esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Applications of GlobeLand30: A Review
## 2 GlobeLand30 and Accuracy Analysis
### Accuracy Analysis  
The accuracy of GlobeLand30 had been evaluated by third-party researchers from more than ten countries for its all classes or one single class via sample-based validation or comparison with existing land cover products [28; 29; 30]. At the country/region level, a satisfactory overall accuracy was estimated as 82.4% for China [31], 80% for Italy [29], 77.90% for all of Iran [14], 80.1% for Nepal [32], and 89.7% for Kyiv Oblast, Ukraine [33]. [PERSON] et al. [31] evaluated the accuracy of seven land cover products over China, namely, International Geosphere-Biosphere Program Data and Information System's land cover dataset (IGBP DISCover), The University of Maryland land cover dataset (UMD), Global Land Cover 2000 dataset (GLC2000) from the European Commission's Joint Research Center (JRC), Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products MOD12Q1 and MCD12Q1, Global Map-Global LC (GLCNMO) dataset from the International Steering Committee for Global Mapping, Climate Change Initiative land cover dataset (CCI-LC) from European Space Agency (ESA), and GlobeLand30; they found that GlobeLand30-2010 has the highest overall accuracy (82.4%). [PERSON] et al. [28] reported that GlobeLand30 has high agreements with CORINE (92.52%), Urban Atlas (85.43%), OpenStreetMap (74.24%), and ATKIS (85.23%) in Germany. [PERSON] [34] found that GlobeLand30 product overlap with a degree of 77% with GLC-Share product in Continental Portugal. However, a lower overall accuracy with 46% for the GlobeLand30-2010 was found in Central Asia, that is, Kazakhstan, Tajikistan, Turkmenistan, Uzbekistan, and Kyrgyzstan [35]. The major classification error might originate from the confusion between bare land and grassland [33; 36] because of discrepancies of land cover type definitions. Table 2 lists the accuracy assessment results for GlobeLand30 in the literature.  
Accuracy assessment has also been conducted for certain single land cover types of GlobeLand30. [PERSON] et al. [30] found that land surface water of GlobeLand30 overlaps 91.9% of the reference data in Thessaly, Greece, whereas the coarser-resolution products are restricted to lower accuracies. [PERSON] et al. [37] compared cropland class of five global cropland datasets in circa 2010 of China, including GlobeLand30, Finer Resolution Observation and Monitoring GLC dataset (FROM-GLC), GlobCover, MODIS Collection 5, and MODIS Cropland. The results showed that the overall accuracy of cropland of GlobeLand30 is 79.61%, which is highest in the five products. [PERSON] et al. [38] evaluated the accuracy of cropland for GlobeLand30, MODIS land cover product, GlobCover2009, and FROM-GLC in Shaanxi, China, and the overall accuracy for GlobeLand30 is 80.63%.  
Figure 2: Data tiles of GlobeLand30.
# Virtual Flux Control Methods for Grid-Forming Converters: A Four-Method Comparison
## 4 HIL Results and Comparison
### Islanded Mode Operation  
For this test, a three-phase resistive load is first connected at point \(v\) in Figure 1. Then, at t = 0.5 s, the grid is disconnected, leaving the converter supplying the load in islanded mode. Figure 17 illustrates the output voltage magnitude of the converter, the frequency measured through a PLL, and the active and reactive power.  
At the moment of grid disconnection, the converter successfully supplies the load while maintaining the nominal voltage (1 p.u.) in all four cases. However, as observed in the first graph of Figure 17, the VFOC algorithm exhibits more noise in the voltage waveform compared to the other control schemes. In the second graph of the same figure, a slight decrease in frequency is observed in the four systems due to the droop control and the value of the connected load. The last two graphs show that once the grid is disconnected, which was initially supplying the load, the converter increases its active and reactive power to the necessary levels to maintain the required voltage and frequency. Regarding dynamic performance, all four control strategies exhibit almost identical behavior in this scenario.  
Furthermore, Figure 18 displays the instantaneous output current waveforms of the converter for the four control schemes at the moment of grid disconnection. As can be seen, their behavior in islanded mode is quite similar across all cases. However, the VFOC system introduces some noise into the waveforms, which aligns with the observations made in Figure 17.  
Figure 17: Voltage, frequency, and active and reactive power during the transition to islanded mode.
