esa-sceva/satcom-chunk-collection huggingface dataset:
# Acid and Volatiles of Commercially-Available Lambic Beers
## Results and Discussion
### Titratable Acidity, Residual Sugar, Lactic Acid, Volatile Acidity, and Ethanol  
Because of the high attenuation rate found in gueuze lambic beer, small to trace amounts of reducing sugars were found (Table 3). In prior research (14), only trace amounts (0.8% \(w/v\)) were reported. The amount of reducing sugars in the eight commercial beers tested ranged from 0.7% \(w/v\) to 1.8% \(w/v\). Beers sweetened with syrups tend to contain a higher percentage of reducing sugars (2% \(w/v\)) because these beers tend to undergo a limited secondary fermentation and are quickly filtered and pasteurized once the fermentation process is complete (1). Cantillon and Boon both contained the highest percentage of reducing sugars at 1.8% \(w/v\), while Oude Artisan had the lowest at 0.7% \(w/v\). A gueuze that is called \"Oude\" is considered an old gueuze that has been allowed to ferment for three years, unlike traditional gueuzes that are fermented for two years. The lactic acid (g/L) measured for the lambic (gueuze) beers ranged between 3.67 and 17.47 g/L. Oude Artisan contained the highest lactic acid at 17.47 g/L while Cantillon had the lowest at 3.67 g/L. The volatile acidity for the lambic (gueuze) beer ranged from 3.97 g/L to 17.27 g/L, where Oude Artisan had the highest volatile acidity, while Boon had the lowest. Volatile acidity refers to the organic acids (such as acetic or butyric acids) that are more volatile or are more easily vaporized than non-volatile or fixed acids. Total acidity (g/L) for the lambic (gueuze) beer ranged from 2.62 to 7.83 g/L with Oude Boon exhibiting the lowest value and Oude Artisan having the highest value. Ethanol ranged from 5.64% to 7.16%. Ethanol concentration for gueuze beers has been previously reported to range between 4.25% and 5.20% (14).  
\begin{table}
\begin{tabular}{c c c} \hline
**Brand** & \(N\) & **Means** \\ \hline Oude Gueuze Vieille & 4 & 6.85 \(\pm\) 1.21  \\ Cantillon & 2 & 8.09 \(\pm\) 0.49  \\
3-Fonteinen & 4 & 8.46 \(\pm\) 0.13  \\ Hanssens Artisan & 4 & 9.06 \(\pm\) 0.33  \\ \hline \end{tabular}
\end{table}
Table 2: Color comparison for commercial lambic (gueuze) beers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Name** & **Sample Size** & **RS \% \(w/v\)** & **TA (g/L)** & **TA-Lactic Acid (g/L)** & **VA (g/L)** & **Ethanol \%** \\ \hline Cantillon & \(n=1\) & 1.8 & 3.29 & 3.67 & 12.59 & 5.64 \\ Cantillon Bio & \(n=2\) & 1.2 & 4.42 \(\pm\) 0.07 & 9.86 \(\pm\) 0.15 & 11.15 \(\pm\) 1.53 & 6.06 \\
3 Fonteinen & \(n=2\) & 1.2 & 5.71 \(\pm\) 0.32 & 12.73 \(\pm\) 0.71 & 7.22 \(\pm\) 1.17 & 6.39 \\ Girardin & \(n=1\) &.90 & 4.96 & 11.07 & 6.30 & 6.43 \\ Boon & \(n=1\) & 1.8 & 2.71 & 6.06 & 3.97 & 6.02 \\ Oude Boon & \(n=1\) & 1.2 & 2.62 & 5.85 & 4.92 & 7.16 \\ Hansams Artisan & \(n=2\) & 0.70 \(\pm\) 0.14 & 7.83 \(\pm\) 0.91 & 17.47 \(\pm\) 2.03 & 17.27 \(\pm\) 1.65 & 5.66 \\ Oude Gueuze Vieille & \(n=2\) & 1.65 \(\pm\) 0.07 & 2.74 & 6.10 \(\pm\) 0.01 & 5.71 \(\pm\) 0.07 & 6.5 \\ \hline \end{tabular}
\end{table}
Table 3: Chemical measurements of lambic beer samples.
