esa-sceva/satcom-chunk-collection huggingface dataset:
# Metabolite Analysis of Hangzhou Gongmei White Tea of Different Varieties
## 3 Results and Discussion
### Differential Analysis of Volatile Metabolites
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Components** & **RI\({}_{\text{r}}\)** & **RI\({}_{\text{c}}\)** & **CAS** & **FD** & **JK** & **LJ** & **YS** \\ \hline
**Alkanes** & & & & & & & & \\ Octane & 800 & 799 & 111-65-9 & 0.50 \(\pm\) 0.07 a & 0.00 \(\pm\) 0.00 c & 0.39 \(\pm\) 0.04 b & 0.37 \(\pm\) 0.03 b \\
2,2,4,6,6-Pentamethylheptane & 943 & 954 & 13475-82-6 & 0.34 \(\pm\) 0.05 a & 0.18 \(\pm\) 0.02 b & 0.32 \(\pm\) 0.06 a & 0.35 \(\pm\) 0.03 a \\
3-Methyllnonane & 965 & 961 & 5911-04-6 & 0.43 \(\pm\) 0.02 a & 0.19 \(\pm\) 0.02 c & 0.28 \(\pm\) 0.05 b & 0.43 \(\pm\) 0.03 a \\ Decane & 1000 & 999 & 124-18-5 & 1.12 \(\pm\) 0.04 a & 0.56 \(\pm\) 0.03 c & 0.66 \(\pm\) 0.09 c & 0.98 \(\pm\) 0.08 b \\  & & & & 2.40 \(\pm\) 0.03 a & 0.94 \(\pm\) 0.06 c & 1.66 \(\pm\) 0.22 b & 2.13 \(\pm\) 0.16 a \\
**Alcohols** & & & & & & & \\ Pent-1-en-3-ol & 1165 & 1170 & 616-25-1 & 1.11 \(\pm\) 0.29 a & 0.74 \(\pm\) 0.26 ab & 0.66 \(\pm\) 0.08 b & 0.78 \(\pm\) 0.16 ab \\ Pentanol & 1259 & 1256 & 71-41-0 & 1.20 \(\pm\) 0.20 a & 0.75 \(\pm\) 0.14 b & 0.90 \(\pm\) 0.20 b & 0.76 \(\pm\) 0.01 b \\ (Z)-Pent-2-en-1-ol & 1334 & 1326 & 1576-95-0 & 1.61 \(\pm\) 0.31 a & 0.90 \(\pm\) 0.13 bc & 0.82 \(\pm\) 0.21 c & 1.29 \(\pm\) 0.10 ab \\ Hexanol & 1361 & 1355 & 111-27-3 & 9.33 \(\pm\) 1.33 a & 5.02 \(\pm\) 0.20 b & 4.48 \(\pm\) 0.66 b & 5.67 \(\pm\) 0.34 b \\ (Z)-Hex-3-en-1-ol & 1391 & 1991 & 928-96-1 & 17.00 \(\pm\) 2.83 a & 4.89 \(\pm\) 0.46 b & 7.68 \(\pm\) 1.27 b & 7.17 \(\pm\) 0.36 b \\ Octan-3-ol & 1393 & 1397 & 589-98-0 & 0.33 \(\pm\) 0.07 a & 0.24 \(\pm\) 0.04 ab & 0.16 \(\pm\) 0.05 b & 0.15 \(\pm\) 0.01 b \\ (_E_)-Hex-2-en-1-ol & 1412 & 1412 & 928-95-0 & 3.22 \(\pm\) 0.36 a & 1.52 \(\pm\) 0.09 c & 1.82 \(\pm\) 0.24 c & 2.57 \(\pm\) 0.27 b \\ Linalool oxide I & 1540 & 1451 & 34995-77-2 & 37.00 \(\pm\) 5.58 a & 17.12 \(\pm\) 2.30 c & 14.43 \(\pm\) 2.94 c & 24.38 \(\pm\) 1.10 b \\ Oct-1-en-3-ol & 1452 & 1456 & 3391-86-4 & 2.96 \(\pm\) 0.27 a & 3.33 \(\pm\) 0.30 a & 3.29 \(\pm\) 0.61 a & 2.32 \(\pm\) 0.04 b \\ Heptanol & 1456 & 1461 & 1111-70-6 & 6.38 \(\pm\) 0.69 a & 3.00 \(\pm\) 0.17 c & 2.89 \(\pm\) 0.47 c & 3.96 \(\pm\) 0.21 b \\
6-Methylhept-5-en-2-ol & 1467 & 1468 & 1569-60-4 & 1.90 \(\pm\) 0.21 a & 0.62 \(\pm\) 0.06 b & 0.72 \(\pm\) 0.26 b & 0.91 \(\pm\) 0.08 b \\
2-Ethylhexan-1-ol & 1496 & 1496 & 1047-67 & 41.82 \(\pm\) 4.60 a & 22.73 \(\pm\) 1.76 c & 9.66 \(\pm\) 1.18 d & 34.52 \(\pm\) 2.02 b \\ Linazol & 1552 & 1558 & 78-70-6 & 358.29 \(\pm\) 46.85 a & 172.38 \(\pm\) 1.85 b & 118.07 \(\pm\) 19.67 c & 217.22 \(\pm\) 9.60 b \\ Octanol & 1562 & 1564 & 1111-87-5 & 5.74 \(\pm\) 0.59 a & 3.43 \(\pm\) 0.28 c & 4.31 \(\pm\) 0.69 b & 4.85 \(\pm\) 0.23 ab \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The content of volatile components in HGW of different varieties (\(\mu\)g/L).
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 3 Open RAN Movement and the Related Technologies
