esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Key Information Extraction  
#### 2.1.1 Traditional Key Information Extraction Methods  
Key information extraction (KIE) is a pivotal task in document image processing. It aims to extract textual content of specified categories, such as names and telephone numbers from documents like certificates or contracts. Traditional key information extraction from document images mainly includes rule-based filtering methods, template matching methods, and traditional natural language processing-based methods [16]. Rule-based filtering and template matching methods often require many pre-designed rules or templates, which are based on location coordinates, with poor generalization performance and high migration costs.  
Deep learning methods based on natural language processing consider key information extraction as the recognition task of named entities; they distinguish the attributes of named entities by calculating strong correlations between sequences using semantic text features. BiLSTM-CRF [8] pre-extracts temporal features of sequences, applies syntactic constraints on the extracted contextual feature vectors, and finally completes the training for sequence labeling. However, these methods only use textual semantic features, ignoring the location of key information and the layout information of the image. With the deepening of research, [PERSON] et al. [17] propose that text block coordinate embedding features are directly added to the BiLSTM-CRF model. Post-OCR [18] attempts to implement coordinate information in the labels of sequence annotation. Although introducing coordinate information can improve extraction accuracy, it cannot effectively construct the correlation between discontinuous entities.
## 1 Introduction
### Methodology Context  
Depending on their specific objective and scope, the literature review highlights that the studies on the impact of the COVID-19 pandemic on UPT have used different methods to characterize mobility as well as for the analysis of the results obtained. Thus, to quantify the general traffic, broad-spectrum sources such as Google Mobility or traffic cameras have been used [48; 49], whilst in order to measure the number of users and their movements in UPT (especially bus and train), the most common source has been the counting of tickets supplied by public administrations in charge of urban transport, usually the municipalities [38; 43; 50]. In addition, surveys have allowed researchers to know other aspects such as the motivation, service perception, needs, and intentions of users [51; 52]. Regarding the analysis of the relationship between variables, simple or multiple regressions have been performed [53; 54], as well as multiple correspondence analysis (MCA) when the relationship between variables is apparently less direct [4; 55; 56].  
Considering the above, as well as the remarkable exploratory nature of this study, in order to analyze the impact of urban bus network characteristics on the recovery of UPT users in the post-pandemic scenario in the city of Almeria, an MCA was performed including both the operating network variables and the evolution of the annual number of users in each bus line during the period 2019-2022. The use of a MCA seemed the most appropriate approach in this study, since it allows the authors to explore to what extent there is a relationship between the variables involved, independently of the statistical significances, which is crucial in studies with small-size samples, as in this case, when only 16 urban bus lines with their operating variables are analyzed.
# Similarity Estimation for Large-Scale Human Action Video Data on Spark
## 3 Proposed Framework
### Preprocessing  
In this phase, we performed the preprocessing on stored video data. Here, at first, we extract the frames from each videos, and then converted this frame into a gray level frame from RGB color frame. After that, we resize each frame so that user input frame and stored database video frame has the same size. Lastly, we perform background subtraction operation on each frame in order to separate the foreground human from the background. In our case, many users performing human activity videos following the reference human activity videos on the database. First of all, all of these videos are stored into the HDFS storage; then, the dataset stored in HDFS is loaded into the Spark cluster. For reference, for human activity videos that are stored in the database, the pre-processing step including the feature extraction step is performed in offline mode, while users performing human activity videos are processed on the fly. All nodes including the master node and worker nodes  
Figure 1: The proposed framework for human action similarity estimation.  
