esa-sceva/satcom-chunk-collection huggingface dataset:
# Hazard Model: Epidemic-Type Aftershock Sequence (ETAS) Model for Hungary
## 4 Modelling Results
### Distance Test  
This test compares the distribution of observed distances between trigger events and their aftershocks against the modelled space response function [8]. If an event \(i\) is triggered by an event \(j\), the standardized distance between the two events can be defined as:  
\[r_{ij}=\frac{\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}}}{ \sqrt{D^{2}.e^{a\left(M_{j}-m_{0}\right)}+\varepsilon^{2}}}. \tag{15}\]  
If the approximation of using a Gaussian space response function in the model is correct, then \(r_{ij}\) should follow a chi distribution with two degrees of freedom (or Rayleigh distribution with \(\sigma=1\)). Since ancestor-descendant relationships between events cannot be identified unambiguously, the weights \(\zeta_{ij}\) are introduced to express the fraction of event \(i\), which is triggered by event \(j\):  
\[\zeta_{ij}=\frac{K.e^{a\left(M_{j}-m_{0}\right)}.g\left(t_{i}-t_{j}\right).f \left(x_{i}-x_{j},y_{i}-y_{j}|M_{j}\right)}{\lambda\left(t_{i},x_{i},y_{i}|H_{ j}\right)}. \tag{16}\]  
In order to build a histogram of the observed distribution of \(r_{ij}\) the sum of the \(\zeta_{ij}\) values is taken in each standardized distance bin, where \(i\) runs over \(H_{V}\) and \(j\) runs over \(H_{ij}\).  
The test results are shown in Figure 8, and they show a good fit. This outcome seems to be in contrast with the results of [PERSON] et al., who conclude that the Gaussian space response form fits poorly to the Japan Meteorological Agency (JMA) catalogue data, as opposed to the power law, which shows good results. However, the authors used the Rayleigh distribution with the wrong parameter for the test, that is, \(\sigma=1/\sqrt{2}\) instead of \(\sigma=1\)[8] (Equation (26), p. 7, Figure 7a, p. 10). With the correct theoretical distribution, the difference between the Gaussian model variant and the JMA data is much less than what the 2004 article suggests. In the case of this study, the additional spatial parameter \(\varepsilon\) also helps to achieve better results in this test.  
Figure 7: Examples of chi-squared Poisson goodness-of-fit tests: The distribution of event counts per interval are compared to the modelled Poisson distribution for (**a**) background events in the core area, \(M\geq 3.0\); (**b**) background events in the core area, \(M\geq 2.5\); (**c**) all events, \(M\geq 3.0\); and (**d**) all events, \(M\geq 2.5\).
# Transient Time Reduction in Time-Varying Digital Filters via Second-Order Section Optimization
## 3 Materials and Methods
### Transient Behavior Analysis  
Transient time in digital filters is the interval required for the filter's output to stabilize after a sudden change in the input signal, such as a step input. The duration of the transient response is influenced by the filter's parameters, including the pole locations, which dictate stability and speed of convergence. Poles closer to the unit circle in the z-plane lead to longer transient times, as the system response decays more slowly. Additionally, the filter order is a significant factor. Higher-order filters typically produce more complex and extended transient behaviors due to the interplay of multiple poles and zeros.  
Figure 1 depicts an exemplary step response of the 4 th-order low-pass Butterworth filter. The measured 1% steady-state transient time has settled at 0.024 s after step excitation.  
Time-varying coefficients can further complicate transient behavior, introducing variability that may prolong or destabilize the response, underscoring the need for careful parameter optimization.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### ICT for in-Home and Building Energy Management  
#### 4.3.1 Ice-Wish  
The ICE-WISH project, which involved participants from 11 countries, aimed at motivating households in developing energy conservation practices and managing energy usage in the best possible way. The project objectives included the construction of a platform in order to collect sensor data and the definition of ICT and communication infrastructure. A system for the user to observe the energy results has also been part of the project. Wireless techniques have been used for the data transmission to the control center from the data concentrator hub [81].
