esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Materials
### Data Source and Preprocessing  
The remote sensing data used in this study mainly come from the MODIS (moderate-resolution imaging spectroradiometer) data provided by the Google Earth Engine (GEE) platform. MODIS is a remote sensing product carried by the Terra and Aqua satellites; it is used to observe global climate change and biological activities. The MODIS data products used in this study are processed and analyzed synthetic images, with imaging time ranging from July to September between 2012 and 2020. This period represents the peak growing season for vegetation in the Xizang region and helps distinguish vegetation areas from bare land and snow-covered areas. Five types of MODIS data products (Table 1) were used in this study. These data products have high quality and have undergone radiometric and atmospheric corrections. However, due to geometric distortions, geometric correction was performed using the reproject and resample functions in the GEE platform. Other data processing tasks, such as mosaicking, mask extraction, and uniform resolution, were also implemented by coding in the GEE platform. Finally, the indicator data for the study area could be calculated.
## 2 Materials and Methods  
### Extract Preparation and LC-MS Analysis  
_S. horneri_ extracted with 70% ethanol was obtained from the National Marine Biodiversity Institute of Korea (Chungchunngam-do, Republic of Korea) and ParaJeju (Jeju-si, Republic of Korea). Fucoxanthin extraction, which is abundant in _S. horneri_, was previously shown to be better extracted using organic solvent than water [33], and the maximum fucoxanthin extraction rate from _S. horneri_ was obtained using ethanol [34]. Moreover, according to prior studies, 70% ethanol extract of _S. horneri_ has an anti-inflammatory impact on RAW264.7 cells [35]. Therefore, 70% ethanol was used for the extraction process in this investigation. The levels of total phenolics and flavonoids were measured using the Folin-Ciocalteu assay [36] and diethylene glycol colorimetric method [37]. Liquidchromatography-mass spectrometry (LC-MS) analysis was performed using an Agilent 6530 Infinity HPLC system (Agilent Technologies, Santa Clara, CA, USA) coupled with a hybrid quadrupole time-of-flight mass spectrometer [38].
## 4 Proposed Method
### Coverage Hole Repair Based on Mobile Node Redeployment  
After rotating the stationary sensor, we expanded the total coverage area of the sensors, but there are still coverage holes in the network. Therefore, the task of this stage is to repair coverage holes and achieve coverage enhancement by redeploying mobile nodes. Our mobile node redeployment plan consists of the following steps: using MEP-ISSA to search for repair paths, calculating the number of required mobile sensors, and redeploying mobile sensors.  
Similar to the previous section, we run the proposed algorithm in the control center (base station) and then broadcast the results to the sensors. Mobile sensors are scheduled for new locations based on the information obtained.
## 3 Results and Discussion
### FT-Raman Spectral Acquisition  
The spectra obtained with the hydroethanolic flowers extract (50:50) shown in Figure 9 display the strong influence of the compounds in this matrix [40]. The intense bands around 2975, 2931 and 2883 cm\({}^{-1}\) were assigned to C-H stretching of some compounds present in the plant extracts, but also of the CH\({}_{2}\) and CH\({}_{3}\) stretching of the ethanol [18], and were influenced by the other substances from the plant extracts. The peak at 1450 cm\({}^{-1}\) corresponded to the C-OH bending of ethanol, but also originated from the combination of bending vibration of CH\({}_{2}\) and the vibration of the COO- group in the flavanol and organic acids [41; 42].  
Figure 8: Principal component analysis of ethanol:water (50:50) extracts from the _A. delluta_ flowers in different maturation stages from two central regions of Portugal. A—region A, B—region B; EF—early flower, MF—mid flower, LF—late flower; TPC—total phenol content, TFC—total flavonoid content; GLUC—\(\alpha\)-glucosidase, ACET—acetylcholinesteraseerase; EcoliA—E.. coli ATCC 29998 TM, Saur—_S. aureus_ ATCC 43300 TM, Calbi—C.. _albičans_ ATCC 10231 TM, _Ecoli_ESA—_E. coli_ ESA37, SaurESA—_S. aureus_ ATCC ESA111, AB_r—amborictorin-B-resistant _Candidia albicans_ ESA 100.  
The band at 1270 cm\({}^{-1}\) was found to be due to the deformation vibration of O-C-H, C-C-H and C-O-H.  
The peaks at 1090 and 1050 cm\({}^{-1}\) were from the in-plane movement of the carbon ring and the substituents, which are regions linked to the characterization of alcohols, ethers, esters, carboxylic acids and anhydrides [43].  
