esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effects of Injection at the Valve and Reactivity Fuels on the Emission Attributes
#### 3.3.4 NOx Emission  
NOx emission was found less at the 4.5-5.5 bar IMEP but increased drastically as the engine load proceeded to 6.5 bar IMEP during the combustion of B5 and biogas injected at the valve, as depicted in Figure 18a. The increase in the NOx emission could be an attribute of the significant rise in the cylinder temperature that reaches the nitrogen-oxygen bond formation temperature and virtually the period of such temperature increase [22]. A similar trend manifested for B5 and biogas in a premixed mode, and the exhibited highest NOx emission was observed at 4.5-5.5 bar IMEP while lowest emission displayed as the load extended to full 6.5 bar IMEP. Besides, the trend of the NOx emission was identical to that of the conventional diesel RCCI combustion for both premixed and biogas injected at the valve, as shown in Figure 18b. The figure further shows that biogas injection at the valve rises the NOx emission at 6-6.5 bar IMEP, which could be an influence of the rise in the cylinder temperature. Figure 18c indicated that the NOx emission increased with the increase in the engine speed at various capacities, as an attribute of the increased cylinder temperature. The NOx emission maintained a closed range at the load of 4.5-5.5 bar IMEP for all the engine speed levels. As shown in Figure 18c, the NOx emission increased with the increase in the engine speed at merely all the load conditions. However, the margin between 1600 rpm and the rest of the speed levels increased with the rise in the engine capacity due to an increase in the cylinder temperature, causing an increased rate of the nitrogen-oxygen bond formation when operated at higher speeds.  
Figure 18: Engine-out NOx emission for (**a**) B5/biogas RCCI for premixed and port injection at the valve, (**b**) diesel/biogas in a premixed and port injection at the valve, and (**c**) B5 and premixed biogas RCCI combustion at various engine speed.
###### Abstract  
First-principles calculations within the density-functional-theory (DFT) approach are conducted in order to explore and explain the effect of small amounts of titanium on phase stability in the U-6 Nb alloy. During rapid quenching from high to room temperature, metastable phases \(\alpha^{\prime}\) (orthorhombic), \(\alpha^{\prime\prime}\) (monoclinic), and \(\gamma^{0}\) (tetragonal) can form, depending on Nb concentration. Important mechanical properties depend on the crystal structure and, therefore, an understanding of the effect of impurities on phase stability is essential. Insights on this issue are obtained from quantum-mechanical DFT calculations. The DFT framework does not rely on any material-specific assumptions and is therefore ideal for an unbiased investigation of the U-Nb system.  
density-functional theory; uranium-niobium alloys; metastable phase; metallic fuels; thermodynamic modeling +
Footnote †: journal: Applied Physics Letters
## 4 Results and Discussion  
### Heat Transfer Character of Adsorbent Bed  
The heat transfer character of the adsorbent bed has a great influence on the result of the adsorbent performance testing. At the same time, the adsorption and desorption process need to be conducted under vacuum conditions. Since the temperature of the inner adsorbent bed cannot be measured directly, we only can measure the temperature of the wall outside the adsorbent bed. It is hoped that the temperature change in the adsorbent bed can be mastered by the measurement results of the wall temperature outside the adsorbent bed as accurately as possible. Thus, the difference between the adsorbent bed inner temperature and the temperature of the wall outside the adsorbent bed is tested. The temperature of inside and outside the adsorbent bed are measured by six thermocouples, top inside bed, middle inside bed, bottom inside bed, top outside bed, middle outside bed, and bottom outside bed. The rate of the temperature inside and outside the adsorbent bed during the process of heating and cooling adsorbent bed are shown in Figure 4. It can be seen that the temperature of the bottom outside bed is very close to the temperature of the bottom inside bed. The temperature variation of inside and outside the bottom adsorbent bed during the process of heating the adsorbent and cooling the adsorbent bed are shown in Figure 5. The temperature difference between the bottom outside bed and the bottom inside bed at the end of heating and cooling was 5.66 \({}^{\circ}\)C and 0.303 \({}^{\circ}\)C, respectively. The deviation will be less than 2.13% and 1.06% in the heating and cooling process, respectively, the average deviation in the whole heating and cooling process is 3.24%. It is feasible to use the temperature of outside bottom adsorbent bed to represent the temperature of the inside bottom adsorbent bed.
# Research on Short-Term Wind Power Forecasting by Data Mining on Historical Wind Resource
## 5 Numerical Case of Interval Prediction
### Prediction Interval Evaluation Indicators  
The prediction interval indicators include mean absolute percentage error, forecasting interval coverage percentage, forecasting interval width.  
