esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Forecasting Models
#### 3.2.2 Prophet  
The Prophet model has an advantage--the parameters are interpretable so that the analyst can intuitively adjust them with domain knowledge of the time series. Tools that help analysts use their expertise effectively enable the reliable and practical forecasting of business time series [22]. The Prophet model is a procedure for forecasting time series data based on an additive model, where nonlinear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. We use the decomposable time series model outlined by [PERSON] and [PERSON] [32], with three main model components: trend, seasonality, and holidays. They are combined in Equation (2).  
\[y(t)=g(t)+s(t)+h(t)+\ \epsilon_{t} \tag{2}\]  
Here, \(g(t)\) is the trend function that models nonperiodic changes in the value of the time series, \(s(t)\) represents periodic changes (e.g., weekly and yearly seasonality), and \(h(t)\) represents the effects of holidays that occur in potentially irregular schedules over one or more days. The error term \(\epsilon_{t}\) represents any idiosyncratic changes that are not accommodated by the model; later, we will make a parametric assumption that \(\epsilon_{t}\) is normally distributed [22]. The Prophet model has various parameters related to \(g(t)\), \(s(t)\), and \(h(t)\) that can be set by the user. It also has parameters such as saturating forecasts to control the upper and lower limits of forecast values, trend changepoints to change trend pointers in the data, seasonality to directly create seasonal attributes and scale the seasons, holiday effects to set holidays and special events, regressors to add additional effects, multiplicative seasonality to apply to models with progressively increasing seasonality, and uncertainty intervals to indicate the range of uncertainty in the predicted values. The user can set these parameters to improve forecasting performance. By default, the Prophet model will automatically detect changepoints and will allow the trend to adapt appropriately. However, we can use a few input arguments to have more control over this process. In our experiment, we increased the input argument change_point_scale above the default value because of the underfit problem. We also used holidays as holiday effects.
## 2 Materials and Methods
### Monte Carlo Simulation
#### 2.2.2 Simulation of Data Collection from an Inexpensive HSI Device  
The data simulation approach of CUDAMCML effectively assumes a perfect sensor capable of collecting spectral information at precisely known wavelengths with an infinitely narrow bandwidth. This assumption is a poor one for real devices and is particularly poor for a device that relies on inexpensive LEDs and/or filters with relatively wide bandwidths for spectral data collection. Table 2 gives peak tolerance and spectral widths, for example, narrowband LEDs in the 520-600 nm range. Thus, to model a more realistic device, the analysis was repeated with a copy of the simulation dataset where the spectra were convolved with a 15 nm FWHM Gaussian filter using the Python function _gaussian_filter1d_ from the _scipy_ library [31]. Importantly, the absorption coefficients for oxygenated and deoxygenated blood were also convolved with the same filter to produce revised coefficients in a manner similar to the integrated absorption technique [32].  
Figure 1a shows the smoothing effect of the Gaussian convolution on an example spectrum. The convolution tends to flatten and broaden the two peaks that result from oxyhemoglobin absorption. This effect is not modeled in the EMLB, so to compensate, we convolve the extinction coefficients for oxy- and deoxyhemoglobin with the same 15 nm Gaussian filter. Figure 1b,c show these coefficients before and after the convolution. Analyses were conducted on the data both before and after con  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline SO\({}_{2}\) & 0.40–1.0 (steps of 0.05) \\ Epidermis Thickness (\(\mu\)m) & 60.0 \\ Dermis Thickness (cm) & 2.0 \\ \(f_{mel}\) & 0.015, 0.15, 0.40 \\ \(f_{blood}\) & 0.05 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters for simulated dataset used in wavelength selection.  
\begin{table}
\begin{tabular}{c c c c} \hline
**LED** & **Peak Wavelength** & **Peak Tolerance** & **FWHM** \\ \hline Lumiled LUXEON \(Z\) (green) [33] & 520–540 nm & \(\pm\)0.5 nm & 30 nm \\ Thorlabs LED 560L [34] & 562 nm & (not given) & 11 nm \\ Thorlabs LED 590L [34] & 590 nm & \(\pm\)10 nm & 15 nm \\ Ocean Insight Visible LED [35] & 533 nm & \(\pm\)5 nm & 18 nm \\ Osram LP-T655-QIR2-25 [36] & 562 nm & \(\pm\)4 nm & 19 nm \\ \hline \end{tabular}
\end{table}
Table 2: Example visible band LEDs in the 520–600 nm range of interest. Spectral widths are provided as FWHM.  
differences in performance that might be expected between a costly medical- or industrial-grade instrument and the sort of less expensive device that could benefit patients in poor and rural areas. We refer to analyses with the original data (i.e., before convolution) as the \"ideal\" case and with the convolved data as the \"convolved\" case.
