esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Temperature Dielectric Response of Strontium Titanate Thin Films Manipulated by Zn Doping
## 3 Results and Discussion
Such an explanation should also be valid in the case of Zn doping on Ti sites of ST. The temperature dependence of the dissipation factor of Zn-doped ST films shown in Figure 3b presents up to three peaks at \(\sim\)17, \(\sim\)100, and \(\sim\)185 K. The tan\(\delta\) magnitude is rather low, staying between 0.3% and 0.6% for all the films, with the only exception being a tan\(\delta\) value increase up to 1% at around 100 K for SrTi\({}_{0.95}\)Zn\({}_{0.05}\)O\({}_{3-\delta}\) film. On the other hand, reduced permittivity is desirable for impedance matching of tunable dielectric components as well as for a decrease in capacitance-induced delays [17]. Therefore, we checked the dielectric tunability of Zn-doped ST films under 125 kV/cm at 10 kHz and compared it to that of similarly prepared undoped SrTiO\({}_{3}\) film, as shown in Figure 3c. For that, the relative tunability was calculated by the equation:  
\[n_{r}=[e^{\prime}(0)-e^{\prime}(E)]/e^{\prime}(0)\times 100\% \tag{1}\]  
where \(e^{\prime}(E)\) and \(e^{\prime}(0)\) denote the real part of dielectric permittivity under the applied bias field \(E\) and after returning to zero field, respectively. As seen in Figure 3c, the temperature variation of \(n_{r}\) shows the peak similar to that observed in \(e^{\prime}(T)\) dependence with the peak temperature decreasing with increasing Zn content. Concerning the relative tunability values, they are close to each other for all the films under study in the \(T\) range above 100 K and are evidently enhanced by Zn doping below 100 K. As a result, the peak tunability value  
Figure 3: Temperature dependence of the real part of dielectric permittivity \(e^{\prime}\) (**a**), dissipation factor tan\(\delta\) (**b**), and relative tunability \(n_{r}\) (**c**) under a 125 kV/cm \(dc\) electric field at 10 kHz for SrTi\({}_{1-x}\)Zn\({}_{x}\)O\({}_{3-\delta}\) thin films with x = 0.01 and 0.05 in comparison to that for SrTiO\({}_{3}\) film. grows from \(\sim\)25.6% at \(\sim\)55 K for undoped SrTiO3 to \(\sim\)30.2% at \(\sim\)40 K for SrTiO99 Zn0.01O3\({}_{3-\delta}\) and further to \(\sim\)32.9% at \(\sim\)40 K for SrTiO95 Zn0.05O3\({}_{3-\delta}\) films. Figure 4 shows \(\varepsilon^{\prime}\) and \(n_{r}\) of 1% and 5% Zn-doped and undoped ST films as a function of the applied DC electric field at selected temperatures of about 15 and 70 K. In agreement with Figure 3, at these temperatures, the permittivity is lower and tunability is higher for Zn-doped ST films compared to the undoped one, whose curves are shown in Figure 4a. Moreover, SrTiO0.99 Zn0.01O3\({}_{3-\delta}\) film presents almost hysteresis-free \(\varepsilon^{\prime}(E)\) variation (see Figure 4b), while some hysteresis is observable for SrTiO0.95 Zn0.05O3\({}_{3-\delta}\) film (see Figure 4c). Since the hysteresis can make tunable device operation ambiguous, 1% Zn-doped ST film is more recommended for tunable component applications.
