esa-sceva/satcom-chunk-collection huggingface dataset:
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
## 4 Simulations and Results
### Performance Under Different Network Loads
The average delay of ROPA was significantly smaller than those of UW-CSMA/CA and SFAMA because it supports concurrent transmission of multiple nodes in a transmission cycle so that the average handshaking overhead is reduced. However, the nodes whose destinations are not the source node still need to wait for another transmission cycle, which limits the performance of ROPA. Compared with ROPA, the proposed method increases the opportunity of concurrent transmission in a  
Figure 3: Performance of UWALOHA, UW-CSMA/CA, SFAMA, and ROPA protocols and that of the proposed method under different network loads. (**a**) Normalized throughput; (**b**) Packet delivery rate; (**c**) Average end-to-end delay; (**d**) Average energy consumption. transmission cycle, so had the lowest average delay in the simulation. DS results in lower average delay than DS without SO due to the more compatible transmission scheduling. Figure 3d shows the average energy consumptions of the four conventional protocols and that of the proposed method. UWALOHA consumes the highest average energy due to the retransmissions caused by the frequent collisions. UW-CSMA/CA and SFAMA have medium average energy consumption because handshaking reduces the collision while introducing the additional transmission of RTS and CTS packet for each data packet. ROPA and the proposed method reduce the average overhead energy consumption by supporting concurrent transmission in each transmission cycle. The increase in the concurrent transmission opportunity in the proposed method leads to lower average overhead energy consumption than ROPA. DS results in lower average energy consumption than DS without SO.
# Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model
## 5 Synthetic Data Manipulation
### Feature Compression  
The feature compression step is a feature fusion process of determining a suitable group of HIs that can represent relevant information about the component performance degradation. In this paper, we use the PCA to transform the selected features into suitable HIs [44; 45]. PCA is an unsupervised feature fusion that transforms the original data set consisting of the selected features to a new subspace of orthogonal new data set called principal components (PCs). The PCA finds the first PC with the highest variance in the latent space, using the covariance matrix and its eigenvalues and eigenvectors. The first components are the ones with the highest variance, which are orthogonal to the others. Before performing PCA, the selected statistical features are normalized and standardized by using Equation (11) in order to obtain zero mean and unit variance.  
\[F_{i}=\frac{(D_{i}-min(D_{i}))}{(max(D_{i})-min(D_{i}))};where\;D_{i,t}=\frac{ min(f_{i})}{f_{i,t}} \tag{11}\]  
where \(f_{i,t}\) is the \(i\)th feature data point at time index \(t(t=1\dots T)\), \(T\) is the feature length, and \(F_{i}\) is the \(i\)th normalized feature. The corresponding eigenvalues, giving the amount of variability associated with each direction, are from the selected features of the used signals: PCA-latent-torque = [5.9920 0.0058 0.0016 0.000 0.0000 0.0000], PCA-latent-current = [5.6676 0.2203 0.0702 0.0392 0.0026 0.0000] and PCA-latent-speed = [5.5743 0.8089 0.4914 0.0676 0.0420 0.0156 0.0000]. The corresponding Pareto charts to the calculated values of PCA-latent that represent the percentage of variance explained by the PCs of torque, stator current, and speed signals are shown in Figure 7. Figure 7a shows that the first component \(PC_{1}\) is sufficient to represent almost 100% of the data variance in the torque signals and can be used as a HI machine. Figure 7b,c show that the first two and three components can represent 100% of the data variance in the stator current and speed signals, respectively.  
In this work, the first PC from each signal presents the machine HI since most of the information within the initially selected features is squeezed into the first components, which retain 99.91%, 95,85%, and 79.89% of all variance PCA.  
To improve the prognostic process performance, it is required to transform the first PC of each signal into a \(HI\). Therefore, a linear whitening transformation is applied to the PCs by removing each first PC data point as follows:  
\[HI=PC-offset \tag{12}\]  
After that, we computed the most suitable HIs, including degradation information about the rotor component health state. A robust RUL estimation can be implemented by showing the validity and the feasibility of the proposed prognostic methodology using synthetic data recorded from the Matlab/Simulink model.  
