esa-sceva/satcom-chunk-collection huggingface dataset:
# Trajectory Optimization for UAV-Aided IoT Secure Communication Against Multiple Eavesdroppers +
## 4 Simulation Results
### Secrecy Rate Versus Iteration Number and UAV Speed  
As shown in Figure 4, the secrecy rate of the secure communication system converges rapidly as the number of iterations of \(\mathbf{P1}^{\text{(0)}}\) increases. The secrecy rate of the secure communication system converges rapidly, typically within about two to three iterations. In addition, the UAV's maximum speed \(v_{max}\) has an impact on the secure transmission performance. Specifically, a higher maximum UAV speed results in a greater achievable secrecy rate. This is because a higher UAV speed allows for increased trajectory design flexibility, thereby improving its capability to evade potential eavesdropping threats.  
Finally, we observed whether the secrecy rate reaches a saturation point as the UAV's speed increases. In Figure 5, we find that when the UAV speed increases to a relatively large value, the secrecy rate gradually stops increasing. Therefore, when the UAV speed is large, it is not necessary to keep increasing the speed of UAV to secure transmission.  
Figure 4: The minimum average secrecy rate versus the number of iterations.  
Figure 3: The UAV’s instantaneous speed changes over time.
## 2 Background
### Data Compression  
Digital vital signal compression has been well established by our research team, from very redundant sequence signals such as Electrocardiography (ECG) [21] to much less diverse (non-redundant) sequence signals such as Electromyography EMG [22]. Redundant data such as ECG has been the primary target for signal processing researchers because of its high redundancy [23; 24]. On the other hand, EMG data compression has been particularly challenging due to its extreme irregularity. Lack of redundancy is a set-back for dictionary-based approaches [25] founded on algorithms such as Lempel-Ziv (LZ) [26; 27], or Huffman and its variants. Accordingly, in the proposed literature's case of handling DNA, our hypothesis is that DNA would also cause some difficulties due to irregular quality scores and metadata.  
Although there have been lossy compression methods for the efficient compression of DNA [25; 28], our research adhered to lossless methods. However, our logic is that even if quality scores and other metadata are noisy, lossless approaches should be adopted under the condition that the reconstructed data is not distorted. Especially, in terms of healthcare, handling important vital signs of human beings should be approached with caution; thus, a lossless approach is deemed optimal.
###### Abstract  
In the existing studies of slope stability under seismic excitation, the non-uniform spatiotemporal dynamic response and amplification effect caused by the slope scale are often overlooked. In order to analyze the dynamic response of large-scale slopes under non-uniform seismic excitation, this study presents an analytical model based on the Hilbert's best approximation problem (BAP), taking into account the scale of slopes under seismic incidence. A methodology for differentiating between the plane wave assumption of [PERSON] principle and BAP principle is proposed. A Stoneley equation considering the scale of the slope on the interface is proposed to analyze the non-uniform spatiotemporal dynamic response. Meanwhile, the Knott equation is subjected to non-uniform seismic waves and adopted to describe the amplification factor of spectral response accelerations (SRA). To visually represent the non-uniform spatiotemporal dynamic response and amplification effect, numerical models based on the finite difference method (FDM) are established to simulate the non-uniform incidence of seismic waves. The numerical simulation results show that the non-uniform dynamic response cannot be disregarded in cases where \(L/h>4:20\). Slopes of this scale subjected to seismic wave incidence satisfy the BAP principle. Conversely, the dynamic response exhibits uniformity in cases where \(L/h<1:20\), which is in accordance with the plane wave assumption of [PERSON] principle.  
seismic wave; non-uniform incidence; slope scale; dynamic response Article
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 4 Simulation Results
Despite the fact that SCMC has its first node dead in an earlier round than the other approaches, it shows very promising results, with the death of all nodes as shown in Table 4 occurring at about 9800 rounds compared to 4035 for MO  
Based on the previous simulation results, we have shown that SCMC provides a higher throughput and a prolonged lifetime of the WSN compared to MO-SCP, MDP-MSC, and LEACH. This is due to the fact that SCMC uses a set cover approach to create clusters, and min-cost max-flow to route data between CHs and the base station. Although MDP-MSC also uses the concept of set covers to create clusters, it remains different from MO-SCP and SCMC in three ways. Firstly, it searches for k paths from the sink to all sensor nodes while MO-SCP and SCMC use a single path approach. Secondly, it calculates maximum disjoint set covers while MO-SCP and SCMC calculate a minimum set cover. Furthermore, MDP-MSC doesn't consider the problem of extending the lifetime of WSNs as an optimization problem, while MO-SCP considers it as a multi-objective optimization problem, and SCMC as a single objective problem. In SCMC the energy consumption is well distributed among nodes and routing is done in an efficient way. Figure 8: Throughput comparison of SCMC, MO-SCP, MDP-MSC and LEACH (Size = 300). Figure 7: Throughput comparison of SCMC, MO-SCP, MDP-MSC and LEACH (Size = 200).
