esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete
## 4 The Application of Machine Learning in Nano-Modified Concrete
### Artificial Neural Network (ANN) Algorithm
[54] proposed a multi-objective ANN model (MOANN model) to illustrate the effect of different porosities on the compressive and flexural strength of cement mortar hardened specimens containing micro-silica (MS) and NS, and the modeling results showed that the MOANN-II model predicts the compressive and flexural strength of cement mortar at the same time by taking porosity as an input parameter with high prediction accuracy. [PERSON] et al. [25] used the ANN to predict the compressive strength of MS and NS cement mortar, but the prediction with ANN I model did not include porosity as an input parameter, which resulted in a high error rate, so the ANN II model was developed by considering the effect of porosity, and the developed model was validated with the collected dataset in Figure 12. The x-axis in Figure 12 refers to the series number of the experimental samples. This follows the format used in the original study by [PERSON] et al. [25]. Each number corresponds to a distinct mix design tested in their dataset. [PERSON] et al. [21] used ANN and Gradient Boosting (GB) modeling to evaluate the effect of important parameters W/C, Flash Ash (FA), etc., on the compressive strength  
Figure 11: Comparison of ANN models [51]. Figure 12: (**a**) Evaluation of experimental and predicted results by ANN-II model: compressive strength [25]. (**b**) Validation of ANN-II model using collected dataset: compressive strength [25]. of nano-titanium-based concrete composites. Based on the modeled evaluation of the parameters, it was found that Gradient Boosting (GB) modeling was the most effective in accurately predicting the compressive strength (CS), especially in determining the mix ratios at the desired CS. [PERSON] et al. [27] developed an intelligent modeling ANN for predicting residual compressive strength (RCS) and gamma ray linear attenuation coefficient (\(\upmu\)) of different concrete mixtures, where type and proportion of nano-additives, temperature and high temperature exposure time were the input variables of the neural network and RCS and \(\upmu\) values were the outputs. The predictive ability of the developed model is better as compared to regression analysis. [PERSON] et al. [55] explored how the dose of colloidal nano-silica (CNS) and its surface area affects the mechanical properties of cement mortar composites by using response surface methodology (RSM) and ANN, and the ANN model was found to outperform the RSM model in terms of performance by comparison. [PERSON] et al. [56] explored the effectiveness of advanced ML algorithms in predicting the CS of concrete modified with nano-silica and supplemental cementitious materials. The final results showed that the RF (R\({}^{2}\) = 0.93) and ANN (R\({}^{2}\) = 0.92) models outperformed the RF and ANN (R\({}^{2}\) = 0.92) models in terms of accuracy, which implies that experimental savings can be achieved in the future.
# The Effect of Different Filament Arrangements on Thermal and Optical Performances of LED Bulss
## 1 Introduction  
As a new light source, light emitting diodes (LEDs) are regarded as the fourth generation of solid-state lighting sources owing to their advantages of environmental friendliness, energy saving, long lifetime, quick start up, and so on [1; 2; 3; 4]. So far, environment protection and energy saving have generated considerable research interest, the white light emitting diodes (WLEDs) have been developed rapidly in the commercial market and widely used in various lighting fields [5; 6; 7]. Early devices only provided lighting in one direction and could not meet the requirements for omnidirectional luminescent applications [8; 9; 10]. The flexible spiral LED filament bulbs combine the bulb shell of traditional incandescent bulbs with a new LED filament to achieve 360-degree illumination [11; 12]. However, there is no effective radiator installed in the limited space, which seriously affects the thermal and optical performance of the filament. The heat generated by the chip is difficult to dissipate from inside of the bulb to the environment [13; 14; 15; 16].  
The study of the effect of filament overheating, is of great value to the thermal design and management of LED filament bulbs. The properties of phosphors, such as the diameters, shapes, thickness, and concentration, may affect the luminous flux and junction temperature [11; 17], [PERSON] [17] et al. reduced the average junction temperature of the LED filament by optimizing the size of the bulb and the structure of the phosphor. Moreover, heat generated from LED chips is transferred to the surface of the filament by conduction, then transferred to the bulb shell by convection, and finally dissipated to the environment [18; 19]. LED filament bulbs mainly rely on convection heat dissipation and have a poor heat dissipation performance [17; 20; 21]. The previous report put forward several plans.  
