esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Theory and Application of the Method Based on the ANN  
Generally, ANNs try to approximate or simulate the neurophysiological structure of the human brain. The large complex of the network consists of interconnected neural cells, also called neurons. Each neuron receives signals from the neurons connected to it through the dendrites and conveys a signal using the axon. A simple mathematical model for this process considers the output of a unit as a function. The function is called the activation function, which is usually nonlinear. The coefficients of the transformation (connections between the neurons) are called biases; the other coefficients called weights determine the response and can be adapted depending on how the connection is activated during the training [42; 43; 44; 45].  
There are many types of ANN with different architectures, activation functions, training functions, etc. In the proposed methodology, the feed-forward back propagation neural network was applied [46]. The ANN architecture is shown in Figure 5. For the ANN, the sigmoid function \(S(x)\) can be used:  
\[S(x)=\ \frac{1}{1\ +\ e^{-x}} \tag{1}\]  
In our case, the symmetrical sigmoid as an alternative to the sigmoid function was used, according to the formula:  
\[S(x)=2s(x)-1=\ \frac{1-\ e^{-x}}{1\ +\ e^{-x}} \tag{2}\]  
As the training algorithm scaled, conjugate gradient (SCG) for the supervised learning was used. The SCG updates weight and bias values according to the scaled conjugate gradient method [47; 48]. The SCG can train any ANN as long as its weight, net-input, and transfer functions have derivative functions. In order to calculate derivatives of the performance with respect to the weight and bias variables \(x\), backpropagation was used [26].  
The architecture of the ANN used for the temperature prediction is in Figure 5. The ANN for the prediction of the temperature fields consists of the input layer with three neurons, three hidden layers and of the output layer [20].  
Figure 4: Turbine blade mesh topology of the iSTC-21v jet engine.  
During the training process, the measured data summarized in Table 1 were used. The detailed results obtained from the CFD analyses, according to the methodology presented in Figure 1, that were used as the inputs for the ANN to predict the temperatures are described in Section 3, Results. The input layer consists of the input temperature \(T_{\text{3t}}\), pressure \(P_{\text{3t}}\) and rotor speed. The output layer is created by the predicted temperatures, in our case calculated for 24 points selected as the specific nodes on the surface of the turbine blade of the iSTC-21v jet engine. In the output layers, there are also temperatures \(T_{\text{3t}}\), estimated using new technology, which is quicker than conventional methods as CFD and other methods.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 3 Background
### Problem Formulation  
Before outlining our specific node-level problem, we first define a node-level structural anomaly in a dynamic graph.  
**Definition 5** (_Node-level Structural Anomaly_).: _Given a dynamic weighted graph, \(\mathcal{G}=(V_{t},E_{t},W_{t})\)\(=\{G_{t}\}_{t=0}^{T}\), consisting of the initial snapshot, \(G_{0}\), where \(V_{t}\) is the set of nodes, \(E_{t}\) is the set of edges, and \(W_{t}\) is the set of edge weights at time \(t\). The total node set is \(V=\cup_{t=0}^{T}V_{t}\), and the total edge set is \(E=\cup_{t=0}^{T}E_{t}\). Each snapshot can have changes in edge events, \(|\Delta E_{t}|\geq 0\), and changes in node events, \(|\Delta V_{t}|\geq 0\). Let \(f:V\rightarrow\mathbb{R}\) be a specified scoring function. The set of anomalous nodes, \(V^{\prime}\subseteq V\), is defined such that \(\forall v^{\prime}\in V^{\prime}\), \(|f(v^{\prime})-f|>c_{0}\), where \(f\) is a summary statistic of the score \(f(v)\), \(\forall v\in V\), and \(c_{0}\) is a predefined threshold indicating a significant deviation._  
**Problem 1**.: _Detect node-level structural anomalies in \(G_{t}\). Specifically, a node, \(\mathbf{u}\), is considered anomalous at time \(\mathbf{t}\) if it experiences a significant deviation characterized by a **sudden transition** in its connectivity, i.e., from its original set of outgoing edges to neighbors, \(\langle v_{1},\dots,v_{\Delta n}\rangle\), to a new set, \(\langle\phi_{1},\dots,\phi_{\Delta n}\rangle\)._  
In Problem 1, \(\Delta_{n}\) is defined as a substantial change in the set of a node's neighbors, \(\langle v_{1},\dots,v_{n}\rangle\), as shown in Equation (3):  
\[\Delta_{n}=N(u,t+1)\setminus N(u,t), \tag{3}\]where \(N(u,t)\) is the set of neighbors of node \(u\) at time \(t\). A _sudden transition_ occurs when \(|\Delta_{n}|\) exceeds a predefined threshold, \(\tau\), indicating a notable structural shift.  