## 4 Requirements and Constraints
### Requirements  
Requirements:  
1. A functional spacecraft shall be designed, developed, deployed and demonstrated.
2. The spacecraft shall be compliant with revision 12 of the CubeSat form factor and launch provider integration standards (with the later taking precedence over the former in case of any conflict).
3. The spacecraft shall be capable of producing imagery with a minimum spatial resolution of 150 meters
4. Software shall be developed which can produce a 3x increase in spatial resolution over the optical resolution.
5. The spacecraft shall be capable of executing critical elements of constellation operations including position determination and attitude correction
6. The mission shall incorporate students in the design, development and operation of the spacecraft.
7. The spacecraft shall have sufficient pointing knowledge to determine whether a desired target has been imaged.
8. Relevant documentation to allow future fabrication of all in-house designed hardware elements shall be produced.
## 4 Electrical System
### Energy Supply
The Breizh Tigresse, for example, used a charge controller to turn off the electronics and actuator power when the battery voltage was lost. The boat was programmed to act like a drifting \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Device** & **Qty.** & **Daily Use (h)** & **Power (watts)** & **Daily Power (Wh)** \\ \hline AT24C512 EEPROM & 1 & 24 & 0.02 & 0.48 \\ Brushless DC Motor & 2 & 2.8 & 8.4 & 47.04 \\ SAMD20J18A & 1 & 24 & 0.3 & 7.2 \\ Stream 211 (Transmitting) & 1 & 0.0222 & 3.96 & 0.087912 \\ Stream 211 (Standby) & 1 & 24 & 0.204 & 4.896 \\ Voltage Regulators & 1 & 24 & 0.085 & 2.04 \\ MAX13448E Transceiver & 1 & 24 & 0.075 & 1.8 \\ GPS & 1 & 24 & 0.066 & 1.584 \\ Compass & 1 & 24 & 0.01485 & 0.3564 \\ Wind sensor & 1 & 24 & 0.114 & 2.736 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Power budget. \begin{table}
\begin{tabular}{l l} \hline \hline
**Boat Name (Year)** & **Energy System** & **Days Lasted** \\ \hline OpenTransat (2019) & Main system: 12.8 V 30 Ah LiFePo4 battery powered by 6, 24, and 18 watt peak \\  & solar panels; \\  & Wing sail: 12.8 V 1600 mAh LiFePo4 powered by 2 \(\times\) 3 W peak solar panels; \\  & Backup for tracking system: 3.7 V 3000 mAh lithium polymer battery. \\ \hline SeaLeon (2018) & 19 \(\times\) 4 D-size lithium thionyl 3.6 V 19 Ah batteries. Voltage of 14.4 V and total \\  & capacity of 361 Ah. \\ \hline Breizh Tigresse (2015) & 20 \(\times\) 3 D-size lithium thionyl 3.6 V 19 Ah batteries. Total capacity of 380 Ah. \\ \hline ABoat Time (2014) & 12 V LiFePo charged by 15 W solar panel; \\  & 6 V LiPo charged by 3 W solar panel. \\ \hline EC Crossing (2019) & 4.5 an lead acid battery with \\  & 10 W peak 12 V solar panel. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The energy system used, and number of days lasted by past Microtransat participants. raft in this case. Adjustments could also be made to have the sailboat enter a'recharge mode' when the battery voltage drops below a certain level, and would limit the boat's daily consumption of power.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 4 Discussion
### Efficient Usage of Power and Energy Resources is Important  
In most of the cases, researchers focus on solving the spectrum scarcity problem without considering the energy or computational cost. MAC protocols must be designed in a very effective manner to reduce power consumption, interference and usage of the computational power of the CR user devices to prolong their lifetime. For example, since spectrum sensing is a time- and power-consuming action, CR users may only sense channels with high availability for transmission, so that the sensing overhead and power consumption are reduced.
