esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
The \(k\)-th user terminal uses the observation from the time slot \(n\) in (2) to decode the two messages in the SIC manner from \(x_{1}(n)\) to \(x_{2}(n)\). For the user \(k\), the signal-to-interference-plus-noise ratio (SINR) for the signal \(x_{j}(n),\ j\leq k\) through the \(i\)-th HDR is as follows:  
\[\gamma_{j}^{k,i}=\frac{|\mathbf{g}_{i,k}\mathbf{w}_{T,i}|^{2}\rho_{j}}{| \mathbf{g}_{i,k}\mathbf{w}_{T,i}|^{2}\sum_{m=j+1}^{2}\rho_{m}+1}. \tag{3}\]  
On the other hand, each HDR is interfered by the other HDR as in (2), though they decode the messages with the same SIC manner as is carried out at user terminals. The SINR for \(x_{j}(n)\) at the \(i\)-th HDR is given as follows  
\[\gamma_{j}^{R,i}=\frac{|\mathbf{w}_{R,i}^{H}\mathbf{h}_{i}|^{2}\rho_{j}P_{s}}{| \mathbf{w}_{R,j}^{H}\mathbf{G}_{i}\mathbf{w}_{T,i}|^{2}+|\mathbf{w}_{R,i}^{H} \mathbf{h}_{i}|^{2}\sum_{m=j+1}^{2}\rho_{m}P_{s}+1}. \tag{4}\]  
For the \(j\)-th message of time slot \(n\)\(x_{j}(n)\), it should be decoded at the HDR and at both of the user terminals. Therfore, \(R_{j}^{i}\), the transmission rate of \(x_{j}(n)\) through the \(i\)-th HDR is determined by \(\log(1+\min[\gamma_{j}^{R,i},\gamma_{j}^{1,i},\gamma_{j}^{2,i}])\) with the DF protocol considered. Then, the average throughput from the two times through the HDRs is  
\[R =\frac{\sum_{i=1,j=1}^{2}R_{j}^{i}}{2}\] \[=\frac{1}{2}\sum_{i=1,j=1}^{2}\log(1+\min[\gamma_{j}^{R,i},\gamma _{j}^{1,i},\gamma_{j}^{2,i}]). \tag{5}\]
## 2 Results and Discussion
### Headspace Composition of the Volatile Organic Compounds Isolated by HS-SPME--Plant and Hydrosol
**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**RI**} & \multicolumn{6}{c}{**Average Area\% \(\pm\) SD**} \\ \cline{3-10}  & & & **HS-Fr (f1)** & **HS-Dr (f1)** & **HS-HY (f1)** & **HS-Fr (f2)** & **HS-Dr (f2)** & **HS-HY (f2)** \\ \hline
1 & Pentanal & \(<\)900 & - & - & 0.04 \(\pm\) 0.02 & - & - & 0.1 \(\pm\) 0.00 \\
2 & Pyridine & \(<\)900 & - & - & 0.04 \(\pm\) 0.01 & - & - & 0.05 \(\pm\) 0.01 \\
3 & Furfurral & \(<\)900 & - & - & 0.10 \(\pm\) 0.01 & - & - & 0.16 \(\pm\) 0.02 \\
4 & (E)-Hex-2-enal & \(<\)900 & - & - & - & 0.03 \(\pm\) 0.01 & - & - \\
5 & Hexan-1-ol & \(<\)900 & - & - & 0.02 \(\pm\) 0.00 & - & - & 0.02 \(\pm\) 0.01 \\
6 & Nonane & 904 & 0.09 \(\pm\) 0.00 & 0.15 \(\pm\) 0.02 & - & 0.14 \(\pm\) 0.01 & 0.07 \(\pm\) 0.01 & - \\
7 & (Z)-Non-2-ene & 920 & 0.01 \(\pm\) 0.00 & - & 0.01 \(\pm\) 0.00 & - & - \\
8 & \(\alpha\)-Thujene & 937 & 0.24 \(\pm\) 0.02 & 0.32 \(\pm\) 0.02 & - & 0.13 \(\pm\) 0.00 & 0.13 \(\pm\) 0.03 & - \\
9 & \(\alpha\)-Pinene & 945 & 0.02 \(\pm\) 0.00 & 0.03 \(\pm\) 0.00 & 0.03 \(\pm\) 0.00 & 0.02 \(\pm\) 0.00 & 0.02 \(\pm\) 0.01 \\
10 & Benzaldehyde & 970 & - & 0.01 \(\pm\) 0.00 & 0.10 \(\pm\) 0.01 & - & 0.01 \(\pm\) 0.00 & 0.10 \(\pm\) 0.03 \\
11 & Sabinetne & 982 & 4.07 \(\pm\) 0.29 & 5.32 \(\pm\) 0.27 & 0.04 \(\pm\) 0.01 & 7.70 \(\pm\) 0.56 & 3.99 \(\pm\) 0.33 & 0.03 \(\pm\) 0.01 \\
