esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Interpretation for Signal Classifier
### Prerequisites  
LIME is an explanation method of machine learning models that is a black box classifier [20]. Given a model \(f\) to be explained and an input instance \(x\), LIME defines explanation \(\xi(x)\) as follows using explainable model \(g\), which has trainable parameters such as a linear model or decision tree:  
\[\xi(x)=\arg\min_{g}\mathcal{L}(f,g,\pi_{x})+\Omega(g) \tag{1}\]  
where \(\pi_{x}\) represent the distance between the input instance and the perturbed sample, as well as \(\Omega(g)\) is the complexity of the explainable model \(g\) such as the number of non-zero weights for a linear model. For the complexity \(\Omega(g)\), we choose a constant so that LIME claims that \(\Omega(g)\) can be a hyper-parameter [20].  
Once the complexity is chosen, the input \(x\) is divided by discrete components that are interpretable. For example, given an image of a dog in the grass and a complexity of 2, it can be split into a set \(\mathcal{Z}\) of two images such as the dog and the grass using a super-pixel such as Felzenszwalb [34]. Then, a trainable linear model \(g\) is obtained by minimizing the loss as below:  
\[\mathcal{L}(f,g,\pi_{x})=\sum_{z,z^{\prime}\in\mathcal{Z}}\pi_{x}(z)\big{(}f(z)- g(z^{\prime})\big{)}^{2} \tag{2}\]  
where \(z\) and \(z^{\prime}\) represent the perturbed sample and the discrete component of the input \(x\) named perturbed sample, respectively, e.g., a perturbed sample \(z\) can be one of the image of the dog or the image of the grass. At that time, the positive model parameter of \(g\) describes the importance of the component because entries of \(z^{\prime}\) are 0 or 1 where those entries represent \"absence\" and \"presence\" of the component, respectively. Consequently, selection of the split method plays an important role. For time series data, former works apply LIME as well [24; 25; 35]. Unlikely for images, they not only focus on how to split the input, but also focus on how to generate perturbed regions. They acquire random split [24; 35] or finding the most probable change points comparing the similarity between window slides [25]. In addition, they attempt to fill the regions to generate perturbed samples using constant value, linear interpolation or both short-time Fourier transform (STFT) and inverse STFT (ISTFT).
## 2 Materials and Methods
### Performance Assessment  
Initially, the annual and seasonal spatial distribution patterns were interpolated to find the product that best fit the rain gauge pattern across Pakistan using ArcGIS 10.2, where the temporal (monthly and seasonal) performance of the products against the observed data was determined using statistical matrices, including linear correlation 'CC', root mean square error 'RMSE', percent bias 'PBias', and Kling-Gupta efficiency 'KGE'. The zero values of the RMSE and PBias represent the significant association of the estimated precipitation with the ground observation [30]; in contrast, a zero value of the CC shows the poor performance of the product, while one exhibits the best agreement between the global precipitation product and the rain gauge data. The KGE has been revealed as a useful statistical parameter for effectively analyzing the reliability of the reanalysis and satellite-based precipitation products, as it summarizes the correlation, bias, and variability ratio value within a single value [39; 40]. It ranges from 1 to negative infinities, with negative values indicating a poor match, 1 showing a perfect match, and 0 indicating the same pattern with a different magnitude [41]. The following equations are mathematical expressions of the selected matrices:  
\[CC=\frac{\sum_{i=1}^{n}\left(Obs_{i}-\overline{Obs_{i}}\right)\left(Est_{i}- \overline{Est_{i}}\right)}{\sqrt{\sum_{i=1}^{n}\left(Obs_{i}-\overline{Obs_{ i}}\right)^{2}\sum_{i=1}^{n}\left(Est_{i}-\overline{Est_{i}}\right)^{2}}} \tag{1}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}(Est_{i}-Obs_{i})^{2}}} \tag{2}\]  
\[PBias=\left[\frac{\frac{1}{n}{\sum_{i=1}^{n}(Est_{i}-Est_{i})}}{\sum_{i=1}^{ n}\overline{Obs_{i}}}\right]\times 100 \tag{3}\]  
\[KGE=1-\sqrt{(CC-1)^{2}+(\frac{\overline{Est}}{Obs}-1)^{2}+(\frac{\sigma_{Est }\overline{Obs}}{\overline{Est}}-1)^{2}} \tag{4}\]  
where \(n\) denotes the amount of data; \(Est_{i}\) and \(Obs_{i}\) are the estimated and observed precipitation at the \(ith\) site, respectively; \(\overline{Est}\) and \(\overline{Obs}\) are the average of \(Est_{i}\) and \(Obs_{i}\); and \(\sigma_{Est}\) and \(\sigma_{Obs}\) are the standard deviation of \(Est_{i}\) and \(Obs_{i}\), respectively.