### Technologies Related to Open RAN
Network slicing: Network slicing will not be a part of a cost-saving element in 5G, but the one that can reduce cost is network sharing. Network sharing can make savings for about 40% TCO. 4. Cloudification and open source: The deployment of cloud and open-sourced software in 5G network can save up to 5% 5G's TCO. The cost efficiency mentioned in this section can also be further improved by implementing Open RAN in 5G networks. Before Open RAN era, the previous RAN hardware infrastructure was the part that had the highest cost of all parts in RAN infrastructure. Basically, to make good RAN hardware, there must be cabinets, radio antennas, baseband processing tools, power tools, cooling tools, and other tools. These tools had made for about 45% until 50% out of RAN's TCO. In 5G technology, the infrastructure cost has increased by 65% in some deployments. This 65% cost is probably the maximum possibility for any Open RAN scenario. This cost can be less than 65% for some Open RAN's lower-cost deployment scenarios. This wide gap depends on some modifications used in the 5G Open RAN architecture. One of those modifications is adopting C-RAN's architecture deployment into the Open RAN. At least, this C-RAN adoption has saved 25%, compared to the previous generation of C-RAN cost, which is D-RAN. While C-RAN adoption has saved 25%, this number is predicted to be up to 45% in the future. While the FH has made its way to reduce the RAN's TCO, the backhaul will not be similar to FH. Because 5G connectivity requires higher capacity with lower latency, backhaul has its cost increased by 55% in some higher Open RAN deployments. This 55% number is not definitive, because the increased cost can probably be lower than this number in some lower Open RAN deployments.
## 4 Discussion
### Meteorological Influence and Spatial Patterns  
The spatial distribution of pollutant concentrations is strongly influenced by meteorological factors such as wind speed, temperature stratification, and turbulence intensity.  
In particular, our simulations demonstrate that low wind velocities (below 1 m/s) at the surface level correlate with significantly higher pollutant accumulation, especially in the central urban zone.  
Temperature gradients also play a critical role: during simulation, a vertical drop of ~7 \({}^{\circ}\)C from 17.4 \({}^{\circ}\)C at the surface to 10.6 \({}^{\circ}\)C at 500 m height was associated with strong stratification that limited vertical mixing. As a result, pollutants were effectively trapped within the lowest 200 m, and only diffusive processes enabled upward transport.  
Figure 6 shows that the concentration decay with height follows an exponential profile, which is typical in stable boundary layers. We also observe horizontal concentration gradients up to 40 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) per kilometer, reflecting the interplay between localized emission sources and heterogeneous ventilation conditions.  
Temporal analysis (see Table 4) shows that pollutant mass decreases by over four orders of magnitude within one hour, indicating that dispersion processes are effective in open domains but depend critically on initial wind velocity and surface temperature.  
