esa-sceva/satcom-chunk-collection huggingface dataset:
# Do Oral Antiseptics Affect the Force Degradation of Elastomeric Chains?
###### Abstract  
Objective: This study aimed to compare the force degradation of elastomeric chains submerged in commonly used mouthrinses. Methods: One hundred samples of elastomeric orthodontic chains from five different brands (Ormco, GC, RMO, Forestandent, and 3M Unitek) were initially activated on double length, and the force was measured with a universal mechanical testing machine. Then, elastomeric modules were thermocycled and immersed into four different mouthrinses: Octenident, Vitis Orthodontic, Perio Plus+, and Listerine through a total number of three cycles which simulated 30 days of intraoral exposure. Force decay was measured after each cycle. Results: All specimens showed statistically significant force degradation over the tested period (\(p<0.001\)). After thermocycling and immersion in oral antiseptics, the lowest measured force was found in Forestadent EOC in Listerine with a median of 70 chN (70-75 chN) and Vitis orthodontic with a median of 70 chN (70-80 chN). On the contrary, the least prone to force reduction was the control group of Ormco 280 chN (275-285 chN) and RMO 280 chN (270-280 chN). Conclusions: Elastomeric chains' force degradation could be exacerbated by the use of mouth rinses. These data could be beneficial in choosing the appropriate combination of elastomeric chain and mouthrinse for optimal results of orthodontic therapy.  
orthodontics; antiseptics; orthodontic appliances; elastomeric chain; dentistry; degradation Article
### Comparison and Discussion  
SPIN and directed diffusion are both application-specific and subject-based communication protocols for wireless sensor networks. Application-specific is the trend of networking service design in sensor networks. Resource management and energy-efficient routing has to be done tightly-coupled with application in order to achieve energy-efficient communication.  
Both protocols, in contrast to the traditional routing protocols, are data-centric which means all communications are based on the named data. In directed diffusion the data is named or identified by the attributes and in SPIN the data is named by metadata.  
Both protocols are designed to be a reliable communication medium. The SPIN protocol is developed to overcome the classic flooding by negotiating between the nodes before the actual data is transmitted. Thus SPIN is sender initiated data communication protocol.  
In the case of directed diffusion, the receiver and sender diffuse interests onto the network and if the interest matches, then the sender publishes the data and the data is drawn down toward the receivingnode. Therefore, in the directed diffusion protocol, the data communication is receiver-initiated.  
Both protocols are energy-aware. In SPIN, the nodes poll the available resources and SPIN nodes can make decisions adaptive to the resource available. Similarly in directed diffusion, the Geographic and Energy Aware Routing (GEAR) helps in propagating interest messages to a specified geographical location defined by latitude and longitude, localizing the flooding in the network and helping to be more application specific. In a sense, both the protocols are flooding-based but vary from the classic-flooding due to the implementation of different mechanisms to control the unwanted flooding in the network.
## 2 Preliminaries
### Fully Secure CP-ABE  
[PERSON] et al. [33] proposed a CP-ABE scheme that is fully secure. The scheme is built using composite order bilinear groups and \(LSSS\). Four algorithms can be executed in polynomial time:  
* \(Setup(\varphi,\mathcal{U})\rightarrow\ PK,MSK\): The setup procedure receives two input parameters: the security parameter \(\varphi\), which determines the level of security required, and the attribute universe \(\mathcal{U}\), which defines the set of attributes. It then generates two output values: the public parameter \(PK\), which can be shared publicly and used for encryption and decryption, and the master key \(MSK\), which is kept secret and used for key generation.
* \(KeyGen(MSK,\mathcal{S},PK)\to SK\): Given the master key \(MSK\), the user's attribute \(\mathcal{S}\), and the public parameter \(PK\) as input, the key generation algorithm computes the decryption key \(SK\) as its output. The decryption key \(SK\) can be used to decrypt data encrypted using the corresponding attribute \(\mathcal{S}\) and the public parameter \(PK\).
