esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### What Are the Proposed Architectures That Support Cognitive Radio LPWAN Based IIoT?
#### 2.2.1 Cognitive IIoT LPWAN Architectures  
Cognitive Radio LPWAN supports artificial intelligent application services, such as automatic driving and smart houses [16]. The heterogeneous wireless communication technologies can be used for the design and implementation of the IIoT infrastructure. To design the IIoT infrastructure for the unlicensed LoRa and Sigfox, the short coverage area BLE and WiFi; and cellular technologies, LTE-m, NBIoT, and 5G, have been suggested [82]. The coverage area of narrowband IoT is less than a distance of 15 km, whereas Bluetooth BLE is less than a length of 10 m; WiFi is less than 100 m; Long Range LoRa is less than 20 km; LTE is less than 11 km; and 5 Gis less than 15 km [83]. The performance parameters such as distance, high data rate, bandwidth, and sensor capacity are discussed in Table 1. The limitations of the different implementations of narrowband IoT base stations using the LTE infrastructures are highlighted. Using LTE radio wave resources can save the power consumption of WAN applications as well as costs. Long Range LoRa is mostly used for cognitive interconnection and transmission and other implementation applications for its enhancement of the unlicensed spectrum. Table 2 presented a summarized comparative analysis of CR LWPAN technologies for the critical Industrial IoT applications. The NBIoT supports environment AI monitoring in smart cities. The WiFi technology supports smart home as AI home, Machine-to-Machine wireless communication technology, and other implementation applications; LoRa supports smart sensing, smart home, and M2M communication technology [84].  
Table 3 presents Industrial IoT characteristics with different parameter requirements (L: Low; M: Medium; H: High). For example, for environments WPWAN CIIoT applications, the power needed is medium due to the scattering of devices around the area; the random distribution probabilities of the long and short distances between the sensor and gateway are similar. Link capacity needed is high due to huge reporting sensors. Coverage is medium as well because for environmental applications, usually the coverage is smaller than cities and larger than campuses. The cost needed is high due to the nature of the environment's application usually at the remote side where no network deployment is available. CR potential interference to other primary users is low, which is due to the environment's application usually at the remote side where there is rarely another network to be deployed at remote or jungle areas, and even though it happens that another foreign network has deployed, the number of devices would not be large.
## 3 Azimuth Spectrum Reconstruction
### Azimuth Spectrum Reconstruction
\begin{array}{ cccc}G_{11}\big{(}f_{\eta}\big{)}&G_{12}\big{(}f_{\eta}+PRF\big{)}&\cdots&G_{1Q} \big{(}f_{\eta}+(Q-1)PRF\big{)}\\ G_{21}\big{(}f_{\eta}\big{)}&G_{22}\big{(}f_{\eta}+PRF\big{)}&\cdots&G_{2Q} \big{(}f_{\eta}+(Q-1)PRF\big{)}\\ \vdots&&\ddots&\vdots\\ G_{Q1}\big{(}f_{\eta}\big{)}&G_{Q2}\big{(}f_{\eta}+PRF\big{)}&\cdots&G_{QQ} \big{(}f_{\eta}+(Q-1)PRF\big{)}\end{array} \right], \tag{11}\]  
where the \(q\)th row corresponds to the reconstruction filter \(G_{q}(f_{\eta})\) of the \(q\)th receiving channel, and the \(q\)th column corresponds to the reconstruction filter function of the \(E_{q}\) frequency range. The SAR azimuth echo signal at any discrete time can be obtained from the impulse response of the multichannel pre-filter banks and the reconstruction filter banks. The reconstructed azimuth signal can be expressed as  
\[\widetilde{h}_{a}(\eta)=\underset{n,q}{\Sigma}h_{a}\big{(}n/PRF-\Delta x_{q} /2L\omega_{s}\big{)}g_{q}(\eta-n/PRF), \tag{12}\]where \(g_{q}(\eta)\) denotes the impulse response of the \(q\)th reconstruction filter. To further obtain the equally spaced azimuth signal, that is, the sequence of which is expressed as \(\{\widetilde{h}_{a}(m/(Q\cdot PRF))\}\). Then, Equation (12) can be rewritten as  
\[\widetilde{h}_{a}\Big{(}\frac{m}{Q\cdot PRF}\Big{)} =\frac{\Sigma\Sigma}{n}q_{a}\Big{(}\frac{n}{PRF}-\frac{\Delta x_{q }}{2L\omega_{s}}\Big{)}g_{q}\Big{(}\frac{m}{Q\cdot PRF}-\frac{n}{PRF}\Big{)} \tag{13}\] \[=\frac{\Sigma\Sigma}{n}q_{a}\Big{(}\frac{Q\cdot n}{Q\cdot PRF}- \frac{\Delta x_{q}}{2L\omega_{s}}\Big{)}g_{q}\Big{(}\frac{m}{Q\cdot PRF}-\frac {Q\cdot n}{Q\cdot PRF}\Big{)}\]  
