esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Fundamental Frequencies in Axially Compressed Rotating Laminated Cylindrical Shells
###### Abstract  
Optimizing composite materials, particularly in rotating structures, offers several practical benefits in the mechanical engineering and aerospace engineering industries. Improved material configurations, such as optimal fiber orientations, enhance the structural performance by maximizing stiffness-to-weight ratios and reducing vibrations. This study optimized the fundamental frequencies of rotating laminated cylindrical shells using the golden section method with respect to fiber orientations. The investigation explored the impact of various factors such as end conditions, shell length, axial compressive force, rotating speed, and the size of the cutout on the maximum fundamental frequencies. Additionally, the associated vibration modes and optimal fiber orientations were demonstrated in relation to these influencing parameters. Generally, it was observed that the optimal frequency decreased with increasing length-to-radius ratio and compressive force.  
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Footnote †: journal: Applied sciences  
program [29]. The material constitutive equations for fiber-reinforced lamina [22; 30; 31; 32] and the principles of the golden section method [5; 22; 26; 28] are well-established in textbooks and research articles; therefore, they are not included in this paper. Instead, the focus is on a brief review of the vibration analysis. The paper then discusses the effects of end conditions, rotational speed, axial compressive force, the length-to-radius ratio, and cutouts on the maximum fundamental natural frequency, optimal fiber orientations, and vibration modes of the laminated cylindrical shells, concluding with significant findings.
# Melody Extraction Using Chroma-Level Note Tracking and Pitch Mapping
## 2 Chroma-Level Note Tracking and Pitch Mapping-Based Melody Extraction
### Voicing Detection  
A preliminary continuous melody is achieved by the chroma-level note tracking and pitch mapping as described in Sections 2.1-2.3. However, melody is not always present in polyphonic music. Voicing detection determines whether the estimated notes belong to melody by the note salience values.  
If the salience value of one note is greater than \(\gamma\overline{S}\), the note frequencies after fine tuning are kept as the melodic pitches among the note duration, otherwise the note is removed and zero frequencies are assigned among the note duration to indicate that the melody is not present.  
The presence of melody over each note duration is determined by the following criterion  
\[v(n_{i})=\left\{\begin{array}{ll}1,&S(n_{i})\ >\ \gamma\overline{S}\\ 0,&\text{otherwise}\end{array}\right. \tag{17}\]  
where \(\gamma\) is the threshold, and \(S(n_{i})\) is the averaged salience of note \(n_{i}\), i.e.,  
\[S(n_{i})=\frac{1}{t_{off}-t_{on}+1}\sum_{t=t_{on}}^{t_{off}}s(f_{n_{i}},t) \tag{18}\]  
where \(t_{on}\) and \(t_{off}\) are the first and last frame indices of note \(n_{i}\), respectively.  
The \(\overline{S}\) in Equation (17) is the average salience of all notes, i.e.,  
\[\overline{S}=\frac{1}{I}\sum_{i=1}^{I}S(n_{i}) \tag{19}\]  
where \(I\) is the number of notes.
## 4 Model Design
### Linear Predictor with Adaptive Time Stepping  
Choosing the appropriate time-stepping size when utilizing a fixed-size LMS linear predictor for traffic load prediction can prove challenging. A larger step size may result in quicker convergence and shorter response time to traffic load changes, but such an approach could result in substantial weight fluctuations in the predictor following convergence. On the contrary, a smaller step size yields slower convergence but a more stable predictor in terms of weight fluctuations. However, due to the dynamic of traffic load changes in 6G satellite networks, fixed-step-size methods are often unable to deliver favorable predictive  
Figure 3: Relationship between computational time step and wave definition.  
performance consistently. An Adaptive Time stepping (ATS) LMS linear predictor is, therefore, a more suitable approach for such applications.  
The ATS-LMS adaptive predictor adjusts its step size in real time, based on the current squared prediction error. Typically, a larger squared error value results in an increase in the step size, providing faster tracking, while a smaller squared error value leads to a decrease in the step size, resulting a in smaller misadjustment.
## 4 Measurement Results
### ML Model 2  
Figure 26a shows that the accuracy of this model is better than that of the previous one for the same participants. Since an accuracy of 74.7% is obtained for P5, it can be inferred that for certain participants, the model's accuracy can achieve rates of about 75%. The precision and sensitivity in Figure 26b also show that Model 2 is much more reliable than Model 1.  
