esa-sceva/satcom-chunk-collection huggingface dataset:
# A Digital Bang-Bang Clock and Data Recovery Circuit Combined with ADC-Based Wireline Receiver
## 6 Conclusions  
In this paper, we proposed a digital bang-bang clock and data recovery circuit combined with an ADC-based wireline receiver. Our digital bang-bang clock and data recovery circuit adopted a partial oversampling system, which only needed one extra sub-ADC and a time interpolator. The DBB-CDR circuit combined with a 32 Gb/s ADC-based wireline receiver was implemented using 28 nm CMOS technology. It achieved 0.42 UIPp JTOL@10 MHz and the minimum JTOL value was 0.362 UIPp under 0.04 UI variance of Gaussian jitter. The whole 32 Gb/s ADC-based receiver, with the exception of the DSP, occupied an area of 0.34 mm \(\times\) 0.8 mm and consumed 233 mW of power. Considering the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **[14]** & **[19]** & **[25]** & **This Work** \\ \hline Technology & 10 nm CMOS & 65 nm CMOS & 7 nm CMOS & 28 nm CMOS \\ RX Architecture & ADC-based & Slicer-based & ADC-based & ADC-based \\ CDR Architecture & MM-CDR & BB-CDR & MM-CDR & DBB-CDR \\ Modulation & PAM4 & NREZ & PAM4 & NREZ \\ Data Rate (Gb/s) & 56 & 40 & 1–60 & 32 \\ Supply (V) & 0.75/0.85/1.2 & 1.2 & 0.75/0.9 & 0.9/1.4 \\ JTOL@10 MHz (UIPp) & 0.072 & 0.27 & 0.18 & 0.42 \\ Min. JTOL (UIPp) & 0.07 & 0.21 & 0.14 & 0.362 \\ Area (mm\({}^{2}\)) & 0.72 & 1.92 & 0.468 & 0.272 \\ Power (mW) & 226.5 & 225 & 244 & 233 \\ \hline \hline \end{tabular}
\end{table}
Table 4: A performance comparison between state-of-the-art wireline receiver.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance** & **DBB-CDR** & **MM-CDR** \\ \hline Stability & High & Low \\ Robustness & High & Low \\ Signal Amplitude & Not Affected & Affected \\ Power (mW) & 0.02 & 0.6442 \\  & 2.02  & 3711 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance comparison between the DBB-CDR and MM-CDR circuits.  
structure of DBBPD is simpler than that of MMPD, the adopted DBB-CDR circuit with the ADC-based wireline receiver did not excessively increase power consumption. Overall, the excellent performance of the circuit in terms of stability, robustness, signal amplitude insensitivity, etc., demonstrated that DBB-CDR circuits could offer a new solution for ADC-based wireline receivers.  
Conceptualization, Y.G., J.W. and Y.C.; methodology, Y.G. and X.F.; software, Y.G., R.C. and X.F.; validation, Y.G., R.C. and X.F.; formal analysis, Y.G., X.F. and Y.C.; investigation, Y.G., X.F. and Y.C.; resources, Y.G., J.W. and Y.C.; data curation, Y.G. and X.F.; writing--original draft preparation, Y.G. and Y.C.; writing--review and editing, Y.G. and Y.C.; visualization, Y.G. and X.F.; supervision, J.W. and Y.C.; project administration, J.W. and Y.C.; funding acquisition, J.W. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China Project, grant numbers: 62090044.  
Not applicable.
# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
## 2 Overview of mmWave HetNets  
In this section, we first introduce the characteristics of 5G networks and devices and then describe the system model. Afterwards, we propose a novel triple-band network structure for the mmWave HetNet.
# Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
## 3 Experimental Setup
The deployed network communicates on a single Hi-Rate channel with a bandwidth of 150 kHz and a baud rate of 166.667 kbit/s [25]. The nodes transmit using an Effective Radiated Power (ERP) of 14 dBm. In order to be able to adjust for static changes in the environment, we do not directly use the RSSI values but convert them to attenuation values relative to the empty environment. We call this process the calibration step, which occurs every night at a fixed time when the subway station is closed. When calibrating, we measure the RSSI values of every link in the network during a 15 min window and average them to obtain a stable value for every link in the environment. These values are stored in the back-end and are subtracted from the collected RSSI data when the system is in operation to become the attenuation vector per cycle used for further processing. Storing the full vector is required in order to allow for calculating the mean attenuation value based on the selected virtual network. The mean attenuation is what is used to feed into the input of our regression models, as we will discuss in detail in the following sections. To train and evaluate the system, we performed 3 ground-truth measurements, in which we manually collected ground-truth data on the number of people standing on the platform, as well as the arrival and departure of rail vehicles in the station. All three of these collection periods had a duration of approximately 1 h and occurred on separate days in order to remove as much bias as possible between the training and test sets. We also added a single 30 min window at 03.00 a.m. at night to the training set. This allowed for collecting many samples where the platform was empty and no terms were present. For all results that we will be presenting in the next sections, we used the first collection period and the nightly period for training and the last two periods for evaluation. Figure 3: This diagram shows a chronological overview of the messages being exchanged in the network during one cycle in between the gateway (GW) and the nodes (0, 1, 2). (**a**) represents the initialization message send from the gateway. (**b–d**) represent the communication and sensing messages.
## 5 Conclusions  
In this paper, we investigated the BC-UAC problem, and proposed a BC-UAC method based on the TFC of the bottlenose dolphin whistle. In the proposed BC-UAC method, the TFC is evenly divided into several segments to increase the communication rate, and the information bits are modulated by time-delay in BC-UAC-TD and frequency-shift in BC-UAC-FS, for each symbol. Moreover, since the whistle is not a constant envelope signal, the performance of the proposed BC-UAC method is more seriously affected by the multipath effect than the traditional UAC. In order to mitigate the multipath effect, the energy compensation technique, channel estimation with the IOMP algorithm and channel equalization with the VTRM technique are considered in the proposed BC-UAC method. Finally, the performance of the proposed BC-UAC method, in terms of the PCC and the BER, is evaluated under simulated and measured underwater channels. Numerical results show that the concealment of the biomimetic modulated signal behaves well in the proposed BC-UAC method. Moreover, BC-UAC-TD and BC-UAC-FS have good performance in terms of the BER. In addition, comparing BC-UAC-TD with BC-UAC-FS, the covertness of the biomimetic modulated signal in BC-UAC-TD is better than that of BC-UAC-FS, although the reliability of BC-UAC-TD is worse than that of BC-UAC-FS.  
In future research, we will study the BC-UAC problem under the time-variant underwater acoustic multipath channel and improve the existing receiving technology. Moreover, we will verify the proposed BC-UAC method in the sea experiment. Furthermore, we will investigate the integrated communication and localization system based on the proposed BC-UAC method.  
Figure 17: The impact of \(M\) on the BER of proposed BC-UAC method under the measured channel. (**a**) BC-UAC-TD; (**b**) BC-UAC-FS.  
**Author Contributions:** Conceptualization, L.X. and Y.J.; methodology, L.X. and Y.J.; software, Y.J.; validation, L.X., J.Z., Y.J. and H.C.; formal analysis, L.X. and Y.J.; investigation, J.Z. and Y.J.; resources, L.X.; data curation, Y.J.; writing--original draft preparation, J.Z. and Y.J.; writing--review and editing, L.X. and J.Z.; visualization, J.Z.; supervision, L.X. and H.C.; project administration, L.X. and H.C.; funding acquisition, L.X. and H.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is partly supported by the National Key Research and Development Program of China (No. 2016 YFC0301206), the National Natural Science Foundation of China (No. 61531017) and the Science and Technology Department of Zhejiang Province (No. 2018R52046).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Development of Transmission Systems for Parallel Hybrid Electric Vehicles
## 2 Materials and Methods
### Parallel Hybrid Transmission Design
#### 2.2.3 Influence of Location of Gearbox  
For the parallel hybrid propulsion system, the transmission system can be placed at three locations. Figure 3 shows the possible locations of the gearbox (GB) with respect to the ICE, EM, TW, and PIM in the parallel hybrid propulsion system. These different locations of the gearbox have different effects on the torque and the rotation speed between the PIM in the hybrid driving or power-split mode. The details of the effect of each location of the gearbox are as follows:  
* The influence of GB1: The torque and rotation speed of engine outputs can be independently adjusted.
* The influence of GB2: The torque and rotation speed of motor outputs can be independently adjusted.
* The Influence of GB3: The torque and rotation speed of PIM outputs can be adjusted, which indicates that the outputs of the engine and motor are simultaneously adjusted. This location of the gearbox alone cannot be used to adjust engine or motor outputs.  
