esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated GNSS/IMU-Gyrocompass with Rotating IMU. Development and Test Results
## 6 Discussion  
First of all let us analyze the heading accuracy under good visibility of GNSS signals from multiple satellites. As seen from the Figure 10, the heading error after the completion of transients in the system including the heating (at 1000-1500 s) reaches 1.2\({}^{\circ}\) and 1\({}^{\circ}\) for the stop (Figure 10a) and in motion (Figure 10b), respectively. This error level is observed without account for synchronization errors between INS and GNSS/IMU gyrocompass prototype. Synchronization error was 100-200 ms and critically degraded the heading error during the maneuver (Figure 10b, shown by an oval). The difference between heading errors during the stop and during motion is assumed to be conditioned mainly by multipath effects. As is known [42], the rate of change of multipath error is directly proportional to the distance between the receiver and the obstruction from which the signal is reflected. During the stop, multipath error becomes a low-frequency one, which complicates its filtering using IMU data. Plots in Figure 10 show that the transient mode (heading determination with error of 1-1.5\({}^{\circ}\)) lasts 1200-1500 s after GNSS/IMU gyrocompass start. This duration of alignment is conditioned by several factors. First, large FOG biases and rhumb drifts should be estimated. Second, since horizon alignment of the system should be performed, to shorten the transient process during FOG drift estimation a delay up to 500 s from the system start was introduced, during which the drifts were not estimated (see Figure 11). The third reason is the system self-heating period. The plots in Figure 12 demonstrate that FOG rhumb drift reaches 1.5 deg/h, and FOG bias reaches 30-40 deg/h.  
As for heading errors for phase measurements (12) from only a single GLONASS satellite it is easy to see by comparison of Figure 13 (single satellite) and Figure 10b (five satellites) that even using phase measurements from a single GLONASS satellite the heading error does not greatly differ from the error with five satellites available: 1.5\({}^{\circ}\) vs 1\({}^{\circ}\). The duration of transient process is also comparable. From the presented results (Figure 13) it follows that the considered prototype GNSS/IMU gyrocompass preserves the acceptable heading accuracy with phase measurements from a single satellite.  
Finally it is important to analyze the heading error after complete loss of satellite signals. The presented results (Figure 16) show that GNSS/IMU gyrocompass can operate with the acceptable heading accuracy even with a long satellite signal outage (up to 1.5 h): the error is max 3\({}^{\circ}\). In this case the duration of GNSS/IMU gyrocompass performance with the required accuracy will be determined mainly by FOG error instability. In its turn FOG bias and rhumb drift can be effectively estimated during the system alignment using effective phase measurements from a single GLONASS satellite. The experimental results demonstrate maximum roll and pitch errors did not exceed 0.2\({}^{\circ}\) for all conditions of car test.
## 7 Deployment Criteria  
As the polarization purity of the waves emitted by the probes and the cross-polarization of the sensor are essential in the intended experiments, the probes and sensors have to be deployed in environments that do not distort the emitted or received waves. This means that the near-field of the antenna, up to 1 or 2 wavelengths (40-80 m at 7 MHz), should be devoid of metal wiring (wire fences, power cables, telephone wire) and large conducting objects. Furthermore, the ground under the antenna wires, which is also in the near-field and influences the antenna diagram, must be homogeneous and flat. Rooftops are generally not suitable, as the structure directly under the antenna may contain conducting supports and wiring, which will distort the antenna diagram. In the far field, resonant objects (e.g., HF antennas) or multi-resonant objects (e.g., telephone and power lines) should preferably not be within line-of-sight. In practice, conforming to all these criteria is not always possible, but the more strictly these criteria are adhered to, the higher the quality of the measurements.  
Figure 6: The block diagram of the probe signal transmitter.  
Figure 7: Example of a sequence of polarized probing signals. This example shows left-hand circular polarization (LHCP), right-hand circular polarization (RHCP), transmitter identification (ID) and linear polarization (LIN). The ‘off’ interval is used to synchronize probe and sensor.  