# Sensitivity Analysis for Microscopic Crowd Simulation
## Appendix A Optimal Steps Model Utilities
### Obstacle Utility  
Additionally, to assure that agents are not colliding with obstacles an obstacle utility \(U_{o,j}\) is defined. Since the floor field is not defined inside obstacles, agents cannot step on an obstacle. The obstacle utility models the natural behavior of pedestrians around obstacles: If there is enough space, pedestrians keep a distance from obstacles. The fewer space they have, the closer they might move towards the obstacle. The obstacle utility is implemented in PotentialFieldObstacleCompactSoftshell. It is based on the theory of [PERSON] [39] and defined in [12] as:  
\[U_{o,j}(x)=-p_{o,j}=-\begin{cases}\psi_{o}^{2}(x)+\psi_{o}^{1}(x)&\text{if }d_{o}(x)<r_{i}\\ \psi_{o}^{1}(x)&\text{if }r_{i}\leq d_{o,j}(x)<w_{o}\\ 0&\text{else}.\end{cases} \tag{10}\]  
with  
\[\psi_{o}^{1}(x)= h_{o}\cdot\exp\left(\frac{2}{\left(\frac{d_{o}(x)}{w_{o}} \right)^{2}-1}\right) \tag{11}\] \[\psi_{o}^{2}(x)= 10^{5}\cdot\exp\left(\frac{1}{\left(\frac{d_{o}(x)}{r}\right)^{ 2}-1}\right) \tag{12}\]  
where \(d_{o,j}(x)\) is the distance between position \(x\) and the closest point of the obstacle, \(h_{o}\) and \(w_{o}\) are the height and width of the obstacle utility, respectively, and \(r_{i}\) is radius of agent \(i\). The obstacle utility function is visualized in Figure A2.
# Assessment of Negative Gingival Recession: A Critical Component of Periodontal Diagnosis
## 4 Gingival Enlargement  
Any deviation from the normal dimensions of the gingival tissue signifies a pathologic event, and all changes involve some degree of inflammation induced by the bacterial biofilm [45]. \"Gingival enlargement\"--or interchangeably, \"gingival overgrowth\"--has many sources and can manifest with various clinical characteristics, including inflammatory enlargement of gingiva due to gingivitis, drug-induced gingiva overgrowth, gingival overgrowth associated with systemic conditions and diseases, gingival fibromatosisis, and other neoplasm-associated gingival enlargements [45; 46]. It could involve only the inter-dental papillae as a localized form or may involve marginal gingiva and papillae as a generalized clinical presentation. Dimensionally, it is difficult to quantify the severity and extent of gingival enlargement; however, indices have been developed to score the overgrowth severity [47; 48; 49]. Positive correlations were found between overgrowth severity and gingival inflammation, probing depths, calculus accumulation, plaque score, and the width of keratinized gingiva [50]. Gingiva enlargement is usually not associated with attachment loss, unless there is concurrent periodontitis; yet, it could be challenging to detect the CEJ in such cases to establish the true CAL. Inflammatory gingival enlargement (gingivitis) commonly originates as a slight ballooning of the interdental papilla and marginal gingiva, and continues to increase in size particularly when a local plaque retentive factor exists [51]. On the other hand, drug-associated gingival enlargement typically begins at the interdental papillae and is more frequently found on the labial surfaces of the anterior segment [52]. It frequently appears within 1 to 3 months following medication initiation, which makes the detection of early changes challenging. The degree of fibrosis and inflammation are associated with the dose, duration and type of drug, oral hygiene, and individual's susceptibility due to genetic and environmental influences [53]. Other pathologic gingival overgrowth associated with systemic, hereditary, or neoplastic conditions are beyond the scope of this review.
## 3 Strong Ground Motion Prediction of the Nankai-Trough Earthquake  
### Modeling of the Seismogenic Fault and Analyzed Points  
The source model of the hypothesized Tonankai earthquake that we used in this article is a non-uniform kinematic source model consisting of three large asperities and a background region. This conforms with the model proposed for the estimation of strong motions by the Headquarters for Earthquake Research Promotion (HERP) where [PERSON]'s recipe [24] is employed for a standardized setting procedure regarding macroscopic (outer) and microscopic (inner) fault parameters. We also refer to the model by [PERSON] and [PERSON] [25] for the Nankai earthquake. The so-called asperity (or SMGA) model assumes that there is a non-uniform slip distribution on the fault surface, where the relative slip amount is twice as much as the average slip as [PERSON] et al. (1999) found from the kinematic source inversions [26]. Here, 22% of the entire fault area is considered to be asperities. As shown in [PERSON] and [PERSON] [17], however, we found that if we consider only a couple of smooth asperities for a mega-thrust event, their sizes seem to be too large to radiate sufficient energy in the intermediate frequency range around 1 Hz. Therefore, we further divide the asperities into small square sections where we assign slip fluctuations  
Figure 2: Statistical time-varying characteristics from observed data; (**a**) relationship between \(\mathrm{Tr}\) versus magnitude, (**b**) \(\mathrm{Td}\) versus magnitude, (**c**) \(\mathrm{Tr}\) versus hypocentral distance, and (**d**) \(\mathrm{Td}\) versus hypocentral distance. We also show (**e**) an example of the comparison of the statistical envelope derived from the regression relationship and the observed acceleration amplitude characteristics normalized by PGA.  
