esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Influence of Geometric Parameter
### Multiple Design Parameters  
The interference results listed in Tables 4-10 were obtained with the variation of single parameters. A simulation of multi-parameter variation was completed in order to investigate the coupling relationship between the geometric parameters of the V-shaped support. In this study, the parameter combination of L = 300 mm/H = L2 was changed into L = 400 mm/H = L2 + 100 mm, and the parameters D and P remain unchanged. The mesh of this study is shown in Figure 3 and the typical results are listed in Table 11. The table, \"\(\Delta Cx\) (L)\", represents the difference caused by the changing of parameter L, while other parameters remained the same. The value of \"\(\Delta Cx\) (L) + \(\Delta Cx\) (H) - \(\Delta Cx\) (L/H)\" reflected the coupling relationship between parameters L and H. The closer this value was to 0, the weaker the coupling relationship was between them.  
In Table 11, the influence of multi-parameter variation is almost equal to the sum of the single parameter influence, indicating that the coupling relationship between parameters L and H was quite weak. The conclusion could be used to predict the interference caused by multi-parameter variation.
# Eight Element Wideband Antenna with Improved Isolation for 5G Mid Band Applications
## 4 Proposed Eight-Element Antenna
### Equivalent Circuit Analysis  
The lumped equivalent circuit model was used to study the simulated scattering properties of the antenna. Figure 11 provides the equivalent circuit representation of the antenna that was analyzed employing circuit theory and the impedance matching method [29; 30]. The circuit model of the single antenna was first created and attached to the modeled lumped components of the decoupling structure using the coupling capacitors (CC1-CC8) to create the antenna's whole equivalent circuit.  
The parallel RLC component of each single element in the figure corresponds to the single resonance of the antenna which represents the real and imaginary part of the antenna impedance. The excitation of the circuit is given by 50 \(\Omega\) ports as shown in the figure. The three LC combinations toward the center of the equivalent circuit represent the stub-integrated connected ground decoupling structure. The port-to-port coupling between the antenna elements was realized by tuning these combinations of inductance and capacitance (LC), which generate band stop characteristics across the entire operating range. The accurate tuning of these LC circuits accounts for the antenna's input impedance, bandwidth, and isolation. The lumped parameters in the whole circuit were finally tuned using NI-AWR software to match the scattering parameters that were obtained from the HFSS software. Figure 11 gives the tuned values of lumped elements. The comparison of the reflection and transmission coefficients from HFSS and the equivalent circuitry is depicted in Figure 12a,b.
# Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan
## 2 Materials and Methods
### Population Estimation at Fine-Scale
#### 2.3.1 Classification of Buildings with the _Tf-IDF_ Method  
Firstly, multi-source data are processed to classify urban buildings. Residential buildings are selected from the classification results. The data used in this method include urban building data, _POI_ vector data for the research area, and administrative division data. Urban building data, _POI_ data, population census data, and administrative division data describe the characteristics of geographical areas in different dimensions, and they have significant spatial correlations. According to the limited spatial scope of the administrative division, subdistrict scale building data, _POI_ data, and population census data are obtained, and relevant statistics are generated. The specific process is shown in Figure 5.  
Figure 4: Population estimation method.  
Figure 5: Data preprocessing.  
The attributes of _POI_ vector data include the ID, latitude, longitude, and category. Building vector data comprise outline data for city buildings. Attribute data includes the building ID, building floor number, building perimeter, and building area. Administrative territorial entity properties include the administrative territorial entity ID, administrative territorial entity name, and administrative territorial entity update time. The demographic data used in this study are provided by the National Bureau of Statistics of the People's Republic of China. The geographic coordinates system for _POI_ data and building data is unified in World Geodetic System 1984, and map projection is conducted on this basis. Based on the administrative territorial entity data, _POIs_ and building spatial data can be associated with administrative territorial entity data to obtain subdistrict scale _POIs_ and building data.
