esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Overview of Analytical Process  
This research examines the performance of the dimensionality reduction techniques PCA and LDA, followed by the machine learning [4; 5] classification methods KNN, NB, and the SVM. In the first part of our comparative analysis, LDA outperformed PCA across various evaluation metrics. While both methods aim to reduce dimensionality and extract essential features from the data, LDA demonstrated better discriminatory power by explicitly considering class information during dimensionality reduction. This allows LDA to more effectively capture the underlying structure of the data, resulting in enhanced separability between different classes. Conversely, PCA, despite its effectiveness in preserving overall variance, may not prioritize features that are discriminative for classification tasks. LDA was chosen as the dimensionality reduction technique based on the objectives and characteristics of the dataset. Figure 24 provides a visualization of the dataset after the application of PCA, while Figure 25 shows the dataset after the application of LDA.  
In the second part of our comparative analysis among the machine learning classifier techniques KNN, NB, and the SVM, the SVM provided better performance across various evaluation metrics. While KNN relies on proximity-based classification and NB assumes feature independence, SVM harnesses the concept of maximum margin hyperplanes to achieve robust classification boundaries, particularly in high-dimensional spaces. This enables SVM to optimize decision boundaries based on the data distribution and maximize the margin between different classes, resulting in better prediction and performance. Therefore, SVM proves to be the preferred choice for our specific application, underscoring the importance of selecting a classifier that aligns with the dataset's characteristics of supporting multiple classes.
# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 3 Background: Challenges in Detecting Three Behaviors
### Loitering  
Loitering detection is critical as it can help to identify the cases where (i) vulnerable people such as older patients with Alzheimer's disease need attention, and (ii) potential suspects involved in illegal activity are noticed [14]. Different studies (e.g., [14; 16]) have defined the act of loitering based on two main characteristics. First, loitering is the act of lingering in a certain area for more than a specified time. Note that the value for the time span must be tunable as the threshold value would differ from one application to another. So, for example, it will be shorter in a critical area such as an authorized building as compared to the corridor of a shopping mall. Second, whereas normal pedestrians typically walk in a straight line with an aim, loitering is characterized by the random and arbitrary movement of an individual. Here, various possibilities about each of these cases are defined. Along the time span dimension, the possibilities are:  
1. A person is detected in a scene and stays in the scene for a specific duration.
2. A person is found in a scene and is moving at a slow speed for a specific duration.  
Similarly, concerning movement type, [PERSON] et al. [14] have found loitering to be a case of movement that forms a certain motion trajectory within a specific activity area. In this way, the following cases are possible:1. A person is detected in a scene and moves within an activity area that forms a regular shape such as a rectangle or ellipse.
2. The person continues to move around a point of interest thus resulting in an activity area that revolves around one point.
# A Novel Intelligent Method for Fault Diagnosis of Steam Turbines Based on T-SNE and XGBoost
## 3 Experiments, Results and Discussion  
### Introduction of Data Set  
The data set in this paper was derived from the time series data of Steam Turbine 2 in a thermal power plant in China. The data set include the following two parts.  
(1) One was from the supervisory control and data acquisition (SCADA) system. The original sampling period of the SCADA system was less than one millisecond. The data set was the interval sampling data of every one second.  
(2) The other was the fault information from the manual record and the system application and product (SAP). The fault information mainly came from the manual record of the power plant, including the fault content and the recorded time. However, some of the fault information was not part of the equipment operation faults and could not be effectively identified by the automated acquisition system. Therefore, in this paper, the fault information was filtered.  
After excluding measurement points with severe data loss or no data records, the steam turbine data set contained 34 variables, such as the time stamp, operating condition parameters and status parameters. The acquisition time was eight months, and the size of the effect data was approximately 340,000.  
Table 2 shows the statistical information of the steam turbine data set. In addition, more detailed information of the data set can be seen in Appendix B, Table 1.  
The fault information was obtained from the fault records manually recorded by the power plant, including the fault content and recording time. Since some fault records were not plant operation faults and could not be effectively identified by the data, the available fault information was screened. The fault records selected for use are shown in Table 3.
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 5 Evaluation
### Heterogeneous Paths  
The next set of experiments considered heterogeneous network paths.  
