esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of the Art
### Stakeholders' Perspectives and Interactions
Municipalities are tasked with ensuring these policies are equitable, enforceable, and accompanied by clear communication strategies. Pilot projects and stakeholder engagement can be effective in facilitating their implementation. Residents and local communities, while often overlooked, are directly impacted by the consequences of freight activities and also represent a crucial social aspect of Urban Governance. They are concerned with noise, air pollution, safety, and the quality of life in their neighborhoods. In some cases, residents may also be e-customers, positioning them as both beneficiaries and affected parties of the same logistics flows, depending on context and time of day. Their support is crucial for the social legitimacy of logistics-related urban policies. Depending on their level of awareness and involvement, residents may support these restrictions when environmental and social benefits become visible and tangible, particularly in terms of reduced traffic, noise, and pollution [29]. The recent literature on urban access control policies highlights several challenges related to their effectiveness and sustainability. In particular, [PERSON] and [PERSON] [30] identify five \"paradoxes\" that can arise during policy implementation: the emission reduction paradox, driven by the overall increase in vehicle numbers; the spillover effect, which shifts traffic to peripheral areas raising equity concerns; the agglomeration effect, caused by insufficient public transport capacity; socioeconomic disparities in the impact of restrictions; and the temporal and spatial limitations of regulations. These findings emphasize the need for integrated policy design and multi-stakeholder engagement to avoid unintended consequences and enhance social acceptance of urban access regulations.
## 4 Experimental Results
### Segmentation Results  
To evaluate the proposed segmentation method, we have used the ROI to specify the important information that can help to identify the abnormal cases. To achieve the evaluation goal, we have investigated the textures of the extracted regions and tested them by using ANN. Moreover, to highlight the effectiveness of the extracted region, we have compared it with the texture features extracted from the whole image.  
Figure 8 shows the result of the features of the traditional fractal dimension extracted from complete images and the ROI extracted from segmented images. The comparison of results of the entire image and ROI showed that the segmentation stage is very important in dealing with specific information (ROI) whilst ignoring the rest of the image.
# A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic
## 5 Performance Evaluation and Results
### Performance Analysis
In addition, Figure 10(b) shows the packet loss ratio with respect to the inter packet arrival interval. When the packet arrival time is short, COOF can handle the packet in the queue effectively and reduces the packet loss ratio. In particular, when the packet arrival interval is 1 and 2 second, COOF reduces the packet loss ratio by approximately 10.9% as compared to SCAOF. The reason for the performance improvement of COOF is that it reacts more accurately to dynamic traffic than others. Average end-to-end delay: It represents the average time difference between the time when the data packet was sent and the time when the data packet was received at the root node. The average end-to-end delay has been evaluated under different inter-packet interval, as shown in Figure 12. The result shows that, as compared to other, COOF achieves the lowest packet delivery delay. The average delay in OF0 is significantly greater than MRHOF, SCAOF, and COOF. It was obvious, because OF0 could not deliver data packets on time and it has to be in the queue for a long time when there was heavy traffic. In contrast, the COOF can handle the traffic dynamicity efficiently and it performs better. When data traffic is high, COOF is aware of resources in terms of queue utilization, and thus traffic is routed through the less congested parent node. As a result, the packets are not queued in the node's buffer for a long time. Figure 11: Packet losses in the queue; (**a**) packet loss ratio in terms of different inter packet arrival interval, (**b**) packet loss ratio with respect to the transmit and receive success ratio. Figure 10: Packet reception ratio on DODAG root; (**a**) PRR with respect to the transmit success and receive success ratio, (**b**) PRR with respect to the inter packet interval. Energy consumption: it is defined as the average residual energy of nodes in the network. It represents the application that requires priority in terms of lifetime. The total energy consumption is the sum of the energy consumed due to the reception and transmission of per successful packet from source to the destination. Figure 13 shows the average energy consumption in the network with respect to different inter-packet interval. The energy consumption of COOF is significantly lower than other approaches. It is because COOF loses fewer packets in the queue, and thus minimizes the re-transmission of packets. In addition, REI gives the better understanding of the network situation during parent selection. As retransmission is high in OF0, MRHOF, and SCAOF, so sending and receiving lost packets due to buffer overflow is necessary and thus waste energy. Moreover, OF0 and MRHOF do not include any energy metric thus performing poorly as compared to SCAOF and COOF.
