esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed ISAC System  
### Concept  
In contemporary technological advancements, the radio frequency front-end architectures in radar and wireless communication technologies are progressively converging. Numerous functions traditionally implemented through hardware components are now transitioning to digital signal processing. Simultaneously, the carrier frequencies utilized in communication systems have shifted into the microwave spectrum, aligning with those historically associated with radar applications. This alignment has paved the way for the seamless integration of both communications and radar functionalities on a shared radio-frequency hardware platform, made easily achievable with today's technology. This innovative platform holds the potential for new system concepts and applications, allowing communication and sensing to operate concurrently, ensuring continuous access to their functions, and partially mitigating the constraints of limited spectral resources.  
Figure 1 depicts the core concept of the proposed ISAC system. The transmitter serves a dual purpose by generating a DSSS signal that fulfills both sensing and communication objectives. To achieve this, a pseudorandom (PN) code comprising \(L\) bits is generated and subsequently modulated with each incoming information bit. An \(N\)-bit information sequence is transmitted to establish a single coherent processing interval (CPI) for radar signal processing.  
Upon reception at the communication side, the transmitted signal undergoes matched filtering (MF) and sampling. The sampled signal is then processed using thresholding after performing the despreading and sum operation. The despreading is performed using the same PN code employed at the transmitter.  
Concurrently, the radar antenna captures the reflected signal resulting from the presence of an object within its detection range. This signal is subsequently directed to the radar signal processor, which is responsible for determining the range of the target object. The process of analyzing the received signals will involve the utilization of signal  
Figure 1: Model of the proposed ISAC system.  
processing techniques, enabling a comprehensive evaluation of the data acquired by the ISAC system.
# Research on Disease Spreading Behavior Oriented to Three-Layer Complex Networks
## 2 Introduction to Basic Concepts
### SIS Disease Spread Mechanism  
Depending on the state of the individual in the procedure of disease spreading, researchers have proposed classical SI-, SIS-, and SIR-spreading mechanisms. Currently, some diseases where the infection originates from animals are transmitted to humans through intermediaries, such as mosquitoes, cockroaches, and rats, and it is eventually cured or inhibited by an immunization antibody or drug treatment, which returns it to a susceptible state; the procedure is similar to the SIS-spreading mechanism. Therefore, this paper begins with a study of spreading behavior on multi-layered complex networks based on the SIS disease-spreading mechanism.  
In the SIS disease-spreading mechanism, the states of nodes are divided into two types: the susceptible state \(S\) and the infected state \(I\). The infected individuals infect the virus to the susceptible individuals with a certain infection rate \(\alpha\), and then it is cured with a certain recovery rate \(\beta\); finally, it becomes the state \(S\) again. The \(s(t)\) and \(i(t)\) are denoted the density of individuals in state \(S\) and state \(I\) at time \(t\), respectively. The behavior of disease spread in the SIS model can be described by the following set of differential equations:  
\[\left\{\begin{array}{l}\frac{ds(t)}{dt}=-\alpha i(t)s(t)+\beta i(t)\\ \frac{di(t)}{dt}=\alpha i(t)s(t)-\beta i(t)\end{array}\right. \tag{3}\]  
Moreover, we set the rate of disease spread as \(\lambda=\alpha/\beta\), the problem of solving the above differential equations can be transformed into the calculation of the following differential equations:  
\[\left\{\begin{array}{l}\frac{di(t)}{\beta dt}=i(t)((\lambda-1)-\lambda i(t) )\\ i(0)=i_{0}\end{array}\right. \tag{4}\]  
Using Equations (1)-(4), it can be obtained as follows:  
\[i(t)=\frac{1-1/\lambda}{1+(\frac{\lambda-1-\lambda i_{0}}{\lambda i_{0}})e^{ -\alpha(1-1/\lambda)\beta t}} \tag{5}\]  
There is a threshold \(\lambda_{c}=1\) for such equations. At moment \(T\), the steady state's solution is \(i(T)=0\), \(\lambda<\lambda_{c}\), when the network spreading tends to a steady state. When \(\lambda\geq\lambda_{c}\), the increase or decrease of \(i(t)\) depends on the size of \(i_{0}\), whose limit value is \(i(\infty)=1-1/\lambda\). As the \(\lambda\) increases, its steady state solution is \(i(T)=c>0\).
