esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Method for Constructing the Static-Voltage-Stability Boundary with a Conversion Strategy
 } & Carry out continuous power flow calculation until divergence & \(\bullet\) & Provides numerical information near the divergent point of power flow & \(\bullet\) & Requires a lot of power flow calculations & Develop a more practical and robust continuous power flow calculation \\ \cline{1-1}  & & & Describes the stable boundary from the voltage perspective & \(\bullet\) & Difficulty in calculating boundary points. & \(\bullet\) \\ \hline \multirow{4}{*}{ \begin{tabular}{c} The fit-ting/approximation method \\ \end{tabular}  } & Fit the boundary of stability with approximate expression & \(\bullet\) & Provides geometric estimates of boundary shapes & \(\bullet\) & The fitting effect is affected by the boundary shape & Develop more practical and concise boundary \\ \cline{1-1}  & & & Implements the analytic expression of the boundary is too complex & \(\bullet\) & \ \begin{tabular}{c} The analytic expression of the \\ boundary is too complex \\ \end{tabular}  & \(\bullet\) & \begin{tabular}{c} Develop more practical and concise boundary \\ fitting methods \\ \end{tabular}  \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & &Recently, reference [25] presents the SVSRB construction method based on the continuous parameter tracking calculation, which uses any boundary point as the initial value and quickly migrates to the entire stable boundary. However, a large number of simulations have found that there is some divergent scenario happened occasionally. Therefore, this paper firstly analyzes the divergence of the method in the predictor and corrector stages mentioned in reference [25], respectively, and analyzes the mathematical mechanism of the calculation failure. Based on the causes of the divergent scenario, adaptive strategies, i.e., the alternate curve prediction strategy and orthogonal-arc-correction conversion strategy, are proposed to alleviate the computational burden and divergence difficulty. To sum up, the original contributions of this paper are listed below:  
1. Divergence mechanism: summarizing the reason for the tracking failure to construct the SVSRB in the predictor stage and corrector stage, respectively. 2. Improving methods: proposing the curve extrapolation and correction conversion strategies to promote the efficiency of constructing the SVSRB. 3. Analytical SVSRB expression: utilizing the intermediate data in the predictor stage to approximate the SVSRB piecewise. The remainder of this paper is structured as follows: In Section 2, the basic definition of the SVSRB and the continuous parameter tracking method are reviewed. In Section 3, the solving process of the parameter tracking method and the typical failure scenario are presented. In Section 4, the improving boundary tracking method is proposed to promote the efficiency of constructing the SVSRB. In Section 5, the proposed method is verified in simple power system, the WECC 3-machine, 9-bus power system. Section 6 provides the conclusion.
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 4 Integration of Artificial Intelligence in LPBF Defect Sensing
### Integrated ML Techniques and Thermal Sensors
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Material** & **Sensor Type** & **ML Technique(s)** & **Defects Observed** \\ \hline IN718 [99; 152; 193] & Photodiode sensing system, STWIP & GMM, SVM, CNN, LSTM & Balling, overheating, LOF, pores, and cracks \\ \hline Ti6 Al4V [194] & X-ray imaging system, IR camera & LR, RF, GBC, GPC & Keyhole pores \\ \hline AISI H13 Steel [195] & High-resolution thermographic camera & CNN & Delamination, splatter \\ \hline SS304 [196] & SWIR & KNN, RF, DT, MLP, LR, AdaBoost & Micropores \\ \hline ATI 718 Plus [197] & Dual-wavelength imaging pyrometer & KNN, SVM, LR, CNN & LOF, keyhole pores \\ \hline IN625 [198] & High-speed coaxial camera, near-infrared wavelengths & CAE & Spatter \\ \hline SS316L [154; 199] & Photodiode sensor, infrared thermal camera (FLIR) & BPNN, SSAE, LSTM, CNN & Cylindrical-shaped voids, warping, spheroidization, cracking, porosity, slag inclusion, LOF \\ \hline \hline \end{tabular}
\end{table} 
Table 11: A compiled list of thermal sensors integrated with ML methods for detecting LPBF defects. rithms. The CNN-based monitoring technique was implemented for SS316L LPBF printing, which utilizes infrared imaging data [154]. A YOLOV5 target recognition algorithm capable of recognizing macro-defects was implemented. The experimental results mentioned the detection of the laser spot, inclusion, surface spheroidization, and scraper damage warping but did not include micro-defects and the molten pool environment. A stacked group of autoencoders was utilized to reduce noises in raw melt pool monitoring images, and a CNN was used to identify the scan direction associated with an individual melt pool monitoring image [198].
