esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### NEP Maps  
The NEP maps were combined into merged cropland and grassland products and summarized to quantify NEP in the U.S. Great Plains at weekly, seasonal, and annual time steps. Specifically, four groups of maps were produced from the merged weekly grassland and cropland NEP maps: (1) mean weekly NEP; (2) annual NEP; (3) mean seasonal NEP; and (4) mean annual NEP. Each of these maps represented the cumulative NEP (in g C\(\cdot\)m\({}^{-2}\) specified time-period\({}^{-1}\)) using ecological-based notation [3; 10; 11], where negative values indicate areas with higher RE than GPP (C source) and positive values indicate areas with higher GPP than RE (C sink). The annual maps (Figure 5) showed the level of variability of cumulative NEP from one year to the next [67], while the mean seasonal (Figure 6) and weekly NEP maps captured the intra-annual dynamics of NEP. The mean annual 2000-2008 map (Figure 7) proved useful in identifying the prevalent NEP condition during 2000-2008.  
To quantify the spatial variation of multiple year NEP uncertainty, a regression tree model was developed to estimate leave one site out cross validation accuracies from vegetation type (crop or grass), soil organic carbon, 30-year mean annual precipitation, and mean annual NEP. This three member committee Cubist model (two rules for each committee model) predicted the leave-one-out site RMSE with a training MAD of 0.18 g C\(\cdot\)m\({}^{-2}\).day\({}^{-1}\) (\(r\) = 0.91) and a random 10-fold cross validation MAD of 0.22 g C\(\cdot\)m\({}^{-2}\).day\({}^{-1}\) (\(r\) = 0.87). The similarity between the training and cross validation MADs implies minimal over fitting tendencies. The resultant RMSE map (Figure 8) was masked by crop and grassland areas and stratified (see legend) by the percentage the three input variables exceeded the data values observed at the flux tower locations (extrapolation index). RMSE tends to be higher in cropland areas and lower in grassland areas similar to the results shown in Table 5, but also reflects grassland domination in drier western portions of the Great Plains. Also, western agriculture is often dominated by irrigation, which results in higher production, NEP, and RMSE.  
These maps present an important NEP archive of a large portion of the U.S. Great Plains for the 2000-2008 time period and, when coupled with the following statistical analysis quantifying and comparing NEP behavior in the U.S. Great Plains, could be utilized for various carbon-cycle-based analyses.  
Figure 4: Grassland NEP mapping model randomized test and training mean (from nine replications) error terms (MAD) with varying test sizes.  
Figure 5: Cumulative annual NEP of the U.S. Great Plains for 2000–2008 [67]. Data published at [68].
Structural Health and Condition Monitoring with Acoustic Emission and Guided Ultrasonic Waves: What about Long-Term Durability of Sensors, Sensor Coupling and Measurement Chain?  
[PERSON]  
Empa, Swiss Federal Laboratories for Materials Science and Technology, CH-8600 Dubendorf, Switzerland; [EMAIL_ADDRESS]  
Footnote †: Abstract  
Acoustic Emission (AE) and Guided Ultrasonic Waves (GUWs) are non-destructive testing (NDT) methods in several industrial sectors for, e.g., proof testing and periodic inspection of pressure vessels, storage tanks, pipes or pipelines and leak or corrosion detection. In materials research, AE and GUW are useful for characterizing damage accumulation and microscopic damage mechanisms. AE and GUW also show potential for long-term Structural Health and Condition Monitoring (SHM and CM). With increasing computational power, even online monitoring of industrial manufacturing processes has become feasible. Combined with Artificial Intelligence (AI) for analysis this may soon allow for efficient, automated online process control. AI also plays a role in predictive maintenance and cost optimization. Long-term SHM, CM and process control require sensor integration together with data acquisition equipment and possibly data analysis. This raises the question of the long-term durability of all components of the measurement system. So far, only scant quantitative data are available. This paper presents and discusses selected aspects of the long-term durability of sensor behavior, sensor coupling and measurement hardware and software. The aim is to identify research and development needs for reliable, cost-effective, long-term SHM and CM with AE and GUW under combined mechanical and environmental service loads.  
