esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Multiport converters (MCs) are widely adopted in many applications, from renewable energy sources and storage integration to automotive applications and distribution systems. They are used in order to interface different energy sources, storage devices and loads with one single, simple converter topology in contrast to the traditional approach, which can require different solutions made by two-port converters. MCs allow for a reduction in the number of components and cascaded conversion stages with respect to an equivalent system of two-port converters, resulting in reduced complexity, dimensions and costs, as well as in improved reliability and enhanced efficiency. Nevertheless, some aspects related to the design of MCs are still worth further discussion when MCs are applied to hybrid AC/DC distribution systems. First, most converters are developed for one specific application and are not modular in structure. Furthermore, many of the proposed solutions are not equally suitable for AC and DC applications and they can introduce significant issues in hybrid distribution systems, with earthing management being particularly critical. Even though most available solutions offer satisfying steady-state and dynamic performances, fault behavior is often not considered and the possibility of maintaining controllability during faults is overlooked. Building on these three aspects, in this paper, a new MC for hybrid distribution systems is presented. An innovative circuit topology integrating three-phase AC ports and three-wire DC ports and characterized by a unique connection between the AC neutral wire and the DC midpoint neutral wire is presented. Its control principles and properties during external faults are highlighted, and extensive numerical simulations support the presented discussion.  
multiport converters; AC/DC converters; DC/DC converters; distributed generation; storage devices; sliding mode control; fault analysis Article
## 3 Review of WBASN MAC Protocols for IEEE 802.15.4 and IEEE 802.15.6
### Cross-Layer-Based Approaches  
These approaches use information from other layers to tune the MAC layer parameters. Although such approaches fulfil the requirement of the applications as they depend on the information of the other layers, there is a high chance that delay will occur, which is not tolerated for medical applications.  
Adaptive access parameter tuning (ADAPT) [84] is proposed as a cross-layer protocol to attain energy efficiency and reliability. The idea of ADAPT is to initially understand the application's reliability levels and then perform the parameter tuning. ADAPT operates under an adaption module that is capable of interacting with other layers of the ZigBee stack.  
Figure 7: Optimisation approach categorisation for IEEE 802.15.4 and IEEE 802.15.6.  
This adaption module collects information from different layers to optimise performance. ADAPT perceives requirements from the application layer and maps them to the MAC layer so that suitable tuning can be performed to the parameters, including macMinBE, macMaxCSMABackoffs and MacMaxFrameRetries. It considers single-hop and multi-hop scenarios. An optimisation problem is developed with the objective function of minimising energy. The proposed model works on a few constraints, i.e., the delay value should not increase more than the threshold and bandwidth allocation to the nodes in the network. An adaptive-variable scheme that considers the requirement of the application and the PHY layer is used to allocate the bandwidth. The framework jointly optimises the performance in terms of energy and bandwidth.  
Timely, reliable, energy-efficient and dynamic (TREnD) [85] is a cross-layer protocol that focuses on various industrial applications. TREnD enables interaction among the routing algorithm, the MAC layer and power modules to achieve the required reliability and latency. TREnD operates in inter-cluster and intra-cluster architecture by accommodating local routing and dynamic routing. A hybrid MAC protocol with TDMA/CSMA is used with local routing cases. The nodes wake up within their predefined slot time in a TDMA-based approach to save energy. The TDMA mechanism is applied in such a way that different clusters are synchronised with it. The receiving nodes send the multicast beacons to stay in the topology.
# Resistance of \(L\). _monocytogenes_ and \(S\). Typhimurium
## Materials and Methods
### Statistical Analysis, Modelling, Parameter Estimation, and Estimation of Sub-Lethal Injury  
For the first part of this research, analysis of variance (ANOVA) tests were performed to determine whether there are any significant differences amongst means of (i) the total mass of the untreated biofilms, (ii) the cell densities prior to CAP treatment, and (iii) the obtained log-reductions following 10 min of CAP treatment. A confidence level of 95.0% (\(\alpha\) = 0.05) was applied and [PERSON]'s Least Significant Difference (LSD) test was used to distinguish which means were significantly different from others. Standardized skewness and standardized kurtosis were used to assess if the data sets came from normal distributions. The analyses were performed using the Statgraphics 18 software (Statistical Graphics, Washington, DC, USA). Significant differences have been indicated with a different letter, with 'a' or 'A' bearing the lowest value.  