# An Application of BOTDR to the Measurement of the Curing of a Bored Pile
## 3 Field Test
### Optical Fiber Sensor and BOTDR Demodulator  
In this experiment, the armored optical fiber sensor is used for strain sensing. The structure of this metallic sensor is a coaxial cylinder covered with multi-layer materials. From outside to the inside, there are the outer sheath, reinforcement fiber, coating, cladding, and fiber core [22]. The optical fiber in this experiment uses the steel wire as fiber reinforcement and plastic sleeve as an outer sheath. The core has a high refractive index and is mainly responsible for transmitting light waves. The cladding is made of material with a low refractive index, which mainly acts as a restraint of light waves in the fiber core. The protective cover and coating mainly protect the fiber core; they enhance the overall strength of optical fiber, isolate the damaging of fiber structure, and reduce light loss. The structure of optical fiber can be seen in Figure 1a and the sensor are shown in Figure 1b.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Soil Layer** & **Soil Name** & **Thickness (m)** & \begin{tabular}{c} **Density** \\ **p (g/cm\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **Elastic** \\ **Modulus** \\ **E (MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Cohesion** \\ **C (kPa)** \\ \end{tabular} &
\begin{tabular}{c} **Internal** \\ **Friction Angle** \\ **\(\varphi\) (\({}^{\circ}\))** \\ \end{tabular} \\ \hline \hline \multirow{3}{*}{2} & 1 & Plain fill & 3.55 & 1.90 & 18.5 & 30.9 & 24.6 \\  & 2 & Clay & 2.58 & 1.97 & 38.5 & 46.6 & 16.6 \\  & 3 & Silty clay & 2.89 & 1.89 & 29.5 & 31.1 & 21.6 \\  & 4 & Silt & 3.45 & 1.88 & 41.0 & 6.2 & 26.8 \\  & 5 & Silty sand & 4.91 & 1.93 & 43.0 & 3.8 & 29.4 \\  & 6 & Silty clay & 6.44 & 1.89 & 23.0 & 30.9 & 20.4 \\  & 7 & Silty clay & 3.08 & 1.88 & 21.5 & 30 & 19.7 \\  & 7 & Silty clay and silt & 3.55 & 1.91 & 41.0 & 3.9 & 28.2 \\  & & interbed & & & & & \\  & 8 & Silty sand & — & 1.93 & 42.0 & 3.8 & 29.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical properties of soil.  
Figure 1: Structure of optical fiber and sensor.  
This metallic armored sensing optical fiber sensor is manufactured by Nanzhi sensing technology company, Suzhou, China. The diameter of sensor is 5 mm, and the core diameter of optical fiber is 0.9 mm. The optical sensing fiber sensor has a relatively rough surface in good combining with the concrete, and the deformation of optical fiber sensor is consistent with concrete deformation. The strain coefficient is 0.04998 MHz/\(\mu\epsilon\) and the temperature coefficient is 1.775 MHz/\({}^{\circ}\)C.  
The data demodulator of BOTDR is AV6419 single-terminal distributed optical fiber demodulator, the measuring range is 0.5 km-80 km, the maximum sampling interval is 0.05 m, the range of strain is \(-\)15,000 \(\mu\epsilon\)\(-\)15,000 \(\mu\epsilon\), and its performance index meets the requirement of this experiment. Figure 2 shows BOTDR demodulator.
# Multi-Feature Behavior Relationship for Multi-Behavior Recommendation
## 5 Experimental Results And Evaluation
### Hyperparameter Study (RQ3)  
To investigate the impact of various parameter settings on experimental outcomes, we selected the key hyperparameters in MFBR and studied their recommendation performance under different values. We show the results in Figure 4; the results can be summarized as follows.  
* **Embedding Dimensionality**, \(d\). The latent dimensionality \(d\) is searched from 4 to 32. It can be seen that the performance of the model gradually improves as the dimension increases from 4 to 16. This is because as the dimension increases, the model can capture more interaction potentials, and therefore the performance gradually improves. However, the performance of the model does not necessarily improve when the dimensionality is further increased, as shown by the fact that the performance on the MovieLens dataset is further increased when the dimensionality is 32, while the performance on the Taobao dataset decreases instead. This may be due to the overfitting phenomenon due to high dimensionality, which causes the recommendation performance to decrease with increasing dimensionality.
* **Layer of Graph Neural Network**, \(L\). The user-item multi-relationship graph is used to capture the higher-order user-item interaction information and learn the relationships between different behaviors in order to construct a more fine-grained representation of user preferences. In our experiments, the number of propagation layers of the graph structure was set to 0, 1, 2, 3. It can be observed that the recommendation performance of MFBR when the number of propagation layers is 1 or 2 far exceeds the performance at layer 0, and the best performance is achieved at layer 2. This illustrates that increasing the number of propagation layers appropriately can better encode the high-value information of user-item interactions. However, when the number of propagation layers is increased to 3, the performance decreases on both datasets. This illustrates that when the number of propagation layers is too high, noise may be introduced, leading to a degradation in the performance of the model.
