esa-sceva/satcom-chunk-collection huggingface dataset:
# PAPR Reduction in UFMC for 5G Cellular Systems
## 3 The Proposed Dcomposed SLM PAPR Reduction Technique
Therefore, in the proposed algorithm, we consider the fundamental complexity of the IFFT as a reference. In other words, the computations complexity of the system is \(\frac{(U+1)N}{2}\mathrm{log}_{2}N\) and \((U+1)N\mathrm{log}_{2}N\) for complex multiplications and additions, respectively, where the extra operation added (\(U\)+1) is for the original operation of the IFFT to which no PAPR reduction process is applied. Accordingly, the proposed Dcomp-SLM is implemented at the same order of magnitude of computational complexity as conventional SLM techniques.
# The Plasma Ozonizer System for Mangosteen Storage Container to Preserve the Quality of Mangosteen
## 1 Introduction
Freshly cut broccoli (Brassica oleraceae L) that had been exposed to aqueous ozone (2.5 mg h\({}^{-1}\)) for 5, 10, and 15 min was examined by [PERSON] et al. [21]. The ozone concentrations of 0.56, 1.00, and 1.50 ppm were the most efficient. In comparison to the control treated with pure water, the results demonstrated reductions in total coliform bacteria, molds, and yeasts of 1.20, 2.50, and 1.80 log\({}_{10}\) CFUg\({}^{1}\), respectively. By the reduction in microbial populations and ethylene oxidation, treatment with ozone appears to improve the shelf life of fresh, uncut commodities, according to a number of research studies [22; 23; 24; 25; 26]. There have not been any previous reports on how ozone affects the quality and shelf life of mangosteen. The objective of this work was to study the use plasma ozonizer system for a storage room prototype to determine the effectiveness of ozone in extending the shelf life and preserving the quality of mangosteen. The studies were conducted to investigate the resonance frequency, oxygen gas flow rate, and discharge time, which are the system's optimum conditions. The appropriate conditions were then used in tests to look at how ozone affected the concentration of ethylene. The final step in determining mangosteen quality involved evaluating L*, a*, b*, weight loss percentage, pericarp firmness value, and sensory evaluation.
# Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms
## 5 Simulation Results  
In this section, we present the results obtained by our proposal. First, we study the performance of the genetic algorithm used within the GARSUD approach to search the solution among all the available locations for the RSUs. Second, we present a comparison between GARSUD and other existing RSU deployment strategies (i.e., geographic and the D-RSU). To ensure that the results we have obtained are representative, all the results included in this paper represent an average of three runs.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline mobility generator & Citymob for Roadmaps (C4R) [34] \\ number of vehicles & \(100,200,300,400\) \\ simulated area & 2000 m \(\times\) 2000 m \\ simulated layouts & Madrid and Valencia \\ mobility models & Downtown model [29] and Krauss [28] \\ maximum acceleration of vehicles & 1.4 m/s\({}^{2}\) \\ maximum deceleration of vehicles & 2.0 m/s\({}^{2}\) \\ driver reaction time (\(\tau\)) & 1 s \\ number of warning mode vehicles & 3 \\ message size & 512 bytes \\ message rate & 1 per second \\ Physical and Medium Access Control layers (PHY/MAC ) & IEEE 802.11p \\ maximum transmission range & 400 m \\ warning message priority & Access Category 3 (AC3) \\ normal message priority & Access Category 1 (AC1) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter values for the simulations.
# A Machine Learning Approach for the Prediction of Thermostable \(\beta\)-Glucosidases
## 4 Results and Discussion
### Thermostability Patterns in Enzymes Obtained from Non-Thermostable Organisms
#### 4.1.3 Differences in the Electrostatic Surface  
Assessing whether a \(\beta\)-glucosidase enzyme is thermostable is not a trivial task. For instance, [PERSON] et al. (2017) suggest that the thermostability of a \(\beta\)-glucosidase may be related to the distribution of charges on the electrostatic surface. They argue that enzymes with a more equal distribution of positive and negative charges on the surface tend to be more thermostable.  
To assess this, I performed electrostatic surface charge distribution analyses of the four case study enzymes using the APBS tool. Figure 6 shows the surface visualization of the four structures--region close to the active site entrance pocket. The surface was colored on the red-white-blue color scale, i.e., redder regions have a more negative charge, while bluer regions have a more positive charge.  
