esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Results and Discussion
### Evaluation of Setup 2: Redundant Transmissions Using FRER via Different 5G Mid-Band Systems  
The results of Setup 2, using two different 5G mid-band systems, are shown in Figure 11. The end-to-end latency of the individual UEs one and two are given in Figure 11a,b as a distribution over time. The combined transmission after elimination on Switch 2 is shown in Figure 11c, with an average latency of 6.08 ms. By using FRER, the maximum latency for 99.99% of the values has dropped from 11.31 ms for UE1 and 11.10 for UE2 to 8.23 ms, again meeting the use case requirements set to 10 ms for 99.99% of messages. As in Setup 1, different outliers have been equalized by the second communication path, smoothening the overall latency and reducing the jitter of the communication. Especially, the communication via UE1 shows different spikes going up to 60 ms compared to fewer spikes in the communication of UE1. Still, UE2 could deliver packets in time to Switch 2 when a latency spike occurred in UE1, achieving an overall stable and reliable communication. Further, it can be seen in Figure 11c that the few smaller spikes left in the communication of Setup 1 could be eliminated in Setup 2. This can be explained by the increased redundancy due to the different 5G systems used. Latency spikes coming for the network were smoothed in Setup 2, where the spike only affected one communication path, whereas latency spikes in the network affected both communication paths in Setup 1.  
As a result of Setup 2, the impact of FRER using redundant 5G systems can be seen. Again, the spikes of the different communication paths could be equalized using the redundant communication paths. Further, an improvement in communication quality could be achieved by using redundancy not only in end devices but also in the 5G system.
## 3 Results and Discussion  
### Three-Dimensional Distribution of Air Voids in Asphalt Mixture during Freeze-Thaw Testing  
3D images of asphalt mixture specimens with air voids before and after freeze-thaw cycles reconstructed via image processing (Figure 3) are provided in Figure 6. A striking difference is observed for specimens with different design air void contents during freeze-thaw cycles. 3D images of samples with a design air void content of 7% are filled with yellow voids (which stands for larger voids with a volume greater than 10 mm\({}^{3}\)); contrastingly, such voids only appeared at the top and bottom of 3% design air void content samples. However, all 3D images are similar in larger void sizes, voids present in the top and bottom parts of samples compared with the middle part, indicate similarities to the 2D slices shown in Figure 5. Thus, it increases the credibility of the 3D model. When comparing the 3D images with the same design air void under different freeze-thaw cycles, two main changes of the internal structure can be observed. The first one is the emergence of new red regions generated during freeze-thaw cycles, marked with rectangles in Figure 6. Second regions are marked with ellipses in Figure 5, and those regions are divided into two parts. The solid ellipse indicates air void volume increase during freeze-thaw cycles, and the dotted ellipse relates to the connection between existing air voids. These changes illustrate how freeze-thaw cycles can damage the internal structure of the asphalt mixture in three ways: (1) expansion of individual voids under freeze-thaw cycles, (2) generation of new voids under freeze-thaw cycles, and (3) consolidation of two separated air voids under freeze-thaw cycles.  
It must be noted that not all colors can be easily distinguished in 3D images containing air voids, so the volume grades defined in these images are not meticulous. For more specific quantitative research, the voids are divided into micropores (\(<\)0.1 mm\({}^{3}\)), mesopores (0.1-5 mm\({}^{3}\)), and macropores (\(>\)5 mm\({}^{3}\)), and more details of volume distribution of voids for which the results of different design air void content are shown in Tables 5-7.
## Appendix B GNSS System Technical Details  
Technical parameters of the used GPS system Titan2 were as follows: GPS ENGINE (U-Blox M8 Concurrent GNSS LCC Module, TCXO, ROM, SAW, LNA); triple GNSS antenna (GPS + GLONASS + GALILEO -SGGP.18A Series Taoglas), high resolution sampling (10 HZ), inertial measurement (1000 HZ accelerometer), battery for 7 hr, the use of SBAS--EGNOS(Satellite Based Augmentation Stations) (European Geostationary Navigation Overlay Service) error correction.
