esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 4 Optimization Algorithms
### The Flow of the Improving Equilibrium Optimizer  
The flow of the improved EO algorithm applied to the multi-objective distribution network reconfiguration problem obtained according to the above improvement strategy is shown in Figure 4, and the specific steps are described as follows.  
Step 1: Input distribution network nodes, branch data, DG-related parameters and parameters of the improved EO algorithm including population size, upper and lower bound vectors of optimization variables \(C_{max}\) and \(C_{min}\), maximum iteration number \(Max\_Iter\), weight constant coefficient \(a_{1}\) for global search, weight constant coefficient \(a_{2}\) for local search, generation probability parameter \(GP\), etc.  
Step 2: Introduce the feasible solution judgment model \(\mathbf{I}\mathbf{R}\) based on adaptive ordered loops, and generate the adaptive ordered loop feasible solution matrix \(\mathbf{J}\mathbf{M}\).  
Step 3: Perform stochastic uniform initialization of the population pop according to Equation (37) to construct the initial concentration of the population with iteration number \(Iter=1\).  
Step 4: Perform the trend calculation for all individuals, and find the fitness values \(F_{1}(C_{i})\), \(F_{2}(C_{i})\) of the individuals in the population according to the objective function, where \(C_{i}\) is the concentration vector of the \(i\)th individual in the current population.  
Step 5: Evaluate each solution with all the objective functions in the objective space, apply the NDS mechanism to judge the dominance relationship of the concentration particles, and at the same time, introduce the elite strategy, and calculate the crowding distance for the individuals in each non-dominated layer, and select the appropriate individuals to be deposited in the external archive according to Equation (38).  
Step 6: Select candidates based on the sorted results and construct the equilibrium pool \(C_{gap,pool}\).  
Step 7: Update the individuals using the Equation (24) concentration update formula under the guidance of the candidates in the balanced pool. The stochastic differential mutation strategy of Equation (39) and the binomial crossover strategy of Equation (41) are introduced to perform the mutation and crossover of particles according to the mutation probabilities in different periods of the distribution network reconfiguration, and the concentration of particles generated is \(U_{i}\).  
Step 8: The selection operator based on Equation (42) will perform a winnowing operation on the test concentration vector \(U_{i}\) and the current concentration vector \(C_{i}\), and take the individual with the better adaptation value in the current \(G\)th generation to be saved in the next generation population.  
Step 9: Judge whether the maximum number of iterations is satisfied, and if not, repeat steps 3 to 7; if satisfied, terminate the iteration and output the optimal reconstruction solution.  
Figure 4: Improved EO algorithm solving flowchart.
## 3 Experiments
### Ortolan Bunting (Emberiza Hortulana): Syllable, Song Variant, and Song Type Classification
In addition, a simple baseline curve normalization is added to the log energy feature. This consists of performing a second order polynomial fit to the log energy curve for each song, then subtracting the baseline curve from the log energy on a per-frame basis. This is done because the amplitude of the individual syllables is commonly the loudest in the middle portion of calls, and the normalization compensates for the longer-term amplitude changes while still allowing shorter-term signal amplitude modulations to be represented by the energy feature and thus used for classification. Each syllable model consists of a 15 emitting state HMM with a single Gaussian, designed to track the time-evolution of each syllable's frequency pattern. Multiple Gaussian mixtures are not necessary  
Figure 4: Ortolan hunting syllables (from [46]). because of the simplicity of the frequency characteristics along each step in the time-frequency pattern. Recognition is implemented using a constrained language model describing the allowable song patterns, as illustrated previously in Figure 3.  
Classification is evaluated in terms of song-type accuracy, syllable accuracy, and song-variant accuracy. Song-type and song-variant accuracy can be computed directly as the percent of songs with the correct classification. However, since there are multiple syllables in each song, syllable accuracy requires a detailed alignment process, where the recognized song is aligned against the correct song transcription and a dynamic programming method is used to determine the minimal number of insertions, deletions, and substitutions differentiating the recognized song or correct song [23]. Final results are shown in Table 3 below. In addition, Table 4 shows the increase in syllable accuracy as the various system improvements described above are added sequentially, including language modeling constraints, feature normalization and signal enhancement, short pause models between syllables, and energy normalization. It can be seen that language model constraints, feature normalization (which reduces individual vocal variability), and front-end signal enhancement each make very substantial contributions to accuracy improvement.
