esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Calibration Method for Parameters of the Model  
Due to the abstraction of the traffic flow in the connected roadway, the vehicles in the queueing system are in batches as the basic unit, and the variables and parameters in the traffic flow model Equation (13) derived based on the queueing model are also in batches as the basic unit. The actual observed traffic flow data are all in the basic unit of vehicle. Therefore, to fit Equation (13) based on the actual observed data, it is necessary to convert Equation (13) to Equation (14).  
\[q=\frac{k^{2}\cdot v_{f}(1-\pi(N,2))}{k_{j}\left(\sum_{i=0}^{L}i\cdot\pi(i,1)+ \sum_{i=0}^{N}i\cdot\pi(i,2)\right)} \tag{14}\]  
The transformation relationship between the variables \(q\) and \(k\) of Equation (14) and \(q_{b}\) and \(k_{b}\) is shown in Equation (1). The parameters of Equation (14) include \(k_{j}\), \(v_{f}\), \(\mu_{1}\), \(\mu_{2}\), \(D\) and \(U\). The relationship between the parameters \(k_{j}\) and \(k_{bj}\) is shown in Equation (1), while \(k_{bj}\) can be determined by \(N\) and \(l\) with \(1/k_{bj}=l/(N+1)\), so \(k_{j}\) can be determined by Equation (15).  
\[k_{j}=\frac{n\cdot m\cdot(N+1)}{l} \tag{15}\]  
In addition, \(v_{f}\) can be obtained from actual observation and can be taken as \(v_{f}=\)200 m/min. The transformation relationship between parameters \(\mu_{1}\), \(\mu_{2}\), \(D\) and \(U\) and \(\mu_{b1}\), \(\mu_{b2}\), \(D_{b}\) and \(U_{b}\) is shown in Equation (16).  
\[\mu_{b1}=\mu_{1}/(n\cdot m),\;\mu_{b2}=\mu_{2}/(n\cdot m),\;D_{b}=D/(n\cdot m),\;U_{b}=U/(n\cdot m) \tag{16}\]  
The parameters to be fitted to Equation (16) are \(\mu_{1}\), \(\mu_{2}\), \(D\) and \(U\). In this paper, the least squares method is used for the fitting [32].
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 4 Energy Consumption Model
### Methodology and Assumptions  
Figure 2 illustrates a possible working sequence of the sensor node and allows defining different operating modes, which are managed by the processing unit.  
Our energy consumption model is based on the following assumptions:  
* As proposed in [1; 4; 14; 15], the processing unit is in on-state along the working sequence. To optimize the consumed power by the MCU unit, this assumption can be improved to make the MCU unit in low-power modes during most of the activity cycle.
* Each step of the sensor working sequence is characterized by a constant time duration.
* The radio module transmits a packet of information at a fixed transmission power level.
* No local storage of information is considered in this model but we assume a real-time transmission of all measured data.
# A Survey on Blockchain-Based Federated Learning
## 2 Preliminary
### Overview of Blockchains  
Blockchains, initially introduced as part of a payment system known as Bitcoin by [PERSON] in 2008 [21], has become one of the most widely adopted disruptive technologies in various financial and industrial applications. It is essentially a distributed and immutable ledger consisting of blocks that are shared among untrusted participants within a peer-to-peer (P2P) network, eliminating the need for a trusted central authority [39]. To ensure the validity of all transactions before they are recorded, consensus algorithms are employed. As illustrated in Figure 2 each block in the chain contains a hash of the preceding block, ensuring the immutability of the blocks [40]. To maintain data integrity, all network participants maintain identical copies of the ledger. When a new transaction is generated, it is disseminated to specific nodes within the network, often referred to as miners. These miners validate the incoming transaction by verifying its associated signature. Upon validation, they proceed to create a new block and distribute it across the network, reaching a consensus through a distributed process. Once the miners reach a consensus and validate the new block, it is appended to the distributed ledger. From a structural perspective, a block consists of two parts: the block header and the block body. Key information in the block header includes the current version number, the hash value of the previous block, a timestamp, a random number (Nonce), and the hash value of the Merkle Root [41].  
