esa-sceva/satcom-chunk-collection huggingface dataset:
# Highly Sensitive Liquid M-Z Waveguide Sensor Based on Polymer Suspended Slot Waveguide Structure
## 3 Mode and Optical Field Optimization of the Slot Waveguide  
The single-mode waveguide ensures the extinction ratio of the MZI and the stability of the device detection. However, to reduce the coupling loss of the device, while maintaining the compactness and fewer modes of the device, the Y-branch of the MZI adopts a size of 2 \(\upmu\)m \(\times\) 2 \(\upmu\)m. The taper structure is used in the coupling area to compress the optical field to the slot waveguide, and the slot region needs to guarantee the single-mode characteristics of the device to realize the stability of the device detection. The slab size was kept at 100 nm to ensure machining accuracy and structural stability. The number of modes, distribution of the optical field, and mode effective refractive index (\(N_{eff}\)) of waveguides with varying slot cross-section sizes were simulated using the finite element method. Figure 2 depicts the relationship between waveguide size and \(N_{eff}\), as well as the distribution of the optical field. As shown in Figure 2, when the section dimension of the slot waveguide is 2 \(\upmu\)m \(\times\) 2 \(\upmu\)m, it becomes two waveguides, which cannot form a relatively high energy ratio in the slot. Meanwhile, when the section size is 0.5 \(\upmu\)m \(\times\) 0.5 \(\upmu\)m, the optical field has a large leakage. The increase in modes is not conducive to the extinction ratio of the device. Meanwhile, when the slot waveguide's size increases, the slab's limitation of the slot on the light field decreases, causing light to leak from the slab. Thus, the waveguide size needs to be reduced horizontally and vertically. Nevertheless, when the waveguide is undersized, the waveguide structure is vulnerable in the process of coupling loss in the redissolving atmosphere. Finally, the slot height was set at 1 \(\upmu\)m to ensure single-mode characteristics and low leakage loss, establishing the basic mode of the slot waveguide. The optical field distribution in Figure 2 shows that when the supports on both sides are larger than 2.2 \(\upmu\)m, there is no effect on the optical field.
## 2 Detection Model and Existing Problems
### Signal Structure  
During the detection model, several sets of B1I and B3I signals transmitted by MEO satellites with corresponding pseudo-random noise (PRN) codes were used. A part of the signals reaches the airborne-based receiver directly, and the other part of the signals is reflected by the ship targets and arrives at the latter receiver. The received echo is the sum of all echoes transmitted by each satellite and reflected by each target. In the simulation experiment, we found the echo modulation information through the proposed geometric model with satellite ephemeris, receiver motion information and target position, altitude, and velocity information. The BDS signals' model structures are expressed as formulae with changes and states in the propagation and receivers, as follows.  
In the formula expression, we set '\(i\)' as the satellite number and '\(j\)' as the target number. The transmitting BDS signal [27; 28] of satellite \(i\) is expressed as:  
\[s^{i}(t)=AC^{i}D^{i}\cos(2\pi f_{0}t+\varphi^{i}) \tag{1}\]  
where \(s^{i}(t)\) contains B1I and B3I signals. \(A\) is the signal amplitude, \(C^{i}\) the signal ranging code, and \(D^{i}\) the signal data code. During the cosine function, \(f_{0}\) is the carrier frequency, and \(\varphi^{i}\) is the original phase. The echoes after propagation and receiving can be expressed as follows:  
\[R^{i}_{d}=a^{i}_{d}\cdot s^{i}(t-\tau^{i}_{d},f^{i}_{d}) \tag{2}\]  
\[R^{i,j}_{r}=a^{i,j}_{r}\cdot s^{i}(t-\tau^{i,j}_{r},f^{i,j}_{d}) \tag{3}\]  
where the subscripts '\(d\)' and '\(r\)' are direct signals and reflected signals, respectively. \(a^{i}_{d}\) is the propagation coefficient and scattering coefficient of direct signals, and \(a^{i,j}_{r}\) is the reflected signal reflected by target \(j\). \(\tau\) and \(fd\) are the responding transmitted delay and Doppler frequency, which are related to the carrier frequency and show different values between the B1I and B3I signals. Finally, the total received B1I and B3I signals transmitted by multiple satellites and propagated through the direct and multiple targets reflection approach is expressed as  
\[R=\sum_{i=1}^{M}(R^{i}_{\text{B1I},d}+\sum_{j=1}^{N}R^{i,j}_{\text{B1I},r})+ \sum_{i=1}^{M}(R^{i}_{\text{B3I},d}+\sum_{j=1}^{N}R^{i,j}_{\text{B3I},r}) \tag{4}\]  
Figure 1: MIMO and multiple target BDS signal detection geometric model.  
