esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Discrete Element Modelling  
Discrete element simulations are used to study the jamming, shearing, and critical state of granular materials. The commercial software PFC3D is used, and the studied granular assembly is made of spherical grains with a uniform diameter \(D\) of 0.17 mm. The motion of grains is calculated with [PERSON]'s law for rigid bodies.  
A linear Coulomb-type model is used for the contacts between grains. Given a spherical grain \(g\) and one of its neighbouring grain \(b\), they have position vectors (\(x_{g}\), \(x_{b}\)), velocities (\(v_{g}\), \(v_{b}\)), angular velocities (\(\omega_{g}\), \(\omega_{b}\)), and diameters (\(D_{g}\), \(D_{b}\)). If the penetration depth \(\delta_{c}=\frac{D_{g}+D_{b}}{2}-|x_{g}-x_{b}|\) is positive, they are in contact (the contact point is denoted as c) and grain \(g\) experiences a force \(f_{gc}=f_{gc,n}+f_{gc,t}\) from grain \(b\), where \(f_{gc,n}\) is the normal contact force, and \(f_{gc,t}\) is the tangential contact force. The contact forces components are also divided into an elastic part and a viscous part (\(f_{gc,*}=f_{gc,*}+f_{gc,*}\)).  
The normal contact force is in the direction of contact normal \(n_{gc}\). Its elastic part is represented by a spring and its viscous part is represented by a dashpot. The equation for the normal contact force is:  
\[f_{gc,n}=\left(f_{gc,ne}+f_{gc,ne}\right)\left(-n_{gc}\right)=\left(k_{n}\delta _{c}+2\beta_{n}\sqrt{m_{*}k_{n}}\dot{\delta_{c}}\right)\left(-n_{gc}\right) \tag{1}\]  
where \(m_{*}=\frac{m_{*}m_{*}}{m_{g}+m_{b}}\) is the effective mass, and \(k_{n}\) and \(\beta_{n}\) are the elastic stiffness and viscous damping constants. The tangential contact force is first calculated as  
\[f_{gc,t}=f_{gc,te}+f_{gc,tv}=\left[f_{gc,te}^{0}+k_{t}\Delta v_{t,c}\Delta t \right]+2\beta_{t}\sqrt{m_{*}k_{t}}\Delta v_{t,c} \tag{2}\]  
where \(\Delta v_{t,c}\) is the relative tangential velocity at the contact \(c\), and \(k_{t}\) and \(\beta_{t}\) are the tangential elastic stiffness and viscous damping constants. There is a slider in the tangential contact force model, which means that the tangential contact force has a Coulomb-type limit, which is imposed as \(|f_{gc,t}|<\mu|f_{gc,n}|\).  
The normal stiffness \(k_{n}\) is calculated as \(k_{n}=\frac{\pi}{2}ED_{*}\), where \(E\) is the elastic modulus of grains and \(D_{*}=\frac{D_{g}D_{b}}{D_{g}+D_{b}}\) is the effective diameter. The ratio between the normal and tangential stiffness \(\kappa=\frac{k_{n}}{k_{n}}\) is fixed for all contacts. If not otherwise stated, the DEM parameters are used as in Table 1.