## 2 Calculation Model and Solution Method
### \(p\)-\(y\) Curve Characteristics and Selection
#### 2.3.2 Comparison of Applications of \(p\)-\(y\) Curves for Homogeneous Soil  
The above calculation method and program can also be applied to homogeneous soil layers. In this paper, site horizontal load test conditions are used for the calculation conditions (see Section 3 for details), and the soil around the pile is regarded as a uniform soil layer. Four \(p\)-\(y\) curve models, the [PERSON], Sullivan, [PERSON], and [PERSON] methods, are used to analyze the internal forces and deformation of the pile body. Table 5 shows that  
Figure 3: The flow chart of the \(p\)-\(y\) curve programming method.  
Tables 2 and 3 show that the pile top displacements and pile shaft bending moments calculated by different methods can differ greatly. Under different horizontal loads (\(H=200\), \(300\), \(350\) kN), the pile top displacement obtained by the [PERSON] method is close to the measured value, as is the maximum pile shaft bending moment obtained by the [PERSON] method. Further analysis shows that both [PERSON]'s method and [PERSON]'s method are more suitable than other methods in calculating relevant parameters for horizontally loaded piles in soft clay areas. However, [PERSON]'s method was selected as the basis for improvement and application. In [PERSON]'s \(p\)-\(y\) curve, \(y_{50}=A\varepsilon_{50}D\), \(A=0.05(1/D+4)\), and \(\varepsilon_{50}\) is the strain value when the maximum principal stress difference is half: \(p_{u}=KAC_{u}D\), \(K=\frac{100D}{3+8.30}+\frac{42/D}{1+0.42/D}\).
## 2 System Description
### ESS Control System  
The ESS control system that was proposed in [14] is briefly described in this subsection. Figure 3 shows the ESS configuration (the block diagram of the ESS in Figure 1). \(P_{\text{loss}}\) is the charge/discharge loss of the ESS, as given by  
\[P_{\text{loss}}(t)=\begin{cases}(1-\eta)\cdot P_{\text{b}}(t)&(P_{\text{b}}(t )>0),\\ (1-\frac{1}{\eta})\cdot P_{\text{b}}(t)&(P_{\text{b}}(t)<0)\end{cases} \tag{8}\]  
where \(\eta\) is the charge/discharge efficiency of the ESS. \(E\) denotes the remaining energy of the ESS that is calculated by integrating \(P_{\text{b}}-P_{\text{loss}}\). In Figure 3, the block diagram in the dashed frame shows the SOC-FB control [8], which regulates the state-of-charge (SOC) to its reference, and \(A\) is the SOC-FB gain. A controller \(K(s)\) that removes short-period components from the WF output is given by  
\[K(s)=K_{\text{p}}\cdot G_{\text{hpf}}(s),\quad G_{\text{hpf}}(s)=\frac{s^{2}} {s^{2}+a_{1}s+a_{0}} \tag{9}\]  
where \(a_{0},a_{1}>0\), \(K_{\text{p}}\) (\(0<K_{\text{p}}\leq 1\)) is a constant gain and \(G_{\text{hpf}}(s)\) is a second-order high-pass filter. The WF output are divided into short and long period components \(P_{\text{WF}}^{\text{s}}\) and \(P_{\text{WF}}^{\text{l}}\). Specifically, \(P_{\text{WF}}^{\text{s}}=s^{2}P_{\text{WF}}(s)/(s^{2}+a_{1}s+a_{0})\), \(P_{\text{WF}}^{\text{l}}=(a_{1}s+a_{0})P_{\text{WF}}(s)/(s^{2}+a_{1}s+a_{0})\) and \(P_{\text{WF}}(s)=P_{\text{WF}}^{\text{s}}+P_{\text{WF}}^{\text{l}}\). Thus, the combined output is obtained by  
\[P_{\text{g}}(s)=(1-K_{\text{p}})\cdot P_{\text{WF}}^{\text{s}}+P_{\text{WF}}^ {\text{l}}. \tag{10}\]  
It is clear that the short-period component is reduced by a factor of \((1-K_{\text{p}})\) and the long-period component is directly supplied to the power system. Moreover, the ESS output is given by  
\[P_{\text{b}}(t)\approx K_{\text{p}}\cdot P_{\text{WF}}^{\text{s}}. \tag{11}\]  
Figure 2: \(C_{\text{p}}\)-\(\lambda\) curve of wind turbine.  
Consequently, the controller proposed in [14] can reduce the required rated power of the ESS by a factor of \(K_{\rm p}\).
