esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager is a unique system that permits simultaneous measurements of vegetation structure, foliar spectra and surface temperatures at very high spatial resolution (\(\sim\)1 m). The complementary nature of LiDAR, optical and thermal data provide an analytical framework for the development of new algorithms to map plant species composition, plant functional types, biodiversity, biomass and carbon stocks and plant growth. G-LiHT data will enhance our ability to validate data from existing satellite missions, design new missions and produce data products related to biodiversity and climate change.  
The scientific rationale and motivation for G-LiHT is similar to other multi-sensor systems (e.g., Carnegie Airborne Observatory, CAO, Alpha and Beta Systems [3]; CAO Airborne Taxonomic Mapping System, AToMS [2]; National Ecological Observatory's Airborne Observation Platform, NEON AOP [4]), and differences largely exist due to specific mission objectives. G-LiHT was specifically designed to simplify worldwide deployment and minimize collection and data processing costs by using commercial off-the-shelf instruments and local general aviation aircraft. In contrast, NEON AOP is an operational observatory that will acquire annual acquisitions over selected NEON sites in the US and additional in-country flights requested by the scientific community. CAO systems have largely focused on sustainable forest management and habitat conservation in global tropical forests, but unlike G-LiHT and NEON, the data is not openly distributed. Other differences between these systems include choice of instrumentation. NEON and CAO systems use a custom-built, high fidelity visible to shortwave infrared spectrometer (380 to 2,510 nm) that covers a wider range of the electromagnetic spectrum and has a greater signal-to-noise ratio than the G-LiHT spectrometer (400 to 1,000 nm), but the instrument cost is one-hundred-fold greater, and a more capable aircraft is needed to accommodate the greater size, mass and power requirements. Additionally, G-LiHT is the only system that currently acquires downwelling irradiance and surface temperature measurements. Comparisons are currently under way at locations where both NEON and G-LiHT data have been collected to evaluate the performance and cross-calibrate the LiDAR and spectrometer data products.  
Additional G-LiHT instrument specifications, campaign information and access to more than 6,500 km\({}^{2}\) of data can be obtained from the through the G-LiHT webpage, [[http://gliht.gsfc.nasa.gov](http://gliht.gsfc.nasa.gov)]([http://gliht.gsfc.nasa.gov](http://gliht.gsfc.nasa.gov)).
# A Multi-Path Semantic Segmentation Network Based on Convolutional Attention Guidance
## 4 Experiment
### Ablation Experiment  
An ablation study is conducted on the ADE20K dataset, investigating the impact of different modules on MCAG. \"Multi-path\" refers to the multi-path mechanism, excluding the main path; \"Attention-guided\" indicates the utilization of the AGFM (attention-guided fusion module); and \"Parallel Aggregation\" signifies the use of the PFM (parallel fusion module). The results are presented in Table 5.  
Figure 8: Visualization results of MCAG on the Cityscapes dataset. The first column displays the input images, the second column represents the corresponding ground truth and the third column shows the segmentation results of the MCAG method.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Multiple Paths** & **Attention Guidance** & **Parallel Aggregation** & **mIoU (\%)** \\ \hline \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & 47.70 \\ \(\checkmark\) & \(\checkmark\) & & 47.55 \\ \(\checkmark\) & & \(\checkmark\) & 46.94 \\ \(\checkmark\) & & \(\checkmark\) & 47.30 \\  & & & 46.72 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiments of each module. \(\checkmark\) indicates that the module is used.  
Among the modules, \"Multiple Paths without Attention Guidance\" represents that the guidance of the main route to the DP and the guidance to the BP are simply added together. It can be observed that each component contributes to the model's final performance. When using both multiple paths and attention guidance, the mIoU is 0.84% higher than having only one main route. If there is no attention guidance mechanism and a simple addition of main route and branch results is performed, the result is lower by 0.4%. These two findings indicate that the proposed multi-path and attention guidance from the main route are both effective and necessary.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 5 The Proposed Security Scheme
### Surveillance UAV Network Operation Phase
#### Leave Procedure:  
The drone \(D_{l}\), which needs to leave for battery recharging, embeds a recharge request in its previous reading message \(v_{l}=(v_{o}|D_{l}|DF_{l}|Recharge)\), encrypts it to \((\bar{h}_{l},\bar{z}_{l})\) and sends the result to the current cluster heads of the group \(D_{z}\) and \(D_{s}\); they include the message \((\tilde{h_{l}},\tilde{z_{l}})\) in the current total aggregated message \(F\), encrypt \(F\) to \(\tilde{F}=\mathbb{E}_{\mathbb{E}_{k_{z}}}(F),\tilde{F}=\mathbb{E}_{k_{s}}(F)\) before forwarding \(\tilde{F},\tilde{F}\) to \(CS\).  