perform this work in parallel. Partitions of this dataset are cached into worker nodes. All loaded data partitions and the created collection of objects are managed by RDD. An RDD is the representation of the information and a collection of partition record. RDD operations are accomplished in parallel using each worker node. In each partition, frames are extracted from the loaded videos. Next, frames are converted into gray scale from RGB mode. Then, these gray level frames are resized to \(720\times 404\). Lastly, we perform the background subtraction. Because our goal is to analyze human action similarity, we only need to analyze foreground objects (e.g., humans). In our work, humans are foreground objects. Here, we use Gaussian probability distribution [22] to process background subtraction. Here, we employed static background images to subtract the background from the frames of each activity video clip. The probability of being a pixel to be background is given as  
\[P(R(x,y))=\frac{1}{C}\sum_{i=1}^{C}\frac{1}{\sqrt{2\pi\sigma^{2}}}exp(\frac{ -(R(x,y)-B_{i}(x,y))^{2}}{2\sigma^{2}}), \tag{1}\]  
where \(C\) is the number of background images. \(P(R(x,y))\) is the probability of being a pixel of the background in \(x\) and \(y\) position of the recent frame. \(B_{i}(x,y)\) and \(R(x,y)\) are the intensity values of the pixel in \(x\)- and \(y\)-position of the \(i\)-th background image and recent frame, respectively.
# Effect of Radial Height of Helical Static Blade on the Cavitation Performance of Inducer
## 4 Results Analysis
### Analysis of Simulation Results  
Figure 6 plots the cavitation performance curves of both the separate inducer and the inducer with helical static blades, the radial height of which is \(k\) = 0.05, 0.1, 0.15, and 0.2, respectively. The curve describes the relationship between the cavitation number and the head, and the cavitation number, a dimensionless number that characterizes inlet pressure, is defined as the following equation:  
\[\sigma=\frac{p_{\text{in}}-p_{\text{v}}}{\frac{1}{2}\rho u^{2}} \tag{8}\]  
where \(p_{\text{in}}\) is the inlet pressure of inducer, \(p_{\text{v}}\) is the vapor pressure at local temperature, \(\rho\) is the fluid density, and \(u\) is the characteristic velocity of the flow. With the decrease in inlet pressure, the bubbles generate and gradually increase. However, the performance of inducer is not affected until the cavitation area develops to a certain scale. When the inlet pressure, or cavitation number, decreases to a certain value, the head then drops rapidly, as shown in Figure 6. In general, when the head drops by 10%, the corresponding cavitation number is defined as the critical cavitation number, which is used to evaluate the cavitation performance of inducer.  
It can be seen that the similarity among different cases lies in that the head remains basically unchanged under the non-cavitation condition and decreases sharply when the cavitation number is below the critical cavitation number. With the decrease in cavitation number, the scale of bubbles continuously increases and gradually blocks the inducer flow channel, resulting in the sharp drop of the head. When the helical static blade is implemented, a portion of bubbles enters the channel of the helical static blade and squeezes the fluid within, which will reduce the leak of the helical static blade and increase the head as well. Therefore, for the inducer with helical static blades, the head increases slightly before the drop in the early cavitation stage, and the increment gets larger with the increase in radial height in a certain stage.  
In order to analyze the effect of radial height of helical static blade, the rest of this section separately discusses the changes in external characteristics, flow field, critical cavitation number, and the cavitation evolution process.  
Figure 5: Results of numerical calculation and experiment. (**a**) Head. (**b**) Efficiency.
## 3 Experimental Analysis of Targeted Outdoor Units
### Experimental Set-Up for Characterizing Noise Performance  
In this study, to characterize the acoustic performance of the targeted outdoor unit, the spectral sound pressure levels were measured in the anechoic chamber. The experiment was conducted in accordance with the national test standard, KS (Korean Industrial Standards) C 9306 for air-conditioners [12]. The tested fan system is fixed at the center of the chamber and the microphone was installed downstream at a distance of 1 m from the center of an axial fan. The acoustic pressure was measured with a sampling frequency of 6400 Hz and a frequency resolution of 1 Hz. The measured overall sound pressure level with increasing rotating speed of fan is represented in Figure 3.
