esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Stand-Alone Wind-Photovoltaic-Battery System with the Hybrid Electrolyzer  
### Structure of Wind-Photovoltaic-Battery System with the Hybrid Electrolyzer  
Under the stand-alone condition, the wind-photovoltaic-battery system with the hydrogen production is illustrated in Figure 1. The hydrogen production infrastructure was designed with three schemes: (A) a single AWE, (B) a single PEME, and (C) a hybrid electrolyzer. The system consists of two primary energy flows: electrical and hydrogen. In terms of electricity energy, the components include wind turbine power generation systems (WTGs), photovoltaic power generation systems (PVGs), battery energy storage systems (BESSs), power loads, and the hybrid electrolyzer. The WTGs and PVGs supply green energy for load demands and hydrogen production. The BESS balances the power discrepancy between the energy supplied by the new energy sources and the electricity consumed by the loads. For hydrogen energy, the system comprises a hydrogen production unit, a hydrogen storage unit, and a hydrogen sales unit. The hydrogen production unit operates as a hybrid electrolyzer, incorporating AWE and PEME. The hydrogen sales unit represents the hydrogen load. This study considered the advantages of both AWE and PEME in terms of hydrogen production cost and efficiency. A hydrogen production mode for a hybrid electrolyzer was designed to enhance the energy utilization and economy of hydrogen production systems. To further discuss and analyze the optimal combination ratio of the hybrid electrolyzer, a mathematical modeling of the wind-photovoltaic-battery system with a hybrid electrolyzer under stand-alone conditions was established. This mathematical model consists of system operation constraints and optimization objective functions.  
Figure 1: A stand-alone wind-photovoltaic-battery system with the hybrid electrolyzer.
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 4 Performance Analysis
### Node-Location Patterns  
Equations (1) and (2) show that the mean number of both the sent messages and the updates for every new location update broadcast over the network depend on the number of nodes at each hop from node \(i\).  
In the following, the nodes were assumed to be uniformly distributed over the network according to a homogeneous Poisson point process of rate \(\lambda\). The radio-transmission range of the nodes was assumed to be \(R\).  
Three different cases were considered: (1) cars located on a highway where all vehicles are on the same axis, moving in the same direction or (2) in the exact opposite direction, or (3) cars located in a city, and the movement of the other vehicles could be considered as a pseudorandom movement on a plane. To provide the complete picture, we also considered a 3D scenario. The most plausible scenario for this last case was not a VANET, but a set of drones flying together.
## 4 Tree-Based Protocols
### Window Based Protocols
Altogether, this tree-based protocol achieves significant energy savings and a reduction in identification time. \[f(L)=k(1-e^{-\beta L}),\hskip 14.226378 pt0<L\leq k \tag{1}\]
* A modification of the QwT is presented in [51] called Standardized Query window Tree protocol (SQwT). This protocol aims to reduce the number of bits that define _ws_ by using a standardized value of 3 bits and approximating it to the nearest power of 2, using \(s=log_{2}ws\) instead. Tags calculate the number of bits to respond by using \(ws=2^{s}\). By using only 3 bits, SQwT can cover window size values from 1 to 128 bits. * Another modification of QwT is presented in [52] and is called Flexible Query window Tree protocol (FQwT). This modification takes advantage of the window to perform an estimation of the tag ID distribution in the interrogation area and improve the identification time of the protocol. The functioning of the protocol is divided into two phases: the estimation of the distribution, and the identification process. During the former phase, the reader estimates the tag ID distribution until the first tag is identified (see Figure 17). After the first tag is identified, the reader begins the identification phase, similar to that of the SQwT. This phase, however uses a different heuristic function when a go-on slot occurs, taking advantage of the \(c_{g}\) parameter (see Equation (2) related to the type of tag ID distribution). \[f(c_{g},L)=\frac{c_{g}}{L}k(1-e^{-\beta L}),\hskip 14.226378 pt0<L\leq k \tag{2}\]  
Equation (2) is adjusted with a value of parameter \(\beta\), preselected to decrease the energy consumed by the proposed protocol. Table 2 compares some of the standard tree-based protocols.
