esa-sceva/satcom-chunk-collection huggingface dataset:
# Functional Data Analysis in Sport Science: Example of Swimmers' Progression Curves Clustering
## 1 Introduction
### Functional Data Analysis (FDA)
Coefficients are slightly longer to compute, but this basis has really good mathematical properties and progressively replaces Fourier basis in several applications [29; 30]. For non periodic data, the classical choice is spline basis, particularly the cubic splines in practice [6]. B-splines are piecewise polynomial functions and require few coefficients to define a good approximation, which make B-splines especially adequate when observations are sparse on the time domain [31]. They allow for approximating a wide range of shapes with a rather good smoothness [30]. From a computational point of view, one can use the \(R\) package _fda_, on whichone can find methods to fit observations into functional data, and way more tools for FDA. An overview of the _fda_ package can be found in [30]. Once the data set is approximated by functions, one may perform analysis on them, and some classical statistical tools have been extended in the functional context. One of the first and most important adapted methods was the functional principal component analysis (FPCA). Although slightly different, FPCA provides analogous information as the finite dimensional version [27]. This method allows for describing data into a non correlated low dimensional space. This is why it provides an excellent explanatory tool to visualize main features of the data as well as a way to reduce the number of informative dimensions. This can be particularly useful when one wants to apply algorithms on the vector \(\{\alpha_{1},\ldots,\alpha_{N}\}\) of coefficients of the basis expansion, with N rather large. It may accelerate calculation while retaining most of the information as well as avoiding the curse of dimension in a big data context. We may also cite several methods presented in [27] such as _functional canonical correlation_, _discriminant analysis_ and _functional linear models_. The purpose of this paper is twofold: at first, it aims at providing a brief review of several methods and references for the theoretical aspects. Secondly, examples of practical tools and useful packages (on the software \(R\) as it is currently the most convenient to perform FDA) of curve clustering state-of-the-art methods are presented. Then, we also detail a specific study on a real data set, coming from our collaboration with the French Swimming Federation. This work focuses on the clustering of performance progression curves of young male swimmers and uses several FDA tools. We emphasise the fact that FDA provides some tools that give information we could not exhibit otherwise, like the study of derivatives for example.
## 1 Introduction  
In autonomous driving systems, understanding the vehicle's surroundings and free-space information [1, 2] is essential in driving the vehicle safely to its destination. Thus, it is crucial to have predefined free-space maps which do not include dynamic objects (i.e., cars, pedestrians, and bicycles) [3]. Semantic segmentation is one way to draw free-space maps, which have become widely used due to an improved performance with advances in fully convolutional neural network (CNN) approaches [4, 5, 6, 7].  
However, it is challenging to obtain a fully free-space segmentation map directly from the real world, where dynamic objects are ubiquitous. As illustrated in Figure 1, the static map contains only static objects, and the dynamic map includes both static and dynamic objects. Therefore, an unsupervised method of deleting dynamic objects from the segmentation map and treating them as static objects is required to obtain a free-space map from the real world (Figure 2).  
In this paper, we propose an unsupervised semantic segmentation inpainting method that replaces dynamic segmentation labels with static labels in situations where the data are unpaired. We employ a generative adversarial network (GAN) [8] for the unsupervised semantic segmentation inpainting and introduce a preprocessing method and loss function to solve the instability of the GAN on the segmentation map. Our main contributions are summarized as follows:  
* We introduce a preprocessing method that matches the maxima of the distributions of the predicted free-map and ground-truth map. This prevents the discriminator from learning the probability distribution and rather focuses on the segmentation inpainting, allowing the generator to learn how to inpaint the masked part.  
* We propose a new cross-entropy total variation loss term to facilitate the generator network to smooth the nearby static segmentation. The proposed term is a denoising loss function for binary data, which removes the noise caused by the instability of the unsupervised learning process.
* The experimental results on the Media Integration and Communication Center Semantic Road Inpainting (MICC-SRI) [9] and Cityscapes [10] datasets validate the effectiveness of the proposed unsupervised semantic segmentation inpainting framework.  
Section 2 introduces the related works of inpainting on an image and segmentation map. Section 3 describes the proposed unsupervised segmentation inpainting method. Section 4 introduces the MICC-SRI and Cityscapes datasets, experimental settings, and the outcomes of the proposed framework. Lastly, Section 5 summarizes the overall research of this paper.
