esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments  
Vibration sensitivity of the reinforced-support FP cavity was tested using two ultra-stable lasers. Both ultra-stable lasers were frequency locked by the PDH method. One of the ultra-stable lasers had a reinforced-support FP cavity. The reinforced support FP cavity was placed on a vibration table. After an acceleration was generated by the vibration table, the vibration sensitivity of the FP cavity was measured by testing the beat frequency variation of the two lasers. The schematic diagram of the vibration sensitivity test is shown in Figure 6. Firstly, the laser 1 was locked to the FP cavity 1. The laser from the laser 1 passed through BS1, EOM1, PBS1 and \(\lambda\)/4 and entered the FP cavity 1. The laser reflected by the FP cavity 1 was collected by PD1 for PDH frequency locking. Secondly, the laser 2 was locked to the FP cavity 2 (reinforced-support FP cavity). The laser locking optical path was same as laser 1. Subsequently, the laser 1 and laser 2 were frequency beat after frequency locking. Subsequently, the lasers from laser 1 and laser 2 passed through BS3 for frequency beating. The frequency-beat signals were collected by PD3 (FPD310 detector from Menlo Systems, Planegg, Germany) and analyzed using a frequency counter (FCA3100 from Tektronix, Beaverton, OR, USA) and a spectrum analyzer (FCA3100 from Tektronix, Beaverton, OR, USA). The FP cavity 2 was placed on a vibration table ([PERSON]'s TS-150), and acceleration was applied by the vibration table. Finally, different accelerations were applied to the FP cavity 2 to measure the beat frequency changes. Vibration sensitivity of the FP cavity was calculated from the amount of acceleration and frequency variations.  
The zero-expansion temperature test of the FP cavity was then carried out. After changing the operating temperature of FP cavity 2, the FP cavity 2 would thermally deform, resulting in the beat frequency of the two lasers being changed. The curve of the beat frequencies variation with temperatures could be obtained by testing the beat frequencies at different temperatures. The zero-expansion temperature of FP cavity 2 was obtained from this curve. The specific method was: firstly, The FP cavity 2 operating temperature was changed. When the FP cavity 2 temperature stabilized, the beat frequency was measured. The operating temperatures of FP cavity 2 were set to 25 \({}^{\circ}\)C, 27.5 \({}^{\circ}\)C, 28 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, and 30.5 \({}^{\circ}\)C, respectively. The beat frequency results at these temperatures were recorded separately. Then, the variation curve of beat frequencies at different operating temperatures  
Figure 5: Stress analysis of the FP cavity at different temperatures. The red arrow indicates the location of maximum stress.  
was plotted. According to this curve, the temperature at which the beat frequency reached the minimum value was derived, and this temperature was the zero-expansion temperature of the FP cavity 2.
# Low Power EEG Data Encoding for Brain Neurostimulation Implants +
###### Abstract  
Neurostimulation devices applied for the treatment of epilepsy that collect, encode, temporarily store, and transfer electroencephalographic (EEG) signals recorded intracranomially from epileptic patients, suffer from short battery life spans. The principal goal of this study is to implement strategies for low power consumption rates during the device's smooth and uninterrupted operation as well as during data transmission. Our approach is organised in three basic levels. The first level regards the initial modelling and creation of the template for the following two stages. The second level regards the development of code for programming integrated circuits and simulation. The third and final stage regards the transmitter's implementation at the evaluation level. In particular, more than one software and device are involved in this phase, in order to achieve realistic performance. Our research aims to evolve such technologies so that they can transmit wireless data with simultaneous energy efficiency.  
Keywords:epilepsy; deep brain stimulation; responsive neurostimulation; wireless transmission; Bluetooth Low Energy; Delta encoding low power
## 4 Materials and Methods
### Schemes for Recovery of Optical Parameters  
#### 4.4.1 Generation of MC Look-Up Table  
Modified MCML (Section 3.1) program was executed to simulate the SRR as well as the epithelial sensitivity at each of the 76 detector fibers for a set of fixed (known) and free (unknown) optical parameter values. The term 'free' refers to the parameters which we aim to reconstruct. In this study the goal is to extract the optical parameters of the epithelium layer and hence the scattering and absorption coefficients of the top layer are considered as the free optical parameters. The values of free parameters for which the simulations are executed are chosen a priori, based on our knowledge of the range of the optical parameters for the tissue to be probed.  