12 & \(\beta\)-Pinene & 986 & 0.27 \(\pm\) 0.00 & 0.37 \(\pm\) 0.02 & - & 0.27 \(\pm\) 0.01 & 0.19 \(\pm\) 0.05 & - \\
13 & \(\beta\)-Myrcene & 995 & 0.49 \(\pm\) 0.04 & 0.46 \(\pm\) 0.02 & - & 0.50 \(\pm\) 0.01 & 0.24 \(\pm\) 0.07 & - \\
14 & Decane & 1003 & - & 0.04 \(\pm\) 0.00 & - & - & 0.01 \(\pm\) 0.00 & - \\
15 & 2,6-Dimethylcyclohexnam-1-ol & 1009 & - & - & 0.03 \(\pm\) 0.01 & - & - & - \\
16 & [Z)-Hex-3-enyl] acetate & 1011 & 0.44 \(\pm\) 0.07 & - & - & - & - & - \\
17 & \(\alpha\)-Phellanderene & 1011 & - & 0.02 \(\pm\) 0.00 & 0.08 \(\pm\) 0.01 & 0.01 \(\pm\) 0.00 & - & 0.07 \(\pm\) 0.03 \\
18 & Hexyl acetate & 1018 & 0.07 \(\pm\) 0.01 & - & - & - & - & - \\
19 & \(\alpha\)-Terpinene & 1023 & 0.22 \(\pm\) 0.02 & 0.11 \(\pm\) 0.00 & 0.10 \(\pm\) 0.00 & 0.09 \(\pm\) 0.01 & 0.04 \(\pm\) 0.01 & 0.09 \(\pm\) 0.03 \\
20 & \(p\)-Cymene & 1032 & 0.54 \(\pm\) 0.01 & 1.21 \(\pm\) 0.03 & - & 0.87 \(\pm\) 0.03 & 0.68 \(\pm\) 0.32 & - \\
21 & \(\beta\)-Phellandrene & 1037 & 0.12 \(\pm\) 0.01 & 0.09 \(\pm\) 0.00 & 0.12 \(\pm\) 0.00 & 0.06 \(\pm\) 0.00 & 0.04 \(\pm\)
## 4 Data Analysis and Interpretation
### Sensibility Analysis for Implementing V2G  
Several power smoothing methods are compared in this section in terms of performance against several key metrics: standard deviation, crest factor, form factor, ripple index, and coefficient of variation. The methods evaluated include the baseline case, MA, RR, and two variants of the V2 GSUN method with different SoCs of 50% and 20%. Figure 18 provides a compact analysis of the power smoothing metrics achieved by the proposed method. The standard deviation, a measure of data dispersion around the mean, showed significant improvement with the V2 GSUN method at 50% SoC, achieving the lowest value (3.540) compared to the baseline (3.950), indicating enhanced consistency and reduced variability in power generation. Although the crest factor, which evaluates the relationship between peak values and the root mean square (RMS) value, showed no notable improvements with the smoothing methods, the baseline case had the lowest value (1.632), indicating fewer extreme peaks. The MA and RR methods showed higher crest factors  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**(20\%) \(SoC_{SC}\) Initial** & **Variance without Compensation** & **Variance with Compensation** & **Variance Reduction** & **Renewable Energy** & **Renewable Energy** & **Energy Difference** \\  & **Frequency without** & **Frequency with (\%)** & **Frequency without (\%)** & **Delivered without Compensation** & **Delivered with Compensation** & **Frequency with Difference** \\  & & & **Frequency with (kWh)** & **Frequency with (kWh)** & **Frequency with (kWh)** & **Frequency with (kWh)** \\ \hline V2 GSUN & 15.63 & 13.12 & 16.05 & 3.21 & 3.06 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of renewable energy production with the application of the compared power smoothing methodologies.  
