esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Coherent Receiver Settings for Optimum Channel Spacing in Gridless Optical Networks
## 2 Clock Recovery in Coherent Optical Receivers
### Sources of Jitter  
Coherent receivers must deal with two main sources of jitter, one is specific to optical impairments and the second is due to typical phase locked-loop (PLL) implementation. Jitter from optical links is due to itemized impairments below:  
Figure 2: Simplified clock and data recovery schemes used in coherent receivers [8].  
* Amplified spontaneous emission (ASE) induced by optical line amplifiers can shift the pulse from its ideal location [17].
* Kerr nonlinearity such as self-phase modulation (SPM) and cross-phase modulation (XPM) have effect on timing jitter as shown, respectively, in [18] and [19].
* CD, which its bulk part should be compensated before being able to digitally recover timing information, although a certain residual dispersion tolerance is anticipated but it comes with a penalty. In [20], when CD is convoluted with the phase noise of the local oscillator it causes timing jitter.  
From the electronics side or radio frequency (RF), on both the transmitter and receiver clocks, the dominant sources of jitter in PLLs are the VCO and charge pump (CP). With respect to the VCO, it is subject to jitter accumulation which manifests itself as high phase noise at low frequency offsets with respect to the carrier frequency. This can be expressed with the following simplified [PERSON]' frequency domain equation [21]:  
\[L(f_{m})=\frac{FKTemp}{2P}[\frac{1}{f_{m}^{\,3}}\frac{f_{c}^{\,2}\frac{f_{1}}{f }}{4\text{Q}_{L}^{\,2}}+\frac{1}{f_{m}^{\,2}}\frac{f_{c}^{\,2}}{\text{Q}_{L}^{ \,2}}+\frac{\frac{f_{1}}{f}}{f_{m}}+1] \tag{2}\]  
with:  
Q: Resonator loaded QP: Resonator power  
\(\frac{f_{1}}{f}\): Flicker noise corner  
\(f_{m}\): Offset from carrier  
\(f_{c}\): RF frequency  
F: Oscillator noise figure  
K: Boltzmann's constant  
Temp: Temperature  
Other sources of jitter, as listed in [22], are thermal noise, and flicker noise in clock buffers, internal aperture of the ADC, supply variation and electromagnetic coupling. The latter is due to the decrease in electronic channel lengths when circuits are integrated on the same substrate. Some jitter, or ripple effects, is caused by the closed-loop control. The design of the loop filter order and bandwidth plays a major role in controlling the total jitter induced.
## 2 Materials and Methods
### Artificial Neural Network (ANN)  
The ANN operates by emulating the functionality of the neurons in the human brain. ANN mimics the memorizing and information processing activities of neuronal networks. Hence, it gives computer and information systems human-like classification and approximation capabilities [40]. ANN was used because of its capability to learn non-linear models and also its capability to learn models in real-time. A commonly used ANN is the multilayer perceptron (MLP) [41]. MLP is comprised of an input layer, one or more hidden layers and an output layer of computation node. The ANN is associated with weights which are adjusted during training to minimize classification errors [42]. It can learn a non-linear function approximator for either classification or regression. It is different from logistic regression, in that between the input and the output layer, there can be one or more non-linear layers, named hidden layers. The advantages of multi-layer perceptron are:  
* The capability to learn non-linear models.
* The capability to learn models in real-time (on-line learning).
Finally, based on the results of the PERT model, the DDPG learning model is applied to achieve automatic optimization of resource demand planning, which is called DDPGAO in this paper. In addition, by using the total time difference parameter, the calculation results of the PERT model are used as inputs for optimizing the resource demand plan. The optimization results of the resource demand plan are also fed back to the PERT model, which achieves multilevel planning management from the enterprise to the workshop and from the workshop to the section. Moreover, the optimization results and response times of the DDPG model are compared with those of traditional reinforcement learning models and greedy algorithms, and the advantages of the DDPG model in dealing with discrete production problems are discussed. The DDPG algorithm has a response time 3.0% lower than that of the DQN algorithm. The DDPG algorithm has a response time that is 8.4% shorter than that of the gray search algorithm. The DDPG algorithm has a response time reduced by 19.7% compared to that of the random search algorithm.