## 3 Experimental Setup
### Photon Counting with PCM
#### 3.3.2 Background and Dark Counts  
The output pulse and dead time, background and dark count can also not be ignored. The dark count is the average number of counts registered by a detector per second when  
Figure 8: The appearance of the photon-counting module: (**a**) dimensions of the module, (**b**) the front view of the module, (**c**) the output waveform from the analog output of conventional photodetector (yellow curve) and PCM (green curve).  
Figure 9: The characteristics of PMT output pulse: (**a**) the single-photoelectron waveform over 2.4 μs, (**b**) the single-photoelectron waveform over 120 ns.  
all input light to the detector is blocked. In practical applications, the dark count and background count cannot be clearly distinguished because the background photons cannot be adequately blocked from reaching a detector. We counted the number of output pulses under the actual experimental environment. The counting time was set to 80 ns which corresponded to the slot time of the communication system. As statistical results are shown in Figure 10, the background and dark counts are 0.001 photo counts per slot time and 0.007 per symbol (32-PPM) time, respectively.
## 2 Related Works
### Cluster-Based Routing Algorithm  
The cluster-based routing algorithm divides routing into intra-cluster routing and inter-cluster routing. Members within a cluster only maintain routing information within their cluster, while inter-cluster routing is decided by the cluster head or gateway through on-demand routing. In large-scale networks, this type of algorithm can effectively reduce the number of nodes involved in routing calculations and shrink the size of the routing table, thereby reducing storage overhead. Therefore, in large-scale networks, it has better performance than other algorithms [14]. For example, Cluster-Based Routing Protocol  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Self-Organizing Routing Algorithm** & **Characteristics** & **Delay** & **Packet Delivery Rate** \\ \hline \multirow{3}{*}{Topology-based routing algorithm} & Route calculation is based on & \multirow{3}{*}{OLSR: up to 2 s [12]} & \multirow{3}{*}{OLSR: 20–80\% [12]} \\  & the network’s topological structure and is divided into & AODV: up to 4 s [12] & AODV: about 85\% [12] \\  & active and passive methods. & & \\ \hline \multirow{3}{*}{Location-based routing algorithm} & Routing is calculated based on & \multirow{3}{*}{GPSR and its improved algorithm: up to 0.45 s [13]} & \multirow{3}{*}{GPSR and its improved algorithm: up to 0.95\% [13]} \\  & the nodes. & & \\ \hline \multirow{3}{*}{Cluster-based routing algorithm} & Divide the network into layers & \multirow{3}{*}{CBRP: up to 2.2 s [14]} & \multirow{3}{*}{CRBP: higher than 90\% [14]} \\  & and categorize routing into & & \\ \cline{1-1}  & inter-cluster and intra-cluster. & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three types of self-organizing routing algorithms.  
(CBRP) confines flooding to cluster heads and gateway nodes, thereby reducing the overhead compared to flooding across all nodes in the entire network [15]. [PERSON] proposed the Cluster-Based On-demand Multi-path Routing Protocol (COMPRP) which searches for multiple paths that meet QoS requirements through the cluster structure. The results showed that the delay of COMRP is lower than 1.5 s, while the delay of CBRP is up to 2.2 s [14]. [PERSON] et al. proposed Efficient Clustering V2V Routing Based on PSO (Particle Swarm Optimization) in VANETs (Vehicular Ad Hoc Networks), whose packet delivery rate is higher than 90% in VANETs [16].  
Although the above algorithms have solved the end-to-end routing problem in self-organizing networks through various methods, they do not consider the overhead of the nodes, which may lead to high queuing delay and high loss rate of packet. Therefore, this paper proposes a new routing algorithm, the shortest queue length-cluster-based routing protocol (SQL-CBRP), which calculates routes based on the queue length of neighbor nodes.