## 4 Integrated Positioning
### Integrated Between-Platform Model  
By combining between-platform observables in Equation (24) and platform array observables in Equation (9), the functional and stochastic models of the integrated system read:  
\[\mathrm{E}\left(\mathcal{Y}\right) = AZ+G\mathcal{R}B_{0} \tag{26}\] \[\mathrm{D}\left(\mathrm{vec}(\mathcal{Y})\right) = P\otimes Q_{yy} \tag{27}\]  
where \(\mathcal{Y}=[Y^{1}\quad Y^{2}\quad y^{12}]\) is the combined observation matrix, \(\mathcal{R}=[R^{1}\quad R^{2}\quad b^{12}]\in\mathbb{R}^{3\times(q_{1}+q_{2}+ 1)}\) is the combined rotation matrices and between-platform baseline, \(\mathcal{Z}=[Z^{1}\quad Z^{2}\quad z^{12}]\in\mathbb{Z}^{fm\times n_{t}}\) is the combined ambiguity matrix, \(B_{0}=\mathrm{blockdiag}\left(B_{0}^{1},\ B_{0}^{2},\ 1\right)\) is the combined local geometry matrix and:  
\[P = \left[\begin{array}{ccc}P_{n_{1}}&0&\frac{1}{2}e_{n_{1}}\\ 0&P_{n_{2}}&-\frac{1}{2}e_{n_{2}}\\ \frac{1}{2}e_{n_{1}}^{T}&-\frac{1}{2}e_{n_{2}}^{T}&1\end{array}\right] \tag{28}\]  
is the combined correlation matrix with \(n_{t}=n_{1}+n_{2}+1\). The above system consists of attitude models of both platforms with unknowns \(Z^{k}\) and \(R^{k}\) and the between-platform baseline model with unknowns \(z^{12}\) and \(b^{12}\). Even though these three subsystems do not have any parameter in common, they are correlated as in Equation (28), due to the use of common observations from pivot antennas.
# Acoustic Characterization of Some Steel Industry Waste Materials
## 2 Materials and Methods
### JCA Model and Inverse Method  
The JCA model assumes that rigid-frame open-cell porous media can be seen as an equivalent fluid of effective, or equivalent, dynamic density \(\rho_{eq}\) and equivalent dynamic bulk modulus \(K_{eq}\). These equivalent properties depend on five transport (or macroscopic-non-acoustic) parameters: open porosity \(\Phi\); static airflow resistivity \(\sigma\); tortuosity \(\alpha_{\infty}\); viscous characteristic length \(\Lambda\); and thermal characteristic length \(\Lambda^{\prime}\). These parameters are referred to the geometry of the porous material and describe the complexity of the porous network. By definition, open porosity \(\Phi\) is a measure of the volume fraction of air (\(V_{fluid}\)) in the total volume (\(V_{tot}\)) or the complement to unit of the ratio between the solid volume of the frame (\(V_{solid}\)) on the total volume [42]:  
\[\Phi=\frac{V_{fluid}}{V_{tot}}=1-\frac{V_{solid}}{V_{tot}} \tag{1}\]  
Airflow resistivity expresses the resistance opposed to the airflow while passing through the material. It can be calculated as [42]  
\[\sigma=\frac{\Delta p}{v_{airflow}d}\qquad\left[\mathrm{Ns/m^{4}}\right] \tag{2}\]  
with \(\Delta p\) as the pressure drop across the medium, \(v_{airflow}\) the amount of airflow passing through the material and \(d\) its thickness. Tortuosity \(\alpha_{\infty}\) is an intrinsic property of the porous frame, related to the microgeometry of the interlinked cavities. It is a dimensionless quantity that expresses the tortuous fluid paths through the porous material. It can be calculated as [42]  
\[\alpha_{\infty}=\frac{1}{V}\int_{V}v^{2}\;dV/\left|\frac{1}{V}\int_{V}v\;dV\right| ^{2} \tag{3}\]  
where \(v\) is the microscopic velocity of an ideal inviscid fluid within the pores and \(V\) a homogenization volume that expresses the volume of free fluid contained in the cavities. Tortuosity cannot be lower than \(1\). Viscous characteristic length \(\Lambda\) is used to describe the viscous forces generating within the cavities at high frequencies and is related to the characteristic dimension of the connection between pores--particularly to the mean diameter of the hole connecting two adjacent cells, expressed in micrometers. It is given by [42]  
\[\Lambda=2\int_{V}|v|^{2}dV/\int_{S}|v|^{2}dS\;\;\;\;\;\;[\upmu\mathrm{m}] \tag{4}\]  
where \(S\) is the specific surface that denotes the total contact surface between the frame and the pores.
