esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results
### Impact of the Random Search Strategy  
In this paper, the impact of the random search strategy on system performance is shown in Figure 15, where the batch size is set to 50, and the learning rates of the MDRLNN and ULNN are set to 0.0008 and 0.0001. With the random search strategy, we can see that the energy efficiency and the sum rate of the NOMA system are significantly improved. This is due to the fact that the random search strategy enables the system to explore a greater number of channel allocation schemes per time slot, which prevents the neural networks of the proposed MDRL-UL algorithm from becoming trapped in local optima.  
However, when the number \(B\) of randomly generated JAs by the NOMA system is set lower, such as 5, the system convergence slows down because the MDRLNN needs more time slots to train to find a better channel allocation scheme. Also, when \(B\) is set larger, e.g., as 15, we find that although the system performance is able to converge to a better level in a shorter period, there is still a large oscillation in the system performance. This is due to the over-reliance on the results of the random search strategy during the training process, which leads to the phenomenon of overfitting. When the system environment, i.e., channel gain \(H_{m,\mu}^{n}\), changes drastically, the neural network is unable to adapt to the change in time. And when \(B\) is set to 10, it can be found that the energy efficiency and the sum rate can converge to a higher value in a shorter time and remain stable. This is because, based on the result of the random search strategy, the system can provide a high-quality action label for the neural network in the early stage of reinforcement learning training to avoid blind exploration of the agents and accelerate convergence.  
As a result, this demonstrates that the random search strategy, combined with a reasonable number \(B\), is able to improve the system performance by finding a better channel allocation scheme for the NOMA system in a time-varying dynamic environment.
## 2 Materials and Methods
### Chemometric Tools
#### 2.2.3 Sequential and Orthogonalized Covariance Selection Linear Discriminant Analysis  
(SO-CovSel-LDA)  
Sequential and Orthogonalized Covariance Selection Linear Discriminant Analysis [31] is a multi-block classifier based on the combination of the regression approach called SO-CovSel and LDA. The algorithm of SO-CovSel is similar to the one for SO-PLS; the main difference being the fact the feature reduction operated by PLS (in SO-PLS) is replaced by the variable selection achieved by CovSel [51]. Briefly, considering the two predictor blocks \(\mathbf{X_{1}}\) and \(\mathbf{X_{2}}\), for the prediction of the response matrix \(\mathbf{y}\), the SO-CovSel algorithm can be summarized as follows.  
1. Variables in \(\mathbf{X_{1}}\) are selected by CovSel, obtaining the reduced matrix \(\mathbf{X_{1 sel}}\)
2. \(\mathbf{X_{1 sel}}\) is used to predict \(\mathbf{y}\) by ordinary least squares
3. \(\mathbf{X_{2}}\) is orthogonalized with respect to \(\mathbf{X_{1 sel}}\), obtaining \(\mathbf{X_{20 th}}\)
4. Variables in \(\mathbf{X_{20 th}}\) are selected by CovSel, obtaining the reduced matrix \(\mathbf{X_{20 th,sel}}\)
5. \(\mathbf{X_{20 th,sel}}\) is used to estimate the residuals from step 2
6. The equation \(\mathbf{y=X_{1}b+X_{2}c+f}\) is solved (\(\mathbf{b}\) and \(\mathbf{c}\) being the regression coefficients and \(\mathbf{f}\) the residuals).  
Eventually, if the aim is to create a classification model, LDA can be calculated on the \(y\) predicted at step 6. For more details on SO-CovSel-LDA, the reader is addressed to the work in [31]. Calculations were made using in-house written functions running under Matlab, which are freely downloadable at [52].
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
## 2 System Model of the IEEE 802.11p
### Transmitter Model  
The transmitter consists of a convolutional encoder for the forward error correction, a puncturing module for higher data rates, the interleaver to oppose burst errors. Then a modulation module is added for the selection of different schemes. The 4 phase tracking pilots are inserted to the 48 data subcarriers in the pilot insertion module. These phase tracking pilots help in nullifying the phase rotations that are caused by the frequency offsets. Additionally, a total of 11 subcarriers and a null subcarrier is also added to complete a set of 64 subcarriers. After that, an Inverse FFT (IFFT) module is placed to transform the data to the time domain and then a GI and preamble modules are placed to make packet ready for transmission.
