esa-sceva/satcom-chunk-collection huggingface dataset:
# Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation
## 3 Methodology
### Overview Pipeline of Open-Set Recognition Based on Audio Segmentation  
Figure 1 illustrates the pipeline for detecting unknown Pansori rhythm patterns, which is extendable to other audio-segmentation challenges. The process comprises seven stages: preprocessing, data augmentation, feature extraction, mapping and classification, loss calculation, unknown detection, and postprocessing.  
1. Preprocessing: The waveform signal is transformed into acoustic representation features and segmented into overlapping patches.
2. Data augmentation: To mitigate the risk of overfitting, data-augmentation techniques are employed to augment the sample size by making slight modifications to existing data.
3. Feature extraction: The encoder processes the low-level representation features and converts them into a high-level abstract representation that retains essential structural information of the input.
4. Mapping and classification: The embedded features are restructured into new vectors corresponding in size to the acoustic representation features, from which predicted probabilities for each audio frame are derived.
5. Loss calculation: The backpropagation method is utilized to minimize the discrepancies between the predicted vectors and the ground truth outputs.
6. Unknown detection: Upon adequate training, test data of known and unknown samples are feed into the network, where the latter are identified using open-set-detection techniques.
7. Postprocessing: For large test datasets, patch-based predictions are executed, and the results from overlapping inputs are merged. Additionally, frame-based prediction timestamps are converted into temporal stamps.  
Training Phase: The dataset, comprising audio signals and their associated known classes, is prepared and preprocessed to form suitable inputs for the network. Randomly cropped patches of the log-Mel spectrogram from the sampled audio signal within the analysis window are utilized to enhance the training data volume. These patches are then input into the backbone encoder to extract temporal features, which are subsequently mapped to the decision classifier for known sound events.  
Inference Phase: During inference, patches of the log-Mel spectrogram in a sequential window are processed by the feature extractor, followed by the mapping and classification block, to determine whether they correspond to known or unknown audio frames.
## 3 Results and Discussions
### Compression Plate Results
The comparison focuses on the deformed state of the pack at 12 mm for the baseline, while for the variant configurations, the displacement is limited to the point where the maximum and critical load of the pack without spacers is reached. The improvement is evaluated by analyzing the overall displacement value during the compression process at the same load. In V1, Figure 8, the press stops at 5 mm for all materials, which is significantly below the 12 mm threshold, suggesting a conservative approach to avoiding the risk of thermal runaway. In V2, Figure 9, the press also halts at 5 mm for aluminum and PLA, while the 60 \({}^{\circ}\)C PLA is allowed to compress slightly further, stopping at 7 mm. This adjustment for 60 \({}^{\circ}\)C PLA still avoids reaching the critical 12 mm limit. In V3, Figure 10, all materials compress to approximately 9 mm. However, none of these versions surpass the 12 mm threshold, which is crucial for preventing the onset of thermal runaway. A key point to note across all versions is that the cells maintain their round shapes, as shown in the figures, suggesting that while compression varies, the structural integrity of the cell pack is preserved up to the respective maximum load. Figure 8: Compression plate, cell configuration V1, center-cut deformation for different spacers material: (**a**) aluminum; (**b**) printed PLA; (**c**) printed PLA at 60 \({}^{\circ}\)C.
## 5 Results and Discussions
### Effect of Crushing Height on the Impact Force  
In general, the veered car crashes at the lower part of the column; for this reason, impact height was chosen as the car ground height. According to EC 1 [22], the position of impact loading on the column for a passenger car collision is approximately 500 mm in height from the column's base. This may be applicable to small and medium-weight passenger cars. On the other hand, for SUV-type seven-seater passenger cars, the height of impact position was found to be approximately 840 mm.  
The effect of the impact height on the behavior of RC column was also studied, and it was found that the variation in impact force histories was very little. The peak impact forces were in between 695 to 713 kN for impact heights between 850 and 1300 mm. The impact force-time histories are given in Figure 13. As many studies have shown that the design ESF can be calculated from the impact force-time history [26], the calculated ESF will be almost the same for a crashing height up to 1300 mm. As the column is more rigid than the car, the effect of impact height (between 850 and 1300 mm) on the impact force is very low. However, if the impact location is at the middle of the column, the column will absorb some impact energy by flexural deformation. Therefore, the ESF value will be slightly lower than the ESF value obtained from the impact at the lower part of the column.  
