esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### The Speed Matrix  
In the previous section, the frequency weighting matrix was obtained. In the FWMB algorithm, another key parameter is the wave velocity matrix. Its role is to accurately match the wave speed in different frequency bands to solve the wave speed error that is caused by the dispersion effect in the flat plate. From the perspective of beamforming theory, wave speed is a critical parameter. An inaccurate wave speed results in the wrong delay, which leads to the wrong location result.  
Due to the dispersion characteristics of Lamb waves, signals with different frequency components have different phase velocities. It was assumed herein that the phase velocities were approximately the same in a narrow band of 10 kHz. The numerical solution of the Rayleigh-Lamb equation of the test plate is shown in Figure 8.  
The data were processed into eight or nine frequency bands by narrow band-pass filtering: 100-110, 110-120,..., 180-190 kHz. The corresponding phase velocities of different frequency components in the dispersion curve were recorded as a velocity matrix. For the low frequency thickness product, between 0.3 and 0.6 MHz \(\cdot\) mm, there were only two wave modes (the A\({}_{0}\) and S\({}_{0}\) modes). Since the A\({}_{0}\) mode is dominant in the energy of the wave [17], the A\({}_{0}\) mode signal is discussed in the rest of this article. Table 1 lists the corresponding phase speeds \(c_{p}\) and the weighting factors in different frequency bands.  
Figure 8: Dispersion curves of the A\({}_{0}\) (symmetric) and S\({}_{0}\) (anti-symmetric) mode waves for test plate.  
Once the phase speeds and the geometry of a sensor array are given, the time delay matrix can be obtained via Equations (1) and (2). With the process in Figure 2, the FWMB algorithm can be produced. When the power gets its maximum, the corresponding assumed angle is the best estimate of the actual direction of arrival.
## 3 Numerical Calculation and Discussion
### Analysis of Stirring Effects and Impact Characteristics in Molten Bath Flow
**(a)** Volume fraction of molten steel at the steel liquid level. **(b)** Velocity vector diagram of gas–liquid interface. **(c)** Velocity vector diagram of molten steel. Combined with the previous analysis, these results imply that the velocity of the molten bath is slow because of the premature dissipation of kinetic energy in the 13\({}^{\circ}\) oxygen lance, resulting in poor steelmaking efficiency. Therefore, in the design of a nozzle-twisted lance, the situations caused by excessive NTAs, such as jet interference, gas collision, and premature dissipation of kinetic energy, should be avoided. Compared with the conventional oxygen lance, the 5\({}^{\circ}\) nozzle has advantages in stirring effect at the liquid level. However, the swirl trend at the lower layer is not remarkable and the improvement is limited. The 8\({}^{\circ}\) oxygen lance induces a substantially improved stirring effect in the molten bath, but its effectiveness in the near-wall area is inferior to that of the conventional nozzle. In the top-blow process, the supersonic gas impinges on the steel liquid level and a depression is formed in the molten steel by the huge kinetic energy generated. Meanwhile, the splash of molten steel in the converter poses a great threat to the nozzle. Therefore, reducing splash in the steel converter is beneficial to improving the service life of an oxygen lance. Figures 12 and 13 track the evolution mechanism of the two-phase interface between the conventional nozzle and the eight-degree nozzle in the preliminary stage of top blowing (the period when splash is most likely to occur). A comparison of these figures shows that the impact depth of the 8\({}^{\circ}\) oxygen lance is shallow and the splash is obviously weakened. This effect is mainly caused by the relatively low kinetic energy and the appropriate tangential velocity component. In addition, the simulation results are similar to the experiment of [PERSON] et al. [10]. The results also suggest that the nozzle-twisted lance not only intensifies the local swirling in molten steel but also reduces the splashing of molten steel, which improves the efficiency of kinetic energy transfer and has substantial application prospects for steelmaking. Figure 11: Molten steel movement in various heights with different multi-nozzle oxygen lances. (**a**) Under a liquid level of 20 mm. (**b**) Under a liquid level of 300 mm. (**c**) Under a liquid level of 600 mm. (**d**) Under a liquid level of 1000 mm.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
###### Abstract  
Remote photo-plethysmography (rPPG) uses a camera to estimate a person's heart rate (HR). Similar to how heart rate can provide useful information about a person's vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine fluctuations in the intervals between heart beats. However, this measure requires temporally locating heart beats with a high degree of precision. We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not only estimates heart rates, but also extracts the pulse waveform to time heart beats and measure heart rate variability. This unsupervised method requires no rPPG specific training and is able to operate in real-time. We also introduce a new multi-modal video dataset, VicarPPG 2, specifically designed to evaluate rPPG algorithms on HR and HRV estimation. We validate and study our method under various conditions on a comprehensive range of public and self-recorded datasets, showing state-of-the-art results and providing useful insights into some unique aspects. Lastly, we make available CleanerPPG, a collection of human-verified ground truth peak/heart-beat annotations for existing rPPG datasets. These verified annotations should make future evaluations and benchmarking of rPPG algorithms more accurate, standardized and fair.  
