esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design Rationale and System Architecture
### Hierarchical SDN-Enabled Point Cloud Service Network  
As a potential technology to improve network performance and reduce management cost, the rationale of SDN is utilized to design a conceptual network architecture for multi-domain PC applications. Since this paper focuses on the Blockchain-based authentication network architecture, the key components and the workflow of the SDN are briefly described. The detailed SDN designs will be presented in our future work. The left part of Figure 1 shows the hierarchy of a point cloud service network according to point cloud application stage: acquisition, aggregation, and analytic. The point cloud data layer acts as an infrastructure layer including multiple domain networks, which are responsible for raw data collection, processing, and delivery. In each domain, point cloud centers interconnect with each others via forwarding switches. The 3D sensors generate cloud points and send them back to the point cloud centers, which are actually local servers, to process and store the data. Given the decisions made by the SDN controllers, the forwarding switches can forward the data traffic flows efficiently to satisfy the QoS requirements.  
The network intelligence and control logic of each domain network are performed by the SDN controller, which can be deployed on fog or cloud computing platforms. By using a pre-defined southbound API, each SDN controller can either update the configuration of forwarding switches to change the network operations or synchronize the status to have the global view of a domain network. Northbound interfaces allow an SDN controller to interact with the upper-level application layer, such as providing domain network status to the system monitoring and accepting the network operation policies' update. Therefore, these SDN controllers construct a control layer, which acts as a broker between point cloud applications and fragmented domain networks, and they can provide network connectivity and data services among heterogeneous domain networks.  
Figure 1: System architecture of SAUSA.  
The application layer can be seen as a \"system brain\" to manage the physical resources of the point cloud data layer with the help of SDN controllers. The application management maintains registered users and their service requirements, while the system monitoring can provide the global status of the point cloud ecosystem. Given the inputs from application management and system monitoring, the dynamic resource coordination adopts machine learning (ML) algorithms, which achieve fast resource (e.g., computation, network, and storage) deployment and efficient service re-adjustments with QoS guarantees.
## 2 Basic Operation of Solid Oxide Fuel Cell  
The high temperature fuel cell operates from 800 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C. The basic operation involves the REDOX systematic. In SOFCs, the conducting species are Oxygen ions. Oxygen gets reduced at the cathode by taking electrons and gets ionized. In a high temperature range, Yttria Stabilized Zirconia (YSZ) electrolyte is mostly used for conduction of oxygen ion. Oxygen is supplied to the porous cathode where it gets reduced into oxygen ions which are carried to the anode through solid electrolyte. At the anode, oxygen ions combine with hydrogen and produces water along with carbon dioxide (CO\({}_{2}\)) as shown in Figure 1. Very hard and rigid materials are required for SOFC components due to the high operating temperature. In view of long term (40,000-50,000 h) efficient cell operation, chemical and thermal stability are also of utmost concentration along with first rate electrical, electrochemical properties, high chemical and thermal consonance in the fuel cell operating environments [8; 12; 23; 24; 25; 30; 31; 32]. Two types of concentration are taken into account during the fabrication: economic and technical. Economic concentration includes lowering the fabrication cost by using low cost materials. The technical concentrations include that the design should be fabrication friendly and cost friendly. In the technical setup, economization friendly means that: the materials should be price conscious with its maximum outcomes, should easily be manufactured into the required mechanism, and be minimized structures with passivity and less investment [8].  
A single basic fuel cell creates an open circuit voltage slightly above 1 V and decreases to 0.5-0.7 V approximately when load is connected to it [12; 31; 32; 33]. During operation the current density may alter from 200 to 1000 mA/cm\({}^{2}\) (DC). To achieve enhanced current and voltage output, many cells are connected in series or parallel to form an assembly (stack). The power plant of SOFC along with stack comprises of different sub-units such as a fuel processing unit for fuel purification, oxygen supply unit, DC to AC conversion, a unit to attain the required temperature and control/safety units. The components (anode, cathode and electrolyte) used in SOFC are all solids [8].
# Enhancing the 3 GPP V2X Architecture with Information-Centric Networking
## 6 Evaluation Methodology  
The objective of the conducted evaluation study was to assess the performance of the proposal and measure the benefits that can be achieved against a legacy non-NDN-based solution, considered as a benchmark. To this purpose, a simulation study was conducted which enables the evaluation of the proposed framework under different workload settings.
