esa-sceva/satcom-chunk-collection huggingface dataset:
# Pressure-Driven Nitrogen Flow in Divergent Microchannels with Isothermal Walls
## 2 Problem Description  
A probabilistic numerical approach based on the direct simulation Monte Carlo method was employed to study steady-state, pressure-driven, Poiseuille gaseous nitrogen flow in divergent microchannels shown schematically in Figure 1. The divergence angle of the channel (\(\phi\)) is defined as follows [39]:  
\[\phi=\frac{100\cdot(H_{\mathrm{o}}-H_{\mathrm{i}})}{L}, \tag{1}\]  
where \(H_{\mathrm{i}}\) and \(H_{\mathrm{o}}\) are the height of the channel at the inlet and the outlet, respectively. The length of the channel (\(L\)) is twenty times larger than its inlet height (i.e., \(L=20\times H_{\mathrm{i}}=8\times 10^{-6}\,\mathrm{m}\)). The problem is assumed to be two-dimensional, which is a valid assumption for channels with considerably large depths [43; 49]. The molecular properties of gaseous nitrogen are summarised in Table 1, where \(d_{\mathrm{P}}\) is molecular diameter, \(m_{\mathrm{P}}\) is molecular mass, \(\omega\) is the viscosity-temperature index, \(T_{\mathrm{ref}}\) is the reference temperature in the viscosity-temperature relation, and \(\mu_{0}\) is the viscosity at the reference temperature.  
Due to the symmetric configuration of the channel and the flow field, one-half of the physical domain (the hatched region in Figure 1) was considered for calculations. Heat and fluid flow in the channel are described in a Cartesian coordinate system. The temperature of the solid walls (\(T_{\mathrm{w}}\)) and the gas at the inlet (\(T_{\mathrm{i}}\)) is constant, equal to \(300\,\mathrm{K}\). The Knudsen number is defined based on the inlet height, i.e., \(\mathrm{Kn}=\lambda/H_{\mathrm{i}}\). The inlet-to-outlet pressure ratio (\(\Pi\)) is defined as \(p_{\mathrm{i}}/p_{\mathrm{o}}\) and the gas pressure at the channel inlet (\(p_{\mathrm{i}}\)) is calculated based on the Knudsen number and the ideal gas law. The effects of tangential momentum accommodation coefficient (\(\alpha\)) on thermal and fluid flow fields were also studied in the present work. The tangential momentum accommodation coefficient ranges between zero (specular reflection) and one (fully-diffused reflections) depending on the internal gas molecule structure, the molecular mass of the gas, and the surface material [50; 51].
# Whisper: Programmable and Flexible Control on Industrial IoT Networks
## 3 Whisper Framework: SDN without SDN
### Algorithms Description
#### 3.4.1 Creating New Topology  
This first algorithm creates a new topology derived and built from the initial one given as input according to the network blueprint and the policies to be applied. This procedure can be addressed either by an optimization algorithm or by an heuristic. In this work, we have chosen the heuristic since it renders the same solutions in significantly less computing time. This heuristic is a greedy procedure that basically consists in first identifying the triggering nodes \(a_{i}\) (e.g., a node that have low battery), and afterwards, for every one of its children \(ch_{a_{i}}\), select the next preferable parent \(d_{t_{j}}\) as the preferred parent other than the triggering node. Finally, each of these topology changes will be rated according to the policy that triggers them and they will be checked to be loop-free. The more preferable solution will be selected and will be used in the next step of the algorithm as desired topology. This algorithm is described in Algorithm 1.  
