esa-sceva/satcom-chunk-collection huggingface dataset:
# In Situ Transformation of Electrospun Nanofibers into Nanofiber-Reinforced Hydrogels
## 3 Results
### Biological Studies  
Agar diffusion tests demonstrated contact-based inhibition of bacterial cultures of pre-hydrated T-free samples GPP and zeinPEO-GPP, i.e., samples containing PEI, while dry GPP and zeinPEO-GPP samples showed no inhibition of bacterial cultures (Figure 6). From the agar plates, it may be assumed that there was bacterial inhibition present initially, but bacterial growth started again at a later point (Figure A2). In contrast, clear inhibition zones were observed both for dry and pre-hydrated T-containing samples (Figure 6).  
Figure 4: Solid-state characterization of the nanofiber scaffolds. (**a**) X-ray diffractograms of different nanofiber scaffolds and the raw materials used to prepare them and (**b**) TGA data of different nanofiber scaffolds. Key: PDA, polydopamine; T, tetracycline hydrochloride; PEO, polyethylene oxide; PEI, polyethyleneenimine; G, fiber sample containing gelatin; GPP, fiber sample containing gelatin crosslinked by polydopamine and polyethyleneenimine; zeinPEO-GPP, fiber sample containing zein and polyethylene oxide in the core and gelatin crosslinked by polydopamine and polyethyleneenimine in the shell; zeinPEO-GPP, fiber sample containing zein, polyethylene oxide and tetracycline hydrochloride in the core and gelatin crosslinked by polydopamine and polyethyleneenimine in the shell.  
Figure 5: Cumulative release from zeinPEOT-GPP scaffolds. Key: zeinPEOT-GPP, fiber sample containing zein, polyethylene oxide and tetracycline hydrochloride in the core and gelatin crosslinked by polydopamine and polyethyleneimine in the shell.  
Figure 6: Results for the antimicrobial studies. Bacterial inhibition of _E. coli_ and _S. aureus_ by dry and pre-hydrated (hyd) nanofiber scaffolds. The data are presented as means with standard deviations of \(p\) < 0.0002 (***) and \(p\) < 0.0001 (***). Key: G, fiber sample containing gelatin; GPP, fiber sample containing gelatin crosslinked by polydopamine and polyethyleneimine; zeinPEO-GPP, fiber sample containing zein and polyethylene oxide in the core and gelatin crosslinked by polydopamine and polyethyleneimine in the shell; zeinPEOT-GPP, fiber sample containing zein, polyethylene oxide and tetracycline hydrochloride in the core and gelatin crosslinked by polydopamine and polyethyleneimine; T, tetracycline hydrochloride-containing control sample.  
All samples containing PEI, including the PEI control samples in two concentrations, demonstrated high cytotoxicity towards fibroblasts according to both LDH and MTT assays (Figure 7). In contrast, sample G as well as the T, PDA and untreated control samples showed good cell viability above 80% (Figure 7a) and a low LDH release (Figure 7b).
