esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### RNA Extraction and cDNA Synthesis  
The mirVanaTM RNA Isolation Kit (Invitrogen, Carlsbad, CA, USA) was used for total RNA extraction from each sample, following the instructions by the manufacturer. The NanoDrop 2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA) was used  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gene** & **Primer Sequence (5′ to 3′)** & **Product Length (bp)** \\ \hline \(CYP\) & FCGCGTGGCAGCTTACTAT; R:TGGTATTAGGCCCAGCAT & 104 \\ \(EF1\)-\(\alpha\) & F:TATTGACAAGCGTGGTGTGCTG; RTATTACAAGCCCCACGCA & 87 \\ \(GAPDH\) & F:ACATGGTGAACACAGA; R:CATGAAGCCGCATTTATT & 80 \\ \(TUB\) & F:GCTGTTTTCATCCGAGCA; RC:AGGCCACAGGTCAACAAT & 103 \\ \(UIBQ\) & FCACCCCTTCCGAGCTATAAC; RTCCGGTAGCGCTTTGA & 98 \\ \(AQP\) & FCGTTTCCTCTCTATTCCCAG;RC:CATGAGCAGTCCATGTATT & 117 \\ \(HIS\) & F:GATTCACAGGCAGTTGAGCTAG; RT:TCAGATCCTTGCTCGCAT & 138 \\ \(PGK\) & FGCGTAAAGGCTGACAC; R:GCAGGTAACACTTGCCTC & 87 \\ \(RPL13\) & FGCAGGTAACACACTTGTGTCC; RC:CTCTCTCTCTCTATTATT & 93 \\ \(POD\) & FCCGAGCCTTTGACAATTA; RCATTGACAAGCAGACACGTGT & 110 \\ \(PAL\) & FCTGTCATCAGGTCATAGAG; RT:TAGCGTGCTCCCCACCAAATA & 111 \\ \(CHI\) & FGCGCACAATAAGATGGCG; R:AGTTGCCTCATAGCTGCAC & 80 \\ \(DIR\) & F:GAGCCGCTATTCAGGCTA; RCGTACTCGAGAGAGATG & 89 \\ \hline \hline \end{tabular}  
* The Tm of all primers in this table is 60 \({}^{\circ}\)C. F, forward primers; R, reverse primers.  
\end{table}
Table 1: Primers of the candidate reference genes and target genes for qRT-PCR analysis.  
to evaluate the yield and quality of RNA. Finally, the RNA integrity was assessed via electrophoresis with 1% agarose gel.  
cDNAs synthesis was carried out in a GeneAmp(r) PCR System 9700 (Applied Biosystems, Foster City, CA, USA). The RT (reverse transcription) reaction consisted of 0.5 \(\upmu\)g RNA, 2 \(\upmu\)L of 5 \(\times\) TransScript All-in-one SuperMix (TransGen Biotech, Beijing, China), 0.5 \(\upmu\)L of gDNA Remover, and a total volume of 10 \(\upmu\)L with ddH\({}_{2}\)O. The RT reaction conditions were 15 min at 42 \({}^{\circ}\)C and 5 s at 85 \({}^{\circ}\)C. Each RT product was diluted ten times with nuclease-free water and then stored at \(-\)20 \({}^{\circ}\)C.
# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
## 3 Support Vector Machine Model
### Parameter Calibration and Optimization  
Parameter selection is very important to find the optimal hyper-plane in the SVM model used in this study. The insensitive loss function parameter \(\varepsilon\) decides the permitted error of function. Therefore, a proper value of \(\varepsilon\) is much needed according to the actual demand, and the penalty parameter \(\mathbf{C}\), which is also called Upper Bound, which denotes the upper bound function. However, existing studies mainly adopted the traditional grid search method, direct determination method, one-dimensional search method, and inverse ratio method to determine the insensitive loss function parameter \(\varepsilon\) and penalty parameter \(\mathbf{C}\). However, there are many shortcomings associated with these methods, and the resulting errors will significantly influence the accuracy of the prediction results.  
