esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Neural Networks--A Review with Case Study
## 4 Methodology  
In the study, it was decided to verify the following fuzzy networks for which the author had access to their code:  
* ANFIS network according to the implementation available on github [36];
* FNN--Fuzzy network with application Ordered Fuzzy Numbers [33];
* SFNN--scalable deep fuzzy neural network using OFN notation [35].  
To compare solutions, the Iris set was also classified using the following algorithms:  
* KNeighborsClassifier;
* Decision Tree Classifier;
* Random Forest Classifier.  
The Iris dataset is a well-known and widely used collection in machine learning and pattern recognition. It consists of 150 instances (samples) of iris flowers, evenly divided among three species: Iris setosa, Iris versicolor, and Iris virginica, each with 50 samples. Each sample in the dataset includes four numerical features (measured in centimeters): the length and width of the sepal and petal.  
The dataset is commonly used for:  
* Classification algorithms (e.g., Decision Trees, SVM, Neural Networks).
* Supervised learning experiments,
* Feature selection and dimensionality reduction.  
Prior to training, the data were scaled individually for each feature to the range [0, 1] using min-max scaling. The dataset was then split into a training set (80%) and a test set (20%), maintaining proportional class distribution through stratified sampling.  
The learning of the network and algorithms was then performed. Finally, the entire dataset was tested. For the fuzzification process, the data were transformed into singleton representations of Ordered Fuzzy Number.
## 4 Statistical Model
### Fractional Factorial Design
#### 4.2.2 Estimation of Marginal Means and 95% Confidence Intervals  
To provide a more comprehensive interpretation of the effects, we employed the method of estimating marginal means and 95% confidence intervals. This method involvescalculating the marginal means for each factor level and providing confidence intervals for these estimates to represent the precision and variability of the effects visually.  
The estimates of the marginal means are given by the following:  
\[\hat{Y}_{A_{i}}=\hat{\mu}+\hat{\alpha}_{i} \tag{13}\]  
\[\hat{Y}_{B_{j}}=\hat{\mu}+\hat{\beta}_{j} \tag{14}\]  
\[\hat{Y}_{ij}=\hat{\mu}+\hat{\alpha}_{i}+\hat{\beta}_{j}+(\alpha\beta)_{ij} \tag{15}\]  
The 95% confidence interval (CI) is given by  
\[CI=\hat{Y}\pm t_{\alpha/2,df}\cdot SE(\hat{Y}) \tag{16}\]  
where \(t_{\alpha/2,df}\) is the critical value from the t-distribution, and \(SE(\hat{Y})\) is the standard error of the estimate, calculated using the residuals from the ANOVA model as follows:  
\[SE(\hat{Y}_{ij})=\sqrt{\frac{MSW}{n_{ij}}} \tag{17}\]  
Through these steps, we can comprehensively analyze the impact of various factors on the performance of the model and further interpret these effects.
## 4 Results and Discussion  
The proposed regression model was simulated by a Python 3.7 software tool with libraries of SciPy, Keras, TensorFlow, SciKit Learn, Matplotlib, Numpy, Polars, and Pandas. The proposed regression model was analyzed on a system with 8 TB hard disk, Linux operating system, 128 GB RAM, and Intel core i7 10 th generation processor. This equipment was sourced from SATA manufacturer, Mumbai, INDIA. The efficacy of the Bi-GRU model was analyzed on the Rossmann and Walmart datasets by means of NRMSE, ND, RMSSE, MAE, R2, and MSE.
## Appendix A Unstructured Data Classification and Grading Framework
### Data Classification Based on Text Classification  
Various classification algorithms can be chosen depending on the specific context or scene. We provide an overview of common text classification algorithms, along with their respective applicable scenarios, advantages, and disadvantages, as shown in Table 11.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Text Classification Algorithm** & **Suitable Scenarios** & **Advantages** & **Disadvantages** \\ \hline FastText & \begin{tabular}{l} Large sample sizes, multiple \\ categories, tasks with limited \\ semantic understanding \\ \end{tabular} & \begin{tabular}{l} Fast, low computational \\ requirements \\ \end{tabular} & \begin{tabular}{l} Limited semantic \\ understanding \\ \end{tabular} \\ CNN & \begin{tabular}{l} Tasks requiring some \\ semantic understanding \\ \end{tabular} & \begin{tabular}{l} Captures more, broader, and \\ finer text features \\ \end{tabular} & Long training time \\ Self-Attention & \begin{tabular}{l} Tasks requiring some \\ semantic understanding \\ \end{tabular} & \
\begin{tabular}{l} Captures more, broader, and \\ finer text features, long-term \\ dependencies within the text \\ \end{tabular} & Long training time \\ Traditional Machine Learning &
\begin{tabular}{l} Short texts (e.g., messages, \\ microblogs, comments) with \\ less than 150 words \\ \end{tabular} & Fast training & Unable to handle long texts \\ BERT & Limited labeled data scenarios & High accuracy & Long training and prediction time \\ \hline \hline \end{tabular}
\end{table}
Table 11: Applications and Pros/Cons of Common Text Classification Algorithms.
