esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### The Cause of Landslide
#### 5.1.3 Earthquake  
Earthquakes are also a predisposing factor for landslides. The Longmenshan Fault Zone is located at the junction of the North China Block, South China Block, and Qiangtang Block, with a length of about 500 km and a width of about 30-50 km. To the northwest is the Songpan Ganzi Ancient Residual Ocean Basin, and to the southeast is the stable Yangtze Craton. It extends in the NE-SW direction, and the fault sliding is mainly characterized by thrust with a right-handed strike-slip component. The main faults of the Longmenshan Fault Zone are distributed from west to east, including the Houshan Fault (Wenchuan Maoxian Fault), the Central Fault (Yingxiu Beichuan Fault), and the Qianshan Fault (Jiangyou Guaxian Fault) [23]. Affected by the overall stress background, the activity speed in this area is stronger than in other areas, making it prone to earthquakes, as shown in Figure 13. On 12 May 2008, a strong earthquake with a magnitude of 8.0 occurred in Wenchuan County, and on 20 April 2013, a strong earthquake with a magnitude of 7.0 occurred in Lushan. The magnitude of both earthquakes in Hanyuan County was 5. Two earthquakes accelerated the deformation process of the slope. Earthquakes are often accompanied by the activity of faults, which cause the rock layers on the slope to be displaced or broken, damaging the integrity of the rock layers and reducing their shear strength. In addition, earthquakes can further develop cracks in the rock mass on the slope, reducing its overall strength and stability, making it easier for the rock mass to slide under stress In addition, earthquakes can cause the upper surface of the slope to rise or the lower surface to settle, directly changing the overall slope of the slope and thus altering the stress distribution of the slope. In addition, the Sichuan Chongqing region experiences frequent rainfall in summer, leading to the occurrence of the Hanyuan landslide under the influence of multiple factors.  
Figure 13: Regional structure and earthquake epicenter distribution map of Longmenshan Fault Zone.
# Advanced Methods for Photovoltaic Output Power Forecasting: A Review
## 3 AI-Based Techniques Used for PV Output Power Forecasting
### Application of Machine Learning in PV Power Forecasting
The ELM outperforms the classical back propagation (BP) algorithm in terms of accuracy. The technique showed the smallest MAE = 1.08% in June, while the biggest MAE = 18.83% and corresponded to February and March. A multiple reservoirs echo state network (MR-ESN) based model has been proposed for in [64]. The quasi Newton algorithm has been used to optimize the reservoir parameters. The results showed a MAPE very close to zero (0.00195%) and, with reference to one-hour forecast horizon, the model performed better than other techniques such as SVM, back-propagation neural networks (BPNNs), support vector regression (SVR-ANN), and wavelet transform (WT). A multi-model ELM-based forecaster was proposed in [65] for the forecasting of the power produced by 250 kWp PV plant installed in Beijing, China. With reference to the accuracy of the multi-model, the MAE was 2.13% in spring and 1.7% in summer, while for an annual single model the MAE was 2.43% in spring and 1.81% in summer. The designed multi-model takes into account the fluctuation of the power output in order to improve the accuracy.
## 3 A reliability Evaluation Framework Based on DBNs  
### Theoretical Basis of DBNs  
In general, a DBN is a new type of generative neural networks which are constructed by stacking restricted Boltzmann machines (RBMs) that are usually trained based on the probability principle [29]. In a DBN, the RBMs are connected between hidden and visible layers. In the visible layer, the nodes are connected without directions. In the pre-training stage, the main training strategy is unsupervised learning, where the fine-tuning stage is trained by a supervised learning method. The outputs of a visible layer are treated as inputs to the hidden layer. In general, DBNs are widely used in the field of image identification. Their application in structural reliability analysis is relatively insufficient. In this study, the DBN and BP networks were utilized to approximate the structural response surfacefunctions. The framework of predicting the effect of structural load effect using the DBN is shown in Figure 4. The DBN principle will be demonstrated below.  
