esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
For the cases of the fringe patterns with only 180\({}^{\circ}\) phase difference, the reconstructed signals, \(s_{k1}\), and \(s_{k2}\) (from the signal \(s_{k1}\)) are generated by using Equation (3) with \(\varphi_{0}=0^{\text{o}}\) and \(\varphi_{0}=\pi\), respectively;
10. The correlation coefficients (CCs) between the original and reconstructed signals, \(s_{k1}\), and \(s_{k2}\), are denoted as \(\text{cor}_{k1}\) and \(\text{cor}_{k2}\) for all the \(k_{f}\) reconstructed fringe patterns, respectively. The CC value \(r\) between the two 2-D fringe patterns \(X(i,j)\) and \(Y(i,j)\) of size \(N\) by \(N\) is defined in Equation (12), which is used to measure the similarity between the reconstructed and original signals. \[r=\frac{\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\left[X(i,j)-\overline{X} \right][Y(i,j)-\overline{Y}]}{\sqrt{\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N} \left[X(i,j)-\overline{X}\right]^{2}}\sqrt{\sum\limits_{i=1}^{N}\sum\limits_{ j=1}^{N}\left[Y(i,j)-\overline{Y}\right]^{2}}}\] (12) Note that \(\overline{X}\) and \(\overline{Y}\) represent the average values of \(X(i,j)\) and \(Y(i,j)\), respectively. 11. By comparing the CC values of \(\text{cor}_{k1}\) and \(\text{cor}_{k2}\), we can determine the constant phase shift \(\varphi_{0}\) denoted in Equation (3). That is, we select the reconstructed signal that has a larger CC value than the other one;
12. Finally, all the \(k_{f}\) separated fringe patterns can be solely reconstructed and the accuracy of each reconstructed fringe pattern can be evaluated by using the CC values.
## 5 Conclusions  
* DISH-IVD, in contrast to IVD obtained from trauma, showed an up-regulation of _EGR2_ and _IL-6_, and _IGF-1_ tended to be up-regulated.
* Dysregulation of IGF-1 has been proposed for DISH-patients to be a serum-specific marker, and our data from IVD tissue pointed in this direction [4].
* _GDF5_ and _GDF6_ tended to be down-regulated in DISH-IVD compared to trauma-IVD.
* The observed differences in gene expression and protein differences could also be a result of factors other than DISH such as age and gender bias [58].  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/app11094072/s1](https://www.mdpi.com/article/10.3390/app11094072/s1)]([https://www.mdpi.com/article/10.3390/app11094072/s1](https://www.mdpi.com/article/10.3390/app11094072/s1)): Figure S1: TGF8 pathway transcriptome analysis of three donors with age- and disc level matched controls.  
**Author Contributions:** [PERSON] and [PERSON] were responsible for the conception and design of the study. [PERSON] performed the experiments, data analysis and prepared the figures. [PERSON] drafted the manuscript; [PERSON] did extensive editing and prepared all of the figures; [PERSON]-L. and K.A.C.O. reviewed and edited the manuscript; [PERSON] and L.M.B. provided donor materials and clinical input and contributed to editing. All authors participated in the interpretation of the findings and approved the final version of the manuscript. B.G. and C.E.A. provided funding. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported from a start-up grant from the Center for Applied Biotechnology and Molecular Medicine (CABMM) to C.E.A. and B.G, and by funds from the Marie Sklodowska Curie International Training Network (ITN) \"discall\" ([[https://cordis.europa.eu/project/id/955735](https://cordis.europa.eu/project/id/955735)]([https://cordis.europa.eu/project/id/955735](https://cordis.europa.eu/project/id/955735)), accessed on 28 April 2021). Further funds were received from the clinical trials unit of Bern University Hospital and by a Eurospine Task Force Research grant #2019_22.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** All primary human cells presented in this article were obtained with written consent from the donors. The study was conducted in accordance with the Declaration of Helsinki, and the Ethics Committee approved the protocol of the Canton of Bern (Ref. 2019-00097).  
**Data Availability Statement:** All data presented in this manuscript can be obtained upon request from the corresponding author.  