Additionally, for the sensor, a location where the radio noise level is very low has to be selected. City locations generally exhibit very high radio noise levels. Photovoltaic sites may also emit high levels of radio noise. Radio noise is generally lowest in remote rural areas, with a very low density of buildings and human activity. As the waves that we observe in the NVIS experiments arrive at very steep angles, typically 70\({}^{\circ}\)-90\({}^{\circ}\), a measurement site located in a valley and shielded from far-away noise sources by mountains may be preferred over hilltop sites with a good view all around. Radio noise measurements with calibrated equipment are preferred to assess the local radio noise level. An example will be given for the measurements in Spain. Firstly, the central low noise sensor location was selected and verified by measurement. Secondly, potential probe locations were identified at distances between 50 and 150 km from the sensor location via friends and colleagues. Screening of potential sites was done firstly via Google Earth, then secondly by personal inspection. Figures 8 and 9 provide an impression of such sites. The site in Cal Cerdanyola was located in a canyon in the lower Pyrenees, with steep mountain ridges all around. This site provided an excellent demonstrator of the performance of NVIS radio systems in areas seemingly cut off from the horizon.
# Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System
## 4 Results and Discussion
### Carbon Emission Analysis  
Table 3 presents the carbon emission parameters of diesel engines [39]. Figure 8 reveals the benefit from the renewable energy. It shows that total carbon emission decreased from 17,064 kg/h to 15,364 kg/h with increasing wind power in Case 1. Compared with Case 1, the greenhouse gas emissions dropped by 5.9% every hour, which is 15,194.4 kg/h in Case 3. The total carbon emission is markedly decreased from 16,151.27 kg/h to 15,194.4 kg/h owing to the optimal allocation of ESSs. It should be noted that the emissions are 17,386 kg/h in Case 3 when the wind power reaches the maximum. The simulation results indicated that the operating range of the diesel engine should be taken into account when optimizing the configuration of the ESS.  
As seen from the above analysis, the operation cost and carbon emission are greatly reduced by the distributed ESS configuration. Meanwhile, the real power loss of the system declines sharply from 26.4 MW to 10.7 MW. Eventually, Buses 12, 25, and 26 are found to be the best places to install ESSs with capacities of 30.7 MW, 18 MW, and 39.46 MW, respectively, which are demonstrated in Table 4.  
Figure 8: (**a**) Carbon emission considering wind distribution; (**b**) Total carbon emission.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Generator** & \(d\) & \(e\) & \(f\) \\ \hline Gen 1 & 22.983 & \(-\)0.9 & 0.0126 \\ Gen 2 & 25.505 & \(-\)0.01 & 0.027 \\ Gen 3 & 24.900 & \(-\)0.005 & 0.0291 \\ Gen 4 & 24.700 & \(-\)0.004 & 0.0290 \\ Gen 5 & 25.300 & \(-\)0.0055 & 0.0271 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Carbon emission parameters of diesel engines.
# Use of Gradient-Based Shadow Detection for Estimating Environmental Illumination Distribution
###### Abstract  
Environmental illumination information is necessary to achieve a consistent integration of virtual objects in a given image. In this paper, we present a gradient-based shadow detection method for estimating the environmental illumination distribution of a given scene, in which a three-dimensional (3-D) augmented reality (AR) marker, a cubic reference object of a known size, is employed. The geometric elements (the corners and sides) of the AR marker constitute the candidate's shadow boundary; they are obtained on a flat surface according to the relationship between the camera and the candidate's light sources. We can then extract the shadow regions by collecting the local features that support the candidate's shadow boundary in the image. To further verify the shadows passed by the local features-based matching, we examine whether significant brightness changes occurred in the intersection region between the shadows. Our proposed method can reduce the unwanted effects caused by the threshold values during edge-based shadow detection, as well as those caused by the sampling position during point-based illumination estimation.  
Keywords:gradient-based shadow detection; environmental illumination; augmented reality marker +
Footnote †: journal: Applied Sciences
## 4 Analytical Results
### MAI Impact  
Figure 3 presents the log(BER), as a function of simultaneous users, for the IM/DD Fast-OFDM-CDMA system compared to AO-OFDM-CDMA and OCDMA-WDM, while using 2D-HC codes.  
As shown in Figure 3, the three PON configurations can support up to 180 simultaneous users with a BER value less than \(10^{-4.5}\) (i.e., less than the typical FEC limit of \(3.8\times 10^{-3}\)) when 2D-Hybrid codes are used as the OCDMA signature. Furthermore, it is shown that the IM/DD Fast-OFDM-CDMA outperforms both the AO-OFDM-OCDMA and OCDMA-WDM systems in terms of BER as a function of simultaneous users. Further, at 100 simultaneous users, the IM/DD Fast-OFDM-CDMA configuration improves the system performance by factors of \(10^{-0.5}\) and \(10^{-1.5}\), in terms of BER, compared to AO-OFDM-OCDMA and OCDMA-WDM, respectively.
