esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks
## 4 Evaluation
### Network Construction  
This study assumes that an NoN comprises a PN and multiple VNs. The PN contains \(N^{P}\) P-nodes and \(E^{P}\) P-links, and similarly VN \(k\) contains \(N^{V_{k}}\) V-nodes and \(E^{V_{k}}\) V-links. If \(N^{P}>N^{V_{k}}\), the VNs are mapped onto the PN so that the fewest P-nodes are shared among the VNs. The connectivity structure for the PN and VNs is determined based on a specific network model, for which we adopt the Erdos-Renyi (ER) model [35] and the Barabasi-Albert (BA) model [36], which generate node degrees following a Poisson distribution and a power-law distribution, respectively. Because it is virtually impossible to predict connectivity patterns in an IoT scenario with numerous types of services, we use the aforementioned models because they have been observed widely in actual networks and used for network evaluation to date [37; 38; 39; 40]. Other types of network model are conceivable, such as the Watts-Strogatz (WS) model [41], the Waxman model [42], and the random geometric graph (RGG) model [43]. However, the Waxman model belongs to the class of random networks; in other words, it is a special case of the ER model. The degree distributions of the WS and RGG models are close to a uniform distribution, making it difficult to evaluate how network influencers and configuration affect assortativity. Furthermore, the WS model is characterized by its small-worldness, which the BA model shows as well.  
Regarding the VN topologies, demands from service providers for connectivity reconfiguration now arise more frequently because of the flexibility and cost-efficiency of virtualized networks [44; 45; 46; 10; 34]. Therefore, this paper deals with the configuration of VN connectivity from the perspective of assortativity. Assortativity is a network metric for evaluating the correlation of node centrality in a given topology [17]. For example, looking at assortativity based on degree centrality, an _assortative_ node is one whose connected neighbors have similarly high (or low) degrees, while a _disassortative_ node is one that has either a high degree compared to its low-degree neighbors or vice versa. In this study, we configure the assortativity of a VN (_intranetwork assortativity_) and that among VNs (_internetwork assortativity_).
# ReDSHIFT: A Global Approach to Space Debris Mitigation
## 3 Additive Manufacturing
### Shielding  
Two baseline shielding concepts were defined for 3D-printing. The shields include a multi-shock panel (MSP) (left panel of Figure 12) and a single corrugated panel (SCP). The right panel of Figure 12 shows the 3D printed prototype of one of the SCPs. The influence of key design parameters, such as overall panel thickness, bumper thickness and spacing, were considered and will be evaluated through hypervelocity impact testing. For the MSP, the default bumper thickness is set to 0.5 mm, but values from 0.25 up to 1 mm were explored. The rear wall thickness in the range of 1.0 mm to 3.0 mm is realistic for most equipment, and an intermediate value of 2.0 mm was chosen for the first phase of investigations. Different stand-off distances between the walls in the MSPs were assumed, from 1.5 cm up to more than 10 cm.  
Two additional concepts were also anticipated, i.e., a double corrugated sandwich panel and a hybrid multi-layered panel with single corrugated bumper. These will be further investigated based on the results of the first testing phase. As a complement to this work, an impact risk analysis was performed using PHS's SHIELD3 model [12] to provide a preliminary evaluation of the shielding performance of the MSP and SCP designs. Almost 700 simulations were run to estimate the probability of penetration of each panel type when it is applied to different faces of a notional spacecraft. The designed shields appear to be suitable to protect against hypervelocity particles in the 2 mm to 3 mm size range. The simulations pointed out that the stand-off distance between the walls in the MSPs is extremely important for increased protection. Further work will be undertaken to confirm the effectiveness of the baseline shield-panels and their enhanced versions. In particular, with regard to the holistic design of ReDSHIFT, it is desirable if the shield-panels provide comparable structural and thermal performance to standard honeycomb panels whilst offering significant improvements in terms of debris shielding (and re-entry demisability).
