esa-sceva/satcom-chunk-collection huggingface dataset:
### IoT System Implementation Challenges  
Every IoT application, either industrial or consumer, is subject to cyber security threats. False data injections (FDI) in industrial control systems can have large impacts on the physical processes [6; 7]. Consumer devices are vulnerable to various attacks [8; 9], such as surveillance video cameras, which can reveal sensitive information through unauthorized video recording and location tracking [10].  
The term _massive_ data is commonly applied to IoT, such as in the work by [5], which describes how smart meters and phasor measurement units (PMUs) use cellular communications to send their periodic reports to the control center. When all these data are transmitted over the Internet, a question arises as to how it is protected.  
Security adds overhead, such as the extra delay in performing the encryption operation, extra messages to exchange keys, and the added bytes at the end of the message to provide integrity. An important question is whether this overhead impacts the availability and safety of these systems.  
According to [PERSON] [11], cryptography is used to provide secrecy, authentication, and integrity. This is accomplished through _protocols_ that define a series of well-defined steps that all the parties must agree to follow during the communication process. To provide secrecy, cryptographic protocols usually employ hybrid crypto-systems, which have symmetric and public-key encryption algorithms.  
Figure 1: IoT applications have different requirements in terms of the CIA triad, in addition to safety. They should be considered when choosing the communication and security protocols.  
Symmetric algorithms use a session key to encrypt the plaintext and decrypt the ciphertext. In public-key algorithms, each node has a key pair, one public and one private. The public key can be sent to other nodes, which they use to encrypt the plaintext. Only the node that has the corresponding private key can decrypt the ciphertext. Most of the time, public-key algorithms are used to safely distribute the session key (some call this _key encipherment_), and the regular exchange of messages uses symmetric encryption. In addition, these hybrid crypto-systems generate digital signatures or message integrity codes (MICs) to ensure the authenticity of the parties and the integrity of the data.  
However, hybrid crypto-systems in IoT devices are a challenge because these devices have limited power, memory, and computational resources [12]. For example, public-key encryption is a mathematically demanding encryption scheme with a high computational cost [13]. Additionally, cryptographic protocols require large keys, which require memory and computing to store and generate keys. To distribute keys, encrypt plaintext, decrypt the ciphertext, and verify digital signatures, we require time, which causes processing delays that can impact the physical process being monitored or controlled. For all these reasons, lightweight cryptographic protocols are necessary to protect IoT systems from malicious users while utilizing as few of the end device's resources as possible.
# Numerical Simulation of Flow Field of Submerged Angular Cavitation Nozzle
## 3 Results and Discussion
### Influence of Expansion Section on Cavitation
#### 3.4.2 Influence of Expansion Section Length on Cavitation  
To investigate the influence of the length of the expansion section on the cavitation effect of the angular cavitation nozzle, keeping the remaining structural parameters unchanged, numerical simulations were conducted by setting different dimensionless expansion section lengths [34] (\(Le/d\) = 3, 3.5, 4, 4.5, 5), and the results of the axial velocity distribution of the nozzle jet and the axial vapor volume fraction distribution at \(d/2\) are shown in Figures 22 and 23, respectively.  
As can be seen from Figure 22, when the axial distance is less than 0 mm, the jet axial velocity corresponding to different Le is approximately the same, and there is essentially no effect on the jet velocity of the nozzle contraction section and the cylindrical section. While the axial distance is greater than 0 mm, the jet axial velocity corresponding to different  
Figure 23: Effect of \(Le\) on the cavitation.  
Figure 22: Effect of \(Le\) on the fluid velocity.  
Le fluctuates within a certain range, in which the jet axial velocity of the length of the dimensionless expansion section with a value of 4 is the largest.  
It can be seen from Figure 23 that when the axial distance is less than 0 mm, the steam volume fraction at \(d/2\) corresponding to different Le is basically the same. When the axial distance is greater than 0 mm, the steam volume fraction at \(d/2\) corresponding to different Le changes dramatically. If Le is too small, it is not conducive to the generation of cavitation bubbles, and the steam volume fraction is also small. Le should not be too large; otherwise, the growth of cavitation clouds is affected due to the fluid resistance. Therefore, the steam volume fraction is the largest and the cavitation effect is the best when \(Le=4d\).