High thermal conductive materials and high thermal conductive gases are chosen to decrease the average junction temperature of the LED filament [15; 22; 23]. To increase the flow of gas inside the bulb, [PERSON] [24] et al. used ionic wind to improve the heat dissipation ability of LED filament bulb. It was also necessary to consider the optical and thermal properties with various currents [24; 25; 26]. Nevertheless, they did not study the effect of different filament arrangements on the thermal and optical performances, nor did they design and simulate the temperature distribution and gas flow path inside the bulbs, so it was worthwhile to analyze the thermal and optical performances during the operating time.  
Hence, the purpose of this paper is to investigate the thermal and optical performances of flexible spiral LED filaments bulbs with three different filament arrangements. The junction temperature, temperature of surface bulb, luminous flux, and luminous efficiency of LED filament bulbs are studied. In addition, for better analysis and understanding of the principle of heat dissipation of LED filament bulbs, simulation models were carried out.
# A Method to Construct an Indoor Air Pollution Monitoring System Based on a Wireless Sensor Network
## 4 Simulation and Results Discussion
### Coverage Holes Detection  
#### 4.2.1 Application Method  
The PSO algorithm allowed us to optimize a total coverage of the RoI and to collect the best positions of the deployed sensors. After a period of time (one hour), the base station updated these positions leading to one of the deployed sensors out of the coverage network. Following this, the VD will be used to define the position of the coverage hole.  
The target area (RoI) A is also a two-dimensional plane (100 m\({}^{2}\)). It is digitized into \(100\times 100\) grids where each grid size is equal to 1. The total number of deployed sensors in coverage optimization is 40 sensors. In this process, we will first randomly move one of these sensors and assume that, after updating the sensor positions, it has been found that the sensor has pulled out of the coverage network as shown in Figure 8. Following this, the 39 sensors remaining in the RoI (their positions are already known) are used to partition the RoI into 39 cells and to discover the location of the coverage hole using the VD assuming that the theorems 1 and 2 are already defined.  
Figure 7: Coverage rate compared to the number of sensor deployment.
# A Road Defect Detection System Using Smartphones
## 3 A Proposed Road Defect Detection System  
In this manuscript, we detail two significant contributions stemming from our innovative road defect detection system. Firstly, we introduce a method for the automation of data collection and labeling geared towards accelerometer-based classification of road defects. Prior studies in this domain, utilizing machine learning or deep learning for the identification of road defects, have consistently encountered challenges in amassing substantial datasets across diverse environments, primarily due to the complexities associated with data collection and labeling. Our solution, an automated data collection system, streamlines the acquisition and categorization of data, thereby facilitating the generation of comprehensive datasets crucial for deep learning applications.  
Secondly, we propose a Convolutional Neural Network (CNN) model specifically designed to leverage this automatically collected dataset. This model is adept at identifying and distinguishing between three common road defects: speed bumps, manholes, and potholes. Through these contributions, our system not only addresses the critical challenges of data collection and labeling in the context of accelerometer-based road defect detection but also presents the potential of deep learning models to enhance the accuracy and efficiency of road defect detection.
# Super-Twisting Sliding Mode Control with SVR Disturbance Observer for PMSM Speed Regulation
## 5 Conclusions  
This study introduced a robust control system for a PMSM with sliding mode control using a super-twisting reaching law and SVR-DOB. The control law suppressed the chattering and improved the disturbance observer. The PMSM speed control system with the proposed reaching law exhibits a better performance than the conventional reaching law; therefore the designed sliding mode speed controller can replace the traditional SMC regulator. An additional term for the disturbance compensation ensures the robustness of the speed controller which was applied to improve the tracking performance. A comparison of the simulation results shows the validity and effectiveness of the proposed reaching law, demonstrating that the STRL-SMC with SVR-DOB is an effective control method. Compared with the traditional SMC controller, STRL-SMC with SVR-DOB performed better in terms of speed tracking and disturbance rejection. SVR-DOB enables SMC to suppress disturbance without the data of system modeling. SVR-DOB can be trained using data of relation between current and rotor speed error. The output of SVR-DOB includes system uncertainty and compensates the disturbance effectively. SVR-DOB estimates the disturbance and model uncertainty that exist in real condition. The proposed controller has the potential to be expanded using appropriate estimators for the control of electrical machines. Using the simulation processed in this study, the experimental results in progress with PMSM hardware will be supplemented in future work. The proposed controller has the potential to be applied to PMSM and can show improved performance. The disturbance estimation of SVR-DOB can improve the performance of the proposed controller in real industrial applications.  
