esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT)
## 3 IIoT and Privacy-Preserving Architectures
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Technology** & **Description** & **Privacy Benefits** & **Challenges** & **Comparison** \\ \hline \hline \multirow{8}{*}{Blockchain transactions} & \multirow{8}{*}{Data Transparency: Blockchain allows participants in the network to have access to a transparent and auditable history of transactions without revealing specific identifying information.} & Scalability: Blockchain networks can face challenges in terms of scalability due to the consensus mechanisms and the need to replicate data across multiple nodes, resulting in slower transaction speeds. & \multirow{8}{*}{Data Handling: Blockchain technology stores data directly on the ledger.} \\  & & & & \\ \cline{1-1}  & \multirow{8}{*}{Blockchain technology is a decentralized distributed ledger system that offers enhanced security and privacy features. It ensures the integrity and immutability of data by storing transactions in a chain of blocks, making it difficult for malicious actors to alter or tamper with the data.} & \multirow{8}{*}{Data Integrity: Decentralized blockchain stores data directly on multiple nodes, resulting in slower transaction speeds.} \\ \cline{1-1}  & & & & Energy Consumption: Some blockchain networks, particularly those utilizing proof-of-work consensus, require significant computation power, for data contents. \\ \cline{1-1}  & & & & Data Privacy: While blockchain technology ensures data integrity and immutability, it does not inherently provide strong privacy protection for the contents of the data. \\ \cline{1-1}  & \multirow{8}{*}{Secure Transactions: blockchain employs cryptographic techniques, such as digital signatures and encryption, to secure data transfers and ensure authentication.} & \multirow{8}{*}{Data Privacy: While blockchain technology does not inherently provide strong privacy protection for the contents of the data.} \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \end{tabular}
\end{table} 
Table 2: Blockchain vs. Federated Learning. It is important to note that these solutions are not mutually exclusive, and their applicability depends on the specific requirements and constraints of the IIoT ecosystem. Organizations may choose to combine these approaches or utilize other privacy-enhancing technologies to achieve the desired level of privacy and security. This paper aims to provide insights into the privacy dimensions of the IIoT and provide recommendations for ensuring secure and privacy-respecting implementation, which can help to build trust in IIoT systems and ensure their successful adoption.
## 3 Experimental Imaging Results without Moving Elements
### Positive Magnifications  
In this section, we examine positive magnifications where the image has the same orientation as the object. These results have not been previously presented, to our knowledge. In each case, the first lens forms a real image between the two lenses that is inverted and serves as a real object for the second lens. This second lens then forms the erect image.  
Figure 7 shows the case where the first SLM is encoded with a focal length of \(f_{1}=50\) cm while the second SLM is encoded with a focal length of \(f_{2}=31.8\) cm. Note that this second focal length is below the Nyquist limit. The composed system has a negative focal length of about \(F=-27\) cm. We now obtain an erect image with a magnification of \(m=+2\) in excellent agreement with the theoretical value. The size of the image is larger than the detector.  
Figure 8 shows the case where the first SLM is encoded with a focal length of \(f_{1}=31.8\) cm while the second SLM is encoded with a focal length of \(f_{2}=50\) cm. Now the first lens has a focal length shorter than the Nyquist limit. The composed system again has a negative focal length of about \(F=-27\) cm. Again we obtain an erect image, but with a smaller magnification of \(m=+0.5\) in excellent agreement with theory. We cannot capture the entire image, unlike the case in Figure 5. In this case because of the short focal length of the first lens, the rays forming the external points of the intermediate image do not enter the second lens. This effect is similar to that shown in the holographic  
Figure 5: Zoom system configuration with \(f_{1}=\infty\) and \(f_{2}=+69.6\) cm and experimental image with magnification of \(m=-0.4\).  
Figure 6: Zoom system configuration with \(f_{1}=+100\) cm and \(f_{2}=+100\) cm and experimental image with magnification of \(m=-1\).
