esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of GPP Modeled by SIF, MuSyQ-GPP, and BEPS Models  
In addition to the SIF-based GPP estimation, we also generated GPP using both the LUE-based MuSyQ-GPP algorithm and the process-based BEPS model. As the BEPS model used in our study was initially designed to run at hourly intervals, we modified it to a half-hourly time step to match the observations in this study. Thus, all approaches were performed every 30 min in our analysis. As the average of the four SIF\({}_{760}\) measurements may represent the situations of our study site better than the individual measurements, we only considered the statistical model generated using the averaged SIF\({}_{760}\) values (GPP = 0.621 \(\times\) SIF\({}_{760}\) + 0.614) in this study. As shown in Figure 6, all three approaches estimate the GPP well. Among which, the BEPS model (R\({}^{2}\) = 0.867, RMSE = 0.153 mg m\({}^{-2}\) s\({}^{-1}\)) performs better than that of SIF\({}_{760}\) (R\({}^{2}\) = 0.624, RMSE = 0.237 mg m\({}^{-2}\) s\({}^{-1}\)) and MuSyQ-GPP (R\({}^{2}\) = 0.702, RMSE = 0.247 mg m\({}^{-2}\) s\({}^{-1}\)) models. We also modeled GPP using the statistical model generated with SIF\({}_{686}\) (GPP = 0.682 \(\times\) SIF\({}_{686}\) + 0.539) (see detailed results in Text S2, Figures S1-S4 in Supplementary Materials). Compared with the SIF\({}_{760}\)-based model, SIF\({}_{686}\)-based GPP model showed a limited accuracy with R\({}^{2}\) = 0.456 and RMSE = 0.363 mg m\({}^{-2}\) s\({}^{-1}\).  
Figure 5: Relationship between: (**a**) individual SIF\({}_{760}\) and GPP; and (**b**) averaged SIF\({}_{760}\) and GPP. The error bars indicate the range of SIF\({}_{760}\) values for the four measurements.  
We noticed that some of the SIF-based GPP estimates were higher than the EC-based estimates when GPP was relatively low, which corresponded to the observations at 8:30 on 21 August and 8:30 on 22 August (dots in the red circle in Figure 6a). When these two groups of data were excluded from the analysis, both SIF based GPP models performed better with R\({}^{2}\) increased to 0.667 and RMSE decreased to 0.236 mg m\({}^{-2}\) s\({}^{-1}\) for the SIF\({}_{760}\)-based model, and R\({}^{2}\) increased to 0.459 and RMSE decreased to 0.352 mg m\({}^{-2}\) s\({}^{-1}\) for the SIF\({}_{686}\)-based model, respectively. The MuSyQ-GPP algorithm was found to underestimate the GPP values at high productivity, which could be attributed to the assignment of a constant \(LUE_{0}\) value in the algorithm as it could not properly handle the contribution of both sunlit and shaded leaves to the total GPP. BEPS model was found to slightly underestimate GPP at high productivity and overestimate GPP at low productivity. Among these three approaches, BEPS model showed better performance than that of SIF-based or LUE-based MuSuQ-GPP models, with higher R\({}^{2}\) and lower RMSE.
## 6 Experimental Results
### Results by Gender and Classifiers  
The previous experiments indicated that the feature selection positively influenced the prediction performance, so we utilized it in further experiments. The number of all features for one subject was 1560, and only 300 features were selected in feature selection.  
For each classifier, we applied two types of cross-validation. In the first case, 75% of the data were used for training the model. Out of these training data, 25% were utilized as validation data, employed for hyperparameter tuning. The classifier model with the best parameters found in the previous step was applied to the test data that were not part of the training dataset. The whole process  
Figure 5: Prediction accuracy as a function of the number of features.  
Figure 6: Prediction accuracy as a function of the number of principal component analysis (PCA) components.  
was repeated 10 times and the results were averaged. As a second validation scheme, we used fourfold cross-validation. The dataset was divided into four folds. Then, three folds were used for training (75%) and one fold for testing (25%). The loop proceeded in such a way that each fold was used once for testing and three times for training. This is cross-validation without resampling. The results of testing both cases are shown in Table 2.  
