esa-sceva/satcom-chunk-collection huggingface dataset:
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **NB-IoT** & **SigFox** & **LoRa** & **Google Thread** & **WirePas Mesh** \\ \hline Channel Bandwidth & 180 kHz & 100 Hz & 125 kHz & 600 kHz\(-\)5 MHz & 2 MHz \\ \hline Full Duplex & No & No & No & No & Yes \\ \hline Downlink & 127 kbps & 100–600 bps & 300 bps–50 kbps & 20–250 kbps & 1 Mbps \\ \hline Uplink & 159 kbps & 100–600 bps & 300 bps–50 kbps & 20–250 kbps & 1 Mbps \\ \hline Latency & \textless{}10 s & \textgreater{}20 s & 1–2 s & \textless{}100 ms, 1 s & \textless{}10 ms \\ \hline Data Limit & NA & \
\begin{tabular}{c} 140 packets/day uplink, \\ 4 packets/day downlink \\ \end{tabular} & NA & NA & NA \\ \hline \multirow{2}{*}{Duty Cycle} & NA & Max 30 s/h (\textless{}1\%) & \textless{}1\% &
\begin{tabular}{c} \textless{}1\% in some \\ frequency bands \\ \end{tabular} & NA \\ \hline Allocated Bandwidth & \multirow{2}{*}{200 kHz} & \multirow{2}{*}{200 kHz} & \multirow{2}{*}{125 kHz} & \multirow{2}{*}{-} & \multirow{2}{*}{-} \\ EU & & & & \\ \hline Maximum & \multirow{2}{*}{14, 20, 23 dBm} & \multirow{2}{*}{22 dBm} & \multirow{2}{*}{22 dBm} & \multirow{2}{*}{0 dBm} & \multirow{2}{*}{\textless{}10 dBm EU} \\ Transmission Power & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of IoT communication protocols. Figure 1: IoT communication protocols. Viable communication protocols that can be integrated into the Internet of Things (IoT) concept. yet in scientific literature, making for another novelty element of this paper. The ability to further test and develop new communication protocols, makes from the LoRa traffic generator an asset that can significantly contributeto large-scale LoRa networks research. This paper has the following structure: First, the presentation of a short introduction explaining the goal of this work, it mentions the communication protocols that can be integrated into the IoT concept. The second section presents the LoRaWAN communication architecture. The third section presents the evaluation of LoRa modulation. This section provides a detailed assessment of performance in terms of Bit Error Rate (BER) when different spreading factors and communication channels bandwidths are used. The fourth section presents a novel LoRa traffic generator based on SDR (Software Defined Radio) technology. The LoRa traffic generator contribute to experimentally measuring the performance provided by LoRa under capture effect and the quasi-orthogonality condition of LoRa spreading factors. The fifth section makes for the conclusion of this work and the discussion of the obtained results.
## 3 Experiments and Results
### New Combinations of Signals Using Four Common Methods
#### 3.3.1 CSP Using New Combinations of Signals
In Table 2, for FB 5 and 6, the calculated \(p\)-value is 0.002 (\(p\)-values = 0.002), which is less than 0.05 and the others. In second place, FB 5 with 9 has a \(p\)-value of 0.003 (\(p\)-value \(<\) 0.05). In Table 6, for FB 3 with 9, the calculated \(p\)-value is 0.0061 (\(p\)-value = 0.0061), which is less than 0.05 and the others. In second place, FB 4 with 9 has a \(p\)-value of 0.0058 (\(p\)-value = 0.0058). The results show that the newest combinations of signals are statistically significant for the left and right hands and the hand and foot.
## 3 RACH Success Probability and Power Consumption Model for LEO Satellite-Based NB-IoT Networks  
In this section, we derive the RACH success probability based on stochastic geometry theory in LEO satellite-based networks. Furthermore, we also analyze the power consumption of NB-IoT terminals using repetition to achieve the CE mechanism.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Link** & **Physical Random Access Channel** & **Number of Repetitions** \\ \hline Uplink & NPRACH & \{1, 2, 4, 8, 16, 32, 64, 128\} \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of repetitions in NB-IoT NPRACH.  
Figure 3: Example of NB-IoT coverage enhancement mechanism.