Figure 2 illustrates node-structure anomalies across two graph snapshots. Sudden changes in node scores and the appearance of new edges could indicate potential cyber-attacks on computer networks or a series of fraudulent transactions. Intuitively, to detect such structural anomalies, our approach focuses on the existence of edges connecting nodes rather than the frequency of edge occurrences between them. Additionally, we consider the rapid occurrence of these edges (insertion or deletion) with fast adaptiveness and, ultimately, an optimized vector score for each node in the dynamic graph.
# High Resolution Imaging Using Micro-Objectives Fabricated by 2-Photon-Polymerization
## 4 High Resolution Imaging: Results and Discussions
In addition to the above-mentioned deviation between the simulation and the experiment, the contrast between the pits and lands on the Blu-ray plays an important role in the valuation of the FOV. In the simulation, the contrast was assumed to be a step function with values of 0 and 1, which  
Figure 5: Demonstration of the high-resolution properties of a biconvex micro lens. The test object is the surface of a BluRay and the illumination is provided by a halogen lamp with a bandpass filter with a central wavelength of 450 nm \(\pm\) 10 nm. (**a**) SEM image of the micro-objective with a biconvex micro lens with a radius of curvature of 12.5 \(\upmu\)m, an aperture diameter of 20 \(\upmu\)m and an index of refraction of \(n\) = 1.54 [25] as the central imaging element. (**b**) Light microscope image of the entire micro-objective, the marked area is shown enlarged in (**c**). In (**b,c**) the fine surface details of the BluRay disk, including the vertical tracks and the characteristic pits and lands of the BluRay surface are clearly resolved. The red lines in (**c**) serve as guidelines for a better visualization of the direction and distance of the BluRay tracks of 320 nm. does not correspond to the contrast in the experiment. Because of the higher contrast in the simulation, the FOV seems larger than in the experiment. To further demonstrate the versatility of 2 PP-fabricated micro-optics for high-resolution imaging, future work will include imaging experiments in a broader range of samples, such as biological cells and nanostructured materials. This will further demonstrate the potential of the presented approach for applications in life sciences and nanotechnology.
## 2 Materials and Methods
### Quantification Analysis of ARGs  
The obtained DNA samples originating from antibiotic resistant bacteria were used for ARG analysis with a conventional polymerase chain reaction (PCR) and a real-time polymerase chain reaction (qPCR) using specific primers. During preliminary testing, the occurrence of seven genes responsible for the resistance to tetracyclines (_tet_A, _tet_C, _tet_L, _tet_M, _tet_O, _tet_A(P), _tet_X) and ten genes responsible for the resistance to beta-lactams (_bla_TEM, _bla_SHV, _bla_OXA, _bla_CTX-M, _bla_CTX-M, _bla_CTX-2, _bla_CTX-M, _bla_VEB, _bla_CMY, _bla_AMP-C) were investigated using the PCR reaction. These genes wereselected based on previous studies of the authors [11; 28; 30]. For ARG quantitative analysis with qPCR, genes (\(bla_{\rm TEM}\), \(bla_{\rm SHV}\), \(bla_{\rm OXA}\), \(tet\)A, \(tet\)M) were selected based on their occurrence in environmental samples in the preliminary testing. Moreover, the presence of gene \(uid\)A in _E. coli_ was also used as a marker of the taxonomic genotype. Antibiotic resistance genes were used to create standard curves derived from wastewater strains. The standard curves were generated by cloning the amplicon from the positive control into vector pCR2.1-TOPO (Invitrogen, Massachusetts, USA). All qPCR reactions were carried out using a Roche Light Cycler 480 device (Roche Applied Science, Denver, CO, USA). The reactions were carried out in triplicate to ensure repeatability, using both the negative and positive control.
