esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Total Phenolic Compounds, Flavonoids, and Antioxidant Capacity  
For the present study, a hydroalcoholic solvent was chosen based on the possibility of industrial applications in food and cosmetic products. Various extraction methods were previously tested (see Supplementary Information) and corroborated with the literature. Different extraction times were tested; however, past 10 min of sonication, the temperature of the mixture would rise beyond our 65 \({}^{\circ}\)C cutoff. [PERSON] et al. (2021) previously concluded that ultrasound-assisted extraction (UAE) produced the best avocado dye recoveries at shorter times [32]. On the other hand, [PERSON] et al. (2015) previously observed that greater ethanol concentrations yielded better antioxidant activity in flower extracts [33]. Lastly, [PERSON] et al. (2021) observed that phenolic compounds were easier to extract using semi-polar organic solvents, which agrees with our findings [34].  
The Gardner index of the AS extract was 15+, resulting in an orange-colored extract with potential applications in the food industry (see Supplementary Information). Other parameters tested included the recovery yield, pH, and absorbance, with 8.11%, 5.97, and 2.89 (\(\lambda_{\text{max}}\) = 450), respectively. The protection of a biological system against oxidative stress and its harmful effects relies on the antioxidant capacity [19]. Therefore, to assess the potential benefits of the Semil 34 AS extract, we studied the antioxidant capacity (TEAC) as well as the total phenolic content (TPC) and total flavonoid content (TFC). Table 1 compares the results obtained in this study with those of previous studies found in the literature for other varieties of avocado. The TEAC, TPC, and TFC obtained for the Semil 34 AS extract were 1743.33 \(\upmu\)M/g seed, 25.86 mg GAE/g extract, and 2.0911 mg QE/g extract, respectively. In a previous study, [PERSON] et al. reported TEAC of 1065.00 \(\upmu\)M/g seed (units were converted) for a Hass cultivar AS extract [18]. Interestingly, another study by [PERSON] et al. reported a TEAC value of 18,400 \(\upmu\)M/g seed and a TPC value of 1303.0 mg GAE/g extract, which differ from our results (see Table 1). The chemical composition of avocado can vary from cultivar to cultivar, depending on several factors, such as the variety, processing method, region, climate, and precipitation [18, 35]; this explains the variability found within the values for the TEAC, TPC, and TFC from different authors, even when the same variety of avocado is evaluated. This also highlights the importance of preliminary studies of the avocado residues prior to processing for the targeted industry.
# Improved SPGD Algorithm for Optical Phased Array Phase Calibration
## 4 Experimental Results and Analysis
A set of voltages calibrated days ago are applied to the same OPA with our temperature-controlled packaging. The same main lobe beam will repeat. Figure 7 shows the far-field 512-channel OPA calibrated by the NSPGD algorithm. The 512-channel OPA is successfully calibrated by NSPGD, and the SMSR is 10.4 dB. Compared with the initial beam, the side lobe is suppressed and the main lobe is optimized. Finally, we calibrated the beam at different angles, and the results are shown in Figure 8. According to the change of selected \(\Omega\) in our PC program, as described in Equation (12), the calibrated beams of different angles were obtained. \(\Delta\varphi\) means the phase difference between adjacent emitters. Grating lobes will emerge with the scanning angle increasing because the distance between adjacent emitters is larger than \(\lambda/2\). The beam in the red rectangle is the main lobe, and the side lobe locates in the white one. Figure 7: The 512-channel OPA (**a**) far-field without calibration, (**b**) far-field with NSPGD calibration, and (**c**) SMSR of far-field after calibration.
# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 2 Materials
### Satellite and Ground Data
#### 2.2.2 Sentinel-2 Data  
The Copernicus Sentinel-2A and -2B satellites were launched on 23 June 2015 and 7 March 2017, respectively. Both carry a wide-swath (290 km) Multi-Spectral Instrument (MSI) that, when combined, can observe the Earth every 5 days. The MSI includes 13 spectral bands covering the visible, NIR, and shortwave infrared spectrum. The blue, green, red, and NIR band images are acquired at 10 m spatial resolution. The rest of the spectral bands are acquired at a 20 or 60 m spatial resolution. We used the 10 m resolution red (band 4) and NIR (band 8) images to compute NDVI in our study.  
