esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Particle Movement and Distribution Characteristics in the Milling Chamber  
The particle movement and the distribution characteristics are influencing factors in the separation of rice bran and broken rice. The simulation is conducted under an air speed of 6-8 m/s, and the jet air speed is 10-20 m/s. The movement tracks of the rice bran and the broken rice under working conditions are shown in Figure 7. At the beginning, the rice bran is deposited in the milling chamber, then discharged through a rice sieve under high-speed airflow, after which it is moved to the air duct. The broken rice particles are mainly distributed in the lower area of the air duct, and no broken rice particles are discharged from the upper air inlet.  
The results show that when the air speed is 6 m/s and the jet air speed is 10 m/s, the rice bran moves upward and a small amount of rice bran is moved at the lower air inlet. No rice bran is discharged from the lower air inlet, only broken rice. When the air speed is 6 m/s, the jet air speed is 20 m/s, the rice bran moves downward and part of the rice bran is discharged from the lower air inlet. When the air speed is 8 m/s, the jet air speed is 10 m/s, the rice bran moves upward and part of the broken rice is discharged from the upper air inlet due to the upward airflow. The particle movement trend is the same when the air speed is 8 m/s and the jet air speed is 20 m/s, and when the air speed is 6 m/s and the jet air speed is 20 m/s.  
The particle mass distribution in the milling chamber is shown in Figure 8. The rice bran particles move upward, no rice bran is discharged from lower air inlet, the separation phenomenon is obvious, and there is no rice bran accumulation.  
The results show that when the air speed is 8 m/s, the jet air speed is 20 m/s and the air speed is 8 m/s, the jet air speed is 10 m/s and the particle mass is distributed evenly in the milling chamber's right vertical direction. The particle mass distribution, when the air speed is 6 m/s and the jet air speed is 10 m/s, is slightly worse. When the air speed is 6-8 m/s, the larger \(v_{o}\) is, the more evenly the particles are distributed in the vertical direction of the right milling chamber. When the air speed is 6-8 m/s, the mass deposited at rice sieve's bottom is smaller.
## 4 Simulation Results and Discussions
### Effects of the Spatial Distribution of the \(K_{\rm s}\) Field  
The spatial distribution of the \(K_{\rm s}\) field is expressed using three statistic parameters: spatial variation variance, characteristic length and anisotropy ratio. This section conducts numerical simulations in either isotropic (\(\lambda=\lambda_{x}=\lambda_{z}=\) 8, 12, 16 m) or anisotropic (\(\lambda_{x}=\) 4 m, \(\lambda_{z}=\) 16, 64, 180 m, that is, \(v=\) 4, 16, 45) soil profiles with \(\sigma=\) 0.5, 1.2, 2.0.  
The inverse results under different soil heterogeneity conditions are illustrated in Figure 8. Because previous figures has shown the case with \(\sigma=\) 1.0, \(\lambda_{x}=\lambda_{z}=\) 4 m, here, results only under other conditions are illustrated. The quantitative metrics are listed in Table 3 using NRMSE. From the figure, it can be observed that overall the inversion effect has a tendency to become better as the spatial variability of the \(K_{\rm s}\) field becomes smaller.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scale** & **Simulation Time (min)** & \(K_{\rm s}\)**-NRMSE** \\ \hline
8 \(\times\) 8 & 59 & 0.0054 \\
16 \(\times\) 16 & 89 & 0.0021 \\
32 \(\times\) 32 & 178 & 0.0005 \\
64 \(\times\) 64 & 534 & 0.0009 \\
128 \(\times\) 128 (fine-scale) & 1067 & 0.0003 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The simulation time with \(K_{\rm s}\)-NRMSE at different scales in Model A.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Scale Size (NRMSE)**} \\ \cline{2-7}  & **8 \(\times\) 8** & **16 \(\times\) 16** & **32 \(\times\) 32** & **64 \(\times\) 64** & **128 \(\times\) 128** \\ \hline A & 0.0054 & 0.0021 & 0.0005 & 0.0009 & 0.0003 \\ B & 0.0640 & 0.0174 & 0.0023 & 0.0005 & 0.0002 \\ C & 0.0317 & 0.0988 & 0.04
## 4 Empirical Study
### Data Collection and Screening
#### 4.1.2 Data Screening  
Text mining from platforms such as Zhihu inherently involves inherent noise and potential inaccuracies, necessitating meticulous text preprocessing, including automated procedures and manual refinements [61,62]. In this study, a series of rigorous preprocessing steps were undertaken before the formal analysis. For example, irrelevant data unrelated to the subject's core experience, such as special characters, blank information, and URLs, were manually excised. Then, phrases prone to fragmentation, such as \"range\", \"driving experience\", and \"monthly sales\", were designated as domain-specific terms prior to tokenization and part-of-speech tagging. Finally, text de-noising was applied, where colloquial words, such as \"please\", \"also\", and \"too\", were categorized as stop words. Moreover, the data-screening process was iteratively refined until it was appropriate for the subsequent analytical procedures.
