esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
###### Abstract  
To carry out required aid operations efficiently and effectively after an occurrence of a disaster such as an earthquake, emergency control centers must determine the effect of disasters precisely and and in a timely manner. Different kinds of data-gathering techniques can be used to collect data from disaster areas, such as sensors, cameras, and unmanned aerial vehicles (UAVs). Furthermore, data-fusion techniques can be adopted to combine the data gathered from different sources to enhance the situation awareness. Recent research and development activities on advanced air mobility (AAM) and related unmanned aerial systems (UASs) provide new opportunities. Unfortunately, designing these systems for disaster situation analysis is a challenging task due to the topological complexity of urban areas, and multiplicity and variability of the available data sources. Although there are a considerable number of research publications on data fusion, almost none of them deal with estimating the optimal set of heterogeneous data sources that provide the best effectiveness and efficiency value in determining the effect of disasters. Moreover, existing publications are generally problem- and system-specific. This article proposes a model-based novel analysis and synthesis framework to determine the optimal data fusion set among possibly many alternatives, before expensive implementation and installation activities are carried out.  
disaster situation awareness; UAVs and data sources; quality of data fusion; model-based framework for determining optimal data fusion; domain model of data sources for earthquake detection; automated synthesis for data fusion +
Footnote †: journal:  
0023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
## 2 Datasets  
At present, numerous publicly available ship recognition datasets exist, primarily categorized into optical remote sensing image datasets and synthetic aperture radar (SAR) image datasets. For example, the FGSCR dataset [29], widely used for ship recognition tasks, and the OpenSARShip dataset [30], mainly designed for ship detection, contain approximately 9000 high-resolution remote sensing images and around 2000 SAR images, respectively. However, existing datasets predominantly consist of simple scenarios, with occluded ship scenes being relatively rare. Therefore, this paper constructs a new ship dataset to address this limitation.  
The ship dataset established in this study incorporates images from publicly available visible light and SAR ship datasets. Visible light images are sourced from the HRSC2016 dataset [31], FGSC-23 dataset, SPOT satellite images provided by Kaggle, and other open datasets. High-resolution SAR images are derived from the OpenSARShip and SSDD datasets [32]. After filtering out abnormal data and removing non-interesting scenes and targets, all images are uniformly re-annotated using the labelme 1.0 software. Additionally, we further select qualified ship targets and scenarios from Sentinel-2, Sentinel-1 satellite  
Figure 1: Common feature occlusion phenomena in remote sensing ship imagery. while **(a,c,d)** depict scenarios with cloud or fog occlusion; **(b,e,f)** illustrate occlusions caused by other ships or port obstacles.  
slices, and Google Earth imagery. These images are manually annotated in labelme to mark ship target locations and categories before being added to the dataset. The final dataset comprises 5392 multi-source (optical and SAR) ship images, covering scenarios such as ports, near-shore environments, and cloudy weather. The dataset includes six categories: military vessels (e.g., aircraft carriers, cruisers, destroyers, helicopter carriers, civilian ships, and special-purpose vessels). The sample sizes of the six types of ships are as follows: 2196 aircraft carrier samples, 1941 cruiser samples, 2521 destroyer samples, 561 helicopter carrier samples, 261 special-purpose vessels, and 6232 civilian ship samples. Examples from our dataset and some other publicly available datasets are shown in Figure 2. Complex scenarios with occluded ship features account for over 60% of the dataset. To validate the robustness of the proposed method, the dataset is divided into training and testing sets in an 8:2 ratio.
# Explainable AI-Enhanced Human Activity Recognition for Human-Robot Collaboration in Agriculture
## 2 Materials and Methods
### Machine Learning Workflow  
#### 2.2.1 Data Pre-Processing and Feature Extraction  
The critical steps for preparing raw sensor data to be clean and consistent and generating supplementary features for input into the ML model to better identify hidden patterns are described in detail in [27]. In summary, these steps include managing outliers and unsynchronized sensor data to ensure data consistency, as well as aligning sensor readings to a common time frame. Outlier identification and elimination were implemented during the initial data pre-processing phase using a z-score-based statistical technique, which identifies data points that deviate significantly from the mean [45]. A median filter with eleven taps was applied to effectively reduce noise in the data while maintaining important details within the dataset [46]. Each window was assigned a label corresponding to the activity occurring during that period. A 50% overlap was applied between consecutive windows to enhance data representation. One-hot encoding was used to represent categorical variables, as they provide a clear and unambiguous way to encode non-numeric data into a format that can be easily interpreted by ML algorithms. The StandardScaler [47] was applied to ensure that each feature had a mean of zero and a standard deviation of one. Subsequently, we split the dataset into training, validation, and test sets using an 80/10/10 ratio [32]. Finally, feature selection was not explicitly performed in this study, as SHAP values naturally assess and highlight the most influential features [48].
