esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Wavelet-Based Algorithm for Detection of QRS Complex
###### Abstract  
Accurate QRS detection is an important first step for almost all automatic electrocardiogram (ECG) analyzing systems. However, QRS detection is difficult, not only because of the wide variety of ECG waveforms but also because of the interferences caused by various types of noise. This study proposes an improved QRS complex detection algorithm based on a four-level biorthogonal spline wavelet transform. A noise evaluation method is proposed to quantify the noise amount and to select a lower-noise wavelet detail signal instead of removing high-frequency components in the preprocessing stage. The QRS peaks can be detected by the extremum pairs in the selected wavelet detail signal and the proposed decision rules. The results show the high accuracy of the proposed algorithm, which achieves a 0.25% detection error rate, 99.84% sensitivity, and 99.92% positive prediction value, evaluated using the MIT-BIT arrhythmia database. The proposed algorithm improves the accuracy of QRS detection in comparison with several wavelet-based and non-wavelet-based approaches.  
Keywords: electrocardiogram (ECG); QRS detection; noise evaluation; biorthogonal wavelet transform +
Footnote †: journal: Applied Sciences
# Central and Periodic Multi-Scale Discrete Radon Transforms
## 4 Periodic DRT  
Equations (3)-(5) remained untouched when defining central DRT, because it computes the same as conventional DRT, only for fewer values. The algorithm had to be modified to operate on half of the values, but the operations carried out on those values did not change. Now, it will be the opposite; the algorithm remains mostly untouched from conventional DRT, but there will be a slight change in Equation (4).  
It is desirable, in the case of periodic DRT, that discrete lines do not abandon the square \(N\times N\) region where the input is defined. Instead, if for a certain slope and displacement a discrete line is going to cross the upper limit through position \(\{x,\,N-1\}\), it will be forced to reenter at position \(\{x+1,0\}\).  
This _line wraparound effect_ is normally undesired because such definition of lines turns the transform useless for tomographic inversion. In Fourier-based transforms, this effect is counteracted by additional padding. However, some applications can benefit from a collateral effect of this redefinition of discrete lines: the forward transform that emerges has a fast inversion algorithm, something that central DRT does not.  
In Figure 13, the expected behavior for discrete lines in periodic DRT is depicted. Lines with parameters \(\{s,d\}\) will finish now on positions \(\{N-1,\,(d+s)\mod N\}\). To accomplish such behavior, the partial transform equation must be redefined:  
\[\widetilde{f}^{\,m}(\widehat{s_{n-m}},\,\widehat{\boldsymbol{\sigma}}\,|\, \mathbf{v}\,|\,d)=\,\sum_{\mathbf{u}\in\mathbb{Z}_{2}^{n}}f\Big{(}\lambda( \mathbf{u},\mathbf{v})\,|\,\left(I_{\lambda(\mathbf{s})}^{m}(\mathbf{u})+d \right)\mod N\Big{)} \tag{7}\]  
That change translates directly into the mapping equation:  
\[\widetilde{f}^{\,m+1}(\widehat{s_{n-m-1}},\,\widehat{\boldsymbol{\sigma}}\,| \,\mathbf{v}|\,d)=\,\widetilde{f}^{\,m}(\boldsymbol{\sigma}\,|\,0,\mathbf{v} |\,d)+\,\widetilde{f}^{\,m}\bigg{(}\boldsymbol{\sigma}\,|\,1,\mathbf{v}|\, \big{(}d+\lambda(\mathbf{s})\big{)}\mod N\bigg{)} \tag{8}\]  
The input image must not be padded now. The algorithm for a quadrant is given in Algorithm A2. This algorithm is a more direct translation of the equations defining the  
Figure 13: A depiction of the whole set of discrete lines covering the displacements and slopes, as computed by periodic DRT.  
transform than before, on central DRT, because operations are performed again at datum level, not on \(N\)-length vectors.  
The output can be rectified by applying a circular shift of magnitude \(\lfloor\frac{s+1}{2}\rfloor\) to each column of the four quadrant outputs, \(\mathfrak{R}f(s,\cdot)\).