## 4 Numerical Results and Discussions
### Bit-Error-Rate  
#### 4.1.1 Bit-Error-Rate in S\({}_{1}\)  
In Figure 4, we present BER performance of both NU and D\({}_{2}\) with respect to SNR, where SNR varies from 0 to 30 dB. In S\({}_{1}\), NU and D\({}_{2}\) have identical BERs because their BERs depend only on the incorrect antenna estimation. This is also true for BER of D\({}_{2}\) in S\({}_{2}\). The terms in legend of Figure 4 represents the number of transmit and receive antennas. We observed that BER decreases with increasing SNR values, as expected. We also observed that the BER performance improves with an increase in the number of receive antennas. This is due to the increase of diversity order with an increase of the receive antenna numbers. On the other hand, an increase in the number of transmit antennas triggers more error. The reason is that when the number of transmit antennas increases, the likelihood of erroneous detection of antennas increases [12]. Figures 5 and 6 illustrate the BER performance of FU in S\({}_{1}\) for different order of PSK and QAM modulation as a function of SNR, respectively, where two different types of antenna configurations are considered. Here, SNR ranges between 0 and 30 dB. The term in legends of the two figures denotes the number of antennas at BS (A\({}_{\text{BS}}\)), D\({}_{1}\) (A\({}_{\text{D}_{1}}\)), FU (A\({}_{\text{FU}}\)), and \(M\)-PSK/QAM modulation used by BS and D\({}_{1}\) to transmit FU data bits. In each instance, BER declines with an increase in SNR which is expected. We noted that in both figures higher order of modulation has higher BER compared to lower order of modulation when BS, D\({}_{1}\) and FU are all equipped with 4 antennas. This is because of the fact that when the number of modulation order increases, the likelihood of erroneous detection of constellation points increases due to the reduction of Euclidean distances among the points. Similar trend is observed in BER where all nodes are equipped with 2 antennas except that the BER curves tend to get closer to each other at high SNR. In Figure 7, we plot BER of the different users in S\({}_{1}\) against SNR for a comparative analysis, where SNR varies between 0 and 30 dB. The legend terms show the corresponding information as we mentioned for the previous figures and additionally includes the transmitted number of data bits. Considering the same order of modulation, it is noticed that FU gives better BER performance with 4 antennas at all involving nodes compared to 2 antennas at all involving nodes. We also noticed similar trend in the BER performance of NU or D\({}_{2}\), i.e., BER shows improved performance when all involving nodes are equipped with 4 antennas. It is worth noting that BER of NU or D\({}_{2}\) outperforms BER of FU for the transmission of the same number of data bits (e.g., 2 bits) when all the nodes in the system are
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 4 Future Trend of ML in IVLC
### Intelligent Physical Layer
#### 4.1.1 Fundamental Electromagnetism Theory and Frontiers in Optical Physics  
The frequency bands available for next-generation wireless communication evolve to higher frequency bands such as millimeter wave, terahertz, and visible light [93]. In this case, the network spectrum and resources are unprecedentedly plentiful. Efforts are underway to sense the space electromagnetic information, to govern the spectrum allocation and beamforming by combining the fundamental electromagnetism theory with the conventional information theory. Intelligent Reflecting Surfaces (IRS) is a promising research direction in mm-waves and terahertz waves [94]. In visible light communication, we can imagine the optical phased array antenna. The feature size of the photonic circuits is far larger than that of the microelectronic circuits. Metamaterials, metasurfaces, and metalenses enable manipulation of the propagation, polarization, amplitude, and phase of  
Figure 8: High-level view of intelligent physical layer and intelligent network layer of IVLC.  
the injected light at a deep subwavelength scale. This kind of optical artificially structured material is ultracompact in a scale of tens or hundreds of nanometers. On one hand, AI technologies can be a useful tool to design such kinds of materials by using optical reverse design. On the other hand, metamaterial can be a powerful way to realize optical neural networks and optical computing. All of these ideas are related to optical physics and could be promising research avenues towards 6G.
