esa-sceva/satcom-chunk-collection huggingface dataset:
# Herbicide Bioassay Using a Multi-Well Plate and Plant Spectral Image Analysis
## 2 Materials and Methods
### Spectral Image Acquisition and Analysis
#### 2.3.1 RGB Images  
RGB images of the crabgrass plants growing in multi-well plates were acquired using a CMOS camera (EOS-600D, Canon, Tokyo, Japan) at 3, 6, 24, 48, 72, and 120 h after herbicide treatment (HAT). The acquired RGB images were then analyzed after white balancing and transforming the RGB image into the Lab color space to segment the crabgrass from the image. Otsu thresholding was then applied in an a* channel in Lab color space to eliminate the background (Figure 1). To measure the visual responses and level of greenness in plants, the modified Normalized Difference Index (mNDI) and Excess Green Index (ExG) were calculated by normalizing the red (R), green (G), and blue (B) values of the crabgrass using the following formulae:  
\[\mathrm{r}=\frac{\mathrm{R}}{R+G+B},\;\mathrm{g}=\frac{\mathrm{G}}{R+G+B},\; \mathrm{b}=\frac{\mathrm{B}}{R+G+B},\;\mathrm{NDI}=\frac{g-r}{g+r} \tag{1}\]  
\[\mathrm{mNDI}=\frac{NDI_{h}}{NDI_{0}}\times 100 \tag{2}\]  
\[\mathrm{ExG}=2\mathrm{g-r-b} \tag{3}\]  
where \(\mathrm{NDI}_{h}\) and \(\mathrm{NDI}_{0}\) indicate the NDI values of the weed plants treated with herbicides and the untreated control, respectively.
## 2 Design and Analysis of DL-LET  
### Design of DL-LET  
The schematic diagram of a DL-LET joint is shown in Figure 1. H18 aluminum foil has the advantages of large Young's modulus and high strength while maintaining good toughness. Beryllium bronze has obvious superiorities in terms of strength, mechanical property, and fatigue resistance. Therefore, the two materials are selected as layers in the DL-LET joint, respectively. Using strong welding adhesive, the LET joint of beryllium bronze is bonded to the H18 aluminum foil, which is consistent with the overall dimensions of the joint.  
The dimension of the DL-LET joint is shown in Figure 2. In the beryllium bronze layer, the length of the torsion segment is \(l_{\rm t1}\), and the width is \(w_{\rm t1}\); the length of bending segment 1 is \(l_{\rm b1}\), and the width is \(w_{\rm b1}\). In the H18 aluminum foil, the length of bending segment 2 is \(l_{\rm b2}\), and the width \(w_{\rm b2}\) is the same as \(l_{\rm b1}\). The thicknesses of the beryllium bronze layer and the H18 aluminum foil layer are \(t_{1}\) and \(t_{2}\), respectively.
## 5 Discussion  
### Effect of Pore Water Salinity on Clay Compressibility  
The influence of pore water salinity on the compressibility of marine clay exhibits significant variability depending on the dominant clay mineralogy. Previous studies ([PERSON], 2018 [27]; [PERSON], 2021 [12]; [PERSON], 2016 [28]; [PERSON], 2015 [29]; [PERSON], 2022 [6]; [PERSON], 2021 [21]) have reported contrasting behaviors, which can be attributed to differences in mineral composition. Montmorillonite-rich clays demonstrate high sensitivity to salinity, where increasing NaCl concentration compresses the double layer, enhances particle reorientation, and reduces both the compression index (\(C_{c}\)) and rebound index (\(C_{r}\)) ([PERSON], 2018 [27]; [PERSON], 2021 [12]; [PERSON], 2016 [28]). Similarly, illite-dominated clays exhibit decreased compressibility and swelling indices under elevated salinity, while their oedometer modulus and consolidation coefficient increase ([PERSON], 2015 [29]). In contrast, kaolinite clays, due to their stable lattice structure, show negligible changes in \(C_{c}\) and \(C_{r}\) with varying salinity ([PERSON], 2018 [27]). [PERSON] (2022) [6] further observed a nonlinear (quadratic polynomial) relationship between salinity and compression index in multi-mineral mudstone, suggesting a threshold effect governed by chemical-mechanical interactions.  
