esa-sceva/satcom-chunk-collection huggingface dataset:
# Affective Stroking: Design Thermal Mid-Air Tactile for Assisting People in Stress Regulation
## 2 Materials and Methods  
### Hardware Design and Components  
The Stroking Device is composed of three components: the Stroking Component, Thermal Control Component, and Hardware Driver Component (Figure 1). The first component is the Stroking Component, consisting of 8 fans, each measuring 4 cm \(\times\) 4 cm. These fans are relatively small and can be combined freely. Therefore, it can be better designed according to the needs of the situation. They are arranged in two columns. Each column contains 4 fans and has a total length of 20.5 cm. The distance between each fan is 1.5 cm. One column is placed on the top of the forearm, while the other is positioned on the outer side (Figure 2). Each corresponding pair of fans from the two columns forms a row, resulting in four rows (P1, P2, P3, P4).  
The second component is the Thermal Control Component (Figure 1). Each column of four fans is fixed onto a metal heat conduction layer, which has three Peltier elements (TEC1-12702) attached to its back. Peltier elements have been frequently used as thermal driving devices in previous studies [62; 63; 64]. The heat generation and transfer by the Peltier elements are quick and easy. A motor driver (L298N Dual H Bridge) regulates the temperature hot/cold changes in the Peltier elements by controlling the current. Heat is transferred through the conductive layer to the back of each fan. The fan blades disperse the heated air, allowing participants to feel the temperature, thereby simulating the human warmth that should be included in stroking. In this setup, we set the constant temperature to 32 \({}^{\circ}\)C [37]. It is within a comfortable temperature range for humans. The DS18B20 temperature sensor detects real-time temperature changes. A PID (Proportional, Integral, Derivative) algorithm is implemented in the software to maintain the temperature at 32 \(\pm\) 0.5 \({}^{\circ}\)C. L298N motor drivers are powered by 12 V power supplies.  
Figure 1: Hardware design: (**a**) hardware of the stroking device and (**b**) framework of the whole system.  
Figure 2: Arrangements of 8 fans and blowing-air pressure on the skin to imitate stroking sensation.  
The third component is the Hardware Driver Component (Figure 1). Because there are many electronic components, the Mega2560 MCU can provide enough interfaces to ensure the normal operation of the system. We use it to control the fan's blowing speed, the variation in the simulated stroking speed, and the output current of the two motor drivers to manage the thermal changes produced by Peltier elements. Additionally, we develop an Android application that connects to the MCU via a JDY31 Bluetooth module. This allows us to trigger various stroking effects, facilitating participant relaxation based on affective touch in subsequent experiments.
## 3 Optimized Joint Signal Processing Schemes
### Sparsity Reconstruction-Based Improved Parameter Estimation
#### 3.1.3 Improved Parameter Estimation
Combined with the detailed derivation process in [15], the CRBs of range and velocity under the proposed system are obtained, which can be formulated as  
\[\text{CRB}(R)= \frac{I_{vv}c^{2}}{4(I_{vv}I_{\tau\tau}-I_{v\tau}I_{vv})} \tag{32}\] \[\text{CRB}(v)= \frac{I_{\tau\tau}}{(I_{vv}I_{\tau\tau}-I_{v\tau}I_{vv})} \tag{33}\]  
where \(I_{vv}\), \(I_{\tau\tau}\), \(I_{v\tau}\), and \(I_{\tau\tau}\) are the corresponding elements in the Fisher information matrix. In addition, for the proposed signal design scheme, the range resolution can be expressed as  
\[R_{res}=\frac{c}{2B}=\frac{c}{2N_{c}\Delta f} \tag{34}\]  
where \(c\) denotes the signal propagation velocity and \(B\) denotes the integrated signal bandwidth. In addition, we can effectively determine the maximum detectable range of the proposed system in the environment through the spacing of subcarriers, which can be written as  
\[\text{vec}(\mathbf{Y})=\text{vec}\Bigg{(}N_{r}\sum_{q=1}^{Q}\beta_{q}\mathbf{\tau}_{q} \otimes\mathbf{D}_{q}^{\text{T}}\Bigg{)}+\text{vec}(\tilde{\mathbf{W}}) \tag{19}\]0  
Similarly, the velocity resolution and the maximum detectable velocity of the system are expressed as  
\[\text{vec}(\mathbf{Y})=\text{vec}\Bigg{(}N_{r}\sum_{q=1}^{Q}\beta_{q}\mathbf{\tau}_{q} \otimes\mathbf{D}_{q}^{\text{T}}\Bigg{)}+\text{vec}(\tilde{\mathbf{W}}) \tag{19}\]1  
