esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Evaluation of the Interactive Formula with Machine Learning Models  
To evaluate the validity of the predictive formula, the results obtained were compared with various classification algorithms known in the scientific literature. The train-test  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Variables** & **Total RR1** & **View \% RR1** & **Total RR2** & **View \% RR2** \\ \hline Outpatient consultations & 777 & 6.61 & 233 & 7.44 \\ Discharged home & 3353 & 28.50 & 1037 & 33.12 \\ Primary care team & 2978 & 25.32 & 846 & 27.02 \\ Deceased in emergency department & 16 & 0.14 & 8 & 0.26 \\ Admitted to hospital & 4019 & 34.17 & 815 & 26.03 \\ \hline \hline \multicolumn{5}{l}{Triage level} \\ I & 24 & 0.21 & 10 & 0.33 \\ II & 1545 & 13.27 & 598 & 19.85 \\ III & 5414 & 46.49 & 1585 & **52.61** \\ IV & 4251 & 36.50 & 787 & 26.12 \\ V & 411 & 3.53 & 33 & 1.10 \\ \hline \hline \multicolumn{5}{l}{Length of stay \textgreater{} 4 h} \\
4 h to 4 h 30 min & 1462 & 12.43 & 388 & 12.39 \\
4 h 30 min to 5 h & 1420 & 12.07 & 341 & 10.89 \\
5 h to 5 h 30 min & 1321 & 11.23 & 288 & 9.20 \\
5 h 30 min to 6 h & 1169 & 9.94 & 280 & 8.94 \\
6 h to 7 h & 1846 & 15.69 & 495 & 15.81 \\
7 h to 8 h & 1354 & 11.51 & 316 & 10.09 \\
8 h to 9 h & 929 & 7.90 & 241 & 7.70 \\
9 h to 10 h & 616 & 5.24 & 153 & 4.89 \\ More than 10 h & 1646 & 13.99 & 629 & **20.09** \\ \hline \hline \multicolumn{5}{l}{Time to first attention} \\ Up to 5 min & 79 & 0.67 & 61 & 1.95 \\
5 to 15 min & 661 & 5.62 & 239 & 7.63 \\
15 to 30 min & 1783 & 15.16 & 534 & 17.06 \\
30 min to 1 h & 2691 & 22.88 & 701 & 22.39 \\
1 h to 1 h 30 min & 1961 & 16.67 & 478 & 15.27 \\
1 h 30 min to 2 h & 1455 & 12.37 & 369 & 11.79 \\
2 h to 2 h 30 min & 981 & 8.34 & 254 & 8.11 \\
2 h 30 min to 3 h & 688 & 5.85 & 161 & 5.14 \\ More than 3 h & 1464 & 12.45 & 334 & 10.67 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Analysis of emergency department outcomes and metrics.  
technique was used for the evaluation, which separates the training data. The statistical data presented in Section 4.1 were used to feed the algorithms, and the 6 months of data following the study interval were used for testing the predictive formula, and comparing them with other classic artificial intelligence classifiers.  
As can be seen in Table 8, the prediction results of the predictive formula are relatively similar to the best automatic techniques, maintaining a good balance between sensitivity and specificity, which makes it perfectly acceptable for use in the prediction of patients with high LOS, taking into account that it is a fully traceable formula.
# Biomass Refined: 99% of Organic Carbon in Soils
## 1 Introduction
### Defining biotic Biomass  
The term Biomass is the quantity of material of organic origin present in a habitat or biome. It generally refers to living or recently dead organisms, plus any byproducts of those organisms be they virus, microbe, fungi, plant, or animal. In a different, though related, sense, it is applied to plant matter or animal waste used or intended as a source of fuel. In a strict sense, it refers to a single species in a limited habitat or, in the broadest view, it encompasses all organisms that are living or dormant, or dead (sometimes labelled Necromass), plus their specific products (e.g., GRSP). In addition to plant, fungal, or microbial exudates, products may include molluscs shells or earthworm calciferous secretions, plus eggshells, bones, etc.--which are generally (mis)classed as inorganic carbon in soil analyses. Biomass is usually expressed as mass for a defined area, or as density per unit area (for soil or land), or per unit volume (for aquatic or marine, often summarized per unit area too), these are averaged and typically multiplied by planimetrically flat area.  