## 3 System Model and Parameterization
### State Representation Using Markov Chain  
The different states during the operation of the SCN can be modelled using Markov models, as shown in Figure 3.  
With respect to the Markov representation of SCN states, let us consider the probabilities of being in the normal, partially failed, and failed states be denoted as \(p_{n}\), \(p_{pf}\), and \(p_{f}\) respectively. The state transition probabilities are \(p_{n-f}\), \(p_{n-pf}\), \(p_{pf-n}p_{pf-f}\), \(p_{f-n}\), and \(p_{f-pf}\) for normal to failed, normal to partially failed, partially failed to normal, partially failed to failed to normal, and failed to partially failed states, respectively. As the  
Figure 3: Markov representation of different states of SCN operation.  
current state decides the next state and the next state does not depend upon the initial state, the system model satisfies the Markov properties. The resiliency of the synchrophasor communication network can also be described using the Markov chain.
###### Abstract  
The purpose of this study was to develop two different functional breads (Pita and Tandoori) supplemented with novel functional ingredients: waxy wheat flour (15%), inulin (8%) and guar gum (2%) independently and in combination. The breads were submitted to sensory characteristics, shelf life, glycaemic and satiety indices assessments. Both guar gum and inulin independently and in combination significantly (\(p<0.05\)) decreased post-prandial blood glucose and glycemic response of Pita (GI of guar gum bread was 55%, inulin 57%) and Tandoori (GI of guar gum bread was 57% and inulin bread was 60%) compared to the control breads (GI 100%). Moreover, the results of the area under the curve of satiety showed that the addition of functional ingredients increased satiety levels as follows: for Pita, control was 355, a combination of all ingredients was 418, inulin was 451 and guar gum was 452; for Tandoori, control was 329, a combination of all ingredients was 420, inulin was 381 and guar gum was 390. The results showed that all sensory characteristics were improved, and breads were acceptable (all obtained more than five points) when the highest proportions of ingredients were added. Similarly, the shelf life of supplemented Pita and Tandoori breads was improved with the addition of ingredients. Therefore, the functional ingredients such as inulin and guar gum can be used independently and in combination to reduce GI and increase satiety of Pita and Tandoori bread with acceptable quality and shelf life.  
glycaemic index; satiety index; sensory evaluation; inulin; guar gum; Pita and Tandoori breads +
Footnote †: journal: Applied Sciences
## 3 Results and Discussions
### Precision of the Results  
For the estimated difference deformation rate at arcs, the posterior root-mean-square (RMS) is about 0.8 mm/year. Since only three interferograms are used in this case study, the RMS may lack statistical meaning and large bias might exist between the \"true\" RMS and the estimated one. In a practical view that the island at the one end of the bridge should keep stable, it is possible to use the points on the island to validate the result. Assuming that no deformation occurred on the island, the RMS of deformation rate of the points on the island is 1.2 mm/year.  
The errors of the InSAR-derived deformation rate can be partly induced by the single master used in the three interferograms. The atmospheric delay and other noises in the master could be propagated into the interferograms, which will behave as an accumulation rather than neutralizing the estimation of the deformation rate. In addition, it is observed in Table 1 that the perpendicular baselines of the three interferograms increase with the time intervals of the three interferograms. This, however, is not in favor of the solution of the LS approach (i.e., Equation (9)), resulting in a possible indistinguishable relation between the elevation error and the deformation rate.
## 3 Results and Discussion  
### The LMO Monitoring Project for LM Canola  
Since 2009, the MOE has conducted a comprehensive LMO monitoring project aimed at managing the unintentional release of LMOs into natural environments, as shown in Figure 1A. The project consists of four key steps: (i) selecting quadrat nationwide and conducting monitoring within a 100-m radius of each site, (ii) identifying LMOs through event-specific PCR and sequencing, (iii) classifying the LMO collection site, and (iv) conducting intensive surveys and post-management of the sites where LMOs were released. The NIE has been responsible for performing LMO monitoring, considering factors such as the growth period and the types of introduced genes in LM canola. Each year, NIE has analyzed and managed the factors contributing to the detection of LM canola.  