## 3 Envelope Detector-Aided, Machine Learning-Based Fault Type Detection Model
### Convolution Neural Network  
The empirically designed CNN architecture for both classification layers is presented in Figure 10. Because the input size can change based on the feature extraction methods, the channel length numbers are only given in Figure 10. The CNN is established with three convolution layers, and the channel lengths of these layers are 16, 32, and 16, respectively. After each convolutional layer, the Leaky ReLU is employed to prevent vanishing problems in the ML layers. As illustrated in Figure 10, the last convolution layer is shaped as flattened layers, and then the flattened layer is connected to two fully connected layers. The lengths of the fully connected layers are empirically adjusted as 64 and 16. The last layer is the softmax layer configured as 4 for both classification layers because the number of labels for each classification layer is the same.  
Support Vector Machine  
SVM is another ML technique that aims to maximize the space between the defined hyperplane and the nearest data points [31; 35]. When dealing with datasets containing more than two labels, optimizing the SVM model can be challenging. For this reason, a polynomial order of four is used to achieve the highest prediction accuracy in this study. Additionally, a one vs. one decision function is employed for the multilabel design.  
Decision Tree and Random Forest Classifier  
The DT algorithm analyzes the training dataset and labels using a tree-like structure consisting of various nodes (root, decision, and leaf). This structure determines the label for the test data by splitting the dataset into subsets based on the most significant feature. The RFC is an improved version of DT constructed from the collection of DT structures.
# Hybrid Renewable Energy Systems--A Review of Optimization Approaches and Future Challenges
## 4 HRES Optimization
### Computation Efficiency  
Several critical factors influence the computation efficiency and speed in HRES optimization (see Figure 7).  
The design and complexity of the algorithm play a pivotal role, with iterative processes, large solution spaces, and non-linear problems significantly increasing computation time. Problem size and scale, including the number of variables, constraints, and objectives, directly affect resource demands, with large-scale tasks such as grid-wide simulations requiring extensive computational resources. Leveraging parallel computing techniques can mitigate these challenges by distributing tasks across multiple processors, although efficient task decomposition and minimizing synchronization overhead are essential for success.  
Figure 7: Optimization tools computations efficiency components weights, where 1 represents low, 2 represents medium and 3 represents high impact on an efficiency component(developed by the authors).  
Hardware architecture greatly influences performance, as do factors such as processor speed, memory bandwidth, and I/O capabilities. Software optimization enhances speed through efficient data structures, memory management, and load balancing, especially in high-performance computing environments. However, data communication overhead in distributed systems can be a bottleneck, making its minimization essential. Additionally, numerical precision affects computational resources, requiring a balance between accuracy and efficiency. In addition, energy-efficient systems, while conserving power, may impose performance limits that also affect computational speed [69]. Besides, it was decided to rank optimization methods based on four key criteria: Numerical Precision and Accuracy, Hardware Architecture, Problem Size and Scale, and Algorithm Design and Complexity. Each of these criteria was assessed and rated on a scale of 1 to 3, with corresponding values: 1--\"Low\", 2--\"Medium\" and 3--\"High\". Where a higher value indicates a more significant impact or higher complexity for that component.
## 2 Materials and Methods
### Spectral Vegetation Indices
Important spectral regions for chlorophyll analysis include approximately 680 nm, which is associated with chlorophyll-a absorption, and 550 nm, where chlorophyll absorption is minimal. The existing literature thoroughly discusses the most effective wavelengths and chlorophyll indices [60]. A notable index is the Modified Chlorophyll Absorption in Reflectance Index (MCARI) [61], which is an enhancement of the original Chlorophyll Absorption in Reflectance Index (CARI) [62] (Equation (1)). \[\mathrm{CARI}=((700_{\mathrm{nm}}-670_{\mathrm{nm}})-0.2(700_{\mathrm{nm}}-550 _{\mathrm{nm}}))\Big{(}\frac{700_{\mathrm{nm}}}{670_{\mathrm{nm}}}\Big{)} \tag{1}\]  
CARI seeks to address the variability in photosynthetically active radiation attributed to non-photosynthetic materials by employing reflectance data from certain wavelengths,specifically 550 nm, 670 nm (the absorption peak for chlorophyll-a), and 700 nm (the transition point between pigment absorption and structural effects). MCARI quantifies chlorophyll absorption depth at 670 nm in relation to reflectance at 550 nm and 700 nm as defined in Equation (2). \[\mathrm{MCARI}=(700_{\mathrm{nm}}-670_{\mathrm{nm}})-0.2\cdot(700_{\mathrm{nm} }-550_{\mathrm{nm}}) \tag{2}\]  