* \(Encrypt((A,\rho),PK,\mathcal{M})\to CT\): To encrypt a plaintext \(\mathcal{M}\), the encryption algorithm takes as input a matrix \(A\), where each row of the matrix is mapped to an attribute \(\rho\), along with the public parameter \(PK\). The encryption algorithm computes the ciphertext \(CT\) as its output.
* \(Decrypt(CT,PK,SK)\rightarrow\ \mathcal{M}\): Given the ciphertext \(CT\), the public parameter \(PK\), and the decryption key \(SK\), the decryption algorithm computes the corresponding plaintext \(\mathcal{M}\) as its output. The decryption key \(SK\) must be associated with an authorization set mapped to rows of the matrix used during encryption, otherwise the decryption will fail.
## 4 Experimental Results
### Price Forecasting
#### 4.3.2 Price Forecasting at the Daily Level  
The results of price prediction for the next 5 and 10 days at a daily level are compared in Table 5 with 30 iterations. The accuracy of the 5-day forecast is better than that of the 10-day  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Hours**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Garlic**} & \multicolumn{3}{c}{**Ginger**} & \multicolumn{3}{c}{**Chili**} \\ \cline{2-10}  & & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline \multirow{4}{*}{5} & GRU & 0.008 & 0.009 & 0.028 & 0.029 & 0.032 & 0.052 & 0.002 & 0.002 & 0.019 \\  & LSTM & 0.01 & 0.011 & 0.034 & 0.046 & 0.048 & 0.083 & 0.003 & 0.003 & 0.03 \\  & TCN & 0.007 & 0.007 & 0.022 & 0.029 & 0.033 & 0.053 & 0.003 & 0.003 & 0.035 \\  & TCN-Attention & 0.006 & 0.008 & 0.021 & 0.045 & 0.047 & 0.082 & 0.003 & 0.004 & 0.038 \\ \hline \multirow{4}{*}{10} & GRU & 0.007 & 0.008 & 0.024 & 0.03 & 0.031 & 0.053 & 0.041 & 0.129 & 0.471 \\  & LSTM & 0.009 & 0.009 & 0.029 & 0.043 & 0.046 & 0.077 & 0.032 & 0.100 & 0.372 \\ \cline{1-1}  & TCN & 0.006 & 0.006 & 0.019 & 0.032 & 0.034 & 0.057 & 0.044 & 0.129 & 0.507 \\ \cline{1-1}  & TCN-Attention & 0.007 & 0.007 & 0.022 & 0.044 & 0.047 & 0.079 & 0.038 & 0.11 & 0.436 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Forecasting at the hourly level.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variety** & **Method** & **MAE** & **RMSE** & **MAPE** & **\(\mathbf{R^{2}}\)** \\ \hline \multirow{4}{*}{Ginger} & GRU & 0.034 & 0.051 & 0.066 & 0.935 & 23,808.89 \\  & LSTM & 0.036 & 0.057 & 0.08 & 0.917 & 19,775.038 \\  & TCN & 0.029 & 0.043 & 0.061 & 0.953 & 4930.271 \\  & TCN-Attention & 0.035 & 0.053 & 0.072 & 0.93 & 5264.069 \\ \hline \multirow{4}{*}{Chili} & GRU & 0.056 & 0.088 & 0.157 & 0.948 & 14,011.354 \\  & LSTM & 0.058 & 0.093 & 0.195 & 0.942 & 11,406.636 \\ \cline{1-1}  & TCN & 0.059 & 0.094 & 0.194 & 0.941 & 2914.418 \\ \cline{1-1}  & TCN-Attention & 0.061 & 0.092 & 0.197 & 0.942 & 3155.873 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 8: The forecasting results for garlic in the next 10 days: (**a**) 30 epochs; and (**b**) 100 epochs.  
forecast. For garlic, TCN achieved the lowest MAE and RMSE in price forecasting. For chili, TCN-Attention demonstrated better predictive performance in terms of MAE, RMSE, and MAPE; however, it is worth noting that the forecasting accuracy at the daily level was lower than that at the hourly level, and the attention mechanism was not always effective.
