esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Transport  
The points of the thermodynamic cycle characteristic of a possible application of the TES system in the field of mobility are presented in Table 6.  
The performances obtained from the simulations on the thermodynamic model are summarized in Figure 9 (for short-term storage) and in Figure 10 (for long-term storage). The conclusions reached for the residential sector remain generally valid. The most interesting difference concerns the zeolite AQSOA FAM Z02/water mixture (CM12), which already in the previous case appeared to be a promising solution. The application in the field of mobility highlights the characteristic strengths of this working pair: unlike other water-based solutions (e.g., CM09, CM13), the mixture offers performances that resist well to the increase in storage time and \(\phi\), as clearly shown in Figure 11. In fact, CM12 involves a percentage reduction in efficiency (from \(\eta\)0 to \(\eta\)4) equal to 14.1%, against an average value of the water-based solutions of 21.2%; for t = \(\infty\), the difference is even more clear (22.6% against 37.9%).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Working Pair** & \(\mathbf{T_{1}}\) & \(\mathbf{T_{2}}\) & \(\mathbf{T_{3}}\) & \(\mathbf{T_{4}}\) & \(\mathbf{T_{cond}}\) & \(\mathbf{T_{exp}}\) & \(\mathbf{P_{exp}}\) = \(\mathbf{P_{1}}\) = \(\mathbf{P_{4}}\) & \(\mathbf{P_{cond}}\) = \(\mathbf{P_{2}}\) = \(\mathbf{P_{3}}\) \\  & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) & (**(mbar)**) & **(mbar)** \\ \hline CM01 & 40 & 71.05 & 90 & 56.11 & 40 & 10 & 72.78 & 348.00 \\ CM02 & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ CM03 & 40 & 71.86 & 90 & 59.53 & 40 & 10 & 72.78 & 348.00 \\ CM04 & 40 & 73.80 & 90 & 49.87 & 40 & 10 & 72.78 & 348.00 \\ CM05 & 40 & 69.64 & 90 & 51.97 & 40 & 10 & 72.78 & 348.00 \\ CM06 & 40 & 71.29 & 90 & 50.65 & 40 & 10 & 31.06 & 178.26 \\ CM07 & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ CM08 & 40 & 72.53 & 90 & 57.00 & 40 & 10 & 31.06 & 178.26 \\ CM09 & 40 & 75.36 & 90 & 49.72 & 40 & 10 & 12.38 & 74.38 \\ CM10 & 40 & 79.35 & 90 & 49.61 & 40 & 10 & 12.38 & 74.38 \\ CM11 & 40 & 69.00 & 90 & 58.01 & 40 & 10 & 12.38 & 74.38 \\ CM12 & 40 & 66.07 & 90 & 57.70 & 40 & 10 & 12.38 & 74.38 \\ CM13 & 40 & 69.77 & 90 & 53.89 & 40 & 10 & 12.38 & 74.38 \\ CM14 & 40 & 65.36 & 90 & 60.56 & 40 & 10 & 12.38 & 74.38 \\ CM15 & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ CM16 & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ CM17 & 40 & 72.66 & 90 & 53.10 & 40 & 10 & 12.38 & 74.38 \\ \hline \end{tabular}
\end{table}
Table 6: Summary of the simulated cycles (transport sector).  
Figure 9: Energy efficiency of short-term storage, for transport application (with \(\phi\) = 0; 0.5; 1; 2; 3).
# Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network
## 4 Experimental Results and Analysis
### Results of Point Cloud Upsampling
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**4\(\times\)**} & \multicolumn{3}{c}{**16\(\times\)**} \\  & **CD** & **HD** & **P2F** & **CD** & **HD** & **P2F** \\ \hline PU-Net [6] & 0.5225 & 4.6083 & 4.3590 & 0.3123 & 3.9111 & 5.0470 \\ PU-GAN [17] & 0.2676 & 4.7379 & 3.6100 & 0.2232 & 6.3243 & 4.5690 \\ PU-GCN [14] & 0.2724 & 3.0455 & 3.3760 & 0.1657 & 3.8224 & 3.5060 \\ Dis-PU [7] & 0.2560 & 4.7277 & 3.1780 & 0.1484 & 6.0934 & 3.7660 \\ PC2-PU [24] & 0.2321 & 2.5942 & 2.7250 & 0.0998 & 2.8692 & 2.9440 \\ Grad-PU [9] & 0.2500 & 2.3700 & 1.8900 & 0.1080 & 2.3520 & 2.1270 \\ \(S^{3}\) U-PVNet [11] & 0.2800 & 1.6400 & 2.5300 & 0.1212 & 1.5250 & 2.4040 \\ SAPCU [25] & 0.4600 & 9.0600 & 3.4500 & 0.3820 & 9.3550 & 3.2900 \\ SSPU-FENet & 0.1898 & 2.2293 & 2.7034 & 0.0991 & 2.0297 & 2.1435 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of 4\(\times\) and 16\(\times\) results under evaluation metrics. Figure 12: Comparison of 16\(\times\) results. **Mesh reconstruction based on upsampled point clouds. ** Point cloud upsampling is pivotal in various downstream tasks, such as mesh reconstruction. Firstly, we verify the impact of our upsampled point cloud model on mesh reconstruction using the Poisson surface reconstruction method [28]. As shown in Figure 13, the first column displays randomly selected ground truth (GT) models from the ESB and PU-GAN datasets. The second and third columns are the 4\(\times\) and 8\(\times\) upsampling results, respectively, with the 512 input points. The varying colors in the last two columns represent the distance between the points and the GT model, ranging from blue (indicating the smallest distance) to red (representing the largest distance). It is evident that the mesh reconstruction closely resembles the GT model, apart from some minor non-smooth edges observed in the 4\(\times\) upsampling. Moreover, we randomly select a test sample from the PU1K dataset and generate the corresponding upsampled point cloud. Subsequently, we employ the ball-pivoting method [29] to reconstruct the meshes, as shown in Figure 14. Our method successfully infers the points at the armrests of the chair, significantly contributing to the subsequent mesh reconstruction process. In contrast, other methods [7; 8; 12; 14; 17; 26; 30] yield incomplete results or loss points at the armrests of the chair, demonstrating the superiority of our approach in handling complex geometric features. Figure 13: Visualization of mesh reconstruction compared to GT models.
## 3 Results
### User Experiments
#### 3.2.4 Surveys Results  
The results of the user experiments provided valuable insights into user expectations and potential usability issues. Tables 3 and 4 present the answers to the surveys and feedback from users. Key findings are as follows:  
* In general, participants were satisfied and impressed with the potential, assistance demonstration, usability, and user interface, giving them high scores.
* Regarding the demo execution--although they found it useful, they found that it lacked speed execution. This is interesting because the movements of the robot were executed at this speed for safety as well as safety expectations.
* Regarding their needs after seeing the demo--they expressed a desire for the robot to pick up objects not only from the table and shelf but also from the floor and cupboard. They explained that they often dropped objects and needed assistance from a nurse to retrieve them. They also suggested that the robot should be able to pick up objects from the wardrobe because they needed to pick up clothes from there without help as well.  
Figure 20: Set-up of the FLM room for user experiments. Map on the left, with furniture tagged in gray, the POIs in red and the patient location in blue. View of the room on right, with furniture and user tagged.  
* Regarding the user interface (UI)--the majority of users were pleased. However, some noted the need for larger typography, while others suggested the inclusion of pictures to facilitate easier understanding.  
Despite the small number of participants, it is interesting to note that they came to similar conclusions regarding their expectations, impressions, and needs. This consistency helps to focus future routine implementations and highlights the need to optimize execution time--or at least manage expectations around it--due to safety concerns.
# Performance Analysis of Physical Layer-Based Multiple-Input Multiple-Output on WiMAX (MIMO-WiMAX) +
## 4 Simulation Results  
The high data rate transmission that arises from the combination of WiMAX with MIMO technology is one way to improve the performance of WiMAX-MIMO systems over normal WiMAX systems. The performance of this technology is superior to that of standard WiMAX, which does not make use of MIMO systems for BER and SNR values. Alamouti systems are implemented using two transmit antennas and nr reception antennas, and are capable of having max. diversity order of 2 Nr. Maximum Likelihood Decoding is used at the receiver for Alamouti code encoding. The performance of MIMO-WiMAX for BER vs. SNR was analyzed. Comparing lower modulation schemes like QPSK to higher modulation schemes like 64-QAM, simulation results illustrated in Figure 6 suggest that lower modulation schemes, such as QPSK, have lower bit error rates. Transmission quality deteriorates as the number of symbols sent in each block increases. This occurs as a direct result of the mapping that is carried out inside the transmission values. The number of possible mapping arrangements has a direct correlation with the error rate, which rises as choice counts increase, as shown in Figure 6.  
Figure 5: MIMO-WiMAX system.  