Measurements of biomass are obtained by two basic methods: Direct field sampling (cores or quadrats with flat surface areas taken perpendicular to the centre of the Earth), and via remote sensing (e.g., tower monitors, aircraft, or satellites). Mean values per unit area are extrapolated for biomass based upon simplistic model assumptions. Although planimetrically flat surface areas are appropriate for aquatic metrics, for the terrestrial realm, a manifest reality is of terrain with undulating topography overlain by rugosity of soils.  
Whereas Ocean or Atmospheric tallies are reported to full depth or altitude, for some reason, Soil budgets are usually reported only for superficial layers, often just cm or m. Apart from true soil depth, other underappreciated factors are friable saprock and the extraction of recalcitrant glomalin/GRSP fractions, as is noted above and as detailed later.  
Terrestrial values may be doubled for depth for inappropriately shallow samples and doubled again for topographical terrain [16], plus other omissions added. A simple example is data from [17] showing Earth's living organic matter (biomass) dominated by autotrophic and photosynthesiszing land organisms. Their total 2402 Gt dry weight for both above- and under-ground parts of plants is equivalent to 1201 Gt C biomass which, doubled for terrain, is \(\sim\)2400 Gt C (>99.9% of Earth's total). In context, their 0.08 Gt C Ocean Phytomass (plant biomass) is 30,000 times smaller.
## 4 Development and Simulation of a Predictive S&OP Methodology
### Simulation Case Study
#### 4.3.1 Design of the Supply Chain Flow and Planning Characteristics for a Manufacturer
Therefore, in cyclical demands, there are periods with mid-low utilization of production capacities, with high utilization employed in other planning periods. Personal planning: both models use demand plans to determine the number of employees that require a planning period. Moreover, the flexibility of the employees can increase or decrease the capacity of shops by six units per day. Based on the production requirements, the system decides whether to increase the quantity of employees or to reduce it. As a result, operational expenses or savings can be obtained. Procurement planning: Both models receive procurement plans from the manufacturer based on the demand forecast. Distribution planning: Both models receive distribution plans from the manufacturer based on the WIP units following a push-strategy. Investment planning: Both models have the capability to decide upon new investments for new capacities at the supplier, the manufacturer, and the distributor level. However, the parameters and implementation time are different. The predictive S&OP model assesses the investment based on the control limits derived from the root cause analysis and preparation of solutions. As indicated in Table 1, the options for the predictive S&OP model are shown in the right column. These options represent the main difference between the two models in comparison. The predictive S&OP model is able to prepare management decisions and their implementations to respond to future potential inefficiencies. By doing so, control limits can be defined. This allows the model to decide on management changes and their implementation efficiently whether if it is: For procurement management due to preagreements between supply chain parties: * Supplier capacity expansion. * Reduction of replenishment lead time due to a new warehouse or due to a new consignment stock due. For production management due to internal decision-making committees: * Expanding production capacities. * Changes in the quantity of employees. * Expanding storage capacity. * Changes in planning method and parameters for volume leveling. For distribution management due to preagreements between supply chain parties: * New warehouse for reducing distribution lead times. In this context, Table 7 shows the main differences in the simulation logic of both models:
# Achievable Rate of NOMA-Based Cooperative Spectrum-Sharing CRN over Nakagami-\(m\) Channels
## 6 Conclusions  
We derived ergodic achievable rate expressions at the PR and SRs for the NOMA-based cooperative spectrum-sharing CRN. In this paper, we assumed that communication channels experience Nakagami-\(m\) fading. The analysis presented can be used for both  
Figure 4: Ergodic achievable rate performance against different values of \(\alpha_{0}\).  
Figure 5: Ergodic achievable rate performance versus the number of SRs, \(K\).  
integer and non-integer fading index values. We compare the results of the derived ergodic achievable rate expressions with the numerically computed achievable rate results. The derived ergodic achievable rate's results match well with the simulation's results.  
The analysis presented in this study will be useful in predicting the performance of the NOMA-based spectrum-sharing CRN in Nakagami-\(m\) fading channels, where fading severity parameter \(m\) can be varied to any integer and non-integer value. In future work, the statistical performance of NOMA-based spectrum-sharing CRNs can be characterized in the presence of intelligent reflecting surfaces (IRSs), which can be useful when direct transmission links between communicating nodes are blocked. The deployment of IRSs can also be utilized to improve the received signal strength in NOMA-based CRNs.  