# A Study on the Seismic Performance of Steel H-Column and T-Beam-Bolted Joints
## 1 Introduction
[12; 13; 14], aiming to study the progressive collapse performance of the unilateral-bolted joints under the failure of the middle column, respectively, carried out experimental studies on the end-plate joints of two types of square steel columns with unilateral bolts. They analyzed the failure modes and damage mechanisms of these joints. The results demonstrate that the load-bearing capacity of the Hollo-Bolt end-plate connection nodes drops rapidly, and their anti-progressive collapse performance is poor. In contrast, the anti-seismic performance of the SCBB end-plate connection nodes is better in the late loading stage owing to the catenary mechanism. This paper systematically varies T-plate stiffness (web thickness: 14-22 mm; flange thickness: 20-28 mm), column wall thickness (10-18 mm), axial compression ratio (0.2-0.6), and bolt preload (180-270 kN). Unlike prior parametric studies [9; 11], which focused on limited parameter ranges or specific failure modes, this study comprehensively evaluates the interactive effects of these parameters on hysteretic behavior, energy dissipation, and failure mechanisms. In practical engineering structures, beam-column joints typically take the form of spatial joints. Previous studies on beam-column joints [3,4] primarily focused on planar joints under unidirectional loads, overlooking the complex spatial coupling effects in practical engineering. Existing parametric analyses [2,12] were limited to specific T-piece dimensions or column-wall thicknesses, lacking systematic investigations into the combined effects of T-plate stiffness, axial compression ratio, and bolt preload. This study addresses these gaps by systematically analyzing spatial joints under bidirectional loads and quantifying the influence of multiple parameters, providing a more comprehensive framework for seismic design. These spatial beam-column joints are subjected to two-way loads, and under such loads, the joints exhibit complex spatial coupling effects. Accurately analyzing the mechanical properties and deformation characteristics of spatial joints under two-way loads, as well as reasonably evaluating the reliability and safety of these joints during earthquakes, is of great significance for the seismic design of steel-frame structures. In this paper, the stiffness, axial compression ratio, bolt preload, and wall thickness of the square steel pipe column are taken as parameters to establish a finite-element model of the column joints in the space of single-bolt-connected-shaped parts. The influence of various parameters on the seismic performance of spatial joints is then analyzed.
## 3 Results and Discussion  
### Forklift Behavior  
The acceleration, duration, and delta velocity response registered for each forklift is shown in Table 4. The average acceleration measured on the forklifts was 2.98 G. The coefficient of variation obtained for these measurements was 37%, reflecting values as low as 1.38 G (for the reach truck) or as high as 5.1 G (for the gas forklift). The average event duration recorded was 13.6 ms. However, durations recorded in the reach truck were 50% higher than for the gas and electric forklifts. Durations in the gas and electric forklift averaged 10.2 ms. The increase in event duration for the reach truck is due to the reach mechanism being fully extended for its impacts. The coefficient of variation obtained for that event duration was 23%. While high, the variations found in this study are considered acceptable as shock is traditionally considered a difficult parameter to characterize [24]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pallet Design** & **Entry Speed (m/s)** & **Top Load (kg)** & **Gas Forklift** & **Electric Forklift** & **Reach Truck** \\ \hline \multirow{4}{*}{Wood} & & 227 & 10 & 10 & 10 \\  & 0.22 & 680 & 10 & 10 & 10 \\  & & 1134 & 10 & 10 & 10 \\ \cline{2-6}  & & 227 & 10 & 10 & 10 \\  & 0.45 & 680 & 10 & 10 & 10 \\  & & 1134 & 10 & 10 & 10 \\ \hline \multirow{4}{*}{Plastic} & & 227 & 10 & 10 & 10 \\  & 0.22 & 680 & 10 & 10 & 10 \\  & & 1134 & 10 & 10 & 10 \\ \cline{1-1} \cline{2-6}  & & 227 & 10 & 10 & 10 \\ \cline{1-1}  & 0.45 & 680 & 10 & 10 & 10 \\ \cline{1-1}  & & 1134 & 10 & 10 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Specifications for experimental circumstances. The acceleration response measured in the forklifts is used as the main response for further analysis. The \(p\)-values obtained in the analysis show that all the main factors, including forklift type, pallet design, entry speed, and top load, significantly affect the acceleration experienced by the forklift during its interaction with the pallet (\(p<0.05\)). Similarly, there are significant two-way interactions between most variables, except between the forklift type and the top load (\(p=0.508\)). Only one three-way interaction was found to be significant in the model (\(p=0.000\)), corresponding to the forklift, pallet design, and top load. The effects of the main variables were further investigated using a main effects plot (Figure 4) and a [PERSON]'s pairwise comparison (Appendix A). The measured acceleration response was significantly higher (15%) when the gas forklift was used, while the reach truck and the electric forklift were not significantly different. However, event durations with the reach truck were 50% longer than the electric forklift.
