esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Good Operation Practice: Minimizing Wastestreams
#### 3.1.2 Filter Backwash Duration  
Filter backwash time was the second operational parameter optimized. At the end of each filter run, backwash duration was randomly varied between 90 and 900 s to assess its impact on filtration performance. A total of 47 experiments were conducted.  
Two metrics were used to evaluate performance:  
1. Post-backwash pressure drop across the filter media.
2. Clean bed filtration efficiency, calculated as the negative logarithm of the effluent-to-influent turbidity ratio \(pC^{*}\).  
The \(pC^{*}\) value, representing turbidity removal [48], is defined by Equation (1):  
\[pC^{*}=-log\left(\frac{NTU_{eff}}{NTU_{inf}}\right) \tag{1}\]  
Negative value indicates more turbidity in the filter effluent than in the influent, therefore suggesting that there are still particles in the filter bed after the backwash cycle is completed. Consequently, it is recommended to run the backwash until the clean bed log removal of turbidity equals 0.  
The results showed that the pressure drop remained unaffected by backwash duration. However, filtration efficiency improved with longer backwash times, up to an optimal duration of 300 s (5 min). Beyond this point, additional backwash time had no measurable benefit. Conversely, shorter durations increased flow but compromised filtration performance and reduced filter run time. Thus, a 5 min backwash was identified as optimal, balancing filtration efficiency and operational throughput. Figure 4 illustrates the relationship between backwash duration and filter performance.  
Figure 3: Multiple experiments testing the sludge drain clogging in varying operation conditions; flow was recorded over the entire shift and is represented as bars indicating minimum, maximum, and average flow rate.  
The duration of the filter runs was dependent on the clarified water turbidity and usually stayed within the range of 6 to 8 h per filter run. With a 5 min backwash, this results in an additional 1.2% of the water lost due to the backwash operation and a total loss of more than 10% of the raw water fed into the system over the entire treatment process.
## First Results of Field Absolute Calibration of the GPS Receiver Antenna at Wuhan University  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 2,*, [PERSON] 1, [PERSON] 1 and [PERSON] 1**  
1 GNSS Research Center, Wuhan University, Luoyu Road No. 129, Wuhan 430079, China; E-Mails: [EMAIL_ADDRESS] (Z.H.); [EMAIL_ADDRESS] (G.W.); [EMAIL_ADDRESS] (Z.D.); [EMAIL_ADDRESS] (T.L.)  
2 School of Geodesy and Geomatics, Wuhan University, Luoyu Road No. 129, Wuhan 430079, China  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (Q.Z.); [PERSON]AIL_ADDRESS] (G.C.); Tel.: +86-186-2770-0081 (Q.Z.); +86-185-7157-1020 (G.C.).  
Academic Editor: [PERSON]  
_Received: 23 June 2015 / Accepted: 7 November 2015 / Published: 13 November 2015_  
**Abstract:** GNSS receiver antenna phase center variations (PCVs), which arise from the non-spherical phase response of GNSS signals have to be well corrected for high-precision GNSS applications. Without using a precise antenna phase center correction (PCC) model, the estimated position of a station monument will lead to a bias of up to several centimeters. The Chinese large-scale research project \"Crustal Movement Observation Network of China\" (CMONOC), which requires high-precision positions in a comprehensive GPS observational network motived establishment of a set of absolute field calibrations of the GPS receiver antenna located at Wuhan University. In this paper the calibration facilities are firstly introduced and then the multipath elimination and PCV estimation strategies currently used are elaborated. The validation of estimated PCV values of test antenna are finally conducted, compared with the International GNSS Service (IGS) type values. Examples of TRM57971.00 NONE antenna calibrations from our calibration facility demonstrate that the derived PCVs and IGS type mean values agree at the 1 mm level.  
