esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing the Long-Term Phenological Trends of Salt Marsh Ecosystem across Coastal LOUISIANA
## Materials and Methods
### Seasonality Analysis and Simple Linear Trend Analysis  
TIMESAT allows its user to derive various seasonality parameters using time-series data (Table 1). We extracted seasonality parameters, such as base value, max value, amplitude, and small seasonal integral, which are major indicators of seasonal photosynthesis and biomass production and accumulation, for all of the salt marsh pixels covering coastal LA for 16-year period. Once extracted, they were analyzed for seasonal fluctuations. Possible reasons behind the seasonal fluctuations were investigated through the careful study of environmental events such as temperature and precipitation anomalies, the landfall of tropical storms, man-made hazards, or other climatic hazards such as drought causing possible localized dieback, which might trigger an early end to a growing season, or a subsequent late/early start of a growing season. After that, we examined the overall trend in these seasonality parameters using simple linear regression. We examined the slope of the fitted trendline to determine any overall positive and negative trends over the 16-year time period based on the positive or negative value of the slope magnitude.
# Brain-Inspired Data Transmission in Dense Wireless Network
## 4 Network Reliability Assessment through the Percolation Theory
### Application of the Percolation Theory
Thus, the distribution of the aggregated interference can be expressed as:  
\[I\sim S\bigg{(}\alpha=1,\beta=1,\gamma=\frac{\pi^{2}}{2}\lambda P_{l}\mathbb{E }\{\Lambda_{i}\}\bigg{)}. \tag{9}\]  
Finally, the probability density function can be computed as:  
\[f(x) =\frac{1}{2\pi}\int_{-\infty}^{\infty}e^{-iux}\omega(w)dw \tag{10}\] \[=\frac{1}{2\pi}\int_{-\infty}^{\infty}e^{-iux}e^{-\gamma|w|\big{[} 1+j\frac{2}{\pi}\text{sign}(w)\ln(w)\big{]}}dw\] \[=\frac{1}{2\pi}\int_{-\infty}^{\infty}e^{-iux}e^{-\frac{\pi^{2}}{ 2}\lambda P_{l}\mathbb{E}\{\Lambda_{i}\}|w|\big{[}1+j\frac{2}{\pi}\text{sign} (w)\ln(w)\big{]}}dw.\]  
The above formula can be calculated numerically and tabulated, allowing for direct calculation of the searched probability \(P\{SINR\geq\theta^{*}\}\).
## 4 Conclusions
**Nomenclature**  
\(a\) distance between neighboring drops' centers [m]  
\(A_{1}\), \(A_{2}\), \(A_{3}\) group parameters  
\(B_{1}\), \(B_{2}\) group parameters  
\(f\) area fraction  
\(g\) gravitational force [m/s\({}^{2}\)]  
\(G\) growth rate  
\(h_{i}\) interfacial heat transfer coefficient [W/m\({}^{2}\).K]  
\(H_{fg}\) latent heat of vaporization [J/kg]  
\(k_{cont}\) coating layer thermal conductivity [W/m.K]  
\(k_{c}\) condensate thermal conductivity [W/m.K]  
\(n\) exponent in drop size distribution  
\(n(r)\) small drop size distribution [m\({}^{-3}\)]  
N(\(r\)) large drop size distribution [m\({}^{-3}\)]  
\(N_{s}\) nucleation sites [m\({}^{-2}\)]  
\(q_{d}\) heat transfer through a single drop [W]  
\(r\) drop radius [m]  
\(r_{e}\) effective radius [m]  
\(r_{max}\) maximum radius [m]  
\(r_{min}\) minimum radius [m]  
\(s\) distance between two drops' centers  
\(R_{D}\) ratio of maximum drop diameter to DWC width  
\(T_{sat}\) saturation temperature [K]  
\(w_{DWC}\) dropwise condensation region width [m]  
**Greek Symbols**  
\(\Delta T\) total temperature drop [\({}^{\circ}\)C]  
\(\theta\) contact angle [\({}^{\circ}\)]  
\(\theta_{a}\) advanced contact angle [\({}^{\circ}\)]  
\(\theta_{r}\) receding contact angle [\({}^{\circ}\)]  
\(\mu\) viscosity [Pa.s]  
\(\sigma\) surface tension [N/m]  
\(\rho\) steam density [kg/m\({}^{3}\)]  
\(\gamma\) ratio of maximum radius  
\(\delta\) coating layer thickness [m]  
**Abbreviations**  
DWC dropwise condensation  
FWC filmwise condensation
## 3 Methodology
### Scenario Simulation Test  
First, we constructed the roundabout scenario in CARLA in the same manner as the raw data was collected. Maps were retrieved from the OpenStreetMap open-source map collection then imported into RoadRunner. We made sure the roads were meticulously refined on the map to align with the actual roundabout, as the downloaded map could have some missing areas that need to be corrected. Then, we imported the map, which was downloaded from RoadRunner, to CARLA. We set the parameters of each road based on the structural dimensions and speed limits of the real-world roundabout. The CARLA road network was obtained as shown in Figure 10.  