# Antiviral Filtering Capacity of GO-Coated Textiles
## 3 Results
### Characterization of Textiles with and without Coating  
Figure 3 shows SEM of the textiles before and after the coating process with 10% GO. The general architecture of textile is maintained after coating with GO. The coating process which upholsters the fibers and smooths their surface extends from one fiber to another in the form of a thin lamina.  
Raman analysis was carried out on GO-coated fibers. Several spectra were acquired directly on GO clusters identified on the three samples treated with different concentrations of GO (1%, 5%, and 10%). Spectra collected for similar clusters found on the three samples  
Figure 3: VP-SEM characterization of textiles without coating (**A**,**B**) and with coating (**C**,**D**). Textile without coating is imaged at 50\(\times\), scale bar 1 mm (**A**). The texture of the weaving is visible. It is formed by bundles of fibers arranged in a messy way (**B**). Textile single fiber is imaged at 2000\(\times\), scale bar 20 μm, it appears similar to a ribbon, longitudinally grooved by a gutter (g), which is limited at the sides by bulging ridges (r). Fiber surface appears wrinkled in some parts, exfoliated in others. (**C**) GO-coated textile is imaged at 50\(\times\), scale bar 1 mm. The texture of the weaving is visible, fiber bundles appear as having a more compact and ordered aspect. (**D**) Coated textile fibers are imaged at 2000\(\times\), scale bar 20 μm. (l).  
are shown, respectively, in Figure 4A (GO 1%, blue curve), Figure 4B (GO 5%, black curve) and Figure 4C (GO 10%, red curve), together with the background signal corresponding to the linen fibers (green curve).  
The spectral features corresponding to the textile background can be identified in all reported spectra, together with some specific bands ascribable to the GO structure, such as the two main bands located at about 1570 cm\({}^{-1}\) (G peak) and 1350 cm\({}^{-1}\) (D peak). The first one shows the graphitic signature of carbon in GO, while the second one is associated with a disorder caused by defects on the sp\({}^{2}\) hybridized hexagonal C sheet. In the case of GO 10%,(Figure 4, panel C), the lower intensity ratio between D and G peaks, is probably due to the larger mean size of the clusters, while a smaller bandwidth for both peaks, and a third GO signature (2D) at about 2683 cm\({}^{-1}\), can be attributed to a second-order overtone of the in-plane vibration.
# A Hybrid Learning Framework for Enhancing Bridge Damage Prediction
## 2 Materials and Methods
### Feature Selection and Dimensionality Reduction  
The hidden patterns and knowledge are extracted from massive amounts of data in the form of association rules, with the assistance of association rule mining. One of these association rule mining algorithms, the Apriori algorithm [22], uses frequent item set mining to generate association rules from a transactional dataset. Additionally, machine learning uses it for classification, feature selection, and dimensionality reduction. Selecting characteristic features from raw data is a fundamental stage in data mining. There are two main objectives for feature extraction. The first is to improve the classifier's efficiency by representing each data object with a limited number of useful features, and the second is to remove features that are not representative of the data object, which will increase classification accuracy [23; 24]. The main objectives of dimensionality reduction and feature selection are [25]:  
1. We can reduce overfitting by eliminating redundant or unnecessary features from the model, especially when working with high-dimensional data.
2. Dimensionality and feature reduction can produce a simpler, more interpretable model that is easier to comprehend and explain.
3. Reducing execution time by shrinking the model's size, or data pruning, assists in speeding up the training and inference processes.
4. By eliminating noisy or unnecessary features, the ability of a model to generalize to previously unobserved data can be enhanced.  
Figure 2: Local binary pattern calculation.  
5. By data pruning, the best trade-off between model size and performance is achieved, resulting in a balance between accuracy and complexity.
