esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### 5G Network Architecture  
This study implements kernel-based virtual machines (KVMs), digital equipment versions. KVM solutions provide similar functions to their physical counterparts, a feature highly valuable for equipment testing. In this study, FortiGate 6.45 and Docker-Ubuntu are dynamic NFV components capable of emulating actual networking hardware activities. FortiGate KVM requires the acquisition of its physical hardware. Therefore, its use is limited to authorized users. In contrast, Ubuntu-Docker is an open-source container service.  
A 5G network is composed of two sections: front-haul and back-haul. The front-haul section refers to the network's wireless access (5G RAN) and establishes a radio connection between the user equipment (UbuntuDockerGuest-1) and the 5G Access Point. In contrast, back-haul access relies on a fiber to the X (FTTX) connection and connects the different network equipment components.  
The network representation of the Industry 4.0 Implementation Center has been conducted with GNS3, as shown in Figure 3. Its architecture consists of public and private networks. The blue highlighted block (Figure 3, left) is the \"known\" part of the network (private). This part of the network uses NFV components to send data. The connection between Ericsson's 5G access point (Small Cell) and L2 Switch (OSI Layer 2) relies on the FTTX connection. A firewall is after the L2 Switch to guarantee network security.  
The red block on the right side of Figure 3 is the highlighted public network, which interconnects to private networks via the L3 switch (OSI Layer 3). The primary function of this part is to guarantee access to the internet.  
Figure 3 represents the network layout of the private network. Therefore, the image represents a dynamic network simulator system rather than graphic icons. The components included in Figure 3 are KVM solutions (UbuntuDocker-1, UbuntuDocker-2, and FortiGate 6.45-1) and open-source NFV components (ethernet switch). Linux command line interface (CLI) commands are required to interact with the system to control each component.  
Figure 3: 5G network architecture.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology** & **Virtual Equipment** & **Physical Equipment** & **Reference Name** \\ \hline NFV & Docker-Ubuntu & 5G UE & Ubuntu-DockerGuest-1 \\ NFV, RAN & Ethernet Switch & Ericsson 5G Small Cell & 5G AP \\ NFV & Ethernet Switch & Cisco 2959X & L2\_Switch \\ NFV & FortiGate 6.45 & FortiGate 81E & FortiGate6.45-1 \\ NFV & Ethernet Switch & Cisco 3850 & L3\_Switch \\ NFV & Docker-Ubuntu & Server & Ubuntu-DockerGuest-2 \\ \hline \hline \end{tabular}
\end{table}
Table 9: List of network components.
## 4 Discussion  
The results of this method are very promising in terms of time revenue savings. Significant time revenue savings were observed during vibration testing. Test specimens were vibrated 42 times faster than those performed with three-point bending testing. Vibration samples were vibrated at 32 Hz, whereas three-point bending specimens were tested at 0.75 Hz.  
To better understand this effect, consider the hypothetical case of testing a material that yields at least 100,000 cycles per data point with equipment having a range of 1-5 Hz.  
As seen in Table 4, the time for obtaining a data point at a frequency of 5 Hz will require at least 5.55 h.  
In contrast, testing at 32 Hz, a frequency not attainable in many conventional legacy systems, would allow a time revenue saving of 84.3%. In cases where various data points are required at each stress ratio, the time needed for geometry design and preparation is justified. However, the proposed methodology is not recommended for just one or two tests. In addition, the temperature increase in the specimens was measured to be less than 2 \({}^{\circ}\)C. This increase was shown to be negligible as compared to the results observed in a previous study that investigated the effects of temperature and frequency from 3 to 15 Hz using a three-point bending configuration, which produced errors of up to 100% for fatigue life [12]. This lack of a temperature increase was most likely due to the convective heat transfer present in the periodic motion of the beam moving through air during vibrations. The results from vibration testing were shown to be reliable, producing a similar S-N curve as given by three-point bending results. When considering the effects of contact stress, all vibration results fell within the 95% confidence intervals of the three-point bending data for all ratios tested.
