esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Image Acquisition Protocol  
In cohort 1, all participants underwent scanning using the same image-acquisition protocols. The imaging data were collected with 3.0 T TOF PET/MR (GE Healthcare, Milwaukee, Wisconsin, USA). The acquisition parameters for fMRI were as follows: data matrix = 64 \(\times\) 64, repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, slice thickness = 4.0 mm, slice number = 28, voxel size = 3.5 \(\times\) 3.5 \(\times\) 4 mm\({}^{3}\). The following parameters were used for the MRI: TR = 6.9 ms, TE = 2.98 ms, SPGR sequence, matrix = 256 \(\times\) 256, slice thickness = 1 mm, slice number = 192, inversion time (TI) = 450 ms, voxel size = 1 \(\times\) 1 \(\times\) 1 mm\({}^{3}\).  
A dynamic scan lasting for 35 min was performed for [18F] AV45 PET, approximately 40 min after administering an intravenous injection of 7-10 mCi [18F] florbetapir. The following parameters were used for PET data acquisition: 8 iterations, 32 subset matrices = 192 \(\times\) 192, half-width height = 3, FOV = 350 \(\times\) 350.  
The fMRI images in cohort 2 were acquired using 3.0-T scanner from SIEMENS. The following were the acquisition parameters for the fMRI data: TR = 3000 ms, TE = 30 ms, number of volumes = 197, slice number = 48, voxel size = 3.44 \(\times\) 3.44 \(\times\) 3.40 mm\({}^{3}\), slice thickness = 3.4 mm. The MRI parameters were as follows: TR = 2300 ms, TE = 2.98 ms, slice thickness = 1 mm, TI = 900 ms, voxel size = 1 \(\times\) 1 \(\times\) 1 mm\({}^{3}\). The parameters for 18-F [AV45] PET were as follows: Number of Columns = 336.0 pixels; Number of Rows = 336.0 pixels; Number of Slices = 109.0; Slice Thickness = 2.0 mm.
## 6 Model Performance Evaluation and Optimization
### Optimization of the Number of Hidden Layers  
The number of hidden layers has a greater impact on the model performance than the size of the hidden layers. Therefore, this paper determines the suitable model depth for the prediction of the aerodynamic performance of the bluff body section [34; 35]. The convolutional layer, pooling layer, and ReLU layer are considered to be downsampling components, and the number of these components (that is, the number of pooling times (N) in Figure 6) is optimized. Considering that the size of the data image is 64 \(\times\) 64, after 6 iterations of max pooling, the input information is converted into multiple channels of 1 \(\times\) 1, and, therefore, the maximum number of downsampling components is set to 6. When naming the model, UNet_2-3 represents two downsampling components, and each downsampling component layer contains three convolution operations. Using the same training and validation sets, the comparison of different models is shown in Table 2.  
From the results in Table 2, it can be seen that as the number of model layers increases, MAE first decreases, then increases, and finally decreases to its lowest value. The model obtains the smallest drag coefficient MAE when using six downsampling components. However, the data in Table 2 are based on two convolution operations. Therefore, it is necessary to continue to observe the effect of the number of convolution operations (Table 3).  
Combining the observations in Tables 2 and 3, it can be seen that when three convolutions are used, the MAE is significantly lower than when two convolutions are used. At the same time, the UNet_3-3 model has the lowest MAE and fewer parameters than the other models, and its computational cost is lower. Considering that increasing the number of convolution operations can significantly improve the model performance, in order to determine the optimal number of convolution operations for the UNet_3 model, UNet_3-4 is added for comparison. The calculation shows that the prediction result of UNet_3-4 is \(MAE=0.1675\) and the parameter quantity is \(8.540\times 10^{6}\). It can be seen that when the number of downsampling components is 3, the MAE does not decrease but increases after the number of convolution operations is increased to 4. In summary, considering the performance and the amount of computation, this paper chooses 3 as the number of downsampling components and uses the UNet network model with 3 convolutions.