**Acknowledgments:** We thank [PERSON], [PERSON], and [PERSON] for laboratory assistance. The microscopes were provided by the microscope core facility of the University of Bern (www.mic.unibe.ch, accessed 28 April 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Conventional Diagonal Loading Methods
### Reconstruction of the Covariance Matrix  
Although Equation (16) can obtain a more accurate steering vector of the target source, the error between \(\mathbf{R}_{\text{ICLC}}\) and \(\mathbf{R}_{\text{IN}}\) cannot be completely eliminated; that is, the components of the target source may still exist in \(\mathbf{\bar{R}}_{\text{IN}}\). Therefore, a new covariance matrix is reconstructed to reduce the sensitivity of the self-cancellation of the main lobe. Using the reconstructed covariance matrix, we hoped the components of the target source would be reduced or that the components of the interference-plus-noise would be highlighted. Based on the eigenvalue decomposition of \(\mathbf{R}_{\text{XX}}\), we define the weights of the target source, interference sources and noise as \(\rho_{\text{target}}\), \(\rho_{\text{interference}}\) and \(\rho_{\text{noise}}\), respectively, as shown in Equation (17). Moreover, based on the spatial sparsity of the sound sources, the uncertain sets of sound sources were used to reconstruct the covariance matrix. Hence, the weights of the entire angular space can be coherently expressed as (17):  
\[\begin{cases}\rho_{\text{target}}=\lambda_{M}\\ \rho_{\text{interference}}=\lambda_{1}\\ \rho_{\text{noise}}=\frac{1}{M-P}\sum\limits_{i=P+1}^{M}\lambda_{i}\end{cases} \Rightarrow\rho(\vartheta)=\begin{cases}\rho_{\text{target}},\,\vartheta\in \Phi_{\text{T}}\\ \rho_{\text{interference}},\,\vartheta\in\overset{\mathbb{G}}{\underset{g=1}{ \cup}}\Phi_{1}\\ \rho_{\text{noise}},\text{otherwise}\end{cases} \tag{17}\]  
where \(\vartheta\) is the angle varying from \(0^{\circ}\) to \(180^{\circ}\) and \(\Phi_{\text{I}}\) is the uncertain set of the interference sources.  
Thus, the reconstructed covariance matrix \(\mathbf{R}_{\text{rec}}\)\(\in\)\(\mathbb{C}^{M\times M}\) can be calculated by the steered spatial power as follows:  
\[\mathbf{R}_{\text{rec}}=\mathbf{R}_{\text{IGLC}}+\int_{0}^{180}\rho(\vartheta )\mathbf{a}(\vartheta)\mathbf{a}^{H}(\vartheta)d\vartheta \tag{18}\]  
where \(\mathbf{a}(\vartheta)\)\(\in\)\(\mathbb{C}^{M\times 1}\) is the steering vector related to angle \(\vartheta\).  
Similarly, in order to reduce the impact caused by the modulus of \(\mathbf{R}_{\text{rec}}\), Equation (18) is also normalized as follows:  
\[\mathbf{\overline{R}}_{\text{rec}}=\frac{[\mathbf{R}_{\text{IGLC}}]}{[ \mathbf{R}_{\text{rec}}]}\mathbf{R}_{\text{rec}} \tag{19}\]  
where the symbol \"\(\lceil\;\rceil\)\" indicates the determinant operation.
###### Abstract  
For decision-making and governance, smart cities depend on tracking data collected via a substantial percentage of wireless sensing nodes. However, several limitations affect Wireless Sensor Network (WSN)-based Internet of Things (IoT) services, such as low battery life, recurrent connectivity problems due to multi-hop connections, and a limited channel capacity. Furthermore, in many systems, clustering and routing are handled independently, which prevents the adaptation of effective strategies for optimal energy usage and prolonged network lifespan. This research gathers data from heterogeneous IoT nodes linked via WSN and distributed across a smart infrastructure. There are two interrelated problems to be addressed with respect to energy efficiency computations: clustering and routing. We provide a new clustering strategy through which efficient routing of critical and regular data is handled. As a result, both clustering and routing have been significantly strengthened, which balances the communication load across different sectors of the smart infrastructure network. Minkowski distance and ranking strategy are used for routing and selecting cluster heads, respectively. Deterministic distributed-time division multiple access (DD-TDMA) scheduling is employed to balance the communication load across the network. The experimental results show that the proposed work outperforms some of the popular cluster-based routing strategies.  
s _et al._ Article
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 3 Results
### Effect of Initial pH of Fermentation Medium on Cellulase and Xylanase Production  
The result of investigation carried out in this study on effect of initial pH on cellulase and xylanase production by _Streptomyces albidoflavus_ strain SAMRC-UFH5 is depicted in Figure 2a,b. Xylanase production by the _Streptomyces_ strain was over a wide pH range but optimal at pH 8, with an activity of 0.21 \(\pm\) 0.01 U/mL and the least being 0.035 \(\pm\) 0.001 U/mL at pH 3. While there was statistical difference between the pH range (\(p\) < 0.05), none was observed between pH 5, 6, 9 and 10, (Figure 2a). On the other hand, cellulase was produced optimally at pH 6 with an activity of 0.028 \(\pm\) 0.001 U/mL and only a slight decline, with no significant difference between production, at pH 8-10; while the least production was observed at pH 3 (Figure 2b).  
