esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology Architecture
### STA/LTA Arrays Windows Sequence (SLAWS)  
The application of STA/LTA arrays is the smartest way for automated recording of critical events. Most of the unique samples or events are traced using STA, and the complete sequence with heterogeneous events is tracked using LTA. The STA/LTA has key initiation parameters that must be automated for non-stationary anomaly events like earthquakes.  
Different seismic frequencies were observed in different earthquakes over the course of time. Let the standard alarming seismic waves frequencies be f\({}_{STANDARD}>\) 0.1 Hz and onwards. Let us generalize that the P-wave has a frequency between 1 and 16 Hz generalized from [42], the S-wave can be [43; 44] generalized as 0.1-8 Hz, and both [45] R- and L-waves have 0.01-2 Hz at maximum. STA and LTA have the following generic event detection parameters (EDP):  
* Wd--Waveform data ('shm_thingspeak.csv')
* Fs--Sampling frequency (as per [PERSON] criterion, i.e., 2F = 2 (1 Hz), 2 (1.5 Hz),... 2 (24 Hz))
* Lts--Length of time series (80,000 samples)
* Tv--Time vector of the waveform (10,000 samples)
* ABSts--Absolute value of time series (40,000)
* STA window size (Dynamic)  
Figure 7: The scaling coefficients array generation sequence (SCAGS) mapping for SP2.  
\(\mathrm{W_{LTA}}=\mathrm{EDP_{2}*F_{s}}-\mathrm{LTA}\) window size (Dynamic)
* \(\mathrm{WL_{LTA}}=\mathrm{WL_{LTA}}-\mathrm{current}\) length of growing LTA window (sizes of the time axis of respective wave)
* \(\mathrm{TR_{ON-STA+LTA}}=\mathrm{EDP_{3}}-\mathrm{trigger}\) on when STA_to_LTA exceeds this threshold
* \(\mathrm{TR_{ON-STA-LTA}}=\mathrm{EDP_{4}}-\mathrm{trigger}\) off when STA_to_LTA drops below the threshold  
In our case with four wave types in one sequence, we will have three STAs and one LTAs array for a single sequence of events as exhibited in Figure 8.
###### Abstract  
Grouting power is a vital parameter that can be used as an indicator for simultaneously controlling grouting pressure and injection rate. Accurate grouting power prediction contributes to the real-time optimization of the grouting process, guaranteeing grouting safety and quality. However, the strong nonlinearity of the grouting power time series makes the forecasting task challenging. Hence, this paper proposes a novel hybrid model for accurate grouting power forecasting. First, empirical wavelet transform (EWT) is employed to decompose the original grouting series into several subseries and one residual adaptively. Second, partial autocorrelation function (PACF) is applied to identify the optimal input variables objectively. Then, support vector regression (SVR) is adopted to obtain prediction outcomes of each subseries, while an improved Jaya (IJaya) algorithm by coupling chaos theory and Levy flights to improve the algorithm's accuracy performance is proposed to optimize the SVR hyperparameters. Finally, the prediction results of decomposed subseries are superimposed to produce the final results. A consolidation grouting project is taken as a case study and the computation results with the RMSE = 0.2672 MPa-L/min, MAE = 0.2165 MPa-L/min, MAPE = 3.85% and EC = 0.9815 demonstrate that the proposed model exhibits superior forecasting ability and can provide a viable reference for grouting construction.  
grouting power prediction; hybrid model; support vector regression; improved Jaya algorithm; hyperparameters optimization +
Footnote †: journal: Applied Sciences
# A Novel Mixed Reality Solution Based on Learning Environment for Sutures in Minor Surgery
## 3 Results
### Users Subjective Evaluation of the Mixed Reality  
In order to detect the user's opinion about learning with both methodologies, we elaborated a short questionnaire with the following three Likert scale questions and one multiple choice question:  
1. _Do you consider that your attention span during the use of Sutture MR has been compromised by the use of new learning technology (Mixed Reality)?_ Please check whether 1 = Strongly Disagree, 5 = Strongly Agree.
