esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Model Configuration  
Several simplified models have been built to optimize and deliver approximate results satisfactorily. They can be classified according to dimensions and work phases. One-dimensional (1D) models consider the tank flow uniform and in the axial direction. The possibility of eddies or turbulence in any plane or radial movement of the fluid was not considered. In the case of two-dimensional (2D) models, they consider the variation of temperature and velocity in the axial and radial directions, constituting an axisymmetric problem. It implies that the circumferential motion from one radius to another does not change. The possibility of eddies or turbulence in the radial plane was evaluated, but in the horizontal plane, they are negligible.  
On the other hand, three-dimensional (3D) models consider the variation of temperature and velocity in the axial, radial, and circumferential directions. It is the most comprehensive solution. In addition, double-phase models (2P) evaluate the temperature of the solid and the fluid in each element. They are considered complex and capable of evaluating the temperature of the wall. Similarly, the one-phase models (1P) evaluate an equivalent temperature, the average temperature between the wall, the fluid, and the solid, in each spatial element. Generally, the 1D-2P or 1D-1P models are usually developed for energy considerations.  
[PERSON]'s model [35] was the first formulation to give a mathematical solution to the equations that govern the heat transfer rate between a fluid and a solid fill material when the former flows through the latter, heating or cooling itself. However, the model has presented an analytical approach without addressing the experimental application of the theory. Later, [PERSON]. [36] presented a 1D-2P model, implementing it through exact equations from [PERSON]. This mathematical model was validated through experimental results from a pilot TES and scaled with a thermocline. It is to study practical and operational problems, such as the compatibility of molten salt with mechanical components, among others.  
Other more complex models were developed by [27; 37], considering the two-dimensional problem. In these cases, the models evaluate the behavior of a TES thermocline. The tank was considered a perfect vertical cylinder containing the porous medium. Furthermore, the transient governing equations for heat transfer and fluid dynamics divide axially into discs and radially into tank rings. Finally, a 1D-2P-type model was presented by [38], with the particularity that the governing equations were reduced to their dimensionless form. This way, a direct solution to the discrete equations was provided without considerable computing power. With its numerical solution, this method was proven to be a fast, efficient, and accurate algorithm.
## 3 Experimental Evidence of Synchronization between Rossler Systems
### Adaptive Generalized Synchronization between Circuit and Computer Model
#### 3.3.1 Circuit Implementation
 \tag{19}\]  
Let \(a=0.2\), \(b=0.2\), \(c=5.7\), \(M=4\). Then \(C_{1}=C_{2}=C_{3}=C_{0}\), \(R_{1}=R_{2}=R_{0}\), \(R_{3}=R_{0}\), \(R_{4}=R_{0}/a=50\) k\(\Omega\), \(R8/R9=40\), \(R13=R_{0}/10=1\) k\(\Omega\). \(R_{10}=R_{0}/4=2500\)\(\Omega\), \(R_{11}=R_{12}=R_{0}\). Formulation (19) may be convenient if different scales of state variables are required or capacitors of different values are to be used, in which cases it becomes difficult to apply the empirical principles described above. Furthermore, (19) may be helpful for more realistic circuit simulation. All operational amplifiers in the circuit in Figure 6 work as inverting amplifiers. Therefore, the variable \(-\hat{y}\) at the output of U\({}_{2}\) is obtained, and an additional op-amp U\({}_{3}\) is required to change the \(\hat{y}\) sign. Second, the circuit does not oscillate immediately after the power is applied, and zero initial conditions should be set for the oscillations to start. For this purpose, resistors \(R_{7}\) and \(R_{14}\) are used, which limit the op-amp gain in the absence of oscillations, meaning that a zero output signal is established. After achieving \(\hat{x}=\hat{y}=\hat{z}=0\) V, the resistor \(R_{7}\) branch may be opened to force the circuit to go on the attractor. Resistor \(R_{14}\) does not affect the chaotic oscillations. A resistor parallel to \(C_{1}\) may also be introduced but was not required in our experiments. Ultra-low distortion, low-noise operational amplifiers OPA2134 (Texas Instruments, Austin, TX, USA) were used to ensure the accuracy of analog computations. Parameters \(b\) and \(c\) were defined as voltages using National Instruments ELVIS III station with a high-precision DAC.
# Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication
## 4 Performance of Different Antenna Configurations  
According to the antenna configurations, rectangular arrays have similar conclusions to circular arc arrays. For a more in-depth study, we can define the aspect ratio \(\xi\).  
\[\xi=m=\frac{d}{d_{s}} \tag{46}\]  
where \(\xi\) is defined as the ratio of distance \(d\) of transceiver antennas and the array element spacing \(d_{s}\).