# Infrared Spectroscopy for Studying Structure and Aging Effects in Rhamolipid Biosurfactants
###### Abstract  
Biosurfactants are produced by microorganisms and represent amphiphilic compounds with polar and non-polar moieties; hence they can be used to stabilize emulsions, e.g., in the cosmetic and food sectors. Their structure and its changes when exposed to light and elevated temperature are yet to be fully understood. In this study, we demonstrate that attenuated total reflection infrared (ATR-IR) spectroscopy is a useful tool for the analysis of biosurfactants, using rhamnoids produced by fermentation as an example. A key feature is that the analytical method does not require sample preparation despite the high viscosity of the purified natural product.  
vibrational spectroscopy; FTIR; _Pseudomonas aeruginosa_; rhamnose; fatty acid +
Footnote †: journal: Applied Physics Letters  
_Applied Physics Letters_  
a good option as it can be applied to opaque samples. In ATR-IR spectroscopy, the infrared radiation is propagating in a material with a refractive index (often referred to as internal reflection element, IRE) that is higher than that of the sample. Consequently, the radiation can undergo total internal reflection at the sample/IRE interface. Due to the light-matter-interaction in the evanescent field, the reflected radiation carries information about the absorption spectrum [9]. The penetration depth of the evanescent field in the sample is typically of the order of one micrometer [10; 11].  
To date, only a few studies reporting the application of vibrational spectroscopy to rhamnoids have been published. [PERSON] et al. [12] applied IR spectroscopy together with a variety of analytical methods to a rhamnoid-layered double hydroxide (RL-LDH) nanocomposite material. They aimed at a comprehensive characterization of the material and at unravelling how the rhamnoid anions are introduced into the interlayers of LDH. ATR-IR spectroscopy for the qualitative identification of rhamnoids produced in fermentation processes has been reported as well [13; 14; 15; 16; 17; 18; 19].  
The present study aims at testing the potential of ATR-IR for the structural analysis and the investigation of aging effects of rhamnoids. For this purpose, rhamnoids have been produced and purified. IR spectroscopic analysis was performed after the purification process and after five months of storage, during which the samples were kept at room temperature.
## 4 Invariant Multi-Mode Vectors Pushover Analysis
### The Generalized Pushover Analysis (GPA)  
[PERSON] and [PERSON] [58] introduced a new method called generalized pushover analysis (GPA). The GPA method uses generalized force vectors to predict building sway during earthquakes. Pushover analyses create an envelope of results, allowing for better understanding of building reactions under different conditions.  
The GPA algorithm is composed of the following five basic steps:  
1. Eigenvalue analysis: calculate natural frequencies \(\omega_{n}\), modal vectors \(u_{n}\), and modal participation factors \(\Gamma_{n}\) from eigenvalue analysis.
2. Response spectrum analysis: obtain modal spectral amplitudes \(A_{n}\), \(D_{n}\) from the corresponding linear elastic spectra.
3. Calculate modal interstory drift ratios at the \(j\)-th story, \(\Delta_{j,max}\), and the maximum interstory drift ratio at the \(j\)-th story, \(\Delta_{j,max}\), from RSA.
4. Generalized force vectors. Compute generalized force vectors \(f_{j}\) that produce the maximum response \(\Delta_{j}\), as calculated from the expression below: \[f_{j}(t_{max})=\sum_{n}\left(\Gamma_{n}m\varphi_{n}A_{n}\frac{\Delta_{j,n}}{ \Delta_{j,max}}\right),\] (23) Target interstory drift demands: calculate the target interstory drift, \(\Delta_{jt}\), at the \(j\)-th story using Equation (24): \[\Delta_{jt}=\sum_{n}\Gamma_{n}\frac{\Delta_{j,n}}{\Delta_{j,max}}D_{n}\big{(} \varphi_{n,j}-\varphi_{n,j-1}\big{)},\] (24)  
Note: If the first mode spectral displacement demand \(D_{1}\) in Equation (24) is replaced with its inelastic counterpart, \(D_{1}^{*}\), then a first mode pushover analysis is conducted. \(D_{1}^{*}\) is obtained from either NRHA or the inelastic response spectrum of the bi-linear SDOFsystem. For higher modes \(n=2-N\), \(D_{n}\) values are obtained from the linear elastic response spectrum.
