esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The photovoltaic electrical energy generation has received a great deal of attention in the last decade, thanks to its zero emission impact, government financed incentives and the zero cost of its primary energy. Indeed, the solar irradiance availability in the planet allows us to get free power once the initial plant investment has been faced [1]. The scientific community has pointed out several improvable aspects, the efficiency of the photovoltaic (PV) cells being of paramount importance [2; 3; 4]. As no cost is associated with solar energy availability, the proximity of the PV operating point to its maximum power produces the same results as the PV cells with improved efficiency. In other words, a solar power plant should always be working as close as possible to its maximum potential in terms of power, in order to really exploit any possible improvement in the efficiency of the PV cells. In order to track the PV maximum power point, several strategies have been proposed. The most diffused applications use the _Perturb and Observe_ technique [5], where the setting of perturbation step can also be adaptive [6], and _Incremental Conductance_[7]. Several low level control algorithms have been proposed in tandem with such control strategies [8; 9]. The overall system efficiency may also depend on whether the Maximum Power Point Tracking (MPPT) is applied with a centralized or distributed approach on the various PV panels [10]. The last solution is mostly suitable for low power application [11]. Most of the MPPT techniques require the panel parameter values [12] and are based on both measurements of PV current and voltage.  
Another important aspect is regarding the priority consumption of the green energy production versus traditional production from fossil energy sources, which is also pursued by means of fault-tolerant converters [13]. This goal can be achieved by means of topology changeable converters [14; 15], capable of reconfiguring themselves via static switches. A different solution is based on interleaved converter topologies [16; 17], where the total power is distributed among two or more identical modules. In the case of a fault occurrence on one of the modules, this solution guarantees the continuity of the operation, even if at reduced maximum power.  
In this paper, a two-channel, three-phase grid connected, interleaved inverter is coupled with a PV array via a LC filter. The proposed tracking algorithm of the PV array is an evolution of the one introduced in [18; 19] and is applied with a centralized approach in the context of a medium power system (around 200 kW). This strategy requires only one voltage measurement and, as it is based on a hysteresis approach, it ensures a very low power ripple around the PV MPP. The MPPT output is then processed by the inverter control algorithm for which a space vector modulation technique is proposed.
# Improving Real-Time Position Estimation Using Correlated Noise Models +
## 4 Quantifying the Filtering Algorithm Performance  
Similar to the method in Section 3.2.1, which issues time-series estimates of position in the form of Gaussian distributions, the UKF produces \(\{\mathbf{x}_{n}\}\) together with \(\{\mathbf{\Sigma}_{n}\}\). We interpret these as the mean and variance parameters of Gaussian posteriors. With this interpretation, the marginal distributions \(\{q(x)\}\) of position are also Gaussian.  
To compare and rank algorithms with different noise models according to their performance, we introduce the log-score [15; 16; 41] (\(\ell=1\) m):  
\[S(q|x)=-\log(q(x)\ell). \tag{4}\]  
This quantity is often interpreted as a \"surprise\" of true value \(x\). If \(q\) is large around the true value, the corresponding log-score, \(S\), is low, i.e., the outcome is not surprising. An algorithm producing lower log-scores will be regarded as superior compared to those achieving higher \(S\) values.
# An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery
## 1 Introduction
The a trous wavelet transformation (ATWT) with 10-m MNDWI yielded the best water body mapping results [32]. In the referenced paper [34], [PERSON] et al. employed principal component analysis to sharpen the 10 m NDWI and the 20 m SWIR bands and, then, the SWIR band was sharpened with the green band to construct the MNDWI to identify exact water bodies. Although the sharpening method between SWIR bands and optical bands can be performed to balance the different spatial resolutions, the efficiency of water body extraction at a large scale will be limited. Currently, most of the research has focused on existing water body indices and has not taken into account the unique vegetation-sensitive red-edge band of Sentinel-2. Therefore, this study first analyzes the spectral response mechanism of different land cover types for Sentinel-2 and then identifies the sensitivity bands of water bodies to calculate a novel NDWI. By testing various water types (freshwater lakes, coastal waters, salt lakes, and ice), the extraction performance and precision of the new index and the NDWI are compared. Finally, the application scope of the novel water index is discussed. This novel water index can assist the understanding of the dynamic changes in surface waters by utilizing large-scale surface water mapping.
