esa-sceva/satcom-chunk-collection huggingface dataset:
# The Development of an Advanced Air Mobility Flight Testing and Simulation Infrastructure
## 3 Development of the Platforms
### Vehicles  
Two different vehicles are considered for representing the real flights. These are the Swift VTOL UAV and the Cranfield multicopter. These UAVs have high endurance and payload capacity with a customizable avionics architecture that allows operators to integrate and test different AAM use cases with fast iteration. The technical details of each UAV are given in the following section, with emphasis on their payload.  
The Swift VTOL is a modified version of a MAKE FLY EASY 2.43 m wingspan VTOL aircraft. The specification of Swift VTOL is given in Table 1. In addition to the standard avionics required for a fully autonomous mission, the Swift VTOL UAV is also fit with approved Electronic Conspicuity that provides ADS-B output. The uAvinox Ping 1090 Mode S module is used for ADS-B output. The UAV location is tracked throughout Cranfield ATC during the AMU-LED demonstration. The telemetry link between the UAV and Ground Control Station (GCS) is established to share the UAV location with USSP ANRA Technologies. A photo of the Swift VTOL UAV is provided in Figure 4.  
The Cranfield Multicopter is a modified version of the YANGDA YD6-1600L Heavy Lift Multicopter. The specifications of the Cranfield Multicopter are given in Table 2. The Cranfield Multicopter is capable of being fully autonomous with a 5 kg payload capacity. With the collaboration of the Satellite Applications Catapult, the Multicopter UAV was equipped with a SATCOM and 4G modem called SkyLink, by the Blue Sky Network. This modem uses the Iridium satellite constellation and GSM/LTE network to provide an unlimited communication range. A photo of the Cranfield Multicopter with the SkyLink modem is given in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Detail** \\ \hline Manufacturer: & Yangda \\ Model: & YD6-1600L Heavy Lift Multicopter \\ Overall Dimensions: (H \(\times\) L \(\times\) W) mm & 500 mm \(\times\) 1385 mm \(\times\) 1600 mm \\ Max Take-Off Weight: & 20 kg \\ Maximum Payload: & 5 kg \\ Maximum Endurance: & Approximately 45 min \\ Maximum Operating Altitude: & 2000 m AMSL \\ Maximum Range: & 30 km capable \\ Cruise Operating Speed: & 12 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the Cranfield Multicopter.  
Figure 4: The Swift VTOL UAV.
## 2 Models and Data  
### Light Use Efficiency Models  
Seven light use efficiency (LUE) models were compared at the global scale in this study, including the Carnegie-Ames-Stanford Approach (CASA, [18, 34]), Carbon Fixation (CFix, [35, 36]), Carbon Flux (CFlux, [20, 37]), Eddy Covariance-Light Use Efficiency (EC-LUE, [38, 39]), Moderate Resolution Imaging Spectroradiometer GPP algorithm (MODIS, [12]), Vegetation Photosynthesis Model (VPM, [19]), and Vegetation Photosynthesis and Respiration Model (VPRM, [21]).  
These models are based on two assumptions: (1) that ecosystem GPP is directly related to absorbed photosynthetically active radiation (APAR) through LUE, where LUE is defined as the amount of carbon produced per unit of APAR and (2) that realized LUE may be reduced below its theoretical potential value by environmental stresses such as low temperatures or water shortages [10, 12]. LUE models are therefore generalized as:  
\[GPP=PAR\times FPAR\times LUE_{\text{max}}\times f\left(T_{s},W_{s},\cdots\right) \tag{1}\]  
where PAR (MJ-m\({}^{-2}\)) is the incident photosynthetically active radiation per time period (e.g., day, month, or year), FPAR is the fraction of PAR absorbed by the vegetation canopy, LUE\({}_{\text{max}}\) (g C\(\cdot\)m\({}^{-2}\cdot\)MJ\({}^{-1}\) APAR) is the potential light use efficiency without environmental stresses, and \(f\) are the scalars quantifying the environmental stresses. Although the seven LUE models can be expressed in the general form, they are different in the explicit realization of Equation (1). For example, not all models use the same FPAR data. Two models (CFlux and MODIS) directly employ a MODIS product (MOD15), and other models use FPAR derived from vegetation indices. Please refer to the supplementary online material (SOM) for the detailed model descriptions and Equations.  
We used 157 FLUXNET eddy covariance sites to calibrate and evaluate these models [26]. Specifically, fifty percent of the sites were randomly selected to calibrate model parameters and the remaining sites were used to evaluate models. This parameterization process was repeated until all possible combinations of the 50% sites were achieved. The performance of these models has been evaluated using CO\({}_{2}\) flux measurements from these sites [26]. In the present study, the global terrestrial GPP were simulated using the seven models each with the mean values of the calibrated parameters.
# Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems
## 4 Results and Discussion
### Single-Flow  
For the single-flow viewpoint, each model was trained and tested with the the CIDDS-001 preprocessed data. Table 9 summarizes the obtained results.  
Even though there are few differences between each model's accuracy results, the values for the other metrics are quite different. LSTM presents the highest precision score  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Single-Flow (\%)**} \\ \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **FPR** \\ \hline LSTM & 99.91 & 98.37 & 71.40 & 74.23 & 0.05 \\ RF & 99.90 & 79.43 & 95.68 & 85.04 & 0.02 \\ MLP & 99.92 & 78.68 & 73.75 & 75.79 & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Single-Flow results for the LSTM, RF and MLP models.  
with little recall, while RF presents the highest recall value with considerably less precision. On the other hand, MLP exhibits a minor difference between its precision and recall values. This balance translates into a better f1-score value than the one related to the LSTM model. The f1-score for the RF model is substantially greater than the ones presented by every other technique. Regarding the FPR, the RF assures fewer occurrences of false alarms than both the LSTM and MLP.  
The combination of high accuracy values and differentiated f1-scores can lead to the conclusion that models performed quite differently on the minority classes. Figure 8 expresses the f1-score values of each model for every class.  
The RF performed better for the Ping Scan class than every other model. Since the Ping Scan class only represents 0.04% of the total sample data, this class's misclassification has little effect on the accuracy value. For the macro-averaged f1-score, this does not happen, because the misclassification of any class, more or less prevalent in the sample, translates into a heavy penalization in its value.  
From the general perspective, choosing the best model for this experiment is not a straightforward process. The RF assures that most of the attacks are detected even with the occurrence of a considerable amount of false-positive errors, while the LSTM fails to detect a significant quantity of attacks but grants a lower occurrence of such errors. Regarding the balance between precision and recall, RF outperforms the remaining techniques, but that is only significant when compliant with the deployment situation's requirements and constraints.
## 1 Introduction  
In recent years, there has been ongoing development of aircraft with low-probability-of-intercept (LPI) capabilities, which are difficult to track using conventional radar systems. To counter these technologies, passive coherent location (PCL) radars using commercial low-frequency broadcast signals, such as frequency modulation (FM) radio [1; 2], digital TV [3; 4; 5; 6], analog TV [7], satellites [8; 9], and mobile communications [10], have been studied extensively. In particular, PCL radars using low-frequency bands, including the very high frequency (VHF) band of FM broadband signals, can effectively counter long-range targets with radar absorbing material (RAM) or radar absorbing structure (RAS). In addition, PCL radars can dramatically reduce system hardware costs, effectively conceal the viewing position, and precisely estimate the target location with multi-static receivers, by using external illuminating sources often referred to as \"illuminators of opportunity\" [11]. In general, an appropriate number of illuminators is required to precisely pinpoint targets in the multi-static receiver PCL system [11]. However, as FM stations cannot cover all regions, additional commercial stations can also be utilized, such as terrain digital multimedia broadcasting (T-DMB, 174-216 MHz), which is similar to European digital audio broadcasting (DAB) communications. In order to extend PCL system performance from FM to T-DMB bands, it is necessary to improve individual antenna characteristics for both the FM band and the T-DMB band. Achieving this multi-band performance at low frequencies has been considered technically challenging.  
In this paper, we propose a novel design of an array antenna element that can achieve multi-frequency band operation by applying a coupled-fed technique to a dipole antenna. This coupled-fed technique, often used for broadband matching characteristics of microstrip patch antennas [12; 13; 14], is first applied to the design of the multi-band dipole antenna for PCL systems. The antenna element comprises an internal dipole and an external coupled-fed dipole. These internal and external dipoles are tightly attached to the housing structure, which includes the circuit board of a balun. Using the coupled-fed structure, the two resonances are appropriately controlled to achieve FM and T-DMB dual-band matching characteristics. To confirm the effectiveness of the proposed design, we fabricate the coupled-fed dipole element and measure antenna characteristics, such as the voltage standing wave ratio (VSWR) and the antenna gain. The proposed antenna element is then applied to an eight-element circular array to form the reference and surveillance beams in the T-DMB band. Finally, the target location is estimated by constructing amplitude-range doppler (ARD) maps for one FM and two T-DMB illuminators in the Seoul-Gyunggi urban area.