```
1:procedureFindNewTopology (givenPolicies, givenTopology)
2:for\(policy\) in givenPolicies do
3:if (\(policy==\) Policy 7 or \(policy==\) Policy 9) then continue
4:for\(a_{i}\) in triggeringNodes do
5:for\(t_{j}\) in \(ch_{a_{i}}\)do
6: selectNextCandidate of \(t_{j}\), \(d_{t_{j}}\)
7: remove link \(l(t_{j},a_{i})\)
8: add link \(l(t_{j},d_{t_{j}})\)
9: save topology and \(d_{t_{j}}\)
10:return rateTopologies()
```  
**Algorithm 1** Create new topology  
The policies that do not require a topology change and manage only the scheduling (e.g., Policies 7 and 9), will skip this step and will go directly to the next step.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Name** & **Description** & **Rule** & **Push** & **Polling** \\ \hline Energy & Minimize traffic in a node that has a low battery level & if Battery \(<\) T r & x & x \\ Temp. (CPU) & Minimize traffic in a node that has high CPU temperature & if Temp. C \(>\) T & x & x \\ Queue (Memory) & Minimize traffic in a node that has high queue usage level & if avgQueue \(>\) T & x & x \\ Load Balancing & Maximize fairness of the routed traffic between the nodes & if loadNode1 \(>\) T + loadNode2 & & x \\ Deny of Service & Minimize effects of a DoS attack & if pkt \(>\) T & x & x \\ Reliability & Maximize end-to-end reliability & if e2e2T explain() + e2e2T explain() & & x \\ Latency & Minimize end-to-end latency & if delay(path1) \(>\) T + delay(path2) & & x \\ P2P traffic & Minimize hop count between two nodes & if p2p-path1 \(\exists\) & x & x \\ Mobility & Minimize packet loss in a parent switch & if RSSI-neigh1 \(>\) T + RSSI-neigh2 & x & \\ Link PDR & Maximize PDR in a given link between two nodes & if cellIPR \(<\) T & & x \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table with example of policies that can be applied. T means Threshold.
# Analysis of Ruggedness of 4H-SiC Power MOSFETs with Various Doping Parameters
## 3 TCAD Simulation Results
### UIS Circuit Simulation for Various Doping Concentrations  
#### 3.2.1 P-Base Region  
Figure 6a shows the \(V_{DS}\) for the various p-base doping concentrations ranging from \(3\times 10^{18}\) to \(9\times 10^{18}\) cm\({}^{-3}\). As the \(N_{phase}\) increased, the inductor current was completely removed, as shown in Figure 6b. This can explain why the holes in the p-base region underwent a complete recombination with the electrons remaining in the p-base region; however, in the case of the \(N_{phase}\) of less than \(6\times 10^{18}\) cm\({}^{-3}\), the holes were not sufficient for a full recombination. After the recombination was completed, the electrons remained in the p-base. Finally, the parasitic BJT was turned on and the current flowed across the p-base region, as shown in Figure 7[17].  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Heat Capacity**} \\ \hline \(a\) & \(1.5778\) [ \(\left\lceil J/\left(\text{K}\cdot\text{cm}^{3}\right)\right\rceil\) \\ \(b\) & \(3.5332\times 10^{-3}\) [ \(\left\lceil J/\left(\text{K}^{2}\cdot\text{cm}^{3}\right)\right\rceil\) \\ \(c\) & \(-1.5447\times 10^{-6}\) [ \(\left\lceil J/\left(\text{K}^{3}\cdot\text{cm}^{3}\right)\right\rceil\) \\ \(d\) & \(2.6052\times 10^{-10}\) [ \(\left\lceil J/\left(\text{K}^{4}\cdot\text{cm}^{3}\right)\right\rceil\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental Data of Thermal Dynamic Parameter.  
Figure 5: (a) Simulated UIS \(V_{DS}\) for various \(t_{on}\) waveforms; (b) simulated UIS \(I_{D}\) for various \(t_{on}\) waveforms; \(V_{DD}=400\) V; \(L=1\) mH.
# Challenges and Future Trends in the Treatment of Psoriasis [PERSON] and [PERSON]
## 4 Future Aspects in the Treatment of Psoriasis
### Advancements in Topical Treatments
#### 4.2.2 Topical Nanocarriers  
Nanoparticles (NPs) have been studied extensively for their potential in transdermal drug delivery, particularly in the treatment of psoriasis. NPs offer advantages such as a large surface-area-to-volume ratio, enabling them to penetrate through hair follicles and the skin's various routes, including the intercellular lipid route, transcellular route, and follicular route [71]. The characteristics of NPs, such as size, molecular weight, surface charge, and pH, influence their drug permeation through the skin. For psoriasis treatment, adjusting the characteristics of NPs is crucial to enhance their permeation through the altered skin conditions.  
