esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Train-to-Ground Communication Architectures with Leaky Waveguide
### On-Train Leaky Lens
#### 5.2.2 Lens' Thickness  
To determine the thickness of the above two leaky lens, the ratios of thickness to radiation aperture need to be calculated. Assume that the lens' thickness is \(d\) and the radiation aperture is \(D\). As for hyperboloid lens, consider the point \(A(d,D/2)\) at edge of the lens, as is shown in Figure 5a, and combine this point with Equation (5), thus the ratio of thickness to radiation aperture can be expressed as  
\[\frac{d}{D}=-\frac{f/D}{n+1}+\sqrt{\frac{\left(f/D\right)^{2}}{\left(1+n\right) ^{2}}+\frac{1}{4\left(n^{2}-1\right)}} \tag{11}\]  
Similarly, combine Equation (10) and the point \(A\) in Figure 5b, the ratio of thickness to radiation aperture of ellipsoid lens can be expressed as  
\[\frac{d}{D}=\frac{n}{n-1}\frac{f}{D}+\sqrt{\frac{n^{2}\left(f/D\right)^{2}}{ \left(n-1\right)^{2}}-\frac{n^{2}}{4\left(n^{2}-1\right)}} \tag{12}\]  
According to Equations (11) and (12), the thickness of the lens can be calculated if the focal length \(f\) and radiation aperture \(D\) are determined. Normally, \(f/D\) is in the range of 1.0 to 1.6 in the design of leaky lens.
## 3 Results
### Effect of HVAC Power Demand on the Residual Range of a BEV  
The influence of HVAC power demands over the battery SoC evolution and the residual driving range estimated throughout the driving mission were studied and the results are reported here. Both conditions of heating and cooling the vehicle cabin were simulated considering ambient temperatures (outside the vehicle) equal to 0 \({}^{\circ}\)C (winter scenario) and 35 \({}^{\circ}\)C (summer scenario), respectively. The HVAC system was assumed to be switched on only 60 s after the beginning of the drive and the relative power demand was calculated according to (23) and Figure 10. A comparison of battery SoC and residual range variation along DM20 with and without HVAC consumption for the winter scenario are reported in Figures 30 and 31, respectively. The evolution of the battery SoC charted in Figure 30 highlights two different trends, with final SoC values differing by about 4%. Consistently, the trajectories of the residual driving range estimated throughout DM20 followed very different traces. Specifically, the curve of the residual driving range estimated when the HVAC was activated (Figure 31, blue curve) shows a relevant decrease after 60 s, which is the time instant at which the HVAC was assumed to be turned on. At this stage, the power consumption demanded for the air conditioning was instantly added to the battery, and hence a drastic fall occurred to the curve. From that time instant onwards, the estimated residual driving range stabilised to a similar decreasing shape of the HVAC-off curve and led to a final variation of \(-31\%\) (\(-35\) km).  
The same analysis was conducted considering the summer scenario with an ambient temperature of 35 \({}^{\circ}\)C and the results for DM20 are reported in Figures 32 and 33. Once again, the influence of the cabin conditioning system assumed a relevant role both on the battery SoC discharge and the residual driving range, which were reduced by 7% and 46% (\(-51\) km) with respect to the HVAC-off condition, respectively.  
Figure 30: Evolution of the battery SoC variation with and without the HVAC system consumption for DM 20 considering the winter scenario (0 \({}^{\circ}\)C).  
Considering the entire dataset under HVAC-on condition, the final values of the battery SoC reduced in the range (4, 12)% for heating mode and (3, 15)% for cooling mode, whereas the average decrease in residual driving range stayed between (28, 57)% for heating mode and from (25, 63)% for cooling mode. The HVAC consumption for cabin conditioning can hence be considered as a major impact for BEV real-world driving missions in both cases of hot and cold ambient temperatures.
# An Electroglottograph Auxiliary Neural Network for Target Speaker Extraction
## 4 Experiments and Results
### Experiments
#### 4.1.2 Experimental Settings  
Based on the SpeakerBeam network, we proposed EGG_Aux and Pre_EGG_Aux networks. We set the batch size to 256, with a learning rate of \(1\times 10^{-4}\). We used sequence summarization with an attention mechanism for the extraction of target speaker features to inform the networks about the target speaker. Due to the attention mechanism, the networks could focus on the more useful information provided by the EGG signals and extract target speech more accurately.  