Furthermore, variable \(Q\cdot n\) in Equation (13) can be substituted by  
\[z_{q}(p)=\left\{ \begin{array}{ll}h_{a}\Big{(}\frac{p}{Q\cdot PRF}-\frac{ \Delta x_{q}}{2L\omega_{s}}\Big{)},&p=Q\cdot n,\\ 0,&p\
eq Q\cdot n.\end{array} \right. \tag{14}\]  
From Equation (13) and Equation (14), the uniformly spaced azimuth sampling can be further expressed as a sequence of convolutions, which is given by  
\[\widetilde{h}_{a}\Big{(}\frac{m}{Q\cdot PRF}\Big{)}=\underset{q}{\Sigma}z_{q} \Big{(}\frac{m}{Q\cdot PRF}\Big{)}\otimes g_{q}\Big{(}\frac{m}{Q\cdot PRF} \Big{)}. \tag{15}\]  
Since the convolution calculation of discrete sequences can be realized by the FFT operation, thus the model of azimuth spectrum reconstruction can be further simplified, which is shown in Figure 3. Here, \(\{G_{q}(k)\}\) is the discrete fourier transform of \(\big{\{}g_{q}(m/\big{(}Q\cdot PRF))\big{\}}\).
###### Abstract  
The time-delayed displacement feedback control is provided to restrain the superharmonic and subharmonic response of the elastic support beams. The nonlinear equations of the controlled elastic beam are obtained with the help of the Euler-Bernoulli beam principle and time-delayed feedback control strategy. Based on Galerkin method, the discrete nonlinear time-delayed equations are derived. Using the multiscale method, the first-order approximate solutions and stability conditions of three superharmonic and 1/3 subharmonic resonance response on controlled beams are derived. The influence of time-delayed parameters and control gain are obtained. The results show that the time-delayed displacement feedback control can effectively suppress the superharmonic and subharmonic resonance response. Selecting reasonably the time-delayed quantity and control gain can avoid the resonance region and unstable multi-solutions and improve the efficiency of the vibration control. Furthermore, with the purpose of suppressing the amplitude peak and governing the resonance stability, appropriate feedback gain and time delay are derived.  
p ezoelectric elastic beam; time-delayed feedback; superharmonic response; subharmonic response 2019 1557; doi:10.3390/app9081557
## 3 Experimental Design
### Numerical Experiment  
The models were trained on a computer equipped with Intel i9-10900k, 64G RAM, and 1T SSD. In order to eliminate the errors caused by sample differences, we modeled each sample individually to detect driving drowsiness. The original data for each model were the extracted physiological features described in Section 3.2. The training targets were the drowsiness states mentioned in Section 3.3 (0--no drowsiness; 1--drowsiness).  
There were two stages in the numerical experiment: drowsiness detection and verification.  
* Drowsiness detection  
In this stage, the original data were input into the proposed model and the three comparison models. As shown in Table 5, data division varied with different classification algorithms. The evaluation indexes of the model are shown Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Drowsiness State** & **KSS Level** & **TOR Level** \\ \hline \multirow{6}{*}{0 No drowsiness} & 1 Extremely alert & \\ \cline{2-3}  & 2 Very alert & \\ \cline{2-3}  & 3 Alert & 0 Not drowsy \\ \cline{2-3}  & 4 Rather alert & \\ \cline{2-3}  & 5 Neither alert nor sleepy & \\ \hline \multirow{6}{*}{1 Drowsiness} & 6 Some signs of sleepiness & 1 Slightly drowsy \\ \cline{2-3}  & 7 Sleepy, but no effort to keep alert & 2 Moderately drowsy \\ \cline{1-1} \cline{2-3}  & 8 Sleepy, some effort to keep alert & 3 Very drowsy \\ \cline{1-1} \cline{2-3}  & 9 Very sleepy, great effort to keep alert, fighting sleep & 4 Extremely drowsy \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correspondence between the KSS, TOR, and drowsiness state defined in the present research.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Algorithms** & **Training Set** & **Validation Set** & **Testing Set** \\ \hline \multirow{2}{*}{Artificial neural networks} & BPNN & 60\% & 20\% & 20\% \\ \cline{2-4}  & CFNN & 60\% & 20\% & 20\% \\ \hline \multirow{2}{*}{Classic machine learning algorithms} & SVM & 60\% & 0\% & 40\% \\ \cline{2-4}  & KNN & 60\% & 0\% & 40\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Data division.  
