esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Cross-Checking with Coeval GPS Datasets  
To confirm our findings and perform a cross-validation of our results with a completely independent dataset, we repeated the same analysis carried out with the EGMS Ortho Vertical Sentinel-1 dataset by using the GPS measurements available for the study area. Data were obtained from the INGV Osservatorio Vesuviano GPS network [82]. In detail, we used the vertical displacement data for twenty-one GPS stations located in the Campi Flegrei area (Figure 8), selecting the measurements from January 2015 to December 2021 [107,108]. For each GPS point, the distance to the deformation center and the 2016-2021 cumulative vertical uplift value were calculated. A value of 3 mm was declared by [PERSON] et al. (2022) [107] for the uncertainty of the vertical deformation in 2016-2021, as measured by the GPS. Then, a series of polynomial curves describing theoretical radial ground deformation with a progressively higher accuracy were generated by using the imported values for each point.  
As for interferometric data, we chose the best fitting polynomial curve considering adjusted R\({}^{2}\) values, which again resulted in the fourth degree (4), characterized by an adjusted R\({}^{2}\) = 0.9935; it was then used for calculating the expected uplift according to a model of radial deformation. The predicted uplift values for each point were calculated based on the following equation:  
\[\text{y}=-4\text{E-14x}^{4}-2\text{E-13x}^{3}+2\text{E-05x}^{2}-0.2105\text{x} +675.49 \tag{4}\]  
where  
x = distance to the deformation center;  
y = vertical uplift value in the vertical GPS dataset.  
Figure 7: Location of epicenters of the earthquakes Md > 3.0 that occurred from 2019 to July 2024. Seismic events according to INGV databases (see text for references).  
Then, the two datasets (vertical GPS and Ortho Vertical Sentinel-1) were compared, showing a very high correlation, as described in the Results section.
## 7 Limitations and Future Works  
Tests revealed several limitations but also revealed opportunities for improvement that could guide more targeted and informed future development.  
Regarding the user test, the university participants, while providing valid usability results, present a limitation in terms of sample diversity. In addition, the sample had a relatively young average age, highlighting the need for a broader demographic range along with better gender balance. Future studies could aim to include potential yacht customers in real-case purchase scenarios, and pair relationships could also be further explored as an alternative to the current semi-random pair generation approach.  
Figure 12: Distribution of SUS scores based on user gender (**left** image) and prior experience with virtual reality (**right** image).  
As far as navigation, the current teleportation method enables rapid exploration by allowing users to reach any point in the scene and then move in real space within physical boundaries. However, as noted by some test participants, it often results in a fragmented experience and poses challenges for less experienced users due to its reliance on dexterity and familiarity with the software. Joystick locomotion can be a valid alternative to simulating walking. Nevertheless, this technique is prone to inducing motion sickness, as noted in the literature [21], and presents challenges when navigating tight spaces, such as the interior of a boat, due to collisions. Introducing a parametric avatar tailored to the user's height and body type could support the implementation of alternative locomotion methods, offering a more natural and immersive experience.  
In terms of performance, professional software has shown significant rendering limitations resulting in an impractical user experience. Under identical hardware conditions, MAGIC performed reasonably well despite minor fluidity constraints. Future tests will incorporate quantitative metrics (e.g., GPU consumption and FPS monitoring) to provide a comprehensive efficiency comparison. The co-located setup was requested by the partner company's marketing staff. In their experience, a common client pattern involves couples either visiting the company or joining remotely, with no specific need for a far-apart configuration. Nevertheless, future work may also investigate latency and bandwidth parameters to assess the feasibility of long-range configurations.  
Moving forward, a promising prospect is the integration of shared co-design tools within a virtual space, where clients and designers can collaborate in real time to create ad hoc solutions beyond pre-configured options. Users would participate in designing interior layouts and structural components, leading to a more proactive and engaging process. However, achieving this will require overcoming technical challenges, such as synchronizing the VR environment with CAD files and finely managing objects within the scene.  