Moreover, in the SVM model, kernel function selection is also an important factor that influences the performance of the SVM. The radial basis kernel function \(K(x_{i},y_{i})=e^{\frac{\|x-x_{i}\|}{\sigma^{2}}}\) (RBF) is an adaptive kernel function for low-dimensional space data and high-dimensional space, which have good convergence domains, and this function can be described as an ideal kernel function. Therefore, the RBF was selected as the classification prediction kernel function of the SVM, in which a kernel parameter \(\sigma\) needs to be optimized.  
Therefore, three parameters need to be optimized, namely the core parameter \(\sigma\), the non-sensitive loss function parameter \(\varepsilon\), and the punishment parameter \(C\). The kernel parameter \(\sigma\) is the distribution or range of the training sample data. The non-sensitive loss function parameter \(\varepsilon\) affects the number of support vectors. The larger the value of \(\varepsilon\), the lower the regression precision, and the fewer the support vectors. The penalty parameter \(C\) is used to control the degree of punishment of samples beyond the allowable error range. The higher the value, the heavier the punishment of samples.  
We used the artificial fish swarm algorithm to optimize the parameters of the regression model. The artificial fish swarm algorithm has unique advantages in parameter optimization and overcomes the blindness of traditional algorithms in parameter optimization and the defects of the linear model and neural network in parameter selection. It can be said that the parallel performance of the artificial fish swarm algorithm can ensure that the model parameters converge faster to the global optimization extreme [32].
# A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms
## 4 Challenges Still Exist in the Field of Clustering Algorithms
### Data Dimensionality  
Several techniques, including K-means, GMM clustering, maximum-margin clustering, and information-theoretic clustering, have trouble dealing with strong datasets. By projecting the actual information into a fairly low domain, clustering on the characteristic embedding, e.g., sparse code, can improve all of these problems [90].
## 2 Materials and Methods
### Rootentin Slices  
Human single-rooted freshly extracted teeth (\(n\) = 36) were cleaned by ultrasonic scaler (SH-2140, Saehansonic, Seoul, Korea) and stored in 0.5% sodium azide. Following decoration, the roots were cut (first vertically then horizontally) and ground to achieve a smooth surface to make standardized root dentin discs (\(7\times 3\times 3\) mm\({}^{3}\)) slices of 3 mm thickness. The dentin slices were autoclaved at 121 \({}^{\circ}\)C for 15 min. Sterility was confirmed by anaerobic incubation at 37 \({}^{\circ}\)C overnight in BHI, trypticase soy, or MRS culture broth, followed by plating onto agar plates containing these respective culture media maintained anaerobically at 37 \({}^{\circ}\)C overnight. Dentin slices were randomly assigned (\(n\) = 9/group) for conditioning as follows: Group I, NaOCI (1.5%, 10 mL/5 min), then EDTA (17%, 10 mL/5 min), followed by NTAPP; Group II, NaOCI, then NTAPP; Group III, NaOCI, then EDTA; Group IV, NaOCI alone (control).
## 3 Results and Discussion
### Background-Temperature Effect on Enhanced Biological Phosphorus Removal (EBPR) without Cu(II)
#### 3.2.3 Anaerobic Glycogen Degradation  
As mentioned above, the presence of Cu(II) did not affect anaerobic phosphate release, but inhibited anaerobic PHA synthesis, regardless of temperature. The result indicated that the inhibition of anaerobic PHA synthesis of PAOs in the presence of Cu(II) was not a result of a lack of energy, which was normally provided by the hydrolysis of poly-P, necessary for synthesizing PHAs. There should be another important factor governing the biochemical reaction of anaerobic PHA synthesis when Cu(II) is present. Besides energy requirements, anaerobic PHA synthesis of PAOs demands a reducing power. About 70% of the reducing power relies on degrading glycogen to acetyl-CoA and oxidizing a partial number of them to CO\({}_{2}\)[32]. Obviously, glycogen degradation is essential for PHA synthesis and PAO proliferation, [33] and it is probably a limiting factor of anaerobic PHA synthesis of PAOs. Thus, we inferred that the most likely reason for decreased PHA synthesis in the presence of Cu(II) was due to the lack of reducing power provided by glycogen degradation. This inference could be further proved from Figure 2e,f, which show that glycogen degradation rates markedly reduced while increasing the Cu(II) concentration. Nevertheless, unlike the case of PHA synthesis in Figure 2c,d, the interaction between temperature and Cu(II) concentration on glycogen degradation was substantial. The lower the temperature, the larger the inhibition of glycogen degradation rate, the larger the inhibition of the oxygen degradation rate, the  
degradation by Cu(II) toxicity was. Especially in the 10 \({}^{\circ}\)C case, the presence of 0.5 mgL\({}^{-1}\) of Cu(II) could completely stop the process of anaerobic glycogen degradation of PAOs, as shown in Figure 2f. On the other hand, 2.0 mgL\({}^{-1}\) of Cu(II) completely inhibited the metabolism of glycogen degradation at 20 \({}^{\circ}\)C, whereas only 75% was inhibited at 30 \({}^{\circ}\)C. This result indicates PAOs in a high temperature environment (30 \({}^{\circ}\)C) had a good tolerance of Cu(II) with respect to anaerobic glycogen metabolism.
