esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Example Application
### Comparative Analysis of Prediction Results  
The results of the comparison between the predicted and measured pre-camber values of the two prediction models for the vertical mould pre-camber of the 11#-13# girder sections of the bridge are shown in Table 3. It can be seen that the accuracy of the GM-BP  
Figure 7: Neural network structure.  
prediction model is more obviously improved compared with that of the GM(1,1) model. Its average relative error is reduced from 8.69% to 3.01%, and the minimum relative error of the GM(1,1) model is 6.38% larger than that of the maximum relative error of the GM-BP model, 4.52%, which indicates that this method can effectively improve the predictions' accuracy and stability.  
In order to further verify the applicability and accuracy of the model, the predicted values of the pre-camber of the overall girder section were plotted as curves for a comparative analysis with the measured values, as shown in Figures 8 and 9.  
As can be seen from Figure 8, although both prediction models can truly reflect the variation rule of the pre-camber, the GM(1,1) prediction model has individual girder segments with large errors, especially in the prediction results of the 11#-13# girder segments, which display large discrepancies with the measured values. The GM-BP combined prediction model, on the other hand, demonstrates a high degree of agreement between the predicted and measured values, and there is no significant increase in dispersion. As can be seen from Figure 9, the relative error of the GM(1,1) prediction model fluctuates greatly, and the relative error in the 13# beam section reaches 12.81%, while the fluctuation amplitude of the GM-BP combined prediction model is relatively smooth, and its maximum relative error is 5.51% in the 3# beam section, which is not easily affected by the irregularity of the original data. This shows that the GM-BP combined prediction model can better combine the advantages of the two methods used, can deal with the multi-parameter coupling problem in the case of less data, has a higher accuracy in linear predictions of the bridge construction process, and is more stable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Beam**} & \multirow{2}{*}{**Measured**} & \multicolumn{2}{c}{**GM(1,1) Model**} & \multicolumn{2}{c}{**GM-BP Model**} \\ \cline{3-6}  & & **Predicted Value (m)** & **Relative Error (\%)** & **Predicted Value (m)** & **Relative Error (\%)** \\ \hline
11\# & 0.0564 & 0.0600 & 6.38 & 0.0547 & 3.01 \\
12\# & 0.0597 & 0.0638 & 6.87 & 0.0570 & 4.52 \\
13\# & 0.0601 & 0.0678 & 12.81 & 0.0592 & 1.50 \\ \hline \multicolumn{6}{c}{Average relative error (\%)} & \multicolumn{2}{c}{8.69} & \multicolumn{2}{c}{3.01} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test table of models’ prediction accuracy.  
Figure 8: Comparison chart of model prediction results.
# Soy Isoflavones and PCOS: Role in Hormonal and Metabolic Mechanisms
## 3 Biochemistry of Soy Isoflavones
### Genistein
Importantly, the course and efficiency of genistein metabolism may be further modulated by a range of environmental and physiological factors, including intestinal transit time, gastrointestinal pH, redox potential, as well as the nutritional and immune status of the host [44]. From a biological activity perspective, the microbial degradation of genistein to DH-GEN, 6\({}^{\prime}\)OH-ODMA, and/or 4 EP is associated with a reduction in oestrogenic potential. Genistein itself has a higher affinity for oestrogen receptor \(\beta\) (ER\(\beta\)) than \(\alpha\)-type (ER\(\alpha\)). However, 4 EP has no detectable affinity for either receptor. Meanwhile, 6\({}^{\prime}\)OH-ODMA binds to ER\(\alpha\) and ER\(\beta\) with comparable potency [45]. Since only about 9-22% of ingested genistein is excreted in the urine, it is presumed that the remaining portion is either eliminated through alternative pathways or distributed among various tissues in the body [46]. The oestrogen-like activity of genistein arises from its chemical structure (Figure 2), particularly the presence of hydroxyl groups at the carbon positions 4 and 7 of the ring structure. Their spatial arrangement is similar to that of the hydroxyl groups in the 17\(\beta\)-oestradiol molecule, which enables genistein to bind to oestrogen receptors--both ER\(\alpha\) and ER\(\beta\). Due to this structural similarity to oestradiol, genistein can exert cellular effects via receptor activation; however, its potency is considerably weaker than that of 17\(\beta\)-oestradiol [47]. Importantly, genistein exhibits approximately 20-fold higher affinity for the ER\(\beta\) receptor compared to ER\(\alpha\)[48; 49]. This is particularly significant from both physiological and safety perspectives. The adverse effects of estrogenic activity are largely associated with ER\(\alpha\) activation, whereas ER\(\beta\) activation is believed to exert protective and health-promoting effects. Consequently, genistein is classified as a phytoestrogen with a favourable action profile [50]. The effects of genistein are influenced by the localisation of oestrogen receptors, which can vary temporally, across tissues, between individuals, and even across populations [51; 52; 53]. Notably, the biological activity of genistein is not limited to interactions with oestrogen receptors. It has also been shown to modulate the activity of various other targets, including 5\(\alpha\)-reductase [54], tyrosine kinase [55], peroxisome proliferatorator-activated receptor gamma (PPAR-\(\gamma\)) [56; 57], glucose transporters (GLUT1 and GLUT4) [58; 59], as well as topoisomerases type I and II [60]. These interactions broaden the range of genistein's potential biological effects.
