esa-sceva/satcom-chunk-collection huggingface dataset:
# Variable Admittance Control in Sliding Mode for Robust Physical Human-Robot Interaction
## 3 Reachability in VAC
### Reachability in VAC
The primary goal of this research is to establish foundational knowledge for the robust and precise application of the proposed sliding mode control law in variable admittance control. The proposed controller is implemented in SIMULINK (MathWorks, Inc. 2022, Natick, MA, USA). The 7-DoF Kinova Gen3 (Kinova, Inc., Boisbraind, QC, Canada) with a Robotiq FT-300 force sensor (Robotiq, Levis, QC, Canada) is modeled using Simscape Multibody(MathWorks, Inc. 2022, Natick, MA, USA) for the closest simulation to reality. The system is then modeled as a planar (\(n=2\)) 3-DoF configuration in Figure 4a, where joints 1, 4, and 6 are activated. Linear motion is of interest for analyzing the system behaviors; hence, high admittance parameters are selected for the rotational motion to allow orientation of the end effector of the robot to have no significant change during the interaction. In addition, a robust damped least-squares method [45,46] is applied in case the system becomes unstable near or at singularity configuration (0\({}^{\circ}\) of joint 4). The proposed control law allows system states to be in sliding mode even in the presence of time-varying admittance parameters. Comparing the results in Figure 5, the method in the literature [35, 36, 37] is insufficient to tackle VAC, while our approach not only ensures relatively accurate tracking but also demonstrates robustness. The robustness can be seen from three aspects: (i) the modeling error is designed to be large; (ii) the numerical solver adopts 1 st-order Newton methods; (iii) and \(\mathbf{C_{w}}\) term is modeled as a bounded uncertainty. In addition, the human-intention-based VAC is realized. Equation (5) allows for the maximum allowable operation velocity to be constrained within 0.1 m/s. According  
Figure 4: (a) Simscape model of the manipulator under a planar 3-DoF configuration and (b) Physical human–robot interaction scene with an experimental setup that has a similar configuration. to the effort-saving law in Equation (2), the velocity is maintained even if the applied force restores to the nominal level between 5-6 s (from \(-7\) N to \(-5\) N).
# Telecommunication Systems for Small Satellites Operating at High Frequencies: A Review
## 2 Asteria  
ASTERIA [1] (Arcsecond Space Telescope Enabling Research in Astrophysics) is a 6U CubeSat designed, integrated, and operated by JPL to demonstrate high-precision pointing control and thermal control, and to conduct photometric measurements of bright, nearby stars to seek transiting exoplanets. The project started in 2014 and ASTERIA was launched in August 2017 as pressurized cargo on aCommercial Resupply Services mission to the International Space Station (ISS). ASTERIA deployed into low-Earth orbit in November 2017 and in February 2018 completed the technology demonstration objectives planned for the 90-day prime mission. Since then, ASTERIA has been performing an extended mission to search for exoplanets and obtain additional operational spacecraft data. As of this writing, ASTERIA has been operating in space for over 2 years, until December 2019 when it lost contact.
# Optimal Power Dispatch of an Offshore Wind Farm under Generator Fault
## 4 Optimal Power Dispatch with Fault Accommodation
### Particle Swarm Optimization  
PSO is a heuristic algorithm introduced by [PERSON] and [PERSON] in 1995. It is an optimization algorithm for simulating bird predation behavior. PSO has many advantages, such as simple implementation, few adjustable parameters, and fast convergence [27]. It has been widely used in function optimization, neural network training, and many other related engineering applications. The basic PSO algorithm can be expressed as follows:  
It is assumed that \(N\) particles in an \(n\)-dimensional space constitute a population. The position \(\mathbf{X}_{i}\), the velocity \(\mathbf{V}_{i}\), the optimal position \(\mathbf{P}_{i}^{L}\) searched by itself of the \(i\)th particle, and the optimal position \(\mathbf{P}_{g}\) of the entire population are respectively recorded as: \(\mathbf{X}_{i}=(x_{i1},...,x_{in})\), \(\mathbf{V}_{i}=(v_{i1},...,v_{in})\), \(\mathbf{P}_{i}^{L}=(p_{i1},...,p_{in})\), and \(\mathbf{P}_{g}=(p_{g1},...,p_{gn})\). The objective value can be calculated by taking \(\mathbf{X}_{i}\) into the objective function. Firstly, \(\mathbf{X}_{i}\) and \(\mathbf{V}_{i}\) are randomly initialized. Then, the search process is conducted by updating (7) and (8):  
\[\mathbf{V}_{i}=w\mathbf{V}_{i}+c_{1}r_{1}(\mathbf{P}_{i}^{L}-\mathbf{X}_{i})+c_{2}r_{2}(\mathbf{P}_ {g}-\mathbf{X}_{i}) \tag{7}\]  
\[\mathbf{X}_{i}=\mathbf{X}_{i}+\mathbf{V}_{i} \tag{8}\]  
where \(i\) is the number of the particle, \(w\) is the inertia weight, \(c_{1}\) and \(c_{2}\) are learning factors, and \(r_{1}\) and \(r_{2}\) are random numbers uniformly distributed between \((0,1)\).  
