esa-sceva/satcom-chunk-collection huggingface dataset:
# Gold Nanofilm-Coated Porous Silicon as Surface-Enhanced Raman Scattering Substrate
## 2 Methods and Materials  
The etching of Si wafer was done as per protocol by [PERSON] et al. with minor modifications [PERSON] et al. (2019). PSi samples were prepared by wet electrochemical etching process at room temperature using p-type, boron-doped one side polished Si wafer in \(\langle 100\rangle\) orientation with a thickness of \(525\pm 25\)\(\upmu\)m and resistivity of 1-10\(\Omega\) cm. Etching was done by placing Si wafers of a specific size (2 \(\times\) 2 cm squares) in a lab-made Teflon cell using a piece of copper foil as a back electrode and a small O-ring to seal the wafer to the cell, exposing an area of approximately 0.8 cm\({}^{2}\) to the solution and applying a constant current density of 50 mA cm\({}^{-2}\) for 20 min in an electrolyte solution of 1:2 volume ratio of hydrofluoric acid (HF) (48 to 51% in water, Acros Organics, USA) and ethanol (99.5%, Shimakyu's Pure Chemicals Co., Japan). A platinum wire was immersed in the solution as a counter electrode. No further chemical or thermal treatment was carried out after etching. The cell and sample were then washed with ethanol and dried under a stream of nitrogen.  
The prepared PSi substrate and the atomically flat Si wafer surface were Au-sputtered by PVD technique, which was first developed by [PERSON] et al. in 2009 [PERSON] et al. (2009) and herein this study, the sputtering procedure was done as per protocol described in the literature with minor modifications [PERSON] et al. (2019). A layer of Au thin film was therefore coated onto PSi and Si surfaces by PVD of pure gold (99.9999%, Sigma-Aldrich, USA) using IJUHV E-400 electron-beam (e-beam) technology. The Au deposition was accomplished by maintaining a vacuum level of \(10^{-7}\) torr with deposition rate 0.6 A per second and an argon flow equal to 50 sccm. Au nanofilm of 5, 7, and 10 nm thickness onto both PSi and Si surface were therefore achieved by regulating the deposition time.  
Aqueous solution of Raman tag was prepared from R6G powder (99%, Acros Organics, USA) and used as a target analyte at a concentration of \(10^{-2}\) and \(10^{-6}\) M. The SERS substrates were prepared by applying the specific amount of R6G solution on the respective substrates and air dried. SERS measurement was performed by a Micro-Raman system HORIBA Jobin Yvon (LabRAM HR800) using a helium-neon laser as the excitation source operating at 633 nm. A 40x objective lens was used to focus the laser spot on the substrate. Raman scattering was collected using a charge coupled device (CCD) at a spectral resolution of 1 cm\({}^{-1}\). All Raman data were collected with an integration time of a few tens of seconds and the intensities were calculated with the unit of cm\({}^{-1}\).
## 6 Discussion
### Challenges and Future Directions
#### 6.3.4 Missing Data Problem  
When using RNNs for traffic prediction, data integrity is crucial for model performance. Traffic data are typically collected from sensors, cameras, or GPS devices, and these data collection tools may result in data missing due to failures, communication interruptions, or processing errors. These missing data can decrease model training and prediction accuracy, as RNNs rely on complete time series data to learn the dynamic changes in traffic flow.  
