esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Batch Desorption Studies
#### 3.6.2 Detailed Adsorption Studies
The current research focuses on the simple and effective removal of the ARV, ZDV (a commonly used ARV drug in South Africa for mother-to-child prevention of the spread of the virus) from wastewater using a modified tea waste adsorbent. It also reports on the parameters such \begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Pollutant** & **Qe Max (mg/g)** & **Adsorption Conditions** & **Adsorbent** & **Author** \\ \hline \multirow{2}{*}{Phenol} & \multirow{2}{*}{134.01} & Column adsorption, optimum pH = 2, contact time = 180 min & Activated waste tea leaves & [17] \\ \hline \multirow{4}{*}{9.49} & \multirow{2}{*}{9.49} & Batch adsorption, optimum pH = 7, adsorbent dose = 15 mg/L & Tea waste biomass & [46] \\ \hline \multirow{4}{*}{5.80} & \multirow{2}{*}{5.80} & Batch adsorption, optimum pH = 5.48, adsorbent dosage = 2.4 g, and contact time = 8 h & Tea waste biochar & [47] \\ \hline \multirow{4}{*}{30.31} & \multirow{2}{*}{30.31} & Batch adsorption, optimum pH = 6, and contact time = 360 min & Hizikia fusiformis, biochar & [48] \\ \hline \multirow{4}{*}{14.61} & \multirow{2}{*}{14.61} & Batch adsorption, optimum pH = 6, and contact time = 120 min & Food waste, biochar & [49] \\ \hline \multirow{4}{*}{10.84} & \multirow{2}{*}{10.84} & Batch adsorption, optimum pH = 8, dosage = 0.6 g and contact time = 60 min & Magnetic palm kernel, biochar & [50] \\ \hline \multirow{4}{*}{49.5} & \multirow{2}{*}{Batch adsorption, optimum pH = 2.5 g, dosage = 1 g and contact time = 25 min} & Ferric chloride green tea waste biochar & [51] \\ \hline \multirow{4}{*}{71.94} & \multirow{2}{*}{Batch adsorption, optimum pH = neutral, dosage = 13 g and contact time = 240 min} & Delonix regia biochar & [52] \\ \hline \multirow{4}{*}{61.65} & \multirow{2}{*}{Batch adsorption, optimum pH = 2, dosage = 1.0 g and contact time = 60 min} & Modified tea waste & Current study \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of biochar with other adsorbents for phenol removal. as the effect of pH solution, adsorbent dose, equilibrium characteristics, and mechanisms involved during adsorption.
## 4 Experimental Validation of STST Based on TVCM-VPF
### Method and System of STST Based on TVC-VPF
#### 4.2.2 Characteristics of the NS  
The TLD was designed such that the frame structure exhibits a linear performance under rare earthquake excitations. Therefore, a simplified linear three-degree-of-freedom (3 DOF) mass-stiffness-damping model was adopted to simulate the NS in STST. The fundamental frequency and the fundamental damping ratio of the steel frame structure was 17.9 rad/s and 0.35%, which were obtained through preliminary tests. The frame was simplified to a shear-type numerical model with three degrees of freedom. Its mass, stiffness, and damping matrices can be expressed as follows:  
\[M_{\text{N}}=\left[\begin{array}{ccc}400.06&0&0\\ 0&355.50&0\\ 0&0&357.50\end{array}\right]\text{kg},\ K_{\text{N}}=\left[\begin{array}{ccc}1 173000&-586500&0\\ -586500&1173000&-586500\\ 0&-586500&586500\end{array}\right]\text{N/m},\]  
\[C_{\text{N}}=\left[\begin{array}{ccc}39.1548&2.7212&0.2753\\ 2.7212&34.4612&2.9367\\ 0.2753&2.9367&37.3788\end{array}\right]\text{N-s/m}.\]
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 4 The Non-Cross Layer Design Method
### Summary  
In Sections 4.1-4.3 we have reviewed the traditional non-cross-layer routing protocols for underwater acoustic sensor networks. These routing protocols relate to the energy consumption, the propagation delay, and the mobility, which are also the main factors of routing protocol design.  