The Sentinel-2 L1C top of atmosphere (TOA) reflectances are available from the European Space Agency (ESA) Copernicus Open Access Hub [31] and the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center [32]. We downloaded the TOA reflectance for the BARC site (tile 18 SUJ) from 2019-2020 and atmospherically corrected the data using the Sen2 Cor (version 2.8) processor [33]. NDVI was computed for all clear pixels based on the Sen2 Cor cloud mask. Figure 2b shows the number of clear Sentinel-2 observations over BARC in 2019 and 2020. BARC is located in an overlap area of two adjacent Sentinel-2 swaths. The eastern part of BARC has fewer observations than the western part. The average of cloud-free pixels per day over BARC for Sentinel-2 was 8.3% for the entire time and was 10.5% over the cover crop termination period highlighted in Figure 2b.
# SPACNet: A Simulation Platform of an Acoustic Cognitive Network
## 4 Simulation Experiment
### Network Parameters Configuration  
The parameters of the simulation experiment are shown in Table 1. The total number of nodes was 21, and the number of sink nodes was 2. A different M-ary DSSS system was applied, and the node communication mode was half-duplex. Moreover, a 15.5 kHz carrier frequency, a 2.35 kHz bandwidth, and a 55.8 b/s data rate after convolutional decoding were used in this simulation experiment. The total runtime of the network was 24 h. The packet generation follows the Poisson distribution \(\lambda\), and the \(1/\lambda\) was set from 100 to 1500 s/packet in the simulation experiment. Note that the specific parameters as given in Table 1 were selected according to the typical marine environmental monitoring scenario of underwater acoustic networks, which feature high transmission reliability and low data load. The deployment location of the simulation marine environmental monitoring UACNs was set to an offshore sea area, corresponding to a shallow water-type channel pattern.  
The topology structure of the simulation experiment is shown in Figure 7a, and the initial network state is shown in Figure 7b.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes & 21 \\ Number of sink nodes & 2 \\ Transducer directivity & Omnidirectional \\ Modulation system & Different Mary-DSSS \\ Node communication mode & Half-duplex \\ Carrier frequency (kHz) & 15.5 \\ Bandwidth (kHz) & 2.35 \\ Data rate after channel decoding (b/s) & 55.8 \\ Maximum number of retransmissions & 3 \\ Runtime of network (h) & 24 \\ Packet generation (s/packet) & 100–1500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the simulation experiment.  
To mimic the real underwater acoustic channel environment of an offshore sea area affected by periodic tide phenomena, artificial time-varying channels were generated by the Bellhop model with a changing depth parameter. Figure 8 shows the channel of node 9, which shows periodical tide-driven time-varying channel characteristics. Moreover, the environmental noise experimentally collected from Xiamen Harbor was adopted as additive noise for the simulation to create a receiving signal with a specific signal-to-noise ratio (SNR).
