esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 8 Conclusions  
In the article, we review the shortcomings of swarm learning (SL) and further propose a new privacy-protection Byzantine-resilient swarm learning for e-health, called PBSL, which ensures the privacy of local gradient and the tolerance toward Byzantine attacks. With the CKKS cryptosystem based on threshold decryption and decentralized computing based on blockchain, each **MC** can safely aggregate other **MCs'** gradients to generate a more accurate global model, while the confidentiality of the final global model can be ensured. And in the process of global model aggregation, our proposed Byzantine-robust aggregation method uses secure cosine similarity to punish malicious **MCs**. Detailed security analysis and extensive experiments show that PBSL has privacy protection capability and efficiency.  
In this paper, our attack detection strategy is based on cosine similarity between the local model gradient and the global model gradient. In future work, in order to prevent new attack vectors, we will continue to study new attack detection strategies that are suitable for blockchain platforms and take into account privacy protection, so as to deal with unforeseen threats in future-proofing systems.  
Conceptualization, X.Z.; Methodology, X.Z.; Software, T.L.; Writing--original draft, X.Z.; Writing--review & editing, T.L.; Supervision, H.L.; Project administration, H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[[http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)]([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)), [[https://github.com/zalandoresearch/fashion-mnist](https://github.com/zalandoresearch/fashion-mnist)]([https://github.com/zalandoresearch/fashion-mnist](https://github.com/zalandoresearch/fashion-mnist))].  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Empirical Analysis
### Forecast Analysis
#### 4.2.1 Application Verification of the \(ARIMA\) Model  
Figure 5 shows the total value of the Baidu Index of the PC and mobile terminals of \"artificial intelligence + education\" in Guizhou province from 1 January 2013 to 31 October 2020, with the monthly average as the statistical unit and a total of 94 sample data. As can be seen from the original figure, the data showed a trend of a gradual increase year by year, and the variation of the fluctuations showed a nonlinear and nonstationary sequence. Therefore, it was necessary to transform the original data appropriately to make it have the characteristics of the \(ARIMA\) model.  
Figure 6 is a diagram of the original sequence converted by the difference method. By comparison, it can be seen that the time series was close to the stationary series in the second difference, and the data showed strong independence. Since the mean value and variance of a stationary sequence would not change significantly, it was necessary to test whether the sequence of second-order difference met the stationarity requirements.  
The stationarity detection results of the sequence of second-order difference are shown in Figure 7. It can be seen that the mean value and variance of the sequence tended to be constant with relatively small fluctuation. At the same time, according to the calculated results of the unit root (Table 1), the statistical value was less than the critical value of 1%, so the time series was stable at 99% confidence, in line with the stationary characteristics required by the \(ARIMA\) model. Therefore, on this basis, the \(ARIMA\) model could be used for prediction.  
Figure 5: Total Baidu search index value of “AI + education” in Guizhou province.  
Figure 6: Sequence after differential treatment.  
Figure 7: Mean and variance of the sequence of second-order difference.  
After the above stationarity detection, the sequence after the second difference was stationarity, so the parameter \(d\) of the model was two. To determine the best \(p\) and \(q\) parameters, this paper adopted the Akaike information criterion to calculate the \(ARIMA(0,2,0)\) model with the lowest \(AIC\) value, as \(ARIMA(0,2,0)\) was selected to predict the attention degree for \"AI + education\" in Guizhou province.  
To analyze the availability of the \(ARIMA(0,2,0)\) model, the monthly mean of the index of \"artificial intelligence + education\" in Guizhou province from June 2016 to May 2020 was selected as the test set. Figure 8 is the fluctuation graph of the original data of the test set and the predicted value of the model. It can be seen from the graph that the predicted value and the actual value change trend were consistent. According to the \(RMSE\) result of 1.8590, the error was relatively small. Therefore, it was feasible to select the \(ARIMA(0,2,0)\) model to predict the attention to \"AI + education\" in Guizhou province.
## 5 System Architecture and Technology  
The system architecture is outlined in Figure 6 and follows a component-based approach. The general idea of the system architecture is to define software building blocks that are compatible and serve as clearly defined interfaces for further extension or are replaced by existing software tools. The overarching goal of the system is to lower the access barrier for all participants.  
Figure 6: Confirmation process within the system architecture.  
