esa-sceva/satcom-chunk-collection huggingface dataset:
# Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint
## 3 Modeling  
### Research Hypothesis  
This study is based on the following assumptions to facilitate the research.  
(1) Express cargo packaging is a regular rectangular box shape; this study does not consider the loading situation of bagged goods.  
(2) The goods are sorted from the largest to smallest in volume to improve the utilization rate of layer theory, with a difference of no more than 5 cm between the adjacent height of goods. With sufficient support by default, when the total volume is less than the pallet volume, the goods can be loaded onto the pallet; that is, the upper layer of goods will not deform the lower layer of goods.  
(3) Gaps created through layer theory are automatically filled by the upper layer of goods moving down, and only the loading rate component is calculated in this study.  
(4) The quality of the goods in the carriage, the gradient in the route, and others do not affect the cost per distance delivered.  
(5) The traditional 3D loading scheme of the vehicle takes at least 0.5 h per customer point for the unloading process by default owing to the need to differentiate the cargo at each customer point.  
(6) Each distribution vehicle is responsible for one distribution route, and any two distribution vehicles do not cross-service the same customer point.
# Effects of Precipitation and Fire on Land Surface Phenology in the Brazilian Savannas (Cerrado)
## 2 Methodology
### Data Analysis
During the period of analysis (2002 to 2023), the ENP was affected by three major human-induced fires: one that began on 1 September 2005, impacting 45% of the park's area; another that started on 12 August 2010 (90% of the park's area); and a third event that ignited on 10 July 2021 (21% of the park's area). Thus, all fire events occurred during the local dry season. In the data analysis, the spectral profiles of the MODIS NDVI and its derived TIMESAT metrics were examined for these three events, using the same set of randomly selected pixels from the previous rainfall analysis (500 pixels for grasslands and 500 pixels for woodlands). Due to the inherent bias in pixel sampling for burned areas, which ranged in extent from 21% (2021 event) to 90% (2010 event), and the challenges in selecting control sites within the 250-m MODIS MOD13Q1 product, our experimental design was redefined. We then focused on the 2021 fire event and on the LSP metrics derived from the reprocessed 5-day, 30-m harmonized HLS product. The 2021 event was the only one of the three fire events covered by this product. Using satellite images from the HLS product, we selected a control site (non-burned area) located near a burned site. Both the control and burned sites (size of 2 km \(\times\) 2 km each) are therefore composed of the same vegetation type (grasslands) and have experienced the same amount of precipitation during a given pre- or post-fire seasonal cycle. For each site (control and burned), with location further indicated in Results (color composites), we randomly sampled 500 pixels without replacement. For each LSP metric, we compared the difference between control and burned sites in the seasonal cycle preceding the fire (2019/2020), during the fire event (2020/2021), and in the post-fire cycles (2021/2022 and 2022/2023). In addition to using \(p\)-values to detect statistically significant differences between the means of two populations (control and burned sites), we calculated the magnitude of the impact caused by fire and burned areas on the determination of vegetation phenological metrics. We measured the standard deviation of the difference through the calculation of Hedge's G metric [49]. The Hedge's G was calculated for each of the 13 HLS-derived LSP metrics for the control and burned site pairs of data across these mentioned seasonal cycles (2019/2020 to 2022/2023). The analysis of the LSP differences between sites in the pre-fire cycle served as an indirect measure of uncertainties in LSP determination using the HLS product. In contrast, the post-fire cycle analysis allowed for the evaluation of vegetation recovery in the burned areas. Additionally, relative frequency histograms of the HLS-derived TIMESAT metrics, along with corresponding images, were generated and plotted to consolidate the analysis and interpretation of results.
