esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Derivation of Boundary Conditions of 4D C/C Composite  
Before deriving the thermal boundary conditions of the two unit cells of 4D C/C composites, it is necessary to determine which types of the symmetry transformation are used to construct the unit cells [38]. The heat flux distributions and relative temperature on the unit cells are different, if they are obtained by distinct symmetry transformations. The common symmetry transformations mainly include rotational, translational, and reflectional symmetries [15]. As shown in Figure 2, the UC1 formed by translational symmetries is presented in black solid lines for the 4D C/C composites. The UC2 shown in blue dash-dotted lines is constructed by two rotational symmetries based on UC1. Figure 3 shows the topological structure of preform of the 4D C/C composites, and the matrix is hidden so that the conformation of UC1 can be observed clearly. In this work, UC1 is defined as a cube, whose domain is \(0\leq x\leq l\), \(0\leq y\leq w\), \(0\leq z\leq h\). The UC2 is constructed by two \(180^{\circ}\) rotational transformations of UC1 about X1-axis \((x,w/2,h/2)\) and Y1-axis \((l/2,y,h/2)\), as shown in Figure 3. It is discussed in detail in Section 2.3.  
Figure 1: Framework of the entire solving process.  
Figure 2: The formulation of UC1 and UC2.
## 4 Experimental Results
### PF and THD Comparisons with and without Feedforward Compensation  
Figure 21 compares the PF and THD of the prototype with the proposed control method, with direct feedforward and without feedforward. In Figure 21a, PF of the prototype is improved via the proposed feedforward over the whole load range. At \(P_{o}=589\) W, the PF with the proposed feedforward is 99.11%. Compared to direct feedforward, an improvement of 0.41% is obtained. Compared to the control method without feedforward, the PF is improved by 1.23%. At \(P_{o}=1570\) W, the proposed method improves the PF from 99.50% to 99.82% compared to the control method without feedforward, and an improvement of 0.3% is achieved. In Figure 21b, the proposed method reduces the THD more than 1.61% compared to the method without feedforward. Compared to direct feedforward, the proposed method reduces the THD more than 0.37%. Because of the proposed reference current phase correction, the improvement in the PF is larger than that of THD.  
Figure 22 shows an efficiency comparison of the prototype. The efficiency is improved at light load, where an improvement of 0.24% is achieved at \(P_{o}=589\) W. The peak efficiency is 98.69% at about 1250 W, and the efficiency is 98.62% at rated power.
## 3 Parametric Study for Determining the Optimal Sampling Strategy
### Sampling from a Single Point
#### 3.1.6 Effect of the Factor of Safety \((fs)\)  
The variation of \(p_{f}\) with respect to \(d_{p}/H\) for different \(FS\) values is shown in Figure 9; the optimal sampling distance from the wall face was also found to be at \(\mathrm{x}/H=0\) for any \(FS\) value. Thus, only the \(\mathrm{x}/H=0\) case is presented here. From Figure 9, it is obvious that the failure probability decreases as \(FS\) increases, but what is not trivial is that the positive effect of targeted field investigation on the reduction of the statistical error is greater for greater \(FS\) values. For example, considering the case of the rotating wall, as shown in Figure 9, the \(p_{f}\) value when \(FS=1.1\) is approximately 0.45 and independent of the \(d_{p}/H\) ratio. For the same soil-wall system, if \(FS=1.5\), \(p_{f}\) ranges from 0.18 for \(d_{p}/H=0\) (and 1.4) to almost zero for \(d_{p}/H=0.6\).
# A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET
## 4 Evaluation
### Comparison of CBR  
The CBR is defined as the ratio between the time the channel is detected as busy and the total observation time. CBR is a useful indicator of the channel load, with higher values indicating higher channel load and vice versa. To monitor the change in CBR throughout the simulation process, each vehicle calculates the current CBR based on the status of the channel before sending each BSM. Figure 4 illustrates the individual CBR values observed by each vehicle over the simulation interval for the 10 Hz 20 mW scenario. This is referred to as \"Real Time CBR\", with the X-axis in seconds. It can be difficult to determine overall variations in the channel CBR from this plot. Hence, we introduce a new metric, \"average CBR\", which calculates the average CBR from all vehicles in 5-second intervals until the end of the simulation time.  