## 5 Numerical Test and Results
### Case 1: Without Voltage Regulation  
The nodal voltage curves are shown in Figure 6. The red, blue, and green curves represent the voltage of nodes 18, 17, and 16 (having voltage problems), respectively. The grey curves of other nodes have had no voltage for the whole day. Since node 18 is farthest from the reference node, the voltage problem of node 18 is most severe. The voltage of node 18 reaches the maximum of 1.013 p.u. at 11:10 and reaches the minimum of 0.896 p.u. at 19:20.
## 3 Methodology
### Dimensionality Reduction
#### 3.3.1 Feature Selection  
Feature selection is crucial for the development of an effective IDS [54]. It simplifies datasets by removing redundant and unnecessary features, thereby enhancing the performance of machine learning algorithms [55]. This process helps reduce the training time required for the model while simultaneously enhancing classification accuracy and mitigating overfitting [56].  
In our study, we employed an embedded feature selection method using the Select-FromModel (SFM) function from the Scikit-learn package. SFM acts as a meta-transformer that applies importance weights to select features according to a specified threshold, which we set to the mean for this experiment. Specifically, we utilized SFM based on the LogitBoost algorithm [57] to identify important features. LogitBoost was chosen for its ability to handle noisy and outlier data effectively [58], address overfitting issues [59], and manage multiclass problems using multiclass logistic loss [60]. It also reduces training errors and improves generalization capabilities [57]. Notably, LogitBoost requires a regression algorithm as its base estimator, for which we used XGBRegressor [61] in our implementation.  
Using LogitBoost, we identified 28 important features from \(D1\) and 35 from \(D2\). Table 4 presents the selected features from both datasets. Algorithm 1 outlines the pre-processing steps integrated with feature selection in our methodology.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Feature Count** & **LogitBoost-Selected Features** \\ \hline \multirow{4}{*}{D1} & \multirow{4}{*}{28} & Dur, service, ct\_src\_d\_dport\_ltm, dmean, ct\_srv\_src, dpkts, sybtes, ct\_dst\_src\_ltm, \\  & & sinpkt, dinpkt, ct\_dst\_ltm, smean, rate, sttl, sload, ct\_dst\_sport\_ltm, dbytes, dtcpb, djit, \\  & & stcpb, dload, dloss, tcprt, synack, ackdat, sjit, ct\_src\_ltm, ct\_srv\_dst. \\  & & Destination Port, Flow IAT Min, Flow IAT Max, Total Fwd Packets, Flow Duration, \\  & & Flow Bytes/s, Fwd Packet Length Std, Flow IAT Std, Fwd Packet Length Mean, Flow IAT Mean, Fwd Packet Length Max, Flow Packets/s, Bwd \\  & & Packet Length Std, Fwd Packets/s, Fwd IAT Std, Bwd IAT Min, Fwd IAT Mean, Bwd IAT Mean, Bwd IAT Mean, Fwd IAT Max, Fwd IAT Min, Bwd IAT Total, Fwd IAT Max, min\_seg\_size\_forward, Max Packet Length, Init\_Win\_bytes\_forward, \\  & & Min Packet Length, Init\_Win\_bytes\_backward, Bwd Packets/s, Average Packet Size, \\  & & PSH Flag Count, Packet Length Std, URG Flag Count. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Feature selected using LogitBoost method.
###### Abstract  
A wide variety of applications require high peak laser intensity in conjunction with a narrow spectral linewidth. Typically, injection-locked amplifiers have been employed for this purpose, where a continuous wave oscillator is amplified in a secondary external resonant amplifier cavity using a pulsed pump laser. In contrast, here we demonstrate a setup that combines a CW Ti:sapphire oscillator and pulsed amplifier in a single optical cavity, resulting in a compact system. Dichroic beam combination of blue wavelength semiconductor diodes and the green wavelength of a Nd:YAG laser allowed the simultaneous excitation of the Ti:sapphire crystal by both continuous wave and pulsed pump sources. A linewidth of \(<\)2  MHz is achieved in continuous wave operation, while the linewidth increases to about 10  MHz in the combined CW + pulsed mode with a pulse duration of 73  ns. A peak pulse intensity of 0.2  kW is achieved, which should enable efficient single-pass second harmonic generation in a nonlinear crystal.  