Figure 16: Result of prioritized SoC charging for storage system using V2G while mitigating generated power peaks.  
Figure 17: Real-time adjustment of data window size (t).  
(1.651 and 1.652, respectively), suggesting more frequent peaks. The form factor, which measures the uniformity of data distribution, also highlighted the V2 GSUN method at 50% SoC, with the lowest value recorded (1.061), suggesting a more consistent signal than the baseline case (1.084). The ripple index, indicating signal variation relative to its mean, was significantly reduced with the V2 GSUN method at 50% SoC (0.354), demonstrating smoother and more stable power generation compared to the baseline (0.418). Similarly, the coefficient of variation, assessing variability relative to the mean, was lowest with the V2 GSUN method at 50% SoC (0.354), showing the least relative dispersion and greatest consistency in power generation. At the same time, the baseline case had the highest value (0.418), reflecting the most significant relative variability.
# A Novel Expectation-Maximization-Based Blind Receiver for Low-Complexity Uplink STLC-NOMA Systems
## 4 Blind Energy Estimation (BEE)
### Energy Estimation (Line 15)  
Finally, the received energy \(P_{n}\gamma_{n}\) for each STA is estimated from the inferred means and weights, where the equation is derived from the following procedure. Without loss of generality, assuming \(\mu_{1}<\mu_{2}<\cdots<\mu_{M}\), the inferred means can correspond to the normalized \(M\)-ASK constellation points as follows:  
\[\mu_{k}=\frac{2k-1-M}{\sqrt{\sum_{m=1}^{M}{(2m-1-M)^{2}/M}}}\sqrt{P_{n}\gamma_ {n}}, \tag{13}\]  
The AP is interested in estimating the received energy of the STA \(\sqrt{P_{n}\gamma_{n}}\ (\geq 0)\) from the inferred constellation points, which can be obtained through the following equation:  
\[\frac{1}{M}\sum_{k=1}^{M}{|\mu_{k}|}=\frac{1}{M}\sum_{k=1}^{M}\frac{|2k-1-M|}{ \sqrt{\sum_{m=1}^{M}{(2m-1-M)^{2}/M}}}\sqrt{P_{n}\gamma_{n}}. \tag{14}\]  
Here, \(1/M\) on the left-hand side represents the weight assuming all clusters have the same weights, i.e., \(\mu_{k}=1/M,\ \forall k\). However, in practice, each point on the constellation may not be expressed with exactly the same probability depending on the generated bit sequence. Hence, the AP can estimate the received energy of the STA from the inferred means and weights as  
\[\sum_{k=1}^{M}{\pi_{k}|\mu_{k}|}=\frac{\sum_{k=1}^{M}{|2k-1-M|}}{\sqrt{M\sum_{ m=1}^{M}{(2m-1-M)^{2}}}}\sqrt{P_{n}\gamma_{n}}. \tag{15}\]  
Finally,  
\[\sqrt{P_{n}\gamma_{n}}=\frac{\sqrt{M\sum_{m=1}^{M}{(2m-1-M)^{2}}}}{\sum_{m=1} ^{M}{|2m-1-M|}}\sum_{k=1}^{M}{\pi_{k}|\mu_{k}|}. \tag{16}\]
## 8 Cooperative Model-Predictive Control Methodology
### Implementation of the MPCs  
The docking controller is designed to have several layers to decide how to perform the docking maneuver based on the distance between the agent \(i\) and the neighboring agents. The layers, as mentioned earlier, are the selector NMPC and LMPC. Essentially, the selector works as a governor and decides the reference trajectory that the LMPC tracks. If the absolute distance between agent \(i\) and neighboring agents is greater than a threshold, the NMPC generates a trajectory toward the neighboring agents using neighboring state information. Note that NMPC does not aim to achieve the terminal state constraint, but it repeatedly minimizes the state error within the given prediction horizon. Then, the generated trajectory, which creeps toward the neighbors, is passed to LMPC. If the distance is smaller than the threshold, LMPC receives the neighboring state information to generate control inputs without needing NMPC. The distance and threshold are denoted as \(\mathbf{e}\ =\ \|\left[\mathbf{p}_{j}\right]  
When triggered, NMPC generates a rough trajectory toward agent \(j\) for a given prediction horizon \(M\) based on the formulation given in (64). Then, a portion of this trajectory that is determined by the control horizon \(M_{u}\) is extracted, and this is denoted as \(x_{mmpc}\). The optimization parameters for NMPC such as \(M\), \(t_{f}\), and \(M_{u}\) are selected empirically to achieve a rough trajectory and allow a time interval to queue a new trajectory to guide the agent \(i\) to the vicinity of other agents. The selection of the optimization parameters with the right-hand-side sparsity template described in [46] provides a longer time to queue a new trajectory. Before the LMPC receives the output of the NMPC or the neighboring state information, i.e., \(x_{mmpc}\) ir \(x_{j}(t-1)\), respectively, a line or a set of line segments are populated based on the prediction horizon of the LMPC. Finally, the tracking based on the optimization provided in (66) is performed by the LMPC.