## 2 Digital Platform--DNS with SWAT Module
### Land Phase
#### 2.1.2 Erosion  
Erosion is a key process that initiates the transport of sediment particles down the basin. At the same time, it is one of the most serious problems of environmental degradation, adversely affecting many natural and man-made ecosystems [104]. In agricultural basins,soil erosion not only reduces the nutrient-rich topsoil in place, but also degrades water quality by moving sediment off site. An example of a compromise between empirical and physical algorithms in the SWAT module is the use of a modified Universal Soil Loss Equation (MUSLE) to predict soil erosion at the HRU level resulting in soil particle transport. MUSLE (4) is a modified version of USLE [105] based on the runoff characteristics as the best single indicator for predicting sediment performance at the basin outlet, and factors influencing soil erosion. By using the energy of surface runoff, not rainfall, MUSLE is suitable for use on a daily basis to estimate the sediment efficiency, and takes into account sediment deposition in HRU [106]. MUSLE eliminates the need for delivery ratios and allows the equation to be applied to individual storm events, and therefore improves prediction of sediment loading [107, 108, 109, 84].  
\[SY=11.8\left(Q_{qp}A\right)^{0.56}\left(C\ P\ K\ LS\ F_{CRF}\right) \tag{4}\]  
where: \(SY=\) HRU sediment yield (t/day); \(Q=\) daily runoff volume (mm); \(qp=\) runoff peak discharge (m\({}^{3}\)/s); \(A=\) HRU area (ha); \(C\), \(P\), \(K\), and \(LS\) are dimensionless factors accounting for HRU crop cover, soil protection, soil erodibility, and topography as defined in the original Universal Soil Loss Equation (USLE); and \(F_{CRF}\) is a dimensionless factor to account for coarse fragment cover (stoniness).  
Although the effectiveness of MUSLE in sediment transport analysis has been confirmed [109, 110], it should be remembered that the quality and reliability of sediment simulations are not only directly related to runoff simulations, but also show high sensitivity to rainfall characteristics. The kinetic energy of the raindrops plays an important role in soil detachment, but also causes runoff disturbance. Ignoring them by the SWAT module, in the case of some basins, may lead to a deterioration in the adjustment of simulated data to real conditions [111].
## 2 Literature Review
### Indirect Measurement Techniques
#### 2.2.4 Optics Method  
Several research papers proposed various types of sensors based on the surface plasmon resonance (SPR) optical phenomenon in a variety of fields such as medical diagnostics, biochemical sensing, and water pollution monitoring [26; 27; 28]. Based on the same technique (SPR) [29; 30; 31; 32; 33], other works proposed a novel method for multi-parameter sensors' realization for simultaneous detection of seawater salinity and temperature. Many other studies used Raman spectroscopy for seawater salinity detection [34; 35]. The authors in [36; 37] proposed a salinity sensor with fiber grating. [PERSON] and [PERSON] [38] employed a multimode optical fiber that uses the principle of surface plasmon resonance to measure the salinity of a liquid. Furthermore, [PERSON] et al. [39] proposed a salinity sensor based on photonic crystal fiber.  
Numerous sensor types based on the interferometer method have been deployed, including the Fabry-Perot interferometer structure, the rectangular optical microfiber Sagnac interferometer structure, and the tapered structure [40; 41; 42]. The fiber interferometer structure has the benefit of high sensitivity, but it is inadaptable in harsh field measurement conditions and is mostly used in laboratories [43].  
The refractive index (RI) measurement technique is an optical technique-based salinity sensor as it is correlated to the density according to the relation of Lorentz-Lorenz and thus can be related to the absolute salinity [44; 45]. Over the past years, many optical salinity sensors based on refractive index measurements have been developed using optics concepts such as the Fraunhofer method, total internal reflection, critical angle, etc. [46; 47; 48; 49; 50; 51].
## 3 Evaluation Metrics
### Auc-Roc  
AUC represents the area under the Receiver Operating Characteristic (ROC) curve, which plots the True Positive Rate (TPR) against the False Positive Rate (FPR).  
AUC-ROC reflects the model's ability to distinguish between collision and non-collision events. A higher AUC indicates better separability and overall classification capability across all decision thresholds.  
\[\text{AUC}=\int\limits_{0}^{1}\text{TRP(f)df} \tag{7}\]  
where f = FPR.  
All the above evaluation metrics were employed to comprehensively assess the deep learning model's performance in car collision detection. Accuracy measures overall correctness but may mislead with imbalanced datasets where collisions are rare. Precision minimizes the incorrect prediction of a collision when there is none, while recall ensures all true collisions are detected, which is crucial for safety-critical applications. The F1-score balances precision and recall, offering a single measure of detection efficacy. IoU measures the overlapping between predicted and ground-truth collision regions whereas AUC reflects the model's ability to distinguish between collision and non-collision events. Together, these metrics provide a robust evaluation framework for this safety-critical application.