## 3 Relative Kinematic-Equation Building for UAV-UGV Formations
The values of each of these variables are as follows:  
\[\Phi_{1}^{r}=\left[ \begin{array}{ccc}\cos\theta_{l}\cos\psi_{vl}&\sin\theta_{l}& -\cos\theta_{l}\sin\psi_{vl}\\ -\sin\theta_{l}\cos\psi_{vl}&\cos\theta_{l}&\sin\theta_{l}\sin\psi_{vl}.\\ \sin\psi_{vl}&0&\cos\psi_{vl}\end{array} \right] \tag{4}\]  
\[\Phi_{2}^{l}=\left[ \begin{array}{ccc}\cos\theta_{f}\cos\psi_{pf}&-\sin\theta _{f}\cos\psi_{pf}&\sin\psi_{pf}\\ \sin\theta_{f}&\cos\theta_{f}&0\\ -\cos\theta_{f}\sin\psi_{pf}&\sin\theta_{f}\sin\psi_{vf}&\cos\psi_{of}\end{array} \right] \tag{5}\]  
\[V_{12}=\left[ \begin{array}{c}V_{I}\\ 0\\ 0\end{array} \right] \tag{6}\]  
\[V_{f2}=\left[ \begin{array}{c}V_{f}\\ 0\\ 0\end{array} \right] \tag{7}\]  
where \(V_{I}\) denotes the velocity magnitude of the UGV in the inertial coordinate system; \(\theta_{l}\) denotes the inclination angle of the UGV; \(\psi_{vl}\) denotes the deflection angle of the UGV; \(V_{f}\) is the velocity magnitude of the UAV in the inertial coordinate system; \(\theta_{f}\) is the inclination angle of the UAV trajectory; \(\psi_{vf}\) is the deflection angle of the UAV trajectory. The relative velocities of the UGV and UAV in the relative coordinate system are expressed as follows:  
\[\mathbf{V}_{r}=\left[ \begin{array}{c}\hat{x}\\ \dot{y}\\ \hat{z}\end{array} \right] \tag{8}\]  
where \(x\), \(y\), and \(z\) are the components of the position vector \(\mathbf{r}\) of the UAV along each axis in the relative coordinate system. The angular velocity of rotation \(\mathbf{\omega}\) in the relative coordinate system with respect to the inertial space can be expressed as follows:  
\[\mathbf{\omega}=\left[ \begin{array}{c}\dot{\psi}_{vl}\sin\theta_{l}\\ \dot{\psi}_{vl}\cos\theta_{l}\\ \dot{\theta}_{l}\end{array} \right] \tag{9}\]  
Then, based on Equation (2), the following equation can be obtained:  
\[\mathbf{V}_{fr}=\left(\mathbf{V}_{fr}-\mathbf{V}_{lr}\right)-\mathbf{\omega}\times\mathbf{r} \tag{10}\]  
The relative motion relationship between the UAV and UGV in the three-dimensional plane can be obtained by combining Equations (2), (8), and (10). \[\left\{ \begin{array}{l}\dot{x}=V_{f}\cos\theta_{f}\cos\theta_{l}\cos\psi_{e }+V_{f}\sin\theta_{f}\sin\theta_{l}-V_{l}+y\dot{\theta}_{l}-z\dot{y}_{vl}\cos \theta_{l}\\ \dot{y}=-V_{f}\cos\theta_{f}\sin\theta_{l}\cos\psi_{e}+V_{f}\sin\theta_{f}\sin \theta_{l}-x\dot{\theta}_{l}+z\dot{y}_{vl}\sin\theta_{l}\\ \dot{z}=V_{f}\cos\theta_{f}\sin\psi_{e}-y\dot{\psi}_{vl}\sin\theta_{l}+x\dot{y}_ {vl}\cos\theta_{l}\\ \psi_{e}=\psi_{vl}-\psi_{vf}\end{array} \right. \tag{11}\]
