esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
Since \(P_{0}\) was held constant for all simulation, it meant that \(N\) had to be increased to achieve the given \(T_{\rm max}\). The results reveal that \(N\) required to achieve \(T_{\rm max}=70\)\({}^{\circ}\)C decreased with tumor size. For instance, \(N\) required to achieve \(T_{\rm max}=70\)\({}^{\circ}\)C decreased from \(112.37\times 10^{14}\) mL\({}^{-1}\) to \(5.54\times 10^{14}\) mL\({}^{-1}\) when \(R_{\rm T}\) was increased from 1 to 5 mm. Lastly, the nanoparticle concentrations that were required to achieve the different values of \(T_{\rm max}\) corresponded to volume fractions in the range between 0.004% and 10.6%. A review of the nanoparticle delivery to tumors in the literature between 2006 and 2016 by [PERSON] et al. [43] revealed that only approximately 1% of administered nanoparticle dose reached the tumor. Therefore, it is important that the \(\phi_{\rm v}\) is kept at the low value for practical applications. This can be achieved by through several means such as increasing the laser power or exploiting the capability of the Fe\({}_{3}\)O\({}_{4}\) NPs to generate synergistic heat during simultaneous exposure to NIR laser and alternating magnetic field as previously reported elsewhere [19]. These predictions are consistent with previously reported experimental and computational results in the literature. [PERSON] et al. [44], developed a treatment planning model for the optimization to parameters such as laser power density, nanoparticle concentration and exposure time in an effort aimed at potential enhancement of treatment outcome. Their predictions showed that any change made to any of the parameters can be compensated by altering the remaining parameters. Using an integrated strategy that combined x-ray computed tomography or ex-vivo with a 4-dimensional FEM model, [PERSON] and co-workers [20] simulated photothermal heating with polyethylene glycol PEGylated gold nanorods (PEG-NR) and used the results to guided pilot therapeutic studies on human xenograft tumors in mice. Their simulations revealed the extension of thermal flux vectors from the region where PEG-NRs were located as well as the expected thermal profile.
# Segmentation of Liver Tumor in CT Scan Using ResU-Net
## 3 Material and Method  
### Liver CT Scan Dataset  
The ResU-Net network is evaluated on the 3D-IRCADb01 dataset [27] publicly provided by the IRCAD Digestive Cancer Institute, which included 3D CT scans of 10 females and 10 males with liver cancer among 75 female and male cases. The 3D CT scan is in the shape of DICOM and is divided into 2D slices, and each has 2800 slices with masks for liver, tumor, bone, arteries, kidneys, and lungs. The general pipeline of the method utilized in this study for liver tumor segmentation as shown in Figure 1.
## 2 System Model for Laser Time Transfer in Common-View
### Models for Optimized Satellite Orbits Design in Multi-Station Common View
\begin{bmatrix}X_{Sr}\\ Y_{Sr}\\ Z_{Sr}\end{bmatrix} = \begin{bmatrix}-\sin\!B_{S}\!\cos\!L_{S}&-\sin\!B_{S}\! \sin\!L_{S}&cosB_{S}\\ -\sin\!L_{S}&\cos\!L_{S}&0\\ cosB_{S}cosL_{S}&cosB_{S}sinL_{S}&\sin\!B_{S}\end{bmatrix}\begin{bmatrix}X_{S} \\ Y_{S}\\ Z_{S}\end{bmatrix} , \tag{7}\]  
The conversion process for the satellite's instant Earth coordinate system is presented in Figure 2. The IERS official website ([[https://www.iers.org/IERS/EN/DataProducts/data.html](https://www.iers.org/IERS/EN/DataProducts/data.html)]([https://www.iers.org/IERS/EN/DataProducts/data.html](https://www.iers.org/IERS/EN/DataProducts/data.html)), accessed on 10 December 2023) provides the most recent versions of the axial precession, nutation, and polar motion that have been used. To calculate the observation elevation angles that align with the station, the coordinates of the station and satellite relative to the centroid coordinate system of the station are needed. This aids in determining the satellite's visibility during the station's observable time period (\(Time_{observe}\)). Technical abbreviations will be clarified upon their initial usage. The procedure for deriving the coordinates of the station coordinate system's center for a specific satellite at a given time remains consistent across stations. It involves assessing whether the observation elevation angle \(\theta_{i}\) exceeds the minimum observation angle \(\theta_{min}\) and identifying the intersection of observation periods from various stations. This method establishes a shared observation period (referred to as \(Time_{com}\)) for a given satellite during its orbital period.