This article was prepared via the collective efforts of all authors. Conceptualization, S.H.A., B.A., J.M., A.K., M.A.J., J.A. and N.H.; critical review, S.H.A., B.A., J.M., A.K., M.A.J., J.A. and N.H.; writing--original draft, S.H.A., B.A. and J.M.; writing--review and editing, A.K., M.A.J., J.A. and N.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data will be available on request from the corresponding author.  
The authors declare no conflicts of interest.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} \multicolumn{2}{l}{ACI} & Adjacent-Channel Interference \\ ATSC & Advanced Television Systems Committee \\ ATSC 3.0 & Advanced Television Systems Committee, third generation \\ AWGN & Additive White Gaussian Noise \\ BCH & Bose-Chaudhuri-Hocquenghem \\ BER & Bit error rate \\ BICM & Bit-Interleaved Coded Modulation \\ CFO & Carrier frequency offset \\ CP & Cyclic prefix \\ CP-OFDM & Cyclic prefix--Orthogonal Frequency-Division Multiplexing \\ CR & Code rate \\ CRs & Code rates \\ DML & Deterministic maximum likelihood \\ DTT & Digital Terrestrial Television \\ DVB & Digital Video Broadcasting \\ DVB-H & Digital Video Broadcasting--Handset \\ DVB-S2 & Digital Video Broadcasting Satellite, second generation \\ DVB-T & Digital Video Broadcasting--Terrestrial, first generation \\ DVB-T2 & Digital Video Broadcasting--Terrestrial, second generation \\ ETSI & European Telecommunications Standards Institute \\ EVM & Error Vector Magnitude \\ F-OFDM & Filtered-Orthogonal Frequency-Division Multiplexing \\ FBMC & Filter Bank Multicarrier \\ FEC & Forward Error Correction \\ FFT & Fast Fourier Transform \\ GFDM & Generalized Frequency-Division Multiplexing \\ HDTV & High-Definition Television \\ ICI & Intercarrier Interference \\ IFFT & Inverse Fast Fourier Transform \\ ISI & Intersymbol Interference \\ ITU & International Telecommunication Union \\ LDPC & Low-Density Parity Check \\ LLR & Log-Likelihood Ratio \\ LoS & Line of sight \\ LTE & Long-Term Evolution \\ MDPI & Multidisciplinary Digital Publishing Institute \\ \end{tabular}
## 3 Measurement Results and Discussion
### Mode Control in Electronic Multiplier Sources  
Initially, the focus of the investigation was attributed to spatial features of THz radiation delivered from electronic multiplier sources. One can note that profile was artificially perturbed by detuning the source from the optimal working regime. The results are shown in Figure 3, where the performance of Ti-mB to record a continuous wave (CW) mode is demonstrated for different polarizations and at three selected frequencies 0.6 THz, 0.3 THz and 0.15 THz. For comparison, measurement results registered by the pyroelectric detector are illustrated in a panel (a) of Figure 3, while the beam profiles at different polarizations are presented in panels (b) and (c). Pyroelectric detector in principle does not detect the polarization of the incident light, thus, registered signal represents total power value of the THz radiation. Despite this fact, the signal values induced in microbolometer and presented in panel (b) are 4-7 times larger than those in panel (a) if compared within the same frequency. In contrast, the microbolometer is polarization-sensitive, thus, not only the mode profiles, but also their fine structure are clearly resolved, which is visible in Figure 3b,c. Comparing the polarization extinction ratio (PER), it amounts to 11 dB, 11.8 dB and 7.8 dB at 0.6 THz, 0.3 THz, 0.15 THz, respectively, which indicates the suitability of the Ti-mB for polarization-sensitive applications. Special emphasis is required to the results obtained at 0.15 THz (panel (c)). Estimates show that the microbolometer connecting wires can act as additional coupling antennas disturbing consequently precise control of the radiation polarization and the mode shape.
# Electrical Equalization Analysis of PAM-4 Transmission in Short-Reach Optical Systems
## 1 Introduction  
In the last years, we have seen a significant increase in the data transmission rate over optical communication systems, which requires the adaptation of both infrastructure and communication methods. One of these is short-range communication in data centers, which requires especially high rates and capacity. A leading solution seen today is four-level pulse amplitude modulation (PAM-4), which enables working at 100 Gbit/s (and beyond) at a relatively low cost and with less losses [1, 2].  
Generally, any optic system, and especially at high data-rates, is significantly influenced by the chromatic dispersion (CD), which introduces inter-symbol interference (ISI) along with other ISI contributors in the transmitter and receiver side, mainly due to the limited bandwidth (BW) of their components.  
