esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The privacy-preserving techniques used in disease prediction systems utilize deep learning, and they explore various approaches, such as cryptographic techniques, attribute-based encryption, homomorphic encryption, and hybrid methods [22]. * To achieve interoperability for heterogeneous IIoT environments, reliable network connectivity is essential. * To attain the best data collection from IIoT devices, the environment must be known with precision, and the data must be captured with suitable granularity. In this case, fault data are identified. * To efficiently find the adaptive threshold, the sensed data should be analyzed in real time. * To improve the network scalability for incorporating a large number of nodes, energy-efficient clusters should be formed.
# Wavenumber-Frequency Analysis of Internal Aerodynamic Noise in Constriction-Expansion Pipe
## 5 Conclusions  
The LES technique and a wavenumber-frequency analysis were combined to estimate the internal compressible acoustic waves and incompressible hydrodynamic pressure waves in a simple constriction-expansion pipe. First, the steady-state flow was numerically simulated, and the result was compared with a quasi-1D theoretical solution, which confirmed the validity of the current numerical method. Then, an unsteady simulation was performed to predict the unsteady pressure fluctuation inside a pipe. Next, the compressible acoustic pressure waves and incompressible hydrodynamic pressure waves in the pipe were separated by applying a wavenumber-frequency transform to the total pressure field. Finally, the inverse wavenumber-frequency transform facilitated the estimation of the acoustic power spectrum by separating the compressible and incompressible pressure fluctuations. The estimated acoustic power level was compared with those that were obtained using the existing empirical models, which highlighted the fact that the current method can provide more detailed information about the acoustic source for the so-called AIV of a piping system, and thus facilitate systematic measures for the mitigation of this AIV.  
This research was supported by the Basic Science Research Program through the National Research Foundation of South Korea (NRF), funded by the Ministry of Education (NRF-2016R1D1A1A099 18456).  
[PERSON] provided the overall numerical analysis schemes. [PERSON] and [PERSON] carried out the numerical simulations and worked on the analysis of numerical results. [PERSON] developed the code of wavenumber-frequency analysis. [PERSON] provided the operation conditions of the target problem and the empirical formulas available.  
The authors declare no conflict of interest.
## 4 Discussion  
### Spatiotemporal Distribution and Change of NPP  
The YRB is rich in natural resources. Against the backdrop of climate change, efforts have been made in ecological and environmental management in the YRB, yielding certain achievements [47; 48; 49]. From 2003 to 2020, there was an overall trend of fluctuating increase in NPP, and the NPP for each river system also exhibited an upward trend.  
Previous research has demonstrated a strong correlation between estimated NPP values and vegetation NDVI [50; 51; 52; 53], with NPP increasing as vegetation quality improves. The NPP in the Sichuan Basin, the middle reaches of Dongting Lake, and the upper reaches of the Jinsha River have exhibited varying degrees of growth. The Sichuan Basin, in particular, has shown a notable increase, with a predominance of moderate stability, along with scattered areas of lower stability [54; 55]. This suggests that the reforestation efforts by the Sichuan municipal government in recent years have been effectively executed. The overall NPP in the Han River Basin is also exhibiting an increasing trend, especially in regions such as Hanzhong and Shiyan, aligning with the findings of [PERSON] et al.'s research [56]. The source area of the YRB is a typical high-altitude and cold plateau region, with an ecosystem highly sensitive and fragile, making its carbon balance particularly responsive to climate and anthropogenic factors. In recent years, there has been a significant increase in NPP in the source area, as indicated in Figure 11, with a substantial contribution from anthropogenic disturbances [57], accounting from 75% to 100%. The Chinese government's efforts in environmental protection in this context should not be underestimated.  
Looking at the fluctuations in NPP over the years, there are significant high-variance regions in the Mintuo River. The central region of the Jinsha River Basin (Sichuan Province)  
Figure 11: Contribution rates of different influencing factors to NPP variations.  
also shows pronounced interannual variations. These areas are primarily located near the boundaries of China's first and second terraces in the vicinity of the Hengduan Mountains (Ranges), characterized by mountainous terrain and tectonic fault zones [58]. Taking the Longmenshan seismic belt as an example, this belt has experienced numerous earthquakes. Frequent earthquakes not only cause damage to local buildings and structures but also trigger events like landslides, mudslides, and avalanches [59], resulting in significant harm to vegetation and its growth environment. In summary, geological disasters lead to a decline in NPP, while post-disaster reconstruction and natural recovery increase vegetation coverage and NPP. However, the instability of fault zones contributes to NPP fluctuations [60; 61; 62; 63].