## 3 Overview of 6G Technologies  
This section introduces the fundamental vision of 6G and outlines its key features, multi-layer architecture, and core enabling technologies. It also highlights how each innovation is tailored to address current limitations in wireless networks and presents emerging 6G applications across different verticals.
## 4 System Development and Experimental Verification
### Experimental Verification
#### 4.2.2 Obstacle Risk Detection and Location Method Experimental Verification  
To verify the obstacle risk detection and location method based on the safety limit model proposed in the previous section of the article, we first built a two-dimensional contour model of the mechanical arm and detection equipment and divided the spatial envelope along the predicted direction of motion. Then, by executing spatial filtering and clustering calculations, the cumulative point cloud data in the safe area was eliminated. As  
Figure 16: Registration accuracy score results of adjacent frames in the experimental tunnel.  
Figure 17: Images before and after tunnel point cloud thickening. (**a**) Point cloud data before thickening. (**b**) Point cloud data after thickening.  
shown in Figure 18, using the system-designed limit detection function module, the spatial filtering algorithm was used to remove the data in the safe area, and the point cloud of the head-end intrusion of the protruding cable frame on the tunnel wall was obtained to reduce the generation of invalid point clouds while detecting risk obstacles, thus improving detection efficiency.  
Finally, we applied the OBB algorithm to the point cloud of the clustered subset to extract features, successfully obtaining the specific location and pose information of the obstacle. Figure 19 shows the pose information of the contact network frame point cloud after the OBB algorithm.  
Through the above experiments, we have verified the effectiveness and feasibility of the obstacle risk detection and location method based on the safety limit model in practical applications.  
Figure 19: Pose information of contact network frame point cloud.  
Figure 18: The cable rack on the tunnel wall and its head-end encroaching point cloud. (**a**) Tunnel wall protruding cable frame. (**b**) Head-end intrusion point cloud.
## 3 Research Model and Methodology
### Research Model
Turn signal usage is an important aspect of driver communication and contributes to the safe and predictable movement of vehicles within the roundabout. Activating the turn signal allows other drivers to anticipate and adjust their driving behavior accordingly, reducing the likelihood of collisions or conflicts. Proper use of turn signals promotes smooth traffic flow and enhances overall safety within the roundabout. Figure 1: Illustration of lane change by car to the second lane while traversing roundabout [38]. The hypotheses of this study aim to examine the relationship between different variables related to roundabouts. Specifically, the study focuses on the variables of lane change, correct lane usage, and turn signal usage, comparing the outcomes between signalized and non-signalized roundabouts. The hypotheses for each variable are as follows:  
1. Lane Change Hypotheses: * H0: There is no significant difference in lane change behavior between signalized and non-signalized roundabouts; * H1: There is a significant difference in lane change behavior between signalized and non-signalized roundabouts. 2. Correct Lane Usage Hypotheses: * H0: There is no significant difference in correct lane usage between signalized and non-signalized roundabouts; * H1: There is a significant difference in correct lane usage between signalized and non-signalized roundabouts. 3. Turn Signal Usage Hypotheses: * H0: There is no significant difference in turn signal usage between signalized and non-signalized roundabouts;  
Figure 3: Illustration of turn signal usage [40]. Figure 2: Illustration of correct lane usage by arrows that indicate movements such as red with right or through turn, blue with through or left turn, and yellow with left or U-turn [39]. \(\rm H_{1}\): There is a significant difference in turn signal usage between signalized and non-signalized roundabouts. These hypotheses are formulated to investigate whether there are notable disparities in the behavior of drivers at signalized and non-signalized roundabouts regarding lane change, correct lane usage, and turn signal usage. By analyzing and comparing the data, the study aims to determine if these variables exhibit significant differences, providing an understanding of driver behavior in the context of roundabouts.
