esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Major Agriculture Applications and Services
### Irrigation  
On the Earth, oceans hold 97% of all water, but it is saline. The other three percent of water is fresh, among which 2% to 3% of the water is in the form of glaciers [31; 32]. Out of three percent, half percent of freshwater is present as surface water, and the remaining freshwater is groundwater. People have to rely on the half percent of freshwater to sustain their lives. This half percent of freshwater includes water storage in lakes, rivers, and other reservoirs. Agriculture uses almost 75% of freshwater [33; 34]. In many countries, such as Brazil, the demand for crops has increased to 75% [35]. Among several constraints, the lack of proper monitoring tools to estimate crop water requirement is the actual cause of the ad hoc application of irrigation water. For example, the US uses about 80% of freshwater for irrigation purposes [5]. In the United Nations Convention to Combat Desertification(UNCCD) held in 2013, it was found that about 168 countries of the world have a shortage of water for irrigation. Research has showed that there would be a huge water shortage for irrigation throughout the world by 2030 [36].  
Due to the increase in irrigation water requirements, freshwater will only be available to those countries that have adopted the best water resources management practice. For this purpose, to reduce the water shortage, awareness programs should be promoted to adopt effective irrigation practices. Many new irrigation techniques, including sprinkler and drip irrigation methods, are used to reduce the losses and water shortage. Traditional approaches, including furrow and flood irrigation, cause the waste of water. Moreover, traditional irrigation approaches (e.g., flooding) reduce many soil nutrients through water losses, which impact crop yield. Robust technologies and tools are needed, which include smart irrigation techniques. The smart irrigation techniques help estimate crop water requirements based on soil types, antecedent soil moisture, and climatic conditions. In this situation, many new tools such as the IoT play a significant role in measuring air and soil humidity, which ultimately leads to the improvement of crops.  
Crop improvement and its quality can be enhanced by the effective management of soil and water. The use of the IoT, such as for irrigation management based on the crop water stress index (CWSI), is expected to improve crop efficiency significantly [30]. For this reason, the calculation of the CWSI requires one to obtain a crop canopy at different times and temperatures. A CWSI is a wireless sensor-based monitoring system that includes several crop parameter measurement systems that help estimate moisture status, which is used to estimate irrigation water requirements [11].
# Energy and Spectrally Efficient Modulation Scheme for IoT Applications
###### Abstract  
Due to the Internet of Things (IoT) requirements for a high-density network with low-cost and low-power physical (PHY) layer design, the low-power budget transceiver systems have drawn momentous attention lately owing to their superior performance enhancement in both energy efficiency and hardware complexity reduction. As the power budget of the classical transceivers is envisioned by using inefficient linear power amplifiers (PAs) at the transmitter (TX) side and by applying high-resolution analog to digital converters (ADCs) at the receiver (RX) side, the transceiver architectures with low-cost PHY layer design (i.e., nonlinear PA at the TX and one-bit ADC at the RX) are mandated to cope with the vast IoT applications. Therefore, in this paper, we propose the orthogonal shaping pulses minimum shift keying (OSP-MSK) as a multiple-input multiple-output (MIMO) modulation/demodulation scheme in order to design the low-cost transceiver architectures associated with the IoT devices. The OSP-MSK fulfills a low-power budget by using constant envelope modulation (CEM) techniques at the TX side, and by applying a low-resolution one-bit ADC at the RX side. Furthermore, the OSP-MSK provides a higher spectral efficiency compared to the recently introduced MIMO-CEM with the one-bit ADC. In this context, the orthogonality between the in-phase and quadrature-phase components of the OSP are exploited to increase the number of transmitted bits per symbol (bps) without the need for extra bandwidth. The performance of the proposed scheme is investigated analytically and via Monte Carlo simulations. For the mathematical analysis, we derive closed-form expressions for assessing the average bit error rate (ABER) performance of the OSP-MSK modulation in conjunction with Rayleigh and Nakagami-m fading channels. Moreover, a closed-form expression for evaluating the power spectral density (PSD) of the proposed scheme is obtained as well. The simulation results corroborate the potency of the conducted analysis by revealing a high consistency with the obtained analytical formulas.  
