esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Contrastive Ensemble Learning Method for Anomaly Sound Detection
## 2 Materials and Methods
### Domain Fusion Model  
In order to be able to unite the time-frequency domain features, this paper ensembles the feature extraction backbone networks from the frequency domain and time domain models. The fusion method is illustrated in Figure 5. The fusion model first concatenates the feature extraction backbone network from the frequency domain and time domain models.  
the feature vectors from both time and frequency domains along the channel dimension. Subsequently, the concatenated feature vector is fed into a classifier. This classifier employs multiple Squeeze-and-Excitation Blocks (SEBlock) [23] to perform channel-wise adaptive weighting on the input features. Finally, for the classification prediction output, the ArcFace function is used to calculate the loss value for model convergence. During the training process, neither the time domain model nor the frequency domain model participates in gradient updates; only the classifier participates in the back-propagation process. The aim is to ensure that the feature representation capabilities of the two models remain unchanged, thereby guaranteeing optimal feature modeling capability. In this paper, the channel attention-based fusion method is employed instead of directly utilizing time-frequency features. This is because the direct utilization of time-frequency domain features may not be able to specifically represent the feature distribution of the input samples. By using the channel attention mechanism, the model can automatically extract the correlation between each feature channel and automatically weight the channels according to the degree of importance, which can help the model focus on feature maps that are more critical to the target task than directly extracting features from the time-frequency domain, thus improving the model performance. The overall fusion classifier structure is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **In Channel** & **Out Channel** & **Kernel Size** \\ \hline layer1 & 2 & 16 & 1 \\ SEBlock & 16 & 16 & 1 \\ layer2 & 16 & 64 & 3 \\ SEBlock & 64 & 64 & 1 \\ layer3 & 64 & 256 & 3 \\ layer4 & 256 & 512 & 6 \\ layer5 & 512 & 128 & 1 \\ FC & 128 & 16 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: The fusion classifier structure: FC represents the fully connected layer, layer represents the convolutional layer and SEBlock represents the channel attention block. The batchnorm is used in each convolutional layer, and the PRELU activation function is used in the first two convolutional layers. In each SEBlock, the mid-channel factor is set to 0.5.  
Figure 5: Time-Spec domain fusion model: \(w\) and \(h\) represent the shape of feature map, \(c\) represents the number of channels. During Squeeze-Excitation, the model first squeezes the feature map along the channel dimension, then derives weights for each channel and finally applies these weights to the corresponding channels of the original map.
## 4 Methodologies and Implementation
### Challenges and Limitations in Implementing DDPM for Biomedical Image Segmentation
#### 4.3.2 Parameter Sensitivity  
DDPM relies on the tuning of various parameters such as the diffusion steps, noise levels, and learning rates. Sensitivity to these parameters may hinder the model's robustness and generalizability across different biomedical imaging scenarios [53; 99].
# VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT
## 2 Related Work  
### Video Temporal Grounding  
For fully-supervised VTG, prior works [1; 9; 10; 11; 13; 14; 15; 16] typically employ encoders to extract visual and textual features, followed by designing a VTG model (e.g., transformer encoder-decoder) to interact and align two modalities, as depicted in Figure 1b. UniVTG [13] designs a multi-modal and multi-task learning pipeline, undergoing pre-training or fine-tuning on dozens of datasets. To accelerate the training convergence of VTG, GPTSee [14] introduces LLMs to generate prior positional information for the transformer decoder. However, these supervised approaches inevitably rely on extensive human-annotated data and training resources. To alleviate the dependence on annotations, PSVL [17], DSCNet [18], and [PERSON] et al. [19] propose unsupervised frameworks that employ clustering to generate pseudo queries from video features. Similarly, PZVMR [20] and [PERSON] et al. [21] leverage CLIP [22] for pseudo query generation. Yet, the above unsupervised methods unavoidably introduce biases from mismatched video-query pairs. In this paper, we adhere to the definitions of unsupervised and zero-shot settings as discussed by [PERSON] et al. [23], classifying these approaches [17; 20; 21] as unsupervised.  
