esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Evaluation
### Experimental Results: Performance of SAP Estimators  
First, we compare the different _a priori_ SAP estimators when used along with our eKF-RTF estimator for MVDR beamforming (i.e., no postfilter is applied yet). This comparison is shown in Table 3 in terms of PESQ and STOI. The techniques evaluated are the multi-channel version of MCRA (MCRA) [4], CDR-based SAP estimation (CDR) [5] and our proposed PLD-based SAP estimator (PLDn) and its combination with CDR-based estimation (P&C), both presented in Section 4.2. Results for the noisy speech signal at the reference microphone (Noisy) are given as a baseline. In addition, we show the results achieved by the eKF-RTF estimator with an oracle estimation of the noise SCM (eKF-OracleN) as a performance upper-bound. This estimation was obtained using the true noise signals in a recursive procedure similar to Equation (50). The speech presence probability obtained from clean speech was used in the eKF-RTF estimator to obtain these oracle results.  
For CT conditions, the best results are obtained for the eKF-PLDn system. The speech power difference between microphones for CT conditions makes that the PLD-based SAP estimator can easily detect those bins where speech is absent. This power difference reduces the CDR ratio, defined in Equation (57), of the multi-channel signal in the presence of speech, leading the CDR-based SAP method to underestimate the speech presence and decrease the performance of the noise tracking algorithm. Therefore, the combination of both approaches does not improve the single decision based on PLD between channels.  
On the other hand, for FT conditions, speech power at both channels is more similar and CDR increases under the presence of speech. This is especially true at higher SNRs, where the CDR-based SAP detector outperforms the PLD-based one. However, the performance of the CDR-based detector degrades more severely at lower SNRs, while the PLD detector is more robust in these conditions. Finally, the combination of both detectors increases the performance in terms of both noise reduction and speech intelligibility, keeping a performance similar to the PLD one at lower SNRs and improving at higher SNRs.  
To sum up, our proposals improve the tracking of the noise statistics in dual-microphone smartphones. The eKF-PLDn proposal is the best solution in CT conditions, with eKF-P&C having a similar performance. The joint decision proposed in eKF-P&C achieves the best results in FT conditions at higher SNRs, while eKF-PLDn performs slightly better at lower SNRs.
## 4 Results and Discussions
### Calorific Value and Possible Electricity Generation
#### 4.2.2 Chemical Composition (Dry) of MSW at RCC  
It is very important to know the chemical composition of the MSW in order to implement proper waste management options. The chemical composition of MSW was tested at the Bangladesh Council of Scientific and Industrial Research (BCSIR). A total of five repetitions were done for the elemental composition analysis. Carbon (C), hydrogen (H), nitrogen (N), sulfur (S) and oxygen (O) contents have been found from the analysis. The mean and standard deviations of the percentage composition of the elements, obtained during the trials, are illustrated in Figure 10. These mean values have been considered for further analysis in this research. As observed from the figure, the average oxygen content  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**MSW Composition** & **Family 1** & **Family 2** & **Family 3** &
\begin{tabular}{c} **Family 4** \\ **(\%)** \\ \end{tabular} & **Family 5** & **Family 6** & **Family 7** & **Average MSW Composition (\%)** \\ \hline Vegetables and food & 70.12 & 65.35 & 69.45 & 80.70 & 69.12 & 71.12 & 80.21 & 72.29 \\ Paper and paper products & 7.29 & 7.32 & 5.73 & 2.02 & 7.29 & 7.29 & 3.65 & 5.79 \\ Polyethylene & 4.10 & 7.99 & 4.31 & 4.10 & 4.10 & 4.60 & 3.98 & 4.74 \\ Textiles and wood & 4.68 & 3.30 & 4.95 & 5.08 & 4.69 & 4.52 & 2.56 & 4.25 \\ Leather and rubber & 1.08 & 0.38 & 0.60 & 1.38 & 1.08 & 0.87 & 0.39 & 0.82 \\ Metals & 1.23 & 0.90 & 3.14 & 0.17 & 2.14 & 1.13 & 0 & 1.24 \\ Glass and ceramics & 0.12 & 0.50 & 0.58 & 0.61 & 0.22 & 0.12 & 1.27 & 0.48 \\ Brick, concrete and stones & 3.48 & 4.18 & 4.35 & 0.27 & 3.46 & 3.07 & 2.76 & 3.08 \\ Dust, ash and mud products & 6.43 & 7.00 & 6.12 & 3.43 & 6.44 & 5.43 & 3.22 & 5.43 \\ Others & 1.80 & 2.75 & 1.80 & 1.60 & 1.70 & 1.70 & 1.87 & 1.88 \\ Total & & & & & & & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Physical composition of MSW collected from seven different families.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MSW Composition** & **Weight before Drying (kg)** & **Weight after Drying (kg)** & **Weight Percentage (dry)** & **Moisture Content (\%)** \\ \hline Food and vegetables & 45.47 & 16.65 & 26.33 & 63.38 \\ Paper and paper products & 3.66 & 3.31 & 5.24 & 9.56 \\ Polyethylene & 3.00 & 2.79 & 4.42 & 7.00 \\ Textiles and wood & 2.69 & 2.62 & 4.15 & 2.60 \\ Rubber and leather & 0.52 & 0.47 & 0.74 & 9.62 \\ Metal and tins & 0.78 & 0.77 & 1.21 & 1.28 \\ Glass and ceramics & 0.30 & 0.29 & 0.47 & 3.33 \\ Brick, concrete and stone & 1.95 & 1.90 & 3.00 & 2.56 \\ Dust, ash and mud products & 3.43 & 2.74 & 4.33 & 20.12 \\ Others & 1.19 & 1.16 & 1.83 & 2.52 \\ Total & 63.23 & 32.70 & & 48.28 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Moisture contents of different MSW components collected from seven families.  