Acoustic Emission; Guided Ultrasonic Waves; Structural Health Monitoring; Condition Monitoring; process monitoring and control; long-term durability; sensor coupling; measurement hardware and software; equipment maintenance
## 3 Experimental Section
### Quantification and Characterization by HPLC-ELSD/MS  
Lipid composition and FAME content were analyzed as previously described [24], using an HPLC (Surveyor LC Pump and Autosampler Plus, Thermo Finnegan, USA) coupled to both ELSD (Sedere Sedex 75) and quadrupole mass spectrometer (Thermo Finnigan MSQ) using a 10:1 line splitter (Analytical Instruments, USA). Xcalibur software controlled operation of the autosampler, pump, and mass spectrometer. ELSD analog data was acquired through an A/D data acquisition box (Agilent Technologies, SS420X) and RS232 PCI data acquisition card (Sea Level Systems, 7406S).  
Lipid standards and extracts were resolved using a YMC Pack PVA-Sil-NP column (250 mm \(\times\) 4.6 mm I.D., 5 \(\upmu\)m bead size) protected by a Waters Guard Pak(tm) guard column containing Nova-Pak(tm) silica inserts. The solvent program is given in Table 1. ELSD was run at 30 \({}^{\circ}\)C at gain setting 8. Mass spectrometer was run in APCI positive mode with probe temperature of 400 \({}^{\circ}\)C.
# An Energy-Efficient Integration of a Digital Modulator and a Class-D Amplifier
## 4 Block Diagram of the Integrated System
### Integrated System  
The block diagram of the class-D amplifier is shown in Figure 3. The first block in the diagram is a PW-modulator. The switching block consists of two metal-oxide semiconductor field-effect transistor (MOSFET) and the corresponding gate drivers. If the three diagrams in Figures 1-3 are compared, we realize that if the low-pass filter is replaced by a band-pass filter, the high-power output signal will be the carrier modulated in phase by \(v_{in}\). To improve power efficiency of the wireless communication system, a question arises: As the class-D amplifier already contains a modulator, is it possible to directly drive the PW-modulator with a signal that, when band-pass filtered, becomes the PSK modulation of the digital input? The idea is shown schematically in Figure 4[15]. Obviously, the coder blocks in the upper and lower parts of Figure 4 will have to be different.  
The coder and the PW-modulator blocks of the lower part of Figure 4 have already been developed [16]. They can be implemented either with a microcontroller or a digital signal processor (DSP) unit with the architecture of Figure 5. The frequency of the clock input is N times larger than the sampling frequency. Additionally in [17], it has been used to directly feed the switching circuit of the class-D power amplifier in audio applications. The question now is how to obtain the FSK-modulated signal. At this point, we realize that by using a rectangular carrier, we can easily obtain a PSK output instead of an FSK.  
Figure 4: Block diagram of a PSK modulator that drives a class-D power amplifier [15].  
Figure 3: Block diagram of a half-bridge class-D power amplifier.  
Figure 2: Switching-circuit phase modulator [10].  
The proposed integrated PSK modulator is sketched in Figure 6. The input data bits are converted to a bipolar voltage signal that eventually multiplies a square bipolar carrier, changing its phase according to the input bit. The square signal then drives the power bridge of the class-D amplifier, whose output goes through a band-pass filter centered at the frequency of the bipolar carrier, to obtain a PSK-modulated signal.