## 3 Results
### Temporal Performance Evaluation  
Table 4 represents the statistical matrices for the datasets against the ground observations based on the monthly and seasonal scales. Overall, comparatively, IMERG7  
Figure 5: Precipitation distribution pattern derived for all the seasons (winter, pre-monsoon, monsoon, and post-monsoon) using rain gauge data and selected datasets. more closely follows the observed values at all the time scales except monsoon season, where MSWEP2.8 exhibits better performance due to showing comparatively less bias (PBias = 21.15%) and a higher KGE of 0.33. However, for the other seasons, MSWEP2.8 performs better than CRU-TS and ERA-5. Although ERA-5 shows a comparatively good association with the RG data compared to CRU TS, it scores a negative KGE value for all the time scales due to the notable bias and higher error values, leading to poor performance for all the time scales. Furthermore, each product demonstrates significant variation in its performance for different time scales. The better agreement of the GPPs with the ground observations is revealed at a monthly scale, followed by the winter season; however, during the post-monsoon, besides the good correlation with the observed values, the significant bias and negative KGE lead to the poor performance of products used to estimate precipitation in the dry season. The box plots in Figure 6 present the linear correlation between the observed values and the precipitation datasets at different time scales. IMERG7 exhibits nearly the same agreement with the rain gauge data for all the time scales; however, the maximum correlation is observed for the post-monsoon season. The performance of MSWEP2.8 significantly varies across the different time scales, ranging from a weak correlation during the pre-monsoon to a strong association during the post-monsoon season. For the other seasons, it shows relatively similar and good correlation with the ground observations. ERA-5 exhibits similar performance for the annual, winter, and post-monsoon, while a weak association is depicted for the pre-monsoon season, followed by the monsoon season. The performance of CRU TS is also not consistent for the different time scales, where a weak linear correlation with the observed values is revealed during the pre-monsoon season and shows the good performance for monthly and winter precipitation prediction, with a moderate mean correlation level. Figure 7 shows the performance of all the datasets for the different time scales based on the RMSE values. The datasets show the best fit to the real data for the post-monsoon season, followed by the monthly precipitation simulation, pre-monsoon, winter, and monsoon season, respectively. The poor performance of all the datasets during the monsoon season could be due to the model's incapability to predict extreme rainfall events accurately. During winter and the pre-monsoon, each product nearly shows the same performance. IMERG7 and MSWEP2.8 exhibit almost similar alignment with the gauge data across the time scales' precipitation estimates; however, IMERG7 shows relatively superior perfor
# Evaluating the Physicochemical Properties of Some Kosovo's and Imported Honey Samples
## 3 Results and Discussion
### Free Acidity  
The free acidity (meq kg \({}^{-1}\)) in Kosovo's honey samples ranged between 23.90 and 69.00 (mean value \(\pm\) SD = 45.95 \(\pm\) 12.30%). On the other hand, the free acidity in imported honey samples varied from 18.40 to 68.60 (mean value \(\pm\) SD = 31.48 \(\pm\) 14.33%). The free acidity was above 50.0 meq kg\({}^{-1}\) which is the maximum value allowed by Council Directive 2001/110/EC [10], in 14 out of 33 samples (42%) collected from beekeepers in Kosovo, while only 2 out of 12 imported honey samples (16.7%) showed higher values than 50.0 meq kg \({}^{-1}\), which is the maximum value permitted by Codex Alimentarius and EU standards. Slightly higher values than 50.0 meq kg\({}^{-1}\) (50.90-57.20) were shown in the following honey samples: KH2 (forest honey, Peja region), KH5 (mixed honey, Ferizaj region), KH7 (forest honey, Prishtina region), KH10 (flower honey, Prishtina region), KH14 (flower honey, Gjilan region), KH19 (forest honey, Gjakova region), KH20 (Acacia honey, Gjilan region), KH 23 (forest honey, Prizren), KH28 (flower honey, Mitrovica region), and in imported honey sample IH11 (chestnut honey, Albania). Free acidity values between 60 and 69 meq kg \({}^{-1}\) were observed in the following Kosovo honey samples: KH17 (Acacia honey, Gjakova region), KH24 (mixed honey, Gjakova region), KH25 (forest honey, Prishtina region), KH26 (mixed honey, Gjilan region), KH27 (mixed honey, Prishtina region), and in imported honey sample IH7 (flower honey, N. Macedonia). Statistical analyses showed significant differences (\(p<0.05\)) in terms of the free acidity values in Kosovo's and imported honey samples. Although the free acidity is usually influenced by the natural presence of organic acids in honey, the harvest season, and the floral origin [12; 44], the values above 50 meq kg\({}^{-1}\) found in 14 Kosovo's honey samples and in 2 imported honey samples are an indication that fermentation may have occurred. Literature data show lower levels of acidity in honey samples in comparison with this study. The average values from this study are higher than those obtained by authors from Turkey, Croatia, as well as those obtained by Italian researchers [29; 30; 31; 45; 46]. The lower value of acidity shows the freshness of honey, but due to the fermentation process, the value can increase during storage [47; 48; 49]. In our case, analyses were carried out in honey samples stored for more than one year, so we assume that acidity increased with storage. Different authors evaluated the free acidity of honey samples stored at room temperature over different periods, and the results showed an increase in free acidity after 20 or 30 months of storage [50; 51].
