esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In dry turning operation, various parameters influence the cutting force and contribute in machining precision. Generally, the numerical cutting models are adopted to establish the optimum cutting parameters and results are substantiated with the experimental findings. In this paper, the optimal turning parameters of AA2024-T351 alloy are determined through Abaqus/Explicit numerical cutting simulations by employing the Johnson-Cook thermo-viscoplastic-damage material model. Turning simulations were verified with published experimental data. Considering the constrained and nonlinear optimization problem, the artificial neural networks (ANN) were executed for training, testing, and performance evaluation of the numerical simulations data. Two feedforward backpropagation neural networks were developed with ten hidden neutrons in each hidden layer. The Log-Sigmoid transfer function and the Levenberg-Marquardt algorithm were applied in the model. The ANN models were studied with four input parameters: the cutting speed (200, 400, and 800 m/min), tool rake angle (5\({}^{\circ}\), 10\({}^{\circ}\), 14.8\({}^{\circ}\), and 17.5\({}^{\circ}\)), cutting feed (0.3 and 0.4 mm), and the contact friction coefficients (0.1 and 0.15).The two target parameters include the tool-chip interface temperature and the cutting reaction force. The performance of the trained data was evaluated using root-mean-square error and correlation coefficients. The ANN predicted values were compared both with the Abaqus simulations and the published experimental findings. All of the results are found in good approximation to each other. The performance of the ANN models demonstrated the fidelity of solving and predicting the optimum process parameters.  
AA2024-T351; cutting simulation; Johnson-Cook material model; artificial neural networks +
Footnote †: journal: Applied Physics Letters A
# A Design Method for Gammachirp Filterbank for Loudness Compensation in Hearing Aids
## 3 Channel Division Method  
Some studies have shown that information in distinct frequency bands contributes differently to speech intelligibility, and the energy of voiceless consonants in Chinese is mainly concentrated in the medium-high frequency band [36; 37]. The loss of sensitivity and resolution in the medium-high frequencies of the hearing impaired will cause difficulties in speech recognition. The relation between frequency ranges and speech intelligibility is shown in Table 1 [36].  
A new method of channel combination that considers the contribution of frequency components for speech intelligibility is proposed in this section. Firstly, the hearing threshold values at the center frequencies of each channel must be obtained according to the audiogram of the hearing-impaired patient by linear interpolation. Then, the difference between hearing thresholds at the center frequency of adjacent subbands is compared with a set threshold. If the difference is less than the _set merging threshold_, the two channels can be directly merged into a new channel. Next, the _combination thresholds_ are determined with the influence of each frequency range on speech intelligibility. The combination threshold of the frequency bands with large contributions to intelligibility is 5 dB, which means that channel combining can be performed when the hearing threshold difference at the center frequency of adjacent channels in the 500-2000 Hz band is less than or equal to 5 dB. Similarly, the threshold can be set according to the intelligibility contribution of other frequency bands. In this study, the threshold for the 0-500 Hz band is 20 dB, and 10 dB for the 2-8 kHz range. This channel division method not only meets the matching requirements of audiograms to ensure the performance, but also reduces the number of subband filters and the computation amount.  
Consider the transfer function of the M-channel analysis filters \(G_{\rm CK}(z),\{k=0,1,\ldots,M-1\}\), which are gained by merging \(l_{i}\) adjacent analysis filters.  
\[\widetilde{G}_{Ci}(z)=\sum_{k=n_{i}}^{n_{i}+l_{i}-1}G_{\rm CK}(z),i=0,1,\ldots,\hat{M}-1 \tag{14}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency Range (Hz)** & **Intelligibility (\%)** \\ \hline
0–250 & 2 \\
250–500 & 3 \\
500–1000 & 35 \\
1000–2000 & 35 \\
2000–4000 & 13 \\
4000–8000 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relation between frequency range and intelligibility.  