## 2 Materials and Methods  
Experimental trials were conducted across two seasons, 2020-2021 and 2021-2022, in a hydroponic greenhouse (28 \(\times\) 32 \(\times\) 4.5 m) at the educational farm (24\({}^{\circ}\)39\({}^{\prime}\) N, 46\({}^{\circ}\)44\({}^{\prime}\) E) of the College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia. To control the indoor climate and hydroponic irrigation system, the greenhouse was equipped with MACQU systems (Geosmart, Athens, Greece) to implement the planned treatments. The climate system was equipped with cooling pads and fans, heaters, circulation fans, shading screen, and overhead ventilation, along with sensors installed at a height of 2.0 m inside the greenhouse to monitor radiation, temperature, and relative humidity. Tomato plants were grown on 12 lines of stainless-steel troughs at a height of 1 m with a slope of 5%. Each experimental line accommodated 27 perlite substrate bags (0.90 \(\times\) 0.22 \(\times\) 0.15 m), each with a capacity of 30 L. The distance between two plant lines was 1.78 m, and the distance between two plants within a line was 0.25 m. The plant density was maintained at 2.7 plants m\({}^{-2}\). Irrigation was performed using self-draining dripers set at a flow rate of 3 L h\({}^{-1}\), with a weekly schedule in an open operation mode. The irrigation water had an EC value of 1.04 dS m\({}^{-1}\) and Na\({}^{+}\), Ca\({}^{2+}\), K\({}^{+}\), HCO\({}_{3}\)\({}^{-}\), Cl\({}^{-}\), and SO\({}_{4}\)\({}^{-}\) content of 3.61, 0.76, 0.19, 0.35, 2.39, and 1.82 meg L\({}^{-1}\), respectively.
# Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
## 3 Bounded-Error Sensor Data Compression (BESDC)
### Spatial Correlation Compression  
For spatial correlation compression, SN processes multiple encoded data from neighboring EDs to further reduce data transmission to the fog-tier sink to extend the SN lifetime and save storage in fog-computing machines. While SN receives packets from neighboring EDs, SN verifies whether each sensor data encoding part (SDEP) in the payloads of two packets from similar geography regions is identical. For our proposed spatial correlation compression, if two packets share exactly the same SDEP, BESDC will use _S_NoOpt to replace one of the SDEPs. _S_NoOpt is encoded as \"1\" and the other SDEPs will be labeled by adding a \"0\" in front of them before they are encoded. Figure 5 illustrates two packets sent from two neighboring EDs and the packet format after they are compressed. The second SDEPs of these two packets are identical, so one of them will be replaced by _S_NoOpt. Then, this spatial correlation encoding scheme can further compress the incoming sensor data from different EDs to reduce the data transmission from SN to the fog-tier sink for more energy saving for SN.  
Figure 4: An example of EB-RLE and EB-HC under bounded error 0.5.  
Figure 5: Spatial correlation encoding for sensor packets from 2 neighboring EDs.