* Indicator 1: Mean absolute percentage error (MAPE) is used to evaluate the error between measured and predicted value [20]. The smaller the \(I_{MAPE}\) value, the higher the prediction accuracy. Its expression is as follows \[I_{MAPE}=\frac{1}{n}\sum\limits_{i=1}^{n}\left|\frac{x_{i}-\overset{\wedge}{x_ {i}}}{x_{i}}\right|\times 100\%\] (16) where \(n\) represents the number of test samples, \(x_{i}\) represents the measured power value and \(\overset{\wedge}{x_{i}}\) represents the predicted value.
* Indicator 2: Forecasting interval coverage percentage (FICP) is used to express the probability of the measured value in the prediction interval. A more measured value falls into the interval when  
Figure 5: Flow chart of wind power prediction interval model based on FIG-Elman method.  
the value of \(I_{FICP}\) is larger, and the reliability of the interval prediction is higher. Its expression is as follows \[I_{FICP}=\frac{1}{n}\sum_{i=1}^{n}c_{i}\] (17) where \(x(i)\in[L_{i},U_{i}]\), \(c_{i}=1\), or \(c_{i}=0\).
* Indicator 3: Forecasting interval average width (FIAW) is used to test the appropriateness of the predictive interval width, which used to prevent excessive interval width. The smaller the \(I_{\text{FIAW}}\) value is, the narrower the interval is, as follows \[I_{\text{FIAW}}=\frac{1}{n}\sum_{i=1}^{n}\left(U(x_{i})-L(x_{i})\right)\] (18) where \(U(x_{i})\) represents the upper limit of the prediction interval of the first prediction sample. \(L(x_{i})\) denotes the lower limit of the prediction interval of the first prediction sample.
# Using Graph Neural Networks for Social Recommendations
## 4 Experimental Evaluation
### Performance Metrics
#### 4.3.2 Hit Rate (HR)  
HR is a metric used to evaluate the performance of the model by calculating the percentage of times that at least one item from the test set of a user was recommended in the top-\(n\) recommendations suggested by the model:  
\[HR@n=\frac{\text{Number of users with at least one hit in top-}n}{\text{Total number of users}} \tag{17}\]  
Hit rate \(HR@n\) for a top-\(n\) recommendation list is defined as follows [30]:  
\[HR@n=\frac{|H_{u}|}{m} \tag{18}\]  
where \(H_{u}=1\) (i.e., a \"hit\") when the algorithm manages to recommend at least one item that was in the user's original list (test set), and 0 otherwise; \(n\) is the size of the recommendation list considered; and \(m\) is the total number of users in the test set. Essentially, HR assesses whether the model is able to recommend at least one relevant item to the user.  
For example, let us consider a test set of 100 users and their corresponding ground truth sets of movies. The RS generates a list of top-5 movie recommendations for each user. After evaluating the recommendations, it is found that 30 users have at least one recommended movie in their ground truth sets. In this case, the hit rate would be calculated as \(30/100=0.3\) or \(30\%\).  
A higher hit rate serves as an indicator of the effectiveness of an RS, as it suggests that the system is successfully recommending items that match users' preferences. However, it is essential to recognize that the hit rate metric solely focuses on whether the recommended items are present in the user's ground truth set, without considering their ranking or relevance. As a result, it is common practice to complement the hit rate with other evaluation metrics to obtain a more comprehensive assessment of the performance of the RS. By incorporating additional metrics, the evaluation process gains insights into the system's ability to provide accurate and highly relevant top-\(n\) recommendations to users.
## 3 Results and Discussion
### Cloning and Expression of Active spPEP
#### 3.7.2 Determination of Km of spPEP  
The spPEP purified from the soluble fraction exhibited a Km similar to that determined by [PERSON] [2] for the enzyme purified from infested wheat. A representative Michaelis-Menten kinetics is shown in Figure 6 using zGPpNA as the substrate. The inset shows the v _vs._ [S] plot. The enzyme Km for the peptide substrate was determined to be \(211\pm 53\)\(\mu\)M in 25 mM ethanolamine, pH 8.5, 0.1 M NaCl, 1 mM DTT. [PERSON] _et al._[2] reported a Km of 65.3 \(\mu\)M for the enzyme purified from the infested wheat and analyzed in 25 mM ethanolamine, pH 8.5. One primary reason for the difference in Km could be the level of purity of the enzyme. The enzyme in [PERSON]'s study was only partially purified, whereas the recombinant protein was greater than 95% pure, as determined from the SDS PAGE (Figure 4B). The presence of contaminating proteins might actually help in the stability and binding of the substrate to the PEP. Additional studies need to be performed, such as the addition of BSA, to the reaction to test this hypothesis. Because the enzyme was pure, the turnover number for the spPEP was able to be determined. The turnover number was \(172\pm 47\)\(\mu\)M of peptide/s/\(\mu\)mole of spPEP.