In the second part of this research, the model of [PERSON] et al. [27], describing a microbial inactivation curve consisting of a log-linear inactivation phase and a tail (Equation (1)), was used to fit the experimental data obtained for the two selected biofilm ages.  
\[N(t)=(N_{0}-N_{res})\cdot e^{-k_{max}\cdot t}+N_{res} \tag{1}\]  
where \(N(t)\) [CFU/cm\({}^{2}\)] is the cell density at time \(t\) [min], \(N_{0}\) [CFU/cm\({}^{2}\)] the initial cell density (prior to treatment), \(N_{res}\) [CFU/cm\({}^{2}\)] is a more resistant subpopulation, and \(k_{max}\) [1/min] is the maximum specific inactivation rate. Based on the difference between \(\log_{10}N_{0}\) and \(\log_{10}N_{res}\), the final log-reduction (following 30 min of CAP treatment) has been calculated for each biofilm age.  
The parameters of the [PERSON] et al. [27] model were estimated via the minimization of the sum of squared errors (SSE), using the lsqnonlin routine of the Opzimization Toolbox of Matlab version R2015b (The Mathworks, Inc., Natick, MA, USA). At the same time, the parameter estimations were determined based on the Jacobian matrix. The Root Mean Squared Error (RMSE) served as an absolute measure of the goodness of the model to fit the actual obtained data.  
To calculate the percentage of sub-lethal injury (% SI), theoretical concentrations that were obtained from the model of [PERSON] et al. [27] were used for both the general and selective counts. The equation of [PERSON] [28] (Equation (2)) was used to determine the percentage of injured cells at each treatment time. As a result, the percentage of sub-lethal injury was plotted as a function of the treatment time.  
\[\%\text{SI}=\frac{\text{CFU general medium}-\text{CFU selective medium}}{\text{CFU general medium}}.100 \tag{2}\]  
Finally, for each parameter of the [PERSON] et al. [27] model, different ANOVA tests (for each microorganism separately) were performed by making use of the Statgraphics 18 software.
## 5 Conclusions  
The inherit asymmetric rate-dependent hysteresis characterization in the DEAP actuator is a barrier to achieving high-precision trajectory tracking control. In this paper, we establish a Hammerstein model to characterize the asymmetric rate-dependent hysteresis and employ LM algorithm to identify the parameters of the MPI model and the ARX model. In order to suppress the asymmetric hysteresis behavior, the inverse MPI model is used to be the hysteresis feedforward compensator. Furthermore, to overcome the challenge of the estimation error \(\tilde{\mathbf{D}}(k)\) and other perturbations, based on the hysteresis feedforward compensator, a novel DTSMC is proposed for the high-precision tracking control of the DEAP actuator, and the system stability is analyzed. A conventional DSMC with the hysteresis compensator is implemented for comparison.  
The above two control schemes are tested under different frequencies' input signals, including 0.1 Hz, 0.2 Hz, 0.3 Hz, 0.4 Hz, 0.5 Hz and a hybrid frequency. Noting that, in the DSMC and DTSMC schemes, the switching control may make the chattering occur. To eliminate the chattering, we employ the smoother saturation function to replace the signum function in this work. When the input signal frequency is increased, or the input signal is a hybrid frequency, compared with the DSMC with hysteresis compensator, experimental results show that the proposed DTSMC with the hysteresis compensator can improve the tracking accuracy, robustness, and error convergence of the DEAP actuator. The hysteresis compensation effect of the proposed control scheme is significantly higher than that of the DSMC scheme.  
[PERSON] proposed the controller and wrote the manuscript. [PERSON] and [PERSON] designed experimental and analyzed the results. [PERSON] and [PERSON] provided some improvement strategies to improve the accuracy of the model and helped verify the effectiveness of the control strategy. [PERSON] and [PERSON] revised and edited the paper.  
This research was funded by the National Science Foundation of China (61472037, 61375100).  