## 2 Materials and Methods
### Assembly of BHJ-OSC Device  
In order to fabricate BJH-OSCs, indium tin oxide (ITO, Samsung electronics, 12-16 \(\Omega\)/sq) glass substrates were washed consecutively using DI water, detergent, acetone and isopropyl alcohol using an ultrasonic bath for 15 min, followed by drying in an oven. A buffer layer of compact c-TiO\({}_{2}\) layer [38] was first spin coated on cleaned ITO substrates at a scan rate of 3000 rpm for 30 s and subsequently heated at 100 \({}^{\circ}\)C in a vacuum oven for 20 min, followed by annealing at 450 \({}^{\circ}\)C in a closed furnace for 30 min. Thereafter, the blend solutions were formulated in chlorobenzene by mixing H3T-4-FOP and PC\({}_{61}\)BM in different weight ratios (H3T-4-FOP: PC\({}_{61}\)BM; 1:1, 12, 1:3 _w/w_) under stirring at 60 \({}^{\circ}\)C for 4 h to obtain well mixed blend solution. H3T-4-FOP: PC\({}_{61}\)BM blend thin film was casted on c-TiO\({}_{2}\) layer at a scan rate of \(\sim\)2100 rpm for 35 s and immediately subjected to annealing at 70 \({}^{\circ}\)C for 10 min. Finally, a thin layer of gold (Au, \(\sim\)100 nm thickness) as top electrode was thermally evaporated over the ITO/c-TiO\({}_{2}\)/H3T-4-FOP:PC\({}_{61}\)BM to complete the device configuration of ITO/c-TiO\({}_{2}\)/H3T-4-FOP:PC\({}_{61}\)BM/Au. In order to check the reproducibility, 20 devices of each BjH-OSCs were tested for extracting the performance of the devices.
# Cooperative Jamming with AF Relay in Power Monitoring and Communication Systems for Mining
## 1 Introduction
Considering the imperfect CSI and multiple eavesdroppers, we use an effective BCD algorithm to design the precoding and jamming matrix at both the source and the relay, in which maximizing the secrecy rate of the FD-AF relay-assisted wireless communications system is emphasized. This paper is organized as follows. Section 2 describes the system model. The secrecy rate optimization problem is proposed and transformed into a solvable form in Section 3, which also gives the algorithm. Section 4 characterizes the numerical results in different scenarios. Finally, the conclusion is presented in Section 5. Notation: The \(\mathbf{W}^{T},\mathbf{W}^{H},\mathrm{vec}(\mathbf{W}),\|\mathbf{W}\|\)and \(\mathrm{tr}(\mathbf{W})\) denote the transpose, conjugate transpose, vectorization, Frobenius norm, and trace of the matrix, respectively. \(\otimes\) denote the Kronecker product, and \(\mathbf{W}^{K}\) represents the \(\mathbf{W}\mathbf{W}^{H}\) along with \(\log|\mathbf{E}+\mathbf{C}\mathbf{D}|=\log|\mathbf{E}+\mathbf{D}\mathbf{C}|\). \(\mathbf{E}\) is the identity matrix.
## 3 Proposed Framework  
We propose RGBSformer, a two-stream Transformer-based approach for RGB video-based human action recognition. An overview of RGBSformer is depicted in Figure 2. In this section, we start with a general overview of the framework and then discuss the differences between skeleton stream and RGB stream. Then, we discuss the detailed architectures, one to process pseudo skeleton heatmaps and the other to process RGB frames, and introduce how to fuse information of the two streams.  
An overview of the framework.Inspired by SlowFast [26], we designed the two-stream framework as one slow stream to capture the spatial feature and the other to focus more on temporal information working on fast frame rate. Due to the lack of appearance information and detailed shape information, skeleton data naturally focus more on motion information. We argue that skeleton data provide more temporal information than spatial information. Hence, we take the skeleton heatmap stream as the fast pathway, and the RGB frame stream as the slow pathway. Then, as shown in Figure 2, an overview of the overall framework can be summarized as follows: Firstly, after acquiring RGB videos, we first use HRNet to acquire skeleton data and generate corresponding skeleton heatmaps. Secondly, the skeleton heatmaps and RGB frames are input to RGBSformer with different temporal and spatial resolutions. Thirdly, after decomposition, they are input to the spatial Transformer encoder, which has fewer layers than the RGB stream for the skeleton stream. Fourthly, the frame-level representations which are obtained from the spatial Transformer encoder of skeleton stream, are fused into the RGB stream, followed by the temporal Transformer encoder and the MLP head afterwards. Finally, after passing the classification header, the classification scores are averaged from two streams to obtain the final classification results.  
Figure 2: The overview of our proposed framework, RGBSformer.  