# Image Steganography Using LSB and Hybrid Encryption Algorithms
## 3 Method
### Implementation Phase
The LSB approach is an essential and widely used technique in steganography. 2. It involves replacing the least significant bits of the cover media with secret data to embed information. The altered bits are typically invisible to the human eye or ear,  
Figure 1: Least significant approach encryption and decryption process. making it difficult to detect the hidden message. Supposing we have an image with pixel values ranging from 0 to 255, we can modify the least significant bit of each pixel to hide a secret message using the LSB approach. For instance, if the pixel value is 110 (in binary: 01101110), and we want to embed a bit of 1, we can change the LSB to 1 (01101111). This slight modification is often visually indistinguishable, especially with large cover media such as images or audio files.
## 6 Comparison Analysis and Research Challenges
### Research Challenges
#### 6.2.8 Hidden Terminal Problem  
The hidden node problem [30] in multi-hop WSNs refers to a collision at a common receiver due to two different senders' simultaneous transmissions located at the other side. These two senders cannot recognize each other's transmission because they are out of one another's transmission range. Applying good duty-cycling may be effective in mitigating this problem, because the duty-cycling may prevent the nodes from simultaneous transmission if the two nodes are set to not overlap their wakeup times. Thus, designing a duty-cycling for the hidden node problem seems to be interesting. On the other hand, a multi-channel procedure can also provide another good solution for the problem. By taking advantage of the multi-channels, DCM-MAC [33] could improve its performance in avoiding collisions.
# Future Internet Architectures on an Emerging Scale--A Systematic Review
## 5 Potential Benefits and Challenges of Future Internet Research  
The future internet continues to evolve as potential technologies continue to emerge. These technologies could transcend the way we see and use the internet in the future. The role of blockchains in the organization and progress of the applications and services within the Metaverse has been discussed in this article as part of the emerging future internet. Technologies such as the Internet of Things (IoT), vehicular communications, artificial intelligence (AI), future 6G networks, and quantum computing serve as few other examples that have the potential to revolutionize the next phase of the future internet. 6G networks, as the considered successors to 5G networks, are currently under development and are expected to offer more promising speeds than 5G, to provide support towards experiencing immersive applications such as AR and VR, and to perform remote surgeries and AI-enabled autonomous driving. As AI advances in the future, it is expected to revolutionizethe development of newer applications to improve business productivity, decision making capabilities, and healthcare outcomes. Quantum computing is another important aspect of the future internet and is expected to revolutionize fields such as drug discovery and cryptography, etc. Vehicular communications are a rapidly evolving field that seeks to address technological, societal, and standardization challenges. It is seen that efforts are currently underway [13] to integrate SDN and network function virtualization (NFV) with fog computing. The market for internet-connected vehicles is rapidly growing and it is predicted that companies will be using such vehicles in some form by the end of 2023.  
Along with the above-mentioned benefits, there are several challenges that need to be addressed along the way in creating a future internet that is safe, more reliable, and secure. For example, there is a need to enforce stronger privacy regulations and to provide end-user control of user data, as vast amount of user data is generated by technologies such as AI and the IoT, which raises concerns regarding user privacy and security. There is a need to design and develop robust security features to fight against cyber-attacks in an environment of increased data transmission. The energy consumption of the future internet is likely to rise with the increasing use of connected devices and data centers. The sustainable development of technologies that support energy efficiency is needed to resolve this problem. This sustainable development of AI and other related technologies may raise several ethical issues such as potential bias, accountability, and transparency. Therefore, by addressing these concerns, one can have a new face of the future internet that is safe, secure, and accessible to everyone.