In future research, to address the issue of missing data, researchers need to develop and apply effective data imputation techniques, such as estimating missing values using historical data trends or employing machine learning methods to fill in missing data automatically. However, ensuring the accuracy and reliability of data imputation methods remains a challenge. Additionally, solutions can be found through techniques such as few-shot learning or transfer learning. Few-shot learning aims to enable models to quickly learn and adapt to new tasks with a small amount of data and is particularly useful in scenarios with scarce data. By pre-training models on large datasets to learn general feature representations and then transferring this knowledge to specific traffic prediction tasks, accuracy can be improved in cases of limited data availability. Furthermore, data augmentation techniques are also a strategy to address missing data, as they can increase the diversity of training samples and reduce the risk of overfitting.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADAS & Advanced Driver-Assistance Systems \\ AI & Artificial Intelligence \\ BTP & Basic Transport Protocol \\ CA & Cooperative Awareness \\ CAM & Cooperative Awareness Message \\ CAMINO & vehiCuIAr coMmunication maNagement framewOrk \\ CCAM & Cooperative, Connected and Automated Mobility \\ CDF & Cumulative Distribution Function \\ C-ITS & Cooperative Intelligent Transport Systems \\ COTS & Commercial Off-the-Shelf \\ CSMA-CA & Carrier-Sense Multiple Access with Collision Avoidance \\ DEN & Decentralized Environment Notification \\ DENM & Decentralized Environment Notification Message \\ DSRC & Dedicated Short Range Communication \\ DUST & Distributed Uniform Streaming \\ EEBL & Electronic Emergency Brake Light \\ GNSS & Global Navigation Satellite System \\ GPSd & GPS deamom \\ HMI & Human-Machine Interface \\ ITS & Intelligent Transport Systems \\ IVI & Infrastructure to Vehicle Information \\ IVIM & Infrastructure to Vehicle Information Message \\ IVS & In-Vehicle Signage \\ KPI & Key Performance Indicator \\ MEC & Mobile Edge Computing \\ ML & Machine Learning \\ NTP & Network Time Protocol \\ OBU & On Board Unit \\ OEM & Original Equipment Manufacturer \\ PDR & Packet Delivery Rate \\ QoS & Quality of Service \\ RAT & Radio Access Technology \\ RHW & Road Hazard Warning \\ RSU & Road Side Unit \\ \end{tabular}
# Research on the Lossless Data Compression System of the Argo Buoy Based on BiLSTM-MHSA-MLP
## 3 Data Compression and Decompression
### Profile Data Preprocessing  
First, to facilitate subsequent reading and training, the collected profile data in the txt format is converted into an npy file. The raw profile data is read character by character, and each unique character is assigned a unique identifier. Different characters are given different identifiers, and the total number of identifiers determines the size of the alphabet. Two dictionaries are also generated, one mapping characters to identifiers and another mapping identifiers to characters. These dictionaries will be used for character matching  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter Name** & **Parameter Size** \\ \hline batch\_size & 64 \\ timesteps & 64 \\ emb\_size & 32 \\ lstm\_size & 128 \\ h & 8 \\ vocab\_size & 14 \\ block\_len & 12 \\ hdim & 1024 \\ \hline \end{tabular}
\end{table}
Table 1: Probabilistic model parameters.  
in later stages. Using the constructed dictionaries, the raw profile data are then converted into an integer-based npy file, one character at a time.  
Second, the npy file is divided into a 2D training array using a sliding window and the introduction of timesteps. The file is split into features X and labels Y. In this paper, the probability of the timesteps+1 st character is estimated by the previous timestep characters. Assuming the character sequence of the training data npy file is \(S^{N}=\{S_{1},S_{2},\ldots,S_{N}\}\), and that the file has N characters, the character stream is first read from the npy file and reshaped into a 1D character vector. Then, using a sliding window and time steps, the 1D vector is split into an overlapping segmented matrix of shape (n, timesteps + 1), as shown in Figure 4.  
The first timestep columns of the overlapping segmentation matrix are used as feature X, and the last column is used as label Y. The number of rows n of the overlapping segmentation matrix is the number of training samples, and the dimensions of feature X of each training sample are the timesteps.  
Finally, the obtained X and Y are used to generate a dataset through DataLoader. Data can be obtained batch by batch later to speed up model training.
# MZM Optimization of PAM-4 Transmission in Data Center Interconnect
## 4 Lab Measurements
### Experimental Results
In this experiment, the two inner power levels (1 and 2) of the 4.08 V bias point (red curve) was configured to even power levels spacing such that both curves are under identical power levels spacing and only the OMAouter improvement is introduced. It is shown that the 4.08 V bias point outperform the quadrature bias point by \(\sim\)1 dB. This value of \(\sim\)1 dB is in a good agreement with the estimated improvement of the OMAouter introduced by the 4.08 V (uneven) PAM-4 case discussed in Figure 4a.