Blockchains can be categorized into four main types: public blockchains [42; 43; 44], consortium blockchains [45; 46; 47], and private blockchains [48; 49; 50; 51]. A public blockchain is a system where anyone in the network can access the blockchain at any time. It is usually considered fully decentralized and highly anonymous, and the data are immutable. Consortium blockchains are managed collectively by a number of enterprises or institutions. Data are recorded and maintained by verified participants, and these nodes have the permission to read the data. A private blockchain is a blockchain controlled by a particular organization or user. The rules for controlling the number of participating nodes are strict, resulting in very fast transaction speeds and a higher level of privacy. It is less susceptible to attacks, and while it offers higher security compared to public blockchains, its degree of decentralization is significantly reduced.
# Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models
## 6 Results
### GARCH Models  
The estimated GARCH coefficients and unconditional volatilities for RAST3, RAST4 and RMST4 are listed in Table 2a-d, along with the minimum, median and maximum of the coefficients from the non-seizure channels. Table 2a shows the results for six one-minute segments preceding the onset of seizure. RAST3 and RAST4 show higher ARCH and lower GARCH parameters across all segments than the majority of the other signals; the sum of the ARCH and GARCH parameters are also lower than the majority of the other channels. Estimates for RMST4 do not appear to be different than the non-seizure channels. The unconditional volatility for all three channels do not show as marked a difference; however, each are less than the median of the unconditional volatility of all other signals for each segment. Similar results can be seen for the awake data (Table 2b). As with the seizure data, RAST3 and RAST4 show higher ARCH and lower GARCH coefficients than RMST4 and all of the other channels. The unconditional volatility for each of the three channels is also lower than the median for the other channels. The sleep to awake and sleep data (Table 2c,d) do not show as large of differences as the seizure and awake data. The estimated ARCH and GARCH coefficients of the sleep to awake and sleep data for RAST3 and RAST4 are higher (lower) than the median for the non-seizure channels. The unconditional volatility for the three channels are also lower than the median for the non-seizure channels and are similar to the those found in the seizure and awake data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Seizure Channels**} & \multicolumn{2}{c}{**Non-seizure Channels**} \\ \hline  & **RAST3** & **RAST4** & **RMST4** & **Half Life** \\ \hline \multirow{4}{*}{**Segment**} & **HL** & **HL** & **HL** & **Median** \\  & **95\% CI** & **95\% CI** & **95\% CI** & **Min** \\  & **Overlapping** & **Overlapping** & **Overlapping** & **Overlapping** & **Max** \\  & **Interval Count** & **Interval Count** & **Interval Count** & **Interval Count** & **Interval Count** \\ \hline \multirow{4}{*}{1} & 1.64 & 1.78 & 6.36 & 12.37 \\  & (1.29,1.98) & (1.55,2.01) & (3.24,9.47) & 6.00 \\  & 5 & 5 & 32 & 69.59 \\  & 1.83 & 1.49 & & 12.24 \\
2 & (1.37,2.29) & (1.26,1.72) & N/A & 4.03 \\  & 5 & 5 & 141.25 \\  & 5.24 & 1.93 & 6.49 & 14.09 \\
3 & (4.27,6.22) & (1.68,2.18) & (3.11,9.88) & 3.24 \\  & 22 & 3 & 26 & 452.16 \\  & 1.90 & 1.79 & 6.33 & 18.61 \\
4 & (1.58,2.23) & (1.55,2.03) & (1.52,11.14) & 4.44 \\  & 5 & 5 & 33 & 106.59 \\  & 1.27 & 1.50 & 6.42 & 8.27 \\
5 & (0.98,1.55) & (1.30,1.70) & (0.10,12.74) & 2.16 \\  & 4 & 5 & 37 & 208.17 \\  & 1.61 & 1.98 & 268.82 & 28.11 \\
6 & (1.30,1.91) & (1.73,2.23) & (0.14,537.50) & 4.82 \\  & 2 & 2 & 53 & 83.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Half-Life and 95% (Delta Method) CIs Pre-seizure data.