The bands around 705 cm\({}^{-1}\) corresponded to the C-O and C-C-O stretching and O-C-O bending, and the bands in the 880 cm\({}^{-1}\) region were assigned to the C-H, C-O-H and CH\({}_{2}\) bending.  
Concerning the results of the PCA (mean centered) made with the spectral data (Figure 10) the separation between regions as observed for the analytical data is clear, as well as the separation between the maturation stages of the flowers.  
These results showed that Raman spectroscopy is a promising technique for discriminating between flower raw materials to identify possible differences in the biological activities of their extracts.  
Figure 10: Score plot of the first two principal components after the PCA performed on the Raman spectra recorded from Acacia flowers samples using the first derivative Savitzky–Golay spectra transform with 15 smoothing points.  
Figure 9: Raman spectrum, baseline corrected spectrum for Acacia flowers samples.  
To our knowledge, this is the first time that these extracts have been studied and these bioactivities were explored together to provide a possible new drug strategy in neurodegenerative treatments, among other possibilities, and to help to contribute to the control of an invasive plant.
###### Abstract  
This study investigates the relationship between geomagnetic activities and ionospheric scintillations, focusing on how solar and geomagnetic parameters influence ionospheric disturbances across varying time frames and latitudes. Utilizing indices such as Kp, Dst, sunspot numbers, and the F10.5 solar flux, we elucidate the dynamics influencing ionospheric conditions, which are vital for the reliability of satellite communications, particularly in low-latitude regions. Our analysis demonstrates a clear correlation between periods of high solar activity and increased geomagnetic disturbances, leading to heightened ionospheric scintillations, such as occurred during the solar maximum of 2015. In contrast, 2020--a solar minimum period--exhibited fewer disturbances, highlighting the impact of solar activity levels on ionospheric conditions. Innovatively employing ConvGRU networks, this research advances the modeling and prediction of ionospheric scintillations by integrating deep learning techniques suited to the spatiotemporal complexities of ionospheric data. The ConvGRU model effectively captures both temporal sequences and spatial distributions, offering enhanced accuracy in depicting ionospheric scintillation patterns crucial for satellite-based navigation and communication systems. Ground-based GNSS data from 121 stations across South America, collected during 2015 and 2020, provide a robust dataset for our analysis. The study highlights the influence of the solar cycle on ionospheric scintillations, with the years of maximum and minimum solar activity showing significant differences in scintillation intensity and frequency. Our evaluation of the ConvGRU models using statistical parameters demonstrated their potential for reliable ionospheric scintillation prediction. The research underscores the necessity of integrating adaptive mechanisms within models to effectively handle the dynamic nature of ionospheric disturbances influenced by external geomagnetic and solar factors. This study enhances the understanding of ionospheric scintillations and significantly advances predictive modeling capabilities using advanced machine learning techniques. The potential establishment of real-time alert systems for ionospheric disturbances could significantly benefit civilian applications, enhancing the operational efficiency of technologies reliant on accurate ionospheric information.  
global navigation satellite systems (GNSS); ionospheric irregularities; convolutional gated recurrent unit (ConvGRU); loss function; total electron content (TEC); amplitude ionospheric scintillation (S4) +
Footnote †: journal: remote sensing  
02024  
162757
# External Validation of Accelerometry-Based Mechanical Loading Prediction Equations
## 2 Materials and Methods
### Statistical Analyses  
The statistical analyses were performed using the R software (version 4.3.2; R Foundation for Statistical Computing, Vienna, Austria). The models selected to be tested in this study were as follows: [PERSON] et al., 2022 [20] and [PERSON] et al., 2014 [17] for walking and running, and [PERSON] et al., 2023 [21] for jumping. We predicted all mechanical loading variables from these studies, using the reported model coefficients, on our data. For the studies from [PERSON] et al., 2022 [20] and [PERSON] et al., 2023 [20], pGRF and pLR were predicted using the resultant vector and the vertical component on hip, lower back, and ankle accelerometers. For the study from [PERSON] et al., 2014 [17], only vertical pGRF using the hip accelerometer was predicted, as it was the only equation from their study that could be used with our data. The study from [PERSON] et al., 2022 [20] included walking and running on a force plate-instrumented treadmill, at 13 speeds, ranging from 2 to 14 km\(\cdot\)h\({}^{-1}\) (with 1 km\(\cdot\)h\({}^{-1}\) increments). [PERSON] et al., 2014 [17] tested walking and running on a 15 m straight path that included a force plate, with between 8 and 10 walking and 8 and 10 running self-selected speeds. [PERSON] et al., 2023 [21] tested several heights of drop jumps, box jumps, and continuous jumps.  