This research was accomplished by all the authors. A.C. and K.Y. conceived the idea, performed the analysis, and designed the simulation; A.C., H.K. and M.H. conducted the numerical simulations; and H.K., H.A. and Y.K. co-wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2019R1A2C1002343) and the BK21 FOUR Project.  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest.  
Figure 6: Disturbance estimation of SVR-DOB.
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 6 Conclusions
### Application of the Model to Regulatory Discussions  
With a physical model as described in previous section, a direct relationship between the ambient electromagnetic noise level and the unwanted emissions of electrical and electronic equipment will be established. Therefore, the creation of such a model and validating it with (existing and new) measurements will--for the first time--relate the compatibility studies in the world of spectrum management to the emission limits in the world of electromagnetic compatibility. The impact that a lowered of increased EMC limit will have on the achieved utility of the spectrum would become visible and computable. This will help to take better informed decisions.  
This research was funded by the European Commission within the INFRAIA-02-2020 program, grant number ID 101007599, as part of the project \"Plasmasphere Ionosphere Thermosphere Integrated Research Environment and Access services: A Network of Research Facilities (PITHIA-NRF)\", [[https://www.pithia-nrf.eu/](https://www.pithia-nrf.eu/)]([https://www.pithia-nrf.eu/](https://www.pithia-nrf.eu/)) (accessed 1 October 2024).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Access to the data in the ITU Radio Noise Data Bank requires ITU membership.  
Figure 11: The Radius of Significance (RoS) contains the sources that predominantly define the man-made noise level at the sensor. The outlined shapes represent houses, the stars in one house depicting that each house contains multiple sources.  
**Acknowledgments:** The author thanks [PERSON] and [PERSON] of Ofcom UK for their valuable discussions on radio noise measurement and the Radio Noise Data Bank. The author also thanks [PERSON] of the Dutch Authority for Digital Infrastructure, The Netherlands, for fruitful discussions on the Radius of Significance (he coined the term). And last but not least [PERSON] of the Federal Network Agency, Germany, and [PERSON] of the Dutch Authority for Digital Infrastructure, The Netherlands, for creating the first international standard for radio noise measurements in 2005.  
**Conflicts of Interest:** The author declares no conflicts of interest.
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
## Appendix A Derivation of \(\omega^{*}\) and \(\gamma^{*}\)
### Optimal Solution  
After the novel PSO algorithm converges, the optimal \(\omega^{*}\) and \(\gamma^{*}\) are obtained from the global best position:  
\[(\omega^{*},\gamma^{*})=\mathbf{g}_{\text{best}} \tag{21}\]  
This is achieved when at each iteration \(t\), every particle \(i\) updates its position based on these three components:  
1. Inertia: \(w(t)\mathbf{v}_{i}(t)\)  
2. Cognitive component: \(r_{1}c_{1}(t)\big{(}\mathbf{p}_{\text{best},i}-\mathbf{x}_{i}(t)\big{)}\)  
3. Social component: \(r_{2}c_{2}(t)\big{(}\mathbf{g}_{\text{best}}-\mathbf{x}_{i}(t)\big{)}\)  
As the algorithm progresses, the inertia weight \(w(t)\) decreases, reducing the influence of the previous velocity. Simultaneously, the cognitive and social components guide the particles toward the best-known solutions.  