T:sapphire laser; amplifier; injection-lock; diode pumping; solid state laser +
Footnote †: journal: Applied Physics Letters  
Acticle  
A Hybrid Self-Seeded Ti:sapphire Laser with a Pumping Scheme Based on Spectral Beam Combination of Continuous Wave Diode and Pulsed DPSS Lasers. _Appl. Sci._**2022**, 12, 4727. [[https://doi.org/10.3390/app12094727](https://doi.org/10.3390/app12094727)]([https://doi.org/10.3390/app12094727](https://doi.org/10.3390/app12094727))
## 2 Reconsideration of TM Fields Difficulties:  
The wave equation formalism in TE case can be written as:  
\[\left[\
abla^{2}+k_{o}^{2}n^{2}(x)\right]E_{y}(x,z)=0 \tag{1}\]  
However, in TM case with \(z\)-invariant refractive index the wave equation for \(H_{y}\) (\(x\), \(z\)) in a \(y\)-invariant structure takes the form [17]:  
\[\left[\
abla^{2}+k_{o}^{2}n^{2}-\frac{1}{n^{2}}\frac{\partial n^{2}}{\partial x }\frac{\partial}{\partial x}\right]H_{y}(x,z)=0 \tag{2}\]Where: \(k_{\rm o}=2\pi/\lambda_{\rm o}\) is the free space wavenumber and \(\lambda_{\rm o}\) being the free-space wavelength. In a \(y\)-invariant structure \(\
abla^{2}=(\partial^{2}/\partial x^{2}+\partial^{2}/\partial z^{2})\) and \(n(x)\) is the refractive index profile of the planar waveguide with core refractive index \(n_{co}\) surrounded by a cladding with index \(n_{\rm cl}\). As stated earlier, the first problem facing the applicability of the FFT-based BPM in the solution of Equation (2) is the term involving the mixed derivative of the field and the inverse of the refractive index profile. [PERSON] et al. [17] suggested a transformation of the TM problem to a TE one via the following transformation:  
\[H_{y}(x,z)=n(x)F(x,z) \tag{3}\]  
Direct substitution of Equation (3) in Equation (2) results in the following wave equation for \(F(x,z)\):  
\[\
abla^{2}F+k_{o}^{2}[n^{2}(x)-\frac{n(x)}{k_{o}^{2}}\frac{\partial^{2}}{ \partial x^{2}}(\frac{1}{n(x)})]F=0 \tag{4}\]  
This can be written as:  
\[[\
abla^{2}+k_{o}^{2}n_{\alpha q}^{2}(x)]F=0 \tag{5}\]  
It is easy to conclude that Equation (5) is a TE problem with an equivalent index medium:  
\[n_{\alpha q}^{2}(x)=n^{2}(x)-\frac{n(x)}{k_{o}^{2}}\frac{\partial^{2}}{ \partial x^{2}}(\frac{1}{n(x)}) \tag{6}\]  
The equivalent problem Equation (5) is amenable -in principle- to be solved via an FFT-based BPM algorithm. However, the step-like index profile \(n(x)\) is the second major problem that should be handled carefully. A judicious solution is to approximate \(n(x)\) by a suitable \"smooth\" function to circumvent the singularity in the transverse derivative \(\partial_{x}^{2}(1/n(x))\) in the equivalent index Equation (6). Accordingly, we adopted a sigmoid-like function \(n_{sig}\) that approximates \(n(x)\) as follows:  
\[n_{sig}(x)=n_{cl}-\frac{\Delta n}{1+e^{-a(x-h/2)}}+\frac{\Delta n}{1+e^{-a(x+ h/2)}} \tag{7}\]  
where \(n_{cl}\) is the cladding refractive index, \(\Delta n\) is the difference between the waveguide cladding and core refractive indices, \(h\) is the waveguide core width, and \(a\) is the smoothing parameter that controls the steepness of the sigmoid function.  
Each term \(f\) in the spatial-dependent part in Equation (7) has the important analytical property:  
\[f\frac{\partial^{2}}{\partial x^{2}}(\frac{1}{f})=1-f \tag{8}\]  
Thus \(n_{sig}\partial_{x}^{2}(1/n_{sig})\) is readily evaluated.  
The extreme simplicity in the numerical evaluation of the above expression avoids the numerical artifacts and the generation of spurious modes that happen when evaluating numerically the second derivative as pointed out by [18].