To avoid any training or fine-tuning of the model, [PERSON] et al. [2] design the first zero-shot framework utilizing CLIP, but its reliance on shot transition detectors for obtaining temporal segments limits performance. Considering that CLIP (InternVideo [24]) pre-trained on 400 M image-text (12 M video-text) pairs can align visual and textual inputs in a shared feature space, [PERSON] et al. [23] develop a bottom-up pipeline to leverage the capabilities of vision-language models. [PERSON] et al. [25] employ the sparse frame-sampling strategy and BLIP2 [26] to reduce the computational cost of inference. However, these zero-shot methods [2; 23; 25] tend to generate redundant video features, introducing new biases that impair model performance. A recent study [27] found that masking over 75% of the input images can effectively train large self-supervised models. Moreover, SeViLA [28] demonstrates that transforming visual signals into textual representations significantly reduces redundant information, thereby boosting performance in tasks such as video question answering and VTG.
## 2 Results and Discussion
### Application of the HS-SPME/GC-MS Method to Strawberry Fruit Samples
#### 2.2.5 Aldehydes and Ketones  
In this study, 19 aldehydes (for example, hexanal, (_E_)-2-hexenal, benzaldehyde, 3_A_-dimethyl benzaldehyde, nonanal, (_E_)-2-nonenal, decanal, furfural, and 5-hydroxymethylfurral) were identified in all treatments, covering 0.74%, 4.16%, 6.81%, 17.05%, 24.10%, and 29.09% of total volatiles in frozen, shade air-dried, freeze-dried, oven-dried (45 \({}^{\circ}\)C), oven-dried (60 \({}^{\circ}\)C), and microwave-dried samples, respectively, compared to 0.82% in fresh samples. An increase in aldehyde content was observed during all drying treatments, particularly at elevated temperatures (oven-dried (45 \({}^{\circ}\)C), oven-dried (60 \({}^{\circ}\)C), and microwave-dried samples), while aldehyde content decreased in only the frozen sample. Hexanal and (_E_)-2-hexenal are primarily responsible for the green/vegetative aroma component in strawberries. (_E_)-2-Hexenal is thought to be responsible for the \"fresh strawberry\" flavor in \"Strawberry Festival\" and \"Florida Radiance\" strawberries [32]. It is also worth noting that some aldehydes such as furfural and 5-hydroxymethylfurral appeared only in heat-drying methods such as oven- and microwave-drying, and these results are in agreement with other authors who reported that 5-hydroxymethylfurral (HMF) and furfural are heat-induced chemical markers generated during strawberry jam processing and baking of cereal-based products [16; 41].  
For ketones, the heat-drying methods showed a significant increase in ketone content, but freezing and freeze-drying decreased the ketone content. Microwave-dried samples (14.57%) possessed the highest total ketone content, whereas frozen (0.02%) and freeze-dried (0.43%) samples showed the lowest values compared to the fresh sample (0.78%). This demonstrated that drying at elevated temperatures altered the overall flavor impression in dried samples by enriching ketone and aldehyde flavor notes, and this is consistent with the literature [11].
# A Review of Research on Secure Aggregation for Federated Learning
## 2 Federated Learning
### Classification of Federated Learning
#### 2.2.3 Network Structure  
Distinguished by network architecture, federated learning consists of centralized federated learning and decentralized federated learning. Centralized federated learning uses a central server to guide aggregation and synchronization. Decentralized federated learning allows clients to communicate directly with each other in a peer-to-peer (P2P) manner.  
Figure 4: A schematic diagram of federated transfer learning.  
Figure 3: A schematic diagram of vertical federated learning.  