# Prediction of Almond Nut Yield and Its Greenhouse Gases Emission Using Different Methodologies
## 1 Introduction
A combination of ANN and fuzzy (ANFIS) provides the advantages of both models and allocates a high capability to interpret non-linear systems. Table 1 summarizes some of the reported studies in the open literature for ANFIS application in agricultural research fields. \begin{table}
\begin{tabular}{c c c} \hline \hline
**System/Process** & **Summary** & **Reference** \\ \hline \multirow{2}{*}{Convective drying} & ANFIS showed a good ability for predicting the drying properties for three products (i.e., potato, garlic, and cantaloupe.) & [29] \\ \cline{2-3}  & The grain yield of the wheat was successfully predicted by & [30] \\ \cline{2-3}  & ANFIS approach. & \\ \cline{2-3}  & Wheat production & Wheat grain yield was successfully predicted by ANFIS based on the energy inputs. & [31] \\ \hline \multirow{4}{*}{Broiler production} & The broiler farms were analyzed to estimate energy outputs using & \\ \cline{2-3}  & ANFIS method. & [22] \\ \cline{2-3}  & To estimate the best body weight and feed the conversion ratio, ANFIS & \\ \cline{2-3}  & was evaluated for determination of the first three limiting amino acids. & [32] \\ \hline \multirow{4}{*}{Landslide susceptibility mapping} & The findings of the ANFIS model were manifested using remote & \\ \cline{2-3}  & sensing data integrated with GIS for landslide & [33] \\ \cline{1-1} \cline{2-3}  & susceptibility evaluation. & \\ \cline{1-1} \cline{2-3}  & Landslide susceptibility plotting was carried out using optimized & \\ \cline{1-1} \cline{2-3}  & ANFIS by the teaching-learning-based optimization and satin & [34] \\ \cline{1-1} \cline{2-3}  & bowerbird optimizer algorithms. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some studies reported for ANFIS application in the agricultural sector. According to the preceding discussion, the main objectives of this work were to analyze GHG emitted from almond production and to predict almond nut yield using energy inputs by means of AI modeling techniques, including ANN and ANFIS models.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 4 Biomedical Applications
### Magnetofection (MF)  
Magnetofection is a technique that combines the use of magnetic nanoparticles with conventional transfection methods to improve the delivery of genetic material (e.g., DNA, RNA, or siRNA) into target cells. The goal is to enhance the efficiency and specificity of gene transfer by leveraging the magnetic properties of nanoparticles [153]. MF was employed to transport nucleic acids into cells for gene delivery purposes [153]. Magnetic nanoparticles (MNPs) that are attached to genetic material are inserted into the body and directed towards specific cells or tissues by using an external magnetic field capable of interacting with the MNPs. This technique improves the effectiveness of gene transfection, offering a potent tool for gene treatment and research purposes. Nucleic acid molecules can be indirectly attached to the MNPs surface using linkers [154] or directly through electrostatic interactions, especially in the case of polyethylenimine coatings [155; 156], as presented in Table 1.  
MF is very efficient due to its swift targeting and decreased cytotoxicity, providing notable benefits compared to conventional chemical transfection techniques [157; 158; 159].  
Besides these applications, MNPs can be employed in efficient DNA isolation directly from biological samples. A higher separation capacity as compared to commercially available kits has been reported [160].
# A Novel Inductor-Based Non-Dissipative Equalizer
## 4 Experimental Results  
Experiments have been carried out to verify the equalization performance of the NIBNDE. Table 9 gives the initial cell voltages. An equalization prototype with four 2.2 Ah Samsung lithium batteries is built and the parameters are summarized in Table 10, which RDS(\(on\)) represents the on-resistance of MOSFET. This section compares the simulation with the experiment. The frequency, the equalization threshold and the initial voltages of the batteries in the simulation are the same as the experiment.  
Figure 9 shows the four cells' voltage waveforms in the equalization process. Figure 10 presents the separate figures for the transient voltages of cells A, B, C, and D. Figure 11 shows a comparison of the four cells  
As shown in Figure 12, the initial voltage gap (59.5 mV) decreases to 4 mV at about 0.2139 s in the simulation. In the experiment, the batteries have internal resistance, the inductors have resistance and magnetic leakage, the MOSFETs have on-resistance, the voltage detection of the battery has errors, etc. As a result, the average voltage in the experiment is lower than the simulation result.  
As shown in Figure 13, the peak value of the inductor current \(i_{L1}\) in the experiment is about 1.51 A, 4.79% lower than the simulation result (1.586 A). Considering the resistance of the experimental circuit and the components used in the experiment are not ideal, the current error is small enough. In summary, the accuracy of the equalization is excellent and experimental results agree with the analysis in Section 2.3.  
