esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To address the noise issue in the measured vibration signals of spillway radial gate discharge, this paper utilizes the Multiverse Optimization Algorithm (MVO) to optimize the number of decomposition modes (K) and the penalty factor (\(\alpha\)) in Variational Mode Decomposition (VMD). This approach ensures improved efficiency of VMD decomposition while maintaining accuracy. Subsequently, the obtained Intrinsic Mode Functions (IMFs) from VMD decomposition are classified based on Multi-scale Permutation Entropy (MPE). IMFs are divided into pure components and noisy components; the noisy components are processed with Wavelet Threshold Denoising (WTD), while the pure components are overlaid and reconstructed to obtain the denoised vibration signal of the gate. Comprehensive comparisons involving artificial signal simulations, gate flow-induced vibration model tests, and numerical simulations lead to the following conclusions: compared to other algorithms, the proposed combined denoising method (MVO-VMD-MPE-WTD) achieves the highest signal-to-noise ratio (SNR) in both the frequency and time domains for artificial signals, while yielding the lowest mean square error (MSE). In the gate flow-induced vibration model tests, the method significantly reduces noise in the vibration signals and effectively preserves characteristic information. The error in preserving characteristic information across model tests and numerical simulations is kept below 1%. Furthermore, compared to other optimization algorithms, the MVO demonstrates higher computational efficiency. The parameter-optimized combined denoising method proposed in this study provides insights into denoising measured vibration signals of hydraulic spillway radial gates and other drainage structures, and it opens possibilities for exploring more efficient optimization algorithms for achieving online monitoring in the future.  
spillway radial gate; vibration noise; VMD; joint denoising; numerical simulation +
Footnote †: journal: Applied Sciences  
Article
## 3 Proposed Algorithm
### Packet forwarding  
Once a source node recognizes the location of the destination, the source node selects one node from the routing table toward the destination direction. If many candidate nodes are selected, the source node selects the node of the minimum hop count and the highest sequence number. Nevertheless, if it still has two or more candidate nodes, it finally selects the one node with the longest distance value. The distance is determined from the distance field value of the routing table. If there is no suitable node for packet forwarding, local repair procedures are executed.  
Figure 4: Local repair flow chart.  
Figure 5 depicts source node S selecting one of the forwarding packets among nodes which locate in its transmission range. First, node S selects A and B nodes having the same direction (direction = 2) to the destination from its routing table. Subsequently, if they have the same hop count value and sequence number, node S then compares the distance field value of each one. As described in Figure 5, node A is finally selected as the packet forwarding node that has a larger distance value than node B.
# Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
## 4 Experimental Evaluation
### Training Model 1  
In this training model, we trained a DNN ([[https://github.com/[PERSON]/Ide_ntifying_earthquakes_in_low_cost_sensors_signals_contaminated_with_vehicular_noise/blob/main/Model_for_Car.ipynb](https://github.com/[PERSON]/Ide_ntifying_earthquakes_in_low_cost_sensors_signals_contaminated_with_vehicular_noise/blob/main/Model_for_Car.ipynb)]([https://github.com/[PERSON]/Ide_ntifying_earthquakes_in_low_cost_sensors_signals_contaminated_with_vehicular_noise/blob/main/Model_for_Car.ipynb](https://github.com/[PERSON]/Ide_ntifying_earthquakes_in_low_cost_sensors_signals_contaminated_with_vehicular_noise/blob/main/Model_for_Car.ipynb)), accessed on 13 September 2023) to identify vehicular signals so that the model can predict when we have a signal from a passing vehicle or pure noise. To achieve that, we had to normalize the data in the range (1, \(-\)1) and additionally balance the data. The balancing was done by undersampling the majority class (noise class) using the method NearMiss (version1), so we have the same amount of data on both classes. After the pre-processing of the data, we created the DNN, which contained four layers (as per Section 3.5). We used one LSTM layer followed by a 'flatten' layer and three dense layers. LSTM layers are widely used for time series predictions as they can learn patterns and correlations within the time series, crucial for earthquake detection. The other layers are simple ones that assist in transforming the data. The data were allocated with 80% for training and 20% for testing. The results of the training are shown in Table 1 while the ROC curve is shown in Figure 6.  
