esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed FDTE with Iterative Channel Estimation
### Analysis of the Computational Complexity  
In this section, the complexity analysis and a comparison for the different turbo equalizations are provided. The complexity comes from four parts, namely the equalization filtering, symbol estimation, computation of the conditional mean, and variance [9]. The main complexity is generally from equalization filtering. In the proposed IFDTE, the parallel block equalization filtering is made using FFT with significant efficient parallel convolution computation, thus improving the computational complexity significantly by reducing many multiplications in the filtering calculation of the TDTE. However, through the parallel implementation of the proposed block equalization filter, the proposed scheme occupies much more memory usage than the parallel FFT calculation, other than for the serial convolutional one, for the cost of fast computation. And the additional memory usage is mainly related to the degree of parallelism of the main convolutional filtering. In addition, the symbol estimation also occupies some complexity in the proposed IFDTE and it is moderate, as shown in Table 2. Then, the complexity comparison can be analyzed as follows. Without a loss of generality, the computation complexity in terms of complex multiplication is adopted. Suppose the number of receiving hydrophones is \(M\), the maximum number of the EP self-iteration is \(S\), the length of FFT is \(K\), the length of the feedforward filter is \(N_{1}\), the length of the feedbackward filter is \(N_{2}\), the total length of the filter for the TDTE is \(N_{s}=N_{1}+N_{2}+1\), and the length of the UWAC channel is \(L\).  
From Table 2, the complexity of the proposed IFDTE is approximately \(S\) times higher than the FDDF-FDTE. This result is due to the self-iteration based on the EP in estimating the actual characteristic of the a posteriori distribution. However, the complexity of TDTE is proportional to the length of the filter and the UWAC channel. The UWAC channels are longer than 50 taps, the length of equalization is longer than that of channels [2], and the number of self-iterations is smaller than 10. Therefore, the computational complexity of the LE and SDFE is larger than that of the IFDTE.
# Fire Activity and Fuel Consumption Dynamics in Sub-Saharan Africa
## 1 Introduction
### Remote Sensing of African Landscape Fires
The product's performance, in terms of active fire detection omission errors, has been demonstrated to be significantly better than all other tested operational active fire products generated from SEVIRI [42] and we use this data here to assess spatio-temporal variations in fire activity across sub-Saharan Africa from 2004 onwards. We also use temporally integrated SEVIRI FRP data to estimate fire radiative energy (FRE), from which we calculate the landscape fire fuel consumption, and we compare these estimates to those related to the accumulated fuel available to burn, which we derive from MODIS vegetation productivity and burned area products. Overall, our aim is to investigate the temporal dynamics of FRP-derived fuel consumption and its relationship to the available fuel load. This will increase our knowledge of fire behaviour and impacts across sub-Saharan Africa.
## 3 Results
Actually, introducing such a parameter in a DFT + \(U\) approach invokes physics that is inappropriate for these metals, unless the parameter is very small and inconsequential. To illustrate this, we performed VASP DFT + \(U\) calculations that include spin-orbit interaction with the proper exclusion of the p states, as discussed. In Figure 4 we show these results that suggest an over-estimation of all the volumes, except for thorium. Accounting for the fact that the pseudopotential approximation tends to slightly under-estimate the volume (Figure 3), the conclusion from Figure 4 is that the DFT + \(U\) volumes are decidedly too large and worse than those of the treatment without a \(U\) (Figure 2). The overall effect of the applied \(U\) is here relatively small, cf. Figures 2 and 4, because we investigate a very small \(U\) (0.4 eV). Using the more common and larger value of \(U\) (\(\sim\)1 eV) exacerbates this over-estimation problem for the volumes, while adopting a larger \(U\) (\(>\)4 eV), often applied for \(\delta\)-plutonium, causes wholly unrealistic volumes for all the actinides (including \(\delta\)-plutonium). Figure 2: Experimental (\(T=0\), see main text) and DFT (VASP) atomic volumes (Å\({}^{3}\)). The calculations include spin–orbit coupling (not on p states) and for Pu also orbital polarization. Another very obvious sign of the improper physics that the Hubbard \(U\) parameter enforces is found in the prediction of the ground-state phases. For plutonium, DFT + \(U\) predicts the wrong ground state, even for the very small \(U\) we have adopted in this study (0.4 eV). In Figure 5 we show our fully  
Figure 4: Experimental (\(T=0\), see main text) and DFT + \(U\) (VASP) atomic volumes (Å\({}^{3}\)) for the actinides. The calculations include a Hubbard \(U=0.4\) eV and spin–orbit coupling (not on p states) but no orbital polarization. Figure 3: Scalar-relativistic (no spin–orbit coupling) atomic volumes for Th to Pu calculated with VASP and FPLMTO. (both structural and magnetic-configurational) relaxed DFT + \(U\) calculations for \(\alpha\)- and \(\delta\)-plutonium. Notice that the \(\delta\)-phase energy is about 7 mRy (per atom) lower than the \(\alpha\) phase. The fact that the \(\delta\) phase is predicted to be the ground state when a large \(U\) (\(\sim\) 4 eV) is employed has been discussed [40], but it is remarkable that even a very small \(U\) promotes the wrong ground state. Actually, the two phases are energetically degenerate for a small \(U\)\(\sim\) 0.2 eV. Here, both the \(\alpha\) and \(\delta\) volumes are significantly under-estimated (18.74 and 24.25 A\({}^{3}\), respectively). We find that increasing \(U\) aggravates the problem and further increases the stability of \(\delta\) over \(\alpha\) (not shown). We emphasize that a theory assuming no ad hoc Coulomb repulsion (\(U\) = 0) but orbital polarization and careful relaxation of all degrees of freedom reproduces the correct ground state in accordance with previous conclusions [18; 40].