Let us examine the expected position of a particle after \(k\) iterations:  
\[\mathbb{E}[\mathbf{x}_{i}(t+k)] =\mathbf{x}_{i}(t)+\sum_{j=0}^{k-1}\mathbb{E}[\mathbf{v}_{i}(t+j+1)] \tag{111}\] \[=\mathbf{x}_{i}(t)+\sum_{j=0}^{k-1}(w(t+j)\mathbb{E}[\mathbf{v}_{i }(t+j)]+\mathbb{E}[\mathbf{U}_{i}(t+j)]) \tag{112}\]  
As \(k\to\infty\), we expect the following:  
1. \(w(t+k)\to 0\) (due to the exponential decay of the inertia weight)  
2. \(\mathbb{E}[\mathbf{v}_{i}(t+k)]\to 0\) (particles stabilize)  
3. \(\mathbb{E}[\mathbf{U}_{i}(t+k)]\to 0\) (particles converge to a common point)  
Under these conditions, the expected position converges:  
\[\lim_{k\to\infty}\mathbb{E}[\mathbf{x}_{i}(t+k)]=\mathbf{x}_{i}(t)+\sum_{j=0}^ {\infty}\mathbb{E}[\mathbf{U}_{i}(t+j)] \tag{113}\]  
the global best position \(\mathbf{g}_{\text{best}}\) is continuously updated to represent the best solution found so far. The convergence of all particles towards this point is driven by the minimization of the fitness function \(f(\omega,\gamma)\).  
Let \(\mathbf{x}^{*}=(\omega^{*},\gamma^{*})\) be the true optimal solution. We can show that  
\[\lim_{t\to\infty}\mathbb{E}[\|\mathbf{g}_{\text{best}}(t)-\mathbf{x}^{*}\|]=0 \tag{114}\]  
This convergence is assured by the properties of the novel PSO:  
1. Decreasing inertia weight allows for fine-tuning of the solution.  
2. Oscillating acceleration coefficients balance exploration and exploitation.  
3. The fitness function guides particles toward the optimal solution.
###### Abstract  
The objective of the present study was to compare recent methods for characterizing cell modifications. We studied the effect of extracellular reduced glutathione (GSH) on an olfactory neuroblast cell line (13s24). Three methods were used to monitor, in label-free, noninvasive real-time experiments, cell surface occupancy by measuring impedance (xCELLigence), cell behavior (HoloMonitor cytometry), cell ultrastructure by measuring refractive index (3D Nanolive microscopy). Reduced glutathione dose-dependently increased cell volume and motility and decreased cell adhesion. Cell sorting analyses revealed that after short-term exposure (6 h), GSH reduced F-actin polymerization and extracellular glycoproteins leading to adhesion strength loss. Results support the hypothesis that excreted GSH could modulate disulfide bound-dependent integrin conformations involved in neurogenesis and/or neuronal plasticity. This is the first evidence of a causal link between GSH and changes in cell volume and motility required for cell division, migration, and/or differentiation. Results show the importance of real-time analysis methods, without labelling, in the study of cell responses under culture conditions. The present findings highlight important criteria in the choice of methods, beyond the parameters studied, such as cell preparation time, plate filling time, number of cells studied, friendly use of the devices, and the complexity of data processing.  
olfactory neuroblast cell line; cell volume; cell adhesion; reduced glutathione; HoloMonitor; xCELLigence; Nanolive; flow cytometry Article
## 5 IEEE 802.15.4 Technology
### Performance Trends of 802.15.4 Based WSNs
The behavior of the mean energy spent as a function of the packet size, for different values of \(N\), is shown. Here we refer to the whole packet size, composed of the PHY and MAC headers (of 15 bytes) and the payload. As we can see, the mean energy spent presents a  
Figure 12: Throughput as a function of packet size for an 802.15.4 network organised in star and tree-based topologies. maximum. This is due to the fact that for low packet sizes, the increase of the size increases the energy as well, since a larger amount of energy is spent for transmitting larger packets. Conversely, when the packet size becomes too large, the energy starts to decrease since the probability that the node succeeds in accessing the channel and transmitting its packet gets lower as well.
