esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Phase Synchronization Scheme
### System Design and Phase Model
the calibration signal is transmitted from the internal calibrator to the synchronization transceiver. Then, the signal is recorded by the radar receiving unit for processing. After the processing of the above calibration loop, the internal calibration compensation phase \(\varphi_{\text{cal}}(t)\), i.e., the phase error introduced by the hardware system, can be expressed as  
\[\varphi_{\text{cal}}(t)= -\frac{1}{2}\Big{(}\varphi_{\text{ST}}^{b}(t)-\varphi_{\text{ST}} ^{a}(t)\Big{)}+\frac{1}{2}\Big{(}\varphi_{\text{SR}}^{b}(t)-\varphi_{\text{SR} }^{a}(t)\Big{)}\] \[-\Big{(}\varphi_{\text{CR}}^{b}(t)-\varphi_{\text{CR}}^{a}(t) \Big{)}+\frac{1}{2}\Big{(}\varphi_{\text{RE}}^{b}(t)-\varphi_{\text{RE}}^{a}( t)\Big{)} \tag{4}\]  
where \(\varphi_{\text{CR}}(t)\), \(\varphi_{\text{RE}}(t)\), \(\varphi_{\text{ST}}(t)\), and \(\varphi_{\text{SR}}(t)\) denote the phase extracted from loop CR, RE, ST, and SR, respectively. The superscript \(\{\cdot\}^{a}\) and \(\{\cdot\}^{b}\) indicate that the phases are extracted at LT-1A and LT-1B, respectively. When in orbit, satellites are flying with high-speed. The velocity difference will lead to phase error introduced by the Doppler effect, which should be corrected according to the orbit parameters [31]. The Doppler frequency shift between LT-1A and LT-1B can be expressed as  
\[f_{d}(t)=f_{c}v_{\text{ab}}(t)/c \tag{5}\]  
where \(v_{\text{ab}}(t)\) and \(c\) indicate the relative velocity and the velocity of light, respectively. Therefore, the Doppler phase error is expressed as  
\[\varphi_{\text{dop}}(t)=-\pi f_{d}(t)\tau_{\text{sys}}. \tag{6}\]  
The high-precision real-time positioning and velocity measurement of the LT-1 system contribute to the accurate correction of the Doppler effect. After combining the internal calibration compensation phase and the Doppler phase error, the accurate compensation phase of the LT-1 system can be written as  
\[\varphi_{\text{ac}}(t)=\varphi_{c}(t)-\varphi_{\text{cal}}(t)-\varphi_{\text{ dop}}(t). \tag{7}\]  
During ground validation, satellites are placed in the microwave darkroom. The radar signal generated by LT-1A is transmitted through the optical delay line and then received by the receivers of LT-1A and LT-1B simultaneously, so as to simulate the BiSAR data acquisition. The optical delay line ensures the high SNR of the imaging signals, which are used for the analysis of synchronization accuracy. After pulse compression of imaging signals, the phase difference can be expressed as  
\[\varphi_{\text{dif}}(t)=\varphi_{b}(t)-\varphi_{a}(t) \tag{8}\]  
where \(\varphi_{a}(t)\) and \(\varphi_{b}(t)\) represent the peak phase of imaging signal received by LT-1A and LT-1B, respectively. Thus, the residual phase error \(\varphi_{\text{res}}(t)\) after synchronization phase compensation can be calculated by  
\[\varphi_{\text{res}}(t)=\varphi_{\text{ac}}(t)-\varphi_{\text{dif}}(t). \tag{9}\]  
Finally, the synchronization accuracy of the LT-1 system can be evaluated by the standard deviation (STD) of \(\varphi_{\text{res}}(t)\).