## 3 Problem Statement
### Energy Consumption Model  
In this study, we use the energy consumption model for network radio parts as outlined in previous work. This model describes how energy is used in the electronic parts of both sending and receiving units. It calculates the energy needed to send or receive a single bit in a data packet, measured in nanojoules per bit [38]. Additionally, it explains the energy used in a WSN for sending one bit from one node to another as \(E_{amp}\), represented in picojoule's per bit per meter. Figure 3 visually represents the energy consumption model for wireless communication in a network. The arrows indicate the direction of data flow, showing how a 1-bit packet is transmitted from the sender to the receiver. The triangles represent the antennas used for wireless communication, where the left triangle symbolizes the transmitting antenna, and the right triangle represents the receiving antenna.  
The power consumption in our model follows the approach used in the LEACH method. This model estimates the energy used by a transmitting node to send a data packet, considering the packet size '\(K\)' bits and the distance '\(d\)' between the sender and receiver.  
\[E_{tx}(i)=k\left(E_{elec}+E_{amp}*d^{2}\right) \tag{2}\]  
Here, \(E_{elec}\) stands for the power used by the electronic circuit, while \(E_{amp}\) represents the power needed to amplify signals for sending and receiving a data bit. Additionally, the power used by the receiving node to process a data packet of size '\(k\)' bits is calculated using a particular equation.  
\[E_{rx}(i)=k*E_{elec} \tag{3}\]  
It is essential to understand that the power used by nodes positioned between the data-sending node and the receiving node equals the total energy needed for both sending and receiving data.  
Figure 3: Model used for energy consumption in radio performance.  
Figure 2: Hierarchical Structure of Congestion Control Strategies in WSNs.  
\[E_{cons}(i)=\sum_{i=1}^{n}[E_{tx}(i)+E_{rx}(i)] \tag{4}\]  
It is important to recognize that the power consumption of any nodes in the network, encompassing both data transmitting and receiving nodes, can be calculated using the formula.
# Position Tracking Control of 4-DOF Underwater Robot Leg Using Deep Learning
## 4 Torque Control Based on the Neural Network
### Hybrid Control Method  
If only joint torque predicted by the neural network is directly input to the motor, it may not respond smoothly to errors. Additionally, as mentioned earlier, relying solely on a PD controller can lead to increased joint torque output and reduced compliance if the control gains are excessively raised for position accuracy. To address these shortcomings, we devised a hybrid controller by combining the task-space PD controller with the neural network. This hybrid approach compensates for both issues. By leveraging deep learning to overcome non-linear dynamic characteristics, we can achieve higher position accuracy without the need for excessively high PD control gain or complex computation. Furthermore, compliance and position accuracy can be secured even for trajectories that were not directly learned due to data limitations.  
