esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland-Urban Interfaces
## 3 Results  
The post-processor module receives the output from the bundles and determines the vegetation and trees in the buffer zones that do not comply with the law and should,  
Figure 9: Example of AI segmentation and the calculation of buffer zones.  
therefore, be cut down. The post processor basically uses data from CHAMELEON bundles BC1 and BC3 to identify the vegetation and trees inside the buffer zones and then applies an algorithm to select the zones where vegetation and trees should be cut down. In the first buffer zone (the 10 m buffer zone), the operation is straightforward, as all vegetation and trees must be removed. However, in the second buffer zone (the 10 m to 50 m buffer zone), the algorithm is more complex, because trees are allowed if their crowns are separated by at least 4 m.  
Figure 10 shows the result of the compliance check for the Castanheira use case. The red masks indicate trees and vegetation inside the buffer zones that do not comply with the law and must, therefore, be cut down.  
This first use case is very simple because only the first buffer zone (10 m) is required. However, the Oleinros use case is more complex because there is a road and also a building, which requires two different buffer zones. The result of the compliance check for this use case can be observed in Figure 11. The red zones represent vegetation and trees that must be cut down to comply with the law and, thus, reduce the risk of wildfire propagation near the building.  
Figure 10: Results of the compliance check for the Castanheira use case.  
The accuracy of the compliance check depends directly on the accuracy of the CHAMELON bundles that extract information from the 3D point-cloud files. As a matter of fact, the bundles are not faultless and generate two types of errors: false positives (areas wrongly classified as trees or vegetation) and false negatives (trees and vegetation not classified as such).  
The accuracy of the bundles was estimated using a black-box testing approach, feeding them the data from the Oleiros use case, and comparing their output with the expected results (ground truth). Their calculated accuracy is shown in Table 1.  
If only the BC3 bundle were used for the compliance check, its high rate of false negatives would have a major impact on the accuracy of the compliance results, as it does not detect a substantial amount of vegetation. However, the compliance check module joins the detection results of bundle BC3 with the results of bundle BC1 (union operation); therefore, the high rate of false negatives of bundle BC3 is compensated by the low rate of false negatives of bundle BC1. To put it simply, vegetation that is not detected by bundle BC1 will most likely be detected by bundle BC3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bundle** & **False Positives** & **False Negatives** \\ \hline BC1 & 2.6\% & 0.8\% \\ BC3 & 3.8\% & 21.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bundle accuracy results.  
Figure 11: Results of the compliance check for the Oleiros use case.
# Topology-Embedded Temporal Attention for Fine-Grained Skeleton-Based Action Recognition
## 4 Experiments  
### Dataset  
The figure skating dataset with 30 classes (FSD-30) [33] was used in our experiments. The dataset was created using video footage in figure skating competitions from 2017 to 2022. To maintain the relative consistency of the dataset, the frame rate of the video in the source video material was standardized to 30 frames per second, and the image size was standardized to 1080 \(\times\) 720. Following that, the Open Pose 2D Pose estimation algorithm [1; 2] was used to extract the bone points of the video, frame-by-frame. Moreover, the dataset was stored in \(npy\) format. The dataset included 30 classes and 4184 samples, with each sample consisting of bone point data from successive frames of action. All classes of samples were randomly and evenly divided into a training set of 2922 samples and two test sets of 628 and 624 samples, respectively. Moreover, we named the three sets \(TrainSet_{1}\), \(TestSet_{1}\), and \(TestSet_{2}\).
# A Comprehensive Review of Micro UAV Charging Techniques
## 8 Potential Challenges in UAV Charging Techniques
### Fast and On-Demand UAV Recharging  
In [130], the authors proposed vehicle-assisted wireless rechargeable UAV networks (VWUNs) which provide a viable approach to support automatic and on-demand recharging services by deploying UGVs with WPT techniques. However, due to limited vehicle roofs, the charging pads installed on UGVs can face capacity limitations. Moreover, as multiple UGVs and UAVs with specific energy requirements can be located in an operational area, their cooperation must be taken into account. Similarly, current works mostly focus on one-to-one charging, while charging multiple UAVs is neglected. Thus, fast recharging methods for multi-UAVs must be designed.