**Keywords:** GPS; IGS; receiver antenna; field absolute calibration; absolute antenna phase center corrections
###### Abstract  
The purpose of the current study is to propose a novel meta-heuristic image analysis approach using multi-objective optimization, named 'Pixel-wise k-Immediate Neighbors' to identify pores and fractures (both natural and induced, even in the micro-level) in the wells of a hydrocarbon reservoir, which presents better identification accuracy in the presence of the grayscale sample rock images. Pores and fractures imaging is currently being used extensively to predict the amount of petroleum under adequate trap conditions in the oil and gas industry. These properties have tremendous applications in contaminant transport, radioactive waste storage in the bedrock, and \(CO_{2}\) storage. A few strategies to automatically identify the pores and fractures from the images can be found in the contemporary literature. Several researchers employed classification technique using support vector machines (SVMs), whereas a few of them adopted deep learning systems. However, in these cases, the reported accuracy was not satisfactory in the presence of grayscale, low quality (poor resolution and chrominance), and irregular geometric-shaped images. The classification accuracy of the proposed multi-objective method outperformed the most influential contemporary approaches using deep learning systems, although with a few restrictions, which have been articulated later in the current work.  
deep CNN; clustering; image processing; micro-pores; micro-fractures; pixel intensity; porosity +
Footnote †: journal:  
0000-0001-6000-0001-6000][PERSON]  
1 Center for Research in Data Science (CeRDAS), Universit Technolog Petronas (UTP), Serl Iskandar 32610, Malaysia 1
# Numerical Simulation of Rock-Breaking Mechanism by Spherical Tooth Impact in Granite Formation
Currently, research on the rock-breaking mechanism of button bits primarily concentrates on the rock fragmentation aspect, with insufficient attention paid to the distribution of the buttons. Additionally, the impact of commonly used hard alloy balls of different diameters bits on rock has not been considered. This paper verifies the applicability of the zero-thickness viscoelastic element through mathematical modeling. By simulating different impact energies, the study investigates their effects on penetration depth, fragmentation area, and the region of stress influence. The aim of this research is to understand the rock-breaking mode to optimize drilling parameters and the arrangement of ball bits on the drill head, thereby reducing the number of bits while ensuring efficient rock fragmentation and minimizing costs.
# Anti-Breast Cancer Activity of Essential Oil:
## 3 Result
### Bioactivity of Essential Oils as Anti-Breast Cancer Agents
#### 3.4.3 Triterpenes  
Triterpene has been reported to be active against breast cancer cells [129; 130]. In comparison to the essential oils from the leaf (IC\({}_{50}\) 2.4 \(\pm\) 0.5 \(\mu\)g/mL and 1.5 \(\pm\) 0.1 \(\mu\)g/mL), triterpenes from _Pallines spinosa_ demonstrated a considerable cytotoxic effect on MCF-7 (IC\({}_{50}\) 0.25 \(\pm\) 0.03 \(\mu\)g/mL) and MDA-MB-231 (IC\({}_{50}\) 0.21 \(\pm\) 0.03 \(\mu\)g/mL) cells. When evaluated on normal MCF-10-2A (IC\({}_{50}\) 1.3 \(\pm\) 0.2 \(\mu\)g/mL) and blood mononuclear cells (2.80 \(\pm\) 0.45 \(\mu\)g/mL) as opposed to breast cancer cell hematology, flower essential oils' toxicity was five to eight times lower. Both essential oils change the levels of the proteins Bcl-2 and Bax and cause caspase-dependent apoptosis in MCF-7 and MDA-MB-231 cells. Additionally, via modifying the expression of cyclin D1, CDK4, and p21 proteins in both cancer cell lines, essential oils suppressed the cell cycle at the G0/G1 phase [60].  
_Boswellia scara_ essential oils which were distilled at 100 \({}^{\circ}\)C were more effective than essential oils prepared at 78 \({}^{\circ}\)C. This is because the extract has higher triterpene content. These compounds have the ability to cause the death of cancer cells, stop the growth of MDA-MB-231 cells in Matrigel, harm multicellular tumor spheroids (T47D cells), and control molecules that control apoptosis, signal transduction, and cell cycle progression [107].  
[PERSON] et al. reported that the ethanolic extract of _Pleutrotus eryngii_ inhibits MCF7 cells. Three triterpene components, 2,3,6,23-tetrahydrooxy-urs-12-en-28 oic acid, 2,3,23-trihydroxy-12-en-28 oic acid, and lupeol, make up most of this extract's composition. The ability of these substances to stop the growth of breast cancer cells was demonstrated by their respective IC\({}_{50}\) values of 15.71, 48, and 66.89 \(\upmu\)M, respectively [131].