# Micro-Focusing of Broadband High-Order Harmonic Radiation by a Double Toroidal Mirror
## 3 Wavefront Measurements and Optimization of the Focal Spot Size
### Alignment of the Focusing Optics
Figure 3: Evolution of the XUV wavefront root mean square (RMS) as a function of the focusing optics relative angle (number of steps) along the different axes: (**a**) rotation stage; (**b**) horizontal goniometer; and (**c**) vertical goniometer. The RMS is plotted in black dots in units of lambda for \(\lambda=42\) nm (left axis), with error bars coming from several single-shot measurements. The corresponding Strehl ratios are plotted in blue dots (right axis). The qualitative trends are plotted in dashed lines. Figure 5 is a comparison between the beam before the focusing optics, intensity and wavefront in Figure 5a,c respectively, and after optimization (Figure 5b,d). The beam before focusing optics has some \(0^{\circ}\) astigmatism, with an RMS value of 0.11 \(\lambda\) (=\(\lambda/9\)). After optimization of the focusing optics, we obtain an RMS value of \(0.028\pm 0.015\)\(\lambda\) (\(\lambda/36\)). It shows that the focusing optics can not only be aligned to introduce the least possible aberrations, but can also correct the pre-existing ones.
## 3 Principles of Infrared Spectroscopic Analysis Techniques
### Attenuated Total Reflection (ATR) Spectroscopy  
Attenuated total reflection (ATR) is mainly but not only a measurement technique applied in conjunction with mid infrared spectroscopy. ATR offers very often the advantage that solid and even liquid state samples can be directly measured without any sample pretreatment procedures necessary [66]. Applying this technique, light undergoes multiple internal reflections in a crystal of high refractive index, in many cases diamond, germanium or zinc selenite is the material of choice. The result of total internal reflection is the so called evanescent wave (Figure 3a). With this technique, infrared light is passed through the ATR crystal in such a way that it reflects at least once in most attempts even more often the internal surface in contact with the sample (Figure 3b). The depth of the evanescent wave is depending onto the exact wavelength, angle of incidence (\(\alpha\)) and the refraction indices for the ATR crystal and the sample of interest according to the following equation [67]:  
\[d_{p}=\frac{\frac{\lambda}{n_{1}}}{2\pi\sqrt{sin^{2}\alpha-\left(n_{2}/n_{1} \right)^{2}}} \tag{6}\]  
The signal-to-noise ratio (SNR) obtained depends on the number of reflections but also on the total length of the optical light path which dampens the intensity.  
The advantages of ATR spectroscopy can be summarized as follows:  
* Non-soluble materials can be analyzed (e.g., duroplasts)
* No KBr pellet is required
* Relatively high penetration depth for optical transparent materials
* Improved signal to noise ratio compared to measurement using KBr pellet
## 5 Numerical Tests
### Comparison with the DL-SCALE and DLTOP Methodologies  
Recently, a new methodology named DL-SCALE [28] has been proposed by the authors that also aims to reduce the computational demand of topology optimization with the use of deep learning under a reduced order modeling framework. In this section, the efficiency of the proposed DLMU scheme is assessed in terms of computing time and objective function value over the past ones proposed by the authors (i.e., the DLTOP [1] and DL-SCALE [28] methodologies) for the case of 200,000 number of FEs. The comparison  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**ne**} & \multicolumn{3}{c}{**SIMP**} & \multicolumn{3}{c}{**DLMU**} & \multirow{2}{*}{**Acceleration (\%)**} & \multirow{2}{*}{**Reduction (\%)**} \\ \cline{2-2} \cline{6-9}  & **Iterations** & **Objective** & **Time (s)** & **Iterations** & **Objective** & **Time (s)** & **Time (s)** \\ \hline
75,000 & 261 & 21.30 & 330.54 & 74 & 21.22 & 204.30 & 38.19 & 0.38 \\
100,000 & 326 & 21.45 & 559.69 & 79 & 21.38 & 252.59 & 54.87 & 0.33 \\
150,000 & 310 & 21.60 & 833.30 & 74 & 21.62 & 321.29 & 61.44 & \(-\)0.06 \\
200,000 & 358 & 21.79 & 1306.80 & 99 & 21.79 & 515.14 & 60.58 & \(-\)0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test example D—Performance of the DLMU scheme.  
Figure 8: Optimized domain for test example D for the case of 200,000 FEs discretization: (**a**) SIMP output and (**b**) DL-SCALE output.  
Figure 9: Optimized domain for test example E for the case of 200,000 FEs discretization: (**a**) SIMP output and (**b**) DL-SCALE output.  
is presented in Table 6. The final optimized configuration achieved by any of the three methodologies (DLMU/DL-SCALE/ DLTOP) is more or less identical to the one obtained using SIMP, and thus a comparison of the results is made only in respect of iterations, objective function value and running time.  