# Optimization of Phase-Only Computer-Generated Holograms Based on the Gradient Descent Method
## 6 Conclusions  
We proposed a new technique for calculating phase-only CGHs using the gradient descent method, which minimizes the MSE defined for the intensity distributions of reconstructed images. We confirmed that the proposed technique optimizes the CGH phase distribution more rapidly than the GS algorithm and provides higher-precision reconstructed images. The proposed technique was also experimentally verified. The proposed technique could control the intensity of the reconstructed images evenly, because the MSE was directly defined for intensity distribution. The GS algorithm could control lower intensities more precisely than the proposed technique because it focused on controlling the amplitude distribution. For the binary images, the GS algorithm could manage the black region more accurately, but the proposed technique could control the white region more precisely. The proposed algorithm based on the gradient descent method can be utilized for the optimization of the Fresnel-type phase-only holograms.  
Conceptualization, S.L.; methodology, S.L.; software, S.L.; validation, Y.T.; formal analysis, S.L.; investigation, S.L.; resources, S.L.; data curation, S.L.; writing-original draft preparation, S.L.; writing-review and editing, Y.T.; visualization, S.L.; supervision, Y.T.; project administration, Y.T.; funding acquisition, Y.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number 19H02189.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector
## 1 Introduction
The choice of the XGBoost algorithm is mainly due to the typically good performance in product classification [30] and sales prediction [31]. Following the experience of the authors in the large-retail sector and in sales prediction estimation, a research industry project focused on a supermarket store DSS was developed by defining multiple attributes by means of collaboration of the industry managers. The work is structured as follows:  
* the DSS platform main functions are defined;
* the multiple attributes influencing the supermarket sales predictions are defined;
* the customer grid (customer segmentation) is structured;
* the AD approach able to increase the DSS performance is defined;
* the platform is implemented;
* the XGBoost algorithm is tested by proving the correct choice of the AD approach for the DSS. Lack of data is a problem that machine learning algorithms face in many fields, especially when there are privacy concerns, such as in the medical field [32; 33]. One method used in this case is the construction of synthetic or surrogate data using the copula method [34]. There are numerous research studies that have focused on data mining means in the field of sales forecasting [17; 35; 36; 37; 38; 39]. In particular, the XGBoost [16; 17], ref. [40] method is widely used for its performances, which also finds applications in e-commerce [40; 41; 42]. However, this powerful method requires a sufficient amount of data to be applied profitably. In our case the data were not enough, so we used the augmented data technique. This new technique, which has been tested in different sectors [43; 44; 45], is being applied for the first time for sales forecasting.
# Temperature Effects on Vibration-Based Damage Detection of a Reinforced Concrete Slab
## 3 Mathematical Model
### Simple Linear Regression Model  
The relationship between eigenfrequency and mean temperature is considered to be linear, which is shown as:  
\[f=kT+b \tag{6}\]  
where \(f\) is the eigenfrequency; \(T\) is the temperature; \(k\) is the slope coefficient; and \(b\) is intercept. The simple linear regression model of each mode is shown in Figure 11.  
Figure 11: Modal frequencies of linear regression model under different temperature. (**a**) 1 st mode. (**b**) 2 nd mode. (**c**) 3 rd mode. (**d**) 4 th mode.  
The fitted regression line is plotted in Figure 8a-d together with the 95% confidence bounds, and the slope coefficients and intercepts for the first four natural frequencies are shown as Table 2.
