esa-sceva/satcom-chunk-collection huggingface dataset:
# Hierarchical Gradient Similarity Based Video Quality Assessment Metric
## 2 Hierarchical Video Quality Assessment
### Modeling the Stream to the Dorsal Pathway  
Areas V1 and V2 contain cells that respond preferentially to linearly-oriented patterns, such as edges, bars, and gratings [14]. Edge detection such as Sobel filter can be used to model the processing of these areas. Furthermore, the MT area is dedicated to visual motion such as motion gradients, motion-defined edges, and locally opposite motions. Thus, the stream to the dorsal pathway can be modeled as a spatio-temporal gradient.  
Since the spatio-temporal gradient vector also contains the components of the spatial version, the similarity of the spatio-temporal gradient vector is used to model the stream to the dorsal pathway. To balance the effect of the temporal and spatial gradients, each component is divided by the sum of positive filter coefficients, respectively. The similarity is evaluated as  
\[S_{dp}(x_{r},x_{t})=\frac{2\|\mathbf{g}^{\prime}\|_{2}\|\mathbf{g}^{\prime}\| _{2}+C_{1}}{(\mathbf{g}^{\prime})^{2}+(\mathbf{g}^{\prime})^{2}+C_{1}}\cdot \frac{\mathbf{g}^{\prime}\cdot\mathbf{g}^{\prime}+C_{1}}{\|\mathbf{g}^{\prime }\|_{2}\|\mathbf{g}^{\prime}\|_{2}+C_{1}}, \tag{2}\]  
where \(S_{dp}(x_{r},x_{t})\) denotes the gradient similarity between \(x_{r}\) and \(x_{t}\), which are the pixels in prediction parts of the reference frame and the distorted frame, respectively. The vectors \(\mathbf{g}^{\prime}\) and \(\mathbf{g}^{\prime}\) denote the corresponding spatio-temporal gradient vectors, which are calculated by the Sobel filter along \(x\), \(y\), and \(t\) directions, respectively; i.e., \(\mathbf{g}=(g_{x},g_{y},g_{t})\). The Sobel kernel for the \(t\) direction is a \(3\times 3\times 3\) matrix [13]. The parameter \(C_{1}\) is a small constant to avoid the denominator being zero, and is set as \(C_{1}=0.03\times 255^{2}\). The first term represents the similarity of the strengths between \(\mathbf{g}^{\prime}\) and \(\mathbf{g}^{\prime}\). The second term represents the similarity of the directions between the two gradient vectors. Equation (2) can be further simplified to:  
\[S_{dp}(x_{r},x_{t})=\frac{2\mathbf{g}^{\prime}\cdot\mathbf{g}^{\prime}+C_{1}} {(\mathbf{g}^{\prime})^{2}+(\mathbf{g}^{\prime})^{2}+C_{1}}. \tag{3}\]  
Using (3), each pixel will get the DP similarity.
