esa-sceva/satcom-chunk-collection huggingface dataset:
# A Jumping Robot Driven by a Dielectric Elastomer Actuator
## 3 Prototype Implementation and Experiment
### Performance of the DEA  
The energy storage of the spring leg of the jumping robot requires a large driving force. In order to measure the relationship between the voltage and driving force of DEAs with different numbers of layers, a force-voltage test device was built as shown in Figure 9. However, it should be noted that when the number of layers increases, it is necessary to increase the k value of the preload spring to ensure that the preload and the contraction force of the multilayer DE film can be balanced at a stable initial position.  
The output forces of the 1-layer, 10-layer, 15-layer, and 20-layer DEAs at 4-7 kV were measured at room temperature. The results are shown in Figure 10a. It can be seen that the output force that the actuator could provide was very limited when a 1-layer DEA was applied, and the output forces of the  
Figure 8: **(a)** The fabrication of single-layer DE. **(b)** The stacking of multilayer DE.  
Figure 9: The force–voltage test device for the DEA.  
actuators also increased significantly with the voltage rise when the number of layers was increased. When the number of DE layers reached 20, the output force increased rapidly with increasing voltage lower than 6.2 kV, but the increase tended to be gentle when the voltage was higher than 6.2 kV. The reason for this is that the 20-layer DEA nearly approached its maximum deformation under the stimulation of 6.2 kV; therefore, further increase in voltage could no longer significantly reduce the contraction force of the film. The output force reached 30 N at 7 kV.  
In addition to providing the output force, the DEA also has certain displacement characteristics which ensure that the transmission rod can be driven to store energy. A laser displacement sensor was used to measure the displacement of the 20-layer DEA, and the measurement result is shown in Figure 10b. It can be seen that the displacement of the DEA showed a higher growth trend at voltage below 6.2 kV. The displacement did not change much when the voltage increased from 6.2 kV to 7 kV, and finally reached 13.9 mm. This was consistent with the last observation that the change in the force-voltage test tended to stabilize after 6.2 kV. In addition, the maximum strain of the DEA was 18.57%.  
By referring to these curves, the net actuation energy E can be evaluated:  
\[\text{E}=\int_{0}^{14.9}Fds=0.2297] \tag{15}\]  
where F is output force, and s is displacement.  
For a given actuator's mass (without end fixations and passive border), the specific net actuation energy density E\({}_{d}\) can be calculated:  
\[E_{d}=\frac{E}{m}=28.71J/Kg. \tag{16}\]
###### Abstract
In this scheme, the pilot signal and the information symbols are carried in the same time slot, and they pass through the same channel, so the real-time channel estimation can be carried out, which improves the ability of the system to resist the time-varying characteristics of the UWA channel. Index modulation (IM) techniques utilize the index of different transmission entities to transmit additional information bits, which has become a promising wireless communication [12; 13; 14] and underwater acoustic communications [15; 16] solution. Inspired by [11] and index modulation, we have proposed an MSSS system with in-phase/quadrature index modulation (MSSS-I/Q-IM) [17]. Therefore, the contributions of this paper are summarized as follows. Relative to the conference paper [17], we further derive the theoretical BER of the MSSS-I/Q-IM system over an AWGN channel and verify by simulations. In addition, we investigate the system performance on the Watermark platform, which is the benchmark acoustic communication simulator driven by channel measurements for different sea scenarios [18]. Finally, we carry out the field experiments in pool. Compared with the MSSS system, the results show that the proposed scheme has advantages in data rate and BER performance. The rest of this paper is organized as follows. Section 2 elaborates on the proposed system model. In Section 3, the theoretical BER is derived. Simulation and experiment results are presented in Section 4. Finally, conclusions are given in Section 5. The following notations are used in this paper. Uppercase and lowercase boldface represent matrices and vectors, respectively. The superscript \((\cdot)^{T}\) stands for the transpose, \((\cdot)^{*}\) denotes the conjugation, \(\lfloor\cdot\rfloor\) denotes floor function, and \(\lfloor\cdot\rfloor\) denotes absolute operator. \(\mathbb{E}\{\cdot\}\) represents the expectation, \(\text{Var}\{\cdot\}\) represents the variance, \(\mathcal{R}\{\cdot\}\) denotes the real part, and \(\mathcal{I}\{\cdot\}\) represents the imaginary part.
