esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Data Used  
The data used for training and evaluation of the approach is owned by the New York Independent System Operator (NYISO) and can be freely accessed [28]. It has been already used in [29] and therefore it will be possible to compare the results of both approaches. NYISO's data consists of integrated hourly load forecasts and corresponding measurements of active load power. Moreover, load information, NYISO additionally offers time series representing the price for power delivery, losses, and congestion in USD and forecasts of ambient and wet bulb temperature in Fahrenheit produced by different weather stations.  
The NYISO data set is almost complete (with a few missing inputs) for all eleven zones. Because it includes also the forecast results of the utility, it is very adequate for research purposes.  
The training, test, and evaluation data used in the presented approach contain load, temperature, and wet bulb time series from the years 2013 until 2015, 2016, and 2017 respectively. The decision to not consider price information was motivated by the very low correlation between load and price data. More details related to the data set are in [29].  
The most strongly correlated features are the load power and the ambient temperature. However, this relationship differs concerning the season as described in [29] and varies strongly depending on the temperature range. Figure 1 shows daily load curves for subsequent working days in May 2017 in the New York City data. Remarkable are strongly different daily peak values causing different load curve shapes. The peak values increase from 6 [GW] on 15.05.17, over 6.5 [GW] on 16.05.17 until 8 [GW] on 17.05.17.  
The corresponding daily temperature curves shown in Figure 2 document the same order of increase of the peak values from 15.05.17 until 17.05.17 indicating a relationship between both variables.  
Figure 3 shows this relationship which is non-linear and differs strongly for each day discussed. Because of this fact, the conclusion is self-evident, which is that the commonly used sliding window approach for the choice of the training data such as for example in [24] cannot be successful here. Instead, the choice of the training is based on similar-day approach [12; 30]. The reason is that following days can have strongly differing load patterns due to changing weather conditions. Therefore, the similar day approach seems to be superior.  
Figure 1: Load curves on subsequent days in May 2017 for the New York City data.  
Figure 2: Temperature curves on subsequent days in May 2017 for the New York City data.  
Before the training, time series with missing entries or outlier values are excluded from the data set. The training, test, and evaluation are executed on the z-score normalized data according to Equation (1) with \(\mu\) the feature mean and \(\sigma\) feature standard deviation. For the calculation of the accuracy of the method, the data are however transformed back to the original value space.  
\[z_{i}=\frac{x_{i}-\mu}{\sigma}. \tag{1}\]
# Model for the Prediction of Rain Attenuation Affecting Free Space Optical Links
## 3 Comparison with Analytical Methods  
The rain attenuation statistics estimated using the methodology described in Section 2 are expected to be accurate based on the prediction results shown in [24; 25], where MultiEXCELL rain maps were used in the context of terrestrial radio links. On the other hand, the whole simulation process is rather complex and time consuming, which prompted us to search for an analytical approach aimed at predicting rain attenuation on FSO links. The rationale is to use the \(P(A)\)s derived from MultiEXCELL maps as the reference curves against which the prediction performance of existing analytical models can be assessed. Specifically, we have taken into account the following models, which were developed to predict rain attenuation on terrestrial radio links: ITU-R P.530-16 [10], Brazilian [11], Lin [12], and Moupfouma [13]. Indeed, all these models rely on analytical expressions that include the \(k\) and \(\alpha\) power-law coefficients for the conversion of the rain rate into specific attenuation: the question addressed in this section is whether these models can be extended to predictions in the optical frequency range by simply using the adequate set of \(k\) and \(\alpha\) coefficients (see Table 1). First, the models are briefly described and then their prediction performance is compared.
## 2 Datasets
### AmeriFlux Site Data  
The AmeriFlux network is a community of sites and scientists measuring the amounts of carbon, water, energy fluxes, and related environmental variables in ecosystems across the Americas [46]. It supplies a long period of field observations and features wide coverage of forests, grasslands, croplands, shrub lands, wetlands, savannas, and other geographies (e.g., urban). A total of 166 sites are distributed in North and South America, of which 109 feature continuous radiation measurement. Level-2 data of downward and upward radiation from these 109 sites were downloaded from the Carbon Dioxide Information Analysis Center (CDIAC) at Oak Ridge National Laboratory (ORNL). The instruments mounted on the tower including [PERSON] and Zonen (CNR1, CM-3, OR CM-6), and the Eppley-PSD albedometer/pyranometer. The instruments were fitted with domes to collect radiation fluxes in the broadband shortwave domain (0.3-2.8 \(\upmu\)m). Data received from each site were reviewed and incorporated into the network of the AmeriFlux database. The data review process includes checking for consistent units, naming conventions, reporting intervals, and reforming to ensure consistency with the larger network-wide database [47]. Radiation data were collected every half hour during the years indicated in Table 1 for each site. The overall range of years recorded at one site or more is 1995 to 2013. A region of 10 \(\times\) 10 km around each site was selected as the research area.