# HYDRUS-1D Simulation of Soil Water Dynamics for Sweet Corn under Tropical Rainfed Condition
## 4 Conclusions  
The HYDRUS-1D simulation model was used to evaluate water flow and water losses during two growing seasons of sweet corn in MARDI, Malaysia under the tropical rainfed conditions in the presence of a shallow groundwater table. HYDRUS-1D (despite considerable demand for input data) has proven to be a reliable tool to evaluate water movement in agricultural fields under various irrigation schemes and different crops [17; 31; 32]. Similarly, HYDRUS-1D was found useful in evaluating water fluxes and to evaluate the HYDRUS-1D model for the rainfall potential to meet the water requirement of sweet corn in the tropical region. The sprinkler system was set up to deal with water-stressed conditions. As rainfall fulfilled the crop water requirement throughout the growing seasons no additional irrigation (sprinkler) was applied. The soil water content and crop uptake did not drop below the threshold level. The results suggest avoiding scheduled irrigation, which is the general practice in Malaysia. Excess irrigation triggers the runoff and leaching losses that result in fertilizer loss. However, the study was carried out in the presence of shallow water table that also contributes to soil water through the capillary rise. Therefore, it is recommended to repeat the research with deep water table scenario to establish the findings for the tropical region.  
[PERSON] carried out the experimental investigation and prepared the manuscript, including the data analysis. [PERSON] supervised the research work and improved the manuscript. [PERSON] assisted for the field experiment, [PERSON] and [PERSON] provided valuable comments for preparing the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
Authors are grateful to Universiti Putra Malaysia for providing research facilities under Putra Grant (Grant No: 9557200). Authors also acknowledge the University of Agriculture Faisalabad and Higher Education Commission (HEC) Islamabad, Pakistan, for providing the scholarship in the PhD study. Authors sincerely express the gratitude to MARDI for providing experimental field.  
All the authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Season**} & **WI** & **ET** & **RO** & **L** & **SS** & \(\Sigma\) \\ \hline \multirow{2}{*}{First} & Simulated & 75.8 & \(-\)30.88 & \(-\)31.10 & \(-\)07.64 & 3.70 & 2.48 \\  & Observed & & \(-\)30.84 & \(-\)36.00 & & & \\ \hline \multirow{2}{*}{Second} & Simulated & 79.7 & \(-\)26.40 & \(-\)22.80 & \(-\)20.90 & 5.90 & 3.70 \\  & Observed & & \(-\)26.70 & \(-\)22.40 & & & \\ \hline \hline \end{tabular}  
* WI—total input water, ET—evapotranspiration, RO—surface runoff, L—water leaching, SS—soil storage, \(\Sigma\) water balance error.  
\end{table}
Table 5: Simulated and observed water balance components (cm) in the soil profile during two seasons of sweet corn in MARDI, Malaysia.
###### Abstract  
UltraViolet & infrared Sensors at high Quantum efficiency onboard a small SATellite (UVSQ-SAT) is a small satellite at the CubeSat standard, whose development began as one of the missions in the International Satellite Program in Research and Education (INSPIRE) consortium in 2017. UVSQ-SAT is an educational, technological and scientific pathfinder CubeSat mission dedicated to the observation of the Earth and the Sun. It was imagined, designed, produced and tested by LATMOS in collaboration with its academic and industrial partners, and the French-speaking radioamateur community. About the size of a Rubik's Cube and weighing about 2 kg, this satellite was put in orbit in January 2021 by the SpaceX Falcon 9 launch vehicle. After briefly introducing the UVSQ-SAT mission, this paper will present the importance of measuring the Earth's radiation budget and its energy imbalance and the scientific objectives related to its various components. Finally, the first in-orbit observations will be shown (maps of the solar radiation reflected by the Earth and of the outgoing longwave radiation at the top of the atmosphere during February 2021). UVSQ-SAT is one of the few CubeSats worldwide with a scientific goal related to climate studies. It represents a research in remote sensing technologies for Climate observation and monitoring.  
