esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### OSP Considering the Evolution of Structural States and Material Properties
#### 3.4.3 Optimal Sensor Placement
Referring to Figure 21i-m, based on the consideration of dam material zoning, when accounting for the degradation of the dam elastic modulus after 10 years, the sensor placement remains relatively unchanged compared to the seamless condition. However, as the elastic modulus degrades over 20, 30, 40, and 45 years, an increase in sensors is observed in the upper-right and lower-left parts of the dam. Sensors in the middle-lower part of the dam also significantly increase, resulting in a progressively more uniform distribution of sensors throughout the dam. In conclusion, dam material zoning and tiny elastic modulus degradation have minimal impact on sensor placement. With increasing degradation of the dam's elastic modulus, additional sensors are deployed in the upper-right, lower-left, and overall middle-lower parts of the dam, leading to a more even sensor distribution. Referring to Figure 21n-r, compared to the sensor placement without cracks, when considering the effects of dam elastic modulus degradation over 10, 20, and 30 years, cracks, and contraction joints, there is an increase in sensors deployed in the upper-right and left-middle-upper parts of the dam. When considering dam elastic modulus degradation over 40 and 45 years, cracks, and contraction joints, the sensors in the upper-right and lower-left parts of the dam increase. With the increase in the degradation period of the elastic modulus, the sensor placement changes from being more concentrated in the middle-upper part of the dam to gradually becoming more concentrated in the middle-lower part. In summary, considering the impact of cracks, contraction joints, and dam elastic modulus degradation, sensors gradually moving from the middle-upper part of the dam to the middle-lower part with an increase in degradation period. Therefore, based on the above study, it is necessary to consider the effects of cracks, contraction joints, and the elastic modulus zoning degradation of a dam body when performing the OSP on the arch dam. For the optimized sensor placement of this arch dam in its current state after 45 years of operation, it is determined that 36 sensors need to be placed for this arch dam after considering factors such as the changes in structural states and material properties, and the results of the spatial location of the sensors in the final placement are shown in Figure 21r.
# Performance Tradeoff Analysis of Hybrid Signaling SWIPT Systems with Nonlinear Power Amplifiers
## 4 SNDR Analysis
### Identical Amplification Coefficient  
In this case, \(y=\alpha(s+e)+d=\alpha x+d\). Because the energy signal and the information signal have an identical amplification factor, the definition of \(\alpha\) is the same as that of the traditional SNDR defined in (23). Unlike the traditional SNDR definition, the power of the useful signal in SNDR definition should be expressed as the power of the information signal. Therefore, according to Equation (25), the SNDR with respect to the information signal \(s\) can be obtained as  
\[\text{SNDR}=\frac{\left|\alpha\right|^{2}\text{E}\left[\left|s\right|^{2}\right]} {\varepsilon_{d}+\sigma_{v}^{2}}. \tag{26}\]  
Here, the linear coefficient \(\alpha\) is a function of the signal envelope \(r\), i.e.,  
\[\alpha=\frac{\int_{0}^{\infty}rg(r)\exp(jf(r))f_{r}(r)dr}{\int_{0}^{\infty}r^ {2}f_{r}(r)dr}. \tag{27}\]  
where \(f_{r}(r)\) denotes the PDF of signal envelope \(r\). The power \(\varepsilon_{d}\) of the distortion term can be evaluated as  
\[\varepsilon_{d}=\int_{0}^{\infty}[g(r)]^{2}f_{r}(r)dr-\left|\alpha\right|^{2} \int_{0}^{\infty}r^{2}f_{r}(r)dr. \tag{28}\]
## 4 Results
### Findings Obtained from the Data Augmentation Process  
In real-world scenarios, achieving a perfectly balanced dataset is often impractical. In this study, data imbalance stems from natural factors such as the irregular nature of user  
Figure 4: Proposed fog–cloud architecture for smart campus services in a multi-campus and multi-building environment.  
feedback and the variability in environmental conditions. Users tend to be more active at certain hours or focus more on specific topics, leading to an uneven data distribution. For instance, variations in air quality and temperature may be more pronounced at specific times, resulting in a limited amount of data collected under certain conditions.  
The initial dataset exhibited imbalanced class distribution, with classes 1 and 4 overrepresented, potentially hindering model performance. This section analyzes datasets augmented using the SMOTE, evaluating class distribution, correlation, and outlier detection.  
