esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[46] have reported the development of polymers based on PEG and chitosan to accelerate the full-thickness wound healing process. This study was confirmed by [PERSON] [47] with the synthesis of chitosan gels based on diglycidyl ethers of ethylene glycol and PEG. These linkers seem to be compatible with native sugars and sugar derivatives. Few studies deal with the use of PEG as a crosslinker for the synthesis of \(\beta\)-CD polymers. These papers report that the conditions as well as the molecular weight (Mw) of the PEG affect the solubility of the obtained polymers, leading to soluble [48] or insoluble polymers [49; 50; 51; 52]. For example, [PERSON] et al. [52] described that bisphenol (BPA) was efficiently removed from an aqueous solution using \(\beta\)-CD polymers obtained with polyethylene glycol diglycidyl ether (PEDGE) as the crosslinking linker. This study has been corroborated by [PERSON] [53]. By adding polyethylenimine in the polymer network, \(\beta\)-CD polymers based on PEDGE can readily remove BPA and copper from wastewater. Recently, the [PERSON] team has reported the synthesis of crosslinked \(\beta\)-CD polymers based on PEDGE and their nitrated derivatives for binder formulation with selected energetics [54; 55; 56]. To validate this linker, methylene blue (MB) was selected as the target dye. MB is a cationic phenothiazazine derivative part of the xanthine family, commonly used as a dye in the textile industry or for medication and often employed as a model to evaluate the adsorption capacities of CD and CD derivatives, such as polymers or nanoparticles [57; 58; 10; 59]. Nevertheless, accumulation of MB affects the environment and threatens the life of microorganisms due to their deep color and low degradation rate [60]. With regards to humans, MB has adverse health effects, such as skin sensitization, eyes burn, etc. [61]. Herein, we report the synthesis of insoluble \(\beta\)-CD polymers using polyethylene glycol diglycidyl ether (PEDGE) and ethylene glycol diglycidyl ether (EDGE) crosslinkers in a one pot process. Polymers were characterized using FTIR spectroscopy, Thermogravimetric Analysis (TGA), Differential Thermal Analysis (DTA), X-ray Diffraction (XRD), Brunauer-Emmett-Teller (BET) analysis and Scanning Electron Microscopy (SEM). Batch adsorption experiments were realized with different parameters, such as adsorbent mass, initial MB concentration and pH. An isotherm model was evaluated to determine the efficiency of the adsorption process. Moreover, the reusability of the adsorbent was also investigated.
# Creating a Remote Choir Performance Recording Based on an Ambisonic Approach
## 4 Realization of Remote Music Recording
### Postproduction of Soundtrack  
It is necessary to correct each of the submitted soundtracks manually in the postproduction phase to achieve the best effect. Unfortunately, as already mentioned, most of the musicians in the academic choir did not have the possibility at home to make a professional recording. Most participants produced their recordings with a smartphone, therefore, most often, they required noise removal. This was performed with noise gates and FIR filters.  
Another issue is corrections to the melodic-rhythmic layer itself. While intonation irregularities in large performance groups can be obscured by other more correct performances, rhythmic irregularities are much more noticeable and detract from a good impression of  
Figure 3: An example of setting up a recording in a home environment; the screen shows the score, the model recording, and a separate device (e.g., laptop) recording the sound [60].  
the production reception. To rectify these problems, available VST plug-ins were used, including the ReaTune plug-in built into the Reaper digital audio workstation (DAW) [76] for correcting melodic lines. Moreover, the Melodyne Studio plug-in [77] appears to be a potent tool that allows improving not only the pitch or duration of a sound but also has algorithms for editing formants, attack time of individual sounds, or transitions between sounds. An illustration of working in this environment is shown in Figure 4.  
Large performance groups are most often recorded in large concert halls rather than individually, each with their own individual voices. A recording realized this way enables the tracks to be treated objectively and spatialized in ambisonics. It is important to ensure that the acoustics of both the virtual room and the rendered \"objects,\" i.e., the choir members, are as faithful as possible. To this end, a directivity pattern can be given to each object source. In addition, the virtual acoustics can be modeled using apparent reflections and also by adding diffuse reverberation. In the considered recording, it was decided to use the VST plug-ins from the IEM Plug-in Suite [78], in particular, DirectivityShaper [79], RoomEncoder [80], and FDNREverb [81]. Figure 5 shows an example setup of these three VST plug-ins.  