## 5 Conclusions
It must be stressed that the obtained results concern specific situations, although their number runs into thousands. Nevertheless, for other situations not covered here, the Appendix A code can still be used to expand the results. **Funding:** This research was funded by VSB--Technical University of Ostrava through its project \"Development of Integrated Management of Industrial Processes through Linking the Principles of Smart Production and Quality Management\", grant number SGS SP2023/043. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are openly available at [[https://doi.org/10.6084/m9.figshare.23899668.v1](https://doi.org/10.6084/m9.figshare.23899668.v1)]([https://doi.org/10.6084/m9.figshare.23899668.v1](https://doi.org/10.6084/m9.figshare.23899668.v1)) (accessed on 16 August 2023), [[https://doi.org/10.6084/m9](https://doi.org/10.6084/m9)]([https://doi.org/10.6084/m9](https://doi.org/10.6084/m9)) _figshare.23899524.v1 (accessed on 16 August 2023), [[https://doi.org/10.6084/m9.figshare.23899455.v1](https://doi.org/10.6084/m9.figshare.23899455.v1)]([https://doi.org/10.6084/m9.figshare.23899455.v1](https://doi.org/10.6084/m9.figshare.23899455.v1)) (accessed on 16 August 2023). The first reference stores files \"power_sc number1_number2.xlsx\", each such file containing test powers for different parameter combinations within scenario \"sc number1\" for sample size \"number2\". The second reference stores files \"d_sc number1_number2.xlsx\", and each such file contains the two \(d\) multiples for the lower and upper percentiles for each scenario \"sc number1\" parameter combination and sample size \"number2\". The third reference stores files of the form \"a_sc number1_number2.xlsx\", which contains significance levels alpha for each scenario \"sc number1\" parameter combination and sample size \"number2\". **Conflicts of Interest:** The author declares no conflict of interest. The funders had no role in the design of the study, in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results. \begin{table}
\begin{tabular}{c c c} \hline \hline \(k_{1}k_{2}\theta_{1}\)**Scenarios** & \(d\)**in** & \(d\)**in** \\ **(See Page 5)** & \(\overset{\alpha}{p_{2.5}}=\overset{\alpha}{var}_{X-Y}^{1/2}\) & \(\overset{\alpha}{p_{97.5}}=\overset{\alpha}{var}_{X-Y}^{1/2}\) \\ \hline
1 & \(-1.96\) & \(1.96\) \\
2 & \(-1.96\) & \(1.96\) \\
3 & \(-1.96\) & \(1.96\) \\
4 & \(-1.92\) & \(2.00\) \\
5 & \(-1.91\) & \(2.01\) \\
6 & \(-1.95\) & \(1.97\) \\ \hline \hline \end{tabular}
\end{table} 
Table 15: The multiples \(d\) to obtain the percentiles \(\hat{p}_{2.5}\), \(\hat{p}_{97.5}\). If \(\hat{p}_{2.5}<\overline{x}-\overline{y}<\hat{p}_{97.5}\), the hypothesis H\({}_{0}\): \(E(X)=E(\gamma)\) is accepted; in all other cases, it is rejected. For calculation of \(\overset{\alpha}{var}_{X-Y}^{1/2}\), see page 6.
## 4 Results
### Modis Rst-Flood  
The RST-FLOOD maps produced by analyzing the two above-mentioned MODIS data are shown in Figure 3, where in the top panels (Figure 3a,b), the areas flagged as anomalous using the ALICE\({}_{\text{NIR-VIS}}\) index (i.e., Equation (1)) are shown, while those related to the ratio index (i.e., Equation (2)) are plotted in Figure 3c,d. In all the maps, indices values less than \(-2\) are depicted in violet and orange colors when the difference and ratio indices have been applied, respectively.  
Several anomalous pixels have been detected by the RST-FLOOD indicators in all the output maps. These pixels are mostly located along the Basento and Lato rivers and are in good agreement with the local information about the flood localization [18; 21; 22; 32], thus indicating that it can be associated to the inundated zones. Moreover, a difference between the two indicators can be observed, with a higher sensitivity of the ALICE\({}_{\text{NIR-VIS}}\) to the flood inundated area (see areas along the Basento and Lato rivers), as well as to the effect of turbid waters in the San Giuliano lake (see Table 1). Concerning flood dynamics, a lesser number of anomalous pixels has been identified in the maps of 5 December 2013 by both indices (Table 1). In more detail, an area of about 16 km\({}^{2}\) was detected as flooded on 4 December 2013, decreasing to 12 km\({}^{2}\) the next day.  
In order to provide a deeper view of the achieved results, a magnification of the area within the red box of Figure 1 is plotted in Figure 4, where for each of the analyzed MODIS images, the results achieved by combining both the indices are highlighted. This aggregation allowed for the better definition of the potentially flooded areas, while the common detections (namely the pixels flagged as anomalous by both indices--green pixels in Figure 4), may be associated to the definitely flooded areas. Furthermore, in these maps, the temporal persistence of the anomalous area is evident, as well as the better sensitivity of the ALICE\({}_{\text{NIR-VIS}}\) index than the ALICE\({}_{\text{NIR/VIS}}\) one.  
