esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Thermal Response for RF Exposure in Human and Rat Models
## 5 Conclusions  
This study investigated the differences in the temperature elevation and thermal time constant between rat and human models exposed to dipole antennas at frequencies of 3-10 GHz. We computationally estimated the time course of temperature elevation by considering the vasodilation. It should be noted that the temperature elevation and time course are quite different between rat and human models, especially in deep tissue such as the brain. The characteristics of temperature elevation vary in humans owing to the shape and size of the head and distance to the antenna. We then proposed the effective SAR volume as a metric to estimate the thermal time constant, indicating that there is a good correlation between them, even if head shapes are significantly different, such as in rats and humans. These findings should be useful for extrapolating small animal studies to humans.  
Conceptualization, A.H.; methodology, A.H.; software, S.K. and A.H.; validation, S.K. and A.H.; formal analysis, S.K. and A.H.; investigation, S.K. and A.H.; resources, S.K. and A.H.; data curation, S.K. and A.H.; writing of the original draft preparation, S.K. and A.H.; writing of review and editing, S.K. and A.H.; visualization, S.K.; supervision, A.H.; project administration, A.H.; funding acquisition, A.H.  
This research was funded by the Committee to Promote Research on the Possible Biological Effects of Electromagnetic Fields of the Ministry of Internal Affairs and Communications, Japan.  
The authors declare no conflicts of interest.
# Water Quality Evaluation and Prediction Based on a Combined Model
## 4 Results and Discussion
### Water Quality Prediction Model
#### 4.2.1 Water Quality Prediction of Lanzhou Xincheng Bridge Section
The original sequence was denoised as shown in Figure 12:  
Figure 11: EMD diagram of dissolved oxygen. Figure 10: ARIMA Prediction Diagram. The denoised sequence is smoother than the original sequence, showing its original fluctuation trend more clearly. Therefore, the denoised sequence can be used for subsequent experimental demonstration and analysis. First, a three-layer forward neural network is created, the parameters of each network are initialized and a training set is constructed to train the wavelet neural network. The data for weeks T-1, T-2, T-3 and T-4 are used to predict the number of neurons in week T. Therefore, the number of neurons in the input layer and output layer is four and one, respectively, and the number of neurons in the hidden layer is nine, as determined by the trial-and-error method; therefore, the structure of the wavelet neural network proposed in this paper is 4-9-1. The Morlet wavelet function is selected as the wavelet basis function, the number of iterations is set as 100, the learning probability is 0.001 and the training target is \(10^{-6}\). The weight and parameters of the network are modified by gradient correction method so that the predicted output is close to the desired output [39]. The predicted result is output and compared with the real value, as shown in Figure 13.  
The bat algorithm is used to determine the coefficients of each model, and the process of determining the optimal weight is transformed into the process of hunting prey by bats; therefore, the combined prediction model of dissolved oxygen in Lanzhou Xincheng Bridge section of the Yellow River Basin is as follows:  
\[P_{DO}=0.1495P_{WNN}+0.8505P_{ARIMA}\]  
Figure 12: Comparison of dissolved oxygen sequence before and after denoising. Figure 13: WNN prediction diagram. The combined prediction model of permanganate and ammonia nitrogen was obtained according to the same steps:  
\[P_{COD_{Mn}} =0.7852P_{WNN}+0.2148P_{ARIMA}\] \[P_{NH_{3}-N} =0.3274P_{WNN}+0.6726P_{ARIMA}\]
## 2 Working Principle and Modeling
### Mathematical Model
\begin{cases}-\left|F_{\text{f1}}\right^{\prime}\right|&\dot{x }_{1}\leq-\frac{\left|F_{\text{f1}}\right^{\prime}\right|}{k_{\text{s}}}\\ k_{\text{s}}\dot{x}_{1}&-\frac{\left|F_{\text{f1}}\right^{\prime}\right|}{k_{ \text{s}}}<\dot{x}_{1}<\frac{\left|F_{\text{f1}}\right^{\prime}\right|}{k_{ \text{s}}}\\ \left|F_{\text{f1}}\right^{\prime}\right|&\dot{x}_{1}\geq\frac{\left|F_{\text{ f1}}\right^{\prime}}{k_{\text{s}}}\end{cases}  \tag{10}\]  