Interestingly, while most studies indicate that higher salinity reduces compressibility--particularly in active clays like montmorillonite--[PERSON] (2021) [21] observed a different trend in low-activity clays (illite and kaolinite), where a peak salinity concentration induced maximum compression. This finding aligns with the present study, highlighting that the role of salinity is not universally compressing but rather mineral-dependent.  
The discrepancies among existing studies underscore the necessity of microstructural analysis (e.g., SEM) to clarify the mechanisms by which pore water salinity influences clay behavior. Additional investigations into the relationships between mineralogical composition, pore fluid chemistry, and mechanical behavior would help establish a more complete understanding of salinity's role in marine clay compressibility.
## 3 Results and Discussion
[PERSON] and co-workers [66] reported that the presence of both Zn\({}_{\text{i}}\) and V\({}_{0}\) in ZnO is beneficial for achieving good transportation and separation of photo-excited charge carriers, thus causing the improvement of the ZnO photocatalytic performance. Hence, our PL and EPR results suggest that the as-prepared samples could be promising candidates for efficient photocatalysts. In addition to the presence of the defects in ZnO, the degree of crystallinity favorably influences the rate of the photocatalytic process. There is great disagreement between studies concerning the role of this structure parameter in the dye photodegradation. The multiple data have revealed that highly crystalline materials are efficient photocatalysts [67], but some researchers have reported that the amorphous or poorly crystallized structure could promote the photocatalytic properties [68; 69]. In our case, the best photocatalyst G2 (it exhibited the highest photocatalytic discoloration rate) had a two-phase crystalline structure: poorly crystallized wurtzite and almost amorphous hydrozinc. [PERSON] et al. [70] also revealed that the combination of crystalline and amorphous phases in ZnO/TiO\({}_{2}\) ensured a larger number of active sites, originating from the lattice defects. In addition, the presence of two or more crystalline phases also favors the photocatalytic performance due to the presence of numerous interphase defects. A similar effect was observed for nanoparticles, consisting of mixed TiO\({}_{2}\) phases [71; 72; 73; 74]. The morphological (particle shape and size) and textural (specific surface area, pore sizes) features are also responsible for the better electron-hole diffusion, mass transportation, and improved activity of the plant-mediated ZnO powders. The use of the plant extract leads to a decrease in the crystallite size (proved by the XRD), which is responsible for the higher specific area. Other researchers have also proved that the enhanced photocatalytic ability of ZnO NPs, prepared via _Hiliscus sabdariffa_ extract, is favored by the smaller crystallite size [75]. The plant extract yields plate-like morphology and well-defined mesoporous structure with the greatest share of mesopores in comparison with the reference sample. The larger number of mesopores ensures more active sites and facilitates the diffusion of large reactant molecules [74]. Microscopic observations have confirmed that application of **MA** leads to more effective particle separation, probably due to the presence of long-chain phytochemicals [76]. The results obtained in the present study have proved the positive role of **MA** extract as a reducing/ capping agent in the control over all of the above-mentioned factors, responsible for the increased photocatalytic efficiency of plant-mediated ZnO powders. We hope that the newly obtained zinc-containing composite material, possessing a bi-phase structure, can find applications not only in the field of photocatalytic processes, but also in some other fields of solid-state materials chemistry. Figure 2:4 _Cont._
## 4 Experiment and Performance Evaluation of the New Screen Sand Production
### Experimental Results and Analysis  
According to the test results, when the inlet end pressure is 0.3-1 MPa, both Class A and Class B screens have relatively small flow resistances. Moreover, under the condition that the production pressure difference is lower than 1 MPa, Class A and Class B screens both have a certain amount of water production. Combined with the conversion and analysis of test completion strings and surface testing equipment, both of the two types of screens can maintain a certain rate and amount of gas production.  
