esa-sceva/satcom-chunk-collection huggingface dataset:
# Image-Based Tactile Deformation Simulation and Pose Estimation for Robot Skill Learning
## 3 Results
### Calculation Time  
The calculation time for NSGA-II was observed to be relatively insensitive to the density of chain points within the same chain but highly dependent on the total number  
Figure 19: Planned grasp poses on tube with Franka gripper.  
Figure 21: Continuous displacement (lower right) and depth (upper right) feedback with gripper holding tube with Pose A.  
Figure 22: Continuous displacement (lower right) and depth (upper right) feedback with gripper holding tube with different Pose B.  
Figure 20: Configuration of Kawasaki RS007L manipulator (Kawasaki Heavy Industries, Akashi, Hyogo, Japan) with Franka gripper (Franka Robotics, München, Germany).  
Figure 21: Continuous displacement (lower right) and depth (upper right) feedback with gripper holding tube with Pose A.  
of chains and their maximum iteration settings. In contrast, SLSQP's calculation time was significantly impacted by both the maximum allowed iteration number and the density of chain points. The time increased proportionally with the maximum iteration but plateaued once the optimization objective value converged. The density of chain points had a more pronounced effect, with time increasing exponentially as the density increased.  
We utilized the distribution pattern of Transverse and Vertical Chains to achieve a result with sufficient accuracy, obtaining less than 10% of summed positional error for all the pins. Moreover, the SLSQP algorithm took an average of 2 min, while the NSGA-II algorithm required 17 min on average when executed on Google Colab with the default settings, which allocate a random CPU from the server.  
* **Chain Point Density:** The calculation time for NSGAII was not significantly influenced by the number of chain points, as it processes all variables simultaneously. Conversely, SLSQP's calculation time increased with the number of chain points due to its gradient-based methodology. Since each chain point corresponds to an unknown variable, the calculation time increases with the number of chain points.
* **Chain Quantity:** Both NSGA-II and SLSQP algorithms exhibited calculation times influenced by the number of chains, due to the structure of the simulation algorithm designed in this study. The Tactip's shape is depicted using multiple chains, with each chain points computed individually.
* **Maximum Iterations:** The maximum iteration count directly impacted the calculation times for both algorithms. However, the SLSQP algorithm might halt before reaching the maximum iteration if optimal results are achieved. In contrast, the NSGA-II, consistent with its genetic algorithm nature, continues to run until the maximum iteration count is reached.
## 4 Analysis and Performance Comparison
### Performance Evaluations in Terms of Energy Waste
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|} \hline  & \multicolumn{4}{|c|}{**CLIENT (mJ)**} & \multicolumn{4}{|c|}{**SERVER (mJ)**} \\ \cline{2-10}  & **Light** & **Battery** & **Toggle** & **Discover** & **Light** & **Battery** & **Toggle** & **Discover** \\ \hline
**RX** & 82.8 & 82.7 & 82.6 & 262.1 & 84.3 & 83.1 & 81.4 & 193.0 \\
**TX** & 15.3 & 15.4 & 15.2 & 67.5 & 15.4 & 15.3 & 15.3 & 174.3 \\
**CPU** & 73.1 & 72.8 & 72.6 & 122.5 & 69.5 & 69.3 & 68.8 & 146.8 \\
**LPM** & 46.6 & 46.6 & 46.6 & 45.1 & 46.9 & 46.9 & 47.0 & 44.9 \\ \hline _Total_ & _218.0_ & _217.7_ & _217.2_ & _497.3_ & _216.2_ & _214.8_ & _212.5_ & _559.2_ \\ \hline \end{tabular}
\end{table} 
Table 6: CoAP-packet Energy Consumption calculation (ContikiMAC RDC Layer). Figure 8: Energy Consumption as a function of the CoAP Packet Size.
# Coupled Vibration Analysis of Submerged Floating Tunnel System in Wave and Current
## 4 Parametric Influence Study
### Effect of Fundamental Structural Parameters  
The gravity-buoyance ratio (GBR) of the tube and the inclined mooring angle (IMA) of the anchor cables are key parameters affecting the structural stability of the SFT [35]. GBR affects the initial tension of the cables; meanwhile, the variation in IMA in turn causes a change of the cables' supporting stiffness on the tube, which jointly determines the natural vibration frequency of the components and further influence the autoparametric vibration of the SFT system.  
In Figure 9, we discuss the influence of GBR/IMA settings on the maximum displacement amplitude of the cables, for the same case given in Figure 4 (undamped parametric vibration of the system without fluid effects).  
