esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 4 Results and Discussion
### Winter Wheat Mapping
#### 4.2.4 Incremental Classification Results Using SAR Data  
In order to assess the ability of SAR data to identify winter wheat early, an incremental classification was conducted with backscatter intensity, coherence, and texture images of SAR data. Every new image acquisition was added to all previously available images and was performed using the RF classifier (Figure 7). As shown in Figure 7, the classification accuracy of winter wheat improved as the season progressed. The combination of all six Sentinel-1A datasets achieved the highest classification accuracy, using only one Sentinel-1A image with the lowest classification accuracy. This shows that multi-temporal SAR data can provide more useful information to improve classification accuracy [20; 66]. In addition, the classification accuracy was satisfactory using four Sentinel-1A images  
Figure 6: Accuracy of winter wheat using each optical image alone for RF (**a**) and SVM (**b**).  
(22 November 2015, 9 January 2016, 26 February 2016 and 21 March 2016) acquired before the jointing period of winter wheat (with an overall accuracy up to 93.91% and a kappa coefficient up to 0.9093, respectively), while the F1 measure was 87.89%. The classification accuracy using four Sentinel-1A datasets is shown in Table 5. As shown in Table 5, for rapeseed fields, the F1 measure was no higher than 75% using the data combination. For others fields, this data combination produced satisfactory accuracy, with both the F1 measure and overall accuracy being higher than 90%. Therefore, using SAR data early in winter wheat, satisfactory classification accuracy can be obtained. Samples of final classification maps are displayed in Figure 8. [PERSON] et al. [11] evaluated the usefulness of the Sentinel-1A early crop type classification as an optical image supplement and found that a satisfactory land cover map could be obtained early in the season, with a significant improvement in accuracy compared to the use of optical images alone.  
Figure 8: Classification results using the RF classifier. (Top) Using the four SAR datasets (22 November 2015, 9 January 2016, 26 February 2016 and 21 March 2016). (Bottom) Using the combination of S + Q.  
Figure 7: Incremental classification accuracy of winter wheat with backscatter intensity, coherence, and texture images by adding every new image acquisition to all previously available images.
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 3 Results
### Effect of Polarization  
All previous simulations were performed considering TE polarization. Since the reflection coefficient is smaller for TM polarization (which provides better penetration into the soil), an improvement in the results could be expected with TM. However, when the surface is rough, there is not only better penetration, but also slightly higher clutter levels with TM. As a result, the contrast between the target and the soil remains almost the same, as shown in Figure 14 for the multibistatic configuration. Nonetheless, there is less clutter below the soil surface, which helps to facilitate the detection.  
Figure 13: SAR image from FDFD simulations with a dielectric target buried under a rough surface (dashed line indicates the soil interface) for multimonostatic (**a**) and multibistatic (**b**) configurations.
## 2 Preliminaries
### Applications of Broadcast
The messaging system uses a publish-subscribe model to ensure that messages are reliably delivered to all subscribers, while the distributed lock service provides a mechanism for achieving mutual exclusion in a distributed environment [73]. Service Fabric also includes built-in support for microservices, which allows developers to build complex distributed systems using a modular architecture [74]. In contrast to our proposed algorithm, these systems are fairly complex and provide broadcast with at least one semantic, which may lead to multiple copies of the same message being received by a process that may not be appropriate for some safety-critical applications, including remote surgeries and transportation applications for which receipt of multiple messages poses a safety risk. In contrast, our proposed algorithm provides the exact one semantics which ensures that every process receives exactly one copy of the message. All the abovementioned algorithms and protocols require an initialization phase before their execution. This initialization process introduces an added layer of complexity to the existing algorithms. Furthermore, this can introduce scalability challenges, especially in large networks. On the other hand, due to being able to start in an \"arbitrary system configuration\", our proposed algorithm does not require a system configuration setup. Furthermore, they are not capable of handling \"spurious messages\", whereas our proposed algorithm prevents the propagation of \"spurious messages\" by implementing 1-safety.