* Verification  
In this stage, drowsiness detection was repeated 100 times, then each evaluation index's average and standard deviation were calculated to verify the drowsiness detection results and evaluate each model's stability.
## 3 Modeling and Design Optimization
### Magnetic Field and Electromagnetic Force Expression
As shown in Figure 2, the square magnets are adopted to guarantee the symmetry of the proposed planar motor. Therefore, the magnet sizes can be fully described only by the pole pitch (\(\tau\)) and the magnet thickness (\(h_{M}\)). Actually, the pole pitch is twice of the length/width of the square magnet (i.e., \(\tau=4a=4b\)). Once we have obtained the magnetic flux density, we can derive the force expression of the planar motor based on Equation (2). As shown in Figure 3, there are always eight effective sides of the coils which are orthogonal to the moving direction when the motor moves either in the \(x\) direction or \(y\) direction. Therefore, the force expression is modified to  
\[F=8 NB_{zm}Il_{cf} \tag{14}\]  
where \(l_{cf}\) is the effective length of coil side and can be expressed as  
\[l_{ef}=\alpha_{i}\tau \tag{15}\]  
where \(\alpha_{i}\) is the pole-arc coefficient. For the proposed planar motor, which is used in the high-accuracy, high-dynamic systems, a high force level is always required to guarantee the dynamic performance. In general, a large value of \(h\) is adopted to provide more space for the coils and to increase the force level. Compared with \(h\), the length of mechanical air-gap is negligible. Therefore, the ampere-turns in Equation (14) can be approximated as  
\[NI=J_{c}w_{c}h \tag{16}\]  
where \(J_{c}\) is the current density of coil cross section and \(w_{c}\) is the width of coil, which can be expressed as  
\[w_{c}=C_{fw}\tau \tag{17}\]  
where \(C_{fw}\) is the flat top width coefficient. \(C_{fw}\) is defined as the ratio between the width range (\(W\)) in which the magnetic flux density \(B\) is higher than \(0.9B_{\max}\) and the pole pitch (i.e., \(C_{fw}=W/\tau\)). Substituting Equations (10) and (15)-(17) into Equation (14), the force expression relating to the main parameters of the planar motor can be derived as  
\[F=\frac{12.8\mu_{0}M_{\gamma}\alpha_{i}J_{c}C_{fw}}{\pi}\tau^{2}h\,X(\lambda,\gamma) \tag{18}\]
## 5 Implementation and Comparison
### Configuration of Experiment Parameters  
The computational device used for the optimization evaluation in this study consists of a high-performance server running Windows Server 2016 Standard. The server is equipped with an Intel(R) Xeon(R) Silver 4314 CPU operating at 2.40 GHz, featuring 32 processing cores, and 128 GB of installed RAM.  
However, all compared algorithms have been implemented and run in the same environment to provide a fair comparison (run = 30, FES = 1000, agents No. = 30).  
The parameter configuration is critical for the consistent evaluation of optimization algorithms using the benchmark functions. However, the CEC 2022 and CEC 2017 parameters utilized in these benchmarks provide a controlled environment to compare the performance of various evolutionary algorithms. These parameters are summarized in Tables 1 and 2.  
A population size of 30 and a dimensionality of 10 provide a balance between computational feasibility and complexity. The maximum number of function evaluations is defined as 1000, offering sufficient iterations for algorithms to demonstrate convergence without excessive computational overhead. The search range of \([-100,100]^{D}\) illustrates a broad and consistent search space across all dimensions.  
The functions often include both rotation and shifting to increase complexity, simulating more realistic optimization challenges. Noise is intentionally omitted to ensure that results focus on algorithm robustness in navigating challenging landscapes, rather than random perturbations. This uniform framework allows a fair comparison of different algorithms, highlighting their strengths and weaknesses across the wide range of benchmark functions.
