esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Mathematical Modeling Results
#### 3.2.3 ODE 1 Models  
ODE 1 models were built as systems of linear differential equations (4) was parametrized by the Nelder-Mead method. A program was developed to parametrize the ODE 1 model. Numerical experiments showed the models (4) to be valid only for local forecasting. For that reason, the time interval was split into several subintervals, each of which was modeled. Building the models of the system of linear differential equations (4) enabled convenient analysis of the stability of trajectories the dynamic system has in the phase space, and provide easy identification of bifurcation points. The systems of differential equations were tested for stability [30] by spectral analysis; testing essentially boiled down to the stability testing of a trivial solution: stationary points. Analysis showed the models built on data  
Figure 11: Diagram of series \(x(t)=X(t)/S(t)\) (blue line) and model values (red line).  
from different periods of observation had different types of stable points. This fact might indicate the presence of bifurcation points.  
Let us take a look at the analysis of one model:  
\[\left\{\begin{array}{l}\frac{dx}{dt}=0.75x+0.49z\\ \frac{dz}{dt}=-0.6x-0.11z\end{array}\right.A=\left[\begin{array}{cc}0.75&0.49 \\ -0.6&-0.11\end{array}\right]\]  
Characteristic values: \(\lambda_{1,2}=0.32\pm 0.33i\), therefore, the stationary point, the unstable focus of the system trajectory that was arbitrarily close to the zero at the initial timepoint deviate from the \(\epsilon\)-neighborhood of the zero as \(t\) grows on.  
Next to a nonsingular point, all vector fields have an identical, simple structure. In order to test the behavior at singularities, use one of the built models:  
Near a nonsingular point, all vector fields have a simple and identical structure. To study the behavior at the points of singularities, we use one of the constructed models:  
\[A=\left[\begin{array}{cc}0.64&0.15\\ -0.58&-0.07\end{array}\right]\]  
\(a_{22}\) can be ignored in view of its smallness. Then, from the system of differential equations with the defined matrix, find:  
\[dx/dz=-(1.1+0.25\;z/x).\]  
According to the general theory [30], obtain:  
\[du/dz=-1,1-(u+1/u),\;u=x/z\]  
The dynamic system is known to be oscillating at \(k=1,1<2\) and change the direction of movement no more than once at \(k=2\). This value of the coefficient can be further used to add the control parameter to the general formulation of the system of differential equations:  
\[\left\{\begin{array}{l}\frac{dx}{dt}=(a_{11}+\mu)x+0.49a_{12}z\\ \frac{dz}{dt}=a_{22}x\end{array}\right.\]  
where \(\mu\) is the control parameter.  
Conclusions. (1) Spectral analysis shows the stationary points of the system to be unstable. (2) Negative attitude to the project is on the rise. (3) Analysis of singularities found out ways to introduce the control parameters. (4) The solution obtained by means of the ODE 1 models \(\ddot{x}\succ\dot{z}\).
# Advancements in Flame-Retardant Systems for Rigid Polyurethane Foam
## 3 Additive-Type Flame Retardants
### Addition of Phosphorous-Containing Flame Retardants  
The addition of phosphorous-containing flame retardants (P-FRs) in rigid polyurethane foam is a common approach to enhance its fire resistance. Phosphorous-containing compounds are considered effective flame retardants due to their ability to promote char formation in the condensed phase and to decrease the release of flammable gases in the condensed phase during combustion [75]. Examples of commonly used P-FRs in rigid polyurethane foam include ammonium polyphosphate (APP), dimethyl methylphosphonate, red phosphorous, and resorcinol bis(diphenyl phosphate) [63; 76]. These flame retardants can provide varying degrees of fire resistance depending on their chemical structure, loading level, and processing conditions. It has been observed that the effectiveness of flame retardancy can be influenced by the valence state of phosphorus.  
DOPO and its derivatives have recently emerged as P-FRs for the RPUF matrix, which release phosphorus species (PO\(\bullet\)) and scavene H\(\bullet\) and OH\(\bullet\) radicals in the flame to prevent the thermal degradation of polymers [77]. In a study by [PERSON] et al. [78], a new flame retardant called DOPO-BA was synthesized and added to a rosin-based RPUF matrix. When 20 wt.% DOPO-BA was incorporated, the LOI value increased from 20.1% to 28.1%. However, there was a significant reduction in the total smoke release.  