# Overview of Predictive Control Technology for Permanent Magnet Synchronous Motor Systems
## 3 An Overview of the Recent Development for Predictive Control Methods of PMSM
### FCS Model Predictive Control of PMSM
#### 3.2.3 Multi-Step Predictive Control  
As shown in Figure 2, when the traditional FCS-MPC acts on the three-phase PMSM it optimizes eight voltage vectors, while the number of vectors of the polyphase motor is more. Therefore, the single-step prediction will cause a delay, as shown in Figure 8.  
In order to prevent prediction delay and low precision of single-step prediction, the \"multi-step prediction\" method was proposed [121, 122, 123]. For example, the model predictive torque control based on two-step prediction is proposed in [124]. First, the state of the  
Figure 8: Prediction sequence diagram. (**a**) Ideal situation; (**b**) actual situation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Conventional** & **Auxiliary Voltage** & **Double Vector** & **Three-Vector** \\  & **MPC** & **Vector MPC** & **MPC** & **MPC** \\ \hline Executive time (s) & 18 & 24 & 31 & 33 \\ Torque ripple (Nm) & 1.21 & 1.06 & 0.78 & 0.86 \\ Current THD & 13.24 & 8.75 & 7.05 & 5.83 \\ analysis (\%) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Numerical Comparison under Different Model Predictive Control Strategies.  
Figure 7: Voltage vector range of three methods based on MPTC. (**a**) One-vector-based. (**b**) Two-vector-based. (**c**) Three-vector-based.  
motor is predicted in one step to obtain the state of the motor in the next sampling period, and then the optimal voltage vector is calculated through a secondary optimization process, which is applied to the inverter drive motor.  
In [125; 126], two-step prediction is used to compensate for the delay. In [127], the Heun method is used to predict the current at the time of \(k+1\), and two-step prediction is carried out on the basis of this method. In [128], a correct PMSM based on numerical analysis is proposed. This method replaces the error caused by traditional forward Euler discretization. In [129; 130], a multi-step predictive control strategy is proposed. First, the optimal and suboptimal current predictive values are calculated by the enumeration method, and then the current. However, multi-step prediction also leads to long calculation times and a more complex prediction process.
## 4 Experimental Results
### Performance Comparisons of the Proposed PAPMCC Algorithm with the Conventional Robust AP Algorithms  
Two conventional robust algorithms were taken into account for comparison to deal with impulsive noise, namely, APS and PAPS algorithms. Herein, the step sizes of the APS and PAPS algorithms were set to 0.005, while the step sizes for the PAP and improved PAP (IPAP) algorithm [49] were set to 0.5, and the bound of set-membership PAP (SM-PAP) algorithm [34] was set to \(\sqrt{2\sigma_{r}^{2}}\) where \(\sigma_{r}^{2}\) represents the power of the noise. The other parameters were consistent with the previous simulations. The results presented in Figure 11 indicate that the proposed PAPMCC algorithm could still achieve the lowest steady-state error.  
Figure 11: Performance comparisons of the proposed PAPMCC algorithm with the conventional robust AP algorithms. Input signal: colored.  
Figure 10: Effects of SNR on the PAPMCC algorithm.
## 3 Results
### Power Density for Different Thermal Conductivity  
Device simulations were conducted for different materials with different thermal conductivity distributions in order to verify that the discrepancy between the concentrations of the maximum power density and the maximum ZT is caused by the thermal conductivity. Materials A, B, and C were obtained by multiplying the thermal conductivity and electrical conductivity of the previously simulated P-type BiSbTe\({}_{3}\) material (Material D) by 0.4, 0.6, and 0.8, respectively. In this case, the ZT distribution of materials A, B, and C is assumed to remain the same as that of material D because the thermal conductivity change in the denominator of the ZT equation can be compensated for by electrical conductivity in the numerator.  