# Nanosheets of CuCo\({}_{2}\)O\({}_{4}\) As a High-Performance Electrocatalyst in Urea Oxidation
###### Abstract  
The urea oxidation reaction (UOR) is a possible solution to solve the world's energy crisis. Fuel cells have been used in the UOR to generate hydrogen with a lower potential compared to water splitting, decreasing the costs of energy production. Urea is abundantly present in agricultural waste and in industrial and human wastewater. Besides generating hydrogen, this reaction provides a pathway to eliminate urea, which is a hazard in the environment and to people's health. In this study, nanosheets of CuCo\({}_{2}\)O\({}_{4}\) grown on nickel foam were synthesized as an electrocataly for urea oxidation to generate hydrogen as a green fuel. The synthesized electrocataly was characterized using X-ray diffraction, scanning electron microscopy, and X-ray photoelectron spectroscopy. The electroactivity of CuCo\({}_{2}\)O\({}_{4}\) towards the oxidation of urea in alkaline solution was evaluated using electrochemical measurements. Nanosheets of CuCo\({}_{2}\)O\({}_{4}\) grown on nickel foam required the potential of 1.36 V in 1 M KOH with 0.33 M urea to deliver a current density of 10 mA/cm\({}^{2}\). The CuCo\({}_{2}\)O\({}_{4}\) electrode was electrochemically stable for over 15 h of continuous measurements. The high catalytic activities for the hydrogen evolution reaction make the CuCo\({}_{2}\)O\({}_{4}\) electrode a bifunctional catalyst and a promising electroactive material for hydrogen production. The two-electrode electrolyzer demanded a potential of 1.45 V, which was 260 mV less than that for the urea-free counterpart. Our study suggests that the CuCo\({}_{2}\)O\({}_{4}\) electrode can be a promising material as an efficient UOR catalyst for fuel cells to generate hydrogen at a low cost.  
urea oxidation reaction; fuel cells; cyclic voltammetry; hydrogen production +
Footnote †: journal: Applied Physics Letters
# A Novel Method for Constructing the S-Box Based on Spatiotemporal Chaotic Dynamics
## 6 Conclusions and Future Research  
A novel generation method for S-boxes by using the NCML spatiotemporal system is presented in this paper. The high dimensional feature of the NCML system can not only resist the degradation of finite precision computation, but also increases the randomness of the constructed S-box. The randomness of the constructed S-box has been verified by using NIST-800-22 statistical tests. In the proposed method, one of chaotic sequences generated by the NCML system is employed to construct the initial S-box. Then, the permutation operation using independent chaotic sequences is performed, which improves the BIC and LP properties of the constructed S-box. According to the simulation results and the performance analysis, we know that the obtained S-box meets the six criteria. Hence, the proposed method is effective. Moreover, we also construct an S-box by using the CML system for comparisons. In comparisons with the former S-boxes, the proposed S-box has a better BIC property and a better ability to resist linear password attacks. Hence, the proposed scheme presents superior cryptographic performance. In future practical research work, we intend to apply the proposed method to image encryptions in a parallel process.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON] wrote the paper.  
