esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### CMC Determination of Surfactants  
Four commercial surfactants (SDS, CTAB, DDAPS, and Tween 20) were used to disperse the particles [14; 15; 16; 17]. In this study, the following surfactants were chosen to investigate the effect of their ionic character on the TiO\({}_{2}\) dispersion owing to their different ionic characteristics: anionic SDS, cationic CTAB, zwitterionic DDAPS, and nonionic Tween 20. Their chemical structures are shown in Figure 1a.  
The CMC of materials determines the critical concentration for formulating self-assembled micelles by oil (air)-water interfacial activation and is referred to as the minimum concentration that can disperse particles far away. In general, the CMC of surfactants is  
Figure 1: (**a**) Chemical structures of sodium dodecyl sulfate (SDS), cetyltrimethylammonium bromide (CTAB), \(N\)-dodecyl-\(N\),\(N\)-dimethyl-3-ammonio-1-propanesulfonate (DDAPS), and polyoxyethylene (20) sorbitan monolaurate (Tween 20). (**b**) Determination of critical micelle concentration for SDS and CTAB by conductivity measurement. (**c**) Determination of critical micelle concentration for DDAPS and Tween 20 by UV-Vis absorption measurement.  
determined via a concentration-dependent conductivity relationship [18]. CMC of SDS and CTAB was determined at a point of intersection between different linear slopes: 8.1 mM (0.233 wt%) and 0.97 mM (0.035 wt%), respectively (Figure 1b). Those are comparable (\(\pm\)5% variation) with values reported in the literature [19]. The conductivity values of DDAPS were constant regardless of concentration, indicating no apparent intersection point (Figure S2). This is because the net charge of the zwitterionic species is close to zero, thus, they rarely contribute to the conductance [20]. For Tween 20, the conductivity could not be measured because of the intrinsic non-charged character of the surfactant.  
As an alternative method for determining the CMC of DDAPS and Tween 20, the inflection point of the concentration-dependent absorbance was used when the surfactant was mixed with R6G (Figure S3) [12]. CMC of DDAPS and Tween 20 was determined at a point of intersection between different linear slopes: 4.1 mM (0.137 wt%) and 0.06 mM (0.007 wt%), respectively (Figure 1c). These values are similar (\(\pm\)5% variation) to that measured in previous studies [21; 22].
## 1 Introduction
### Automated Inspection and Robotic Systems
In 2016, Donecle developed its own autonomous UAV [34], offering visual inspection of the exterior of an aircraft. In that direction, [PERSON] et al. [16] conducted research on developing an autonomous UAV equipped with a high-definition camera to inspect aluminium panels, including various defects, such as lightning strikes and hail damage. The UAV was outfitted with multiple distance and trajectory sensors to maintain an optimal distance from the aircraft and to avoid obstacles. Data transmission between the UAV and a PC-based ground station was facilitated through a Wi-Fi link, and a graphical user interface (GUI) was developed in MATLAB/LabVIEW for data processing and presentation. While this was a preliminary design, initial tests demonstrated promising results in defect detection. However, further refinements in fully autonomous trajectory planning are needed. The RC EYE One Xtreme was selected as the micro-UAV for this project, featuring a weight of only 260 g, a payload capacity of 150 g, and a typical flight time of 10 min. The author also highlighted several advantages of using drone-based GVI. First, this approach could be cost-effective and time-efficient compared to conventional visual inspection methods. Significantly reducing the aircraft's maintenance time in the hangar by accelerating visual checks. Additionally, UAVs could safely access hard-to-reach areas, eliminating operator health risks. Defect detection accuracy could be increased by comparing the acquired images with a database containing thousands of previously captured images. Moreover, various data types could be gathered by integrating non-destructive sensors, such as thermal cameras and ultrasound probes. In 2024, [PERSON] et al. [35], in collaboration with TUI's airline maintenance team, introduced a drone-based General Visual Inspection (GVI) system. They used a Parrot Anafi drone, which featured a camera mounted on an integrated gimbal, to inspect the surface of a TUI aircraft within a hangar. The data collected was used to train a machine learning model to detect and classify five distinct types of defects automatically. Additionally, they developed a size estimation algorithm to assess the identified defects. The primary objective was to detect dents, and the model demonstrated a precision of 71% with an Area Under the Curve (AUC) of 0.69.