## 1 Introduction
Geographically weighted regression (GWR) models can be used to study the correlation between multiple variables characterized using spatial distribution and the dependent variable by creating local regression equations in each grid, and they are increasingly being used for the spatial analysis of social and environmental data. They allow for \"the study of spatial heterogeneity in processes and relationships through a series of local regression models rather than a single global regression model\" [29; 30; 31; 32]. In view of this, the GWR model was introduced in this study to explore the spatial heterogeneity of the effects of various drivers on the ESV gradient in the study area, enabling the results to shed more scientific light on the possible reasons behind the changes in the ESV gradient in the study area. The Xijiang River, a major component of the Pearl River system, is the third-largest river in China, the longest river in South China, and the second-largest river in China in terms of navigation volume, flowing successively through five provinces and regions, namely Yunnan, Guizhou, Hunan, Guangxi, and Guangdong. Scholars are concerned about the impact of climate and land use changes on the runoff hydrology of the Xijiang River Basin [33]. At the same time, some scholars also focus on the spatial and temporal changes of the river [34]. In this paper, the land-water interface zone for the upper, middle,and lower reaches of the basin in the Guangdong section of the Xijiang River Basin is divided into the along-river gradient and the upper and lower-reaches gradient, and the gradient analysis of ESV was carried out by using the ecosystem service value valuation methodology based on the area of a unit to explore the gradient change law of ESV in the Guangdong section of the Xijiang River Basin.
# DELER: Distributed Clustering with Local Direction Centrality and Density Measure
###### Abstract  
Clustering by Measuring Local Direction Centrality (CDC) is a recently proposed innovative clustering method. It identifies clusters by assessing the direction centrality of data points, i.e., the distribution of their \(k\)-nearest neighbors. Although CDC has shown promising results, it still faces challenges in terms of both effectiveness and efficiency. In this paper, we propose a novel algorithm, Distributed Clustering with Local Direction Centrality and Density Measure (DEALER). DEALER addresses the problem of weak connectivity by using a well-designed hybrid metric of direction centrality and density. In contrast to traditional density-based methods, this metric does not require a user-specified neighborhood radius, thus alleviating the parameter-setting burden on the user. Further, we propose a distributed clustering technique empowered by \(z\)-value filtering, which significantly reduces the cost of \(k\)-nearest neighbor computations in the direction centrality metric, lowering the time complexity from \(O(n^{2})\) to \(O(n\log n)\). Extensive experiments on both real and synthetic datasets validate the effectiveness and efficiency of our proposed DEALER algorithm.  
distributed clustering; data mining; distributed; \(z\)-value filling curve
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 2 Related Work
### Region-Based Location-Service Management Protocol (RLSMP)  
The RLSMP [12] is a rendezvous-based location service that divides the network into regions (called segments) that are further divided into cells. Each cell has a cluster leader (CL) that manages the location of all nodes in the cell, and sends its location information to the cell responsible for the cluster, called the location-service cell (LSC). A vehicle querying for destination co-ordinates sends its location query to its LSC. If the requested information is not found in this LSC, the query is forwarded to neighboring LSCs, following a spiral shape until the co-ordinates of the destination are found. The RLSMP generates a significant signaling load, as the same query may visit several LSCs before reaching the one holding the requested information. This approach provides reliable location information. However, a lot of traffic must be generated to keep the cluster leaders up to date.
