esa-sceva/satcom-chunk-collection huggingface dataset:
# Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
## 5 Conclusions
### Comparison with Traditional Inspection Methods  
The revolutionary MR-based quality control system, seamlessly incorporating smart glasses and advanced image recognition, represents a paradigmatic shift from traditional inspection methodologies. In this comparative analysis, we accentuate the advantages that position the proposed system as a pivotal advancement in industrial quality control.  
Regarding speed and efficiency, traditional manual inspections reliant on human visual assessments are time-intensive and susceptible to inconsistencies. By contrast, the proposed MR-based system, armed with real-time object detection, expedites inspections. Digital overlays on smart glasses facilitate swift identification, eliminating the need for laborious manual cross-referencing against plans or physical checklists.  
Accuracy and consistency, paramount in quality control, are often compromised by human error, fatigue, and subjectivity in traditional methods. Integrating cutting-edge technologies, specifically YOLOv8 and OpenCV, ensures precise and consistent inspections. Advanced computer vision algorithms detect defects with accuracy that surpass human assessments, eliminating the variability associated with traditional approaches.  
The early detection and prevention capabilities of the MR-based system further distinguish it from conventional methods. This system mitigates downstream complications by proactively identifying and rectifying issues in the early stages of the manufacturing process, minimizing the risk of rework, reducing recalls, and fortifying the brand's image. The user-friendly interface presented through smart glasses and the system's adaptability to varied manufacturing scenarios underscores its transformative impact on operational efficiency. Real-time tracking and data logging provide actionable insights, allowing managers to optimize workflows, identify bottlenecks, and enhance productivity. In summary, the proposed MR-based quality control system transcends the limitations of traditional methods, ushering in a new era of technology-driven, proactive quality control aligned with the demands of modern industrial settings.
## 1 Introduction
The purpose of the study was to assess the potential of using NIR HSI techniques to inspect the contamination of peanut and walnut powders in whole wheat flour. Peanut and walnut powders, as well as the whole wheat flour used in our study, were of the similar small particle sizes. The mixture samples were homogeneous. The specific objectives were to (1) analyze and compare the spectra of pure peanut, walnut powders, and flour in order to extract the distinct spectral features between whole wheat flour and the contaminants; (2) select feature wavelengths and develop the multispectral model to quantify peanut and walnut contaminants in flour; (3) develop an individual identification model for peanut and walnut contaminants in flour, as well as a general model for combined samples of the two; and (4) generate a chemical distribution map to graphically display the contamination concentration variation in the spatial dimension.
# Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms
###### Abstract  
Enhanced oil recovery (EOR) is a complex process which has high investment cost and involves multiple disciplines including reservoir engineering, chemical engineering, geological engineering, etc. Finding the most suitable EOR technique for the candidate reservoir is time consuming and critical for reservoir engineers. The objective of this research is to propose a new methodology to assist engineers to make fast and scientific decisions on the EOR selection process by implementing machine learning algorithms to worldwide EOR projects. First, worldwide EOR project information were collected from oil companies, the extensive literature, and reports. Then, exploratory data analysis methods were employed to reveal the distribution and relationships among different reservoir/fluid parameters. Random forest, artificial neural networks, naive Bayes, support vector machines, and decision trees were applied to the dataset to establish classification models, and five-fold cross-validation was performed to fully apply the dataset and ensure the performance of the model. Utilizing random search, we optimized the model's hyper parameters to achieve optimal classification results. The results show that the random forest classification model has the highest accuracy and the accuracy of the test set increased from 88.54% to 91.15% without or with the optimization process, achieving an accuracy improvement of 2.61%. The prediction accuracy in the three categories of thermal flooding, gas injection, and chemical flooding were 100%, 96.51%, and 88.46%, respectively. The results also show that the established RF classification model has good capability to make recommendations of the EOR technique for a new candidate oil reservoir.  
enhanced oil recovery; EOR screening; machine learning; random forest; ANNs; naive Bayes; SVM; decision trees
## 2 Methodology
We also prepared analogous solutions for the PPP approach using GAMP software [32], but only for the investigated 25 stations. A basic PPP model was used in the analysis. Undifferenced dual-frequency codes and phase measurements were converted into an ionosphere-free linear combination [33]. In this way, we estimated the station coordinates, the clock correction of the receiver, the wet component of the tropospheric delay, and real ambiguity values. To avoid the impact of less accurate coordinates, we skipped the first epochs (1 h) in the final solution. A shortlist of used parameters for both approaches is shown in Table 3. For both approaches, daily coordinate solutions were finally prepared. Figure 1: EPN stations providing the Galileo (GAL) observations between 30 June and 6 July 2019 (GPS week 2060) used in this study. NYA200 NOR is outside of the presented area and is not plotted on the map.
