esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Soft X-Rays: Two Color Operation  
X-ray pump/X-ray probe applications are made possible at the European XFEL SASE3 beamline by generating two X-ray pulses with different photon energies. The controllable temporal delay is enabled by a magnetic chicane and in the future also by an optical delay line, which is an ongoing upgrade project to be implemented between the two undulator sections of cell 2-12 (U1) and cell 14-23 (U2) A schematic for the hardware configuration for two-color operation is depicted in Figure 5a. The measured spectral distribution for single bunches where the same electron bunch is lasing first at 945 eV in U1 and subsequently at 905 eV in U2 is displayed in Figure 5b. The target gas is neon. The flight-tube retardation voltages for the sxPES are individually controlled, and thus we can independently select one group for a certain photon energy and another group for the other photon energy. Detectors at 157.5\({}^{\circ}\), 180\({}^{\circ}\) and 202.5\({}^{\circ}\) (blue) are tuned to \(-60\) V to achieve optimal resolving power for the 945 eV spectrum. Detectors at 22.5\({}^{\circ}\), 0\({}^{\circ}\) and 337.5\({}^{\circ}\) (red) are tuned to \(-20\) V to achieve optimal resolving power for the 905 eV spectrum. Drift tubes on the horizontal plane are selected to take advantage of the higher yield due to the horizontal polarization of the X-rays and the positive \(\beta\) value of the Ne 1s photo line. Figure 5b shows four typical subsequent single-shot spectra where we combined the intensity from three detectors for each color. Flight tubes set to the higher retardation voltage discriminate against photoelectrons emitted by the lower photon energy pulse (blue line). The lower retardation voltage has as a consequence that also the Ne 1s peak emitted from the 945 eV pulse is included in the spectra but with lower spectral resolution (red line). This data set was collected together with the invasive high-resolution grating spectrometer (GS) (black line) [9] to enable us to confirm the PES calibration and also to verify that the PES spectra are representative of the actual spectral distribution.  
Furthermore, two-color operation invalidates information from the X-ray Gas Monitor (XGM) which can only deliver absolutely calibrated pulse energy data for pulses with a single photon energy [6]. Therefore, in addition to spectral and polarization characterization, pulse energy diagnostics also becomes the responsibility of the sxPES during two-color operation. The routine is to first produce lasing only with U1 segments and to correlate the pulse energy measured by the XGM with the peak intensity of the PES for that particular setting. The procedure is then repeated for U2. When two-color operation is activated and after preparing with the described calibrations, the pulse energy determination then relies solely on the PES for both colors [24]. Clearly, Figure 5 shows that not only the spectral distribution changes over the trains, but also the intensity ratio varies dramatically.
# Research on Coal Structure Prediction Method Based on Genetic Algorithm-BP Neural Network
## 2 Geological Overview
### Division of Coal Structure in the Study Area  
Coal structure refers to the characteristics of the internal damage and deformation degree of coal, which occur after the coal seam has undergone various geological actions in the geological history evolution process. Coal structure is divided into primary structural coal and tectonic coal. Primary structural coal refers to a coal seam that retains the primary sedimentary structure and primary structural characteristics. During geological structural movements, coal seams are subjected to stresses such as compression, shear, or tension. These stresses result in physical deformations of the coal body, such as fracturing, wrinkling, or mylonitization, and are accompanied by chemical changes such as the degradation or polymerization of organic matter. These changes lead to significant alterations in the structure and composition of the coal, thus forming structural coal. Structural coal can be further divided into three types according to the degree of coal crushing: crushed-structure coal, crushed coal, and mylonitic coal.  
The coal-core data in the study area show that the main coal rock in the study area has a fragmented structure, with a small amount of coal in primary structure and fragmented structure. Therefore, this study divides the coal structure into three types: primary-structure coal, crushed-structure coal, and crushed coal (Figure 4). Primary-structure coal: the macroscopic coal rock types are clearly defined and the macroscopic coal rock composition can be distinguished. The primary structure is complete, with continuous bedding. The coal body is intact and the cracks do not cross the bedding. There is no crumbling and no structural sliding surface. The hand-tested strength is hard or relatively hard. Crushed-structure coal: the boundaries of macroscopic coal-rock types are clear and the macroscopic coal-rock components are identifiable. There is slight local dislocation and the original structure is slightly damaged. The bedding is easy to identify and the coal body is relatively intact or fragmented. The particle size of the fragments is generally larger than 5 mm, exogenous cracks are well developed, the cracks slightly offset the bedding, wrinkles are not developed, structural slip marks are occasionally seen, and the hand test strength is relatively hard. Crushed coal: the boundaries of macroscopic coal-rock types are generally indistinguishable. The primary structure has been severely damaged, and the bedding is  
Figure 3: Comparison between coal seams and logging curve in Shizhuang block.  
difficult to distinguish. The coal body is broken, with a particle size mostly below 5 mm. The coal body is mostly cut into blocks by cracks, and the hand-tested strength is low.