Figure 1: Unrooted phylogenetic tree construct showing the relationship of _S. albidoflavus_ strain SAMRS-UFH5 (bold face) with selected actinomycetes in GenBank database. The Bootstrap values of 1000 replicates are shown next to the branches. The scale bar indicates the number of substitutions per site.
# Optimized-Based Fault-Tolerant Control of an Electro-Hydraulic System with Disturbance Rejection
## 1 Introduction
Moreover, a reinforcement learning (RL) control strategy for a flexible two-link manipulator to enable vibration suppression was developed in Ref. [33], while retaining trajectory tracking. Although some published papers applied the RL for control issues, the achievement of FTC to solve tracking control problems using powerful learning algorithms and tackling both disturbance and actuator fault for the EHA systems has not been investigated. Therefore, the development of the FTC scheme with optimal performance for related EHA applications subject to actuator faults and disturbances motivates this paper. Through the above analysis, it is revealed that most EHA systems have the requirement of optimal performance, while simultaneously suffering from disturbances/uncertainties and the internal leakage fault, which need to be addressed. Inspired by the abovementioned reasons, the proposed FTC scheme in this article for the EHA is investigated by using a cascade control technique, including the two control loops (position and force controls) and two DOs that warrant the system stability and the optimal control performance subject to the above disadvantages. The main contributions of this paper can be summarized in the following. 1. Compared with Refs. [34; 35], this work firstly investigates the FTC problem for the EHA system to simultaneously solve the lumped disturbances, bias fault, so that the force control of the EHA system is optimized. 2. To effectively attenuate the influences of the disturbances and the fault in the EHA system, the combination of DOs, a nonsingular terminal sliding-mode (NTSMC) scheme for the position tracking control loop, and an optimized force control method is developed. 3. The system stability and asymptotic tracking error convergence is warranted by Lyapunov theory, and the effectiveness and feasibility of the suggested methodology are verified based on the simulation results. The rest of this article is arranged as follows: In Section 2, the mathematical model with the actuator fault model of the EHA is described. The proposed cascade FTC comprises of the two loops (position and force controls) and two DOs, which is designed in Section 3. Section 4 presents the numerical simulation and comparison studies, respectively. Finally, the brief conclusions and future works are drawn in Section 5.
## 2 Principle  
### Phased Array Ultrasonic Sector Scan Imaging  
A schematic diagram of the beam forming process for phased array sector scanning imaging is shown in Figure 1. Assume that the linear array consists of \(N\) elements, whose index number is \(n(1\leq n\leq N)\), and the center distance between the adjacent two elements is \(d_{pitch}\). Figure 1a shows that the coordinate system \(xOz\) is established with the array center as the coordinate origin. The array element arrangement direction and screw depth direction are the positive directions of the \(x-\)axis and \(z-\)axis, respectively. Let there be a total of \(M\) emission angles and keep the focal length \(D_{F}\) constant. The \(k\)th emission angle focuses on point \(F\) at the sound speed \(c_{l}\), and the deflection angle between point \(F\) and the \(z-\)axis is \(\mid\theta_{k}\mid\). Then, the delay time \(\tau_{n}\) of the \(n\)th array element is given by:  
\[\tau_{n}=\frac{D_{F}}{c_{l}}\left[1-\sqrt{1+\left(\frac{nd_{pitch}}{D_{F}} \right)^{2}-2\frac{nd_{pitch}}{D_{F}}\sin\lvert\theta_{k}\rvert}\right] \tag{1}\]  
In the unit of emission angle, the RF signal is stored in the form of a \(2-\)D data matrix \(N_{f}\times N\), where \(N_{f}\) represents the number of samples, as shown in Figure 1b. After coordinate transformation, the ultrasonic sector scanning imaging shown in Figure 1d can be obtained by adding along the direction of the array elements. Then, the amplitude of the RF signal at the \(k\)th emission angle \(\theta_{k}\) can be expressed as:  
\[I_{\theta_{k}}=\sum_{n=1}^{N}S_{\tau n} \tag{2}\]  
where \(S_{\tau n}\) is the delayed data received by the \(n\)th array element at the \(k\)th emission angle \(\theta_{k}\).
