esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Response Characteristics and Parameter Influence Analysis  
Based on the analytical methods in Sections 3.1 and 3.2, this section discusses the stability of the amplitude-frequency response curve and the trivial solution of the pipe-NES coupling system, and we analyzed the influence of the nonlinear stiffness and damping of an NES on the motion of the pipe (the main system).  
Figure 3: Response time history diagram when \(\sigma=0\), \(\kappa=97.97\), \(\zeta_{2}=0.0085\), \(\rho_{2}=0.02\); (**a**) Analytical solution of \(x_{1}\); (**b**) approximate analytical solution of \(Z\); (**c**) numerical solution of \(x_{1}\); and (**d**) numerical solution of \(Z\).
###### Abstract  
Cloud radio access networks (C-RANs) are emerging architectural solutions to anticipate the increased capacity and quality demands of future 5G cellular networks at a reduced cost. In C-RANs, a transport segment referred to as fronthaul has been defined, which become a major constraint in practical implementations due to its high cost. A transport protocol referred to as eCPRI (enhanced common public radio interface), which was specifically designed for the fronthaul networks, imposes stringent end-to-end (E2E) latency and capacity requirements, which can be satisfied through the extortionate optical links. The high implementation cost of optical fronthaul networks significantly increased the system cost and made the fronthaul a hurdle to accomplish the cost-benefits of the C-RANs' architecture. The globally deployed Ethernet networks could be leveraging solutions, but are inadequate to comply with the eCPRI requirements in fronthaul bridged networks and result in intolerable latencies due to ineffectual traditional quality of service aware forwarding schemes. Therefore, to realize the cost-benefits of ubiquitously deployed Ethernet infrastructure, this paper proposes the E2E latency aware path computation and packet forwarding schemes, which ameliorate the performance of Ethernet-based fronthaul bridged networks to transport the eCPRI traffic at tolerable latencies. The simulation results verify the feasibility of low-cost Ethernet to carry the eCPRI traffic streams up to 100 Gbps with the proposed schemes in fronthaul bridged networks.  
5G networks; BBU centralization; cloud radio access networks; eCPRI; fronthaul
## 2 Experimental Procedure and Analysis  
### Experiment  
Tellurite-based glass samples doped with Tm\({}_{2}\)O\({}_{3}\) were prepared by using the melt quenching technique which was described in our previous study [29]. Three kinds of tellurite-based glass samples were prepared with the glass network modifiers niobium oxide, zinc oxide, and a mixture of potassium and niobium oxide. The Tm\({}_{2}\)O\({}_{3}\) concentration was varied between 0.125% and 1%. Two tellurite-based glass samples were prepared by using niobium oxide as a glass modifier: (x)Tm\({}_{2}\)O\({}_{3}\)-(95)TeO\({}_{2}\)-(5-x)Nb\({}_{2}\)O\({}_{5}\), (TeNbTm) where x = 1.0 and 0.25 (1.0 and 0.25 mol %); four samples were prepared at different concentrations using zinc oxide as a glass modifier: (x)Tm\({}_{2}\)O\({}_{3}\)-(80)TeO\({}_{2}\)-(20-x)ZnO (TeZnTm), where x = 1.0, 0.5, 0.25 and 0.125 (1.0, 0.5, 0.25 and 0.125 mol %); and four thulium-doped tellurite glasses with potassium and niobium oxide were prepared at different Tm\({}_{2}\)O\({}_{3}\) concentrations (x)Tm\({}_{2}\)O\({}_{3}\)-(70)TeO\({}_{2}\)-(15)K\({}_{2}\)O-(15)Nb\({}_{2}\)O\({}_{5}\) (TeNbKTm) where x = 1.0, 0.5, 0.25 and 0.125 (1.0, 0.5, 0.25 and 0.125 mol %).  
The absorption spectra of the glass samples were recorded by using a commercial spectrophotometer. In order to measure the emission spectrum, a home-made, 60-ns pulsed,tunable Ti:sapphire laser was employed as the excitation source. The emission spectra and lifetime measurements were conducted as described in detail in [29]. All measurements were carried out at room temperature.
# Reliability and Maintenance Analysis of Unmanned Aerial Vehicles +
## 5 Multiplexed Systems  
Redundancy of the most critical systems does not always lead to an increase in the reliability of the whole system. In fact, the redundancy of a quite unreliable system means increases in the total failure rate. In other words, the overall safety of the system will be increased, but certainly not the reliability. A higher failure rate brings an increase in expenses for spare parts and in person-hours, increasing operating costs [11].  