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Routing Decision Process
#### 4.3.3 Calculating Normalized Distance  
For the custodian selection, the sender calculates the normalized distance to incorporate the distance information with the congestion information successfully. Considering a two-hop neighbor, the sender UAV calculates the average distance (\(avg\_d_{n_{i}d}\)) from the current and possible future distance between the considered two-hop neighbor and destination, as follows:  
\[avg\_d_{n_{i}d}=\alpha\times d_{n_{i}d}^{1}+(1-\alpha)\times d_{n_{i}d}^{2}, \tag{4}\]  
where \(\alpha\) is constant with a value of 0.5. Similarly, considering a one-hop neighbor, the sender calculates the average distance (\(avg\_d_{n_{i}d}\)) as follows:  
\[avg\_d_{n_{i}d}=\alpha\times d_{n_{i}d}^{1}+(1-\alpha)\times d_{n_{i}d}^{2}. \tag{5}\]  
Generally, a one-hop neighbor connects to multiple two-hop neighbors. The sender creates pairs consisting of a two-hop neighbor and the one-hop neighbor through which the two-hop neighbor is connected to the sender. Such pairs are made for each of the two-hop neighbors. Then, the final average distance is calculated for each pair, as follows:  
\[F\_avg\_d_{n_{i}d}=\beta\times avg\_d_{n_{i}d}+(1-\beta)\times avg\_d_{n_{i}d}, \tag{6}\]where \(\beta\) is a constant with a value of 0.5. The sender also calculates a \(F\_avg\_d_{n_{i}d}\) for itself considering its current and future distance to the destination, as follows:  
\[F\_avg\_d_{sd}=\alpha\times d_{sd}^{1}+(1-\alpha)\times d_{sd}^{2}. \tag{7}\]  
Finally, the sender considers the maximum possible distance (_max_distance_) between two UAVs in the considered mission area and calculates the normalized distance (\(\Pi\)) as follows:  
\[\Pi=\frac{F\_avg\_d_{sd}}{max\_distance}. \tag{8}\]  
The sender calculates \(\Pi\) for itself and each pair of one- and two-hop neighbors.
## 4 Results  
### Rail Accelearance and Mobility  
The rail accelearance and mobility are helpful to the investigation of vibration characteristics, which are of great importance to explain the rail sound power characteristics. The amplitudes of the direct vertical rail accelearance and mobility are shown in Figures 10 and 11, respectively.  
Figure 10: Amplitude of direct vertical rail accelearance.  
Figure 9: The accelerometer and the hammer impact positions.  
According to Figure 10, the simulated amplitude of the direct mid-span rail accelerance increases with the increase of the frequency in the low-frequency range and reaches the peak at 163 Hz where the vertical rail resonance occurs. The resonance frequency is denoted by \(f_{\mathrm{v}}\), and it depends on the vertical fastener stiffness. In the frequency range of 200-1000 Hz, the accelerance amplitude does not change much. Another peak appears at 1063 Hz where the first-order vertical pinned-pinned rail resonance occurs. This resonance frequency is denoted by \(f_{\mathrm{p}}\). The corresponding modal shape turns out to be a standing wave with nodes at the fastener, and the half-wavelength equals to the fastener spacing. The on-support and mid-span rail accelerance amplitude curves coincide with each other besides the parts near \(f_{\mathrm{p}}\). As the nodes of the standing wave are located at the fasteners, there is a trough at \(f_{\mathrm{p}}\) in the on-support rail accelerance amplitude curve.  
Due to the vertical rail resonance and the first-order vertical pinned-pinned rail resonance, there are also two peaks at \(f_{\mathrm{v}}\) and \(f_{\mathrm{p}}\) in the direct mid-span rail mobility amplitude curve. However, the rail mobility amplitude decreases with the increase of the frequency above \(f_{\mathrm{v}}\), which is different from the accelerance amplitude curve. The on-support and mid-span rail mobility amplitude curves also coincide with each other besides the parts near \(f_{\mathrm{p}}\).  
The measurement and the simulation of the mid-span rail accelerance amplitude match well. The peaks at \(f_{\mathrm{v}}\) and \(f_{\mathrm{p}}\) are obvious in the measured accelerance amplitude curve. Therefore, the Timoshenko-beam track model and the SEM are adequate for studying the rail vibration characteristics of the monolithic track bed structure.
# Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs
## 2 Background and Motivation
### GPU Architecture  
In this section, we explain a GPU architecture and then describe how OpenCL kernels and memory regions are mapped and executed on a GPU architecture. Although each vendor has its own implementation of GPUs, their high-level architecture is common. This section is based on a state-of-the-art GPU architecture for OpenCL execution [18]. The basic execution unit of GPU is a _compute unit (CU)_. Generally, a high-performance GPU can process a huge amount of data with many CUs.  
Figure 3 shows the architecture of a CU. A CU generally includes multiple single instruction, multiple data (SIMD) vector units, scalar units, local data share (LDS) memory, cache memory, texture units, and schedulers. For example, a CU in the GPUs used in this paper has four SIMD vector units, one scalar unit, and LDS memory. An SIMD unit consists of 16-lane vector arithmetic logic units (ALUs), and each lane is designed to compute four work-items. Thus, an SIMD unit can process 64 work-items, which is referred to as a _wavefront_. In addition, an SIMD unit has a buffer for ten wavefront instructions, as shown on the left side of Figure 3. The reason for this instruction buffer is to maximize the utilization of CUs. If data accessed by the work-item are not in memory, the work-item must wait until data are loaded to memory; this decreases the CU utilization. In this case, the execution of the next wavefront instruction can hide the latency derived from the memory load.  
A CU can activate at most 40 wavefronts simultaneously because it has four SIMD units, and each unit has ten wavefront instruction buffers. Therefore, a GPU having eight CUs can process up to 320 wavefronts concurrently. Furthermore, since a wavefront handles 64 work-items, the GPU can compute up to 20,480 work-items simultaneously (320 wavefronts \(\times\) 64 work-items per wavefront).  
As described in Section 2.1, the abstract memory structure of OpenCL supports various computing devices. Figure 2 shows the correlation between the OpenCL memory abstraction and the GPU's memory structure. In OpenCL, global and constant memories are accessible from all work-items, and these memories are mapped to the GPU memory (graphics double data rate (GDDR) memory of a discrete GPU or the system memory of an APU). Furthermore, local memory is local to work-items within the same work-group and is mapped to the GPU's LDS memory, which is a fast memory private to each CU. Finally, private memory is mostly mapped to registers in a CU, but if registers are not enough to hold private memory, data on private memory can spill onto the GPU memory.
## 3 Performance Improvement of Multiple Receiver WPT
### Overall System Design and Parameter Estimation  
The design procedure of the dynamic multiple Rx system by considering the frequency splitting, cross-coupling issues with selective uniform power transfer is illustrated in Figure 9. From the above analysis, the complete system parameter estimation of the proposed method with the selected operating frequency of 1.0 MHz, 1.5 MHz, 2.0 MHz, 3 MHz along with the circuit parameters are presented in Table 3, and different scenarios are considered for the calculation. In scenario 1 the Rx and Tx coils are considered at a fixed distance of 3.5 c.m and in scenario 2 the distance between Tx and the Rx1, Rx2 and Rx3, Rx4 are maintained at 3 c.m, 5.5 c.m respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Distance**} & \multicolumn{3}{c}{\(d_{\text{R1}}=\) 