IoT; one-bit ADC; MIMO-CEM; OSP-MSK; energy efficiency; spectral efficiency +
Footnote †: journal: Sensors
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 2 Materials and Methods
### Frequency-Selective Surface (FSS) Structure  
The frequency-selective surface (FSS) structure is developed on an FR-4 substrate to improve the gain and bandwidth of the proposed antenna at a frequency of 3.5 GHz. The FSS is developed to resonate at 3.5 GHz. To achieve this, four 5 mm \(\times\) 5 mm square copper metal stubs, situated at a distance of 10 mm from each other, are etched on a single-layer FR-4 printed circuit board (PCB). It will behave as an LC resonating circuit that is tuned at 3.5 GHz. In this FSS structure, every square section behaves as an LC circuit. The total size of the FSS layer is 35.94 mm \(\times\) 33.96 mm \(\times\) 1.6 mm. The width of the FSS is exactly equal to the width of the antenna, i.e., 35.94 mm, while the length of the FSS is 33.96 mm, i.e., 5.01 mm (38.97 mm-33.96 mm) less than the length of the main antenna substrate. The final FSS structure is shown in Figure 4a. Every square copper stub of 5 mm \(\times\) 5 mm will behave as an inductor, while the 10 mm separation between two stubs gives the value of the capacitor. Therefore, the electrical equivalent of the FSS is equal to the parallel combination of the two series combinations of two L and C circuits, as shown in Figure 4b. The overall circuit will behave as a single-tuned series LC circuit [53].
## 7 Numerical Testing  
To show the effectiveness of our proposed multi-step iterative method, we solve a carefully-chosen set of ODEs and PDEs. It is also important to verify the claimed order of convergence numerically, so we adopt the following definition to compute the computational order of convergence (COC):  
\[\text{COC}=\frac{\log\left(\mid\mid\mathbf{F}(\mathbf{x}_{\text{k}+1})\mid \mid_{\infty}/\mid\mid\mathbf{F}(\mathbf{x}_{\text{k}})\mid\mid\mid_{\infty} \right)}{\log\left(\mid\mid\mathbf{F}(\mathbf{x}_{\text{k}})\mid\mid\mid_{ \infty}/\mid\mid\mathbf{F}(\mathbf{x}_{\text{k}-1})\mid\mid\mid_{\infty} \right)}\]  
Consider a system of four nonlinear equations:  
\[\begin{split}&\text{F}_{1}(\mathbf{x})=\text{x}_{2}\,\text{x}_{3}+ \text{x}_{4}\left(\text{x}_{2}+\text{x}_{3}\right)=0\\ &\text{F}_{2}(\mathbf{x})=\text{x}_{1}\,\text{x}_{3}+\text{x}_{4 }\left(\text{x}_{1}+\text{x}_{3}\right)=0\\ &\text{F}_{3}(\mathbf{x})=\text{x}_{1}\,\text{x}_{2}+\text{x}_{4 }\left(\text{x}_{1}+\text{x}_{2}\right)=0\\ &\text{F}_{4}(\mathbf{x})=\text{x}_{1}\,\text{x}_{2}+\text{x}_{3 }\left(\text{x}_{1}+\text{x}_{2}\right)-1=0\end{split} \tag{33}\]  
Let \(\mathbf{w}=[\text{w}_{1},\text{w}_{2},\text{w}_{3},\text{w}_{4}]^{\text{T}}\) be a constant vector, and \(\mathbf{F}^{\prime}(\mathbf{x})\) and \(\mathbf{F}^{\prime\prime}(\mathbf{x})\mathbf{w}=\left(\mathbf{F}^{\prime}( \mathbf{x})\mathbf{w}\right)^{\prime}\) can be written as:  
