esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
## 5 Conclusions and Future Work  
Multi-party privacy protected machine learning proposed in this paper can help multiple users to perform machine learning without leaking their own private data due to the integration of homomorphic encryption and federated learning. Especially in privacy data protection, the algorithm can train common models in the case of data areolation. Experiments of the PFMLP algorithm show that the model trained by PFMLP has a similar effect as the model trained using all data on a single machine. All parties just transmit the gradient data and gradient fusion is performed by homomorphic operations in the central computing sever. The learning model is updated based on the new gradient data after homomorphic operations. However, homomorphic encryption will inevitably cause some performance problems, such as the additional overhead of the encryption and decryption process which will greatly affect the training efficiency. In addition, the network structure, encryption/decryption key length, and key replacement frequency, etc. also affect the final performance.  
About the future work, firstly, more powerful and scalable federated learning should be considered, like vertical federated learning algorithms splitting the features into different clients. Secondly, highly efficient homomorphic encryption algorithms will accelerate the learning performance. Finally, a more robust privacy protected learning algorithm should be given more attention, including those hybrid algorithms, anti-malicious attack clients algorithms, etc.  
Figure 12: Accuracy comparisons among single-node MLP, 2-client and 4-client PFMLP on the fatigue dataset.  
**Author Contributions:** Conceptualization, H.F. and Q.Q.; methodology, H.F. and Q.Q.; software, H.F.; validation, H.F.; formal analysis, H.F. and Q.Q.; investigation, H.F. and Q.Q.; data curation, H.F.; writing--original draft preparation, H.F.; writing--review and editing, Q.Q.; visualization, H.F.; supervision, Q.Q.; project administration, Q.Q.; funding acquisition, Q.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the \"National Key Research and Development Program of China, Grant No. 2018 YFB0704400\" and the \"Key Program of Science and Technology of Yunnan Province, Grant No. 202002 AB080001-2\".  
**Data Availability Statement:** Data that support the findings of this study is available from the corresponding author upon reasonable request.  
**Acknowledgments:** This work is partially sponsored by the National Key Research and Development Program of China (2018 YFB0704400), Key Program of Science and Technology of Yunnan Province (202002 AB080001-2), Research and Development Program in Key Areas of Guangdong Province (2018B010113001). The authors gratefully appreciate the anonymous reviewers for their valuable comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussion
### Comparison Fabricated Inductor to Previous Work  
A comparison table to compare the proposed techniques, wire-bonded and wire-wound inductors with two other solenoid inductors is provided in Table 5. The wire-bonded and wire-wound inductors show a maximum tuning ratio of 81% and 90.6% at 310- and 300 MHz with EMG901, respectively, which is quite a bit higher than the one achieved by ([PERSON] et al. 1999b) at 5 MHz, but comparable to the one achieved by ([PERSON] et al. 2006) at 100 MHz. However, the quality factor of the proposed inductor (29.88 and 45.2 at 310 and 300 MHz) are much higher than the one measured by ([PERSON] et al. 2004). This shows that the proposed techniques maintain a high quality factor, which is necessary for RF applications.  
The measured peak quality factor for wire-bond and wire-wound solenoid inductors are 18.4 at 108 MHz and 69 at 30 MHz, respectively, when EMG901 is fully injected. However, depending on the application and the desired tuning range, the quality factor can be increased in lower-permeability EMG liquids are selected. In this case, a lower tuning ratio is achieved.
## 3 Case Study
### TEP Dataset
#### 3.1.3 Comparative Study on Few-Shot Fault Classification  
In practical fault diagnosis, few-shot analysis often resorts to transfer learning methods. To benchmark against these methods, two deep neural networks renowned for their learning capabilities, namely VGG-11 [32] and Resnet-18 [33], were selected as the backbone architectures for knowledge transfer. During this process, fault data from the source  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer Number** & **Module 1** \\ \hline L1 & Conv1D (3, 64) \\ L2 & ReLU \\ L3 & BN \\ L4 & Maxpool1D (2) \\ L5 & LSTM (100) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of each module for the TEP dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Module Number** & **Base Learner** \\ \hline
2 & Module 1 \\
3 & Module 1 \\
4 & Module 1 \\
5 & FC (Dense Layer) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Structure of the base learner for TEP dataset.  
domain were utilized to pretrain the base network, followed by fine-tuning with fault data from the target domain. Preliminary experiments indicated that fine-tuning the entire network achieved greater accuracy compared with merely adjusting the classifier. Hence, the experimental results of transfer learning were all based on the fine-tuning of the entire network. Crucially, to ensure a fair comparison, all comparative methods employed an N-way K-shot experimental setup.  
In the domain of few-shot learning applications, the GOPML algorithm demonstrated its efficacy in fault diagnosis by optimizing the entire parameter set \(\theta\) through a bi-level gradient descent strategy, as evidenced by the results presented in Table 4. GOPML distinguished itself from traditional transfer learning methods that primarily focus on updating initialization parameters during the pretraining phase. Instead, GOPML employs a comprehensive update strategy across all tasks, thereby focusing on a more generalized initialization of parameters \(\theta\) that facilitates rapid adaptation to new tasks.  
The empirical results, as detailed in Table 4, underscore the significant accuracy improvements achieved by GOPML in few-shot fault diagnosis tasks. In particular, GOPML's accuracy reached 93.53% and 97.48% in 3-way 5-shot and 6-shot tasks, respectively, and 84.01% and 86.59% in 8-way tasks for 5-shot and 6-shot scenarios. These figures substantially surpassed those of conventional transfer learning methods and highlighted the robustness of GOPML, especially when the number of training samples was limited. The improvements confirmed the effectiveness of the algorithm's strategy in optimizing the parameters \(\theta\) for better generalization across tasks.
## 7 Results and Analysis
### Robustness Analysis  
The robustness analysis highlights the LSTM scheduler's capability to rapidly adapt to abrupt load variations, specifically when PRB utilization shifts from 50% to 90% within a 5 s window. The system achieved stable latency performance with less than 5% degradation, demonstrating recovery times of 2.3 s for eMBB traffic, 1.8 s for URLLC services, and 3.1 s for mMTC bursts. These results underline the scheduler's ability to handle sudden surges in traffic demand while preserving service-level agreements across diverse slice types. The robustness analysis was extended to discuss timing-related trade-offs. Although the current framework meets the Near-RT latency requirements, scaling the system to larger networks comprising over 100 Distributed Units (DUs) may necessitate the deployment of distributed aggregation servers to mitigate potential bottlenecks in the aggregation phase. Additionally, while the 4.8 s convergence time is deemed acceptable for Non-Real-Time RIC policy orchestration, further optimizations will be required to accommodate dynamic scenarios such as rapid handovers or emergency call prioritization. Nevertheless, certain limitations emerged under extreme burstiness scenarios, particularly when traffic spikes exceeded 300%, where the DU wake-up success rate declined to 84.7%. To address this challenge, we propose the implementation of adaptive wake-up thresholds as shown in Equation (22):  
\[T_{\text{wake}}=\alpha\log\left(1+\frac{\partial\text{Load}}{\partial t} \right)+\beta\text{PLR}_{\text{hist}} \tag{22}\]  
where \(\alpha=0.7\) and \(\beta=0.3\) are empirically derived coefficients. This formulation leverages the rate of change in load and historical packet loss ratio to dynamically adjust the DU wake-up decision threshold, thereby enhancing responsiveness to extreme traffic surges while mitigating the observed degradation in wake-up success rate.
## 4 Results and Discussion
### Model Comparison for Predicting Air Pollutant Concentrations  
The performance of the MLR and RF models in predicting air pollutant concentrations (PM10, NO, NO2, and CO) based on meteorological parameters was compared. Table 3 summarizes the results using performance metrics such as R2, mean squared error (MSE), and root mean squared error (RMSE).  
For PM10, the RF model demonstrated better performance, with an R2 value of 0.78 compared to 0.36 for MLR. RF also had a lower MSE (68.56 vs. 162.9) and RMSE (8.28 vs. 12.76). These results highlight RF's ability to model non-linear interactions between PM10 and meteorological factors, such as WS and RainF. The findings also demonstrate superior performance compared to recent PM10 prediction efforts, achieving an R2 value of 0.38 [32] and 0.53 [33].  
In the case of NO, RF outperformed MLR, with an R2 value of 0.71 versus 0.32, alongside lower MSE (139.48 vs. 217.6) and RMSE (11.81 vs. 14.75). This underscores RF's effectiveness in capturing non-linearities in NO behavior. In addition, for NO2, RF slightly outperformed MLR, achieving an R2 of 0.65 compared to MLR's 0.5. RF also reported marginally better error metrics, with lower MSE (49.14 vs. 67.7) and RMSE (7.01 vs. 8.23).  
Both models struggled with CO prediction. RF achieved an R2 value of 0.39, slightly higher than MLR's 0.18, but MSE (0.02) and RMSE (0.31) were identical for both models. The low predictive power aligns with research highlighting the challenges of modeling CO due to its diverse sources (e.g., traffic, industry, and residential heating) and weak correlations with meteorological variables. This indicates a need for advanced modeling approaches to improve CO predictions.  
These results emphasize the importance of selecting appropriate models based on pollutant characteristics. RF performed better for PM10 NO, and NO2, which exhibit complex, non-linear relationships with meteorological parameters. The low predictive power for CO highlights the need for alternative approaches.  
To improve prediction accuracy, future studies should consider incorporating additional data sources, such as emission inventories and chemical transformation mechanisms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Pollutants**} & \multicolumn{6}{c}{**Models**} \\ \cline{2-7}  & \multicolumn{3}{c}{**RF**} & \multicolumn{3}{c}{**MLR**} \\ \cline{2-7}  & **R2** & **MSE** & **RMSE** & **R2** & **MSE** & **RMSE** \\ \hline PM10 & 0.78 & 68.56 & 8.28 & 0.36 & 162.9 & 12.76 \\ NO & 0.71 & 139.48 & 11.81 & 0.32 & 217.6 & 14.75 \\ NO2 & 0.65 & 49.14 & 7.01 & 0.5 & 67.7 & 8.23 \\ CO & 0.39 & 0.02 & 0.31 & 0.18 & 0.13 & 0.36 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative performance of RF and MLR models.  
Advanced modeling techniques, such as hybrid models or deep learning algorithms, could also be explored to better address pollutants with diverse and variable emission sources, particularly CO.
# Illegal Nitrite Treatment of Red Tuna and Prolonged Storage: What About Other Food Safety Risks?
## 1 Introduction
The illegal treatments of tuna with high amounts of nitrite to stabilize color characteristics obtain a significant improvement in the appearance and could mask a significant food safety risk since high levels of amines, particularly biogenic amines (BAs), can be produced during storage. Among these, histamine (2-(4-imidazolyl) ethylamine, HIM) is frequently implicated in poisoning outbreaks characterized by symptoms encountered with IgE-mediated food allergies [17]. Scombroid fish, such as tuna, may accumulate high HIM levels within fish muscles because of the presence of high concentrations of histidine, which is converted to HIM by the bacterial enzyme histidine-decarboxylase (HDS). Ingestion of food containing low amounts of histamine can cause an allergy-like syndrome referred as \"histamine intolerance\" [18]. However, in large doses, the normal metabolic mechanisms are insufficient for the detoxification. An HIM intake of 70-1000 mg per single meal may cause the so-called \"scombroid poisoning\", characterized by an incubation period ranging from a few minutes to hours and symptoms such as hypotension, headache, tachycardia,nasal secretions, and possible death in sensitive subjects [19]. The nitrite-treated product, therefore, although fresh in appearance, may hide a state of degradation which makes it no longer fit for consumption. The microbial count, growth, and the possible presence of pathogens are other concerns to consider as a possible consequence of the prolonged storage of adulterated red tuna samples. With this background, a study of nitrite-treated tuna samples was carried out in the present work to produce data, both chemical and microbiological, that are useful and in the interest of health.
