esa-sceva/satcom-chunk-collection huggingface dataset:
# Air Entrainment and Air Demand in the Spillway Tunnel at the Jinping-I Dam
## 4 Discussion
### Estimation Method of Air Demand
#### 4.2.2 The Second Type of Formula  
The second type of formula is obtained by means of analyzing the prototype data, establishing the relationship between dimensionless air entrainment rate \(\beta\) (denotes the ratio of unit width air demand to unit width flow rate) with Froude number Fr, aerator device parameters \((t,d,\alpha,\theta,\ldots)\), and cavity sub-pressure \(\Delta P\). The general expression of this type is:  
\[\beta=f[\text{Fr},\ (t,d,\alpha,\theta,\ldots),\ \Delta P] \tag{4}\]  
Empirical formulas proposed by [PERSON] [39], [PERSON] [3], [PERSON] [26], [PERSON] et al. [17], [PERSON] [28] and [PERSON] et al. [40], for example, belong to this type of formula. Some of these typical formulas are described below.  
The general form of [PERSON] and [PERSON] [39] formula is:  
\[\beta=K_{1}(\text{Fr}-K_{2})-K_{3}\bigg{(}\frac{\Delta P}{\rho gh}\bigg{)}^{x} \tag{5}\]  
where \(K_{1}\), \(K_{2}\) and \(K_{3}\) are the empirical coefficients depending on the aerator's type, and \(x\) is the empirical index.  
With the engineering information of Foz do Areia, [PERSON] [26] obtained an empirical formula as follows:  
\[\beta=0.47(\text{Fr}-4.5)^{0.59}\bigg{(}\frac{C_{\text{d}}A}{Bh}\bigg{)}^{0.6} \tag{6}\]  
where \(C_{\text{d}}\) is the flow rate coefficient; and \(B\) is the chute width.  
The basic type of [PERSON] et al. [40] formula is:  
\[\beta=\sqrt{\cos\alpha}\Big{(}0.00358+0.0684X-0.0387X^{-1}\Big{)} \tag{7}\]  
where \(X\) is a comprehensive hydraulic parameter, related to Froude number, aerator parameters, and water depth. It is expressed as follows:  
\[X=\text{Fr}\cdot\Big{(}\sqrt{t/h}/\cos\alpha\cdot\cos\theta\Big{)} \tag{8}\]  
Compared with the first type formula, the second type formula avoids estimating cavity length in advance, instead applying a Froude number and aerator parameters, so it is more practical. However, the Froude number or water depth should be known by numerical calculation or model tests. Moreover, as each formula is derived from prototype observations of one or several projects, the calculation results of different formulas differ greatly from each other, and this type of formula still lacks universal applicability.
# An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features
## 5 Experimental Evaluation
### Example 2: Eliminating Noisy Features  
In this section, we evaluate the performances of the compared approaches in eliminating noisy features. We take emotions, birds, and enron as the benchmarks, and measure the residual feature redundancy in the selected feature subset \(\mathbf{\hat{F}}\) as follows:  
\[R(\mathbf{\hat{F}})=\frac{1}{k^{\prime}(k^{\prime}-1)}\sum_{\hat{t}_{j}\hat{x}_{ j}\in\mathbf{\hat{F}}}\sum_{c=1}^{l}\rho_{\hat{t}_{j}y_{c}}^{2}\rho_{\hat{t}_{j}y_{c}}^{ 2}, \tag{16}\]  
where \(\rho_{\hat{t}_{j}y_{c}}\) and \(\rho_{\hat{t}_{j}y_{c}}\) are the Pearson correlation coefficients of the features \(\mathbf{\hat{f}}_{i}\) and \(\mathbf{\hat{f}}_{j}\) with the target label \(\mathbf{y}_{l}\), and \(k^{\prime}\) and \(l\) are the numbers of the selected features and labels, respectively. When \(R(\mathbf{\hat{F}})\) reaches its maximum value, the maximal redundant information exists in \(\mathbf{\hat{F}}\), which interprets as the inferior ability of the selection approach in removing noisy features.  
The feature redundancy of \(k^{\prime}\) selected features for each approach is demonstrated in Figure 1, where \(k^{\prime}\in\{d/10,2d/10,\ldots,9d/10\}\) and \(d\) is the total number of original features. It illustrates that ELC is superior in reducing feature redundancy. In other words, ELC can effectively remove redundant features in its multi-label feature selection process. This is one of the crucial factors leading to the excellent discriminative ability of ELC. It should be pointed out that in contrast to the case of single-label feature selection, eliminating noisy features has not received sufficient attention from existing multi-label feature selection approaches. While the issue of noisy features is an obstacle of yielding high selection performance not only for the single-label learning but also for the multi-label cases, we devised ELC to comprehensively tackle this problem. Moreover, the reduced feature redundancy in the majority of redundancy elimination-based approaches is not directly relevant to the target labels. In contrast, ELC quantitatively reduces target-relevant redundancy without any prior probability knowledge, which is conducive to its superiority in multi-label feature selection.