## 3 Methodology
### Accessibility Score Computation
#### 3.2.3 Overall Accessibility Score  
The overall accessibility score is computed on the basis of the mathematical summation of the accessibility counts via Mauve++ and TAW, as shown in Equation (16). The complete procedure for the overall accessibility score is described in Algorithm 3 under the _ComputeScore_ function. \[\text{Overall accessibility score count, }\rho=\frac{\sum\rho_{(Mauve)}+\rho_{(Taw)}}{n} \tag{16}\]```
Input: URL (\(\alpha\)) Output: Accessibility Score Count \(\rho\) Function ComputeScore 0
1. \(\rho_{\langle\textit{Mauve}\rangle}\) = ComputeAccessibility.Mauve (\(\alpha\))
2. \(\rho_{\langle\textit{Test}\rangle}\) = ComputeAccessibility.TAW (\(\alpha\))
3. \(\rho=\frac{\sum(\rho_{\langle\textit{Mauve}\rangle}+\rho_{\langle\textit{Test} \rangle})}{n}\)
```
4. return (\(\rho\))
5. End ```

**Algorithm 3**Compute Accessibility

## 4 Computed Score Analysis

The primary focus of this work is on the healthcare sector, to measure its accessibility status and to compute its accessibility score via the proposed variable-magnitude approach by incorporating two automated accessibility-testing results. The initial application was performed by considering the home pages of hospital and clinical websites in Hungary. A consideration of the entire website is beyond the scope of this work. The hospitals and clinics were chosen from the capital city, other large cities, and small towns in Hungary. The URLs of the considered hospitals' and clinics' web pages are listed in Table 2.

To compute the integrated accessibility score, first, the accessibility scores were computed by incorporating Mauve \(\rho_{\langle\textit{Mauve}\rangle}\) via the _ComputeAccessibility.Mauve_ process (Algorithm 1). The computed results of 16 healthcare institutions' web pages are shown in
## 2 Introduction of Relevant Domain Knowledge
### Classical Zero-Order TSK Fuzzy Classifier  
The classical zero-order TSK fuzzy classifier [50] is one of the most commonly used fuzzy classifiers, and its basic principle is as follows:  
Let \(x=[x_{1},x_{2},\ldots,x_{n}]^{\text{T}}\) be the input vector. Each component \(x_{i}\) is a fuzzy linguistic variable, \(\boldsymbol{\mu_{A_{i}^{k}}}(x_{i})(i=1,2,\ldots,n,k=1,2,\ldots,K)\) is each component's corresponding membership function, and \(A_{i}^{k}\) (\(k=1,2,\ldots,K\)) is the input vector \(x_{i}\) in the \(k\) fuzzy subsets under three rules. Then, in the \(kth\) fuzzy rule, the output is \(y^{k}\). The expression is:  
\[\text{Rule }k\text{: If }x_{1}\text{ \ is }A_{1}^{k}\text{ And }x_{2}\text{ \ is }A_{2}^{k}\text{ And }\ldots\text{ }x_{n}\text{ \ is }A_{n}^{k},\text{ then }y^{k}=p_{0}^{k},k=1,2,\ldots,K.\]  
where Rule \(k\) represents the first \(k\) rule and is the fuzzy connection operator, \(K\) is the total number of fuzzy rules, and \(p_{0}^{k}\) represents the first \(k\) output of the fuzzy rules.  
According to [51; 52; 53], after a series of processing on the input vector, the final output of the TSK fuzzy classifier can be expressed as:  
\[y_{0}=\frac{\sum\limits_{k=1}^{K}\mu^{k}(x)p_{0}^{k}}{\sum\limits_{k=1}^{K}\mu^{ k^{\prime}}(x)}=\sum\limits_{k=1}^{K}\widetilde{\mu}^{k}(x)p_{0}^{k}=\sum \limits_{k=1}^{K}\mu^{k}(x)p_{0}^{k} \tag{1}\]  
where the fuzzy membership function \(\mu^{k}(\mathbf{x})\) can be written as:  
\[\mu^{k}(\mathbf{x})=\prod\limits_{i=1}^{d}\mu^{k}_{A^{k}_{i}}(x_{i}) \tag{2}\]  
where the normalized fuzzy membership function \(\mu^{k}(\mathbf{x})\) can be written as:  
\[\widetilde{\mu}^{k}(\mathbf{x})=\frac{\mu^{k}(\mathbf{x})}{\sum\limits_{k^{\prime}=1} ^{K}\mu^{k^{\prime}}(\mathbf{x})} \tag{3}\]  
Under normal circumstances, we use the Gaussian fuzzy membership function as the fuzzy membership function, and its expression is:  
\[\mu_{A^{k}_{i}}(x_{i})=\exp(\frac{-(x_{i}-c^{k}_{i})^{2}}{2\delta^{k}_{i}}) \tag{4}\]  
Two of the parameters, \(c^{k}_{i}\) and \(\delta^{k}_{i}\), can be calculated by using a clustering algorithm or other methods [54].