# A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data
## 2 Data Preprocessing
### Space-Time Mapping and Discretization
The travel volume \(q_{k}\) in cell \(k\) can be calculated using Equation (4), where \(a_{V_{1}}^{k}\) denotes the area occupied by the Tyson polygonal region \(V_{1}\) in cell \(k\), \(E_{1}\) denotes the total number of trips corresponding to site \(s_{1}\), \(A_{V_{1}}\) denotes the total area of the Tyson polygonal region \(V_{1}\), and \(m\) denotes the number of polygons intersecting with \(q_{k}\). \[q_{k}=\frac{a_{V_{1}}^{k}\cdot E_{1}}{A_{v_{1}}}+\frac{a_{V_{2}}^{k}\cdot E_{2 }}{A_{v_{2}}}+\ldots+\frac{a_{V_{m}}^{k}\cdot E_{m}}{A_{v_{m}}} \tag{4}\]  
On the basis of spatial discretization, according to the fixed time interval, the total number of passengers boarding and alighting at each station in each time period of the day is counted, and the similarity of passenger flow time distribution between stations is calculated using the correlation coefficient. The calculation method is shown in Equations (5) and (6). \[F_{i}=[f_{1i},f_{2},\ldots,f_{ii}],i=1,2,\ldots,n \tag{5}\]  
\[\rho_{ij}=\frac{E[(F_{i}-\mu_{i})(F_{j}-\mu_{j})]}{\sigma_{i}\sigma_{j}} \tag{6}\]  
where \(f_{ii}\) is the total number of passengers getting on and off the bus at station \(i\) in time period \(t\). There are 48 time periods \(t\), of which the first 24 represent the passenger flow at the first bus station, and the last 24 represent the passenger flow at the second bus station; \(n\) is the number of bus stations; \(u_{i}\) and \(u_{j}\) are the average passenger flow of stations \(i\) and \(j\), respectively; and \(\sigma_{i}\) and \(\sigma_{j}\) are the passenger flow variance of stations \(i\) and \(j\), respectively. The calculation result is a matrix of \(n\times n\), which represents the similarity of passenger flow time distribution between two bus stations. Figure 3: Discrete cell schematic diagram of bus stations.
## 4 Interference Detection Based on the Reassigned Spectrogram
### Reassignment Method
In practice, a more efficient implementation of the calculation of reassigned location can be obtained, written as follows:  
\[\hat{t}(y_{a};t,\upomega)=t-\mathit{Re}\bigg{\{}\frac{STFT_{y_{a}}(t,\upomega;t_{ h})STFT_{y_{a}}^{*}(t,\upomega;h)}{\left|STFT_{y_{a}}(t,\upomega;h)\right|^{2}} \bigg{\}} \tag{37}\]  
\[\hat{\upomega}(y_{a};t,\upomega)=\upomega+\mathit{Im}\bigg{\{}\frac{STFT_{y_ {a}}(t,\upomega;D_{h})STFT_{y_{a}}^{*}(t,\upomega;h)}{\left|STFT_{y_{a}}(t, \upomega;h)\right|^{2}}\bigg{\}} \tag{38}\]  
where \(\mathit{Re}\) and \(\mathit{Im}\) represent real and imaginary parts, respectively; \(t_{h}=th(t)\) means the time ramped smoothing window multiplied by the time variable \(t\); \(D_{h}(t)=\mathsf{d}h(t)/\mathsf{d}t\) denotes the smoothing window using the first time derivative of the window function \(h(t)\); and \(*\) indicates a complex conjugate. Explicit phase differentiations can be avoided by computing two additional STFTs \(STFT_{y_{a}}(t,\upomega;t_{h})\) and \(STFT_{y_{a}}(t,\upomega;D_{h})\). These new expressions in Equations (37) and (38) do not focus any more on the phase of the STFT, and for computational simplicity, the reassigned point \((\hat{t}(y_{a};t,\upomega),\hat{\upomega}(y_{a};t,\upomega))\) is computed from the ratios of the STFTs rather than from the phase derivatives. The reassigned spectrogram \(S^{(r)}(t^{\prime},\upomega^{\prime})\) can be adopted to improve TF energy aggregation properties in the TF plane, and it is also free of cross-term artifacts; therefore, in this work, the TF distribution by using the reassigned spectrogram has been proposed in interference detection for GNSS receivers, which is clearly illustrated in Figure 5. The two-dimensional convolution of the WVD \(W_{y_{a}}(t,\upomega)\) of the analytical signal \(y_{a}(t)\) with the WVD \(W_{h}(t,\upomega)\) of the window function \(h(t)\) (\(i.e.\), the two-dimensional smoothing kernel \(\Pi(t,\upomega)=W_{h}(t,\upomega)\)) is performed to obtain the spectrogram \(S(t,\upomega)\); after the reassignment is performed in \(S(t,\upomega)\), the obtained reassigned spectrogram \(S^{(r)}(t^{\prime},\upomega^{\prime})\) has good TF readability, which is suitable for multi-component signal analysis. Therefore, the proposed TF analysis by using the reassigned spectrogram is able to clearly differentiate the spectral characteristic of the interfering signal from that of the useful GNSS signal; thus, the instantaneous frequency of the disturbing term in the interfered GNSS signals can be effectively estimated by detecting the peaks of the reassigned spectrogram. Figure 5: Interference detection by using the reassigned spectrogram for GNSS receivers.
