esa-sceva/satcom-chunk-collection huggingface dataset:
# Multicell Interference Management in Device to Device Underlay Cellular Networks
## 6 Performance Evaluation  
In this section, we provide a series of numerical results evaluating the operational features and the performance of the proposed approach. Initially, in Section 6.1, we focus on the operation performance achievements of our proposed methodology, in terms of interference, uplink transmission power and achievable data rate. The achievable performance and contribution of the proposed RB allocation process (i.e., first step) is examined and evaluated in Section 6.1.1, while subsequently in Section 6.1.2 the additional benefits obtained by the second stage of our approach (i.e., power control) are studied and its fast convergence to the final optimal power values is shown. Then, in Section 6.2 we provide a comparative evaluation of our proposed approach against another existing approach in the recent literature that targets rate maximization [26], through the efficient resource allocation in a multi-cell network supporting D2D communication. The aforementioned approaches are thoroughly compared in order to illustrate the obtained gain of our proposed approach in terms of uplink transmission power required to meet the users' SINR requirements, number of users that can be supported by each approach (i.e., users that their SINR meets or exceeds the pre-specified threshold), rate and interference. It should be noted that unless otherwise is explicitly mentioned, the majority of the numerical results is the statistical average outcome of several random topologies with respect to the distribution of the cellular and D2D users within each cell.
## 3 Results
### The Ozone Degradation of Contaminants
#### 3.2.1 The HPLC-DAD-MS Analysis  
The mixture specimens collected after 1, 2, 4, 6, and 8 h of treatment were analyzed using HPLC-UV-MS to evaluate the parent molecules' disappearance. No detailed analysis of the degradation products of each compound was performed since this procedure was out of the scope of this work  
The chromatographic peak positions of each mixture component obtained via the HPLC-UV-MS analysis of the untreated mixture were collected in Figure 3.  
Figure 3: HPLC-UV-MS/MS analysis of the mixture at 0.1 mg/L. (**a**) individual ion current of each contaminant; (**b**) UV chromatogram at \(\lambda=285\) nm.  
Figure 2: The chemical structure of the active principles mixed in solution to test the degradation power of the ozone treatment.  
In Table 4, the average values of the pollutants' concentration in treated and untreated mixture are reported and compared with the respective LOD values.  
After 1 h of treatment, most of the parent molecules disappeared; the concentrations were under the limit of determination by the mass spectrometry analysis. This was an interesting result, since the concentration of each compound was about 0.5 mg/L, definitively higher than those present in wastewater or environmental samples. Conversely, 4 h of treatment were necessary to obtain an ofloxacin concentration lower than its LOD, confirming the high stability of the fluoroquinolone antibiotics and their long persistence once dispersed in the environment [43].  
The most recalcitrant compound, however, was the insecticide, Imidadoprid. At the end of treatment, its concentration decreased only by 40%. This insecticide was recently banned in the European Union [44], but its resistance to degradation accounts for the concern about its accumulation in the environment, soil, and surface water.
# OBU for Accurate Navigation through Sensor Fusion in the Framework of the EMERGE Project
## 3 Sensor Fusion: Loosely Coupled Algorithm Implementation
### Setting Initialisation Parameters for IMU Errors  
Inertial navigation systems are renowned for delivering highly accurate position, velocity, and attitude information, particularly over short time spans. However, this precision degrades significantly over extended periods due to inherent error sources within the sensors. To address this challenge, the algorithm's first step focuses on precisely defining the initialisation parameters related to IMU errors. This meticulous calibration lays the groundwork for mitigating sensor inaccuracies and ensures the subsequent integration of INS-GNSS data yields precise and reliable navigation estimates.  
The Allan variance method is used to characterise various error types present in inertial sensor data. This method allows for representing root mean square (RMS) random drift error as a function of averaging time [32]. Inertial measurements were analysed using the Allan variance method; precisely, specific force and angular velocity measurements collected from the IMU during a stationary five-hour test were utilized. These measurements were crucial in deriving parameters such as velocity random walk, angle random walk, angle rate random walk, bias instability, correlation times, and dynamic bias root-PSD for the accelerometer and gyroscope.  
