esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods and Results
### Sar
#### 3.2.4 Shape from Shading  
Iceberg surface topography can be extracted from the shape-from-shading technique [52; 53]. In this technique, the relationship between the backscatter coefficient is linked to the local incidence angle (governed by the slope angle) used to reconstruct elevation information (topography) of a smooth surface based on its initial height. This method is feasible to measure topography from only one SAR intensity image, but it also requires information of coarse scale elevation. In our case, we used the freeboard estimated using the height-from-shadow technique described above.  
This method considers that the SAR intensity is a function of the backscatter coefficient, resolution, incidence angle and slope. The ice backscattered coefficient is assumed to be constant, and it is known for X-band. Using an intensity image, we can estimate and consequently calculate an elevation increment (dH) for every resolution cell. The overall shape is calculated by integrating dH over the entire image. The results are strongly affected by speckle noise, and using speckle filtering is required to make this method robust.  
The resulting DEM (Figure 16, top), generated using the shape-from-shading method applied to a single TerraSAR-X image, was compared with the interferometric TDM DEM (shown in Figure 15) by calculating the statistics of DEM differences (Figure 16, bottom). The comparison on a large iceberg (length greater than 120 m) demonstrated a good correlation between DEMs. The bias of DEM difference was 0.6 m, and the standard deviation was 2.7 m. It can be observed that the highest values of DEM difference (Figure 16, bottom) correspond to the edge of the iceberg, caused by a layover phenomenon.
# Enhancing IoT Data Dependability through a Blockchain Mirror Model
## 2 State of the Art  
Trust is a well-recognized property of data and data exchanges [6], but the requirement of data trust has highly increased in relevance in recent years when large IoT networks began operating in different and critical domains (health, finance, military and similar) [7; 8]. The intuitive concept of adding trust to a dataset that will be exchanged among several parties emerged several years ago, with a problem of stability and discoverability of the Internet resources over time (http 404 error). The issue has been addressed by implementing persistent identifiers (e.g., CoolURI, Digital Object Identifier, National Bibliography Number) and digital preservation services [9; 10] in order to guarantee the trustworthiness of the digital resources exploited. The introduction of blockchain technology intuitively opens new technical possibilities to build trust for digital datasets that have been only partially explored in the current publications [11; 12]. In particular, a formal argumentation of the precise meaning of adding trust to a dataset has not been provided yet. One of the reasons for this surprising omission may lay in the novelty of the blockchain technology (the technology aimed at providing trust) and its origin (Bitcoin). Technological issues are prominent in any review of blockchain and blockchain in the context of the IoT [13; 14], and a more abstract and detached reflection on the conceptual meaning of trust is seldom considered. The fundamental step on the way toward more abstraction layers is the recognition of the separation of concerns between reality and representation and between representation and trust, as shown in this paper. The idea of separation of concerns through different layers of abstraction is well known in traditional areas such as database research, where it is common practice designing a conceptual model that, in turn, will be transformed in a database schema. Indeed, even in the area of blockchain this separation has been highlighted [15]. The substantial differences between database and distributed ledger technologies (and blockchain) are clear and proposals in the form of decision trees are also available to assist in the choice and use of both technologies. Nevertheless, solutions based on so-called \"blockchain databases\" [16], whose aim is the hybridization of databases and blockchain, are not so uncommon. This is not our position. We believe that databases pertain to the representation domain and blockchains to the trust domain, thus providing a clean and fruitful separation of concerns.
# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 4 Future Trends
### Future Challenges and Opportunities for Smart Ship Visual Navigation Systems  
Smart ships are becoming more autonomous, but the challenges that come with this cannot be ignored. Human-machine collaboration still plays a crucial role in ensuring safe ship operation. Future smart ship designs will need to focus more on optimizing human-machine interaction, providing intuitive user interfaces and decision support tools that will enable crew members to effectively supervise and control ship automation systems. In addition, with the development of technology, how to ensure data security and privacy protection against cyberattacks is also a major challenge that smart ship visual navigation systems need to face. The core of ship visual navigation is security and risk management. With the advancement of data analysis technology, intelligent systems will be able to assess navigation risks more effectively, issue timely warnings to crews and help them take preventive measures, thus improving the safety of navigation. However,this also requires intelligent systems to have a high degree of reliability and stability, and any technical failure or miscalculation may lead to serious consequences. In future developments, the field of ship navigation will focus more on environmental adaptability and sustainability, adopting more environmentally friendly technologies, optimizing energy consumption and reducing the impact on the marine environment. At the same time, the application fields of ship visual navigation technology will continue to expand, extending from the traditional shipping field to a wider range of fields, such as marine scientific research, marine operations and environmental monitoring, providing more diversified technical support for the development and protection of marine resources and promoting the sustainable development of the marine economy.  
The future of visual navigation systems for smart ships at sea is full of challenges and opportunities. Continuing advances in technology will drive smart ships towards higher levels of autonomy and safety. However, this will also bring new requirements, and the smart ship industry needs to work closely with governments, research institutes and international organizations to address the challenges and ensure the healthy development and widespread application of smart ship technology [116]. This includes the development of new regulations and standards to accommodate the special needs of smart ships, as well as the development of new education and training programs to ensure that crews are able to adapt to future technological changes. It is only through these combined efforts that smart ship visual navigation systems can realize their full potential and revolutionize the shipping industry.
## 3 Materials and Methods
### Methodology Framework  
This study integrates multisource geospatial, meteorological, and hydrological data to model groundwater variations using deep learning techniques. The entire methodology workflow is presented in Figure 4. As indicated above, data collection encompasses geomorphic parameters (slope, aspect, DEM, and GRACE gravity data), meteorological variables (precipitation, temperature, humidity, evapotranspiration, land surface temperature, and soil moisture), spectral indices (NDVI, GPP, NDWI, LAI, and FPAR), displacement rate (land subsidence), and in situ groundwater measurements. These datasets, spanning multiple temporal resolutions, provide a comprehensive foundation for subsequent analysis.  
Figure 3: Spatial and temporal correlations of hydrological and meteorological variables in the Marvdasht basin, (**a**) Spatial correlation, (**b**) Temporal correlation.  
Pre-processing involves data cleaning, interpolation of missing values, normalization, and feature selection to ensure consistency across diverse data sources. Structured datasets are compiled in standardized formats and partitioned into training and testing subsets. Raster data are transformed into a matrix representation to facilitate integration with auxiliary variables. This step ensures that all input features are optimized for deep learning and transformer models while preserving their spatial and temporal integrity.  
InSAR time series analysis is conducted using the SBAS approach to derive ground deformation patterns indicative of subsurface hydrological variations. Deformation maps are extracted and combined with ancillary datasets to serve as covariates in predictive modeling. The processed data are subsequently utilized to train deep learning models, including LSTM, Bi-LSTM, CNN-LSTM, Transformer, ConvTransformer, and Informer. These models capture spatiotemporal dependencies, enabling high-accuracy groundwater level predictions. The final stage involves data validation, model performance assessment, and spatiotemporal prediction of groundwater fluctuations. Based on validation metrics, the most robust model outputs are selected, followed by visualization and interpretation of results. Predicted groundwater trends are analyzed in conjunction with environmental and climatic variables to derive insights into subsurface water dynamics. This integrated methodology supports the potential of deep learning and InSAR synergy for groundwater monitoring and sustainable water resource management.
