esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Using a recently developed X-ray streaking technique [29; 30], we have probed the photo-absorption cross-section of a Ni thin film at the Ni \(M_{2,3}\)-edges using circularly polarized XUV pulses for five probing energies, and both helicities and directions of the applied magnetic field. This allowed us to measure the time-dependent absorption and magnetic circular dichroism (MCD) time traces, and to retrieve information about transient changes occurring in the electronic and spin sub-systems close to the Fermi level. Our results show that the absorption and MCD dynamics both depend on the probing XUV wavelength. While providing a detailed microscopic model that can account for our observations is well beyond the scope of the present paper, we see that, to a first approximation, a redshift of the absorption and MCD spectra on very short timescales (<100-200 fs) can rationalize our data, which is in agreement with recent findings [34]. However, our analysis also hints at more complex modifications of those spectra, and we hope that our experimental findings will stimulate further work in order to obtain a truly quantitative picture of femtosecond charge and spin dynamics in magnetic thin films excited by ultrashort laser pulses.  
Figure 5: Calculated static MCD spectra (black line) and measured MCD spectra for different time delays (colored symbols).  
**Author Contributions:** [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] conceived the experiments, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] fabricated the zone plate optics, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], I.L.-Q., [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] performed the experiment at FERMI. [PERSON]. ([PERSON]) and [PERSON] grew the sample. [PERSON]. ([PERSON]), [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] interpreted and discussed the results. [PERSON]. ([PERSON]) and [PERSON] prepared the figures and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded within the EU-H2020 Research and Innovation Programme, No. 654360 NFFA-Europe (BR). The authors are grateful for the financial support received from CNRS-MOMENTUM, the SNSF project (No. 200021-160186), UMAMI ANR-15-CE24-0009, and the CNRS-PICS programs.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** We acknowledge the great support of the technical team of FERMI, at Trieste, as well as the help of [PERSON] and [PERSON] from Sorbonne Universite. We also thank [PERSON] for stimulating discussions.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Detection of Surface Water and Floods with Multispectral Satellites
###### Abstract  
The use of multispectral satellite imagery for water monitoring is a fast and cost-effective method that can benefit from the growing availability of medium-high-resolution and free remote sensing data. Since the 1970s, multispectral satellite imagery has been exploited by adopting different techniques and spectral indices. The high number of available sensors and their differences in spectral and spatial characteristics led to a proliferation of outcomes that depicts a nice picture of the potential and limitations of each. This paper provides a review of satellite remote sensing applications for water extent delineation and flood monitoring, highlighting trends in research studies that adopted freely available optical imagery. The performances of the most common spectral indices for water segmentation are qualitatively analyzed and assessed according to different land cover types to provide guidance for targeted applications in specific contexts. The comparison is carried out by collecting evidence obtained from several applications identifying the overall accuracy (OA) obtained with each specific configuration. In addition, common issues faced when dealing with optical imagery are discussed, together with opportunities offered by new-generation passive satellites.  
multispectral satellite imagery; water detection; flood mapping; multispectral indices 2022146005[[https://doi.org/10.3390/rs14236005](https://doi.org/10.3390/rs14236005)]([https://doi.org/10.3390/rs14236005](https://doi.org/10.3390/rs14236005))
###### Abstract  
Rolling bearings are the core component of rotating machinery. In order to solve the problem that the distribution of collected rolling bearing data is inconsistent during the operation of bearings under complex working conditions, which results in poor fault identification effects, a fault diagnosis method based on multi-source deep sub-domain adaptation (MSDSA) is proposed in this paper. The proposed method uses CMOR wavelet transform to transform the collected vibration signal into time-frequency maps and construct multiple sets of source-target domain data pairs, and a rolling bearing fault diagnosis network based on a multi-source deep sub-domain adaptive network is established. The network uses shared and domain-specific feature extraction networks to extract data features together. At the same time, the local maximum mean discrepancy (LMMD) was introduced to effectively capture the fine-grained information of the category. Each set of data was used to train the corresponding classifier. Finally, multiple sets of classifiers were combined to reduce the classification loss of the target domain samples at the classification boundary to achieve fault identification. In order to make the training process more stable, the network used the Ranger optimizer for parameter tuning. This paper verifies the effectiveness of the proposed method through two sets of comparative experiments. The proposed method achieves 97.78%, 99.65%, and 99.34% in three migration tasks. The experimental results show that the proposed method has a high recognition rate and generalization performance.  