Figure 4: Anomalous pixels detected within the red box shown in Figure 1 by MODIS-based RST-FLOOD on (**a**) 4 December 2013 at 12:25 GMT and (**b**) 5 December 2013 at 11:30 GMT using ALICE\({}_{\text{NIR-VIS}}\) (violet pixels) and ALICE\({}_{\text{NIR/VIS}}\) (orange pixels). In green, the pixels detected as anomalous by both indices are highlighted.  
Figure 3: Anomalous pixels detected by Moderate Resolution Imaging Spectroradiometer (MODIS)-based Robust Satellite Techniques for detecting flooded areas (RST-FLOOD) using: (**a**) Absolutely Local Index of Change of the Environment Near Infrared-Visible (ALICE\({}_{\text{NIR-VIS}}\)), (**b**) ALICE\({}_{\text{NIR/VIS}}\) on 4 December 2013 at 12:25 GMT and (**c**) ALICE\({}_{\text{NIR-VIS}}\), (**d**) ALICE\({}_{\text{NIR/VIS}}\) on 5 December 2013 at 11:30 GMT.
# Improving the RST-OIL Algorithm for Oil Spill Detection under Severe Sun Glint Conditions
## Materials and Methods  
### Data  
MODIS Level 1B (L1B) and the corresponding Geolocation (GEO) data acquired by Terra and Aqua satellites were used in this work. Such L1B data, downloaded from the NASA-LAADS DAAC (National Aeronautics and Space Administration-Level-1 and Atmosphere Archive and Distribution System Distributed Active Archive Center) website [40], were processed over the Gulf of Mexico for the months of April and May within the 2003-2010 time interval. In detail, 348 and 472 images were collected for April and May respectively, producing four different datasets, one for each month and satellite considered. Then, a spectral/spatial sub-setting was carried out by means of HDFLook software (developed under collaboration between the Laboratoire d'Optique Atmospherique and the Goddard Earth Sciences Distributed Active Archive Center), which was aimed at producing historical series of 0.8 and 12 \(\upmu\)m (TIR) band signals for a sub-set centered on the DeepWater Horizon Platform (DWH) platform location (i.e., 28.74 Lat N, 88.39 Lon W), and re-projected in Lat/Lon WGS-84 (Figure 1). The above-mentioned sub-set (at a 1-km spatial resolution) was also produced starting from the GEO (i.e., geolocation) file to create the same output for the sensor and solar azimuth and zenith angles. NIR data were used at a full spatial resolution (i.e., 250 m) to implement the methodology proposed in this paper, while TIR data (at a 1-km spatial resolution) allowed for cloud screening, which will be discussed later. The azimuth and zenith angles were exploited for identifying sun glint conditions.  
Figure 1: (**a**) DeepWater Horizon Platform (DWH) localization in the Gulf of Mexico. (**b**) True color Red Green Blue (RGB) of the Region of Interest (ROI) used in this work.