The energy function of a joint structure (\(V,H\)) of visible and hidden units can be written as:  
\[E(V,H)=-\sum_{i=1}^{m}a_{i}v_{i}-\sum_{j=1}^{n}b_{j}h_{j}-\sum_{j=1}^{n}\sum_{i =1}^{m}v_{i}h_{j}w_{ij} \tag{13}\]  
where \(V\) = (\(v_{1}\), \(v_{2}\), \(v_{3}\),..., \(v_{m}\)) is a visible layer, \(H\) = (\(h_{1}\), \(h_{2}\), \(h_{3}\),..., \(h_{n}\)) is a hidden layer, \(m\) and \(n\) are the number of element in the visible and the hidden layer, respectively; \(v_{i}\) and \(h_{j}\) are the \(i\)th and \(j\)th neurons in the visible and hidden layers, respectively; \(w\) = (\(w_{ij}\)) is the weight of elements, and \(a_{i}\) and \(b_{j}\) are the bias in the visible layer and the hidden layer, respectively.  
As it can be seen in Figure 4, the parameters \(a_{i}\), \(b_{i}\), and \(w_{\bar{y}}\) are trained and optimized from bottom to top based on an unsupervised learning method. Subsequently, the parameters are adjusted from top to bottom. The training function is:  
\[w=w+\zeta\Big{[}P(h_{j}^{(k)}=1\big{|}v^{(k)}\,){(v^{(k)})}^{\rm T}-P(h_{j}^{(k+ 1)}=1\big{|}v^{(k+1)}\,){(v^{(k+1)})}^{\rm T}\Big{]} \tag{14}\]  
\[a=a+\zeta(v^{(k)}-v^{(k+1)}) \tag{15}\]  
Figure 4: Flowchart of structural load effect prediction based on DBNs.  
\[b=b+\zeta\Big{[}P(h_{j}^{(k)}=1\Big{|}p^{(k)})-P(h_{j}^{(k+1)}=1\Big{|}p^{(k+1)}) \ \Big{]} \tag{16}\]  
where \(P\) is a probability function, \(\zeta\) is the training rate which is in the range of 0.05 and 0.2, and \(h_{j}^{(k)}\) is the \(k\)th optimization of the \(j\)th neuron in the hidden layer.
## 3 Results and Discussion  
The detection wavelengths for the simultaneous analysis of the 13 prohibited synthetic colorants by HPLC-DAD were set in consideration of the maximum absorption wavelength (\(\lambda_{\rm max}\)) for each compound. As shown in Figure 2d, three wavelengths were selected to effectively separate the compounds: 254 nm for Basic Red 2, Disperse Yellow 3, HC Blue No. 2, Solvent Orange 7, and Solvent Red 24; 430 nm for Basic Yellow 28, Disperse Brown 1, Disperse Orange 3, HC Yellow No. 5, Solvent Orange 4, Solvent Yellow 1, and Solvent Yellow 3; and 590 nm for Basic Blue 26. The ultraviolet (UV) spectra are shown in Figure 2d. Figure 2: HPLC chromatogram and UV spectra of blank sample and 13 illegal synthetic colorants: (**a**) tattoo lipstick blank sample; (**b**) tattoo eyebrow blank sample; (**c**) hair tint blank sample; (**d**) 13 illegal synthetic colorants. The HPLC-DAD method was validated in terms of specificity, LOD, LOQ, linearity, recovery, accuracy, and precision. The specificity was confirmed as peaks of interfering compounds were not observed at the retention times of the colorants in all blank samples. The LODs and LOQs of colorants in tattoo eyebrow samples were 0.10-1.20 ug/mL and 0.30-3.60 ug/mL, respectively, while those for tattoo lipstick and hair tint samples were 0.01-0.30 ug/mL and 0.02-0.90 ug/mL, respectively (Table 1). The calibration curves for all prohibited colorants showed excellent correlation coefficients (\(R^{2}>0.999\), Table 1). The recovery was determined at three concentrations (low, medium, and high) based on the LOQ concentration of each compound. Low, medium, and high concentrations were set to the LOQ, 10 times the LOQ, and 50 times the LOQ, respectively. The recoveries of the tattoo lipstick, tattoo eyebrow, and hair tint samples were 86.11-108.39%, 83.32-105.33%, and 93.49-106.48%, respectively, and the corresponding relative standard deviations (RSDs) were <4.66%, 3.58%, and 9.63%, respectively (Tables 2-4). The recoveries and RSDs were within the appropriate ranges. Accuracy was evaluated as the average of recovery, and precision was evaluated as RSD% (Tables 2-4), with satisfactory results obtained in all cases. For tattoo lipstick samples, the intra- and inter-day accuracies were 85.97-106.50% and 89.22-108.28%, respectively, while the intra- and inter-day precisions were 0.53-8.96% and 0.04-6.96%, respectively. For tattoo eyebrow samples, the intra- and inter-day accuracies were 80.23-103.90% and 80.70-103.74%, respectively, while the intra- and inter-day precisions were 0.14-9.68% and 0.02-7.64%, respectively. For hair tint samples, the intra- and inter-day accuracies were 81.20-107.44% and 86.34-104.02%, respectively, while the intra- and inter-day precisions were 0.08-9.99% and 0.45-9.80%, respectively.