# Federated Learning-Based Service Caching in Multi-Access Edge Computing System
## 5 Experiment and Evaluation
### Counterpart  
To verify the effectiveness of the proposed framework, we evaluate SCFL and the following approaches.  
* Random Caching (RC) [31]: This approach employs a random selection process for determining the service types deployed on the MEC server.
* Least Recently Used (LRU) [22]: The prioritization in service deployment on MEC servers is based on the recency of their requests.
* Least Frequently Used (LFU) [22]: This approach leverages historical statistical data from past time slots to replace the services that have received the lowest requests.
* Federated Learning with AE (FLAE): This method employs the traditional autoencoder model to extract data features that are then used to select the appropriate service.
## 4 Conclusions  
In this study, the immobilized lipase CALB@MCM-41-C\({}_{8}\) was developed by immobilizing the lipase CALB on a hydrophobically modified mesoporous molecular sieve MCM-41 for the enzymatic deacidification of vegetable oils such as rapeseed oil, tea seed oil, rice bran oil, and soybean oil. The large surface area and suitable pore size of MCM-41-C\({}_{8}\) due to its densely and orderly distributed hexagonal mesopores, the catalytic activity of immobilized lipase was significantly improved to facilitate the acid value of rapeseed oil to decrease from 15.2 mg KOH/g to 1.7 mg KOH/g, and DAG content was increased from 1.8% to 40.1% simultaneously. Moreover, the deacidified rapeseed oil also exhibited fatty, bitter almond aromas based on the aroma characteristics evaluation. To accelerate the industrial application of enzymatic deacidification, finding an easier method to separate immobilized lipase from the water absorbent of molecular sieves and high-yield enzymatic systems such as continuous-flow reactions would be a valuable future research direction. Overall, the self-made immobilized lipase CALB@MCM-41-C\({}_{8}\) with good catalytic stability and broad applicability shows great potential in the greener processing of edible oils and cleaner production of functional lipids.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods13162539/s1](https://www.mdpi.com/article/10.3390/foods13162539/s1)]([https://www.mdpi.com/article/10.3390/foods13162539/s1](https://www.mdpi.com/article/10.3390/foods13162539/s1)), Table S1: Text parameters of different molecular sieves. Figure S1: Protein standard curve; Figure S2: Water contact angles of MCM-41 and MCM-41-C\({}_{8}\).  
**Author Contributions:** Conceptualization, investigation, writing--original draft, Z.L.; methodology, data curation, T.L.; visualization, formal analysis, R.L.; methodology, data curation, Q.Z.; resources, supervision, Y.Z. ([PERSON]); writing--review & editing, supervision, Y.Z. ([PERSON]); formal analysis, funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the National Key Research and Development Project of China (2021 YFD2100303), the National Natural Science Foundation of China (32272271, 32302021), the Hubei Province Natural Science Foundation of China (2021 CFB209, 2023 AFB324), Young Elite Scientists Sponsorship Program by CAST (2022 QNRC001), State Key Laboratory of Heavy Oil Processing (WX20230155), the Major Project of Hubei Hongshan Laboratory (2022 hszd002), and Central Public-interest Scientific Institution Basal Research Fund (No. 1610172024002).  
