esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Physical Properties
#### 2.1.2 Hardness  
The hardness test is one of the most commonly adopted methods for evaluating the aging degree of composite insulators under different aging times. In this paper, a Shore A durometer was used to analyze the hardness of three samples with the same aging time. The test methods for hardness are as follow.  
The hardness test criterion is in accordance to GB/T 531.1-2008 [21]. Six points, denoted _a-f_, from the rod to the edge of the shed of each composite insulator sample, were tested, and the hardness of each point was the average value of the hardness of the same three samples at this point. The average hardness of six points, denoted \(A\), was obtained for comparing the hardness between samples aged for different years.  
The hardness test results are listed in Table 2.  
Table 2 shows the following:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**Aging Time/Year**} & \multicolumn{5}{c}{**Test Results**} \\ \cline{3-7}  & & & \(\theta_{av}\)/\({}^{c}\) & **HC** \\ \hline A\({}_{1}\) & 1 & 130.2 & 1 \\ A\({}_{2}\) & 3.5 & 122.5 & 1 \\ A\({}_{3}\) & 6 & 111.8 & 2 \\ A\({}_{4}\) & 8 & 100.6 & 3 \\ A\({}_{5}\) & 10 & 94.3 & 4 \\ A\({}_{6}\) & 12 & 84.1 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hydrophobicity test results on samples.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{5}{c}{**Hardness (Shore A)**} \\ \cline{2-9}  & \(a\) & \(b\) & \(c\) & \(d\) & \(e\) & \(f\) & \(A\) \\ \hline A\({}_{1}\) & 70.3 & 72.5 & 73.1 & 73.4 & 74 & 74.1 & 72.9 \\ A\({}_{2}\) & 72.6 & 73.2 & 73.5 & 74.8 & 75.3 & 75.6 & 74.2 \\ A\({}_{3}\) & 74 & 74.2 & 74.3 & 74.5 & 74.8 & 75.2 & 74.5 \\ A\({}_{4}  
(1) When aging time increases, it leads to an increase of \(A\) of composite insulators. For example, the average hardness of sample A\({}_{1}\) aged for 1 year is 72.9. When aging time reaches 8 years, the average hardness of sample A\({}_{4}\) increases to 74.8.  
(2) In one single sample, the hardness tends to decrease from the edge of the shed to the rod of the composite insulator. For example, the hardness measurements of six points of sample A\({}_{2}\) aged for 3.5 years from the rod to the edge of the shed are 72.6, 73.2, 73.5, 74.8, 75.3 and 75.6, respectively.
###### Abstract  
Near-infrared spectroscopy (NIRS) is widely used as a non-invasive method to monitor the hemodynamics of biological tissue. A common approach of NIRS relies on continuous wave (CW) methodology, i.e., utilizing intensity-only measurements, and, in general, assumes homogeneity in the optical properties of the biological tissue. However, in monitoring cerebral hemodynamics within humans, this assumption is not valid due to complex layered structure of the head. The NIRS signal that contains information about cerebral blood hemoglobin levels is also contaminated with extra-cereb tissue hemodynamics, and any recovery method based on such a priori homogenous approximation would lead to erroneous results. In this work, utilization of hyper-spectral intensity only measurements (i.e., CW) at multiple distances are presented and are shown to recover two-layered tissue properties along with the thickness of top layer, using an analytical solution for a two-layered semi-infinite geometry. It is demonstrated that the recovery of tissue oxygenation index (TOI) of both layers can be achieved with an error of 4.4%, with the recovered tissue thickness of 4% error. When the data is measured on a complex tissue such as the human head, it is shown that the semi-infinite recovery model can lead to uncertain results, whereas, when using an appropriate model accounting for the tissue-boundary structure, the tissue oxygenation levels are recovered with an error of 4.2%, and the extra-cereb tissue thickness with an error of 11.8%. The algorithm is finally used together with human subject data, demonstrating robustness in application and repeatability in the recovered parameters that adhere well to expected published parameters.  