## 8 How Small Molecules Inhibit RNA Transcription

Table 3: Structural information on bacterial RNAP and small molecule inhibitor complexes *. inhibition: myxopyronin inhibits transcription (1) by preventing the opening of the RNAP clamp that permits entry and unwinding of DNA (hinge jamming mechanism) [85] or (2) by interfering with interactions between RNAP and the unwound DNA template strand [86]. A new structure of a bacterial RNAP-inhibitor complex was obtained by using salinamide, which binds the N-terminal end of the bridge helix and prevents nucleotide addition (PDB: 4 MEX) [37]. Squaramide is a non-natural bacterial RNAP inhibitor isolated by high-throughput screening, and its binding site was predicted at the switch region, based on analysis of squaramide-resistant RNAP mutants followed by computational modeling using the structure of the _T. thermophilus_ RNAP-myxopyronin complex [88]. Crystal structures of _E. coli_ RNAP in complex with squaramides were determined, structurally confirming that squaramides bind to RNAP switches (PDB: 4 YFK, 4 YFN) [87]. During the past decade, our understanding of bacterial transcription processes has drastically improvedas a result of structural studies on bacterial RNAP. I hope this review will be a useful reference  
Figure 3: Three-dimensional representation of targets of small-molecule RNAP inhibitors. _E. coli_ RNAP holoenzyme is depicted as an \(\alpha\)-carbon backbone traced with partially transparent molecular surfaces (\(\alpha\) subunits: white; \(\beta\) subunit: cyan; \(\beta^{\prime}\) subunit: pink; \(\omega\) subunit: gray; \(\sigma^{70}\): orange). Small-molecule inhibitors bound to RNAP are depicted as CPK models. Chemical structures of small-molecule inhibitors and mechanisms of transcription inhibition are indicated. for researchers who study the mechanism, structure and function of bacterial RNAP transcription and that structures presented in this review will provide guidelines for designing new experiments.
## 1 Introduction
Calibrate Prediction: Due to the LSTM prediction error, this paper uses STBC to adjust the predicted stock prices by avoiding inaccuracies caused by stock price volatility. It thereby achieves the goal of reducing model prediction errors. 3. Determine Trading Points: This paper uses GAs to evolve the optimal parameters of the membership functions of fuzzy rules to form a fuzzy system, which determines the buying and selling points. 4. Optimize Trading Strategy: The predicted lowest and highest prices are used as the buying and selling prices for executing trades. In financial markets, stock price prediction is not only critical for investors' trading decisions but also helps assess the financial health of companies, thereby predicting bankruptcy risk. The Z-Score model [9], widely used for bankruptcy prediction, is based on financial ratios that assess a company's risk of insolvency. These ratios include operating capital, reinvested income, earnings before interest and taxes, market value of equity, and sales revenue, all of which reflect a company's stability and debt-paying ability. Therefore, stock price fluctuations are not only tied to market sentiment and supply-demand dynamics but also have a close relationship with the underlying financial risks of a company. This study aims to predict stock prices of the Taiwan Stock Exchange ETF50 using LSTM networks with STBC calibration. By training on historical stock data, we can learn market trends and further estimate a company's financial status and bankruptcy risk. Such a stock prediction model provides investors with a deeper risk management perspective, particularly in volatile markets, enhancing decision-making accuracy. This paper is divided into five sections. Section 1 addresses the research motivation of the paper. Section 2 introduces the relevant literature on LSTM, fuzzy systems, GAs, and stock trading strategies. Section 3 explains the experimental process and framework to determine the trading points and the trading strategy. Section 4 presents the experimental results and analysis, identifying the optimal trading strategy. Finally, Section 5 summarizes the research findings and discusses any shortcomings and possible directions for future improvements and extensions.
## 3 Results
### Test Outcome  
Figure 7 shows the results of the example case described in Section 2.6. In each plot, the schedules of all units within the TVPP are stacked upon each other by addition, while the blue line shows the aggregated schedule. The red areas describe the transformers upper and lower bound that has been derived by calculations in Pandapower.  
The left plot shows the case in which the TVPP does not offer any congestion management while the right plot exhibits the results when the TVPP optimizes their scheduled units towards the mitigation of grid congestions. In the latter case, the aggregated load of the TVPP remains within the requested boundaries, because parts of the industrial loads are shifted towards the period in which high feed-in from the wind turbines is forecasted. The results show that the TVPP is able to optimize the schedules of its units such that the requirements calculated in the power system analysis are met.  
Figure 6: SPARQL query to find suitable simulation models.  
Figure 7: Simulation results of the technical virtual power plant (TVPP) in the example case described in Section 2.6. The left plot shows the situation before optimization of the TVPP and the right plot after optimization.  