Regarding the implementation details, DLTOP requires performing 36 conventional steps of SIMP, then the already trained DBN is used to perform a close to final density distribution for the case of three classes classification (i.e., 0, 0.5 and 1), followed by the refinement steps. Regarding the DL-SCALE methodology, its implementation requires a set of \(n_{\mathit{probs}}\) auxiliary STO problems that rely on reduced order models compared to the fully refined \(TOP^{f}\) one. Based on the investigation performed in [28], the number of these problems is equal to \(n_{\mathit{probs}}=5\) and the number of FEs used to discretize these coarse meshes are equal to [3000, 4000, 5000, 7000, 10,000] elements, respectively. Evidently both DLMU and DL-SCALE outperform DLTOP in all test examples considered, both in terms of acceleration and reduction of the objective function value, all compared with the time required and the result obtained by the corresponding implementation of SIMP approach. Furthermore, DLMU slightly outperforms DL-SCALE in terms of the quality of the solution achieved as denoted by the reduction of the objective function value compared to the reference one obtained by SIMP.
# Rapid Prototyping of Multi-Functional Battery Energy Storage System Applications
## 2 Multi-Functional Energy Storage System Application Development
### Specification and Development Process  
The realization of EMS control applications requires following certain stages, which involves specification, design, proof-of-concept, implementation, and deployment [14]. Those steps are addressed in this section.  
At the specification stage, the stakeholders, DERs, and Intelligent Electronic Devices (IEDs), involved in the system under study, are identified. In addition to this, the communication and physical architecture are suggested. This results in a list of requirements to be addressed during the design phase. Subsequently, at the design level, solutions to define the specific behavior and structure of control strategies embedded by an EMS are evaluated. Hence, a list of variables encompassing control, setpoint and measurement signals are properly described. This description also gathers information related to protocols communication. Thus, the communication interfaces that allow interconnection across the power distribution application are also specified. Furthermore, the allocation of control strategies through the ICT hardware infrastructure is determined as well.  
The validation of the proposed control strategies is carried out at the proof-of-concept phase. At that stage, models of IEDs (e.g., smart meters) and DERs (e.g., BESS) are built and deployed into a power system emulator (e.g., MATLAB/Simulink, DlgSILENT PowerFactory), along with the control logic of the EMS [15]. The transition from design to proof-of-concept may entail communication and stability issues that were not considered at the design stage. Hence, an iterative refinement of the control algorithms are considered. Afterwards, the deployment of the validated control algorithms takes place at the implementation phase. A programming language compatible with a specific hardware controller is chosen to deploy the logic, usually this is driven by automation standards such as IEC 61499 [9] and IEC 61131-3 [10]. The validation of implementation phase is carried out by real-time simulations, controller-hardware-in-the-loop, and/or laboratory-based tests [16].
# A Semantically Aware Multi-View 3D Reconstruction Method for Urban Applications
## 3 Results and Discussion
### Semantic Segmentation for Complex Urban Scenes
The formula is as follows:  
\[P_{A}=\frac{\sum_{i=0}^{k}p_{ii}}{\sum_{i=0}^{k}\sum_{j=0}^{k}p_{ij}} \tag{18}\]  
where \(k\) denotes the number of target classes, and \(p_{ij}\) refers to the number of pixels of class \(i\) predicted as class \(j\). Another critical metric in the field of semantic segmentation is intersection over union (IoU). The IoU metric is based on the calculation that involves taking the intersection and the union of the predicted segmentation results and the actual segmentation results, followed by computing the ratio of the intersection to the union:  
\[IoU=\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}+\sum_{j=0}^{k}p_{ji}-p_{ii}} \tag{19}\]  
A lower IoU value signifies a closer approximation of the predicted segmentation to the actual segmentation, thus indicating higher accuracy and effectiveness of the model. To validate the segmentation accuracy of the training results based on the Cityscape dataset and the reconstructed dataset, we annotated 10% of the reconstructed dataset and created a validation dataset for verification. We compared the semantic segmentation accuracy of the three best semantic segmentation networks using the reconstructed dataset. The results are shown in Figure 6.  