In the traffic scenarios concerning areas where vehicles merge into roundabouts, it is often the responsibility of the drivers in the merging vehicles to make safety judgments to decide when and at what speed to enter the roundabout. In contrast, vehicles circulating within the roundabout typically continue their current path of travel, with drivers rarely needing to consider yielding to merging vehicles. Therefore, during virtual simulation testing, rather than focusing on the functionality of autonomous vehicles driving withinthe roundabout, it is more crucial to test how autonomous vehicles make decisions when merging into the roundabout.  
In the second step, we added two vehicles running within the constructed roundabout environment in CARLA. Vehicle1 was designated as a vehicle circulating within the roundabout, with its vehicle type defined as \"CAR\". We used the interface to ensure that the trajectory of V1 followed the trajectory data of the generated nearby roundabout vehicle (NRV). The other vehicle, Vehicle2, represents a vehicle merging into the roundabout. Its vehicle type was defined as \"CAV\". The starting position of V2 was specified to ensure it aligned with the starting trajectory data of the generated main subject vehicle (MSV). By setting the endpoint within the internal roads of the roundabout, the objective of V1's movement was achieved, entering the roundabout and circulating within it. Once the CARLA simulation was up and running, V2 was free to travel, using certain deceleration and avoidance measures when a collision was detected or there was a risk of collision based on the agent from CARLA.  
All the generated samples were tested according to the above process, and the trajectories of the \"CAV\" in the simulated scenario were exported and analyzed together with the trajectories of the \"CAR\" to determine the danger level of the scenario. The potential encounter time (PET) metric was employed to quantify the severity of conflicts between the merging vehicles and the vehicles circulating within the roundabout.
## 3 Benchmarking
### Benchmarking Lumawig on Larger Data Sets  
We perform a second stage of benchmarking against the current state-of-the-art implementation of the bottleneck distance in Hera. We again simulate 100 0-dimensional persistence diagrams and pair each diagram with another simulated diagram not necessarily with the same number of points. Similar descriptive measures as in the earlier stage are considered from the 100 pairs of diagrams with increasing number of points from 1000 to 30,000. The choice of benchmarking bottleneck computation to at most 30,000 points was to draw comparison with that of Hera in [13]. One difference in our benchmarking is that the number of points in the two diagrams we are comparing need not be equal.  
Figure 5 shows the running time distribution of 100 dimension zero bottleneck distance computations over increasing diagram sizes. Please note that the vertical axis is displayed in logarithmic scale. Only five boxplots for the running time of the original algorithm implemented in Dionysus are superimposed to provide reference for the state-of-the-art Hera and our two implementations of Lumawig. A quick inspection reveals that both implementations of Lumawig are consistently several orders of magnitude faster than the current state-of-the-art Hera. The use of the same pairs of simulated persistence diagrams for bottleneck computations across implementations allowed for paired tests of significant difference in running time relative to Hera. These significant values, computed at \(\alpha=0.95\) level of confidence, appear in Table 1.  
Figure 4: Boxplots of relative differences from Dionysus of the bottleneck computation outputs in (a) Hera, (b) Persim, (c) Lumawig, and (d) Lumawig.  
Figure 5: Running time (seconds in log scale) of Lumawig versus the current state-of-the-art implementation in Hera. Five boxplots for the running time of the original algorithm in Dionysus are superimposed for reference.  