6. Feature reduction helps to enhance model performance and lower the chance of overfitting.  
We used min-support = 0.9 and min-confidence = 0.9 as a threshold for feature selection. After applying the Apriori algorithm to the features derived from LBP-BoVW, the number of features that are selected in one image differs from the number of features that are selected in another image. So, the resulting features need to be processed to obtain an equal number of features for each image. To treat these missing values, we used the imputation mean method. When data are imputed, missing values are substituted with values that make sense given the information at hand. Data can be imputed in a way that maintains the set's size and structure, prevents the removal of information that can be valuable, and lowers the predictability and bias of the estimations. Table 1 explains the number of features after Apriori feature selection and the size of the LBP-BoVW features for the used datasets.
## 3 Instrument Characteristics and Tests
### ELBARA II Characteristics  
The radiometer output voltages, \(U_{\rm RS}\) and \(U_{\rm ACS}\), recorded for the two frequency channels with the RS and the ACS switched to the MA are used to determine the most important system parameters. The same settings as these given in Section 3.1.4 applied to calibrate the ACS are used (sampling with \(f_{\rm ADC}=\) 800 Hz during 10 seconds). The data set used consists of measurements performed every 10 minutes for 4 hours, resulting in a total of 2:24 data records for the RS and the ACS.  
The characteristics measured for \(T_{0}=\) 313 K (40 \({}^{\circ}\)C) are summarized in Tables 2 and 3. The parameters measured are: The radiometer gain \(G_{\rm RM}\), the residual noise temperature \(T_{\rm RM\,0}\), the time bandwidth product \(B\,\tau\) of a single measurement associated with the smallest possible integration time (nominally 2.5 ms), the voltage noise \(\sigma U_{\rm PDA}\) of the PDA, the overall accuracies \(\sigma U_{\rm RM}\) of radiometer output voltages, and the corresponding accuracies \(\sigma T_{\rm B}\) of brightness temperatures measured.  
The equations used to derive \(G_{\rm RM}\), \(T_{\rm RM\,0}\), \(B\,\tau\), \(\sigma U_{\rm PDA}\), and \(\sigma U_{\rm RM}\), \(\sigma T_{\rm B}\) from the measurements \(U_{\rm RS}\) and \(U_{\rm ACS}\) are discussed below. The following conventions are used: (i) Volt  
\begin{table}
\begin{tabular}{l|c|c|c} \hline \hline
**System Parameter** & **LSB** & **USB** & **both** \\
**channel** & **channel** & **channel** & **channels** \\ \hline \(G_{\rm RM}\) [mV K-1] & 1.93 \(\pm\) 0.01 & 1.79 \(\pm\) 0.01 & 1.86 \\ \(T_{\rm RM,\,0}\) [K] & 147.0 \(\pm\) 0.3 & 158.8 \(\pm\) 0.3 & 153 \\ \(B\,\tau\) [Hz s] & 15,908 \(\pm\) 291 & 15,828 \(\pm\) 271 & 15,868 \\ \(\sigma U_{\rm PDA}\) [mV] & 0.849 \(\pm\) 0.150 & 0.449 \(\pm\) 0.093 & 0.649 \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters of the ELBARA II radiometer measured at \(T_{0}=\) 313 K (40 \({}^{\circ}\)C).  
data record of 10 seconds duration are \(\langle U_{\rm RS}\rangle\), \(\langle U_{\rm ACS}\rangle\) and their standard deviations are \(\sigma U_{\rm ACS}\), \(\sigma U_{\rm RS}\); (ii) the noise temperature of the RS is \(T_{\rm RS}\) = \(T_{0}\) (measured once for each data record); (iii) the expression (9) evaluated for measured \(T_{0}\) is used for \(T_{\rm ACS}\); (vi) the values given in Table 2 are mean values derived from the 2:24 data records \(U_{\rm RS}\), \(U_{\rm ACS}\).
