esa-sceva/satcom-chunk-collection huggingface dataset:
# Compliant Human-Robot Collaboration with Accurate Path-Tracking Ability for a Robot Manipulator
## 3 Experiments and Results
### RMSE Evaluation and Comparison  
Following our objective of accurate path tracking in human-robot collaboration tasks, we evaluate the error between the reference path and the actual recorded path. It is important for the robot to continue tracking the path accurately, despite forces being applied or not during the human-robot interaction. In this context, the addition of directional compliant motion should have as small influence as possible on path tracking accuracy.  
Figure 7: Helix path-tracking test detailed data for free motion (FM), force-applied motion (FAM), and combined motion with virtual forces (CVF) in the TNB frame. (**a**) External Forces-FM. (**b**) Parameter s-FM. (**c**) Desired Velocities-FM. (**d**) External Forces-FAM. (**e**) Parameter s-FAM. (**f**) Desired Velocities-FAM. (**g**) External Forces–CVF. (**h**) Parameter s–CVF. (**i**) Desired Velocities-CVF.  
Thus, we must ensure the error difference is small among the proposed cases that represent the interaction assistance range.  
Figure 8 shows the reprojection of the RMSE residuals to the 3D helix path calculated between the reference helix path \(r_{p}(s)\) in black dashed line and the recorded test data \(r(s)\) in red solid line, also green dotted lines connect the points from \(r(s)\) to \(r_{p}(s)\) to display the error every 10 samples. Figure 9 presents for each of the three evaluated cases, the RMSE values in green dashed line and the tracking error between the actual and reference samples in solid blue line. For every joint position data collected in the test, we find the shortest distance \(d_{p,j,1,r_{p}}\) between the \(i\)th point and the reference curve \(r_{p}(s)\), which was sampled from (20)-(22) to have the same number of points as the test data, see (23). Then, we find the RMSE as in (24), where \(p_{j,i}\) is the \(i\)th test point of the \(j\)th component, \(j=x,y,z\), and \(N\) is the total number of points in the test.  
\[d_{p,j,1,r_{p}}=\min_{s}\Big{(}\sqrt{(r_{p,x}(s)-p_{xj})^{2}+(r_{p,y}(s)-p_{y, i})^{2}+(r_{p,z}(s)-p_{z,i})^{2}}\Big{)},s\in[0,1] \tag{23}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{i=N}\Big{(}d_{p,j,1,r_{p}}\Big{)}^{2}}{N}} \tag{24}\]  
In Table 3, we can see that the RMSE values obtained between the FM and FAM motion have a very small difference of 0.021 mm. This shows path tracking has a good performance even when external forces are applied to guide the motion. In the other case for the combined test with virtual forces, the error difference with the free motion test is slightly higher, 0.038 mm; however, it is still less than 0.1 mm, which is the pose accuracy value reported for commercial robots used in collaborative applications [36; 37; 38].  
Figure 8: Helix path tracking. \(r_{p}(s)\)-black dashed line, \(r(s)\)-red solid line, distance from each point of \(r(s)\) to \(r_{p}(s)\)-green dotted line. (**a**) FM. (**b**) FAM. (**c**) CVF.  
Figure 9: RMSE and distance error plot for each test. (**a**) FM. (**b**) FAM. (**c**) CVF.
## 5 Conclusions  
A novel design method and geometry of the waveguide bandpass filter are suggested. Substantial size-reduction and excellent bandpass filtering functions are made possible by coming up with the waveguide CRLH resonator which leads to a very thin structure much shorter than the half-wavelength for the conventional cavities. The ZOR phenomenon is generated with the transverse geometrical parameters of the waveguide cross-section, unlike other metamaterials utilizing longitudinal line segments. The ZOR as the thin waveguide part does not block the incoming RF signal but passes it to the next ZOR. By cascading the ZORs through transmission-sections as the coupling elements, the passband becomes distinct with the steeper skirt in the stopband. An eighth-order ZOR BPF is designed and simulated, moving to the step of fabrication. It is manufactured into the aluminum waveguide filter. The prototyped BPF is measured and compared with the simulated result. As for the passband, insertion loss and reflection coefficient are around \(\leq\)1 dB and \(\leq\)\(-\)15 dB from simulation to measurement. The attenuation of \(\geq\)40 dB at 7 GHz and 8 GHz is achieved as desired in the specifications. The length of the WG ZOR BPF is 3.5 \(\lambda_{g}\) for the eighth-pole, but the conventional one has 5 \(\lambda_{g}\) even for the seven-pole case. Furthermore, the BPF was combined with the horn antenna in order to provide it with channel selectivity. The broad-band horn antenna is made possible to select the signals through the passband. The frequency responses are acceptable for the use of a satellite transponder. The proposed filters make the LEO- and scientific satellites much lighter with the weight of 54 g, which is greatly reduced from the conventional WG filters.  