## 3 Results
### Impact of the Signal-to-Noise Ratio in the Measurements
When the spectral correction is not applied, the mean quantification errors in \(\Delta C_{HbO_{2}}\) and \(\Delta C_{Hb}\) reach a plateau for an SNR value equal to 200.
## 1 Introduction
Assuming the typical phase noise of \(-130\) dBc/Hz at 1 Hz Fourier frequency limited by the resonator self noise, the increase in \(Q\)-factor at 4K should see frequency instabilities as low as \(2\times 10^{-16}\)[28], and if this can be pushed down further due to power optimisations it is strongly feasible to push the stability into the \(10^{-17}\) regime. The other advantage at low temperatures, is the significantly reduced temperature coefficient, in the standard quartz BAW, the coefficient is annulled at around 1 K in operating temperature [29]. However, this value may be raised by varying the cut angle with respect to the anisotropy of quartz [30]. Although, in general all devices are nonlinear in nature, the feasibility to achieve nonlinear regimes are usually limited for a variety of reasons. For example, these regimes usually require considerably high amounts of incident power that may also cause nonlinear effects in auxiliary components, induce noise processes such as flicker noise, and cause the heating. So, in order to reduce this threshold of nonlinearity, it is required to reduce the system losses and increase nonlinear interactions. These requirement, in particular for BAW devices, usually contradict each other leading to trade offs. Another possible solutions is to utilise ultra high Quality factor resonators and couple them to a nonlinear superconducting circuit with a low threshold power. The former can thus provide very narrow spectral lines, while the latter adds the strong nonlinearities required at low powers. Thus, our goal to achieve better performance is to couple BAWs at low temperatures [27] to SQUID amplifiers [31] and operate them with low power oscillations of order \(-80\) dBm. To further increase the power output, a chain of regular cryogenic ultra-low noise amplifiers will be used allowing us to generate reasonable power of order 10 dBm, with fractional frequency instabilities of \(10^{-16}\) or better. In this work we make the first step towards this goal and show the large signal characteristics of a SQUID coupled to a BAW resonator. Similar SQUID-mechanical resonator systems have been used in the past as Gravity Wave detectors [32; 33; 34] and more recently as quantum hybrid systems [35; 36] but only in the non-driven or weakly driven regimes.
# Energy Efficient Power Allocation for the Uplink of Distributed Massive MIMO Systems
## 4 Energy Efficiency Optimization
### Optimal \(P\) for Given \(N\)  
After obtaining \(\theta^{*}\), the optimization problem \(\mathcal{P}_{1}\) can be reformulated as \(\mathcal{P}_{5}\) for given \(N\).  
\[\begin{split}\mathcal{P}_{5}:&\max_{N,P}\quad\quad \frac{K\log_{2}\left[\frac{PN\Phi}{K\sigma^{2}}\right]}{P_{0}+NP_{c}+KP_{c}+ \frac{p}{\xi}}\\ & s.t.\quad\quad P>0,\end{split} \tag{22}\]  
where \(\Phi=\left(\prod_{k=1}^{K}\mathcal{I}_{k}\right)^{\frac{1}{K}}\).  