2. _Would you recommend the use of this tool, based on Mixed Reality, as a learning method?_ Please check whether 1 = Strongly Disagree, 5 = Strongly Agree.
3. _To continue learning, would you recommend Sutture MR among the two learning methodologies?_ Please check whether 1 = Strongly Disagree, 5 = Strongly Agree.
4. _Choose the option you agree with: \"1 believe the learning has been greater after using... \" (A) Text-Reading Methodology; (B) Mixed Reality Tool (Suture MR); (C) Both methodologies are comparable._  
Regarding how the use of new technologies can affect attention, the survey results indicate that 10 of the 32 participants noticed limitations in their attention span caused by the need to adapt to technology, 10 were neutral, and the remaining 12 considered that their attention was slightly or not affected at all by the technology. As for the overall assessment of our learning tool, 93.3% agreed or strongly agreed to recommend its use for learning (see Figure 7). Note that 48% of those surveyed consider that their learning has been greater through using Sutture MR, 38.7% consider that both methodologies are comparable, and  
Figure 6: Graphic representation of SUS score.  
the remaining 12.9% indicate that they have learned more by following the traditional methodology (see Figure 8).
# Trajectory Tracking Outer Loop Regressor-Based Adaptive Controller for a Quadrotor
## 4 Experimental Results
### Implemented Controllers
#### 4.2.3 Annismc  
This controller was presented in [33] and was developed for the dynamic models in (1)-(2). Hence, it is not only an outer loop controller but has a two-loop configuration. In this scenario, controlling both position and attitude dynamics without access restrictions is possible. In this control scheme, the outer loop is an adaptive RBFNN scheme that governs  
Figure 3: Optitrack® motion capture system and communication protocol.  
the position dynamics and the inner loop is an integral sliding mode scheme that handles the attitude dynamics. This scheme will be referred to as ANNISMC (Adaptive neural network integral sliding modes control) in the remainder of this document. The ANNISMC was implemented using the following gains  
\[\mathbf{c} =[-1.5\;-1\;-0.5\;0\;0.5\;1\;1.5]^{\top}, k_{w} =4.5,\] \[\mathbf{\sigma} =[5\;5\;5\;5\;5\;5\;5]^{\top}, \rho_{w} =1\times 10^{-6},\] \[\Lambda =\text{diag}\{2.55\;2.55\;3.55\}, k_{\eta} =15,\] \[A =0.15, \rho_{\eta} =1\times 10^{-4},\] \[k_{v} =1.257.\]  
The definition of each gain can be consulted in [24].
## 3 Variable Pole Pitch (VPP) Electromagnetic (EM) Propulsion
### Arrangement of VPP DLIMs  
We define the primaries with the same dimensions as a section of the EM propulsion, as shown in Figure 3. It is possible that one section may contain several primaries due to the kinetic requirements. Due to practical production and operational consideration, especially to circumvent the risk of adverse effects caused by altered slot combinations as the LS-secondary travels to other sections, several principles should be followed:  
1. The secondary dimensions, including the cogging ratio, ladder width, and slot width, should be remain constant during the whole operating process.
2. The slot combination between the primary and secondary must be the same among sections.
3. The primary width is maintained all along the propelling direction.
4. The maximum synchronous speed of the LS-secondary DLIMs is the same as the terminal speed of the EM propulsion.  
Thus, to realize variable pole pitch, the primary slots per pole per phase \(q\) should vary piecewise. Based on the design principles of the DLIM [19], the aforementioned principles lead to the following restrictions:  
\[\tau=m_{1}qb_{1}\]  
\[V_{\mathrm{s}}=2\tau f\Rightarrow\frac{V_{\mathrm{s}}}{f}\propto q, \tag{13}\]  
\[a=k_{a}\tau\Rightarrow k_{a}\propto\frac{1}{q}, \tag{14}\]  
which result in two categories of VPP EM propulsion:  
1. Sections with a constant \(V_{\mathrm{s}}\), have the same synchronous speed.
2. Sections with variable \(V_{\mathrm{s}}\), have different synchronous speeds.  
The first category is applicable to sections with a constant \(V_{\mathrm{s}}\), and the \(V_{\mathrm{s}}\) of the primaries in all sections are set to \(V_{\mathrm{T}}/(1-s)\). Since this arrangement avoids limiting the synchronous speed, the sections can be arranged for different kinetic optimization targets by altering their order.  
