esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Exchange in Cluster Structure for Longevity of IoT
## 1 Introduction
Furthermore, when data exchange between clusters is required, since the property of the requested data is utilized, only the clusters relevant to the property participate in the data exchange process. In the experiment, the proposed method exhibits a better performance than the existing method. In particular, in terms of the average energy consumption rate, the proposed method exhibits a saving effect of more than 10 times. Through the experiments, we demonstrate that the proposed method contributes to a more reliable network and improves the longevity of IoT networks. The remainder of this paper is organized as follows. Section 2 describes the related works based on WSNs, routing algorithms, and the IoT environment for efficient data transfer. Section 3 describes the proposed methods to construct clusters of local things and to exchange data on the basis of the cluster property in IoT network environments. Section 4 presents the performance evaluations of the proposed method. Section 5 concludes the paper and suggests future works.
# Classifying X-Ray Tube Malfunctions: AI-Powered CT Predictive Maintenance System
## 2 Related Work
used external sensors installed on a CT to detect abnormalities in sensory data that could signal malfunction in two studies [13; 14]. Both studies used five external sensors measuring current, radiation, humidity, temperature, and vibration (acceleration in the x and y axes) to predict the chance of a CT breakdown as \"low\" or \"high\". Both studies used synthetically generated data because of the lack of abnormal readings, where they expanded the data distribution. Both studies used an ANN with an input layer of 6 neurons (mentioned values, as single points) and two hidden layers of 6 neurons, each with RELU activations. The output layer had a single neuron with a sigmoid activation function. They achieved an average accuracy of 95.91 % and 97.58 %, respectively. [PERSON] et al. [15] used their custom SAX-HCBOP feature representation, which merges the Histogram-based Information Gain Binning (HIGB) and Coefficient improved Bag of Pattern (CoBOP) alongside the LightGBDT model to predict the X-ray tube arcing. After an analysis, the authors asserted that X-ray tube arcing might be a good indicator of CT malfunction, as it may lead to damage of some of the components or deterioration of CT image quality. They used a private dataset provided by Sichuan University that contains a time series with five features (including arcing used as a label). They were able to achieve an accuracy of 90.4% and a recall of 74.7%. [PERSON] et al. [16] created a system to predict both future cathode filament currents of an X-ray tube, based on the historical values, and filament failure. In order to be able to demask this degradation, they needed to maintain consistent settings of the CT. Afterwards, they extracted historical data about filament current and used three modules with different sizes of 1D convolutions (1, 3, 5) followed by a scale attention module and MLP to produce their proposed MSAP model (\"multiscale attention prediction model\"). Afterwards, on the basis of the measured currents and their changes, they were also able to formulate a framework for the prediction of filament failure. To sum up the state of the art, not much work has been conducted in the area of PdM of medical devices. Even then, most of the works, pretty much all outside of [15; 16], either work with singular data points (such as [9; 10; 13; 14]) or are concerned more with probabilities of transitioning to states based on the past observed behaviour than based on current data itself [11; 12]. Both [15; 16] try to detect a partial problem that is a possible sign of X-ray tube failure. Our work concerns itself with detection of any failure from historical data monitoring most of the available CT variables. To the best of our knowledge, this is one of the first works to address the problem of PdM, based on the long-term data, in the medical equipment field.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 8 Submerged HF Membrane Integrity and Failure
### Future Trends for Integrity Assessment  
The current state-of-the-art tools for monitoring membrane integrity are limited to detecting compromises via a variety of in situ and ex situ techniques and tools. Despite extensive research performed on membrane-failure mechanisms and their resultant effects, these studies often are based on ex situ or offline analytical techniques, which can only provide information when a serious breach in membrane integrity is detected. Therefore, this underscores the need for the development of non-destructive, computer-aided modeling techniques to predict membrane failure and optimize module design. One possible approach is the prediction of failure in membrane systems via the use of finite element analysis (FEA). FEA is a modeling technique that is widely used by structural,mechanical, and biomedical engineers to perform mechanical analyses on complex structures to determine displacements from applied loads [330; 331]. Through FEA, high stress locations along the membrane fiber can be determined that will help identify areas where failure is most likely to occur. Such analyses would aid in membrane and membrane-module design as well as the optimization of operating strategies. FEA can also be used as a diagnostic tool to provide supportive interpretations at similar operating conditions when performing autopsies on failed membranes and modules.