# Applications of Digital Twin across Industries: A Review
## 7 Oil and Gas  
The oil and gas sector generates about USD 3.3 trillion annually in revenue, making it one of the largest sectors in the world in terms of dollar value [116]. The emergence of DT technology in the oil and gas industry has brought tremendous value to this industry by being an effective tool in, (i) reducing and a better understanding of risks, (ii) creating and managing executable work schedules, and (iii) identifying any changes in the process and responding accordingly [117]. Additionally, DT can be a critical resource for oil companies when it comes to extracting offshore resources [118]. Due to all these benefits of DT, Accenturem, in one of their surveys from 2017, found that out of 200 refineries, 66% of them were planning to increase their investments in digital transformation in the coming years, and 57% already had invested more than the previous year [119].  
BP (British Petroleum) [120], one of the largest oil and gas companies in the world, has been using DTs for several of their plants in the North Sea called APEX. APEX helps them in safely optimizing the production, remote surveillance, predictive maintenance, saving time in optimizing, and testing. The most notable benefit contributed by DTs for BP was a boost in oil and gas production by 30,000 additional barrels per day globally [120].  
Another oil and gas company to embrace the DT technology is Royal Dutch Shell. By collecting more than 10 million operational data per minute, they use it for maintenance, improvement in productivity and safety, and reduction in emissions by anticipating the conditions to optimize asset performance and managing them autonomously [121]. They also developed an improved fatigue model for their assets using the DT concept by combining the data from sensors with a structural finite element model in order to accurately predict the life of assets so that they can optimize the inspection planning and safety cases around it [122]. Other companies, namely Eni [123] and Equinor [124], are also exploiting the technology for maximizing productivity while keeping the costs and risks at a minimum.  
Siemens [125] also studied the benefits of deploying DTs in the oil and gas industry and found out that the project cycle was shortened by four-eighteen weeks in total: four weeks in stabilizing the operation and eight weeks in the engineering phase. Moreover, the capital expenditure and operation expenditure were also reduced by USD 4-7 million and USD 60-100 million, respectively. It was also suggested that creating a DT as a part of the project rather than retrofitting it to the existing plant is more economical.  
Since the oil and gas industry relies on heavy and sophisticated plants as well as machinery located in remote areas under extreme environmental conditions, a DT makes it safer to monitor and control the activities/processes, thus reducing the risks involved. Moreover, by predicting the downtime, the DT improves the overall process, which directly translates into savings both in terms of time and money.
## 5 Conclusions  
This study investigated the use of Artificial Neural Networks (ANNs) in combination with various data balancing techniques--namely SMOTE, ADASYN, and BorderlineSMOTE--to classify firewall packets, a critical task in network security. The findings from our research demonstrate that while ANNs are highly effective for this purpose; their performance is significantly enhanced when combined with appropriate data balancing techniques to address the inherent challenges of class imbalance.  
The ANN model combined with BorderlineSMOTE emerged as the most effective approach, achieving an accuracy of 97% and a weighted average precision and recall of 96%. This model demonstrated strong performance across all classes, including minority ones, making it a robust tool for real-world applications where security accuracy is paramount. The integration of SMOTE and ADASYN also provided improvements, though to varying degrees, with SMOTE showing better overall performance in balancing the dataset than ADASYN.  
Moreover, the real-time performance metrics indicate that the integration of these balancing techniques not only improves classification accuracy but also enhances the model's efficiency in processing real-time data. This dual benefit underscores the critical importance of addressing class imbalance in machine learning models, particularly in cybersecurity contexts where the cost of misclassification can be severe.  
These results underscore the importance of addressing class imbalance in machine learning models, particularly in cybersecurity applications where the cost of misclassification can be severe. The research highlights that the combination of ANNs with data balancing techniques not only improves overall accuracy but also ensures that the model performs consistently across all classes, including those that are underrepresented.