For pulse accumulation and slow-time Doppler frequency detection, the echoes expressed in \(Q\) ranging code periods is (5), after data code demodulation processing.  
\[\mathcal{Q}R=\sum\limits_{q=1}^{Q}u_{T_{P}}(t-q\cdot T_{P})\cdot R \tag{5}\]  
where \(\mathcal{Q}R\) represents the echoes in \(Q\) ranging code periods, and \(u_{T_{P}}(t-q\cdot T_{P})=1\), \(q\cdot T_{P}<t<(q+1)T_{P}\) is the window function. \(T_{P}\) is the ranging code cycle period.
## 5 Discussion
### Thermal Fatigue Test (TF)  
The repetition of heating cycles (TF) showed fewer fluctuations than the TT, although some rocks were very sensitive to this thermal regime (Figure 10).  
The behaviour of the chalk to the TF test was similar to the TT test, with no variations in its microstructure but a grain crushing and loss of material on the surface, which produced only an increase in the CRI (Figures 9a and 10). During a temperature increase on chalk, the calcite observed at SEM is likely to expand freely [80], however, none of these dilatations or mineralogical modifications have generated significant changes in the bulk microstructure of the chalk [3].  
The Fontainebleau sandstone (FS) showed a microstructural redistribution during the first heating at 200 \({}^{\circ}\)C, with an increase in porosity measured by MIP, and a decrease in C. Through the thermal fatigue, FS experienced better connectivity of the porosity thanks to the creation of intergranular cracks (Figure 4) which lead to a higher C. Actually, the porous volume is connected in 3D with a larger intergranular matrix size than in the initial state (Figure 9b). The big macropores and some grain detachment contributed to the augmentation of cooling velocities through the thermal cycles (Figure 10).  
From the very first heating at 200 \({}^{\circ}\)C, the Gris Alba granite (GA) showed a significant modification of its porous network, observed by SEM and quantified by MIP, with a more intense microcracking in feldspar [60] and an increase in the pore radius access greater than 100 \(\upmu\)m. The pores with access radii greater than 10 \(\upmu\)m constituted preferential flow paths during capillary water uptake. Nevertheless, they were not the largest part of the porous network of GA, which explained the little variation in C (Figure 10). During the following heating cycles, all the properties as well as the observations did not show great changes, indicating that the first heating produced a great redistribution in the GA microstructure and that it was not affected by thermal fatigue.  
Similar to the TT test, the microstructural parameters of the Golden Ski granite (GS) hardly changed through the TF test, although for lower temperatures and fatigue of 30 cycles, an increase in porosity was reported [28]. Only the CRI experienced a decrease explained by a mineral expansion that closed the bigger cracks and clay remobilisation that occluded some of the surface voids [21]. The clays placed on the edge of the pore walls allowed the continuous water circulation ([PERSON], 2004) that was in agreement with the CRI and capillary results (Figure 10).
## 2 Results and Discussion
### Analysis of Free Amino Acids (FAAs)  
FAAs are precursors of volatile flavor compounds and biogenic amines [27]. In the present study, the contents of Asp, Thr, Glu, Ala, Met, Ile, Leu, Tyr, Phe, His, Arg, Pro, and total FAs increased at the beginning and afterwards gradually decreased (Table 1). The MAP treatments retarded the changes of some special FAs, including Val, Met, Ile, Leu, Phe, Lys, and His. At the same time, Ala and Gly were the most abundant FAs in turbid samples at day 15 and the AP treated samples had a higher concentration than that of VP and MAP treated samples. His increased from initial value of 1.91 mg/100 g to 9.65, 8.23, 7.45, 4.98, 6.14, and 6.24 mg/100 g at day 15 for AP, VP, MAP1, MAP2, MAP3, and MAP4, respectively. Then His decreased at the end of storage as the precursor of VOCs [28]. Good acceptance of MAP2 treated samples can be explained by its higher Glu and Asp concentrations than others except day 15 during storage and the two FAs are responsible for stronger unami taste [29]. The MAP treatments could select different predominate bacteria and affect their growth rate and metabolic activity, which can illuminate the difference on FAs with different treated turbid samples during storage. Overall, the above results demonstrated that flavor deterioration was associated with the decrease of special flavor-enhancing amino acids and accumulation of flavor-detracting amino acids, and MAP combined with superchilling could effectively slow down the process and maintain a good edible value of turbid samples during storage.