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 3 Method
### Loss Function  
The loss function is based on a summation of the commonly used dice loss [31] and cross-entropy loss [32]. Dice loss improves the model's sensitivity to small targets, while CE loss ensures the model's ability to classify all pixels. With this complementarity. It is defined as follows:  
\[Loss=w_{1}(1-\frac{2\sum_{i=1}^{N}p_{i}g_{i}}{\sum_{i=1}^{N}p_{i}^{2}+\sum_{i=1 }^{N}g_{i}^{2}})+w_{2}(-\frac{1}{N}\sum_{i=1}^{N}(g_{i}log(p_{i})+(1-g_{i})log (1-p_{i}))) \tag{4}\]  
where \(p_{i}\) is the predicted probability of the \(i\)th pixel, \(g_{i}\) is the true label (0 or 1) of the \(i\)th pixel, \(N\) is the total number of pixels in the image, \(\alpha\) and \(\beta\) are weighting coefficients used to balance the contribution of the two types of losses to the total loss.
# Shape Memory-Enhanced Electrical Self-Healing of Stretchable Electrodes
## 3 Results and Discussion  
### Fabricationof the Healable Stretchable Electrodes  
The traditional shape memory-assisted self-healing typically involved a two-step procedure, i.e., exposure upon the cracks in the original state and thermal-triggered self-healing [3]. Except for the crack areas, there were not visible macro-scale shape changes during the healing process. Unlike the traditional procedure, the explored shape memory-enhanced self-healing contained three steps, which are illustrated in Figure 1a. The samples were firstly pre-stretched and fixed into the temporary shapes via a typical shape memory thermo-mechanical programming. Afterwards, scratch in the direction perpendicular to the pre-stretching destroyed the conductive networks. Thirdly, heating was applied to trigger the heal-ability, meanwhile the shape recovery was simultaneously occurred. The pre-stretching and creation of the surface scratches meant that changes in the shape and length of the electrode caused resistance variation. Furthermore, the release of the strain energy along with the shape recovery dramatically improved the recovery of the conductivity. Compared with the conductions in the cracked and thermally-triggered recovered states, the electrodes exhibited better reconstruction of the conductive networks as well as enhanced heal-ability of the conduction. Figure 1b shows SEM image of the resultant electrodes in bi-layer structure. The conductive AgNPs network was embedded into the healable matrix, constructing pathways for the electrons transportation. On the other hand, the polymer matrix exhibited well compatibility due to the PCL segments in the compositions. The resistance of the electrodes significantly decreased as the increase of the AgNPs loads, the plot of which is shown in Figure 1c.The weight amounts of the AgNPs utilized for one time ranged 0.8 and 4 mg. Correspondingly, the area densities of the AgNPs were calculated ranged 0.43 and 2.12 ug/mm\({}^{2}\).According to the percolating theory, the conduction approached to constant once the content of the conductive nano-fillers reached threshold value. The threshold value for the AgNPs network was determined to be around 2.8 mg-(1.49 ug/mm\({}^{2}\)). Thus, the samples containing 3.0 mg of the AgNPs were utilized in this study. The conduction could be seriously damaged by the cracks in the conductive surface layer. Figure 1d shows a representative scratch in the width of dozens of micrometers. The gap made the AgNPs separated in the local area. As a result, the conductive network was temporarily damaged.  
Figure 1: (**a**) Schematic illustration of the shape memory-enhanced self-healable electrode; (**b**) Scanning Electron Microscope (SEM) image of the electrode cross section; (**c**) The plot of the sheet resistance against the AgNPs loads. (**d**) SEM image of the surface morphology with micro-scale scratch.