## 4 Results and Discussion
### Discussion of the Proposed Work's Scalability, Implications, and Insights  
The proposed system is designed to handle large-scale high-speed 5G networks and has been validated through NS-3 simulations, demonstrating high accuracy and decision rates. It has a setup parameter of 50 mobile users and 8 access points spread across 500 m \(\times\) 500 m, which can be extrapolated into real-world scenarios. The system integrates blockchain technology for secure, decentralized authentication and authorization and is energy-efficient for sustainable operation. It adapts to evolving threats using AI and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **[PERSON]’s Algorithm** & **Mud Ring Algorithm (Proposed)** \\ \hline Bandwidth (Mbps) & 52.4679 & 69.325 \\ \hline Jitter (ms) & 0.3501 & 0.241 \\ \hline Delay (ms) & 1 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of MRA with [PERSON]’s shortest path algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Evaluation Metrics**} \\ \cline{2-4}  & **Detection Rate (\%)** & **Authentication Time (s)** & **Throughput (Mbps)** \\ \hline ML-IDP [17] & 94.08 & 21.9 & 384 \\ \hline CNN [22] & 93 & 20 & 276 \\ \hline SEE-BF [21] & 92.3 & 19.65 & 386 \\ \hline BSHNN (Proposed) & 96.1 & 16.2 & 468 \\ \hline \hline \end{tabular}
\end{table}
Table 8: State-of-the-art comparison.  
blockchain, ensuring effectiveness against new security challenges. Future steps include pilot testing in real-world 5G environments.  
NS-3 is an abstract model of network protocols and components, which may not accurately represent real-world hardware and real-time conditions. It is effective for simulating network behaviour and performance but may oversimplify aspects of network operation and hardware limitations. Hardware-in-the-Loop (HIL) allows for interactive testing with actual hardware, providing a closer approximation of real-time behaviour. NS-3 can simulate large-scale networks with numerous nodes and complex topologies but may be limited by physical hardware and the cost of scaling up to large or complex networks. HIL is more suited for detailed testing of specific components or subsystems due to physical constraints and costs.  
This research integrates user authentication, registration, and key generation into the data acquisition layer. The switch layer manages tasks such as switch selection, flow rule security, and hashing. In the domain control layer, activities involve packet analysis and classification. The smart control layer focuses on efficient packet transmission. The proposed system, incorporating blockchain and advanced neural networks, enhances 5G network security by addressing resource constraints and demonstrating superior attack mitigation, contributing to network resilience in a connected and evolving era.
## 5 Experiments and Results
### Comparison with Other Methodologies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Paper** & **Methodology** & **ACC (\%)** & **AUC** \\ \hline \multirow{3}{*}{2016} & \multirow{3}{*}{[PERSON] et al. [11]} & Lower-order and higher-order histogram features & \multirow{3}{*}{87.4 \%} & \multirow{3}{*}{0.968} \\  & & Local binary patterns & & \\  & & Gray-level co-occurrence matrix & & \\ \hline
2017 & [PERSON] et al. [12] & Stain normalization and ConvNet & 79.66\% & - \\ \hline
2017 & [PERSON] et al. [31] & Convolutional neural networks bilinear (BCNN) & 92.6\% & 0.985 \\ \hline
2017 & [PERSON] et al. [13] & Variation of LBP with point-to-average thresholding & 93.4 \% & - \\ \hline
2017 & [PERSON] et al. [10] & Cyclical learning rates & 82.2\% & - \\ \hline
2018 & [PERSON] et al. [32] & Reduction strategy based on the cross-correlation & 84.8\% & - \\ \hline
2019 & [PERSON] et al. [7] & Polynomial learning rate & 94.54\% & - \\ \hline
2020 & [PERSON] et al. [14] & Reduced Deep Features & 95.40\% & 0.9961 \\  & & Fine-Tuning & 95.02\% & 0.9976 \\ \hline
2020 & [PERSON] et al. [8] & Fixed learning rate & 92.8\% & 0.97 \\ \hline
2021 & [PERSON] et al. [9] & Dynamic learning rates & 91.84\% & 0.97 \\ \hline  & Proposed method & Transfer Learning & Test: 94.4\% & 0.98 \\ \hline  & Proposed method & Schedule Learning Rate (Polynomial decay) & Test: 95.3\% & 0.98 \\ \hline  & Proposed method & Cyclical Learning Rate (Triangular) & Test: 96.4\% & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of related work available in the literature on the study of the learning rate and multi-class data-set classification. Figure 10: Results obtained by applying discriminative fine tuning with a decreasing LR in epochs. The confusion matrix, the Receiver Operating Characteristic Curve (ROC curve) and the Area Under the Curve (AUC), the confidence matrix and, in the lower right corner, the accuracy and the training loss function versus epochs are shown. In this example, a larger number of epochs has been considered to appreciate the graph.