This is also the reason for why a mobile and web app was created, and it can be accessed from a browser on any smartphone, tablet, or desktop computer for all interactions with the system. The core of the system is the Management Backend component. It coordinates and encapsulates all interactions between the client, Blockchain Communication Backend, Payment Provider, and Company Identity Provider. The Blockchain Communication Backend combines all blockchain interactions in a dedicated component, which helps to make all connected components independent from technological decisions and implementation details of the underlying blockchain technology. Having a separate component means that the selected blockchain infrastructure can adapt to new requirements regarding data privacy, compliance, and security, change control, and access management processes, or it can switch to a combination of multiple blockchains. The Signing Backend plays an important role regarding control and access management, as it handles the signing identities for each participant in the system.
# Generating Super Spatial Resolution Products from Sentinel-2 Satellite Images +
## 4 Conclusions  
In this paper, the susceptibility of satellite images, such as Sentinel-2, to super-resolved pixelation images was addressed. The proposed method in this study involved utilizing spatial features in the model input. Implementing this approach managed to enhance the network's performance and improve the visual results. Therefore, investigating and focusing on the selection or extraction of appropriate features is an important consideration.  
Conceptualization, M.R.Z. and M.H.; methodology, M.R.Z.; software, M.R.Z.; validation, M.H. and M.R.Z.; data curation, M.R.Z.; writing--original draft preparation, M.R.Z.; writing--review and editing, M.R.Z. and M.H.; visualization, M.R.Z.; supervision, M.H.; project administration, M.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
## 4 Data Pre-Processing
### Data Analysis and Processing
#### 4.2.2 Classification of MTSA-Related Accidents  
The marine accident data utilized in this study included the maritime routes of passenger ships involved in accidents. Therefore, by combining the reviewed sailing data on maritime routes, it is possible to distinguish between ships operating on MTSA-related routes and marine accidents on MTSA-unrelated routes. In addition, the association between marine accidents and MTSA was examined. This process allowed for the identification of MTSA-related accidents and MTSA-unrelated accidents. This process is illustrated in Figure 6.  
Figure 4: Classification of “MTSA-related route” and “MTSA-unrelated routes” and derivation of sailing information.  
Figure 5: MTSA-related route and MTSA-unrelated route.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Number** \\ \hline MTSA-related route & 94 \\ \hline MTSA-unrelated sea route & 31 \\ \hline Total & 125 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Features of each maritime route.  
First, an overlay analysis of marine accidents and MTSA-related route data was conducted to identify marine accidents involving coastal passenger ships on MTSA-related routes. Subsequently, these data were implemented in the GIS software, and the location information of past MTSA-assessed project sites was input. Marine accidents occurring within 3 NM of the centroid of the project site boundary objects were extracted. Then, based on the maritime spatial characteristics of the extracted marine accident locations and the criteria mentioned in Table 2 of Section 3.2.2 regarding the classification of MTSA-related accidents, MTSA-related accidents on MTSA-related routes were confirmed.  
Second, marine accident data were overlaid with data on MTSA-unrelated routes, and an analysis was conducted to identify marine accidents involving coastal passenger ships on MTSA-unrelated routes. These data were visualized in GIS software to verify whether marine accident locations were within 3 NM of various nearby structures (such as ports, piers, and bridges) where MTSA was not previously conducted. Marine accident data beyond 3 NM were excluded from this study. The marine accident classification is illustrated in Figure 7, and the corresponding results are presented in Figure 8 and Table 6.  
Figure 6: Process of deriving MTSA-related accidents by route.  
Figure 7: Distribution range of related/unrelated accidents.
# Improving Deep Learning Anomaly Diagnostics with a Physics-Based Simulation Model
## 4 Validation with the Long Short-Term Memory Autoencoder
### Simulation Validation with SN and SA Data  
The SA data was generated using the Simulink uniform-random-number block with a multiplication range from 0.8 to 1.3. Any occurrences more than 1.25 (+25%) times the SN signal x6 were recorded and annotated as an anomaly label SA. The model-generated SA occurrences were randomly assigned to the x6 data column presented in DS3\({}_{\text{output}}\) Equation (6). However, the number of randomly generated x6 torque anomalies was governed by the anomaly partition depicted in the Diagnostics validation section using the DS2 data set.  
