esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Statistical Analysis  
All measurements were performed by two orthodontists with at least ten years of clinical experience and advanced expertise in digital orthodontic technologies. To assess inter-operator consistency, a reproducibility analysis was conducted using the intraclass correlation coefficient (ICC), which demonstrated high agreement and confirmed excellent measurement reliability.  
The statistical analysis included both descriptive and inferential methods. Descriptive statistics were calculated for each outcome variable, including the mean, standard deviation, and minimum and maximum values (Table 1). The normality of the angular deviation data between the planned and actual miniscrew insertion axes was verified using the Shapiro-Wilk test, which confirmed a normal distribution.  
Accordingly, a paired \(t\)-test was applied to assess whether the discrepancies between planned and actual positions were statistically significant (Table 2). No significant differences were observed between the right and left sides for any of the evaluated variables. As a result, the analysis was conducted on the entire sample without distinguishing between sides, focusing solely on the spatial reference points. All analyses were conducted in Stata software (version 17; StataCorp, College Station, TX, USA).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Descriptive Statistics**} \\ \hline
**Outcomes** & **Mean** & **Sd** & **Min** & **Max** \\ \hline Hor Head Dmax (mm) & 0.3 & 0.16 & 0.05 & 0.54 \\ Hor Tip Dmax (mm) & 0.66 & 0.21 & 0.38 & 1.04 \\ MAD (\({}^{\circ}\)) & 2.95 & 1.13 & 0.9 & 5.19 \\ Vert HD (mm) & 0.35 & 0.21 & 0.06 & 0.66 \\ Vert TD (mm) & 0.67 & 0.67 & 0.07 & 2.16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics. Means, standard deviations, and minimum and maximum mean values.  
Figure 7: Software interface: the maximum vertical head distance (VHD), maximum horizontal head discrepancy (HHDmax), and minimum horizontal head discrepancy (HHDmin).
## 3 Data and Methods
### Accuracy Assessment and Estimated Uncertainty  
We conducted an accuracy assessment of the main maps derived from our methodology: (1) the stand density map, and (2) the burn severity map. Validation for the stand density calibration equation (Section 3.5) was made using a field-based stand count conducted during a field survey by a private company of environmental planning (Miller-Blum & Co. Environmental Planning Ltd.) in 2016 in an area nearby the WUI study area (2 km apart). A map of 29 plots of 10 \(\times\) 10 m with a number of trees and their relative location within the plot area was provided by Miller-Blum & Co. Environmental Planning Ltd. This map was then used to compare with the stand density derived from Planet GCC image and the calibration equation. Validation was conducted by collocating 3 \(\times\) 3 Planet pixels within the plot area while averaging the stand density over the 3 \(\times\) 3 pixels. The coefficient of determination (R\({}^{2}\)) was used as an accuracy metric for this validation.  
The burn severity classification map derived from satellite data (Section 3.7) was validated with a vector map created from a very-high-spatial resolution aerial photograph (7.5 cm) taken in 28 November 2016 and the information derived in the field, as in [23], using a confusion matrix technique. The overall accuracy (OA) and the kappa of the confusion matrix were used as accuracy metrics for the satellite-derived burn severity map.  
We used the mean absolute error (MAE) of the regressions obtained from Section 3.5 and the different ranges in percentages of consumed woody biomass per burn-severity class given in Section 3.8 to provide the uncertainty estimations of the consumed woody biomass, the stand density, and the economic loss through maps of low-to-high estimates of these variables (Results presented in Figure A3, Figure A4, Figure A5 in the Appendix A).