## 9 Quickest Spectrum Sensing
### Finite Horizon Case  
Now, we can apply the powerful tool of dynamic programming to our problem. We first consider finite horizon case, i.e., we consider a time window \([1,\Gamma]\) and close the communications over all channels after time \(\Gamma\). Then, it is easy to verify that  
\[J_{\Gamma}\left(S_{m}^{\Omega}|X_{0}^{\Gamma}\right)=\sum_{m\in\Omega}P(T_{n}>\Gamma) \tag{76}\]  
i.e., the sum of false alarm probabilities for the remaining active channels. For \(1\leq t<\Gamma\), we can apply the Bellman's equation to compute the cost-to-go function \(J_{t}\left(S_{m}^{\Omega}|X_{0}^{t}\right)\). The details can be found in [158].  
One problem with the above approach of dynamic programming is that we need to record all observations \(X_{0}^{\Gamma}\), which requires prohibitively large memory. Fortunately, we can show that the _a posteriori_ probabilities \(\left\{P\left(T_{m}\leq t|X_{0}^{t}\right)\right\}_{m=1,\ldots,M}\) are sufficient statistics for the cost-to-go functions, i.e., for time slot \(t\), the cost-to-go function \(J_{t}\left(S_{m}^{\Omega}|X_{0}^{t}\right)\) can be written as \(J_{t}\left(S_{m}^{\Omega}|\mathbf{p}_{t}\right)\), where  
\[\left(\mathbf{p}_{t}\right)_{m}=P\left(T_{m}\leq t|X_{0}^{t}\right) \tag{77}\]  
Therefore, we need to record and update only the \(M\)-vector \(\mathbf{p}_{t}\), which requires only constant amount of memory. The _a posteriori_ probabilities can be computed in a recursive manner (the recursive expression can be found in [158]), thus being quite efficient. Note that each cost-to-go function is a function of \(\mathbf{p}_{t}\), instead of a constant. Therefore, in numerical computation of the cost-to-go functions, we need to discretize the \(M\)-vector \(\mathbf{p}_{t}\) first.
## 3 Mathematical Model for Optimal Trajectory Planning
### Objective Function Design  
We defined the objective function as an optimization criterion to improve the quality of path planning [1]. Therefore, we define the objective function as a weighted component of energy consumption, flight risk estimation, and important area surveillance and formulated the objective function as in Equation (4),  
\[F_{obj}=\ -w_{1}F_{EC}-w_{2}F_{FRE}+w_{3}F_{SAI} \tag{4}\]  
where \(F_{EC}\), \(F_{FRE}\), and \(F_{SAI}\) are defined to be in the range of [0, 1] and \(w_{i}\) (\(i=1,2,3\)) were used for expressing the weight of the objective component. Our main focus was on generating the optimum path with less energy consumption, environmental flight risk, but higher surveillance. Therefore, we designate surveillance area importance as the positive and the rest areas negative values.
## 3 Numerical Model  
The set of equations previously defined is solved using the commercial software ANSYS FLUENT 18.2. The quadratic upwind differencing (QUICK) method is used for the discretization of the convective terms in the momentum and energy equations. Also, semi-implicit pressure-linked equation (SIMPLE) scheme is employed to overcome the velocity-pressure coupling. The pressure staggering option (PRESTO) algorithm is also used for pressure correction equation. The enthalpy-porosity formulation is used to solve the mass, velocity, and energy equations. The green-Gauss cell-based method is used to evaluate all gradients. In order to improve the convergence stability, the under-relaxation factors have been fixed to 0.3, 0.2, 1, and 0.9 for pressure, velocity, energy, and liquid fraction, respectively. The residuals for the continuity, momentum, and energy equations are adjusted to be less than \(10^{-3}\), \(10^{-3}\), and \(10^{-6}\), respectively.  
In the present study, the effects of operational and geometrical parameters on the melting process are investigated numerically in the shell and coil tube ice storage. The investigated operational parameters are the inlet mass flowrate and temperature of the heat transfer fluid (HTF), ethylene glycol. The analyzed geometrical parameters are the pitch, the diameter and the height of the coil. The melting process is simulated in the ice storage system put horizontally.  