## 6 Application Cases
### EMU Bogie Wheel Pair System Fault Data Prediction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Failure Mode** & **Predicted Data** \\ \hline
1 & Wheel tread damage & 6 \\ \hline
2 & Wheel diameter is not qualified & 3 \\ \hline
3 & Axle box damage & 1 \\ \hline
4 & Wheel peeling & 6 \\ \hline
5 & Wheel edge loss & 1 \\ \hline
6 & Crack in wheel & 2 \\ \hline
7 & Wheel tread is partially concave & 3 \\ \hline
8 & Wheel outside rim is cracking & 2 \\ \hline
9 & Bearing burn & 2 \\ \hline
10 & Bearing fatigue spalling & 3 \\ \hline
11 & Brake clamp clearance reduced & 2 \\ \hline
12 & Gearbox wheel seal opposite side failed & 3 \\ \hline
13 & Shaft antislip valve failure & 4 \\ \hline
14 & Internal failure of the board & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Bogie wheelset system fault prediction data. Figure 9: Curves of true and predicted values. According to the failure data and prediction data in Tables 1 and 2, it is shown that the number of component failures of the bogie wheelset system is relatively high. The wheelset system failed 31 times in 2021, and the number of failures predicted for the next year is 33. From the results, it can be seen that in the wheel pair system, the wheel tread surface was damaged six times, the wheel surface peeling occurred six times, axle damage occurred seven times, the wheel diameter failed three times, the wheel concaved three times, and the rest of the system experienced damage eight times. Therefore, it is necessary to pay close attention to the operating state of the bogie wheel pair system and the manufacturing quality of the wheelset, formulate the corresponding repair scheme in advance, and feed data back to the manufacturing department and design department. In the meantime, the fault cause of each part is traced, and the specific problems are avoided and optimized in advance.
# Extraction of Flaw Signals from the Mixed 1-D Signals by Denoising Autoencoder
## 3 Ultrasonic Signal Database
### Neural Network Architecture  
To implement 1-D A-scan flaw signal data into the neural network, a DAE was designed and modified. The DAE was designed in Keras of Tensorflow, an open-source deep learning software by Google. The algorithm included an input layer with the same number of nodes as the sampling point of the given signals, i.e., 4096. Next, the encoder with three convolutional layers and two max pool layers follows. The max pool layers are in between the first, second, and third convolutional layers. The bottleneck hidden layer as known as the latent space was designed with the max pool layer where the number of points of input is compressed to 512 points. Then, the decoder has two convolutional layers and three up-sampling layers. Through the decoder, the output becomes the same size as the input. For learning complex relationships of data, the rectified linear unit (ReLU) is used as an activation function for adding nonlinearity to the network [12]. Then, the mean square error is used for the calculation of loss between the reconstructed output and the zero-padded flaw signal that is manually made earlier. Figure 8 shows the training process of the DAE proposed in this study as a diagram. In addition, Figure 9 presents the architecture of the denoising autoencoder. The model is saturated at the 45 th epoch. The dropout technique is added to the overall procedure of encoding and decoding because even though the denoising autoencoder is more robust than the ordinary autoencoder, there still exists a risk of over-fitting.  
\begin{table}
\begin{tabular}{c c c} \hline
**Flaw Type** & **Data (\#)** & **Total (\#)** \\ \hline Crack & 630 & \\ Lack of Fusion & 378 & \\ Slag Inclusion & 623 & 2463 \\ Porosity & 344 & \\ Incomplete Penetration & 488 & \\ \hline \end{tabular}
\end{table}
Table 2: Data acquired from the ultrasonic testing.  
Figure 8: The diagram of training procedure for the proposed DAE used in this study.  
Figure 7: The sample flaw signals of every flaw types in the specimens: (**a**) the A-scan crack signal; (**b**) the A-scan lack of fusion signal; (**c**) the A-scan slag inclusion signal; (**d**) the A-scan porosity signal; (**e**) the A-scan incomplete penetration signal.  
Figure 9: The architecture of DAE used in this study.