# Probabilistic Models for the Shear Strength of RC Deep Beams
## 2 Database  
An extensive database (listed in Table 1) of 645 tests was used to develop probabilistic models covering a wide range of structural parameters. Figure 1 provides an overview of the distributions of main influencing parameters, including compressive strength (\(f_{c}\)), the reinforcement ratio(\(\rho\)), stirrup rate (\(\rho_{\rm v}\)), shear span ratio (\(a/d\)), shear-depth ratio (\(l_{0}/h\)), and width-depth ratio (\(b/h\)). Approximately 457 beams had concrete compressive strengths lower than 40 MPa. Most members had a shear-depth ratio (\(l_{0}/\)h) between 1.5 and 4. More than half of the test specimens had longitudinal reinforcement ratios greater than 1.5%, and 96 beams had less than 1.0% reinforcement ratios. About three quarters of the members had a shear span ratio of 0.6\(-\)1.8, and the ratios of 123 beams were 0.2\(-\)0.6. \begin{table}
\begin{tabular}{c c c c c} \hline
**Literature** & **Quantity** & \(f_{\rm c}\)**/MPa** & \(a\)**/\(d\)** & \(V_{\rm test}\)**/\(k\)N** \\ \hline [PERSON] et al., 1970 [30] & 33 & 18.6–26.8 & 0.35–0.18 & 78.0–308.0 \\ \hline [PERSON] et al., 1997 [31] & 18 & 86.3–56.2 & 0.85–1.69 & 185.0–775.0 \\ \hline [PERSON] et al., 2000 [32] & 5 & 19.6–26.1 & 0.5–2.50 & 64.7–180.2 \\ \hline [PERSON] et al., 1971 [33] & 12 & 30.1–44.8 & 0.30–1.00 & 226.9–258.0 \\ \hline [PERSON] et al., 1982 [34] & 52 & 20.4–28.7 & 0.77–2.01 & 73.4–178.5 \\ \hline [PERSON] 1982 [35] & 39 & 18.3–30.1 & 0.36–1.94 & 67.6–411.6 \\ \hline [PERSON] et al., 1984 [36] & 10 & 20.6–83.8 & 1.50–2.94 & 87.700–558.1 \\ \hline [PERSON] 1990 [37] & 5 & 33.7–37.0 & 0.75–0.84 & 434.0–472.0 \\ \hline [PERSON] et al., 1995 [38] & 19 & 41.1–58.8 & 0.27–2.70 & 105.0–675.0 \\ \hline [PERSON] et al., 1997 [39] & 15 & 72.1–64.6 & 0.28–1.28 & 150.0–925.0 \\ \hline [PERSON] et al., 1998 [28] & 5 & 120.0–89.0 & 0.87–0.88 & 950.0–2000.0 \\ \hline [PERSON] et al., 1999 [40] & 13 & 52.0–73.0 & 1.50–2.00 & 90.0–287.1 \\ \hline [PERSON] et al., 1999 [41] & 12 & 30.8–49.1 & 0.56–1.13 & 435.0–1636.0 \\ \hline [PERSON] et al., 2008 [42] & 4 & 32.7–37.6 & 0.42–0.84 & 331.5–1305.0 \\ \hline [PERSON] et al., 2001 [43] & 53 & 23.7–73.6 & 0.50–2.00 & 112.5–745.6 \\ \hline [PERSON] 1967 [44] & 5 & 26.7–31.4 & 1.00–1.03 & 155.2–585.4 \\ \hline [PERSON] et al., 1986 [45] & 16 & 26.1–43.2 & 1.02–2.08 & 185.0–875.0 \\ \hline [PERSON] et al., 2013 [46] & 13 & 34.6–67.8 & 0.61–0.83 & 1156.0–2018.0 \\ \hline [PERSON] et al., 2000 [47] & 6 & 34.0–41.0 & 1.71 & 225.0–450.0 \\ \hline [PERSON] et al., 1954 [48] & 14 & 17.2–25.4 & 1.52 & 267.6–507.1 \\ \hline \end{tabular}
\end{table} 
Table 1: Deep-Beam Database.
