esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Freeze Drying on Bioactivity and Physical Properties of Food Products
## 3 Parameters of Freeze Drying
### Size  
Large or small sizes, which are other parameters of FD, affect positively or negatively the drying time which impacts the protection of the bioactive and physical properties of the sample, as well as improves the quality of the final product. An experiment on blueberries found that the default moisture value of large berries was maintained for a longer time than that of small berries, and these results suggested that small samples led to a reduction in the drying time [65]. The outcomes provided that the sample size directly affects the drying time of the FD. Moreover, shorter drying times obtained from small samples resulted in higher biological value and more preserved polyphenic compounds. Additionally, the decrease in the drying time reduced the cost of the technique.  
In another study, different cuttings, including transverse and longitudinal cuts, were used to examine the effects of the size and shape of ginger (_Zingiber officinal Roscoe_) on the drying processes and product quality [66]. Additionally, FD, hot air drying, vacuum drying, and catalytic infrared drying were applied to the ginger in this study. The study explained not only the effect of the size on the ginger quality in FD processes but also determined the best drying technique to obtain the best dried ginger quality. The longitudinal cuts resulted in shorter drying times and better preservation of physical properties compared with other cuts. However, no change in the bioactive properties was observed when these cuts were applied to the ginger. Even though FD had a longer drying time compared with other techniques, it was chosen as the best technique among other drying techniques to obtain products that have maximum quality, especially in terms of the bioactive and physical properties, because FD perfectly minimized the degradation of gingerol content, total phenolic, flavonoid content, and volatile compounds as bioactive properties of the sample. When longitudinal cut samples were dried via catalytic infrared drying, excellent results were obtained from reducing the drying time and developing the final product quality.  
In some cases, the sample size may not be a determining factor in obtaining the maximum quality of freeze-dried products. For example, in the study of [PERSON] et al.,ginger was prepared as grated, sliced, and whole to determine the impact of the size of the sample on product quality and the drying rate of the FD [67]. Grating or slicing applications were ineffective in determining the effect of the sample size on the product quality compared with whole ginger.
## 2 Biosorbent-Based Extraction Phases
### Concluding Remarks about Biosorbents  
In general, biosorbents demonstrated great versatility for the extraction of the different classes of compounds. Lignocellulosic biosorbents, such as cork, bract and _Moringa oleifera_ seeds, are mainly composed of lignin, cellulose and hemicellulose. These macromolecules have a number of chemical groups that are capable of interacting with a wide range of analytes with different polarities. The works reported in this review showed studies in which cork was able to satisfactorily extract non-polar compounds, such as PAH's, and compounds with intermediate polarity, such as parabens, benzophenone and tricolcarban. Bract biosorbent demonstrated similar behavior, presenting satisfactory extraction for compounds with low polarity, such as OCPs, and for those with intermediate polarity, such as steroid estrogens.  
The extraction efficiency of these lignocellulosic materials is mostly explained by the \(\uppi\)-\(\uppi\) interactions between lignin and the analytes, or through hydrogen-bonding and dipole-dipole interactions between O-H groups presented in the cellulose and hemicellulose with the O-H, N-H bonds and Cl present in the analytes. When biosorbents were used with SPME for PAH extraction in water samples, bract fiber showed lower LODs than cork and diatomaceous earth fiber. The same was observed for OCP determination in water samples. Bract has a higher percentage of lignin in its structure than cork, which could explain the higher extraction efficiency for the non-polar compounds.  
By using BA\(\upmu\)E as the microextraction technique, the diatomaceous earth bar provided lower LODs than the cork bar for the extraction of parabens, benzophenone and tricolcarban in water samples. Although diatomaceous earth has been used for PAHs determination, it has shown good extraction efficiency for compounds with intermediate polarity, which was mainly due to interactions through O-H groups. It is also worth mentioning that the porous structure of these biosorbents plays an important role in the extraction through physical interaction with the analytes.  
As a final remark, cork has been the material most used with different microextraction techniques and for a large variety of compounds. This fact can be related to the ease with which it is obtained through the reuse of wine bottle corks. The other biosorbents are more limited, such as bract, which is obtained from trees in southern Brazil and in eastern Argentina. Similarly, diatomaceous earth is a sub-product from the beer filtration and clarification process. Most of the works report the comparison with commercial extraction phases. In general, the results are similar or even better, in some cases. However, the procedures employed in the preparation of the devices, in particular for SPME, may be a limitation for the widespread use of these bio-based extraction phases.