## 3 Engineering Case Study
### Establishment of the Landslide Seepage Field Model
#### 3.3.3 Model Parameter Range  
The model materials from top to bottom were gravel soil, fractured highly weathered rock, and moderately weathered bedrock. Due to the strong heterogeneity of the saturated permeability coefficient of gravel soil and complexity of the fracture development in the rock mass, the permeability coefficients in Table 1 obtained from the tests cannot be directly used for numerical calculations. Based on the results of permeability tests in boreholes and preliminary calculations of initial parameters using numerical models, this article established the order-of-magnitude range of permeability coefficients for three types of strata materials (see Table 2).
## 3 Results and Discussion
### LSTM Network  
The LSTM network is trained to determine the structure parameters based on the training dataset. The optimal number of neural network layers is 2 and the number of neurons in each layer is 32. The batch size and epochs are 256 and 150. The tanh activation function and Adam optimizer are used in the training process. To validate the shale gas productive prediction model based on LSTM, we also select 100 groups of different geological and fracturing reservoir parameters and compare the prediction results of the model with the real values. The comparison of the true values and values predicted by LSTM networks of daily shale gas production and the relative error between the true values and predictions are shown in Figure 4. The predictions of the model are in accordance with the true values, which indicates that the model has a good prediction effect. Figure 4 also shows the relative error between the predicted values and the true values of the neural network under the constraint of geological and fracturing reservoir parameters. The average relative error is 0.68% and the maximum relative error is 3.08%, which is far lower than the average relative error of the MLP network.  
Figure 3: Relative error between the values predicted by MLP networks and true values under different production time.  
Figure 2: Comparison of the predictions by MLP networks and the true values of the daily production under three constraints.  
In this section, we analyze the LSTM prediction results for shale gas production. The prediction performance for the daily shale gas production under three groups of geological and fracturing reservoir parameters is shown in Figure 5. The geological and fracturing reservoir parameters in case 1 are the maximum values in the dataset, and the parameters in case 3 are the minimum values in the dataset. The comparison between the LSTM predictions and the true values under different constraint parameters is shown in Figure 6. The data of the first 6 days are input data, and the prediction results start from the 7 th day. It can be seen that the LSTM predictions agree satisfactorily with the true values. The relative error of the LSTM predictions and the true value of shale gas production on the 10 th day, 100 th day, and 1000 th day are shown in Figure 6. The relative errors of case 2 are the smallest, and the relative errors on the 10 th, 100 th, and 1000 th day are 0.04%, 0.13%, and 1.18% respectively. The relative errors of case 3 on the 10 th, 100 th, and 1000 th day are the largest, which are 0.95%, 0.73%, and 1.85%, respectively, of the production. However, they are far lower than the relative errors between the MLP predictions and the true values.  
Figure 4: Comparison of the true values and values predicted by LSTM networks for validation data of daily shale gas production and the relative error between the true values and the predictions.  
Figure 5: Comparison of the values predicted by the LSTM networks and the true values of the daily shale gas production under three constraint conditions.
## 5 Finite Element Analysis of Air Film Load-Bearing Characteristics
The resultant data, encompassing both theoretical calculations and simulation analyses, were meticulously collated and presented in the form of scatter plots, then subsequently subjected to rigorous curve fitting for enhanced interpretative insights. As depicted in Figure 8, the theoretical calculations for the three specifications of the axial load-bearing air film closely aligned with the curve trends observed in the simulation results. With a progressive increment in the thickness of the axial load-bearing air film, there was a discernible decline in the air film's load-bearing capacity, eventually approaching zero. As the thickness of the radial load-bearing air film was augmented, its load-bearing capacity gradually converged towards the force exerted by atmospheric pressure upon the cylindrical roller. These observed trends are in accordance with fundamental principles in fluid mechanics. Error analysis: As depicted in Figure 8a, the theoretical calculations for the 5 \(\times\) 5 specification of the axial gas film exhibited a commendable concordance with the results obtained through simulation analysis. The curves exhibited a substantial overlap. However, for the 10 \(\times\) 10 and 13 \(\times\) 13 specifications of the axial gas film, the simulation results slightly deviated from the theoretical calculations, with the load-bearing capacity in the simulation demonstrating a negative value. This deviation arose from an enlarged outlet for the axial load-bearing gas film, resulting in an excessive outflow of gas from the outlet ports. Consequently, localized pressure levels dipped below atmospheric pressure, which was particularly evident in the central region, as illustrated in Figure 7a. It is noteworthy that the simplified algorithm employed in theoretical calculations did not account for this phenomenon. Figure 7: Air film pressure distribution cloud. (**a**) Axial load-bearing. (**b**) Radial load-bearing.