## 3 Optical Receiver Filter Equation-MGF Optimisation Procedure
### Optimum Filter Impulse Response Function  
We should notice that the analytical solutions in Equations (15) and (17) are not optimum. The same is true for Equation (19) numerical solution. To get the optimum solutions, we need to apply the \(J\) operator on Equations (14), (17) and (19). The \(J\) operator execution transforms these equations to the following optimum filter equations:  
\[2\lambda_{0}Exp[h_{f}]-(\lambda_{1}+\lambda_{0})\quad=0,\text{ NO AGTN} \tag{20}\]  
\[\lambda_{1}Exp[-h_{f}]-\lambda_{0}Exp[\ h_{f}]=4N_{0}\ h_{f},\text{ White AGTN} \tag{21}\]  
\[\lambda_{1}Exp[-h_{f}]+\lambda_{0}Exp[h_{f}]=\frac{N_{0}h^{\prime\prime}_{f}} {w_{0}^{2}}-N_{0}\ h_{f},\text{ Colored AGTN}. \tag{22}\]  
Equation (20) is the filter equation without AGTN has the following analytical optimum solution:  
\[h_{f}=ln\left[\frac{(\lambda_{1}+\lambda_{0})}{2(\lambda_{0})}\ \ \right]. \tag{23}\]  
Equations (21) and (22) need a numerical optimum solution. In summary, the equations from Equations (14)-(19) are the general solution of \(h_{f}(t)\), while the equations from Equations (20)-(23) provide the optimum solution of \(h_{f}(t)\). It is worth noting that all these equations show that \(h_{f}(t)\), as a general solution or optimum, depends on the \(\lambda(t)\) given in Equation (6). Equation (6b) reveals that \(\lambda(t)\) embeds the dependency on the transmitted optical pulse \(h_{p}(t)\) and the propagation channel impulse response \(h_{c}(t)\). Equation (6c) exposes the fact that the ISI is rooted in the digital stream of bits around the zero-position bit. Hence, the ISI in the UWOC optical receiver will have two sources: (1) the digital signal itself and (2) the scattering and scintillation impairments that cause the stretching of the optical pulse tails. This tail elongation is not related to the pulse center shift.
## 2 Materials and Methods
### Data
#### 2.1.4 Aerosol Robotic Network (AERONET) Data  
To verify the accuracy of the AHI AOD in the retrieval of this study, we downloaded the AERONET [43] level 2.0 data of 2017 and 2018 at six stations: Bamboo (25.19\({}^{\circ}\) N, 121.54\({}^{\circ}\) E), Hong Kong PolyU (22.3\({}^{\circ}\) N, 114.18\({}^{\circ}\) E), Hong Kong Sheung (22.48\({}^{\circ}\) N, 114.12\({}^{\circ}\) E), Beijing CAMS (39.93\({}^{\circ}\) N,116.32\({}^{\circ}\) E), Xiang He (39.75\({}^{\circ}\) N, 116.96\({}^{\circ}\) E), and Beijing (39.98\({}^{\circ}\) N, 116.38\({}^{\circ}\) E). The AERONET AODs were interpolated using 440 nm and 675 nm with the help of Angstrom exponential to be consistent with the AHI AOD 550 \(\upmu\)m band [44]. During the validation process, to ensure consistency in space and time, the average value  
Figure 1: Location of PM\({}_{2.5}\) and meteorology monitoring stations in study area.  
of 3 \(\times\) 3 images was calculated with AERONET sites as the center, and the average value of AERONET AOD before and after half an hour of satellite transit time was calculated, with a total of 4544 validation data. The validation comparison scatter plot is shown in Figure 2, with a high correlation coefficient value of r = 0.82 and a fit slope close to 1, RMSE = 0.38.
## 5 Modeling of Power Supply
### Calculation of Solar Panel and Battery Requirements  
The power output of solar panels is directly related to their area and efficiency. To determine the area needed to meet the energy demand for each charging station, the following equation is used:  
\[A_{m^{2}}=\frac{P_{kW}\times t_{h}}{\eta\times I} \tag{10}\]  
where* \(A_{m^{2}}\): required solar panel area in square meters;
* \(\eta\): solar panel efficiency (assumed 20%);
* \(I\): solar irradiance (kWh/m\({}^{2}\)/day).  
The following solar panel areas are needed for various charging station capacities:  
* A 150 kW charging station requires a solar panel area of approximately 1529.7 m\({}^{2}\).
* A 50 kW charging station requires a solar panel area of approximately 507 m\({}^{2}\).
* A 22 kW charging station requires a solar panel area of approximately 75.6 m\({}^{2}\).  
In addition, battery storage is crucial for balancing supply and demand to ensure reliability during peak usage. The following battery storage requirements were estimated based on the daily energy consumption of each station:  
* A 150 kW charging station requires a battery area of approximately 60 m\({}^{2}\).
* A 50 kW charging station requires a battery area of approximately 22 m\({}^{2}\).
* A 22 kW charging station requires a battery area of approximately 4 m\({}^{2}\).  