## 2 Results and Discussion
### Fingerprinting of Cinnamon NMR Dataset
#### 2.4.1 Primary Metabolites  
The appearance of doublets at \(\delta\) 4.46 (\(J\) = 7.8 Hz), 5.09 (\(J\) = 3.6 Hz), and 5.14 (\(J\) = 3 Hz) ppm were readily assigned to anomeric protons of \(\beta\)-glucose (N3), \(\alpha\)-glucose (N4), and sucrose (N6), respectively, and verified by cross-peak correlation at \(\delta\) 98.1, 94.2 and 93.9 ppm, respectively. Moreover, the presence of doublet of doublets at \(\delta\) 4.01 ppm (\(J\) = 1.2, 12.6 Hz) is characteristic of fructose (N5).  
Signals characteristic for unsaturated fatty acid could be assigned in \({}^{1}\)H-NMR spectra following the same identification reported in [44] assigned to \(\omega\)-6 fatty acid (N1). Glycerol (N2) was readily detected from its characteristic pair of doublet of doublets at \(\delta\) 3.5 and 3.58 ppm related to C1 and C3 methylene protons. All previous assignments were confirmed from 2D-NMR spectral data (Table S2). Primary metabolites were detected in nearly all authenticated and commercial samples except for CP-3, which showed the presence of \(\beta\)-glucose only with the absence of all other primary metabolites, Figure 4.
# Notes on Common Misconceptions in Microplastics Removal from Water
## 3 Microplastics Removal from Water
### Degradation
#### 3.2.3 Ozonation  
Ozonation is defined as a method leading to the degradation of the exposed material via ozone formation. Ozone molecules act as a reactive species, attacking bonds in organic molecules [49].  
Ozonation is often applied on various pollutants removal from water [94; 95; 96]. However, these and other studies have a major drawback--they only study the effect of ozone as gas on the surface of plastic material. On the other hand, it is expected that plastic materials will undergo degradation using the ozonation process [97], but an aquatic environment will reflect a different behaviour, compared to an atmospheric environment.  
[PERSON] et al. [98] studied ozonation for the preparation of a model microplastic solution (PS, 1-200 \(\upmu\)m), because, when a solution of microplastics in water is being prepared, microparticles tend to aggregate and ozone dissolution helps to separate them. However, an increase in DOC (dissolved organic carbon) in the tested solution was observed, due to the partial oxidation of the PS surface yielding water-soluble oxidation products. [PERSON] and [PERSON] reported on the successful removal of microplastics (particles smaller than 1.2 \(\upmu\)m) by ozonation on the tertiary stage of WWTPs and the efficiency was nearly 90%, compared to removal by disc-filtration (79%), or rapid sand filtration (74%). The main disadvantages associated with ozone utilization are high energy consumption and formation of oxidizing organic by-products, which may reveal significant toxicity properties, e.g., carboxylic acids, or aldehydes [21]. Another recent study focused on pre-oxidative treatment of PE particles (<0.5 mm) by ozonation, hypochlorite and permanganate. It was shown that such pre-oxidation did not enhance the removal of microparticles by conventional drinking water treatment methods. On the other hand, the change in surface properties resulted in an increase in hydrophobicity; this resulted in better adsorption of hydrophobic organic micropollutants, which might cause a significant increase in particles toxicity [99].  
Ozonation itself affects the surface properties of microparticles; however, very often, it contributes to an increase in toxic properties of the treated effluent. The size of microparticles also varies a lot (as for other methods), thus further research for better understanding is desired. On the other hand, for the preparation of a model solution, ozonation might help in disabling microparticles aggregation.