Figure 4a shows the ZT and thermal conductivity distribution according to the carrier concentration of each material. The power density values of the device for materials A, B, C, and D are shown in Figure 4b. The distribution of power density changed with the change of the thermal conductivity of the material, although ZT is the same. As the thermal conductivity decreases, the power generation of the device increases on the human body, which is consistent with the result of [PERSON] et al. However, it is observed that the carrier concentration of maximum power density approaches that of the maximum ZT as the material thermal conductivity becomes lower.  
Therefore, in order to compensate for the power loss caused by the high thermal conductivity at the ZT maximum point, the carrier concentration of the maximum power density needs to shift to the left by reducing the thermal conductivity, although ZT decreases. If the thermal conductivity is low enough at the ZT maximum point, as with material A, the maximum point of power density is barely shifted.  
Figure 3: The material ZT and the power density of P-type BiSbTe\({}_{3}\) according to carrier concentration. The carrier concentration of the maximum power density shifted to the left side from the ZT maximum point.  
As a result, in order to use the thermoelectric material to harvest human body heat, it is necessary to design a material at the optimum carrier concentration calculated from the device simulation, rather than engineering it at the maximum ZT. However, if the thermal conductivity of the material is distributed below a certain value, a shift of maximum power density is hardly observed. In this case, the power density at the carrier concentration of the maximum ZT is close to the maximum power density.
## 4 Data Collection
### Traffic Data and Accident Data  
The traffic volume on the analyzed existing road sections during the reference period (9 years from 2014 to 2022) was extracted from the regional web portal [44]. In particular, the traffic is known at three survey stations located close to the road section under study. Historical accident data were collected for the analyzed road section for the same time interval (2014-2022). Crash data at the selected sites were retrieved both from the web-page of the municipality [45] and the portal of the National Statistical Institute (ISTAT) [46]. The Annual Average Daily Traffic (AADT) values and the overall number of crashes that occurred during the considered time interval on the analyzed road are synthesized in Table 1. The traffic values refer to both directions of travel as these are single carriageway roads. Consequently, the number of observed accidents associated with each road section includes accidents occurred in both directions. The location of the available traffic sections on the map is illustrated in Figure 1a.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multicolumn{6}{c}{**Annual Average Daily Traffic—AADT**} & \multicolumn{6}{c}{**Observed Crashes with Injuries and Fatalities (\(\mathbf{N_{obs}}\))**} \\ \hline \multirow{3}{*}{**Year**} & \multicolumn{3}{c}{**Traffic Section**} & \multirow{3}{*}{**Year**} & \multirow{3}{*}{**Crashes**} & \multirow{3}{*}{**Injuries**} & \multirow{3}{*}{**Fatalities**} \\  & **North** & **Centre** & & & & & \\ \hline
**2014** & 13,665 & 12,416 & 7883 & **2014** & 19 & 27 & 0 \\
**2015** & 13,468 & 12,602 & 8136 & **2015** & 22 & 42 & 0 \\
**2016** & 13,448 & 12,649 & 8249 & **2016** & 24 & 35 & 0 \\
**2017** & 11,866 & 12,682 & 8377 & **2017** & 23 & 40 & 0 \\
**2018** & 10,850 & 13,331 & 8499 & **2018** & 32 & 53 & 3 \\
**2019** & 10,861 & 12,393 & 8401 & **2019** & 20 & 38 & 1 \\
**2020** & 8873 & 9464 & 7030 & **2020** & 28 & 47 & 0 \\
**2021** & 10,882 & 11,033 & 8052 & **2021** & 21 & 47 & 0 \\
**2022** & 12,370 & 11,783 & 8226 & **2022** & 32 & 48 & 1 \\ \hline
**Average** & 12,010 & 12,068 & 8073 & **Total** & & & \\ (_Standard deviation_) & (1637) & (1097) & (422) & (9 years) & 221 & 377 & 5 \\
**Minimum** & 8873 & 9464 & 7030 & **Average** & & & \\ (_1 year_) & 25 & 42 & 1 & & & \\
**Maximum** & 13,817 & 13,331 & 8499 & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Summary statistics of traffic data (AADT) and crash data.
# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
## 5 Experimental Results and Discussion
### Experiment 4: Comparing the Impact of Different Exploration Strategies on the K-L Algorithm  
To further validate the advantages of the K-L algorithm in exploring strategies, using the same area with the same start point and end point, we compare the SSA exploration strategies of the K-L algorithm with different representative strategies, including the \(\varepsilon\)-decreasing strategy [24], the action selection strategy [15], and the traditional \(\varepsilon\)-greedy strategy. As shown in Figure 10, three raster maps are used in the experiments, sized 10 \(\times\) 10, 20 \(\times\) 20, and 30 \(\times\) 30, with randomly placed static obstacles, as well as a dynamic obstacle. The dynamic obstacle moves back and forth between the upper raster and the lower raster of its starting position. The average results of path lengths and convergence times of 1500 repeated experiments are shown in Figures 11 and 12.  
As shown in Figure 11, the exploration strategy based on the SSA achieves better results in terms of path length, followed by the \(\varepsilon\)-decreasing strategy, action selection strategy, and \(\varepsilon\)-greedy strategy. Because the \(\varepsilon\)-decreasing strategy is based on linear changes, it cannot be adjusted dynamically according to the current situation, so it is poorly optimized against dynamic obstacles. On the other hand, the action selection strategy is only good at avoiding dynamic obstacles, whereas the SSA strategy is not easily affected by map changes due to its ability to adaptively adjust.  
Figure 12 shows that SSA has an advantage in terms of convergence time. The \(\varepsilon\)-decreasing strategy is the fastest among the investigated strategies, followed by the action selection strategy and the \(\varepsilon\)-greedy strategy. Because SSA is dynamically adaptive, it can effectively balance exploration and utilization as the map changes; as a result, the K-L algorithm converges quickly and can find the optimal path with high probability.
## 2 Materials and Methods
### Implementation of the NPF Encoding  
The implementation of the proposed scheme is available at [[https://github.com/qua11q7/NPFcoding](https://github.com/qua11q7/NPFcoding)]([https://github.com/qua11q7/NPFcoding](https://github.com/qua11q7/NPFcoding)). The Figure 3 presents the speed performance of the implementation on randomly generated files of size 1, 10, 100, and 1000 megabytes respectively. The machine used in the experiments had 16 GB memory and Intel i5-6600 CPU with Ubuntu 18.04.2 operating system. For fast processing the implementation follows a similar scheme as in the case of Huffman decoding with lookup tables [19]. Empirical observations showed that this type of look up make sense on small \(d\) values, particularly  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**d**} & \multicolumn{2}{c}{**Block Size in Bits**} \\ \cline{2-3}  & **min** & **max** \\ \hline
6 & 384 & 875 \\
8 & 2 K & 6.7 K \\
10 & 10 K & 43 K \\
12 & 49 K & 256 K \\
14 & 230 K & 144 K \\
16 & 1 M & 7 M \\
18 & 4.7 M & 39 M \\
20 & 21 M & 194 M \\ \hline \hline \end{tabular}
\end{table}
Table 4: The minimum (\(d\cdot 2^{d}\)) and maximum (\(d\cdot 2^{d}\cdot(\frac{d-1.44}{2})\)) block sizes that are appropriate according to the proposed non-UD coding scheme for selected \(d\) values.  