climate observation and monitoring; earth radiation budget; nanosatellite; IPCC 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Model Implementation
### Weighting Score for Expert Elicitation  
The CPr calculated through the RTO data are validated by assessing their coherence with domain expert knowledge. After IRB approval, the survey questionnaire was sent to at least 45 experts. These experts are from academia and the power industry and have vast experience with research in power grid systems. The selection of these experts was based on consultation with a panel of researchers assisting the authors of this study. After two reminders in one month of surveying, twenty-one responses were received, with only eighteen being completed in all aspects. These eighteen responses were analyzed using statistical techniques for completeness and coherence. Three responses were declared outliers due to inconsistent points outside the \(\sigma\pm 1\). To ensure a comprehensive analysis that incorporates diverse perspectives and expertise on power grid failures, only fifteen responses were selected for a final evaluation. Outliers were excluded using the interquartile range (IQR) technique to maintain the integrity and reliability of the data. These responses were chosen from a group of experts with varied backgrounds in industry and academia, each bringing unique knowledge and experience to the power grid failure mechanism. This selection process ensures that the analysis reflects a broad range of viewpoints, addressing the complex nature of power grid failures and enhancing the overall depth of the findings.  
The weight for each variable received in linguistic terms was added to the total weight using Equation (2), and the weighted values were calculated using Equation (3). The results of expert elicitation from experts showing their PP, Exp, EdU, and age are shown in Table 3.
# A Knowledge Graph-Based Consistency Detection Method for Network Security Policies
## 3 Proposed Methods  
The method proposed in this article mainly includes two parts: the construction of knowledge graph and the consistency detection method.  
The flowchart of the method for constructing a knowledge graph of network security policies is shown in Figure 1. The knowledge graph proposed in this paper includes four layers, including the network information layer graph obtained by topology information; the security knowledge layer graph obtained by entity recognition, knowledge extraction, and knowledge fusion from security knowledge data sources; the detection logic layer graph obtained by transforming the policy text into a formal method related to ontology modeling; the logical dependency layer graph obtained by persistently storing default information and semantic parsing functions for policy text. In the four-layer graph, the detection logic layer graph is the core and contains the main content of the policy; the form of policy storage is event sequence and response. The network information layer graph is a collection of detection objects, including the detection targets of policies. The security knowledge layer graph is the basis for detection, and persistent and maintainable security domain knowledge provides the basis for detection. The logical dependency layer graph is an auxiliary tool for detection, mainly used to achieve automated and standardized consistency detection tasks.  
The flowchart of the method for policy consistency detection based on the knowledge graph is shown in Figure 2. Simply put, first, the event sequence and response of the target policy are obtained from the detection logic layer graph. Then, segment the policy text and query the default information and the semantic parsing functions related to it in the logical dependency layer graph. Subsequently, the semantic parsing function is introduced into the event sequence to retrieve unknown parameters from the security knowledge layer graph and network information layer graph, and the events in the event sequence are executed sequentially (written in a formal language similar to BMSL that combines Cyphersyntax). Finally, consistency detection is achieved by comparing the execution results of the event sequence with the response corresponding to three different detection tasks.