## 5 ML Intersection with 6G Enabling Technologies
### ML and UAV Integration for Future 6G Networks
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref** & **ML Algorithm** & **Application** & **Outcome** \\ \hline
[144] & EE-CSL & Power optimization & Significantly minimized energy consumption for low computational complexity and achieved a more significant sum rate than conventional MIMO orthogonal multiple access. \\ \hline
[143] & LSTM & Signal detection & Outperformed the (SIC) receiver and the limited radio resources. \\ \hline
[142] & Combined unsupervised and supervised learning & Spectrum sensing & Provided accurate and effective spectrum sensing while maintaining optimal power allocation. \\ \hline
[147] & LSTM & Channel coefficients prediction & Provided reliable performance even when cell capacity increased. \\ \hline
[146] & DREAM-FL & Client selection & Provided more qualified clients with higher model accuracy than FDMA- and TDMA-based solutions. \\ \hline
[145] & DDQL & Transmission power optimization & Converged successfully in 91\% of the test cases with a value better than the target, and performed better than the SLSQP and TCONS algorithms. \\ \hline
[148] & DNN & Power optimization & The proposed approach achieved similar results as the conventional optimization approach but with faster computational time. \\ \hline
[149] & CDNN & Power allocation & The proposed CDNN framework enhanced the performance of MIMO-NOMA in terms of sum data rate and energy efficiency and outperformed all other approaches. \\ \hline
[150] & k-means, MA-DQN, 0-\(\gamma\) MA-DQN & Power control and spectral efficiency & The developed method improved the computational efficiency and overall resource allocation. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of ML integration with NOMA for optimized 6G network. and more sustainable green communication. A summary of the different ML-assisted UAV networks has been provided in Table 6.
## 3 Results and Discussion of Biosensor Formation and Its Validation
### Repeatability  
Repeatability refers to results obtained under the same analytical conditions, meaning analyte concentrations in samples determined in a given laboratory, by means of a specific analytical method and by one analyst. To determine the repeatability of the developed analytical method, using standard solutions, five repetitions of three selected analyte concentrations (\(\mathrm{C_{RM}}\)) were performed, and the mean value of the concentration (\(\mathrm{X_{C}}\)) was calculated, in addition to SD, recovery, and CV. Similarly, determinations were performed on a real sample, and \(\mathrm{X_{C}}\), SD, and CV were calculated. The concentrations of the studied analytes in a sample with unknown concentrations were quantified five times. Table 5 summarizes the data obtained during these tests.  
The facts that the coefficients of variation (CV) did not exceed 4%, the recovery values lay in the range 98.60-107.35, and the standard deviations were small for both methods confirm that they give reproducible results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Biomarker**} \\ \cline{2-3}  & **VEGF-A** & **FGF-2** \\ \hline \(\mathrm{C_{real\,sample}}\) [pg/mL] & 45.62 & 5.75 \\ \(\mathrm{C_{standard}}\) [pg/mL] & 100.00 & 10.00 \\ \(\mathrm{C_{quantif}}\) [pg/mL] & 147.32 & 16.03 \\ \(\mathrm{C_{add}}\) * [pg/mL] & 101.7 & 10.28 \\ Recovery [\%] & 101.7 & 102.8 \\ \hline \hline \end{tabular}  
* \(\mathrm{C_{quantif}}\)-\(\mathrm{C_{real\,sample}}\).  
\end{table}
Table 4: Recovery test.