The impact of data augmentation on addressing class imbalance by increasing minority class examples is examined. The analysis of class distribution clearly visualizes these effects. Figure 5 displays the class distribution of the original dataset, while Figure 6 illustrates the improvements achieved after applying the SMOTE.  
The original dataset exhibited a highly imbalanced class distribution, with classes 1 and 4 being overrepresented. After applying the SMOTE for data augmentation, the class distribution became significantly more balanced. Figure 5 shows the original distribution, while Figure 6 illustrates the improvement after the SMOTE, with a more even representation of all classes. This balance is expected to reduce the model's bias toward the overrepresented classes and improve its overall performance.
## 2 Basics of QDI Circuits
### RTZ Handshake Signaling  
According to RTZ handshaking, firstly, the dual rail data bus initially assumes the spacer and ACKIP = 1. After the current stage register bank sends data, rising signal transitions (i.e., zero to one) will occur on one of the rails of each dual rail encoded input of the dual rail data bus. Secondly, the next stage register bank, after receiving the processed data from the QDI circuit, would drive ACKOP to 1. Thirdly, the current stage register bank would wait for ACKIP to become zero, and after this happens, the dual rail data bus would once again assume the spacer. Finally (i.e., fourthly), after a finite and positive unbounded time duration elapses, the next stage register bank would receive the spacer from the QDI circuit and would drive ACKOP to 0. Consequently, ACKIP will become one. With this, one data transaction is deemed to have been completed and the next data transaction may commence. According to RTZ handshaking, the inputs are supplied following the sequence of data, spacer, and so forth.
# Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach
## 3 Proposed Signal Processing Methodology
### Pre-Processing Algorithms
#### 3.1.2 Wavelet Decomposition  
The next phase of pre-processing algorithms is developed to extract signal patterns that are buried inside the noise. For data with higher sampling rates, the noise floor will be significant. This noise is eliminated by stationary wavelet decomposition. Stationary Wavelet Transform (SWT) is a variant of Discrete Wavelet Transform (DWT). This allows for better time-frequency localisation as the number of wavelets sampled can be used to locate a particular temporal interval within data, creating a logarithmic frequency scale.  
Different types of wavelet can be sampled in the transform and each wavelet has different characteristics that can change how the transformation of the data is applied. This new representation retains important information about the data whilst compresses it. Overall, the signal was demonstrably smoothened more than the spike subtraction signal in the previous stage. SWT completely removed the noise as shown in Figure 3.  
In addition to the time series analysis, a exploratory processing task utilising power spectral density (PSD) to compare the spectra of signal before and after denoising using SWT based pre-processing methods is also implemented. The resulting figures are presented in Figure 4. As it is event from the plots, the PSD estimates has dropped significant energy of at least 20 dB&/Hz after 1 kHz in both healthy and defect data. Moreover, the noise buried in the SWT processed health data has become neglectable between 1 kHz and 9 kHz. This shows that the noise is completely eliminated.  
Figure 3: Before and after de-noising using Stationary Wavelet Transform.  
Figure 2: Demonstration of spike subtraction algorithm on open source IMS bearing data set.
## 3 Results  
### Spatial Distribution Patterns of Natural Grassland Fire Susceptibility  
In the MaxEnt model, the MTSS and TPT values were 0.4639 and 0.1042, respectively. Based on these values, the natural grassland fire susceptibility area (Figure 3a) was categorized into three susceptibility areas: low [0-0.1042), medium [0.1042-0.4639), and high [0.4639-1]. The high-susceptibility zone is primarily located in the southeastern and northeastern regions of Qinghai Province, encompassing most of the Guoluo Tibetan Autonomous Prefecture, Zeku County, and Henan Mongolian Autonomous County within Hainan Tibetan Autonomous Prefecture, as well as the western part of Yushu Tibetan Autonomous Prefecture. Medium-susceptibility areas are mainly concentrated in the east and south of Qinghai Province, with sporadic occurrences in the west. Low-susceptibility areas are scattered, predominantly in the central and western parts of the province. These areas display distinct geographic characteristics, typically found in grasslands, near rivers, and abundant in water sources. This distribution suggests that natural grassland fires are likely to occur in alpine marsh meadows, characterized by dense vegetation and abundant combustible materials.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Typology** & **Hierarchy** & **Area Proportion (\%)** & **Fire points Proportion (\%)** & **G** \\ \hline \multirow{3}{*}{Natural grassland fire} & Upper Hierarchy & 8.98 & 70 & 0.87 \\  & Middle Hierarchy & 36.63 & 30 & \(-0.22\) \\  & Lower Hierarchy & 54.39 & 0 & 1 \\ \hline \multirow{3}{*}{Anthropogenic grassland} & Upper Hierarchy & 7.82 & 95.35 & 0.92 \\  & Middle Hierarchy & 10.11 & 4.65 & \(-1.17\) \\ \cline{1-1}  & Lower Hierarchy & 82.07 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grassland fire spontaneous combustion process gain statistics of grassland fire susceptibility evaluation model in Qinghai Province.