# Hybrid LSTM-Attention and CNN Model for Enhanced Speech Emotion Recognition
## 4 Experiments and Discussion
### Comparison with Existing Models
[49], a streamlined one-dimensional deep convolutional neural network (1-D DCNN) is employed to reduce processing demands and effectively capture the prolonged patterns within the speech emotion signal. For the TESS dataset, various methods were explored to determine the most effective combination of features for emotion recognition. Reference [44], incorporating STFT, RMS, MFCC, and ZCR as features, achieved a commendable accuracy of 99.0%. In contrast, Ref. [45], which solely relied on MFCC, managed to slightly outperform it with an accuracy of 99.5%. The study in Ref. [46] combined RMS, MFCC, and ZCR, attaining an accuracy of 99.6%. Yet, when comparing all these methods, our proposed technique stood out. By using the same combination of features as those used in Ref. [46], our method exceeded the others, achieving an impressive accuracy rate of 99.8% on the TESS dataset. Turning our attention to the RAVDESS dataset, the performances varied more widely. Using just the MFCC feature, Ref. [45] obtained an accuracy of 88.7%. However, when Ref. [47] combined \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Benchmarks** & **Features** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{TESS} & [44] & STFT+RMS+MFCC+ZCR & 99.0 \\ \cline{2-4}  & [45] & MFCC & 99.5 \\ \cline{2-4}  & [46] & RMS+MFCC+ZCR & 99.6 \\ \cline{2-4}  & Our & RMS+MFCC+ZCR & 99.8 \\ \hline \multirow{4}{*}{RAVDESS} & [45] & MFCC & 88.7 \\ \cline{2-4}  & [47] & RMS+MFCC+ZCR & 79.41 \\ \cline{1-1} \cline{2-4}  & [48] & MFCC+ZCR+Energy+HNR & 81.0 \\ \cline{1-1} \cline{2-4}  & [49] & MFCC+LPC+WPT+ZCR & 94.18 \\ \cline{1-1} \cline{2-4}  & Our & RMS+MFCC+ZCR & 95.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis of the proposed method’s performance against other rival techniques. RMS, MFCC, and ZCR, the accuracy dropped to 79.41%, showcasing the intricate dynamics of the RAVDESS dataset. Ref. [48] experimented with a combination of MFCC, ZCR, energy, and HNR, achieving a slightly better result of 81.0%. Diversifying the feature set further, Ref. [49] combined MFCC, LPCC, WPT, and ZCR, achieving a significantly higher accuracy of 94.18%. But among all these diverse approaches and feature combinations, our model, emphasizing RMS, MFCC, and ZCR, emerged as the most effective, recording the highest accuracy of 95.70% for the RAVDESS dataset. These evaluations underscore the crucial role that feature selection plays in emotion recognition and highlight the competitive edge our proposed approach offers.
## 4 Simulation Results
### System Performance with Ideal Channel Coefficients
A wrong decision is very probable for them. Similar conclusions can be drawn based on Figure 7, in which the plots for the values of \(g_{2}\) between 0.5 and 0.2 are drawn. The worst performance for both types of PNC algorithm occurs for \(g_{2}=0.2\), however, the proposed PNC algorithm is about 2 dB better than the reference one. One can also notice that for \(g_{2}=0.5\), the difference in performance is much more significant and is equal to about 5.5 dB. The general conclusion is that if any of the analyzed PNC receivers is applied, aiming to receive the same receive power of the signals received from both terminals is not the best strategy. The best performance is achieved when, for \(g_{1}=1\), \(g_{2}=0.5\), which is equivalent to a 6 dB difference in power. Figure 6: BER on the output of the receiver of terminal A (RxA) and terminal B (RxB) for several values of the weighting coefficient \(g_{2}\) after two-way data relaying (Prop.—the proposed PNC algorithm, Ref.—the reference PNC algorithm).