## 2 Materials and Methods
### Mathematical Models
#### 2.2.1 One-Dimensional Riccati Model  
In this case, the sound-absorbing structures are modelled as continuous segments of tapered waveguides with compliant walls, following the original paper [3]--see Figure 2. Within this model, the ends of the ribs in the case of the SBH and the MPP in the case of the MPP-SBH form a locally compliant metamaterial wall of a tapered waveguide with cross-section \(2a(x)\times B\), and the specific acoustic admittance of this wall is \(\hat{Y}_{\text{w}}(x)\). The function \(a(x)\) defines the SAS's profile, and it has the minimum value \(A_{\text{min}}>0\) at the SAS's apex (\(x=0\)), namely \(a(0)~{}=~{}A_{\text{min}}\). In all the studied cases, the maximum frequency of interest is below the cut-on frequency  
\[f_{\text{cut-on}}~{}=~{}\frac{c_{0}}{4A},\]  
Figure 1: Geometry of an SBH (**left**) and MPP-SBH (**right**) attached to a rectangular waveguide (with the front wall removed). Figure 2: Simplified model of SBH (**left**) and MPP-SBH (**right**). where \(c_{0}\) is the sound speed, so that all the acoustic quantities are assumed to only depend on the longitudinal coordinate \(x\). Even if the acoustic field in the sound-absorbing structures can be calculated employing the modified Webster equation [3], in this case, the Riccati equation is employed (see e.g., [15; 26]); compared to the Webster equation, it is represented by a first-order differential equation. The Riccati equation for the specific acoustic admittance \(\tilde{Y}(x)\) in the geometries depicted in Figure 2 reads  
\[\frac{\mathrm{d}\tilde{Y}}{\mathrm{d}x}\ =\ \mathrm{i}\omega\tilde{p}_{0 \mathrm{g}}\tilde{Y}^{2}-\frac{\mathrm{i}\omega}{\tilde{\rho}_{0\mathrm{g}} \tilde{\epsilon}_{0\mathrm{g}}^{2}}-\frac{1}{a}\frac{\mathrm{d}a}{\mathrm{d}x }\tilde{Y}-\frac{\tilde{Y}_{\mathrm{w}}}{a},\qquad x\in\langle 0,L\rangle, \tag{1}\]  
see [26], where \(\tilde{\rho}_{0\mathrm{g}}\ =\ \tilde{\rho}_{0\mathrm{g}}(x)\), and \(\tilde{c}_{0\mathrm{g}}\ =\ \tilde{c}_{0\mathrm{g}}(x)\) are the effective fluid density and sound speed in the main duct of the SASs. They model the thermoviscous losses in the acoustic boundary layer formed along the tapered wall and they are calculated using [PERSON]'s model [29] for a slit with a width of \(2a\). In the case of the SBH, where (as it is explained below) the structure of the ribs and slits is rather fine, the wall admittance is introduced as \(\tilde{Y}_{\mathrm{w}}\ =\ \xi\tilde{Y}_{\mathrm{s}}\), where \(\xi\ =\ h_{\mathrm{s}}/H\) is the wall porosity, and  
\[\tilde{Y}_{\mathrm{s}}(x)\ =\ \frac{\mathrm{i}}{\tilde{\rho}_{0\mathrm{s}} \tilde{c}_{0\mathrm{s}}}\tan\biggl{\{}\frac{\omega}{\tilde{c}_{0\mathrm{s}}} [A-a(x)]\biggr{\}} \tag{2}\]  
is the input specific acoustic admittance of the cavities between the ribs, whose depth is \(A-a(x)\). Here, \(\tilde{\rho}_{0\mathrm{s}}\) and \(\tilde{c}_{0\mathrm{s}}\) are the density and sound speed of equivalent fluid in the cavities between the ribs, respectively, which account for the thermoviscous losses in the boundary layers; [PERSON]'s model [29] for a slit with width \(h_{\mathrm{s}}\) is used.
## 5 Application of CFD-Based Digital Twins  
For developing Digital Twins to analyse the two-phase flow in pipes during filling and emptying procedures, real-time measured data are unified and, consequently, CFD simulations are performed. In this scenario, tests are performed in an experimental setup, recording data with the various equipment described in Section 2.2. For the CFD simulations, HVoF equations and turbulence models are defined. Experimental and simulation data are integrated into a dataset to calibrate the CFD model using mesh sensitivity analysis. By applying AI/machine learning tools, predictive analyses are performed that lead the user to identify the magnitude of the effects of air-water interaction in the study network through real-time records and, finally, represent these findings through a virtual model (DT) to test the most efficient conditions for decision-making on pressure conditions, trapped air volume, as well as the need for air valves in the study network. Figure 8 illustrates a DT framework which integrates CFD modelling to analyse hydraulic procedures.  
In this section, an application of the DT based on CFD modelling is shown, taking two example cases as the filling and emptying procedures, as well as a summary of the correlational analysis of the pressure values between the real-time measurements and the DT with the dataset.  
