esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
## 3 Main Reflectance Indices
### Water Indices  
Water is an important factor limiting plant growth and development. Particularly, the estimation of the water content in crops is necessary for controlled irrigation and the mitigation of drought. The several water light absorption bands were shown in the range from 800 to 2500 nm for single leaves [113]. It should be noted that a spectral range more than 1000 nm can be difficult for the reflectance measurements because more expensive and complicated cameras with the InGaAs matrices are necessary [46].  
It is important that the light absorption by water in a leaf is low, at about 750 to 900 nm, and high, at 1240, 1450, and 1940 nm [45]. Combinations of these wavelengths seem to be promising for the estimation of the water content and can be used for the development of reflectance indices [45]. The main reflectance indices that are sensitive to the water content in plants are summarized in Part 2 of Table 1.  
The normalized difference water index (NDWI) [75] based on reflectance at 860 and 1240 nm shows high sensitivity to changes in the moisture content in the vegetation cover and stability to atmospheric influences [75; 114; 115]. There are a number of other reflectance indices that are sensitive to the water content, including the moisture stress index (MSI, [78]), normalized difference infrared index (NDII, [77]), and normalized multi-band drought index (NMDI, [76]), which are calculated on the basis of wavelengths at slopes near local spectral maximums of the light absorption by water in plants. These reflectance indices can be used not only for the direct remote sensing of the water content in plants but can show processes related to this content; e.g., the NMDI can be effective for the prediction of fires [116].  
Reflectance at 950-970 nm, which is related to the NIR spectral band of the light absorption by water, can also be used for the water content estimation; the light absorption in this spectral region is weaker than this absorption in the range of SWIR [10] [PERSON] et al. [79] proposed the water index (WI) based on the ratio of reflectance at 900 to reflectance at 970 nm; the wavelengths are the local minimum and maximum of light absorption by water, respectively [79; 117].  
Additionally, broadband indices can also be sensitive to the water content; particularly, changes in the normalized reflectance indices based on 500-600 and 700-800 nm spectral ranges and on 600-700 and 700-800 nm spectral ranges (RI(500-600, 700-800) and RI(600-700, 700-800), respectively) are strongly correlated with water changes after the propagation of electrical signals and under drought conditions [80].  
Thus, water indices are promising for agricultural and ecological remote sensing. The monitoring of the water in vegetation and soil can be used for the estimation of the water availability and detection of water restrictions for the increase in the plant biomass.
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 4 Engineering Background and Numerical Simulation Model  
### Project Profile  
The Changsha International Financial Center, located in Hunan Province, China, stands as the tallest building in the region, reaching a maximum height of 452 m. The foundation pit associated with this structure has an excavation depth of 31.80 m and covers a substantial area of 76,700 square meters. With its considerable depth and size, the foundation pit can be classified as deep and large. The foundation pit exhibits a predominantly rectangular shape, with the following approximate lengths for each side: east (168 m), south (488 m), west (136 m), and north (546 m). Please refer to Figure 4 for a visual representation.  
The challenging aspect of this project is that the foundation pit is situated within a densely populated urban area, where existing buildings, roads, and underground pipes are in close proximity. The strata within the foundation pit are characterized by their unstable nature, and any instability in the retaining structure can potentially result in  
Figure 4: Schematic diagram of foundation pit.  
severe disasters. The specific parameters of the strata materials are outlined in Table 1, which have been compiled based on the field investigation report.  
Based on the design scheme, a cast-in-place pile and anchor cable retaining structure was adopted for the west side of the foundation pit. The piles are 40 m long, made of C30 grade concrete, with a diameter of 1.3 m and a depth of 7 m embedded in the soil. The pile spacing is 2.4 m. There are a total of 11 anchor cables arranged on the piles, with the first to sixth anchor cables spaced 2 m apart, and the seventh to eleventh anchor cables spaced 3 m apart. The anchor cables are 36 m long and have an included angle of 15\({}^{\circ}\) with the horizontal direction. The crown beam is 1.4 \(\times\) 1.0 m in size, and the waist beam is 0.1 \(\times\) 0.1 m, both made of C30 grade concrete. The design of the retaining structure and the dimensions of each structure are presented in Figure 5.
