esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Robustness Analysis with NLDI and MNLDI
The error difference between the desired attitude angle and the actual response of both flight control technique proves that MNLDI is more robust against wind disturbances and model uncertainties that NLDI. Figure 14: Attitudes response with NLDI and MNLDI with roll angle excitation with wind disturbances and uncertainties. Figure 15: Attitude angle error for outer roll angle excitation with wind and uncertainties. For more robustness analysis of the designed flight controller using MDI, a cross wind with speed 10 [m/s] and heading 90\({}^{\circ}\) was applied. Figure 17 ensures of the asymptotic stability of the designed control law that guarantees best tracking performance with severs flight conditions. In Figure 18, a comparison between the performance of angular rates tracking loop in response to roll angle command with high-speed cross wind is illustrated. MNLDI flight control technique is an internally stable system as the UAV attitude rates are still within the standard limit despite the cross wind. Figure 7:0 Inner loop rate response for outer roll angle excitation with wind and uncertainties. Figure 7:1 Attitudes response with NLDI and MNLDI with roll angle excitation with wind disturbances and uncertainties. In Figure 19, the difference between the desired and the actual attitude angle of both flight control technique confirms that MNLDI is more robust against wind disturbances and model uncertainties than NLDI. The yaw channel response shown in Figure 20 is similar to the pitch and yaw channel's response with a confirmation of the greater capability of MNLDI compared to pure NLDI in tracking reference commands with a strong wind disturbance and model uncertainties. Figure 7:2 Inner loop rate response for outer roll angle excitation with wind and uncertainties. Figure 7:3 Attitude angle error for outer roll angle excitation with wind and uncertainties. The associated angular rates in responding to yaw attitude command are shown in Figure 21, which show the need for modification on NLDI. In Figure 22, the difference between the desired attitude angle and the actual response of both flight control technique confirms that MNLDI is more robust against wind disturbances and model uncertainties that NLDI. Figure 7:4 Inner loop rate response for outer side-slip angle excitation with wind and uncertainties. Figure 7:5 Attitudes response with NLDI and MNLDI with side-slip angle excitation with wind disturbances and uncertainties. To summarize the flight control segment, any small variations in the model will be accompanied with less tracking performance using NLDI flight controller. Therefore, NLDI flight controller is prone to parameter variations and misleads the desired command. In contrast, MNLDI is robust against model uncertainties; consequently, it is guaranteed as a stand-alone flight control system.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 6 Conclusions  
We proposed a direct approach to validate coarse resolution LAI products that involved the scaling up of field-measured LAI via the validation and recalibration of the Sentinel-2 LAI product. MODIS, GEOV3 and GEOV2 LAI products showed good performance in the magnitude of LAI (\(RMSE\) < 0.3) and the timing of phenology metrics in maize crops (within \(-2\) d difference for the timing of the start of season (SoS), \(\pm 1\) d for the peak of season (PoS) and \(\pm 8\) d for the end of season (EoS)). EPS LAI showed, on the opposite, high differences in terms of magnitude of LAI with an underestimation of high LAI values, \(R^{2}\) = 0.69 and \(RMSE\) = 0.68, and a delay in the timing of phenology metrics as compared to other products and the reference LAI (+6 d for the SoS, +20 d for the PoS and +24 d for the EoS). GLASS also showed a delay in the second half of the growing season after the peak of LAI. C3S V2 showed high correlation with reference LAI values but a systematic underestimation of LAI values with a negative \(BIAS\) of \(-0.29\) LAI and a systematic advance of \(-4\)d for the SoS and \(-6\)d for the PoS and EoS. More validation activities and comparison with ground-based phenological metrics are necessary to further verify these findings. This study demonstrated the potential of using the decametric Sentinel-2 LAI product with minimum ground-based calibration as a reference to validate the hectometric and kilometric LAI products over cropland areas. This approach may constitute an alternative to the standard CEOS up-scaling approach when a limited number of ground measurements are available. The methodology may also be applied to other land cover types, but it is limited to the conditions of validity of the physical model used for training Sentinel-2 LAI.  