\[\text{DS3}_{\text{output}}=\begin{bmatrix}y_{1,2}&x1_{1,3}&x2_{1,3}&x3_{1,3}&x4 _{1,3}&x6_{1,3}\\ \dots&\dots&\dots&\dots&\dots&\dots\\ y_{11470,2}&x1_{11470,3}&x2_{11470,3}&x3_{11470,3}&x4_{11470,3}&x6_{11470,3}\\ \end{bmatrix} \tag{6}\]  
In DS3\({}_{\text{output}}\), the total anomaly share of 17.8% of the total data points resulted in the 2042 anomalistic data points appointed to column x6, considering the PMA-averaging function for the signal. The share of binary classified states for the DS3\({}_{\text{output}}\) is summarized in Table 8.  
As was done for the Diagnostics validation, the results from the LSTM autoencoder classification for the simulation data (Classification 2) are presented in a confusion matrix format in Table 9.  
Figure 9: Reconstruction error with a threshold of 0.3 for the Diagnostics validation DS2\({}_{\text{output}}\) data set.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Total Data Points** & **(\%)** & **Normal State (SN)** & **(\%)** & **Anomaly State (SA)** & **(\%)** \\ \hline
11,470 & 100.0 & 9428 & 82.2 & 2042 & 17.8 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Labeled simulated x6 data normal and anomaly states of the DS3\({}_{\text{output}}\).  
The results in the confusion matrix show that the simulated torque signal x6 in both SN and SA categories can be reliably created. The true-positive value remains dominant in the matrix yet it shows an improvement in negative classification (TN/FN) ratios. However, a direct comparison of the results between data sets DS2 output and DS3 output is cumbersome due to the differences in the data set-input-variable statistics. Similar to the the Diagnostics validation-results, a more comprehensive analysis of the results is performed in the Comparative validation-results.
## 4 Proposal: Tabu Search Approach
### Extension: Wireless Communication Network Connectivity  
In this section, we present the extended version of Tabu Search deployment presented above. The extension consists to guarantee the communication network connectivity for any communication range value, so all constraints of optimization problem (minimize number of sensors, reduce the gap between the generated and requested event detection probabilities, network connectivity) are included formally in our proposal. We adapt the initialization stage and the selection process of solutions in the neighborhood to build the network connectivity. The remainder steps and cost functions are identical to those presented in section 4.1. Hereafter, we detail the modifications made to the initialization stage and the selection of new solution process.  
1. _Initialization stage_ Compared to the initialization stage described in section 4.1., the new version differs in the fact that once the [PERSON] decision to deploy the first sensor is made, the only points which will be tested (i.e. [PERSON] decision to deploy or not to deploy a sensor) are those which are located inside the communication range \(R_{c}\) of any deployed sensor. The connectivity is thus guaranteed by construction (gradually). Moreover, the selection order of the cell to be tested follows a decreasing order of the difference between the requested and the generated detection probabilities. That is, the next tested point is \(p_{(i,j)}=\max_{p_{(x,y)}\in\mathcal{B}}\left(\mathcal{P}_{(x,y)}-r_{(x,y)}\right)\), where \(\mathcal{B}\) denotes the set of points of \(\mathcal{A}\) which have not been tested yet at this stage. The positions of the resulting deployed sensors are considered as the initial solution \(s_{0}\). The latter one is saved in the algorithm's memory (Tabu List).
2. _Selection of new solution_ After the neighborhood exploration (a suppression or an addition oriented stage) during the \(n^{th}\) iteration, an elected solution \(s_{n}\) must be chosen among the \(V\) (size of the neighborhood) explored candidates. For each candidate, say \(C\), we construct the graph of connectivity, denoted \(G(V,E)\). Here, \(V\) is the vertices set. The latter one is exclusively composed of points where, according to solution \(C\), a sensor will be deployed. Moreover, \(E\) is the edges set. Formally, \[E
## 1 Introduction
Considering existing acoustic channel models and respective features, it can be found that most of the approaches either suffer from computational complexity or are unable to deal with dynamic acoustic channel conditions. With the intent to alleviate such these limitations, in this paper, a novel statistical acoustic channel model has been derived by considering the key dynamic channel conditions under varied operating conditions [15]. The derived statistical channel model has been applied to examine the effectiveness of an enhanced systematic random linear network coding (SRLNC) based delay tolerant, energy efficient and QoS routing protocol for UWSN. Generally, UWSN comprises sensors and Autonomous Underwater Vehicles (AUVs), deployed under water to perform collaborative monitoring and communication [16]. Practically, the main intricacy for the implementation of AUVs in UWSNs is to design a robust Medium Access Control (MAC) protocol well-suited for the acoustic channel environment. The UWSN MAC routing protocol must enable maximum network throughput, minimal bandwidth consumption and access delay, and minimal energy consumption, and no doubt transmission security under acoustic channel conditions [17]. To ensure energy efficiency, QoS enriched, reliable communication enhancing UWSN MAC is a must. Similarly, reducing signalling overheads, primarily in forward error check (FEC), can be significant. With this motivation, in this paper, a novel systematic random linear network coding (SRLNC) based transmission model is proposed for UWSNs. The proposed SRLNC based transmission mechanism has been applied over a derived statistical channel model [18]. The results obtained exhibited higher throughput, minimal data loss and minimal retransmission probability, even under varying link loss probability caused due to scattering and acoustic dynamism.