# Synthesis of Silicon Carbide Powders from Methyl-Modified Silica Aerogels
###### Abstract  
\(\beta\)-silicon carbide (SiC) powders were synthesized by the carbothermal reduction of methyl-modified silica aerogel/carbon mixtures. The correlations between the phase evolution and morphologies of the SiC powders and the C/SiO\({}_{2}\) ratio were investigated. At a C/SiO\({}_{2}\) ratio of 3, \(\beta\)-SiC formed at 1425 \({}^{\circ}\)C and single-phase SiC powders were obtained at 1525 \({}^{\circ}\)C. The methyl groups (-CH\({}_{3}\)) on the silica aerogel surfaces played important roles in the formation of SiC during the carbothermal reduction. SiC could be synthesized from the silica aerogel/carbon mixtures under lower temperature and C/SiO\({}_{2}\) ratios than those needed for quartz or hydrophilic silica. The morphology of the SiC powder depended on the C/SiO\({}_{2}\) ratio. A low C/SiO\({}_{2}\) ratio resulted in \(\beta\)-SiC powder with spherical morphology, while agglomerates consisting of fine SiC particles were obtained at the C/SiO\({}_{2}\) ratio of 3. High-purity SiC powder (99.95%) could be obtained with C/SiO\({}_{2}\) = 0.5 and 3 at 1525 \({}^{\circ}\)C for 5 h.  
silicon carbide (SiC); silica (SiO\({}_{2}\)); aerogel; surface methyl (-CH\({}_{3}\)) group; carbothermal reduction
## 2 Materials and Methods  
### Sampling during Standard Dental Treatment  
The study was performed by sampling hospital surfaces during patient visits to the National University Centre for Oral Health Singapore (NUCOHS) by evaluating aerosols and splatters produced during ultrasonic scaling. Ethics approval was obtained from the Domain Specific Review Board, National Healthcare Group (NHG DSRB Ref.: 2021/00059). The sample size was calculated based on the study objective to compare the contamination pattern between the experimental group and the control group. With a Cohen's effect size of 1.2 between the experimental group (D-DART) and the control group, a total of 32 subjects (16 subjects per group) was required for the study at 5% statistical significance and 90% statistical power. Informed consent was obtained from all patients. The effect size was selected according to the following reasons: (i) preliminary in vitro data showed promising results that the D-DART was able to contain the aerosols with a large difference compared with the Control; (ii) vaccines were not yet widely available at the time this study was initiated; and (iii) the efficacy of rapid antigen test was not ascertained, and polymerase chain reaction (PCR) testing was prioritized for suspected COVID-19-positive cases at the time this study was initiated. Hence, to avoid unnecessary exposure, risks of clinicians and staff to get infected, and the potential spreading COVID-19 in the population, we opted for an effect size of 1.2.  
The exclusion criteria were patients below the legal age of consent or who were unable to give consent; pregnant females; patients who had received antibiotics within 1 month of the time of treatment; and patients with known transmissible infectious diseases. As a pre-treatment standard procedure, patients rinsed their mouths with 10 mL of 0.05%  
Figure 1: (**A**) D-DART design. (**B**) Dental suction cannula attachment to the extraction valve (red arrow). (**C**) Dental aerosols are directed to the extraction valve (green arrows). (**D**) The distances from the back and lateral panels allow for ample movement of the instruments.  
cetylpyridinium chloride for 1 min. The treatment was performed with only the dentist, patient, and dental assistant present in the room.  
Deep ultrasonic scaling was performed on 32 patients randomized 1:1 and receiving the treatment with the D-DART (experimental group) or without (control group). A single operator performed the scaling using a piezoelectric ultrasonic scaler (SiroSonic TL or Dentsply Sirona, Charlotte, NC, USA). Dental unit water was used concurrent with the ultrasonic instruments to achieve cooling. Evacuation was performed by a dental assistant during the treatment using a dental suction (canula diameter 6 mm) and high-volume evacuator (HVE; canula diameter 16 mm) with a suction flow of 300 L/min.