# Generation and Propagation of a Hermite-Gaussian Correlated Schell-Model LG\({}_{0l}\) Beam
## 2 Propagation Equation of a HGCSMLG\({}_{0l}\) Beam
The first rows of Figures 5-7 show that the variation of the modulus of the DOC of a focused HGCSM beam, for different beam  
Figure 4: Normalized focal plane intensity of an HGCSMLG\({}_{0l}\) beam with \(l=2\) for different beam orders \(m\), \(n\) and the spatial coherence width \(\delta_{0}\). Figure 3: Normalized intensity of a focused HGCSMLG\({}_{0l}\) beam at different distances with \(m=1\), \(n=1\) and increasing values of the TC. order, with increasing distance z. The distribution of the DOC of the HGCSM beam displays an array distribution near the source plane (\(z=1\) mm) which is the same as the source plane [19] and remains array distribution as the beam propagates. It is not until it reaches the focal plane that the DOC distribution evolves into one beam spot. The second and the third rows of Figures 5-7 show the propagation properties of the DOC of a focused HGCSMLG\({}_{0l}\) beam at different distances for different beam order m, n and increasing values of the TC. One can find the vortex phase brings about obvious changes of the DOC distribution even in the plane (\(z=1\) mm) near the source plane. The DOC distribution exhibits a \"rotating effect\" as the beam propagates along the z axis. We can decide the handedness of the TC by the rotation direction of the distribution of the DOC. As the rotation direction of windmills (see Figure 7b2-c2) is anti-clockwise, the value of TC is positive (\(l>0\)). While on the contrary, the value of TC is negative (\(l<0\)) (see Figure 7b3-c3). Figure 8 shows the modulus of the focal plane DOC of an HGCSMLG\({}_{0l}\) beam for different \(m\), \(n\) and the TC. The dark rings' number of the focal plane DOC increases as the value of TC increases. For \(m=0\), \(n=1\) (see Figure 8a1-d1) and \(m=1\), \(n=0\) the dark rings' number along the y direction and the x direction equals to the TC number \(l\) separately. For \(m=1\), \(n=1\) (see Figure 8a3-d3), the dark rings' number along the \(x\) or \(y\) direction is one less than the TC number \(l\) if \(l>2\). Thus, the TC number can be determined by the number of the dark rings. It can be concluded that we can distinguish the handedness and value of the TC from the DOC distribution, in spite of form changes of the DOC distribution, caused by the new correlation function. Figure 5: The modulus of the DOC (\(|\mu(\rho,0;z)|\)) of a focused HGCSMLG\({}_{0l}\) beam at different distances with \(m=0\), \(n=1\) and increasing values of the TC. Figure 6: The modulus of the DOC (\(|\mu(\rho,0;z)|\)) of a focused HGCSMLG\({}_{0l}\) beam at different distances with \(m=1\), \(n=0\) and increasing values of the TC. Figure 7: The modulus of the DOC (\(|\mu(\rho,0;z)|\)) of a focused HGCSMLG\({}_{0l}\) beam at different distances with \(m=1\), \(n=1\) and increasing values of the TC.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 4 Results and Discussion
### Discussion of mRSU and sRSU for Propagation Models regarding Packet Size  
Regarding constant parameters, this instance takes Sector F5 in Islamabad, Pakistan, with routing algorithm AODV and a simulation time of 360 s, vehicle velocity of 20 m/s, and the random way point mobility model. Every node transmits power and the broadcast range is similarly set. The performance characteristics of the propagation model are evaluated with the packet size modifications. Figure 10(a) shows that the Friis model and three-log distance model outperform the other models both for the mobile RSU and sRSU in terms of end-to-end delay, because the receiving power is inversely proportional to the distance, and as the mRSU remains in motion with the vehicles, the distance does not increase rapidly and the delay remains the lowest. Meanwhile, the Nakagami model gives the worst result regarding the delay due to the variations in signal strength due to multi-path fading.  
For the packet delivery ratio, the mRSU gives the best results at all packet sizes in the different models, e.g., at 100 KB, the mRSU in the range propagation and three-log models gives the best results, while, at 200 KB, 300 KB, and 400 KB, the mRSU in the Nakagami model gives the best results, and at 500 KB, the Friis model gives the best result. Overall, the mRSU gives best packet delivery ratio at all packet sizes because the possibility of packet drop reduces as the RSU is in motion along with the vehicles. On the other hand, the throughput, as shown in Figure 10(e), shows better performance with the increase in the packet size for the mRSU in the Nakagami, Rician, and Rayleigh propagation models and shows a gradual increment. Thus, the throughput presents better results for the mRSU, especially for Nakagami, because, through the multi-path process, the MIMO helps to receive multiple inputs at the receiver and achieves greater throughput. On the other hand, regarding the energy consumption, the sRSU consumes less energy than the mRSU in all propagation models because of its continuous mobility; however, it is necessary to determine the quantity of energy in the mRSU that should be reserved for emergency packets per day. Overall, the mRSU outperforms the sRSU in terms of the throughput, end-to-end delay, and packet delivery ratio.
# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 2 Materials
### Satellite and Ground Data  
#### 2.2.1 VEN\(\upmu\)S Data  
BARC is one of the 123 acquisition sites selected by the VEN\(\upmu\)S scientific satellite mission developed jointly by France and Israel [17]. The VEN\(\upmu\)S instrument was launched on 1 August 2017, and acquires imagery in 12 spectral bands that cover the visible and near-infrared (NIR) spectrum. All images at a given sampling site are acquired at a nadir (or zenith) viewing angle and at the same overpass time (10:30 am) every 2 days at 5 m spatial resolution. The constant view angle attribute for VEN\(\upmu\)S reduces time series reflectivity anomalies induced by widely changing view angles.  
VEN\(\upmu\)S data were acquired over the BARC site starting from November 2018 to the present, and were downloaded from the Theia Data Center [29]. The level-2A VEN\(\upmu\)S data products have been atmospherically corrected to surface reflectance by the Centre National d'Etudes Spatiales (CNES), France, using the MAJA methods [30] and are provided at 5 m resolution (10 m in an earlier version for 2019). For the BARC site, most of the fields are around 1-10 ha in size, and 5-10 m resolution is sufficient to resolve these regularly shaped fields (1 ha = 400 of 5 m pixels or 100 of 10 m pixels). In this paper, we selected relatively large fields (>2 ha) for assessing the WIST algorithm and validating results.  
VEN\(\upmu\)S surface reflectance products include a cloud mask layer. We use the cloud mask to exclude cloud and cloud shadow pixels in subsequent processing. In order to compare to the 10 m Sentinel-2 data, the 5 m VEN\(\upmu\)S surface reflectances in 2020 were resampled (2 \(\times\) 2 average) to 10 m resolution. NDVI was computed using red (VEN\(\upmu\)S band 7) and near-infrared (NIR) band (VEN\(\upmu\)S band 11) surface reflectance data at 10 m resolution. Figure 2a shows the number of clear pixels over BARC in 2019 and 2020. The average of cloud-free pixels per day (number_clear_pixels/(number_image_pixels \(\times\) days_revisit)) over BARC during the entire period (2019 and 2020) was 12.8% (or 25.6% for the 2 days VEN\(\upmu\)S mission). For the critical periods that include cover crop and/or alfalfa determinations (light green areas in Figure 2a), the average of cloud-free pixels per day over BARC was 14.8% (or 29.6% for the 2 days VEN\(\upmu\)S mission). However, there were several large temporal gaps (>9 days) without any clear observations in the 2019 and 2020 termination periods.  
Figure 1: Beltsville Agricultural Research Center (BARC) study site (yellow rectangle and zoom-in figure, northeast suburbs of Washington D.C.). A VEN\(\upmu\)S false color composite image (Red: near-infrared band; Green: red band; Blue: green band) acquired on 13 July 2019, is overlaid on an Esri ArcGIS online base map.
# A Hybrid Hidden Markov Model for Pipeline Leakage Detection
## 3 Pipeline Leakage Detection
### Setup of Experiment  
The purpose of experiment is to detect pipeline leakage utilizing PZT transducers. The negative pressure wave is generated by leakage in the pipeline and propagated along the pipeline from the leakage point to both ends. The experimental pipeline was built at the University of Houston, as shown in Figure 5.  
Figure 4: DBN-DNN-HMM architecture.  
The experimental pipeline consists of a series of Plain-End PVC pipe sections connected together to form a pipeline with a total length of 55.78 m. Six PZT transducers (\(P_{1}\) to \(P_{6}\), size is 15 mm \(\times\) 10 mm) are directly mounted on the pipeline by using epoxy to detect negative pressure wave signal. A NI PXI\(-\)5105 digitizer is used as a data acquisition system. The digitizer is triggered by the voltage signal of PZT No. 1 with the trigger level at \(-\)0.02 V and all the signals from six PZT sensors are recorded simultaneously at a sampling rate of 100 KS/s. The experiment was presented in a published paper using the latency of signal to locate leakage [4]. In [4], the PZT sensors were used to detect the arrival time of NPW, and then the arrival time was used to calculate the exact location of leakage. We use the same experimental setting and data to validate the proposed DNN-HMM model which is for detecting leakage section and sequential changes of damage states. The hybrid model is implemented based on the hmmlearn [33] and deep-belief-network Python package [34], ran on a desktop with Windows 10 64-bit and Intel core i7-8700 CPU. The seqHMM R package is used for Gaussian mixture model component number \(K\) estimation.  