## 6 Simulation and Case Study
### Simulation of Multiple Temporal Stages of Frequency Control
#### 6.1.2 Simulation of SFR  
The SFR for the same load step change is shown in Figure 8. The SFR kicks in after the frequency deviation lasting for 20 s.  
From Figure 8, the SFR is activated after the PFR has stabilized the system frequency. When SFR kicks in, the load reference settings of selected SGs are regulated. The system frequency is gradually restored. The power increment of PFR is overridden by the power increment from the SFR loop.
# One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
## 4 Discussion  
The proposed method of multi-scale feature fusion realizes the extraction of context information and the detection of maize leaf diseases. However, in the detection of edge targets and dense small targets, the prefetching of small target detection cannot be divided, and even the problem of missed detection occurs. This is because the limitations of convolution operation cannot model the image globally, resulting in insufficient global context information extraction. To solve the above problems, in the next stage of work, we use the idea of a transformer to achieve the optimization of the baseline and use a DropBlock [50] convolutional regularization method to improve the detection accuracy.  
In our model, we treat the images in a simple way such that the data are enhanced with the help of flip transform, random clipping, and scale transform. In some techniques of segmentation and classification using color [51], they are capable of processing trivial features such as shadows, noise, pixel saturation, low light, different crop varieties, and intrinsic camera parameters to improve model quality. We will attempt to use these methods in our model to improve detection performance in the future.  
The quality of the dataset plays an important role in the detection effect of the algorithm. In a future study, we will sample maize leaves of different varieties, fertility stages, and shooting angles under field conditions, and strictly label them to establish a large dataset of maize leaves.  
Moreover, through learning and analysis of big data, we will develop and design plant disease models applicable to maize leaves in general, explore deep learning networks with better performance, improve the accuracy of maize leaf disease detection, and apply them to early plant diagnosis and intelligent monitoring.  
Meanwhile, to improve the practical application capability, the MFF-CNN model was developed into a corresponding access point installed on mobile devices such as UAVs and smartphones to provide timely, accurate, and wide-range, real-time monitoring information for maize leaf disease identification. In addition, we will attempt to use the MFF-CNN for research on the detection of biotic and abiotic stresses in agriculture, such as saline stress and drought stress.
## 5 Experiment
### Comparison with State-of-the-Art Methods  
#### 5.3.1 Qualitative Comparison  
The qualitative comparison results by the proposed method and other eight methods are shown in Figure 5. It's shown that most saliency detection methods can handle well simple images with relatively homogenous background (e.g., row 4, 5, 7 and 8). They can uniformly highlight the whole defect object and generate high-quality saliency map and segmentation image. However, for some complex defect images containing multiple objects (e.g., row 5, 6, 10 and 11), having a cluttered background (e.g., row 6), and showing there are similarities between the defect objects and background (e.g., row 2 and 9), the whole defect objects could not be uniformly highlighted, and parts of the background being falsely taken as the defect objects. It can be seen that the contrast of saliency maps obtained by RPCA, DSR and RS is low and ambiguous, especially for Patch defects (e.g., row 5 and 6), which is difficult to define a proper threshold to segment the defects. The saliency maps obtained by RBD and SBD miss detecting parts of the defect objects, while some incorrectly include background regions into detection results. Hence, there are some missing defects and fake defects in their final segmentation image. Differently, although IS, ULR and SMF produce the good saliency map, there are  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**IndexSNR** & **No Noise** & **22 dB** & **18 dB** & **14 dB** & **10 dB** \\ \hline AUC & 0.8350 & 0.8216 & 0.7922 & 0.7414 & 0.6918 \\ MAE & 0.1584 & 0.1638 & 0.1837 & 0.2114 & 0.2384 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results with different noise.  