We tabulated the synthetic SRR data for top layer absorption coefficients: \(\mu_{a}^{t}=~{}1,~{}3,~{}5,~{}7\) and 9 cm\({}^{-1}\) and scattering coefficients: \(\mu_{s}^{t}=~{}20,~{}40,~{}60,~{}80\), and 100 cm\({}^{-1}\). We further generated SRR data for absorption coefficients: 1-9 cm\({}^{-1}\) in steps of 0.05 cm\({}^{-1}\) and scattering coefficient: 20-100 cm\({}^{-1}\) in steps of 1 cm\({}^{-1}\) using cubic interpolation. This process builds the three-dimensional MC-LUT of size (\(76\times 161\times 81\)).
## 3 The Proposed Model
### Depth Prediction Network  
Our network learns how to predict pixel-wise inverse depth and disparity in a single image \(I\) in an unsupervised approach by training stereo images (i.e., USI model). Although this method may provide only general training for depth prediction, as compared to the depth supervision methods, it has the advantage of being able to train with the easily acquired larger datasets and then improve its accuracy and performance by using good loss functions. The depth \(p\) is calculated according to the formula \(p=(b\times f)/d\) (\(b\): baseline distance between two cameras, \(f\): camera focal length, \(d\): disparity map). Figure 1 shows the structure of the proposed model. The network is a simple ResNet50-based auto-encoder, which predicts a disparity map as required to simulate an image from a different viewpoint. The network needs pairs of images (provided from the KITTI dataset) for training. In other words, it learned how to reconstruct an input left image \(l_{l}\) into a target right image \(l_{r}\). Our network is inspired by [6], and we used a ResNet50 backbone network as an encoder. The decoder consists of six up-convolution layers and performs upsampling based on a bilinear interpolation with a scale-factor of two in each layer. In the decoding process, three disparity maps \(d^{f/m,c}\) of different sizes (a scale factor of two) are predicted, where \(f\), \(m\) and \(c\) represent the fine, medium, and coarse view, respectively. We calculate the loss (image-reconstruction loss, left-right consistency loss, and smoothness loss) for each of these three disparity maps. The proposed network generates a disparity map \(d_{r}\) used to synthesize \(l_{l}\) into \(\hat{l}_{r}\) (i.e., warping process \(w_{+}\)). In addition, it also predicts another disparity map \(d_{l}\) used to synthesize \(l_{r}\) into \(\hat{l}_{l}\) (i.e., warping process \(w_{-}\)) simultaneously. After that, the post-processing method suggested by [6] is applied to further improve the accuracy.
## 3 Results and Discussion
### Detection of Wetting Zones through GPR Data
In the subsequent phase, as the advancing wetting front reached the underlying limestone stratum at a depth of 0.3 m, the infiltration rates observed at the soil surface diminished owing to the reduced hydraulic conductivity of the stratum. The three-dimensional representation facilitated the identification of flow channeling through the limestone layer (Figure 2g), corroborated by observations in Figure 1.  
In this scenario, the wetted region exhibited an extended and uneven form (Figure 2g,h), aligning with the interface of the limestone layer (red arrows in Figure 1 Step 6, Figure 2e,g). Both the 3D diagram and the patterns from dye staining indicated that water infiltrated through this impermeable layer via a funneling mechanism. Specifically, the limestone layer, characterized by low hydraulic conductivity, acted as a capillary barrier that directed flow into a relatively confined path, a common phenomenon known as funneled flow [46]. Consequently, flow channeling was observed at the layer interfaces, specifically at a depth of 0.3 m.
## 4 Discussion
conducted a study to assess the combined expressions of p53, cyclin D1, and EGFR as well as their prognostic value in 97 (stages III-IV) oral cancer patients undergoing CRT. The authors discovered that Cyclin-D1 and p53 expression was associated with poor response to treatment, and that co-expression of Cyclin-D1, EGFR, and p53 was associated with poor survival. As a result, they concluded that these biomarkers could be used as prognostic indicators in patients with locally advanced OSCC. Ref. [21] According to [PERSON] et al., a study was conducted to investigate the expression of cyclin D1, EGFR, Ki67, p21, p27, p53, and CD31 in the primary tumour after CRT. They found that patients with high expression of p21, p27, ki-67, and CD31 and low expression of p53, cyclin D1, and EGFR have better disease-free survival [22]. [PERSON] et al. conducted a study to assess the relationship of cyclin D1, EGFR, and p53 with the pattern of recurrence in OSCC patients undergoing CRT. Increased cyclin D1, EGFR, and p53 levels were found to be associated with tumour recurrence. The researchers concluded that tumours that overexpress cyclin D1, EGFR, and p53 are resistant to CRT and that these markers may serve as predictors of metastasis and locoregional recurrence in OSCC patients [23]. According to [PERSON] et al., a study was conducted to assess the relationship of EGFR, p53, Bcl-2, p16, and cyclin D1 expression with treatment outcomes in 178 patients with advanced (stage III-IV) OSCC treated with CRT. Only p53 was found to be an independent prognostic marker in this study, identifying patients at higher risk of recurrence. The study concluded that the panel of p53, EGFR, and p16 has clinical relevance, with p53 serving as a prognostic marker and EGFR and p16 being useful for developing personalized treatment protocols [24]. The treatment plan can be created based on the relevant clinical conditions and molecular characteristics of individual cancers. Comprehensive knowledge of the tumour's biological aggressiveness and customized treatment protocols will significantly affect the cancer patient's quality of life [24].