# Nonlinear Integral Type Observer Design for State Estimation and Unknown Input Reconstruction
###### Abstract
By applying \(\xi>\zeta(t)\), the closed-loop observer dynamics can be governed by the following auxiliary system  
\[\dot{\mathbf{x}}=\mathcal{A}_{\mathcal{C}}\mathbf{x}+\mathcal{B}\Big{(}-\overline{ \xi}\text{sgn}(\overline{\mathbf{\epsilon}}_{2})\Big{)} \tag{45}\]  
where \(\overline{\xi}=diag\Big{(}\widetilde{\xi}_{i}\Big{)}\), \(\widetilde{\xi}_{i}\in\Big{[}\widetilde{\xi}_{i}^{+},\widetilde{\xi}_{i}^{-} \Big{]}\), \(\widetilde{\xi}_{i}^{+}=\|\mathbf{\upvarepsilon}\|+\max(\zeta(t))>0\) and \(\widetilde{\xi}_{i}^{-}=\|\mathbf{\upvarepsilon}\|-\max(\zeta(t))>0\). For (45), the following [PERSON] candidate is proposed  
\[\mathcal{V}=\mathbf{\mathbf{x}}^{T}\mathcal{P}\mathbf{x}+2\sum_{i=1}^{p}\mathcal{P}_{22}^ {i}\widetilde{\xi}_{i}\Big{|}\vec{\mathbf{\epsilon}}_{2}^{i}\Big{|} \tag{46}\]  
where \(\mathcal{P}_{22}^{i}\) and \(\vec{\mathbf{\epsilon}}_{2}^{i}\) stands for the \(i\)-th diagonal element in the \(\mathcal{P}_{23}\) and the \(i\)-th element in the \(\overline{\mathbf{\epsilon}}_{2}\),  
\[\overline{\mathcal{P}}\mathcal{A}^{T}+\mathcal{A}\overline{\mathcal{P}}-\mathcal{L}^ {T}\mathcal{B}^{T}-\mathcal{BL}+2\beta\overline{\mathcal{P}}<0 \tag{51}\]  
where \(\mathcal{L}=\mathcal{K}\overline{\mathcal{P}}\) is applied. Moreover, control gain minimization can be achieved by imposing the following minimization process further  
\[\text{min }\gamma\text{ subject to }\left[ \begin{array}{cc}-\gamma& \mathcal{L}^{T}\\ \mathcal{L}&-\gamma\end{array} \right]<0\text{ \ and \ }\overline{\mathcal{P}}>\mathbf{I} \tag{52}\]  
**Remark 2**. : _By using the similar concept of the equivalent control injection, since it has been proven that the estimation errors approach to zero asymptotically, from system (36) it reveals that \(\mathbf{v}\rightarrow\mathbf{D}_{22}\mathbf{f}(\mathbf{x},t)\). In other words, the perturbation term can be reconstructed by measuring the control signals (37) and thus the disturbance can be easily recovered._  
**Remark 3**. : _When applying SMO, low pass filters (LPFs) should be used for disturbance recovery. As point out in [1], the time constant applied in the LPFs needs to be sufficiently small to pass the slow component of the equivalent effort but is large enough to eliminate the high frequency component [26]. In other words, the cut-off frequency needs to be sufficient larger than the frequency of external disturbance so as to reach good estimation precision. The other way to reconstruct the unknown perturbation is to applied boundary layer technique [20; 21; 27] and the estimation precision depends on the size of the boundary layer. For the proposed PIO, the disturbance can be observed without incorporating extra LPFs and the resulting estimation precision can reach asymptotic level. Moreover, since the control algorithm used in the PIO is continuous, it is suitable for practical computer implementations as demonstrated in the previous works [24; 25]._
## 2 Materials and Methods
### Preparation of the Samples and Graphene Nano-Coatings
#### 2.1.3 Percolative Graphene by Self-Assembly of Graphene Flakes  
An Ultrathin Graphene Film (UGF) can be produced by self-assembly of graphene nano-platelets (GNPs). This method is a variant of the solution-processing-based methods of graphene flakes and follows the procedure described by [PERSON] et al. [37]. Graphene flakes were prepared in-house by electrochemical exfoliation from a bulk graphite rod (Sigma-Aldrich, Darmstadt, Germany) within an electrolyte solution, applying 10 V bias to a platinum wire as grounded