## 4 Wireless Access for Vactrain
### Traditional Solutions  
At present, several technologies can be used to link the train to the ground. These solutions can be divided into categories as follows [18].  
* Satellite solution.
* Public cellular networks solutions.
* Solutions based on WiFi or WiMAX.  
Communication satellites, together with aggregation networks, can provide a vast coverage area and enable Internet access for the HSR. Nevertheless, this type of solution remains expensive and provides limited throughputs. Satellite communications also face the long propagation delay of 500-600 ms, vulnerability to weather, and significant burst errors due to obstacles and mobility. As for public cellular networks solutions (e.g., UMTS), a large number of costly Base Stations (BSs) need to be set along the vacuum tube line. Thus, most of the track-side wireless radio devices remains idle state, which implies a severe \"tidal effect\" and great waste of resources. WiMAX is a solutions provide for \"the last miles broadband access\", so-called IEEE 802.16 wireless metropolitan area network. However, WiMAX has a poor availability of commercial-off-the-shelf equipments due to its unpopularity in railway systems. Solutions based on WiFi are usually adopted in the low-speed scenarios. Note that its performance indicators will be greatly affected with the increase of speed.  
If the above, wireless access solutions are adopted in the vactrains; the traditional antennas need to be deployed inside the vacuum tube. Assume that the tube is a vacuum cylinder with radius of 2 m and the train runs at a speed of 1000 km/h. As is shown in Figure 1, the antennas are set inside the tube every 200 m, which operates at 5 GHz with the bandwidth of 300 MHz. Figure 1 shows the doppler spectrum of this scenery. The large doppler frequency shift can be observed, which is change with respect to time.
# Differential Expression of MicroRNAs in Dark-Cutting Meat from Beef Carcasses
## 3 Results
### Identification of Differentially Expressed miRNAs  
After mapping sequences to _Bos taurus_ miRNAs in miRbase (greater than 90% of the total small RNA sequence reads mapped to _B. taurus_ miRNAs), 542 miRNAs passed the initial filtering within the DESeq2 analysis software (Supplemental Table S1). Because this experiment's goal was to determine whether miRNAs might play a role in the DFD phenotype, other small RNAs were not investigated and we did not attempt to determine potentially novel miRNAs in this dataset. Nearly half of the miRNAs were expressed similarly between the dark-cutting and normal (DFD and NON) groups, exhibiting expression ratios (DFD/ NON) between 0.90 and 1.10 (Figure 2). Because miRNAs can be potent cellular regulators of gene expression and small changes may have great effects, a cut-off for differential expression between DFD and NON was set to a level of at least 20% (ratio of DFD/NON \(\leq\) 0.8 or \(\geq\)1.2). Although messenger RNA (mRNA) transcriptome analyses often utilize a two-fold expression difference as a cut-off, much smaller differences in miRNA expression can have powerful downstream impacts on gene expression. As a result, we chose a 20% difference in expression as a reasonable measure for examining potential differences between DFD and NON samples. Of 170 miRNAs meeting the differential expression threshold, 28 miRNAs also met the initial threshold for statistical significance (\(p\leq\) 0.05; Table 2). However, after a correction for multiple testing was applied, only a single miRNA, _beta-miR-2422_, approached significance with a 5.4% probability of being a false positive (\(p_{\text{adj}}\) = 0.0541; Table 2). Seven additional bovine miRNAs met the cut-off if the FDR threshold was relaxed to 30% (_miR-10174-5p_, _miR-1260b_, _miR-144_, _miR-142-5p_, _miR-2285 at_, _miR-2285e_, _miR-3613a_). These data for the \"top\" miRNAs presented in Table 2 are also displayed in Supplemental Figure S1 as an expression heatmap of raw read counts across all animals. As was also reflected in the DESeq2 analysis of differential expression, animal-to-animal variability is observed, and small changes in expression are somewhat difficult to visualize graphically. The \"myomiRs\" are miRNAs known to be specifically expressed and have a regulatory function within skeletal muscle [35]. These microRNAs (_miR-1, miR-133a_, _miR-133b_, _miR-206_, _miR-208a_, _miR-208b_, _miR-486_, _miR-499_) are included in the table of differentially expressed microRNAs as a reference because of their specificity to skeletal muscle. All of  
Figure 1: Incidence of DFD in offspring of sires with at least 4 progeny in the contemporary group cohort (\(n=78\)). Letters designate sire, followed by its number of offspring within the cohort. the myomiRs were detected but were not differentially expressed between the two groups (Table 2).
