esa-sceva/satcom-chunk-collection huggingface dataset:
# Wettability of Carbonate Reservoir Rocks:
## 3 Materials and Experiments  
### Plug Samples and Routine Tests  
Nineteen core plug samples (with 1.5-inch diameter and 2-inch length) were collected from different depths of a single well in a carbonate reservoir located in the southwest of Iran. The American Petroleum Institute (API) standard of RP40 was employed to prepare the core plugs for routine and special core analysis (RCAL and SCAL) tests. The core plugs were first cleaned using toluene in the Soxhlet extractor in which clearness of the solvent was used as the indicator of a hydrocarbon-free sample. Plugs were then dried in the oven for 48 h. The cores were then cleaned using methanol to eliminate any existing salt crystals where clearness of the extracts in the presence of Silver Nitrate (AgNO\({}_{3}\)) was adopted as the indicator of a salt-free sample. Consequently, the samples were again dried in the oven at 90 \({}^{\circ}\)C for 48 h. After that, the samples were cooled down in desiccators and used for the helium porosity and air permeability measurements.  
The petrophysical characteristics (helium porosity and air permeability) of the rock samples are summarized in Table 1. [PERSON] et al. [67] proposed global hydraulic element-star (GHE*) as a graphical template to compare single-phase rock types from different sources (i.e., formations, reservoirs, fields, etc.). Their approach is based on the value of the reservoir quality index (\(RQI\)) of [PERSON] [68] that is used widely in the literature, e.g., by [PERSON] [69] and [PERSON] et al. [70]. The \(RQI\) is given by  
\[RQI=0.0314\sqrt{\frac{k}{\phi}} \tag{13}\]  
where \(\phi\) is the porosity in fraction, \(k\) is the permeability in millidarcy, and \(RQI\) is the reservoir quality index in microns.  
The GHE* framework consists of a template comprising ten preset classes, as presented in Figure 1. In this figure, each line on the template corresponds to a given \(RQI\) value and describes the lower boundary of a prebuilt reference GHE* class. We used this technique to identify single-phase rock types.  
Figure 1: Permeability against porosity for the 19 carbonate rock samples as well as the ten preset classes shown by different GHE* curves. Each line on the template corresponds to a given \(RQI\) value and describes the lower boundary of a prebuilt reference GHE* class. As can be seen, the plug samples have been distributed in GHE* classes 3 and 4.  
Table 1 includes the GHE* number and _RQI_ value corresponding to each sample. As reported in Table 1, the plugs studied here belong to GHE* classes 3 and 4. This limited number of rock types is probably because porosity and permeability only span a factor of nearly two and seven, respectively, in the studied database. It is noteworthy to mention that the original GHE technique was presented by [PERSON] and [PERSON] [71] based on the flow zone indicator (_FZI_) proposed by [PERSON] et al. [72] as follows  
\[FZI=\frac{0.0314\sqrt{\frac{k}{\phi}}}{\frac{\phi}{1-\phi}} \tag{14}\]
# Prediction of Residual Stress of Carburized Steel Based on Machine Learning
## 4 Results and Discussion
### Prediction Results and Analysis of Residual Stress  
#### 4.3.1 Residual Stress Prediction Principle  
Residual stress is caused by stresses that include thermal and phase transition stresses. There are two explanations for the residual stress in the quenched part. One explanation involves the thermal stress caused by the temperature difference between the surface and the core of the quenched part, and the second explanation involves the structural stress caused by the phase change and the integrated value. However, because the surface of the sample is carburized, the carbon content of the carburized layer changes only gradually; the carbon content is high at the surface but low in the core. The change in carbon content plays a leading role in the residual stress value. Therefore, the residual stress is mainly attributable to the cooling process, and the volume expansion of the martensite transformation varies due to the differences in carbon content in different parts of the surface layer. In addition, after quenching, the surface morphologies of the martensite and transition layers are inconsistent and form a structural gradient. The carburized surface layer consists of acicular martensite and residual austenite, the transition layer consists of acicular martensite and batten martensite plus some residual austenite, and the core consists of batten martensite and a small number of residual austenite bodies. After the martensite transformation occurs during quenching, the martensite solid-solution carbon content in the surface carburized layer is high, resulting in increased hardness compared with the core after quenching and the formation of a hardness gradient. When the high carbon content of the surface layer is martensitic, it undergoes a greater volume expansion than that of the low-carbon-content core. Thus,  
Figure 8: (**a–e**), comparison of the retained austenite measurements and the predicted values after deep learning identification; (**f**) Surface microstructure picture.  
