esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Boreal Vegetation Phenology Changes  
#### 3.1.1 Comparison of Multi-Year Mean Phenology for Two Distinct Periods  
As shown in Figure 4, we generated spatial maps of multi-year mean data and calculated the overall and latitudinal mean of SOS, EOS, and LOS for the three major vegetation types. While the spatial differences in multi-year mean phenological metrics between the two periods appear relatively small, latitudinal variations become more pronounced with increasing latitude, particularly in high-latitude regions. This latitudinal enlargement of phenology differences between these two periods was primarily attributed to climatological temperature [28]. Statistical results (Figure 4d,h,l) indicate that phenological metrics for forestlands and shrublands areas were more tightly clustered compared to grasslands, with shrublands showing the most concentrated variation. In general, the SOS for all vegetation types had advanced, the EOS had been delayed, and the LOS had extended, with variations in these changes observed among different vegetation communities. Shrublands experienced the most significant changes between the two periods. The mean SOS of shrublands in period P2 (DOY 141) was 4 days earlier, and the mean EOS (DOY 259) was 3 days later than in period P1, resulting in a 7-day extension of the LOS.  
Figure 4: Climatology comparison of phenology metrics for the two distinct periods. Panels (**a,e,i**), and (**b,f,i**) present the spatial distributions of multi-year mean phenology metrics (SOS, EOS, LOS) for two distinct periods: 1982–2000 (P1) and 2001–2018 (P2), respectively. Panels (**c,g,k**) and (**d,h,l**) show the latitudinal mean and overall mean phenology metrics of three vegetation types for the two periods, respectively.
## 2 Materials and Methods
### Experimental Setup  
#### 2.4.1 Photo-Fenton System  
The photo-Fenton system (Figure 1a,b) was designed and developed in collaboration with Enviolet GmbH (Germany). It consists of three borosilicate reactors connected in serial, each containing a different UV lamp. The lamps installed are two medium-pressure (MP) lamps of 500 W (marked with D in 1a) and 150 W (marked with C in Figure 1a, C) and a low-pressure (LP) lamp of 40 W (marked with B in Figure 1a, B). Irradiation profiles are provided in the Supplementary Information (SI) (Table S1). Each reactor has an irradiated volume of 1.2 L and an external surface of 0.1 m\({}^{2}\). The reactors are fed from a polyethylene tank (75 L capacity) connected to a Schmitt MPN115 (0.25 kW) centrifugal pump at a flow rate of 800 L min\({}^{-1}\), ensuring a turbulent regime inside the reactors. The temperature was maintained between 18 and 23 \({}^{\circ}\)C through two cooling coils to avoid lamps overheating.  
The system was operated in batch mode, treating 60 L of solution each time. For the experiments, different lamps were used in single lamp configuration; besides that, the simultaneous use of MP and LP lamps was tested. The reactor was filled with the water matrix, adding 25 mg L\({}^{-1}\) of indigo carmine, and recirculated for 15 min until homogeneity was reached. Afterwards, the iron complex was added, and the pH was then adjusted to simulate the natural pH of the wastewater. The time zero of the experiment was taken when the hydrogen peroxide was added and the lamps were switched on.  
The iron complex was prepared by dissolving iron in acidic water until complete dissolution. Then, the chelating agent was added (molar concentration 1:1) to the iron solution and stirred until the complete complex formation, being EDDS or NTA.  
The treatment time was established to be 20 min in batch mode, resulting in 0.4 min of \"relative contact time\" (meaning the time that the effluent has been inside the lamp, or rather the irradiation time) inside the borosilicate reactor. This setup intended to simulate the real operational time ranges of HRT in a continuous process at full scale, which usually isin the order of seconds [32; 33]. The residual H\({}_{2}\)O\({}_{2}\) of each sample was quenched with a solution of 1 g L\({}^{-1}\) of bovine catalase to stop the Fenton reaction.