In psoriasis management, lipid-based NPs have been widely investigated. These include solid lipid nanoparticles (SLNs), nanostructured lipid carriers (NLCs), liposomes, transfersomes, ethosomes, and niosomes. Lipid-based NPs are advantageous for transdermal drug delivery due to their flexibility, which allows them to pass through the extracellular matrix in the skin's stratum corneum layer. For instance, NLCs loaded with anti-psoriatic drugs like methotrexate have shown promising results in enhancing drug permeation and therapeutic effects [72]. Metallic nanoparticles, such as gold and silver nanoparticles, have also been explored for drug delivery in psoriasis. These rigid nanocarriers offer tunable size and surface characteristics. Functionalized gold nanoparticles, for example, have demonstrated improved drug delivery and immunomodulatory effects in psoriatic skin [73; 74; 75]. Polymeric nanoparticles, including chitosan and poly(lactide-co-glycolic acid) (PLGA) nanoparticles, have also been studied extensively. Synthetic polymeric NPs offer defined characteristics for drug encapsulation and release. PLGA NPs loaded with curcumin have shown enhanced drug permeation through psoriatic skin and improved therapeutic effects [76]. Nanofibers, fabricated through electrospinning and other methods, have emerged as potential vehicles for drug delivery in psoriasis treatment. These nanofiber-based delivery systems have been used to load anti-psoriatic agents for controlled release and enhanced treatment efficacy [77; 78].  
In conclusion, NPs and nanofibers hold great promise for transdermal drug delivery in psoriasis management. However, further research is needed to fully understand the specific penetration and drug release mechanisms of different types of NPs and to develop targeted NPs for improved therapeutic efficiency and reduced side effects in psoriatic skin.
# An Active Path-Associated Cache Scheme for Mobile Scenes
## 4 Cooperative Caching Mechanism
### Caching Mechanism Based on Node Cache Value
#### 4.2.2 The Process of Cache Decision  
Algorithm 1 describes how our caching strategy marks cacheable nodes on the path. Based on the neighbor selection mechanism of Section 3.1, after the source AP determines the set of neighbor APs, the source AP will send a lightweight detection packet. The detection packet contains the number of hops, the current timestamp, the content id, and the popularity of the content. The detection packet is used to transfer the current maximum cache value on the path. The cache node will determine whether it is cached based on the cache value in the detection packet. We describe the complete marking process of the source AP sending the detection packet to a neighbor AP in Figure 5. In this process, we use the has_cache() method to filter out APs, that is, AP devices do not cache content. As can be seen in Figure 5, through the decision of detecting packet and node information, our policy marks that node1 and node3 are worth caching.
## 2 Basic Theories
### Efficiency Calculation of SBG
#### 2.1.4 Friction and Efficiency Calculation Model  
The friction coefficient \(\mu\) is usually defined by fluid shear stress \(\tau\) and load \(W\):  
\[\mu=\frac{\iint\tau dxdy}{W} \tag{10}\]  
To balance computing speed and accuracy in optimal design, the fitting formula of friction coefficient \(\mu\) proposed by [PERSON] and [PERSON] based on friction calculation and multiple linear regression [33], which contains many key parameters including maximum contact pressure \(p_{h}\), radius of curvature along minor axis direction of contact ellipse \(r_{x}\), slide-roll ratio \(SR\), viscosity of lubricating oil \(\eta\), relative sliding velocity and entrainment velocity \(V\) and \(U\), root-mean-square roughness (RMS) \(\sigma\):  
\[\mu=e^{f(SR,p_{h},\eta_{0},\sigma)}p_{h}^{b_{2}}|SR|^{b_{3}}U^{b_{6}}\eta_{0}^{ p_{r}}r_{x}^{b_{8}} \tag{11}\]  
where:  
\[f(SR,p_{h},\eta_{0},\sigma)=b_{1}+b_{4}|SR|p_{h}\log_{10}(\eta_{0})+b_{5}e^{-| SR|p_{h}\log_{10}(\eta_{0})}+b_{9}e^{\sigma} \tag{12}\]  
where \(b_{1}=-8.916465\), \(b_{2}=1.03303\), \(b_{3}=1.036077\), \(b_{4}=-0.354068\), \(b_{5}=2.812084\), \(b_{6}=-0.100601\), \(b_{7}=0.752755\), \(b_{8}=-0.390958\), \(b_{9}=0.620305\) are fitting coefficients.  