with the amount of slip 1.5 times of the average asperity slip (parts denoted with \(\blacksquare\) symbols) and the remaining areas should have slips less than the average. The earthquake moment is always conserved. The target analyzed points are K-NET, KiK-net, and JMA sites within 400 km of the assumed fault area of the Tonankai earthquake and are mostly in the Kinki and Chubu regions. Figure 3 shows the positions of asperities, together with the higher slip elements (\(\blacksquare\)), in the three-segment seismogenic fault of the hypothesized Tonankai earthquake, the center of the background region (*), and the assumed rupture starting point (\(\bigstar\)). The target analyzed sites are shown as circles. Table 1 shows the major fault parameters of this Tonankai earthquake. The aforementioned statistical Green's functions are used together with the waveform summation method by [PERSON] [13] to predict strong ground motions of the hypothesized Tonankai earthquake over a wide region in western Japan. As we show later, the estimated seismic intensity of the past earthquakes that repeatedly happen at the Nankai Trough (e.g., 1854 Tokai earthquake and 1944 Tonankai earthquake) was reproduced. At the same time, predicted waveforms correspond well with the empirical attenuation function [10] frequently referred to in Japan.
## 3 Results
### Overall Registration of TLS and HLS Point Cloud for Simulated Mining
This is shown in Figure 4c, where the number of identical points extracted from the TLS and HLS point clouds were 4,282,765 and 4,842,785, respectively, and the processing time was 538 s. The recall rate of the identical area point cloud was 99.8%, which showed that the identical area detection algorithm has better robustness and lower requirements on point cloud density and quality. Furthermore, the improved 3D-NDT algorithm was adapted to register the multi-platform point cloud of the identical areas, and the registration parameters were used to transform the HLS point cloud to the coordinate system located by the TLS point cloud. The registration results are shown in Figure 4d, where the fine registration accuracy is 4.3 cm and the processing time is 462 s.  
Furthermore, the cloud-to-cloud (C2C) distance of the TLS and HLS point cloud was calculated to analyze the registration accuracy (Figure 5). The C2C distance was mainly 0-8 cm, with a mean value of 5.5 cm, which demonstrates that the proposed method can achieve the fine registration of a multi-platform point cloud. Roughness metrics vary between TLS and HLS datasets due to different capture angles and resolutions, making it difficult to apply a uniform downsampling criterion. This can lead to inconsistent point density and data quality across the fused point cloud. Therefore, the roughness of a two-platform point cloud was calculated in our study (Figure 6a,b). The means of the TLS and HLS point cloud roughness are 6 mm and 12 mm and the standard deviations 9 mm and 10 mm, respectively. To ensure the accuracy and efficiency of point cloud and deformation analysis, it is necessary to downsample the fused point cloud. In our study, the points were removed when their roughness was greater than twice the standard deviation, and the fusion point cloud is shown in Figure 6c. Figure 5: Distance between corresponding points of the TLS and HLS point clouds. Figure 6: Results of fusion of point clouds of the two platforms. (**a**) Roughness of the TLS point cloud; (**b**) roughness of the HLS point cloud; (**c**) fusion of the TLS and HLS point clouds.