The \(CS\) then sends a forward request to the group heads \(D_{z}\) and \(D_{s}\); this request asks them to forward the future aggregated messages of the group via the returning drone \(D_{l}\), which becomes the relay \(R_{l}\). The forward requests are encrypted by the cluster heads session keys \(k_{z}\) and \(k_{s}\):  
\[c_{forward-z}=\mathbb{E}_{k_{z}}(m_{forward-z}),\]  
\[c_{forward-s}=\mathbb{E}_{k_{s}}(m_{forward-s}).\]  
The forward request messages reach the cluster heads via the previously returning relays, e.g., \(R_{j}\) and \(R_{k}\).  
\[CS\xrightarrow{c_{forward-z}}R_{j}\xrightarrow{c_{forward-z}}D_{z},\]  
\[CS\xrightarrow{c_{forward-z}}R_{k}\xrightarrow{c_{forward-z}}D_{s}.\]  
Then, \(D_{z}\) and \(D_{s}\) aggregate the group readings in one message \(P\) and send their encrypted group messages \(\hat{P},\hat{P}\) to the relay drone \(R_{l}\):  
\[D_{z}\xrightarrow{\hat{P}}R_{l},\]  
\[D_{s}\xrightarrow{\hat{P}}R_{l}.\]  
\(R_{l}\) stores \(\hat{P},\hat{P}\) until it enters the \(CS\)'s range and then forwards them to \(CS\). Figure 4b shows the leave procedures:  
\[R_{l}\xrightarrow{\hat{P},\hat{P}}CS.\]  
In join/leave procedures, the symmetric session keys guarantee the confidentiality of the exchanged messages with tiny computation delay; in addition, the keys are only used for the current reading round, i.e., the probability of breaking the key is very low.  
Figure 3: Surveillance UAV network operation phase collecting the area information.  
Figure 4: Surveillance UAV network operation phase—join/leave procedures.
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 3 Energy Management of an HRES Based on Deep Q-Network  
### Introduction of the Proposed HRES  
EMS is one of the most important parts to ensure the system is in reliable and efficient operation. The main function of the EMS is to balance the power flow between the system components, and simultaneously reduce the amount of fossil fuel and cost of energy production. A proposed DC/AC-bus system for power generation is presented in Figure 1. Excess energy from PV and WT will be stored in the battery and hydrogen system by controlling the K_Battery and K_Electrolyzer switches. In case PV and WT cannot fulfill the load demand, based on the available energy levels of system components, EMS will discharge battery or turn on FC and DG by K_Fuel-Cell and K_Diesel switches, respectively.  
The proposed EMS control schema is presented in Figure 2. It is a learning-based approach, so no explicit mathematical model of the system is needed. A Markov Decision Process (MDP) of the EMS is needed for the implementation of the DQN algorithm. Based on the MDP model, the objective is to find the optimal policy for dispatch control of the system components to ensure a stable operation of the power system with the lowest cost of energy. An MDP model of the EMS is firstly defined in Section 3.2, including states (S), actions (A), transition probabilities (P), and rewards (R). It is considered as a tuple S, A, P, R. In which, \"S\" is a finite set of states which describes the all the operating point of thesystem. \"A\" is the control action. \"P\" is the probability of moving from one state to another one. \"R\" is an immediate return given to an agent when he or she performs specific action or task. Good action will receive positive reward while bad action will get punished.  