The geometry considered now is a shell and coil tube ice storage tank as displayed in Figure 1. The length of the tank, coil tube diameter, and pitch are 400, 10, and 80 mm, respectively. Ethylene glycol is used as HTF fluid within the coil tube.  
For the grid sensitivity analysis, four different mesh grids have been considered with a total of 436,400, 584,300, 1,391,200, and 1,631,500 cells, respectively. No noticeable difference was obtained for the three thinnest grids such that all the results presented hereafter have been obtained using the grid with 584,300 cells (248,304 cells in the shell and 335,996 cells in the coil tube). The mesh is unstructured and based on tetrahedral elements. A mesh refinement close to the walls is applied through 10 prismatic layers to guarantee the good resolution in the boundary layers where the highest  
Figure 1: Schematic of the shell and coil tube PCM storage with relevant geometrical parameters.  
gradients are usually observed. The time step is fixed to 0.5 s for all simulations. The shell is full of ice at the initial condition.
# A CSI-Based Multi-Environment Human Activity Recognition Framework
###### Abstract
In the fourth phase, a set of features is extracted from the activity segments. In addition, a feature selection procedure is applied to select the best combination of features that can be used to recognize different human activities. Finally, the fifth phase utilizes the extracted features to construct support vector machine (SVM) classifiers to distinguish between the CSI signals associated with different human activities. To validate the performance of our proposed approach, we have collected a large dataset composed of CSI signals acquired from 20 subjects while performing six different daily-life human activities, including walking, falling, sitting on a chair, picking up a pen from the ground, and turning. The dataset was collected in two different environments to evaluate the capability of our proposed approach to recognize human activities that are performed in different environments. In addition, in this work, we are performing a leave-one-subject-out cross-validation technique to test the ability of the developed models to recognize activities from new subjects. The main contribution of this work can be summarized in the following points:  
* We addressed a multi-environment human activity identification problem. * We evaluated the proposed system using a leave-one-subject-out manner in which no activity trace from that subject is used to train the classification model. * A novel segmentation method to remove the part of the signal that contains no-movements is developed. * A methodology for denoising the collected CSI values, which are obtained from the Wi-Fi exchanged packets, is introduced. * A large number of handcrafted features were investigated to determine their effectiveness in recognizing the different performed human activities in a multi-environment domain. * To add to the credibility of this work, ample experiments are conducted using the publicly available dataset to demonstrate the effectiveness of the proposed methodology in determining the differently performed human activities. The rest of this paper is organized as follows: In the \"Related Work\" Section, we provide a summary of the recent work proposed in the area of activity recognition. After that, in the \"Background\" Section, we provide a brief background about the CSI. In the \"Methodology\" Section, we provide a detailed description of the acquired dataset including the performed activities and the environments in which the datasets were recorded. A detailed description of the extracted features and the process we used to collect these features are also provided in the \"Methodology\" Section. In the \"Results\" Section, we present and discuss the results obtained using our proposed approach. Finally, the conclusions are provided in the \"Conclusions\" section.
# Mobile Robot Path Planning Considering Obstacle Gap Features
## 2 Path Planning Method with Clearance Characteristics
### Global Path Planning Based on Gap Map
#### 2.1.3 The Path Optimization Based on Gap Extraction  
In the traditional A* path planning method that generates a path based on a grid map, the improvement of related redundant point deletion takes a long time to calculate, and the calculation of obstacle collision detection with different angles is difficult. Therefore, it does not meet the actual movement needs of robots. And the nodes are optimized by the obstacle gap map as follows:  
* Extracting the gap map layer by obtaining the gap grid number of each obstacle in the global map;
* All the gap points represented by {P\({}_{1}\), P\({}_{2}\)...P\({}_{n}\)} passed by the robot are obtained by matching the grid matrix obtained by path planning with the gap map layer matrix. Then, put the starting and ending points into the set to generate the set of all nodes to be computed, which is represented by {Start Point, P\({}_{1}\), P\({}_{2}\)...P\({}_{n}\), Target Point}. And the non-colliding node is obtained by calculating whether the neighboring path nodes of all nodes in the obtained node set. The calculation process is shown in Figure 3.