## 3 Path Loss Models  
Path loss models are mathematical models used to predict the attenuation of radio signals as they propagate through a medium. These models consider factors like distance, frequency, environmental conditions, and obstacles to estimate the signal strength at a receiver. They are essential for network planning and optimization in wireless communication systems. The transition from one path loss model to another occurs to improve accuracy and account for more relevant factors like frequency, environment, technological advancements, and standardization requirements [31].
# Patient-Specific Dosimetry Evaluations in Theranostics Software for Internal Radiotherapy
## 3 Results  
**Phantom Study**  
Mean absorbed doses and TIAs of the phantom calculated with OLINDA 2.0 and Velocity Theranostics are included in Table 2 and plotted in Figure 4.  
Clinical Cases  
The absorbed doses in OLINDA 2.0 and Velocity Theranostics for the kidneys, spleen and liver of the cohort of patients are shown in Table 3. TIAs and absorbed doses calculated with OLINDA 2.0 and Velocity Theranostics are plotted in Figure 5.  
Figures 6 and 7 show the results of the second comparison between absorbed doses. The data included in the columns denoted as \"OLINDA 2.0 with TIAs of Velocity Theranostics\" (OLINDA(Theranostics TIA)) in Table 3 were considered in place of data in the columns denoted as \"OLINDA 2.0\". For this purpose, OLINDA 2.0 was run while inserting the same TIA values calculated by Velocity Theranostics, to better investigate the performance of the dosimetry algorit  
Figure 5: Dispersion graph of the results for the complete cohort of patients for TIA and absorbed dose.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{4}{c}{**Kidneys**} & \multicolumn{4}{c}{**Liver**} & \multicolumn{4}{c}{**Spleen**} \\ \cline{2-10}  & **Olinda** & **Velocity** & **OLINDA(Theraostics TIA)** & **Olinda 2.0** & **Velocity** & **OLINDA(Theraostics TIA)** & **Olinda 2.0** & **Velocity** & **OLINDA(Theraostics TIA)** \\ \hline
1 & 0.304 & 0.467 & 0.461 & 0.0605 & 0.705 & 0.643 & — & — & — \\ \hline
2 & 0.440 & 0  
The Bland-Altman analysis in Figure 7 shows lower differences when the same TIA is used in absorbed dose calculations (OLINDA(\(\mathrm{\textsc{Theranostics}\textsc{TIA}}\))). This is also evident in Figure 8, where differences (%) are visually compared using box-plots.  
Figure 6: Dispersion graph of the results for the cohort of patients for absorbed doses calculated by running OLINDA 2.0 with the same TIA values calculated by Velocity Theranostics.  
Figure 7: Bland-Altman plots for absorbed doses of Theranostics vs OLINDA 2.0 (on the left) and OLINDA(\(\mathrm{\textsc{Theranostics}\textsc{TIA}}\)) (on the right). The difference was evaluated as the percentage difference of Velocity Theranostics in comparison with the OLINDA 2.0 dose. Dashed lines represent the limits of agreement (\(\pm\)1.96 SD). The horizontal solid lines represent the average percentage differences.  
The absorbed doses were found to be poorly correlated (Lin's coefficient = 0.672) between OLINDA 2.0 and Velocity Theranostics, while better correlation was found between OLINDA(Theranostics TIA) and Velocity Theranostics (Lin's coefficient = 0.894).
## 4 Results and Discussion
### SISO and MISO  
Figure 3 displays the wavelet-transformed BER performance for the SISO and MISO channel diversity techniques. Data were transmitted through the channel and received by the receiver through the signal output channel when transmit diversity was used at the transmitter. Because of the array gain at the transmitter, the SNR was increased, which reduced the BER. Because the SNR value at \(10^{-3}\) for the MISO DWT was 12 dB and that for the SISO DWT was higher than 12 dB, the MISO DWT performed better than the SISO DWT diversity approach.  
Figure 3: MISO diversity with DWT-OFDM.  
Figure 2: SIMO diversity with DWT-OFDM.  
The simulation parameters are given Table 2. The wavelet family was dB8, and the modulation scheme was QAM.
