esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Analysis
### Performance of Conventional Methods  
Table 3 summarizes the SER values obtained for each conventional method (MLD, MMSE, and ZF) across different SNR levels for the case where the MIMO system has an equal number of transmitters and receivers. Additionally, the table presents the corresponding CTs for each method, highlighting the trade-off between performance and complexity. The results are further visualized in Figure 2. As illustrated, the MLD method generally exhibits lower SER compared to ZF and MMSE, indicating superior performance. However, Figure 2 also reveals that MLD has the highest computational time among the three methods.  
In Figure 2, the CT for MLD is significantly higher than that of MMSE and ZF, making the values for MMSE and ZF nearly invisible on the graph. However, as depicted in Table 3, the CT for ZF is very small compared to MLD, and for MMSE, it is even smaller, approaching zero. This illustrates the much greater CT required for MLD in comparison to MMSE and ZF.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**SER**} & \multicolumn{3}{c}{**CT**} \\ \hline
**N** & **M** & **MLD** & **MMSE** & **ZF** & **MLD** & **MMSE** & **ZF** \\ \hline
2 & 2 & 0.000325 & 0.00567 & 0.00823 & 0.11714 & 0.00132964 & 0.0016907 \\ \hline
4 & 4 & \(7.5\times 10^{-6}\) & 0.005705 & 0.015575 & 0.518657 & 0.00273238 & 0.0033708 \\ \hline
6 & 6 & \(5\times 10^{-6}\) & 0.00602 & 0.0257517 & 5.41947 & 0.00967891 & 0.0118022 \\ \hline
8 & 8 & \(3.875\times 10^{-5}\) & 0.00395 & 0.0307313 & 36.4324 & - & 0.0193294 \\ \hline
10 & 10 & \(8\times 10^{-6}\) & 0.002195 & 0.030952 & 390.642 & - & - \\ \hline
12 & 12 & \(1.67\times 10^{-6}\) & 0.00260917 & 0.0370992 & 288.854 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of MLD, MMSE, and ZF in a case with the same number of transmitters and receivers.  
Figure 2: Comparison of MLD, MMSE, and ZF in the case of the same number of transmitters and receivers.  
Furthermore, the evaluation was extended to scenarios where the MIMO system possesses different numbers of transmitters and receivers. Similar to the previous case, Table 4 and Figure 3 present the SER and computational time results for MLD, MMSE, and ZF. The findings reiterate the trend observed previously, with MLD achieving lower SER but incurring higher computational costs.  
The particular setup and settings of the investigation are the main reasons for the high SER values shown in Figures 1(a) and 2(a). These graphs show how well the DNN-based detector performs in different settings, with high noise and little training data, in comparison to the traditional approaches (MLD, MMSE, and ZF). Rather than optimizing for absolute performance, the main goal at this stage was to evaluate the relative performance of several detection systems in challenging circumstances. Future research will focus on improving the DNN architecture and training procedure and investigating new methods for reducing noise and interference in order to obtain SER values that are more appropriate for real-world implementation.
# A PSO-Based Uneven Dynamic Clustering Multi-Hop Routing Protocol for Wireless Sensor Networks
## 5 Proposed Algorithm
### Particle Representation and Initialization  
In PSO, a particle swarm represents a complete solution. For the clustering process of the proposed algorithm, a particles represents optimal positions of the center of the circular areas where candidate CH nodes are located. Each component \(P_{i}(t)=(X_{i,1}(t),\,X_{i,2}(t))=(x_{i}(t),\,y_{i}(t))\) denotes the coordinates of the center of a circular area.
