esa-sceva/satcom-chunk-collection huggingface dataset:
# Transformative Technology for FLASH Radiation Therapy
## 2 Beam Conditions for the FLASH Effect
### FLASH with Photon Beams  
Megavoltage (MV) photon dose rates produced using clinical electron linacs are too low, and no FLASH effects have been observed. The first photon FLASH dose rates were produced using dedicated pulsed kVp X-ray tubes built in the 1950s and 1960s at the Atomic Energy Research Establishment (AERE). An in vitro study published in 1969 using high-dose-rate X-rays delivered with a single sinusoidal nanosecond pulse of \(\sim\)10 ns or 50 ns lengths to mammalian cells showed cell survival sparing effects compared to 60 Co gamma rays of a conventional dose rate [9]. In recent years, Monte Carlo simulations and experimental validation with scintillators provided evidence that unfiltered 160 kVp X-ray tubes can provide FLASH dose rates exceeding 40 Gy/s or even 100 Gy/s but only over a shallow-water-equivalent penetration depth of <2 mm [17; 18]. A pair of opposing X-ray sources can achieve a better dose uniformity at the depth. Based on this concept, a system for small-animal FLASH research has recently been developed by a group of investigators at Johns Hopkins University using commercially available high-capacity 150 kVp X-ray sources with rotating anode technology and validated with GEANT4 Monte  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Electron Beam** & **Min. for Observed FLASH** & **Optimal for FLASH** \\ \hline Average dose rate & 30 Gy/s & 100 Gy/s \\ Intrapulse dose rate & \(\sim\)10\({}^{5}\) Gy/s & \(\geq\)10\({}^{6}\) Gy/s \\ Total dose & \(<\)10 Gy & \(\geq\)10 Gy—tissue dependent \\ Delivery time for 10 Gy & \(<\)1 s & 1 μs–10 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Preclinical electron FLASH properties relevant to a clinical application of FLASH.  
Carlo simulations [19]. A FLASH dose rate can also be generated using a high-intensity superconducting radio-frequency (SRF) electron linac with a tungsten target producing intense higher-energy photon beams (6-8 MeV) at the Chengdu THz Free Electron Laser facility. A significant FLASH effect in tumor-bearing mice irradiated to the lungs and intestine was reported by [PERSON] et al. [20].  
High dose rates of photons can also be produced using light-source synchrotrons, notably synchrotron broad-beam radiation therapy (SBBR) and microbeam radiation therapy (MRT), which generates an array of parallel microbeams, each separated by a few microns, thus spatially separating the delivered dose into 'peaks' and 'valleys'. For the SBBR, one study did not show a FLASH effect (37-41 Gy/s, 4-28 Gy); see Figure 1, ANSTO Rx (2018) [21]. Still, a second study involving mouse-brain irradiation (37 Gy/s, 10 Gy) showed significant cognitive sparing similar to the experience with electron FLASH [22]. The main difference between the two studies was that the second had a vertical beam size that was 20 times smaller than the first.
# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 3 Related Work
The features were extracted from the aggregated packets in the same direction and then transformed into images to be used in CNN. When tested over OpenVPN and ISCX-tor datasets, the proposed method achieved 97.20% and 93.31%, respectively, outperforming other state-of-the-art approaches. On the other hand, [PERSON] et al. [20], proposed a multiclass imbalanced and concept drift network traffic classification framework based on online active learning (MicFool). Experimental results showed that MicFool is an efficient algorithm and performs better than several state-of-the-art approaches. From the above review of literature, it is noteworthy that there are a very limited number of papers that focus on QUIC traffic classification using machine learning. Most of the works are on deep learning techniques and only reference [4] used ML for intrusion detection using QUIC traffic. Therefore, we can conclude that the ensemble techniques are under-studied for encrypted classification in general and QUIC traffic classification in specific. In the coming sections, more details about ensemble techniques and QUIC traffic classification are entertained.
## 2 Results and Discussion
### Method Evaluation and Application to Real Samples  
Under the optimal conditions, a series of parameters were investigated to validate the online in-tube-SPME-HPLC method, which included linear range, correlation coefficient (r), inter-day repeatability, intra-day repeatability, limits of detection (LODs), and limits of quantification (LOQs). As shown in Table 1, the analysis method was established with the linear range of 0.5-10.0 \(\upmu\)g/L, the correlation coefficient (R\({}^{2}\)) of 0.9794-0.9910, and the LOD of 0.08-0.14 \(\upmu\)g/L for the six estrogens. The relative standard deviations (\(n=3\)) for the intra-day (\(\leq\)2.1%) and inter-day (\(\leq\)4.8%) tests demonstrated that satisfactory repeatability was obtained for the superhydrophobic wire. Each estrogen component on the extraction tube was enriched effectively with their EF values in the range of 20-177.  