a residual compressive stress gradient is formed. Therefore, the carbon content and microstructure can be used to predict the residual stress. This article trained a model using 1200 sets of data from Process 1 to Process 5 ranging from the surface to the heart. Some of these data are shown in Table 4. Each dataset is a \(256\times 256\) pixel picture; based on deep learning image segmentation, each picture is analyzed for acicular martensite, retained austenite, lath martensite, and the residual stress at the corresponding points.
## 2 Materials and Methods
### Search Strategy and Data Sources  
The search terms included combinations of \"telemedicine,\" \"eHealth,\" \"digital health,\" AND \"Poland,\" using Boolean operators (AND, OR). The search was limited to English-language publications. The initial query identified 1012 records, of which 852 were published between 2014 and 2024. After restricting the results to this timeframe and conducting deduplication and relevance screening, a final dataset comprising 212 peer-reviewed publications was selected for in-depth review. Duplicates were removed using EndNote's deduplication function, followed by manual screening to ensure accuracy.  
Based on the number of articles on specific specializations and fields, the most frequently represented ones (four or more articles from a given specialization) were selected, and field-specific comments were added. For comparison, the remaining articles (three or fewer articles) were analyzed and described collectively with references to Supplementary Table S1. To enrich the dataset and provide a policy context, the national context was considered based on gray literature, national reports, and strategic policy documents using targeted searches in the Semantic Scholar database, digital archives of the MH, NFZ, and Center of eHealth (formerly Central Center for Information and Health Systems (CSIOZ), and selected European health repositories. However, these studies were excluded from analysis.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 5 Results and Testing
### Steganography Result  
After the image encryption, testing, and video segmentation process, the masking process occurs, where the encrypted image is hidden in the selected video and transmitted. PSNR assessed the stego-video's quality, which stayed above 42.67 dB, guaranteeing no perceptual deterioration. Also, SSIM measures above 0.98 confirmed the strong resemblance between the original and stego-video. The steganographic embedding and its visual outcome are illustrated in Table 10, which demonstrates the process of hiding the encrypted image into a video frame and reconstructing the stego-video.  
The system recovers and rebuilds the concealed data after it embeds the encrypted image into the chosen video frames. While maintaining the integrity of the video, the extraction procedure guarantees that it can entirely retrieve the original encrypted image.
# Full-Field Vibration Measurements by Using High-Speed Two-Dimensional Digital Image Correlation
## 3 Experiment and Result
### Single-Frequency Vibration Measurement  
To test the vibration measurement capability of an HS-2D-DIC, single-frequency vibration experiments were also carried out. A fascia gun was used as the excitation source and a flat plate with a speckle pattern was used as the specimen, as shown in Figure 7a. In the experiment, the out-of-plane displacements of the specimen with three different frequencies were generated using the excitation source, and the specimen's movements were measured using the HS-2D-DIC system. The working distance and sampling rate of the HS-2D-DIC system are 500 mm and 1000 _f_ps, respectively. The reference vibration frequencies of the specimen are 19.82 Hz, 29.33 Hz, and 38.85 Hz, respectively, and are measured using a tachometer. The out-of-plane displacements measured by the HS-2D-DIC are shown in Figure 7b-d. The frequencies estimated from the measured displacements are 20.02 Hz, 29.08 Hz, and 38.95 Hz, respectively, which are very close to the reference values. The absolute values of the relative errors are less than 1%, validating the effectiveness of the proposed method in measuring the specimen's vibrations.  