The experimental results of Class A screens are shown in Figure 5:  
1. For the two 50-60 mesh screens, the total water output is about 80-100 L, with an amount of sand and water in the production. This is mainly because the liquid has a better sand-carrying performance than the gas.
2. The 60-70 mesh screen has a good overflow performance, with less sand in the process of production. The inlet pressure of the three 70-80 mesh screens shows an increasing trend and the water output is less, indicating that the three screens have larger flow resistance and poor flow performance. Meanwhile, the sand output is very little, mainly because the formation sand migration capacity of the argillaceous fine sand reservoirs is reduced under the action of the screen with poor flow performance. According to the comprehensive analysis of three 70-80 mesh screens, mainly according to the indicators of sand production, water output and overflow performance, the Class A screen 2 (60-70 mesh) not only has a good sand control accuracy (60-70 mesh), but also has a good overflow performance. It can be used in the process of sand control and trial production testing requirements of argillaceous fine silt reservoirs.  
Figure 4: Schematic diagram of experimental equipment.  
Figure 5: Performance evaluation experiments of 5 different screens in Class A types.  
The experimental results of Class B screens are shown in Figure 6:  
1. The 30-50 mesh screen has a good overflow performance with a relatively large amount of sand output; the 40-70 mesh screen also has a good flow performance with a relatively small amount of sand output.
2. Comprehensive analysis shows that the inlet pressure of the Class B 40-70 mesh screen is larger, the sand production is smaller, and it has a good sand control performance. The total water output can reach 300 L, which can meet the requirements of sand control as well as trial testing requirements of argillaceous fine silt gas hydrate reservoirs.
## 2 Materials and Methods
### Cost Optimisation Methodology
#### 2.1.3 Calculation of Global Costs of Sets of Measures  
The calculation of global costs of sets of measures is made for a period of 30 years, as established by the Delegated Regulation 244/2012/EU for public buildings.  
The global costs include those related to the intervention itself, building consumption and maintenance during the calculation period. Following the regulation, for cost calculation of each set of measures, the initial investment is considered (C\({}_{\text{I}}\)), as well as replacement costs, disposal costs, annual energy costs and annual rise in energy price, the annual maintenance cost of measures, as well as the residual value of elements added.  
The Equation (1) used for global cost calculation is:  
\[\text{C}_{\text{g}}\left(\tau\right)=\text{C}_{\text{I}}+\sum\text{j}\left[ \sum\
olimits_{\text{i=1}\left(\tau\right)}\left(\text{C}_{\text{a,i}}\text{ (j)}\times\text{Rd (i)}\right)-\text{V}_{\ell,\tau}\text{ (j)}\right] \tag{1}\]  
In which: \(\tau\) indicates the calculation period; \(\text{C}_{\text{g}}\left(\tau\right)\) indicates global cost (referred to starting year \(\tau_{0}\)) over the calculation period; CI indicates initial investment costs for implementing measure or set of measures j; \(\text{C}_{\text{a,i}}\text{ (j)}\) indicates the cost per year, i for measure or set of measures, j; \(\text{V}_{\ell,\tau}\text{ (j)}\) indicates residual value of measure or set of measures j at the end of the calculation period (discounted of the starting year \(\tau_{0}\)); and Rd (i) indicates discount factor for year i.  
Consequently, the optimal cost of measures or sets of measures would be that with the lowest energy consumption at the lowest global cost per m\({}^{2}\) and year. As an example, to facilitate understanding and subsequent interpretation of global cost graphs, a graph is included (Figure 3). It shows the resulting cost-optimal curve (orange line), the initial state of the building (L0), the global cost in the initial state (red line) and the optimal-cost sets of measures (green line).  
The sets of measures with global cost above the optimal cost in the initial state (L0) have a period to recover initial investment longer than the calculation period itself, in this case over 30 years.