# Effect of Washing Temperature on Adsorption of Cationic Dyes by Raw Lignocellulosic Biomass
## 3 Results and Discussion
### Characterization of Materials
#### 3.1.2 Chemical Characterization
* The intensities of the bands corresponding to O-H hydroxyl group vibrations (-3428 cm\({}^{-1}\)) and C=O elongation vibrations (1740, 1649, and 1628 cm\({}^{-1}\)) decrease with increasing wash water temperature for both materials. This trend aligns with the reduction in the acidity observed in the pHpzc measurements and Boehm titrations. This decrease may be attributed to the breaking of certain chemical bonds at higher temperatures or to structural damage, which can limit the accessibility of specific surface chemical functions.
# Design of a New Non-Coherent Cross-QAM-Based \(M\)-ary DCSK Communication System
## 3 Performance Analysis
### XQAM-MDCSK (\(M\geq 32\))  
For the XQAM-MDCSK constellation (\(M=32,128,...\)), \(P_{b\_{,}i_{2}}(\gamma_{s})\), \(P_{b\_{,}q_{2}}(\gamma_{s})\), and \(P_{b\_{,}i_{3}}(\gamma_{s})\) are special, while the BER expressions of the other bits can be derived according to the method in [21]. For the bit \(i_{1}\), the decision boundary is the \(y\)-axis, while the decision domain is the \(x\)-axis for the bit \(q_{1}\), the conditional BER of \(i_{1}\) is equal to the one of \(q_{1}\), which is given by  
\[P_{b\_{,}i_{1}}(\gamma_{s})=P_{b\_{,}q_{1}}(\gamma_{s})=\frac{4}{M}\left[ \frac{3}{4}\sqrt{\frac{M}{2}}\sum_{t=1}^{\frac{2\sqrt{2M}}{8}}q(2t-1)+\frac{1 }{2}\sqrt{\frac{M}{2}}\sum_{t=1+\frac{2\sqrt{2M}}{8}}^{\frac{2\sqrt{2M}}{8}}q (2t-1)\right]. \tag{6}\]  
For the bit \(i_{2}\), its decision boundary is a square in the center, which can be divided into inside and outside the square. The conditional BER of \(i_{2}\) can be calculated as  
\[P_{b\_{,}i_{2}}(\gamma_{s}) = \frac{4}{M}\left[2\sum_{t=1}^{2^{m-2}}\left(q(2t-1)-q\left(2t-1+2 ^{m+1}\right)\right)\left(2^{m-1}-\sum_{t=1}^{2^{m}}q(2t-1)\right)\right. \tag{7}\] \[+ \left[\frac{M}{8}-\left(2^{m-1}-\sum_{t=1}^{2^{m}}q(2t-1)\right) ^{2}\right]\right].\]  
For the bit \(q_{2}\), the judgment domain is divided into two parts. The first part is the boundary of two horizontal judgments, and the second part is the concave and convex judgment boundaries near the \(y\)-axis. The conditional BER of \(q_{2}\) can be calculated as \[P_{b_{-}q_{2}}(\gamma_{s}) =\frac{4}{M}\Bigg{[}\Bigg{[}2^{m-2}\times\sum_{t=1}^{2^{m-2}}\Big{(}q( 2t-1)-q\Big{(}2^{m+1}+2t-1\Big{)}\Big{)}+3\times\Big{(}2^{m-2}\Big{)}\Bigg{(} \sum_{t=1}^{2^{m-2}}q(2t-1)\] \[+\sum_{t=1+2^{m-2}}^{2m-1}q(2t-1)-\sum_{t=1+2^{m-1}}^{3\times 2^{m-2 }}q(2t-1)\Bigg{)}\Bigg{]}+\Bigg{[}\Bigg{(}2^{m-2}-\sum_{t=1}^{2^{m-2}}\big{(}q( 2t-1)\] \[-q\Big{(}2t-1+2^{m+1}\Big{)}\Big{)}\Big{)}\times\Bigg{(}\sum_{t=1}^{2^ {m-2}}q(2t-1)\,+\sum_{t=1+2^{m-2}}^{2^{m-1}}q(2t-1)-\sum_{t=1+2^{m-1}}^{3\times 2 ^{m-2}}q(2t-1)\Bigg{)}\] \[+\Bigg{(}\sum_{t=1}^{2^{m}}(-1)^{t-1}q(2t-1)\Bigg{)}\times\Bigg{(} 2^{m-2}-\sum_{t=1+2^{m-1}}^{2^{m}}q(2t-1)\Bigg{)}\Bigg{]}\Bigg{]}. \tag{8}\]  
For the bit \(i_{3}\), the judgment domain is also divided into two parts. The first part is two open rectangles, and the second part is four oblique triangular regions, for example \(X_{1}\), \(X_{2}\), \(X_{3}\), and \(X_{4}\) in Figure 2a.