**Theorem 3**.: _Given \(N\), the optimal transmit power \(P^{*}\) which maximizes the EE \(\tilde{\eta}_{\rm EE}\) is given as_  
\[P*=\frac{K\sigma^{2}\exp\left[W\left(\chi\right)+1\right]}{N\Phi}, \tag{23}\]_where \(\chi=\frac{\zeta N\Phi\left(P_{0}+NP_{c}+KP_{c}\right)}{K\sigma^{2}}\), and the corresponding EE is_  
\[\hat{\eta}_{\text{EE}}^{*}=\frac{KW\left(\frac{\zeta N\Phi\left(P_{0}+NP_{c}+KP _{c}\right)}{K\sigma^{2}}\right)}{\ln 2\left(P_{0}+NP_{c}+KP_{c}\right)}. \tag{24}\]  
Proof.: Since EE is a quasiconcave function w.r.t. \(P\), the first-order derivative shall be zero to maximize EE. Thus, the optimal transmit power should satisfy the following equation:  
\[\frac{d\frac{K\log_{2}\left[\frac{PNb}{K\sigma^{2}}\right]}{P_{0}+NP_{c}+KP_{c }+\frac{\zeta}{\xi}}}{dP}=0, \tag{25}\]  
which can be reformulated as  
\[\frac{P_{0}+NP_{c}+KP_{c}+\frac{P}{\xi}}{\ln 2P}-\log_{2}\left(\frac{PN\Phi}{K \sigma^{2}}\right)=0. \tag{26}\]  
Then, we can conclude that  
\[\frac{\xi\left(P_{0}+NP_{c}+KP_{c}\right)}{P}=\ln\left(\frac{PN\Phi}{K\sigma^{ 2}}\right)-1. \tag{27}\]  
Substituting \(t=\ln\left(\frac{[PERSON]\[PERSON]}{K\sigma^{2}}\right)-1\), we have  
\[\frac{\xi N\[PERSON]\left(P_{0}+NP_{c}+KP_{c}\right)}{K\sigma^{2}}=te^{t}. \tag{28}\]  
Thus, \(t\) can be expressed as  
\[t=W\left(\frac{\xi N\[PERSON]\left(P_{0}+NP_{c}+KP_{c}\right)}{K\sigma^{2}}\right), \tag{29}\]  
where \(W\) is the Lambert \(W\) function defined as \(t=W(t)e^{W(t)}\). Thus, the optimal transmit power \(P^{*}\) can be given as  
\[P^{*}=\frac{K\sigma^{2}\exp\left[W\left(\chi\right)+1\right]}{N\Phi}. \tag{30}\]  
Substituting (30) into the objective function of \(\mathcal{P}_{5}\), we have  
\[\hat{\eta}_{\text{EE}}^{*} =\frac{K\left[W\left(\chi\right)+1\right]}{\ln 2\left(P_{0}+NP_{c}+KP _{c}+\frac{K\sigma^{2}\exp\left[W\left(\chi\right)+1\right]}{\xi N\Phi}\right)} \tag{31}\] \[=\frac{KW\left(\frac{\zeta N\Phi\left(P_{0}+NP_{c}+KP_{c}\right) }{K\sigma^{2}}\right)}{\ln 2\left(P_{0}+NP_{c}+KP_{c}\right)}.\]  
This completes the proof.
# GPU-Accelerated Fock Matrix Computation with Efficient Reduction
## 4 Proposed Method
### Distributed Atomic Reduction Through Replicated Fock Matrix Update
Memory re quirements become a significant consideration when handling very large molecules/basis sets with thousands of basis functions \(M\) on a single GPU. Assuming double-precision floating-point numbers for the Fock matrix, the memory usage of its replicas can be estimated as \(M^{2}\times N_{\text{Fock}}\times 8\) [Byte], scaling quadratically with the number of basis functions \(M\). As shown in Figure 4, for \(M\leq 4000\), the additional memory usage due to the Fock matrix replication has minimal impact on the computable molecular size. For \(M>4000\), the number of replicas \(N_{\text{Fock}}\) becomes somewhat limited. Considering the memory required for other matrices involved in the SCF procedure, the upper limits for the number of replicas can be estimated as \(N_{\text{Fock}}=32\) for \(M=8000\) and \(N_{\text{Fock}}=8\) for \(M=16{,}000\). Nevertheless, the experimental results in Section 5 show that sufficient speedup can be achieved with the proposed method even with a small number of Fock replicas. \[\left(\mu\
u|\lambda\sigma\right) =\sum_{a=1}^{K_{\mu}}\sum_{b=1}^{K_{\
u}}\sum_{c=1}^{K_{A}}\sum_{d= 1}^{K_{\sigma}}g_{abcd}\] \[g_{abcd} =w_{a}w_{b}w_{c}w_{d}\left[ab|cd\right]\] \[F_{\mu\
u} =F_{\mu\
u}+4D_{\lambda\sigma}g_{abcd}\] \[F_{\lambda\sigma} =F_{\lambda\sigma}+4D_{\mu\
u}g_{abcd}\] \[F_{\mu\lambda} =F_{\mu\lambda}-D_{\
u\sigma}g_{abcd}\] \[F_{\
u\sigma} =F_{\
u\sigma}-D_{\mu\lambda}g_{abcd}\] \[F_{\mu\sigma} =F_{\mu\sigma}-D_{\
u\lambda}g_{abcd}\] \[F_{\