# On the Needs and Requirements Arising from Connected and Automated Driving
## 4 Technical Requirements: from Automotive to Network Perspective
### 5 GCAR Use Case Technical Requirements
#### 4.3.2 Cooperative Perception Based on See-Through
On the other hand, if specific image processing algorithms require high-quality image data to be transmitted, a data rate of about 29 Mbps or higher would be necessary (video codec without motion compensation). The latency requirement (i.e., deadline) depends on the speed of the vehicle, its heading, as well as pitch angle changes. A delay of at most 50 ms should be kept, as both videos from the front vehicle and the rear have to be stitched together in the rear vehicle. Lower values would enhance the experience of this function, whereas additional delays would increase buffering in the rear vehicle. In addition, the jitter (variation of latency between received transmissions) should be kept small in order to avoid the loss of video frames. A reliability of 99% is required in order to avoid massive artifacts in the real-time video stream, as this would be used only to indicate to the driver if overtaking is possible or not. For high automation levels, this value will be further increased, due to the need for transmitting object detection messages. The availability requirement is considered to be aligned with the reliability value and, therefore, 99% is required.
###### Abstract  
To address the challenges in identifying NAPL contamination within low-permeability clay sites, this study innovatively integrates high-density electrical resistivity tomography (ERT) with a UNet deep learning model to establish an intelligent contamination detection system. Taking an industrial site in Shanghai as the research object, we collected apparent resistivity data using the WGMD-9 system, obtained resistivity profiles through inversion imaging, and constructed training sets by generating contamination labels via K-means clustering. A semantic segmentation model with skip connections and multi-scale feature fusion was developed based on the UNet architecture to achieve automatic identification of contaminated areas. Experimental results demonstrate that the model achieves a mean Intersection over Union (mIoU) of 86.58%, an accuracy (Acc) of 99.42%, a precision (Pre) of 75.72%, a recall (Rec) of 76.80%, and an F1 score (f1) of 76.23%, effectively overcoming the noise interference in electrical anomaly interpretation through conventional geophysical methods in low-permeability clay, while outperforming DeepLabV3, DeepLabV3+, PSPNet, and LinkNet models. Time-lapse resistivity imaging verifies the feasibility of dynamic monitoring for contaminant migration, while the integration of the VGG-16 encoder and hyperparameter optimization (learning rate of 0.0001 and batch size of 8) significantly enhances model performance. Case visualization reveals high consistency between segmentation results and actual contamination distribution, enabling precise localization of spatial morphology for contamination plumes. This technological breakthrough overcomes the high-cost and low-efficiency limitations of traditional borehole sampling, providing a high-precision, non-destructive intelligent detection solution for contaminated site remediation.  
NAPLs contamination identification; low-permeability sites; electrical resistivity tomography; UNet; dynamic contamination monitoring Article
# Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms
## 5 Results and Discussion
### Blasting Parameters Optimization  
Reducing the size of ore fragmentation in blasting operations can help control hazardous factors and facilitate ore handling. However, smaller fragmentation implies higher blasting costs. Therefore, it is crucial to find blasting parameters that strike an optimal balance between fragmentation and cost. In this section, we established blasting fragmenta  
Figure 8: Comparison of predicted results: (**a**) training sets; (**b**) testing sets.  
tion and blasting cost models related to blasting parameters, respectively serving as two objective functions for blasting parameter optimization. The Pareto front representing the relatively optimal conditions was obtained. By the 90 th iteration, a stable Pareto front was formed, resulting in a Pareto optimal solution set for different blasting design parameters, as shown in Figure 9. The distribution of the optimal solution set in the graph indicates uniformity, demonstrating that the surrogate model obtained accurate optimization results in the MOPSO algorithm.  
Three sets of typical Pareto optimal solutions were extracted, and the blasting parameter optimization values corresponding to the variable denormalization are shown in Table 5. The optimal solutions at points A and C have characteristics where one objective is relatively small while the other is relatively large. Point B, with its two objectives balanced between the optimization extremes, corresponds to a more balanced blasting fragmentation and blasting cost. The blasting design parameters extracted at this point represent the ideal blasting design scheme for this mine, satisfying the relatively optimal solution. Therefore, considering the optimized blasting design parameters at point B in the blasting plan can further enhance the overall production efficiency of the mine. The corresponding optimized blasting design parameters are \(S\) = 5.5 m, \(B\) = 4.3 m, \(q\) = 0.51 kg/m\({}^{3}\), and \(H_{0}\) = 2.0 m.