After the calculation of the friction coefficient \(\mu\), the instantaneous sliding friction could be written as:  
\[F_{\mu}(\varphi_{i})=\mu(\varphi_{i})W(\varphi_{i}) \tag{13}\]  
where \(\varphi_{i}\) represents each rotational angle (\(i\) = 1,2,3... ). \(\mu(\varphi_{i})\) and \(W(\varphi_{i})\) are the friction coefficient and normal load at contact position \(i\).  
The rolling friction force \(F_{r}\) is also needed before efficiency calculation, which could be obtained by [34; 35]:  
\[F_{r}(\varphi_{i})=\Phi_{T}(\varphi_{i})F_{ro}(\varphi_{i}) \tag{14}\]  
where \(\Phi_{T}\) is heat impact factor and defined as:  
\[\Phi_{T}(\varphi_{i})=\frac{1-13.2(\frac{D_{h}(\varphi_{i})}{L^{2}})L_{s}^{0.4 2}}{1+0.213(1+2.23S^{R.083})L_{s}^{0.64}} \tag{15}\]  
\[L_{s}=(\frac{d\eta}{dT}|T_{0}\,)\frac{U^{2}}{K_{f}} \tag{16}\]  
where \(K_{f}\) is the coefficient of heat conduction, while the function of the isothermal rolling friction force \(F_{ro}\) is:  
\[F_{ro}=4.318(G\widetilde{U})^{0.658}\widetilde{W}^{0.0126}R_{x}/\kappa \tag{17}\]  
where \(G=\alpha E^{\prime}\) is the nondimensional material parameter; \(\widetilde{U}=\frac{\eta_{0}U}{E^{\prime}R_{x}}\) is the nondimensional entrainment velocity; \(\widetilde{W}=\frac{W}{E^{\prime}R_{x}}\) is the nondimensional load.  
Based on the calculation of sliding and rolling friction, the instantaneous transmission efficiency \(\eta_{e}(\varphi_{i})\) could be written as:  
\[\eta_{e}(\varphi_{i})=1-\frac{1}{T_{g}\omega_{g}}(\left|F_{p}(\varphi_{i}) \cdot V(\varphi_{i})\right|+\left|F_{r}(\varphi_{i})\cdot U(\varphi_{i})\right|) \tag{18}\]  
where \(T_{g}\) and \(\omega_{g}\) are the torque and rotational speed of gear. The average transmission efficiency \(\eta_{e}\) could be obtained by averaging the instantaneous transmission efficiency \(\eta_{e}(\varphi_{i})\).
## 3 Materials and Methods
### Statistical Analysis  
Statistical analyses were performed with Statistica version 13.3 (StatSoft, Krakow, Poland). In vivo samples were analyzed in duplicate; the values for each group represent the mean of eight animals (n = 8). All results were expressed as means and standard deviation, and all variables were normalized using the Shapiro-Wilk test. The data obtained were subjected to analysis of variance (ANOVA) using a linear model procedure. The  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gene Symbol** & **Forward Primer** & **Reverse Primer** \\ \hline _Mmp1_ & 5\({}^{\prime}\)-CCACATAACATTCGAAAGGCTT-3\({}^{\prime}\) & 5\({}^{\prime}\)-GGTCCATCAAATGGGTTATTG-3\({}^{\prime}\) \\ _Hmgr_ & 5\({}^{\prime}\)-CTTGACGCTCTGTGGAATG-3\({}^{\prime}\) & 5\({}^{\prime}\)-GTTGGCAAGCCAGGACATA-3\({}^{\prime}\) \\ _Tnf\(\upmu\)_ & 5\({}^{\prime}\)-GCCCAGACCCTC-3\({}^{\prime}\) & 5\({}^{\prime}\)-CCACTCCAGCTGCCTCT-3\({}^{\prime}\) \\ _Pparx_ & 5\({}^{\prime}\)-TCACACAATGCAATCGCTT-3\({}^{\prime}\) & 5\({}^{\prime}\)-GGCCTTGACCTTGTGTCTAGT-3\({}^{\prime}\) \\ _Pparx_ & 5\({}^{\prime}\)-GAGATCCCTGTTGACCAGCTCTCTGGACG-3\({}^{\prime}\) & 5\({}^{\prime}\)-CCACAGGCTGTATCC-3\({}^{\prime}\) \\ _Ac1_ & 5\({}^{\prime}\)-CCACAGCCTTGACCGAACT-3\({}^{\prime}\) & 5\({}^{\prime}\)-CAGCCTCTGCTGACCACC-3\({}^{\prime}\) \\ _Ac2_ & 5\({}^{\prime}\)-GGAACTACAGGTTGAGCC-3\({}^{\prime}\) & 5\({}^{\prime}\)-CACATAACCTCAGGAGGCC-3\({}^{\prime}\) \\ _Lxr1_ & 5\({}^{\prime}\)-TGATGCTGGAATTGCCTGC-3\({}^{\prime}\) & 5\({}^{\prime}\)-GGCTCACCACCACCCTTATG-3\({}^{\prime}\) \\ _Srb1c_ & 5\({}^{\prime}\)-TGCCCTAAGGCTTCAAATCC-3\({}^{\prime}\) & 5\({}^{\prime}\)-TGGGGGCCACTACTAGGAA-3\({}^{\prime}\) \\ _Lax1_ & 5\({}^{\prime}\)-TGATGCCTGAAATTGCCTCTGC-3\({}^{\prime}\) & 5\({}^{\prime}\)-GGCTCACCACCCTTCTTAGC-3\({}^{\prime}\) \\ _Nos3_ & 5\({}^{\prime}\)-GGGCCTCCCTCCTCCCGCCG-3\({}^{\prime}\) & 5\({}^{\prime}\)-GGATCCCGGAAAAGGGCG-3\({}^{\prime}\) \\ _Nox1_ & 5\({}^{\prime}\)-TCTTGTGTTGACACTTGGC-3\({}^{\prime}\) & 5\({}^{\prime}\)-TATGGGGGAGTGGGAATCTTG-3\({}^{\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Primer sequences. _MMP1_, matrix metalloproteinase-1; _HMGR_, 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase; _TNFa_, tumor necrosis factor alpha; _PPARx_, peroxisome proliferatorator-activated receptor alpha; _PPARy_, peroxisome proliferatorator-activated receptor gamma; _ACAT_1, acetyl-CoA acetyltransferase 1; _ACCI_, acetyl-CoA carboxylase 1; _ACC_2, acetyl-CoA carboxylase 2; _LXR1_, liver X receptor; _SREB1c_, sterol regulatory element-binding protein 1; _LOXI_, lectin-type oxidized LDL receptor; _NOS3_, nitric oxide synthase 3; _NOXI_, NADPH oxidase 1.  
significance of the differences between the test results was determined by the NIR test. The significance level was established for \(p<0.05\) to evaluate the differences between the studied groups.
## 2 Materials and Methods
### Data Analysis  
Temporal trends were assessed using the nonparametric Mann-Kendall (MK) test [33; 34; 35], a widely applied method for detecting monotonic trends in environmental time series. A positive standardized test statistic (Z) indicates an increasing trend, while a negative Z denotes a decreasing trend. Statistical significance is evaluated by comparing the absolute value of Z to a critical threshold; specifically, if \(|Z|>1.96\) (corresponding to a 0.05 significance level under the standard normal distribution), the null hypothesis (H\({}_{0}\)) of no trend is rejected, indicating a statistically significant temporal change [33].  
To identify key environmental predictors of mortality transitions, we employed Generalized Additive Models (GAMs) using the _gam_ function from the _mgcv_ and _MuMin_ packages [36]. GAMs allow for flexible, nonlinear relationships between predictors and the response variable. Predictor variables considered include GWT, LAI, NEE, and Rn, based on prior ecological understanding and correlation analysis. Model performance was evaluated using the Adjusted R\({}^{2}\) for goodness-of-fit, Akaike Information Criterion (AIC) for model parsimony, and significance levels for each predictor variable [36].  
Early warning thresholds for tree mortality risk were identified using Receiver Operating Characteristic (ROC) curve analysis [37]. For each continuous predictor variable, optimal thresholds were determined using Youden's Index, which maximizes the difference between the true positive rate (sensitivity) and the false positive rate (1-specificity) [37]. To assess the overall classification performance, the area under the ROC curve (AUC) was calculated and reported.  
Linear regression and anomaly detection were applied to assess interannual and seasonal trends in NEE, GPP, and RE under both flooded and non-flooded conditions. Graphics are performed using ggplot2 [38], ggpubr, plotly [39], _tidyverse_[40], and _reshape_[41]. All analyses were performed using R Statistical Language Tool [42].