As \(\text{LUMAWIG}_{\text{R}}\) yields exact values for the bottleneck distance relative to the original Dionysus implementation, we use it as basis in the computation of relative differences in this stage. Figure 6a,b show the relative difference in the computed dimension zero bottleneck distance respectively of Hera and \(\text{LUMAWIG}_{\text{PY}}\) with respect to that of \(\text{LUMAWIG}_{\text{R}}\). Consistent with the comparison between the outputs of Hera and Dionysus in Figure 4a, Hera consistently overestimates the dimension zero bottleneck distance with respect to that of \(\text{LUMAWIG}_{\text{R}}\). In contrast, relative differences between the two implementations of \(\text{LUMAWIG}\) can be attributed to rounding differences between Python and R.
## 3 Results
### Mycoremediation Assay
The aromatic hydrocarbons with chains EC\({}_{12}\)-EC\({}_{16}\) and EC\({}_{21}\)-EC\({}_{35}\) were one-fold lower that EC\({}_{16}\)-EC\({}_{21}\). Finally, the lightest (EC\({}_{10}\)-EC\({}_{12}\)) and heaviest (>EC\({}_{35}\)) aromatic hydrocarbons were the less abundant with concentrations 2-fold lower than the most abundant aromatic fraction. Three of the four bioaugmented treatments, _L. edodes_, _P. ostreatus_ and _A. bisporus_, got significant lower concentrations of total aromatic hydrocarbons than the control treatment. Between the effective treatments, _A. bisporus_ microcosms achieved a significantly lower concentration of total aromatic hydrocarbons. However, the improvement in the degradation effectiveness of SMS treatments was only detected for the light aromatic hydrocarbons EC\({}_{10}\)-EC\({}_{12}\), EC\({}_{12}\)-EC\({}_{16}\) and EC\({}_{16}\)-EC\({}_{21}\). Therefore, these treatments were not effective to improve the degradation rate of heavy aromatic hydrocarbons with respect to the autochthonous microbiota. For the light fractions, _A. bisporus_ SMS got a significantly lower concentration of these contaminants than _L. edodes_, _P. ostreatus_ and _P. eryngii_ microcosms. Despite _P. eryngii_ SMS did not show significant differences with respect to the control treatment for the total aromatic hydrocarbons, this treatment reached a significantly lower concentration for the two most light fractions (EC\({}_{10}\)-EC\({}_{12}\) and EC\({}_{12}\)-EC\({}_{16}\)). The PCA analysis of the remaining concentration of TPHs and aliphatic and aromatic chains for the control treatment and the four SMSs microcosms are shown in Figure 2. The percent variability explained by PC1 was 66.4% and for the PC2 was 14.9%. The PC1 was positively related to the remaining concentration of TPHs and all the hydrocarbon fractions except to >C\({}_{35}\) and the heaviest aromatic fractions (EC\({}_{21}\)-EC\({}_{35}\) and >EC\({}_{35}\)) which were positively related to the PC2. The control microcosm was located at the right of the PCA diagram denoting the high concentration of hydrocarbons at the end of the incubation time. The bioaugmented microcosms were progressively displaced to the left of the PCA diagram according to the effectivity of hydrocarbons degradation. _P. eryngii_ microcosms were closer to the control, both in the positive section of PC1. The other bioaugmented microcosms were in the negative section of the PC1 denoting their ability to remove TPHs and a high variety of aliphatic and aromatic chains. _L. edodes_ and _P. ostreatus_ were overlapped denoting similar effectivity to remove petroleum hydrocarbons. Finally, _A. bisporus_ microcosms were clearly away from the rest of the microcosms with negative values of PC1. This location was negatively related to a high concentration of hydrocarbons demonstrating once again the effectivity of this SMS to remove petroleum hydrocarbons. The low score of PC2 of _A. bisporus_ and the other bioaugmented treatments or even the positive score of _P. eryngii_ showed the low effectiveness of these SMSs to remove heavy aromatic hydrocarbons.
# Suppressing Salt Transport through Composite Pervaporation Membranes for Brine Desalination
## 3 Results
### Incorporation of GO into PVA Matrix
#### 3.2.2. Pervaporation Performance Using Single Salt Brine
Upon the completion of the long-term brine desalination test, the membranes were re-tested with Milli-Q water to investigate their permeation flux recovery. The results suggested even though the initial flux was much lower, it can gradually recover to the original Milli-Q water flux prior to the brine desalination test in less than 2 h, shown in Figure S8. This observation confirmed flux decline during the brine desalination test was mainly due to the blockage of water transport pathway by the bulkier hydrated salt ions. In all, with the increase of GO loading in PVA matrix, the water flux tended to decrease for both Milli-Q water and brine, while the salt rejection firstly increasedthen decreased. Similar membrane behavior has been reported for the PVA-GO-based composite pervaporation membrane for organic solution dehydration [54].
# Study of Noise Effect of Slag Storage Technology on Surrounding Environment
## 2 Materials and Methods
### Methods
#### 2.2.3 Methodology for Calculating Equivalent Noise Levels  
A general procedure was used to predict noise propagation:  
* dividing the entire problem to be solved into subproblems that address a specific noise emission rate, in the form of inputting the sound power level A or sound pressure level A, from the point sources of acoustic energy used in the isophone prediction;
* for noise sources whose sound power levels A vary over the time period under consideration, an equivalent value for the time period under consideration shall be determined;
* for each of the point sources used, the direct propagation of acoustic energy in the outdoor environment, corrected for possible reflections from existing terrain and attenuation from existing objects, shall be addressed;
* the resulting noise level at the site under consideration is calculated as a superposition of the effects from the individual elementary sources and the acoustic energy;
* only the airborne noise component is considered;
* the sound pressure values \(L_{p,Aeq}\) are calculated for the reference time intervals day (6 am-6 pm), evening (6 pm-10 pm), and night (10 pm-6 am) [29; 30].
###### Abstract
A WDM-FSO system integrated with a fiber access network for wireless traffic was experimentally investigated in [19]. FSO systems were integrated with PONs in [20], where 10 Gbps OOK data were transmitted. A full-duplex architecture for the transmission of ultrawideband (UWB) signals over an FSO network that offers the benefits of installation simplicity and mobility was presented in [21]. All-optical generation and transmission of multiple ultrawideband signals to radio access units over an FSO link was demonstrated in [11] using a centralized frequency comb source. The performance of an all-optical relay-assisted FSO system was investigated in [22] for OOK and DPSK modulation formats. In [23], a single lightwave source was used for delivering baseband, FSO, and MMW traffic simultaneously in a PON architecture. A Hybrid PON-FSO strategy was introduced in [24] to create a more cost-effective solution for fronthaul networks in congested metropolitan areas compared to solo PON systems, primarily owing to the FSO technology. The main optical wireless communication (OWC) technologies, algorithms, concepts, and configurations that could improve the performance of the next generation of data centers are discussed in [25]. Recent developments in FSO DCN design have been discussed in [26], as well as current designs that utilize cellular technology to realize point-to-point Line-of-Sight (LOS) links. An OWC DCN architecture based on a diffractive grating and a fast tunable transmitter was demonstrated in [27], where different modulation formats (NRZ-OOK and PAM4), as well as different sizes of DCN, have been evaluated. This paper reviews recent and prospective DCN technologies to meet aggregate capacity demands. Moreover, we propose and demonstrate a novel bidirectional WDM-FSO system based on IM/DD OFDM for employment in OWC-DCNs. In order to increase capacity and reduce costs, a wavelength scheme based on reflective semiconductor optical amplifiers (RSOAs) has been implemented, which requires no optical source for upstream transmissions. The findings here are comparable to those found in the literature, such as [19; 23; 24]. In contrast to the basic modulation formats used in the literature, we have employed a novel technique based on OFDM technology and wavelength reuse. Here, 10 Gbps OFDM and OOK FSO signals can be transmitted over a 1000 m free space link for downstream and upstream traffic, respectively. The proposed WDM FSO system offers cost-effective transmission of high-data-rate signals, as well as mobility and a shortened deployment time.