Near-infrared spectroscopy; tissue optics; superficial layer contamination; Hyperspectral spectroscopy +
Footnote †: journal: Applied Physics Letters
# Enhancing Privacy in Large Language Model with Homomorphic Encryption and Sparse Attention
## 3 Materials and Methods
### Proposed Method
#### 3.3.4 Dynamic Sparse Attention Module  
In natural language processing, the attention mechanism has become a key component for enhancing model performance, especially within the transformer model's multi-head attention mechanism. However, the computational cost becomes exceedingly high when dealing with large datasets or long sequences, which is particularly pronounced in the context of homomorphic encryption. To address this, a dynamic sparse attention (DSA) module is introduced to reduce computational demands while maintaining or even improving model performance. This chapter will detail the differences between the DSA module and the original multi-head attention mechanism, its design details, and its advantages when applied to the tasks addressed in this paper.  
The core idea of the DSA module is to reduce the consumption of computational resources while preserving the essential functions of the attention mechanism by dynamically selecting key parts of the sequence. In the traditional multi-head attention mechanism, attention scores are computed at every position in the sequence, leading to a computational complexity of \(O(n^{2})\). The DSA module, through a predefined strategy such as gradient-based or task-relevance metrics, selects the most critical information to reduce computational volume. To illustrate this, suppose there is a sequence of length \(n\), where the computational complexity is \(O(n^{2})\) in the traditional attention mechanism. In our dynamic sparse model, if only \(k\) most important positions are selected for attention score computation, the computational complexity can be reduced to \(O(nk)\), where \(k\ll n\). This process can be mathematically formulated as follows:  
\[\text{Attention}(Q,K,V)=\text{Softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}} \bigg{)}V \tag{20}\]  
In the dynamic sparse model, the above formula is modified to:  
\[\text{SparseAttention}(Q,K,V)=\text{Softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k} }}\bigg{)}\bar{V} \tag{21}\]  
where \(\bar{K}\) and \(\bar{V}\) are the \(k\) most important keys and values selected from the original \(K\) and \(V\) based on some strategy. The primary reason for designing the DSA module is to address the computational challenges faced when processing large-scale natural language tasks in a homomorphic encryption environment. Homomorphic encryption itself introduces additional computational overhead, and using the computationally expensive standard multi-head attention mechanism would make the model impractical for real-world scenarios. By implementing sparsity, our model significantly reduces computational costs while maintaining focus on the most important information in the sequence.
# A Comparative Analysis of 3D Software for Modeling Fatigue Crack Growth: A Review
## 2 Software
### Numerical-Based Approaches
#### 2.1.7. ANSYS Workbench
While the SMART Crack Growth feature in ANSYS is undeniably powerful and useful, it is crucial to recognize that it does have certain limitations that should be taken into account. Here are a few key limitations to consider when utilizing the SMART Crack Growth feature in ANSYS:  
* The material properties assigned to the predefined materials must be complete and accurate. For instance, if you are using the Paris crack growth law, ensure that you define the coefficient and constant for Paris's law in the engineering data. Incomplete or incorrect material definitions can affect the accuracy of crack growth predictions. * The SMART Crack Growth method relies on mesh refinement around the crack tip. However, mesh quality and element size play a crucial role in capturing accurate stress fields. If the mesh is not adequately refined near the crack tip, the results may be less reliable [97]. * The SMART Crack Growth feature is primarily designed for fatigue crack growth analysis in metallic materials with LEFM behavior. It may not be suitable for other types of materials, such as composites or polymers, or for crack growth phenomena influenced by factors beyond fatigue, such as environmental effects or creep [95]. * The SMART Crack Growth feature typically assumes that the crack will propagate in the direction of the maximum principal stress or strain. While this assumption is often valid, it may not accurately represent complex crack growth patterns influenced by factors such as material anisotropy, stress gradients, or geometrical constraints. * The SMART Crack Growth feature primarily focuses on fatigue crack growth in structural components subjected to cyclic loading. It may not adequately consider the influence of environmental factors, such as corrosion, temperature, humidity, or aggressive media, which can significantly affect crack growth behavior. * Fatigue crack growth in SMART is based on Paris's law, and it may not account for plasticity effects, nonlinear geometry effects, load-compression effects, and crack-tip-closure effects [109]. Table 1 provides a comprehensive comparison of the fatigue modeling approaches, crack growth methods, and mesh types utilized in seven discussed software packages. It offers valuable insights into how each software package handles fatigue analyses and examines crack growth phenomena. However, it is essential to acknowledge that the information presented in the table is contingent upon the individual capabilities and constraints of each software package. It is crucial to thoroughly evaluate the suitability of the chosen software for the specific application and to consider any additional factors or considerations beyond the scope of Table 1. When evaluating different 3D modeling software options, it is crucial to consider several important criteria to make a well-informed decision. In this context, Table 2 provides a comprehensive comparison of the mentioned software tools specifically in terms of their ability to simulate fatigue crack growth.