## 3 Metasurface Simulation Results
Far-field simulations indicated that the three-stripe configuration for a steering angle of \(\theta\) = 10\({}^{\circ}\) showed an almost 0.7\({}^{\circ}\) difference (see Figure 10) compared to the uniform one-stripe configuration, which was about a 1.2-1.4\({}^{\circ}\) difference (see Figure 9) for the same frequencies. Table 4 summarizes the bandwidth simulation results. Figure 7: Beam steering far-field radar cross section (RCS) results at 28 GHz in the three-stripe configuration (green) vs. the one-stripe configuration (red). (**a**) Steering angle simulation results of 5\({}^{\circ}\). (**b**) Steering angle simulation results of 7\({}^{\circ}\). (**c**) Steering angle simulation results of 9\({}^{\circ}\). Figure 8: Results of far-field co-polarization and cross polarization for beam steering at 28 GHz in the three-stripe configuration. Steering angle simulation results of 9\({}^{\circ}\). Figure 10: Results of bandwidth for beam steering 10\({}^{\circ}\) at 28 GHz in the three-stripe configuration. \[\theta=\sin-1\ (\lambda\cdot\Delta\phi_{x}/360\cdot\Delta X) \tag{1}\]0 Results of bandwidth for beam steering 10\({}^{\circ}\) at 28 GHz in the one-stripe configuration. The phase dynamic range for 12\({}^{\circ}\) and 31\({}^{\circ}\) are given in \[\theta=\sin-1\ (\lambda\cdot\Delta\phi_{x}/360\cdot\Delta X) \tag{1}\]1 The three-stripe configuration composed of uniform-type A-uniform was able to cover a much larger dynamic range of the steering angle, as can be seen in Figure 12, whereas the type A (one stripe) configuration was unable to realize this. \begin{table}
\begin{tabular}{c c c} \hline
**Monitor Far-Field (GHz)** & **Reflected Beam (Deg) One Stripe** & **Reflected Beam (Deg) Three Stripes** \\ \hline
27.75 & 11.1 & 10.7 \\
28 & 9.9 & 10 \\
28.25 & 8.5 & 9.2 \\ \hline \end{tabular}
\end{table} 
Table 4: Results of far-field radar cross section (RCS) for beam steering around 28 GHz for a uniform configuration compared to a three-stripe configuration. Figure 11: Beam steering far-field radar cross section (RCS) results at 28 GHz in the three-stripe (green) vs. one-stripe configuration (red). Steering angle simulation results of 8\({}^{\circ}\). Figure 12: _Cont._
## 5 Feature Challenges  
### Integration of Autonomous Vehicles  
The era of 5G networks coincides with scientific progress in other related fields, such as the development of smart electric grids (SEGs). In this framework, bandwidth demanding and zero latency applications will be deployed and supported, such as the use of Autonomous Vehicles [84; 85]. The vision for AVs is to be able to navigate without human interaction and perform additional related tasks, such as dynamic update of city maps and battery charging. As it is expected to be extremely costly for electricity companies and electric network operators to develop and set up their own wireless infrastructure to fully support the concept of EVs, a promising solution is to rent 5G infrastructures. Therefore, AVs will be a new type of cargo for 5G [86; 87; 88]. Smart driving poses high requirements on the transmission latency, reliability, and data rate of 5G networks. In smart driving scenarios, 5G network coverage can be affected by various factors, such as road conditions, other vehicles, and travelling speed. This describes an urgent need for more complex and refined scenario-based planning solutions.  
In this context, Vehicle-to-everything (V2X) communication [89] is defined as information transmission from a vehicle to any entity that may affect the vehicle, and vice versa (Figure 7). It is a vehicular communication system that incorporates other more specific types of communication as V2I (vehicle-to-infrastructure), V2N (vehicle-to-network), V2V (vehicle-to-vehicle), V2P (vehicle-to-pedestrian), V2D (vehicle-to-device), and V2G (vehicle-to-grid). Therefore, the appropriate channel modeling of V2X channels should be incorporated in 5G network simulators as well [90]. In [91], a survey of recent developments in the area of propagation and channel modeling for vehicular communications is provided. In [92], the required steps needed for enabling V2X systems through 5G, along with the related requirements for channel modeling are discussed. In [93], the authors describe methods for deriving channel propagation models via ray-tracing simulations for mmWave transmissions with applications to V2X communications. Finally, in [94], the Internet of Vehicles (IoV) concept is described, which can be viewed as part of intelligent cities and it is characterized as an open and integrated network system, composed by several components, including vehicles, people, and things [95]. In this context, the provision of an IoV application (entertainment on board services delivery) has been evaluated through the ns-3 simulator, employing mmWave communications.