# Decision-Maker's Preference-Driven Dynamic Multi-Objective Optimization
## 2 Background
### Mathematics for Newly Proposed Performance Measures
#### 2.2.1 Deviation of Violating Decisions  
Solution space vectors whose objective values are outside the preference set are referred to as violating decisions, since they violate DM preferences. Depending on the control parameters used in the implementation of the penalty function of the proposed algorithms, the violating decisions may occasionally find their way into the archive, especially in situations where all the non-dominated solutions violate DM preferences and non-violating decisions are not found. However, it is a rare scenario: the non-violating decisions, if they are found in the archive, are very likely to dominate the violating and penalized decisions in the Pareto-dominance sense. However, when violating decisions find their way into the archive, a measure of the proximity of these violating decisions to the preferred bounding box is required. The smaller the total proximity, the better the violating decisions are. This section presents the mathematics underlying the calculation of the total proximity/deviation of the violating decisions.  
Let \(\mathbf{p}\), \(r\), and the distance measure \(d(\mathbf{z},\mathbf{p})\) be as defined in Section 2.1, and let a set of violating decisions, \(Z\), be defined as follows:  
\[Z=\{\mathbf{z}_{k}\in O|d(\mathbf{z},\mathbf{p})>r\},\,k=1,\ldots,|Z| \tag{8}\]  
Let the cardinality, \(N\), of \(Z\) be defined as  
\[N=|Z| \tag{9}\]  
Let the deviation of \(\mathbf{z}_{k}\in Z\) be defined as  
\[d_{k}=d(\mathbf{z}_{k},\mathbf{p})-r\left(d_{k}>0\right) \tag{10}\]  
Then the total deviation of all elements in \(Z\) is  
\[\mathrm{dVD}=\sqrt{\frac{\sum(1+d_{k})^{2}}{N}} \tag{11}\]
# Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications
## 1 Introduction
This article investigates the potential of OWC for bidirectional, full-duplex, on-axis rotary scenarios and describes how to design a monolithic, hybrid TX-RX lens. In Section 2 a channel model is used to derive some adequate figures of merit. The use of a hybrid lens is motivated by discussing several realization approaches of rotary OWC. Next, the design procedure of a hybrid lens is described and the choice of design parameters is discussed. In Section 3, the performance of the lens and a second system is evaluated and compared using optical ray tracing simulations. Thereby, the alignment-depended optical signal power at the receiver and optical crosstalk is studied. The results are discussed in Section 4. Finally, Section 5 provides a short summary. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Group** & **Contact** & \multicolumn{4}{c}{**Contactless**} \\ \hline
**Type** & **Slip Ring** & **Capacitive** & **RF** & **Fiber (FORJ)** & **OWC** \\ \hline  & & & & **Single** & **Multi** & \\
**Ref.** & [5; 6; 16; 17; 18] & [6; 9; 11; 13] & [1; 6; 10; 11; 13] & [4; 7; 8] & [6; 13] & [19] \\ \hline data rate/Gbit/s & 0.1... 3 & \(5\cdot 10^{-4}...\textgreater{}1\) & 0.054...5 & 10...40 & \textgreater{}40 & \textgreater{}10\textasci{10}\textasci{10}\textasci{10}\textasci{10} \\ max speed / rpm & \(10^{1}...10^{4}\) & \(10^{3}...10^{4}\) & \(10^{3}...10^{4}\) & \(10^{3}...10^{4}\) & \(10^{2}...10^{3}\) & \textgreater{}1400\textasci{10}\textasci{10}\textasci{10}\textasci{10}\textasci{10} \\ max revolutions & \(10^{7}...2\cdot 10^{8}\) & \textgreater{}10^{8}\) & \textgreater{}10^{8}\) & \textgreater{}10^{8}\) & \textgreater{}10^{8}\) & \textgreater{}10^{8}\) \\ cost (initial) & medium & low/medium & low/medium & high & very high & low \\ cost (maintenance) & high & low & low & low & high & low \\ RF immunity & weak & medium & weak & strong & strong & strong \\ on-/off axis & off-axis & off-axis & off-axis & on-axis & on-axis & on-axis \textasci{10}\textasci{10}\textasci{10}\textasci{10}\textasci{10}\textasci{10} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Common principles for rotary data transmission and their typical state-of-the-art performance. Values should be understood as orders of magnitude rather than exact values. * Higher values are possible but have not been published yet; ‡ this work is limited to on-axis scenarios.