In contrast, it is much more likely for sections with variable \(V_{\mathrm{s}}\) to get better workouts on the supply frequency. When \(V_{\mathrm{s}}<V_{\mathrm{T}}/(1-s)\) is assumed, the supply frequency of former sections will be reduced compared to those for the sections with a constant \(V_{\mathrm{s}}\). This category of VPP EM propulsion is suitable for the multi-target optimization of extremely high-speed large-thrust applications, especially EM launching systems, where the maximum frequency of the power electronic devices is more significant than the financial expense and operating space.
## 3 Types of Scanning Patterns
### Spiral Scanning  
#### 3.1.1 Spiral Scan Trajectory  
The spiral scan begins from the center of the uncertainty area (the region of high probability) and moves outward toward the region of low probability. The spiral path is defined in polar coordinates by the following equation [40; 46]:  
\[r=\frac{n}{2\pi}\theta, \tag{11}\]  
where \(n\) is the step size and the pitch of the spiral, which is defined by the beam divergence \(\theta_{b}\). The pitch of the spiral is the distance between successive turns, and \(r\) is the distance from the center of the uncertainty area. The step size can be varied by changing the overlap factor \(a\) (\(0\leq a\leq 1\)), and the relationship is defined by the following equation [39; 46]:  
\[n=\theta_{b}(1-a). \tag{12}\]  
Figure 1a,b show the spiral path for the uncertainty region with a radius of \(250\upmu\)rad, beam divergence \(\theta_{b}=25\upmu\)rad, and overlap factor values \(a=0\) and \(a=0.2\). As the overlap factor increases, the step size decreases, but the number of scan points increases, which, in turn, increases the acquisition time. Similarly, increasing the beam divergence reduces the  
number of scanning points and, thus, the acquisition time. However, increasing the beam divergence has power limitations and can make the communication system more complex.
# Spectrum Based Power Management for Congested IoT Networks
## 7 Conclusions  
To minimize power waste at IoT sensor during network congestion a spectrum based power management system is proposed. The system scans channel conditions before packet routing and utilizes the least congested channel for IoT packet routing.  
We used a Freescale Freedom Development Board (FREDEVPLA) as a channel coordinator in our solution. While coordinating channel allocation at the FREDEVPLA level, our system configures MAC and Physical layer of IoT devices in a way that it can have the outstanding power utilization based on the operating network in active, idle and sleep modes compared to the basic IoT standard. A prototype of our solution has been set up by using a number of freescale launchpads that act as IoT devices and can simulate a congested network. A designated FREDEVPLA device was programmed and utilized to execute the proposed spectrum based power management in a congested IoT network scenario. Energy consumption by a channel during connected state, idle state, initial start-up and retransmission over congested IoT networks are measured for a test duration of 6 h. Test data show that battery life of IoT devices using proposed spectrum based power management increases by at least 30% more than non-spectrum based power management methods embedded within IoT devices itself. Additionally, the proposed system eliminates the need for packet retransmission, decreases latency, saves lot of memory for IoT coordinator, improves overall IoT network performance and, more importantly, reduces the risk of losing communication packets. A detailed analysis in this paper will help further research in channel scanning and spectrum based power control.  
Figure 12: Comparison power consumption.  