## 6 FWA Transport Network Solutions
### WDM/OTN  
The WDM/OTN scheme implementation helps in the transmission of multiplexed and transparent signals over the network links to multiple DU sites. Consequently, the implementation enhances the fiber capacity by supporting multiple channels on a shared fiber optic installation [47; 371; 385]. This can be achieved through the in-phase and quadrature components (I/Q) data encapsulation in the OTN frame. Then, the resulting frame is subsequently WDM multiplexed. Therefore, the resulting frame can be routed to the destination port using any wavelength [361]. In addition to its ability to minimize the required fiber resources, other remarkable benefits of this scheme are the provision for network protection, OAM capabilities, service reliability, service level agreement management, and network demarcation. Besides, this scheme offers promising features concerning high bandwidth and low latency. Furthermore, the WDM/OTN scheme is an effective solution for addressing the required pluggable colored optical interface at CU and DU by the passive WDM solution. Additionally, due to the fact that a colored optical interface is not required at RRH by the OTN [371], the challenges of wireless equipment deployment are significantly lessened by the solution. The scheme also presents an uncomplicated system scalability advantage and an improved optical network reach [372; 386], because, there is no need for wireless equipment optical interface replacement in the process of a system upgrade from non-C-RAN architecture to the C-RAN. Nevertheless, the main weakness of the scheme is that the equipment cost is comparatively higher. Furthermore, the OTN employment for CPRI transport over OTN infrastructure demands substantial precaution. This is due to the fact that, in the CPRI/eCPRI clients mapping and transportation, OTN standards are not aligned. Additionally, the introduced feature such as forward error correction (FEC) can further contribute to the system latency. It is remarkable that though the power supply is not mandatory for WDM transport in the solution, it is needed for active management as well as wavelength translation [47; 371; 372; 385; 386].
## 3 Algorithm Implementation and Case Study
### Algorithm Applications
#### 3.3.1 Case 1  
The travel begins at the intersection of Boxue Road and Fengnian Road in Jiading District and ends at the intersection of Zhongshan North Road and Wuning Road in Putuo District. Jiading District is located in the Shanghai suburbs. Near Fengnian Road and Boxue Road, there are many residential communities. Many new Shanghaies buy homes and settle down here. Putuo, located in the heart of Shanghai, between Zhongshan North Road and Wuning Road, is home to various firms and businesses. This is a typical instance of a working and living travel algorithm. The transfer algorithm presented in this study generates three better transfer plans.  
Figure 8 presents the route map of Plan 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Plan** & **Transfer Number** & **Walk Distance (m)** & **Cost (CNY)** & **Time (min)** \\ \hline Plan 1 & 1 & 700 & 14 & 48 \\ Plan 2 & 1 & 2400 & 6 & 90 \\ Plan 3 & 1 & 2000 & 7 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Plan Comparison for path analysis of Case 1.  
Figure 8: Query result for Plan 1 in Case 1.  
Figure 10 presents the route map of Plan 3.  
Figure 10: Query result for Plan 3 in Case 1.  
Figure 9: Query result for Plan 2 in Case 1.  