## 4 Evaluation of Model Developments
### Evaluation of Updated Snow
#### 4.4.1 Site Evaluation at ESM-SnowMIP  
Figure 15 shows summary statistics for Snow Water equivalent (SWE) and snow depth (SD) using the SL and ML5 snow schemes, evaluated at the ESM-SnowMIP sites. ML5 improves the bias, correlation and standard deviation of the errors at most of the sites considered. The largest improvements are found at Col de Porte (cdp), as reported already in [PERSON] et al. [41]. Senator Beck (snb) is the only site showing a consistent degradation in both SWE and SD, for which both correlations and errors increase for ML5, see [41] and discussion therein. SL has an overall positive bias in SWE and SD at most of the ESM-SnowMIP sites, at some sites exceeding 20%. ML5 generally reduces the biases, changing sign (negative) at some sites.  
Figure 14: In-Situ evaluation of the vegetation related variables when using the growth model. (**a**) Observed LAI in m\({}^{2}\)/m\({}^{2}\) for 2004 at Fluxnet Hu-mat station (green with red dots), LAI climatology (purple), and prognostic LAI (black); (**b**) Above-ground biomass in kg/m\({}^{2}\) from WOFOST (red), ECLand with climatological LAI (green) and ECLand with prognostic LAI (black).  
Figure 15: Taylor diagram of Snow Water Equivalent (SWE, **left**) and snow depth (SD, **right**) for offline simulations using the single-layer (CTL SL, red) and multi-layer (ML5, blue) snow schemes over the ESM-SnowMIP sites; see Section 4.2 for details on the ESM-SnowMIP sites.  
The impact of the new vertical discretisation on mountain sites is shown in Figure 16, by comparing a simulation at the Swamp Angel site with and without the snow depth-dependent vertical discretisation over complex terrains. The latter configuration, called ML5-noOro, corresponds to a simulation with the same vertical discretization of the snowpack as used by [PERSON] et al. (2018). ML5-noOro shows an overestimation of melting during the end of the snow season in particular years, which is alleviated in ML5. Considering the entire time period, the impact over mountain sites of the new parametrization is generally positive (not shown).
# A Lightweight and Practical Anonymous Authentication Protocol Based on Bit-Self-Test PUF
## 3 Proposed Scheme
### Proposed Authentication Protocol
After the server receives the above information, it checks the temporary \(ID_{1}\) to find the corresponding \(k_{1}\) and \(k_{old}\) in the database, and uses the key \(k_{1}\) and \(m_{1}\parallel m_{2}\) as input to generate five pseudorandom numbers \(s^{\prime}_{1},\ldots,s^{\prime}_{5}\) using the same \(PRF\) as on the device. If the device is legal, then \(s^{\prime}_{1},\ldots,s^{\prime}_{5}\) will be the same as \(s_{1},\ldots,s_{5}\). Therefore, the server should first verify whether \(s^{\prime}_{1}\) and \(s_{1}\) are equal; if they are equal and the MAC \(v\): =\(PRF^{\prime}(s^{\prime}_{3},m_{2}\parallel u)\) are verified, the server will successfully authenticate the device. After this, it performs the later operation \(k_{2}:=s^{\prime}_{3}\bigoplus u\), \(ID_{2}:=s^{\prime}_{5}\bigoplus ID_{1}\), updates the stored data on the server:\((ID_{1},k_{1}),(ID_{old},k_{old}):=(ID_{2},k_{2}),(ID_{1},k_{1})\), and sends the random number \(s^{\prime}_{4}\) to the device. We need to consider a situation wherein \(s^{\prime}_{4}\) cannot be received by the device due to some attacks. This will make the device unable to update the authentication information and cause the information update with the server to not be synchronized. To solve this problem, if \(s^{\prime}_{1}\) is not equal to \(s_{1}\), the server will use \(k_{old}\), which replaces \(k_{1}\) as the input to the \(PRF\), and computes \(s^{\prime}_{1},\ldots,s^{\prime}_{5}\) again, verifying whether \(s^{\prime}_{1}\) is equal to \(s_{1}\). If they are still not equal, the authentication will fail and the server will return a random value to the device. In this way, the proposed authentication protocol can resist DoS and desynchronization attacks. After the device receives the data \(s^{\prime}_{4}\), it will verify whether the \(s^{\prime}_{4}\) is same as the pseudorandom numbers \(s_{4}\), and if they are equal, it performs the operation \(ID_{2}:=s_{5}\bigoplus ID_{1}\) and updates the stored data \(ID_{1},(c_{1},h_{1}):=ID_{2},(c_{2},h_{2})\) on the device as the authentication information for the next authentication; otherwise, the authentication fails.