## 5 Conclusions  
The dissolution and diffusion characteristics of alkaline ions in fly ash play a crucial role in the mineralization reaction of CO\({}_{2}\). To address this scientific issue, a multi-scale dynamic diffusion model of ions was developed based on the micro-nano pore structure of fly ash. This model established the correlation between the ion leaching rate and micro pore structure, as well as elucidated the dissolution and diffusion process of the alkaline ions in fly ash. The model also analyzed the change in medium mass concentration over time. The experimental validation of the model confirmed the relationship between the fly ash soaking time and the ion leaching concentration, the link between the ion leaching concentration and the mineralization degree, and the impact of the mineralization degree on the mechanical strength of specimens. This study revealed the influence of alkaline ion concentration on the mineralization degree and mechanical strength of fly ash materials. The main findings are as follows:  
1. A mathematical model was developed to study ion leaching and diffusion in the micro-nano pores of fly ash. The model effectively predicts the evolution of the alkaline ion concentration during the dissolution and diffusion process.
2. Alkaline ions dissolve and diffuse rapidly, leading to a rapid increase in the ion concentration. As the time progresses, the ion concentration stabilizes.
3. The mineralization degree shows an initial increase, followed by a slight decrease, and eventually levels off with soaking time.
4. The compressive strength initially sharply increases with the mineralization degree, followed by a slight decrease.  
B.X.: conceptualization, methodology, writing--review and editing, supervision, project administration, funding acquisition. W.G.: methodology, investigation, writing--original draft, formal analysis. J.P.: methodology, investigation, validation, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number No. 51974042), and the Shanxi Science and Technology Plan Announced Bidding Project (grant number No. 20191101015).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Fault Detection of Bearing Systems through EEMD and Optimization Algorithm
## 2 Background Theories
### EEMD and Feature Extraction  
It is widely known that EMD has suffered from a mode mixing problem, such that the individual IMFs tend to be blended together [19; 20]. Obviously, EEMD is proposed to tackle the issue of mode mixing by inserting Gaussian noise into the original signal before proceeding to EMD, and iterating the process of averaging out of IMFs to eliminate mixed modes [21]. In this section, we compared the EMD and EEMD by showing how the sinusoidal component is isolated in IMF. Figure 1 shows that in the first IMF of the EMD result, sine waves of different frequencies were mixed. Because of the presence of mode mixing in low IMFs, higher IMFs also failed to decompose two different sinusoidal signals. One the other hand, the EEMD successfully separated the high frequency portion in the low IMFs, so that high-frequency and low-frequency signals were clearly isolated in IMF5 and IMF7, respectively.  
While recognizing the IMFs of EEMD from bearing signals is one of the convenient representations of bearing conditions, it is impractical to use the IMF without extracting damage-sensitive features. Here, we considered several statistical parameters, such as standard deviation, root mean square (RMS), kurtosis, zero-crossing rate, and entropy from each IMF, to evaluate the condition before and after the presence of defect in the bearing system. Table 1 shows such parameters that were extracted from the IMFs of EEMD to develop a feature vector for both healthy and damaged bearings. To reduce deviations towards the sensitivity of the bearing defect, all the elements of the feature vector have been equally weighted and initially normalized.
# Machine Learning Based Design of Railway Prestressed Concrete Sleepers
## 3 Methodology  
In terms of the design of railway prestressed concrete sleepers, this study mainly implements EN13230 \"Railway Application-Track-Concrete sleepers and bearers\" [4]. The core design sections are in the second part of the standard (EN13230-2 Concrete Sleepers and Bearers) and the sixth part (EN13230-6 Design). In this study, Python has been adopted. First, Python code is relatively versatile for coding. Second, it is easy to be complied with and translated by users. Third, object-oriented programming (OOP) in Python has great flexibility. Fourth, Python offers more options in terms of graphics packages and toolsets [31].  
The design of the railway prestressed concrete sleepers includes multiple iterative steps for the rail seat section and the centre section (or midspan) of the sleepers. In this study, these steps will be automated. The machine learning application has been developed to design and optimise the top width of sleepers, the bottom width of sleepers, the height of sleepers, the eccentric distance, area, and tensile strength of the prestressed reinforcement, and the compressive strength of the concrete. The maximum positive and negative bending moment capacities that the sleeper sections can bear will be predicted by using the machine learning algorithms developed. The purpose of this machine learning application is to aid in the design optimisation for railway concrete sleepers, which is the precursor to better materials' waste reduction and end-of-life management of railway concrete sleepers in practice.  
Deep learning, Bayesian ridge, and random forest will be adopted and customised for data learning and prediction. The suitability of each machine learning algorithm will be assessed by comparisons of prediction errors and key performance metrics. The design database containing about 3000 groups of data will be used for training and testing the machine learning algorithms.