Under the parameter settings in Table 1 (i.e., GBR = 0.65, IMA = \(90^{\circ}/\omega_{t1}\):\(\omega_{t1}\) = 2:1), the principal autoparametric resonance occurs and the lateral dis. amplitude of the cable reaches its maximum. This is an extreme situation and should be avoided in engineering practice.  
Once GBR \(\geq\) 0.7, the parametric vibration of the system will not cause large deformation of the anchor cables. However, when GBR < 0.7, if IMA meets certain conditions, an intensive vibration of the anchor cables can still be excited. This indicates that the parametric vibration of system is more sensitive to GBR, which should be given priority in the design process.  
The IMA should take into consideration the support stiffness and the construction of the underwater foundation, thus \(45^{\circ}\) is determined as a reasonable value.  
Figure 8: Maximum amplitude of cables or tube with different wave period \(T\) and wave height \(H\) (frozen parameters: GBR = 0.65, IMA = \(90^{\circ}\) and \(v_{0}\) = 1.18 m/s): (a) \(A_{ci}/d_{i}\) of cables; (b) \(A_{t}\) of tube.  
Figure 9: Maximum displacement amplitude of SFT cable with different combinations of GBR and IMA (frozen parameters: \(v_{0}\) = 0, \(H\) = 0 and \(T\) = 0).
## 2 Materials and Methods
### 3-PG Model and Parameter Setting  
The 3-PG model is a physiological-ecological process model based on allometric equations and a monthly time scale [27]. The model has a relatively simple structure and few input parameters [42]. It can simulate many tree species including eucalyptus, and is widely used in Australia, Brazil, Canada, and China [30; 43; 44; 45]. The model was initially developed to simulate even-aged evergreen forest species, and now is able to simulate deciduous, uneven-aged, and mixed forest, and assess the forest growth under different management measures [46]. Many studies utilized the model to simulate forest growth of eucalyptus, Masson pine, and larch at the plot level [31; 47; 48]. The model has four submodules: the light sub-model, the biomass production and allocation sub-model, the water balance sub-model, and the mortality sub-model. More details about 3-PG model are provided in [27; 45]. The tree growth was simulated at monthly intervals by inputting monthly meteorological data (maximum and minimum temperatures, average temperature, precipitation, and solar radiation), site conditions (latitude, soil class, and soil fertility), planting time, and initial organ biomass, management measures, and parameters for the tree species. The model can output many variables such as GPP, NPP, DBH, H, organ biomass (monthly), etc. The DBH, H, and ACS (calculated from biomass) were selected for output and evaluation in this study. All the simulations were performed with the r3 PG package in the R platform [49].
# Decentralized Public Transport Management System Based on Blockchain Technology +
## 5 Discussion and Future Perspectives
### System Advantages  
The introduction of such a system has several advantages, as follows:  
Reduction in labor costs.  
Reduction in administrative management costs.  
Improvement in the quality of driving equipment on the route.  
Increase in the speed of decision making in the transport system.  
Reduction in maintenance and repair errors.  
Improvement in the reliability and service life of vehicles.  
For this model, improving the quality of transport organizations' management systems is fundamental. The necessary information about transport processes is safely stored inthe blockchain; thanks to the compression algorithm, data transmission is faster, and infrastructure costs are reduced. At the same time, the blockchain can still be private, for example, for one organization or city.
## 4 Description of the Proposed Topology Construction
### Communication Abstraction of the Proposed Topology
#### 4.6.1 Communications within a Chain  
Within a chain, sensor nodes communicate with each other to disseminate control information and sensed data. Communications among the sensor nodes are restricted to only the successive sensor nodes. Figure 19 shows the communication pattern inside a chain. In this figure, six sensor nodes (\(C_{0}\) to \(C_{5}\)) construct a chain. \(C_{2}\) is the lower-level leader of the chain. The lower-level leaders disseminate information and control messages to all the member nodes of their chains. These information and control messages are propagated hop-by-hop from one sensor node to its successive neighbouring node. For example, Figure 19(a) shows that the leader node \(C_{2}\) sends the control information to the nodes \(C_{1}\) and \(C_{3}\). After copying the control message, the node \(C_{1}\) sends the control message to the node \(C_{0}\) and  
Figure 18: Energy consumption differences between two-layered and three-layered chains.  
sends the message to \(C_{4}\), which then sends it to \(C_{5}\). As the nodes \(C_{0}\) and \(C_{5}\) are the end nodes of the chain, they refrain from sending the control message any further.  