## 5 Discussion
### Limits and Future Works  
An effective and efficient approach was proposed in this study for aquaculture area mapping over large areas; however, some issues should be further investigated. Firstly, some narrow rivers adjacent to aquaculture ponds were still misclassified. Thus, more accurate classifier and post-processing methods are still worth investigating, and the combination of post-processing and river vector boundaries may be a potential solution. Secondly, the aquaculture areas were roughly classified into two types in this study: aquaculture ponds and matriculture areas, and more specific aquaculture types are needed to be investigated. Finally, only one global threshold was used to extract waterbodies (containing aquaculture areas); it was not always optimal for different and complex image scenes, and thus a locally adaptive thresholding approach is a potential solution to improve the segmentation of the water surface. Only the aquaculture maps in 2020 were obtained in this study, and they are not sufficient for more potential applications. With historic earth observation images, it is necessary to analyze the long-time spatial-temporal changes of aquaculture areas and their impacts on economy and ecological systems and further to provide supports for the sustainable developments of the study area.
# Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
###### Abstract  
The Sentinel-1 constellation provides an effective new radar instrument with a short revisit time of six days for the monitoring of intensive mining surface deformations. Our goal is to investigate in detail and to bring new comprehension of the mine life cycle. The dynamics of mining, especially in the case of horizontally evolving longwall technology, exhibit rapid surface changes. We use the classical approach of differential radar interferometry (DInSAR) with short temporal baselines (six days), which results in deformation maps with a low decorrelation between the satellite images. For the same time intervals, we compare the radar results with prediction models based on the Knothe-Budryk theory for mining subsidence. The validation of the results with ground levelling measurements reveals a high level of resemblance of the DInSAR subsidence maps (\(-0.04\) m bias with respect to the levelling). On the other hand, aside from the explicable exaggeration, the location of the subsidence trough needs improvement in the forecasted deformations (\(0.2\) km shift in location, a deformation velocity four times higher than in DInSAR). In addition, a time lag between DInSAR (compatible with extraction) and prediction is revealed. The model improvement can be achieved by including the DInSAR results in the elaboration of the model parameters.  
m 2019111745 [1]1 Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, 50-375 Wroclaw, Poland; [EMAIL_ADDRESS] (A.B.); [EMAIL_ADDRESS] (K.S.); [EMAIL_ADDRESS] (W.R.)  
m 201911745 [1]1 Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, 50-375 Wroclaw, Poland; [EMAIL_ADDRESS] (A.B.); [EMAIL_ADDRESS] (K.S.); [EMAIL_ADDRESS] (W.R.)
# _Arthrospira platensis_ as a Feasible Feedstock for Bioethanol Production
## 2 Growth Requirements for Biomass Production
_Spirulina_, like many other algae grown photoautotrophically, depends on light as its main energy source. [PERSON] [34] was the first to study the response of _Arthrospira maxima_ to light and concluded that growth rate reached a maximum when cultures were grown under a light irradiance between 340-400 umol photon m\({}^{2}\) s\({}^{-1}\). However, the study of [PERSON] et al. [44] showed that growth of _A. fusiformis_ strain M2 became light-saturated in the range of 150-200 m mol photon m\({}^{2}\) s\({}^{-1}\). This value for irradiance is about one order of magnitude less than recorded outdoors in summer days (1850-2000 m mol photon m\({}^{2}\) s\({}^{-1}\)). [PERSON] et al. [45] observed that the maximum biomass production and biomass productivity of _Arthrospira platensis_ increased with increasing light intensity from 100 to 700 umol photons m\({}^{-2}\) s\({}^{-1}\) while they remained nearly unchanged when the light intensity was higher than 900 umol photons m\({}^{-2}\) s\({}^{-1}\), which may be related to excessive illumination that would inhibit the biomass production and CO\({}_{2}\) fixation efficiency [46]. Indeed, photoinhibition is defined as a loss of photosynthetic capacity due to damage caused by photon flux densities (PFD) in excess of that required to saturate photosynthesis. The phenomenon of photoinhibition in laboratory _Arthrospira_ cultures was first studied by [PERSON] [47], who observed a reduction in the photosynthetic activity when cells were exposed to high light. However, different strains may differ in their sensitivity to the light stress [8]. This difference could be due to a different rate of turnover of a specific protein, D1, which is part of PS II. The different response of strains to a photoinhibitory stress may thus have a genotypic origin, but can also arise from growth conditions. Indeed, cultures grown at high light intensity exhibit a higher resistance to photoinhibition, showing that an adaptation mechanism exists, probably explaining why outdoor cultivated strains can resist many intense irradiances. Finally, regarding nitrogen source, _Arthrospira_ strains can use different ones, even if the most widely used are nitrates. Nitrates enter the cells by a specific transporter (NRT type), are reduced into nitrites by nitrate reductase, and are further reduced into ammonium by nitrite reductase [48]. Ammonium assimilation appears to be more energetically favorable as there is no need for reduction steps, and is then the preferred source at low concentration. However, at high pH, ammonium is transformed into ammoniaic, which has been demonstrated to be toxic for the cells [49; 50], with a toxicity degree depending on the amount of ammonium and the pH of the medium [51; 52] and the cellular target being photosynthetic [53; 54]. In some studies, urea has also been shown to be a possible nitrogen source for _Arthrospira_ species [55].
