esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Ultrasound Signal Processing  
The ultrasound data were processed in MATLAB 2021b (MathWorks Inc., Natick, MA, USA). A flow chart of signal processing steps for the simulation and experiment is illustrated in Figure 4. The echo data from each transmission angle were first demodulated into in-phase and quadratic (IQ) data and then beamformed by a delay-and-sum (DAS) beamformer. Receive angles of \(-10^{\circ}\), 0, and \(10^{\circ}\) were used in beamforming. A singular value decomposition (SVD) filter [37] was used to minimize the influence of clutter on the velocity estimation. After clutter filtering, the lag-one autocorrelation algorithm [38] was applied to each filtered signal to derive the Doppler frequency in each pixel of the imaging regions. The ensemble length used in estimating the Doppler frequency was 40. The multi-angle vector Doppler method was performed to calculate the blood flow velocities in a vector form [16]. A wall detection algorithm based on intensity differences between the vessel wall and flow [39] was used to locate the region of interest (ROI) for displaying velocity vectors and WSS. In brief, the vessel wall location, i.e., the ROI, was obtained by searching for the peaks of the intensity profile through each vertical line of beamformed images.  
The velocity component can be solved by the equation below:  
\[\begin{bmatrix}cos\theta_{t_{i}}+cos\theta_{r_{i}}&sin\theta_{t_{i}}+sin\theta _{r_{i}}\end{bmatrix}\begin{bmatrix}v_{z}\\ v_{x}\end{bmatrix}=\frac{\zeta}{f_{r}}f_{d}, \tag{3}\]  
where \(v_{z}\) and \(v_{x}\) are the estimated velocity vectors along the horizontal and vertical axes, \(\theta t_{i}\) is the \(i\)th transmission angle, \(\theta r_{i}\) is the receive angle, \(f_{c}\) is the probe transmission frequency, and \(f_{d}\) is the estimated Doppler frequency. An overdetermined system was formed if \(i\ \geq\ 3\). The horizontal and vertical velocity components were solved by the least-squares method and filtered by a 0.7 mm 2D median filter. The B-mode image used for displaying the vector  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Simulation** & **Experiment** \\ \hline Probe & N/A & L11-5v \\ Steering angle & \(-10^{\circ}\), 0, \(10^{\circ}\) & \(-10^{\circ}\), 0, \(10^{\circ}\) \\ Transmission frequency & 6.25 MHz & 6.25 MHz \\ Number of elements & 128 & 128 \\ Number of transmit/receive elements & 128 & 128 \\ PRF & 10 kHz & 10 kHz \\ Sampling frequency & 50 MHz & 25 MHz \\ Pulse length & 2.5 cycles & 2.5 cycles \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in simulation and experiment.  
flow field and WSS map was coherently summed by three individual angles' data [36]. The resultant velocity magnitude \(v\) was calculated using the Pythagorean theorem as follows:  
\[v=\sqrt{v_{x}^{2}+v_{z}^{2}}. \tag{4}\]
## 5 Simulation Results
### BER Performance  
To find out how the receiver behaves in the spectrum-saving scheme, we provide BER results in this subsection, where \(M=5\). Note that we only consider the unaligned beamforming degradation factors and receiving capability differences in building the HS and UT channel gains. Specifically, 4-QAM with gray mapping is applied in simulations where the bit length is \(1024\times 10^{4}\). Compared with UTs, the receiving capability advantage of the HS is 7 dB. We define the link SNR at UTs as \((P_{0}+M\times P)/\sigma^{2}\), and the \(x\)-axis values are presented as \(SNR_{UT}=P_{0}/\sigma^{2}\) and \(SNR_{HS}=P/\sigma^{2}\) for UT and HS receivers, respectively. To show the results clearly, we present BER curves of three UTs instead of all the UTs. It can be understood from Figure 8 that the BER slightly degrades when applying the spectrum-saving scheme since signals of other UTs are regarded as interferences. Further, we can find from Figure 9 that BER performance at the HS is comparable to the spectrum isolation method since SIC and parallel filtering is considered.  
Figure 8: BER with variable \(\text{SNR}_{\text{UT}}\) when \(M=5\).  
Figure 7: Ergodic sum rate with variable \(\tau\).