# Fatigue Crack Detection Based on Semantic Segmentation Using DeepLabV3+ for Steel Girder Bridges
## 3 Proposed Fatigue Crack Detection Model Based on Sematic Segmentation  
### Overview of the Proposed Model  
The semantic segmentation model for fatigue crack detection proposed in this paper is shown in Figure 2. First, based on the ResNet50 network [10], the image sample dataset is used for training the classification network ResNet50_crack. Compared with ResNet50, ResNet50_crack captures crack features and facilitates semantic segmentation network training in the next step. Therefore, the DeepLabv3+ semantic segmentation network, with ResNet50_crack as the backbone network, can treat crack semantic segmentation in complex backgrounds with smaller generalization errors and more accurate results. Active learning [11; 12] is employed to screen more valuable training images. The new dataset is chosen to train the semantic segmentation network DeepLabv3+, and finally, the category threshold of the model is optimized, as shown in Figure 2. The following section will describe in detail the method of dividing the dataset, the screening method for effective samples, and the training of the classification network ResNet50_crack and the semantic segmentation network DeepLabv3+.  
Figure 1: DeepLabv3+ model.
# Inverse Kinematics of Large Hydraulic Manipulator Arm Based on ASWO Optimized BP Neural Network
## 5 Simulation and Experimental Validation
### Simulation Verification
The ASWO-BP algorithm's end position error ranges are (\(-33\)-\(37\)) mm, (\(-42\)-\(40\)) mm, and (\(-60\)-\(54\)) mm, which is lower than the SWO-BP, which is the best among the other three algorithm models. The same target position (7950, 1800, 1540) mm is inversely solved by four methods respectively, and the four sets of joint values are substituted into the simulation model. As can be seen from Figure 10, there are three position points in each figure: the theoretical position obtained by the DH forward solution, the actual position after compensation for the forward solution error model, and the preset target position. The ASWO-BP inverse solution position error is (\(-12\), \(20\), \(4\)) mm, the SWO-BP inverse solution position error is (\(-18\), \(-30\), \(-29\)) mm, the PSO-BP inverse solution position error is (\(-33\), \(-37\), \(35\)) mm, and the GA-BP inverse solution position error is (\(-51\), \(-24\), \(-52\)) mm. The simulation results show that the ASWO algorithm is able to optimize the initial weights and biases more efficiently, thereby assisting the BP neural network in the learning and training process and helping the network avoid local optima and find the global optimum or a better local optimum. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **GA-BP** & **PSO-BP** & **SWO-BP** & **ASWO-BP** \\ \hline \(\Delta\theta_{1}(^{\circ})\) & \(-0.941\)-\(0.86\) & \(-0.634\)–\(0.754\) & \(-0.536\)–\(0.417\) & \(-0.305\)–\(0.368\) \\ \(\Delta\theta_{2}(^{\circ})\) & \(-1.121\)–\(0.953\) & \(-0.802\)–\(0.741\) & \(-0.382\)–\(0.459\) & \(-0.412\)–\(0.374\) \\ \(\Delta d_{3}(\text{mm})\) & \(-113.6\)–\(102.7\) & \(-90.3\)–\(92.5\) & \(-85.2\)–\(79.6\) & \(-44.3\)–\(56.5\) \\ MSE & 1.562 & 0.686 & 0.394 & 0.173 \\ \(\Delta X(\text{mm})\) & \(-86\)–\(110\) & \(-94\)–\(86\) & \(-61\)–\(49\) & \(-33\)–\(37\) \\ \(\Delta Y(\text{mm})\) & \(-139\)–\(113\) & \(-89\)–\(100\) & \(-47\)–\(65\) & \(-42\)–\(40\) \\ \(\Delta Z(\text{mm})\) & \(-155\)–\(124\) & \(-107\)–\(92\) & \(-66\)–\(73\) & \(-60\)–\(54\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation results of the four algorithmic models. Figure 10: Comparison of end positioning accuracy of four inverse solution methods for robotic arms.
## 5 Results and Discussion
### Results of Optimizing Input Channel Data  
The final experimental results show that the model performed best when Channel 1 and Channel 2 contained the answer features and Channel 3 contained the question number feature. This combination not only effectively reduced the complexity of the data but also improved the accuracy and stability of the model. Specifically, this configuration reduced interference from extraneous information while maintaining the model's efficient learning capability, leading to better training results. The results also indicate that using a zero array might lead to information loss; although it successfully reduced complexity, it also diminished model performance.  
This finding suggests that by carefully selecting the data content of the input channels, the training efficiency and performance of deep learning models can be significantly improved. Simplifying the question images to question number features and effectively combining them with answer features not only simplifies the data structure but also helps the model converge more quickly, achieving optimal prediction accuracy. Table 4 shows the results of optimizing input channel data.