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 5 Evaluation
### Bandwidth Throughput  
Next, we use the benchmark _Netperf_ to test the improvement of system bandwidth throughput based on SR-IOV virtual network resource allocation framework. _Netperf_ is a common network performance testing tool based on TCP or UDP transmission. In this test, we open a remote TCP connection to the server in each VM on the client side. Figure 7c shows a comparison of bandwidth throughput before and after system optimization when the number of VMs increases from 1 to 63. From the experimental results, we can see that the bandwidth throughput of the virtual resource allocation framework based on SR-IOV can always be at the maximum and keep in a relatively stable range, while the bandwidth throughput of the system before optimization is gradually decreasing with the increase of the number of VMs. It can be seen from the graph that the optimized bandwidth throughput curve will continue to be better than that before optimization. Compared with the default scheduling strategy, the virtual resource allocation framework based on SR-IOV can maximize the bandwidth throughput by 5%, that is, when the number of VMs is 63, it will increase from 9018.04 Mbps to 9428.92 Mbps.  
When using the default scheduling strategy of KVM, it is expected that the bandwidth throughput of the system decreases as the number of VMs increases. As the number of VMs increases, VMs will compete highly for network resources, and the overhead of scheduling and interrupt processing will lead to the decrease of system bandwidth throughput, even the fluctuation of system bandwidth throughput and other unpredictable situations. When the number of system VMs increases, the use of SR-IOV-based virtual resource allocation framework can always ensure that network-intensive VMs get sufficient bandwidth resources. Despite the slight overhead of scheduling and interrupt processing, the bandwidth throughput of the system can always be at its maximum and remain within a relatively stable range.
# Orientation of Liquid Crystalline Molecules on PDMS Surfaces and within PDMS Microfluidic Systems
## 2 Materials and Methods
### Mold Manufacturing
The main milling process was performed with the 0.5 mm diameter endmill (a feed of 500 mm/min with a step of 0.05 mm), and the final surface was processed using a 0.05 mm-diameter endmill (a feed of 500 mm/min with a step of 0.01 mm). The mold could be immediately used after cleaning from the material residue, while no burrs were visible on the sides of the drilled channels. Their walls, with the retained (and sharp) edges, were almost perpendicular to the base. The higher roughness was found at the upper surface of the mold, showing periodic unevenness related to the tool geometry, but it seems to be completely negligible in further applications. The height of the convex pattern obtained in PMMA was about 200 \(\upmu\)m, and the mold itself could be used more than once. Completing the list of cheap and simple fabrication techniques, we come to photolithography, one of the most reliable methods in terms of mold quality. In this case, the master was made of SU-8 25 negative photoresist (Kayaku Advanced Materials, Inc., Westborough, MA, USA), providing a high-aspect ratio with nearly vertical sidewalls and superb chemical and temperature resistance. A standard process, including (i) substrate preparation (6'' silicon wafer cleaned, dried, and baked at 200 \({}^{\circ}\)C for 5 min on a hotplate for dehydration); (ii) SU-8 static dispersion (8 mL, which gives approximately 1 mL per inch of substrate diameter); (iii) spin coating (spread cycle: ramp to 500 rpm at 100 rpm/s acceleration for 7 s; spin cycle, i.e., ramping to a spin speed of 1700 rpm at an acceleration of 300 rpm/s and holding for a total of 30 s); (iv) soft baking (on a closed hotplate at 95 \({}^{\circ}\)C for 6 min); (v) i-Line UV light exposure (through a 4'' square chrome mask in 1:1 contact mode with an exposure energy of 180 mJ/cm\({}^{2}\)); (vi) post-exposure baking (two-step contact hotplate process with a PEB time of 1 min at 65 \({}^{\circ}\)C and 6 min at 95 \({}^{\circ}\)C); (vii) SU-8 develop (immersion for about 3 min in a dedicated SU-8 developer to dissolve the unexposed part of the photoresist); (viii) rinsing (for about 30 s with the fresh developer); and (ix) drying (with a stream of pressurized nitrogen), resulted in a mold with a height of about 30 \(\upmu\)m. The master could be used repeatedly to prepare the PDMS structures without compromising their quality.