# Model-Based Slippage Estimation to Enhance Planetary Rover Localization with Wheel Odometry
## 3 Traversability Analyses
### Trajectory Integration with the Dynamical Model  
A first step in our analysis is a strict comparison between the rover's trajectories obtained through the integration of the kinematic [31] and dynamical equations. The kinematic approach does not depend on the properties of the soil, which strongly impact on the rover's dynamical forces. In this set of numerical simulations, we assumed a smooth flat cloudy soil. The rover is moving with a steering angle \(\delta=2^{\circ}\) for the two front wheels. The commanded speed \(\overline{V}\) is imposed to match the ExoMars nominal speed \(v_{c}=40\) m/h [25]. The rover's initial heading angle is \(\psi_{0}=\pi/2\). By applying the dynamical model, an initial speed \(u_{0}=v_{c}\) is adopted, whereas the rover's speed is equal to \(v_{c}\) for the kinematic model. The rover's equations of motion were integrated for \(\sim\)5.5 h to complete a loop with a radius \(R\approx 38.66\) m.  
Figure 3 shows the differences \(\Delta x,\ \Delta y\) and \(\Delta\psi\) between the trajectory integrated with the kinematic and dynamical equations. The trajectory resulting from the kinematic model is along a precise circular path, since the interaction between the wheeled vehicle and terrain is neglected. Those differences are then interpretated as an error in the rover's position. The kinematic and dynamical integrated trajectories differ by \(\Delta x=7.89\) m, \(\Delta y=10.20\) m and \(\Delta\psi=17.80^{\circ}\) at the end of the simulation. These significant errors in both position and orientation indicate the crucial role of an accurate dynamical modeling for rover's navigation.  
Figure 2: A 3D model of the rover in a Martian-like environment.
# Linking Remotely Sensed Carbon and Water Use Efficiencies with In Situ Soil Properties
## 4 Discussion
Moreover, soil chemical properties vary significantly as a function of topographic position. Although we controlled for land cover type (for Landsat data), we did not control for topographic variability. In addition, the scale mismatch between Landsat and MODIS can result in differences in the land cover type assigned for calculating GPP and NPP. Thus, MODIS GPP and NPP values might not represent the same land cover type as Landsat derived GPP and NPP. The scale mismatch among site, MODIS, and GOSIF GPP could influence the results since no common soil variables were detected between MODIS and GOSIF GPP CART results for CUE and WUE. Moreover, the difference in algorithms MODIS and GOSIF GPP products (Table 4) could have contributed to the differences in our results as evident in the low correlation between MODIS GPP and GOSIF GPP. Finally, in implementing our approach, we limited the soil sampling to two soil orders to control for the effect of the variability in soil type on the results. Limiting our sampling to two soil orders could affect the broad application of our results. We anticipated that the observed relationships of CUE and WUE with soil chemical properties could apply to these two soil orders in different climatic regions, but more research is needed to verify the correlations that we observed. The robustness of our results is dependent on the accuracy of satellite data, used to estimate CUE and WUE. Newer, higher spatial resolution data on WUE from ECOSTRESS have the potential to significantly refine these results further. ECOSTRESS produces an operational WUE product at 70 \(\times\) 70 m spatial resolution every 1-5 days from the International Space Station [42, 83]. ECOSTRESS measures thermal infrared radiance, which is converted into a land surface temperature and emissivity (L2_LSTE) product; L2_LSTE is then converted into ET (L3_ET_PT-JPL) using the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) method [84]. ET is then combined with GPP from MOD17 to create the WUE product (L4_WUE); newer versions of ECOSTRESS replace MOD17 with a native 70 m GPP based on the Breathing Earth System Simulator [85]. ECOSTRESS can detect spatial variability in WUE never seen before (Figure S8). These differences are evident throughout the study region landscape, especially in relation to topography, hydrology, and land cover, and land use. Nevertheless, to our knowledge, our effort is one of the few that relate satellite data to soil properties for investigating the soil-vegetation interactions. CUE and WUE terms are important indicators of plants' adaptability to changing environmental conditions, such as precipitation and temperature. Understanding changes in CUE and WUE is thus critical to quantify the response of the terrestrial ecosystem to climate change.