## 2 System Architecture
### Wireless Recording Unit  
Figure 1 shows the block diagram of the IC which acts as recording and transmitting module. The circuit consists of \(16\) low-noise amplifiers (LNAs), which perform low-power and low-noise amplification of neural signals. The amplifier outputs are sampled by a time-division multiplexer (TDM) at \(20\) kS/s onto one data lead before being further amplified by a variable-gain amplifier (VGA) and converted by an \(8\) bit successive approximation register (SAR) analog to digital converter (ADC). The digital data are then processed by a logic block, performing data compression, assembly and Manchester encoding of the bit stream. Finally, the digital signal is sent to a \(400\) MHz binary FSK wireless transmitter based on direct modulation of a Voltage Controlled Oscillator (VCO), enabling a \(1.25\) Mbit/s data rate. A crystal Clapp oscillator employing an external \(20\) MHz quartz and a power-on/reset (POR) circuit complete the system, providing a clean \(20\) MHz frequency clock and a synchronization signal at the start-up. It is worth pointing out that, to reduce silicon area and costs, only \(16\) amplifiers have been integrated. The remaining \(48\) channels have been added by connecting a voltage reference to the multiplexer input. In this way all the stages following the amplifier array are designed to process signals from a \(64\) channel front-end, thus making it possible to reliably assess the actual performance of a complete \(64\) channel system.
## 3 Results
### Results of Risk Analysis Using Machine Learning
#### 3.1.1 Initial Model Performance Evaluation  
The testing results of the initial models confirmed the limitations of both linear and nonlinear models due to the small dataset size:  
* Linear Regression: MAE = 0.0161, R\({}^{2}\) = \(-0.35\) (indicating some linear patterns but with significant deviations);
* Random Forest: MAE = 0.0162, R\({}^{2}\) = \(-0.60\) (failed to capture significant nonlinear patterns);
* Neural Network: MAE = 0.2374, R\({}^{2}\) = \(-137165.96\) (catastrophic generalization failure due to insufficient data).
# Nonlinear Robust Adaptive Control of Universal Manipulators Based on Desired Trajectory
## 3 Controller Design
### Lyapunov Feedback Compensation Design
\begin{array}{ll}\dot{V}_{1}&=b_{1}\parallel e\parallel\parallel r\parallel+b_{2 }\parallel e\parallel^{2}\parallel r\parallel+b_{3}\parallel r\parallel^{2}+b_{4 }\parallel r\parallel^{2}\parallel e\parallel-r^{T}K_{r}r-e^{T}K_{e}\Lambda e\\ &-r^{T}K_{c}\parallel e\parallel^{2}r\\ &\leq b_{1}\parallel e\parallel\parallel r\parallel+b_{2}\parallel e\parallel^{ 2}\parallel r\parallel+b_{3}\parallel r\parallel^{2}+b_{4}\parallel r \parallel^{2}\parallel e\parallel-\lambda_{min}(K_{r})\parallel r\parallel ^{2}\\ &-\lambda_{min}(K_{e}\Lambda)\parallel e\parallel^{2}-\lambda_{min}(K_{c}) \parallel e\parallel^{2}\parallel r\parallel^{2}\\ &=b_{1}\parallel e\parallel\parallel r\parallel-b_{2}\parallel e\parallel^{ 2}\Big{(}\parallel r\parallel-\frac{1}{2}\Big{)}^{2}-b_{4}\parallel r \parallel^{2}\Big{(}\parallel e\parallel-\frac{1}{2}\Big{)}^{2}\\ &-(\lambda_{min}(K_{c})-b_{2}-b_{4})\parallel e\parallel^{2}\parallel r \parallel^{2}-\Big{(}\lambda_{min}(K_{r})-b_{3}-\frac{1}{4}b_{4}\Big{)} \parallel r\parallel^{2}\\ &-\Big{(}\lambda_{min}(K_{e}\Lambda)-\frac{1}{4}b_{2}\Big{)}\parallel e \parallel^{2}\end{array}  \tag{24}\]  
where \(\lambda_{min}(\star)\) is the smallest eigenvalue of the matrix \(\star\). Note that \(b_{2},b_{4}\) are nonnegative. \(K_{c}\) is chosen to satisfy  
\[\lambda_{min}(K_{c})-b_{2}-b_{4}\geq 0 \tag{25}\]  
Equation (24) can be further written as:  
\[ \begin{array}{ll}\dot{V_{1}}&\leq b_{1}\|e\|\|r\|-\Big{(}\lambda_{min}(K_{r}) -b_{3}-\frac{1}{4}b_{4}\Big{)}\left\|r\right\|^{2}-\Big{(}\lambda_{min}(K_{e} \Lambda)-\frac{1}{4}b_{2}\Big{)}\left\|e\right\|^{2}\\ &=-\left[\left\|r\right\|\left\|e\right\|\right]\begin{bmatrix}\lambda_{min}(K_ {r})-b_{3}-\frac{1}{4}b_{4}&-\frac{1}{2}b_{1}\\ -\frac{1}{2}b_{1}&\lambda_{min}(K_{e}\Lambda)-\frac{1}{4}b_{2}\end{bmatrix} \left[\left\|r\right\|\right]\end{array}  \tag{26}\]  
