esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Capability of Capturing Abrupt Variations in Land Surface Albedo  
Abrupt changes in surface albedo are often due to drastic changes in surface characteristics over a short period, which are difficult to capture using remote sensing data [57]. The background field as obtained from MODIS historical period data can reflect abrupt changes to a certain extent, but the effect is not obvious. If TM data are available during the period of abrupt change, the model prediction is updated and the estimated results are closer to the observed values; the abrupt change in surface albedo can be captured effectively.  
In our time-series albedo estimation for farmland stations, surface albedo jump occurred on the 230 th day at the DE-Kli site, the 150 th day at the IT-Bci site, and the 230 th and 250 th days at the FR-Gri site. There are TM data available before and after these days, and the trajectory of the estimated results was corrected in time after adding the observed data. On the 170 th day at the DE-Kli site, the 140 th day at the IT-Bci site, and the 245 th day at the FR-Gri site, there were no TM data available to update the model prediction and only the background field trajectory could be used for forward predictions. The abrupt variation in surface albedo is difficult to show and the estimated results contain deviations. The jump is more severe during snowy periods (especially snowfall and melting), where the surface albedo deviation may be higher than 0.5 and the EnKF algorithm cannot be applied effectively without support from TM data. To operate the surface albedo data assimilation algorithm, it is necessary to secure more high-quality satellite data as input observations. With support from more observations, the data assimilation algorithm is better able to capture abrupt variations and produce accurate estimations.
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
## 4 Dataset Generation
### Geomagnetic Field Modeling
#### 4.3.2 CHAOS-7 Model  
To simulate more accurate magnetometer measurements, the CHAOS-7 model was used. The CHAOS-7 model [16] is the most recent version of a series of time-dependent geomagnetic field models developed at the Technical University of Denmark (DTU) spanning from 1999 to 2020. This model exploits both satellite and ground observatory data and provides regular updates using the latest data available every 4 to 6 months. Satellite data were provided by the satellites Orsted (from 1999 to 2013), CHAMP (from 2000 to 2010), SAC-C (from 2001 to 2004), and CryoSat-2 (from 2010 to 2014) and the three Swarm satellites (from 2014 to 2020). The model also used annual differences in the revised monthly means of ground observatory data (from 1999 to 2019) from 182 ground observatories (see Figure 4).  
Figure 4: Locations of ground magnetic observatories providing data for derivation of CHAOS-7 from [16], published under license CC BY 4.0.  
The model followed the source-free approximation of Equation (28), with the scalar potential being the sum of two terms:  
\[V=V_{int}+V_{ext}, \tag{29}\]  
where \(V_{int}\) includes the internal core and lithospheric sources while \(V_{ext}\) consists of the magnetospheric-assumed external sources and their internal Earth-induced counterparts. A spherical harmonics expansion was used for both terms of \(V\), with a maximum order of truncation of 70 for the internal field and 2 for the external field. The induced fields were computed using a \(Q\)-responses-based method assuming an Earth-conductivity model [23; 24]. A more detailed description of the scalar potential modeling can be found in [16].  
Depending on the latitude and longitude, the difference between the two geomagnetic field models varies up to 400 nT in polar regions. Figure 5 shows the difference between the two models at a fixed altitude of 567 km with a resolution of 5\({}^{\circ}\) and the ground track of the satellite over the entire duration of the simulation (from 3 January to 3 May 2019).
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 6 Implementation and Evaluation
### Evaluation
#### 6.3.2 Increasing LoRaWAN Coverage  
We evaluated our forwarding solutions' reliability compared to that of fixed-path forwarding using the network topology in Figure 6a. Actually, the topology is not realistic as all end-devices are located out of the gateway's coverage. However, it is a good topology to evaluate the extension of coverage through a relay. As shown in Figure 8, both OOD and OODC showed a similar result with better reliability compared to that of fixed-path forwarding in both SDR and FDR scenarios. This was mainly owing to the multi-reception benefits of opportunistic forwarding.  
Under fast data rate settings, both OOD and OODC maintained performance above 90% of the PDR, even increasing the number of active devices up to 1200. Conversely, both solutions can serve only up to hundreds of end-devices under slow data rate settings to achieve similar reliability performance. This result shows that the difference in transmission times between the SDR and FDR settings affects the network capacity. As more end-devices are in the contention, the packet collision increases.  
Figure 7: Packet delivery ratio (PDR) comparison between Pure-ALOHA without capture effect and Pure-ALOHA and LBT-ALOHA with capture effect. The capture effect and LBT approach contribute to in improving LoRa reliability.