Figures 5 and 6 compare the average CBR of the four approaches with 300 and 500 vehicles, respectively. In both cases, the 10 Hz transmission rate consistently results in the highest average CBR because all vehicles use it to send BSMs. The average CBR of 5 Hz is lower than 10 Hz. In both traffic models, QBACC consistently demonstrates stability with all average CBR values remaining below 0.6, which was defined in the reward function. In contrast, MDPRP has a higher average CBR in both traffic models.  
Figure 4: Real Time CBR.  
Figure 5: Average CBR with 300 Vehicles.
## 3 Experiment and Results Discussion
### Discussion  
The UV-Vis spectroscopy used for COD detection in this research spans a spectral wavelength range from 193.91 nm to 1121.69 nm, with a spectral resolution of 0.45 nm, yielding a total of 2048 wavelength features. It may result in excessively high dimensionality for the input variables in the water COD prediction model using the full spectrum. This increases the complexity of the model and makes it prone to overfitting because the number of samples is significantly smaller than the number of spectral features. Consequently, the model's prediction accuracy is compromised, with an \(R_{pred}^{2}\) of just 0.8481 and an RMSEP of 10.86. Therefore, reducing the dimensionality of features is essential to simplifying the model and enhancing its predictive performance. The model had improved performance. \(R_{pred}^{2}\) increased to 0.8943, and the RMSEP decreased to 7.83. However, it is still a problem that environmental factors influence the UV-Vis spectroscopy, thereby influencing the stability and accuracy of COD detection. So, the influence of three environmental factors on UV-Vis spectroscopy was researched using a standard solution and real water samples. The mechanism of the influence of various environmental factors in water on UV-Vis spectroscopy was analyzed. Considering the complexity of the influence of environmental factors, data fusion was introduced to comprehensively compensate for the influence. Firstly, a COD prediction model based on the fusion of spectral characteristic wavelength and single environmental factor feature level was established to compensate for the influence of a single environmental factor. Through the fusion of spectral features and a single environmental factor, the accuracy of COD prediction was improved. Among the three factors, the pH single feature wavelength fusion model was the best, as the model's \(R_{pred}^{2}\) increased to 0.9459 and the RMSEP decreased to 4.46. However, the actual influence is not from a single environmental factor but the result of the interaction of multiple environmental factors. Therefore, a COD compensation model based on WSTEF was established, which combined spectral features and three environmental factors. The prediction accuracy of the model was further improved, and the optimal COD prediction model was obtained. The model's \(R_{pred}^{2}\) increased to 0.9602, and the RMSEP decreased to 3.52. Therefore, the fusion of UV-Vis spectroscopy and environmental factors achieved the more accurate detection of COD.