Figure 4: Segmentation results of the proposed DLRSD-based method: (**a**) input image; (**b**) original defect foreground image; (**c**) enhanced defect foreground image; (**d**) segmentation image by Otsu’s method; (**e**) manual-labeled ground-truth image.  
many pixels that belonging to the background are misjudged by defect, and some background regions also stand out with the defect regions. By contrast, our proposed method separates the defect objects from the background successfully and locates various defects precisely. It more efficiently highlights the complete defect object with well-defined boundaries and effectively suppresses the backgrounds than the other saliency detection methods. These results illustrate our proposed method not only enhances the contrast between surface defect and background effectively but also improves the robustness to the different illumination conditions, various shapes, scales, directions and locations of surface defect.  
Figure 5: Qualitative comparisons: (**a**) ground-truth; (**b**) RPCA; (**c**) IS; (**d**) ULR; (**e**) RBD; (**f**) SBD; (**g**) DSR; (**h**) RS; (**i**) SMF; (**j**) Ours. We can see that our segmentation results, which are produced by simple Otsu’s method on the saliency map, are very closer to the ground truth.
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 3 Geospatial Data Processing
### ICESat Profile Processing  
Data filtering was performed in three steps: (1) spatial filtering of profiles with data collected on Upsala, Viedma glaciers, and in the north region of SPI (near GRCS and GBCS stations). The glacier basin outlines were obtained from the 2000 Randolph Glacier Inventory (RGI v6.0) provided by GLIMS ([[https://www.glims.org/RGI/rgi60_dl.html](https://www.glims.org/RGI/rgi60_dl.html)]([https://www.glims.org/RGI/rgi60_dl.html](https://www.glims.org/RGI/rgi60_dl.html)), accessed on 4 July 2021); (2) in the case of ICESat-1, the parameters used to filter the data were those used by [PERSON] et al. [13]; ICESAT-2 data were used as long as their elevations had the condition atl06_quality_summary = 0, indicating that no elevation quality problems were identified [36]; (3) only elevations with ICESat values differing by less than 50 m from SRTM were accepted since even after applying step (2), anomalous elevations with positive differences against SRTM were observed. The SRTM elevation model was co-registered to ICESat-1 elevation profiles using the methodology and parameter calculations described by [PERSON] et al. [13] to estimate elevation changes.  
For a better estimation of elevation changes, two filters were performed: first, it is well known that the error in high mountainous areas increases with a slope \(>\)25\({}^{\circ}\)[51]; hence, all points with values equal or higher than this threshold were filtered out. Second, to obtain the seasonality elevation change, we separated the results according to the ablation and accumulation area defined, respectively, by the ELA of the main glaciers [29]. We estimated the relative errors by comparing the differencing result of the SRTM and elevation profiles over stable terrain (off-glacier zones) using the median and the normalized median absolute deviation (NMAD) [52]. Note that the median as the central measurement, and NMAD as the dispersion measurement are robust statistical indicators [53]. The relative random error (\(\sigma_{\Delta h_{rand}}\)) of the median difference for each profile (\(\Delta h\) (m)) was calculated using the NMAD of \(\Delta h\), Equation (6), in off-glacier zones for all the dates:  
\[\sigma_{\Delta h_{rand}}=\frac{NMAD}{\sqrt{n}} \tag{6}\]  
where \(n\) is the number of observations.