## 5 Performance Evaluations
Figure 10 shows the BER performances for the GS pre-coding based on the number of optimal iterations, the GS pre-coding based on the proposed estimation scheme and the ZF pre-coding in \(256\times 64\) (\(\alpha=4\)) and \(256\times 128\) (\(\alpha=2\)) systems when an initial solution is \(\mathbf{s}_{\text{NS,Stair}}^{(0)}\) and the 16-QAM is used. In Figure 10, the calculated \(\delta_{4}\) and \(\delta_{2}\) are 0.07 and 0.28, respectively. The estimation errors are similar to the results in Figure 7 since the property of the massive MIMO system mainly depends on \(\alpha\) where the estimation performance is not varied according to the number of absolute transmit antennas and users. Figure 11 shows the BER performances for the GS pre-coding based on the number of optimal iterations, the GS pre-coding based on the proposed estimation scheme and the ZF pre-coding in \(100\times 20\), \(100\times 30\), \(100\times 40\), and \(100\times 50\) systems when the initial solution is \(\mathbf{s}_{\text{NS,Stair}}^{(0)}\) and the 16-QAM is used in Rician fading. In Figure 11, the line of sight (LoS) is applied to the first path in the eight-path fading and the \(K\) factor, which denotes the LoS path to non-LoS (NLoS) path power ratio, is set as 5 dB. The GS pre-coding requires more iterations to obtain the same BER performance compared to the results in Figure 7 where the fading channel is modeled as Rayleigh fading since the LoS path is applied. The calculated \(\delta_{5}\), \(\delta_{3,3}\), \(\delta_{2,5}\), and \(\delta_{2}\) are 0.13, 0.18, 0.47, and 0.75, respectively, which are less than one and the accuracy of estimation is not seriously reduced compared to the results in Figure 7 since the proposed estimation scheme uses relative methods which calculate the NED. Figure 12 shows the number of required multiplications for the GS pre-coding based on the proposed estimation scheme when an initial solution is \(\mathbf{s}_{\text{NS,Stair}}^{(0)}\). In Figure 12, the values of \(i_{\text{Avg}}\) are results in Figure 7. The number of multiplications for the GS pre-coding based on the proposed estimation scheme is increased slightly with respect to increased \(i_{\text{Avg}}\). In addition, the number of multiplications for the GS pre-coding based on the proposed estimation scheme is slightly higher than the number of multiplications for the conventional GS pre-coding since an additional complexity for the proposed scheme is only \(iN_{u}\) multiplications for calculating NEDs. Figure 6.0 The BER performances for GS pre-coding based on the number of optimal iterations, GS pre-coding based on the proposed estimation scheme, and ZF pre-coding in \(256\times 64\) and \(256\times 128\) systems when an initial solution is \(\mathbf{s}_{\text{NS,Stair}}^{(0)}\) and 64-QAM is used.
# Removal of Cesium from Radioactive Waste Liquids Using Geomaterials
## 3 Results and Discussion
### Adsorption Experiment
#### 3.1.2 Effect of the Adsorbent Dosage on the Adsorption of Cs  
The amount of adsorbent used is an important factor affecting the adsorption capacity. In order to determine the effect of the amount of adsorbent on the removal of Cs\({}^{+}\), the adsorbent dosage was varied (0.05 g, 0.1 g, 0.2 g, 0.4 g, 0.6 g, and 0.8 g) under other fixed conditions (temperature: 298 K; contact time: 24 h; and initial concentration: 0.79 mg.L\({}^{-1}\)). The results are shown in Figure 2. It can be seen from the figure that as the adsorbent dosage increased, the removal rate increased significantly. For the adsorbent dosage of 0.6 g, it can be observed that more than 80% of Cs\({}^{+}\) was removed; when the adsorbent dosage was greater than 0.6 g, the adsorption capacity did not increase significantly. Therefore, 0.6 g was considered to be the optimal dosage for Cs adsorption for further study.
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 4 Performance Evaluation
### Performance Measures
#### 4.2.1 HO KPIs  
The dense deployment of SCs can increase the probability of handovers. In this study, various handover metrics are considered to evaluate overall network performance.  
* HOs attempts: Refers to the total number of times the handover process is initiated to transfer the UE connection from the source cell to the target cell.
* Successful HOs: Occur when the UE seamlessly changes the connection between two cells. The handover success rate is obtained as the ratio of the number of successful handovers to the total number of HO attempts.
* Failed HOs: Occurs when the handover between two cells is unsuccessful owing to inefficient resources. Failed HOs are defined as the ratio of the number of failed handovers to the total number of HO attempts.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Network area & \(100\times 100\) km\({}^{2}\) \\ \hline Network sectors & 25 sectors \\ \hline Date Rate & 100–400 Mbps -Mid-Band 5G \\ \hline Q-Learning & Learning rate \(\alpha\) = 0.1 \\ parameters & Discount factor \(\gamma\) = 0.9 \\  & Exploration Rate = 0.9 \\ \hline  & Queue delay(s) = 5 ms \\ Network & Radio resource control (RRC) time = 20 ms \\ configuration & Path switch time = 0.03 ms \\  & RACH procedure time = 0.01 ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation settings.  
