esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Ethyl Ester (EE) Production from Palm Olein  
Low production of EE (<25%) was observed, even after 70 h, for all BTL2 and CAL covalent derivatives and for that of TLL immobilized in the presence of DTT whether dried or not (Table 5); for BTL2, this low transesterification activity was also seen when it was immobilized on poly-hydroxybutyrate beads [53], whereas the low EE yield seen for CAL and TLL derivatives produced by immobilizations mediated by DTT is probably related to the deleterious effect of the additive on enzyme activity, as discussed in Section 2. By contrast, the aminated TLL derivative obtained under classic immobilization conditions at pH 9.0/10.1 (51.6% EE\({}_{\text{14 h}}\)), the novel TLL derivative immobilized at pH 7.0 in presence of AA (71.5% EE\({}_{\text{14 h}}\)), and the commercial biocatalyst reference Novozyme(r) 435 (75.1% EE\({}_{\text{14 h}}\)) were the most active biocatalysts after drying (Table 5). For the same derivatives but without the drying pretreatment, EE\({}_{\text{14 h}}\) yield drops to 11.7%, 14.3%, and 47.2%, respectively, implying that a low water content is positive for the EE\({}_{\text{14 h}}\) yield of all the biocatalysts, as determined for other systems where an excess of water may denote a competitive hydrolytic reaction [1; 9]; it is also possible that the pretreatment with _tert_-butanol during drying (Section 3.5), as in other biocatalytic transesterification [54], helps to improve the performance of TLL-Gx derivatives. It is very likely that the fact that Novozyme(r) 435 results were less affected by moisture than TLL-Gx is due principally to the hydrophobic nature of its constituent support: Lewatit(r) VP OC 1600 is based on a DVB-crosslinked polymer that probably excludes water and favors contact with the reacting oil in the microenvironment of the enzyme to a higher degree than the highly hydrophilic crosslinked- agarose matrix present in Gx-TLL derivatives. Differences in terms of EE yield between Gx derivatives under the same enzyme and drying conditions may be due to the different protein structure acquired after immobilization (Section 2.1). For example, unlike the FTIR-ATR spectra of the more active Gx-TLL derivatives in EE production (reference and immobilized at pH 7.0 with AA, Table 5), the less active one (immobilized at pH 7.0 with DTT) has a band present between 1700-1800 cm\({}^{-1}\) (Figure 5). This could be the result of a different local state of the peptide backbone [55; 56] caused by DTT, which results in a less active enzyme structure in EE production after the immobilization process. The fact that both the commercial catalyst and the TLL-Gx derivatives present conversions below 80% even after 44 h may be explained by taking into account chemical equilibrium considerations [1; 9; 15]: in the reactions conditions assayed herein, a stoichiometric and \"greener\" ethanol to oil molar ratio (3:1) was selected, while considerably higher ratios were necessary to obtain conversions above 90% using analog covalent TLL derivatives in other studies (9:1-18:1) [3; 4].
# A New Hybrid Positioning Method by Fusion of BDS and 5G Signal Using the Particle Swarm Method
## 4 Performance Analysis
### Test of the BDS-5G Signal MOPSO-TOA/TDOA Algorithm in the Complex Environment  
To verify the ability of the BDS-5G signal fusion positioning algorithm based on MOPSO-TOA/TDOA to improve the localization of a single BDS in complex scenes, such as occlusion, test experiments were carried out in actual scenes (city urban canyon, unilateral buildings, and under the bridge). The control group (single BDS positioning test) and the experimental group (BDS-5G signals MOPSO-TOA/TDOA test) were set up to conduct a comparative analysis of the positioning experiments in three environments. The test scenario is shown in the following Figure 5a,c,e. The blue square represents the 5G base stations distribution, the red pentagram represents the distribution of the BeiDou satellites, and the light blue rectangular area is the building sheltered area. Two hundred positioning experiments were carried out in these three scenarios. The cumulative distribution of errors of positioning results and real positions is shown in Figure 5b,d,f. Red represents the results of the BDS-5G MOPSO-TOA/TDOA algorithm, and blue represents the BDS positioning results. The 1\(\sigma\) value of the overall error is shown in Table 1.  