With these developments, MAGIC could evolve into an even more flexible and powerful tool capable of meeting the technical needs of designers and the expectations of end users.
# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
###### Abstract  
With the ongoing changes in global climate, ocean data play a crucial role in understanding the complex variations in the earth system. These variations pose significant challenges to human efforts in addressing the changes. As a data hub for the satellite geodetic technique, unmanned aerial vehicles (UAVs) instill new vitality into ocean data collection due to their flexibility and mobility. At the same time, the dual-functional radar-communication (DFRC) system is considered a promising technology to empower ubiquitous communication and high-accuracy localization. In this paper, we explore a new fusion of UAV and DFRC to assist data acquisition in the ocean surveillance scenario. The floating buoys transmit uplink data transmission to the UAV with non-orthogonal multiple access (NOMA) and attempt to localize the target cooperatively. With the mobility of the UAV and power control at the buoys, the system throughput and the target localization performance can be improved simultaneously. To balance the communication and sensing performance, a two-objective optimization problem is formulated by jointly optimizing the UAV's location and buoy's transmit power to maximize the system throughput and minimize the attainable localization mean-square error. We propose a joint communication and radar-sensing many-objective optimization (CRMOP) algorithm to meliorate the communication and radar-sensing performance simultaneously. Simulation results demonstrate that compared with the baseline, the proposed algorithm achieves superior performance in balancing the system throughput and target localization.  
oean monitoring; UAV; DFRC; many-objective optimization Article
## 3 Experimental Results
### Performance Evaluation
#### 3.3.2 Dataset of 800 Videos  
The models were trained, validated, and tested using a dataset of 800 videos, as shown in Table 2. SynthSecureNet performed better than both MobileNetV2 and ResNet50V2. The results in Table 2 were obtained during the testing phase, and the training duration that was observed during the training phase is also included in Table 2.  
The SynthSecureNet architecture demonstrated a high degree of performance in violence detection with an F1 score of 98%, a recall of 98%, a precision of 98%, and an accuracy of 98%. However, ResNetV2 outperformed MobileNetV2 with an F1 score of 90% compared with 89% for MobileNetV2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **F1 Score** & **Recall** & **Precision** & **Accuracy** & **Training Duration** \\ \hline SynthSecureNet & 96\% & 96\% & 96\% & 96\% & 15 min \\ ResNetV2 & 91\% & 88\% & 89\% & 89\% & 9 min \\ MobileNetV2 & 89\% & 89\% & 88\% & 88\% & 5 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison using the 400-video dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **F1 Score** & **Recall** & **Precision** & **Accuracy** & **Training Duration** \\ \hline SynthSecureNet & 98\% & 98\% & 98\% & 98\% & 30 min \\ ResNetV2 & 90\% & 88\% & 92\% & 89\% & 14 min \\ MobileNetV2 & 89\% & 89\% & 89\% & 89\% & 9 min \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison using the 800-video dataset.  
Figure 2: Confusion matrices for SynthSecureNet, ResNetV2, and MobileNetV2 (400-video dataset).  
The training duration of the SynthSecureNet model, which lasted for 30 min, exceeded that of the ResNet50V2 model, which took 14 min, and the MobileNetV2 model, which took 9 min.  
Figure 3 displays the confusion matrices acquired throughout the testing process. SynthSecureNet achieved a high accuracy in classifying between violent and non-violent activities, with 4567 right predictions and just 110 incorrect predictions.
# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Analytical Modeling
#### 3.2.6 Other Methods
(2019) compared four machine learning methods--neural networks, decision trees, reinforcement learning, and multilayer perceptual machines--for taxi time prediction, and the results of the model evaluation showed that the decision tree-based prediction model had the best accuracy and the smallest mean error. Previous studies have shown that the combined model has a greater propelling effect on the accuracy of taxi time prediction. In 2019, [PERSON] (2019) first built models based on searching for improved K-nearest neighbors and based on a non-parsimonious Bayesian classifier to predict the taxi route of aircraft surface and the number of flights with short takeoff times, respectively. After the results obtained from the prediction and other influencing factors constituting the set of feature variables, the aircraft taxi time is then predicted based on the gradient boosting tree method. From the perspective of feature selection, some scholars have improved the taxi time prediction model. In 2020, [PERSON] (2020) divided the aircraft departure process into the process from the ramp to the taxiway to the runway to the final takeoff, and then analyzed the factors affecting the taxi time in terms of the reference time, taxiway congestion waiting time, and departure queuing and waiting time involved in these processes, so as to select the feature variables. By comparing the four regression algorithms, it can be seen that the prediction model based on the gradient boosting regression algorithm has the best fitting effect. Existing studies rarely consider the effect of the interaction between features on taxi time. However, in 2021, [PERSON] et al. (2021) innovatively introduced interaction features; that is, the use of the product of two features with one-time feature to constitute a candidate feature set. They then compared the K nearest neighbor, Support Vector Regression, and decision tree methods. They found that the decision tree algorithm was the best method to construct a taxi time prediction model, compared with using only one-time feature. In 2022, [PERSON] et al. (2022) introduced \"cross-region features\" to construct a feature set. They compared six commonly used machine learning methods, and selected the gradient boosting regression tree with the highest degree of model fit to construct a taxi time prediction model. They then analyzed the importance level of each feature. After analyzing the degree of importance of each feature, it was concluded that the surface flow feature is the most important, with the highest correlation with the taxi time, and the newly introduced \"cross-area feature\" ranks third in importance among all the features. This indicates that this feature is meaningful for the prediction of taxi time.
# Safety of Nuclear Power Plants with Respect to the Fault Displacement Hazard
## 4 Baseline Information for Plant Response to Permanent Ground Displacement
### Structural Features of the New Nuclear Power Plant Designs  
For understanding the potential safety issues due to PGD, the design features of the nuclear power plants should be considered.  
As an example, typical structural features of a new Pressurized Water Reactor (PWR) will be considered. In the modern designs, such as the European Pressurized Walter Reactor, the AP-1000 or the Russian designs, the reactor is placed in a double containment that is surrounded by safety buildings. The 1.2 to 1.5 m thick prestressed concrete inner containment sized for pressures (0.5 to 0.7 MPa) and high temperatures due to loss-of-coolant accident. The \(\approx\)2 m thick outer cylindrical shell (diameter \(\sim\)50 m, height \(\sim\)70 m) protects the reactor in the internal containment from the effects of crashing of large commercial airplane, like A380. The safety-related buildings should be designed for 10\({}^{-4}\)/year (in some countries 10\({}^{-5}\)/year) earthquake. The structures inside of containment supporting the extreme heavy components and the reactor shaft that serves as shielding increases the overall mass and stiffness of the structure. The containment is surrounded by safety buildings housing the redundant trains of safety systems and the control building. The building complex of nuclear island was built up on the common base mat with a footing pressure between 0.5 and 0.9 MPa. The dimensions and stiffness of the foundation complies with the mentioned above features of the superstructure.  
Regarding basic safety functions, the reactor can be shut down by solid neutron absorbers. The injection of boron into primary coolant is the diver and redundant system that can be used in case the solid absorbers could not be inserted into the reactor core (anticipated transients without scram--ATWS). In design basis accidents, the heat from the reactor and spent fuel is removed by redundant emergency cooling systems outside of the containment either to a water body (sea, river, artificial pond) or to the air via cooling towers.  
The primary systems in the inner containment are connected with the \"outside\" structures and systems by the piping of feedwater and steam lines for the steam generator. The piping of the emergency heat removal, and cables for emergency energy supply and cables for control and instrumentation connect the SSCs housing in the inner containment and safety and control buildings. The modern designs are equipped with passive safety systems that can remove the heat from the core and inner containment directly to the air without external power supply. The systems of passive heat removal are located in the containment building.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 4 Aggregation-Based Compressed Sensing
### Aggregator Selection  
Data collection and aggregation at intermediate nodes for efficient transmission have to be considered in the network design itself. In the proposed work, data aggregation plays an effective role in compressing the sensor data before transmitting it towards the border router. The aggregator in each region is selected by the following Algorithm 1.  