# A Control-Oriented Linear Parameter-Varying Model of a Commercial Vehicle Air Brake System
## 3 Simulation and Experimental Validation  
### Experiment Setup  
To experimentally validate the developed model, an air brake system test bench is constructed, consisting of an air chamber C3519 VS05D, a switch valve, a pressure regulator, an air supply tank, a compact pneumatic pressure sensor PSE540, and DSpace; see Figure 5 for details.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Title** & **Value** & **Title** & **Value** \\ \hline \(\tau\) & 0.005 & \(k_{3}\)/N/m & 2955 \\ \(C_{d}\) & 0.82 & \(f_{20}\)/N & 10.2 \\ \(\gamma\) & 1.4 & \(f_{30}\)/N & 7.8 \\ R & 287 & \(a_{1}\)/m\({}^{2}\) & 1.96 \(\times\) 10\({}^{-3}\) \\ \(T_{0}\)/K & 298 & \(a_{2}\)/m\({}^{2}\) & 1.96 \(\times\) 10\({}^{-7}\) \\ \(V_{0}\)/m\({}^{3}\) & \(1.5\times\) 10\({}^{-6}\) & \(k_{b}\)/N/m & 15,000 \\ \(A_{p}\)/m\({}^{2}\) & \(2\times 10^{-4}\) & \(x_{pre}\)/m & 0.0057 \\ \(k_{1}\)/N/m & 2637 & \(A_{b}\)/m\({}^{2}\) & 0.0132 \\ \(k_{2}\)/N/m & 2955 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for simulation.  
Figure 4: Model block diagram.  
Experiments were conducted under two different supply pressure levels, 4.5 and 5.8 bar. At the same time, the brake chamber pressure was maintained at 2, 3, and 4 bar through a proportional-integral-derivative (PID) controller. The main reason for using the closed-loop response for model validation is due to the high open-loop system gains that make maintaining an open-loop brake chamber pressure challenge.
# Smart Contract-Based Right-of-Way Trade with Game Theory in Blockchain-Enabled Internet of Vehicles
## 4 Right-of-Way Trade Strategy
### Theoretical Performance Comparison and Analysis
#### 4.6.1 Trade Success Probability  
According to the analyzed trade success conditions in Sections 4.2 and 4.3, a deal is made only if \(\Delta C_{A}\leq S_{A}^{*}\leq S_{B}^{*}\leq\Delta C_{B}\). The trade success probability of Solution 1 is derived under the optimal bids in Section 4.4.2.  
\[P_{1}=\left\{\begin{matrix}\frac{2\alpha_{B}\Delta t_{BA}}{3\Delta t_{AB}},& \text{if }0<\alpha_{B}<\frac{3\Delta t_{AB}}{\Delta t_{BA}}\\ 1,&\text{if }\ \alpha_{B}>\frac{3\Delta t_{AB}}{2\Delta t_{BA}}\end{matrix}\right. \tag{35}\]  
However, the trade success probability of Solution 2 is derived under the optimal bids in Section 4.5.  
\[P_{2}=\left\{\begin{matrix}\frac{\alpha_{B}\Delta t_{BA}}{2\Delta t_{AB}},& \text{if }0<\alpha_{B}<\frac{2\Delta t_{AB}}{\Delta t_{BA}}\\ 1,&\text{if }\ \alpha_{B}>\frac{2\Delta t_{AB}}{\Delta t_{BA}}\end{matrix}\right. \tag{36}\]  
According to Equations (35) and (36), the trade success probability curves are shown in Figure 4a. When \(\alpha_{B}\) is relatively small, the trade success probability increases as \(\alpha_{B}\) increases, with Solution 1 having a higher trade success probability than Solution 2 at the same \(\alpha_{B}\). As \(\alpha_{B}\) increases, the trade success probabilities of Solution 1 and Solution 2 successively reach 1. As \(\alpha_{B}\) continues to increase, both solutions are certain to make a trade. Therefore, the trade success probability performance of Solution 1 is better than that of Solution 2.
# A Survey of Information Extraction Based on Deep Learning
## 2 Entity Relationship Extraction Based on Deep Learning
### Summary  
As mentioned in this section, the pipeline extraction-based method is relatively straightforward to implement; the flexibility of the extraction models is high; and the entity and relationship models can use independent data sets without requiring both annotated entities and relations. However, it involves the following shortcomings: 1. the error accumulation affects the performance of the next relationship extraction, causing error propagation, 2. because the extracted entities are paired and then classified, the redundant information of un-related candidate entities increases the error rate and increases the computational complexity, and 3. the missing interaction ignores the internal connection and dependence between the two tasks. The joint extraction framework focuses on extracting entities and relationships using individual models to capture the linguistic dependencies inherent between relationships and entity parameters, thereby addressing some problems of pipeline extraction. Some progress has been made in existing methods of remote-supervised relationship extraction, whose paradigm can automatically collect training data for relationship extractors but often encounters incorrect labeling problems. Remote monitoring inevitably introduces noise to the resulting training data set, and thus reduces the relationship extraction performance. They still face two challenges, including designing more efficient sentence encoders to generate more expressive sentence-level features, and figuring out how to make full use of the informative sentences in the package and then integrating them to generate package-level features to predict a given relationship.