Figure 12: Relationship between PIFs and car crashing velocity.
## 4 Evaluation  
### Experimental Setup  
For evaluation, we used a cluster composed of 8 physical compute nodes, each equipped with an AMD EPYC 7713 processor (64 cores/128 threads @ 2.0 GHz), 130 GB of DDR4 memory, and a Seagate FireCuda 530 NVMe SSD with 2 TB capacity. All nodes run Ubuntu 22.04.3 LTS with Linux kernel version 6.6.2. All nodes communicate through the Message Passing Interface (MPI).  
To evaluate subset-level encryption under realistic conditions, we used the ECU-IoHT dataset [79]. We gradually increased the file size from 10 GB to 100 GB to assess scalability across various data volumes. For key management, we adopted the Kyber512 algorithm from the OQS library [78] for PQC-based encapsulation of a 256-bit AES key [76; 77], and used the Qiskit Aer simulator [62] to emulate BB84-based QKD session key generation for secret key masking. To create a realistic evaluation scenario, we used a macro-benchmark from real application environments to assess the feasibility of our proposed scheme. We utilized the Yahoo Cloud Serving Benchmark (YCSB) [80], which is widely used for read/write-intensive workloads in cloud systems. For key-value storage, we configuredYCSB with RocksDB. During evaluation, we executed YCSB workloads A, B, C, and D under two separate conditions: once while running AES (Original) and once while running our proposed scheme. This allows for measuring and comparing the encryption overhead imposed on application-level performance.
# Hybrid Energy Routing Approach for Energy Internet
## 5 Improved ACO-Based Energy Routing Protocol (IACO-ERP)
The probability depends on the amount of pheromone and the power loss to the next hop: \[Prob_{ij}=\frac{\left(\tau_{ij}\right)^{\alpha}\left(\eta_{ij}\right)^{\beta}} {\Sigma_{j\in F_{i}}\left(\tau_{ij}\right)^{\alpha}\left(\eta_{ij}\right)^{\beta}}\] (28) \[\eta_{ij}=\frac{1}{w_{i}+w_{ij}}\] (29) where \(F_{i}\) is the neighboring list of node \(v_{i}\), in the graph \(G2\), while \(\eta_{ij}\) represents the quality of the power line (edge) \(e_{ij}\), \(\alpha\) and \(\beta\) are two parameters that control, respectively, the importance of the pheromone intensity and the quality of the power line. 5. The algorithm runs in iterations, and at each iteration an energy efficient path is selected (set \(S\) in Figure 5), and the amount of the pheromone is updated according to Equations (26) and (27). 6. The energy efficient path is the path with the minimum power transmission loss in set \(S\) (Equation (21)). A virtual circuit, constructed using the selected energy efficient path, is allocated to transfer the power between the producer-consumer pair. The exchange information between routers during the creation of energy transmission circuits and the verification of capacity constraints before the construction of the paths prevent the congestion and overhead problems.
# A Novel Fault Diagnosis Method for TE Process Based on Optimal Extreme Learning Machine
## 3 Results
### The Performance of Purposed Method
#### 3.1.3 TSNE  
To directly show the extent to which the fault states are identified by the method in this paper; the final output t-distributed random neighbor embedding (TSNE) plots of the method in this paper are shown in Figure 7, where different colored points indicate different fault states. The plotted data consists of 480 pieces of data from each fault training set. It can be seen that fault 1 and fault 2, which have a fault diagnosis rate of 95% or more, are both well-classified in the current sample, while fault 3, which has a fault diagnosis rate of 0.7354, contains many points from both fault 1 and fault 2, and has fewer points from fault 1 than from fault 2, which is a reflection of the higher fault diagnosis rate for fault 1 relative to fault 2. As shown in Figure 8, fault 15, with a fault diagnostic rate of 58.85%, is  
\begin{table}
\begin{tabular}{c c c c} \hline
**Fault** & **Precision** & **Recall** & **F1** \\ \hline
1 & 0.9816 & 1.0000 & 0.9907 \\
2 & 0.8696 & 1.0000 & 0.9302 \\
3 & 0.3763 & 0.8938 & 0.5296 \\
4 & 1.0000 & 1.0000 & 1.0000 \\
5 & 0.9143 & 1.0000 & 0.9552 \\
6 & 1.0000 & 1.0000 & 1.0000 \\
7 & 1.0000 & 1.0000 & 1.0000 \\
8 & 0.6957 & 1.0000 & 0.8205 \\
9 & 0.3606 & 0.9375 & 0.5208 \\
10 & 0.7182 & 0.9875 & 0.8316 \\
11 & 0.5490 & 0.9438 & 0.6943 \\
12 & 0.6178 & 1.0000 & 0.7637 \\
13 & 0.5412 & 0.8625 & 0.6651 \\
14 & 0.9938 & 1.0000 & 0.9969 \\
15 & 0.2440 & 0.7000 & 0.3619 \\
16 & 0.6653 & 0.9813 & 0.7929 added to the TSNE for faults 1, 2 and 3. The relatively similar and difficult-to-identify mix of fault 15 and fault 3 occurs, which is a direct reflection of the low fault diagnostic rate of fault 15, and, in fact, the relatively similar and difficult-to-identify mix of fault 15 and other faults occurs in essentially all of the different TSNE plots. In fact, in the different TSNE diagrams, there is a relatively similar and unidentifiable mix of fault 15 and other faults.  
