esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis Results
### Effect of Distance of Linkage Butts  
At the same time, it is necessary to investigate the influence of the distance between the adjacent linkage butts on the natural frequencies of reflector structure. Three models based on different distance L of linkage butt design are carried out and the results are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Frequency** & **Model 1** & **Model 7** & **Model 8** & **Model 9** & **Model 10** \\
**Order** & **P = 3.5 N** & **P = 3.0 N** & **P = 2.5 N** & **P = 2.0 N** & **P = 0.0 N** \\ \hline
1 & 0.7847 & 0.7457 & 0.7022 & 0.6533 & 0.3647 \\
2 & 0.8301 & 0.7846 & 0.7360 & 0.6833 & 0.3926 \\
3 & 0.8533 & 0.8092 & 0.7621 & 0.7113 & 0.4123 \\
4 & 0.8819 & 0.8329 & 0.7802 & 0.7232 & 0.4223 \\
5 & 0.9268 & 0.8766 & 0.8233 & 0.7664 & 0.4650 \\
6 & 0.9858 & 0.9309 & 0.8722 & 0.8092 & 0.5091 \\
7 & 1.0077 & 0.9593 & 0.9081 & 0.8536 & 0.5848 \\
8 & 1.0359 & 0.9961 & 0.9558 & 0.9145 & 0.6007 \\
9 & 1.0896 & 1.0400 & 0.9870 & 0.9301 & 0.6461 \\
10 & 1.1364 & 1.0780 & 1.0159 & 0.9491 & 0.7251 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Natural frequencies (Hz) of Mode 1, 7, 8, 9, and 10.  
According to the results of three levels of linkage butt distance L, there is an approximately linear variation trend between the natural frequencies and linkage butt distance L. With the enlargement of linkage butt distance L, this downward trend of natural frequencies decreases slowly. The longer the distance between two adjacent linkage butts is, the more flexible the drag spring becomes. It is demonstrated that the linkage butts should be set on the boundary of the petal for model design.
# A Method for Estimating the Resultant Expanded Uncertainty Value Based on Interval Arithmetic
## 3 Coherence Factors Values Estimation
### Case of Uncorrelated Error Signals
Taking into account the conclusions presented, a universal correction is proposed to address the phenomenon in question:  
\[p_{a,b}=p_{b,a}=\sqrt{\frac{\min(U_{a},U_{b})}{\max(U_{a},U_{b})}}, \tag{13}\]  
Nevertheless, it is essential to note that this correction may not consistently capture the dynamics observed in Figure 2.  
Lastly, in scenarios involving uncorrelated error signals, the coherence coefficient value can be determined based on the adjusted shape coefficient value:  
\[h_{i,j}=h_{j,i}=s_{ij}p_{i,j}k_{i,j}=s_{ij}\sqrt{\frac{\min\left(U_{i},U_{j} \right)}{\max(U_{i},U_{j})}}\left(\frac{U_{i}^{2}+U_{j}^{2}}{\sum_{k=0}^{N-1}U _{k}^{2}}\right), \tag{14}\]  
while in the case of the original method [18], these values were determined according to the equation:  
\[h_{i,j}=h_{j,i}=s_{ij}k_{i,j}=s_{ij}\left(\frac{U_{i}^{2}+U_{j}^{2}}{\sum_{k=0 }^{N-1}U_{k}^{2}}\right). \tag{15}\]Figure 2: The value of the coherence coefficient depending on the ratio of the expanded uncertainty values of the analyzed signals (case for uncorrelated signals).
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 5 Results and Discussion
### Simulation 2: Performance Evaluation under Average Density Nodes Scenario (49 Nodes)
#### 5.2.1 Throughput Performance Analysis  
Figure 14 shows the throughput for both sinkhole and framework scenarios as the first 5000 ms were set to be the time at which the application starts. In the sinkhole scenario, the next 8833 milliseconds showed a positive like to 1.522 Mbps in throughput followed by a steady decrease to 0.88 Mbps. The highest network throughput achieved is 1.525 Mbps at 13,604.24 ms of simulation time. The scenario's output significantly dropped after 10,000 milliseconds. The framework on the other hand had a positive hike in figures throughout with a steady increase until the end of the simulation. The highest network throughput achieved was 2.37 Mbps at 94,282.78 ms. The framework scenario's throughput grew over time, unlike the sinkhole scenario which dropped performance just after a tenth of the simulation time.