## 3 Results and Discussion
### Pharmacotechnical Tests
#### 3.4.3 Mass Uniformity  
The values of mean masses (Table 7) and calculation methodology to determine if the tablet mass is uniform according to the standards (Table 8) are noted.  
Since the mean masses of CP1, CP3, and CP4 are all between 231 mg and 250 mg, it is necessary to refer to the standards to verify whether these controlled tablets satisfied the mass uniformity test.  
To declare that the mass of tablets for a lot is uniform, it is necessary to check the condition that the 20 controlled tablets do not stray from \(\pm\)7.5% of the mean mass. If no more than 2 of the 20 tablets stray by \(\pm\)7.5% from the mean mass, none of the tablets shall stray by \(\pm\)15% from the mean mass.  
On the other hand, the mean mass of CP2 is greater than 250 mg, so, to declare that the tablet mass for the CP2 batch is uniform, it is necessary to check the condition that the 20 controlled tablets do not stray by \(\pm\)5% of the mean mass. If no more than 2 of the 20 tablets stray by \(\pm\)5% from the mean mass, none of the tablets shall stray by \(\pm\)10% from the mean mass.  
By comparing the results (Table 7) to the standards (Table 8), it has been found that the individual mass of the 20 tablets of _Atriplex halimus_ for the three controlled formulations CP1 (100-200 \(\upmu\)m), CP3 (300-400 \(\upmu\)m), and CP4 (400-500 \(\upmu\)m) range from 230 mg to 250 mg; therefore, none of the tablets stray from \(\pm\)7.5% of the mean mass of the tablets (235.1 mg to 246.3 mg). It is then concluded that the tablets comply with the mass uniformity test.  
For the CP2 formulation (200-300 \(\upmu\)m), it is found that the individual mass of 20 controlled tablets varies from 251 mg to 261.4 mg; therefore, none of the tablets stray by \(\pm\)5% of the mean mass of tablets (259.6 mg). It is, therefore, concluded that the CP2 tablets comply with the mass uniformity test.
# MODIS Cloud Detection Evaluation Using CALIOP over Polluted Eastern China
## 3 Results and Discussion
### MODIS-CALIOP Cloud Detection Difference Depending on Aerosol and SZA  
To further investigate what caused the discrepancy for cloud detection between MODIS and CALIOP, the possible impacts from aerosol and SZA were analyzed. Figure 4 shows the dependence of the ratio of MODIS cloud pixel numbers to CALIOP cloud-free pixel numbers (r_MODcloud_CALfree) on AOD and SZA over dark, bright, and water surfaces. It clearly shows that r_MODcloud_CALfree increased with an increase in AOD and SZA over the three surface types (Figure 4a-c). The r_MODcloud_CALfree at 0-0.5 AOD range was just 0.14, 0.21, and 0.21 over dark, bright, and water surfaces, respectively, while it increased to 0.73, 1, and 0.71 at high AOD (>3.2) over dark, bright, and water surfaces, respectively (Figure 5a). Those clouds observed by MODIS but undetected by CALIOP had a low CTH (<1.5 km) (Figure 2a). That suggests that most of the discrepancies between MODIS and CALIOP may be due to MODIS misclassifying heavy aerosol pollution as clouds at low altitude [21; 22; 23]. Similarly, the r_MODcloud_CALfree increased from 0.10, 0.03, and 0.14 at low SZA (20-24\({}^{\circ}\)) to 0.97, 0.66, and 0.73 at high SZA (68-70\({}^{\circ}\)) over dark, bright, and water surface, respectively (Figure 5b). This may be caused by an increase in the satellite retrieval biases of COT and cloud effective radius (CER) with an increase in SZA [34]. The increase in COT and decrease in CER at high SZA both contribute to an overall increase in a cloud droplet concentration of 40% to 70% between low and high SZA [34], resulting in more MODIS cloud at high SZA. On average, the r_MODcloud_CALfree at high AOD increased by 421%, 376%, and 238% relative to that at low AOD, and the r_MODcloud_CALfree at high SZA increased by 870%, 2100%, and 421% relatively to that at low SZA over dark, bright, and water surfaces, respectively.  