These findings highlight the importance of wind channeling and temperature inversion for pollution control strategies. For example, zoning policies should avoid placing sensitive infrastructure (schools, hospitals) in topographically enclosed areas with historically weak ventilation.
# An Image Hashing-Based Authentication and Secure Group Communication Scheme for IoT-Enabled MANETs
## 4 Proposed Image Hashing-Based Authentication Scheme  
In this section, we present an image hashing-based authentication scheme (IHA). Security is a vital requirement for authentication-based IoT applications. Our scheme provides a solution for secure communication and authentication schemes for emergencies in war. The proposed scheme is subdivided into three phases, namely, node registration, node joining in a group, and the migration of nodes in a group, as shown in Figure 2.  
In the registration phase, a soldier is registered at the time of his recruitment. The NA takes the image and other credentials of the soldier stored in the local repositories and further uploads them to the private cloud server for permanent storage. Furthermore, the NA embeds this info (e.g., id, login id, password, and image hash) into the mobile device of the soldier. There will be separate mobile allocations to each soldier. In a war scenario, several soldiers leave and join the groups based on the emergency scenario. In this context, effective authentication and communication are required.  
Therefore, the proposed scheme employs a fog node that locally processes the information at the edge of the network. Fog nodes act as a NA that manages the information on the count of groups in the specific network. Moreover, the node count is also considered within these groups. NA forwards the request message for the joining of a soldier in a specific group or the migration of the soldier to a specific group in war situations. A list of notations for the proposed scheme is shown in Table 1.
# Change of Lip Curvature Through Extraction and Non-Extraction Orthodontic Treatment
## 2 Materials and Methods
### Study Sample  
Patients meeting the following criteria and treated at the orthodontic department of Ajou University Hospital dental clinic, between January 2012 and June 2021, were included in this study.  
The inclusion criteria for the study were as follows:  
1. Skeletal Class I malocclusion (0\({}^{\circ}\) < ANB angle < 4\({}^{\circ}\));
2. Adults aged 19 years and older;
3. Extraction or non-extraction orthodontic treatment;
4. Less than 6 mm crowding of each dentition.  
The exclusion criteria were as follows:  
1. Congenital tooth defects;
2. Craniofacial deformities that might affect tooth movements such as cleidocranial dysplasia or a cleft lip/palate;
3. Orthongathic surgery;
4. More than 6 mm crowding of each dentition.  
A total of 62 patients met the inclusion and exclusion criteria, with 37 in the extraction group and 25 in the non-extraction group. To compare the extraction and non-extraction factors, age-matched and similar skeletal, dental, and soft tissue cases were selected (Table 1).  
Skeletal Class II and Class III patients are often treated with diverse plans, such as extraction on one jaw and non-extraction on the other, to compensate for their skeletal discrepancies. These discrepancies frequently coincide with functional factors that can affect the soft tissue. To ensure sample homogeneity, this study exclusively included Class I patients.  
All human research procedures were followed in accordance with the ethical standards of the committee responsible for human experimentation (institutional and national), and with the Helsinki Declaration of 1975, as revised in 2013. This study was reviewed and approved by the Institutional Review Board (IRB) of Ajou University Hospital (IRB no.: AJIRB-MED-MDB-21-540). The IRB Committee waived the requirement for obtaining patient informed consent.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Demographic** & \multicolumn{2}{c}{**Extraction**} & \multicolumn{2}{c}{**Non-Extraction**} & \multirow{2}{*}{\(p\)**Value**} \\
**Data** & & **Group** & & **Group** \\ \hline Count & Male & 7 & 1 & 0.018 * \\  & Female & 30 & 24 & 0.018 * \\ Mean & Age (y) & 24.541 & 26.480 & 0.226 \\  & ANB (deg) & 2.389 & 3.090 & 0.047 \\  & FMA (deg) & 27.609 & 28.629 & 0.466 \\  & UI to FH (deg) & 117.972 & 113.904 & 0.022 \\  & IMPA (deg) & 96.181 & 95.824 & 0.856 \\  & SAL depth (mm) & \(-1.436\) & \(-1.338\) & 0.507 \\  & SAL angle (deg) & 154.727 & 156.690 & 0.438 \\  & PBL depth (mm) & \(-4.384\) & \(-4.686\) & 0.307 \\  & PBL angle (deg) & 130.552 & 124.492 & 0.081 \\  & Interlabial (gap) & 3.243 & 2.793 & 0.422 \\  & (mm) & & & \\ \hline \hline \end{tabular}  
* A Chi-Squre Test was performed. Other variables were analyzed using a \(t\)-test.  