In Table 2, each column shows the highest value (bold font) achieved within each type of testing method and for each classifier. As can be seen, more bold values are in rows representing results for 300 selected features. Feature selection led to an improvement in overall accuracy. Another finding is that the SVM classifier achieved the best results.
## 5 Data-Specific Solutions of the Framework  
In this section, the previously modeled framework is implemented with a real-world problem of an actual manufacturing SME in Pakistan. The company's manufacturing facility, situated near the city of Sialkot, produces different types of sports equipment including footballs, gloves, shin pads, etc. Production of these items solely depends upon a variety of raw materials supplied by different vendors, thus requiring long-term sustainable business relationships. The company is currently working with 10 different suppliers. The top management of the company wants a methodical prioritization of these suppliers based on their compatibility with the current and future production needs of the company so that it may shortlist some of these companies for long-term integration. Therefore, the problem lies within the domain of multi-objective optimization, which is the central methodology of our presented framework. The first step of our framework is exploration and prioritization of all the factors, which can affect the SI decision. During the factor-prioritization phase of this study, a total of 400 questionnaires were distributed among the top management experts of 30 different SMEs of Pakistan. Out of these distributed questionnaires, 242 were returned to the authors. The questionnaires contained a rating scale for each of the factors with a range of 1 to 5, taking '1' as the least significant and '5' as the most significant. An average (arithmetic mean) was then calculated to assign absolute weights for all the factors. The factors Quality, Delivery, Costs, and Technical capability were computed as the most crucial, having weights of 4.78, 4.51, 4.33, and 4.24, respectively. As a next step, the performance of the selected suppliers was evaluated based on their previous performance with respect to the screened out factors. This performance data was then utilized to implement the developed algorithm for further analysis. The past performance or historical data of the ten suppliers currently in business relations with the company are compiled in Table 2.  
These data are related to the past two-year performance of these 10 main vendors where the ranges for all factors are expressed as percentages. The approximate demand of the company is about 200 units per month. The capacity of each supplier to meet this monthly demand is also presented. The company wants to select a maximum of four suppliers for long-term business integration. The ranges of the factors are given in Table 3. The data-specific problem was solved using the proposed AABC algorithm. The results were further validated using basic ABC algorithm and particle swarm optimization (PSO) algorithm. The results obtained from the simulation are presented in the next section. The proposed AABC algorithm is shown in Figure 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factors** & Quality & Delivery & Cost & Technical \\  & & & & Capability \\ \hline
**Weightage** & 4.78 & 4.51 & 4.33 & 4.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Weighted Factors.
## 1 Introduction
### Advantages of Using CR in WSNs
#### Global Operability  
Each country has its own spectrum regulation rules. A certain band available in one country might not be available in another. Traditional wireless sensors with a preset working frequency might not work in cases where the manufactured wireless sensors are deployed in different regions. On the other hand, if nodes are equipped with cognitive radio capability, they can overcome the spectrum incompatibility problem by changing their communication frequency band. Therefore, CR wireless sensors have the potential to be operated almost anywhere in the world.
## Experimental Results
### Comparative Study of Ground-Level and Above-Ground Deployment
#### 5.1.4 Link Asymmetry  
Link asymmetry is another property that must be taken into account when designing WSN protocols. Looking at the RSSI or PRR measured on forward and backward links of pairs of nodes (1,2) and (3,4), we noticed a significant difference: for a ground surface deployment, Figure 5b shows a much higher PRR on the forward link \(3\to 4\) than on the backward link \(4\to 3\).  
Beyond this illustrative example, Table 1 presents statistics concerning the difference of PRR and RSSI values measured on forward and backward links on all nodes, as well as the number of unidirectional links. These results show that the asymmetry problem is general in WSN, and not restricted to ground-level deployments. Indeed, Table 1 shows that, while the average difference of measured PRR on forward and backward links was higher at ground level, the maximum difference of the measured PRR and the maximum or the average difference of measured RSSI on forward and backward links were even higher for a deployment at height. However, we also note that, while there were no unidirectional links when the sensors were deployed at height, we had 8% unidirectional links in the case of ground-surface deployment. This means that, for a deployment at height, even if there might temporarily exist a significant difference in values measured between two nodes, the forward and backward links are usually available in the two communication directions.
