esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study: Users Play with Animal Dolls in Mobile AR Application
### Pilot Study
#### 5.5.2 Procedures and Measurements  
We prepared an online survey consisting of five pages. The first page was a brief instruction for our study, followed by a pre-questionnaire (e.g., demographics, AR familiarity). For the next three pages, each page contained three video clips for each condition, followed by a questionnaire that was only visible after the participant finished watching the video. The order of conditions was predefined in a counter-balanced manner, and the order of questions was randomized. The questionnaire comprises 15 questions, three for each of the five aforementioned aspects, which we extracted and modified from the previous work [7; 49]. The order of the questions was provided randomly, and all questions were answered on a seven-point Likert scale. On the last page, we asked the participants to leave a comment. Below is our questionnaire.  
* **Spatial Awareness** 1. _I was able to imagine the arrangement of virtual agents with regard to the physical space very well._ 2. _I was able to make a reasonable estimate of the virtual agent's size regarding the presented space._ 3. _I was able to estimate how far apart the virtual agent and the doll were from each other.
* **Spatial Presence** 4. _I felt like virtual agent was a part of the environment in the presentation._ 5. _I felt like virtual agent was actually there in the environment with the doll._ 6. _I felt like virtual agent was physically present in the environment of the presentation._
* **Perceived Naturalness** 1. _I thought virtual agent movement/behavior in real space seemed natural._ 2. _I thought the virtual agent was naturally moving around to avoid a doll._ 3. _I thought virtual agent looked natural when it overlapped with a doll._
* **Object Occlusion** 1. _I felt that virtual agent was properly occluded behind the doll._ 2. _I thought part of a virtual human body is occluded the same as the silhouette of a doll._ 3. _I thought virtual agent's appearance was out of harmony with the background space/things._
* **Physical Ability** 3. _I felt that virtual agent could not pass through a doll._ 3. _I felt that virtual agent could touch a doll._ 3. _I felt virtual agents were well aware of the space around the doll's front and back sides._  
Figure 10: Photo illustrations of interaction scenarios with the silhouette meshes applied. (**a**) One doll that is similar in size to a virtual human, (**b**) two dolls that are similar in size to a virtual human, and (**c**) one doll that is larger than a virtual human.
## 5 Simulation Study
As is evident from Figures 2 and 3, both the SOBE and MOBE methods enable the system state \(x_{1}\) to track the designated reference \(x_{r}\). However, the angular position of the robot converges faster to the reference when employing the SOBE controller, resulting in smaller disparities between the system state and the reference signal. Additionally, Figures 4 and 5 clearly illustrate that the SOBE method yields smaller position tracking errors, which converge to 0 more rapidly. Notably, the predefined performance boundaries are consistently upheld throughout the entire identification and control process. Conversely, the MOBE method exhibits a larger  
Figure 4: Angular position error for joint 1. Figure 3: Angular position for joint 2. Figure 2: Angular position for joint 1. Figure 5: Angular position error for joint 2. Figure 6: Angular velocity for joint 1. Figure 7: Angular velocity for joint 2. Figure 8: Velocity tracking error for joint 1. Figure 10: Tracking results of \(\tau_{1}\) for joint 1. Figure 9: Velocity tracking error for joint 2. Figure 4:0 Tracking results of \(\tau_{1}\) for joint 2. Figure 4:1 NN estimation results for joint 1. Figure 4:2 NN estimation results for joint 2. Figure 4:3 Control signal for joint 1. Figure 4:4 NN estimation errors for joint 1. Figure 4:5 NN estimation errors for joint 2. The control signals generated by both the SOBE and MOBE methodologies are illustrated in Figures 16 and 17. Evidently, during the transient phase, the amplitude of \(u_{f}\) is substantial, facilitating the swift convergence of the fast state \(\tau_{1}\) to its equilibrium value \(h(x,t)\). Subsequently, \(u_{f}\) diminishes to near-zero levels at steady state. This characteristic response exemplifies the inherent behavior of a singularly perturbed system. The figures presented in this study demonstrate that the SOBE method, which incorporates tracking errors into the NN weight updating law and utilizes the PPC to constrain error boundaries, significantly outperforms the MOBE method. Specifically, the SOBE method exhibits superior identification and control performance, as compared to the MOBE method, which relies solely on a regular sliding mode control approach and updates NN weights based solely on identification errors.
# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
## 4 Experimental Evaluation  
To assess the performance of our MCNN method, we choose AlexNet [42] as the baseline due to its noteworthy achievements in image classification tasks. Additionally, for implementing comparative analysis, we incorporate another popular method, CMwv [48], to evaluate the effectiveness of the MCNN technique. In order to ensure an equitable comparison between MCNN and the two alternative CNN-based methodologies, as delineated in Section 4.1, we adopt identical training methodologies, following the protocols introduced by [PERSON] et al. [48]. A subset of 320 images per class is randomly selected as input from the annotated TenGeoP-SARwv dataset, categorized by WV1 and WV2 modes. It is pertinent to acknowledge that the input size is relatively modest in comparison to the overall dataset. Seventy percent (70%) and thirty percent (30%) of the input dataset are designated for the training and validation subsets, respectively, in accordance with the previously outlined methodologies for feature extraction and neural network weight optimization. A distinct evaluation dataset, consisting of 5000 vignettes captured under the WV1 and WV2 modes, has been established to quantitatively evaluate the effect of different classification models. Subsequently, a geophysical application is exemplified through the utilization of a classified map depicting rain cells (RCs). This map is then juxtaposed with precipitation data obtained from the Global Precipitation Measurement (GPM) Program (available on the NASA data archive website: [[https://pmm.nasa.gov/data-access/downloads/gpm](https://pmm.nasa.gov/data-access/downloads/gpm)]([https://pmm.nasa.gov/data-access/downloads/gpm](https://pmm.nasa.gov/data-access/downloads/gpm)) (accessed on 7 February 2024)).
# QoS-Aware Flexible Handover Management in Software-Defined Mobile Networks
## 4 Performance Evaluation  
In this section, we analyze the performance of the proposed scheme in terms of the handover signaling cost, UL/DL handover delay, and packet loss compared to the SDN-based PMIPv6 [16; 17] and SDN-based EPC [23; 24; 49]. Among the conventional schemes, the basic handover procedure for SDN-based PMIPv6 is same as that of Class 1 in Figure 2a, such as the SDN mobility signaling flow [17] and reactive OpenFlow-PMIPv6 handover signaling flow [18]. In the case of the SDN-based EPC, we assume that the architecture is the full-SDN EPC architecture [24], where all of the control plane entities such as MME and HSS are implemented in the SDN controller for a fair comparison with the SDN-based PMIPv6. whose LMA and MAG entities are implemented in the SDN controller. In this SDN-based EPC architecture, almost the same handover procedures as those of the current EPC [4] are provided based on the virtualized MME and gateway entities [23; 49]. In addition, we assume that the wireless access point has no information on the handover decision compared to the eNodeB in the EPC which determines the next eNodeB after handover and commands the handover to the MN. This means that proactive tunneling between the candidate wireless access point and SGW and buffering at the candidate wireless access points are also utilized, in the same way as the current EPC, but the functions of eNBs are not considered in this paper for fair comparison with other schemes. This means that the handover procedure in the SDN-based EPC with pro-active buffering and user plane updates [23; 49] is the same as that of Class 4. Therefore, from here on, it can be observed that the operations of the SDN-based PMIPv6 and Class 1 as well as the SDN-based EPC and Class 3 have the same procedures, respectively.  
Based on the previous research works [1; 14], we developed an analytical model. For the sake of simplicity, the user movement pattern is assumed to be the fluid-flow model and has a constant speed in one direction. The parameters for the performance analysis are described in Table 2.