**Data Availability Statement:** The original contributions presented in the study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Reliability Engineering Approach for Managing Risks in CubeSats
## 3 Materials and Methods
### Step 3. Perform the Criticality Analysis  
This step allows for a structured systematic analysis of failure modes, based on three semi-quantitative indexes. In this sense, the proposed approach starts from ESA (European Space Agency) guideline (ECSS-Q-ST-30-02C), which takes into account severity, ranging from 1 (negligible damage) to 4 (catastrophic damage), and occurrence, ranging from 1 (probable, probability greater than 1E\(-\)1) to 4 (extremely remote, probability minor than 1E\(-\)5). These two terms are multiplied to obtain the Criticality Number (CN), which is used to filter the failures modes: the critical failure modes are the ones that are catastrophic (severity score equals to 4), and the ones whose CN is greater or equal to 6. On the contrary, the US standards for FMECA (SAE ARP5580) at a component level calculates an RPN (Risk Priority Number) defined as the product of severity (in a 1-10 range), occurrence (in a 1-10 range) and likelihood of mitigation--if the design can be revised, or detectability--if the design cannot be revised (both in a 1-10 range). Note that similar concepts are present in ESA guidelines for FMECA at process level. Therefore, the approach used in this study, extends ESA guidelines for functional FMECA, following the influence of the US satellite standard for FMECA--also documented in a recent FMECA space-based application [19]--in order to produce a three-dimensional risk index. This latter will include three indicators, each one rated in a 1-5 scale. The odd number of slots allows assigning intermediate values to variables, in order to support the analysts in avoiding overestimation or underestimation of factors, as emerged from initial categorizations performed in a real case scenario. The indexes are occurrence, severity, and detectability.
## 4 Comprehensive Performance Evaluation
### Indoor Environment
#### 4.1.2 Mapping
The scanning characteristics of LiDAR result in uniform point clouds on the same plane of corridor walls, making it challenging to distinguish features and positions of individual objects in finer detail. As a result, the visual discernibility is comparatively lower in LiDAR SLAM. The second aspect is deficiencies caused by indoor lighting sources. Defects, such as a lack of point cloud, are observable in the reconstruction based on Visual SLAM on both the floor and ceiling. This is attributed to the installation of fluorescent lights on the ceiling, causing reflections on the floor that introduce errors in depth measurements based on the binocular disparity of Zed2. These errors are subsequently mitigated through filtering during the mapping process. Conversely, the scanning nature of 3D LiDAR in LiDAR SLAM is unaffected by indoor lighting sources and it does not experience the same issue. The third aspect is the blind spot of local maps. Due to the limited FOV inherent in the camera's imaging principle, certain parts of the scene may not be captured, such as corners during turns at intersections. This issue results in the absence of objects around corners, as depicted in Figure 8. However, in LiDAR SLAM, the 3D LiDAR used in this experiment provides a 360-degree scan of the environment, eliminating blind spots in the LiDAR reconstruction results. Figure 8: Blind spots in local mapping around corners.
# Numerical Analysis of EOD Helmet under Blast Load Events Using Human Head Model
## 3 Results and Discussion
### Intracranial Pressure (ICP)  
Another criterion used to analyze the injury risk is intracranial pressure proposed by [PERSON] et al. [54] and is based on inner cranial pressure generated. In order to determine the potential injuries, based on maximum intracranial pressure, three ranges have been suggested [54]:  
* If peak pressure is higher than 235 kPa, severe brain injuries like a concussion are likely to be.
* If peak pressure is in the range 173-235 kPa, the injuries produced are of a mild nature, such as hemorrhage in the cerebral cortex.
* Finally, if peak pressure is lower than 173 kPa, there are no brain injuries or they are likely to be very slight.  
Figure 12 shows the ICP history for all studied cases of the brain area that receives the peak pressure is the external part of the frontal lobe, the place where the wave makes the first contact with the brain. The peak pressure is found somewhat higher on the forehead in the model with the EOD helmet.  
Figure 11: HIC parameter evolution with the explosive load.  
Table 8 shows the peak pressure for each configuration studied. From obtained data, it is possible to affirm that the use of protective helmet reduces the intracranial pressure around 70% approximately in all the studied cases, decreasing the injuries of severe nature to very minor or no injuries presence.
## 5 Use Case Description with the OGC SensorThings Standard
### Tested Sensors  
The proposed architecture focuses on translating the payloads sent by sensors from different manufacturers. In fact, each measurement sent by the sensors is formatted according to the technical specifications provided by the respective manufacturers, and specifically, two sensors from different companies, such as SensorData [98] and Libellium [99], have been tested.