The TC-PIM that was in this study used a spur gear train to design and analyze the parallel hybrid transmission system. Spur gears are widely used in power transmission mechanisms. It is a simple mechanism to transfer mechanical power. The theory of spur gear-type transmission is that a gear set comprises drive and driven gears. These gears have different dimensions and, thus, it can adjust the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Design Demand** & **Value** \\ \hline Maximum traction force of tire & 3500 N \\ Maximum slope at 10 km/h & 25\% \\ Top vehicle speed & ICE: 161 km/h \\ Hybrid: 176 km/h \\ Engine max stat rpm & 3000 rpm \\ Motor max power in EV mode & 8.5 kW \\ Engine max power in ICE mode & 25 kW \\ \(\varphi_{2}\) & 1.1 \\ \hline
**Vehicle Parameters** & **Value** \\ \hline Weight & 1200 kg \\ Effective radius of traction wheel & 0.3 m \\ Front area & 2.16 \(m^{2}\) \\ Drag coefficient & 0.26 \\ Rolling resistance coefficient & 0.02 \\ Maximum grade resistance & 2855.1 N \\ Rolling resistance & 288.41 N \\ Drag resistance & 2.6042 N \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design requirement of transmission and corresponding parameters.  
Figure 3: Gearbox location of parallel hybrid driveline.  
torque and rotation speed. Equations (15) and (16) can express the relationship of the torque value and rotation speed value for drive and driven gears. The term \(\frac{r_{dn}}{r_{d}}\) represents the gear ratio.  
\[\eta\frac{T_{dn}}{T_{d}}=\eta\cdot\frac{r_{dn}}{r_{d}} \tag{15}\]  
\[\frac{\omega_{d}}{\omega_{dn}}=\frac{r_{dn}}{r_{d}} \tag{16}\]
## 4 Experimental Results
### Price Forecasting  
The experiment leveraged electronic transaction data to assess the effectiveness of the proposed model. The garlic, ginger, and chili data sets were utilized for forecasting at various intervals, including hourly and daily scales. Each data set comprised 10 features relevant to prediction, structured in hourly units, with seven data points chosen per day. Daily data were derived from the averaging of hourly data points for each day.
## 4 Results and Discussions
### Effect of Sensor Placement  
Even though WNN and BLSTM have smaller network sizes, as seen from Table 1, BLSTM has performed well regardless. Hence, to reduce the training time only the BLSTM network was further trained using the TID, SID, and FID to determine the best sensor position for the first set of joint angles. The result of the training is shown in Table 2. Except for the right hip angle, the inertial data of the sensor attached to the tibia/shank bone has better accuracy compared to the other three with minimum and maximum MAEs of 3.02\({}^{\circ}\) and 4.33\({}^{\circ}\), respectively. This is because the shank motion is directly associated with the rotations of the knee and hip joints during walking. In other words, we cannot move the lower part of the body without moving the shank part, but we can move the lower part of the body without too much movement on the pelvis. Hence, the shank part captures most of the lower-body kinematics when walking. The performance for the estimation of the second eight joint angles is lower than the estimation for the four joint angles set. It can be particularly seen from the ankle and hip abduction/adduction angle columns of Table 3 that the BLSTM network did not perform well for the two joints.  
Hence, the overall estimation accuracy was affected. However, the result for the TID is slightly better than the result of SID in the second set of joint angles as shown in Table 3. Therefore, for lower extremity joint-angle estimation, attaching an IMU sensor on the tibia bone right below the knee or on the side of the thigh works well for the four joint angles. It can be concluded that by using a single inertial sensor, the general pose of walking of a person can be estimated with good accuracy. Not only from the pelvis inertial data but the pose of the lower half of the body can also be estimated from other bone segment inertial data of either leg. In other words, general walking parameters such as forward walking speed, sagittal joint angles, and step/stride sizes could be computed from a single inertial data-based estimation method.