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 6 The 6G Network Infrastructure to Manage V2X Application Lifecycles
### Over-the-Air (OTA) Software Updates  
Regarding the last step of vehicle operations management, we have the particularization of vehicle software update, which encompasses the over-the-air (OTA) software updates that represent a process in which software enhancements are transmitted remotely from a cloud-based server via a cellular connection to a connected vehicle. The primary objective is to furnish the vehicle's software systems with new features and updates, and it represents a paradigm shift in how vehicles receive software upgrades, eliminating the need for physical interventions and ensuring timely updates. This mechanism enhances vehicle performance and features and addresses potential vulnerabilities, ensuring robust vehicular cybersecurity. These updates encompass potential modifications to any software governing the vehicle's physical components or electronic signal processing systems. In practice, OTA updates predominantly impact user interfaces, such as infotainment screens, navigation systems, and vehicle maps. The execution of OTA updates empowers continuous enhancement and upkeep of a vehicle's performance and features. The necessity for physical visits to repair or service centers is obviated by leveraging advanced data analytics and automated, remote service delivery. Two user stories are associated with the OTA scenario:  
* Scheduled Updates: In this scenario, users can schedule updates during off-peak hours or when the vehicle is not in use, ensuring minimal disruption.
* Incremental Updates: Rather than fetching the complete software package, vehicles exclusively retrieve the modifications from the previous version, resulting in quicker update times and decreased data usage.  
Considering different variables like current and predicted future network coverage, number of surrounding vehicles impacting the available bandwidth, remaining battery life, available storage, and others, the vehicular operations management system can decide to schedule the update according to off-peak hours, prioritize the updates based on urgency, have redundancy mechanisms ensuring continuous operation and implement robust and encryption and authentication protocols that guarantees the security of OTA updates. Additionally, this technological evolution in updates allows vehicle manufacturers to rejuvenate and refine their products continuously remotely. Notably, the effectiveness of the firmware over-the-air (FOTA) process hinges significantly on C-V2X technology, which facilitates efficient and scalable wireless communication between vehicles and software management platforms.  
OTA software updates, as a component of vehicle operations management, highlight the significance of ongoing software improvements in the automotive sector. As vehicles become more interconnected and reliant on software, these methods guarantee peak performance and security.
# CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords
## 3 Proposed System
### Mobile Multi-Factor CNN (MMCNN)  
In addition, this study presents a mobile multi-factor CNN (MMCNN) with a lightweight structure of only four layers for use in mobile devices, the experimental results for which revealed an accuracy rate of 99.6%. The MMCNN passes through one convolutional layer for each input factor, then concatenates the three factors. Subsequently, it passes through one convolutional layer and two FC layers (Affine layers), the result of which is output through SoftMax classification. Figure 4 displays the schematic structure of the MMCNN, and Table 3 provides detailed specifications for each layer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Input 1** & **Input 2** & **Input 3** \\ \hline Input pixel size & \(227\times 227\times 3\) & \(65\times 65\times 3\) & \(65\times 65\times 3\) \\ \hline \multirow{4}{*}{First convolutional layer} & Filter: 96 & Filter: 96 & Filter: 96 \\  & Channel: 3 & Channel: 3 & Channel: 3 \\  & Width, Height: 11 & Width, Height: 11 & Width, Height: 11 \\  & Stride: 4 & Stride: 1 & Stride: 1 \\  & Zero padding: 0 & Zero padding: 0 & Zero padding: 0 \\  & Output: \(55\times 55\times 96\) & Output: \(55\times 55\times 96\) & Output: \(55\times 55\times 96\) \\ \hline \multirow{4}{*}{First max pooling} & Width, Height: 7 & Width, Height: 7 & Width, Height: 7 \\  & Stride: 3 & Stride: 3 & Stride: 3 \\  & Output: \(17\times 17\times 96\) & Output: \(17\times 17\times 96\) & Output: \(17\times 17\times 96\) \\ \hline \multirow{4}{*}{Concatenate} & \multicolumn{2}{c}{\(17\times 17\times 96\times 3\)} \\ \cline{2-4}  & & Filter: 256, Channel: 288 \\ \cline{1-1} \cline{2-4}  & & Width, Height: 3 \\ \cline{1-1} \cline{2-4}  & & Stride: 1, Zero padding: 1 \\ \cline{1-1} \cline{2-4}  & & Output: \(17\times 17\times 256\) \\ \hline \multirow{2}{*}{Second max pooling} & Width, Height: 5, Stride: 2 \\  & Output: \(7\times 7\times 96\) & \\ \hline \multirow{4}{*}{First affine layer} & Flatten: 12,544, Neuron: 1024, \\  & Dropout: 0.5 \\ \cline{1-1}  & Output: 1024 \\ \hline \multirow{2}{*}{Second affine layer} & Neuron: 100 \\ \cline{1-1} \cline{2-4}  & & Output: 100 \\ \hline \end{tabular}
\end{table}
Table 3: Detailed specifications of the MMCNN.  