* **Low-rank Decomposition Dimension**, \(d^{\prime}\). In this component of MFBR, we design a low-rank decomposition self-attentive network to learn the sequential patterns of user behavioral interactions in order to capture the sequential information between interactions. From the evaluation results, it can be seen that the configuration of \(d^{\prime}=4\) yields the better recommendation quality. The recommendation performance of the model decreases when the low-rank decomposition dimension is further increased. This experimental result demonstrates that our designed low-rank decomposition scheme is reasonable and effective in reducing computational complexity and improving the robustness of the model.
# Improved ECG Watermarking Technique Using Curvelet Transform
## 2 Curvelet Transform
### Conversion of the ECG Signal into an Image  
The biomedical ECG 1-D signal from the MIT-BIH arrhythmia database [19] sampled at 128 Hz was in this study. Figure 1 shows the 1D-ECG signal. P waves and T waves in the ECG QRS complex are the fundamental attributes for diagnosing any disease. The Tompkins algorithm [26] is used to identify P waves and T waves in ECG. In this, band-pass filters are used to filter the ECG signal. The signals are squared, and these squared signals are averaged by the window function to exclude noise, hence forming the ECG signal. The fiducial mask is attained by focusing the QRS attributes on a distinct instantaneous time. Then, its explorations back for missed QRS complexes and removes the duplicate detections. Lastly, the R-wave and QRS attributes are identified. P waves and T are utilized to alter the 1D-ECG signal into a 2-D image. Sampling rate 128 Hz is used and for each ECG trained signal fiducial points are calculated. From each calculated fiducial mask 64 points are utilized [12]. Now the ECG signal is changed into the image. The size of the 2-D image depends upon the number of ECG trains. There are some data losses due to the selected arguments employed to transform the 2D-ECG image into a 1D-ECG signal. However, this data loss is negligible and does not affect the ability to diagnose of the disease. The 2D-ECG image produced from Figure 1 is shown in Figure 2.  
Figure 1: Original 1-D electrocardiogram.  
Figure 2: 2-DECG image.
## 4 Results
### PWCT2 Performance
These findings highlight the substantial resource demands of code generation in PWCT1, particularly as the number of steps increases. The statistically significant data and strong performance metrics allow us to generalize the findings when discussing the performance improvements of PWCT2 over PWCT1. On average, PWCT2 is approximately 36.7 times faster in code generation time (CGT) per step and uses approximately 20.9 times less storage per step compared to PWCT1. This significant improvement is evidenced by the lower average CGT and storage per step for PWCT2, as well as the low RMSE values, indicating high efficiency and consistency. These \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Generation** & **VPL** & **File** & **Storage (KB)** & **Steps** & **LT (ms)** & **CGT (ms)** \\ \hline PWCT2 & RingPWCT & GameOfLife & 773 & 1253 & 1068 & 43 \\ PWCT2 & RingPWCT & Checkers & 845 & 1307 & 1077 & 43 \\ PWCT2 & RingPWCT & GoGame & 946 & 1453 & 1166 & 47 \\ PWCT2 & RingPWCT & Chess & 1012 & 1560 & 1270 & 52 \\ PWCT1 & CPWCT & Vmfuncs & 7966 & 1000 & 549 & 1748 \\ PWCT1 & CPWCT & Refmeta\_ext & 8243 & 1075 & 593 & 1993 \\ PWCT1 & CPWCT & File\_ext & 9799 & 1235 & 747 & 2651 \\ PWCT1 & CPWCT & Vm\_oop & 11397 & 1497 & 862 & 3862 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: A table of some visual source files containing steps equal to or greater than 1000 steps. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute** & **PWCT1** & **PWCT2** \\ \hline Visual Programming Language & CPWCT & RingPWCT \\ Visual Source Files Count (Sample Size) & 43 & 25 \\ Pearson Correlation (Storage vs. Steps) & 0.8693 & 0.9662 \\ Pearson Correlation (CGT vs. Steps) & 0.9105 & 0.9947 \\ Spearman Correlation (Storage vs. Steps) & 0.8198 & 0.9867 \\ Spearman Correlation (CGT vs. Steps) & 0.9914 & 0.9855 \\ Average Storage per Step (KB/step) & 13.6751 & 0.6543 \\ Average CGT per Step (ms/step) & 1.2956 & 0.0353 \\ RMSE for Storage & 23.4063 & 0.1082 \\ RMSE for CGT & 1.0539 & 0.0032 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Statistical Analysis of RingPWCT and CPWCT samples. results highlight the advancements and effectiveness of PWCT2, making it a more efficient tool for visual programming tasks.