# Experimental Research on Mechanical Performance of SSRC Columns under Eccentric Compression
## 5 Crack Width Analysis  
### Load-Crack Curve  
The ultimate load-average crack curves of specimens are depicted in Figure 12. The average crack widths of the SSRC columns at failure were between 0.13 and 0.42 mm. Under the same eccentricity, the average crack width increased with a decrease in the reinforcement ratio. For the SSRC columns under small eccentric compression, the change in the reinforcement ratio has a negligible effect on the crack development when compared with the eccentricity. In addition, the development of the crack widths is similar for SSRC6 and HRC9 under the same conditions.
# Posterior Probability-Based Symbol Detection Algorithm for CPM in Underwater Acoustic Channels
## 3 Proposed Algorithms
### PS-FDE for CPM Signals
The received data \(\hat{a}_{n}\) are given as follows [29]:  
\[\hat{a}_{n}=\frac{1}{\pi h}\mathrm{arg}(r_{n,q}r_{n-1,q}^{*}). \tag{22}\]  
Ensuring a computational stability, the algorithm is executed within the logarithmic domain as follows:  
\[\widetilde{\Pr}(b_{n,q}^{(l)}=\sum_{p=0}^{K-1}\vartheta_{n,p,q}^{(l)})=\ln[ \Pr(b_{n,q}^{(l)}=\sum_{p=0}^{K-1}\vartheta_{n,p,q}^{(l)})]. \tag{23}\]  
According to \(c(p,q;l)\), an increase in \(l\) leads to a reduction in the energy ratio of the \((n+l)^{\mathrm{th}}\) symbol information within the \(n\)th symbol interval. Consequently, the detection gain derived from \(b_{n-l,q}^{(l)}\) becomes negligible. To further simplify the algorithm, only the \(l=\{1-L_{m},\ldots,-1,0,1,\ldots L_{m}-1\}\) symbol with \(L_{m}\leq L\) is utilized in the computation of the likelihood value \(\hat{\mathrm{L}}(r_{n,q})^{(k)}\). Accordingly, Equation (23) is used to reformulate the log-likelihood values \(\mathrm{L}(r_{n,q})\) and \(\hat{\mathrm{L}}(r_{n,q})^{(k)}\):  
\[\mathrm{L}(r_{n,q})=\ln\{\exp[\widetilde{\Pr}(b_{n,q}^{(0)}=\sum_{p=0}^{K-1} \vartheta_{n,p,q}^{(0)})]\} \tag{24}\]  
\[\hat{F}_{k,q}=\frac{\hat{H}_{k}^{*}W_{k,q}}{\left|\hat{H}_{k}W_{k,q}\right|^{2 }+N_{0}}, \tag{16}\]0
## 4 Energy Balanced Strategies for Sparsely Deployed UWA-SNs
### Overview of EBH
By the same way, the control message is forwarded hop by hop along the linear network until it reaches the end node in the chain. The end node will not receive any ACK message after it relays the control message. Second, what happens if one node dies suddenly due to reasons other than energy depletion? It depends on specific applications: some applications may demand that all nodes should alive in order to get the integrity of the whole datasets. Under such cases, when one node dies in the chain, the lifetime of network is over. Of course, some applications may survive the death of some nodes. In that case, we can change our algorithm a little bit to adjust to the new situation. For example, when a node dies suddenly, we can just let the dead node's upstream neighbor fix on \(MODE1\). In that way, the chain turns into two smaller chains and these chains will maintain energy balance. Third, what is the impact of the overhead costs, such as the linear network maintenance? Because the control message is always much shorter compared with the normal sensed data and the transmit power is often 100 times more expensive than that of the receiving, the major part of energy consumption underwater is the transmission energy consumption. In addition, the overheads will consume energy and affect the residual energy of node. So, if the overheads lead to uneven energy consumption among nodes, the algorithm will make the nodes which have higher energy relay more data packets and eventually make the whole chain evenly, because the basic idea behind the algorithm is that the node with higher residual energy should do more work. Now, we show EBH in **Algorithm 1**. At initialization phase, each node sets working mode to \(MODE0\) and computes the unit of energy using the optimum grades \(m\). The sink takes charge of setting up the linear networks. It broadcasts a neighbor-finding message to its one hop neighbor. After receiving the neighbor-finding message, the node records its downstream neighbor. Then it answers the sender with an ACK message including its own ID. After that, it relays the neighbor-finding message including its own id to the next node in the chain. When the downstream node receives the ACK message, it records the upstream neighbor. When a node has consumed one gradation of energy, it sends out a control message containing its residual energy to the upstream neighbor, and then it checks whether its mode is \(MODE1\). If so, it changes to \(MODE0\). When a node receives a control message from downstream node, it compares residual energy of itself with the information contained in the control message and decides whether it changes working mode or relays the control message. If it has higher residual energy than the downstream neighbor then it sets working mode to \(MODE1\). Otherwise, it just sends out the control message to the upstream neighbor.