Each year, the classification of LMO collection sites is used to develop post-management and survey strategies for the following year. Based on this information, we classified the collection sites into four categories: roads, stockbreeding farms, feed factories, and \"other\", which includes festival sites. Figure 1B-E shows typical monitoring sites where LM canola is surveyed, along with the surrounding natural ecosystem, based on the detailed address.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Target Event** & **Primer Name** & **Sequence (5\({}^{\prime}\)-3\({}^{\prime}\))** & **Product Size (bp)** & **Reference** \\ \hline MON88302 & MON88302-F & TCAGATTGTCGTTTTCCCCGCCTTCA & & [49] \\  & MON88302-R & GTCTTTGCTTTGGCTCTTTACTTTTTGCG & 304 & [50] \\ \hline Ms8 & Ms8-F & CCAAATAGCCTCCCACCCTA & & [23] \\  & Ms8-R & GGAGGGTTTTTTGGTTTCC & 249 & [51] \\ \hline GT73 & GT73-F & GCTTATACGAAGGCAAAGAAGA & & [52] \\  & GT3-R & GAAGTTTTCTCATCAAGCCCCATTTG & 321 & This study \\ \hline DP-073496-4 & DP-073496-4-F & GCTGTGCCAATTCAGATATATGGGT & 350 & This study \\  & DP-073496-4-R & CAAACCCTCCTAGAGTTCAACATCTTAA & 350 & [53] \\ \hline T45 & T45-F & CAAGCGTGCTGCTCCCACCATG & 378 & [23] \\  & T45-R & GAACATAGATGAGTCTCCCCA & 378 & [54] \\ \hline MS11 & MS11-F & CAAGATGGGAATTAACATCTACAAATTG & & [55] \\  & MS11-R & GCAGCACTGTACTGGTTCAA & 355 & This study \\ \hline \(Cru\ A\) & Cru A -F & GTCAAGGCCAAGGACAACAG & & \\  & Cru A-R & CCGTTGTAGAACCATTGG & 154 & [55] \\ \hline \end{tabular}
\end{table}
Table 1: Cont.
## 3 UAV Transition Corridor
### Deflection Angle-Speed Envelope Based on Power Limit in Ducted Lift-Increasing System  
The envelope of the equilibrium state gives the deflection angle-speed envelope of the ducted lift-increasing system on the premise that the wing does not stall. However, in actual flight, as long as the power is sufficient, the UAV can complete transition well even if the wing is stalled. In this section, the deflection angle-velocity envelope of the ducted lift-increasing system is determined according to the available power of the ducted lift-increasing system as a constraint [19].  
The ducted lift-increasing system must satisfy the balance of the lift fan and ducted lift-increasing system aerodynamic force and the airframe aerodynamic force with the gravity of the UAV during the deflection process, and the available power of the lift fan and ducted lift-increasing system are required during the transitional flight.  
The required power \(P_{r}\) of the lift fan and the duct is the same, which is composed of the induced power \(P_{i}\), type resistance power \(P_{pr}\), waste resistance power \(P_{p}\), and climbing power \(P_{c}\).  
\[P_{r}=\frac{2}{\eta_{p}}\left(P_{i}+P_{pr}+P_{p}+P_{c}\right) \tag{16}\]  
where \(\eta_{p}\) is the transmission loss coefficient from the engine to the blades.  
According to the conservation of energy and momentum theorem, the expressions of the waste resistance power \(P_{p}\), induced power \(P_{i}\) and climb power \(P_{c}\) can be expressed as follows:  
\[P_{ipc}=T(U_{c}+v_{i}) \tag{17}\]  
where \(U_{c}\) is the vertical velocity of the blade plane and \(v_{i}\) is the induced velocity of the rotor blade.  
Considering the existence of multi-blade blades in the lift fan and duct, the induced speed is non-uniform. In this paper, the induced speed is modified by \(K_{ind}\), and the above formula becomes  
\[P_{ipc}=T(U_{c}+K_{ind}v_{i}) \tag{18}\]  
According to the blade element theory, the rotor-type resistance power can be expressed as  
\[P_{pr}=P_{pr0}(1+4.7\mu) \tag{19}\]  
where \(P_{pr0}=\sigma\pi R^{2}\rho V_{c}^{3}c_{d}/8\), \(\sigma\) is the solidity of the rotor, \(c_{d}\) is the drag coefficient of the blade, \(V_{t}\) is the tip speed of the rotor, and \(\mu\) is the forward ratio. For lift fans and duct power components, their power can be obtained by the above formula [12].  