To confirm the applicability of the analytical method, tap water taken from our laboratory were selected for the analysis. As shown in Table 2, none of analytes were detected in the tap water. The spike recoveries of the analytes were determined by the standard addition method. The concentration of addition in the sample was 0, 5, and 10 \(\upmu\)g/L. The recoveries of the six analytes were in the range of 73.2-91.1%, indicating that this method could be applied to analyze trace estrogens in real samples.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Analytes**} & **LODs** & **LOQs** & **Linear** & & **EF** & **Extraction Repeatability** & **Preparation Repeatability** \\  & **(\(\upmu\)g/L)** & **(\(\upmu\)g/L)** & **Ranges (\(\upmu\)g/L)** & & & **(\(\upmu\)g/L)** & **Interday** & **Interday** \\ \hline \multirow{3}{*}{Bisphenol A} & 0.097 & 0.323 & 0.5–10.0 & 0.9887 & 20 & 2.0 & 4.6 & 10.4 \\  & Estradiol & 0.135 & 0.450 & 0.5–10.0 & 0.9886 & 33 & 2.1 & 4.8 & 8.9 \\ Ethynyl estradiol & 0.131 & 0.437 & 0.5–10.0 & 0.9886 & 28 & 1.3 & 3.2 & 5.2 \\  & Estrone & 0.079 & 0.263 & 0.5–10.0 & 0.9867 & 177 & 0.8 & 4.5 & 14.3 \\  & Dethylistibestrol & 0.132 & 0.440 & 0.5–10.0 & 0.9794 & 43 & 1.7 & 4.0 & 2.3 \\  & Hexestrol & 0.092 & 0.307 & 0.5–10.0 & 0.9910 & 154 & 0.9 & 2.9 & 9.4 \\ \hline \hline \end{tabular}  
* Extraction repeatability was investigated by extracting 10 \(\upmu\)g/L of estrogens three times. b Preparation repeatability was investigated by extracting 10 \(\upmu\)g/L of estrogens with three extraction tubes.  
\end{table}
Table 1: Analytical performances and EF values of six estrogens using in-tube SPME-HPLC method.  
Figure 8: Comparative HPLC chromatograms before and after enrichment using the superhydrophobic wire or in-tube SPME. Peak order and other experimental conditions are the same as Figure 6.
###### Abstract
However, the current systems have been found to have deficiencies in switch rail surface cleaning and power consumption efficiency. Therefore, an effective and efficient solution is needed, to comply with current operational standards of railway switch heating systems. This can be achieved through the integration of data collection and control functions into the system, as discussed in article [12]. The electronic data processing unit, with special software, continuously receives signals from rail temperature, outdoor temperature, and precipitation sensors. The signals are then compared, according to a certain algorithm, and the first and second stepless voltage control units transmit voltage to flat oval electric heaters. This maintains the required heating temperature of the frame rails, depending on air temperature and ambient humidity. This device for cleaning snow and ice by electrical heating, can reduce energy consumption by up to 60% compared to existing systems, preventing icing and removing ice and snow in the area of the wits and frame rails. It ensures uninterrupted train movement during snowfalls and blizzards, and provides continuous operation of turnouts in winter, regardless of precipitation [13]. To evaluate the efficiency and performance of systems for maintaining required temperature parameters of railway switch control surfaces without using expensive equipment, computer simulation modeling is currently used for testing and experimenting in applied research. SIMULINK, a general-purpose simulation environment, is a powerful tool for testing and analyzing simulated preventive adjustment control systems applied to railway switch electric heating systems. Simulation allows for the testing and verification of models for performance. The algorithm for measuring snow and ice buildup presence on the control surface, required development and testing of a computer simulation model in the MATLAB environment [14]. The aim of this paper is to propose a GSM control system for fully or partially decommissioned railway stations, which could improve the flow and efficiency of train traffic, by reducing the number of minutes of delay on partially decommissioned railway lines,with the added benefit of SCADA compatibility. It also proposes a way to protect the aforementioned system from accidental or malicious attempts to interfere with the wireless network and its adjacent components, and to perform an estimated analysis of the reduction in delays and costs this method could achieve. The main hypothesis tested is whether the proposed control method would result in significant delay and cost reductions on railway lines with decommissioned stations.