## 4 Conclusions  
The proposed control scheme has been validated on a hardware prototype experimental setup of the system and for the experimental environment as show in Figure 9.  
In the sun tracking system, the LBS controller enables system stability and high efficiency power production. Under sunshine conditions, the reference-tracking controller provides tracking accuracy to help HCPV power production as needed. Under cloudy conditions, the path follow tracking controller moves to follow the sun trajectory, which is stored in the memory. The sun trajectory table is sought and calculated by interpolation when required any time with location between two points. In practice, some error is introduced by calculation and by interpolation. However, the path follow tracking controller is to enable stable tracking. The other way, a reference-tracking controller is used. PID control with a sun position sensor, results in a high accuracy tracking to \(\pm\)0.2 degree. However, the  
Figure 12: Comparison of power output between LBS control and PID control  
transition time is between the path follow tracking controller transfers to reference tracking controller or reference tracking controller transfers to path follow tracking controller. The transition time is a key element of LBS for the control system. In the sun tracker system, the transition time has no significant influence on the system performance. Because the system response time of the sun tracker is almost 15 sec with the requirement of accuracy tracking \(\pm\)0.2 degree.  
This paper discusses the control method of stability for the HCPV system, a LBS controller for trackers of 1 MW HCPV demo plant application. Comparing the experimental results has shown that the control method not only stabilizes trackers of the HCPV system, but also can significantly improve power production of the HCPV system due to sun light changes. The LBS control is a switching control because of its reference tracking and path follow tracking controller and is suitable to control HCPV systems.  
The authors would like to thank the colleagues on the Tracking & Power team of INER HCPV project.
## 3 Result and Discussion
### Engine Emissions
#### Nitrogen Oxides (NOx)  
The variation of nitrogen oxides (NOx) emission with different fuel blends is shown in Figure 10. The amount of nitrogen oxides (NOx) emission of pure diesel fuel is somewhat lower than B10 and B20. The nitrogen oxides (NOx) for B10 and B20 are 13.11% and 22.54% higher than net-diesel (B0), respectively, due to the fact that the biodiesel has a higher oxygen content than diesel. Therefore, by using the biodiesel, nitrogen oxides (NOx) emission increased relative to diesel. The excess oxygen in the biodiesel increases the local temperature due to excess hydrocarbon oxidation, and when the oxygen levels increase, the maximum temperature during combustion increases. Then the formation of nitrogen oxides (NOx) increases [76].