For sending the sensed data, each sensor node sends data to its successive node towards the leader of the chain. For example, in Figure 19(b), the node \(C_{0}\) sends its sensed data to the node \(C_{1}\), while the node \(C_{1}\) merges its own data with \(C_{0}\)'s data, and sends them to the leader node \(C_{2}\). Similarly, the node \(C_{5}\) sends its data to the node \(C_{4}\), and \(C_{4}\) then sends \(C_{5}\)'s data and its own data to the node \(C_{3}\). The node \(C_{3}\) accumulates this data with its own data, and sends them all to the leader node \(C_{2}\).
## 2 Literature Review
### LoRa
#### 2.2.1 Features of LoRa  
_Adaptive Power Level:_ based on data rate and link conditions among other devices, the power levels of LoRa devices are adaptive. Speed of transmission and power are directly related; the higher the speed, the higher the power consumption and vice versa. Hence, battery life span is extended to the maximum level while maintaining the capacity of the network.  
_Adaptive Data Rate:_ Variable bandwidth and spreading factors (SF2-SF12) are a crucial combination to implement an adaptive data-rate in a trade-off with transmission distance. For long distance communication, higher spreading factors are used compromising the low data rate and vice versa. The data rate can range from 18 bps to 40 Kbps.  
_Modulation:_ LoRa uses the Chirp Spread Spectrum (CSS) modulation, similar to the Frequency Shift Keying (FSK) modulation physical layer-based radios, CSS modulation helps to achieve a high communication range and low power consumption characteristics. In military and space communication applications, CSS was well-known technique, but LoRa is the first commercial application of CSS at a low cost [39].
## 4 Conclusions  
In this work, an energy management model for the entire electric vehicle including vehicle cabin, electric motors, battery, and heating-cooling system was prepared in the MATLAB Simulink environment. The model was run under different ambient conditions with the internationally recognized road operating conditions cycles as NEDC, FT75, and the constant speeds of 20 km/h, 50 km/h, 80 km/h, and 110 km/h for one hour. Using the prepared model, the effect of the energy consumption of the heating-cooling system on the total energy consumption and range of the vehicle was examined in detail. The main conclusions of the study are given below:  
In the heating-cooling system in electric vehicles, the energy consumption in the cooling process is higher than in the heating process. The compressor always consumes the most energy among the system elements.  
When NEDC and FT75 road operating conditions were compared, it was calculated that 15% more energy was consumed in the heating process for NEDC and 25% more energy was consumed in the cooling process for NEDC in the heating and cooling process when the outdoor ambient conditions were \(-10\)\({}^{\circ}\)C and \(40\)\({}^{\circ}\)C. Since the vehicle's standstill time was much less in NEDC than in the FT75 driving cycle, more convection heat transfer was realized between the vehicle and the ambient conditions. Therefore, more energy was consumed to sustain the target temperature inside the cabin.  
Figure 19: Effect of different target temperature of heating and cooling systems on range and charging rate.  
The energy consumption of the system decreases as the outdoor ambient temperatures approach the target temperature inside the cabin under all considered road operating conditions. The decrease in energy consumption leads to an increase in the total range of the vehicle and the amount of remaining charge.  
As the vehicle speed increases, the convection heat transfer rate and the energy consumption of the heating and cooling system increase. Despite the increase in total consumption as a result of the increase in vehicle speed, the range loss and deterioration rate caused by the heating-cooling system decreases. At higher vehicle speeds, although the total consumption was higher, more range was achieved.  
In general, the energy consumption in the cooling process was higher than in the heating process. A change of \(1\)\({}^{\circ}\)C in cabin target temperature affects the total range by approximately 3 km.  
Conceptualization, M.K. and M.O.K.; methodology, M.K. and M.O.K.; software, M.K. and M.O.K.; validation, M.K. and M.O.K.; formal analysis, M.K. and M.O.K.; investigation, M.K. and M.O.K.; resources, M.K. and M.O.K.; data curation, M.K. and M.O.K.; writing--original draft preparation, M.K. and M.O.K.; writing--review and editing, M.K. and M.O.K.; visualization, M.K. and M.O.K.; supervision, M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used to support the findings of this study are included within the article.  
The authors declare no conflicts of interest.
## 4 Results
### Smart Gate Processes
#### 4.2.2. Checkpoint 2--Container, Vehicle, and Driver Identification  
As mentioned above, drivers will take different routes depending on whether they have a prior appointment or not. Regardless of the situation, in both alternatives (lanes for scheduled and unscheduled services), a lane equipped with scales is essential, both for those who are marked and for those who are not.  