In this case, the cyclic prefix (CP) choices utilized are 1/4, 1/8, and 1/16 of the symbol time, which corresponds to the number of FFT samples. Findings indicate that the cyclic prefix of 1/4 had a lower BER compared to the cyclic prefixes of 1/8 and 1/16. The greater the cyclic prefix, the more effective it is in avoiding interference between symbols; nevertheless, the higher the full time of symbol, the longer it takes to communicate the same information [15]. Therefore, a lower cyclic prefix, such as 1/4, has a better bit error rate than higher cyclic prefixes, such as 1/8 and 1/16, as shown in Figure 7.  
In this part of the article, BER vs. SNR graphs for MIMO-WiMAX are generated using Alamouti 2 \(\times\) 1 and 2 \(\times\) 2 schemes [16]. The simulation uses QPSK modulation, and the bit error rate (BER) of 2 \(\times\) 1, 2 \(\times\) 2, and 1 \(\times\) 1 (basic WiMAX) are studied and compared with one another, as shown in Figure 8.  
Figure 6: BER estimations for various modulation approaches in MIMO\(-\)WiMAX.  
Figure 7: BER performance of 16\(-\)QAM at different cyclic prefixes.  
The simulation results of Alamouti 2 \(\times\) 1, 2 \(\times\) 2 are compared with each other in the above figure, demonstrating a steady drop in the BER rate as receive diversity increases. Results obtained with Alamouti 2 \(\times\) 2 have been shown to be superior to those obtained with Alamouti 2 \(\times\) 1 and 1 \(\times\) 1 [17]. It is clear from the results that MIMO (2 \(\times\) 2) has a reduced BER in comparison to both MISO (2 \(\times\) 1) and standard WiMAX (1 \(\times\) 1). According to the findings, combining the MIMO strategy with WiMAX results in a better bit error rate (BER) than using WiMAX alone [18; 19; 20].
## 4 Design Procedures and Simulation Results  
In this section, three of the major virtual inertia topologies were restudied in a diesel generator based remote microgrid system. The design procedures and simulation results presented are aimed to supplement the concepts of virtual inertia topologies reviewed in Section 3. Three of the topologies--the synchronverter, the Ise lab's topology, and the VSG--were implemented and their performance was studied in a common benchmark. Moreover, a procedure is provided to choose appropriate parameters for the virtual inertia systems. The three virtual inertia systems were designed in a common framework so that the different parameters used are more relatable to each other. To this end, constants in each topology were selected such that the virtual inertia system injects/absorbs the same amount of active power for a given frequency change. Furthermore, the inertial constant and the damping constant have the same proportion and were related through a time constant \(T_{f}\) of 0.01 s in all the simulations. This led to an inertia constant \(H\) of 1 s in all simulation cases for the virtual inertia unit. The schematic used for the virtual inertia simulation benchmark is shown in Figure 11. The generator was rated at 13 kVA, while the PV unit was rated at 6 kW[22]. A separate, dedicated inverter unit rated at 10 kW was used as the virtual inertia unit. In all the cases, the steady-state power output from the inverter was set to 1000 W. It was assumed that, the DC side of the inverter was connected to a 400 V DC source which remained constant in all the simulations. Step changes in the load were used to emulate the change in load or PV generation in all the systems. For simplicity, the inverter was modeled as either a controlled current source or a controlled voltage source (depending on the virtual inertia topology used) neglecting the switching behavior.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Control Technique** & **Key Features** & **Weaknesses** \\ \hline Synchronous generator (SG) model based & * Accurate replication of SG dynamics * Frequency derivative not required * Phase locked loop (PLL) used only for synchronization & * Numerical instability concerns * Typically voltage-source implementation; no over-current protection \\ \hline Swing equation based & * Simpler model compared to SG based model * Frequency derivative not required * PLL used only for synchronization & * Power and frequency oscillations * Typically voltage-source implementation; no over-current protection \\ \hline Frequency-power response based & * Straightforward implementation * Typically current-source implementation; inherent over-current protection & * Instability due to PLL, particularly in weak grids * Frequency derivative required, system susceptible to noise \\ \hline Droop-based approach & * Communication-less Concepts similar to traditional droop control in SGs & * Slow transient response * Improper transient active power sharing \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Virtual Inertia Control Topologies.
