esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Dilatometer Tests Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Zone** & **Description** & \(\rho\) **(t/m3)** & \(\rho_{d}\) **(t/m3)** & **Valid Ratio \(\epsilon\) (\(\chi\))** & **\(w_{w}\) (\%)** & **\(p_{1}\) (MPa)** & **\(I_{SDMT}\) (\(\chi\))** & \(\tau_{p_{d}}\) (MPa) from FV** \\ \hline \multicolumn{8}{c}{Residual mineral soils} \\ \hline
1 & _Carne sand (CSo)_ & 1.70 \(\div\) 2.15 & 1.65 \(\div\) 2.0 & 0.20 \(\div\) 0.48 & 4.0 \(\div\) 25 & 0.2 \(\div\) \(p_{1}\)\(\leq\) 10 & 0.0 \(\div\) 0.1 & 0.10 \(\div\) \(\tau_{p_{d}}\) 0.30 \\
2 & _Medium sand (MSa)_ & 1.70 \(\div\) 2.15 & 1.65 \(\div\) 2.0 & 0.20 \(\div\) 0.48 & 4.0 \(\div\) 25 & 0.2 \(\div\) \(p_{1}\)\(\leq\) 10 & 0.1 \(\div\) 0.2 & 0.10 \(\div\) \(\tau_{p_{d}}\) 0.30 \\
3 & _Fine sand (Fai)_ & 1.70 \(\div\) 2.15 & 1.60 \(\div\) 2.0 & 0.20 \(\div\) 0.48 & 5.0 \(\div\) 28 & 0.2 \(\div\) \(p_{1}\)\(\leq\) 10 & 0.2 \(\div\) 0.3 & 0.10 \(\div\) \(\tau_{p_{d}}\) 0.30 \\
4 & _Silty sand(sai)_ & 1.70 \(\div\) 2.15 & 1.59 \(\div\) 2.0 & 0.20 \(\div\) 0.48 & 5.0 \(\div\) 28 & 0.2 \(\div\) \(p_{1}\)\(\leq\) 10 & 0.3 \(\div\) 0.4 & 0.10 \(\div\) \(\tau_{p_{d}}\) 0.30 \\
5 & SLITUS & 1.60 \(\div\) 2.10 & 1.40 \(\div\) 1.90 & 0.18 \(\div\) 0.70 & 10 \(\div\) 30 & 2.0 \(\div\) \(p_{1}\)\(\leq\) 10 & 0.4 \(\div\) 0.75 & 0.02 \(\div\) \(\tau_{p_{d}}\) 0.5 \\
6 & CLAY(Cl) & 1.60 \(\div\) 2.10 & 1.50 \(\div\) 1.70 & 0.18 \(\div\) 0.40 & 20 \(\div\) 40 & 0.5 \(\div\) \(p_{1}\)\(\leq\) 2 & 0.4 \(\div\) 0.75 & 0.02 \(\div\) \(\tau_{p_{d}}\) 0.6 \\ \hline \multicolumn{8}{c}{Organic soils} \\ \hline
7 & Cytta (Gy) & 1.20 \(\div\) 1.00 & 0.50 \(\div\) 0.60 & 2.5 \(\div\) 3.2 & 110\(\div\) 1500 & 0.09 \(\div\) 10.2 & & 0.0125 \(\div\) \(\tau_{p_{d}}\) 0.025 \\
8 & Mud (M) or Organic mud (Mord) & 1.25 \(\div\) 1.70 & 0.54 \(\div\) 0.67 & 2.6 \(\div\) 3.2 & 110\(\div\) 1400 & 0.2 \(\div\) \(p_{1}\)\(\leq\) 0.5 & 0.40 \(\div\) \(I_{SDMT}\)\(\leq\) 1.0 & 0.0255 \(\div\) \(\tau_{p_{d}}\) 0.0505 \\
9 & & & & & & & \(\tau_{p_{d}}\) 0.0125 \\
10 & & & & & & & \(\tau_{p_{d}}\) 0.0125 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Proposed soil classification based on \(I_{SDMT}\) and \(p_{1}\) from in situ (DMT) tests. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Soil Types** /**Parameters**} & \multicolumn{3}{c}{**Residual Mineral Soils**} & \multicolumn{3}{c}{**Organic Soils**} \\ \cline{2-5}  & **Non-Cohesive Soils** & **Coftija (Gy)** & **Mud (M)** & **Organic Mud (M\({}_{\text{eq}}\))** & **Peat** \\ \hline \(I_{SDMT}\) & 0.0 \(<I_{SDMT}\)\(\leq\) 0.4 & 0.4 \(<I_{SDMT}\)\(\leq\) 0.75 & 0.4 \(<I_{SDMT}\)\(\leq\) 1.0 & \\ \(P_{1}\) (MPa) & 0.1 \(<p_{1}\)\(\leq\) 10 & 1.0 \(<p_{1}\)\(\leq\) 10 & 0.09 \(\div\) 1.0 \(<p_{1}\)\(\leq\) 0.2 & 0.2 \(<p_{1}\)\(\leq\) 0.5 & \(p_{1}\)\(<\) 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Proposed soil classification based on \(I_{SDMT}\) and \(p_{1}\) from in situ (DMT) tests. Figure 11: Chart estimating the type and state of residual mineral soils and organic soils.
# Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article
## 5 Clinical Applications of Ultrasound Elastography
### Breast
#### 5.3.4 Technical Limitations in Thyroid Elastography  
The primary problem with thyroid elastography is that external artificial compression in strain scanning causes operator-dependent variability. Moreover, the unpredictable nature of the stiffness of a tissue causes higher stiffness measurements. Furthermore, ultrasound is ineffective for nodules with cystic components, as fluid movement does not indicate solid component stiffness [112; 113].
## 2 Fatigue Test Conditions and Results  
### Test Material and Fatigue Test Conditions  
The material used in this study was a metastable \(\beta\) titanium alloy Ti-22V-4 Al (Material code DAT51 [18]), and the chemical composition of this alloy is listed in Table 1. The as-received materials were \(\Phi\)18 mm \(\times\) 250 mm round bars. [PERSON] et al. investigated the effect of heat treatment conditions on the mechanical properties of this alloy [19]. Based on their results, we conducted experiments to obtain a good balance between tensile strength and elongation. The optimal heat treatment process for the as-received materials was determined to be as follows: solution treatment at 1023 K for 1 h followed by water quenching, and aging at 823 K for 4 h followed by air cooling. The heating process of solution treatment and aging were carried out as follows: first, the bars were set into an ambient temperature furnace and then heated gradually to the target temperature at a heating rate of 50 K/h to obtain a homogenous and fine \(\alpha\)-phase precipitation in the \(\beta\) grain, as shown in Figure 1. The average grain size of the heat-treated material was approximately 20 \(\upmu\)m. The mechanical properties, which are the average values from three tests, are listed in Table 2. The tensile strength (1235 MPa) is higher than that of widely used \(\alpha+\beta\) titanium alloys, which exhibits around 900 to 1100 MPa. In addition, the ductility (Elongation: 9.3%, Reduction of Area: 21.9%) provides an excellent workability. The specimens used for the fatigue test had an hourglass configuration with a 4-mm diameter and 6-mm straight length of the control volume. To diminish the influence of the work-hardened layer caused by the lathe turning process, the specimen surface was finished by polishing with #120 to #2000 emery papers.  
Figure 1: Microstructure of the applied Ti-22V-4 Al alloy with a homogenous and fine \(\alpha\)-phase precipitation from SEM analysis: (**a**) low-magnification and (**b**) high-magnification images. The material was polished to a mirror finish and then etched with [PERSON]’s reagent (HF:HNO\({}_{3}\):H\({}_{2}\)O = 1:2:50) for 8–10 s.  
A uniaxial hydraulic servo fatigue testing machine developed in our laboratory was used to provide a sinusoidal loading of 120 Hz. Three stress ratios (\(R=\) minimum stress/maximum stress) were chosen for fatigue tests (\(R=-1\); \(R=-0.5\); \(R=0.1\)), and the experimental run-out was set at \(10^{8}\) cycles.
# Fault Detection and State Estimation in Automatic Control
## 3 Conclusions  
This editorial letter describes the roles of fault detection and state estimation in automatic control systems and highlights the applications of advanced methods and cutting-edge technologies in recent years. In particular, with the development of artificial intelligence, the convergence of deep learning and integration methods has attracted significant attention. These powerful AI-driven methods are designed for fine-grained action recognition, predictive models for fault identification, and fault-tolerant control in complex multi-sensor systems. Other research efforts in the Special Issue advance fault detection and state estimation in both theory and practice. These studies show that fault detection and state estimation have become particularly important in industrial production processes as the level of technology and the scale, complexity, and automation of modern industrial production increase.