## 6 Precise Orbit Determination Methods
### High-Performance Orbit Determination Methods for Large-Scale Satellite Cluster Targets  
Due to the characteristics of space missions, large-scale constellations of satellites are commonly positioned in LEO, where they encounter specific perturbations from atmospheric drag. Compounding this, since these constellations tend to share similar orbital altitudes, the perturbations affecting their orbits may display similarities. The paramount challenge is to adeptly and precisely forecast the orbits of these extensive satellite constellations using their characteristics while operating within the constraints of limited on-orbit computational resources. Addressing this issue is imperative to fulfill the stringent requirements for avoiding space collisions arising from on-orbit activities of large constellations, elevating it to a pressing matter that demands attention.
## 4 Formulations of Green Tea Extracts and EGCG in Pharmaceutical and Cosmetic Applications
However, further study on the formulation with gelatin showed that a real food processing condition during biscuit preparations could be harmful to catechin molecules. Thus, this issue needs to be more deeply analyzed in future. Gelatin was also used to obtain a stable catechin formulation using the layer-by-layer (LbL) assembly method performed by [PERSON] et al. [54]. Some other natural EGCG formulations with starch, soybean lecithin, and \(\beta\)-glucan were prepared by a unique method called 'particles from gas saturated solutions drying' (PGSS-drying). This technique allows the obtaining of stable products with preserved bioactive properties for further development [55]. Lecithin formulations of green tea extract named \"Greenselect Phytosome\" (GSP) were developed by [PERSON] et al. and allowed the increase of the bioavailability of EGCG in breast cancer treatment [56]. Improved absorption of EGCG was also achieved by [PERSON] et al. [57; 58], who created self-double-emulsifying drug delivery systems (SDEDDS) for oral and transdermal administration of catechin. These formulations when suspended in water can easily form emulsions, thus demonstrating an increased cellular uptake of EGCG. (\(-\))-Epigallocatechin-3-gallate has also been used as an adjuvant in drug formulations. EGCG when combined with tannic acid, was applied as a solubilizing agent for the preparation of better soluble drug formulations of previously mentioned APIs, such as paclitaxel, docetaxel, amphotericin B, curcumin, and rapamycin, significantly improving the solubility of these drugs in aqueous solutions [59]. [PERSON] et al. studied EGCG encapsulated in transferrin-bearing vesicles for intravenous administration [60], which revealed significant tumor suppression. A separate group of catechin formulations for pharmaceutical purposes are polymers. EGCG-loaded microspheres with Eudragit(r) S100, non-ionic block copolymer Pluronic F127 or PLGA were prepared and investigated towards intestinal diseases, antihyperlipidemic and anti-obesity activity [61; 62; 63]. The main formulations of EGCG were summarized in Figure 2.  
Figure 2: The main formulations of EGCG mentioned in this review.
# Comparison of Water Pretreatment Devices for the Measurement of Polar Odorous Compounds
## 3 Results and Discussion  
### Quality Assurance  
To evaluate the reliability of the detector, a calibration curve was prepared by diluting the mixed standard gas (5 Odor mix, Rigas Co., Ltd., Republic of Korea) with 10, 20, 40, 80, and 100 ppb. The determination coefficients for MEK, MIBK, i-BuAl, BuAc, and styrene were 0.9999, 0.9997, 0.9998, 0.9993, and 0.999, respectively. Thus, they were reliable values because they were all above 0.98, as suggested in the test method. In order to verify the reliability of the measured data, the MDL was calculated by repeatedly analyzing the sample for seven times at a concentration of 0.5 ppb. The MDL values of MEK, MIBK, i-BuAl, BuAc, and styrene were 0.17, 0.12, 0.39, 0.09, and 0.06 ppb, respectively. In the test method, MDL target values were less than 1 ppb of styrene, and the other compounds were less than 10 ppb. The calculated MDL values were found to be acceptable with respect to concentration  
Figure 1: Sampling train for the comparison of the target chemical recovery rate: (**a**) Without water pretreatment device; (**b**) with water pretreatment device.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Concentration**} & \multicolumn{3}{c}{**Volume**} \\ \hline
**Primary Standard Gas** & **RH (\%)** & **Humid Air (mL)** & **Primary Standard Gas** & **Working Gas** **Concentration (ppb)** \\ \hline
10 * & 50 & 9980 & 20 & 20 range (20.6–20.8) \\  & & 9900 & 100 & 100 range (103–104) \\ \hline
10 * & 80 & 9980 & 20 & 20 range (20.6–20.8) \\  & & 9900 & 100 & 100 range (103–104) \\ \hline \hline \end{tabular} Note: * 10.3 ppm for MEK and 10.4 ppm for i-BuAl, MIBK, BuAc and styrene. Working gas concentration will be presented in this study as 20 ppb or 10 ppb instead of a concentration range.  