## 3 Results and Discussion
### Isotherm
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Si/Al**} & \multicolumn{4}{c}{**Langmuir Isotherm**} \\ \cline{3-5}  & & \(q_{m}\) **(mg/g)** & \(K_{L}\) **(dm3/mg)** & \(R^{2}\) \\ \hline Nat-CLI & 4.37 & 48.07 & 0.3598 & 0.9939 \\ HCLI-0.1 M & 5.31 & 41.49 & 0.2175 & 0.9970 \\ HCLI-0.2 M & 7.91 & 23.47 & 0.1969 & 0.9948 \\  & 9.04 & 10.67 & 0.0146 & 0.9163 \\  & 9.50 & 9.30 & 0.0081 & 0.8816 \\ \hline \multicolumn{5}{c}{**Freundlich Isotherm**} \\ \hline
**Sample** & **Si/Al** & \(K_{F}\) **(mg g\({}^{-1}\)) (L mg\({}^{-1}\))\({}^{1/n}\)** & \(n\) & \(R^{2}\) \\ HCLI-0.5 M & 9.04 & 0.701 & 2.279 & 0.99 \\ HCLI-1.0 M & 9.52 & 0.295 & 1.849 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of Pb(II) sorption by Nat-CLI and H-CLI from the parameters obtained applying the Langmuir and Freundlich models. Figure 6: Isotherms for Pb(II) sorption of by non- and acid-modified clinoptiolite-rich tuffs. Table 4 shows the maximum sorption capacity (\(q_{m}\)) of different materials for Pb(II) obtained from the Langmuir model at different initial concentrations, as was obtained in the present work. It can be observed that carbon foam has 10 times more sorption capacity than clinoptilolite- rich tuff for this heavy metal. However, the zeolitic material has 1.9 times more capacity to remove Pb(II) than pectin-containing substances. In the case of the acid-modified clinoptilolite-rich tuff the maximum sorption capacity is 5.1 times lower than that of the unmodified material. Other carbonaceous materials among them biochar, multi-walled carbon nanotubes functionalized by selenophosphoryl groups, and sulfone-modified magnetic activated carbon, present similar maximum sorption capacities for Pb(II).
## 4 Analysis of Experimental Result
\begin{table}
\begin{tabular}{c c c c c} \hline
**Input Image Size** & **Precision** & **Recall** & **F-1 Score** & **mIoU** \\ \hline \(64\times 64\) & 0.7092 & 0.6832 & 0.6959 & 0.7495 \\ \hline \(128\times 128\) & 0.7561 & 0.7315 & 0.7436 & 0.7752 \\ \hline \(256\times 256\) & 0.7352 & 0.6926 & 0.7133 & 0.7170 \\ \hline \(512\times 512\) & 0.7310 & 0.6883 & 0.7090 & 0.5962 \\ \hline \end{tabular}
\end{table} 
Table 2: Training results by input size. Figure 10: Segmentation results 2. (**a**) input image, (**b**) ground truth, (**c**) ours, (**d**) U-Net, (**e**) FCN-8s. Figure 9: Segmentation result 1. (**a**) input image, (**b**) ground truth, (**c**) ours, (**d**) U-net, (**e**) FCN-8s. The comparison of the prediction results among the three models demonstrated the proposed network's relative strength in detailed detection. Although the network did not perfectly capture the connectivity present within fine cracks, it demonstrated a more sensitive response than the other models. Table 3 summarizes the comparative results between our network and those proposed by [PERSON] et al. [7], [PERSON] et al. [9], [PERSON] et al. [10], [PERSON] et al. [12], and [PERSON] et al. [27]. From these results, we can observe that the proposed method significantly outperformed the other networks in terms of mIoU. Specifically, the proposed method achieved an F-1 score of 0.7436 and mIoU of 0.7752. Notably, it exhibited higher performance when the BRFB module was used, demonstrating the effectiveness of the BRFB in conjunction with the attention gate designed to mitigate feature loss. The substantial difference in mIoU, reaching over 0.2 in some cases, confirms a considerable overlap between the predicted areas and actual regions of interest across all test images. Table 4 shows the results based on whether the BRFB module is used. This result shows that the use of the BRFB module yields statistically significant results in the metrics and demonstrates its effectiveness in aiding crack detection. Table 5 shows the results of training with different batch sizes, with the best performance observed for a batch size of 16. The network incorporating the BRBF module ultimately achieved the highest mIoU, demonstrating its ability to effectively learn and segment key features even for low-resolution images.
