esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this paper, we consider the physical layer security problem of the wireless communication system. For the multiple-input, multiple-output (MIMO) wireless communication system, secrecy capacity optimization artificial noise (SCO\(-\)AN) is introduced and studied. Unlike its traditional counterpart, SCO\(-\)AN is an artificial noise located in the range space of the channel state information space and thus results in a significant increase in the secrecy capacity. Due to the limitation of transmission power, making rational use of this power is crucial to effectively increase the secrecy capacity. Hence, in this paper, the objective function of transmission power allocation is constructed. We also consider the imperfect channel estimation in the power allocation problems. In traditional AN research conducted in the past, the expression of the imperfect channel estimation effect was left unknown. Still, the extent to which the channel estimation error impacts the accuracy of secrecy capacity computation is not negligible. We derive the expression of channel estimation error for least square (LS) and minimum mean squared error (MMSE) channel estimation. The objective function for transmission power allocation is non-convex. That is, the traditional gradient method cannot be used to solve this non-convex optimization problem of power allocation. An improved sequence quadratic program (ISQP) is therefore applied to solve this optimization problem. The numerical result shows that the ISQP is better than other algorithms, and the power allocation as derived from ISQP significantly increases secrecy capacity.  
physical layer security; secure transmission; secrecy capacity; secrecy capacity optimization artificial noise; power allocation; channel estimation error +
Footnote †: journal: Applied  
13 May 2021  
Footnote †: journal: Applied  
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**Copyright:** © 2021 by the authors. License MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 System Model
### Receiver  
At the receiver, a structured decoding process is applied to reconstruct the semantic information from the received signal. The received bitstream undergoes a sequence of transformations, including demodulation, error correction, semantic feature recovery, and final waveform reconstruction.  
First, demodulation maps the received symbols back to a bitstream representation. The demodulated bits are then passed through channel decoding to achieve optimal error correction by minimizing the bit error rate (BER). The recovered bitstream is subsequently mapped back to its integer and floating-point representations:  
\[\mathbf{\hat{B}}=f_{\text{int}}(\mathbf{\hat{b}}) \tag{8}\]  
\[\mathbf{\hat{Z}}=f_{\text{float}}(\mathbf{\hat{B}}) \tag{9}\]  
\[\mathbf{\hat{X}}_{s}=\text{reshape}(\mathbf{\hat{Z}}) \tag{10}\]  
Following this, the semantic expander reconstructs the full-resolution semantic feature tensor representation:  
\[\mathbf{\hat{F}}=T_{E}(\mathbf{\hat{X}}_{s};\theta_{E}) \tag{11}\]  
The semantic interpreter synthesizes the original speech waveform:  
\[\hat{s}=T_{D}(\mathbf{\hat{F}};\theta_{D}) \tag{12}\]  
where \(T_{D}(\cdot)\) represents a learned mapping function that reconstructs the speech signal while preserving perceptual quality and intelligibility. The final output \(\hat{s}\) approximates the original speech signal with minimal distortion. This architecture bridges conventional communication systems with emerging semantic communication paradigms. The detailed model is shown in Figure 2.
## 5 Service Life Predictions
### Reinforced Concrete in the Marine Section
#### 5.1.2 Pier Concrete  
Corrosion-resistant reinforced bars were used to improve the durability of the reinforced concrete. The characteristic value of the concrete cover \(x\) is 52 mm, and the margin for the concrete cover \(\Delta x\) is 5.21 mm. After curing for 28 days, the measured apparent chloride diffusion coefficient of the pier concrete was 2.32\(\times\)10\({}^{-12}\)m\({}^{2}\)/s. The main durability parameters used in the service life prediction model are shown in Table 11. The reliability indexes of the pier concrete with carbon reinforced bars and with corrosion-resistant reinforced bars were calculated and are shown in Figure 16.  
Table 11 shows that the critical chloride concentration for the carbon reinforced bars in the splash zone is 0.9%. On the other hand, as the corrosion-resistant reinforced bars are more corrosion-resistant, the critical chloride concentration for the corrosion-resistant reinforced bars in the splash zone is 4.5% [41; 42; 43]. For the pier concrete with carbon reinforced bars, the reliability index of the reinforced concrete after 100 years of service is only 0.03. On the other hand, for the pier concrete with corrosion-resistant reinforced bars, the reliability index of the reinforced concrete after 100 years of service is nearly 2.0. The higher reliability index can be attributed to the higher critical chloride concentration in the corrosion-resistant reinforced bars as compared to that in the carbon reinforced bars. Further, the service life of the pier with corrosion-resistant reinforced bars in the marine section is 263.2 years, and its reliability index is 2.01 when serving for 100 years.