## 3 Materials and Methods
### Determination of Volatile Compounds  
The volatile compounds were determined by solid phase microextraction-gas chromatography (SPME-GC) including a preconcentration step carried out on a multipurpose sample autosampler (Gerstel, Mulheim an der Ruhr, Germany) in which temperature and time of the process were automatically controlled by Gerstel Maestro (v1.4) software (Gerstel GmbH et Co.KG, Mulheim an der Ruhr, Germany).  
The oil sample (2 g) was placed in a 20 mL glass vial, tightly capped with polytetrafluoroethylene (PTFE) septum and left for 10 min at 40 \({}^{\circ}\)C to allow for the equilibration of the volatiles in the headspace. After the equilibration time, the septum covering each vial was pierced with a SPME needle and the fiber was exposed to the headspace for 40 min. A 1-cm StableFlexic divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) composite SPME fiber (50/30 \(\upmu\)m film thickness) was used (Supelco, Bellefonte, PA, USA). The fiber was previously conditioned following the instructions of the supplier.  
The volatiles adsorbed by the fiber were thermally desorbed in the hot injection port of a 7820A gas chromatograph (Agilent Technologies, Madrid, Spain) with a flame ionization detector for 5 min at 260 \({}^{\circ}\)C, with the purge valve off (splitless mode). An Agilent J&W GC DB-WAX capillary column (60 m \(\times\) 0.25 mm internal diameter, 0.25 \(\upmu\)m coating) (Agilent Technologies, Santa Clara, CA, USA) was used. The carrier gas was hydrogen, at a flow rate of 1.5 mL/min. The oven temperature was held at 40 \({}^{\circ}\)C for 10 min and then programmed to rise 3 \({}^{\circ}\)C/min to a final temperature of 200 \({}^{\circ}\)C.  
The identification of the volatiles was carried out with standards [29]. Additionally, the identification was checked by analyzing the samples with mass spectrometry (GC7820-MSD5975, Agilent Technology, Santa Clara, CA, USA) following the strategy described in previous works [38; 50]. Thus, the information from mass spectra, and their comparison with standards in the FID chromatograms, and linear retention indexes (LRIs) were considered for a full identification. The quantification of volatiles was carried out by using 4-methyl-2-pentanol as internal standard and correcting the concentrations by the relative response factors. These factors were calculated following the procedure described by [PERSON] et al. [51] using two ranges of concentration, 0.05-5.0 and 0.5-30 mg/kg.  
The odor activity values (OAVs) [52; 53] of the volatile compounds were calculated in all the monthly collected samples, in order to study the sensory influence of each compound on the total aroma of VOO and its evolution during the storage.
## 1 Introduction
Two approaches determined the spatial representativeness: one is the fraction of target vegetation type (FTVT), which was used to detect to what degree the flux measurement is related to the target vegetation. The other is the spatial extent to which the EC measurements can be upscaled. Our objectives are to (1) characterize the temporal variation of EC source areas, (2) examine the spatial heterogeneity in different target areas around the EC flux site, and (3) evaluate seasonal changes in spatial representativeness of the EC measurements. This work will be helpful in understanding the uncertainties in the carbon and water flux models caused by the changes in source area on a heterogeneous surface.
# Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil
## 2 Materials and Methods
### Analysis
#### 2.6.3 SfM-MVS Products  
Measures of benthic surface complexity computed from SfM-MVS models are robust and repeatable metrics. Aquatic biodiversity has been shown to be strongly related to habitat structural complexity (e.g., [69; 70]). Among the various ways in which complexity is defined, here we refer to it as the heterogeneity in the arrangement of substrate types [71]. As examples of complexity metrics that can be readily determined from SfM-MVS products, we computed two spatial properties of the Ilha do Lair DSM, vector ruggedness and the 3D Mindowski-Bouligand fractal dimension.  
Figure 8: Illustration of the SfM-MVS workflow carried out in Pix4D Mapper. The inputs to the workflow (red) were the UAS photographs. The products of interest for this study (blue) were the dense point cloud, DSM and orthomosaic.  