# 1D CNN Based Detection and Localisation of Defective Droppers in Railway Catenary
## 4 Fault Classification through CNNs
### Structure of the Integrated 1D CNN Method  
In 1D CNN, the forward propagation (FP) from the \(l-1\) convolution layer (output) to the \(l\) layer (input) is expressed as Equation (5), where \(x_{k}^{l}\) is a neuron of the input layer; \(b_{k}^{l}\) is the scalar bias of the \(k\)th neuron of the \(l\)th input layer; \(s_{i}^{l-1}\) is the \(i\)th neuron from the \(l-1\) output layer; and \(\omega_{ik}^{l-1}\) is the kernel from the \(i\)th neuron of the \((l-1)\)th output layer to the \(k\)th neuron of the \(l\)th input layer.  
\[x_{k}^{l}=\sum_{i=1}^{N_{l-1}}conv1D\left(\omega_{ik}^{l-1},s_{i}^{l-1}\right) +b_{k}^{l} \tag{5}\]  
The neuron of intermediate output is defined as \(y_{k}^{l}\), which can be obtained from input \(x_{k}^{l}\). It can be presented as Equation (6), where \(s_{k}^{l}\) is a neuron of the output layer; \(\downarrow ss\) is the down-sampling operation with the scalar factor (ss).  
\[\begin{array}{l}y_{k}^{l}=f(x_{k}^{l})\\ s_{k}^{l}=y_{k}^{l}\downarrow ss\end{array} \tag{6}\]  
In this paper, a method based on integrating multiple 1D CNNs (integrated 1D CNN) is applied to classify the damage degree and the position of defective droppers as two independent tasks. The structure of the integrated 1D CNN model is shown in Figure 5. Specifically, Sub-model 1 considers the damage degree of droppers defined in Table 3. Sub-model 2 considers the location of defective droppers that comprises nine labels representing nine positions. It should be noted that when the dropper is identified as being healthy, it is not necessary to identify its position. Hence, Sub-model 2 is applied to train the part of  
Figure 4: Quantified impact of defective droppers on pantograph–catenary dynamic behaviour.  
the dataset which only contains the data labelled as 'low risk' and 'high risk' (i.e., data not labelled as \"healthy\").  
To integrate the two trained sub-models, a method to transmit the output result from Sub-model 1 to Sub-model 2 is proposed.  
The result from Sub-model 1 is given as a set of predicted probabilities corresponding to the three damage conditions. The probabilities of 'healthy', 'low risk' and 'high risk' are given as \(P_{1}\), \(P_{2}\) and \(P_{3}\), respectively. The \(P_{\text{max}}\) is the maximum value of \(P_{1}\), \(P_{2}\) and \(P_{3}\). If the \(P_{\text{max}}\) corresponds to 'healthy' condition (\(P_{1}\)), the process will stop. The predicted output of Sub-model 1 will be saved. If the \(P_{\text{max}}\) belongs to 'low risk' or 'high risk' (\(P_{2}\) or \(P_{3}\)), it will be fed into Sub-model 2 for further prediction. The predicted output of Sub-model 2 represents the location of the defective dropper. The testing performance of the integrated CNN model is the product of the predicted results of Sub-model 1 and Sub-model 2.
# Performance of Retro-Reflective Building Envelope Materials with Fixed Glass Beads
## 4 Results and Discussion  
### Angular Retro-Reflectivity of RR Samples  
The angular retro-reflectivity of 10 developed RR samples and two types of RR materials (capsule sheet and prism sheet) commercially available in the market was evaluated and is detailed in Figure 4. It was found that the prism sheet has the largest retro-reflectivity of approximately 0.41 at the incident angle of 0\({}^{\circ}\), and its retro-reflectivity decreased as the incident angle was increased from 0 to 85\({}^{\circ}\) (the retro-reflectivity decreased sharply from the incident angle of about 75\({}^{\circ}\)). The capsule sheet had a similar change in retro-reflectivity to the prism sheet. Its retro-reflectivity decreased sharply from the incident angle of 70\({}^{\circ}\). Compared to the prism and capsule sheets, samples 2, 4, and 6 (white, yellow and gray) have a relatively smaller angular retro-reflectivity of approximately 0.27 at the incident angle of 0\({}^{\circ}\) and have a relatively stable change in angular retro-reflectivity until the incident angle is increased to 80\({}^{\circ}\). Sample 10 (transparent paint) has a relatively larger retro-reflectivity of about 0.28 at the incident angle of 0\({}^{\circ}\), however, its retro-reflectivity decreased sharply from the incident angle of 30\({}^{\circ}\).  
