esa-sceva/satcom-chunk-collection huggingface dataset:
# Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals
## 5 Conclusions  
This article introduces a portable information system, consisting of a PPG signals recording device and web-based application for processing and mathematical analysis of PRV while using serverless architecture.  
The prospect of introducing PPG-based equipment is due to the following advantages: mobility (recording PPG signals is performed while the patient is working, traveling, sporting (except excessively fast movement, such as running; sports involving running or fast movement), resting, and in extreme situations); security (24-h patient monitoring is possible, and short messages (SMS) may be sent to the attending physician when a deviation is recorded in the patient's results); prevention (identifyingthe disease at an early stage before it has become a serious health problem); diagnostics (long-term follow-up allows accurate diagnosis); and, others.  
The preprocessing of the registered PPG signals involves the determination of the PP interval series to which linear and nonlinear methods for the data analysis are applied. The quantitative measurements of the studied parameters in the linear analysis have significant clinical application because the boundaries of norm-pathology are known. The nonlinear mathematical methods provide additional information on the formation of the parametric and the graphical assessment of patients' health status.  
The established information system can find application in modern clinical practice, offering an approach for long-term monitoring and investigation of changes in cardiac activity of the subjects studied.  
[PERSON] and [PERSON] conceived this study. [PERSON] and [PERSON] worked to create the PPG device. [PERSON] measured photoplethysmographic signals and created PPG extraction and processing software. [PERSON] and [PERSON] created a software system for mathematical analysis of photoplethysmographic and electrocardiographic data, performed the experiments and wrote the manuscript. Finally, [PERSON] reviewed the manuscript and contributed to the final version. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NATIONAL SCIENCE FUND OF BULGARIA (scientific project \"Investigation of the application of new mathematical methods for the analysis of cardiac data\"), Grant Number KP-06-N22/5, 07.12.2018.  
The authors declare no conflict of interest.
## 3 Experiments and Results
### Evaluation Criteria  
The performance of the proposed method was evaluated using four criteria, including the number of matched tie points (\(N_{\alpha p}\)), the used time for image registration (\(T\)), the accuracy of the transformation model obtained using the final tie points (\(RMSE_{M}\)), and the geometric accuracy of the registered image using some verification points (\(RMSE_{T}\)).  
The \(RMSE_{M}\) and \(RMSE_{T}\) are computed using Equation (16),  
\[RMSE=\sqrt{\frac{1}{N}{\sum_{i=1}^{N}{\left({{{\left({x_{i}-X_{i}}\right)}^{2} +{{\left({y_{i}-Y_{i}}\right)}^{2}}}\right)}}}}, \tag{16}\]  
where \((x_{i},y_{i})\) are the projected coordinates of the \(i\)th point on the pre-earthquake image, \((X_{i},Y_{i})\) are the projected coordinates of the \(i\)th point after transformation, and \(N\) is the number of points. The \(RMSE_{M}\) is calculated using all matched tie points obtained by the three methods. The \(RMSE_{T}\) can be calculated using some manually selected verification points. In this case, \((X_{i},Y_{i})\) are the projected coordinates of the validation points in the reference image, whereas \((x_{i},y_{i})\) are the coordinates of the corresponding points in the registered image.  
To verify the geometric accuracies of the registered images, 18, 10, and 10 verification points were selected for the three datasets, respectively, using ENVI software. As shown in the reference images in Figures 8-10, these verification points (shown in red), which are evenly distributed, were used to calculate the \(RMSE_{T}\) of the three methods.
