esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Interactive BM Segmentation Based on Supervised Learning Models
### Supervised Classification of Pathological and Healthy Tissues
#### 2.2.2 BM Delineation Using V-Net  
The V-Net model is a Fully Convolutional Neural Network proposed by [PERSON] et al. [34] for volumetric medical image segmentation. The name V-Net comes from the fact that the network can be drawn with the symmetric shape like the letter V. Salient aspects of the model proposed are the use of volumetric convolution to overcome the slice-by-slice processing of input image volumes and the use of a novel objective function based on Dice coefficient maximisation, optimised during training. Both these aspects are significant for our segmentation task, which is based on volumetric data characterised by a strong imbalance between the number of voxels belonging to the pathological area and background.  
In our experiment, we adopt the V-Net configuration proposed for Lung Tumour Segmentation in [40] (see Figure 2). The inputs of the network are T1c MRI sub-volumes by dimension \(64\times 64\times 64\). The V-Net has an increasing number of convolutional filters (16, 32, 64, 128, 256) with a total of 116 layers and 128 connections. The output layer is a custom Dice loss layer [41].  
Figure 2: The architecture of 3D convolutional V-Net.  
Figure 2 shows a schematic representation of V-Net architecture adopted, which is composed of two parts implementing compression and decompression paths. In the left part, we found a deep residual learning strategy: all the outputs of the convolutional layers, after non-linearities processing, are added to the output of the last convolutional layer [42].  
In each stage, the compression part of the network computes features two times higher in number than the one of the previous layers. In the decompression part, the network extracts the features and expands the spatial support of the lower resolution feature maps. The last convolutional layer implements two outputs of the same size of the input image volume. A voxel-wise soft-max operation is applied, converting the two outputs as probabilistic segmentation of background and BM regions.  
Results obtained in several segmentation studies show how necessary it is to use large-scale datasets for effective application of deep learning methods [11; 43]. Whereas in our context, as in many other biomedical domains, manually segmented reference volumes are not easy to obtain, our strategy includes data augmentation and transfer learning tasks to compensate for the limited dataset available and reaching the double goal of increasing robustness and generalisation capability of the network respectively, as described in Section 3.
## 3 Results
### Osteoconductivity Tests: SEM/EDX and Fluorescence Microscope  
Mineralization pattern of SaOS-2 cell-grown apatite on the three samples was provided by SEM/EDX, as shown in Figure 12a-c. SEM images, collected at 500X, showed significant differences among the samples. In particular CS/PEO did not show high-mineralized matrix formation. Few small spots were located randomly on the surface and they were related to calcium (Ca) (blue) and phosphorous (P) (green), indicating the beginning of mineralization as shown by EDX. The carbon (C) signal arises from the fiber substrate and not from the presence of an organic matrix. The Ti-alloy SEM image presented bigger mineralized areas associated to bone formation. This was confirmed by the EDX image where Ca-blue and P-green stained areas of mineral components were surrounded by organic matrix (C-red stained). As with Ti-alloy, the CS/PEO/BG substrate also presented some mineralized nodules having a larger size, compared to the ones in CS/PEO. The EDX image confirmed that these were related to mineral and organic components of mineralized tissue. In particular, red-stained areas associated to the matrix indicated how the mineralization process was progressing faster. An important aspect displayed by SEM images was also that no scratches, holes, or fiber dissolution were involved, indicating how the crosslinking increased stability in the solution.  
Figure 13 shows the results of fluorescence microscopy obtained after treatment with different staining in order to verify the presence of osteoclast and osteopontin, two osteoblast differentiation markers used to detect the bone formation process. CS/PEO/BG images display a homogeneous distribution of cells and related osteoclastin and osteopontin small spots located on the entire surface (Figure 13c) and associated with a mineralized matrix enhancement. Differently, on the fiber-coated sample without BG, a very limited bioactive response to the cells, homogeneously distributed on the substrate, was detected due to weak signals of osteopontin and osteocalcin (Figure 13b).  