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 3 The Importance of Quantum Key Distribution (QKD)
### The Challenges of QKD in 5G Implementation
#### 3.1.3 Efficiency  
The third challenge is efficiency. Efficiency can be justified by the number of classical and quantum resources used to transfer an amount of information. Currently, the QKD protocol only provides a steadiness key over 50 km with the only secure key rate of 1 Mb/s. With that, no QKD protocol can prove its efficiency to travel over 300 km using optical fiber. Thus, it is necessary to improve the efficiency over quantum channel. However, this approach is not able to guarantee end-to-end security.
# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 4 Detection Methods
### Methods Based on Filtering
#### 4.2.3 Joint Spatial/Frequency Analysis Methods  
Gabor Transform  
Since the Fourier transform lacks spatial localization ability, it has a poor performance in practical applications. In order to solve this problem, the windowed Fourier transform was  
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Name** & **Strengths** & **Weaknesses** \\ \hline Spatial domain & A more centralized test-based approach (in which the segmentation of the text file is separate from the image). & Difficult to determine the optimal filter parameters. \\ \hline Frequency analysis & Spatial frequency spectrum is invariant to shift, rotation, and scaling. & Lack ability of spatial orientation. \\ \cline{2-3}  & Suitable for the detection of global and local defects. & Not suitable for random texture detection. \\ \cline{2-3}  & FFT Fast Fourier transform) calculation time is short (600 pixels with 2.2 ms). & Difficult to determine the optimal filter parameters. \\ \hline Gabor Transform & Suitable for high dimensional feature space. & No rotation invariance. \\ \cline{2-3}  & An adaptive filter selection method is implemented to reduce the computational complexity. & filter parameters. \\ \cline{2-3}  & Suitable for defect detection in airspace and frequency domain. & No rotation invariance. \\ \hline Wavelet transform & Suitable for multi-scale image analysis. & Easily to be affected by feature correlations between the scales. \\ \cline{2-3}  & High detection rate (83-79\%). & Easily to be affected by feature correlations between the scales. \\ \cline{2-3}  & Efficient image compression with less information loss. & \\ \hline Multiscale Geometric Analysis & Suitable for the optimal and sparse representation of high-dimension data. & Redundancy problem \\ \cline{2-3}  & Good at image processing of strong noise background. & cannot be solved. \\ \cline{2-3}  & Compression with less information. & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Strengths and weaknesses of filtering-based detection methods for steel defects.  
developed in 1946; this is known as the Gabor transform if the window function is a Gaussian function. [PERSON] et al. [19] applied a Gabor filter optimized by genetic algorithm (GA) for the detection of corner cracking and thin cracking defects. [PERSON] et al. [66] adopted the Gabor filter to perform edge-pair detection in order to reduce the influence of lighting conditions, with satisfactory effects. [PERSON] et al. [67] used a Gabor filter with morphological defect detection algorithms to detect pinholes on the surface of the steel plate. [PERSON] [50] employed Gabor filtering and dual-threshold segmentation detection methods for the crack detection of steel plates. Among these methods, [PERSON] [50] achieved the best results, with a detection rate of up to 94.43%.
## 3 Soil Features as a Consequence of Reduced Fertilization
### Soil Diversity
#### 3.3.1 Microbiome  
A study conducted by [PERSON] et al. [92] demonstrated that N fertilization results in an increased relative abundance of Proteobacteria and Actinobacteria while concurrently decreasing the abundance of Acidobacteria, aligning with the general life history strategy theory for bacteria. Acidobacteria, recognized as more fastidious oligotrophic bacteria with the capacity to catabolize recalcitrant C, are suppressed due to elevated N inputs, as noted by [PERSON] et al. [93]. Conversely, Proteobacteria and Actinobacteria, classified as copiotrophic organisms, exhibit a preference for nutrient-rich environments, demonstrating lower C-use efficiency compared to oligotrophic bacteria while maintaining a high growth rate [91; 94]. The coexistence of oligotrophic and copiotrophic bacteria enhances microbial diversity within the soil, which is beneficial for overall soil health. The increased abundance and metabolic activity of these bacterial groups lead to the accelerated decomposition of OM, with copiotrophic bacteria often being more effective at decomposing readily available organic substrates, thereby facilitating nutrient release. In contrast, oligotrophic bacteria are instrumental in decomposing more recalcitrant organic materials, playing a crucial role in the long-term nutrient cycling process [94; 95]. Similar results were reported by [PERSON] et al. [91], who indicated a decline in the abundance of Acidobacteria and Alphaproteobacteria as well as Bacteroidetes and Chloroflexi. In this study, Actinobacteria and Betaproteobacteria exhibited an opposite trend, suggesting that N fertilization serves as a stimulant for their growth. Interestingly, bacterial taxa within the same group do not always respond uniformly, underscoring the importance of studying changes in microbial biodiversity under reduced fertilization regimes to maintain the stability of soil ecosystems and the resilience of microbial communities [88; 91; 92].  