# An Early Investigation of the HHL Quantum Linear Solver for Scientific Applications
## 2 Quantum Linear Systems and the Implementation of the Solver
### Overview of the Harrow-Hassidim-Lloyd (HHL) Algorithm
#### 2.1.1 Mathematical Foundation of HHL  
[PERSON], [PERSON], and [PERSON] first developed the HHL algorithm to solve the quantum linear-system problem [3]. The fundamental idea behind the HHL algorithm is that the eigenstates of the Hermitian matrix \(A\) (noted as \(\left\{\left|v_{j}\right\rangle\right\}\)) form a complete orthonormal basis of \(\mathbb{C}^{N}\) (i.e., \(\left\langle v_{j}\right|v_{k}\rangle=\delta_{jk}\)), and hence the state \(\left|b\right\rangle\) can always be decomposed by this basis as \(\left|b\right\rangle=\sum_{j=0}^{N-1}b_{j}\left|v_{j}\right\rangle\). Similarly,  
\[\left|x\right\rangle =\frac{A^{-1}\left|b\right\rangle}{\left\|A^{-1}\left|b\right\rangle \right\|_{2}}\] \[=\frac{1}{\left\|A^{-1}\right\|b\right\|_{2}}\sum_{j=0}^{N-1} \frac{1}{\lambda_{j}}\left|v_{j}\right\rangle\left\langle v_{j}\right|\sum_{j =0}^{N-1}b_{j}\left|v_{j}\right\rangle\] \[=\frac{1}{\sqrt{\sum_{j=0}^{N-1}\frac{\left|b\right|^{2}}{ \lambda_{j}^{2}}}}\sum_{j=0}^{N-1}\frac{b_{j}}{\lambda_{j}}\left|v_{j}\right\rangle. \tag{2}\]  
In other words, the HHL algorithm needs a quantum computer to perform eigen-decomposition of \(A\) and eigenvalue inversion. Figure 1 shows a general description of the circuit that exactly serves the purpose, with an additional \(n_{d}\)-qubit data-loading block to load \(\left|b\right\rangle\) into the quantum computer and \(n_{d}=\lceil\log(N)\rceil\).
## 4 Conclusions  
For systems as chemically and geometrically heterogeneous as caprocks and inter-layers, the standard rock core characterisation techniques have been seen to lead to conflictual findings concerning the actual impact that scCO\({}_{2}\) exposure has on the pore structure and transport properties of the rock. However, recent research described here has offered some alternatives that will often resolve the issues with standard methods and thus deliver new insights into how scCO\({}_{2}\) treatment changes the rock properties. In particular, it has been found that the ISO BET surface area measurement is insufficient for systems as chemically and geometrically heterogeneous as rocks, but the homotattic patch model offers more accurate void space parameter estimations that are also predictive of the changes to mass transport following exposure to scCO\({}_{2}\). Overcondensation has been shown to allow gas sorption to be used to probe changes to rock pores from the molecular scale all the way up to very large macroopores, for large samples sizes, exceeding the pore size range possible with any single imaging modality, and without the need for the high pressures involved in mercury porosimetry that can damage mechanically weak rock structures. The potential value of the rarely used adsorption calorimetry technique for characterising the structural evolution of rocks has been demonstrated. Alternatives, involving gas sorption  
Figure 11: Vertical plane slice through a reservoir model showing a comparison of the CO\({}_{2}\) plume behaviour between models of sandstone aquifers with shale inter-layers (dashed line) with thicknesses of 0.3 m (**a**) and 3 m (**b**) for the simulation of 120 years of injection. The colour scale represents increasing concentrations of CO\({}_{2}\).  
scanning, to the more abstract void space segmentation schemes used in direct imaging have been shown to identify physically meaningful sub-sections of the void space that are critical for controlling mass transport following exposure to scCO\({}_{2}\). In addition, it has been demonstrated that field-scale reservoir simulations of carbon sequestration need to include all three processes--coupled flow, geochemical and geomechanical processes--to truly provide accurate predictions of plume behaviour that incorporate the critical but counter-intuitive effects that would not otherwise be anticipated for geologically heterogeneous reservoirs. For instance, it has been observed that the complex interaction of these three types of processes leads to thinner inter-layers providing a greater barrier to plume migration than slightly thicker layers.  
All authors have read and agreed to the published version of the manuscript.  
The work of RS was supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant Reference number EP/L016362/1. The work of AA was supported by the GeoNetZero CDT and the University of Nottingham.  
The authors declare no conflict of interest.
## 2 Antenna Array Specifications Based on Link Budget for LEO Connectivity
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline E\({}_{b}\)/N\({}_{0}\) required for 16 APSK 2/3 & \(E_{b}\)/N\({}_{0}\) & 4.76 & dB \\ Data rate required & \(DR\) & 25 & Mbps \\ Channel BW & \(CBW\) & 10 & MHz \\ Receiver noise BW & \(RBW\) & 36 & MHz \\ Carrier-to-noise ratio required & \(C\)/N & 8.74 & dB \\ Carrier-to-noise density required & \(C\)/N\({}_{0}\) & 84.3 & dB \\ Satellite EIRP & \(EIRP\) & 34.6 & dBW \\ Downlink frequency & \(f_{d}\) & 12.6 & GHz \\ Path distance & \(d\) & 1200 & km \\ Free-space path loss & \(FSPL\) & 176.09 & dB \\ Atmospheric losses & \(L_{atm}\) & 5.0 & dB \\ System noise temperature & \(T_{sys}\) & 314 & K \\ Roll-off factor & \(f_{ro}\) & 0.1 & \\ Antenna quality factor required & G/T\({}_{sys}\) & \(-\)2.31 & dB/K \\ Realized gain required & \(G\) & 27.4 & dB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Link budget calculations in the Ku-band (downlink).