Figure 5: Loosely coupled INS/GNSS coupling scheme.
# Quantitative Set-Based Design to Inform Design Teams
## 1 Introduction  
The design of complex engineered systems requires detailed analyses performed by a large number of experts over a specific period. A traditional design approach, point-based design (PBD), uses system decomposition, modeling, simulation, optimization, and analysis to find and compare discrete design alternatives. PBD is a well-researched area [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. An alternative to PBD is set-based design (SBD), which explores a large number of design alternatives grouped into sets and uses uncertainty resolution to select the most promising sets. SBD research dates back to the early 1990s [12, 13].  
Recent advances in SBD research have increased the adoption of SBD by several organizations. For example, in 2008 the then Commander of the Naval Sea Systems Command sent a memo entitled \"Ship Design and Analysis Tool Goals\", which required the use of SBD and the use of new tools for trade-off analysis [14]. In 2018, the United States Air Force in their Capability Development Guidance mandated that \"Development planners should use a set-based design modeling, simulation, and analysis approach, which supports the thorough exploration of alternative solutions while maintaining maximum design trade space\".  
The purpose of this paper is to show how tradespace exploration in early system design using an integrated model-based engineering (MBE), SBD, and trade-off analysismethod can help develop requirements and identify high-performing design alternatives that have an affordable cost. We propose a qualitative SBD conceptual framework and quantitative SBD approach to inform decision-making by design teams. The quantitative SBD approach fills a gap in the literature on how to implement quantitative SBD. We use an unmanned aerial vehicle (UAV) case study with an integrated model-based engineering framework to demonstrate the benefits of SBD. Specifically, we demonstrate how SBD helps to (1) analyze requirements to inform requirement developers and (2) assess design decisions using design sets to better inform design teams when selecting design options.  
We organize the rest of this paper in the following manner. Section 2, entitled Set-Based Design, provides insight into SBD as a design process and an overview of the recent SBD literature. Additionally, it contains the proposed quantitative SBD implementation methodology, which informs design teams. We then describe the UAV case study in Section 3 (Unmanned Aerial Vehicle Case Study). Section 4 (Insights from Quantitative Set-Based Design) uses this case study to describe and demonstrate how to use SBD to inform requirement analysis and select design options. Finally, Section 5 (Summary and Future Work) concludes with a summary and discussion of future research.
# On the Reliability Assessment of Artificial Neural Networks Running on AI-Oriented MPSoCs
## 4 Case Study
Specifically, a complete CNN inference cycle (a single prediction) takes 276,529 clock cycles (15,772 ms at 18 MHz). Initially, the network parameters (weights, biases) are stored in the 512 KB L2 memory; before each layer computation, the DMA is in charge of transferring the current layer parameters from L2 memory to the cluster's shared 128 KB L1 memory. Further architectural details are provided in [16]. Moreover, the RISC-V cores of GAP-8 do not have a hardware floating-point unit, and all computations are executed in fixed-point arithmetic. Therefore, the targeted LeNet CNN was quantized to comply with the PULP-NN library requirements [61] and to fit into PULP memories. Unfortunately, due to memory constraints, full-precision neural networks cannot be easily ported into resource-constrained devices, as stated in Section 2. Then, the CNN parameters were quantized to 8-bit signed integers. Specifically, the full-precision PyTorch model's parameters (weights and biases) were quantized, and then a new model running on the multiprocessor SoC was created in the C programming language by exploiting the kernel functions of PULP-NN. The accuracy of the new quantized network was computed by running the MNIST test set, and it slightly decreased by 1.1%. The reader should note that the final network-oriented score map did not change after the quantization step.
