esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Routing Maintenance and Reconstruction Algorithm  
### Routing Maintenance Principle  
Because of the openness and time variability of the low-voltage power line network, bad nodes will appear during the operation of the network. Two principles to maintain the route are proposed to ensure the success rate of the data collection for the BS:  
1 when bad nodes caused by routing interrupt the operation of the completed network, a process to reconstruct the local routing of the bad nodes begins (in Section 3.2).
2 during the idle periods of the network, the BS initiates the routing detection instructions. If a bad node is found, principle (1) is implemented to establish new communication routes.  
In the secondary side of a 100 m \(\times\) 100 m tree-type grid, we set 39 end-user nodes and a base station node (Node 1) in Figure 5. The results for the artificial cobweb routing algorithm (ACRA) are shown in Figure 6. Each node in a grid with tree topology only has a single point-to-point link shown in Figure 5. If one communication link (such as links 21-31) is interrupted, unless the entire network is reconstructed, the interrupted communication link cannot be restored. In the ACRA network shown in Figure 6, the nodes in the same layer (such as nodes 23, 28, 33, 36) can communicate with other nodes. A peripheral node may continue to communicate with central node owing to the additional tangential  
Figure 4: Result of networking.  
connections when one communication link is interrupted. For example, when the links 28-33 are interrupted, peripheral node 28 can communicate with central node 33 by choosing peripheral node 23 as a relay. After networking, the BS can communicate with any node in the networking via the central node, which acts as a relay node. An example is shown in Table 1. It is obvious that the communication quality is more effective using the ACRA than using tree topology. The ACRA can effectively reduce the network reconfiguration frequency.
# Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review
## 4 Partial Discharge Measurement Using Different Types of Sensors on the Power Transformer and GIS
### Mechanism in Optical Sensors
The electrical signal undergoes amplification before being sent to a high-speed signal processor or a digital oscilloscope. The silica diaphragm and single-mode fiber Fabry-Perot interferometer sensor is built within the sensor lead. The Fabry-Perot interferometer sensor's capacity to track acoustic signals produced by partial discharge inside the transformer has been proven in multiple testing. On the other hand, in addition to those aforementioned earlier in the manuscript, Tables 2 and 3 show the recent timelines of partial discharge detection activities in power transformers and gas-insulated switchgear, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Reference, Year**} & \multicolumn{2}{c}{**Artificial Defect/PD Test**} & \multirow{2}{*}{**Techniques**} & \multirow{2}{*}{**Significant Outcomes**} \\  & & **Cell/Electrodes Configuration** & \\ \hline \multirow{4}{*}{[128], 2018} & \multirow{4}{*}{Needle–plane model} & A three-phase oil-filled transformer’s whole internal structure was employed to present the propagation properties of electromagnetic waves & The EM signal’s amplitude reduces nonlinearly as its distance from the FD source grows, and the rate of damppering slows as it does so. \\  & & Transformer oil characteristics for a temperature range of 30–75° C may be identified via the AE technique & Due to alterations in factors like viscosity and BDV, the AE signal’s amplitude decreased from 65° C to 75° C at 17 kV. \\ \hline \multirow{4}{*}{[129], 2018} & \multirow{4}{*}{Needle–plane electrode} & Fabry–[PERSON] optical fiber sensor array-based AE technique with a steered response power sound-source localization algorithm & Enhanced accurateness compared to the more common piezoelectric transducer. \\ \cline{1-1} \cline{3-3}
# Noise Reduction in CWRU Data Using DAE and Classification with ViT
## 3 Application Example
### ViT-Based Classification Results  
#### 3.3.1 Fine-Tuning  
The pre-trained ViT model introduced in Section 2.3 was fine-tuned using the bearing data from the CWRU dataset. To prevent overfitting and improve generalization performance, the learning rate was set to 0.0001, and the model was trained for 10 epochs with the Adam optimizer. During the fine-tuning process, the loss of the training dataset was monitored, and the model's performance was evaluated on the test dataset. Among 280 images for each class, 224 images were used to fine-tune the ViT model, and 56 images were used as test data.