## 3 Experiment and Results Discussion
### COD Detection Compensation Based on Fusion of UV-Vis Spectroscopy and Environmental Factors
#### 3.2.3 Fusion Modeling of Spectra and Single Environmental Factor  
After data processing, the PLS algorithm was used to establish a COD prediction model that fused spectral absorbance and a single environmental factor. The performance of the models is shown in Table 4.  
It can be seen that the \(R_{pred}^{2}\) of the modeling results of the three environmental factors are pH, conductivity, and temperature in descending order from Table 4. Therefore, the order of influence of the three factors on the model is pH, conductivity, and temperature in descending order. It can be analyzed and understood from a chemical perspective that  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Data Processing**} & \multirow{2}{*}{**Data Dimension**} & \multicolumn{2}{c}{**Calibration**} & \multicolumn{2}{c}{**Prediction**} \\ \cline{3-5}  & & \(R_{val}^{2}\) & **RMSEC** & \(R_{pred}^{2}\) & **RMSEP** \\ \hline Raw spectrum + PLS & 2048 & 0.8744 & 9.14 & 0.8481 & 10.86 \\ SCARS + PLS & 14 & 0.9138 & 6.56 & 0.8943 & 7.83 \\ SCARS + pH + PLS & 15 & 0.9485 & 4.29 & 0.9459 & 4.46 \\ SCARS + Temperature + PLS & 15 & 0.9177 & 6.30 & 0.9054 & 7.11 \\ SCARS + Conductivity + PLS & 15 & 0.9395 & 4.87 & 0.9317 & 5.39 \\ \hline \end{tabular}
\end{table}
Table 4: The model performance of compensation for single environmental factors.  
Figure 11: Process of feature extraction using SCARS method.  
pH causes a red shift and a blue shift in the spectral absorption curve, accompanied by an up- and downshift, thus having the greatest influence. The conductivity includes multiple components in water, which can cause significant interference in the detection of COD. Temperature needs to change over a large range to influence the spectroscopy data. In reality, temperature changes are within the room temperature range, and they have the lowest influence on the COD prediction model.
## 1 Introduction
### Design Consideration of HG-SIQBC Configuration  
#### 2.5.1 Current Ripple of Inductors for HG-SIQBC  
The current of all inductors increases as the input voltage is applied. The peak-to-peak ripple in the current of inductors \(L_{1}\), \(L_{2}\), and \(L_{3}\) can be derived as follows:  
\[\left\{\begin{array}{l}\Delta I_{L_{1}}=\frac{V_{L_{1}}}{L_{1}}DT_{s}=\frac {DV_{in}}{2L_{1}f_{s}}\\ \Delta I_{L_{2}}=\frac{V_{L_{2}}}{T_{s}}DT_{s}=\frac{DV_{in}}{2L_{2}f_{s}}\\ \Delta I_{L_{3}}=\frac{V_{L_{3}}}{L_{3}}(1-D)T_{s}=\frac{D\left(D-2\right)V_ {in}}{2\left(1-D\right)L_{3}f_{s}}\end{array}\right., \tag{39}\]  
where \(f_{s}\) is the switching frequency of the power switches.  
The design values of inductors are calculated with the known values of \(D\), \(V_{in}\), and \(f_{s}\).
# Uplink Transmit Power Control for Single-Carrier Grouped FDMA with Iterative Multiuser Detection
## 5 Conclusions  
This paper presented a power control algorithm for an uplink SC-GFDMA system. We formulated and simplified the maximization problem of spectral efficiency and used a simple approximation to determine a power control criterion that exploits the non-orthogonal characteristic of the SC-GFDMA structure. The specific criterion was derived from the SINR analysis performed based on SNR-variance density evolution. The spectral efficiency was considerably improved by limiting the signal power of each user according to the proposed criterion. In simulations, the proposed algorithm provided significantly higher spectral efficiency than the conventional methods, even with a considerably reduced total transmit power.  
Future WorkIn this study, to emphasize the notable impact of power control, we considered a relatively simple system using the SC-GFDMA structure. Thus, a further study on power control may consider a more complicated SC-GFDMA system involving hybrid subcarrier mapping and different MCSs for different users in the same chunk. As the power control algorithm is derived independent of user scheduling in this study, one may also consider a joint power control and scheduling scheme. In addition, as the proposed method was derived by tracking the SINR performance of the IMD, one may attempt to find further applications of the proposed method for any system using an iterative detection, even if the system does not use the SC-GFDMA structure.  