## 2 Baseline-Free Damage Detection Using the Time Reversal Method of Lamb Wave
The \(DI\) value falls within the range of \((0,1)\). If the two waveforms are fully coincident with each other, \(DI=0\). If these two waveforms gradually deviate from each other, the \(DI\) value increases and approaches 1. It is noted that when Lamb waves propagate in composite laminates, a response signal consists of many wave components. Such as, wave components propagate through the direct path (from actuator to sensor) and those reflected off from the edges of laminates. Because Lamb wave is easily affected by the boundary conditions, the extracted re-emitted and reconstructed components corresponding only to the direct path. Therefore, the determination of the two time intervals, in which the re-emitted and reconstructed components are extracted respectively, are very difficult. If the time intervals are too narrow, the direct wave components are incomplete. If they are too long, the extracted signal will contain other mode components and noises. Both will result in a large DI value, creating a false alarm of damage. Moreover, the selection of the excitation parameters is also very important. In order to alleviate the influence of the time reversal operator and improve the SNR, narrowband signal is usually used as the original input signal. However, the time duration of narrowband signal is large. This will lead to the superposition of components in the response signal and reconstructed signal, and then produce a large DI value. In a word, the traditional TRP requires a large number of experiments to optimize the two time intervals and the excitation parameters, and further each experiment includes two actuating-receiving steps, which can make the experiments time-consuming.
# Energy Efficient Underlaid D2D Communication for 5G Applications
## 6 Conclusions  
Device-to-device communication is paving the way for many future realities such as the Internet of Things and smart cities. This improves the spectrum efficiency and energy efficiency, reduces the latency, and enhances the network throughput. In this work, a modified derivative algorithm was proposed to optimize the energy efficiency of the entire cellular network underlaid with device-to-device communication. We developed the model for the derivative algorithm and the modified derivative algorithm in a MATLAB and we compared the energy efficiency with the cellular density users and energy efficiency with the D2D density users of both the models. From our simulation results, we assumed an uplink environment where 5, 8 multiple bands of the spectrum with bandwidth 25 MHz are shared by cellular and device users. The modified derivative algorithm energy efficiency improved when compared with the derivative algorithm. Numerical results confirmed the proposed algorithm's nearly ideal performance. It is possible to look at the optimal D2D user and cellular density for each band as well as the associated trade-off analyses. We concluded that this algorithm can be used for 5G applications due to its high energy efficiency. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Total Energy Efficiency**} & \multicolumn{2}{c}{**Total Energy Efficiency**} & \multicolumn{2}{c}{**Total Energy Efficiency**} \\
**D2D User** & \multicolumn{2}{c}{**(Kbps/J)**} & \multicolumn{2}{c}{**(Kbps/J)**} & \multicolumn{2}{c}{**(Kbps/J)**} \\
**Density** & \multicolumn{2}{c}{**with**} & \multicolumn{2}{c}{**with**} & \multicolumn{2}{c}{**with**} & **with** \\
**(user/m\({}^{2}\))** & \multicolumn{2}{c}{**C\({}_{\text{p,I}}\) = 325 mW**} & \multicolumn{2}{c}{**C\({}_{\text{p,I}}\) = 375 mW**} & \multicolumn{2}{c}{**C\({}_{\text{p,I}}\) = 425 mW**} \\  & \multicolumn{2}{c}{**Modified**} & \multicolumn{2}{c}{**Derivative**} & \multicolumn{2}{c}{**Modified**} & **Derivative**} & **Derivative** & **Modified** \\  & **Algorithm** & **Derivative** & **Algorithm** & **Derivative** & **Algorithm** & **Algorithm** \\ \hline \(1\times 10^{-4}\) & 4.230 & 5.750 & 3.683 & 1.564 & 3.252 & 1.398 \\ \(2\times 10^{-4}\) & 10.056 & 11.573 & 8.766 & 8.262 & 7.750 & 7.265 \\ \(3\times 10^{-4}\) & 15.664 & 20.004 & 13.604 & 16.373 & 11.980 & 14.393 \\ \(4\times 10^{-4}\) & 18.772 & 26.609 & 16.289 & 22.486 & 14.330 & 19.766 \\ \(5\times 10^{-4}\) & 20.064 & 30.182 & 17.403 & 25.811 & 15.305 & 22.688 \\ \(6\times 10^{-4}\) & 19.803 & 30.981 & 17.174 & 26.640 & 15.101 & 23.417 \\ \(7\times 10^{-4}\) & 18.445 & 29.718 & 15.994 & 25.629 & 14.062 & 22.529 \\ \(8\times 10^{-4}\) & 16.450 & 27.148 & 14.263 & 23.453 & 12.539 & 20.616 \\ \(9\times 10^{-4}\) & 14.187 & 23.904 & 12.300 & 20.673 & 10.813 & 18.172 \\ \(1.0\times 10^{-3}\) & 11.915 & 20.451 & 10.330 & 17.699 & 9.081 & 15.558 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of energy efficiency with the D2D density users (users/m\({}^{2}\)).