## 1 Introduction
However, in the detecting process, this technology is vulnerable to a high-temperature environment, and it has drawbacks such as a short service life, inconvenient installation, and high maintenance costs. To address the above problems, other industrial fluid detection techniques, such as the ultrasonic detection method [11], infrared detection method [12], and vibration detection method [13], have been developed. The vibration detection technique has been extensively applied in the industrial fluid detection due to its benefits of high detection stability and accuracy. It has become one of the most important tools for increasing the detection success rate of flow-field states. This approach, however, must comply with stringent technical standards. The vibration detection sensor needs to have a high sensitivity, good anti-interference performance, and a long service life. The detection sensor may then be mounted in a suitable detecting location, and the vibration signal created by the fluid can be collected and sent to the industrial computer. To determine the liquid-state transition process, an appropriate signal-processing technique must be applied to the vibration signal. This technique is used to determine the state of molten steel. In light of the method's exceptional performance, this paper describes the underlying concept of vortex-induced vibration detection technology and its research development. The most important scientific concerns are described and assessed, and current issues and research trends are forecasted. It can not only offer a useful theoretical reference for researchers working on multiphase vortex cross-scale modeling, vibration evolution mechanisms, and signal detection methods, but it also has promising application prospects in some industrial fields, such as those of energy-saving process, the vibration-optimization design of thin-walled shells, and the development of new emission energy systems. This paper is organized as follows. In Section 2, the basic principle of vortex state detection technology and the research status of vortex-induced vibration detection are introduced. Section 3 discusses the key technology for vortex-induced vibration detection. The research trend and prospects for this technology are detailed in Section 4. The conclusions are reported in Section 5.
# On Vulnerability of Selected IoT Systems to Radio Jamming--A Proposal of Deployment Practices
###### Abstract  
Weightless and SigFox are both narrowband communication systems designed for the Internet of Things, along with some other counterparts such as LoRa (Long Range) and narrowband Internet of Things (NB-IoT). As systems dedicated specifically for long-range operations, they possess considerable processing gain for energetic link budget improvement and a remarkable immunity to interference. The paper describes outcomes of a measurement campaign during which the Weightless and SigFox performance was tested against variable interference, generated in an anechoic chamber. Results allow the quantitative appraisal of the system behavior under these harsh conditions with respect to different operational modes of the two investigated IoT systems. The outcomes are then investigated with respect to an intentional radio jammer attempting to block a base station (BS) operation by directly radiating an interfering signal towards it. An Interference Margin is proposed for a quantitative expression of a system's resilience to jamming. This margin, calculated for all available configuration settings, allows the clear assessment of which combination of a system's operational parameters does and which does not provide immunity to this type of radio attack.  
machine-type communications; ultra-narrowband; carrier to interference; packet error rate; interference; anechoic chamber +
Footnote †: journal: Sensors
# Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS
## 1 Introduction
The structure of this paper is as follows: Section 2 introduces the overview of the study area, MODIS LAI and sentinel-2 image preprocessing and the acquisition of ground measurement data. The theory of the LAI inversion method and normalized LAI growth function are introduced. Section 3 shows the inversion process, using remote sensing images and ground measurement data to carry out fast time-series forest land LAI inversion in the Saihanba area. Section 4 discusses the results and puts forward suggestions for future research. Finally, the conclusion is given in Section 5.
# An Application of Simulated Annealing in Compensation of Nonlinearity of Scanners
###### Abstract  
Galvo scanners are popular devices for fast transversal scanning. A triangular signal is usually employed to drive galvo scanners at scanning rates close to the inverse of their response time where scanning deflection becomes a nonlinear function of applied voltage. To address this, the triangular signal is synthesized from several short ramps with different slopes. An optimization algorithm similar to a simulated annealing algorithm is used for finding the optimal signal shape to drive the galvo scanners. As a result, a significant reduction in the nonlinearity of the galvo scanning is obtained.  