\[\begin{split}&\mathbf{F}^{\prime}(\mathbf{x})=\begin{bmatrix}0&\text{x}_{3}+ \text{x}_{4}&\text{x}_{2}+\text{x}_{4}&\text{x}_{2}+\text{x}_{3}\\ \text{x}_{3}+\text{x}_{4}&0&\text{x}_{1}+\text{x}_{4}&\text{x}_{1}+\text{x}_{3}\\ \text{x}_{2}+\text{x}_{4}&\text{x}_{1}+\text{x}_{4}&0&\text{x}_{1}+\text{x}_{2}\\ \text{x}_{2}+\text{x}_{3}&\text{x}_{1}+\text{x}_{3}&\text{x}_{1}+\text{x}_{2}&0 \end{bmatrix}\\ &\mathbf{F}^{\prime\prime}(\mathbf{x})\mathbf{w}=\begin{bmatrix}0&\text{w}_{3}+ \text{w}_{4}&\text{w}_{2}+\text{w}_{4}&\text{w}_{2}+\text{w}_{3}\\ \text{w}_{3}+\text{w}_{4}&0&\text{w}_{1}+\text{w}_{4}&\text{w}_{1}+\text{w}_{3}\\ \text{w}_{2}+\text{w}_{4}&\text{w}_{1}+\text{w}_{4}&0&\text{w}_{1}+\text{w}_{2}\\ \text{w}_{2}+\text{w}_{3}&\text{w}_{1}+\text{w}_{3}&\text{w}_{1}+\text{w}_{2}&0 \end{bmatrix}\end{split} \tag{34}\]  
In Table 1, we computed the COC's of different iterative methods, and numerical results confirm the theoretically-claimed convergence orders. The last two columns of Table 1 are similar because the structure of iterative methods Equations (21) and (22) is similar, and iterative method Equation (22) has an extra parameter that does not enhance the convergence order. Table 2 shows the COC for different values of \(\mathbf{m}\) for the iterative method Equation (31), and the COC satisfies the formula \(2(\mathbf{m}+1)\) that was claimed. The numerical computations in Tables 1 and 2 are performed in Mathematica\({}^{\textregistered}\) 10 by setting $\(\$\text{MinPrecision}=\) 82,000 and $\(\text{$\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\
## 2 Problem Formulation  
The overall integrated approach used in this paper for individual wheel brake force estimation consists of the following three steps:  
(i) Vehicle mass estimation.  
(ii) Road friction identification of each wheel (front-left (FL), front-right (FR), rear-left (RL), and rear-right (RR)) using an artificial neural network (ANN) classifier.  
(iii) Individual wheel brake force estimation via a data-driven approach collaborated with estimated mass and road condition.  
For a clear understanding of the proposed integrated system, the structure of the entire estimation system is described in Figure 1.  
In the first step, the vehicle mass is estimated based on suspension deflections under the particular condition of vehicle velocity. A set of measurements for vehicle status is processed and then fed to a trained neural network for road f  
Figure 1: Entire structure of the proposed estimation systems.  
second step. Finally, the estimated values are taken as the known parameters in the third step to estimate the brake forces.  
As the proposed estimator relies on longitudinal dynamics, similar to other model-based methods, the following pre-conditions for the activation of estimation are established:  
(i) The estimation is activated when the brake is applied.  
(ii) The steering wheel angle does not exceed 20 degrees.  
(iii) The acceleration, yaw rate, longitudinal velocity, wheel speed, steering angle, brake pedal, and suspension deflection are assumed to be available during the estimation period.  
When any of the above conditions are not satisfied, the estimation becomes inactive until all conditions are met again.