###### Abstract  
The total column water vapor (TCWV) is a relatively active component in the atmosphere and an important detection object of climate change. Exploring the spatiotemporal modes characteristics of TCWV and predicting its changing trends can provide a reference for human beings to deal with climate change and formulate corresponding countermeasures. The TCWV data over China region by using the Atmospheric Infrared Sounder (AIRS) dataset from 2002 to 2022 were obtained. The empirical orthogonal function (EOF) analysis, linear regression, Mann-Kendall (M-K) mutation test, Seasonal Autoregressive Integrated Moving Average (SARIMA) model and other methods were used to discuss the spatiotemporal modes characteristics of TCWV in the China region on the monthly, seasonal, and annual scales and verify the rationality of the forecast of the monthly average trend of TCWV in the next year. The obtained results show that: (1) The annual and seasonal scales spatial distributions of TCWV in China are roughly consistent, with obvious latitudinal distribution characteristics. That is, the TCWV in the low latitude region, especially in the tropical region, is larger, and it gradually decreases with the increase of the latitude. Furthermore, the TCWV in the eastern region is higher than that in the western region at the same latitude; (2) The EOF analysis results show that its first mode can better reflect the typical distribution characteristics of the southeast-northwest positive distribution in China; (3) From 2002 to 2022, the TCWV in China shows an upward trend and the TCW increases at a rate of 0.0413 kg/m\({}^{2}\) per year, which may be related to the long-term increase of air temperature in recent years; (4) The inter-monthly variation of TCWV shows a slightly positive skewed 'bell-shaped' curve, with the maximum in summer, the minimum in winter and the similar distribution in spring and autumn. As can be seen from the M-K curves of the four seasons, each season has different mutation points; (5) Forecasting the TCW was done using time series monthly average values from September 2002 to February 2022. SARIMA (3, 1, 3) \(\times\) (0, 1, 12) was identified as the best model. This model passed the residual normality test and the forecasting evaluation statistics show that MAPE = 2.65%, MSE = 0.3229 and the R2-score = 0.9949. As demonstrated by the results, the SARIMA model is a good model for forecasting TCWV in the China region.  
water vapor column; spatiotemporal distribution; EOF modes analysis; Mann-Kendall mutation test; SARIMA model +
Footnote †: journal: _arxiv.org/abs/1208.00022_  
1
## 3 Data
### Satellite data  
The Normalized Difference Vegetation Index (NDVI) has been the most common vegetation index used throughout the history of satellite data applications. NDVI represents the absorption of photosynthetic active radiation and hence is a measurement of the photosynthetic capacity of the canopy. NDVI is calculated from reflectance in the red (R) and near-infrared (NIR) bands using the following equation:  
\[NDVI=\frac{(NIR-R)}{(NIR+R)} \tag{7}\]  
NDVI has been established to be highly correlated to green-leaf density, absorbed fraction of photosynthetically active radiation and above-ground biomass and can be viewed as a surrogate for photosynthetic capacity [42, 43].  
10-day 1-km NDVI data set for the growing season (April-October) 2004 was obtained from SPOT-Vegetation satellite data. The data set had been generated using a maximum value composite procedure, which selects the maximum NDVI value within a 10-day period for every pixel. On this way, non-vegetated noise originally presented in the row data was significantly reduced [44]. Nonetheless, some noised pixels remained in the NDVI data set and were removed by employing a spatial filter, which calculated a new value for each noised pixel from values of neighbouring non-noised pixels.
# Experimental Study of LoRa Transmission over Seawater
## 3 Materials, Experimental Setup and Methods
### Traffic Setup  
In all experiments, the transmitters were configured to send packets consisting of an 8 byte preamble, 9 byte payload and CRC. The payload of each packet was populated with the packet's unique identification number (ID) which was consecutively incremented in each sending cycle starting from the value \(1D=1\). An inter-packet sending delay of \(T_{d}=200\,\mathrm{ms}\) was set to enable also the investigation of short-term variabilities in reception quality. However, it should be noted that this  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Parameter** & **Link AC: LOS Path** & **Link AD: Obstructed LOS Path** \\  & **(868, 434) MHz** & **(868, 434) MHz** \\ \hline Free space path loss & \((118.33,112.31)\) dB & - \\ Longley-Rice path loss & \((118.26,112.25)\) dB & \((176.70,166.12)\) dB \\ Attenuation due to terrain shielding & \((0.07,0.06)\) dB & \((56.47,51.91)\) dB \\ Mode of propagation & line-of-sight mode & double horizon, diffraction dominant \\ \hline \hline \end{tabular}
\end{table}
Table 2: BotRf simulation results for 868 MHz and 434 MHz.  
Figure 4: Map showing line-of-sight trajectory (A–C) and obstructed trajectory (A–D).  
