esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
The WL\({}_{i}\) with respect to the local geoid is calculated using the following equation:  
\[\text{WL}_{i}=altitude-range-geoid-\Delta range_{geo} \tag{1}\]  
where _altitude_ is the distance between the satellite center of mass and the reference ellipsoid WGS84, _range_ is the distance between the satellite and the targeted water surface, _geoid_ is the distance between the reference ellipsoid and the EGM2008 model of the equipotential surface of the gravity field of the Earth, and \(\Delta range_{geo}\) represents the geophysical corrections applied to the _range_ due to variations in the Earth's surface height and echo travel-time delays produced by the atmosphere. These corrections are explored further in Section 3.3.  
In our study, we obtain the _range_ directly from L1A products. This is achieved by processing L1A products with our in-house FFSAR processor to obtain FFSAR L1B products and then retracking the waveforms with the Gaussian-Peak retracker.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Target Name**} & \multicolumn{2}{c}{**Type**} & \multicolumn{2}{c}{**Coordinates**} & \multicolumn{2}{c}{**Mean WL\({}_{t}\)**} & \multirow{2}{*}{**S6 Track**} & \multicolumn{2}{c}{**Transect Length**} \\  & **(R/r)** & & & & **Lat** & & **Lon** [m]** & **[m]** \\ \hline Yesa & R & 42.610 & \(-\)1.122 & 480 & 248 & 2000 \\ Itoiz & R & 42.872 & \(-\)1.331 & 568 & 248 & 150 \\ Mequinensa North & R & 41.401 & 0.172 & 116 & 111 & 1000 \\ Mequinensa South \({}^{1}\) & R & 42.320 & 0.114 & 116 & 111 & 2000 \\ Mequinensa Caspe & R & 41.271 & \(-\)0.081 & 116 & 248 & 300 \\ Cinca & r & 41.568 & 0.299 & – & 111 & 50 \\ \hline \hline \end{tabular} \({}^{\dagger}\) The width of the sinuous reservoir at this location is about 500 m (Figure 1).  
\end{table}
Table 1: Information about the inland water targets studied, including the type of target (reservoir (R) or river (r)), reported coordinates obtained from the center of the crossing between the target and the satellite’s nominal track, and the transect length obtained from the crossing of the satellite’s nominal track with the water extension of the target as indicated in the Global Surface Water dataset [33].  
A polygon representing the targeted reservoir, also known as a water mask, is applied to select only the WL\({}_{i}\)s containing information about the target. This polygon is based on the water occurrence provided by the Global Surface Water (GSW) dataset [33]. Finally, WL\({}_{i}\) filtering is applied to estimate a WL\({}_{a}\) of the satellite pass based on the distribution of observed WL\({}_{i}\)s.  
Figure 2 shows a schematic of the methodology followed, with details of each step and inputs provided in the following subsections.
## 5 Results
### Experimental Results  
In the validation tests, the responses from the 7-_dof_ model are compared against the measured responses in Figure 11. It has been observed that the experimental measurements are in accordance with the simulation results with the parameter estimates obtained via the co-simulation study in the previous section. The axle point acceleration recorded field test responses show narrow peaks and wider troughs due to the fact that the tire radius is comparable to the track wavelength, and this has been captured well in the simulation as well owing to the road kinematic tire model as described in Section 4.3.  
In Table 9, the final estimated parameters using the proposed method and the true parameters are presented. It is observed that the estimation error is less than 16% for all the parameters and as low as 3% for some of the parameters. Some of the parameters are estimated better than others. For instance, the error in the estimation of the sprung mass is only 6% as opposed to the unsprung mass estimation errors of 13 and 16% for the front and rear, respectively. In our modelling approach, we made some modelling assumptions: for example, the vehicle is represented by a 7-_dof_ ride model that pitches and rolls about its centre of gravity, while in actual practice, these out-of-plane motions are encountered about the pitch and roll axes, respectively. It was assumed that the tire contact is maintained with the road surface at all times, but we observed that during field testing the wheel jumps off the road surface, leading to inaccuracies in the estimation of the unsprung mass. Moreover, it was assumed that the vehicle is a symmetric body with no product-of-inertia terms, which is certainly not true for an actual vehicle, leading to large deviations in the estimation of inertia properties; for instance, the error in the estimation of the roll inertia \(I_{xx}\) is 16%.  