# Ultraviolet Transient Absorption Spectrometer with Sub-20-fs Time Resolution
## 3 Experimental Setup  
### UV Pump Pulse Generation  
In our TA setup we generate short UV pulses by frequency up-conversion of a broadband NOPA [71; 72; 73], which is a source of few-optical-cycle visible pulses with moderate energy (a few \(\upmu\)Js) and high shot-to-shot stability. The experimental setup for the generation of the UV pump pulses is shown in Figure 1. The system starts with a regeneratively amplified Ti:sapphire laser (Libra, Coherent, Santa Clara, CA, USA), which delivers vertically polarized 100-fs pulses at 1 kHz repetition rate and at 800 nm wavelength. A 500-\(\upmu\)J fraction of the laser light drives a single-pass visible NOPA [74], pumped by the SH and seeded by a WLC generated in a 2-mm-thick sapphire plate. This NOPA produces vertically polarized broadband pulses with a spectrum extending from 525 to 700 nm, corresponding to a transform-limited (TL) pulse duration of 6.2 fs, and with energy of the order of 10 \(\upmu\)J. The NOPA pulses are compressed by 12 reflections onto a pair of dispersion-matched dielectric double-chirped mirrors (DCMs) until nearly flat spectral phase is achieved. For SHG (Figure 1a) the vertically polarized NOPA pulses are focused by an f = 250 mm spherical mirror onto a 20-\(\upmu\)m-thick free-standing Type I BBO crystal cut at \(\theta=42.4^{\circ}\) which produces horizontally polarized UV pulses. Subsequently FW and SH pulses are collimated by an identical spherical mirror and sent to a Brewster-cut MgF\({}_{2}\) prism pair (tip to tip distance: 215 mm) which serves the dual purpose of filtering out the FW light and of compensating the dispersion introduced by air and by the cuvette window on the pump beam path. By adjusting the tilt angle of the BBO crystal and simultaneously the NOPA bandwidth, it is possible to tune the carrier wavelength of the UV pulses from 260 to 300 nm, maintaining in all cases a pulse energy of 40-50 nJ at the prism pair output.  
For SFG (Figure 1b) a 30-\(\upmu\)J fraction of the laser FW is split off, sent through a half-wave plate which rotates its polarization to horizontal, synchronized by a delay line and collinearly combined with the compressed NOPA output by a dichroic mirror, which reflects the NOPA and transmits the FW. The collinear FW and NOPA pulses are focused by a f = 250 mm mirror to a 50-\(\upmu\)m-thick BBO crystal (Type II, eoe, \(\theta\) = 46.8\({}^{\circ}\)), recollimated by a mirror and sent to a set of three identical UV dielectric mirrors, which filter out the FW of the laser and of the NOPA. Typical pulse energies of up to 1 \(\upmu\)J are obtained.
## 2 Concepts of Clustering Approaches
### Single-Hop vs. Multi-Hop Models  
Data are transmitted between different nodes and the sink node in one of two methods, the single-hop model and the multi-hop model. In the single-hop model, all sensor nodes transmit their data to the sink node. This model speeds up the data process and makes the implementation easy, resulting in reduced energy consumption. However, single-hop models are infeasible in large-scale environments, as the transmission cost consumes high energy levels, and in the worst case the sink node cannot be reached. Figure 2 illustrates an example of a military surveillance application using the single-hop clustering model.  
Figure 1: Classification of energy reducing schemes in WSNs.  
On the other side, the multi-hop clustering model keeps the sensor nodes at a low level of energy consumption, as multiple cluster heads collect data and transfer them to the sink node. However, the synchronization of multi-hop time in the cluster core produces a very high load and data transmission among nodes, causing more packet delay and packet loss and, therefore, quick energy depletion. Consequently, the multi-hop clustering model is suitable for a large-scale area network [14]. Figure 3 shows an example of a precision agriculture application using the multi-hop clustering model.
# Performance and Measurement Devices for Membrane Buildings in Civil Engineering: A Review
## 3 Performance
### Mechanical Performance
#### 3.1.3 Wind Load  
In practical application, the membrane is generally located on the outer surface of the structure as a sheltering or facade element [57]. The negative pressure of airflow on the membrane is the leading cause of its failure, which means it is difficult to ignore the influence of wind load. Wind tunnel tests and field measurements are direct and efficient methods to acquire the wind load response of membrane buildings. Some studies achieve this purpose through specially designed devices (Figure 9). The advantage of laboratory experiments (such as through wind tunnels) is that conditions can be tailored to fulfill the necessities [24]. However, most wind tunnel experimental models are rigid rather than aeroelastic [36], and the model is simplified after scaling. These two factors make field measurement research unavoidable, even though obtaining a stable reference pressure and determining a time segment to calculate the average wind speed remain difficult [36].