# Topological Phase Diagram of BiTeX-Graphene Hybrid Structures
## 3 Methods  
The optimized geometry and ground state Hamiltonian and overlap matrix elements of each structure were self consistently obtained by the SIESTA implementation of density functional theory (DFT) [31; 32]. For every calculation, the spin-orbit interaction was included as it was implemented in SIESTA by Dr. [PERSON] based on the original on-site SO formalism and implementation developed by Prof. [PERSON] [33]. SIESTA employs norm-conserving pseudopotentials to account for the core electrons and linear combination of atomic orbitals to construct the valence states. We used the pseudopotentials optimized by [PERSON] et al. [34]. For all cases, the considered samples were separated with a minimum of 18.5 A  thick vacuum in the perpendicular direction. The generalized gradient approximation of the exchange and the correlation functional was used with Perdew-Burke-Ernzerhof parametrization [35], as the pseudopotentials were created to the PBE functionals, with a double-\(\zeta\) polarized basis set and a real-space grid defined with an equivalent energy cutoff of 1000 Ry. The Brillouin zone integration was sampled by a 24 \(\times\) 24 \(\times\) 1 Monkhorst-Pack \(\mathbf{k}\)-grid [36]. The geometry optimizations were performed until the forces were smaller than 0.01 eV/A.  
The choice of pseudopotentials optimized by [PERSON] et al. ensures that both the obtained geometrical structures and the electronic band properties are reliable. As a benchmark we validated our method by comparing the electronic properties of the bulk BiTeI with the experimental data. This approach gave us 130 meV band gap and 4.6 eVA  as Rashba parameter for BiTeI bulk. The corresponding experimental results are 190 meV and 3.8 eVA  respectively [37].  
We extracted the topological phase information of the systems by determining the Wannier center flow. The center of the Wannier function can be expressed as the phase of eigenvalues of a matrix obtained as the product of the Berry connection along the \"Wilson loop\" by our own post-processing tool. The used real space Hamiltonian was calculated by the SIESTA self-consistent cycle. The  
Figure 2: The calculated in-plane tensile–out-of-plane compressive strain phase diagram of the BiTeCl/BiTeBr/BiTeI–graphene structures (**a–c**) respectively. The blue colors indicate that the gap is topological, while the red hue means that is trivial. The metallic shade marks the phase boundary. Subfigures (I)–(IV) are the corresponding band structures of the marked phases near the \(\Gamma\) point in momentum space (color online).  
topological numbers were expressed as the number of times mod 2 of the partner switching of these phases during a complete period of the \"time reversal pumping\" process [38; 39; 40].
# Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application
## 4 Methodology  
This section describes the enhanced deep-learning approach used for debris recognition and classification. Figure 2 illustrates the functional description of the proposed scheme.  
The entire design flow is divided into two phases--the training phase and the testing phase. The proposed scheme uses MobileNet v2 for feature extraction and SSD for detection and classification of the floor debris into solid and liquid spillage debris. Then SVM is used to classify the liquid spillage debris regions based on spillage size as being small or large. Typically, identifying the size of the object from the boundary box outputs of CNN is the possible method, but this method faces several drawbacks. Firstly, it is based on the hard-coded threshold which is set manually and is hard to determine accurately [18; 19]. Secondly, for finding the actual object size by boundary box method, we need very accurate intrinsic and extrinsic cameras parameters. The problem of camera calibration errors between the detected object in world frame and detected objects in the image frame are difficult to address in dynamic environments. Using SVM and with given training data, the input boundary box can be classified autonomously into two classes, i.e., hard-to-clean or easy-to-clean objects.
