esa-sceva/satcom-chunk-collection huggingface dataset:
# Resonant X-ray Emission Spectroscopy with a SASE Beam
## 4 Conclusions  
In this work, we further investigated the possibility to perform simultaneous X-ray absorption spectroscopy (XAS) and X-ray emission spectroscopy (XES) measurement at XFELs with the stochastic SASE beam. The described methodology allows obtaining the combined XAS and XES data in the form of an RXES plane reconstructed from the measured incident SASE and X-ray emission spectra [10]. Conventional XAS spectrum or RXES plane acquisition involves special treatment of the incident X-ray beam, e.g., monochromatization [19], beam splitting [20], or seeding [21], which reduces the XFEL beam intensity on the sample or extends the XFEL pulse duration [22]. The described approach does not require any XFEL beam treatment and engages a scanning-free X-ray emission spectrometer, thus allowing a quick acquisition of the entire RXES plane at high energy resolution using the bright SASE beam and offering temporal resolution solely restricted to the pulse duration. The number of pulses required to reach the reconstructed RXES plane's precision of interest can be determined in a single measurement.  
Conceptualization, Y.K., [PERSON] ([PERSON]) and W.B.; methodology, Y.K. and W.B.; software, All authors; validation, [PERSON] ([PERSON]) and C.M.; formal analysis, W.B.; investigation, [PERSON] ([PERSON]) and W.B.; resources, All authors; data curation, W.B. and Y.K.; writing--original draft preparation, [PERSON], [PERSON] ([PERSON]) and [PERSON]; writing--review and editing, All authors; visualization, [PERSON], [PERSON] and [PERSON]. ([PERSON]); supervision, All authors; project administration, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science Centre (Poland) under Grant No. 2017/27/B/ST2/01890 (for spectral and temporal properties of XFEL beams) and under Grant No. 2020/37/B/ST3/00555 (for electronic structure studies of nanoparticle systems).  
Not applicable.  
Figure 2: The dependence of the reconstructed 3D RXES plane’s noisiness on the number of XFEL shots recorded (black) calculated with Equation (4) and estimation based on a function \(\sim 1/\sqrt{k}\) (blue). Also shown is the methodological threshold \(m\) (red)—minimum number of shots required for reconstruction, equal to the number of points in the incident spectra (in the present study equal to 225).  
Data Availability Statement: Raw data were generated at the X-ray Pump Probe instrument of the Linac Coherent Light Source. Derived data supporting the findings of the presented study are available from the corresponding authors on reasonable request.  
Not applicable.  
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# XGBoost-Enhanced Graph Neural Networks: A New Architecture for Heterogeneous Tabular Data
## 3 Models and Methods
### Graph Neural Networks (GNNs)  
Given an attribute graph G = (V, E, X), where x\({}_{i}\) is the d-dimensional feature vector of node vi, the GNN algorithm can learn to generate a node representation for each node \(v_{i}\in\) V by implementing the aggregate function combining the function sums \(g_{i}\). These two functions are typically iterated multiple times in each graph neural network layer in order to continuously update the node representation as information is passed. Assuming that we are training an m-layer GNN, the nodes embedded in the mth layer, i.e., \(g_{i}(m)\), can be obtained by using Equations (9) and (10):  
\[\text{a}_{i}^{(k)}=\text{aggregate}^{(k)}(\text{h}_{j}^{(k-1)}:v_{j}\in\mathcal{ N}(v_{i})), \tag{9}\]  
\[\text{h}_{i}^{(k)}=\text{combine}^{(k)}(\text{h}_{i}^{(k-1)},\text{a}_{i}^{(k )}), \tag{10}\]  
of which \(g_{i}(0)=\text{x}_{i}\), \(g_{i}=g_{i}(m)\), the aggregate\({}^{(m)}(.)\) and combine\({}^{(m)}(.)\) are the aggregation and combination functions of the mth layer, respectively.  
So how does the GNN learn from a graph representation? Again, in a graph G = (V, E, X) with attributes, for each node \(v_{i}\in\) V, we obtain its derived node representation \(g_{i}\). By reading out the function R\((.)\), the embedding of all nodes is mapped to a representation \(g_{G}\) of the whole graph G. This readout function can be a simple permutation-invariant function, such as a summation or pooling method.  