# Fourier Domain Adaptation for the Identification of Grape Leaf Diseases
## 2 Materials and Methods
### Methods
#### 2.2.3 FDA for Image Identification  
We proposed our identification approach of grape leaf diseases based on the Fourier domain adaptation technique. Firstly, it consists of swapping the low-frequency amplitudes of the source image spectrum with those of those of the target image, the style of which should be borrowed. Then, the generated new image and the source domain image are combined into a new dataset, which is put into AlexNet, VGG13, and ResNet101 for image identification training and tested with the target domain dataset to obtain the identification results. It shows an image identification model for grape leaf diseases based on Fourier domain adaptation with VGG13 as an example in Figure 4.  
Figure 4: FDA for image identification, taking VGG13 as an example.  
Figure 3: Effect of the size of the domain \(\beta\).
# A Passive RF Testbed for Human Posture Classification in FM Radio Bands
## 5 Conclusions  
The main goal of this work was to design a system capable of detecting and classifying human postures based on passive RF sensing techniques. Firstly, an RF sensing solution based on two SDRs was described. The RF sensing system was used to collect data and generate datasets. These datasets were created taking into consideration three proposed classification scenarios (human postures) to be detected, which were labeled as classes. A features selection step was carried out applying an ANOVA feature selection algorithm. Regarding the classification stage, three different classification techniques were used: the sum of distances to all clusters' points, SVM, and KNN. The SVM classifier with an RBF kernel achieved the highest performance, reaching an overall classification accuracy of 87.896%, a mean specificity of 93.948%, and a mean F1-score of 87.908%. The main conclusion of this work relies on the capacity of the proposed testbed to passively recognize human postures through FM radio bands, despite the simple classification algorithms proposed in this initial study. Future work includes the investigation and identification of different classification features and the evaluation of more adequate classification methods leveraged by the recent advances in machine learning and deep learning.  
Conceptualization, J.P. and R.O.; Methodology, J.P. and R.O.; Software, J.P.; Validation, J.P. and E.C.; Investigation, J.P., R.O. and E.C.; Writing--original draft, J.P., R.O. and E.C.; Writing--review & editing, R.O. and E.C. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Fundacao para a Ciencia e Tecnologia through the projects CELL-LES86G (2022.08786.PTDC) and UIDB/50008/2020.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Conflicts of Interest: The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **Specificity (\%)** & **F1-Score (\%)** \\ \hline Class 0 & 90.832 & 85.407 & 87.434 & 92.530 & 86.409 \\ \hline Class 1 & 88.475 & 83.043 & 82.212 & 91.606 & 82.625 \\ \hline Class 2 & 96.485 & 95.350 & 94.040 & 97.707 & 94.691 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Accuracy of the performance evaluation metrics considering the data from the confusion matrix for all classes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Number of Datasets** & **Percentage** \\ \hline Feature1 and Feature2 & 2 & 9.091\% \\ \hline Feature6 and Feature1 & 2 & 9.091\% \\ \hline Feature6 and Feature2 & 18 & 81.818\% \\ \hline Total & 22 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 17: Frequency of each combination of the selected features for all 180 s datasets.