In addition, the ratio of CALIOP-alone cloud pixel numbers to MODIS cloud-free pixel numbers (r_CALcloud_MODfree) has apparent maxima at low COTs, of 0.146, 0.137, and 0.132 for COT \(<\) 0.4 over dark, bright, and water surfaces, respectively (Figure 5c). The r_CAL  
Figure 4: Dependence of r_MODcloud_CALfree on aerosol optical depth (AOD) and solar zenith angle (SZA) over (**a**) dark; (**b**) bright; and (**c**) water surfaces.  
Figure 5: Variations in r_MODcloud_CALfree with (**a**) AOD and (**b**) SZA, and (**c**) variations in r_CALcloud_MODfree with COT.  
to less than 0.03 sharply for COT \(>\) 0.4. This suggests that most of the discrepancies are caused by optically thin clouds (COT \(<\) 0.4) that were undetected by MODIS but readily observed by CALIOP.
## 4 Discussion
### Validation of the Two Model-Based Estimates of Vocal Fold Collision Pressure  
Although using the ISP probe is not practical for routine data collection, the data collected from the direct measurement of intraglottal, collision, and subglottal pressures using the ISP probe in a select group of individuals can be used as references in numerical models that can be optimized to estimate these important parameters in patients with voice disorders or vocally healthy speakers. Two numerical modeling approaches were tested in this paper to validate model-based estimates of vocal fold collision pressure using only data from the laryngeal high-speed videoendoscopy recording, a procedure that is feasible to perform in practice. Good agreement was exhibited between the model-estimated collision pressures and measured vocal fold collision pressures. This agreement provides valuable initial in vivo validation of the vocal fold collision pressure estimates derived by these types of models, which is critical, as these engineering methods are expected to be more easily translated into clinical practice.  
To avoid confirmation bias and given the pilot nature of this study, further in vivo validation is needed to assess the robustness of the indirect estimation approaches. However, prior validation against silicone vocal fold models and prior studies has been successfully performed for both modeling approaches utilized in this study [45; 46]. It is interesting to note that both numerical and experimental findings point to a close relation between the peak contact pressure and the driving subglottal pressure, near a 1:1 ratio. The assessment of this relation is of clinical relevance and requires further attention, as other factors are hypothesized to influence this relation, such as muscle activation level, loudness condition, mode of vibration, and supraglottal compression.
###### Abstract
Furthermore, STAMP/STPA is also applicable to systems in early development stages, such as autonomous vehicles and autonomous ships, which may lack fine-grained design details, making it an important tool to guide safety-centered design and decision-making processes [35; 36; 37; 38]. The main objectives of this research are:  
1. To identify and analyze the potential hazards and challenges associated with integrating UAS into public safety/law enforcement systems in urban environments. 2. To develop a comprehensive set of safety requirements and guidelines for the safe and effective deployment of UAS in public safety missions. 3. To propose strategies for efficient infrastructure investment and resource allocation based on STAMP/STPA results. By applying STAMP/STPA, this study aims to deliver a comprehensive hazard analysis, a set of safety requirements and constraints, and recommendations for efficient infrastructure investment and resource allocation. These outcomes will support the safe integration of UAS into public safety systems in rapidly urbanizing areas, providing valuable insights and guidance for policymakers, system designers, and public safety agencies in addressing the challenges of UAS integration and ensuring their safe and effective deployment in urban environments while optimizing resources and investments.