# Efficient QC-LDPC Encoder for 5G New Radio
## 2 5G NR QC-LDPC Codes
### Preliminary  
Let \(Z\) be the size of a circulant permutation matrix and \(P_{i,j}\) be the shift value. For any integer value \(P_{i,j}\), \(0\leq P_{i,j}\leq Z\), a \(Z\times Z\) circulant permutation matrix shifts the \(Z\times Z\) identity matrix \(I\) to the right by \(P_{i,j}\) times for the \((i,j)\)-th non-zero element in a base matrix. This binary circulant permutation matrix is denoted as \(Q(P_{i,j})\). Considering \(Q(1)\) as an example,\[Q(1)=\begin{bmatrix}0&1&0&\cdots&0\\ 0&0&1&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&0&\cdots&1\\ 1&0&0&\cdots&0\end{bmatrix}. \tag{1}\]  
For simple notation, \(Q(-1)\) denotes the null matrix (all elements equal to zero) of the same size.
# Compositional Modeling of Dimethyl Ether-CO2 Mixed Solvent for Enhanced Oil Recovery
## 4 Conclusions  
A model using a mixed solvent of CO\({}_{2}\)-DME was developed to explain the change in oil recovery when CO\({}_{2}\) WAG and DME were added. To evaluate the reliability of the compositional simulation, results from fluid modeling were matched with experimental  
Figure 10: DME injection and DME production in Case 4.  
Figure 9: Oil recovery according to DME and the CO\({}_{2}\) ratio.  
data from real Weyburn W3 fluids. A cross-sectional 2D model was designed to examine the sweep efficiency considering the gravity override phenomenon during WAG. Since DME is an expensive solvent, a mixed solvent of DME and CO2 was used and the effect was analyzed. Four cases with different DME addition ratios were modeled and the oil recovery was analyzed through changes in the sweep efficiency and displacement efficiency. DME in CO2 decreases the IFT and MMP, which increases the displacement efficiency. Adding DME to the CO2 flood also increases the vertical sweep efficiency. The developed compositional model illustrated that DME increases the sweep efficiency and displacement efficiency. In addition, the vertical sweep efficiency and displacement efficiency was presented quantitatively with viscous gravitational numbers, MMP, and IFT. When the DME content was 25%, the oil recovery was 72%, a 31% increase over CO2 only. This study confirmed the favorable effect of DME addition into the CO2 WAG process over simple CO2 flooding. Since DME is an expensive solvent, the optimum design with an integrative techno-economical model is required for successful field implementation of the process. In addition, a full 3D model is required to analyze the effect of vertical and areal reservoir heterogeneities on the oil recovery from actual fields.  
Conceptualization, Y.W.L. and K.S.L.; methodology, Y.W.L., J.C. and K.S.L.; software, Y.W.L. and J.C.; validation, Y.W.L. and K.S.L.; formal analysis, Y.W.L.; investigation, Y.W.L., M.S.J. and H.S.L.; resources, Y.W.L. and K.S.L.; data curation, Y.W.L.; writing--original draft preparation, Y.W.L.; writing--review and editing, Y.W.L. and K.S.L.; visualization, Y.W.L., M.S.J. and H.S.L.; supervision, K.S.L.; project administration, K.S.L.; funding acquisition, K.S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1F1A1070406). [PERSON] was supported by the NRF grant (No. 2018R1A6A1A08025520 and No. 2020R11A1A01067015).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors are grateful to the Computer Modelling Group Ltd. (CMG) for technical support.  
The authors declare no conflict of interest.  
References
## 4 Discussion
Although the convolutional neural network applied to 2D spectra gives it the form of image analysis, it still involves the feature processing of spectra in essence. Compared with our study, their model is a little more complicated, while the performance needs to be further improved. In addition, this study analyzes the importance of variables in prediction models by introducing the SHAP method to quantify the contribution of variables, which is useful for exploring the interpretation of machine learning models with high accuracy. Overall, the visualization method of coal quality indices implemented in this study can better realize the migration of the prediction model from the spectral dimension to the spatial dimension, which offers the advantages of being fast, nondestructive, and accurate compared with the traditional measurement methods. It should be noted that the quantity and diversity of the sample data still remains the key opportunity to improve the accuracy and robustness of the model. Deep learning models demonstrate significant potential in feature extraction and model construction from hyperspectral data due to their capacity for efficient multi-dimensional data processing and robust nonlinear modeling [66]. In addition, deep learning can also facilitate the development of the end-to-end models, which is highly advantageous for real-world applications [67]. Moreover, further advancement of the method can be widely applied in many promising ways for daily analyses of large quantities of coal. With hyperspectral coal quality testing applied, coal washing plants producing large quantities of commercial coal can avoid quality problems caused by hours of delay in traditional testing methods [68]. In coal trading, coal analysis based on hyperspectral imaging allows the thermal power plantsto quantitatively evaluate bulk coal quality during transactions, preventing the adulteration of low-quality coal to stabilize pricing and minimize economic losses [69]. The rapid and accurate determination of coal quality indices is also crucial for energy regulation in coal utilization [70]. In coal-fired power plants, coal blending, which often involves substantial quantities of coal and occurs frequently, could benefit from rapid coal quality analysis, thereby conserving resources and reducing carbon emissions [71].