# Adaptive Parameters for LoRa-Based Networks Physical-Layer
## 3 Proposal
### Network and System Model
This can be calculated by \(E_{b}/N_{0}\) and is represented by a complementary error function (called function \(Q\) or function \(erfc\)) as shown on Equation (5). Finally, Equation (6) correlates \(E_{b}/N_{0}\) with the SNR of a signal. \[R_{b}=SF\times\frac{1}{\frac{2^{SF}}{BW}} \tag{4}\]  
\[BER=Q\bigg{(}\frac{\log_{12}(SF)}{\sqrt{2}}\times\frac{E_{b}}{N_{0}}\bigg{)} \tag{5}\]  
\[\frac{E_{b}}{N_{0}}[dB]=SNR[dB]+10\log_{10}\bigg{(}\frac{BW}{R_{b}}\bigg{)} \tag{6}\]  
Equation (7) is obtained by substituting Equation (4) on Equation (6), which correlates the BER of a LoRa communication on an AWGN channel with the Spreading Factor and the SNR measured from the signal. It is important to highlight that although the equation does not depend on the channel's signal bandwidth, it depends on the SNR value and the SF. \[BER=Q\bigg{\{}\frac{\log_{12}(SF)}{\sqrt{2}}\cdot\bigg{[}SNR[dB]+10\log_{10} \bigg{(}\frac{2^{SF}}{SF}\bigg{)}\bigg{]}\bigg{\}} \tag{7}\]  
Finally, Figure 3 illustrates the relationships between BER and SNR for a LoRa modulation on an AWGN channel considering different SF values. We can observe that a higher SNR of a signal provides a lower BER. Additionally, considering the same SNR value, higher SF values performed better on communication.
## 3 Results and Discussion
### Numerical Results
#### 3.2.4 Comparison of Numerical and Analytical Results  
Figure 16 provides a comparison of the resulting strain distributions from the numerical models, considering the basis parameter set acc. to Table 2, with the analytical solution. It is observed that, for the numerical models with an interface friction angle of \(\varphi=0^{\circ}\), an almost perfect fit for the resulting radial (\(\varepsilon_{r}\)) and vertical (\(\varepsilon_{z}\)) strain distributions with the analytical solution is achieved. This clearly demonstrates that the chosen approach for inducing the required axial stress level of 10 MPa within the simulations is sufficient. Also, the selected procedure for the determination of the axial and vertical strains from the numerical models seem to provide reliable results.  
Figure 16: Comparison of the numerical and analytical results for the radial and vertical strains, \(\varepsilon_{r}\) and \(\varepsilon_{z}\), evaluated at specific cross sections H# for various isotropy plane inclinations \(\beta\) and interface friction angles \(\varphi\).  
The boundary effects caused by friction between the sample and platens have a negligible effect on the resulting strain distributions at the central cross section H50 for the models with \(\varphi=15^{\circ}\) and isotropy plane inclinations \(\beta\) of \(0^{\circ}\) and \(90^{\circ}\). From the resulting radial strains \(\varepsilon_{r}\) of the model with \(\beta=45^{\circ}\) and \(\varphi=15^{\circ}\), it is evident that the distributions at cross sections H25 and H75, closer to the boundaries, take on asymmetric shapes. Values from the curves associated with these two cross sections show the biggest divergence from the analytical solution in the \(x\)-axis direction. At the sample's central cross section H50, symmetry in the radial strain curve is kept. Nevertheless, deviations from the analytical solution are still visible and are largest again in the direction of the \(x\)-axis.  
The specific direction for the occurrence of the largest deviation from the analytical solution was confirmed along the \(x\)-axis for samples with \(\beta=45^{\circ}\) from further simulations considering the extended parameter sets acc. to Table 2. This suggests that, for moderately anisotropic rocks with \(E/E^{\prime}\) of \(\sim\)2, the transverse shear modulus \(G^{\prime}\) is the elastic calibration parameter affected the most by interface friction when calibrated based on the corresponding radial strain measure.  
Regarding the distributions of vertical strains \(\varepsilon_{z}\) in Figure 16, it can be seen that the influence of interface friction is only visible in terms of a slight non-uniformity in the generated distribution for the sample with \(\beta=45^{\circ}\). However, as vertical strains are usually evaluated as a single averaged value from multiple local measurements around the central cross section, the small deviations can be considered negligible for practical purposes.
# TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks
## 5 Performance Evaluation
### Evaluation Scenario  
For the process of performance analysis, two scenarios were taken. Firstly, parameters such as energy efficiency, throughput, packet delivery ratio, end-to-end delay, packet loss, and network overhead were calculated with varying numbers of vehicles with a vehicle coverage area of 200 m and with a static speed of 50 Km/h. The values were calculated and the performance analyzed. Secondly, parameters such as cluster head efficiency, cluster member efficiency, and average cluster number were calculated with varying vehicle  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Simulator Version & NS-2.35 \\ Simulation Time & 100 ms \\ Network Coverage Area & 1500 m \(\times\) 1500 m \\ MAC Model & MAC/802.11 \\ Vehicles Count & 200 vehicles \\ Channel Type & Channel/Wireless \\ Radio Propagation Model & Two Ray Propagation Model \\ Antenna Type & Omni-directional Antenna \\ Transmission Range & 5 Km \\ Data Packet Size & 512 bytes \\ Mobility Pattern & Random Mobility Pattern \\ Agent Type & Transmission Control Protocol \\ Routing Protocol & TACRP \\ Initial Energy & 1000 Joules \\ Transmission Energy & 0.05 Joules \\ Speed & 100 Km/H \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameter Description.  
speeds, with a static number of vehicles and with a vehicle coverage area of 200 m. The values were calculated and the results compared with the ECHS and NRHCS protocols.