All authors declare that there is no conflict of interest
# Recent Progress in Synthesis and Photonic Applications of Two-Dimensional Bismuthene
## 4 Photonic Applications Based on 2D Bismuth
### Photodetectors
By utilizing such a structure, the separation of carriers and light absorption are more effective than those of Bi/Si, leading to the detectivity of up to 1.36 \(\times\) 10\({}^{13}\) Jones, which outperforms TMDs-based photodetectors [94; 95; 96]. Furthermore, the detectivity of the device maintains a constant value of 10\({}^{10}\) Jones under different external biases (Figure 6f), which can be explained by the equally balanced trade-off between the photocurrent and the dark current.
## 4 Evaluation  
### Stretching Force  
An experiment is designed to measure the stretching force of VSFH to quantitatively demonstrate that VSFH can provide precious stretching force for stroke patients and the validity of the relationship between hinge thickness and force in the simulation model. Referring to the finger stiffness of stroke patients with spasm [29], the MCP joint of the index finger requires extension torque of 0.46 Nm so that VSFH needs to provide a 5.51 N stretching force.  
The experiment setup is on a stationary platform that provided the slide track. The motor and VSFH for the test are fixed on the platform by a fixed block. The body and the force end of the dynamometer are connected to the motor and the free end of VSFH by ropes, respectively. In addition, the VSFH for the test includes a support plate for fixation, a VSFH 3D printed according to (21) and (22) and Table 1 (\(h_{1}\), \(h_{2}>>\) printing accuracy), and a limit board used to simulate the connecting rope. In all experiments where the motor is used, the working parameters of the motor are the same, including 10 s forward rotation time and 10 s reversal time in one cycle, the corresponding maximum rotation angle is 270\({}^{\circ}\), and the diameter of the capstan is 25 mm. Figure 7 shows the experimental setup.  
The limit board at the free end of VSFH imitates the function of the connection rope in its original state by providing a limit so that the length of the limit board is \(l_{\rm r}\) and the maximum flexion angle of VSFH is \(\theta_{\rm a}\). In the process of measurement, the motor pulls the dynamometer at a constant speed to bend VSFH with or without the limit board. The stretching forces provided by VSFH are recorded in real-time and compared. In addition, in the measurement experiment with the limit board, the motor is reversed in advance artificially to protect the limit board. Figure 8 shows the results of the experiment.  
Results with limit board show that the stretching force changed abruptly at a certain point in time, indicating that the limit board touches the support plate used to imitate a human finger at this time and the value of abrupt point is the magnitude of the original stretching force. Results without limit board show the variation of the stretching force during a complete driving cycle, which indicates that we can change the material of the hinge to reduce the rate of change in stretching force, thus further improving the stability of hinge flexion.  
Compared to the required stretching force for stroke patients, the actual stretching force can provide 5.70 N with an error of 3.4%, indicating that the simulation model is correct. The corresponding VSFH can be designed to provide different precise stretching forces and resistance according to the needs of patients.  
Figure 8: Results of the stretching force of VSFH for stroke patients.  
Figure 7: Experiment setup for measuring the stretching force of VSFH.
# Vulnerability Detection and Classification of Ethereum Smart Contracts Using Deep Learning
## 3 Methodology
### Performance Metrics  
Performance metrics such as precision, recall, accuracy, and F1-score are calculated to evaluate the effectiveness of the algorithms used in classifying instances with multiple labels and binary labels.  
Confusion Matrix: The confusion matrix evaluates the performance of classification models by measuring metrics such as Accuracy, Recall, and Precision using values defined in Table 1. These metrics can be used to determine the success of the used model [58]. The confusion matrix also gives future insights into the maintenance, processing, and comparison of the models, to improve the classification.  
* Accuracy: This is one of the most popular metrics in multi-class classification. Accuracy represents the ratio of correctly predicted instances to the overall correctness of predictions, as illustrated in Equation (1) [58; 59]. \[\text{Accuracy}=\frac{TP+TN}{P+TN+FP+FN}\] (1)
* Recall: This is the fraction between True Positive elements (correctly predicted positives) divided by the total number of positively classified units (all actual positives) [58; 59]. \[\text{Recall}=\frac{TP}{TP+FN}\] (2)  
Figure 14: Multi-Label classification processes.  