Figure 4: Structure of the MMCNN.
# Simulation of Electromagnetic Generator as Biomechanical Energy Harvester
## 2 Electromagnetic Generator Modelling
### Air Gap Thickness of the Generator
Hence, the 1 mm air gap thickness is suitable for the electromagnetic generator design as it exhibits moderate electromagnetic force which ranges between 1.314 N and 1.550 N and does not require huge effort from the user. The optimum magnet size for the electromagnetic generator is chosen based on magnetic flux density in the air gap region. Therefore, based on 1 mm air gap thickness, the magnetic flux density at air gap region was determined by varying the magnet sizes as shown in Figure 7.  
Magnet C, Magnet E and Magnet F demonstrate an almost similar level of magnetic flux density. Out of three magnet sizes, Magnet E is chosen for this study as the electromagnetic force is lower than Magnet F and the dimension is wider than Magnet C.  
Similarly, the T shape stator is analyzed by using the Magnet E by varying the air gap thickness. Figure 8 shows the electromagnetic force and magnetic flux density for T shape stator design. Figure 8: Reaction due to air gap thickness. (**a**) Electromagnetic force. (**b**) Magnetic flux density at the center of the air gap region. Figure 7: Magnetic flux density at the centre of the air gap region. Similar to the previous analysis, the T shape stator with 0.5 mm air gap thickness also exhibited a higher electromagnetic force which will need higher user effort. Hence, the 1 mm air gap thickness was found to be the optimum choice as the magnetic flux density is at the 1 Tesla region and other air gap thicknesses is exhibiting lower than 0.8 Tesla magnetic flux density and are not suitable for an electromagnetic generator to produce high power. The air gap thickness of 1 mm was able to produce optimum magnetic flux density for both square edge stator and T shape stator.
# Machine Learning-Based Lithium Battery State of Health Prediction Research
###### Abstract  
To address the problem of predicting the state of health (SOH) of lithium-ion batteries, this study develops three models optimized using the particle swarm optimization (PSO) algorithm, including the long short-term memory (LSTM) network, convolutional neural network (CNN), and support vector regression (SVR), for accurate SOH estimation. Key features were extracted by analyzing the temperature, voltage, and current curves of the battery, and health factors with high correlation to SOH were selected as model inputs using the Pearson correlation coefficient. The PSO algorithm was employed to optimize model parameters, resulting in the construction of three predictive models: PSO-LSTM, PSO-CNN, and PSO-SVR. The models were validated using the NASA PCoE battery aging datasets B0005, B0006, and B0007, with prediction accuracy evaluated based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R\({}^{2}\)). Results indicate that the optimized models achieved significant improvements in prediction accuracy, with RMSE and MAE reduced by over 0.5%, a minimum reduction of 38% in MAPE, and R\({}^{2}\) exceeding 0.8, demonstrating strong fitting capabilities and validating the effectiveness of the PSO strategy. Among the three models, PSO-LSTM exhibited the best predictive performance, achieving a minimum MAE of 0.67%, RMSE of 0.94%, MAPE of 45.82%, and R\({}^{2}\) as high as 0.9298 across the three datasets. These findings suggest that the PSO-LSTM model provides a robust reference for accurate SOH prediction of lithium-ion batteries and shows promising potential for practical applications.  