## 1 Introduction
Despite the appeal of using elliptical rings instead of circular ones, not much effort (besides the just mentioned works) has been made to validate and approve/disapprove of the advantages of using elliptical geometries. From the modeling point of view, even less works dedicated to restrained shrinkage-induced cracking in concrete rings are found in the literature. We cite for instance the work of [11; 19; 20; 21], proposing theoretical/analytical models to estimate the cracking age of concrete and the residual stress development. These models, despite their simplicity, showed good estimations. Unfortunately, the same equations are not well adapted when dealing with elliptical rings. The same authors who initially proposed the elliptical ring test proposed in their newest work [17] a fracture mechanics-based numerical approach using the R-curve method to predict crack initiation in restrained concrete elliptical rings. The latter uses notched elliptical rings with an initial crack located on the major axis. The cracking ages matched the ones found experimentally. None of the above models can offer a complete scenario starting from a sound to partially damaged then to fully damaged ring. In addition, no information about the crack path and its evolution is available. The present work is a first attempt using the promising Thick Level Set approach (TLS), a regularized damage model with automatic transition to fracture [22; 23; 24; 25], to model damage evolution and crack propagation in concrete structures due to restrained shrinkage. The TLS model acts as a bridge between damage mechanics and fracture mechanics. Starting from a perfectly sound material, an initial damage can be detected automatically. Once the material is fully degraded in a zone, a crack is automatically initiated. Crack branching and interactions are straightforwardly handled as well. A brief description of the TLS damage model is presented in Section 2 with a special emphasis on the time-independent evolution law adopted in this work (see Section 2.1), as well as on the shrinkage contribution (see Section 2.1). Following the promising preliminary results presented in [26], the study dealing with shrinkage-induced cracking in elliptical concrete rings is extended in Section 3. The effect of ring geometry, more precisely rings' oblateness, is investigated. Numerical results issued by the TLS are analyzed. Crack positions are superposed on those obtained in [17; 27]. The capacity of this novel TLS model is evaluated for concrete applications. Ideas for future generalization and enrichment are thus proposed accordingly in Section 4.
## 3 Results
### Evaluation of the Influence of the Number of Planning Periods on the Incremental Approach
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & **OWT** & **Plans** & **OWT** & **Plans** & **OWT** & **Plans** & **OWT** & **Plans** & **OWT** & **Plans** & **OWT** & **Plans** \\
**T/P** & **84/1** & & **84/2** & & **84/3** & & **168/1** & & **168/2** & & **168/3** \\ \hline Apr. & 16 & 10 & 17 & 8 & 19 & 8 & 16 & 5 & 17 & 4 & 18 & 4 \\ May & 17 & 10 & 18 & 8 & 18 & 8 & 17 & 5 & 17 & 5 & 18 & 4 \\ Jun. & 13 & 11 & 17 & 8 & 17 & 9 & 16 & 5 & 16 & 5 & 16 & 5 \\ Jul. & 14 & 10 & 18 & 8 & 17 & 8 & 16 & 6 & 17 & 4 & 17 & 5 \\ Aug. & 18 & 10 & 19 & 8 & 18 & 8 & 18 & 5 & 18 & 4 & 18 & 4 \\ Sep. & 12 & 10 & 18 & 8 & 18 & 8 & 18 & 5 & 17 & 5 & 16 & 5 \\ Oct. & 12 & 9 & 13 & 9 & 14 & 9 & 13 & 6 & 13 & 6 & 13 & 6 \\ \hline Sum & 102 & 70 & 120 & 57 & 121 & 58 & 114 & 37 & 115 & 33 & 116 & 33 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: MPC results for different T and P.  
comparing all simulated months. Figure 8 shows the cost per finished turbine per month (left) and the average across all simulated months for each combination of \(T\) and \(P\) (right). Therefore, only the cost part of the cost function in Equation (1) was recalculated for all operations, which finished within the first four weeks of the generated plans, ignoring the penalty terms. As can be seen, the average cost of the shorter sampling step size for \(T\) is generally lower, which can be explained by a lower amount of wrongly estimated durations. In both cases, the cost for \(P=2\) is slightly lower, which indicates more efficient plans in terms of offshore times. Although choosing \(P=3\) provides a marginally better result when focusing on finished turbines, a planning period of \(P=2\) provides a more reliable choice and slightly lower costs per turbine. For the shorter prediction horizon, the optimizer retrieves optimal plans in all cases after some minutes. For the longer prediction horizon, several cases occurred, which finished due to stagnation, each after two iterations, respectively, after six hours. This stagnation indicates that the optimizer found at least a close-to-optimal solution, but that it was not able to proof optimality in time.
