esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experiment Site and Data Collection
#### 2.1.1 Eddy Flux and Micrometeorological Measurements  
The eddy covariance system (IRGASON, Campbell Scientific, Logan, UT, USA) with an installation height of 2.85 m collected the 10 Hz three-dimensional wind speed and direction, ambient CO\({}_{2}\) and H\({}_{2}\)O density, sonic temperature, and air pressure. The corresponding meteorological and soil elements, including air temperature and humidity (HMP155A, Vaisala Oly, Vantaa, Finland), incoming outgoing shortwave radiations (CNR4-L, Kipp & Zonen, Delft, The Netherlands), four-layer soil moisture, and temperature at 5 cm, 10 cm, 20 cm, and 40 cm (TDR310N, Acclima, Meridian, ID, USA) were measured.  
The 30 min eddy fluxes, including net ecosystem exchange (\(NEE\)), latent heat flux (\(LE\)), sensible heat flux (\(H\)), and friction velocity (\(u\)-), were generated from 10 Hz raw data of the EC system using the EddyPro software v7.0.9 (www.licor.com/). The data quality of eddy flux was evaluated using the scheme proposed by [PERSON] et al. [(43)], which classifies the flux data into 9 levels. The data labeled as levels 7-9 were regarded as low-quality and removed. In addition, the flux data were dropped when \(u\)- was less than a specific threshold that indicated the period of low turbulent mixing. The \(u^{*}\) threshold was calculated for each season using the moving point method [(43)]. The seasonal \(u\)- threshold is shown in Table S1 (see Supplementary Material Section S2). Then, the missing flux data were filled using the marginal distribution sampling method [(44)]. Finally, the 30 min \(NEE\) observation was partitioned into \(GPP\) and ecosystem respiration (\(R_{co}\)) using the night-time flux partitioning method [(44)]. The processes of \(u\)- filter, gap filling, and flux partitioning were conducted using the REddyProc package [(45)].  
To determine the spatial extent of the EC system measurement, the flux footprint was calculated using the Kljun model [(46)], which requires the standard deviation of lateral velocity fluctuations (\(\sigma_{y}\)), friction velocity (\(u\)-), Obukhov length (\(L\)), wind speed (\(u\)), and boundary layer height (\(bll\)) (see Supplementary Material, Section S2). \(\sigma_{y}\), \(u_{*}\), \(L\), and \(u\) were measured from the EC system. There was no direct measurement of \(bll\), so we adopted the \(bll\) data from ERA5 hourly data on singe-level products ([[https://cds.climate.copernicus](https://cds.climate.copernicus)]([https://cds.climate.copernicus](https://cds.climate.copernicus)).  
Figure 2: The location of the experiment site. The red five-pointed star represents the EC system. The contour lines of the footprint during the growing period in (3) are shown in steps of 10% from 10 to 90%. The background maps in (**a,b**) are RGB images from Google Earth. The background map in (c) is the RGB image from the UAV platform.  
eu/datasets/reanalysis-era5-single-levels, accessed on 17 October 2024) in this study. Then, an 80% footprint was regarded as the measurement range of the EC system.
# Adaptive Beamforming Damage Imaging of Lamb Wave Based on CNN
## 3 Adaptive Beamforming Model Based on CNN
### Network Training Strategies  
The network is trained using the Adam optimizer [24], an adaptive learning rate algorithm that combines the advantages of momentum-based gradient descent and RMSProp. Adam dynamically adjusts learning rates for each parameter by estimating the first and second moments of gradients, enabling efficient convergence even with sparse or noisy data. The initial learning rate is set to \(1\times 10^{-4}\), which balances the training stability and convergence speed for our task. The training process optimizes the loss function by processing each random batch, which contains all the pixels from a single image. The stochastic training strategy ensures that the sequence of data provided to the network in each training round is randomized. The total training loss of the network includes the image loss, which is designed to promote similarity between the target image and the image generated by the network. Since the proposed CNN model predicts continuous weighting factors rather than discrete classes, regression metrics are used instead of classification metrics like confusion matrices. The traditional MSE loss function is computationally efficient and straightforward to optimize, measuring the error magnitude by calculating the squared difference between predicted and true values. This approach not only reduces noise sensitivity but also provides a robust reflection of the model's performance, aligning well with the regression nature of the task. Therefore, the network training image loss function used is MSE, denoted as \(\mathrm{L}_{\mathrm{MSE}}\).