## 1 Introduction
### Interconnections of Islands in Greece  
Before its completion, [PERSON] et al. [19] conducted research on the subject of the interconnection between Peloponnese and Crete. Per the authors, the connectivity would be completed in two stages. The electric grid of Crete would be coupled with the system of Peloponnese in phase 1 of the project, with a capacity for transmitting power ranging from 150 MW to 180 MW. During phase 2, the electric grid of Crete is expected to be integrated with the system of Attica, and the capacity for transmitting power will be 1000 MW [20].  
[PERSON] et al. [21] conducted research on the current production of power on the non-interconnected islands in the Aegean Sea, with the exception of the islands of Crete and Rhodos. According to the authors, the yearly contribution of renewable energies in the overall energy mix has remained relatively constant at roughly 15% to 18% due to the current technological limits. They also claimed that the cost of generating energy on tiny islands using thermal power plants is significant, coming in at roughly 600 EUR/MWh. They suggested the creation of electricity storage devices as a means of increasing the proportion of renewable energies that are included in the overall energy mix on these islands.  
[PERSON] [22] has focused on the process of energy transition in the islands of Greece that are not connected with each other. According to what was stated in the article, customized transitional policies need to be devised. He also noted that the national objectives for the year 2030 include a decrease in CO2 emissions of forty (40) percent and a penetration of renewable energy sources of thirty-two (32) percent in the overall energy mix. Regarding the transportation industry, it is anticipated that there will be a complete shift in the kind of fuels that are used, in addition to an increase in the number of electric cars and trains.
## 3 Simulation Analysis and Method Comparison  
### Simulation Analysis of the Coupled Lorenz System  
When the tested signal is input into the coupled Lorenz system as a disturbance term, it will have an impact on the running state of the system, and the phase space trajectory of the two chaotic systems will lose synchronization. The synchronization error \(e(t)\) is the average value of the sum of the corresponding deviations of the three state variables. The equation is as follows.  
\[e(t)=\frac{1}{3}\left(e_{x}+e_{y}+e_{z}\right). \tag{9}\]  
According to the effect of the white noise and the periodic signal on the phase space trajectory, the synchronization error between the two ends \(e(t)\) is mainly influenced by the periodic signal, and the SNR of the synchronization error data will be greatly improved compared with the tested signal.  
The structural parameters of the Lorenz system are set to \(\sigma=10\), \(\beta=2.6\). Referring to Figure 3b, when \(\rho=50\), the Lorenz system runs in the chaotic state, which can better identify the periodic signal. Therefore, we configured the chaotic system as the above parameters. To verify the inference proposed above, a sinusoidal signal \(0.001\sin\left(2\pi t\right)\) and a white noise (\(N(0,0.05)\)) are superimposed as the tested signal \(r(t)\), and its SNR is  
\[SNR_{r(t)}=10\lg\left(P_{s(t)}/P_{n(t)}\right)=-50\,dB. \tag{10}\]  
The time domain waveform and power spectral density of the tested signal and the synchronization error are shown in Figure 4.  
It can be seen that the weak sinusoidal signal has been masked in the noise in Figure 4a. It is difficult to directly distinguish in the frequency domain. In Figure 4b, a considerable amount of noise is filtered out in the synchronization error, and its SNR is greatly improved. By finding the peak value of the power spectral density, the frequency of the sinusoidal signal obtained from this figure is \(1\) Hz, which is consistent with the actual value. The simulation result shows that the new coupled Lorenz system has a good noise reduction function.  
Figure 4: The time domain waveform and power spectral density of the tested signal (**a**) and the synchronization error (**b**).
# Optical Cable Lifespan Prediction Method Based on Autoformer
## 4 Autoformer-Based Optical Cable Life Prediction Model
### Prediction Process Based on the Autoformer Model  
This paper employs the Autoformer model for predicting the lifespan of optical cables. The proposed forecasting approach is primarily divided into two segments: data preprocessing and model construction, with the specific workflow illustrated in Figure 2.  
Figure 2: Prediction process.  
Data Preprocessing: This study utilizes measured meteorological data from January 2011 to December 2023 in Guangzhou, including daily maximum and minimum temperatures and wind speed. Subsequently, leveraging the daily average temperature and wind speed, the corresponding optical cable lengths are calculated using a dedicated formula. For anomalous values, the Lagrange interpolation method is employed for estimation and imputation.  