## 1 Introduction
However, blending is no \"silver bullet\", as it often introduces unforeseen spatial and temporal variances ([PERSON] et al., 2020). Therefore, it is critical to systematically evaluate the benefits of blending and identify where and when blending helps to improve monitoring. Blending satellite data with complementary frequency and spatial resolution characteristics, such as MODIS (herein considered as an HTF and low-spatial-resolution (LSR) sensor) and Landsat (herein considered as a low-temporal-frequency (LTF) and HSR sensor), provides a solution of synthetic imagery that is both HTF and HSR ([PERSON] et al., 2020). Current literature such as [PERSON] et al. (2020) found that using Landsat images provides a higher crop yield prediction accuracy for field scales over MODIS images, and a further improvement can be achieved by combining Landsat and MODIS (L-M) blended data with the incomplete Landsat series. To date, the utility of blended output is not yet tested for regional and national crop yield mapping ([PERSON] et al., 2020; [PERSON] et al., 2020; [PERSON] et al., 2020; [PERSON] et al., 2020; [PERSON] et al., 2020; [PERSON] et al., 2020; [PERSON] et al., 2020; [PERSON] et al., 2020) (Table 1); this study fills that niche. This study quantifies and evaluates the benefits of blending satellite data of different temporal frequencies and spatial resolutions for crop yield prediction. The specific objectives of this study are to (i) estimate yields using MODIS, Landsat, and L-M blended data and then compare the yield prediction at both pixel and field scales, (ii) identify the fraction of missing Landsat data during a growing season considering the 16-day acquisition cycle to determine a threshold where the blended data can improve the prediction, and (iii) quantify the improvements in the yield prediction accuracy based on the threshold.
## 2 Materials and Methods
### Metabolic Reconstruction
#### 2.1.2 Refinement of Reconstruction  
After the first draft model was obtained, a great deal of work was required until the model represented the phenotypic states of the organism [50]. Gap-find was utilized to identify network pathologies which include root no-consumption, root no-production, downstream no-production, and upstream no-consumption and blocked reactions [28; 51].  
Figure 1: Iterative workflow from the bottom-up process in _C. sativa_ reconstruction.  
Identify Candidate Reactions to Fill Gaps  
An exhaustive review of the literature was carried out to identify reactions related to the secondary metabolism of the plant that could fill the gaps and facilitate integrating diverse metabolic pathways taking place in the different cellular compartments [6; 11; 14; 16; 52; 53; 54; 55]. Furthermore, KEGG tools were used to complement the metabolic information of the reconstruction through a second functional annotation carried out based on BlastKOALA (KEGG Orthology and Links Annotation) [56]. This was aided by the updated annotation release of the _C. sativa_ reference genome [44].  
Add Gap Reactions to Reconstruction  
Regarding the manual curation of models, one of the most complex problems researchers face is the diversity of terminology in reference databases. The present model relies on the Model SEED repository [57], which involves several databases (KEGG, Metacycl, AraGEM, BiGG, Maize_C4 GEM, PlantCyc, and TS_Athaliana, among others) and adds a unique identifier to them [12].  
An iterative workflow was carried out to add reactions identified previously to the reconstruction. First, reactions were transformed to the ModelSEED nomenclature taking into account reference ModelSEED database information. Next, renamed reactions were integrated into the reconstruction, considering each compartment of the reconstruction. Finally, a network evaluation was carried out, looking for additional gaps that could be generated for new reactions (Figure 1).  
Once the reconstruction was obtained, successive flux balance analyses (FBAs) were carried out. FBA is a mathematical approach that calculates the flow of metabolites through metabolic reconstruction, making it possible to predict the growth rate of an organism [58]. This is done by taking advantage of the constraints imposed by the stoichiometric coefficients of each reaction in the metabolic fluxes. FBAs of _C. sativa_ are based on the biomass composition of the plant cell [49] as the objective function of the model (Supplementary Table S1) and then evaluating the flow distribution within the system.
## 4 Conclusions  
In this paper, so as to quickly solve the extreme loads and make it easily usable for preliminary blade design, a new simplified method, called as PSO-ELPM, was developed and validated in this study. Following this, the effects of the status parameters on the extreme loads were analyzed. Some conclusions are given below:  
1. The extreme root loads computed by PSO-ELPM and FOCUS were very close. The error between them was less than 10%. The extreme CMF_ROOT was lower than 5%. Moreover, PSO-ELPM needs much less computation cost than FOCUS. Hence, PSO-ELPM can be used for predicting the extreme load and is suitable for preliminary blade design.
2. Higher rotor speed and smaller pitch angle will generate larger extreme bending moments at the root. It is for this reason that we need to pitch the blade to feather at high wind speed and activate security strategies when the rotor speed is larger than overspeed. In addition, negative yaw angle easily generates the extreme bending moments.