# Interclass Interference Suppression in Multi-Class Problems
## 5 Experiments
### Comparison Among the One-Class Classifier and Binary Classifier-Based Approach
By comparing the best performance obtained by the two methods, it is observed that the classification accuracy of the binary SVM is significantly better than one-class SVM, which is consistent with the previous research results of one-class classifiers. The performance of one-class classifiers is usually worse than binary classifiers, and it is difficult to implement one-class classification algorithm in many multi-classifiers. Therefore, the interclass interference suppression method based on binary classifiers is the best choice to suppress the interclass interference of multi-classifiers.
# A Comparative Study of the Pullout Strength of Geostraps and Geogrids in Reinforced Soil
## 4 Discussion  
The findings of this study highlight the importance of assessing the pullout performance of reinforcement materials to support sustainable geotechnical infrastructure development. The experimental pullout tests conducted on sandy soil provided valuable insights into the interface behaviour between the soil and the geostraps under controlled boundary conditions. A significant contribution of this study is the integration of experimental data into a numerical model for validation and extended analysis. The model demonstrated high consistency with the test results, with deviations well within acceptable limits. This confirms the reliability of the numerical approach in predicting geostrap behaviour under various conditions, enabling a more comprehensive evaluation beyond the limitations of physical testing. Based on the results, the applications of different grades of geostraps based on the maximum pullout resistance are shown in Table 5.  
Higher-grade geostraps could be suitable for critical infrastructure requiring a higher tensile capacity, whereas lower grades may suffice for temporary or less critical applications. A comparison of the M65 grade of geostrap with the biaxial geogrid for sandy soil was also performed. Based on the findings, the area of application for both types of reinforcement is shown in Table 6.  
Figure 6: Plot of pullout force per unit length and displacement of geostrap vs. the geogrid.  
Both geostraps and geogrids are polymeric compositions that may lead to longer service life, corrosion resistance, and potentially lower environmental impact if manufactured from recycled or low-energy input materials. The flexibility of the materials also suggests easier handling, transportation, and installation, which can contribute to reducing the overall carbon footprint of infrastructure projects.
# FORCE--Landsat + Sentinel-2 Analysis Ready Data and Beyond
###### Abstract  
Ever increasing data volumes of satellite constellations call for multi-sensor analysis ready data (ARD) that relieve users from the burden of all costly preprocessing steps. This paper describes the scientific software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring), an 'all-in-one' solution for the mass-processing and analysis of Landsat and Sentinel-2 image archives. FORCE is increasingly used to support a wide range of scientific to operational applications that are in need of both large area, as well as deep and dense temporal information. FORCE is capable of generating Level 2 ARD, and higher-level products. Level 2 processing is comprised of state-of-the-art cloud masking and radiometric correction (including corrections that go beyond ARD specification, e.g., topographic or bidirectional reflectance distribution function correction). It further includes data cubing, i.e., spatial reorganization of the data into a non-overlapping grid system for enhanced efficiency and simplicity of ARD usage. However, the usage barrier of Level 2 ARD is still high due to the considerable data volume and spatial incompleteness of valid observations (e.g., clouds). Thus, the higher-level modules temporally condense multi-temporal ARD into manageable amounts of spatially seamless data. For data mining purposes, per-pixel statistics of clear sky data availability can be generated. FORCE provides functionality for compiling best-available-pixel composites and spectral temporal metrics, which both utilize all available observations within a defined temporal window using selection and statistical aggregation techniques, respectively. These products are immediately fit for common Earth observation analysis workflows, such as machine learning-based image classification, and are thus referred to as highly analysis ready data (hARD). FORCE provides data fusion functionality to improve the spatial resolution of (i) coarse continuous fields like land surface phenology and (ii) Landsat ARD using Sentinel-2 ARD as prediction targets. Quality controlled time series preparation and analysis functionality with a number of aggregation and interpolation techniques, land surface phenology retrieval, and change and trend analyses are provided. Outputs of this module can be directly ingested into a geographic information system (GIS) to fuel research questions without any further processing, i.e., hARD+. FORCE is open source software under the terms of the GNU General Public License v. >= 3, and can be downloaded from [[http://force.feut.de](http://force.feut.de)]([http://force.feut.de](http://force.feut.de)).  