of MSW in RCC is 40.57%, which is the highest among all the elements present in MSW. The oxygen content of MSW from RCC was also found to be more (40.17%) than in previous studies [9].
# Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning
## 6 Implementation and Results  
Based on the HITL simulation environment built in Section 5, the decision policy for the enemy avoidance problem will be learned with the new framework constructed in Section 4. Furthermore, a fixed-rule policy and a random policy are designed to compare with the policy learned by the Sarsa based framework.  
The experiment settings are as follows. The region for the enemy avoidance problem has a length of \(l=600\) m and a width of \(w=450\) m. The outside loiter center of each UAV is 100 meters away from the nearest region edge, and the loiter radium is 100 m. The goal point \(G_{f}\) is inside the region, which is 150 m away from the right edge of the region. The decision making UAV loiters around the goal point \(G_{f}\) with a loiter radium of 50 m when it has arrived the goal point \(G_{f}\), and the goal radius is \(r_{g}=100\) m. The collision radius is \(r_{c}=40\) m. Set \(\Delta V=1.0\) m/s and \(\Delta\phi=5.0\) degree.
# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## 3 Related Works
### Streaming Novelty Detection
In the first case, the parameters of existing clusters are updated to include the information provided by the last point [50]. In [51], a recursive algorithm for clustering and drift detection is proposed, in which clusters are updated according to the similarity between the data and the changes occurring in the data flow. For this reason, the algorithm is more robust to outliers and noise. However, these algorithms need to know the cluster membership function in advance. In contrast, in [52], the membership function based on the input data density and distribution, therefore, is extracted from the data, and represents their density and distribution. In this context, the concept of cluster is replaced by the concept of cloud, which, unlike clusters, has no boundaries, could have any shape, and does not have a center. Therefore, the variables affecting the cloud shape and the assignment of points to the cloud are the local and global density, which are calculated recursively as new data arrive. Based on these concepts, ref. [53] proposed a two-step methodology for fault detection and identification in streaming data. First, faults are detected through autonomous anomalous detection (AAD). Then, a fuzzy classifier named AutoClass assigns the data sample to existing clouds or new clouds, depending on their closeness to the cloud center. Contrary to the AutoClass algorithm, the autonomous data partitioning (ADP) algorithm proposed in [54] is an evolving clustering algorithm that does not impose a data generation model on the empirical observation and is free from user-specific parameters. To summarize, novelty detection approaches may be classified according to the learning paradigm they adopt, which can be supervised or unsupervised, and the necessity of a training phase before the streaming application. Consequently, three approaches can be distinguished:  
1. Classification-based approaches that require a training phase on one or more classes (e.g., COMPOSE);
2. Clustering-based approaches that require a training phase on one or more classes (e.g., OLINDDA and MINAS);
3. Clustering-based approaches that can be applied from scratch (e.g., ADP). More recently, deep learning models, such as autoencoders and long short-term memory (LSTM) networks [55; 56], are used to detect novelties. However, they require a large amount of data and a high computational effort for training. In addition, they have to be retrained each time a novel behavior is detected [57].