Scenario 4 had heterogeneous paths that differed in their bandwidth. We set the delay (150 ms) and random packet loss probability (4%) to be the same for both paths. We set the bandwidth to a fixed value of 20 Mbps for one path while varying the capacity of the other one from 10 to 80 Mbps. We then measured the RSF-DB performance when downloading a 10 MB file using different MPTCP algorithms.  
Figure 5 shows the impact on performance. In general, the throughputs achieved by the existing MPTCP algorithms were fairly low and remained almost constant, even with the increasing bandwidth on the second path. That is, they did not exploit the additional bandwidth well. In contrast, the RSF-DB algorithm showed consistent improvement in throughput under the same network settings. When packet losses were frequent, the existing MPTCP algorithms suffered a lot, but our algorithm was able to discover heterogeneous paths quickly and improved the load balancing across paths.  
Figure 4: Fairness results for 4 MB files in Scenario 2: homogeneous paths with 10 Mbps bandwidth, 150 ms delay, and 2% random packet loss.  
Figure 3: Throughput results for different file sizes in Scenario 2: homogeneous paths with 10 Mbps bandwidth, 150 ms delay, and 2% random packet loss.
## 1 Introduction
### Motivation  
Most existing multi-path routing techniques use multi-path as an alternative path when the communication link is disconnected [9]. However, when large amounts of data are generated, transmission through a single path can be challenging to achieve by using queue and bandwidth management techniques only. In the case of low-priority data, the transfer may not be possible [10]. In this study, we attempted to guarantee the reliability and QoS by distributing and transmitting mission-critical data through multiple paths that satisfy the trustworthiness and QoS requirements of each data packet. The proposed scheme enables the effective detection of malicious attacks that might occur in tactical ad-hoc networks and ensures the reliable transfer of mission-critical data. As a result, the command and control center quickly yet accurately collects mission-critical data, which is essential for tactical operations and situational awareness.
## Appendix A  
Let us assume the set \(S_{A}\) (20) of the initial tracking error when the trajectory crosses the point \(A\), and let \(\delta_{zA}\) be its generic instance. At the end of the trajectory segment \(T_{AB}\), each instance will be subject to the following equation:  
\[\delta_{zN_{AB}}=\bar{\Phi}_{n}^{N_{AB}}\delta_{zA}+\Big{[}\bar{\Phi}_{n}^{N_{ AB}-1}\bar{B}_{D}\ \ \bar{\Phi}_{n}^{N_{AB}-2}\bar{B}_{D}\ \ \ \cdots\ \ \ \bar{B}_{D}\Big{]}\hat{D} \tag{1}\]  
with \(n=1,\cdots,N_{v}\) and \(\hat{D}=\begin{bmatrix}\mu_{0}^{T}&\mu_{1}^{T}&\cdots&\mu_{N_{AB}}^{T}\end{bmatrix}^ {T}\). For each \(n-th\) vertex of polytopic linear time invariant system (19) with \(n=1\cdots N_{v}\), it is possible to define the smallest ellipsoidal set  
\[S_{N_{AB}}^{n}=\{\delta_{z}\in\Gamma_{0}:\delta_{z}^{T}P_{0}\delta_{z}\leq \bar{\gamma}_{n},\ \ 0\leq\bar{\gamma}_{n}\leq 1\} \tag{2}\]  
containing all allowable tracking errors at the time instant \(t_{N_{AB}}\) according to Equation (1). Such a set can be found by solving the following semi-definite programming minimization problem:  
\[\min_{\tau_{0},\cdots,\tau_{N_{AB}-1},\lambda,\bar{\gamma}_{n}}\bar{\gamma}_{n} \tag{3}\]  
such that  
\[0\leq\bar{\gamma}_{n}\leq 1 \tag{4}\]  
\[\tau_{0},\cdots,\tau_{N_{AB}-1} \geq 0 \tag{5}\] \[\lambda \geq 0 \tag{6}\]  
\[\bar{\gamma_{n}}-\sum_{h=0}^{N_{AB}-1}\tau_{h}d_{max}^{2}-\lambda\geq 0 \tag{7}\]  
\[\begin{bmatrix}-\bar{\Phi}_{n}^{T}P_{0}\bar{\Phi}_{n}+\lambda_{n}P_{A}&-\bar{ \Phi}_{n}^{T}P_{0}\bar{\Phi}_{n}\\ *&-\bar{J}_{n}^{T}P_{0}I_{n}+\sum_{h=0}^{N_{AB}-1}\tau_{h}\Lambda_{h}^{T} \Lambda_{h}\end{bmatrix}\geq 0 \tag{8}\]  
where \(\bar{\Phi}_{n}=\bar{\Phi}_{n}^{N_{AB}}\),  
\[\bar{J}_{n}=\begin{bmatrix}\bar{\Phi}_{n}^{N_{AB}-1}B_{D}\ \ \bar{\Phi}_{n}^{N_{AB}-2}B_{D}\ \ \ \cdots\ \ \ B_{D}\end{bmatrix} \tag{9}\]  
and \(\Lambda_{h}\) is the matrix such that \(\Lambda_{h}\hat{D}=d_{h}\).  