Figure 7 illustrates the diagram of the hybrid controller, combining the neural network and the task-space PD controller. The torque input \(\tau\) to each joint of the robot leg is as follows:  
\[\tau=\int^{T}\Big{\{}K_{p}\Big{(}P_{ref}-P_{act}\Big{)}+K_{d}\Big{(}\dot{P}_{ ref}-\dot{P}_{act}\Big{)}\Big{\}}+\tau_{ref} \tag{5}\]  
\(J\) is the Jacobian matrix. \(P_{ref}\), \(\dot{P}_{ref}\) are the reference position and velocity of the foot tip, and \(P_{act}\), \(\dot{P}_{act}\) are the actual position and velocity of the foot tip. Obtained through forward kinematics based on motor encoder reading. The actual value may differ from the exact value due to errors in motor encoder reading. However, we assumed it was correct. Subsequently, we determined the PD control signal based on \(P_{act}\) and \(P_{ref}\) and \(\dot{P}_{act}\) and \(\dot{P}_{ref}\), and then, multiply the control signal by the Jacobian matrix to implement PD control in the task-space.
# A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images
## 5 Experiments
### Results of the Fine Localization Stage
#### 5.2.2 Results of the Regression CNNs  
After training the networks, their performances were analyzed and the localization results are summed up in Table 3. Additionally, to assess the performance of the method, the following values are included in the table: the average, maximum and minimum error, along the \(X\)-axis, along the \(Y\)-axis and global (error measured as Euclidean distance). Additionally, the deviation of the error is included in the table. The \(X\) and \(Y\) axes can be seen in Figure 5. Regarding the performance for the stairs area, the \(X\) coordinate results are good, but the \(Y\) coordinates are worse, because they reach extreme values. Nevertheless, the deviation value is low, and hence the extreme values can be aisle cases. Concerning the performance related to the toilet, the average error is similar for both coordinates and they are relatively low. The deviation values are also low. Hence, the CNN trained for this room exhibits good performances. As for the corridor, the table shows that despite the low average error values, there are extreme error values. This is also noticed by observing the deviation values. From these results, it is concluded that this room needs a special treatment with the aim of obtaining a network capable of addressing the pose estimation more accurately. Regarding the printer area, the related error values are accurate enough, since the average error for the euclidean distance is around 30 cm and the training images presented an average distance of 20 cm. Furthermore, the deviation values are not significantly high. Last, concerning the office, the results shown in the Table 3 are not good enough if we take into consideration the size of this room. This drawback can be introduced  
Figure 4: Transfer learning process addressed from the CNN for the room retrieval task (**left**) to develop the CNN that estimates the the \(X,Y\) coordinates (**right**).  
because the trajectory addressed by the robot in the test dataset differs substantially from the trajectory addressed during the training.  
Table 3 also shows a column with the average results for the five rooms. Moreover, Figure 5 shows the trajectory map of the ground truth data and the pose estimations provided by the CNNs. In general, pose predictions fit real poses. There are critical results concerning the office and the extreme parts of the corridor. In these areas, the predictions are significantly inaccurate. Therefore, improvements should be addressed in those two rooms.
## 2 The Experimental Data
### Constant Sound Speed in the Water and a Constant Velocity Gradient in the Sub-Bottom Layer  
For a sub-bottom layer with a vertical velocity gradient, the range \(x_{2}\) and time \(t_{2}\) of diving rays refracting from this layer can be described according to [35; 56] as:  
\[\begin{array}{c}x_{2}=\frac{2}{Kp}\sqrt{1-p^{2}c_{0}^{2}}\\ t_{2}=\frac{2}{K}\left[\log\frac{1+\sqrt{1-(p\zeta_{0})^{2}}}{c_{0}p}\right] \end{array}\Bigg{\}},\,0\leq p<\frac{1}{c_{0}}, \tag{4}\]  
where \(c_{0}\) is the sound speed at the seafloor and \(K\) is the gradient in the sub-bottom layer.  
When the source and receivers are located in the homogeneous water layer, the travel-time curve consists of two parts: the reflection travel-time curve is given by Equation (3) and the travel-time curve of the refraction of diving rays is given by the summation of Equations (3) and (4). The reflection travel-time curve is a hyperbola. The refraction forms a bow-tie curve with up to two cusps (i.e., turning points), depending on the parameters of \(c_{w}\), \(c_{0}\), \(H_{1}\), and \(K\).