## 2 Materials and Methods
### Two-Dimensional Resistivity Description and Data Acquisition
The quality of the data acquisition was entirely controlled by the computer software, which, before starting any measurement, checked the electrode connectivity to the soil and the magnitude of the contact resistances. The 1 m electrode spacing was chosen with an estimated investigation depth about 20 m below the ground. The apparent resistivity data were inverted to create a model of the resistivities of the subsurface using AGI Earthimager 2-D Software (after Advanced Geosciences, Inc., Austin, TX, USA). With this software, the data collected with the AGI SuperSting earth imaging resistivity instruments can be interpreted into easy-to-read 2-D earth sections (resistivity tomograms). A more accurate subsurface model is the two-dimensional (2-D) model, where the resistivity changes in the vertical and horizontal directions along the survey line are recorded. 2-D resistivity profiling was carried out by making many measurements at different profile locations and offsets. Finally, the 2-D resistivity profiling data were inverted using AGI Earthimager 2-D Software (after Advanced Geosciences, Inc., Austin, TX, USA) to create a tomogram-like resistivity model along a subsurface section that detected different salinity water zones [4]. AGI EarthImager 2-D software (after AGI Inc., Austin, TX, USA) was used for resistivity data inversion. AGI EarthImager 2-D software is a two-dimensional inversion modeling software for affordable resistivity and induced polarization (IP) imaging. Using this software, any array or mixed data from Schlumberger, pole-pole, pole-dipole, dipole-dipole, or Wenner electrode arrays can be inverted.
# Application of Riemannian Seismic Ray Path Tracing in Salt Dome Prospecting
## 2 Materials and Methods (Theoretical Background)
### Curvature and Curvature Tensor  
To determine whether a space is intrinsically curved or not, it is necessary to evaluate the components of the Riemann curvature tensor, which characterises the curvature in a tensor way. Under a coordinate system \(\{q^{i}\}\) in \(\mathbb{M}\), the components of the Riemann \(\binom{i}{3}\) tensor take the form  
\[R^{k}_{lmn}=\partial_{m}\Gamma^{k}_{ln}-\partial_{n}\Gamma^{k}_{lm}+\sum_{i=1} ^{d}\Gamma^{k}_{im}\Gamma^{i}_{ln}-\sum_{i=1}^{d}\Gamma^{k}_{in}\Gamma^{i}_{lm }\;. \tag{7}\]  
In this way, a flat manifold is one in which \(R^{k}_{lmn}=0\;\forall k,l,m,n=1,\cdots,d\). The tensor \(R^{k}_{lmn}\) has \(\left(d^{4}-d^{2}\right)/12\) independent components, since it satisfies the properties of symmetry \(R_{abcd}=R_{cdab}\) and anti-symmetry \(R_{abcd}=-R_{bacd}=-R_{abdc}\) as well as the identities of Bianchi. Moreover, any other tensor that describes the intrinsic curvature of a manifold can be deduced from this tensor. Such is the case of the Ricci tensor,  
\[R_{mn}=\sum_{k=1}^{d}R^{k}_{mkn}\;, \tag{8}\]  
and of the Ricci scalar field  
\[R=\sum_{m,n=1}^{d}g^{mn}R_{mn}\;. \tag{9}\]  
The latter is the simplest curvature invariant in a Riemannian manifold. Being a scalar invariant, it assigns a real number to each point of \(\mathbb{M}\) according to the geometry of that manifold in the vicinity of the point, whose value does not depend on the coordinate system that parametrizes \(\mathbb{M}\).