remote photoplethysmography; heart rate variability; unsupervised; clean ground truth +
Footnote †: journal: Applied Sciences
# Reviews on Corrugated Diaphrags in Miniature Fiber-Optic Pressure Sensors
## 2 The Principles of Operation of Miniature Fiber-Optic Pressure Sensors
### Demodulation Methods of EFPI Sensors
#### 2.2.1 Intensity demodulation  
As shown in Figure 2, intensity demodulation usually uses a narrow-band light source and detects the external signal according to the change of the reflected light intensity, which is directly received by the photodetector.  
The intensity demodulation method has the advantages of high sensitivity, fast demodulation speed, simple processing and low cost. So, it is very effective in measuring small dynamic disturbance signals. However, this method has the disadvantages of a small dynamic range, it is difficult to control and stabilize the working point and it is very susceptible to fluctuations in light intensity. In order to overcome the shortcomings of the intensity demodulation algorithm, the operating point control technique [28; 29], self-compensation method [30; 31], quadrature phase method [32; 33], and three-wavelength demodulation method [34] have been proposed, and so on.
# Multi-Objective Motion Control Optimization for the Bridge Crane System
## 1 Introduction
Although the LQR method is simple and highly efficient, the system states can possibly deviate from the equilibrium points when the system is disturbed by external signals, and thus, it is difficult to guarantee the stability of the control performance. To solve this problem, the constraint of the payload swing and the capability of anti-disturbances need to be taken into account, and some combined algorithms of open loop and closed loop methods are introduced [30; 31; 32]. In [30], [PERSON] and [PERSON] employed the feedforward control and the feedback PID control together, according to the inverse dynamics analysis. However, this method lacks the stability analysis, and it is difficult to satisfy the demand of the payload angle and crane position simultaneously with the PID control. Aiming at these considerations, a combined open loop and closed loop method is herein proposed to satisfy the constraint of the payload swing, and precisely realize the position control of the crane. First, the motion path of the crane is designed by TP, which keeps the payload swing states in the vicinity of the equilibrium point due to the underactuated characteristics. By this manner, the constraints of the swing angle can be satisfied, and the residual swing can be eliminated. Then, an updated LQR algorithm is introduced to improve the precision of the crane position and the payload swing control, which are immune from external disturbance. Here, for the sake of the multi-freedom control for the crane position, a differential and integral control loop is added to track the crane position with fast speed. In order to gain preferable control effects, a multi-objective GA (MOGA) algorithm is employed to achieve the different motion state optimization by searching the weighed matrices of the LQR [33]. The MOGA is characterized by the weight of the multi-objective functions being assigned randomly; thus, its search direction is not fixed. A set of Pareto optimal solutions can be retained and uniformly distributed during implementation [34; 35]. By this manner, the MOGA exhibits the advantage of the diversity of solutions, and meanwhile, the computational complexity is lower compared with the traditional GA [36; 37]. Finally, the quasioptimal control of the crane is reached by simulation and experiment validation. The remainder of this paper is structured as follows. The detailed modeling and associated constraints are introduced in Section 2. The controller design and its stability proof are provided in Section 3. The feasibility of the proposed method is verified by the simulation and experiments in Sections 4 and 5, respectively. Finally, conclusions are drawn in Section 6.