Figure 5: Three-dimensional reconstruction data and semantic segmentation results: (**a**) Tsinghua University’s Old Gate, and (**b**) semantic segmentation results using Mask2 Former. Figure 6: Comparison of the accuracy of three semantic segmentation networks: (**a**) validation accuracy using the Cityscape dataset, and (**b**) validation accuracy using the 3D reconstructed dataset. Figure 6 shows the validation accuracy when the networks were trained internally on the Cityscape dataset and the validation accuracy when trained on the reconstructed dataset. Since the dynamic-other and static-other classes inherently occupy a small proportion of the scenes, and static targets do not significantly affect our reconstruction, the segmentation accuracy is overall acceptable. In this comparison, we examined the Mask2 Former, OCRNet, and DeepLabV3+ networks, which are three types of semantic segmentation networks. Among them, Mask2 Former achieved the best accuracy performance, and thus, we used the training results of Mask2 Former to segment the reconstructed images. The specific validation accuracy of Mask2 Former on the reconstructed dataset is shown in Table 4.  
As can be seen from Table 4, the segmentation accuracy for buildings and people, which are our primary concerns, reached over 95%, and the accuracy for cars and bicycles also exceeded 85%. These segmentation results met the requirements for our subsequent semantic reconstruction.
## 2 Materials and Methods  
### Printing of Specimens  
Flexural specimens were prepared from SUNLU acrylonitrile-butadiene-styrene (ABS) with a diameter of 1.75 mm. This grade of material corresponds to simple \"ABS\" as provided by the manufacturer. CreatBot F430 FDM printer was used for the fabrication of these specimens. Dimensions of the specimens were kept as per the guidelines mentioned in ASTM D790 [25]. Samples were designed in PTC Creo CAD software with specified dimensions, as shown in Figure 4, while Ultimaker CURA was used to set process parameters. These process parameters were divided into two categories, i.e., constant and variable. For the effect of raster angle, other printing parameters, including nozzle diameter, layer height, printing speed, nozzle, bed, and chamber temperatures, were kept constant. Samples were printed at raster angles of 0\({}^{\circ}\), 90\({}^{\circ}\), 0/90\({}^{\circ}\), 45\({}^{\circ}\)/\(-\)45\({}^{\circ}\), 60/30\({}^{\circ}\), 75/15\({}^{\circ}\), and 60\({}^{\circ}\) with an infill density of 100%. Whereas for observing the effect of infill patterns, the infill density was reduced from 100% to 60% so that the effect of infill patterns becomes more pronounced. Samples were printed at five different infill patterns, i.e., line, triangular, grid, tri-hexagon, and quarter-cubic, with an infill density of 60%, as presented in Figure 5. Table 1 summarizes the values of the process parameters used. At the same time, the overall experimental plan is shown in Figure 6.  
Figure 4: (a) Schematic of Experimental Setup; (b) dimensions of bending specimen used in this study.  
Figure 3: Raster angle representation with the help of flatwise samples.  
Figure 5: Infill patterns assessed in this study.  
Figure 6: Experimental plan.  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**Process Parameters**} & **Value** \\ \hline Nozzle diameter (mm) & 0.4 \\ Layer height (mm) & 0.3 \\ Chamber Temperature (\({}^{\circ}\)C) & 28 \\ Nozzle temperature (\({}^{\circ}\)C) & 250 \\ Bed temperature (\({}^{\circ}\)C) & 100 \\ Printing speed (mm/s) & 60 \\ Number of bottom layers with 100\% infill density (for infill pattern evaluation) & 3 \\ Number of top layers 100\% infill density (for infill pattern evaluation) & 3 \\ Number of layers inner layers contributing to 60\% infill density (for infill pattern evaluation) * & 8 \\ \hline \hline \end{tabular}  
* It must be noted that, for the samples fabricated for evaluating the effect of infill pattern on the flexural properties, the number of top and bottom layers was 6, i.e., 3 at the bottom and 3 at the top. These layers had 100\% infill, while the inner layers were printed with lower infill density, i.e., 60\%.  
\end{table}
Table 1: Process parameters used for 3D printing of specimens in this study.
## 1 Introduction  
Metasurfaces, as advanced two-dimensional materials, offer unique electromagnetic properties, making them ideal for applications such as energy harvesting sensors and antennas. Designing these metasurfaces, particularly their unit cells, is complex and time-consuming. To streamline this process, we propose using an artificial neural network, specifically a multilayer perceptron (MLP), to automatically generate metasurface geometries based on the desired resonant frequencies. This approach, tested with simulation data, demonstrates the potential of AI tools in optimizing metasurface design for practical applications.