## 5 Empirical Analysis and Results
### Assessment of Measurement Model
#### 5.2.2 Internal Consistency Reliability  
Internal consistency reliability refers to the extent to which all components are measured by a particular (similar concept [58]. Composite reliability coefficient and Cronbach's alpha coefficient are the most frequently used to estimate the internal consistency reliability of items in marketing research [55]. The composite reliability coefficient was chosen to ascertain the internal consistency reliability of measures adopted in the present study.  
The justification for using composite reliability coefficient, is because composite reliability coefficient provides a better estimate of reliability bias; [PERSON]'s alpha coefficient assumes that all items contribute equally to its construct without considering the actual contribution of individual loadings [56] and may over- or underestimate the reliability scale. Nevertheless, [PERSON]'s Alpha as a whole for all items is above the 0.7, which is considered as good reliability as it is above [56] recommended value.  
Composite reliability takes into account that indicators have different loadings; it can be explained in the same way as [PERSON]'s alpha, that is, no matter which particular reliability coefficient is used, an internal consistency reliability value above 0.70 is regarded as satisfactory, whereas a value below 0.60 indicates a lack of reliability. However, in this study the explanation of internal consistency reliability using the composite reliability coefficient was based on the rule of thumb provided by [66]. Furthermore, as recently suggested by [55] composite reliability coefficient should be at least 0.70 or more. See Table 3 illustrates the composite reliability, the coefficients of the latent constructs.
## 3 Wireless Sensor Networks
### Wireless Sensor and Ad Hoc Networks  
Wireless _ad hoc_ networking techniques can be deployed for wireless sensor network applications. Although many protocols and algorithms have been proposed for traditional wireless _ad hoc_ networks, they are not well suited for the unique features and application requirements of sensor networks. In this subsection we briefly compare sensor and _ad hoc_ networks to emphasize the important role of wireless sensor networks, especially in monitoring of the human body. To illustrate this point, the differences between sensor networks and _ad hoc_ networks can be summarized as follows:  
* Numbers of Sensor Nodes: the number of sensor nodes in a sensor network can be many orders of magnitude higher than the nodes in an _ad hoc_ network.
* Efficiency: sensor nodes are densely deployed.
* Failure: sensor nodes are prone to failures.
* Network Topology: the topology of a sensor network changes very frequently.
* Communication Ability: sensor nodes mainly use broadcast communication paradigm whereas most _ad hoc_ networks are based on point-to-point communications.
* Computing: sensor nodes are limited in power, computational capacities, and memory.
* Cost: Sensor nodes may not have global identification because of the large amount of overhead and large number of sensors.
# LAMDA Controller Applied to the Trajectory Tracking of an Aerial Manipulator
## 3 Proposed Controllers
### Inverse Dynamics Controller  
The inverse dynamics controller is applied to systems with known dynamics, for which it is essential to obtain the system model and its dynamics be as close as possible to the real system so that it can be controlled. In addition, it is important to mention that it is not a robust controller because it depends directly on the changes that may exist in the dynamics of the system. The proposed controller scheme is shown in Figure 3.  
Figure 3: Proposed inverse dynamics controller scheme.  