# Integrating Prior Knowledge into Attention for Ship Detection in SAR Images
## 5 Experiments and Results
### Hyper-Parameter Analysis  
As mentioned in Section 3.3, clustering parameters are the key point of the attention mechanism. Table 3 displays the brightness intervals of the ships obtained with brightness clustering in the datasets.  
From the table, we observe that the same class of targets may have different brightness intervals in different datasets. The cause is the different system parameters used in the acquisition of different datasets. Therefore, it is necessary to use brightness clustering to learn the brightness intervals of the targets in the dataset in advance.  
Figure 8 shows the performance of different _Eps_ and _MinPts_ on three datasets. The more leftward the horizontal coordinate in this graph, the smaller the ratio of _MinPts_ to _Eps\({}^{2}\)_. It can be found that as the ratio rises, the curves of SSDD and HRSID will first rise to a certain value and then start to fluctuate in a small range, while the curve of LS-SSDD starts to fall after fluctuating for a period of time. According to the trend of the curve changes in Figure 8, the combination used for the experiments on SSDD is \(Eps=5\) and \(MinPts=5\), while the combination of \(Eps=7\) and \(MinPts=7\) is used for the experiments on HRSID. And for the LS-SSDD, we choose the combination of \(Eps=6\) and \(MinPts=5\). Using these parameters, the average IoUs obtained on SSDD, LS-SSDD, and HRSID are 0.754, 0.463, and 0.632.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Training** & **Validation** & **Testing** & **Total** \\ \hline SSDD & 812 & 116 & 232 & 1160 \\ HRSID & 4538 & 505 & 561 & 5604 \\ LS-SSDD & 5000 & 1000 & 3000 & 9000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Datasets** & **Median of the Interval** & **Interval Width** \\ \hline SSDD & 0.9531 & 0.0928 \\ HRSID & 0.8245 & 0.3443 \\ LS-SSDD & 0.8507 & 0.2346 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The brightness intervals of SSDD, LS-SSDD, and HRSID.  
Figure 9 is the visualization of the clustering results on some images in the training datasets. For the image of ships on the sea, the filtered points are all on the ships. Even for a complex background as in the image of ships near the shore, our approach filtered out most noisy points.
# Deep-Learning Based Algorithm for Detecting Targets in Infrared Images
## 4 CMF-3 DLSTM
### Attentional Mechanism  
Attention mechanisms are generally used in natural language processing. As the length of text sequence increases in practical applications, the more advanced information in the series is lost more seriously, leading to a significant decline in model performance. A common solution is to input text sequences in both sequential and reverse order or LSTM. Although the two methods can improve the model performance to a certain extent, it is still difficult to effectively solve the problem of a too-long sequence.  
The final output size of the feature graph of 3 DLSTM is 3256, which adopts bi-LSTM to output vectors with the length of 256 from the three dimensions of length, width, and height of the 3D feature graph, respectively. Each element in the vector represents the neuron's output under a time step. Such a feature map size has the problem of a too-long sequence. Moreover, the weights of the three vectors are different, and the weight of each element in each vector should also be different. Therefore, an attention mechanism is adopted that can selectively screen out a small amount of important information from a large amount of information and focus on this vital information.  
A source in attention consists of a series of key-value pairs. The weight coefficient of each key corresponding to value is obtained by calculating the similarity of each key in input vector query and source. Formula (3) is the calculation of the similarity between the query and the key. Formula (4) determines the weight coefficient of each key corresponding to value by the \(Soft_{max}\) function.  
\[Sim_{i}(Query,Key_{i})=\frac{Query\cdot Key_{i}}{\|Query\|\cdot\|Key_{i}\|} \tag{3}\]  
\[a_{i}=Soft_{max}(Sim_{i})=\frac{e^{Sim_{i}}}{\sum_{j=1}^{N}e^{Sim_{j}}} \tag{4}\]  
The weighted sum of values obtains the final attention value according to these weight coefficients. The calculation formula of attention value is as follows:  
\[Attention(Query,Source)=\sum_{i=1}^{N}a_{i}\cdot Value_{i} \tag{5}\]  
Using the attention mechanism, you can assign different weights to the context information collected by 3 DLSTM in different directions and to the different elements in the context information in each direction. This enables the target detection model CMF-3 DLSTM to pay more attention to the salient features of the target, thus improving the target detection performance of CMF-3 DLSTM.