In Case 1, the transfer numbers of the three plans are all one, as demonstrated in Table 3. The P&R transfer parking mode is used in Plan 1. It takes 18 min to self-drive, covering 11 km to the Nanxiang P&R parking lot, then transfer to the rail transit line 11, and then walk 700 m to the destination from Caoyang Road station. The complete journey takes 48 min and costs 14 CNY, comprising a P&R parking fee of 10 CNY and a rail transit fee of 4 CNY. Plan 2 and Plan 3 both adopt bus-to-rail transportation systems. Plan 2 has a total walking distance of 2.4 km, whereas Plan 3 has a total walking distance of 2 km. Both plans take 90 min to complete the journey. For individuals residing in the suburbs and working in the city on weekdays, Plan 1 offers a daily reduction of 3 km in travel distance, an 84-min time savings, and an additional cost of 7 CNY. Considering most people's living patterns and consumption conceptions, travelers prefer to use less time and less walking distance to reach their destination, and most people can overlook the cost if it is less than 10 CNY per day. Therefore, Plan 1 is the best option after careful evaluation. Of course, each traveler may tailor his or her itinerary to meet his or her requirements. In Case 2, the number of transfers for both plans is two. As shown in Table 4, Plan 1 takes 15 min to drive 9 km from the starting point to the P&R transfer station in Songjiang University Town, and then transfers to lines 9 and 8 to reach the end point. Compared to Plan 2, Plan 1 is more expensive but takes 40 min less time and 1.4 km less walking. Under the current low-carbon travel concept, Plan 1 is undoubtedly the best choice for most people living in the suburbs when they want to go into the city center to play, do business, or have a party. There are also several options for travelers to choose from to suit their individual needs.
## Materials and Methods
### Data Analysis
#### 2.3.2 Second Step: Multiple Linear Regressions  
The researchers used multiple linear regressions to discover the publication volumes (Category a), publication components (Category b), publication moments (Categories c and d), and publication topics (Category e) that increased the recognition of content.  
In the context of social network services, multiple linear regressions focus on quantitative analyses of activity metrics from organizations and users [16].  
The information, in the form of metrics that is analyzed by applying multiple linear regressions is generally regarded as structured information. These data are collected in predefined fields and presented using tables of values in which fields and cases are represented in columns and rows, respectively.  
The analyses of activity metrics on these platforms can be carried out using techniques such as simple linear regressions (SLR), structural equation modeling (SEM), or even descriptive explorations. Examples of studies that use these techniques are the works of [PERSON] and [PERSON] [53], [PERSON] and [PERSON] [28], or [PERSON] [54], among others. However, although these techniques are widely accepted, multiple linear regression is probably the most effective technique when the purpose is knowing not only the influence of the independent variables on the dependent ones individually, but also the joint potential of these within the predictive model [16].  
The general equation, which is used to represent the multiple linear regression, is expressed as:  
\[\text{Y}_{i}=\alpha+\beta_{1}\,\text{X}_{i1}+\beta_{2}\,\text{X}_{i2}+\ldots +\beta_{k}\,\text{X}_{ik}+\varepsilon_{i} \tag{2}\]  
where \(\alpha\) is the constant term of the model, Y\(i\) is the dependent variable, Xi represents the independent variables, \(\beta\) represents the regression coefficients, and \(\varepsilon i\) is the error or average of residuals.  
The multiple linear regression allowed us to reveal the volumes, components, publication moments, and publication topics that increased the recognition of content. In this analysis, the authors took all the thirty-nine variables considered in the study. Thirty-seven acted as independent variables and two as dependent variables. These thirty-seven independent variables corresponded to the categories (a) Publication volumes, (b) Publication components, (c) Publication day of the week, (d) Publication time slot, and (e) Publication topic. The two dependent variables were those corresponding to category (f) Recognition obtained by the publication (Retweeted Pubs. and Favorite Pubs.).
## 5 Discussion
### Models' Performance Analysis  
We evaluated six lightweight models in this study. InceptionV4 [18], NanoViT [19], EfficientNet [20], EdgeNeXt [21], ConvNeXt [22], and MobileNetV3 [23] showed diverse yet significant insights on the selected benchmark datasets. Due to the nature of the dataset and model's architecture, some achieved high accuracy and others struggled in generalizing the training data.  