[PERSON] and [PERSON] conceived the experiments; [PERSON] performed the experiments, and wrote the paper; [PERSON] implemented the field experiments; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant (41971282; 42001303), the Sichuan Science and Technology Program (2021)DJQ0007; 2020 JDTD0003), the Copernicus Global Land Service (CGLOPS-1, 199494-JRC), and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 835541.  
Not applicable.  
The data presented in this study are available on request from the authors.  
We are thankful for various data depositaries who made the global LAI products available (Table 1). This work also represents a contribution to the CSIC Thematic Interdisciplinary Platform PTI TELEDETECT.  
The authors declare no conflict of interest.
## 6 Results and Discussion
### Overview of Performance Measurement
#### 6.2.4 F-Measure (F1-Score)  
The F1-score combines precision and sensitivity into a single metric, providing a balanced assessment of the model's performance. It considers both false positives and false negatives and is particularly useful for evaluating models on imbalanced datasets.  
\[F\text{-}measure=\frac{2\times(Precision\times Sensitivity)}{Precision+Sensitivity}\]
# Silicon 3D Microdosimeters for Advanced Quality Assurance in Particle Therapy
## 4 Application of SOI Mushroom Microdosimeters
### Irradiation Facilities
#### 4.1.3 Proton Therapy Facility in Ithemba Labs, South Africa  
Microdosimetric measurements were conducted with the Trenched-3D Mushroom microdosimeter in the double scattering proton beamline at the iThemba Labs, South Africa, and compared against in-vitro cell experiments. A proton beam with initial energy of 200 MeV was delivered from the cyclotron to the treatment nozzle where the beam was variably degraded to form a 50 mm SOBP with a maximum energy of approximately 127 MeV at the surface of the water phantom (with a range of approximately 120 mm in water). A field size of 10 cm diameter was delivered in this study.
# In Search of Lightweight Deployable Tensegrity Columns
## 3 Description of the Columns
### X Column  
The authors analyzed three possibilities of deployment for this column that seemed to be most natural.  
The basic equations that describe the lengths of the cables and struts in this module are given below in the final form in Table 4.  
The first approach is to control the height of the structure by erecting or flattening single modules according to the formulas provided in Table 4 (compare the designations with Figure 5). The biggest disadvantage is the necessity to control all the cable lengths. On the other hand, this method maximizes the space savings for storing. Just like in the previous approach, here, applying elastic elements in parallel with the cables would be a way to keep the layout of the structure during the whole process and prevent the entanglement of cables. This would result in decreasing the amount of active cables.  
Another approach is to obtain a flat structure that can be packaged either as a bundle or a plane. It can be achieved by elongating and shortening pairs of base cables to have a rotation of the neighboring modules. This approach is applicable only by assuming minimum global stiffness during the deployment, i.e., by applying elastic elements in parallel with the cables that would keep the layout of the structure during the whole process and prevent the entanglement of cables. This scheme was not analyzed further in this paper.  
The second approach is to rotate the modules along the rotation axes between them. Those axes are stabilized by cables with no self-stress, and only these cables are used for the deployment. Single modules stay prestressed during the whole process and keep the predefined self-stress values all the time.  
The third scheme makes use of elastic elements to stabilize the structure instead of active cables. The base cables are loosened as a function of the vertical cables' shortening. The diagonal cables' lengths stay constant so there is a need to stabilize the structure with elastic elements. Single modules stay prestressed during the whole process.  
Figure 4: Overlapped simplex column—designations.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Member** & **Equation** \\ \hline Strut & \(L_{\text{S}}=\sqrt{2r^{2}(1-cos(140^{\circ}))+h^{2}}\) \\ Outer base cable & \(L_{\text{B}}=\sqrt{3}r^{2}\) \\ Intermediate base cable & \(L_{\text{BM}}=\sqrt{r^{2}+m^{2}}\) \\ Module diagonal cable & \(L_{\text{D}}=\sqrt{2r^{2}(1-cos(20^{\circ}))+h^{2}}\) \\ Intermodular diagonal cable & \(L_{\text{DM}}=\sqrt{2r^{2}(1-cos(80^{\circ}))+(h-m)^{2}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overlapped simplex column—functions of the member lengths.
# HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN
## 5 Training
### Preprocess  
In order to build a network for a broader application, we do not use fixed exposures, but a random exposure among \(-1\), 0, +1 EV for the input dataset. For the input dataset, we first choose a single image among 3 multiple-exposure LDR images randomly. We resize and crop the chosen image's short side into \(500\times 500\) resolution. Then, the image is randomly cropped into 4 pieces with \(256\times 256\) resolution to solve a small dataset problem. For random cropping, we use the low-discrepancy sequence proposed by [PERSON] [39] to avoid overlapping. The ground truth dataset is perfectly paired with the input dataset. Finally, we attain 2432 images of input and ground truth images. Train and test datasets are separated from 2432 images by randomly selecting the group of 4 cropped images which are from the same original image. Consequently, a train dataset has 2000 images from 500 original images and a test dataset has 432 images from 108 original images. All images are converted to HSV color space before training. \(H\) is divided by 360 to be normalized in the range \([0,1]\). \(S\) of input and ground truth images are already well normalized in the range \([0,1]\) when converted to HSV color space. \(V\) of ground truth images is not in the range \([0,1]\), while \(V\) of input images is in the range. To normalize \(V\) of ground truth images, we use a gamma correction, \(f(x)=\frac{x}{x+1}^{1/\gamma}\), where \(\gamma=2.2\).  
The network has two inputs (the normalized input images in HSV color and the corresponding mask) and one target input (the normalized target images in HSV color). Through the network, the mask is multiplied to the loss function as a weight.
# Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
## 3 Fault Diagnosis Methods
### Signal Processing Methods
#### 3.2.5 Wigner-Ville Distribution  
For an input signal \(x(t)\), the definition of Wigner-Ville distribution (WVD) is:  
\[W_{x}(t,v)=\int x(t+\frac{\tau}{2})x^{*}(t-\frac{\tau}{2})e^{-j2\pi v\tau} \mathrm{d}\tau \tag{12}\]  
where \(x(t+\frac{\tau}{2})x^{*}(t-\frac{\tau}{2})\) is the transient correlation. As can be seen, WVD is a time-frequency energy density which is obtained by correlating \(x(t)\) with a time and frequency translation of itself. According to the study [68], the joint time and frequency resolution provided by the WVD is the best, better than other time-frequency analysis methods and its calculation cost is not high. However, if the signal to be analyzed contains more than one frequency component, the result of WVD will be subject to cross-term interference, which is undoubtedly quite unfavorable for fault diagnostic. To solve this problem, some researchers have combined WVD with EMD, included in HHT, to analyze the current by analyzing the demodulated IMF with WVD [76; 77]. Besides, [PERSON] et al. used a smoothed pseudo Wigner Ville distribution (SPWVD) and Zao-Atlas-Marks distribution (ZAM), which can eliminate interference in WVD-based PMSM fault diagnosis [78; 79]. Even so, its application on PMSM remains to be further studied.
## 2 Materials and Methods
### Data Source
#### 2.2.3 Normalized Difference Vegetation Index (NDVI)  
Landsat remote sensing image data were selected for vegetation index analysis, and the data were obtained from the Google Earth Engine (GEE) Cloud Computing Platform at [[https://code.earthegine.google.com/](https://code.earthegine.google.com/)]([https://code.earthegine.google.com/](https://code.earthegine.google.com/)) (accessed on 22 August 2024). In this study, Landsat 5 and 8 satellite data were used; they provide medium resolution multispectral imagery suitable for vegetation monitoring. All image preprocessing and NDVI calculations were performed using the GEE platform. And the GEE platform directly invoked the preprocessed Landsat surface reflectance dataset to ensure the consistency of data quality. NDVI data for the period of May to September 2008-2023 in Northeast China were selected. The NDVI data used in this study are 16-day composite products generated using the Maximum Value Composite (MVC) method, which integrates multi-view imagery. This method selects the highest NDVI value for each raster cell within the 16-day period as the output. Given that vegetation indices typically peak under clear-sky conditions and are significantly depressed by cloud cover, the MVC approach effectively filters out cloud interference. The 16-day integration period ensures that even with temporary cloud cover, each raster cell has a high probability of being observed under clear skies, thereby producing a vegetation index that closely approximates ground truth. Consequently, this NDVI product offers high data integrity and reliability, effectively overcoming the common issue of cloud interference in single-time-phase imagery.