Through the analysis of the above results, BDS-5G signal particle filter fusion positioning is improved in all scenarios compared with single BDS positioning. In the three occluded scenes, the MOP-SO-TOA/TDOA algorithm has greatly improved the positioning accuracy compared to the single BDS. The positioning accuracy has been improved by 50-80%, and the overall average positioning errors are 5.63 m and 16.43 m, respectively, which increased by 65.7%. BDS-5G signal integrated positioning technology can effectively improve the positioning ability of BDS, which is more significant in sheltered environments.  
Figure 4: Comparison between the multiobjective particle filter algorithm and Kalman filter algorithm: (**a**) signal receiving terminal; (**b**) test location and route (The words on the map are Chinese place names); (**c**) convergence time; (**d**) error comparison.  
Figure 5: Comparison of positioning results in complex scenes: (**a**) BDS/5G base station distribution in canyon environment; (**b**) positioning error in canyon environment; (**c**) BDS/5G base station distribution in single-side building environment; (**d**) positioning error in unilateral building environment; (**e**) BDS/5G base station distribution in under bridge environment; and (**f**) positioning error in under bridge environment.
## 2 Materials and Methods  
### Study Area and In Situ Data  
Hangzhou Bay is located in the northern Zhejiang Province and southern Shanghai (29.92\({}^{\circ}\)-30.86\({}^{\circ}\)N, 120.95\({}^{\circ}\)-122.07\({}^{\circ}\)E), China. As the estuary of the Qiantang River and a globally renowned strongly tidal estuarine bay, approximately 435 million tons of sediment (recorded at Datong Station during 1951-2000) is discharged annually from the Yangtze River mouth estuary into the East China Sea, with about 230 million tons transported southward into Hangzhou Bay, thereby constituting the primary sediment source for the bay ([PERSON] et al., 2006) [27]. In this region, the inflow from the upper Qiantang River basin is about one-hundredth of the water coming in with the rising tide from the East China Sea; consequently, the influence of river discharge is minimal, and tidal dynamics predominantly govern the system. Pronounced diurnal variations in tidal current speed and direction, driven by strong tidal currents and circulations, maintain persistently high SSC in Hangzhou Bay.  
Between 2 December and 13 December 2011, a 12-day field campaign was conducted in Hangzhou Bay employing a fixed-point observation strategy for synchronous water monitoring. Fixed observations were carried out at three designated stations during high, medium, and low tidal phases. Sampling was performed from 07:30 to 17:30 at hourly intervals at stations Zhapu 1 (S01), Andong 2 (S02), and Jinshan 3 (S03) (see Figure 1). The images used in this study were acquired on 12 December 2011, when the in situ measurements were taken at station Andong 2 under relatively calm conditions, with small wind waves, clear skies, and minimal whitecaps on the water surface.  
During the campaign, spectral measurements were obtained using an ASD spectroradiometer via an above-water measurement technique. Spectral data were screened based on solar zenith angle [28], yielding 49 valid spectral curves (see Figure 2). Flow velocity data were recorded using an ADCP instrument, and in situ concentration measurements were performed by on-site filtration. The filters were preserved and later analyzed in the laboratory to extract SSC and other water quality parameters.
# Predicting the Colouration between Binaural Signals
## 3 Validation
### Discussion  
The PBC method performed comparably with three alternative publicly available and widely used methods as an objective prediction of the colouration between binaural signals. This was shown first with specific test scenarios, intended to isolate the features of the method, for which the presented method produced colouration values that fit with expectations.  
Second, the correlation between the subjective results of a listening test conducted on perceived similarity of binaural signals and the colouration values of the four tested methods was measured. All four tested methods produced a statistically significant (\(p<0.001\)) overall negative correlation between colouration values and perceived similarity, though the presented PBC method produced the highest overall correlation value (\(r=-0.95\)), which is notably higher than the other tested methods. One reason for this could be the some loudness scale, allowing for a greater perceptual weight for louder frequency bands.  