Figure 8: Digital Twin applied to filling and emptying processes.  
Scenarios of Table A1 were used for simulating filling procedures. On the other hand, information of Table A2 were used to simulate emptying procedures. Some modelling conditions are defined and shown in Table 6.  
The CFD-based DTs were simulated using a two-way coupling approach with HVoF equations. This ensures a reciprocal interaction between water and air, allowing each phase to influence the other's dynamics. Furthermore, the DTs were represented through the OpenFOAM v2412 software using a solver entitled _compressibleInterFoam_, which considers the compressibility of both air and water, with their respective compressibility differences (air is 20,000 times more compressible than water [70; 71]). The effects of surface tension due to the application of the filling and emptying process in a small inner diameter pipe were considered (equal to 51.4 mm).  
On the other hand, the modelling conditions for the filling and emptying procedures were based on the modelling conditions of [PERSON] et al. [59] for the filling procedures, as well as [PERSON] et al. [29] and [PERSON] et al. [54] for the emptying procedures, highlighting some aspects such as mesh conditions with mesh independence analysis and physical boundary conditions. In this case, a turbulence model such as SST \(k-\omega\) was applied for the filling procedures due to the good performance concerning adaptation in scenarios with high-pressure gradients. It was also used in emptying procedures since this turbulence model can represent the effects of the viscous layer as a function of the dynamic viscosity thus adequately simulating the fluid-pipe wall interaction independently in air and water.
###### Abstract  
Deep reinforcement learning-based approaches to mapless navigation have relied on the distance to the goal state being known a priori or that the distance to the goal can be obtained at each timestep. In artificial or simulated environments, obtaining the distance to the goal is considered a trivial task. Still, when applied to a real-world scenario, the distance must be obtained through complex localization techniques, and the use of localization techniques increases the complexity of the agent design. However, for agents navigating in unknown environments, using information about the goal to either form part of the state representation or act as the reward mechanism is usually expensive for both the robot design and for computing costs. This paper proposes using a pre-trained Siamese convolutional neural network (SCNN) to estimate the distance between an agent and its goal, thus enabling agents equipped with onboard cameras to navigate an unknown environment without needing localization sensors. This technique can be applied to environments where a goal location may be unknown, and the only information regarding the goal maybe a description of the goal state. Our experiments show that the Siamese network can learn the distance between the agent and its goal using relatively few training samples. Therefore, it is useful for mapless navigation using only visual state information and reduces the need for complex localization techniques.  
few-shot learning; mapless navigation; reinforcement learning; Siamese convolutional neural networks; mobile robot +
Footnote †: journal: Applied  
Acticle
# Simulation Study on Ground Vibration Reduction Measures of the Elevated Subway Line
## 3 Numerical Results and Discussion  
The subway line's total length is 23 km, including 16 stations, vehicle sections, etc., basically all sections of the elevated mode. The line runs through the residential, commercial, and administrative areas, and the line runs near the existence of more sensitive points of vibration and noise; this paper focuses on the most stringent ground vibration requirements of a large hospital, as shown in Figure 7. The building is a concrete frame structure, six floors above ground, and the nearest distance between the building and the centerline of the line to be constructed is 18.5 m. The maximum speed of trains in this area is 80 km/h, and the minimum speed is 50 km/h.  
Figure 6: Pier–soil–building finite element model.  
Figure 7: Sensitive site: Hospital.  
According to the requirements of the local document, the ground vibration velocity limit in the area is 0.05 mm/s, which requires that when the train passes through the building, the ground vibration speed at a distance of 0.5 m from the building should not exceed 0.05 mm/s. The vibration velocity results are taken as the mean-square velocity of 1 s in the time period of the largest vibration velocity.  
Because the maximum speed of the train in this area is 80 km/h, the minimum speed is 50 km/h, in order to explore the ground vibration caused by the train running to meet the requirements of the limit value of the region. Therefore, in this paper, numerical simulation is carried out for the average speed of 65 km/h of train operation. At the initial stage of design, the parameters of the fasteners used were 12.0 \(\times\) 10\({}^{6}\) N/m for vertical stiffness, 13.5 \(\times\) 10\({}^{6}\) N/m for lateral stiffness, 1361.12 Ns/m for vertical damping, and 947.27 Ns/m for lateral damping.  
