esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Gc/ms  
The GC/MS system consisted of 6890 (Agilent Technologies, Santa Clara, CA, USA) equipped with a 59772 MSD Mass selective detector and a 30 m \(\times\) 25 mm fused silica column coated with 0.25 \(\upmu\)m HP-5 MS-column. The injector temperature was set at 250 \({}^{\circ}\)C and the injection volume was 1 \(\upmu\)L under a split relation of 1:10. Helium was the carrier gas at a velocity of 1.4 mL/min in the constant flow mode. The oven temperature raised from 60 \({}^{\circ}\)C to 320 \({}^{\circ}\)C at a rate of 6 \({}^{\circ}\)C/min. The MSD recorded the mass fragments in the range \(m/z\) 40 to 500. The spectral libraries Wiley 275 and NIST 08 were used to identify the mass spectra. Additionally, retention indices were calculated using a mix of C8-C30 n-alkanes analyzed under the same conditions [15; 16]. The peak areas from the total ion current without any correction were taken to calculate the percent composition of the essential oils and hydrolates.
## 2 GaN on SiC HEMTs
At low frequencies, parasitic capacitance is generally considered to have no effect on the gain amplitude of the amplifier, but as the frequency increases, the parasitic capacitance will cause the gain amplitude of the amplifier to roll down. Therefore, in order to obtain the best matching transistor performance, the large signal parameters of the transistor are obtained. The saturated output power density of the transistor is 5 W/mm, the PAE is 65%, and power gain is 14.1 dB. When the input power of GaN devices is 27 dBm at 10 GHz, the transistor's PAE reaches its maximum, but the gain is severely compressed at this time. The small-signal equivalent circuit of the GaN HEMT device is shown in Figure 3. R\({}_{\text{s}}\), R\({}_{\text{d}}\), and R\({}_{\text{g}}\) represent the source, drain, and gate parasitic resistances, respectively. C\({  
to increase the cutoff frequency, a slightly smaller-sized transistor should be selected, and a trade-off should be made with the gain.
## 4 Results and Discussion
### Storage and Loss Modulus Results  
The results for storage modulus (G\({}^{\prime}\)) and loss modulus (G\({}^{\prime\prime}\)) at two different temperatures, 15 \({}^{\circ}\)C and 25 \({}^{\circ}\)C, reveal significant temperature-dependent behavior in both modified and unmodified bitumen samples as shown in Tables 3-6. As expected, the moduli are generally higher at 15 \({}^{\circ}\)C than at 25 \({}^{\circ}\)C, indicating that bitumen is stiffer and more elastic at lower temperatures. This is a typical response since, at lower temperatures, bitumen becomes more resistant to deformation, behaving more like a solid (elastic), while at higher temperatures, it becomes more viscous and pliable. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Bitumen Type** & **Unmodified** & **4\% SBS** & **5\% SBS** & **4\% AB** & **5\% AB** & **6\% AB** & **3\% WCO** & **4\% WCO** & **5\% WCO** \\ \hline
**Angular Frequency** & & & & & & & & \\
**[rad/s]** & & & & & & & & \\ \hline
100 & \(5.71\times 10^{3}\) & \(8.76\times 10^{3}\) & \(9.89\times 10^{3}\) & \(6.15\times 10^{3}\) & \(9.71\times 10^{3}\) & \(4.82\times 10^{3}\) & \(4.82\times 10^{3}\) & \(2.96\times 10^{3}\) & \(4.78\times 10^{3}\) \\ \hline
68.1 & \(5.17\times 10^{3}\) & \(8.18\times 10^{3}\) & \(8.16\times 10^{3}\) & \(5.50\times 10^{3}\) & \(7.93\times 10^{3}\) & \(3.79\times 10^{3}\) & \(3.79\times 10^{3}\) & \(2.29\times 10^{3}\) & \(3.80\times 10^{3}\) \\ \hline
46.4 & \(4.72\times 10^{3}\) & \(7.84\times 10^{3}\) & \(6.66\times 10^{3}\) & \(5.01\times 10^{3}\) & \(6.38\times 10^{3}\) & \(2.95\times 10^{3}\) & \(2.95\times 10^{3}\) & \(1.77\times 10^{3}\) & \(2.98\times 10^{3}\) \\ \hline
31.6 & \(4.32\times 10^{3}\) & \(8.91\times 10^{3}\) & \(5.36\times 10^{3}\) & \(4.54\times 10^{3}\) & \(5.10\times 10^{3}\) & \(2.28\times 10^{3}\) & \(2.28\times 10^{3}\) & \(1.35\times 10^{3}\) & \(2.23\times 10^{3}\) \\ \hline
21.5 & \(3.92\times 10^{3}\) & \(7.50\times 10^{3}\) & \(4.30\times 10^{3}\) & \(3.90\times 10^{3}\) & \(4.06\times 10^{3}\) & \(1.73\times 10^{3}\) & \(1.73\times 10^{3}\) & \(1.02\times 10^{3}\) & \(1.79\times 10^{3}\) \\ \hline
14.7 & \(3.55\times 10^{3}\) & \(6.26\times 10^{3}\) & \(3.43\times 10^{3}\) & \(3.13\times 10^{3}\) & \(3.18\times 10^{3}\) & \(1.31\times 10^{3}\) & \(1.31\times 10^{3}\) & \(7.62\times 10^{5}\) & \(1.37\times 10^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Storage modulus [kPa] at 15 \({}^{\circ}\)C.