## 8 Experimental Results and Discussion
### Accuracy  
The degree of similarity between a measurement and its real value is referred to as accuracy. A limited number of EEG channels recorded concurrently can improve the accuracy. Two distinct types of tests were conducted to evaluate brain activity and lasted 30 min. The first experiment was performed without a cell phone (GSM); however, the second experiment included a mobile phone while subtracting brain activity. Interesting discoveries were made, and it was revealed that, while utilizing a cell phone, the accuracy was somewhat reduced.  
Figure 8 demonstrates the accuracy and compares the average spectrum exposure with and without GSM for the ranges of delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta \(\beta\) (13-32 Hz). Figure 8a indicates that the accuracy without a mobile phone was 99.95%, whereas the accuracy with a mobile phone was 99.78% utilizing a delta range of (1-4 Hz). Figure 8b shows that, with the theta (4-8 Hz) frequency range, 99.88% accuracy was obtained without a mobile phone, whereas 99.69% accuracy was obtained with a mobile phone.  
Figure 8: (**a**) The accuracy of the average spectrum exposure with and without GSM using the frequency range of delta \(\bar{\circ}\) (1–4 Hz). (**b**) The accuracy of the average spectrum exposure with and without GSM using the frequency range of theta \(\bar{\circ}\) (4–8 Hz). (**c**) The accuracy of the average spectrum exposure with and without GSM using the frequency range of alpha \(\alpha\) (8–13 Hz). (**d**) The accuracy of the average spectrum exposure with and without GSM using the frequency range of beta \(\beta\) (13–32 Hz).  
Figure 8c depicts the 99.81% accuracy with the alpha (8-13 Hz) range in the absence of a mobile phone. A cell phone, on the other hand, achieved 99.58% accuracy in the same frequency band. Figure 8d exhibits 99.80% accuracy without a mobile phone utilizing a beta (13-32 Hz) frequency range and 99.59% accuracy with a mobile phone. It was shown that the GSM had a negligible impact on the signal accuracy while monitoring brain activity.
## 3 Results
### Detection of CDH by Flow Cytometry  
In order to test if there is potential to use developed resazurin assay for detecting CDH activity by flow cytometry in water-in-oil-in-water double emulsions series of enzymatic reactions was set up and analyzed by flow cytometry (Figure 6).  
\begin{table}
\begin{tabular}{l l l l l l} \hline  & **Lactose** & **Lactose** & **Cellobiose** & **Cellobiose** & **Mutations** \\ \hline  & Vmax (RFU \(\times\) & Km (mM) & Vmax (RFU \(\times\) & & \\  & 10\({}^{5}\)/OD-min) & & 10\({}^{5}\)/OD-min) & Km (\(\mu\)M) & \\ tmCDH & 1.70 \(\pm\) 0.11 & 0.144 \(\pm\) 0.063 & 2.71 \(\pm\) 0.05 & 2.7 \(\pm\) 0.4 & D20N, A64T, V592M \\ H5 & 2.80 \(\pm\) 0.16 & 0.225 \(\pm\) 0.099 & 3.51 \(\pm\) 0.18 & 5.5 \(\pm\) 1.3 & D20N, A64T, V592M, V22A, T99T \\  & & & & & D20N, A64T, V592M, T84A, A261P, E674G, N715S \\ \hline \end{tabular}
\end{table}
Table 1: Kinetics properties of CDH mutants. Enzymatic assay was done with resazurin (0.1 mM), lactose/cellobiose (different concentrations), sodium-phosphate buffer (0.1 M; pH 5.5), and fluorescence measured at ex/em 540/590 nm.  
Figure 6: Flow cytometry analysis of the resazurin assay for cellobiose dehydrogenase activity in double emulsions. (**a**) Emulsions with resazurin; (**b**) resazurin plus soluble enzyme; (**c**) resazurin plus cells not expressing CDH; (**d**) resazurin plus cells expressing tmCDH. The plots show bivariate histograms of forward scattering and resorufin fluorescence.  