When dealing with heterogeneous graphs, traditional GNN models (e.g., GCN, GAT, APPNP, and AGNN) all require some adjustments or special designs to accommodate the diversity of nodes and edges. By introducing node-type encoding, considering edge-type information, utilizing meta-paths, designing multi-type attention modules, or employing type-aware aggregation and hybrid strategies, these models can better capture complex structures and relationships in heterogeneous graphs and improve performance on heterogeneous graphs. For processing heterogeneous data, we chose a traditional GNN model over a specially designed heterogeneous GNN model combined with XGBoost. This was done because of model complexity, data characteristics, the difficulty of combining models, computational resources and efficiency, and the ability to understand and withstand errors in the model.
## 3 Routing Protocols for UWSN
### Protocols Facturing Node Mobility  
This category of routing protocols considers the dynamic nature of the sensor nodes and the movement thus consider the dynamic depth of the nodes while choosing the route from source to the sink. VBF, HH-VBF, DBR, CoDBR and VTPfall into this category. Even though DMBR falls in the category of energy efficiency and EEDBR falls in the category of energy balancing, they are discussed in this group along with DBR as they are the evolution of DBR. They give better performance in dense network in comparison to the sparse one. So, these routing protocols are deployed for the tasks like underwater pipeline monitoring, where the sensor density is high.
# On the Track to Application Architectures in Public Transport Service Companies
## 4 Current State of Use-Cases from Different Transportation Service Providers
### AI in Rail-Inspect at SBB
#### 4.3.2 Pipelines and Release-Management  
A pipeline consists of all steps from data acquisition to data analysis and the necessary post-processing to deliver the data to the experts in the desired format. We have two types of pipelines: (1) a training pipeline, which is used for (re-)training our models and (2) an inferencing pipeline, which is used for analyzing production data from the inspection runs. Both pipelines must be highly automated and traceable. Especially for fulfilling the traceability requirement, it is crucial to version the code, data and the model. To allow this traceability, a well established ML meta data management is essential, that allows for tracking all images that have been used to train a given ML model.  
Thus, a well-defined release management needs to be established which incorporates code, data, models and the ML meta data. New models can only be deployed if they are carefully tested beforehand and if it can be ensured, that on one hand the performance of the current models can be assured, and on the other hand, the models infer also correctly on new data. This requires a continuous monitoring and means of automatically evaluating new model versions based on clearly defined evaluation data (see expert labelling).
## 3 Experiments
### Experiments on EEI-100 Dataset
#### 3.3.2 Comparison Experiment with Existing Methods  
To demonstrate the superiority of our proposed method in the classification of electrical equipment images, this section presents a comparative experiment with three existing methods, namely, CGCS, Neg-Cosine, and HGNN, on the EEI-100 dataset. These three methods have recently achieved good performance on public datasets. The classification accuracy of the test set is presented in Table 3. Specifically, our method achieves the highest classification accuracy (up to 94.12%) compared with the other methods.  
Figure 4: Test experiments under different values of \(\alpha_{1}\) and \(\alpha_{2}\) on EEI-100 dataset. (**a**) 1-shot and 5-shot test accuracy under different values of \(\alpha_{1}\); (**b**) 1-shot and 5-shot test accuracy under different values of \(\alpha_{2}\).
## 2 Simulations  
For better comparison with our previous work on SHG FROG, we used the same simulated test pulses as in those previous simulations, in which we generated three pulse trains, each containing 5000 unique pulses, as discussed in that work [59]. Each pulse was constructed with a stable short pulse plus a longer unstable component [53]. The temporal full width at half-maximum (FWHM) of the stable component was set at 12 fs for all three trains. Because the random components were necessarily longer, they were assigned higher energies before being added to the stable train, resulting in average temporal FWHMs of 26, 54, and 108 fs for the unstable trains. The pulse energies were then tailored to match a normal distribution with a coefficient of variation of 10%. Simulations of PG and TG FROG measurements were created by taking the mean of the individual pulses in each of the three trains. Additionally, 3% additive and 5% multiplicative noise [64] were applied to the traces. Also, before the retrieval process, the random trace noise was suppressed using the same preprocessing techniques as are always performed when retrieving pulses from experimental traces [57].  
