esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Validation with the Landscape Indicators  
Urban areas, rural areas, and urban-rural fringes often have significant differences in landscape diversity [42, 63, 64]. To calculate the landscape indicators, we chose a publicly available dataset of land cover in China, also dated from 2019, with a resolution of 30 m [65]. The dataset included multiple land cover types, and we excluded those land types that were too small and finally retained the five types of croplands, forest, grassland, water, and impervious surface. Then, we calculated two landscape indicators, namely the percent of landscape (%LAND) and the Shannon's diversity index (SHDI) [66]. The former represents the proportion of each land cover in the total area, while the latter reflects the diversity of the landscape and is calculated using the proportions of the five types of landscape. Table 1 presents the landscape indicators for the three land types. First, the SHDI was the lowest in urban areas at 0.41 and the highest in rural areas at 1.59, while the urban-rural fringe's SHDI was in between with a value of 0.98. This is due to the fact that urban areas are dominated by highly intensive buildings and infrastructure, resulting in a homogeneous landscape ([PERSON] et al., 2023) [1]. Land cover in rural areas is more diverse, including cropland, forest, grassland and other natural landscapes. Second, these three land types also had obvious differences in the percent of landscape. For the cropland, urban areas accounted for 6.51%, while urban-rural fringes and rural areas accounted for more than  
Figure 8: Population dynamics in urban–rural fringes in a single day: (a) 6 a.m., (b) 11 a.m., (c) 3 p.m., (d) 10 p.m.  
a quarter. This showed that cropland was distributed in urban areas in small quantities and was one of the main landscapes in urban-rural fringes and rural areas. Forests were widespread in rural areas, accounting for 56.82%, but rare in other areas. Grassland was similar, mainly distributed in rural areas, accounting for about 3.07%. Water bodies were scattered among the three land types, accounting for a small proportion of about 1%. The area of impervious surface in urban areas, urban-rural fringes, and rural areas accounted for 92.79%, 72.22%, and 12.17%, respectively. The first two areas were subject to more intense man-made transformation, while rural areas had large amounts of undeveloped land. This evidence was a reflection of the different degrees of urbanization of the three types, and it further validated the identification results.
## 3 Results
### Atom% of \({}^{13}\)C and \({}^{15}\)N in POM and Mussel Samples  
The atom% of \({}^{13}\)C and \({}^{15}\)N in the POC and PON of POM (mostly _Microystis_) in each chamber showed similar trends (Figure 4a). Within 1 d of the addition of the tracers, the atom% of \({}^{13}\)C and \({}^{15}\)N in POM was enriched through active phytoplankton assimilation, peaking at 16 h (max. 4.15%, 1.07-4.15%) and 12 h (max. 1.10%, 0.37-1.10%), respectively. However, the values became saturated and were slightly decreased by the end of the experiment. The atom% of \({}^{13}\)C and \({}^{15}\)N in feces and pseudofeces (FECAL) peaked at 20 h (max. 2.81%, range of 2.03-2.81%) and 60h (max. 1.26%, 0.77-1.26%), respectively. The atom% of pseudofeces was 1.26-2.81% throughout the 72 h of the experiment, which was higher than the background value of 1.01%. This indicates that the bivalve fed on \({}^{13}\)C-enriched phytoplankton (_Microystis_), assimilated them into the body, and then discharged them in the form of pseudofeces (Figure 4b). Unlike POM and FECAL samples, _Unio douglasiae_ samples showed continuous enrichment of \({}^{13}\)C and \({}^{15}\)N atom% in mussel tissues (BODY), ranging from 1.083-1.102% and 0.369-0.373% for \({}^{13}\)C and \({}^{15}\)N, respectively (Figure 4c). Overall, the slope of enrichment for carbon was slightly higher than that for nitrogen throughout the experimental period.  
The accumulation of carbon and nitrogen in the muscle and digestive tissues of _Unio douglasiae_ indicates that these components were newly synthesized by the organisms through dietary assimilation; however, a major portion of these components was not assimilated and discharged in the form of fecal pellets (Figure 4). Of tissues, the gut accumulated the highest amount of carbon and nitrogen over a similar time (\({}^{13}\)C atom% of 1.138% at 60h and \({}^{15}\)N atom% of 0.379% at 72 h, respectively), but the values were generally low for the remaining tissues. Overall at 72 h, carbon and nitrogen accumulation in tissues was in the order of gut-> gill-stomach>mantle>muscle, regardless of the isotope kind (Figure 4, Supplement-2).  