###### Abstract  
Background: This study aimed to determine the effect of the Nordic hamstring curl training (NHCT) program on athletic performance elements such as linear speed, change of direction (COD), jump performance (CMJ), and eccentric muscle strength (Nordic Hamstring Curl Breaking Point Angle--NHCBP) in young football players. Methods: The study group consisted of 40 male football players who were actively trained and had no previous eccentric training experience or reported any injuries. The participants were randomly divided into a training group (TG = 20) and a control group (CG = 20). The TG performed the NHCT program twice a week for eight weeks, in addition to their standard football training. The CG continued with standard football training. Results: The study findings revealed no statistically significant differences in the interaction between group and time in 10, 20, and 30 m sprint performance. However, statistically significant differences were found in CMJ performance in terms of group-time interaction (F = 19.99, \(p<0.001\), \(\eta^{2}=0.52\)), COD (F = 11.10, \(p<0.004\), \(\eta^{2}=0.38\)), and NHCBP (F = 6.14; \(p<0.02\), \(\eta^{2}=0.25\)). After the eight-week NHCT program, the experimental group showed improvements of 18% in CMJ performance, 8% in COD performance, and 25% in NHCBP performance. Conclusions: The NHCT program significantly increased hamstring muscle strength in football players, and this increase positively affected players' linear speed and change of direction performance. NHCT has the potential to enhance athletic performance in football.  
change of direction; speed; agility; eccentric strength +
Footnote †: journal: Applied sciences
# Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR
## 2 Accurate Modeling of SINR in the Presence of RFI
### SINR in Different System Parameters of LEO SARs and GEO SARs  
According to the parameters in Table 2, the SINR of images are shown in Figures 7 and 8, while the GEO SAR and the LEO SAR demonstrate various bandwidth and pulse width.  
Obviously, the impact is more serious when the disturbed system has a larger bandwidth and the RFI source has a smaller bandwidth. For example, it is better for LEO SAR images if the LEO SAR has a small bandwidth and the GEO SAR has a large bandwidth. Similarly, it is better for GEO SAR images if the GEO SAR has a small bandwidth and the LEO SAR has a large bandwidth.  
In addition, the large ratio of the pulse width of the RFI source to the disturbed system can have serious effects on SINR; for example, GEO SAR images will be destroyed more seriously as the pulse width of the LEO SAR increases compared with the GEO SAR. Therefore, high-quality images of LEO SARs and GEO SARs have conflicting requirements for pulse width, which should be chosen based on the demand from designers.
## 2 Description of Experiments  
### Description of gearbox Testing (NAVAIR gearbox Test)  
NAVAIR-4.4.2 has built an experimental test stand to replicate the failure progression under complex loading and determine the ideal NDE strategies. Figure 1a shows the test stand and the mechanical details. The 2.5 h block cycle in the controlled environment simulates 2.5 flight hours in an average mission. EMD (Electrical discharge machining) notch is introduced to control the crack growth location. The test rig can produce the multilevel block cycle with torque, trust, and bending moment. The hub moment is identified as the primary driver of crack growth and creates one per revolution cyclic stress in the spline gear; and also, the direction of hub moment is changed for simulating different flight scenarios. The bench test includes standard strain sensors, proximity probes, thermocouples, accelerometers, and load sensors, in addition to the AE sensors. The AE data acquisition and sensing system consist of PCI-2 (Peripheral Component Interconnect) data acquisition system, and two different sensor types, including WD (wideband sensor) and micro 30 sensors manufactured by Mistras Group Inc. (Princeton Junction, NJ, USA). WD sensor has wideband response spanning 100 kHz to 1 MHz; micro 30 sensor has the bandwidth of 150-400 kHz. The resonant behaviors of the AE sensorssignificantly influence the output signal [15]. Figure 1c shows the calibration curves of WD and micro 30 sensors. It is important to utilize the same sensors in the laboratory testing. The AE sensors are coupled using vacuum grease and the coupling state is secured with aluminum brackets. Two sensors of each type are placed at different locations on the gearbox, as shown in Figure 1b. Time-dependent approach independent from threshold is implemented to record the AE data. The streamed AE waveforms with 150 ms duration and 2 MHz sampling rate are recorded at every 5 s.
# Optoelectronic Instrumentation and Measurement Strategies for Optical Chemical (Bio)Sensing
## 2 Optical Devices
### Optical Sensors
The main applications for FOCS are in the sensing gases and vapors, medical and chemical analysis, molecular biotechnology, marine and environmental analysis, industrial production monitoring, and bioprocess control. Many chemical analytes can be sensed. These range from various gaseous species (NH\({}_{3}\), H\({}_{2}\), CH\({}_{4}\), H\({}_{2}\)S, CO\({}_{2}\), NO\({}_{2}\), O\({}_{2}\)) to volatile organic compounds (such as ethanol, methanol, acetone, toluene, and formaldehyde), as well as heavy metal ions such as Hg\({}^{2+}\), Pb\({}^{2+}\), Mg\({}^{2+}\), Cd\({}^{2+}\), Ni\({}^{2+}\), and Mn\({}^{2+}\). Other applications are for sensing of temperature and relative humidity (RH), water fractions in organic solvents, pH values, ions (Mn\({}^{2+}\), Cd\({}^{2+}\), Pb\({}^{2+}\), Hg\({}^{2+}\), Zn\({}^{2+}\)), and organic species (glucose, fructose, oils, etc.). Figure 5: Basic constitution of a (bio)chemical sensor sensitive to a specific analyte: R = receptor (molecular recognition element); T = transducer; A = amplifier.