Ecologically important for the proper functioning of soil are Bacteroid, which were found to depend on the agricultural practice and the season [96]. Furthermore, the genera _Mucilaginibacter_ and _Edalphobaculum_ were proven to be the most sensitive to soil chemistry over the changing seasons; thus, their reduced abundance may indicate inadequate soil chemistry parameters and, consequently, the fatigue of agricultural soils [96] in the context of over-fertilization or under-fertilization. On the other hand, the genus _Flavobacterium_ was shown to be the most sensitive to agricultural practices, and its high abundance may indicate the good quality of agricultural soils [96]. [PERSON] et al. [96] also proved that near-neutral or alkaline pH are the best conditions for Bacteroid community presence and abundance. Additionally, these bacteria have been shown to be intolerant of high soil salinity and excessive soil moisture [96]. Efforts are currently in progress to study the dependence of Bacteroid on the dose of reduced fertilization according to the F2F strategy.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 3 Metal Selenide Thin Films
### Zinc Indium Selenide Films  
The nanostructured n-type ZnInSe films were deposited on a glass slide at room temperature by using indium (II) chloride (InCl\({}_{2}\)), ZnCl\({}_{2}\), and Na\({}_{2}\)SeSO\({}_{3}\) solutions. These films were located in the family of the II-III-VI Group. According to the research results, these compounds showed a tetragonal phase, with specific lattice constants (a = 5.7095 A, b = 11.449 A) in the I-42m space group. Energy dispersive X-ray fluorescence results confirmed that the obtained films had cation-rich conditions; the sample compositions were 2% zinc, 30% selenium, and 68% indium. Based on the SEM analysis, small spherical grains completely covered the substrate surfaces. The band gap was 2.09 eV, according to the optical absorption investigations [109].
# Research on Key Technologies of Quantum-Safe Metro-Optimized Optical Transport Networks
###### Abstract  
This research introduces a novel physical-layer encryption technique for metropolitan-optimized optical transport networks (M-OTNs) that integrates real-time optical signal time-domain scrambling/descrambling with decoy-state quantum key distribution (DS-QKD). The method processes real-time optical data from the optical service unit (OSU) using a series of tunable Fabry-Perot cavities (FPCs), synchronized and updated with a running key. Experimental validation demonstrates secure communication within the optical network's physical layer during standard OTU2 data transmission (10.709 Gbps), achieving an online transmission distance exceeding 100 km over typical single-mode fiber with a power loss of approximately 1.77 dB. The results indicate that this integrated approach significantly enhances the security of the optical physical layer in M-OTNs.  
decoy-state quantum key distribution; metropolitan optimized optical transport network; optical service unit; tunable Fabry-Perot cavity 2025 15 2809
## 4 Experiments
### Performance Evaluation and Metrics  
To evaluate the proposed method's performance appropriately, this study applied voxel-grid downsampling and removed the floor and ceiling from the point cloud data. The ground truth data was manually labelled and classified into structural elements and indoor clutter objects. The red points indicate the structural elements, while the blue points indicate indoor clutter objects. This study compared the ground truth labels with the results of classifying the six experimental datasets into structural elements and indoor clutter objects using the proposed method.  
Moreover, the proposed method's performance was compared with the Auto-Classify Indoor function of commercial point cloud processing software. Auto-Classify Indoor automatically classifies the point cloud into indoor elements, including walls, floors, ceilings, and other remaining parts, using feature-based methods. The performance of the proposed method was evaluated using metrics such as accuracy, precision, recall, and F1 score. These metrics are calculated based on the values from the confusion matrix. True Positive (TP) refers to the test results correctly identified as structural elements. True Negative (TN) refers to the test results correctly identified as non-structural elements. False Positive (FP) refers to the test results incorrectly identified as structural elements. False Negative (FN) refers to the test results incorrectly identified as non-structural elements. Each metric is calculated according to Equations (7)-(10).  
\[Accuracy=\frac{TP+TN}{TP+FN+FP+TN} \tag{7}\]  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
\[F1\;score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{10}\]  
Figure 10: Experimental data downsampled to a voxel size of 0.05 m from where the floors and ceilings were removed: (**a**) parking lot, (**b**) basement, (**c**) apartment 1, (**d**) apartment 2, (**e**) lecture room 1, and (**f**) lecture room 2.