## 6 Results and Discussion
### Comparison between Various Versions of BHHO with Basic and Time Varying V-Shaped TFs
In terms of fitness rates, as provided in Table 6, it is clear that all time-varying V-shaped TFs based methods outperform their peers (basic V-shaped-based techniques) in terms of fitness rates. Consid \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Dataset** & **BHHO\({}_{\mathbf{V1}}\)** & **BHHO\({}_{\mathbf{TV1}}\)** & **BHHO\({}_{\mathbf{V2}}\)** & **BHHO\({}_{\mathbf{TV2}}\)** & **BHHO\({}_{\mathbf{V3}}\)** & **BHHO\({}_{\mathbf{TV3}}\)** & **BHHO\({}_{\mathbf{V4}}\)** & **BHHO\({}_{\mathbf{TV4}}\)** \\ \hline Breastcancer & 0.9693 & **0.9783** & **0.9998** & 0.9924 & 0.9779 & **0.9848** & **0.9929** & 0.9781 \\ BreastEW & 0.9702 & **0.9819** & **0.9909** & 0.9883 & 0.9813 & **0.9918** & 0.9737 & **0.9792** \\ CongressEW & **0.9939** & 0.9801 & 0.9889 & **0.9992** & 0.9655 & **0.9816** & 0.9774 & **1.0000** \\ Exactly & **1.0000** & 0.9828 & 0.9135 & **0.9965** & **0.9997** & **0.9997** & 0.9993 & **0.9998** \\ Exactly2 & 0.7918 & **0.8137** & **0.8148** & 0.7263 & 0.7565 & **0.7975** & 0.7712 & **0.7885** \\ HeartEW & **0.9370** & 0.8877 & 0.8988 & **0.9037** & 0.8704 & **0.8957** & 0.9074 & **0.9105** \\ IonosphereEW & 0.9620 & **0.9695** & 0.9418 & **0.9507** & 0.9596 & **0.9615** & 0.9531 & **0.9728** \\ KrvskpEW & 0.9735 & **0.9791** & 0.9724 & **0.9728** & **0.9735** & 0.9701 & 0.9735 & **0.9789** \\ Lymphography & **0.9822** & 0.9133 & 0.8878 & **0.9489** & **0.9511** & 0.9267 & 0.9656 & **0.9811** \\ M-of-n & **0.9998** & **0.9998** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\ penglungEW & **1.0000** & **1.0000** & **1.0000** & 0.9444 & 0.9933 & **1.0000** & **1.0000** & **1.0000** \\ SonarEW & 0.9421 & **0.9556** & 0.9492 & **0.9833** & **0.9595** & 0.9341 & 0.9508 & **0.9754** \\ SpectEW & **0.9056** & 0.8883 & 0.8549 & **0.8778** & 0.8605 & **0.9296** & **0.9111** & 0.9093 \\ Tic-tac-toe & 0.8267 & **0.8542** & 0.8410 & **0.8594** & 0.8316 & **0.8418** & 0.8163 & **0.8333** \\ Vote & 0.9639 & **0.9994** & 0.9833 & **0.9872** & **0.9883** & 0.9861 & 0.9867 & **0.9872** \\ WaveformEW & **0.8023** & 0.7971 & 0.8036 & **0.8083** & **0.8056** & 0.7916 & **0.8003** & 0.7973 \\ WineEW & **1.0000** & **1.0000** & **1.0000** & 0.9926 & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\ Zoo & **1.0000** & 0.9444 & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\ \hline \(W|T|L\) & \(7|3|8\) & \(8|3|7\) & \(5|2|11\) & \(11|2|5\) & \(5|4|9\) & \(9|4|5\) & \(3|4|11\) & \(11|4|3\) \\ Rank (F-Test) & 4.56 & 4.61 & 4.83 & 4.44 & 5 & 4.53 & 4.61 & 3.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of BHHO with the basic and time-varying V-shaped variants in terms of accuracy rates. ering F-test results, BHHO\({}_{TV4}\) is ranked as the best place compared to all other competitors. In this work, we consider that classification accuracy has higher importance compared to the number of selected features. Based on results, we found that HHO with time-varying V-shaped TV4 can realize the best performance.