# The Sensitivity Analysis of Parameters in the 1D-2D Coupled Model for Urban Flooding
## 2 Method
### Parameter Sensitivity Analysis Method
#### 2.2.2 Parameter Sensitivity Analysis
It indicates the proportion of the total variance contributed by a single parameter \(X_{i}\) to the model output variance. To measure the independent impact of this parameter on the output, the formula for \(S_{1}\) is as follows:  
\[S_{1}=\frac{V_{i}}{V}=\frac{Var[E(Y|X_{i})]}{Var(Y)} \tag{10}\]  
In the formula, \(S_{1}\) represents the first-order sensitivity index of the input parameter \(X_{i}\), and \(Var[E(Y|X_{i})]\) denotes the variance of the conditional expectation of the model output \(Y\) given \(X_{i}\), while other parameters are held constant. This reflects the contribution of \(X_{i}\) to the uncertainty of the output. To measure the overall impact of the input variable \(X_{i}\), including interactions, on the output, the total sensitivity index \(S_{T}\) in the Sobol method is introduced. \(S_{T}\) represents the contribution of \(X_{i}\), accounting for all interactions, to the variance of the model output. This includes both the direct effect of the parameter and its interactive sensitivity with other parameters. The formula for calculating \(S_{T}\) is as follows:  
\[S_{T}=1-\frac{Var[E(Y|X_{\sim i})]}{Var(Y)} \tag{11}\]  
In the formula, \(S_{T}\) represents the total sensitivity index of the input parameter \(X_{i}\). \(Var[E(Y|X_{\sim i})]\) is the variance of the conditional expectation of the output \(Y\) when all parameters except \(X_{i}\) are held constant. The \(S_{T}\) value reflects the total contribution of parameter \(X_{i}\), including its direct effect and its interactions with other parameters. A large \(S_{T}\) value indicates that the parameter and its interactions with other parameters have a significant impact on the output variance.
# How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks
## 7 Results on the Impact of Increasing DVs on 5G Network EE
### Impact of Increasing DVs on the Power Consumption of 5G Network  
Since increasing DVs impact both EE metrics of 5G networks, an increase in DVs will also have an influence on instant 5G network power consumption. The instant network power consumption represents the total average instant power consumption of all (small and macro) BSs located in the analyzed device density class that are in an active and operating state (Table 2). Obtained results presenting the impact of DV increase on data EE metrics and instant 5G network power consumption for indoor hotspot, dense urban, urban and rural device density classes are shown in Figure 2(a)-d, respectively. Results presented in each figure have been obtained for all of the five different deployment and operation approaches and for DVs characteristic for every year in 2020s.  
According to Figure 2(a)-d, for analyzed Approaches 1, 2, 4 and 5, an increase in DV of each device density class will impose an increase in the instant 5G network power consumption. This is due to the fact that transferring larger DVs requires more network resources in terms of activating an additional number of BSs, exploiting more capacity of active BSs (in terms of transceivers, channels, subcarriers and multiplexing slots) and transmitting at higher Tx power levels. This consequently results in higher instant power consumption of individual BSs, which jointly contributes to an increasing trend of total instant network power consumption in every device density class. The only exception from this power consumption trend is the instant power consumption of Approach 3 (Figure 2(a)-d). Since this approach lacks any adaptation of BSs deployment dynamics and operation activity according to DV variations, instant power consumption of the network will be constantly at maximal levels (Figure 2(a)-d). In comparison with other deployment and operation approaches, this results in the worse data EE metric of Approach 3.  
Therefore, for any of the analyzed approaches (Approaches 1, 2, 4 and 5) which exploit adaptation of BSs deployment dynamics and operation activity according to increasing DV trend, this DV increase will have a negative impact in terms of increasing the instant power consumption of the 5G network. This negative impact is not translated to data EE metrics, which will increase with the increase of DV in all analyzed device density classes (Figure 2(a)-d). This is the consequence of the fact according to which instant power consumption and DV have different increasing rates. Although instant 5G network power consumption and, therefore, overall network energy consumption increase during the analyzed period of 10 years, the overall DV during this period also increases with higher rates, which in total contributes to the increase of data EE metrics calculated based on the Equation (1).