Figure 7: Pareto chart of the variance explained by the first principal components, (**a**) torque signals, (**b**) stator current signals, and (**c**) speed signals.
# A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing
## 4 Reputation-Based Collaborative User Recruitment Algorithm
### Collaborative Sensing Method  
Random movement by the vast number of mobile users in the city brings great opportunity for MCS, but the significance of user mobility is two-fold. It makes ubiquitous sensing possible, but it may lead to the uneven distribution of users in some regions. For instance, because of the random movement of users, some sensing areas may have insufficient or even very few users, which would prevent the cloud platform \(P\) from recruiting a suitable user to finish the task. In this case, pre-deployed static nodes with powerful sensing, collecting, computing, and communicating capabilities in the city can supplement and collect the sensing information for the tasks.  
In our system, there are some pre-deployed smart static nodes \(S_{n}\) that generally perform other work or just stay asleep. When the cloud platform \(P\) is unable to find a suitable user to execute the task, these static nodes can be the best substitute to sense the expected data and achieve optimal coverage collaboratively. As these static nodes \(S_{n}\) usually have more powerful sensing ability and radius than mobile users, we comprehensively consider three factors: location, sensing radius, and cost. Hence, we deduce the fitness function for the task to recruit the optimal static node, which is described as follows:  
\[f_{S_{n}}=\eta D_{S_{n}}+\mu\frac{R_{S_{n}}}{R_{\theta_{j}}}+\varphi\left(1- \frac{C_{S_{n}}^{\theta_{j}}}{R\omega_{\theta_{j}}}\right), \tag{14}\]  
where \(D_{S_{n}}\) is the distance factor for the static node \(S_{n}\) similar to \(D_{i}\). \(R_{S_{n}}/R_{\theta_{j}}\) represents the coverage ability for \(S_{n}\). \(\eta\), \(\mu\) and \(\varphi\) are the balancing coefficients and \(\eta+\mu+\varphi=1\). In addition, \(C_{S_{n}}^{\theta_{j}}\leq R\omega_{\theta_{j}}\).
## 2 Data Used for Typhoons Hagibis and Bualoi
### Super Typhoon Hagibis  
_Hagibis_ (No. 1919) was named by the Japan Meteorological Agency (JMA) at 19:00 UTC on 5 October 2019, and it began a process of rapid strengthening. It was upgraded to a typhoon at approximately 21:00 UTC on 6 October and upgraded to a super typhoon by the CMA at 06:00 UTC on 7 October. It continued to strengthen and became the strongest typhoon since 2019 recognized by the CMA. Afterward, it passed near Anatahan Island in the northern Mariana Islands, turned north and moved north by east, and its intensity weakened slowly. It approached the Tokyo capital circle and landed off the coast of the Izu Peninsula, Japan, at approximately 10:00 UTC on 12 October. The CMA finally stopped numbering it at 8:00 on 13 October.
# A 60 GHz CMOS I/Q Receiver for High-Speed Wireless Communication System
## 4 Conclusions  
This paper presents a wideband 60 GHz I/Q receiver in bulk 40 nm CMOS technology. The passive I/Q generator with a tandem coupler and Marchand balun is implemented to generate the accurate I/Q signals at 60 GHz. To achieve the wide gain control range, the resistive feedback with the PMOS switch is implemented in the 60 GHz amplifiers. The receiver achieves a conversion gain of 51 dB at 60 GHz with a gain control range is nearly 48 dB. The 3 dB baseband bandwidth of 300 MHz is achieved from 57 GHz to 60 GHz. The I/Q receiver chip dissipates a 172 mW of power from a 0.9 V supply, and the chip size is \(2.01\times 1.45\) mm\({}^{2}\). With these specifications, the proposed 60 GHz I/Q receiver with passive I/Q generation is well suited for a variety of high-speed wireless communication applications.  