Further analysis on specific test sound locations showed that the PBC produced the highest correlation values for all but one tested sound location, and the correlation plots in Figure 11 show the PBC as consistently producing a colouration result close to the linear regression line. However, the BSD, CLL, and PBC values are less correlated with the listening test results at lower levels of signal similarity, which could be an avenue for further work.  
The validation has shown that the PBC method for predicting the colouration between binaural signals correlates highly with the perceptual results of a listening test on similarity. These are the highest correlation values out of the four colouration calculations tested. However, other relevant methods of predicting colouration exist that were not included in this study due to the availability of source code, and should be included in further validation [50,51]. Additionally, further validations could utilise a wider range of test stimuli, with impulsive noises such as click trains, tonal noises such as sine waves, as well as real-world sounds with greater dynamic ranges, such as speech and music. Note that the number of participants in the listening test was limited by COVID-19 regulations. Further validation should aim for more participants with a greater diversity; nonetheless, the results with the current number of participants is sufficient to conclude that the presented method is highly correlated with the listening test results.
## 3 Overview of RF-Based Localisation Techniques  
Loosely speaking, localisation based on radio signals can be divided into range-based and fingerprinting methods.  
* Range-based techniques: Localisation is achieved by inferring the distance or angle of the target from a node based on the measurements. Time Of Arrival (TOA), Time Difference Of Arrival (TDOA), and Received Signal Strength (RSS) provide ranges, while Angle Of Arrival (AOA) provides bearings measurements. In a sensor framework, two or several of these methods can be combined, which might result in a better outcome. In the next stage, the extracted ranges or bearings are used to estimate the location, taking advantage of various mathematical tools such as Maximum Likelihood (ML), the Least Squares (LS) approach, the Bayesian model, or different types of filters such as the Kalman filter (KF), extended Kalman (EKF), Unscented Kalman filter (UKF), and Particle filter (PF). In the next section, we will explain the methods used for range-based localisation.
* Range-free or Fingerprinting: Instead of calculating the distance or direction, the environmental survey is performed to obtain fingerprints or features recorded on a database, such as the location-RSS pair's value, and then in online mode, for every new measurement, the localisation is performed by finding the best match in the data set. More generally, this method consists of mapping and matching. Compared to the range-based approach, fingerprinting techniques are more accurate and demanding to implement, requiring a pretest to create an extensive database. In addition, fingerprinting methods differ in generating and updating the data set and the matching process. Nevertheless, fingerprinting is widely used for CSI and RSS-based localisation.
## 4 Results and Discussion
### Different Latitudes Area Results  
From the above analysis, it can be concluded that the advantage of TIC2 compared to TIC1 was not obvious in the MS period, so the whole number of GPS stations was divided into three latitudinal bands (high-latitude band, middle-latitude band, and low-latitude band). The TIC1 and TIC2 localization performance of SF PPP during MS and NMS periods were analyzed.
## 3 Problem Description and Motivation
Furthermore, the ATLAS protocol is designed to minimize the delay and maximize the channel capacity utilization. Figure 1: Superframe structure of LDTA-MAC.
# OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles
## 4 Algorithms
### A Genetic-Algorithm-Based Path Planning with VRAOA (GA w/VRAOA)
#### 4.2.2 Fitness Function Evaluation  
The objective of the OADC scheme is to minimize the total traveling distances for multiple UAVs. In GA w/VRAOA, the fitness value for each chromosome is the total traveling distance of a UAV tour. The smallest fitness value for each chromosome illustrates the smallest distance of a tour, which also represents a good solution. At each iteration, a new chromosome is produced, and the fitness value \(F_{l}\) for each chromosome can be represented as  
\[F_{l}(C_{l}^{m})=\sum_{i\in\mathbb{N}}\sum_{j\in\mathbb{N},j\
eq i}w_{ij}^{m}d _{ij} \tag{13}\]  
where \(i\) and \(j\) are the sensors in chromosome \(C_{l}^{m}\), and \(1\leq i\) and \(j\leq B_{m}\). The value of \(w_{ij}^{m}\) is 1 when UAV \(m\) visits sensor \(j\) right after \(i\) in chromosome \(C_{l}^{m}\); otherwise, 0. Furthermore, \(d_{ij}\) is the traveling distance from sensor \(i\) to sensor \(j\) and is calculated from the Dijkstra algorithm, which is explained below.  