_applied_  
_sciences_  
_Article_  
Continuous Adsorption of Acid Wood Dyes onto an Activated Carbon Prepared from Pine Sawdust  
**[PERSON] \({}^{\copyright}\), [PERSON] \({}^{\star}\)\({}^{\copyright}\), [PERSON] \({}^{\copyright}\) and [PERSON] \({}^{\copyright}\)**  
Department of Chemical Engineering, School of Engineering, Universidade de Santiago de Compostela,  
15782 Santiago de Compostela, Spain; [PERSON]@usces (C.H.P.);  
[PERSON]@usces (D.G.-D.); [PERSON]@usces.cs (J.G.-A.)  
\({}^{\star}\) Correspondence: mariasonia.freire@usces; Tel.: +34-888186758  
**Featured Application: This study aimed to synthesize a novel biomass-based, chemically activated carbon from _Pinus radiata_ sawdust and to investigate its effectiveness in the removal of wood dyes from aqueous solutions in a fixed-bed column.**  
**Abstract: In this paper, an activated carbon obtained from _Pinus radiata_ sawdust is applied to remove blue, red, and black wood dyes from aqueous solutions in a fixed-bed column. The flow rate (7.7-30.8 mL min\({}^{-1}\)), initial dye concentration (25-500 mg L\({}^{-1}\)), and bed height (2-4 cm) highly influence the breakthrough-curves' features. The results indicate that the adsorption capacity increased by decreasing the flow rate and increasing the initial dye concentration, except for the black dye, and increasing bed height, except for the red dye. In addition, the breakthrough time changed by modifying the studied variables. The curves became steeper as the flow rate increased and as the bed height decreased. Also, by increasing the inlet dye concentration, the breakthrough time decreased significantly, and sharper breakthrough curves were obtained. The activated carbon with a surface area of 2826 m\({}^{2}\) g\({}^{-1}\) led to high values of the adsorption capacity between 150 and 1300 mg g\({}^{-1}\). The Yoon-Nelson and [PERSON] models were the ones that best described the adsorption data. The activated carbon saturated with black dye could be used in three successive cycles after regeneration with H\({}_{2}\)O\({}_{2}\).**  
**Keywords:**pine sawdust; wood dyes; fixed-bed adsorption; breakthrough curves; kinetic models; regeneration
## 6 Investigating Several RL-Based Routing Methods
### [PERSON] et al. [PERSON] et al. in [66] have suggested a cluster-based routing protocol for VANETs. In this approach, it is assumed that each vehicle exchanges its information such as location and speed through hello messages with neighboring vehicles. In the first step, a decentralized clustering approach based on fuzzy logic is designed to choose CHs. In this fuzzy system, there are three inputs, including mobility factor (MF), leadership factor (LF), and signal quality factor (SQF). The mobility factor is obtained from the velocity of vehicles in the network. The leadership factor is evaluated with regard to the density of nodes moving in the same direction of the current vehicle and the signal quality factor is also computed based on the hello reception rate. These parameters are extracted from the hello message. Upon receiving hello message, each node gets the fitness value of itself and its own single-hop neighbors using the fuzzy system. Then, a node with the highest fitness is chosen as a CH. It announces itself as the CH node through the hello message. In this clustering process, the communication overhead for joining/leaving clusters is zero because this scheme does not require the joining/leaving messages for maintaining the information of cluster member nodes. Moreover, cluster head nodes can be directly connected with other neighboring CH nodes. In the next step, a coalitional game theory-based model implements the clustering process to select a better path. In this model, players indicates single-hop neighbors, and each coalition value expresses an agreement between vehicles to use the cluster-based transmission process. The coalition value is determined by the average collision probability. Initially, each RSU initializes payoff and sends it to CHs. Ultimately, the payoff is evenly allocated by each CH to its single-hop neighbors. In this process, if a vehicle is close to RSU, it receives the payoff directly from the RSU. Otherwise, it must choose a CH vehicle that is closest to the RSU. Note that vehicles tend to send their data through CHs because the payoff is only distributed by CHs and RSU. This reduces the number of transmitter nodes and improves the network throughput. This payoff evaluates routing paths in the RL-based routing operation. In the RL-based route selection process, the network nodes are considered as the learning agent that learns the network environment by exchanging the hello message. The action is the choice of the latter vehicle. This learning structure is depicted in Figure 21. Each vehicle stores a Q-table that is refreshed by hello messages. Note that the Q-value is related to the number of hops, the payoff value obtained from the game theory, and the signal quality factor (SQF). Moreover, fixed values are considered for the learning rate and the discount factor.  
Figure 20: Learning structure in RSAR.
