esa-sceva/satcom-chunk-collection huggingface dataset:
# MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane
## 5 Performance
### XCH\({}_{4}\) Error Budget
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Random Error** & **Impact on XCH4** \\ \hline Differential Atmospheric Optical Depth (DAOD) & 1.3\% & 23.14 ppb \\ SSE & 10 m & 2.57 ppb \\ Surface pressure (Numerical Weather Prediction (NWP)) & 2 hPa & 4.27 ppb \\ Temperature & 2 K & 5.2 ppb \\ Total budget based on specified values & & 24.3 ppb \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Total budget for XCH\({}_{4}\) random error obtained by combining specified values for contributors. The current best estimate of the budget (24.3 ppb) is compliant when accounting for the actual weighting function with enhanced sensitivity in the boundary layer (27 ppb, see Table 3). \begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Systematic Errors** & **Impact on XCH4** \\ \hline DAOD & 0.13\% & 2.31 ppb \\ SSE & 6 m & 1.54 ppb \\ Surface pressure (NWP) & 0.2 hPa & 0.43 ppb \\ Temperature & 2 profiles of bias & 0.5 ppb \\ Water vapor & 4\% & 0.1 ppb \\ Methane cross section & 0.2\% & 0.62 ppb \\ Frequency & 7 MHz offset, 8 MHz systematic & 0.3 ppb \\ Laser width knowledge & 5\% for 100 MHz & 0.17 ppb \\ Spectral purity & - & 0.23 ppb \\ Processing & & 1 ppb \\ Total budget based on specified values & & 3.1 ppb \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Total budget for XCH4 systematic error obtained by combining specified values for contributors. The current best estimate of the budget (3.1 ppb) is compliant to the observational requirements when accounting for the actual weighting function with enhanced sensitivity in the boundary layer (3.7 ppb, see Table 3). combine the uncertainty of the emitted wavelength, the FRU characteristics, the Doppler effect, and so forth. Then, they are summed quadratically as they are independent. A more detailed description of the modelling of the error is outside the scope of this paper and will be given in a further publication.
## 2 Literature Review
### Our Contributions  
This research presents the design and implementation of a novel system that integrates artificial intelligence (AI) voice recognition, natural language processing (NLP), and sentiment analysis to evaluate learners' emotional engagement based on textual data derived from learning sheets and real-time discussions. The study employed a structured methodology for collecting and processing educational text data, utilizing learning sheets completed by 129 first- and second-year university students during a two-week computational thinking (CT) course. The researchers introduced a participation recognition system that leverages AI speech-to-text conversion and emotion scoring to assess real-time engagement during student discussions. We provide empirical evidence of the system's effectiveness through an analysis of students' learning texts, which yields a dictionary of feelings and engagement indicators. Although sentiment analysis has been widely applied in other domains, this work uniquely adapts it to the STEAM education context, focusing on CT and inquiry-based learning. By linking learners' textual responses and emotional scores to their perceptions of course utility, the researchers bridged the gap between emotional engagement and educational outcomes, thus contributing to the growing body of research on AI-enhanced pedagogy.
## 2 Materials and Methods
### Data Collection and Preprocessing
#### 2.2.3 Vertical Ground Motion Data  
The European Ground Motion Service (EGMS) is based on Earth observation products using Synthetic Aperture Radar Interferometry (InSAR) data from the Sentinel-1 mission [71; 72]. Its goal is to determine and measure vertical ground movements across the European Territory caused by natural phenomena including tectonic activity, groundwater extraction, mining activities, etc. It is expected that the EGMS can assist a variety of interdisciplinary stakeholders in monitoring potential structural hazards in constructions (e.g., dams, buildings, road infrastructure, bridges), as well as promote data-driven decision-making in the fields of spatial planning and natural hazard risk mitigation [73; 74].  