Finally, in the present study, we assumed the suspension to be represented by a linear Kelvin-Voight model. The vehicle is equipped with double-wishbone front and semi-trailing-arm rear suspensions, for which the equivalent suspension properties are  
Figure 10: MATLAB versus ADAMS simulation results for low-speed-low-severity herringbone test.  
considered at the wheels by using the motion ratio, which is the usual practice followed by suspension designers. These are assumed constant in the present study, while in actual practice, the suspensions are usually non-linear and the motion ratio is a function of suspension geometry, which varies with wheel travel. This is a modelling assumption, and the variation with wheel travel can be considered in order to increase the model fidelity. A model of _intermediate_ complexity can be developed to reduce the estimation errors; this can be a topic of research for future work.
## 2 Materials and Experiments  
### Materials  
Ordinary Portland cement P-I 42.5 (OPC) and CSA used in this study meet the Chinese standards GB8076-2008 [29] and GB/T 37125-2018 [30], respectively. The chemical and mineralogical compositions of OPC and CSA were analyzed by XRF and QXRD, and the results are listed in Table 1. The specific surface areas of OPC and CSA were 1.57 m\({}^{2}\)/g and 1.85 m\({}^{2}\)/g, respectively, as determined by the nitrogen adsorption method (Belsorp-max II, MicrotracBEL, Osaka, Japan). NC was provided by Rhawn Chemical Reagent Co., Ltd. (Shanghai, China) with a mean particle size of 50 nm. The particle size distributions of OPC and CSA were measured by the laser scattering technique, and the results are shown in Figure 1. Aluminum sulfate octadechydrate with analytical purity was obtained from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). Deionized water was used in this study.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \
\begin{tabular}{c} **Chemical** \\ **Composition** \\ \end{tabular} & **OPC** & **CSA** &
\begin{tabular}{c} **Mineralogical** \\ **Composition** \\ \end{tabular} & **OPC** & **CSA** \\ \hline CaO & 62.38 & 34.71 & C\({}_{3}\)S & 66.90 & - \\ SiO\({}_{2}\) & 20.78 & 14.35 & C\({}_{2}\)S & 12.43 & 18.12 \\ Al\({}_{2}\)O\({}_{3}\) & 4.53 & 27.14 & C\({}_{3}\)A & 7.42 & - \\ Fe\({}_{2}\)O\({}_{3}\) & 3.22 & 3.09 & C\({}_{4}\)AF & 8.96 & - \\ MgO & 3.29 & 2.37 & Gypsum & 3.61 & 7.01 \\ Na\({}_{2}\)O & 0.32 & 0.21 & Anhydrite & 0.68 & 22.07 \\ K\({}_{2}\)O & 0.87 & 0.42 & Ye’elimite & - & 52.80 \\ SO\({}_{3}\) & 3.97 & 15.88 & & & \\ TiO\({}_{2}\) & 0.34 & 1.02 & & & \\ Other & 0.30 & 0.81 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical and mineral composition of OPC and CSA in this study (%).  
Figure 1: Particle size distributions of OPC and CSA.
## Materials and Methods
### Characterization and Classification of Rock Mass Based on \(Rmr_{89}\)  
The geomechanics classification system, also known the rock mass rating classification system, was developed by [PERSON] [5]. This classification system has been continuously refined over several years, and its characterization criteria have been revised from its creation onwards [5; 6; 7; 15; 20; 21; 22; 23],as summarized in Table 1. Major revisions to the system were proposed in 1989 [6]. These include new charts and tables, which were added for the rating of intact rock strength (\(\sigma_{c}\)), rock quality designation (\(RQD\)), discontinuities/joint spacing, and the condition of discontinuity parameters. The refined \(RMR\) system is termed as the \(RMR_{89}\) version. The basic \(RMR\) (\(RMR_{b}\)) value is the sum of the ratings of five declared parameters. It includes the rating for intact rock strength (\(R_{1}\)), \(RQD\) (\(R_{2}\)), joint spacing (\(R_{3}\)), joint condition (\(R_{4}\)), and groundwater condition (\(R_{5}\)). The \(RMR_{89}\) system is the result reached after adjusting \(RMR_{b}\) for discontinuity orientation with respect to the tunnel orientation and excavation direction rating (\(R_{6}\)). These six parameters describe the rock mass rating, \(RMR_{89}\), and the corresponding quality is obtained using Equation (1):  
\[RMR_{89}=R_{1}+R_{2}+R_{3}+R_{4}+R_{5}+R_{6}. \tag{1}\]  