## 2 Materials and Experimental Procedures
### Experimental Procedures
#### 2.2.3. Leaching Tests  
In order to assess the possible release of the MWCNTs to groundwater aquifers once applied on-site, further leaching tests were performed. These tests were conducted for both contaminated soil with and without MWCNTs, with a concentration of 0.01% (_w/w_) of MWCNTs in the last case. The samples were prepared in the same way as described for the percolation tests. The leaching tests were performed in accordance with the standard NEN 7345 [62] with some adaptations as follows: (i) the contaminated soil samples were wraped in a 5 um mesh nylon tissue and submerged in ultrapure water at least 2 cm below the water surface; (ii) the leaching test was done under constant temperature  
Figure 2: Schematics of the percolation tests: soil cells, beakers and injection system composed by a pressure regulator, air compressor and interface air/water.  
(22 \({}^{\circ}\)C) and immediately after each leachate collection (at 1, 3, 7, and 14 days), the ultrapure water was renewed; (iii) for each leachate sample, the pH was measured and the presence of carbon nanotubes was verified through SEM images of the powder obtained after drying the leachate samples. The equipment used to acquire the SEM images was a JEOL JSM-5310 model, from the Instituto Pedro Nunes in Coimbra, Portugal.
## 4 Experiments and Result Analyses
### Experimental Results
#### 4.4.3 Performance Improvement by Each Module  
The total loss of the proposed network consists of three parts: final feature loss \(L_{final}\), body feature loss \(L_{body}\), and edge feature loss \(L_{edge}\). Different weight settings for each loss will affect the performance of the network differently. The experiments indicate that \(L_{edge}\) is much higher than \(L_{final}\) and \(L_{body}\), which results in an extremely large proportion of \(L_{edge}\) in the total loss, causing the network to fail to learn the final and body features well. Therefore, the weight (\(\lambda_{edge}\)) of \(L_{edge}\) must be small, but an extremely small weight will also cause the network to fail to learn the edge feature well. Experiments were conducted to explore the impact of different loss weight settings on network performance and find an ideal setting. Given that \(L_{final}\) is near \(L_{body}\), the final feature loss weight (\(\lambda_{final}\)) and the body feature loss weight (\(\lambda_{body}\)) can be fixed to 1. For \(\lambda_{edge}\), several representative weights were selected: 0.01, 0.05, 0.1, 0.5, and 1. Experiments were conducted on the wheel defect dataset, and the results are shown in Table 3.  
In Table 3, when \(\lambda_{edge}\) gradually decreases from 1 to 0.1, F1 score and IoU will gradually increase; when \(\lambda_{edge}\) gradually decreases from 0.1 to 0.01, F1 score and IoU will gradually decrease. The experimental result shows that \(\lambda_{edge}\) needs to be set to a smaller value than \(\lambda_{final}\) and \(\lambda_{body}\). When \(\lambda_{edge}\) is set to 0.1, the defect detection effect is better than other settings.
# Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals
## 1 Introduction
### The Purpose of This Article  
The purpose of this article is to present a portable system for recording, processing, storing, and mathematically analyzing PPG signals to assist cardiologists in the diagnostic process and in taking preventive measures to protect the health of at-risk patients. The system combines the convenience of photoplethysmographic devices, the accuracy of the results of PPG analyzes performed with modern mathematical technologies, and the advantages of serverless architecture in web-based software implementation. The new photoplethysmographic system enables the expansion of hardware with additional sensors and other components, as needed (to solve new future tasks).