## 3 Simulation Results
### Multi Objective Optimization  
#### 3.2.1 Without Considering the Power Exchange Limitation with the Network  
In this section, in order to solve the problem in a multi objective format, the limitation of power exchange with the upstream network is not considered. Figure 6 shows that the different approaches have come to close conclusions and the Pareto Front is very close to all three methods. Where NSIDE is non-dominated solution improved differential evolution, NSDE defines non-dominated solution differential evolution and NDPSO is non-dominated solution particle swarm optimization. Table 13 also shows the output of a sample response from the Pareto front of the NSIDE method. While the power generation capacity of all units is within the permissible range, it can be observed that at some times the network has largely purchased or sold electricity from the upstream network, which was due to the importance of cost and pollution in the optimization. When this problem was solved as a single objective, values of 618 and 136 were obtained for cost and pollution, respectively, whereas in the sample, these values are 753.97 and 394.74.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Best Answers** & **Average** & **Worst Answers** & **Standard** \\  & **(s)** & **Answers (s)** & **(s)** & **Deviation (S)** \\ \hline PSO & 965.08 & 1051.35 & 1123.25 & 124.25 \\ DE & 682.3 & 682.15 & 695.21 & 11.22 \\ IDE & 661.37 & 677.27 & 687.5 & 10.81 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of the proposed method with other methods for reducing costs with considering the limitations of upstream network power exchange.  
Figure 5: Convergence diagrams of gas emission in different methods considering the limitations of power exchange with upstream network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Best Answers** & **Average** & **Worst Answers** & **Standard** \\  & **(kg)** & **Answers (kg)** & **(kg)** & **Deviation (kg)** \\ \hline PSO & 1253.44 & 2494 & 3632.23 & 837.5 \\ DE & 461.4 & 479.77 & 499.77 & 11.47 \\ IDE & 449.16 & 463.4 & 485.38 & 10.46 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison of the proposed method with other methods for reducing gas emission with considering the limitations of upstream network power exchange.
# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 3 Methods
### Object Shape Extraction Module
#### 3.2.1 Utilizing Backbone Network Information  
This module takes image gradients, the outputs of the first, third, and the last convolutional layers of the backbone network, as well as the memory reader results from the backbone network as inputs, and then generates the boundary information of the object as output. The architecture of this module consists of a series of residual blocks interleaved with gated convolutional layers (GCLs) [25]. The presence of GCLs [25] ensures that the OSEM processes only information related to the object's shape. Since we can obtain precise object shape information from the ground truth object segmentation mask, we can train the OSEM using cross-entropy loss.
# Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User
## 3 The Proposed Model
### The Framework Description  
Generally, the proposed model incorporates several tasks starting from collecting data, followed by a series of analysis processes ending with assigning the proper labels to users, as shown in Figure 1. Collecting review data has been performed by scraping customers' feedback available on the online markets (e.g., Amazon, BestBuy, Lenovo, Dell). The gathered data include the review text, date and time of writing the review, ratings, and information of the person who wrote this comment. Therefore, the next step is to extract the username field from the collected data. Commonly, websites do not allow spaces to be used when creating a username during the registration or submission of the review. Therefore, customers may find no alternative to writing concatenated names, making gender identification tasks more difficult. Therefore, word segmentation appears to be a suitable solution to handle this task.  
Typically, a username may contain letters, numbers, and special characters. Additionally, many people prefer to use common substitutions of specific letters in the word with specific numbers, known as the Leet language. Recognizing these numbers and substituting them with the correct letters is the next task to be achieved. Afterward, the remaining numbers and/or symbols need to be removed so that after finishing this step, only alphabetical letters are being maintained.  
People's names of the same gender converge in groups, and each group shares morphologies. The forms of affinity vary from the convergence in word structure to parts of words such as prefixes, suffixes, root words, and stems. Searching for matching names in the dataset starts with the input pattern and creates the n-gram combination, where \(n\) represents the number of characters in the input word. In the continued absence of matching words, \(n\) is decreased by one letter and becomes \((n-1)\) until finding matching or reaching a certain threshold; Dynamic Pruned N-Gram refers to this process.  
Commonly, websites limit the number of characters and symbols that can synthesize the username, and the practice of some users ignore some letters, usually the vowels  
Figure 1: The Experimental Framework of the Gender Identification Model.  
from the middle of the word. From Figure 1, we can observe that the process of searching for matching names is incorporated with another task. Handling the misspelling word problems is the current task. Therefore, while searching for matchings, we consider the probability of missing characters, with or without intention.  
Therefore, the previous tasks' results are a set of nominations for the gender of the extracted parts of the names associated with specific probabilities. By considering these results and frequencies of matchings, a final decision on the gender of the user will be made. Algorithm 1 presents the proposed model's mechanism with more details and clarifies how the different sub-procedures can interact and cooperate to output the final results.