l +
Footnote †: journal: Applied sciences  
1
Footnote †: journal: Applied sciences
## 3 Results
The GPP in Kursk was strongly influenced by soil water content, due to a long dry period. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Site**} & **NEE Cumulated** & **Reco Cumulated** & **GPP Cumulated** & **Light Use Efficiency** \\  & **(g C CO\({}_{2}\) m\({}^{-2}\))** & **(g C CO\({}_{2}\) m\({}^{-2}\))** & **(g C CO\({}_{2}\) m\({}^{-2}\))** & **(g C CO\({}_{2}\) m\({}^{-2}\)/\(\mu\)mol ph m\({}^{-2}\))** \\ \hline Moscow & \(-86\) & 248 & 334 & 0.86 \\ Kursk & \(-89\) & 525 & 613 & 0.67 \\ \hline \end{tabular}
\end{table} 
Table 1: Cumulative values of carbon balance of the two study sites. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Degrees of Freedom**} & \multicolumn{1}{c}{**Sum of Squares**} & \multicolumn{1}{c}{**\% Total**} & \multicolumn{1}{c}{**Mean Squares**} & \multicolumn{1}{c}{**F Value**} & \multicolumn{1}{c}{**Significance**} \\ \hline
**Kursk** & & & & & & \\ \hline \hline \multicolumn{2}{c}{Tsoil} & 1 & 62,603 & 1.7\% & 62,603 & 15.489 & ***1 \\ \multicolumn{2}{c}{Tair} & 1 & 191,195 & 5.1\% & 191,195 & 47.305 & *** \\ \multicolumn{2}{c}{PPFD} & 1 & 255,245 & 6.9\% & 255,245 & 63.153 & *** \\ \multicolumn{2}{c}{SWC} & 1 & 1,305,364 & 35.1\% & 1,305,364 & 322.973 & *** \\ \multicolumn{2}{c}{Phase} & 8 & 1,430,050 & 38.5\% & 178,756 & 44.228 & *** \\ \multicolumn{2}{c}{Residuals} & 117 & 472,881 & 12.7\% & 4042 & \\ \multicolumn{2}{c}{Total} & & 3,717,338 & 100.0\% & & \\ \hline \multicolumn{2}{c}{**Moscow**} & & & & & \\ \hline \hline \multicolumn{2}{c}{Tsoil} & 1 & 10,037 & 1.0\% & 10,037 & 7.4275 & ** \\ \multicolumn{2}{c}{Tair} & 1 & 7436 & 0.7\% & 7436 & 5.5025 & * \\ \multicolumn{2}{c}{PPFD} & 1 & 5364 & 0.5\% & 5364 & 3.9698 & * \\ \multicolumn{2}{c}{SWC} & 1 & 134,794 & 13.1\% & 134,794 & 99.7504 & *** \\ \multicolumn{2}{c}{Phase} & 8 & 748,859 & 72.9\% & 93,607 & 69.2715 & *** \\ \multicolumn{2}{c}{Residuals} & 89 & 120,267 & 11.7\% & 1351 & & \\ \multicolumn{2}{c}{Total} & & 1,026,757 & 100.0\% & & & \\ \hline \hline \end{tabular}  
* Significance codes: ***-\(p<0.001\), ***-\(p<0.01\), ***-\(p>0.05\).  
\end{table} 
Table 2: ANOVA of the linear mixed models predicting GPP dynamics in relation to five parameters: Tsoil, Tair, PAR, SWC and phase of barley development for EC station in Moscow and station in Kursk. Figure 5: GPP in growing season shifted at the seeds time (0–point) for EC stations in Moscow (small crosses) and Kursk (empty dots). Lines represent 7-day running mean values for station in Moscow (the dotted line) and for station in Kursk (solid line). Vertical lines show the conventional boundaries of the stages of crop development: a—seeds, b—germination, c—growting, d—tilering, e—leaf tube formation, f—milky ripeness, g—wax ripeness, h—complete ripeness and hharvest.