esa-sceva/satcom-chunk-collection huggingface dataset:
# Hierarchical Phoneme Classification for Improved Speech Recognition
## 2 Phoneme Clustering
### Phoneme Clustering Using Confusion Matrix
The lower four rows of Table 3 are the phoneme groups based on the dendrogram in Figure 2 using a 0.6 cut-off with some additional adjustments. The first group is a set of nasal sounds, [m], [n], and [ng]. The second one is a group of fricatives, sounds with air friction with longer pronunciation time. Voiced sounds ([z] [zh] [v] [dh]) and their corresponding unvoiced sounds ([s] [f] [th]) are grouped together. The voiceless glottal fricative sound, [hh], also belongs to this group. The third is the affricates group ([jh] [ch]), weak sounds with friction. The stop sounds such as [t], [d], [p], and [b] are closely clustered. The clustering result is similar to the basic phonetic categories. Our resultant clusters are analogous to the conventional linguistic categories, so we can confirm that it provides reasonable results. To confirm that the phoneme categories are well clustered, we drew a confusion matrix with the new phoneme groups in Table 3, and it is shown in Figure 3a. The phonemes are reordered so that all the phonemes in the same group are located adjacently. The red-colored rectangles indicate phoneme groups, and Figure 3b is the table below the figure is a confusion matrix with the phoneme groups as classification targets. The diagonal ones represent that the accuracy of the group labeling is very high, which implies that the different groups are well distinguished. In other words, the phonemes within the same group are similar and confusing, so we focus on improving the recognition performance of these phonemes inside the groups. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Group** & **Phonemes of the Group** \\  & & **(Written in ARPAbet)** \\ \hline \multirow{2}{*}{**Vowels**} & & iy ih eha eax uv uh \\  & & ao ey ay oy aow er \\ \hline \multirow{2}{*}{**Mixed**} & & l r w y \\ \cline{2-3}  & & Nasals & m n n g \\ \cline{2-3}  & & Fricatives & s z zh f v th dh hh \\ \cline{2-3}  & & Affricates & jh ch \\ \cline{2-3}  & & Stops & b d g k p t dx \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Phoneme groups obtained by agglomerative hierarchical clustering on the TIMIT training dataset.
# Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms
## 5 Results and Discussion  
### Modeling for Prediction  
The models for predicting blast fragmentation were trained using the training dataset, resulting in four intelligent prediction models (GA-LSSVM, RIME-LSSVM, PSO-LSSVM, LSSVM). After incorporating relevant parameters, the Kuz-Ram model was adjusted. Additionally, integrating GA-LSSVM with blast cost formulas into the MOPSO optimization model facilitated the optimization of mining blast parameter design, providing precise and reliable guidance for engineering applications.
# A Comparative Analysis of Big Data Frameworks:
## 4 Big Data Processing Frameworks
### 4.5. Flink
#### 4.5.1 Architecture  
Flink has a layered architecture that consists of four layers (Figure 9). Primarily, Flink is a stream processing engine that doesn't offer a storage and resource management system of its own, instead it is designed to read data from various streaming and various storage systems. The core of Flink is a distributed data flow engine that receives user programs in the form of a DAG. The DAG in Flink is a parallel data flow graph which contains a series of tasks that produce and consume data streams. Flink has two main APIs: The DataSet API for processing bounded data streams i.e., batch processing and the DataStream API for processing potentially unbounded data streams. DataStream and DataSet APIs are interfaces that programmers can use to define jobs. When compiling the program, these APIs will generate a data flow graph.  
The Flink runtime mainly consists of three processes: Job Manager, Task Manager and Client. The client receives the application code, converts it into a data flow graph, and then sends it to the JobManager. This conversion stage will also create serializers and other type-specific code. In addition, the program will go through a cost-based query optimization phase. JobManager is the master process that controls the execution of a single application. It is responsible for all activities that need to be centrally coordinated, such as distributed execution of data streams, monitoring the status and progress of each task, scheduling new tasks and coordinating checkpoints. Task Managers are the worker/slave processes that actually processes the data stream. Each application is managed by a different JobManager that receives the application from client. JobManager then requests the necessary resources from the ResouceManager. When it receives enough TaskManager, tasks will be distributed to the executing TaskManagers.