u\lambda} =F_{\
u\lambda}-D_{\mu\sigma}g_{abcd}\]  
Figure 3: Distributed atomic reduction method through the replicated Fock matrix update. ```
0: Primitive shells \(S_{0},S_{1},\ldots,S_{N-1}\)
1: Upper bound factors for each task \(Q_{00},Q_{01},\ldots,Q_{N-1N-1}\)
2: Density matrix \(\boldsymbol{D}\)
3:for each GPU thread in parallel do
4: Get a quartet of primitive shells \(S_{\alpha}\), \(S_{\beta}\), \(S_{\gamma}\), and \(S_{\delta}\)
5:if\(Q_{\alpha\beta}Q_{\gamma\delta}\geq\theta\)then
6:\(k\gets I\) mod \(N_{\text{Fock}}\)
7:for each combination \(G_{\alpha}\), \(G_{b}\), \(G_{c}\), and \(G_{d}\) from \(S_{\alpha}\otimes S_{\beta}\otimes S_{\gamma}\otimes S_{\delta}\)do
8:if\(\sqrt{|ab|ab|}\sqrt{[cd|cd]}\geq\theta\)then
9:\(g_{abcd}\gets w_{a}w_{b}w_{c}w_{d}[ab|cd]\)
10:\(F_{k}(\mu,\
u)\gets F_{k}(\mu,\
u)+4D_{\lambda\sigma}\times S_{abcd}\)
11:\(F_{k}(\lambda,\sigma)\gets F_{k}(\lambda,\sigma)+4D_{\mu\
u}\times g_{abcd}\)
12:\(F_{k}(\mu,\lambda)\gets F_{k}(\mu,\lambda)-D_{\
u\sigma}\times S_{abcd}\)
13:\(F_{k}(\
u,\sigma)\gets F_{k}(\
u,\sigma)-D_{\mu\lambda}\times S_{abcd}\)
14:\(F_{k}(\mu,\sigma)\gets F_{k}(\mu,\sigma)-D_{\
u\lambda}\times S_{abcd}\)
15:\(F_{k}(\
u,\lambda)\gets F_{k}(\
u,\lambda)-D_{\mu\sigma}\times S_{abcd}\)
16:endif
17:endfor
18:endif
19:endfor
20:\(F\gets F_{0}+F_{1}+\cdots+F_{N_{\text{Fock}}-1}\)
```  
**Algorithm 1** Fock matrix construction using the distributed atomic reduction method
factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.  
Keywords:landslide; evolutionary optimization algorithm; prediction accuracy; goodness-of-fit; machine learning; China +
Footnote †: journal:
## 2 Related Work
### MEC Implementation and Verification  
Here, we discuss two points with respect to the various implementations of MEC: the orchestrators and the PoC that are distributed worldwide. First, regarding orchestrators, in [45], the MEC Service Function (MSF) is considered an orchestrator. Furthermore, the authors discuss that in MSF, applications are deployed either in MEC or the cloud. In [46], the authors describe signaling to MEC and discuss how users connect with MEC applications.  
However, based on the above, there is no discussion about who will hold what functions as MEC/Cloud Orchestrator, and this is an important issue to be discussed. Thus, the present paper proposes who should have what functions in the MEC/Cloud Orchestrator.  
Next, in [47], performance is evaluated by implementing the edge computing system in a chip set. In [48], the authors implemented a framework linking edge computing and cloud computing as a use case. In addition, other researchers have implemented prototypes for IoT devices [49]. There are examples of implementing a controller for an edge computing platform. For example, [50] implements container-based Network Functions Virtualization Infrastructure (NFVI) control using Kubernetes. References [51; 52; 53; 54; 55] discuss the comparison of Fog computing, cloudlet, and MEC. Regarding to application implementation in edge computing, several studies have implemented and demonstrated experiments focusing on Augmented Reality (AR) and image analysis and discussed processing effectiveness at the edge. Based on the above explanation, it is not easy to understand the effect of MEC on PoC because the system has not been implemented in terms of E2E, and thus the effect of MEC on PoC cannot be evaluated by comparing the performances offered by the E2E network without any MEC and offered by the MEC deployment. Therefore, in the present paper, the system is implemented in E2E and deployed outdoors. By evaluating PoC, we validate MEC in E2E and show its effectiveness.  