Note that  
\[Q= \begin{bmatrix}\lambda_{min}(K_{r})-b_{3}-\frac{1}{4}b_{4}&-\frac{1}{2}b_{1} \\ -\frac{1}{2}b_{1}&\lambda_{min}(K_{e}\Lambda)-\frac{1}{4}b_{2}\end{bmatrix}  \tag{27}\]  
Then, the appropriate \(K_{r}\), \(K_{e}\), and \(\Lambda\) are selected to satisfy that the matrix \(Q\) is a positive definite matrix, which can ensure that \(\dot{V_{1}}<0\). At this point, the design of the compensation control term can be completed by satisfying Equation (25) and also satisfying that \(Q\) is a positive definite matrix.
## 3 Discussion
### Practical Significance of PSD Simplifications  
A major contribution of this research is the demonstration that simplifying PSDs--whether through normalization, reduction of characteristic points, or interpolating envelopes--does not substantially affect the precision of fatigue life predictions. This is particularly significant for industries where time and computational resources are at a premium. The simplifications allow for faster simulations and data analysis, making it feasible to analyse a broader range of design scenarios in less time.  
Thus, this approach is likely to be especially useful in fields like automotive and aerospace engineering, where large-scale simulations are often required to ensure the durability and safety of components subjected to complex vibration loads.
## 1 Introduction
[PERSON], [PERSON], and [PERSON] used a Gaussian process to achieve near-optimal sensor placements [27]. [PERSON] and [PERSON] proposed an approximation approach to covariance functions of Gaussian process for spatial data [28]. [PERSON], [PERSON], and [PERSON] applied Gaussian process models to study the spatiotemporal correlation of a 2D or 3D field [29]. However, only limited sensor observations are collected, because only sparse sensors are deployed in sensor networks given the high costs of sensors and limited budget. Due to limited sensing observations and the existence of various data uncertainties, temperature field estimation purely using statistical approaches fails to achieve an accurate estimation result. Therefore, an accurate temperature field estimation requires combining appropriate physical laws and statistical approaches to finely characterize temperature distribution and associated evolution. However, few studies have addressed this issue to the best of our knowledge. In this paper, we develop a field estimation approach to model spatiotemporal dynamics of temperature fields based on grain storage sensor networks by combining a physics-specific model and spatiotemporal stochastic processes. First, a three-dimensional thermodynamic model, that is, the 3D heat transmission model for the grain temperature field, is employed to describe the global temperature profile under ideal conditions by considering extrinsic factors. Then, we capture local temperature changes caused by intrinsic factors by a spatiotemporal Gaussian process based on the deviance of  
Figure 1: A 3D grain temperature field in a granary. (Note: the cloud is the grain temperature field, and the black dots are the sensor locations). sensing observations from the global temperature profile. Directly estimating the spatiotemporal dynamics with conventional Gaussian process models will generally be computationally infeasible, as \(\mathcal{O}\!\left(n^{3}\right)\) operations is required for a dataset of size \(n\). Hence, we borrow a tapering covariance function and integrate it into the Gaussian process model to reduce the computational burden. We conduct a real case of temperature fields in grain storage to validate the model performance of our field estimation method. The remainder of this article is organized in the following. Section 2 introduces the methodology of the temperature field estimation. Section 3 discusses a real case study in grain storage to validate the proposed method. Section 4 provides the discussion, the conclusion, and the future work.