## 3 Proposed Job Recommendation Method
### Ranking Stage  
In employment, the degree of professional alignment between job seekers and job positions is a crucial factor influencing job search outcomes. With the growing gap between job availability and the demand for skilled workers, employers are emphasizing professional compatibility between job roles and candidates [36]. Rational choice theory posits that individuals are rational decision-makers who weigh the costs and potential benefits when arriving at decisions [40]. Researchers, such as [PERSON] et al. [41], have used the potential Dirichlet distribution method online to extract themes from a substantial volume of employee comments, and their analysis has highlighted salary as a pivotal factor influencing job satisfaction. Hence, salary alignment emerges as a significant determinant affecting the satisfaction of job recommendations. Regular equivalence theory, a fundamental doctrine within communication theory, posits that an individual's behavior is influenced by the information, attitudes, and actions of others within a network [37]. When two individuals share more common social circles, their behavioral preferences tend to exhibit greater similarity, fostering a propensity to establish connections [42]. Thus, this study applies social networks in recommending jobs to recent college graduates, drawing on the employment decisions of alumni to effectively enhance satisfaction and reciprocity in job recommendations.  
In the ranking stage, this study used three critical dimensions: professional matching, salary matching, and social network, to perform entropy-based aggregation. The recommended job set is generated by calculating the entropy aggregation. Professional matching assesses the compatibility between college graduates' skills and qualifications and the requirements specified in job postings. Salary matching evaluates the level of correspondence between the expected salary of college graduates and the actual salary offered by the employing companies. Social networks analyze the social connections and relationships between college graduates and other graduate students, taking into consideration potential networking opportunities or affiliations that may influence job recommendations.
# An Improved Hilbert-Huang Transform for Vibration-Based Damage Detection of Utility Timber Poles
## 2 Limitation of Original Hilbert Huang Transform:
### Hilbert-Huang Transform
Accordingly, the shifting process will be terminated when \(r_{n}(t)\) and \(c_{n}(t)\) meets pre-determined stopping criteria and \(r_{n}(t)\) becomes constant or a monotonic function from which no more IMF can be extracted [38]. The input signal \(x(t)\) can be reconstructed by adding all the IMFs and final residue \(r_{n}(t)\). \[x(t)=\sum_{i=1}^{n}c_{i}(t)+r_{n}(t) \tag{8}\]  
These IMFs are symmetric, and they will have different frequency at a particular time. Hilbert Transform is then applied to each IMF and instantaneous frequency and amplitude are calculated according to Equations (3) and (5). Now the original signal \(x(t)\) is expressed as  
\[x=\sum_{j=1}^{n}a_{j}(t)\exp\biggl{(}i\int\omega_{j}(t)dt\biggr{)} \tag{9}\]The EMD process combined with Hilbert transform is known as Hilbert-Huang transform (HHT) and using HHT, original signal can be represented in its instantaneous amplitude, frequency, and time in a three-dimensional plot. Thus, a full energy-frequency-time distribution can be obtained which is known as Hilbert spectrum.
# Predicting Choices Driven by Emotional Stimuli Using EEG-Based Analysis and Deep Learning
## 6 Results and Discussion
### Comparison with Classical Classifiers  
We used different existing classic ML classifiers such as KNN, SVM, RF, LG, AdaBoost, and XGBoost to compare their performance with the proposed DL models. Our extracted features are fed into these classifiers, and we investigated several parameters for each one. We evaluated the performance of each classifier in the same dataset twice (before and after resampling) using the three evaluation metrics: accuracy, precision, and recall. Table 3 shows the hyperparameters used in the classical classifiers as follows:  
* For KNN, we used 10 for the number of neighbors using all processors.
* For SVM, we used the polynomial kernel function and a cost of 10.
* For LR, we used the liblinear solver with a 100 regularization strength.
* For RF, we built 500 decision trees during the training and produced the class that represents the mode of the choice.
* For XGBoost, we used binary logistics.  
Figure 10 shows that for the dataset before resampling, the performance of all classifiers was similar in terms of accuracy (about 70%). However, for the dataset after resampling, the performance of all classifiers, except for KNN, increased significantly. The KNN performance decreased after resampling to about 55% because of the enlarged number of samples, and KNN performs better with the smallest datasets. The highest performance  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Classifier** & **Parameters** \\ \hline KNN & n\_neighbors = 10, n\_jobs = \(-1\) \\ \hline SVM & Kernel = ‘rbf’, C = 10) \\ \hline LR & solver = ‘liblinear’, C = 100 \\ \hline RF & n\_estimators = 500, max\_features = ‘auto’, n\_jobs = \(-1\) \\ \hline AdaBoost & Random\_state = 0 \\ \hline XGBoost & Seed = 0, objective = ‘binary:logistic’ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classifier Parameters.  