## 4 Classification of Routing Protocols
### Temperature-Aware Routing Protocols
#### 4.2.5 Hotspot Preventing Routing (HPR)  
The Hotspot Preventing Routing is a thermal-aware routing protocol for delay sensitive data, presented in [29]. The aim of this algorithm is to prevent the formation of the hotspots in the network as well as to minimize the average end-to-end delay. The two phases of HPR are: setup phase and routing phase. In the setup phase, all nodes exchange the information of the shortest path and initial temperature. Based on this information, each node constructs its own routing table. While in the second phase, _i.e._, routing phase, each node considers the following:  
* Each node forwards the packets towards the destination using the shortest hop route. A hop-count is associated with each packet, incremented by one once a node forwards it. A packet is discarded if its hop-count exceeds the threshold value.  
Figure 11: An example of LTRT [15].  
* If the destination node is one of the neighbor nodes, the packet is forwarded directly. Otherwise it is forwarded to the next-hop node in the shortest path to the destination with temperature less than or equal to the threshold value. The threshold temperature of a node is derived from the average temperature of the neighbor nodes and the own temperature of the node.
* If the temperature of the next-hop in the shortest path to the destination is higher than the summation of the source node's temperature and the threshold temperature, then the node identifies it as a hotspot and bypasses it by forwarding the packet to non-visited neighbor node with least temperature. Each packet maintains a list of the visited nodes to avoid routing loops.  
In this scheme, each node calculates the threshold value based on temperature of its neighbor nodes and its own temperature. This makes it different from the other thermal-aware routing protocols where threshold is a predefined value [15].
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
## 2 Related Work
### Why Multiple Instances?
Table 1 shows the diversity of the delay tolerance and reliability for the different NAN applications [5]. Thus, for a wireless sensor network, different criteria have to be taken into consideration in order to achieve a proper communication with the following requirements: reliability, latency, auto-configuration, auto-adaptation, network scaling and data prioritization [6]. From here the need of an objective function with multi-objective metric for RPL.
## 3 Algorithm Description
### Information Exchange  
The topology of the satellite network exhibits time-varying characteristics, as the connectivity between the satellites dynamically changes over time. Consequently, agents must periodically update their neighbor's information to optimize the forwarding strategy upon the arrival of data packets. In practice, satellites employ \"hello\" packets to acquire and maintain neighbor information [23]. To enhance the efficiency of data exchange for reinforcement learning and optimize the formulation of forwarding strategies, we adopt a periodic broadcasting mechanism for link-state information. At regular time intervals T, the link-state information within the network is updated, and the agents correspondingly update their Q-tables based on the received information. Furthermore, if a satellite fails to receive the ISL state information within a predefined time interval, it is flagged as faulty, and all associated links are deactivated. In contrast to Q-routing algorithms that propagate \"hello\" packets solely through data packet forwarding, we employ periodic broadcasting to accelerate link-state awareness and neighbor discovery. Furthermore, differing from ground-station-based approaches that disseminate global link information to all nodes, \"hello\" packets only transmit local neighbor and link-state information from the originating node. It significantly reduces network overhead and decreases nodal computational loads.
###### Abstract  
Chemical flooding, such as alkaline-surfactant (AS) or nanoparticles-surfactant (NS) flooding, is an enhanced oil recovery (EOR) technique that has been increasingly utilized to enhance the oil production rate and recovery factor while reducing chemical adsorption. The AS/NS flooding process involves the injection of a mixture of surfactant and alkali/nanoparticles solutions into an oil reservoir to reduce the interfacial tension between the oil and water phases by surfactant and lower surfactant adsorption by alkali or nanoparticles (NPs) to improve the residual oil recovery. In this study, the AS/NS flooding is evaluated for a Kazakhstan oilfield by systematically screening the chemical constituents involved. Field A in Kazakhstan, one of the oldest fields in the country, has been water-flooded for decades and has not produced even 50% of the original oil in place (OOIP). Currently, the water cut of the field is more than 90%, with a high residual oil saturation. Therefore, besides polymer flooding to control mobility, chemical EOR is proposed as a tertiary recovery method to mobilize residual oil. This study aimed to screen chemicals, including surfactant, alkali, and NPs, to design an effective AS/NS flooding program for the target field. The study focused on conducting laboratory experiments to identify the most effective surfactant and further optimize its performance by screening suitable alkaline and NPs based on their compatibility, stability, and adsorption behavior under reservoir conditions. The performance of the screened chemicals in the porous media was analyzed by a set of core-flood experiments. The findings of the study indicated that alkali agents, particularly sodium carbonate, positively affected surfactant performance by reducing its adsorption by 9-21%. The most effective surfactant combination was found, which gave Winsor type III microemulsion and the lowest interfacial tension (IFT) of 0.2 mN/m. The core-flood tests were conducted with the screened surfactant, alkali, and NPs. Both AS and NS tests demonstrated high residual oil recovery and microemulsion production. However, NS flooding performed better as the incremental oil recovery by NS flooding was 5% higher than standalone surfactant flooding and 9% higher than AS flooding. The results of this screening study helped in designing an efficient chemical formulation to improve the remaining oil recovery from Field A. The findings of this study can be used to design EOR projects for oil fields similar to Field A.  