# Design of a Hand-Launched Solar-Powered Unmanned Aerial Vehicle (UAV) System for Plateau
## 4 UAV Flight Control
### Analysis of Lateral-Directional Maneuverability  
The UAVs with conventional configuration use aileron and rudder to control roll and sideslip, while MP Solar UAV with aileron-free design uses rudder to controls both roll and sideslip. Therefore, it is necessary to analyze the maneuvering characteristics of aileron-free UAV [19]. Figure 12 shows the free response of rudder deflection during cruise at altitudes of 500 m and 5000 m.  
According to Figure 12, the rudder deflection of the UAV can cause both sideslip angle and roll angle, scilicet, and the rudder can be used for rolling and yaw control. There was an obvious phase difference between the roll angular rate and the sideslip angle, which indicates that the roll angle of the UAV was mainly caused by the sideslip angle, rather than the rudder deflection. Compared with aileron control roll, rudder control is an indirect process with slower response, which needs to be considered when designing the control laws.  
Compared with the flight at low altitude, the response frequency of the UAV at high altitude was almost the same, but the amplitude was larger, which is consistent with the result in the Figure 11 that the system damping decreased as the altitude increased. After the convergence of shock, the effect of rudder control sideslip at different altitudes was basically the same, while the effect of high-altitude control was better than that of low-altitude control.
## 3 Results and Discussion
### Numerical Predictions
While using the DDA method, the wavelength of longitudinal SPR of a single GNR was around 810 nm. This discrepancy results from the fact that the width and length of the GNRs in the solution could vary by up to 10% of this size, resulting in an aspect ratio that could vary from 3.36 to 4.33. The difference could also be caused by the aggregations of GNRs in the solutions. Clustering alone can shift the wavelength of the SPR from 700 nm to 1100 nm depending on the configuration, as demonstrated in Figures 10 and 11. Figure 8: The linear relationship between the absorption efficiency and the orientation. Figure 9: Linear increase of the wavelength of longitudinal surface plasmonic resonance (SPR) and of maximum absorption efficiency with aspect ratio. Figures 10 and 11 present the shift in the wavelength of the SPR for two basic aggregation types, end-to-end and side-by-side, with various gaps between the nanorods. The distance between GNRs varied from 1 nm to 50 nm. As can be seen from both figures, the optical properties of the GNRs approach the fully dispersed case of a single GNR when the distance between two nanorods was increased up to 50 nm. As shown in Figure 10, the SPR of the end-to-end assembly experienced a red shift from 810 nm to over 1100 nm when the gap was 1 nm because this type of aggregation of GNRs effectively enlarged the value of aspect ratio of a single GNR, which was in agreement with results predicted by [PERSON] and [PERSON] [29] and [PERSON], et al. [28]. Although the absorption efficiency of the end-to-end assembly with a small gap was significantly higher than a single independent GNR, the longitudinal SPR wavelength was around 1100 nm, which was not within the range of the optical tissue window. Therefore, the aspect ratio of either GNRs or GNRs aggregate should not be too large. For the side by side configuration shown in Figure 11, when the distance between GNRs was about 50 nm or less a blue shift and broadening of the resonance peak in the optical spectrum were obtained. The most significant shift was observed when the distance was smaller than 10 nm. The resulting blue shift when the gap was shorter than 50 nm can be attributed to an effective decrease of the aspect ratio when the side-by-side assembly was used. Figure 11: Absorption efficiency of side-by-side assembly with various gaps. Figure 10: Absorption efficiency of end-to-end assembly with various gaps. By analyzing the experimental and numerical results shown in Figures 6, 10, and 11, it can be concluded that the GNRs in the samples preferentially formed numerous side-by-side assemblies because the longitudinal SPR measured in the experiment was around 770 nm wavelength. With the increase of the concentration, the GNRs also formed some end-to-end assemblies, providing a slight red shift of SPR in Figure 6.