## 3 Overall System  
This paper presents a rebar intersection recognition and planning system based on autonomous environmental sensing to support automated rebar tying operations. The system comprises three main modules: visual recognition, path planning, and coordinate transformation. The visual recognition module uses computer vision techniques to capture and process real-time image data from the construction site, accurately identifying the locations of rebar intersections. Based on the detected points, the path planning module applies optimization algorithms to generate an efficient traversal sequence, ensuring safe and smooth operation. The coordinate transformation module converts the image coordinates from the recognition system into spatial coordinates required for robotic manipulation, enabling precise localization and actuation. Through the integrated operation of these modules, the system autonomously perceives and adapts to complex construction environments, providing reliable position and path information for robotic tying tasks. This improves operational accuracy and efficiency while reducing labor intensity in reinforced concrete construction. Figure 1 shows the overall architecture of the system.  
Figure 1: System framework for rebar intersection detection and path planning.
## 2 Structure of Equilibrium Optimizer (EO)
### Concentration Updating  
Concentration updating serves as the core mechanism of EO to effectively search optimization space. Under the guidance of the equilibrium candidates, the particle concentration in EO is updated based on the mass balance equation, which is expressed as  
\[\begin{split}&\mathbf{P}_{i}(g+1)=\mathbf{P}_{\text{eq}}(g)+\big{(}\mathbf{P}_ {i}(g)-\mathbf{P}_{\text{eq}}(g)\big{)}\mathbf{H}_{i}(g)+(1-\mathbf{H}_{i}(g))\frac{\mathbf{F}_ {i}(g)}{\mathbf{\xi}_{i}(g)\mathbf{V}},\end{split} \tag{3}\]  
where \(g\) indicates the current iteration number; \(\mathbf{V}\) signifies a unit, which is designated as \(1\); \(\mathbf{\xi}_{i}(g)\) signifies a turnover rate and is generated by a random vector within the scope of \(0\) to \(1\); and \(\mathbf{P}_{i}(g)\) and \(\mathbf{P}_{i}(g+1)\), respectively, denote the \(i\)-th particle concentration before and after updating in the \(g\)-th iteration.  
\(\mathbf{H}_{i}(g)\) is an important term that aids in balancing the exploration performance and exploitation performance of EO, and it is calculated by  
\[\begin{split}&\mathbf{H}_{i}(g)=\eta_{1}\,\text{sign}(\mathbf{r}_{2}-0.5) \Big{(}e^{-\xi_{i}(g)t(g)}-1\Big{)},\end{split} \tag{4}\]  
where \(\eta_{1}\) and \(\eta_{2}\) are two constants utilized to, respectively, modulate exploration performance and exploitation performance, and they are designated as \(2\) and \(1\). \(g_{\text{max}}\) representsthe maximum iteration number for the whole optimization process and \(r_{2}\) signifies a random vector within the scope of 0 to 1; by referring to \(r_{2}\), \(\text{sign}(r_{2}-0.5)\) regulates the direction of exploratory and exploitative operation.  