# Scale-Free Random SAT Instances
###### Abstract
[13] generalize the notion of scale-free random k-SAT formulas and prove that there exists an asymptotic satisfiability threshold (in the sense of [14]) for \(\beta<1-1/k\), when the number of clauses is linear in the number of variables. [PERSON] and [PERSON] [15] find sufficient conditions for the sharpness of this threshold, generalizing the results of [PERSON] [14] for uniform random formulas. [PERSON]. [16] generalizes the notion of scale-free formulas to the notion of _non-uniform random formulas_, only assuming that variable \(x_{i}\) is selected with probability \(p_{i}\) (where \(p_{1}\geq p_{2}\geq\cdots\geq p_{n}\)) and determine the position of the threshold for \(k=2\). [PERSON] et al. [17] and [PERSON] and [PERSON] [18; 19; 20] analyzed the _configuration model_ for 2-SAT where, instead of fixing the probability of every variable, they fixed the degree of every variable. If these degrees follow a power-law distribution, the location of the satisfiability threshold (for \(k=2\)) is the same as in our model. [PERSON] and [PERSON] [21; 22] propose a new model of random formulas that, besides heterogeneity, also consider the notion of locality in formulas. [PERSON] et al. [23] computes the number of assignments of uniform random 2-SAT formulas, using the cavity method. Finally, [PERSON] et al. [24] analyze the hardness of random instances according to their heterogeneity and locality. This article proceeds as follows: In Section 2 we review some methods to generate scale-free random graphs. One of these methods is the basis of the definition of scale-free random formulas, introduced in Section 3. In Section 4, we summarize some properties of industrial or real-world SAT instances, described in detail in our work presented in [5]. We prove the existence of a SAT-UNSAT phase transition phenomenon in scale-free random 2-SAT instances in Section 5. This is achieved using percolation techniques. In Section 6, we prove that when the \(\beta\) parameter that regulates the scale-free structure of formulas exceeds a particular value, the SAT-UNSAT phase transition phenomena vanishes, and most formulas become unsatisfiable due to the small cores of unsatisfiable clauses.
# Biomass Refined: 99% of Organic Carbon in Soils
## 3 Results and Discussion
### Total Global Soil SOC Refined
#### 3.5.1 Mineral Soils  
Mineral soils are formed from biotic weathering of parent rocks and are primarily composed of inorganic material usually defined as having less than 17% living SOC compared to _\"Organic soils\"_ like Peat (not to be confused with the carbon-enriched soils as found on organic farms). According to the Soil Classification Working Group, organic soil horizons may contain >17% SOC (or >30% of SOM) by weight, and these occur in Organic soils, or may be present at the surface of mineral soils. All soils are living entities, and a precise definition of a mineral soil is _\"A soil consisting predominantly of, and having its properties predominantly determined by, mineral matter. It usually contains <20% organic matter_ [i.e., <10% SOC] _but may contain an organic surface horizon up to 30 cm thick_\". Permafrost is perhaps entirely (or mostly?) excluded from this definition except where discontinuous or sporadically intergrading thus possibly overlapping with other soil types, including Peat.  
Mineral soils are most productive for forests, woodlands, pasture, or farmland. Total mineral SOC is difficult to determine accurately but can be calculated from best estimates or deduced from a total global SOC tally, less defined Peat and Permafrost components.  
Perhaps a reasonable starting point is [18] who explicitly account for mineral soils with 1263 Gt SOC up to two metres depth plus 199 Gt SOC in 2-3 m deep soil (with zero below this, thus omitting deeper delta, alluvia, or loess) to total (1263 + 199 =) 1462 Gt SOC. Doubled for depth, and again for terrain is 5848 Gt SOC. Adding 25-30% glomalin/GRSP is \(\sim\)7000 Gt SOC plus 26% saprock C possibly sums to total 8650 Gt SOC. Interestingly, this is similar to an estimated total in [16] of 8580 Gt SOC.  
These are the most conservative totals as other calculations of basic mineral soil SOC are higher. For example, tables S1,S2 in ref. [111] for global _\"mineral soil\"_ (excluding tundra, peatlands, and deserts) for what they call _\"topsoil\"_ (<0.3 m) plus _\"subsoil\"_ (0.3-1.0 m depth only!) from SoilGrids of 1401-1765 Gt SOC, with median value about 1583 Gt SOC. This is above mineral soils in [PERSON] et al. to fully 3 m depth (1462 Gt SOC) but seems to ignore [PERSON] et al.'s 199 Gt SOC in >2-3 m or deeper.  