rolling bearing; multi-source subdomain adaptive; Ranger optimization algorithm; deep residual network +
Footnote †: journal: Applied Sciences
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 4 Adaptive GLAN Using Reinforcement Learning  
In this section, we present an adaptive GLAN (AGLAN) protocol for UAV networks. We aim to reduce the memory and computational requirements by applying RL to the GLAN algorithm. Learning is carried out to converge the angle of the FR determined by the GLAN algorithm to an optimal angle using attention-enabled deep Q-networks (\(aDQN\)). A customized RL algorithm, \(aDQN\), learns quickly and accurately by combining the attention mechanism and the existing DQN.  
RL is an algorithm that finds a goal by learning through mistakes and rewards. The objective is to learn the optimal behavior or policy. Since it is a UAV environment where missions must be performed quickly with little data, a value-based algorithm is more suitable than a policy-based algorithm. Furthermore, by applying the attention mechanism to DQN, we speed up the learning rate of DQN. In addition, \(aDQN\) can set target policies so that the UAV network can be optimized, establish optimal policies through sufficient exploration, and improve the learning accuracy by reducing correlations between states using experience replay.  
We obtain location information and set the routing address of each node. By associating the IP address with the geographic location, the FR is determined. Thus, in the routing process, the unnecessary waste of resources can be prevented. However, this FR is crucial. To obtainan optimal FR, several aspects should be considered, besides the positions of UAVs.
## 2 Study Background
[33] selected five factors from the literature: strategy, economics, risk, quality and environment. They proposed a multi-criteria model based on these factors by using AHP and ELECTRE III. The AHP technique provides the weights of the criteria, whereas the ELECTRE III calculates the final rankings. In the context of intuitionist two tuple linguistic information, [PERSON] et al. [34] suggested two techniques for best-worst methods, namely the linear best-worst method and the Euclidean best-worst method, to produce the best criteria priority vector for Multi-Criteria Group Decision Making (MCGDM) situations. Similarly, [PERSON] et al. [35] presented a DSS based on multi-criteria decision analysis (MCDA) techniques for sustainable evaluation. The capabilities of the system were demonstrated through the use of renewable energy sources in European countries. [PERSON] et al. [36] solved a numerical example by six different MCDA techniques that resulted in a complete ranking. [PERSON] et al. [37] devised a system based on the AHP framework's criteria, with a decision matrix that was built by utilizing consistent fuzzy preference relations. The symmetrical decision matrix technique was used. The suggested methodology's rationale and effectiveness were demonstrated through a range of numerical explanations and an analysis of quantitative findings. A summary of the related work is given in the following Table 1. \begin{table}
\begin{tabular}{c c c} \hline
**Techniques** & **Source** & **Description** \\ \hline \multirow{3}{*}{Fuzzy logic [14]} & \multirow{3}{*}{Empirical study} & Breach of contract, lack of expertise and experience, cultural difference, no project management experience, costly amendments in contract, disputes and litigation, financial stability of providers, security breach, contract is not flexible and lack of innovation. \\  & & Literature & Confidentiality, availability, integrity and six sub factors. \\  & & Literature review & Impact, perspective, dependency, type and sub factors. \\  & & Literature & Performance, functionality, maintenance. \\  & & Literature, interviews & Software economic, software quality and sub factors. \\  & & Literature review & Project setup, project management, project complexity and sub factors. \\  & & Empirical study & Confidentiality, perturbability, integrity, availability and other sub factors. \\  & & Fuzzy AHP [21] & Literature, experts & Availability, CPU, transaction cost, security, storage and performance. \\  & & & Portability, efficiency, reliability, maintainability, functionality, usability and sub factors. \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the study background.