The results, despite being promising, are far from optimal because of the complexity of the data we used for training. Also, signals were labeled manually using the synchronized audio files as ground truth, which means that minor discrepancies between the audio and the file may have occurred.
###### Abstract  
In most quantum key distribution (QKD) systems, a decoy-state protocol is implemented for preventing potential quantum attacks and higher mean photon rates. An optical intensity modulator attenuating the signal intensity is used to implement it in a QKD system adopting a one-way architecture. However, in the case of the plug-and-play (or two-way) architecture, there are technical issues, including random polarization of the input signal pulse and long-term stability. In this study, we propose a method for generating decoy pulses through amplification using an optical amplifier. The proposed scheme operates regardless of the input signal polarization. In addition, a circulator was added to adjust the signal intensity when the signal enters the input and exits the QKD transmitter by monitoring the intensity of the output signal pulse. It also helps to defend against Trojan horse attacks. A test setup for the proof-of-principle experiment was implemented and tested, and it was shown that the system operated stably with a quantum bit error rate (QBER) value of less than 5% over 26 h using a quantum channel (QC) of 25 km.  
plug-and-play quantum key distribution; decoy-state protocol; quantum optics; quantum communication; QKD; information security
# LIransformer: A Transformer-Based Framework for Task Offloading in Vehicular Edge Computing
## 4 Experiment and Analysis
### Parameter Adjustment  
The hyperparameters of the LTransformer are parameters whose values control the deep learning process and determine the values of parameters that the algorithm ends up learning. Different hyperparameters of the LTransformer were analyzed in the experiment. We tested the accuracy of the model by adjusting the value of one parameter, while keeping the training set and other parameters constant. Figure 7 shows the accuracy of the LTransformer model with different dimensions.  
First, experiments were conducted to compare and analyze the embedded dimensions of the LTransformer. The results show that the LTransformer had the best prediction performance with 512 dimensions. This is because the LTransformer goes through pre-training, which reduces the number of dimensions to be fitted. Meanwhile, all of the embeddings are summed together instead of going through concatenation, which also reduces the number of dimensions.  
In addition, the experiment also tested the other hyperparameters, and finally set the other hyperparameters to \(N=6\), \(p=16\), and \(q=4096\), for which the LTransformer had the best prediction performance.  
Furthermore, the experiment tested the prediction time of the LTransformer for a single trajectory datum, which only requires 0.008 s. Therefore, the experimental results  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Memory Consumption in Different Models**} \\  & **GRU** & **LSTM** & **RNN** & **LTransformer** \\ \hline Sample 1 & 5.5033 & 5.5063 & 5.5036 & 3.7122 \\ Sample 2 & 5.5069 & 5.5115 & 5.5032 & 3.7133 \\ Sample 3 & 5.5142 & 5.5253 & 5.5015 & 3.7228 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Memory consumption in different models.  
Figure 7: Comparison of accuracy of the LTransformer with different dimensions.  
indicate that the LTransformer achieves a higher accuracy compared to other baseline algorithms and meets the requirement of real-time prediction, making it more suitable for deployment in VEC. Meanwhile, the experiments also demonstrated that the LTransformer can be applied in traffic control, in-vehicle services, and other VEC applications.
# Sand and Dust Storms: Impact Mitigation
## 5 Policy Measures for SDS Impact Mitigation
### A Policy Framework for Improving SDS Hazard Mitigation  
Following the recent resolutions from the UNGA, UNCCD has been developing a draft policy framework on SDS to assist countries and regions in developing more proactive SDS policies and better predictive mechanisms. Mitigation of SDS hazards involves actions that address both source area mitigation--the on-site issues surrounding wind erosion and sand encroachment--and mitigation of hazards brought by atmospheric dust in suspension and on deposition. The policy framework aims to establish a mode of SDS management with a major focus on disaster risk reduction, as advocated by the Sendai Framework. The ultimate goal is to reduce societal vulnerability to this recurrent hazard, using policy actions under three headings:  
1. post-impact crisis management (emergency response procedures);
2. pre-impact governance programs to strengthen resilience, reduce vulnerability and minimize impacts (mitigation); and
3. preparedness plans and policies.