To date, \(RMR_{89}\) continues to be used in the tunneling field even after its latest version, called \(RMR_{14}\), was made effective. Although there are graphical approaches for the precise rating of \(RQD\), intact rock strength and discontinuity spacing [6], most users select the discrete/lump rating values using the \(RMR_{89}\) table [24; 25; 26; 27]. The continuous rating concept through using equations was introduced by [PERSON] and [PERSON] using the lump rating system of the \(RMR_{73}\). This reveals that the minimum value of the worst \(RMR\) value is 8 [28], but the worst is actually 0 in \(RMR_{89}\)[23]. The rating for intact rock strength (\(R_{1}\)) is from 0 to 15. A rock with a uniaxial compressive strength (UCS) greater than 250 MPa is assigned maximum points, and that with less than 1 MPa is assigned minimum points. The continuous rating between them can be determined using Equation (2):  
\[R_{1}=0.126\sigma_{c}-0.0004\sigma_{c}^{2},\qquad(\sigma_{c}\leq 110\;\text{MPa}), \tag{2a}\] \[R_{1}=0.475\sigma_{c}^{0.626},\qquad(\sigma\geq 110\;\text{MPa}). \tag{2b}\]  
Since its development [29], \(RQD\) has been used in \(RMR\) and \(Q\) systems because of its historical background. Because of several inherent limitations, and ignoring the basic definition [4; 30], this parameter is not used in the latest version of \(RMR\). Along with the joint spacing, \(RQD\) was replaced by joint frequency [15]. It remains a mandatory parameter in the \(Q\) system. According to \(RMR_{89}\), the rating of \(RQD\) (\(R_{2}\)) is from 3 to 20 based on the average values.
# Performance Comparison of NB-Fi, Sigfox, and LoRaWAN
## 3 An Overview of NB-Fi, Sigfox, and LoRaWAN
### Modulation  
#### 3.1.1 NB-Fi  
NB-Fi sensors communicate with BSs using binary phase-shift keying (BPSK) or differential binary phase-shift keying (DBPSK) in the downlink and DBPSK in the uplink.  
In NB-Fi, frames can be transmitted at four different rates: 50, 400, 3200, or 25,600 bps. The rate affects the frame duration and the bandwidth used for the frame transmission (see Figure 1). As the use of a narrow band increases the power spectral density, the lower is the rate, the higher can be the transmission range, i.e., the maximal distance between the transmitter and the receiver at which the communication is still reliable.  
The receiver sensitivity is calculated as  
\[S_{crit}=10\log_{10}kT\Delta f+N_{base}+SNR_{BER}, \tag{1}\]  
where \(N_{base}\) is the input noise, \(SNR_{BER}\) is the signal-to-noise ratio (SNR) corresponding to the given bit error rate, \(k\) is the Boltzmann constant, \(T\) is the temperature, and \(\Delta f\) is the signal frequency band. Therefore, for the values given in the standard [24]: \(T=290\) K, \(BER=10^{-5}\), \(SNR_{BER}=5\) dB, \(N_{base}=2\) dB, we obtain the values of sensitivity for different bitrates, shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **NB-Fi** & **Sigfox** & **LoRaWAN** \\ \hline Frequency Band & Sub-GHz ISM & Sub-GHz ISM & Sub-GHz ISM \\ \hline Topology & star of stars & star of stars & star of stars \\ \hline Bitrate, bps & (a) 50; (b) 400; (c) 3200; (d) 25,600. & (a) 100; (b) 600. & (a) 250; (b) 440; (c) 980; (d) 1760; (e) 3125; (f) 5470. \\ \hline BW, kHz & UL: \(\geq\) 51.2 & 192 & 125, 250, 500 \\  & DL: \(\geq\) 102.4 & & \\ \hline PL size, bytes & 8 & \(\leq\) 12 & (a) \(\leq\)51; (b) \(\leq\)51; (c) \(\leq\)51; (d) \(\leq\)115; (e) \(\leq\)242; (f) \(\leq\)242. \\ \hline Modulation & UL: DBPSK & UL: DBPSK & LoRa \\  & DL: DBPSK, BPSK & DL: GFSK & \\ \hline \(S_{crit}\), dBm & (a) \(-\)150; (b) \(-\)141; (c) \(-\)132; (d) \(-\)123. & UL: \(-\)135; DL: \(-\)126 & (a) \(-\)137; (b) \(-\)136; (c) \(-\)134; (d) \(-\)131; (e) \(-\)128; (f) \(-\)125. \\ \hline Operating & NRX, DRX, CRX & U-, B- procedure & Class A, B, C \\ modes & & S-, M- frames 1  & \\ \hline ACK mode & + & – & + \\ \hline Block & + & + & not considered \\ transmission & & & \\ \hline Coverage range, & urban: 10 & urban: 10 & urban: 5 \\ km [20,25,26] & rural: 40 & rural: 40 & rural: 20 \\ \hline Encoding & UL: Convolutional or & UL: Convolutional & \\ non-systematic polar & DL: BCH15-11 & Hamming \\ DL: Zigzag & & & \\ \hline Code rates & UL: 5/s & 1/3 & 4/5,4/6,4/7,4/8 \\  & DL: 1/2 & & \\ \hline Encryption & Magma & AES-128 & AES-128 \\ [21,22,24] & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of nominal properties of the considered technologies.  