Figure 1: Network Model.
# Blockchain Applications in Education: A Systematic Literature Review
## 4 Discussion
### Research Question 1: What Is the Current State of the Usage Scenarios for Blockchain in Education?
#### 4.1.5 Education Resources  
Four articles (14%) present initiatives to combine blockchain with educational resources according to different approaches, so that they can be safely shared and protected from unauthorized access, copied, modified or even deleted.  
[PERSON] et al. [18] discuss a platform to authenticate and check the integrity of learning resources in MOOCs for teachers and learners, while [PERSON] et al. [21] focus on digital rights management of multimedia resources in an online educational scenario.  
[PERSON] et al. [22] propose and analyze a framework to share exam questions that can be safely kept from being accessed until a certain date and time.
## 4 Proposed System Model
### Appliances' Categorization and Their Energy Models
#### 4.2.2 Elastic Load  
These are those appliances that can be fully managed, i.e., they can be shifted to any time slot and can also be interrupted at any time keeping in view the minimization of PAR and electricity bill [24]. These include: dish washer, washing machine, spin dryer, electric car, laptop, desktop computer, vacuum cleaner, oven, cook top and microwave oven [31]. The energy consumed by all elastic appliances in the total time interval of 24 h with 48 time-slots can be found as in Equation (2).  
\[E_{e}=\sum_{ap_{e}\in AP_{e}}\left(\sum_{n=1}^{48}\left(\rho_{e,n}\times X_{e,n} \times\tau_{e}\right)\right) \tag{2}\]  
\[\text{subject to:}\qquad\alpha_{e}\leq\tau_{e}\leq\beta_{e}\]  
where \(ap_{e}\) denotes each elastic appliance, \(AP_{e}\) is the set of elastic appliances, \(\rho_{e}\) is the power rating of elastic appliances, \(X_{e,n}\) is the ON (1) and OFF (0) states of the n\({}^{t}\)_exth_ elastic appliance, \(\tau_{e}\) is the LOT for each elastic appliance, \(\alpha_{e}\) is the earliest starting time and \(\beta_{e}\) is the latest ending time of elastic appliances, respectively. The total energy consumption of the end-user \"\(E_{T}\)\" is given by:  
\[E_{T}=E_{f}+E_{e} \tag{3}\]  
where \"\(E_{f}\)\" is the fixed appliances total energy and \"\(E_{e}\)\" is the total energy of the elastic appliances.  
Figure 1: A typical overview of a house with smart appliances. EMC, Energy Management Controller.
# Investigation on Numerical Simulation of VIV of Deep-Sea Flexible Risers
## 2 Numerical Simulation Method of VIV of Riser
### Overset Mesh Technique  
Traditional moving mesh technology has the drawback that even slight movement in one part can affect the overall situation, which consumes significant computing time and requires high grid quality. Moreover, when the grid is moving fast or has large deformations, it is prone to distortion, leading to negative volume during dynamic updates.  
To address these issues, Fluent has introduced a new mesh function called the overset mesh technique. With this technique, the entire computational domain is divided into a background area and a foreground area corresponding to the moving parts. Mesh division is performed separately for each area, and an overset interface is created to link the foreground and background meshes. Data transfer between the two regions is achieved through interpolation.  
[PERSON] et al. [31] used the overset mesh technology to build a full-scale KRISO container ship model for nonlinear unsteady RANS simulation. The authors found that important flow characteristics, such as flow separation, slamming, wave breaking and high vortices around the hull, remained within the overset domain.