# The Sun and Space Weather
## 8 Solar Cycle Variation of Space Weather Events
### CME Rate-Sunspot Number Relationship  
The overall solar cycle variation of the daily rate of CMEs is very similar to that of SSN in phase. However, the amplitudes of the two phenomena are different in different solar cycles (Figure 19): the CME rates are similar in SCs 23 and 24, but the SSN is much smaller in SC 24. However, fast and wide (FW) CMEs relevant for space weather are very different between the two cycles and consistent with the reduction in SSN. The number of major flares is also smaller in SC 24. The occurrence of less energetic CMEs in SC 24 is further supported by the smaller average CME speed in that cycle (Figure 19c). The rise phase of SC 25 has witnessed CME and SSN behavior similar to those in SC 24.  
Figure 20 further examines the relation between the CME rate and SSN using a scatter plot. Considering all CMEs observed by SOHO/LASCO from 1996 to 2021, we see that the CME rate-SSN correlation is high (r = 0.82). There is a large scatter in the SSN range 100-150, which corresponds to the maximum phase. The scatter is drastically reduced when the data points are separated according to the solar cycle they belong to. The correlation improves significantly to r = 0.88 for SC 23 and 0.92 for SC 24. The different slopes of the regression lines are consistent with the higher amplitude in the CME rate, as shown in Figure 19. However, it must be noted that the CME identification was made by several people, so one cannot rule out the effect of subjectivity on the CME rate in cycle 23 (especially of the slow and narrow CMEs). In addition to the inter-cycle variations, the correlation shows intra-cycle variations as well (Figure 21). While the CME rate-SSN correlation  
is similar in the rise and declining phases of the two cycles, it is relatively small in the maximum phases (r = 0.63 in SC 23 and 0.71 in SC 24). The reduction in the correlation has been attributed to the non-spot CME sources that are abundant during the maximum phase [184; 185]. Figure 22 illustrates this using the locations of prominence eruptions and their occurrence rates obtained from the Nobeyama Radioheliograph images [186]. While the sunspots occur only at low latitudes, prominences occur at all latitudes. There is an abundance of prominence eruptions at latitudes 30-60\({}^{\circ}\), with an additional population at latitudes >60\({}^{\circ}\) due to the rush-to-the-poles phenomena. The occurrence rate and source distribution of prominence eruptions are very similar to those of regular CMEs because the two phenomena are physically related (see [50; 187] and references therein). Thus, the non-spot CME rate is not expected to correlate with SSN, resulting in the overall reduction in the CME rate-SSN correlation.
## 3 Methodology
### Evaluation Matrices  
Precision is the number of correctly predicted samples out of all predicted samples, which is a measure of classifier exactness. Recall is the number of correctly predicted samples out of the number of actual samples, which is a measure of classifier completeness.  
Figure 2: Histogram-equalized image from KDEF dataset.  
The F-measure is a harmonized mean of precision and recall, useful with uneven class distributions [76].  
\[\text{Accuracy }=\ \frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
\[\text{Precision }=\ \frac{TP}{TP+FP} \tag{5}\]  
\[\text{Recall }=\ \frac{TP}{TP+FN} \tag{6}\]  
\[\text{F-measure }=\ \frac{2\times\text{Recall}\times\text{Precision}}{\text{ Recall}+\text{Precision}} \tag{7}\]  
Here, true positive (TP) is when a positive sample is correctly classified as positive, and true negative (TN) is when a negative sample is correctly classified as negative. False negative (FN) is when a positive sample is classified as negative, and false positive (FP) is when a negative sample is classified as positive.  
The AUC-ROC and AUC-PRC are performance metrics commonly used to evaluate the performances of binary classification models. The false positive rate (fpr) is calculated to determine these evaluation metrics. Here is the equation for the AUC-ROC:  
\[\text{AUC-ROC}=\int_{0}^{1}\text{TPR}(fpr)\,d(fpr) \tag{8}\]  
Here, TPR (fpr) represents the true positive rate (TPR) at a given false positive rate (fpr), and d (fpr) represents the differential element for fpr. Here is the equation for the AIC-PRC:  
\[\text{AUC-PRC}=\int_{0}^{1}\text{Precision}(recall)\,d(recall) \tag{9}\]  
Here, Precision (recall) represents the precision at a given recall value, and d (recall) represents the differential element for recall.