This research was funded by the National Natural Science Foundation of China grant numbers [61672124, 61173183, and 61370145], the Program for New Century Excellent Talents in Fujian Province University, the Natural Science Foundation of Fujian Province of China grant number [2018J01100], the Zhangzhou Science and Technology Project grant number [ZZ2018J23], the Program for Liaoning Excellent Talents in University grant number [LR2012003], and the Password Theory Project of the 13 th Five-Year Plan National Cryptography Development Fund grant number [MM]J20170203].  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### Validation and interrogation of 2-stage NN models
\begin{table}
\begin{tabular}{|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|} \hline Product types\({}^{\mathrm{z}}\) & Experimental model inputs & \multicolumn{4}{|c|}{Validation with commercial harvests} \\ \cline{3-7}  & & Cases\({}^{\mathrm{w}}\) & Measure & Model validation & \(fa=1^{\circ}\)C\({}^{\mathrm{U}}\) & \(fa=21^{\circ}\)C\({}^{\mathrm{U}}\) \\ \hline Living (n=66) & \(pm\) (\(m\)=3)\({}^{\mathrm{Y}}\), \(ps\) & 28 & R\({}^{\mathrm{2}}\) & 0.026 & 0.36 & 0.019 \\ \hline  & & & Error (day) & 4.5 (0.44)\({}^{\mathrm{V}}\) & 5.7 (0.39) & -0.2 (0.36) \\ \hline  & & & Error (\%) & 100\% (10.8\%) & 136\% (15.1\%) & 7\% (6.1\%) \\ \hline  & & & & & \\ \hline  & & & & & \\ \hline  & \(pm\) (\(m\)=5)\({}^{\mathrm{X}}\), \(ps\) & 26 & R\({}^{\mathrm{2}}\) & 0.66 & 0.53 & 0.65 \\ \hline  & & & Error (day) & 4.1 (0.32) & 5.5 (0.39) & 0.02 (0.29) \\ \hline  & & & Error (\%) & 154\% (19.7\%) & 212\% (26.6\%) & 23\% (12.9\%) \\ \hline Butter (n=38) & \(pm\) (\(m\)=3)\({}^{\mathrm{Y}}\), \(ps\) & 10 & R\({}^{\mathrm{2}}\) & 0.89 & 0.92 & 0.81 \\ \hline  & & & Error (day) & -1.8 (0.56) & -1.2 (0.66) & -3.7 (0.86) \\ \hline  & & & Error (\%) & -25\% (6.6\%) & -16\% (6.8\%) & -59\% (3.9\%) \\ \hline  & & & & & \\ \hline  & \(pm\) (\(m\)=5)\({}^{\mathrm{X}}\), \(ps\) & 9 & R\({}^{\mathrm{2}}\) & 0.96 & 0.95 & 0.96 \\ \hline  & & & Error (day) & -1.8 (0.49) & -1.6 (0.54) & -3.0 (0.64) \\ \hline  & & & Error (\%) & -34\% (6.1\%) & -23\% (9.6\%) & -66\% (2.5\%) \\ \hline \end{tabular} \({}^{\mathrm{Z}}\) Product type: Individually wrapped lettuce with roots (living) or without roots (butter).  
\({}^{\mathrm{Y}}\) Refer to section “Generic BPNN” and equations (4) and (5). \(pm\)={\(t\)(\(c\)), \(t\)(\(c\)-1), \(t\)(\(c\)-2)} and associated \(ps\).  
\({}^{\mathrm{X}}\) Refer to section “Generic BPNN” and equations (4) and (5). \(pm\)={\(t\)(\(c\)), \(t\)(\(c\)-1), \(t\)(\(c\)-2), \(t\)(\(c\)-3), \(t\)(\(c\)-4)} and associated \(ps\).  
\({}^{\mathrm{W}}\) Number of validation cases in commercial data.  
\({}^{\mathrm{V}}\) Mean followed by standard error in bracket.  
\({}^{\mathrm{U}}\) Future average temperature (\(fa\)) was specified as 1\({}^{\circ}\)C or 21\({}^{\circ}\)C for 2-stage NN model interrogation.  
\end{table} 
Table 1: Validation of 2-stage NN experimental models with commercial harvests for predicting remaining shelf life (RSL in days) of living and butter lettuce.
# Registration of Dental Tomographic Volume Data and Scan Surface Data Using Dynamic Segmentation
## 2 Proposed Method: MICP
### Automatic Registration Process  
#### 2.2.1 Defining an Edge Point on Intensity Profile  
Based on a single point of scan data, an intensity profile can be generated along the normal direction of the point by 3D interpolating with a uniform interval. For this intensity profile generating process, two parameters are needed, the maximum distance and the interval. In this study, we used 10 voxels as the maximum distance and 1 voxel as the interval, and a total of 21 interpolation values are calculated for 1 intensity profile. With this intensity profile, the presence of step edges can be determined. The first derivative of the line profile can be used to determine the presence of a step edge. The determined step edge could correspond to an intensity increasing shape or an intensity decreasing shape.  