For overcoming the weakness that the energy consumption of an underwater acoustic sensor network is much higher than that of a terrestrial wireless sensor network, energy-effective routing protocols are proposed. These routing protocols use the location information or the depth information to reduce unnecessary broadcasts and hop counts to save energy, such as in [40; 42; 45]; or work with the assistance of AUVs, such as in [41; 44; 47].  
The propagation delay is another factor which can affect the routing performance. As analyzed in Section 3, the reasons for propagation delay are the low transmission speed of acoustic waves and the unreliable underwater acoustic channel, so approaches used to reduce propagation delay mainly concentrate on three aspects: first, using location information to reduce unnecessary routing discovery; second, reducing the distance between two relay nodes; third, reducing retransmissions as much as possible. The mobility also has a great effect on routing performance. To this end, the location information and the hop-by-hop transmission model are used to reduce the routing maintenance and routing rebuilding, which can obviously reduce the energy consumption and propagation delay.
## 2 Materials and Method  
A short overview of the physical processes implemented in DOSE is given for completeness. A charged particle, such as a proton, moving in a material, loses energy almost continuously by inelastic Coulomb interaction with electrons and is slightly deflected from its initial direction. The interaction with the atomic nuclei gives rise either to elastic/inelastic collisions or nuclear reactions. In particular:  
* Inelastic collisions with the atomic electrons of the material produce ionization or excitation of the atoms of the medium and energy loss of the charged incident particle.
* Elastic collisions in the Coulomb field of nuclei, although less frequent, lead to negligible energy loss while significantly changing the trajectory of the incident particle.  
The amount of energy loss in each collision in both cases is a very small fraction of the initial kinetic energy of the incident particle. However, since the number of collisions per unit of travel is very high, the result is a significant cumulative energy loss with small fluctuations. The interaction of the charged particles is essentially of the electromagnetic type. Electromagnetic interactions are predominantly elastic collisions, i.e., collisions in which energy is transferred from one particle to another, but the oncoming particles retain their identity. However, there are numerous mechanisms of particle production in the interactions of high-energy particles in matter. The high energy protons (up to 0.1 TeV) that interact with a material give rise to nuclear collisions with the atomic nuclei in the shield and may produce hadrons (protons, neutrons, charged pions) or other nuclei, which in turn can have further nuclear collisions; other particles, such as neutral pions will decay almost instantly and whose products may initiate electromagnetic showers. In addition to fast hadrons, nuclear collisions also give rise to low-energy (MeV) neutrons, protons, light ions (alpha particles), and gamma rays, which are emitted during the de-excitation of the target nuclei. Neutrons can be highly penetrating and can escape the material unless they undergo multiple elastic nuclear collisions until they thermalize and are captured. Eventually, the residual nuclei of elastic and inelastic collisions can also further interact with neighboring nuclei [16].  
In this scenario, the type of secondary particles and their absorption inside the shield are critical properties of the shielding material. Secondary radiation escaping the shield might be less harmful than the primary one (preferred feature) or can be even more harmful, depending on the nature of the incident flux and the chemical composition of the shield. The effects of nuclear interactions on the radiation environment behind the shield are very different in the two cases: proton-nucleus collisions tend to make the environment behind the shield more dangerous [17; 18; 19], whereas nucleus-nucleus collisions tend to do the opposite [20].