The architecture of the networks consisted of bidirectional long short-term memory (BLSTM) layers and convolution layers. The loss function was MSE loss between the amplitudes of target speech and estimated signals. Additionally, although SDRi has been selected as the metric to compare performance in previous studies, in our work, we considered SISDRi metric in order to compare the outcomes more reasonably.
# A Comparative Study of Local Descriptors and Classifiers for Facial Expression Recognition
## 5 Experimental Results and Discussion
### Comparison with the State-of-the-Art  
Several local descriptors have been proposed in recent years to tackle the FER problem [33; 37; 38]. Table 8 gives the performances of various existing FER systems compared to our proposed best combinations of local descriptors and classifiers. The table shows that several local descriptors work as well as deep neural networks. Furthermore, [PERSON] et al. [39] proposed the Pyramid Histogram of Oriented Gradients (PHOG) descriptor and KNN classifier, achieving an average accuracy of 100% on the CK+ dataset. Results such as these are difficult to compare with other approaches because they depend on the selection of the train and testing images. The more recent studies prefer 10-fold cross-validation as a more reliable measure of the accuracy of FER classifiers. This is because each sample is used for training and testing once. Hence, a high accuracy during a 67-33% split, as in [39], is not always a good indicator of performance.  
It is clear from the table that our approach using HOG and CLBP either performs better or as efficiently as other approaches. Although some existing approaches have produced higher accuracies, they generally produce larger feature vectors. The proposed method finds the best balance between a small feature vector and high accuracy by exploring several feature sets. Our findings reveal that a small feature vector can achieve the same  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Descriptor** & **WLD** & **LDP** & **ALDP** & **LBP** & **CLBP** & **HOG** & **Average** \\ \hline SVM & 1300 & 530 & 1240 & 1050 & 2520 & 1970 & 1435 \\ NB & 150 & 240 & 180 & 200 & 200 & 210 & 197 \\ KNN & 3010 & 500 & 3260 & 3560 & 6360 & 1160 & 2975 \\ MLP & 310 & 140 & 460 & 460 & 680 & 580 & 438 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction times of various classifiers (times are given in milliseconds).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**WLD** & **LDP** & **ALDP** & **LBP** & **CLBP** & **HOG** & **Average** \\ \hline
0.54 & 3.79 & 3.9 & 5.69 & 12.49 & 1.72 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Extraction times (in seconds).  
accuracy as a large feature vector if the extraction parameters are appropriately set, as seen in Figure 4. This finding could greatly benefit existing methods with high dimensionality and large memory footprints. It should be noted that, unlike many recent approaches, this study did not use any dimensionality reduction or subspace-learning methods and still achieved competitive results.
## 4 Simulation Results and Discussion
### Case 1: Sensitivity Analysis for Criterion Variation Within \(\pm 5\%\)
#### 4.2.1 Scenario 1: Variation in Upload Speed  
In this scenario, we randomly vary the initial upload speed values while keeping the values of other criteria unchanged. Table 5 shows the random variation rates applied to the initial upload speed values of each satellite. The resulting decision matrix is shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Satellite ID**} & \multicolumn{2}{c}{**Upload Speed**} & \multicolumn{2}{c}{**Download Speed**} & \multicolumn{2}{c}{**Ping [ms]**} & \multicolumn{2}{c}{**Packet Loss**} \\  & \multicolumn{2}{c}{**[Mbps]**} & \multicolumn{2}{c}{**[Mbps]**} & \multicolumn{2}{c}{**[Mbps]**} & \multicolumn{2}{c}{**[\%]**} \\ \hline
1 & 10.97 & 188.94 & 49.50 & 0.01 \\ \hline
2 & 12.46 & 220.21 & 62.67 & 0.16 \\ \hline
3 & 13.31 & 213.23 & 62.34 & 2.88 \\ \hline
4 & 15.88 & 221.39 & 64.16 & 19.46 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Decision matrix of Scenario 1, for criterion variation within \(\pm 5\%\).  