It is shown that the samples 1, 3, 5, 7, and 9 (beads with a refractive index of 1.5) have smaller retro-reflectivity compared to the other RR samples.  
Figure 4: Change in angular retro-reflectivity of 10 developed RR samples and two RR materials commercially available in Japan.  
Moreover, the developed RR samples with the same color layer and different refractive index of 1.5 or 1.9 were compared and shown in Figures 5-10. It was found that the angular retro-reflectivity of samples with a refractive index of 1.9 is larger (often 2-5 times higher or more) than that of samples with refractive index of 1.5 applied to the same color reflective layer.  
Figure 5: Angular retro-reflectivity of RR sample 1, (**a**) white layer and refractive index of 1.5 and RR sample 2 (**b**) white layer and refractive index of 1.9.  
Figure 6: Angular retro-reflectivity of RR sample 3, (**a**) yellow layer and refractive index of 1.5 and RR sample 4 (**b**) yellow layer and refractive index of 1.9.  
Figure 7: Angular retro-reflectivity of RR sample 5 (**a**) gray layer and refractive index of 1.5 and RR sample 6 (**b**) gray layer and refractive index of 1.9.
## 2 Materials and Methods
### Multi-Angle Spectral Observations  
#### 2.3.1 iAMSPEC II System  
Multi-angle canopy spectral data were obtained from an improved tower-based observation system stemmed from AMSPEC II, which was first developed by [PERSON] _et al._[63]. The new system is named iAMSPEC II, and was mounted at 31 m (about 16 m above the canopy) on the flux tower in QYZ in January of 2013. The system (Appendix A) is mainly composed of a dual channel spectrometer, a dual axis rotating device, a computer, and some accessories (e.g., uninterruptible power supply (UPS), fans, temperature controller, wireless internet).  
The spectrometer used is Unispec-DC (PP Systems, Amesbury, MA, USA) featuring 256 contiguous bands for both upwelling and downwelling channels covering a nominal spectral range from approximately 305 nm to 1135 nm with about 3 nm nominal bandwidth (10 nm full width, half max). The upwelling channel measures incoming solar irradiance while the downwelling channel simultaneously measures canopy-reflected radiance with fiber optics. This instrument allows measurements to be conducted under different weather conditions [63]. The upward pointing fiber optic equipped with a cosine receptor is used to acquire hemisphere sky irradiance for varying solar altitudes. The downward-looking fiber optic, with an instantaneous field of view (IFOV) of 20\({}^{\circ}\), is attached to an axis of the rotating device to measure canopy reflectance at various angles.  
The rotating device is a pan-tilt unit, PTU-D46 (FLIR Systems, Goleta, CA, USA), possessing two rotatable parts (pan axis is horizontal and tilt axis is vertical). The downward-looking probe is mounted on the tilt axis with a 45\({}^{\circ}\) angular holder (Appendix A), which is different with AMSPEC II [63]. This improvement allows the sensor head to be moved at a full range of zenith angle (\(\theta\)v), while the inherent motion range of the tilt axis is only between \(-37^{\circ}\) and \(42^{\circ}\) around the vertical axis. Due to obstruction by the tower, view azimuth angles range actually from 45\({}^{\circ}\) to 325\({}^{\circ}\) (defined from geodetic north).  
Both Unispec-DC and PTU are connected to a computer (Thinkpad T430U) via the RS232 serial port. However, due to the long distance (about 50 m) from the tower to the control house, the RS422 serial communication (using RJ45 cables) is used to connect Unispec-DC and PTU with the computer (Appendix A). This system is controlled by a MATLAB GUI program, which is able to automatically initialize PTU and Unispec-DC, acquire spectra from the spectrometer, and save data every 15 min. Canopy spectra were sampled every 2-3 s all day at a 10\({}^{\circ}\) angular step width (horizontally and vertically).
## 7 Research Challenges and Recommendations for Future Works
### Compromised IoT Device and Open Environment  
Since LPWANs support enormous number of IoT devices, the deployment of these devices that operate in an open environment makes them vulnerable to varieties of security threats. Therefore, effective security schemes to detect, isolate and classify malicious nodes such as intrusion detection systems [19] and key management mechanisms for IoT devices should be considered for increased communication reliability and improved quality of service. Also, ensuring the physical layer security issues are addressed such as deploying efficient tamper proofing protections.
