esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussions and Conclusions
### Conclusions  
In this paper, an improved homotopy analysis method (IHAM) is applied to derive complete expressions of vertical and horizontal displacements of a cantilever beam under point load at the free end. In fact, the proposed method is a semi-analytical method since many numerical algorithms are used.  
The linear solution is only valid for small \(\alpha\). For large \(\alpha\), the nonlinear effect of the equation cannot be neglected. A family of the explicit solutions of the rotation angle, the vertical and horizontal displacements of a cantilever beam under a point load at the free end are obtained by IHAM, which are summarized in Table A1 for 30 th-order approximation. The current explicit solutions with different parameters and solution schemes match well with the exact solutions from elliptical integrals; however, our solutions for the current problem by the proposed IHAM, especially Equations (3.36) and (4.31), are explicit and simple compared with the exact solution from the elliptical integrals which requires solving a transcendental equation. The current solution is easy to calculate with the explicit polynomial expressions, which should be, thus, prioritized for practical engineering applications with minimal requirements on the calculation and computation.  
By solving the exact bending equation of a cantilever beam, we demonstrate the superior proficiency of IHAM over a class of existing equations in solid mechanics. The improved homotopy analysis method has great superiority compared over the traditional homotopy analysis method in view of the very much increased rate and precision of convergence as well as the substantially enlarged convergence range.  
The convergence ranges of the rotation angle and vertical displacement are extended by the homotopy-Pade approximation, whereas it does not avail horizontal displacement.  
In summary, the 30 th-order improved homotopy analysis solutions and exact solutions of the angular, vertical and horizontal displacements of large deflection cantilever beams by Equations (3.42), (3.47) and (4.35) are compared in the following Appendix A table. It can be concluded from this study that the proposed IHAM can be applied to solve a general strong nonlinear problem in structural analysis.  
Conceptualization and methodology, Y.L. and X.L.; validation and data curation, C.X. and S.H.; formal analysis, Y.L.; investigation, X.L.; writing--original draft preparation, C.X.; writing--review and editing, X.L.; visualization, S.H.; supervision, Y.L.; project administration, Y.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 11972145) and Tianjin Excellent Science and Technology Commissioner Support Project (Grant No. 20 YDTPJC0080).  
No application.  
This work was funded by the National Natural Science Foundation of China (Grant No. 11972145) and Tianjin Excellent Science and Technology Commissioner Support Project (Grant No. 20 YDTPJC0080).  
The authors declare no conflict of interest.
## 3 Methodology  
### Dual-Branch Low-Band Speech Enhancement Network LB-Net  
The architecture of TS-CGANet is presented in Figure 4. LB-Net comprises two sub-networks, namely, MLB-Net for processing magnitude and CLB-Net for processing real  
Figure 3: CGA block architecture.  
Figure 2: Convolution-augmented gated attention unit.  
and imaginary components. The input signal, which is the noisy speech signal, is first converted into the frequency domain \(Y_{o}\in\mathbb{R}^{B\times T\times F\times 1}\) using STFT. Subsequently, a power law compression is applied to the spectrum with a compression exponent of 0.5.  
\[Y=|Y_{o}|^{c}e^{jY_{p}}=Y_{m}e^{jY_{p}}=Y_{r}+jY_{i}=\left[Y^{lb};Y^{mb};Y^{lb}\right] \tag{1}\]  
where \(c\) is the compression exponent and \(Y_{p}\) is the phase of noisy speech. Then, we divide the spectrum \(Y\) to \(\left[Y^{lb};Y^{mb};Y^{bb}\right]\) according to three frequency bands and send the speech magnitude \(Y^{lb}_{m}\) of LB band into the encoder of MLB-Net. The real component \(Y^{lb}_{r}\) and the imaginary component \(Y^{lb}_{i}\) are concatenated as \(\left[Y^{lb}_{r};Y^{lb}_{i}\right]\in\mathbb{R}^{B\times T\times F\times 2}\) as the input of the encoder of the CLB-Net.  