Conceptualization, S.K. and J.C.; methodology, S.K. and J.C.; software, J.C. and Y.S.; validation, J.C. and W.S.; formal analysis, J.C.; investigation, J.C., Y.S., S.K. and W.S.; resources, S.K. and E.L.; data curation, J.C.; writing--original draft preparation, J.C.; writing--review and editing, S.K. and J.C.; visualization, J.C.; supervision, S.K.; project administration, S.K., W.S. and E.L.; funding acquisition, S.K. and E.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Agency for Infrastructure Technology Advancement (KIA) grant funded by the Korea government (MOLIT) (No. 22 ABCD-CC164547-02, Development of Self-Powered and Wireless Safety Monitoring Technology for Railway Power Supply Systems.  
Not applicable.  
Incheon National University is thankful to ETRI for the Financial Support.  
The authors declare no conflict of interest.
### Nanomaterials/Nano-Modified Coatings as Corrosion Inhibitors
Interestingly, Figure 6b depicts a reasonably thick covered film and the existence of ZnO NPs in the protective coating. It was intriguing to discover that the inclusion of NiO NPs in the protective coating (Figure 6c) exerts a significant impact upon the interfacial layer's width, preventing it from thickening. Employing combined oxides  
Figure 5: FT-IR spectra of magnetic hydroxyapatite NPs. Reprinted with permission from Ref. [44]. MDPI (2022). of NiO and ZnO NPs results in a noticeable alteration in the coating thickness because it significantly thickens the interfacial film of protective coatings. EIS evaluations were also performed in order to verify the information acquired from the PCP operations. When reporting on corrosion and its prevention for metals in a variety of corrosive conditions, the EIS approach could be effectively used. Following being submerged for 1 h in a 3.5% NaCl medium, Nyquist graphs were recorded for the LCS, LCS + Sn, LCS + Sn + ZnO, LCS + Sn + NiO, and LCS + Sn + ZnO + NiO specimens. Figure 7 illustrates how every spectrum only displays one semicircle, whose diameter is the shortest for the LCS specimen. The width of the semicircle increased after applying a Sn layer to the steel, demonstrating that the layer's inclusion enhances resistance to corrosion. A broader semicircle resulted from the addition of 0.25% ZnO NPs to the Sn-coated film upon the substrate of the LCS specimen, while adding 0.25% NiO NPs to the Sn covering had a significantly greater impact. The steel specimen that contained both NiO and ZnO NPs together with the Sn covering offered the maximum semicircle radius. Following introducing NiO and ZnO, presumably separately or together, the resistance indices RS, RP1, and RP2 rose. Additionally, the existence of Q (with \"n\" values that ranged from 0.67 to 0.80) might be viewed as a double-layer capacitance with certain apertures to lessen the charged specimen interfaces. Furthermore, it was seen that the values of CdI and YQ decreased with the addition of the Sn protective film. Significant declines were noted with the addition of NiO and ZnO NPs, with the LCS + Sn + ZnO + NiO specimen showing the lowest value. The EIS and PDP investigations both show that Sn covering upon steel improves corrosion protection in the chloride medium and that this action was increased by the addition of NiO and ZnO NPs. Various nanomaterials have been used by numerous research teams based on their suitability for anti-corrosion purposes as listed in Table 1.  
Figure 6: Micrography of four distinct covered substrate and interfacial coatings of steel: (**a**) pristine Sn covering (S1); (**b**) Sn + ZnO NPs covering (S2); (**c**) Sn + NiO NPs covering (S3); and (**d**) Sn + ZnO + NiO NPs covering (S4). Reprinted with permission from Ref. [45] MDPI (2022).
## 5 Discussion and Conclusions  
This article analyzed one-way Trojan-horse attacks on CV-QKD systems. We derived and calculated the numerical changes of excess noise under attacks with the same and different wavelengths as the original pulse. We turned attacks into an information leakage problem and then quantified the excess noise bounds caused by the Trojan horse attacks to specify the security bounds of the system. From the simulation results, it can be seen that the Trojan password attacks in both cases will affect the excessive noise of the system. When the excess noise is too large, the existence of [PERSON] will be discovered. In order to successfully implement the attack, we can achieve it in two ways: on the one hand, based on the imperfection that the beam splitter has a wavelength-dependent property, [PERSON] can estimate the deviation of shot noise and then correct the excess noise by inserting different wavelength pulses. On the other hand, we can calculate the security bounds and then control the amount of crosstalk by controlling the time when the Trojan horse pulse enters the system, or the input power of the Trojan horse pulse to control the amount of excess noise. As a consequence, the transmission errors within the security bounds can be negligible and the legal users will not perceive the presence of [PERSON].  