# Effects of Sinusoidal Model on Non-Parallel Voice Conversion with Adversarial Learning
## 5 Evaluation and Discussion
### Experimental Setup  
Unlike [25; 33], this work is conducted on the CSTR VCTK corpus [38], which contains 46 h of English speech from 109 speakers with various accents. From this database, our proposed system was evaluated on data of 8 speakers (4 males and 4 females). The training examples of each speaker are split into 90% training and 10% test sets. As the conversion setting is non-parallel, we did not use any time alignment procedures for training and each speaker reads a different set of sentences. All the speech signals were resampled to a sampling rate of 16 kHz. The 36-dimensional MGC, 1-dimensional MVF, and 1-dimensional contF0 are extracted from the speech of the speakers, and the corresponding stats (means and standard deviations) computed. The acoustic features were computed using a 25 ms window and 5 ms frameshift. We conduct 8 inter-gender (female-to-male and male-to-female) conversions and 4 intra-gender (male-to-male and female-to-female) conversions.  
As there are 8 speakers involved in our experiments, \(c\) is represented as an 8-dimensional one-hot vector and there were 12 different combinations of source and target speakers in total. Thus, there are 960 samples (10 utterances * 8 speakers * 12 tests) that needed to be evaluated in total. An NVidia Titan X GPU is used for training. The state-of-the-art system in this study follows the structure of the StarGAN-VC model [25] that achieved greater naturalness of the converted speech in many-to-many non-parallel VC. Thus, we assess our proposed system with it. For comparative evaluation, we choose to exclude any parallel data in the training and testing data.
## 4 Numerical Simulation Results  
This section consists of two essential parts: the first one proposes an isolator prototype design and selects parameters for each component based on the derived equations in Section 3. The second part is a numerical simulation, which is adopted to verify the authenticity of the proposed isolator theory and its analytical results.
# Fault Sensing Using Fractal Dimension and Wavelet
###### Abstract  
A new fusion sensing (FS) method was proposed by using the improved fractal box dimension (IFBD) and a developed maximum wavelet coefficient (DMWC) for fault sensing of an online power cable. There are four strategies that were used. Firstly, the traditional fractal box dimension was improved to enlarge the feature distances between the different fault classes. Secondly, the IFBD recognition algorithm was proposed by using the improved fractal dimension feature extracted from the three-phase currents for the first stage of fault recognition. Thirdly, the DMWC recognition algorithm was developed based on the K-transform and wavelet analysis to establish the relationship between the maximum wavelet coefficient and the fault class. Fourthly, the FS method was formed by combining the IFBD algorithm and the DMWC algorithm in order to recognize the 10 types of short circuit faults of online power. The designed test system proved that the FS method increased the fault recognition accuracy obviously. In addition, the parameters of the initial angle, transient resistance, and fault distance had no influence on the FS method.  
f 1 2016 9 66 10.3390/a9040066 11 October 2016
# Robust Input Shapers for Acceleration-Limit Actuators
## 2 Robust Input Shapers for Acceleration-Limit Actuators
### EI\({}_{\rm{AL}}\) Input Shaper for Ramp Actuators
Then, the amplitude condition is obtained as  
\[|\overrightarrow{V_{1}}|=|\overrightarrow{V_{3}}| \tag{38}\]  
By setting the resultant magnitude equal to the oscillation limit, \(V_{lim}\) at \(\omega/\omega_{n}=1\), the resultant constraint with Equation (38), \(\theta_{3}=2\theta_{2}\) and \(\theta_{1}=0\), can be expressed as:  
\[\{|\overrightarrow{V_{2}}|sin\theta_{2}+|\overrightarrow{V_{3}}|sin(2\theta _{2})\}^{2}+\{|\overrightarrow{V_{1}}|+|\overrightarrow{V_{2}}|cos\theta_ {2}+|\overrightarrow{V_{3}}|cos(2\theta_{2})\}^{2}=V_{lim}\,^{\rightarrow} \,|\overrightarrow{V_{unshaped}}|^{2}=V_{total} \tag{39}\]  