## 1 Introduction
Remote sensing is an effective means of monitoring the development of green tides, and scholars have used remote sensing to estimate the coverage and biomass of macroalgae blooms [32; 33]. Nevertheless, remote sensing can be easily interfered with by bad weather conditions, such as clouds and fog over the sea [15]. Numerical simulation, e.g., with a Lagrangian particle-tracking model, is an economical and efficient tool to hindcast or predict the spatiotemporal variations in the locations and biomass of macroalgae. In the past decade, a number of numerical studies have been carried out to investigate the sources, trajectories, and destinations of the drifting macroalgae in the Yellow Sea [21; 34; 35; 36; 37; 38; 39], while no numerical modeling research has been implemented in the green macroalgae blooms in Jinmeng Bay, Qinhuangdao, which forms the major content of this paper. Since field observations and measurements have been carried out in Jinmeng Bay, which are costly and time-consuming, a rapid pre-warning system based on numerical modeling is desirable for the disaster management of green tides. Compared with the Subei Shoal and the extensive Yellow Sea, Jinmeng Bay is a region of a relatively smaller scale in addition to a highly developed coast with multiple artificial structures, e.g., Lianhua Island, Hailuo Island, and submerged breakwaters [2; 31]. These structures create low-energy semi-enclosed waters with a long water exchange time, which is unfavorable for the dispersion of nutrients or temperature, which have been proven to be the control factors of the intensity of the green tides in the Yellow Sea [20; 27; 40]; therefore, it is worth investigating the effect of these structures on the migration of the green macroalgae in Jinmeng Bay. Wind plays a vital role in the migration of macroalgae, not only driving the surface currents but also exerting a direct momentum on the macroalgae by friction which is defined as windage or leeway [35; 41], and thus is another emphasis in this research. Different from the green tides in the Yellow Sea and the golden tides in the Atlantic Ocean, in which the macroalgae are floating on the sea surface, the green macroalgae in Jinmeng Bay were suspended throughout the water column. Therefore, the macroalgae particles in this model are cast randomly vertically and the depth-averaged currents are redistributed vertically using theoretical velocity profiles to move the particles. This paper is organized as follows: Section 2 introduces the background of the study area, the hydrodynamic model, the particle-tracking model, and the validation of these two models. Section 3 shows the model results of the hydrodynamics and the spatiotemporal distribution of the green tide that occurred in August 2021, in Jinmeng Bay. Section 4 discusses the influence of winds and artificial structures on green tide distribution, the sensitivity analysis of the effect of wind, and the future improvement of the models. Section 5 concludes the whole paper.
## 2 Materials and Methods
### Chromatographical Analysis  
The composition of volatiles emitted by the black cherry leaves was determined by the solid phase microextraction (SPME) method for sampling volatile organic compounds. The analyses were performed separately for plant material from each sapling at three collection dates. Fresh leaves of _P. serotina_ were cut in pieces about 2 cm long, then 1.5 g of plant material was inserted into a test tube and it was tightly closed with a rubber membrane. The membrane was pierced by the stainless steel SPME (DVB/CAR/PDMS 50/30 \(\upmu\)m, 2 cm) needle protecting the fiber (Supelco Co., Bellefonte, PA, USA). The volatile compounds were exposed to fiber for about 30 min at 23 \({}^{\circ}\)C. After this period the fiber was retracted into the needle. The chemical composition of the volatile compounds absorbed on the fiber was analyzed using a gas chromatograph (GC) coupled to a mass spectrometer (MS) Saturn 2000 (GC-MS system, Varian, Walnut Creek, CA, USA) with a ZB-5 (Phenomenex, Shim-Pol, Warsaw, Poland) column (30 m \(\times\) 0.25 mm film \(\times\) 0.25 \(\upmu\)m film thickness), conducting desorption for 5 min. The MS was equipped with an ion-trap analyzer set at 1508 for all analyses with an electron multiplier voltage of 1350 V. Scanning (1 scan/s) was performed in the range of 39-400 \(m/z\) using electron impact ionization at 70 eV. The analyses were carried out using helium as a carrier gas at a flow rate of 1.0 mL min\({}^{-1}\) in a split ratio of 1:10 and the following program: 40 \({}^{\circ}\)C at the beginning and hold 3 min; 5 \({}^{\circ}\)C min\({}^{-1}\) to 110 \({}^{\circ}\)C; 20 \({}^{\circ}\)C min\({}^{-1}\) to 300 \({}^{\circ}\)C. The injector and detector were held at 200 and 300 \({}^{\circ}\)C, respectively. Identification of compounds were based on three methods: (i) comparison of obtained spectra with databases NIST 17 (National Institute of Standards and Technology, Gaithersburg, MD, USA) and FFNSC [25], (ii) comparison of calculated retention indices using a retention indices calculator [26] with values presented in NIST 17 and FFNSC and (iii) comparison of retention times of unknown compounds with authentic standards. For comparison of mass spectra we used the AMDIS (v. 2.73) (provided by Phenomenex, Shim-Pol, Warsaw, Poland) and GCMS solution (v. 4.20) (provided by Phenomenex, Shimadzu, Kyoto, Japan) programs.