## 2 Materials and Methods
### Biovolume Estimation  
Equations for the estimation of cell volume (biovolume) for a wide range of microorganisms have become increasingly precise and powerful, especially since the introduction of allometric models [42]. Although laser and digital confocal microscopy can acquire 3D images, these techniques are time-consuming and expensive. In this regard, digital two-dimensional images acquired with an optical microscope have been recently proposed as a very promising alternative low-cost technique for biovolume mathematical estimation [43; 44]. Thus, approximately 100 microscopy images of individual randomly selected cells (colonies were not considered) were taken for each yeast strain with a BA410 optical microscope (Motic, Kowloon, Hong Kong) provided with a Motic 580 5.0 MP camera and Motic Image Plus 3.0 software. These microscopy images were analysed with the aid of the R _EBImage_ package [45]. Thus, the cell volumes were estimated from the 2D cell images by applying Equation (6) [43]. In this equation, \(A\) represents the estimated area of the cell cross-section in pixels according to the _EBImage_ functionalities, \(d\) is the major axis of the ellipse that better fits their section and \(E\) is the exact area of the ellipse. In addition, the \(\sqrt{\frac{A}{E}}\) term is a correction factor to obtain a more realistic approximation of the real cell volume.  
\[V=\frac{2}{3}Ad\sqrt{\frac{A}{E}} \tag{6}\]  
This method produces an average error below 1% even when some complex-shaped cells are analysed [42]. Therefore, since the analysed shapes of this study are quite simple (elliptical), the error must be almost negligible.  
The biovolume differences found between \(Sc\) and \(Sc_{b}\) was confirmed with the use of a unilateral \(t\)-test [46]. The Kolmogorov-Smirnov test [47] was also performed in order to reject the hypothesis that the two samples are drawn from the same continuous distribution.
# Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand
## 2 Materials and Methods
### Data Collection  
The data used in this study involved three types of primary data, namely (1) the historical measurements of daily rainfall, daily maximum and minimum temperatures, and daily river discharge; (2) digital descriptions of the landscape gathered from the Land Development Department (LDD), i.e., Digital Elevation Model (DEM) with 30 m horizontal spatial resolution, land use, and soil type; and (3) statistics data such as population and factory information, which were used to calculate the water demand for household and industry, respectively. Regarding the daily observations during 2000-2017, the rainfall and maximum and minimum temperature data were collected from seven rain gauge stations and one meteorological station of the Thai Meteorological Department (TMD), respectively, whereas the discharge recorded at gauging station E.68A (Ban Khong Po, Mueang District, Nong Bua Lamphu) were collected from the Royal Irrigation Department (see Figure 1 for locations).  
Figure 1: Location of the study area, and its topography and land use.
###### Abstract
Additionally, intra-cluster and inter-cluster SWIPT mechanisms are introduced to allow for the immediate energy replenishment for CHs or relays with insufficient energy while transmitting data, thereby preventing data transmission failures due to energy depletion. 2. To prevent the death of CRSN nodes due to insufficient energy, and to avoid the energy waste caused by excessive activation of the SWIPT mechanism, an energy status control mechanism is introduced. When the residual energy of nodes falls below the dormancy threshold, nodes only engage in EH and do not participate in data transmission or forwarding. Simulation results indicate that the proposed EH- and SWIPT-based multi-hop uneven clustering routing protocol (ES-MUCRP) markedly prolongs the network lifespan, improves the balance of energy consumption among nodes, and enhances the network monitoring capabilities when compared to existing clustering routing protocols for CRSNs.