\[\text{vec}(\mathbf{Y})=\text{vec}\Bigg{(}N_{r}\sum_{q=1}^{Q}\beta_{q}\mathbf{\tau}_{q} \otimes\mathbf{D}_{q}^{\text{T}}\Bigg{)}+\text{vec}(\tilde{\mathbf{W}}) \tag{19}\]2  
According to (35), we can realize a larger detection range of the system by reducing the subcarrier spacing. However, (37) shows that the subcarrier spacing should be increased for a higher detectable velocity. Therefore, it is necessary to choose the appropriate subcarrier spacing according to different requirements in practical applications. Next, we discuss the computational complexity of the proposed parameter estimation method. First, define one complex multiplication operation as a unit of computational complexity; it is certain that the greater the number of complex multiplications, the higher the computational complexity. According to the processing flow of the above algorithm, the computational complexity of the direct sparse reconstruction of \(P\) pulse data is \(\text{O}(QKIN_{c}P^{2})\), while the computational complexity of the proposed segmented coherent accumulation method is \(H\cdot\text{O}(QKIN_{c}P^{2}/H^{2})+P^{2}\cdot\text{O}(N_{c}\text{lg}N_{c})\).
###### Abstract  
COSMIC-2 is a remote sensing satellite mission that mainly provides scientific data for weather forecasting, ionosphere, and climate research. High precise orbit is the basis for the application of remote sensing satellite data. In order to realize the precise orbit determination (POD) of COSMIC-2, we have assessed the quality of space-borne GPS observation in detail, including the utilization of GPS observations, cycle slip ratio (o/slps), multipath error, single-noise ratio (SNR) and ionospheric delay rate (\(IOD\)) of the data, realized the POD of COSMIC-2 with the reduced dynamic (RD) method, and evaluated the accuracy of the solved orbit by means of the carrier-phase residual, overlapping orbit comparison and the reference orbit comparison. The data quality assessments show that the data is less affected by the multipath effect, the utilization of the data is low, cycle slips occur frequently, and the carrier-phase data is often interrupted. The POD results indicate that the root mean square (RMS) values of the carrier-phase residuals of six COSMIC-2 satellites are between 6.0 mm and 7.5 mm, The mean RMS values of the overlapping orbit are better than 0.92 cm, 1.33 cm and 1.03 cm in the radial (R), tangential (T) and normal (N) directions respectively, and the mean RMS values of the six satellites in the 3D direction are between 1.38 cm and 1.75 cm. The mean RMS values in R, T and N directions orbit determination accuracy of the reference orbit comparison are better than 5.61 cm, 6.59 cm and 2.29 cm respectively, and the mean RMS values of the six satellites in the 3D direction are between 7.35 cm and 8.79 cm.  
COSMIC-2; space-borne GPS; quality assessment; reduced dynamic method; precise orbit determination Article
## 5 Discussion
In our work, the area of the sensor node, including the antenna, is only 9 cm\({}^{2}\) (3 \(\times\) 3 cm\({}^{2}\)) with a thickness of just 4.2 mm, allowing placement in very small cavities, such as inside the engine compartment. It should also be pointed out that neither [27] nor [28] include any sensing on the node. Regarding the low-power node specifications, the energy requirement for an active cycle results to be between 1.45 and 1.65 mJ, whereas the current consumption is less than 150 nA in sleep mode. This allows for reducing the capacity of the storage elements to only 1 mF, which is critical for reducing its leakage currents and size. The capacity is reduced by a factor 5 with respect to [5], a node which consequently presents a size significantly larger (3 \(\times\) 5 cm\({}^{2}\), without accounting for the thermoelectric generator (TEG) and the antenna). To the authors' best knowledge, this work represents the first demonstration of a fully self-sustainable, battery-free multi-node sensor system designed and validated for hostile metal-rich scenarios, demonstrating that communication and power transfer to the sensor nodes is robust across different configurations of RF sources and receiving elements. With respect to solutions relying on different energy sources [2; 3; 4; 5; 6; 7], the energy source itself is integrated and designed as part of the system, guaranteeing full control on the amount of available energy and continuous operation of the wireless sensor nodes.