PSO has been proven to be effective for many different optimization problems. However, the original PSO is proposed for the single objective continuous problem. In order to solve the constrained multi-objective optimization problem that is common in practical applications, the basic PSO should be modified [28; 29].
## 2 Study Area and Datasets
### Satellite Imagery
#### 2.3.2 Spectral Resolution  
The spectral range of the RapidEye satellite begins at 440 nm, a comparable wavelength with the coastal/aerosol band of Landsat (433 nm), maximising radiance measurements from the seabed. Although RapidEye has a dedicated \"Red-Edge\" band at 690 nm-730 nm for vegetation studies that is sensitive to changes in chlorophyll content, we used the NIR band (760 nm-850 nm) in this study, as it outperformed the \"Red Edge\" band in delineating the land-sea interface.
## 3 Modeling and Analysis
### Simulation Results Analysis
#### 3.4.1 Comparison of Rotor Stress  
Figure 8 shows the stress cloud diagrams of rotors in the motors at the moment of maximum stress occurs. It can be seen that during the high-impact process, the maximum stress of rotor of the TRUM-1 reaches 481.4 MPa, which exceeds the yield strength of the rotor material (325 MPa), so the rotor will be plastically deformed and operation of the motor will be badly influenced. For the TRUM-2, however, the maximum stress value of the rotor is 287.0 MPa, which does not exceed the yield strength of the rotor material. The rotor deforms elastically. When the impact acceleration is removed, the rotor will return to its original shape without affecting the structure of the motor. Similarly, comparing the motors in group 2, it is shown that the maximum stress values of the rotors in TRUM-3 and TRUM-4 are 432.6 MPa and 259.3 MPa respectively, which means that there is plastic deformation in TRUM-3's rotor, but not in TRUM-4's rotor. Thus, it can be concluded that the disc spring does have a good buffering effect during the high impact to the motor and the rotor is protected. It can also be observed from the cloud diagrams that during the impact process, the stress is concentrated at the root of the middle boss of the rotor where the maximum stress occurs. Therefore, the improved design of the rotor can be considered from the perspective of reducing the stress concentration at the junction of the boss and web.  
Figure 8: Stress-cloud diagrams of the rotors at the moment that maximum stress occurs: (**a**) TRUM-1; (**b**) TRUM-2; (**c**) TRUM-3; (**d**) TRUM-4.
## 5 Results and Discussion
### Quantifying Post-Attack Impact for Iterative Improvement  
The ability to quantify post-attack impact using the PAVI is an essential element of the HAVA. Unlike traditional accuracy metrics, which only capture absolute performance degradation, the PAVI contextualizes the severity of these losses by relating them to the model's baseline reliability. For instance, as shown in Figure 4 and Table 3, the adversarial FGSM and adversarial evasion attacks reduced accuracy from 97.36% to 61.40% and 62.28%, respectively. These attacks yielded a high PAVI of 38.60% and 37.72%, indicating a substantial compromise in the model's performance. In contrast, data poisoning caused a milder reduction to 89.47% accuracy and a low PAVI of 10.53%. This standardized metric enables a nuanced comparison across attack types and facilitates identifying vulnerabilities that demand immediate intervention. Without the PAVI, the relative impact of different attacks could be misinterpreted, especially when working with models of high baseline accuracies.  