## 2 The Overall Strategy of D-PMU Placement
### Initial Placement of D-PMU  
If a bus is identified that can cause personal casualties and heavy economic losses after power supply interruption, a D-PMU needs to be placed on the bus to monitor the operation status in real time. In addition, D-PMUs should be placed in advance according to practical applications, such as feeders with voltage regulating equipment, access points of photovoltaic power plants, etc. As a result of the access of new energy and electric vehicles, voltage instability caused by excessive voltage amplitude has become one of the main factors threatening the stable operation of the power grid. The real-time and accurate measurements of D-PMUs can capture the out-of-limit information of voltage in time. D-PMUs can be pre-placed on buses with a high probability of displaying out-of-limit voltage [15].
## 3 Experiment
### Comparative Experiment  
Two traditional denoising methods (WNNM [34] and GDGF [35]) and two neural network denoising algorithms (DNCNN [29] and ADNet [30]) were chosen for the experiment since they had better image denoising effects. The denoising effect of the algorithm is evaluated subjectively through two display modes of the single-band image and false color image. The qualitative evaluation indices include the subjective visual effect of human eyes and the column mean curve of the image. Figure 6 shows the effect of several methods on noise removal in some bands of hyperspectral images. The part specially marked is for the biological detail characteristics of tumor hyperspectral images, which plays an important role in the subsequent image processing. Therefore, cleaner noise removal and clearer details result in the best experimental results being obtained.  
The experiment selected different single-band images of tumor hyperspectral images, which are important reference bands for the classification and diagnosis of subsequent tumor hyperspectral images, and some details are biological tissues or biological tissue boundaries with obvious characteristics. The four original band pictures of the images all exhibit rather significant fringe noise, as can be observed. Although the two conventional techniques have the same effect of eliminating fringe noise, they both suffer from image distortion due to excessive edge smoothing. The fringe noise after removal is still severe even if the two neural networks' denoised images are clean and maintain the highest amount of visual information. It is evident that the separation of the fringe components is incomplete. The approach presented in this paper can successfully achieve denoising while effectively separating the fringe components.  
In order to better display the denoising effect, 630.25 nm (band 105), 518.05 nm (band 56), and 480.17 nm (band 39) were selected in the experiment to synthesize pseudo  
Figure 6: Comparison of single-band images.  
As can be observed, the standard filter denoising method leaves some noise in some areas of the image while being overly smooth in others, causing artifacts to form at the edges of the image. The removal of noise by a neural network do not show a significant contribution, and it may potentially add new fringe noise from the subjective influence of human vision. The detailed part specially marked includes the blood vessels of brain tissue, the boundary between tumor and normal tissue, and the gray matter of the brain, which are important references for tumor hyperspectral diagnosis methods. Therefore, the denoising effect of this part is directly related to the accuracy of the diagnostic results. The method suggested in this work may successfully eliminate non-uniform fringe noise while maintaining the image edge features, as can be convincingly demonstrated in the sections that follow.
# Metabolite Profiling of _Christia__vevertilionis_ Leaf Metabolome via Molecular Network Approach
## 3 Results and Discussion
### C-glycosflavone Derivatives
#### 3.1.10 Other Compounds  
Inspection of the MS/MS spectral data of the peaks that appear in the TICs of both positive and negative ion modes (Figure S1a) also allowed the identification of other mis-cellaneous compounds, comprising organic acids, a nucleoside, and amino acids. All these compounds appeared as \"self-loop\" nodes in the generated MN of C. _respetinitis_ leaf.  