# Starch Hydrogels for Slow and Controlled-Release Fertilizers: A Review
## 1 Introduction
In soils, SHs and their composites have been investigated for various applications, including (i) nutrient delivery, (ii) soil remediation, and (iii) protective coatings for herbicides and pesticides. Additionally, these materials contribute to improving several soil properties, such as enhancing permeability and water-use efficiency, extending irrigation intervals, increasing water retention across different soil types, minimizing soil erosion and runoff, reducing soil compaction, and improving drainage [14; 15; 16; 17]. SHs have shown significant potential in the controlled transport of fertilizers, enabling their slow and sustained release in both plants and soils [18]. These hydrogels facilitate nutrient delivery by minimizing nutrient washout, reducing fertilization frequency, and limiting leaching through decreased runoff. Although numerous studies have explored the formation and application of SHs, many reviews have focused primarily on laboratory-scale production, often overlooking methodologies for large-scale manufacturing. Therefore, this review aims to provide a comprehensive overview of the characteristics and structure of SHs and their composites with potential agricultural applications. In particular, it emphasizes large-scale production strategies and summarizes controlled delivery systems for macronutrients such as nitrogen (N), phosphorus (P), and potassium (K) in soils and plants. These nutrients are applied in large quantities in crop cultivation, often resulting in substantial environmental and economic losses. The controlled delivery of these essential nutrients using SHs and their composites can help mitigate these issues by reducing runoff and leaching, enhancing fertilizer efficiency, and ultimately improving crop yields while minimizing environmental impact. The following section highlights various starch sources that can be used to develop cost-effective SHs for application in controlled fertilizer release.
## 4 Experimental Results and Discussion
### Noise Sensitivity Analysis of PL-MSMM  
To assess the robustness of the PL-MSMM algorithm, we added noise to the input image and compared it with the case without added noise. We chose the MSMM for comparison. For this experiment, we employed 10-fold cross-validation to evaluate the fault diagnosis accuracy of both methods. Table 4 presents the average testing and training accuracy, precision, and F1-score of the experimental methods. It should be noted that \"Clean\" means using the original input image without added noise and \"SPN\" means using the input image with salt and pepper noise added.  
It is evident that the proposed algorithm outperforms the MSMM method, indicating its effective handling of noise in the input data while maintaining high classification accuracy and robustness. In contrast, the MSMM method performs poorly when noise is added, suggesting its difficulty in coping with noisy data, which leads to a degraded classification performance. The results show that the proposed algorithm exhibits a smaller accuracy difference of 0.99 compared to the MSMM method's difference of 1.62 when noise is present. The proposed algorithm demonstrates relatively smaller fluctuations in results when affected by noise. Furthermore, when considering the relative percentage difference (RPD) as a metric, the RPD value for the PL-MSMM method, relative to the accuracy without noise, is 1.00%. In comparison, the RPD value for the MSMM method is 1.65%. This indicates that the percentage decrease in performance when handling noisy data is smaller when using the PL-MSMM method. Owing to the noise insensitivity of the pinball loss function, the PL-MSMM achieves better results than the MSMM on noise-contaminated datasets. This shows that the PLMSMM can handle noise-containing turnout fault datasets more effectively, with robustness and reliability.
# Mapping Based Quality Metrics for Mesh Deformation Algorithms Using Radial Basis Functions
## 3 Mapping-Based Metrics
### Size Metrics  
Size metrics is to judge whether the deformed elements are too large or too small compared to the initial elements. According to Equation (20), denote \(f_{size}\) as the mapping-based size metric of area change, and:  
\[f_{size}=\min(J,\frac{1}{J}) \tag{23}\]  
where \(J=\det(\mathbf{F})=\det(\mathbf{H}+\mathbf{I})\). With deformation information, the mapping-based size metrics can be calculated with Equations (6) and (11).  
\(f_{size}=1\) means that the deformed element has the same area as the original one.  
\(f_{size}\leq 0\) means that the deformed element is degenerate.  
In a traditional method [6; 22], mesh-based size metrics measuring area change could be expressed as:  
\[\vec{f}_{size}=\min(\tau,\frac{1}{\tau}) \tag{24}\]  
where \(\tau\) is the ratio of the area of the deformed elements to the area of the initial elements. Coordinates of each deformed and initial elements and calculation of the area are required.