\end{table}
Table 1: Demographic data table.
# Laser Induced Aggregation of Light Absorbing Particles by Marangoni Convection
###### Abstract  
Laser induced Marangoni convection can be used to accumulate micro-particles. In this paper, a method is developed to control and accumulate the light absorbing particles dispersed in a thin solution layer. The particles are irradiated by a focused laser beam. Due to the photothermal effect of the particles, the laser heating generates a thermal gradient and induces a convective flow around the laser's heating center. The convective flow drives the particles to accumulate and form a particle aggregate close to the laser's heating center. The motion of particles is dominated by the Marangoni convection. When the laser power is high, the vapor bubbles generated by laser heating on particles strengthen the convection, which accelerates the particles' aggregation.  
Keywords: photothermal effects; Marangoni convection; thermal gradient; particle aggregation; liquid-water interface +
Footnote †: journal: Applied sciences
# Calibration of a Low-Cost 8\(\times\)8 Active Phased Array Antenna
## 3 Active Phased Array Calibration
### Active Antenna Measurement
The comparison of the horizontal cut of the phased array radiation pattern at the center frequency of 18.95 GHz with and without calibration is shown in Figure 11a for two different reference columns. As can be observed, the radiation pattern without any calibration stage shows a diffraction lobe in the green circle, which causes a loss in the maximum directivity of the array. By selecting an arbitrary column for calibration, Figure 11a presents two calibrations that use different columns as references. A 0.5 dB improvement in the maximum value of the radiation pattern can be observed, with increased power in the main lobe and an improvement in the secondary lobes (including the diffraction lobe present in Figure 11a, with deeper nulls in both cases). Figure 11b presents the comparison of the radiation pattern using the phase calibration and REV method. The obtained results are similar, and the improvement using the REV method is insignificant, with respect to the phase calibration method proposed. Figure 11: (**a**) Measured horizontal cut of the phased array with (W) and without (WO) calibration, taking Column 4 (Col4) and Column 5 (Col5) as a reference. (**b**) Measured horizontal cut of the phased array with phase calibration and REV calibration. Figure 10: (**a**) Normalized measured initial phase response of the subarray. (**b**) Normalized measured calibrated phase response of the subarray.
# Feasibility Study of Real-Time Monitoring of Pin Connection Wear Using Acoustic Emission
## 2 Experimental Setup  
### The Specim  
To assess the performance of AE technique in monitoring the wear process of low rotational speed pin connections, four specimens of pins were fabricated. Its dimensions are shown in Figure 1. The material of the specimens was high speed steel (HSS), which is widely used to manufacture pins, bolts, and other machine parts. Considering that HSS is a common material to manufacture pins, this paper chooses HSS as the material of the pins. The chemical compositions of the specimens are summarized in Table 1. In addition, since the experiment was to study the wear process of low rotational speed pin connections, the force applied on the specimens was low, and it could not cause shear failure. The name of lubricant used in the experiment was #3 lithium base grease, which is made of lithium hydroxy fatty acid, medium viscosity mineral lubricating oil, antioxidants, and others. Since the mechanical stability, antioxidant, and antirust stability of #3 lithium base grease are excellent, it has been widely applied in water pumps, blowing machines, motors, and others. Before a test, the #3 lithium base grease would be evenly applied on the exterior surface of pins, and on the inner face of the U-shaped parts and the circular parts.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Elements** & **C** & **Mn** & **P** & **S** & **Si** & **Cr** & **Ni** \\ \hline Proportion & \(\leq\)0.07 & \(\leq\)2.00 & \(\leq\)0.035 & \(\leq\)0.030 & \(\leq\)1.00 & 17.00–19.00 & 8.00–11.00 \\ \hline \end{tabular}
\end{table}
Table 1: The content of trace elements in specimens.  
Figure 1: Dimension of pin (mm): (**a**) Side view; (**b**) front view.