## 3 The VSWT-PMSG Control System  
The block diagram of control system for VSWT-PMSG proposed in this paper is shown in Figure 6. The VSWT-PMSG system consists of the following components: a direct drive PMSG, blade pitch controller, AC/DC/AC converters based on two levels of IGBT which are composed of stator side converter (SSC) and grid side converter (GSC), a DC-link circuit composed of a chopper with a resistance (\(R_{c}\)) and a capacitor (\(C_{dc}\)), two voltage source converter controllers ( stator side controller and grid side controller), and LC filter with passive damping resistance. The SSC is connected to the stator of PMSG, and it converts the three-phase AC voltage generated by PMSG to DC voltage. The three-phase voltage and current of PMSG are detected on the stator terminal. The rotor speed of PMSG is detected from the rotor of the generator. All outputs of the sensors are fed to the stator side controller as input signals in order to control the voltage references of the stator side converter for modulation. In the GSC, the converter converts the DC voltage into the three-phase AC voltage of the grid frequency. The converter is connected to the grid system through an LC filter and a step up transformer. The grid current and the grid voltage sensors are detected on the converter side of the LC filter and the high voltage side of the transformer, respectively. The DC voltage (\(V_{dc}\)) is detected on the  
Figure 5: Block diagram of LC filter. Figure 6: VSWT-PMSG control system. DC capacitor. Using the grid side controller controls the voltage reference of grid side voltage source converter for modulation. When a fault occurs in the grid, the \(V_{dc}\) increases significantly due to power unbalance between SSC and GSC. In order to protect the DC-link circuit, the controller activates the chopper by a trigger command (_Ctrl_). Output power of a wind generator always fluctuates due to the wind speed variations. To maintain the output power of generator under the rated level, a pitch controller is used to regulate rotational speed of PMSG under its rated value. In modulation technique, Third Harmonic Injection Pulse Wave Modulation (THIPWM) is used in this work. Injection of the third harmonic in the reference voltage makes it possible to utilize the voltage reference without over modulation. In addition, the THIPWM can maximize fundamental amplitude of the output voltage [14]. In this paper, a 2.5 MVA class of VSWT-PMSG is considered. Table 1 presents the system parameters of the grid connected VSWT-PMSG. In order to determine the LC filter parameters, step by step procedures and limitations of the filter parameters presented in [7] are adopted.
## 3 Materials and Methods
### Preprocessing and Exploratory Data Analysis  
Preprocessing and exploratory data analysis (EDA) are critical steps in the machine learning workflow. Preprocessing involves preparing the raw data for analysis. This step includes handling missing values, converting data types, normalizing or standardizing data, and encoding categorical variables [24; 25]. Preprocessing ensures that the dataset is consistent, complete, and formatted correctly for analysis. Effective preprocessing can significantly improve the performance of machine learning models by removing noise and irrelevant information. EDA involves analyzing datasets to summarize their main characteristics, often using visual methods. EDA helps to identify patterns, spot anomalies, and form hypotheses that can be further tested using more formal techniques [26].  
In our study, we began with a review of the dataset to gather basic information, such as the number of entries and columns, with their respective data types. This initial overview confirmed that our dataset consists of 330,936 entries and 33 features, including numerical and categorical data types. Next, we verified the dataset for anomalies, including missing values and outliers. Subsequently, categorical features representing hexadecimal protocol-level values were directly converted to their numerical equivalents (decimal format) to preserve their inherent numeric semantics and ensure compatibility with machine learning algorithms. The target column consists of six traffic classes (legitimate, DoS, brute force, malformed, SlowITe, and flood), suitable for multiclass classification. For binary classification, we converted these into binary values: 0 for legitimate and 1 for attack classes. The data are balanced, with approximately 50% of the instances being attacks.  