# Sketch-Guided Topology Optimization with Enhanced Diversity for Innovative Structural Design
## 4 Experiments
### Comparative Analysis with Existing Methods
\begin{table}
\begin{tabular}{l l l l l l l} \hline \multicolumn{6}{c}{**Case 1**} \\ \hline
**Setting** & \(\mathbf{\mu}_{comp}\) & \(\mathbf{\mu}_{vol}\) & \(\mathbf{CV}_{comp}\) & \(\mathbf{CV}_{vol}\) & \(\overline{\mathbf{S}SIM}\) & \(\overline{\mathbf{MAE}}\) \\ \hline \(\beta_{Inc}=0\) & 2.5831 & 0.3892 & 0.0204 & 0.0074 & 0.6461 & 0.1385 \\ \(\beta_{Inc}=5\;\times\;10^{-4}\) & 3.1602 & 0.3970 & 0.0687 & 0.0111 & 0.6069 & 0.1634 \\ \(\beta_{Inc}=5\;\times\;10^{-4}\), \(\lambda=0.05\) & 3.1263 & 0.3948 & 0.0523 & 0.0073 & 0.7585 & 0.0841 \\ \(\beta_{Inc}=5\;\times\;10^{-4}\), \(\lambda=0.05\), \(l_{min}=12\) & 3.4158 & 0.3952 & 0.0798 & 0.0068 & 0.7170 & 0.1058 \\ \hline \multicolumn{6}{c}{**Case 2**} \\ \hline
**Setting** & \(\mathbf{\mu}_{comp}\) & \(\mathbf{\mu}_{vol}\) & \(\mathbf{CV}_{comp}\) & \(\mathbf{CV}_{vol}\) & \(\overline{\mathbf{S}SIM}\) & \(\overline{\mathbf{MAE}}\) \\ \hline \(\beta_{Inc}=0\) & 2.5979 & 0.3884 & 0.0230 & 0.0079 & 0.6457 & 0.1386 \\ \(\beta_{Inc}=5\;\times\;10^{-4}\) & 2.9896 & 0.3919 & 0.0939 & 0.0094 & 0.6199 & 0.1579 \\ \(\beta_{Inc}=5\;\times\;10^{-4}\), \(\lambda=0.1\) & 2.8207 & 0.3894 & 0.0558 & 0.0070 & 0.7235 & 0.1111 \\ \(\beta_{Inc}=5\;\times\;10^{-4}\), \(\lambda=0.1\), \(l_{min}=12\) & 2.9787 & 0.3928 & 0.0565 & 0.0138 & 0.8138 & 0.0659 \\ \hline \end{tabular}
\end{table} 
Table 2: Quantitative experimental results of ablation study. baseline 1, as well as outperforming baseline 2 when it has a small perturbation amplitude (<0.4). It only slightly lags behind baseline 2 when the perturbation amplitude is large (=0.4) in terms of morphological diversity. For baseline 2, as the amplitude increases, the compliance and volume increase significantly, even violating the volume constraint, as indicated by the \(\mu_{comp}\) metric shown in bold. While baseline 2 can generate the most diverse solutions (\(\overline{SSIM}=0.7358\), \(\overline{MAE}=0.0663\)), it excessively sacrifices solution quality and struggles to reasonably balance the conflict between solution quality and diversity by only adjusting the perturbation amplitude. The above comparative analysis verifies the contribution of the proposed framework in achieving both high design quality and diversity.
## 3 Proposed ML Method
### Feature Extraction
#### 3.1.2 Envelope Analysis  
An Envelope Analysis (EA) approach, involving the extraction of the signal envelope of the discrete-time vibration signals, was also used as an alternative method to extract time-frequency features. Two different filtering approaches were applied to the non-stationary signals, linear and non-linear. The linear filtering process uses equidistant frequency spacing, whilst the non-linear option involves applying the one-third octave band scale to the vibration signal in the time domain. This produced 25 representations of the signal for both the linear and the non-linear cases for classification performance comparison. The filter employed was a 25 Finite Impulse Response (FIR) sixth order Butterworth filter.  
The discrete-time domain signal is demodulated by taking the absolute value of the non-stationary signal points, to produce \(x_{r}[n]\). By taking the Hilbert Transform of this rectified signal, we can produce the signal, \(x_{i}[n]\), which enables the creation of the complex analytic signal, defined as \(z(n)\) and shown in Equation (2).  
\[z[n]=x_{r}[n]+jx_{i}[n] \tag{2}\]  
The final step to calculate the envelope signal is to take the magnitude value of the complex analytic signal, as described in Equation (3).  
\[EA[n]=(x_{r}^{2}[n]+x_{i}^{2}[n])^{\frac{1}{2}} \tag{3}\]  
where \(EA[n]\) represents the envelope signal.
# Software-Defined Visible Light Communication for Internet of Things: A Low-Complexity Approach
## 4 Modeling of CAP16 Modulator and SDLMS Equalizer for SD-VLC System
Figure 8: The BER performance with CAP16 modulation and SDLMS equalization comparison between AWGN and multipath channels over a range of SNR values, with an inset showing the received constellation diagram at a high SNR of 10 dB. Figure 7: The error of the SDLMS equalizer with different step sizes \(\mu\). (**a**) \(\mu=0.01\). (**b**) \(\mu=0.05\).