\end{table}
Table 3: Dilution method for recovery experiment by water pretreatment devices.  
ranges in this study. In addition, the LOQ was 0.55, 0.38 1.23, 0.30, and 0.20 ppb for MEK, MIBK, i-BuAl, BuAc, and styrene, respectively. In terms of precision, 80 ppb concentration was selected for a precision test and analyzed three times and evaluated as standard deviation (SD). The calculated SD of MEK, MIBK, i-BuAl, BuAc, and styrene was 1.82%, 5.03%, 2.55, 4.76%, and 9.84%, respectively. Precision presented in the test method was less than 10% SD.
# Reinforced Drapery Meshes: A Design Method Accounting for Retaining Ropes Contribution
## 3 Example  
Focusing on the mesh and rope design, in this section, an example of the novel methodology is proposed. A squared anchor pattern with both \(i_{x}\) and \(i_{y}\) equal to 3 m is installed, as commonly adopted in current practice. Two cases are considered: Case 1 represents a squared rope pattern (Figure 4c), and Case 2 is a rhomboidal rope pattern (Figure 4e). The slope angle \(\beta\) is equal to \(70^{\circ}\), while the worst joint inclination \(\alpha\) is equal to 40\({}^{\circ}\), with a friction angle of 30\({}^{\circ}\). Applying the partial safety factors proposed by the Italian regulation [35] to the effect of the actions, the obtained \(F_{m,Ed}\) due to the possible detached block is equal to 44.3 kN for Case 1 and, due to the halved area \(A_{m}\), is equal to 22.2 kN for Case 2. Similarly, the punching action \(P_{m,Ed}\) on the mesh is equal to 22.4 kN in Case 1 and 11.2 kN in Case 2. In this example, retaining ropes with a diameter of 14 mm, a cross-sectional area \(A_{f}\) equal to 115 mm\({}^{2}\), a tensile strength grade of 1570 MPa, a minimum breaking force of 162 kN [36], and an apparent elastic modulus \(E_{r}\) of 120 GPa are considered. The distance between anchors \(l\) is equal to 3 m in Case 1 and 4.24 m in Case 2, resulting in a maximum elongation \(\Delta l_{max}\) equal to 0.02 and 0.028 m for Case 1 and Case 2, respectively, i.e., about 1% of the rope length. In Case 1, the maximum deflection \(f_{max}\) is 0.153 m, the ultimate load \(q_{r,max}\) is equal to 25.9 kN/m, the real total deflection \(f_{r,Ed}\) is 0.142 m, and the acting load \(q_{r,Ed}\) is equal to 9.7 kN/m. In Case 2, \(f_{max}\) is 0.217 m, \(q_{r,max}\) is 15.8 kN/m, \(f_{r,Ed}\) is 0.201 m, and \(q_{r,Ed}\) is 6.8 kN/m. The working rate of the rope is around 37% in Case 1 and around 43% in Case 2, showing that the latter would be slightly more effective from a design point of view.  
This reveals that the working rates of ropes are similar, while the mesh-puncturing action in Case 2 is halved compared to Case 1. From a design point of view, the rhomboidal configuration of Case 2 provides a considerable cost reduction for the required mesh type.  