Model Construction: The model is constructed following the architectural schematic of Autoformer, with input comprising the processed data and labels representing the residual lifespan at that time step. The output is a time series for the remaining lifespan prediction.  
Model Training and Testing: The model is trained on the training dataset and then evaluated on a separate test dataset.
## 3 Experiments and Result Analysis
### Results and Analysis
#### 3.3.3 The Performance for the Different Size of Input Sample  
The size of input sample for classifiers also affects the performance for crop classification. After the compression data with NC-SAE, Table 7 gives the classification results of MSCDN classifier with different sample size, the corresponding training curves are shown in Figure 9. Firstly, we set the size of input samples for the MSCDN classifier to \(15\times 15\). In this scenario, slightly over fitting has been observed when training the MSCDN, which is shown in Figure 9a. This problem has been ultimately solved by increasing the size of input sample. Figure 9b shows the training curve for input samples with size of \(35\times 35\). We see that the over-fitting can be completely eliminated by expanding the input size. Observing Table 7, we see that the OA and Kappa increased by 4.12% and 4.98%, ultimately rise to 99.33% and 99.19%, respectively, when increasing the input size from \(15\times 15\) to \(35\times 35\), whereas when the size of input samples was expanded to \(55\times 55\), both OA and Kappa only raised approximately 0.3% relative to the \(35\times 35\), while considerable computational burden is needed. Therefore, a moderate size \(35\times 35\) for input samples is recommended in real applications.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Ground Truth**} \\ \cline{2-10}  & \multicolumn{4}{c}{**CNN**} & \multicolumn{4}{c}{**MSCDN**} \\ \cline{2-10}  & **Duw** & **Mip** & **Mih** & **Chf** & **Duw** & **Mip** & **Mih** & **Chf** \\ \hline \multirow{8}{*}{**Classified**} & **Len** & 0.58 & 1.93 & 0.79 & **55.9** & 0.09 & 0.04 & 0.26 & **8.98** \\  & **Duw** & **69.2** & 1.80 & 0.01 & 0 & **98.4** & 0.65 & 0 & 0 \\  & **Spw** & **22.9** & 1.31 & 1.75 & 0 & **0.78** & 0.14 & 0.38 & 0 \\  & **Fip** & 0.25 & 0.07 & 0.13 & 0.33 & 0.18 & 0.04 & 0.08 & 0.67 \\  & **Oat** & 1.18 & 0.02 & 0.36 & 0 & 0 & 0.61 & 0.01 & 0 \\  & **Can** & 0.59 & 0 & 0.53 & 0 & 0.22 & 0 & 0.46 & 0 \\  & **Gra** & 0 & **9.03** & **11.7** & 0.07 & 0 & **0** & **2.10** & 0.26 \\
**Image** & **Mip** & 0.01 & **69.0** & 4.84 & 0 & 0.01 & **97.3** & 0.05 & 0 \\  & **Mih** & 0.07 & 3.25 & **76.3** & 0 & 0 & 0.73 & **96.3** & 0 \\  & **Bar** & 3.98 & 0.06 & 0.21 & 0 & 0.21 & 0.07 & 0 & 0 \\  & **Suf** & 0 & **9.54** & 0 & 0 & 0.04 & **0** & 0.23 & 0 \\  & **Fla** & 0.92 & 3.99 & 3.30 & 0.70 & 0.01 & 0.33 & 0.01 & 0.11 \\  & **Cas** & 0.21 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & **Chf** & 0 & 0 & 0 & **42.9** & 0 & 0 & 0 & **89.9** \\ \hline \hline \end{tabular} Note: The numbers using bold represent the accuracy of easily confused crops.  
\end{table}
Table 6: The partial confusion matrix of crop classification for CNN and MSCDN.  
Figure 9: Training curves with the sample size of (**a**) \(15\times 15\), (**b**) \(35\times 35\).