From the above, we now briefly explain the differences between the two points of the edge computing demonstration experiment and the implementation of the orchestrator, respectively. There has been no discussion about the kinds of applications running on virtualization platforms in edge computing regarding the former. Therefore, the effect of edge computing is difficult to understand because it has not been implemented based on the discussed architecture even in demonstration experiments. Regarding the latter, the use case of the orchestrator has not been defined, with only the control of virtualization implemented. Nonetheless, it is a known technology, and there has been discussion about the orchestrator, including Management and Orchestration (MANO). However, many uncertainties remain regarding E2E system implementation and the design of MEC/Cloud Orchestrator including scenarios.
## 8 Discussion
* Assuming the applicability of a single-attack mitigation solution to defend composite ones would not be a feasible strategy. Composite ones require more collaborative and distributed mechanisms to be effectively detected and mitigated. * It is challenging to mitigate hybrid attacks without addressing the different RPL routing attacks with a one-solution-fits-all approach. Jointly addressing and optimizing multiple network security aspects at different levels need to be achieved in a customizable and efficient manner. The fusion of multiple technological advancements into an RPL security architecture would be a feasible consideration in this regard. * Validating the feasibility of any solution in only small-scale scenarios is not sufficient to ensure its efficiency and avoid scalability issues. More emphasis needs to be placed on large-scale experimentation when validating RPL security solutions. * It is important to emphasize not adding much to the complexity of the RPL design when enhancing RPL functionality to a further security limit. Security solutions need to be effective without imposing additional entities, high computational complexity, and unnecessary communication overhead. This can be approached with effective modifications to certain RPL operational properties, particularly those related to topology establishment and maintenance.
# Noise Cancellation Method Based on TVF-EMD with Bayesian Parameter Optimization
## 2 Preliminaries
### Bayesian Optimization  
The BO-TPE works by assuming that the black-box function is sampled from a Gaussian mixture model and maintains the posterior distribution of that function while making observations. The posterior probabilities are updated using new sample points at each iteration. To prevent the results from falling into local optima, the BO algorithm adds a stochastic algorithm to balance stochastic exploration and posterior distribution.  
The probabilistic surrogate model and acquisition function are the core components of the BO method, and the TPE is the probabilistic surrogate model of the study. The BO algorithm is highly efficient for hyperparameter estimation, as it exhibits excellent convergence.  
The objective of Bayesian optimization is to determine the global maximum or minimum value of the objective function in the hyperparameter space. This study aimed to determine the maximum value of the true fitness objective function on a bounded set \(\chi\) in a limited number of iterations. The mathematical model of the BO algorithm is as follows:  
\[x^{*}=\operatorname*{argmax}_{x\in\chi}f(x) \tag{4}\]  
where the point \(x^{*}\) that maximizes the surrogate function is the proposed point for evaluating the objective function \(f(x)\).  
Let us suppose that the observation values are of the form \(D_{1:t}=\{x_{i},y_{i}\}_{i=1}^{t}\), where \(y_{i}\) is the generalization accuracy of the algorithm under \(x_{i}\). This can be considered the generalization accuracy of random observations \(y=f(x)+\varepsilon\), where the noise introduced into the observation \(\varepsilon\) is assumed to satisfy \(\varepsilon\sim N\big{(}0,\sigma_{\varepsilon}^{2}\big{)}\). To explore \(x^{*}\) in the hyperparameter space, the acquisition functions were obtained, in which the maximum of the function was calculated as  
\[x_{t+1}=\operatorname*{argmax}\,x_{t}(x,D) \tag{5}\]  
In this study, the bandwidth threshold \(\xi\) and B-spline order \(n\) were the independent variables \(x\) of the BO model, and the flowchart of the Bayesian optimization was outlined as follows (see Figure 1). First, we determined whether the parameters of the model, \(\xi\) and \(n\), were initialized, and if not, the initial parameters were generated randomly. If the parameters were initialized, the values were brought into the tree Parzer estimator surrogate model. Subsequently, it was judged whether the acquisition function reached its maximum value when given \(\xi\) and \(n\). If the maximum value of the target function was satisfied, the value of the two parameters were output. If not, the values of the surrogate model parameters were updated until the requirements were met.