esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hyperledger Fabric-Based System Framework for Healthcare Data Management
## 5 Discussion  
The blockchain-based healthcare data management scheme that is presented in this paper focuses on privacy preservation and interoperability. This solution is implemented through the privacy-preserving mechanism of the Hyperledger Fabric framework and the smart contract for hierarchical access control. Moreover, due to the advantages of Hyperledger Fabric, such as enterprise-level distributed ledger technology; pluggable functions (e.g., consensus algorithms, components, member management services, etc. ); modular; and universal design, this blockchain development platform satisfies a large number of use cases in the healthcare industry. Compared with the mainstream blockchain development platforms, especially enterprise-level frameworks that are based on Ethereum, such as Hyperledger Besu and Quorum, although the cost of deploying and maintaining the Hyperledger Fabric network is higher, companies within large alliances are more inclined to use consortium chains. In the healthcare industry, due to the need to transmit valuable information among hospitals, research institutions, medical groups, large pharmaceutical companies, and insurance companies, the adoption of consortium chains based on the Hyperledger Fabric framework become a more suitable choice. Moreover, due to the high maintenance cost of the Hyperledger Fabric network, it also raises barriers to
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
###### Abstract  
In a frequency hopping spread spectrum (FHSS) network, the hopping pattern plays an important role in user authentication at the physical layer. However, recently, it has been possible to trace the hopping pattern through a blind estimation method for frequency hopping (FH) signals. If the hopping pattern can be reproduced, the attacker can imitate the FH signal and send the fake data to the FHSS system. To prevent this situation, a non-replicable authentication system that targets the physical layer of an FHSS network is required. In this study, a radio frequency fingerprinting-based emitter identification method targeting FH signals was proposed. A signal fingerprint (SF) was extracted and transformed into a spectrogram representing the time-frequency behavior of the SF. This spectrogram was trained on a deep inception network-based classifier, and an ensemble approach utilizing the multimodality of the SFs was applied. A detection algorithm was applied to the output vectors of the ensemble classifier for attacker detection. The results showed that the SF spectrogram can be effectively utilized to identify the emitter with 97% accuracy, and the output vectors of the classifier can be effectively utilized to detect the attacker with an area under the receiver operating characteristic curve of 0.99.  
frequency hopping signals; radio frequency fingerprinting; emitter identification; outlier detection; physical layer security; inception block; deep learning classifier +
Footnote †: 2021: 11, 10812. [[https://doi.org/10.3390/app112210812](https://doi.org/10.3390/app112210812)]([https://doi.org/10.3390/app112210812](https://doi.org/10.3390/app112210812))
# Landmark Generation in Visual Place Recognition Using Multi-Scale Sliding Window for Robotics
## 3 Proposed Method of Landmark Generation via MSW
The bottom right four images show the examples of landmarks extracted by MSW with four different scales. Figure 2: Overview of our MSW based visual place recognition method. First, 100 landmarks are extracted from different scales by MSW strategy. Next, 1024-dimensional CNN features are computed for representing the landmarks before the bi-directional nearest neighbor search conducted to match pairwise landmarks. Thus, the best pairwise match is obtained by pooling all the pairwise matching results for similarity measure. With the landmarks produced by Algorithm 1, visual place recognition can proceed as shown in Figure 2. For completeness of the presentation, we briefly describe the system in Figure 2, which is based on the classical method in [12]. From each landmark of either a query or a reference image, we compute a 64,896-dimensional CNN feature vector using the third convolutional layer (conv3) of the pre-trained AlexNet [20]. Next, this vector is dimension-reduced to 1024 by Gaussian random projection [35]. To match images, their landmarks are matched by nearest neighbor search on cosine distance between the landmark feature vectors, and only reciprocal matches are accepted. The overall similarity between two images is calculated by [12]:  
\[Sim=\frac{1}{\sqrt{n_{a}\cdot n_{b}}}\sum_{ij}1-(d_{ij}\cdot shape_{i,j}) \tag{2}\]  
where \(d_{ij}\) is the cosine distance between two matched landmarks; \(n_{a}\) and \(n_{b}\) are the number of extracted landmarks in two images, including the non-matched ones; and \(shape_{i,j}\) is defined as shape similarity calculated as [12]:  
\[shape_{i,j}=exp\Big{(}\frac{1}{2}\big{(}\frac{|w_{i}-w_{j}|}{max(w_{i},w_{j}) }+\frac{|h_{i}-h_{j}|}{max(h_{i},h_{j})}\big{)}\Big{)} \tag{3}\]  
to penalize shape incompatibility between two landmarks. The reference image with the highest similarity score is returned as a potential matching place, subject to a verification step before the final decision is rendered.