## 4 Experiments and Results
### Ablation Experiment
#### 4.4.2 Ablation Experiment on C2f_D2 CN  
To verify the effectiveness of the deformable convolution and dynamic convolution in the C2f_D2 CN module, we conducted experiments on the CrowdHuman dataset. The experimental results in Table 3 clearly demonstrate the improvement in both accuracy and efficiency brought by the C2f_D2 CN module. With the addition of deformable convolution and dynamic convolution, the FLOPs only slightly increased, while the AP improved by 1.5% and the MR\({}^{-2}\) decreased by 1.1%. This proves that our proposed module can effectively enhance detection accuracy while maintaining computational efficiency.
# 3D Stress Analysis of Multilayered Functionally Graded Plates and Shells under Moisture Conditions
## 4 Results  
This section is of fundamental importance. First of all, it defines the properties of the Functionally Graded (FM) layers, presenting the mechanical and hygrometric properties of their constituents and the law defining their variation in composition. Then, it features two subsections. The first one validates this exact 3D solution for shells embedding layers made of FGM. Validations of new solutions are often conducted comparing the new outputs with those already available in the literature. Besides verifying the accuracy of this solution, this phase helps define how many mathematical layers should be used to consider with confidence the effects of the curvature and those of the FGM law and the order of expansion to use in the exponential matrix calculation. Strengthened by those results, the second subsection presents a set of new results. The effect of the moisture content field is studied on different geometries, featuring different stacking sequences, thickness ratios, and moisture content boundary conditions. In all the assessments and benchmarks, the FGM layers rely on two constituents: a metal and ceramic. The metal is Monel, 70 Ni30 Cu, a nickel-based alloy; the ceramic is Zirconia. An estimate of the mechanical properties is given in terms of the bulk modulus and the shear modulus \(\mu\) of the two materials; the moisture expansion coefficients \(\eta\) and the moisture diffusion coefficients \(\mathcal{D}\) are given explicitly:  
\[K_{m}=227.24\,\mathrm{GPa},\quad\mu_{m}=65.55\,\mathrm{GPa},\quad \eta_{m}=2\times 10^{-3}\frac{1}{\%},\quad\mathcal{D}_{m}=10^{-9}\,\frac{ \mathrm{kg}}{\mathrm{ms}}, \tag{79}\] \[K_{c}=125.83\,\mathrm{GPa},\quad\mu_{c}=58.077\,\mathrm{GPa},\quad \eta_{c}=1\times 10^{-3}\frac{1}{\%},\quad\mathcal{D}_{c}=10^{-10}\,\frac{ \mathrm{kg}}{\mathrm{ms}}, \tag{80}\]  
The metal and the ceramic phases are denoted by the subscripts \(m\) and \(c\). The estimate of the thermal properties is also given, as they will be necessary in the assessment phase:  
\[\gamma_{m}=15\times 10^{-6}\frac{1}{K},\quad\kappa_{m}=25\frac{ \mathrm{W}}{\mathrm{mK}} \tag{81}\] \[\gamma_{c}=10\times 10^{-6}\frac{1}{K},\quad\kappa_{c}=2.09\frac{ \mathrm{W}}{\mathrm{mK}} \tag{82}\]  
\(\gamma\) denotes the thermal expansion coefficient, while \(\kappa\) the conductivity coefficient. This work assumes that the volume fraction of the ceramic phase follows a power law of order \(p\). Introducing the thickness of the FG layer \(h_{{}_{FG}}\) and a local thickness coordinate \(\tilde{z}_{{}_{FG}}\) inside it (0 at its bottom, \(h\) at its top), the FG law takes the following expression:  
\[V_{c}=(\tilde{z}_{{}_{FG}}/h_{{}_{FG}})^{p} \tag{83}\]  
At the bottom of the FG layer, where \(\tilde{z}_{{}_{FG}}=0\), Equation (83) implies \(V_{c}=0\), meaning that it is made of metal only. At the top of the FG layer, where \(\tilde{z}_{{}_{FG}}=h_{{}_{FG}}\), Equation (83) implies \(V_{c}=1\), meaning that it is made of ceramic only.