# Traffic-Predictive Routing Strategy for Satellite Networks
## 5 Route Maintenance
### Node Stability  
Having multiple neighboring nodes can provide redundant paths, thereby enhancing the stability of the network in the event of link failures. When the primary link experiences a fault or interruption, nodes can still communicate with other nodes using alternate paths. The existence of these redundant paths makes the network more resilient, enabling it to better adapt to changes and failures in the links. Having multiple neighboring nodes increases the communication opportunities for a node, as each neighbor can be considered a potential communication path. Consequently, the connections between nodes become more stable, reducing the impact of a single point of failure on network accessibility. Therefore, the stability of a node can be reflected by the number of neighboring nodes, indicating the density of connections and accessibility in the network.  
Assuming that the number of satellite nodes is \(N\), the satellite network topology at \(t\) moment can be represented by \(G(t)=\langle V,E(t)\rangle\). Where \(V=\{1,2,\ldots N\}\) constitutes a node set, \(S\) is the source node, \(S\in V\), \(D\) is the destination node, and \(D\in V\). \(E(t)=\{e_{1},e_{2},\ldots e_{m}\}\) forms the set of links at \(t\) moment. If there is an edge between node \(i\) and node \(i+1\) at time \(t\), then node \(i+1\) is the neighbor node of node \(i\), and all the neighbor nodes of node \(i\) form the neighbor set of node \(i\), denoted \(V_{i}(t)\).  
Figure 3: Local repair example diagram.  
Figure 2: Example of source node repair.  
Suppose the neighbor set of the node \(i\) at time \(t_{1}\) is \(V_{i}(t_{1})\), and the neighbor set of the node \(i\) at time \(t_{2}\) is \(V_{i}(t_{2})\). Then, the stability of the node \(i\) is:  
\[STA_{i}=\frac{V_{i}(t_{1})\cap V_{i}(t_{2})}{V_{i}(t_{1})\cup V_{i}(t_{2})} \tag{3}\]  
\(STA_{i}\) in Formula (3) reflects the stability of the node \(i\). Among the one-hop nodes adjacent to a node \(i\), the higher the \(STA_{i}\), the more neighbor nodes remain connected to the node \(i\), and the more stable the node \(i\) is considered. On the contrary, it means that the fewer neighbor nodes that remain connected to the node, the higher the probability of the node breaking. In this paper, the time interval for calculating node stability is set as the survival time of the link after the node receives the HELLO message sent by the neighbor node.
## 2 Patients, Materials and Methods
### In Vivo Reflectance Confocal Laser Scanning Microscopy  
In order to microscopically evaluate the initial EDs and their changes (disappearance, reduction, invariability or worsening) at the end of the CPP-ACP treatment, a series of in vivo confocal images were collected. For the purpose, a commercially available reflectance confocal microscope (RCM) was used (Vivascope3000(r), first version, Lucid, Rochester, NY, USA) to image in vivo the enamel surface, at microscopic resolution, with no pain or discomfort for the patients. Each picture corresponds to ahorizontal \(500\times 500\) um optical section of the dental enamel, 5 um in thickness and with a 0.5-1 um lateral resolution with correspondence to standard histology [26; 28]. RCM allows to vertically scan the enamel layers until a maximum depth of 300 um from the surface. The system operates with a diode laser class 3A (European version) at 830 nm wavelength and with a 309 water immersion objective lens and a 0.9 numerical aperture. The laser power varies from 5 to 10 mW according to the layer to evaluate, and it causes neither tissue damage nor dental heating [28; 35].  
In order to obtain confocal images of the tissue from the plans parallel to the RCM lens (confocality), the device needs to work in close contact to the dental surface. Ultrasound gel is interposed between the lens and the window, and the window and the tooth, to optimize the incident and the refracted lights transmission. Hence, only vestibular surfaces of front teeth (incisors and canines) could be imaged, because both the size of the RCM sensor and the encumbrance of the handpiece impede to reach the back teeth and lingual dental surfaces.