Figure 5: Case study structures. (**A**) Structural alignment among the 3D structures. Generated using PyMOL. (**B**) Matrix of sequence identity. Generated using Clustal Omega (details available in Supplementary Table S3). PP: real positive and predicted as positive; NN: real negative and predicted as negative; NP: real negative and predicted as positive; and PN: real positive and predicted as negative.  
Figure 6 indicates that the electrostatic surfaces of A0A0B3 BP14 and A0A087E2J5 (both belonging to the thermophilic group) have more evenly distributed charges when compared to the others (note the distribution of red and blue dots). On the other hand, A0A0B2A6F6 (non-thermophilic group) appears to have more negative charges (more red dots) when compared to the others. However, visually, its surface is not comparable to A0A0G0 PX59, which also belongs to the non-thermophilic group but was wrongly classified by our model.  
A priori, this could be an indicator that A0A0G0 PX59 has characteristics that differentiate it from the non-thermophilic group. It is important to emphasize that the non-thermophilic group was obtained by randomly selecting sequences. Therefore, there is no guarantee that they do not really have thermostable characteristics. Furthermore, these initial results indicate that the method proposed here could be used to detect new thermostable \(\beta\)-glucosidase enzymes. Also, the results obtained by the TemStaPro software in case study 1 corroborate the idea that A0A0G0 PX59 may have thermostable characteristics. However, further experiments need to be performed to further evaluate this.  
Figure 6: Charge distribution on the electrostatic surface. Force field: AMBER—pH: 7.0. Generated using the APBS software v3.4.1.
# The Use of Smart Rings in Health Monitoring--A Meta-Analysis
## 4 Discussion
### Expanding the Role of Smart Rings in Digital Health  
This systematic review underscores the growing interest in SR technology as a health monitoring tool. The expansion of the IoMT industry [64; 65; 66; 67] suggests that healthcare professionals and patients will have access to a broader array of health data [68]. The integration of AI, big data, and machine learning algorithms could enable real-time data analysis and insights for clinicians and patients [69; 70]. The results are in line with the current performance of other smart wearables. For example, a recent meta-analysis assessing the accuracy of smartwatches in detecting AF showed a specificity of 94% and a sensitivity of 93%. Moreover, wearable devices show promising results in monitoring the athlete's internal and external workloads, though their technology readiness level needs substantial developments, particularly in acquiring more effective biomarkers [71; 72].  
As part of the digital health ecosystem, SRs offer unique opportunities for building Digital Twins and developing personalized treatment plans [73; 74]. Data acquired through SRs can be used to develop treatment plans tailored to the patient's specific needs, improving the efficacy of the treatment and reducing the risk of adverse events [75]. This could lead to advances in patient care and clinical decision making and potentially enhance patient-doctor relationships by streamlining communication, providing access to health information, and enabling remote monitoring and care [76; 77]. However, digital health technologies must complement and empower the patient-doctor relationship, which is essential for fostering trust and empathy, both correlated with better health outcomes [78]. SRs have the potential to promote patient engagement by providing feedback and monitoring progress, which can motivate patients to make healthy lifestyle choices [79]. However, more studies are needed to understand the power of this technology in promoting a healthy lifestyle [80] and to improve adherence to treatment plans [81].
## 3 Application Examples and Discussion
It is known that the ground node of a nullor-based oscillator can be chosen arbitrarily without affecting the characteristic equation [24]. Therefore, the grounded capacitor oscillator can be obtained by selecting Node A in Figure 7b as the ground. In addition, more oscillators can be obtained by applying adjoint network theorem or RC-CR transformation. It must be noted that the proposed method can be applied to any band pass filters, so it has better generality compared to the transformed method in [25], which can only be applied to the filters with specific configuration and cascadable property. Furthermore, since the oscillation frequency of the synthesized oscillator is identical to the center frequency of the original band pass filter, one can obtain high-performance oscillators by choosing the suitable band pass filters for transformation. For example, the aforementioned band pass filters with low-sensitivity or electronically adjustability can be selected to obtain useful oscillators. Furthermore, current-mode linear oscillators can also be derived according to the proposed method, because the complementary and inverse transformations can be applied to current-mode circuits. Figure 4:5 **(a)** A CCII-based band pass filter; **(b)** the obtained filter after applying the transformations in Figure 4:6  
Figure 4:7 **(a)** Grounding input of Figure 6b; **(b)** realization of the obtained oscillator in **(a)**.