# A Moment-Fitted Extended Spectral Cell Method for Structural Health Monitoring Applications
## 2 Problem Statement and Main Strategy  
In this contribution, we consider a linear elastic domain \(\Omega\) in \(d=\{1,2,3\}\) dimensions in the presence of complex geometrical features and/or damage (Figure 1a). These features are either voids or cracks, and their boundaries are considered stress-free; however, they are allowed to intersect. The governing equations can be expressed in weak form as  
\[\int_{\Omega}\rho\ddot{\mathbf{u}}(t)\cdot\mathbf{v}\ \mathrm{d}\Omega+\int_{ \Omega}\mathbf{\sigma}(\mathbf{u}(t)):\mathbf{\epsilon}(\mathbf{v})\ \mathrm{d}\Omega=\int_{\mathbf{\Gamma}_{\mathrm{s}}}\mathbf{p}_{ \mathrm{s}}(t)\cdot\mathbf{v}\ \mathrm{d}\Gamma_{\mathrm{s}}+\int_{\mathbf{\Gamma}_{ \mathrm{cs}}}\mathbf{p}_{\mathrm{c}}(t)\cdot\mathbf{v}\ \mathrm{d}\Gamma_{\mathrm{cs}}, \tag{1}\]where \(\square\) denotes time differentiation; \(\mathbf{u}(t)\) is the displacement solution at time \(t\),  
\[\mathcal{U}_{t}=\bigg{\{}\mathbf{u}(t)|\mathbf{u}(t)\in\Big{(}H^{1}(\Omega)\Big{)}^{d}, \mathbf{u}(t)=\bar{\mathbf{u}}\text{ on }\Gamma_{\text{u}}\bigg{\}}, \tag{2}\]  
conforming to Dirichlet boundary conditions \(\mathbf{u}(t)=\bar{\mathbf{u}}\) defined on \(\Gamma_{\text{u}}\); \(\mathbf{v}\) is the test or weighting function [17],  
\[\mathcal{V}^{0}=\bigg{\{}\mathbf{v}|\mathbf{v}\in\Big{(}H^{1}(\Omega)\Big{)}^{d},\mathbf{ v}=0\text{ on }\Gamma_{\text{u}}\bigg{\}}; \tag{3}\]  
\(\rho\) is the material density; \(\mathbf{\sigma}\) is the Cauchy stress tensor; and \(\mathbf{\epsilon}\) is the linear strain. In order to facilitate the modeling process, mesh-independent geometrical descriptions of the domain details and damage are introduced so that, in the ideal case, a structured SE mesh can be used to discretize the domain. In the finite cell method (FCM) [79; 80; 81] and the SCM, this might also be considered as complementing the physical domain \(\Omega\) with a void domain \(\Omega_{\text{v}}\), which can become relevant in addressing the poor conditioning of the system of equations.
## 3 Network and Adversary Models
### Network Model  
In this section, we present the network model used in this work. This model is inspired by the panda hunter game model suggested in [39]. With a single strong BS in the middle, the sensor nodes are distributed uniformly throughout the network. The network under consideration has a circular structure, and it is envisaged that sensors form rings around the base station, as illustrated in Figure 1. The sensor nodes are uniform and static in design. In other words, before the network starts up, every node in it has the same amount of initial energy, computational power, radio range, and storage capacity. The communication range of the sensor nodes is constrained, so two nodes can only connect when they are close, i.e., within each other's sensing range. Therefore, multi-hop routing is used for communication between nodes that are not neighbors. Each sensor can determine its position in the network using the existing localization techniques [48]. Here, we take into account the situation of habitat monitoring, in which a network asset could randomly arrive at any point in the network. The asset is assumed to be tagged with an RFID (radio frequency identifier) and sensor nodes are equipped with an RFID reader. A source node continues transmitting the packets to its neighbors after spotting an asset within its radio range to send information packets to the base station. It is expected that the packets have been encrypted using cryptographic methods to prevent unwanted parties from reading their contents [49]. Each node divides its neighbors into three groups: (i) lower neighbor set, (ii) equal neighbor set, and (iii) far-away neighbor set, based on the depth of each node as measured with respect to BS, after the initialization phase (see Section 4).
## 5 Comparisons of Speeds with the Conventional SMC-Based SMO  
Figures 24-29 show the comparison results of the conventional SMC and proposed DSMC-based sliding mode observers offering stable speed measurements when the BSRM was subjected to different electrical and mechanical parameter variations. The actual and estimated speeds of the DSMC-based SMO converge on the desired speed (9000 rpm) at 0.01 s, and conventional SMC-based SMO converges at 0.008 s. Even though the SMC-based SMO method has a less settling time than the DSMC-based SMO, it suffers from high chattering and significant steady-state errors. In the case of the DSMC-based SMO method, the chattering phenomena was reduced to a very lesser extent, which shows a smooth and strong robustness against all parameter variations and less steady-state errors.  
Figure 24: Changes in the supply voltage. [Note: ACT_SMC: actual speed under sliding mode controller; EST_SMC: estimated speed under sliding mode controller; ACT_DSMC: actual speed under dynamic sliding mode controller; EST_DSMC: estimated speed under dynamic sliding mode controller; EST_DSMC: estimated speed under dynamic sliding mode controller; SPEED_REF: reference speed].  
Figure 25: Changes in the load torque. [Note: ACT_SMC: actual speed under sliding mode controller; EST_SMC: estimated speed under sliding mode controller; ACT_DSMC: actual speed under dynamic sliding mode controller; EST_DSMC: estimated speed under dynamic sliding mode controller; SPEED_REF: reference speed].  