# Evaluation and Prediction of Ecological Benefits in Song-Liao River Basin
## 3 Results
### Result of Prediction  
The average values of EQI and ESI at the county level are computed before training to adjust to the model's training environment and assess changes in these scores (Figure 3c step 3). The predictions made by the four prediction models for ESI are displayed in Figure 7a-d. The maximum RMSE is 0.93, the lowest is 0.09, the highest determinable coefficient is 0.97, and the lowest is 0.46. Following the model screening, the BP neural network possessed the highest prediction accuracy and largest training effect. The Song-Liao River Basin's 2030 ESI spatial distribution features, as projected by the BP neural network, are displayed in Figure 7e. The forecast results indicate that the average ESI is lower in areas with frequent human activity and fragile ecology, like central and western areas, and higher in areas with more vegetation coverage and less human activity, like the northern Changbai Mountains, the Lesser Khingan Mountains, and the Greater Khingan Mountains.  
The predictions of four prediction models for EQI are displayed in Figure 7f-i. The biggest root mean square error is 1.13; the lowest is 0.06; the highest determinable coefficient is 0.93; and the lowest is 0.46. Following screening, the maximum training effect and highest prediction accuracy were attained by the Support Vector Machine. The Support Vector Machine-predicted spatial distribution characteristics of EQI in the Song-Liao River Basin in 2030 are displayed in Figure 7j. EQI and ESI hold comparable spatial distribution properties.  
Figure 6: Ecological quality index in SRB during 1990–2020. Note: (**a–d**) are the spatial distribution characteristics of EQI in 1990, 2000, 2010, and 2020, respectively; (**e–h**) are the proportions of five categories (Best, Good, Normal, Bad, and Worst) of EQI in the current year after reclassification; (**i–l**) are the spatial distribution characteristics of the four sub-variables (Greenness, Wetness, Dryness, and Heat).
## 3 Integration of 5G NPN Deployment Options and Edge Computing Scenarios
### Standardone NPN with Private, On-Premises Edge
#### 3.1.1 Standalone Edge Data Centers  
This alternative is suitable for such industry scenarios, where each component of an application software runs locally at the edge. A typical example could be the edge-enabled (collaborative) control of the fleet of mobile robots, where all of the control components, which are offloaded from the industrial end device, have to be deployed on the on-premises edge due to low latency and on-premises data handling.  
One deployment option is to have only a single edge data center on the factory premises. Even in this case, multiple execution environments can be deployed at the edge in order to support the various requirements of applications, e.g., OpenStack supports virtual machines, bare-metal servers, and containers from one control plane or virtual machines and Kubernetes clusters can be run in the same data center when the Kubernetes clusters are deployed on virtual machines.  
Alternatively, multiple standalone data centers could be deployed, with different hardware infrastructure and connectivity, as well as platform capabilities. This enables the different data center infrastructures, as well as platforms, to meet the requirements (real-time execution environment, support of hardware acceleration, etc.) of the different industrial applications (e.g., real-time device control applications vs. analytics tools). The data centers are handled as separate clusters from the cloud management perspective.
## 2 Materials and Methods
### Matching Principle and Error Analysis of NmF2/hmF2 between FY3C/GNOS and COSMIC  
To validate the profile-level accuracy of FY3C/GNOS IRO products with that of COSMIC, the comparability between them must be guaranteed, so the NmF2/hmF2 of FY3C/GNOS and COSMIC were matched into data pairs using the matching principle. Two factors were weighed before we established the principle, one is the matching criteria that other researchers used to verify the accuracy of peak ionospheric parameters [3; 56; 55], and the other is our need for quantity of the matched samples in error statistics. If the matching conditions are too stringent, the number of matched samples is too small to support the error statistics, if too loose, there will be large discrepancies in statistical errors due to spatial-temporal change of the ionosphere. After comprehensive consideration of the two factors, the NmF2/hmF2 between FY3C/GNOS and COSMIC observed within \(\pm\) 1 h and \(\pm\)2\({}^{\circ}\) in time and space were scanned for matched pairs. Since the diurnal variation of NmF2 can sometimes be more than one order of magnitude, we used relative error in error analysis of NmF2 between FY3C/GNOS and COSMIC, and the absolute error was employed to express the error characteristics of hmF2. Based on these, the bias and std of absolute/relative errors served as the criteria for accuracy judgement of FY3C/GNOS IRO data.  