5. Generalized pushover analysis (GPA): Conduct \(N\) GPAs. In the \(j\)-th GPA (\(j=1-N\)), incrementally push the structure in the lateral direction with a force distribution proportional to \(f_{j}\). At the end of each loading increment, \(i\), compare the interstory drift \(\Delta_{ji}\) obtained at the \(j\)-th story with the target interstory drift \(\Delta_{ji}\) calculated from Equation (24). Continue displacement-controlled incremental loading (\(i=1\), \(2\), \(\ldots\)) at the \(j\)-th GPA until \(\Delta_{ji}\) reaches \(\Delta_{ji}\).
###### Abstract  
The use of smart antenna systems (SASs) in mobile ad hoc networks (MANETs) has been promoted as the best choice to improve spatial division multiple access (SDMA) and throughput. Although directional communications are expected to provide great advantages in terms of network performance, directional MAC (medium access control) protocols introduce several issues. One of the most known problems in this context is represented by the fact that, when attempting to solve or at least mitigate the problems introduced by these kinds of antennas especially at MAC layer, a large amount of energy consumption is achieved; for example, due to excessive retransmissions introduced by very frequently issue such as deafness and handoff. The expedients proposed in order to reduce these drawbacks attempting to limit beamforming time of nodes in cooperation with a round-robin scheduling can grant high performance in terms of fairness and throughput. However, the overall energy distribution in the network is not efficient due to static approach. In view of this, we propose adaptive beamforming time with round-robin MAC providing a dynamic assignment of the beamforming time with the aim to limit the waste of energy of nodes. The proposed approach provides benefits in terms of energy consumption distribution among nodes in sectorized antennas environments and, simultaneously, improves MAC packet performance.  
beamforming; energy consumption; MAC, MANET, round-robin; SAS Article
## 2 Proposed Methodology
### New Signal Combination Model with Four Common Methods  
Most researchers [15; 16; 17; 80] have used a large-scale frequency domain consisting of known brain activities and noise representing unknown brain activities. A large-scale frequency filter can divide frequencies into overlapping narrow frequency filters to extract highly discriminative features [19; 20]. The disadvantage of decomposing the EEG signal into narrow-pass filters is the degradation of the raw EEG signals. The essential information of each distribution event is in a large frequency band. The degradation is related to the removal of much of the EEG frequency range for processing. The main EEG signals of the events are lost. The main goal of the model is to find the most important patterns of different events in small frequency bands and to find more frequency ranges of more similar patterns. Choosing an appropriate scale for frequency filters is essential to avoid degrading raw EEG signals and to reduce noise in EEG signals. Our model reduces degradation. For collecting important distinctive patterns of brain activities, it is necessary to combine several essential small-scale frequency bands for classification. The combination of two fixed filter banks introduces new combinations of signals. These models investigate three feature extraction methods and one feature selection method.  
To the best of our knowledge, at the time of writing this paper, using the combination of two fixed filter banks with a 4 Hz domain (limited frequency bands) is reported for the first time at this scale in this paper. This leads to creating new combinations of signals with 8 Hz domains in total. The performance accuracy of new signals was studied using two feature extraction methods, i.e., CSP and FBCSP, and one feature selection method, i.e., PCA. In addition to the three common methods, a Lagrangian polynomial equation was introduced to convert the data structure to a formula structure for classification. The purpose of the Lagrangian polynomial is to detect important coefficients for increasing the distinction of brain activities. It may be noted that the concept of the Lagrangian polynomial is different from auto-regression (AR), in which features are coefficients for classification.  
In the following subsections, we explain the details of the four main contributions of this paper by describing the models. We show an overview of the four implemented models in Figures 1-4. Figure 5 provides an overview of the database, filter banks, and the model of the new combinations of signals with the classifiers.