Precision: This is the fraction between predicted correct labels (True Positive elements) divided by the total number of positively predicted units [58; 59]. Precision = \(\frac{\text{TP}}{\text{TP}+\text{FP}}\) (3)
* F1-Score: This is the harmonic meaning between precision and accuracy. Also, the F1-score is used to provide a balanced assessment of a model's performance and handle the complexities introduced by the multiple labels in multi-label classification [60].
# A Joining Procedure and Synchronization for TSCH-RPL Wireless Sensor Networks
## 3 Related Work
The different simulations in this work consist of varying the density of nodes as well as the transmission period of the EBs, and the authors arrive at the conclusion that a random period manages to improve the connection times in denser networks. Vallati et al. [30] is one of the few works that analyses problems with the joining time in TSCH networks while considering the entire protocol stack, including the routing protocol RPL. A comparison between a minimal configuration defined by 6 TiSCH and a dynamic resource management algorithm has been proposed, showing the improvements that can be made to the transmission of EBs and RPL control messages during network formation. In this work, a study is carried out to improve the synchronization process and the initial connection in a WSN, analysing the basic parameters that directly intervene in the synchronization phase, regardless of the number of nearby devices of the waiting/joining node. In addition, unlike the abovementioned studies, the nodes' behaviour is analysed to obtain a fully deployed network, using TSCH to synchronize the devices and the RPL protocol for the construction of the topology and the creation and maintenance of routes. This is relevant because all of the traffic that circulates through the network during both the synchronization phase and the connection phase is taken into account, and allows us analyse the impact of the RPL protocol when used in combination with a time-synchronized and frequency-hopped access mechanism.
## 5 Fall Detection Application
### Experimental Results
#### 5.2.2 Sitting Experiment  
For the scenario of a sitting person, we measured the test subject's spectral signature using the radar, as shown in Figure 6b. We also tracked the movement of his head using video processing. Figure 9 shows three frames from the video of the sitting test subject; the red markings show the tracking of his head movement. We chose to track the head because it is the uppermost part of the body and therefore has the highest velocity during this motion. Figure 10 shows the results of the two experiments described above.  
The fusion of the sensor's radar and video helps to verify the results. However, in practice, the system will be radar-based only. Therefore, the following analysis deals with the results obtained from the spectrogram. Figure 10 clearly shows the accuracy of the spectrogram result. We split the head motion into four parts in order to better understand the spectrogram result. First, in the time interval 2.6 s \(<t<\) 3.4 s, the head is accelerating forward with the test subject's body and the frequency changes in the range \(0<f_{d}<\) 0.695 kHz; therefore, there is a positive slope of \(m_{p}=0.868\) kHz/s. Second, in the time interval 3.4 s \(<t<\) 3.7 s, the subject accelerated backward while their velocity was still in a forward direction up to the point where their velocity reached 0.  
Figure 8: Presentation of the measurement results for a falling man. The spectral signature was measured using the MMW radar, and the red line indicates the data obtained through head tracking in a recorded video.  
Figure 9: Process for the video analysis of a sitting man, tracking head motion: (**a**) standing; (**b**) sitting; (**c**) seated.  