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Source of Information  
The video content life cycle includes three stages. The first stage is the capture stage, during which the recording or capturing of the video takes place. The second stage is the production stage; during this stage, a message or a story is conveyed through the format the video has been converted into after being edited. The third stage is the viewing stage, during which the video is shown to a targeted audience. Through these stages, the video content is desemanticized, and then the various audiovisual elements are extracted [11]. Based on the source of information, personalized video summarization techniques can be divided into three categories: internal personalized summarization techniques, external personalized summarization techniques, and hybrid personalized summarization techniques.
# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 2 Proposed Method
### Ssam  
Shallow features produced by the encoder, despite containing rich spatial details, are short of semantic information, easily affected by noise, and predominantly contain non-water body interferences. Referring to existing research ideas [33], we design the SSAM using a multi-attention combination to refine feature information and enhance the network's anti-interference ability.  
The structure of the SSAM is shown in Figure 3; it mainly includes two parts: spectral attention (channel dimension compression) and multi-scale spatial attention (parallel dilated convolution). Specifically, the spectral attention branch performs global pooling on the input feature map along the spatial dimensions (height H and width W) through a global average pooling layer to obtain channel information with a shape of N \(\times\) C \(\times\) 1 \(\times\) 1 (N is the batch size). Then, two 1 \(\times\) 1 convolution operations are used to downsample the channel count from C to C/16, and a nonlinear activation function, ReLU, is introduced. Thereafter, the channel count is recovered to C, and a spectral attention map is produced using the Sigmoid function.  
The multi-scale spatial attention branch defines three parallel convolution branches, each using a 3 \(\times\) 3 convolution kernel with different dilation rates (dilation = 1, dilation = 2, and dilation = 3). Then, the outputs of the three branches are concatenated along the channel dimension and fused through a 1 \(\times\) 1 convolution layer. Finally, a spatial attention map is generated through the Sigmoid function.  
The spectral and spatial attention maps are applied to the input feature map, and the original input is superimposed to achieve residual learning to obtain the final enhanced feature map.
# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 3 Results
### No Flooding  
The changes related to anthropogenic activities (hereafter named \"No flooding\", NF) that occurred in relation to the periods 1997-1998 and 2017-2018 and were identified in the 39 working SAR pairs, are 212 in total (26.40 km2). These are distributed throughout the Rimac River basin area, and many of these changes are due to new urban settlements, growth in trade activities, and development of new roads. Changes related to anthropogenic activities have been divided into 13 categories, which are described in Table 6. Figure 13 shows the distribution of the areas in which changes in the radar backscatter have been observed as a consequence of anthropogenic activities (i.e., categories NF1, NF2, NF3, NF7, NF9, NF12, and NF13). To assess the risk of flooding:  
* Changes due to anthropogenic factors falling within the categories NF1, NF3, and NF13, were all considered because of their spatial position with respect to the hazard factors (natural, geological and anthropogenic) that could suggest a potential impact on increasing the risk locally;
* Of the changes falling within the categories NF2, NF7, NF9, and NF12, we considered only those located in areas that were very likely to be affected by any future flooding events, given the local slope or because of their proximity to the Rimac River;
* In contrast, categories NF4, NF5, NF6, NF8, NF10, and NF11, were not taken into consideration since they were changes not relevant for the hydraulic risk zoning. Figure 12: Google Earth synoptic view of the riverbank (red circle; see Figure 10e) and the Talavera Bridge (red square; see Figure 11) collapses on 26/03/2017. The above criteria led us to select 82 out of the total 212 NF changes for the hydraulic risk interpretation. The key characteristics of the NF1, NF2, NF3, NF9, and NF13 changes can be summarized as follows:  
* Motorway construction: the 37 radar backscatter changes corresponding to the construction of the New Central Highway (NAC), show both increases and decreases in the radar signal,
## 2 Materials and Methods  
The raw materials were powders of Al (purity of at least 99.0%) with a particle size of about 450 \(\upmu\)m and rutile TiO\({}_{2}\) (purity of at least 92.0%) with a particle size of about 1 \(\upmu\)m. The content of TiO\({}_{2}\) in the studied materials was 5 wt.%. Materials with two different compositions were studied. In one case, an additive of 5 wt.% Cu (purity not less than 99.5%) was used in the form of a powder for improving milling efficiency. The particle size was not more than 100 \(\upmu\)m. In the other case an additive of stearic acid was used as a PCA in an amount of 2 wt.%. The powder mixtures were processed in a Retsch PM400 planetary ball mill at a rotation speed of 300 rpm in an argon atmosphere for 10 h. The ball-to-powder mass ratio was 10:1. Composite granules were compacted by initial cold pressing at room temperature and subsequent hot pressing at 400 \({}^{\circ}\)C. The compaction pressure was 500 MPa in both cases. The consolidated samples had a diameter of 6 mm and a height of 9 mm.  