# LSTM Attention-Driven Similarity Learning for Effective Bug Localization
## 5 Experimental Result  
The hyperparameters for the LSTM-Attention model were carefully chosen to balance computational efficiency and predictive accuracy, ensuring optimal performance for the task of bug localization. The specific hyperparameters and their rationale are as follows:  
* hidden_units = 128: This value determines the number of units in the LSTM hidden layer. A setting of 128 strikes a balance between model complexity and computational efficiency, enabling the network to effectively capture sequential dependencies in these input data without overfitting.
* num_layers = 1: Using a single LSTM layer simplifies the model while maintaining sufficient capacity to process the input sequences. Adding more layers could increase  
Figure 5: Overview of LSTM-Attention.  
computational overhead without significant performance gains, given the nature of the dataset.
* max_sequence_length = 100: This parameter limits the length of input sequences processed by the model. It was set to 100 to focus on the most relevant parts of the input while reducing noise and ensuring manageable computational costs.
* input_length = 300: This value defines the maximum length of the tokenized bug report and source code sequences used during training. A length of 300 allows for the inclusion of sufficient context from both the bug report and source code without introducing unnecessary complexity.
* validation_split = 0.01: This parameter reserves 1% of the dataset for validation purposes. Given the size of the dataset, this split was chosen to provide a representative validation set while preserving the majority of these data for training.
* learning_rate = \(5\times 10^{-2}\): The learning rate was set to 0.05 to enable stable convergence of the model during training. This value ensures that the updates to the model weights are neither too large (which could lead to overshooting the optimal point) nor too small (which could slow down convergence).  
A detailed summary of the LSTM-Attention model's architecture is presented in Table 3.  
These hyperparameters and architectural choices were informed by the characteristics of the dataset and task. The model needed to process textual data (bug reports and source code), capture sequential and contextual relationships, and output accurate predictions for buggy files. The combination of LSTM and attention mechanisms enabled the model to handle lengthy input sequences effectively while maintaining high predictive accuracy.
## 3 Results and Discussions
### The Measurement Model
Therefore, the discriminated validity condition was again met using cross-load size as an alternative to AVE. The discriminated validity of a suitable model can be easily detected using the HTMT report. It expresses the ratio between the geometric average of the correlations of the indicators from the constructs that have the role of measuring different phenomena and the average of the correlations of the articles from the same construction [115]. Various studies have shown that for reflective models the HTMT value should be below 1.0 [116] or more strictly, be lower than 0.9 [117] or even 0.85 [118]. The results demonstrated maximum HTMT values between PE and PR (0.793) and minimum between PA and EE factors (0.268), conforming to the value threshold of 0.85 [119]. Multicollinearity in OLS regression is problematic if the variance inflation coefficient (VIF) is greater than 4.0 (some studies use a milder limit of 5.0) [112,117]. The reflective construct model also indicates very good multicollinearity, as the variance inflation coefficients (VIF) for each indicator, calculated with the OLS regression method, are lower than 4.0 [105]. VIF highs were reached by indicators AT2 (3117) and AT3 (3175), and lows of BI2 (1011), PA4 (1147), AT4 (1170) and PP4 (1210) (Figure 7). The GoF size of 0.595 indicates, after evaluating the data sets, a good level of fit of the proposed measurement model, being located almost in the middle of the allowed range (between 0 and 1) [120]
## 3 Related Work
### Relevant Studies
The simulation framework proposed by [PERSON] et al. [2018] integrated cutting-edge robotics, communication, and control system components such as ROS2, Gazebo, OMNeT++, Artery, MATLAB, and Simulink to simulate the cooperative autonomous driving scenarios and their required technologies. The proposed framework can also be used to integrate new tools since the integration is done in a modular fashion. The platooning scenariounder cooperative adaptive cruise control (CACC) and the ETSI ITS-G5 communication architecture was used by the authors to further highlight the framework. In another study, [PERSON] et al. [17] presented SUMMIT, a simulator to generate high-fidelity interactive data to develop, train, and test crowd-driving algorithms. The simulator generated unregulated congested traffic at any location in the world using online maps. It could generate complex and realistic crowds that closely resemble uncontrolled traffic in the real world by fusing topological road contexts with an optimization-based crowd behavior model. Moreover, the authors also formulated a Context-POMDP as a reference planning algorithm for future developments. In conclusion, they stated that SUMMIT can support a wide range of applications such as perception, control, planning, and learning for driving in unregulated dense urban traffic. [PERSON] et al. [18] addressed the problem of tracking algorithms based on deep learning. To this end, they designed and developed four different deep learning algorithm--deep convolutional neural networks, deep convolutional neural networks with fine-tuning, transfer learning with a deep convolutional neural network, and fine-tuning deep convolutional neural networks with transfer learning to track the targeted object. The proposed algorithms achieved significantly good results than the state-of-the-art algorithms. In another research, [PERSON] [19] worked on a similar problem. They leveraged the Kalman filter and deep learning algorithms to detect and track the multiple dynamic targeted objects.