Battery cooling and ventilation must also be considered, ideally through underground installations with ventilation systems to maintain optimal temperatures and efficient battery operation [31]. In addition, the balance between power consumption and generation has to be taken into account. To achieve and maintain it, the following steps are proposed:  
1. **Load Scheduling:** Develop detailed load schedules for each station, considering peak and off-peak usage patterns.
2. **Solar Panel Sizing:** Calculate the total number of solar panels required using Formula (10).
3. **Battery Storage Sizing:** Determine the required battery storage capacity to meet peak demand and ensure reliability.
4. **Cost Analysis:** Perform a cost analysis, calculating the total cost of solar panels and battery storage for each station. An analysis of ROI and the payback period based on energy savings is included.
5. **Cooling and Ventilation:** Design efficient cooling systems for battery storage, preferably underground with proper ventilation systems.
6. **Integration with the Grid:** Evaluate the potential for integrating the charging stations with the local grid and using smart grid technologies for energy distribution and load management [36].
# ARCFIRE: Experimentation with the Recursive InterNetwork Architecture
## 5 Discussion of Experimental Results  
### Resiliency Experiments  
Different resiliency schemes are possible per layer, but in the experiment reported in Section 3.1 we focused on using Loop-Free Alternates (LFAs) in a single layer. We first showed that the recovery scheme functionally works, on a very simple setup, three nodes connected in a triangle. Then, we measured an upper bound for the recovery time. Fast failure recovery, i.e., below 50 ms, is definitely achievable. We recover in at most 13 ms. We then turned towards a bigger experiment, by emulating the GEANT network, which is deployed all over Europe. We measured the bandwidth before and after a link failure, for regular link state routing and when using LFAs. We showed that traffic is recovered in case of a failure, at the cost of higher bandwidth usage in the network. We also presented latency measurements of different flows. We see that after recovery, there is not only a higher bandwidth usage but also an increase in delay. All average round trip times stay below four milliseconds. Both observations are of course simply because more hops are used to reach the destination. We demonstrated that the experiment is very reproducible, largely thanks to [PERSON] [10].
Estimating Satellite Navigation Broadcast Ephemeris via Inter-Satellite and Ground-to-Satellite Ranging +
Footnote †: This paper investigates the potential of performing orbit determination directly in the Earth-fixed frame based on Inter-Satellite Ranging (ISR) measurements as primary observables, combined with Ground-to-Satellite Ranging (GSR) measurements from a small regional ground network. Current Global Navigation Satellite Systems (GNSSs) use L-band pseudo-range and carrier phase measurements from global or regional ground station networks to perform dynamic Orbit Determination and Time Synchronization (ODTS), whereas sparse Satellite Laser Ranging measurements are mainly used for validation. Future GNSSs may be equipped with inter-satellite links (ISLs) to enable inter-satellite clock offset estimation, ranging and data relay. These capabilities carry the potential to significantly improve ODTS procedures. In this work, we assume a fully connected constellation via pair-wise ISLs, with measurement topology assigned by appropriate link schedulers. The satellite orbits are parametrized with the standard 15 Galileo broadcast perturbed Keplerian elements, estimated by using ISR and GSR measurements. This processing strategy eliminates the complex modeling of gravitational and non-gravitational forces, making it particularly suitable for on-board applications and offering an alternative to classical GNSS orbit determination processing architectures. The proposed orbit determination scheme can be used in case of a ground segment failure as a back-up procedure to estimate the orbits of the GNSS satellites onboard of each satellite and guaranteeing a continuous navigation message generation for the system users. The performance of the proposed method depends on a number of factors, such as the length of the data fitting interval, the measurement quality (precision and accuracy), the scheduling and geometry of ISR and GSR measurements, the number and distribution of ground stations, and the accuracy of the ground station coordinates. Preliminary results show that an orbit-only Signal-in-Space Range Error (SiSRE) in the order of 7-9 cm can be obtained by processing 2 to 3 h data with a limited set of supporting ground stations. In this study, the orbit determination scheme proposed is tested on different scenarios, providing a first assessment of attainable performance.  
On-board orbit determination, inter-satellite links, laser ranging, ground-to-satellite ranging, perturbed Keplerian elements, signal-in-space range error
# Design of a Compact Microstrip Decoupled Array
## 3 Result  
The array antenna was connected to the feeding unit. The measured results are shown in Figure 16a,b. The measurement environment is shown in Figure 17. The \(S\) parameter test used Anritsu's vector network analyzer, which measured 201 points in the band 2.4-2.6 GHz. LB-10180-SF wideband horn antennas with a frequency of 1-18 GHZ were used for the measurement of antenna gain.  