A description of the DQN algorithm for EMS control is shown in the following part. In the DQN approach, a deep neural network is designed to approximate the action-value function and the DQN algorithm is adopted to train the neural network. It takes the state of the HRES as inputs, and outputs are the signals for dispatch control of the system components. The combination of the states of K_Battery, K_Electrolyzer, K_Fuel-Cell, and K_Diesel basically determines the system modes of operations. Finally, in Section 3.4, a conventional-based EMS is also applied for the validation of the proposed method.
## 4 Results and Discussion
### Steady-State Analysis  
#### 4.1.1 Phase 1: WG Enthalpy Loss Study (Baseline Engine)  
The results in Figure 12 show that the energy loss through the wastegate for the model with the original size of compressor and turbine (Case 9) can reach the amount of 5 kW at high engine speeds and loads. The amount of the potential energy dismissed at medium load and engine speed conditions is of lower magnitude, up to 2 kW. At low loads and speeds, there is no wasted energy, as the WG is completely closed.  
Figure 11: Driving cycles conditions: (**a**) engine speed; (**b**) engine BMEP. NEDC, New European Driving Cycle; WLTC, Worldwide harmonized Light vehicles Test Cycle.  
Figure 12: Phase 1: DoE analysis of the waste-gated enthalpy loss for the different compressor and turbine sizes; the blue line represents the maximum torque line of the baseline engine’s model as provided by the manufacturer (\(\lambda<1\)); the \(\bigcirc\) symbols indicate data points; the \(\bigotimes\) symbol indicates a limit violation at specific points.  
Figure 12 highlights the effect of a smaller turbine and compressor on the amount of waste-gated flow and the maximum power of the engine. As can be seen, a smaller compressor (Cases 7 and 8) cannot provide the boosting requirements needed to achieve maximum engine's power at medium and high engine speeds. On the other hand, a smaller turbine (Cases 3 and 6) can slightly increase the amount of waste-gated flow at medium engine speed and load conditions. However, a smaller turbine leads to increased pre-turbine pressure, in some cases violating the maximum limit set, and reduces the maximum torque of the engine.
# Enhancing BER Performance Limit of BCH and RS Codes Using Multipath Diversity +
## 6 Column Weight Multipath Combiner (CWMC)  
This paper proposes a combiner based on Hamming weight (\(w_{h}\)) to combine the LoS and NLoS signals. This combiner receives \(L\) copies from one transmitted signal as the input (\(L\) should be an odd number and include the LoS signal), then arranges them in an \(L\times N\) matrix:  
\[L_{c}=\left[\begin{array}{cccc}c_{11}&c_{21}&\ldots&c_{1\times N}\\ c_{21}&c_{22}&\ldots&c_{2\times N}\\ c_{31}&c_{32}&\ldots&c_{3\times N}\\ \ldots&\ldots&\ddots&\vdots\\ c_{L1}&c_{L2}&\ldots&c_{L\times N}\\ \end{array}\right] \tag{6}\]  
\[y_{ij}=\left\{\begin{array}{ll}0\;if&w_{hij}\big{(}c_{1j},c_{2j},c_{3j}, \cdots,C_{Lj}\big{)}<\frac{L+1}{2}\\ 1\;if&w_{hij}\big{(}c_{1j},c_{2j},c_{3j},\cdots,C_{Lj}\big{)}\geq\frac{L+1}{2} \\ \end{array}\right. \tag{7}\]  
where \(i=1,2,\cdots,L\), \(j=1,2,\cdots,N\), and \(y_{ij}\). represents the combiner output. For example, we assume five different codewords (\(L=5\)) which are ten bits long (\(N=10\)) each:  
\[C_{1}=\bigg{[}\begin{array}{cccccccccc}0&1&0&1&0&1&1&0&0&0\\ \end{array}\bigg{]}, \tag{8}\]  
\[C_{2}=\bigg{[}\begin{array}{cccccccccc}1&1&1&1&0&1&1&0&0&0\\ \end{array}\bigg{]}, \tag{9}\]  
\[C_{3}=\bigg{[}\begin{array}{cccccccccc}0&0&0&0&0&1&1&0&0&0\\ \end{array}\bigg{]}, \tag{10}\]  
\[C_{4}=\bigg{[}\begin{array}{cccccccccc}1&1&1&1&1&1&1&1&1&1\\ \end{array}\bigg{]}, \tag{11}\]  
and:  
\[C_{5}=\bigg{[}\begin{array}{cccccccccc}0&0&0&0&0&0&0&0&0\\ \end{array}\bigg{]} \tag{12}\]  
The matrix will be like the below:  