\[F_{\text{f2}}= \begin{cases}-\left|F_{\text{f2}}\right^{\prime}\right|&\dot{x }_{2}\leq-\frac{\left|F_{\text{f2}}\right^{\prime}\right|}{k_{\text{s}}}\\ k_{\text{s}}\dot{x}_{2}&-\frac{\left|F_{\text{f2}}\right^{\prime}\right|}{k_{ \text{s}}}<\dot{x}_{2}<\frac{\left|F_{\text{f2}}\right^{\prime}\right|}{k_{ \text{s}}}\\ \left|F_{\text{f2}}\right^{\prime}\right|&\dot{x}_{2}\geq\frac{\left|F_{\text{ f2}}\right^{\prime}\rfloor}{k_{\text{s}}}\end{cases}  \tag{11}\]  
where F\({}_{\text{f1}}\)\({}^{\prime}\) and F\({}_{\text{f2}}\)\({}^{\prime}\) are the maximum static friction forces of shafts 1 and 2, and k\({}_{\text{s}}\) is a constant with a value much greater than c\({}_{\text{v1}}\) and c\({}_{\text{v2}}\). Through this design, the static friction forces can be dynamically and smoothly changed whenever the objects are stable and moving. Since the reduction in the RD volume chamber is equal to the increase in the CD volume chamber, the expression is  
\[V_{1}=V_{2} \tag{12}\]  
where V\({}_{1}\) is the reduction in the RD volume chamber, and V\({}_{2}\) is the increase in the CD volume chamber. The variations in the volume chamber of the RD and the CD are related to their displacements, respectively, and the expressions are  
\[V_{1}=f(x_{1}) \tag{13}\]  
\[V_{2}=f(x_{2}) \tag{14}\]  
where f(x\({}_{1}\)), f(x\({}_{2}\)) are functions related to the displacement that need to be determined through simulation. The relationship between the displacement of the RD and the displacement of the CD must be defined. Combining with Equations (12)-(14), the displacement proportional coefficient kp1 is defined, and the expression is  
\[k_{\mathrm{p1}}=\frac{x_{1}}{x_{2}} \tag{15}\]  
Combining Equations (1)-(15), a dynamic model of the HFAM has been formulated in Simulink, as shown in Figure 7. In the dynamic model, the input is the voltage acting on the voice coil motor, which is converted into the driving force F\({}_{\mathrm{U}}\) by Equations (3) and (4), and the output is the displacement of the CD x\({}_{2}\). The model can be divided into the RD part and the CD part. The two parts are established based on Equation (1) and Equation (2), respectively, and are connected through Equations (5) and (15). Figure 7: Dynamic model of HFAM in Simulink.
# Traffic-Predictive Routing Strategy for Satellite Networks
## 4 Route Discovery
### Forwarding Control Mechanism
\begin{table}
\begin{tabular}{c c c c c} \hline Type & R & A & Reserved & Prefix Sz & Hop Count \\ \hline \multicolumn{5}{c}{Destination IP Address} \\ \hline \multicolumn{5}{c}{Destination Sequence Number} \\ \hline \multicolumn{5}{c}{Originator IP Address} \\ \hline \multicolumn{5}{c}{Lifetime} \\ \hline \multicolumn{5}{c}{Curr\_traffic} \\ \hline \multicolumn{5}{c}{Next\_traffic} \\ \hline \end{tabular}
\end{table} 
Table 2: Improving the HELLO package. packet is forwarded; otherwise, a congestion control signal is sent to the upstream node, and the upstream node performs path replacement at the same time. \[\left\{ \begin{array}{l}Next\_traffic_{i}>L,\text{The node is congested and the path needs to be replaced.}\\ Next\_traffic_{i}\leq L,\text{Receive RREQ packet and forward it}\end{array} \right. \tag{2}\]
## 6 Castalia Simulator
### Energy Consumption Model  
The resource manager module of Castalia is responsible for calculating the energy use per operation of the node. Castalia models the energy source as an AA battery of 18,720 Joules. Energy is linearly subtracted based on the overall power drawn and simulation time. Modules that model hardware devices send messages to the resource manager in order to signal their power needs [28].  
Power consumption in Castalia has two components: radio consumption and baseline consumption. The default baseline consumption value is 6 mW; this corresponds to the energy consumption of a more when the radio is off and the microcontroller is active. The power consumption corresponding to the radio modes depends on the specific radio chip. Therefore, for sensor nodes based on the CC2420 transceivers, the radio draws 62 mW per second when it is in the listening or receiving state, 1.4 mW when it is in sleeping state, while the power consumption in the transmission state will depend on the transmission power level used.  