## 4 Hybrid Algorithm Based on DQN and DWA
### Hybrid Algorithms
If action == 0:
22. new_pose = follow_global_path(global_path,current path)
23. elif: action == 1:
24. velocity_window = generate_velocity_window()
25. new_velocity = dwa(current_pose, velocity_window, map)
26. new_pose = update_pose(current_pose, new_velocity)
27. reward = calculate_reward(new_pose, goal, map)
28. next_state = get_state(new_pose, global_path, map)
29. done = (new_pose == goal)
30. dqn.remember(state, action, reward, next_state, done)
31. memory.append((state, action, reward, next_state, done))
32. If len(memory) > memory_size:
33. memory.pop(0)
34. If len(memory) >= batch_size:
35. dqn.reply(batch_size)
36. current_pose = new_pose
37. return final_path
```  
**Algorithm 1** (The Hybrid Algorithm(S,G))  
To verify the feasibility and effectiveness of the previously proposed hybrid algorithm's route planning, the local optimal state of the DWA algorithm may be directly realized. The appropriate simulation is run under identical simulation settings. (30.5, 1.5) remains the beginning position, while (2.5, 29.5) is the ending. The trajectory of global route planning using the conventional A* method is shown by the blue path in the picture. The green route in the picture shows the hybrid algorithm's search result, which is based on the integration of the DWA algorithm. Figure 25 displays the outcome. In contrast to the traditional A* algorithm, Figure 25 shows how the fusion approach proposed in this study avoids long paths and unnecessary turning angles. Furthermore, as Table 4 illustrates, the enhanced hybrid approach increases planning efficiency by cutting the route length by 3.62% and the conventional A* algorithm by 50%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Transition** \\ **Node** \\ \end{tabular} & \begin{tabular}{c} **Smoothness** \\ **Ostacles** \\ \end{tabular} & \begin{tabular}{c} **Avoid Dynamic** \\ **Ostacles** \\ \end{tabular} & \
\begin{tabular}{c} **Path Length** \\ **Variance** \\ \end{tabular} &
\begin{tabular}{c} **Variance** \\ **Experiments** \\ \end{tabular} \\ \hline A* & 7 & \(\times\) & \(\times\) & 43.079 & 7.963 & 20 \\ \hline Improved A* & 5 & \(\surd\) & \(\times\) & 41.072 & 2.369 & 20 \\ \hline DWA & - & \(\surd\) & \(\surd\) & - & 9.006 & 20 \\ \hline Hybrid A* & 4 & \(\surd\) & \(\surd\) & 40.603 & 1.045 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of algorithm performance indicators. Figure 23: Hybrid algorithm diagram. Figure 24: Diagram for path planning. Figure 25: Hybrid algorithm path planning simulation diagram. Above is a description of the hybrid algorithm's trajectory. By accounting for the DWA algorithm's failure to fall into optimal errors in local route planning and addressing the A* algorithm's incapacity to avoid dynamic obstacles, the combined advantage of smoothing curves enables tracking to fulfill motor and angular smoothing requirements.
# A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
## 2 Model Design
### Component-Specific Prediction Submodels  
The component-specific prediction submodels are classified as follows: the BiLSTM model for trend component prediction, the SARIMAX model for seasonal component prediction, and the 1D-CNN model for remainder component prediction. A detailed explanation of each model is provided below.
## 2 Material and Methods  
### The Examined Space Heating Systems  
The examined space heating configuration includes a high-temperature air-to-water heat pump that produces hot water for feeding radiators, as Figure 1a shows. The competitive system is an oil boiler that produces hot water for feeding the same radiators, as Figure 1b depicts. The working medium in the heat pump was selected to be the R1234 ze(E), which is a promising working fluid with various advantages. More specifically, this working fluid has zero ozone-depleting potential (ODP), has a low global warming potential (GWP) equal to 6, is non-toxic, and also presents restricted flammability (A2L--ASHRAE safety Classification) [22]. Practically, the GWP of R1234 ze(E) is significantly lower when compared to other usual fluids, such as R404A, R134a, and R32, which are equal to 3922, 1300, and 677, respectively [25].  
Figure 1: The examined heating systems: (**a**) system with high-temperature heat pump, and (**b**) systems with oil boiler.  