Conceptualization, A.B. and J.-G.K.; methodology, A.B. and J.-G.K.; investigation, A.B. and J.-G.K.; writing--original draft preparation, A.B., D.B. and J.-G.K.; writing--review and editing, A.B. and J.-G.K.; supervision, J.-G.K.; project administration, D.B. and J.-G.K.; funding acquisition, J.-G.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2018-0-01663, Development of Communication Sensing Converged B5G Millimeter Wave System).  
Not applicable.  
The data can be obtained from the authors on request.  
The authors declare no conflict of interest.
# Three-Dimensional Finite Element Analysis for Pressure on Flexible Wall Silos
## 3 Three-Dimensional Analysis of Square Silo  
### Pressure in Flexible Wall  
A three-dimensional curve for Leighton Buzzard sand, based on the finite element results, was created to show the change in lateral wall pressure distributions across the silo, as shown in Figure 11. The effect of wall flexibility can be seen clearly in these curves by cutting various vertical sections of bulk materials.  
Figure 11: 3-D lateral wall pressure in squat flexible wall silo filled with sand.  
Figure 10: Finite element results and [PERSON] predictions along the silo height in case of the rigid walled silo.  
As seen in Figure 11, the results show that the horizontal pressure distribution is not uniform at any depth throughout the filling phase. The lateral wall pressures steadily grow as they approach the corner column, reaching their maximum value at that point.  
The middle section of bulk solids has the lowest values of lateral wall pressure due to large displacements taking place at the silo's center. The corner section experiences the most lateral wall pressure as a result of the boundary conditions of the steel column that supported the structure. Thus, flexible wall silos behave differently from rigid wall silos, with pressure governed by the relative stiffness of the granular solid and the wall. Close to the bottom, the wall pressures are influenced by the boundary conditions at the base and the interaction of the ensiled material with it.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 4 Simulation Results and Comparative Analysis
### Harmonic Contents Comparison  
Figure 14 presents a comparison of the frequency spectrum of the output current of the grid-connected inverter along with respective THD values. Table 2 summarizes the performance of the different current control techniques discussed in this paper in terms of the THD, overshoot, response time, and number of switching that occurred per 60-Hz AC cycle. The THD was calculated using six full AC cycles of the output current waveform as recommended in IEEE std. 519 [52]. When using the PI controller, the THD was highest among all the controllers at 2.30%, but the constant switching frequency means that the lower order harmonics, which are of the most concern, were concentrated together, which makes the processes of filtering out these harmonics easier. The PR controller was able to achieve a lower THD of 1.01%, and the harmonics were concentrated around lower order as in the case of the PI controller. With the hysteresis and the MPC controllers, on other hand, the frequency spectrum was widespread because of the variable switching frequency. Although the THD values were quite low at 1.60% and 1.35%, respectively, there may be difficulties in the filter design while implementing these controllers. The supplementary controller was able to reduce the overshoot in the response, while achieving a lower THD of 1.68% and response time than that of a PI controller. The THD was however higher than with the hysteresis and/or the MPC control.  
Figure 14: Comparison of the harmonic spectrum of the output current. (**a**) PI controller. (**b**) PR controller. (**c**) Hysteresis controller. (**d**) MPC. (**e**) Supplementary ADP controller. THD: Total Harmonic Distortion.
## 4 Analysis of Results
#### 4.2.2 Spatial Panel Measurement Model Inspection and Selection
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{3}{c}{**Agricultural Technology R\&D**} & \multicolumn{3}{c}{**Application of Agricultural Technology**} \\ \cline{2-7}  & **LLC** & **Fisher-ADF** & **Fisher-PP** & **LLC** & **Fisher-ADF** & **Fisher-PP** \\ \hline InGad & \(-5.581\)*** & 121.438*** & 90.619*** & \(-5.581\)*** & 121.438*** & 90.619*** \\ Inat & \(-3.517\)*** & 120.151*** & 136.382*** & \(-5.280\)*** & 143.264*** & 227.995*** \\ Infsa & \(-1.647\)** & 140.164*** & 179.808*** & \(-1.647\)** & 140.164*** & 179.808*** \\ Immii & \(-3.385\)*** & 140.730*** & 227.857*** & \(-3.385\)*** & 140.730*** & 227.857*** \\ Inadr & \(-3.282\)*** & 138.931*** & 255.860*** & \(-3.282\)*** & 138.931** & 255.860*** \\ Incps & \(-9.266\)** & 88.812*** & 110.232*** & \(-9.266\)** & 88.812*** & 110.232*** \\ Immci & \(-1.858\)** & 102.278*** & 105.064*** & \(-1.858\)** & 102.278*** & 105.064*** \\ \hline \hline \end{tabular} Note: ***, represent significance levels of 1%, 5% in turn.  