The absolute error between FY3C/GNOS and COSMIC measurement is as follows:  
\[Ab\_err_{i}=data_{i}^{FY}-data_{i}^{COSMIC}, \tag{2}\]  
where \(Ab\_err_{i}\) represents the absolute error between FY3C/GNOS and COSMIC measurement in the \(i\)-th data pair.  
The relative error between FY3C/GNOS and COSMIC measurement is calculated below:  
\[Re\_err_{i}=\frac{Ab\_err_{i}}{data_{i}^{COSMIC}}, \tag{3}\]  
where \(Re\_err_{i}\) represents the relative error between FY3C/GNOS and COSMIC measurement in the \(i\)-th data pair.  
The bias of the absolute/relative errors is expressed as below:  
\[E(err)=\frac{\sum\limits_{i=1}^{N}err_{i}}{N}, \tag{4}\]  
where \(N\) represents the number of matched data pairs, \(E(err)\) represents the statistical mathematics expectation of errors of \(N\) data pairs, namely, the bias. \(err_{i}\) represents the absolute/relative error of the \(i\)-th data pair.  
The std of the absolute/relative errors can be calculated using the following equation:  
\[\sigma(err)=\sqrt{\frac{\sum\limits_{i=1}^{N}\left(err_{i}-E(err)\right)^{2} }{N}}, \tag{5}\]  
where \(\sigma(err)\) represents the std of errors of \(N\) data pairs.
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## Materials and Methods
### Methods
#### 2.3.4 Net Biosphere Exchange  
The NBE is determined from \(R_{e}\) and FE as carbon sources and the GPP as a carbon absorption (Equation (9), Figure 2). To effectively conduct validation within mixtures of positive (+) and negative  
Figure 4: Scatter plots of the daily gross primary production (GPP) data (g C/m\({}^{2}\)/day) derived from the drained forest (\(DF\)) tower (\(x\)-axis) and MODIS (\(y\)-axis). Linear fitting equations were obtained from the results of least squares method (red line) and robust regression method (green line).  
(-) values of CO\({}_{2}\) fluxes, the RMSE of the NBE was normalized (NRMSE) by using \(y_{max}-y_{min}\) (Equation (10)) where \(y\) is the result of \(R_{e}\), FE, GPP, or NBE in this study.  
\[\text{NBE}=(R_{e}+\text{FE})-\text{GPP} \tag{9}\]  
\[NRMSE=\frac{RMSE}{y_{max}-y_{min}} \tag{10}\]
# The Current Development Status of Agricultural Machinery
## 4. Chassis Leveling Systems of Agricultural Machinery
### Control Systems of Leveling
As operational requirements in complex terrains continue to grow, emerging intelligent control strategies--including sliding mode variable structure \begin{table}
\begin{tabular}{p{71.1 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Control Strategy** & **Core Advantages** & **Performance Defects** & **Performance Indicators** \\ \hline Dual-Loop Fuzzy PID Control & Excellent stability; rapid response speed & Difficult to design parameters; over-regulation problem; steady-state error problem & Maximum slope: 30–35\({}^{\circ}\); energy consumption per unit mass: 2.0–2.7 kJ/kg\(\cdot\)km; response time: 80–150 ms; overshoot: 3–8\% Maximum slope: 35–40\({}^{\circ}\); energy consumption per unit mass: 1.7–2.3 kJ/kg\(\cdot\)km; response time: 200–350 ms; overshoot: 1–5\% Maximum slope: 38\({}^{\circ}\)+; energy consumption per unit mass: 1.8–2.5 kJ/kg\(\cdot\)km; response time: 40–100 ms; overshoot: 0–3\% \\ \hline Sliding Mode Control (SMC) & Fast response; strong robustness; strong anti-interference ability & High-frequency chattering & Maximum slope: 38\({}^{\circ}\)+; energy consumption per unit mass: 1.8–2.5 kJ/kg\(\cdot\)km; response time: 40–100 ms; overshoot: 0–3\% \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 17: Dual-loop PID control block diagram. control, model predictive control (MPC), reinforcement learning control, and adaptive robust control--are anticipated to become key research areas within domestic studies. Furthermore, multi-modal control architectures--such as hybrid decision layers combining MPC with reinforcement learning and execution layers integrating adaptive robust control with fuzzy logic--will drive future technological convergence. Simultaneously, the development of high-precision domestic MEMS attitude sensors and low-latency hydraulic servo valves is crucial to support the practical implementation of advanced algorithms. This will gradually reduce the performance gap with international standards in comprehensive leveling capabilities under complex operating conditions.