Figure 8: TSNE visual output of fault 1, 2, 3, 15.  
Figure 7: TSNE visual output of fault 1, 2, 3.
###### Abstract  
We propose a new method for constructing a virtual sound source (VSS) based on the direct-to-reverberant ratio (DRR) of room impulse responses (RIRs), using multiple pairs of parametric-array loudspeakers (PALs) and conventional loudspeakers (hereafter referred to simply as loudspeakers). In this paper, we focus on the differences in the DRRs of the RIRs generated by PALs and loudspeakers. The DRR of an RIR is recognized as a key cue for distance perception. A PAL can achieve super-directivity using an array of ultrasonic transducers. Its RIR exhibits a high DRR, characterized by a large-amplitude direct wave and low-amplitude reverberations. Consequently, a PAL makes the VSS appear to be closer to the listener. In contrast, a loudspeaker causes the VSS to be perceived as farther away because the sound it emits has a low DRR. The proposed method leverages the differences in the DRRs of the RIRs between PALs and loudspeakers. It controls the perceived distance of the VSS by reproducing the desired DRR at the listener's position through a weighted combination of the RIRs emitted from PALs and loudspeakers into the air. Additionally, the proposed method adjusts the direction of the VSS using vector-based amplitude panning (VBAP). Finally, we have confirmed the effectiveness of the proposed method through evaluation experiments.  
virtual sound source; direct-to-reverberant ratio; room impulse responses; parametric-array loudspeaker; vector-based amplitude panning Article
# Traceability of the Micro Scale Pipe Viscometer for Traceable Calibration of Dynamic Viscosity
## 3 Results and Discussion
### Comparison with Glass Capillary Visometers  
The measurement uncertainty of the micro pipe viscometer has also been validated by a comparison between several metrology institutes, which use either a pipe viscometer or a glass capillary viscosity meter to determine the dynamic viscosity or the kinematic viscosity, which is transformed into the dynamic viscosity with the measured density of the liquid [2; 23]. Eight different liquids were used and all the details of the comparison can be found in the references mentioned above. Only the results of two liquids measured by three pipe viscometers (METAS, RISE and NEL) and two glass capillary viscosity meters (NQIS/EIM and KRISS) with the corresponding reference value for the dynamic viscosity of the liquids are listed in Table 10. Liquid C is a 10%wt glucose solution and liquid E is a solution of 0.22%wt NaCl and 5.55%wt glucose. The calculation of the reference value and the consistency check of the results is explained in detail in [2]. The results shown here are consistent within the measurement uncertainty and strengthen the stated measurement uncertainty of the pipe viscometer described in this paper.
# Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX
## 5 Conclusions  
In this paper, we propose a hyperspectral anomaly detection algorithm with a channel attention mechanism and LRX. The channel attention mechanism is utilized to guide the feature learning process of the auto-encoder, which aims to explore the effective bands of different land covers and make the reconstruction image more discriminative and less disturbed by noise. Then, the LRX algorithm is applied to the obtained reconstruction image from the auto-encoder network for anomaly detection on the reconstruction image in order to improve the performance of hyperspectral anomaly detection. The effectiveness of the proposed algorithm is verified on three different real datasets in this paper. Experimental results show that the proposed algorithm can obtain higher detection accuracy compared with other comparative algorithms.  