where \(\left|\overrightarrow{V_{unshaped}}\right|\) represents the magnitude of unshaped vibrations. Using \(\frac{\mathrm{d}V_{total}}{\mathrm{d}\omega}=0\) at \(\omega/\omega_{n}\)=1, \(\theta_{2}=\pi\) is obtained. From Equations (32)-(35) and (39), the time locations of the \(\mathrm{EI_{AL}}\) shaper are expressed as:  
\[t_{2}=\frac{1}{\omega_{n}}\bigg{\{}\pi-\frac{(1-3V_{lim})V_{d}\omega_{n}}{8 \kappa_{a}}\bigg{\}} \tag{40}\]  
\[t_{3}=\frac{2\pi}{\omega_{n}} \tag{41}\]  
The switch time for stop operations was the same as that for start operations. The sign of the impulse magnitude changed asymmetrically. In stop operations, deceleration (\(\kappa_{d}\)) is used instead of acceleration (\(\kappa_{a}\)) in start operations, and the impulse times \(t_{5}\) and \(t_{6}\) for the stop operation are given by:  
\[t_{5}=t_{4}+\frac{1}{\omega_{n}}\bigg{\{}\pi-\frac{(1-V_{lim})V_{d}\omega_{n} }{8\kappa_{d}}\bigg{\}} \tag{42}\]  
Figure 7: Vector diagram of \(\mathrm{EI_{AL}}\) shaper. \[t_{6}=t_{4}+\frac{2\pi}{\omega_{n}} \tag{43}\]  
where \(t_{4}=t_{p}\) represents the pulse duration at the start of the operation. The EI\({}_{\text{AL}}\) shaper must be used under the constraint of Equation (26) owing to the slope of the ramp-shaped velocity profile, similar to the ZVD\({}_{\text{AL}}\) shaper presented in the previous subsection. Under the condition of a ramped velocity profile, the EI\({}_{\text{AL}}\) shaper reduced the residual deflection in a flexible system with a ramp actuator. Using Equations (40)-(43), the EI\({}_{\text{AL}}\) shaper for the start and stop operations of the flexible system can be expressed as  
\[ \begin{bmatrix}A_{i}&t_{i}\end{bmatrix} = \begin{bmatrix}\frac{(1+V_{lim})}{4}& \frac{(1-V_{lim})}{2}&\frac{(1+V_{lim})}{4}&\frac{-(1+V_{lim})}{4}&\frac{-(1-V_ {lim})}{2}&\frac{-(1+V_{lim})}{4}\\ 0&t_{2}&t_{3}&t_{4}&t_{5}&t_{6}\end{bmatrix}  \tag{44}\]  
Equation (44) is used to generate a ramp-shaped command using an actuator with asymmetric acceleration and braking through convolution on a robust input command. The analytical development procedure for the EI\({}_{\text{AL}}\) shaper can be expanded to generate commands for various shapes using acceleration-limit actuators.
# ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant
## 8 Number of Neurons in a Dual Layer ANN  
In this work, networks with two hidden layers between the input and the output have also been analyzed. This network topology has two degrees of freedom related to the optimum number of neurons. Therefore, different criteria can be adopted, such as keeping the number of neurons in one layer constant while varying them in the other, or keeping a constant ratio of neurons between the layers, etc. Several simulations have been performed, but hereafter only the Levenberg-Marquardt training algorithm with a slow convergence setting is considered.  
Figure 5 shows the mean _NMAE_% trends of the dual layer networks as a function of the neurons of the first layer, for a total of 50 forecasts for each layout. From 20 to 120 neurons were employed, with an increasing rate of 20 neurons. In Figure 6, the neurons of the second layer were fixed at 25%, 50%, and 75% of the first layer neurons, rounded up, and originating in this way three different curves. The best result is comparable to the one reached by the single layer networks. However, here it is possible, by analyzing the gradient of the orange curve, to have small margins of improvement while the number of neurons is increasing. Similarly to the analysis carried out for a single layer network, the _NMAE_% is calculated for the ensemble of the forecasts, and it is shown together with the mean error of the single outputs. Also in this case, the shape of the curves suggests looking for the optimum layout towards a higher number of neurons. Moreover, the smallest value of the error for the ensemble output is slightly less than the one obtained by the single layer networks. Therefore, the dual layer networks seem to provide better performance.  
Figure 4: _NMAE_% of ten random ensemble (grey lines) as a function of the growing number of trials. The artificial neural network (ANN) has 120 neurons and a “Levenberg–Marquardt (LM) slow” training algorithm. The mean of the ten ensemble forecasts \(\overline{NMAE}_{p}\) is in red, the mean of one thousand forecasts is the dashed green line, and the ensemble _NMAE_% of one thousand forecasts is the dash-dotted blue line.  