Here, \(l_{i}\) is the total number of channels to be combined, \(n_{i}\) is the upper band-edge frequency (\(n_{0}=0<n_{1}<n_{2}<\ldots<n_{\hat{M}}=m=M\)), and \(\hat{M}\) is the channel number of the new gammachirp filterbank. The synthesis filter \(\bar{F}_{Ci}(z)\) can be gained using a similar method.  
\[\bar{F}_{Ci}(z)=\frac{1}{l_{i}}\sum_{k=n_{i}}^{n_{i}+l_{i}-1}F_{\text{CK}}(z),i= 0,1,\ldots,\hat{M}-1 \tag{15}\]
# Forewarning Model for Glacial Lake Outburst in Southeast Tibet
## 1 Introduction
Moraine lake outburst disasters are non-periodic sudden natural disasters affected by the geographical climate and environment. Considering its high accuracy and rationality, this study applied logistic regression to establish a model for assessing the outburst susceptibility of moraine lakes in southeast Tibet. Southeast Tibet lies in the eastern part of the Himalayas, and it is characterized by a dense distribution of glacial lakes. This area is dominated by moraines, which have high glacier activity and poor stability and are sensitive to climate change. Glacial avalanches, glacial mudslides, and GLOFs caused by glacier retreat have become the main natural disasters in this area. This study aimed to establish a risk evaluation and prediction model of GLOFs in southeast Tibet. To this end, the morphological characteristics of typical glacial lakes in southeast Tibet were investigated, the evaluation indexes of the GLOF probability were summarized and selected, and the mechanism and influence of GLOFs were identified using the logistic regression method. The findings have reference value for disaster prevention and mitigation in Tibet.
# Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning
## 6 Performance Evaluation
### Convergence and Complexity Analysis
One prevalent issue with machine learning-based solutions is that they require a learning phase before providing an optimal solution. The learning phase is a computational cost of the Q-learning process, as is the case with other learning methods. During this phase, an agent investigates several possible actions in each potential condition to learn about the environment. In the case of using experience relay, the training part requires registering and reading experiences. However, after the environment has been learned, the best action in every given condition can be taken, resulting in the optimal solution. For a given state \(s\), Q-learning based selection of optimal action \(a\) requires an average time of 0.018 ms (average over 100 runs). In general, the total time required to report an optimal action requires 1 s (wireless signal measurement) + 1.032 s (processing time of technology recognition classification + saturation sensing) + 0.018 ms (Q-matrix based decision ) \(\approx\) 2.032 s. This time is the processing time required in real-time applications, as it includes the time required by the technology recognition solution [26] to capture, pre-process, and classify WiFi frames on the medium. This means our proposed scheme can estimate the traffic and report an optimal action every 2.032 s. This interval is practically suitable as accurate traffic load prediction of real-time applications can be achieved even with a higher prediction interval (up to 5 s) [36]. However, we employ a discrete event simulator (ns-3) in our simulation, and parameter parsing is done via functions, thus the collection of WiFi frame statistics occurs with negligible processing time. On the other hand, the proposed coexistence schemes do not necessitate information exchange between the LTE and WiFi networks and this makes the solutions less complex as there is no need to make any modifications to the WiFi network elements.
# Experimental Investigations on the Effects of Fatigue Crack in Urban Metro Welded Bogie Frame
## 3 Factors Relative to Crack Initiation and Propagation  
### Wheel Polygon  
The roundness measurement results of No.1 and No.2 wheels installed on the tested bogie are shown in Figure 12a,b. No.1 wheel was a worn wheel after profiling, showing a good state. No.2 wheel was a worn wheel before profiling, demonstrating a significant polygonal worn phenomenon, with eccentric characteristics and 14- to 16-side polygon.  
The wheel polygon will worsen the wheel-rail force and cause the high vibration of the axle box and bogie frame. The excitation frequencies of 14- to 16-side polygon wheel are coincided with the running speed linearly, shown in Figure 13. As indicated by the 15-sided wheel polygon analysis, for an example, when the operating speed is 54 km/h, the dominant frequency of the wheel-rail excitation is 86 Hz. Further, according to the analysis results of Figures 8 and 10, it can be observed that the dominant frequency of the wheel-rail excitation (86 Hz) is similar to the dominant frequency (89 Hz) of dynamic stress amplitude at point D02 in 54 km/h.  