## 2 Materials and Methods
### Experimental Procedure
#### 2.1.2 Mathematical Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mask Types** & **Mask Layers** & **V0 (m/s)** & **V1 (m/s)** & **V (m/s)** & **Average Pore** & **Pictures** \\ \hline \multirow{4}{*}{**Surgical**} & 1 st layer & 37.76 & 8.65 & 36.42 & 2254 \\ \cline{2-6}  & 2 nd layer & 38.65 & 1.31 & 39.20 & 180 \\ \cline{2-6}  & 3 rd layer & 37.61 & 6.2 & 36.60 & 2207 \\ \cline{2-6}  & **Whole mask** & **38.55** & **1.56** & **39.12** & **1570** \\ \hline \multirow{4}{*}{**Reusable Cotton**} & 1 st layer & 38.01 & 3.78 & 38.72 & 4902 \\ \cline{2-6}  & 2 nd layer & 37.85 & 5.46 & 37.96 & 1056 \\ \cline{2-6}  & 3 rd layer & 38.60 & 1.77 & 39.16 & 7624 \\ \cline{2-6}  & 4 th layer (Filter holder) & 38.59 & 2.56 & 39.10 & 7163 \\ \cline{2-6}  & **Whole mask** & **38.52** & **1.25** & **39.18** & **4936** \\ \hline \multirow{4}{*}{**FFP2**} & 1 st layer & 38.21 & 7.27 & 37.72 & 2445 \\ \cline{2-6}  & 2 nd layer & 37.27 & 9.57 & 36.74 & 4715 \\ \cline{1-1} \cline{2-6}  & 3 rd layer & 38.90 & 1.64 & 39.39 & 63 \\ \cline{1-1} \cline{2-6}  & 4 th layer & 38.71 & 1.61 & 39.39 & 39 \\ \cline{1-1} \cline{2-6}  & 5 th layer & 37.81 & 9.94 & 36.40 & 1221 \\ \cline{1-1} \cline{2-6}  & **Whole mask** & **39.02** & **0.46** & **39.39** & **1603** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The mask layers with their entry and exit velocities and pore sizes. Another example applies to the filtering efficiencies of the six _FFP3_ layers: 58, 15, 86, 86, 83, and 34%, respectively. Thus, the above formula can be used to calculate the \(F\) (total) = 1 - (0.58 \(\times\) 0.15 \(\times\) 0.86 \(\times\) 0.86 \(\times\) 0.83 \(\times\) 0.34) = 0.98, or 98%, which again compares well with the experimental data (F whole mask = 97%). The analysis is based on the assumption of the existence of the maximum central velocity within the air duct mask container. This measure provides a convenient comparison of each mask's performance.
## 3 Results and Discussion
### FT-IR Spectroscopy
The paired EO peaks at 1070 and 1058 cm\({}^{-1}\) were correlated with the broadened shoulder of the main b-CD spectral feature at 1066 cm\({}^{-1}\) in the b-CD-EO-T system. A strong EO 1000 cm\({}^{-1}\) peak was noticed in the b-CD-EO-T profile in the indented shoulder of the main spectral feature, which occurred at 1008 cm\({}^{-1}\). The b-CD-EO-T at 948 cm\({}^{-1}\) was the resulting overlap of the 950 and 938 cm\({}^{-1}\) peaks, which were added to the strong EO peak that occurred at 938 cm\({}^{-1}\). It is worth noting that the two strong EO peaks at 870 and 814 cm\({}^{-1}\), which were correlated with the gamma-C-H out-of-plane vibrations of the abundant aromatic components, were severely hampered in the b-CD-EO-T pattern, providing small broadened features at 831 and 806 cm\({}^{-1}\). The b-CD 614 cm\({}^{-1}\) peak was extinguished in the complex, while the 579 cm\({}^{-1}\) was maintained and possibly overlapped with the EO 575 cm\({}^{-1}\) peak. The EO peak at 468 cm\({}^{-1}\) was greatly enhanced and broadened in the b-CD-EO-T mixture at 470 cm\({}^{-1}\) (Figure 1D). In both reacted mixtures, the observed spectral shifts were comprehensively higher than the applied 2 cm\({}^{-1}\) spectral resolution; however, the 1 cm\({}^{-1}\) high-resolution FT-IR spectra were obtained to support and corroborate the reported results. Analysis of the IR spectral profile of the reacted b-CD-EO solid-state mixture supported the actual molecular inclusion phenomena of different oil components inside the lipophilic folder of b-CD. By comparison with the IR patterns of the single separated components, general broadening of signals accompanied by different relative spectral intensities were noticed for both reacted systems, with the spectra not being a mere superposition of the parent patterns [22]. In previous studies, these observed comprehensive spectral scenarios were established as being correlated with host-guest molecular encapsulation and the subsequent molecular complex formation [22; 23].