Figure 2: Traffic distribution.  
The initial statistics revealed that 12 features have constant values (zero mean and zero standard deviation). In other words, these features lack variability and provide no discriminatory power for predictive modeling. Additionally, we identified an outlier in one of the features, significantly distant from the rest of the dataset. Consequently, we dropped the 12 zero-variance features and removed the outlier to maintain data quality and interpretability. This reduced our feature set from 33 to 20.
## 2 Literature Review
### SatLoc--Romania  
The SATLOC system was designed by the UIC in cooperation with eleven organizations from six countries with the support of the European Space Agency (ESA), Paris, France. The project focused on the development and innovative use of the GNSS for the support of railway transport control on regional lines. The project also aimed to design new operating standards, as well as software and hardware solutions and services. The SATLOC system is now operated in testing mode on a single-track line of 27 km length. The designed and tested system corresponds with the ETCS L2 standards [23; 24].
# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 5 Experimental Results and Performance Evaluation
### Standard F1-Score (F1)  
The F1-score is the harmonic mean of _Precision_ and _Recall_, measuring the balance between false positives and false negatives.  
\[F1=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{20}\]  
where  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{21}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{22}\]  
Here, _TP_ (True Positive) represents the correctly detected anomalies, _FP_ (False Positive) denotes normal points incorrectly identified as anomalies, and _FN_ (False Negative) corresponds to undetected anomalies.  
The sequential and temporal nature of anomalies makes it difficult to apply the standard F1-score to time-series anomaly detection, even though it works well for discrete classification problems. Direct comparison between predicted and ground-truth anomalies may lead to unfair penalization or overestimation because anomalies happen over a range of time steps rather than isolated points.
# Robust Space Time Adaptive Processing Methods for Synthetic Aperture Radar
## 3 The Piecewise Constrained STAP Based on Sub-Apertures Framework
The calculation of \(w_{2}\) is formulated as:  
\[\text{min}w_{2}^{H}R_{2}w_{2}\text{ }subject\text{ to }w_{2}^{H}C_{2}=f_{2} \tag{13}\]  
\[C_{2}=[v(\theta),x(Q-L/2+1),\cdots x(Q+L/2)] \tag{14}\]  
\[f_{2}=[1,w_{1}^{H}x(Q-L/2+1),\cdots w_{1}^{H}x(Q+L/2)] \tag{15}\]  
The solution of (13) is:  
\[w_{2}=R_{2}^{-1}C_{2}[C_{2}^{H}R_{2}^{-1}C_{2}]^{-1}f_{2} \tag{16}\]  
The adaptive weight vectors in the remaining sub-apertures can be obtained similarly:  
\[w_{m}=R_{m}^{-1}C_{m}[C_{m}^{H}R_{m}^{-1}C_{m}]^{-1}f_{m} \tag{17}\]  
\[C_{m}=[v(\theta),x(Q(m-1)-L/2+1),\cdots x(Q(m-1)+L/2)] \tag{18}\]  
\[f_{m}=[1,w_{m-1}^{H}x(Q(m-1)-L/2+1),\cdots w_{m-1}^{H}x(Q(m-1)+L/2)] \tag{19}\]  
Then the SAR image can be obtained by two-dimensional processing of the entire aperture data containing all sub-apertures with classical SAR algorithms.
## 4 Stock Price Prediction Model Methodology and Research Design
### General Approach to Stock Price Movement Prediction
This approach is not only more practical but also aligns with the efficient utilisation of computational resources, as the simplicity of classification models makes them computationally less intensive compared to regression models that predict exact price changes. Lastly, the success of classification models in prior works within the field serves as a compelling argument for adopting this methodology. Numerous instances of effective applications demonstrate the reliability and utility of classification-based approaches [10; 33; 34; 35] in extracting meaningful insights from high-frequency market data. Considering these factors, framing the stock price movement prediction objective as a classification problem was deemed more sensible than approaching it as a regression problem. As highlighted in Section 2, deep supervised learning algorithms, exemplified in state-of-the-art research [10; 35], surpass conventional statistical and machine learning models in predicting stock price movements. Deep neural networks offer the advantage of effectively handling high-dimensional data and, when trained optimally, can replicate any function, regardless of its complexity, by defining outputs based on inputs. However, there are two complications associated with using deep learning. Firstly, these models necessitate significantly larger datasets for training compared to traditional statistical or machine learning models. To address this, the dataset utilised in this experiment comprises more than 1.5 million records in total, which should be adequate for training the most well-known deep learning models. The second complication is the heightened computational intensity of these models compared to simpler ones. In fact, training some of these models on standard personal computers would be impractical due to prohibitively long processing times. To surmount these challenges, the experiment was conducted on a computer cluster equipped with a Tesla V100 GPU. Given the above-mentioned advantages and ability to address the related challenges, it was decided to use deep supervised learning to address the stock price direction classification problem outlined above. More details on the proposed deep supervised learning model are presented in Section 4.3. Figure 1 presents a schematic diagram outlining the proposed methodology. The detailed description of the methodology is presented in the following subsections.