# A Cost-Efficient Approach towards Computational Fluid Dynamics Simulations on Quantum Devices
## 4 Quantum Reduced Finite Element Method
### Additional Quantum Speedup  
As mentioned before, measuring the quantum states and preparing the input at each time step requires \(\mathcal{O}(N)\) time. This \(\mathcal{O}(N)\) multiplier can cancel out the speedup gained from using the quantum solver; therefore, if one aims for an exponential speedup, this problem needs to be addressed. However, with the use of snapshot-based MOR methods, it is possible to slightly speed up the computational time in favor of the quantum algorithm. The reason is that, in snapshot-based reduction methods, such as the POD described in Section 3.3.1, knowing the solution is not required at every time step. Rather, we need solutions sampled at different and evenly spaced times. This means that measuring the quantum state is only required at each snapshot. Since the described MOR methods do not require any addition preprocessing steps for the measured and prepared solutions, the \(\mathcal{O}(N)\) time complexity remains unaffected. Nevertheless, finding the sufficient number of snapshots depends on the desired accuracy, and can vary greatly between applications. This makes it difficult to estimate what the optimal number of snapshots is in general, and requires a deep analysis and careful experimenting of the problem at hand. However, it is safe to say that, in the case where we select only every \(k\)th time step as a snapshot, then one can reduce the \(\mathcal{O}(N)\) time complexity caused by measuring the quantum state at each time step by a factor of \(k\). If every time step is selected as a snapshot, then there would be no additional speedup, and we would arrive back at the original time complexity.  
Throughout this paper, we assumed that the snapshots are evenly spaced in the time domain, but there are more advanced adaptive methods for the snapshot selection [62; 63; 64]. With the use of such methods, the additional speedup could be even more significant.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Comparison of ROMs**} \\ \hline Number of POD basis functions: & 14 & 33 & 54 & 75 & 95 \\ Cumulative energy: & 99.1\% & 99.91\% & 99.99\% & 99.999\% & 99.9999\% \\ Pre-processing: & 43.55s & 59.24s & 87.81s & 160.09s & 286.38s \\ Computation: & 1.67s & 11.09s & 44.7s & 118.52s & 245.52s \\ Post-processing & 18.35s & 20.18s & 21.31s & 22.37s & 23.8s \\ Overall time: & 63.57s & 90.51s & 153.82s & 300.98s & 555.7s \\ Speedup factor: & 1297x & 911x & 536x & 274x & 148x \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of ROMs calculated with different values of the POD velocity space dimension. The higher the number of POD basis functions is, the more accurate the results are, but with a decrease in the speedup factor.
## 2 Background
### Cycle Crossover (CX)  
The CX [24] operator creates two children from two parents as follows. It first selects an index into one parent uniformly at random. It computes the permutation cycle for the pair of parents that includes the chosen element. Child \(c_{1}\) receives the positions of the elements that are in the cycle from \(p_{2}\) and the positions of the other elements from \(p_{1}\). Likewise, child \(c_{2}\) receives the positions of the elements that are in the cycle from \(p_{1}\) and the positions of the others from \(p_{2}\). The runtime to apply CX is \(\Theta(n)\), where \(n\) is the permutation length.  
Consider an example where the parents are the permutations of Equation (1), which consist of three permutation cycles (see Equation (3)). The result of CX depends upon the random starting element. If element 0 or 2 begins the cycle, then the children are:  
\[\begin{split} c_{1}&=[2,1,0,3,4,5,6,7,8,9]\\ c_{2}&=[0,3,2,5,6,7,8,9,4,1].\end{split} \tag{4}\]  
If one of the elements \(\{1,3,5,7,9\}\) begins the cycle, then the children are:  
\[\begin{split} c_{1}&=[0,3,2,5,4,7,6,9,8,1]\\ c_{2}&=[2,1,0,3,6,5,8,7,4,9].\end{split} \tag{5}\]  
Otherwise, if one of the elements \(\{4,6,8\}\) begins the cycle, then the children are:  
\[\begin{split} c_{1}&=[0,1,2,3,6,5,8,7,4,9]\\ c_{2}&=[2,3,0,5,4,7,6,9,8,1].\end{split} \tag{6}\]  
Since the starting element is chosen uniformly at random, larger cycles are chosen with greater probability. In this example, the probability of generating the first pair of children above is 0.2, while the probability of generating the second pair of children is 0.5, and the probability of generating the last pair of children is 0.3.  
One characteristic of CX is that every element of each child has its absolute position within the permutation from one or the other of the two parents. In this way, it is particularly well suited to permutation problems where absolute position has greatest effect on fitness, since the children are inheriting absolute element position from the parents.