Figure 11: Voltages of the four cells before and after equalization.  
Figure 10: The cells’ voltage waveforms in the equalization process for A, B, C and D (**a**) A. (**b**) B. (**c**) C. (**d**) D.
# Browser Forensic Investigations of Instagram Utilizing IndexedDB Persistent Storage
## 1 Introduction  
Technology is advancing very rapidly. As a result, the volume and complexity of digital data are growing at an accelerated rate. On average, an individual living in the U.S. spends approximately two hours on social media sites every day, while leaving a substantial number of digital artifacts on various sites [1]. The situation is not any different offshore. A striking example is the case of [PERSON], an Austrian privacy activist, who made a request to see his personal information stored in Facebook servers and received 1222 pages of documents [2]. As the amount of data usage increases, so does the need to store it in innovative ways. This phenomenon creates a heavy burden on all individuals and forensic investigators, as the ways we communicate and utilize information are subject to constant change. Therefore, renovation and diversification of the technologies subject to digital forensics is a constant obligation.  
IndexedDB is a fairly new technology utilizing NoSQL (Not Only SQL) transactional database systems to provide persistent storage in web browsers [3]. It can provide over 50 MB of storage for each origin (distinct protocol, domain, host, and URL to access a source on the web) [4]. This is over a minimum of 45 MB of increased data storage compared to previous Web Storage technology [5]. Due to advantageous storage capability and fast access to persistent data with the utilization of JavaScript and JSON (JavaScript Object Notation) objects [6], the utilization of IndexedDB is increasing rapidly [7]. One of the websites that recently joined this trend is the popular social media site Instagram, which has over 1074 billion users worldwide [8].  
This paper scrutinizes the Instagram-generated data stored in IndexedDB technology for its digital forensic value. In particular, the extraction, processing, and presentation of artifacts are scrutinized for Mozilla Firefox implementation of IndexedDB and verified through another implementation utilized by Google Chrome. Additionally, a proof-of-concept tool is developed for the application of the given techniques. In this scope, the paper raises two research questions.  
* Is it possible to obtain information about the actions of a suspect on Instagram through artifacts populated in IndexedDB storage?
* Are the existent IndexedDB data convenient for construction of time frame analysis for the actions of Instagram users?  
The rest of the paper is structured as follows: presentation of related work, background on IndexedDB, methods of experimentation, results, discussion, description of the proof-of-concept tool, and conclusions.
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 2 The Concept of Watermarking
### Embedding Domain
#### 2.1.8 Pixel Pair Matching  
Pixel pair matching is another interesting watermarking technology. These embedding algorithms typically employ the pixel pair \((p_{i,1},\ p_{i,2})\) as a reference station to find additional positions \((p^{\prime}_{i,1},\ p^{\prime}_{i,2})\) inside a preset nearby set of \(\phi(p^{\prime}_{i,1},\ p^{\prime}_{i,2})\) to satisfy \(f(p^{\prime}_{i,1},\ p^{\prime}_{i,2})=SB\). Where\(f\) denotes the extraction function and \(SB\) is the secret number in the B-ary coding system [47]. Moreover, the authors in [48] proposed the adaptive pixel pair matching approach. As an embedding unit, two pixels are scanned, together with a specifically built neighbouring array.
