esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of the New Compound-Carrier-Modulated Signal for Future Navigation Signals
## 4 Evaluation and Analysis of NSCC with Representative Parameters
### Tracking Performance  
With the front-end bandwidth ranging from 0 to 30 MHz, the Gabor bandwidth is computed and the corresponding result is illustrated graphically in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**SSCs(dB)** & **C/A** & **MSK** & **BOC(1,1)** & **MBOC(6,1,1/11)** & **QPSK(10)** & **P** & **NSCCSelf** \\ \hline C/A & \(-\)61.9 & & & & & & & \\ MSK & \(-\)62.3 & \(-\)62.4 & & & & & & \\ BOC(1,1) & \(-\)67.9 & \(-\)66.3 & \(-\)64.9 & & & & & \\ MBOC(6,1,1/11) & \(-\)68.3 & \(-\)66.7 & \(-\)65.3 & \(-\)65.7 & & & & \\ QPSK(10) & \(-\)67.2 & \(-\)67.1 & \(-\)67.5 & \(-\)67.9 & \(-\)65.9 & & & \\ P & \(-\)70.2 & \(-\)70.1 & \(-\)70.6 & \(-\)70.9 & \(-\)68.9 & \(-\)71.9 & & \\ NSCC(19) & \(-\)73.0 & \(-\)72.9 & \(-\)73.1 & \(-\)73.1 & \(-\)70.4 & \(-\)73.4 & \(-\)73.1 & \\ NSCC(9) & \(-\)72.9 & \(-\)72.7 & \(-\)73.1 & \(-\)73.5 & \(-\)71.2 & \(-\)74.2 & \(-\)73.0 \\ NSCC(4) & \(-\)72.6 & \(-\)72.5 & \(-\)73.1 & \(-\)73.5 & \(-\)73.5 & \(-\)76.5 & \(-\)72.9 \\ NSCC(3) & \(-\)72.5 & \(-\)71.9 & \(-\)73.0 & \(-\)73.4 & \(-\)74.5 & \(-\)77.5 & \(-\)72.7 \\ NSCC Mono & \(-\)69.8 & \(-\)69.7 & \(-\)70.1 & \(-\)70.5 & \(-\)68.2 & \(-\)71.2 & \(-\)70.0 \\ NSCC Bimo & \(-\)87.0 & \(-\)89.9 & \(-\)84.3 & \(-\)79.6 & \(-\)75.7 & \(-\)78.7 & \(-\)69.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SSC values for BPSK, QPSK, BOC, MBOC, MSK and NSCCs for 20 MHz pre-filtering bandwidth.  
Figure 6 shows that when the front-end bandwidth is <6 MHz, the Gabor bandwidth of NSCC(19) is lower than that of the BPSK(10), BOC(1,1) and MBOC(6,1,1/11) modulation. As the front-end bandwidth increases, the Gabor bandwidth becomes large until 20 MHz, which is the main lobe bandwidth for NSCC(19). The Gabor bandwidth of NSCC outperforms BPSK(10) until the pre-filtering bandwidth approximately equals 12 MHz. For the very large front-end bandwidth, NSCC presents the best code-tracking performance. Therefore, the NSCC shows the best code-tracking performance among the analyzed signals for large pre-filtering bandwidths.  
Figure 7 illustrates the Gabor bandwidth for NSCC modulation with different parameter configurations. Obviously, the NSCCs Gabor bandwidths all appear to be approaching their peak values above 20 MHz, which is the main lobe bandwidth of the NSCCs for our simulation. Note that the less the chip rate, the greater the Gabor bandwidth peak value is. As for the reference baseline, the NSCC bimodal presents excellent code-tracking performance compared favorably with NSCCs occupied 20 MHz bandwidth, while the NSCC monomodal just gets approximately half of the bimodal Gabor bandwidth.  
Figure 6: Gabor bandwidth of the legacy navigation signals and NSCC modulation.  
Figure 7: Gabor bandwidth for NSCCs.
# Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds
## 1 Introduction
UAV Lidar is also used for monitoring the vegetation around PLs, which has generally not been paid as much attention as direct monitoring of the power line components [3]. Some other studies have concentrated on the integration of ALS data and aerial images [17; 18]. Nevertheless, the irregular distribution and complexity of Lidar data make it challenging to detect anomalies in power transmission objects and their surroundings without knowing their spatial relationships [19; 20]. Therefore, one must first extract transmission wires and HV pylons from the rest of the point cloud and distinguish them from the environmental elements. To provide comprehensive guidance for facility maintenance, a thorough UAV Lidar processing method of UAV Lidar inspection data was demonstrated in this paper, including the precise extraction of transmission lines, the 3D localization of pylons, the accurate detection of insulators, and the modeling of power transmission components.
## 5 Case Studies
### Cost Comparison  
A comparison of the costs for implementation, configuration, and hardware support between the two approaches is shown in Table 3. It must be highlighted that direct comparability is limited because of the different procedures and cost types. In the first case, it took approximately one hour of work to mount the camera and configure the vendor's IoT cloud. This same amount of effort would be required for each additional camera sensor. In the second case, the LoRa base server, the LoRaWAN infrastructure, and the LoRa payload had to be configured and tested, which took about three hours. Each CT bridge was then integrated into the LoRaWAN infrastructure, which took a few minutes.  
In the EMS, the respective data source's interface had to be implemented as a device driver with a graphical user interface. The representational state transfer (REST) API for the IoT cloud required 16 days of effort, including a simple, interactive configuration of the data source and mapping to measurement points. For the second case, it took 90 days to implement a universal OPC UA client, including security profiles and browsing through the offered OPC UA address space. The main effort in both cases was implementing the user interfaces. The configuration per setup and per sensor in the EMS was similar in both cases; the configuration of the data source (IoT cloud or OPC UA server) only occurred once per setup.  
While our costs for the OCR camera were known in the first case, at \(\copyright\)210 net without a quantity discount, we could only quote the invoice for the LoRa base server at \(\copyright\)900 in the second case. For the CT bridge, which is not yet available on the market, a price of about \(\copyright\)60 was estimated.  
Beyond cost, it must also be considered that no IoT cloud of a third-party provider was involved in the second case. The data, thus, remains in the company's sphere of sovereignty at all times.
## 3 Numerical Simulation and Experimental Analysis
### Crack Mode
#### 3.1.2 Experimental Analysis  
Experiments are performed in the rig according to the conditions listed in Table 2. The shaft is driven by a servo motor and rotate at 3000 rpm. The stimulator gives the shaft an instant force to stimulate the elastic waves, and then the wave begins to propagate from the stimulated end to the other, just as propagating from location \(x_{0}\) to location \(x_{l}\) in Figure 1b. Acceleration sensors are set on both ends of the shaft, which can capture the waves. As mentioned before, the uniform shaft having the same mass with the non-uniform shaft is also studied here for deducing the propagation constants by comparison. The propagation characteristics of the elastic waves in the real shafts can be deduced by comparing the spectrum of the signals of the uniform shaft and the non-uniform shaft [17]. The result is shown in Figure 7.  
As shown in the upper of Figure 7, the time history of the acceleration signals at location \(x_{l}\) shown in Figure 1 is truncated to 0.01 s for analysis, and these signals are captured after propagating through the shaft. The lower part is the spectrum of the time history, and the spectrum are derived by FFT (Fast Fourier Transform). The real lines stand for the uniform shaft while the dotted ones stand for the non-uniform shaft. In the spectrum curve, if the real line is higher than the dotted line, which means the energy distributed in the range is much higher, then, the corresponding range can be treated as a stopband [17]. In the above figure, three stopbands can be found, and they are [30; 120], [335; 520] and [810; 1275] separately.  
Only the individual mode is considered, and the crack is supposed to be a transverse one in the paper. It can be found by comparing the experimental result with the numerical one listed in Table 2 that the experimental result matches well with the numerical result deduced with Mode III, though there are errors between them. Thus, it can be inferred that Mode III is the most suitable for the non-uniform shaft in the experiment. In addition, it is of great importance to study the crack mode in a real practice.  
Figure 7: Experimental Stop Bands derived by spectrum comparison.