# Estimation of the Achievable Performance of Mobile Ad Hoc Networks with Optimal Link State Routing
## 3 Results
### Evaluation Metrics and Performance Analysis in MANETs  
We will use the following evaluation metrics to analyze the achievable performance of the MANET:  
* Route acquisition time
* Routing overhead
* End-to-end delay
* Losses on the radio signal propagation path between nodes
* Network scalability
# 5G-MEC Testbeds for V2X Applications
## 3 Existing Testbeds and Implementations
### 5G-Mec V2X Testbeds
#### 3.3.4. Akraino Connected Vehicle  
The Akraino connected vehicle blueprint [56] focuses on the development of the MEC platform, specifically for V2X applications. Akraino Edge Stack [57] is an open source software stack that was created by the Linux Foundation in February 2018. It supports access methods such as 5G, LTE, Wireline, and Wi-Fi and provides high availability that is optimized for edge computing systems and application cloud services. It is designed to improve the state of the edge cloud infrastructure for enterprise edge, Over-The-Top (OTT) edge, and carrier edge networks, giving users a new level of flexibility to rapidly scale edge cloud services, maximizing the number of applications or users supported, and helping to ensure the reliability of the system at runtime. The Akraino open source project is organized around blueprints, using them to implement diverse edge computing-based use cases. Each blueprint contains the declarative configuration of the three-layer stacks described above, such as cloud platforms, APIs, and applications. Specifically, it includes the configuration statement, hardware, software, tools for managing the entire stack, and delivery point (PoD, pod of delivery) of edge computing applications or architectures in a specific scenario. Each published blueprint enables automated edge infrastructure builds, edge stack-site deployment, upstream and downstream project integration, automated orchestration, etc.
# Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera's Video Stream
## 7 Future Work  
Extreme weather conditions (e.g., snow, rain, fog) may affect the accuracy of the proposed method, since such conditions can affect the quality of the images captured by the video camera. Additional research could be performed to validate the robustness of our method in extreme weather conditions.  
In addition, other types of video cameras may improve the accuracy of the proposed method. For example, a thermal camera may provide improved accuracy in extremely dark conditions. In future research, the method's robustness with frames obtained from other kinds of cameras could be examined.  
We also note that in our real-world tests the drone's speed was slower than the speed used in the simulator. Our ability to fly the drone at a high speed was limited, as we were instructed by our institution to fly the drone in a controlled manner, so as not to pose a risk to the environment. In future work, we suggest evaluating the method's robustness at higher speeds in a real-would experimental setting.
# Early Results on GNSS Receiver Antenna Calibration System Development +
## 2 Materials and Methods
### Antenna Calibration Method at LMMT  
To efficiently sample the entire antenna-under-test (AUT) hemisphere during calibration, at LMMT, a 6-axis industrial robot Mitsubishi MELFA RV-4 FLM-Q is utilized.  
Figure 1: Definition and geometrical interpretation of the adopted GNSS receiver antenna phase center correction (PCC) model; ARP—antenna reference point; MPC—mean phase center; APC—actual phase center; PCO—phase center offset; PCV—phase center variation.  
The robot turns the AUT in 2088 different antenna orientations, with stationary 2.5 s at every single orientation. Therefore, depending on the calibration timing parameters, a full calibration at LMMT lasts approx. 2 h. During calibration, a reference station (REF) on a 5 m short baseline is used. On both stations (REF and AUT), simultaneous 10 Hz raw carrier phase observations are registered with equal receiver settings. Afterwards, prior to PCC LSQ estimation, the carrier phase measurements are preprocessed to eliminate the majority of the GNSS error sources.  