Surprisingly, figure 5 in ref. [112] had yet higher values (of Mineral-Associated OM and Particulate OM) to just 1 m depth from various data sets of 1390-2470 Gt SOC excluding Peat, but with Permafrost contribution to the values unclear (if at all).  
In summary, superficial mineral soils say of \(\sim\)1500 Gt SOC, doubled for terrain then full depth (\(\times\) 4), is \(\sim\)6000 Gt SOC; adding \(\sim\)25% GRSP and \(\sim\)25% saprock C, totals \(\sim\)9000 Gt SOC. Conversely, \(\sim\)1500 Gt plus 30% GRSP + 25% saprock is 2500 Gt \(\times\) 4 = \(\sim\)10,000 Gt SOC.
###### Abstract  
The development of hybrid satellite-terrestrial relay networks (HSTRNs) is one of the driving forces for revolutionizing satellite communications in the modern era. Although there are many unique features of conventional satellite networks, their evolution pace is much slower than the terrestrial wireless networks. As a result, it is becoming more important to use HSTRNs for the seamless integration of terrestrial cellular and satellite communications. With this intent, this paper provides a comprehensive performance evaluation of HSTRNs employing non-orthogonal multiple access technique. The terrestrial relay is considered to be wireless-powered and harvests energy from the radio signal of the satellite. For the sake of comparison, both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols are considered. Subsequently, the closed-form expressions of outage probabilities and ergodic capacities are derived for each relaying protocol. Extensive simulations are performed to verify the accuracy of the obtained closed-form expressions. The results provided in this work characterize the outage and capacity performance of such a HSTRN.  
energy harvesting; ergodic capacity; NOMA; outage probability; satellite communications 2020, 20, 4296; doi:10.3390/s20154296
## 3 Experimental Setup  
Figure 5 shows the schematic diagram of our experimental setup. Nine lead zirconate titanate (PZT) transducers (Baoding Yitian Ultrasonic Technology Corporation, Baoding, China) are homogenously mounted on a circular pedestal to generate the desired sound field within a circular region (radius = 0.5\(\lambda\)). The plastic pedestal with radius 88.8 mm is placed at the center of the Petri dish that is filled with deionized water. The center frequency of the PZT transducers is 2 MHz, while the width, height and thickness are 20 mm, 10 mm, and 5 mm, respectively. To minimize the reflected wave, each transducer is designed with an epoxy matching layer on the front surface and a tungsten-loaded absorbing backing layer. Without vertical force to balance the gravity, the particle will sink to bottom where the sound intensity is much lower. To solve this problem, the center bottom of the pedestal is covered by agar with 5 mm thickness. The agar has very similar acoustic properties to water and will not disturb the synthesized sound field [56]. Continuous sinusoidal voltage that is generated by the multichannel function generator (homemade PXI platform using FPGA) is amplified by the power amplifiers and then applied to the transducers. The amplitude and phase of each driving signal can be independently modulated with a computer. A visual recording system composed of a CCD camera (GCO-260202, Daheng Imaging, Hangzhou, China), an image acquisition card and a motion platform is used to record the micro-particle position. The actual apparatus is shown in Figure 5b and the related acoustic parameters are listed in Table 1.  
Figure 4: Spatial transformation of the synthesized sound field with an appointed distance 0.5\(\lambda\) along different directions. (**a**) 45\({}^{\circ}\), (**b**) 135\({}^{\circ}\), (**c**) 225\({}^{\circ}\), and (**d**) 315\({}^{\circ}\).  
Figure 5: (**a**) Schematic diagram of the experimental setup; (**b**) Actual setup.