# Solar Irradiance Forecasting with Transformer Model
## 4 Discussion  
Currently, the decoder (simulator) part of the model is used to predict the future in the form of daily data. In the future, a partition of the latent space among several independent decoder blocks will be possible. In this way, each of the independent decoder blocks could predict a different time unit. In addition to daily data, NASA POWER also provides monthly data containing the annual average and hourly data. The disadvantage of hourly data is that it does not contain regional data and thus cannot be used on the encoder input side. Using independent decoder blocks architecture, the Transformer can be used to aggregate gradients from multiple nodes (decoders) in the encoder part, thus strengthening the accuracy of the training process of the encoder part, assuming there is a replenishment of data in each time category. It is also a prerequisite to have synchronized windows across all time categories.
## 2 Basic Models
### Operating Constraints  
Considering the multi-period operation of a power system with renewable plants and ESUs, the constraints are gathered as shown below:  
\[\sum_{i\in G}p_{it}^{g}+\sum_{j\in S}\Bigl{(}p_{it}^{sd}-p_{it}^{sc}\Bigr{)}+ \sum_{k\in W}p_{kt}^{r}=\sum_{q\in D}p_{qt}^{d},\forall t\in T \tag{1}\]  
\[-f_{l}\leq\sum_{i\in B}\pi_{il}p_{it}^{n}\leq f_{l},\forall l\in L,\forall t\in T \tag{2}\]  
\[p_{i}^{gn}\leq p_{it}^{g}\leq p_{it}^{gn},-r_{i}^{-}\leq p_{it+1}^{g}-p_{it}^{ g}\leq r_{i}^{+},\forall i\in G,\forall t\in T \tag{3}\]  
\[0\leq p_{it}^{sc}\leq p_{j}^{g}b_{jt}^{g},0\leq p_{it}^{sd}\leq p_{j}^{s}\Bigl{(} 1-b_{jt}^{s}\Bigr{)},\forall j\in S,\forall t\in T \tag{4}\]  
\[\alpha_{j}^{l}E_{j}^{s}\leq e_{\beta}+h\sum_{\tau=1}^{t}\Bigl{(}p_{j\tau}^{sc }\eta_{j}^{c}-p_{j\tau}^{sd}/\eta_{j}^{d}\Bigr{)}\leq E_{j}^{s},\forall j\in S,\forall t\in T \tag{5}\]  
\[\sum_{\forall t\in T}p_{j\tau}^{sr}\eta_{j}^{c}=\sum_{\forall t\in T}p_{jt}^{ sd}/\eta_{j}^{c},\forall j\in S \tag{6}\]  
\[0\leq p_{kt}^{r}\leq p_{kt}^{rm},\Delta p_{kt}^{rm}=p_{kt}^{rm}-p_{kt}^{r}, \forall k\in W,\forall t\in T \tag{7}\]where (1) describes the system-wise power balance. Moreover, (2) says the active power flow in line \(l\) cannot exceed its transmission capacity; \(\pi_{il}\) is the power transfer distribution factor from bus \(i\) to line \(l\); \(p_{it}^{n}\) represents the net power injection at bus \(i\) in period \(t\), which depends on the production of generator (\(p_{it}^{s}\)), the power exchange with ESU (\(p_{it}^{sc}/p_{it}^{sd}\)), the dispatched renewable power (\(p_{it}^{r}\)), as well as the load (\(p_{it}^{d}\)), if there is any. Moreover, (3) restricts the output and ramping rates of generators.  
The charging and discharging behaviors of ESUs are constrained by (4), where \(P_{j}^{s}\) is the power capacity; when the binary indicator is \(b_{jt}^{s}=1/0\), the ESU \(j\) operates under the charging/discharging mode in period \(t\), with a corresponding power lower than \(P_{j}^{s}\). The state-of-charge (SoC) dynamics of ESU is expressed by (5), where \(E_{j}^{s}\) is the energy capacity, and \(a_{j}^{t}\) is a constant coefficient indicating the lower bound of SoC; \(e_{j0}\) is the initial level of SoC, and \(h\) is the duration of one time slot, i.e., one hour; besides, the terminal SoC is set to the initial state \(e_{j0}\), implied by (6).  
In (7), \(p_{it}^{m}\) is the available renewable power in renewable plant \(k\) in period \(t\), which is uncertain and determined by the weather conditions; hence, the dispatched renewable power, \(p_{kt}^{r}\), cannot exceed \(p_{kt}^{vm}\). Meanwhile, the curtailed renewable power is defined as \(\Delta p_{kt}^{r}\).