Figure 5: Terrain profiles for: (**a**) Line-of-Sight path over sea. (**b**) Obstructed path over sea. Brown area indicates the curvature of the earth, modified by the refraction index; Green area is the terrain profile as seen by the radio wave and magenta line corresponds to 60% of the first Fresnel zone at 434 MHz; and blue line is the optical line of sight.  
sending rate violates the European constraints of maximum duty-cycle [8], and should not be embraced in commercial solutions. On the receive side, time stamp, packet ID, RSS, and SNR were logged.  
Experiments were carried out within a bandwidth of \(BW=125\,\mathrm{kHz}\) and a coding rate of 4/6, while the spreading factor was varied, \(SF\in[7,10,12]\). The bandwidth was fixed at 125 kHz since it is the value standardized by the LoRaWAN specification, while the coding rate of 4/6 was chosen as a mid-value among those supported by the LoRa modem.  
It is worth remembering that different LoRa parameters yield different packet air times. Since 20,000 packets were sent in each experiment, the duration of the experiments was different. Table 3 summarizes packet air time, equivalent bit rate, theoretical sensitivity and experiments' duration for the LoRa parameters used in the measurements.
## 3 Results and Discussion
### Influence of Temperature on the Adsorption of Chromium(III)  
The adsorption of chromium(III) (0.01 g L\({}^{-1}\)) in 0.1 M NaOH by 1 g L\({}^{-1}\) of the adsorbent was studied over a temperature range of 20-60 \({}^{\circ}\)C. Under these conditions, increasing temperature gave a decrease in chromium adsorption (84% at 20 \({}^{\circ}\)C versus 52% at 60 \({}^{\circ}\)C), and defined the metal distribution coefficient between the adsorbent and the aqueous solution as:  
\[\mathrm{D_{Cr}}=\frac{\left[\mathrm{Cr}(\mathrm{III})\right]_{\mathrm{cnt,e}} }{\left[\mathrm{Cr}(\mathrm{III})\right]_{\mathrm{aq,0}}} \tag{6}\]  
The plot of the log D\({}_{\mathrm{Cr}}\) versus 1000/T was linear (r\({}^{2}\) = 0.9959) (Figure 4). From this plot, the value of \(\mathrm{\Delta H^{\circ}}\) was estimated to be \(-\)32 kJ mol\({}^{-1}\); thus, the adsorption was exothermic, and \(\mathrm{\Delta S^{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{ \mathrm{\mathrm{  \mathrm{   }}}}}}}}}}}}}}}\) = \(-\)95 J mol\({}^{-1}\) K\({}^{-1}\), indicating a decrease in the randomness at the solid-liquid interface during the adsorption process. The estimated \(\mathrm{\Delta G^{0}}\) value was \(-\)4 kJ mol\({}^{-1}\), which represented a spontaneous adsorption process.  
Chromium(III) Removal from the Aqueous Solution Using Different Adsorbents/Ion Exchangers: A Comparison  
Different adsorbents or anion exchange resins were used to compare chromium(III) adsorption results with that obtained with the present multiwalled carbon nanotubes. In the present case, the aqueous solution was of 0.01 g L\({}^{-1}\) Cr(III) at 0.1 M NaOH, with an adsorbent dosage of 1 g L\({}^{-1}\). The temperature of the experiments was 20 \({}^{\circ}\)C. The results from this comparison are summarized in Table 5. As shown, the worst results were obtained when the nonfunctionalized Lewatit EP63 resin was used to remove Cr(III) from the solution, whereas the use of active carbon achieved near 94% Cr(III) removal value, which was very near the yield reached with the use of the multiwalled carbon nanotubes.  
Figure 4: Plot of log D versus 1000/T for the adsorption of chromium(III) at various temperatures. Dotted lines represented a 95% confidence interval of the regression line.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Adsorbent** & **Functional Group** & **a** **Cr(III) (mg g\({}^{-1}\))** \\ \hline MWCNTs & none & 8.4 \\ Ionac SR7 & b  & QAS & 7.3 \\ Lewatit EP63 & none & 1.1 \\ ox-MWCNTs & carboxylic & 5.0 \\ Active carbon [19; 20] & none & 9.4 \\ \hline \hline \end{tabular}  
* At equilibrium. b  
\end{table}
Table 5: Chromium(III) adsorption using various adsorbents.