# Fungi of the _Trichoderma_ Genus: Future Perspectives of Benefits in Sustainable Agriculture
## 4 Application of _Trichoderma_ Fungi in the Control of Fungal Plant Pathogens
### Antibiosis
\begin{table}
\begin{tabular}{c c c c} \hline
**Species of _Trichoderma_** & **Pathogens** & **Secondary Metabolites** & **Reference** \\ \hline _Trichoderma arundinaceum_ & _Botrytis cinerea_ & aspinolide C & [64] \\ \hline \multirow{4}{*}{_Trichoderma asperellum_} & _Alternaria alternata_ & \multirow{4}{*}{trichotoxin} & \multirow{4}{*}{[65]} \\  & _Botrytis cinerea_ & & \\  & _Colletotrichum gloososporoides_ & & \\  & _Fusarium oxysporum_ & & \\ \hline \multirow{4}{*}{_Trichoderma asperellum_} & _Botrytis cinerea_ & \multirow{4}{*}{volatile organic compounds:} & \multirow{4}{*}{[66]} \\  & _Sclerotinia sclerootrim_ & & alkanes, alcohols, aldehydes, \\  & & alkenes, amines, benzenes, ketone & \\ \hline \multirow{4}{*}{_Trichoderma asperellum_} & _Corynespora cassicola_ & \multirow{4}{*}{volatile organic compounds:} & \multirow{4}{*}{[67]} \\  & _Cruvalaria aeria_ & & acids, alcohols, aldehydes, \\ \hline \multirow{4}{*}{_Trichoderma asperellum_} & _Fusarium graminearum_ & & \\  & _T_ribavirin, 6-pentyl-\(\alpha\)-pyrone & & \\ \hline \multirow{4}{*}{_Trichoderma asperellum_} & _Magnaporthiopsis maydis_ & 6-pentyl-\(\alpha\)-pyrone & [69] \\ \cline{2-2} \cline{4-2} \  
Complex in vivo and in vitro studies show that secondary metabolites with antifungal properties produced by _Trichoderma_ sp. have a high potential for use in plant production. [PERSON] et al. [65] reported that trichotoxin extract obtained from _T. asperellum_ reduced the growth of _C. gloesporoioides_ (growth inhibition \(-\) 92.2%), _B. cinerea_ (growth inhibition \(-\) 74.29%), _A. alternata_ (growth inhibition \(-\) 58.4%), and _F. oxysporum_ (growth inhibition \(-\) 36.2%). Moreover, the incidence of _A. alternata_ disease in tomatoes was found to be 92.5% under in vivo control conditions. In turn, treating seeds with trichotoxin extract reduced the incidence of infections to 0%. Similarly, [PERSON] et al. [76] demonstrated that in a potting experiment, a harzianolide extract obtained from _T. harzianum_ stimulated tomato seed growth and also induced systemic resistance in the plant to infection with _S. sclerotorium_.
## 3 Experiments and Comparisons
### Model Hyperparameter Settings  
The setting of neural network hyperparameters has a crucial impact on the model's training performance, capacity to generalize to complex data, training speed, and its overall performance in practical applications. The learning rate regulates the step size of gradient updates for each weight adjustment. Reference [6] recommends a value of 0.001. Experiments demonstrate that this learning rate effectively facilitates the updating of model weights and accelerates the convergence process while ensuring model stability. Epochs denote the frequency at which the training dataset is fully processed and weights are updated. Its optimal value is determined through experimentation. Setting the number of epochs to 100 avoids overfitting, thereby optimizing computational resources and time while ensuring comprehensive learning. Batch size refers to the number of samples used in each iteration during neural network training. Additional parameters were determined through comparative validation based on the aforementioned experimental conditions to identify the hyperparameters yielding optimal experimental outcomes. The detailed hyperparameter configurations are presented in Table 1.
## 2 Materials and Methods
### Classification Methods
#### 2.3.1 Decision Trees  
Decision trees (DTs) are commonly used in data mining and classification [27]. DTs are built by splitting the source data set, constituting the tree's root node, into subsets, which comprise the successor children. In these tree structures, leaves represent class labels, and branches represent conjunctions of features that lead to those class labels. The splitting is based on a set of splitting rules based on classification features. This process is repeated for each derived subset recursively until splitting no longer improves the classifications.
## 3 Materials and Methods
### Anomaly Classification
#### 3.5.3 Late Temporal Modeling  
The features extracted from both the spatial and temporal streams are flattened and concatenated to perform feature fusion. In order to learn the long-term temporal characteristics of a video, late temporal modeling is performed from a concatenated feature set. Long Short-Term Memory (LSTM), bidirectional-LSTM, and Gated Recurrent Units (GRUs) are the three modeling approaches that are experimented with, and it is observed that GRUs provide the best temporal modeling characteristics, with a slight margin over LSTMs and bi-LSTMs. A possible explanation for the better performance of GRUs over LSTMs is the smaller size of the training dataset necessary to train a GRU. The GRU is followed by a dense layer and classification of the video into one of eight classes.