## 2 Materials and Methods
### Methodology  
The study methodology included the following steps:  
1. Acquisition of raw data for satellites Landsat 2, 8 and Sentinel 2 of the Marshlands area of the US Geological Survey site [22]. Landsat 2 images were obtained on 10 June 1977, and 3 August 1977; because there were no serial images. Landsat 8 images were also 2 October 2017 and 11 October 2017. Sentinel 2 was obtained on 2 and 3 October 2017.
2. Merging the spectral bands of images for the study area to obtained composite images.
3. Mosaic for the images of each satellite separately to get one large image while keeping all the data in it.
4. Cutting the study area (Clip) from the resulting image of the mosaic.
5. Calculation of the NDVI for each satellite image separately. This indicator is one of the most useful methods for monitoring and distinguishing vegetation from water. It depends on the relationship between the spectral bands NIR and Red band R, due to the high reflectivity of the plant in the NIR range and its low reflectivity in R (Red band) for the VIS spectrum. The value of the NDVI is between \(-1\) and \(+1\), where the value from \(-1\) to \(0\) represents water and other surface types, while the plant represents values from \(0\) to \(+1\). NDVI is calculated according to the following Equation [23]: \[NDVI=\frac{NIR(band)-R(band)}{NIR(band)+R(band)}\] (1)
6. The ArcGIS 10.4.1 program is used to perform the supervised classification process, which is the division of the image according to their digital elements (DN) values. This requires the availability of samples representing the categories of classification in certain areas of the image and the production of land cover maps for the study area. The calculation of the number of changes in the area of water and plants in the marshes of southern Iraq for 1977 and 2017
## 3 Results and Discussion
### The Impact of Roasting on Primary Metabolites Patterns
Strecker degradation of amino acids in the presence of \(\alpha\)-dicarbonyls form 2-/3-Methylbutanal and phenylacetaldehyde from Ile, Leu, and Phe, respectively [56]. The intermediates from Strecker reaction, \(\alpha\)-amino carbonyl compounds, can dimerize to dihydropyazines which can either oxidize into pyrazines or further react with aldehydes to form substituted pyrazines [57]. Reactions between Ala, Arg, Lys, Pro and Orn, and de  
Figure 3: Principal component analysis (PCA) including all analyzed samples (36 runs) and based on % response (TIC 2D volumes) of re-aligned 2D peaks the feature template. (**A**) corresponds to 430 UT peaks while in (**B**) the computation is based on aroma precursors (see Table 2). oxyosomes (i.e., sugar degradation products) form 2-acetyl and 2-propionylpyrroline that can be further oxidized to pyrrole derivatives [2]. As detailed in Section 3.2, aroma precursors have characteristic signatures in raw hazelnuts (Figure 3A green indicators, larger intra-class variability) with strong correlations to cultivar and origin, but when lab-scale roasting is applied, they start to react and the resulting distribution patterns in roasted and over-roasted samples are more homogeneous. Roasting is a variable that dominates phenotype variations of key-aroma precursors. Finally, within roasted samples, the impact of post-harvest drying, i.e., E1 vs. E2 conditions, was analyzed. The variable importance in projection (VIP) selection, conducted on roasted samples (23 runs) and considering all targeted analytes (\(n=80\)), highlighted the presence of glucose (glucose 5 TMS), fructose (fructose 5 TMS syn- and anti-), 3-\(\alpha\)-mannobiose 8 TMS, and threonine and malonic acid within the first 10 variables with the highest ranking (high VIP value and low standard deviation-SD). In particular, sugars were present in higher relative amounts for E1 drying, conducted in-field at higher temperatures, while acids were present in higher amounts for low-temperature drying (E2 conditions). These data should be interpreted in the light of previous results on volatiles signatures from raw and roasted hazelnuts (Tonda Romana and Ordu) submitted to post-harvest drying in similar conditions [55]. Low-temperature drying (E2) enabled effective stabilization of kernels along 12 months of storage and higher relative amounts of key odorants were developed during roasting. In contrast, E1 drying was correlated with an early incidence of autoxidation on raw hazelnuts that, from one side, developed higher amounts of linear saturated and unsaturated aldehydes from fatty acids hydroperoxides decomposition, while, on the other side, when roasted along shelf-life checkpoints, resulted in weaker flavor profiles with lower amounts of key odorants. Figure 4: Histograms illustrating the % response evolution across the roasting profile for targeted metabolites. Error bars correspond to \(\pm\) SD over samples’ classes (raw-green bars; roasted-pink bars; over roasted-blue bars).