[PERSON] et al. [79] addressed the migration issue of liquid flame-retardant dimethyl methylphosphonate (DMMP) in RPUF by synthesizing a biobased polyol from limonene and incorporating it with DMMP. They found that the addition of 2 wt.% DMMP can significantly reduce the burning time of RPUF by 83%. In addition, RPUF with TSPB exhibited improved water resistance. [PERSON] et al. [80] observed that adding 10.6 wt.% toluidine spirocyclic pentaerythnitol bisphosphonate (TSPB) to RPUF resulted in an improved LOI value and a UL-94 V-0 rating, which was attributed to the char-forming effect.
## 3 Study Area and Data
### Research Data Acquisition and Processing
#### 3.2.2 Traffic Accident Data Acquisition and Processing  
1. Traffic accident data acquisition  
The traffic accident data used from 2014 to 2015 were obtained from the Hong Kong Traffic Management Department. The data attributes include latitude and longitude coordinates, weather conditions, and accident severity, etc., and there are a total of 10,392 entries.  
The acquired traffic accident data attributes are shown in Table 5.  
Figure 5: Example of multi-lane treatment in Kowloon Peninsula.  
Figure 6: Results of road network treatment in Kowloon Peninsula.  
Figure 8: Illustration of the location of the intersection at Kowloon Peninsula.  
\begin{table}
\begin{tabular}{c c} \hline
**Property Name** & **Hidden Meaning** \\ \hline fid & The “fid” can be used to uniquely identify an element on the map. \\ \hline shape & The “shape” attribute is usually used to indicate the shape of a line element. \\ \hline year & The “year” attribute is usually used to indicate the year of a traffic accident. \\ \hline severity & The “severity” attribute is usually used to indicate the severity of the crash. \\ \hline \end{tabular}
\end{table}
Table 5: Table of attributes of traffic accidents.  
Figure 7: Illustration of the location of local road intersections in Kowloon Peninsula.  
According to the research objectives of this experiment, focusing on selecting fid attribute, year attribute, latitude attribute, and longitude attribute, the content of the attribute table of traffic accident data is displayed, as shown in Table 6.  
The main purpose of the experiment is to remove the traffic accident data with missing latitude and longitude attributes, and it also includes some steps such as filling the missing values and processing the outliers (the latitude and longitude coordinates are not on the road network, etc.). After processing, a total of 8374 pieces of experimentally available traffic accident data were obtained.  
A buffer zone will be established using MapGIS 10.6 with the road intersection node as the center of the circle, as shown by the black dot in Figure 9, with a radius of 100 m.  
Then, combining the 8374 traffic accident data points, using MapGIS to import them into the established road intersection buffer and unify the coordinate system, the final results are displayed as shown in Figure 10.  
If the number of traffic accidents in this buffer zone exceeds twenty or more, this defines the road intersection as a traffic black spot. After counting, there are 584 traffic black spots in total, and the final results are displayed as shown in Figure 11, where the red nodes denote the traffic black spots, which are replaced by the number 1, and the blue nodes denote the non-traffic black spots, which are replaced by the number 0.  
Figure 11: Distribution of traffic black spots.  
Figure 10: Graphical representation of traffic accident data falling within the intersection buffer zone.  
The MapGIS platform was also used to count the distribution of traffic black spots in the five districts of Kowloon Peninsula, as shown in Table 7.
## 4 Conclusions  
In this work, we have demonstrated that a low-cost Fabry-Perot laser diode works very well to drive a THz TDS setup. We found that the commercially available laser diode is mode-locked. With 84 m of additional single mode fiber, the linear chirped pulse can be compressed to 544 fs. Without any further amplification, a THz pulse train is generated. The spectral bandwidth of the THz signal is 1.4 THz with a peak dynamic range of 51 dB from a single measurement.  
For comparison, the measurement with the state-of-the-art fiber laser under otherwise same conditions shows a peak dynamic range of 70 dB and a bandwidth of more than 4 THz. Keeping in mind that the laser diode costs less than a tenth of the fiber laser, Fabry-Perot laser diodes are an excellent alternative to fiber lasers for future low-cost terahertz systems. For applications like the detection of water content of leaves or the detection of germinable sugar beet seeds, a bandwidth below 1 THz suffices [29; 30]. These applications can be realized with this cheap and compact laser source. Further, we see that the bandwidth is now limited by the pulse duration of the laser. If the pulse duration can be further reduced, for example by the modulation of the laser diode as in Reference [31], the THz bandwidth may be further increased.  
Conceptualization, S.P. and J.C.B.; Data curation, K.-H.T. and F.F.; Formal analysis, K.-H.T.; Investigation, K.-H.T.; Software, K.K.; Supervision, S.P. and J.C.B.; Validation, K.K. and F.F.; Writing--original draft, K.-H.T.  