## 4 Results and Discussion
### Geometrical Parameters  
The geometrical parameters, namely, bond lengths, bond angles and dihedral angles for C-I and its dimer D\({}_{1}\), are collected in Tables 3 and S2. The C-C bond lengths of the phenyl ring are found in the range 1.388-1.408 A, in conformity with the C-C bond lengths (1.396 A) in benzene. The two N-H bond lengths of the NH\({}_{2}\) group are not equal due to the different surroundings of the two H-atoms. The C-O(H) bond length of the carboxylic group was found to be smaller as compared to the other two C-O(H) bond lengths. However, the O-H bond length of the carboxylic group was found to be larger than the other two O-H bond lengths due to strong N...H interaction. In the monomeric form, the H\({}_{11}\) atom is involved in the intra-molecular hydrogen bonding (O\({}_{10}\)-H\({}_{11}\)...O\({}_{12}\)), while, in the dimeric form, the H\({}_{13}\) atom is involved in the inter-molecular hydrogen bonding (O\({}_{12}\)-H\({}_{13}\)...O\({}^{\prime}{}_{46}\)) (where O\({}^{\prime}{}_{46}\) is the O atom in the second molecule M\({}_{2}\), corresponding to the O\({}_{21}\) atom of the first molecule M\({}_{1}\), Figure 3). Hence, the bond length O\({}_{10}\)H\({}_{11}\) was found to be larger than the bond length O\({}_{12}\)H\({}_{13}\) bond in monomeric form. However, in the dimeric form, the O\({}_{12}\)-H\({}_{13}\) was found to be larger than the O\({}_{10}\)-H\({}_{11}\) bond. In going from the monomer to the dimer, the C\({}_{1}\)-O\({}_{12}\) bond length is reduced by 0.015 A due to enhancement of the APT charge at the site O\({}_{12}\), which results in a strong attracting force between the C\({}_{1}\) and O\({}_{12}\) atoms. The inter-molecular H-bonding enhances the C=O bond length in the dimeric form as compared to the monomeric form. In going from the monomer to the dimer, the bond length N\({}_{22}\)-H\({}_{23}\) is increased, whereas the other bond length N\({}_{22}\)-H\({}_{25}\) remains unchanged.  
The C-C-C bond angles of the phenyl ring were found to lie in the range 118.3-121.1\({}^{\circ}\) in both the monomeric and dimeric forms. In going from the monomer to the dimer, the bond angles C\({}_{1}\)-O\({}_{12}\)-H\({}_{13}\) and C\({}_{17}\)-C\({}_{19}\)-O\({}_{21}\) were found to reduce by 1.7\({}^{\circ}\) and \(-\)1.4\({}^{\circ}\). The bond angles reported by earlier authors [11; 12] show noticeable changes from the present values in several cases (Table S2), causing considerable changes in the vibrational frequencies (Table S4). The geometrical parameters reported previously [11; 12] differ from the present values. This is due to the fact that the conformers reported by them differ from each other and also differ from the presently reported conformer C-I.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### ETX-Based Solutions
#### 4.2.1 Fuzzy Logic Based Energy-Aware Routing Protocol (FLEA-RPL)  
Authors [60] proposed Fuzzy Logic Based Energy Aware Routing Protocol (FLEA-RPL) whose routing metrics are based on load, Residual Energy (RER), and Expected Transmission count (ETX). The load is the amount of data traveling across the network for a specific instant of time. Residual energy represents the variation of initial and current energy in the node. ETX is a measure of the quality of a path between two nodes in a wireless packet data network. The input fuzzy set is a combination of different metric Loads, ETX, and RER. Terminologies used in fuzzy logic are the linguistic variable and membership function. A linguistic variable is a structure of language and belongs to a set of values between true and false. The linguistic variable is evaluated by the membership function to provide accurate input and output variables.  
As an example, if the load is less than or equal to 3, the fuzzy subset of a node is Light and the membership value is 1. If the load is between 3 to 6, the membership value decreases to 0, and the linguistic variable as Heavy. Similarly, when RER is less than or equal to 75, the membership value is 1 and the fuzzy subset is Low and when RER is in the range of 25 to 75, the membership function is 0 and the linguistic variable is Average. For ETX values less than or equal to 10, membership is 1 and the fuzzy subset is Short. For selecting a quality neighbor node or preferred parent node linguistic variable contains seven sets namely awful, low bad, bad, good, very good, and excellent. By seeing a fuzzy set of Load, RER, and ETX, the quality of the linguistic variable is selected, which helps in selecting the preferred parent node. Simulation results show the number of parent changes per hour is 0.2 in basic RPL, 0.25 in MRHOF-RPL [39], 0.27 in FL-RPL, and 0.26 in FLEA-RPL [60]. The average end-to-end delay of a packet in RPL shows low latency in FLEA-RPL as compared to other protocols. FLEA-RPL with 6 packets per minute throughput shows the result of no packet loss up to a network size of 20.