Differences in energy occur because the radio is ON for different periods of time in each node. The parameters, beacon order (BO) and superframe order (SO), define the duty cycle between the active and inactive periods. In Castalia, BO equals six and SO equals four, while a duty cycle of 25% is the default value for MAC protocol 802.15.4.  
In this work, the energy model included in Castalia has been used for evaluating the impact of our transmission power control policies. We have focused on the results related to the transmission term, deriving the results under the application of our TPC policies. Moreover, the path loss model has been closely integrated with the energy model in order to relate our results to the A-LQE approach.
## 3 Results and Discussion
### Mineral Analysis
The same trend is observed in AMs species, although with some differences. In the case of AS species, it presents a weak negative correlation for Hem, Fib, and NDF, while, conversely, it presents a weak positive correlation for H, protein, ash, and fat. AR stands out as the species with the highest amount of ash, followed by AC and AS, and all other parameters, except NFC, show a negative correlation. The results of the mineral content analysis reveal significant disparities among the species under investigation. Mn emerged as the mineral exhibiting a notable positive correlation and stood out as the most prevalent mineral in the AD species. This species demonstrated positive correlations with Cd, Mg, Fe, Cu, and Ni and a weak positive correlation for Cr. Conversely, the AC, AR, and AS species confirmed their status as the species where most of the analyzed minerals were detected (Ca, Zn, Cd, P, K, Na, and Cu), thus exhibiting positive correlations for these minerals, albeit with significant differences observed between the species. Conversely, a negative correlation between the AD and AP species was found. As previously stated, AMs displayed a higher Fe content compared to other species, thus manifesting a strong positive correlation. Notably, AMs was the sole species demonstrating a negative correlation for all analyzed minerals except Cu.
# Does Organic Farming Increase Raspberry Quality, Aroma and Beneficial Bacterial Biodiversity?
## 2 Materials and Methods
### Assessment of Fruit Quality Parameters  
To measure soluble solids content (SSC) and fruit titratable acidity (TA), fruit juice was obtained homogenising berries and sieving them through a cloth. SSC of fruits was determined by digital refractometer (Atago-PAL1, Tokyo, Japan) and expressed as \"Brix. Titratable acidity was determined manually by adding 0.1 M NaOH dropwise until reaching the titration end-point. Analyses were performed on nine replicates consisting of the juice from a pool of three berries each. Prior to homogenisation, fruit colour was determined using a CR-400 Chroma meter Colorimeter (Konica Minolta, Tokyo, Japan) on each berry. Three measurements were performed on the same fruit.  
To determine the anthocyanins content, 3 g of homogenized fruit were suspended in 25 mL of methanol and incubated 24 h at room temperature. Samples were then centrifuged for 5 min at 4000\(\times\)\(g\) and the supernatant was stored at \(-\)20 \({}^{\circ}\)C. Anthocyanin content was determined on the supernatant by using high-performance liquid chromatography (HPLC, Waters 1525, Waters, Milford, MA) at 520 nm according to [47].  
Quantification was performed with the external standard method with the standards calibration curves (different concentrations were used to build the curve: 1-100 \(\upmu\)mL\({}^{-1}\)). Malvidin-3-glucoside chloride (Sigma Aldrich, St. Louis, MO, USA) was used as standard. The reliability of the quantification method was assessed by the R\({}^{2}\) value of the standard and assessing that the concentration of each quantified compound was within the concentration range of the standard. Anthocyanin identification was performed according to [48].
# Analyzing the Characteristics of Soil Moisture Using GLDAS Data: A Case Study in Eastern China
## 1 Introduction
In this paper, we analyzed the characteristics of soil moisture using GLDAS data, focusing on the effects and differences between temperature and precipitation in different seasons from 1961 to 2011 in eastern China. As soil moisture is an important reference for climate change, the evaluation of dry and wet climate conditions is not only significant with regard to meteorology, but also to natural environment and farming cultivation.
## 2 Design and Simulation
### Simulation of the Relative Sensitivity with Different Loaded Areas on Surface of SAWR
We simulated the variation of the frequency response generated by the particle loaded on each area of the SAWR's surface. Additionally, a 300 nm dense SiO\({}_{2}\) layer was used to cover the IDT as the particle loaded on the chip surface, and the loaded area was set as a pair of interdigital electrodes. The response of each area of the chip was calculated numerically and compared with the center frequency before loading to obtain the normalized sensitivity distribution of the chip to the mass loading effect in Figure 6.  