Finally, the set \(S_{N_{AB}}\in\Gamma_{0}\) the smallest ellipsoidal set  
\[S_{N_{AB}}=\{\delta_{z}\in\Gamma_{0}:\delta_{z}^{T}P_{0}\delta_{z}\leq\bar{ \gamma},\ \ 0\leq\bar{\gamma}\leq 1\} \tag{10}\]  
embedding all allowable tracking errors at the time instant \(t_{N_{AB}}\) can be obtained as the minimum volume ellipsoid containing \(S_{N_{AB}}^{n},n=1,\ldots,N_{v}\) sets [56].
# Enhanced Local Fisher Discriminant Analysis for Indoor Positioning in Wireless Local Area Network
## 1 Introduction
In contrast to LDA, LFDA further preserves the within-class local structure of signal space and thus obtaining more separate location features. Second, a perturbation method is proposed to further enhance the performance of LFDA. Generally, the number of training samples is always limited, since RSS samples collection is a time-consuming work. Through perturbation method, more number of representative samples is generated and thus improving the generalization ability of LFDA. Our experiments in realistic indoor environment demonstrate that, the proposed E-LFDA method achieves significant accuracy improvement than classical feature extraction methods including LDA and PCA.
# Affect Analysis in Arabic Text: Further Pre-Training Language Models for Sentiment and Emotion
## 2 Literature Review
### Emotion Detection in Arabic
The evolution that has occurred in the NLP area, especially in the exploitation of transfer learning and advanced pre-trained language models (Transformer-based models), has not been significantly investigated in the Arabic language. However, compared to English, in Arabic studies, machine learning or deep learning algorithms were utilised to analyse sentiment and emotion more often than current pre-trained language models. In particular, the Arabic emotion detection task has limited studies compared to the Arabic sentiment analysis task. In addition, most of the studies have used a limited number of datasets (i.e., experiments using one or two datasets), or small datasets; for example, in emotion detection studies, most have used the AIT dataset. The reason could be the limited number of resources available for Arabic sentiment and emotion classification tasks. Furthermore, model adaptation approaches have given rise to large performance gains in the English language, whether using domain-adaptation approaches [10; 11; 12] or task-adaptation approaches [13; 14]. Adapting the pre-trained language model to a specific domain or task means continuing the pre-training of language models on this task or domain using an unlabelled domain-specific or task-specific dataset. To the best of our knowledge, model adaptation approaches, in particular, further pre-training the language model on a specific domain have only been undertaken in two studies for the Arabic language [15; 16], whereas classifying sentiment and emotion were not the focus of these studies. Additionally, further pre-training the language model on a specific task (i.e., within-task and cross-task adaptation) has not yet been investigated for Arabic in general, and for the sentiment and emotion task in particular. Nevertheless, amid the emerging advancements in Arabic sentiment and emotion classification, further study and experimentation are still needed to address the existing gaps and enhance performance in this field.