4.0 c,} & \multicolumn{3}{c}{\(d_{\text{R1}}=\) 4.5 c,m} & \multicolumn{3}{c}{\(d_{\text{R1}}=\) 4.5 c,m} & \multicolumn{3}{c}{\(d_{\text{R1}}=\) 4.5 c,m} & \multicolumn{3}{c}{\(d_{\text{R1}}=\) 4.0 c,m} \\  & \multicolumn{3}{c}{\(d_{\text{R2}}=\) 4.0 c,} & \multicolumn{3}{c}{\(d_{\text{R2}}=\) 4.0 c,} & \multicolumn{3}{c}{\(d_{\text{R2}}=\) 4.0 c,m} & \multicolumn{3}{c}{\(d_{\text{R2}}=\) 4.5 c,m} & \multicolumn{3}{c}{\(d_{\text{R2}}=\) 4.0 c,m} \\  & \multicolumn{3}{c}{\(d_{\text{R3}}=\) 4.0 c,} & \multicolumn{3}{c}{\(d_{\text{R3}}=\) 3.5 c,m} & \multicolumn{3}{c}{\(d_{\text{R4}}=\) 3.0 c,m} & \multicolumn{3}{c}{\(d_{\text{R3}}=\) 3.0 c,m} & \multicolumn{3}{c}{\(d_{\text{R3}}=\) 3.5 c,m} \\  & \multicolumn{3}{c}{\(d_{\text{R4}}=\) 4.0 c,m} & \multicolumn{3}{c}{\(d_{\text{R4}}=\) 3.0 c,m} & \multicolumn{3}{c}{\(d_{\text{R4}}=\) 3.0 c,m} & \multicolumn{3}{c}{\(d_{\text{R4}}=\) 3.0 c,m} & \multicolumn{3}{c}{\(d_{\text{R4}}=\) 3.0 c,m} \\ \hline \multicolumn{12}{c}{**Tx Dia**} \\ \multicolumn{12}{c}{**(c.m)**} & \multicolumn{12}{c}{**6.5**} & \multicolumn{1}{c}{**12.5**} & \multicolumn{1}{c}{**14.5**} & \multicolumn{1}{c}{**15.5**} & \multicolumn{1}{c}{**6.5**} & \multicolumn{1}{c}{**12.5**} & \multicolumn{1}{c}{**14.5**} & \multicolumn{1}{c}{**15.5**} & \multicolumn{1}{c}{**6.5**} & \multicolumn{1}{c}{**12.5**} & \multicolumn{1}{c}{**14.5**} & \multicolumn{1}{c}{**15.5**} \\ \hline
1.0 & 0.95 & 0.68 & 0.57 & 0.36 & 0.94 & 0.68 & 0.55 & 0.35 & 1.51 & 0.68 & 0.56 & 0.36 & 0.85 & 0.68 & 0.47 & 0.26 \\
2.0 & 0.93 & 0.68 & 0.57 & 0.35 & 0.92 & 0.68 & 0.55 & 0.34 & 1.30 & 0.68 & 0.  
In scenario 3 and 4 the Rx's positions are changed to 3.5 c.m, 3 c.m, 5.5 c.m, 6.0 c.m and 4 c.m, 3.5 c.m, 4.5 c.m, 7 c.m respectively. In all the cases it is observed the variation in the compensation  
Figure 9: Design Flowchart of Proposed Method.  
capacitance at the load side is about 0.02 pF to 0.12 pF which can be achieved by using a discrete capacitor at each load coils. Similarly, for the Tx coil diameter of 12.5 c.m, the mutual inductance variation in all the scenarios are from 0.6 to 0.51 which will not deviate the operating frequency of the circuit.
# Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning
## 3 Methodology  
### Problem Formulation  
Markov decision processes (MDPs) are mathematical frameworks used to make optimal decision in situations where outcomes are partly random and partly under the control of a decision maker [40]. An MDP is defined by a set of states, a set of actions, a transition function that determines the probability of moving from one state to another given an action, and a reward function that assigns a numerical reward for each action taken in a given state. The goal in an MDP is to find a policy--a mapping from states to actions--that maximizes the expected sum of rewards over time.  
In many real-world scenarios, however, the agent does not have full observability of the environment's state. This leads to the framework of partially observable Markov decision processes [41]. POMDPs extend MDPs by incorporating a set of observations and an observation function. This function provides a probability distribution over possible observations, given the actual state and action taken. The agent maintains a belief state, a probability distribution over all possible states, based on the history of actions and observations. This belief state serves as a sufficient statistic for making decisions in a POMDP.  