Accuracy indices, including mean absolute error (MAE), mean absolute percent error (MAPE), and root mean square error (RMSE), were computed. Bland-Altman plots were used to visually analyze agreement between predicted and measured values, with bias and limits of agreement calculated and plotted.
## 2 Materials and Methods
### Laboratory Investigations
#### 2.3.4 Ion Chromatography Analysis  
Prior to the analysis, the samples were dried in an oven at 60 \({}^{\circ}\)C for 24 h and then weighed using a Sartorius microbalance. The samples were placed in 20 mL vials containing 5, 10, or 20 mL (depending on the weight of the sample) of deionized water and shaken for 1 h to ensure the complete extraction of ionic compounds. The resulting solution was filtered using a 0.45 \(\upmu\)m filter and analyzed via ion chromatography (IC). The analysis was performed using an ICS1000 Thermo-Fisher instrument (Sunnyvale, CA, USA). Cations, Na\({}^{+}\) (sodium), NH\({}_{4}\)\({}^{+}\) (ammonium), K\({}^{+}\) (potassium), Mg\({}^{2+}\) (magnesium), and Ca\({}^{2+}\) (calcium) were determined using column ION PAC CS12A-4 mm, pre-column ION PAC CG12A-4 mm, and CSRS-ULTRA-4 mm suppressor. Methane sulfonic acid in isocratic mode was used as the eluent (20 mM, flow rate of 1.2 mL/min). Anions, F\({}^{-}\) (fluorine), Cl\({}^{-}\) (chloride), NO\({}_{2}\)\({}^{-}\) (nitrite), NO\({}_{3}\)\({}^{-}\) (nitrate), SO\({}_{4}\)\({}^{2-}\) (sulphate), C\({}_{2}\)O\({}_{4}\)\({}^{2-}\) (oxalate), and PO\({}_{4}\)\({}^{3-}\) (phosphate) were determined using column AS11-4 mm, pre-column AG11-4 mm, and ASRS-ULTRA 4 mm suppressor. Potassium hydroxide, generated by ECG40 EGC II KOH Thermo-Fisher in gradient mode (from 10 to 60 mM), was used as the eluent. All samples (S\({}_{1}\)-S\({}_{11}\), S\({}_{4b}\), S\({}_{5b}\), S\({}_{6b}\) and S\({}_{8}\)) were investigated with respect to their specific alteration phenomenology (Table 1 (S\({}_{1}\))).
## 2 Experimental Setup
### Gc  
A modified Agilent GC (7890B)/MS (5977B), similar to the one described by [PERSON] et al. [25], was used in this study. A thermal conductivity detector (TCD) and two flame ionization detectors (FID) were used for quantifying the stable gases (CO, CO\({}_{2}\), O\({}_{2}\), H\({}_{2}\) and H\({}_{2}\)O) and hydrocarbons (HC), respectively. Six different columns (Agilent) were integrated into the GC. Two PlotQs with lengths of 1.5 and 3 m and a MS5A (1.5 m) were used for the TCD. For the FIDs, the gas was split after a 30 m HP5 column using a Dean's switch. One detector was used to analyze short HC (\(\leq\)C\({}_{5}\)). Here, an additional 30 m PlotU was used. For analyzing longer HC (\(>\)C\({}_{5}\)), an additional HP5 (30 m) was used at the second FID.  
A quartz probe with an orifice diameter of \(d_{O}=1\) mm was used for sampling. A total of 15 storage loops enabled sampling at different heights above the burner (HAB) of a flame. The probe as well as the sampling system and analyzing routines are described in more detail in [25].  
Statistical (each measurement was repeated three times) and systematic uncertainties (calibration and gas standards) were considered to determine the overall uncertainty of the GC measurements. Depending on the sampling position, this resulted in an uncertainty of about 5% for the main species and 20-50% for the higher hydrocarbons.
beam was split using a beam splitter. One beam was directed into a reference detector. The other was directed through the flame into the main detector (both Thorlabs PDA100A). The signal from the reference detector was used to monitor and, if necessary, correct the temporal power variations of the laser. Mirrors and apertures were used to minimize background radiation from the flame incident on the detectors. The signals from the detectors were digitized and stored using a 16-bit data acquisition system (Nation Instruments NI USB-6361).  