## 4 Simulation Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{6}{c}{**Routing Protocol**} \\ \cline{2-10}  & \multicolumn{3}{c}{**STAR-LORA**} & \multicolumn{3}{c}{**OLSR**} & \multicolumn{3}{c}{**LAR1**} \\ \cline{2-10}  & **Telnet** & **S-Frame** & **Gen-FTP** & **Telnet** & **S-Frame** & **Gen-FTP** & **Telnet** & **S-Frame** & **Gen-FTP** \\ \hline Avg. tioan delay (micro s) & 58 & 60 & 64 & 65 & 68 & 69 & 78 & 64 & 80 \\ Rx power consption(mWh) & 0.15 & 0.2 & 0.019 & 0.35 & 0.024 & 0.35 & 0.14 & 0.036 & 0.078 \\ Tx power consption (mWh) & 0.16 & 0.028 & 0.14 & 0.04 & 0.07 & 0.04 & 0.03 & 0.006 & 0.03 \\ Idle power consption (mWh) & 0.85 & 0.65 & 0.68 & 0.65 & 0.75 & 0.77 & 0.85 & 0.95 & 0.85 \\ Time spent transmitting (m s) & 28 & 50 & 62 & 16 & 17 & 22 & 11 & 20 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters analysis when deploying Telnet, S-frame, and Gen-FTP of the STAR-LORA, OLSR, and LAR1 routing protocols. Figure 7: Available Energy when deploying Telnet, S-frame, and Gen-FTP in Receive Mode of the STAR-LORA, OLSR, and LAR1 routing protocols. Aveg. Txion Delay (Micro Sec) When Deploying Telnet, S-Frame, and Gen-FTP of the STAR-LORA, OLSR, and LAR1 Routing Protocols  
Figure 9 illustrates the typical transmission delay (in microseconds) experienced by 240 nodes when STAR-LORA, OLSR, and LAR1 are used in conjunction with Telnet, S-frame, and Gen-FTP. Table 4 illustrates that the smallest amount of minimum necessary average transmission time for STAR-LORA in the Telnet deployment application is 58 microseconds. Time Spent Transmitting (m s) When Deploying Telnet, S-Frame, and Gen-FTP of the STAR-LORA, OLSR, and LAR1 Routing Protocols  
Figure 10 illustrates the amount of power that is consumed by 240 nodes when STAR-LORA, OLSR, and LAR1 are used in conjunction with Telnet, S-frame, and Gen-FTP. Table 4 demonstrates that 11 milliseconds is the minimum amount of time that must be spent sending in order to meet the requirements for LAR1 in the Telnet deployment application. Figure 8: Available Energy when deploying Telnet, S-frame, and Gen-FTP in Idle Mode of the STAR-LORA, OLSR, and LAR1 routing protocols. Figure 9: Avg. Txion delay (micro sec) when deploying Telnet, S-frame, and Gen-FTP of the STAR-LORA, OLSR, and LAR1 routing protocols. Figures A1-A15 illustrate the conclusions of the study, which include the utilization of available energy during the deployment of Telnet. Figures A16-A30 illustrate the conclusions of the study, which include the utilization of available energy during the deployment of the S-frame. Figures A31-A45 illustrate the conclusions of the study, which include the utilization of available energy during the deployment of Gen-FTP for a variety of routing protocols, such as STAR-LORA, OLSR, and LAR1.
## 3 Materials and Methods
### Feature Extraction Module
CNNs are particularly adept at capturing spatial patterns within data, while the proposed architecture leverages the inherent ability to learn hierarchies of features, enabling them to uncover intricate relationships within the data streams. This feature amplifies the model's potential to detect anomalies hidden within complex data patterns in real-time. The CM-DANA architecture's uniqueness further emerges in its fusion of features across modalities. Extracted features from distinct subnetworks are merged through a fusion layer, creating a unified representation that encodes the combined knowledge of different data streams. By integrating CNNs for feature extraction, CM-DANA elevates this fusion process, as it now incorporates spatial insights that other architectures might overlook. This enables the architecture to capture cross-modal interactions and dependencies more effectively. In addition, the incorporation of CNNs amplifies the architecture's ability to capture localized and global spatial features. As anomalies within smart communication environments often manifest as intricate spatial irregularities, the proposed model's innovative CNN-based feature extraction enhances its precision in pinpointing subtle anomalies that might be missed by traditional methods. This leads to more accurate and efficient anomaly detection in complex, evolving data streams. Finally, it must be noted that the CNNs within the CM-DANA model do not operate in isolation. They serve as integral components within the cross-modal learning module, collaborating with other components to decipher complex interactions between data modalities. By enriching the feature extraction step with CNNs, the model contributes to more informative feature representations that empower subsequent modules in making more accurate anomaly detection decisions. By leveraging CNNs to extract features, the CM-DANA architecture stands out as a promising method for capturing complex interactions between data modalities and advancing anomaly detection capabilities.