X-ray diffraction (XRD) analysis was carried out on a D8 Discover diffractometer (Bruker-AXS) in CuK\(\alpha\) radiation. Primary processing of X-ray peaks and calculation of the centers of gravity with the integral widths were performed using the OUTSET software [66]. The microstructure was studied under a TESCAN VEGA 3 LMf scanning electron microscope (SEM) with an XMAX-80 energy dispersive analyzer in the backscattered electron mode. Differential scanning calorimetry (DSC) was carried out on a SetaramLab-sys 1600 calorimeter in an argon atmosphere with a heating rate of 20 K/min. An empty corundum crucible served as a reference sample. The microhardness was measured using the Vickers method on a 402 MVD instrument at a load of 25-50 g. Compression tests were carried out with the use of a Zwick Z250 universal testing machine at room temperature and 300 \({}^{\circ}\)C. The machine's crosshead rate was 4 mm/min. The nominal strain was calculated by dividing crosshead displacement by the initial sample height.
# A Conceptual Design Methodology for e-VTOL Aircraft for Urban Air Mobility
## 2 Design Methodology
### Aerodynamics  
The _Aerodynamics_ module evaluates aircraft drag polar, which is necessary to properly evaluate the overall aircraft performance, both in terms of power required (see Section 2.3) and in mission performance (see Section 2.4). The total drag of the aircraft, in fixed-wing configuration, is evaluated by using the software AVL [57], based on a Vortex-Lattice Method (VLM), and the component build-up method [49; 58]. Specifically, the induced drag is evaluated by AVL, and parasitic and friction drag is evaluated by the component build-up method. NACA23018 has been selected as airfoil for all the sections of both front and rear wings, whereas NACA0017 has be adopted in the vertical tailplanes. The vertical tip-wings have variable airfoils along their span, since they connect the upper surface of the front wing to the lower surface of the rear wing, and vice versa.  
This methodology has already been used for the design of hybrid-electric aircraft; further explanation is detailed in [68].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Passengers (N\({}_{\text{parx}}\))** & **Very from 2 to 6** \\ \hline Range & 50-60 nm \\ \hline Cruise speed (V\({}_{\text{CR}}\)) & 45-55 m/s \\ \hline Cruise height & 500 m Above Ground Level (AGL) \\ \hline Diversion length & 5 nm \\ \hline \hline \end{tabular}
\end{table}
Table 3: TLARs for UAM.  
It is worth emphasising that wing-propeller interactions have not been implemented in the aerodynamic analysis to keep the workflow simple and fast, which are fundamental characteristics of approaches adopted in the early stages of the design.
## 4 System Modelling
### Shortest Path and Road Rerouting  
As mentioned earlier, the objective of DIFTOS is to find for each vehicle the shortest path from its source to the destination point, such that all the road segments' weights are positive during all the time slots (8). In addition to that, the priority quota of each road segment should be wisely assigned to the legitimate vehicles.  