# A Method for 5G-ICN Seamless Mobility Support Based on Router Buffered Data
## 3 Bdmm
### How to Achieve Seamless Mobility Support?
The packet forwarding path after switching is Content Server \(\rightarrow\) R3 \(\rightarrow\) R2 \(\rightarrow\) R1 \(\rightarrow\) UPF1 \(\rightarrow\) BS1 \(\rightarrow\) UE. Our method utilizes the ability of ICN routers to forward packets based on identifiers to solve the problem of session interruption caused by UE cross-UPF handover in 5G networks. Our method also makes full use of the buffering packet capability of the ICN router to buffer packets during UE handover to achieve seamless handover. In addition, in the cross-UPF handover scenario, our method optimizes the handover path by propagating Mobile Path Notification.
# Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots
## 4 Homography Estimation for a Non-Center-Aligned Image Pair  
We extended our study to estimate the homography for a non-center-aligned image pair and used the MS-COCO 2014 dataset to generate enough training and test samples for network model training and testing. The trained models were used to directly estimate the four-point homography between the camera images and the wall maps of wall-climbing robots in the next section.
# Effect of Tide Level Change on Typhoon Waves in the Taiwan Strait and Its Adjacent Waters
## 4 Model Setup and Validation
### Model Setup
#### 4.1.2 Computational Time Step  
In this paper, the wind field model is adopted at a scale of 18\({}^{\circ}\) N-30\({}^{\circ}\) N and 116\({}^{\circ}\) E-128\({}^{\circ}\) E, with a grid point arrangement of 145 \(\times\) 145 and a spatial resolution of 5\({}^{\prime}\times\) 5\({}^{\prime}\). Here, the mesh is used to calculate the waves generated by the wind, while another one in Section 2.1 is for the wind. Moreover, a two-dimensional time-varying spherical coordinate system model is used. Its frequency range is 0.04 to 1.00 Hz, and the direction is divided into 24 segments with a resolution of 15\({}^{\circ}\). The time step of the model calculation is 15 min, delivering outcomes once per hour.
## 1 Introduction
### Purpose and Novelty of the Current Paper  
The process design, operating parameters, cost assessment, and system aims (efficiency and purity) studied in the previous papers revealed an incomplete demonstration of membranes' possibility to be integrated with a particular application. Therefore, in this study, a systematic comparison of three different membrane designs with several CO2 permeabilities and operation parameters for the membrane technology was conducted to present a complete realization of the economic and technical results for the considered power plant (600 MW) that uses lignite coal as fuel. The current article aims to set the optimum membrane process configuration for at least 99% CO2 purity and 90% efficiency with the lowest LCOE obtained. By the process simulation, different cases were analyzed based on the effect of membrane number of stages, membrane surface area, and feed compression utilized to analyze their impact on the electrical energy requirements and membrane system performance. For a specific membrane process, high power consumption is the main obstruction; therefore, in the current paper, multi-stage membrane designs with several variables were introduced to improve and decrease the power required for the auxiliaries, thus reducing the total cost.  
Numerous detailed technical and economic factors have been investigated in this article, such as SPECCA and LCOE, CO2 emissions factor, etc., to present a fully comprehensive analysis compared with other research papers published until now and to provide a wide understanding regarding CO2-capture technologies based on membranes. This research also evaluates the impact of different CO2 permeabilities (300, 1000, 3000 GPU) for post-combustion CO2 capture used in fuel-based supercritical power generation. In the end, the optimal design obtained based on the separation requirements and economic assessment was compared with different power plants' integrated membrane systems published in articles to present the features of the results achieved.
# Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network
## 3 Model Establishment
### Fault Feature Extraction  
The gray level co-occurrence matrix with texture information in the spatial distribution relationship between pixels, can accurately describe the roughness, vertical direction and complexity of the image. Therefore, it is often used as a feature parameter in image analysis [27]. The gray level co-occurrence matrix is generated as follows: take one pixel \(A\) (\(x\), \(y\)) and another pixel \(B\) (\(x\) + \(D\)x, \(y\) + \(Dy\)) in the gray image, then make statistics on the probability of their simultaneous occurrence \(P(i,j,d,\theta)\). The equation of \(P(i,j,d,\theta)\) is as follows:  
\[\begin{array}{l}P(i,j,d,\theta)=\{[(x,y),(x+Dx,y+Dy)|\\ f(x,y)=i,f(x+Dx,y+Dy)=j]\}\end{array} \tag{10}\]  
where \(i,j\) = 0, 1, 2,..., \(N-\)1, and \(N\) is the gray value of the image; \(x\), \(y\) are the horizontal and vertical values of coordinate of pixel \(A\) in the image; the deviation between pixel \(A\) and pixel \(B\) in \(x\) direction and \(y\) direction are \(Dx\) and \(Dy\), respectively; \(d\) is the distance between  
Figure 2: Schematic diagram of symmetrical polar coordinates algorithm.  