The motivation for the current research is the formalization of information technology in the composition of models, methods, and algorithms to control the information interaction process of a set of terminals and base stations in the 5 Ge ecosystem that are focused on applications in e-commerce. The hierarchy of the applied tasks that allow this motivation to be achieved is formulated in Section 3.1. This article represents the first published work by the current authors in this specific area. Thus, the research object is the operation process of the e-commerce-oriented ecosystem of the base station 5 Ge, which supports the functioning of independent virtual network segments. The main contributions of this paper can be summarized as follows:  
1. We have created a mathematical model for the operation process of the e-commerce-oriented ecosystem of the 5 Ge base station, the information environment of which supports the operation of independent virtual network segments that provide terminal-segment information interaction services;2. We have designed a functional algorithm for the forced termination of an active terminal-segment information interaction section in an overloaded virtual network segment;
3. We have formulated the control mechanism of the distribution of the released system resources between other virtual network segments by taking into account the degree of their overload;
4. We conducted simulation and computational experiments showing that the implemented forced termination algorithm and system resource management mechanism allow the 5 Ge base station to continue receiving incoming requests despite the overload of individual virtual network segments. The structure of the article is as follows: 1. The Section 2 provides a critical analysis of the state of the research related to the topic discussed in the present article. The results of the analysis revealed an area that has so far been ignored in general studies. The subject of the research was also selected in the identified area; 2. In Section 3, the research objectives are set, and the proposed information technology for describing the process of information interaction in the e-commerce-oriented ecosystem of the 5 Ge base station is theoretically substantiated; 3. The Sections 4 and 5 are devoted to the simulation modeling of the presented information technology and the analysis of the adequacy of the results demonstrated by it; 4. In Section 6, the conclusions concerning the urgency of the chosen theme and the received theoretical and applied results are formulated. Directions for future research are also determined.
# Effect of Fuel Composition on Carbon Black Formation Pathways
## 3 Results and Discussion
### Electron Energy Loss Spectroscopy (EELS)  
EELS is used to illustrate particle-to-particle variation (or lack thereof) of sp\({}^{2}\) to sp\({}^{3}\) hybridized carbon. Quantification in EELS is dependent on an appropriate calibration standard [16]. Graphitized carbon black has been used as the EELS calibration standard in this work with the assumption of 100% sp\({}^{2}\) content, its quasi-spherical geometry making it free of orientation effects--a potential disadvantage when using graphite as a standard [17].  
For the carbon black samples in this work, EELS spectra were collected and averaged from 7-15 particles for each carbon. The integrated EELS intensities are the basis for differentiating sp\({}^{2}\) and sp\({}^{3}\) orbitals, based on the corresponding electronic transition intensities and associated energy loss of the incident electrons [16; 17; 18]. Normalized percentages are reported in Table 2. Commonly such values are reported after normalization by a graphitic standard to correct for aromatic ring (and hence orbital) orientations. As a common constant, it preserves the ordering but diminishes the differences in the unprocessed data, listed in Table 2, along with the corrected values normalized to the graphitic standard. The difference between particles originating from the varied feedstocks is apparent and  
Figure 4: Lattice fringe analysis showing differences in fringe lengths of particle core lamellae subtracted from particle shell lamellae for (**A**) CT-CB and (**B**) FCC-CB blacks.  
consistent with projections for particle formation and growth based on the compositional differences.  
The CT-CB possesses the lowest sp\({}^{2}\) content with higher sp\({}^{3}\) bonding and hydrogen content. Differently, the FCC derived particles exhibit a substantially higher sp\({}^{2}\) content. Interestingly the ET-CB particles exhibit an intermediate value of sp\({}^{2}\) percent. Our inference is an intermediate compositional balance of aromatic and aliphatic components, but further resolution by feedstock analysis is not presently available.  
CT- and FCC-CB have thus far displayed physical differences in primary particle size, nanostructure, and chemical bonding differences. Such divergence in structure and chemistry brought about by changing the feedstock can further influence material reactivity such as its response to thermal treatment. This is demonstrated in the subsequent section.  