## 4 Design of Proposed Algorithm
### System Architecture  
The MTD3 CO algorithm makes full use of the MEC system capabilities by network training the algorithm on both mobile devices and edge servers. It splits the training process into two loops, with the inner-loop training for task-specific policies and the outer-loop training for meta-policies, with the inner-loop training performed on the mobile device and the outer-loop training performed on the edge server. Figure 2 shows a task offloading system that includes a mobile device and an edge server.  
Because mobile devices have a variety of different devices to perform application tasks with different foci, the DAGs used to describe the applications will change. Firstly, the inner-loop learner must download the meta-strategy from the outer-loop learner, initialize the strategy's parameters to the inner-loop learner's network, and then try some offloading with the specific application for which the device is responsible. Secondly, on the basis of these offloading experiences, the inner-loop learner's network is trained to get its specific offloading strategy. Lastly, the edge server receives the inner-loop learner strategy for this device. This completes the inner-loop learning.  
At the level of the edge server, it is responsible for collecting the inner-loop training experience of all inner-loop learners and extracting their shared characteristics. Using the collected data, the edge server trains the outer-loop learner network, gets the new meta-strategy, and performs the next round of training. Once a more stable meta-strategy has been obtained, outer-loop training can be stopped. The inner-loop learner can use the learning experience contained in this meta-strategy to quickly learn the specific offloading strategy for the task it is responsible for, and, because the meta-strategy contains experience shared by all inner-loop learners, only a few loops are required to achieve better results. With this fast iterative learning, the algorithm can adapt to different DAG tasks and environments.
## 1 Introduction
The objective of this study is to benchmark AES encryption performance across various CPU and GPU hardware models, highlighting the computational benefits provided by GPU acceleration. Additionally, we propose an optimized hybrid CPU-GPU workflow designed to balance workload distribution efficiently, tailored to specific data sizes and operational scenarios. To achieve this, we first formulate the research problem and identify key computational bottlenecks in AES encryption (Section 2.1). We then analyze CPU and GPU architectures (Section 2.2) and describe the AES-128 algorithm, emphasizing CBC and CTR modes suitable for parallel computing (Section 2.3). Next, we introduce our proposed conceptual hybrid CPU-GPU workflow and the structured experimental approach adopted for its evaluation (Section 2.4). Subsequently, we present detailed descriptions of five hybrid encryption methods--data shuffling, I/O bottleneck reduction, adaptive workload distribution, ping-pong processing, and bit-slicing parallelization (Section 2.5). Computational models and analyses applied to quantitatively evaluate performance improvements, resource utilization, and energy efficiency are detailed in (Section 2.6). Finally, we describe the experimental environment, hardware specifications, and software tools employed to ensure reproducibility and validation of our results (Section 2.7). The outcomes of our comprehensive evaluations demonstrate the effectiveness of the hybrid CPU-GPU encryption tion methods, offering valuable insights and practical guidelines for efficient large-scale data encryption.