# Seismic Performance Assessments of RC Frame Structures Strengthened by External Precast Wall Panel
## 1 Introduction  
As a large number of RC frame structures with non-seismic details have been severely damaged, many researchers [1; 2; 3; 4; 5; 6] have investigated the infill wall-strengthening method to improve seismic performance by inserting cast-in-place concrete or precast (PC) walls inside the beam-column frame structure. The infill wall-strengthening method has the advantages that it is cost effective, while greatly increasing shear strength. However, it is not easy to secure the sufficient shear resistance performance of the connection due to the complex connection details between the RC frame and the infill wall; moreover, during the construction period, the use of internal space is very limited. In order to overcome the shortcomings of the conventional infill wall-strengthening method, various strengthening methods have been proposed. [PERSON] [2] presented the shear key connection details of PC panels to improve the infill wall-strengthening method, and [PERSON] and [PERSON] [3] proposed a strengthening method using the fiber reinforce polymer (FRP). [PERSON] et al. [4] investigated a strengthening method using CFRP hollow clay tiles, and [PERSON] and [PERSON] [5] suggested a PC panel-strengthening methodusing epoxy mortars. This study proposes an advanced seismic strengthening method using the externally anchored precast wall panel (EPCW). The EPCW method proposed in this study can significantly shorten the construction period, because the precast wall panels are connected with the existing frames on the outside of the building. Furthermore, there is an advantage in that the internal space can be used, even during construction. In this study, the specimens of RC frame structures with non-seismic details that are strengthened by the EPCW method are subjected to cyclic loading tests, to verify the strengthening performance of the EPCW. In addition, an analysis method is proposed, and the accuracy of the proposed model is verified by comparing the analytical and test results.
## 3 Results
### Determination of Cell Rescue Systems and NADPH Concentration  
The total network of twofold changed genes, including cell rescue systems, was visualized via StringGO-based genetic interaction. As shown in Figure 6, approximately half of the two-fold-changed genes formed a genetic network; however, the rest remained networked. Stress-responsive genes, including _FMP16_ encoding protein involved in responding to stress conditions, _PHM7_ encoding vacuolar transport chaperone, _MSC1_ encoding protein with ubiquitin ligase activity, _NQM1_ encoding transaldolase, _CTT1_ encoding cytosolic CAT, and _ALD3_ encoding ALD, formed the central core of the genetic network. Future studies of the genetic interaction for the unconnected single genes should be entailed to further elucidate the sophisticated tolerance mechanism to FT2 stress.  
Thereafter, immunoblotting analysis and NADPH assay were performed to demonstrate the change from transcriptional to translational levels. As shown in Figure 7, compared with WT cells with empty vector alone, protein expression coding hexokinase (Hxt2), ALD, alcohol dehydrogenase (Adh6), 6-phosphogluconate dehydrogenase (Gnd2), CAT (Ctt1), GSH transferase (Gto1), GAPDH (Gpd1), and Hsp12 showed high accumulation in DM cells expressing _DaMDHAR_ under FT2 stress, whereas the protein level encoding thioredoxin 3 (Trx3) and Hsp30 in DM cells decreased under FT2 stress (Figure 7A). Protein expression patterns mimicked the changes in mRNA expression of each gene, despite the imperfect relationship between mRNA and protein level observed. The moderate differences observed between mRNA and protein levels were likely due to post-transcriptional modification. The difference between mRNA and protein levels was high for Hsp30, suggesting that the degraded Hsp0 protein was capable of binding to Hsp30.  
Figure 6: StringGO-based genetic network of multiple genes (**A**) and single gene (**B**) of twofold changed genes in _DaMDHAR_-expressing yeast cells under repeated FT (FT2) stress.  
Thereafter, we estimated the intracellular NADPH levels in WT and DM cells under FT2 stress using spectrophotometric quantification. The NADPH concentration of DM cells was 2.25-fold higher than that of WT cells under the same stress (Figure 7B). High NADPH levels resulted from gene activation associated with the PPP. This increase in NADPH in DM cells likely reflected the overall levels, including changes of cytosol and mitochondrial compartment. Therefore, these results suggested that gene expression provides, at least partially, a direct relationship between mRNA transcripts and protein levels.
# Applications for Microwave Kinetic Induction Detectors in Advanced Instrumentation
## 4 MKIDs in Particle Physics  
The above might lead the reader to believe that MKIDs are only useful for astronomical applications. Instead, Microwave Kinetic Inductance Detectors are widely employed in the context of particle and astro-particle physics. Some research groups are studying the feasibility of using MKIDs for rare events experiments such as the experimental evaluation of the neutrino mass and the detection of Dark Matter particles such as WIMPs (Weakly Interacting Massive Particles). In both cases, MKIDs based instruments have been proposed both as means of direct detection and to further reduce the background signal of current experiments.