The mask \(\hat{X}^{lb\prime}_{m}\) obtained after MLB-Net processing is multiplied by the original spectrum \(Y^{lb}_{m}\) to obtain the coarse-estimated spectrum magnitude and then coupled with \(Y^{lb}_{p}\) to obtain the roughly estimated real parts and imaginary parts. \(Y^{lb}_{p}\) is the phase of the LB noisy speech \(Y^{lb}\):  
\[Y^{lb}_{p}=\arctan\left(\frac{Y^{lb}_{i}}{Y^{lb}_{r}}\right) \tag{2}\]  
As a supplement, CLB-Net estimates the complex spectrum \((Y^{lb}_{r},Y^{lb}_{i})\) to get the residual mapping \((\hat{X}^{lb\prime}_{r},\hat{X}^{lb\prime}_{i})\). Finally, the roughly estimated real and imaginary parts are added with the output of CLB-Net to get the final enhanced LB speech \(\hat{X}^{lb}\):  
\[\hat{X}^{lb}_{r}=\hat{X}^{lb\prime}_{m}Y^{lb}_{m}\cos Y^{lb}_{p}+\hat{X}^{lb \prime}_{r} \tag{3}\]  
\[\hat{X}^{lb}_{i}=\hat{X}^{lb\prime}_{m}Y^{lb}_{m}\sin Y^{lb}_{p}+\hat{X}^{lb \prime}_{i} \tag{4}\]  
\[\hat{X}^{lb}_{m}=\sqrt{\hat{X}^{lb^{2}}_{r}+\hat{X}^{lb^{2}}_{i}} \tag{5}\]
# LBTM: Listen-before-Talk Protocol for Multiclass UHF RFID Networks
## 8 Lbtm+DIM Mode Analysis  
As described in the introduction, the EPCglobal Class 1 Gen 2 standard [1] uses DIM and MIM modes to deal with reader-to-reader interferences. LBTM protocol can be used together with these modes, adding some soft QoS control to them. In this section, we make a comparison of a LBTM+DIM operating protocol versus a standalone DIM network with the transmit mask of \(-30\) dBch (henceforth, this parameter is called Adjacent Channel Power Rejection -ACPR-, following nomenclature from [50]) as specified in the standard [1].  
First, we simulated a DIM configuration, where we assumed that each channel is serving a number \(N\) of interrogators (from 1 to 20). The scenario layout corresponds to the hospital scenario, given in Figure 7. Channel access times correspond to the ones specified in Figure 1. That is, the reader occupies the channel until the batch of tags is read, and then waits for 100 ms before accessing the channel again. The \(N\) active readers are changed randomly every 20 s, while the total simulated time is 8000 s. The transmission power of the readers is 0.1 W (0.5 W EIRP), and interference among readers is considered  
Figure 12: \(\Delta\) evolution. (**a**) High priority readers for patient monitoring. (**b**) Medium priority readers of entrances. (**c**) Low priority readers for storage monitoring.  
additive, taking into account the antenna directivity and distances among readers. The access is done with DFSA assuming perfect tag count estimation.  
In this experiment, the average number of cycles that interrogators read batches per second is measured. Results are shown in Figure 13. While the aggregated interfering power of the readers is low due to the transmission mask applied, it can still cause a slight performance degradation. This effect is related to the increased number of misidentifications under the capture effect. That is, when the small additive noise coming from neighbor interrogators adds to the interfering noise in FSA slots accessed by several tags, the ratio of tags recovered by capture effect is reduced. This is the leading cause of the performance degradation observed in Figure 13.  
This result suggests that using some time multiplexing strategy between readers may result in an improved access rate for a selected set of readers because the global level of interferences will be kept controlled. Thus, we have applied LBTM for implementing this idea. When the reader senses power in the tags response channel over a given interference threshold (I\({}_{\text{th}}\)), then it delays its channel access (using the LBTM procedure). Results are shown in Figure 14 for the case of \(N\) = 20 readers. The figure also contains the standalone DIM method performance (\(\phi_{\text{DIM}}\)).  