## 4 Discussion
### Effect of Fissure Penetration on Sidewall Failure
#### 4.1.2 Influence of Fissure Penetration on Tunnel Stability
At 62.00 MPa, the maximum stress level for specimen 'S-0-25', the sidewall failure in this specimen changed significantly. (Figure 19b). The left and right sidewalls, including their backs, showed lamination from outside to inside, while the rock slab near the crack on the right sidewall was thicker and remained partially intact. In contrast, the failure in specimen \"S-0-0\" showed no notable change compared to the previous stage (Figure 19a). However, in specimen \"S-0-50\", spalling occurred under the right sidewall (Figure 19c), with cracks forming in the upper-left shoulder, indicating a marked difference in damage across the three rectangular holes. These observations demonstrate that the presence of fissures reduced the stability of the rectangular tunnels, with fully penetrated fissures resulting in the poorest stability. This conclusion is further supported by the reduction in peak failure stress in the sandstone specimens.
## 3 Experimental Flight and System Identification
### Dynamic Model Analysis and Verification
Both dynamic models had small gains in the low-frequency range, which resulted in a large steady state error. To reduce the steady state error, the loop gain should be increased. However, this may decrease the stability margins. Figure 9 shows experimental flight data and simulation results of the GRAD1 dynamic model using the PD feedback controller. As shown in Figure 9a, the pitch angle response of the GRAD1 dynamic model was closer to the pitch angle from the experimental flight data compared to that of the dynamic model from CFD. The peak and valley points predicted using the GRAD1 dynamic model were more accurate than those of the CFD dynamic model. Figure 9b also shows that the GRAD1 dynamic model was better than the CFD dynamic model in following the pitch rate from the experimental flight data, neglecting the high-frequency noise. The high-frequency noise was due to wing flapping, which degraded the performances of system identification and control. Figure 8: Frequency responses of extended dynamic models with a PD controller: (**a**) Bode plot; (**b**) Nyquist diagram. Figure 7: Frequency responses (basic dynamic models only): (**a**) Bode plot; (**b**) Nyquist diagram.
## 5 Conclusions
The study presents some limitations related to the use of traditional machine-learning methods, such as logistic regression, which, while offering interpretability, do not fully capture the complexity of nonlinear relationships in the data. Additionally, the SHAP (SHapley Additive exPlanations) algorithm has not been fully explored in interpreting models on large datasets or complex nonlinear relationships, limiting transparency in decision making in these scenarios. In the future, adopting more advanced techniques, such as deep learning or ensemble methods, could improve performance, especially for larger or more complex datasets. These approaches, although more computationally intensive, can reveal deeper patterns and correlations in healthcare data. Integrating the SHAP algorithm into these advanced models could enhance the interpretability of AI-driven decisions, helping to overcome the \"black box\" problem and fostering greater trust and acceptance among clinicians and patients. Despite these limitations, the prospects of AI in healthcare management are broad and promising. Ongoing research and the development of more transparent and interpretable AI models can improve trust and adoption of these technologies. Increasing focus on explainable artificial intelligence (XAI) aims to make AI systems more understandable and reliable for clinicians and patients. Simultaneously, the evolution of privacy and data security regulations can provide a more solid framework for sensitive information protection. In conclusion, AI represents a powerful catalyst for change in the healthcare sector, with the potential to significantly improve healthcare management. Its impacts are vast, the limitations non-negligible, and the prospects full of opportunities to overcome current challenges. As AI continues to evolve, a deep understanding and a balanced approach will be essential to fully leverage its potential for the benefit of global health. Not applicable Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Anomaly Identification of Wind Turbine Yaw System Based on Two-Stage Attention-Informer Algorithm
## 4 Case Analysis  
### Prediction Effect Analysis Based on TSA-Informer Model  
The effectiveness of the prediction performance based on the TSA-Informer model was analyzed using SCADA data, with the time series of residuals exceeding the threshold marked as abnormal segments. Figure 3 shows the time series of actual values and predicted values for a specific day (00:00-24:00) of normal operation in a wind turbine unit. Most data points have residuals within 1.8\({}^{\circ}\), indicating that the TSA-Informer anomaly detection model can effectively track the actual operating state. Based on the data from six units operating normally for two months, the F1 score (a comprehensive evaluation metric combining accuracy and recall) of the model was found to be 0.921, indicating a high level of recognition accuracy.