# Intrusion Detection Method Based on Preprocessing of Highly Correlated and Imbalanced Data
## 1 Introduction
### State of the Art
Multilayer perceptron (ML) is effective for modeling complex non-linear relationships in classification but require large datasets, are susceptible to overfitting, and have longer training times than other methods [12]. Given the availability of large datasets and complex features, deep learning methods such as convolutional neural networks (CNNs) [13], recurrent neural networks (RNNs) [14], long short-term memory (LSTM) [15], and generative adversarial networks (GANs) are increasingly used. However, neural networks and deep learning methods require a large amount of data for training, can be prone to overtraining, and are sensitive to data outliers. Ensemble classifiers are also a powerful machine learning technique that combines multiple weak learners (models) to create a stronger overall learner. They can capture a broader range of patterns in the data, leading to more reliable and accurate predictions. They also reduce variance by averaging out the errors of base models, resulting in predictions with lower variance and higher accuracy. On the other hand, training ensemble methods can be computationally expensive compared to training a single model. This can be a factor if there are limited computational resources, or fast training times are required. Ensemble models based on meta-algorithms such as bagging [16], boosting [17], and stacking [18] have become particularly popular today. The choice of the most advanced ensemble classifier depends on your specific data and task requirements. Additionally, efficient model construction includes data preprocessing. This stage is crucial for ensuring the data are in a suitable format and quality for the model to learn from effectively, especially when faced with imbalanced and highly correlated data. Thus, despite significant progress in intrusion detection using machine learning and deep learning, there is a need for the continuous improvement of methods and technologies used for monitoring and identifying the state of computer systems and networks.
# Effective Dereverberation with a Lower Complexity at Presence of the Noise
## 4 Experiments and Evaluation
### Analysis and Comparison of the Test Results
#### 4.3.5 Comparison of Computational Complexity  
In order to compare the computational complexity of the \"proposed\" method and the \"proposed-kron\" method, Table 5 lists the operations required for the multiplication, addition, and division of two proposed algorithms. The update Equations (31), (32), (49),  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(T_{60}\) **(ms)** & **100** & **400** & **500** & **600** & **700** & **800** \\ \hline \multicolumn{8}{c}{CD (dB)} \\ \hline unprocessed & 4.64 & 4.78 & 5.00 & 5.21 & 5.38 & 5.53 \\ ISCLP & 5.80 & 6.73 & 6.84 & 6.94 & 7.02 & 7.10 \\ WPE & **4.41** & **4.20** & **4.19** & **4.20** & **4.21** & **4.21** \\ proposed & 5.64 & 6.29 & 6.27 & 6.26 & 6.25 & 6.24 \\ proposed-kron & 5.32 & 5.71 & 5.75 & 5.79 & 5.82 & 5.85 \\ \hline \multicolumn{8}{c}{SIR (dB)} \\ \hline unprocessed & **14.19** & \(-\)5.53 & \(-\)6.67 & \(-\)7.32 & \(-\)7.81 & \(-\)8.62 \\ ISCLP & 9.55 & \(\mathbf{-0.98}\) & \(\mathbf{-1.57}\) & \(\mathbf{-1.96}\) & \(\mathbf{-2.21}\) & \(\mathbf{-2.42}\) \\ WPE & 6.78 & \(-\)1.90 & \(-\)2.99 & \(-\)3.84 & \(-\)4.54 & \(-\)5.13 \\ proposed & \(-\)1.91 & \(-\)6.79 & \(-\)6.80 & \(-\)6.67 & \(-\)6.49 & \(-\)6.38 \\ proposed-kron & \(-\)3.03 & \(-\)8.05 & \(-\)8.09 & \(-\)7.92 & \(-\)7.82 & \(-\)7.73 \\ \hline \multicolumn{8}{c}{fwSegSNR (dB)} \\ \hline unprocessed & 6.39 & 5.86 & 5.65 & 5.43 & 5.22 & 5.04 \\ ISCLP & **7.33** & 4.14 & 3.84 & 3.63 & 3.45 & 3.28 \\ WPE & 6.55 & **6.53** & **6.56** & **6.55** & **6.54** & **6.51** \\ proposed & 5.86 & 5.24 & 5.13 & 5.04 & 4.94 & 4.86 \\ proposed-kron & 6.27 & 5.76 & 5.64 & 5.53 & 5.40 & 5.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 5: Spectrogram comparison in the case of _SNR_ = 10 dB and \(T_{60}=800\) ms.  