# Concentration Influence of Complexing Agent on Electrodeposited Zn-Ni Alloy
## 4 Conclusions  
This study aimed to investigate the impact of TEPA concentration on electrodeposited Zn-Ni alloy coatings. The results indicate that an increase in TEPA concentration from 0.035 M to 0.1 M led to the improved surface gloss of coatings at low current densities, as well as an increase in the crystallization of Zn-Ni alloy coating at low current density. While the crystallite size decreased with increasing TEPA concentration, the increase in TEPA concentration resulted in a coarser morphology of the Zn-Ni coating. Additionally, the increase in TEPA concentration led to a decrease in the thickness of coatings from approximately 10 \(\upmu\)m to lower than 5 \(\upmu\)m at a current density higher than 2 ASD. On the other hand, the variation in Ni content was proportional to the change in TEPA concentration, and the content of Ni could reach up to 20% at 8 ASD and TEPA concentration higher than 0.035 M. Notably, the study found that TEPA concentration did not influence the electrochemical behavior of Zn-Ni alloy deposition. In conclusion, this study highlights the vital role of TEPA concentration in controlling the properties of Zn-Ni alloy coatings. The findings have important implications for the development of high-performance coatings in a range of industrial applications. Further research is needed to explore the underlying mechanisms of the observed effects and identify strategies for further improving the properties of Zn-Ni alloy coatings.  
Conceptualization, J.-W.C.; data curation, J.-W.C.; formal analysis, J.-W.C.; funding acquisition, B.-K.S.; investigation, J.-W.C.; methodology, J.-W.C.; project administration, S.-B.J., J.-W.C. and I.-J.S.; supervision, B.-K.S. and I.-J.S.; validation, B.-K.S., J.-W.C. and I.-J.S.; visualization, I.-J.S.; writing--original draft, J.-W.C.; writing--review and editing, B.-K.S. and J.-W.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors confirm that the data supporting the findings of this study are available within the article.  
The authors declare no conflict of interest.
# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
## 1 Introduction
However, in order for this localization approach to work, all Evolved Node Bs (eNBs) need to be synchronized, which is a complex and expensive task for operators. Therefore, the OTDoA feature has not yet been implemented in many countries. On top of that, an OTDoA-capable UE requires the presence of highly accurate clocks, which is often not the case in low-cost IoT devices. Thus, there is a need for more energy-efficient, low-complexity and low-cost localization methods in NB-IoT. In previous research, we performed the first real-world Received Signal Strength (RSS)-based outdoor localization experiments with NB-IoT [3]. It was found that only a single eNB antenna could be used to localize an MT. This is in contrast to Sigfox and LoRaWAN, where multiple nearby gateways receive the message from the MT. In this paper, we extend our work with the following contributions. Firstly, we elaborate on the accuracy of RSS-based localization algorithms using a single NB-IoT cell. Secondly, we address the mobility issues of NB-IoT in a localization context. More specifically, we investigate the reason why only the serving cell can be used to locate a transmitter and we evaluate the cell sector reliability, as well as the cell reselection time. The remainder of this paper is structured as follows. Section 2 provides an overview of recent outdoor localization studies using NB-IoT. In Section 3, the data sets used for both localization and cell reselection evaluation are described. Next, three optimized RSS-based localization algorithms are proposed in Section 4, namely proximity, range-based and fingerprint-based localization. Section 5 shows the setup to evaluate the cell reselection in NB-IoT. The results in terms of localization accuracy and mobility performance are shown in Section 6. Finally, the main observations regarding localization accuracy and mobility of NB-IoT are discussed in Section 7 and summarized in Section 8.
# Power Grid Reliability Evaluation Considering Wind Farm Cyber Security and Ramping Events
## 6 Case Studies and Results
### Reliability Assessment Considering Worst-Case Attack  
In the worst-case analysis, a sequential Monte Carlo simulation was conducted to estimate the system reliability indices. The load power contained the seasonal daily and hourly feature of a year. For the WPRs, the absolute value of ramping rate ranged from 133 to 2282 MW/h. The mean values  
Figure 4: The flow of estimating the time-varying failure rates of wind turbines (WTs).  
of the upward and downward ramping rates were 536.5 and \(-\)494.8 MW/h, respectively. The mean ramping duration was 0.24 h. The limitation of the ramping rate in the wind farm was set as the 5% of the wind farm power capacity per minute. It was noted that when the penetration rate changed, the conventional power capacity was adjusted in proportion to keep the total install power capacity of the RTS79 system on 3405 MW.