Figure 4: Before and after the recording correction in Melodyne Studio (the alto part) [60].  
Figure 5: VST from IEM Plug-in Suite: DirectivityShaper, RoomEncoder, FDNREverb [60].
# BDS Satellite Clock Prediction Considering Periodic Variations
## 2 Materials and Methods
### Analysis of Periodic Variations in BDS Satellite Clock Offsets
For C14, special periodic terms with decreasing frequency can be found in the spectrogram. A special periodic term can be identified both from WHU and GFZ clock offsets. It may be caused by anomalies of the satellite atomic clock itself. Based on the analysis of this section, it can be found that the main periodic term of BDS satellite clock offsets is time-varying, and the variations occur frequently in all satellites. Therefore, the frequencies of the periodic terms obtained by STFT are more accurate for compensating the periodic noise than FFT. These time-varying periodic terms should be considered to improve the clock prediction model, especially for satellites with marked periodic variations.
# A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
## 4 Experimental Results
### Field Test  
In order to verify the tower tilt model and the algorithm's accuracy, several field tests were conducted, and the model was corrected based on the results of these tests. The field test setup is shown in Figure 13.  
Testing procedure:  
1. Collect the parameters such as gear pitch, tower height, and tower width;
2. Measure the field data through the rangefinder;
3. Calculate the distance between the tower and the camera and the distance between its own horizontal components according to the tower tilt model and algorithm, combined with the parameters of the tower;
4. Compare the calculated data with the measured data, and correct the distance based on the comparison results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Order Number**} & \multicolumn{2}{c}{ \begin{tabular}{c} **The Camera Is Far away from** \\ **the Pole and Tower Pz (m)** \\ \end{tabular}  } & \multirow{2}{*}{**Range Error (\%)**} & \multicolumn{2}{c}{**Axial Eccentric**} & \multirow{2}{*}{**Range Error (\%)**} \\  & & & & **Distance (cm)** & \\ \hline
1 & 14.8 & 1.33 & 1.6 & 2 \\
2 & 14.6 & 2.67 & 1.7 & 1.5 \\
3 & 14.8 & 1.33 & 1.8 & 1 \\
4 & 14.5 & 3. As shown in Table 3, the angle error calculated directly according to the unprocessed camera is significant. In contrast, the angle calculated by the complementary filtering method camera image is reduced to a certain extent under different coefficients. When the inclination angle measured by the camera occupies a higher weight, the output angle of the complementary filter is closer. According to the analysis, the reason should be that the acceleration on the rod is mainly gravity acceleration, and the acceleration of the bar can be suppressed after preprocessing from the perspective of relative accuracy. When the weight coefficient A = 0.25, the relative accuracy of the measured data can be controlled within 1.40%, which can fully meet the accuracy requirements of the tower online monitoring. The inclination perception end is placed in the standard using an electronic level instrument to verify the error analysis further to measure a precise uniaxial rotary platform. One sampling point is taken every 20\({}^{\circ}\) during the rotary shaft rotation, and the obtained error is statistically analyzed after measurement and data processing. The measurement results and correction results are shown in Table 4.