\(A\) and \(B\); \(\theta\) is the angle between the connecting line of two pixels and the horizontal line [28]. Generally, \(\theta\) = 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\) and 135\({}^{\circ}\)[29], as shown in Figure 3.  
Due to the large amount of data in the gray level co-occurrence matrix, it is generally not used as a feature to distinguish image texture directly, but based on some statistics obtained from its calculation as a feature of image texture classification. Therefore, the researchers proposed 14 statistical feature quantities obtained from the calculation of gray level co-occurrence matrix [26]. In the study, six commonly and effective statistical features were used as the characteristic parameter: max probability (\(mp\)), entropy (\(ent\)), contrast (\(con\)), correlation (\(cor\)), energy (\(ene\)) and inverse difference moment (\(idm\)).  
Before extracting the feature statistics of the gray level co-occurrence matrix, the normalization process is generally performed according to Equation (11).  
\[g(i,j)=\frac{P(i,j)}{\sum\limits_{i=0}^{N-1}\sum\limits_{j=0}^{N-1}P(i,j)} \tag{11}\]
# Vibration Velocity Prediction with Regression and Forecasting Techniques for Axial Piston Pump
###### Abstract  
Measuring vibration velocity is one of the most common techniques to estimate the condition of industrial machines. At a constant operating point, as the vibration velocity value increases, the machine's condition worsens. However, there are no precise thresholds that indicate the condition of a machine at different operating points. Also, the axial piston pump, which is the subject of the article, is a device that generates stronger vibrations by design and cannot be enclosed in general vibration norms. Due to different use cases and work regimes of axial piston pumps, the need to determine whether the device is working correctly for a broad spectra of operating points emerges. This article aims to present and compare different methods for vibration velocity prediction for axial piston pumps with use of neural networks including dense networks, variants of recurrent neural networks, and ensemble methods. The result of this research consists of models that have performance metrics that clearly indicate whether the monitored pump has malfunctioned or not across a wide variety of operating points, working conditions, and in case of reassembling. A detailed analysis of the influence of available measured variables on the performance of models is also provided. The conclusion is that the application of commercial implementation of developed models is reasonable in the context of both performance quality and costs of sensors needed to provide the necessary data.  
neural networks; virtual sensor; vibrations; sensor fusion; axial piston pump +
Footnote †: journal: _Applied_  
to different possible regimes of work and use cases for axial piston pumps, a tool for estimating the vibration velocity is desired.  
The article presents several techniques for predicting future vibration velocity values. As a target, deviations from the nominal condition of the machine will be used to determine whether the monitored device has somehow malfunctioned. The approach presented in this article is correlated with the concept of a virtual sensor presented in article [4]. Such an approach, in different use cases and domains, was presented in publication [5].  
In addition, ensemble learning [6] was used to mix different techniques together and train one strong-learner instead of several weak-learners. Such an ensemble approach can potentially give better results than using single models without blending them [7]. All described methods were evaluated and compared. The Python programming language with packages Tensorflow [8] and Keras [9] was used to perform analysis and neural network training, scikit-learn [10] for training machine learning models, numpy [11], Pandas [12] matplotlib [13] and seaborn [14] for general data analysis and model evaluation.  
Piston pump domain experts from PONAR Wadowice S.A. were consulted while writing the present article. Measurement experiments also took place there.