On the other hand, the duplication of the most critical or vital systems is not the ideal solution, as it increases the cost and the complications of the system, so it is necessary to find another way to improve reliability [60].  
A classic case is that related to propulsion systems: many UAVs have only one engine, but this is a highly reliable system, even if its loss compromises the entire mission. The installation of two engines would seem to be an ideal solution because the loss of one of these would not compromise the final mission. However, the duplication of a motor means the duplication of all ancillary systems. This in turn leads to a decline in the system's overall reliability [61].  
The ideal solution is based on two keywords: _oversize_ and _derating_. We will therefore choose an engine with characteristics that exceed the UAV requirements, and will work under ordinary operating conditions, i.e., derated, or in a very \"relaxed\" way. In this case, we will see that, while remaining a single point failure according to FMECA analysis, the engine has a considerably higher reliability, as it will work at less than 50% of its capability; this condition reduces the rate of failure occurrence.
## 2 Modeling Using Fully Cartesian Coordinates
### Calculation of the Global Center of Mass of the System  
When the model of a body is made with a pair of \"basic points\", a vector of four coordinates \(\mathbf{q}_{b}\) representing the positions vector of body \(b\) is introduced and can be expressed as follows:  
Figure 2: A model of a body in fully Cartesian Coordinates using three basic points: \(A,B,\) and \(C\).  
\[\mathbf{q}_{b}=\begin{bmatrix}x_{A}&y_{A}&x_{B}&y_{B}\end{bmatrix}^{T}\equiv \begin{bmatrix}\mathbf{q}_{A}{}^{T}&\mathbf{q}_{B}{}^{T}\end{bmatrix}^{T} \tag{15}\]  
where \(\mathbf{q}_{A}\) and \(\mathbf{q}_{B}\) are the position vectors of points \(A\) and \(B\), respectively.  
In this way, the position \(\mathbf{g}_{b}\) of the CoM of body \(b\) with respect to the global reference frame can be calculated by the following:  
\[m_{b}\mathbf{g}_{b}=\left(\mathbf{M}_{1b}+\mathbf{M}_{3b}\right)\mathbf{q}_{A }+\left(\mathbf{M}_{2b}+\mathbf{M}_{4b}\right)\mathbf{q}_{B} \tag{16}\]  
In matrix form, it can be expressed as follows:  
\[m_{b}\mathbf{g}_{b}=\mathbf{B}_{b}\mathbf{M}_{b}\mathbf{q}_{b} \tag{17}\]  
where the matrix \(\mathbf{B}_{b}\) is introduced here to arrange the terms of the matrix multiplication to obtain the result in Equation (16). This matrix is composed by a set of \((2\times 2)\) identity matrices, \(\mathbf{I}_{2}\), that in number are equal to the number of basic points involved in the multiplication. In this case, matrix \(\mathbf{B}_{b}\) is as follows:  
\[\mathbf{B}_{b}=\begin{bmatrix}\mathbf{I}_{2}&\mathbf{I}_{2}\end{bmatrix} \tag{18}\]  
As aforementioned, the system can be seen as a cloud of points coming from the corresponding model of each body. Thus, for a system with \(n\) bodies, the general position of the CoM, \(\mathbf{g}\), can be calculated by the following:  
\[m\mathbf{g}=\sum_{b=1}^{n}m_{b}\mathbf{g}_{b}=\sum_{b=1}^{n}\mathbf{B}_{b} \mathbf{M}_{b}\mathbf{q}_{b} \tag{19}\]  
where the total mass of the system, \(m\), is:  
\[m=\sum_{b=1}^{n}m_{b} \tag{20}\]  
Using the points shared among bodies and following a proper assemblage procedure, the global CoM of the system can be calculated in a simple and easy to automated matrix multiplication as follows:  
\[m\mathbf{g}=\mathbf{B}\mathbf{M}\mathbf{q} \tag{21}\]  
The terms in Equation (21) will be exemplified in following sections by examples. The assembling procedure will be clear then.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 7 Challenges for Future Work
### Interoperability Challenge  
SDN, ML, and MSA are very different technologies from each other as they have been developed for different purposes. The proposed methodology brings them together to make the IoMT networks resilient to failures. Hence, the proposed methodology faces some interoperability challenges, as follows:  
1. Protocol and Communication Standard In a network, a common communication standard is mandatory for data transmission. SDN is used for the smooth conduction of data transmission in the proposed methodology for this purpose. There are different types of controllers used in SDN for various purposes. One of the protocol standards used by SDN is OpenFlow. Can one protocol be sufficient enough to support all three technologies or is another protocol required to solve the compatibility issue? This protocol testing needs to be conducted through the experimentation stage in the future. The issues, such as which specific protocol standard should be used in the methodology so that the rest of the supporting technologies (ML and MSA) can be compatible with that protocol, can be resolved by this analysis and the framework of the proposed methodology.