Figure 4: Satellites scores for the initial decision matrix.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Criteria** & **Sat 1** & **Sat 2** & **Sat 3** & **Sat 4** & **Sat 5** & **Sat 6** & **Sat 7** & **Sat 8** & **Sat 9** & **Sat 10** \\ \hline SD-TOPSIS & 0.43 & 0.39 & 0.42 & 0.54 & 0.77 & 0.53 & 0.45 & 0.56 & 0.55 & 0.46 \\ \hline Entropy-TOPSIS & 0.41 & 0.36 & 0.40 & 0.56 & 0.76 & 0.50 & 0.46 & 0.54 & 0.56 & 0.47 \\ \hline Importance-TOPSIS & 0.39 & 0.37 & 0.41 & 0.58 & 0.74 & 0.51 & 0.47 & 0.54 & 0.59 & 0.49 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Satellites scores values for the initial decision matrix.
# A Review of the Artificial Neural Network Models for Water Quality Prediction
###### Abstract  
Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi)-output, Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.  
ANNs; feedforward; recurrent; hybrid; water quality prediction +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
### Materials  
Natural rubber latex (NR; 60% dry rubber content with high ammonia) was obtained from Chemical and Materials Co., Ltd. (Bangkok, Thailand). Sodium dodecyl sulfate (SDS) and acrylamide (AM) monomer were purchased from Loba Chemie Pvt. Ltd. (Mumbai, India). Acrylic acid (AA) monomer and cumene hydroperoxide (CHP) initiator were obtained from Aldrich (St. Louis, MO, USA). AA was purified by using a column packed with alumina adsorbent before polymerization [24]. Tetraethylene pentamine (TEPA) activator was purchased from Acros organics (Geel, Belgium). Urea and toluene were purchased from RCI Labscan Limited (Bangkok, Thailand). Terric16A (10 wt%) and 1,3-diphenyl guanidine (DPG) were obtained from the Rubber Authority of Thailand (Bangkok, Thailand). Ethanol was purchased from Duksan Reagents (Ansan, South Korea). The compounding ingredients, such as zinc oxide (ZnO), stearic acid, N-cyclohexyl-2-benzothazole sulfenamide (CBS), and sulfur, were received from Chemical Innovation Co., Ltd. (Bangkok, Thailand). Precipitated silica (JS-185, surface area (BET) of 170-200 m\({}^{2}\)/g, a DBP absorption value of 2.0-2.6 cm\({}^{3}\)/g, and a residue from a 75 \(\upmu\)m sieve \(\leq\) 10%) was obtained from Jinsha Precipitated Silica Manufacturing Co., Ltd. (Fujian, China). Silica was used without further purification. Deionized water was used throughout the study.  
Preparation of Poly(acrylic acid-co-acrylamide)-Grafted Deproteinized Natural Rubber ((PAA-co-PAM)-DPNR)  
The deproteinized natural rubber was firstly prepared according to [PERSON] et.al. [25] by incubation with 0.1 wt% of urea and 1 wt% of SDS for 60 min. The dispersion was centrifuged at 15,000 rpm for 30 min. The cream fraction was then redispersed with 1 wt% of SDS, followed by centrifugation. The washing step was performed two times. The cream fraction was collected, redispersed with deionized water, and kept for further modification.  
The modification of the DPNR by graft copolymerization with a comonomer of acrylic acid and acrylamide was carried out by the CHP/TEPA initiating system. The DPNR latex and Terric16A were charged into a glass reactor equipped with a mechanical stirrer. The latex was then purged with nitrogen gas for 45 min at a controlled temperature under stirring at 100 rpm. The CHP was charged into the reactor, followed by the addition of acrylic acid (40 mol% of acrylic acid was neutralized with 20 wt% of NaOH solution) and acrylamide. In this case, the comonomer ratio of the acrylic acid and acrylamide was kept constant at 50:50 by weight percentage. The momomer contents were varied as 10 and 30 phr, as shown in Table 1. Then, a TEPA solution was injected by dropwise addition to initiate polymerization. The weight ratio of CHP and TEPA was fixed at 1. The total solid content was kept constant at 20 wt%. Polymerization was continued in a nitrogen atmosphere under stirring. After that, the obtained latex was collected for characterization.