Generally, the calibration system at LMMT is based on the triple-difference (TD) approach, i.e., time-differenced double-difference carrier phase observations [15]. A generic GNSS observation equation from receiver A to satellite \(i\), in units of length is expressed as follows [16]:  
\[\Phi_{\rm A}^{i}=\rho_{\rm A}^{i}+c\Big{(}\delta t_{\rm A}-\delta t^{i}+dt^{ \rm rel}\Big{)}+\xi_{\rm A}^{i}+c\Big{(}dt_{\rm A}-dt^{i}\Big{)}+T_{\rm A}^{i}- I_{\rm A}^{i}+\lambda N_{\rm A}^{i}+\lambda\omega_{\rm A}^{i}+MP_{\rm A}^{i}+ \varepsilon_{\rm A}^{i}, \tag{2}\]  
where \(\rho_{\rm A}^{i}\) is the geometric distance, \(c\) is the speed of light, \(\delta t_{\rm A}\) and \(\delta t^{i}\) are the receiver and satellite clock errors, \(dt^{\rm rel}\) is the relativistic effects term, \(\xi_{\rm A}^{i}\) is the combined satellite and receiver antennas' PCC value, \(dt_{\rm A}\) and \(dt^{i}\) are the receiver and satellite hardware delays, \(T_{\rm A}^{i}\) is the tropospheric delay, \(I_{\rm A}^{i}\) is the ionospheric delay, \(\lambda\) is the signal wavelength, \(N_{\rm A}^{i}\) is the integer phase ambiguity, \(\omega_{\rm A}^{i}\) is the carrier phase wind-up (PWU) effect, \(MP_{\rm A}^{i}\) is the multipath term, and \(\varepsilon_{\rm A}^{i}\) is the phase observation noise term.  
Forming the TD carrier phase observations needed for antenna calibration includes eight raw observations, from Equation (2), on the REF (R) and AUT (T) receivers for two satellites \(i\) and \(j\), in two epochs \(t_{k}\) and \(t_{k+1}\):  
\[TD_{\rm T,R}^{ij}(t_{k},t_{k+1}) = \Phi_{\rm R}^{j}(t_{k+1})-\Phi_{\rm T}^{j}(t_{k+1})-\Phi_{\rm R}^ {i}
# Detecting and Mitigating Attacks on GPS Devices
## 6 Countermeasures and Their Comparison
### Countermeasures for GPS-Denied Environment--Alternate Positioning
#### 6.3.5 SLAM Approaches  
Simultaneous Localization and Mapping (SLAM) is a special category of navigation and positioning that aims to characterize and navigate a local GPS-denied environment via its onboard sensors. SLAM methods generally fall into three categories:  
1. Light Detection and Ranging (LIDAR)-based SLAM [170; 171; 172; 173; 174; 175; 176; 177; 178; 179; 180; 181; 182; 183; 184; 185; 186; 187; 188; 189; 190];
2. Visual-SLAM (vSLAM) [178; 179; 191];
3. Hybrid Visual-LiDAR SLAM [192; 191].  
These SLAM approaches typically consider the scenario of an unmanned vehicle navigating a localized region, such as a small UAS exploring an underground cave system. These approaches can be either map-based, where the UAS is attempting to navigate an area from a known map, or mapless, where the unmanned system is attempting to navigate an area while simultaneously creating a map of the region. In both cases, the unmanned system attempts to detect features in the environment and use those features to determine its location. LiDAR-SLAM approaches detect regional features via LiDAR sensors. A primary limitation of LiDAR is cost, since LiDAR sensors are expensive compared with other sensor types, limiting their applicability. vSLAM approaches use the onboard camera sensor. Most vSLAM techniques propose monocular camera sensors that serve vSLAM or VO purposes [193; 194; 195]. A primary limitation of vSLAM is its computational complexity, which also limits its applicability. There is a relatively even mixture of 2D versus 3D LiDAR-SLAM papers in the literature; however, 2D LiDAR-SLAM is far more mature. Most 3D LiDAR-SLAM papers focus on finding solutions to the various challenges of 3D SLAM.  
These approaches are mature and have demonstrated their ability to map and navigate highly complex environments, including obstacle avoidance; however, they typically do not provide absolute positioning solutions. SLAM methods may not directly apply to generalized positioning and navigation; however, much of the research within the SLAM community is directly applicable to approaches for generalized positioning. For example, significant research within the vSLAM community is focused on improving object and feature detection performance in low-light conditions, which is also a limitation within the generalized vision approaches to positioning, and lessons learned across these communities will benefit both groups.