Figure 6: Results of the out-of-plane translation tests with working distances of (**a**) 500 mm, (**b**) 1000 mm, and (**c**) 1500 mm.
# Potential of Liquid-Crystal Materials for Millimeter-Wave Application
## 2 Measurement of Complex Refractive Indices in Millimeter Wave
### Evaluation of Refractive Index and Absorption Coefficient
Although the reason for the discrepancy is not clear at this stage, a precise interference model of the reflection MMWs should be introduced to determine the loss parameter instead of the simple calculation model used here. Although some challenges still remain for measuring the LC materials, since a cell structure must be introduced onto the Si substrate surface, the potential of the optical short working as a noncontact movable reflector was verified in the experiment.
## 3 Experimental and Analysis
### Qualitative Experiments  
The loss and mAP curves during the training process before and after the model improvement are shown in Figure 12. As seen in Figure 12a, within the first 110 training epochs, the mAP value of the YOLOv5s model was higher than that of the proposed model. Around epoch 130, the mAP values of both models were equal. Subsequently, the mAP value of the proposed model surpassed that of YOLOv5s and eventually stabilized at around 0.98. As seen in Figure 12b, the initial loss values of both models were around 0.16. In the first 50 training epochs, the loss value of the proposed model decreased rapidly, indicating higher learning efficiency in the early stages. After approximately 100 training epochs, the loss values of both models tended to stabilize, and the models gradually converged. Throughout the entire iteration process, the loss value of the proposed model remained consistently lower than that of the original model. At the end of 300 training epochs, the loss value of the proposed model was around 0.035, while the loss value of YOLOv5s was around 0.045. The results indicated that, under the same conditions, the proposed model demonstrated a more significant performance advantage in the target detection task.  
Table 2 shows the detection results for each category of engineering vehicles before and after the model improvement. It was observed that the mAP value of the improved CER-YOLOv5s model reached 98.3%, an increase of 2.9% compared with the original YOLOv5s. The average precision for all four categories of engineering vehicles also showed significant improvement, with the truck category seeing the most substantial increase, improving by 4.8 percentage points compared with before the improvement. The experiments indicated that the improved model enhanced the overall detection capability for various types of targets.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Numerical Value** \\ \hline epochs & 300 \\ batch size & 16 \\ image size & 640 \(\times\) 640 \\ initial learning rate & 0.01 \\ optimizer & SGD \\ momentum & 0.937 \\ weight decay & 0.0005 \\ \hline \end{tabular}
\end{table}
Table 1: Experimental parameter setting.
# Demand Analysis of Lubricating Oil in Spur Gear Pairs
## 4 Effect of Gear Parameters
### Effect of Modulus  
Modulus is a basic parameter of the gear that determines its size and exerts a significant impact on transmission performance. Moduli are set to 3, 4, 5, 6 and 8, and the other parameters listed in Table 1 remain the same. Several lubrication simulations are conducted to analyse the influence of modulus on lubricating oil demand.  
The variation in oil volume for the lubrication of the gear under different moduli during the meshing process is obtained, as shown in Figure 5(a).  
This figure indicates that the length of the actual meshing line, the width of the first two teeth area, the width of the single tooth area and the width of the second two tooth area increase as the modulus of the gear increases. In addition, the oil volume for the lubrication of the gear increases with increasing modulus of the gear because the diameter of the gear increases with increasing modulus. The entrainment velocity and oil film thickness at the same rotational speed increase as the modulus of the gear increases. Notably, the gaps of the oil volume for the lubrication between the two tooth meshing and the single tooth meshing areas increase as the modulus of the gear increases.  