# Influence of Right Triangular Prism Rough Beds on Hydraulic Jumps
###### Abstract  
A hydraulic jump phenomenon is exciting in turbulent flow as it causes large-scale turbulence and high-energy loss. This paper investigates the hydraulic jump characteristics of right triangular prism rough beds. The renormalization group RNG \(k\)-\(\varepsilon\) turbulent model and the volume of fluid (VOF) method in a CFD model are utilized to simulate hydraulic jumps. A total of 210 numerical simulations of four new types of rough beds were performed with an initial Froude number (\(Fr_{1}\)) ranging from 4.8 to 9.4, the non-dimensionless wave steepness values of \(0.67\leq t/s\leq 1.33\), and the distances between roughness elements of \(0\leq L_{\mathrm{s}}/y_{1}\leq 2.67\). This study found that arranging the right triangular prism rough elements in a stilling basin increased bed shear stress and energy loss. At the same time, they reduced sequent depth and jump length by about 22% and 50% compared to a smooth bed, respectively. In addition, the entropy production rates are also used to analyze energy dissipation, which clearly shows that the characteristic shape of a rough bed significantly influences the hydraulic jump length. Equations and plots that specify the relationships between the hydraulic jumps and study parameters are helpful guidelines for defining the rough bed dimension when designing or repairing a stilling basin for low-head irrigation works and highway sewers.  
CFD method; entropy production; hydraulic jumps; rough beds +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Data Processing
#### 2.3.2 Clearness Index  
The clearness index (_CI_) was defined as the ratio of the radiation received on the ground (\(S_{r}\)) to the extraterrestrial solar radiation at a plane parallel to the Earth's surface (\(S_{e}\)) [35]:  
\[CI=\frac{S_{r}}{S_{e}} \tag{1}\]  
\[S_{e}=S_{\rm sc}\times\left[1+0.033\times\cos(360t_{d}/365)\right]\times\sin\varepsilon \tag{2}\]  
\[\sin\varepsilon=\sin\varphi\times\sin\delta+\cos\varphi\times\cos\delta\times \cos\omega \tag{3}\]  
where \(S_{\rm sc}\) is the solar constant (1370 W m\({}^{-2}\)), \(\varepsilon\) is the solar elevation angle, and \(t_{d}\) is the day of the year; \(\varphi\) is the local latitude, \(\delta\) is the declination of the sun, and \(\omega\) is the hour angle. When the daily mean _CI_ was greater than 0.50, the day was defined as sunny; otherwise, the day was defined as cloudy [36].
## 4 Shaking Table Test
### Test Results  
#### 4.5.1 Comparison with the Proposed Solution  
The bending moments obtained by tests are used to compared with those from the analytical solution with the PGA of 0.4 g. The bending moments of the tests can be obtained from the following equation:  
\[M=\frac{1}{2}(\sigma_{1}-\sigma_{2})W=\frac{1}{64}E\pi D^{3}(1-\frac{d^{4}}{D^{ 4}})(\varepsilon_{1}-\varepsilon_{2}) \tag{24}\]  
where \(\sigma_{1}\) and \(\sigma_{2}\) are stresses at the left sidewall and right sidewall of monitoring sections, \(\varepsilon_{1}\) and \(\varepsilon_{2}\) are maximum strains monitored corresponding to stresses \(\sigma_{1}\) and \(\sigma_{2}\), \(W\) is section modulus, and \(D\) and \(d\) are the outer diameter and inner diameter of the tunnel, correspondingly.  
The comparison of bending moments driven from the theoretical analyses and experiments is demonstrated in Figure 13. Note that the monitoring positions are determined by using the stratum interface as the baseline in terms of the analytical solution. All bending moments are given in prototype scale. As illustrated in Figure 13, the law is consistent for the two methods. The differences between the two methods may be due to the assumption that each section of surrounding rock is homogeneous isotropic and viscoelastic in the analytical method. Moreover, the model soil is nonlinear and can be disturbed during the shaking model tests, especially the soft rock in model tests. Therefore, the proposed analytical solution is verified and can provide first estimates or a preliminary design for scientific research and practical engineering.  
Figure 12: Input earthquake wave. (**a**) Acceleration time histories; (**b**) Fourier spectra.  
Figure 13: Comparison of bending moments of lining between the theoretical analysis and the model tests.
## 3 Shield Tunneling Anomaly Detection and Scenario Identification  
### System Framework  
The framework of the Anomaly Detection and Scenario Identification model (AD_SI) is represented in Figure 1, and it can be divided into two phases: (1) detect abnormal sections in the shield tunneling process, and (2) identify the specific scenarios of anomalies based on anomaly detection. The first phase focuses more on the abnormal change within the time series and the second phase focuses on the correlation among multidimensional parameters.  