###### Abstract  
The use of continuous monitoring systems to control aspects such as noise pollution has grown in recent years. The commercial monitoring systems used to date only provide information on noise levels but do not identify the noise sources that generate them. The identification of noise sources is an important aspect in order to apply corrective measures to mitigate the noise levels. In this sense, new technological advances like machine listening can enable the addition of other capabilities to sound monitoring systems such as the detection and classification of noise sources. Despite the increasing development of these systems, researchers have to face some shortcomings. The most frequent ones are on the one hand, the lack of data recorded in real environments and on the other hand, the need for automatic labelling of large volumes of data collected by working monitoring systems. In order to address these needs, in this paper, we present our own sound database recorded in an urban environment. Some baseline results for the database are provided using two original convolutional neural network based sound events classification systems. Additionally, a state of the art transformer-based audio classification system (AST) has been applied to obtain some baseline results. Furthermore, the database has been used for evaluating a semi-supervised strategy to train a classifier for automatic labelling that can be refined by human labellers afterwards.  
machine listening; supervised and semi-supervised learning; noise monitoring systems; urban sounds database; sound classification; deep neural networks +
Footnote †: journal: _sciences_  
_Article_  
NoisenseDB: An Urban Sound Event Database to Develop Neural Classification Systems for Noise-Monitoring Applications
## 4 Model Framework
### Feature Extraction  
**Text:** In our dataset, each video is precisely paired with its corresponding Chinese script. To achieve efficient encoding of the quintuple annotations, we employ the pre-trained Chinese RoBERTa-cn model [28] and fine-tune the encoder using the SentiLARE approach [29]. Specifically, we extract sentence-level semantic embeddings from the fine-tuned model, transforming each sentence into a 768-dimensional dense vector representation. This high-dimensional vectorization not only captures the deep semantic features of the text but also provides a rich representational foundation for subsequent fine-grained emotion analysis.  
**Video:** We utilize the OpenFace 2.0 toolkit [30] to extract visual features, including 68 facial landmarks, 17 facial action units, head pose, gaze direction, and orientation measurements.  
**Audio:** Using the Librosa toolkit [31], we extract key audio characteristics such as the fundamental frequency (log scale), Mel-Frequency Cepstral Coefficients (MFCCs), and Constant Q Transform (CQT). These features, crucial for identifying emotion and intonation, are then combined to create a 33-dimensional set of acoustic features at the frame level for each video.
# Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill
## 2 Literature Review
### Predictive Maintenance  
The availability of massive data brings new opportunities for evolving maintenance practices. In networked factories, new technologies permit operators (or even intelligent systems) to monitor the machinery conditions. This has led to the investigation of PdM strategies. In the Industry 4.0 context, PdM strongly relies on IoT communications. IoT satisfies timely and correct information exchange, which is critical for the safe and effective behavior of the monitored components. An example of an industrial IoT solution that allows advanced PdM applications is presented in [PERSON], [PERSON] et al. [12].  
Predictive maintenance assumes that the monitored machine parts go through a measurable process of degradation, hence enabling the estimation of temporal windows for carrying out repair operations [2; 13]. PdM comes to benefit from technological advances and predict the RUL of components through degradation measuring. Some examples of computer models for PdM in manufacturing nowadays include Auto-Regressive Integrated Moving Average (ARIMA) models for non-stationary processes, condition-based maintenance techniques [1], as well as Prognostics and Health Management (PHM), approaches using physics-based representations and analysis [14].
## 4 Results
### Training Duration of the Model  
Table 4 records the training duration of all methods. The hardware environment for model training is Intel i7-8700 (CPU), 64 G (RAM), 1050 Ti (GPU). The six linear regression methods outlined in this paper had similar imputation effects, but their training time varied greatly, with MLR being the fastest (less than 1 s) and ARD being the slowest (more than 5 min on average).  
In both the tree-based categories, the RF was the slowest, taking an average training time of nearly 1 min. However, it had the best imputation effect among all algorithms. A method that is faster than the RF and has a similar imputation effect was Bagging, taking 8 s as an average training time. Its R\({}^{2}\) is 1% lower than that of RF, and its SMAPE is 3% higher than that of the RF.  