# Simulink Model of a Thermoelectric Generator for Vehicle Waste Heat Recovery
###### Abstract  
More than 50% of the energy released through combustion in the internal combustion engine (ICE) is rejected to the environment. Recovering only a part of this energy can significantly improve the overall use of resources and the economic efficiency of road transport. One solution to recoup a part of this otherwise wasted thermal energy is to use thermoelectric generator (TEG) modules for the conversion of heat directly into electricity. To aid in development of this technology this effort covers the derivation of a respectively simple steady-state Simulink model that can be utilized to estimate and optimize TEG system performance for ICEs. The model was validated against experimental data found in literature utilizing water cooling for the cold side. Overall, relatively good agreement was found with the maximum error in generated power around 10%. Following it was investigated whether air can be used as a cooling medium. It was established that, at the same temperature as the water (18.4 \({}^{\circ}\)C), a flow velocity of 13.1 m/s (or 47.2 km/h) is required to achieve a similar cold junction temperature and power output. Subsequently using the model with air cooling, the performance of a TEG installed on a heavy-duty vehicle traveling at 50, 80, 90, and 120 km/h under different ambient temperatures was analyzed. It was determined that both a lower temperature and a higher flow velocity can improve power output. A further increase of the power output requires a larger temperature gradient across the module, which can be achieved by a higher heat input on the hot side.  
thernoelectric generator; thermoelectric module; waste heat recovery; internal combustion engine; steady-state model +
Footnote †: journal: Applied Scientific
## 3 Predict and Forward Scheme Based on Node Profile
### Phase Transition: The Optimal Stopping Theory  
As we defined, there are two phases in the Predict and Forward scheme: studying the state of the network and making delivery decisions for Prelearn messages. During the Predict phase, if the Prelearn message studies the state of the network for a long time, excessive statistics may cause a lack of computing resources and storage spaces of the node, and the network latency will increase dramatically, so the Prelearn message may miss the optimal candidate in the data transmission process. On the contrary, a short-lived Predict phase may imply that the Prelearn message does not obtain enough information about the attribute characteristics of nodes. Consequently, it is vital for Prelearn messages to make a precise phase transition in the data transmission process.  
To solve the  
Based on the calculation results, \(1/e\) is the maximum value for \(P_{K}\). Consequently, the optimal transition phase \(K\) is \(n/e\), which indicates that the \(n/e\)-th node is the last node to be routed by Prelearn messages and it begins to send Prelearn messages to the destination profile.
# An Advanced Vehicle Body Part Inspection Scheme Based on Scattered Point Cloud Data
## 3 Materials and Methods
### Methods
#### 3.2.2 Denoising of PCD Based on a Hybrid Filtering Algorithm  
The initial PCD generally features a great deal of noise and many outliers, as shown in Figures 2 and 3. These factors will affect subsequent analyses of the point cloud. Therefore, to avoid the impact of noise points on the simplified registration process, the PCD first needs to be filtered. The noise-denoising process is performed using the following steps. **Step 1. ** Invalid point removal based on straight-through filtering [34]; the calculation process is shown in Equation (1). \[\left\{ \begin{array}{l}x_{min}\leq x_{i}\leq x_{max}\\ y_{min}\leq y_{i}\leq y_{max}\\ z_{min}\leq z_{i}\leq z_{max}\end{array} \right., \tag{1}\]  
where \(x_{i}\), \(y_{i}\) and \(z_{i}\) are the coordinate values of point \(p_{i}\); \(x_{min}\), \(y_{min}\) and \(z_{min}\) are the minimum coordinate values in the \(x\), \(y\) and \(z\) directions; and \(x_{max}\), \(y_{max}\) and \(z_{max}\) are the minimum coordinate values in the \(x\), \(y\) and \(z\) directions. Thus, the invalid point cloud is removed by setting the part range of the PCD. However, the pass-through filtering algorithm needs to get the specific size of the parts according to the drawing information firstly and set the parameters manually. This method is subjective to some extent. **Step 2. ** Outlier removal based on statistical filtering. Because of the large amount of the PCD, the \(k\)- dimensional (\(k\)-d) tree [35] and KNN [36] are introduced into the traditional statistical filtering algorithm to further improve the efficiency of the algorithm [37]. The algorithm flow is as follows. (1). Read into the PCD set \(P_{i}(p_{1},p_{2},\ldots,p_{n})\) and build the \(k\)-d tree;  
(2). For each point \(p_{i}\), calculate the average distance between point \(p_{i}\) and its \(k\) neighborhood point. \(d_{ij}\) is the distance between point \(p_{i}\) and \(p_{j}\), and \(\overline{d_{i}}\) is the mean distance of the \(k\) neighborhood point in \(p_{i}\). This process is shown in Equations (2) and (3).