# Evaluating Explainable Artificial Intelligence for X-ray Image Analysis
## 3 Results and Discussion
### RQ1: What Is the Purpose of the Article?  
With this question, we aimed to identify the main purpose of each study. Because we reviewed articles centered on X-ray images in a medical context, most of the articles were centered on the prediction or identification of a health problem.  
We found 17 different purposes in the reviewed articles; see Figure 6. We classified these 19 goals into three classes: thoracic problems (86.52% of the reviewed articles), bone-related problems (9.93%), and others (3.55%). It is noteworthy that most of the articles were centered on the detection of thoracic diseases. This trend had increased in 2020 and 2021 with the appearance of COVID-19. We observed that the main thoracic disease handled by the reviewed articles was pneumonia (50.35%), whether it was COVID-19-related (42.55%) or not (7.80%). Other articles addressed this disease, but with a more general goal to detect multiple pulmonary diseases (28.37%). Further, 4.26% of the reviewed articles had the detection of tuberculosis as the main purpose. Finally, from the rest of the articles that handled thoracic diseases, we found multiple purposes: the detection of pneumothorax (1.42%), the detection of cystic fibrosis (0.71%), the detection of malposition of feeding tubes (0.71%), the segmentation of tracheal tubes (0.71%), and the detection of congestive heart failure (0.71%).  
Figure 6: Answers to RQ1. Vertical axis indicates the percentage of articles for each category.  
We found another set of articles centered on bone-related diseases. We classified these into three types, depending on the purposes addressed: the detection of bone fractures (3.55%), the identification of prosthesis type (3.55%), and the identification of osteoarthritis (2.13%).  
Finally, one set of articles did not fall into any of the two previous categories: one study identified different anatomy zones from the image, and another one classified different CXR views. One study aimed to predict the age of the patients, another one diagnosed periodontitis, and one study recognized causes of shoulder pain.  
Taking into consideration the results obtained from RQ1, we observed that the majority of the reviewed articles used X-ray images to detect either lung diseases (e.g., pneumonia) or bone-related problems. From these results, we can make two conclusions: that, in the medical community, apart from some particular cases, X-ray images are used for these two kinds of problems, and that these two kinds of problems are the only ones that are suitable for the usage of AI methods with X-ray images.
# Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication
## 3 Analysis of Antenna Configuration
### Rectangular 2 \(\times\) 2 Transceiver Antenna Array  
The rectangular array is simpler to configure than the circular arc array in wireless communication systems. The rectangular transceiver antenna array is shown in Figure 4.  
In Figure 4, \(d_{s}\) is the array element spacing, \(d\) shows the distance of the transceiver antennas, and \(d_{11},\ d_{12},\ d_{21},\) and \(d_{22}\) are the sub-channel propagation paths.  
According to Equations (4), (19), and (23), the expression of channel capacity can be simplified as:  
\[C=log_{2}\bigg{\{}(\rho+1)^{2}-\frac{\rho^{2}}{2}\Big{[}1+cos\Big{(}4\pi n \Big{(}\sqrt{1+m^{2}}-m\Big{)}\Big{)}\Big{]}\bigg{\}} \tag{31}\]  
If it satisfies Equation (32), the channel capacity of the rectangular array can also reach the maximum value.  
\[cos\Big{(}4\pi n\Big{(}\sqrt{1+m^{2}}-m\Big{)}\Big{)}=-1 \tag{32}\]  
Then, the relationship between \(m\) and \(n\) can be represented by Equation (33):  
\[m=\frac{2n}{2k+1}-\frac{2k+1}{8n},k\in N \tag{33}\]  
According to Equation (33), it is clear that \(m\) reaches the maximum value when \(k=0\).  
\[m_{max}=2n-\frac{1}{8n} \tag{34}\]  
According to Equations (23) and (34), the expressions can be simplified as:  
\[d=F(\lambda,d_{s})=\frac{2d_{s}^{2}}{\lambda}-\frac{\lambda}{8} \tag{35}\]  
In summary, Equation (35) is a general expression for 2 \(\times\) 2 LoS MIMO systems in the rectangular array, which corresponds to the parameters setting in [43].  