Figure 16: Antenna diagram. (**a**) The top surface. (**b**) The bottom surface.  
Figure 14: The feed network simulation results. (**a**) Sum port. (**b**) Difference port.  
Figure 15: The difference in \(S\) parameters of the mono-pulse antenna before and after adding the decoupling network.  
The simulated \(S\) parameters are depicted in Figure 18, which shows a frequency discrepancy of 3 MHz in the reflection coefficient between the sum and difference ports. The simulation results show that the sum return loss is 16.14 dB, the difference return loss is 15.2 dB, and the isolation between ports is 36.6 dB. The measurement results show that the sum return loss is 14.7 dB, the difference return loss is 16.7 dB, and the isolation between ports is 28.3 dB.  
The proposed antenna is a one-dimensional sum and difference beamforming antenna, and the observed principal plane is the radiation pattern on the y-z plane. From Figure 19a,b, the measured and simulated results of the mono-pulse antenna fit well at the center frequency of 2.45 GHz.  
Figure 19: Simulation and measured gain results of the antenna. (**a**,**b**) are radiation patterns.  
Figure 17: The antenna measurement environment.  
Figure 18: The antenna simulation and measurement results.  
When the antenna spacing is greater than 0.5 \(\lambda\), the antennas have good isolation. Compared with the previous work, the designed antenna unit size is smaller, and the isolation between the two ports is good. Table 3 shows a comparison between the proposed work and previous research results.
# A Methodology to Evaluate Standards and Platforms within Cyber Threat Intelligence
## 4 Standards Evaluation Results
### Evaluation of the Standards
#### Intelligence Process  
From the process perspective, STIX has the capability to meet most of the proposed requirements. The use of serialization in JSON format provides a common and structured format, with relatively low overhead and machine-readability. The twelve objects that compose STIX architecture are well described and documented providing an unambiguous data model with coupled relationship mechanisms. When used together with TAXII, it offers a reliable transport mechanism. Finally, since it has a significant practical application, most of the TI platforms and tools have integration methods with this standard.  
IODEF and OpenIOC are based in the XML format, so they also provide a common and structured format with machine-readability. However, IODEF can present some problems due to free text fields that compose its data model. Regarding the relationship mechanisms, OpenIOC provides logic operators (AND/OR) to create connections between indicators, on the other hand, besides the interconnections on the data model, IODEF does not present specific mechanisms to relate information.  
IODEF and OpenIOC are supported in many platforms and tools and can be integrated with different systems. IODEF can be used together with the RID protocol, providing an efficient and secure transport, while OpenIOC does not focus on implementing transport mechanisms.
## 3 Results and Discussion
### Effect of the Reaction Time  
In order to evaluate the correlation between the reaction time and the DRs achieved by the hydrothermal process under study, a number of tests were carried out using different reaction times, as follows: 35 min (P2), 80 min (P4), and 120 min (P3). The rest of the variables remained unchanged: 300 \({}^{\circ}\)C, 100-130 bar, 60 rpm, 300 ml\({}_{\rm solv}\)/g\({}_{\rm comp}\) solvent/sample ratio, 5 wt.% IPA solution as solvent, and 0.1 M KOH as catalyst. The resulting DRs (%) can be seen in Figure 5.  
Figure 4: Decomposition rates obtained with and without stirring at 300 \({}^{\circ}\)C, 130 bar, 35 min, 300 ml\({}_{\rm solv}\)/g\({}_{\rm comp}\), IPA 5 wt.%, KOH 0.1 M.  
According to the DRs obtained, it could be confirmed that the test with the longest reaction time (120 min) achieved the highest decomposition rate (98%), while the tests that used 80 and 35 min as reaction times registered DRs of 95% and 51%, respectively.  
A clear connection can be inferred from these results with respect to the subcritical conditions under which these tests were carried out (300 \({}^{\circ}\)C): namely, the longer the reaction time, the higher the resin decomposition rate. Nevertheless, the comparison between the DRs obtained from the 80 and the 120 min tests revealed that the actual increases in DR were not so significant. This led us to conclude that, from 80 min onwards, longer reaction times would result in gradually smaller increases of the DRs.  
[PERSON] et al. (2009) [32] studied the influence of the reaction time on the DRs by using water at 260 \({}^{\circ}\)C to treat the CFRPs for times ranging between 45 and 105 min. The resulting DRs were between 0% and 100%. This corroborates the results obtained from the experiments conducted under subcritical conditions in the present study, where it was observed that a minimum time is required to start degrading the epoxy resin, and emphasizes the relevance of this variable with regard to the potential degradation rates that can be achieved.