Figure 11: Relationship between the vibration acceleration and stress response: (**a**) time domain diagram; (**b**) power spectrum diagram.  
Figure 12: Polar diagram of roundness: (**a**) No.1 wheel; (**b**) No.2 wheel.  
Figure 14 indicates the comparison results between the time-frequency diagram at point D11 and the frequency excited by the wheel polygon. It can be observed that the characteristic frequency of 15-sides polygon (red points in Figure 13) is highly consistent with the dominant frequency of dynamic stress.  
Figure 15 shows the comparison of the maximum dynamic stresses at D11 of the profiled and worn wheels. It can be observed that the maximum stress amplitude of the profiled wheel is significantly lower than that of the worn wheel, and the maximum reduction is 74%, indicating that the wheel polygon has a significant influence on the stress amplitude of the frame.  
Figure 14: The frequency comparison between dynamic stress at point D11 and excitation from the wheel polygon phenomenon.  
Figure 13: Relationship between wheel polygon and running speed.
## 2 Materials and Methods
### Flowchart  
This study attempted to develop a model to address the problems mentioned above and to predict the CAI. Figure 2 shows a flowchart for developing a CAI prediction model. The calculation process for predicting the CAI, which was the purpose of this study, was as follows. (1) Network methods were applied to air-quality stations located across the country to identify clusters with similar characteristics. (2) As for the dependent and independent variables, Jeong-wang's six-pollutant hourly data were collected from 1 January 2015 to 31 December 2020. In addition, a CAI was calculated using the six pollutants. (3) In order to develop a model for predicting the six pollutants, the model was divided into learning and evaluation sections. Data from 2015 to 2018 were used for the learning part, and data from 2019 to 2020 were used for the evaluation part. Six pollutants were predicted using LSTM. (4) In order to develop a CAI prediction model, the model was divided into learning and evaluation sections. Data from 2015 to 2018 were used for the learning part, and data from 2019 to 2020 were used for the evaluation part. A comprehensive air-quality index was predicted using the DNN model. (5) The prediction accuracy (predictive power) of each model was evaluated using a normalized root mean squared error (NRMSE), a Nash-Sutcliffe efficiency coefficient (NSE), and a correlation coefficient (CC). (6) If the prediction were made using point data, there would be the disadvantage that only the value located at the point could be predicted. To solve this problem, the predicted values were spatially distributed by applying ordinary kriging.  
Figure 1: Study area. Black pentagonal (right panel) points are air pollutant monitoring stations in Korea (data source: Korea Environment Corporation).
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Bio-Inspired Energy-Efficient Routing Protocols (Bio-Inspired E2 RPs)
#### 5.2.3. Energy-Efficient Layer-Based Routing Protocol (E2 LRP)  
An energy-efficient routing protocol (E2 LRP) based on layer for UWSNs was presented by the author in [138]. In a competition amongst the sensor Rx nodes to become a CH, the CH is chosen based on waiting time (WT), which is determined by the amount of energy left. A node becomes the CH and informs its nearby communicating nodes of this shift when the waiting period for a sensor Rx node ends. Clusters of communicating nodes located at greater depths in the sea have more communicating nodes per cluster than clusters located close to the water/ocean's surface. Therefore, it will have a negative impact on CH forwarding processes that are located close to the sink node. If the CH's remaining energy is less than the network's cluster average energy, the CH is considered as changed or damaged. The technique used to alter the CH helps to distribute the burden across the sensor Rx nodes. The use of flooding methods is expected to cause network congestion and considerable routing overheads.