# ISAR Image Quality Assessment Based on Visual Attention Model
###### Abstract  
The quality of ISAR (Inverse Synthetic Aperture Radar) images has a significant impact on the detection and recognition of targets. Therefore, ISAR image quality assessment is a fundamental prerequisite and primary link in the utilization of ISAR images. Previous ISAR image quality assessment methods typically extract hand-crafted features or use simple multi-layer networks to extract local features. Hand-crafted features and local features from networks usually lack the global information of ISAR images. Furthermore, most deep neural networks obtain feature representations by abridging the prediction quality score and the ground truth, neglecting to explore the strong correlations between features and quality scores in the stage of feature extraction. This study proposes a Gramin Transformer to explore the similarity and diversity of features extracted from different images, thus obtaining features containing quality-related information. The Gramin matrix of features is computed to obtain the score token through the self-attention layer. It prompts the network to learn more discriminative features, which are closely associated with quality scores. Despite the Transformer architecture's ability to extract global information, the Channel Attention Block (CAB) can capture complementary information from different channels in an image, aggregating and mining information from these channels to provide a more comprehensive evaluation of ISAR images. ISAR images are formed from target scattering points with a background containing substantial silent noise, and the Inter-Region Attention Block (IRAB) is utilized to extract local scattering point features, which decide the clarity of target. In addition, extensive experiments are conducted on the ISAR image dataset (including space stations, ships, aircraft, etc.). The evaluation results of our method on the dataset are significantly superior to those of traditional feature extraction methods and existing image quality assessment methods.  
ISAR image; quality assessment; Gramin Transformer; attention block +
Footnote †: journal: Medical Image Quality Assessment  
2025  
2025
# Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization
## 4 Artificial Intelligence Application in MEMS System: Current Trends  
Due to infrastructure advancement and data availability, disciplines such as data science and data analytics have gained significant attention from an application perspective in different domains, such as the MEMS sensors manufacturing process. [PERSON] et al. [32], [PERSON] et al. [33], [PERSON] et al. [34], [PERSON] et al. [35], [PERSON] et al. [36], [PERSON] et al. [37, 38], and [PERSON] et al. [39] have provided extensive overviews of ML applications for predictive maintenance application in the manufacturing domain of Industry 4.0. This provides a strong motivation for exploring AI benefits in the MEMS domain. AI algorithms can efficiently and effectively analyze massive amounts of data and identify particular trends and patterns humans cannot see, enabling process automation and discarding human intervention. Real data contains noise, complex patterns, multi-modal distributions, variance, etc., which makes the analysis computationally extensive and difficult using traditional methods. AI algorithms are adept at managing multidimensional and multivariate data in dynamic or uncertain environments. It is not predisposed toward particular datasets; thus, learning can, therefore, aid in minimizing bias in corporate choices. Data scarcity and confidentiality can be challenging in certain domains in finding the optimal solution. This issue can be handled by different RNN variants or DL-based synthetic data generation. The benefits of the AI algorithms are discussed, in detail, in Section 6.2.  
In this section, we surveyed the current trends of AI applications in different process parts for MEMS-based sensors. MEMS gyroscopes and accelerometers have been scorching research topics due to their diverse application in consumer, automotive, industrial, and medical fields. Thus, more attention is given to improving and optimizing the hardware and software system for these two applications. A brief overview of AI algorithm implementation is discussed in Section 4.1. In Section 4.2, we investigated different application areas of AI in MEMS-based sensors. In Section 4.3, AI application for the overall MEMS manufacturing and design is covered.
## 4 The Conductors Analyzed and the Experimental Setup
### Aluminum Conductor Steel-Reinforced (ACSR) Conductors  
ACSR conductors have been used extensively in transmission lines for the past 100 years [26; 27]. ACSR conductors consist of a core of steel strands surrounded by aluminum strands. All strands are wound helically, while the strands of adjacent layers are wound in opposite directions [20]. Due to aluminum's much higher electrical conductivity, the aluminum strands carry most of the current [28], while the steel strands provide the mechanical strength [22]. However, eddy current and hysteresis losses occur in the core due to the 50 Hz magnetic flux [23]. The combined effect of the uneven current distribution in the aluminum layers, the magnetism in the core, and the transformer effect tends to increase the \(r_{AC}\)[22].  
Figure 3 shows the configuration of the studied ACSR conductors.  
By carefully removing the outer layer of the aluminum strands, the two-layer ACSR conductor was converted to a single-layer ACSR conductor.