# Efficient Wireless Charger Deployment for Wireless Reshapeable Sensor Networks
## 6 Experiments and Simulations
### Experiments

Table 2: Received power (mW) for different distances and vertical angles. Figure 16: The illustration of distance \(D\), horizontal angle \(\Phi\), and vertical angle \(\theta\). and 30\({}^{\circ}\). For example, Figure 17 shows the functions derived by performing power regression analysis on experimental data of received energy (or power) for horizontal angel \(\Phi\) = 0\({}^{\circ}\), 15\({}^{\circ}\) and 30\({}^{\circ}\), and vertical angle \(\theta\) = 0\({}^{\circ}\). Moreover, Figure 18 shows the functions derived by performing power regression analysis on experimental data of received power for horizontal angel \(\Phi\) =0\({}^{\circ}\) and vertical angle \(\theta\) = 0\({}^{\circ}\), 15\({}^{\circ}\) and 30\({}^{\circ}\). We can see that the functions, namely \(y\) = 5.019\(x^{-2.217}\), \(y\) = 3.8881\(x^{-2.225}\), \(y\) = 2.6394\(x^{-1.821}\), \(y\) = 1.3522\(x^{-1.939}\), \(y\) = 0.7134\(x^{-2.01}\), fit well with the experimental data, as they all have coefficient of determination (i.e., \(R^{2}\)) [26] values larger than 0.958, which is very close to the maximum value 1. They also comply with the Friis equation, since the function exponents are approximately-2. Therefore, we may well estimate the power received by the harvester with the functions. In practice, we interpolate the values of the functions to estimate the received power for given parameters, like distance \(D\), horizontal angle \(\Phi\), and vertical angle \(\theta\).
###### Abstract  
The relative motion of transverse cable dampers is smaller than predicted by the taut string model because of the effects of bending stiffness and fixed support conditions. As a result of the reduced damper motion, the dissipated energy per cycle is reduced as well, which may explain why damping measurements on real stay cables with transverse dampers often show lower cable damping ratios than expected from the taut string theory. To compensate for the reduced damper motion and damper efficiency, respectively, a semi-active cable damper is proposed. The controllable damper is realized by a hydraulic oil damper with real-time controlled bypass valve whereby the resulting damper force is purely dissipative. The proposed control law is clipped viscous damping with negative stiffness. The viscous coefficient is adjusted in real time to the actual frequency of vibration to generate optimum modal damping while the negative stiffness component partially compensates for the reduced damper motion due to the flexural rigidity and fixed support conditions of the cable. The measurements of the prototype semi-active hydraulic damper are used to derive a precise model of the semi-active damper force including the control force constraints due to the fully open and fully closed bypass valve. This model is used to compute the cable damping ratios of the first four cable modes, for typical damper positions, for a taut string model and for a cable model with flexural rigidity and fixed supported ends. The obtained cable damping ratios are compared to those resulting from the passive linear viscous damper being optimized to the first four cable modes. The results demonstrate that the proposed semi-active cable damper with the consideration of the minimum and maximum control force constraints significantly enhances the cable damping of the first four modes compared to the linear viscous damper.  
cable; control; damping; semi-active; negative stiffness; vibration Article
# Coupled Vibration Analysis of Submerged Floating Tunnel System in Wave and Current
## 5 Conclusions  
This paper analyzes the dynamic response of SFT subjected to the external hydrodynamic loads using a theoretical approach. The proposed model focuses on the coupled tube-cable system rather than a separate marine tether or a slender tube on elastic supports. In comparison with the existing methods, it is thus more in line with the actual characteristics of the SFT and help us better understand the vibration mechanism. The significant resonance phenomena of the system are obtained under artificial parameter settings. Also, several cases with different working conditions are presented to describe the vibration characteristics and prove the correctness of this model. Although only the vertical vibrations of the tube are demonstrated in the case study, the proposed procedure can be extended to the actual structure, with more complex dynamic behaviors. Furthermore, parametric studies are performed to provide some references for engineering practice. From this research, the main conclusions can be summarized as follows:  
1. [label=(0)]
2. When the fundamental frequency of the cables reaches one-half that of the tube (i.e., \(\omega_{t1}\):\(\omega_{i1}\) = 2:1), the autoparametric resonance of the SFT system is significant, which is more adverse to the cable.
3. VIV of the anchor-cables can excite the resonance of the tube-cable system. Although the coupling effect of the system suppresses the lateral displacement of the cables to some degree, the VIV of the cables may stimulate the vibration of the tube body, which leads to a significant tension variation in the cables.