For all the datasets, InceptionV4 [18] and EfficientNet [20] performed the best, followed by MobileNetV3 [23], showing their ability to manage variations in the data while maintaining excellent accuracy. Even with noise and irregular motions, strong feature extraction was made possible using InceptionV4's Inception modules, which can analyze  
Figure 8: Radar charts showcasing the visual representation of the performance metrics from the experimental results presented in Tables 3–8 (**a**) InceptionV4. (**b**) EfficientNetV2. (**c**) NanoViT. (**d**) EdgeNeXt. (**e**) ConvNeXt. (**f**) MobileNetV3.  
spatial and temporal information with remarkable accuracy. Similarly, because of the compound scaling technique in EfficientNet [20], it can maintain the input data's dimensions and keep the network size compact, while it is yet to reach maximum performance accuracy with minimal computing. This has made it possible to perform well across all the datasets when generalizing the model. Hence, both models were able to perform reasonably when exposed to subjective testing under different test scenarios as listed in Tables 2, 7, and 9.  
On the other hand, ConvNeXt [22] and EdgeNeXt [21] performed less than ideal on a number of datasets. Although effective, ConvNeXt's [22] solely convolutional architecture lacks the depth necessary to perform well in generalization on datasets with substantial intra-class variability and irregular gesture execution. Its inability to grasp fine-grained spatial connections that are essential for hand gesture identification was caused by its dependence on conventional convolutional hierarchies. However, because of its lower model complexity, EdgeNeXt's [21] lightweight transformer-based architecture has trouble extracting spatial and temporal characteristics from gesture sequences, even though it was designed for resource-constrained contexts. Their overall effectiveness and suitability for difficult datasets were hampered by these constraints.  
The lightweight vision transformer NanoViT [19] performed in the middle tier, outperforming ConvNeXt [22] and EdgeNeXt [21] but not InceptionV4 [45] or EfficientNet [20]. Because NanoViT's self-attention mechanisms enabled it to grasp the global context well, it was able to withstand a certain amount of dataset fluctuation. However, it was unable to fully use complicated patterns due to its relatively shallow design and smaller parameter count, especially in datasets that required in-depth spatial and temporal knowledge. Although NanoViT [19] provided a balance between accuracy and computing efficiency, its trade-offs made it less useful for datasets with a lot of different gestures.
# Adversarial Attack for Deep Steganography Based on Surrogate Training and Knowledge Diffusion
## 3 Adversarial Attack Method for Deep Steganography  
### Preliminaries  
In general, the end-to-end DS methods are mostly based on autoencoders. The encoder takes the secret image \(S\) and the cover image \(C\) simultaneously as the input and then outputs the container image \(C^{\prime}\). The decoder takes the container image \(C^{\prime}\) generated by the encoder as input, and the output is the revealed image \(S^{\prime}\). The encoder-decoder network is trained to minimize the differences between \((C,S)\) and \((C^{\prime},S^{\prime})\), as described in Equation (1).  
\[\min\left\{d(C,C^{\prime})+\beta d\big{(}S,S^{\prime}\big{)}\right\} \tag{1}\]  
where \(d\) is the visual distance (e.g., mean square error (MSE), structural similarity index measure (SSIM), or a linear combination of both) between two images [11; 12; 14].