\(F_{i}(g)\) denotes the formation rate, which aids in boosting the exploitation capability, and it is calculated by  
\[\mathbf{F}_{i}(g)=\mathbf{F}_{i,0}(g)\mathbf{H}_{i}(g), \tag{7}\] \[\mathbf{F}_{i,0}(g)=\mathbf{F}\mathbf{C}_{i}(g)\big{(}\mathbf{P} _{\alpha q}(g)-\zeta_{i}(g)\mathbf{P}_{i}(g)\big{)},\] (8) \[\mathbf{F}\mathbf{C}_{i}(g)=\left\{\begin{array}{cc}0.5r_{3},&r_ {4}\geq FP\\ 0,&r_{4}<FP\end{array}\right., \tag{9}\]  
where \(FP\) is fixed to 0.5, which denotes the formation probability; \(r_{3}\) and \(r_{4}\) indicate two random numbers ranging within the interval \([0,1]\); and \(\mathbf{F}\mathbf{C}_{i}(g)\) represents the formation rate control parameter, which is adjusted by \(FP\).
# Safety Analysis of Buried Corrosion-Defect Gas Pipeline in Ceramic Soil Layer Under Blasting Action
## 4 Numerical Simulation of Corroded Defective Gas Pipelines  
### Numerical Model Establishment  
According to the actual field test, a 1:1 field modeling analysis of the pipeline was conducted. The dynamic response characteristics of the different pipelines were investigated.  
The model size is 40 m \(\times\) 10 m \(\times\) 20 m, and the model units adopt eight-node SOLID164 entity units, in the form cm-g-\(\mu\). A Euler grid was used by trial and error to study the influence of different grid sizes on the numerical simulation parameters. The pipeline mesh was refined using the minimum mesh method. Different contact surfaces adopt the contact mode. In addition, both ends of the pipeline and the surface of the model adopt the free surface mode, i.e., there is no reflection boundary condition. The numerical model is shown in Figure 5 below. The defect diagram of the pipeline is shown in Figure 6.  
In the model, the explosive material used is exactly the same as the #2 rock explosive actually applied at the experiment site. The high explosive material model (MAT_HIGH_EXPLOSIVE_BURN) built into the software was used to build the explosive model. The detailed parameters of the explosives are shown in Table 2. Samples were taken according to the site, and mechanical tests were carried out in the laboratory. The mechanical parameters of different materials were then studied, as shown in Table 3. The linear elastic material model in LS-DYNA was used to simulate the stress-strain behavior of rock materials and cast-iron gas pipelines.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Density** & \(\mathbf{A}\) & \(\mathbf{B}\) & \(\mathbf{R_{1}}\) & \(\mathbf{R_{2}}\) & **Omega** & \(\mathbf{E_{0}}\) & \(\mathbf{V}\) \\
**(g-cm\({}^{3}\))** & _/_(GPa)_ & _/_(GPa)_ & \(\mathbf{R_{1}}\) & \(\mathbf{R_{2}}\) & **/(GPa)** & _/_(GPa)_ & _/_(cm\({}^{3}\))_ \\ \hline
1.25 & 214 & 18.2 & 4.2 & 0.9 & 0.15 & 4.19 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of detonation products.  
Figure 5: Schematic diagram of numerical model.  
Figure 6: Defect diagram of the pipeline.  