The EGMS provides consistent, reliable, and interoperable information on ground motion phenomena through three different levels of products that are updated on an annual basis [71; 72]:  
1. Basic/L2a: This category provides line-of-sight velocity maps--in descending and ascending satellite orbits--including geolocalization and quality measures per point. These products refer to a local reference point.
2. Calibrated/L2b: Compared to the basic level of products, the calibrated ones are absolute in value since they do not refer to a local reference point. They include line-of-sight velocity maps referenced to a model derived from GNSS time-series data.  
Ortho/L3: This level of products includes components of motion (horizontal and vertical) anchored to the reference geodetic model. These products follow a raster format of 100 m resolution.  
EGMS's initial dataset covered the period between 2015 and 2021 [75]. Its results were verified for usability in a variety of applications according to documented user requirements and the quality and consistency of the provided data with the technical specifications of various application scenarios [76].  
The present research used the EGMS L3 product as it provides a raster version of the intended dataset that is easier to process. As can be seen, in the Mediterranean Coastal Zone, the recorded vertical ground motion using SAR algorithms varies from \(-91\) mm/year (located particularly in the Southern parts of Greece) to \(+63\) mm/year (Figure 3). The preprocessing stage included the mosaicking of the necessary tiles that were downloaded from the EGMS portal and the conversion of the mean velocity value from mm/year to m/year. The data were resampled from 100 m resolution to 30 m to match the pixel size of GLO-30 DEM and were clipped to the area of interest's extent. The annual mean values were considered constant and projected to the target years of study, i.e., 2050 and 2100, by applying a simple raster calculation.
# Noise Reduction in CWRU Data Using DAE and Classification with ViT
## 2 Research Method
### Vision Transformer  
Among artificial intelligence methodologies, the CNN is used most widely in the computer vision field. The CNN is a model that learns and classifies an image by performing convolutions of filters represented as matrices through pooling and convolution layers [28]. However, in recent research, the ViT has outperformed CNN-based models in accurately classifying images [29].  
The ViT uses a transformer, a deep learning model primarily used in natural language processing [30]. Unlike CNN models, the ViT structure learns and classifies images using only self-attention [28]. In this study, the bidirectional encoder representations from transformers, i.e., the transformer encoder structure and attention mechanism used in natural language processing, were maintained while introducing changes at the image input stage [30]. The input images were divided into patches and arranged in one dimension, creating sequence-like data similar to natural language for input [28].  
Similar to the sequence data in natural language, the images were divided and flattened for data processing, as shown in Figure 6. The flattening process involved patch embedding across channels, image dimensions (height \(\times\) width), number of patches, patch sizes, etc. Additionally, a class token was added to represent the entire image. After a token was added as the transformer model, positional embedding was performed to store the position information, and learning proceeded through the encoder.  
Figure 6: Image division for ViT learning.  
The ViT adopts the transformer encoder as its model architecture (Figure 7) and performs multi-head self-attention. However, normalization is performed before residual connection, and a multi-layer perceptron (MLP) head, where all the layers have the same nodes, classifies the data instead of feedforward neural networks. Then, a Gaussian error linear unit, a nonlinear activation function, is used in the MLP [28].  
The ViT_b_16 model from Torchvision was utilized in this study. This model accepts input images with sizes of 224 \(\times\) 224 and divides them into patches of 16 \(\times\) 16 pixels. Therefore, a 224 \(\times\) 224 image is divided into 14 \(\times\) 14 patches. Each 16 \(\times\) 16 patch is converted into a high-dimensional (768 dimensions) vector through embedding via a linear transformation (fully connected layer). It then passes through a transformer encoder layer composed of a self-attention layer, feedforward network, layer normalization, and dropout. Moreover, an additional class token passes through the transformer encoder and summarizes the entire information of the image. The specific details of the ViT_b_16 model are shown in Table 1.