## 4 Experimental Results  
In this section, we discuss the experimental results achieved by TruMuzic. Since the nature of music is subjective, it is a challenging task to evaluate music plagiarism detection models. Therefore, we established various test datasets with ground truths alongside a series of experiments to assess and compare the results produced by TruMuzic with those from other SOTA approaches. There are two primary goals used in our experimental process. The first is to evaluate the weights of the data features used in the model. The second is to assess the accuracy and efficiency of our model against other SOTA approaches. In this paper, we focus our model primarily on accuracy. However, for experimental purposes, we also evaluate its efficiency by measuring the time taken by TruMuzic to process a result compared to existing approaches. It is always up for debate which method works best for music similarity comparison because the goals of the various available models and the data features they use for their comparison are entirely different and application specific. Therefore, we developed several test datasets to evaluate the accuracy of the overall process as well as look at the individual contributions from various data features to the final result.  
As mentioned in the experimental goals, we performed the experiments in two phases: (1) internal comparisons and (2) external comparisons. In the internal or distinctive comparisons, we evaluate the similarity contribution of a particular data feature or various combinations of these features and their impact on the model's overall accuracy. In the external or comprehensive comparisons, we combine all features and evaluate the model's overall accuracy against other SOTA methods. In this section, we will also explain our experimental setup. We describe the internal evaluation of features in Section 4.1, followed by the external evaluation against other methods in Section 4.2.
## 3 Results and Discussion
### Response of Bacterial and Archaeal Communities
#### 3.4.2 Effects of Mixing Ratio on Archaeal Community  
Comparatively, the archaeal community had much lower richness and diversity compared to the bacterial community at both the phylum and genus levels. As depicted in Figure 4c, _Euryarchaeota_ was the only dominant phylum of methanogens, and its relative abundance (93.61-97.85%) showed no observable differences among the various groups.  
Due to the heterogeneities in various precursor substrates applied to produce biomethane, methanogenic pathways are divided into four types: acetoclastic, hydrogenotrophic, methylotrophic, and electron-dependent methanogenesis [43], all of which play crucial roles in biomethane production. To further clarify the methanogenesis pathways affected by different combinations of FW and MA, the genus distribution of the methanogenic community was analyzed. At the genus level, the predominant genera (>2%) identified in the archaeal community were _Methanoseta_ (41.16-73.31%), _Methanobacterium_ (8.82-31.35%), _Methanolinea_ (5.74-9.15%), _Candidatus_Methanofistidosum_ (1.28-5.17%), and _Methanospirillum_ (2.08-6.49%), all of which exhibited significant changes in each group (Figure 4d). Notably, the abundance of Methanospirillum increased from 2.70% in the mono-digestion of MA (FW/MA = 0:1) and 2.92% in the mono-digestion of FW (FW/MA = 1:0) to 6.49% in the co-digestion of FW/MA = 1:1, indicating that it was selectively enriched during co-digestion. Interestingly, similar to _Methanospirillum_, the relative abundances of _Methanocorpusulum_, Methanomethylovans, and _Methanosarcina_ were also enriched in the co-digestion of FW/MA = 1:1 (Figure 4d). It is worth noting that most of these genera carry out hydrogenotrophic methanogenesis. Compared with mono-digestion, higher _Methanosaeta  
Figure 4: Microbial community analysis. (**a**) The microbial community distributions at the phylum level. (**b**) The relative abundance of bacteria at the genus level. (**c**) The archaeal community distributions at the phylum level. (**d**) The relative abundance of archaea at the genus level.  
was seen in the FW/MA = 0:1 group (73.31%), and higher _Candidatus_Methanofistidiosum_ was seen in the FW/MA = 1:0 group (4.87%), contributing to acetoclastic methanogenesis [36]. It is reasonable to conclude that the optimal mixture of FW and MA potentially changed the metabolic routes for biomethane generation, resulting in better anaerobic co-digestion performance.
## 3 Materials and Methods
### Feature Extraction  
Classical parameters, such as MFCCs and LPCCs, are used in this study. MFCCs are standard methods for feature extraction that exploit the knowledge of human hearing systems [1]. The first step of linear predictive (LP) analysis is to estimate a source signal using inverse filtering. After that, the spectrum is computed using the source signal. The computed spectrum is used to study the energy distribution in both normal and pathological voices. The number of LP coefficients is one of the key elements of LP analysis to determine the formant peaks. This is because removing the effect of the formants from the speech signal can provide an accurate estimation of the source signal [25]. In this work, 20-dimensional MFCCs and LPCCs were extracted from a 40-ms window signal using a 20-ms frameshift.  