* Ping-pong HOs: Describes an unwanted situation where a UE rapidly transitions back and forth between SCs, causing unnecessary power consumption, inducing signal overhead, and inefficient resource utilization. Ping-pong HOs quantify the ratio of the number of occurrences of ping-pong HOs to the total number of handovers performed.
* Prevented unnecessary HOs: The unnecessary handover occurred as a consequence of the UE attempting to change its current connection, despite the fact that it is sufficient for communication.
# Cartesian Aerial Manipulator with Compliant Arm
## 3 Modelling  
### Kinematic Model  
In the definition of an aerial manipulation task, three reference frames can be identified: the Earth fixed frame \(\{\mathbf{E}\}\) (inertial), the multirotor base frame \(\{\mathbf{B}\}\), and the manipulator base frame \(\{\mathbf{0}\}\). The position and orientation of the multirotor, \({}^{\mathbf{E}}\mathbf{r_{B}}=[x,y,z]^{T}\) and \({}^{\mathbf{E}}\mathbf{r_{B}}=[\phi,\theta,\psi]^{T}\), are referred to the Earth fixed frame, whereas the position of the tool center point (TCP) of the manipulator, \({}^{\mathbf{0}}\mathbf{r}_{TCP}=[x_{TCP},y_{TCP},2_{TCP}]^{T}\), is expressed in \(\{\mathbf{0}\}\) for convenience, considering that the onboard perception system usually gives the reference positions in the local axes. Figure 7 depicts the reference frames, position vectors and the joint variables in the Cartesian aerial manipulator. Here \(d_{1}\) and \(d_{2}\) denote the displacement of the Cartesian base along the \(\mathbf{X_{0}}\mathbf{Y_{0}}\) axes, respectively, \(q_{3}\) is the rotation angle of the joint in the pitch angle, whereas \(D_{1}\), \(D_{2}\) and \(L\) are the corresponding link lengths.  
Figure 6: Components and architecture of the Cartesian aerial manipulator with ground control station (GCS).  
Figure 7: Kinematic model of the Cartesian aerial manipulator.  
The position of the tool center point referred to \(\{\emptyset\}\) can be computed easily from the joint variables applying the forward kinematic model \(\mathbf{FK}\colon\ \Re^{3}\to\Re^{3}\):  
\[\mathbf{\mathsf{o}}_{\mathbf{TCP}}=\left[\begin{array}{c}x_{TCP}\\ y_{TCP}\\ z_{TCP}\end{array}\right]=\mathbf{FK}\left(\left[\begin{array}{c}d_{1}\\ d_{2}\\ q_{3}\end{array}\right]\right)=\left[\begin{array}{c}d_{1}+L\cdot\sin(q_{3}) \\ d_{2}\\ -L\cdot\cos(q_{3})\end{array}\right] \tag{1}\]  
Note that \(x_{TCP}\) depends on two joint variables (\(d_{1}\) and \(q_{3}\)), although \(q_{3}\) is determined by \(z_{TCP}\). The inverse kinematic model, \(\mathbf{IK}\colon\ \Re^{3}\to\Re^{3}\), provides two analytical solutions for \(q_{3}\) if \(|x_{TCP}|<D_{1}/2\), and only one solution if \(|x_{TCP}|\geq D_{1}/2\). The double solution is determined by the sign of \(q_{3}\), and indicates the pointing direction of the link, either forwards or backwards:  
\[\left[\begin{array}{c}d_{1}\\ d_{2}\\ q_{3}\end{array}\right]=\mathbf{IK}\left(\left[\begin{array}{c}x_{TCP}\\ y_{TCP}\\ z_{TCP}\end{array}\right]\right)=\left[\begin{array}{c}x_{TCP}\mp L\cdot \sin(\alpha)\\ y_{TCP}\\ \pm\alpha\end{array}\right] \tag{2}\]  
where angle \(\alpha\) is defined in the following way:  
\[\alpha=\left|\cos^{-1}\left(\frac{z_{TCP}}{L}\right)\right| \tag{3}\]
# A Survey of Secure Time Synchronization
## 2 Time Synchronization
### Time Synchronization Requirements
#### 2.4.4 Robustness  
In cases of some nodes' failures, synchronization schemes must be be up and running for the remaining nodes, because sensor networks are left unattended for large amounts of time in hostile environments [35]. However, the available protocols are not efficient with this option, for example, the TPSN is unable to handle topology changes efficiently. In the case of root node failure, the whole process of synchronization must be repeated which increases energy consumption [31]. The TS/MS protocols have high convergence time and, in the case of node failure, resynchronization will take longer. The FTSP has high robustness against node failure and topology changes; however, as mentioned above, it increases computational resources and energy consumption which is limited for sensor nodes [30].