# The Plasma Ozonizer System for Mangosteen Storage Container to Preserve the Quality of Mangosteen
## 4 Discussion
### Ethylene Concentration of Mangosteen Fruit  
Mangosteen is a climacter fruit that ripens via the hormone ethylene [2]. Ethylene is a gaseous plant hormone important in inducing the ripening process. The pattern of ethylene concentration of untreated ozone mangosteen at 15 \({}^{\circ}\)C in this study is approximately 0.56 ppm on day 1, 1.4 ppm on day 8, and rapidly increases to a maximum of about 17.64 ppm by day 19.  
The results showed that at the maximum ethylene concentration day, the concentration of untreated fruit was about 11, 4, and 3-fold higher than treated fruit after ozone fumigation from oxygen flow rates 2, 3, and 4 L/min (ozone concentration about 80, 55, and 30 ppm), respectively. When ozone concentration increased, so did a trend showing a decrease in ethylene concentration. This result agreed with the findings mentioned by [PERSON] et al. [29], [PERSON] et al. [30], [PERSON] et al. [19]. It was indicated that ethylene may potentially be oxidized by ozone and that this would be more significant as ozone exposure concentrations increase. When ethylene is exposed to ozone over an extended period of time in a humid cold storage environment, it eventually breaks down into carbon dioxide, carbon monoxide, and water [31].  
In climacteric fruits, ethylene is the main regulator of ripening [32]. Mangosteen is a fruit that displays climacteric characteristics, so ethylene plays a significant role in ripening [33]. Our results imply that ozone can prolong the life of fruit after harvest by effectively inhibiting the ripening and maturity processes. Longer shelf life is achieved by inhibiting ethylene biosynthesis or ethylene activity, which effectively slows down the ripening process [29]. According to [PERSON] et al. [34], using ozone to treat cantaloupe melons for 13 days at 6 \({}^{\circ}\)C decreased the activities of cell wall enzymes, lowering the ethylene concentration and extending the shelf life.
## 2 Synchronization Protocols in WSN
### Common Synchronization Protocols in WSN  
In the last 10 years, many works have been published in the WSN area. Most time synchronization approaches in the scientific literature can be grouped into two main fields: The first group finds its foundations in obtaining the synchronization through the acquisition and the processing of data [7] to estimate the delays using mathematical models. The second group obtains the synchronization by analyzing the delay sources and removing them from the communication in the root node [8]. Both approaches have reached several advances in synchronization protocols [2, 3, 9, 10, 11].  
[PERSON] _et al._ proposed the Reference Broadcast Synchronization Protocol [9] (RBS). The beacon frames sent by the reference node do not contain any time information. These frames are used by the nodes to determine the arrival time and, thus, to compare their local clocks using message transfer.  
With the goal of improving the RBS protocol, [PERSON] _et al._ proposed the Timing-sync Protocol for Sensor Networks (TSPN) [10]. It uses a hierarchical topology to create a network with N levels, all of them synchronized with the root node. Two phases are applied to obtain the complete synchronization: the level discovery phase and the synchronization phase. In the level discovery phase, the topology and the levels of the network are created. The synchronization phase is responsible for carrying out the message exchanging between levels to achieve the synchronization in the whole network using MACtime-stamping. The major problem of this approach is in the message exchange. When the network has a large number of nodes, the energy consumption due to send and receive packets is very high.  
The Flooding Time Synchronization Protocol (FTSP) was proposed by [PERSON] _et al._[3]. It is based in the concept of flooding (broadcasting) packets in WSNs. Each broadcast packet contains synchronization information about the root node of the network. It sends broadcast packets periodically with its local time (which is considered to be the global time of the network). When the broadcast message arrives at its destination, the arrival time is stored using MAC time-stamping. Using the retrieved information and the local MAC time-stamping, a reference point is obtained. It contains information about the global time and the local time of the receiving node. When a certain number of reference points is reached, the nodes can obtain both the offset and the clock skew with high accuracy using linear regression.  
Besides the above mentioned mechanisms, a great amount of protocols have been proposed in the literature to synchronize a WSN as, for example, the Reliable Slotted Broadcast Protocol (RBSP), which is based on the concept of broadcast time slots.