Because the normal direction of the surface data and the gradient direction of the volume data are opposite, the sign of the first gradient of the step edge must be negative. In conclusion, if an intensity profile has a high negative value for the first derivatives, that intensity profile has a step edge and the point of the surface data becomes a corresponding point for ICP. Then, the minimum value of the first derivatives is defined. Many studies have evaluated the HU values of materials in CBCT volume data [31; 32; 33; 34]. There are two step edges which define the teeth boundaries, whether bone--air or bone--skin. To detect and use both step edges, the step edge defined by the relatively low first derivatives value, the bone--skin value, becomes the reference. Based on the previous studies, \(-800\) was defined as a reasonable slope value of the first derivatives for defining the step edge [19]. All experiments in Section 3 used this slope value to define step edges. This edge point is physically the same as a zero-crossing edge point [35]. The detected edge point on the intensity profile is a 3D point because the intensity profile is defined by 3D interpolation from volume data.  
Figure 3: Overall procedure of the proposed method.  
Volume data is highly complex data containing not only teeth, but also bones and tissues. Therefore, more than two step edges could be found. These intensity profiles are not used for the registration process. The proposed registration works based on only the defined points of surface data which are near the step edges. Because of this strict standard, the proposed registration method is robust to cases with metal artifacts.  
Once the edge point is found, the sub-voxel level edge point is defined by local 3D interpolation back and forth along the intensity profile. This is the final step for a single intensity profile. The sub-voxel level edge point becomes a corresponding point to the point defining the intensity profile.
# An Open Source Framework Approach to Support Condition Monitoring and Maintenance
## 5 The Solution
### Monitoring of Industrial Machines
The prediction application programming interface permits clients to ask for predictions from the models, taking into account the available data and the training data (which is also inserted through this interface). Internally, this part of the system is built of the Intelligent Maintenance Decision Support System (IMDSS), which handles the models, namely, model generation, selection, training, and testing, i.e., the training data or responds when the API is contacted. In addition, the HMI module, which is part of the Local Cloud 2, provides support for the visualization of data as well as for system management. The HMI allows us to view historical and live data as they were received from the middleware and inspects the results from the data analysis component (alarms for unusual data, warnings of impending failures, etc.). This solution builds on the solution proposed in [35] and improves it by leveraging the Arrowhead Framework, to provide to our prototype which is more capable of adapting to changes in the infrastructure, thanks to the orchestrator and system registry services. The prototype also enjoys improved security capabilities, with stronger authentication, and it is capable of interoperating with other technologies, allowing the proposed architecture to evolve easily, and to support other systems and functionalities. The architecture, based on a multiple Arrowhead local cloud, allows to perform analytics-related actions remotely with respect to data collection, and finally, the implementation of the solution was made less challenging and expensive since the Arrowhead Framework is made up of open source components.
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 4 Results and Discussion
### Shaker Test
#### 4.1.3 Effect of Varying Resistance  
To further study the performance of the circuits, analysis is carried out by investigating the effect of varying load resistors (R1-R4) on rectified voltage and output power with an input voltage of 5 V. For the purpose of illustration, the outcome obtained with Capacitor C2 is presented. At an excitation frequency of 1 Hz, the rectified voltage by both circuits with different input voltage is plotted against different resistances, as shown in Figure 16.  
From Figure 16, it can be seen that the rectified voltage increases with the increasing resistance, regardless of the type of rectifier circuit. The H-Bridge circuit also consistently provide higher rectified voltage, in comparison with the FBR circuit. The reason is similar to the description provided for Figure 13.  
It is found that the rectified voltage in C1 and C2 are similar, so the outcome from C1 is omitted. However, since the capacitance of the C2 is higher than the C1, the subsequently rectified voltage through H-Bridge circuit is found to be more stable than the FBR circuit. However, the charging time of C2 is much longer than C1 due to its large capacitance ratings [18].  
Figure 17 demonstrates the output power stored in C2 through both circuits at different resistances (R1-R4) and constant input voltage of 5V. Contrary to the rectified voltage, the trend of output power against resistance is rather different. The output power produced by H-Bridge increases slightly at a lower resistance and remains stagnant at higher resistance. On the other hand, those produced by the FBR circuit show inconsistent trend.  
Figure 16: Rectified voltage (stored in C2) against resistance at 1 Hz: (**a**) H-Bridge, (**b**) FBR circuits with input voltage of 5 V.  