## 3 Detailed Operators
### Mathematical Model of Reliability-Based and Cost-Oriented Optimization  
This process corresponds to Stage V in Figure 1, and is described in Section 2. Reliability optimization is an important guarantee for the correct progression of later product detailed design. The generalized cost function is regarded as a starting point where other factors are considered. The mathematical model of reliability-based and cost-oriented product optimization is established, where the minimum product reliability allowable and the scope of subsystem reliability are regarded as constraint conditions, and the product reliability and cost are set as optimization objectives. Product cost \(C\) includes the development cost \(C_{1}\) and maintenance cost \(C_{2}\).  
\[C^{1}=\sum\limits_{i=1}^{n}C_{i}^{1}\left(R_{i},f_{i},R_{i,\;min},R_{i,\;max},w_{i}^{f}\right)=\sum\limits_{i=1}^{n}\exp\frac{w_{i}^{f}(1-f_{i})\left(R_{i} -R_{i,\;min}\right)}{\left(R_{i,\;max}-R_{i}\right)} \tag{16}\]  
\[C^{2}=\sum\limits_{i=1}^{n}B_{i}^{2}\left(R_{i},w_{i}^{d},C_{i}^{2},\beta_{i} \right)=\sum\limits_{i=1}^{n}B_{i}^{2}w_{i}^{d}\exp[\beta_{i}(1-R_{i})] \tag{17}\]  
where \(f_{i}\) is the feasible degree to improve the reliability of the \(i\)th subsystem; \(R_{i,min}\) is the minimum reliability of the \(i\)th subsystem after working for a while; \(R_{i,max}\) is the maximum reliability of the \(i\)th subsystem; \(C_{i}^{1}\) is the development cost of the \(i\)th subsystem; \(R_{i}\) is the reliability designed of the \(i\)th subsystem which is within [\(R_{i,min}\), \(R_{i,max}\)]; \(w_{i}^{f}\) is the weight of the \(i\)th subsystem considering the development cost; \(C_{i}^{2}\) is the maintenance cost of the \(i\)th subsystem (all the subsystems are supposed to be repairable); \(B_{i}^{2}\) is the purchase cost of the \(i\)th subsystem; \(w_{i}^{d}\) is the weight of the \(i\)th subsystem considering the product failure which is calculated by the FRPN model; and \(\beta_{i}^{2}\) is the difficulty degree of the \(i\)th subsystem. The mathematical model of reliability-based and cost-oriented product optimization is established:  
\[\left\{\begin{array}{l}\min C=\sum\limits_{i=1}^{n}\left(C_{i}^{1}+C_{i}^{2} \right),\\ \max R=\prod\limits_{i=1}^{n}R_{i},\\ s.t.\prod\limits_{i}^{n}R_{i}\geq R^{*},\\ R_{i,\;min}\leq R_{i}\leq R_{i,\;max}\end{array}\right.\;\;\;\;\;\;\;\;\;\;\;i=1, \;2,\ldots,n \tag{18}\]  
where \(R^{*}\) is the target reliability for the product.