# A Review of Research on Secure Aggregation for Federated Learning
## 2 Federated Learning
### Applications
#### 2.3.4 Renewable Energy  
In the field of renewable energy, wind and solar power generation heavily rely on sensitive data such as geographical and climatic conditions. Traditional centralized model training often suffers from privacy leakage and data silo issues. Thus, federated learning has emerged as a key approach to preserving data privacy while enabling collaborative model development across multiple parties.  
In [45], deep reinforcement learning is integrated with federated learning to develop an ultrashort-term wind power forecasting method, which effectively protects data privacy while maintaining forecasting accuracy compared to traditional approaches.  
In [46], a hybrid prediction model based on federated learning is proposed to address the limitations of traditional photovoltaic (PV) power prediction methods, particularly their inability to share data and their poor generalization performance. Experimental results show that the prediction accuracy of the proposed model improves by more than 20%.
# Shedding of GPP130 by PC7 and Furin: Potential Implication in Lung Cancer Progression
## 2 Results
### Identification of GPP130 Sites Shed by PC7 and Furin  
To identify GPP130 cleavage sites, we replaced arginines or lysines with alanines within potential consensus cleavage motifs (R/K)-2 Xn-(R/K)\(\downarrow\) using site-directed mutagenesis and assessed the shedding of GPP130 by analyzing its release into the media [1]. Western blot analysis of HEK293 cells overexpressing cDNAs encoding wild type (WT) GPP130, its mutant forms (H67A, R68A, R70A, K73A, R148A, K274A or R277A) or empty vector control, demonstrated that the R68A and R70A mutants are resistant to PC7 shedding and that basic residues variants in the stretch from H67A to K73A are resistant to Furin shedding within the motif H67 RSRLEK73 (S1-site) (Figure 3A,B). Also, while R148A did not affect PC7 or Furin shedding, K274A and R277A variants of GPP130 were resistant to Furin's second shedding (S2-site) (Figure 3A,B). These results suggest that GPP130 is cleaved (S1 site) at HRSR\({}_{70}\)\(\downarrow\) by PC7 and HRSR\({}_{70}\)LEK\({}_{73}\)\(\downarrow\) by Furin but that only Furin cleaves GPP130 (S2 site) at KPTR\({}_{277}\)\(\downarrow\)EV (Figure 3B). To further emphasize that these are the cleavage sites, we generated mutant proteins harboring an optimized PC-recognition site [1] RRRR\({}_{71}\)EL (hereafter called 4 REL).  
WB analysis demonstrated that this motif is indeed the cleavage site because the expression in HEK293 cells of cDNAs coding for the GPP130-4 REL mutant generated a soluble form of the protein secreted into the media, even when co-expressed with an EV control, with the same molecular size as the WT construct co-expressed with PC7 or Furin (Figure 3C). Furthermore, the expression of the R70A mutant prevented cleavage at the S1-site by both PC7 and Furin but still allowed the Furin S2-site cleavage (Figure 3B,C). These data suggest that the S2 cleavage by Furin does not depend on the prior generation of the S1 fragment and that both S1 and S2 fragment formation by Furin could occur independently.  
Figure 2: GPP130 is localized primarily in the TGN when overexpressed in HeLa cells. Immunofluorescence-orescence analysis of permeabilized HeLa cells overexpressing human GPP130-V5 colocalizing (white arrows) with TGN marker (Golgin-97), early endosome marker (EEA1), endoplasmic reticulum marker (calnexin) and plasma membrane marker low-density lipoprotein receptor (LDLR) (non-permeabilized condition) [\(n=3\)]. The images shown are representative of three independent experiments. Scale: 10 \(\upmu\)m.