## 4 Discussion
### Perceived Workload (NASA-TLX) Scores  
The results indicated that using paper-based methods for complex tasks increased the mean score ratings of physical demand, performance, and frustration compared to AR-based instruction. For the complex task, the AR instruction method reduced physical workload by 12.18%, performance improved by 8.69%, and frustration improved by 60.14%. AR instruction resulted in greater temporal demand ratings and worse performance ratings for the simple task than paper instruction. AR instruction for the simple task increased temporal demand by 43.63% and decreased performance and frustration by 27.64% and 24.97%, respectively, compared to paper-based instruction. Overall, the AR-based method reduced performance, frustration, and mean scores by 18.13%, 50.82%, and 6.50%, respectively. Additionally, it was observed that the average ratings for mental workload, physical workload, temporal demand, and frustration all rose as the task difficulty went up, regardless of which instruction method was used. Some previous studies have shown that the utilization of AR for the purpose of delivering visual assistance and task-specific information has beenfound to effectively decrease cognitive load and result in enhanced performance [15; 80; 81]. Conversely, several studies have indicated that AR may be seen as mentally demanding and consequently result in diminished performance [82; 83]. The outcomes of this study align with the findings from previous research [15; 80; 81]. It was demonstrated that factors such as the type of AR display device used, the relevance and timeliness of the content, the manner of information presentation, and user and task characteristics all profoundly influence mental effort and task performance [84]. The observed discrepancies in results could potentially be attributed to variations in study types, technologies employed, and research strategies.
## 5 Discussion
### Robustness  
Through the MVS network modeling channel based on deep learning, the post-earthquake housing damage assessment work is completed--that is, from the public close-range experimental data migration to the real image of the UAV in the post-earthquake area and reconstruction of the 3D model--so whether the model has good robustness is extremely important. MVS network modeling based on deep learning completes 3D modeling in the form of calculated depth maps, so higher-resolution depth maps can reconstruct finer 3D models. The dilated convolution used by \(D^{2}\)HC-RMVSNet takes into account the features of a larger field of view, and AA-RMVSNet refines feature extraction and filter matching results, both of which improve when rebuilding 3D models. However, when used for high-resolution post-earthquake regional UAV image reconstruction, the series of models obtain higher-resolution depth maps by fine-tuning the \(max\_w\) and \(max\_h\) of the input image. However, from the experimental results, both methods have large fluctuations in time consumption and memory consumption. However, the CasMVSNet method constructs a smaller cost volume, so it has better stability at this time.  
The MVS series networks based on deep learning calculate the pixel depth information based on the assumption of discrete depth intervals. Therefore, when using the UAV image data of large scenes to reconstruct the 3D model of the post-earthquake region, the single-stage networks of \(D^{2}\)HC-RMVSNet and AA-RMVSNet can fine-tune and refine the discrete depth intervals. However, the above two networks use the RNN method, so it will increase the workload of the recurrent neural network when fine-tuning the assumed discrete intervals. CasMVSNet can not only improve the robustness of 3D reconstruction by refining the discrete depth intervals of each stage of the network, but also refine the depth map output in the final stage by increasing the level of the network. In this way,CasMVSNet realizes the 3D reconstruction of UAV image data in large scenes after the earthquake. Whether the number of stages of the network is adjusted or the discrete depth interval is refined, it will refine the calculation of depth information and provide better stability.
## 5 Discussion  
The designed and analyzed integrated transportation and energy system is an extreme hypothetical scenario, because:  
1. The city area is not connected to any national electricity or natural gas grid or a transportation fuel network. It is self-sufficient and stand-alone.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Hamburg** & & & **Murcia** & \\ \hline
**Annual Cost of Energy for Households** & & **Near** & **Mid** & **Present** & **Near** & **Mid** \\
**(Without Taxes and Levies)** & & **Future** & **Century** & **Present** & **Future** & **Century** \\ \hline Building CoE\({}_{\text{hh,B}}\) & [\(\ell\)/hh/year] & 1050 & 1820 & 480 & 1120 & 1360 & 340 \\ Transportation CoE\({}_{\text{hh,T}}\) & [\(\ell\)/hh/year] & 460 & 790 & 290 & 350 & 570 & 180 \\ Total CoE\({}_{\text{hh}}\) & [\(\ell\)/hh/year] & 1510 & 2610 & 770 & 1470 & 1930 & 520 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The annual cost of energy for households (CoE\({}_{\text{hh}}\)) without taxes and levies for the Present, Near Future, and Mid Century scenarios in Hamburg and Murcia.  