# A Geospatial Framework of Food Demand Mapping
### Novel Food Demand Mapping Framework
This methodological innovation parallels the comprehensive use of household data by [PERSON] et al. [28] to reveal food security patterns in Uganda, and is further informed by the work of [PERSON] et al. [36], who explore the spatial interconnections between food insecurity, BMI, and socio-economic factors. The significance of this study extends beyond its methodological contributions; it presents a pivotal opportunity for the establishment of cross-national collaborations aimed at assessing dietary patterns across diverse geographical contexts. Such international research endeavors, informed by the granular-level data analysis advanced in our methodology, hold the potential to unveil region-specific dietary behaviors, thereby informing the development of tailored nutritional interventions. Through this collaborative and targeted approach, the study not only advances the scientific understanding of nutritional epidemiology but also contributes to the global effort in improving public health outcomes related to diet and nutrition. However, it is important to note the primary limitation of this study: its reliance on a small sample size. This was a deliberate choice given its nature as a pilot study aimed at testing the proposed food demand mapping framework. Future research will aim to expand the sample size to enhance the robustness and generalizability of the findings. Lookingahead, the research intends to develop an agent-based model of the food supply chain, utilizing the insights gained from the mapping exercise. To circumvent the challenges associated with conducting extensive surveys, we plan to integrate satellite imagery as a novel approach for predicting food demand at a granular level. This methodology promises to streamline the data collection process, offering a more efficient and scalable means of understanding food demand dynamics without the logistical complexities of survey distribution and collection. The primary goal of this research is to develop a comprehensive framework for mapping food demand, integrating individual dietary behaviors with spatial data to address disparities in food access. To validate the proposed framework a case example of Lithuania will be conducted. The specific objectives of this study are:  
1. Create a robust methodology for mapping food demand based on survey data that capture both individual and regional consumption patterns. 2. Implement a pilot survey in Lithuania to gather preliminary data on individual eating habits and preferences, laying the groundwork for more comprehensive future studies. 3. Employ advanced spatial analysis techniques to generate visual maps representing food demand across different regions, with a focus on identifying and highlighting areas of concern such as food deserts and food swamps, thereby validating the proposed framework.
## 3 Results and Discussion
### Bubble Size Distribution on the Anode--Experiment 1  
Figure 5 shows the size distribution of the bubbles on the anode under different apparent current densities and anode geometries.  
Figure 5: The size distribution of the bubbles on the flat and slotted anodes within 60 s. (**a**) 50 mm \(\times\) 22 mm flat anode (**b**) 50 mm \(\times\) 22 mm slotted anode.  
From Figure 5, for both 50 mm \(\times\) 22 mm flat and slotted anodes, the general bubble size showed an overall decreasing trend with the increase in the apparent current density. The proportion of the large bubbles decreased, while the percentage of small bubbles increased significantly. For example, in Figure 5a, the dimensions of the bubbles are mostly distributed in the range of 0-500 mm\({}^{2}\), and the average bubble sizes at apparent current density of 0.9 A\(\cdot\)cm\({}^{-2}\), 1.1 A\(\cdot\)cm\({}^{-2}\), and 1.3 A\(\cdot\)cm\({}^{-2}\) are 324 mm\({}^{2}\), 223 mm\({}^{2}\), and 73 mm\({}^{2}\), respectively. The existence of large bubbles is obvious, and the proportions of large bubbles (a bubble covering more than 1/3 of the anode surface) are 40.8%, 23.1%, and 10.4%, respectively, at 0.9 A\(\cdot\)cm\({}^{-2}\), 1.1 A\(\cdot\)cm\({}^{-2}\), and 1.3 A\(\cdot\)cm\({}^{-2}\).  
In Figure 5b, most of the bubbles are less than 170 mm\({}^{2}\), which is different from the case with the 50 mm \(\times\) 22 mm flat anode (Figure 5a). It is apparent that the anode slot can reduce the bubble dimension. As a contrast, the average bubble sizes at 0.9 A\(\cdot\)cm\({}^{-2}\), 1.1 A\(\cdot\)cm\({}^{-2}\), and 1.3 A\(\cdot\)cm\({}^{-2}\) are 40 mm\({}^{2}\), 34 mm\({}^{2}\), and 26 mm\({}^{2}\), respectively. No bubble is found over the size of 1/3 anode bottom surface. Additionally, with the increase in the apparent current density, the proportion of the small bubbles increase significantly.