## 6 Discussion
### Broader Implications  
Optimised energy efficiency is one of the broader implications of this research study in the context of the IoT and smart buildings. The implications of the results indicate that the use of Fog computing significantly optimises energy consumption in IoT network environments. Furthermore, this is essential due to the increased use of smart IoT devices, leading to increased energy consumption. The adoption of Fog computing in the planning and design of smart buildings will optimise energy consumption, which in turn will lead to the more sustainable construction of smart buildings, which will subsequently lead to addressing global issues such as the United Nations Sustainable Development Goals, particularly SDG 11 on sustainable cities and communities.  
Optimised latency is another broader implication of this study in the context of the IoT. The optimised latency benefits of Fog computing are suitable for applications with real-time response requirements in an IoT network architecture. Furthermore, this benefits automation, real-time data processing, and response to requests in a smart building network architecture environment. The adoption of Fog computing within a smart building can improve and optimise security, environment control, and emergency system response times, which is beneficial for making critical decisions.  
The importance of network bandwidth usage efficiency to the IoT network environment cannot be overstated. The optimised efficiency of network bandwidth usage improves data flow and reduces network bandwidth usage congestion. Adopting Fog computing in a smart building environment optimises and supports reliability and performance. Should this be adopted in all smart buildings in a smart city with a large volume of smart IoT devices, this would result in enhanced and efficient network bandwidth usage for the improved performance and reliability of other smart systems in the smart city.  
The scalability approach is also important in the broader context, with smart IoT devices' ability to be scaled up, which benefits the IoT smart building network architecture environment. Consequently, with advancements in smart building development and innovative functions and capabilities will improve current smart buildings and support the retrofitting of existing non-smart buildings to benefit from Fog computing.
## 4 Results
### Annual Validation
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Region**} & **Validation** & **Plant** & \multicolumn{2}{c}{**RMSE (Mt CO2)**} & \multicolumn{2}{c}{**MBE (Mt CO2)**} \\  & **Type** & **Count** & **ML [CI]** & **Baseline** & **ML [CI]** & **Baseline** \\ \hline US & cross & 77 & 2.11 [1.73, 2.36] & 2.68 & 0.37 [ 0.08, 0.67] & \(-\)1.13 \\ US & external & 5 & 1.12 [0.37, 1.43] & 2.25 & \(-\)0.61 [\(-\)1.08, \(-\)0.10] & \(-\)0.56 \\ EU & cross & 58 & 2.27 [1.70, 2.68] & 3.04 & 0.57 [ 0.19, 0.97] & \(-\)0.18 \\ EU & external & 36 & 1.00 [0.76, 1.16] & 1.05 & \(-\)0.41 [\(-\)0.58, \(-\)0.23] & 0.25 \\ Australia & cross & 6 & 4.17 [2.91, 5.12] & 3.67 & \(-\)1.40 [\(-\)2.98, 0.34] & \(-\)0.84 \\ Australia & external & 1 & 3.23 [N/A] & 2.63 & \(-\)2.06 [N/A] & \(-\)1.21 \\ India & external & 24 & 2.74 [2.41, 3.01] & 4.06 & 0.33 [\(-\)0.02, 0.68] & 2.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Annual emissions estimation performance compared to the reported data for the US, EU, Australia, and India during the period 2019–2022 the generalizability of our ML emissions estimation approach beyond training regions. Furthermore, these errors are small enough relative to the total emissions of major power plants that, although it may not measure emissions perfectly, the ML technique presented here is immediately useful in helping the world track relative comparisons between power plants and thus quantify marginal progress toward urgent emission reduction goals. Figure 7: A comparison for 2019–2022 of ML estimated vs. reported (**a**) capacity factor with Pearson correlation 0.80, (**b**) electricity generation (terawatt hour, TWh) with Pearson correlation 0.93, and (**c**) CO\({}_{2}\) emissions (megatonnes, Mt) with Pearson correlation 0.90 [50]. Each dot represents an individual plant and year matched to a reported electricity generation or emissions source from the respective region’s reporting agency. Note: reported CO\({}_{2}\) emissions were not plotted for Turkishye as no emission data are available (Appendix A.5).