## 4 Improved Routing Protocol
### Route Discovery
#### 4.2.2 Route Reply  
Every node (destination node included) receiving RREQs saves GPS position information of source nodes and sends back a route reply (RREP) with its position information. The node receiving RREP will decide whether the precursor node belongs to its forward list or backward list based on the GPS position information. As shown in Figure 5, node \(i\) receives RREP from node \(j\). Given the position vector of node \(I\), \(\overrightarrow{\mathbf{r}}_{i}=(\phi,\lambda,h)\) in WGS84 (World Geodetic System 1984) coordinates parsed from RREP, the earth-centered geometric coordinate \(\overrightarrow{\mathbf{a}}_{i}=(x,y,z)\) can be calculated [29]. The angle \(\theta\), defined as Equation (15), is used to describe the relative position of node \(i\) in the route map. Since the \(\theta\) is a value of relative position, another variable \(\theta_{\rm th}\) is introduced to limit the range. The distance of node \(i\) and node \(j\), denoted by \(d_{ij}\), is calculated by Equation (16). In addition, variable \(d_{\rm th}\) is introduced to judge whether node \(i\) and node \(j\) are in the same tower:  
\[\theta=\arccos\frac{\left(\overrightarrow{\mathbf{a}}_{i}-\overrightarrow{ \mathbf{a}}_{s}\right)\cdot\left(\overrightarrow{\mathbf{a}}_{i}- \overrightarrow{\mathbf{a}}_{j}\right)}{\left|\overrightarrow{\mathbf{a}}_ {i}-\overrightarrow{\mathbf{a}}_{s}\right|\left|\overrightarrow{\mathbf{a}}_ {i}-\overrightarrow{\mathbf{a}}_{j}\right|}, \tag{15}\]  
\[d_{ij}=\left|\overrightarrow{\mathbf{a}}_{i}-\overrightarrow{\mathbf{a}}_{j}\right|. \tag{16}\]  
The judgment process of the forward/backward list is presented as follows:  
1. As shown in Figure 5a, if \(\theta>\theta_{\rm th}\), node \(j\) is added to the forward list of node \(i\). Namely, node \(i\) is the intermediate node from the source to node \(j\).
2. As shown in Figure 5b, if \(\theta<\theta_{\rm th}\), node \(j\) is added to the backward list of node \(i\). Namely, node \(j\) is the intermediate node from the destination to node \(i\).
3. As shown in Figure 5c, if \(d_{ij}<d_{\rm th}\), node B will not be added to any list of node A. It indicates that node \(i\) and node \(j\) will not exist on the forward list or the backward list of each other when they are in the same tower range.  
The threshold variables \(\theta_{\rm th}\) and \(d_{\rm th}\) can be set as empirical values. As a rule of thumb, \(\theta_{\rm th}\) and \(d_{\rm th}\) are set to \(90^{\circ}\) and \(10\) m, respectively. It should be mentioned that the threshold variables for some corner towers are specifically set according to geographical constraints.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Address of Nodes** & **Distance** & **Link Quality Indicator (LQI)** & **Link Status** \\ \hline Address 1 & \(d_{1}\) & \(LQI_{1}\) & Valid/invalid \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ Address \(n\) & \(d_{n}\) & \(LQI_{n}\) & Valid/invalid \\ \hline \hline \end{tabular}
\end{table}
Table 1: Route discovery neighbor table of the forward/backward list.