## 2 Materials and Methods  
### Experimental Setup  
The experimental campaign was conducted at the University of Jaen (Spain, with a Mediterranean-Continental climate), a small-size city (113,000 inhabitants) where periodic Saharan dust episodes occur in summer, together with high levels of olive-tree pollen during May. A comprehensive description of this site in atmospheric terms was carried out in a previous work [32].  
The empirical data used in this paper was collected at the outdoor research facilities located on the flat roof of one of the buildings of the High Technical School of the Engineering and Technology campus, located within the city. These facilities were described in detail in previous studies [37; 38].  
The direct spectrum was measured by an EKOTM MS700 spectroradiometer equipped with a collimator tube and fixed to a two-axis BSQTM D1506 solar tracker for concentrating PV applications, as shown in Figure 1. The collimator tube was made of black PVC and was devised according to the values of the slope angle (1\({}^{\circ}\)) and aperture angle (5\({}^{\circ}\)) specified in the MS700 spectroradiometer datasheet. This experimental setup has been successfully used in earlier work [39].  
The specifications of the above instrumentation include a 10-nm spectral resolution, a wavelength interval of \(\approx\)3.3 nm between 350 and 1050 nm, and a temperature dependency of \(\pm\)1% between \(-\)20 and +50 \({}^{\circ}\)C. The 12-month monitoring period extended from March 2013 to February 2014 at different time intervals. Observations were normally done every 20 s, but the interval had to be relaxed to 5 min during a few periods; this did not influence the results, as discussed in Section 2. The instruments were calibrated by the manufacturer  
Figure 1: Pyheliometer (**a**) and spectroradiometer with collimator tube (**b**) used in this work.  
just before the experimental campaign and after it, in October 2014, with no significant change in sensitivity. Additionally, the broadband DNI was measured with a first-class Kipp & Zonen CHP 1 pyrheliometer. Spectral instances recorded while the broadband DNI was below 50 W\(\cdot\)m\({}^{-2}\) were excluded to avoid the noise introduced by a low signal. Neglecting these measured spectra does not affect the calculations to be presented in what follows because the annual DNI fraction collected below this threshold only accounts for \(\approx\)1% in Jaen [38]. After filtering of the data, 78,772 valid DNI spectra were available for further analysis.  
As discussed earlier, the possible bijectivity between APE and spectral DNI for a given site was apparently investigated in only one study so far [34]. Thus, it is worth mentioning that the same spectroradiometer brand and model that was used in their work is also used here, thus making any comparison of results impervious to experimental differences. Their data filtering and its subsequent processing do not differ substantially from that presented in this paper either, except in a few aspects that will be described hereafter.
## 3 Background
### Amdahl's Law  
Amdahl's law is a mathematical method that allows for having an idea about the maximum improvement that can be achieved through improving a particular part of the system. It was named after [PERSON] and first introduced in 1967. [PERSON]'s law considers P the parallel portion of the system and 1-P the serial part of the system. The maximum speedup is then calculated using the following formula [20]: S(\(N\)) = \(1/(1-\)P) + (P/\(N\)), \(N\) being the number of processors. This formula implies that as we scale up in the number of processor, the speedup tends towards \(1/(1-\)P).
# Management of Obstructive Sleep Apnea in Hospitalized Patients
## 6 Considerations in Specific Inpatient Populations
### OSA and Pulmonary Disorders and Infections  
The presence of OSA and COPD together is termed overlap syndrome [90]. These patients have more profound nocturnal hypoxemia than patients with either disease alone [91]. The management of overlap syndrome is focused on the concurrent treatment of both underlying diseases [92]. CPAP is still an accepted treatment for overlap syndrome and has been shown to improve PaO2, PCO2, pulmonary artery pressures, maximum inspiratory force, and exercise capacity in small studies [93; 94; 95]. Since CPAP does not significantly affect ventilation, the mechanism of action is thought to be related to the off-loading of respiratory muscles as they work against upper airway resistance [92]. For patients who do not improve with CPAP, bilevel ventilation (BPAP) effectively improves hypercarbia and provides ventilation support [90]. If hypoxemia does not resolve with PAP therapy alone, oxygen supplementation may be added to PAP therapy [90].  