###### Abstract
At higher concentrations (about 10-40 MSCs per 100 stimulated lymphocytes), MSCs are able to inhibit the proliferation of T lymphocytes. On the contrary, lower concentrations of MSCs (0.1-1%) can stimulate lymphocyte proliferation in mixed lymphocyte culture [25]. An accurate understanding of how reactions communicate with the interacting population of MSCs and lymphocytes is important for the therapeutic use of MSCs as well as for the optimization of the applied dose of cells. Immunomodulatory properties as well as immunosuppressive effect of MSCs were used multiple times in patients with graft versus host disease (HVGR), after allogeneic bone marrow transplantation [26; 27]. Recent studies show that several types of immune cells affect the immunomodulatory function of MSCs. The detailed course of these interactions remains unclear. Several mechanisms of interaction of MSCs and immune cells were described. In the intercellular contact of MSCs and immune system cells, activation secretion is triggered and in some cases immune cell apoptosis can be induced. Individual subpopulations of T regulatory (Treg) lymphocytes are characterized by specific phenotypes. Treg lymphocytes have regulatory properties and an ability to control the immune response. Mechanisms involved in the development of the regulatory function of Treg lymphocytes are more complex and require multiple signals [28]. CD4+, CD25+, and FoxP3+ Treg lymphocytes play an important role in the control of immunological tolerance and homeostasis of the immune system [29]. MSCs not only have the ability to influence the proliferation of activated lymphocytes, but they can also differentiate them. They preferably act on the differentiation and expansion of T lymphocytes and their subgroups with regulatory phenotype [30]. They are involved in modulating the subpopulation of CD4+ and CD8+ T lymphocytes, resulting in their differentiation into Treg lymphocytes which are known for their inhibitory action on activated lymphocytes [31; 32]. CD4+, CD25+, and FoxP3+ Treg lymphocytes suppress autoimmune reactions and produce IL-10, TGF-\(\beta\) cytokines. They act primarily through direct contact. MSCs are involved in coordinating the function of immune cells in a particular environment. Immune system and cells associated with the immune system are linked by complex processes of expression and production of cytokines. The topic of the study of the biological and immunomodulatory properties of MSCs isolated from fetal packaging is highly relevant. So far, all the developments and interactions in this area have not been explored.
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 4 Experiments
### Further Study
#### 4.5.2 Effects of Kernelization  
In this paper, DSAH adopts a kernelization technique to handle the nonlinear relations between different heterogeneous modalities to improve the retrieval accuracy and efficiency. To demonstrate the effects of kernelization, we denote a variant of DSAH, named DSAH-ke, which directly uses the original features to learn the hash codes. We conduct experiments on two datasets with the code length varying from 8 bits to 128 bits to evaluate the performance of DSAH-ke. The mAP results of DSAH-ke are reported in Tables 4 and 5. From the tables, we can observe that the lack of using kernelization will reduce the retrieval performance.
## 2 Experimental Setup and Detection Method
### Data Collection Process
#### 2.2.2 Labelling Process for the Dataset  
After all the images were collected and properly filtered, they were transferred from a storage device to OneDrive for easy access on multiple platforms. The labelling process uses an online labelling platform called Labelbox [31]. As this is an online service, labelling the dataset can be done with any computer as long as they have access to the project. This again helps when collaboratively working on labeling the dataset as there is no need in transferring and downloading large number of photos. This platform was also chosen as it keeps track of how many photos still need labelling and the estimated time it took per photo. The platform was also very easy to use as there were no other external setup needed other than an account to the service.  
With the help of hired undergraduate students at the University of Guelph, it took about a month to completely label 1499 photos. The photos consisted of a mixture of close-up and zoomed-out photos which involved classes, labelled as \"Fruit Flower Single\", \"Fruit Flower Cluster\" and \"Tree\". An example of the training dataset can be seen in below in Figure 5.  
Figure 5: Training dataset in its first iteration.  