Figure 5: Schematic graph of force during transition flight.  
Thus, in the case of the power limitation, the VTOL fixed-wing UAV's angle-speed envelope boundary must satisfy the force balance, and, at the same time, the total power of the lift fan and duct must be less than the available power of the system \(P_{n}\), i.e., \(P_{r}<P_{n}\).
# An Efficient Trajectory Planning Method for High-Speed Interception of Invasive Drones
## 6 Performance Verification and Error Analysis  
This section aims to conduct a comprehensive comparative analysis of the planning effects of the method proposed in this paper and the one described in reference [6], under both single-objective and multi-objective scenarios. This will be achieved through rigorous experimental verification within the Gazebo [31] physical simulation environment. The study will primarily focus on assessing the performance advantages of the algorithm presented in this paper in high-speed flight scenarios, as well as its efficacy in managing complex multi-moving target environments. To provide a holistic evaluation of these two methods, we will systematically test them in both low-speed and high-speed flight scenarios, conducting an in-depth analysis of their respective planning results and performance differences during simulation experiments. To ensure the comparability of experimental conditions throughout the comparison process, both methods will utilize the same drone model for simulation, with key parameters detailed in Table 1:  
Figure 6: Flowchart for efficient recursive testing of trajectory feasibility.  
The method in [6] does not consider air resistance, so its simulation actual thrust output is slightly higher than the planned value. This additional thrust is mainly used to compensate for the rotor self-resistance and the body air resistance.  
However, the actual output thrust of our method under low-speed conditions is slightly lower than the planned value, which is consistent with the linear approximation analysis in Section 3.1.  
Although there are differences between them, both methods are within the actual capability range of drones in terms of actual thrust demand. Under the effective regulation of the feedback controller, both methods can achieve relatively accurate tracking of the planned trajectory. This shows that both methods have certain effectiveness in low-speed flight scenarios, although they adopt different strategies to deal with dynamic models and external resistance effects.
## 2 Results and Discussion
### Repeatability of the Extraction Methods  
Table 1 displays the coefficients of variation (CVs) obtained for the volatile compounds extracted using the above-mentioned extraction methods, except for the D-HS-trilayer method, which leads to non-exploitable results. Carboxylic acids display a higher CV compared to compounds from other chemical classes, whatever the extraction method used. These compounds are strongly retained on polar columns, such as the DB Wax used here, leading to spreading shaped peaks, rendering integration complicated.  
The CVs of compounds extracted by D-HS-Tenax are lower than the CVs obtained for the HS-SPME-Car/PDMS method (CVmean 13% \(<\) 35%, respectively). Indeed, the smallness of peaks obtained with the HS-SPME-Car/PDMS method leads to unavoidable variations in the integration of the peak area between replicates leading to high values of CVs. This result contrasts with several papers comparing these two methods on bovine milk [44; 51]. However, the volumes of milk used in these studies were higher and did not result in this bias. Regarding the static and dynamic MultiSPME methods, the mean CV of compounds is 18 and 21%, respectively. The repeatability of these methods is improved compared to using a single fiber under static conditions, which the higher associated peak areas can explain. Finally, the mean CV for the D-HS-MultiSPME/Tenax is 15%, which is in the range of the CV obtained from the D-HS-Tenax procedure and in the range of those obtained from validated extraction methods commonly applied to dairy products [44; 51; 60].  
In conclusion, among the six extraction procedures tested in the present study, i.e., HS-SPME-Car/PDMS, D-HS-Tenax, D-HS-trilayer, S-HS-MultiSPME, and D-HS-MultiSPME/Tenax methods, the latter leads to the best performance in terms of the range of identified compounds and extraction capacity. The dynamic mode used in the D-HS-MultiSPME/Tenax method enables us to extract a large number of terpen. In contrast, the use of SPME fibers enables us to recover a large number of carboxylic acids. In addition, the large quantity of Tenax in the trap ensures a high recovery of aldehydes, ketones and alcohols. Finally, this latter method demonstrates suitable repeatability, which is in the range of currently applied methods [44; 51; 60]. Among the different methods tested, the D-HS-MultiSPME/Tenax is the most efficient way to explore human milk's volatile fraction. This method was further evaluated from a discriminability point of view.