## 4 Protocol Adaptation for Modbus and 5G  
By analyzing the characteristics for the frame structure of Modbus RTU, Modbus TCP and 5G in Section 3, we propose the protocol adaptation scheme in Figure 4 to establish the end-to-end link for Modbus and 5G. Specifically, we first judge the message's frame structure and screen out the valid data. Then, we resolve the valid data and extract part of the data for storage. After that, we encapsulate and transmit the stored data. Lastly, we decapsulate the 5G packet and recover the complete frame structure of the Modbus protocol.  
Figure 4: The protocol adaptation process between 5G and Modbus.  
Figure 3: The uplink frame structure of the user plane in the data link layer.  
In the following, we will introduce the protocol adaptation process for Modbus RTU and Modbus TCP in detail.
# Ultra-Wideband Bandpass Filters Using Tapered Resonators
## 4 UWB Bandpass Filter Design
The results of the lumped circuit equivalence of the tapered short-circuited resonators calculated from (9) and (10) demonstrate the filter performance over the entire band (3.3 GHz--11 GHz).
# Compact 15 mJ Fiber-Solid Hybrid Hundred-Picosecond Laser Source for Laser Ablation on Copper
## 3 Experimental Results and Discussion
### The Output Characteristics of Fiber-Solid Hybrid Laser Source  
Figure 7a shows the output energy of the Nd:YAG double-pass amplifier. The all-fiber master oscillator delivers pulse energy of 9.6 mJ and pulse width of 341 ps at a repetition rate of 28.49 MHz and a 3 dB spectral bandwidth of 0.78 nm. The seed pulses are amplified to 4 mJ at a repetition rate of 500 Hz by a QCW side-pumped Nd:YAG regenerative amplifier. By precisely controlling the energy of the incident pulses, these pulses are further amplified to 14.58 mJ by a QCW side-pumped Nd:YAG double-pass amplifier. The corresponding magnification is 18.2. By using a negative lens to compensate the thermal lens effect, 90\({}^{\circ}\) polarization rotation to compensate for thermal birefringence, and spatial filter to achieve relay image, the depolarization loss can be controlled to below 1.5%. The output power stability was tested over 8 h, and an RMS power fluctuation of 1.9% was achieved, as shown in the illustration of Figure 7a. Figure 7b shows the spectrum distributions of the regenerative amplifier and double-pass amplifier. The pulse stretcher significantly suppresses the spectral broadening, and the relatively narrow spectral width of 0.78 nm allows efficient energy amplification in Nd:YAG amplifier with a gain bandwidth of 0.45 nm. The 3 dB bandwidth of the regenerative amplifier is narrowed from 0.78 nm to 0.35 nm due to the gain-narrowing effect. After being double-pass amplified, the 3 dB spectrum bandwidth is slightly broadened to 0.47 nm. This is because SPM-induced new spectral components to be generated during high peak power double-pass amplification, resulting in the broadening of spectrum and narrowing of pulse width. Therefore, the pulse width of the Nd:YAG double-pass amplifier is narrowed to 228 ps, as shown in Figure 7c. The beam quality of the laser source is recorded by a beam quality analyzer (Ophir-Spiricon, M2-200). When the laser source delivers an output repetition frequency of 500 Hz and a pulse width of 228 ps at a central wavelength of 1064.1 nm, the intensity distributions of the far field are as shown in Figure 8. The beam quality factors are \({M_{x}}^{2}=1.49\) in the \(x\) direction and \({M_{y}}^{2}=1.51\) in the \(y\) direction at the output energy of 14.58 mJ. It is evident that the high beam quality benefits from the effective compensation for the thermal effects.  
Figure 6: Schematic diagram of image relaying optical path of double-pass amplifier.  
Figure 8: Beam quality of the laser source.  
Figure 7: (**a**) Output energy and magnification of the Nd:YAG double-pass amplifier. The inset shows the power stability of the laser source in 8 h. (**b**) Spectrum distribution of regenerative amplifier and double-pass amplifier. (**c**) Temporal profiles of the regenerative amplifier and double-pass amplifier.
## 4 Fault Monitoring Using KPCA-KDE-SSGLR  
This research does establish a detection technique to identify a diverse variety of process shifts for nonlinear multivariate frameworks. Significantly, the suggested approach combined KPCA-KDE and GLR statistics using sequential sampling. Figure 1 shows the flow chart of the KPCA-KDE-SSGLR procedure. The KPCA is considered to minimize the dimensionality in the procedure, and the SSGLR is being used as a tracking statistic. The current technique consists of two stages, stage I for offline training and stage II for online fault detection. Step 1 uses standard data to create a measurement structure and approximate the related parameters. For Step 2, new data obtained through the virtual system are being used to determine the system's state; details of the two phases are given below.