We generated three multi-shot simulated measured PG/TG FROG traces by adding together the 5000 traces of all the pulses for each of the three trains. We then retrieved pulses from the three resulting traces using both the standard GP algorithm [57] and the RANA approach [61] (which incorporated the GP algorithm), each with 100 different initial guesses. For the pulse trains with average temporal FWHMs of 26, 54, and 108 fs, we used trace sizes of 128 \(\times\) 128, 256 \(\times\) 256, and 512 \(\times\) 512, respectively, ensuring that the intensities at the trace perimeters were less than \(10^{-4}\) of the maximum intensity. We then calculated the \(G\) and \(G^{\prime}\) errors (non-normalized and normalized rms difference between measured and retrieved traces, respectively), the \(TBP_{rms}\), and the temporal FWHMs of the retrieved fields for the three unstable trains. The maximum number of iterations was set to 1500 for the GP algorithm and 375 for the RANA approach. Iterations were stopped when the average change in \(G\) error values over the previous 10 iterations was less than \(10^{-7}\).  
We evaluated the performance of the GP and RANA approaches by comparing their \(G^{\prime}\) errors. Recall that RANA involves obtaining a significantly improved initial guess (IG) for the pulse spectrum directly from the measured trace and also using a multi-grid approach for the iterations, using only small numbers of the trace data points for early iterations and only using the entire trace for the final few [61]. The quantity of first guesses and iterations are shown horizontally in Table 1. Each row indicates the values used for a different pulse train.
# A Clustering WSN Routing Protocol Based on k-d Tree Algorithm
## 5 Simulation and Results Analysis
### Simulation Parameters  
There were two simulation scenarios for LEACH, LEACH-C and H-kdtree. Scenario 1 consisted of a random deployment of sensors. Scenario 2 was a sensor deployment with higher density in the central zone of a deterministic scenario, as shown in Figure 10.  
The use of random and deterministic node deployment scenarios aimed at abstracting network traffic behavior to evaluate QoS. Node deployment was done in a 100 m \(\times\) 100 m area, maintaining the same density in both scenarios. The deterministic scenario aimed to evaluate network traffic in a scenario with higher density in its central area, with the objective of analyzing the influence of clustering in both types of scenarios.  
Table 2 shows the simulation parameters used. These parameters are used in literature mainly to evaluate the performance of LEACH and LEACH-C [49; 50; 51; 52].  
Hierarchical protocols present two types of networks, according to their energy: homogeneous and heterogeneous networks. In homogeneous networks, the initial energy level is the same for all the nodes in the network. In heterogeneous networks, the nodes in the network have different initial energy values.  
In the scenarios shown in Figure 10, the network is divided into two energy levels. This energy division is represented by the parameter \(m\), which is used to calculate cluster energy \((E_{cls})\)[53]. \((E_{cls})\) can be calculated as follows:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Protocols & LEACH, LEACH-C and H-kdtree \\ Initial energy & 0.5 J \\ \(E_{ec}\) & 50 nJ/bit \\ \(\epsilon_{amp}\) & 0.0013 pJ/b/m\({}^{4}\) \\ \(\epsilon_{fs}\) & 10 pJ/bit/m\({}^{2}\) \\ Data aggregation \(E_{DA}\) & 5 nJ/bit/signal \\ Message size & 4000 bits \\ Additional energy & \(\alpha=1\) \\ Heterogeneity & \(m=0.5\) \\ CH probability & \(p=0.3\), \(k=4\) \\ Scene & Random, Deterministic \\ Number of nodes & 200 \\ Position node sink/BS \((x,y)\) & (50, 150) \\ Type of traffic & UDP \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 10: Evaluated scenarios.  
\[E_{cls}=N_{cls}\cdot E_{0}\cdot(1-m)+N_{cls}\cdot m\cdot E_{0}\cdot(1+\alpha), \tag{4}\]  
where \(E_{0}\) is the initial energy of a regular node, \(N_{cls}\) is the number of clusters, and \(m\) is the percentage of nodes in the network with an advanced energy level. The quantity of advanced energy is represented by \(\alpha\). H-kddree uses the simulation parameter \(k\) to obtain the depth with which the nodes in the network will be partitioned. This partition is similar to LEACH's \(p\) parameter, which is used to estimate the expected number of CH nodes.