## 2 Study Region and Data
### Data  
Measurements from a dense gauge network, which consists of 2401 gauges in the study domain (Figure 1), were used as the reference for this study. The average rainfall error of gauge observations is 6.52% [32], which is much smaller than the remote-sensing-based estimation. The satellite-based quantitative precipitation estimation (QPE) products to be evaluated include the V7 real time TMPA product 3B42 RT and the gauge-adjusted post-real-time research product 3B42V7.  
Figure 1: Study Region including Hebei province, Beijing city and Tianjin City (HBT).  
The V7 TMPA products provide a number of scientific and data processing enhancements over the previous Version 6 products. Both versions have high temporal-spatial resolution of 3-hourly/0.25\({}^{\circ}\) with coverage of 60\({}^{\circ}\) N-S for real time product and 50\({}^{\circ}\) N-S for the gauge-adjusted post-real-time research product. The V7 TMPA products integrate multiple sources of passive microwave satellite products, which include TRMM Microwave Imager (TMI), Special Sensor Microwave Imager (SSMI), Special Sensor Microwave Imager/Sounder (SSMIS, 3B42V7 only), Advanced Microwave Scanning Radiometer-EOS (AMSR-E), Advanced Microwave Sounding Unit-B (AMSU-B), and Microwave Humidity Sounder (MHS). The IR sensors applied by TMPA come from all available geostationary satellites (GOES-8/10, METEOSAT-7/5 & GMS). The precipitation product estimated from MW data is named 3B40 RT and is used to calibrate IR-based product 3B41 RT (variable rain rate, or VAR). VAR data are used to fill in the field where 3B40 RT data does not exist. The combination of 3B40 RT and VAR data yields the real time QPE product 3B42 RT. In addition, the new Global Precipitation Climatology Centre (GPCC) \"full\" gauge analysis, whenever available, and the GPCC \"monitoring\" gauge analysis since 2010 were incorporated in 3B42V7; whereas the previous monitoring product covered up to April 2005 and used the Climate Assessment and Monitoring System (CAMS) analysis thereafter [33]. Compared to its previous version-6 TMPA, V7 TMPA applied the enhanced V7 TMPA Level-2 PR product that gained small to moderate improvements over the V6 Level-2 PR product (_i.e._, 2A25 or PRV6 [34,35]. In addition, V7 TMPA makes use of the GPCC data with improved climatology and anomaly analysis, especially in the complex terrain [33]. Thirdly, more satellite data were blended in V7 TMPA, including SSMIS and 0.07\({}^{\circ}\) Grisat-B1 infrared data, which is a superset of the International Satellite Cloud Climatology Program B1 data [36] and represent an improvement in resolution and areal coverage over the infrared data (1\({}^{\circ}\)/24 classes) used in the V6 algorithm. More details can be seen in [33,37]. With these improvement input sources, we ask the question: Is the V7 TMPA ready for extreme events? With this question in mind, we conducted the evaluation of V7 TMPA during the 60-year return period extreme storm that hit Beijing and its adjacent regions with the latest V7 TMPA released in December of 2012.
# BRNN-LSTM for Initial Access in Millimeter Wave Communications
## 3 Beam Prediction Access Scheme
### Operating Modes  
The network operates in two modes, i.e., the learning (Mode I) and training (Mode II).  
Learning Mode (Mode I)The network here operates in normal mode, where beam scanning is performed at the MS and BS using conventional schemes, e.g., codebook-based iterative search. Namely, once a MS transits from sleep to active mode and joins the mmWave network, then search is conducted over all beamforming and combining vectors to determine the best beam index and its affiliated direction at time step \(t\), i.e., yielding the highest signal level. In notations,  
\[(b_{i}^{\text{MS}},b_{i}^{\text{BS}})_{bst}\vDash\ \arg\max\left|y_{\text{MS}}\right| \tag{6}\]  
Thereafter, the BS and MS will feed the best beam index at every time index (step) for use in the training mode. After the model is trained well, the MS and BS leverage it to predict the next best beam used, as presented next in Mode II.  
Training Mode (Mode II)Given the sequences of beam indices with the highest selection over time step \(t\), retrieved from the dataset, the MS and BS now predict the next most likely beam to be used at time step \(t+1\). Namely, the prediction scheme leverages parametric information of previous time steps (periods) and then labels the next to predict the beam index that returns the highest signal. The BRNN-LSTM scheme here recursively processes beam sequences at every time step of the input. It then maintains a hidden state which is a function of the previous state and the current input.  