Figure 4: Rectangular 2 \(\times\) 2 transceiver antenna array.
## 2 Materials and Methods
### Measurements  
An electrochemical cell with a three-electrode configuration was used in this study. A graphite rod, saturated calomel electrode (SCE), and glassy carbon electrode (GCE) (GAMRY Instrument Company, \(A\) = 0.071 cm\({}^{2}\), Warminster, PA, USA) were used as counter, reference, and working electrodes, respectively. All potentials will be presented with respect to SCE. The GCE was cleaned by mechanical polishing with aqueous slurries of successively finer alumina powder (down to 0.06 \(\upmu\)m) and was then washed thoroughly with deionized water. All electrochemical experiments, e.g., cylic voltammetry, linear sweep voltammetry and chronoamperometry (CV, LSV, and CA) were performed using a Gamry potentiostat (model 600) operated by Gamry software (version 6.23.2982, Gamry, Warminster, PA, USA). The surface morphologies and the chemical composition of the deposits was identified using field emission scanning electron microscope, FE-SEM, (QUANTA FEG 250, Thermo Fisher Scientific, Hillsboro, OR, USA) equipped with an energy EDX (Energy Dispersive X-ray) unit. XRD, (PANalytical, X'Pert PRO) operated with a Cu target (\(\hat{x}\) = 1.54 A) was used to identify the crystallographic structure of the deposited materials.
# Sustainable Parking Space Management Using Machine Learning and Swarm Theory--The SPARK System
## 3 Methodology Concept
### Advanced Computer Vision and Real-Time Parking Space Detection
[61] proposed Criss-cross and Multi-spectral Channel Attention (CMCA)-YOLO, integrating attention mechanisms to improve the detection of small and overlapping objects. [PERSON], et al. [62] introduced YOLO-CIR (YOLO and ConvNext for infrared), a novel combination of YOLO and ConvNeXt for infrared object detection, achieving robust performance in low-visibility environments. [PERSON] and [PERSON] [45] presented the Automated Parking Space Detection and Occupancy Classification (APSD-OC) algorithm, combining YOLOv5 for vehicle detection with ResNet34 for parking slot occupancy classification. Recent developments have seen the use of various YOLO versions to tackle specific challenges in vehicle segmentation and parking space identification. For instance, YOLOv3 has been employed by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [60], YOLOv5 by [PERSON], [PERSON], [PERSON], and [PERSON] [56], and YOLOv7 by [PERSON], [PERSON], and [PERSON] [59]. Each version brings unique benefits, such as improved accuracy, faster processing times, and better handling of complex scenes. In this study, YOLOv9, the latest version with segmentation capabilities, was employed, offering significant enhancements in performance and accuracy. Using the most up-to-date version is important for achieving the highest accuracy rates and speeds [55]. In this study, due to the aforementioned reasons, YOLOv9e-seg was employed for vehicle segmentation. Object segmentation was performed, rather than object detection, to accurately determine the precise locations of available parking spaces. While object detection focuses on identifying and locating individual objects within an image, segmentation takes this a step further by classifying each pixel in an image, enabling the system to distinguish between multiple regions within the same frame. In the context of parking space detection, segmentation allows for more precise identification of occupied and empty spaces by analyzing the boundaries of vehicles. This approach enabled the precise delineation of the boundaries of occupied parking spaces by segmenting the detected vehicles. A wide range of image sources can be utilized for YOLO-based segmentation, including Internet Protocol (IP) cameras [40; 63], dashboard camera videos [58], UAV imagery [57; 59; 64], satellite images [65], and bird's-eye view wide-angle images [66]. Inthis study, real-time footage from outdoor cameras provided by volunteers was used for parking space detection.