**Difference between the two streams.** We propose three ways to make the fast pathway capture temporal information precisely. Compared to the slow pathway, the RGB stream, the skeleton stream has three differences.  
1. **Higher temporal resolution.** The skeleton heatmap stream works at a higher frame rate (temporal resolution) than the RGB stream. The fast refreshing frames can effectively model the potentially fast changing motion;
2. **Lower spatial resolution.** As presented in Section 3.1, the heatmaps have smaller size than original frames. Noisy background and uninteresting information have been discarded in the cropping process. Meanwhile, we feed the generated heatmaps into Transformer directly without any cropping or resizing operations;
3. **Less attention layers.** Compared to original RGB frames, skeleton heatmaps are already mid-level features for human action recognition. In Transformer architecture, the attention blocks are repeatedly stacking on top of each other to construct the final model. Compared to raw RGB data, the fast pathway with less attention block layers can extract features more effectively.
## 5 Deposition Methods
### Sputtering  
Sputtering is a widely used deposition method for a wide variety of thin films. It is particularly suited for GST because of its multi-element composite nature. Sputtering can produce high-quality dense films with preservation of the stoichiometry upon deposition. In sputtering, a target with the correct GST composition is typically used as the material source, see Figure 18a. Ar\({}^{+}\) atoms are accelerated by an electrical field towards the target where the atomic species are sputtered out. These atoms and clusters land on the substrate to form the thin films. One of the strengths in sputtering is the preservation of the target stoichiometry, which occurs only after a certain length of time (known as target conditioning) to account for the different sputter yields of the constituent elements of the target. The sputtered films properties can be controlled by adjusting the working pressure, power, and the plasma gas. During DC sputtering, a higher argon pressure leads to a lower phase transition temperature [109], see Figure 18b. The argon gas also has an effect on the sputtered thin film surface morphology, [PERSON] et al. [110] reported the sputtered GST film tends to form cracks with a higher Ar flow rate (higher pressure), presumably due to the accumulation of stress. Therefore, the higher quality GST can be deposited by sputtering at a lower argon pressure.
# Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods
## 6 Discussion
### Coordinate System Positioning  
The object tracking methods the application uses to establish CSs for the assembly and the component box are suitable for relaying graphics in relation to the CSs. The more complex and asymmetrical objects are, the less reflecting of light, the more they are suitable for creating accurate CSs in dynamic environments. Nevertheless, other methods should be explored for effective and efficient tracking of simple, symmetric, and reflective objects.  
The proposed methods for calibrating the MR application's CSs using optical recognition software to process built-in camera input proved to have applicable solutions for the scenario presented in this work. Based on the calibrated CSs, graphic information relevant to the real state of the assembly in question can be derived and implemented to help the user with an immersive and comprehensive experience, featuring graphics, audio, and text overlay. The specific solutions implemented in various steps of the application that have been presented in Section 4 are not robust enough to be suitable for all assembly scenarios, and it is not ideal to program parts of the MR application through trials-and-errors, rather than having an underlying architecture to guide the developers through the process. This is, therefore, the next necessary step towards establishing this kind of MR application for training and guidance during assembly operations.
## 2 Mathematical Modeling
### Energy Conservation Equation  
The energy conservation in the adsorption bed is shown by the following equation [24]:  
\[\varepsilon_{t}\rho_{g}C_{pg}\frac{\partial T_{g}}{\partial t}+\rho_{g}C_{pg} v_{g}\frac{\partial T_{g}}{\partial z}=\varepsilon_{b}K_{g}\frac{\partial^{2}T_{g} }{\partial z^{2}}-P\frac{\partial v_{g}}{\partial z}+a_{p}h_{gs}\big{(}T_{s}- T_{g}\big{)}+\frac{4h_{\text{in}}}{d_{b}}\big{(}T_{w}-T_{g}\big{)} \tag{2}\]  
where \(\varepsilon_{t}\) is the total porosity; \(\rho_{g}\) is the gas density; \(C_{pg}\) is the specific heat capacity of the gas; \(T_{g}\) is the temperature of the gas; \(K_{g}\) is the axial thermal conductivity of the gas; \(P\) is the pressure; \(a_{p}\) is the specific surface area of the adsorbent; \(h_{gs}\) is the heat transfer coefficient between the gas and solid; \(T_{s}\) is the temperature of the solid; \(h_{\text{in}}\) is the heat transfer coefficient between the gas and wall; \(d_{b}\) is the inside diameter of the bed; and \(T_{w}\) is the temperature of the wall.  