The intensity modulation and direct detection techniques indeed have the advantage of simplicity; however, they can cause critical problems, especially at high rates, such as chirping, signal-dependent noise, and non-linearity of the system. In the presence of ISI, the non-linearity impact is even greater and can stifle the detection of the data.  
One of the most common and effective ways to deal with the ISI impairment is the use of electrical equalizers [3, 4].  
Research on electrical equalizers in optical systems started in the early 1990s [5] and was specifically enhanced around 2000 [3, 4, 6, 7] and until the last few years [8, 9, 10, 11, 12]. To our knowledge, this is the first time that the performance analysis of the feedforward equalizer (FFE) and decision feedback equalizer (DFE) in a short-reach optical system has been described, emphasizing the impact of the system critical link parameters: transmitter and receiver low-pass filter (LPF) 3 dB cutoff frequency (\(f_{c}\)), fiber length and numbers of the equalizer taps.  
Most of the research on electrical equalizers in optical systems neglect the filter BW and the number of taps impact, and those parameters are fixed and not discussed [4, 6, 9, 10, 11, 12]. We note that [3, 8] presented a comparison between the variable number of taps but the results and the reason was not discussed broadly; moreover, they referred to long-distance fiber.  
The remainder of this paper is divided into four sections. Section 2 presents theoretical details and equations, Section 3 covers the structure and main properties of the simulation, Section 4 shows the results of the simulations, and Section 5 presents the conclusions of this research.
# A Photonically-Excited Leaky-Wave Antenna Array at E-Band for 1-D Beam Steering
## 2 Leaky-Wave Antenna  
This design is motivated by the necessity of increasing the emitted power by a scalable layout, with beam steering capabilities, and a 10% relative bandwidth around the center frequency (81 GHz). One of the simplest beam steering methods is frequency steering with a 1-D periodic LWA. In this configuration, a traveling wave encounters periodic perturbations in one direction, so that it leaks progressively. If the electrical path between successive perturbations is such that the traveling wave encounters them in phase, the antenna will radiate a beam at or near broadside, as is clear from the array factor. Conversely, as frequency increases, the electrical path between elements will also increase, and the array factor will produce a beam moving from the backward to the forward quadrant [19].  
Figure 1 shows a top view of the proposed antenna, its different parts, and the unit-cell of the radiating section, all described in detail hereinafter. The geometrical features were printed on a grounded Duroid 5880 substrate (\(\epsilon_{\rm r}=2.24\), tan\(\delta=0.004\) at 60 GHz), suitable for high-frequency applications. At mm-wave frequencies, these values are a more exact determination of the relative permittivity than the typical values used (\(\epsilon_{\rm r}=2.20\), tan\(\delta=0.0009\) at 10 GHz) [20]. The chosen thickness is the minimum among the standards (127 \(\upmu\)m) to minimize the radiation losses and the power lost to surface-waves [21] of the different printed structures. The antenna is excited by a \(2\times 1\) array of UTC-PD on InP. Each UTC-PD presents a 50-\(\Omega\) grounded coplanar waveguide (G-CPW) output and feeds two rows of the array. The length of each row is truncated by a matched patch, where a longer row will  
feature a more directive beam. Additionally, this design is scalable in the direction perpendicular to the rows, so that more rows can be added. By controlling the number of rows and their length, one can easily accomplish symmetrical patterns (at E- and H-planes) or increase the directivity or the emitted power. In this respect, the proposed structure is advantageous to other classical sub-millimeter wave antennas, such as horns or on-chip antennas with silicon substrate lenses, which are more challenging to escalate. The antenna was designed using a full-wave simulator (ANSYS HFSS, version 19.0.0) and transmission line modeling for fast and preliminary layouts.