## 3 Model Development
### Groundwater Flow Model
#### 3.1.4 Simulated Pump-Storage Operations Scenarios  
Four pumping-discharge scenarios (Figure 4) have been considered (Smartwater project, 2018; [4,7]). The pumping-discharge scenarios, which were implemented as boundary conditions in the models, are based on real electricity data. These scenarios describe the emptying and filling of the upper reservoir and the alternating phases of pumping, discharge, and no-activity over a period of fourteen days. In Figure 3, negative flow rate values correspond to pumping operations in the quarry and filling of the upper reservoir. Positive flow rate values correspond to discharge steps of the upper reservoir through the turbines and injection in the quarry. Three of the pumping-discharge scenarios are based on the electricity market daily data at different seasons of the year (spring, summer, and winter). The pumping and discharge periods take approximately five hours. The succession of the different phases (pumping/discharge/no-activity) is faster in winter, intermediate in spring, and slower in summer. The last scenario was generated randomly, with a change of phase (i.e., pumping/discharge/no-activity) every 15 min, following a uniform law. This scenario has been developed in line with PSH stations potentially connected to renewable and intermittent energy sources, whose production management would probably require higher frequencies, with periods shorter than an hour. The pumping-discharge scenarios were used as input data for the numerical model and were implemented by prescribing the flow rates (i.e., Neuman boundary conditions) in the cells corresponding to the quarry. The pumping and discharge flow rates were chosen according to the volume of the upper reservoir (\(\approx\)1 million m\({}^{3}\)) to satisfy that it can be filled or emptied during one pumping/discharge cycle. Thus, for an example cycle of 4.8 h, the pumping/discharge rate was 55.56 m\({}^{3}\)/s.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Alluvium** & **Chalk** & **Quary** \\ \hline Hydraulic & \(10^{-4}\) & \(10^{-5}\) & \(10^{-1}\) \\ conductivity [m/s] & 30 & 16 & 100 \\ Specific yield [\%] & 30 & 16 & 100 \\ \hline \end{tabular}
\end{table}
Table 2: Calibrated hydraulic parameters values used in the groundwater flow model of the Obourg quarries.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Resource Allocation** & **Power Allocation** & **Energy Efficiency** & \begin{tabular}{c} **Dynamic Resource** \\ **Allocation** \\ \end{tabular} & \
\begin{tabular}{c} **Computational** \\ **Efficiency** \\ \end{tabular} &
\begin{tabular}{c} **Computation** \\ **Offloading** \\ \end{tabular} \\ \hline [PERSON] [93] & Yes & No & No & No & No & No \\ \hline [PERSON] et al. [64] & Yes & No & Yes & No & No & No \\ \hline [PERSON] et al. [94] & No & Yes & No & No & No & No \\ \hline [PERSON] et al. [95] & Yes & No & No & No & No & No \\ \hline [PERSON] et al. [96] & No & No & No & No & No & Yes \\ \hline [PERSON] et al. [97] & No & Yes & No & No & No & No \\ \hline [PERSON] et al. [98] & Yes & No & No & No & No & No \\ \hline [PERSON], [PERSON], and [PERSON] [99] & Yes & No & No & No & No & No \\ \hline [PERSON], [PERSON], and [PERSON] [100] & No & No & Yes & No & No & No \\ \hline [PERSON], [PERSON] \& [PERSON] [101] & No & Yes & No & No & No \\ \hline [PERSON] [102] & No & No & Yes & No & No & No \\ \hline [PERSON] et al. [103] & No & No & Yes & No & No & Yes \\ \hline [PERSON], [PERSON] and [PERSON] [104] & No & No & No & No & Yes & No \\ \hline [PERSON] et al. [105] & Yes & No & No & No & No & No \\ \hline [PERSON] et al. [106] & Yes & No & No & No & No & Yes \\ \hline [PERSON] et al. [107] & No & Yes & Yes & No & No & No \\ \hline [PERSON] et al. [108] & Yes & No & No & No & No & Yes \\ \hline [PERSON] and [PERSON] [109] & No & No & No & No & No & No \\ \hline [PERSON] et al. [110] & No & Yes & No & No & No & No \\ \hline [PERSON] et al. [111] & No & No & No & No & No & Yes \\ \hline [PERSON] et al. [112] & No & Yes & No & No & No & No \\ \hline [PERSON] et al. [113] & No & No & Yes & No & No & No \\ \hline [PERSON] et al. [114] & Yes & No & No & No & No & No \\ \hline [PERSON] et al. [115] & Yes & No & No & No & No & No \\ \hline [PERSON] et al. [116] & No & Yes & No & No & No & No \\ \hline [PERSON] et al. [117] & No & No & Yes & No & No & Yes \\ \hline [PERSON] et al. [118] & No & No & No & No & No & Yes \\ \hline [PERSON] et al. [119] & No & No & Yes & No & Yes & No \\ \hline [PERSON], [PERSON], and [PERSON] [120] & X & No & No & No & No & No \\ \hline [PERSON] et al. [121] & No & Yes & No & No & No & No \\ \hline [PERSON] et al. [122] & No & No & No & No & No & No \\ \hline [PERSON] et al. [123] & Yes & No & No & No & No & No \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Category-wise data for reviewed research articles.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 5 BackCom: Wireless Communications
### BackCom Communication Modes
#### 5.1.2 Half-Duplex BackCom  
Half-duplex mode in BackCom considers data to be transmitted one way at a time. This mode requires less energy than the counterpart, due to the single way transmission at an instant. [PERSON] et al. suggested the optimal policies for time allocation in the half-duplex BAWPCN to maximize the system throughput [95]. The half-duplex mode of communication was also used in BackCom for relaying and for extension of the communication range [109]. The data rate of the half-duplex BackCom is lower than the full-duplex mode due to one-way communication on the same frequency spectrum. This is the reason that the half-duplex mode was used only in bandwidth constraint applications, with a low data rate requirement.  
**Discussion:** Table 6 summarizes the communication modes. Both communication modes of BackCom have importance in different applications. However, the full-duplex mode is widely used, due to the advantage of dual way communications. The usage of both methods in various scenarios is enumerated below:  
1. Full-duplex mode of BackCom is used in multiple access [77], in-band communication [78], H-AP [92], relaying [93], and feedback channels [94].
2. The simultaneous transmission and reception are supported in full-duplex mode. This enables the concurrent reception of energy while transmitting data [93]. Moreover, this feature of simultaneous transmissions and reception enhances the data rate compared to the half-duplex mode.
3. The half-duplex mode of transmission has the benefits of lower energy consumption and low bandwidth utilization [109].
# Bloch Oscillations in the Chains of Artificial Atoms Dressed with Photons
###### Abstract  
We present a model of one-dimensional chain of two-level artificial atoms driven with DC field and quantum light simultaneously in a strong coupling regime. The interaction of atoms with light leads to electron-photon entanglement (dressing of the atoms with light). The driving via dc field leads to the Bloch oscillations (BO) in the chain of dressed atoms. We consider the mutual influence of dressing and BO and show that scenario of oscillations dramatically differs from predicted by the Jaynes-Cummings and Bloch-Zener models. We study the evolution of the population inversion, tunneling current, photon probability distribution, mean number of photons, and photon number variance, and show the influence of BO on the quantum-statistical characteristics of light. For example, the collapse-revivals picture and vacuum Rabi-oscillations are strongly modulated with Bloch frequency. As a result, quantum properties of light and degree of electron-photon entanglement become controllable via adiabatic dc field turning. On the other hand, the low-frequency tunneling current depends on the quantum light statistics (in particular, for coherent initial state it is modulated accordingly the collapse-revivals picture). The developed model is universal with respect to the physical origin of artificial atom and frequency range of atom-light interaction. The model is adapted to the 2D-heterostructures (THz frequencies), semiconductor quantum dots (optical range), and Josephson junctions (microwaves). The data for numerical simulations are taken from recently published experiments. The obtained results open a new way in quantum state engineering and nano-photonic spectroscopy.  