## 1 Introduction  
Diabetes mellitus (DM) is a group of metabolic disorders characterized by chronic hyperglycemia due to defects in insulin secretion, insulin action, or both (American Diabetes Association, 2013). It is a global health challenge, with the International Diabetes Federation (IDF) reporting that 1 in 10 adults--approximately 537 million people--are living with DM (IDF, 2022). The disease disproportionately affects developing countries, particularly in South Asia, where the prevalence is rising. In Sri Lanka, the prevalence of DM is 9.8%, with an age-sex standardized prevalence of 21.8% [1]. Chronic hyperglycemia leads to long-term complications affecting various organ systems, including the retina, kidneys, nerves, cardiovascular system, and oral cavity [2]. Among the many complications of DM, oral manifestations are often overlooked despite their significant impact on quality of life. DM is associated with an increased risk of periodontal diseases, diabetic sialadenitis, xerostomia, burning mouth syndrome, oral candidiasis, and angular cheilitis [3]. Oral candidiasis is frequently observed in diabetic patients, especially those with poor glycemic control.  
_Candida_ species, which normally exist as commensals in the oral cavity, can become opportunistic pathogens under conditions such as immunosuppression, high salivary glucose levels, and reduced salivary flow, leading to the development of various oral lesions [4]. The most commonly isolated species in oral candidiasis is _Candida albicans_[5]. _Candida_ spp. exhibit several virulence factors that contribute to their pathogenicity, including morphological switching, biofilm formation, adherence mechanisms, and environmental adaptability [6]. When transitioning from the yeast form to the filamentous (hyphal) form, _C. albicans_ gains invasive properties, allowing it to breach mucosal barriers and cause lesions such as Pseudomonas Candidiasis (PMC), Erytheratous candidiasis (EC), Chronic Hyperplastic Candidiasis (CHC), Median Rhomboid Glossitis (MRG), and Deture Stomatitis (DS) [7]. Studies indicate that the prevalence of oral candidiasis in DM patients ranges from 13.7% to 64%, with PMC being the most common presentation [8,9]. Despite extensive global research on DM-associated oral candidiasis, there is a notable gap in local data from Sri Lanka. To the best of our knowledge, no studies have specifically examined the prevalence and types of oral candidal lesions in Sri Lankan DM patients. This study aimed to address this gap by identifying the proportions of type 2 DM patients presenting with oral candidal lesions. Additionally, this study will raise awareness among affected individuals, allowing for early intervention and appropriate treatment. In this study, periodic acid Schiff (PAS) staining, a cost-effective and reliable method, was used to identify the hyphal form of _Candida_ in oral mucosal lesions [10]. The findings will provide valuable insights for clinical practice, research, and healthcare professional training, ultimately improving the management of oral health in diabetic patients.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 2 Background
### Device Composition  
Whether a big and complex machine for car manufacturing or a small and smart bracelet for wearing, they contain relatively fixed components such as chips, flash, firmware, and so on. Its composition mainly includes hardware and software parts.  
(1) _Hardware parts:_  
* **Logic chip.** For complex devices, it has an operating system so that it needs multiple logic chips or CPU. Simple embedded devices may only use a single microprocessor to run programs.
* **Memory.** Provides the storage space for system and program running, ranging from a few KB to GB.  
Figure 1: Internet of Things (IoT) architecture on the consumer-level.  
Figure 2: IoT architecture on the industry-level.  
* **Flash storage.** The location where the IoT device firmware is stored. Part of the device's bootloader is also stored in the flash.
* **Network module.** The difference between IoT devices and traditional embedded devices is that they connect to the Internet. They generally adopt wireless technology to connect to the Internet with the hub, such as access points (APs).
* **Serial debug interface.** The IoT device often requires means for communicating with the external world for debugging. The serial debug interface could be to send and receive commands to and from the vendor developers. One of the most commonly used interfaces is the universal asynchronous receiver/transmitter (UART). _(2) Software parts:_
* **BootLoader.** It is a small program. Before the IoT device system runs, it initializes the hardware device and loads the firmware to the boot device. Thus, it brings the system's software and hardware environment to a suitable state to prepare the correct environment.