## 3 Results
### Statistical Tests  
#### 3.4.1 McNemar's Test  
The [PERSON]'s test was employed to assess statistically significant differences in classification performance across referent models--Random Forest (RF), Artificial Neural Network (ANN), Logistic Regression (LR), Support Vector Machine (SVM), LightGBM (LGBM), CatBoost (CB), and XGBoost (XGB)--(Table 6). A pronounced divergence was observed in all pairwise comparisons involving LR, which exhibited consistent under-performance relative to other models, as evidenced by universally significant \(p\)-values (\(p<0.001\)) and elevated chi-squared statistics (e.g., \(\chi^{2}\) = 47.70 vs. RF; \(\chi^{2}\) = 50.08 vs. ANN; \(\chi^{2}\) = 54.28 vs. CB). These results underscore LR's inferior discriminative capacity within the evaluated framework.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**10-Fold CV**} & \multicolumn{6}{c}{**10 \(\times\) 10-Fold CV**} \\ \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **test\_f1** & **roc\_auc** & **Accuracy** & **Precision** & **Recall** & **f1** & **roc\_auc** \\ \hline
**RF** & 0.800 & 0.796 & 0.813 & 0.804 & 0.863 & 0.793 & 0.788 & 0.808 & 0.797 & 0.861 \\  & [0.784, 0.815] & [0.776, 0.815] & [0.791, 0.835] & [0.820]  
Statistically significant differences (\(\alpha\) = 0.05) were further identified exclusively in two pairwise comparisons: CB versus RF (\(p\) = 0.0315, \(\chi^{2}\) = 4.63) and CB versus LGBM (\(p\) = 0.0036, \(\chi^{2}\) = 8.49). The absence of significance in the CB-XGB comparison (\(p\) = 0.210) was noted, with the chi-squared value reported as 'nan', a condition typically arising when contingency table cells contain zero counts, precluding conventional test computation. This outcome, however, aligns with the non-significant \(p\)-value derived via exact binomial approximation, suggesting parity in classification efficacy between CB and XGB.
## 5 Results: Impact of Faults in LSHT Machines
### Evaluation of Shared Faults Across 10-Pole and 16-Pole LSHT Machines
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Parameter** & **Symbol** & **PMSM** & **SynRM** & **SCIM** \\ \hline \multirow{4}{*}{Healthy} & Mean Torque & \(\rm T_{mean}\;[Nm]\) & 1360.9 & 716.8 & 700.1 \\  & Torque Ripple & \(\rm T_{rp}\;[\%]\) & 4.5 & 42.9 & 3.9 \\  & Efficiency & \(\rm\eta\;[\%]\) & 97.3 & 94.4 & 94.2 \\  & Active Losses & \(\rm L_{cp+fe+edd}\;[W]\) & 2329 & 2650 & 2699 \\  & Power Factor & \(\rm pf\;[-]\) & 0.98 & 0.47 & 0.78 \\ \hline \multirow{4}{*}{Dynamic \(\Delta_{\rm de}=0.25\)} & Mean Torque & \(\rm T_{mean}\;[Nm]\) & 1361 & 719.2 & 700.8 \\  & Torque Ripple & \(\rm T_{rp}\;[\%]\) & 4.6 & 44.0 & 3.5 \\  & Efficiency & \(\rm\eta\;[\%]\) & 97.3 & 94.4 & 94.2 \\  & Active Losses & \(\rm L_{cp+fe+edd}\;[W]\) & 2323 & 2661 & 2695 \\  & Power Factor & \(\rm pf\;[-]\) & 0.98 & 0.47 & 0.78 \\ \hline \multirow{4}{*}{Dynamic \(\Delta_{\rm de}=0.5\)} & Mean Torque & \(\rm T_{mean}\;[Nm]\) & 1361.0 & 717.9 & 700.5 \\  & Torque Ripple & \(\rm T_{rp}\;[\%]\) & 4.6 & 44.2 & 5.4 \\  & Efficiency & \(\rm\eta\;[\%]\) & 97.3 & 94.4 & 94.2 \\  & Active Losses & \(\rm L_{cp+fe+edd}\;[W]\) & 2323 & 2650 & 2695 \\  & Power Factor & \(\rm pf\;[-]\) & 0.98 & 0.47 & 0.78 \\ \hline \multirow{4}{*}{Static \(\Delta_{\rm se}=0.25\)} & Mean Torque & \(\rm T_{mean}\;[Nm]\) & 1360.9 & 713.7 & 703.5 \\  & Torque Ripple & \(\rm T_{rp}\;[\%]\) & 4.6 & 44.5 & 3.47 \\  & Efficiency & \(\rm\eta\;[\%]\) & 97.3 & 94.4 & 94.2 \\  & Active Losses & \(\rm L_{cp+fe+edd}\;[W]\) & 2323 & 2664 & 2696 \\  & Power Factor & \(\rm pf\;[-]\) & 0.98 & 0.47 & 0.78 \\ \hline \multirow{4}{*}{Static \(\Delta_{\rm se}=0.5\)} & Mean Torque & \(\rm T_{mean}\;[Nm]\) & 1360.5 & 714.4 & 704.1 \\  & Torque Ripple & \(\rm T_{rp}\;[\%]\) & 4.7 & 45.0 & 4.8 \\ \cline{1-1}  & Efficiency & \(\rm\eta\;[\%]\) & 97.3 & 94.4 & 94.2 \\ \cline{1-1}  & Active Losses & \(\rm L_{cp+fe+edd}\;[W]\) & 2327 & 2661 & 2698 \\ \cline{1-1}  & Power Factor & \(\rm pf\;[-]\) & 0.98 & 0.47 & 0.78 \\ \hline \end{tabular}
\end{table} 