Patients with OSA have a higher risk of community-acquired pneumonia [96]. Possible mechanisms include swallowing dysfunction, recurrent aspirations, and an increased risk of GERD [97]. In a retrospective cohort study of 250,907 patients, the incidence of OSA was 6.2% (15,569) [98]. Patients with pneumonia and comorbid OSA tend to be men with a higher BMI compared to the control population, with a higher incidence of CHF and COPD. In addition, patients with OSA and pneumonia were at higher risk of being transferred to the ICU, requiring invasive mechanical ventilation, tracheostomies, and extended hospital stays [98; 99]. Interestingly, mortality in two large retrospective cohorts of patients with OSA and pneumonia was lower than in patients without OSA [98; 99]. This may be related to the chronic intermittent hypoxia associated with OSA and the morbidity-mortality paradox of obesity [27; 100]. Providers caring for pneumonia patients should continue screening and watching for SDB.  
Patients with OSA have been found to have an increased risk of hospitalization with respiratory viral illnesses, such as coronavirus disease 2019 (COVID-19) [101] and influenza [102]. Patients with OSA are at a higher risk of developing an influenza infection based on a population-based cohort study [103]. Patients with comorbid OSA and COVID-19 were found to be at a higher risk for risk-adjusted mortality [104; 105]. In another study, OSA was not only associated with a higher risk of hospitalization but also with a progression to the development of respiratory failure, although the prevalence of OSA was much lower in the control group compared to the reported OSA prevalence in the literature [106]. Hospitalized COVID-19 patients with OSA have been shown to have increased LOS in the hospital as compared to patients without OSA [105].
## 5 Numerical Modeling
### Calculation Stages and Physical-Mechanical Parameters  
Based on the geological-evolutionary model (Figure 4), different stages were developed in the numerical model. These stages allow to simulate the progressive debuttressing of the slope and the consequent variation of the stress-strain state of the rock mass. Considering the information available on the development of the DSGSD, time-dependent rheological models were applied only in the last stages of analyses. A calculation time of 90,000 years was applied to the Late Pleistocene phase, while a calculation time of 30,000 years was applied to the Latest Pleistocene - Holocene phase following the indication in [PERSON] et al. [39]. To limit calculation errors associated with the removal of large volumes of rock, the upper part of the mass present at the beginning of the Late Pleistocene was removed before the time-dependent analyses.  
The previously described parameters, together with the continuous and discontinuous physical-mechanical models, allowed us to characterize the 102 regions in which the section has been divided. All regions have been characterized with an equivalent continuum  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Region** & **Lithology** & **Model** & **Unit** & **Geological** & **UCS** & **Intact Rock** \\  & & & **Weight** & **Strength Index** & **Strength** & **Constant** \\ \hline - & - & - & **7** & **GSI** & \(\sigma_{c}\) & **m\({}_{i}\)** \\ - & - & - & **kN/m\({}^{3}\)** & **-** & **MPa** & **-** \\ \hline \multirow{3}{*}{44} & Upper Cretaceous & Burgers/Hoek- & \multirow{3}{*}{25.9} & \multirow{3}{*}{50} & \multirow{3}{*}{82.4} & \multirow{3}{*}{12} \\  & limestones & Brown & & & \\ \cline{1-1}  & Upper Cretaceous & Burgers/Hoek- & & & \\ \cline{1-1}  & Impestones & Brown & & & \\ \cline{1-1}  & Upper Cretaceous & Burgers/Hoek- & & & \\ \cline{1-1}  & limestones & Brown & & & \\ \hline \multirow{3}{*}{98} & Highly-Jointed Level & Burgers/Hoek- & \multirow{3}{*}{25.6} & \multirow{3}{*}{35} & \multirow{3}{*}{24.9} & \multirow{3}{*}{12} \\  & (HJL) & Brown & & & \\ \cline{1-1}  & Highly-Jointed Level & Burgers/Hoek- & & & \\ \cline{1-1}  & (HJL) & Brown & & & \\ \cline{1-1}  & Highly-Jointed Level & Burgers/Hoek- & & & \\ \cline{1-1}  & (HJL) & Brown & & & \\ \cline{1-1}  & Highly-Jointed Level & Burgers/Hoek- & & & \\ \cline{1-1}  & (HJL) & Brown & & & \\ \hline \end{tabular}
\end{table}
Table 3: Synthesis of physical and strength parameters used for the rock mass. In this table, only the characteristics of the regions directly involved in DSGSD are reported. For location of different regions, please refer to Figure 7.  