## 2 Method
### SLIC Superpixel Generation and Superpixel Constraint
#### 2.3.1 SLIC Superpixel Generation  
This paper uses the SLIC algorithm to obtain superpixels from PolSAR images. Considering that the target decomposition parameter is an important feature for describing the class information, the three components of the Pauli decomposition have obvious physical meaning. Therefore, the PauliRGB is used to replace the spectral features in the traditional optical image. The main details of the processing steps are as follows:  
1. Generate center seed points: Firstly, we generate a PauliRGB gradient image. Secondly, select the seed point as the initial center of the superpixel according to the step S sampling. Finally, adjust the seed point to the lowest point of the gradient image in the local S \(*\) S range;
2. Local K-means: First, the distance of each pixel to the center of the superpixel is calculated in the range of 2S \(*\) 2S of each superpixel center and divide the pixel into the nearest superpixel. Second, SLIC's search scope is limited to 2S \(*\) 2S, which speeds up algorithm convergence. The distance between two pixels is measured in d. Third, we assume that the Pauli decomposition feature vectors of pixel (\(x_{i}\), \(y_{i}\)) and pixel (\(x_{j}\), \(y_{j}\)) are (\(P_{a_{i}}\), \(P_{b_{i}}\), \(P_{c_{i}}\)) and (\(P_{a_{j}}\), \(P_{b_{j}}\), \(P_{c_{j}}\)). The computational formula of spatial distance, Pauli distance, and distance d is defined as follows: \[d_{s}=\sqrt{\big{(}x_{i}-x_{j}\big{)}^{2}+\big{(}y_{i}-y_{j}\big{)}^{2}}\] (22)\[d_{p}=\sqrt{\left(P_{a_{i}}-P_{a_{i}}\right)^{2}+\left(P_{b_{i}}-P_{b_{i}}\right)^ {2}+\left(P_{c_{i}}-P_{c_{i}}\right)^{2}} \tag{23}\]  
\[d=\sqrt{\left(\frac{d_{p}}{maxd_{p}}\right)+\left(\frac{d_{s}}{S}\right)^{2}} \tag{24}\]  
After the calculation, we update the center of each superpixel. Next, we repeat the above steps until convergence or it reaches the maximum number of iterations. Finally, a superpixel of approximately \(\mathrm{S}*\mathrm{S}\) size can generate;
3. Post-cluster processing: The superpixels with less than a certain number of pixels are merged into the nearest superpixel to obtain the final PolSAR superpixel image.
## 3 Results  
### Clustering Results  
All 361,746 events have been recognized as entries that may impact the QoE level of viewers. A total of 49,629 events were generated by 212 devices and marked by the algorithm as the lowest grade. A total of 1093 devices reached an unsatisfactory penultimate grade. In total, 1305 (0.036%) devices were affected by the two lowest quality levels. On the other hand, 32,319 (86.7%) decoders reported at least one event with the highest possible quality (Table 4). The perceptible but not annoying level was determined for 13,017 STBs, and a slightly annoying stage was characteristic for 7738 devices. The single STB may report several grades during the study.  
The ACR assignment is reflected by cluster allocation referenced to SCI, STCSI, SCTI, and VSBCT features. Every single entry consists of four features during the clustering process, and the returned cluster is considered ACR grade. The VSBCT feature implies the most on the returned cluster during considering the given dataset (Figure 8). The VSBCT parameter can reach lower values than SCI, SCTI, and STCSI since in the dataset can be found devices that log an enormous number of negative bitrate switches. Those devices are often connected wirelessly with decreased signal power. The VSBCT for 5 varies between \(-1\) and \(-2.585\). Those values mean that no degradation occurred or the significance of degradation related to a smaller bitrate than the source is low. Single bitrate changes could occur, but if there is no following negative bitrate switch in time, the VSCBT is improving and aims to \(-1\). For the lowest cluster 1, the values are between \(-11.1693\) and \(-7.5314\), which means that degradation is evident. Many bitrate switches occur over the whole watching session. There is no period when the signal quality can improve, and as a result the customer can observe degradation almost over the whole watching session.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grade** & **No. Devices** & **No. Events** \\ \hline
5 & 32,319 & 103,270 \\
4 & 13,017 & 43,073 \\
3 & 7738 & 69,334 \\
2 & 1093 & 96,440 \\
1 & 212 & 49,629 \\ \hline \hline \end{tabular}
\end{table}
Table 4: ACR assignment of unique devices and clustered events.