# Real-Time 3D Reconstruction Method for Holographic Telepresence
## 1 Introduction
This research was inspired by earlier works related to 3D reconstruction for telepresence. Researchers have been intrigued by the prospect of 3D telepresence for decades, but because of technological limitations in the past, prototypes have only just begun to appear in the marketplace. Multiple cameras are deployed in prior works in order to build a 3D reconstruction of a room-scaled scene [11], and their images are constantly changed to include the moving user, utilizing a variety of stereo reconstruction [18] techniques. This enables the reconstruction of the scene into 3D representation. When inexpensive depth sensors, for instance, the Microsoft Kinect [19], became widely available at low cost  
Figure 1: The schematic illustration of stage setup for Pepper’s Ghost. and were capable of acquiring video images as well as per-pixel depth information and were accessible, the total amount of studies and developments related to 3D reconstruction merged with telepresence systems increased substantially [20]. Therefore, this paper further discusses the 3D reconstruction process involved to reconstruct a 3D representation of the captured user using RGB-D camera implemented with holographic telepresence. The test application and the results are also presented, and this paper ends with a conclusion. The first phase is designed to produce the real-time 3D reconstruction of the local user where the flow is as demonstrated in Figure 2. When the data is obtained using the commodity depth sensor, which has been pre-processed, the next step is to remove the background in order to get the foreground mask which contains only the captured user's body without the background. After we obtain the foreground mask of the captured data, the 3D point cloud is extracted. For the surface production of the 3D model, the marching cube algorithm is then used with point cloud data as the input. The resulting 3D model has then applied the texture mapping. After the textured 3D model of the local user has been reconstructed, the data, which includes mesh, texture and audio, will be transmitted to the remote location through transmission over the network. The experimental setup for the 3D telepresence will then be set up for the test application.
# Pharmacological and Cosmeceutical Potential of Seaweed Beach-Casts of Macaronesia
## 3 Results and Discussion
### Antigaging Activity
#### 3.4.2 UV-Protection  
All the extracts tested at 250 \(\upmu\)g/mL provided UV protection comparable to the SPF 30 commercial sunblock standard Darphin Soleil at the same concentration, i.e., between 31.12 SPF and 34.37 SPF (Table 4).  
The exhibited activity seems to be independent of the beach-cast composition. This is an excellent feature in the context of beach casts biomass valorization, converting it into products with antiaging properties, since many photoaging changes in skin are due to action of the UV component of sunlight [67]. Therefore, in addition to the antioxidant activities expressed above, the protection against UV also helps to protect ECM structures against further degradation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **SPF** & **Sample** & **SPF** \\ \hline
3.E & \(32.95\pm 0.49\) & 8.M & \(32.88\pm 1.30\) \\
3.M & \(33.93\pm 1.83\) & 9.E & \(33.37\pm 1.25\) \\
4.E & \(31.27\pm 0.52\) & 9.M & \(33.54\pm 2.23\) \\
4.M & \(33.15\pm 1.95\) & 10.E & \(31.34\pm 0.48\) \\
5.E & \(34.03\pm 1.03\) & 10.M & \(30.48\pm 0.59\) \\
5.M & \(33.38\pm 1.68\) & 11.E & \(33.19\pm 0.52\) \\
6.E & \(32.41\pm 1.34\) & 11.M & \(34.37\pm 3.32\) \\
6.M & \(34.37\pm 0.95\) & 12.E & \(31.31\pm 0.88\) \\
7.E & \(31.12\pm 0.78\) & 12.M & \(32.48\pm 1.51\) \\
7.M & \(32.82\pm 0.34\) & & \\
8.E & \(33.62\pm 1.67\) & Standard & \(32.86\pm 1.46\) \\ \hline \hline \end{tabular} Standard–Standard Darphin Soleil SPF30 sunblock.  
\end{table}
Table 4: UV protection of samples at a concentration of 250 \(\upmu\)g/mL.  
Figure 6: IC\({}_{50}\) values of ABTS radical scavenging activity by extracts (light bars) and fractions thereof (dark bars). Trolox was used as reference.
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
## 3 Key Transmission and Distribution System Components
### Lightning Current Monitoring Systems
These sensors can perform contactless sensing with higher dielectric strength, stronger anti-interference ability, no bandwidth limitation and no magnetic saturation issues.