## 4 Simulation Results and Discussion
#### 4.2.2 Effect of the Junction Nodes of the Power Line  
The number and position of nodes are usually different for the power lines with different topologies. In order to study the influence of the junction nodes of the power line on the total cost, we add a branch with a load in the middle of each section of the test power line, which is the feeder 4 connected to bus 6 of the IEEE RBTS. The adding nodes are numbered from 24 to 45 accordingly, and the load power is set as the average power value of the test system. Using the same method to solve the optimal configuration model, the optimal installation number of the acquisition unit of the fault indicator increases from 14 to 29, and the corresponding installation position is point 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 15, 18, 22, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 40, 42, 43, 44, and point 45, as shown in Figure 11. The optimal installation number of the collection unit still equals 1, but the corresponding installation position changed to point 28, as shown in Figure 11. The impact of the junction nodes of the power line on the optimal configuration scheme and the influence of the total cost is shown in Table 5.  
It can be seen from Figure 11 and Table 5 that the number of IoT-based acquisition units of the fault indicators will increase with the increment of the power line junction nodes. The total cost will increase from 228.48 USD to 429.36 USD when the number of junction nodes increases from 23 to 45. The reason is that with the addition of nodes and loads, the interruption cost, which is given by Equations (2)-(4) will increase greatly. Hence, it is necessary to install more fault indicators to ensure the reliability of power supply and the investment performance at the same time. In conclusion, for different power lines, the total cost will be higher with the addition of nodes.  
Figure 11: The installation position of IoT-based fault indicator when the junction nodes of the power line change.
## 4 A Mixed Sieving Algorithm
Then, among the uniformly sampled vectors, we set aside \(2^{c_{p}n}\) vector pairs as centers for each hypercube. Thus, in Theorem 3, we replace \(|\mathcal{C}|\) by \(2^{c^{(p)}n+o(n)}\). Thus, space complexity is \(2^{c_{space}n+o(n)}\) where \(c_{space}=c_{s}+\max(c^{(p)},c_{b}/2)\). It takes \(O(n)\) time to map each vector to a large hypercube, and then at most \(2^{c_{p}n+o(n)}\) time to compare it with the \"AKS sieve-centers\" within each hypercube. Thus, the time complexity is \(2^{c_{time}n+o(n)}\) where \(c_{time}=\max(c_{space}+c_{p},c_{b})\). Theorem 4: _Let \(\gamma\in(0,1),\xi>1/2\) and \(A\) be some constant. Given a full-rank lattice \(\mathcal{L}\subset\mathbb{Q}^{n}\), there is a randomized algorithm for \(\mathcal{SVP}^{(p)}\) with a success probability of at least \(1/2\), a space complexity of at most \(2^{c_{space}n+o(n)}\), and a running time of at most \(2^{c_{time}n+o(n)}\). Here, \(c_{space}=c_{s}+\max(c^{(p)},\frac{c_{b}}{2})\) and \(c_{time}=\max(c_{space}+c_{p},c_{b})\). \(c_{s}=-\log\Bigl{(}0.5-\frac{1}{4\xi}\Bigr{)}\), \(c_{b}=\log\Bigl{(}1+\frac{2\xi(2-\gamma)}{1-\gamma}\Bigr{)}\), \(c_{p}=\log(1+A)\) and \(c^{(p)}=\log\Bigl{(}2+\frac{2}{A\gamma}\Bigr{)}+c_{p}\)._  
_In the Euclidean norm, we have \(c_{2}=0.401-\log\bigl{(}\frac{2}{A}\bigr{)}\), \(c^{(2)}=\log\Bigl{(}2+\frac{2}{A\gamma}\Bigr{)}+c_{2}\), \(c_{s}^{(2)}=-0.5\log\Bigl{(}1-\frac{1}{4\xi^{2}}\Bigr{)}\) and \(c_{b}^{(2)}=0.401+\log\Bigl{(}\frac{2\xi(2-\gamma)}{1-\gamma}\Bigr{)}\). Comparison with previous provable sieving algorithms [20; 27; 28]_  
In the Euclidean norm with parameters \(\gamma=0.645,\xi=0.946\) and \(A=2^{0.599}\), we obtain a space and time complexity of \(2^{2.25n+o(n)}\), while the List Sieve Birthday [26; 28] has space and time complexities of \(2^{1.233n+o(n)}\) and \(2^{2.465n+o(n)}\), respectively. We can also use a  
Figure 4: One iteration of the mixed sieve in the \(\ell_{2}\) norm. (**a**) A number of vector pairs (solid black dots) with a (Euclidean) length of at most \(R\) are sampled. (**b**) The space is divided into hypercubes with diagonal length \(r^{\prime}\), and the vector pairs are mapped into each hypercube. (**c**) Within each hypercube, some vector pairs are selected as centers (red dots) and a hypercube is further sub-divided into a number of \(\ell_{2}\) balls, centered around these red dots. Then, vector subtraction is performed between the center and the vector pairs in each \(\ell_{2}\) ball (like AKS). different sieve in the second level, such as List Sieve [27], etc., which works in \(\ell_{2}\) norm and is faster than the AKS sieve. We can therefore expect to achieve a better running time. The Discrete Gaussian-based sieving algorithm of [PERSON] et al. [20] with a time complexity of \(2^{n+o(n)}\) performs better than both our sieving techniques. However, their algorithm works for the Euclidean norm and, to the best of our knowledge, it has not been generalized to any other norm.