## 3 Modeling and Performance Analysis  
As described in the previous section, 17 states are defined for two types and three cases. The following state transition matrix (\(\mathbf{P}\)) can be obtained using the transition probability between states.  
\[\begin{array}{|c|Note that the sum of the transition probabilities from each state to other states is 1. In a typical case, it can be confirmed that the sum of transition probabilities from \(E_{21}^{I}\) to other states (\(E_{21}^{I}\), \(E_{12}^{I}\), \(I_{21}^{I}\) and \(E_{21}^{II}\)) is \(1\)\((=\frac{3(1-\theta)}{5}m+\theta m+1-m+\frac{2(1-\theta)}{5}m)\). The steady-state probability (\(\mathbf{\pi}\)) of each state can be obtained using \(\mathbf{P}\) in Equation (5) and the Markov chain method [26]. However, since the time staying in each state differs for each case, it is necessary to compute an accurate steady-state probability by applying the semi-Markov process theory [26] as described in the following subsection. Note that a Markov process considers transitions between states, determining future state probabilities based solely on the current state. On the other hand, a semi-Markov process incorporates both state transitions and the time taken for transitions, modeling the time required to reach future states alongside state transitions.
# Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy
## Appendix B Pre-Initialization  
The principal axes of the fat pad, which are the normals to the faces of the bounding box, are computed from the segmentation, using principal component analysis (PCA) on the points on the fat pad surface. Let the data matrix with \(n\) surface points be, \(P=[p_{1},\,p_{2},\,p_{3}\dots p_{n}]\in\mathbb{R}^{3\times n}\) where each \(p_{i}\) is a boundary voxel. The eigenvectors of the covariance, _Cov_, of the data matrix give the principal axes Equation (A2),  
\[(Cov-\lambda I)v=0\;;\;v^{\mathsf{T}}.v=1\] (A2)  
where \(v\in\mathbb{R}^{3}\) is the eigenvector, and the \(\lambda\) is the eigenvalue. Of the three eigenvectors obtained, the vector, \(v_{si}\), which goes from superior to inferior direction of the fat pad, gives the rotation matrix for the US volume. In addition, the point on the boundary of the fat pad through which \(v_{si}\) passes, i.e., intercepts \(0^{\circ}\) or \(180^{\circ}\) angle, gives the required translation for the US volume. This point can be calculated by computing the dot product of \(v_{si}\) and all boundary voxels, Equation (A3).  
\[\cos\theta=\frac{(p_{i}-c).v_{si}}{|p_{i}-c|.|v_{si}|}\;;\;p_{i}\in P\;and\;C=( \hat{x},\hat{y},\hat{z}),\] (A3)  
where centroid \(C\) is the mean of all points in \(P\). There will be two closest points on the boundary which intercept \(0^{\circ}\) and \(180^{\circ}\) with \(v_{si}\), and the one farther away from the centre of the MRI image is the point that gives the translation vector for the US. Let \(p_{1}\) and \(p_{2}\) be the obtained closest points, and \(O\) be the centre of the cropped MRI image. The required point \(p\) is determined by Equation (A4),  
\[p=\begin{cases}p_{1},&||p_{1}-O||>||p_{2}-O||\\ p_{2},&||p_{1}-O||<||p_{2}-O||\end{cases}\] (A4)  
The rotation matrix given by \(v_{si}\) and the translation vector given by obtained point \(p\) together give us the initial transform of the US.
## 1 Introduction
For each path, the elevation angle and slant distance between the interference source and ground station are calculated during the data communication period according to the altitudes of the airborne interference source. In all scenarios, the period of data communication between the LEO satellite and the ground station is assumed to be approximately 600 s. We examine the \(J/S\) ratio results according to satellite trajectories, with maximum elevation angles of \(86.8^{\circ}\) (Trajectory 1), \(62.7^{\circ}\) (Trajectory 2), and \(37.3^{\circ}\) (Trajectory 3). The trends in relative angle difference and \(J/S\) ratio according to the paths and altitudes of the interference source are observed, and the results confirm that the \(J/S\) ratio increases as the relative angle difference decreases. These results show that although the distances between ground station, LEO satellite, and interference source are important from a \(J/S\) ratio perspective, the relative angle difference between the interference source and satellite is an even more critical factor.