\end{table} 
Table 7: Unit root test of each variable. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**LM Test**} & \multicolumn{2}{c}{**Agricultural Technology**} & \multicolumn{2}{c}{**Application of Agricultural Technology**} \\ \cline{2-5}  & **LM Value** & \(p\)**-Value** & **LM Value** & \(p\)**-Value** \\ \hline LM-lag test & 110.743*** & \(\leq\)0.01 & 104.314*** & \(\leq\)0.01 \\ LM-error test & 94.167*** & \(\leq\)0.01 & 97.720*** & \(\leq\)0.01 \\ Robust LM-lag test & 18.179*** & \(\leq\)0.01 & 6.975*** & \(\leq\)0.01 \\ Robust LM-error test & 1.602 & \(>\)0.1 & 0.382 & \(>\)0.1 \\ \hline \hline \end{tabular} Note: *** represent significance levels of 1% in turn.  
\end{table} 
Table 8: LM test of regression results between agricultural science and technology innovation and green agricultural development under economic matrix. The LM test result can preliminarily indicate that the spatial Dubin model (SDM) should be used under the economic matrix, in addition to the LR and Wald test, as shown in Tables 9 and 10 and the test results are significant at the 1% significance level. The assumptions of \(\varsigma=0\) and \(\varsigma+\rho\beta=0\) are rejected, indicating that it will not degenerate into a spatial lag model or a spatial error model, and the spatial Dubin model should be selected for analysis.
## 3 Results and Discussion
### Changes in Substances during Fermentation
#### 3.3.2 Free Amino Nitrogen
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Amino Acid Type** & \begin{tabular}{c} **LS (10\({}^{\circ}\)P Substrate)** \\ **(mg/100 mL)** \\ \end{tabular} & \begin{tabular}{c} **10\({}^{\circ}\)P OWBL (mg/100 mL)** \\ \end{tabular} & \
\begin{tabular}{c} **Control Wort (mg/100 mL)** \\ \end{tabular} &
\begin{tabular}{c} **Sugar Cane Juice** \\ **(mg/100 mL)** \\ \end{tabular} \\ \hline Asp & 5.85 \(\pm\) 0.47 & 7.22 \(\pm\) 0.64 & 9.17 \(\pm\) 0.78 & 13.45 \(\pm\) 1.04 \\ Thr & 0.93 \(\pm\) 0.04 & 2.13 \(\pm\) 0.18 & 6.79 \(\pm\) 0.47 & 2.92 \(\pm\) 0.34 \\ Ser & 1.91 \(\pm\) 0.16 & 2.35 \(\pm\) 0.28 & 6.88 \(\pm\) 5.35 & 8.41 \(\pm\) 0.96 \\ Glu & 14.29 \(\pm\) 1.54 & 8.03 \(\pm\) 0.78 & 13.24 \(\pm\) 1.64 & 81.56 \(\pm\) 8.13 \\ Gly & 2.96 \(\pm\) 0.13 & 4.16 \(\pm\) 0.31 & 3.88 \(\pm\) 0.28 & 0.79 \(\pm\) 0.04 \\ Ala & 8.99 \(\pm\) 0.68 & 12.2 \(\pm\) 1.51 & 11.2 \(\pm\) 1.67 & 7.35 \(\pm\) 0.76 \\ Cys & 0.24 \(\pm\) 0.03 & 0.37 \(\pm\) 0.02 & 0.54 \(\pm\) 0.06 & 0.11 \(\pm\) 0.01 \\ Val & 2.54 \(\pm\) 0.03 & 8.72 \(\pm\) 0.79 & 12.32 \(\pm\) 1.36 & 5.25 \(\pm\) 0.04 \\ Met & 0.47 \(\pm\) 0.04 & 2.12 \(\pm\) 0.31 & 4.33 \(\pm\) 0.42 & 0.64 \(\pm\) 0.05 \\ Ile & 0.76 \(\pm\) 0.06 & 4.02 \(\pm\) 0.36 & 7.54 \(\pm\) 0.69 & 2.83 \(\pm\) 0.29 \\ Leu & 0.78 \(\pm\) 0.08 & 7.12 \(\pm\) 0.82 & 16.60 \(\pm\) 1.72 & 2.16 \(\pm\) 0.23 \\ Tyr & 6.06 \(\pm\) 0.53 & 10.78 \(\pm\) 1.32 & 12.96 \(\pm\) 1.32 & 2.26 \(\pm\) 0.19 \\ Phe & 2.77 \(\pm\) 0.31 & 11.26 \(\pm\) 1.41 & 16.33 \(\pm\) 1.75 & 2.12 \(\pm\) 0.18 \\ Lys & 