At checkpoint 2, the driver initially crosses a barrier, which is open by default, and data are collected: (i) truck registration using LPR; (ii) container data (if available) with ACCR; and (iii) container status records, checking the container for damage (images of the container from both sides and the top) accessed by ADDR.  
After collecting data while the vehicle is in motion, the first barrier of checkpoint 2 closes, preventing a second vehicle from entering the control area. Subsequently, the driver parks next to a kiosk (hardware: TOUCH HMI--touch screen and intercom) where the following takes place:  
* The driver's identification (if they have a card and a valid entry permit) using a card and biometric identification;
* Verification of the logistics service (if they have a valid prior appointment) through hardware: PIN or QR code (laser scanner);
* The confirmation of the service data by the driver;
* Weighing of the cargo (if they have not done so before going to the terminal) by means of a weightridge);
* If necessary, the help mechanism can be activated.  
Based on this information, the driver will be told where to go next, whether to checkpoint 3 or checkpoint 4. If the appointment and authorization are valid, the driver receives information on his/her phone with the location where he/she should go and the drop-off/pick-up points for the container(s) inside the terminal. If the conditions for entering the terminal are not met, then the driver will not receive any message, and he/she will be guided to park/checkpoint 3. Figure 5 illustrates the checkpoint 2 process.
## 4 Simulation Results
### Quadrature Phase Shift Keying (QPSK)
#### 4.2.2 Noncoherent QPSK  
* AWGN Channel  
Figure 10 shows the performance of the noncoherent QPSK in the case of the AWGN. The simulated system is of \(N_{s}=10^{5}\) (number of symbols) and SNR = 0-10 dB. An AWGN is added with variance = 1/(symbol power). A noncoherent detector is used to detect the received noisy signal. Then, the noisy signal goes through the AdaBoost algorithm of \(T=10\) (number of weak classifiers) and \(K=4\) (number of classes) to improve the BER curve.  
The figure shows the theoretical and simulation curves of the noncoherent QPSK system. The simulated curve is improved over the theoretical one. At the beginning, the simulated curve appears to go slightly above the theoretical one. After that, it coincides with the theoretical curve up to a SNR of 7 dB. Then, starting from 7 dB and up to 10 dB, the simulated curve appears to improve as it goes under the theoretical one by about 0.00022. The improvement is caused by the added AdaBoost algorithm which helps in correcting the BER of the received noisy signal. This is done by the prediction capability of the algorithm that detects the original bit stream of the data sent.  
* Rayleigh Fading Channel  
In Figure 11, the performance of the noncoherent QPSK is evaluated by simulating a system of \(N_{s}\) = \(10^{5}\) (number of symbols) and SNR = 10-40 dB. An AWGN is added of variance = 1/(symbol power). The signal is sent through a Rayleigh channel of \(f_{\rm D}\) = 100 Hz (Doppler frequency), \(T_{s}=1\times 10^{-4}\) s (sampling time), and \(c=3\times 10^{8}\) m/s. A noncoherent detector is used to detect the received noisy signal. Then, the noisy signal goes through the AdaBoost algorithm of \(T=5\) (number of weak classifiers) and \(K=4\) (number of classes) to improve the BER curve.  
Figure 10: BER vs. SNR for noncoherent QPSK modulation over the AWGN channel.  
Figure 11 shows the theoretical and simulation curves of the noncoherent QPSK system. The simulated curve is improved over the theoretical one. It appears to go under the theoretical curve by around 0.046 for SNR = 10-20 dB. Starting from SNR = 20-35 dB, the simulated curve goes under by 0.0040 to 0.0011 and then from SNR = 35-40 dB it goes under the theoretical curve by 0.0004. Again, and as stated before, the AdaBoost algorithm is able to achieve a coherent performance for the noncoherent system.  
In comparison to the theoretical bit error rate (BER) vs. SNR curve, AdaBoost shows an improvement in the BER of coherent detection of BPSK and QPSK. The curve is coincided with that of each modulation technique, proving its improvement effect on the BER vs. SNR curve. As for the noncoherent system, the BER curve shows an improvement over that of the theoretical one. The AdaBoost algorithm improved BER curve and the BER curve became closer in result to that of the coherent detection. This result shows that by using the AdaBoost detection characteristics or capabilities, the value of the phase of the received signal could be calculated, which was unknown or highly noisy in the case of noncoherent detection.