Summing up, LBTM can improve the operation of priority readers, also when used in combination with DIM. This is, of course, done at the expense of worsening the other traffics.
## 2 Conventional Edge-Based MTF Measurement Methods
### Analysis of Conventional Edge Method  
The main steps of the existing modified edge methods derived from ISO 12233: 2017 [4] have not changed essentially, and they still include the following contents: ROI extraction, edge detection, ESF construction based on projection binning, LSF construction by difference or derivation, MTF calculation from Fourier transform and MTF compensation. Among these steps, the condition of the STAS edge greatly limits the practical application range of the edge method in the field of RS. If this constraint condition cannot be relaxed, the high-precision angle measuring model and ESF curve fitting model can hardly exert their effectiveness for those RS images lacking the STAS edge feature.  
The ROI extraction and ESF construction step in the edge method correspond to the applicable limit of \"straight edge\" and \"small tilt angle (STA),\" respectively. This paper focuses on solving the STA application limitation for straight edges, that is, studying the conditions under which projection binning with arbitrary edge angle can be realized. For the \"vertical edge\", there are two main projection schemes along the edge: projecting to the horizontal direction or the direction perpendicular to the edge. The two projection schemes are approximately the same when the edge angle is small. However, the two projection schemes cannot be approximated when the edge angle increases. ESF oversampling binning mainly includes two schemes based on unit pixel width (called OMNI binning [11]) or horizontal adjacent pixel projection path width (called ISO-cos binning [22]), both of which involve the determination of OSR and binning PS. A comparison of different schemes for projection and binning is shown in Figure 1. When adopting a fixed and same OSR (such as 4), the essential difference between ISO-cos and OMNI binning methods lies in whether the width of the bin changes with the edge angle. The bin width of ISO-cos is less than or equal to OMNI for arbitrary edge angle, which makes the possibility of the empty bin in OMNI less than ISO-cos under the condition of fixed OSR.  
Figure 1: Diagrams of projection and binning in edge method (OSR = 4): (**a**) when the edge angle is small (\(\theta\) = 8 deg), (**b**) when the edge angle is big (\(\theta\) = 30 deg).
## 4 Validations
### Example 2: RC Beam Falling in Shear (AW1 from [26])  
Example 2 is a RC wide beam without shear reinforcement (span to depth ratio or \(a/d=3.4\)), tested experimentally at the University of Toronto [26]. Figure 6a shows the geometry of the beam, and Table 2 gives the input parameters. Mean values of \(f_{c}^{\prime}\) and \(f_{y}\) are from the experiment and COV values are identical to the ones used in [3], for comparison purposes with ANFEA results.  
Figure 5: Example 1: comparison of numerical and theoretical results.  
Figure 6: Example 2: RC beam AW1 from [26], falling in shear: (**a**) Geometry of the beam; (**b**) Compressive stresses at failure; (**c**) Damage pattern at failure; (**d**) Deformed shape at failure.  
When assessing an existing RC structure, the initial load applied at the beginning of the analysis or _preload_ corresponds to service load level (Figures 1(a) and 1(a)). The difference between service load and ultimate load levels is due to the overload factors as discussed in Section 3. For an experimental program, the service load level is a proportion of the failure load of the tested structure or structural element. In this case, the preload value would be a proportion of the experimental failure load. It was therefore decided for this example and for the next example to apply half of the experimental failure load (50% \(P_{u}\)) during the loading phase. Sensitivity analyses showed that preload values between 40% and 100% \(P_{u}\) contributed to minor fluctuations in the obtained numerical failure load.  
The purpose of this example is to apply the global resistance factor reliability framework and compare SRAM results with: (i) experimental results (in terms of the average value of ultimate strength); and (ii) numerical reliability results conducted in [3] using ANFEA (in terms of the average and c.o.v values of ultimate strength).  