## 4 Experiments
### Experimental Results
#### 4.5.5 Fault Tolerance Comparison  
Due to sensor maintenance and breakdown, there are partially missing values in the traffic data. To evaluate the fault-tolerant ability of the model, we randomly discarded a fraction \(\alpha\) of the historical traffic data, and trained the model using the remaining data. In the experiment, we set the \(\alpha\) ranging from 10% to 90%. We conducted experiments on the GSTGCN, Graph Wavenet, DCRNN, STGCN, and ST-MetaNet using the dataset PeMSD7 separately, and the prediction MAE are shown in Figure 9b. Our proposed model, GSTGCN, has better fault tolerance than all the other baselines. It indicates that GSTGCN learn complex spatiotemporal correlations more effectively from sparse and noisy real-world datasets.
## 3 Results and Discussion
### TCN Hidden Layer Setting  
It is vital to select the number of channels of TCN hidden layers. Too few channels will lead to a decrease in the learning ability of the network and even a decrease in the ability to predict information. Conversely, too many choices will lead to a more complex network. Such a network not only fails to improve its performance but also tends to fall into local minima during the training process, which will reduce the learning speed of the network. In order to select the most suitable number of hidden layers, the network performance is tested in this paper when the hidden layers are 32 \(\times\) 1, 64 \(\times\) 1, 128 \(\times\) 1, and 64 \(\times\) 1. Figure 7 shows the comparison results of recognition rate, F1 score, and Cohen's Kappa (\(\kappa\)) with different numbers of channels. It is clear that the 64 \(\times\) 1 network model has better learning ability.  
Figure 6: Comparison results of recognition rate, F1 score, and Cohen’s Kappa (\(\kappa\)) under different network layers.
# Predictive Analysis of Vehicular Lane Changes: An Integrated LSTM Approach
## 2 Methodologies
### Data Extraction and Feature Engineering
#### 2.1.1 Data Extraction  
While the original dataset is expansive, not every piece of data is pertinent to our primary focus on vehicle motion. Thus, it becomes imperative to identify and retain only the most crucial features.  
The original dataset encompasses 25 features. However, features unrelated to vehicular motion, such as basic vehicle information and information about surrounding vehicles not associated with movement, should be omitted. Within the timestamp category, only a singular feature is required to pinpoint the exact moment of the motion; hence, \(Global\_Time\) is retained. For denoting the precise vehicle position, features such as \(Local_{X}\), \(Local_{Y}\), and \(Lane_{ID}\) are vital.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input Parameters** & **Descriptions** \\ \hline \(t\) & the timestamp \\ \(\sigma\) & the vehicle ID \\ \(\delta\) & the lane ID \\ \(v\) & the vehicle speed \\ \(v_{x}\) & the lateral speed \\ \(\theta\) & the steering angle \\ \(a\) & the vehicle acceleration \\ \(x\) & the lateral position \\ \(y\) & the longitudinal position \\ \hline \hline
**Decision variable** & **Descriptions** \\ \hline \hline m & the lane-changing maneuver (\(-1/1\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Maneuver classification parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input Parameters** & **Descriptions** \\ \hline \(t\) & the timestamp \\ \(\sigma\) & the vehicle ID \\ \(\delta\) & the lane ID \\ \(v\) & the vehicle speed \\ \(v_{x}\) & the lateral speed \\ \(\theta\) & the steering angle \\ \(a\) & the vehicle acceleration \\ \hline \hline
**Decision variable** & **Descriptions** \\ \hline \hline \(x\) & the lateral position \\ \(y\) & the longitudinal position \\ \hline \hline \end{tabular}
\end{table}
Table 2: Trajectory prediction parameters.  
