esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
### Novelty of the Proposed Methodology  
The proposed method is different from the traditional methods relying on theoretical computational models such as finite element analysis or the limit equilibrium method, which are widely used in engineering practice but have certain limitations in dealing with slope stability problems under complex geological conditions. In contrast, the method in this study, by combining real-time slope deformation rate monitoring data, is able to provide a more intuitive and precise way to assess the stability of slopes, being more in line with the actual deformation and monitoring of slopes and improving the practical significance and accuracy of the calculation of the coefficient of safety.
# Oil Press-Cakes and Meals Valorization through Circular Economy Approaches: A Review
## 5 Possible Ways to Capitalize the by-Products Obtained from the Oil Industry
### Extraction of Bioactive Compounds
#### 5.1.3 Dietary Fiber  
Dietary fiber (DF) is defined as a group of carbohydrate polymers (oligosaccharides and polysaccharides) that cannot be hydrolyzed (digested and absorbed) in the small intestine by human digestive enzymes [142]. In this category are included insoluble components (cellulose, hemicelluloses, lignin), soluble components (gums, pectin substances, mucilages), inulin, and resistant starch [143; 144]. DF has different functions and effects, depending on the physicochemical properties. For example, DF with high water-holding and/or swelling capacities reduce defecation time and produce satiety, on the other hand, those with high binding activity hinder DNA and epithelial cell damage [76].  
A diet rich in dietary fiber brings many health benefits, such as reducing the risk of coronary heart disease, cancer, obesity, and diabetes. In the food sector DF is used to improve nutritive, organoleptic (color, flavor, taste), and textural properties (increasing water/oil holding, emulsion, or/and gel formation capacities and avoiding syneresis). They can also prolong the shelf life and improve oxidative stability [76]. Recovery and utilization of fiber from wastes represent key factors in implementing a sustainable production (they have health benefits, are available in large quantities, have low cost and are environment friendly), zero wastes, and circular economy [119; 145].  
DF can be extracted through different methods: traditional (dry/wet processing, chemical, enzymatic gravimetric, microbial) and green/innovative extraction (with ethanol, water, steam, ultrasonic, hydrostatic pressure, and pulsed electric field). The method used can influence the composition, trait, and behavior (in food and human body) of DF. From the first category, the most rentable, effective (purity of final product 50-90%) is wet processing. The utilization of alkali/acid can damage the structure and through enzymatic path the extraction can be incomplete. The innovative methods present advantages such as high recovery rate and low environmental impact [119; 143].  
Dietary fiber extracted from by-products can be used in foods and pharmaceutics as a functional ingredient, supplements, or additive [144]. [PERSON] and [PERSON] [76] studied how particles size, extraction with cellulase, and acid treatment affect the structure and properties of the dietary fiber from coconut oil cake. Reduction of the first factor (particle size) led to an increase in entrapment, water holding, and swelling capacities. The cellulase hydrolysis had an undesirable effect on color and oil holding capacity but increased carbohydrate content, porosity, water holding, swelling and alpha-amylase inhibition capacities. Compared to the previous (cellulase hydrolysis), the last parameter (acid treatment) led to inverse results.