## 2 System Synthesis
### Optional Subsystems  
The subsystems described next are optional and are not required for a SailREC System to work. Some of them might have a positive impact on the energy yield or lead to additional revenue.  
An **Ocean Cleanup Subsystem** installed on the SailREC System can be used to clean oceans of floating plastics. A collection mechanism that does not require the use of any power would be ideal. The collected plastic can also be transferred to shore when fuel is transferred. Furthermore, to collect plastics, the subsystem can--depending on the design--increase the hydrodynamic drag in the wind direction, thereby acting as an Anti-Wind Force Subsystem. This would facilitate increased power production by the wind turbine.  
The incorporation of additional energy sources, such as solar, wave, or even ocean thermal energy conversion, can be thought of. While such additional subsystems, falling into the Energy Group, will increase the energy output of the SailREC System, their utilisation might not result in a lower cost per unit of energy due to more costly and complex construction and design.  
The SailREC System can also transport cargo during its usual operation. The optimal path for maximum energy yield may coincide in part with trading routes. So the transportation of cargo could bring additional revenue. Furthermore, as the mass of the whole system increases, the inertia is increased as well, which can lead to lower drift velocities and, thus, a higher effective wind speed at the wind turbine and, in turn, more generated power.  
Just as with cargo, the concept can be extended to include the transport of people with a passenger module. Additional safety features might be required in this case.  
An aquafarm incorporated into the SailREC System can also be used for carbon capture and to generate additional resources. The carbon capture can be performed by feeding farmed algae to a pyrolysis reactor.
# Detecting AI-Generated Images Using a Hybrid ResNet-SE Attention Model
###### Abstract  
The rapid advancements in generative artificial intelligence (AI), particularly through models like Generative Adversarial Networks (GANs) and diffusion-based architectures, have made it increasingly difficult to distinguish between real and synthetically generated images. While these technologies offer benefits in creative domains, they also pose serious risks in terms of misinformation, digital forgery, and identity manipulation. This paper presents a novel hybrid deep learning model for detecting AI-generated images by integrating the ResNet-50 architecture with Squeeze-and-Excitation (SE) attention blocks. The proposed SE-ResNet50 model enhances channel-wise feature recalibration and interpretability by integrating Squeeze-and-Excitation (SE) blocks into the ResNet-50 backbone, enabling dynamic emphasis on subtle generative artifacts such as unnatural textures and semantic inconsistencies, thereby improving classification fidelity. Experimental evaluation on the CIFAKE dataset demonstrates the model's effectiveness, achieving a test accuracy of 96.12%, precision of 97.04%, recall of 88.94%, F1-score of 92.82%, and an AUC score of 0.9862. The model shows strong generalization, minimal overfitting, and superior performance compared with transformer-based models and standard architectures like ResNet-50, VGGNet, and DenseNet. These results confirm the hybrid model's suitability for real-time and resource-constrained applications in media forensics, content authentication, and ethical AI governance.  
deep learning; AI-generated images; SE-ResNet34; squeeze-and-excitation; image classification; CIFAKE dataset Article
# CourseKG: An Educational Knowledge Graph Based on Course Information for Precision Teaching
## 3 Proposed Method
### Entity Recognition
#### 3.3.2 BiGRU Module  
The gated recurrent unit (GRU) [43] is a variant of the long short-term memory neural network (LSTM). Traditional recurrent neural network (RNN) training frequently faces challenges such as gradient vanishing and explosion. In contrast, LSTM addresses these issues by incorporating input gates, output gates, and forget gates. Although the LSTM partially addresses the gradient disappearance problem, its computational process is time-intensive. The GRU structure streamlines the LSTM by merging the input gate and forget gate into an update gate. Consequently, the GRU preserves the strengths of the LSTM while simplifying its architecture. In the context of entity recognition in educational records, the GRU proves effective in extracting features.  