## 1 Introduction
As a result, typical intrusion detection technology cannot be used directly to a power industrial control system, and associated research is limited. Investigation of intrusion detection technologies for power grid industrial control systems is crucial. To address the above-mentioned problems, this paper presents an intrusion detection approach for power industrial control systems based on hyperparameter optimization. The main contributions are as follows:  
(1) We collect performance data that captures the roc_auc score of the random forest (RF) classifier with hyperparameter settings. We then fit an RF model to this data and use functional ANOVA to decompose the variance of predictions into contributions due to every single hyperparameter. The significance of RF hyperparameters is determined by the magnitude of variance contribution. (2) To further improve the intrusion detection model's performance, we propose an improved grid search that performs a local grid search for each hyperparameter in turn, based on the importance of the RF hyperparameters. The optimal hyperparameter combination is obtained by substituting the optimal hyperparameter value for the matching base classifier hyperparameter value. (3) Based on the objective, structure and characteristics of the power industrial control system, we built an RF-based intrusion detection model, taking full advantage of the RF algorithm's advantages of processing high-dimensional data, fast training speed, and good performance. To ensure that our proposed method is effective for power industrial control system intrusion detection and simple to validate, we use publicly available power industrial control system datasets. The rest of this paper is organized as follows. Section 2 provides the background in intrusion detection. Section 3 discusses our methodology when applying our experiments. In Section 4 we describe our results and compare with similar work. Finally, we draw a conclusion of this article in Section 5.
# Prediction of Concrete Failure Time Based on Statistical Properties of Compressive Strength
## 5 Scale Parameter and Shape Parameter of Gamma Distribution  
A reliability analysis was applied in this paper to predict the concrete failure time with high accuracy. In order to develop a time-varying reliability, the parameters (\(\alpha\) and \(\lambda\)) of the gamma distribution function at different service years need to be investigated. Different from \(\alpha\), \(\lambda\) varies with different values of \(f_{c}\). The parameters and the corresponding fitting curves are observed in Figures 11 and 12.  
Figure 11: Scale parameter (\(\lambda_{1}\)) fitting (\(f_{c}=18.0\) N/mm\({}^{2}\)).  
Figure 12: Shape parameter (\(\alpha\)) fitting.  
Equations (14) and (15) are the predicted models of these two parameters obtained by curve fitting.  
\[\lambda\ _{1}(t)=12.94\times\exp(-0.072t) \tag{14}\]  
\[\alpha(t)=428.4\times\exp(-0.08t) \tag{15}\]  
Another method for computing the time-varying scale parameter is proposed without fitting \(\lambda(t)\). The proposed method is presented in the subsequent section.  
In this paper, MVCS was computed by Equation (16a). Besides, based on the property of gamma distribution, MVCS can also be expressed as Equation (16b).  
\[\mu_{Rs}(t)=Rs_{n}(t)\times f_{c} \tag{16a}\]  
\[\mu_{g}(t)=\alpha(t)/\lambda_{n}(t) \tag{16b}\]  
where \(\mu_{Rs}\) and \(\mu_{g}\) are both the mean value of compressive strength; and \(n\) is used to distinguish \(Rs\) and \(\lambda\) with different \(f_{c}\).  
Figure 13 shows two prediction models of \(\mu_{Rs}\) and \(\mu_{g}\) of \(f_{c}=18.0\) N/mm\({}^{2}\), which are expressed based on Equations (10), (16a) and (14), (15), (16b), respectively. The difference between the predicted results exists because \(\mu_{Rs}\) and \(\mu_{g}\) were both obtained based on curve fitting. The errors between the predicted results were less than 2%.  