2. Diversity in Platform Utilization Each technology of this integration not only varies in the communication protocol standard but also in the type of platforms used; each technology runs on different platforms and through different programming languages. For example, SDN can be simulated on platforms such as mininet, OMNeT++, Cooja, etc. Microservices can be simulated on platforms such as Kubert, docker, etc. Machine learning can be implemented on platforms such as Scikit-learn by Python or Rstudio, etc.  
Potential Course of Action  
The potential solution to the above challenges can be determined by the development of a physical testbed with a set of interconnected networks to test the resilience in case of route failure. This strategy will conduct anomaly detection through ML and reflect the changes on traffic routes through SDN in real time.  
Testing can be conducted to check the compatibility within these technologies (ML, SDN, and MSA) by using the docker image of all these technologies as a software stack so that all the operational and configurational issues can be addressed and resolved adequately.
## 2 Proposed Methods
### Adaptive Focus Window
#### 2.1.3 Mask Fusion and Optimization  
The adaptive window is chosen by fusing the obtained \(Mask_{FOV}\) and \(Mask_{SL}\), mathematically expressed as their intersection, \(Mask_{FOV}\cap\overline{Mask_{SL}}\). To further optimize the window, appropriate structural elements are selected for morphological opening-closing operations to fill small holes and cracks while overcoming the edge diffusion of the SL region. Finally, the largest connected domain in the selected region is positioned as the final window (Figure 2c).
## 4 Results and Discussion
### Spectral-Shaped THz Generation
The generated THz spectrum, shown in Figure 7a by the red curves, has a narrow peak centered at 0.65 THz with a 20% of bandwidth respect to uniform comb beam. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Comb Beam 1**} & \multicolumn{3}{c}{**Comb Beam 2**} & \multicolumn{3}{c}{**Ramp**} \\  & \multicolumn{3}{c}{**Inter-Distance (4)-(5) = 1.6 ps**} & \multicolumn{3}{c}{**Inter-Distance (4)-(5) = 0.5 ps**} & \multicolumn{3}{c}{**of Charge**} \\ \hline  & **Bunch** & **Position** & **Charge** & **Bunch** & **Position** & **Charge** & **Bunch** & **Position** & **Charge** \\  & **Length (ps)** & **(ps)** & **(pC)** & **Length (ps)** & **(ps)** & **(pC)** & **Length (ps)** & **(ps)** & **(pC)** \\ \hline Beam 1 & 0.38 & 0 & 60 & 0.38 & 0 & 55 & 0.37 & 0 & 14 \\ Beam 2 & 0.32 & 0.95 & 55 & 0.32 & 1.1 & 55 & 0.56 & 0.9 & 36 \\ Beam 3 & 0.29 & 1.88 & 50 & 0.20 & 2.1 & 48 & 0.48 & 1.7 & 61 \\ Beam 4 & 0.20 & 3.5 & 50 & 0.23 & 3.6 & 46 & 0.40 & 3.0 & 85 \\ Beam 5 & 0.16 & 5.1 & 23 & 0.29 & 4.1 & 18 & 0.27 & 4.6 & 23 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Electron beam parameters for the three different configurations of the five bunches. Figure 5: THz generation via Coherent Transition Radiation (CTR) by a five comb beam: (**a**) Experimental data (**b**) Numerical calculation from measured current distribution reported in the inset.
# Wavelet Packet Decomposition to Characterize Injection Molding Tool Damage +
## 3 Results and Discussion  
AE monitoring is a very promising method for the detection of injection mold defects. Namely, the rapid increase of mold pressure during the filling and holding stages of the injection molding process can trigger defects (acoustic emission sources) in the tool steel. Acoustic emission testing can detect dynamic processes associated with the degradation of structural integrity.  