and (50) of the Kalman filtering are used for the calculation of the first step, whereas update Equations (33)-(35) and (51)-(53) are utilized for the calculation of the second step. Furthermore, the update Equations (36), (37), (54), and (55) are employed for the calculation of the third step. The complexity reduction factor is defined as the ratio between the operations required for the \"proposed-kron\" and \"proposed\" methods in each step. We can see that the \"proposed-kron\" algorithm has greatly improved computational efficiency compared to the \"proposed\" algorithm. Especially, the multiplication operations in the first and third steps are reduced much more. The \"proposed-kron\" method not only effectively reduces the computational complexity but also guarantees the speech quality compared to the \"proposed\" method.
# Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models
## 5 Experimental Results and Discussion
### Statistical Analysis  
To prove that our ensemble model performs better than the individual base models, we have performed the statistical hypothesis testing using the paired sample \(t\)-test, also known as Student's \(t\)-test [61]. It is one of the trusted statistical tests performed to find if there exists a significant difference between two sets of observations. It only allows testing the pairs of observations, and a conclusion can be drawn depending upon the significance level and obtained \(p\)-values. Therefore, we have compared all the independent DL models and the ensemble model, selecting two at a time with the final stacking ensemble framework. The null hypothesis is assumed as there exists no significant difference between the performance of the individual models and the ensemble framework. On the other hand, an alternative hypothesis states there is a statistically significant difference between the performances of the two compared models. All the statistical trials are performed at a significance level of 5% or the alpha is 0.05. The obtained \(p\)-values are shown in Table 3. From the resulting \(p\)-values, which are all less than 0.05, we reject our assumed null hypothesis. This proves that the better performance of the ensemble framework is statistically significant and not a result obtained by chance.  
Table 4 compares the proposed model for the prediction of the COVID-19 disease with a few related past methods on the SARS-CoV-2 CT scan dataset. From this table, it can be observed that for the prediction of COVID-19, [PERSON] et al. [50] used an explainable DL method while [PERSON] et al. [47] used DenseNet201, a pre-trained DCNN model. The authors mentioned in [47,50] achieved 97.38% and 96.25% recognition accuracies, respectively. In contrast, our proposed model successfully detects COVID-19 cases with 98.79% correct classification accuracy, which is higher than in [47,50].
## 1 Introduction
During the degradation process of the outer casing, the heavy metals contained therein can pose serious chemical risks to the human body. Consequently, the development of a wireless power supply scheme for implanted devices represents an important issue that demands urgent attention. Battery-free systems exhibit a greater ability to conform to human tissues while concurrently reducing the system's volume. The power source of such systems can be derived either from within the body [7; 8; 9; 10] or from external sources [11; 12; 13]. During the actual design phase, it is essential to take into account the device's practical usage as well as the pros and cons of each scheme. For implantable sensors engineered for short-term monitoring purposes, such as assessing the postoperative recovery status, devices fabricated and encapsulated with biodegradable materials offer substantial advantages. These devices will spontaneously degrade after functioning appropriately within the body for a specific duration, which can range from several days to a few weeks. The degradation products are either assimilated by the human body or excreted harmlessly from the body. This obviates the necessity for a secondary surgical procedure to remove the device and minimizes the potential harm to the patient's body to the greatest possible extent. This article conducts a comprehensive review of the most recent advancements in biodegradable wireless implantable sensors and meticulously analyzes the pros and cons of different sensing techniques. Concerning wireless data transmission and wireless power supply, this paper presents several currently prevalent wireless communication implementations approaches and contrasts their applicable scopes. Additionally, this article puts forward strategies for powering sensors without batteries, enabling diverse types of devices to choose appropriate solutions. Furthermore, this article places particular emphasis on the performance and selection strategies of biodegradable materials. A variety of biodegradable materials and their degradation characteristics are enumerated in the article, offering references and convenience for the design of biodegradable devices. The article deliberates on each of the aforementioned modules and introduces the development status of implantable sensors, implantable miniature antennas, and biodegradable materials.