# The CYGNSS Mission: On-Going Science Team Investigations
## 2 Methodology, Results, and Discussions
### Data Products
Similar to the CYGNSS-NOAA product, this product has been released through the PODAAC (Figure 5), with data availability from March 2017 through the present (with a 1-month data release latency) [26]. NASA JPL serves as the primary data lead for CYGNSS within PODAAC, and continually provides updates to the CYGNSS Science Team regarding data publication. At the time of the meeting, the L1 ocean product remained the most popular CYGNSS product being downloaded, and the CYGNSS usage from October 2019 to May 2020 rebounded to pre-FTP retirement levels. It was also noted that by early 2021, cloud services for select datasets would become operational. Around the time of the meeting, PODAAC's web portal went through a revitalization to improve the user experience. This includes listing  
Figure 4: Full day of wind speed observations (m/s) on 30 August 2020 from the NOAA CYGNSS L2 Science Wind Speed 25-km v1.1 product. Figure 5: Full day of latent heat flux (W/m\({}^{2}\)) observations on 30 August 2020 from the CYGNSS L2 Ocean Surface Heat Flux CDR v1.0 product. all of the CYGNSS's data products on a single organized page, allowing users to have a better access to the CYGNSS's scientific data products.
## 4 Impact of the Constellation Mesh Area on Localization Accuracy
### Discussion about the Simulated Positioning Results  
As emerges from the previous sections, the behavior of the localization accuracy allows to identify a trade-off for wider observation domains, where an overall acceptable accuracy is easily achievable without using a high number of anchors by exploiting larger meshes. By using a simple model like the one discussed in Section 4.2, the system architect can tune positioning accuracy by reducing their area and increasing the number of anchors, so that the localization system can be considered scalable for different application scenarios, [28]. This latter consideration can be sustained by comparing Figures 13 and 14, which show the maps of the localization error obtained with the three mesh dimensions;  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & & **A** & **B** & **C** \\  & & **Large** & **Medium** & **Tiny** \\ \hline \multirow{2}{*}{**Full Room**} & **Mean Error** & 0.70 m & 0.85 m & 1.75 m \\  & **Coverage \({}^{*}\)** & 79.13\% & 72.12\% & 34.35\% \\ \hline \multirow{2}{*}{**Within the Mesh Area**} & **Mean Error** & 0.66 m & 0.47 m & 0.32 m \\  & **Coverage \({}^{*}\)** & 82.09\% & 96\% & 100\% \\ \hline \hline \end{tabular}  
* Percentage of the full room area with sub-metrical localization error (\(\leq\)1 m).  
\end{table}
Table 3: Measured localization error results.  
the results come from simulations and measurements respectively, whose summaries are provided in Tables 2 and 3. The simulation model shows a significant level of agreement with the measured results, in particular for the large and medium meshes, for what concerns both the accuracy and the distribution of localization error. The accuracy of this model can be attributed to the adoption of circular polarized antenna that are inherently robust to multipath, [29; 30]. This is due to the change of polarization rotation, i.e., left hand versus right hand, once that the electromagnetic wave bounces a conductive object, thus resulting in a cross-polarized wave at the receiver antenna that is therefore rejected. This occurrence justifies the adoption of a coarse channel model, like the one adopted in this model, although the adoption of more case of studies should be considered for a complete model validation.
# Neural Network Approach to MPPT Control and Irradiance Estimation
###### Abstract  
Photovoltaic (PV) modules require maximum power point tracking (MPPT) algorithms to ensure that the amount of power extracted is maximized. In this paper, we propose a low-complexity MPPT algorithm that is based on the neural network (NN) model of the photovoltaic module. Namely, the expression for the output current of the NN model is used to derive the analytical, iterative rules for determining the maximal power point (MPP) voltage and irradiance estimation. In this way, the computational complexity is reduced compared to the other NN-based MPPT methods, in which the optimal voltage is predicted directly from the measurements. The proposed algorithm cannot instantaneously determine the optimal voltage, but it contains a tunable parameter for controlling the trade-off between the tracking speed and computational complexity. Numerical results indicate that the relative error between the actual maximum power and the one obtained by the proposed algorithm is less than 0.1%, which is up to ten times smaller than in the available algorithms.  