The preceding analysis demonstrates that the friction coefficient is the primary influencing factor of the oil volume for cooling. The variations in the friction coefficient at different moduli with gear rotation are obtained, as shown in Figure 5(b), including a partially enlarged image. The friction coefficient decreases as the modulus increases, because the radius of the tooth surface curvature increases as the modulus of the gear increases and the friction coefficient is inversely proportional to the radius of the curvature.  
A series of simulations is performed to examine the influence of different moduli of the gear on the oil volume for cooling, and an enlarged image of the single meshing area is shown in Figure 5(c). The oil volume for cooling decreases as the modulus of the gear increases due to the friction coefficient.  
Figure 6: Oil volume for lubrication (**a**), friction coefficient (**b**), and the oil volume for cooling (**c**) under different moduli during the meshing process.
## 3 Results and Discussions
### Carbon Mass Balance during the AD Process  
One of the main reasons for the lower biogas produced during the AD process is possibly the residual carbon in the CSTR. Therefore, the carbon mass balance was evaluated during the AD process. The carbon fed inside the reactor was present in the solid and liquid fractions of SE pretreated \(L\). _grandiflora_. The carbon content in the pretreated solid and liquid fractions was 42.9 \(\pm\) 0.5%TS and 28.0 \(\pm\) 0.5%TS, respectively (Table 2). The carbon mass balance during the AD operation can be seen in Table 3. The feeding in the CSTR reactor was performed every 2 days, leading to an increase in the carbon amount inside the reactor (Table 3). However, because of the lower degradability and high lignin of \(L\). _grandiflora_, as discussed above, the carbon content in the biogas and effluent was significantly less compared with that in the fed carbon. Therefore, the carbon continued to accumulate inside the digester measured as residual carbon. The carbon kept accumulating (85.20\(-\)98.88%) in the reactor, indicating a large amount of undigested carbon, which could be a potential source for stable AD processes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Operational** & **Fed Carbon** & **Biogas** & **Effluent** & **Residual** & **Residual/Fed** \\
**Day** & **(kg)** & **Carbon (kg)** & **Carbon (kg)** & **Carbon (kg)** & **Carbon (\%)** \\ \hline
0 & 0.12 & 0 & 0 & 0.12 & 100 \\
7 & 1.96 & 0.15 & 0.14 & 1.67 & 85.20 \\
21 & 14.64 & 0.40 & 0.39 & 13.85 & 94.60 \\
35 & 39.32 & 0.66 & 0.60 & 38.06 & 96.79 \\
42 & 53.88 & 0.76 & 0.69 & 52.43 & 97.30 \\
58 & 76.51 & 0.87 & 0.77 & 74.87 & 97.85 \\
70 & 107.33 & 0.94 & 0.86 & 105.54 & 98.33 \\
84 & 159.22 & 1.10 & 1.02 & 157.10 & 98.67 \\
91 & 188.66 & 1.19 & 1.10 & 186.37 & 98.79 \\
98 & 211.10 & 1.29 & 1.18 & 208.63 & 98.88 \\ \hline \hline \end{tabular}  
* Fed carbon = the carbon content in the solid and liquid fractions of the pretreated \(L\). _grandiflora_. Biogas carbon = the carbon content in methane and carbon dioxide. Effluent carbon = the carbon content in the digestate removed from the AD reactor. Residual carbon = the remaining carbon in the AD reactor. Residual carbon = \(|\).Fed carbon (solid + liquid fraction) \(-\)\(\sum\)|Biogas carbon + Effluent carbon\(|\)].  
\end{table}
Table 3: Carbon mass balance during the AD process.
## 4 Experimental Results and Discussion
### Experimental Results and Comparative Evaluation
#### 4.3.3 Ablation Experiment  
An ablation experiment was performed on the same dataset to evaluate the influence of the introduced modules on the detection performance of infrared devices, with the results presented in Table 2. The comparison of results highlights that the YOLOv8n-C, YOLOv8n-CG, YOLOv8n-CG, and YOLOv8_Adv models demonstrate notable enhancements in [EMAIL_ADDRESS] values over the baseline YOLOv8n model, achieving improvements of 2.1%, 2.7%, 3.4%, and 4.1%, respectively. This indicates that the introduced modules have played a positive role in enhancing detection performance.  