It could be seen that population of droplet with increased red fluorescence was visible in enzymatic reaction mixtures with soluble enzyme compared to the blank reaction. Also, smaller population of droplets with increased fluorescence was observed in the case of cells encapsulated within double emulsion expressing active tmCDH compared with cells without expressed CDH. Cross-boundary reactions during analyzing cells were not significant due to the small number of encapsulated cells compared to the number of double emulsion compartments.
## 3 Methodology
### Error Rate Calculation and Correction  
Here, error rate calculation and correction were carried out from \(\varrho^{\prime}\) using Shannon entropy as an approximation of the Von Neumann Entropy (VNE), followed by Information Reconciliation using Low-Density Parity-Check (IR-LDPC). While the VNE is formally defined using the density matrix of a quantum system, in this context, we adopted the binary Shannon entropy as a practical approximation for quantifying the uncertainty in the measurement outcomes, after the quantum states had been projected into classical bitstrings.  
Let the probability of error in \(\varrho^{\prime}\) be denoted as \(\varrho^{*}\). The error in the keys is then calculated using  
\[H=-(1-\rho^{*})\log_{2}(1-\rho^{*})-\rho^{*}\log_{2}\rho^{*} \tag{40}\]  
Here, \(H\) represents the estimated uncertainty. It should be noted that this formulation assumes the error values in the bitstring are statistically independent and valid within that approximation. This entropy estimate is used to guide the subsequent error correction steps.  
Here, \(H\) denotes the computed error utilizing VNE, and represents the logarithmic function. Following calculation of the error, error repair was executed using Information-Reconciliation-based Low-Density Parity-Check (IR-LDPC). Information reconciliation entailed the employment of classical communication to resolve discrepancies and guarantee the presence of identical keys for both parties. LDPC codes possess superior error-correcting abilities, especially in channels characterized by moderate to high noise levels. Here, \(H\) was encoded using the subsequent equation:  
\[E_{nc}=H*g^{cn} \tag{41}\]  
Here, \(E_{nc}\) signifies the encoded keys and \(g^{cn}\) represents the generator matrix. Then, to correct the errors, the decoding process is carried out.  
\[\delta_{corr}=\Psi^{pur}\times(E_{nc})^{trans} \tag{42}\]  
Here, \(\delta_{corr}\) signifies the corrected key, \(\Psi^{par}\) signifies the parity check matrix, and \((E_{nc})^{trans}\) characterizes the transpose of \(E_{nc}\).
# Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
## 2 Data and Methods
### Methods
#### 2.2.2 Functional Neural Networks (FNN)
The architecture of FNN can vary widely, depending on the specific application and the complexity of the data. The training typically involves backpropagation through functional derivatives or basis expansion, a method that adjusts the weights of the network to minimize the error between the predicted output and the actual output. The choice of loss function and optimization algorithm also impacts the performance of the network. Figure 4: The general architecture of Functional Neural Network (FNN).
# RESEMBLE: A Real-Time Stack for Synchronized Mesh Mobile Bluetooth Low Energy Networks
## 3 Overview of the Bluetooth Mesh Architecture
### Limits of Bluetooth Mesh in Supporting Real-Time Communications
#### 3.1.2 Bluetooth Low Energy v.5.3  
The BLE v.5.3 specifications [31] introduce several innovations in the transmission of ADVs while maintaining the interoperability with the previous specifications. The most important innovation is the ability to use all 37 BLE data channels for the transmission of auxiliary messages within the same transmission event. The specifications provide two kinds of channels for the transmission of ADVs, i.e., the primary and the secondary channels. The primary channels are the three channels dedicated to the ADV that were already used in the previous version of BLE, while the secondary channels are the remaining 37 channels that, in the previous versions of BLE, could not be used for transmitting ADVs.  