# Vibration Suppression of Collaborative Robot Servo with Dual Encoders
## 1 Introduction
The integration of dual encoders introduces significant changes to the original system, necessitating structural modifications across both mechanical and control domains. On the mechanical side, the framework must be adapted to accommodate the installation of an encoder on the reducer side. On the control side, this adaptation impacts the system's control architecture and dynamic model, particularly altering the original system's response characteristics. Notably, the rotational speed response measured from the reducer-side encoder is significantly slower than that from the motor-side encoder, primarily due to the inherent elasticity and mechanical play in the reducer. This discrepancy necessitates careful adjustments to the control parameters to ensure system stability and performance. Achieving satisfactory performance after the implementation of dual encoders poses a challenge, as it requires meticulous tuning of the control system to harmonize the dynamics between the motor-side and reducer-side measurements. This tuning process is critical to fully realizing the benefits of the dual-encoder configuration in enhancing system precision and vibration suppression. The dual encoder solution proposed in this paper, when economically feasible, can be applied to all joints, enabling each joint to achieve effective vibration suppression. Incases where cost and space constraints apply, adding dual encoders to the second axis with the greatest inertia variation can still yield significant overall vibration suppression effects. Using the second axis of the collaborative robot as an example, the application of dual encoders has demonstrated promising results in practical scenarios and provided a practical and effective solution for controlling the end vibration of collaborative robots. This paper analyzes the causes of vibrations in collaborative robots. It is structured into eight sections: the composition and hardware design of collaborative robots, the software implementation of the collaborative robot servo system, common vibration suppression methods, the acquisition of Bidirectional Synchronous Serial (BiSS) data, control principle of dual encoder vibration suppression, experimental effect, and the conclusion.
## 3 System Architecture and Design of Intelligent Reflecting Surface (IRS)
### Composite Materials of Individual Element  
An IRS comprises of a large 2D artificial array of passive reconfigurable dielectric patches printed on a dielectric substrate possessing the ability to manipulate the direction of incident signals. An IRS is composed of a large number of low-cost components such as varactors and diodes [82]. A unit cell patch is modeled independently as a passive scattering element patch. The intercell distance is in the order of a wavelength, usually half a wavelength or five to ten times smaller than the wavelength [34].
# Efficient FPGA Implementation of Automatic Nuclei Detection in Histopathology Images
## 2 Materials and Methods
### 2.2. 2-D gLoG Filter  
Because the cell nuclei in digital histopathological images typically have circular or elliptical shapes, the 2-D gLoG filters are used for nuclei detection [1]. The nuclei are detected by convolving the image \(H\) with a 2-D gLoG filter. The gLoG filters are generated from a bank of gLoG kernels \(\
abla^{2}G(x,y)\) as defined below [1]:  
\[\
abla^{2}G(x,y)=\frac{\partial^{2}G(x,y)}{\partial x^{2}}+\frac{\partial^{2} G(x,y)}{\partial y^{2}},\]  
where \(G(x,y)\) is a 2-D Gaussian function defined as follows.  
\[G(x,y)=\lambda\cdot e^{-(ax^{2}+2 kny+cy^{2})}\]  
Note that \(a\), \(b\) and \(c\) are functions of scale \((\sigma_{x},\sigma_{y})\) and orientation \(\theta\) of the Gaussian kernels [1,4]. By changing the scales and the orientation, a set of gLoG kernels can be obtained. In this paper, we generate gLoG kernels \((\sigma_{x},\sigma_{y})\) with \(\sigma_{x}>\sigma_{y}\) ranging from 6 to 12 insteps of 0.5 and nine orientations \(\theta,\{\theta=n\pi/9,n=0,1,...,8\}\). The nine gLoG filters corresponding to nine orientations are generated by adding up gLoG kernels of the same orientation, but with different scales. Special kernels, whose \(\sigma_{x}=\sigma_{y}\) are rotational symmetric and their structures are independent of the orientation, are summed separately to form a rotationally symmetric gLoG filter. In this paper, 10 gLoG filters are used (see  
Figure 4: Convolution module. Multiplication of data and coefficients are shown for \(M\)th column.  
Figure 5), with nine filters of different orientations and one rotationally symmetric (RS) filter. A total of 10 response maps, with one response map from each gLoG filter, are generated.  