photovoltaic (PV); solar cell; modeling; neural network; model-based MPPT control Article
# Introduction to Photonics: Principles and the Most Recent Applications of Microstructures
## 5 Classification of Optical Fiber Sensors
### Extrinsic Optical Fiber Sensors  
This type of fiber optic sensor makes use of optical fiber cables, usually the multimode type, to pass controlled light from either an electronic sensor linked to an optical transmitter or a non-fiber optical sensor. The advantage of extrinsic sensors is that they extend to places that cannot be otherwise accessible--for example, measuring the inside of aircraft engines using fibers to pass radiation to a radiation pyrometer that is situated on the exterior part of the machines. Similarly, extrinsic fiber optic sensors can be utilized in measuring the internal temperature of electrical transformers, in which the presence of a high electromagnetic field makes it impossible to measure using other measurement techniques. Extrinsic fiber optic sensors offer an outstanding shield of the frequency signal from being corrupted by noise. Regrettably, several traditional sensors release electrical outputs that must be changed to optical signals for fiber use. Extrinsic sensors found application in measuring temperature, rotation, acceleration, vibration, velocity, as well as displacement [83].  
Figure 9: Polisher design setup; (**a**) the stage used to hold the fiber; and (**b**) the polishing process, where the light is figuring out from the fiber due to a removal of the cladding.
## 4 Analysis and Discussion  
The above experimental results offers the following analyses:  
1. According to the repetitive measurement experiment in Section 3.1, it can be seen that in the absence of error compensation algorithm and under measuring speed of 4 mm/s and white pixel threshold of 100, the measurement error of each time is about 0.05 mm and does not exceed 0.1 mm; the fluctuation range of standard deviation is between 0.038 mm and 0.073 mm. Above accuracy and stability of the designed measuring device are sufficient to meet the hole depth measuring requirements for automatic drilling and riveting of large-scale composite board parts.
2. Through the experiment on the effect of feed speed on measurement accuracy in Section 3.2, it can be seen that with the increase of probe feed speed, the measurement stability of the device decreases gradually. Considering the factors of measurement efficiency, the optimal probe feed speed is 5 mm/s, while ensuring its measurement stability.
3. Based on the effect of pixel threshold on measurement values in Section 3.3, it can be seen that with the increase of the pixel threshold, the measured hole depth gradually decreases, which is in line with expectations. Moreover, with the increase of pixel threshold, the stability of measurement fluctuates. Therefore, in order to bring the measuring result closer to the true value, the optimal pixel threshold in this experiment should be 100. In practical applications, the optimal pixel threshold needs to be adjusted according to the measurement object due to effects such as flash, burrs, iron filings, etc.
4. In follow-up studies, we can further design and optimize an error compensation algorithm and automatic calibration method, study the corresponding relationship between feed speed and measurement error and find an effective compensation method, study the mapping relationship between pixel threshold and measurement error and offer a compensation method, study the specific relationship between the diameter of the laser spot and the measurement accuracy, etc., so as to further improve the measurement accuracy and efficiency of the device.
5. This paper only focuses on the design of measuring methods and measuring devices, and preliminarily proves the feasibility of the proposed method. Follow-up research can integrate the prototype device into the automatic riveting manipulator arm and measure the actual engineering sample, and further explore the feasibility and stability of its engineering application.  
Figure 14: The average value and standard deviation in pixel threshold experiment.
# Portable L-Band Radiometer (PoLRa): Design and Characterization
## 3 Radiometer Characterization
### Uncertainty Characterization
Though many modern radiometers have recently used high sample rate digital back ends for frequency-domain RFI mitigation, this approach still results in residual RFI, and is not foolproof [32]. Gaussian fitting of samples in the time domain is also an adequate means of RFI detection, as demonstrated in [28; 33; 34]. The Portable L-band Radiometer (PoLRa) discussed here uses the direct-detection architecture with total power detection for stability, simplicity, and low power consumption. Digital back ends of similar radiometers have been shown to consume at least 19 W [35], which is significantly more than the \(\sim\)4 W used by PoLRa. Conversion from antenna temperatures to footprint brightness temperatures used for retrieval of geophysical state parameters may also require a correction to consider the relatively large field-of-view of the antenna. When viewing the ground at non-nadir incidence angles, the linear polarizations at the antenna plane only correspond to the same linear polarizations at the boresight of the antenna. At off-nadir angles, the emissions from the ground must be corrected for polarization mixing; a detailed description of this procedure is provided in Appendix A of [20]. PoLRa-based retrievals of geophysical parameters, such as soil moisture, will be validated in the future using a network of in-situ soil moisture sensors.