# A Data-Intelligence-Driven Digital Twin Framework for Improving Sustainability in Logistics
## 2 Materials and Methods
### Digital Twin Framework  
Digital twins serve as a replica of a physical object in the digital space with a two-way automatic flow of data and information, unlike a digital model or shadow that simply has a one-sided manual flow [26]. Our work utilizes a DT with a two-way automatic flow using an end-to-end pipeline that leverages Supply Chain 4.0 technologies. A digital twin framework that utilizes cloud computing, the Internet of Things (IoT), real-time/soft-real-time data processing, and machine learning is necessary. The requirement to handle data storage, multiple data sources, ETL/ELT, API handling, and cloud and on-premises data systems constitutes the need for a distributed, modular approach with two-way feedback, which can perform efficiently in terms of time and resource constraints such as API call limits, latency, and response time. This work adopted a distributed DT framework that uses edge/fog computing from [3] to implement the solution. Section 3 describes the architectural framework adopted in more detail.  
Tools  
Our digital twin instance is based on the following suite of tools, enabling us to perform real-time data ingestion, pre-processing and insights discovery.  
* **Azure Digital Twin (ADT)**: A platform for creating digital models of real-world assets, processes and events [27]. Using the Digital Twin Definition Language (DTDL) [28], we created a JSON-based model depicting the virtual representation of our operations, syncing in real time with our data lakehouse.
* **Databricks**: An all-in-one data intelligence platform enabling data and AI/ML workloads [29]. The use of Apache Spark for real-time processing with the Delta Lake format allows a seamless integration of the ADT with our intelligence models powered by Mosaic AI [30].
## 2 Materials and Methods
### Methodology Flow Chart  
The work's methodology (Figure 4) is described below, containing five main steps:  
1. Collecting data: Input parameters including beam length (L), beam end opening distance (d\({}_{0}\)), opening diameter (D), inter-opening distance (d), section height (H), web thickness (t\({}_{\text{web}}\)), flange width (w\({}_{\text{flange}}\)), flange thickness (t\({}_{\text{flange}}\)), and the output parameter of the critical buckling load (q\({}_{\text{c}}\)) were collected from the literature published in [PERSON] et al. [31].
2. Dataset preparation to train ML models: The input and output parameters were used to create a complete set of data. A number of 70% data (2551 training samples) were extracted from the initial dataset for training the ML models. The remaining 30% data (1094 testing samples) were used for validation the AI models.
3. Training models: The ML models were trained using the training dataset. Three ML algorithms based on ANFIS with three optimization methods RCSA, CA, and SFLA. The concepts of these models were introduced in the previous sections. This step was repeated until the models successfully trained within a preselected tolerance error criterion.
4. Models validation: After successfully training three ML models, the validation process was performed using the testing dataset. The models were verified using different statistical measures such as RMSE, MAE, and R.  
Sensitivity analysis: After validation of the ML models, the sensitivity of input parameters was performed using the best model to identify the influence between input parameters and the critical buckling load of I-shaped cellular beams.
## 4 Performance Evaluation
### Reliability  
In this section, a reliability analysis of EPON-based communication network between OLT and WT-ONUs for a small-size offshore WPF is given. Five basic topologies without any redundancy (architecture A, B, C, D, and E) are constructed. Since redundancy for collector systems in WPFs considers additional cables for protection, we consider additional FF, POS, and DF as network protection components. Failure rate and mean time to repair (MTTR) are considered, based on network components from reference [20; 21; 22].  