Figure 15: Rectified voltage (input voltage = 5 V) versus output power through (**a**) H-Bridge, and (**b**) FBR circuits.  
This can be explained by [PERSON]'s law, where the rectified voltage generally increases with resistance, but the current usually decreases with higher resistance. Thus, the output power achieved through both circuits shows a different trend. Regardless of the resistance, the performance of H-Bridge circuit is once again superior. The output power from H-Bridge circuit is significantly higher than the FBR circuit due to its unique features of MOSFET, which are explained in Section 2.3. Note that the general trend and observation for excitation at 2 Hz is similar to the case of 1 Hz and is herein omitted. The effect of varying frequency is discussed in the following subsection.
## 4 Experimental Analysis
### Results of the Unoptimized Neural Network Experiments
#### 4.1.2 Analysis of Low-Level Electric Power Experiment  
The performance of different network architectures varied at low battery levels. The graph below compares the max errors of various networks when the battery SOC was above 20% (including 20%) and below 20%.  
In Figure 14, the black area represents the error when the battery SOC was above 20%, and the red area represents the error when the battery SOC was below 20%. The error remained stable when the battery SOC was above 20%, but it increased significantly when the battery SOC was below 20%. Table 3 provides the error and the MSE for different networks, comparing a battery SOC of above 20% with one of below 20%.  
In Figure 15 and Table 3, it can be observed that when the battery SOC was equal to or above 20%, both the max error and the MSE were relatively small, which is better than the corresponding values for below 20%. In the DST dataset, the maximum error of the BP neural network for a battery SOC of above 20% was 2% lower than the corresponding values for one of below 20%. The CNN, RNN, and TSCNN were lower by 0.49%, 1.67%, and 1.02%, respectively. In the UDDS dataset, the max errors for a battery SOC of above 20% were lower than those of below 20% by 1%, 0.66%, 2%, and 0.15%, respectively. Therefore, we conclude that the performance of the neural network deteriorates when the battery SOC is below 20%.  
Figure 14: (**a**) Error of BP on DST; (**b**) error of CNN on DST; (**c**) error of RNN on DST; (**d**) error of TSCNN on DST; (**e**) error of BP on UDDS; (**f**) error of CNN on UDDS; (**g**) error of RNN on UDDS; (**h**) error of TSCNN on UDDS.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**DST**} & \multicolumn{3}{c}{**UDDS**} \\  & \multirow{2}{*}{\(\geq\)**20\%**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**MSE**} \\  & & & & & & & & \\ \hline BP & 3.10 & 15\% & 3.11 & 17\% & 6.5 & 8\% & 7.05 & 9\% \\ CNN & 12.02 & 13.65\% & 14.43 & 14.14\% & 6.47 & 8.94\% & 20.27 & 9.6\% \\ RNN & 19.55 & 12.47\% & 24.44 & 14.14\% & 10.09 & 10.48\% & 59.78 & 12.48\% \\ TSCNN & 3.07 & 5.24\% & 3.5 & 6.26\% & 3.78 & 4.24\% & 4.49 & 4.39\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Max error and MSE for SOC of different batteries on two datasets.