It can be seen from Figure 6 that when the particles fall onto each area of SAWR's surface, the response was different: The chip's central transducer was the most sensitive area of the mass loading effect. As the distance between the particle and the chip center increased, the frequency offset decreased accordingly. The yellow line in the figure indicated a gap area between the central and lateral transducers. This area produced the same frequency offset for the loading effect under the same particle mass. The reflector area near the outer edge of the chip was almost insensitive to the mass loading effect. Taking the corresponding area within 50% of the highest frequency offset  
Figure 5: The contrast of the theoretical results and measured results: (**a**) Amplitude-frequency response; (**b**) Phase-frequency response. Figure 6: The normalized sensitivity distribution of the SAWR’s surface to the mass loading effect. responding area, we can produce the most sensitive response to the particle while having a larger attachment area. The optimal particle attachment area is indicated by \(L\) in Figure 6. Therefore, we should let most of the particles fall within this range after being ejected from the acceleration nozzle of the impactor to obtain a good response effect.
## 2 Materials and Methods
### Data Analysis
#### 2.2.1 Data Preprocessing  
The temporal segmentation, a technique previously developed in our lab, was used to automatically extract the timestamps of the vibratory onsets and offsets of the vocalized segments in the HSV recording [15]. Subsequently, a denoising algorithm and motion compensation were implemented across the video frames of each vocalization to track the location of the vocal folds inside a window--encompassing the vibrating vocal folds [18; 57]. The HSV frames were then cropped based on the center and size of the motion window. The kymograms were extracted next at various cross sections along the anteroposterior length of the vocal folds from the cropped HSV frames for each vocalization. The \(y\)-axis of the kymogram image represents the left-right dimension of the video frame, while the \(x\)-axis refers to time (frame number).  
Each kymogram was smoothed using a moving average 1D filter with a window size of 5 pixels (along the \(y\)-axis) to mitigate the impact of noise in the images. Therefore, each pixel intensity in the kymogram was calculated by taking the average intensity of the four neighborhood pixels (two pixels above and below the pixel of interest). Since our area of interest, i.e., the glottal area, was located in the middle section of the kymograms, a Tukey window function (window size of 15 pixels) was applied next to provide higher weights to the pixels located in the middle and less weights to the pixels located in the top and bottom of the kymograms. With these preprocessing steps, the kymograms became ready for the feature extraction, explained below.
## 2 Materials and Methods
### Introduction of Observation Instruments
These accuracies are maintained through regular calibrations, including the use of a precision target for liquid nitrogen (LN2) calibration, which ensures an absolute accuracy of \(\pm\)0.25 K and a calibration repeatability better than 25 mK. A key advantage of the MWR is its ability to maintain continuous operation even under challenging weather conditions, such as thick cloud cover or precipitation, where other instruments may encounter limitations or produce less reliable data. This robust capability supports a variety of applications, including weather forecasting, atmospheric research, boundary layer analyses, and studies of cloud and precipitation dynamics. The real-time nature of the MWR's measurements allows for the continuous tracking of atmospheric changes, making it an invaluable tool for meteorologists and atmospheric scientists. The MWR contributes significantly to the study of atmospheric science, particularly in areas such as atmospheric stability, water vapor and cloud dynamics, and boundary layer turbulence. Its high sensitivity and precision in measuring key thermodynamic parameters make it an indispensable instrument for advancing our understanding of weather and climate processes. For more information on the RPG-HATPRO-G5 microwave radiometer, please visit [[http://www.radiometer-physics.de](http://www.radiometer-physics.de)]([http://www.radiometer-physics.de](http://www.radiometer-physics.de)) (accessed on 4 December 2024).
## 3 Results and Discussion
### X-ray Diffraction and Physical-Chemical Analysis  
X-ray diffraction analysis (Figure 5) identified compounds already reported for _Sargassum_ spp. in earlier studies, such as calcium carbonate and dolomite (calcium-magnesium carbonate) [56; 57]. The planes characteristics of each phase are shown in Figure 5. These compounds were present throughout the anatomy of _Sargassum_ spp., from stem to leaves, as was previously recorded in other scientific studies [56; 57; 58; 59] and demonstrated in this study using scanning electron microscopy (SEM, Figure 6).  
Figure 4: Calculation of the maximum degree of advance (\(\alpha\)) at the highest degradation rate to calculate the reaction order (\(n\)) of _Sargassum_ spp. using the Horowitz-Metzger method.  
Figure 5: X-ray diffraction (DRX) analysis of _Sargassum_ spp.  