## 6 Strengths, Limitations, and Future Research Directions  
The review made it possible to identify not only the strengths of each architecture (outlined in Section 6.1), but also their potential for improvement (described in Section 6.2). Future research directions were also derived from this and are presented in Section 6.3.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 4 Physical Layer Tests Analysis
### Synchronization Analysis  
In this part of the study, we conducted some experiments to determine the best PN sequence to be used for synchronization in this long-haul link. The testbench is based on blocks of 3 PN sequences [30] called training blocks. Two types of PN sequences were tested: m-sequences [28,29] and CAZAC [31]. For each type of sequence, four chip lengths were used: 255, 511, 1023 and 2047. Three different upsample factors (6, 8 and 10) were applied to each of the sequences. The test bandwidths were chosen based on the knowledge obtained from previous tests that used spread spectrum techniques [8,9].  
This testbench has two different objectives: (i) identifying the differences in the performance between m-sequences and CAZAC sequences and (ii) obtaining the combination of chip length and bandwidth, which provides the best performance for our scenario.  
Maximum peak signal versus the mean signal amplitude of the synchronization triplet correlated with the received one (from now, Max/Mean) is the selected figure of merit in the performed tests. The choice of this figure is based on the statement that in terms of synchronization, it is crucial to find the acquisition point minimizing the false alarm probability. The Max/Mean value fulfills this condition. The Max/Mean value is then normalized dividing it by the length of each sequence. The resulting measure gives a sense of synchronization taking into account the length of each tested sequence. If the measure was not normalized, the longest sequence in terms of Max/Mean would always be the best.  
Table 3 displays the tests of the synchronization analysis from which we can reach the conclusion that the normalized Max/Mean is a PN sequence with length of 255 chips. Moreover, when comparing the bandwidths, the best situation is presented by the wider one, as it performs better against channel selective fading. Another conclusion extracted from the table is that the results show no evidence of significant differences between CAZAC and m-sequences, because high noise and lossy environment dissipates the difference in terms of auto-correlation between both types of sequence.  
According to our results we conclude that the most suitable synchronization sequence is the 255 chip, 16 kHz, m-sequence. This synchronization block contains three sequences and lasts for 15.3 ms per sequence, becoming 45.9 ms for the entire block.
# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 2 Materials and Methods  
### Study Area  
The study area was Poyang Lake (115\({}^{\circ}\)47\({}^{\prime}\) E-116\({}^{\circ}\)45\({}^{\prime}\) E, 28\({}^{\circ}\)22\({}^{\prime}\) N-29\({}^{\circ}\)15\({}^{\prime}\) N), Jiangxi province, China, in the lower Yangtze River Basin (Figure 1). Poyang Lake is the largest freshwater lake in China and constitutes a major hydrological subsystem of the middle Yangtze basin [29]. The climate is characterized as a sub-tropical, humid monsoon climate with 1620 mm mean annual precipitation and an annual average temperature of about 17 \({}^{\circ}\)C [2; 63]. Poyang Lake receives inflows from five rivers--namely, the Xiushui, Ganjiang, Fuhe, Xinjiang, and Raohe [37].  
Poyang Lake exhibits large inter-annual variations in its water level (Figure 2), which reflects in changes of the water surface area. The water level rises during the wet season, and the water surface expands. In the wet season, it is the largest freshwater body in China and can cover up to 3500 km\({}^{2}\) by the end of the season (April to September). In the dry season (October to March), the water level drops and Poyang Lake becomes smaller than 1000 km\({}^{2}\), with several small lakes remaining.  
Figure 1: Spatial location and extent of the study area of Poyang Lake in Jiangxi Province, People’s Republic of China. (**a**) shows the administrative divisions of China, the green polygon in (**b**) shows Jiangxi Province, and (**c**) shows Poyang Lake. The blue polygons in (**b**) show the swaths of Sentinel-1 SAR data and the red polygon in (**c**) shows the study area.  