An advertisement event always starts with the transmission of an ADV on one of the three primary channels, and this limits the possibility of exploiting channel diversity to avoid collisions. This limit can be partially overcome defining suitable mechanisms to choose the channel for the auxiliary messages, but a mechanism to avoid collisions on the primary channels still needs to be defined. The considerations of the determinism for BLE v.4.2 are also valid for BLE v.5.3. However, the BLE v.5.3 specifications define several mechanisms and multiple transmission methods for the ADVs. This increases the protocol complexity but, at the same time, opens new ways to design medium access mechanisms that support real-time communications. For example, the chance to use all 37 channels for the transmission of the ADVs, combined with the ability to set the channel to be used, enables the adoption of multichannel communication mechanisms that allow for multiple nodes to transmit at the same time on different channels.
# Personalized Contextual Information Delivery Using Road Sign Recognition
## 4 Experiments and Results
### Road Sign Recognition Performance  
The performance of the proposed system was evaluated across three key tasks: object detection, text detection and recognition, and directional information extraction. For object detection, the Faster-RCNN model demonstrated high accuracy in identifying textual and symbolic elements within road sign images. Optimized anchor box configurations and aspect ratios enabled the effective detection of small objects such as text and directional arrows. The anchor box sizes were set to [8 \(\times\) 8, 16 \(\times\) 16, 32 \(\times\) 32, 64 \(\times\) 64, 128 \(\times\) 128], and the aspect ratios were configured as [1:1, 1:2, 2:1] to match the observed size distributions in the dataset. The model was trained with a learning rate of 0.001, a weight decay of 0.0001, and a batch size of 16, over 100 epochs, with early stopping applied to prevent overfitting. These settings achieved a Precision of 94.3% and Recall of 92.1% for text detection, and 91.5% and 89.4% for arrow detection, with an average IOU of 0.84. Robust detection performance was maintained under various conditions, including low-light and partially occluded scenarios [1; 6; 7].  
Text detection and recognition were carried out using the CRAFT model combined with OCR techniques, yielding a Precision of 93.7%, Recall of 91.9%, and Word-Level and Character-Level Accuracies of 89.5% and 96.2%, respectively. The use of data augmentation techniques, such as rotation, scaling, and brightness adjustments, enhanced recognition performance, particularly under challenging conditions, with a 3.4% improvement in Word-Level Accuracy compared to non-augmented data [8; 12].  
For directional information extraction, the DeepLabV3 model segmented road sign images into distinct regions, enabling the accurate grouping of objects based on their spatial alignment and orientation. This process achieved a Precision of 92.3%, Recall of 90.8%, and a directional grouping accuracy of 88.1%. Despite this, some errors were noted when text overlapped with directional symbols, highlighting potential areas for further optimization. Overall, the system demonstrated robust performance across all modules, confirming its capability to handle real-world scenarios and laying the groundwork for effective contextual information integration [6; 7; 16].
# A Predictive Reliability Model to Assess the Performance of Photovoltaic Systems
### The Device Factors  
The device factors are the technical specifications of the array and battery. These factors might differ from one model to another and indicate the quality of the PV system. The following is a list of the important device factors:  
1. Array efficiency \(\eta_{arr}\): this is defined as the energy at the array output relative to the irradiance on the total area of the collector and given by: \[\eta_{arr}\ =\ \frac{E_{arr1}}{G_{i}}\] (2) where \(E_{arr1}\) is the energy produced at the output of the array, in one hour, while the battery is charging.
2. The array performance ratio \(APR\): this is defined by the array yield to the reference yield as: \[APR\ =\ \frac{Y_{a}}{Y_{r}}\] (3) where \(Y_{r}\) is the reference yield of the system in kWh/m\({}^{2}\)/day, which is the amount of energy received by the array collector in one day, and \(Y_{a}\) is the array yield or the daily production of the array in kWh/kWp/day. The unit kWh/kWp means the produced electrical energy from the rough area of the array, so, there is no conflict in units of \(Y_{r}\) and \(Y_{a}\).