## 4 Experimental Results
### Efficiency of the Proposed FeatureNetLite and MsvNetLite  
The second experiment was carried out to evaluate the efficiency of the proposed lightweight neural networks. We compared optimal accuracy, parameters, FLOPs, and inference latency with other learning-based machining feature recognition approaches. Optimal accuracy is the highest achievable accuracy attained by a particular approach. In the context of our study, the optimal accuracy was determined by examining the results presented in Table 3. For instance, upon analysis of Table 3, it was observed that MsvNetLite achieved the highest accuracy value of 98.84, therefore, the optimal accuracy of MsvNetLite is considered to be 98.84. Additionally, we have included confusion matrices and receiver operating characteristic (ROC) curves for each approach to more thoroughly analyze the distinctions in classification performance. The confusion matrices and ROC curves are shown in Appendix A.  
In Table 4, FeatureNetLite achieves nearly 18\(\times\) reduction in parameters and 89\(\times\) reduction in FLOPs compared to FeatureNet and even outperforms other voxel-based approaches in classification performance. Similarly, when compared to MsvNet, MsvNetLite has only  
Figure 9: The t-SNE visualization of the visual representation on test set: (**a**) the backbone output of MsvNetLite after fine-tuning using the SGDM optimizer in semi-supervised learning, (**b**) the backbone output of MsvNetLite initialized with ImageNet pre-trained weights, (**c**) the backbone output of MsvNetLite with random initialization, (**d**) the backbone output of MsvNetLite after the self-supervised stage in semi-supervised learning, (**e**) the backbone output of MsvNetLite after supervised learning, (**f**) the backbone output of MsvNetLite after fine-tuning using the Adam optimizer in semi-supervised learning.  
1/76 parameters and 1/47 FLOPs, but their classification performances are approximately equivalent (about \(-0.3\%\)). Furthermore, this table also shows the results of the inference latency tested on [EMAIL_ADDRESS] GHz CPU and GTX1070 GPU. Both on the CPU and GPU, FeatureNetLite is more than twice as fast as FeatureNet in inference speed. When compared to MsvNet, the inference speed of MsvNetLite is just 2 ms faster on the GPU but over six times faster on the CPU. Although the proposed networks are neither the fastest nor the most accurate, they achieve the best balance between accuracy and inference speed. It has been noted that the acceleration effects of the two lightweight networks on CPU and GPU are not consistent, which is caused by the disparate hardware architecture. As GPU is likely used to render 3D objects in CAD software, inference speed on CPU is more significant. In general, fewer network parameters and faster inference speed mean lower memory usage and quicker response of machining feature recognition application, respectively.
## 5 Proposed Scheme
### Resource Sharing Optimization  
The main objective of the proposed scheme is to maximize the overall system throughput compared to other existing schemes. In order to evaluate the throughput of cellular users and D2D pairs, we consider mathematical expression of [PERSON]'s formula. Therefore, throughput of the overall system is defined as:  
\[T_{overall}=\ \sum_{c=1}^{N}T_{c}+\ \sum_{D=1}^{M}T_{D} \tag{22}\]  
where \(T_{c}\) and \(T_{D}\) are the throughput of cellular user and D2D pairs, respectively, which is defined by:  
\[T_{c}=Wlog_{2}(1+\gamma_{k}) \tag{23}\]  
\[T_{D}=W\Big{[}log_{2}\big{(}1+\gamma_{j}\big{)}+log_{2}\Big{(}1+\gamma_{j^{ \prime}}\big{)}\Big{]} \tag{24}\]  
where \(W\) is the channel bandwidth.  