# Self-Configuring IoT Service QoS Guarantee Using QBAIoT
## 3 QoS Guarantee within Internet of Things Environment
### Self-Configuring QBAIoT
The HL-Gw monitors the changes of the IoT environment by considering the different gSLAs stored in the knowledge base. For each LL-Gw, the monitor function groups the corresponding gSLAs into different bundles. Each bundle is specific to a LL-Gw and includes all the gSLAs of the IoT services available through that LL-Gw. Consequently, the characteristics of the LL-Gw environment are available in the bundle. The analyze function counts the number of QoS classes (Nb_QoS_Classes) and counts the number of RTC (RT_Classes) in each bundle. The analyze function deducs also the number of NRTC (NRT_Classes). Then, the HL-Gw Plan function searches in the knowledge base the corresponding entry to the number of RTC and NRTC retrieved by the analyze function. Finally, the Execute function extracts the values from the knowledge base entry and sends them to the effector interface on the LL-Gw. The effector updates the configuration values to be sent in the beacon. This control loop is called at each time the IoT-SP makes changes concerning the IoT environment. If an iSLA is added, removed or modified, the self-configuring control loop is executed in order to adapt the IoT LL-Gw configuration according to IoT environment changes without human intervention. Figure 12: QBAIoT MAPE-K control loop implementation. Figure 13 shows the detailed self-configuring process deployed on the HL-Gw along with the different MAPE-K functions. The HL-Gw monitors the environment changes and updates the values of Nb_QoS_Classes and RT_Classes. When Nb_QoS_Classes is equal to zero, the IoT-SP has no gSLA concerning the LL-Gw. In this case, no QoS classes are available on the LL-Gw environment and there is no need for configurations. If Nb_QoS_Classes is equal to 1, a single QoS class exists within the LL-Gw environment, then BO and SO are set to 14, and the single QoS CAP that exists in the superframe is allocated the 16 slots. If there is more than a single QoS class, Nb_QoS_Classes is greater than 1 and RT_Classes is equal to 0 then there are two classes that are NRTC. Consequently, the BO and SO values are set to 3 and the Superframe includes two QoS CAPs. The first QoS CAP, corresponding to the streaming traffic, is allocated 13 slots. Whereas, the second QoS CAP, corresponding to the NRT traffic, is allocated 3 slots. If Nb_QoS_Classes is greater than 1 and RT_Classes is equal to or greater than 1 then a nested loop is to be browsed in order to allow the HL-Gw to choose the corresponding slots configuration for the considered IoT environment.
## 3 Materials and Methods
### Metrics and Validation
#### 3.6.1 Intersection over Union (IoU)  
This is a metric that measures the overlap between two bounding boxes, the predicted one and the ground truth according to Equation (1).  
\[J(B_{p},B_{gt})=IoU=\frac{area(B_{p}\cap B_{gt})}{area(B_{p}\cup B_{gt})} \tag{1}\]  
For detection, the calculated \(IoU\) value is compared with a predefined threshold. \(IoU\) values above the threshold are considered as correct prediction and values below as incorrect prediction.
# Research on Safe Following Distance on an Expressway Based on Braking Process Analysis
## Modeling
### Determination of Parameter Values in the Model
#### 3.3.2 Determination of Parameters \(j_{\rm{Amax}}\) and \(j_{\rm{Bmax}}\)  
During the braking process, the braking deceleration produced by a car reflects the value of the ground braking force, which is not only related to the brake force when the wheel is rolling, but is also related to the adhesion provided by the road when the anti-lock system drags or slips. Specifically, the braking deceleration is related to the type of brakes, wheel load, road adhesion coefficient and road slope. In general, the road adhesion condition is the key factor affecting the braking deceleration. Due to different road conditions and a different adhesion coefficient, the car's ground braking force varies, so the time the car takes to achieve the maximum braking deceleration differs.  