Figure 8 gives the time and frequency domain results of ground vibration under the fastener 1 scheme when the vehicle speed is 65 km/h. The result of ground vibration under this scheme is 0.08 mm/s, which does not meet the vibration limit
# Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM
## 2 Related Work
Reference [41] studies whether scrubbing is really necessary for cache memories. Considering the cache size and the FIT (failure in time), the authors calculate the DUE (detected unrecoverable errors) rate, and they assert that scrubbing is necessary only in very large (from hundreds of megabytes) caches. Regarding adaptive mechanisms that can modify the memory operation, we have found in the literature some proposals. In Reference [47], [PERSON] et al. propose a technique that allows the operating system to adaptively change the cache size and ECC capability to adjust to cache operating conditions, particularly when switching between normal and low voltage operation modes. In [48], [PERSON] & [PERSON] propose a similar approach to phase change memories (PCM). Again, the ECC and the memory size are adapted over time as the number of failed cells in the phase change memory accumulates. The operating system monitors the number of errors corrected on each memory line, and when the number of errors on a line begins to exceed the strength of the currently used ECC, the ECC strength is adaptively increased while the memory capacity reduces. In Reference [49], [PERSON] et al. propose an adaptive memory controller that can opt between multiple scheduling schemes to enhance memory performance. These adaptive systems are software implemented, and they are not applied to tolerate intermittent faults. Particularly, the proposal in [49] is devoted exclusively to enhance performance. More recently, there are efforts to develop adaptive hardware schemes to enhance the reliability and/or yield of NAND flash memories ([50; 51], out of the scope of this work) and PCM. Reference [52] proposes to use a separate DRAM to store check bits for faulty rows in a PCM array. When a non-faulty row is read, data are extracted directly from the PCM array. When a faulty row is read, a long codeword (including check bits) is read from the PCM and the DRAM, and data are decoded from it. In Reference [53], [PERSON] et al. propose an SEU monitor (basically, an error counter) to be integrated into an SRAM with EDAC (error detection and correction) and scrubbing. To end up, there exist in the literature a few works related to detect intermittent faults. Apart from the works mentioned in the Introduction (theoretical/simulated [7; 8; 9], or using an identification system [10]), there are others implemented at high abstraction levels. For instance, [PERSON] et al. [54] propose a software intermittent fault detector based on signature analysis, and [PERSON] and [PERSON] propose a system to detect intermittent resistive faults at the board level [55; 56].
# Energy Efficiency Analysis of MIMO Wideband RF Front-End Receivers
## 2 Preliminaries
### Communication Model
Furthermore, the instantaneous SNR at the receiver is  
\[\xi=\lVert\mathbf{H}\rVert_{\text{F}}^{2}\cdot\overline{\xi}, \tag{7}\]  
where \(\lVert.\rVert_{\text{F}}\) is the Frobenius norm and the average SNR at each receive antenna with respect to each transmit antenna is given by  
\[\overline{\xi}=\frac{P_{\text{r}}}{N_{0}\,B\,F_{\text{rx}}}, \tag{8}\]  
in which \(P_{\text{r}}=\frac{\kappa\,P_{\text{out}}}{n_{\text{t}}}\) is the received power. In addition, it is worth noting that \(F_{\text{rx}}\) affects the average SNR at the receiver and, therefore, the EE of the communication system.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 5 Clustering Process Optimization
### Cluster Head Selection Phase
#### 5.1.1 Probability-Based Clustering Optimization  
Probability-based clustering schemes rely on the pre-assigned probability of each SN to determine the initial CH [48]. This pre-assigned probability is the primary criterion for determining the chances of selecting an individual node as a CH. The other conditions considered during CH selection are the residual energy, average network energy, initial energy, etc. These probability-based clustering algorithms ensure efficient energy usage and usually achieve faster convergence times and low packet exchange rates.