In this study, we frame the problem of a robotic manipulator completing tasks with joint malfunctions as a POMDP. This formulation allows us to account for uncertainties in joint functionality and train the robot to adapt to varying conditions. Our experimental platform utilizes the Isaac Lab environment, which provides a high-fidelity simulation for robotic manipulation tasks [42]. The platform allows for precise control and the measurement of the robot's movements, as well as the ability to simulate various joint failure scenarios. The robot used in our experiments is [PERSON]'s Panda, a 7-DOF robotic manipulator known for its precision and dexterity. The Franka robot modeled in Isaac Lab has been accurately simulated with high fidelity to match the physical characteristics and kinematic properties of the real robot. The task environment was set up to simulate a typical industrial workspace where the Franka robot would be required to open a drawer. The following subsections describe details of the RL framework and reward functions.
# The Effects of Augmented Reality Interaction Techniques on Egocentric Distance Estimation Accuracy
###### Abstract  
Recent developments in virtual environment applications allow users to interact with three-dimensional (3D) objects in virtual environments. As interaction with 3D objects in virtual environments becomes more established, it is important to investigate user performance with such interaction techniques within a specific task. This study investigated two interaction modes, direct and indirect, depending on how the users interacted with the 3D objects, by measuring the accuracy of egocentric distance estimation in a stereoscopic environment. Fourteen participants were recruited to perform an acquisition task with both direct pointing and indirect cursor techniques at three egocentric distances and three task difficulty levels. The accuracy of the egocentric distance estimation, throughput, and task completion time were analyzed for each interaction technique. The indirect cursor technique was found to be more accurate than the direct pointing one. On the other hand, a higher throughput was observed with the direct pointing technique than with the indirect cursor technique. However, there were no significant differences in task completion time between the two interaction techniques. The results also showed accuracy to be higher at the greatest distance (150 cm from the participant) than at the closer distances of 90 cm and 120 cm. Furthermore, the difficulty of the task also significantly affected the accuracy, with accuracy lower in the highest difficulty condition than in the medium and low difficulty conditions. The findings of this study contribute to the understanding of user-interaction techniques in a stereoscopic environment. Furthermore, developers of virtual environments may refer to these findings in designing effective user interactions, especially those in which performance relies on accuracy.  
Keywords:direct pointing interaction; indirect cursor interaction; stereoscopic environment +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Setup of the Optimal Conditions for microRNAs Adsorption on PDMS Planar Surfaces
After three washes with the same buffer as for incubation, the fluorescence of the adsorbed microRNA was measured (F\({}_{\text{A}}\)). Panel (**b**): fluorescence of the eluted microRNA (F\({}_{\text{E}}\)). The microRNA adsorbed at different pH was eluted from the surfaces with buffers at three different pH values: pH = 5 (white squares), pH = 7 (gray squares) and pH = 9 (black squares). After elution, surfaces were imaged and the resulting signal was subtracted from F\({}_{\text{A}}\), obtaining values of the eluted microRNA (F\({}_{\text{E}}\)). Data are means of at least three independent replicates, while error bars represent standard errors. at pH 9 was complete also when the microRNA adsorption step occurred in water (the measured fluorescence value was \(25.7\times 10^{3}\pm 180\)). Experiments run on PDMS planar surfaces took to the conclusion that best conditions for microRNA adsorption are the dilution in water followed by elution at pH 9. Importantly, the adsorption at physiological pH is also high, allowing to process biological samples without changing the pH of solution before microRNA capture.