Figure 25: Precision and Sensitivity of ML Model 1 for Participants 1–11.  
**Intersubject classification**  
Figure 27 shows the precision and sensitivity of participants 1 to 11. Even though the sensitivity for the RV pair is quite low at 28.5%, the intersubject classification of model 2 is better than that of model 1. The accuracy achieved is 57.88%.  
Since Model 2 has shown improved performance for both intrasubject and intersubject classification compared to Model 1, it suggests that the explanations for Model 1's low performance are valid to some extent. One possible reason for the lower scores in Model 1 could be the binary classification approach used in the CSP method. Figure 28 presents a segment of the framework depicted in Figure 20 for easier reference. The three scalogram pairs--FW_GP, FW_RL, and FW_RV--are all distinct because CSP ensures that only a pair of two classes is distinguishable from each other. In the FW_GP pair, CSP maximizes the distinguishability between FW and GP, and the same is done for the other pairs. This results in the two FW groups from FW_GP and FW_RL being different, as CSP ensures that FW is distinct from GP in the first pair and distinct from RL in the second pair.  
Figure 26: (**a**) Accuracy of ML Model 1. (**b**) Precision and sensitivity of ML Model 2.  
Figure 27: Precision and Sensitivity of ML Model 2 for Participants 1–11.  
The distinctions made by CSP in binary classification can lead to issues. For instance, FW from the FW_GP pair might exhibit similar characteristics to RL from the FW_RL pair. This problem can extend to all scalogram pairs used for creating the training and test sets. Since this issue arises from the binary classification nature of CSP, it is necessary to adopt a method capable of multiclass classification. Consequently, a third model is implemented using the CSP-OVR method, which is designed for multiclass classification.
## 4 Application to Power Electronics
### The Choice of Parameters for the Simulation  
In the context of the dynamic simulation process, it is imperative to judiciously select the initial step size to prevent numerical complications during the simulation. In this instance, a notably small value, such as \(10^{-9}\), is required, because the aircraft power channel has fast commutation frequencies.  
Furthermore, the number of points in the frequency spectrum must be meticulously determined to ensure an accurate evaluation of the FFT, according to the Nyquist-Shannon theorem. As the last harmonic needs to occur at 15 kHz, the FFT has to be carried out based on at least 30 000 points, in order to respect the theorem. The simulation step size is automatically adapted to reach each sampling point.  
Figure 13: Implementation of the aircraft power channel model in NoLOAD.
# Role of Blockchain Technology in Combating COVID-19 Crisis
## 5 Blockchain-COVID 19 Ongoing Research
### Controlling Disease from Expanding Using Anonymous Contact-Tracing-VIRI Platform  
Restriction of the spreading of transmissible infections, such as COVID-19, is one of the objectives of digital contact tracing. By identifying infection hotspots and locating infected people, user information secrecy is affected. The main technical variations of the contact tracing resolutions and the platforms, as mentioned earlier, can change the flexibility and efficiency of digital contact tracing's proposed technologies. Among these, the cross-entity VIRI's platform has proposed a universal platform to solve such research problems widely. It also guarantees to conserve the user's data privacy.  
VIRI was designed to trace the scope of the disease in different regions, which can effectively handle locating the growth of COVID-19 infection at several spots. Whenever a user has had close contact with a COVID-19 positive-tested person, it informs him/her by pushing a notification. The system can warn the user regarding dangerous illnesses in the following step according to the hazard scale. For example, when a user has had direct contact with an infected case, VIRI can modify the state of a user of a \"cleared case attribute\" to a \"likely COVID-19 case\".  
By open-source APIs, there is an option of easily integrating the platform with already existing enterprise technologies. Furthermore, VIRI confirms that the individual has full authority over their information, and it is saved on their device anonymously using blockchain technology by benefiting from its privacy-preserving characteristics. Such can support AI-based means to predict the COVID-19 pandemic globally [55,56].