# A Review of Transformer-Based Approaches for Image Captioning
## 5 Vision-Language Pre-Training in Captioning
### VLP in Image Captioning
#### 5.2.4. VIVO: Visual Vocabulary Pre-Training for Novel Object Captioning  
[PERSON] et al. [40] present a method of pre-training on image-tag pairs rather than image-caption pairs to build a visual vocabulary. The model is then later fine-tuned using image-caption pairs and then tested on a dataset with objects not seen during the image-caption training or fine-tuning. The visual vocabulary enables the model to reasonably generate captions on the objects not seen in the fine-tuning dataset. The visual vocabulary is a joint embedding space where image region features and tags of semantically similar objects are mapped into vectors that are close to each other. The cosine similarity is used to measure the distance between image region and tag.  
The perceived benefit of this is the possibility of making use of many other datasets that only contain images and tags to pre-train for the downstream task of image captioning, given that image-caption datasets may be smaller or fewer and contain less diverse visual objects. Images paired with machine-generated tags can also be used as weak supervision signals. Since the tags for training are not ordered, they employ a Hungarian matching loss  
Figure 11: OSCAR overview. The object tags are used as anchor points to align image regions with word embeddings. The objective function is based on the masked token loss and a contrastive loss.  
with masked tag prediction to conduct the pre-training. The training objective is to predict the masked tags given a bag of image-level tags and image regions.
# A Comprehensive Survey on Knowledge-Defined Networking
## 5 Control Plane
### Interfaces and Sub-Planes Connected with the Controller  
The low-level architecture of KDN with sub-planes and interfaces between the planes [272] is shown in Figure 6.  
Note that, as shown in Figure 6 and as explained in Section 2, the control plane, the management plane, and the knowledge plane can be abstracted to a combined control plane [62]. This combined control plane in KDN additionally generates knowledge and rules, and the controller's actions are influenced by knowledge and rules compared to the controller in SDN.
# Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification
## 2 Materials and Methods
### Pre-Trained Networks Used  
In this work, several of the best-known pre-trained CNN architectures have been used in order to identify the most performing one.  
Moreover, for each architecture used, the various layers were analyzed in order to find the most discriminating one for the classification problem addressed.  
The families of CNN architectures that have been used are the following:  
* **AlexNet**[20]: This network has been trained on 1.3 million high-resolution images in the LSVRC-2010 ImageNet training set into the 1000 different object classes. Rectified Linear Units (ReLU) is used as a non-linear activation function at each layer;
* **googleNET**[21]: This architectures makes use of so-called inception blocks. Inception blocks can be interpreted as a network-in-a-network, where the input is branched into several different convolutional sub-networks, which are concatenated at the end of the block;
* **VGG**[22]: The main idea of this architecture is to increase depth and reduce the dimension of convolution filters. The image is passed through a stack of convolutional (conv.) layers, where are usedfilters with a very small receptive field: 3 \(\times\) 3 (which is the smallest size to capture the notion of left/right, up/down, center);
* **ResNet**[23]: This architecture is currently the best performing deep architecture, being the winner of the ImageNet challenge in 2015. The authors propose deeper CNN for solving the problem of performance degradation due to depth with a residual learning framework. Instead of hoping each few stacked layers directly fit a desired underlying mapping, the authors explicitly let these layers fit a residual mapping;
* **Densenet**[24]: This pre-trained CNN has an architecture that connects each layer with all the others with depth greater than its own (feed forward mode). One of the peculiarities is that the concatenation of the features from the previous layers is made by concatenating them;
* **Sequeerenet**[25]: The authors have built a smaller architecture with three main advantages: smaller CNNs require less communication across servers during distributed training; smaller CNNs require less bandwidth to export a new model from the cloud to an autonomous car; smaller CNNs are more feasible to deploy on FPGAs and other hardware with limited memory.
# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 1 Introduction
### Rise of Electric Vessels and Their Power
It also presents a comparative analysis of various lithium-ion battery chemistries (e.g., NMC, LFP, LCO, and NCA) and emerging technologies like solid-state and lithium-air batteries, with a particular emphasis on marine-specific performance criteria, including safety, thermal stability, and energy density. Moreover, the manuscript highlights practical challenges, such as corrosion in saltwater environments, and explores the potential of wireless charging as a robust, maintenance-friendly solution--an aspect frequently overlooked in reviews of land-based electric vehicle infrastructure.