# The IoT/IoE Integrated Security & Safety System of Pompeii Archeological Park
## 11 The Monitoring Systems
In the field of Cultural Heritage, COSMO-SkyMed plays a crucial role by continuously observing monuments, archeological areas, historic buildings, and cultural landscapes in both Italy and around the world. Through repeat satellite passes and interferometric analysis, it is possible to detect and track temporal changes, such as subsidence, structural deformation, unauthorized construction, and environmental degradation, offering early warning capabilities and valuable long-term trend data. The integration of these satellite data sources into the overarching Smart Mapping Interface (SMI) component--described in detail in the following sections--enables a highly advanced form of spatiotemporal monitoring. With this interface, users can not only visualize but also predict the evolution over time of numerous complex and interrelated phenomena. These include, but are not limited to, the stability of soils and archeological structures, ongoing structural deformations, hydrogeological risks, anthropogenic pressures (such as illegal excavation or unauthorized access), the proliferation of weed vegetation, and the presence and diffusion of airborne or surface-level pollutants, which may negatively impact exposed heritage materials and surfaces. To support this vast and multi-faceted ecosystem, a powerful artificial intelligence (AI) engine has been developed and deployed. This AI system is underpinned by a variety of advanced technologies and methodologies, including Big Data analysis, video analytics, data mining techniques, and in some cases, blockchain-based data certification and traceability. These tools enable the system to perform predictive analytics across a wide range of parameters and threat vectors. For example, the AI engine can identify patterns that precede environmental deterioration, detect subtle changes in vegetation that signal underground anomalies, or correlate climate data with structural stresses, thereby helping experts anticipate critical situations before they manifest visibly. In this way, the technological platform not only serves as a real-time monitoring and management system, but it also evolves into a proactive and preventive decision-support tool, contributing to the preservation, security, and sustainable management of one of the world's most valuable archeological and cultural heritage areas.
## 3 Component Selection and Criteria
### Estimation of \(C_{L},C_{D}\), and Moment of Inertia  
#### 4.3.1 Vertical Take-Off  
The aerodynamic force equilibrium of this UAV for vertical takeoff and landing was defined using Equation (11) as follows:  
\[\text{Lift }=\text{Weight }+\text{ Drag }\Rightarrow\text{ Lift }-\text{ Weight }-\text{ Drag }=0 \tag{11}\]  
In the multirotor-based propulsive system, the performance factors such as coefficient of lift and coefficient of drag to be estimated as follow as:  
\[\frac{1}{2}\rho(\text{V}_{\text{UAV}})^{2}\text{S}_{\text{P}}\text{C}_{\text{ L}}=\text{Lift}=\text{Thrust}=\text{T}=0.5\times\rho\times\text{A}_{\text{P}} \times[(\text{V}_{\text{UAV}})^{2}-(\text{V}_{0})^{2}];\]  
\[\text{C}_{\text{L}}=\frac{0.5\times\rho\times\text{A}_{\text{P}}\times[(\text{ V}_{\text{UAV}})^{2}-(\text{V}_{0})^{2}]}{\frac{1}{2}\times\rho\times\text{S}_{ \text{P}}}\Rightarrow\text{C}_{\text{L}}=1-\frac{\text{V}_{0}^{2}}{(\text{V}_ {\text{UAV}})^{2}};\text{ and}\]  
\[\text{C}_{\text{L}}=1-\frac{4^{2}}{(33.248)^{2}}\Rightarrow\text{C}_{\text{L}} =0.98552.\]  
From Equation (11), relatzionships can be derived as follows:  
\[\left(\frac{1}{2}\right)\times\rho\times(\text{V}_{\text{UAV}})^{2}\times \text{S}_{\text{P}}\times\text{C}_{\text{L}}=(4.43\times 9.81)+\left(\frac{1}{2} \right)\times\rho\times(\text{V}_{\text{UAV}})^{2}\times\text{S}_{\text{D}} \times\text{C}_{\text{D}};\]  
\[0.5\times 1.2256\times 33.25\times 33.25\times 8\times 0.0163\times 0.98552=(4.4 3\times 9.81)+\left(\frac{1}{2}\right)\times\rho\times(\text{V}_{\text{UAV}})^{2} \times\text{S}_{\text{D}}\times\text{C}_{\text{D}};\]  
\[86.85-43.426908=\left(\frac{1}{2}\right)\times\rho\times(\text{V}_{\text{UAV} })^{2}\times\text{S}_{\text{D}}\times\text{C}_{\text{D}};\text{and}\]  
\[\text{C}_{\text{D}}=\frac{70.8601}{(\text{V}_{\text{UAV}})^{2}\text{S}_{\text{ D}}}\Rightarrow\text{C}_{\text{D}}=\frac{0.06411}{[8*0.0163+0.032+0.0314]} \Rightarrow\text{C}_{\text{D}}=0.3314.\]  
where, \"\(\text{C}_{\text{L}}\)\" is coefficient of Lift, \"\(\text{C}_{\text{D}}\)\" is coefficient of Drag, \"\(\text{S}_{\text{P}}\)\" is planform area of Propeller, \"\(\text{A}_{\text{P}}\)\" is cross sectional area of the propeller, \"\(\text{S}_{\text{D}}\)\" is drag contributing outer area of the octocopter, \(\text{V}_{\text{UAV}}\) is the induced velocity by the UAV's propeller in m/s, and \(\text{V}_{0}\) is the atmospheric fluid velocity in m/s.