Upon further testing, which will be explained within Section 3, the previously annotated dataset was not sufficient for the use case of this project and additional labelling was to be conducted. This resulted in the two separate classes, \"Clusters\" and \"Tree\". The purpose of the two classes was to be able to detect the fruit flower clusters while also being able to determine the number of clusters per tree. The \"Tree\" class was also changed to show a larger view of the tree, instead of the zoomed-in, partially seen tree. An example of the updated \"Tree\" bounding boxes can be seen in Figure 6.  
The next few iterations of the annotation process consisted of experimenting on the approach that would produce the best possible outcome. It was concluded that the training images should be as similar to the actual use case of the robot which was zoomed-out images. Therefore, only images that encompassed the entire tree was used in the training data. This involved using the mobile vehicle to collect data in the following year. Figure 7 is an example of the finalized dataset. In general, the final dataset consisted of a very large number of bounding boxes, due to the significant amount of fruit flower clusters present in each tree.
###### Abstract  
Due to rapid development in mobile communication technology in recent years, the demand for high quality and high capacity networks with thorough coverage has become a major necessity. Several models have been developed for predicting wireless signal coverage in urban areas, but these models suffer from inadequately calculating certain conditions, such as weather and building materials, especially window size. In this paper, we propose a new path loss prediction model based on the measurement of new indicators, such as window size, temperature, and humidity conditions, after which an extensive statistical analysis using a linear regression technique was implemented in order to validate the new indicators. As the new indicators were incorporated into the Okumura model to derive a new path loss model, the results showed that the proposed model provides an accurate prediction of the received signal strength in a given propagation environment. Our model enhanced the prediction of path loss by 10% when compared to the Okumura and by 15% when compared to the COST-Hata.  
Keywords:path loss; signal; millimeter wave; Okumara; COST-Hata; window size; temperature; humidity +
Footnote †: journal: Applied Sciences
## 1 Introduction
Optimizing parameters like activation temperature and the ratio of sodium carbonate to raw materials is essential to maximize adsorption capacity. Several articles compare different activation methods, emphasizing that chemical activation with Na\({}_{2}\)CO\({}_{3}\) offers advantages over other activators due to the lower corrosion if compared to alkalis and to safer management in real applications [20; 35]. For example, using Na\({}_{2}\)CO\({}_{3}\) to produce activated carbon from wastepaper resulted in materials with superior properties for adsorbing dyes like methylene blue [33]. The use of Na\({}_{2}\)CO\({}_{3}\) is not limited to biomass-derived materials. The activation of chars obtained from post-consumer mixed plastic waste proved that Na\({}_{2}\)CO\({}_{3}\)-activated carbon (Na\({}_{2}\)CO\({}_{3}\)-AC) exhibits superior performance in the adsorption of heavy metals such as Pb (40 mg g\({}^{-1}\)), Cd (13 mg g\({}^{-1}\)), and Cu (12 mg g\({}^{-1}\)) [22]  
The optimization of activation processes and the exploration of new feedstock sources remain areas of ongoing interest, promising a bright future for the use of sustainable materials in various industrial applications. Furthermore, integrating chemical activation methods can significantly mitigate environmental issues, making Na\({}_{2}\)CO\({}_{3}\) activation a valuable option in the circular economy and developing cleaner technologies. In addition to Na\({}_{2}\)CO\({}_{3}\) activation, modifying activated carbon with citric acid (CA) has gained attention for further improving its adsorption capabilities. Citric acid is a biodegradable, non-toxic organic compound that introduces carboxyl groups to the carbon surface, making citric acid-modified materials suitable for a wide range of applications, particularly for water treatment strategies [36; 37; 38; 39; 40; 41; 42; 43]. For instance, citric acid-based residues were employed to produce activated carbon formulas with a superior adsorption uptake of volatile organic compounds such as ethyl acetate [38]. Acid citric can be used to add carboxyl groups onto the surface of the adsorbent, which improves the binding potential of organic adsorbates such as metolachlor, raising the uptake of the non-treated adsorbent from \(\sim\)51 mg L\({}^{-1}\) to \(\sim\)74 mg L\({}^{-1}\) following citric acid treatment [40]. Citric acid can be used as a green cross-linker with potential applications in bio-adsorbents, which highlights them for biomedical applications such as tissue engineering and drug delivery [36]  
Based on the aforementioned context, the primary objectives of this research are to (1) prepare activated carbon from plastic waste char through sodium carbonate activation and citric acid modification; (2) comprehensively characterize the resulting activated carbon in terms of surface area, porosity, elemental composition, proximate analysis, and others; (3) evaluate its effectiveness in removing specific contaminants from wastewater; and (4) evaluate the economic viability and environmental sustainability of the process.