In order to accurately simulate the pressure, volume, and energy characteristics of explosive in the process of explosion, the equation of state (EOS_JWL) of the explosive is developed. The JWL equation can describe the relationship between the detonation pressure of the explosive and its relative volume and internal energy. The specific expression is shown in Equation (14), thus realizing the real simulation of the explosive process.  
\[p=A\bigg{(}1-\frac{\omega}{R_{1}V}\bigg{)}e^{-R_{1}V}+B\bigg{(}1-\frac{\omega} {R_{1}V}\bigg{)}e^{-R_{1}V}+\frac{\omega E_{0}}{V} \tag{14}\]  
In the above formula, \(p\) is the pressure of the explosive product; \(V\) is the relative volume of the explosive product; \(R_{1}\), \(R_{2}\), \(\omega\), \(A\), and \(B\) are the parameters of the explosive material; and \(E_{0}\) is the initial specific internal energy. The relevant parameters of explosive detonation products are shown in Table 2.
## 1 Introduction
This approach converts the disturbances into chance constraints on the velocity. In [23], [PERSON] et al. designed a tight bound on the collision probability approximation and presented a nonlinear model predictive control method to obtain trajectories of each robot. Considering the above factors, a model predictive priority contouring control method is proposed. First, the reference paths are generated by the ICBS planner. The low-level planner of the CBS planner is replaced by the hybrid A* planner. The reference paths are utilized as the initial guess of model predictive priority contouring control. Second, double-layer corridors are devised to provide robust safety guarantees. The static-layer corridors are generated based on reference paths and the dynamic-layer corridors are generated based on relative positions and velocities of robots. The double-layer corridors are applied as safety constraints of model predictive priority contouring control. Third, a prioritization mechanism is proposed to improve the computational efficiency of model predictive priority contouring control. The key contributions of this paper are as follows. * The construction method of double-layer corridors is devised, and the chance constraints introduced by disturbances are transformed into linear deterministic safety constraints. * Model predictive priority contouring control with the double-layer corridors is proposed to generate multi-robot trajectories, which ensures security while greatly improving computational efficiency. * Extensive evaluation of the method through a great number of simulations. The rest of the paper is organized as follows. In Section 2, the problem of multi-robot robust motion planning is stated. In Section 3, double-layer corridors are devised. InSection 4, the model predictive priority contouring control with double-layer corridors is proposed. In Section 5, simulations are present. Finally, Section 6 concludes our work.
## 3 Results and Discussion
### Response Surface Modelling and Optimization
#### 3.6.2 Cod
Graphically (Figure 6b), it was demonstrated that the response surface showed an arc with the optimal region for turbidity at the higher levels of A (catalyst load), ranging from 0.48 g/L to 0.54 g/L, whilst the COD removal remained constant along the varying reaction time and therefore did not have as significant an effect as the catalyst load. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of** & **Degree of** & **Mean** & & & \\
**Squares** & **Freedom** & **Square** & & & & \\ \hline Model & 2356.51 & 3 & 785.50 & 6.85 & 0.0106 & significant \\ A-Catalyst load & 1222.08 & 1 & 1222.08 & 10.66 & 0.0098 & \\ B-Reaction time & 144.5 & 1 & 14.45 & 0.1260 & 0.7308 & \\ A\({}^{2}\) & 1119.98 & 1 & 1119.98 & 9.77 & 0.0122 & \\ Residual & 1032.12 & 9 & 114.68 & & & \\ Lack of Fit & 474.44 & 5 & 94.89 & 0.6806 & 0.6632 &
\begin{tabular}{c} not \\ significant \\ \end{tabular} \\ Pure Error & 557.69 & 4 & 139.42 & & & \\ Cor Total & 3388.63 & 12 & & & \\ \hline R\({}^{2}\) & Adjusted R\({}^{2}\) & C.V.\% & Predicted & Adeq. Pr & Mean & SD \\
0.6954 & 0.5939 & 39.25 & R\({}^{2}\) 0.4378 & 6.0593 & 27.28 & 10.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: ANOVA for quadratic model. Figure 6: (a) Actual vs. predicted values of COD removal; (b) 3D surface plot of COD removal. \[\text{COD}=35.88+14.27A+1.55B-18.62A^{2} \tag{4}\]