# Intelligent Evaluation of Chinese Hard-Pen Calligraphy Using a Siamese Transformer Network
## 3 Proposed Methodology
### Backbone Feature Extraction Network  
The calligraphy evaluation model adopts the Siamese network as the overarching structural framework, utilizing VGG16 as the backbone feature extraction network within the Siamese structure. Input images undergo convolutional processing by VGG16 to derive effective features. Images of size 256 \(\times\) 256 \(\times\) 3 are fed into the VGG16 network for hierarchical feature extraction. Through multiple convolutional and pooling operations, and by employing the ReLU activation function after each convolution operation to strengthen the inter-layer connections, the image features are extracted, ultimately resulting in a feature matrix with a size of 4 \(\times\) 4 \(\times\) 512. Stacks of smaller convolutional kernels are a key element of VGG16 [34]. To address the issue of diminished model accuracy due to the small feature maps resulting from multiple convolution operations, an enhancement is made to the VGG16 network. A combination of upsampling and pointwise convolution is applied to the vector processed by VGG16 to augment the feature map size and alter the feature map dimensions. The improved structure is illustrated in Figure 7. Upsampling employs bilinear interpolation [35], utilizing interpolation algorithms to calculate new values inserted between the existing matrix elements.  
Figure 6: YOLOv5 network architecture.  
Figure 7: VGG network enhancement module.
## 3 Results
### Model Verification  
For the silver paste, \(\alpha\) of \(81^{\circ}\) and a nominal line spacing \(l_{s0}\) of \(409~{}\upmu\)m are determined by testing. Figure 7 shows the white light interference images of the ink surface obtained at different line spacings, as well as a comparison of the experimental and theoretical cross-sectional profiles. Figure 8 shows a comparison between the maximum height difference of the ink surface obtained experimentally and that predicted by the theoretical model for different line spacings. The average value of height measured for each peak of the ripple are calculated as the \(\Delta H\). It is shown that the experimental results agree well with the theoretical model. Furthermore, it is suggested from the model that the smaller the line spacing \(l_{s}\), the smaller the height difference \(\Delta H\). However, in practical experiments, there is a lower limit for the line spacing \(l_{sm}\), below which the flatness of the ink surface cannot be further improved. In this case, the surface roughness is dominated by the inclusion particle sizes, which are greater than the height of ripples. Moreover, the critical line spacing \(l_{sm}\) for the silver paste is experimentally calibrated around \(204~{}\upmu\)m.  
The layer thickness can be adjusted by controlling the extrusion volume per unit length. As shown in Figure 6a, based on the volume conservation for the printing ink, the layer thickness is determined by:  
\[h_{l}=\frac{aS_{p}}{l_{sm}}, \tag{4}\]  
where \(S_{p}\) is the cross-sectional area of a single printed ink line. As shown in Figure 4, if \(S_{p}\) is calculated using the ink line width \(l\), then \(S_{p}=bl^{2}/4\), where \(b\) is a fixed value determined by the contact angle \(\theta\) and is given by \(b=\theta/\sin^{2}\theta-\cos\theta/\sin\theta\). The symbol \(h_{l}\) is the overall thickness of the cured ink surface, and a is the solid content. If the layer thickness is estimated based on the width of a single ink line, then:  
\[h_{l}=\frac{abl^{2}}{4l_{sm}}. \tag{5}\]For detailed derivation of the equation for calculating layer thickness, please see Supplementary Materials.  
Figure 7: White light interference images and corresponding cross-sectional profiles and maximum height differences \(\Delta H\) under different \(l_{s}\). The solid line is the cross-sectional profile measured by the experiment, and the dashed line is the cross-sectional profile predicted by the model.
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## Appendix A R-Package 'Hopbyhop' Tutorial
### Full-Reliable and Semi-Reliable Communication  
Considering the chain topology presented in Figure 11 with \(N=3\) hops, and supposing that the probability for each hop is \(p=0.5\), the expected global number of transmissions and the probability values (between source and sink) in a full-reliable (unlimited \(L\)) multi-hop network can be calculated by the R-package 'Hopbyhop' using the function.  