Table 2 presents the results, where SRAM values are considered (corresponding to the case 100% \(P_{u}\)). As anticipated, SRAM results for ultimate shear load were on the conservative side, with an underestimation of 30% of the experimental shear failure load. The result was also 24% lower if compared to numerical failure load obtained by ANFEA in [3]. Comparison of the four SRAM Rosenblueth's analyses shows that the parameter \(f_{y}\) has no influence on the final resistances of the beam, which is consistent with the shear type of failure, or more precisely, the strut crushing failure obtained by SRAM as shown in Figure 5(d). The c.o.v obtained by SRAM was slightly higher than the one obtained by ANFEA [3].
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 3 Results
### Trend Detection Model Comparison
#### 3.1.2 Evaluation on Accelerated Wear Test Data  
The data from the accelerated wear test was analysed using the two chosen models. The results are depicted in Figure 9. The blue line shows the health indicator values, the dots indicate the models' decision of whether a trend is present in the time window of the last 504 samples (which equals a time frame of 2.5 months) while the horizontal yellow line shows, when more then 50 percent of the last 504 decisions were positive.  
In such a case, a maintenance action should be planned. It can be seen that both models show similar behaviour for the beginning of the data set where they both detect a trend in the data after the initialisation phase of the first 504 measurements. The outlier at measurement 1000 leads to the rejection of the hypothesis that a trend is present for the following measurements in the MK model. It can be assumed that the CS model interprets the outlier correctly so that even for the following measurements a trend is detected. Both models detect the more stationary behaviour of the time series at its end. As the CS model handles the outlier around measurement 1000 better compared to the MK model, it is suggested to use the CS model in CATS. In this experiment, the confidence level parameters from the ROC curve of synthetic data set 2 were chosen for the models that yielded the highest TPR values with the lowest FPR at the same time.
## 4 Comparison Analysis of Prediction Models
### Training Convergence and Feature Importance Interpretation Analysis
Additionally, XGBoost places relatively more importance on OV, with a value of 33, compared to the BP and GA-BP models, which assign lower values to OV. Despite the differences in feature importance across the models, STV and OTV remain the most significant features for all three models. This highlights that traffic volume in the same and opposite directions plays a central role in the prediction process. Moreover, the models consistently emphasize the importance of vehicle speed, as reflected in the significance of SV and OV across all models. In addition to STV, OTV, SV, and OV, other features also play a role in the prediction process. For instance, S (whether a vehicle traveling in the same direction is encountered) has an importance value of 45 in the BP model, 42 in the GA-BP model, and 40 in the XGBoost model, indicating that this feature has a relatively smaller but notable influence. SS (whether the encountered same-direction vehicle is a car) has importance values of 35, 32, and 30, respectively, across the models, suggesting that the type of vehicle in the same direction has some effect on the prediction. SM (whether the encountered same-direction vehicle is an electric bicycle) has importance values of 33 for BP and GA-BP and 29 for XGBoost, showing that electric bicycles have a smaller influence than cars. Conversely, O (whether a vehicle traveling in the opposite direction is encountered) has a lower importance value in all three models. BP at 40, GA-BP at 38, and XGBoost at 36 indicate that, while it influences the prediction, its effect is relatively small. OS (whether the encountered opposite-direction vehicle is a car) has importance values of 35, 33, and 32, suggesting that the type of vehicle in the opposite direction is relevant to the prediction. OM (whether the encountered opposite-direction vehicle is an electric bicycle) has lower  
Figure 11: Feature Importance of BP, GA-BP, and XGBoost models. importance values, with BP at 30, GA-BP at 28, and XGBoost at 25, reflecting that the impact of electric bicycles in the opposite direction is minimal. P (whether pedestrians are walking together) shows relatively higher importance in all models, with BP at 60, GA-BP at 65, and XGBoost at 58, indicating that whether pedestrians are walking with others affects the prediction. Finally, W (road width) has lower importance in all models, with BP at 25, GA-BP at 22, and XGBoost at 20, suggesting that while road width can influence pedestrian behavior, its effect on prediction is relatively small. In conclusion, despite the variations in how each model ranks the importance of different features, STV and OTV remain the most important features for all three models. These findings align with the experimental objectives and reinforce the critical role of traffic volume and vehicle speed in the prediction task. Additionally, vehicle type (car or electric bicycle) and whether pedestrians are walking together influence the prediction, although their impact is smaller than traffic volume and speed.