## Results and Discussion
### Ecological Profiles of the Different Areas: Cluster Analysis
_Corynebacterium_ spp., present in the sump in the deboning room and tools storage cabinet, also has a global distribution and is found in soils, water, animals, and plants, especially in temperate areas [40]. The less common _Leifsonia aquatica_, isolated from slicer B and the tool storage cabinet (Figure 3), could come from the water since it inhabits the aquatic environment. It is characterized by having a low growth rate and the capacity to form biofilms [41]. In this second cluster, areas 6 (work room floor), 7 (cured meat carts cleaning room floor), and 10 (Iberian sausage transportation carts) were mostly colonized by _Bacillus_ spp. (Figure 4). This genus includes many of the most ubiquitous bacteria [42]. These areas are physically close and the same personnel and equipment, such as the transportation carts, raw material, and fresh products, circulate there. The fresh meat is processed in the work room and it is transported by the carts, so the same residues can be found in the work room as in the fresh meat carts cleaning room floor. Microbial communities are representative of each processing area and are influenced to a large extent by food debris, liquid effluents, and wash water [25]. _R. radiobacter_, isolated in the three areas that comprise this cluster, is also a ubiquitous bacterium and is usually found in soil, plants, etc. [43]. _Serratia_ spp., another common microorganism in the area between the work room floor and the sink, has also been isolated from industries that manufacture sausages [44]. _S. liquefaciens_ is one of the dominant enterobacteria in raw meat working plants. Another species of the genus _Serratia_ spp. that is sometimes identified is _S. marcescens_[44]. The results obtained in the present study are in concordance with these findings, as both species were found on the surfaces in Plant B. _Enterobacter_ spp., belonging to the family _Enterobacteriaceae_ and widely distributed in nature, were found between the floor of the fresh meat cart cleaning room and the Iberian sausage transportation carts. They can be found in soil, water, and as part of the microbiota of animals, insects, and the human gastrointestinal tract [45]. Clusters 3 and 4 correspond to areas 8 (cured meat carts cleaning room floor) and 2 (slicer A), respectively. The analysis by hierarchical clusters determined that these two profiles were distant from the other two main clusters (1 and 2). This could be explained by the presence of non-isolated species such as _Staphylococcus_ spp., _Stenotrophomonas maltophilis_ in the other areas, and the strong presence of _M. haemolytica_ in area 8 (cured meat carts cleaning room floor) (Figure 5). The atypical profile of location (8) could be explained by the fact that the carts transport a different raw material. They do not displace fresh products, but rather genera that have undergone a process of healing or maturation in which the microbiota is modified [35]. In addition, this room is on the upper floor of Plant B, unlike almost all the other areas where sensors were located (except for slicer B).
_Appl. Sci._**2012**, \(2\), 303-314; doi:10.3390/app2020303  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Effects of Superparamagnetic Nanoparticle Clusters on the Polymerase Chain Reaction**  
**[PERSON] *, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**  
Bio-Nano Electronics Research Centre, Toyo University, 2100 Kujirai, Kawagoe, Saitama 350-8585, Japan; E-Mails: [EMAIL_ADDRESS] (H.M.); [PERSON]ADDRESS] (Y.N.);  
[EMAIL_ADDRESS] (T.F.); [PERSON]_ADDRESS] (A.E.); [EMAIL_ADDRESS] (R.U.); [PERSON]ADDRESS] (T.M.);  
[PERSON]DDRESS] (T.H.).  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +81-49-239-1375; Fax: +81-49-234-2502.  
_Received: 16 February 2012; in revised form: 19 March 2012 / Accepted: 23 March 2012 / Published: 2 April 2012_  
**Abstract:** The polymerase chain reaction (PCR) method is widely used for the reproduction and amplification of specific DNA segments, and a novel PCR method using nanomaterials such as gold nanoparticles has recently been reported. This paper reports on the effects of superparamagnetic nanoparticles on PCR amplification without an external magnetic field, and clarifies the mechanism behind the effects of superparamagnetic particle clusters on PCR efficiency by estimating the structures of such clusters in PCR. It was found that superparamagnetic nanoparticles tend to inhibit PCR amplification depending on the structure of the magnetic nanoparticle clusters. The paper also clarifies that Taq polymerase is captured in the spaces formed among magnetic nanoparticle clusters, and that it is captured more efficiently as a result of their motion from heat treatment in PCR thermal cycles. Consequently, Taq polymerase that should be used in PCR is reduced in the PCR solution. These outcomes will be applied to novel PCR techniques using magnetic particles in an external magnetic field.  
**Keywords:** magnetic nanoparticle; superparamagnetic; DNA; polymerase chain reaction; Taq polymerase