Figure 12: SEM images of bone formation on the three substrates and EDX elemental maps labeled calcium (blue), carbon (red) and phosphorous (blue) to identify the presence of the mineral. SEM images were collected at 500X (Ti-alloy (**a**), CS/PEO (**b**), CS/PEO/BG (**c**)), while EDX maps and related SEM images were collected at 5000X.  
On Ti-alloy substrates, as illustrated in Figure 13a, some big stained spots of osteoclast and osteepontin are visible, even if not homogeneously distributed as on the CS/PEO/BG surface. Furthermore, different areas of nuclei seem distributed over most parts of the surface, indicating a cell adhesion promotion.
## 2 Background
### Machine-Type Communication (M2M)  
Mobile cellular networks have been deployed and optimized in recent years to accommodate human-to-human communication (H2H) traffic, which in most cases is characterized by a high demand on the downlink stream. However, with the application of IoT, H2H is being slowly overshadowed by M2M, which imposes high demands on the uplink traffic stream [11]. In machine-type communication, devices communicate between them or interacting with a remote server, with minimal human interaction. The use of smart electricity meters, intelligent vending machines, and intelligent traffic control devices are examples of machine-type communications. These devices are equipped with SIM cards that facilitate a unique identification in the network, using an IMSI (international mobile subscriber identity) and an IMEI (international mobile equipment identity). The upgrade of the current cellular infrastructure, to accommodate M2M, has been a topic of study for many researchers. Although 5G cellular support is viewed as favorable for IoT and M2M connections [5], research is highlighting the growth and challenges of M2M traffic on existing cellular networks [12].  
[PERSON] et al. [13] studied the parallelism between M2M and H2H traffic by analyzing a week of data collected from a mobile operator in the USA. The study used vital parameters such as data volume on the uplink and the downlink. It was shown that M2M generates heavy uplink traffic. The nature of the M2M data stream was also reviewed in [14], which proved the complexity of M2M traffic patterns. [PERSON] et al. [3] introduced a mechanism for congestion control in M2M, improving energy efficiency, evaluating the impact of M2M compared to H2H, and analyzing traffic priorities on different classes of devices. The research on M2M shows the preventive measures that mobile operators need to take to efficiently adjust to high, unbalanced, and possibly unstructured data traffic.
# Current Trends in Fully Automated On-Line Analytical Techniques for Beverage Analysis
## 2 On-Line Solid-Phase Extraction (On-Line SPE)
### SPE Sorbent Phases  
SPE is a selective method that offers innumerable sorbent material options from traditional materials as reverse phase sorbents (C18, C8), normal phase sorbents (silica, alumina), ion exchange sorbents, and polymers (styrene-divinylbenzene, SDVB) or mixtures of them, to new materials such as bonded silica, carbon-based and selective materials such as molecularly imprinted polymers (MIP) [19], immobilized receptors or antibodies (IMS) and restricted access materials (RAM) [20; 21; 22].  
Silica-based materials are the most commonly used and commercially available but they present a great disadvantage, which is the presence of residual silanol groups that interact with the water present in the sample covering the active sites available for the extraction [23]. Thus, as the particles employed in high-performance liquid chromatography (HPLC), the tendency is to minimize the number of residual silanol groups through end capping these groups with a short alkyl chain bonded silica to obtain a better extraction efficiency.  
In recent years polymer-based sorbents are also available for SPE, which have advantages such as not requiring acid / basic elution modifiers, nor presenting pH limitation (are stable from pH 1 to 14). Also, has a high sample capacity, great flexibility during method development, and the absence of silanols groups. Altogether polymer-based phases drives to the predominance of a retention mechanism in opposition to bonded silica that might allow different retention mechanisms in the same sorbent.  
The most commonly used polymeric sorbents are (poly) styrene-divinylbenzene copolymers (STY-DVB) [24].  