According to car theory [25], \(j_{\rm{max}}=\varphi g\). Obviously, the maximum braking deceleration varies with the road adhesion coefficient. However, if the car is driving on a sloped section of road, the gravity of the car is decomposed along the longitudinal slope, and the direction of the supporting force is perpendicular to the road. In an uphill section, the gravity component along the longitudinal slope and the ground braking force jointly provide braking force for the vehicle. In a downhill section, gravity along the longitudinal slope of the component force will offset part of the vehicle's braking force, which is not conducive to vehicle braking. According to JTG B01-2014 Highway Engineering and Technical Standards, the maximum longitudinal slope of an expressway shall not exceed 5%. Therefore, the maximum braking acceleration \(j_{\rm{max}}\) under different slopes can be obtained by the formula \(j_{\max}=(\varphi+i)g\) by simplification. When the slip rate is in the range of 15-30%, the wheel-road adhesion coefficient is at the maximum value, and the braking force can reach the maximum value. Asphalt and cement are widely used as the pavement materials in China (2016). The values of the wheel-road adhesion coefficient \(\varphi\) are shown in Table 1.
## 3 Results and Discussion
### Accuracy Comparison of Single Feature Classification under Different Classification Methods
For the Naive Bayes classifier, the highest accuracy of discriminating fish meal samples with different quality was obtained using the average differential value and relation steady-state response average value as eigenvectors, and the lowest accuracy of discriminating fish meal samples with different qualities was obtained using the maximum gradient value as eigenvectors. Therefore, for the accuracy of single feature classification, the highest accuracy was obtained using the integral value, relation steady-state response average value and average differential value as eigenvectors to discriminate different quality fish meal samples, and the lowest accuracy was obtained using the maximum gradient value as eigenvector to discriminate different quality fish meal samples. These findings were almost identical to the results of the previously mentioned PCA and LDA analysis. The similarity may be attributed to the observation that the test response signal of the electronic nose to the sample is relatively stable. The feature points extracted by the maximum gradient method of the response curve of a sample will be observed at different times of the response curve, which is worse than the analysis of the relation steady-state response average value, while the relation steady-state response average value and integral value method can extract the stable signal of the electronic nose signal, so the prediction results are better.
## Materials and Methods
### Phantom Study
#### 2.1.2 MRI Specification and Protocol  
(1) MRI specification  
For this study, we used a Siemens Skyra 3.0T model MRI and 20 ch head coil, as shown in Figure 1c.  
(2) Sequence and parameters  
We used a conventional SE sequence for T1 imaging. First, the MR phantom was loaded into the head coil, as shown in Figure 1b. TE, which was the parameter of interest in this study, decreased from 25 to 7 msec in seven steps, as shown in Table 2. All other parameters were kept constant.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Saline** & **GBCA** & **Gadoteridol** & **Dilution** & \multirow{2}{*}{**No.**} & **Saline** & **GBCA** & **Gadoteridol** & **Dilution** \\  & **(mL)** & **(mL)** & **(mmol)** & **Rate (\%)** & & **(mL)** & **(mL)** & **(mmol)** & **Rate (\%)** \\ \hline
1 & 0.00 & 30 & 500 & 100 & 16 & 29.52 & 0.48 & 8 & 1.60 \\
2 & 6.00 & 24 & 400 & 80 & 17 & 29.58 & 0.42 & 7 & 1.40 \\
3 & 12.00 & 18 & 300 & 60 & 18 & 29.64 & 0.36 & 6 & 1.20 \\
4 & 18.00 & 12 & 200 & 40 & 19 & 29.70 & 0.3 & 5 & 1.00 \\
5 & 24.00 & 6 & 100 & 20 & 20 & 29.76 & 0.24 & 4 & 0.80 \\
6 & 24.60 & 5.4 & 90 & 18 & 21 & 29.82 & 0.18 & 3 & 0.60 \\
7 & 25.20 & 4.8 & 80 & 16 & 22 & 29.88 & 0.12 & 2 & 0.40 \\
8 & 25.80 & 4.2 & 70 & 14 & 23 & 29.94 & 0.06 & 1 & 0.20 \\
9 & 26.40 & 3.6 & 60 & 12 & 24 & 29.95 & 0.048 & 0.8 & 0.16 \\
10 & 27.00 & 3 & 50 & 10 & 25 & 29.96 & 0.036 & 0.6 & 0.12 \\
11 & 27.60 & 2.4 & 40 & 8 & 26 & 29.97 & 0.024 & 0.4 & 0.08 \\
12 & 28.20 & 1.8 & 30 & 6 & 27 & 29.98 & 0.012 & 0.2 & 0.04 \\
13 & 28.80 & 1.2 & 20 & 4 & 28 & 29.99 & 0.006 & 0.1 & 0.02 \\
14 & 29.40 & 0.6 & 10 & 2 & 29 & 30.00 & 0 & 0 & 0.00 \\
15 & 29.50 & 0.54 & 9 & 1.80 & 30 & fat & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: GBCA concentration and dilution rate in the phantom study.  