## 4 Research Methodology  
### Sample and Data Collection  
This study aims to evaluate undergraduate satisfaction with online learning in Saudi Arabia following the pandemic. As universities now offer online courses to undergraduates, this evaluation will help enhance the quality of online learning outcomes to ensure theyalign with the universities' objectives. To collect the data, a closed-ended questionnaire was distributed to undergraduate students at King Saud University who were registered for the second semester of the 2023-2024 academic year. Two steps were implemented to ensure the precision and clarity of the questionnaire. The survey was translated from English to Arabic, given that the recipients' primary native language was Arabic. Afterward, it was reviewed by three translation experts to verify consistency and alignment between the Arabic and English versions. The survey contained a total of 22 questions. Questionnaire items were evaluated using a five-point Likert scale, where 1 indicated strong disagreement and 5 represented strong agreement. The 22 questions used in this research were created based on validated items from previous studies and modified to meet the purpose of this research. The measurement items utilized in this study, along with their corresponding sources, are detailed in Table 2.  
We redesigned the questionnaire to include an equal number of positively and negatively worded questions for each construct, aiming to enhance validity and reduce acquiescence bias. For instance, a positive statement like \"The e-learning system is easy to use\" was paired with a negative counterpart, such as \"The e-learning system is difficult to navigate.\" This approach ensures a balanced assessment of participants' attitudes. Additionally, we randomized the order of all items to prevent participants from identifying response patterns, further improving the quality and reliability of the collected data.
# Quasi-Static Sliding Wear Analysis of 3D Rough Surface Considering Changes in the Point of Contact
## 3 Result and Discussion
### Wear Simulation by Deterministic Quasi-Static Model  
For wear analysis using the quasi-static model, an upper moving surface and a lower stationary surface were created, and the two surfaces were assumed to be symmetrical with respect to the contact surface to confirm the difference in wear depth according to the upper/lower positions. Three surfaces with different statistical parameters were analyzed to compare the wear progress according to skewness \(Sk\) and kurtosis \(Ku\). The input values for surface generation, contact, and wear analysis conditions are shown in Tables 1 and 2.  
Figure 8a-c show the probability density functions of surfaces 1, 2, and 3, and Figure 8d-f show the generated rough surface. As shown in Figure 8a, the probability density function of surface 1 is consistent with the normal distribution, which means it can be verified that a Gaussian surface with values of \(Sk=0,Ku=3\) was properly generated. The probability density function of surface 2 in Figure 8b shows a positively skewed distribution that is skewed to the left, the data are slightly concentrated at a location smaller than the mean, and the right tail of the distribution is longer. Moreover, due to the high kurtosis, the maximum value of the probability density function is higher than that of the normal distribution curve. Surface 2 in Figure 8e shows more peaks than valleys due to skewness. The probability density function of surface 3 in Figure 8c shows a negatively skewed distribution that is skewed to the right, the data are concentrated at a location higher than the mean, and the left tail of the distribution is longer. Surface 3 in Figure 8f has almost no peak and the contact surface is smooth.
# Development of a Low-Cost Automated Demand Response Controller for Home Energy Management
## 2 Materials and Methods  
DR means that users adjust their load (or power demand) in response to some message from the power company (e.g., price or emergency notification), or users who are capable of suppressing consumption suppress their energy consumption to reduce the electricity bill when the hourly electricity price in the wholesale market skyrockets, or independent dispatch agencies request electricity users to shed load to reduce the electricity consumption during peak periods to avoid the practice of rolling blackouts due to a system emergency. DR programs present great flexibility and interactivity, with the advantages of more selections for system operators in an emergency, making users who suppress load create potential economic benefits, shifting the power supply of generation companies, and introducing flexible demand to cope with high electricity prices. DR programs are generally classified into load response and price response. So far, most demand responsesbelong to load response, focusing on releasing system load to maintain the reliability of the power supply. Regarding the contribution of load suppression, load response is regarded as the major DR resource. Such programs have been preceded in the USA for years with a successful practice. The interruptible load program and rationable load program are the representatives of load response. In price response, dynamic price-setting approaches, such as Critical Peak Pricing (CPP), Peak Time Rebate (PTR), and Real-Time Pricing (RTP) are included in price response programs. The basic concept is price transparency, allowing users to understand market conditions and relevant costs.  
The price response program enhances the emergence of a demand transaction market, a short-period peak demand transaction. It is operated by notifying users involved in demand transactions with the electricity price per hour; the users could propose a guarantee to the demand exchange of reducing electricity consumption (demand) and the condition to reduce electricity consumption (price and time). Demand exchange accepts the user end's guarantee of reducing electricity consumption and the conditions. Then, the users have to fulfill the promise, and the demand exchange will pay users as contracted.