## 5 Design of the Antenna Pointing Control System
### Design  
First, the interference between the two control systems can be calculated by the interference factor [21]. After the interference is found to be small enough for the gain margin and phase margin of classical control theory, the two control systems can be designed independently. The interference factor is obtained by converting the block diagram, as shown in Figure 16, for an example, into the form of a block diagram shown in Figure 17 and by calculating Equation (24). For simplicity's sake and for a worst-case scenario, equations of motion in Equations (17)-(19) are used, and the converted block elements are expressed in Equations (18)-(21).  
\[I_{\rm zz}\ddot{\psi}=T_{\rm Z}-I_{\rm GZ}\ddot{\psi}_{\rm G} \tag{17}\]  
\[I_{\rm GZ}\ddot{\psi}_{\rm G}=T_{\rm GZ}-I_{\rm GZ}\ddot{\psi} \tag{18}\]  
\[\psi_{\rm A}=K_{\rm B}\psi_{\rm G}+\psi \tag{19}\]  
\[H_{11}=\frac{1}{I_{\rm zz}-I_{\rm GZ}}.\frac{1}{s^{2}} \tag{20}\]  
\[H_{12}=-\frac{1}{I_{\rm zz}-I_{\rm GZ}}.\frac{1}{s^{2}} \tag{21}\]  
\[H_{21}=\frac{1-K_{\rm B}}{I_{\rm zz}-I_{\rm GZ}}.\frac{1}{s^{2}} \tag{22}\]  
\[H_{22}=\frac{1}{I_{\rm GZ}}.\frac{K_{\rm B}-(I_{\rm GZ}/I_{\rm zz})}{1-(I_{ \rm GZ}/I_{\rm zz})}.\frac{1}{s^{2}} \tag{23}\]  
\[\lambda=\sqrt{\frac{H_{12}H_{21}}{H_{11}H_{22}}}=\sqrt{\left|\frac{1-K_{\rm B }}{K_{\rm B}-(I_{\rm GZ}/I_{\rm zz})}\right|} \tag{24}\]  
In the control system, the ratio, \(K_{\rm B}\), of the fluctuation angle of the antenna's beam to the drive angle of the two-axis gimbal is 1, meaning that interference factor \(\lambda\) is 0. The same result was obtained for the elevation angle of the two-axis gimbal. Those outcomes show that the effect of interference between the two control systems is nil.  
Figure 16: Block diagram.
## 4 Results and Analysis  
### Fluid-Structure Interaction Analysis  
In the finite element model of the fluid domain, we created three planes perpendicular to the direction of fluid flow, corresponding to the locations where PVDF piezoelectric thin films were attached, namely, Plane1, Plane2, and Plane3, as shown in Figure 5.  
Three data extraction points, namely, Point1, Point2, and Point3, were set in pipe wall, corresponding to the three planes in the fluid domain, as shown in Figure 6.  
Considering the circular shape of cross-sections, we compared the circumferential stress and strain signals at Point1 with the fluid pressure signal, as shown in Figure 7a,b. In Figure 7, the \(Y\)-axis on the left stands for pressure, which is  
Figure 4: The finite element model of pipe and polyvinylidene fluoride (PVDF) piezoelectric thin films.  
Figure 5: Diagram of cross-sections.  
Figure 6: Diagram of data extraction points.  
\(Y\)-axis on the right represents circumferential strain or circumferential stress, which is the green curve. Figure 7a,b shows intuitively that the variation of circumferential stress and strain signals at Point1 was very close to the fluid pressure fluctuation signal. To study the correlation between two signals more accurately, we used correlation coefficient to quantitatively describe their degree of correlation. The correlation coefficient is defined as  
\[R(i,j)=C(i,j)/\left[\sqrt{D(i)}\cdot\sqrt{D(j)}\right], \tag{9}\]  
where \(R(i,j)\) denotes the correlation coefficient of signal \(i\) and signal \(j\), \(C(i,j)\) denotes the covariance of signal \(i\) and signal \(j\), \(D(i)\) denotes the variance of signal \(i\), and \(D(j)\) denotes the variance of signal \(j\). According to Equation (9), a correlation of +1 indicates a perfect positive correlation, meaning that signal \(i\) and signal \(j\) change in the same direction simultaneously.  
The correlation coefficients of the circumferential strain and circumferential stress signals at Point1 with the fluid pressure signal calculated by Equation (9) were 0.9256 and 0.9225, respectively, which were both very close to 1. A conclusion can be drawn that the variation of circumferential stress and strain at Point1 was highly positively correlated to the pressure fluctuation of internal fluid. We also compared the circumferential stress and strain signals at Point2 and Point3 with the fluid pressure signal. Similar conclusions can be drawn from the correlation coefficients, as shown in Table 1. Thus, the pressure fluctuation of internal fluid can be measured indirectly by measuring the variation of circumferential stress and strain of the pipe wall.