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 3 Bridge Experiment
### Improved Accuracy of GNSS Positioning Solution
#### 3.3.1 Enhanced Vertical Positioning  
With regard to height, a GNSS receiver installed on a bridge can assess the deflection of the deck. This deflection analysis provides information on the changed time-series of vertical deflection to the BHMS. However, if the positioning solution is contained in outliers, the delivery of false information cannot be avoided. Therefore, in implementation of the BHMS, accurate and reliable delivery of information on vertical deflection is critical. Figures 13 and 14 show the results of the comparison before and after applying the developed algorithm for quality control. As shown in the figures, a more normal deflection was monitored with a more significantly reduced RMS due to the elimination of the outliers. Table 1 summarizes the results.
# Algorithms for Calculating Generalized Trigonometric Functions
## 4 Methods for Implementing Calculations of Hyperbolic _Ateb_-Functions  
We use the Fourier series expansion to implement the calculations of _Ateb_-functions. Here is a well-known theorem from mathematical analysis: if a periodic function with period 2T is piecewise monotone and bounded on the interval \(\left[-T;T\right]\), then the Fourier series constructed for this function is convergent at all points in this interval. For hyperbolic _Ateb_-sine and _Ateb_-cosine, these conditions are executed in the interval \(\left[-\Pi^{*};\Pi^{*}\right]\). So these functions can be expanded on this segment with a Fourier series. Since hyperbolic functions are differentiable, the Fourier series are convergent in the interval \(\left[-\Pi^{*};\Pi^{*}\right]\). And the hyperbolic cosine \(cha(m,n,\omega^{*})\) is an even function, so we obtain  
\[cha(m,n,\omega^{*})=\frac{a_{0}}{2}+\sum_{k=1}^{\infty}  
Since the hyperbolic sine \(sha(m,n,\omega^{*})\) is an odd function, we obtain the following Fourier series  
\[sha(n,m,\omega^{*})=\sum_{k=1}^{\infty}b_{k}sin\frac{k\pi\omega^{*}}{\Pi^{*}}, \tag{26}\]  
Coefficients in this series are calculated according to the formulas  
\[b_{k} =\frac{1}{2\Pi^{*}(n,m)}\int_{-\Pi^{*}}^{\Pi^{*}}sha(m,n,y)cos\frac {k\pi y}{\Pi^{*}}dy=\] \[=\frac{n+1}{2\Pi^{*}(n,m)}\int_{-\Pi^{*}}^{\Pi^{*}}sin\frac{k\pi y }{\Pi^{*}}\int_{0}^{-1\leq y\leq 1}\frac{dy}{(1-g^{n+1})^{\frac{m}{n+1}}}dy. \tag{27}\]  
The algorithm for hyperbolic _Ateb_-sine calculation contains the following stages (see Figure 1)  
Define parameters \(n,m,\) and \(K\)-count of elements in Fourier series;  
Calculate interval \(\Pi^{*}(n,m)\) according to Formula (23);  
Define step h for numerical calculation integrals and calculate coefficients of Fourier series according to Formula (27);  
Calculate \(sha(m,n,\omega^{*})\) according to Formula (24).  
For this algorithm's practical realization, we used \(k=5,h=0.01\), and quadrature formulas to calculate the defined integral. When we have a value of \(sha(m,n,\omega^{*})\) to obtain the value of \(cha(m,n,\omega^{*})\) with the same parameters of \(n\) and \(m\), we have two choices. The first one is the realization of the same scheme for calculation:  
Define parameters \(n,m,\) and \(K\)-count of elements in Fourier series (this step is the same as for hyperbolic sine);  
Figure 1: Schema for hyperbolic _Ateb_-function calculations.  
Calculate interval \(\Pi^{*}(n,m)\) according to Formula (23) (this step is also realized for hyperbolic sine);
3. Define step h for numerical calculation integrals for coefficients of Fourier series according to Formula (25);
4. Calculate \(cha(m,n,\omega^{*})\) according to Formula (26).  
The second way for the calculation of hyperbolic cosine is using Formula (22). It is clear that the second way is easier and needs a lower count of calculations.
## 2 System Model
### Direct Adaptive Multi-Resampling Turbo Equalizer  
Based on the above transmission signal model, this paper proposes the DAM-TEQ, which adaptively realizes the processing of the transmitted signal through a two-stage structure. The two-stage structure of the DAM-TEQ is shown in Figure 2. The first stage is an adaptive multi-resampling equalizer, and the second stage is a parallel turbo equalizer.