## 3 Network Model  
Only unicast from source node \(S\) to destination node \(D\) was considered in this paper. The network consisted of \(N+2\) wireless mobile nodes (\(S\), \(D\), and \(N\) relay nodes) connected in multi-lines, and the links were time-varying. The network was assumed to be sparse: Contemporaneous end-to-end paths were not guaranteed, and the ratio between the coverage area of all nodes and the total area was low so that we neglected interference. We also assumed that two mobile nodes exchanged data via short-range interfaces (Bluetooth, WIFI, etc.), and that communications were bidirectional. Messages were buffered at each intermediate node and waited until the next hop was available, which could take a long period of time. Each node had the same and limited bandwidth and cache size. In addition, studies have shown that many popular movement models, such as random waypoints, random walks, and community-based models, have the property that the inter-contact time of nodes is exponentially distributed or has an exponential tail [36; 37]. In order to guarantee the universality of our method, this paper does not assume a specific movement model, but adopts the assumption that the inter-meeting interval between node pairs are independent random variables that follow the same exponential distribution (rate parameter is \(\lambda\)). The proposed analytical framework is also effective for other realistic movement models.  
This paper only considers coding at the source node: The file was cut into \(K\) pieces by the source node and transmitted as coded packets in cycles, where the RaptorQ was employed. The destination node could decode the source file with high probability after collecting several arbitrary coded packets more than \(K\). To ensure that the source node could get the number of coded packets delivered in time, the destination node sent multiple ACKs in each cycle: When destination node \(D\) received a new coded packet, an ACK \(q\) was sent back to the source. The index value \(q\) reflects the number of coded packets needed by the destination currently to recover the source file.  
When routing coded packets, we adopted time-limited epidemic routing, which utilized multiple possible data propagation paths from the source to the destination to reduce the latency [38]. On the feedback path of ACK, unlimited epidemic routing was used to send it to the source node as soon as possible.
# Scaled Model Simulation and Experimental Verification of Submarine Flexible Pipeline Laying System
## 2 Similarity Theory and Scaled Model of Flexible Pipeline-Laying System
### Ambient Load of Paving System and Ship Motion Response
#### 2.1.3 Research on Dynamic Performance of Pipe-Laying Ship  
The ship can be considered as a rigid body when studying its motion in waves. The wave forces acting on the ship are usually solved from two aspects: diffraction and radiation. The motion of the ship is neglected in the calculation of the diffraction method, while the radiation method is mainly used to calculate the influence of the hydrodynamic coefficient  
Figure 2: Attitude of underwater pipeline (**a**) and Force of pipeline unit (**b**).  
on the motion of the ship [21]. In this paper, the AQWA sub-module of ANSYS is used to analyze the hydrodynamic performance of pipeline-laying ships. Through the frequency domain, hydrodynamic analysis of the ship by AQWA, the hydrostatic stiffness matrix, hull additional mass, hull damping coefficient, first-order wave force, and second-order wave load of the ship can be obtained [22].  
HYSY201 is a pipeline laying vessel of China National Offshore Oil Corporation (CNOOC). Based on the size parameters of HYSY201, the model of pipeline laying vessel is established. The size parameters and physical characteristics of the selected laying vessel are shown in Appendix B. The parameters provided by the ship's profile drawing are imported into the DM module of ANSYS to complete the drawing of the ship's geometric surface by the generated surface, and finally, the ship's surface model is established. Then the hydrodynamic model can be obtained by meshing the underwater wet surface of the ship with plate units in ANSYS-AQWA, as shown in Figure 3.  
When laying pipelines at sea, the inherent frequency of the hull should avoid the peak frequency of the spectrum under the working sea condition to avoid hull resonance. \(RAO\) is used to describe the motion response of a floating structure under incident waves of unit amplitude [23; 24]. The definition of RAO is as Equation (8).  
\[RAO(\omega)=\zeta(\omega)/\zeta \tag{8}\]  
where \(\zeta(\omega)\) is the response of the hull to waves of a certain frequency and \(\zeta\) is the amplitude of the corresponding frequency wave.