surfactant flooding; interfacial tension (IFT); adsorption; nanofluids; residual oil saturation (S\({}_{\text{or}}\)) 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Study on Microseismic Monitoring of Landslide Induced by Blasting Caving
## 3 Microseismic Monitoring Results of Blasting-Induced Landslide
### Microseismic Monitoring Results of the Major Blasting Event  
The microseismic monitoring system recorded the precise blasting time as 21 June 2012, at 07:19:31.565. The waveforms of the major blasting event captured by the microseismic monitoring system are shown in Figure 6.  
Within 5 min after the major blasting event, two clusters of densely located microseismic events (blue stripes) were recorded by the microseismic monitoring system (see Figure 7), totaling 85 microseismic events. The first cluster occurred within 3 min post-blasting, with spatial distribution primarily concentrated in Zone I. The second cluster, observed between 3 and 5 min post-blasting, was mainly localized in Zone II, indicating that the major blasting induced two distinct phases of rock mass fracturing activity.  
Spatially, the post-blasting located events were distributed across two primary regions (Figure 8):  
* Zone I: Elevations below 630 m, covering the P1-P3 and C6-C8 zones.
* Zone II: Elevations above 630 m, covering the P4-P5 and C1-C6 zones.  
Figure 5: Drilling layout of the blasting project: (**a**) plan of the blasting area, (**b**) plan view at the 558 m level, (**c**) 3H sectional view, (**d**) 3Z sectional view.  
Figure 6: Microseismic monitoring waveforms of the major blasting event.  
The location error statistics for the 85 microseismic events are presented in Figure 9. The average location error was 22.3 m, with a standard deviation of 19.3 m and a modal error of 7.5 m. These results confirm the high spatial accuracy of the microseismic event localization. Field observations revealed that 5 min after the blasting, a large-scale landslide occurred in the overlying mountain body above the blasting area. Cameras installed outside the warning zone recorded the entire landslide process triggered by the blasting. Sequential video frames from the event are shown in Figure 10, illustrating the temporal progression of the landslide.  
Figure 8: Microseismic event location map: (**a**) plan view, (**b**) sectional view.  
Figure 7: Real-time monitoring interface.  
Figure 9: Microseismic event location error statistics.
## 3 The Proposed Method
### ESVM for Multi-Fault Diagnosis  
After the discriminative feature selection, one ESVM is constructed and trained for multi-fault intelligent diagnosis of rolling bearings. To be consistent, denote the training samples and the corresponding labels set as \(TR=\{(\textbf{x}_{i},y_{i})\), \(i=1,2,\ldots,N\}\), where \(\textbf{x}_{i}=\)(\(f_{i,1}\),\(f_{i,2}\),...,\(f_{i,d}\)) is the \(d\)-dimensional discriminative features for the \(i\)-sample and \(y_{i}\in\{1\), \(2\),..., \(Q\}\) is the label from the \(Q\) fault conditions of rolling bearings. From the statistical view, the training sample sets of each individual SVM (i.e., base learner) should be as different as possible to obtain higher improvement of the ensemble aggregation. Thus, the bagging technique is considered to construct an individual SVM classifier in this paper. Suppose the ensemble number of SVM is \(P\); bootstrapping then builds replicated data sets \(TR_{p}\) (\(p=1\), \(2\),..., \(P\)) by randomly resampling with replacement from the original training data set \(TR\). In this way, each pair \(\textbf{x}_{i}\) and \(y_{i}\) in the \(N\) given training set may appear repeated or not at all in any particular replication. With the replicated data sets \(TR_{p}\), an individual SVM is independently trained to act as one classifier as in Equation (5) by the one-against-one strategy.  