The Doppler frequency, from the spectrogram, decreased to \(f_{d}=0.38\) kHz and the slope of this part was \(m_{n}=0.393\) kHz/s. Third, in the time interval \(3.7\) s \(<t<4.2\) s, the subject's head movement and velocity were in a backwards direction. The frequency increased to \(f_{d}=0.8\) kHz with a slope of \(m_{p}=0.814\) kHz/s. Finally, in the time interval \(4.2\) s \(<t<5\) s, the frequency decreased to \(f_{d}=0\). In this interval, the frequency has a decreasing slope of \(m_{n}=1\) kHz/s.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Total (SD)** \\ **N = 74** \\ \end{tabular} & \begin{tabular}{c} **Injured (SD)** \\ **N = 31** \\ \end{tabular} & \begin{tabular}{c} **Healthy** \\ **Control (SD)** \\ **N = 43** \\ \end{tabular} & \
\begin{tabular}{c} **_p_** \\ **(95\% CI)** \\ \end{tabular} &
\begin{tabular}{c} **Mean Differences** \\ **_p_** \\ \end{tabular} \\ \hline
**Gait cycle [s]** & 1.14 (0.19) & 1.23 (0.26) & 1.07 (0.06) & \textless{}0.001 & 0.16 (0.06–0.26) & 0.0029 \\ \hline
**Cadence [gait cycle/s]** & 99.29 (20.95) & 83.30 (20.31) & 110.81 (11.99) & \textless{}0.001 & 27.51 (19.29–35.73) & \textless{}0.0001 \\ \hline
**Stride [cm]** & 112.05 (13.63) & 104.2 (14.84) & 117.7 (9.34) & \textless{}0.001 & 13.5 (7.42–19.58) & \textless{}0.0001 \\ \hline
**Step time [s]** & 0.55 (0.11) & 0.57 (0.16) & 0.53 (0.04) & 0.002 & 0.06 (0.02–0.10) & 0.133 \\ \hline
**Step lenght [cm]** & 56.03 (6.8) & 52.1 (7.37) & 58.85 (4.67) & \textless{}0.001 & 6.75 (3.72–9.78) & \textless{}0.0001 \\ \hline
**Double support [s]** & 0.49 (0.13) & 0.58 (0.14) & 0.42 (0.07) & \textless{}0.001 & 0.16 (0.10–0.22) & \textless{}0.0001 \\ \hline
**Unipodal support [s]** & 0.34 (0.09) & 0.33 (0.013) & 0.35 (0.02) & 0.28 & 0.05 (0.02–0.08) & 0.262 \\ \hline
**Speed [m/s]** & 0.94 (0.26) & 0.73 (0.25) & 1.08 (0.12) & \textless{}0.001 & 0.35 (0.25–0.45) & \textless{}0.0001 \\ \hline
**Acceleration [m/s\({}^{2}\)]** & 0.0003 (0.004) & 0.00032 (0.004) & 0.00023 (0.002) & 1.0 & 0.0019 (0.0020–0.0018) & 0.925 \\ \hline
**Contact phase [s]** & 0.81 (0.13) & 0.88 (0.15) & 0.74 (0.05) & \textless{}0.001 & 0.14 (0.08–0.20) & \textless{}0.0001 \\ \hline
**Load response [s]** & 0.25 (0.06) & 0.29 (0.06) & 0.21 (0.03) & \textless{}0.001 & 0.08 (0.05–0.11) & \textless{}0.0001 \\ \hline
**Phase flatfoot [s]** & 0.39 (0.13) & 0.33 (0.16) & 0.44 (0.09) & 0.0023 & 0.11 (0.05–0.17) & 0.0011 \\ \hline
**Preswing [s]** & 0.24 (0.06) & 0.29 (0.06) & 0.21 (0.03) & \textless{}0.001 & 0.08 (0.05–0.11) & \textless{}0.0001 \\ \hline
**Propulsion [s]** & 0.31 (0.17) & 0.45 (0.15) & 0.21 (0.1) & \textless{}0.001 & 0.23 (0.17–0.29) & \textless{}0.0001 \\ \hline
**Swing phase [s]** & 0.34 (0.09) & 0.35 (0.13) & 0.32 (0.02) & 0.28 & 0.05 (0.02–0.08) & 0.262 \\ \hline \hline \end{tabular} Note: SD = standard deviation; 95% CI = 95% confidence interval; cm: centimeter; m: meter; s: second.  
\end{table} 
Table 2: Values of the spatio-temporal gait parameters, and mean differences between groups. Figure 3: _Cont._
## 3 Our System Model and Primer
### Distances and Base Station Moving (d)  
The flying base station can control its physical location, and the flying base station location with respect to the user; the stationary base station affects the wireless communication performance. The distance between the user (\(U\)) and the _flying_ base station (\(F\)) is \(d_{F\!U}\) and the distance between the flying base station (\(F\)) and the _stationary_ base station (\(S\)) is \(d_{FS}\), as shown in Figure 1. The distance between the user and the stationary base station is \(d_{S\!U}\). The flying base station--due to its mobility--can control its distance to the user (\(d_{F\!U}\)) and to the stationary base station (\(d_{FS}\)).