One of the current but rarely described approaches with great potential is the use of nanofibers as materials for solid phase extraction. Thanks to the small size of the fiber, with diameters below 1000 nm, the nanofibers have a large surface offering a great extraction capacity. Several approaches to nanofibers have already been described, the most common form being electrospinning, during which nanofibers are formed from a solution or melt of a polymer with the help of a strong electrostatic field [25]. There are only a few articles that focus on the application of nanofibers as SPE sorbents [26; 27; 28], and these studies carried out extraction employing an off-line mode using either disc tips or pipettes SPE devices, which is tedious and time-consuming. Therefore, the number of articles published in the last decade using nanofibers for on-line extraction directly coupled to a chromatographic system is still limited [26]. Another work reported the production of polyamides nanofibers using magnetic and electric fields, obtaining nanofibers that were utilized for SPE extraction on-line with HPLC [29].  
In addition to the large number of sorbents reported in the literature, a considerable amount of manufacturers and distributors offer a large number of SPE sorbent phases, formats, and sizes, making this technique easily affordable to analysts almost everywhere.
# Correction of Field-Measured Wind Speed Affected by Deterministic Interference Factors
## 3 Wind Speed Revise
### NLDI Site
#### 3.1.2 Numerical Simulation Verification
[23] (2017), the SST \(k\)-\(\omega\) model with the exponential mean velocity profile, turbulent kinetic energy, and its dissipation rate in numerical simulation are given by the following:  
\[u_{z}=\frac{u_{*}}{k}\ln(\frac{z+z_{0}*l_{s}}{z_{0}*l_{s}}) \tag{1}\]  
\[k=\frac{u_{*}^{2}}{\sqrt{C_{\mu}}}\sqrt{C_{1}\ln(\frac{z+z_{0}*l_{s}}{z_{0}*l_ {s}})+C_{2}} \tag{2}\]  
\[\omega=\frac{u_{*}}{k\sqrt{C_{\mu}}}\frac{1}{z+z_{0}*l_{s}} \tag{3}\]  
where \(z\) is the height from the ground, and \(U_{z}\) is the mean velocity at the height of \(z\). \(u_{*}\) is the friction velocity with the value of 0.6 m/s. \(C_{\mu}\) is the turbulence model constant given as 0.028. The two constants \(C_{1}\) and \(C_{2}\) are \(-0.36\) and \(5\), respectively. \(z_{0}\) is the aerodynamic ground roughness length related to terrain types, \(\kappa\) is the von Karman constant of 0.42, and the scale ratio \(l_{s}\) is 0.001. All the boundary conditions were defined through the user-defined functions provided by the fluent. Figure 5: Computational domains and grids. The computational settings include the pressure-based steady-state solver, the Semi-Implicit Method Pressure-Linked Equations (SIMPLEC) algorithm for the pressure-velocity coupling, and the second-order upwind format for the diffusion terms in the momentum and turbulence model equations. The convergence criteria of the scaled residuals for all the variables and the continuity equation are set to \(10^{-5}\). The relaxation factors of pressure, momentum and turbulent kinetic energy are 0.3, 0.7 and 0.8, respectively.
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 5 Constraints of IPCB Geometry Configuration
### Time Interval Constraint  
Due to the low-density atmosphere at IPCB flight altitudes and the limited capacity of fans, IPCB geometry configuration adjustment requires a long time. So, the time interval between adjacent adjustment actuations should be greater than the maximum trajectory adjustment time required by all the IPBs in the constellation. In this paper, Equations (14) and (15) are used to estimate the time required for an IPB trajectory adjustment.
# Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms
## 4 Simulations and Practical Considerations
Figure 4: Performance of the RLS-T, RLS-DCD-T, and RLS algorithms, for the identification of the global impulse response \(\mathbf{g}\). The tensor-based filters have \(q_{1}=4\), \(q_{2}=3\), and \(q_{3}=q_{4}=2\). The input signal is an AR(1) sequence with the pole 0.85, \(N=4\), and \(L=2048\). Both RLS versions have forgetting factors with the form \(\lambda=1-1/(2^{k}L)\). Figure 3: Performance of the RLS-T, RLS-DCD-T, and RLS algorithms, for the identification of the global impulse response \(\mathbf{g}\). The tensor-based filters have \(q_{1}=4\), \(q_{2}=3\), and \(q_{3}=q_{4}=2\). The input signal is a Gaussian noise, \(N=4\), and \(L=2048\). Both RLS versions have forgetting factors with the form \(\lambda=1-1/(2^{k}L)\). The performance of the RLS-T and the RLS-DCD-T is almost identical. The final scenario analyzes the robustness of the RLS-DCD-T adaptive algorithm in low SNR conditions. The employed MISO configuration is for a global impulse response with \(L=4096\), where the tensor based decomposition is performed for \(N=4\) channels with \(L_{1}=L_{2}=L_{3}=L_{4}=8\). For an interval of 5000 iterations, the SNR changes from the value of 15 dB to \(-15\) dB. The performances corresponding to the RLS-DCD-T, respectively the RLS-T, are illustrated in Figure 5. The RLS-DCD-T demonstrates the same robustness as the RLS-T when affected by low SNR conditions, with a similar number of iterations required to update its filter coefficients and to achieve again the previous misalignment for the global system estimate \(\widehat{\mathbf{g}}(n)\).