\[L_{c}=\left[\begin{array}{c}C_{1}\\ C_{2}\\ C_{3}\\ C_{4}\\ C_{5}\\ \end{array}\right]=\left[\begin{array}{cccccccccc}0&1&0&1&0&1&1&0&0&0\\ 1&1&1&1&0&1&1&0&0&0\\ 0&0&0&0&0&1&1&0&0&0\\ 1&1&1&1&1&1&1&1&1&1\\ 0&0&0&0&0&0&0&0&0&0\\ \end{array}\right] \tag{13}\]  
After calculating \(w_{1,1},w_{1,2},w_{1,3},\cdots,w_{1,10}\), \(Y\) will be:  
\[Y=\bigg{[}\begin{array}{cccccccccc}0&1&0&1&0&1&1&0&0&0\\ \end{array}\bigg{]} \tag{14}\]  
Figure 4 shows that the combined packets improved the system BER performance, where the improvement is increased when the number of combined paths are increased. Additionally, the BER performance improvement depends on the threshold value (see Figure 5). In Figures 4 and 5, the red slope represents the uncoded signal (LoS), and the blue, brown, green, and magenta slopes represent the combining of the uncoded LoS with two, four, six, and eight uncoded NLoS signals, respectively.
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 4 Case Studies, Results, and Discussion  
In this paper, a simple three-phase radial distribution network was used to demonstrate the proposed fault classification and location method. The distribution network has 120 kV balanced generation, one 120 kV/25 kV step down transformer, a 30 km distribution line, one 25 kV/575 V step down transformer, and a lumped load.  
The distribution network was modelled on Simulink as a four-bus radial system, as shown in Figure 4. The distribution line was modelled using its pi-equivalent. The fault was modelled by  
Figure 4: Simulink model for the distribution grid.  
Figure 3: Multilayer perceptron (MLP) architecture for fault location.  
Figure 2: Neural network architecture for both scenarios listed as: (**a**) artificial neural network (ANN) training model for fault detection and classification for the balanced load scenario (11 outputs = 10 fault types + 1 non-fault case), and (**b**) ANN training model for fault detection and classification for the unbalanced scenario (12 outputs = 11 fault types + 1 non-fault case).  
dividing the line into two, with the first part representing the line on the sending side of the fault and the second part representing the section of the line after the fault. Two scenarios were considered to test the robustness of the proposed methodology. The first scenario was under a balanced load condition while the second scenario was an unbalanced loading condition. The details of the parameters for each scenario are summarized in the following sections.
## 6 Matching User Preferences to Network Response
### Combining Evaluation Metrics for Comparison
#### 6.1.1 Scaling Variables  
Scaling variables is performed for two reasons: (1) to add contextual information with regard to the criteria and (2) to correct an imbalance introduced while scoring. Adding contextual scaling is domain specific and is applied to the criteria on a case by case basis. When considering the criteria involved in the current example, there is a rationale to apply a scaling factor to the reliability criterion. One can argue that a reliability of less than 30% is of little to no benefit to the network. Figure 8 shows a scaling factor can be added to remove any additional score from the reliability criterion to the solutions overall score should the reliability value be lower than 30%.  
Correcting an imbalance is required if the scoring scheme is imbalanced with a single or set of criteria overshadowing the overall score. The scaling is used to reduce the effect of the criterion that would otherwise exaggerate the difference between solutions. This imbalance can occur where the scale for scoring a certain criterion has a much wider range than the others. A simple weight correction can be applied to redress this imbalance. This is usually in the form of a multiplicative scaling factor. A weight correction is applied to both the efficiency and stability criteria seen in the example in Section 6.2.