Although the dual layer networks seem to provide slightly better performance, single layer networks seem to be more appropriate for this kind of forecast. In fact, dual layer networks with larger numbers of neurons need remarkably higher calculation times, and this might represent a great drawback for their employment. As an example, the training of only one single layer network with 120 neurons required an average of 50 s, while the training of a unique dual layer network with 120 and 90 neurons required nearly 350 s on a quad-core Intel(r), Santa Clara, CA, USA, CoreTM i7-2640M CPU, with an operating frequency of 2.8 GHz, and 8 GB ram.
# Upgrading of the L-P Band Cryogenic Receiver of the Sardinia Radio Telescope: A Feasibility Study
## 4 Results and Discussion  
On the basis of results concerning the RFI analysis of Section 3, it appears that there are several unwanted signals that can be rejected with targeted changes in the receiver. In this upgrade feasibility study, some hardware changes for RFIs mitigation are proposed, such as the replacement of original electronic control system with a next-generation board and the removal of unused microwave components (i.e., the switches of the Dewar block). In addition, the microwave filters of the receiver need to be replaced with more performant filters. The requirement for the design of these components is the attenuation of at least 30 dB for the highest RFIs present in the receiver bandwidth (i.e., TETRA signals, mobile communications signals and Italian army radars signals).
## 2 Results and Discussion  
### Analysis of SDOM Fatty Acids from Evaporated to Drupess Samples  
#### 2.1.1 Thermochemolysis: Fingerprints of Total Fatty Acids in SDOM  
Thermochemolysis in the C9-C18 Fatty Acids Range  
To have an overview of the SDOM fatty acids, optimal analytical conditions of thermochemolysis in the presence of TMAH were first determined on a standard mixture of C9 to C18 fatty acids. An amount of 25%TMAH in methanol at 600 \({}^{\circ}\)C was found to be the best conditions as it gave the highest peak intensity and a good overall molecular response (Figure 1).  
Figure 1: %mol (Ai/\(\sum\)Ai) of fatty acid methyl esters (C9 to C18) in function of (**A**) the temperature of thermochemolysis (with 25% TMAH in MeOH) and (**B**) the percentage of TMAH in methanol (at 600 \({}^{\circ}\)C, 15 \(\upmu\)L TMAH solution).  
SDOM Analysis by Thermochemolysis  
The fatty acid distribution of percolating waters from urban green waste amended soil and its non-amended reference was investigated to validate thermoc hemolysis for SDOM analysis. In total, 500 mL of each sample (ranging from 8 to 61 mgC/L, Figure S1) was evaporated at 40 \({}^{\circ}\)C to dryness and optimal thermoc hemolysis was performed on the resulting residues.  
Fatty acids, observed as methyl esters, ranged in all samples from C6 to C30. The bimodal distribution was dominated by C16 for short mode (C6-C18) and C28 and C30 for long-chain acids (Figure 2). In reference soil solutions, even-over-odd predominance in the C24-C32 range was a marker of higher plant vegetation (Figure 2A). In amended soil solutions, branched isomers of C15 and C17 were as abundant as long-chain compounds in the first 30 cm depth, showing higher microbial activity than in reference soil solutions (Figure 2B). The higher input of vegetation was at 30 cm in amended soil whereas it was at 100 cm in reference soil solutions. The influence of amendment performed seven years before the sampling was thus still significant at 30 cm depth.  
1.2 Boron Trifluoride-Methanol Complex (BF3/MeOH) Derivatization: Fingerprints of Free Fatty Acids in SDOM  
To identify only the free fatty acids, a derivatization with BF3/MeOH was then evaluated.  
When performing BF3/MeOH derivatization on evaporated samples, the resulting distribution was only due to free fatty acids (Figure 3). The bimodal distribution ranged from C12 to C32. High concentrations in 30 cm reference soil solutions compared to thermoc hemolysis results showed that high concentrations of fatty acids at 100 cm were rather associated to macromolecules such as humic substances than to free molecules. This procedure is, however, not suitable for analyzing carboxylic acids of low molecular weight (<C12) since their increased volatility after derivatization can lead to unquantifiable losses related to evaporation.  
Figure 2: Thermochemolysis of 500 mL reference (**A**) and amended (**B**) soil solutions from 15 to 100 cm depth after a 40 \({}^{\circ}\)C evaporation step (TMAH 25%, 600 \({}^{\circ}\)C).  
To have a rapid overview of low molecular weight compounds, we have developed an in situ derivatization on a 2 mL sample.