## 2 Materials and Methods
### Indentation-Type Atomic Force Microscopy (IT-AFM)  
IT-AFM was carried out as described in [PERSON] et al., 2020 [26]. A NanoWizard I AFM (JPK Instruments, Berlin, Germany) was used in combination with an inverted optical microscope (Axiovert 200, Carl Zeiss Micro Imaging GmbH, Gottingen, Germany) for accurate lateral positioning of the AFM-tip. The entire set-up was placed on an active vibration isolation table (Micro 60, Halcyonics, Gottingen, Germany) inside a 1 m\({}^{3}\) soundproof box to reduce external noise. Indentation experiments were performed using silicone-nitride cantilevers (MLCT, Cantilever E, Bruker, Karlsruhe, Germany) with a nominal spring constant of 0.1 N/m, and sharp pyramidal tips with a nominal tip radius of 20 nm. For each cantilever, the spring constant was determined individually using the thermal noise method [28]. During measurements, the tissue sections were immersed in PBS (Dulbecco PBS w/o Mg\({}^{2+}\)/Ca\({}^{2+}\), pH 7.4, Biochrom, Berlin, Germany). The vertical tip velocity during the indentation experiments was 10 \(\upmu\)m/s. The elasticity of the ITM of each cartilage zone (superficial, middle and deep) was assessed on two different tissue sections with a total of 1875 force-indentation curves distributed over 3 different 9 \(\upmu\)m\({}^{2}\) areas. Subsequently, a modified Hertz-Sneddon model was fitted onto the approach curve up to 500 nm using the JPK Data Processing Software (Version 5.0.96, JPK Instruments), and the [PERSON]'s modulus (E) was extracted. The resulting [PERSON]'s modulus values were summarized in histograms using the Igor Pro software (Version 6.3.7.2, WaveMetrics, Portland, OR, USA) and the two maxima of the bimodal distributions were determined by fitting a linear combination of two Gaussian distributions to the data. Previous studies observed similar bimodal nano-stiffness distributions in cartilaginous structures, where the first peak was attributed to the proteoglycan phase and the second peak to the collagen fibrils [13; 14].
## 4 Experiments and Analysis
### Comparison with Other Methods  
#### 4.3.1 Quantitative Results  
We utilized YOLO11 as the backbone of our algorithm. YOLO11 is the latest generation of object detection models introduced by Ultralytics. Compared to previous versions, YOLO11 incorporates several improvements in architecture design and training processes, significantly enhancing the model's performance and flexibility. The YOLOv11 network primarily consists of the Backbone, Neck, and Head. The Backbone employs the C3k2f module, enhancing feature capabilities through Bottleneck Block, SPPF, and C2 PSA modules. The Neck, situated between the Backbone and Head networks, is responsible for feature fusion and enhancement. The Head network is the decision-making component of the object detection model, tasked with producing the final detection results. YOLO11 offers various models designated as n, s, m, l, and x, each differing in parameter size and computational complexity. In this paper, we utilize the YOLOv11m model as the benchmark model, which comprises 409 layers, 20,114,688 parameters, 20,114,672 gradients, and a theoretical computational complexity of 68.5 FLOPs.  
We report a quantitative comparison of the performance of the proposed algorithm with other state-of-the-art algorithms on the IRSTD-1k and IRMT-UAV datasets, including EFLNet [42], MDvsFA [17], YOLOSR-IST [4], ACM [18], ISNet [41], ALCNet [33], and DNANet [43], as shown in Table 3. On the IRSTD-1k dataset, compared to the SOTA algorithms, our precision improved by 2.4%, recall increased by 1.9%, and mAP50 improved by 1.4%.  
Additionally, we carried out comparative studies using our self-created dataset, IRMT-UAV. The suggested dataset encompasses complex situations, including intensely cluttered backdrops, faint, diminutive targets, and incomplete occlusion. The technique we use utilizes a multi-faceted channel focus system for small and faint targets against intricate backdrops, resulting in a notable enhancement of the algorithm's efficiency. In the case  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Value** \\ \hline Learning Rate & 0.001 \\ Momentum & 0.937 \\ Epochs & 300 \\ Batch Size & 64 \\ Image Size & 640 \(\times\) 640 \\ Optimizer & SGD \\ Weight Decay & 0.0005 \\ Patience & 100 \\ Work & 16 \\ Pretrained & False \\ Warmup Epochs & 3 \\ Warmup Momentum & 0.8 \\ Warmup Bias Learning Rate & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The specific hyperparameter information of the model.  
of the IRMT-UAV dataset, our precision improved by 3.7%, recall increased by 4.3%, and mAP50 improved by 4.9%.