## 5 Experiments and Results
### Training from Scratch, Transfer Learning and Frozen Layers  
Deep learning can be approached by building an architecture from scratch (by setting up different types of layers and connections), by using an existing network (Transfer Learning), and by Transfer Learning with frozen layers. In this section, we show the results obtained by training our model in these three ways using the LR found in the previous section. In this section, the simulations are performed with a \(LR=5\cdot 10^{-4}\).  
Learning from scratch means creating a completely new model and starting with random values in the weights of the neural model. In this first experiment, we train our model, with the parameters of Table 1, imposing the condition of non-pretrained weights with ImageNet, where we obtain an accuracy of 88%.  
Figure 8: On the left is a graph of learning rate vs. loss to find the range of optimal values of LR; on the right, the iterations vs. learning rate.  
Figure 7: Dataset examples of the different tissues with random augmentation.  
In transfer learning, there are a large number of convolutional networks that are trained by default with a set of images and focus on machine vision. In this process, information learned from one problem is used and applied to a related problem. This process usually involves training the final layers of the network or adding new ones, resulting in a reduction in the time required to train the network. We trained our VGG19 model using ImageNet pre-trained weights and re-trained all the layers. It is observed that the accuracy and training speed improve compared to training from scratch, obtaining an accuracy of 94%.  
In the process of frozen layers, the aim is to slightly adjust the weights of some of the intermediate layers of the network in order to fine-tune the new problem to which Transfer Learning is applied. When the new model is initialized, the first layers are \"frozen\" so as not to change their weights, as these are more general feature extraction layers, and the remaining layers are trained with the new database [28]. In this part of the experiment, we freeze 15%, 25% and 50% of the 27 layers that complete our model, obtaining an accuracy of 87%, 83% and 49%, respectively. With this method of freezing layers, the model needs to be trained with fewer parameters and therefore takes less time to train, but the results are worse than with the transfer learning process without freezing layers. Table 2 shows the results obtained in this section, the Precision, Recall, F1 score for each tissue class, Accuracy, Training time and the parameters that can be trained.  
Since the best results are obtained with the Transfer Learning training, all subsequent training with our model will follow this strategy.
# Implementing Federated Governance in Data Mesh Architecture +
## 4 Introducing Blockchain-Powered Mesh Platform
### Using Blockchain for Policy and Metadata Management  
Blockchain technology offers multiple features that are well-suited for building a Type II metadata catalog. Its immutable, append-only ledger ensures that metadata records \(\mathcal{M}\) are stored permanently, providing a version history for each record. The hashing function of blocks introduces a layer of trust crucial for verification and audit processes. In a blockchain network, each node maintains a copy of the ledger that contains all metadata records. This is analogous to nodes \(N\) deploying replicas \(R\), ensuring redundancy and availability.  
Furthermore, the EVM-like ledgers with smart contract support enable the automated enforcement of consistent, globally defined governance policies, represented by a set of governing methods \(T\). In fact, a similar approach has already been proposed for building the data sharing systems [33].  
We argue that it is also possible to adopt a smart contract as a policy approach for promoting data product-sharing practices based on executable data contracts. Upon the signature of the data contract between the data provider and data consumer, the data mesh governance system will automatically generate and register the agreement in the form of a new \"smart policy\" by using a contract factory pattern. Then, the processing system can pro-actively verify the state of this agreement and perform the necessary actions if the breach is detected [34].  
The distributed consensus algorithm ensures the ledger remains synchronized across all replicas, acting as a consistency function \(s\). This mechanism also facilitates the seamless adaptation and automatic testing of policy or contract updated in response to evolving business processes.  
The distinction between public and private blockchains is critical, with public blockchains operating anonymously and treating all participants equally. This openness, however, makes them more susceptible to malicious acts. Private blockchains, like Fabric, offer a more controlled environment by pre-identifying participants, which enhances security and simplifies access management. Such a setup is inherently more secure and suitable for applications requiring strict access controls and data privacy.  