Figure 27: Decrements of the moment of inertia by 10%. [Note: ACT_SMC: actual speed under sliding mode controller; EST_SMC: estimated speed under sliding mode controller; ACT_DSMC: actual speed under dynamic sliding mode controller; EST_DSMC: estimated speed under sliding mode controller; AST_DSMC: actual speed under dynamic sliding mode controller; EST_DSMC: estimated speed under dynamic sliding mode controller; EST_DSMC: estimated speed under dynamic sliding mode controller; EST_DSMC: estimated speed under dynamic sliding mode controller; EST_DSMC: estimated speed under dynamic sliding mode controller; EST_DSMC: estimated speed under dynamic sliding mode controller; EST_DSMC: estimated speed under dynamic sliding mode controller; SPEED_REF: reference speed).  
Figure 28: Changes in the switching angle to 11 degrees. [Note: ACT_SMC: actual speed under sliding mode controller; EST_SMC: estimated speed under sliding mode controller; ACT_DSMC: actual speed under dynamic sliding mode controller; EST_DSMC: estimated speed under dynamic sliding mode controller; EST_DSMC: estimated speed under dynamic sliding mode controller; SPEED_REF: reference speed).  
Figure 26: Increments of the moment of inertia by 10%. [Note: ACT_SMC: actual speed under sliding mode controller; EST_SMC: estimated speed under sliding mode controller; ACT_DSMC: actual speed under dynamic sliding mode controller; EST_DSMC: estimated speed under dynamic sliding mode controller; SPEED_REF: reference speed).  
The overall speed performance comparison of the SMC-based SMO and DSMC-based SMO is presented in Table 4.
## 2 Materials and Methods
### Principal Component Analysis
If the data obtained, \(C_{\varepsilon}\) versus \(\varepsilon\), are arranged in a straight line in a log-log  
Figure 8: From the dynamic variable \(x\) of the Lorenz attractor: (**a**) principal component analysis (PCA). The inner box clearly shows how the dynamic behavior of the attractor can be characterized with only three directions. (**b**) Correlation dimension (\(D_{2}\)). It can be seen how from \(m=3\), the correlation dimension is confined to a constant value in an intermediate and reduced range of scales \(\varepsilon\). plot, especially for the intermediate values of \(\varepsilon\), that means that the correlation sum follows a power law. For extreme values of \(\varepsilon\), the statistical estimation of \(C_{\varepsilon}\) is not reliable, and therefore, the points obtained usually move away from the straight line. Consequently, a straight line, in a log-log plot and for a central region of values of \(\varepsilon\), suggests a power law, which in this case, obeys:  
\[C_{\varepsilon}\propto\varepsilon^{\dim_{C_{\varepsilon}}}, \tag{11}\]  
where the exponent \(\dim_{C_{\varepsilon}}\equiv D_{2}(\varepsilon)\), often a non-integer number, denotes the slope of the line, and it is called the correlation dimension. For different values of \(m\), different values of the slope of the line are generally obtained. The minimum correlation dimension value, for which additional increments of \(m\) do not clearly modify its value, implicitly defines the appropriate reconstruction dimension, say \(m>2\left\lceil D_{2}\right\rceil\), in accordance with [PERSON]'s theorem [10], so that the attractor can fully deploy its dynamics (see Figure 8b). If the correlation dimension grows continuously with each value of \(m\) initially chosen, the data suggest a random behavior.
# A Framework for the Joint Placement of Edge Service Infrastructure and User Plane Functions for 5G
## 3 5G Reference Architecture  
The 3 GPP architecture for 5G networks [21] is an evolution of the current 4G system, based on the concepts of CUPS, service-based architecture and network slicing. This architecture mainly consists of the following elements: Network Slice Selection Function (NSSF), Authentication Server Function (AUSF), Unified Data Management (UDM), Access and Mobility Management Function (AMF), Session Management Function (SMF), Policy Control Function (PCF), Application Function (AF), UPF, UE, (Radio) Access Network ((R)AN) and Data Network (DN). The separation of the control and user planes guarantees that the resources of each plane can be scaled independently and that the UPFs can be distributed and deployed near the users. This approach allows satisfying increasing traffic demands at a lower cost-per-bit and to serve low-latency applications hosted at the network edge [22].  
The main component of the 5G user plane is the UPF while the control plane is formed by the NSSF, AUSF, UDM, AMF, SMF, PCF and AF. Figure 1 illustrates the 5G architecture and the interactions among its different entities. The UPFs process data plane packets between the (R)AN and the DN and act as PDU session anchor points to provide intra/inter-Radio Access Technologies (RATs) mobility. Additionally, UPFs provide packet routing and forwarding functionalities, access control, QoS handling, and lawful interception. To perform all these functions they communicate with the SMFs located in the control plane. The SMFs are responsible for selecting, controlling and managing the UPFs for the establishment of PDU sessions.