## 2 Materials and Methods
### CEEMDAN-CNN-ATT-LSTM Model
The table shows that the model's accuracy generally decreases with longer forecast horizons, especially in summer, where the higher seasonal variability leads to larger MAE and RMSE values. The best results are typically observed in the 1 h resampling, particularly for winter, where the MAE and RMSE values are the lowest for the 12 h ahead forecast. These results highlight the impact of both resampling intervals and forecast horizons on the overall prediction accuracy of the model, with winter predictions demonstrating higher accuracy than summer predictions across all time intervals. Figure 13: Predictions 12 h ahead with different resamplings. (**a**) 10 min resampled to 12 h forecasts in winter. (**b**) 10 min resampled to 12 h forecasts in summer. (**c**) 30 min resampled to 12 h forecasts in winter. (**d**) 30 min resampled to 12 h forecasts in summer. (**e**) 1 h resampled to 12 h forecasts in winter. (**f**) 1 h resampled to 12 h forecasts in summer. Table 8 presents the performance metrics for forecasting irradiance 12 h in advance in winter and summer, with 1 h resampling intervals. The results compare the proposed model with a reference LSTM, an autoencoder, and a transformer-based model. The transformer model is configured with three encoder layers, three decoder layers, and eight attention heads. The proposed model outperforms the other approaches on MAE and \(R^{2}\), showing higher accuracy, especially in summer, where it achieves an MAE of 110.43 and an \(R^{2}\) of 0.64. The transformer model, while competitive, exhibits slightly higher MAE and RMSE values (118.00 and 148.50 in winter; 113.50 and 140.75 in summer) and a slightly lower \(R^{2}\) (0.56 in winter and 0.61 in summer) compared to the proposed model. Both the reference LSTM and the autoencoder show slightly higher errors in both seasons, highlighting the proposed model's greater efficiency for accurate irradiance forecasting under varying seasonal conditions. The RMSE and MAPE values confirm the proposed model's robustness and performance compared to the alternatives, with the transformer model demonstrating robust performance, albeit with marginally higher error metrics. Figure 14 shows two box plots illustrating the MAE distribution for solar irradiance forecasting at different horizons: 1 h, 6 h and 12 h. Figure 14a shows the summer forecast errors and Figure 14b the winter ones. Each box illustrates the MAE interquartile limits, and the red line within each box the MAE mean. The overall dispersion of the MAE is greater as the forecast horizon increases, as illustrated by the boxes for the 6 h and 12 h horizons. This, in turn, is consistent with expectations: longer forecast windows imply more significant uncertainty. However, despite this variability, the median MAE is reasonably stable over all horizons, including winter, which is reflective of the robustness of the model also for
# SPACNet: A Simulation Platform of an Acoustic Cognitive Network
## 5 Field Experimental Validation
### Network Configuration of the Field Experiment  
To guarantee the comparability of the field experiment with respect to the simulation, the network parameter configuration of the field experiment was set according to that in simulation, as shown in Table 2. Note that the number of nodes, the network runtime, and the packet generation parameter were different from that of simulation due to practical limitation caused by hardware overheads and field operation costs associated with the trial test.  
The location of the field experiment, Xiamen harbor sea area, is a typical shallow water offshore area with an average depth of about 20 m, which is governed by a semididurnal tide. The topology of the field experimental underwater acoustic network is given in Figure 10(a), which consists of five nodes, with the largest distance between adjacent nodes being 5.9 km. The network node adopted for the field UACNs experiment is shown in Figure 10(b). To be consistent with the simulation network scenario, the field UACNs was configured to a  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes & 5 \\ Number of sink nodes & 1 \\ Transducer directivity & Omnidirectional \\ Modulation system & Mary-DSSS \\ Node communication mode & Half-duplex \\ Carrier frequency (kHz) & 15.5 \\ Bandwidth (kHz) & 2.35 \\ Data rate after channel decoding (b/s) & 55.8 \\ Maximum number of retransmissions & 3 \\ Runtime of network (h) & 24 \\ Packet generation (s/packet) & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the field experiment.  
Figure 10: Results of the simulation experiment. (**a**) A2D. (**b**) AETX.  
five-node marine environmental monitoring sensor network with node 1 as the sink node, and the other four nodes as the sensor nodes, as shown in Figure 11. Moreover, to facilitate the comparison with the simulation results, the 24 h running time of the field UACNs was similarly divided into two stages, the first 12 h running in fixed routing mode and the second 12 h running in cognitive routing.
## 3 Numerical Model
### Validation of the Numerical Model  
The flow chart of the partial discharge test and simulated volt-ampere characteristic curve measurement for insulation defects in air switchgear is shown in Figure 5.  
The system for testing and simulating insulation defects and partial discharge, constructed in this paper, investigates the discharge current of insulation defects when subjected to a sine cycle (20 ms) power supply voltage. During the experiment, the ambient temperature was 300 K and the atmospheric pressure was 0.1 MPa. The discharge current is tested using the HIOKI CT6701 current probe (Hioki E.E. Corporation, Nagano, Japan).  
Figure 4: Partial discharge test platforms.  
The time-varying characteristic curves of current and voltage for experimental testing and numerical simulation are shown in Figure 6. From Figure 6, it can be observed that the simulated current calculated by the numerical model has the same trend as the experimental current. During the rising period of the positive half axis of the input voltage, the discharge current first remains constant, and then rapidly increases in the opposite direction to the voltage direction. During the falling period when the input voltage reaches the positive half axis, the discharge current reverses. This phenomenon indicates that in a discharge cycle, it can be essentially divided into two discharges of opposite polarity. During the rise of the negative half axis of the input voltage, the discharge current first remains constant and then rapidly increases in the opposite direction to the voltage direction. The time interval between two discharges of the positive and negative half axes of the input voltage is 1 ns\(\sim\)2 ns.  