## 3 Results and Discussion
### The Reconstruction Results of the AGES-AHK Method
The nature of this temperature distribution, which changes with \(\text{coef}_{\text{HE}}\), is consistent with the theoretical temperature phantoms in Figure 7.  
In comparison to the theoretical temperature phantoms in Figure 7, the AGES-AHK reconstructed temperature contour maps in Figure 10 shows an extremely high reproduction performance. At \(\text{coef}_{\text{HE}}\) values of 0.1, 0.2 and 0.5, the reconstructed results of the AGES-AHK method can accurately reproduce the overall temperature field topography, the extent of the hot zone, the location of hotspots, the peak temperature and the overall temperature range. In the extreme case of \(\text{coef}_{\text{HE}}=1\), AGES-AHK is able to reproduce the overall temperature field topography, the extent of the hot zone, and qualitatively track the location of hotspots and the peak temperature well. These results illustrate the validity of the AGES-AHK method. As shown in Figure 10, the white lines represent the nonuniform discretization of AGES, which is in accordance with the intrinsic gradient of the theoretical temperature field. In the domain discretization of all three temperature phantoms, AGES gives local refinement of grids near identified hotspots and sparse meshing across thermally uniform regions. This improves the resolution of grid reconstruction in the vicinity of the identified hotspot, whereas in larger grids, the reconstruction results are overaveraged, thus relatively reducing the reconstruction error. It is noteworthy that markedly nonuniform grid collocations are exhibited in _Tfield_1, whereas the smallest grid precisely encloses the theoretical hotspot. In summary, the proposed AGES-AHK method shows great improvement compared to previous studies [25; 26; 30; 36; 38]. The AHK method combines the sharp spatial localization characteristics of G with the global characterization of MQ, thereby enhancing the suitability for reconstructing large-gradient temperature fields [26; 36; 38], as evidenced by Table 2. The AGES method modifies the grid density distribution to match the temperature field topography at a finite number of measurement points, improving the fixed-center non-uniform distribution [30] and the spatial moving of hexagonal grids [25], as evidenced by Figure 10.
# Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
## 3 Approach Methods and Procedures
### Data Processing
This reduces biases and errors introduced by differences in feature magnitudes, allowing the model to learn more about trends between numerical values. In this study, Z-score normalization is utilized for data standardization. The Z-score is calculated using the formula shown in Equation (1), where the variables are defined as follows: z represents the standardized value, \(x\) denotes the raw data points after feature extractions, \(\mu\) corresponds to the mean of the dataset, and \(\sigma\) is the standard deviation of the dataset. After this processing, the mean of the standardized dataset is 0, and the standard deviation is 1. \[\text{z}=\frac{x-\mu}{\sigma} \tag{1}\]  
Figure 9: Segmentation on y-component cutting force signal. After standardizing the training and test data, feature selection is subsequently performed. Feature extraction is based on the three cutting force component signals after data cleaning, and it involves three main aspects:  
1. For the cleaned cutting force signals, 9 time-domain features and 4 frequency-domain features were extracted. 2. Each cutting force component signal is segmented into 10 windows, and 9 time-domain features are extracted in each window. 3. The cleaned cutting force signals are transformed into frequency-domain, and the amplitudes correspond to spindle speed and tooth passing frequencies, and their harmonics are extracted. After feature engineering, the extraction of features related to cutting-tool wear results in a relatively large number of features. Therefore, feature selection is conducted in order to find an optimal subset of features. Feature selection is a crucial step in machine learning, as an excessive number of features can lead to overfitting, causing a decline in prediction model performance. Through feature selection, irrelevant or redundant features can be eliminated, and those that contribute to predicting the target variable are retained. Reducing the number of features can also lower the computational cost of model training and prediction. In this study, the method employed for feature selection is based on the feature importance in random forests.