Thermal treatment is a widely used processing tool for carbon materials such as carbon black. Therein it is necessary to understand material response based on its initial chemistry and structure. Here CT- and FCC-carbon have been subjected to a moderate (500 \({}^{\circ}\)C) oxidation under isothermal conditions using a TGA and a comparatively high-temperature (>3000 \({}^{\circ}\)C) laser heat treatment. The response of the two carbon blacks is recorded and related back to their nanostructure and chemistry--demonstrating how reactivity is ultimately influenced by feedstock, mediated by particle nanostructure and chemistry.
## 4 Results and Discussion
### Vibration Isolation Performance
In reference [30], as the damping ratio increased from 0.05 to 0.3, the peak response only decreased from 17.87 m/s\({}^{2}\) to 15.60 m/s\({}^{2}\). The damping ratio mainly affects the decay rate. In this study, in the later stage of the decay process, such as around 0.1 s, the peak responses of LS, CS-6 mm, and CS-8 mm are 4.51 m/s\({}^{2}\), 3.02 m/s\({}^{2}\), and 1.72 m/s\({}^{2}\), respectively, showing a significant convergence speed of the shock response. In reference [26], the damping ratio of the 316 L-made BCC lattice structure after filling with hard damping material is 0.007. In conclusion, the damping effect of the method presented in this paper is significant.
## 2 Simulation Analysis of the Cubic FP Cavity with Reinforced Support  
The FP cavity is the reference unit of the ultra-stable laser. Since being proposed by the National Physical Laboratory (NPL), the cubic FP cavity has presented potential for transportable and space applications [21]. The reinforced-support cubic FP cavity structure was designed in our previous work, which successfully passed the aerospace-grade mechanical vibration test [20]. To clearly understand the thermal-mechanical characteristics of the reinforced-support cavity (mainly including vibration sensitivity and zero-expansion temperature), this cavity was first modeled and analyzed.
## 2 Challenges and State of the Art
### Operational Context  
The context in which the system will operate is crucial to determine the required security level for certification. Security requirements in a smart home differ from those in a medical environment. Approaches like CC, the European Cybersecurity Certification Scheme (EUCC), or the European Cybersecurity Certification Scheme for Cloud Services (EUCS) rely on security profiles to define the minimum security level for each context, while others, such as CPA, do not adequately consider the operational context [11]. Inspired by CC, our methodology reuses the notion of protection profile (PP) to establish the security level for each context, serving as the certification basis.
## 5 Gaussian Mixture Modeling
### Rationale for Using Gaussian Mixture Modeling  
GMM has been successfully applied in problems represented as time series in other domains, including motor current simulations, electrocardiogram recordings, and speech waveforms [33]; in other tasks such as forecasting and interpolation [34]; structural damage detection [35]; outliers detection in traffic data [36]; and, even on the prediction of apnea episodes based on wireless sensor signals [37]; among many other uses. Accordingly, based on previous prominent results, we resorted to GMM to gain a deeper understanding of CPAP adherence.  
GMMs define a superset of the, so-called, _hard clustering_ methods such as k-means [38]. The difference is that samples closer to the centroid (mean) of a GMM component have a probability of pertaining to that \"cluster\"; rather than categorically pertaining, or not. Furthermore, a GMM model describes the data in such a way that it can generate synthetic data similar to the original data. In our problem setting, the use of hard clustering and density-based methods stumbled in the fact that our CPAP use dataset defines a single continuum in the time series space; that is, there is no gap nor regions of lower density. In such a scenario, hard clustering will detect only one big cluster and the cloud of outliers, which is of no good use. By contrast, our approach was to fit a set of Gaussian distributions able to reproduce the data characteristics. The number of distribution components was defined using the method Bayesian Information Criterion, as we explain next.
