esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Photogrammetric Processing Chain
### MicMac Overview  
MicMac (acronym for \"Multi-Images Correspondances, Methodes Automatiques de Correlation\") is an open-source photogrammetric software suite developed by IGN(r) for computing 3D models from  
Figure 2: Main steps of the SfM-MVS photogrammetry workflow.  
sets of images [31; 32] Micmac(r) chain is open and most of the parameters can be finely tuned. In this study, we use the version v.6213 for Windows.  
The standard \"pipeline\" for transforming a set of aerial images in a 3D model and generating an orthophotograph with MicMac consists of four steps:  
1. [leftmargin=*]
2. Tie point computation: the Pastis tool uses the SIFT++ algorithm [33] for the tie points pairs generation. Here, we used Tapioca, the simplified tool interface, since the features available using Tapioca are sufficient for the purpose of this study. For this step, it is possible to limit processing time by reducing the images size by a factor 2 to 3. By default, the images have been therefore shrunk to a scaling of 0.3.
3. External orientation and intrinsic calibration: the Apero tool generates external and internal orientations of the camera. A large panel of distortion models can be used. As mentioned later, two of them are tested in this study. Using GCPs, the images are transformed from relative orientations into an absolute orientation within the local coordinate system using a 3D spatial similarity (\"GCP Baseline\" tool). Finally the Campari command is used to refine camera orientation by compensation of heterogeneous measures.
4. Matching: from the resulting oriented images, MicMac computes 3D models according to a multi-resolution approach, the result obtained at a given resolution being used to predict the next step solution.
5. Orthophotograph generation: the tool used to generate orthophotographs is Tawny, the interface of the Porto tool. The individual rectified images that have been previously generated are merged in a global orthophotograph. Optionally, some radiometric equalization can be applied.  
With MicMac, at each step, the user can choose any numerical value, whereas PhotoScan only offers preset values (\"low\", \"medium\" and \"high\"), which is more limiting. As for PhotoScan, the intermediate results can be checked and saved at each step. At the end of the process, a DEM and an orthophotograph are exported in GeoTiff format.  
For both software packages, the processing time depends on the RAM capacity of the computer, as memory requirements increase with the size and number of images and with the desired resolution.
## 4 Results and Discussion
### Spatial and Temporal Distribution of Maize and Sunflower  
Maize and sunflower distribution maps in the study area from 2009 to 2015 are shown in Figure 9. Most large maize and sunflower fields are concentrated in the eastern three counties (Hangjinhouqi, Linhe and Wuyuan), and fields are patchier in Dengkou where the terrain is mainly desert and Gobi. Maize is mainly distributed in Hangjinhouqi and Linhe, while sunflower mainly in Wuyuan. The distribution of maize and sunflower is coherent during the study years, which is in agreement with official statistical results. This verifies the accuracy of the classifiers' performance of this optimal classifier again.  
Figure 9: Crop maps of maize and sunflower from 2009 to 2015 (**a–g**).  
According to the color depth in Figure 9, we can see that the planting areas of maize and sunflower are increasing from 2009 to 2015. Especially for the sunflower, the planting sites are gradually expanded from Wuyuan to the northern part of Hangjinhouqi and Linhe, and these results are in agreement with the local economic policy. Sunflower seeds are the main raw material of sunflower oil and sunflower is an important economic crop in the Hetao Irrigation District. Therefore, planting sunflower can bring more benefit to farmers than other crops. Compared to wheat, local farmers prefer to plant sunflower to get more profits, resulting in an increase in the planting area of sunflower and decrease of wheat. Hence, it is more important to identify sunflower more accurately in Hetao Irrigation District. External factors, such as economic factors, are the main reason for the change of crop plant structure. This result is similar to [PERSON] et al. (2016), who reported that biofuel mandates led to a significant increase in the planting area of maize in Laurentian Great Lakes Basin.
## 3 Results and Discussion  
The redundancy of both the test campaigns and the analyses prove fundamental for the final outcomes, as the correct estimation of the modal parameters has been possible only by exploiting different combinations. As an example, Figure 4 reports the FRF-sum amplitudes obtained from burst random (excitation level 1 V) and Chirp periodic (excitation level 0.5 V) tests, respectively. The former is globally less scattered, thus making the response peaks emerge very clearly. On the other hand, the latter can catch resonances of modes that are poorly excited in the previous case.
# Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance
## 4 Simulation Results
### Experiment #2: Benchmark Dataset  
This section aims to compare our proposed method against established predictive approaches using a synthetic dataset that has been used in prior research [12; 36]. The selected benchmark dataset is particularly suitable for this evaluation as it incorporates both informative and noninformative variables, with varying degrees of noise, thus providing a comprehensive framework for assessing model performance under realistic conditions.  
The dataset is generated using a set of equations designed to create correlated predictor variables that combine both signal and noise components, allowing for a critical evaluation of each method's ability to discern relevant features in the presence of spurious or irrelevant variables. The data generation process is defined as follows:  
\[\mathbf{y}=0.25\exp(4\mathbf{x}_{1})+\frac{4}{1+\exp(-20(\mathbf{x}_{2}-0.5))}+ 3\mathbf{x}_{3}+\eta, \tag{15}\]  
where \(\mathbf{y}\) is the output variable, \(\eta\) is normally distributed with mean 0 and standard deviation 0.2, and the predictor variables \(\mathbf{x}_{1}\), \(\mathbf{x}_{2}\), and \(\mathbf{x}_{3}\) are uniformly distributed between 0 and 1 (\(U(0,1)\)).  
Each relationship in Equation (15) contributes uniquely to \(\mathbf{y}\), reflecting the distinct characteristics of their functional forms. The exponential dependency on \(\mathbf{x}_{1}\) acts as the primary driver of variability in \(\mathbf{y}\). Due to its exponential nature, even small increases in \(\mathbf{x}_{1}\) can produce substantial shifts in \(\mathbf{y}\), especially when \(\mathbf{x}_{1}\) reaches higher values within its range. This makes \(\mathbf{x}_{1}\) a dominant factor influencing the output variable. In contrast, the logistic dependency on \(\mathbf{x}_{2}\) provides a significant but bounded effect on \(\mathbf{y}\). The logistic function has its strongest impact around the midpoint (\(\mathbf{x}_{2}=0.5\)), where it introduces notable changes in \(\mathbf{y}\). However, this effect stabilizes outside of that range, introducing a controlled nonlinear influence that is considerable near the midpoint but limited in other regions. The linear dependency on \(\mathbf{x}_{3}\) has a more modest contribution to \(\mathbf{y}\). Although this linear relationship ensures proportional adjustments in \(\mathbf{y}\) with changes in \(\mathbf{x}_{3}\), its effect can be easily overshadowed by the more pronounced variations introduced by \(\mathbf{x}_{1}\) and \(\mathbf{x}_{2}\), as well as by the error term \(\eta\). This suggests that, in the presence of more dominant influences and noise, the contribution of \(\mathbf{x}_{3}\) may be less perceptible and could, therefore, receive a lower score in certain attribute importance measures.
## 4 Simulation Results
Figure 9: Effective femtocell coverage _versus_ algorithm (coverage target of QoS-SP-IM set to \(\infty\)). The difference is that it still allows the uplink QoS to be part of the dimensioning process and thus guaranteed. When exploring in more detail the uplink performance _versus_ the QoS target, we need to compare uplink throughput available to UE _versus_ what NR that UE produces towards the overlay macro. For the settings here, we have allocated to each HNB an own interference contribution to nearest MNB of \(-11.7\) dB compared to the noise floor. We achieve this value by reserving 3 dB noise rise for MUEs and then divide the remaining noise rise contribution (3 dB) to 30 simultaneously active HNBs per macro site. We combine simulations for 200 kbps and 1 Mbps QoS target respectively and the per-HUE interference at MNB is plotted in Figure 10.  
Investigating the user statistics, the outage for the 200 kbps case is 3.8%, thereby indicating good accuracy of the algorithm. As we are achieving a compromise between desired downlink coverage and desired uplink rate in the algorithm, we are slightly sacrificing the uplink performance when setting the QoS target to high values. For the 1 Mbps case, we observe that 76% of users are able to achieve the uplink throughput. 90% of users exceed 500 kbps and 95% of users achieve 256 kbps. The reported HUE UL performance is found reasonable, considering that the proposed QoS-SP-IM algorithm preserves the MUE UL performance, see Table 2. For both scenarios we can see from Figure 10 that no HUE exceeds the set NR limit.