## 4 Result and Discussion
### Comparison of LSGB and BRFFNN Based on UAV Movement Pattern
#### 4.8.3 Standard Deviation Analysis of Delay and Throughput  
Each experiment was carried out ten times using various random seeds and UAV initialization parameters in order to increase the reliability of the findings and provide information on the simulations' variability. In order to demonstrate the statistical robustness of the experiments, the reported metrics include the mean values for each run along with the standard deviation (SD) and 95% confidence intervals (CIs). The following figures illustrate the analysis results for delay and throughput for bandwidth allocation using the BRFFNN and LSGB models. The shaded region represents the 95% confidence interval for each model. The negligible variability indicates that the models maintain robust delivery performance within the coverage area.  
The results of mean \(\pm\)95% CI for the PDR over a 150 s simulation period show that both the BRFFNN and LSGB maintain a consistently high PDR (\(\sim\)90-100%) across all time instances. For the data transmission delay, the result of mean \(\pm\)95% CI is shown in Figure 13. The BRFFNN consistently exhibits lower delay (\(\sim\)1.5-2.0 \(\times\) 10\({}^{-3}\) ms) compared to LSGB (\(\sim\)2.0-2.5 \(\times\) 10\({}^{-3}\) ms). The confidence intervals for the BRFFNN are narrower, indicating more stable delay performance. LSGB delay values have slightly higher variability due to less precise bandwidth allocation.  
Figure 14 shows the mean \(\pm\)95% CI of throughput in packets/second. The BRFFNN achieves higher throughput (\(\sim\)4.0-4.2 million pkt/s) compared to LSGB (\(\sim\)3.1-3.3 million pkt/s). The confidence intervals for the BRFFNN are more compact, showing stable bandwidth utilization, while LSGB shows slightly larger fluctuations.  
The BRFFNN performs better than LSGB in terms of decreased delay, increased throughput, and more stable performance under random UAV movement patterns, as demonstrated by the standard deviation and confidence interval analysis. In dynamic  
Figure 12: Results for (**a**) PDR, (**b**) throughput, and (**c**) delay for random UAV movement.  
UAV-GS communication environments, the BRFFNN model is more suited to bandwidth allocation due to its superior robustness and reliability.
## 2 Materials and Methods
### Data Acquisition and Reference Point Cloud
#### 2.3.2 Reference Point Cloud from Terrestrial Laser Scanning  
A reference point cloud is used for most of the evaluation aspects described in Section 2.2. The point cloud for the first study area was acquired with a Leica ScanStation P50 TLS and a target-based registration using BOTA8 (Bonn Target 8) laser scanning targets [42], shown in Figure 4a. To cover the entire study area, 16 viewpoints with 29 target locations were required, which were measured with the scanner resolution setting of 3.1 mm @ 10 m. In addition, the viewpoints were planned next to the objects along the road and near the control points, which are defined by attached targets (pillar or building) shown in Figure 3. These control points are used for georeferencing the point cloud and are part of an evaluation environment [1], which consists of a dense network of 37 control points in total. The control points are determined in a network adjustment based on measurements from total station, GNSS and leveling, which results in an estimated standard deviation of \(<\)0.1 cm. Measurements of the network were carried out in two consecutive years to verify the stability of the control points included. Further information about the measurements and methodology can be found in [PERSON] [1]. The coordinates  
Figure 4: Study area for the analysis of single strip measurement with TLS measurement for the reference point cloud.  
of all control points are given in the official German coordinate system ETRS89/UTM32 (European Terrestrial Reference System 1989, Universal Transverse Mercator, Zone 32) for positions. Furthermore, the heights are given in physical heights (DHHN16, German Main Height Network 2016) and also ellipsoidal heights (GRS80, Geodetic Reference System 1980). Subsequently, the control point coordinates are used in the registration of TLS viewpoints to process the georeferenced point cloud. The processing of TLS measurements was done using the Leica Cyclone processing software. The registration error related to control points is given with a RMSE (root mean square error) of 0.5 cm. To control the reference point cloud, additional RTK-GNSS measurements were performed at two different epochs on this day, which showed a RMSE of the reference point cloud \(<\)1 cm for the height component. For the second study area with the multiple strip measurements, the creation of the reference point cloud is done quite similar as for the first study area, using a Leica ScanStation P50 and a georeferencing based on known control points as already described. The registration error related to the control points is given with an RMSE of 0.8 cm for the second study area and is further verified by RTK-GNSS measurements of the distributed targets, which are again in a range \(<\)1 cm. Therefore, the absolute accuracy described by the RMSE of the georeferenced point cloud in both study areas is much higher than the expected accuracy of the UAV-based laser scanning system measurements and can be used as a reliable reference.