Conceptualization, Z.R.; Methodology, H.G.; Software, H.W.; Validation, H.W.; Formal analysis, Z.R.; Data curation, H.W.; Writing--original draft, H.G.; Writing--review & editing, X.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2023-JC-QC-0714.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Mesogen Orientation of PAz Films
#### 3.1.3. PAz Film with Top-Coated Amorphous Polymers
In the left profiles, red, black and blue lines show the out-of-plane and in-plane directions and 55\({}^{\circ}\) to the normal direction, respectively. (**c**) Schematics on the mesogen orientation induced by the free surface (left) and glass plate or polymer coat on the top (right) are displayed. (**d**) Schematic illustrations of the structure and orientation of the smectic C phase at room temperature (left) and vertically oriented smectic A phase at 90 \({}^{\circ}\)C (right) of PAz film covered with a polymer layer. Figure 5: 2D GI-SAXS data for thicker PAz films (thickness: 400 nm) covered with PBMA (**left**), P4 VP (**middle**) and PVP (**right**) layer on top at room temperature. For the white arrow, see the text.
## 4 Video Application Based on Deep Reinforcement Learning Algorithm
### Multi-Agent Deep Reinforcement Learning  
The DDPG algorithm becomes helpless when faced with more than one agent. [PERSON] et al. [85] proposed a DRL model of cooperation and competition among multi-agents, in which each agent is trained by independent DQN algorithm, and rewards are designed according to the goal of each agent. According to the different return functions designed, multi-agent deep reinforcement learning can be divided into three ways: complete cooperation, complete competition and mutual cooperation and competition. When the environment of video transmission over CVIS is so complex that a single agent cannot solve the problems, multi-agent DRL enters the attention of researchers.  
[PERSON] et al. [86] proposed a computing offload scheme, based on multi-agent DRL, which minimizes the total task processing delay in the long run. In order to evaluate the performance of the proposed unloading scheme, a large number of simulations were carried out. Simulation results verify the effectiveness and superiority of the proposed scheme.  
[PERSON] et al. [87] studied the problem of service migration in MEC-enabled vehicle networks to minimize the total service delay and migration costs. In order to obtain an effective solution, it is modeled as a multi-agent MDP and solved by the deep Q-learning (DQL) algorithm. Simulation results show that the proposed scheme achieves near-optimal performance.  
Figure 9: The structure of the soft-ACDRL algorithm.  
[PERSON] et al. [94] used deep learning technology to predict the movement pattern of vehicles. An architecture composed of a centralized decision-making unit and distributed channel allocation is proposed to maximize the spectrum efficiency of all related vehicles. In order to achieve the above goals, the authors used DQN and A2C technology and further integrated LSTM [58] into DQN and A2C technology. The performance of the proposed scheme is verified by simulation.  
In order to solve the problem of the abnormal growth of computational complexity and the explosive growth of state space and action space in multitasking and multi-agent (MTMA) environments, [PERSON] et al. [95] used a value decomposition mechanism (VDM) to decompose complex value functions into several small blocks, and then calculated each sub-function separately. The proposed paradigm can greatly accelerate the learning speed and help significantly in a smaller state and action space, but will not degrade the performance. Simulation results showed the effectiveness of the proposed mechanism.
## 3 Experimental Results and Discussion
### Pushover Tests on Isolated Structural Wall
#### 3.1.1 Diagram of Horizontal Load versus Drift  
Figure 13 shows the diagram of horizontal load versus lateral drift (hysteresis loops). In this case, positive values mean push, and negative values mean pull.  
Some interesting conclusions can be obtained from Figure 13. First, a symmetric behavior under push and pull is obeserved, as expected. Second, it can be observed that the behavior of the specimen is linear elastic up to the critical drift \(\varphi_{\text{y}}\) (which is 0.5% according to ACI374.2R-13 [34]). In consequence, this solution agrees with one of the requirements of the American standard mentioned above.  
Once the drift is greater than 0.5%, a progressive plastic behavior is observed, i.e., the specimen begins to dissipate energy at the expense of a higher deformation. The area enclosed by the hysteresis loop is proportional to the energy dissipated during the testing and represents the structural element capacity to mitigate the earthquake effect inelastically.  