## 1 Introduction
[21] compared the actual burned area, which was obtained by phone call surveys of land managers and prescribed burn contractors, with burned areas from permits in Georgia, and found a high correlation (R\({}^{2}>\) 0.64) between the two. The study also compared burned areas from permits, Blended-BBEP, and GFED4s in Georgia and Florida at a relatively coarse resolution (state-based or county-based). The correlation between satellite-derived and permit-reported burned areas is relatively low compared to the correlation between surveyed land and permits provided to the burned area. To enhance the accuracy of emissions estimation and burned area comparisons, it is important to differentiate between burn types. However, this is often not possible with satellite data as fires are typically detected by thermal energy, which makes it difficult to distinguish between prescribed burns and wildfires. Despite this, some recent studies and emission products have made progress in differentiating burn types. For instance, CFIRE [22] used a combination of remote sensing data and permit records to provide burn-type information, allowing for separate calculations of emissions based on different types of wildland fires. [PERSON] [23] employed a spatiotemporal clustering algorithm to estimate the growth pattern and lasting duration of wildfires, suggesting that it is possible to separate long-term fires from prescribed burns. In this work, we differentiate prescribed burns from wildland fires in FINN and compare the daily burned areas of prescribed burns from FINN to permits under different spatial resolutions. The ultimate goal of the research is to generate three-dimensional prescribed burning emissions for chemical transport modeling. The study includes three parts. In the first part, we developed a method to identify fire types by differentiating agricultural burning, wildfires, and prescribed burning in FINN data. In the second part, we compared the burned area for prescribed burning from FINN and permits by developing and applying novel algorithms to match records from these two sources. A linear regression model was employed to capture the relationship between the burned area from FINN and permits and adjust the burned area of prescribed burning from FINN. In the third part, we estimated the magnitude and vertical structure of emissions based on the adjusted burned area and BlueSky Smoke Modeling Framework [24]. We compared the emissions magnitudes from different methods (FINN and BlueSky) and from different data sources (adjusted FINN and permits) to evaluate and understand the uncertainties in prescribed fire emissions. Then, we used a plume rise model incorporated in BlueSky to generate vertical emission profiles, which we combined with emission magnitudes to obtain three-dimensional prescribed burning emissions. Our results and the following discussion highlight the various challenges associated with obtaining fire activity information suitable for estimating prescribed burning emissions.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 5 Future Research Directions
### Energy-Efficient Localization  
Battery life remains a critical limitation for wearable and mobile localization devices. Research should aim to develop low-power localization techniques that extend the operational lifespan of devices without sacrificing accuracy. Innovations in energy harvesting, such as utilizing ambient light, motion, or thermal energy, can help power devices in resource-constrained environments [65]. Additionally, optimizing protocols for technologies like BLE and ZigBee can further reduce power consumption while maintaining reliable performance [22].
## 3 The Model of Flexible Arm  
### Deformation Angle Modeling  
Underwater vehicles can experience deformation due to impacts from water currents and their own loads. This deformation can cause changes in the lever arms between SRINSs installed at different positions, leading to changes in the positional relationship between the two SRINSs [21]. The flexural deformation can be represented as the sum of static and dynamic deformation angles. The static deformation angle does not change over time, while the dynamic deformation angle can be described using a second-order Markov process [25]:  
\[\begin{array}{l}\ddot{\theta}_{i}+2\beta_{i}\dot{\theta}_{i}+\beta_{i}^{2} \theta_{i}=w^{\prime},\quad i=x,y,z\\ \dot{\theta}_{i}=2.146/\tau_{i},\quad i=x,y,z\end{array} \tag{12}\]  
where \(\tau_{i}\) represents the correlation time of the deformation angles along three axes, which is determined based on the operating environment and motion conditions of different carriers; \(\sigma_{i}^{2}\) is the estimated variance of \(\theta_{i}\), and \(w^{\prime}_{i}\) is a random white noise with variance satisfying:  