Figure 9: Proposed LSTM model accuracy and loss.  
was reached with LSTM (96%), followed by CNN (88%), XGBoost (86%), SVM (83%), LR and RF (82%), and AdaBoost (78%).  
Figure 11 shows the comparison of the performance measurements (accuracy, precision, and recall) of the models after resampling. The proposed LSTM achieved the best accuracy at 96% over the hybrid model (91%), whereas the KNN achieved the lowest at 55%. AdaBoost achieved a higher accuracy of 78% compared with KNN. The performance of the three classifiers, SVM, LR, and RF, was very similar at 83%, 82%, and 82%, respectively. Of all classical classifiers, XGBoost had the best performance (86%), which is close to that of our proposed CNN (88%).
# Formation of a Colloidal CdSe and ZnSe Quantum Dots via a Gamma Radiolytic Technique
## 4 Results and Discussion
### Structural Analysis (XRD)  
The crystal structure and crystallite size of the synthesized nanoparticles were investigated by generating diffraction patterns, XRD, from the crystalline powder samples at ambient temperature.  
Figure 1 shows the XRD pattern of CdSe nanoparticles synthesized with a 120 kGy dose showing the formation of the nanoparticles. The peaks observed in the XRD patterns at 20 value of 25.1, 26.5, 28.2, 36.8, 43.8, 47.9, 51.9, 54.6 and 72.4\({}^{\circ}\) nearly match with the (100), (002), (101), (110), (103), (200), (112) and (211) crystalline planes, respectively, of the hexagonal CdSe structure reported in The International Centre for Diffraction Data (ICDD) PDF 02-0330.  
From the width of the XRD peak, the mean crystalline size can be calculated using the Debye-Scherrer equation [34]:  
\[\mathrm{D}\ =\ \mathrm{k}\lambda/\beta\mathrm{cos}\theta \tag{9}\]  
where k is a constant equal to 0.9, \(\lambda\) is the wavelength of the X-ray radiation (0.1542 nm), \(\theta\) is the diffraction angle, and \(\beta\) is the full width half maximum (FWHM). The average crystalline size of CdSe nanoparticles was determined from the full width half maximum of the (002) reflection of the XRD pattern using the Debye-Scherrer equation. The calculation showed that the average crystalline size is 16.3 nm. Figure 2 shows the XRD pattern of ZnSe nanoparticles synthesized with a 120 kGy dose. The strong and sharp peaks at 20 values of 26.40, 28.60, and 30.50\({}^{\circ}\) virtually match those of the (100), (002) and (101) crystalline planes, respectively, of the hexagonal ZnSe structure reported in the ICDD PDF 89-2940. Furthermore the peaks observed at 20 values of 34.9, 43.6, 47.2 49.3, 50.5, 51.9, 54.4, 57.7 and 60.4\({}^{\circ}\) were also assigned to the (102), (110), (103), (200), (112), (201), (004), (202) and (104) crystalline planes of the hexagonal phase ZnSe, respectively. However, some extra peaks not identified as ZnSe may be attributed to some impurity such as a zinc complex formed during the reactions.  
The average crystallite size of the ZnSe nanoparticles was calculated to be 10.7 nm from the sharp reflection peaks of (100) using the Debye-Scherrer equation.  
Figure 1: X-ray diffraction pattern of CdSe nanoparticles.
# Multi-Scale Indoor Scene Geometry Modeling Algorithm Based on Segmentation Results
## 2 Related Work  
In order to understand the scene and reconstruct individual objects or the entire scene using the segmented 3D data, usually only a few simple geometric primitives are needed, such as planes, cylinders, and spheres [8]. However, acquiring complete object models from real scenes can be challenging, making some matching-based methods unsuitable. Therefore, methods that combine object segmentation and geometric modeling have been proposed, mainly categorized into two types: entity-based modeling and surface-based modeling [9].  
Entity-based modeling methods directly operate on the segmented objects, identifying geometries and fitting parameters based on extracted features of the entire object [10]. For example, [PERSON] et al. [11] proposed an approach based on iterative Gaussian mapping, reconstructing geometric objects in indoor scenes based on the distribution of normals on the Gaussian sphere and using improved RANSAC. In addition to traditional algorithms, [PERSON] et al. [12] combined deep neural networks and proposed the BAGSFit framework, which uses a fully convolutional neural network to achieve scene instance segmentation. The framework estimates the probabilities of associated geometric types based on the boundary of the entire object, enabling the modeling of three-dimensional primitives with multiple modes. Entity-based modeling methods do not depend on accurate segmentation results but are only suitable for modeling simple shapes.  