Prior to the actual measurements, a reference measurement without flame had to be performed to determine the original light intensity without absorption (\(I_{0}\)). In addition, the background signal caused by the flame was determined. For the measurements, at least 30 s of data were recorded for each measurement point and then averaged. Soot volume fractions were calculated assuming a refractive index of 1.75 + 0.58i [27].
## 3 Materials and Methods
### Face Image Completion  
#### 3.5.1 GLCIC Algorithm  
The Global and Local Consistent Image Completion (GLCIC) algorithm is a deep convolutional neural network-based algorithm specifically designed for image completion tasks [14]. This algorithm consists of three neural networks: a completion network and two context discriminator networks. The completion network is used for image completion, while the two context discriminator networks, namely the global and local discriminators, are used to train the completion network to achieve realistic image completion. The global discriminator network takes the entire image as input, whereas the local discriminator network only takes a small region around the completion area as input. Both discriminator networks are trained to determine whether an image is real or completed by the completion network, while the completion network is trained to deceive these two discriminator networks. Only by jointly training these three networks can the completion network achieve the realistic completion of diverse images. Unlike other methods that focus on image generation, this method does not generate images from noise, which helps stabilize the initial training process.  
However, the GLCIC algorithm encountered several challenges while restoring the facial features of the Tang Dynasty terracotta figurines. Firstly, the number of existing Tang figurines is limited, making it difficult to support the large data requirements needed for training. Secondly, due to the erosion of time, most Tang female terracotta figurines' faces are nottled and damaged, with the main facial features generally missing or incomplete. Lastly, due to the characteristics of the completion network, the image completion is based on regional features, which can result in the smoothing of features with the surrounding area after completing the facial features of Tang female terracotta figurines, as shown in Figure 5. Figures 5 and 6 below show the results of training and restoring the existing images of Tang female terracotta figurines using the GLCIC algorithm. It can be seen that the main facial features of the Tang figurines have not been well restored.
## 3 Results
### PPG Sensors  
We further conducted preliminary tests to measure vital signs. The obtained PPG is displayed on an oscilloscope (Figure 11--top). The emitting green LED has the peak wavelength of 515 nm and peak wavelength of photodiode is 565 nm. The reflected light from transmissive LED is measured by APDS-9008. The raw signal needs to be pre-processed by filtering the noise and also amplification. The output signal is followed by a composition of RC as the first passive low pass filter (LPF). Furthermore, the signal is filtered by a high pass filter (HPF) by the second composition of CR. We have set the cut-off frequency in the range of approximately 0.07-3.4 Hz. Output signals of physiological parameters are in a very small range of amplitude (\(\upmu\)V). Therefore, to feed the signal to the microcontroller, we have adjusted the gain to 330. As the signal of PPG is in the range of 20-40 Hz, therefore, according to [PERSON] sampling theory, the sampling frequency must be greater than or equal to twice of original signal (\(f\geq f\)s). In order to meet the conditions of the theory and be in the safe side to restore the signal, the sampling frequency of the microcontroller has been set to 500 Hz. The detected peak of the PPG signal is 280 mV. In each iteration of 15 s, the PPG signal is acquired for 3 s. Except some saturation artefacts (Figure 11--top, left panel), the curves nicely correspond to the expected shape of a PPG signal (Figure 11--top, right panel), as described, for instance, in Reference [56].  
From our measurement, we can extract clearly the systolic amplitude, which indicate the pulsating changes in the volume of blood [57] and relate to stroke [58]; and the diastolic amplitude, which is mathematically the point where the first derivative is zero [59]. Furthermore, the peak-to-peak interval is easily derived from our measured data. The 180 ms correspond to the R-wave interval of the ECG signal [56]. The amplitude and time differences of systolic and diastolic amplitude (in our test 120 mV and 70 ms, respectively) provide valuable information for understanding the cardiovascular system [60], as this parameter changes on gender and age of the subject [61; 62].
# Weld Defect Detection of a CMT Arc-Welded Aluminum Alloy Sheet Based on Arc Sound Signal Processing
## 2 Materials and Methods
### Customized CNN Model
#### 2.3.2 The Customized CNN Model  
The customized CNN model is shown in Figure 1. The CNN model consists of three convolutional blocks and three fully connected layers. Each convolutional block includes a convolutional layer, a max pooling layer, and a batch normalization layer. The first convolutional layer uses 16 kernels (size: 3 \(\times\) 3) to convolve with the n-channel time-frequency spectrum (size: 128 \(\times\) 50), producing 16 feature maps (size: 128 \(\times\) 50). The kernels' stride was 1 pixel in both the horizontal and vertical directions. Similarly, the second convolutional layer produces 32 feature maps, and the third convolutional layer produces 32 feature maps. A pooling layer following each convolutional layer uses max-pooling function to reduce the dimensions of the feature maps. There is a batch normalization layer following each pooling layer. Finally, the fully connected layer is used for classification.