Landsat; Sentinel-2; software; analysis ready data; cloud masking; atmospheric correction; image compositing; spectral-temporal metrics; large area; time series analysis; data fusion; land surface phenology; trend analysis +
Footnote †: journal: remote sensing
# Train-Induced Unsteady Airflow in a Metro Tunnel with a Ventilation Shaft
## 3 Results and Discussions
### Ventilation Rate  
The previous sections focus on the effect of shaft dimensions and locations on the piston wind speed. To analyze the cumulative effect of unstable airflow caused by trains, the concept of ventilation rate [4,6,31] is introduced. For a metro tunnel without a ventilation shaft, the ventilation rate can be expressed as Equation (17):  
\[\eta=\frac{\int_{t=0}^{t=t_{t}}vA_{L}dt}{A_{L}L} \tag{17}\]  
For the metro tunnel with a shaft investigated, the ventilation rate can be defined as Equation (18):  
\[\eta=\frac{Q_{T-in}+Q_{s-int}}{A_{L}L+A_{s}H_{s}}=\frac{Q_{T-out}+Q_{s-out}}{ A_{L}L+A_{s}H_{s}} \tag{18}\]  
As shown in Figure 5a, \(Q_{T-in}=\int_{t=0}^{t=t_{t}}v_{u}A_{L}dt\) represents the total volume of air flowing into the tunnel from the entrance of the tunnel, and \(Q_{T-out}=\int_{t=0}^{t=t_{t}}v_{d}A_{L}dt\) represents the total volume of air flowing out of the tunnel from the tunnel exit. Correspondingly, \(Q_{s-in}=\int_{t=t_{t=t_{t=t_{t=t_{t=t_{t=t}}}}}}^{t=t_{t}}v_{s}A_{s}dt\) represents the total volume of air flowing into the tunnel through the shaft, and \(Q_{s-out}=\int_{t=0}^{t=t_{t}=t_{t=t_{t=t_{t=t_{t=t}}}}}v_{s}A_{s}dt\) represents the total volume of air flowing out of the tunnel through the shaft, as shown in Figure 7.  
Figure 11 shows the ventilation rates with different ventilation shaft parameters, and the ventilation rate without shafts is also shown in the figure. The influence of the shaft location on theventilation rate is shown in Figure (a)a. The ventilation rate firstly increases and then decreases with the increase in \(\varphi\). As \(\varphi\) increases from 0.25 to 0.75 at intervals of 0.125, the ventilation rate increases by 10.4%, 11.2%, 11.3%, 10.2%, and 7.4% compared to the ventilation rate in PW-NVS mode. When the shaft is close to the middle of the tunnel, it is practically useful to increase the ventilation rate. A quadratic polynomial fitting is performed to quantify the effect of \(\varphi\) on the ventilation rate, as shown in Figure (a)a. As shown in Figure (b)b, the cross-sectional area of the shaft has a significant impact on the ventilation rate. As \(\beta\) increases from 0.293 to 0.781, the ventilation rate increases by 8.8%, 10.12%, 11.3%, 12.10.%, and 12.7% compared to the ventilation rate in PW-NVS mode. The relationship between the ventilation rate and \(\beta\) can be well correlated by a power function, and the fitting results are given in Figure (b)b. The analysis in Section 3.3 shows that the shaft height has little effect on the piston wind. Therefore, the ventilation rate shows a very faint difference with the increase in the shaft height, as shown in Figure (c)c.
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 3 Training Data Selection
### Target Pre-Detection
After applying the proposed pre-detection algorithm which also cancels the target, the actual false alarm rate around the target region gets drastically improved. The value is in the order of the adjacent ranges which contain no target. However, the ratio is still higher in the near and mid ranges of the HH and VV channels. This is due to the fact that the K-distribution generally is not well suited for the RC F-SAR data. A further explanation and discussion on the proper selection of the optimum sea clutter model is given later in Section 4. In addition to this, the Doppler centroid map of the data is also generated to further examine the potential of the pre-detection algorithm. The Doppler centroid is estimated from the data using the energy balance algorithm proposed in [34]. A moving window of 512 range samples and 128 azimuth samples for estimating the Doppler centroid were applied over linearly and circularly acquired RC F-SAR L-band data. The results are shown in Figure 7:5  
Figure 7:6 Ratio of the false alarm rates over range for X-band HH (top), VV (middle) and HV (bottom) polarization before (blue) and after pre-detection and target cancellation (red). Note that a ship target is present at around 42\({}^{\circ}\) incidence angle. Figure 7:7 Doppler centroid map estimated from (**a**) linearly and (**b**) circularly acquired F-SAR L-band HH polarized RC data. The ship histories in both (**a**,**b**) can be clearly seen. (**c**,**d**) Doppler centroid maps re-estimated after cancelling the potential targets using the proposed pre-detection algorithm. It can be clearly seen in the second row of Figure 13 that any bright target signals, contained in the first row, were cancelled after applying the proposed pre-detection module. It has to be further noted, that the Doppler centroid in Figure 13d matches well with the one computed using the aircraft's Euler angles (cf. Figure 3 where a non-moving ocean surface is assumed). The observable difference is among others given by the fact that the ocean surface moves itself. Principally the ocean surface velocity can be estimated by exploiting the Doppler centroid differences. However, this is not the scope of the paper. The strange Doppler centroid values in Figure 13c are due to the presence of sandbanks in the Wadensee area which lies in the southeastern part of the North Sea close to the town Cuxhaven. The ocean surface is very heterogeneous in this area. This is confirmed by comparing the RC data and the fully focused Pauli image of the scene with an optical Google Earth image (cf. Figure 14).