## 2 The Proposed Video QoE Metric for Dynamic Adaptive Streaming Services
### Modeling Method  
Due to a correlation between the feature parameters, such as the deep spatial-temporal features of video quality, fluency and volatility as well as due to small data amount, we chose the ridge regression method in this study, to establish the mapping relationship between the input feature parameter vector and MOS.  
Ridge regression is a method of regularization of ill-posed problems; it is particularly useful to mitigate the problem of multi-collinearity in linear regression, which commonly occurs in models  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classification** & **Extracted Parameters** \\ \hline Video quality & Average bitrate \\  & Proportion of video bitrate \\  & Frame rate \\ Fluency & Average playback interruption length per second \\  & Average re-buffering times per second \\  & Initial buffering time \\ Volatility & Average video bitrate switching count per second \\  & The variance in the proportion of each video bitrate \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters representing influencing factors of QoE extracted based on DASH standard.  
with large number of parameters. In general, ridge regression improves the efficiency of parameter estimation, but at the same time it also leads to increased estimation bias.  
In essence, ridge regression is an improved least square estimation method. By giving up the unbiasedness of the least square method, the regression coefficient obtained at the cost of losing part of the information and reducing accuracy is more practical and reliable. The fitting of ill conditioned data is better than the least square method.  
The loss function of ridge regression can be expressed as:  
\[J(\theta)=\frac{1}{2m}\sum_{i=1}^{m}\left(y_{i}-\sum_{j=0}^{p}\omega_{j}x_{ij }\right)^{2}+\lambda\sum_{j=0}^{p}\omega_{j}^{2} \tag{6}\]  
where \(y_{i}\) represents the score for \(i^{\text{th}}\) video, \(x_{ij}\) represents influencing factor, \(w_{j}\) is regression coefficients and \(\lambda\) is regularization parameter. Equation (6) shows that the ridge regression expression is obtained by adding a regular term to the least square method. This regular term is the penalty for the coefficient to be obtained. Adding the regular term can avoid the occurrence of overfitting.
## 1 Introduction
### UC3M4 Safety and Bindi  
The research shown on this paper is part of a system called Bindi, a smart solution for women's safety by the UC3M4 Safety group [3]. The UC3M4 Safety is a multidisciplinary team for detecting, preventing and combating violence against women from a technological point of view at University Carlos III Madrid, Spain. The goal of this project is to develop a wearable solution for detecting through physiological sensory data, speech and audio analysis, and machine-learning algorithms, when a user is under an intense emotional state, such as panic, fear or stress, caused by a gender-based violence situation [4].  
As shown in Figure 1, the Bindi system consists of two wearable devices and a smartphone application. One of the wearables, the bracelet, is in charge of measuring biometrics such as heart rate, galvanic skin response and temperature [5; 6] through physiological sensors. The second device is a pendant, which incorporates a panic button and a microphone that records audio and speech, presumably from the user who is wearing it but also from the environment and other speakers. The bracelet is constantly registering and analyzing physiological data to identify any significant deviations with respect to the basal state. When an abnormality is detected, a trigger is sent to the pendant to start a microphone recording. This information is collected and sent via Bluetooth to the smartphone equipped with a software application that after examining and interpreting the audio signal--detecting sound events, voice levels, distinctive noises, etc.--under certain conditions, sends an alert to a group of people, previously selected by the victim, or to emergency services in order to alert of a threatening situation. The audio recorded is then stored in an only-read Cloud with several encrypting processes and accessibility restrictions.  
Figure 1: Conceptualization of the Bindi System.  
Regarding speech, there are two main tasks to be performed within Bindi, Stress Detection [7] and Speaker Identification [1]. The former focuses in the detection of stress in the victim's voice whereas the latter relies on the proper identification of the victim despite the emotional conditions that may be present in her voice. These two tasks are interdependent as one needs from the help of the other. In this paper, we direct our attention to the latter, the development of a stress-robust speaker identification system.  
Developing a personalized speaker identification module would allow the system to identify whether the voice belongs to the user or to someone else. Moreover, a system which is robust to stress could be able to recognize more accurately the main speaker when her voice exhibits stress conditions. Thus, this research is focused on speaker identification to recognize who does the voice captured by the microphone belong to even under emotional or stress conditions. This is further used to allow speaker adapted voice commands only obeying the victim but not the aggressor or even as judicial evidence.