## 4 Results and Discussion
### Variation in Thalweg Migration Distance  
The section-scale thalweg migration distance was determined based on the repeatedly surveyed profiles of sedimentation sections in the Xiaobeiganliu reach in 1986-2001. Figure 6 shows the average, maximum, and minimum annual thalweg migration distances at 25 sedimentation sections along the Xiaobeiganliu reach. The section of CS63 had a maximum average thalweg migration distance of 1950 m, with a maximum annual migration distance of 4290 m in 1998. The average thalweg migration distances at the sections of CS59, CS58, CS42, and CS41 were relatively small, with magnitudes less than 500 m. This is because there were several natural nodes and river training works that can control the river regime at these sections. The cross-sectional thalweg migration distances in the upper reach were relatively greater than those in the middle and lower reaches. The results also indicate that thalweg migration distance varied greatly along the Xiaobeiganliu reach, and the variation tendency in one section cannot represent the tendency of the whole reach. Therefore, it is necessary to investigate the changes in the reach-scale thalweg migration distance.  
Table 1 shows the reach-scale thalweg migration distances in the Xiaobeiganliu reach and three sub-reaches. The thalweg migration distance in the whole reach ranged from 210 to 707 m, and the maximum thalweg migration occurred in 1991. Additionally, the average reach-scale thalweg migration distance in this reach was 385 m. In the three sub-reaches, the LM-MQ reach had a maximum annual thalweg migration distance of 1603 m, and the maximum annual thalweg migration distances were 930 and 510 m in the MQ-JMK and JMK-TG reaches, respectively. This result is because the middle reach is located at the top of the anticline of the Tertiary laterite layer, and the erosion resistance of the soil is stronger than that in the upper reach. In addition, some natural nodes and river training works in the middle and lower reaches played a significant role in restricting lateral migration. Thalweg migration in the Xiaobeiganliu reach was beneficial for the aquatic and riverine ecosystems, but the rapid migration significantly affected the stability of river channel and damage riparian hydraulic structures, which may threaten the safety of flood control.
# Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery
## 3 Results
### Jufra
#### 3.2.1 True Positives  
The producer's accuracy of 68% reflects 15 correct identifications of change which threaten the surviving sites in the Jufra oases, highlighting sites where urgent recording is a priority. Agricultural changes are the most pressing form of damage to archaeological sites in the region. The change detection found that a foggara to the south-east of Waddan, EAMENA-0001547, had been very badly damaged during the analysis period (Figures 18 and 19), although the size of the changed area was underestimated (Figure 19). Many similar features have already been damaged in this way since 2011 [20].  
In some cases, the damage had already been done, and relatively insignificant changes were detected. At EAMENA-0001544 (a structure), a field which had been fallow since 2016 was partially re-planted, and a tree cut down. An area of burning had eroded. A patch of recent discolouration at this site was missed, however. Non-seasonal changes in planting in existing fields and continued growth of existing palms was detected where traces of ancient agriculture and irrigation were recorded (EAMENA-0001500, EAMENA-0001563, EAMENA-0001550). Soil discolouration, which may represent moisture or dumping was detected, but underestimated at EAMENA-0001537 (foggara).  
At several sites to the north of Hun, some or all of the positive change result related to minor surface moisture differences. These include settlement sites EAMENA-0095146 (Figure 20), EAMENA-0001488 and structure EAMENA-0001486. More concerning is a short-lived pool of water north of Waddan at EAMENA-0001513 and EAMENA-0001512 where there are a medieval town and an outlying rectangular structure. The water derives from a wadi draining the jebel to the north-east, and its flow is dammed by the modern road which truncates the edge of the site (Figures 21-23). As well as moisture changes, bulldozing had also occurred within the changed area at EAMENA-0095146 (confirmed in the field in 2020).  
Rubbish dumping affects several sites in Jufra, with a range of undesirable effects: Rubbish dumps are frequently the site of fires and are often bulldozed, are subject to pollution and the impact of multiple vehicle tracks. Although outside the buffered area of the site, an extensive dump was detected close to the medieval town of EAMENA-0001513. A change in the colour of an existing road which truncates an area of cairns (EAMENA-0095144) may also be due to dumping/burning of rubbish.  
Figure 20: Moisture changes at EAMENA-0095146 have long been an issue. Photograph by [PERSON] in 2013.  
Figure 21: The pool of water and rubbish dumping at EAMENA-0001513. Google Earth images.  
Figure 22: Map of detected changed area at EAMENA-0001513. Google Earth images (top) and Google image 2020 imported as an xyz tile to QGIS (bottom).