## 5 Conclusions  
In summary, we have successfully prepared PVDF and PVDF GO NFs by the electrospinning method. The effect of nanofiller and the alignment of the nanofibers on the nucleation of electroactive \(\beta\) phase also evaluated and found that GO-loaded aligned NF exhibited increased \(\beta\) phase compared to the GO-loaded random NFs and pristine PVDF nanofibers due to the active interaction of GO with the PVDF matrix and the uniaxial stretching. The structural characterisation using XRD and FTIR confirms the enhanced nucleation and stabilised polar \(\beta\) phase. The PGANF exhibited the highest piezoelectric output voltage of 2.1 V and it was successfully tested its efficacy for biomechanical piezoelectric sensors as a glove sensor, smart chair or locomotion detector. The enhanced piezoelectric efficiency suggests the possibility of these nanofibers in electronic and biomedical applications.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/10/3493/s1](http://www.mdpi.com/2076-3417/10/10/3493/s1)]([http://www.mdpi.com/2076-3417/10/10/3493/s1](http://www.mdpi.com/2076-3417/10/10/3493/s1)), Figure S1: FESEM image showing the PVDF GO NF, Figure S2: FTIR showing the characteristic peak of GO in the PGRNF and PGANF. Figure S3: Comparison of the relative proportion of \(\beta\) phase obtained from both XRD and FTIR. Figure S4: Photograph showing the flexibility of PEGANF FENG's, Figure S5: The piezoelectric voltage generated from the PEGANF FENG under different mechanical vibration frequency.  
[PERSON] designed the research and M.K. conducted the experiments. V.K.K. assisted M.K. to fabricate FENG device. A.R.U. guided in the overall electrospinning process. [PERSON] and [PERSON] wrote the manuscript. [PERSON] and [PERSON] conducted the overall analysis and guided the experiments. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by grant from the Basic Science Research Program through National Research Foundation of Korea (NRF) by Ministry of Education, Science and Technology (Project no. 2016R1D1A1B03934124 and 2019R1A2C1003988) and also partially supported by the program for fostering next-generation researchers  
Figure 6: Biomechanical piezoelectric sensors studies: Piezoelectric output voltage and photograph of PGANF FENG as (**A,B**) Bio E-Skin, (**C,D**) locomotion detector, (**E,F**) smart chair.  
in engineering of National Research Foundation of Korea (NRF) funded by the Ministry of Science, (Project no. 2017H1D8A2030449). The authors declare no conflict of interest.
# Investigation on Ship Hydroelastic Vibrational Responses in Waves
## 2 Ship Hydroelasticity Theory in Regular and Irregular Waves
### Hydrodynamic Theory in Ship Hydroelasticity
#### 2.3.1. Hydrostatic Restoring Force  
For both regular and irregular wave conditions, the nonlinear hydrostatic restoring forces acting on the elastic hull are calculated as follows:  
\[F_{S}(t)=-\rho g\sum_{k=1}^{m}p_{ka}\iint\limits_{S(t)}\mathbf{n}\cdot\mathbf{u}_ {r}w_{k}ds-\int\limits_{L}F_{g}(x)w_{r}(x)dx(r=1,\;2,\cdots,m) \tag{7}\]  
where \(S(t)\) denotes the instantaneous wetted body surface in waves, \(\rho\) is the fluid mass density, \(p_{ka}\) is the \(k\)-th principal coordinate, \(w_{k}\) denotes the \(k\)-th mode of vertical displacement of the hull, \(F_{g}\) denotes the sectional hull weight, \(L\) is the longitudinal length of the ship.