# Designed Circularly Polarized Two-Port Microstrip MIMO Antenna for WLAN Applications
## 7 Conclusions  
In this paper, we designed a simple MIMO-CP antenna for the WLAN band. The MIMO-CP antenna has an easy structure with dimensions of \(22.5\,\mathrm{mm}\times 50\,\mathrm{mm}\), and it operates at 5.6 GHz. To achieve CP, the simple square patch was cut at the corner using the concept of CP. Improving impedance matching, high isolation, and better AR can be archived using a simple technique that produces a slot in the ground. The antenna impedance bandwidth ranged from 5.23 to 6.42 GHz and \(AR\leq 3\) dB from 5.37 to 5.72 GHz, with better isolation \(|S21|\leq-37\) dB within the S11 frequency range. At the resonant frequency, peak gain and radiation efficiency were 6 dB and 84%, respectively. The ECC was 0.001 and DG was nearly 10 dB lower. The designed MIMO-CP antenna results (simulated and measured) are suitable for WLAN applications.  
Conceptualization, I.K. and LU.; methodology, I.K.; software, I.K.; validation, I.K., S.U.R. and K.Z.; formal analysis, K.Z.; investigation, I.K.; resources, K.Z.; data curation, I.K.; writing-original draft preparation, I.K. and I.U.; writing-review and editing, K.Z. and H.U.; visualization, Q.W.; supervision, K.Z.; project administration, K.Z. and Q.W.; funding acquisition, K.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science Foundation of China, grant number 62171165.  
Not applicable.  
Figure 16: (**a**) Proposed simulated and measured ECC. (**b**) Proposed simulated and measured diversity gain.  
Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors acknowledge the support from the Natural Science Foundation of Heilongjiang Province under grant no. YQ2020F002, and the open project of the Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing.  
The authors declare no conflict of interest.
## 5 Computer Simulations
### Spectral Efficiency Performance in Rich Scattering Environment
In summary, the proposed algorithm achieves near optimal performance in a consistent manner under various system parameters when Rayleigh fading channel comes into play. Moreover, the proposed scheme shows a slightly higher performance when compared with conventional relay selection techniques such as BHM and SVD.
## 3 Results
### Prediction of the Secondary Structure of Pm TPSs  
The secondary structure of Pm TPS amino acid sequences was predicted using SOPMA software (Figure 3a). The main secondary structures of the six Pm TPSs were \(\alpha\)-helices, accounting for 63.27%, 63.91%, 63.04%, 63.62%, 68.95%, and 68.28%, respectively (Table S4). The \(\alpha\)-helix content of sesqui-TPSs was slightly higher than that of mono-TPSs. The random coil contents were 26.66%, 26.22%, 29.25%, 30.56%, 29.45%, 24.01%, and 23.79%, respectively. The content of \(\beta\)-sheet/corner (2.92%-4.14%) and extension chain (3.18%-5.52%) was the lowest. The \(\beta\)-sheet/turn and extended strands are interspersed in the \(\alpha\)-helix structure, and random coils connect all the structures. Owing to the presence of a signal peptide, the \(\alpha\)-helix content in the first 100 amino acids of Pm mono-TPSs was less than that of Pm sesqui-TPSs. All Pm mono-TPSs had a higher content of extended strands than Pm sesqui-TPSs, except for Pm TPS (\(-\))-\(\beta\)-pinene.  