# Delay-Sensitive Service Provisioning in Software-Defined Low-Earth-Orbit Satellite Networks
## 3 Network Model and Problem Formulation  
### Network Topology Model  
We model an LEO satellite network as a directed graph \(G(V,E)\), where \(V\) is the set of satellites and \(E\) is the set of all possible links between satellites. In order to cope with the dynamic changes of topology, this paper adopts the virtual topology approach. The virtual topology method divides the motion period \(T\) of the satellite network into \(|\mathcal{T}|\) time slots. Each time slot \(i\) corresponds to a topology snapshot [17; 18], indicating that there are no changes in the topology snapshot during time slot \(i\). For simplicity, we assume that all time  
Figure 1: Routing strategies with different number of path handovers.  
Figure 2: Routing strategies with different load balancing performances.  
slots have the same length. However, the algorithm designed in this paper can also be used directly in the cases of non-equal time slots.  
Since satellites move continuously over time, a link \(e\in E\) may be disconnected during time slot \(i\). Therefore, we use \(y_{e}^{i}\in\{0,1\}\) to indicate whether link \(e\) exists in the \(i\)-th time slot. Additionally, the propagation delay of links varies over time, so we use \(d_{e}^{i}\) to represent the maximum propagation delay of link \(e\) in time slot \(i\).  
In this paper, we mainly focus on scheduling time-sensitive (TS) flows. The set of TS flows to be scheduled is denoted as \(F\), where the \(j\)-th TS flow is represented by \(f_{j}\). Let \(b_{j}\) and \(r_{j}\) represent the bandwidth requirement and maximum transmission delay constraint of flow \(f_{j}\).
## 4 Experimental Analysis
### Experimental Results and Analysis  
#### 4.3.1 Comparative Experiments with Classical Feature Extraction Networks  
In the backbone feature extraction stage, Table 2 illustrates the performance evaluation of classical network models, including Vgg16, Resnet34, Resnet18, and DenseNet121, on the CBSD training image dataset. The MFS-GC model achieves an accuracy of 95.5% in the Top-15, surpassing the performance of other models. Furthermore, the training loss of our proposed model is 0.024, which is lower compared to other models. Among the six models considered, our model demonstrates state-of-the-art performance.  
The experiments indicate that the MFS-GC model introduced in this paper enhances the underlying fine-grained features of bone stick images, yielding overall results superior to alternative methods on the dataset employed in this study. Figures 8 and 9 illustrate the training loss and accuracy curves for each algorithm. From the graphs, it is evident that all models tend to converge with the network model in this paper achieving the highest accuracy of 95.5% after 30 training epochs, which is accompanied by a training loss of 0.24. This performance is, in general, superior to that of other networks. DenseNet121 exhibits the slowest convergence speed with an accuracy as low as 73.5%, while the Vgg16 model converges rapidly but achieves a relatively lower accuracy of only 75%. Resnet18 and Resnet34 demonstrate accuracies of 83% and 89%, respectively, indicating that for the bone stick image dataset in this study, the performance of residual networks surpasses that of other networks. Additionally, a comparison among different residual networks reveals  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Missed Detection** & **Loss** \\ \hline Vgg16 & 0.750 & 0.192 & 0.25 & 1.456 \\ Resnet34 & 0.890 & 0.301 & 0.11 & 0.369 \\ Resnet18 & 0.83 & 0.225 & 0.17 & 0.384 \\ DenseNet121 & 0.735 & 0.175 & 0.264 & 0.91 \\ MFS-GC & 0.955 & 0.356 & 0.045 & 0.24 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the performance of different backbone networks.  
that the performance of the MFS-GC model, utilizing the Resnet50 backbone network, outperforms Resnet18 and Resnet34.
## 3 Materials and Method
### Statistical Analysis and Results  
Through the application of the bespoke system developed for this study, continuous monitoring and computation of the head angle in relation to its initial positioning were conducted in real-time. Within each 2-min recording session, four distinct metrics were systematically derived to assess each individual's performance across multiple sessions and days, encompassing evaluations before, during, and after feedback. Among these metrics, paramount attention was directed to the maximal computed value, representing the zenith of head angle deviation from its initial alignment. Simultaneously, the arithmetic mean of these values was computed to provide an overview of the average head angle deviation. Furthermore, meticulous calculation of the standard deviation was performed for each recording, serving as a pivotal statistical measure that conveyed the effective amplitude governing fluctuations in head angle deviation. In concert with these parameters, the calculation of the DFA number was undertaken during each recording, contributing to a comprehensive evaluation of performance and responsiveness within the experimental paradigm.