Problem Formulation: Let \(\hat{s}_{t}\) be the prediction status at time step \(t\). Hence, the beam access problem is defined as a prediction of the best beam direction at time step \(t+1\), given the status at time step \(t\). Thus, the goal is to maximize the probability of successful beam prediction at the BS using the proposed BRNN-LSTM deep learning model.
## 5 Experiments and Results
### Comparison of SC-M* to Baselines
#### 5.3.3 Scalability  
We compared the scalability of the three SC-based MAPP solvers in terms of planning for a large system size (\(m>50\)). Figure 9 presents the success rate, average additional cost (i.e., how much more cost than the individually optimal path), and run-time ratio over SC-CBS under different thresholds \(T\), where the run-time ratio of SC-CBS is compared to itself and thus is constant. SC-A* has the slackest constraint (\(T=0.35,\delta=9.0\)) but poorest performance because of the prohibitively large search space. SC-CBS has the best success rate because of the property of the decoupled searching. However, this is at the expense of path cost. SC-M* performs decently in terms of both the success rate (significantly superior to SC-A*) and cost (noticeably lower than SC-CBS) as the number of agents increases.  
The run time of the SC-M* is generally longer than that of SC-CBS. In another experiment, we observe that the run-time ratio of SC-M* over that of the SC-CBS starts to decrease after a peak. This is because we force all algorithms to terminate after 1000 s, and both curves will converge to value one when their success rates decline to zero. We conducted another scalability experiment with different offsets \(\delta\) (given \(T=0.25\)) and observe the same results in terms of scalability. Figure 10 shows the experimental results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(m\) & SC-CBS & SC-M* & SC-A* \\ \hline
4 & 1.971 & 2.002 & 47.35 \\
5 & 1.798 & 3.312 & 473.7 \\
6 & 1.942 & 2.969 & 390.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average run time of SC-based MAPP solvers in the one-resource-one-type context.  
Figure 9: Success rate, cost, and run time ratio of the three SC-based MAPP solvers under different \(T\).  
Considering the scalability and path cost altogether, SC-M* demonstrates its overall advantages over alternative SC-based solvers.
## 3 Results and Discussion
### Determination of Phenolic Compounds in Dried Sourdoughs
However, it cannot be related to the production of phenyllactic acid with the antifungal capacity of the strain [15]. Otherwise, studies such as those of [PERSON] et al. [27] and [PERSON] et al. [14] reported the relationship of the presence of phenyllactic acid to fungal inhibition. Both studies elucidated the synergy of the organic acids and phenolic compounds as antifungal compounds. The results highlight the sourdoughs fermented with _P. pentosaceus_ TI6, which produces the maximum amount of phenolic compounds, such as DL-3-phenyllactic acid, benzoic acid, 3,4-dihydrohihydrocinamic acid, 3-(4-hidroxy-3-metoxyphenyl) propionic acid, and chlorogenic acid. It is noted that the phenolic compounds determined in the dry sourdoughs included 3,4-Dihydroxyhydrocinnamic acid, p-coumaric acid, phenyllactic acid, 3-(4-hydroxy-3-methoxyphenyl) propionic acid, and benzoic acid, similar to those of the study of [PERSON] et al. [52] produced by LAB strains isolated from Andean fermented products. Furthermore, in vitro studies such as that of [PERSON] et al. [53] showed that some LAB strains metabolize the primary phenolic acids, giving rise to bioactive compounds with antioxidant, antimicrobial, and anticarcinogenic effects. The acidic environment during fermentation causes enzymes to hydrolyze phenolic compounds, making them active and generating antifungal compounds against fungal growth, providing the sourdough with a higher value and shelf life [54]. Thus, studying antifungal compounds, their action mechanism, and the discovery of appropriate applications can lead to a novel use of these LAB, especially _P. pentosaceus_ TI6, to control the fungal contamination in fermented cereal products and increase their value. However, previous studies focused on the phenolic profile of sourdoughs, while the application of different drying techniques is still in development. Therefore, the literature lacks information about the combination of both techniques.