As seen from previous analysis, BiLSTM, LSTM, and TCN are three neural network imputation methods with a better performance. However, due to their structure, LSTM and BiLSTM can only read and parse one record at a time, and they have to process each record sequentially. Hence, LSTM and BiLSTM take a long time to train. In our experiment, the longest training time of LSTM was 4680.28 s, and that of BiLSTM was 6061.58 s, which is too slow for data imputation tasks at the hourly level. TCN is a novel structure that applies convolutional networks to time series learning tasks, solving the problem of LSTM and BiLSTM being hard to parallelize and significantly reducing the training time.  
Figure 8: The best and worst imputation results at the 2008 meteorological observation site.
# MonoMR: Synthesizing Pseudo-2.5D Mixed Reality Content from Monocular Videos
## 4 Performance Evaluation
### Accuracy of Texture Extraction  
We evaluated the accuracy of the person texture extraction method applied to MonoMR. We created ground truth data for 500 images through the mask R-CNN [33], one of the state-of-the-art algorithms of segmentation, and measured the mask intersection over union (IoU). Consequently, the obtained average mask IoU is 0.72 (sd = 0.05). Figure 10 is the visualization result of extracting texture through the mask R-CNN and the proposed method.  
The mask image obtained through our method generally does not have significant artifacts. However, compared with the mask obtained through the mask R-CNN, the results of our method may contain noise such as shadow and missing body parts. Nevertheless, given that the proposed method has more advantages than the mask R-CNN regarding processing speed, we applied the background extraction-based texture extraction method in this study to enhance the possibility of real-time processing.  
Figure 10: Visualization results of texture extraction methods. (**a**) Mask R-CNN (blue region), (**b**) ours (red region), and (**c**) overlapped two methods (purple region is the intersection area).  
Figure 9: Mean absolute error of depth estimation for each distance section in a long-range space.
# Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods
## 6 Results
### Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Phase** & **Accuracy** & **Recall** & **Precision** & **F-1 Score** &
\begin{tabular}{c} **Balanced Error** \\ **Rate** \\ \end{tabular} \\ \hline Logistic & Train & 0.80 & 0.10 & 0.63 & 0.18 & 0.46 \\ \cline{2-7} Regression & Test & 0.78 & 0.08 & 0.55 & 0.15 & 0.47 \\ \hline \multirow{2}{*}{Naïve Bayes} & Train & 0.29 & **0.96** & 0.22 & 0.36 & 0.47 \\ \cline{2-7}  & Test & 0.28 & **0.92** & 0.22 & **0.36** & 0.49 \\ \hline \multirow{2}{*}{ANN} & Train & 0.81 & 0.20 & 0.65 & 0.31 & 0.41 \\ \cline{2-7}  & Test & 0.77 & 0.13 & 0.46 & 0.21 & 0.45 \\ \hline \multirow{2}{*}{XGBoost} & Train & **0.89** & 0.53 & 0.93 & 0.68 & **0.24** \\ \cline{2-7}  & Test & 0.79 & 0.25 & 0.58 & 0.35 & **0.40** \\ \hline \multirow{2}{*}{Random Forest} & Train & 0.79 & 0.00 & 0.00 & 0.00 & 0.50 \\ \cline{2-7}  & Test & 0.78 & 0.00 & 0.00 & 0.00 & 0.50 \\ \hline \multirow{2}{*}{CatBoost} & Train & 0.88 & 0.42 & **0.98** & 0.59 & 0.29 \\ \cline{2-7}  & Test & **0.80** & 0.15 & **0.69** & 0.24 & 0.43 \\ \hline \multirow{2}{*}{LightGBM} & Train & **0.89** & 0.53 & 0.93 & **0.89** & **0.24** \\ \cline{2-7}  & Test & 0.79 & 0.24 & 0.58 & 0.34 & **0.40** \\ \hline \hline \end{tabular} Best values are shown in bold.  