electrode. The flakes were collected by filtration and then dispersed in dimethylformamide. To get high-quality flakes and narrow the flakes thickness distribution, the solution thus obtained was centrifuged to remove side products like thick graphite particles. Centrifuged flakes were re-dispersed in ethanol with a mass concentration of 1 mg/mL and the ethanol solution injected on the surface of the DI water. The graphene flakes spread on the DI water surface due to the Marangoni effect and tied with each other as ethanol evaporated. The shaping of the continuous UGF by the self-assembly process was facilitated by sonicating the DI bath. In this work, two sonication times were tried out: 3 h and 6 h. Previous Raman analysis [38] showed that with increasing the sonication time, the amount of graphene agglomerations increases too. The UGF was transferred onto the sample surface by immersion of the sample into the DI water solution, and the samples were finally dried at 75 \({}^{\circ}\)C.  
The few-layered graphene structure thus obtained, also called percolative graphene, is said to have high structural uniformity [37], even though it may suffer from clamping-up of flakes due to imperfect binding of flakes. Although relatively cheaper and easier to produce, there are several factors that may potentially play a role in the overall quality of the graphene layer, e.g., size of flakes, quality of flakes, and sonication time.
###### Abstract  
Terrestrial gross primary productivity (GPP) is the major carbon input to the terrestrial ecosystem. The Yangtze River Basin (YRB) holds a key role in shaping China's economic and social progress, as well as in ecological and environmental protection. However, how the GPP in the YRB responds to the climate factors remain unclear. In this research, we applied the Vegetation Photosynthesis Model (VPM) GPP data to explore the spatial and temporal variations of GPP in the YRB during 2000-2018. Based on the China Meteorological Forcing Dataset (CMFD), the partial least squares regression (PLSR) method was employed to identify the GPP responses to changes in precipitation, temperature, and shortwave radiation between 2000 and 2018. The findings showed that the long-term average of GPP in the YRB was 1153.5 \(\pm\) 472.4 g C m\({}^{-2}\) yr\({}^{-1}\) between 2000 and 2018. The GPP of the Han River Basin, the Yibin-Yichang section of the Yangtze River mainstream, and the Poyang Lake Basin were relatively high, while the GPP of the Jinsha River Basin above Shigu and the Taihu Lake Basin were relatively low. A significant upward trend in GPP was observed over the 19-year period, with an annual increase rate of 8.86 g C m\({}^{-2}\) yr\({}^{-1}\) per year. The GPP of the Poyang Lake Basin and Jialing River Basin grew much faster than other water resource regions. Savannas and forests also had relatively higher GPP rate of increase compared to other vegetation types. The relative contributions of precipitation, temperature, and shortwave radiation to GPP variations in the YRB were 13.85 \(\pm\) 13.86%, 58.87 \(\pm\) 9.79%, and 27.07 \(\pm\) 15.92%, respectively. Our results indicated that temperature was the main climatic driver on the changes of GPP in the YRB. This study contributes to an in-depth understanding of the variations and climate-impacting factors of vegetation productivity in the YRB.  
terrestrial gross primary productivity (GPP); Yangtze River Basin; climate change 2002-2018. Forensics (2023), 14, 1898. [[https://doi.org/10.3390/114091898](https://doi.org/10.3390/114091898)]([https://doi.org/10.3390/114091898](https://doi.org/10.3390/114091898))