The maximum loads obtained during the testing were 13.0 kN in the push phase and 12.0 kN in the pull phase. In both cases, these loads correspond to a drift of 2%, which is four times greater than the critical drift. The loads obtained at the critical drift were 6.5 kN in the push phase and 6.2 kN in the pull phase.  
Figure 13: Diagram of load vs. drift. Pushover test on isolated structural wall.
# Identification of the Surface Cracks of Concrete Based on ResNet-18 Depth Residual Network
## 4 Model Performance Analysis and Discussion
### Performance of Real Bridge Images Outside the Dataset  
Finally, in order to investigate the practical applicability of the model in real-world scenarios, two approaches are employed for gathering authentic images depicting bridge cracks. The first approach involves conducting on-site visits to directly observe the cracks present on various bridges and gather corresponding images. The second approach entails searching for authentic images available on the Internet. Subsequently, a total of 30 photographs were collected from each of the four categories and employed to evaluate the precision of the trained model through recognition. The evaluation of the model's performance in real-world scenarios and the provision of feedback for further improvement and optimization were facilitated. The objective of this identification verification experiment was to validate the practicality of the model, thereby providing a reliable tool and judgment basis for bridge safety detection in various fields. An example of the collected live crack images is shown in Figure 16, and the model output is shown in Table 4.  
Figure 15: Crack image recognition results under local shadow.  
According to the results in the table, it can be seen that the model performed best in recognizing crack-free images with 100% accuracy, where the best recognition rate was 99.90%. The recognition accuracy for cracks and longitudinal cracks was relatively low, but the best recognition rate was 99.90%. Based on the analysis of the image, it is plausible that the recognition of the model could be compromised due to bifurcation of vertical cracks, resulting in a certain probability of misclassification as craquelures.  
In summary, the model demonstrated robust recognition capabilities in practical applications and was able to accurately classify various types of cracks. Relevant professionals can promptly comprehend and implement appropriate measures to ensure the smooth operation of structures and facilities by effectively categorizing cracks. Consequently, the exemplary performance of this model will yield positive ramifications for real-world engineering applications by enhancing efficiency and mitigating risks. As a result, it holds significant implications for both the engineering industry and societal development.
# Biflavonoid Profiling of _Juniperus_ Species: The Influence of Plant Part and Growing Location
## 3 Results
### Geography-Dependent Biflavonoid Accumulation in J. communis
Bilobetin was present in all unripe (green) cone samples but was only found in ripe cones from three locations (Proskok, Igman, and Trebovic) and in four out of six locations where needle samples were collected. The cupressuflavone content ranged from 4.69 \(\upmu\)g/g (Nahorevo ripe cones) to 1987.54 \(\upmu\)g/g (Trnovo needles). Amentoflavone concentrations varied between 107.29 \(\upmu\)g/g (Visocica ripe cones) and 3486.68 \(\upmu\)g/g (Nahorevo needles). Bilobetin levels ranged from undetectable to 388.01 \(\upmu\)g/g (Trebovic unripe cones), while hinokiflavone was detected in amounts from 0 \(\upmu\)g/g (Trebovic unripe cones) to 602.58 \(\upmu\)g/g (Nahorevo needles). Interestingly, in unripe cones from the Trebovic location, we did not detect hinokiflavone. However, these cones contained the highest amounts of cupressuflavone and bilobetin among all unripe cone samples. In needles, the concentrations of cupressuflavone, amentoflavone, and hinokiflavone were significantly higher than in both unripe and ripe cones. This reflects the total biflavonoid content, which ranged from 201.03 \(\upmu\)g/g (ripe cones from Konjic) to 6033.24 \(\upmu\)g/g (needles from Trnovo). Notably, samples from Konjic had significantly lower levels of all detected biflavonoids compared to other locations, with particularly reduced amounts of amentoflavone and hinokiflavone. The biflavonoid content of samples collected from different locations was also subjected to principal component analysis (PCA), with the results presented in Figure 10. In this analysis, the first two principal components accounted for 99.7% of the total variance, indicating a strong representation of the data structure. The distinct separation of needle samples on the right side of the PCA plot highlights their significantly different biflavonoid profiles compared to other analyzed plant parts, a trend that is also evident in Table 1. These findings suggest that plant part has a greater influence on biflavonoid accumulation than geographic location.