###### Abstract  
The effects of wave and current on floating offshore wind turbines (FOWTs) are usually treated separately without considering their inherent interaction. In this study, the coupling effect of wave and current on the dynamic responses of a semi-submerged FOWT carrying a 5-MW NREL turbine is investigated. A numerical model considering the wave-current interaction is introduced, which accounts for the frequency shifts and surface profile changes for waves traveling over currents. The dynamic structural model of the semi-submerged FOWT is established in ANSYS AQWA, where the aero-servo-structural loadings on tower and turbine were obtained from the FAST platform by using the FAST-to-AQWA coding program. Irregular waves with 1- and 50-year return periods, in conjunction with a uniform current, were adopted to evaluate the coupling interaction effects. Waves traveling on positive and on opposite currents are examined in different cases with waves and currents propagating along the surge or sway direction. Waves consistently propagate along positive surge or sway direction. Waves interacting with positive or opposite currents have dramatically different modifications on the wave spectrum. Differences of up to 22% are recorded by comparing both the main motions and mooring tension when the interaction of waves and currents is considered or not. The coupling interaction between waves and currents has a limited influence on the tower base shear forces and bending moments. It was found that a straightforward superposition approach to evaluate the effect of the waves and the currents may underestimate the dynamic motions and mooring tension of FOWTs.  
xxxxxxxxxx-current interaction; semi-submerged floating offshore wind turbine; coupled dynamic analysis 2025
# How to Prevent Aseptic Loosening in Cementless Arthroplasty: A Review
## 4 Implant Properties  
The cementless total hip replacements systems are made up of four elements: the acetabular cup, insert, femoral head, and femoral stem. The most common alloy used in acetabular cups and femoral stems is 90 Ti-6 Al-4V ELI. The acetabular insert can be made up of polyethylene or ceramic, while the femoral head can be made up of metal alloy or ceramic. The interaction of the femoral head and the insert is called the bearing surface. The bearing surfaces available today are ceramic-on-ceramic, ceramic-on-polyethylene and metal-on-polyethylene. There are three main characteristics of the implant which can influence the rate of aseptic loosening: implant design, alloy, and surface treatment.  
Implant design has a direct impact on the survivorship of an uncomented total hip replacement according to multiple studies. Different femoral stem designs, which can be classified according to the Mont group, have different contact areas between the implant and bone [6]. So far, there have been described three generations of acetabular cups and seven femoral stem types [6]. The contact area can affect the primary osseointegration process and even further, the rate of aseptic loosening.  
The metal alloys used directly influence the implant's biocompatibility, Young's modulus of elasticity, strength, and corrosion resistance, which all have a great impact on implant survivorship, as previously shown [7]. Moreover, surface treatments such as hydroxyapatite coating, acid etching, alkali heat treatment, plasma treatment, ion implementation, sandblasting, and nanotechnology can prevent aseptic loosening by affecting the primary osseointegration process [7]. We will further describe the most recommended implant characteristics in terms of reducing the rate of aseptic loosening.
# Online Phase Measurement Profilometry for a Fast-Moving Object
## Principle
### Equivalent Deformed Patterns  
Using the previous MAP super-resolution image reconstruction method, we can reconstruct a set of high-resolution deformed patterns in different positions of an online moving object. In order to obtain equivalent deformed patterns, which means that an object in each deformed pattern is in the same position, this paper adopts Fast 3D measurement based on improved optical flow for dynamic objects by [PERSON] et al. (2014) for pixel matching. As shown in Figure 2, Figure 2a is the modulation of the deformed pattern \(I_{1}\)', Figure 2b is the modulation of the deformed pattern \(I_{N}\)'. Figure 2a is used as a reference, the motion displacement of the object is calculated by pixel matching of Figure 2a,b; at the same time, according to the calculated motion displacement, the modulation with the same position of the object is obtained, as shown in Figure 2d, where the black area is the part of the missing information after the left shift of the pixel matching; phase demodulation can be carried out for the region of interest (ROI), which is the dotted line region in Figure 2c,d. \(I_{N}\)' is moved in the reverse direction according to the calculated motion displacement, and the equivalent deformed patterns \(I_{1}\) and \(I_{N}\) can be obtained by intercepting \(I_{1}\)' and \(I_{N}\)' on the ROI in Figure 2d. Similarly, by matching the modulation of \(I_{2}\)' - \(I_{N-1}\)' with the modulation of \(I_{1}\)', and intercepting on the ROI in Figure 2d, a set of equivalent deformed patterns \(I_{1},I_{2},I_{3},\ldots,I_{N}\) can be obtained. The phase \(\varphi(x,y)\) of the online object can be obtained by substituting \(I_{1}\), \(I_{2}\), \(I_{3},\ldots,I_{N}\) into Equation (3). The continuous phase \(\Phi(x,y)\) of the online object can be obtained by using the rhombic phase unwrapping algorithm and 3D shape of the online object can be obtained by substituting \(\Phi(x,y)\) into Equation (4).  