## Abbreviations  
The following abbreviations are used in this manuscript:  
5G 5 th Generation Networks AES Advanced Encryption Standard AP Access Point APT Advanced Persistent Threats AR Augmented Reality BE Back End BLE BLuetooth Low Energy BS Base station CCTV Closed-circuit television CSA Cloud Security Alliance CSP Cloud service providers DDoS Distributed Denial of Service DeS Denial of Service FE Front End FN Fog Nodes GCM Galois/Counter Mode HTTP Hypertext Transfer Protocol LTE Long Term Evolution IaaS Infrastructure as a service IBC Identity Based Cryptography IBE Identity-Based Encryption ICT Information and Communication Technology IDS Intrusion Detection System IoE Internet of Everything IoWT Internet of Wearable Things MAC Medial Access Control MITM Man-in-the-Middle Attack MR Mixed Reality NIST National Institute of 66 Standards and Technology OS Operating System OSI Open Systems Interconnection model PaaS Platform as a Service PKI Public Key Infrastructure PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses QoS Quality of Service SaaS Software as a Service SLA Service Level Agreement SQL Structured Query Language SSL Secure Socket Layer
## 3 Results and Discussion
### Narrowband Signal Analysis and Direction Finding  
After downconversion and filtering the wideband data to isolate the JJI (22.2 kHz) signal, MSK demodulation was applied. The demodulation results are shown in Figure 16. The three subplots illustrate the relationships between amplitude, frequency, and phase over time between the original signal and the reconstructed demodulated signal. The reconstructed demodulated signal closely matches the original signal, indicating a high degree of fit.  
Figure 14: Time-frequency spectrum of three channels within one minute.  
Figure 15: Power spectral density of three antennas of the main receiver within one minute.  
Direction finding was performed on the JJI (22.2 kHz) signal without removing the MSK phase modulation. The phase difference between the two receivers is shown in Figure 17. To avoid filter boundary effects, the data at both ends were truncated. The average phase difference measured over five seconds was \(-\)0.90873 radians, resulting in a direction finding result of 112.5624\({}^{\circ}\) (due to the two receivers not being strictly aligned north-south, a compensation of +0.42\({}^{\circ}\) was applied). The actual azimuth angle was 112.6771\({}^{\circ}\), yielding a direction finding error of 0.1147\({}^{\circ}\). The phase difference introduces minor errors due to MSK modulation; however, averaging over a certain time period can partially mitigate these errors. Experimental results indicate that, without removing the MSK phase modulation, the direction finding results sufficiently meet the requirements. After removing the MSK phase modulation from the same data segment and applying carrier phase direction finding, the result was 112.6596\({}^{\circ}\), with a direction finding error of 0.0175\({}^{\circ}\), improving accuracy.  
Direction finding was also performed on the JJY60 (60.0 kHz) signal. Since JJY uses ASK modulation, noise can easily interfere with the signal phase during low-power periods. Furthermore, phase unwrapping introduces phase ambiguities. To obtain stable and reliable results, all stable time segments within a period were extracted, and phase ambiguities were eliminated. The phase analysis results are presented in Figure 18. \"Global\" refers to the complete segment of raw, unprocessed direction finding data, while \"Stable\" refers to the phase-stable segment obtained by removing the low-power, phase-unstable segment of the ASK modulation. Within five seconds of data, five phase-stable segments were identified, with an average phase difference of \(-\)1.7852 radians. The direction finding result was  
Figure 16: MSK demodulation results of the main receiver for the JJI (22.2 kHz) signal.  
106.3202\({}^{\circ}\), compared to the actual azimuth angle of 106.3412\({}^{\circ}\), yielding a direction finding error of 0.0210\({}^{\circ}\).
# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
## 3 Methods
### Detecting Sand Dunes and Deserts Using Thermal Information  
Different crop fields (such as wheat and corn and sunflower) and non-vegetative land (including sand dune area, bare soil area, residential area, and abandoned salinized farmland) in Hetao were successfully detected by the \(K\)-means clustering and shape-model-fitting methods. However, the methods failed to subdivide the non-vegetative land with a similar temporal VI pattern. Thermal information was used to mask the sand dune area (or desert) out of the retrieved non-vegetative land using their significant difference in brightness temperature (Figures 5 and 6). Evapotranspiration (ET) of sand dune areas is largely determined by rainfall, resulting in a high-level net radiation (Rn) partition to the sensible heat flux (H). In contrast, abandoned salinized farmland (Figure 5) and residential areas (Figure 6) show lower surface temperatures than sand dune areas due to the higher air-specific humidity and soil moisture.  