Compound **1**, with a deprotonated molecular ion [M - H]\({}^{-}\) at \(m/z\) 195.050, was identified as D-gluconic acid. It displayed fragment ions at \(m/z\) 177 for [(M\(-\)H)-H\({}_{2}\)O]\({}^{-}\), 147 for [(M\(-\)H)-H\({}_{2}\)O-CH\({}_{2}\)O]\({}^{-}\), 129 for [(M\(-\)H)-2H\({}_{2}\)O-CH\({}_{2}\)O]\({}^{-}\), and 111 for [(M\(-\)H)-3H\({}_{2}\)O-CH\({}_{2}\)O]\({}^{-}\)[38]. Compound **3** was putatively characterized as the nucleoside molecule, namely, adenosine, which presented a protonated molecular ion [M + H]\({}^{+}\) at \(m/z\) 268.104. The MS/MS spectra of this compound exhibited a fragment ion at \(m/z\) 136 for [(M+H)-132]\({}^{+}\), which corresponded to the formation of the adenine ion, which, in turn, lost a molecule of NH\({}_{3}\) to give to the fragment ion at \(m/z\) 119. This fragmentation pattern was in accordance with that reported by [PERSON] et al. [39]. Compound **4** showed a deprotonated molecular ion [M - H]\({}^{-}\) at \(m/z\) 191.019, and fragment ions at \(m/z\) 173 for [(M\(-\)H)-H\({}_{2}\)O]\({}^{-}\), 129 for [(M\(-\)H)-H\({}_{2}\)O-CO\({}_{2}\)], and 111 (base peak) for [(M\(-\)H)-CO\({}_{2}\)-2H\({}_{2}\)O]\({}^{-}\), all of which matched with the characteristic fragment ions of citric acid [38]. Compounds **5**, **8**, and **12**, with protonated molecular ions [M + H]\({}^{+}\) at \(m/z\) 182.081, 166.086, and 205.097, respectively, were characterized as amino acids based on the common neutral losses of 46, 63, and/or 17 Da, resulting in the formation of main fragment ions, viz., [(M+H)-H\({}_{2}\)O-CO]\({}^{+}\), [(M+H)-H\({}_{2}\)OCO-NH\({}_{3}\)]\({}^{*}\) and/or [(M+H)-NH\({}_{3}\)]\({}^{+}\) in their MS/MS data. Thus, compounds **5**, **8**, and **12** were putatively assigned as tyrosine, phenylalanine, and tryptophan, respectively [40; 41].
## 5 Performance Analysis and Results
### Encryption Quality Metrics
#### 5.2.2 Correlation Analysis  
Correlation is defined as a statistical relationship that measures the relativity between two variables. The correlation between the original image and encrypted image is measured between two vertically adjacent pixels: a plain image/cipher image, respectively [4]. If the correlation coefficient values are closer to 1, it reflects highly dependent variables between the original and deciphered image (i.e., good decryption quality). If the correlation coefficients are closer to 0, it refers to highly independent variables between the original and cipher image (i.e., totally different, no features between original image and encrypted one, high-quality encryption algorithm). Smaller values of the correlation coefficients assess a successful encryption/decryption process. The correlation between original and encrypted images for the proposed systems MPFrFT and FrFT are tabulated in Table 12. These correlation coefficient values ensure the immunity of the proposed schemes. The correlation coefficient \(\left(r_{xy}\right)\) is defined as [3]:  
\[r_{xy}=\frac{Cov(x,y)}{\sigma_{x}\times\sigma_{y}} \tag{30}\]  
\[Corr=\frac{\sum_{i=1}^{N}\left(x_{i}-E(x)\right)\left(x_{y}-E(y)\right)}{ \sqrt{\left(x_{i}-E(x)\right)^{2}}\times\sqrt{\left(x_{i}-E(x)\right)^{2}}} \tag{31}\]  
where \(E(x)=\frac{1}{N}\times\sum_{i=1}^{N}x_{i}\), \(x,y\) are the gray-scale pixel values of the source and enciphered images.
## 3 Analysis of Calculation Examples  
To validate the analytical calculation method proposed in this paper, a simply supported bridge-rail system under the moving loads of four motor car groups was considered as an example, and the analytical calculation method proposed in this paper and ANSYS finite-element numerical method were used to calculate its dynamic response. The simply supported bridge-rail system can be simplified as a simply supported double-beam, and the rail fasteners can be simulated with springs in interlaminar distribution. The specific parameters of the simply supported double-beam system are as follows: length of beams \(l=32\) m; length of springs is \(1\)m; damping ratio is \(0\); primary beam: elastic modulus \(E_{1}=2.06\times 10^{5}\) MPa, horizontal moment of inertia \(I_{1}=3.217\times 10^{-5}\) m\({}^{4}\), mass per unit length \(m_{1}=60\) kg/m; secondary beam: elastic modulus \(E_{2}=3.5\times 10^{4}\) MPa, horizontal moment of inertia \(I_{2}=10.42\) m\({}^{4}\), mass per unit length \(m_{2}=36,000\) kg/m; spring stiffness \(\kappa=6\times 10^{7}\) N/m. As shown in Figure 2, four load-groups contain four moving loads, where load \(F=160\) kN, distance of load \(L_{1}=2.5\) m, \(L_{2}=14.875\) m, \(L_{3}=4.9\) m, \(L_{4}=24.775\) m.