###### Abstract  
The thermal process of green amaranth leads to the partial or complete degradation of chlorophyll pigments and loss of green colour due to the formation of chlorophyll derivatives. This study aimed to evaluate a stabilisation process utilising metal ions to obtain a stable green colour of metal-chlorophyll derivative complexes. In this study, the effects of CuSO\({}_{4}\) (0-240 ppm), ZnCl\({}_{2}\) (0-1800 ppm) ions, pH (4-9), and temperature (60-100 \({}^{\circ}\)C) on green amaranth pures with a constant time of 15 min were investigated. In tapered leaf amaranths, the sample depicted higher contents of chlorophyll\(\pm\) (0.33 mg/g), chlorophyll\(\pm\) (0.34 mg/g), and total chlorophyll (0.68 mg/g) than round leaf amaranths (chlorophyll \(\pm\) 0.28 mg/g, chlorophyll\(\pm\) 0.29 mg/g, and total chlorophyll\(=\) 0.58 mg/g). A higher chlorophyll derivative content (0.62 mg/g), DPPH scavenging activity (93 mM TE/g), and FRAP value (54 mM TE/g) of Cu-amaranth pures were successfully achieved using 210 ppm of CuSO\({}_{4}\) after heating at pH 6 and 80 \({}^{\circ}\)C. Zn-amaranth pures were produced using 1500 ppm of ZnCl\({}_{2}\) at pH 8 and 90 \({}^{\circ}\)C for 15 min with chlorophyll derivative content of 0.39 mg/g, DPPH scavenging activity of 79 mM TE/g, and FRAP value of 57 mM TE/g. In HPLC chromatograms, two major peaks were identified as chlorophylls \(a\) and \(b\) in fresh amaranths. Nevertheless, these two peaks disappeared in Cu- and Zn-amaranth pures, presumably due to the formation of metallo-chlorophyll derivatives.  
chlorophylls; green colour; antioxidant activity; amaranth; metal ions +
Footnote †: journal: Applied Sciences
## 3 Results  
A subset of some of the acceleration-derived HVSR results is shown in Figure 3, with the station and \(V_{\rm S30}\) indicated in each graph. Figure 3a shows results for station YSUK demonstrating an obvious peak for the station-specific HVSR at \(f_{\rm P}\) = 1.4 Hz derived from a collection of earthquake events shown in gray. The dashed lines represent \(\pm\)1\(\sigma\). The lower boundary of the green shaded area represents the mean of the HVSR, while the solid blue line is 1.5 \(\times\) HVSRavg. This shows the HVSR peak characteristics satisfy all three conditions for being a relevant peak, i.e.,\(f_{\rm P}\) > 1.0, \(A_{\rm P}\) > 2.0, \(A_{\rm P}\) > 1.5 \(\times\) HVSRavg. Figure 3b shows results for station HCH, which has multiple HVSR peaks, but the absolute maxima occurs at \(f_{\rm P}\) = 0.4 Hz. For frequencies considered above 1 Hz,\(f_{\rm P}\) = 6.7 Hz, which is also true for velocity-derived HVSR. Note that \(A_{\rm P}\) = conditions are satisfied for both \(f_{\rm P}\) = 0.4 and 6.7 Hz. Figure 3c brings it a step further and shows results for station WDL. In this case, the major peaks occur at frequencies greater than 1 Hz with \(A_{\rm P}\) > 2.0 but <1.5 \(\times\) HVSRavg. Figure 3d shows the opposite for station YSUM, where \(A_{\rm P}\) < 2.0, but >1.5 \(\times\) HVSRavg. After processing, 11 stations produced a valid \(f_{\rm P}\) using acceleration-derived HVSR, while 10 stations produced a valid \(f_{\rm P}\) using velocity-derived HVSR.  
Figure 3: Examples of HVSR versus frequency with the indicated station abbreviation and \(V_{\rm S30}\). The light gray lines represent the HVSRs derived from individual earthquake events. The solid red line represents the mean of the logarithmically distributed HVSR per frequency, and the dashed red line is \(\pm\)1 standard deviation. The lower boundary of the green shaded area represents the mean of the HVSR, while the solid blue line is 1.5 \(\times\) the averaged HVSR. (**a**) station YSUK, showing a well-defined peak; (**b**) station HCH, showing multiple peaks but \(f_{\rm P}\) < 1 Hz; (**c**) station WDL, showing \(A_{\rm P}\) > 2, but <1.5 \(\times\) HVSRavg; and (**d**) station YSUM, showing \(A_{\rm P}\) < 2, but >1.5 \(\times\) HVSRavg.