This article takes the transmission distance of 50 km as an example to quantify the threshold of excess noise. Through calculation and simulation results, it can be known that when Trojan horse attacks use the same wavelength as the original, the degree of crosstalk should be less than 0.3. When using Trojan horse attacks with a different wavelength from the original, the input power of the attack pulse should be less than 0.7 mW.  
Conceptualization and formal analysis, L.Z.; methodology, investigation, writing--original draft preparation, Y.P.; writing--review and editing and supervision, D.H. All authors have read and agreed to the published version of the manuscript. This research was funded by National Nature Science Foundation of China (Grant Nos. 11904410 and 61801522), and National Nature Science Foundation of Hunan Province, China (Grant No. 2019J40352). The authors declare no conflict of interest. The following abbreviations are used in this manuscript:
## 4 Predictive Model for Bearing Capacity of Columns with Stirrup Corrosion
### Buckling Bearing Capacity of Longitudinal Reinforcing Steel Bars  
After stirrups corrosion, in addition to the core concrete restraint being reduced, the lateral restraint of the longitudinal reinforcing steel bars is weakened at the same time, resulting in flexural damage to the longitudinal reinforcing steel bars before reaching the yield strength. The longitudinal tendons will have an axial tensile effect on the corroded stirrups when flexural deformation occurs. Accordingly, the corroded stirrups have a lateral restraint reaction on the longitudinal tendons, similar to the spring restraint, so the corroded stirrups arranged along the column height will be modeled as multiple springs of variable stiffness acting on the longitudinal tendons, as shown in Figure 14 (the letter S is the stirrup spacing in the figure). The compression and flexure deformation of the longitudinal reinforcement is related to the degree of stirrup corrosion (when the compression and flexure deformation curve of the longitudinal reinforcement spans n-1 stirrup, it is called the n-order flexural mode). The more serious the corrosion of the stirrup, the weaker the lateral restraint effect of the stirrup on the longitudinal reinforcement, the more the longitudinal reinforcement spans in compression and flexure length, and the compression and flexure mode of the longitudinal reinforcement may jump from first order to second order or higher order (Figure 14).  
A previous study [19] investigated the bearing capacity of longitudinal bars after stirrup corrosion under different compression and flexure modes, establishing the relationship between the compression and flexure modes of longitudinal bars after stirrup corrosion through compression and flexure tests on reinforcing steel bars with different diameters, lengths, and corrosion rates.  
\[N_{sc}=\bigg{[}0.9606-0.007\frac{ns}{d}-0.0002\Big{(}\frac{ns}{d}\Big{)}^{2} \bigg{]}f_{y}{}^{\prime}A_{S}{}^{\prime} \tag{6}\]  
where \(N_{sc}\) is the flexural bearing capacity of the longitudinal bars after stirrup corrosion; \(n\) is the compression-flexion modulus order; \(s\) is the stirrup spacing; \(d\) is the stirrup diameter before corrosion; \(f_{y}{}^{\prime}\) is the yield strength of the longitudinal bars before corrosion; and \(A_{s}{}^{\prime}\) is the cross-sectional area of the longitudinal bars before corrosion.  
The resulting model for predicting the bearing capacity of axial compression columns with stirrup corrosion can be expressed as:  
\[N_{\text{uc}}=\varphi\bigg{\{}A_{\text{c1}}f_{\text{c}}+K_{\text{c}}A_{\text{c2 }}f_{\text{c}}+\bigg{[}0.9606-0.007\frac{ns}{d}-0.0002\left(\frac{ns}{d}\right) ^{2}\bigg{]}f_{y}^{{}^{\prime}}A_{S}{{}^{\prime}}\bigg{\}} \tag{7}\]  
where \(\varphi\) is the reliability adjustment factor [30], taken as 0.9.