The Hyperledger Fabric (HLF) platform is an open-source blockchain initiative under the Linux Foundation [35]. Its flexible architecture supports the integration of diverse computational elements tailored to specific system needs, including customizable consensus protocols, identity verification services, and transaction endorsement policies. To enhance security measures, it also facilitates network partitioning through the use of channels and algorithms for private data sharing.  
We elaborate on building the data mesh governance with HLF thereafter.
## 6 Analysis of the Obtained Results

Table 17: Comparison of the obtained indicator values for testing the quality of classifiers for the models obtained with decision trees (DT) and the rough set theory (RST). and DT in the class of 30-50% (NVP = 1). It is the lowest for the class of 70-85% for covering and genetic algorithms (NPV = 0.67). The results of the compared values of the [PERSON]'s correlation coefficient (MCC) measure (correlation coefficient between real classes and projected classes by the model) indicate that the best result was achieved in the class of 30-50% for DT (MCC = 1). The lowest result was for a genetic algorithm in the class of 70-85% (MCC = 0.17). By analyzing the results obtained for F1 (harmonic mean of precision and sensitivity of a model) and J (the sum of sensitivity and specificity reduced by 1) for all the models, it can be seen that the best classifier is DT in the class of 30-50%, while the worst is a genetic algorithm for the same class. The remaining indicators should take the lowest possible values. A general classifier error (Err) is more favorable in case of using the DT classifier for the class of 30-50% (Err = 0.00), and the worst for the class of 70-85% is a genetic algorithm. The FPR indicator (probability of false alarms, i.e., the objects incorrectly assigned to a highlighted class, among all objects actually non-highlighted) and the FDR indicator (probability of false alarms among all the objects recognized by the classifier as highlighted) achieves the best values for the class of 30-50% (FPR = 0 and FDR = 0). However, the best FNR values (the probability of missing the highlighted objects, that is, their assignment by the classifier to the non-highlighted class) were also obtained for the class of 30-50% (FNR = 0).
# A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
###### Abstract
Standard detection and mitigation of harmonics are crucial for power system reliability, particularly given the increasing presence of loads like electric vehicles, which can introduce significant harmonic distortion; in addition, due to harmonics in large electrified railway systems, considerable power quality problems are induced [10; 11]. This paper reviews existing technologies and measures designed to manage harmonic content, providing a reference point for researchers working in this area. Additionally, the challenges posed by large-scale, complex power networks--similar to the issues faced by large-scale information networks--demand an integrated approach to harmonic management. A comprehensive strategy for harmonic detection, suppression, aggregation, and estimation is vital to maintaining the overall health of modern power systems. This paper is structured as follows: Section 2 presents an overview of harmonic detection methods, including both traditional and modern approaches. Section 3 discusses harmonic suppression strategies, highlighting passive, active, and hybrid filtering methods. Section 4 focuses on harmonic aggregation, examining the challenges of managing multiple harmonic sources in complex systems. Finally, Section 5 explores harmonic estimation models and their role in predicting harmonic behavior in evolving power grids.
## 2 Overview of Near-Infrared Reflectance Spectroscopy (NIRS)
### Main Components and Modes of Operation of the NIRS  
A light source, beam splitter system, sample detector, optical detector, and data processing system are the main components of an NIR spectrometer. Most NIR spectrometer systems use tungsten halogen lamps as the source of light due to the low cost and high intensity for a continuous spectral output. The role of the beam splitter system is to translate multicolor light to single-color light. Usually, the NIR system works with a computer system with software installed for data acquisition. The older NIRS equipment, which was highly sophisticated and expensive and could only be used in the laboratory, has now been replaced by new designs that have the advantage of being lighter, smaller, and available in portable versions suitable for field applications [39].  
The performance of the NIRS has also improved in terms of speed, spectrum acquisition, and signal processing. NIRS can operate in three modes: transmittance, reflectance, or transfection. The transmittance mode collects spectra data from the entire sample volume incident by light, whereas the reflectance mode only collects data from the sample surface. Transflectance, however, combines the reflectance and transmittance modes, which are applicable to liquid samples [40]. NIRS application requires calibration, which is the key to its successful use [41]. However, the reliable calibration of the NIR systems requiresmany samples with physical and chemical characteristics variations. Calibration models are developed by combining the NIRS spectra data with the reference data (biophysical or biochemical) obtained from laboratory analysis using chemometric tools. The developed models then predict the constituents of interest from similar unknown samples. Principal component analysis, multiple linear regression, and partial least squares regression (PLSR) are the most commonly used chemometrics tools. They establish a mathematical relationship between variations in the NIR spectra of the samples and variations in the measured laboratory parameters.