# Design of Micro-Phasor Measurement System to Identify Phasor of Distribution Transformers
## 2 Design of the Micro-Phasor Measurement System
### Voltage Angle Calculation  
The GPS 1 PPS signals were adopted as the standards for synchronous signals sampling in two locations. Once the sampling synchronization is completed, the DSP receives 1 PPS that serves as the starting point of time measurement. To identify the absolute phase angle of voltage, the voltage passed through the sampling and zero-crossing circuits to produce zero-crossing signals. Once the DSP received the signals, time measurement is stopped. The time difference was then calculated and converted into a phase angle.  
In Figure 3, \(\Delta T_{1}\) is the time difference between the reception of the 1 PPS signal and that of the voltage zero-crossing signal. The entire period could be converted to 360\({}^{\circ}\), and be expressed as the reciprocal of frequency (\(T=\) 1/\(f\)), \(\Delta T_{2}\) could be calculated as follows:  
\[\Delta T_{2}=\frac{1}{f}-\Delta T_{1} \tag{1}\]  
\[\theta=360\frac{\Delta T_{2}}{T}=360\Delta T_{2}f \tag{2}\]  
where \(\Delta T_{1}\) is the time obtained through measurement, \(\Delta T_{2}\) the time acquired through calculation, \(\theta\) the absolute angle converted from \(\Delta T_{2}\), and \(f\) is the frequency of voltage signals. Subsequently, (1) was substituted into (2) to obtain (3).  
\[\theta=360(1-\Delta T_{1}f) \tag{3}\]  
Therefore, the absolute angle \(\theta\) could be calculated once the time difference between 1 PPS and voltage zero-crossing signals is measured.  
Figure 3: Method to measure voltage angles.
# Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis
## 4 Dataset Description and Properties
### Feature Engineering and Data Preparing  
To provide a collection of input features that will be utilized to train a machine learning model, feature engineering is the process of choosing, altering, and extracting features from raw data. Based on network analysis, a new feature called \"network_bytes\" has been built to represent the transmitted bytes over the network [35]. Network bytes are total bytes transferred by bytes (source to destination bytes) and dbytes (destination to source bytes). In the context of improving the performance of the machine learning model, it is important to standardize the data for the numerical data standard scalar method. This helps in streamlining numerical data by transforming variables so that they are on the same scale, that aims to enhance the efficiency of the used machine learning algorithms and make the variables more interpretable [36]. One hot encoding process has been used to process categorical data. This is a process which transforms the categorical variables into a format that is suitable for modelling. Categorical variables are variables that take on values that are drawn from a limited set of categories. In this experiment, Onehot encoding has been applied to proto, service and state columns. After applying the one-hot function, the encoded data had 197 columns.
## 4 Discussion
### Remote Sensing of Boreal Peatland Species Diversity  
In low-diversity ecosystems such as peatlands, the loss of species diversity may have cascading effects on ecosystem function and productivity [44]. The low functional redundancy of peatlands is such that the loss of a species may also represent the loss of an entire plant type, thereby negatively effecting ecological productivity [47]. We demonstrate that drought in peatlands negatively affects diversity, particularly of mosses and herbaceous species. Specifically, variation and heterogeneity in spectral reflectance increased with increasing Shannon diversity among plots. Increasing spectral heterogeneity has been previously used as a proxy for diversity in studies that have mapped peatland ecological diversity using remote sensing [91; 92; 93]. This type of study is based on the species-area relationship in which species richness increases with scale across a heterogeneous landscape [94]. These studies have extrapolated species richness by calculating indices such as the normalized difference vegetation index (NDVI), then calculating heterogeneity for pixel aggregates of varying spatial scales [53; 55]. Where these approaches have modeled diversity as a function of area, our approach captures diversity directly by comparing the spectral heterogeneity among plots with varying levels of diversity [53]. Alternative approaches have attempted to map diversity through the identification of unique species [95; 96; 97; 54]. However, this approach is challenging in non-forested systems in which individual organisms do not occupy an entire pixel. Our results indicate remote sensing may be used to estimate relative levels of diversity directly by comparing the spectral heterogeneity of measurements performed over a variety of different canopy types. We suggest that these results offer the ability to track diversity in systems with mixed or heterogeneous community assemblages that include trees, shrubs, graminiods, or other plant types [98].