To summarize, out of the 25 feature columns, we distilled the dataset to a set of 7 pivotal features that encapsulate the essence of vehicular movement. These are the timestamp (\(t\)), vehicle ID (\(\sigma\)), lane ID (\(\delta\)), speed (\(v\)), acceleration (\(a\)), longitudinal position (\(y\)), and lateral position (\(x\)).  
Given the vast amount of data rows, we will focus solely on data for vehicles with an ID less than 1500 and those that have executed a lane change. This refined dataset comprises 54,463 rows and 7 columns.
## 2 Materials and Methods
### LDAS-Monde
#### 2.1.2 Data Assimilation  
LDAS-Monde routinely uses a simplified extended Kalman filter (SEKF, [38]) to assimilate observations of SSM and LAI. This is a sequential approach with a forecast step followed by an analysis step (see Figure 2 for schematic diagram describing how it works). The forecast step propagates the initial state over a 24-h assimilation window with the ISBA LSM. Then, the analysis step corrects the forecast by assimilating observations. This step involves an observation operator defined as the product of the model propagation of control variables over the 24-h assimilation window with the projection of those variables to observation equivalents. The analysis requires the calculation of the Jacobian of the observation operator. It is computed using finite differences obtained by perturbed model runs over 24-h assimilation windows. For a given grid point and vegetation patch, each control variable requires a perturbed model run obtained by initializing ISBA with the initial state perturbed for the selected control variable (0.1% typically, see [19]). In this study, the analysis step updates the modeled LAI and soil moisture from layer 2 (1-4 cm) to layer 7 (60-80 cm). The approach is fully detailed in [19].  
A mean volumetric standard deviation error of 0.04 m\({}^{3}\cdot\)m\({}^{-3}\) was affected to soil moisture in the second layer of soil (i.e., the model equivalent of the SSM observations). Then, for deeper layers, the mean volumetric standard deviation error of 0.02 m\({}^{3}\cdot\)m\({}^{-3}\) was used, as suggested by several authors for RZSM [37; 38; 40; 47]. The observational SSM error is set to 0.05 m\({}^{3}\cdot\)m\({}^{-3}\) as in [37]. This value is consistent with errors typically expected for remotely sensed soil moisture (e.g., [47; 48; 49]). Soil moisture observational and background errors are assumed to be proportional to the soil moisture range (the difference between the volumetric field capacity (\(w_{fc}\)) and the wilting point (\(w_{wilt}\)), calculated as a function of the soil type, as given by [24]). The standard deviation of errors of GEOV2 LAI is assumed to be 20% of GEOV2 LAI. The same assumption is made for the standard deviation of errors of the modelled LAI (20% of modelled LAI) for modelled LAI values higher than 2 m\({}^{2}\cdot\)m\({}^{-2}\). For modelled LAI values lower than 2 m\({}^{2}\cdot\)m\({}^{-2}\), a constant error of 0.4 m\({}^{2}\cdot\)m\({}^{-2}\) is assumed, according to [40].
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
## 5 Simulation Results  
This section is devoted to evaluating the effectiveness of the proposed control schema where a nonlinear tracking controller is used for comparison purposes. The simulation conditions are set up, where the disturbance function and the desired attitude are defined. Furthermore, the vehicle's parameters are specified; namely, maximum allowed control torque and inertia matrix. For simulation purposes, the inertia matrix considered is the one for the Crazyflie 2.0 [53], which is a tiny quadrotor. Due to the small size and low weight, this vehicle is very susceptible to external disturbances, allowing assessment of the approach. Particular interest is put in the evolution of the desired and current angular velocity and quaternion. The error evolution is analyzed in terms of ISE index.