## 5 Experimental Results
### Ablation Experiment
This method can more effectively highlight the representative behavioral features in malicious communications and improve the model's perception of changes in behavioral patterns. In short, attention pooling implements a dynamic aggregation strategy based on feature content, which not only improves the representation strength of key features but also avoids the shortcomings of traditional pooling methods in information utilization. Removing residual connections results in a 4.84% drop in accuracy, confirming their importance in stabilizing training, mitigating gradient vanishing, and facilitating the learning of deep hierarchical features. As a standard design in deep networks, residual connections ensure smoother gradient flow and enhance convergence during training. The four-behavior pattern encoding, combined with subsequent fusion, is crucial for constructing a holistic view of traffic behavior and identifying anomalies that manifest across multiple dimensions. While removing modules reduces the parameter count, it also leads to pronounced accuracy degradation. This confirms the effectiveness of EFTransformer's multi-level architecture in achieving fine-grained, high-precision encrypted traffic detection. EFTransformer minimizes the FPR and FNR by striking a balance between feature modeling and component control through a tightly integrated architectural design. By leveraging multi-head attention mechanisms and feature fusion on four behavioral channels, it captures temporal and semantic patterns in encrypted traffic, thereby achieving fine-grained differentiation between benign and malicious traffic. The design achieves a significantly low FPR of 6.19% and an FNR of 5.85%, outperforming all ablation variants. Ablation studies confirm that removing key modules such as attention mechanisms, feature fusion, or residual connections significantly improves the FPR and FNR to 12.44% and 11.39%, highlighting the role of multi-dimensional encoding, dynamic aggregation, and gradient stabilization in suppressing false positives. Together, these strategies ensure robust detection performance even under dynamic and unbalanced traffic conditions.
## 5 Results
### Exergy Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Stream** & **Units** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \(\hat{m}\) & kg/s & 45.58 & 45.60 & 73.77 & 59.29 & 71.74 & 71.74 & 73.77 & 22.79 \\ \(t\) & \({}^{\circ}\)C & 155 & 155.13 & 634.11 & 550.00 & 700.00 & 360.87 & 798.65 & 15.00 \\ \(p\) & bar & 9.90 & 9.19 & 30.00 & 9.00 & 8.64 & 29.50 & 29.70 & 5.00 \\ \(x_{H_{2}}\) & \% (mole) & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 25.60 & 68.82 & 0.00 \\ \(x_{CO}\) & \% (mole) & 0.00 & 0.00 & 50.01 & 0.00 & 0.34 & 52.11 & 22.42 & 0.00 \\ \(x_{CO_{2}}\) & \% (mole) & 0.70 & 0.72 & 0.34 & 1.00 & 64.52 & 6.99 & 0.78 & 0.70 \\ \(x_{CH_{4}}\) & \% (mole) & 94.90 & 94.87 & 47.37 & 0.00 & 32.15 & 0.31 & 3.08 & 94.90 \\ \(x_{H_{2}O}\) & \% (mole) & 0.00 & 0.02 & 51.03 & 0.00 & 2.48 & 14.67 & 4.48 & 0.00 \\ \(x_{CH_{4}OH}\) & \% (mole) & 0.20 & 0.20 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.20 \\ \(e\) & MJ/kg & 49.73 & 49.70 & 24.33 & 0.82 & 8.81 & 10.42 & 28.56 & 49.57 \\ \hline
**Stream** & **Units** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **16** \\ \hline \(\hat{m}\) & kg/s & 1996.38 & 1996.38 & 1904.12 & 420.90 & 420.90 & 69.46 & 58.41 & 1996.38 \\ \(t\) & \({}^{\circ}\)C & 1151.02 & 893.41 & 15.00 & 250.00 & 38.00 & 38.01 & 70.96 & 320.08 \\ \(p\) & bar & 4.50 & 4.00 & 1.01 & 50.00 & 47.00 & 48.50 & 1.30 & 2.25 \\ \(x_{H_{2}}\) & \% (mole) & 0.00 & 0.00 & 0.00 & 54.35 & 47.55 & 52.35 & 0.00 & 0.00 \\ \(x_{CO}\) & \% (mole) & 0.00 & 0.00 & 0.00 & 31.71 & 28.33 & 31.17 & 0.00 & 0.00 \\ \(x_{CO_{2}}\) & \% (mole) & 4.99 & 4.99 & 0.00 & 7.01 & 8.61 & 9.21 & 0.00 & 4.99 \\ \(x_{CH_{4}}\) & \% (mole) & 0.00 & 0.00 & 0.00 & 4.34 & 5.03 & 5.52 & 0.00 & 0.00 \\ \(x_{H_{2}O}\) & \% (mole) & 8.05 & 8.05 & 0.00 & 0.62 & 0.24 & 0.00 & 0.00 & 8.05 \\ \(x_{CH_{5}OH}\) & \% (mole) & 0.61 & 0.00 & 0.00 & 1.23 & 9.39 & 0.79 & 1.00 & 0.00 \\ \(e\) & MJ/kg & 25.22 & 0.71 & 0.01 & 18.70 & 18.29 & 17.67 & 22.43 & 0.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Stream data of the methanol plant with parallel steam-methane and dry reforming (Case III).