Figure 6: SEM analysis: (**a**) energy-dispersive X-ray spectroscopy (EDS) of the stem; (**b**) EDS of the leaves; (**c**) macroporous morphology; (**d**) vesicles; (**e**) leaves.  
SEM analysis (Figure 6) revealed the morphology of _Sargassum_ spp., showing that the results of the elemental chemical composition determined by _energy_-dispersive _X-ray spectroscopy_ (_EDS_) (Figure 6a,b) correspond to the phases of the compounds identified by DRX (Figure 6). Another observation is the presence of macroporous structures (Figure 6c-e) that are characteristic of seagrasses that contain calcite and dolomite [56]. The chemical composition revealed by SEM and DRX was consistent with the results of infrared spectroscopy (Figure 7).  
The infrared spectroscopy analysis (FTIR), shows a strong and broad absorption band at 3406 cm\({}^{-1}\) and the medium peak at 1408 cm\({}^{-1}\) (Figure 7) which correspond to O-H stretching vibrations that characterize the presence of hydroxyl groups in compounds like cellulose, hemicellulose, and lignin [60; 61; 62; 63]. The intensities of the high bands between 1630 and 1406 cm\({}^{-1}\) represent C=C stretching and suggest the presence of lignin and aromatic compounds, while the weak peak at 1250 cm\({}^{-1}\) can be attributed to S=O stretching in sulfate esters and CO stretching in phenols. The band affiliated at 1055 cm\({}^{-1}\) is indicative of COC stretching in the xylan of the hemicellulose. These bands are characteristics of different types of _Sargassum_ spp., including those reported on Mexico's Caribbean coasts [60; 61; 62; 63].  
The characterization by FTIR and SEM is consistent with the chemical composition of the principle polymeric components -cellulose, hemicellulose, and lignin- detected by fiber analysis in the _Sargassum_ spp. These results are presented in Table 1 as values in ranges determined for each compound, with reference to the samples collected from the Mexican Caribbean coast.
## 2 Theoretical Analysis
### Image Encryption Based on Binarized Computational Ghost Imaging (BCGI)
The spatial light modulator contains a random phase matrix \(\{\delta_{i}(x,y)\}\)\(i=1,2,\ldots,N\) and a spatially incoherent beam is generated after SLM. When we know the random phase and the incident light field \(I(x,y)\), the intensity of the light field after SLM can be calculated as \(I_{i}(x,y)\). \[I_{i}(x,y)=I(x,y)e^{\delta_{i}(x,y)} \tag{4}\]  
**Step 2:** After Fresnel diffraction, light transmits to the object plane. Assume that the distance between the object plane and the spatial modulator is z and the intensity of light projected onto the object after random phase modulation is \(I_{r}(x,y)\). \[I_{r}(x,y)=\left|I_{i}(x,y)\otimes h_{z}(x,y)\right|^{2} \tag{5}\]  
where \(h_{z}(x,y)\) is the optical transfer function at distance z in the spatial domain and \(\otimes\) represents the convolution operation. **Step 3:** Data detected by single pixel cameras \(\{C_{r}\}\) is as follows:  
\[C_{r}=\iint{I_{r}(x,y)m(x,y)dxdy} \tag{6}\]  
where \(m(x,y)\) represents distribution function of the target object. Figure 2: Flowchart of the watermarking method based on binarized computational ghost imaging. **Step 4:** To facilitate concealment, the ciphertext \(\{C_{r}\}\) is binarized as follows:  
\[D_{r}=\left\{ \begin{array}{cc}1&C_{r}>V\\ 0&C_{r}\leq V\end{array} \right\} \tag{7}\]  
where \(V=[max(C)+min(C)]/2\) is the critical value, \(max(C)\) is the maximum of \(\{C_{r}\}\) and \(min(C)\) is the minimum of \(\{C_{r}\}\). **Step 5:** The purpose of the decryption process is to reconstruct the distribution function \(m(x,y)\) of the target object. We associate the binarized \(\{D_{r}\}\) with the light intensity \(I_{r}(x,y)\) projected onto the object. The core arithmetic formula for correlation is as follows:  
\[T(x,y)=\frac{1}{N}\sum_{i=1}^{N}{(B_{r}-\langle B\rangle)(I_{r}(x,y)-\langle I (x,y)\rangle)}=\langle BI(x,y)\rangle-\langle B\rangle\langle I(x,y)\rangle \tag{8}\]  
where \(T(x,y)\) is the distribution function of the object obtained after decryption and \(\langle B\rangle\) is the average value of \(B\).