# Augmented Reality in Surgical Navigation: A Review of Evaluation and Validation Metrics
## 3 Evaluation and Validation of AR Surgical Technology  
The clinical practice of simulation surgery utilizing 3D images has recently been accomplished, and AR is an effective tool in conducting surgical procedures, due to advancement of diagnostic imaging and creative computer programs [78]. In recent decades, surgery procedures aiming at minimizing invasiveness have grown in popularity [79]. AR is a technique that adds computer generated information to the perception of reality. When surgeons make decisions about anatomical structures and associated environment, they use their perceptual and spatial thinking skills. It is critical to develop methods and approaches to operationalize objective metrics of performance in tasks that involve the perception of and spatial reasoning regarding anatomical structures in order to study the facilitation offered by AR interactive displays of 2D and 3D, volumetric, biomedical visualization [80]. The driving force behind AR's development was the need for an optimal head tracking system that continuously and perfectly tracks all subtle changes of the surgical area and transparently modifies the baseline registration all throughout the surgical intervention [81; 82; 83]. The display device must fit seamlessly into the physician's workflow because AR visualization is supposed to make the physician's task easier. As a result, the optimal presenting technique is one that blends in with the surroundings while delivering the necessary details. The crucial point is that the human visual system responds to the resultant surface's high frequency components, forming a sparse 3D picture of the surface. The buried virtual
## 4 Signal Preprocessing
### Interference Filtering
#### 4.3.1 CFAR Algorithm  
The received signal \(X_{r}^{t}\) from the FoV can be expressed as:  
\[X_{r}^{t}=\ X_{s}^{t}+g^{t}, \tag{5}\]  
where \(\ X_{s}^{t}\) is the target reflection and \(g^{t}\) is the white Gaussian noise in a certain frame \(t\).  
From the received signal, the CFAR algorithm [42] is applied to detect the presence or absence of the target. A fixed threshold value is used in traditional detectors such as the Neyman-Pearson detector [38]. The assumption is that interference (noise or clutter) is spread similarly across the test range bins such that, if the signal in the test bin exceeds a specific threshold \(\gamma\), the bin contains a target. This results in false alarm conditions, as shown in the following equations:  
\[X_{s}^{t}+g^{t}\ \geq\ \gamma,\ \text{true detection} \tag{6}\]  
\[g^{t}\geq\ \gamma,\ \text{false alarm} \tag{7}\]  
A CFAR detector maintains a constant false alarm rate, which adjusts the detection threshold within the range bins. The detector calculates the noise level inside a sliding window and uses this estimate to assess the presence or absence of a target in the test bin. If a target is found in a bin, the algorithm returns the target range-Doppler localization [38]. Finally, all CFAR-identified targets are organized into groups based on their positions in a 3D matrix. In certain cases, this assumption might be deceptive, such as when the target returns contain only interference that surpasses the detection threshold. Therefore, additional filters with clustering are applied, as described in Section 5.
# A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
## 2 Intra-Vehicle Security
### General Overview  
A nice overview of the technologies involved in intra-vehicle systems is provided in [9; 10]: the authors highlight the complexity of modern intra-vehicle networks and state that substantial efforts are spent by various organizations to explore standard solutions for the communication stack. The impact of complexity on security is twofold: on the one hand, the complexity makes it easier for malicious actors to spot possible vulnerabilities; on the other hand, it makes it difficult to implement effective security solutions. Some of the specific challenges and possible solutions are analyzed in [11]: in particular, in that paper the proposed solutions are grouped into a few categories, namely machine learning techniques, cryptographic solutions, communication protocols (e.g., CAN bus, FlexRay, Automotive Ethernet), traffic analysis and intrusion detection, physical access and sensor data security, and hardware security modules (HSM). Among the open challenges, the authors mention the implementation in real-scenarios of the solutions. With respect to these alternatives, in the present paper we propose a solution relying on state-of-the-art hybrid cryptographic features to secure intra-vehicle network nodes: the solution has been developed and tested in a real-world scenario in the context of the already mentioned industrial research project EMERGE. To the best of our knowledge, the Crypto-Engine is the first solution that considers topology-authenticated hybrid cryptography as the core mechanism to secure automotive intra-vehicle networks.