## 4 Related Work  
### Base Station Control Communication Security  
The base station provides connectivity to many users utilizing multiple-antenna/MIMO and channelization/multiplexing technologies (where the channels coexist but are separate in frequency, time, code, and/or space-direction) and the medium access control (MAC) protocol (which sets up and synchronizes such wireless channel resources to use for data communications across multiple users). Previous research secured the integrity of the MAC and control communications for the availability of data channel resources, which is especially important because the DoS on the flying base station's channel resources can disrupt and disable the channel connectivity to the other legitimate users. Such research includes securing MAC protocol and radio-control handshaking in the dynamic and sophisticated dynamic spectrum environment against the channel control against insider, credential-compromising, and dynamic jamming [17; 18; 19], against MAC injection and handshaking-manipulating threats [20; 21], and against the threats on channel-state-information (CSI) feedback [22; 23]. While these research use models are abstracted from the 5G protocol details and generally applicable to wireless communications, the threats apply to the concrete protocols of 5G NR RRC protocol standardized by 3 GPP [24; 25; 26]. However, the research solutions for securing the specific 5G RRC protocol between the user and the base station have been lacking; rather, the 5G security research has focused on incorporating and implementing security on the backend core network beyond the base station, e.g., [27; 28; 29; 30].
## 2 Case Study Location and Building Data  
### Description of the Case Study Location  
The case study corresponds to an ambulatory medical center for ophthalmological care located in the historical center of San Francisco de Campeche, Mexico (19\({}^{\circ}\)50\({}^{\prime}\)41\({}^{\prime\prime}\) N, 90\({}^{\circ}\)32\({}^{\prime}\)23\({}^{\prime\prime}\) W). The city's zone is a completely urbanized area (with practically no natural vegetation) comprised of masonry buildings and concrete streets, which has fostered the heat island phenomenon. According to the Koppen climate classification, the city is characterized by a hot-humid climate with rains in summer, and an average monthly temperature between 22 \({}^{\circ}\)C and 31 \({}^{\circ}\)C, with May being the hottest month and January the coldest [21]. The average solar radiation in the city is over 16.2 MJ/m\({}^{2}\)-day, reaching values higher than 18 MJ/m\({}^{2}\)-day at summer. The region is also characterized by a high percentage of relative humidity throughout the year, between 70% and 98% [21]. Figure 1 illustrates the perceived heat index in the city's historical center, where the blue dots indicate the ambient temperatures recorded for 2022. More than 60% of the year, the buildings in the region are within the margin of caution, which increases dangerous heat and extreme danger indices the closer one is to the summer. In addition, it is essential to emphasize that non-operational hours (night hours) are mainly those where it is outside of this risk zone. All of them drastically impact the thermal comfort of this zone buildings' occupants and increase energy use to counteract it. Therefore, the analysis of the building is critical because it forms part of a compendium of facilities that currently perform functions for which and are susceptible to thermal restorations.
## 3 Materials and Methods
### Algal Growth  
Microalga _Nannochloropsis gaditana_ (CCAP 849/5) was obtained from the Scottish Association for Marine Science. Its cultivation occurred via a modified F/2 [17] made in artificial seawater (6.3 mM KCl, 2.0 mM NaHCO\({}_{3}\), 7.1 mM KBr, 0.36 mM H\({}_{3}\)BO\({}_{3}\), 0.024 M Na\({}_{2}\)SO\({}_{4}\), 9 mM CaCl\({}_{2}\)\({}_{2}\)H\({}_{2}\)O, 0.046 M MgCl\({}_{2}\)\(\cdot\)6H\({}_{2}\)O, 0.35 M NaCl) with the following composition: 3.5 mM NaNO\({}_{3}\), 0.036 mM NaH\({}_{2}\)PO\({}_{4}\)\(\cdot\)H\({}_{2}\)O, 0.12 \(\upmu\)M FeCl\({}_{3}\)\(\cdot\)6H\({}_{2}\)O, 0.12 \(\upmu\)M Na\({}_{2}\)EDTA, 0.04 \(\upmu\)M CuSO\({}_{4}\)\(\cdot\)5H\({}_{2}\)O, 0.076 \(\upmu\)M ZnSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O, 0.042 \(\upmu\)M CoCl\({}_{2}\)\(\cdot\)6H\({}_{2}\)O, 0.91 \(\upmu\)M MnCl\({}_{2}\)\(\cdot\)4H\({}_{2}\)O, 0.025 \(\upmu\)M Na\({}_{2}\)MoO\({}_{4}\)\(\cdot\)2H\({}_{2}\)O. Before each cultivation, the culture medium, all the tubes and the filters were autoclave sterilized while the reactors were sterilized in an oven (TIPE) at 170 \({}^{\circ}\)C for 1 h. Cellular concentration was monitored daily by reading the absorbance at 750 nm (Agilent Cary 60 UV-Vis) inside 1.5 mL cuvettes and the following calibration curve was experimentally obtained from 16 data points with concentrations between 0.05 g L\({}^{-1}\) and 1 g L\({}^{-1}\):  
\[X[g\,L^{-1}]=0.2198\times abs_{750} \tag{11}\]  
Light intensity was measured by a photometer (HD 9021, Delta OHM, Padua, Italy) with a photosynthetic active radiation (PAR) probe (LP 9021 PAR, Delta OHM, Padua, Italy).  