Calculating the Distance between Two Sensors:  
In GA w/VRAOA, the Dijkstra algorithm, together with VRAOA, is used to calculate the distance between two sensors when there is an obstacle between them, as shown in Figure 4. Assume that there is an obstacle between sensors \(i\) and \(j\), and the obstacle's shape is converted to a convex polygon ABCDEFA using the VRAOA algorithm. Consider a graph \(G\) that has vertices \(i\) and \(j\), and all the points in the polygon ABCDEFA, i.e., A, B, C, D, E, and F, where sensor \(i\) is the source node, and \(j\) is the destination node. The edges between the vertices of the convex polygon (A, B, C, D, E, and F) follow the shape of the polygon, as shown in the figure, i.e., A-B, B-C, C-D, D-E, E-F, and F-A. The source and destination sensor nodes \(i\) and \(j\) have edges with vertices in the sets \(G_{i}(G_{i}\subseteq G)\) and \(G_{j}(G_{j}\subseteq G)\), respectively. \(G_{i}\) and \(G_{j}\) contain the points of a convex polygon that are directly connectable to the sensor, \(i\) and \(j\), respectively. For example, in Figure 4, the points in \(G_{i}\) are F, E, and D, whereas \(G_{j}=\{A,B\}\). In this graph \(G\), applying the Dijkstra algorithm [33], the shortest path between two sensors \(i\) and \(j\) can be found. If there is no obstacle between two sensors, then graph \(G\) only contains vertices \(i\) and \(j\) with a direct edge between them, and the distance can be calculated using the Euclidean distance formula.
## 4 SLA Specification for the Fog Storage Services
### SLA Specification  
In the context of Fog computing, one of the most important components of SLA are the terms of operation, which define SLA parameters of consideration and their respective Service Level Objectives (SLOs). For instance, given that cost is an SLA parameter, then an SLO for it could be  
\[\text{cost}\leq\$5/\text{month}.\]  
Therefore, SLOs behave as constraints to the agreed SLA parameters. Essentially, SLA parameters can be considered to be the QoS attributes (e.g., availability, throughput and operation cost), whereas software engineers' QoS requirements can be considered as the SLOs. In the scope of this work, the contract is made between two entities: the service customer and the service provider. Essentially, the service customers are the software engineers, whereas the service providers are the Fog node providers. In this paper, it is considered that software engineers that develop applications for various IoT scenarios, have specific QoS requirements and demand their data to be dynamically stored in the Fog. Fog node providers, on the other hand, own the Fog nodes whose computing and storage resources belong to the pool of available sources and are responsible of assuring that their Fog nodes deliver the guaranteed QoS.  
SLAs are specified either formally or sometimes via natural language, as a standalone agreement or as a part of general terms of service conditions. For autonomous computing, a representation in natural language is not desired because computers are still not good enough at interpreting natural text in a precise way. Thus, several proposed SLA specification formats and frameworks exist; some of them are standardised.  
Bilateral [41] is Java,.NET and Web Service-based protocol, originating from resource reservation in Grids. It supports negotiation, dynamic establishment and management, metrics specification, definition of management actions, reuse, type systems and most of the SLA life cycle steps. Standard Web Services Agreement (WS-Agreement) [39], defined by the Open Grid Forum, offers a framework and protocol, both based on XML syntax. Contrary to the bilateral protocol, WS-Agreement does not support metrics specification, but it does support all the SLA life cycle steps. Web SLA (WSLA) [42], proposed by IBM, is similar and also supports metrics specification. Additionally, a language for Quality of  
Figure 2: Modelling and implementing the SLA life cycle for Fog storage.  