## 2 Materials and Methods  
Lowari Top is situated at a height of 3100 m and connects Chiral in the north to other regions of Pakistan in the south (Figure 1). Due to significant snowfall throughout the winter season, it remains closed. This terrain is characterized by snow avalanches, rockfalls, and landslides, making road building and maintenance challenging and expensive. Consequently, in 1960, a horseshoe-shaped rail tunnel with a length of about 8.5 km and a cross-section size of 52.3 m\({}^{2}\) was planned [40]. The construction started in 1975 and ended in 1977, 2 years later. After a lengthy delay, efforts resumed in 2006, and in 2009, a breakthrough was made. In 2012, the design of the rail tunnel was changed to a road tunnel with a cross-section size of 87.2 m\({}^{2}\). In 2017, the building of the modified road tunnel was finished, and it began operating. The south portal of the Lowari Tunnel is situated at 35\({}^{\circ}\)18\({}^{\prime}\)59.39\({}^{\prime\prime}\) N and 71\({}^{\circ}\)50\({}^{\prime}\)08.17\({}^{\prime\prime}\) E, while the north portal is located at 35\({}^{\circ}\)22\({}^{\prime}\)41.11\({}^{\prime\prime}\) N and 71\({}^{\circ}\)47\({}^{\prime}\)05.19\({}^{\prime\prime}\) E. This tunnel connects the districts of Dir and Chitral in Khyber Pakhtunkhwa,Pakistan. The tunnel has a south-to-north slope of 1.8% and crosses many geological units, such as granite, granite biotite, metasediments, meta-igneous rocks, meta-volcanic, and shear zones. It has a maximum overburden of 1100 m and considerable water infiltration. In the summer, the average daily traffic volume is 350 vehicles and 2000 passengers, while in the winter, it drops to 200 vehicles and 1000 passengers. The vehicle traffic consists of automobiles (50%), trucks (40%), and four-wheel-drive vehicles (10%).
## 2 Related Work
They proposed to squeeze global spatial information into a channel descriptor and modeled channel-wise relationships using a lightweight gating mechanism. They demonstrated that SE blocks brought significant improvements in the performance of the state-of-the-art CNNs at a minimal additional computational cost. CRFs have been employed to enforce structure consistency in semantic segmentation. In [34], a fully connected CRF is used to consider the structural properties of the segmentation outputs. More recently, deep learning models integrating the densely connected CRF are proposed in many studies. DeepLab [8] proposes deep CNNs with atrous convolution, which is convolution with upsampled filters, and combines the responses at the final layer with a fully connected CRF. In [9], an RNN is introduced to approximate the mean-field iterations of CRF optimization, allowing for an end-to-end training of both the fully convolutional network and the RNN. In this section, we describe our weakly supervised AED model, referred to as DSNet. The overall structure of the DSNet is depicted in Figure 1. The input of DSNet is a log mel spectrogram image \(X\in R^{N\times M}\), where \(N\) denotes the number of segments and \(M\) is the number of mel filterbanks. First, convolution is performed on the log mel spectrogram images to extract feature maps. We use four DS blocks, which consist of a dense block, an SE block, and a max-pooling layer. Two fully connected layers are applied for segment-level prediction. To detect overlapping audio events simultaneously, we define AED as a multi-label classification problem. For this, segment-level predictions are calculated using sigmoid activation functions at the final fully connected layer. A global pooling layer is applied for clip-level prediction. For structured prediction, DSNet with an RNN is proposed and a fully connected CRF is applied as a post-processing method. The detailed architectures of DSNet and DSNet-RNN are given in Section 4.