In order to obtain the kinetic parameters of the Monod equation, \(\mu_{max}\) and \(I_{K}\), batch cultivations were made inside the quasi-isoactinic flat photobioreactor for 10 days at a fixed temperature (25 \({}^{\circ}\)C) and incident light intensities \(I_{0}\) of 20, 50, 100, 140, 210, 300 and 450 \(\upmu\)E s\({}^{-1}\)m\({}^{-2}\).  
Model results were then compared with experimental growth curves obtained for 13 days in the annular column PBR and in the two flat-plate PBRs, at an incident light intensity \(I_{0}\) = 210 \(\upmu\)E s\({}^{-1}\)m\({}^{-2}\). The other culture conditions (temperature and air flow) were the same as those in the cultivation for the kinetic parameters.
# Experimental Investigation of Clearance Influences on Cage Motion and Wear in Ball Bearings
## 2 Experimental Approach  
A bearing test rig was developed to measure the motions and wear, in which the cage was specifically redesigned with diverse clearances including the pocket and guiding ones. The pocket clearance is the radius differences between cage pocket and ball, can be expressed as \(C_{p}=0.5(D_{p}-D_{b})\), as shown in Figure 1. Additionally, guiding clearance as the radius differences between the two guiding surfaces of cage and inner ring can be expressed as \(C_{g}=0.5(D_{ci}-D_{i\bar{g}})\), as shown in Figure 1, where \(D_{p}\), \(D_{b}\), \(D_{ci}\), \(D_{i\bar{g}}\), \(B_{g}\) are the diameters of cage pocket and ball, the inner diameter of the cage, outer diameter of inner ring, and the width of cage, respectively. As a certain bearing, different clearances can be obtained by changing pocket and inner diameter of the cage.
## 2 Data and Methods
### Development of \(n_{p}\) Estimation Model
#### 2.5.3 Derivation of the PSD Slope, j, from TSM  
A close relationship between TSM and \(b_{\text{p}}(488)\) could be established with relatively high fitting accuracy (Figure 5A), which was also consistent with that demonstrated in the [PERSON] et al. [32] study. Meanwhile, very close relationships between \(b_{\text{p}}(\lambda)\) and \(c_{\text{p}}(\lambda)\) could also be found in our study area, as observed in previous studies [5; 35; 36; 37]. As shown in Figure 5B, their relationships could be modeled well, with an extremely high \(R^{2}\) of 0.998 (\(p<0.001\)). Such good relationships provide a stable basis for accurately deriving \(c_{\text{p}}(\lambda)\) at different wavelengths from TSM. According to Equations (2) and (3), we obtained the parameter \(j\), which was then used as an input of the model estimating \(n_{\text{p}}\) in this study.  
Figure 4: (**A**) Correlation coefficient spectra between \(R_{\text{rs}}(\lambda)\) and \(b_{\text{bp}}(\lambda)\) and \(\log_{10}(b_{\text{bp}}(\lambda))\) in the range of 400–700 nm; (**B**) correlation coefficients between \(b_{\text{bp}}(\lambda)\) and eight band combination forms (X1 to X8), which are the same as those in Figure 4. (**C**) Scatter plots of X7 (i.e., (\(R_{\text{rs}}(555)+R_{\text{rs}}(660)\))/(\(R_{\text{rs}}(555)\)/\(R_{\text{rs}}(660)\))) and \(b_{\text{bp}}(488)\) using a power function; the solid red lines are the fitted function curves, and the dotted red lines are the 95% confidence bounds.  