## 3 Simulation Scheme
### Basic Model  
The traditional five-point well pattern layout is used as the research object, and a quarter of the extraction area near the bottom of the well is selected as the simulation area (Figure 6). The actual size is 100 \(\times\) 100 m. A two-dimensional intercept line is set on the diagonal of the model, and observation points B and C are set near the injection well and the production well. The coordinates of point B and point C are (20 m, 20 m) and (80 m, 80 m) respectively. The initial formation pressure of the coal seam is 1.6 MPa, and the temperature of the coal seam is 289 K. The gas injection boundary condition is constant pressure boundary condition. The production well is connected to the surface, and the bottom hole flowing pressure is 0.1 MPa. Table 5 illustrates the numerical simulation core parameters.  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Plan** & **Model** & **Coupled Mode** & **Question** \\ \hline A & \(CBM\) & H-M coupling & Effect of \(CO_{2}\) Injection on \\  & \(CO_{2}\)-\(ECBM\) & H-M coupling & \(CBM\) Exploitation \\  & injection pressure: 2 MPa & & \\ B & injection pressure: 4 MPa & T-H-M coupling & Effect of \(CO_{2}\) Injection \\  & injection pressure: 6 MPa & & Pressure on \(CO_{2}\)-\(ECBM\) \\  & injection temperature: 289 K & & \\ C & injection temperature: 300 K & T-H-M coupling & The effect of injection well \\  & injection temperature: 320 K & & temperature on \(CO_{2}\)-\(ECBM\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation program.  
Figure 6: Numerical simulation model for \(CO_{2}\)-\(ECBM\).
## 3 AoI Minimization in UAV-Assisted WSN/IoT Application Architectures
### Trajectory and Energy Management (TE)  
#### 4.2.1 _T_e with Node Sampling Policy  
In practice, sensor nodes can sample data using different policies. For instance, it is not in all cases that sensor nodes generate packets at will. In many situations, autonomous sensing is applicable where the data sensed and generated may follow a unique pattern. For example, environmental sensors can monitor humidity, wind, and temperature after regular time intervals. In other words, sensor node transmissions might follow a regular pattern rather than transmit whenever they find the opportunity. This motivates the authors in [105] to study AoI-aware data collection using a learning-based approach. In the considered multi-UAV model (see Figure 7b), sensor nodes sample the environment at fixed or random intervals based on their sampling mode. The update packet containing the latest sampled information is stored in a buffer. The SNs' update packets are delivered via multiple energy-constrained UAVs. In the first scenario, the base station gathers real-time information (globally) about the network and decides on the UAV flight direction and data collection schedule using its collected information. In the second case, each UAV acts as an agent that (autonomously) make decisions based on what they locally observe. Additionally, flight directions are chosen based on the system state in each time slot in accordance with the optimal strategy. Using Sarsa and a multi-agent VDN-based learning algorithm with partial and full observation, the AoI-optimal trajectory of the UAV was found.  
Figure 11: Illustrative architecture for UAV-assisted data collection for EH-IoT (**a**) and multi-UAV-aided data collection (**b**) based on [85; 91] respectively.
# Metal Bioaccumulation by Carp and Catfish Cultured in Lake Chapala, and Weekly Intake Assessment
## 2 Materials and Methods  
### Study Design and Sampling  
About one thousand catfish fingerlings (_Ictalurus punctatus_) aged 15 weeks (average weight 4.80 \(\pm\) 2.96 g and length 8.51 \(\pm\) 2.04 cm), and two hundred adult carps (_Cyprius carpio_) aged 30 weeks (average weight 30.04 \(\pm\) 15.55 g, and length 11.08 \(\pm\) 4.69 cm) were cultured in the same floating net cage in Lake Chapala, Jalisco, Mexico (coordinates 20\({}^{\circ}\)17\({}^{\prime}\)2.9\({}^{\prime\prime}\) N, \(-\)10\({}^{\circ}\)10\({}^{\prime}\)27.2\({}^{\prime\prime}\) W) (Figure 1). These fish were called the experimental group. The cage measured 6 m in diameter and 2 m deep, with a surface area of 100.13 m\({}^{2}\) and volume of 56 m\({}^{3}\). The mesh size was 1 inch. It was placed 3 m above the bottom of the lake. During the fingerling age period, the catfish were kept in a nursery cage within the larger cage, and after they remained in it for 3 months, fish were released. In order to detect any factor that might affect metal bioaccumulation, a control group of fish with the same characteristics was cultured in an earth pond in Jocotepec, Jalisco, a lakeside community (Figure 1).  