For hardware implementation, the architecture of the gLoG filter module is similar to that of the Gaussian filter described in the previous section, except the filter size and coefficients. In this work, the size of the gLoG filter is set to 25 \(\times\) 25 in order to match the size of typical nuclei in the input data. As the gLoG filter coefficients are independent of the image data, they are calculated offline, converted into 16-bit precision (with 14-bit fractional value), and stored in ROM IP cores on the FPGA. The output response map (denoted by \(I\)) from each gLoG filter is stored with 8-bit precision in the block RAM on the FPGA.
## 5 Results and Discussion
### Selection of Available Satellites
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Satellite Number** & **(S/J)** & \(r_{n}\) & **Satellite Number** & **(S/J)** & \(r_{n}\) \\ \hline PRN 04 & S & \textless{}0.4 & PRN 01 & J & \textless{}0.4 \\ PRN 09 & S & \textless{}0.4 & PRN 03 & J & \textless{}0.4 \\ PRN 11 & S & \textless{}0.4 & PRN 11 & J & \textless{}0.4 \\ PRN 13 & S & \textless{}0.4 & PRN 16 & J & \textless{}0.4 \\ PRN 27 & S & \textless{}0.4 & PRN 22 & J & \textless{}0.4 \\ PRN 31 & S & \textless{}0.4 & PRN 23 & J & \textless{}0.4 \\ PRN 22 & S & \textless{}0.4 & PRN 27 & J & \textless{}0.4 \\ PRN 03 & S & \textless{}0.4 & PRN 18 & J & \textless{}0.4 \\ PRN 32 & S & 0.4–0.5 & PRN 21 & J & \textless{}0.4 \\ PRN 19 & S & 0.4–0.5 & PRN 24 & J & \textless{}0.4 \\ PRN 24 & S & 0.5–0.6 & PRN 31 & J & \textless{}0.4 \\ PRN 01 & S & 0.5–0.6 & PRN 15 & J & \textless{}0.4 \\ PRN 16 & S & 0.5–0.6 & PRN 13 & J & 0.5–0.6 \\ PRN 14 & S & 0.6–0.7/0.7–0.8 & PRN 32 & J & 0.5–0.6 \\ PRN 30 & S & 0.6–0.7/0.7–0.8 & PRN 09 & J & 0.6–0.7 \\ PRN 23 & S & 0.6–0.7/0.7–0.8 & PRN 04 & J & 0.6–0.7 \\ PRN 07 & S & 0.6–0.7/0.7–0.8 & PRN 14 & J & 0.7–0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Satellite statistics for different threshold ranges of the cross-correlation coefficient (\(r_{n}\) ). To further verify the dispersion degree of the phases from the selected satellites in different \(r_{n}\) ranges, the satellites with repeated ascending and descending segments were eliminated. Then, 12 satellites were left to be analyzed, as shown in Figure 9. It can be seen that when the range of \(r_{n}\) was set lower, some of the selected satellites, such as PRN19, PRN24, PRN01, and PRN16, had poor quality and showed more outliers. As the range of \(r_{n}\) increased, the selected satellites acquired a better observation quality. When the value of \(r_{n}\) was greater than 0.700, the phases of the selected satellites basically showed no abnormal values. To this end, the satellite phases selected by CCSS were used to retrieve SM; schemes are formulated in the next section.
# Network Performance Optimization for Low-Voltage Power Line Communications
## 1 Introduction
We propose using the improved adaptive \(p\)-persistent CSMA-based dynamic game theory to optimize the saturation performance of the improved LVPLC artificial cobweb and address the problem of how to guarantee the saturation performance under unknown numbers of active stations. Each station independently estimates the number of competitive stations using a hidden Markov model (HMM), adopts the optimum probability for sending the data packets, and achieves performance optimization. This paper is organized as follows: Section 2 presents the low-voltage distribution network topology; Section 3 describes the LVPLC network scheme; Section 4 describes the incompletely cooperative dynamic game model that optimizes the saturation throughput and the access delay performance; Section 5 presents some numerical simulation results not previously reported; and Section 6 draws the conclusions.