# A Hybrid Metaheuristic Algorithm for the Efficient Placement of UAVs
## 3 System Model and Problem Formulation
### System Model
Therefore, the term \(W_{ij}\) in Equation (1) simplifies to \(W_{ij}=1,\forall i,j\in N\). Moreover, because the problem formulation is posed as a minimization optimization problem, we consider the inverse of each link capacity as the corresponding link cost, i.e., the time needed to transmit a bit. Therefore, we let  
\[T_{ij}=\frac{1}{C_{ij}} \tag{8}\]  
to express the link cost between node \(i\) and node \(j\), i.e., a link either connecting a user \(i\) to an UAV \(j\) (A2G link), or an UAV \(i\) to an UAV \(j\) (A2A link). Note that, according to the general description of the \(p\)-hub median problem, the only locations that are searched for placing the hubs are the locations of the users themselves. In our case, this means that every UAV will be placed right above a distinct user on the ground. Consequently, because all of the UAVs are located at the same altitude \(h\), the total cost introduced by the corresponding \(p\) A2G links to users right below the UAVs are constant across all possible combination of UAV placements. For this reason, we do not need to take those costs into account. Finally, we can treat the optimization of UAV placement as a \(p\)-hub median problem after appropriate modifications to the original model presented in Equation (1), as follows  
Minimize: \[\sum_{i,j,k,l\in N}T_{ik}X_{ik}+T_{kl}X_{ik}X_{jl}+T_{jl}X_{jl}\] (9a) Subject to: \[\sum_{k=1}^{n}X_{kk}=p,\] (9b) \[\sum_{k=1}^{n}X_{ik}=1,\forall\ i\in N\] (9c) \[X_{ik}\leq X_{kk}\quad\forall\ i,k\in N\] (9d) \[X_{ik}\in\{0,1\}\ \ \forall\ i,k\in N\] (9e)  
Figure 1 depicts the general network scenario of our problem. The full red lines indicate the links between UAVs, while the dashed blue lines show the links between ground users and UAVs. It also displays the key variables used in the formulation of the problem, such as the altitude \(h\) of UAVs, the LOS distances between users and UAVs (e.g., \(d_{ij}\)), and Euclidean distances between users and UAVs' projections on the ground (e.g., \(r_{ij}\)). Figure 1 shows an example where user \(i\) can communicate with user \(j\) by using the three-hop path that contains UAV \(k\) (with link cost \(T_{ik}\)), UAV \(l\) (with link cost \(T_{kl}\)), and user \(j\) (with link cost \(T_{jl}\)). Figure 1: Illustration of a communication between users through user-UAV and UAV-UAV links. The quadratic integer linear program that is posed by Equation (9a)-(9e) can be linearized and solved by well-known optimization tools, e.g., CPLEX's branch-and-cut optimizer. Nevertheless, as discussed in Section 3.1, the problem may become intractable due to the excessive number of variables and constraints, thus, in the following, we also propose a hybrid optimization methodology integrating a biased randomized (BR) technique with the popular Iterated Local Search (ILS) algorithm, which provides further improvements for the execution time and problem size.
## 2 Materials and Methods
### Retrieval of Reflectance Indices and Solar Induced Fluorescence  
As leaf and white reference reflectance measurements were not collected simultaneously, changes in ambient light intensity (typically caused by patchy cloud cover) occasionally resulted in a mismatch between reflected radiance measurements. To overcome this, simultaneous global PAR measurements were used to identify and match each leaf spectrum with a white reference counterpart taken within \(\pm\)15 min. This time period was chosen, because changes in solar altitude were found to have a negligible effect on radiance computation during this time frame. The matching was performed in the R programming language and resulted in a mean absolute time mismatch \(\pm\) standard deviation (SD) of 4.22 \(\pm\) 4.89 min and a PAR mismatch of 7.89 \(\pm\) 17.93 \(\upmu\)mol photons\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\), respectively. Leaf target measurements, where the closest matching white reference measurement differed in PAR by \(>\)10 \(\upmu\)mol photons\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\), were discarded.  
NDVI, PRI and SIF at 687 (red) and 760 nm (far-red; FR) were retrieved from screened radiance data. The NDVI and PRI were calculated according to Rouse Jr. [32] and Gamon [15] respectively, using a 5 nm wavelength averaging. Since changes in SIF\({}_{\text{red}}\) and SIF\({}_{\text{FR}}\) were found to influence NDVI values, with a high SIF corresponding to increases in NDVI, the red reference wavelength was shifted to 647 nm and the FR reference wavelength was taken at 868 nm to minimise this influence. Retrieval of SIF was performed using the Fraunhofer Line Discriminator (FLD) [33] and the 3 FLD [34] methodologies within both the O\({}_{2}\)-A and O\({}_{2}\)-B absorption spectral regions. Under low levels of leaf irradiance (\(<\)150 \(\upmu\)mol photons\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)), the 3 FLD SIF retrieval occasionally resulted in negative estimates of SIF. As such, all negative SIF retrievals were reclassified to 0 mW\(\cdot\)m\({}^{-1}\cdot\)sr\({}^{-1}\cdot\)nm\({}^{-1}\). Solar induced fluorescence yields (YSIF) for both the red and FR retrievals were calculated as the SIF value normalized to the absorbed PAR:  
\[\text{YSIF}_{\lambda}=\frac{\text{SIF}_{\lambda}}{\text{APAR}} \tag{9}\]  
where the APAR was calculated as the incident light intensity at the leaf surface multiplied by the absorbance of the leaf (400 to 700 nm).
# Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image
## 3 Materials and Methods
### Data Processing and Method  
For the LiDAR waveform data, we propose a multi-Gaussian function fitting method to extract water depth. The bathymetric values were converted into mean lower low water (MLLW) data to unify the standards of measurement. A series of preprocessing steps for the QuickBird image, such as radiation calibration and atmospheric correction, were needed. The improved logarithmic conversion ratio model (ILCRM) was used for the active and passive combination of water depth inversion in this study.
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## 5 Conclusions  
In this study, we comprehensively evaluated the ecosystem carbon balance of tropical peatlands by using satellite-based data from 2002 to 2018 combined with in-situ observations in Indonesia. First, the \(GWT\) was estimated by using a typical drought index (KBDI). The implemented spatial distributions of the \(GWT\) were able to generate the spatiotemporal dynamics of \(R_{e}\). A MODIS-based FE dataset was used as a source of anthropogenic fire emissions. For the data for CO\({}_{2}\) uptake, a product of the MODIS gross primary production (GPP) was applied to calculate the NBE after modification with flux tower-based GPPs in Palangkaraya by using a robust regression method. We compared three types of flux tower observations for modeling \(R_{e}\), i.e., undrained forest (\(UF\)), drained forest (\(DF\)), and burned forest (\(BF\)). The highest accuracy among the three equations was found in the \(UF\) site. Both \(DF\) and \(BF\) showed a lower respiration than that of \(UF\) because the forests were already degraded and productivities had decreased. As such, we used a calibrated model that used \(UF\) to estimate the NBE time series. As a result of maps of various factors, \(R_{e}\) and the GPP showed even distributions across the whole study area, although FE showed a heterogeneous distribution because the fires are episodic. The NBE was always greater than zero, meaning peatlands are a net emitter of carbon to the atmosphere. The average NBE was 353.51 Mt C/year from 2002 to 2018 when the peatland ecosystems were considered intact (without any disturbances). To investigate the impact of climatic changes, the Nino.3 index was compared with the NBE. El Nino periods, during which Nino.3 was greater than zero, might have accelerated \(R_{e}\) and FE, which could have increased the NBE from tropical peatland ecosystems. The remote sensing-based diagnostic simulation of the C budget in this study provides spatiotemporal distributions of the net carbon balance, including natural emissions, anthropogenic-induced emissions, and natural absorptions in detail. The findings are expected to contribute to the creation of a feasible and robust assessment system for peatland ecosystems in terms of the daily carbon cycle by using satellite-based datasets.  
Conceptualization, W.T., and H.P.; methodology, W.T.; software, W.T.; validation, H.P.; formal analysis, H.P.; investigation, H.P.; resources, K.I. and W.T.; data curation, K.I. and W.T.; writing--original draft preparation, H.P.; writing--review and editing, W.T., and K.I.; visualization, H.P.; supervision, W.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We deeply appreciated to [PERSON] of Hokkaido University and his group associated with AsiaFlux. Groundwater observation and CO\({}_{2}\) flux observation data of Palangkaraya, Kalimantan, in Indonesia were provided by [PERSON].  
The authors declare no conflict of interest.
## Materials and Methods
### Mathematical Modeling and Description  
In addressing the path optimization problem in urban transport networks, this paper aims to minimize the total travel time and cost from Beijing Union University to Beijing South Railway Station. To achieve this goal, a multi-objective optimization mathematical model is constructed, integrating the combined paths of the subway and taxi. The model is  
Figure 1: Modes of transportation.  
solved by using a genetic algorithm to perform a weighted summation of the distances and costs of different paths.  