## 2 Materials and Methods
### Footprint of Spectral Observation  
The measured upwelling irradiance \((E)\) can be calculated by integrating the radiance \((L)\) over all the propagation directions (defined using the view zenith angle (\(\theta\)) and view azimuth angle (\(\varphi\))) within the FOV. For hemispherical measurements, neglecting the influence of atmospheric scattering, the measured irradiance of the signal within the FOV can be expressed as [13; 31; 32]:  
\[E(\text{FOV})=\int_{0}^{2\pi}\int_{0}^{\frac{ FOV}{2}}L_{0}(\theta,\varphi)\cdot T_{\uparrow}(\theta)\cdot\cos\theta\cdot\sin \theta d\theta d\varphi. \tag{3}\]  
where \(T_{\uparrow}(\theta)\) is the upwelling atmospheric transmittance at the given view zenith angle, \(\theta\). For bands free from atmospheric absorption, the transmittance can be assumed independent of the RTPL (i.e., \(T_{\uparrow}\) is independent of \(\theta\)). Assuming that the ground surface is homogeneous and Lambertian (i.e., \(L\) is independent of the propagation direction), Equation (3) can be expressed as:  
\[E(\text{FOV})=2\pi\cdot L_{0}\cdot T_{\uparrow}\cdot\int_{0}^{\frac{ FOV}{2}}\cos\theta\cdot\sin\theta d\theta=\pi\cdot L_{0}\cdot T_{\uparrow} \cdot\left(\sin\theta\right)^{2} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Description** & **Value/Range** & **Unit** \\ \hline Cab & Leaf chlorophyll a + b content & 20, 40, 60, 80 & \(\mu\)g/cm\({}^{2}\) \\ LAI & Leaf area index & 1, 2, 4, 6 & m\({}^{2}\)/m\({}^{2}\) \\ \hline  & & Planophile & \\  & & Erectophile & \\ LIDF & Leaf inclination distribution & Plagiophile & - \\  & & Extremophile & \\  & & Spherical & \\ \hline SZA & Solar zenith angle & 30 & degree \\ VZA & View zenith angle & 0-89, in steps of 1 & degree \\ RAA & Relative azimuth angle & 0–360, in steps of 10 & degree \\ Height & Surface elevation & 20 & m \\ Atmospheric Profile & Atmospheric Profile & Mid-latitude summer & - \\ AOD550 & Aerosol optical depth at 550 nm & 0.1 & - \\ Aerosol Model & Aerosol Model & Rural & - \\ H\({}_{2}\)O & Water vapor column & 3 & g/cm\({}^{2}\) \\ O\({}_{3}\) & Ozone column & 300 & Dobson unit (DU) \\ \hline \end{tabular}
\end{table}
Table 1: Main parameters for simulation in SCOPE and MODTRAN 5 models.  
According to Equation (4), for hemispherical measurements made with the cosine-corrected foreoptic, the measured irradiance of the signal within the FOV is proportional to the square of \(\sin\theta\). Using Equation (4) and discretizing the FOV to \(1^{\circ}\) intervals, it is possible to model the fractional contribution of the signal originating from each \(1^{\circ}\) interval of the zenith angle to the hemispherical irradiance (see the blue circles in Figure 2), as well as the accumulated contribution of the signal within the FOV (see the red diamonds in Figure 2). It can be seen from Figure 1 that the signal within an FOV of \(72^{\circ}\) contributes 90% of the total hemispherical irradiance, of which the maximum contribution comes from a view zenith angle of around \(45^{\circ}\).