# Evaluation of Nitrogen and Phosphorus Removal
## 4 Discussion
### Phosphorus Removal Performance  
#### 4.3.1 Treatment Tanks with MBBR  
Throughout the sampling season, moderate removal occurred in the entire system for both TP and SRP, as measured in both mass loads and concentration reductions (Table 2 and Figure 5). Analysis of the subset of storms without tank bypass storms suggest that most of the load removal occurred in the bioreactors, as opposed to the treatment tanks with MBBR (Table 3 and Figure 6). Phosphorus concentrations and loads generally increased from System Inflow to Tank Outflow locations. This supports the hypothesis that undiluted silage leachate enters the tanks between monitored storm events, increasing nutrient concentrations in Tank 1, and subsequently the concentrations entering the bioreactors from Tank Outflow.
# The World of Cyclic Dinucleotides in Bacterial Behavior
### GGDEF Domain Structure and Catalysis
(**A**) residue frequency in GGDEF domains. Using the Dali server [79], 23 sequences of GGDEF domain structures were used to create a multiple sequence alignment, and the sequence logo was created with the WebLogo server [90]. The sequence shown below the logo and the secondary structure elements belong to PleD of _Caulobacter vibrioides_ (PDBID: 2V0N). Residues colored in red are involved in ligand or magnesium binding (for underlined residues, only the main chain is involved) and those colored in green are located in the I-sites. The GGDEF motif is placed in a red box. On the right, the structure of the GGDEF domain of PleD is shown as a cartoon. The topology of GGDEF is shown below the structure, and the CATH topology name and code are also shown [91]; (**B**) interaction network between the GGDEF domain of PleD binding pocket with the substrate, GTP. In the bottom, the PleD structure in the inactive conformation is shown, in which the two inhibitory sites are shown (I-site and I’-site). On the right, it is shown in more detail the residues involved in the (c-di-GMP)\({}_{2}\) interactions at the inhibitory sites. Gray dotted lines represent hydrogen bonds. The magnesium ions are colored in green. GTP and the protein residues involved in its binding are shown as sticks. Carbons are colored white, oxygens are red, nitrogen atoms are blue, and phosphorous atoms are orange. Some GGDEF domains have diverged from the canonical GG(D/E)EF amino acid sequence motif and are described as degenerate GGDEF domains, due to the loss of their catalytic activity. These degenerate GGDEF domains can evolve to possess different biological functions, and two examples have been described in the literature: 1-a degenerate GGDEF domain that is a sensor domain and binds GTP to activate the phosphodiesterase active in the neighboring EAL domain of the _Caulobacter crescentus_ CC3396 protein [38]; and 2-a degenerate GGDEF domain of the _Bacillus subtilis_ YybT protein that has unexpected ATPase activity [92]. Allosteric inhibition in proteins containing GGDEF domain. The DGC activity of proteins containing GGDEF domain are inhibited by an allosteric noncompetitive product inhibition. A GGDEF dimer contains two symmetrical allosteric sites (I and I' sites), in which each allosteric site binds a c-di-GMP dimer (c-di-GMP)\({}_{2}\) (Figure 3B). Both sites are formed by four residues, three of them from one GGDEF molecule, the RxxD motif (R359 and D362 of PleD) and an arginine (R390 of PleD), and the fourth residue is an arginine from the adjacent GGDEF molecule (R313 of PleD). The two (c-di-GMP)\({}_{2}\) dimers are expected to crosslink allosteric sites on opposite GGDEF domains, resulting in their immobilization in an inactive orientation [70,80,87,93] (Figure 3B). The RxxD motif and the positively charged residue (R390 in the case of PleD) are conserved in GGDEF members (Figure 3A).
# Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio
## 3 Design of Reconfigurable P-DFTFB  
This section provides the design of improved P-DFTFB for real signals and the reconfiguration method of RFB.  
Figure 1: Rough estimation and fine sensing of wideband spectrum.  
Figure 2: Wideband spectrum sensing with a RFB.