approach, while only to the shallow regions directly affected by the DSGSD discontinuous systems have been applied to simulate the joints sets. The physical and mechanical parameters of rock mass are reported in Tables 3 and 4, while the main characteristics of joint sets are summarized in Table 5. For simplicity, only the characteristics of the regions directly affected by the deformation process are shown in these tables.
# Classification of Systems and Maintenance Models
## 3 Classification of Systems
The ILS was operated in intermittent mode (\(l=2\)) because avionics operation is associated with the alternation of flights and stops of aircraft. Thus, the ILS belongs to the subgroup \(I_{1112}\). As shown in Table 1, the maintenance effectiveness indicators were \(P_{O}\) and \(C_{2}^{r}\). **Example 2**. : _We determined the maintenance effectiveness indicators for an aircraft's satellite communication (SATCOM) system._ \begin{table}
\begin{tabular}{c c c} \hline \hline
**System Subgroup** & **Number of a Subgroup** & **Maintenance Effectiveness Indicators** \\ \hline \(I_{1111}\) & 1 & \(P_{O}\), \(C_{1}^{r}\) \\ \hline \(I_{1112}\) & 2 & \(P_{O}\), \(C_{2}^{r}\) \\ \hline \(I_{1212}\) & 3 & \(A_{ma}\) \\ \hline \(I_{1311}\) & 4 & \(A_{a}^{r}\) \\ \hline \(I_{1321}\) & 5 & \(W_{1}^{r}\) or \(V_{1}^{r}\) \\ \hline \(I_{1412}\) & 6 & \(A_{j}^{r}\) \\ \hline \(I_{1422}\) & 7 & \(W_{2}^{r}\) or \(V_{2}^{r}\) \\ \hline \(I_{2111}\) & 8 & \(P_{A}\), \(C_{1}^{n}\) \\ \hline \(I_{2112}\) & 9 & \(P_{A}\), \(C_{2}^{n}\) \\ \hline \(I_{2511}\) & 10 & \(A_{a}^{n}\) \\ \hline \(I_{2512}\) & 11 & \(A_{j}^{n}\) \\ \hline \(I_{2521}\) & 12 & \(W_{1}^{n}\) or \(V_{1}^{n}\) \\ \hline \(I_{2522}\) & 13 & \(W_{2}^{n}\) or \(V_{2}^{n}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Subgroups of technical systems and corresponding maintenance effectiveness indicators. The SATCOM system was repairable (\(i=1\)). The SATCOM system was included in the MMEL [85], which means that the system has little to no effect on flight safety. Hence, the failure consequence is the downtime of the system in a state of unrevealed failure when used as intended, regardless of the downtime associated with scheduled maintenance (\(j=4\)), because scheduled maintenance can be carried out during time intervals when the system is not in flight. An economic assessment of the consequences of failure is impossible (\(k=1\)). The SATCOM was operated in intermittent mode (\(l=2\)). Thus, the SATCOM system belongs to the subgroup \(I_{1412}\). As shown in Table 1, the maintenance effectiveness indicator was \(A_{i}^{r}\).