## 3 Results
### RSM Model and Analysis of Variance
Positive regression coefficients imply that the increment of the parameter levels will have a direct proportion to the production of the product, vice-versa [54]. Interaction between any two parameters X1, X2 and X3 are highly significant to the yield of ED-F1 (\(p<0.001\)), but on the opposite, interaction of X1-X3 is not significant for the formation of HMF. Similar occurrence was reported by [55] in optimizing the hydrolysis conditions for xylans from beech wood and corn cob. Their results indicated that temperature and time of hydrolysis plays a pivotal factor in the conversion of hemicelluloses to oligosaccharides using the minimum concentration of sulfuric acid, thus reducing the degradation of xylose monomers to furfural by-product. Temperature and H2 SO4 concentration also showed a significant effect on the hydrolysis yield of ED-F1 mainly with the temperature around 120-130 \({}^{\circ}\)C and H2 SO4 concentration around 0.9-1.6 M (Figure 4). Temperatures below 120 \({}^{\circ}\)C and higher than 130 \({}^{\circ}\)C showed reduction in hydrolysis yield of ED-F1 which can be confirmed by the negative quadratic term of temperature in the reduced mathematical model. Similarly, H\({}_{2}\)SO\({}_{4}\) concentration range below 0.09 M and higher than 0.16 M showed negative impact on ED-F1 yield. Subcritical temperatures beyond the boiling point of water with the addition of mild acid have been shown to efficiently reduce the hydrolysis time for a higher yield of oligosaccharidesacharide fraction. According to [PERSON] et al. (2018), the highest yield (45.18%) of xylooligosaccharide (XOS) fraction using subcritical water with 1% (0.1 M) sulfuric acid from hemicellulose was the highest at 120 \({}^{\circ}\)C for 60 min [56]. Temperatures beyond 120 \({}^{\circ}\)C have been noted to produce more xylose monomers. This observation could be explained by the different hydrolysis rate constant of the substrate at different temperature and acid concentration that affects the scission of terminal-nonreducing bonds, interior glycosidic bonds and terminal-reducing C-O bonds; the chain length of the produced oligosaccharides and the constant have been shown to be proportionally related [57].
## 5 Results and Discussion  
This section presents the results and discussion of the study. The data collection, analysis, and system design and development were performed according to the study's objectives. Before demonstrating the system's requirements, it is worth identifying the stakeholders of the system to clarify where each requirement falls. The stakeholders were divided into four categories, as shown below:  
* Primary stakeholders: patients are the pivotal actors and primary beneficiaries of their records as the owners and data controllers at the first level;
* At the same level, primary stakeholders are also physicians, therapists, and those who provide advice and treatment to the patient in addition to that of pharmacies and laboratories, and who add the results of tests and other processes to a health record;
* Next, secondary stakeholders include insurance organizations who are responsible for medical approvals, relatives authorized by the patient on behalf of their health record, and employees responsible for administrative matters;
* Finally, tertiary stakeholders include government or private agencies, researchers, and medical institutions.  
Figure 3 illustrates the stakeholders.  
Figure 2: Summary of the design science methodology.  
The first section illustrates the requirements collected from the academic literature review and the interviews with PHR stakeholders. These requirements are categorized as functional, nonfunctional, and system design requirements.
## 3 Experiments and Results
### Classification Analysis Based on Grad-CAM  
Class activation mapping (CAM) can give a good visual explanation for classification results, and can achieve weak supervised positioning of the target object (the butterfly in this study) [25; 36]. For different network models, even if all of them make the identical prediction, Grad-CAM can tell us which network is a \"stronger\" classification network. We used Grad-CAM to provide visual explanations of the classification performance for our proposed SESADRN. The butterfly images of the China butterfly dataset and their Grad-CAMs are shown in Figure 8a,b, respectively. The butterfly images of Leeds Butterfly Dataset and their Grad-CAMs are shown in Figure 8c,d, respectively. By visualizing a specific prediction area of the image with Grad-CAM, we can see that the SESADRN classification model locates the butterfly area well in the image.  
Figure 7: Accuracy and loss of training and validation (SESADRNNoPretrained, SESADRN). (**a**) Training loss of 300 epochs. (**b**) Validation loss of 300 epochs. (**c**) Training accuracy of 300 epochs. (**d**) Validation accuracy of 300 epochs.  
We compared the Grad-CAMs of the proposed SESADRN with those of DADRN [32], which achieved the second best performance as shown in Tables 2 and 3. The original sample images are shown in Figure 9a. Their Grad-CAMs obtained by SESADRN and DADRN are shown in Figure 9b,c, respectively. For the SESADRN model and the DADRN model, the heatmaps shown in Figure 9b,c, respectively. As well as the results shown in Figure 8, the proposed SESADRN can localize the target object (butterfly) well and enhance the and enhance the meaningful features (Figure 9b), while DADRN could not localize the target object (butterfly) well (Figure 9c). The visualization of the Grad-CAMs provided us a visual explanation why the proposed SESADRN is superior than DADRN.  