The heat pump cycle is a simple typical cycle with an evaporator, a compressor, a condenser, and a throttling valve, as Figure 1a illustrates. The superheating in the evaporator outlet was selected at 10 K, and there was no subcooling in the condenser outlet. The expansion in the throttling valve was assumed to be ideal, which means that there are no thermal losses to the environment. The compressor was selected to be a semi-hermetic reciprocating device, more specifically the model \"2 EES-3Y-40S\" from the Bitzer manufacturer [26]. This compressor is able to produce high-temperature water for different ambient conditions, and, thus, it was selected. Data from the manufacturer software [27] were used in order to determine the isentropic efficiency of the compressor, and these values were inserted in the present developed thermodynamic model. The condensing temperature in the heat pump was selected at 80 \({}^{\circ}\)C in order to make it possible to operate the radiators close to the nominal conditions. The evaporator temperature was selected to be 7 K lower than the ambient temperature in order to achieve proper operation. Figure 1b shows the alternative system, which includes an oil boiler coupled to the radiator for providing heating in the building. The present work aims to examine the substitution of a relatively old system with a heat pump, and, thus, a boiler with a reduced overall efficiency of 75% [28] was selected to be studied. The lower heating value of the oil was chosen at 42,000 kJ/kg, and its density was chosen at 0.86 kg/m\({}^{3}\)[29]. In the heating system of both subfigures, the red line indicates the hot water in the inlet of the radiator, and the orange line indicates the return of the water with a lower temperature level.
# Experimental Measurements of a Joint 5G-VLC Communication for Future Vehicular Networks
## 3 5G Field Trials
### Use Cases
Network Slicing: The 5G network is able to provide specific network slice for V2X communications in order to manage its own features independently on the other services. However, how slices can efficiently share the resources is still a challenging issue. The studying of practical algorithms is ongoing considering both the computational complexity and the ability to reconfigure the resources allocation following the variability of the vehicular network topology. In particular, one of the main challenges of the infrastructure layer is the virtualization and division of the RAN into slices due to spectrum limitation. In addition, the coexistence communications with the network (V2N) and among vehicles requires a high flexibility and dynamicity of the RAN. 4. MEC: Reduced network congestion and improved applications performance can be obtained by using the multi-access edge computing (MEC) paradigm, which introduces cloud-computing capabilities closer to the end-user within the access network. Data generated from vehicles and infrastructure can be efficiently processed by the MEC thus delivering locally-relevant contents to support smart driving services. The MEC allows ultra-low latency, high bandwidth and real-time access to the access network that can be leveraged by the applications. 5. Access point densification: Network capacity can be improved by deploying a large number of small cells in addition to traditional macrocells. Moreover, in case of emergency or network unavailability, vehicles themselves could complement the public network becoming moving cells. Anyway, in case of coexistence of multiple cell-layers, a careful investigation on resource usage is required as well as on coordination strategies among all the cells. 6. Multi RATs: In the smart mobility paradigm, multiple radio access technologies (Multi-RAT) can be integrated into vehicles, which become a powerful mobile gateway. Both V2V and V2N communications (e.g., 802.11p, LTE, C-V2X, 5G, VLC) could ask for multi-RATs integration, although an accurate managing should be done for the exploitation of benefits and limitation of their drawbacks. In this context, one of the most original experimentations is the integration of VLC in the proposed platform for V2N and V2V communications. A measurement campaign on the integration of 5G system and VLC for vehicular communications have been carried out in the project, and it is described in the following section.