optimization; algorithm; galvo scanner; nonlinearity; simulation +
Footnote †: journal: Applied
# Effect of Height and Geometry of Stepped Spillway on Inception Point Location
## 2 Materials and Methods
### Realizable k-\(\varepsilon\) Model  
The steps in a spillway have a significant effect on increasing the roughness. Owing to the effect of roughness, the flow over a stepped spillway is highly turbulent. Two-phase flow over a stepped spillway can generate an even higher turbulence rate. Thus, a turbulent model is used to predict the turbulent behavior. The realizable k-\(\varepsilon\) model was introduced by [PERSON] et al. [45] for high Reynolds number turbulent flow, which is based upon the realizable constraints. The realizable k-\(\varepsilon\) model gives better results than other turbulence models because a new equation for the dissipation rate (\(\varepsilon\)) is proposed, which is based upon the large turbulent Reynolds number. The performance of the realizable k-\(\varepsilon\) model is improved and provides good results for the recirculating flow. Other Reynolds-averaged Navier-Stokes (RANS) models such as the standard k-\(\varepsilon\) model, RNG k-\(\varepsilon\), standard k-\(\omega\), and shear stress transport k-\(\omega\) models perform well when used for simulating other types of flows like pipe or laminar flows. Because of the unsatisfactory results from other RANS models for recirculating flow,considering that the flow in the stepped spillway is recirculating, realizable k-\(\epsilon\) is used in the present study. The transport equations for turbulent kinetic energy (k) and turbulent dissipation rate (\(\epsilon\)) are expressed as Equations (6) and (7), respectively. The meaning of all the symbols in Equations (6) and (7) is shown at the end of the text.  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho ku_{j })=\frac{\partial}{\partial x_{i}}\Bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{\sigma_{ k}}\bigg{)}\frac{\partial k}{\partial x_{j}}\bigg{]}+G_{k}+G_{b}-\rho\varepsilon-Y_{M}+S_{k} \tag{6}\]  
\[\frac{\partial}{\partial t}(\rho\varepsilon)+\frac{\partial}{\partial x_{j}}( \rho\varepsilon\mu_{j})=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\mu+ \frac{\mu_{t}}{\sigma_{\varepsilon}}\bigg{)}\frac{\partial\varepsilon}{ \partial x_{j}}\bigg{]}+\rho C_{1}S_{\varepsilon}-\rho C_{2}\frac{\varepsilon ^{2}}{k+\sqrt{\
u\varepsilon}}+C_{1\varepsilon}\frac{\varepsilon}{k}C_{3 \varepsilon}G_{b}+S_{\varepsilon} \tag{7}\]
# Co-Operative Binary Bat Optimizer with Rough Set Reducts for Text Feature Selection
## 5 Results and Discussion
### External Evaluation
This way, optimizing each part separately improves the performance and controls the diversity of the population. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**NB**} & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**KNN**} \\ \cline{2-10}  & **P** & **R** & **F** & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline Earn & 94.31 & 92.66 & 93.48 & 96.54 & 91.41 & 93.90 & 91.74 & 93.94 & 92.83 \\ Acquisition & 90.87 & 97.25 & 93.95 & 89.17 & 98.36 & 93.54 & 91.77 & 96.56 & 94.10 \\ Trade & 94.22 & 91.18 & 92.68 & 96.06 & 91.28 & 93.61 & 94.59 & 88.91 & 91.66 \\ Ship & 97.56 & 78.43 & 86.96 & 93.62 & 77.39 & 84.73 & 95.37 & 70.55 & 81.10 \\ Grain & 94.92 & 77.78 & 85.50 & 93.73 & 82.64 & 87.84 & 89.13 & 76.47 & 82.32 \\ Crude & 96.49 & 92.12 & 94.25 & 95.16 & 91.57 & 93.33 & 93.99 & 95.54 & 94.76 \\ Interest & 93.29 & 89.87 & 91.55 & 91.53 & 88.87 & 90.18 & 93.29 & 93.86 & 93.57 \\ Money-fx & 93.80 & 90.81 & 92.28 & 92.07 & 87.69 & 89.83 & 95.62 & 92.49 & 94.03 \\ Corn & 90.31 & 79.52 & 84.57 & 90.64 & 82.59 & 86.43 & 86.89 & 82.98 & 84.89 \\ Wheat & 82.87 & 87.45 & 85.10 & 88.26 & 85.98 & 87.11 & 86.97 & 84.35 & 85.64 \\ \hline Average & 92.86 & 87.71 & 90.03 & 92.68 & 87.78 & 90.05 & 91.94 & 87.56 & 89.