# Analytical Attitude Guidance Planner for Multiple Ground Targets Acquisitions
## 4 Attitude Point-to-Point Generator
### Final Strategy
#### 4.2.1 Selection of the Minimum Maneuver Times  
Of all the \(n\) and \(m\) roots, only the real and positive ones are considered, while the complex and negative are discarded. Among the remaining, only the ones satisfying both constraints are maintained. The roots that verify both constraints could be selected by substituting the \(n\) roots in the constrain \(||\dot{\mathbf{\omega}}(t)||\leq\dot{\omega}_{max}\) and the \(m\) roots in the condition \(||\mathbf{\omega}(t)||\leq\omega_{max}\). Indeed, not all the roots verify both constraints, as a consequence some solutions can be discarded. This approach is schematized in the following:  
\[(||\mathbf{\omega}(t)||\leq\omega_{max}) \rightarrow\ n_{roots}\,\text{of}\,t_{f}^{\omega} \rightarrow\ (||\dot{\mathbf{\omega}}(t)||\leq\dot{\omega}_{max})\ \rightarrow\ l_{roots}\,\text{of}\,t_{f}^{\omega,\dot{\omega}} \tag{50}\] \[(||\dot{\mathbf{\omega}}(t)||\leq\dot{\omega}_{max}) \rightarrow\ m_{roots}\,\text{of}\,t_{f}^{\omega} \rightarrow\ (||\mathbf{\omega}(t)||\leq\omega_{max})\ \rightarrow\ p_{roots}\,\text{of}\,t_{f}^{\omega,\dot{\omega}}\]  
In general, only \(l\) of the \(n\) roots and \(p\) of the \(m\) roots verify both conditions, thus only the (\(l\)+\(p\)) roots are suitable to obtain a feasible maneuver, satisfying the boundary conditions. The optimal solution is carried out by taking among the \(l\)+\(p\) solutions the one corresponding to the minimum time value. The method can be repeated for each \(k^{th}\) step (with \(k=1,2,3\)), and for each \(i^{th}\) component (\(i=x,y,z\)).  
\[T_{f_{k}}^{i}=\min((l_{k,i}+p_{k,i})\,t_{f_{k}}^{\omega_{i},\omega_{i}}) \tag{51}\]  
where \(T_{f_{k}}^{i}\) is the minimum final time of the \(k^{th}\) step, for each \(i^{th}\) component. The minimum time of each step is then evaluated by taking  
\[T_{f_{k}}=\max_{i=x,y,z}(\,T_{f_{k}}^{i}) \tag{52}\]  
In this way, it is possible to find the \(k^{th}\) solution that satisfies both the boundary conditions and the physical constraints Equations (4) and (9) in each step. \(T_{f_{k}}\) represents the duration time of each step and it was the problem's unknown. As a consequence, the coefficients of the three polynomials, which approximate the guidance law, are immediately evaluable by means of Equations (19) and (32) and the attitude guidance path of each step is solvable in a closed form, as reported in Equation (15) and in Equation (29).
###### Abstract  
Demand response (DR) aggregator controlling and aggregating flexible resource of residential users to participate in DR market will contribute the performance of DR project. However, DR aggregator has to face the risk that users may break the contract signed with aggregator and refuse to be controlled by aggregator due to the uncertainty factors of electricity consumption. Therefore, in this paper, community operator (i.e., DR aggregator) is proposed to equip auxiliary equipment, such as energy storage and gas boiler, to compensate for power shortage caused by users' breach behavior. DR aggregated resource with different auxiliary equipment will have different characteristics, such as breach rate of DR resource. In the proposed DR framework, for selling the aggregated resource, community operator has to compete the market share with other operators in day-ahead DR market. In the competition, each operator will try its best to make the optimal bidding strategy by knowing as much information of its opponents as possible. But, some information of community operator (e.g., DR resource's characteristic) belongs to privacy information, which is unknown to other operators. Accordingly, this paper focuses on the application of incomplete information game-theoretic framework to model the competition among community operators in DR bidding market. To optimize bidding strategy for the high profit with incomplete information, a Bayesian game approach is formulated. And, an effective iterative algorithm is also presented to search the equilibrium for the proposed Bayesian game model. Finally, a case study is performed to show the effectiveness of the proposed framework and Bayesian game approach.  