Computation of the Probability of Successful Transmission in A Network with Different Values of \(L\)  
In the second and third energy saving protocols proposed in Sections 5.3 and 5.4, it is required the computation of the success probability in a network where the value of \(L\) is not unique for all the nodes in the route. For instance, consider Figure 11 with \(N=3\) and \(p=0.5\), define \(L=4\) for the first node \(\mathcal{N}_{1}^{[1,\bar{n}_{1}]}\) and \(L=\infty\) for the other nodes \((\mathcal{N}_{2}^{[1,\bar{n}_{1}]},\mathcal{N}_{3}^{[1,\bar{n}_{1}]},\mathcal{ N}_{4}^{[1,\bar{n}_{1}]})\). The probability of successful transmission between the first and last nodes can be calculated by implementing a cluster in the network, that is, splitting the transmission of the packet from the source to the sink by a set of hops with the same value of \(L\). In the example, \(N_{1,2}=1\) denotes a sub-network composed by a unique hop between \(\mathcal{N}_{1}^{[1,\bar{n}_{1}]}\) to \(\mathcal{N}_{2}^{[1,\bar{n}_{1}]}\), while \(N_{2,\mathcal{A}}=2\) represents a sub-network composed by two hops between \(\mathcal{N}_{2}^{[1,\bar{n}_{1}]}\) to \(\mathcal{N}_{4}^{[1,\bar{n}_{1}]}\), such that the success probability for the route connecting the source and the sink is given by  
\[P_{S}=P_{S1,2}(p=0.5,N=N_{1,2}=1,L=4)\times P_{S2,\mathcal{A}}(p=0.5,N=N_{2, \mathcal{A}}=2,L=\infty).\]  
Note that \(P_{S1,2}(p=0.5,N=N_{1,2}=1,L=4)\) and \(P_{S2,\mathcal{A}}(p=0.5,N=N_{2,\mathcal{A}}=2,L=\infty)\) can be computed by the R-package by using, respectively, the following commands:  
> HBH(0.5, 0.5, 4, 1) and  
> HBH(0.5, 0.5, Inf, 2).  
Analogously, the first sub-net has an expected number of transmissions given by \(\mathcal{E}(M)_{1,2}\), while the second half expected number of transmissions is denoted by \(\mathcal{E}(M)_{2,\mathcal{A}}\). The value for the entire network is given by  
\[\mathcal{E}(M)=\mathcal{E}(M)_{1,2}+P_{S1,2}\times\mathcal{E}(M)_{2,\mathcal{A}},\]  
where the value of the expected number of transmission in the second half is weighted by the probability of the packet reaching this subnet (\(P_{S1,2}\)).  
Using the procedure described above, it is possible to determine the values presented in Tables 6 and 7 of the paper.
## 2 Theoretical Background and Related Works
### CPS-Based Occupational Safety and Health  
The cyber-physical system (CPS) is also known as an intelligent ICT system. This system consists of interconnected, interdependent, cooperative, and autonomous features. It provides computing and communication capabilities to monitor and control physical objects in various applications. The concept and functionalities of CPS can be applied in the workplace to enhance the OSH [18; 20; 84; 82; 85; 86; 87]. Generally, the notion of CPS can be demonstrated as an imagination infrastructure. It is comprised of two layers, including the physical and cyber layers [88; 89; 90; 91; 92]. The physical layer consists of physical entities located in physical 3D space to implement particular jobs and communicate with each other physically. The entities might be composed of sensors and actuators or equipped with proper sensors and actuators. Spatially distributed telecommunication nodes and computing networks are part of a cyber-layer. This is connected to sensors or actuators and immersed in the natural environment [93]. As mentioned above, CPS contains various sensors and actuators employed to monitor abundant parameters of workers' health and the surrounding environment. These assume that the data processing units, transmitters, and receivers are installed in sensors and actuators. Hence, the workers' health states and corresponding working environment can be monitored and controlled by data processing to guarantee the OSH [88; 89; 90; 91; 92]. Utilizing real-time monitoring, the health status of laborers can be observed by measuring primary physiological coefficients like heart rate, body temperature, breathing rate, and work comforts like work posture, underclothing temperature, and humidity. Moreover, workplace hazards such as exposure to poisonous chemical matters, noise, and optical radiation can be detected. After analysis, these collected data will warn workers about the emergence of risky situations. Furthermore, the protective systems will activate after surpassing a high-risk threshold value. Therefore, CPS should be considered a tool to improve OSH. Hence, it should be adequate to prevent workers from being exposed to the risks of health hazards and prone to disease. Furthermore, healthcare and medicine applications are on a medium scale. The adoption of CPS in this domain helps to monitor the workers' health conditions and take necessary responses to ensure their safety and health.