The control law is given by:  
\[\mu_{D}(t)=M(q(t))\sigma+C\big{(}q(t),\dot{q}(t)\big{)}\mu(t)+G(q(t)) \tag{13}\]  
where \(\mu_{D}(t)\) is the control action and \(\sigma\) is defined as:  
\[\sigma=\dot{\mu}_{c}(t)+Q_{D}tanh\Big{(}\ Q_{D}^{-1}\ K_{D}\ \widetilde{\mu}(t) \Big{)} \tag{14}\]  
where \(\dot{\mu}_{c}(t)\) is the derivative of the velocity of the Kinematic controller; \(Q_{D}\) and \(K_{D}\) are the positive definite calibration matrices; \(\widetilde{\mu}(t)=\mu_{c}(t)-\mu(t)\) represents the velocity error; \(\mu(t)\) is the velocity vector of the aerial manipulator robot, and \(\mu_{c}(t)\) is the velocity vector of the Kinematic controller.  
To analyze the stability of the controller, the system is considered in a closed loop, where it is established that \(\dot{\mu}(t)=\sigma\) and replacing in Equation (14), we obtain:  
\[0=\dot{\widetilde{\mu}}(t)+Q_{D}tanh\Big{(}\ Q_{D}^{-1}\ K_{D}\ \widetilde{\mu}(t) \Big{)} \tag{15}\]  
From (15) it can be verified that \(\widetilde{\mu}\to 0\) When \(t\rightarrow\infty\).
## 6 Defenses against Model and Data Attacks
### Data and Update Analysis
The Multi-Krum algorithm defends against poisoning attacks by rejecting updates that deviate from the majority. Differential privacy and secure aggregation protect against information leakage by adding noise to updates and obfuscating individual updates. The blockchain provides transparency and auditability to verify the training process integrity. Biscotti achieves decentralization, reducing the risk of a single point of failure, and enhances robustness against attacks. It scales efficiently with participants and handles peers joining and leaving. By integrating Multi-Krum, differential privacy, and secure aggregation, Biscotti offers a multi-layered defense against poisoning and information leakage attacks, empowering users to control their sensitive information and enhancing privacy and trust. The authors introduced FL-Defender [69], a framework designed to counteract targeted poisoning attacks in FL. It engineers robust features by analyzing the last layer gradients of local model updates and computed cosine similarities to identify gradient direction differences indicative of attacks. Principal Component Analysis (PCA) compresses the gradient similarity matrix, enhancing attack detection. FL-Defender aggregates these features into a centroid and measures each worker's deviation from it, using accumulated similarities over training rounds to assign long-term trust scores. Updates from workers far from the centroid receive lower weights during federated averaging. This method, which focuses on the last layer gradients, was shown to be effective against label flipping and backdoor attacks across various datasets.
## 2 Mathematical Model
### Sunrise and Sunset Hour Angle  
The solar altitude angle is zero at the sunrise and sunset. To find the sunrise (or sunset) hour angle \(\omega_{r}\) (\(\omega_{s}\)) on a south-facing (\(\gamma=0\)) tilt surface, one can use the following formulas [32]:  
\[\omega_{1}=\cos^{-1}(-A/(2B)), \tag{3}\]  
\[\omega_{2}=\cos^{-1}(-\tan\phi\tan\delta). \tag{4}\]  
Furthermore, the sunrise (or sunset) hour angle \(\omega_{r}\) (\(\omega_{s}\)) for the inclined surface is given by [31]:  
\[\omega_{r}=\max(-\omega_{1},-\omega_{2}), \tag{5}\]  
\[\omega_{s}=\min(\omega_{1},\omega_{2}), \tag{6}\]  
with  
\[A=2(\sin\delta\sin\phi\cos\beta-\sin\delta\cos\phi\sin\beta\cos\gamma)\times( \cos\delta\cos\phi\cos\beta+\cos\delta\sin\phi\sin\beta\cos\gamma), \tag{7}\]  
and  
\[B=(\cos\delta\cos\phi\cos\beta+\cos\delta\sin\phi\sin\beta\cos\gamma)^{2}\times (\cos\delta\sin\beta\cos\gamma)^{2}, \tag{8}\]  
where \(A\) and \(B\) are calculated by Equation (2), which were used to obtain the values of sunrise (or sunset) hour angle in Equations (3)-(6).
