esa-sceva/satcom-chunk-collection huggingface dataset:
# An Insightful Overview of the Wiener Filter for System Identification
## 6 Wiener Filter with the Quadratic Eigenvalue Problem  
Assume that some knowledge is available on the impulse response, \(\mathbf{h}\), i.e., \(\|\mathbf{h}\|_{2}^{2}=\alpha^{2}\) with \(\alpha^{2}>0\). This information can be used to exactly regularize (with the Tikhonov technique) the Wiener filter with the help of the so-called quadratic eigenvalue problem (QEP) [29; 30]. This approach is explained in this section. The criterion is  
\[\min_{\mathbf{h}}\mathcal{J}\left(\widehat{\mathbf{h}}\right)\ \ \ \text{ subject to}\ \ \widehat{\mathbf{h}}^{T}\widehat{\mathbf{h}}=\alpha^{2}. \tag{43}\]  
From the above criterion, the Lagrange function can be written:  
\[\mathcal{L}\left(\widehat{\mathbf{h}},\lambda\right)=\sigma_{d}^{2}-2\widehat{ \mathbf{h}}^{T}\mathbf{p}+\widehat{\mathbf{h}}^{T}\mathbf{R}\widehat{ \mathbf{h}}-\lambda\left(\widehat{\mathbf{h}}^{T}\widehat{\mathbf{h}}-\alpha ^{2}\right), \tag{44}\]  
where \(\lambda\) is a Lagrange multiplier. Differentiating \(\mathcal{L}\left(\widehat{\mathbf{h}},\lambda\right)\) with respect to \(\widehat{\mathbf{h}}\) and \(\lambda\) and equating to zero yields the equations:  
\[\mathbf{R}\widehat{\mathbf{h}}-\lambda\widehat{\mathbf{h}} =\mathbf{p}, \tag{45}\] \[\widehat{\mathbf{h}}^{T}\widehat{\mathbf{h}} =\alpha^{2}. \tag{46}\]  
It can be shown [31; 32] that the smallest solution \(\lambda\) of the two previous equations is needed to solve (43). Assume that \(\lambda\) is not an eigenvalue of \(\mathbf{R}\) and let  
\[\overline{\mathbf{h}} =(\mathbf{R}-\lambda\mathbf{I}_{L})^{-1}(\mathbf{R}-\lambda\mathbf{ I}_{L})^{-1}\mathbf{p} \tag{47}\] \[=(\mathbf{R}-\lambda\mathbf{I}_{L})^{-1}\widehat{\mathbf{h}}.\]  
In fact, because \(\mathbf{R}>0\), all the eigenvalues of \(\mathbf{R}\) are real and strictly positive. To be in accordance with the regularization technique, \(\mathbf{R}-\lambda\mathbf{I}_{L}\) also needs to be positive definite. Consequently, \(\lambda\) needs to always be negative.
# A Multi-Dimension Spatial Method for Topology Awareness and Multipath Generating
## 3 Constructing Multi-Dimensional Topology View
### Merging Topology Views  
We merge independent views by relations of locations and embedding SPs. The information of SPs is hard to obtain due to privacy and security policy, so we infer SPs from MM(i,j) based on deployment characteristics, e.g., exchange point for two autonomous systems is usually in the same location, and latency between RSes near the exchange point is relatively small. To reduce search complexity, we only search MR() between RSes in locations of specific distance. i.e., \(0\leq NB(L_{p},L_{q})\leq DT\), and DT is the location distance threshold. In addition, only top h minimum MM() within each city are chosen, and LT sets the exchange point latency threshold according to empirical measure, so that exchange point would exist if \(MM()\leq LT\). The embedding procedure is shown in Algorithm 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Definition and Description** \\ \hline \(T_{set}=\{T_{1}\sim T_{n}\}\) & topology view set, \(T_{i}\) denotes the view of \(AS_{i}\) \\ \(T_{i}=\{RS_{1}\sim RS_{m}\}\) & \(T_{i}\) has m RSes \\ \(L_{set}=\{L_{1}\sim L_{p}\}\) & location set, city is the basic unit of location \\ \(RS_{i}^{j}\in L_{j}\) & \(RS_{i}\) is deployed in \(L_{j}\) \\ \(NB(i,j)\) & distance between \(L_{i}\) and \(L_{j}\), or \(RS_{i}\), and \(RS_{j}\) \\ \(L_{k}(p,q)=1\) & \(T_{p}\) and \(T_{q}\) have SPs in \(L_{k}\) \\ \(MP(i,j)=\{P_{1}\sim P_{q}\}\) & q multiple paths between \(RS_{i}\) and \(RS_{j}\) \\ \(RS_{i}\in P_{j}\) & \(RS_{i}\) is on \(P_{i}\) \\ \(P_{j}\subseteq T_{k}\) & \(\forall RS_{i}\in P_{j},RS_{i}\in T_{k}\) \\ \(MP(i,j)\) & the multipath diversity of \(MP(i,j)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and Definitions in Underlay Diversity.  