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 6 Results
### Sparse Jamming Estimation Error
The MSE of the GSJE algorithm increases as the uncertainty of prior knowledge increases. For example, the MSE of the GSJE with perfect knowledge is 0.0446 at a JNR of 10 dB, which is higher than that of BSJE; however, the MSE decreases to 0.0880 under the same conditions, except for the condition in which prior knowledge is unknown. Its MSE value with unknown prior knowledge is lower than that of BSJE. * MSE performance according to JNR The performance of both algorithms degrades as the JNR decreases. However, the degree of performance degradation is greater for the GSJE algorithm. For example, the MSEs of the GSJE with perfect knowledge are 0.193 at a JNR of 5 dB and 0.754 at a JNR of 0 dB, which are lower than those of BSJE. It can be recalled that the MSE of GSJE is higher than that of BSJE at a JNR of 10 dB. Thus, BSJE is more suitable for environments with large JNR ranges than the GSJE. This agrees with the theoretical expectation that the output noise model of GSJE becomes more distinguishable with the true signal model as JNR decreases from 10 dB to 0 dB. * Running time The results indicate that the BSJE algorithm is far slower than the GSJE algorithm. However, the running time of GSJE increases when it suffers from a lack of prior knowledge. For example, GSJE is more than ten times faster when it has perfect knowledge and approximately four times faster when it does not have any prior knowledge. In contrast, the running time of BSJE is almost constant regardless of the prior knowledge since BSJE does not explicitly exploit the information.
## 2 Materials and Methods
### Quality Control  
Quality was maintained starting from soil sampling to PAH determination. Soil samples were collected using a clean auger, dried under shade condition, and stored in refrigerator using labeled aluminum zipper bags. A blank was considered to find out the extent of contamination. The process was replicated three times to ensure data accuracy. All the glassware was properly cleaned, sonicated, and rinsed with ultrapure water (Type 1 water). The method detection limit was determined by comparing it to a signal-to-noise ratio of 3:1. The pretreatment method achieves a detection limit below 1 ppb, a recovery rate from 104% to 114%, and a measurement accuracy within \(\pm\)4% for repeated measurements of a 500 ppb mixed standard of 16 PAHs.
## 4 Determination of Indicator Weights Based on Combination Weighting Method  
### Preprocessing of the Evaluation Indicators  
We chose the Delphi method to quantify qualitative indicators. Several experts complete the scoring process according to the uniform criteria. Suppose that \(M\) is the number of experts and \(S_{k}^{i}\) is the score of the qualitative indicator \(k\) assessed by expert \(i\). After the scores of all the experts are finished, the quantified score \(S_{k}\) of indicator \(k\) is given, as follows:  
\[S_{k}=\frac{1}{M}\sum_{i=1}^{M}S_{k}^{i} \tag{1}\]  
Figure 2: The three-level evaluation indicator system of the energy saving and emission reduction effects for electricity retailers in China.  
In order to eliminate the influence of units, types, and economic implications of different indicators on the evaluation results, the raw data of evaluation indicators needs to be preprocessed firstly. Suppose that \(x_{ij}^{0}\) is the indicator original value. Then, the normalization of benefit type and cost type indicators is given, as follows:  
\[x_{ij}={x_{ij}}^{0}/{maxx_{ij}}^{0} \tag{2}\]  
\[x_{ij}={minx_{ij}}^{0}/{x_{ij}}^{0} \tag{3}\]  
where \(x_{ij}\) is the normalized value. We have the normalization matrix \(X^{*}\), as follows:  
\[X^{*}=\left[\begin{array}{cccc}x_{11}&x_{12}&\cdots&x_{1m}\\ x_{21}&x_{21}&\cdots&x_{2m}\\ \vdots&\vdots&\ddots&\vdots\\ x_{n1}&x_{n1}&\cdots&x_{nm}\end{array}\right]\]  
where \(n\) is the number of evaluation indicators and \(m\) is that of evaluation schemes. \(i=1,2,\cdots,n\), \(j=1,2,\ldots,m\).
## 2 Test Sites and Data Preparation
### Collections of UAV Stereo Imagery  
The acquisition system of stereo imagery was built on a six-rotor helicopter [27]. The camera mounted on the helicopter pointing at nadir was a Sony NEX-5T, which could collect images with 16.10 megapixels. The ground station associated with the UAV system enabled to automatically program the flying routes and the positions to take photos according to system settings, including focal lengths of camera, flying heights, forward overlaps, side overlaps, and flying speeds. The duration of each flight was determined by the battery capacity. Taking the battery of 16000 mAh used in this study for example, it was about 18-20 minutes flying at about 300 m AGL with a flying speed of 10 m/s. The forward overlap used in this study was about 90% while the side overlap was about 60%. The stereo imagery used in this study was collected by flying along the same routes on 25 August 2014 and 5 May 2015, corresponding to leaf-on and leaf-off seasons, respectively. The focal length and shutter speed used in image collection was 16 mm and 1/600 s, respectively. 776 images stored in the RGB format were used in this study. The spatial resolution or the ground sample distance (GSD) of the stereo imagery was about 8.6 cm.