According to Figure 13, it is concluded that \(\mu_{Rs}\) and \(\mu_{g}\) were equivalent to evaluate the compressive strength, which is expressed as Equation (17a). Therefore, an approximate expression, formulated based on Equation (17a) for the time-varying scale parameter, is elaborated as presented by Equation (17b).  
\[Rs_{n}(t)\times f_{c}=\frac{\alpha(t)}{\lambda_{n}(t)} \tag{17a}\]  
\[\lambda_{n}(t)=\frac{\alpha(t)}{Rs_{n}(t)\times f_{c}} \tag{17b}\]  
In light of Equation (17b), it is feasible to obtain \(\lambda\) without curve fitting. By using the proposed method, \(\lambda_{2}\) of \(f_{c}=21.0\) N/mm\({}^{2}\) is obtained based on Equations (9), (15), and (17b), as shown in Figure 14.  
Figure 13: Comparison of results calculated by \(\mu_{Rs}\) and \(\mu_{g}\) (\(f_{c}=18.0\) N/mm\({}^{2}\)).
## 2 Materials and Methods
### Datasets and Data Processing
#### Evapotranspiration, Gross Primary Production, and Sun-Induced Fluorescence  
Independent datasets of evapotranspiration and gross primary production (GPP) are used to assess the quality of the LDAS-Monde reanalysis of LSVs.  
Terrestrial evapotranspiration estimates are from the GLEAM (Global Land Evaporation Amsterdam Model) v3.1. product [61]. They cover the period of 1980-2016 and are available at a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). The GLEAM dataset is widely used for investigating both trend and spatial variability in the terrestrial water cycle (e.g., [62; 63; 64]) as well as land atmosphere interactions (e.g., [65; 66]). In short, the model computes the terrestrial evaporation and root-zone soil moisture [55] and is mainly driven by microwave remote sensing observations, the potential evaporation amount being constrained by satellite-derived soil moisture.  
For the evaluation of GPP, we use estimates derived from meteorological parameters through the use of machine learning algorithms within the FLUXCOM project [67]. This set of observations can be found at the Max Planck Institute for Biogeo-chemistry data portal ([[https://www.bgc-jena.mpg.de/geodb/projects/Home.php](https://www.bgc-jena.mpg.de/geodb/projects/Home.php)]([https://www.bgc-jena.mpg.de/geodb/projects/Home.php](https://www.bgc-jena.mpg.de/geodb/projects/Home.php)), last access: November 2018) and is available at a 0.5\({}^{\circ}\) x 0.5\({}^{\circ}\) spatial resolution with a monthly temporal frequency over the 1982-2013 period. In this study, GPP products were used over the 2001-2013 time period.  
We also use estimates of sun-induced fluorescence (SIF) from the GOME-2 (Global Ozone Monitoring Experiment-2) scanning spectrometer [68; 69] to evaluate GPP. [PERSON] et al. [43] has shown that observations of SIF can be used as a proxy to evaluate the influence of data assimilation  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & **2007** & **2008** & **2009** & **2010** & **2011** & **2012** & **2013** & **2014** & **2015** & **2016** & **2017** & **2018** \\ \hline SWI & 36,154 & 39,624 & 38,761 & 41,687 & 41,699 & 41,652 & 41,086 & 41,129 & 173,818 & 319,565 & 319,097 & 157,476 \\ \hline LAI & 37,107 & 37,500 & 37,342 & 37,425 & 37,269 & 36,706 & 37,024 & 38,778 & 38,839 & 38,757 & 38,243 & 19,908 \\ \hline \end{tabular}
\end{table}
Table 1: Yearly N points of SWI and LAI from 2007 to 2018–06.  
on simulated GPP using correlations. We use in this study the Level-3 v27 SIF product, giving a daily-averaged SIF at 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) resolution over the 2010-2016 period.  
Observations are rescaled to match the spatial resolution of the two atmospheric forcing data-sets, 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and 0.50\({}^{\circ}\)\(\times\) 0.50\({}^{\circ}\) for ERA5 and ERA-Interim, respectively.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 4 Imaging Fusion Algorithms  
There are three levels of image fusion: the pixel level, feature level and decision level. Pixel level fusion is the most basic image fusion method, which directly acts on the pixels in the image and does not need to extract features, but requires strict image registration. Feature level fusion requires extracting features in the image, such as size, edge, shape, texture information and other details. Decision-level fusion is able to extract, identify and classify valuable objects in the fusion image, and perform fusion at a higher level. For medical image fusion, both the pixel level and feature level are usually applied. Due to the particularity of liver imaging, it is necessary to combine several methods to achieve image fusion. The following are the commonly used image fusion methods.