## 2 Preliminaries and Problem Description
### Radial Basis Function Neural Network  
As one of the most useful tools for dealing with unknown nonlinear functions, the radial basis function neural network (RBFNN) has been widely applied in many studies. Using an RBFNN to approximate the unknown nonlinear function \(F_{nn}(x):R^{n}\to R\) can be described as  
\[F_{nn}(x)=\Phi^{T}\zeta(x) \tag{1}\]  
where \(x\in R^{n}\) is the input of RBFNN, \(\Phi=[\psi_{1},\cdots,\psi_{l}]^{T}\in R^{l}\) is the weight vector, \(l>1\) is the neural network node number, \(\zeta(x)=[\zeta_{1}(x),\cdots,\zeta_{l}(x)]^{T}\in R^{l}\) is the basis function vector, and \(\zeta_{i}(x)\) is the Gaussian basis function with the following form  
\[\zeta_{i}(x)=\exp\left(-\frac{(x-\ell_{i})^{T}(x-\ell_{i})}{b_{i}^{2}}\right),\ i=1,\cdots,l \tag{2}\]  
where \(\ell_{i}=[\ell_{1},\cdots,\ell_{in}]^{T}\) is the center of the basis function and \(b_{i}\) is the width of the Gaussian function.  
**Lemma 1** ([24]).: _For any continuous nonlinear function \(F(x)\), which is defined over a compact set \(\Omega_{x}\subset R^{n}\), there exists an RBFNN \((\Phi^{*})^{T}\zeta(x)\) such that_  
\[F(x)=(\Phi^{*})^{T}\zeta(x)+\varepsilon(x) \tag{3}\]_where \(\varepsilon(x)\) is the approximation error and bounded \(\Phi^{*}\) is the ideal weight vector with the following form_  
\[\Phi^{*}:=\arg\min_{\Phi\in R^{n}}\left\{\sup_{x\in\Omega_{x}}\Bigl{|}F(x)-\Phi^{ T}\zeta(x)\Bigr{|}\right\} \tag{4}\]
## 1 Introduction
The developed performance analysis model basically reflects only static operating conditions and, therefore, does not take into account the transient characteristics, such as due to a disturbance in flight; however, it is constructed to suit the iterative calculation for initial sizing or optimal design based on simple equations. Finally, the developed model was verified by comparison with data sheets and static thrust test results for actual commercial products. Validation has confirmed that the performance analysis of the electric propulsion system for large-size multicopter UAVs can be more reliably performed through major improvements, including the armature reaction model. However, in order to use the proposed model, a preliminary experiment for each motor product is still needed in order to estimate the armature reaction constant and thermal profile; thus, follow-up research is underway to secure the estimated model.
## 4 Collaborative Distributed Scheduling Approach
### One-Step Collaborative Distributed Scheduling Approach  
The reason for calling this approach a one-step collaborative distributed scheduling approach is because only one step -- transmitting statistical variables to upstream -- is required in the exchange process at the SU stage.
# On the Entropy of Oscillator-Based True Random Number Generators under Ionizing Radiation
## 2 Background
### Entropy Tests  
TRNGs are typically subject to exhaustive tests due to their critical role in security systems. There are currently three trends which can be used to evaluate the quality of a TRNG:  
* **Output Statistical Analysis:** the most extended way of assessing the TRNG quality is testing the statistical distribution of the output using statistical tests [16]. Traditionally, widely known test suites as NIST or Diehard have been used to obtain an initial evaluation of TRNGs [5; 6]. These tests cannot guarantee the entropy of the TRNG because they check the final output (after the post-processing) of the TRNG.
* **Entropy Source Statistical Analysis:** a new trend in TRNG evaluation was introduced in AIS-31 [17] where not only the final TRNG output is evaluated but also the entropy source. Among the different testing approaches stand out the NIST recommendations about entropy sources that include some statistical tests intended for estimating the min-entropy of a random number generator [18]. Of particular interest are the tests intended for generators that may have dependencies in time and/or state, which are commonly known as non independent and identically distributed (non-IID) number generators. These tests are particularly designed to avoid an overestimation of the entropy of these generators.
* **Physical Parameters Analysis:** the estimation of entropy must be based on a carefully constructed model of the random number generation process. Once the stochastic model is set, the measurement of some physical parameters (e.g., jitter measurement) can be used to estimate entropy at the output of the generator. In this line, some interesting proposals have been presented [19; 20].