Figure 1: MR phantom and MRI apparatus. (**a**) Schematic diagram of the MRI phantom; (**b**,**c**) photographs of the MR phantom and apparatus. MR, magnetic resonance; MRI, magnetic resonance imaging.
# Formation Mechanism of Metro Rail Corrugation Based on Wheel-Rail Stick-Slip Behaviors
## 2 Statistics of Measured Corrugation on Metro Lines  
The measured line was located in the Tianjin Metro Line 6. The north section of the line was opened to traffic in December 2016, and the south section was opened to traffic in April 2018. The train adopts 6B marshaling, and the maximum design speed was 80 km/h. The design situations of line and track are shown in Table 1.  
By measuring in the Tianjin Metro Line 6, the corresponding corrugation state of the line can be obtained, as shown in Table 2. It can be seen that most of the corrugations occur on the track with a curve radius of less than 700 m. In addition, rail corrugation also occurs in a 500 m long straight section. Furthermore, combined with the field investigation, it is found that rail corrugation mainly occurs on the inner rail surface, and there is also corrugation on some sections of the outer rail, but the occurrence range is relatively small. The field pictures of rail corrugation are shown in Figure 1.  
For the convenience of comparison, the wave depth amplitudes of rail corrugation corresponding to tracks with different curve radii are plotted in Figure 2. It can be concluded that 75% of rail corrugations occur in curve tracks with a radius less than 400 m, and the wave depth amplitudes are relatively large. At the same time, it can be seen from Figure 2 that rail corrugations with amplitudes greater than 0.4 mm only exist on curve tracks with radii of 300-400 m, which indicates that the rail corrugation will be more serious with the decrease of curve radius. In order to explain the serious corrugation phenomenon of small radius curves, the cause of rail corrugation was explored from the wheel-rail contact stick-slip theory by analyzing the curve passing form of a single wheelset. \begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{1}{c}{**Line Design Situation**} & \multicolumn{2}{c}{**Track Design Situation**} \\ \hline Line length & 41 km, the north section is 19 km, the south section is 22 km. & rail & 60 kg/m, U75V. \\ \hline Number of small & R \(\leq\) 450 m: 38; & common track bed & ZX-2 type fastener and long sleeper track bed \\ radius curves & R \(\leq\) 350 m: 18. & & track bed \\ \hline Average number of small & R \(\leq\) 450 m: 0.93/km; & moderate vibration & Double layer nonlinear damping \\ radius curves per kilometer & R \(\leq\) 350 m: 0.44/km. & attenuation & fastener and long sleeper track bed \\ \hline Minimum curve radius & 300 m. & advanced vibration & Floating slab of damping pad and floating slab of steel spring \\ \hline - & - & special vibration & Floating slab of liquid damping \\ attenuation & & steel spring \\ \hline - & - & distribution of short & Transition section of ditch on both sides of floating slab, part of cast-in-place floating slab section \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Line and track design situations.