## 4 Applications of AI in Modern Communication Networks
### Traffic Prediction and Load Balancing
#### 4.5.2 Analysis of AI-Based Traffic Prediction Results  
The graph in Figure 7 presents a comparison between the predicted and observed traffic volume (in Mbps) across specified time intervals, indicating how effectively the AI model forecasts network demands. The time labels (e.g., \"Hour 1\", \"Hour 2\") denote sequential hours starting from the beginning of the observation period. This relative representation allows for general analysis of the prediction trends over time without tying the data to specific clock times.  
* Trend Comparison: The predicted and observed traffic trends show a strong alignment throughout the time intervals. Both the green line (predicted traffic) and the orange line (observed traffic) demonstrate a similar progression, suggesting that the AI model accurately captures the general fluctuations in traffic.
* Prediction Accuracy: Observing each time interval, the predicted values are consistently close to the observed values, with deviations rarely exceeding 5 Mbps. This minimal error range indicates that the AI-based model is well calibrated for traffic prediction, offering reliable insights for network resource planning.
* Handling of Peak Volumes: As time progresses, both predicted and observed traffic volumes increase, reaching peak levels close to 150 Mbps. The model accurately captures this peak, showcasing its capability to anticipate high traffic loads. Effective peak prediction is crucial for bandwidth management and can help minimize latency during peak hours.
* Error Distribution: The error between predicted and observed values is minimal during low-traffic periods and increases slightly during peak times. This behavior is typical for prediction models, where rapid traffic surges present a challenge. Nevertheless, the AI model maintains acceptable error margins, highlighting its robustness.  
Figure 7: Comparison between predicted and observed traffic over sequential hours using RNNs for AI-based traffic prediction.  
* Implications for Network Management: This predictive capability, demonstrated by the AI model in Figure 7, is advantageous for network administrators. With such a model, administrators can dynamically allocate bandwidth based on predicted traffic, reducing the risk of congestion and enhancing user experience.  
Future analysis could incorporate metrics such as mean absolute error (MAE) or root mean squared error (RMSE) to further quantify prediction accuracy and validate the model's robustness.
## 2 Individual Methods
### Data Collection  
The corrosion data for this study were obtained from a pipeline located in the western region of China. The input parameters collected included seven key factors: hydrogen sulfide percentage (X1), temperature (X2), chloride ion concentration (X3), carbon dioxide percentage (X4), pH (X5), fluid flow rate (X6), and corrosion inhibitor dosing (X7), comprising a total of 300 data sets. All input parameters were collected directly from the gas field production site. The training set and test set were split in a ratio of 7:3. Table 2 presents the descriptive statistics of the collected natural gas pipeline corrosion data, including the maximum, minimum, mean, standard deviation, kurtosis, and skewness. The seven parameters related to corrosion exhibit a wide distribution range, and the obtained data cover various conventional distribution types. Therefore, it is feasible to conduct corrosion prediction studies using these data.
# Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools
## 3 Task 1. Building the Foundation for Organizing and Curating the Raw Image Data
### Curation: Image Preparation Using Cleaning and Preprocessing
#### 3.1.5 Other Preprocessing Methods for Feature Extraction  
The feature extraction procedure makes use of a number of image-processing methods and statistical analyses to discover common patterns within the pictures. Texture characteristics have been identified as one of the most critical imaging aspects in the field of classification [31]. Preprocessing a picture can significantly influence the quality of extracted features and image analysis outcomes. Preprocessing an image is comparable to the mathematical normalization of data collection, which is a frequent stage in developing several feature descriptor algorithms [16].  
As [PERSON] et al. [32] discussed, background removal is one of the preprocessing procedures for preparing pictures for feature extraction. They stated that if an image has a light-colored object on a dark background, it is possible to distinguish the grayscale pixels of the object and background by identifying two main parts. A commonly used technique for removing the background in this situation is selecting a threshold value that separates these parts.  
The extraction of an image's features is a fundamental and critical element for image modeling [33]. Based on the work by [PERSON] [34] to employ feature engineering, the feature vector of a system is enlarged by introducing new features that are calculated from the existing features. Manual feature engineering is eliminated by utilizing a CNN to learn the visual representation for the collection challenge. However, such a presentation needs to be as simple as feasible to facilitate the retrieval and preservation of document images [35]. Some detection algorithms receive the extracted features of unannotated pictures, annotated images, and a code book as the input [36]. The critical stage in evaluating an image's numerous properties is feature extraction. One goal of feature selection and extraction may be to reduce the dimensions of data for better analysis [37]. Then, in the next section, we cover how to manage image collection summarization.