Different leakage locations are chosen as different states in the HMM. Two damage indices, i.e., one time domain damage index and one frequency domain index, as we used in [14], are extracted from the original signals. The damage indices are adopted to indicate the signal variations and serve as observations of the HMM. The first damage index (\(DI_{1}\)) is a time-domain damage index, defined as:  
\[DI_{1}=1-\sqrt{\frac{\sum_{t=0}^{t^{\prime}}(s_{1}(t)-s_{1})(s_{2}(t)-s_{2})}{ \sum_{t=0}^{t^{\prime}}(s_{1}(t)-s_{1})^{2}\sum_{t=0}^{t^{\prime}}(s_{2}(t)-s_ {2})^{2}}}\quad, \tag{7}\]  
where \(s_{1}(t)\) is the baseline waveform and \(s_{2}(t)\) is the comparison waveform at time \(t\). \(\tilde{s}_{1}\) and \(\tilde{s}_{2}\) are the average values of \(s_{1}(t)\) and \(s_{2}(t)\).  
The second one is a frequency-domain damage index \(DI_{2}\), the amplitude of peak frequency, defined as:  
\[DI_{2}=\max_{0\leq K\leq L-1}(|X(K)|)\quad, \tag{8}\]  
where \(X(K)=\sum_{0}^{N-1}X(n)e^{-i2\pi/L}\), \(i=\sqrt{-1}\). \(K\) is frequency corresponding to the selected frequency spectrum window and \(L\) is length of signal. The leakage signal collected by the first sensor \(P_{1}\) serves as the original data for damage indices extraction.
# Pharmaceutical Industry in Vietnam: Sluggish Sector in a Growing Market
## 3 Evolution of Pharmaceutical Industry
### The Evolution of Government Policies in the Pharmaceutical Industry  
In Vietnam, the key central government agency responsible for healthcare issues in general, and pharmaceutical management in particular, is the Ministry of Health (MoH). The MoH's basic functions, authority and responsibilities are set forth in the government's State Decree No. 15/CP dated 3 February 1993. In 1996, The Drug Administration of Vietnam (DVA) was specially established with the responsibility for state management of pharmaceuticals. Since then, DVA acted as the pharmaceutical regulatory authority under the supervision of MoH.  
In general, the ultimate goal for the development of pharmaceutical industry in Vietnam is to \"fully meeting the domestic demand\" [40], reflecting the role of an essential sector rather than of a profit-seeking industry. To achieve this, the government together with MoH and DVA has managed short-term policies based on long-term development strategy up to next 10-20 years. For example, the National Drug Policy issued in 1996 is a development strategy up to 2000 with the outlook to 2010 while the current development strategy (issued in 2015) is up to 2020 with the outlook to 2035.  
Investigating the government planning strategies related to the sector, it is possible to identify three main development periods of Vietnam's pharmaceutical industry from 1990 to 2017.
## 3 The Proposed Method
### Iterative Fine-Tuning Based on Sensitivity  
CP decomposition often leads to a drop in the accuracy of CNNs due to the optimization errors of decomposition. To restore the lost accuracy, fine-tuning is a necessary process. Nevertheless, the application of CP decomposition to each layer with one-time fine-tuning has not been effective because of its inherent instability [11]. Therefore, after decomposing each layer, we fine-tune the entire model iteratively to overcome the instability and prevent the errors from becoming unrecoverable.  
The arrangement of convolutional layers plays a crucial role in iterative CP decomposition and fine-tuning. In general, the performance of a network using iterative decomposition and fine-tuning is influenced by the varying sensitivity information of each individual layer. Therefore, we introduce sensitivity to determine the sequence of decomposition and fine-tuning for each convolutional layer. Specifically, layers with higher sensitivity have a greater impact on the output of CNNs, and it is more difficult to recover their accuracy by fine-tuning after decomposition, so we prioritize compressing convolutional layers with higher sensitivity in the original network, and then iterate until the whole model is compressed. By using this iterative fine-tuning approach that considers sensitivity, we anticipate that the overall error of the compressed model will be effectively controlled, preventing it from reaching an unrecoverable magnitude.  
As illustrated in Figure 2, the procedure of our method to compress the original model includes four essential steps:  
Figure 2: Overall workflow of the proposed iterative low-rank tensor decomposition based on the sensitivity of convolution layers.  
Estimating the sensitivity of each layer in the original model.
2. Sorting each network layer according to the order of the sensitivity information content of each network layer from large to small.