1. Distance Calculation Calculate the total distance between the stations in the path: \[D=\sum_{i=0}^{N-1}\sum_{j=1}^{N}d_{ij}\cdot x_{ij}\] (1)
2. Time Calculation To calculate the total travel time from Beijing Union University to Beijing South Railway Station, the formula is as follows: \[T=\sum_{i=0}^{N-1}\sum_{j=1}^{N}t_{ij}\cdot x_{ij}\] (2) The specific time calculations include the following cases:  
* Special cases: The distance between stations 12-13 and 16-17 has a fixed time of 4.5 min (transfer time).
* Neighboring stations: If you take the subway from the first station, it is a 4.5 min walk from the starting point (Beijing Union University) to the subway station. Subsequently, if the difference between the two stations is 1 (taking the subway), the first time is increased by 4.5 min (including waiting time), and thereafter it is increased by 2 min each time (average running time between two neighboring subway stations).
* Other cases: Time calculation based on distance. \[t_{ij}=\frac{d_{ij}}{v}\cdot 60\ (v\ \text{is the speed at which the taxi is traveling}.)\] (3)
3. Cost Calculation The specific cost calculation formula is as follows:
* For subway fare, \[c_{1}=\sum_{i=0}^{N-1}\sum_{j=1}^{N}c_{ij}\cdot x_{ij}\] (4)
* For taxi fare, \[c_{2}=\begin{cases}13+1,&if\ d_{ij}\leq 3\\ 13+1+\left(d_{ij}-3\right)\times 2.3,&if\ d_{ij}>3\end{cases}\] (5) Note: A CNY \(1\) fuel charge is added to each taxi fare.
4. Constraints
* Starting point constraint: The path from the starting point is chosen once. \[\sum_{j=1}^{N}x_{Sj}=1\] (6)
* End point constraint: The end point can only be reached once. \[\sum_{i=1}^{N}x_{iP}=1\] (7)
* Path uniqueness constraint: Each intermediate site can only be accessed once. \[\sum_{i=1}^{N}x_{ik}=\sum_{j=1}^{N}x_{kj},\forall k\
eq s,t\] (8)
* Non-negative constraint: The path selection variable is non-negative. \[x_{ij}\in\{0,1\},\forall i,j\] (9)* Distance constraint: Ensure that the selected path is valid. \[d_{ij}>0,\forall i,j\] (10)
* Cost constraint: Ensure that the selected path is valid. \[c_{ij}>0,\forall i,j\] (11)
* Parameter definitions \(N\): Total number of subway stations; \(S\): Starting point (BUU); \(P\): End point (Beijing South Railway Station); \(d_{ij}\): Distance from station \(i\) to station \(j\) with \(i<j\); \(t_{ij}\): Time from site \(i\) to site \(j\) with \(i<j\); \(c_{ij}\): Cost from site \(i\) to site \(j\) with \(i<j\); \(x_{ij}\): Decision variable indicating whether the path from site \(i\) to site \(j\) is selected. It is 1 if selected and 0 otherwise.
## 4 Results  
### Review of Existing Agricultural and NRM Software/Data Repositories to Inform EASY Concept Design  
Given the need to build on strengths of existing systems, workshop participants were asked to discuss existing software or data repositories that were used to provide information for groups within or outside their organisation. A 'SWOT' (Strengths, Weaknesses, Opportunities, Threats) analysis was then applied to these existing approaches as a way to inform EASY concept design.  
In all, workshop participants were presented with a list of 17 different existing systems available for NRM and agricultural applications. The strengths of these systems included that many were available at no charge via the Web, had high value as extension tools, were built on existing knowledge and assisted in communication among landholders, extension staff and researchers. Perceived weaknesses of the current systems included limited data coverage, little or no opportunity for two-way information sharing--_i.e._, farmer feedback, inclusion of recent scientific results, varying data quality, lack of integration of data--and that landholders needed greater support to interpret the information on these systems. Workshop participants felt there were many opportunities to develop a system that provides better linkage among a number of existing complementary web-based systems, caters to a wider range of end-users, improves data sharing with better environmental metrics, and provides for two-way information exchange.  