* **Firmware.** The firmware includes the operating system, file system, and service programs. Security research on IoT devices generally starts with firmware analysis.
# Artificial Intelligence-Based Controller for DC-DC Flyback Converter
## 1 Introduction
In Reference [29], a comparison was made between fuzzy and PI-based voltage controllers for an integrated buck flyback converter. Similarly, in Reference [30], the performance of the FC compensated system was analyzed via a PID controller and FLC. In these references, FLCs have outshone PIDs in terms of performance. Although an FL can make decisions based on the knowledge supplied to it, due to the lack of a training mechanism, it cannot make its knowledge adaptive. More intelligence can be introduced to fuzzy rules by retuning them with artificial neural networks (ANN). ANFIS is the fusion of ANN and FL and utilizes the salient features of neural and fuzzy networks, respectively. The main advantage of ANFIS is that it converges quickly since it minimizes the search space dimensions of the backpropagation method used in NN [31; 32; 33]. An ANFIS-based maximum power point tracking (MPPT) system for a PV module with a DC-DC converter is presented in References [34; 35; 36; 37]. In Reference [38], the authors proposed an ANFIS-based MPPT system comprising a PV module connected to a load through a DC-DC Cuk converter to track the maximum power point (MPP). The MATLAB/Simulink environment was used to simulate the proposed model. Very limited research can be found in the literature regarding the control of switching converters using ANFIS. This paper thus proposes an ANFIS-based controller to regulate and improve the output voltage of FC. Line and load regulation and response to reference voltage changes are presented and the static and dynamic performance of the proposed controller is analyzed. Furthermore, its performance was also compared with FLC and PID controllers to validate its superiority. The paper is organized as follows. Section 2 describes the modeling of an FC in continuous conduction mode (CCM). The design parameter selection of the FC used throughout the paper for the design of the controllers is described in Section 3. Section 4 presents the detailed controller design procedure for the FLC, ANFIS-based controller, and PID controller. Section 5 shows the results obtained after performing the simulations of FC with feedback controllers. Finally, the results are concluded in Section 6.
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 6 System Implementation
### System Function Comparison  
This scheme integrates various cutting-edge security technologies with cryptography as the core, achieving comprehensive security protection for data assets throughout their entire lifecycle. The specific technical capabilities at each stage are as follows.
# Compact Dual-Band Tapered Open-Ended Slot-Loop Antenna For Energy Harvesting Systems
## 1 Introduction
The design is based on stacking two single-port patch antennas back-to-back in order to implement a compact dual-port patch antenna with high isolation between ports. The previously reported antennas are not simply integrated with wireless applications, despite the previous attempts that were conducted to solve the antenna compactness and poor radiation pattern at the lower frequency band. Indeed, the reported designs were not straightforward and they require rigorous mathematical analysis. Therefore, dual-band compact and high efficient antennas are highly recommended. The objective of this study is to introduce a simple dual-band compact loop-slot combined architecture suitable for RFID, Wi-Fi, and energy harvesting applications. In this paper, a compact 0.92 GHz and 2.45 GHz dual-band combined loop/slot antenna is proposed in order to generate good radiation and electromagnetic wave propagation at the specified frequencies. The OEL and OES are used to provide the microwave propagation at 0.92 GHz and 2.45 GHz, respectively. The OEL is a trapezoidal rectangular shape with a total length of 0.7\(\lambda_{g}\) at 0.92 GHz. Additionally, the OES is based on a tapered slot, the overall length is \(\lambda_{g}/2\) at 2.45 GHz. This paper is organized, as follows: the structure and configuration of the antenna are introduced in Section 2 as well as antenna analysis. In the same manner, discussions are carried out to investigate the operation principle of the antenna. The experimental results and comparison are presented in Section 3. In the last part, the manuscript is summarized with the conclusions.