# Extended-Reach Drilling (ERD)--The Main Problems and Current Achievements
## 3 Drilling Fluids for Extended-Reach Wells
### Torque and Drag
The friction reduction performance of the lubricant for coiled tubing (CT) application in ERWs depends on its concentration and on the presence of polyacrylamide (a viscosifer and fluid friction reducer), salt and sand in the fluid. Laboratory tests have shown that polyacrylamide in a concentration greater than 1% adversely affects the performance of the lubricant while, when increasing the lubricant concentration, the friction coefficient decreases, which is especially pronounced at higher salinity and sand conditions [9]. Besides adding lubricants to the drilling fluid, another solution for additional torque reduction is the use of different mechanical tools. [PERSON] et al. studied and analyzed the use of mechanical tools to reduce torque in ERD wells. The use of mechanical torque reduction tools consisting of an inner mandrel and an outer sleeve on two wells resulted in significant torque reduction [70].
# Experimental Research on Braking Feedback and Taxing Feedback System of New Energy Vehicles
## 3 Complete Vehicle Integration and Test System for Brake Energy Recovery System
### Brake Energy Recovery System Energy Economy Test Results  
#### 3.4.1 Comparison of Coordinated and Stacked Braking Energy Recovery Strategies  
The energy economy was compared based on the energy recovery strategy in the previous section and the superimposed brake energy recovery strategy. The superimposed braking energy recovery strategy does not change the original air pressure braking based on the superimposed on the motor feedback braking torque, in order to get a better braking feeling, the motor feedback braking torque can not be too large. And in the coordinated braking energy recovery strategy, the maximum torque that can be fed back from the motor can be used effectively.  
The braking process with an initial braking speed of 43.1 km/h and a braking deceleration of 0.58 m/s2 was selected, and the test results are shown in Figures 8 and 9.  
As can be seen from Figure 9, in the superimposed braking energy recovery strategy, the maximum braking torque of the motor was approximately 200 N\(\cdot\)m, and the maximum battery charging current was approximately 100 A. In the coordinated braking energy recovery strategy, the braking torque of the motor can reach 500 N\(\cdot\)m, and the battery charging current can reach more than 140 A, which greatly improves the braking energy recovery. The two braking energy recovery strategies were tested in a typical Chinese urban bus cycle, and the results are shown in Figures 10 and 11.  
Figure 8: Coordinated braking energy recovery strategy for a particular braking process.  
Figure 9: Stacked brake energy recovery strategy for a particular braking process.  
The analysis of the test results calculates that the contribution of the coordinated strategy is 21.28% and the contribution of the stacked strategy is 14.57%. The test results can prove that the energy economy of the coordinated braking energy recovery strategy has a significant improvement over the stacked strategy.
# A New Generation of Fast and Low-Memory Smart Digital/Geometrical Beamforming MIMO Antenna
## 4 Numerical Evaluations
### Performance of Proposed Smart MIMO System  
Compared to an isotropic antenna, the directivity of the new digital/geometrical MIMO is very high. This means that it can precisely transmit or receive signals in a specific direction. The directivity of an antenna is expressed as a ratio of the power transmitted or received in a particular direction to the average power radiated or received by an isotropic antenna and is usually measured in decibels (dB). According to the numerical calculation, the directivity of this transmitted beam of the new digital/geometrical MIMO for the example presented above is 11.6 (10.65 dBi), while the obtained side lobe reduction is 0.05. When we use a similar antenna as the receiver end of this system, the eight-element dipoles proved to be a highly directive receiving side. On the receiving side, the directivity of the vertical plane of half-wave dipole antenna affects the overall antenna gain. According to the calculations performed, this value is 2.2 (3.42 dBi) for the proposed MIMO receiver system. Hence, the overall antenna gain for this proposed system is 25.52 (14.07 dBi). Moreover, the signal processing gain at the receiver antenna contributes to the overall power gain of the system. The signal processing gain at the receiver is 64 (18.06 dBi). Therefore, the overall power gain of the proposed new digital/geometrical smart MIMO system, calculated using Equation (7), is 1633.28 (32.13 dBi).  
Using Equation (8), we calculated the overall power gain of the MIMO system when both the receiving and transmitting antennas were pointing at each other. This was 134.56 (21.28 dBi).