###### Abstract  
The rapid development of unmanned aerial vehicles (UAVs) has led to many security problems. In order to prevent UAVs from invading restricted areas or famous buildings, an anti-UAV defense system (AUDS) has been developed and become a research topic of interest. Topics under research in relation to this include electromagnetic interference guns for UAVs, high-energy laser guns, US military net warheads, and AUDSs with net guns. However, these AUDSs use either manual aiming or expensive radar to trace drones. This research proposes a dual-axis mechanism with UAVs automatic tracing. The tracing platform uses visual image processing technology to trace and lock the dynamic displacement of a drone. When a target UAV is locked, the system uses a nine-axis attitude meter and laser rangers to measure its flight altitude and calculates its longitude and latitude coordinates through sphere coordinates to provide drone monitoring for further defense or attack missions. Tracing tests of UAV flights in the air were carried out using a DJI MAVIC UAV at a height of 30 m to 100 m. It was set up for drone image capture and visual identification for tracing under various weather conditions by a thermal imaging camera and a full-color camera, respectively. When there was no cloud during the daytime, the images acquired by the thermal imaging camera and full-color camera provide a high-quality image identification result. However, under dark weather, black clouds will emit radiant energy and seriously affect the capture of images by a thermal imaging camera. When there is no cloud at night, the thermal imaging camera performs well in drone image capture. When the drone is traced and locked, the system can effectively obtain the flight altitude and longitude and latitude coordinate values.  
unmanned aerial vehicles (UAVs), dynamic coordinate tracing; computer vision; anti-UAV defense system +
Footnote †: journal: Applied
# CFD Simulation of Dry Pressure Drop in a Cross-Flow Rotating Packed Bed
## 4 Results and Discussion
### Outlet Zone  
#### 4.5.1 Effect of Rotating Speed  
The effect of rotating speed on the pressure drop in the outlet zone is shown in Figure 13. Figure 13a shows that pressure drop is kept at 60 Pa with the increase of rotating speed from 200 rpm to 800 rpm at gas flow rate of 800 m\({}^{3}\cdot\)h\({}^{-1}\), even if the section of the outlet area is reduced. It should also be noted that both the static pressure drop (Figure 13b) and dynamic pressure drop (Figure 13c) are kept constant with the increase of the rotating speed. In conclusion, rotating speed has no significant effect on the pressure drop in the outlet zone.
## 4 Multi-Antenna Frequency-Selective Channels
### LSTD Channel Model  
The channel model (4) implies that the channel vector \(h_{l,f}\) in (7) and (8) can be written as the product of a frame-dependent \(N_{R}N_{T}W\times D\) matrix \(T_{f}\) and of a slot-dependent \(D\times 1\) vector \(\beta_{l,f}\) as in [41],\[h_{l,f}=T_{f}\beta_{l,f}, \tag{23}\]  
where \(T_{f}\) collects space-time signatures of the \(D\) paths as  
\[T_{f}=[\Omega_{1,f}^{1/2}\mathbf{g}(\tau_{1,f})\otimes\text{vec}(a_{1,f}), \ldots,\Omega_{D,f}^{1/2}\mathbf{g}(\tau_{D,f})\otimes\text{vec}(a_{D,f})], \tag{24}\]  
with \(\mathbf{g}(\tau_{d,f})=[g(-\tau_{d,f}),\ldots,g((W-1)T-\tau_{d,f})]^{\top}\) being the \(W\times 1\) vector that collects the Nyquist-rate samples of the delayed waveform \(g(\tau-\tau_{d,f})\), and the \(D\times 1\) fading amplitude vector being defined as \(\beta_{l,f}=[\exp(-jw_{1,f}t_{1}),\ldots,\exp(-jw_{D,f}t_{l})]^{\top}\).  
