esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Study
### Domain Interpretation of Considered Application Scenarios  
This sub-section presents domain interpretation of the considered application scenarios provided by a domain specialist (M.V.I.) regarding the obtained process models, the applicability of process mining in the target cases, and insights from the discovered process models.  
Process optimization is crucial for healthcare as there is always a lack of human and time resources. It is even more pronounced in the management of hypertension and COVID-19. In the first case, it is mainly due to the enormous amount of patients (who are mostly outpatients) as around 33% of the adult population is hypertensive around the globe [57]. With the introduction of telehealth home blood pressure (BP) monitoring was given a \"second life\". Home BP telemonitoring and remote counseling are now feasible and practical approaches for hypertension management [58; 59]. In addition, during the COVID-19 pandemic, telehealth played a key role in chronic disease management, notably hypertension [60]. A team approach is based on health behavior and psychological support and on close physician supervision. In the observed BP telemonitoring scenarios (\"Monitoring Process\"), we suppose it could be wrong to ping-pong a patient from an operator (or case manager) to a doctor and vice versa. Both doctor and nurse may act similarly in most cases. In the meantime, optimization of the home BP telemonitoring supports multidisciplinary patient care and early doctor alerts but only when there is something wrong with the patients' BP data (\"red zone\" or missed measurements). Optimized management process ensures patient safety and adherence. The proposed way of the process model optimization approach (Table 4) seems plausible. A number of interim steps may be taken off, and the scheme of BP telemonitoring may be distilled into 3-4 interpretable steps without any loss in quality of patient care. The unnecessary closed loops of actions were also suppressed.  
also play a crucial role in mortality and morbidity benefit, especially in elderly cases [62]. So, it is of great importance to look inside the business processes and to try to improve them as process optimization may save not only time and money but also health and lives. Here, (\"Nurse Workflow\" and \"Physician Workflow\") the optimized process models describe emergency patient care with a structured and more reasonable workflow for nurse and physician. These provide early targeted therapy; therefore improved inpatient care gives an opportunity to shorten hospital stays without posing a risk to patients but with the possibility to treat more critically ill patients with faster hospital bed rotation. One may argue that the nurse's workflow seems to be more complex after optimization and aggregation, but it should be noted that some physicians' activities have been rescheduled. These are mostly technical ones, and task shifting is one of the most important steps when it comes to emergency care.
###### Abstract  
The investigation into potential hazards linked with millimeter-wave (mmWave) radiation is crucial, given the widespread adoption of body-centric wireless sensor nodes operating within this frequency band. This is particularly pertinent in light of its envisaged use for the upcoming 5G/6G networks and beyond. As 6G is anticipated to leverage a broad spectrum, including both sub-6 GHz and mmWave bands (30-300 GHz), concerns arise regarding increased human exposure to non-ionizing radiation (NIR). This work highlights the advantages of deploying 6G in the mmWave band, focusing on evaluating human body exposure to NIR interactions. Additionally, this research aims to address mmWave NIR exposure by introducing a Distributed Base Station (DBS) network. Utilizing low-power remote antennas to extend network coverage, the DBS architecture seeks to effectively minimize NIR's impact without compromising overall network performance. The findings underscore the significant potential of the DBS approach in mitigating NIR-related concerns associated with mmWave utilization in 6G networks.  
6G; non-ionizing radiation; millimeter wave; distributed base station Article
# OQPSK Synchronization Parameter Estimation Based on Burst Signal Detection
## 2 Algorithm Description
### Synchronization Parameter Estimation
#### 2.2.2 Phase Offset Estimation  
We perform phase offset estimation after frequency offset estimation is completed. The first step of phase offset estimation is to compensate for the frequency offset in the frame header:  
\[y(k) =r(k)*exp(-2\pi\hat{f}_{d}kT)\] \[=\{exp(j(\theta+2\pi f_{d}kT+\theta_{0}))+n(k)\}*exp(-j2\pi\hat{f} _{d}kT) \tag{15}\] \[=exp(j(\theta+\theta_{0}))+n^{\prime\prime}(k)\]  
where \(y(k)\) is the frame header after removing the frequency offset and \(n^{\prime\prime}(k)=n(k)*exp(-j2\pi\hat{f}_{d}kT)\) is the noise item. According to the principle of ML, the DA phase offset estimation algorithm can be obtained as [19]:  
\[\hat{\theta}_{0}=arg\Bigg{\{}\sum_{k=0}^{N-1}y(k)c^{*}(k)\Bigg{\}} \tag{16}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Multiplication** & **Addition** & **Complex Angle** \\ \hline L\&R & \(12 NN_{0}-6N_{0}^{2}-6N_{0}\) & \((2N-N_{0}-1)(N_{0}-1)\) & 1 \\ [PERSON] & \(12 NN_{0}-6N_{0}^{2}-6N_{0}\) & \((2N-N_{0}-1)(N_{0}-1)\) & \(N_{0}\) \\ [PERSON] & \(14(N-1)\) & \(2(N-1)\) & 1 \\ L\&W & \(14(N-1)\) & \(2(N-1)\) & 1 \\ This article & \(1\) & \(0\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the complexity of frequency offset estimation algorithms.