As a summary, Figure 6 shows the main processes of our developed model in the current study to estimate the bulk refractive index \(n_{\rm P}\) of particles by \(R_{\rm rs}(\lambda)\). A Mie-theory-based relationship model was essentially adopted to derive \(n_{\rm P}\) values from the so-called input parameters, namely, \(B_{\rm P}\) and \(j\) (Equation (1) by [PERSON] et al. (2001) [2]), considering the fact that there are currently hardly any methods that can be used to directly measure the particle bulk refraction. Although some empirical relationships were used in the framework of our model development, the proposed \(n_{\rm P}\) estimation model is a meaningful attempt to obtain satellite-derived \(n_{\rm P}\) records.  
Figure 5: Relationships (**A**) between TSM and \(b_{\rm p}\)(488), and (**B**) between \(b_{\rm p}\)(488) and \(c_{\rm p}\)(532) and \(c_{\rm p}\)(555), which were observed in January 2017. These close relationships all show a very high confidence level, with \(p<0.001\).  
Figure 6: Flow diagram of our developed \(n_{\rm P}\) estimation model that shows the steps of deriving \(n_{\rm P}\) from \(R_{\rm rs}(\lambda)\). The shown different colors in the legend refer to different processes, namely, inputs, calculations, models, and outputs.
## 4 Results and Discussion
### The SE of Textile and 3D-Printed Fabrics
Instead, the macroscopic and microscopic sample structure also plays an important role. This finding indicates that crocheted or three-dimensional textile fabrics, e.g., knitted fabrics, should be investigated further, ideally combining their relatively thick 3D structure with significantly reduced pore sizes. The results obtained from \(SE\) measurements (Figure 8) again show that there are two types of materials that have significantly different properties. The first set of samples includes the highly conductive silver and copper coatings on dense woven fabrics, for which the \(SE\) is about 82 dB and 74 dB, respectively. The second type is the open-pore, less conductive crocheted fabrics whose \(SE\) does not exceed 35 dB, but which still have a much higher EMI shielding effectiveness than the other--partly more conductive--samples under examination. Theoretically, the \(SE\) should increase with frequency. According to our measurements on textile fabrics, this value is almost constant (silver and copper coating on woven fabrics) or even decreases (magnetic yarn, magnetic+conductive yarn). Only for the conductive yarn, the \(SE\) increases slightly with frequency. The observed differences from the theory stem from the inhomogeneous nature of the woven materials and the electrical dimensions of their pores (physical dimensions related to the operating wavelength \(\lambda\)), which increase with frequency. For this reason, the \(SE\) of the fabric can decrease with frequency. The metalized woven fabrics show a high \(SE\) of around 80 dB, comparable to the value of 85 dB previously reported by another research group for a 4.5 mm thick carbon/polyamide 6 nonwoven sample [30]. Only for PPy-modified Mxene sheets coated on PET fabrics, a higher EMI shielding effectiveness of approximately 90 dB was found in a previous study [14], thus applying a much more complicated method to reach a high conductivity, as compared to using simple metallized, commercially available woven fabrics. The other \(SE\) values presented in the literature are significantly smaller, such as MXene-coated fabrics [31; 32], different copper- or nickel-coated fabrics [33; 34], carbon nanotube/graphene coatings [35], or coatings with conductive polymers [36; 37]. Thus, our investigations show the great potential of some readily available metalized fabrics for shielding purposes. The future experiments will aim to improve the washability of the examined textile fabrics to increase their longevity.
# Rock Emissivity Measurement for Infrared Thermography Engineering Geological Applications
## 2 IRT Background
With reference to rock applications, available and quickly usable values belong to rocks employed for decorative and construction purposes, such as fired clay (\(\varepsilon\) = 0.91), granite (\(\varepsilon\) = 0.77-0.97), gypsum (\(\varepsilon\) = 0.8-0.9), sand(\(\varepsilon\) = 0.6-0.9), sandstone (\(\varepsilon\) = 0.909-0.935), and soil (\(\varepsilon\) = 0.92-0.95 in dry and saturated condition, respectively) [20], thus highlighting the need of implementing such scientific casuistry, which is the aim of this paper. Figure 1: The electromagnetic spectrum with the range of occurrence of thermal radiation and the infrared band highlighted.