###### Abstract  
Measurement campaigns in wind energy research are becoming increasingly complex, which has exacerbated the difficulty of taking optimal measurements using light detection and ranging (LiDAR) systems. Compromises between spatial and temporal resolutions are always necessary in the study of heterogeneous flows, like wind turbine wakes. Below, we develop a method for space-time conversion that acts as a temporal fluid-dynamic interpolation without incurring the immense computing costs of a 4D flow solver. We tested this space-time conversion with synthetic LiDAR data extracted from a large-eddy-simulation (LES) of a neutrally stable single-turbine wake field. The data was synthesised with a numerical LiDAR simulator. Then, we performed a parametric study of 11 different scanning velocities. We found that temporal error dominates the mapping error at low scanning speeds and that spatial error becomes dominant at fast scanning speeds. Our space-time conversion method increases the temporal resolution of the LiDAR data by a factor 2.4 to 40 to correct the scan-containing temporal shift and to synchronise the scan with the time code of the LES data. The mean-value error of the test case is reduced to a minimum relative error of 0.13% and the standard-deviation error is reduced to a minimum of 0.6% when the optimal scanning velocity is used. When working with the original unprocessed LiDAR measurements, the space-time-conversion yielded a maximal error reduction of 69% in the mean value and 58% in the standard deviation with the parameters identified with our analysis.  
improvement; synchronisation; statistics; wakes; scanning measurements; flow solver +
Footnote †: journal:  
Article
## 5 Conclusions  
The results of this research work allow us to draw the following conclusions.  
* The protocol designed to be implemented in the INESSCOM monitoring system allows different parameters to be followed up, of which corrosion intensity and (i\({}_{\text{CORR}}\)) double layer capacity (C\({}_{\text{DL}}\)) are highlighted.
* The proposed multivariate classification model (i\({}_{\text{CORR}}\) and C\({}_{\text{DL}}\)) quickly and simply allows the precursor agent of corrosion to be identified, even when corrosion kinetics are similar. The developed model simplifies the techniques used for defining classification and estimation models, commonly employed in voltammetric sensor systems. Some of these techniques, such as PCA and PLS, require advanced computational software, whereas the proposed model can be processed with simple tools like spreadsheets. This makes it easier for non-expert users to apply it and allows its more widespread use.
* Implementing the proposed measurement protocol is simple and does not require modifying changes in technology, but merely a change in the data management of the control software. So, it can be implemented in already existing remote-testing systems.
* The study data were validated with in situ measurement tools, with deviation close to 10% when the remote-sensing system INESSCOM was used.  
The results presented in this study, along with those found in Part I, demonstrate the suitability of the modification designed to be implemented in the INESSCOM monitoring system and the repercussions that it would have on corrosion assessments of RCSs by  
Figure 12: Data comparison: results obtained by means of the INESSCOM monitoring system (sensors) and commercial equipment (rebars). Regression line (red dotter line).  
remote sensing. The proposal improves structural corrosion monitoring through the innovative use of highly reliable voltammetric sensor systems by optimising data processing resources and employing simpler models than those typically used in these systems.
## 2 Materials and Methods
### Uncombined Dual- and Triple-Frequency Multi-GNSS Decoupled Clock Model
#### 2.1.3 Reference Satellite and Multi-GNSS Considerations
A cross in frequency band column means that measurements on selected frequency are used to estimate state term. The fact that the datum of the carrier-phase measurements is set by the reference satellite's ambiguities means that the reference satellite choice must depend on the number of frequencies available for the constellation in question. Indeed, for the GPS constellation for example, if only dual-frequency satellites are visible to the receiver, then the reference satellite can be dual-frequency. However, as soon as a triple-frequency satellite is included in the processing, the reference satellite must be switched to one of the triple-frequency satellites to set the datum on the third frequency. The reference satellite must therefore contain as many frequencies as the maximum number of frequencies in its constellation. A summary of the strategy of reference satellite choice is provided in Figure 1. The summary only focuses on the impact of the number of frequencies on the reference satellite choice. Other parameters need to be taken into account when making the choice, such as choosing a satellite that was processed for a long enough time that its ambiguities are correctly estimated, and a satellite that has the highest elevation angle and signal-to-noise ratio among the available satellites, etc. Note that another approach can be used when setting the datum for phase measurements, as the datum on each frequency does not have to be set by the same satellite. As a consequence, instead of requiring the reference satellite to have as many frequencies as processed for its constellation, the datum on the first two frequencies can be set by a dual-frequency satellite, while the datum on the third frequency can be set by one of the triple-frequency satellites' third frequency phase measurement.