Figure 8: Visualization of the proposed SESADRN’s Gradient-weighted Class Activation Mapping (Grad-CAM). Butterfly sample images of China butterfly dataset (**a**) and their Grad-CAM (**b**); butterfly sample images of Leeds Butterfly Dataset (**c**) and their Grad-CAM (**d**).  
Figure 9: (**a**) Butterfly sample images, (**b**) Grad-CAM obtained by the proposed SESADRN model, (**c**) Grad-CAM obtained by the existing state-of-the-art model (DADRN) [32].
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 2 Materials and Methods
### Model Construction and Evaluation Methods
#### 2.4.2 Evaluation Methods  
The confusion matrix, also referred to as the error matrix, is a visual tool primarily employed to compare the classification outcomes with the actual observed values, thereby providing a clear representation of the classification accuracy. The performance metrics of the classification model include accuracy, precision, recall, and the F1 score. By utilizing the confusion matrix, one can assess the misclassifications made by the model, facilitating subsequent adjustments to model parameters or data augmentation. The calculation methods are as follows (5)-(8):  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
\[F1-score=\frac{2\times(Precision\times Recall)}{Precision+Recall} \tag{8}\]  
where \(TP\) denotes the number of true positives in the positive category, \(FN\) represents the number of false negatives in the positive category, \(FP\) indicates the number of false positives in the negative category, and \(TN\) refers to the number of true negatives in the negative category.
## 3 Materials and Methodology
### Surface Subsidence Prediction and Impact Analysis
#### 3.4.1 Probability Integral Method (PIM)
\begin{array}{c}U(x,y)_{j}=U_{x}C_{u}^{\prime}cos\varphi+U_{y}C_{x}^{\prime }sin\varphi\\ U(x,y)_{j+90}=U_{x}C_{y}^{\prime}cos\varphi+U_{y}C_{x}^{\prime}sin\varphi\\ U(x,y)_{cm}=U_{x}C_{y}sin\varphi_{u}+U_{y}C_{x}cos\varphi_{u}\end{array} \] (4) where \(\varphi_{u}\) is the angle between the maximum horizontal movement direction and the OX axis, \(\varphi_{u}=arctg(U_{y}C_{x}^{\prime}/U_{x}C_{y}^{\prime})\), and \(U_{x}\) and \(U_{y}\) are the horizontal movement values of the point to be determined along the strike and dip at the projection point of the main section, mm. For the tilt direction, add \(C_{y}^{\prime}.W_{cm}.cfg\). * The horizontal deformation \(\varepsilon(x,y)_{j}\) value of any point \(A(x,y)\) on the surface in the \(\varphi\) direction is shown in Equation (5): \[ \begin{array}{c}\varepsilon(x,y)_{j}=\varepsilon_{x}C_{y}\prime cos^{2} \varphi+\varepsilon_{y}C_{x}\prime sin^{2}\varphi+\big{[}\big{(}U_{x}T_{y}+U_ {y}T_{x}/W_{cm}\big{)}\big{]}sin\varphi cos\varphi\\ \varepsilon(x,y)_{j+90}=\varepsilon_{x}C_{y}^{\prime}sin^{2}\varphi+\varepsilon _{y}C_{x}^{\prime}cos^{2}\varphi-\big{[}\big{(}U_{x}T_{y}+U_{y}T_{x}/W_{cm} \big{)}\big{]}sin\varphi cos\varphi\\ \varepsilon(x,y)_{max}=\varepsilon_{x}C_{y}^{\prime}cos^{2}\varphi_{e}+ \varepsilon_{y}C_{x}^{\prime}sin^{2}\varphi_{e}+\big{[}\big{(}U_{x}T_{y}+U_ {y}T_{x}/W_{cm}\big{)}\big{]}sin\varphi_{e}cos\varphi_{e}\\ \varepsilon(x,y)_{min}=\varepsilon(x,y)_{j}+\varepsilon(x,y)_{j+90}-\varepsilon (x,y)_{max}\end{array} \] (5) where \(\varphi_{e}=\frac{1}{2}arctg\frac{U_{x}T_{y}+U_{y}T_{x}}{W_{cm}\big{(} \varepsilon_{x}C_{y}-\varepsilon_{y}C_{y}^{\prime}\big{)}}\); \(\varepsilon(x,y)_{max}\) and \(\varepsilon(x,y)_{min}\) are the maximum and minimum horizontal deformation values of the point to be determined; and \(\varepsilon_{x}\) and \(\varepsilon_{y}\) are the horizontal deformation values of the point to be determined after its superposition along the strike and dip at the projection of the main section.