Author [PERSON] acknowledges the Deutsche Forschungsgemeinschaft (DFG) for funding project number 278381540 (REPHCON). We acknowledge support by the Open Access Publication Fund of the University of Duisburg-Essen.  
The authors declare no conflict of interest.  
Figure 4: THz-TDS time-trace (**a**) and spectrum (**b**) with the commercial Menlo System and the FPL1009P. The black spectrum has a dynamic range of 70 dB and a bandwidth of more than 4 THz. The light red spectrum has a bandwidth of 1.4 THz and a peak dynamic range of 51 dB.
## 1 Introduction
* To the best of our knowledge, this is the first study of class imbalance modulation recognition in low-data regimes, and it will serve as a reference for researchers and the community in order to better understand the problem of class imbalance modulation recognition using data augmentation methods;
* We demonstrate that existing rebalancing methods are limited in their ability to solve the class imbalance and small sample size problems in AMR tasks, both because they increase training costs and provide very limited improvements in recognition performance;
* We propose a new automated data augmentation method for modulated signal data, called SigAugment, which can be used without additional hyperparameters compared to existing methods and can be adapted to models with different structures. Experimental results demonstrate that SigAugment can outperform existing SOTA methods on different types of datasets. In particular, the gain is significant on small datasets with unbalanced classes.
## 3 Materials and Methods
### Modeling
#### 3.3.1 Feature Engineering  
In feature engineering, it is vital to balance including informative variables and avoiding unrelated ones. Including informative variables can improve the outcome, while unrelated variables can add unnecessary noise to the model. Exploring the data is essential in selecting the appropriate models to forecast the internal temperature in the greenhouse.  
Data exploration revealed that each season exhibits a unique trend and number of samples in their variables, as shown in Table 2, which displays the greenhouse's internal temperature variations. This analysis resembles the one presented by [PERSON] et al. (2014), where they divided the data into seasons of the year to avoid underfitting during model training due to varied temperature trends throughout the year.  
Splitting the data by seasons allows for better analysis and modeling of internal temperature ([PERSON] et al., 2016). When trying to forecast the internal temperature using all the data, it is hard to capture the complete trend and its correlation with other variables. Therefore, it is important to understand the data's origin to create viable model candidates for accurate forecasting.  
Figure 4 shows the correlation diagrams for each season; fall (Figure 4a), summer (Figure 4b), spring (Figure 4c) and winter (Figure 4d). The correlation analysis reveals noteworthy findings.  
* During the seasons when it is hotter, such as summer and spring, there is a stronger relationship between solar radiation and internal temperature. The correlation coefficients for these seasons are 0.75 and 0.66, respectively. In contrast, during the cooler seasons of winter and autumn, the correlation coefficients are lower at 0.24 and 0.28, respectively.
* The internal dew presents high concordance values in fall, winter, and summer, whereas there is no significant correlation in winter.
* The external temperature has significant correlation values with the external and internal humidity. The value due to the initial selection of predictors in the models is remarkable; these correlations between independent variables indicate multicollinearity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Season** & **Samples Number** & **Mean** & **Standard Deviation** & **Minimum Value** & **Maximum Value** \\ \hline Spring & 28,684 & 29.57 & 15.03 & 2.60 & 66.49 \\ Summer & 14,508 & 31.00 & 13.31 & 16.20 & 71.00 \\ Fall & 24,870 & 25.10 & 13.62 & 6.20 & 63.20 \\ Winter & 17,927 & 23.73 & 16.41 & \(-\)0.61 & 67.70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive analysis of internal temperature by the season of the year.
## 5 Implementation of the Proposed Model
### Implementation Phase
#### 5.3.1 Coding  
In this stage, the user profile acquisition stage is used as a basis to define the user preferences as an input (speeds, music, and the number of obstacles), and the reward matrix as an output (Figure A3 presents a fragment of the coding of the algorithm in the virtual environment). Through two classes, the data preprocessing is in charge of mapping the preferences to their respective satisfaction states. On the other hand, the interaction stage algorithm is in charge of transitioning the states, elements, and clues of the video game; mainly, this is centered on the Q-learning algorithm, which takes as input the initial state \(S_{t}\), the reward set \(R\), the learning rate \(\alpha\), discount factor rate \(\gamma\), and initializes a Q matrix of the values \(q_{*}(s,a)\), the only output of the interaction algorithm is the transition of states and tracks.  
