculate the Lyapunov exponent: One is based on a dynamical model, and the other is based on time series. Due to the time delay, time interval, evolution time and embedding dimension of the time series are difficult to determine. Therefore, this paper calculates the Lyapunov exponent based on the dynamics model. It is calculated as follows:  
\[\lambda=\lim_{n\rightarrow\infty}\frac{1}{n}\sum_{i=0}^{n-1}\ln\left|\frac{df( \mathbf{X})}{dX}\right|_{X_{i}} \tag{25}\]  
The equation of state is obtained by transforming the dynamical equation of a nonlinear system. The magnitude of the Lyapunov exponent \(\lambda\) is determined by the Jacobian matrix \(\left.\left|df(\mathbf{X})\right\rangle\right\rangle/dX\left.\right|\), where the function \(f(\mathbf{X})\) is at \(\mathbf{X}_{i}\). The formula for calculating the Jacobian matrix is as follows:  
\[\left|\frac{df(\mathbf{X})}{dX}\right|=\begin{vmatrix}\frac{\frac{\partial\dot{X}_ {1}}{\partial X_{1}}}{\frac{\partial\dot{X}_{1}}{\partial X_{1}}}&\cdots& \frac{\partial\dot{X}_{12}}{\partial X_{1}}\\ \frac{\partial\dot{X}_{1}}{\partial X_{2}}&\frac{\partial\dot{X}_{2}}{ \partial X_{2}}&\cdots&\frac{\partial\dot{X}_{12}}{\partial X_{2}}\\ \vdots&\vdots&\ddots&\vdots\\ \frac{\partial\dot{X}_{1}}{\partial X_{12}}&\frac{\partial\dot{X}_{2}}{ \partial X_{12}}&\cdots&\frac{\partial\dot{X}_{12}}{\partial X_{12}}\\ \end{vmatrix} \tag{26}\]  
where \(\dot{\mathbf{X}}_{i}(i=1,2,\cdots,12)\) denotes the element \(\mathbf{X}_{i}\) (\(i=1,2,\ldots,12\)) in row \(i\), column \(1\) of the derivative \(\dot{\mathbf{X}}=\left[\dot{\mathbf{q}},\dot{\mathbf{p}}\right]^{T}\) of the system state equation denotes the elemenl \(\mathbf{X}_{i}\) in row \(i\), column \(1\) of the system state equation \(\mathbf{X}=[\mathbf{q},\mathbf{p}]^{T}\). This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, and the experimental conclusions that can be drawn.  
Based on the established dynamics model, the Lyapunov exponential spectrum can be calculated by bringing Equation (24) into Equation (25). The specific flow of tethered machine motion stability analysis based on the Lyapunov exponent is shown in Figure 6. Among them, the number of iterations is 100, the step size is 0.5, the sampling time is 100 s, and the initial values of the system state quantities are all zero.