## 2 Methods
### Methodology
#### 2.2.3 Improved Vision Transformer Module
The position information of each patch is preserved by inserting the learnable positional embedding into the patch embedding to obtain \(\mathbf{Z}_{0}\in\mathbb{R}^{N\times D}\). After standardization, \(\mathbf{Q},\mathbf{K},\mathbf{V}\) are obtained and sent to Multi-Head Attention for attention operations. Transformers primarily use self-attention techniques to extract global contextual semantic information from images, yet this process is known to be computationally intensive. The time complexity during the calculation process is \(O(N^{2}D)\). In the context of optimizing the time complexity of calculating attention in transformer models, we attempt to sparsify the attention matrix. Inspired by [35], row-column attention (RCA) is introduced to replace the multi-head attention block, as shown in Figure 3. Meanwhile, in order to make use of the position relationship between different regions, positional encoding is added to the RCA. In this work, the improved module refers to the convolution process, which calculates within a fixed receptive field range. Therefore, the receptive field is first fixed as each column on the feature map \(((H/P)\times(P^{2}C))\). \(H/P\) patches of columns are projected onto a \(D\)-dimensional space. By adding the positional encoding \(E^{{}^{\prime}}_{pos}\in\mathbb{R}^{1\times H/P\times C}\), \(\Big{\{}\mathbf{Z}_{j}\in\mathbb{R}^{H/P\times D}\Big{|}j=1,2,\dots,W/P\Big{\}}\) is obtained. Finally, the attention results are calculated row by row and concatenated as the output features. Obviously, the features fully cover the contextual information of elements between rows in the vertical direction. Meanwhile, the receptive field is fixed on each row of the output feature map and perform similar operations to obtain features with global information interaction. The time complexity during the calculation process is \(O(ND(H+W)/P)\). In addition, the layer norm (LN) layer and multi-layer perceptron (MLP) layer are replaced with an asymmetric convolutional block (ACB) and a Leaky ReLU activation layer to reduce the computational burden brought by MLP and make up for the local information of RCA.