\end{table} 
Table 7: Results from the combined use of airport and weather datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Phase** & **Accuracy** & **Recall** & **Precision** & **F-1 Score** &
\begin{tabular}{c} **Balanced Error** \\ **Rate** \\ \end{tabular} \\ \hline Logistic & Train & 0.66 & 0.66 & 0.65 & 0.66 & 0.34 \\ \cline{2-7} Regression & Test & 0.65 & 0.60 & 0.33 & **0.43** & **0.36** \\ \hline \multirow{2}{*}{Naïve Bayes} & Train & 0.52 & 0.99 & 0.51 & 0.67 & 0.48 \\ \cline{2-7}  & Test & 0.25 & **0.95** & 0.22 & 0.36 & 0.50 \\ \hline \multirow{2}{*}{ANN} & Train & 0.91 & 0.90 & 0.92 & 0.91 & 0.09 \\ \cline{2-7}  & Test & 0.70 & 0.32 & 0.33 & 0.32 & 0.43 \\ \hline \multirow{2}{*}{XGBoost} & Train & 0.99 & 0.97 & 0.99 & 0.98 & 0.02 \\ \cline{2-7}  & Test & 0.79 & 0.27 & 0.56 & 0.35 & 0.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of weather and airport data using the SMOTE technique.
## 2 Our Proposed Context-Aware Smart Classroom Architecture
### The Context-Aware Smart Classroom Prototype
#### 2.1.3 Smart Classroom with Context Awareness  
When environment parameters are changing, classrooms with context awareness should be able to determine the reactions to take based on certain rules or AI algorithms. Therefore, a context-aware smart classroom should have sensors to retrieve environment data and highly integrated with the school information systems for getting user context.  
1. An information board to show classroom context * Classroom schedule should be shown in the information board. Therefore, as shown in Figure 4a, the information about the classroom schedule is always correct even if a class is canceled or moved to another classroom. * Classroom physical environment parameters, such as CO\({}_{2}\) level, humidity, and temperature, can be shown in the information board, as shown in Figure 7.
2. Control actions should link to backend school information systems to get user context related to the classroom. * When the classroom is empty according to the correct classroom schedule from the school information system, classroom door should be locked and all devices, except controlling devices, should be turned off automatically to reduce energy consumption. * When the classroom is not empty according to the correct classroom schedule from the school information system, classroom door should be unlocked automatically and keep unlocked until the class is over. * The door access system should link to teachers and students' private class schedule. Teachers and students who get to the classroom before the class can unlock the door and turn devices on with ID cards. * When a user passes the door access system, devices in the classroom, which are essential to a class, should be turned on automatically.
3. Environment parameters related to teaching and learning performance should link to control actions * CO\({}_{2}\) level triggers or suggests exhaust fan actions. * Temperature and humidity trigger or suggest air conditioner actions.  
Figure 6: Image and audio recording: (**a**) a camera on the projector pointing towards the whiteboard; (**b**) a camera pointing towards students and a multi-function sensor which measures environment parameters including temperature, humidity, CO\({}_{2}\) level, and PM2.5 level. PM2.5 means particulate matter which is smaller than 2.5 micrometers in diameter.
## 4 Simulation Experiments and Discussion
### Experiment 2: Analysis of the Reflector TDE Impacts on ABP Accuracy  
According to Section 3.4, we can see that the thermally induced BPE was related to a total of 18 TDEs (\(\phi_{\text{SSR},ma}\), \(\theta_{\text{SSR},ma}\), \(\psi_{\text{SSR},ma}\), \(\cdots\), \(\phi_{\text{MR},ma}\),  
\(z_{\text{SSR},mn}\), \(\cdots\), \(x_{\text{MR},ma}\), \(y_{\text{MR},ma}\), and \(z_{\text{MR},ma}\)) belonging to 3 reflectors of the examined antenna. These 18 TDEs could be classified into two categories: (1) thermal misalignment errors (\(\varphi_{\text{SSR},ma}\), \(\theta_{\text{SSR},ma}\), \(\psi_{\text{SSR},ma}\), \(\cdots\), \(\varphi_{\text{MR},ma}\), \(\theta_{\text{MR},ma}\), and (2) thermal translation errors (\(x_{\text{SSR},ma}\), \(y_{\text{SSR},ma}\), \(z_{\text{SSR},ma}\), \(\cdots\), \(x_{\text{MR},ma}\), \(y_{\text{MR},ma}\), and \(z_{\text{MR},ma}\)). Because thermal misalignment errors and thermal translation errors have different physical dimensions, it was inappropriate to compare these two kinds of errors horizontally. Accordingly, the impacts of these two kinds of TDEs will be discussed separately in this subsection.