## 4 Route Planning Algorithm  
As previously discussed, the XRP algorithm can be thought of as the outcome of two different consecutive steps. First, a standalone, shortest path solution from runway to apron (or _vice versa_), neglecting the presence of other aircraft on the airport surface, is sought for all aircraft in the planning horizon. This route, which is clearly the one yielding the minimum time the aircraft can potentially spend on the taxiways, does not take into account the constraints due to other traffic on the airport surface. Thus, this solution is amended by a conflict detection and resolution task that attempts to reduce and possibly nullify the number of conflicts generated in the first phase.
## 3 Review of Previous Works
### Throughput Estimation Model
#### 3.1.4 Model Parameter Optimization  
The throughput estimation model has multiple parameters whose value determines the accuracy of the estimation. The _parameter optimization tool_ is adopted to optimize these values. It adopts a local search method that adopts the _tabu table_ and the _hill-climbing function_ together in order to avoid a local minimum convergence [23].  
Table 2 demonstrates the summary of the parameter optimization results for throughput estimation models that are used for two fields in this paper for 11n and 11 ac, _Field #1_ and _Field #2_[24]. _Field #1_ represents the _third floor of Engineering Building #2_ in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Field \#1**} & \multicolumn{4}{c}{**Field \#2**} \\ \cline{2-7}
**Parameter** & \multicolumn{2}{c}{**Group A**} & \multicolumn{2}{c}{**Group B**} & \\ \cline{2-7}  & **11n** & **11 ac** & **11n** & **11 ac** & **11n** & **11 ac** \\ \hline P1 & \(-\)28.9 & \(-\)31.0 & \(-\)31.1 & \(-\)36.1 & \(-\)28.5 & \(-\)30.5 \\ \(\alpha\) & 2.20 & 2.15 & 1.6 & 2.18 & 1.7 & 2.0 \\ W1 & 7.21 & 2.1 & 6.5 & 4.2 & 6.5 & 2.3 \\ W2 & 6.9 & 8.5 & 3.5 & 4.1 & 4.2 & 6.4 \\ W3 & 3.4 & 3.7 & 3.5 & 4.4 & 3.1 & 1.8 \\ W4 & 4.7 & 1.8 & 3.5 & 4.55 & 1.5 & 4.2 \\ W5 & 2.11 & 7.0 & 2.5 & 2.1 & 2 & 4.3 \\ W6 & 2.5 & 1.5 & 1.5 & 1.5 & 2 & 5.3 \\ a & 63.5 & 133 & 66 & 77 & 65 & 134.5 \\ b & 62 & 58 & 70 & 54.1 & 62 & 58.5 \\ c & 6.78 & 6.30 & 5.2 & 5.2 & 6.78 & 6.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter optimization results under CB for 11n and 11 ac.  
_Field #2_ represents the _second floor of Graduate School Building_ at Okayama University, as shown in Figure 3.
## 3 Experimental Results and Analysis
### Microscopic Fracture Characteristics
AE cumulative energy (\(E_{AE}\)), AE signal amplitude (\(d_{AE}\)), and 3D morphology factor (VA-3d) initiate accelerated growth at the dilation threshold (Point 3), followed by nonlinear escalation near peak strength (Point 4), ultimately culminating in pre-failure critical state amplification. Conversely, crack flatness (\(\delta_{3}/\delta_{2}\)) and orientation angles (\(\theta\), \(\varphi\)) display progressive attenuation from Point 3 onward, with rapid deterioration emerging post-peak. Notably, crack systems demonstrate preferential axial propagation (\(\theta\)) over lateral development (\(\varphi\)), which reflects stress-aligned damage progression. Synergistic analysis of crack morphology and AE signals reveals two critical mechanical transitions: the dilation threshold marks the onset of localized damage coalescence, while peak strength signifies percolation threshold attainment. Early-stage deformation (Points 1-2) involves quasi-static microcrack accumulation with limited AE activity. Post-dilation (Point 3 onward), both fracture network complexity and AE signal intensity escalate nonlinearly. Fracture nucleation originates from shear-activated microdefects, progressing through subcritical coalescence until post-peak autonomous propagation (Point 4+), where catastrophic failure progresses independent of external loading. These findings establish the dilation threshold and peak strength as fundamental bifurcation points governing sandstone's failure continuum. Figure 8: Evolutionary patterns of AE signal parameters and crack morphology during the loading process of each monitoring feature point: (**a**) AE signal parameter \(E_{AE}\); (**b**) AE signal amplitude \(d_{AE}\); (**c**) crack morphology parameter \(\delta_{3}/\delta_{2}\); (**d**) crack morphology parameter VA-3d; (**e**) crack morphology parameter \(\theta(^{\circ})\); (**f**) crack morphology parameter \(\varphi\).