A selection of 11 of these existing systems is presented in Table 1. In general, softwares designed to support mapping applications and spatial data display (e.g., ArcInfo(tm) and GoogleEarth(tm)) have strengths in data visualisation and database management, and support a wide range of users. However, the generic mapping capability targeted by such software means that the resulting softwares do not have embedded strengths for farm and landscape management. Those that have a primary focus of farm and landscape management (e.g., ifarm(tm), Back Paddock(tm)) provide strong support for specific farm and landscape management activities. However, their farm application specificity can limit their adaptability and capacity to meet the needs of a wide range of users--a limitation that the EASY project specifically wanted to overcome. Data repositories--for example, Victorian Resources Online [22] and the Western Australian Shared Land Information Portal--provide access to critical data, but do not provide the capability to link datasets in ways that may improve their value for farm and landscape management.
# Untargeted Urinary Volatilomics Reveals Hexadecanal as a Potential Biomarker for Preeclampsia
## 4 Materials and Methods
### GS-MS Analysis  
Following sample preparation, GC-MS was employed to identify volatile compounds isolated from the urine samples. The samples were analyzed using an Agilent 8890 GC system coupled with a 7000D triple quadrupole mass spectrometer (GC/TQ, Agilent Technologies, Santa Clara, CA, USA). The separation of volatile compounds was performed using an HP-5 MS capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent Technologies, Agilent Technologies, Santa Clara, CA, USA). The SPME fiber was inserted into the GC inlet for desorption at 220 \({}^{\circ}\)C for 5 min. The GC oven temperature program was set to an initial temperature of 40 \({}^{\circ}\)C, held for 2 min, and then increased at a rate of 5 \({}^{\circ}\)C/min to a final temperature of 220 \({}^{\circ}\)C, which was maintained for 4 min, resulting in a total run time of 42 min.  
A solvent delay of 4 min was applied to prevent solvent peaks from entering the mass spectrometer. The mass spectrometer operated in positive electron ionization mode (EI+), scanning for a mass range of 10-300 \(m\)/z. The scan time was set to 0.1 s, with a resolution of 1000 at full-width half maximum (FWHM). Helium was used as the carrier gas at a flow rate of 1 mL/min.
## 3 Gangue-Side Lateral Pressure-Control Technology
### Design of the Numerical Model for the Cutting Roof Scheme
#### 3.3.3 The Calculation of the Cutting Angle  
According to the specific engineering geological conditions of the 15,150 working face, the deformation characteristics of the gob-side entry retaining surrounding rock were simulated for top-cutting angles of 0\({}^{\circ}\), 10\({}^{\circ}\), 20\({}^{\circ}\), 30\({}^{\circ}\), 40\({}^{\circ}\), and 45\({}^{\circ}\). The changes in the elastic-plastic zone were observed, with the results shown in Figure 21.  
Figure 21: Lateral load distribution of roadway support under different cutting angles: (**a**) horizontal load curve of roadside support under different roof cutting angles; (**b**) the change of the elastic–plastic region inside the roadside support body under different roof cutting angles; (**c**) horizontal load of roadside support under different roof cutting angles.  
The lateral load distribution on the roadway-side support at different top-cutting angles is shown in Figure 21. As the top-cutting angle increases from 0\({}^{\circ}\) to 40\({}^{\circ}\), the lateral load on the roadway-side support is primarily concentrated at the lower corner near the gob side, with the load at the middle of the support being smaller. The values are 0.46 MPa, 0.53 MPa, 0.49 MPa, 0.37 MPa, 0.39 MPa, and 0.32 MPa, respectively. The load at the top corner is slightly higher. As the cutting angle increases, the deformation of the support side corner, influenced by the torque from the roof rotation, initially decreases and then increases, with the peak horizontal stress within the support gradually decreasing. When the cutting angle reaches 20\({}^{\circ}\), the plastic failure zone of the roadway-side support transitions from primarily compressive failure to shear failure. The support's internal integrity remains with a relatively complete elastic region. However, when the cutting angle reaches 40\({}^{\circ}\), the support is completely destroyed, and its bearing capacity is significantly reduced. Therefore, the top-cutting angle should not exceed 20\({}^{\circ}\).  
Considering the on-site conditions of the gob-side entry retaining and the use of drilling equipment, if the top-cutting angle is too small, the operational workload will increase. After comprehensive consideration, a cutting angle of 20\({}^{\circ}\) is recommended.