## 5 Performance Test Results and Discussion
### Speed Tests  
Speed of the input and output axles was registered. The AC two-pole motor accelerated the input axle until its maximum of 3000 rpm. The results of the measurement are shown in Figure 11.  
Figure 11: Input and output speed measurement.  
Figure 10: Cooling process: temperature _vs._ time.  
In Figure 11, it can be observed the reduction and inversion of the speed in the output shaft in respect to the speed of the input. The reduction ratio calculated for all the speeds was -20, as expected from the design. The maximum speed achieved in the input axle was that of the AC motor, 3000 rpm. It is the first time that a HD can operate at this high an input speed in these temperature and vacuum conditions. A HD operating in cryogenic temperatures, moving faster than 500 rpm in the input axle, has never been registered before.  
The temperature variation operating at different speeds is shown in Figure 12. The system was operating at 3000 rpm for 50 seconds with a tolerable increase of the temperature along the static part (tolerable limit: \(>\)90 K, superconductors change of state).  
As expected, the higher the speed, the larger the eddy current, and so a temperature raise occurred. At the highest speed, the efficiency was evaluated at about 65%, while below 500 rpm, the efficiencies were higher than 80%-85%. As there is no friction, the only source of power losses is the eddy current losses; therefore, efficiency is only dependent on the input/speed and not on the torque loads.  
SMB performed correctly at those speeds and temperatures, absorbing misalignments and vibrations. The maximum speed of the SMB cannot be determined because of the limitation of the AC motor speed, but in literature they reached up to 15000-20000 rpm. Therefore, in MAGDRIVE prototype, the only limitation for the prototype input speed is the eddy current generation.
# Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1
## 2 Theory and Method
### Data Processing  
#### 2.3.1 Dataset Selection and Noise Floor Subtraction  
As introduced above, a large number of GNSS-R DDMs have been collected over the Arctic region since the launch of TDS-1. The DDMs generated onboard the TDS-1 satellite are available on the data service platform MERRByS, which provides data track view to help select DDM dataset of the area of interest. Since the purpose is to explore methods for distinguishing between sea ice and water, so only DDMs collected over sea ice and water fields are selected for testing. Those datasets acquired over land are excluded with the geographic information obtained from the U.S. National Oceanic and Atmospheric Administration (NOAA).  
Before noise floor subtraction, data alignment is employed to each DDM through adjusting the peak power point to the location without delay and Doppler shift (\(\tau\) = 0, \(f\) = 0). Note that some DDMs may be seriously affected by noise and cannot demonstrate the characteristics of reflected surface correctly. Thus, DDMs with peak Signal-to-Noise Ratio (SNR) lower than 0 dB are not used. SNR can be calculated as follows:  
\[\text{SNR}=(DDM(\tau,f)-DDM_{noise})/DDM_{noise}, \tag{7}\]where \(DDM(\tau,f)\) is the raw power value of DDM, \(DDM_{noise}\) is the noise floor, which can be calculated by:  
\[DDM_{noise}=\frac{1}{N}\sum_{\tau_{1}}^{\tau_{m}}\sum_{f_{1}}^{f_{m}}DDM(\tau,f), \tag{8}\]  
where \(DDM_{noise}\) represents the noise floor value; \(\tau_{1}\) and \(\tau_{m}\) are the pixel limits of delay rows of noise box; \(f_{1}\) and \(f_{m}\) are the pixel limits of Doppler shift of noise box; \(N\) represents the number of pixels of the noise box. The noise box chosen in this paper spans twenty Doppler bins and five delay chips starting from 1 st to 20 th in the standard DDM (shown in Figure 2). Then, the DDM values are respectively subtracted by the corresponding noise floor.
## 5 Numerical Results  
In this section, the simulation results are presented to assess the proposed DRL-based task offloading and resource allocation schemes in the IRS-assisted ISAC system. The simulation is based on Python 3.8 and PyTorch 1.8.0. We assume that the BS and the IRS are located at \([-10,0,0]\) m and \([90,0,2]\) m. UEs are randomly distributed in a radius of \(1\) m below the IRS [21]. The channel matrix \(\mathbf{H}_{1}\) and \(\mathbf{h}_{k,2}\) with \(k\in\mathcal{K}\) follow the Rician distribution with the Rician factor \(\gamma_{1}=3\) dB [42]. According to [43], the carrier frequency is set to 30 GHz, and the shadowing standard deviation is 7.8 dB. The path-loss exponent of BS-IRS and IRS-UE are set to 2.8 and 2.5 [44]. The noise power \(\sigma^{2}=-174\) dBm/Hz. Meanwhile, we set noise power \(\sigma^{2}=-85\) dBm and the bandwidth \(B_{k}=2\) MHz [43]. The input data size of task \(d_{k}\), required computation cost \(c_{k}\), and CPU frequency of BS server \(f_{i,k}\) are randomly generated in the interval [1, 2] Mbits, [1, 3] Kcycles/bit, and [1, 2] Gcycles/s, respectively [45]. CPU frequency of BS server \(F_{o}^{\text{tol}}\), effective capacitance coefficients \(\kappa_{o}\), and \(\kappa_{i}\) are set to 10 Gcycles/s, \(10^{-26}\), and \(3\times 10^{-26}\)[45]. The default simulation parameter is listed in Table 2.