The first phase constructs an anomaly detection model using energy consumption as the monitoring parameter. Considering the advantages of unsupervised methods, the proposed anomaly detection model is based on an improved reconstruction model VAE-LSTM (Variational Autoencoder-Long Short-Term Memory). It can report potential abnormal segments by detecting changes in the state of energy consumption time-series  
Figure 1: Graphical representation of the methodological framework.  
data. In addition, recognizing that threshold setting can affect the model's detection accuracy, we introduce a dynamic threshold setting within the model.  
The first phase is accomplished through three steps: (1) Training the anomaly detection model to learn the data features of energy consumption in normal scenarios. (2) Setting a dynamic threshold based on the mean and variance of the reconstruction error of the training set. This threshold will be continuously adjusted with the updating of the training set after the completion of each tunneling ring. (3) Detecting abnormal sections by comparing the reconstruction error of testing data with the dynamic threshold. Data with reconstruction errors exceeding the threshold will be detected as potential anomalies.  
The core task of the second phase is to identify scenarios of the abnormal sections. Based on the analysis of the feasibility and advantages of using the parameter correlation to realize abnormal identification in Section 2.2, we represent and identify scenarios of the abnormal sections detected in the first phase by using correlations among different construction parameters.  
Therefore, we use two steps to realize the second phase: (1) Extracting parameter correlation from multidimensional data of known scenarios in historical projects. The correlation is used to create a feature matrix representing each known scenario, which is then stored in the parameter correlation base of known shield tunneling scenarios. (2) The second step extracts the parameter correlation of abnormal sections detected in the first phase. By comparing the parameter correlation of abnormal sections with that of known scenarios, we can obtain the specific abnormal scenario and thus achieve anomaly identification.
## 3 Results and Discussion
Poor hygiene conditions in the farm environment as well as at the farm level could have resulted in the infection of milk with a certain pathogen. A previous study reports the contamination of approximately 16% of various food samples by _Staphylococcus saprophyticus_[41], without milk participating in the examined samples, however. This particular pathogen can be transferred by hand contact to food and, also, through it being transferred more readily than other pathogens [41]. The number of positive results between the bovine and caprine samples were statistically similar (\(p=0.19\)), while the positive percentages between the bovine, caprine, and ovine samples were statistically different (\(p<0.005\)). Concerning the PCR analysis results, there was agreement between them and those determined by culture-based tests in all analyzed samples. Further to our analyses, all of the samples were tested for seven well-known pathogens related to public health: _Shigella_ spp. _E. coli_, _Salmonella_ spp., _Campylobacter_ spp., _Adenovirus F_, _Rotavirus A_, _Norovirus 2 genotype_, and _Astrovirus_ (analyzed with the kit of Saccace) were negative. According to the results of the present study, pathogens related to mastitis in animals and humans were identified in milk samples intended for further human consumption either as pasteurized milk or processed for the production of other dairy products. Both implemented techniques (the traditional culture-based approach and PCR) showed similar results and, in some cases, an uncommon level of 100% detection per milk sample. Interestingly, common contagious mastitis-causing pathogens were not isolated but rather pathogens related to environmental mastitis. The presence of such pathogens is related to poor hygiene conditions. Even though great progress in terms of milk quality in Greece over the last three decades has been reported [42], the need for further improvements including hygiene conditions is ever present. However, it is important to note that the identified pathogens are related to resistance to antibiotics, which may further raise serious concerns about public health. Antibiotic-resistant pathogens are involved in the spread of antibiotic resistance traits in populations of bacteria of the same species or other species belonging to the same family [35]. Therefore, in farming practice, it is crucial to eliminate the use of antibiotics without authorization. In addition, pasteurization seems unable to eliminate Enterococci at a zero level, as previous studies have reported. Thus, a possible strategy could be potentially increasing the applied temperature or the extension of the holding time. More extensive monitoring of these pathogens, at the farming level which does not belong to the mandatory conducted analyses of milk intended for human consumption, can also be considered by responsible authorities. Possibly, animals and livestock could probably serve as a reservoir for which food (animal products like milk) is the vehicle for their transmission into the human environment.