In centralized federated learning, each client trains a local model with its data, and the central server is responsible for model aggregation. Centralized federated learning is mainly for multi-user federated learning scenarios, where the enterprise acts as a server and coordinates the global model. The network topology of this model is shown in Figure 5.  
In decentralized federated learning, each client trains a local model based on its private data. After training, the client may choose to communicate with other clients, and the clients exchange or fuse local models. The network topology diagram adopted for decentralized federated learning is shown in Figure 6. Decentralized federated learning eliminates the dependence on a central server for model aggregation, and replaces it with algorithms that build trust and reliability, thus reducing the presence of untrusted servers and increasing resilience to network failures.
# Realistic Multi-Agent Formation Using Discretionary Group Behavior (DGB)
## 3 Algorithm: Discretionary Group Behavior
### Sensing the Presence of Obstacles  
In this part of the process, each agent can measure the density of obstacles by generating an ellipse around itself. The center of the ellipse, \((x_{c},y_{c})\), is determined at \(C_{e}\) units direction ahead of the agent's trajectory. To expound it in more details, we placed \((x_{c},y_{c})\) at the \((x,y)+C_{e}*agentUnitVelocity\). \((x,y)\) and \(C_{e}\) (see Section 4 for more details) are the agent's location and a constant coefficient for moving the ellipse's center respectively. The reason for moving the ellipse in the lead of the agent's position is to make the agent focuses more on its forward way for detecting obstacles. An obstacle falls into the ellipse if the condition of Equation (2) is satisfied [5]. In the equation below, \((a,b)\) are the ellipse's major and minor radiuses through the process.  
\[\frac{(x-x_{c})^{2}}{a^{2}}+\frac{(y-y_{c})^{2}}{b^{2}}\leq 1. \tag{2}\]
# Development and Characterization of a Flexible Soundproofing Metapanel for Noise Reduction
## 2 Materials and Methods
### SPL Test for FSM  
We fabricated FSM with the geometric parameters designed (Figure 2a) and conducted comprehensive acoustic testing with an open field test for enclosure-type soundproofing. Our acoustic measurement methodology, featuring a carefully curated sound source, employed a meticulously controlled environment. To ensure acoustic fidelity, we utilized a direct recording of vacuum cleaner noise transmitted via a Bluetooth speaker. This approach not only mitigated potential sound leakage associated with wired connections but also provided a known noise spectrum encompassing our target frequency range of 2000-5000 Hz. The experimental setup incorporated the FSM sample and strategically positioned microphones, enabling precise measurement of sound pressure levels both with  
Figure 1: FE model used to simulate STL of the soundproofing MAM unit cell. Boundary probes can calculate the space-averaged pressure at a certain distance, similar to the microphones of the impedance tube.  
and without the FSM. These measurements provided a direct comparison of the acoustic performance between the FSM and the control conditions.  
As illustrated in Figure 2b-d, our test enclosure consisted of a reference box (0.2 m diameter, 0.5 m height). Sound waves were generated at one end, with the FSM blocking sides and opposite ends. This setup allowed us to measure the noise reduction achieved by the FSM across the frequency spectrum, focusing on our target range. We employed FSM-identical material for enclosure sealing to maintain acoustic integrity and minimize experimental error, ensuring optimal sound isolation. The experimental samples comprising the FSM and mass-law-based control group were precisely aligned perpendicular to the sound wave propagation direction. Microphones were strategically placed at points 1 and 2 to capture SPL measurements inside and outside the enclosure. The measured SPL represents structure-borne noise generated by the vibrating back plate of the FSM. These results indicate that the FSM blocks waves with high dynamic effective density and specific acoustic impedance. To ensure the accuracy and reproducibility of our results, we conducted  
Figure 2: (**a**) Optical image of Flexible Soundproofing Metapanel (FSM). (**b**) Schematic diagram of the experimental setup for acoustic testing of the FSM. (**c**) Casted enclosure-type FSM (**right**) and control (**left**). (**d**) Experimental setup for the SPL test.  
multiple trials for each experimental configuration. The sound source was calibrated before each test session, and environmental factors such as temperature and humidity were carefully monitored and recorded. Additionally, we employed a high-precision laser vibrometer to measure the vibration characteristics of the FSM during acoustic excitation, providing valuable insights into its dynamic behavior.