# Study on Early Warning Methods for Shipping Input Risks Under Consideration of Public Health Events
## 3 Design of Source Country Risk Propagation Model Based on Environmental Factors  
### Improved SEIR-SEI Transmission Model  
Based on the human-mosquito transmission mechanism of mosquito-borne diseases such as dengue, considering climatic factors affecting mosquito population size, and expressing the mosquito transmission rate in terms of economic and health factors, this paper establishes the improved SEIR-SEI model as follows:  
\[\frac{dS_{H}}{d_{t}}=\mu_{H}N_{H}-\beta_{1}\frac{S_{H}}{N_{H}}I_{M}-d_{H}S_{H} \tag{1}\]  
\[\frac{dE_{H}}{d_{t}}=\beta_{1}\frac{S_{H}}{N_{H}}I_{M}-\sigma_{1}E_{H}-d_{H}E_ {H} \tag{2}\]  
\[\frac{d_{H}}{d_{t}}=\sigma_{1}E_{H}-\gamma I_{H}-d_{H}I_{H} \tag{3}\]  
\[\frac{dR_{H}}{d_{t}}=\gamma I_{H}-d_{H}R_{H} \tag{4}\]  
\[\frac{dS_{M}}{d_{t}}=\mu_{M}N_{M}-\beta_{2}\frac{I_{H}}{N_{H}}S_{M}-\mu_{M}S_{M} \tag{5}\]  
\[\frac{dE_{M}}{d_{t}}=\beta_{2}\frac{I_{H}}{N_{H}}S_{M}-\sigma_{2}E_{M}-\mu_{M} E_{M} \tag{6}\]  
\[\frac{dI_{M}}{d_{t}}=\sigma_{2}E_{M}-\mu_{M}I_{M} \tag{7}\]  
\[N_{M}=(0.12X_{1}-0.12X_{2}-0.32X_{3}+10.03)S \tag{8}\]  
\[\beta_{1}=-0.097X_{4}-0.049X_{5}+0.313 \tag{9}\]  
Here, \(S_{H}\) denotes the susceptible population, \(E_{H}\) denotes the exposed population, \(I_{H}\) denotes the infected population, and \(R_{H}\) denotes the recovered population. \(S_{M}\), \(E_{M}\), and \(I_{M}\) represent the susceptible mosquitoes, exposed mosquitoes, and infected mosquitoes, respectively. \(\beta_{1}\) is the transmission rate from infected mosquitoes to the susceptible population, which depends on the biting rate of mosquitoes, the probability of the vector transmitting the virus to the host, and the proportion of vectors to humans in a specific area. \(\sigma_{1}\) is the rate of transition from the exposed population to the infected population, and \(\gamma\) is the recovery rate from the infected population to the recovered population. \(\beta_{2}\) is the transmission rate from the infected population to the susceptible mosquito population, which depends on the biting rate of mosquitoes and the probability of virus transmission from host to vector, but not on the proportion of vectors to hosts. \(\sigma_{2}\) is the rate of transition from the exposed mosquitoes to the infected mosquito population. \(X_{1}\) represents the average daily temperature, \(X_{2}\) represents the average daily relative humidity, \(X_{3}\) represents the average wind speed, \(S\) is the area of the region, \(X_{4}\) represents economic factors, \(X_{5}\) represents health factors, and \(N_{M}\) is the mosquito population size.
## 2 Numerical Modelling
In the designed model, the electromagnetic wave propagation in the computational domain is described by the Helmholtz equation [34]:  
\[\mathbf{\
abla^{2}}\mathbf{E}+\Big{(}\mu\epsilon\omega^{2}-i\mu\sigma\omega\Big{)} \mathbf{E}=0 \tag{1}\]  
where \(\mu\) denotes the magnetic permeability, \(\epsilon\) is the electric permittivity, \(\sigma\) represents the electric conductivity, \(\omega\) is the angular frequency, and E is the electric field vector. This formulation accounts for both dielectric and conductive losses, which are critical for modeling wave interactions within heterogeneous materials like reinforced concrete. Thermal behavior is governed by the heat equation [35]:  
\[\rho C_{p}\Big{(}\frac{\partial T}{\partial t}\Big{)}-\
abla\cdot(\mathrm{k} \
abla\mathrm{T})=\mathrm{Q} \tag{2}\]  
where \(\rho\) is the material density, \(C_{p}\) denotes the material heat capacity, \(q\) is the heat flux associated with the convection phenomenon, \(T\) is the temperature, k denotes the thermal conductivity, and Q denotes the external heat source. The electromagnetic loss-induced heat source is defined as follows [34]:  
\[\mathrm{Q}=1/2\mathrm{Re}\Big{(}(\sigma-j\omega\epsilon)\mathbf{E}\cdot \mathbf{E}^{*}\Big{)} \tag{3}\]  
where Re indicates the real part of the value. The simulations considered two excitation modes: continuous and modulated. In both cases, the heat source was applied across the entire sample, encompassing both concrete and steel. For continuous heating, the total electromagnetic loss was used directly. For modulated heating, the loss was scaled by a time-dependent function (see Figure 3) to mimic periodic power variations. The model used a TE\({}_{10}\) mode at 2.45 GHz, a standard ISM frequency, with a 300 s heating duration. Excitation was applied via a port boundary condition on one waveguide wall, replicating the experimental setup. The key material parameters used in the model are summarized in Figure 3.0  
Figure 3.1 Model geometry. The generated thermograms were used as the exclusive input for training the deep learning model, allowing the network to learn and generalize spatial and thermal cues associated with rebar locations. This simulation-driven approach enabled efficient, repeatable dataset generation without the need for extensive experimental annotation, while maintaining physical realism and measurement relevance.