## 2 Related Works
### NOMA for Dense Network Connectivity  
NOMA is a leading candidate technology for extensive connectivity to next-generation networks. The authors of [3] explore power-domain NOMA, focusing on optimizing PA to achieve proportional fairness and minimize outage probabilities under block fading conditions. The authors evaluated system throughput and latency using hybrid automatic repeat request (HARQ) protocols and maximum ratio combining (MRC), providing numerical results for both symmetric and asymmetric scenarios. In [22], the PA optimization in NOMA systems to enhance SSE and UE fairness is investigated. The study compares three metaheuristic algorithms--differential evolution (DE), particle swarm optimization (PSO), and artificial bee colony (ABC)--to solve the non-convex optimization problem. The results show that DE optimization maximizes SSE by \(\sim\)1.8% to \(\sim\)10% with lower complexity, reducing signal-to-noise ratio (SNR) requirements for UE.  
The authors of [6] explored the performance of integrated sensing and communication (ISAC) systems using NOMA and signal alignment techniques. The study investigates different precoding designs, such as sensing-centric, communications-centric, and Pareto optimal, and evaluates key performance metrics such as communication rate and outage probability. Numerical results demonstrate that ISAC outperforms conventional frequency-division sensing in terms of spectrum, energy, and hardware efficiency, showcasing its superiority in achieving a broader rate region. The authors of [23] suggested a joint design framework that leverages the generalized polarization effect among UE, decomposing the NOMA channel into multiple-bit polarized channels. The recommended schemes, sequential UE partition and parallel UE partition, improve system performance by optimizing the NOMA decoding order and reducing latency.
## 4 Experimental Results
### Training Results Derived by Applying the DA-S Technique
Table 6 presents the comparison or results of model performance by combining saturation weights (\(w\)) of 0, 2, and 3 in pairs for the DA-S technique, with the mean image saturations of each weight pair being 0%, 99.49%, and 99.85%. The comparison was conducted using 33,576 data samples, comprising 11,192 samples from Original DB-T and 22,384 samples (11,192 data samples per weight) of the DB augmented with the DA-S technique. When combining saturation weights 0 and 2, the mean accuracies of ResNet18 and ResNet34 were calculated as 91.15% and 91.02%, respectively. This result shows a difference of 0.13%, with ResNet18 performing better than ResNet34. When the saturation weights 0 and 3 were combined, the mean accuracies of the ResNet18 and ResNet34 models were calculated as 89.03% and 89.15%, respectively. This result shows a difference of 0.12%, with ResNet34 performing better than ResNet18. When saturation weights 2 and 3 were combined, the mean accuracies of the ResNet18 and ResNet34 models were calculated as 89.53% and 89.79%, respectively. This result shows a difference of 0.26%, with ResNet34 performing better than ResNet18. The comparison results \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Mean Accuracy [\%]**} \\ \cline{3-5}  & **Original DB-T** & **DAS 0\%** & **DAS 99.49\%** & **DAS 99.85\%** \\  & & _(w = 0)_ & _(w = 2)_ & _(w = 3)_ \\ \hline ResNet18 & 56.96 (\(\pm\)0.003) & 76.78 (\(\pm\)0.038) & 69.52 (\(\pm\)0.216) & 80.36 (\(\pm\)0.183) \\ ResNet34 & 57.02 (\(\pm\)0.006) & 65.38 (\(\pm\)0.172) & 81.86 (\(\pm\)0.018) & 76.86 (\(\pm\)0.061) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CNN training results were derived using the original DB and the DB augmented with the DA-S technique: performance comparison. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Original DB-A** & **Original DB-T** & **DAS 99.85\%** \\  & & _(w = 3)_ \\ \hline Number of data & 5596 & 11,192 & 11,192 \\ Mean Accuracy & 48.61\% & 56.96\% & 80.36\% \\ (ResNet18 Model) & (\(\pm\)0.023) & (\(\pm\)0.003) & (\(\pm\)0.018) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of performance before and after data augmentation. confirmed that the combination of saturation weights 0 and 2 resulted in the highest mean accuracies for ResNet18 and ResNet34, achieving 91.15% and 91.02%, respectively. Furthermore, the mean accuracy of ResNet18 based on this combination, 91.15%, was the highest among the mean accuracies of ResNet18 and ResNet34 derived from different combinations of saturation weights.