```
0: MM(N,N), N is the number of RSes \(T_{W}\), W is the number of autonomous systems DT, LT Output: \(L_{M}(N,N)\), M is the number of locations (cities) \(NB_{M}(RS_{P},RS_{Q})\), P,Q are the numbers of neighbor RSes. C(N,N), coordinate of exchange point, center of two RSes
1:while\(T_{i}\in T_{W}\textbf{and}T_{f}\in T_{W}\)do
2:while\(L_{c}\in L_{M}\textbf{and}L_{d}\in L_{M}\textbf{and}0\leq NB(L_{c},L_{d})\leq DT\)do
3:while\(RS_{f}\in T_{i}\textbf{and}RS_{f}\in L_{c}\textbf{and}RS_{g}\in T_{j}\textbf{and}RS_{g} \in L_{d}\)do
4: MM(f,g) \(\leq\) LT and Get top h minimum MM(f,g)
5:\(NB_{k}()\leftarrow(RS_{f},RS_{g}),L_{k}(f,g)=1\) (k= c or d or c-d)
6:\(C(f,g)\gets center(C_{f},C_{g})\)
7:endwhile
8:endwhile
9:endwhile
```  
**Algorithm 1** Embedding exchange points
## 3 Results and Discussion
#### 3.1.1 Intelligent Homes
developed a wearable healthcare monitoring system for elderly individuals, using a wearable device designed as a T-shirt that connects to the ThingSpeak IoT platform [71]. Othershave described the creation of a patient monitoring system with vital sign sensors, including a microcontroller, a Message Queuing Telemetry Transport (MQTT) broker for communication, and a mobile application or website as a receptor [72]. The study of [PERSON] et al. presented a robust and efficient system for remote health monitoring by utilizing IoT devices and sensors. This system was designed to provide the real-time tracking of medical equipment locations and the continuous monitoring of essential health metrics [73]. [PERSON] et al. proposed prototypes for geriatric health monitoring, using various sensors to send alert messages to doctors and family members when vital signs are abnormal [74]. In addition, one group has developed an advanced system for the continuous real-time monitoring of physiological parameters, including the temperature, heart rate, and blood oxygen saturation (SPO2). The system is designed to transmit the collected data directly to healthcare providers, facilitating the enhanced management and treatment of chronic conditions [75]. [PERSON] et al. introduced a telemedicine infrastructure for e-healthcare driven by artificial intelligence and the Internet of Medical Things (IoMT) [76]. A biomedical health monitoring and diagnosis system was developed in [77]. A fuzzy-assisted machine learning framework for remote healthcare monitoring using IoT devices and fog computing was described in [78]; finally, the work in [79] proposes a wearable device integrated with various IoT sensors for the elderly's smart homes to promote sustainable living. The studies discussed in this subsection are summarized in Table 2.
## 3 Methodology
### Hybrid HS-SA Algorithm  
HS has been successfully hybridized with other metaheuristic techniques to improve its performance. The main objective with hybridization is to combine the strengths of different techniques to find high quality solutions of complex problems. In [25], HS is combined  
Figure 2: Flowchart of the implemented HS algorithm.  
with an artificial electric field algorithm for feature selection in high dimensional data sets. In [28], the authors hybridized a cultural algorithm with HS to solve the constrained optimization problem of diesel blending. In [26], HS was combined with a neural network for forecasting electricity prices. A comprehensive review of the development of the HS algorithm along with hybrid versions and applications can be consulted in [38].  
In this paper, the hybrid HS-SA approach proposed in [22] was implemented. SA is a metaheuristic technique inspired by the annealing of certain metals proposed in [39]. This technique starts with a randomly selected solution \(S\) and an initial temperature \(T\) denoted as \(T_{0}\). In every iteration of the algorithm, a new solution \(S^{\prime}\) in the neighborhood of \(S\) is generated and the temperature is updated. A better solution is always accepted; nonetheless, inferior solutions are also accepted with a probability determined by its fitness and current temperature. Usually, low quality solutions might be accepted at the early stage of the algorithm to enforce diversification of the search, but as the temperature is updated, the probability of accepting inferior solutions decreases.  