## 3 Experimental Results and Discussion
### Defect Identification Threshold Calculation and Result Analysis
According to Equation (9), a scatter plot was used to determine the threshold of the c value of the substrate's lower part (see Figure 15). The c value maximum of the normal group images was 0.661, and the c value minimum of the substrate damaged images was 0.688. There was no overlapping interval between the normal group and the substrate damaged images. The c value threshold interval was selected as (0.661, 0.688). Finally, the c value threshold was taken as the intermediate value of 0.675 in the experiment.
## 3 Application Fields of Smart Manufacturing and Tactile Internet Powered by 5G
### Education  
During Industry 4.0, engineers have focused on the digitization and digitalization of manufacturing and production systems, i.e., the machine-to-machine (M2M) interface. As a result, modern equipment is already, or soon will be, a thing on the internet (Internet of Things (IoT)). Consequently, machines/systems have become smarter, and are capable of producing and utilizing data (see big data sets). On the other hand, in order to achieve a harmonic coexistence between humans and machines, it is imperative to focus on improving the human-to-machine interface (HMI), which is the central concept of the adaptive, cognitive, human-centered manufacturing initiatives around the world. Therefore, in order to overcome the emerging challenges, education is a key driver for the next generation of engineers. Further to this, new engineers require different skills that will enable them to  
Figure 12: Physical coupling of several users via VR simulation with haptic feedback [13].  
communicate/coexist with machines more easily/intuitively. This is similar to the skill gap between the current generation and the previous generation which have been directly impacted by the introduction of computers in our everyday lives.  
The 5G-based TI will enable enhanced learning based on the educator and learner's haptic overlay. Comprehension-based learning will increase learner interest and outperform traditional learning methods by combining TI with VR and AR. Following that, the learner will look for opportunities to adapt in general by exploring and discovering the available content. Additionally, there will be less interference from the educator and no educator pressure on the learner to study. The incorporation of VR-based study-related applications into the learner's curriculum would immerse the learner in tactile learning by allowing them to touch virtual objects that provide haptic feedback [57].
## 2 Related Works  
### Integrated Sensor and Satellite Network  
Recently, wireless sensor networking has emerged as a low-cost technology for unmanned monitoring of a wide range of environments [17]. These sensor nodes usually consist of sensing, data processing and communication components, which can monitor and process physical data, such as temperature, humidity, vibrations, sounds, pictures and other data [18, 19]. However, these low-cost sensors usually have a short ground transmitting range. They transmit information to respective base stations. Then,these base stations send the data to the receiver over a terrestrial network [20]. In areas that lack the appropriate terrestrial infrastructure [21], such as a disaster area, desert areas, ocean areas, _etc._, it is hard for sensors to transmit their information to the destination over a terrestrial network. Satellite links are an essential element of long-distance telecommunications [22]. Satellites have been used to provide many services, such as mobile communications, television, broadband Internet services, _etc._ It is envisaged that in the future, satellite networks will be integrated with terrestrial ones in order to provide a wider coverage area and data transmission to or from remote, inaccessible areas [23].  
Integration of existing terrestrial sensor networks and satellite networks is one kind of integrated terrestrial and satellite network. It allows senors achieving ubiquitous information exchange between geographically-separated sites at an affordable cost. How to design and optimize the integrated sensor and satellite network schedules provides the first step in order to have a seamless and efficient integration between different technologies. In [24, 25, 26], [PERSON] _et al._ studied the information distribution methods in integrated sensor and satellite networks. They developed methods to select the Earth station that assures the improvement of network performance in terms of energy consumption, load and total time spent by information packets in the network. In [27], [PERSON] _et al._ proposed a delay tolerant network (DTN) bundle layer implementation for the throughput enhancement in integrated sensor and satellite networks. They used the bundle layer to solve the problem of data transmission efficiently. In [28], [PERSON] _et al._ studied the performance of different integrated sensor and satellite network architectures. The packet loss rate and average end-to-end packet delay were compared in different network architectures. In [29], [PERSON] _et al._ considered the integration of a dense machine to machine (M2M) sensor network and satellite network. The M2M sensor devices are locally grouped into clusters and communicate with satellites. Although integrated terrestrial and satellite networks have been studied in many works, to our best knowledge, there are no previous results on how to evaluate the integrated sensor and satellite network capacity.