# Enhanced Osteoblast Adhesion and Proliferation on Vacuum Plasma-Treated Implant Surface
## 3 Results
### Improvement of Cell Adhesion and Proliferation by Plasma Treatment  
Then, to evaluate the cell adhesion characteristics of three types of implants, osteoblasts were adhered to the implant surface by immersing the implants within the dense cell suspension. To investigate the initial adhesion of cells, the number of viable cells that adhered to the implant surface after incubation for 2 h was measured by determining the quantity of soluble formazan using CCK-8 assay. The number of cells on CaSLA did not differ considerably from that on SLA (Figure 4B). However, the number of cells on PCaSLA increased by 49.6% when compared to CaSLA, and 54.6% when compared to SLA (Figure 4B). Each experiment was carried out seven times, and the difference was shown to be statistically significant.  
The number of cells was quantified after 3 and 7 days of incubation to further define the cell proliferation pattern. As shown in Figure 4C, the number of cells on CaSLA slightly increased by 9.9% on day 3 and by 14.2% on day 7 compared to SLA. The number of cells on plasma-treated CaSLA (PCaSLA) increased significantly by 52.4% and 43.4% on day 3 and 7, respectively, in comparison to the non-treated group (CaSLA). Furthermore, when compared to SLA, the number of cells on PCaSLA increased by 67.4% and 63.7% at day 3 and 7, respectively.
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 5 Security Evaluation
### Burrows-Abadi-Needham (BAN) Logic  
Burrows-Abadi-Needham (BAN) logic is a set of logic rules to verify the logical correctness of an authentication protocol [32]. Assuming that the cryptographic functions in the protocol are perfect, BAN logic can systematically find out all incorrect designs in an authentication protocol. To apply BAN logic to our 5 GSBA protocol, we formalize our protocol into the idealized form. Then, we use BAN logic symbols and rules [32] such as the message meaning rule, belief rule, nonce verification rule, jurisdiction rule, etc., to validate if our 5 GSBA protocol fulfills the targeted security goals.
## 4 Results and Discussion
### Spatial Vulnerability  
In Figure 4, it is presented a map that integrates the accessibility of emergency services with the fire risk assessment conducted for each analyzed building. The unique feature of this city center is that it is surrounded by large roads, allowing emergency services to access most buildings. However, due to its medieval nature, the buildings in this city center were constructed very near the protective wall of the castle.  
Figure 4: Spatial vulnerability map.  
Figure 3: Mapping of the criticality of each part of the critical infrastructure.  
As a result, some parts of the city, even today, can only be accessed by pedestrians or by directly entering the buildings (such as Zones 4 and 5). These areas are particularly vulnerable during extreme events, as a fire originating in one of these zones could easily spread throughout the entire historic city center. This vulnerability is further exacerbated by the high Fire Risk Index observed in most buildings in this area.
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 3 Image Tampering Techniques
### Generative Adversarial Networks  
GAN, a machine learning framework, can produce new virtual images that appear at least superficially genuine to human observers when trained on different images. The photos have many realistic characteristics. GAN is well known for generating images/videos of fake faces. The altering of faces (deepfakes) can primarily be created in the following ways: full face synthesis; swapping of expression; swapping of identity; and attribute manipulation over the faces. Figure 11 shows an example of a face swap where the celebrity's face is swapped to a different person's image/video.  
GAN is a neural network and comes under reinforcement learning. The system here is that the network learns dynamically by tuning actions based on continuous feedback.  
GAN components are the generator and discriminator. The generator creates fake images, and the discriminator detects them as fake. The process is repeated until the Nash equilibrium is achieved or nearly achieved [19]. The discriminator's stochastic gradient is updated by ascending to maximize the loss function. On the contrary, the generator's stochastic gradient is updated by descending as there is a need to minimize the loss function.
## 6 Optimization Model  
An iterative hybrid optimization algorithm is developed. This algorithm works on a combined global and local level. At the global level, the global algorithm based on the simulated annealing (SA) method is used, and on the local level, local algorithm based on the conjugate gradient (CG) method is used. Search for the solution space starts with the SA method, because local optima need to be avoided. After a global optimum is located, the algorithm of the CG method is activated, with a goal to precisely find a local optimum.