# C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTM
## 5 Experimental Results
### Ablation Study
#### 5.5.2 Removing Attention Component from C2B Hybrid Model  
The C2B hybrid model without the attention mechanism was also subject to our evaluation. Without an attention mechanism, the results demonstrated a substantial decline in performance, with precision dropping to 0.82, recall to 0.78, F1-score to 0.80, and accuracy to 0.85, as shown in Table 7. The results indicate the critical importance of the attention mechanism in this model. The attention helps enhance focus between relevant portions of code and queries. The absence of an attention mechanism means that it is not capable of properly interpreting or matching code snippets with natural language queries. This emphasizes the need of using an attention strategy to attain the best outcomes in code search activities.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline Original C2B model & 0.897 & 0.907 & 0.820 & 0.902 \\ Without CodeT5 & 0.700 & 0.650 & 0.670 & 0.750 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance metrics of the C2B hybrid model with and without CodeT5.  
Figure 8: Learning code embeddings by minimizing binary cross-entropy loss.
## 7 Results and Discussions
### Optimal Configuration from the HPRALC, MPRAMC, and LPRAHC-Based Configurations
#### 7.5.3 High-Efficiency Appliances Usage-Based Scenario (LPRAHC) (with DSM)  
Based on the results of the HPRALC-based situation, it was determined that the nickel-iron-battery-based IRES (base case) offered the lowest LCC. As a result, we conducted additional research on this configuration using the LPRAHC-based scenario to gain further insights. Table 3 demonstrates that the LCC provided by the base case using HPRALC and MPRAMC-based scenarios and the LCC provided by the LPRAHC-based scenario is approximately 44% and 17% lower than the HPRALC and MPRAMC-based scenarios, respectively. For this case, the optimal values for the individual components are (NPV = 852) and (N\({}_{\text{BAT(Ni-Fe)}}\) = 496). When compared with the HPRALC-based scenario, the number of necessary PV panels is reduced by 617, from 1469 to 852, while the number of required batteries is decreased by 454, from 950 to 496. The scenario values from the MPRAMC  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Configuration** & **Q\&C** & **GA** & **PSO** & **DE** & **GWO** & **ALO** & **DA** & **MFO** & **GOA** & **SSA** \\ \hline
**PV/** & NPV & **1005** & **1005** & 1008 & **1005** & **1005** & **1005** & **1005** & **1005** & **1005** \\ \cline{2-10}
**NH-Fe** & N\({}_{\text{BAT}}\) & **612** & **612** & 625 & **612** & **612** & **612** & **612** & **612** & **612** \\ \cline{2-10}
**@DOD = 80\%** & LCC (\$) & **635,271** & **635,271** & 643,385 & **635,271** & **635,271** & **635,271** & **635,271** & **635,271** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimization results of the IRESs using MPRAMC-based scenario at an LPSP Value of 0%.  
enabled us to reduce the number of PV panels from 1005 to 852, a decrease of 153 panels, and the number of batteries from 612 to 496, a drop of 116. The adoption of an LPRAHC-based scenario has a substantial impact on the system's performance, as it reduces both the LCC and the number of components needed relative to the optimum values determined by the use of medium and low-efficiency appliances. Finally, it can be concluded that among various battery-based IRESs and efficiency-based scenarios, the LPRAHC-based scenario is the most suitable for electriying the study area.  
Figures 7 and 8 show the energy graphs of the components of the IRES for one week throughout both the summer and winter months for the LPRAHC-based scenario of the nickel-iron-battery-based IRES.
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 5 Traffic Prediction-Related Datasets
### Stationary Traffic Data  
A fixed traffic dataset is a collection of traffic state information, such as the road traffic flow, speed, and lane occupancy, which is captured in real time by stationary traffic data collection devices like cameras and lidar sensors. Since the data are collected using specialized equipment, they are generally more accurate and continuous.  