# Research on the Dynamic Characteristics Analysis and Power Control Method of Heat Pipe Reactors
## 3 Analysis of Dynamic Characteristics of HPR
### The Disturbance of SCO\({}_{2}\) Mass Flow Rate  
At \(t=100s\), the SCO\({}_{2}\) mass flow rate in the heat exchanger is reduced by a step of 5%. The response curves of the HPR under this disturbance are shown in Figure 9.  
As indicated by Equation (4), reducing the SCO\({}_{2}\) mass flow rate leads to a decreased capacity for heat transfer from the reactor, resulting in the heat transferred by the heat pipe to SCO\({}_{2}\) being higher than the heat taken away by SCO\({}_{2}\). Consequently, the heat exchanger outlet temperature of SCO\({}_{2}\) is initially elevated. During the first 100 s, the fuel and monolith are gradually increased because the heat production by the core is higher than the heat that SCO\({}_{2}\) can take away, and the negative reactivity resulting from its negative feedback effect causes the core power to gradually decrease. Subsequently, the decrease in reactor power causes the fuel and monolith to transfer more heat outward than they receive, leading to their temperatures decreasing. But the fuel and monolith temperatures are still higher than their initial temperatures during 200-330 s, which means that they still introduce negative reactivity into the core due to the negative feedback effect, resulting in a continuous decrease in reactor power. As the temperatures of the fuel and monolith fall below their initial values, the temperature feedback effect causes a positive reactivity to be introduced into the core, leading to a subsequent increase in reactor power. Then, the system gradually enters a new steady state, and all parameters are changed from the initial state.  
In summary, the HPR can overcome the impact of the above two typical disturbances on its own and finally reach the new steady state, indicating that it has good self-stability and self-regulation. Although the HPR can reach the new stable state only relying on the reactivity feedback of fuel and monolith under the uncontrolled condition, the regulation time is too long, and the parameter dynamics change greatly under various disturbances. After stabilization, all parameters vary from the initial state, which makes it difficult to meet the requirements for HPR operation. For example, under reactivity disturbance, the nuclear power of the HPR varies greatly, and the power deviates from the power set point. Therefore, an automatic controller needs to be designed to suppress changes in parameters and shorten the regulation time during the dynamic process.
## 4 Results and Discussion
### Verification of CFIP Model
#### 4.3.1 Determination of \(\kappa_{z}\)  
Before simulating the displacement field, the relation between \(k_{\eta}\) and \(\kappa_{z}\) was determined from Equation (61). As shown in Figure A3, as the value of \(\kappa_{z}\) increased, the value of \(k_{\eta}\) gradually decreased until \(\kappa_{z}\)= 15, while above \(\kappa_{z}\) = 15, the value of \(k_{\eta}\) is almost independent of the \(\kappa_{z}\) value. The angular-dependence of the simulated displacement field is very effective in determining the \(\kappa_{z}\) value. The angular dependence of the observed AE amplitudes appears to be related to the Bessel function \(J_{0}(\kappa_{z}\xi)\), which is involved in the Green's function in Equation (20). The Bessel function goes to zero at a certain value of \(\xi\), depending on the value of \(\kappa_{z}\). As shown in Figure A4, no minimum appears until \(\kappa_{z}=2\). Above this value, a single minimum appears at \(\vartheta=176^{\circ}\) for \(\kappa_{z}=3\), \(\vartheta=141^{\circ}\) for \(\kappa_{z}=5\), and \(\vartheta=87^{\circ}\) for \(\kappa_{z}=8\). Above \(\kappa_{z}=8\), a well of multiple minima appears: for \(\kappa_{z}=9\), there are two minima position at \(\vartheta=78^{\circ}\) and \(164^{\circ}\). In the simulation, we set \(\kappa_{z}=9\), and set FRS = 91 N/m\({}^{2}\) for p\({}_{0}\) = 2 bar and 250 N/m\({}^{2}\) for p\({}_{0}\) = 4 bar.