# EEG Feature Extraction Using Genetic Programming for the Classification of Mental States
## 2 Reference System
### Classification  
To approximate a normal distribution of the data, the logarithm of the variance obtained from the input data transformation by \(\mathbf{X}\mathbf{W}\) is calculated. This is stored in a matrix \(\mathbf{Z}\), and is given by  
\[\begin{split}\mathbf{Z}=log(var(\mathbf{F}))=\\ \begin{pmatrix}log(var(\mathbf{X}_{1}\mathbf{W}_{1}))&\cdots& log(var(\mathbf{X}_{1}\mathbf{W}_{j}))\\ \vdots&\ddots&\vdots\\ log(var(\mathbf{X}_{i}\mathbf{W}_{1}))&the index \(m\) for testing and keeping the remaining folds for training. The reason for using this type of partitioning is because the recognition is performed at the individual level (grouping trials from the same individual) rather than at the trial level. We want to avoid training and testing the classifier using trials from the same subject, which could lead to misleading results.  
The classifier used is LDA. This technique is commonly used in BCI systems due to its ease of implementation and competitive performance compared with more sophisticated algorithms [50]. Briefly, in LDA, a hyperplane is calculated based on the covariances of the data distribution, and it optimally separates two classes. If we assume that both classes belong to a normal distribution and have the same covariance, then we can calculate  
\[\mathbf{w}=\mathbf{\Sigma}^{-1}(\mu_{\text{C1}}-\mu_{\text{C2}}) \tag{7}\]  
and  
\[b=\mathbf{w}^{T}\frac{\mu_{\text{C1}}+\mu_{\text{C2}}}{2}, \tag{8}\]  
where \(\mathbf{\Sigma}\) is the shared covariance matrix for both classes, while \(\mu_{\text{C1}}\) and \(\mu_{\text{C2}}\) are the corresponding means for each class. Classification is performed by assigning a label (either C1 or C2) to a label vector \(\hat{\mathbf{\gamma}}\) depending on a score vector \(\mathbf{s}\), given by  
\[\mathbf{s}=b+\mathbf{w}\mathbf{\Sigma}, \tag{9}\]  
and  
\[\hat{\mathbf{\gamma}}=\begin{cases}\text{C1}&\quad\text{if }\mathbf{s}\geq 0\\ \text{C2}&\quad\text{otherwise}\end{cases}. \tag{10}\]  
Until this point, we employed the training data to obtain the CSP filter set \(\mathbf{W}\) and the vector \(\hat{\mathbf{\gamma}}\). During the testing phase, a prediction vector \(\hat{\mathbf{\gamma}}\) is computed using the hyperplane found during training but using a projected testing data based on the pre-calculated \(\mathbf{W}\).  
Since this is calculated for a given fold in the LOOCV procedure, after \(q\) repetitions, an average of the accuracy results of all folds is computed and is considered as the prediction accuracy. Results from the classification performance can be seen in Figure 3, where the average and fold-wise accuracy are presented corresponding to the training and testing partitions. For training, an average accuracy of 76.6% was obtained, and 75.7% for testing. Please note that over-fitting exists (or a failure to generalize) for some folds, namely 3, 6, 8, and 12.  
Figure 3: Classification performance for the reference system.
## 4 Experiments and Discussion
### Evaluation measures  
According to the 2015 ISBI challenges on cephalometry landmark detection [6], we use the mean radial error (MRE) to make evaluation. The radial error is defined in Equation (2). \(\Delta x\) and \(\Delta y\) are the absolute differences of the estimated coordinate and ground-truth coordinate of \(x\)-axis and \(y\)-axis.  
\[R=\sqrt{\Delta x^{2}+\Delta y^{2}} \tag{2}\]  
Figure 7: The architecture of ResNet50, the identity block and convolutional block add the former input to the later output. For identity block, it directly adds the former input to the later output. While for convolutional block, before adding, the former input first goes through a convolutional layer to make it the same size with later output.  
The mean radial error (MRE) is defined in Equation (3):  
\[MRE=\frac{\sum_{i=1}^{n}R_{i}}{n} \tag{3}\]  
Because the predicted results have some difference with the ground-truth. If the difference is within some range, we consider it correct in that range. In our experiment, we evaluate the range of \(z\) mm (where \(z=2,2.5,3,4\)). For example, if the radial error is 1.5 mm, we consider it as a 2 mm success. Equation (4) explains the meaning of successful detection rate (SDR), where \(N_{a}\) indicates the number of accurate detections, and \(N\) indicates total number of detections.  
\[SDR=\frac{N_{a}}{N}*100\% \tag{4}\]
## 2 System Structure and Working Principle  
Figure 1 shows the structure of the PV-HESS. The system consists of an ESS comprising two energy storage components, a lithium-ion battery and an SC, PV panels, a load, and current converters.