1. PeMS: PeMS is a traffic flow database for California, containing real-time data from more than 39,000 independent detectors on California highways. Among them, the detector data are mainly from highways and metropolitan areas. These data include information such as vehicle speeds, flows, congestion, etc., which provide an important basis for traffic management, planning, and research. The minimum time interval of the data is 5 min, making them highly suitable for short-term prediction. The historical average method can be used to automatically fill in missing data. In the task of traffic flow prediction, the most commonly used sub-datasets are PeMS03, PeMS04, PeMS07, PeMS08, PeMS-BAY, etc.
2. The METR-LA traffic dataset contains traffic information collected from detectors on freeway loops in Los Angeles County. The Los Angeles Freeway Dataset contains traffic data detected by 207 detectors from 1 March to 30 June 2012, with a sampling interval of 5 min.
3. The Loop dataset contains mainly loop data from the Seattle area, covering data from four highways: I-5, I-405, I-90, and SR-520. The Loop dataset contains traffic status data from 323 sensor stations, with a sampling interval of 5 min [39].
4. The Korean urban area dataset (UVDS) contains data on major urban roads collected by 104 VDS sensors, with traffic characteristics such as traffic flows, vehicle types, traffic speeds, and occupancy rates [40].
# Improvements of the xAAL Home Automation System
## 5 The xAAL Communication Protocol
### The Application Layer  
The Application Layer is a CBOR array of size 4 or 5, depending if there is a _body_ field or not:  
* [0]: _source_--A definite bytesting[16]; the xAAL address (UUID) of the sender of the message.
* [1]: _dev_type_--A definite string; the schema name of the sender (The pair _classe.variant_.)
* [2]: _msg_type_--An assigned int of value: 0:notify, 1:request, 2:reply. Other values must be rejected, the message is ignored.
* [3]: _action_--A definite string; the name of the action brought by the message from the list of _methods_ and _notifications_ described in the considered schema. The considered schema is the one of the sender.
* [4]: _body_--An optional field. If present, it must be a map. Keys of this map are definite strings, the names of parameters associated with the corresponding action according to the schema and their value. Depending on the data model specified in the schema, values may have CBOR tags. Note that the body may be absent if the schema does not specify parameters for the corresponding action. Multiple identical keys are not allowed, the message should be ignored.  
The above-described CBOR items must have no CBOR tags. However, value items within the _body_ may be tagged, according to the corresponding schema.  
Figure 3 gives the CDDL specification of the xAAL Application Layer.
## 5 Evaluation Metrics
### Spearman Rank-Order Correlation Coefficient (SROCC)
#### 5.1.2 Kendall Rank-Order Correlation Coefficient (KROCC)  
KROCC assesses the ordinal relationship between two variables. It is used in BIQA to assess the relationship between expected and actual quality ratings. The equation of KROCC is defined as,  
\[\tau=\frac{(\text{number of concordant pairs})-(\text{number of discordant pairs})}{(\frac{m}{2})}, \tag{11}\]  
where \(m\) is the total number of images within the database, and a pair of images \((p_{l},q_{l})\) is considered to be concordant if their rankings in their predicted and actual scores are the same, and otherwise discordant. KROCC has a value between \(-1\) and \(1\), with \(1\) indicating perfect positive correlation, \(0\) indicating no correlation, and \(-1\) indicating a perfect negative correlation.
# A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery
## 2 Materials and Methods
### Methodology--Model Development  
Several techniques have been adopted in order to extract bathymetric information from multispectral imagery to date [14; 15]. The initial steps for SDB analysis comprise: (a) radiometric, geometric and atmospheric correction, such as the Sentinel-2A Level-2A products evaluated in this work; (b) sub-setting the imagery to the study region, namely the Paros-Antiparos Strait; and (c) imposing a sea-land mask using the NIR band (B8) of Sentinel-2A to emphasize the marine features. Following the above pre-processing phases, the overall methodology implemented in this study is described in the work-flow chart of Figure 2, and analyzed further below.  
Figure 1: The study area of the Paros–Antiparos Strait in the Cyclades Island Cluster of the Aegean Sea in Greece. Highlighted are the sub-areas (1) and (2) mentioned within the text.