## 3 Stray Current Corrosion Morphology, Measurement Method, and Influencing Factors
### Factors Affecting Stray Current Corrosion
#### 3.4.1 Environmental Factors
These results show that the properties of the soil itself, temperature, humidity, pH value, and other factors greatly affect the corrosion of buried pipelines by stray currents. Generally speaking, soil with elevated temperatures, high moisture, and low pH will not only accelerate the transmission of stray currents to the surfaces of buried pipelines, but also increase the tendency of metal ions on the surface of the pipeline to dissolve, further aggravating corrosion.
# An Enhanced Lightweight IoT-based Authentication Scheme in Cloud Computing Circumstances
## 4 Security Analysis and Performance Evaluation
### Countermeasures
#### 4.3.2 Mutual Authentication  
In our scheme, \(U_{i}\) and \(S_{j}\) verify that each other is a legitimate user in the system and want to establish a secure communication through Equations (37) to (40). In this case, \(U_{i}\) sends a challenge to \(S_{j}\) for carrying out the mutual authentication process. The response message contains \(\left(n_{CS}^{new}\parallel T_{CS}^{new}\right)\) which represents the fresh of the communication. Moreover, \(U_{i}\) knows the same value, thus avoiding a man-in-the-middle attack.
# Enhancing Tactile Internet Reliability: AI-Driven Resilience in NG-EPON Networks
## 4 Proposed Architecture
### Simulation-Based Evaluation  
To validate the proposed approach, the simulation-based evaluation setup was built using OptiSystem 21.0 software. OptiSystem is an innovative, rapidly evolving, and powerful software design tool that enables users to plan, test, and simulate almost every type of optical link in the transmission layer of a broad spectrum of optical networks, from LAN, SAN, and MAN to ultra-long-haul. It offers transmission layer optical communication system design and planning from component to system level and visually presents analysis and scenarios [35]. The setup comprises an OLT connected to the 8 ONUs with a passive splitter. The distance between the OLT and ONUs ranges from 15 to 20 km, with a feeder fiber length of 15 km and branch lengths varying from 2 km to 7 km. The optical transmitter frequency is set to 1550 nm with a power of 7 dBm, using NRZ modulation. The attenuation loss is 0.2 dB/km, and the splitter loss varies from 4 dB to 8 dB. Two scenarios were simulated: normal and faulty scenarios. For the faulty scenarios, different anomalies were introduced, including macro-bending, micro-bending, fiber cut, and bad splitter. The simulation generated 709.054 samples. The data set was constructed, normalized, and divided into a training (60%), a validation (20%), and a test set (20%) for fault and normal scenario eye diagrams using OTDR traces, obtained from [12]. It is worth mentioning that the eye diagrams were used for anomaly detection, while the OTDR was used to localize the fault. A BER analyzer was placed at the end of each branch to capture the eye diagrams. The dataset is balanced, with an approximately equal number of samples representing normal and faulty conditions. To mimic anomalies (such as fiber bending, bad splitter) and fiber faults, attenuators were placed at the 2 km, 3 km, 5 km, and 7 km branches, respectively. The termination at the end of the 7 km branch was removed to simulate a fiber fault. Figure 7 shows a simulation-based evaluation setup for generating faulty branch  
Figure 6: Proposed framework for fault detection and localization.  
data using OptiSystem in the passive optical network. Furthermore, the normal samples are derived from the simulation-based evaluation setup conducted without any attenuator.