The principle of energy conservation is applied to the solid phase of the adsorption bed:  
\[\rho_{b}C_{pg}\frac{\partial T_{s}}{\partial t}+\rho_{b}\sum_{j=1}^{N}\big{(} C_{pai}q_{j}\big{)}\frac{\partial T_{s}}{\partial t}=K_{s}\frac{\partial^{2}T_{s} }{\partial z^{2}}+\rho_{b}\sum_{j=1}^{N}\left(-\Delta H_{ads,j}\frac{\partial q _{j}}{\partial t}\right)+a_{p}h_{gs}\big{(}T_{g}-T_{s}\big{)} \tag{3}\]  
where \(\rho_{b}\) is the bulk density of the adsorbent; \(C_{pg}\) is the heat capacity of the solid; \(C_{pa}\) is the heat capacity of the adsorbed phase; \(K_{s}\) is the thermal conductivity of the solid; and \(\Delta H_{ads}\) is the isosteric adsorption enthalpy.  
The energy conservation relationship on the wall of the adsorption bed is [24]:  
\[\rho_{w}C_{pw}\frac{\partial T_{w}}{\partial t}=K_{w}\frac{\partial^{2}T_{w}}{ \partial z^{2}}+\frac{4d_{b}h_{\text{in}}\big{(}T_{g}-T_{w}\big{)}+4(d_{b}+2 \delta_{w})h_{\text{out}}(T_{amb}-T_{w})}{(d_{b}+2\delta_{w})^{2}-d_{b}^{2}} \tag{4}\]  
where \(\rho_{w}\) is the wall density; \(C_{pw}\) is the heat capacity of the wall; \(K_{w}\) is the thermal conductivity of the wall; \(h_{\text{in}}\) is the heat transfer coefficient between the gas and wall; \(\delta_{w}\) is the thickness of the wall; \(h_{\text{out}}\) is the heat transfer coefficient between the wall and environment; and \(T_{amb}\) is the ambient temperature.
## 2 Requirements Specifications and Measurements Gaps
### Sea Ice Drift  
Nowadays, the Copernicus Marine Environment Monitoring Service offers daily sea ice drift data over the Arctic and Antarctic from active (Sentinel-1, ASCAT) and passive microwave sensors (SSM/L AMSR-E) and optical passive (the Advanced Very High Resolution Radiometer /3, AVHRR/3). With the data combined from different sensors, a global dataset of the sea ice drift is obtained with a spatial resolution of 10 km. The data latency is 5 h. In this regard, the data latency has to be improved down to \(<\)1 h and the revisit time down to \(<\)3 h. Potential sensing technologies are: microwave radiometers, SAR, multispectral optical and microwave scatterometers.
# Measuring Maritime Paint Thickness under Water Using THz Cross-Correlation Spectroscopy
## 2 Materials and Methods
### Reflection Setup  
The THs-CCS system is operated in a reflection setup with a nonzero angle of incidence, as seen in Figure 2A. With this setup, the THz beam is directed at an angle (**1**) towards the sample located at the focal point (**4**). Due to the nonzero angle of incidence and the different refractive indices of the media at the interface, light is refracted onto the paint sample. This is illustrated in Figure 2E.  