# Progress in Remote Sensing of Photosynthetic Activity over the Amazon Basin
## 4 Case Study of Seasonal Variation in Light-Use Efficiency and SIF across the Amazon Basin
### Results
#### 4.2.1 Photochemical Reflectance Index and Sun-Induced Fluorescence
Figures A1 and A2 in the Appendix A show seasonal changes in water storage measured from the Gravity Recovery and Climate Experiment (GRACE) along with rainfall estimates acquired from the Tropical Rainfall Measuring Mission (TRMM). Almost no rainfall was measured between June and August in the southern part of the basin, with moderate rainfall of up to 200 mm per month starting in October. The lowest water content was reached between July and September with an increase in storage beginning again in November, about a month behind the commencement of significant rainfall. Increases in water storage lagged about a month. We normalized estimates of PRI and SIF by dividing monthly values by the standard deviations of the entire dataset (total number of months). Pixels were plotted in which changes for a given month were within one standard deviation of the mean (refer to Figure 9). Those with variability in excess of with >1 standard deviation are also designated. GOME-2 provides only one measurement at every 40 km, and the retrieval shows consistently a high variation, for seasonal means and when normalized for the year. Figure 8: Seasonal variation of SIF from Global Ozone Monitoring Experiment-2 (GOME-2) from 2007 to 2012. The annual mean (average across all years) was subtracted from the seasonal mean (average of June, July and August (JJA) (**a**) and December, January and February (DJF) (**b**) for the 2007 to 2012 period) to show fluorescence seasonality. The resolution of this figure is coarse because GOME-2 has a relatively large footprint (approximately 40 km \(\times\) 80 km at nadir gridded to a spatial resolution of 0.5\({}^{\circ}\) latitude by 0.5\({}^{\circ}\) longitude). Areas in grey were not evaluated.
###### Abstract  
Construction-induced ground settlement is a serious hazard in underground tunnel construction. Accurate ground settlement prediction has great significance in ensuring the surface building's stability and human safety. To that end, 148 sets of data were collected from the Singapore Circle Line rail traffic project containing seven defining parameters to create a database for predicting ground settlement. These parameters are the tunnel depth (\(H\)), the tunnel advance rate (\(AR\)), the EPB earth pressure (\(EP\)), the mean SPTN value from the soil crown to the surface (\(Sm\)), the mean water content of the soil layer (\(MC\)), the mean modulus of elasticity of the soil layer (\(E\)), and the spot pressure used for injecting great into the tail void (\(GP\)). Three hybrid models consisting of random forest (RF) and three types of meta-heuristics, Ant Lion Optimizier (ALO), Multi-Verse Optimizer (MVO), and Grasshopper Optimization Algorithm (GOA), were developed to predict ground settlement. Furthermore, the mean absolute error (MAE), the mean absolute percentage error (MAPE), the coefficient of determination (R\({}^{2}\)) and the root mean square error (RMSE) were used to assess predictive performance of the constructed models for predicting ground settlement. The evaluation results demonstrated that the GOA-RF with a population size of 10 has achieved the most outstanding predictive capability with the indices of MAE (Training set: 2.8224; Test set: 2.3507), MAPE (Training set: 40.5629; Test set: 38.5637), R\({}^{2}\) (Training set: 0.9487; Test set: 0.9282), and RMSE (Training set: 4.93; Test set: 3.1576). Finally, the sensitivity analysis results indicated that \(MC\), \(AR\), \(Sm\), and \(GP\) have a significant impact on ground settlement prediction based on the GOA-RF model.  
ground settlement; random forest; ant lion optimizer; multi-verse optimizer; grasshopper optimization  
1  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Hybrid Random Forest-Based Models for Earth Pressure Balance Tunneling-Induced Ground Settlement Prediction. _Appl. Sci._**2023**, _13_, 2574. [[https://doi.org/10.3390/app13042574](https://doi.org/10.3390/app13042574)]([https://doi.org/10.3390/app13042574](https://doi.org/10.3390/app13042574))