## 4 Result and Discussion
### Results for Throughput with Circular-Shaped UAV Movement  
Figure 7 illustrates the throughput results, demonstrating the effective data transfer between the UAV and GS. The results highlight the UAV's capability to maintain high throughput during swift movement, optimizing communication performance in real-time applications.  
Figure 6: Delay using BRFFNN and LSGB for bandwidth allocation with circular UAV movement.  
Figure 7: Throughput using BRFFNN and LSGB for bandwidth allocation with circular UAV movement.  
In the figure, the throughput for the BRFFNN model is represented by the red line, with an average of 3.843 million pps. The blue line represents the throughput for the LSGB model, which achieved an average of 3.232 million pps. These results indicate that bandwidth allocation using the BRFFNN model provides superior throughput compared to the LSGB model, suggesting that BRFFNN is more effective in maintaining higher data transmission rates.
## Materials and Methods
### DNA Extraction, Illumina 16 SRNA Gene Sequencing, and Taxonomic Classification  
A next generation-targeted amplicon sequencing (NG-TAS) analysis of archaeal and bacterial communities was performed in sediments (300-500 mg of sample) of the studied site prior to starting the experiment. The extraction of the environmental DNA was performed using the EZNA Soil DNA isolation kit (Omega Bio-Tek Inc., Norcross, GA, USA). After a NanoDrop quantification of samples, the sequencing of the region V4 of the 16S rRNA gene was done using the Illumina MiSeq system (\(2\times 250\) bp) as previously described in [PERSON] et al. [32]. Briefly, Illumina compatible, dual indexed amplicon libraries of the 16S-V4 rRNA hypervariable region were created with primers 515f/806r. The PCR reactions were made following [PERSON] et al. [33]. Completed libraries were batch normalized using Invitrogen SegualPrep DNA Normalization Plates. Then, the Qubit quantified pool was loaded on a standard Illumina MiSeq v2 flow cell and sequencing was performed in a \(2\times 250\) bp paired end format using a MiSeq v2 500 cycle reagent cartridge. The sequences obtained were then processed using the UPARSE pipeline implemented in USEARCH v11.0.667 [34]. After merging of read pairs, the dataset was filtered by a maximum number of expected errors of 0.5. Chimeric sequences were removed with USEARCH v11.0.667 utilizing the UCHIME [34], against the SILVA 132 database. Filtered sequences were clustered in zero-radius operational taxonomic units (ZOTUs), which are sequences with 100% of identity. Later on, the alignments and taxonomic assignments were done with SINA v1.2.1152 using SILVA 132 database [35]. SINA uses the lowest common ancestor method (LCA). We configured a \"Min identity\" of 0.8 and a maximum number of search results of 1 per sequence results in \"best match\" type. Sequences with low alignment quality (\(<75\%\)) and sequences identified as mitochondria or chloroplasts were removed from the analysis. All the sequence data from this study have been deposited in the Sequence Read Archive (SRA) of the National Centre for Biotechnology Information (NCBI), with BioProject accession number PRJNA635639.
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 2 Principle of the Algorithm
### Algorithm Block Diagram
Although not shown in the block diagram in Figure 4 and not topic of the paper, by using the tracking information the RC data belonging to each detected ship can be extracted and used for generating high-resolution ISAR image sequences [9; 24]. Furthermore, in the case of using multi-channel data, the target track or the target positions can also be converted easily to geographical coordinates using the estimated direction-of-arrival angle. Even a tracking directly applied on the geographical positions is then possible, either as the main tracking step or as a second tracking step, with the main goal of improving the accuracy of the geographical positions. The proposed algorithm is mainly foreseen for airborne radar but not for spaceborne radar vessel detection. The major reason is because the algorithm only uses a relatively small number of azimuth samples for the azimuth FFT and the ship detection. This generally is too less for obtaining a sufficiently high peak signal-to-noise ratio (SNR) required for successful spaceborne vessel detection. In contrast, for the airborne case, a short integration time in the order of a few milliseconds, and hence, a small number of azimuth samples is sufficient for obtaining high enough peak SNR values. Therefore, for the spaceborne radars, either single-channel algorithms optimized for fully focused SAR images [10] or more sophisticated multi-channel algorithms, for instance the powerful EDPCA or ISTAP algorithms [25], should be used.
# Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods
## 2 Materials and Methods
### Combination of Multi-Weight Methods  
To address the problem of insufficient accuracy of urban flood risk characteristics caused by a single assignment, the subjective-objective combination assignment method was adopted for the risk of disaster-causing factors, the CRITIC-entropy weight objective assignment method was adopted for the exposure of disaster-containing environments and the vulnerability of disaster-bearing bodies, and the AHP subjective assignment method was adopted for the criterion layer. This assignment makes up for the shortcomings of single assignments, achieves unity and complementarity between the subjective and objective assignments, and enables the risk of urban flooding to be evaluated in a more scientific way.  
Due to the different data sources and their dimensions and large range of values, it is necessary to standardize the data between 0 and 1. The Grid Calculator in ARCGIS 10.0 is used to standardize rainfall grid data, topographic standard deviation data, etc. Therefore, prior to the assignment, the data matrix was constructed, and the data were normalized. The specific calculation method is as follows:  
1. Construction of data matrix By constructing \(m\) samples and \(n\) evaluation indicators, we obtained the original data matrix (\(x_{ij}\) is the evaluation value of the \(i\)th item under the \(j\)th indicator): \[X=\left(x_{ij}\right)_{m\times n}\] (1) Among them, \(i=1\), \(2\), \(\ldots\), \(m\); \(j=1\), \(2\), \(\ldots\), \(n\).
2. Numerical standardization Individual evaluation factors were quantified without outlines to eliminate unit effects between different variables. If the \(i\)th indicator factor value of the \(j\)th evaluation object was as high as possible, i.e., when the \(j\)th indicator \(x_{j}\) was a positive indicator, it was transformed as follows: \[r_{ij}=\frac{x_{ij}-\min\left(x_{ij}\right)}{\max\left(x_{ij}\right)-\min \left(x_{ij}\right)}\] (2) If the \(i\)th indicator factor value of the \(j\)th evaluation object was as low as possible, i.e., when the \(j\)th indicator \(x_{j}\) was a negative indicator, it was transformed as follows: \[r_{ij}=\frac{\max\left(x_{ij}\right)-x_{ij}}{\max\left(x_{ij}\right)-\min \left(x_{ij}\right)}\] (3) where \(i=1\), \(2\), \(\ldots\), \(m\); \(j=1\), \(2\), \(\ldots\), \(n\), \(r_{ij}\) is the standardized value of the \(i\)th indicator in the evaluation metrics, \(x_{ij}\) is the original value of the \(i\)th indicator in the evaluation metrics, \(\min\left(x_{ij}\right)\) is the minimum value of the \(i\)th indicator in the evaluation metrics, and \(\max\left(x_{ij}\right)\) is the maximum value of the \(i\)th indicator in the evaluation metrics.
## 2 Model Description and Transformation
### Model Transformation
In addition, the residual set is given by \(\bar{\vartheta}=\left\{\mathbf{x}|V(\mathbf{x})\leq\min\left\{\left(\frac{\alpha}{ \bar{\alpha}}\right)^{\frac{1}{p}},\left(\frac{\alpha}{\bar{\beta}}\right)^{ \frac{1}{q}}\right\}\right\}\) with \(\varrho=\frac{\vartheta}{1-\omega}\)._  
**Lemma 2** ([41]). : _For any \(x,y\ \in\mathcal{R}\), \(|x+y|^{v_{1}}\leq 2^{v_{1}-1}|x^{v_{1}}+y^{v_{1}}|\), if \(v_{1}\in\mathcal{R}^{+}\) and \(v_{1}>1\)._  
**Lemma 3** ([37]). : _For any \(x,y\ \in\mathcal{R}\), \((|x|+|y|)^{v_{2}}\leq|x|^{v_{2}}+|y|^{v_{2}}\) with \(0<v_{2}\leq 1\)._  
**Assumption 1**. : _Without loss of generality, we can make the following assumption from [42; 43]. The parameter uncertainties \(\mathbf{f}_{p},\mathbf{f}_{a}\) are bounded by the following functions:_  
\[\left\|\mathbf{f}_{p}\right\|\leq\alpha_{2}+\alpha_{3}\left\|\mathbf{\zeta}\right\|+ \alpha_{4}\left\|\mathbf{\zeta}\right\|^{2},\left\|\mathbf{f}_{a}\right\|\leq\alpha_{ 5}+\alpha_{6}\left\|\mathbf{\Theta}\right\|+\alpha_{7}\left\|\mathbf{\Theta}\right\|^{2},\]  
_where \(\alpha_{2},\alpha_{3},\alpha_{4},\alpha_{5},\alpha_{6}\), and \(\alpha_{7}\) are unknown, but bounded positive constants._  
**Assumption 2**. : _The external continuous disturbances \(\mathbf{u}_{d}\), \(\mathbf{\tau}_{d}\) under the frame \(\mathbf{\xi}_{E}\) and the system matrices \(\mathbf{g}_{i}\) are norm bounded (i.e., \(\left\|\mathbf{u}_{d}\right\|\leq u_{\tilde{d}}\), \(\left\|\mathbf{\tau}_{d}\right\|\leq\tau_{\tilde{d}}\), and \(\left\|\mathbf{g}_{i}\right\|\leq\bar{g}_{i}\)), where \(u_{\tilde{d}}\), \(\tau_{\tilde{d}}\), and \(\bar{g}_{i}\) are positive constants._  
Figure 2: Framework for ADP-based robust tracking control of the quadrotor UAV system.