## AI-Enabled 6G Network Challenges
### Mobility Management  
\"Mobility\" is a characteristic of mobile technology. Fifth-generation (5G) mobile networks were unable to satisfy performance specifications; hence, sixth-generation (6G)mobile network technology will establish new benchmarks. This is due to the strong demand for a more intelligent network, extremely low latency, lightning-fast network connections, and support for a wide range of linked applications. When controlling mobility, neither 5G nor pre-5G networks took network node movement into consideration. The rapidly moving satellites will provide considerable challenges for 6G. The mobility options with 6G will be even more complex. The inter-satellite linkages alter when the satellites' positions vary. The network topology, handover control methods, and other elements will change as a result of the relocation [34].
# Comparative Analysis of Local Differential Privacy Schemes in Healthcare Datasets
## 2 Preliminaries
### LOCOP Scheme  
Estimation, LASSO with Gaussian Copula  
After users encode and perturb their data, they transmit it to the central server, which receives the distribution of users with random noise added by RR. Initially, the server estimates the one-dimensional and two-dimensional distributions using the steps proposed by [PERSON] [10]. Subsequently, it calculates the Pearson correlation coefficient matrix \(\hat{R}\) for the \(d\)-dimensional attributes. Ensuring that this matrix is a positive definite matrix (PDM) is essential, signifying that all its eigenvalues are positive.  
In the realm of copulas, PDMs play a crucial role, as they are utilized to model the dependence structure between random variables. Copulas, acting as functions, describe the joint distribution of random variables by incorporating their marginal distributions and a dependence structure. PDMs enable the specification of the variable's correlation matrix, illustrating the linear relationship between variable pairs. By defining the correlation matrix, it becomes feasible to model the dependence structure between variables with flexibility and accuracy. Therefore, it is imperative to verify that \(\hat{R}\) is a PDM.  
[PERSON] [10] propose an algorithm to check this condition, and if \(\hat{R}\) fails to be a PDM, a postprocessing step is implemented  
```
\(U_{i}(i=1,\ldots,d)\): attribute \((i=1,2,\ldots,d)\) for each\(U_{i}\)do  Generating marginal distribution function \(F_{i}\)  Computing the inverse cumulative distribution function \(F_{i}^{-1}\) endfor  Computing R Generating d-dimensional random vector \((r_{1},\ldots,r_{d})\) following \(\Xi(0,R)\)  Computing \(X_{1}=\varphi(r-i)\), where \(\varphi\) is the standard normal distribution  Computing \(\widehat{U}=(F_{i}^{-1}(X_{1}),\ldots,F_{d}^{-1}(X_{d}))\) return\(\widehat{U}\)
```
**Algorithm 1** Copula-based Multiple Variable Sampling and Synthesizing
## 2 The Selective Energy Transfer (SET) Theory
### Peptide Bond Formation:  
As mentioned in the Introduction, we concentrate here on the formation of the polypeptides and proteins, because of their fundamental importance in life processes. Although the formation of amino acids under reductive atmosphere has been achieved [11], the second sequence (i.e., the formation of polypeptides [3]) seems to be a much more formidable task. The formation of the peptide bond under thermodynamic control is not favorable [4; 5], compared to the free energy of hydrolysis of an internal peptide bond with \(\Delta G\sim-2\) to \(-6\) kJ mol\({}^{-1}\).The formation of long peptides at realistic concentrations of \(\alpha\)-amino acids remains highly unfavorable [4; 5]. It is therefore essential that this overall free energy cost must be compensated by coupling the formation of each peptide bond by an activating agent to make the reaction favorable. In addition, a metal ion to \"mediate\" this process into an integrated whole would seem ideal for the formation of polypeptides under the hydrothermal condition of the undersea vents.  