Using the SC-GFDMA structure, with the proposed power control, the available spectral efficiency in each chunk can be significantly increased compared with that available in the typical SC-FDMA. As each chunk is used as a unit for resource allocation in practical systems [8; 9], the proposed system can be regarded as an alternative to SC-FDMA while having resource grids with improved potential performance. Thus, the system-level resource allocation and management techniques, which are applied across subcarriers, can be further studied based on the analysis framework derived in this paper to improve the system-level performance in the network.  
Figure 15: Computational complexity with and without power control; the number of iterations is 20.  
**Author Contributions:** Conceptualization, Y-S.C.; Data curation, Y-S.C.; Formal analysis, M.M.; Funding acquisition, M.M.; Methodology, Y-S.C.; Software, Y-S.C.; Supervision, M.M.; Validation, Y-S.K.; Visualization, Y-S.K.; Writing--original draft, Y-S.C. and M.M.; Writing--review & editing, M.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2017R1C1B5074268).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Current Status of Development of Geophysical Exploration Techniques
### Exploration Techniques Based on Dielectric Constant
#### 2.3.2 Ground-Penetrating Radar Method  
The ground-penetrating radar (GPR) method [55] is a geophysical exploration technique that uses high-frequency electromagnetic waves to determine the distribution pattern of a material within a medium based on the differences in the relative permittivity and conductivity of the medium [56]. It probes the internal structure and distribution of the target body based on differences in the electrical properties of the subsurface medium [57]. If a leakage path exists in the shallow layer (<10 m) of an earth and rock-fill dam, the high-frequency radar antenna can achieve high-resolution detection, and this technology has been applied in several earth and rock dams [22]. In recent years, with the increasing application of the GPR method in the hazard detection of earth and rock dams, the detection speed has increased, and the detection depth can reach approximately 15 m [58]. The reflection image of a radar [59] contains rich kinematic and dynamic information, which can be used to analyze and locate hidden dangers such as holes, seams, soil loosening, and leakage in earth and rock dams. Based on the contradiction between the complex changes in the structural morphology and medium properties of earth and rock dams and the depth and resolution of detection, [PERSON] et al. [60] and [PERSON] et al. [61] conducted corresponding experimental research and made some progress, which should be improved in engineering practice. If the water content of the embankment is high [62], the attenuation of the electromagnetic waves will be very serious, resulting in a shorter effective detection distance and low detection for deeper seepage points. Figure 9 shows the results of the GPR method of probing for a project.
## 1 Introduction
### BJPA and Curing Temperature as the Major Factors to Study
However, this is also a problem. Although the applied steam may facilitate the cement hydration reaction, accelerate the generation of hydration substances, and quickly increase the CS of the concrete [21; 22; 23], adverse or unknown effects may occur to the microstructures, and in turn, to the CS measured at a later stage and the durability of the concrete material [4; 24; 25; 26]. Some studies have reported that the positive effects of increasing the curing temperature are observed only at the earlier stages of the process. For example, compared to the case at room temperature, increasing the curing temperature and using ground coal bottom ash helped increase the CS of cement mortar [27]. Other studies have also pointed out that the benefits are observed at the earlier curing stage. Recycled brick powder was used in cement paste, and by increasing the curing temperature, the CS increased earlier [6]. Therefore, this was regarded as a'solution' to avoid low CS at the early curing stage of cement mortar. A combination of materials (metakaolin, silica fume, bagasse ash, and calcined bagasse ash) was used to replace the cement binding material, and the effects of the increased curing temperature were studied [28]. The results revealed that the increase in the curing temperature did not affect the pozzolanic reactions. Moreover, the substances produced by the hydration reactions after raising the curing temperature may have been the reason why the structure of the cement paste or sand mortar was enhanced. Hence, the current study investigates the combined effects of increasing the curing temperature and using different substitution ratios of BJPA (SRs-BJPA) on the CS of concrete samples. Three water/binder (W/B) ratios were tested, and this parameter was also treated as a factor during the post-experimental data analysis. Furthermore, the CS data were recorded for each sample at six different time periods after the sample was prepared. That is, the W/B ratio and curing age were also factors, although not the primary ones, investigated in this study. Watching only CS is reasonable because a recent thorough data study [29] has indicated that other mechanical (strength) properties of concrete samples are strongly related to CS.