# A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs
## 3 Results
### Effectiveness Analyses
#### 3.2.3 Comparison with State-Of-The-Art Methods  
The proposed method is compared with state-of-the-art methods [16; 23; 26; 53; 54] on the same dataset. In [23], the Overfeat model proposed by [PERSON] et al. achieved the highest accuracy on the TT100K, which obtained 91% precision, 93% recall, and 93% mAP. However, the image pyramid must be adopted, which greatly increases the computing cost. [PERSON] et al. [54] proposed MSA-YOLOv3, which adopted the augmentation path merge with FPN to detect targets with different sizes. Compared with [23], the speed is significantly improved; the detection precision and recall rate are slightly reduced, and the main reason is that our model has as many as 152 detection categories, which is much higher than the 45 categories in [23; 54]. Nevertheless, as shown in Table 4, the detection speed is much higher than the method in [23], the precision and recall rate are also higher than that in [54]. And our model achieves the fastest detection speed on 45 categories of traffic signs, and mAP is slightly less than [23]. Detailed comparisons of the average precision (AP) of different signs on the enhanced TT100K are shown in Table 5, including Zhu [23], YOLOv3, and improved YOLOv3. It is evident that the model proposed in this paper is capable of detecting more traffic signs and achieving higher average precision of different categories. Moreover, it effectively improves the performance of fine classification for subordinate classes with highly similar appearance such as height limit signs (e.g., ph2.1, ph2.2, etc.) and weight limit signs (e.g., pm10, pm15, pm55, etc.). Figure 6: The comparison of the (**a**) Avg Loss Curve and (**b**) P-R Curve between YOLOv3 and improved YOLOv3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Size** & **Precision 1** & **Precision 2** & **Recall 1** & **Recall 2** & **mAP 1** & **mAP 2** \\ \hline (0,32] & 0.64 & 0.74 & 0.67 & 0.76 & 0.62 & 0.75 \\ (32,96] & 0.83 & 0.94 & 0.84 & 0.92 & 0.81 & 0.87 \\ (96,400] & 0.85 & 0.93 & 0.86 & 0.94 & 0.83 & 0.88 \\ \hline \hline \end{tabular} (1: YOLOv3, 2: Improved YOLOv3).  
\end{table} 
Table 3: YOLOv3 and Improved YOLOv3 Test Results for Targets at Different Sizes ([EMAIL_ADDRESS]).
## 4 Results  
### Lifting Model  
A hypothetical example was analyzed to illustrate the application of the model to developing an opinion based on the NIOSH threshold of 3400 N for spinal compression force. A stoop lift was selected for analysis. The lifting posture, which was defined as body segment relative to horizontal, was \(-68^{\circ}\), \(-120^{\circ}\), \(0^{\circ}\), \(121^{\circ}\), and \(83^{\circ}\) for the elbow, shoulder, torso, knee, and ankle, respectively. The standard deviations, which represent uncertainty in measurement from photographs [18], was \(11.8^{\circ}\), \(7.9^{\circ}\), \(6.3^{\circ}\), \(9.4^{\circ}\), and \(9.4^{\circ}\) for the elbow, shoulder, torso, knee, and ankle, respectively. The central element of this simulation that illustrates its usefulness is demonstrated by setting the _disc injury_ Boolean input node to true. Prior to specifying that value, AgenaRisk computed the _prior_ probability of the L5/S1 compression force exceeding 3400 N (no injury specified condition); setting the value to true produced the _posterior_ probability that incorporated knowledge of disc status. For completeness, _disc injury_ status was also set to false. The _mass in hands_ input mean was varied from 0 to 50 kg in 5 kg increments to demonstrate the relationship between hand load and probability of the L5/S1 compression force exceeding 3400 N.  
Including injury status in the hybrid Bayesian network analysis strongly affected the probability of L5/S1 spinal compression force exceeding 3400 N (Figure 3). Although the largest difference in probability predictions was between the _disc injury_ node states of true and false, the most relevant comparison is between the _disc injury_ unspecified and true states because this represented the comparison between what a traditional analysis would produce and an analysis that incorporated information about the injury status of the plaintiff. At a hand load of 25 kg, for example, the probability of exceeding 3400 N would be 0.40 and 0.61 for the unspecified and true disc injury conditions, respectively. The former value of 0.40 meant that it was less likely than not that the force threshold was exceeded; the probability of 0.61 indicated it was more likely than not. Thus, there was a region of hand loads that lead to two different opinions depending on the _disc injury_ status. However, there were also levels of hand load that produced consistent results for all three _disc injury_ conditions. The probability of exceeding the threshold was less than 0.5 for all _disc injury_ states (true, false, and unspecified) below a hand load of 20 kg; similarly, above 35 kg, both _disc injury_ states produced probability estimates greater than 0.5 for all conditions.  