## 2 Materials and Methods
### Extraction of Volatiles, GC-MS Analysis and Data Processing  
Short-cut rapeseed, short-cut woodchips and mineral wool samples were analysed for their volatile content using gas chromatography coupled to mass spectrometry (GC-MS). To avoid instrumental sensitivity changes, samples were measured in one sequence. For volatile organic compound collection, solid-phase microextraction fiber with a divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS 50/30 \(\upmu\)m) coating from Supelco (Supelco Inc., Bellefonte, PA, USA) was employed. Vials were incubated for 10 min to increase volatiles emission from the sample and then volatiles were collected onto a fiber stationary phase for the next 10 min, both at 40 \({}^{\circ}\)C.  
GC-MS was applied for VOC separation and identification. Basic measurements were performed using Quadrupole Shimadzu GC-MS QP2010 SE-Ultra (Kyoto, Japan), applying an SLB-5 MS capillary column (30 m, 0.25 mm i.d., 0.25 \(\upmu\)m film thickness) from Supelco. The injection was performed at 250 \({}^{\circ}\)C, while the transfer line was kept at 280 \({}^{\circ}\)C. The temperature program was as follows: 40 \({}^{\circ}\)C for 1 min and then with grad 5 \({}^{\circ}\)C min\({}^{-1}\) to 250 \({}^{\circ}\)C and held for 2 min. Total run time was 45 min. Helium was used as a carrier gas at a flow rate of 1 ml min\({}^{-1}\).  
In order to not focus only on a few compounds, the mass analyser was operated in a SCAN mode (scan speed 2000 ns, range 30-400 \(m\)/\(z\). Identification of chemical compounds was based on mass spectral similarity with the in-built NIST MS library (NIST, Gaithersburg, MD, USA; 2017 released version). A group of approximately fifteen main volatile chemical compounds was identified through a literature review to be followed in all of the samples for their comparison. A group of key compounds for each material was defined. Reported intensities are areas of unique mass--the specific mass of compounds' mass spectrum, which were not coeluting with another compounds signal at a signal's retention time.
# Surface Defect Detection Methods for Industrial Products: A Review
## 3 Surface Defect Detection Method of Industrial Products Based on Deep Learning
### Unsupervised Method
#### 3.2.4 Self-Organizing Map  
The self-organizing map simulates the different division of labor of neural network cells in different regions of the human brain, and classifies the set of input patterns by searching for the optimal set of reference vectors.  
Literature [71] proposed a detection method using SOM to distinguish normal wood from defective wood. The suspected defect area is detected in the first stage, and the defect area is inspected separately in the second stage. The test in the pine wood data set has obtained relatively ideal results. Literature [72] combined Otsu with SOM to realize the detection and location of TSV defects.  
The unsupervised method effectively makes up for the deficiency of the supervised method, but it still has some problems due to its own characteristics. Since only positive examples are trained, the unsupervised method cannot determine what is the correct output, so it cannot guarantee a good detection effect for every type of defect samples (which did not appear during training). Therefore, the accuracy of the unsupervised method still has a large room for improvement, and in general, the unsupervised method has a better detection effect on texture images.