In the present proposal, the -NH\({}_{2}\) group of one amino acid moiety and the -COOH groups of another one are held in close proximity by, for example, a Ni (II) or a Cu(II) ion (as shown in Scheme 1). As discussed further on in Section 2.2, the COS molecule transfers quanta of vibrational energy to the coordinated -NH\({}_{2}\) group through a matching resonance vibration, leading to the condensation reaction involving the nearby -OH group, thus forming the peptide link, -(C=O) - NH-, and releasing a molecule of water. The COS molecule delivers the energy necessary for this reaction, \"without itself being consumed thereby\" [20; 21].  
**Scheme 1.** \"Condensation\" of two amino acids catalyzed by COS.  
In view of these results, one can ask, how did the SET model work when meeting the demands of life-creating reactions some billions of years past (and possibly continuing even today)?: It is now generally assumed [10] that the first and most primitive cells (prokaryotes) were formed in the niche environments of continuous supply of primary chemical compounds. However, what can we tell about the role of the amino acids when building the biologically important structures of multi-peptides or possibly proteins?  
In the present investigation, we used SET to try to answer these questions and speculate on the possible formation of simple proteins based on reactions of amino acids under catalytic conditions. We found it necessary here to include, besides COS, a water-soluble salt of a 2+ - metal ion. However, the burning question is, how did this possibly catalytic reaction manifest itself? What happened? Was COS a true catalyst, or was it consumed in the reaction as assumed by [PERSON] et al. [22]? This is good ground to test the SET theory for catalysis.
## 3 Requirements Analysis  
### C-V2X Large-Scale Testing System Architecture  
There is no doubt that C-V2X technologies could alleviate communication congestion, which is a severe problem in Dedicated Short-Range Communications (DSRC) [21]. However, OEMs also have some reservations regarding communication performance. Therefore, in 2020, the IMT2020 (5G)-C-V2X group advocated for and organised large-scale tests at the Songhong demonstration area in Shanghai, China. While the test campaign was successful, the offline test data analysis model was controversial. To solve this problem, we developed an automated C-V2X large-scale testing system (LSTS), as shown in Figure 1.  
The C-V2X LSTS includes two elements: the testing system (TS) and the Device Under Test (DUT). The validity of the data exchange procedure between the TS and DUT should influence the test experience, e.g., the time required and the accuracy of the results.  
Then, an industry standard [23] published by the Ministry of Industry and Information Technology of the People's Republic of China, \"YD/T 4771-2024 Technical Requirements for C-V2X Large-Scale Testing System and Data Interface\", which defined the corresponding data exchange procedure, was compiled, as shown in Figure 2.  
Figure 1: C-V2X large-scale testing system architecture. System architecture is the fundamental organization of a system, embodied in by components, their relationships to each other and the environment, and the principles guiding its design and evolution [22].
## 4 Experiments
### Experiments about Influencing Factors
#### 4.2.3 Results on Segmented Length and Discussion  
The variation of data length is a common phenomenon in time series analysis. This subsection aims to analyze the effect of data length on classification performance. We truncate time series into segments with different lengths (5 time-steps to 100 time-steps, stepped by 5 time-steps).  
The classification results on the dataset \"OSU Leaf\" are shown in Figure 10. As seen in Figure 10, the sensitivity of these optimization methods to data length is definitely different. The accuracies of RN-RNN and BI-RNN do not fluctuate significantly with the increase in data length, which indicates that random mapping and bidirectional transmission can enhance long-term dependence to a greater or lesser degree.  
Moreover, random mapping has more outstanding performance by remembering the previous predicted values compulsorily. Correspondingly, the convolutional operation lacks the direct temporal-dependency learning capability, especially in long time-steps, so the accuracy of CO-RNN declines more significantly.  
However, as speculated from the results of RN-RNN and BICORN-RNN, the convolutional operation and bidirectional transmission help to improve the effect of random mapping indirectly, and only BICORN-RNN achieves a satisfactory classification accuracy of over 80%, regardless of the length of sequence. It also suggests that in some long time-step classification problems, we can try to remove the convolutional operation to simplify the networks, and still obtain adequate results.