# Effectiveness of a Nutrition Education Program for Patients with Type 2 Diabetes Mellitus
## 2 Participants and Methods
### Data Collection and Measures  
At baseline, patients filled out the questionnaire (demographic characteristics, personal information, complications of the disease), and data were recorded for biochemical indicators, including fasting glucose, blood glucose, HbA1c, total cholesterol, HDL cholesterol, LDL cholesterol and triglycerides, and also medications. Anthropometric data were also collected. Participants' weight was measured in light clothing and without shoes using a portable calibrated electronic weighing scale precision scale (TAN- ITA MC-780U Multi Frequency Segmental Body Composition Analyzer, Amsterdam, the Netherlands). Height was measured with portable measuring inflexible bars (Seca model 220, Seca, Hamburg, Germany). Waist (at umbilicus) and hip (at widest point) circumferences (WC and HC) were measured according to standard conditions using a measuring tape, and waist/hip circumference ratio (WIR) was calculated. Blood pressure was measured with a clever blood monitor (FYGB-869). All measurements were taken twice, and the average of the two values was reported, as suggested [28]. The follow-up was planned after 3 months and aimed to evaluate potential changes in the collected variables. Subjects recorded their physical activity using the International Physical Activity Questionnaire [29] and a validated diet recall Food Frequency Intake form [30].  
Low-density lipoprotein cholesterol (LDL) was calculated according to the Friedewald formula [31]. Fasting glucose was calculated [32], and glycated hemoglobin (HbA1c) was measured by high-performance liquid chromatography (HPLC). All participants were evaluated for their changes in the collected variables.
## 2 Basic Theory
### CNN and LSTM  
CNN has excelled in many areas because it can learn from older agents through multiple layers. A CNN consists of input, output and several hidden layers. In the hidden layer, there are convolutional layers, pooling layers and fully linked layers [31]. First, convolution is performed by inputting a one-dimensional or two-dimensional input and a convolution kernel. Second, after convolution, a non-linear activation function is added. Third, to reduce the size of the output feature map, pooling is usually performed. Fourth, after a series of iterations of convolution and subsampling, fully connected layers are used for classification. Finally, a softmax function is performed. Furthermore, backpropagation is used to optimise the parameters of the CNN by minimising the classification loss.  
In a sense, the recurrent neural network (RNN) is the most detailed model [32]. RNNs can only address problems with short-term dependence. A unique RNN that can handle both short-term and long-term dependence problems is the LSTM, as time-series data, signals from smart manufacturing make the LSTM a promising tool for micro-fault diagnosis.
## 4 Results and Discussion
### Network Planning Results and Performance  
#### 4.2.1 Wireless Network Planning for 664 Mbps  
In a second phase, we can now use the quick and accurate ML model for network planning, using the GA as explained in Section 3.4. The reception quality specifications for the floors of the five buildings (Section 3.2) were taken from Table 2. Figure A1 (Appendix A) show the resulting network deployment for the floors of the five buildings. The deployments were designed based on the setting 12/256 QAM/27 dB/664 Mbps/-56 dBm from Table 2, APs transmitting with an EIRP of 20 dBm, and without taking into consideration the maximum \(AVE_{Rx}\) for the whole floor.  
Table 3 compares the results from the WHIPP optimization with the one obtained using the proposed ML + GA approach. The coverage percentage of the GA-proposed deployment is evaluated by applying the WHIPP IDP PL model to the proposed ML-GA deployment. Both tools were built on the same set of candidate APs, obtaining a deployment with the same number of APs in almost all the cases. For the Koutitas building, however, only 10 APs were required (23% reduction) using the ML + GA approach, while 13 APs were required for the WHIPP tool. Hence, in terms of the required APs for network planning, our solution has similar, or better, performance than the WHIPP tool.  
Table 3 confirms that the GA-proposed solution is reliable, as the coverage percentage with the aimed throughput is at least 99.37% (iGent building, 1105/1112 receiver points covered). In the Koutitas building, coverage was 99.56% (909/913) of the receiver points. The rest of the buildings have all grid points covered with the throughput that was aimed for. Furthermore, related to the time to make the planning, the GA optimization is much faster. It should be noted, though, that the ML PL calculation for all possible AP-Rx links requires a one-time calculation (Pre-processing Time in Table 3). However, these values can be re-used when another throughput requirement is set (see Section 4.2.3), when room-specific requirements are set, or when a maximal Rx is strived for (see Section 4.2.2), etc. The heuristic WHIPP algorithm, on the other hand, needs to re-execute the entire optimization algorithm for another throughput requirement and does not offer any flexibility that the GA can account for via its cost function.