## 1 Introduction  
_Phoenix dactylifera_ L. is a vital plant for the desert region in Algeria, where it plays a very important ecological and socio-economic role for the population living in this region. Indeed, the fruit is the subject of important commercial activity, in particular the famous variety 'Deglet-Nour', which is a worldwide commercial variety of choice because it is flavorful, mellow and has an exquisite taste. Deglet-Nour derives its vernacular name from the expression finger of light, it is the most popular cultivar in all the palm groves of southeastern Algeria, and it reaches maturity in October-November.  
During its maturation, it goes through four essential stages called Kimri, Khalal, Rutab and Tamar, where the fruit gradually loses its water and intensifies in color. In the final Tamar stage, Deglet-Nour undergoes a decrease in astringency, and it is light red in color with yellowish tints. Polyphenols are micronutrients, particularly abundant in fruit, grains and vegetables [1]. Their involvement in biological activities of dates was confirmed [2; 3]. Degradation of fruit phenolics alters their nutritional quality, especially since these molecules characterize the sensorial properties of fruits and vegetables [4]. Their preservation within fruit and vegetables is a great necessity, since their oxidation by polyphenoxidase (PPO) and peroxidase (POD) actions leads to installation of fruit browning [5], which drops their market value. The first experiments of food irradiation were carried out to avoid germination of fruits and vegetables. Moreover, food irradiation can control insect infestations and delay ripening of fresh fruit and vegetables [6]. Otherwise, it should be noted that UV-C radiation has not been tested to slow browning and degradation of related molecules, phenolics, in fruit and vegetables.  
The main objective of this work was to study the effect of UV-C radiation on the quality of the Deglet-Nour date by monitoring changes in some of its organoleptic and pharmacological properties during five months of storage after irradiation. The color alteration was targeted by study of fruit browning, in addition to other quality criteria such as changes in antioxidant activity and content of major components.
Analyzing a multitude of compliance functions, we found significant differences between the results achieved for certain crack depths, resulting in a negative influence on the accuracy of the damage assessment methods based on this approach. Recent research focuses on detecting damage by involving artificial intelligence (AI). Examples of current methods aiming to detect damage in beams can be found in [21; 22; 23], while in [24; 25; 26] are exemplified methods applicable to complex structures. These approaches are based on the analysis of the vibration signal parameters in the time domain (acceleration, damping), or in the frequency domain (mode shapes and curvatures, frequencies). The training data are obtained from simulation or measurements, thus initially it involves a limited number of damage cases. If for a given structure it is possible to determine the relationship between the damage parameters and the vibration signal parameters, it is possible to generate a multitude of damage cases [27] including the case of imperfect clamping. In this way, the training process can be improved, and the AI algorithms provide more accurate prediction results. In prior research, we determined a mathematical relation to finding the severity of closed or open cracks [28]. The data used to calculate the severity are the deflections at the free end of a cantilever beam for the healthy and damaged case, respectively. Because the severity is an intrinsic parameter of the damage, it is the same for beams with any boundary conditions. Thus, it is sufficient to determine the severity just for the cantilever beam. For the reasons presented in the next section, determining the deflection of the damaged beam implies regression analysis followed by extrapolation. Thus, the results are influenced by the nature of the regression curve used and may vary accordingly. In this paper, we propose a mathematical relation to calculate the effect of a crack located anywhere on the beam on its deflection. This relation is used to find the damage severity from static tests made with cracks having a random position on the beam. Instead of regression analysis, we use Stochastic Hill Climbing (SHC) as an optimization method. To the best of our knowledge, there is no research to determine the severity of the defect using AI. Using this procedure to find the damage severity we avoid obtaining results that depend on the analysis strategy, thus these are very accurate. The paper is structured as follows: the expression of the cracked beam deflection is deduced in Section 2, then we present the procedure to determine the damage severity (Section 3) and the achieved results for several beams and crack dimensions (Section 4), while in Section 5 we test the capacity of estimating frequency changes due to damage by involving the achieved damage severities. Finally, we present the conclusions of the research in Section 6.