## 2 Preliminaries
### Variational Autoencoders  
In recent years, variational autoencoders (VAE) [38] have become one of the most popular methods for unsupervised learning of complex distributions [48]. The VAE model uses deep neural networks [49] as standard function approximators and can be trained using stochastic gradient descent [50]. In image generation [48; 51], the VAE model has been proven to be effective in generating wide varieties of complex data. In RL [24], many works use VAE to learn low-dimensional representations of the environment, leading to significant improvements [52; 53].  
Assume that samples from a data set \(x\in\mathcal{X}\) are generated from some hidden (latent) random variables \(z\) based on a generative distribution \(p_{\alpha}(x|z)\) with unknown parameters \(\alpha\). The prior distribution over the latent variables \(z\) is a Gaussian distribution with \(\mathbf{0}\) mean and unit variance \(p(z)=\mathcal{N}(z;\mathbf{0},\mathbf{I})\). The VAE model learns a variational parametric distribution \(q_{\sigma}(z|x)=\mathcal{N}(z;\mathbf{\mu},\mathbf{\Sigma},\sigma)\) to approximate the true posterior probability \(p(z|x)\). In addition, the approximation of the true posterior starts with the computation of the KL divergence \(D_{KL}(q_{\sigma}(z|x)\|p(z|x)\,)\). Moreover, the lower bound of evidence \(\log p(x)\) (ELBO) is derived as follows:  
\[\log p(x)\geq\mathbb{E}_{z\sim q_{\sigma}(z|x)}[\log p_{\alpha}(x|z)]-D_{KL}(q _{\sigma}(z|x)\|p(z)\,) \tag{4}\]  
A VAE model is composed of an encoder and a decoder. Specifically, the encoder receives samples and generates a Gaussian variational distribution \(p(z|x;\sigma)\). The decoder accepts the samples generated by the Gaussian variational distribution and reconstructs the original input \(x\). In addition, the VAE model is usually trained by a parameterization trick [38]. \(\beta\)-VAE [54] proposes taking advantage of the parameter \(\beta\) which is positive to control the balance between the KL divergence and the reconstruction loss:  
\[L(x;\sigma,\alpha)=\mathbb{E}_{z\sim q_{\sigma}(z|x)}[\log p_{\alpha}(x|z)]- \beta D_{KL}(q_{\sigma}(z|x)\|p(z)\,) \tag{5}\]
## 3 Packet Delays within a Router
### Measurement of Delays  
Identical tests were undertaken using the ping command, which transmits ICMP packets, to quantify the delay across a router using an OS with basic and then with advanced functionality. The variation in times in the individual results, which can be as great as 100%, is due to the variation of the processes in the Operating System. Results were analyzed using histogram techniques and plotted in Figure 2 where the x-axis shows the measured delay in \(\upmu\)secs and the y axis shows the number of times this value was obtained in a set of 1000 pings _i.e._, 5000 ICMP request and replies.  
\begin{table}
\begin{tabular}{l c c c} \hline  & **OS Size** & **Number of Processes** & **Active Processes \textgreater{} 2** \\ \hline Basic Functionality & 12 MBytes & 73 & 32 \\ Advanced Functionality & 29 MBytes & 184 & 51 \\ \hline \end{tabular}
\end{table}
Table 1: Operating System (OS) Comparison.  
Figure 2: Delay through router with different OS.  