# A Compact Instrument for Temperature-Programming-Assisted Capillary-Nanoliquid Chromatography
## 3 Results  
### Performance of the Developed Capillary-NanoLC Pump  
The developed pumping system demonstrated excellent performance for high-pressure capillary and nanoliquid chromatography, operating up to 350 bar with an operational flow ranging from 50 nL/min to 250 nL/min. Flow calibration was initially conducted using the gravimetric method, where the pump was filled with water and set to specific flow rates. Effluent aliquots were collected every five minutes in 0.5 mL Eppendorf flasks and weighed on an AG285 analytical balance (Mettler Toledo). As illustrated in Figure 4, across the nine tested flow rates, flow accuracy ranged between 99.9% and 101.71%, with the highest deviation observed at the lowest flow rate (100 nL/min). This minimal deviation demonstrates the pump's high precision and ability to maintain set flow rates with minimal fluctuation. The accuracy range indicates the pump performed excellently, with deviations within \(\pm\)1.71% across most tested flow rates. The slight increase in deviation at the lowest flow rate was expected due to the inherent challenges of controlling such small volumes. However, accuracy significantly improved above 500 nL/min, confirming the pump's stability and precision at typical operational conditions. This performance eliminates the need for software flow correction, ensuring that the flow outputs closely match the set flow rates.  
Figure 3: The assembly process of the capillary–NanoLC detection cell: (**a**) anodized aluminum blocks used for supporting the optical path length; (**b**) bending process of the capillary tube to form the optical path; (**c**) optical path length featuring 7.9 mm; (**d**) fully assembled detection cell, including the central metallic tube supporting the optical path length.  
Performance in actual operational conditions was tested using a 250 \(\upmu\)m i.d. \(\times\)15 cm capillary column packed with 3.0 \(\upmu\)m C18 particles, with mobile-phase flow rates (50/50 \(\mathrm{ACN}/\mathrm{H}_{2}\mathrm{O}\)) set at 1, 3, and 5 \(\upmu\)L/min. After system stabilization, indicated by steady column pressure, uracil (a non-retained compound) was injected every 10 min for flow estimation. The injection of uracil, which does not interact with the stationary phase, provided accurate flow estimates, allowing for the direct measurement of the pump's flow rate without interference from sample retention. The average flow rates obtained were 1.01 \(\upmu\)L/min with a relative standard deviation (RSD) of 0.97%, 3.00 \(\upmu\)L/min with an RSD of 0.83%, and 4.99 \(\upmu\)L/min with an RSD of 0.73%.
# Palm Date Leaf Clipping: A New Method to Reduce PAPR in OFDM Systems
## 5 Simulation Results
### Comparative Study  
In this subsection, we present a comparative study of the proposed method and some of the state-of-the-art methods. We begin with a comparison of the PAPR with different variants of PTS. Secondly, we compare the proposed method with SLM and its variants in terms of PAPR and BER. Finally, we compare the proposed method with other clipping techniques by integrating the High-Power Amplifier (HPA). The number of subcarriers was set to 64 and the modulation was set to 16-QAM over the AWGN channel.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Cloud Radio Access Network
#### 2.3.3 Fronthaul Link  
Fronthaul is the connection layer between a BBU and a set of RRHs. It provides high bandwidth and low latency links to handle the requirements of multiple RRHs. This link can be setup using different technologies including optical fiber communication, cellular communication, and millimeter-wave (mmWave) communication [29]. Although optical fibers provide the highest bandwidth requirement, they are highly expensive and not flexible for deployment. In the case of cellular or mmWave communication, they are comparatively less expensive and more convenient for implementation. However, these do not provide the required bandwidth as an optical fiber connection and also cause more latency. The deployment of a C-RAN with cost-optimal fronthaul is discussed in [32].