# COTS Battery Charge Equalizer for Small Satellite Applications
## 4 Experimental Setup
### Balancing Scenario  
To evaluate the performance of the proposed balancing methods, four test scenarios were defined, each with a different initial voltage distribution in the battery cells. These scenarios were carefully selected to represent possible exaggerated imbalance conditions in order to assess the performance of each topology. This range of scenarios enabled a comparison of balancing speed and energy consumption. The initial cell voltages for all scenarios are presented in Table 8 and served as the starting point for the experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Cell 1 [V]** & **Cell 2 [V]** & **Cell 3 [V]** & **Cell 4 [V]** \\ \hline Scenario 1 & 3.7 & 3.8 & 3.9 & 4 \\ Scenario 2 & 4 & 3.7 & 3.7 & 4 \\ Scenario 3 & 3.7 & 4 & 4 & 3.7 \\ Scenario 4 & 4 & 3.7 & 4 & 3.7 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Initial cell voltages in each scenario.  
Figure 6: (**A**) SFCBE implementation; (**B**) DSSCE implementation.  
Figure 7: Setup used during tests.  
In order to facilitate a more intuitive understanding of the initial conditions in each scenario, Figure 8 represents a visual of the cell voltages described in the previous table. Each subplot corresponds to one of the four test scenarios.
## 3 Spectral Scattering Imaging
### Quality Evaluation for Horticultural Products
#### Apple
Mean and SD spectra were extracted from hyperspectral scattering images for NN modelling, resulting in prediction accuracies with \(r^{2}=0.79\) and SEP = 0.72 \({}^{\circ}\)Brix and \(r^{2}=0.64\) and SEP = 0.81 \({}^{\circ}\)Brix for 'Golden Delicious' and 'Delicious' apples, respectively. [PERSON] and [PERSON] [54] investigated 10 MLD functions to characterize hyperspectral scattering profiles. The best prediction with \(r^{2}=0.78\) and SEP = 0.73 \({}^{\circ}\)Brix for 'Golden Delicious' apples was obtained by using the three-parameter MLD function combined with step-wise MLR for model calibration. Compared to the mean and SD spectra method, the MLD method did not achieve better accuracy, but instead needed considerably more computation. [PERSON] et al. [70] reported on using the fused data from hyperspectral scattering and NIRS for SSC prediction. Compared to the data from a single sensor, the fused data led to more accurate and robust results. However, it is technically difficult and more costly to implement the sensor fusion technique for practical use. Later, [PERSON] et al. [66] built an online grading system by integrating hyperspectral scattering imaging and visible and near-infrared (VIS/NIR) spectroscopy, for inspecting apples at a speed of 0.5 fruit/s. Three varieties of apples harvested from two harvest seasons, were graded into two classes for SSC by linear discriminant analysis (LDA), with accuracies varying between 62.0% and 91.7%. The fruit grading system, however, is not ready for practical adoption because of lower grading accuracy, slow speed and costly instrumentation. Multispectral and hyperspectral scattering imaging has shown good performance in predicting fruit firmness, but they generally do not achieve better SSC prediction than conventional NIRS. This is because scattering imaging is primarily designed to enhance the measurement of scattering related properties like firmness, which may not be optimal for SSC assessment. Since NIRS and spectral scattering provide different aspects of information about optical properties of fruit, integrating two techniques could help improve prediction accuracies for both SSC and firmness. Although the concept of using multispectral and hyperspectral scattering imaging for online apple firmness and SSC evaluation has been demonstrated experimentally, considerable improvements are still needed to meet the speed requirement for online sorting and grading.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 4 Photocatalytic Oxidation
### Residence Time  
Residence time, or contact time, refers to the duration of a gaseous molecule remaining near the catalyst surface. This parameter significantly influences the photocatalytic degradation efficiency of pollutants. Increasing the residence time allows more contact between pollutants and the catalyst, facilitating reactions with hydroxyl radicals and positively charged holes, thus enhancing efficiency. For instance, studies have shown that extending the residence time of formaldehyde from 10 to 40 s increased the conversion rates from 36% to 48%, respectively (Figure 3) [95].  
Most research analyzes photocatalytic oxidation with residence times ranging from seconds to minutes; however, practical indoor conditions often see residence times in the tens to hundreds of milliseconds. Consequently, results from laboratory studies may differ significantly from those obtained in real indoor environments [89].