3. Determining the rank of each layer according to its sensitivity and an average rank we set in advance.
4. Iterative compressing and fine-tuning based on the sensitivity of each layer. The sorting results obtained from the second step and the rank obtained from the third step are used to iteratively compress and fine-tune the network layer-by-layer.
## Materials and Methods
### Active Fire Data  
Active fire data over the area from the Moderate Resolution Imaging Spectroradiometer (MODIS) were acquired from the Fire Information for Resource Management System (FIRMS) MODIS Fire Archive Download webpage [40]. The data are from the Collection 5 Near Real Time (NRT), extracted from the standard MCD14 ML fire product produced at the MODIS Fire Science Computing Facility (SCF).  
The MODIS data are available from 2000 to date, comprising of 1 km detections from the combined 4 overpasses of the sensors on the Aqua and Terra satellites. The fires that are actively burning at the time of the satellite overpass are detected using thresholds in the middle and thermal infra-red channels [41]. A quality control channel accompanies the data. Although such data have a number of sampling issues, the comparison of the current number of fire counts in a given region with the long term (i.e., 2000-2015) average can be useful to highlight possible anomalies.
## 3 Test Results, Analyses, and Discussion
### Rheological Properties
#### 3.1.3 LAS Analysis  
To evaluate the fatigue life (\(N_{\ell}\)) of the asphalt-binders with the parameters A and B, strain levels of 2.5%, 5%, and 10% were used in this study. The fatigue behavior of the rejuvenated SBSMA significantly depends on the applied strain loading and the rheological property of asphalt-binder as well as the type of rejuvenators. The results in Figure 9 show a significant decrease in the fatigue life of asphalt-binders at elevated strain levels. That is, high strain loading significantly weakened the fatigue resistance of the SBSMA.  
At low strain levels, thermal oxidative aging did not appear to significantly affect the fatigue life of the asphalt-binders. In fact, there was an increase in the fatigue life at lower strain levels. However, this was not the case at the 10% strain level. This may be explained from the perspective that the increase in viscosity and stiffness of the asphalt-binder due to aging promoted the asphalt-binders to convert to more linear-elastic behavior at low strain levels with a logical decline in the fatigue life under the adverse effects of aging at high strain levels [37]. As the aging and rejuvenation cycles increased, both the aged and rejuvenated asphalt-binders roughly showed a decreasing trend in fatigue life at the high strain level.  
Figure 8: MSCR rutting evaluation of repetitively aged asphalt-binders: (**a**) recovery; (**b**) non-recoverable creep compliance (\(J_{\rm irr}\)) (Red bars: the asphalt-binders rejuvenated with Type I; blue bars: the asphalt-binders rejuvenated with Type II; green bars: the asphalt-binders rejuvenated with Type III).
## 3 Electrical Tomography Principles and Applications
### Fundamentals of Electrical Tomography  
[PERSON] [58] was the first to present a theoretical model connecting the permittivity/conductivity of a two-phase mixture to the volume fraction of one the compounds present in the mixture dispersed in the second. For his calculations, he used small spheres of a certain material possessing equal but small size when compared with the distance between them; additionally, he presumed that in a certain domain, the spheres were distributed in a uniform way along the continuous phase of a second material, and that consequently their presence disturbs a homogeneous electrical field passing through the domain. Extrapolating, by measuring quantities such as conductance/capacitance, between a pair of electrodes placed around the pipe wall, the particle distribution inside that pipe can be obtained by means of the distribution of the electrical properties and through the usage of an adequate mathematic procedure: this mathematic algorithm links the measurements with the particle distribution. The relation between the homogeneity of a multiphase flow and this idea has been used for some time to observe different systems, such as liquid-solid and gas-liquid. For instance, to quantify localised amounts of solids in a gas flow, small capacitive probes have been used [59; 60]. In the gas and oil industry, similar probes have been used with analogous objectives [61]. Typically, in electrical tomography, the excitation sources, either a voltage or a current source, operate at low frequencies (below 5 MHz). Consequently, the systems in which these types of sources are used are described by the governing equations of the electrostatic field. When the flux (or current) lines encounter an interface with different permittivity or conductivity, it gets deflected. In order to allow the mining of the maximum amount of information about the process, the electrodes are usually mounted over the periphery of the pipe at intervals of equal distance. In the case of capacitive systems, the measuring electrodes are installed outside of the process vessel or pipe which is made of a dielectric material (i.e., they are mounted in a non-invasive way). Furthermore, the electrode area must be high, making it possible to detect a large range of capacitance changes. For system measuring Resistivity changes, the sensing electrodes are quite small when compared to those used in capacitive systems. They will typically be in contact with the flow under inspection, mounted level along the inner side of the pipe wall.