4. The displacement amplitude of SFT is very sensitive to the change of the uniform flow velocity. When the flow velocity \(v_{0}\) = 1.18 m/s (reduced velocity \(U_{r}\) = 5.00), the displacement amplitude of the cables reaches its maximum.
5. Vibration frequency of the tube and cable is simultaneously decided by GBR and IMA. The parametric vibration of the system is more sensitive to the changes in GBR. Based on  
Figure 10: Structural component amplitude with different SFT installation depth (frozen parameters: \(v_{0}\) = 1.18 m/s, \(H\) = 8.2 m, \(T\) = 10.8 s, GBR = 0.65, IMA = 90\({}^{\circ}\) and \(d\) = 190 m).  
the case study, IMA is recommended at around 45\({}^{\circ}\), and a reasonable GBR should be greater than 0.7.
* The displacement of the structure increases with the increase in the wave height. The amplitude of the tube is maximized when the wave period is about 9 s. Meanwhile, in the practical engineering, we can reduce the impact of the wave force by properly increasing the underwater installation depth of the SFT.  
[PERSON] guided the research. The manuscript was written by [PERSON] and confirmed by all the authors.  
This work was financially supported by the (National Natural Science Foundation of China) grant numbers (51541810) and (51279178).  
The authors declare no conflict of interest.
## 4 Simulation Results
In the case of the beamforming scheme, it had a relatively high energy consumption in a high SNR. Thus, the normalized throughput performance of the ABF and the DBF schemes had a lower performance than the IDF scheme. In terms of the DF, the AF schemes had a lower performance than the multi-hop scheme in a high SNR ranged between 8 and 30 dB because they considered additionally the source and destination node link, which means that the destination node spent one more time to decode the source node signal.
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 4 CLOR Protocol
### Main Idea
#### 4.1.2 Negotiation Strategy  
Control-based candidate coordination is an efficient procedure to accomplish hybrid candidate set selection. We employ a handshake before each transmission. However, this strategy suffers from an exposed terminal problem. Carrier sensing is a promising method to resolve this problem [2]. Nevertheless, this problem cannot be well addressed only by using carrier sensing due to the long propagation delay underwater. Since the control packet can be overheard by adjacent nodes, we exploit a novel negotiation mechanism that adopts competition together with carrier sensing in the negotiation phase. In addition, we introduce a back-off strategy in CLOR to alleviate the collision caused by the burst transmission [47].
# Power-Efficient Random Access Design for Machine Type Communication
## 4 Link Budget Analysis and Simulation Results
### Simulation Results
\begin{array}{ll}C_{\text{LTE-eMTC}}&=t_{\text{Tx}}I_{\text{Tx}}+t_{\text{Rx}} I_{\text{Rx}}\\ &=(T_{\text{Tx1}}+T_{\text{Tx2}}+T_{\text{Tx3}})I_{\text{Tx}}+(T_{\text{Rx1}}+T_{ \text{Rx2}})I_{\text{Rx}}\\ &=(0.08+0.85+0.13)\text{ s }\times\text{ }260\text{ mA}\\ &+(1.564+0.574)\text{ s }\times\text{ }150\text{ mA}\\ &\approx 0.166\text{ mAh}\end{array}  \tag{28}\]  
or 0.0033% two AA battery capacity. This is an 87% reduction in power consumption for random access. Figure 10 shows the comparison of power consumption (in terms of two AA batteries) between LTE-\(n\)MTC and LTE-eMTC under different coverage classes. Longer air time not only means higher power consumption, it also means a higher resource overhead. The total time-frequency resources used is \(180\text{ kHz}\times 0.288\text{ s}=51{,}840\) for LTE-\(n\)MTC, whereas 1,210,680 for LTE-eMTC. The reduction in resource usage for each random access is therefore around 95.7%. Figure 11 shows the uplink timing performance of our proposed random access signal for different coverage classes with operating SNR \(\Gamma\) listed in Table 4. The timing estimation method is according to [21]. In our design, the CP of the OFDM symbol for the traffic/data channels is \(25\text{ }\upmu\)s. It is used to absorb the timing error of random access and the multipath delay spread. We observe that the timing error is in the range of \(\pm 5\text{ }\upmu\)s (95 percentile) which consumes about \(10\text{ }\upmu\)s of the CP, leaving \(15\text{ }\upmu\)s for the multipath delay spread, which is sufficient for the traffic channels. Figure 11: Timing estimation performance of LTE-nMTC random access signal. Figure 10: Power consumption of LTE-nMTC random access and LTE-eMTC random access.