## 3 Results
### Data Characteristics and Details  
Table 2 presents a list of the data used from the 17 reviewed articles. The sources of the data can be categorized into two, which are the patients and images. Most of the articles used subjects that were patients to obtain images, and only four articles [18; 19; 20; 21] used solely readily images as their dataset. Three studies used private datasets, and one paper did not state the source of the dataset. The number of subjects or data that participated in the studies varied with the highest being 3240 images and the lowest being 10 patients. Three studies involved a wide age range of individuals (aged between 11 and 86) [20; 22; 23], and ten studies focused on adolescents and middle-aged individuals (aged between 10 and 30 years old) [18; 24; 25; 26; 27; 28; 29; 30; 31; 32], while four studies did not provide the age information of the dataset. Fourteen studies provided the required details of the data included in the investigations, such as the inclusion and exclusion criteria. The other three studies did not provide extensive and clear details on the dataset, which led to bias. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Authors and Year**} & \multicolumn{8}{c}{**Questions**} & \multicolumn{8}{c}{**Overall**} & \multirow{2}{*}{
\begin{tabular}{c} **Overall** \\ **(\%)** \\ \end{tabular} } \\ \cline{2-2} \cline{6-11}  & **1** & & & **2** & & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & \\ \[PERSON] et al. (2014) [24] & 1 & 2 & 2 & 1 & 1 & 2 & 2 & 2 & 2 & 1 & 16/20 & 80.0 \\ [PERSON] et al. (2021) [25] & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 1 & 2 & 19/20 & 95.0 \\ [PERSON] et al. (2019) [18] & 2 & 1 & 2 & 2 & 2 & NA & 1 & 2 & 1 & 1 & 14/18 & 77.8 \\ [PERSON] et al. (2021) [26] & 2 & 2 & 2 & 2 & 1 & 2 & 2 & 1 & 0 & 16/20 & 80.0 \\ [PERSON] et al. (2023) [22] & 1 & 2 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 16/20 & 80.0 \\ [PERSON] et al. (2027) & 2 & 1 & 2 & 2 & 2 & 1 & 2 & 1 & 2 & 17/20 & 85.0 \\ [PERSON] et al. (2022) [19] & 2 & 2 & 2 & 2 & 2 & 1 & 2 & 2 & 1 & 17/20 & 85.0 \\ [PERSON] et al. (2015) [28] & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 1 & 1 & 18/20 & 90.0 \\ [PERSON] et al. (2016) [29] & 2 & 2 & 2 & 2 & 1 & 1 & 1 & 2 & 2 & 17/20 & 85.0 \\ [PERSON] et al. (2019) [30] & 2 & 2 & 2 & 2 & 1 & 1 & 2 & 1 & 2 & 17/20 & 85.0 \\ [PERSON] et al. (2019) [31] & 2 & 2 & 2 & 2 & 1 & 1 & 2 & 2 & 2 & 18/20 & 90.0 \\ [PERSON] et al. (2015) [23] & 2 & 1 & 2 & 2 & 0 & 2 & 2 & 2 & 0 & 1 & 14/20 & 70.0 \\ [PERSON] et al. (2022) [33] & 2 & 2 & 1 & 2 & 2 & 2 & 2 & 2 & 1 & 2 & 18/20 & 90.0 \\ [PERSON] et al. (2022) [32] & 2 & 2 & 2 & 2 & 0 & 1 & 2 & 2 & 1 & 2 & 16/20 & 80.0 \\ [PERSON] et al. (2020) [20] & 2 & 2 & 1 & 2 & 2 & 2 & 2 & 1 & 1 & 2 & 17/20 & 85.0 \\ [PERSON] et al. (2022) [21] & 2 & 2 & 1 & 1 & 2 & 2 & 2 & 2 & 1 & 2 & 17/20 & 85.0 \\ [PERSON] et al. (2020) [34] & 2 & 2 & 1 & 2 & 2 & 2 & 2 & 2 & 0 & 1 & 16/20 & 80.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall Rating Score of the Reviewed Articles. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
## 3 Results and Discussion
### Comparison of Interannual Variations (IAVs)  
Figures 9 and 10 show 2D histograms similar to Figures 7 and 8, but this time of normalized GPP differences from the climatology (also known as anomalies or IAVs). To compute the normalized IAVs, the seasonal cycle of GPP was first removed, then the remaining differences were normalized by the range of observed climatological values from the FLUXNET 2015 data for each individual location (known as the min-max normalization). For example, a normalized value of 0.5 means that a positive difference is 50% of the range of observed GPP. If the min-max normalization was not performed, then analyses suggested that the models did not capture IAVs well.  
All models were able to reproduce GPP IAVs well. FluxSat-7 provided improved results as compared with the other models. This was the case particularly for the points with the largest IAVs (both positive and negative). All correlations were different with high statistical significance.  
Figure 10: Similar to Figure 9, but showing two-dimensional histograms of the normalized GPP interannual variability in monthly averages at 0.0833\({}^{\circ}\) for (**a**) FluxSat-7 and (**b**) NIR\({}_{V}\) and 0.05\({}^{\circ}\) resolution for (**c**) VPM versus FLUXNET 2015 GPP. Units of GPP are g C m\({}^{-2}\) d\({}^{-1}\).  
Figure 9: Similar to Figure 7, but showing two-dimensional histograms of the normalized GPP interannual variations in eight-day averages at 0.0833\({}^{\circ}\) resolution computed using (**a**) FluxSat-7, (**b**) NIR\({}_{V}\)-based, and (**c**) FLUXCOM-RS versus FLUXNET 2015 GPP. Units of GPP are g C m\({}^{-2}\) d\({}^{-1}\).