## 5 Modeling Results and Discussion
### Full Bayesian Estimation
These findings suggested that simultaneously accounting for the unobserved heterogeneity across individual observations and spatial correlation among adjacent crashes could further improve the model fit. Similar findings were found by [PERSON] et al. [65] and [PERSON] et al. [66] who pointed out that ignoring spatial correlation and unobserved heterogeneity in crash analysis will lead to biased estimates and incorrect inferences. Meanwhile, the significant spatial variations exhibit in Table 5 can further confirm the importance of accommodating spatial effects in rural SV crash severity analysis. In detail, the spatial variation ratios of no injury and slight injury associated with passenger cars were 0.712 and 0.543, respectively. In motorcycle crashes, the proportions of spatial variation in slight injury and FS injuries were statistically significant (the values were 0.691 and 0.850, respectively). The spatial variation of no injury in pickup collisions was significant (0.723) and of no injury and slight injury in truck crashes were significant (the values were 0.595 and 0.749, respectively). Third, the numbers of effective parameters (_pD_) of passenger car crash functions established by the MN-logit model, RI-logit model, RP-logit model, and SRP-logit model were 62, 83, 115, and 138, respectively, highlighting a progressive increasing trend. This finding suggested that the SRP-logit model had higher complexity than other models because both the unstructured error term and the structured spatial error term were considered. [PERSON] et al. (2019) obtained similar conclusions and pointed out that capturing spatial effects through the first-order neighborhoods will generate a larger number of effective parameters and a better model fit compared to other definition methods. Interestingly, the greater number of effective parameters did not exhibit a negative effect for the fitting performance of the SRP-logit model. Because the posterior mean deviance (\(\overline{D}\)) in the SRP-logit model has a greater degree of reduction; the posterior mean deviance of the MN-logit model, RI-logit model, RP-logit model, and SRP-logit model for passenger car crashes were 8797, 8756, 8712, and 8683, respectively. Similar conclusions can be drawn by checking the number of effective parameters and the posterior mean deviance of other vehicle types. This may be related to the fact that both the unobserved heterogeneity and the spatial correlation were contained in the SRP-logit model ([PERSON] et al., 2019). Finally, considering spatial correlation will result in some significant risk factors not being identical. According to the regression results of the SRP-logit model, there was no significant correlation between traffic control and FS injuries in passenger car crashes and between dark with street lighting and FS injuries in motorcycle crashes, but they became totally significant once spatial effect was not accommodated. This inconsistency may be due to model misspecification, including omission of spatially relevant variables ([PERSON], 2018).
# Fire Video Recognition Based on Local and Global Adaptive Enhancement
## 2 Related Work
### Methods Based on Deep Learning  
Recently, fire detection approaches based on deep learning have witnessed rapid development. In uncertain environments, [PERSON] et al. [21] proposed a fire detection method, which used the lightweight MobileNetV2 [22]. To solve the issue that Convolutional Neural Network (CNN) is not good at processing fire information of arbitrary shapes, sizes, and occlusions, [PERSON] et al. [23] incorporated attention into CNN. A network based on CNN was designed to detect and locate fires [24]. This network produced masks through segmentation in the RGB image. Based on SLIC-DBSCAN-enhanced CNN, [PERSON] et al. [25] proposed a linear iterative clustering and density-based noise application spatial clustering method to identify fire and smoke. Based on the attention mechanism, an algorithm for fire video recognition was devised to reduce the probability of false alarms [26]. On the basis of transfer learning, [PERSON] et al. [27] designed an algorithm, which used Grad-CAM [28] to visualize and localize fires in images. As previous methods were difficult to use in complex scenes with few detection targets, a method based on YOLOX [29] was devised [30]. An approach based on multi-task learning was designed to detect forest fires [31]. A method based on deep learning was devised to detect flames in videos [32]. For fire detection, a data augment method based on random images was proposed to enhance the generalization capacity [33]. A method based on PP-YOLO [34] was designed to enhance the performance of fire detection [35].  
Unlike the above methods, the distinctive features of LGAE are outlined below. First, LAME was devised to enhance the local motion information about flames. Second, GAME was designed to depict the global motion information. Third, LGAE learns both global and local motion information through the utilization of GAME and LAME.
# Multiellipsoidal Mapping Algorithm
## 4 Experiments  
To show the capabilities of the proposed algorithm, in this section, the results of experiments are presented. For all of the following experiments, DE is used with parameter settings: \(F=1.2\) and \(CR=0.7\) and a fixed population of 10 particles and 50 iterations. The objective function has the parameter \(\alpha=0.8\), that was chosen heuristically for a good granularity balance. The point clouds are provided by an SRI-500 Laser Rangefinder from Acuity Technologies capable of scanning 800,000 points per second at distances of up to 500 feet (150 meters approximately).
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 2 The Energy-Efficient Internet-of-Things Model
### The Service-Aware Clustering Model  
The efficiency of an IoT traffic engineering model depends on its routing configuration, which usually reveals the actual routing paths followed by the sensor readings from their environment to the base station during operation. An efficient routing configuration is obtained by engineering the traffic flows carrying the sensor readings to route these readings from their controlling locations to the base station with the expectation of optimizing an objective function (maximizing a reward function or minimizing a penalty function) subject to some constraints on routing metrics, such as the delay, reliability or energy consumption.