# Large Eddy Simulation and Dynamic Mode Decomposition of Turbulent Mixing Layers
## 2 Large Eddy Simulation of Turbulent Mixing Layer and Dynamic Mode Decomposition  
### Freestream Parameters of the Turbulent Mixing Layer  
The turbulent mixing layer adopts the mixing layer with different free flow velocities on upper and lower sides and the same temperature and composition. The freestream temperature \(T_{\infty}\) is 298 K and the freestream composition is air. The upper air freestream velocity \(U_{1}\) equals to 173 m/s, and the lower air freestream velocity \(U_{2}\) equals to 86.5 m/s. The initial vorticity thickness is given by  
\[\delta_{\omega}(0)=(U_{1}-U_{2})/\left|\frac{\partial u}{\partial y}\right|_{ y=0}=1.27\times 10^{-4}\text{m} \tag{1}\]  
The Reynolds number based on the initial vorticity thickness and the freestream velocity difference across the mixing layer  
\[Re=(U_{1}-U_{2})\delta_{\omega}(0)/v=720 \tag{2}\]where \(v\) is the kinematic viscosity of air at \(T_{\infty}\). The velocity ratio of the mixing layer,  
\[\eta=(U_{1}-U_{2})/(U_{1}+U_{2})=1/3 \tag{3}\]  
The convective velocity  
\[U_{c}=(U_{1}+U_{2})/2=0.375c_{\infty} \tag{4}\]  
The convective Mach number  
\[M_{c}=(U_{1}-U_{2})/(2c_{\infty})=0.125 \tag{5}\]  
where \(c_{\infty}\) is the speed of sound of air at \(T_{\infty}\).
# Benefits of Probiotics--Biodetoxification
## 2. Probiotics--Biodetoxification of Food
### The Role of Probiotics in Food Biodetoxification
#### 2.2.4 Aromatic Hydrocarbons  
Polycyclic aromatic hydrocarbons are well-documented food contaminates, especially benzo(a)pyrene (B(a)P), which is classified by the International Agency for Research on Cancer (IARC) in the first group, which means there is sufficient evidence of its carcinogenicity in humans [44]. PAHs occur in various types of food, such as meat, cereals, and vegetable oils, usually when grilling, roasting, smoking, heating, and cooking [41; 45]. Probiotic microorganisms annihilate B(a)P due to cell wall peptidoglycan binding capacity [41]. In the study by [PERSON] et al., _Bifidobacterium lactis_ BI-04 HN019 and _Bifidobacterium infantits_ BY12 were demonstrated to bind over 70% of B(a)P [46]. In the study by [PERSON] et al., fifteen LAB strains were investigated for B(a)P binding ability. _L. pentosus_ CICC 23163 and _L. plantarum_ CICC 22135 showed a B(a)P binding rate of over 60%, which was comparable to the results for heat-deactivated, non-viable cells [47].
## 1 Introduction
(3) Based on the statistics and observation of the shape parameters of TIR ship targets selected from VAIS database, a statistical shape knowledge is generated and utilized to further extract true ship targets from candidate targets. Because the VAIS database contains 1242 TIR ship images composed of 264 different types of ships captured during the daytime and nighttime with variable view-angles and diverse distances, the shape knowledge obtained by statistics and observation on this database could be more widely used in the field of TIR small ship detection. (4) Combining the above methods and their advantages, an efficient and robust infrared small ship detection scheme is developed and is superior to the state-of-the-art ship target detection methods. The structure of this paper is organized as follows: The morphological reconstruction and multi-feature analysis based on the intrinsic TIR imaging characteristics between ship targets and sea clutter are discussed in Section 2. The TIR ship detection algorithm based on morphological reconstruction and multi-feature analysis is proposed, and the whole details of the novel and robust scheme are shown in Section 3. Extensive experiments are included in Section 4 to evaluate the performance of the proposed algorithm, and the results show that the ship detection performance by the proposed method is significantly enhanced. Finally, Section 5 gives the conclusions of this paper. Figure 1: The flow chart of the proposed thermal infrared (TIR) small ship target detection method.