A simple way to calculate the thickness of a medium is by comparing the time difference between the reflection from the paint and the metal underneath. The time difference in reflections can be seen as \(\Delta t\) in Figure 2E and in the time-domain in Figure 4A. This leads to a description of the paint thickness (\(T\)) as:  
\[T=\cos\left(\sin^{-1}\left(\frac{n_{1}}{n_{2}}\sin(\theta_{1})\right)\right) \frac{c\Delta t}{2n_{2}} \tag{1}\]  
with \(n_{1}\) and \(n_{2}\) the refractive index of the material above the paint and the paint, respectively. \(c\) is the speed of light, and \(\theta_{1}\) is the angle of incidence.
## 3 Numerical Experiment and Discussion
### Results, Discussion, and Comparison with Baseline Approaches  
In this subsection, the different simulation results of the two developed techniques are presented, discussed, and compared with two baseline techniques. In addition, we include the effects of the mutual coupling that exists between elements by using measured samples from experiment, which are the ultimate ground truth to test any method. Hence, the measured data from this experiment were further employed to evaluate the performance of the PV, SPV, PWD-SRP, and DPD-MUSIC.
## 3 Derivation of the Governing Equations  
Based on the above basic equations of classical elastic shell theory and orthotropic shell theory, the corresponding deformation compatibility equation of equivalent anisotropic shells is derived from the geometric equations given above in Equation (6):  
\[\frac{\partial^{2}\varepsilon_{x}}{\partial y^{2}}+\frac{\partial^{2} \varepsilon_{y}}{\partial x^{2}}-\frac{\partial^{2}\gamma_{xy}}{\partial x \partial y}={(\frac{\partial^{2}w}{\partial x\partial y})}^{2}-\frac{ \partial^{2}w}{\partial x^{2}}\frac{\partial^{2}w}{\partial y^{2}}-\frac{1}{R} \frac{\partial^{2}w}{\partial y^{2}} \tag{8}\]  
The internal force of the shell can be obtained by the integration of the stress in the cross section expressed as the relation with displacement. Equation (9) is the displacement expression of the bending (\(M_{x}\) and \(M_{y}\)) or torsional moment:  
\[M_{x}=-D_{x}(\frac{\partial^{2}w}{\partial x^{2}}+v_{y}\frac{\partial^{2}w}{ \partial y^{2}}),M_{y}=-D_{y}(\frac{\partial^{2}w}{\partial y^{2}}+v_{x}\frac {\partial^{2}w}{\partial x^{2}}),M_{xy}=-\frac{Et^{3}}{12(1+v)}\frac{\partial^{ 2}w}{\partial x\partial y} \tag{9}\]  
where \(D_{x}\) and \(D_{y}\) are the equivalent flexural stiffness in the \(x\)- and \(y\)-direction per unit length, respectively. \(D_{x}=\frac{E_{x}t^{3}}{12(1-v_{x}v_{y})}\), and \(D_{y}=\frac{E_{y}t^{3}}{12(1-v_{x}v_{y})}\). This equation indicates that the equivalent shell is different from the isotropic shell in the physical mechanism, and the differences in the elastic modulus and Poisson's ratio are reflected in the two perpendicular directions (i.e., the tangential and vertical directions) from the physical angle, which can further reflect the flexural rigidity in two directions. Substituting Equation (9) into Equations (4) and (5), the following equations concerning displacement expressions of shear forces can be obtained:  
\[Q_{x}=-\frac{\partial}{\partial x}(D_{x}\frac{\partial^{2}w}{\partial x^{2}}+ \frac{D_{xy}}{2}\frac{\partial^{2}w}{\partial y^{2}}),Q_{y}=-\frac{\partial}{ \partial y}(\frac{D_{xy}}{2}\frac{\partial^{2}w}{\partial x^{2}}+D_{y}\frac{ \partial^{2}w}{\partial y^{2}}) \tag{10}\]  
where \(D_{xy}\) is the equivalent torsional stiffness in the \(x\)-\(y\) plane per unit length; \(D_{xy}=2v_{y}D_{x}+\frac{Et^{3}}{6(1+v)}\).