\[Q^{\prime}_{i}=4\beta_{i}^{3}\sigma_{i}^{2}(i=x,y,z)\]  
Let \(\theta(t)\) be the flexural deformation angle and \(\mu(t)\) be the rate of change of the flexural deformation angle, satisfying \(\mu(t)=\dot{\theta}(t)\) at time \(t\), then we have:  
\[\left\{\begin{array}{l}\dot{\theta}_{x}(t)=\mu_{x}(t)\\ \dot{\theta}_{y}(t)=\mu_{y}(t)\\ \dot{\theta}_{z}(t)=\mu_{z}(t)\end{array}\right. \tag{13}\]  
Then, the differential equation for the dynamic deformation angle can be written as:  
\[\left\{\begin{array}{l}\dot{\mu}_{x}(t)=-\beta_{x}^{2}\theta_{x}(t)-2\beta _{x}\omega_{x}(t)+w^{\prime}{}_{x}(t)\\ \dot{\mu}_{y}(t)=-\beta_{y}^{2}\theta_{y}(t)-2\beta_{y}\omega_{y}(t)+w^{ \prime}{}_{y}(t)\\ \dot{\mu}_{z}(t)=-\beta_{z}^{2}\theta_{z}(t)-2\beta_{z}\omega_{z}(t)+w^{ \prime}{}_{z}(t)\end{array}\right. \tag{14}\]
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 5 Integrating Cognitive Radio in LPWAN for the Internet of Things
### Network Architecture to Support CR-LPWAN
#### 5.3.1 CR at the LPWAN End Node  
In CR at the LPWAN end node (CREN) scheme, CR is embedded in the LPWAN technology deployed in different IoT end nodes; thus, such end nodes, for example, IoT sensors such as smart meters, are responsible for executing all CR functions. In this scheme, IoT end nodes are designed to be robust and complex to conduct SS, decision making, mobility, and spectrum access. This may impact on cost of the end node, and increase the power consumption rates of such end nodes; nevertheless, it reduces the network overhead since end devices and GWs would communicate less in terms of channel convergence and control messaging. As an example, the CREN scheme was adopted in [46] wherein end nodes were embedded with a channel manager for decision making, spectrum detection, and prediction.
## Data and Methods
### Summary of Differences between CLARA-A2 and ISCCP-H  
Apart from the detailed information about CLARA-A2 and ISCCP-H cloud Climate Data Records (CDRs) reported in Sections 2.1 and 2.2, their main differences are summarized in Table 1, which also lists the Fundamental Climate Data Records (FCDRs) and ancillary data used for their production. In addition, Table 2 shows the temporal coverage of the satellites used by the two data records, as well as which satellites are included in each one of them. \begin{table}
\begin{tabular}{l l l} \hline \hline  & \multicolumn{1}{c}{**CLARA-A2**} & \multicolumn{1}{c}{**ISCCP-H**} \\ \hline \multirow{4}{*}{**Fundamental Climate Data Records (FCDRs)**} & Based on an AVHRR GAC FCDR & \multirow{4}{*}{Generated by applying HBT tables to B1U geostationary satellite data and AVHRR GAC radiances.} \\  & described in [7], including shortwave & \\  & calibration with MODIS Collection 6 as a reference, removal of corrupt data, and & \\  & noise reduction for AVHRR channel 3b. & \\ \hline \multirow{2}{*}{**Cloud Detection Algorithms**} & Based on an upgraded version (patch 1) of the PPS (Polar Platform System) Version 2014 cloud processing package. & Improved version of the D-series algorithm with applied modifications that mostly serve to reduce uncertainties. \\ \hline \multirow{4}{*}{**Ancillary data**} & Various parameters from ERA-Interim & \multirow{4}{*}{Atmospheric temperature-humidity profiles, surface skin temperature,} \\  & (atmospheric temperature and humidity profiles, surface skin temperature, \\  & integrated water vapor, snow cover), land & \\  & use from Unites States Geological Survey (USGS) in [35], surface elevation from & (MACv1, SAGEH) [43], land snow cover (NOAA) [44; 45] and ocean sea ice cover (OSI-SAF, SSM/I) [46; 47], land surface \\  & total ozone column from [36], sea ice & type (MODIS IGBP) [48] and topographic height information (USGS EROS) and land water mask (USGS AVHRR). \\ \hline \multirow{4}{*}{**Satellites**} & Combines day and night measurements from all available satellites. The number of satellites varies from one in the early part of the record to at least four in the last decade. With each satellite having two (daytime plus nighttime) or—towards & \multirow{4}{*}{Between 55\({}^{\circ}\)S and 55\({}^{\circ}\)N geostationary satellites are used. At higher latitudes, ISCCP uses both morning and afternoon polar orbiting satellites simultaneously for the gridded data products. \\  & specific location per day. & \\ \hline
**Spectral Channels** & Incorporates six spectral channels. & Incorporates one VIS and one IR channel. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of main differences between the CLARA-A2 and ISCCP-H cloud data records.