Therefore, the throughput maximization problem can be formulated by:  
\[\max_{c\in N,\ D\in M}\left(\sum_{c=1}^{N}T_{c}+\sum_{D=1}^{M}T_{D}\right)\Bigg{\}} \tag{25}\]  
subject to  
\[P_{C}^{min}\leq P_{C}\leq\ P_{C}^{max};P_{D}^{min}\leq P_{D}\leq\ P_{D}^{max}, \forall\ C\ e\ N,\ D\ e\ M\\ \gamma_{C}^{min}\ \leq\ \gamma_{C};\ \gamma_{C}^{min}\ \leq\ \gamma_{C},\ \forall\ C\ e\ N,\ D\ e\ M\ \ \Bigg{\}} \tag{26}\]  
where \(\gamma_{C}^{min}\) and \(\gamma_{D}^{min}\) are the minimum allowable SINRs of the cellular user and D2D pair respectively. \(P_{C}^{min}\) and \(P_{C}^{max}\) are the minimum and maximum power range for the cellular user, and \(P_{D}^{min}\) and \(P_{D}^{max}\) are the minimum and maximum power ranges of the D2D pair. Equation (26) ensures that the transmit power of both cellular users and D2D pairs should be within the minimum and maximum transmit power range. This is the arbitration between system capacity and interference. Similarly, Equation (26) ensures that the SINR of both cellular users and D2D pairs should be greater than or equal to minimum SINR threshold. Hence, the upper and lower bounds transmit power of cellular user should satisy the following criteria.  
\[P_{C}^{min}\leq\ \frac{\gamma_{k}(P_{i}.l_{i,B}^{-\alpha}\left|h_{i,B}\right|^ {2}+P_{i^{\prime}}.l_{i^{\prime},B}^{-\alpha}\left|h_{i^{\prime},B}\right|^{2} +\sum_{ne\
eq A1}I_{B,ne}+\ P_{N})}{l_{k,B}^{-\alpha}\left|h_{k,B}\right|^{2} }\leq\ P_{C}^{max} \tag{27}\]  
Similarly, the upper bound and lower bounds transmit power of both D2D-Rxs are derived as:  
\[P_{D}^{min}\leq\ P_{i},P_{i^{\prime}}\ \leq\ P_{D}^{max} \tag{28}\]\[\begin{split} P_{D}^{min}\leq\ \frac{\gamma_{j}(P_{h}\cdot I_{k_{f}^{-a}}^{-a} \left|h_{kj}\right|^{2}+I_{f}+\ \sum_{n\
eq A1}I_{j\
eq n}+P_{N})}{P_{I}\cdot I_{f}^{-a}\cdot\left|h_{f,B} \right|^{2}},\\ \frac{\gamma_{f}(P_{h}\cdot I_{k_{f}^{-a}}^{-a}\left|h_{k_{f}} \right|^{2}+I_{+}\ \sum_{n\
eq A1}I_{j\
eq n}+P_{N})}{P_{I}\cdot I_{f}^{-a}\cdot \left|h_{f,B}\right|^{2}}\ \leq\ P_{D}^{max}\end{split} \tag{29}\]
## 5 Results
### Scenario 2--Charge at the Line Terminals  
In the second scenario, two additional 150 kW chargers are located at the line terminals Gare and Aramont (see Figure 6). The batteries' SoCs of the buses are given in Figure 10. Buses recharge during their dwell time between consecutive trips without a delay. According to the number of times they pass through a charging station, bus n^1 charges approximately ten times and bus n^2 twelve times (cf. Section 4). The minimal values of the batteries' SoCs are obtained at 79% for bus n^1 and 73% for bus n^2. This means that the on-board battery capacity can be reduced if charging stations are located at  
Figure 8: Travelled distance and battery SoC of buses n^1 (**a**) and n^2 (**b**)—scenario 1.  
Figure 9: Total charging power and PV production over time—scenario 1.  
the line terminals. The total energy consumption is 373 kWh, which represents an average consumption per travelled distance of 1.01 kWh/km.  
It can be seen in Figure 11 that the two buses recharge during a short period at line terminals, usually not at the same time. However, the total charging power reaches 300 kW for 4 min at 18:36 when both buses are charging at bus terminals and 200 kW for 3 min at 19:02 when bus n\({}^{\circ}\)2 arrives at the terminal and bus n\({}^{\circ}\)1 is at the depot. The charging process occurs mainly during the day, when electricity is produced by PV panels. As for PV energy, 81 kWh are used directly to charge buses during the day, but additional stationary storage would increase the PV share during the day and allow the use of PV energy during the night.