To identify speech impairments, this work used a novel set of HOS parameters extracted from the time domain. A primary advantage of this method is that periodic or quasi-periodic signals are not required for reliable analysis [1; 2; 3; 4; 5; 6]. HOS parameters obtained from time domains provide promising results in this field [1; 2; 3]. Inspired by the above three studies, this work aims to extract HOS parameters from the time domain to detect and classify speech impairments. Among the various HOSs, the 3 rd- and 4 th-order cumulants are used as characteristic parameters in this study. These parameters are called normalized skewness, \(\gamma_{3}\), and normalized kurtosis, \(\gamma_{4}\), and are defined as shown in (1).  
\[\gamma_{3}=\frac{\sum_{n=1}^{N}(x_{n}-\mu)^{3}}{(N-1)\sigma^{3}},\,\gamma_{4}= \frac{\sum_{n=1}^{N}(x_{n}-\mu)^{4}}{(N-1)\sigma^{4}} \tag{1}\]  
where \(x_{n}\) is the n-th sample value, \(N\) is the number of samples, and \(\mu\) and \(\sigma\) represents the mean and the standard deviations, respectively.
## 4 Results and Discussion on the Survey
### Analysis of the Main Results of the Survey
The data highlight a significant gap in the adoption of advanced technologies, such as closed-loop recycling systems, which are crucial for reducing environmental impact and promoting a fully circular production process. Technological innovation theories in the context of the circular economy suggest that the transition to a circular model requires a strong commitment to research and development (R&D) [39]. However, these investments are difficult for small companies to sustain, as they are limited by financial and infrastructural resources. To support companies in this innovation process, targeted public policies are necessary to encourage investment in R&D through funding, subsidies, and tax incentives, which can make the adoption of sustainable technologies more accessible (see Table 16). \begin{table}
\begin{tabular}{l l} \hline
**Main Challenges** & **Focus** \\ \hline Fragmented approach to & Integrating circular models across the \\ sustainable practices & entire production process \\ Diversity in certifications & Creating common standards for \\ sustainability practices & sustainability practices \\ \hline \end{tabular}
\end{table} 
Table 15: Challenges and focus related to sustainability certifications. Figure 9: Answers to Question 16. Barriers and needs for circularity. The adoption of circular economy models in Italian SMEs is hindered by a series of barriers, as shown in Figure 10, that prevent a smooth transition to more sustainable practices. Financial barriers, which account for 20% of responses, are among the main difficulties highlighted by companies. The high upfront costs required to implement advanced technologies and circular processes limit SMEs' ability to adopt these models on a large scale. Organizational barriers (17%) are another significant obstacle. Barriers related to market demand (13%) also emerge as a limiting factor. To overcome these barriers, companies have identified several needs (see Figure 11), such as sustainability training (23%) and the introduction of traceability systems (21%). Circular economy theories suggest that a collaborative approach between businesses, research entities, and governments could facilitate the removal of these obstacles [40]. The creation of supportive policies that encourage the adoption of sustainable practices, through tax incentives, research funds, or promoting synergies between different companies, would be fundamental to accelerating the transition to a more circular and sustainable business model. \begin{table}
\begin{tabular}{l l} \hline \hline
**Main Challenges** & **Focus** \\ \hline Limited adoption of & Encouraging the use of green technologies \\ advanced technologies & through funding and public support \\ High implementation costs & Overcoming economic barriers through \\ incentives and R\&D support \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Challenges and focus related to sustainability technologies. Figure 10: Answers to Question 17.
## 4 Results
### Step 3: Interpretive Ranking Process (IRP) Model
#### 4.3.1 Cross-Interaction Matrix (CIM)  
The CIM produces the relationship between the LBs and lean-based performance criteria. Thus, the interaction matrix is realized using the feedback of the expert team. An expert team critically compares the LBs and their relationships against lean performance criteria. A binary value of '1' or '0' may be used if the relationship between LBs and lean performance criteria exists or otherwise. The cross-interaction matrix so prepared is shown in Table 7.
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 4 Simulation Evaluation  
We evaluated the proposed method with clustering, collaborative caching, and content popularity inference (C-CoCach-Pop) using Scenargie [23]: a commercial simulator supporting V2V communications.