# A Hybrid SWIM Data Naming Scheme Based on TLC Structure
## 4 Experiment and Result Analysis
### Comparative Analysis  
#### 4.4.1 Query Time Analysis of Different Naming Schemes  
To obtain the comparison results of the average query time based on TLC structure and hierarchical naming and flat naming under different naming levels. First, each information element in the collected data set is divided into the form of TLC, and different layers are divided into one layer, two layers, three layers, and up to six layers. Under the division of different naming layers, the average query time comparison results based on TLC structure and hierarchical naming and flat naming are shown in Figure 12.  
Figure 11: Comparison of query time for large-scale content names.  
Figure 12: Comparison of the average query time under different numbers of layers.  
As can be seen from Figure 12, when the number of layers is low (generally less than three layers), the query time of the three naming methods is almost the same. But when the number of layers is greater than two layers, the three naming methods begin to change. The query time of the naming method based on the TLC structure is significantly shorter than that of the flat naming method and hierarchical naming method. Through the above comparative experiments, it is proved that the naming method based on the TLC structure has a great advantage in query time compared with hierarchical naming and flat naming.
## 4 Results
### Comparison with Individual Task Results  
Overall Improved Performance: When combining all signals, the model showed better precision, recall and F1 score metrics compared to most individual tasks. This suggested that signals from multiple tasks contributed complementarily to improving classification between fallers and non-fallers. Reduced Variability in Metrics: The results for individual tasks showed more variability in metrics, with some tasks showing high precision but low recall or low specificity. By combining all signals, these discrepancies appeared to be reduced, suggesting that using all signals together provided a more balanced and stable model. Higher Specificity and Recall Together: Individual tasks showed challenges with either specificity or recall in several cases. For example, \"Sitting to Standing\" had a high recall (0.9610) but low specificity (0.2672), while \"Correction with a Step Back\" had a recall of 0.8248 and excellent specificity of 0.9371. By combining all signals, the model achieved high specificity (0.8924) and recall (0.8793) simultaneously, which was a significant improvement in overall performance.  
The radar chart (Figure 5) illustrates the classification performance metrics (accuracy, precision, recall, F1 score, specificity, and balanced accuracy) for each task in the Mini-BESTest, grouped by task type (Postural Control, Reactive Control, Sensory Orientation, Dynamic Gait, and All Combined). Tasks within each group share a common color, with different line styles distinguishing individual tasks. The radial axis represents the performance of each task across the six metrics, allowing for a clear comparison of their effectiveness in distinguishing fallers from non-fallers.
## 6 Discussion  
In medical scenarios, communication difficulties between doctors and patients often occur, leading to the inaccurate transmission of information. Improving communication efficiency and service quality based on artificial intelligence has become a key area of focus. Our cognitive approach aims to address the uncertainty arising from collective semantic spaces in interactions with individual users. We refer to the entities involved in communication as cognitive agents. Human-machine bidirectional semantic communication can be understood as a process driven by the specific purposes of the cognitive agents. In this process, DIKWP content is interlinked across different cognitive agents. The target cognitive agent adjusts or forms new cognitive structures through semantic associations, probabilistic confirmation, and knowledge reasoning. This newly formed cognitive structure creates new pathways within the semantic space of the cognitive agent, facilitating the transmission and understanding of purposes.  
In a pediatric consultation case, effective communication often requires the involvement of the doctor, the child, and the parents due to the patient's young age. Using infants as an example helps illustrate how the semantic space gradually forms in a non-verbal environment. Infants receive signals through their senses and gradually form a network of semantic associations, which later become understandable content. This process corresponds to infant patients who cannot communicate directly with doctors. Additionally, the patient's parents are another cognitive subject involved in the interaction, but not the patient themselves. There is generally individual variation between the parents and the doctor. This variation corresponds to the DIKWP model's 3-No problem. Our approach addresses and recognizes these 3-No issues, forming a cognitive-semantic-conceptual framework. The concept space is a higher-level product generated by the abstraction and classification of the semantic space, such as written words and language. We classify the relationships between data, information, knowledge, wisdom, and purpose concepts within the concept space through cognitive processing and assign semantic elements to them.
# Is One Teacher Model Enough to Transfer Knowledge to a Student Model?
## 1 Introduction  
Nowadays, a large part of the research on transfer learning is applied to neural models [1, 2, 3, 4, 5, 6], and most of this research tries to solve the problem of the transfer of learning from one already trained neural network to another. It is rare to find research works in the literature that try to transfer knowledge from more than one trained model to a new model that needs to be trained.  
The best transfer learning results are obtained when the learning is transferred from the same network architecture trained on different datasets [7]: it is common to start the training of classification models from a pre-trained model on ImageNet [8]. Only a small part of the research tries to obtain better results with a less powerful network--and many of these only use specific network structures [2] that one must find by experimentation.  