Figure 2 clearly shows the difference in delays attributed to the OS version where it can be seen that there is an increased delay introduced by the Advanced OS of about 25%.
## 2 Methods
### EEG Data Acquisition and Analysis  
The subjects sat on a chair in a room with the ambient noise blocked. Their resting state quantitative EEG was recorded with their eyes open for 4 minutes, and then with their eyes closed for 4 minutes. It was recorded both before and after the participants had watched the 3D VR video clip. EEG signals were recorded using a Quick Cap with 62 Ag-AgCl electrodes and NeuroScan SynAmps (Compumedics USA, El Paso, TX, USA). The electrodes were attached in accordance with the extended 10-20 system. The vertical electrooculogram (EOG) was recorded by attaching the electrodes above and below the left eye, while the horizontal EOG was recorded by attaching the electrodes to the outer cannths of each eye. We analyzed the resting EEG data with the participants' eyes open and closed using CURRY7 (Compumedics USA, Charlotte, NC, USA), which is commonly used for EEG pre-processing. Gross artifacts were removed from the recorded data by visual inspection. Artifacts caused by eye blinks and eye movements were removed using the covariance analysis of CURRY 7 [26]. The pre-processed EEG data were divided into multiple epochs by a length of 2 seconds. Any epochs including significant physiological artifacts (amplitude exceeding \(\pm\)100 \(\mu\)V) at any site, over all 62 electrodes, were excluded from the analysis. Power spectral analysis was used to compress the rhythmic information of the brain wave signals. In power spectral analysis, periodogram function in MATLAB R2017b (MathWorks, Natick, MA, USA) was used to calculate power spectral density of each epoch. The spectral power was then averaged with respect to randomly selected 30 epochs.  
The band powers were classified into seven frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha1 (8-10 Hz), alpha2 (10-12 Hz), beta1 (12-18 Hz), beta2 (18-30 Hz), and gamma (30-50 Hz). The powers were averaged into three regions: anterior (FP1, AF3, F1, F3, F5, F7, Fpz, Fz, FP2, AF4, F2, F4, F6, and F8), middle (T7, C1, C3, C5, Cz, T8, C2, C4, and C6), posterior (P1, P3, P5, P7, PO3, PO5, PO7, O1, Pz, Poz, Oz, P2, P4, P6, P8, PO2, PO6, PO8, and O2), and global.
## 4 Case Study
These data were collected continuously and systematically to ensure a representative sample of actual operations. Next, the data were cleaned and prepared, eliminating any anomalies or errors that could have distorted analysis results. Following the completion of the data preparation procedures, a statistical analysis was conducted to identify the distribution that best represents the set-up time distribution. After carefully analyzing and comparing opinions with experienced personnel, it was determined that the normal log distribution was the most appropriate for this context. Once the distribution analyses were complete, a further operation was conducted to satisfy the software's request to enter the set-up time distribution per individual machine. The operation involved the parameters calculation of the normal log distribution among the machines according to a proportional logic. This was achieved through observations, in-place data collection, and inquiries from operating and line management personnel. Based on the parameters, a percentage weight was assigned to each machine to reflect the relative complexity, importance, and time requirements of each machine compared to the overall set-up time of the entire line. By multiplying the parameters of the distribution of the entire line by the corresponding attributed weight, the software can accurately simulate set-up times during process operations. Figure 4 illustrates the resource utilization and operational states for various machines in the production system, highlighting the effectiveness of resource allocation and identifying inefficiencies. The bar chart categorizes machine states into multiple segments, such as Working, Setting up, Blocked, Waiting, and Failed, providing a detailed breakdown of machine activity percentages. The simulations accurately reflected the observed cycle times, set-up times, and failure rates. Moreover, the model exhibited the capacity to adapt to disparate operational scenarios, substantiating its utility as an instrument for optimizing and administering the production process. The data analysis and operational staff feedback confirmed the model's reliability. As illustrated in Figure 3, the cartooning machine has been identified as the main bottleneck in the production line, causing significant inefficiencies. By analyzing utilization data, it was found that the cartooning machine had the highest rates due to delays and lack of synchronization with other equipment. Strategies such as upstream optimization, improved synchronization, predictive maintenance, and capacity expansion were proposed to address these issues. The findings, enabled by Digital Twin, provide a systematic approach to improving productivity.