Specifically, YOLOv8n-C successfully reduced the computational load by incorporating the C2f-fast module into the backbone network, leading to an 8.9% decrease in GFLOPs. This result demonstrates the effectiveness of its lightweight improvement, enabling the model to perform computations more efficiently while maintaining a certain level of accuracy. YOLOv8n-CG adopted the GSConv module in the neck part, which not only improved the model's accuracy but also reduced GFLOPs by 13.7%. This improvement highlights the dual advantages of the GSConv module in enhancing performance and reducing computational complexity, thereby increasing the model's applicability in real-world scenarios.  
Furthermore, YOLOv8n-CGV combined the GSConv and VoVGSCSP modules in the neck part, resulting in an 18.5% reduction in GFLOPs. This combination not only optimized the model's computational efficiency but also enhanced its detection capability in complex scenarios, demonstrating the effectiveness of multi-module collaborative work. Finally, expanding upon YOLOv8n-CGV, the YOLOv8_Adv model incorporates the EMA attention mechanism, which significantly strengthens its ability to extract and utilize key features, leading to substantial accuracy gains. These experimental results validate the contributions of individual modules and highlight the strategic advantage of modular design in optimizing object detection systems.
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
## 2 Evolution of Single Antenna Element
### Performance Comparison of the Single Antenna with Previous Works  
The comparison in Table 2 highlights the circular-shaped design's performance against existing 5G antennas at sub-6 GHz. The suggested antenna exhibits higher gain and a broader operational frequency range compared to the maximum bandwidth and gain of previously published antennas [30; 31; 32]. Although antenna [31] is relatively compact in size, the proposed design has a greater gain and wider bandwidth than it.  
Figure 8: Parametric analysis results for variation of (a) r and (b) c for (S11).  
Figure 7: Current distribution for (a) the centered slot and (b) the shifted down slot.  
The proposed single element design improves max percentage gain compared with previously described designs [30; 31; 32] by 72%, 202%, and 86%, respectively.
# Building Fire Evacuation: An IoT-Aided Perspective in the 5G Era
## 4 Preliminary Design of an IoT-Aided Building Fire Evacuation Control System
### Information Sources and Data Transmission  
To enable the collection of the information needs as mentioned above in building fire evacuation, there is the need to determine the perception devices and their means of communication, which formulate the perception and the network layers in the IoT architecture.
## 2 Materials and Methods  
There were three phases of the research methodology for this study to address the three objectives. Phase one addresses the first objective, which is to develop a seismic microzonation map of the Malay Basin for offshore facilities. It consists of a data collection process for ground response analysis input, such as seismic time histories, peak ground acceleration, and soil properties. During this phase, one developed program, known as the nonlinear earthquake site response analysis of layered soil deposits (NERA), has been adopted. This one-dimensional nonlinear analysis produced output, such as peak surface acceleration (PSa) and response spectra (RS). Peak surface accelerations were analyzed to develop seismic microzonation mapping for the Malay Basin to address objective number one.  
Phase two of this study addresses the second objective. PSa was used to determine soil amplification factors. The obtained soil amplification factors were used to produce maps of soil amplification factors for the Malay Basin. In phase three, horizontal RS were analyzed to produce design horizontal response spectral acceleration and site coefficient values (Ca and Cv) for soil types D and E of the available data in the Malay Basin to address objective number three. As stated in ISO 19901-2 [26], soil type D represents stiff to very stiff soil, while soil type E is classified as soft to firm soil. Soil classification in this study is based on the average soil shear wave velocity (Vs) of the top 30 m of the seabed. Equation (1) was used in determining _Vs_ for the study area. The detailed flowchart for this research methodology is shown in Figure 4.  
\[V_{S}=30/\sum_{i=1}^{n}\frac{d_{i}}{Vs_{i}} \tag{1}\]