Figure 2: The water level data and a statistic histogram for Poyang gauging stations in Poyang Lake from October 2014 to March 2021 (Wusong water level datum). Some data are missing due to the maintenance of the website. The red horizontal line represents the warning water level, 19.5 m. The blue line is for the trend of water level, and the red dots are for water level records on 27 May 2020, 2 June 2020, and 8 June 2020. The green bars are for the count in each water level interval (64 bins).
# Upgrading of the L-P Band Cryogenic Receiver of the Sardinia Radio Telescope: A Feasibility Study
## 3 Study of the Receiver Criticalities and Workflow for the Upgrading Feasibility Study  
The receiver upgrading feasibility study of this paper considers those technical aspects that can improve the performances of the system. One of them regards certainly the RFIs mitigation, that can derive from the surrounding area, described in Section 3.1, or that can be self-produced by internal components (i.e., the original electronic control system and the feeding process of the active microwave components), aspects addressed in Section 3.2. Finally, some logistical changes are proposed in Section 3.3.
## 3 Effects of Joints on the Tensile Properties
### Effects of Parallel joints on the Tensile Properties
#### 3.2.1 Effects of Joint Spacing and Dip Angle of Two Parallel Joints  
Joint spacing is the perpendicular distance between adjacent joints, and usually determines the sizes of the blocks making up the rock mass [2]. To investigate the effects of joint spacing (\(d\)) and dip angle (\(\alpha\)) of two parallel joints on \(\sigma_{t}\) and \(E_{t}\), four sets of models with different dip angles (\(\alpha=20\), \(30\), \(40\), and \(50^{\circ}\)) were built (as illustrated in Figure 4) under the condition of \(n=2\), and for each set of the model, four subsets with different joint spacing (\(d=10\), \(20\), \(30\), and \(40\) mm) were analyzed. The test program specifics are shown in Table 4, and the stress-strain curves under uniaxial tensile load are shown in Figure 5. The results are shown in Table 5.  
As can be seen, the slopes and peaks of the curves, with same dip angle but different joint spacing, are almost the same, which means that the effect of joint spacing on \(\sigma_{t}\) and \(E_{t}\) are negligible. When \(d\) decreases from \(40\) to \(10\) mm, \(\sigma_{t}\) and \(E_{t}\) merely decrease by \(0.003\) MPa and \(0.002\) GPa, respectively. This  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sets of models** & \(\alpha\) & **Subsets with respect to \(d\)** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{\(20^{\circ}\)} & 10 mm \\  & & 20 mm \\  & & 30 mm \\  & & 40 mm \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{\(30^{\circ}\)} & 10 mm \\  & & 20 mm \\  & & 30 mm \\  & & 40 mm \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{\(40^{\circ}\)} & 10 mm \\  & & 20 mm \\  & & 30 mm \\  & & 40 mm \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{\(50^{\circ}\)} & 10 mm \\  & & 20 mm \\  & & 30 mm \\  & & 40 mm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model designs for rock specimens with two parallel joints.  
Figure 3: The relationship between joint dip angle \(\alpha\) and \(\sigma_{t}\).  
result is obtained under the premise that no joint development is considered. In practice, joints will develop, and adjacent joints will connect and merge under mechanical loading. However, this study focuses on the effects of existing joints on the tensile properties. Similar to the results from Section 3.1, \(\sigma_{t}\) and \(E_{t}\) are affected by the dip angle of the two parallel joints, but the effects vary. The dip angle has a greater effect on \(\sigma_{t}\) than \(E_{t}\). As the dip angle increases from 20 to 50\({}^{\circ}\), \(\sigma_{t}\) increases from 0.88 to 1.05 MPa, while \(E_{t}\) lightly increases from 0.71 to 0.73 GPa. When comparing with Table 3, it can be noticed that the value of \(\sigma_{t}\) is identical in the cases of single-jointed and double-jointed models with the same dip angle.  
Figure 4: Rock specimen model with parallel joints.  
Figure 5: Tensile behaviors of double-jointed rock specimens with different joint dip angles.