Bloch oscillations; Rabi-waves; electron-photon entanglement; artificial atoms +
Footnote †: journal: Applied Physics Letters
## 3 Results
### Bilayered Glass-Ceramic Implant  
Figure 7 shows the interfacial TT-CEL2/TT-SCNA region of the bilayered glass-ceramic cylindrical implant. This image was acquired by SEM set in back-scattered mode (BSM) to better distinguish the materials of the two layers. In addition, BSM is useful to detect the different phases constituting each layer. As regards TT-SCNA, needle-shaped crystals of wollastonite (white regions) ranging within 10-30 \(\upmu\)m in length are clearly distinguishable from the amorphous phase (gray regions). On the contrary, the crystalline phases of TT-CEL2 cannot be clearly detected at this magnification due to the small size of the crystals (below 5 \(\upmu\)m) and the low contrast between glassy and crystalline phases.  
As shown in Figure 7, TT-CEL2 and TT-SCNA layers were successfully joined together without any interfacial defects or cracks. Pores ranging within 5-30 \(\upmu\)m are visible in both materials: in fact, as mentioned in the Section 3.2.2, a residual porosity is present in the sintered bodies. Future optimization of the glass particle size and pressing conditions will allow a reduction of the pore content of the materials, thus leading to a further improvement of their mechanical properties.  
In vitro tests resulted in evidence for the different behaviour of the two regions of the implant put into contact with biological fluids. After immersion for seven days in SBF, no surface modifications occurred on the TT-SCNA layer (Figure 8a), as already mentioned in Section 3.2.3. On the contrary, a continuous and homogeneous layer of HA, with a thickness of \(\sim\)40 \(\upmu\)m, is clearly visible on the TT-CEL2 surface (Figure 8b). Similar results were observed after immersion for one month in SBF (Figure 9): TT-SCNA continued to exhibit a bioinert behaviour, whereas a HA layer grew in thickness up to 100-150 \(\upmu\)m on the TT-CEL2 surface.  
Figure 6: In vitro bioactivity tests carried out on TT-FaGC samples: (**a**) sample surface after soaking for seven days in SBF and (**b**) corresponding EDS pattern.  
Figure 7: Bilayered glass-ceramic implant: interface between TT-CEL2 and TT-SCNA layers.
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 4 Fault Prediction Results and Discussion
### Fault Prediction Results and Discussion of Current Signal
#### 4.2.2 The Results of LSSVM Optimized by QGA  
For the current signal, Grid Search_LSSVM, GA_LSSVM and QGA_LSSVM are also used for prediction. The same first 45 current samples are also used to train the LSSVM. The other 15 samples are also used as the testing set. The optimized parameters \([\gamma,\sigma^{2}]\) obtained by the three prediction models are listed in Table 12.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Data Set** & **RMSE** & **MAE** & \(R^{2}\) & **Time(s)** \\ \hline \multirow{2}{*}{MGM (1,11)} & Fitted values & 0.0898 & 0.0690 & 0.6267 & \multirow{2}{*}{1.9489} \\  & **Predicted values** & **0.4594** & **0.3732** & **0.2725** & \\ \hline \multirow{2}{*}{IMGM (1,11)} & Fitted values & 0.0893 & 0.0563 & 0.7265 & \multirow{2}{*}{1.8974} \\  & **Predicted values** & **0.1723** & **0.1216** & **0.7546** & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of forecasting evaluation indexes of MGM (1,11) and IMGM (1,11) for the current signal.  
Figure 6: The result of MGM (1,11) and IMGM (1,11) for the current signal. (**a**) The result of MGM (1,11); (**b**) the result of IMGM (1,11).  
The three forecasting models are established based on the optimized parameters in Table 12. The corresponding forecasting evaluation indexes are listed in Table 13.  
For the training set, the Grid Search_LSSVM has smaller errors than GA_LSSVM and QGA_LSSVM. For the testing set, all of forecasting evaluation indexes of QGA_LSSVM are better than those of GA_LSSVM and Grid Search_LSSVM in Table 13. Thus, QGA_LSSVM has a good prediction effect not only for the vibration signal but also for the current signal, and QGA_LSSVM can be one of the single forecasting models input to the combined forecasting model for the current signal.