It was developed from dielectric measurements from 15 soil types, covering a wide range of moisture and frequency conditions at the temperature of 20 \({}^{\circ}\)C. In contrast to the Dobson model, the Mironov model employs the spectra that are explicitly related to the surface of water either bound to soil or free to the soil [26]. However, the direct relationship between dielectric constant and soil moisture remains unknown. This paper presents a straightforward approach for computing the quantitative value of dielectric constant using GNSS-R data that were measured by the CYGNSS satellites. This work is part of on-going research at the GNSS Laboratory at York University, Toronto, Canada, being funded by the Canadian Space Agency (CSA) with a GNSS-R soil moisture research grant. Reflectivity, which defined as the ratio of SNR from direct and reflected signals from GNSS satellites, is converted to the Fresnel coefficient to estimate dielectric constants. In order to implement this approach, low incidence angles of reflected signals have been chosen because it is known that the magnitude of horizontal and vertical Fresnel coefficients is identical at low incident angles of less than 60\({}^{\circ}\)[30; 31]. It is also known that scattering features turn into incoherent scattering when the incident angle is close to normal direction of the reflection surface [21]. The dielectric constant calculated at different locations has showed a positive correlation with the radiometer data from the SMAP satellite, which is used to validate the presented analysis by means of conversion from soil moisture to dielectric constant using the semi-empirical model for simplicity. It is noticed that the semi-empirical model delivers soil moisture content, including unexpected errors when the remote sensing data are applied for soil moisture calculation, as the semi-empirical model was based on in-situ measurements by microwave. Section 2 provides brief descriptions of the CYGNSS and SMAP missions. In Section 3, the developed methodology for calculating dielectric constant is described in detail, followed by the results of calculations with the method shown in Section 4. Further validation results for dielectric constant by comparing SMAP data are provided in Section 5. Finally, conclusions and future work are summarized in Section 6.
## 3 Results and Discussion
### Compressive Strength
a\({}_{0}\) is the intercept term, a\({}_{\text{i}}\) are the linear coefficients, a\({}_{\text{ii}}\) are the quadratic coefficients, a\({}_{\text{ij}}\) is the interaction coefficient, and \(\varepsilon\) represents the random error in the estimated model. Experimental data were collected from various mix proportion combinations and analyzed using the Minitab (version 20) platform to estimate the coefficients of the model. The resulting model is expressed as follows:  
\[\text{y}=50.701-0.166\text{x}_{1}+0.866\text{x}_{2}+0.00006\text{x}_{1}^{2}+0. 01206\text{x}_{2}^{2}+0.0604\text{x}_{1}\text{x}_{2}+\varepsilon \tag{2}\]  
The F and \(p\) values were calculated to evaluate the significance of the model and its coefficients. Typically, model terms with _p_-values below 0.05 are significant, while those above 0.05 are not. The coefficient of determination, R\({}^{2}\), measures how well the quadratic polynomial equation fits the data, with values closer to 1 indicating a better fit. Model testing (Tables 4 and 5) revealed a \(p\) value of less than 0.001, confirming that the quadratic polynomial model effectively describes the relationship between FA and QZ content and 28-day compressive strength. The R\({}^{2}\) value of 99.08% further confirms the model's good fit to the data. In the analysis of the various terms in the model, the linear term of QZ and the interaction term between FA and QZ are found to be significant (0.001 for QZ, 0.001 for FA\(\times\)QZ). This indicates that the dosage of QZ and the interaction between FA and QZ have a significant impact on the 28-day compressive strength of concrete. However, the linear and quadratic terms of FA are not statistically significant in the model (0.198 for FA, 0.992 for FA\({}^{2}\)). This suggests that within a substitution range of 20%, the substitution of cement with FA has little effect on the 28-day compressive strength of concrete. In other words, within this dosage range, the incorporation of FA does not significantly promote or inhibit strength development. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Group** & **C100** & **SFA** & **10 FA** & **15 FA** & **20 FA** & **5 QZ** & **10 QZ** & **15 QZ** & **20 QZ** & **5 FA5 QZ** & **10 FA10 QZ** \\ \hline Strength & 50.6 & 49.8 & 48.8 & 48 & 47.6 & 46.8 & 43 & 40 & 38.5 & 48.3 & 47.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Compressive strength results.