Load shifting has been conducted via employment of different schedules for the industrial loads. In other words, the representing agent of a load has the information of all acceptable schedules for that load. In this example, all of these schedules are treated as equally valid and no monetary constraints are associated with the changing of schedules. While this simplification allows for a more comprehensible demonstration case, ISAAC still provides all capabilities for more complex modeling of individual DER constraints.
## 3 Bottom up Meets Top down-The Emerging Use of Google Earth by Grass Root Initiatives
### Bottom-up Example: Quarry and Barge Loading Facility on Texada Island
#### 3.1.2 Methods  
Friends of Davie Bay used a multi-media approach in their submissions requesting a full environmental assessment to the provincial and federal government, including letters, scientific reports, PowerPoint presentations, photographs, and movies. Friends of Davie Bay also commissioned a Google Earth landscape visualization of the proposed barge loading facility, which was combined with overlapping and nearby sensitive ecological features. Lehigh's application included plan and sectional engineering drawings prepared in AutoCAD format. Friends of Davie Bay requested these digital data, but Lehigh declined to make them available. Based on the author's experience with resource extraction projects, the lack of publicly available industry (and sometimes government) geo-data is quite common in grass-roots advocacy contexts [35]. Therefore, Lehigh's hard-copy drawings were scanned on a large-format scanner and then geo-referenced in ArcGIS, a common workaround to ensure the accuracy and credibility of resulting geo-data. The 3D conveyor belt was drawn in Google SketchUp software while the tugboat, barge, and figures of workers were downloaded from the SketchUp 3D Warehouse [36].  
Overlapping and nearby sensitive ecological features included polygon data (\"Coastal Douglas Fir Zone\" and the \"Rockfish Conservation Area\") and point data (\"Eelgrass Bed\", \"Sandspit Eelgrass Meadow\", \"Forage Fish Sandspit West and North\"). Using Google Earth, the polygon data were digitized from maps in government reports [37,38]. The point data also included hyper-link \"balloons\" to photographs by Friends of Davie Bay. All features used pre-defined \"snapshot views\" so that Google Earth would automatically move to defined viewpoints when specific features were clicked.
# Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks
## 1 Introduction  
A traditional mobile network is a communication network where the link to and from end nodes is wireless, and the network is distributed over land areas called \"cells\", each served by at least one fixed-location transceiver. Currently, it is getting harder to offer the necessary quality of service (QoS) criteria [1; 2; 3], especially for users with high mobility, due to the state of technology and the volume of traffic consumed by users [4]. Mobility management is one of the major functions of network communication in traditional cellular networks or terrestrial networks (TN) and non-terrestrial networks (NTN), and mobility management aims to track where the users are, allowing calls and other user services to be delivered to them. Therefore, one of the main problems for these users is the proper initiation of the handover. Many wireless system issues, including decision-making, resource optimization, and network administration, can be presented in a way that is ideal for machine learning (ML) [5; 6; 7; 8]; through intelligent adaptive learning and decision-making, ML can be utilized to improve radio communication. Since applicationsfor NTNs include telecommunications, navigation, and remote sensing, these networks can provide coverage to remote or hard-to-reach areas and can also be used as a backup to terrestrial networks in case of failure or disaster. In Release 16 of the 3 rd generation partnership project (3 GPP) specification, the first 5G wireless technology in the world was standardized. In Release 17, work is being conducted to create 5G wireless technology to support non-terrestrial satellite networks [9]. Massive amounts of data are being produced while 5G networks are growing more sophisticated. To effectively manage the 5G networks, data-driven management, and artificial intelligence (AI) solutions are essential. Research on AI-enabled NG-RAN was carried out by 3 GPP for Version 17; this study examined the high-level principles, functional framework, potential use cases, and related solutions for AI-enabled RAN intelligence [10]. For a selection of AI-based use cases, including network energy conservation, load balancing, and mobility optimization, 3 GPP Release 18 will specify data collection upgrades and signalling support. Enabling 5G systems to support non-terrestrial networks (NTN) is one of the research areas of the 3 GPP, with a focus on common scenarios where non-terrestrial networks provide access to user equipment (UE). Spaceborne platforms such as low earth orbiting (LEO), medium earth orbiting (MEO), geosynchronous earth orbiting (GEO), and high elliptical orbit (HEO) satellites are used by satellite communication networks [11]. Another unmanned aerial system (UAS) is the high-altitude platform station (HAPS) that can potentially be used to provide both fixed broadband connectivity for end-users and transmission links between the mobile and core networks used for backhauling traffic.