## 5 Experimental Results and Discussion  
### Hardware Implementation  
In order to verify the feasibility of the proposed method, a Digital Signal Processor (DSP) based prototype is designed, as shown in Figure 7. A TI TMS320F28033 DSP is used for the algorithm implementation, which is a 32-bit MCU with a 60 MHz clock frequency. The hardware circuit also comprises a current transformer (CT), a bandpass filter, and an Analog-to-Digital converter (ADC). The CT is responsible for measuring the required current signal, where a Pulse PA3655 NL CT is adopted. The PA3655 NL is designed for arc fault detection circuits within a bandwidth of 50-500 kHz and a maximum peak current of 34 A. Moreover, a 4-order bandpass filter is designed based on a low noise precision operation amplifier of TISM73307 with a bandwidth of 33-100 kHz. An external ADC of SM73201 is combined with the MCU to guarantee a better signal quality. The SM73201 has a 16-bit resolution with a maximum sampling rate of 250 kSPS and features a differential analog input with an excellent common-mode signal rejection ratio of 85 dB, making the SM73201 suitable for noisy environments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Time Window/ms**} & **Correlation Coefficient** & **Correlation Coefficient** \\  & **Variance of Arc** & **Variance of Normal** \\ \hline
1 & 0.05939 & 0.01268 \\
2 & 0.07528 & 0.00265 \\
7 & 0.06233 & 0.00195 \\
10 & 0.24262 & 0.00140 \\
12 & 0.02257 & 0.00085 \\
15 & 0.18921 & 0.00110 \\
17 & 0.17401 & 0.00151 \\
20 & 0.21711 & 0.00806 \\ \hline \hline
## 2 Experimental
### Investigation Techniques  
The structure of island films formed at the initial stages of growth was studied using the medium-energy ion scattering (MEIS) method. The samples were bombarded with helium ions with an initial energy of 227 keV. The ion energy after interaction with atoms of the film was registered using an electrostatic analyzer. The crystal structure and phase composition of continuous films were controlled using X-ray phase analysis (XRD) on a DRON-6 diffractometer at the Cu K\(\alpha\)1 emission spectral line (\(\lambda\) = 1.54 A). Measurements were carried out in continuous mode at diffraction angles from 20\({}^{\circ}\) to 60\({}^{\circ}\) with a scanning speed of 2\({}^{\circ}\)/min. Crystal phases were identified using the PDF-2 powder diffractometry database. To calculate the unit cell parameters of STO films investigated, the angular positions of their reflections were used, taking the peak from the substrate as a reference. The surface morphology was investigated using the atomic force microscopy method (AFM) on a scanning probe microscope NTEGRA PRIMA from NT-MDT in semi-contact mode. The scanning resolution was 512 \(\times\) 512 pixels. Cantilevers of the NSG01 type with an average resonant frequency of 150 kHz and an average radius of curvature of the tip of 6 nm were used. Before the measurements, the device was calibrated using NT-MDT TGS1 test grids.  
The electrical properties of STO films were investigated using planar capacitors with a gap width of 6 \(\upmu\)m. Copper electrodes were applied on the surface of the STO film using the thermal evaporation technique using an adhesive chromium sublayer, followed by lithography and chemical etching. The capacitance C and quality factor Q = 1/tan \(\delta\) of the capacitors were measured at a frequency of 2 GHz using a half-wave stripline resonator and an HP 8719C vector analyzer. The resonator design provides an unloaded quality factor of 1000, which ensures the accuracy of capacitance and quality factor measurements of 1 and 5%, respectively. A control voltage of up to 300 V was applied directly to the capacitor plates, providing a field strength E in the capacitor gap of up to 50 V/\(\upmu\)m. The tunability of the capacitors was calculated as the ratio of capacitances at zero and maximum applied control voltage n = (C\({}_{\text{max}}-\text{C}_{\text{min}}\))/C\({}_{\text{max}}\).
# Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation
## 4 Methodology
### Kernel Density Estimation  
The probability distribution function (pdf) can be estimated using the kernel density estimator [53; 54]. For argument \(x\), the estimated density is given by  
\[\hat{f}_{h}(x)=\frac{1}{Nh}\sum_{i=1}^{N}W\left(\frac{x-x_{i}}{h}\right), \tag{2}\]  
where \(x_{1},x_{2},\ldots,x_{N}\) are observations, \(W(\cdot)\) is the kernel smoothing function, \(N\) is the sample size, and \(h\) is the bandwidth. In this paper, a Gaussian kernel is used. The Gaussian kernel is the most common one used in various applications. It corresponds to the Gaussian probability density function; however, it is also used to estimate the probability density functions from other distributions. The Gaussian kernel has the properties of having no overshoot to a step function input while minimizing the rise and fall time. The value of the bandwidth is obtained using the so-called Silverman's rule of thumb [54]. For the Gaussian kernel, the optimal choice for \(h\) (that is, the bandwidth that minimizes the mean integrated squared error) is  
\[h=\left(\frac{4\hat{\sigma}^{5}}{3N}\right)^{\frac{1}{5}}\approx 1.06\hat{ \sigma}N^{-1/5}, \tag{3}\]  
where \(\hat{\sigma}\) is the sample standard deviation and \(N\) is the number of observations.