Figure 1: Different frame widths and durations in NB-Fi.
## 4 Verification Analysis in Current Conditions (Step 1 of the Design Procedure)
### Time-History Verification and Performance Assessment Analysis
As discussed in the following Sections, this suggests incorporating the dissipaters on the upper level only, in order to adequately exploit their damping capacity and limit the cost of the retrofit intervention. In \(X\) direction, \(ILD_{r,max}\) is equal to 0.2% (BDE), 0.25% (MCE) on the first level, and 0.36% (BDE), 0.45% (MCE) on the second level. The interlevel drift profile depicts a frame-like layout along this axis, which approaches a shear-type shape on the second level, as a consequence of the high flexural stiffness of the roof beams in the \(X\)-\(Z\) vertical plan (which determines nearly a sliding-clamped constraint condition on the top section of the columns). The BDE- and MCE-related response was assessed also in terms of stress levels. The shear-related checks are met in both directions and for both levels, up to the MCE. On the other hand, the combined axial-force and biaxial-bending-moment stress state checks are met only for the internal columns (C3 through C16, according to the numbering in Figure 4) on the first level at the BDE. The response of  
Figure 8: Normative pseudo-acceleration elastic response spectra for Florence. the corner columns (C1, C2, C17 and C18) at this level, as well as of all columns on the second level is unsafe starting from the BDE. By way of example, the \(M_{X,c}\)-\(M_{Y,c}\) biaxial moment interaction curves (being \(M_{X,c}\), \(M_{Y,c}\) the bending moments around the \(X\) and \(Y\) axes) graphed by jointly plotting the two bending moment response histories obtained from the most demanding among the seven groups of MCE-scaled accelerograms, are plotted in Figure 9 for a corner column, namely C17. The boundary of the \(M_{X,c}\)-\(M_{Y,c}\) elastic interaction domain--which is a function of the time-history variation of the axial force in the columns--is also traced out in the two graphs for the value of the axial force conventionally referred to the basic combination of gravity loads, i.e., \(N_{c}\) = 104 kN. The response curves relevant to the first level highlight maximum \(M_{X,c}\)-\(M_{Y,c}\) combined values slightly exceeding the safe domain boundary at the BDE, and 1.77 times greater than the corresponding values situated on the boundary, with prevailing contribution of \(M_{Y,c}\), at the MCE. The curves traced out for the second level show more marked unsafe conditions at the BDE, as compared to the first-level ones, and exceed the boundary by a factor equal to 2.07 at the MCE, but with inverted role of the moments (i.e., with prevailing contribution of \(M_{X,c}\) for the second level).
# Extended Reality Application Framework for a Digital-Twin-Based Smart Crane
## 2 Materials and Methods
### Network Layer
#### 2.4.1 OPC UA-MQTT Wrapper  
MQTT (Message Queuing Telemetry Transport) is a publish/subscribe messaging transport designed for remote devices, especially machine-to-machine (M2M) communication with poor network conditions and limited network bandwidth. Since there is no direct connection between publisher and subscriber under MQTT communication, it is necessary to set up a broker to decouple the message [58].  
OPC UA is a robust, secure, and scalable industrial standard communication protocol. MQTT is lightweight and therefore suitable for remote data transmission. Hence, an OPC UA-MQTT wrapper was developed in our research, which can transmit the data from the OPC UA server to the MQTT broker. The OPC UA-MQTT wrapper contains an OPC UA client, an OPC UA-MQTT gateway that transfers data read from the server to the MQTT message format, an MQTT client that sends the data, and an MQTT broker deployed in the open network. The network layer of the monitoring application was realized by the OPC UA-MQTT wrapper. The source code is published as open-source on GitHub [59].