## 3 Results
### Smartphone-Based Colorimetric Assay  
The expensive, delicate, and costly UV-Vis spectrometer is not appropriate for on-site application. The portable, inexpensive, and sturdy smartphone is more suitable for point-of-care sensors [2,11], especially in resource-constrained countries. Therefore, we carried out the experiment assay with the help of a smartphone equipped with a color scan application software (ColorAssist), specially designed for photo analysis instead of a UV-Vis spectrometer. After taking, The software can analyze a photo of the MA-capped Au NPs solution producing a color card and RGB (red, green, and blue) values. As we know, colorimetry is a method that is used to determine the concentration of colored compound in solution by the application of the Beer-Lambert law, which is highly dependent on the use of UV-Vis spectrometer. Unlike colorimetry, RGB, as three primary of light, has been widely used in the field of image display since any color could be decoded by RGB value. The RGB components of light can represent any color, as shown in Figure 4. Small differences in the color of solutions of MA-capped Au NPs with different concentration of target could also be reflected by the ratio of green to red values displayed on the screen. Figure 5 shows that the ratio of green to red can be used to establish a correlation with Cr(VI) concentration. A linear calibration curve (y = 0.48297 + 0.49794x) was obtained and showed good linearity with a correlation coefficient of 0.99036 in the concentration range of 0.2 ug L\({}^{-1}\) to 1 ug L\({}^{-1}\). The detection limit could reach as low as 0.1 ug L\({}^{-1}\) based on the definition of three times the deviation of the blank signal (3 \(\sigma\)). Our detection limit was found to be very near to the limitations which were reported by [PERSON] et al. (0.52 ug L\({}^{-1}\)), [PERSON] et al. (0.26 \(\mu\)g L\({}^{-1}\)), and [PERSON] et al. (0.4 \(\mu\)M) [2,7,32]. However, our detection limit was found to be more reliable than the method reported by [PERSON] et al. (1.3-11.6 \(\mu\)g L\({}^{-1}\)) [11].  
Figure 3: (**A**) Optical response of MA-capped Au NPs in the presence of different concentrations of Cr(VI) and their corresponding UV-Vis spectra (**B**). (**C**) The dose-response curve for Cr(VI) detection and the insert within is the linear calibration plot for Cr(VI) in the range of 0.2 to 2.0 \(\mu\)g L\({}^{-1}\).
## 3 Methodology  
### D-InSAR  
D-InSAR is a technique capable of detecting land-surface deformation by analyzing a single interferogram that is derived from a pair of SAR images with the addition of a DEM [38]. In this study, the two-pass interferometry method was implemented by using two ALOS-PALSAR Single Look Complex (SLC) images for interferogram generation, and then, the topographic phase was removed using the SRTM DEM data. To remove noise and to smooth the interferogram, the Goldstein-Werner filtering process was applied [39], and the coefficient in the filtering process was set to 0.2. Finally,  
Figure 1: (**a**) Geographic location of the study area; (**b**) Administrative districts of the study area superimposed on the topographic map; (**c**) ALOS-Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) false color composite image of the study area on 15 July 2008. The red and blue dashed lines represent the coverage of ENVISAT-ASAR and ALOS-PALSAR data sets. ALOS: Advanced Land Observing Satellite; ENVISAT: Environmental Satellite; ASAR: Advanced Synthetic Aperture Radar; PALSAR: Phased Array L-band Synthetic Aperture Radar.  
the InSAR products were geocoded from the Range-Doppler coordinates to the map geometry with a pixel resolution of 25 m. The SARscape(r) Modules (5.1) for ENVI (5.3) software suit was employed to process the level-1.1 data and perform interferometric analyses.
## 2 Materials and Methods
### EV Charging Load  
The EV charging load represents the electrical energy needed to charge a specific number of EVs that serve visitors in the five hotels for tourist tours in this region. This load was estimated based on the expected daily travel distance of the vehicles, in addition to the distance the vehicles can travel when the battery is fully charged. The total number of electric vehicles (EVs) entering the charging station during the day is estimated at 30. It is assumed that this station consists of ten chargers to accommodate the charging needs of the 30 EVs during the day. The number of EVs is estimated based on the number of tourists per day visiting tourist sites in Fayoum, which was collected from the selected hotels according to the historical data on tourist visits. It is considered that all the EVs are of the same type, namely the Nissan Leaf [18]. The technical data for the EVs are provided in Table 1 [18]. The daily energy required to charge an electric car was estimated as follows [18]:  
\[E_{daily}=\frac{R_{\alpha\times D}}{R} \tag{1}\]  
where:  
\(B_{\alpha}\) is the capacity of the EV battery in kWh, which is estimated to be 40 kWh [18].  