demand response; DR aggregator; DR resource's breach; Bayesian game; energy storage; gas boiler +
Footnote †: journal: Applied Sciences
## 4 Analysis and Experimental Verification
### Verification of the Analytical Model  
Figure 18 shows the load-deflection characteristics of US1 and US2. Similar to Ozaki [37], the determination of the friction coefficient depends on the measurement results, and the obtained value is \(\mu_{e}\) = 0.3. As shown in Figure 18a, the proposed model overestimates the load in the work region of 0-2.5 mm, but with a further increase in the deflection, the theoretical and experimental load-deflection curves perform much more consistently with each other, and the maximum error is less than 3%. However, for the specimen US2, the proposed model overestimates the load's overall working regions, and the divergence increases with a rising deflection, as shown in Figure 18b. The proposed model has a better performance in characterizing the load-deflection relationships of US1 because the proposed model neglects the influences of the shape of the slotted segment, which has been simplified as a linearly gradientgradient width.  
The load-deflection relationships of LGTS1-1, LGTS1-2, LGTS2-1, and LGTS2-2 are presented in Figure 19. It is illustrated that the proposed model has good agreement with the experimental data of the LGTS disk springs with rectangular channels (i.e., LGTS1-1 and LGTS1-2). The proposed model overestimates the load of LGTS2-1 and LGTS2-2, which is caused by neglecting the influences of the slot shapes in the transition region. As shown in Figure 19d, the load increases sharply when the deflection is larger than 4.2 mm. The reason for this is that the compressive force is applied by a circular plate and the force no longer acts on the upper edge of the LGTS disk spring when the deflection increases to a certain extent. Overall, the proposed model has the ability to accurately predict the  
Figure 16: Comparisons of the experimental data of slotted disk springs: (a) US1 vs. LGTS1-1, (b) US2 vs. LGTS2-1.  
Figure 17: Load variation rate curves with different \(C\) and \(C_{1}\) values.  
load-deflection characteristics of both uniform slotted disk springs and LGTS disk springs considering the hysteresis characteristics due to the edge friction.
###### Abstract  
In this study, hydrocalumite-like Ca\({}_{2}\)Al-NO\({}_{3}\)- layered double hydroxides (Ca-Al LDHs) with different microstructures were synthesized. The crystalline properties, structure composition, morphology and particle size distribution of the Ca-Al LDH (CAL) samples were illustrated. To obtain the chloride uptake performances of CAL, the influences of contact time, initial concentration of Cl-, pH of reaction solution and coexistence anions on the chloride uptake were examined systematically. Compared to the CAL samples obtained at a higher aging temperature, CAL synthesized at 60 \({}^{\circ}\)C demonstrated the minimum average particle size (6.148 \(\upmu\)m) and the best Cl- adsorption capacity (211.324 mg/g). Based on the test results, the main adsorption mechanism of chloride ion on CAL was recognized as an interlayer anion exchanging reaction other than the dissolution-precipitate mode. With the increase in the pH value of reaction solution from 7 to 13, it was found that the amount of chloride ion adsorbed by CAL increased slightly, and the solution could remain at relatively high pH value even after the adsorption. The presence of CO\({}_{3}\)2- and SO\({}_{4}\)2- reduced the adsorption capacity of CAL dramatically as compared with OH- due to the destruction of layered structure and the formation of precipitates (CaCO\({}_{3}\) or CaSO\({}_{4}\)). The interference sequence of the investigated anions on the chloride uptake of CAL was SO\({}_{4}\)2-, CO\({}_{3}\)2- and OH-, and the order of interlayer anionic affinity was Cl- \(>\) OH- \(>\) NO\({}_{3}\)-. The results illustrated that the synthesized CAL could be used as a promising chloride ion adsorbent for the corrosion inhibition of reinforcement embedded cement-based materials.  
Keywords:Ca\({}_{2}\)Al-NO\({}_{3}\)- LDHs; aging temperature; particle size; chloride uptake; adsorption capacity; kinetic model +
Footnote †: journal: Applied Physics Letters
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 4 Comparison of Forest AGB Maps over Large Regions  
For a specific region, several forest AGB maps might be available, but they may exhibit discrepancies in both magnitude and spatial distribution. Their agreements and discrepancies were assessed by metrics including the difference maps, the Fuzzy Numerical (FN) index, and variograms used in previous studies, for example, the comparison of available Uganda forest AGB maps in [PERSON] et al. (2018), for the conterminous USA by [PERSON] and [PERSON] (2019), and for pan-tropical maps in [PERSON] et al. (2010). In this section, the comparison of AGB regional and global forest maps over Europe, the conterminous U.S.A., Southeast Asia, tropical Africa and South America are presented.  