## 4 Evaluation
### Pre-Processing Techniques (Normalization)  
The features dataset contains values that have different scale ranges during learning and minimize the loss function. These scales affect each iteration step during the gradient optimization process, which tends to affect the learning rate optimization, since we want the model to converge to the global or local minimum quickly and accurately. Min-Max normalization has several advantages over the standard scaling methods. Min-Max scaling has the ability to deal with the distribution of features that are not Gaussian, since the anomaly detection applications have no specific distribution to follow, unlike the signature-based method in NIDS, which is very suitable in our NSL-KDD dataset. The Min-Max normalization technique is proposed to prevent the gradient while optimizing the loss function from the un-smoothing path toward the global minimum. It takes the column's minimum value and maximum value, where the output values range between 0 and 1, as shown in the following equation.  
\[X^{\prime}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{1}\]  
where \(X-min\) is the smallest number in the column, \(X-max\) is the largest number in the column, and \(X\) is the original data sample value to be normalized.
## 5 Discussion and Technological Prospects
### Discussion on Further Improving the Performance of MCF Cables and Their Future Applications  
Through our field trial, we also found that the splicing loss of 4-core fiber was too large compared to that of SMF, which thereby increased the span loss of the field-deployed MCF cable. By reducing the splicing losses of MCFs, the transmission distance of the single-span unrepeated transmission in the field trial could be further extended. Thus, in our future work, we will further reduce the splicing losses of MCFs in the following ways to promote their implementation in existing networks. Firstly, it is necessary to optimize the MCF itself. By appropriately designing MCFs with a larger effective mode field area, the splicing losses caused by misalignments during the splicing process can be reduced. Moreover, the alignment accuracy during the splicing process will be improved to reduce the splicing losses. This requires improving the monitoring ability of the MCF interface and enhancing the automatic alignment algorithm during the splicing process. Additionally, considering the fact that the relatively large splicing losses of MCFs are also related to the uneven heating of the fiber cores during the arc discharge process, it is important to optimize the approach of arc discharging, such as by using a 3-electronic arc-discharging system [14]. Finally, as regards the field implementation, it is recommended that we splice the MCFs produced in the same batch to further reduce the misalignments between different fiber cores. It can easily be evaluated that if the average splicing loss of each single core is further reduced by 0.1 dB, the span loss of each fiber core channel of the 106 km MCF link can be reduced by approximately 6.5 dB. Under the same conditions, the transmission distance can be extended by about 25 km. In addition to reducing the splicing losses, the span loss can be reduced by optimizing the MCFs, such as by designing ultra-low-loss MCFs, which will further extend the single-span unrepeated transmission distance. The high-speed CDM transmission system in our field trial has the potential to be practically implemented in existing network scenarios, such as high-speed long-span metropolitan area networks or high-speed unrepeated interconnections between data centers, which can greatly improve the transmission rate of data information and reduce the number of amplifiers at the same time. It should also be noted that in order to scale the system toward widespread deployment, the splicing time of MCFs also needs to be further reduced to ensure that the MCFs can be repaired in a timely manner when they are broken.
## 2 Materials and Methods
### Binocular Distance Measurement Algorithm
Subsequently, the cv2.initIndistorRectifyMap() function generates a mapping matrix for both image and distortion correction. Finally, the cv2.remap() function is utilized to accurately calibrate the images from the left and right cameras, ensuring the precision of the subsequent stereo matching. The binocular stereo-matching process employs the Semi-Global Block Matching (SGBM) algorithm [32], implemented in OpenCV. The SGBM algorithm incorporates both local pixel correlations and global visual information in its pixel-level matching, markedly enhancing the accuracy and robustness of the matching, particularly in areas with sparse or repetitive textures. Since crack widths are usually narrow, the method of using similar triangles for width measurements after extracting feature points using a binocular camera may lead to significant errors. The accuracy of the measurements can be significantly improved by using a camera pinhole model. This model combines the focal length, shooting distance, and unit length of the sensor to calculate the physical dimensions of a pixel point at a specific shooting distance. Then, it calculates crack width based on the physical dimensions. The camera pinhole model is used to calculate the physical size of each pixel at the corresponding distance, as shown in Figure 5. The calculation is shown in Equations (11) and (12) after substituting the calibrated camera parameters:  
\[m=\frac{fM}{D} \tag{11}\]  
\[M=\frac{w_{p}D}{P_{c}f} \tag{12}\]  
where the variables are defined as follows:  
\(m\) represents the size of the object on the image plane;  
\(f\) denotes the camera's focal length;  
\(M\) is the actual size of the object in the x-axis direction;  
\(D\) is the distance from the object to the camera;  
\(w_{p}\) indicates the number of pixels in the object's width;  
\(P_{c}\) refers to the number of pixels on the camera's sensor corresponding to 1 cm.