The HAVA leverages these insights for iterative improvement by integrating the feedback from the PAVI evaluations into the model development cycle. For example, a high PAVI for adversarial FGSM attacks highlights the need to prioritize adversarial training in subsequent iterations. Similarly, a lower PAVI for data poisoning suggests that less resource-intensive defenses, such as anomaly detection or federated learning, may suffice. During the mitigation workflow, discussed in the subsection below, this iterative process allows developers and cybersecurity researchers to focus resources on the most critical vulnerabilities to optimize the balance between resilience and computational efficiency.  
Figure 4: Model accuracy with the PAVI: original vs. attacks.  
Figure 3: Training loss over epochs.
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 3 Related Work
### Adaptive Approaches
#### 3.2.5 Oscar  
The scheduler proposed in [13], optimized scheduling cell allocation algorithm for convergecast (OSCAR), exploits the principles of Orchestra. The design of the scheduler is based on the assumption that for convergecast traffic, the nodes closer to the root have higher traffic loads. The node's rank is used to attribute a class to each node, and nodes closer to the root receive additional cells. Allocated cells are reduced if the traffic load decreases but are re-allocated if the traffic load increases again. The authors compare OSCAR with RBS Orchestra and, in the evaluated scenarios, OSCAR demonstrates to be the superior scheduler in terms of average latency and average PDR.
## 4 Discussion
### Distribution of REEs
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Element** & **Original Sample** & **X\({}_{\text{goethite}}\)** & **X\({}_{\text{cancinrite}}\)** & **X\({}_{\text{coarse}}\)** & **E\({}_{\text{end/goe}}\)** & **E\({}_{\text{end/coarse}}\)** \\ \hline La & 237.90 & 207.62 & 256.51 & 46.95 & 1.24 & 5.46 \\ Sc & 82.90 & 71.88 & 91.14 & 54.65 & 1.27 & 1.67 \\ Ce & 438.90 & 380.36 & 468.88 & 116.75 & 1.23 & 4.02 \\ Pr & 49.26 & 46.75 & 53.35 & 12.62 & 1.14 & 4.23 \\ Nd & 193.63 & 178.91 & 206.94 & 52.47 & 1.16 & 3.94 \\ Sm & 37.29 & 36.00 & 38.82 & 12.21 & 1.08 & 3.18 \\ Eu & 8.49 & 7.98 & 8.95 & 2.94 & 1.12 & 3.04 \\ Gd & 33.48 & 24.72 & 39.50 & 12.76 & 1.60 & 3.10 \\ Tb & 4.64 & 4.09 & 5.17 & 2.02 & 1.26 & 2.56 \\ Dy & 27.01 & 22.94 & 29.65 & 12.79 & 1.29 & 2.32 \\ Ho & 5.18 & 4.88 & 5.33 & 2.75 & 1.09 & 1.94 \\ Er & 15.36 & 13.26 & 16.74 & 7.79 & 1.26 & 2.15 \\ Tm & 2.24 & 1.95 & 2.29 & 1.25 & 1.17 & 1.83 \\ Yb & 14.84 & 11.39 & 16.91 & 8.85 & 1.48 & 1.91 \\ Lu & 2.21 & 1.70 & 2.55 & 1.41 & 1.50 & 1.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Calculated REE concentrations (all in ppm) in pure cancinrite, goethite, and coarse-grained fractions as well as the enrichment factors (E). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Element** & **Measured** & **Estimated** & **Element** & **Measured** & **Estimated** \\ \hline La & 237.90 & 236.96 & Tb & 4.64 & 4.74 \\ Sc & 82.90 & 83.44 & Dy & 27.01 & 26.96 \\ Ce & 438.90 & 433.47 & Ho & 5.18 & 5.15 \\ Pr & 49.26 & 50.71 & Er & 15.36 & 15.35 \\ Nd & 193.63 & 195.73 & Tm & 2.24 & 2.15 \\ Sm & 37.29 & 37.69 & Yb & 14.84 & 14.70 \\ Eu & 8.49 & 8.56 & Lu & 2.21 & 2.21 \\ Gd & 33.48 & 33.59 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of linear mixing modelling. has been proposed as the possible hosts of the REEs [28; 29]. Although it was found hard to measure analytically, [PERSON] et al. [17] report canrinite a probable host for Sc in red muds from Greece. Nevertheless, physical and chemical circumstances are not known in detail yet, as there is not enough experimental result available concerning REE adsorption on the two key minerals of the Almasfuzito red mud. If physical adsorption has a significant role, adsorption-desorption mechanisms of REEs in the red mud, pure goethite and canrinite should be studied first in order to find an effective industrial tool to remobilize these elements.