Figure 3: Clearance rate (L g\({}^{-1}\) h\({}^{-1}\)) of _Unio douglasiae_ for suspended particulate matter (SPM), particulate organic matter (POM), and chlorophyll-_a_ (CHL). Each value is the average of three subsamples (standard errors are presented in Supplement-1).
# sRrsR-Net: A New Low-Light Image Enhancement Network via Raw Image Reconstruction
## 3 Methods
### Overall Architecture  
As illustrated in Figure 2, our low-light image enhancement model, sRrsR-Net, consists of four primary modules: Sampler, RGB-iRGB, RAWFormer, and RRAW-sRGB. The Sampler module first extracts the main metadata \(M\) (i.e., \(Metadata(I)\)) from the input image \(I\) by a max-pooling operation. These metadata can assist in the reconstruction of the raw image of the original sRGB image \(I\). Then, the RGB-iRGB module takes both the extracted metadata \(M\) and the initial low-light sRGB image \(I\) as inputs and produces an RGB intermediate feature map \(\hat{F}_{r}\). This feature map is subsequently processed by RAWFormer, a Transformer-based module, to produce the reconstructed raw image \(I_{r}\). Finally, the reconstructed raw image \(I_{r}\) is fed into the last RRAW-sRGB module, which can partly simulate ISP to generate our ultimate enhanced low-light sRGB image \(\hat{I}\).
## Materials and Methods  
### Measuring Principle  
According to the definition of an ion migration number, the Y(III) ion migration number (\(t_{\rm Y}\)) is the ratio of the current it carries to the total current. Therefore, the quantity of the Y (III) ion migration material \(n_{\rm Migrate,Y}\) and the total electric quantity \(n_{\rm eq}\) passing through the electrode are measured respectively. The migration number of the Y(III) ion can be calculated by \(t_{\rm Y}=Z\,n_{\rm Migrate,Y}/n_{\rm eq}\). Because Y(III) ion is +3 valence, here, \(Z=3\).  
In this study, the Hittorf method was employed to determine the Y(III) ion migration number by calculating the number of cations that migrate out of the anode region or the number of anions that migrate out of the cathode region, the quantity of materials undergoing electrode reactions, and the current generated in the electrolyte near the electrode using Equation (1). For this purpose, a three-chamber tank containing an anode chamber, a cathode chamber, and a middle electrode chamber (Figure 1) was designed to study the variation in the migration number of anions and cations [17].  
\[n_{\rm After,Y}=n_{\rm Before,Y}\pm\eta_{\rm Powering,Y}\pm\eta_{\rm Migrate,Y} \tag{1}\]  
In this formula, \(n_{\rm After,Y}\) is the number of Y(III) ions after the reaction; \(n_{\rm Before,Y}\) is the number of Y(III) ions before the reaction; \(n_{\rm Powering,Y}\) is the increase in the number of Y(III) ions due to the electrode reaction or the amount of the reduced compound (here, \"+\" denotes increase, \"\(-\)\" denotes decrease, and 0 represents non-reaction); \(n_{\rm Migrate,Y}\) is the number of migrated Y(III) ions (\"+\" denotes \"moving in\" and \"\(-\)\" designates \"moving out\").  
During the electrolysis process, an oxidation reaction occurs in the anode area, which produces Y(III) positive ions and leads to a positive \(n_{\rm Powering,Y}\) term. When these ions move out of the anode area, the \(n_{\rm Migrate,Y}\) term has a negative sign. Meanwhile, a reduction reaction occurs in the cathode area, which decreases the number of Y(III) positive ions, leading to a negative \(n_{\rm Powering,Y}\) term. When Y(III) ions enter the cathode area, the \(n_{\rm Migrate,Y}\) term becomes positive.  
Figure 1: Measuring principle and experimental setup for the determination of the Y(III) ion mobility number.
# Solving the Container Relocation Problem by Using a Metaheuristic Genetic Algorithm
## 2 Container Relocation Problem--Model Set Up  
In this paper, we focus on solving the two-dimensional, static, offline, and restricted container relocation problem. A two-dimensional problem means that the container relocation problem is solved for one bay with certain numbers of stacks and tiers (Figure 1).  