## 5 Energy Management Strategy
### MPPT Control Strategy for PV Array  
Perturbation and observation method with adaptive variable step is adopted, which achieves self-selection of the step size by adding an adaptive algorithm when setting the step size. This method not only improves the steady state performance of the power system, but also improves the dynamic performance. The step size calculation can be described as follows.  
\[S(k+1)=N\frac{P(k)-P(k-1)}{S(k)} \tag{22}\]  
where \(S(k)\) is step size (\(0<S(k)<1\)); \(N\) is the constant determined by the sensitivity of the adaptive variable step size adjustment; \(P(k)\) is power.  
The flowchart of the perturbation and observation method with adaptive variable step is given in Figure 10.  
The perturbation and observation method with adaptive variable step obtained \(I(k-1)\), \(I(k)\), \(U(k-1)\) and \(U(k)\) to calculate \(P\) (\(k-1\)) and \(P(k)\). We got the value of difference of power \(dP\) (Figure 10). The threshold of the power difference (\(e_{p}\)) was set. The direction of perturbation can be determined by calculating \(dP\)-\(e_{p}\). Then the step size \(S(k+1)\) can be adjusted by Equation (21). Until \(dP=0\), the maximum power point can be considered to be reached.  
Figure 9: The simulation results of (a) the wind speed, (b) wind turbine output power and (c) rotational speed of wind turbine at abrupt wind speeds.
# Deep Learning-Based Next-Generation Waveform for Multiuser VLC Systems
## 4 Results and Discussions
By contrast, OAM-IM maintains its performance, even for high SNR values. It can be observed that OAM-IM outperforms OFDM-IM for both LoS and NLoS VLC channels. The benefit that OAM-IM has an edge in the sense of spectral efficiency, which is difficult to achieve in OFDM-IM, is also highlighted here. The performance of OFDM-IM with low power degrades when using the maximum likelihood detection method; however, CNN-based receiver design improves the BER for both LoS and NLoS VLC channels for all ranges of SNR values. The results show that the CNN-based receiver design performs better than the ML method. The CNN-based system improves the detection rate by up to 20% and 30% for low and high SNR values, especially for low-power users. The CNN-based VLC system is practically suitable for a higher number of users and activation modes because, due to the increasing number of elements to be optimally detected and the avoidance of interference ML-based systems, system complexity is heightened, which leads to a high computational cost and higher chances of system failure. It is important to mention that, for these results, the channel matrix is known and assumed to be static.
# Review of Traffic Assignment and Future Challenges
## 3 Traffic Assignment: Theoretical Background
### Dynamic Traffic Assignment (DTA)
#### 3.2.2 Resolving Approaches
It uses a heuristic to minimize the gap (similar to the relative gap given in Equation (13) but adapted to the transit travel time and there is no denominator) function, coupled with \(MSA\) to estimate the travel time. Advancements in computing capabilities, coupled with the utilization of heuristics and iterative approaches, have created the opportunity to address two challenges simultaneously--namely, planning the public transport network while considering its impact on users. This is exemplified by the approach presented in [130]. Similar to the dynamic assignment of road traffic, transit assignment has benefited from microscopic traffic simulators. These simulators incorporate departure times, account for delays caused by congestion, and consider the time users spend boarding and alighting at stops. Given the complexity of the problem, agent-based approaches seem inevitable. In this approach, agents learn based on their criteria through numerous iterations of decision-making. Such approaches have been successfully employed in refs. [15; 131]. An agent-based approach has also proven beneficial in addressing the complexity of the carpooling assignment problem [132]. Carpooling introduces the challenge of matching routes and determining the balance between itinerary extension and the reward, influencing the decision on the number of individuals sharing the same itineraries.