# Thermal Stability Design of Asymmetric Support Structure for an Off-Axis Space Camera
## 3 Design of Support Structure with Low Expansion Coefficient  
CFRP is widely used in the design of space camera structure because of its good per-formance, such as its light weight, high rigidity and low thermal expansion [13; 18; 22]. Based on the above calculations and discussion, a support structure made of M40 carbon fiber composite for the remote sensing camera was designed and developed. The supporting structure consists of front frame, rear frame and central bearing cylinder, as shown in Figure 5.  
The camera support structure needs to have high stiffness, strength and low weight to ensure that the remote sensing camera has good mechanical properties. By optimizing the mechanical properties of the whole structure of the camera, the optimal size of the supporting structure of each part can be obtained. After optimization, the thickness of front and rear frames is 4 mm and the thickness of center bearing cylinder is 1.5 mm.  
For this kind of structure, the common design method is to use isotropic ply to ensure the consistency of the structure in all directions. However, the thermal stability of the sup-port structure of the remote sensor is not considered in the isotropic stacking mode, and the thermal deformation in the direction of structural asymmetry will especially affect  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\pm\)**0/0\({}^{\circ}\)/90\({}^{\circ}\)** & \multicolumn{2}{c}{\(\pm\)**0/0\({}^{\circ}\)/\(\mp\)\(\oplus\)**} & \multicolumn{2}{c}{\(\pm\)**0/90\({}^{\circ}\)/\(\mp\)\(\oplus\)**} \\ \hline
**Ply Angle \(\theta\)** & **0\({}^{\circ}\)  
the imaging quality. According to the existing optical element layout and form of the support structure, the remote sensor structure has good symmetry in the X direction, so we need to strictly control the thermal expansion deformation of the structure in the Y and Z asym-metric directions.
## 2 Materials and Methods
### Characterization of Physical Properties of Spray-Dried Anthraquinone-Loaded Casein Micelles  
#### 2.3.1 Encapsulation Efficiency  
The anthraquinones present on the surface of the microcapsules and inside the core of the casein micelles were determined. The amount of surface anthraquinones was determined by adding 0.1 g of spray-dried microcapsules to 4 mL of methanol and vortex mixed for 60 s. Then, this mixture was centrifuged at 30,000\(\times\)\(g\) for 5 min to obtain the supernatant. The anthraquinones present inside the casein micelles were determined by the same method as above, although the mixtures were sonicated for 10 min in an ultrasonic bath to ensure the complete release of anthraquinones from the interior of casein micelles.  
The recovered supernatants after centrifugation were taken carefully and filtered by a 0.45 \(\upmu\)m membrane filter for all samples prior to HPLC analysis, as described in our previous article [14]. In general, stock solutions of 1000 \(\upmu\)g/mL and working solutions of mixed standards (aloin, aloe-emodin and rhein) were prepared at concentrations of 5 ppm, 10 ppm, 20 ppm and 40 ppm by diluting the stock solution in a volumetric flask with methanol of were prepared by dissolving in HPLC grade methanol and used as standards. HPLC measurements were performed on an Agilent series1250 infinity gradient HPLC (Agilent Technologies, Santa Clara, CA, USA) equipped with a 600 solvent pump and a C18 reversed-phase packing column (Phenomenex XB-C18, 250 mm \(\times\) 4.60 mm, 3.6 \(\upmu\)m Aeris). Gradient elution was performed at 0.7 mL/min, using a binary mobile phase consisting of water with 1% formic acid (A) and methanol (B). The elution was monitored at 254 nm and the injection volume was 20 \(\upmu\)L. The HPLC run started with 20% methanol and increased to 30%, 45% and 60% at 3, 10 and 15 min, respectively. The methanol percentage was increased to 70% at 18 min and maintained for 8 min, followed by a gradual decrease to 60%, 40% and 20% at 30, 32 and 35 min, respectively. A calibration curve was established for each standard as a function of their peak areas. The contents of anthraquinones present in the samples were quantified against the standards. T, which was then analyzed by HPLC after filtering through a 0.45-micron filter. The amount of anthraquinones present inside the core of casein micelles were determined by subtracting the surface-attached anthraquinones and the encapsulation efficiency was determined as below:  
The following equation was used to calculate \[Encapsulation\ efficiency\ (EE\%)=Total\ added\ AQ(\text{mg})-surface\ attached\ AQ(\text{mg})/Total\ added\ AQ\ (\text{mg})\times 100 \tag{1}\]  
where \(AQ\) stands for anthraquinones in mg's that are present per gram of spray dried microcapsules