## 4 Experiment and Model Validation  
### Experimental Setup  
Semi-circular bending (SCB) tests were conducted at \(-10\)\({}^{\circ}\)C and \(0\)\({}^{\circ}\)C to investigate the fracture behavior of asphalt concrete and validate the CZM-based FE model. The aggregate gradation used in the experiments was consistent with that used in the FE model. Cylindrical specimens with a height of 150 mm and a diameter of 150 mm were compacted using a Superpave rotary compactor (SGC) under a compaction pressure of 600 kPa. The SCB specimens were drilled and cut from the central part of the cylindrical specimens to avoid uneven porosity. The specimens used in the SCB tests were 150 mm in diameter and 25 mm in thickness. A notch with 20 mm in depth was precut, as shown in Figure 6.  
The SCB tests were conducted by a multi-functional testing machine (UTM-250) with an ambient chamber. For each test, the specimen was pre-placed in the ambient chamber for more than 4 h. The temperature in the ambient chamber was set to be constant (\(-10\)\({}^{\circ}\)C or \(0\)\({}^{\circ}\)C) during the test. The vertical loading was applied with a loading rate of 5 mm/min for each test.  
Figure 5: Example of the 2-D FE model.
# Quantum Game Application to Recovery Problem in Mobile Database
## 4 Discussion
### Simulation Results
#### 4.2.6 Experiment Six  
Aim: The last set of experiments was implemented to compare the suggested recovery model (the extended version with three players) with our previous version (modelling by two players). The precision (accuracy) rate has been used for assessment according to the recovery probabilities for multi degrees of handoff threshold values that are considered as a very influential factor to complete the retrieval process.  
Main Results: As expected, Form Figure 9, utilizing three players for game modelling, increases the precision rate by an average of 5% compared with two players for a game.  
Discussion: This improvement comes from increasing competition through utilizing more than two recovery algorithms that gave the opportunity to select the best protocol from a pool of algorithms according to the current MDS environmental conditions. Therefore, some protocols used in our previous work [21] will be retreated in this work due to the entry of other protocols in the competition.  
Figure 8: A loss packet analysis with a comparative model.  
Figure 9: Accuracy analysis between two-player- and three-player-based game modeling.
## 4 Numerical Modeling
### Stability Analysis with SLOPE/W  
In order to determine the overall stability of the system after plastic loading, analyses were made for two different groundwater levels. The overall stability analysis results for the two groundwater levels are presented in Figure 9. For the analysis with the initial groundwater level of +3.00 m, the factor of safety is obtained as 1.976, and for the analysis with the groundwater level of +2.50 m, the factor of safety is obtained as 1.922. After the revision of the project, the critical factor of safety did not change, and the obtained factor of safety values are above 1.50 in all cases. So, there is no stability problem in the system.  
Figure 9: Stability analysis results. (**a**) Factor of safety for initial water level at GWL = 3.00 m. (**b**) Factor of safety for initial water level at GWL = 2.50 m.
## 2 Bridge Structural Health Monitoring (SHM) System
### The Importance of Using the SHM System
As yet another tragic example, in the summer of 2018, the Polcevera bridge (Ponte Morandi or Ponte delle Condotte) in Genoa collapsed. This collapse, that killed 43 people, was caused by corrosion in some steel cables, which in turn snapped as a result of structural deficiencies in the building's design, construction, and subsequent maintenance. Because of this, in 2020, engineers employed  
Figure 2: A summary of the procedure of the SHM system to monitor the functioning of bridges. cutting-edge technologies and instruments to keep tabs on the condition of the replacement bridge for the Genoa structure [47]. After these kinds of incidents, engineers have tried to come up with ways to stop them from happening again.
## 2 Materials and Methods
### Summary of the Computer Vision Algorithm for LLD Assessment  
The image processing algorithm for detecting the visual markers and computing leg alignment is based on the approach presented in our previous work [20], where a non-invasive, HSV color space-based segmentation technique was employed to detect green-colored markers on the subject and background wall. First, RGB images are converted to HSV space, and a binary mask is created using empirically defined threshold ranges for hue, saturation, and value. Morphological operations, specifically dilation, are applied to close gaps between segmented regions.  
Once the markers are isolated, their centroids are computed using first-order spatial image moments. Marker pairs are used to define lines for alignment: the prosthetic leg (knee line), the middle wall markers (middle line), and upper wall markers (upper line). The angle of each line relative to the image's X-axis is computed using the _atan2_ function.  
The alignment is assessed by comparing the knee line to the middle and upper wall lines. During calibration, baseline angles (_\(\theta\_middle\)_, \(\theta\_upper\)) are computed, and each new frame's alignment is checked against these baselines using computed tolerances (_tol_middle_, _tol_upper_). Further details, including the full algorithm flow and marker configuration, are available in [20].