It is worth mentioning that, generally speaking, considering a pinned drapery (namely, Case 3), the action \(F_{m,Ed}\) and \(P_{m,Ed}\) are equivalent to Case 1. The difference lies in the fact that the applied safety factor \(\gamma_{z}\) in Case 3 should also encompass the model uncertainties related to the assumed static scheme and to the greater difference in boundary conditions between laboratory and real cases. Furthermore, the mesh should also be verified to prevent anchor punching, ensuring that the tensile resistance of the steel wire intercepted by the plate is higher than \(P_{m,Ed}\). Consequently, mesh in which a single wire has very high tensile resistance is often required.
# Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia
## 2 Materials and Methods
### Flight Mission  
The UAV used in the study is a DJI Mavic Pro fitted with a 1/2.3\({}^{\prime\prime}\) complementary metal-oxide-semiconductor (CMOS) 12 MP 4K camera. We tabulate the characteristics of the camera in Table 1. We carry out the flight operations twice within three months. The flight mission comprised reconnaissance of the mapped site and pre-flight fieldwork. The H83 mm \(\times\) W83 mm \(\times\) L198 mm copter has an upright take-off weight of 734 g. Its fully charged lithium polymer (LiPo) 3830 mAh batteries provide a maximum flight time of 27 min. The DJI Mavic Pro can withstand wind speed up to 7 m/s, which was never exceeded during the flight mission. The flight altitude while capturing the images was 70 m, which the UAV climbed with an ascent speed of 4 m/s. The resulting ground sample distance was (GSD) 2.4 cm. To observe variation in flow width and vegetation growth, we execute a flight mission to capture UAV images on 31 July 2019 at 09:04 am and 6 November 2019 at 09:20 am at the location 5\({}^{\circ}\)7\({}^{\prime}\)40\({}^{\prime\prime}\) N, 100\({}^{\circ}\)29\({}^{\prime}\)48\({}^{\prime\prime}\) E in Bandar Baharu, Kedah. We name the sites S1 and S2 on 31 July 2019 and 6 November 2019, respectively. The wind speed recorded was 1.67 m/s with the partly cloudy conditions at S1 and 1.11 m/s with broken clouds at S2. The temperature recorded on these two days was 29 \({}^{\circ}\)C and 31 \({}^{\circ}\)C, respectively. The level of tides was 1.69 m at S1 and 1.73 m at S2.
# Fault Location and Restoration of Microgrids via Particle Swarm Optimization
###### Abstract  
This aim of this work was to develop an integrated fault location and restoration approach for microgrids (MGs). The work contains two parts. Part I presents the fault location algorithm, and Part II shows the restoration algorithm. The proposed algorithms are implemented by particle swarm optimization (PSO). The fault location algorithm is based on network connection matrices, which are the modifications of bus-injection to branch-current and branch-current to bus-voltage (\(BCBV\)) matrices, to form the new system topology. The backward/forward sweep approach is used for the prefault power flow analysis. After the occurrence of a fault, the voltage variation at each bus is calculated by using the \(Z_{\text{bus}}\) modification algorithm to modify \(Z_{\text{bus}}\). Subsequently, the voltage error matrix is computed to search for the fault section by using PSO. After the allocation of the fault section, the multi-objective function is implemented by PSO for optimal restoration with its constraints. Finally, the IEEE 37-bus test system connected to distributed generations was utilized as the sample system for a series simulation and analysis. The outcomes demonstrated that the proposed optimal algorithm can effectively solve fault location and restoration problems in MGs.  
fault location; service restoration; particle swarm optimization; microgrid; power flow; short-circuit fault +
Footnote †: journal: Applied Sciences
# Evaluating Retrieval-Augmented Generation Models for Financial Report Question and Answering
### Problem Statement  
The integration of RAG into LLMs has rapidly gained traction, and it has emerged as a key strategy to increase the practical applicability of LLMs [8]. However, despite its potential to reduce factual inaccuracies or \"hallucinations\", there remains a notable gap in the literature regarding RAG's application in question and answering for a specific use case: private investors seeking to analyze the financial reports of banks effectively (see Section 2.4). The manual analysis of such reports is both time-consuming and labor-intensive. However, by leveraging RAG, investors can access domain-specific information from half-yearly or quarterly reports previously published by several banks. Through RAG-facilitated question-and-answer sessions, investors can make informed decisions based on accurate insights.  