## 3 Results & Discussion
### Factors Controlling Pore Cast Quality
#### 3.2.3 Viscosity Modifiers  
Some authors suggested that adding solvents, such as toluene or acetone, lowers the viscosity of the epoxy mixture and results in better impregnation. We added toluene to the epoxy mixture at three different weight ratios: 5%, 10%, and 30%. We used the viscosity behavior of the different epoxy and toluene compositions, depicted in Figure 2, to estimate the resin impregnation penetration length according to Equation (5). Figure 11 shows \(h\) plotted against time for three epoxy mixtures under two different impregnation schemes.  
The modeled behavior indicated no substantial change in the resin impregnation rate or volume unless the toluene weight ratio was more than 30%. However, the mechanical properties of the epoxy mixture with 30% of toluene deteriorated severely, i.e., lower hardness and toughness were observed. Figure 12 features two micromodels impregnated with different compositions of resin and toluene. We observed mechanical property deterioration and severe shrinkage defects, both type 2A and 2B, with a higher toluene/resin ratio.  
Moreover, Figure 12A shows a clear phase boundary between toluene and resin mixtures despite the single-phase injected into the micromodel. The phase separation likely occurred during the epoxy resin curing, followed by the expulsion of excess toluene.  
Figure 11: Estimated lengths of impregnation penetration into Indiana limestone versus time. Three resin and toluene (tol.) compositions were tested: (a) pure resin, (b) resin + 5% tol., and (c) resin + 30% tol. Both vacuum and high pressure impregnation schemes were considered for all compositions. The resin penetration length did not change significantly unless the toluene content was more than 30%; however, the mechanical properties for this composition greatly deteriorated.  
Figure 10: Confocal images of two types of the micromodels impregnated with resin under high pressure (65 bar) released to atmospheric pressure before complete curing took place. (**A**) Silicon micromodel, and (**B**) sintered glass beads. Defects in both models occurred when nitrogen gas dissolved at 65 bar was abruptly released when the pressure was dropped to atmospheric and the resin was incompletely cured. The gas release in model B was severe enough to debond some of the sintered glass beads.  
Therefore, we conclude that adding solvents, such as toluene, impairs the quality of pore casts. This will not cause a significant increase in the impregnation volume but will cause severe defects that may lead to false interpretations.
## 3 Results and Discussion  
### Workability  
Figure 6 shows the flow table test and the flow table test results are presented in Table 4. The flow table test values were determined by averaging the diameters of each mixes test. All mixes showed flow values in the range of 140-215 mm, without segregation or bleeding. The results revealed that addition of EGA increased the workability of cement mortar up to 26.6% and 41.25% for the E50 and E100 mixes respectively compared to the control mix (CS). The increment trend in workability despite the decreasing on the amount of superplasticizer in E50 and E100 mixes is contributed to the smooth surface and spherical shape of EGA [40; 41; 42]. Adding to this, the increase in the flow values can be due to the increase in the amount of entrapped air voids. Furthermore, the workability of CT, E50T, and E100T mixes increased by 5.35%, 30.4%, and 53.7% respectively in comparison to the CS, E50, and E100 mixes respectively, which is attributed to the induction of the microbubble in the water solution during the sonication process and consequently increased in small air voids in the mixes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mix ID** & **Average Flow Diameter (mm)** \\ \hline CS & 140.0 \\ E50 & 177.3 \\ E100 & 201.3 \\ CT & 147.5 \\ E50T & 182.5 \\ E100T & 215.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Flow results of mixes.  
Figure 5: Infrared thermography test.