## 1 Introduction
Despite these advantages, few studies provide confidence intervals for cost contingency estimation [5]. To equip project planners with a realistic range of potential cost outcomes in the form of contingency intervals, advanced uncertainty estimation techniques are needed. Therefore, an important question arises: how can ML techniques enhance the forecast performance of infrastructure cost contingency materialization, especially for projects with high complexity levels? To answer this question, this study has three main research objectives:  
1. To select high-performing ML models for cost contingency prediction;
2. To investigate whether integrating a complexity index can improve prediction performance;
3. To develop interval predictions and assess whether the incorporation of a complexity index improves their accuracy. To achieve these objectives, this study used a dataset of 122 Hong Kong infrastructure projects for model development. Seven ML algorithms were examined for cost contingency prediction. Linear regression and support vector regression (SVR) serve as baseline models, offering interpretability but limited capability in capturing non-linear relationships. Tree-based methods, including decision trees, random forest, gradient boosting, and extreme gradient boosting (XGBoost), leverage hierarchical structures to model variable interactions. Finally, ANNs employ interconnected layers of neurons to capture intricate, non-linear relationships. In addition to point predictions, this study incorporates two interval estimation methods, Monte Carlo dropout and bootstrapping, to quantify model uncertainty. Monte Carlo dropout introduces randomness in neural network predictions, while bootstrapping generates multiple resampled datasets to assess prediction variability. This research contributes to the cost contingency literature in three key ways. First, it employs and compares seven ML algorithms by systematically assessing their predictive performance in cost contingency estimation. Second, it introduces interval prediction methods to capture model uncertainty. To the best of the authors' knowledge, this is the first study to apply interval estimations in cost contingency prediction. Third, it develops a complexity index that accounts for project scope and duration to improve the accuracy of point and interval estimates. These contributions collectively advance the application of ML in cost contingency estimation by improving predictive accuracy and uncertainty quantification.
## 2 Experimental Equipment and Method  
### Experimental System and Measuring Equipment  
The experimental system for milling machining is shown in Figure 1. The experimental system includes the force measuring system, the temperature measuring system, the MQL system, the machine tool, the workpiece, and the milling cutter. The model of the machine tool is a TH5650 vertical machining center. The spindle of the machine tool is controlled by an encoder, which can realize infinitely adjustable speed. The maximum speed of the main motion can reach 6000 r/min. The rapid feed of the \(x\)-axis and y-axis can reach 24 m/min and the \(z\)-axis can reach 15 m/min. The force measuring system mainly includes the Kistler 9257B force measuring instrument, the Kistler 5070 charge signal amplifier, the data acquisition card, and the signal analysis software. The measuring accuracy is 0.01 N, and the data acquisition frequency is 4 kHz. The temperature measuring system includes the \(k\)-type thermocouple, the multichannel acquisition box, the signal amplifier, and the display devices. The data acquisition frequency of milling temperature is 20 kHz.  
The MQL system is shown in Figure 2. The MQL system includes the air pump, the air pressure gauge, the oil tank, the air and oil amount adjustment valves, the frequency generator, and the nozzle. Accu-Lube precision oil spraying device is used for the MQL system. The lubricant used for MQL is Accu-Lube LB-2000 super-lubricated spray-type vegetable cutting oil. The viscosity of cutting oil is 36 cSt at 40 \({}^{\circ}\)C. The strength of the oil film is 60 kg/cm\({}^{2}\).  
Figure 3 shows the measuring equipment, including the 3D measurement laser microscope and the microhardness tester. The 3D laser microscope (LEXT OLS41003D) is selected to measure the surface roughness and observe the microscopic topography. The total magnification is 108 times-17,280 times. The accuracy of the worktable movement along the \(x\)-axis and \(y\)-axis directions is 0.12 \(\upmu\)m and 0.01 \(\upmu\)m in the \(z\)-axis direction. The microhardness tester (MH500) is used for hardness inspection and hardening layer depth measurement of titanium alloy TC21. The dwell time is 10 s, and the test load is 200 gf. The magnifications of the eyepiece and objective are 10 times and 40 times, respectively.  
Figure 1: The experimental system for milling machining.  
Figure 3: Measuring equipment: (**a**) MH500 microhardness tester; (**b**) 3D laser microscope.  
Figure 2: The MQL system: (**a**) air pump, (**b**) oil injector, and (**c**) nozzle.  
Due to the measurement uncertainty and noise, the measurement results may contain large errors [21; 22; 23]. To guarantee measuring accuracy, the surface roughness and milling force have been automatically noising reduced in the measurement software. In addition, to eliminate the effect of measurement uncertainty, the average value of multiple sample points is used as the final measurement result of the microhardness.