\(D\) is the daily commute distance in km, which is estimated to be 50 km.  
\(R\) is the range of EV in km, which refers to the distance it can travel on a single charge before requiring recharging. The range of the EV Nissan leaf is estimated at 311 km [18].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No of EV Population** & **30** \\ \hline Max charge power per EV (kW) & 6.6 \\ \hline Required charge energy per EV (kWh) & 6.5 \\ \hline Charger output power (kW) & 10 \\ \hline No. of charger & 10 \\ \hline Scaled avg sessions/day & 30 \\ \hline Time connected hours & 7 \\ \hline Charging price (USD/kWh) & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical data of EV charging station.  
Figure 2: Seasonal (winter (**A**) and summer (**B**)) daily electric load curves in kW for the primary electricity load.  
HOMER Grid facilitates the definition of the load profile for EV charging stations, allowing users to specify power requirements and charging duration. Users can input technical details, such as the number of EVs, maximum charge power, charger output power, the quantity of charging stations, and charging price. As Egypt currently has a limited number of EVs and EVCS, the selling price of EV charging has not been officially declared. Given that the unit cost of purchasing power from the grid in Egypt is 0.07 USD/kWh, the assumed EV charging selling price is also 0.07 USD/kWh.
## 1 Introduction  
The use of magnetic fields in medicine, specifically pulsed magnetic fields (PEMF) generated by electric current, has been approved by the FDA for the treatment of bone fractures since 1979 [1]. Later, the effects of static magnetic fields (SMF) were also studied to evaluate their effects in orthopedics. Many studies have shown that the use of magnetic fields in orthopedics and oral implantology can be highly beneficial for healing bone fractures and osseointegration in terms of bone-to-implant contact (BIC) of dental implants [2; 3; 4].  
In 2017, we investigated the proliferation rate and gene expression of osteoblasts like MG63 cells at 24, 48, and 72 h after SMF stimulation generated by a small, custom-made, helical neodymium-iron-boron (NeFeB) magnet placed inside the cavity of a dental implant [5]. As a result, we found that applying an SMF to osteoblast-like cells slightly decreased the cell proliferation rate, while it increased the expression of those genes that correlate with differentiation and mineralization. In gene expression profiling, we found that in the samples with implants with magnetic Sc (test group) compared with those with Sc made of titanium (control group), the expression of the following genes was increased: (1) CSF-3 (colony-stimulating factor 3), IGF-1 (insulin-like growth factor 1) by 2.2-fold after 24 h of exposure; (2) COL10A1 by 2.5-fold; (3) TGF-81 by 2.5-fold; (4) VEGF-A by 1.9-fold; (5) PDGF-A (platelet-derived growth factor alfa polypeptide) increased 2.3-fold; (6) BMP-2 increased 3.3-fold; (7) AMELY (amelogenin y-linked) increased 2.5-fold after 72 h of exposure. These results represent the first clinically operational technique for dental implants demonstrating the ability of SMF to promote osteogenesis in vitro.  
In another study aimed to investigate BIC and newly formed bone volume around dental implants placed in the tibia of New Zealand rabbits after SMF stimulation generated by a small, custom-made Sc-shaped neodymium-iron-boron magnet placed in the inner cavity of the dental implants, [PERSON] et al. demonstrated the significant role of SMF field generated around the dental implants in improving bone healing in the animal model [6]. This proved for the first time the ability of SMF to promote osteogenesis in vivo and proposed a ready-to-use clinical technique for dental implants. Until the introduction of the possibility of replacing titanium Sc with NeFeB Sc, which is coated with titanium by PVD (physical vapor deposition), it was not possible to use static magnetic field SMF in dentistry in a way that is accessible to every dentist. As far as we know, this is the first study to investigate the role of SFM on implant stability and success in humans.  
The objective of this case series report was to investigate the effects of SMF on implant stability by RFA (resonance frequency analysis), implant success, and surrounding soft tissue health over a 90-day observation period. The null hypothesis was that the magnetic screw would not affect implant stability.