## 2 Numerical Simulator
### Hologram Analysis by Fractional Fourier Transform  
The next step is the reconstruction of the objects from the holograms. Different tools can be used: wavelet transform, Fresnel transform. We use here the 2 Dimensional-FRactional-order Fourier Transform (2D-FRFT) [28; 29; 30; 31]. The definition of the 2D-FRFT that we use is:  
\[\mathscr{F}_{\alpha_{x},\alpha_{y}}[I(x,y)](x_{\alpha},y_{\alpha})=\int_{ \mathbb{R}^{2}}N_{\alpha_{x}}(x,x_{\alpha})\,N_{\alpha_{y}}(y,y_{\alpha})I(x,y )\,dx\,dy \tag{6}\]with \(\alpha_{p}=\frac{a_{p}\pi}{2}\) (\(p=x,y\)) and \(0\leq|\alpha_{p}|\leq\pi/2\). The kernel of the fractional operator is defined by:  
\[N_{\alpha_{p}}(x,x_{\alpha})=C(\alpha_{p})\exp\left(i\pi\frac{x^{2}+x_{\alpha}^ {2}}{s_{p}^{2}\tan\alpha_{p}}\right)\exp\left(-\frac{i2\pi x_{\alpha}x}{s_{p}^ {2}\sin\alpha_{p}}\right) \tag{7}\]  
with \(C(\alpha_{p})\) defined by:  
\[C(\alpha_{p})=\frac{\exp(-i(\frac{\pi}{4}\text{sign}(\sin\alpha_{p})-\frac{ \alpha_{p}}{2}))}{|s_{p}^{2}\sin\alpha_{p}|^{1/2}} \tag{8}\]  
Coefficient \(C(\alpha_{p})\) enables conservation of energy. Parameters \(s_{p}\) (\(p=x\) or \(y\)) are defined from the experimental set-up according to [32]:  
\[s_{p}^{2}=N_{p}\cdot\delta_{p}^{2} \tag{9}\]  
where \(N_{p}\) are the numbers of pixels of the image along \(x\) or \(y\) axes, and the constants \(\delta_{p}\) are the pixel sizes along both axes (\(p=x\) or \(y\)). In practice \(\delta_{x}=\delta_{y}\). To reconstruct the image of the particle, we evaluate the 2D-FRFT of the diffraction pattern. The diffraction pattern contains a term with a linear chirp \(\varphi\). This linear chirp is easily recovered through 2D-FRFT operation. The kernel of the 2D-FRFT operator is indeed composed of a linearly-chirped function, leading to applications in spatial optics when considering diffraction patterns (as here) or in temporal optics when considering chirped pulses [33]. The best reconstruction plane is reached when [19]:  
\[\varphi_{a}\pm\varphi=0 \tag{10}\]  
where \(\varphi_{a}\) is the phase contained in 2D-FRFT and \(\varphi\) is the phase contained in the diffraction pattern. From this condition, the optimal fractional orders of reconstruction \(\alpha_{x}^{opt}\) and \(\alpha_{y}^{opt}\) can be obtained.  
The existence of analytical relations that link optimal order of reconstruction to experimental parameters of a set-up are particularly interesting for different reasons: first, it allows 3D-localization of the particles under study, whatever the set-up is. In addition, it allows localization of defaults, present in the optical system, that create their own diffraction pattern on the sensor (which acts as noise). For example, we could localize the position of a scratch on a plexiglas cylinder. It was deduced from the analysis of the experimental hologram with these analytical relations. It was then confirmed experimentally watching the inner of the pipe once the set-up was uninstalled [23]. As a perspective, we think that our simulator combined to such analytical relations will allow to understand the sources of speckle noise that can alter the quality of holograms.
## 5 Discussion  
This paper focuses on the decay characteristics of the mechanical properties of asphalt pavements of different depths in hot and humid areas of southern China caused by sudden temperature drops due to rainfall.  
Sudden temperature drops will lead to sharp contractions in the asphalt mixture, resulting in the emergence of microcracks and the degradation of its properties. Past studies tend to favor winter temperature shrinkage and cracking in cold regions, but there is a similar problem in that temperature induces asphalt pavement performance degradation in the coastal areas of southern China. In this region, summer is almost hot and rainy, and asphalt pavements experience rapid cooling and shrinkage in a short period of time. Therefore, there is a temperature gradient from top to bottom of the asphalt pavement in this region, and internal microcracks also gradually expand downward from the upper part where the temperature change is the largest, resulting in a difference in performance degradation at different depths.  
In order to study the decay law of asphalt mixtures under the above conditions, a large-scale wet temperature cycle test device was developed independently to simulate the indoor environment of the southern coastal region. The mechanical properties of the specimens were determined by cutting the layers, and the mechanical properties at different depths from top to bottom were analyzed. According to the results, the decay in the mechanical properties of the asphalt mixture at each cut layer firstly rapidly increased and then gradually decreased, which is consistent with the results in some studies on the damage law of asphalt mixture properties under the action of wet temperature coupling [15,26].  