# Energy-Efficient Resource Provisioning Strategy for Reduced Power Consumption in Edge Computing +
Our contributions can be summarized as follows:  
* We first implement a normal distribution-based task generation model and add the maximum tolerated delay characteristic of the tasks. We introduce the sleep mode with the lower power consumption for the edge node. * Based on that, we approach the reduced power consumption problem by virtualizing the edge nodes into master and slave nodes and propose a scheduling strategy through balancing the resources of virtual nodes that reducing the power consumption and guarantees the user's delay as well. * We conduct various simulations for our scheduling strategy by using iFogSim. The results are shown from different perspectives to provide conclusions. The remainder of this paper is organized as follows. Section 2 surveys related works. Section 3 describes the model and then formulates the problem. Section 4 investigates the reduced power consumption problem for users and introduces a scheduling strategy. Section 5 presents the simulations and results. Finally, Section 6 concludes the paper. ```
1:Topology \(G\), set of users \(U\);
2:Scheduling scheme \(X\), total power consumption \(E\);
3:for\(i=1\) to \(|V|\)do
4:for\(i=1\) to \(|U|\)do
5: initial \(L=\infty\);
6:for\(k=1\) to \(|u_{i}|\)do
7: initial \(S=\emptyset\);
8: Calculate \(E_{c}(u_{i}^{k})\) and \(E_{t}(u_{i}^{k})\), respectively;
9: Send \(E_{c}(u_{i}^{k})\) and \(E_{t}(u_{i}^{k})\) to \(S\);
10: Find \(\delta\) by setting \(\arg\min_{h}\{S_{h}\}\);
11: Calculate \(E(u_{i})=\sum_{u_{i}^{l}\in U^{l}}E(u_{i}^{l})+S_{\delta}\);
12:if\(E(u_{i})\leq L\) and \(D_{i}\leq D_{i}^{d}\)then
13:if\(k==|u_{i}|\)then
14: Update \(L=E(u_{i})\);
15: break;
16:else
17: Continue to calculate next sub-task;
18:endif
19:else
20: Update \(S=S/S_{\delta}\) and go to line 7;
21:endif
22:endfor
23: Calculate \(E(U_{\eta_{j}})\);
24:endfor
25: Calculate the total power consumption \(E\);
26:endfor
27:return\(X\);
```  
**Algorithm 1** Scheduling Scheme based on Branch and Bound Method (SSBB)  
```
1:Topology \(G\), set of users \(U\);
2:Scheduling scheme \(X\), total power consumption \(E\);
3:for\(i=1\) to \(|V|\)do
4:for\(i=1\) to \(|U|\)do
5:for\(k=1\) to \(|u_{i}|\)do
6: Same as Algorithm 1 in lines 5-6;
7: Find \(\delta\) by setting \(\arg\min_{h}\{S_{h}\}\);
8:if\(D_{i}\leq D_{i}^{d}\)then
9: Calculate \(E(u_{i})=\sum_{u_{i}^{l}\in U^{l}}E(u_{i}^{l})+S_{\delta}\);
10: break;
11:else
12: Update \(S=S/S_{\delta}\) and go to line 6;
13:endif
14:endif
15: Calculate \(E(U_{\eta_{j}})\);
16:endfor
17: Calculate the total power consumption \(E\);
18:endfor
19:Return \(X\) and \(E\);
```  
**Algorithm 2** Scheduling Scheme based on Backtracking Method (SSBM)
# A Review of the Performance of Infilled RC Structures in Recent Earthquakes
## 2 Seismic Behaviour of Infilled RC Structures: Definition of Damage Type
### Representative Damage Type Observed in RC Buildings Due to Earthquakes
#### 2.1.6. Damage Type 6: Damage Associated with Short-Column Mechanism
Figure 10: Damage type 6—example of post-earthquake damage in RC structures: short-column mechanism due to adjacent infill panels with openings. Figure 12 shows two residential buildings that partially collapsed due to the soft-storey mechanism. Figure 12a shows the front view of a six-storey building with a commercial storey on the ground-floor. Figure 12b shows a residential building, 4-storeys' high, with a garage on the ground floor that collapsed during the L'Aquila earthquake. This type of damage can be prevented by ensuring a uniform in-elevation distribution of the infill panels and structural elements (columns and shear walls). Figure 11: Structural configurations vulnerable to trigger out soft-storey mechanisms: (**a**) stiffness difference between storeys; (**b**) columns discontinuity; (**c**) height variations among the storeys. Figure 12: Damage type 7—example of buildings collapsed due to the soft-storey mechanism. In Portugal, particularly in the Lisboa