# Smart Grid Protection, Automation and Control: Challenges and Opportunities
## 3 Evolution of Substation Architectures and Digital Modernizations for Smart Substations
### Virtualization of Protection, Automation and Control (VPAC)
The recommended solution is as follows: investing in a high-capacity communication infrastructure with accommodation for evolving standards in future hardware. Licensing and subscription models present additional considerations regarding upgrades, redundancy, and lifetime costs. It is crucial for organizations to evaluate whether their licensing models are perpetual or subscription-based. It is essential to ensure that redundancy is created via the duplication of VMs to maintain high availability. This requires careful planning to avoid any disruptions during updates and to mitigate the risk associated with limited license durations. VPAC systems represent a significant shift in power system management, offering flexibility, scalability, and enhanced security. However, integration, standardization, cost, and licensing issues must be sorted out to achieve this. With regard to centralized protection, ABB's SCC600 [37] represents one such platform, while SCC600 SW [48] introduces the idea of virtualization to impart much higher flexibility (Table 10). In summary, in the process ahead, as the industry moves forward on these issues of standardization, cybersecurity, and judicious engineering, VPAC systems will be leading from the front. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Feature** & **SSC600** & **SSC600 SW** \\ \hline
**Type of Protection** & **Centralized** & **Virtualized** \\ \hline Function Integration & Protection, control, monitoring, and communication & Virtualized protection and control \\ \hline Interoperability & Compliance with IEC 61850 & Compliance with IEC 61850 \\ \hline Cybersecurity & Advanced measures aligned with IEC 62443 and NERC CIP & Advanced measures aligned with IEC 62443 and NERC CIP \\ \hline Flexibility & More rigid structure due to dedicated hardware & High flexibility and scalability \\ \hline Hardware & Requires multiple physical devices & Reduces the need for physical hardware \\ \hline Costs & Potentially higher due to additional hardware & Potentially lower due to reduced hardware \\ \hline Implementation and Updates & Requires hardware installation and updates & Faster and more efficient implementation and updates \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison between SSC600 (CPAC) and SSC60 SW (VPAC).
# Drowsiness Transitions Detection Using a Wearable Device
## 5 Discussion  
Focusing on the results achieved, in the previous section, it was possible to verify that most of the out-of-control points for the heart rate variability are not being influenced by signal noise. This was a concern in the study conducted in [20], where the addition of the accelerometer information was mentioned as future work.  
Nevertheless, note that the drowsiness classification was made manually, considering the video recording and the participant's behavior. This turned out to be subjective, and it is important to find solutions to combat this problem. A similar approach was used in [15], where two trained individuals classified separately the same videos. The classification, in the same period, was different such that it was necessary to evaluate and discuss it together. This is also a bit subjective since the opinion of one evaluator can influence the other. In [44], the drowsiness classification was made by the participant, after the simulation, considering the Karolinska Sleepiness Scale. This may be questionable as it is not always easy to classify our state of drowsiness through a video.  
Other alternatives have emerged such as identifying the duration of the blink automatically and using blink metrics to classify the drowsiness phases [32; 45]. In our experience, this may not be reliable since external factors influence the results. The algorithm can have several weaknesses that cannot be controlled such as wearing glasses, lighting, the participant's facial movement, or even looking back. In addition, it is not always possible to calibrate the participant's eyes well to extract reliable metrics.  
Besides that, evaluating the out-of-control points reached using the heart rate variability, it was possible to improve the drowsiness classification methodology mentioned in [21]. It is essential to evaluate each out-of-control point obtained to prove if there is actually a drowsiness transition. This means that it always needs to be verified to see if such is happening, regardless of the participant in question. In mathematical terms, 83% of the out-of-control points represent a drowsiness transition (precision value), and only 47% of the drowsiness transitions are correctly detected (recall value), using the improved classification. However, if the method developed in [21] is considered, it reaches 37% and 21% for precision and recall metrics, respectively. With the proposed method, it is possible to counter most of the problems mentioned before using the MSPC-PCA methodology, and it is possible to clearly detect the drowsiness transitions. However, this method is not perfect, as it does not identify all the drowsiness transitions. However, regarding the performed analyses, whenever there is an out-of-control point, there is, in most of them, a drowsiness transition. That is a good achievement.
# Query Rewriting for Incremental Continuous Query Evaluation in HIFUN
## 5 Results
### Results
#### 5.2.1 Defining the Queries  
Evaluation of CGMR RuleIn order to evaluate the _Common Grouping and Measuring Rewriting Rule_, the grouping attribute \(g_{1}\) and the measuring attribute, \(m_{1}\) was used to create a set \(Q\) of five queries with five different aggregation operations applicable to measuring attribute \(m_{1}\). The query set \(Q\) is defined as follows:  
\[Q=\{(g_{1},m_{1},sum),(g_{1},m_{1},min),(g_{1},m_{1},max),(g_{1},m_{1},count), (g_{1},m_{1},avg)\} \tag{5}\]  
The equivalent rewritten of Q by this rule is the following query:  
\[Q=\{(g_{1},m_{1}),(sum,min,max,count,avg)\} \tag{6}\]  
Evaluation of the CGR RuleIn order to evaluate the Common Grouping Rewriting Rule, the grouping attribute \(g_{1}\) and five measuring attributes, \(m_{1}\dots m_{5}\) are used to create a set \(Q\) of five queries with five different aggregation operations applicable to those measuring attributes. The query set \(Q\) is defined as follows:  
\[Q=\{(g_{1},m_{1},sum),(g_{1},m_{2},min),(g_{1},m_{3},max),(g_{1},m_{4},count), (g_{1},m_{5},avg)\} \tag{7}\]  
Figure 6: Analysis context of the unstructured dataset.  