Scene diagram. The scene diagram presented in Figure 14 allows specifying the user's interaction with the interface. The user has the opportunity to enter a menu and navigate  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**State**} & \multirow{2}{*}{**Track**} & \multicolumn{5}{c}{**Parameters**} \\ \cline{3-6}  & & **Speed** & **Music** & **Number of Obstacles** \\ \hline \(S_{0}\) & 0 & least preferred & least preferred & least preferred \\ \(S_{1}\) & 1 & least preferred & neutral preferred & least preferred \\ \(S_{2}\) & 2 & least preferred & neutral preferred & neutral preferred \\ \(S_{3}\) & 3 & neutral preferred & neutral preferred & neutral preferred \\ \(S_{4}\) & 4 & most preferred & neutral preferred & neutral preferred \\ \(S_{5}\) & 5 & most preferred & most preferred & neutral preferred \\ \(S_{6}\) & 6 & most preferred & most preferred & most preferred \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mapping of the user’s preference selection to the video game states/tracks: speed, music, and the number of obstacles. The parameters are selected at the user profile acquisition stage and are defined in three values: least preferred, neutral preferred, and most preferred.  
to settings, controls, or return to the game. Within the game, the user can view the total number of coins in the track, coins collected, and the user can view the lap time, best time, and total time per track.  
Figure 15a illustrates the user's profile acquisition interface, and also shows a main rectangle (enclosed in blue) where the player chooses the speed, music, and the number of obstacles. At the bottom of the same image, some buttons are observed wherein \"Main\", the instructions of the virtual environment are indicated; \"Settings\", user preferences configuration; \"Controls\", the controls to move the cart; and \"Credits\", developer credits. The track interface Figure 15b presents in the upper right corner feedback information to the user, such as time per lap, current time, and current lap number, in the upper left corner we can find the collected coins and totals in the track.
## 2 Study Area and Data Collection
### Satellite Data  
In this study, we utilized surface reflectance and surface temperature data from Landsat 7 and Landsat 8 (specifically, Level 2, Collection 2, Tier 1 data) for the estimation of water and carbon fluxes. The surface reflectance data encompassed six spectral bands: blue, green, red, near-infrared, shortwave infrared 1, and shortwave infrared 2. Detailed information on these bands is provided in Table 1. These datasets underwent geometric correction, radiometric calibration, and atmospheric correction. Additionally, we employed the Quality Assurance (QA) band from both Landsat 7 and Landsat 8 for cloud masking prior to data analysis. The entire satellite data processing was carried out using the Google Earth Engine (GEE) platform. Among them, due to sensor issues, Landsat 7's observation data suffer from stripe missing problems. This study employs the focal statistics-based method provided by the GEE platform for interpolation. For specifics, please refer to [[https://code.eartheengine.google.com/16e86](https://code.eartheengine.google.com/16e86) cfbd845a7b583100 cb8c27348e9]([https://code.eartheengine.google.com/16e86](https://code.eartheengine.google.com/16e86) cfbd845a7b583100 cb8c27348e9), accessed on 25 January 2024.
# Effects of Invasive Watermilfoil on Primary Production in Littoral Zones of North-Temperate Lakes
## Materials and Methods
### Open-Water Metabolism  
At the center of each plot, we deployed a YSI 6920 sonde or MiniDOT logger (PME, Vista, CA, USA) in conjunction with surface mounted Hobo light and temperature pendant loggers (Onset, Bourne, MA, USA) for 3-9 days spanning the days of primary producer bottle production and standing crop sampling. All sensors were programmed to log dissolved oxygen, temperature, and light at 10-min intervals.  
A modified one-station metabolism model for multiple observation days was used to estimate GPP, ecosystem respiration (ER), and air-water gas exchange [55; 56]:  
\[\mathrm{O_{2,(t)}}=\mathrm{O_{2,(t-1)}}+\left(\frac{\mathrm{GPP}}{\mathrm{z}} \times\frac{\mathrm{L_{(t-1)}}}{\sum\mathrm{L_{24h}}}\right)+\frac{\mathrm{ER \times t}}{\mathrm{z}}+\mathrm{K_{O_{2}}}t\left(\mathrm{O_{28 at}}-\mathrm{O_{2,(t-1)}}\right), \tag{1}\]  
where GPP are positive rates and ER are negative rates of O\({}_{2}\) production (g O\({}_{2}\) m\({}^{-2}\) d\({}^{-1}\)), O\({}_{2}\) is the measured oxygen concentration (g O\({}_{2}\) m\({}^{-3}\)), z is the depth at the location of sensor deployment (m), t is the time between measurements (d), L is irradiance, K\({}_{\mathrm{O}2}\) is temperature-corrected O\({}_{2}\) gas exchange rate (d\({}^{-1}\)), and O\({}_{\mathrm{2 sat}}\) is O\({}_{2}\) saturation concentration (g O\({}_{2}\) m\({}^{-3}\)).  