## 5 Example Analysis of Deformation Allowance of Loess Tunnel
### Analysis of Assurance Rate of Deformation Allowance for Crown Settlement  
The statistical range of the crown settlement V\({}_{\max}\) is (\(-\)17.1\(-\)201.1) mm. Tunnel construction deformations exhibit inherent randomness due to the influence of numerous factors. Assuming the data follow a normal distribution, a 95% confidence interval can be derived from the sample for statistical analysis. This interval allows for the determination of the upper and lower confidence bounds for deformation at a specified confidence level. Based on the statistical analysis of the data in Figure 16a, the 95% confidence interval of the crown settlement V\({}_{\max}\) is [\(-\)51.53, \(-\)65.11]. Sections where crown settlement remains below 90.0 mm are primarily found in the tunnel with the cover depth h \(\leq\) 3(H + B). For tunnel cover depth h \(>\) 3(H + B), the crown settlement exhibits an upward trend, as depicted in Figure 16b. This is primarily due to the increase in tunnel cover depth, which results in higher soil moisture content (15.0-23.0%), reduced soil stability, and significant overlying load, leading to substantial deformation. With a deformation allowance of 10 cm for crown  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**[PERSON]**} & \multicolumn{2}{c}{**[PERSON]**} & \multicolumn{2}{c}{**[PERSON]**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Lane Type (Excavation Span)**} & \multicolumn{2}{c}{**Lane Type (Excavation Span)**} & \multicolumn{2}{c}{**Lane Type (Excavation Span)**} \\ \cline{2-7}  & **Two Lane** & **Three Lane** & **Two Lane** & **Three Lane** & **Two Lane** & **Three Lane** \\  & **(<14 m)** & **(>14 m)** & **(<14 m)** & **(>14 m)** & **(<14 m)** & **(>14 m)** \\ \hline Grade IV & 8–12 cm & 12–15 cm & 8–12 cm & 12–15 cm & 12–15 cm & 15–20 cm \\ surrounding rock & \textless{}15 cm & 15–25 cm & \textless{}12 cm & 12–15 cm & 15–35 cm & 35–47.5 cm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recommended values of deformation allowance in reference to specifications.  
settlement, the assurance rate is 85.8%. This rate increases to 94.9% when the allowance is increased to 15 cm. Based on the analysis above, a deformation allowance can be considered reasonable if it results in a guarantee rate greater than 80%.
# Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
###### Abstract  
Two novel antennas are presented for mobile devices to enable them to access both licensed shared access (LSA) bands (1452-1492 and 2300-2400 MHz) and all the long-term evolution (LTE) mid (1427-2690 MHz) and high (3400-3800 MHz) bands, together with the GSM1800, GSM1900, UMTS, and 3.3 GHz WiMAX bands. These antennas do not require any passive or active lumped elements for input impedance matching. One of them is a dual-band antenna and the other is a wideband antenna. Both antennas have high efficiency in all the LSA bands, as well as the mid- and high-LTE bands, and nearly omnidirectional radiation patterns in the mid band. In the high band, the radiation patterns of the wideband antenna are less directional than those of the dual-band antenna. The wideband antenna was fabricated and tested and the measurements demonstrated that it had good wideband performance in a wide frequency range from 1.37 to 4 GHz, covering all the above-mentioned bands.  
licensed shared access (LSA); 5G; long-term evolution (LTE); dual band; wideband; hand-held device antenna +
Footnote †: journal: Accepted: 8 February 2023
## 2 Materials and Methods
### Radomics Features Extraction  
Radiomics features were extracted from the VOIs using a dedicated 3D Slicer radiomics extension (pyradiomics library, [26]), as shown in Figure 1. No spatial filter alteration scaling was applied to the radiomic features. In particular, several radiomics features of the first and second order were extracted as follows: (a) first-order statistics (19 features), (b) 2D and 3D shape-based features (26 features), (c) gray level co-occurrence matrix (\(GLCM\), 24 features), (d) gray level run length matrix (\(GLRLM\), 16 features), (e) gray level size zone matrix (\(GLSZM\), 16 features), (f) neighboring gray tone difference matrix (\(NGTDM\), 5 features), and (g) gray level dependence matrix (\(GLDM\), 14 features). From baseline and post-pChT CT images, a total of 120 radiomic features were extracted. Additionally, a \(\Delta\)Radiomics number was obtained using the following formula: