esa-sceva/satcom-chunk-collection huggingface dataset:
# Phased System Reliability Modeling and Assessment for Construction of Lunar Scientific Base
## 6 Conclusions  
In light of the prevalence of small sample tests and zero-failure data in aerospace product reliability tests, traditional methods such as the LM and MML methods are not applicable. This paper proposes a confidence limit calculation method for the product of subsystem reliability to assess the reliability of LSB construction. A novel phased reliability model is developed to facilitate the building block reliability assessment of LSB construction. The reliability of the three main processes of fixed-point delivery, lunar surface transport, and docking assembly is assessed using reliability test information. The corresponding confidence limit calculation formulas are derived for identical and different reliabilities. The LCL for LSB construction reliability is determined with high confidence based on the assessed reliability of the three main processes. Two comparative examples are used to verify the advantage of the BBA method, demonstrating its ability to handle zero-failure subsystems in series systems.  
The application of the proposed method for the LCL of LSB construction reliability is illustrated. Results indicate that the reliability of LSB construction reaches 0.9544 under a high confidence degree index, meeting the engineering requirements. In engineering practice, many aerospace mechanisms can be regarded as series systems, such as solid rockets, the connecting and separating mechanisms of spacecraft, and the landing buffer mechanism of lunar landers. This method can also be extended to assess the reliability confidence limits of such series systems.  
Although this study considers subtasks as the fundamental level of the hierarchical reliability model, it is necessary to note that subtasks may be further decomposed into corresponding functional units when direct reliability information is difficult to obtain. This preliminary theoretical exploration only divides the construction process to the subtask level; future research will incorporate specific task requirements to construct a more comprehensive reliability model.  
Conceptualization, Z.L. and H.F.; methodology, Z.L. and H.F.; software, Z.L.; validation, Z.L., H.F. and Q.W.; formal analysis, Z.L.; investigation, Z.L. and H.F.; resources, Z.L., H.F. and Q.W.; data curation, Z.L.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L., H.F. and Q.W.; visualization, Z.L.; supervision, Q.W.; project administration, H.F.; funding acquisition, H.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant No. U2037602.  
Not applicable.  
The authors gratefully acknowledge the financial support for this project from the National Natural Science Foundation of China grant No. U2037602.  
The authors declare no conflict of interest.
## 2 Numerical Modeling
### Verification of the Numerical Model
#### 2.5.1 Smooth Pipe Verification  
The numerical domain has two smooth sections, which are the inlet and exit sections. The grid distribution in the radial direction at the test section of the ribbed tube is the same as that of the smooth sections. Figure 4 depicts comparisons of the average Nusselt number of a smooth pipe, Nu\({}_{s}\), obtained from the current numerical simulation to that obtained from the Gnielinski and Dittus-Boelter correlations, Equations (13) and (14), respectively. Two grids were applied. Grid1 has 87,271 nodes, while Grid2 has 238,321 nodes. Heat flux was applied at the inlet section of the computational domain, as it represents a smooth pipe of a length more than 10 times its diameter to ensure fully developed flow. The Nusselt number was calculated 15 d from the inlet. The Prandtl number was 0.71, which may represent the flow of air as a working fluid. The k-\(\varepsilon\) and k-\(\omega\) models were investigated. The numerical results show a very high degree of agreement with the Dittus-Boelter correlation.  
Figure 3: Partial view of the computational grid, with 87,271 nodes.  
Figure 2: Schematic diagram of the computational domain.  
On the other hand, the numerical results overestimate the values obtained by the Gnielinski correlation. The two grids almost indicate identical results.  
The friction factor for a smooth tube was compared against the Petukhov correlation, Equation (12), as shown in Figure 4b. Very good predictions of the friction factor were obtained, and the two grids have almost identical prediction values. The discrepancies between the predicted and correlation results decrease as Re increases. The predicted results of the two tested numerical models are close, though are marginally better for the the k-\(\epsilon\) model than the k-\(\omega\) prediction model.
# A Transfer Reinforcement Learning Approach for Capacity Sharing in Beyond 5G Networks
## 4 Performance Evaluation
### Training Performance
Table 4 shows that the increase in the training duration is generally more pronounced when the TRL approach is not used. For instance, the training duration between \(N^{\prime}=7\) cells and \(N^{\prime}=8\) cells increases by approximately 300% when TRL is not used, while this increase is 160% when TRL is used. Figure 5: Standard deviation of \(R\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Parameter**} & \multicolumn{8}{c}{**Values**} \\ \hline Number of cells & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 \\ \hline Initial training steps & 50 & 100 & 300 & \(1\times 10^{3}\) & \(3\times 10^{3}\) & \(8\times 10^{3}\) & \(2\times 10^{4}\) & \(6.5\times 10^{3}\) \\ \hline \multirow{4}{*}{ANN config.} & Input layer (nodes) & 7 & 12 & 17 & 22 & 27 & 32 & 37 & 42 \\ \cline{2-10}  & Fully connected layer & \multicolumn{8}{c}{1 Layer (100 nodes)} \\ \cline{1-1} \cline{2-10}  & Output layer (nodes) & 3 & 9 & 27 & 81 & 243 & 729 & 2187 & 6561 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: DQN configuration for different deployments. Furthermore, the results in Table 4 indicate that shorter training durations are required in the case of using the TRL approach. To provide further insight, the percentage of training duration reduction when using the \(TRL\) mode with respect to the _Non-TRL_ mode has also been included in the last column of Table 4. For \(N^{\prime}\leq 5\) cells, reductions of around 50% are obtained, while larger reductions are achieved for \(N^{\prime}\geq 6\). The reason is that increasing the number of cells leads to an increase in the number of states and actions, and thus, the ANN size and the complexity of the training. These results highlight that the gains of the proposed TRL approach are higher when it is applied in RAN deployments with a larger number of cells.
## 3 Optimal Decision-Level Fusion Criterion (O-DFC) and Its Simplified Form
### Optimal Decision-Level Fusion Criterion (O-DFC)
Assuming the BER for the \(m\)th user at each detector be \(P_{in}\), \(i=1\), \(2\), \(3\), this proposition reveals that the BER achieved at the fusion center, which is denoted as \(P_{ecm\_O}\), is:  
\[P_{ecm\_O}=\texttt{min}\left\{P_{e0m},P_{e1m},P_{e2m},P_{e3m}\right\} \tag{20}\]  
where \(P_{e0m}=P_{e1m}P_{e2m}+P_{e1m}P_{e3m}+P_{e2m}P_{e3m}-2P_{e1m}P_{e2m}P_{e3m}\). Therefore, from Equation (20), we can see that to achieve the improvement of performance by this fusion scheme, the following condition should be satisfied  
\[P_{e0m}<\texttt{min}\left\{P_{e1m},P_{e2m},P_{e3m}\right\} \tag{21}\]  
Otherwise, we should simply choose the detector having the lowest BER for the best performance, which also means that there is no performance gain in the fusion of these three detectors.
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 5 Future Challenges  
While an explosive growth in the number of studies of resource allocation involving RISs has been witnessed in the past few years, there are still challenging problems remaining to be investigated. From communication and wireless network perspectives, when RISs are employed together with other emerging technologies, unique challenges will occur. These include, but are not limited to, full-duplex communications, integrated sensing and communications, Terahertz communications, new forms of multiple access, and mobile edge computing. As these challenges and open issues have been covered in existing RIS review articles [1, 2, 5, 6, 7, 8, 9, 10, 11, 12], this paper focuses on the challenges in signal processing and AI. Below, four such challenges are described, and potential solutions are also discussed.
## 3 Results
### A Traceability System for Timber Supply Chain Monitoring and Compliance
Traceability-critical data stored on-chain along with the cryptographic digest of the traceability-relevant data: blockchain address of batch owner, number of remaining units in the batch, batch inputs, as a JSON record with pairs: \([(n:org\_id,\,u:units)]\), where \(org\_id\) represents the ID of the batch providing inputs and \(units\) is the number of units in that batch provided as input. This information is stored on-chain in the MariaDB relational database. 3. Complementary data included information such as product pictures, geolocation data, full content of logging permits, product catalogues, etc. This information is also stored in the MariaDB relational database. The process of creating and notarising a new batch is as follows:  
1. The company that generates the batch stores all batch data in the backend, including the inputs from previously notarised batches. 1. At the time of creation, a unique identifier would be assigned to the batch. 2. Inputs (batch of origin and amount supplied) are stored as DAG arcs in the backend. In addition, a JSON is generated with the list of inputs, as described above. 3. The remaining units of each input batch would be updated both on-chain (by means of a function provided by the traceability smart contract) and the backend. 2. The company generating the batch, once its data are fully checked and validated, selects the traceability-relevant items and generates a cryptographic summary. Note that this information, besides being relevant, is immutable by nature. To facilitate the generation of the hash key, a view of the relational database with the information to be included in its calculation is provided. 3. The company submits to the traceability smart contract the (newly created) ID of the new batch, the computed hash key, the number of units in the new batch, and a JSON structure with the inputs, using a specific function in the smart contract. As a consequence, a new node is created in the smart contract's traceability mapping. The process of computing the upstream trace from any batch ID, using exclusively the information stored in the blockchain, would be as follows:  
1. With the batch ID, traceability-critical data are obtained from the blockchain, that is, the remaining units and the JSON structure with the inputs. With this information, the following data are obtained:1. The number of remaining units of the batch. 2. A list of pairs (supplying batch ID, supplied quantity) of all batch inputs. 2. With information (b), proceed from step 1 for each supplying batch, if that batch has not yet been processed, until there are no inputs left to process. Note that to reconstruct the upstream trace from the blockchain, a single read query is performed for each batch participating in that trace (i.e., no actual blockchain transactions are required). It would also be possible to reconstruct the trace of any batch in a comprehensive way, including all desired data, both upstream and downstream, from the DAG and the batch data stored in the backend.
## 4 Computer Simulation Experiments
### Algorithm Precision Analysis  
To evaluate the estimation accuracy of the ASTKF estimation method, two sets of GPS L1 C/A signals that differed in terms of _C_/_N\({}_{0}\)_ were generated by the SDX satellite signal simulator. For one set, _C_/_N\({}_{0}\)_ was set to 55 dB-Hz, which corresponds to a strong satellite signal; for the other set, _C_/_N\({}_{0}\)_ was set to 18 dB-Hz, which corresponds to a weak satellite signal. The digital IF signals were post-processed by the software receiver. Two classical methods, the NWPR and the VSM were selected as the reference. To analyse the effect of the averaging time, four averaging times (0.5 s, 1 s, 3 s and 5 s) were used.  
In the simulation tests, the _C_/_N\({}_{0}\)_ estimation results are smaller than the set values due to signal attenuation that is caused by the simulator, the RF front-end, and the connection cables. Therefore, the accuracy of the estimation is measured in terms of the standard deviation (Std) of the estimation results.
# On-Demand Scheduling of Command and Responses for Low-Power Multihop Wireless Networks +
## 5 Evaluation  
We evaluate SCoRe by comparing it to legacy protocols in terms of \"packet reception ratio (PRR)\", command-to-response \"latency\", and \"number of retransmissions\". PRR is calculated as a product of downward PRR (command-phase) and upward PRR (response-phase). Latency is measured as the round trip time (RTT) from the transmission of a command to until the last successful response arrived at the command originator, the root. The number of packet retransmissions is averaged for response packets only as command dissemination is based on link broadcast. We conduct both experiments and simulations with 31 devices: 30 embedded nodes and 1 gateway (as a root).
# Internet of Things (IoT) Cybersecurity: Literature Review and IoT Cyber Risk Management
## 2 Literature Review
### Cybersecurity in IoT Architecture
#### 2.1.2 Cybersecurity at the Network Layer  
In the IoT system, the network layer plays a critical role for the overall IoT security performance, since secure data transmission over the network is essential for the function of devices, processing stations, and the entire IoT system. An intrusion detection system (IDS) is used to detect attacks, take corrective measures, and monitor packets [25]. The IDS deploys various intrusion detection techniques: statistical analysis for anomaly detection [26]; evolutionary algorithm for classifying intrusions based on error conditions, behavior, and attempted intrusions [27]; protocol verification for classifying suspicious behaviors; data mining techniques such as random forest method [28]; and deep learning for classifying network breach patterns [29].  
Deep learning models show promising results for the detection of DDoS attacks with the highest accuracy at 97.16% [29]. Hybrid methods using dimension reduction and classification techniques for detecting malicious activities on the IoT networks also show promising results [30]. The network security segment of the cybersecurity market is estimated to constitute the highest component of cybersecurity between 2018 and 2023, and the rising adoption of IoT applications is a key contributing factor to the growth [5].
## 1 Introduction
The proposed ultrasonic data processing approach estimates the delamination depth by solving two optimization problems: the first problem is to minimize the objective function derived from the Rayleigh characteristic equation and the second problem is to minimize the discrepancy between the analytical and experimental Lamb wave dispersion curves. The proposed approach is evaluated using numerical simulations and laboratory-scale experiments. It offers several advantages in the nondestructive inspection of concrete structures over the abovementioned approaches: (1) it uses a 64-channel noncontact MEMS ultrasonic sensor array that enables the rapid collection of linear ultrasonic scanning data; (2) it enables the accurate estimation of the depth of shallow delamination in concrete; and (3) it effectively uses multiple Lamb wave modes.
## 5 Conclusions  
This study aimed to investigate SA in producing an electrospun patch to deliver probiotics or postbiotics locally to the middle ear. The developed system incorporated probiotics into SA microspheres obtained via electrospray, which were subsequently inserted within SA/PEO fiber meshes to create a patch. The rheological behavior of the solutions was studied, and the swelling behavior of the produced fibers was evaluated. The patch resulted in being cytocompatible with HaCaT cells. In addition, the probiotics were viable and active against _E. coli_ after encapsulation, which demonstrates the safety and effectiveness of the developed processing method. The developed strategy seems promising to produce 0D, 1D and 2D delivery systems for the treatment of chronic otitis media. However, further studies are necessary to improve the electro-spinnability of alginate and assess the efficacy of bacteriotherapy in in vivo models.  
**Author Contributions:** Conceptualization, L.B. and S.D.; methodology, G.B., M.M. and S.D.; validation, R.R., B.C., G.B., M.M. and S.D.; formal analysis, R.R., B.C., L.Z., B.A., T.M. and S.E.; investigation, R.R., B.C., L.Z., B.A., T.M., E.K. and S.E.; resources, G.B. and S.D.; data curation, R.R., B.C. and L.Z.; writing--original draft preparation, R.R., B.C., M.M., G.B. and S.D.; writing--review and editing, R.R., G.B., L.Z., M.M. and S.D.; visualization, R.R., B.C., M.M. and S.D.; supervision, G.B., M.M. and S.D.; project administration, S.D.; funding acquisition, G.B. and S.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Italian Ministry of University and Research (MIUR) under the framework Action II of PON \"Ricerca e Innovazione\" 2014-2020, project \"Sviluppo di nutraceutical di fonti natural--BIONUTRA\" (CUP B14I20001320005), and by PNNR \"THE--Tuscany Health Ecosystem\"; Spoke 7--Innovating Translational Medicine-Sub-project 5--Innovative models for management of infections caused by antibiotic-resistant bacteria (Project code: ECS00000017; CUP I53C22000780001).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are available from the corresponding author, upon reasonable request.  
**Acknowledgments:** The authors thank [PERSON] and [PERSON] (University of Pisa) for their technical support for rheology and scanning electron microscopy analyses, respectively. The authors thank Linari s.r.l. for supporting the research activities.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Study on Temperature Correction of Asphalt Pavement Deflection Based on the Deflection Change Rate
## 6 Conclusions  
In this paper, the influence range of temperature on a deflection basin is studied through long-term FWD deflection detection on in-service highways, and a temperature correction formula for the deflection basin based on the deflection change rate is established. The conclusions are as follows:  
1. When performing the deflection temperature correction of an asphalt pavement in Shanghai, the temperature at the middle depth of the asphalt layer is more suitable for use as the representative temperature of the whole asphalt layer. In other regions, the location of the representative temperature may be different due to the different forms of pavement structures. Further validation analysis will be conducted later to clarify its applicability.