**Author Contributions:** Conceptualization, K.M.B. and J.I.N.-H.; methodology, K.M.B.; software, K.M.B.; validation, K.M.B. and M.Z.A.; formal analysis, K.M.B. and R.B.-M.; investigation, K.M.B.; resources, J.I.N.-H.; data curation, J.I.N.-H.; writing--original draft preparation, K.M.B. and J.I.N.-H.; writing--review and editing, K.M.B., J.I.N.-H., M.Z.A., and R.B.-M.; visualization, K.M.B.; supervision, J.I.N.-H., and M.Z.A.; project administration, K.M.B.; funding acquisition, J.I.N.-H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Special thanks for CONACYT(UABC-FIAD), the sponsor for this publication. Moreover, the authors would like to thank [PERSON] for their support and advise during this work.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Signal Processing
### Respiration Signal Analysis
Therefore, the zero crossings (ZCs) can be determined by (5) in order to calculate the respiration rate: \[\text{ZC}=\sum\limits_{n=1}^{N-1}\mathbf{1}_{A}((x_{n})(x_{n+1})<0).\] (5) Saving all locations of ZC ocurings ZC_loc_ in the given window, the respiration rate \(\text{RR}_{ZC}\) can be determined by converting twice the mean value of the differentiated locations into BrPM: \[\text{RR}_{ZC}=\frac{60\cdot Fs}{2\overline{\lambda ZC}_{loc}}.\] (6)
* **Fast Fourier transform**: The frequency components of a time signal can additionally be determined using the fast Fourier transform (FFT). Moreover, all signals are windowed with a Hann window of the same length before applying the FFT. The frequency spectrum is calculated  
Figure 8: Comparison of low- and bandpass filtered respiration signals. (**b**) Reference (dashed) and radar (solid) respiration signal after applying a lowpass filter; (**b**) Reference (dashed) and radar (solid) respiration signal after applying a bandpass filter. for each window and the maximum of the spectrum in the range from 0.05  Hz to 0.5  Hz is determined. Afterwards it is converted to \(\mathrm{RR}_{FFT}\). After determining the respiration rates, the RMSE is calculated for each measurement using:  
\[\mathrm{RMSE}=\sqrt{\frac{\sum_{n=1}^{N}(\hat{x}_{n}-x_{n})^{2}}{N}}, \tag{7}\]  
with \(\hat{x}\) as the respiration rate of the \(N\) reference breathing windows and \(x\) the rate of the \(N\) radar breathing windows. Next to the respiratory rate the cross-correlation coefficient of radar and reference breathing is calculated as a measure for similarity. Despite synchronization, distance and impedance are different physical parameters that might have an offset between each other. The correlation is calculated according to (4), whereby two different signals are correlated, not one signal with itself. In the end the maximum value of the correlation \(\hat{R}_{xy,max}\) is determined and the corresponding lag \(m_{max}\).
# Robust Transceiver Design for IRS-Assisted Cascaded MIMO Communication Systems
###### Abstract  
Intelligent reconfigurable surfaces (IRSs) have gained much attention due to their passive behavior that can be a successor to relays in many applications. However, traditional relay systems might still be a perfect choice when reliability and throughput are the main concerns in a communication system. In this work, we use an IRS along with a decode-and-forward relay to provide a possible solution to address one of the main challenges of future wireless networks which is providing reliability. We investigate a robust transceiver design against the residual self-interference (RSI), which maximizes the throughput rate under self-interference channel uncertainty-bound constraints. The yielded problem turns out to be a non-convex optimization problem, where the non-convex objective is optimized over the cone of semidefinite matrices. We propose a novel mathematical method to find a lower bound on the performance of the IRS that can be used as a benchmark. Eventually, we show an important result in which, for the worst-case scenario, IRS can be helpful only if the number of IRS elements are at least as large as the size of the interference channel. Moreover, a novel method based on majorization theory and singular value decomposition (SVD) is proposed to find the best response of the transmitters and relay against worst-case RSI. Furthermore, we propose a multi-level water-filling algorithm to obtain a locally optimal solution iteratively. We show that our algorithm performs better that the state of the art in terms of time complexity as well as robustness. For instance, our numerical results show that the acheivable rate can be increased twofold and almost sixfold, respectively, for the case of small and large antenna array at transceivers.  
IRS; robust design; worst case; decode-and-forward; MIMO 202  
the relay have their own limitations that can possibly be compensated for by exploiting each other. The objective of this paper is to maximize the achievable rate of the system by jointly optimizing the impact of the IRS as well as the covariance matrices of the source and the relay.