For one test sample \(\mathbf{x}=(f_{1},f_{2},\ldots,f_{d})\), it is input to the trained SVMs to output a total of \(P\) fault labels. To obtain the fault class label of the new sample, some kind of aggregating rule should be designed. Among the frequently used ensemble rules, majority voting is the simplest one for combining several SVMs and is also utilized here. Let \(f_{p}(\mathbf{x})\) be the decision function for the \(p\)th SVM in the ESVM and \(\#(p|\langle f_{p}(\mathbf{x})=q\rangle)\) be the counted number of SVMs in which the decisions are made to the \(q\)th class label. Then, final fault class label of the ESVM for the test sample \(\mathbf{x}\) is  
\[f_{ensemble}(\mathbf{x})=\max_{q}\Bigl{(}\#\Bigl{(}p\big{|}f_{p}(\mathbf{x} )=q\Bigr{)}\Bigr{)} \tag{8}\]
## 4 The Proposed NSGA-II-MOEA/D-CIS Algorithm
### Design of the Upper-Level NSGA-II Algorithm
#### 4.2.2 Block Crossover Operator  
The purpose of the crossover operation is to exchange genes between parent chromosomes to better explore new regions of the solution space. Since the algorithm adopts an integer matrix encoding scheme, the commonly used simulated binary crossover (SBX) method [33], which is typically applied to real-valued encoding and generates continuous values, is not well suited for integer matrix encoding. Therefore, this paper adopts a block crossover method tailored to the specific problem. In this approach, during each crossover, the chromosome segments corresponding to a specific product specification are entirely exchanged to preserve the integrity of the decision segment information. The detailed process is illustrated in Figure 4.
## 3 Methodology
### Synergistic Adaptive Genetic Algorithm
#### 3.1.2 Adaptive Probabilities of Crossover and Mutation  
In GAs, \(P_{c}\) and \(P_{m}\) play a vital role in preventing premature convergence [33]. In AGA, the \(P_{c}\) and \(P_{m}\) of the best individuals are both zero, which means they will be preserved into the next generation [34]. However, they may not be the global optimal individuals. With the advance of evolution, the algorithm will get stuck at a local optimum easily [32]. To overcome the weakness of AGA, we adopt a new method based on normal distribution to calculate \(P_{c}\) and \(P_{m}\), and the method can be expressed as:  
\[P_{m}=\left\{\begin{array}{ll}P_{Mmin}+(P_{Mmax}-P_{Mmin}).\exp[\frac{(c_{2} -\frac{f-\overline{f}}{f_{max}-\overline{f}})^{2}}{-2}],&f\geqslant\overline{ f}\\ P_{Mmax},&f\leqslant\overline{f}\end{array}\right. \tag{12}\]  
\[P_{c}=\left\{\begin{array}{ll}P_{Cmin}+(P_{Cmax}-P_{Cmin}).\exp[\frac{(c_{3} -\frac{f^{\prime}-\overline{f}}{f_{max}-\overline{f}})^{2}}{-2}],&f\geqslant \overline{f}\\ P_{Cmax},&f\leqslant\overline{f}\end{array}\right. \tag{13}\]  
where \(P_{Mmax}\), \(P_{Mmin}\), \(P_{Cmax}\) and \(P_{Cmin}\) are the maximum and minimum value of \(P_{c}\) and \(P_{m}\), respectively. \(f_{max}\) is the maximum fitness value of population. \(\overline{f}\) is denoted as the average fitness value, and \(f^{{}^{\prime}}\) is the larger fitness value of the individuals to be crossed. \(c_{2}\) and \(c_{3}\) are the controlled parameters, and both equal to 3 in general.  
The adaptive adjustment curves of \(P_{c}\) and \(P_{m}\) are shown in Figures 3 and 4.