Only the residential, services, and road transportation sectors have been taken into account as energy consumers (e.g., not industry, agriculture, rail, or air transportation sectors).
* Space heating and hot water production are all-electric.
* It uses a single set of technologies for road transportation, transportation fuel, energy storage, and balancing, namely hydrogen, hydrogen production, and fuel cells (FCEVs), (no batteries or Battery Electric Vehicles, BEVs).
* The city area is relatively small, based on approximately 5000 people.  
In the future, a mix of multiple energy carriers, storage methods, and energy technologies could all work together. Cities in Europe already have connections to national electricity and sometimes natural gas grids. In addition, all sectors should be considered, not only the residential, services, and road transportation sectors. Increasing the system size and combining several different sectors would create more integration opportunities and could reduce costs.  
However, the calculated energy costs of the designed system are affordable and in line with other studies. This gives reason to explore whether variations in system designs and balancing methods can reduce total system costs even further. The system designs and balancing methods discussed below are a non-exhaustive selection of possible options.
## 3 Results and Discussions
### X-ray Diffraction Results  
Figure 6 shows the XRD photos of Cu-Cr-Zr and pure Cu thin-film samples manufactured in the same way. It indicates that the other two elements of Cr and Zr almost have no effect on the crystal structure of the Cu film, which accounts for the relatively low resistivity of Cu-Cr-Zr thin films.  
Figure 4: XPS spectra of Cr2p core levels on the Cu-Cr-Zr thin film surface before and after annealing. The spectrum of annealed sample (circle dots) was fitted by the Shirley background (dashed line) and fitted with peaks of Cr2p3/2 (573.1 eV), Cr2p3/2 (573.9 eV), and Cr2p3/2 (575.1 eV) (dotted line).  
Figure 5: Focused Ion Beam (FIB)-TEM micrographs of the alloy thin films: (**a**) Cu-Cr-Zr film before annealing; (**b**) Cu-Cr-Zr film after annealing at 300 \({}^{\circ}\)C.  
Figure 7 shows the X-ray diffraction patterns of Cu-Cr-Zr thin-film samples after annealing at various temperatures. As a comparison, the X-ray diffraction pattern of the as-deposited thin-film sample is also included. As shown in the figure, all the samples exhibit five peaks at 26 values of 43.45\({}^{\circ}\), 50.55\({}^{\circ}\), 74.25\({}^{\circ}\), 90.05\({}^{\circ}\), and 95.25\({}^{\circ}\), corresponding to Cu (111), (200), (220), (311), and (222) diffraction peaks, respectively, and grow along the Cu (111) preferential orientation. The preferential orientation is Cu (111) because copper, as a face-centered cubic (fcc) crystal, has the maximum atomic density and the least surface energy in the crystal indices, which is beneficial to the growth of the crystal face. In addition, with the annealing process, the Cu peak intensities are strengthened evidently and the Cu peaks become narrower, which indicates grain growth and the densification of the Cu film [17]. The Cu crystallinity becomes better as the annealing temperature increases, but when the temperature exceeds 400 \({}^{\circ}\)C, the Cu peak intensities become a little weaker, with some new Cu\({}_{2}\)O peaks appearing. It is worth noting that a Cu\({}_{2}\)O (111) peak appears when the annealing temperature exceeds 350 \({}^{\circ}\)C, which explains the very slight increase in resistivity, as shown in Figure 2. In addition, at 450 \({}^{\circ}\)C some peaks such as Cu\({}_{2}\)O (110), Cu\({}_{2}\)O (200), and Cu\({}_{2}\)O (220) appear and the Cu\({}_{2}\)O (111) peak intensity increases dramatically, resulting in the rapid deterioration of the membrane resistivity.  
Figure 6: The XRD of Cu-Cr-Zr and pure Cu thin-film samples.  
Figure 7: The XRD of Cu-Cr-Zr thin-film samples (**a**) at room temperature (RT), and after annealing at (**b**) 300 \({}^{\circ}\)C; (**c**) 350 \({}^{\circ}\)C; (**d**) 400 \({}^{\circ}\)C; and (**e**) 450 \({}^{\circ}\)C.