# Coverage Extension for the UK Smart Meter Implementation Programme Using Mesh Connectivity
## 4 Conclusions and Future Work  
In this paper, we investigated the use of mesh networks at 868 MHz to increase coverage and proposed an additional mounted antenna to increase outside coverage while providing the necessary coverage extension for hard-to-reach indoor locations. Extensive measurements carried out in Newbury in both suburban and open environments for the 868 MHz band in UK conurbations shows that the 868 MHz mesh radio system propagates well in suburban environments and will provide a valuable contribution to coverage obligations required by the SMIP, providing coverage to those hard-to-reach locations. The outlook for the UK SMIP remains on track, to deliver the benefit promised to the UK population.  
Coverage being a challenge for large-scale IoT deployment can be perturbed by costs associated with coverage extension. With the measurements, models, and coverage enhancement proposed in this work, we are able to make greener choices and manage our homes and their energy usage more efficiently. With the three types of practical mesh deployments discussed in this article, further wide-scale deployments can be carried out across the UK and globally. In this work, we showed how with our approach, only about 1% of UK homes will require mesh connectivity to achieve the desired coverage.  
Understanding how these mesh constellations perform is vital to confirm the levels of coverage that can be provided. In addition, further testing of the 868 MHz propagation of the slope intercept model should be carried out. Investigation into more deterministic modelling will be the scope for future research, which will require more work and cost due to the required database describing the building outlines.  
Figure 8: The complexity of the structure of the mesh networks.  
**Author Contributions:** The work was developed in collaboration with all the authors. The work was conceived by [PERSON] and S.A. The paper was mainly drafted by [PERSON] and S.A. The paper was reviewed and updated by [PERSON], [PERSON], [PERSON], R.T. and M.A.I. All authors have read and agreed to the published version of the manuscript.  
Funding: Funding for this work was provided by Telefonica.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The unaltered raw data can be made available for research projects upon request, without undue reservation, by contacting the authors.  
**Acknowledgments:** Telefonica Smart Radio Team: [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. BRE Team: [PERSON], for access to BRE and his continued support of our research. SiRoDa Team: [PERSON], [PERSON], and [PERSON]. Student Measurement Team: [PERSON] and others.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# UAV-BS Site Planning Based on Circular Coverage Strategy
###### Abstract  
Future mobile communication technology will be used to build an integrated global coverage network. Unmanned aerial vehicles (UAVs) are the first choice for low-altitude networks due to their low cost, flexibility, and ease of operation. The characteristics of UAVs also bring new challenges to communication networks, such as short flight time, complex networking, and unstable communication quality. Therefore, it has become an urgent problem to reasonably plan the location of UAV Base Stations (UAV-BSs), reduce communication power consumption, optimize network performance, and build an efficient and stable UAV communication network (UAVCN). The traditional strategy only pays attention to the signal coverage, and ignores the influence of system transmission power on the network, which reduces the performance of the communication system. In this study, a circular coverage power optimization strategy (CCPO) based on system transmit power is proposed. The mathematical model of the circular coverage problem is used to describe the full coverage process of the UAV base station to ground users, and the deployment optimization is carried out with the goal of minimizing system transmit power, so as to obtain an efficient and reliable site planning scheme. In this paper, the binomial power function is used to continuously fit the discrete solution of the circle covering problem, and the circle covering power optimization formula is rearranged. By analyzing the convexity of the objective function under the circular coverage model, the convex optimization theory is used to solve the objective problem, and the optimal deployment number of UAVs and site planning scheme under the circular coverage power optimization strategy are given. Simulation verifies the superiority of the proposed method. Compared with the traditional hexagon strategy and the minimum power loss strategy, the circular coverage power optimization station location planning strategy can save 14.75% and 6.52% of power resources, providing a valuable reference for the design and optimization of UAV communication systems. It provides a promising solution for further improving the performance and efficiency of UAVCNs.  
A _Article_  
UAV-BS Site Planning Based on Circular Coverage Strategy  
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