## 4 Experiments
### Ablation Studies
#### 4.3.1 Traditional Time Series Decomposition vs. Frequency Decomposition  
To investigate the difference between the frequency domain decomposition technique applied in the DESTformer and the traditional method of decomposing sequence data into seasonal and trend terms, the frequency domain decomposition technique in the original DESTformer is replaced with a common time series decomposition method, and this version of the model is denoted as DESTformer-f. The experimental results are shown in Table 4, and it can be seen that under our proposed frequency domain decomposition technique, the DESTformer can reduce the MSE by an average of 1.21% compared to DESTformer-f in all step length settings on various experimental datasets.
# Periodic Surface Defect Detection in Steel Plates Based on Deep Learning
###### Abstract  
It is difficult to detect roll marks on hot-rolled steel plates as they have a low contrast in the images. A periodical defect detection method based on a convolutional neural network (CNN) and long short-term memory (LSTM) is proposed to detect periodic defects, such as roll marks, according to the strong time-sequenced characteristics of such defects. Firstly, the features of the defect image are extracted through a CNN network, and then the extracted feature vectors are inputted into an LSTM network for defect recognition. The experiment shows that the detection rate of this method is 81.9%, which is 10.2% higher than a CNN method. In order to make more accurate use of the previous information, the method is improved with the attention mechanism. The improved method specifies the importance of inputted information at each previous moment, and gives the quantitative weight according to the importance. The experiment shows that the detection rate of the improved method is increased to 86.2%.  
periodic defect; deep learning; CNN; LSTM; attention mechanism +
Footnote †: journal: Applied Physics Letters  
12 June 2019; accepted: 27 July 2019; Published: 1 August 2019
# XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
## 8 Conclusions  
Given the serious security threats to drones using 6G NTN protocols, it is important to detect, classify, and identify protocol attacks. However, classifying uncovered protocol attacks and identifying anomalous protocol data remain challenging. To address these issues, this paper presents an interpretable anomaly classification and identification method for 6G NTN protocols. We design an interpretable anomaly detection framework that introduces XAI techniques and can be used in conjunction with existing detection methods, thereby ensuring both interpretability and cooperation with other approaches. Using this framework, we develop a self-evolving classification method to identify uncovered protocol attacks. The rule detection process and baseline detection results are made transparent through XAI technologies; this information is used synergistically to extract and learn from the fingerprint features of uncovered protocol attacks, and these features are used in the classification process. Additionally, an online identification method is designed within the proposed framework to capture and output anomalous protocol data during the detection process on the basis of an intrinsic interpretable model. The simulation results show that the proposed classification and identification methods can effectively classify uncovered protocol attacks and identify anomalous protocol sequences. Compared with existing methods, the precision of the self-evolving classification method is improved by a maximum of 32.8%, and that of the online identification method for anomalous protocol sequences is increased by at least 26%.  
Conceptualization, Q.S. and J.Z.; methodology, Q.S. and L.D.; software, L.D. and Y.H.; validation, L.D. and Y.H.; formal analysis, Q.S. and J.Z.; investigation, L.D. and Y.H.; resources, L.T. and J.Z.; data curation, Y.H.; writing--original draft preparation, Q.S.; writing--review and editing, J.Z. and Q.S.; visualization, L.D. and Y.H.; supervision, J.Z. and L.T.; project administration, L.T.; funding acquisition, L.T. and J.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 15: Identification of sequence number desynchronization attacks.  
**Funding:** This work was supported by the Beijing Natural Science Foundation (No. L222004) and the National Natural Science Foundation of China (No. 62371039).  