The fish were fed Winfish-Zeigler 3506 (3.5 mm) until they reached a weight of 50 g (6 months), when this was replaced with Winfish-Zeigler 2505 (5.5 mm). Culture performance was monitored monthly in terms of biometrics, morbidity, and mortality. The properties of the lake, monitored and recorded weekly, were pH, temperature, and dissolved oxygen. Table 1 shows the metals concentration of the feedstuffs.  
Figure 1: Location of the experimental cage in Lake Chapala. (**a**) Map of Lake Chapala showing with the red star the experimental cage and the yellow star the control group. (**b**) Experimental cage. (**c**) General view of Lake Chapala environment.  
The collection of fish samples involved taking 3-5 individuals from each species and for each experimental group at intervals of 6-7 weeks of exposure, concretely on days 0, 38, 81, 123, 179, 241, 298, 369, 424, and 473. For sampling, the fish were transferred to containers with 0.2 g/L of MS 222 anesthetic (Sigma Aldrich). When dead, the fish were placed into new plastic bags and transported to the laboratory in a container to maintain the chain of command. The length and weight of each fish and its tissues were recorded before dissection. Liver and muscle tissue were removed, weighed, and homogenized individually, except for catfish, at the three first sampling stages where the fish were very small, and the same tissues from different fish were placed in the same bag. Samples were frozen at \(-20\)\({}^{\circ}\)C for further analysis.
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 4 Random Errors
### Doppler Centroid Decorrelation Decorrelation (\(\delta_{DC}\))  
Doppler centroid decorrelation is the equivalent of geometric decorrelation but in the azimuth (or along-track) direction [28]. Doppler centroid (\(f_{DC}\)) is related to the frequency of the azimuth beam center. Ideally, the Doppler centroid is zero. However, due to the Earth's rotation, the center of the illuminating beam on the ground varies while the satellite is orbiting. The difference of Doppler centroid (\(\Delta f_{DC}\)) between the primary and secondary images would cause a mismatch of two spectrums in the azimuth direction, and a lack of spectrum overlap will reduce the coherence (\(\gamma_{DC}\)), thereby resulting in Doppler centroid decorrelation [55]. Similar to geometric decorrelation, the coherence factor \(\gamma_{DC}\) decreases as the difference of Doppler centroid increases (e.g., Equation (29), where \(B_{A}\) is the bandwidth in azimuth direction) [28].  
\[\gamma_{DC}=\begin{cases}1-\left|\frac{\Delta f_{DC}}{B_{A}}\right|\left|\Delta f_{ DC}\right|\ \leq B_{A}\\ \quad 0\left|\Delta f_{DC}\right|>B_{A}\end{cases} \tag{29}\]  
One way to avoid Doppler centroid decorrelation is by filtering the bursts of two images to a common bandwidth which is known as azimuth spectral shift filtering (or azimuth filtering). The drawback of this method is that the azimuth resolution is downgraded [55]. However, Doppler centroid decorrelation generally creates a very limited coherence drop due to that the advanced attitude steering capabilities of modern spaceborne satellites. The yaw-steering mode applied on ERS-1/2 back in 1991 was already capable of minimizing the effect of Doppler centroid decorrelation without azimuth filtering [28; 154]. The zero-Doppler attitude steering mode that is applied during Sentinel-1 operation is a significant improvement from the ERS-1/2 era as the zero-Doppler attitude steering mode combines yaw-steering with an additional pitch-steering to reduce the Doppler centroid to 0 Hz. Studies have assessed and proven that Sentinel-1 possesses an excellent attitude steering system, which means no azimuth filtering is necessary unless pre-processed SLC images are used or many interferograms need to be stacked [28; 55].