Posterior probability distributions of GPP, ER, and K were simulated using Bayesian parameter estimation with uninformative priors via a random walk Metropolis algorithm and Markov chain Monte Carlo using RSTAN 2.17.3 (Stan Development Team, [[https://mc-stan.org/](https://mc-stan.org/)]([https://mc-stan.org/](https://mc-stan.org/))) in R version 3.4.4 (R Core Team 2018, [[https://www.r-project.org/](https://www.r-project.org/)]([https://www.r-project.org/](https://www.r-project.org/))). Initially, the model was set to integrate across all measurement days to provide more robust estimates of K integrating day-to-date variability in environmental conditions [57]. Yet, when there is wide variation in weather conditions among sampling dates and/or when physical parameters override biological signals [58], these models can produce poor fits or unrealistic estimates of K, GPP, or ER. Therefore, multi-day model outputs were screened for fit and ecologically unrealistic values (negative GPP or positive ER). First, days with no or poor model fit were removed (2 days for Iron Lake -IWM, 1 day for Torch Lake +IWM, 3 days for Torch Lake -IWM). All days with ecologically unrealistic values yet good model fits (1 day for Sturgeon Sloughs -IWM, 2 days for Horseshoe Lake -IWM, 1 day for Horseshoe Lake +IWM) were additionally analyzed with single day metabolism models that estimated GPP, ER, and K from independent 24-h periods without integrating day-to-date variability of K. These single day metabolism models also produced ecologically unrealistic values for these days, so they were removed from analyses. Net ecosystem production (NEP) of plots was calculated as the sum of GPP and ER.
## 2 Theoretical Development
### Static Residual Force Algorithm
In the following theory development, single-damage and multiple-damage cases are discussed separately. Next, single-damage cases and multiple-damage cases are discussed, respectively. Assuming that the \(q\) th element is damaged and the damage extent is \(\alpha_{q}\), Equation (12) reduces for the single-damage case as follows:  
\[\Delta k_{i}=\alpha_{q}k_{i}^{q} \tag{13}\]Substituting Equation (13) into (7) yields the following:  
\[\xi_{i}=\alpha_{q}(k_{i}^{\beta}\cdot u_{d}) \tag{14}\]  
From Equation (14), the damage extent can be calculated as the following:  
\[\alpha_{q}=\frac{\xi_{i}}{k_{i}^{\beta}\cdot u_{d}} \tag{15}\]  
For multiple-damage cases without loss of generality, Equation (12) can be rewritten by supposing that the damage extents are \(\alpha_{1}\), \(\alpha_{2}\), and \(\alpha_{r}\) as follows:  
\[\Delta k_{i}=\sum_{j=1}^{r}\alpha_{j}k_{i}^{j} \tag{16}\]  
Similarly, \(r\) equations for the damaged degrees of freedom are selected from Equation (7) to compute the damage extents:  
\[\left[ \begin{array}{c}\Delta k_{1}\\ \Delta k_{2}\\ \vdots\\ \Delta k_{r}\end{array} \right]\cdot u_{d}=\left\{ \begin{array}{c}\xi_{1}\\ \xi_{2}\\ \vdots\\ \xi_{r}\end{array} \right\} \tag{17}\]  
Substituting Equation (16) into (17) and rearranging yields the following:  
\[\left[ \begin{array}{cccc}k_{1}^{1}u_{d}&k_{1}^{2}u_{d}&\cdots&k_{1}^{r}u_{d }\\ k_{2}^{2}u_{d}&k_{2}^{2}u_{d}&\cdots&k_{2}^{2}u_{d}\\ \vdots&\vdots&\vdots&\vdots\\ k_{r}^{1}u_{d}&k_{r}^{2}u_{d}&\cdots&k_{r}^{r}u_{d}\end{array} \right]\left\{ \begin{array}{c}\alpha_{1}\\ \alpha_{2}\\ \vdots\\ \alpha_{r}\end{array} \right\}=\left\{ \begin{array}{c}\xi_{1}\\ \xi_{2}\\ \vdots\\ \xi_{r}\end{array} \right\} \tag{18}\]  
Then, damage extents \(\alpha_{1}\), \(\alpha_{2}\), and \(\alpha_{r}\) can be calculated from the solution of Equation (18) using the Gauss elimination method.