In particular, [PERSON] [2] proposed to iterate over the epochs two times: the first time to transfer the learning using the distillation loss to obtain a pre-trained student; and a second time to train the student with classical cross-entropy. Our proposal instead also improves the accuracy of weaker neural networks.  
The transfer learning is today intended as one to one: one teacher and one student. Our proposal instead shows a method to transfer the learning from multiple teachers to one student.  
Thus, the primary contributions of this paper are:  
* We propose an approach to simultaneously transfer learning from multiple teachers to another neural network: more than one neural network can simultaneously transfer its learning to another neural network;* Improve [PERSON]'s idea [2] by reducing to only one cycle of training instead of two and by effectively transferring the learning between any kind of network.
## 3 Conclusions and Future Perspectives
This is especially important if the off-target effects are likely to occur in organisms other than the target pest. To address this issue, in silico analysis of sequence homology between all possible nucleotide sequences in the designed dsRNAs and the genomic or transcriptomic sequences of relevant organisms is required [110; 111]. In this regard, because it is impossible that the genome sequences of all organisms on Earth will be revealed, off-target effects can never be completely ruled out. Nevertheless, it is essential to carefully design dsRNA sequences to avoid unintended RNAi effects on non-targeted organisms using bioinformatic analyses of the latest available gene databases. This review has principally summarized the first technical barriers to the practical application of RNAi-based products. However, wider issues must also be considered. To realize the benefits of RNAi-based products, communication among agricultural product consumers, RNAi-based product users (farmers), and producers of RNA pesticides, in cooperation with public institutions, will be essential to ensure accurate information sharing and mutual understanding based on scientific evidence of RNAi-based products. Then, appropriate management, supervision, and legislation will be necessary, and RNAi-based products must go through appropriate safety evaluation and approval processes. Sprayed or externally applied dsRNA-based pesticides are under development, but there are currently no legal regulations specific to such RNAi-based pesticides [112]. There are no specific guidance documents defining the data requirements for the approval of dsRNA-based pesticides in the European Union or the United States. The first review and recommendations on this matter have been presented by the Organisation for Economic Co-operation and Development (OECD) [113]. Once low-cost dsRNA can be procured, various RNAi-based products can be applied practically not only in agriculture and aquaculture, but also in the control of sanitary pests such as cockroaches, fire ants, termites, and vectors of infectious disease [114, 115, 116, 117, 118]. In this way, sustainable RNAi-based products are expected to have a beneficial effect on the lives of humankind. Climate change will promote the propagation and spread of intimidating crop pests. Environmentally friendly pest control methods that can deal with the potential food crisis caused by crop pests are extremely important, and RNAi-based products are expected to play a critical role in addressing this challenge. Conceptualization, S.H. and H.Y. ; writing--original draft preparation, S.H. and H.Y. ; writing--review and editing, S.H. and H.Y. All authors have read and agreed to the published version of the manuscript. There was no special funding for writing this review. We thank [PERSON], [PERSON], from Edanz for editing a draft of this manuscript. The authors declare no conflict of interest.
## 4 Experimental Results and Discussion
### Experimental Results and Analysis
#### 4.4.3 Other Experiments  
To further analyze the model performance, we also conducted supplementary experiments on CMRC2018 and DuReader-checklist datasets. Figure 4 shows the performance plot of F1 score and EM value in the two datasets as the epoch change. It can be seen from the figure that, when the epoch is 5, F1 scores in the two datasets reach 87.51% and 87.39%, and the EM values reach 65.52% and 74.45%, respectively. After five iterations, the model performance gradually tends to be stable, Therefore, through the performance curve, it can be concluded that the model in this paper can converge to the optimal result with only a few iterations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **EM**\% & **F1**/\% \\ \hline Bert-base(Chinese) & 70.57 & 84.71 \\ AT Reader & 73.32 & 87.11 \\ AT-CRF Reader & 74.45 & 87.39 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The effect of CRF on the AT-CRF Reader.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **EM**/\% & **F1**/\% \\ \hline Bert-base(Chinese) & 61.07 & 84.86 \\ Ernie & 62.91 & 86.52 \\ RoBERTa-wwm-ext & 63.87 & 86.36 \\ ELECTRA & 64.43 & 86.73 \\ MacBERT-Chinese & 65.34 & 86.84 \\ ChineseBERT & 65.83 & 88.09 \\ AT-CRF Reader & 65.52 & 87.51 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The performance on the CMRC2018 dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **EM**/\% & **F1**/\% \\ \hline Bert-base(Chinese) & 70.57 & 84.71 \\ Remie & 71.48 & 85.77 \\ [PERSON] & 73.11 & 86.58 \\ ELECTRA & 73.82 & 86.83 \\ MacBERT-Chinese & 73.96 & 87.14 \\ ChineseBERT & 74.62 & 87.76 \\ AT-CRF Reader & 74.45 & 87.39 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance of the DuReader-checklist dataset.  