The frame-dependent matrix \(T_{f}\) is typically rank-deficient, because paths are generally not all resolvable [54; 55]. To account for this structural property of the channel, as in [41], we introduce a \(N_{R}N_{T}W\times K\) full-rank unitary matrix \(B_{f}\), such that \(\text{span}\{T_{f}\}=\text{span}\{B_{f}\}\) and redefine (23) as  
\[h_{l,f}=B_{f}d_{l,f}. \tag{25}\]  
As an example, the unitary matrix \(B_{f}\) can be obtained from the singular value decomposition of matrix \(T_{f}\), i.e., \(T_{f}=B_{f}\Lambda_{f}^{1/2}U_{f}^{\dagger}\), by introducing the \(K\times 1\) vector \(d_{l,f}=\Lambda_{f}^{1/2}U_{f}^{\dagger}\beta_{l,f}\)[41]. For future reference, we also rewrite (25) as  
\[h_{l,f}=\sum_{k=1}^{K}b_{f}^{k}d_{l,f}^{k}, \tag{26}\]  
where \(d_{l,f}^{k}\) is the \(k\)-th element of the vector \(d_{l,f}\) and \(b_{f}^{k}\) is the \(k\)-th column of the matrix \(B_{f}\).  
We will refer to matrix \(B_{f}\) in (26) as the long-term space-time feature matrix, or feature matrix for short, whereas vector \(d_{l,f}\) will be referred as the short-term corresponding amplitude vector. Parametrization (25) and (26) are particularly efficient when the feature matrix \(B_{f}\) can be accurately estimated from the available data. For conventional learning, this requires observing a sufficiently large number of slots per frame, i.e., a large \(L^{\text{new}}\)[41], as well as a channel that varies sufficiently quickly across each frame. In contrast, as we will explore, transfer and meta-learning can potentially leverage data from multiple frames in order to enhance the estimation of the feature matrix.
# Maintenance Prediction through Sensing Using Hidden Markov Models--A Case Study
## 2 Theoretically Background
### Features Generation  
The principle of predictive Feature Generation (FG) is used to maximize the exploitation of information generated exclusively from time and process data, with compact and informative representations of the obtained data [27; 28].  
According to [PERSON] and [PERSON] [29], Machine Learning (ML) algorithms can be seen as techniques to derive one or more hypotheses from a set of observations, thus being one of the ways to improve input (observations) and output (hypothesis) in selecting resources that maximize the performance of architecture.  
If the transformation is chosen correctly, the transformation features can display high-information packing properties compared to the original input samples.  
In summary, FG is of paramount importance in any pattern recognition task, whose objective is to discover compact and informative representations of the obtained data [30].
# Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms
## 2 Materials and Methods
### Model Parameter Optimization and Evaluation  
Based on the python machine learning toolkit (Scikit-learn), 10 types of supervised machine learning algorithms were used to train the intelligent surrounding rock classification model: the support vector machine (SVM), back propagation neural network (BP) [55; 56], radial basis function neural network (RBF), K-nearest neighbor (KNN), Gaussian naive Bayes (GNB), decision tree (DT), random forest (RF), extra trees (ET), bootstrap aggregating (Bagging) and gradient boosting (GB) algorithms.  
The model parameters were optimized and evaluated by the grid search K-flod cross validation method (\(k=5\)), and the model performance was externally unbiased when using the method of K-flod cross validation (\(k=3\)).  
In the model performance optimization process, the average accuracy was adopted to determine the optimum hyperparameter combinations. The average accuracy was calculated by first averaging the accuracy of each validation set over the three surrounding rock grades, and then  
The following conclusions can be drawn from Figure 9 and Table 7.  
Among 10 machine learning algorithm models, the average precision, recall, F1 score and accuracy were above 0.7, except for the recall of grade-IV surrounding rock. In particular, the average recalls of grade-III and -V were greater than 0.90.  
Thus, these machine learning models established by the drilling parameters are feasible and reliable in the intelligent classification of surrounding rocks.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Performance** \\ **Index** \\ \end{tabular} } & \multirow{2}{*}{**Rock Grade**} & \multicolumn{8}{c}{**Supervised Learning Algorithm**} & \multirow{2}{*}{**Average**} \\ \cline{3-14}  & & & **SVM** & **BP** & **RBF** & **KNN** & **GNB** & **DT** & **RT** & **ET** & **Bag-ging** & **GB** \\ \hline \multirow{3}{*}{Precision} & III & 0.81 & 0.79 & 0.73 & 0.77 & 0.75 & 0.75 & 0.81 & 0.80 & 0.80 & 0.81 & 0.78 \\  & IV & 0.90 & 0.87 & 0.76 &