_Applied_  
_sciences_  
_Article_  
**Displacement Characteristics of Shallow-Buried**  
**Large-Section Loess Tunnel with Different Types of Pre-Supports: A Case Study of New Badaling Tunnel**  
**[PERSON], [PERSON] \({}^{\star}\), [PERSON], [PERSON], [PERSON]\({}^{\copyright}\) and [PERSON]**  
Key Laboratory for Urban Underground Engineering of Ministry of Education, Beijing Jiaotong University, Beijing 100044, China; [EMAIL_ADDRESS] (D.L.); [EMAIL_ADDRESS] (Q.F.); [EMAIL_ADDRESS] (Z.S.); [EMAIL_ADDRESS] (L.C.); [EMAIL_ADDRESS] (A.L)  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +86-10-5168-8111  
Received: 6 December 2019; Accepted: 24 December 2019; Published: 25 December 2019  
**Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory.**  
**Abstract:** Three-bench seven-step excavation method (TSEM) has been widely used in large-section loess tunnels for high-speed railway in China. As the most commonly applied pre-supports, pipe roofs and leading ductules are broadly used in the ground reinforcement of loess tunnels. Their application is to ensure face stability and prevent tunnel collapse during construction. This study focused on the impacts of the TSEM on the ground surface settlement (GSS), as well as the tunnel displacement characteristics for the high-speed railway tunnels with large cross-sections in loess ground. Furthermore, the reinforcement effects of the two kinds of pre-supports were compared in this study. In-situ tests for a total of 12 sections were conducted to reveal the GSSs and displacement characteristics for the shallow-buried large-section loess tunnels. The monitoring results showed that the excavation process plays a significant role on the GSSs and tunnel displacements. A maximum value was observed for the tunnel displacement rate at the excavation of the upper and middle benches, where the face instability or collapse were prone to occur. The GSS trough curves were deviated to the early excavation side, with no conformation to the Gauss distribution. After a series of comparisons, we concluded that the pre-reinforcement effect of the pipe roof is better than that of the leading ductule for the loess tunnels.  
**Keywords:** loess ground; large-span tunnel; three-bench seven-step excavation method; pre-support; ground surface settlement; arch settlement; horizontal convergence
## 4 Experimental Evaluation
### Function Comparison  
Table 2 offers a functional comparison between our scheme and other existing schemes. The schemes in refs. [11; 24; 25] did not consider the possibility of incorrect search results and do not possess the capability for result verification. The schemes in [31; 33] and our scheme all take into account the scenario where the server maliciously returns incorrect search results, and they all rely on blockchain technology to accomplish the verification of search results. However, the schemes in refs. [31; 33] lack consideration for forward security during the update, and they are only applicable to single-user scenarios. The scheme in refs. [33] also fails to achieve dynamic verification of search results.  
Our scheme ensures forward security during the data update by a state chain structure and achieves dynamic verification of search results using blockchain and bitmap index. Additionally, we employ access control operations to enable the scheme to achieve secure search functionality in multi-user scenarios.
## 4 Performance Evaluation
### Experimental Results
#### 4.3.4 Power Consumption Results  
The y-axis in Figure 8 shows the percentage of power consumed by the servers, while the x-axis shows time steps from 1 to 2000.  