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 3 Results
### Drought Impacts on Vegetation Growth
#### 3.3.2. Diverse Droughts Responses by NDVI and SIF  
In order to analyze the sensitivity of NDVI and SIF in detecting the impact of drought, typical agricultural areas in YRB were selected to compare the drought response of NDVI and SIF to SPEI (Figure 10). The correlation curves of NDVI, SIF, and SPEI of different land cover types (farmland, grassland and forest) were compared using the scatter plot method. The results show that the linear correlation between SIF and SPEI is greater than that between NDVI and SPEI. The correlation coefficients of SIF and SPEI of farmland and grassland are 0.3424 (\(p<0.01\)) and 0.2434 (\(p<0.05\)), respectively, as shown in Figure 10d,e. The correlation coefficients of NDVI and SPEI of farmland and grassland are 0.2670 (\(p<0.01\)) and 0.1625 (\(p<0.05\)), respectively, as shown in Figure 10a,b. The results showed that there was a significant correlation between SIF and SPEI, and the correlation between SIF and SPEI was higher than NDVI, which further showed that SIF was better than NDVI in response to drought. In conclusion, SIF is superior to NDVI in detecting the impact of drought.  
Figure 9. Response of NDVI to SPEI in different seasons (\(p<0.05\)). (**a**) Annual; (**b**) Spring; (**c**) Summer; (**d**) Autumn. Response of SIF to SPEI in different seasons (\(p<0.05\)). (**e**) Annual; (**f**) Spring; (**g**) Summer; (**h**) Autumn.
## 6 Discussion
The reference methods chosen were photogrammetry and TLS. MLS data were obtained using the RIEGL VMAX-450 mobile laser scanner, capable of measuring up to 1.1 million points and 400 profiles per second. The result of MLS was six separate point clouds with a mean spatial resolution of 2-3 cm. For obtaining TLS data, the Leica HDS7000 TOF CW laser scanner was used, which scanned the historic center of the city from 15 positions. The resulting point cloud was characterized by a mean spatial resolution of 3 mm, with a final registration error of less than 4 mm, and contained over 240 million points. The standard deviation for buildings when comparing TLS and MLS data after filtering ranged from 0.3 to 1.2 cm. Comparisons between point clouds obtained through TLS and MLS at the same epoch provided a Gaussian estimate of mean absolute deviation (MAD) on the order of \(\pm\)5.2 mm. The estimated errors for buildings in the Duomo square were characterized by an average dispersion of \(\pm\)3.6 mm when comparing discrepancies between MLS and TLS. Publication [53] focuses on mapping road infrastructure using Lidar technology from various platforms. The article primarily evaluated the usability of data obtained from the iPhone 14 Pro with the viDoc RTK Rover compared to commonly used laser scanners, Phoenix Alpha-32 and Velodyne. The authors considered the data from these sensors as a reference, with the Phoenix Alpha-32 mounted on a UAV platform and the Velodyne mounted on a car equipped with GNSS antennas, a Ladybug5 camera, and an IMU. For this research, a 90-meter-long asphalt road located on the campus of Istanbul Technical University was selected. Data processing from the iPhone 14 Pro was conducted using the Pix4 Dmatic software, achieving an RMSE accuracy of 4 cm. For the other sensors, the RMSE accuracy was at the level of 3 cm. The results indicate that using the iPhone 14 Pro with the viDoc RTK Rover is feasible due to the quality and precision of the resulting dataset. Compared to other sensors, mobile phones are capable of collecting valuable data to create accurate models to support the digital reconstruction of infrastructure. The authors concluded that higher quality and accuracy of results could be achieved through the use of RAW data.