Firstly, the initial shortest path is computed based on the link travel delay with the weight constraint, after that the set of predicted congested road segments within the shortest path is determined. Following that, a rerouting procedure will be applied to each one of these segments or sub-paths formed by the predicted congested road segments (a set of continuously connected road segments).
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
###### Abstract  
Soil temperature patterns are of great importance for any agro-based economy like Bangladesh since they significantly affect biological, chemical, and physical processes that take place in the soil. Unfortunately, there have been no forecast studies on soil temperature in Bangladesh until now. In this article, we used five tree-based models (decision tree, random forest, gradient boosting tree, a hybrid of decision tree and gradient boosting tree, and a hybrid of random forest and gradient boosting tree) to mine strong links among different meteorological factors and soil temperature at different time window sizes. We found that a hybrid of random forest and gradient boosting tree with all the meteorological factors and a five-day time window is optimal for forecasting soil temperature at depths of 10 cm and 30 cm for all lead times (one, three, or five days), whereas the random forest with the same input scenario and time window is optimal for forecasting soil temperature at a depth of 50 cm for long lead times (five days). Since our study includes the first soil temperature forecast model in Bangladesh, it provides valuable insights for agricultural soil management, fertilizer application, and water resource optimization in Bangladesh, as well as in other South Asian countries that share the same climate patterns as Bangladesh.  
soil temperature forecast; hybrid models; optimization; Bangladesh Article
# Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging
## 3 Experimental Results and Discussions
### Overfitting
#### 3.3.2 Principal Component Analysis (PCA) and Overfitting Mitigation  
Figure 6 demonstrates that applying Principal Component Analysis (PCA) significantly reduces overfitting in all models compared with their performance without PCA. Previously, models such as Random Forest exhibited substantial overfitting, with a large discrepancy between training and test accuracies. After applying PCA, overfitting was mitigated, resulting in more balanced performance across training and test datasets. For example, Random Forest, which previously had a training accuracy of 98.74% and a test accuracy of 84.36%, now shows more balanced results with a training accuracy of 83.43% and a test accuracy of 83.24%. This indicates that PCA effectively reduces model complexity, helping to prevent overfitting by removing noise from the training data, resulting in improved generalization to unseen data.  
Figure 7 compares the models with and without PCA, focusing on the difference between training and test accuracies, a direct indicator of overfitting. A large difference suggests that the model is overfitting, as it performs well on the training data but poorly on the test data.  
Figure 5: Overfitting check by all models with normalization.  
Figure 6: Overfitting check by all models with PCA.  
Before applying PCA, models like Random Forest and XGBoost exhibited significant overfitting. For Random Forest, the gap between training and test accuracies was 14.38% (98.74% vs. 84.36%), and for XGBoost, the gap was 14.79% (98.03% vs. 83.24%). After applying PCA, these gaps were dramatically reduced to 0.19% (83.43% vs. 83.24%) for Random Forest and 0.88% (83.00% vs. 82.12%) for XGBoost, demonstrating that PCA was very effective in controlling overfitting in these models.  
For other models, the initial overfitting was less severe but still present. For example, Logistic Regression showed a small gap between training and test accuracies before PCA (0.85% difference, 85.25% vs. 84.36%), and after PCA, the gap remained minimal (0.74%, 83.42% vs. 82.68%). Similarly, LightGBM saw its training-test accuracy gap reduce from 10.17% (97.05% vs. 86.03%) to 0.60% (82.72% vs. 82.12%) after applying PCA. CatBoost also benefited from PCA, with the overfitting gap decreasing from 5.41% (92.56% vs. 87.15%) to 1.02% (82.59% vs. 81.56%).  
In conclusion, PCA proved to be an effective strategy in mitigating overfitting for all the models evaluated, particularly for complex models like Random Forest and XGBoost, where the initial overfitting was more pronounced. Across all models, PCA helped bring training and test accuracies closer together, thereby improving generalization and ensuring better performance on unseen data.