## 4 Materials and Methods
### Pre-Processing and Transformation  
We created two separate datasets, one for controversial artworks and one for non-controversial artworks. The original csv (comma separated value) files displayed tabular data. Both datasets contained seven columns, including the artwork's id, comments, likes, saved to collection, has more comments, Facebook shares, and text emotion. The JSON (JavaScript Object Notation) strings were converted to csv (comma separated value) using Python modules from the Pandas library [102]. When necessary, we transformed categorical data to numeric, such as in columns saved to collection and has more comments when the values were binary (TRUE or FALSE). The remaining columns, including id, likes, comments, and Facebook shares, were initially formatted as numerals. To integrate the accompanying text with the posts in the datasets, we employed a different tactic. The original text order in each post were maintained. Using the OpenRefine software, we removed spelling errors (\"MUSEum Dayzzzz\" to \"Museum Day\"), special characters (@, #, &, %, \", +), stop words (and, the, to, etc.), punctuation (!,?), and other symbols [103]. While translating posts from multiple different languages to English using the proper Python modules, we also converted all uppercase letters to lowercase. After cleaning the data, we were able to extract the emotions from the text.
# Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis
## 4 Experiment
### Parameter Analysis and Comparisons  
In Algorithm 1, the parameters are defined based on a random distance and do not require user specification. Parameter \(I_{\mathit{lte}}\), the number of iterations, according to our experiment, has a minor impact on the distribution when the number of points is relatively small, e.g., less than 30. If \(N>30\), we set \(I_{\mathit{lte}}=2500\) times as the default value.  
Figure 8: Thirty-one viewpoints in front of the object in the upper hemisphere are used to construct viewpoint-related projection images, and a neighbor topology link graph with three neighbor angles. The colors of lines and fonts are only used for differentiation and have no other meaning. The number next to the vertex is the sequence number of the sampling viewpoint. (**a**) The neighbor ship is built according to \(\delta_{0}=0\); (**b**) The neighbor ship is built according to \(\delta_{0}=\pi/6\); (**c**) The neighbor ship is built according to \(\delta_{0}=\pi/3\).  
Figure 9: Thirty-one images are projected from the same chair along 31 different viewpoints. The top-left sub-image is a 3D model, while the remaining images are 31 projection images from different viewpoints.  
In Sections 3.3 and 3.4, parameter \(\delta_{0}\), the maximum tolerable viewpoint deviation angle, significantly affects the accuracy value. If \(\delta_{0}=0\), an estimated viewpoint is considered correct only if it perfectly coincides with the ground truth viewpoint; otherwise, it is considered incorrect. In this case, the formula for calculating the classification accuracy follows the standard accuracy formula used in image classification. The accuracy calculation method for viewpoint estimation proposed in this study is an extension of the traditional accuracy calculation method for image classification. The smaller the value, the stricter the accuracy criterion, allowing for less error tolerance.  
We experimented to demonstrate the effectiveness of using our method for viewpoint estimation. Using \(\delta_{0}=0,\pi/6,\pi/3\), the nearest neighbor relationships of the 31 viewpoints are represented by a distance matrix, as shown in Figure 10.
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 6 Security Assurances and IoT Threat Model
### Definitions of Security Assurances
#### 6.1.4 Availability  
The property of availability [38] guarantees that services (e.g., web server, email server, data server, telephone, power, network bandwidth, and so on) and resources (e.g., data, storage engine, computational engine) are fully available when needed or requested by their consumers such as human users, computing nodes, processes or other entities.  
In the context of IoT, the availability property ensures that the components of IoT networks, such as sensor nodes, gateway servers, and data servers, among others, are available to each other and their users or owners. For instance, in an industrial IoT, temperature sensors must be available for measuring temperature throughout the operational time. In an agricultural IoT, losing equipment such as humidity and pesticide level measuring sensors, among others, can highly disrupt the production and supply chain [43]. Another issue affecting IoT data availability may arise due to the proprietary lock-in effect, where manufacturers or suppliers are forced to use outdated IoT products and services and sometimes be denied to export IoT data ([[https://bit.ly/3](https://bit.ly/3) WsFfgA]([https://bit.ly/3](https://bit.ly/3) WsFfgA) (accessed on 29 December 2022)).  
Figure 4: (**a**) A symmetric encryption scheme uses a single key, \(K\) for the encryption of a message, \(m\) and decryption of the encrypted message (cipher), \(c\). (**b**) An asymmetric encryption scheme uses two different keys, \(K_{U}\) and \(K_{R}\), for encryption and decryption.