## 4 Simulation Results  
We use the following simulated measurement data to test the performance of the proposed algorithm, which is widely used in the current study and some representative PF-TBD algorithms [2; 3; 4; 5; 6]. Meanwhile, the CV (constant velocity) and CT (coordinate turn) motion models are used for testing.  
The simulated measurement data consists of a total of 30 frames, with the target appearing in the data at Frame 7 and disappearing at Frame 22. Each frame consists of an array of 20 \(\times\) 20 pixels, \(n=m=20\); each cell has unit dimensions \(\Delta_{x}=\Delta_{y}=1\). The probabilities of birth and death are set as \(P_{b}=P_{d}=0.05\). The standard deviation of the target spread function is \(\sum=0.7\). The target initial state was given by \(x_{0}=\left[4.2\,0.45\,7.2\,0.25\,20\right]^{T}\). The birth particles proposal functions \(x_{k}^{b}\sim U(0,n)\), \(y_{k}^{b}\sim U(0,m)\), \(\dot{x}_{k}^{b}\sim U(-1,1)\), \(\dot{y}_{k}^{b}\sim U(-1,1)\) and \(I_{k}^{b}\sim U(10,30)\). Set the observation noise variance \(\sigma^{2}\) to get a different SNR, which is defined by \(SNR=10\log\left(\dfrac{I^{2}}{\sigma^{2}}\right)\). For example, if the SNR is 6 dB, the standard deviation of the background noise level in each pixel is \(\sigma=10\) units. The HDE parameters were set according to [16]. \(F_{n}=09\); \(C=0.6\); the annealing initial temperature \(T_{st}=100\); the annealing parameter \(\rho=0.9\); and the pre-specified temperature \(T0=5\).  
For the CV model, the transition matrix \(\mathbf{F}_{C^{\prime}}\) is given by Equation (2) and \(\mathbf{Q}_{C^{\prime}}\) by Equation (3), with \(q_{1}=0.001\), \(q_{2}=0.01\), \(T_{CV}=1\) and the target detection threshold \(\mathrm{thr}=0.6\).  
For the CT model, the system dynamics model function is given by Equation (1), and the covariances of noise process \(\mathbf{Q}_{C^{\prime}}\) and the process transition matrix \(\mathbf{F}_{C^{\prime}}\) are defined by:  
\[\mathbf{F}_{C^{\prime}}=\begin{bmatrix}1&\dfrac{\sin\left(w(k)T_{C^{\prime}} \right)}{w(k)}&0&-\dfrac{1-\cos\left(w(k)T_{C^{\prime}}\right)}{w(k)}&0\\ 0&\cos\left(w(k)T_{C^{\prime}}\right)&0&-\sin\left(w(k)T_{C^{\prime}}\right)&0 \\ 0&\dfrac{1-\cos\left(w(k)T_{C^{\prime}}\right)}{w(k)}&1&\dfrac{\sin\left(w(k) T_{C^{\prime}}\right)}{w(k)}&0\\ 0&\sin\left(w(k)T_{C^{\prime}}\right)&0&\cos\left(w(k)T_{C^{\prime}}\right)&0 \\ 0&0&0&0&1\end{bmatrix} \tag{17}\]
## 3 Materials and Methods
### Learning Step for Models \(f\)
#### 3.3.3 PLSR Averaging and Stacking  
Contrary to models PLSR and PLSR-P, models AVG, AVG-CV, -AIC, -BIC, and STACK were not preliminarily optimized on training sets. They were directly fitted on \(\{\mathbf{X}_{\text{train}},y_{\text{train}}\}\) after setting an \"omnibus\" maximal number of LVs (\(A\) in Equations (2) and (4)). To keep the operational interest of the methods, \(A\) must be given _a priori_. A heuristic has therefore to be defined (this point is discussed in the final section). Based on our expert experiences on PLSR and our knowledge on the intrinsic heterogeneity of forages data, we defined the following heuristic rule: \(A=50\) LVs for training sizes \(n_{\text{train}}>400\) observations and \(A=30\) LVs for training sizes \(n_{\text{train}}\leq 400\) observations (in this paper, this concerned only WAL datasets). The decrease from 50 LVs to 30 LVs (\(n_{\text{train}}\leq 400\)) relates to the fact that small datasets cannot support dimensionalities that are too high. Other simple heuristics could be studied (e.g., with functional relations to sample size \(n_{train}\)) but this goes beyond the objective of this paper.  