As can be seen in Figure 1, the containers are marked with a different retrieval priority (ordinal numbers) based on the transport documentation that specifies the departure order. The retrieval represents the lifting of the target container and loading it onto a truck or other internal transport unit [17]. Therefore, the retrieval time for each container is known in advance (offline variant). A container with a smaller number has a higher priority and must be retrieved earlier. If some container blocks access to a particular container that needs to be retrieved next, it must be relocated to another free stack within the bay. These  
Figure 1: Yard bay with 3 tiers and 7 stacks.  
relocations of blocking containers, called \"reshuffles\", slow down the entire process of retrieving and shipping containers from the bay. The CRP focuses on finding the best sequence of relocation positions to retrieve all containers within the given bay (respecting their priority) with a minimum number of relocations [17]. The retrieval and relocation of containers is performed by a yard stacking crane called a rubber tired gantry crane (RTG), as shown in Figure 2.  
During the retrieval and shipping of containers within/from the bay, no new containers arrive in that bay. This is the static variant of the CRP. As mentioned earlier, the restricted CRP is limited to moving only the blocking containers and retrieving the container that is currently being shipped. Therefore, containers that are not blocking the next shipping container cannot be moved to optimize the number of additional relocations. Thus, this restricted CRP is much more complicated than the unrestricted CRP.  
The definition of the problem can be formalized and relies on the assumptions A1 to A13 and constraints C1 to C4 according to [17].
###### Abstract  
This paper described an improved method for high-throughput and sensitive determination of zeralenone and its five metabolites (zearalanone, \(\alpha\)-zearalenol, \(\beta\)-zearalenol, \(\alpha\)-zearalanol and \(\beta\)-zearalanol) in human serum. Serum samples were measured both before and after enzyme hydrolysis to assess the free and total amount of each compound by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) in multi reaction monitoring (MRM) mode following off-line 96-well \(\upmu\)Elution solid-phase extraction (SPE). All the analytes were completely separated on a C18 column within 6 min. It enabled multi-sample preparation at the same time eliminating tedious evaporation and reconstitution steps, allowing 96 (one plate) samples to be processed and analyzed within 24 h. Using an isotope labelled internal standard (\({}^{13}\)C-ZEN), high recoveries were achieved for all the compounds in the range 91.6%-119.5%, with intra-day and inter-day relative standard deviations (RSDs) of less than 8%. The limits of detection (LOD) and the limits of quantification (LOQ) were 0.02-0.06 ng mL\({}^{-1}\) (0.6-2 fmol) and 0.1-0.2 ng mL\({}^{-1}\) (3-6 fmol), respectively, demonstrating a notable enhancement in sensitivity compared to the existing methods. The validated method was applied to the analysis of paired urine and serum samples collected from 125 healthy individuals in Henan Province, locating in the middle area of China. ZEN metabolites in human serum were significantly lower than those in urine. Only one serum sample was positive for ZEN after enzyme digestion, whereas at least one of ZEN biomarkers was detected in 75.2% of the paired urine samples. Some comparison and discussion were also included in this paper.  
Keywords:zeralenone; metabolites; serum; 96-well \(\upmu\)Elution SPE; UPLC-MS/MS +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion
### Discriminability of Land Use/Cover Classes for Cropland Mapping in Harare  
The TDSI values ranged from 1.01 to 1.95 in 1984. In 2018, the TDSI values ranged between 1.27 and 2, showing a general improvement compared to 1984 (Table 3). In 1984, croplands and high-density residential classes were the least separable, while in 2018, it was the CBD and high-density residential areas. The most separable were CBD and vegetation and vegetation and water classes in 1984 and 2018, respectively. A higher TDSI in 2018 than in 1984 can be explained by the fact that Landsat 8 provided data with higher quality than the Landsat 5 that was used in 1984. Landsat 8 has a reduced noise-to-signal ratio as well as high radiometric and spectral resolution, which enhances mapping capabilities. The other reason could be that ground truth points obtained by extrapolation of 2018 survey points to 1984 become less accurate as one goes back further in time.