The vector ruggedness was calculated at window sizes of 20 and 80 cm for a subset of the DSM with the Benthic Terrain Modeler toolbox in ArcMap [72]. This measure quantifies ruggedness as the variation in the 3D orientation of the surface incorporating both slope and aspect heterogeneity [73]. Unlike other measures that are correlated with slope (e.g., terrain ruggedness index), this metric quantifies local variations in the terrain independently.  
For the same subset area of the Ilha do Lair data, the dense point cloud was converted to a triangular mesh in CloudCompare Stereo v.2.11.3 ([[https://www.danielgm.net/cc/](https://www.danielgm.net/cc/)]([https://www.danielgm.net/cc/](https://www.danielgm.net/cc/)), accessed on 29 May 2021) at the original GSD and scales of 20 and 80 cm. These meshes were used to compute the 3D Mindowski-Bouligand fractal dimension of the substrate [74,75]. The value of the fractal dimension, D, varies from 0-3 where higher values indicate a more complex surface.  
With ecological knowledge of the fish fauna, products quantifying the three dimensional characteristics of the substrate (e.g., elevation, slope, vector ruggedness, etc.), can further be used to classify the habitat according to the preferences of different fish or plant species. For example, rheophile cichlid and _Loricariae_ species require caves of varied sizes for breeding, and slope and surface texture influence the biocover and microinvertebrate fauna that many rheophile fishes feed on [29].
## 5 Key 6G Technologies
### Other Perspectives
#### 5.3.6 Free-Space Optics Fronthaul/Backhaul Network  
In places where optical fiber connectivity is impractical, like remote areas, free-space optics (FSO) networks emerge as a possible 6G solution for communication systems [219; 220; 221; 222]. Offering characteristics similar to optical fiber networks, FSO ensures robust data transfer, making it a fantastic option for 6G fronthaul and backhaul connectivity. With the ability to support long-range communications over 10,000 km, FSO plays a critical role, particularly in 6G, where higher fronthaul/backhaul capacity and increased remote connectivity are vital [223].
## 3 Wireless Communication Technologies  
In the previous section, we focused on different networking architectures between UAVs and GS. According to the architectures presented in Figure 2, many wireless communication technologies are potential choices to provide reliable and flexible communication links for the fast deployment of FANET. A list of likely candidates that can be selected for the different links of UAV-to-UAV (U2U) and UAV-to-Infrastructure (U2I) communications is tabulated in Table 1. The table shows their main characteristics such as mobility, data rates, communication range, latency, and network topology. The choice of suitable technology depends on the nature of the application and the type of mission. However, demand for the control traffic of FANET is low and can be supported by all wireless technologies. Wireless technologies can be divided into two main categories: short-range and long-range communication technologies. Short-range communication technologies, such as Wi-Fi, ZigBee, and Bluetooth, are used for short distance communication, while the long-range communication technologies, such as cellular, WiMAX, and satellite, can be employed for larger areas. Both of the technologies are being further explored in the following paragraphs.
# Patient-Tailored Dementia Diagnosis with CNN-Based Brain MRI Classification
### Research Aims  
The primary objectives of this study were as follows:  
* To demonstrate how CNN-based models can aid clinical decision-making for dementia patients;
* To utilize dementia-specific neuroimaging biomarkers to detect brain atrophy at the earliest possible stage and monitor disease progression from MCI to AD;
* To support the use of the DL tools for the development of personalized dementia care plans.  
The innovations of the proposed approach include the following:  
1. A lightweight 2D-CNN architecture That employs an optimized, low-complexity 2D-CNN model to balance diagnostic accuracy with reduced computational cost and memory usage;
2. A single-slice classification strategy that implements a slice-wise classification approach using a single representative MRI slice per subject, minimizing data redundancy and model overfitting;
3. An efficient training process that reduces training time significantly compared with volumetric 3D models, facilitating rapid experimentation and model adjustment;
4. Robust performance on limited data, achieving high diagnostic accuracy despite limited labeled data, demonstrating strong generalization capabilities;
5. Potential for real-time use, whereby the low computational burden and fast inference time make the model suitable for integration into real-time clinical research frameworks.