# The Value of Fractal Analysis in Ultrasound Imaging: Exploring Intricate Patterns
## 2 Materials and Methods  
This study is a narrative review of the evidence surrounding the use of fractal analysis in US imaging. We searched the existing literature, focusing on PubMed and Web of Science from inception to July 2024 due to the high availability of published data on the topic. Furthermore, the references of the included papers were thoroughly checked to find eligible publications. The MeSH keywords used were as follows: \"Fractal Analysis\", \"Fractal Dimension\", \"lacunarity\", \"Ultrasound Imaging\", \"Ultrasound\" and \"Ultrasound examination\". The search strategy set for the topic was the following: (\"Fractal Analysis\") OR (\"Fractal Analysis\" AND \"Ultrasound Imaging\") OR (\"Fractal Analysis\" AND \"Ultrasound\") OR (\"Fractal Analysis\" AND \"Ultrasound examination\") OR (\"Fractal Dimension\" AND \"Ultrasound Imaging\") OR (\"Fractal Dimension\" AND \"Ultrasonography\") OR(\"Fractal Dimension\" AND \"Ultrasound\") OR (\"Fractal Dimension\" AND \"Ultrasound examination\") OR (\"Lacunarity\") OR (\"Lacunarity\") OR (\"Lacunarity\") OR (\"Lacunarity\") OR (\"Lacunarity\") OR (\"Lacunarity\") AND \"Ultrasound examination\"). All relevant English-language publications were examined for potential inclusion. References were further screened by selecting the papers that we believed to provide a fair and accurate depiction of the use of fractal analysis for US imaging and that were not yet fully understood but not too speculative. The selection of studies was guided by predefined criteria for inclusion, ensuring relevance, methodological quality and consistency with the objective of our review. The inclusion criteria were as follows: (1) original studies involving the use of ultrasound imaging combined with fractal analysis; (2) studies with appropriate methodological rigor and transparent reporting of both ultrasound imaging protocols and fractal analysis techniques. Exclusion criteria were meticulously applied to filter out peripheral content, thereby concentrating on primary research efforts. The specific exclusion criteria were as follows: (1) papers that did not transparently report the use of fractal analysis combined with ultrasound imaging; (2) papers that did not use a quantitative or standardized approach for fractal analysis or that lacked sufficient methodological details; (3) papers that did not address the use of US imaging for diagnosis; and (4) papers that were not published in English. We conducted an initial screening of all studies by title and abstract, followed by a thorough review of the full texts of the eligible studies. Additionally, references within these studies were examined to identify any further relevant publications to be included. The literature search was conducted by one reviewer (N.P.) and subsequently verified by a senior researcher (C.P.). Any discrepancies were resolved through consensus among the authors (Figure 1).  
Figure 1: Flowchart of study selection.
While consumer tests obviously cannot be performed frequently, instrumental assessment can imply high investment or purchase costs, unlikely to be afforded by a small or middle producer. Furthermore, though instrumental methods can be used and provide a quantitative reference for the fabric hand-feel-related properties--as far as these are recognized by all parties as such--this does not mean that the resulting numbers always predict human perception. Consequently, a human reference needs to be consulted at some point. A human reference remains essential in the development of textile surfaces due to the complexity of human perception as well as to the diversity of the textile field, where various construction and treatment parameters play different roles in influencing human sensorial perception. Fabric hand feel can be analyzed into different components, which can be attributed to defined hand actions. This can allow joint understanding and therefore a reliable scaling or ranking, at least within the compared group of fabrics [3]. The challenge is to ensure representativeness and reproducibility, as humans have different perceptions and are not always consistent [13]. Accepting the complexity of this topic, and the fact that up to now no single absolute quantification system is available, subjective and objective hand-feel evaluations are performed on defined fabric groups to provide a relative judgment. A panel with a limited number of experts (personnel trained in textile and material assessment) could provide a compromise between consumer tests and purely instrumental measurement. Various works have been published on training, maintaining, and monitoring expert panels. The approach has also been normed, for example by AATCC [14] and HESC [15]. Trained panels are offered by various institutions and can usually provide a reliable assessment of hand feel [13]. The drawback is that maintaining such panels can be costly in terms of manpower. Also, the costs of consulting external expert panels are usually higher than instrumental measurements. A praxis-near approach is to train inside the company a semiexpert hand-feel panel from within the field to provide a fast and reliable evaluation of developments. The group deals with a limited number of fabric variations and can focus on fine-tuning within the same quality range and provide a reliable comparison to a set reference. Regular investigations on the effects of different parameters such as fiber content, fabric construction parameters, and surface treatment on fabric hand feel are possible when focused on comparable fabric collections and a defined task. This paper presents the case of a simplified method to train an open semiexpert group that is workable in an industrial environment. The results are presented in comparison with other human, as well as with instrumental, assessments.