**Data Availability Statement:** Restrictions apply to the availability of these data. Data were obtained from Canadian Institute for Cybersecurity (CIC) and are available at [[https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)]([https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)) with the permission of CIC.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Overview of Smart Water Grid Technologies and Systems
### Technological Description
#### 2.2.2 System Controls--Smart Pumps and Valves  
Smart valves and pumps adjust their operations based on environmental conditions or signals from sensors. These adjustments can happen automatically or remotely by a human controller. The main benefit of smart controllers is increased efficiency. For example, variable speed pumps sense water conditions and will ramp up or down depending on those conditions. These pumps can also be equipped to sense clogs in the system and respond by breaking up clogs and/or reversing the flow. This is especially useful for wastewater and raw water conveyance. One manufacturer of a smart pump estimates up to 70% cost savings over the life cycle of the pump [13]. Smart valves adjust or block the flow of water in pipes based on environmental conditions. They can be used as part of pressure management strategies, as a part of leak detection activities, or to prevent environmental contamination due to combined sewer overflows [21; 22].  
At the end-use level, smart irrigation controllers show promise in helping to save water that is wasted on landscape irrigation. Smart irrigation controllers can receive and/or collect weather data or sense soil moisture levels, as well as other parameters, which helps determine proper water scheduling. Using this information, the watering schedule can be updated automatically on a daily basis. The valves and pumps that implement the actual watering of the landscape will then turn on and off at best times possible [23].
## 6 Case Data and Simulation Results
### Simulations Result and Analysis in Different Seasons  
To verify the effectiveness of the proposed strategy, this paper selects the typical days in spring, autumn, and winter for simulation. The data source is the same as that in summer.  
The simulation results in the spring are shown in Figure 11. In the spring, the AC is only turned on and heated at the lower temperatures of 8:00, 9:00, 20:00, and 21:00 due to the pleasant outdoor temperature. When VES is considered, the power consumed by the AC is further elevated due to the lower electricity price at the 8:00, and the building stores cool air for usage at the 9:00. In general, VES utility is provided by EWHVES primarily in the spring to reduce the operating costs of the system.  
The simulation results in autumn are shown in Figure 12. In the autumn, the AC generally works on heating mode due to the lower temperatures. From 12:00 to 13:00, the outside temperature is comfortable and thus the AC is turned off. At 8:00, when the outside temperature is low, the AC works at its full capacity to raise the temperature in the building.  
The simulation results in winter are shown in Figure 13. In the winter, PV generation decreases due to low irradiation, and the system purchases more power to increase the temperature in the building. Thus, the operating costs in winter are relatively high. At 8:00 and 9:00, the temperature in the building is very low, and thus the AC works at its full capacity to raise the temperature. In the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenarios** & **Cost/\$** & **Economize/\$** \\ \hline Without VES & 165.06 & / \\ EWHVES & 152.46 & 12.60 \\ ACVES & 155.70 & 9.36 \\ EWHVES and ACVES & 144.69 & 20.37 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Daily operating costs of the system under 4 scenarios.  
Figure 11: The optimal scheduling results in spring: (**a**) VES is not considered; (**b**) VES is considered.  
Figure 12: The optimal scheduling results in autumn: (**a**) VES is not considered; (**b**) VES is considered.  
following several hours, the solar radiation and the indoor heat sources can provide heat to the room. Thus, the air conditioner operates at lower power to maintain the indoor temperature within a comfort zone. Furthermore, with increased demand for hot water during the winter months, EWHVES offers more virtual energy storage utility. Table 5 shows the operating costs of the system under different seasons.  
From Table 5, it can be seen that the proposed strategy is effective in reducing the operating costs of the system in all four seasons. Among them, the lowest operating costs are incurred during the summer months due to the high volume of photovoltaic generation. VES works best in the summer because the AC show great potential to provide VES in the summer.