region, several infilled RC structures built in the 1950s decade were designed with this structural configuration, inspired by [PERSON], where the primary system is supported by pillotis to allow the circulation of the people, garage and commerce, as shown in Figure 13. Residential buildings with vertical irregularities, located in Lisboa and Silves, are shown in Figure 13a-c. Additionally, Figure 13d presents a school with the same structural irregularity. Other examples of stiffness irregularities were found at the upper-storeys, which resulted in their collapse without leading to the total failure of the building structure. For example, Figure 14a,b shows a 5-storey building severely damaged due to aftershock following the Gorkha (Nepal) earthquake in 2015. The collapsed storey corresponded in the past to a restaurant with a lower number of infill panels when compared with the remaining storeys. This irregularity resulted in the deformation concentration and high shear demands and thus the collapse of this level. In 2017, the same phenomenon was observed in Mexico, where the partial collapse of the fourth storey of an 8-storey building occurred (Figure 14c). Figure 14: Cont. Figure 13: Examples of buildings with soft-storey configuration in Portugal: (**a**) 11-storey building; (**b**) general view of 11-storey structures with soft-storey configuration; (**c**) 4-storey building; and (**d**) school building. Another issue that can trigger the soft-storey mechanism is the abrupt in-elevation reduction of the cross-section of the column. Another common irregularity is associated with modifying the position between consecutive storeys, of the vertical structural elements, or/and of the non-structural elements.
# An Iterative Optimization Algorithm for Planning Spacecraft Pathways Through Asteroids
## 6 Experiments
### Results in the Informed Setting  
The analyses of average and peak performance discussed in Section 6.2 were also conducted in the informed setting, where algorithms are initialized with the solution generated by the GNN heuristic. Tables 3 and 4, respectively, present the average and best results achieved by FAT-RLS, CEGO, UMM, and GNN in the informed scenario.  
The results indicate that all three meta-heuristic algorithms--FAT-RLS, CEGO, and UMM--showed improvement over the initial GNN-generated solution. However, the performance gains over the baseline were less substantial compared to the uninformed setting, likely because the initial solution in the informed scenario is heuristically constructed, rather than generated randomly, providing a stronger starting point.  
Figure 3: Relative percentage deviations recorded by the executions of the algorithms, aggregated by instance size \(n\), in the uninformed setting.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Instance** & **FAT-RLS** & **CEGO** & **UMM** & **GNN** \\ \hline
10\_42 & 9.97 & 8.12 \(\triangledown\) & 10.29 \(\blacktriangle\) & 12.86 \(\blacktriangle\) \\
10\_73 & 19.69 & 9.59 \(\triangledown\) & 18.54 \(\triangledown\) & 22.67 \(\blacktriangle\) \\
15\_42 & 1.08 & 1.28 \(\blacktriangle\) & 2.21 \(\blacktriangle\) & 3.51 \(\blacktriangle\) \\
15\_73 & 3.11 & 2.60 & 6.38 \(\blacktriangle\) & 12.50 \(\blacktriangle\) \\
20\_42 & 6.45 & 6.92 & 15.71 \(\blacktriangle\) & 22.12 \(\blacktriangle\) \\
20\_73 & 1.29 & 1.42 & 4.93 \(\blacktriangle\) & 5.98 \(\blacktriangle\) \\
25\_42 & 6.34 & 9.46 \(\blacktriangle\) & 14.45 \(\blacktriangle\) & 17.50 \(\blacktriangle\) \\
25\_73 & 1.62 & 8.18 \(\blacktriangle\) & 12.93 \(\blacktriangle\) & 13.72 \(\blacktriangle\) \\
30\_42 & 3.43 & 3.75 & 6.71 \(\blacktriangle\) & 8.27 \(\blacktriangle\) \\
30\_73 & 1.93 & 1.65 \(\triangledown\) & 6.85 \(\blacktriangle\) & 7.63 \(\blacktriangle\) \\ \hline \end{tabular}
\end{table}
Table 3: Average relative percentage deviations for informed experiments. Algorithms marked with \(\blacktriangle\) were significantly outperformed by FAT-RLS, whereas those indicated by \(\triangledown\) showed a significant performance advantage over FAT-RLS.