Finally, the prediction equations of mechanical properties at different depths under such conditions were established. The established prediction equation is intended to provide more basis for the design of asphalt pavement in this area.  
However, the research in this paper is limited by conditions, and the extension of its internal microcracks can be investigated subsequently by using instruments such as ICT.
## 2 Materials and Methods
### Cytokine and NO Production  
RAW264.7 macrophage cells were seeded into 96-well flat-bottomed microplates overnight at a density of 1 \(\times\) 10\({}^{5}\) cells/well. The RAW264.7 macrophage cells were treated with KRG or KRG-P (final concentration: 7.8, 15.6, 31.2, 62.5, 125, and 250 ug/mL) for 24 h, the control group was treated with DMEM medium, and the positive group was treated with LPS (final concentration: 500 ng/mL). The supernatants were then collected and used to measure the production of IL-6, TNF-\(\alpha\), and NO. IL-6 and TNF-\(\alpha\) were measured according to the manufacturer's instructions using sandwich ELISA kits from eBioscience (San Diego, CA, USA). The production of nitrite oxide was measured with the Griess reagent by mixing the same volume of supernatant for 10 min and measuring the absorbance at 550 nm. The production of NO was calculated using a standard curve established with sodium nitrite.
# Strategies of Success for Social Networks: Mermaids and Temporal Evolution
## 5 Experimental Results
### Datasets
Indeed, since cumulative degree distributions \(P_{cum}\) have tails that decay as a power law with exponents equal to 2.5 and 2.7 (see Figure 5) and maximum degree \(k_{max}\) is higher compared to the average \(\langle k\rangle\), they could be classified as scale-free networks. The plots in Figure 6 and the assortativity values \(\rho\) in Table 1 suggest that both networks are disassortative (Pearson correlation equal to \(-0.59\) and \(-0.30\) respectively). This means that, on average, users with many connections tend to connect to users with few friends (see classification in [31]). Many other studies found the same correlation pattern in online social networks, like for instance in the Youtube ([32]), pussokram ([2]) or Cyworld ([33]) networks. Being elite in online social networks simply means to have many connections and is just a matter of clicks (cf. [34]). However, this assortative pattern is the opposite compared to the real world where establishing and maintaining friendships require time and effort and where many other factors might influence the likelihood of being a friend of a person, such as cultural, economic, and geographical circumstances. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Communities** & **Virtual Tourist** & **Randomized CM** & **Randomized VT** \\ \hline \(|V|\) & 12,479 & 57,639 & 12,479 & 57,639 \\ \(|E|\) & 60,209 & 211,415 & 60,209 & 211,415 \\ \(\langle k\rangle\) & 9.64 & 7.34 & 9.64 & 7.34 \\ \(k_{max}\) & 656 & 963 & 24 & 21 \\ \(L\) & 4.18 & 4.95 & 4.42 & 5.72 \\ \(C\) & 0.1067 & 0.04425 & 0.0006 & 0.0001 \\ \(E_{glob}\) & 0.238683 & 0.201822 & 0.23296 & 0.17817 \\ \(E_{loc}\) & 0.131466 & 0.056248 & 0.00074 & 0.00013 \\ \(Cost\) (density) & 0.00077 & 0.00013 & 0.00077 & 0.00013 \\ \(Cost/E_{glob}\) & 0.00324 & 0.00063 & 0.00332 & 0.00073 \\ \(\gamma\) & 2.5 & 2.7 & \(\sim\)0 & \(\sim\)0 \\ \#CC & 161 & 2078 & 3 & 43 \\ \(\rho\) & \(-0.027\) & \(-0.027\) & \(-0.002\) & 0.00082 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical features of the Communities and Virtualtourist online social networks, together with randomized versions of the same networks: number of nodes \(|V|\), number of edges \(|E|\), average node degree \(\langle k\rangle\), maximum degree \(k_{max}\), average shortest path \(L\) and average clustering coefficient \(C\) (for the largest connected component), global efficiency \(E_{glob}\), local efficiency \(E_{loc}\), cost, cost over efficiency, exponent of the cumulative degree distribution \(\gamma\), number of connected clusters \(\#CC\), and the correlation pattern \(\rho\).