# Comparative Vulnerability Analysis of Thai and Non-Thai Mobile Banking Applications
## 3 Materials and Methods
### Vulnerability Analysis and Data Collection  
This research employed a comprehensive approach to identify vulnerabilities within the selected mobile banking applications. The methodology involved integrating automated vulnerability assessment tools with a manual examination of the applications' code.  
Three primary SAST tools were used: AndroBugs version 1.0.0, MobSF version 3.7.9 Beta, and QARK version 4.0.0. AndroBugs, a Python-based SAST tool, was employed for its efficiency in rapid vulnerability detection. Its focus on predefined vulnerability patterns allowed for identification of common security weaknesses within the applications. MobSF provided a comprehensive suite of functionalities, including static code analysis, network traffic inspection, and API scanning capabilities. This allowed for a broad initial screening of potential vulnerabilities across various security threats. Finally, QARK, specifically designed for Android applications, offered a deeper analysis of the application's source code, focusing on identifying vulnerabilities related to insecure components, communication channels, and cryptographic implementations.  
The rationale behind using a combination of these SAST tools lies in their complementary functionalities. MobSF's broad scope provided a starting point, while QARK's specialisation in Android applications offered a more targeted analysis. AndroBugs' efficiency helped ensure a more comprehensive assessment. Moreover, by integrating these tools, we enhance the robustness of our findings, ensuring a thorough examination of the vulnerabilities present in the selected mobile banking applications. This multi-faceted approach not only increases the likelihood of identifying critical security weaknesses but also provides a better understanding of the vulnerabilities specific to each application. Consequently, our findings are not only more reliable but also offer actionable insights for developers and financial institutions seeking to improve the security of their mobile banking platforms. This methodological thoroughness highlights the originality and significance of our research in the context of mobile banking security.  
The next step was the data collection process, which involved a systematic analysis of each mobile banking application using the chosen SAST tools. Each application was uploaded to each of the SAST tools' platforms, and the automated analysis features were executed. The generated reports were then reviewed, identifying potential vulnerabilities and their corresponding details. Moreover, the tools provided functionalities for exporting the application's code in a more human-readable format (e.g., Smali code for QARK). This made the subsequent manual code review stage possible.
## 3 Results and Discussion
### Electronic Nose Results
Rt—retention time for Brazil Santos, Guatema Comal, Costa Rica La Pastora, Ethiopia Sidamo, and Peru El Palto Organic coffee beans. The results obtained with the Agrinose showed an effect of the CC roasting method on the sensor responses. The presence of coal tar on the coffee beans resulted in an undesirable aroma profile characterized by increased amounts of aromatic volatile compounds [14]. The aroma in this case was more intense than in the CCR roasting process, hence the higher values of the responses of sensors AS-MLV-P2, TGS2600, TGS2602, TGS2610, TGS2612, and TGS2611. Sensors TGS2620 and TGS2603 responded in the opposite way. This was probably related to their sensitivity in detection of ethanol, which was more abundant in the aroma profile obtained in the CCR roaster (e.g., 2-furanmethanol and 2-buten-1-ol). The intensity of the VOC profile expressed by the \(\Delta\)R/R\({}_{\text{max}}\) parameter was more stable in the case of the CCR roasting method, and its values differed between the examined coffee varieties to a lesser extent [21]. This may indicate the repeatability of the roasting process in this type of roaster. The maximum response value in this case, i.e., \(\Delta\)R/R\({}_{\text{max}}\) = 2.92, was recorded for sensor TGS2602 (high sensitivity to low concentrations of odorous gases such as those generated from waste materials in office and home environments), and the lowest value, \(\Delta\)R/R\({}_{\text{max}}\) = 0.05, was noted for TGS2610 (very high sensitivity to LP gas). These values in the case of the CC roaster were \(\Delta\)R/R\({}_{\text{max}}\) = 3.81 for TGS2602 and \(\Delta\)R/R\({}_{\text{max}}\) = 0.06 for TGS2610, respectively.