0 \(\pm\) 0 & 5.56 \(\pm\) 0.53 & 10.07 \(\pm\) 0.99 & 0.79 \(\pm\) 0.09 \\ NH3 & 1.46 \(\pm\) 0.14 & 2.56 \(\pm\) 0.28 & 3.12 \(\pm\) 0.35 & 0.68 \(\pm\) 0.07 \\ His & 1.49 \(\pm\) 0.11 & 4.15 \(\pm\) 0.40 & 5.61 \(\pm\) 0.61 & 2.67 \(\pm\) 0.19 \\ Arg & 1.19 \(\pm\) 0.09 & 8.49 \(\pm\) 0.72 & 11.7 \(\pm\) 1.25 & 1.34 \(\pm\) 0.13 \\ Hyp & 1.53 \(\pm\) 0.08 & 2.81 \(\pm\) 0.19 & 7.94 \(\pm\) 0.81 & 3.19 \(\pm\) 0.27 \\ Pro & 35.81 \(\pm\) 2.67 & 41.71 \(\pm\) 5.96 & 41.98 \(\pm\) 3.78 & 3.16 \(\pm\) 0.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Amount of change in free amino acids and available nitrogen sources before and after fermentation.
## 4 Fault Diagnosis Based on Traditional and Interval Analytical Redundancy Relations
### Traditional Analytical Redundancy Relations Method
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault Type Number** & **Components Involved** & **Meaning** \\ \hline A & \(R_{ln-l}\) & Internal leakage of actuator \\ B & \(R_{Ex-l}\) & External leakage of actuator \\ C & \(R_{PA}R_{PB}R_{AT}R_{BT}\) & Landing gear selector valve stuck \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fault type and corresponding parameters. \[\left\{ \begin{array}{l}p_{27}=\frac{p_{20}}{A_{Rad}}=\frac{5 Sp_{2}}{A_{Rad}}\\ p_{28}=A_{Rondless}p_{24}=A_{Rondless}SSp_{4}\\ p_{29}=p_{30}+p_{31}=R_{E_{26}-f}SSQ_{2}+F_{ex}(x)\end{array} \right. \tag{24}\]  
\[r_{4}=A_{Rondless}SSp_{4}-\frac{SSp_{3}}{A_{Rad}}-R_{Eq-f}SSQ_{2}-F_{ex}(x) \tag{25}\]  
Define the residual \(r_{i}\), which has no dimensions of its own. In accordance with the correlation between parameters and ARRs, the fault signature matrix (FSM) is obtained. FSM is shown in Table 4. For the elements \(M_{mn}\), \(m\) is the number of rows, \(n\) is the number of columns in the matrix, and the relationship is:  
\[M_{mn}=\left\{ \begin{array}{ll}1&Parameter\in\text{ARR}_{i}(r_{i})\\ 0&Parameter\
otin\text{ARR}_{i}(r_{i})\end{array} \right. \tag{26}\]  
For any parameter corresponding to a lateral quantity such as \(M_{1n}=[M_{11},M_{12},M_{13},\cdots,M_{1n}]\), detectability \(D_{b}\) and isolatability \(I_{b}\) are:  
\[D_{b}=\left\{ \begin{array}{ll}1&M_{1n}=[M_{11},M_{12},M_{13},\cdots,M_{1n}] \
eq[0,\cdots,0]\\ 0&M_{1n}=[M_{11},M_{12},M_{13},\cdots,M_{1n}]=[0,\cdots,0]\end{array} \right. \tag{27}\]  
\[I_{b}=\left\{ \begin{array}{ll}1&M_{1n}=[M_{11},M_{12},M_{13},\cdots,M_{1n}] \text{ {is unique}}\\ 0&M_{1n}=[M_{11},M_{12},M_{13},\cdots,M_{1n}]\text{ {is not unique}}\end{array} \right. \tag{28}\]  
\[\left\{ \begin{array}{ll}r_{i}=\text{ARR}_{i}=0&No\ fault\\ r_{i}=\text{ARR}_{i}\
eq 0&Faulty\end{array} \right. \tag{29}\]  
When a parameter in the FSM deviates from the nominal value, the detectability indicates that the fault can be detected, but not all parameters are separable. The FSM's non-zero elements have been identified by a distinctive hue that denotes the following:  