## 4 Results and Discussion
### Domain Decomposition Comparison and Parallel Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**RCB Method**} & \multicolumn{3}{c}{**MRCB Method**} \\ \cline{2-7}
**Subdomain** & \(\mathbf{N_{tol}}\) & \(\mathbf{N_{con}}\) & \(\mathbf{N_{fSI}}\) & \(\mathbf{N_{tol}}\) & \(\mathbf{N_{con}}\) & \(\mathbf{N_{fSI}}\) \\ \hline SD1 & 120,880 & 8828 & 5515 & 120,037 & 11,252 & 5790 \\ SD2 & 122,157 & 11,548 & 6188 & 121,002 & 11,06
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 3 Materials and Methods
### Hybrid Random Walk-Optimized Simulated Annealing for Tourism Sentiment Chain Modeling
#### 3.3.1 Tourism Sentiment Chain Representation  
The Tourism Sentiment Chain (TSC) is a structured representation of tourists' dynamic decision paths between attractions and is defined as follows:  
\[\begin{split} Chain=\{Path,CAIs,\ Force\},\\ Path=[Attr_{0},Attr_{1},\cdots,Attr_{i},\cdots,Attr_{n_{c}}],\\ CAIs=[CAI_{1},CAI_{2},\cdots,CAI_{i_{t}},\cdots,CAI_{n_{c}}],\\ Force=\Big{[}force_{1},force_{2},\cdots,force_{i},\cdots,force_{n_{c} }\Big{]},\end{split} \tag{14}\]  
where \(i\) is the attraction identifier. \(n_{c}\) is the length of TSC, that is, the number of Attraction Transitions. \(Path\) is the attraction sequence of the chain, which represents the tour sequence from \(Attr_{0}\) to \(Attr_{n_{c}}\). \(CAIs\) is the sequence of comprehensive attractions. \(Force\) is the sequence of driving factors in which \(force_{i}\) is the dominant correlation type of transferring \(Attr_{i-1}\to Attr_{i}\).  
The proposed model facilitates the multidimensional dynamic feature modeling of tourist behavior by explicitly representing the spatial trajectories, transition probabilities, and underlying driving mechanisms of travel paths.
# A Novel Security Architecture for WSN-Based Applications in Smart Grid
## 6 Conclusions  
In this paper, we propose a new security architecture for WSN-based applications in SG. The architecture consists on a distributed PKI where the role of the centralized CA is delegated to a set of sensor nodes dynamically elected in the network using a clustering algorithm. The objective is to ensure that the CA services are available as long as possible and to overcome the problem of a single point of failure of the centralized CA. The clustering algorithm is based on a trust metric that reflects the behavior of the sensor nodes; if one CA is compromised, only its cluster will be affected. The simulation results show that almost all sensor nodes are members of clusters. Furthermore, the lifetime of the elected CA is high which confirms the stability of the distributed PKI. In addition, the proposed architecture is scalable, the more sensor nodes enter the network, the more clusters are created which avoids any denial of service on the existing CAs. From a security perspective, the proposed architecture allows the establishment of secure communications among the sensor nodes. Having a legitimate certificate and keys will allow mitigating against Sybil attacks in addition to the encryption which avoids eavesdropping.  
In our future work, we expect to design a trust management framework for WSN in SG aiming to assess and maintain the trust metrics of the sensor nodes. Furthermore, the inner processing of the PKI will be developed particularly the certification and revocation processes.  
Methodology, T.G.; Validation, T.G.; Resources, N.A.; Writing--Original Draft Preparation, N.A.; Writing--Review and Editing, T.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University of Jeddah, Saudi Arabia, grant number UJ-20-124-DR.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 8: The lifetime of the CA as a function of the percentage of malicious sensor nodes.
# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
## 2 Fundamentals of the Classification Algorithms Applied
### Logistic Regression  
Logistic regression (LR) is a statistical model that can be used to describe the relationship between a dichotomous dependent variable and a series of independent variables [11]. LR has the function of predicting the result of a categorical variable in relation to the predictor variables [18]. It is an efficient tool for burned area mapping since it offers the chance to obtain the probability of a pixel to be classified as burned or unburned [9; 26]. LR provides a good probabilistic framework for the development of burned area algorithms since the models obtained are consistent with variations in environmental variables [21].
# Design of an Optically Transparent Microwave Absorber Based on Coding Metasurface
## 4 Discussion  
Finally, the characteristics of optically transparent or flexible microwave absorbers reported in recent years are summarized in Figure 13. In comparison to previously reported optically transparent microwave absorbers, the proposed receivers have higher reflection coefficients and better flexible and optically transparent properties.