Although MCARI is effective, it is sensitive to background reflectance, which complicates its interpretation, particularly at low leaf area indices (LAI) [53]. Our investigation of the modified chlorophyll absorption ratio index (MCARI) is motivated by its promising applications in remote sensing for precision agriculture. In contrast to other indices that rely heavily on multiple narrow spectral bands and have a strong correlation with chlorophyll concentration, MCARI offers greater versatility. To overcome these limitations, the paper employs an enhanced version of the MCARI designed to improve sensitivity at low chlorophyll concentrations. Some researchers have proposed augmenting MCARI with soil-adjusted indices, such as the Optimized Soil-Adjusted Vegetation Index (OSAVI), to reduce the influence of background reflectance and improve the detection of leaf chlorophyll variability [63]. OSAVI is mathematically derived from the Soil-Adjusted Vegetation Index (SAVI) to effectively account for soil effects (Equations (3)-(5)). \[\mathrm{SAVI}=\frac{(800_{\mathrm{nm}}-670_{\mathrm{nm}})}{(800_{\mathrm{nm} }+670_{\mathrm{nm}}+\mathrm{L})}\cdot(1+\mathrm{L}) \tag{3}\]  
\[\mathrm{CSAVI}=\frac{(800_{\mathrm{nm}}-670_{\mathrm{nm}})}{(800_{\mathrm{nm} }+670_{\mathrm{nm}}+\mathrm{Y})} \tag{4}\]  
\[\mathrm{Y}=0.16\]  
\[\mathrm{TCARI}=3\bigg{(}\left(700_{\mathrm{nm}}-670_{\mathrm{nm}}\right)-0.2 (700_{\mathrm{nm}}-550_{\mathrm{nm}})\frac{(700_{\mathrm{nm}})}{(670_{\mathrm{ nm}})}\bigg{)} \tag{5}\]  
This paper presents the TCARI/OSAVI ratio as an effective approach for accurately estimating crop chlorophyll content through multispectral remote sensing imagery.
## 3 Experimental Test Procedure
### Image Matching  
Image-matching algorithms are used to detect identical points features between RGB+D images from different epochs. The results of image matching are the pixel location of matched identical points in two images from different epochs. As stated before, multiple algorithms are applicable for this use case. To determine the optimal algorithm for this use case, empirical tests were conducted with different image datasets.  
Figure 8: Image and point cloud overlap (Source: Own compilation).  
Matching algorithms SURF, BRISK, ORB, and KAZE were used and tested as they have a direct implementation in MATLAB. All of the test results of matching were also filtered for outliers. In this test case, filtering was done based on the calculated projective transformation between the images and measured maximum displacements from LVDT.  
The first test consisted of testing the algorithms with all combinations of images in a dataset. Test parameters will be the average number of features in images, the number of matched features between images with and without outlier removal, processing time and normalized time for two images and matching between them, and maximum RAM usage of the algorithm.  
The second test consisted of testing the matching points from between epoch located on the top of the beam within the ROI. Points were selected manually to match the area of the wooden beam that was modeled as a reference. The test compared the number of used points in ROI and statistical parameters of the calculated residuals. The residuals were dif  
Figure 10: Distance (D) channel of an RGB+D image within an ROI (Source: Own compilation).  
Figure 9: Composition of RGB channels of an RGB+D image (Source: Own compilation).  
ferences between height displacements calculated from the control data and measurements of RGB+D data.  
Results of both tests of image matching algorithms will be further discussed in the results section, and they will be used as criteria for algorithm selection for the data processing.
# Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique
## 2 Materials and Methods
### KPCA Algorithm for Feature Extraction  
In this study, the feature extraction was based on actual survey data and employed the Gaussian Radial Basis Function (RBF) as the kernel function. MATLAB R2020a programming was used to determine the component contribution and calculate the cumulative contribution. Based on this analysis, factors accounting for 85% of the total variation were retained as principal components. The correlation results are shown in Figure 8.  
The first 4 principal components were selected to replace the 10 corrosion factor evaluation indexes in the original data for analysis. This ensured the orthogonal independence of the feature vectors for each principal component. As shown in Table 1, the analysis of the KPCA algorithm shows that the cumulative contribution of the first 4 principal components  
Figure 8: Cumulative contribution of the main elements.  
Figure 7: Internal correlation matrix of corrosion data.  
was 91.33%. Its cumulative contribution exceeded the threshold of 85%. This was better than the 3 principal components (90.66%) in the journal article [53].  
The approach reduced the original \(60\cdot 10\) dimensional data to \(60\cdot 4\) dimensional indicator data processed by KPCA. This ensured the orthogonal independence of the feature vectors for each principal component. This simplified the model and reduced the time complexity. It still accurately predicted the external corrosion rate of the buried pipelines.
# A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
## 3 Harmonic Suppression Methods  
Harmonic suppression, also known as harmonic mitigation, is a technique used to reduce or eliminate harmonic distortion in electrical power systems. Several methods of harmonic suppression can be used, including passive filters, active filters, and harmonic traps. Passive filters are the most common method, and inductors and capacitors are used to filter out harmonic frequencies. Active filters use power electronics and control algorithms to actively cancel out harmonic frequencies. Harmonic traps are specialized passive filters that are designed to trap and filter out specific harmonic frequencies. Figure 3 presents the overview of harmonic mitigation techniques.  