In addition, we also designed a set of experiments to test the impact of passage lengths on the model. We chose an interval of (50, 550) with a step of 50 for the experiment, and plotted the curve of F1 score and EM value. The final results are shown in Figures 5 and 6. From Figure 5, it can be concluded that the model performs best on the CMRC2018 dataset with the highest F1 scores and EM values when the length of the passage is within the interval of 300-350. The same conclusion can be drawn from Figure 6 that the overall performance is optimal on the DuReader-checklist dataset when the model input passage length is within the 300-350 interval, with the highest F1 scores and EM values. In addition, the relative gap between the F1 scores and EM values of the two models, AT Reader and AT-CRF Reader, on the DuReader-checklist dataset is larger compared to that on the CMRC2018 dataset, which indicates that the model proposed in this paper is more applicable to the dataset with unanswerable questions.  
Figure 4: F1 scores and EM values on the two datasets under different training epochs.  
Figure 5: The effect of input length on model performance in the CMRC2018 dataset. In this experiment, only the input length parameter is changed, and other experimental parameters are kept constant.
## 1 Introduction
### FiWi Network Architecture
This results in significant cost reduction for the implementations at the physical and at the link level; (2) The mathematical optimization model considers the deployment of the FiWi network under planning and scalability over time and space; (3) The model provides a near optimal route map with georeferenced coordinates. This model uses the haversine equation for the calculation of distances. Moreover, the model is able to provide accurate data about the topology of the network and the roadmap for the hybrid near optimal communication path for the deployment in AMI; (4) The heuristic providesanswers to the challenges of UDAPs' placement, identification of target groups, routing, capacity, coverage and reduction of the end to end delay. Hereinafter, the paper is structured as follows. Section 2 describes the need to update the concept of a \"conventional electric network\" with the purpose of migrating to the concept of a smart grid, and discusses the importance of AMI for optimal deployment of microgrids. Section 3 sets out the approach to the problem. Section 4 presents the results and simulations. Finally, in Section 5, conclusions are drawn.
# Fault ride-Through Control Strategy for Variable Speed Pumped Storage Unit with Full-Size Converter
## 5 Simulation Verification
### Simulation Characteristics of LVRT  
Firstly, the LVRT characteristics are simulated. The system voltage signal command is shown in Figure 11. The simulation results under low voltage faults in the power generation and pump conditions without applying fault ride-through control measures are shown in Figure 12.  
\begin{table}
\begin{tabular}{c c} \hline
**Pumped Storage Unit Parameters** & **Value** \\ \hline Rated voltage & 3.3 kV \\ \hline Rated capacity & 5.67 MVA \\ \hline Synchronous reactance (d-axis) & 1.5 pu \\ \hline Synchronous reactance (q-axis) & 1.5 pu \\ \hline Stator Resistance & 0.054 pu \\ \hline Nominal frequency & 50 Hz \\ \hline \end{tabular}
\end{table}
Table 1: The simulation parameters of the pumped storage unit.  
\begin{table}
\begin{tabular}{c c c} \hline
**Chapter** & **Simulation Content** & **The Experimental Steps** \\ \hline
5.1 & Low-voltage fault & Without adopting the LVRT strategy, the system voltage drops to 0.5 pu. \\  & characteristics & Observe the simulation results under different operating conditions. \\ \hline
5.2 & LVRT strategy validation & Adopting the LVRT strategy, the system voltage drops to 0.15 pu. Observe the simulation results under different operating conditions. \\ \hline
5.3 & High-voltage fault & The system voltage rises to 1.35 pu. \\  & characteristics and HVRT & Observe the simulation results under different operating conditions with and without HVRT strategy. \\ \hline \end{tabular}
\end{table}
Table 3: Simulation experiment design.  
Figure 11: Command for voltage changes in the power grid.  
When using the fast speed mode for power generation, the active power on the grid side significantly decreases. Due to the inability to decouple reactive power from active power during transient processes, the reactive power briefly increases before returning to zero. The power on the rotor side slightly decreases and the DC voltage increases significantly, with a slight change in the rotational speed. When using the fast power mode for power generation, the characteristics of active and reactive power changes on the generator side and the unit speed are consistent, but the magnitude of the changes varies due to differences in control modes.  
For clear comparison, the power direction was not distinguished under pumping conditions. Under pumping conditions, the active power on the grid side decreases significantly and oscillates. The active power waveform on the generator side is similar to that on the grid side, while the unit speed and DC voltage decrease significantly. The simulation results align with the analysis of LVRT characteristics presented in Section 3.1.