**Red:** residual elements in the matrix that are not 0. It means that the residual in this column contains the corresponding parameter on the left side. **Blue**: detectability element that is not 0. It means that the parameters in this line have the detectability of faults. **Green**: isolability element that is not 0. It means that the parameters in this line have fault isolation.
# Technology Acceptance in Healthcare: A Systematic Review
## 4 Results
### Main Confirmed Relationships among the Influential Factors  
The classification analysis in this study included an investigation for the most confirmed hypotheses as per the recent literature. Those hypotheses were developed as a part of the proposed models within various studies, confirmed by several scholars, and considered significant for technology acceptance in the healthcare domain. It is crucial to understand those common hypotheses to let researchers understand the potential correlation between the factors within the model. Similar to the determination of key factors, understanding the potential significant correlations can help to develop and enhance acceptance theories based on the findings of previous studies [72].  
As seen in Figure 4, the most confirmed hypotheses were the significant correlation between the \"perceived usefulness\" and the behavioral intention to use a specific technology (\(N=61\)) and between the \"perceived ease of use\" and \"perceived usefulness\" (\(N=59\)). In general, the results confirmed the key relationships as hypothesized in TAM and UTAUT models. On the other hand, we cannot disregard the extensive impact of social influence, trust, anxiety, innovativeness, and computer self-efficacy factors on technology acceptance in healthcare. In other words, the frequency in Figure 4 presents the number of studies that have confirmed the significance of each hypothesis.  
Figure 4: The most confirmed hypotheses in the reviewed literature.  
Figure 3: Key factors affecting technology acceptance in healthcare.
## 3 System Design
### GNSS Output Products  
The proposed receiver produces GNSS signal products in standard open formats, enabling integration with other positioning technologies where available. These formats include Receiver-Independent Exchange Format (RINEX) Files, Radio Technical Commission for Maritime Services (RTCM) messages with configurable rates, GPS Exchange Format (GPX), Keyhole Markup Language (KML), GeoJSON, and National Marine Electronics Association (NMEA-0183) messages for sensor integration. PVT solutions are generated at a configurable rate in the World Geodetic System (WGS-84) based on Least Squares or Kalman filtering. For each visible satellite, the receiver provides time-tagged measurements of pseudorange (in m), carrier phase (in cycles) or phase range (in m), Doppler shift (in Hz) or pseudorange rate (in m/s), received signal strength (in dB-Hz), Dilution of Precision (DOP), raw navigation data, tracking correlators' output, position and Velocity (WGS-84), GPS Time, Galileo Time, and UTC time.