Following the operating principle of the GRU unit, the GRU module excels at discarding redundant information, while its straightforward model structure reduces computational complexity. However, the naive GRU alone fails to fully leverage the contextual information present in educational records. Therefore, the backward GRU is introduced to  
Figure 3: The entity recognition framework.  
capture backward semantics in this paper. By incorporating both forward and backward GRU neural networks, the model, named the BiGRU model, aims to extract key features of named entities in educational records. The BiGRU model employs two sets of GRUs to extract features from sentences, ensuring that each token in a sequence is influenced by both its past and future contexts. This dual-directional approach provides access to both backward and forward information at each step.  
The word vectors generated by BERT serve as the input to the BiGRU at each time step. At step \(t\), a forward hidden layer processes the sequence from step 1 to step \(t\), producing a forward hidden sequence \(\overset{\rightarrow}{h_{t}}=\overset{\rightarrow}{GRU}\). Concurrently, a backward hidden layer handles the same sequence from \(t\) to 1, resulting in a backward hidden sequence \(\overset{\rightarrow}{h_{t}}=\overset{\rightarrow}{GRU}\). Finally, the preceding and subsequent semantic features are combined to obtain \(h_{t}=\textless{}\overset{\rightarrow}{h_{t}}>\).
## 3 Results and Discussion  
### Performance Comparison on Seven Benchmark Datasets  
To demonstrate the effectiveness of the proposed method, we compared its performance with three other existing state-of-the-art methods, including MM-6 mAPred [21], SNNRice6 mA [33], and Deep6 mA [34]. MM-6 mAPred is traditional machine learning method training the model by hand-made features extracted from original DNA sequences. SNNRice6 mA and Deep6 mA are the deep learning methods. All the predictors are evaluated on the seven benchmark datasets by using 5-fold cross validation. Table 2 lists the performances of the proposed method, namely 6 mAPred-MSFF, and four existing predictors on the seven datasets. For the 6 mA-rice-Lv dataset, when compared to the second-best method Deep6 mA, our proposed method achieves an SN of 97.88%, an SP of 96.64%, an ACC of 96.26%, and an MCC of 0.926, yielding a relative improvement over Deep6 mA of 3.15%, 0.92%, 2.03%, and 4.63%, respectively. Compared with the machine learning method MM-6 mAPred, our proposed method improves the performance of SN, SP, ACC, and MCC by 4.61%, 5.8%, 5.12%, and 12.65%, respectively. For the 6 mA-rice-Chen dataset, compared with the runner-up predictor Deep6 mA, with the only exception that the value of SP is better than ours, our proposed method improves the performance of SN, ACC, and MCC by 9.89%, 2.79%, and 6.81%, respectively. For the _A. thaliana_ and _R. chinensis_ datasets, with the exception that the predictor Deep6 mA achieves the best value of SP, and our proposed method improves the performance of SN, ACC, and MCC by 0.28-5.2%. Moreover, we can see that the performance of 6 mAPred-MSFF is also better than all other methods on the _F. vesca, H. sapiens_, and _D. melanogaster_ datasets.
# Projection onto the Set of Rank-Constrained Structured Matrices for Reduced-Order Controller Design
## 4 Numerical Examples
### Stabilizing Static Controllers  
We consider all benchmark linear time-invariant models whose order is less than 1000 from COMPL\({}_{\text{c}}\)ib. There are 95 benchmark models to be checked. For these models, we solve Problem 1 by five methods:1. Nonsmooth \(H^{\infty}\) synthesis [36] with hinfstruct funciton in MATLAB (NSH\({}^{\infty}\))
2. Cone complementarity linearization algorithm [12] (CCL)
3. Nuclear norm minimization [15] (NNM)
4. Alternating projection with approximate projection onto \(\Omega_{r}\)[13] (GS96)
5. Alternating projection with the proposed precise projection in Section 3 (Proposed)  
Table 1 summarizes the number of successful results (i.e., a static stabilizing controller is obtained), and the average computational time.  