2. The effective influence distance of temperature on the deflection basin is 90 cm. When the temperature correction of the deflection basin is performed, the deflection with a further radial distance can be uncorrected.
3. We found that using the deflection change rate can obtain better deflection-temperature correction fitting results than the deflection difference. Then, we obtained a new model for deflection temperature correction based on the deflection change rate. On the basis of the new temperature correction model, we can evaluate the structural load-carrying capacity more precisely.  
Conceptualization, X.H. and L.S.; Methodology, X.H.; Software, Y.H.; Formal analysis, X.H. and Y.H.; Investigation, X.H. and J.L.; Data curation, J.L.; Writing--Original draft, X.H.; Writing--Review & editing, Y.H.; Visualization, X.H.; Funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2018 YFB1600100.  
Not applicable.  
The authors declare no conflict of interest.
# Self-Organized Fuzzy Neural Network Nonlinear System Modeling Method Based on Clustering Algorithm
## 4 Simulation Analysis
### Prediction of Benzene Levels in Air  
In this example, we use the air quality dataset from UCI [42; 43]. This dataset contains responses from gas multisensor devices deployed at urban road sites in Italy with high levels of Class I pollutants and contains 1232 sets of hourly averaged response instances from five metal oxide chemical sensor arrays embedded in air quality chemical multisensor devices. After processing the missing values, we obtained 800 data, using 750 of them for training and 50 of them for testing. The input samples are carbon monoxide (CO), Non-Metanic Hydrocarbons (NMHC), Total Nitrogen Oxides (No\({}_{\text{x}}\)) content, and air temperature (T) in the air, and the output is benzene (C\({}_{6}\)H\({}_{6}\)) content. The parameters of SOFNN-CA are set as follows: md-addh is 0.2, e-min is 0.2, den-th is 12, and S-th is 12. The experimental results are shown in Figure 11.  
As can be seen from Figure 10a, the initial neurons generated by the clustering algorithm are set to three, and the number of neurons changes more obviously and stabilizes at the end. Figure 11b shows the RMSE of the training process, while Figure 11c shows the prediction results; it can be seen that the prediction accuracy is high, and the error is between [\(-\)3 3].  
As shown in Table 4, with only three neurons, SOFNN-CA reaches the best test performance, and the RMSE is 1.0280. At the same time, the test error is smaller than the training error, indicating that the network has a strong generalization ability, and the neuron activation intensity index and density index helped to improve the generalization ability of the network. SOFNN-ALA has a smaller training error during training, but its generalization is weaker because the test error of the network is larger than the training error. ATO-RBF uses an error correction algorithm to increase the number of neurons, so more neurons must be added in order to reduce the error. Therefore, the network is larger, and its test error is 1.4900 higher than those of SOFNN-CA and SOFNN-ALA. It can be seen that the algorithm proposed in this paper has a better performance index. In the experiment of the prediction of benzene content in the air, the prediction can be achieved with high accuracy, and the practical problems can be solved effectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Rule Neurons** & **Training RMSE** & **Testing RMSE** & **CPU Times (s)** \\ \hline SOFNN-CA & 3 & 4.1616 & 3.6034 & 11.26 \\ SOFNN-HPS [8] & 7 & 4.0718 & 3.7469 & - \\ GEBF-OSFNN [41] & 9 1 & 4.2483 1 & 3.8327 1 & - \\ GD-FNN [11] & 15 1 & 3.8945 1 & 4.8510 1 & - \\ D-FNN [12] & 19 1 & 3.7485 1 & 4.2545 1 & - \\ \hline \hline \end{tabular} 1  
\end{table}
Table 3: Comparison of different methods under M-G experiment.