Figure 3: Probability of the L5/S1 compression force exceeding 3400 N for different evidence (true, false, and unspecified) entered at the _disc injury_ node.
###### Abstract  
This study is the first report on the synthesis, characterization and application of amine-functionalized mesoporous nanocomposites based on natural rubber (NR) and wormhole-like mesostructured silica (WMS). In comparison with amine-functionalized WMS (WMS-NH\({}_{2}\)), a series of NR/WMS-NH\({}_{2}\) composites were synthesized via an in situ sol-gel method in which the organo-amine group was grafted onto the nanocomposite surface via co-condensation with 3-aminopropyltrimethoxysilane (APS) as the amine-functional group precursor. The NR/WMS-NH\({}_{2}\) materials had a high specific surface area (115-492 m\({}^{2}\) g\({}^{-1}\)) and total pore volume (0.14-1.34 cm\({}^{3}\) g\({}^{-1}\)) with uniform wormhole-like mesoporous frameworks. The amine concentration of NR/WMS-NH\({}_{2}\) (0.43-1.84 mmol g\({}^{-1}\)) was increased with an increase in the APS concentration, corresponding to high levels of functionalization with the amine groups of 53-84%. The H\({}_{2}\)O adsorption-desorption measurement revealed that NR/WMS-NH\({}_{2}\) possessed higher hydrophobicity than WMS-NH\({}_{2}\). The removal of clofibric acid (CFA), a xenobiotic metabolite of the lipid-lowering drug clofibrate, from the aqueous solution using WMS-NH\({}_{2}\) and NR/WMS-NH\({}_{2}\) materials was investigated using a batch adsorption experiment. The adsorption was a chemical process in which the pseudo-second order kinetic model expressed the sorption kinetic data better than the pseudo first-order and Ritchie-second kinetic order model. In addition, the CFA adsorption sorption equilibrium data of the NR/WMS-NH\({}_{2}\) materials were fitted to the Langmuir isotherm model. The NR/WMS-NH\({}_{2}\) with 5% amine loading had the highest CFA adsorption capacity (6.29 mg g\({}^{-1}\)).  
mesoporous materials; nanocomposite; adsorption; amine group; clofibric acid Article
## 2 Materials and Methods
### Datasets  
Remotely sensed Landsat satellite images between 15 August and 1 October (dry season) were collected for each of the three sample years and used to quantify Land Use and Land Cover (LULC) change. The imagery consisted of 2019 Landsat 8 Operational Land Imager (OLI), and 2009 and 2001 Landsat Thermal Infrared Sensor (TIRS) sensor Surface Reflectance Tier 1 images, both produced by the United States Geological Survey (USGS) but sourced through the Google Earth Engine [44] (see Table 1). The Landsat imagery products were already atmospherically corrected and radiometrically calibrated. Cloud-free imagery was derived by masking cloud pixels by using the Landsat cloud quality metadata. Each image collection (15 August to 1 October) was composited, and the median values were applied to each stacked collection, resulting in 3 groups of 3 monthly composite surface reflectance images for 2001, 2009 and 2019. The administrative boundaries for the Dedza and Ntcheu districts were obtained from the Food and Agriculture Organization of the United Nations (FAO-UN), Global Administrative Unit Layer (GAUL), a second-level administrative units' dataset. To determine agricultural land use, a time series (2001-2017) of mean Gross Primary Productivity (GPP) estimates derived from Moderate Resolution Imaging Spectroradiometer (MODIS) for the growing season were used. GPP shows differences in greenness and photosynthesis in crop phenology such as the normalized difference vegetation index (NDVI). However, GPP estimates can potentially be used to estimate yields [45]. A total of 150 observation points were extracted within the 500 m-by-500 m GPP pixel grid, across four study sites and surrounding areas of the districts and assessed in the field in Summer 2018 over a 200 km\({}^{2}\) area (Figure 2). Field-observed sample points were collected within the 500 m-by-500 m GPP pixel grid while approximate Global Positioning System (GPS) locations were recorded as close to the sample points as possible where land was inaccessible [46]. Figure 3 shows various field site photographs taken of agricultural fields during the summer of 2018 field work.