# An Insightful Overview of the Wiener Filter for System Identification
## 7 Simulations  
In this section, simulation results are provided to support the main theoretical findings related to the regularization of the Wiener filter, which were presented in Sections 4-6. All the experiments were performed using MATLAB R2018b on an Asus GL552 VX device (Windows 10 OS), having an Intel Core i7-6700 HQ CPU @ 2.60 GHz, with 4 Cores, 8 Logical Processors, and 16 GB of RAM.
## 4 Test Results Analysis
### Deformation Capacity  
Ductility factor (\(\mu\)) is defined as the ratio of ultimate displacement to yield displacement (i.e., \(\mu_{\Delta}=\Delta_{\text{u}}/\Delta_{\gamma}\), \(\mu_{\Theta}=\theta_{\text{u}}/\theta_{\text{y}}\)). Inter-story drift ratio \(\gamma\) is the ratio of lateral displacement to specimen length (i.e. \(\gamma_{\text{cr}}=\Delta_{\text{cr}}/H_{\text{r}}\)\(\gamma_{\text{u}}=\Delta_{\text{u}}/H\)). The values of \(\mu\) and \(\gamma\) were listed in Table 2.  
(1) With the increase of the T/M ratio the twist ductility coefficient increased, while the trend is the opposite with the increase of the axial compression ratio. Specifically, With the increasing T/M ratio (0.17, 0.28, 0.36), the average twist ductility for the specimen RCL-5 and RCL-6 increased by 10.5% and 17.8%, respectively, compared to the specimen RCL-1 with T/M ratio of 0.17. While, the reduction of 32.1% in the twist ductility coefficient for the RCL-3 (_n_ = 0.19) was observed compared to the RCL-5 (_n_ = 0.23).  
(2) For the displacement ductility, it decreases with the increase of _T/M_ ratio. It was attributed to the high _T/M_ ratio result in low plastic zone height. The specimens with _T/M_ ratio of 0.36 and 0.17, respectively, attained 73.4% and 71.1% of the displacement ductility factor for the specimen with _T/M_ ratio of 0.  
(3) The limit of elastic story drift ratio and elastoplastic story drift ratio for the CFST frame structure in design code GB50936-2014 (2014) were 0.18% and 2.00%. The average value of the elastic story drift ratio for all specimens is 0.36% and the average value of the ultimate story drift ratio is 2.81%. It is indicated that the RC L-shaped column met the requirement of the standard in regions of high seismic intensity.  
Figure 8: Skeleton curves for the specimens.
# Design and Implementation of DSLMS Algorithm Based Photoelectric Detection of Weak Signals
## 1 Introduction
Reference [15] employs a gradient statistical averaging-based approach to modulate the step-size factor, aiming to speed up the algorithm's convergence and optimize its steady-state error performance. However, the drawback of this approach lies in the weak immunity to interference caused by the introduction of a judgment threshold that increases the algorithm's complexity. Reference [16] introduced a variable-step-size LMS algorithm based on exponential functions by using exponential functions. Still, the algorithm also suffers from high complexity due to the frequent execution of exponential operations. To solve this problem, reference [17] improved on [16] by applying the variable-step-size method to the partial update of the filter weight coefficients, improving the convergence speed and effectively reducing the algorithm's complexity. However, in low-signal-to-noise-ratio environments, the algorithm has a more significant step size when approaching convergence, and the steady-state performance is poor. In summary, the existing variable-step-size LMS algorithms cannot simultaneously solve the problems of noise interference, slow convergence speed, and higher steady-state error. In order to effectively reduce the influence of environmental noise, speed up the convergence of the algorithm to the optimal solution, and achieve a lower error level after reaching the steady state, this paper proposes a variable-step-length adaptive filter based on the improved skip-link function, in which the DSLMS adaptive filtering algorithm is used, with the following main contributions:  
1. A new variable-step-size adaptive filtering algorithm is proposed based on the minutiae function, which is combined with the pairwise elimination system to effectively reduce the noise component in the error by utilizing the characteristic of low correlation of the noise signal and improve the anti-noise-interference capability of the adaptive filter. 2. The adaptive filter effectively improves the convergence speed and significantly reduces the steady-state error by segmentally adjusting the step factor of the weight coefficients. 3. Using FPGA technology as an experimental platform, this study modularizes the design of the DSLMS algorithm and compares it with the traditional LMS algorithm through experiments. The results show that the proposed algorithm has better performance.
# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 3 IberSpeech-RTVE 2022 Evaluation Tasks
### Speech to Text Challenge  
The speech-to-text transcription evaluation consists of automatically transcribing different types of TV shows. The main objective is to evaluate the state-of-the-art in automatic speech recognition (ASR) for the Spanish language in the broadcast sector. There is no specific training partition, thus participants are free to use the previous RTVE datasets (2018 and 2020) or any other data to train their systems provided that these data are fully documented in the system's description paper. For public databases, the name of the database must be provided. For private databases, a brief description of the origin of the data must be provided. Each participant team should submit at least a primary system, but they could also submit up to three contrastive systems.  
Performance Metric  
The ASR system outputs are ranked by the word error rate (WER). All the participants have to provide as output for evaluation a free-form text file per test file, encoded using the UTF-8 charset ([[http://www.utf-8.com/](http://www.utf-8.com/)]([http://www.utf-8.com/](http://www.utf-8.com/)) (accessed on 24 July 2023)), with no page, paragraphs, sentence, or speaker breaks. The text is normalized as follows: all the punctuation marks are removed, numbers are written with letters and all characters are lower-cased. The WER is defined as:  
\[WER=\frac{S+D+I}{N_{r}}, \tag{1}\]  
where \(N_{r}\) is the total number of words in the reference transcription, S is the number of substituted words in the automatic transcription, D is the number of words from the reference deleted in the automatic transcription, and I is the number of words inserted in the automatic transcription not appearing in the reference.
## 5 Parameter Influences  
### Influence and Normalization of the Angular Natural Frequency  
Changing the angular natural frequency \(\omega_{0}\) has a huge effect on the frequency-response curve, because its center changes to another excitation frequency \(\Omega\), depending on \(\omega_{0}\). This has a large impact due to the \(\Omega^{2}\) characteristic of the right-hand side and its appearance in the argument of the cosine. Wanting to apply an obtained frequency-response curve to another excitation frequency requires not only a simple introduction of a frequency ratio but, also, an adjustment of the other parameters. Therefore, it is necessary to have a system that leads to the same resulting equation. In this angular frequency normalization, the units are used similar to Section 4. The units were considered during normalization; however, note that we omitted the unit label in the following for readability. Using Equation (4) with a new natural frequency according to \(\omega_{0,new}=n\omega_{0,old}\), the excitation frequency to achieve the same plot is \(\Omega_{new}=n\Omega_{old}\). Inserting this and equalizing Equation (4) yields  
\[\ddot{u}_{new}+\varepsilon^{2}d^{*}ni_{new}+\omega_{0,new}^{2}u_{new}+ \varepsilon\kappa^{*}n^{2}u_{new}^{3}=\varepsilon^{2}\Omega_{new}^{2}\varphi_ {i,j}q^{*}cos(\Omega_{new}t) \tag{69}\]  
with \(u\) being some kind of response related to \(cos(\Omega_{old}nt)\) as discussed above. Therefore, \(\ddot{u}_{new}=\ddot{u}n^{2}\), \(\ddot{u}_{new}=\dot{u}n\) and \(u_{new}=u\). So \(\alpha_{new}^{*}=\alpha^{*}\omega_{0,new}^{2}\) and \(d_{new}^{*}=d^{*}\omega_{0,new}\) can give the same structure as the original differential equation and, therefore, the same frequency-response curve as for \(\omega_{0}=1s^{-1}\). This way of adapting the parameters can also be used when varying one of the others and wanting to achieve the same frequency-response curve.  
Again, a consideration of a friction damper requires a deeper understanding of the method. The way as shown above would lead to \(\mu_{new}^{*}=\mu^{*}\omega_{0,new}^{2}\), whereas the correct  
Figure 4: Frequency-response curve with markings for the point of maximum amplitude and the point of the linear natural frequency (**a**) and the influence of the damping and nonlinearity coefficient on the maximum amplitude (\(\omega_{0}=1000_{s}^{1},\ \varphi_{i,j}\beta=0.0001\ rad\)) (**b**).  
solution is \(\mu^{*}_{new}=\mu^{*}\omega_{0,new}^{3}\), because the approach via Fourier series or equivalent damping ration includes an additional division by \(\omega_{0}\). This must be observed to get the same frequency-response curve.