## 3 Results and Discussion
### Sem/ds  
Surface and fracture observations, as well as miscibility of the additives with the PLA matrix, were analyzed using a scanning electron microscope equipped with an energy dispersive spectrometer (EDS) to assess the dispersion of the introduced organosilicon compound by the L-FDM technique into the PLA matrix (Figures 4 and 5). EDS mapping was performed for carbon (marked as CK), oxygen (marked as OK), silicon (marked as SiK), and sulfur (marked as SK). The microscopic photographs of the PLA/BRB442/MeOH  
Figure 3: Photos of L-FDM printed samples. (**A**)—photo of bars printed in vertical orientation, (**B**)—photo of paddles printed in vertical orientation, (**D**)—photo of bars printed in horizontal orientation, (**E**)—photo of paddles printed in horizontal orientation, (**C**,**F**)—diagrams showing the arrangement of the fitting in the working space of the printer ((**C**)—vertical, (**B**)—horizontal).  
sample (Figure 4B) reveal a uniform distribution of silicon and sulfur within the polymer matrix, with no observable particle agglomeration. EDS maps of the fractures show that individual layers have a higher element content on their outer edges. The surface of the PLA/BRB295 sample also displays good silicon dispersion and no agglomerates. SEM images of the PLA/BRB 442 (Figure 4A) and PLA/BRB 294 (Figure 5A) samples show the presence of spherulites with a size of around 40 \(\upmu\)m. Based on these analyses, it can be concluded that the modifier is effectively integrated into the filament during its immersion in the reservoir, and the silane is evenly distributed in the PLA matrix during the extrusion process on the 3D printer. EDS analysis confirms the presence of elements in the polymer matrix of L-FDM prints, indicating the potential use of this technique as a tool for introducing various chemicals into the polymer matrix.
# Dynamic Stall Characteristics of the Bionic Airfoil with Different Waviness Ratios
## 2 Materials and Methods  
### The Geometric Model and Grids  
The humpback whales have pectoral fins with leading-edge tubercles (Figure 1). Due to these tubercles, humpback whales are significantly more mobile than other mammals and a unique way of rotary predation is formed.  
The wavy leading edge in the spanwise is described by Equation (1).  
\[C(z)=C+\frac{a}{2}\Big{[}1+cos\Big{(}\frac{2\pi z}{\lambda}\Big{)}\Big{]} \tag{1}\]  
where \(C(z)\) is the local chord length of the airfoil, \(a\) is the wavy amplitude, and \(\lambda\) is the wavelength. In the present research study, the wavy amplitude is 0.025 \(C\) and the wavelength is 0.2 \(C\). \(C\) is the chord length.  
The purpose of this study was to numerically investigate the effect of the wavy leading edge on the dynamic aerodynamic characteristics of the three-dimensional viscous flow around a low aspect ratio airfoil. The aspect ratio of 1.5 belongs to the proportion range of the wing of the micro air vehicles. Therefore, the airfoil aspect ratio (A\({}_{\rm R}\)) is denoted by \(A_{R}\)= \(S\)/\(C\) =1.5 and the waviness ratio (R) is represented by \(R\)= \(S_{W}\), where \(S\). \(S\) is the span of an airfoil and \(Sw\) is the span of waviness. According to the literature [17], we studied the aerodynamic characteristics of the airflow around the wavy wing with five different waviness ratios (0.2, 0.4, 0.6, 0.8, and 1.0). The schematics of the baseline airfoil and wavy airfoil with five waviness ratios are shown in Figure 2.  
Figure 1: Humpback whale flippers with tubercles.  
Figure 3a is a schematic of the computational domain and boundary conditions. The size of the calculation domain forming a cylindrical shape was defined by a radius of 10 C and a height of 1.5 C. The inlet and outlet boundaries were 10 C from the airfoil surface to avoid the boundary reflections, while the far-field boundary was set as the periodic boundary. In this case, the velocity inlet was set to 22 m/s and the pressure outlet was set as free-stream pressure. As shown in Figure 3b,c, an unstructured grid arrangement was used. A no-slip boundary condition was applied to the airfoil surface. When using the SST k-\(\omega\) turbulence model, it was necessary to control the height of the grid perpendicular to the wall direction so that the wall nodes were in the viscous sublayer and \(y+<1.0\). Consequently, these grid distributions were generated such that the y+ in the first layer of the grids adjacent to the airfoil surface was kept near 1.