## 4 Experiment and Analysis
### Tectonic Deformation along Weihe Fault  
The vertical deformation from the last section was decomposed by wavelet analyses to distinguish the long-wavelength tectonic movement and small-scale human-activities-induced deformation, as shown in Figure 6. It can be seen that the deformation fields decomposed by different scale factors manifested clear differences. The deformations composed with small-scale factors generally presented large-scale tectonic deformation characteristics, while deformation fields composed of large-scale factors were mainly from the non-tectonic deformation. As analyzed in Section 3.3, \(q\) = 5-10 was used as the secular vertical tectonic deformation (Figure 6d) and \(q\) = 11-12 was used as the local human-activities-induced deformation (Figure 6f). The estimated vertical field in Figure 6d reveals that the deformation on both sides of the Weihe Fault exhibit completely different patterns: the northern side is uplifting and the southern side is undergoing subsidence. The largest uplift area is located in Liquan county with a magnitude up to \(\sim\)4.5 mm/yr, and the largest subsidence area is located to the south of the Weihe Fault with a magnitude up to  
Figure 4: InSAR-derived deformation rate of the Weihe Fault, where the red cross represents the reference point.  
\(\sim\)6 mm/yr. Meanwhile, it can be seen in Figure 6d that the Weihe Fault exhibits the obvious vertical velocity gradient, implying stress accumulation and posing a risk of moderate to strong earthquakes in the Weihe Fault. The residual deformation (Figure 6f) reveals a spatial pattern dominated by strong subsidence or uplift signals, which are mainly caused by anthropogenic activities.  
Figure 5: (**a**) East–west, (**b**) north–south and (**c**) vertical deformation rates through the integration of GNSS and InSAR observations. The gray arrows represent the leveling observations; the gray lines represent the faults; the black rectangle represent the location of cities.
# Simulation Analysis and Experimental Study of the Cooker Hoods of High-Rise Residential Buildings  
[PERSON]  
1 School of Mechanical Instrumental Engineering, Xi'an University of Technology, Xi'an 710048, China; [EMAIL_ADDRESS]  
[PERSON]  
1 School of Mechanical Instrumental Engineering, Xi'an University of Technology, Xi'an 710048, China; [EMAIL_ADDRESS]  
[PERSON]  
2 Kitchenware Technology Center, Ningbo Fotile Kitchenware Co., Ltd., Ningbo 315336, China; 2 [EMAIL_ADDRESS] (X.-Q.L.); [EMAIL_ADDRESS] (Y.-D.Z.); [EMAIL_ADDRESS] (Y.-X.Y.)  
[PERSON]  
Furthermore, there has been much research of the centralized flue of high-rise buildings, the sources of kitchen pollution, and the design and exhaust characteristics of cooker hoods, which has provided an important reference for improving the exhaust effectiveness. [PERSON] et al. carried out a concentration field analysis of home kitchens and compared the concentration distribution of sunken and side-suction cooker hoods [11; 12]. [PERSON] et al. analyzed the composition, the size of particulate matters, and the diffusion velocity of kitchen oil fumes, and the exhaust effectiveness of cooker hoods. The results show that small particulate matters had obvious diffusion characteristics, and the overall exhaust efficiency was about 90% [13; 14]. [PERSON] et al. carried out structural design and optimization research for cooker hoods, and the effect of structure improved on the exhaust effectiveness of cooker hoods is analyzed [15; 16; 17]. [PERSON] et al. studied the exhaust effectiveness of high-rise buildings from the structure of centralized flues and carried out a simulation analysis of exhaust characteristics in centralized flues of high-rise building [7; 18]. The above research results provide important theoretical and engineering references for improving the exhaust effectiveness of kitchens in high-rise building. However, the comprehensive consideration of the above-mentioned influencing factors in the exhaust effectiveness analysis and the cooker hood design study is still lacking. For indoor air quality or the selection and structure design of the cooker hood, it is necessary to carry out research on the exhaust effectiveness of cooker hoods in high-rise residential buildings based on the real environment or similar conditions.  
In the current research, taking into account the impact of the installed floor and the operation rate of cooker hoods, the boundary parameters of cooker hood outlets are obtained according to a high-rise residential building laboratory, and a simulation calculation model of standard kitchens with cooker hoods for high-rise residential buildings is established based on CFD fluid simulation technology. The characteristics of the exterior flow field and the exhaust effectiveness of cooker hoods are simulated and analyzed.