The implemented HS-SA algorithm is described in Algorithm 1. In this case, HMCR stands for harmony memory considering rate, which is the probability at which a new individual is generated from those already existing in the HM. PAR is the pitch adjusting rate, which is the probability value used for the generation of a new individual. HMS stands for the harmony memory size, which defines the number of individuals stored in the HM, and BW corresponds to the pitch bandwidth. As regards SA, \(T_{0}\) is the initial temperature and \(\alpha\) is the temperature reduction rate.  
```
1 Let:\(f()\) be the proposed objective function Initialize parameters:\(HMCR\), \(PAR\), \(HMS\), \(T_{0}\), \(\alpha\) Initialize harmony memory (HM) Set:\(T=T_{0}\)while(stopping criteria is not reached)do
2 Find the current worst harmonic and the best harmony in HM for\(i=1\) to \(D\)do
3 if(\(rand\leq HMCR\))then
4\(H_{i}=HM_{i}^{j}\) where \(j=rand\_int(1,HMS)\)if(\(rand\leq PAR\))then
5\(H_{i}=H_{i}\pm rand\times BW\) where \(j=rand\_int(1,HMS)\) endif
6
7 else
8 Generate \(H_{i}\) within the allowed bounds (randomly) endif
9
10 endfor
11\(\Delta Cost=f(H)-f(Worst)\)if(\(\Delta Cost<0\) or \(rand<e^{-\Delta Cost/T}\))then
12 Update HM by replacing the worst harmony by H
13 endif
14 Set \(T=\alpha T\)
15 endwhile
16 Save the best harmony as the solution
```  
**Algorithm 1**Hybrid HS-SA algorithm.
## 5 Requirements of UWSNs
### Longevity  
Network lifetime is one of the key requirements of UWSNs. It has a significance impact on the cost, time, maintenance tasks and performing underwater sensor nodes. It is crucial for maximizing the network lifetime, especially for mobile sensor nodes operations. Therefore, firmware has a vital responsibility in ensuring an effective practice of hardware features such as sleep modes, allows interruptions to replace polling and easy to set up. In addition, routing protocol and deployment of nodes have a huge role in controlling the energy consumption. It leads to a significant amount of research works on the development and evaluation processes.
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 2 Related Work
### Image Classification Methods
#### 2.3.1 Manual Feature-Based Image Classification Methods  
Image classification methods based on manual feature extraction are designed by experts based on an understanding of images and domain knowledge. In specific application scenarios, these methods often demonstrate good stability and effectiveness. Table 2 displays image classification methods based on manual features.  
Although image classification methods based on manual features may demonstrate good performance in specific tasks and have the advantage of being easy to understand, their efficiency may not be high when dealing with unknown or complex classification tasks.
## 2 Operation Principle of the Compressed Air Foam System  
The CAFS is a fire-extinguishing facility which releases foam which is transferred from a mixing chamber into air through a discharge nozzle or through a nozzle of a discharging device which is connected to a pipe network. The role of the mixing chamber is to blend the air and the aqueous foam solution by compulsion. The compressed air foam created from the combination of water and an undiluted solution of foam is itself combined with pressurized air or nitrogen to become stable and homogeneous. The operating principle of the existing foam fire-extinguishing facility (for example, GAMS) is to provide bubbles which are produced by the aqueous foam solution in the foam mixer and then released through the nozzle and sucked into the external air simultaneously. The operating principle of the CAFS is as follows (see Figure 1): First, the system forms bubbles which are sent by pressured air to a mixing chamber. These are then discharged through a nozzle and transferred by a network of pipes. In other words, the compressed air is an important factor to produce stable and homogeneous bubbles [8].  
The CAFS works through a choking effect by expanding the volume of the compressed air-foam mixture and increasing the fire-extinguishing effectiveness by increasing the surface area of the compressed air-foam mixture, as the volume and the surface area of the compressed air-foam mixture can be significantly expanded and broadened according to a discharge of the foam with the forcing of air into the aqueous foam solution. Therefore, when the CAFS is applied to the fire-extinguishing facility, it can utilize the relationship of the aqueous foam solution and the fo  
Figure 1: Operating principle of the compressed air foam system.  
of air which is included in the compressed air foam. If foam with accumulated intake air is generated, it can considerably reduce the degree of damage by water and lead to a substantial time of more than 10 minutes during which the flow is poor when adhering to the surface of the target object, as the reduction time as well as the viscosity of the foam is increased. Because the aqueous foam solution which is consumed is less than the typical great quantity of water used, as illustrated above, the CAFS is also economically beneficial.
# Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review
## 4 Discussion
### Quantification of Acoustic Emission Signals  
Quantifying AE parameters into physical unit representations provides a crucial rationalization permitting the interpretation of monitored responses as real deformation indicators. Early investigations qualitatively classified activity levels as shown below:  
1. Low: below 1000 AE counts with a low probability of failure [129].
2. Moderate: 1000-10,000 counts indicating marginal stability requiring monitoring [118].
3. High: 10,000-100,000 counts, representing an unstable state demanding remediation [119].
4. Very high: above 100,000 counts, signaling failure onset necessitating evacuation [118].  
Nevertheless, establishing AE parameters as objective indicators correlated with externally measured deformation rates has been essential for the development of failure prediction models that incorporate specific warning thresholds, with the following capabilities.  
1. Establishing linear scaling between the natural logarithm of the AE event rate (normalized by duration) and monitored landslide flume displacement rates [121].
2. Rating slope stability into four categories of \"essentially stable\", \"marginally stable\", \"unstable\", and \"rapidly deforming\" through gradient classification of scaled AE-displacement rate relation [121].
3. Demonstrating proportional correlation between normalized cumulative AE energy and measured daily displacements of slow-sliding landslide monitored using optical leveling for validation [109].  
In addition to quantification, damage classification and the identification of deforming zones have utilized pattern recognition tools, in the following ways:  
1. Through identifying slope instabilities from stable regions through cluster analyses of AE dataset attributes like b-value, partition coefficient, and skewness [130].
2. Through the spatio-temporal tracking of slope structural failure processes through Hidden Markov Models, decomposing AE sequences into discrete states signifying damage evolution stages [122].  
Overall, quantitative indices constitute the main focus of contemporary AE monitoring towards enabling dependable early warnings through establishing objective success/failure criteria scientifically validated against reference measurements. In the long run, advanced analytical techniques like machine learning may revolutionize data-driven quantitative evaluations of monitored slope health.
## 4 Discussion
\begin{tabular}{l l} \(dN_{max}\) & quantity of large droplets \\ \(dN_{min}\) & quantity of sub-micron and micron droplets \\ DSD & Droplet Size Distribution \\ \(e_{i}\) & channel elbows \\ \(HR\) & humidity ratio, \\ \(L_{e}i\) & length of the elbow channel, m \\ \(l_{m}\) & Prandtl mixing path, m \\ \(L_{SCh}\) & length of the straight channel, m \\ DQ & Droplet Quantity \\ K & Constant, \(\frac{1}{\mathrm{s}}\) \\ LVG & local velocity Gradient \\ \(\dot{m}_{a}\) & mass flow rate of air at the inlet of the measurement channels, \(\frac{\mathrm{kg}}{\mathrm{s}}\) \\ \(\dot{m}_{f}\) & air pressure at the inlet of the measurement channels, \(\frac{\mathrm{kg}}{\mathrm{s}}\) \\ \(\dot{m}_{w}\) & mass flow rate of water at the inlet in the pneumatic nozzle, \(\frac{\mathrm{kg}}{\mathrm{n}}\) \\ M & Magnus lift force, N \\ \(n\) & fan frequency, Hz \\ \(\overline{p}_{a}\) & mean pressure of air at the inlet in the pneumatic nozzle, bar \\ \(\overline{p}_{atm}\) & atmospheric pressure, bar \\ \(\overline{p}_{f}\) & mean pressure of air at the inlet of the measurement channels, kPa \\ \(\overline{p}_{tot}\) & \\ \(\overline{p}_{w}\) & mean pressure of water at the inlet in the pneumatic nozzle, bar \\ PIV & Particle Image Velocimetry \\ S & Saffman lift force, N \\ s & stopping distance, m \\ \(SCh\) & straight channel, m \\ Tu & turbulence intensity \\ \(T_{amb}\) & ambient temperature, \({}^{\circ}\)C \\ \(T_{hum}\) & temperature of the hygrometer, \({}^{\circ}\)C \\ u & velocity of the continuous phase, \(\frac{m}{s}\) \\ \(\dot{V}_{a}\) & volumetric flow rate of air at the inlet in the pneumatic nozzle, \(\frac{\mathrm{Nm}^{3}}{\mathrm{h}}\) \\ \(v_{s}\) & shear velocity, \(\frac{m}{s}\) \\ \(x\) & vertical axis; length, m \\ \(y\) & horizontal axis \\ \(\alpha\) & bending angle, angle of slope, \({}^{\circ}\) \\ \(\delta\) & thickness of the boundary layer, m \\ \(\kappa\) & turbulence constant in the Prandtl mixing model \\ \(\mu\) & dynamic viscosity, \(Pa\cdot s\) \\ \(v\) & kinematic viscosity, \(\frac{m^{2}}{\mathrm{s}}\) \\ \(\rho\) & density, \(\frac{kg}{m^{3}}\) \\ \(\tau\) & shear stresses, Pa \\ \(\epsilon\) & diffusivity of droplets, \(\frac{m^{2}}{\mathrm{s}}\) \\
**Subscripts** & \\ a & air \\ bl & boundary layer \\ M & Magnus \\ ref & reference \\ res & resultant \\ tot & total \\ w & water \\ \end{tabular}
# Explainable Two-Layer Mode Machine Learning Method for Hyperspectral Image Classification
## 3 HSI Classification Method in Explainable Two-Layer Mode  
This section introduces a new paradigm for HSI classification: explainable two-layer mode. We divide the classification algorithm into two layers: front-end learning and back-end learning [31]. In the front-end learning layer, the band selection is realized by combining superpixel segmentation and SSC, resulting in a low-dimensional HSI data re-expression. In the back-end learning layer, support vector machine (SVM) or K-nearest neighbor (KNN) is employed to classify low-dimensional data. Band selection and classification tasks are combined through the front-end and back-end integration strategy.