This study focuses on half-yearly and quarterly reports due to their regular publication frequency, ensuring an ample number of samples for analysis. In addition, these reports contain a better-manageable number of pages than annual reports, which may overwhelm an RAG system with their extensive content. Further details about the data collection process are provided in Section 3.2.  
Currently, there is a lack of a straightforward RAG approach tailored to this use case, which hinders the empirical demonstrations of how RAG can be effectively adapted (Section 2.3). These experiments with different model configurations should focus on both the retrieval and generation components (Section 2.2) to achieve desirable outcomes across evaluation aspects (context relevance, answer faithfulness, and answer relevance, Section 2.3).  
Therefore, the motivation to research RAG encompasses several crucial aspects. First, it aims to enhance the quality of responses across evaluation dimensions such as context relevance, answer faithfulness, and answer relevance by exploring different model configurations (e.g., different LLMs enriched with the same data sources). Second, it attempts to address the persistent challenges encountered by state-of-the-art LLMs, such as factual inaccuracies and hallucinations. By examining the practical implementation and effectiveness of RAG, particularly in specific domains like financial analysis for private investors, this research aims to provide valuable insights that bridge the gap between theoretical advancements and real-world applicability.
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 6 Measurement of Fruit Maturity
### Thermal Time from Flowering  
#### 6.1.1 Heat Units/Growing Degree Days  
Plant development is sensitive to temperature, and many models exist for crop maturation based on accumulated heat units, also known as Growing Degree Days (GDDs). The GDD value is typically calculated using the average of the daily minimum and maximum temperature minus a base temperature. Some models also include a maximum temperature limit. Daily GDD values are accumulated for the development period of the plant organ of interest and a total GDD requirement is established for the development of a plant organ from one phenological stage to another. This requirement is commodity- and cultivar-specific.  
The estimation of fruit maturation time using heat units has two sources of measurement error. The first source is the assessment of the start and end points of the maturation period, i.e., estimation of the time of flowering and of harvest maturity. Potential methods to improve these assessments are discussed in the next sections. The second error source is in the measurement of temperature. Temperatures from previous seasons can be used to forward predict when fruits will reach an established GDD threshold for maturity, with the use of current season data up to the current date in season. Obviously, an error is introduced to the harvest timing estimation if temperatures in the current season differ to the historical record. Another error relates to the measurement of current season temperature, in terms of the device used and its location relative to the block. The advent of low-cost, remotely logged temperature sensors opens potential for more localized inputs to GDD estimates.  
The use of GDD with wine grapes has been heavily documented, in terms of the required GDD to reach multiple phenological stages of the crop and the use of GDD in combination with other inputs. For example, grape cultivar-specific GDD 10 models that incorporated information on calendar day of soil thaw, photoperiod, and solar accumulation achieved validation results of 1.9, 1.3, 1.0 and 1.7 days to achieve budbreak, bloom, veraison and harvest, respectively, compared to the use of models based on cultivar and GDD 10 alone (10.1, 2.5, 4.2 and 5.0 days, respectively) [109].  
For other fruit crops, more work is required to clarify GDD requirements. For example, GDD documented for the major Australian mango cultivars vary in terms of the flower stage taken as the start point for the GDD measurement, and uncertainties on the assessment of time of flowering and harvest maturity likely account for the variation in estimates of GDD requirements of a single cultivar [110; 111; 112].
## 3 Results and Discussion
The experimental strain data is also used to demonstrate the localization capability of the SHM approach, resulting from the model architecture and a known sensor grid layout. Figure 12 shows the damage heat map due to the interpolation of residual values  
Figure 11: Each point represents the damage index of a single transformed experimental test sample. The lowest bound calculated from the benchmark set and the threshold value are additionally displayed. \(\delta_{j}\). Unfortunately, on the spoiler demonstrator, the damage is placed outside of the sensor grid, making the experimental samples not ideal for the evaluation of the model's localization performance, since the assumption of zero residuals at the sensor grid perimeter significantly influences the result there. Nonetheless, the actual location of the damage and its prediction match quite well.