## 1 Introduction
However, significant internal interference still persists. In our opinion, the UAV swarm network is a specific distributed communication system, and large amounts of distributed coordination signals and control data for drones must be transmitted in the network. The UAV swarm network must be a reliable and stable system for special applications such as rescuing, searching, attacking, etc. Packet loss, especially the collision loss of coordination signals, may cause networks to become unstable, which is especially important for distributed networks because the stability and reliability of distributed networks are inherently difficult. Hence, the premise of designing a stable and reliable UAV swarm ad hoc network is the absence of conflict and collision. In this paper, a distributed slot allocation algorithm for UAV swarm ad hoc networks based on time coalition formation game is proposed. Instead of forming coalitions from UAVs within a contiguous block area as in prior studies, UAV nodes with no interference from each other form a coalition, which can be called a time coalition. UAVs belonging to one coalition can share their transmitting slots with each other, and thus, every UAV node in the coalition achieves the whole transmitting slots of the coalition members. They can transmit data packets simultaneously with no interference. By dividing the UAV nodes into different time coalitions, interference between UAV nodes can be avoided, and thus, the performance of network throughput and time delay is improved. The main contributions of our paper are summarized as follows:  
1. We formulate the slot allocation problem of the UAV swarm ad hoc network based on the TDMA protocol as a coalition formation game among UAV nodes with different transmission time slots. Within the framework of the proposed game, UAV nodes with no interference from each other form a coalition, share their time slots, and transmit data packets simultaneously. 2. A simple distributed coalition formation algorithm based on the join-and-leave rule is proposed, through which each UAV node autonomously determines the most beneficial coalition to join. Subsequently, the whole network quickly converges to a final stable partition. 3. Based on the distributed time coalition formation algorithm, we design a TDMA MAC protocol (called DCFG-TDMA) for the UAV swarm ad hoc network. UAV nodes with a DCFG-TDMA protocol can achieve time slots adaptively with dynamically changing network topology. The rest of the paper is arranged as follows. Section 2 provides a comprehensive review of the existing literature pertinent to the topic of this paper. In Section 3, we present the system model and problem formulation. In Section 4, we describe the proposed coalition formation game model and the distributed coalition formation algorithm. In Section 5, the TDMA protocol based on distributed formation game for UAV swarm ad hoc networks is proposed. The simulation configurations and numerical results are discussed in Section 6. Finally, our conclusions are drawn in Section 7. For convenience, the abbreviations used in this paper are listed in Table 1.
## 2 Materials and Methods
### Materials Characterisation  
The evaluation of the compatibility of the three abovementioned materials with the four ATFs was based on the ASTM-D7216 standard, which determines the processes for testing changes in volume, tensile strength, and hardness. All of these properties were measured before and after the ageing of the materials in the ATFs.  
Changes in volume were determined using a Radway AS 310.R2 Analytical Balance (readability: 0.1 mg). A recipient full of water was weighed in the balance with the specimens hanging from a hook, and thus not adding their weight to that of the water but displacing a certain amount of water corresponding to the volume of the specimen. The volume before and after the ageing period was calculated by the difference in weight. The hardness of three specimens of each material was measured with a SautTER HBD 100 durometer before the ageing process to be taken as a reference. Later, the hardness of each aged specimen was measured at four different points. The mean value of these four measurements was considered to be the result to be compared with the reference value. The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Properties** & **Oil A** & **Oil B** & **Oil C** & **Oil D** \\ \hline Density, 15 \({}^{\circ}\)C [g/cm\({}^{3}\)] & 0.869 & 0.847 & 0.854 & 0.854 \\ Viscosity, 100 \({}^{\circ}\)C [cSt] & 7.8 & 6.0 & 7.2 & 6.9 \\ Viscosity, 40 \({}^{\circ}\)C [cSt] & 43 & 30 & 35 & 34 \\ Viscosity Index & 161 & 151 & 181 & 165 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the ATFs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **PEEK (\({}^{\text{th}}\))** & **PTFE (\({}^{\text{20}}\))** & **PA66 (\({}^{\text{3}}\))** \\ \hline Density [g/cm\({}^{3}\)] & 1.31 & 2.15 & 1.15 \\ Young’s modulus [MPa] & 4200 & 22 & 3500 \\ Tensile strength [MPa] & 116 & - & 84 \\ Elongation at break [\%] & 15 & 220 & 70 \\ Hardness & 253 (\({}^{\text{4}}\)) & 55 (\({}^{\text{5}}\)) & 175 (\({}^{\text{4}}\)) \\ Max. op. temperature [\({}^{\circ}\)C] & 260 & 260 & 100 \\ \hline \hline \end{tabular} \({}^{\text{(t)}}\) Polyether ether ketone. (\({}^{\text{2}}\)) Polytetrafluoroethylene. (\({}^{\text{3}}\)) Polyamide 66. (\({}^{\text{4}}\)) Ball indentation hardness ISO 2039-1 [N/mm\({}^{2}\)]. (\({}^{\text{5}}\)) Shore D.  
\end{table}
Table 3: Polymer properties.  
tensile strength was measured with an MTS(r) Sinergy traction machine with a charge cell of 5 kN (MTS-166). The traction speed was set depending on the material and immersion time (Table 4), according to the recommendations of the ASTM D6838 to keep the testing time below 5 min.