# A Multi-Band LNA Covering 17-38 GHz in 45 nm CMOS SOI
## 3 Measurement Results  
The LNA is designed and implemented in the 45-nm CMOS SOI process. Figure 13 shows the die micrograph of the LNA, occupying a chip area of 0.94 \(\times\) 0.34 mm\({}^{2}\). The power consumption is 59 mW from 1-V supply. The chip is tested under the probe station in the measurement process. The input and output of the LNA are directly connected to the test instrument through GSG RF pads on the chip using Cascade RF probes. The block diagram of the measurement setup is shown in Figure 14. An Agilent E3646A DC source provides a power supply of 1 V. An Agilent N5245A Vector Network Analyzer (VNA) is used to measure the S-parameters, gain, and NF. The connection cables and probes are calibrated before measurement to ensure the accuracy.  
Figure 12: S11 of LNA with switch capacitors in different states.  
Figure 13: The die micrograph.  
Figure 15 shows the measurement results of the S21 and NF in 17-21 GHz, 21-28 GHz and 28-38 GHz. The maximum gain in each band is 18.3 dB, 19.5 dB and 23.0 dB, while the minimum NF is 5.2 dB, 4.7 dB and 4.0 dB, respectively. The measured 1-dB gain bandwidth is 3 GHz, 4.7 GHz and 5.4 GHz, respectively, in each operating band. Table 1 summarizes the maximum gain, and the minimum NF.  
Figure 14: The measurement setup.  
Figure 15: _Cont._  
Figure 16 shows the synthesis of gain and NF measurement results in three bands exhibiting that the LNA can provide high gain and low NF across the full frequency band. A figure of merit (FOM) for evaluating the performance of a broadband LNA is defined as Equation (2).  
\[FOM[\frac{GHz}{mW}]=\frac{S_{21}[mag]*BW[GHz]}{(NF-1)[mag]*P_{DC}[mW]} \tag{2}\]  
Table 2 compares the performance of this design with the state-of-the-art wideband LNAs. The LNA in [9], which can operate under two supply voltages, has a low power consumption but low gain and a narrow band. The works [16; 17; 23; 24] also adopted the cascode structure. However, [16; 23] used low gain, [17] had narrow bands and [24] had an inferior NF. It is evident that this work achieves the best FOM of 4.9 GHz/mW. The  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency Band (GHz)** & **Maximum Gain (dB)** & **Minimum NF (dB)** \\ \hline
17–21 & 18.3 & 5.2 \\
21–28 & 19.5 & 4.7 \\
28–38 & 23.0 & 4.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance summary.  
Figure 16: The synthesis of (**a**) gain and (**b**) NF measurement results in three bands.  
Figure 15: The measured gain, and NF at (**a**) 17–21 GHz, (**b**) 21–28 GHz and (**c**) 28–38 GHz.  
proposed LNA obtains high gain, low NF, and high FOM with a wide operating band covering 17-38 GHz.
## 3 Results and Discussion
### ICA on Cattle Sera with QD Amplification
When we tested 50 sera obtained from animals without signs of brucellosis, false-positive results were not observed in any of the assay systems. To characterize cross-reactions with other pathogens, washed preparations of alternative microbial cells were immobilized on the working membrane. _Br. Melitensis, Br. Suis, Yersinia enterocolitica_ O:9, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cattle Sera**} & \multicolumn{6}{c}{**Label**} \\ \cline{2-7}  & \multicolumn{2}{c}{**GNPs**} & \multicolumn{2}{c}{**QDs**} & \multicolumn{2}{c}{**GNPs + QDs**} \\ \cline{2-7}  & \multicolumn{6}{c}{**Detection**} \\ \cline{2-7}  & **Instrumental \({}^{*}\)** & **Visual** & **Instrumental \({}^{*}\)** & **Visual** & **Instrumental \({}^{*}\)** & **Visual** \\ \hline
1 & 0.0 & \(-\) & 0.6 & +/\(-\) & 1.8 & + \\
2 & 0.2 & +/\(-\) & 1.8 & + & 3.7 & + \\
3 & 0.5 & +/\(-\) & 1.2 & + & 4.8 & + \\ Cutoff & 0.0 & \(-\) & 0.7 & +/\(-\) & 1.3 & + \\ Negative serum & 0.0 & \(-\) & 0.0 & \(-\) & 0.0 & \(-\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of digitization of images of test systems for immunohistochemical serodiagnostics of cattle brucellosis. Figure 5: Testing of diluted standard serum sample by immunohistochemicalographic systems for serodiagnostics of cattle brucellosis with GNPs (**A**) and GNPs+QDs (**B**). AZ—analytical zone; CZ—control zone. Y. enterocolitica 287, Francisella tulareis, Vibrio cholerae Inaba, Salmonella enteritidis yena, S. typhimurium TA 100, Escherichia coli 565, E. coli 113-3, Shigella sonne, [PERSON]. [PERSON], Staphylococcus aureus and Citrobacter freundii were used for this purpose at concentrations of 10\({}^{9}\) cells/ml. The only causative agent of yesriniosis Y. enterocolitica O:9 gave nonspecific staining for cattle sera testing, whereas, all other compounds did not demonstrate cross-reactivity. The reason for the observed exception is the similarity of the O-specific polysaccharide, which is part of the LPS structure of Br. abortus 19 and Y. enterocolitica O:9 [41; 42].