## 3 Analysis and Discussion
### Characterization of Changes in Phosphogypsum Leachate  
#### 3.4.1 Characterization of pH Changes  
The experiments monitored pH variations in the leachate, generating characteristic pH evolution curves over time under different initial pH conditions (Figure 6). The time in the graph represents the time from the discovery of the lysate in the outlet section of the reactor. Specimens HY10, HY11, and HY12 required 2 h, 4 h, and 5 h, respectively, to reach leachate breakthrough, while other specimens exhibited comparable breakthrough times (2-3 h) (where 1 d = 24 h).  
Figure 5: Relative change rate of fracture aperture (HY01–HY06 in static experiments, HY07–HY012 in dynamic experiments).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rock Specimens ID** & **Fracture Aperture [mm]** &
\begin{tabular}{c} **Dissolution Amount Per** \\ **Unit Area [g/cm\({}^{2}\)]** \\ \end{tabular} \\ \hline HY04 & 0.6–0.1 & 0.0252 \\ HY05 & 0.3–0.1 & 0.0261 \\ HY06 & 0.05–0.01 & 0.0289 \\ HY10 & 0.7–0.1 & 0.3886 \\ HY11 & 0.2–0.1 & 0.3392 \\ HY12 & 0.05–0.01 & 0.2901 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Dissolution amount per unit area for selected rock specimens.  
In static dissolution experiments, higher initial acidity of the phosphogypsum leachate correlated with extended effective reaction durations, prolonged equilibrium attainment times, and greater pH increases. This occurred due to progressive H\({}^{+}\) ion consumption during acid-carbonate neutralization, which elevated pH over time. Notably, fracture aperture exerted negligible influence on dissolution rates in static systems.  
Conversely, dynamic dissolution experiments demonstrated pH decreases throughout the reaction period, opposing the static trend. Leachates with higher initial acidity exhibited more pronounced pH declines. Additionally, smaller fracture apertures amplified pH variation magnitudes, extended stabilization periods, and resulted in higher stabilized pH values compared to wider apertures.
## 2 The \({}^{3}\)ColStar Satellite Mission
### Concept of Operations (ConOps)
The COMMS subsystem is transmitting scientific data in the following scenarios:  
Figure 5: Launch (a), LEOP (b), IOP (c), FOP (d), and DECO (e) phases. * Nominal IoT Dowlink (Nid): IoT application data download. * Nominal IoT Uplink (Niu): Receiving data from the IoT ground sensors. * Nominal Space Weather (Ns): Nominal operation of the Space Weather payload (ON). * Nominal Space Weather Dowlink (Nsd): Space Weather payload data download. * Sun Safe (SS): If the battery levels become critically low (below 1.5 V, see Table 13), the satellite enters this mode. From the power budget point of view, this mode is identical to the Dd mode. * Ground Mode (GM): Mode designed for ground testing, but also accessible in orbit. No limitation in terms of function execution, designed for contingency scenarios. It gives to the operator a total control of the spacecraft. * Survival (Sv): After an unexpected anomaly, the satellite enters in this mode. The payloads are disabled, the ADCS kepts the desired attitude, and COMMS transmits scientific data. * Satellite Off (OFF): Satellite is turned off during its operation due to unexpected events. EPS, OBC and COMMS are ON. The satellite will receive TCs from ground. * Decommissioning (DC): The satellite is deactivated and safely manage the end of its useful life. Typical activities in Decommissioning mode include:
* Systems Decommissioning: The satellite's operating systems, such as scientific instruments, transmitters, and other electronic components, are shut down in an orderly fashion [59]. * Battery Passivation: Batteries are discharged to minimize the risk of explosions or malfunctions that could generate more space debris. * Final Transmission and Power Shutdown: A final transmission is sent and then the satellite power is permanently shut down, disconnecting the solar panels from the EPS.