## 3 Simulation Results and Analysis
### Mechanism of Coupling Effect on CH\({}_{4}\) Adsorption  
The effect of different actions on CH\({}_{4}\) adsorption can be obtained through analysis, as follows: coupling action > single temperature action > single water content action. First of all, the structure of the coal model is complex, containing a variety of functional groups. The structure of each functional group is different, with different intermolecular forces. The adsorption capacity of the gas is different, which will form adsorption sites with different adsorption capacities [26]. The interaction force between coal and H\({}_{2}\)O is stronger than that between coal and CH\({}_{4}\), and H\({}_{2}\)O is easier to combine with oxygen-containing functional groups in coal, thus occupying adsorption sites. When the water content increases, more CH\({}_{4}\) is displaced from the coal, which manifests as a decrease in adsorption. In addition, the molecular dynamics radius of H\({}_{2}\)O is smaller than that of CH\({}_{4}\). H\({}_{2}\)O enters the coal and can detect a richer specific surface area. Part of the pore surface will be covered by H\({}_{2}\)O, resulting in the weakening of the adsorption of CH\({}_{4}\) by coal and preventing CH\({}_{4}\) from binding to the adsorption sites on the coal surface. Adsorption is an exothermic reaction, with increasing temperature inhibiting CH\({}_{4}\) adsorption and decreasing temperature, promoting CH\({}_{4}\) adsorption. When water content and temperature are coupled, the addition of temperature changes the average molecular kinetic energy of CH\({}_{4}\), and CH\({}_{4}\) is more susceptible to van der Waals forces, which increases the extent of CH\({}_{4}\) adsorption effects on coal. The mechanism of the effect of coupling on CH\({}_{4}\) adsorption is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c} \hline
**Coupling Effect** & \(R^{2}\) \\ \hline  
\hline \end{tabular}
\end{table}
Table 7: \(R^{2}\) after fitting the Langmuir equation with coupled effects.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 5 Enhancing the EPR Effect  
Strategies to enhance the EPR effect have been widely studied in both preclinical and clinical settings. These settings can be broadly divided into two main categories: physical and pharmacological approaches. Physical approaches involve using external stimuli like radiation or heat to temporarily increase the permeability of tumor tissues. On the other hand, pharmacological approaches involve administering drugs that alter the tumor microenvironment to promote better nanoparticle accumulation.
## 2 Experimental Program  
### Test Specimens  
The experimental part of the presented research was carried out on a model of a reinforced concrete column, which was then strengthened with a steel tube jacket. Strengthening was achieved on a premade RC column placed in the steel tube, and the remaining empty space was then filled with fresh concrete (Figure 1). Column models with their dimensions correspond to real structure with an aspect ratio of 1 by 3.3, i.e., 3.3 times smaller than a column of a real structure. The scale of the test models was chosen in consideration of the limitations of the laboratory testing equipment, in which the largest model could be around 90 cm high. Then, an aspect ratio of 1 by 3.3 was obtained by considering that a typical RC building has a 300 cm high story. Other parameters of the test specimens, such as the column's cross-section dimensions, the steel tube wall thickness, the rebar diameter, and the aggregate sizes, were scaled appropriately. The height to diameter ratio of 4.6 for the test models was chosen, which is within the interval from three to five, as it is common in literature (e.g., [19; 21; 29]).  
A total of 14 stub axially compressed columns were tested; among those, nine were strengthened specimens, and five were plain RC specimens. Column models were divided into four groups. The first group of five models consisted of control specimens of plane RC columns (Label S0x, where x, in all series, represents the specimen number). The next  
Figure 1: A schematic view of the strengthened column (**a**), a picture of pouring fresh concrete in a steel tube (**b**), and the reinforcement arrangement (**c**).  
three groups of three models were RC columns strengthened with steel tubes and infilled with fine-grain concrete (Labels B2x, 84x, and P0x). The columns without strengthening and the columns that were strengthened had a square cross-section with dimensions of 12 by 12 cm and a height of 90 cm. All columns were made from class C35/45 concrete. The main reinforcements in columns were 8 bars with a diameter of 6 mm and closed stirrups with a 4 mm diameter. Stirrups were spaced 3 cm at the top and the bottom 20 cm of the column length and spaced 6 cm in the middle (Figure 1c).  
The compression strength of filler concrete was the main parameter that was varied in tests. Three different strength classes were used. In the first group of strengthened columns, specimens were filled with class C20/25 concrete, which is approximately half of the core RC column concrete class. The second group of specimens was filled with class C40/50 concrete, which is close to the strength class of the core RC column concrete. The specimens of the third group were filled with special, commercially available premade two-component, high-quality mortar named Polimag(r)HK-04, which had compressive strength as class C70/85 concrete.  
The main characteristics of all tested specimens are summarized in Table 1, where B is the width of the square section, D and t are the outside diameter and the wall thickness of the steel tube, respectively, and L is the length of the specimen.