# Integrated GNSS/IMU-Gyrocompass with Rotating IMU. Development and Test Results
## 7 Conclusions  
The results of automobile tests have confirmed the operability of the GNSS/IMU gyrocompass prototype. The system provided max heading error within \(\pm\)1.5\({}^{\circ}\), and roll and pitch errors, within \(\pm\)0.2\({}^{\circ}\) in steady mode during the car motion in urban conditions.  
It is shown that with the use of a single-axis rotating platform with installed IMU and two satellite antennas with two satellite navigation receivers with a common clock the proposed GNSS/IMU gyrocompass provides the heading solution based on phase measurements from a single satellite.  
It is noted that IMU rotation and application of tactical grade FOGs helps to keep the heading, roll and pitch accuracy during long GNSS signal outages if INS is updated by additional external linear velocity and altitude measurements. The test results demonstrate that GNSS/IMU gyrocompass provides heading solution with error of max 3\({}^{\circ}\) for 1.5 h after satellite signal loss. The duration of GNSS/IMU gyrocompass performance with the required accuracy is determined mainly by errors in estimation of FOG bias and rhumb drifts realized in the presence of satellite signals.  
Conceptualization, G.E.; methodology, G.E., M.E., and A.S.; software, A.S. and B.B.; investigation, B.B.; resources, M.E. and D.E.; writing--original draft preparation, E.D. and A.S.; writing--review and editing, O.S., and D.E.; visualization, E.D.; supervision, O.S.; project administration, D.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was financially supported by Government of Russian Federation (Grant 08-08).  
**Acknowledgments:** The authors are grateful to [PERSON] (Head of sector, Concern CSRI Elektropribor) for the support of manufacturing and development of GNSS/IMU gyrocompass prototype and model, [PERSON] (First category engineer, Concern CSRI Elektropirbor) for preparing and conducting the model car tests, [PERSON] (First category engineer) and [PERSON] (Leading engineer, Concern CSRI Elektropirbor) for the development of electronics and software for gyrocompass prototype.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 6 Evaluation
### Results and Discussion  
#### 6.2.1 Performance Results of PQC Algorithm Key Generation  
In order to evaluate the performance implications of integrating PQC algorithms, we measured the average key generation time (Avg. Key Gen.), key encapsulation time (Avg. Encap.), and key decapsulation time (Avg. Decap.) for the PQC KEM algorithms, as well as the signature key generation time (Sig. Gen. Time), signing time, and verification time for the PQC signature algorithms across the client and server systems. Tables 2 and 3 show the average time it took to generate the PQC algorithm keys (PQC KEM and signature keys), encapsulate and decapsulate the KEM keys, and sign and verify PQC signatures on the client and server systems.  
For the KEM algorithms, Table 2 shows that for BIKE KEM at all security levels, BIKE-L1 achieved the fastest average key generation time on the client system of 0.43 ms, while BIKE-L5 required 1.95 ms, reflecting a progressive increase in computational demand with higher security levels. Conversely, the server-side key generation times for BIKE-L1, BIKE-L3, and BIKE-L5 were 1.23 ms, 2.63 ms, and 5.05 ms, respectively, demonstrating a more pronounced rise compared to the client-side metrics. The encapsulation and decapsulation times also increased with security levels, particularly for the BIKE and HQC algorithms.  
Furthermore, as shown in the table, BIKE-L5 decapsulation required 14.42 ms on the client system and 15.94 ms on the server system, while HQC-256 decapsulation required 49.77 ms (client) and 53.40 ms (server). However, the CRYSTALS-Kyber algorithms diverged from this trend: K768 achieved the fastest client-side key generation time of 0.19 ms, surpassing both K512 (0.92 ms) and K1024 (1.00 ms). On the server side, the CRYSTALS-Kyber results showed that K1024 was the most efficient for key generation (0.49 ms), encapsulation (0.09 ms), and decapsulation (0.11 ms), with K512 and K768 achieving marginally higher times.  
Figure 3: Experimental settings.  
Furthermore, Table 3 shows the performance of the PQC signature algorithms. According to the results, F512 required 7.01 ms for key generation on the client system, significantly faster than F1024's 19.00 ms. A similar trend was observed on the server system, with F512 and F1024 requiring 9.68 ms and 24.51 ms, respectively. The signing times for F512 were 0.33 ms (client) and 0.37 ms (server), whereas F1024 took 1.43 ms (client) and 0.67 ms (server), illustrating a reversal in efficiency, favoring the server for higher security levels. The verification times remained consistent for F512 across the systems (0.28 ms client vs. 0.82 ms server) but diverged slightly for F1024 (0.31 ms client vs. 0.79 ms server). These results underscore the trade-offs between security levels and computational overhead, particularly for server-side operations.