## 3 Results
### Automatic Calibration  
#### 3.2.1 Calibration A (First Floor)  
Initially, \(k\) = 33 parameters, involving thermal bridges, infiltration, walls' layers, emissivity and reflectance, windows and shading properties, were considered (details reported in Table 9).  
Subsequently, considering for each parameter a number of EEs \(r\) = 10, a number of discretized levels \(L\) = 6 to search in a fixed uncertainty range of \(\pm\)20% from first guessed value, SA was performed and, after 17 h of computational time, the number of parameters was reduced to 10 (Figure 11).  
Figure 10: Results from Calibration C. First floor, North-West exposure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Element** & **First Guess** & **Lower Bound** & **Upper Bound** \\ \hline \multirow{6}{*}{Linear Transmittance of Thermal Bridges} & Ext. Wall-Int.I Wall [W/(K-m)] & 0.03 & 0.024 & 0.036 \\  & Ext. Wall-Ext. Wall [W/(K-m)] & 0.08 & 0.064 & 0.096 \\  & Window Perimeter [W/(K-m)] & 0.03 & 0.024 & 0.036 \\  & Door Perimeter [W/(K-m)] & 0.03 & 0.024 & 0.036 \\  & Ext. Wall-Root [W/(K-m)] & 0.09 & 0.072 & 0.102 \\  & Int.Wall-Root [W/(K-m)] & 0.03 & 0.024 & 0.036 \\ \hline \multirow{6}{*}{Ext. Wall -Brick} & ACH & 0.5 & 0.4 & 0.6 \\ \cline{2-5}  & s Int. Brick [m] & 0.12 & 0.1 & 0.14 \\  & s Air [m] & 0.04 & 0.03 & 0.05 \\  & s Ext. Brick [m] & 0.12 & 0.1 & 0.14 \\  & E Ext. Surface & 0.9 & 0.72 & 1 \\  & R Ext. Surf & 0.5 & 0.4 & 0.6 \\ \hline \multirow{6}{*}{Ext. Wall-Marble} & s Int. Brick [m] & 0.12 & 0.1 & 0.14 \\  & s Air [m] & 0.04 & 0.03 & 0.05 \\  & s Ext. Brick [m] & 0.08 & 0.06 & 0.1 \\  & E Ext. Surface & 0.9 & 0.72 & 1 \\ \cline{2-5}  & R Ext. Surf & 0.5 & 0.4 & 0.6 \\ \hline \multirow{6}{*}{Roof} & s Creeed [m] & 0.07 & 0.05 & 0.09 \\  & s Air [m] & 0.04 & 0.03 & 0.05 \\  & s Slab [m] & 0.04 & 0.03 & 0.05 \\  & s Jost [m] & 0.16 & 0.12 & 0.2 \\  & E Ext. Surface & 0.9 & 0.72 & 1 \\  & R Ext. Surf & 0.5 & 0.4 & 0.6 \\ \hline \multirow{6}{*}{Material Properties} & \(\lambda\) Brick [W/(m-K)] & 0.387 & 0.3096 & 0.4644 \\  & \(\lambda\) Concrete [W/(m-K)] & 1.23 & 0.984 & 1.476 \\ \cline{1-1}  & \(\lambda\) Str. Concrete [W/(m-K)] & 1.91 & 1.528 & 2.292 \\ \cline{1-1}  & \(\lambda\) Concrete-Brick [W/(m-K)] & 0.6 & 0.48 & 0.72 \\ \hline \multirow{6}{*}{Shading (original windows)} & mult. g & 0.15 & 0.12 & 0.18 \\ \cline{1-1}  & mult. U & 0.9 & 0.72 & 1 \\ \hline \hline \end{tabular}  
* *The input parameters were then selected, and it was possible to set and run the optimization problem for the calibration. Once started, the optimization needed 437 simulations and about 5 h of total computation time to achieve the final solution (Figure 12).