Figure 7: Analysis context of the structured dataset.  
The equivalent rewritten of \(Q\) by this rule is the following query:  
\[Q=\{g_{1},(m_{1},sum),(m_{2},min),(m_{3},max),(m_{4},count),(m_{5},avg)\} \tag{8}\]  
Evaluation of the CMOR RuleIn order to evaluate the _Common Measuring and Operation Rewriting Rule_, the attributes \(g_{1}\ldots g_{5}\) are used as grouping attributes, the attribute \(m_{1}\) is used as a measuring attribute and the aggregation operation is a sum applied to measuring attribute \(m_{1}\).  
\[Q^{\prime}=\{(g_{1},m_{1},sum),(g_{2},m_{1},sum),(g_{3},m_{1},sum),(g_{4},m_{1 },sum),(g_{5},m_{1},sum)\} \tag{9}\]  
The equivalent rewritten of \(Q\) by this rule is the following query:  
\[\begin{array}{l}Q^{\prime}=\{(g_{1}\wedge g_{2}\wedge g_{3}\wedge g_{4} \wedge g_{5},m_{1},sum),\\ (proj_{G_{1}},(g_{1}\wedge g_{2}\wedge g_{3}\wedge g_{4}\wedge g_{5},m_{1},sum ),sum)\\ \ldots\\ (proj_{G_{5}},(g_{1}\wedge g_{2}\wedge g_{3}\wedge g_{4}\wedge g_{5},m_{1},sum ),sum)\}\end{array} \tag{10}\]  
Evaluation of the BR RuleWe define the following set of queries:  
\[Q=\{(g_{11}\circ g_{1},m_{1},sum),\ldots,(g_{15}\circ g_{1},m_{1},sum)\} \tag{11}\]  
\(Q\) contains five queries, and all of them have the same distributive operation applicable to the same measuring attribute \(m_{1}\). As described by the rewriting theory, \(Q\) can be equivalently rewritten by the basic rewriting rule as follows:  
\[Q^{\prime}=\{(g_{11},(g_{1},m_{1},sum),sum),\ldots,(g_{15},(g_{1},m_{1},sum), sum))\} \tag{12}\]
## 5 Discussion  
### Comparisons of Models  
We made several assumptions on semantic segmentation models as mentioned in Section 3.3. First, we assumed that DeepLab V3+ has the best invariant performance under different spatiotemporal conditions. This was demonstrated in Tables 1 and 2. The ASPP module in DeepLab V3+ enabled the extraction of multi-scale information of cabbage fields by applying _atrous_ convolutions with different dilation rates. Receptive fields of representation vectors had a positive influence on the detection performance [42]. As can be seen from Figures 7-9, DeepLab V3+ accurately predicted cabbage fields of different sizes. Next, the assumption on U-Net's mirroring strategy did not hold. As shown in the U-Net visualizations of Figure 7, pixels in the border regions were still misclassified. Finally, SegNet's max-pooling indices received from each encoder did not sharpen prediction boundaries from Figures 7-9. In summary, DeepLab V3+ demonstrated superior performance compared to U-Net and SegNet. Especially, we claim that multi-scale feature map aggregation induced by the ASPP module was critical when identifying the complex environment of the South Korea highlands under different spatiotemporal conditions. This has also been emphasized in several previous studies on extracting multi-scale information in UAV images for agriculture [42; 43].
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 4 Results and Discussion  
This section provides a summary of the results concerning the absolute and relative errors observed when estimating the noise figure using polynomial models, compared to values derived from the datasets. It also details the computation times for noise figure calculations from the datasets and the creation of the model itself. First, we consider the uncertainty associated with OSA measurements to set realistic expectations for the model's accuracy. Then, we evaluate the performance of each model in two scenarios: initially using a 70%/30% split of the datasets, and subsequently with complete datasets for broader application. We further analyze the relationship between dataset size and accuracy. This assessment aims to determine whether or not further improvements are possible, if accuracy has reached its peak, or if it is feasible to reduce the number or size of datasets while maintaining similar accuracy levels. Next, we evaluate performance metrics such as model speed and memory usage. Finally, we conduct a test where this model is integrated into a deep-learning framework designed to estimate the power spectral density at the output of an EDFA, based on configuration parameters and input PSD.