Table 4: Main performance comparison of optimized 10-pole LSHT machines including eccentricities faults. Compared to the 10-pole machines, the most significant effect on the torque ripple is observed in the 16-pole PMSM. A significant torque ripple increase was obtained, which can be explained from the alteration of rotor symmetry, leading to higher cogging torque that contributes to the ripple waveform. Nevertheless, torque ripple variations are influenced by multiple factors and cannot be directly linked to eccentricity, making it difficult to predict eccentricity from ripple observation alone. It can be concluded that SE and DE cannot be directly detected from monitoring the basic performance metrics in the 16-pole LSHT PMSM, SynRM, and SCIM.
## 3 Federated Kalman Filter Design  
In this paper, the federated Kalman filter was designed as the inertial/odometer dead-recursive sub-Kalman filter, the inertial/satellite integrated navigation sub-Kalman filter, and a main filter.
## 1 Introduction
After classifying the PV power data and associated weather information in five weather classes: rainy, heavy cloudy, cloudy, light cloudy, and sunny, the CNN is applied to predict the next day's weather type. To this end, five CNN models are constructed and SSA is applied to optimize each model. However, using several CNN models makes this hybrid approach not suitable for real-time forecasting. In Reference [38], a method combining deep convolutional neural network and wavelet transform technique is proposed for deterministic PV power forecasting. Then, the PV power uncertainty is quantified using quantile regression. Results demonstrated the deterministic model possesses reasonable forecasting stability and robustness. Of course, deep learning models possess the capacity to efficiently learn nonlinear features and pertinent information in time-series data that should be exploited in a wide range of applications. This study offers a threefold contribution. Firstly, to the best of our knowledge, this the first study introducing a variational autoencoder (VAE) and Restricted Boltzmann Machine (RBM) methods to forecast PV power. Secondly, this study provides a comparison of forecasting outputs of eight deep learning models, including simple RNN, LSTM, ConvLSTM, Bidirectional LSTM (BiLSTM), GRUs, stacked autoencoders, VAE, and RBM, which takes into account temporal dependencies inherently and nonlinear characteristics. The eight deep learning methods and two commonly used machine learning methods, namely logistic regression (LR) and support vector regression (SVR), were applied to forecast PV power time-series data. Finally, for the guidance of short- and long-term operational strategies for PV systems, both single- and multi-step-ahead forecasting are examined and compared in this paper. Data sets from two grid-connected plants are adopted to assess the outputs of the deep learning-driven forecasting methods. Section 2 introduces the eight used deep learning methods. Section 3 describes the deep learning-based PV power forecasting strategy. Section 4 assesses the forecasting methods and compares their performance using two actual datasets. Finally, Section 5 concludes this study and sheds light on potential future research lines.
## 4 Experimental Process and Discussion
### Comparison  
We conducted a large number of experiments using a 15-cm single-pass cell and an 80-m multi-pass cell, which clarifies the increase in detection sensitivity caused by modulation and the increase in sensitivity attributable to the multi-reflection cell. Many researchers have recently conducted in-depth research on C\({}_{2}\)H\({}_{2}\) detection technology in the near-infrared band. The performance comparison between the proposed C\({}_{2}\)H\({}_{2}\) detection system and the reported near-infrared C\({}_{2}\)H\({}_{2}\) detection system is shown in Table 1. The C\({}_{2}\)H\({}_{2}\) detection system in References [13; 15; 19; 24] uses the conventional driving method of the TDLAS-WMS technique. The laser's driving signal is generated by super-imposing low-frequency sawtooth waves and high-frequency sine waves. Precision and error are greater than 3%. Time division multiplexing differential modulation is used to eliminate light intensity fluctuations, and system stability has been further improved, with a lower detection limit and higher precision.