# Research on Disease Spreading Behavior Oriented to Three-Layer Complex Networks
## 4 Modeling and Analysis of Three-Layer SIS Disease Spread Model
### Influence of Diffusion on Spreading Procedure  
In the SIS disease-spreading model, the disease diffusion rate depends on the biological characteristics of the pathogen, such as spreading rate changes and recovery rate changes. The disease diffusion rate is defined as \(\delta=\lambda/\mu\), and the effect of the disease diffusion rate on the global spread of the network is tested by keeping the other network parameters constant. The effect of disease-spreading rate on spreading procedure in the three-layer EEE network, three-layer EWE network, and three-layer EBE network is shown in Figures 10-12, in which it can be concluded that the spreading speed of the network gradually decreases as the diffusion rate decreases in these three types of networks, and the number of infected people gradually decreases and the number of the susceptible people gradually increases. Conversely, the spreading rate increases as the diffusion rate gradually increases, the proportion of the infected people progressively increases, and the number of susceptible people progressively decreases. In contrast to real-life disease spread, this experiment and its results reveal the dynamics of disease spreading in multi-layer networks from a macro-perspective and provide a theoretical reference for policy makers to effectively curb the spread of the disease.  
Figure 11: Influence of diffusion rate on spread in three-layer EWE network.  
Figure 9: Influence analysis of the three-layer complex network EBE’s parameters on spreading procedure, **(a)** shows the influence of the changing \(m_{0}\) and \(m\), **(b)** shows the influence of inter-layers link probability.
# Impacts of Integration of Wind Farms on Power System Transient Stability
## 2 Basic Theory of Transient Stability Analysis
### Power Network Couples Between Nodal Voltage and Current  
The network equations \(G(X,Y,t)\) in Equation (1) are used to describe the relationships between nodal voltages and currents as Equation (8)  
\[YV=I \tag{8}\]  
where \(Y\) is the power network admittance matrix, \(V\) and \(I\) are nodal voltages and injected currents, respectively.  
The transient stability of a large-scale power system is usually related to a large number of nodes and directly solving Equation (8) is very complicated, and therefore Equation (8) is usually rewritten in a more concise form. If we denote the nodal current vector in two parts, one is for the generator bus and the other is for the non-generator bus, then the nodal current could be rewritten as  
\[I=[\begin{array}{c}I_{m}\\ 0\end{array}] \tag{9}\]  
Then Equation (8) will be rearranged as (10)  
\[\left[\begin{array}{c}I_{m}\\ 0\end{array}\right]=\left[\begin{array}{cc}Y_{mm}&Y_{mr}\\ Y_{rm}&Y_{rr}\end{array}\right]\left[\begin{array}{c}V_{m}\\ Vr\end{array}\right] \tag{10}\]  
where \(Y_{rr}\)_and_\(Y_{mm}\) denote the self-admittance matrix for non-generator nodes and generator nodes, respectively. \(Y_{mr}\) and \(Y_{rm}\) denote the mutual admittance matrix between non-generator and generator nodes. If we rewrite Equation (10) in two equations, we have  
\[I_{m} =Y_{mm}V_{m}+Y_{mr}V_{r} \tag{11}\] \[0 =Y_{rm}V_{m}+Y_{rr}V_{r} \tag{12}\]  
Figure 1: Block function of the IEEE type I excitation system.  
If expressed in terms of \(V_{m}\) and afterward be substituted in (11) to eliminate \(V_{r}\), then we have the form as (13)  
\[I_{m}=\left(Y_{mm}-Y_{mr}Y_{rr}^{-1}Y_{rn}\right)V_{m} \tag{13}\]  
This matrix \(\left(Y_{mm}-Y_{mr}Y_{rr}^{-1}Y_{rm}\right)\) is the required reduced \(Y_{Bus}\) matrix. It is an \(\left(m\times m\right)\) matrix where \(m\) denotes the number of generators.