Figure 5: Example of the difference in brightness temperature between sand dune areas and abandoned salinized farmland. The top left subplot is the brightness temperature data (K) calculated from Landsat 8 OLI imagery (acquired on 10 July 2019). The top right is the corresponding composed RGB imagery. The red area of interest (AOI) denotes the sand dune area, and the green area is the abandoned salinized farmland. The lower subplot is the corresponding Google Earth map that shows the details of AOIs.  
[PERSON]'s method [36], which searches for the threshold by maximizing inter-class variance (between-class variance), was used to distinguish the sand dune area automatically from other non-vegetative surfaces. Specifically, the brightness temperature was calibrated from the Landsat thermal band (band 6 for TM and ETM+, and band 10 for OLI), and optimal threshold \(k^{*}\) between two classes was iteratively calculated as follows:  
\[\sigma_{B}^{2}(k^{*})=\max_{1\leq k<L}\sigma_{B}^{2}(k) \tag{13}\]  
where \(\sigma^{2}{}_{B}\) is the between-class variance, and \(L\) denotes the maximum value of the investigated variable.
## 3 Computational Approach
### User Similarity Calculation  
The objective of determining user similarity is to decipher the intricate relationships and affinities that exist between individual users. This study calculated three types of similarity, which are formed by tag-based weight, time-based weight, and hybrid weight. Leveraging the widely-adopted cosine similarity methodology, when we implement each of these weight vectors, the similarity measure between any two users, specifically user '\(u\)' and user '\(v\)', can be articulated through the subsequent equation [63]:  
\[sim_{i}(u,v)=cos(\overrightarrow{u},\overrightarrow{v})=\frac{\sum_{r\in R}W_{ i}(u,r)\times W_{i}(v,r)}{\sqrt{\sum_{r\in R}w_{i}^{2}(u,r)}\times\sqrt{\sum_{r\in R }w_{i}^{2}(v,r)}} \tag{4}\]  
Here, \(i\) = (Tag, Time, Hybrid), and R is the set of resources. Consequently, there are three types of similarity: tag-based similarity (\(S_{tag}\)), time-based similarity (\(S_{time}\)), and hybrid similarity (\(S_{hybrid}\)). \(W_{i}(u,r)\) and \(W_{i}(v,r)\) symbolize the respective weight vectors corresponding to users '\(u\)' and '\(v\)'. Employing the aforementioned Equation (4), this research endeavors to pinpoint the 'K'-closest neighbors pertaining to a specific user. It is paramount to note that a user's score is indicative of their similarity level: a loftier score implies a greater degree of resemblance to the intended or reference user.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hybrid** & **R1** & **R2** & **R3** & **R4** & **R5** \\ \hline U1 & 0.75 & 0 & 0.5 & 0.625 & 0 \\ U2 & 0 & 0.835 & 0 & 0.625 & 0 \\ U3 & 0.835 & 0 & 0.335 & 0.21 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Example of hybrid weight.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tag** & **R1** & **R2** & **R3** & **R4** & **R5** \\ \hline U1 & 0.5 & 0 & 0.75 & 0.75 & 0 \\ U2 & 0 & 0.67 & 0 & 1.0 & 0 \\ U3 & 0.67 & 0 & 0.17 & 0.17 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of tag-based weight.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time** & **R1** & **R2** & **R3** & **R4** & **R5** \\ \hline U1 & 1.0 & 0 & 0.25 & 0.5 & 0 \\ U2 & 0 & 1.0 & 0 & 0.25 & 0 \\ U3 & 1.0 & 0 & 0.5 & 0.25 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Example of time-based weight.