Figure 1: Experimental setup for AE monitoring during the injection molding process.  
Figure 2: Cracks in the injection molding tool and accompanying PP specimen.  
Experiments were conducted using a new injection mold tool and a damaged tool, respectively. Surface cracks were generated by successive local laser surface hardening of the tool steel. Both tools were tested using magnetic particle testing with fluorescent magnetic suspension. The cracks detected on the damaged tool are presented in Figure 2. After 24 h, the standard injection-molded polypropylene test specimens were scanned with an optical ATOS II SO 3D-digitizer. The ATOS II SO 3D digitizer is based on the triangulation principle whereby a sensor unit first projects different fringe patterns onto the scanned object's surface, which is then recorded by two cameras. Each measurement generates up to 4 million data points. In order to digitize a complete object, several individual measurements from different angles are required. The cracks on the damaged tool are also reflected on the surface of injection molded specimens. Shape deviations (Figure 2) in two dimensions from the ideal shape are stated in mm.  
Figure 3 shows peak amplitudes and energy of AE bursts during the manufacturing of test specimens produced in the new and damaged mold, respectively. Absence of damage in the new tool offers us the opportunity of classifying measured AE bursts as process-orientated signals. On the other hand, the presence of a damaged area in the injection molding tool causes a considerable increase in the number of AE bursts detected during the filling and holding phases, above all in an amplitude  
Figure 3: AE bursts’ amplitude and energy during injection molding with (**a**) a new mold and a (**b**) damaged mold.  
range between 40 and 60 dB. Consequently, we can ascribe the additional AE bursts measured during the filling and holding phase to active AE sources from injection mold defects. A rapid increase in mold pressure during the filling phase could stimulate plastic zone formation, stacking, and slipping of dislocations ahead of the crack tip and crack extension [8; 9] in the tool steel. Plastic deformations in the vicinity of the crack in the steel, mostly related to sliding dislocations, and crack growth cause distinctive AE bursts [20]. We connected AE signals with propagation of damage in the insert but this incorporates, based on the research of other authors, several physical phenomena that we described in the article as possible sources. The injection molding cycle ends after 16 s when the part is ejected out of the mold.
## 2 Materials and Methods
### Double Channel and Double Stage Image Dehazing Network  
The framework of our previous two-channel and two-stage image dehazing network (DTD-Net) is shown in Figure 1. The main modules of network structure include RDB module, basic module, up-down sampling, and attention mechanism. Specifically, the RDB module is mainly composed of two convolution layers of different sizes to better obtain the hazy image features. In order to reuse features to avoid information loss, the output of each convolution layer of the RDB module is taken as the input of the subsequent convolution layer. Then, a convolution layer of size 3 and ReLU activation function constitute the basic module. Bilinear difference up-sampling is introduced to expand the network channel, and attention mechanism is used to weight the expanded features to improve the effectiveness of the network. The visual effect and quantitative indexes demonstrate that the deraining network designed has strong dehazing performance. For more details, please refer to our paper [29].  
Figure 1: The framework of dehazing network [29].
# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
## 3 Methodology
### Point Cloud Partition
#### 3.3.2 Representative Layer Selection  
Although the vast majority of buildings share the same geometry, the number of boundary points varies in different layers. The changing number of boundary points reflects the changes of structures (see Figure 9c). Therefore, we constructed a histogram from the boundary points along the upward direction to select representative layers. In practice, starting from the floor to the ceiling level, we set a given bin size \(b_{h}\) (e.g., 0.05 m) for the voting process and identified the local maximum from the histogram. Figure 10 shows the histogram of boundary points. The point clouds are partitioned into 67 blocks with the bin size of 0.05 m. Through identifying the peaks of histogram, we can extract nine layers to represent the whole building.  
Figure 9: The process of boundary points extraction: (**a**) Convex hull, (**b**) key points on the convex polygon, (**c**) boundary points.  
Given the peaking elevations of the histogram, a set of representative layers can be determined as depicted in Figure 11. The enlarged view of Figure 11a indicates that these selected layers could retain the structural changes along the vertical direction. As a consequence, the shapes of the bay windows could be kept. By means of representative layers selection, the large number of layers could be consolidated without dropping out structural information. Moreover, we converted the 3D reconstruction problem to several 2D layer reconstruction problem which was more feasible for further processing.