## 3 Discussion
### Ground-Based Radar Interferometry System  
The ground-based radar interferometry methods can be involved in both static and dynamic deformation measurements. Here, we focus on the application of GB-InSAR in static monitoring for heritage sites. In recent years, GB-InSAR has proven to be a consistent and reliable technique for monitoring sudden changes over a limited area (up to a few square kilometers). Compared to the space-borne system, GB-InSAR can provide images of a local-scale area in much shorter time intervals (about every 10 min) with precision at the millimeter or even sub-millimeter levels. Thus, GB-InSAR allows the displacement measurements of fast phenomenon, such as landslides and collapse, in heritage sites. Moreover, GB-InSAR can operate in steep slopes thanks to the flexible choices of its installation sites, while the satellite data are limited in areas with layover, foreshortening and shadowing effects. In addition, the retrieval of the actual interferometric phase caused by surface deformation is easier in GB-InSAR for two reasons: first, owing to its fixed observation geometry, GB-InSAR always satisfies the zero baseline condition, and topography-independent interferograms can be generated without resorting to the need for flattening processing; second, the significantly higher image acquisition rate of GB-InSAR largely reduces the temporal decorrelation in the interferometric data [45]. To make full use of these obvious advantages, there are some matters that need attention in the application of GB-InSAR for local-scale displacement measurements over heritage sites. The GB-InSAR devices should be placed in the best locations within sites in order to obtain optimal viewing geometry, especially for the monitoring of a slope or archaeological structures. Besides, since the spatial resolution of images decreases with increased observation distance, there is a trade-off between measurement accuracy and spatial coverage.  
With respect to the monitoring of a relatively large-scale scene over low coherence areas (e.g., forested and vegetated archaeological sites; ruins with an extremely low density of stable radar targets), the usage of artificial reflectors coupled with the GB-InSAR technique can be taken into consideration in order to realize a displacement estimation accuracy at the submillimeter level. This is because artificial reflectors can exhibit a high radar cross-section (RCS) or, at least, a good signal-to-clutter ratio (SCR) that is stable over time. These features allow us to improve the signal-to-noise ratio (SNR) of interferometric data and to minimize the impact of _in situ_, as well as atmospheric effects. This technique can be also used in space-borne radar interferometry [68].
## 3 Diversity and Reliability
### Energy Efficiency (EE) under Reliability Constraints  
The objective of the analysis is to find the energy efficiency of each scheme and discuss more on performance and resource usage. The basic definition of EE is [46]  
\[EE\ [\mathrm{bits/joule/Hz}]=\frac{Data\ rate\ [\mathrm{bits/s/Hz}]}{Power\ consumption\ [\mathrm{Joule/s}]} \tag{14}\]  
where data rate for silencing, TAs with silencing and MRT can be evaluated from (11)-(13) respectively. We adopt the unified power consumption model as proposed in [47]. This model approximates the BSs power consumption as a piecewise linear function of the transmit power \(\mathrm{P_{T}}\):  
\[\hat{\mathrm{P}}_{i}=\begin{cases}\eta\mathrm{P_{T}}+\mathrm{P_{i,active}^{ \mathrm{RRH}}};\ \mathrm{if\ RRH}_{i,active}\\ \mathrm{P_{i,sleep}^{\mathrm{RRH}}};\ \mathrm{if\ RRH}_{i,sleep}\end{cases} \tag{15}\]  
where \(\eta\) is a constant reflecting the power amplifier efficiency, feeder loss and other loss factor due to power supply and cooling for \(\mathrm{RRH}_{i}\), \(\mathrm{P_{T}}\) is the transmit power, \(\mathrm{P_{i,active}^{\mathrm{RRH}}}\ \mathrm{P_{i,sleep}^{\mathrm{RRH}}}\) are the power consumption at a RRH corresponding to active and sleep modes, respectively. Typically, \(\mathrm{P_{i,active}^{\mathrm{RRH}}}>\mathrm{P_{i,sleep}^{\mathrm{RRH}}}\) so that it is beneficial to silent RRH, whenever possible for energy saving. Based on the above power consumption model in (15), we can write the total power consumption as  
\[\hat{\mathrm{P}}=\sum_{i=0}^{k}\hat{\mathrm{P}}_{i} \tag{16}\]  
where, \(k\) is the number of cooperating RRH. We assume \(\mathrm{P_{T}}\), \(\mathrm{P_{i,active}^{\mathrm{RRH}}}\) and \(\mathrm{P_{i,sleep}^{\mathrm{RRH}}}\) are same for all the RRHs. Based on (15) and (16) we can write the power consumption model for each considered scheme as  
\[\hat{\mathrm{P}}=\begin{cases}\eta\mathrm{P_{T}}+\mathrm{P_{active}^{\mathrm{ RRH}}}+k\mathrm{P_{sleep}^{\mathrm{RRH}}}\ \mathrm{for\ silencing\ and\ TAS}\\ (k+1)(\eta\mathrm{P_{T}}+\mathrm{P_{active}^{\mathrm{RRH}}}),\ \mathrm{for\ MRT}.\end{cases} \tag{17}\]As we can see from (17), as \(k\) increases, there is an increase in power consumption. However, consumption seems more in the case of MRT compared to silencing and TAS with silencing.