Figure 1: The flow-process diagram of maximum a posteriori (MAP).  
Figure 2: Modulation of high-resolution fringe pattern: (**a**) Modulation of \(I_{1}\)′; (**b**) Modulation of \(I_{N}\)′; (**c**) Modulation of \(I_{1}\)′ with ROI; (**d**) Modulation of \(I_{N}\)′ with ROI. ROI: region of interest.
## 3 Model Calculation Results  
Through MATLAB (R2022b) software, the fourth-order Runge-Kutta method is used to calculate the dynamic equations. The initial displacements and velocities in the x and y directions are 0.000001 m, the time step is set \(\Delta t=1\times 10^{-6}\) s, the simulation duration is 5 s; the additional loads are Fx = 50 N, Fy = 0 N, and Fz = 0 N; the rotational speed is set to 600 r/min; and simulation calculations are carried out for the axial position angles \(\tau\) of the defect being \(0^{\circ}\) and \(15^{\circ}\). The calculation flow is shown in Figure 8. First, rolling body  
Figure 7: Vibration and shock in three-dimensional space.  
position and load distribution area are calculated, and then the response of the rolling body is calculated by impact simulation in the fault area, while the non-fault area is calculated by Hertz contact mode. The computational data are stored and iterated until the iteration ends.  
It can be found from Figure 9 that when the axial position angle of the fault is \(\tau=0^{\circ}\), vibration responses occur in both the x and y directions, but there is no vibration response in the z direction. This indicates that when only subjected to a radially constant direction load, there will be no axial vibration impact.  
In Figure 10, when the axial position angle of the fault is \(\tau=15^{\circ}\), vibration signals appear in the x, y, and z directions. After the vibration signals are processed by the root mean square, they are shown in Table 2.  
Figure 8: Model solving flowchart.  
Figure 9: Simulation results of fault axial position \(0^{\circ}\).
# Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya
[20] characterized cropping practices (crop type and harvest mode) of sugarcane-based cropping systems in Kenya using a resampled 15 m multi-temporal Landsat dataset and a maximum likelihood classifier. However, sugarcane is usually grown on large-scale commercial and homogeneous fields that can be easily discriminated [21]. To the best of our knowledge, no study has yet attempted to map maize-cropping systems in heterogeneous landscapes in Africa and, moreover, no study is known that utilized RapidEye time-series data using a machine learning classification approach in this regard. Accordingly, the main objective of the study was to examine the utility of random forest (RF) classifier and new-generation RapidEye imagery with enhanced waveband coverage in the red-edge spectral region for cropping systems mapping. Specifically, we aimed to develop a (semi-automated) processing scheme to find the optimal RF model parameters by analyzing the relative model contribution of the RapidEye spectral indices and individual waveband regions(bands) for crop systems mapping. Having information on the spatial distributions of maize systems would ultimately help to better understand factors that contribute to crop productivity and farm or field level yield variability [22].
## 6 Discussion and Verification
### Apc-855
#### 6.1.1 Out-Of-Plane Vibration  
As shown in Figure 4, nine mode shapes were measured in the out-of-plane vibrations, reaching around 10 kHz. \"RM\" indicates the results of inputting the parameters derived using RM into FEM calculations, and \"Ref [27]\" indicates the analysis results obtained in literature for APC-855. The simulation results of these two sets of material constants were both consistent with the mode shapes measured in the piezoelectric specimen experiments. However, the RM calculation results for the resonant frequencies were precise, the errors remaining within 6.472%. The errors in Ref [27], on the other hand, were all greater than 10% and increased as the frequency increased. This demonstrates that the material constants obtained using RM in this study have a certain degree of reliability.  
Figure 4: _Cont._