Figure 5: Flow chart of volt-ampere characteristic curve measurement.  
Figure 6: Discharge current characteristic curves of test and simulation platform. (**a**) Voltage and discharge current. (**b**) Discharge current of the simulation platform.
## 4 Simulated Profile Retrievals Using Radiosonde Data
### Profile Retrievals Using OEM
While, the presence of associated errors in operation is not dismissed, this is one of the known drawbacks of applying OEM in operation. In controlled experiments (simulations), the \"associated errors\"including forward model are absent by choice. However, to test the outcome of tweaking S\({}_{e}\), the input measurement error covariance values for OEM have been increased by ten times (1000% of its original), shown in Figure 6e,f. It is observed that the solution is improved by increasing the values of S\({}_{e}\), but still inferior to RTLS solution. The \"increase of S\({}_{e}\)\" in this experiment is objectively reasonable according to regularization theory, which affects the solution in a way that the regularization strength is now reduced further on top of the tweaking of S\({}_{a}\), therefore, facilitating extraction of some information from the measurements. However, the major argument is that the \"increase of S\({}_{e}\)\" does not have any scientific basis from stochastic covariance theory at least in the context of simulations where additional errors are absent. Figure 6: Retrievals in OEM for two different true profiles from a reasonable a priori. Upper-panels (**a,b**): CrI’S; Middle-panels (**c,d**): TES with exact measurement error covariance (S\({}_{e}\)) at the rate of SNR = 300. Bottom-panels (**e,f**): same as in (**c,d**) but OEM input with a higher noise level of SNR = 30. Color code: true profiles in black; a priori in solid green; OEM (exact S\({}_{a}\) and S\({}_{e}\)) in solid red; OEM20, OEM50 and OEM100 with tweaked S\({}_{a}\) by increase of 20%, 50% and 100% in solid blue, dashed red and dashed blue, respectively. Left-panels: slightly complex true profile; Right-panels: comparatively simpler true profile shape.
## 2 Materials and Methods
### Principal Component Background Suppression with Peak Threshold Detection
#### 2.2.1 Fast Fourier Transform Frequency Analysis  
The chosen frequency analysis method was the fast Fourier transform (FFT). The fast Fourier transform is an optimized algorithm to compute the discrete Fourier transform  
Figure 6: Simulated v2e ON event rates generated by the overwhelming sources and targets. Target event rates were calculated from scenarios absent of the overwhelming source. The overwhelming sources are the same average frequency but increase in power by an order of magnitude between successive sources.  
(DFT) of a signal in order to retrieve its frequency spectrum, amplitude, and phase [33]. Before an FFT algorithm can be applied on event data, the events must first be separated by pixel address and modified into a form accepted by the FFT. The events must be modified due to the non-uniform time intervals between generated events. Events were separated by pixel before the FFT was applied to ensure that each pixel's spectrum contained frequency content corresponding to only that pixel's field of view. While there were a few ways to modify the event data, a binning method was chosen such that events were summed into discrete temporal bins over the length of the recording. In this work, only ON events were used as it was observed that OFF events exhibit a decay feature that can exceed the time length of the intensity drop and introduce extraneous frequency information and unnecessary additional noise. Figure 7 shows the resulting maximum frequency spectra of a simple example scenario with no overwhelming source and three 17 Hz targets, using only ON as well as both ON/OFF events. When both ON and OFF events are used, energy is lost in the target frequencies and overall noise is increased. However, tests were performed on the four simulated scenarios using both ON and OFF events to verify the effects on target detection, with their results included in Section 3.  
Five millisecond time bins were chosen to create the FFT inputs as that bin size was small enough to resolve the frequencies of the sources used per the Nyquist frequency limit (see Section 2.1) but large enough to prevent the need to simulate event \"frames\" at an extremely high rate which would drastically increase an already large simulation computation time. Other time bin sizes were tested on the simulated scenarios with their results included in Section 3. Figure 8 displays a flow chart where ON events from a fluctuating signal are summed into time bins and transformed into the frequency domain through an FFT.  
Figure 7: Maximum frequency spectra of a simple example scenario comparing using only ON and both ON/OFF events. Scenario contained no overwhelming source and three 17 Hz targets.
# Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests
## 2 Materials and Methods
### Terrestrial NFI NPP
Although all our terrestrial forest inventory data assess properties of trees, there are different sampling methods and increment calculation by country in place, which may strongly affect the resulting estimates [33; 34]. Four different methods to estimate tree carbon increment CARBINC are used in our data: (1) repeated observations of fixed area plots (used in Norway, Poland, Belgium); (2) repeated angle count sampling (for Austria, Germany, Finland); (3) increment cores (France, Romania, Italy); as well as increment predictions from (4) tree growth models (Czech Republic, Estonia, Italy). Tree growth model predictions were used if no increment observations, either from repeated observations or from increment cores, were available. In the Supplementary Material, we provide all details for our 12 inventory data sets, the local sampling system, the available data and the used increment method (Table S1 in the Supplementary Material). The tree carbon results for determining carbon increment CARBINC (Equation (3)) were estimated using the carbon calculation method applied by the local forest inventory organization and compiled in [32]. Local biomass functions and biomass expansion factors were used to derive tree biomass and carbon fractions to convert biomass into carbon. In the Supplementary Material, we provide a detailed description on processing the NFI data, the tree carbon estimates and stand variables to describe the represented forests (e.g., mean age, basal area or stand density index). Using this methodology, we processed the forest inventory data from the 12 countries (Table S1) and derived harmonized carbon stocks for all inventory plots. The forest inventory data set consists of 196.434 plots, harmonized across 12 European countries. We applied the carbon increment method for each country and calculated NPP by inventory plot (hereafter called NFI NPP) using Equations (3)-(5).
## 2 The Formulation and Resolution of Equations for Tunnel Load and Deformation
### The Formulation of Equations Accounting for the Influence of Lateral Soil Action  
When the tunnel experiences settlement-induced deformation due to additional loads, the soil foundation beneath the tunnel and its adjacent lateral soil exert restraining forces on the tunnel, as illustrated in Figure 1b. The deformation of the lateral soil mass in response to tunnel deformation is depicted in Figure 2, and the resultant reaction force exerted by the lateral soil mass on the tunnel is presented in Figure 3.  
When analyzing the influence of lateral soil on tunnel deformation, the following assumptions are made:  
Figure 3: Diagram of restraining force exerted by the lateral soil on tunnel.  
Figure 2: Schematic diagram of lateral soil deformation.  
1. The properties of the lateral soil surrounding the tunnel are consistent with those of the soil beneath the tunnel.
2. The restraining forces on the tunnel are \(T1\) and \(T2\), which are transmitted through the soil shear layer on both sides of the tunnel.
3. The soil used in the calculations is treated as isotropic and elastic.
4. The deformation of the tunnel is well-coordinated with the surrounding soil, and there is no detachment or separation between the tunnel and the soil.  
For a plane defined as \(x=x_{0}\), the deformation equilibrium equation for the lateral soil mass surrounding the tunnel is as follows:  
\[0=k\overline{U}(y)-2t\frac{\mathrm{d}^{2}\overline{U}(y)}{\mathrm{d}y^{2}} \tag{5}\]  
where \(\overline{U}(y)\) represents the deformation of the lateral soil shear layer along the \(y\)-axis direction of the tunnel. By solving Equation (5), the general solution can be obtained as:  
\[\overline{U}(y)=C_{1}e^{-\sqrt{k/2t}(y-D/2)} \tag{6}\]  
As shown in Figure 2, when \(y=D/2\), the lateral soil and the tunnel share the same settlement; that is, \(\overline{U}(x,D/2)=u(x)\). For a position that is infinitely far from the tunnel, denoted as \(y=Y\), the corresponding settlement of the lateral soil becomes \(\overline{U}(Y)=0\). The above two equations give two boundary conditions for the model and determine the unknown coefficient \(C_{1}=u(x)\) in Equation (6), which thereby determines the settlement of the lateral soil as:  
\[\overline{U}(x,y)=u(x)e^{-\sqrt{k/2t}(y-D/2)} \tag{7}\]  
Furthermore, the force exerted on the tunnel by the later soil is obtained as:  
\[T_{1}(x)=T_{2}(x)=2t\frac{\mathrm{d}\overline{U}(x,y)}{\mathrm{d}y}\bigg{|}_{ y=\frac{D}{2}}=u(x)\sqrt{2 tk} \tag{8}\]  
From above, the deformation formulation of the tunnel under the restraining effect of lateral soil is given below:  
\[EI\frac{\mathrm{d}^{4}u(x)}{\mathrm{d}x^{4}}+2\sqrt{2 tk}u(x)=q(x)D \tag{9}\]