Surface-based modeling methods, on the other hand, utilize different surface characteristics of geometric primitives to accurately fit the segmented point cloud data with different surfaces. This is followed by operations such as intersection, extension, and merging to obtain complete geometric models [13]. [PERSON] et al. [14] also extract surface features from the scene to identify geometric primitives and construct a graph model based on the color and geometric features of the primitives. They perform graph segmentation to achieve scene segmentation and modeling. [PERSON] et al. [15] proposed a method for semantic segmentation and structure modeling of dense point clouds. They utilize an improved RANSAC approach for fitting and refining the geometric primitives, combined with convex hull and support vector machines for classification and merging of the primitives, resulting in structural modeling of indoor scenes. Surface-based modeling methods can reconstruct complex shapes, but the results depend on the accuracy of surface extraction and fitting.
## 3 Results and Discussion
Regarding the chemical states of iron (high-resolution Fe2p envelopes, Figure 4b), all samples present a high-intensity broad signal ascribed to metallic Fe\({}^{0}\) 2p\({}_{3/2}\) at about 706 eV, representing the dominating contribution [55]. Fe in higher oxidation states, such as Fe\({}^{2+}\) 2p\({}_{3/2}\) at \(\sim\)710 eV [56] is present on the surface of the steel samples submitted to photo-Fenton. Iron at its maximally oxidized state--i.e., Fe\({}^{3+}\)--can be detected at \(\sim\)715 eV [57] in very low amounts on the surface of the initial stainless steel sample, but most notably on the surface of the samples submitted to photo-Fenton. Fe\({}^{2+}\) seems to represent the highest oxidized-state iron contribution. Since the ferrous ions are the main species responsible for electron transfer to the peroxide and persulfate oxidants, their presence on the material's surface confirms the depicted photogeneration of radicals and dye photodegradation mechanism schemes.
## Abbreviations  
\begin{tabular}{l l} \(i,j,k\) & Index of trains \\ \(s\) & Index of standard trains \\ \(i\overline{j}\) & The index of combined train of train \(i\) and train \(j\) \\ \(L\) & Continuous train group, \(L=\{l_{1},l_{2},\ldots,l_{m}\}\), and the index of combined train of \(L\) \\ \(l\) & Index of trains in continuous train group \\ \(m\) & Number of trains in continuous train group \\ \(l_{s}\) & Minimum tracking headway between two standard trains \\ \(l_{is}\) & Minimum tracking headway between train \(i\) and a standard train \\ \(l_{si}\) & Minimum tracking headway between a standard train and train \(i\) \\ \(l_{js}\) & Minimum tracking headway between train \(j\) and a standard train \\ \(l_{Ls}\) & Minimum tracking headway between train \(L\) and a standard train \\ \(d_{i}\) & Departure time of train \(i\) at departure station \\ \(d_{j}\) & Departure time of train \(j\) at departure station \\ \(d_{L}\) & Departure time of the combined train of \(L\) at departure station \\ \(a_{i}\) & Arrival time of train \(i\) at departure station \\ \(a_{j}\) & Arrival time of train \(j\) at departure station \\ \(a_{L}\) & a Arrival time of the combined train of \(L\) at departure station \\ \(OT_{i}\) & Occupancy time of train \(i\) \\ \(OT_{j}\) & Occupancy time of train \(j\) \\ \(OT_{ij}\) & Total occupancy time of train \(i\) and train \(j\) \\ \(OT_{L}\) & Total occupancy time of the continuous train group \(L\) \\ \(RT_{ij}\) & Repeated occupancy time of train \(i\) and train \(j\) \\ \(TT_{i}\) & Travel time of train \(i\) in the section \\ \(TT_{j}\) & Travel time of train \(j\) in the section \\ \(TT_{s}\) & Travel time of a standard train in the section \\ \(TT_{\overline{j}}\) & Travel time of combined train \(i\overline{j}\) in the section \\ \(TT_{L}\) & Travel time of the continuous train group \(L\) in the section \\ \(T_{maintenance}\) & Time occupied by maintenance \\ \(T_{triangle}\) & Invalid time caused by the triangle area \\ \(T_{available}\) & Available time of the train timetable \\ \(\theta\) & Length of the section \\ \(v\) & Average speed of standard trains in the section \\ \(\mu\) & Capacity utilization rate of timetable \\ \(OT_{total}\) & Total occupancy time of all trains on the train timetable \\ \(N_{total}\) & Total Number of trains on timetable \\ \(N_{capacity}\) & Maximum number of trains in the timetable (carrying capacity) \\ \end{tabular}