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Precision (P), Recall (R) and F-measure (F) of each class in the Reuters dataset using \(\text{BBA}_{\text{CO}}\). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**NB**} & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**KNN**} \\ \cline{2-10}  & **P** & **R** & **F** & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline Project & 95.88 & 92.29 & 94.05 & 96.67 & 91.57 & 94.05 & 91.76 & 93.35 & 92.55 \\ Course & 87.72 & 93.57 & 90.55 & 97.18 & 89.33 & 93.09 & 93.55 & 86.74 & 90.02 \\ Faculty & 88.61 & 79.89 & 84.02 & 81.13 & 88.22 & 84.53 & 85.57 & 92.17 & 88.75 \\ Student & 97.69 & 98.42 & 98.05 & 86.85 & 94.20 & 90.38 & 90.79 & 93.58 & 92.16 \\ \hline Average & 92.48 & 91.04 & 91.67 & 90.46 & 90.83 & 90.51 & 90.42 & 91.46 & 90.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Precision (P), Recall (R) and F-measure (F) of each class in the WebKB dataset using \(\text{BBA}_{\text{CO}}\).
## 4 Results and Discussion  
### Uranium Speciation in Groundwater  
In the study samples from wells near the sludge repository, uranium at concentrations of up to 1.56 mg/L was found (Table 1). Cascade filtration of the water samples from Wells 2 and 3 in an anaerobic box under field conditions allowed us to establish the size distribution of the main particles associated with uranium migration. Chemical elements in groundwater can migrate as suspended particles (0.1 \(\mathrm{\SIUnitSymbolMicro m}\)), colloids (1-100 nm), and molecular solutions (\(\leq\)1 nm). It is generally accepted that suspended particles are nearly nonexistent in groundwater, colloids play a subordinate role, and truly dissolved formsdominate. Exceptions are elements such as Si, Al, Mn, Fe, U, and Th. We therefore studied their behaviour (Figure 1). More than 6 g/L of nitrate ions was found in the water of Sample 3, contributing to slightly oxidative conditions (Eh 90 mV), and uranium was found mainly in the form of uranyl ion carbonate complexes, in which form uranium easily migrates (Figure 2 fraction < 0.05 um, yellow colour). Iron and manganese are transported mainly as colloids, and the large fraction (2.4-4.5 um) accounted for 30-40% of these elements. For iron, this fractionation is explained by the formation of insoluble Fe(III) (hydro)oxide; the explanation for manganese is not as apparent. According to the calculations (Table 2), manganese should be present in the form of Mn\({}^{2+}\) and MnSO\({}_{4(\mathrm{aq})}\) without solid phases. Moreover, the aerobic oxidation of bivalent compounds to insoluble forms with the highest valence, followed by subsequent separation, is used to precipitate manganese from contaminated water. Currently, we can only assume that the concentrated Mn colloids are organomineral compounds. The water of Sample 2 presents a nitrate content of approximately 1 g/L (Eh \(-\)28 mV). Iron is mainly in the dissolved forms of Fe(II)--Fe\({}^{2+}\), FeHCO\({}_{3}\)+ and FeSO\({}_{4(\mathrm{aq})}\)--while manganese is in the solution (40%) and in all fractions of colloids. Note that in this water, the concentration of manganese (2.3 mg/L) is much higher than the MAC. According to the calculations, in zone 2 at a distance of tens of metres from the sludge pond, the uranium in solution is mainly in the form of U(OH)\({}_{4(\mathrm{aq})}\) and in the form of true and pseudocolloidal UO\({}_{2(\mathrm{am})}\). Figure 1: The content of elements in different fractions (% of initial concentration) after cascade filtration of groundwater samples: **A**—Sample 2, **B**—Sample 3. The pore diameters of the filters (μm) are displayed at the bottom. Nitrates, with which the solutions of Samples 2 and 3 are saturated, form oxidative conditions in neutral groundwater in the territory of the NCCP and prevent the removal of uranium. Moreover, a positive shift is evident even when the NO3\({}^{-}\) content decreases from 6169 mg/L to 1124 mg/L: (a) the oxidation state of uranium changes from U(VI) to U(IV),