The findings on power consumption have important ramifications for edge cloud environments' sustainability and energy efficiency. When there are limited energy resources available, the effectiveness of algorithms in controlling power usage becomes critical. One notable feature of the power-saving method is that it stabilizes at the lowest average consumption of 5.4 W per server. Because of this, it is especially well-suited for settings with strict energy limits. It ensures sustainability by cutting down on operating expenses and prolongs the life of edge devices by managing energy efficiently.  
On the other hand, the worst-fit method shows the highest average power usage of 5.64 W per server, indicating that it is not very effective at balancing energy consumption with workload demands. This increased energy consumption makes it less appropriate for edge cloud scenarios where energy efficiency is a top concern and could result in higher operational expenses and decreased sustainability. Resources may be strained by the worst-fit algorithm's high power consumption, especially in isolated or off-grid areas where energy supplies are scarce and costly.  
The best-fit algorithm exhibits a more balanced approach, blending an efficient workload distribution with comparatively low energy usage, with an average of 5.45 W per server. It is therefore a wise option for settings that require both consistent performance and energy efficiency. Its capacity to sustain steady power consumption without appreciable spikes guarantees that it can deliver dependable service without placing undue strain on energy resources. The location-based algorithm maintains lower average spikes even with its unpredictability and continuous power usage variations. This indicates that it successfully strikes a balance between workload needs and energy usage, even though frequent reallocations may put some strain on the servers. In dynamic contexts, where low latency and energy efficiency are crucial, this balance is crucial. By reallocating workloads according to user proximity, the location-based algorithm can minimize latency and optimize resource use, improving user experience without appreciably consuming more energy.  
Figure 8: Power consumption per algorithm.  
To sum up, the patterns of power usage illustrate the compromises that each algorithm makes between energy efficiency and workload management. The best-fit and location-based algorithms provide balanced solutions for environments requiring both efficient power usage and an effective workload distribution, while the power-saving algorithm performs well in environments with limited energy due to its low-energy profile, making it ideal for stable environments. The worst-fit algorithm is less desirable because of its high energy demand. These understandings are essential for creating energy-efficient edge cloud environments that maximize performance.
## 1 Introduction
To solve the coverage control problem of nodes, the following problems need to be addressed: (1) The limited energy carried by underwater nodes and the mobility of underwater nodes cause deformation of the topology of UWSNs, leading to coverage holes in some areas, thus affecting the quality of service. (2) The existing covering research involves mainly the geometric method or the use of node force between particles. The former has poor operability in the underwater environment, while the latter is prone to some coverage fluctuations during final network operation. (3) To ensure that the UWSNs cover as few holes as possible, it is necessary to introduce artificial intelligence optimization algorithms to drive underwater nodes to appropriate positions. Given that there are many covered holes in underwater networks in the dynamic open underwater environment, this study makes full use of the combination of particle swarm optimization (PSO) and VFA to propose a three-dimensional iterative enhanced underwater covered hole recovery method. For this article, we make the following contributions: (1) Put forward the improved PSO strategy for complete underwater node deployment, design the iterative steps for nonlinear decreases, and automatically adjust the inertia weight and acceleration factor. It can achieve a balance between local and global search capabilities. (2) To avoid the problem of local optima in the search algorithm, underwater nodes are considered as particles to calculate their mutual forces, driving them to move towards positions that can increase coverage rate. Disturbance operations are carried out in the later stages of iteration to weaken the oscillations of coverage rate. (3) Experimental studies on network topology, coverage rate, coverage efficiency, coverage holes, and average remaining energy before and after utilization of the proposed coverage algorithm were conducted. In terms of enhancing UWSN coverage, the proposed coverage algorithm was verified to be superior to BES, 3D-IVFA, and DABVF. The remainder of this article is structured as follows. Section 2 presents a three-dimensional iterative enhancement coverage algorithm; Section 3 evaluates multiple experiments for various performance metrics; Section 4 gives the conclusions.