# Parameterised Enumeration for Modification Problems +
## 2 Preliminaries
### Graph Modification Problems
For this problem, a solution is a set of edges which have to be added to the graph to make the graph triangular. Observe that, in this special case of the modification problem, the underlying property \(\mathcal{P}\), \"to be triangular\", does not have a finite forbidden set characterisation (since cycles of any length are problematic). Nevertheless, we will see later, that one can efficiently enumerate all minimal solutions as well. A cluster is a graph such that all its connected components are cliques. In order to transform (or modify) a graph \(G\) we allow here only two kinds of operations: Adding or removing an edge. Cluster-Editing asks, given a graph \(G\) and a parameter \(k\), whether there exists a consistent set of operations of cardinality at most \(k\) such that \(S(G)\) is cluster. It was shown by [PERSON] et al., that the problem is NP complete [14]. The problem Triangle-Deletion asks whether a given graph can be transformed into a triangle-free graph by deletion of at most \(k\) vertices. [PERSON] has shown that the problem is NP complete [16]. Analogous problems can be defined for many other classes of graphs, e.g., line graphs, claw-free graphs, Helly circular-arc graphs, etc., see [30]. Now, we turn towards the main focus of the paper. Here, we are interested in corresponding enumeration problems with ordering. In particular, we will focus on two well-known preorders, lexicographic ordering and ordering by size. Since our solutions are subsets of an ordered set of operations, they can be encoded as binary strings in which the \(i\)th bit from right indicates whether the \(i\)th operation is in the subset. We define the lexicographic ordering of solutions as the lexicographic ordering of these strings. Then, the size of a solution simply is its cardinality. \begin{tabular}{l l} \hline \hline
**Problem:** & Enum-\(\mathcal{M}_{\mathcal{P}}^{\text{\tiny{SE}}}\) \\ \hline
**Input:** & \((G,k,O)\), \(G\) undirected graph, \(k\in\mathbb{N}\), \(O\) ordered set of operations on \(G\). \\
**Parameter:** & The integer \(k\). \\
**Output:** & All solutions of \((G,k,O)\) with respect to \(\mathcal{P}\) in lexicographic order. \\ \hline \hline
**Problem:** & Enum-\(\mathcal{M}_{\mathcal{P}}^{\text{\tiny{SE}}}\) \\ \hline
**Input:** & \((G,k,O)\), \(G\) undirected graph, \(k\in\mathbb{N}\), \(O\) set of operations on \(G\). \\
**Parameter:** & The integer \(k\). \\
**Output:** & All solutions of \((G,k,O)\) with respect to \(\mathcal{P}\) in non-decreasing size. \\ \hline \hline \end{tabular}   
If the context is clear, we omit the subscript \(\mathcal{P}\) for the graph modification problem and simply write \(\mathcal{M}\). Furthermore, we write \(\text{Sol}_{\mathcal{M}}(x)\) for the function associating solutions to a given instance, and also \(\mathcal{S}_{\mathcal{M}}\) for the set of all solutions of \(\mathcal{M}\).
## 2 Materials and Methods
### Equipment Instrumental Analysis
#### 2.4.4 Matrix-Assisted Laser Desorption coupled to Time-of-Flight Mass Spectrometry (MALDI-TOF MS)  
The MALDI-TOF MS analyses were performed on an Autoflex III MALDI TOF-TOF from Bruker Daltonics (Bremer, Germany) with a smartbeam-I laser. All spectra were acquired in the positive-ion reflector mode at an accelerating voltage of 25 kV. Trans-2-[3-(4-terbutylphenyl)-2-methyl-2-propenylidene]malononitrile (DCTB), 20 mg/mL, was used as MALDI matrix, sodium trifluoroacetate (NaTFA), 10 mg/mL, as the cationization agent, and tetrahydrofuran (THF) as dissolvent. For the analysis, the preparation made was matrix/sample/cationization agent (10:2:1). Bruker FlexControl software was used for data acquisition and Bruker FlexAnalysis software for data processing. Ions in the mass range (\(m/z\)) of 350\(-\)4000 were detected. Peptide calibration standard II from Bruker was used as external calibrant, enabling a mass accuracy of about 10 ppm.