The following assumptions are considered in our study:  
* The WTs used in this study are 5 MW. The distance between two WTs is 1 km.
* Reliability analysis considers only the communication network between the OLT and ONUs. It consists of OLT, FF, POS, DF, and ONU.
* We assume that the failure rate of the OLT and ONU is small, compared with the number of failures in cables. Therefore, the equipment failures of OLT and ONUs are neglected in our analysis.
* The communication network equipment inside each WT is not considered, and only the ONU is considered in our calculation.
# Review of Adaptive Shock Control Systems
## 2 Literature Review of Adaptive Concepts for Shock Control
### Using Pressure Differences in the Shock Region
#### 2.3.2. Actuated Thin Flexible Plate  
An advancement of the flexible plate approach from Section 2.3.1 is an actuated solution in which [PERSON] et al. [81] use a Haydon-Kerk 25443 actuator with a maximum displacement of 18 mm. This actuator is located in the middle of the flexible plate. This actuator can be used to extend the airfoil surface by pushing against it and the actuator can, conversely, retract and thus hold the surface down, which is depicted in Figure 12.  
In the retracted case (Figure 11(a)) the bump crest position is further upstream while with extending the actuator (Figure 11(b)) the bump moves towards the trailing edge. It could be detected that the cavity pressure has a distinctively smaller effect on the bump geometry compared to the non-actuated flexible plate [81].  
While in [81] (2015) the maximum stress in the single actuation point is approximately 69% of the yield stress, [PERSON] et al. [86] (2016) could reduce this value to a reported 58% yield stress. Hence, in both approaches the stresses are far below 90% yield stress (safety factor: 10%) which the authors chose as limit.  
Whilst a smaller skin thickness will allow for lower actuation forces for the same deflection, questions relating to the robustness of such low thicknesses (e.g., impact damage and corresponding maintenance and repair time and costs) still remain.
## 1 Introduction  
Multiple chronic conditions (MCC) are defined as \"conditions that last a year or more and require ongoing medical attention and/or limit activities of daily living\" [1]. Among the most common chronic conditions are hypertension, heart disease, cardiovascular disease, arthritis, diabetes, cancer, dementia, and various cognitive disorders. The number of people with MCC has increased over the years and, combined with aging, has become a major global health challenge [2]. Because people with MCC must frequently be hospitalized, expenses related to medical care have also risen. In addition, health services worldwidehave been focusing on individual chronic diseases, so there are few projects that address the prediction of factors such as mortality from MCC. This situation makes it necessary to create a tool that can predict different factors, such as mortality, from MCC. This tool would help health professionals to make decisions and subsequently treat patients suffering from these conditions.  
Not surprisingly, multiple chronic diseases and cardiovascular pathologies are associated with impaired quality of life, an increased risk of disability, institutionalization, and mortality, and higher healthcare utilization and associated costs. For example, the annual risk of hospitalization increases from 4% among individuals with zero to one chronic condition to more than 60% for those with six or more chronic conditions. Furthermore, 30-day readmission rates increase progressively as the number of chronic conditions rises, and per capita, Medicare expenditures increase exponentially as a function of the number of chronic conditions [3]. Additionally, physician care is based on treating one disease at a time and does not consider the possible effects of a combination of different pathologies.  
Performing mortality prediction based on multiple chronic conditions and cardiovascular diseases can be a significant challenge for a healthcare professional. Correct prediction can identify patients with a higher risk to their health, allowing physicians to take appropriate measures to avoid or minimize this risk and, in turn, improve the quality of care and avoid potential hospital admissions [4]. The related work section shows that the use of machine learning algorithms to predict mortality and other pathologies has been increasing in recent years. This increase is because machine learning algorithms provide the tools to make an automatic and accurate mortality prediction, which is why they greatly support the decision-making process of health professionals. In addition, the literature review reveals that the most used models are those based on decision trees and support vector machines, with an increase in the use of neural networks at the present time [5].  
This study aimed to predict patients' mortality risk from cardiovascular variables, medical history, medications, and electrocardiogram variables. Supervised learning algorithms were used to perform the predictions.