## 2 Results  
### Characterization of TiO\({}_{2}\)-MWCNT-MIP and MMWCNTs-PS-DVB Composites  
The synthesized TiO\({}_{2}\)-MWCNT-MIP composite was characterized by Raman spectrometry (Figure 1A). The registered Raman spectra showed various scattering bands involving the characteristic D (1324 cm\({}^{-1}\)) and G (1605 cm\({}^{-1}\)) band of the MWCNT as well as E\({}_{\text{g}}\) (148 cm\({}^{-1}\)), B\({}_{\text{lg}}\) (397 cm\({}^{-1}\)), A\({}_{\text{lg}}\) (517 cm\({}^{-1}\)), and E\({}_{\text{g}}\) (640 cm\({}^{-1}\)) bands from TiO\({}_{2}\). SEM technique was used to obtain information about the morphology of TiO\({}_{2}\)-MWCNT-MIP, as shown in Figure 1B. At 50.00 K\(\times\) magnification, it was possible to observe the formation of homogeneous agglomerates, formed by spherical particles. The detailed view (100.00 K\(\times\) magnification) clearly reveals the presence of MWCNTs in the TiO\({}_{2}\)-MWCNT-MIP.  
To confirm the synthesis of the MMWCNTs-PS-DVB composite, the synthesized material was characterized by FT-IR, Raman spectroscopy, and TEM, available in the Supplementary Material (Figure S1). In the FT-IR spectra, the characteristic bands at \(\sim\)1700 cm\({}^{-1}\) of aromatic (C=C) bonding and \(\sim\)2800 cm\({}^{-1}\) of alkyl (C-H) stretches resulting from the PS-DVB were observed. A band at \(\sim\)3085 cm\({}^{-1}\) corresponding to the aromatic (C=C) stretching and a broad band at \(\sim\)3400 cm\({}^{-1}\) indicating the presence of hydroxyl groups onto the surface nanomaterials, were also observed (Figure S1a). In Raman spectra, the characteristic D band at \(\sim\)1351 cm\({}^{-1}\) and G band at \(\sim\)1588 cm\({}^{-1}\) for MWCNTs were observed in the prepared MMWCNTs-PS-DVB composite, as shown in Figure S1b. The TEM micrograph of the MMWCNTs-PS-DVB composite appeared to show the incorporation of the magnetic nanoparticles onto the surface of MWCNTs and PS-DVB (Figure S1c).
This approach provides a more accurate representation of complex propagation characteristics, and the advantages of modeling the measurement data in two parts instead of one are demonstrated in terms of accuracy and flexibility with a piecewise modeling method that considers environmental changes. For this purpose, comprehensive measurements and model evaluations were conducted for shore-to-ship communication in various scenarios. As a result, this research presents an innovative approach to increase the reliability and efficiency of shore-to-ship communication as an important step towards the development of propagation models that form the basis of maritime communication systems. It is believed that this study will make significant contributions to the existing literature on maritime propagation models and to other researchers in the field by drawing attention to the importance of new technological t trends and international regulations in maritime communication, especially the use of the 5 GHz frequency band.
# Control for Autonomous Intersection Management Based on Adaptive Control Barrier Function
## 1 Introduction
### Traffic Light Optimization  
Traffic light optimization is still the primary method for managing intersections, but it can result in parking delays. To mitigate these delays, traffic light systems are often optimized based on real-time measurements of traffic density using different sensors installed on the lights [6; 7; 8; 9; 10; 11].  
The AIM system based on traffic light optimzation incorporates various sensor types. In [7], IR sensors were utilized to enable the traffic light system to accurately assess traffic density and adjust the green light duration accordingly, resulting in an adaptive traffic light system. An adaptive traffic signal controller based on reinforcement learning that can learn from high-dimensional sensory data without pre-set parameters was proposed in [16], reducing intersection delays. However, this controller is only effective under low-traffic conditions. Image processing techniques are used in [8; 9; 10] to analyze traffic density but suffer from poor anti-interference ability and weather sensitivity; accuracy may significantly decrease on rainy or foggy days when cameras cannot capture clear images of intersections. In complex environments, a deep reinforcement learning model was introduced in [11] for controlling the traffic light cycle by analyzing data collected from different sensors to determine traffic density. It should be noted that although these approaches optimize control of the traffic lights, they do not eliminate parking delays at intersections.