# On the Effect of DLC and WCC Coatings on the Efficiency of Manual Transmission Gear Pairs
## 2 Methodology
### Experimental Measurements  
In order to have realistic input data for the numerical simulations, experimental measurements of the surface characteristics of DLC-type coatings, commonly used in automotive applications, were conducted. Two different coatings were chosen--DLC and WCC. The plain DLC coating consists of sp2 graphite and sp3 graphite, while the WCC coating also has layers of tungsten carbine (WC). For the measurement of the surface properties of both materials, steel discs with 58 mm radius and 10 mm thickness were manufactured while the coating was applied by using the Physical Vapor Deposition (PVD) method. The coating film thickness was 3 um. In order to obtain the coefficients of boundary shear strength of the asperities [27], an atomic force microscope (AFM) in lateral force microscopy (LFM) mode was used, as shown in Figure 6.  
Bruker DNP-10 probes with four tips were used for the AFM measurement. The tip is placed on a cantilever with spring constant of 0.350 Nm\({}^{-1}\) and tip radius 20 nm. Prior to each measurement calibration was conducted. The pressure coefficient of boundary shear strength of the two examined coatings was measured under both dry and wet contact conditions for a range of applied loads with a scan area of 3 \(\upmu\)m\({}^{2}\), measuring 1024 data points. For the wet measurements, transmission oil was used. Figure 7 shows the normal load vs. friction variation obtained from the AFM for a DLC disc.  
Similar results were gathered for the rest of the discs and the WCC coated ones. Through these friction measurements, the pressure coefficient of boundary shear strength can be obtained. Table 2 summarizes the measurements taken for both coatings (wet and dry conditions).  
Figure 6: Atomic force microscope (AFM).  
Figure 7: AFM measurement of diamond-like-carbon (DLC)-coated disc dry.  
The results presented show that the values measured under wet contacts are considerably lower than those in dry contacts. The results also show that the two coatings tend to have slightly different properties, and these might lead to different contribution on power loss since it also depends on the characteristics of the gears and their operating conditions.  
Cronbach Alpha statistical method was applied to verify that the measurements taken are reliable. The method requires the calculation of the alpha (a) parameter for the sample of data examined, and any value above 0.7 is acceptable.  
\[\text{a}=\frac{n}{n-1}\bigg{[}1-\frac{\sum\var_{i}}{var_{total}}\bigg{]} \tag{26}\]  
where \(n\) is the sample size, \(var_{i}\) is the variance of disc \(i\), and \(var_{total}\) is the variance of the observed total test scores. For the measurements of the coating properties seven different discs were used. The DLC ones scored an alpha value of 0.80 while the WCC scored 0.70, making both sets of measurements reliable to use for simulating coated surfaces.
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
### Brief OCR Process  
Arabic OCR systems involve several complex steps, and Figure 2 shows the brief overflow of specific OCR steps to be followed. The image is first preprocessed to improve its quality and make recognization easier, which includes operations such as skew correction, noise reduction, and contrast enhancement. The text area is then broken down into individual segments of characters or words. The segmented characters are then recognized, and the best match is selected using a database of known characters. The recognized text then undergoes further processing to correct errors and improve accuracy. The final result is a machine-readable text document that software applications can edit, search, and analyze.