Figure 2: (**a**) Amino acid sequence alignment of six terpene synthases (TPSs), Pm TPS (\(+\))-\(\alpha\)-pinene, Pm TPS (\(-\))-\(\alpha\)-pinene, Pm TPS (\(-\))-\(\beta\)-pinene, Pm TPS limonene, Pm TPS selinene, and Pm TPS longfiolene. The nucleotide sequences were aligned using DNAMAN 6.0. The conserved terpene synthase motifs, RRXsW, RXR, DDX, are underlined. Amino acid residues are shaded in light gray, gray, and black, showing 50, 75 and 100% identity, respectively, while dashes represent the gaps used for optimal alignment. (**b**) Phylogenetic tree of six Pm TPSs together with TPSs of other _Pinus_ spp. The alignment of nucleotide sequences was performed using Clustal X 2.1, and the tree was constructed using the maximum likelihood method, with 500 replicates for bootstrapping, using MEGA 7 software (Version 7.0.26). All _Pm TPSs_ are classified into TPS-d1 and TPS-d2 subfamilies based on the taxonomic distribution of TPS families. The accession numbers of all genes are given in Supplementary Table S1. Missing data and positions with gaps were eliminated. (**c**) Motif analysis of mono- and sesqui-TPSs in _P. massoniana_.  
SMART software was used to analyze the functional domains of Pm TPSs (Figure 3b). Two Pfam domains, terpene-synth and Terpene-synth-C, have been characterized. The Terpene-synth domain of Pm mono-TPSs starts from the N-terminal 73-75 aa region and ends at the 264-270 aa region, while the terpene-synth-C domain starts from the N-terminal 302-312 aa region and ends at the 492-577 aa region. In Pm sesqui-TPSs, the terpene-synth domain starts from 18/27 and ends at 211, while the terpene-synth-C domain starts from 247/257 and ends at 512/523. In addition, there is a low-complexity region in the N-terminus of Pm TPS (+)-\(\alpha\)-pinene, as shown in purple. The sequences were SIVPSMSMSSTTSVS and SMSMSSTTSVS, both serine-rich.
## 3 Results
### Accuracy of Block Level Yield Maps  
In order to extrapolate the measured yield of the selected trees to the entire orchard block, the process described in Figure 3 was employed for the 2016 and 2017 harvest seasons. Estimations of total fruit weight (t-ha-1) per block were compared against actual commercially harvested yield as well as to grower 'eye ball' estimates (Figure 7). The results demonstrate that for all blocks the predicted yield derived from the remotely sensed imagery calibrated with the sampling of strategically located trees, was much closer to actual yield than that provided by the growers 'eye ball' estimate. Note, the growers estimated result for Block 5 was not undertaken. The estimation accuracies ranged from an under estimation of 20% (Block 4, 2017) to an over estimation of 19% (Block 1, 2016). The average estimation accuracy for all five blocks was 98.2% in 2016 and 99.5% in 2017.  
Using the relationship developed with the VIs, yield maps (kg-tree-1) were derived for each block using the process presented in Figure 3. The yield maps of Block 2 in 2016 and 2017 are shown as an example in Figure 8. Maps of average fruit size (g) per tree were also derived for each block, with the maps of Block 2 for the 2016 and 2017 harvest season presented in Figure 9.  
Figure 7: Comparison between the actual yield (t-ha-1) to that predicted from satellite imagery and that made by visual assessment from growers in (**a**) 2016 and (**b**) 2017. Note that no growers estimate was supplied for Block 5.
# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 3 Dataset Description
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{2}{c}{**Indian Pines**} & \multicolumn{4}{c}{**Salinas**} \\ \hline
**Name of** & **Number of** & **Number of** & **Name of** & **Number of** \\
**the Class** & **Pixel Samples** & **Class Description** & **the Class** & **Pixel Samples** & **Class Description** \\  & **per Band** & & & **per Band** & \\ \hline Alfalfa & 46 & Type of green grass & \(\text{Broo}_{\text{l}}\_{\text{green\_{w}eds\_1}}\) & 2009 & Green colored vegetable \\ \hline Corn-notill & 1428 & Corn crop cultivation without tillage & \(\text{Broo}_{\text{l}}\_{\text{green\_{w}eds\_2}}\) & 3726 & Green colored vegetable \\ \hline Corn-mintill & 830 & \begin{tabular}{c} Corn crop cultivation with \\ minimum tillage \\ \end{tabular} & \begin{tabular}{c} Follow \\ \end{tabular} & 1976 & Land Region \\ \hline Corn & 237 & Corn crop & \(\text{Fallow\_{\text{r}}}\_{\text{r}}\_{\text{r}}\_{\text{r}}\_{\text{r}}\_ {\text{r}}\_{\text{r}}\) & 1394 & Land Region \\ \hline Grass-pasture & 483 & Type of green grass & \(\text{Fallow\_{\text{r}}}\_{\text{r}}\_{\text{r}}\_{\text{r}}\_{\text{r}}\_ {\text{r}}\_{\text{r}}\_{\text{r}}\) & 2678 & Land Region \\ \hline Grass-trees & 730 & Type of green grass & \(\text{Stubble}\) & 3959 & Land Region \\ \hline Grass-pasture-moved & 28 & Type of green grass & \(\text{Celeery}\) & 3579 & Green colored Plant region \\ \hline Hay-windrowed & 478 & Row of cut small grain grass & \(\text{Grapes\_{\text{untrained}}}\) & 11,271 & Type of Vineyard \\ \hline Oats & 20 & Plant in Brown color & \(\text{Soil\_{\text{v}}}\_{\text{v}}\_{\text{i}}\_{\text{v}}\_{\text{a}}\)velop & 6203 & Soil Region \\ \hline Soybean-notill & 972 & Soya crop cultivation without tillage & \(\text{Cor\_{\text{s}}}\_{\text{s}}\_{\text{s}}\_{\text{s}}\_{\text{s}}\) & 3278 & Green colored Plant \\ \hline Soybean-mintill & 2455 & \begin{tabular}{c} Soya crop cultivation with minimum \\ tillage \\ \end{tabular} & \begin{tabular}{c} Lettuce\_romaine\_4 wk \\ \end{tabular} & 1068 & \begin{tabular}{c} lettuce crop that grows in the fourth week \\ \end{tabular} \\ \hline Soybean-clean & 593 & Soya Plant & \(\text{Lettuce\_romaine\_5 wk}\) & 1927 & \begin{tabular}{c} lettuce crop that grows in the fifth week \\ \end{tabular} \\ \hline Wheat & 205 & Brown colored Wheat Plant & \(\text{Lettuce\_romaine\_6 wk}\) & 916 & \
\begin{tabular}{c} lettuce crop that grows in the sixth week \\ \end{tabular} \\ \hline Woods & 1265 & Type of Tree & \(\text{Lettuce\_romaine\_7 wk}\) & 1070 &
\begin{tabular}{c} lettuce crop that grows in the seventh week \\ \end{tabular} \\ \hline Buildings-Grass-Trees-Drives & 386 & Building area & \(\text{Vinyard\_{\text{untrained}}}\) & 7268 & Type of Vineyard \\ \hline Stone-Steel-Towers & 93 & Tower area & \(\text{Vinyard\_{\text{vertical\_trellis}}}\) & 1807 & Type of Vineyard \\ \hline Background & 10,776 & & & 56,975 & \\ \hline \end{tabular}
\end{table} 
Table 1: Number of samples per class and description of each class for two standard data sets of AVIRIS Sensor.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 2 Multi-Wavelength QKD with a PUF
### Multi-Wavelength QKD  
Multi-wavelength QKD involves adding multiple wavelengths, allowing for different protocols and data to be sent. With the traditional single wavelength, Alice only has four polarization states to encode bits with (two per basis). Adding a second wavelength doubles the amount of polarization options that Alice can encode data with, since Alice can choose from four states per wavelength for a total of eight possible states. These additional states allow for different encoding schemes based on ternary or quaternary state input stream values (as opposed to binary for bits). Just as binary means we have two states to send data with, using ternary or quaternary data means that we have three or four states to send data with. Figure 1a,b show two potential encoding schemes for a ternary or quaternary protocol, respectively [6].