## 6 Security
### Data Confidentiality (User Privacy)  
Symmetric homomorphic encryption, which is used for metering data aggregation, is unconditionally secure, that is, perfectly secure [40]. Thus, the knowledge of a ciphertext does not provide any information about either the corresponding plaintext or the key. The path key which is used as the decryption key for an aggregated ciphertext on the aggregation path can be obtained only by the concentrator as long as the smart meters and the concentrator do not collude with each other, and every smart meter participates in the path key establishment procedure on aggregation paths it belongs. In addition, under the same assumption, each intermediate smart meter can by no means obtain the other meter's secret key \(k_{i}\) during the path key establishment procedure since the path key aggregation and distribution are done through the secure channel like SSL/TLS. Without \(k_{i}\), it is computationally infeasible to decrypt \(c_{i}=Enc_{k_{i}}(m_{i})\). Therefore, each \(c_{i}\) does not leak information about \(m_{i}\) without \(k_{i}\). Thus, any curious smart meters on the path or passive eavesdroppers cannot obtain any information about the plaintext.  
In addition, the concentrator also cannot guess individual metering data from each encrypted metering measurement sent from smart meters on the routing path because it cannot determine each individual symmetric key from the combined path key. Specifically, the security of the path key establishment protocol is guaranteed by the randomly generated initial key \(k_{init}\) of the leaf smart meter in the aggregation tree. Since the randomly generated initial key is added with real secret keys, the possibility of guessing \(k_{init}\) and the real secret path keys depends on the length of the secret keys. Thus, it is impossible to determine either \(k_{init}\) or the path keys from their composed values when \(M\) is large. When an aggregation path consists of \(n\) intermediate meters to the concentrator and the attacker can compromise \(m\) meters, the attacker cannot guess the path key as long as \(m<n\), or the secret keys of non-compromised meters.  
Therefore, user privacy can be preserved in the proposed scheme against passive adversaries.
# Theoretical Study of the Wear of a Reduced-Diameter Wheel for Freight Wagons, Based on Its Diameter
## 2 Methods and Materials
### Calculation Model
#### 2.4.2 Obtention of the Kinematic Parameters  
Refs. [18; 19; 20; 23; 24; 25] explain how to obtain the kinematic parameters for the wheelsets through relations dependent on the railway line geometry. Ref. [17] collects these relations and applies them to all of the possible geometries that can be found in a railway line: straight or circular sections, where the circular section can either be a circular curve or a transition curve (clothoid, quadratic parabola, or cubic parabola).  
As for the curves, a circular curve is that whose radius holds constant, while the transition curves are those whose radii are variable: the radius of a clothoid, also called an Euler or Cornu spiral, is inversely proportional to the distance run, while the radii of quadratic and cubic parabolas depend on the distance as per those mathematical functions.  
The parameters obtained at these geometries are listed next according to the order in which the relations were collected and generalized in Ref. [17]:  
* Average uncentering and its change rate.
* Yaw angle and its change rate.
* Average sinus of yaw angle and of yaw angle change rate.
* Average yaw angle.
* Combination of the uncentering and yaw angle effects.
* Angle of longitudinal displacement of the contact area.
* Tilt and its change rate.  
An example of the parameters' obtention is given next: uncentering. This parameter (\(y\)) can be defined as the distance between the track center and the wheelset center of  
Figure 4: Reference frame definition. Source: [1].  
gravity. Uncentering happens because of wheel conicity: wheels are slightly tapered to make wheel negotiation possible for wheelsets, which lack a differential. In this way, the wheel whose position is inner in relation to the curve can roll with a lower rolling radius than the nominal radius, while the opposite is true for the wheel whose position is outer in relation to the curve. Therefore, in the end, the outer wheel rolls a longer distance than the inner one, and the wheelset can negotiate the curve with a lower slip.  
The uncentering parameter is associated with some track and wheelset parameters, which are shown in Figure 5, except for the (equivalent) conicity (\(k\)), which is an intrinsic parameter of each wheel. These parameters can be interrelated by computing the linear velocities of each wheel and equating them or by applying the Thales's Theorem to the triangles drawn in Figure 5. The formula for uncentering is named after [PERSON], who developed it in the 19 th century:  
\[y=\frac{r_{o}b_{o}}{kR} \tag{1}\]