The three main measures for harmonic suppression are as follows: First, systems and equipment affected by harmonic interference are improved to enhance their ability to resist it, which indirectly helps suppress harmonic interference. Second, modifications are made to the harmonic generation device to decrease the production of harmonics at the source [25; 26]. Third, an external filter is used to eliminate harmonics, preventing them from entering the power system or electrical equipment and thus suppressing harmonics. These measures are called receiving, active, and passive, respectively. The last two techniques need only a slight transformation of the existing equipment/system; these two are relatively low cost as the system/equipment have already been put into use.  
Harmonic interference capabilities and functions must be taken into account when designing a reasonable suppression measure or transforming the system/equipment. Passive suppression methods are more feasible and relatively simple to implement, as they can achieve a better suppression effect. Therefore, in the current industrial situation, passive  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Ref.** & **Advantage** & **Disadvantage** & **Applications** \\ \hline Fourier Transform & [13; 14] & Widely used, decomposes signals into frequencies & Not suitable for time-varying or non-stationary signals & Power systems with steady-state harmonics \\ \hline Wavelet Transform & [13; 19] & Analyzes non-stationary signals, time-varying frequency & Complex, spectrum leakage, band aliasing & Dynamic systems, transient analysis \\ \hline Adaptive Detection & [16; 17; 18] & Independent of component parameters, high accuracy & Complex parameter adjustment, dependent on circuit components & Detection in variable grid voltage \\ \hline Neural Network-Based & [20; 21; 22; 23; 24] & Adaptive, can learn from data & Computationally intensive, requires large datasets & Advanced AI-driven detection models \\ \hline \hline \end{tabular}
\end{table}
Table 1: Harmonic Detection Methods.  
methods are in the mainstream. These methods involve hardware, specifically a filtering device that compensates for harmonics and prevents the harmonics from spreading between the grid and loads. Passive methods include passive power filters, active power filters, and hybrid active power filters.
# Machine Learning Prediction of Airfoil Aerodynamic Performance Using Neural Network Ensembles
## 3 Results  
To rigorously assess the sensitivity, stability, and reproducibility of the aerodynamic predictions, the results are structured according to three seed-based experimental groups, as detailed in Section 2.4. This analysis covers both single-seed (Groups A and B) and ensemble-based (Group C) setups designed to probe the deterministic effects of pseudo-random initialization in an aerodynamic prediction application.  
The performance metrics reported in this section are obtained by evaluating the trained models on a dedicated test set of 21 NACA four-digit airfoils that were completely excluded from the training process. This separation ensures that the results reflect the model's true generalization capability across unseen geometries and flow scenarios. For each airfoil, predictions include both lift and drag coefficients across 10 distinct Reynolds numbers and 20 AoA, enabling a comprehensive assessment of generalization under varied flow conditions. In the following Table 2, the airfoil sets used for training and testing the model are presented:
# An Overview on Atomization and Its Drug Delivery and Biomedical Applications
## 8 Biomedical Applications
It is known that the proteins and peptide molecules when encapsulated using electrospray technique in a polymeric shell, the chances of these proteins and peptides to get denatured is very less compared to other conventional methods, so this advantage has been exploited in developing a vaccine delivering machine [109]. [PERSON] and his group succeeded in developing an antigen incorporated polymer biomolecules to study the immune response in mice. They generated an ovalbumin ingested chitosan particles and studied its characteristics to find that the particle generated from electrospray technique were round, smooth and uniform with a diameter of 1 to 3 \(\upmu\)m. The in vitro and in vivo study showed that there was no cytotoxic effect, and the particles were orally administered into BALB-C mice. The microparticles coated with 250 \(\upmu\)g ovalbumin demonstrated the highest immune response [110]. Another group encapsulated synthetic antigen of _E. coli_ into microparticles made of chitosan and water-soluble derivative trimethylated chitosan. These particles were directed to the BALB-C mice through the nose and the dosages were on day 1, 28 and 42. The results showed that the mice produced antibodies (IgG & IgA) against the introduced antigen, and which was quantitatively measured using ELISA [111]. Recently scientists are working on inhaling type vaccines rather than injections for the cure of the current pandemic COVID-19. China has developed and approved clinical study of vaccines that can be inhaled, and experts say that efficiency of such vaccines need to be demonstrated in clinical experiments. Researchers are working on it to develop a much easier way of vaccination. Thus, electrospray can be modified and optimized to take full advantage of this technique in various medical fields as it is more user friendly and cost effective compared to other conventional methods of working.