3. When the control system is inactive, the extreme CMF_ROOT is increased but the extreme CME_ROOT is almost unchanged. It shows that the control system has significant influence on reducing the extreme loads, especially the extreme CMF_ROOT.
4. By comparison of the extreme load distribution, flapwise bending moments are close to the results of FOCUS, while edgewise bending moments need be improved in the future.
5. By the analysis of the effects of \(\Delta\Omega\), there is little influence on the extreme flapwise bending moments. However, the edgewise bending moments change a lot while the maximum rotor speed reduces.  
The PSO-ELPM model is a new method, which gives a different way to solve the extreme loads exerted on wind turbine blades. It has some advantages--it is simple and fast, but there are some aspects which have not been considered. There are also some defects that need to be further improved. For example, some parameters that change with the wind turbine model and not easily determined, and it needs to be fixed to make this model more generic. A further example is that the load calculation model needs to be improved so as to obtain better results. For example, the \(\Delta\beta_{2}\) should be set as a variable changing with the wind speed. Storm conditions can be the most challenging conditions and should be added in the improved model. The load calculation could consider the aeroelastic effect. The \(\Delta\Omega\) should be fixed to make this model more accurate.  
[PERSON] developed the new method, did the numerical computation and wrote the article; [PERSON] supervised the work and did the results analysis; [PERSON] discussed with the results and given some useful comments for writing.  
This research was funded by the National Natural Science Foundation of China (No.51606196).  
I would like to thank the China Scholarship Council (CSC) and the International Clean Energy Talent (iCET) Program.  
The authors declare no conflict of interest.
# FAmesh: Generating Frequency Adaptive Meshes from Single Images under 2D Hole Constraints
## 3 Method
### Loss Function  
The loss function used consists of two parts: a global loss \(\mathcal{L}_{global}\) and a local loss \(\mathcal{L}_{local}\). In \(\mathcal{L}_{global}\), we use chamfer distance as the primary loss function (see in [12]).  
\[\mathcal{L}_{cd}=\sum_{x\in G}\min_{y\in S}\lVert x-y\rVert_{2}^{2}+\sum_{y \in S}\min_{x\in G}\lVert x-y\rVert_{2}^{2} \tag{3}\]  
where \(x\in G\) and \(y\in S\) represent the set of points sampled from the generated mesh \(G\) and ground truth \(S\), respectively.  
In this paper, similar to TMNET, we utilize the L\({}_{2}\) loss as \(\mathcal{L}_{error}\) to train the error estimation network [18]. In order to avoid severe self-intersection during the process of global deformation and to improve the mesh's smoothness, we employ the commonly used geometric regularization term [16; 40]. The normal loss \(\mathcal{L}_{normal}\) is used to maintain the normal consistency between the generated mesh and the ground truth, and the smooth loss \(\mathcal{L}_{smooth}\) is used to maintain a smooth target surface. The edge loss \(\mathcal{L}_{edge}\) is used to penalize overly long edges and prevent outliers.  
The final global shape recovery loss \(\mathcal{L}_{global}\) is as follows:  
\[\mathcal{L}_{global}=\mathcal{L}_{cd}+\mathcal{L}_{error}+\lambda_{1}\mathcal{ L}_{normal}+\lambda_{2}\mathcal{L}_{smooth}+\lambda_{3}\mathcal{L}_{edge} \tag{4}\]  
We use local loss \(\mathcal{L}_{local}\) to fine-tune local patches. Different from \(\mathcal{L}_{global}\), each item of \(\mathcal{L}_{local}\) is calculated on the subgraph and the corresponding ground truth. Note that the one-way edge of the extended patch is involved in the calculation of the regularization term to maintain the smoothness of the seam. In addition, we introduce a move loss \(\mathcal{L}_{move}^{p}\) to penalize the points with excessive offset.  
\[\mathcal{L}_{move}^{p}=\sum\lVert v_{i}-v_{i}r\rVert_{2}^{2} \tag{5}\]  
where \(v_{i}\) and \(v_{i}^{f}\) are the vertices of subgraph \(g\) before and after fine-tuning, respectively.  
The final local patch fine-tuning loss \(\mathcal{L}_{local}\) is as follows:  
\[\mathcal{L}_{local}=\mathcal{L}_{cd}^{p}+\lambda_{1}\mathcal{L}_{cd}^{p}+ \lambda_{3}\mathcal{L}_{edge}^{p}+\lambda_{4}\mathcal{L}_{move}^{p} \tag{6}\]