# Development of Korean Indigenous Low-Temperature-Tolerant Yeast for Enhancing the Flavor of _Yakju_
## 2 Materials and Methods
### Analysis of Low-Temperature-Fermented Yakju
#### 2.3.3 Volatile Aromatic Compound Analysis by Gas Chromatography  
The volatile aromatic compounds were determined using a gas chromatography mass spectrometer (7890A GC-MS; Agilent, Santa Clara, CA, USA) equipped with a flame ionization detector, and analysis methods were referred to previously described methods [31]. The separation was performed using a DB-WAX column (60 m \(\times\) 250 um \(\times\) 0.25 mm; Waters, Milford, MA, USA). The detector was an Agilent 5975C Inert XL MSD with a Triple-Axis Detector. Helium was used as a carrier gas at a constant flow rate of 1 mL/min. The temperature of the chromatographic oven was initially held at 40 \({}^{\circ}\)C for 2 min, increased at 2 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C, increased continuously at 20 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C, and then maintained at 240 \({}^{\circ}\)C for 5 min. The volatile ester compounds were collected using a solid-phase microextraction fiber (50/30 \(\upmu\)m DVB/CAR/PDMS; Supelco, Bellefonte, PA, USA). Extraction of the volatile ester compounds from _yakju_ was performed in a headspace (HS) mode with magnetic stirring. Briefly, 5 mL of the sample was placed in an HS vial (20 mm, PTFE/silicone septum, magnetic cap), and 1.25 g of NaCl was added to increase the volatile ester compound concentration in the HS. Before extraction, the sample was shaken in a water bath at 35 \({}^{\circ}\)C for 20 min to achieve equilibrium. The volatile ester compounds were identified based on a comparison of their GC retention times and mass spectrometry with spectral data from the Wiley 9 th Edition/Nist 2008 library program(Wiley 9 th Edition/Nist 2008 library version 5.0; John Wiley & Sons Inc., Hoboken, NJ, USA).
## 4 Discussion and Conclusions  
### Discussion  
#### 4.1.1 Characteristics of Unit Integration Method  
In this study, the unit integration method was proposed to calculate the excavation force of the grab. Regarding the traditional research on the excavation force of the grab, the excavation mechanics model and excavation force equation given by [PERSON] [27] are shown in Figure 21.  
Figure 21: Excavation force (given by [PERSON]).  
Figure 20: Flat digging precision: (**a**) Dry sand for test, (**b**) Wet soil for test.  
For the balance state of the excavating tool and the material, the force on the X-axis and the Y-axis is zero, the equation is as follows:  
\[\sum F_{x}=F\sin(\alpha+\delta)+c_{a}L\cos\alpha-R\sin(\beta+\phi)-cL_{1}\cos \beta=0 \tag{50}\]  
\[\sum F_{Z}=-F\cos(\alpha+\delta)+c_{a}L\sin\alpha-R\cos(\beta+\phi)+cL_{1}\sin \beta+W+Q=0 \tag{51}\]  
With further simplification, the equation for the digging force \(F\) can be obtained.  
\[F=(\gamma gd^{2}N_{\gamma}+cdN_{c}+qdN_{q}+c_{a}dN_{ca})w \tag{52}\]  
where \(\gamma\) is material density; \(g\) is the acceleration of gravity; \(c\) is the material cohesion; \(d\) is the excavation depth; \(w\) is the excavation width; \(q\) is the load on the material; \(c_{a}\) is the adhesion coefficient; \(N_{ca}\) is the influence coefficient of digging force; \(N_{\gamma}\) is the density influence coefficient; \(N_{c}\) is the viscosity influence coefficient of the material; \(N_{q}\) is the influence coefficient of the load.  
The research reported in this paper suggests that it is reasonable to use the unit integration method to calculate the relationship between the excavation force and the excavation parameters. Compared with the McKyes method, this method has the following advantages:  
1. The grab has complex structural geometric parameters, and the analysis of the unit body mechanics model avoids the influence of the grab's geometric parameters. Using the unit integration method, it can be converted into active pressure \(F_{B}\) and passive pressure \(F_{E}\).
2. For the characteristic analysis of the interaction force between the grab and the material, the unit body model can consider the friction between the materials. The construction of the mechanical model of the grab excavating material is more consistent with the actual grab excavation mechanical model.
# Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation
## 3 Materials and Methods
### Slope Factor Extraction  
By referencing the existing mature algorithms [28; 53; 54; 55], the LSW and LS factors of the test area were obtained. The extraction of slope width first requires obtaining the catchment accumulation based on the depression-free DEM, and the key to sub-basin division lies exactly in the selection of the catchment accumulation threshold, which  
Figure 2: Location and display of the six feature areas.  
determines the shape of the river network. In this paper, the second order derivative analysis of river network density is applied to determine the appropriate threshold for flow accumulation [56], and the specific extraction process is as follows:  
Using the hydrology tool, a total of 12 catchment accumulation thresholds from 5000 to 60,000 are set at equal intervals of 5000, generating the river network separately. To obtain the function of flow accumulation threshold and river network density, multi-class functions such as logarithmic function and composite function were used to carry out regression fitting. The results showed that the power function had the best fit, and the R2 (coefficient of determination) of the fit was up to 0.9995. The obtained function expression is shown in Equation (1):  
\[\text{y}=23.432\text{x}^{-0.491} \tag{1}\]  
where y is the density of the river network (km/km\({}^{2}\)); x is the threshold of catchment accumulation.  
The second order derivative of the power function relation yields Equation (2).  
\[\text{y}^{\prime\prime}=17.154\text{x}^{-2.491} \tag{2}\]  
The geometric information is calculated and counted, while the accumulation threshold is brought into Equation (2), resulting in the corresponding hydrological information shown in Figure 3.  
Analysis of Figure 3 shows that when the river network threshold was raised from the initial value to 25,000, the reduction of river network density was remarkable, followed by a sudden change and a slow flattening trend. The inflection point of the second derivative function of river network density occurs at the river network threshold of 55,000, and the tendency of hydrological characteristic parameters such as river network density remains basically unchanged. Finally, the optimal threshold value of 55,000 was selected to extract the river network. Subsequently, the \"Basin\", \"Snap Pour Point\" and \"Watershed\" functions were executed in the Hydrology toolbox of ArcGIS 10.7 to divide the sub-watersheds. On this basis, the delineated watershed data were imported into the WEPP (Water Erosion Prediction Project). After adopting the function of \"Watershed Delineation Analysis\" to determine the two parameters of CSA (critical source area) and MSCL (Minimum Source Channel Length), we simulate the slope flow process to generalize the slope surface. Finally, we visualize the slope width with ArcGIS 10.7 [57].  
Figure 3: Second order derivative of threshold versus river network density.
# Determination of the Optimum Amount of Superplasticizer Additive for Self-Compacting Concrete
## 4 Conclusions  
The optimum quantity of SPA is very important when designing an SCC since it is the key factor in its workability. In this context, having a tool able to indirectly determine, in real time, this optimum quantity of SPA will reduce the time, materials and money dedicated to the proper definition of the mix proportions of an SCC. In this work, in has been proven that there is a measurable relationship between the concrete mixer power computation and the concrete flowability, so it is possible with just one test to define the optimum quantity of SPA required.  
* After analyzing the results, it can be seen that the optimum quantity of SPA/C for the study case is 2%.
* Cement pastes and concretes with an SPA/C of 3% can show, in some cases, greater fluidity than for 2%, but the segregation of their elements is notable, and this is a situation to be avoided.
* Regarding the proposed concrete mixer test method, it has been revealed as a valid methodology to obtain the optimum amount of additive.
* This method is quicker than the current technique, since it allows results to be obtained from a single mix and also eliminates variables in the study when working on the real components of the concrete.
* On the other hand, it has been proven that obtaining the saturation point of the Marsh cone test is more precise when using a higher volume than that recommended by current standards.
* The measurement of the power of the three electrical phases has proved to be similar, so the methodology  
**Author Contributions:** Conceptualization, C.T.; J.A.S.-A., I.A.C. Data curation, S.D.; Formal analysis, J.A.S.-A., I.A.C. and J.A.P.; Investigation, J.A.S.-A., I.A.C., J.A.P., I.S. and J.C.; Methodology, I.A.C., J.A.P. and C.T.; Resources, S.D.; Supervision, J.C.; Writing--original draft, J.A.S.-A., Writing--review & editing, C.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Spanish Ministry of Economy and Competitiveness of Spain, grant number MAT2014-57544-R\".  
**Acknowledgments:** The authors would like to thank: LADICIM, the Laboratory of Materials Science and Engineering of the University of Cantabria, for making the facilities used in this research available to the authors.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.