Service Specification (QML) [43], proposed by HP, also supports type systems. Web Service Offerings Language (WSOL) [44] is not suitable for negotiation. SLAng [45] is feature-wise similar to WS-Agreements, while covering less SLA life cycle steps. Finally, QUO [46] is a CORBA-specific framework with similar features to SLAng.  
So far, not much effort has been devoted towards using standards (e.g., WSLA and WS-Agreements) or designing standards for SLA specifications, while most of the existing papers rely on ad hoc representation of SLAs [37].
# Document Image Shadow Removal Based on Illumination Correction Method
## 4 Experiments
### Quantitative Analysis  
In this work, the SSIM, RMSE, and PSNR are used to quantitatively analyze the performance of the proposed DICNet. All the images in the testing set of each dataset are used. We recorded the SSIM/RMSE/PSNR values of each method on different datasets and counted the average and standard deviation. The quantitative analysis results of SSIM are shown in Table 2. The closer the SSIM value is to 1, the higher the structural fidelity. The DICNet achieved higher SSIM values on all datasets, especially on Adobe (0.9978) and Kligler (0.9956) datasets. Besides that, its standard deviation is the smallest, indicating its performance is more stable than others. The reason may be the SSCABlock's dual-channel attention mechanism: It not only captures the global illumination distribution through channel attention, but also focuses on the local texture of text and background through spatial attention (e.g., small characters and complex typographic in [PERSON] dataset). On the other hand, traditional methods (e.g., ISR, 3D-PC) may lose the local structure due to only focusing on global illumination. Figure 5: The visual comparison on the SDCSRD dataset: (**a**) input image; (**b**) WF method; (**c**) BE method; (**d**) LGBC method; (**e**) JWF method; (**f**) BEATE method; (**g**) CBENet; (**h**) DICNet; and (**i**) the ground truth. As shown in Table 3, DICNet achieves the lowest RMSE across all datasets, with an average RMSE of 8.1467, which is only 75% of that of the suboptimal method BEATE (10.8854). Additionally, its standard deviation is the smallest. The key to achieving such results lies in the accuracy of the soft shadow mask: the mask output by the shadow detection branch can distinguish pixel-level differences between shadow regions and non-shadow regions (such as the hard shadow boundaries in the HS dataset), enabling the illumination correction block to only supplement light to the shadow regions and avoid the over-correction of non-shadow regions, thereby reducing the overall pixel error. Since lacking of refined shadow masks, methods such as LGBC and CBENet may cause the incorrect correction of non-shadow regions, leading to an increase in RMSE. For example, the RMSE of LGBC on the Jung dataset is 20.8406, which is twice that of DICNet. The PSNRs of all comparison methods are shown in Table 4. Higher PSNR values indicating less noise and better quality. The average PSNR of DICNet (30.3618) far exceeds that of BEATE (28.1850) and CBENet (23.9249), with particularly outstanding performance on the Adobe (33.0658) and Kligler (31.1705) datasets. This benefit from the hierarchical adjustment mechanism of the illumination correction block: combined with the multi-round feature optimization of the SSCABlock, the light supplementation for shadow areas is smoother (for example, when processing light shadows in Kligler, it avoids \"overexposure\" noise caused by excessive correction). In contrast, methods like BEATE may cause local brightness jumps due to a single correction strategy, reducing PSNR.
# A Multimode Fusion-Based Aviation Communication System
## 2 System Design
### Overall Architecture  
The new aviation multimode fusion communication system integrates various communication links, such as ad hoc network communication, 5G public network communication, BeiDou satellite communication, RTK high-precision positioning and navigation, and ADS-B air broadcasting communication. This system creates a seamlessly integrated aviation communication network that connects aircraft with ground control centers and other aircraft in real time. This network enables precise data exchange during navigation, providing reliable and prompt data information services for aircraft. Please refer to Figure 1 for a visual representation of how the system works.