## 3 System Design
### Computation of the Navigation Solutions  
The embedded processor computes navigation solutions using the GNSS basic measurements: pseudorange, carrier phase, and Doppler shift. The computation of the navigation solutions is implemented using the RTKLIB open-source package for standard and precise positioning with GNSS [43].  
The estimated receiver positions might include invalid solutions due to unmodeled measurement errors. The processor performs a series of validation tests on the estimated receiver positions, including a residuals test and a Geometric Dilution of Precision (GDOP) Test [43]. If any of these tests fails, the solution is rejected as an outlier. In addition, the software implements Receiver Autonomous Integrity Monitoring Fault Detection and Exclusion (RAIM-FDE) [44]. RAIM-FDE attempts to exclude invalid measurements due to satellite malfunction, receiver fault, or large multipath by iteratively estimating the receiver's position while excluding one visible satellite at a time.  
When operating in high-sensitivity mode, the receiver utilizes assistance ephemeris data to determine PVT. This is necessary because the receiver may encounter challenges in correctly demodulating the telemetry messages of weak Galileo E1b/c signals, primarily due to the presence of bit errors [36; 37]. If the telemetry decoder successfully demodulates the telemetry messages during operation, the receiver then updates the assistance ephemeris data with the latest information received from the satellites. Assistance ephemeris data can be optionally employed for the GPS and the Galileo E5a signals as well.
## 5 Results and Discussion
### Data Augmentation  
As depicted in Table 2, the Nucleus-Based Cervical Lesion Dataset is highly imbalanced, with a positive correlation between the number of annotations for each class and the respective detection performance (see Figure 6) being observable. Therefore, the underrepresented classes were augmented via random basic image transformations, such as 90-degree rotation, horizontal and vertical flips, blur, and sharpening. Table 4 shows the number of annotations before and after the data augmentation procedure.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **ASC-US** & **LSIL** & **ASC-H** & **HSIL** & **Normal** & **Patches w/ Ann.** & **Empty Patches** \\ \hline \multicolumn{8}{c}{**Original**} \\ \hline Train & 596 & 82 & 101 & 315 & 897 & 669 & 1094 \\ Fold 1 & 384 & 44 & 50 & 215 & 561 & 432 & 724 \\ Fold 2 & 412 & 64 & 83 & 210 & 620 & 462 & 728 \\ Fold 3 & 396 & 56 & 69 & 205 & 613 & 444 & 736 \\ \hline \multicolumn{8}{c}{**Augmented**} \\ \hline Train & 708 & 572 & 681 & 654 & 1803 & 1561 & 1094 \\ Fold 1 & 463 & 307 & 375 & 441 & 1011 & 988 & 724 \\ Fold 2 & 497 & 446 & 548 & 438 & 1335 & 1113 & 728 \\ Fold 3 & 457 & 391 & 439 & 429 & 1260 & 1021 & 736 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of annotation and patches before and after data augmentation.  
Figure 5: [EMAIL_ADDRESS] IoU results for the different optimization steps. (i) Hyperparameter tuning: each color represents a different combination, according to the indexes used in Table 3. (ii) Transfer learning: weight initialization using models pre-trained on the Adequacy Assessment (NUCLEI) and COCO Datasets. (iii) Detected class adjustments: exclusion and inclusion of normal squamous nucleus class (+Sqm.).  
Using the augmented version of the dataset, a new set of experiments was performed by using the best transfer learning approach previously found (NUCLEI pre-trained model) and training with the five different LR-BS combinations detailed in Table 3. The results depicted in Figure 7 indicate that the data augmentation strategy did not improve detection performance.  
One possible cause for this outcome might be the large volume of instances generated through basic image manipulation that was added to the highly underrepresented classes, which probably provided mostly redundant information. This leads us to conclude that the usage of data augmentation via basic image manipulations in the target scenario negatively affects the model's generalization capability by potentially infusing bias during model training. Nevertheless, alternative data augmentation approaches that promote higher and more reliable per-class variability should be explored in the future, for instance, generative deep learning, such as generative adversarial networks (GANs) or latent diffusion models.