## 2 Food Safety Management Systems around the World
### Managerial Systematic Approach to Food Safety
#### 2.1.1 Prerequisites: FSMS Foundation in Developing and Developed Countries  
Various studies highlight the significance of prerequisite programs as the foundation of HACCP-based food safety management systems in both developing and developed countries. These programs play a crucial role in preventing food contamination throughout the entire food production process, encompassing aspects such as food handling, storage, hygiene, sanitation, pest control, training, and maintenance (Figure 3) [6; 7; 34]. Moreover, organizational Food Safety Culture Works in Symbiosis with PRPs and HACCPs.  
Figure 2: Managerial Systematic Approach to Food Safety Using HACCP and PRPs (Adapted from What is a Management System? Available online: [[https://images.app.goo.gl/Aqez5](https://images.app.goo.gl/Aqez5) sSfpbvjQqmK7]([https://images.app.goo.gl/Aqez5](https://images.app.goo.gl/Aqez5) sSfpbvjQqmK7) (accessed 20 June 2021). [PERSON] et al. 2021 [6; 33].  
Developed countries, such as the United States and the European Union, have established guidelines and regulations for prerequisite programs through regulatory agencies like the FDA and EFSA. In contrast, implementing and complying with these programs may pose challenges in developing countries due to limited resources, cultural factors, and lack of knowledge and awareness.  
Nevertheless, studies demonstrate that implementing prerequisite programs in developing countries can lead to significant improvements in food safety and a reduction in foodborne illnesses. For instance, a study conducted in Ghana revealed that the implementation of a food safety management system incorporating prerequisite programs resulted in a notable decrease in foodborne illnesses. Similarly, a study conducted in China demonstrated that the implementation of prerequisite programs in food processing plants contributed to a reduction in the prevalence of microbial contaminants in food products, thus mitigating the risk of foodborne illnesses [1; 11; 34].  
Overall, prerequisite programs play a critical role in preventing food contamination and ensuring food safety. Their effectiveness in both developing and developed countries may vary depending on the extent of implementation and adherence to regulatory guidelines and standards.
## 2 Materials and Methods
### LC-MS Analysis of Phenolic Compounds  
Qualitative and quantitative (%) characterization of herbal aqueous extracts were performed on a Shimadzu LC-MS-2010A equipped with a LC-10 ADvp binary pump, a DGU-14A degasser, a SIL-10 ADvp auto sampler, a SPD-M10 Avp Photo Diode Array Detector (DAD), and a quadrupole mass detector (MSD) with an electron spray ion source (MS-ESI, ElectroSpray Ionization, negative mode). Separation was achieved using a reversed phase column Supelco Discovery HS C18 (250 mm \(\times\) 4 mm, 5 \(\upmu\)m) (Bellefonte, PA, USA) at 25 \({}^{\circ}\)C. The mobile phase consisted of water +0.1% formic acid (A) and methanol (B), that were used in the following gradient elution: 75% A, 25% B; 2 min 75% A, 25% B; 40 min 10% A, 90% B; 45 min 10% A, 90% B; 50 min 75% A, 25% B; 60 min 75% A, 25% B (60 min duration). The flow rate was set to 0.4 mL/min and the detection wavelengths to 260, 280, and 330 nm. The injection volume was 20 \(\upmu\)L and was performed for each of the three repetitions. The processing of analysis and chromatographs was carried out using Lab Solutions software (Shimatzu version 3.40.307). The relative percentages of the individual phenolic components were qualitatively determined from relative peak areas (%). Phenolic compounds were identified according to the corresponding spectral characteristics: molecular ion, mass spectra, characteristic fragmentation, and retention time. Identification of most compounds in the samples was achieved by comparison of their spectral characteristics with the standard compounds, while some of them were identified tentatively.
## 3 Integrity Monitoring Method of Space-Ground Integration  
One of the objectives of the next-generation BeiDou system would be providing global high-integrity navigation services for civil aviation (e.g., supporting CAT-I operations) and other emerging applications such as automotive and urban air mobility. To achieve this goal, this section proposes an integrity monitoring method of space-ground integration.