# Piezoelectric Electro-Mechanical Impedance (EMI) Based Structural Crack Monitoring
## 1 Introduction
By analyzing the piezoelectric admittance formula of the electromechanical coupling system and combining with the harmonic response analysis of the finite element method (FEM), it found that the local dynamic characteristics of the structure would change when cracks occur and propagate in the beam. This dynamic variation is represented by the shift of peak frequency in the imaginary part of the piezoelectric admittance spectrum. The method of piezoelectric admittance peak frequency shift was proposed for the detection of the small-scale crack detection with a relatively higher monitoring frequency band. An experimental device was constructed to measure the admittance signal of a piezoelectric patch installed on the structure by a precision impedance meter. The relationship between the peak frequency in the piezoelectric admittance spectrum and the change of the crack size was analyzed. The finite element simulation and experimental results show that the small crack in the beam can be detected by the proposed method.
# Study on Correction Method of Internal Joint Operation Curve Based on Unsteady Flow
## 5 Conclusions  
In this article, the internal joint operation curve has been proposed and tested. The turbocharger performance is investigated under the unsteady flow boundary conditions of the engine, and the turbocharger unsteady flow correction concept and correction method and calculation method are presented, and the curve is corrected. The main conclusions obtained from the results are as follows:  
1. There is a difference in performance between the unsteady flow and the steady flow of the compressor, with the average value of the unsteady flow performance being 4.4% to 5.4% higher than the corresponding steady flow value. The unsteady flow power consumption coefficient for the combined conditions is calculated as the correction coefficient for the compressor unsteady flow, and the pulse energy utilization coefficient is used as the correction coefficient for the turbine unsteady flow.
2. The turbocharger performance is closer to the actual operating condition after accounting for the unsteady coefficient correction at the compressor and the turbine. The internal joint operating curve shifts both to the left and up, with the left shift being larger.
3. The main difference between the corrected internal joint operating curves of the two turbochargers is at the medium-low-speed range. The original turbocharger's surge margin is reduced by 4.6% to 11.8%, and the optimized turbocharger's surge margin is reduced by 15.2% to 21.9% in the medium-low-speed range.
4. The concept of the turbocharger unsteady flow energy utilization factor is proposed to quantitatively assess the turbocharger energy utilization. The overall unsteady flow energy utilization advantage of the optimized turbocharger is significant, especially in the medium-low-speed range, where it is more than 14.5% higher than the original turbocharger. The optimized turbocharger increases the maximum torque by 9.6% and decreases the minimum BSFC by 4.4% compared to the original engine. Therefore, compared with the internal joint operation curve with steady flow, the curve with unsteady flow correction was closer to the actual engine performance.  
Conceptualization, S.Y. and H.F.; methodology, S.Y.; software, H.F.; validation, S.Y., H.F., and R.H.; formal analysis, H.F.; writing--original draft preparation, S.Y.; writing--review and editing, S.Y.; project administration, J.N.; funding acquisition, J.N. and X.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China youth Science Foundation project (Grant No. 22102116) and the Nanchang Intelligent New Energy Vehicle Research Institute Foresight Project (TPD-TC202110-12).  
Figure 16: (**a**) The effect of turbocharger structure on engine torque; (**b**) the effect of turbocharger structure on engine BSFC.