## 3 Analysis of Calculation Results
### Wind Turbine Wake Flow Field Characterization
#### 3.2.2 Wake Vortex Strength  
The spin of the fluid velocity vector is called the vorticity. Blade deformation causes perturbation of the surrounding air, which affects the change in vorticity around the blade. For wind turbine wake vorticity, the center vortex and blade tip vortex mainly dominate. Figure 8 shows the vortex curves of five kinds of wind turbines in the wake flow field of different cross-sections. This shows that the vortex distribution of the larger region is mainly behind the wind turbine. There is a tip vortex at the height of the tip of the blade, there is a center vortex at the height of the hub, and there is a tower vortex below the height of the hub. With the increase in the distance to the wind turbine, the vortex peak gradually decreases, but the distribution trend is almost unchanged. The tip vortex and the center vortex occupy the dominant position of vortex distribution in the cross-section from 1 to 3\(D\) after the wind turbine; the center vortex generated by the wind turbine has been weakened in the 3\(D\) cross-section; and the tip vortex occupies the dominant position of vortex distribution after 3\(D\) from the wind turbine. By comparing the vortex distribution of the blade wake flow field at the same position after the wind turbine with a different number of layers, it can be found in Figure 8 that the tip vortex with 16 layers has the highest vortex, develops backward for the longest distance, and has obvious asymmetry, and its center vortex has the slowest decay speed. As the number of layers increases, the tip vortex decreases and the center vortex decay rate increases. The vortex of the blade along the leaf spreading direction from the root to the tip of the blade shows a trend of decreasing and then increasing. The vortex of the five types of wind turbine blades with different numbers of pavement layers has a large change in the relative radius of 0.4-1.0\(R\), but the trend of the vortex change is gradually flattened with the increase in the number of pavement layers.  
This is because with the increase in the number of pavement layers, the maximum deformation of the blade gradually decreases, and the growth trend of the deformation along the blade spread gradually flattens out. Hence, the disturbance caused by the deformation of the fluid flowing through the blade that needs to be overcome is reduced accordingly. Therefore, with the increase in the number of pavement layers of the blade, the tip vortex of the wake stream decreases, the recovery speed is accelerated, and the vortex change along the blade spread is reduced.
## 3 Results and Discussion
### After Aging: Evaluation of Paint Samples  
#### 3.3.1 Morphological Observations  
As soon as the samples were taken out of the weathering chamber, they all presented liquid droplets on the surface, as shown in Supplementary Table S10. For the Oil_V_COMM, Alk_V, and Sty_V samples, the droplets were uniformly spread over the entire surface, whereas in Oil_V and Acr_V, the droplets were only found on the edges of the paint. The pH value of the droplets was measured with a litmus paper, obtaining a value around pH = 1. Considering the high acidity value and the aging conditions inside the weathering chamber, it can be assumed that sulfuric acid (H\({}_{2}\)SO\({}_{4}\)) was formed inside the chamber by a reaction between sulfur dioxide and the humidified water [60]. In addition, surface changes begin to occur as early as three days after the end of accelerated aging. Specifically, a color change from violet to grey was observed above all at the edges. Over time, the color change seems to affect a more noticeable area of the surface. However, it is important to note that in the samples where the droplets were all over the surface, the color change is widespread, whereas in the Oil_V and Acr_V samples, it is limited to a few points along the edges. This may be due to the different drying phases of the binder. However, it is evident that in all paint films, the color variation starts from the edges.  
Digital Optical Microscopy was used to obtain a more detailed understanding of the primary visual inspection. The microscopic observations (Supplementary Table S11) show that small crystals appear to grow on the droplets formed during accelerated aging, as soon as the samples are removed from the weathering chamber. Over time, the droplets dry out and the crystals become more defined, appearing to have a rhombohedral shape. A second type of monoclinic crystal also appears to form over time. However, as shown in Supplementary Table S11, these considerations exclude sample Oil_V, as it is difficult to observe from the microscopic photos whether crystals have formed or not due to the long drying phase of the liquid droplets on the surface.