## 4 Clustering Ordered Permutations
### Clustering Performance Criteria
#### 4.2.1 Misclassification  
Once clusters are identified, their centers are determined. Then, if a member of a cluster is closer to the centroid of another cluster to which it does not belong, it is said to be misclassified. Hence, misclassification plays a significant role in this investigation. For example, consider a cluster \(i\), which contains \(P_{i}\) number of permutations, and each permutation is defined as \(p=1,2,\ldots,P_{i}\). Then, the cartesian coordinate of each permutation is represented as \(\Pi_{p}=\left\{O_{1,p},O_{2,p},\ldots,O_{n,p}\right\}\). Here, \(O_{n,p}\) (\(n=1,2,\cdots,N\), where \(N\) is the total number of objects) indicates the location of an object (\(n\)) in the permutation (\(p\)). Now, the cartesian coordinate of the centroid of cluster \(i\) along the \(n\)th axis can be calculated using Equation (7), as follows:  
Figure 3: Limitation of binary conversion method-2.  
\[C_{i,n}=\frac{\sum\limits_{p=1}^{P_{i}}O_{n,p}}{N};\;\forall n \tag{7}\]  
The centroid of cluster \(i\) is defined as \(C_{i}=\{C_{i,1},C_{i,2},\ldots,C_{i,N}\}\). The distance (\(D_{p,C_{i}}\)) between any permutation \(p\) and a cluster centroid \(C_{i}\) can be calculated as shown in Equation (8)  
\[D_{p,C_{i}}=\sqrt{\sum\limits_{n=1}^{N}\left(C_{i,n}-O_{n,p}\right)^{2}} \tag{8}\]  
Assuming permutation \(p\) belongs to cluster \(i\), if distance \(D_{p,C_{i}}\) is greater than the distance of this permutation from at least one other cluster \(i^{\prime}\
eq i\), then the permeation is termed to be misclassified. In this way, the total number of misclassified permutations in a given cluster can be computed.
## 4 Dispersing the Light
### Arrayed Waveguide Gratings (AWGs)
#### 4.1.2 Fabrication  
Photonic AWG is the most explored on-chip dispersion technique. Availability of electron-beam lithography has allowed imprinting nanoscale structures on silicon-based platforms. In the last few years, silicon nitride on silica (\(Si_{3}N_{4}/SiO_{2}\)) has emerged as a low-loss and high index contrast material for photonic devices [63; 64]. Single-mode propagation loss of less than 1 dB/m has been demonstrated at 1550 nm with wide waveguide geometries [65]. At the same time, high index contrast allows for strongly guided modes, thus smaller bend radii (\(r_{bend}\sim 2\) mm) are possible with low bending losses and low cross-talk among the waveguides in the array.  
However, a key drawback with high index contrast waveguides (i.e., strongly guided modes) is that they are difficult to couple with SMFs, due to a large mismatch in the mode shape and size. Recent work at the University of Maryland has demonstrated \(\sim\)95% coupling efficiency with high numerical aperture SMFs at 1550 nm by using an adiabatic taper-down profile and a rectangular region of appropriate width added to the waveguide before it is butt-coupled with the fiber [46]. Application of this technique has produced a moderate-resolution (R \(\sim\) 1500), wide-band (1450-1650 nm) AWG with \(\sim\)25-30% end-to-end throughput (fiber-chip-fiber) at 1600 nm [62; 66]. With more improvements underway, the \(Si_{3}N_{4}/SiO_{2}\) platform has a lot to offer towards building high-throughput astrophotonic instruments on a chip.  
Another technique called ultra-fast laser inscription (ULI) or femto-second laser direct writing and is used for writing micron-scale structures in glass substrates [67]. ULI-based AWG has been demonstrated for the first time this year [68]. Thanks to the one-step fabrication process, this is a quick, scalable method with easier process control or repeatability. This technique has been demonstrated for building optical, NIR [69], and mid-IR devices (using chalcogenide glass substrates) [70; 71]. ULI allows fabrication of a 3D structures in glass without introducing additional steps, which opens a great avenue for integrating photonic lanterns, pupil remappers, beam combiners, and/or AWGs in a single glass block. However, the low-index contrast (\(\sim\)10\({}^{-3}\)) of ULI means the waveguide separation needs to be large to prevent crosstalk and the bending radii need to be large (\(\sim\)25 mm) to avoid high bending losses. This increases the size of the device, which could cause unwanted systemic variations. Despite these issues, with rapid advancements in this field, this versatile technique shows great promise for integrated photonic devices over wide wavebands.