on \(d=4\) and \(d=8\). However, on larger \(d\) values due to the expansion of the number of tables as well as their sizes the performance is not improved. In the regarding tables, the \"NoMap\" option means look up tables are not used. We observed that the NPF coding maximizes its speed both in coding and decoding process, when \(d\) is chosen to be 8, where around 170 megabyte per second is achieved. The performance does not change much with the file size, and seems totally dependent on the \(d\) parameter. The payload and disambiguation sizes and overall percentages are also given and observed to be consistent with the theoretical calculations. The decoding speed is about two times of the encoding speed when \(d\) is chosen to be 16 or 20, where it reaches 200 MB per second for \(d=16\) on 1 GB files. More details about the implementation is available on the mentioned code distribution web site.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Next-Hop Selection  
As with all routing protocols, each sensor selects the next-hop towards the destination according to a set of information. The next-hop can be selected in different ways such as Broadcast based, location-based, content-based, probabilistic, opportunistic and hierarchical-based protocols. We detail each of these categories in the following.
## 2 Challenging Open Issues
### Other Open Issues
#### 2.2.8 Quality of Services  
In every application, Quality of Services, or QoS, must be carefully considered. Each application has its own requirements: maximum delay, data rate, packet loss, bit error rate, _etc_. When these requirements are not met, there might be issues for the applications. For example, an electrocardiogram used for monitoring heart rate in the middle of surgery must provide a real-time measurement of the heart rate of the patient. If it exceeds the acceptable delay or latency value, then it becomes useless. The routing protocols in [18; 19; 20] try to improve the QoS.
# Effects of Fractional Time Delay as a Low-Power True Time Delay Digital Beamforming Architecture
## 5 Implementation Results and Analysis  
The Simulink fractional delay processing chain in Figure 4 was implemented in Verilog to determine the resource and power consumption for a single channel. An FPGA target  
Figure 5: Antenna pattern of proposed architecture when steering to \(\theta_{QSLL}\). Arrow denotes worst-case quantization sidelobe.  
Figure 6: Beamsqunit investigation of the proposed architecture.  
is chosen for quick design and implementation results. While an FPGA will not be able to achieve the desired 10 GHz \(F_{s}\), it will provide insight when compared to similar peer-reviewed works with respect to resource utilization and power consumption. Synthesis and implementation were performed using a Virtex Ultrascale xxvu440-flgb2377-3-e. To directly compare to [10], the system clock frequency is set to 250 MHz, with 16-bit input data and 16-bit filter coefficients. The results are shown in Table 2.  
Based on FPGA implementation, it was found that the VFD low-pass filter design requires roughly 10 times less dynamic power and has anywhere from a 5- to 25-time reduction in logic resources needed to implement compared to similar designs in the literature. However, the proposed architecture does require approximately 5 times more multipliers (DSP slices) and achieves only 48.6% of usable bandwidth.
## 3 Theoretical Foundation and Numerical Model Construction  
### Theoretical Foundation  
#### 3.1.1 Percolation Control Equation  
Based on the mass conservation equation and Darcy's law, the governing equation of the seepage field in a porous elastic medium is expressed as follows [20]:  
\[\begin{array}{l}c_{1}\frac{\partial\varepsilon_{v}}{\partial t}+c_{2}\frac{ \partial P}{\partial t}=\
abla[k(\
abla P+\rho_{1}g\
abla z)]\\ c_{1}=1-\frac{k^{\prime}}{K_{s}},\,\,c_{2}=\frac{\overline{\sigma}}{\beta_{1 }}+\frac{(1-\overline{\sigma})}{K_{s}}\end{array} \tag{1}\]  
In the equation, \(\varepsilon_{v}\) denotes the volumetric strain, \(t\) is time (s), \(p\) represents pore pressure, \(k\) is permeability (m\({}^{2}\)), \(g\) is gravitational acceleration (m/s\({}^{2}\)), \(\rho_{1}\) is fluid density (kg/m\({}^{3}\)), \(z\) is the hydraulic head (m), and \(\beta_{1}\) is the bulk modulus of the fluid (Pa). Porosity is denoted by \(\varphi\). \(K^{\prime}\) and \(K_{s}\) are the drained bulk modulus and the effective bulk modulus of the porous medium, respectively (Pa).