From this table, we can say that the Nonsmooth \(H^{\infty}\) method [36] is the best among the five methods. We note that the alternating methods by [13] and our method show a very long average CPU time since there are a few models (e.g., EB5 and JE1) for which it takes a very long CPU time by the se methods. For example, GS96 takes 53,450 [s] and the proposed method takes 52,315 [s] for EB5. However, there are models for which the proposed method successfully gives a stabilizing static controller while some of the other methods fail. We summarize the results in Table 2.  
An advantage of the proposed method is found in this table. Since stabilizing static controller design is in general a non-convex problem, and hence there may be no unified approach that gives a solution for any plant models. For example, no methods but the proposed method can compute a stabilizing static controller for TF1. We emphasize our method is an effective method that may provide a solution to a reduced-order controller design problem that cannot be solved by standard methods as nonsmooth \(H^{\infty}\), cone complementarity linearization, and nuclear norm minimization.
# Quality of Experience Experimentation Prediction Framework Through Programmable Network Management
###### Abstract  
Quality of experience (QoE) metrics can be used to assess user perception and satisfaction in data services applications delivered over the Internet. End-to-end metrics are formed because QoE is dependent on both the users' perception and the service used. Traditionally, network optimization has focused on improving network properties such as the quality of service (QoS). In this paper we examine adaptive streaming over a software-defined network environment. We aimed to evaluate and study the media streams, aspects affecting the stream, and the network. This was undertaken to eventually reach a stage of analysing the network's features and their direct relationship with the perceived QoE. We then use machine learning to build a prediction model based on subjective user experiments. This will help to eliminate future physical experiments and automate the process of predicting QoE.  
QoE; fairness; SDN; classification prediction; DASH; multimedia Article
## 3 Results
### Comparison of Gain and Offset Via Two Fiber Types  
We performed field radiometric calibrations using different fiber types (Table 2). The two different fibers are from Ocean Optics (OC) and Hygirel (HY). The OC fiber is manufactured by Ocean Optics and has a spectral range of 200-1000 nm, and its core diameter is 600 \(\upmu\)m and its length is 2 m; HY is manufactured by HAIJILEKEJI and has a spectral range of 200-1100 nm, and its core diameter is 1000 \(\upmu\)m and its length is 2 m. Figure 4 shows the results of the field radiometric calibration coefficients gain and offset, which we evaluated using \(r\) for least squares regression coefficient accuracy. We found that (a)-1, (a)-2 and (a)-3 have a large variation in gain coefficients across fiber types, but there is a linear relationship, with r reaching 0.99 for Ocean Optics' gain and Hygirel's gain. The gain data are again noticeably jitter at 760 nm, which we attribute to the different integration times measured by Trios and STS-VIS at the time of measurement and to the variation in atmospheric gases. We post-processed the gain data by means of smoothing in subsequent processing. We also found for (a)-1, (a)-2 and (a)-3 that the offset coefficients vary considerably across fiber types and do not have any linear relationship, with \(r\) reaching 0.99 for offset obtained on sumy days. We found that the overall offset coefficient of HY is much larger than that of OC, which we believe is due to the different core diameters of the two fibers and the different amounts of sunlight energy entering the micro-spectrometer at the same integration time for HY and OC.  
We found that for a fixed integration time and different fiber types, there are different field radiometric calibration coefficients for gain and offset. We have made the gain and offset for sunny days for Ocean Optics and Hygirel fiber types publicly available for use so that a field radiometric calibration can be performed before use if a more accurate R\(r\)s is required. Different fiber types mainly affect the calibration coefficients of gain and offset, and the field radiometric calibration coefficients of gain and offset for different  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Ocean Optics (OC)** & **Hygirel (HY)** \\ \hline Wavelength (nm) & 200–1100 & 200–1100 \\ \hline Core Diameter (\(\upmu\)m) & 600 & 1000 \\ \hline Length (m) & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter table of different fiber types.  
Figure 4: Graphs of gain and offset of micro-spectrometer connection with different fiber types.  
fiber types can be found at [[https://github.com/765302995/FRC_Micro-spec](https://github.com/765302995/FRC_Micro-spec)]([https://github.com/765302995/FRC_Micro-spec](https://github.com/765302995/FRC_Micro-spec)) (accessed on 15 November 2022).