For AVG-CV, -AIC, and -BIC, two values of parameter \(\alpha\) used in the bi-weight function (Section 2.2.2) were considered to study the sensitivity of the methods to this parameter:  
* \(\alpha=0\), i.e., \(q=\max\{d_{0},\,d_{1},\,\ldots,\,d_{a}\}\), which means that only the less performant model within \(r=0\),..., 50 LVs is removed from the average;
* \(\alpha=0.3\), which means that the 30% less performant models within \(r=0\),..., 50 LVs are removed.  
Figures 5-7 show illustrations of AIC and BIC curves and related weights \(w_{r}\).  
Figure 5: Example of AIC and BIC curves for PLSR: \(X\)-data TROP1 and \(y\)-variable ADF.  
Figure 6: Example of CV, AIC, and BIC weights for AVG models in which \(\alpha=0\): \(X\)-data TROP1 and \(y\)-variable ADF.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Method** \\ \hline PLSR \({}^{1}\) & Dimensionality is selected by minimal RMSECV. \\ PLSR-P & Parsimonious dimensionality (Wold criterion on RMSECV). \\ “Omnibus” methods & \\ AVG & Averaging with uniform weights. \\ AVG-CV & Averaging with weights computed from CV errors. \\ AVG-AIC & Averaging with weights computed from AIC errors. \\ AVG-BIC & Averaging with weights computed from BIC errors. \\ AVG-SHENK & Averaging with the LOCAL weights [38] \\ STACK & Stacking with MLR as “top” model. \\ \hline \hline \end{tabular} \({}^{\dagger}\) Model taken as reference in this paper.  
\end{table}
Table 4: PLSR models compared in this study.  
Figure 7: Same as Figure 6 but with \(\alpha=0.3\).
# Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments
## 4 Optimization of Detection Algorithms for Vertical Curve Segments  
### Analysis of Factors Affecting Detection Accuracy Due to Vertical Curves  
A segment of simulation data from a specific condition in Section 3 was selected for spectral analysis, as shown in Figure 8. Figure 8 indicates that, in the time domain, significant differences appear between the high-pass filtered virtual irregularities and excitation irregularities near the tangent-to-curve and curve-to-tangent transition points. In the frequency domain, notable discrepancies occur in the low-frequency range. This is attributed to the high-pass filter's cutoff frequency of 120 m, which does not fully eliminate the low-frequency components--specifically, the track alignment--in the acceleration integration results.  
The above analysis demonstrates that the longitudinal profile alignment significantly impacts the accuracy of track dynamic irregularity detection, resulting in increased detection errors near gradient transition points and occasionally triggering abnormal exceedances in practical inspections. This is due to the spatial frequency of track alignment  
Figure 8: Error analysis of the acceleration integral method (**a**) Track irregularity comparison (**b**) Detection result amplitude-spatial frequency spectrum.  
Figure 7: Time–frequency domain comparison of virtual and excitation irregularity metrics under varying vertical curve radii and lengths (**a**) Comparison of error standard deviation (**b**) Comparison of power spectral density.  
changes near transition points being close to the 100-120 m long-wavelength irregularity frequency. Variations in train speed and vertical curve radius further shift the low-frequency components induced by track alignment in the frequency domain, making it challenging to set an appropriate spatial cutoff frequency that preserves relevant track irregularity data while effectively eliminating low-frequency interference from track alignment. Consequently, relying solely on high-pass filtering presents inherent limitations.