## 2 Methodology
### Measurement Results with the R&S\({}^{\circledR}\)TSME6 Scanner
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Measurement Points**} & \multirow{2}{*}{**Technology**} & \multicolumn{4}{c}{**Operator**} \\ \cline{3-5}  & & **MEO** & **NOS** & **Vodafone** \\ \hline \multirow{5}{*}{13} & 5G NR (RSSI) & \(-\)100.22 & \multirow{5}{*}{\(-\)78.23} \\  & LTE (Power) & & & \\  & NB-IoT (NRSSI) & \(-\)104.34 & & \\  & UMTS (RSCP) & & \(-\)89.2 & \\ \hline \multirow{5}{*}{17} & 5G NR (RSSI) & \(-\)100.21 & \multirow{5}{*}{\(-\)74.02} \\  & LTE (Power) & & & \\  & NB-IoT (NRSSI) & & & \\  & UMTS (RSCP) & & & \\ \hline \multirow{5}{*}{19} & 5G NR (RSSI) & \(-\)100.52 & \multirow{5}{*}{\(-\)78.43} \\  & LTE (Power) & & & \\  & NB-IoT (NRSSI) & \(-\)92.15 & & \\  & UMTS (RSCP) & & \(-\)95.4 & \\ \hline \multirow{5}{*}{21} & 5G NR (RSSI) & \(-\)97.70 & \multirow{5}{*}{\(-\)83.11} \\  & LTE (Power) & & & \\  & NB-IoT (NRSSI) & \(-\)98.22 & & \\  & UMTS (RSCP) & & & \\ \hline \multirow{5}{*}{23} & 5G NR (RSSI) & \(-\)97.70 & \multirow{5}{*}{\(-\)80.88} \\  & LTE (Power) & & & \\ \cline{1-1}  & NB-IoT (NRSSI) & \(-\)98.74 & \multirow{5}{*}{\(-\)102.1} \\ \cline{1-1}  & UMTS (RSCP) & & & \\ \hline \multirow{5}{*}{Entrance to the outpatient consultations} & 5G NR (RSSI) & & \(-\)94.5 \\ \cline{1-1}  & LTE (Power) & & \(-\)98 & \\ \cline{1-1}  & NB-IoT (NRSSI) & \(-\)100.31 & \multirow{5}{*}{\(-\)98.6} \\ \cline{1-1}  & UMTS (RSCP) & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of the best received power values for each operator at different measurement points for different technologies. (RSSI, Power, NRSSI, RSCP) for 5G NR, LTE, NB-IoT, and UMTS, respectively, highlighting the operator with the best coverage at each measurement point. Figure 18: RSCP Graph for UMTS measurements at point 17.
## 2 Theory, Requirements and Synthesis
### Practical Implementations
#### 2.4.1 Closed-Loop Control (CLC)  
The CLC prototype was manufactured with through-hole technology elements as there are no high-frequency requirements for this part of the system. Figure 7 is a schematic with the orange block showing the power-supply adapter and the yellow block implementing the concept from the block diagram in Figure 6. The input buffer stage is designed as an active band-pass filter with approximately 14 dB of gain. The lower cut-off frequency is set to block the (near) DC term \(\overset{\sim}{U}\), while the upper cut-off frequency limits the bandwidth to reduce the higher-frequency noise. The mixer is built of a differential amplifier and a CMOS switch, which is triggered by a rectified square-wave voltage. So, in this case, the mixing behavior is achieved by multiplying the signal from the differentiator by \(-1\) or \(1\), alternately. The square and triangle -wave generator is realized as a combination of a Schmitt-trigger circuit and an integrator op-amp circuit, where the trimmer R29 adjusts the frequency. The tunable phase shift is achieved with an additional Schmitt-trigger circuit with adjustable threshold (R17) that is triggered by a triangle-wave output from the generator. A summation amplifier combines all the signals, while letting their magnitudes be varied by means of R21, R22 and R24.
# A Frame-to-Frame Scan Matching Algorithm for 2D Lidar Based on Attention
## 6 Experiments
### Efficiency Test  
In addition to accuracy, computational efficiency is also very important for evaluating the scan matching algorithm. Similar to the accuracy test, the scan matching algorithm was repeatedly run on each dataset 100 times, and the average computation time required per each algorithm was used as the final evaluation standard. The statistical results are shown in Table 2.  
It can be seen from Table 2 that the time spent by each algorithm in different environments is similar, and this is because that the algorithm matching time is only related to the point cloud data and has nothing to do with the size of the environment. Although the office and office building environments are small in size, the number of point clouds in each frame of the laser data is similar, so the final matching efficiency is also similar. The most time-consuming algorithm is the IMLS-ICP algorithm because it needs to build a KD tree to find the nearest neighbors, which is time-consuming. The time spent by the PP-ICP and PL-ICP algorithms is basically the same, whereas the time spent by the ASM algorithm lower than that of the other algorithms. This is because the ASM algorithm only uses landmarks for matching tasks, and in most cases, the number of points in a landmark is less than 1/10 of the total number of points, so the matching speed can be greatly improved.