## 5 Conclusions  
Artificial Neural Networks along with traditional regression methods were applied in the long-term power output forecasting of a PV park, aimed at the reliable assessment of PV panels' efficiency degradation rate and fault diagnosis. The models employed are a linear regression model and a feed-forward ANN model. Available data from the 8-year monitoring period were employed. Initially, data from the first three years of operation  
Figure 17: Evolution of nRMSE and MAPE over the years for the regression and ANN models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**2019**} & \multicolumn{3}{c}{**2020**} \\ \hline  & **M1\({}^{\ddagger}\)** & **M2\({}^{\ddagger}\)** & **M3\({}^{\ddagger}\)** & **M1\({}^{\ddagger}\)** & **M2\({}^{\ddagger}\)** & **M3\({}^{\ddagger}\)** \\ \hline N & \multicolumn{3}{c}{14,312} & \multicolumn{3}{c}{14,477} \\ \hline RMSE [W] & 6.4 & 5.7 & 5.5 & 0.1 & 0.1 & 0.1 \\ \hline MBE [W] & 302.6 & 314.5 & 276.2 & 43.4 & 83.7 & 46.6 \\ \hline MAPE [\%] & \(-5.75\) & \(-5.97\) & \(-5.24\) & \(-0.82\) & \(-1.59\) & \(-0.89\) \\ \hline MAE [W] & 411.8 & 328.6 & 300.2 & 395.4 & 349.9 & 346.4 \\ \hline nRMSE [\%] & 12.5 & 11.2 & 10.8 & 9.7 & 9.8 & 9.2 \\ \hline COR & 0.977 & 0.982 & 0.983 & 0.981 & 0.983 & 0.984 \\ \hline PR & \multicolumn{3}{c}{0.84} & \multicolumn{3}{c}{0.89} \\ \hline kWh/kWp & \multicolumn{3}{c}{1527.2} & \multicolumn{3}{c}{1545.89} \\ \hline nAverage [\%] & \multicolumn{3}{c}{13.47} & \multicolumn{3}{c}{13.97} \\ \hline \multicolumn{7}{l}{\({}^{\ddagger}\)Standard deviation [\%]} & \multicolumn{3}{c}{1.56} & \multicolumn{3}{c}{1.36} \\ \hline \end{tabular} \({}^{\dagger}\) Regression model. \({}^{\ddagger}\) Feed-forward artificial network (3-year training period). \({}^{\ddagger}\) Feed-forward artificial network (5-year training period).  
\end{table}
Table 3: Statistical and energy metrics for 2019–2020 (all three model versions).
# Recent Advances in Wearable Devices for Non-Invasive Sensing
## 5 Wireless Technologies  
A wireless power supply and data transmission are essential techniques for a fully-wearable system. These wireless technologies enable continuous, on-demand monitoring of health without bulky, wired instruments, and this further facilitates the real-time reporting of the obtained data to clinicians, thereby allowing timely and convenient diagnoses.
## 3 EMF-Aware Routing Framework for WMNs
### EMF-Aware Routing Algorithm
#### 3.2.2 An Optimization Problem to Minimize the EMF Exposure  
Once we have presented the network model, we pose the optimization problem that needs to be solved, in order to establish those routes that would minimize the exposure. Since the overall cost (at least the one related to the transmission power) is proportional to the flow traversing an edge, we can state a problem similar to the Minimum Cost Flow Problem (MCF) one, whose main goal is to distribute the overall traffic throughout the  
Figure 2: Example of transformation of a graph embracing 3 nodes. Original and transformed graph are shown in (**a,b**), respectively.  
graph, so that the global transportation cost is minimized. We represent values assigned to edges using the indexes of the nodes connected by the corresponding edge. This way, the edge connecting nodes \(i\) and \(j\) are represented by the pair \((i,j)\). Then, for any edge \((i,j)\) in graph \(G\), the problem can be expressed analytically as follows:  
\[\min\sum_{\forall(i,j)\in\mathcal{E}}c_{ij}\cdot x_{ij} \tag{5}\]  
\[\textbf{s.t.}\quad\forall i\in\mathcal{V}\quad\sum_{j:(i,j)\in \mathcal{E}}x_{ij}-\sum_{j:(j,i)\in\mathcal{E}}x_{ji}=b(i) \tag{6a}\] \[\forall(i,j)\in\mathcal{E}\quad u_{ij}\geq x_{ij}\geq\ 0\] (6b) \[\sum_{i=1}^{|V|}b(i)=0 \tag{6c}\]  
where \(c_{ij}\) and \(u_{ij}\) represent the cost per flow unit and the edge capacity, respectively; \(b(i)\) accounts for the traffic originated (\(b(i)>0\)) or received (\(b(i)<0\)) by each node; \(x_{ij}\) is the flow traversing every link of the graph. Constraint (6a) corresponds to the flow balance at every node (if \(b(i)\) equals 0, the node is neither a source or a sink), constraint (6b) ensures that the edge capacities are respected, and constraint (6c) ensures that the global flow balance is maintained.