## 2 Materials and Methods
### Features in Soil Surface Image  
In this study, we extracted various features from soil surface images for use in a DNN model and multiple regression analysis. Soil color is an effective factor in predicting soil  
Figure 1: Camera setting for in situ image acquisition: (**a**) camera and lighting setting; (**b**) darkroom and remote shutter release.  
characteristics [26]. Digital images are composed of pixels, each representing a point with color information. The most commonly used color system is RGB, which stands for red, green, and blue, and represents the amount of each color in each pixel. However, since each component of RGB is highly correlated with each other, it is difficult to intuitively infer the color of an image by looking at RGB values only. Therefore, color systems composed of features representing different characteristics have been proposed. One such system is HSV, which stands for hue, saturation, and value. Hue refers to the dominant color, with red at 0 degrees, green at 120 degrees, and blue at 240 degrees. Saturation means the purity of color and value refers to the brightness of color. These three features, hue, saturation and value, were extracted from color images.  
Grayscale images represent the overall lightness and darkness in the images. To represent the brightness of the grayscale image, the average value of the gray values of all pixels was obtained and set as one of the features. Skewness and kurtosis were calculated by means of a histogram of the grayscale image to reflect the distribution of gray values. Skewness represents the direction and degree of skew in the gray value histogram, while kurtosis represents the tailedness of the histogram.  
The gray level co-occurrence matrix (GLCM) was used to extract texture features to reflect the texture characteristics of soil surface images. The GLCM is a matrix that describes the distribution of gray levels between neighboring pixels in an image. Five texture features were extracted from the GLCM: contrast, dissimilarity, homogeneity, energy, correlation [27]. The Scikit image library in Python was used for the calculation of GMCM texture features.  
Hu moments were extracted to reflect the shape of the soil surface. Hu moments are a set of mathematical features used to describe the shape of an object [28]. Hu moments have been widely used in image processing and computer vision applications, such as object recognition, face recognition, fingerprint recognition, and medical image analysis. The Open CV library for Python was used for the extraction of Hu moments with a log scale. Seven Hu moments were extracted and denoted as H0 to H6.
# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 4 The PingBasedDown Algorithm
### Link Evaluation Model
#### 4.1.3 Latency Estimating Module  
In order to make the algorithm adapt to different levels of latency, this module is proposed to estimate the pure latency of the link. The pure latency denoted by \(L_{R}\) means how long a RTT of the message is without triggering the retransmission mechanism. Through a normal link from \(v_{i}\) to \(v_{j}\), \(L_{R}\) is expected to be: \(L_{R}(v_{i},v_{j})=\mathcal{E}\big{(}\hat{I}_{n}(v_{i},v_{j})\big{)}\), where \(\mathcal{E}:\mathbb{R}^{N_{w}}\rightarrow\mathbb{R}\) is the operator of mathematical expectation. However, when messages transmit through an abnormal link with stochastic packet loss, the RTT sometimes may be much longer than the pure latency because of the retransmit mechanism. Thus, we introduce an approach. It cuts off the bigger part of the collected RTTs which are supposed to be caused by retransmission process. Then it calculates the average value of the rest, i.e., the smaller part, of RTTs which are supposed to be transmitted without packet loss. This average value is used as the estimate of pure latency. The cut off action is similar to that of the noise filtering algorithm presented in Algorithm 1 with the replacement from \(F_{S}\) to \(L_{pos}\).
## 4 Discussion  
The analysis results detailed in Section 3 strongly suggest that the proposed model, including its presented DBSCAN-based alternative, provides an effective framework to match features based on consecutive ILIs, addressing challenges in spatial variability and interactions between adjacent defects. This section discusses the results of the analysis, demonstrating key findings and validating the study purpose. First, the improvements in feature matching, brought about by clustering, are discussed in Section 4.1, highlighting the key differences in model performance utilizing DENC (an anisotropic-based clustering) and DBSCAN (an isotropic-based clustering). Then, the effectiveness and extensibility of the proposed cluster classification process are discussed in Section 4.2. Next, a discussion of the sensitivity of the model's performance to its parameters, including the DBSCAN-based alternative, is presented in Section 4.3. Furthermore, a discussion of the model's applicability to external defects and insights into runtime performance are outlined in Sections 4.4 and 4.5, respectively. Finally, considerations for detection limitations and pipeline materials are highlighted in Section 4.6.