## 4 Simulations and Analysis
### Storage Effectiveness Analysis
Meanwhile, the probability that the MS directly establishes a session key with sensor nodes could increase with the increasing number of polynomials and keys stored in sensor nodes, and could decrease with the increasing of the size of polynomial pool. Figure 7 shows that the directly connected probability of the proposed scheme is higher than that of the Amar scheme and Liu scheme, but it is a little bit lower than the modified Liu scheme. The reason is that the modified Liu scheme only considers assigning a different number of polynomials to mobile nodes and fixed sensor nodes, while the proposed scheme also takes into account the probability of using the shared keys simultaneously to improve the robustness of the network in the process of calculating the direct connectivity.
# A Two-Stage Routing Protocol for Partitioned Underwater Wireless Sensor Networks
## 4 Results and Discussion
### Partitioned Network
#### 4.2.1 Packet Delivery Ratio  
Figure 15 shows the number of delivered packet by PH and NPH for 25, 50, 75 and 100 nodes. The proposed scheme (PH) achieves higher packet delivery ratio in all the cases. The reason for the better performance is the partition handling mechanism which allows the partitioned nodes, which cannot receive a beacon because of being out of the range of all the beacon-carrying nodes, to request beacon with increased transmission power. Secondly, nodes stay alive longer in case of PH due to the residual energy threshold policy. As the number of delivered packet is just count, an alive node adds to it if its packet is delivered. However, if a node dies, the count goes down by one as a dead node  
Figure 14: Energy Consumption.  
cannot send data. On the other hand, in case of No-PH, a partition handling mechanism does not exist. Therefore, in case of No-PH, partitioned nodes cannot receive a beacon and, as a result, cannot send their packets thus cutting down the number of packets by at least the number of partitioned nodes. A further reduction in the number of delivered packets is caused by connectivity holes and early death of nodes with higher loads.
## 3 Analysis Methods  
### Magnetic Flux Leakage Calculation  
For static magnetic fields, the magnetic flux density and magnetic flux leakage can be obtained using the 3D finite element method, via the following equations.  
\[\
abla\times H=J, \tag{1}\]  
\[\
abla B=0, \tag{2}\]  
\[B=\mu_{0}\mu_{r}H, \tag{3}\]  
where \(J,H,\) and \(B,\) are the current density, magnetic field strength, and magnetic flux density, respectively, and \(\mu_{r}\) is the relative permeability and \(\mu_{0}\) is the vacuum permeability.  
For some of the materials, the relationship between \(B\) and \(H\) exhibits both non-linearity and hysteresis, in which \(B\) is not a single-valued function of \(H,\) but also depends on the history of the magnetization. It is useful to consider the incremental permeability, which is defined as follows:  
\[B=f(|H|), \tag{4}\]  
This is called the B-H curve, the slope of which indicates the permeability of the material. In this analysis, only electromagnetic parts are needed, such as a yoke, a top plate, a magnet, and a voice coil. After solving the equations, the magnetic flux density for every node in the modeling can be obtained.
# Parameter Identification Methods for Hyperelastic and Hyper-Viscoelastic Models
## 2 Parameter Identification Method for the Hyperelastic Model
### The Identified Parameters and the Numerical Verification
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Iterations**} & \multirow{2}{*}{**Treloar**} & \multicolumn{2}{c}{\(N=3\)} & \multicolumn{2}{c}{\(N=4\)} \\ \cline{3-6}  & & **PSA 169000+** & **LMA 48** & **PSA 280000+** & **LMA 65** \\ \hline \(\mu_{1}\) & 0.6174 & 0.37282 & 0.45262 & 0.64979 & 0.806 \\ \(\mu_{2}\) & 0.001176 & \(1.0209\times 10^{-5}\) & 2.642\(\times 10^{-5}\) & 0.027261 & 0.0327533 \\ \(\mu_{3}\) & \(-0.0098\) & 8.2746\(\times 10^{-3}\) & \(-0.01191\) & 0.0032134 & 2.3601\(\times 10^{-7}\) \\ \(\mu_{4}\) & - & - & - & \(-0.0095667\) & \(-0.004861\) \\ \(\alpha_{1}\) & 1.3 & 1.8464 & 1.60617 & 1.13221 & 0.80939 \\ \(\alpha_{2}\) & 5 & 7.29012 & 5.70992 & 4.526 & 8.9157 \\ \(\alpha_{3}\) & \(-2\) & \(-2.09521\) & \(-1.98197\) & 1.1313 & 3.19726 \\ \(\alpha_{4}\) & - & - & - & \(-2.0548\) & \(-2.26312\) \\ \(S_{1}\) & 0.106 & 0.0618 & 0.0333 & 0.0598 & 0.0120 \\ \hline \hline \end{tabular} PSA: pattern search algorithm; LMA: Levenberg-Marquardt algorithm.  