In addition to the spatial distribution of forest AGB maps, we counted the percentage of pixels with AGB values of 10 Mg/ha bins in each dataset, and compared their similarities and differences in frequency distributions across Europe, the conterminous U.S.A., Southeast Asia, tropical Africa and South America. Different definitions of forests were adopted in the generation of these AGB datasets to separate forest and nonforest, which could cause significant discrepancies in the frequency statistics, particularly in regions with lower AGB that were mainly surrounded by woody savannas and savannas. Therefore, the global land cover climatology dataset derived from MODIS Land Cover Type from 2000 to 2010 was used to distinguish the main forest types ([PERSON] et al., 2010). Furthermore, the spatial agreements and disagreements in dominant types of forests in these AGB datasets were assessed with the FN index separately. The FN index in this paper was the average of pixel-level numerical similarities, and thus measured the overall similarity of spatial patterns between two forest AGB maps. Details about the calculation of pixel-level numerical similarity could be found in previous studies ([PERSON] et al., 2010). The FN index ranged from 0 to 1, which represented the two datasets were fully distinct and fully identical, respectively.
# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 4 Discussion
### Research Objectives
#### 4.3.3 Vineyard's Variability Structure
This statement could implicate two main causes: in the sloped areas, the soil variability marks the grapevine growth expression more than random environmental factors [51, 52], or the structure of the variability recorded from flat vineyards is much more well defined in favour of unpredictable patterns. Analysing the nugget and the sill, it seems the nugget from sloped vineyards showed a stable trend, and it is always lower than the nugget recorded from flat vineyards. Hence, the first hypothesis is confirmed. Concluding, the CI range included values lower than 25%, supporting the strong spatial dependence of the VIs [55].
# Unsupervised Low-Light Image Enhancement in the Fourier Transform Domain
## 4 Experiments
### Experimental Comparison  
We compared the results with some classic methods, LLnet [14], RetinexNet [15], Zero-DCE [24], Zero-DCE++ [25], SCI [26], EnlightenGAN [18], and RUAS [17] on 10 pairs of the same test images. Experiments consisted of copying their original test code and testing it in the same environment settings. We calculated several classic evaluation indexes commonly used in low-light image enhancement, such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and learned perceptual image patch similarity (LPIPS) [41]. Unlike traditional metrics that focus on pixel-wise differences, LPIPS evaluates the similarity based on learned representations of image patches. It utilizes a deep neural network trained on large-scale human perceptual judgments to compute the perceptual distance between images. In addition, LPIPS takes into account various visual factors, such as color, texture, and structure, providing a more comprehensive assessment of image similarity. The experimental results are shown in Figure 3.  
From Figure 3, it can be intuitively found that our method is closest to the original image after enhancement and has the best result. Among them, Zero-DCE [24], Zero-DCE++ [25], SCI [26], and RUAS [17] are under-exposed and do not enhance the luminance significantly. RetinexNet [15] has serious artifacts, and the background information is lost. In addition, LLNet [14] and EnlightenGAN [18] also achieve poor recovery. Although all the images become clear, these images have problems of underexposure and color distortion. The quantitative experimental results are shown in Table 1. Our method has achieved the best results on the three evaluation indicators. Meanwhile, we have also done experiments on facial image enhancement in computer vision. In Figure 4, our method and EnlightenGAN [18] yield natural exposure and clear details; our method has a little better  
Figure 3: Enhancement results of each method. (**a**) Input. (**b**) LLNet [14]. (**c**) RetinexNet [15]. (**d**) Zero-DCE [24]. (**e**) Zero-DCE++ [25]. (**f**) SCI [26]. (**g**) EnlightenGAN [18]. (**h**) RUAS [17]. (**i**) Ours. (j) “GT” is the ground truth.  
exposure. In comparison, RetinexNet [15] also produces over-exposed artifacts. There is an obvious overexposure problem of RUAS [17], and the background of LLNet [14] is severely blurred. Other methods cannot recover face information and details well either.