# Novel Data-Driven Models Applied to Short-Term Electric Load Forecasting
## 3 Materials and Methods
### Models Description
In the basic gaNet model, all blocks have the same layer configuration (architecture) and all blocks in the sequence are similar, but not identical, as random initialization of their weights and end-to-end training will induce different weights in each of the blocks. There are also variants on this basic configuration by allowing different block architectures as well as other training options. In [11], the gaNet model is presented in detail with several variants (types), according to whether: the blocks architecture are all identical or not, if they share their weights, or if the loss function is a unique function or is formed by adding the loss functions of the intermediate outputs. Considering all the variants in [11], we chose only two that we named ensemble Type I and II, and that corresponded to types III and IV in [11]. The ensemble model Type I is made up of identical blocks where each block is formed by small DL networks consisting of 1D-Conv, LSTM, and FCN layers. We also differentiated a subgroup of Type I models when all the blocks shared their weights. In this case, we had blocks not only with identical architecture but with identical weights. It is interesting to investigate this specific subgroup because they are models with very few weights, which can be important to avoid overfitting. In the ensemble Type II model, blocks are separated into groups where each group can have a different architecture, with all blocks in the same groups sharing the same architecture. For this model, we indicate separately the number of repetitions of identical blocks per group. There is freedom in the number of groups and blocks per group. In this case, blocks with similar architectures could also share weights, but it is a possibility that has not been explored. We deviated from the original Type II models presented in [11] where all blocks shared the same inputs, by allowing blocks with different architectures to receive different inputs. In this case, we used three different types of inputs: (a) a sequence of scalars formed exclusively by the load values that corresponds to a value of the parameter \(f\) equal to 1, (b) a sequence of vectors formed by the load values plus the date/time features that corresponds to a value of the parameter \(f\) equal to 45, and (c) a sequence of vectors formed by the load values plus the date/time and weather features that corresponds to a value of the parameter \(f\) equal to 57. All these options are presented in the results tables in Section 4 and Figure A1 (Appendix A), and, in those cases where there are different inputs per block, we have marked that in the tables by an NA in the \(f\) column, reporting the type of input associated with each block by an additional letter within a parenthesis and immediately after the layer type. Possible values for these additional letters are: an (L) for inputs formed exclusively by the load values, a (D) for inputs formed by the load plus the date/time features, and a (A) for inputs formed by the load plus the date/time and weather features.
## 4 Results
### CYGNSS SMC Validation Using SMAP Data

Table 1: Linear robust regression model of least absolute residual method: “SMC\({}_{\text{SMAP}}\) = \(\beta_{0}\) + \(\beta_{1}\). SMC\({}_{\text{CYGNSS}}\)”. The corresponding scatter-plot is shown in Figure 11.  
Figure 11: In (**a**) we see the location of the validation SPs (CYGNSS \(\cap\) SMAP) for April 2019 (days shown in Figure 5). In (**b**), the scatter-plot shows the SMAP estimates versus the CYGNSS estimates of SMC [cm\({}^{3}\)-cm\({}^{3}\)]. Parameters of the regression model are shown in Table 1. Northern regions (summer monsoon and dry winter, Cwa) are in blue and southern regions (without dry season, Cfa) are in red.
## 4 Computation and Communication Performance Modeling Approaches
### Computation Modeling Approach
#### 4.1.1 Compute Time Representation  
The most simple method we use to model the execution time of an actor is calculation the average of the observed delays.  
Different compute delays can lead to different communication and bus contention behavior and so to different iteration delays of the whole application. Fitting a Gaussian distribution allows a more precise representation of the execution behavior. With this approach, the fact that an actor can have different execution times gets introduced into the model. The width of the distribution also pushes the execution time for a possible worst case higher, and for the best case lower than the worst/best observed execution time.  
Generalizing the delays of software with a single Gaussian distribution comes with an huge error. To address the individual distributions of different actors, we apply Kernel Density Estimation (KDE). This is a method to estimate the probability density function based on an incomplete set of samples. Basically KDE assumes that each sample of our measured data represents the median of a well defined distribution function (in our case Gaussian distribution). The KDE processed data represents the sum of all individual distribution functions. This approach allows us to not only cover observed execution time during our simulation but also other delays that are likely to appear when using the actor with different stimuli. We apply a standard KDE with multiple Gaussian distributions as kernels. The bandwidth of the kernel is 1% of the worst observed execution time in the data set the KDE gets applied on. Using a large bandwidth makes it more likely to cover the best and worst execution time. For the KDE, we use _scikit-learn_[29].