\end{table} 
Table 1: Results of the identified parameters in Ogden model. Figure 1: Verification of the determined parameters in Ogden model.
# Energy Efficient Transceiver Design for NOMA VLC Downlinks with Finite-Alphabet Inputs
###### Abstract  
Non-orthogonal multiple access (NOMA) is considered as a promising technique in visible light communication (VLC) systems for performance enhancement. In order to improve the system's energy efficiency, we carry out transceiver design for an NOMA VLC system. We develop an optimal power allocation scheme for the system based on finite-alphabet inputs, and we propose a joint detection and decoding (JDD) algorithm for signal detection. Comprehensive simulation results demonstrate that our proposed NOMA strategy can achieve significant performance gains over the time division multiple access (TDMA) scheme, and our designed JDD algorithm outperforms the conventional successive interference cancellation (SIC) algorithm for NOMA.  
non-orthogonal multiple access (NOMA); visible light communication (VLC); power allocation; finite-alphabet inputs; joint detection and decoding (JDD)
## 2 Materials and Methods
### Laboratory Setup
#### 2.1.2 Electronic Amplifier  
The developed amplifier is in the form of a linear electronic converter. It represents a linear electronic circuit whose main component is the operational amplifier TDA2030(A) (STMicroelectronics, Geneva, Switzerland), powered by a DC supply via a single-phase transformer with two secondary windings and a diode rectifier. This amplifier can be used either as a voltage-to-current or voltage-to-voltage converter, for operation in two-quadrant or four-quadrant mode.  
Figure 2b shows the electronic schematic of the amplifier used to supply the DC motor. The input voltage of the amplifier represents the control voltage \(U_{\mathrm{c}}\). The output of the amplifier is, depending on the mode, either the motor armature current \(I_{\mathrm{a}}\) or the reference armature voltage \(U_{\mathrm{m}}\).  
The gain values of the amplifier are: in current modes \(K_{\mathrm{U}\mathrm{c}\mathrm{a}}\) = 1/5 A/V, and in voltage mode \(K_{\mathrm{U}\mathrm{c}\mathrm{l}\mathrm{c}\mathrm{l}\mathrm{m}}\) = 12/5 V/V. The accuracy of the amplifier operation is 1% of the range. In the experiments, the electronics were set to voltage-to-voltage, two-quadrant mode. Table 1 presents the data specifications of the DC motor and the amplifier.  
\begin{table}
\begin{tabular}{c c} \hline Nominal armature voltage & \(U_{\mathrm{an}}\) = 12 V \\ Nominal armature current & \(I_{\mathrm{an}}\) = 0.629 A \\ Nominal motor speed & \(n_{\mathrm{n}}\) = 2750 min\({}^{-1}\) \\ Motor moment of inertia & \(J_{\mathrm{m}}\) = 1.3\(\cdot\)10\({}^{-6}\) kgm\({}^{2}\) * \\ Gear reducer moment of inertia & \(J_{\mathrm{gr}}\) = 0.6\(\cdot\)10\({}^{-7}\) kgm\({}^{2}\) * \\ Encoder moment of inertia & \(J_{\mathrm{e}}\) = 0.7\(\cdot\)10\({}^{-7}\) kgm\({}^{2}\) * \\ Viscous friction coefficient & \(B\) = 1.8\(\cdot\)10\({}^{-6}\) Nm/rad/s ** \\ Coulomb friction & \(M_{\mathrm{c}}\) = 0.48\(\cdot\)10\({}^{-3}\) Nm ** \\ Motor torque constant & \(K_{\mathrm{T}}\) = 25.5\(\cdot\)10\({}^{-3}\) Nm/A \\ Back electromotive force (EMF) constant & \(K_{\mathrm{e}}\) = 25.5\(\cdot\)10\({}^{-3}\) V/rad/s \\ Armature resistance & \(R_{\mathrm{a}}\) = 7.41 \(\Omega\) \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of the A-max 26 A DC motor (\(\OZ6\) mm, 4.5 W, with CLL precious metal brushes) and the amplifier.  
Figure 2: Main components of the laboratory setup: (**a**) DC micro-motor (1), with encoder (2), and gearbox (3); (**b**) Electronic amplifier-schematic representation.