# The Role of Epigenetics in Periodontal and Systemic Diseases and Smoking: A Systematic Review
## 2 Methods and Materials
### PECO Question: Population, Exposure, Comparison, and Outcomes  
The focus question for the present systematic review was developed using the population, exposure, comparisons, and outcomes (PECO) criteria.  
In human adults of any race (population), what are the effects of DNA methylation and histone modification profiles (outcomes) on periodontal diseases and systemic diseases or smoking (exposures) compared to no periodontal and no systemic diseases and non-smokers (comparison)?
## 2 Materials and Methods
### Procedures
#### 2.3.2 Muscle Architecture and Ultrasound  
Ultrasound images were obtained in the morning hours, 48 h after the last training session. Participants remained in a supine position on a physiotherapy bed, for 20 min with their muscles relaxed, their hips positioned in 180\({}^{\circ}\) and their knees extended (\(\sim\)170\({}^{\circ}\)). VL muscle architecture (fascicle length, penation angle and muscle thickness) of the right leg was measured at the middle part of the muscle, 50% of the distance from the central palpable point of the greater trochanter to the lateral condyle of the femur [32]. Panoramic B-mode ultrasound images from VL were obtained with a 10 MHz linear probe (38 mm) via extended field-of-view mode (Product model Z5, Shenzhen, Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, China). The acoustic contact between the probe and the skin was provided via an Aquasonic clear ultrasound transmission gel (Parker laboratories, Inc., New Fairfield, NJ, USA). The transducer was placed longitudinal at femur, oriented in parallel to the muscle fascicles and perpendicular to the skin. The field of view was determined as the distance between two probe lengths, one before and one after the 50% marker of muscle length. By using a permanent pen, a dashed line (\(\sim\)10 cm) was marked on the right and the left of the point of 50% and a probe path was drawn on the skin [33]. Image analysis was obtained with software (Motic Images Plus, 2.0, Motic, Hong Kong, China). For each participant, two different fascicle lengths and their respective penation  
Figure 1: Schematic representation of the study protocol, ILP: isometric leg press, CMJ: countermovement jump.  
angles were assessed (Figure 2). Muscle thickness was determined as the perpendicular distance from the deep aponeurosis to the superficial aponeurosis and was evaluated by two separate measurements (Figure 2). Average values were used for further analysis. Test-retest reliability was determined on 11 participants, on two separate days. Intraclass correlation coefficient (ICC) for muscle thickness was 0.970 (90% CI: 0.913-0.990, standard error of measurement; SEM = 1.7%), for pennation angle ICC was 0.942 (90% CI: 0.836-0.980; SEM = 2.2%) and for fascicle length ICC was 0.948 (90% CI: 0.854-0.982; SEM = 2.1%).
## 1 Introduction
Graphite nanoplatelets could represent a well-balanced trade-off between performance enhancement and material cost, making their utilization a more economical alternative to pure graphene reinforcement [34]. However, despite the promising characteristics of PA12/carbon-based composite powders processed through laser sintering, further research is required to thoroughly investigate their mechanical, electrical, and thermal properties under various operating conditions [35]. This study aimed to investigate the fabrication and characterization of nano-graphite-reinforced PA composites using laser sintering technology. The starting polymeric material and the processing machine were both commercially available products. The mixing technique was a simple yet efficient process based on mechanical roto-translation mixing. A comprehensive analysis of the material properties, including flowability, sintering window, electrical conductivity, and mechanical strength, was conducted to evaluate their suitability for next-generation smart applications. The potential applications of these advanced composites range from wearable electronics and biomedical devices to aerospace components and structural health monitoring systems, where lightweight, durable, and multifunctional materials are highly desirable [29]. By leveraging the advantages of laser sintering and nano-graphite reinforcement, this research contributes to the ongoing development of high-performance, multifunctional materials that bridge the gap between traditional polymer composites and advanced smart materials. The findings from this study provide insights into the processing-structure-property relationships of these novel composites, paving the way for future innovations in the field of AM and materials science by offering a streamlined technological solution for producing smart materials based on conductive polymer composites.