esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm
## 4 Trajectory Optimization
### Sevent-Order Polynomial Optimization Based on Minimum Snap  
First, the trajectory is segmented according to the path nodes, and assuming that the path is divided into \(m\) segments in total and each segment is fitted as a polynomial curve, the expression of the \(n\)th segment path is shown in Equation (17).  
\[F_{n}(t)=p_{n,0}+p_{n,1}t+p_{n,2}t^{2}+\ldots+p_{n,k}t^{k}=\sum\limits_{i=0}^{k}p _{n,i}t^{i}\quad(k=7) \tag{17}\]  
\[F_{n}(t)=\begin{pmatrix}t^{0}&t^{1}&\ldots&t^{k}\end{pmatrix}\begin{pmatrix}p_{n,0}\\ p_{n,1}\\ \vdots\\ p_{n,k}\end{pmatrix}\quad(k=7) \tag{18}\]  
where \(p_{n,i}\) means the \(n\)th path polynomial coefficient and \(t\) means time. For the overall path, then the following expression (18) is given.  
\[F(t)=\left\{\begin{array}{lclcl}\sum\limits_{i=0}^{k}p_{1,i}t^{i}=\begin{pmatrix} t^{0}&t^{1}&\ldots&t^{k}\end{pmatrix}\begin{pmatrix}p_{1,0}&p_{1,1}&\cdots&p_{1,k} \end{pmatrix}^{T}&(T_{0}\leq t\leq T_{1};k=7)\\ \sum\limits_{i=0}^{k}p_{2,i}t^{i}=\begin{pmatrix}t^{0}&t^{1}&\ldots&t^{k} \end{pmatrix}\begin{pmatrix}p_{2,0}&p_{2,1}&\cdots&p_{2,k}\end{pmatrix}^{T}&(T_ {1}\leq t\leq T_{2};k=7)\\ \sum\limits_{i=0}^{k}p_{3,i}t^{i}=\begin{pmatrix}t^{0}&t^{1}&\ldots&t^{k} \end{pmatrix}\begin{pmatrix}p_{3,0}&p_{3,1}&\cdots&p_{3,k}\end{pmatrix}^{T}&(T_ {2}\leq t\leq T_{3};k=7)\\ \vdots\\ \sum\limits_{i=0}^{k}p_{m,i}t^{i}=\begin{pmatrix}t^{0}&t^{1}&\ldots&t^{k} \end{pmatrix}\begin{pmatrix}p_{m,0}&p_{m,1}&\cdots&p_{m,k}\end{pmatrix}^{T}&(T_ {m-1}\leq t\leq T_{m};k=7)\end{array}\right. \tag{19}\]  
where \(T_{0}T_{1}\ldots T_{m}\) denote the times of the path interruption points. From Equation (19), the position, velocity, acceleration, jerk function, and snap function at any moment \(t\) can be obtained by deriving the equation as shown below.  
\[\left\{\begin{array}{lclclcl}v_{n}(t)=\begin{pmatrix}0&1&2t&\cdots&kt^{k-1} \end{pmatrix}\begin{pmatrix}p_{n,0}&p_{n,1}&\cdots&p_{n,k}\end{pmatrix}^{T}&&(k =7)\\ a_{n}(t)=\begin{pmatrix}0&0&2&6t&\cdots&k(k-1)t^{k-2}\end{pmatrix}\begin{pmatrix} p_{n,0}&p_{n,1}&\cdots&p_{n,k}\end{pmatrix}^{T}&&(k=7)\\ jerk_{n}(t)=\left(\begin{array}{  
\[J_{m}=\min\sum\limits_{i=1}^{m}P^{T}\begin{pmatrix}Q_{1}&&\\ &\ddots&\\ &&Q_{m}\end{pmatrix}P \tag{23}\]  
The constraints for polynomial optimization are shown in Equation (24).  
\[s.t.\begin{cases}&F_{1}(T_{0})=P_{1}\\ &F_{m}(T_{m})=P_{m+1}\\ &F_{i}(^{k})(T_{i})-F_{i+1}^{(k)}(T_{i})=0\quad(i=1,2,\ldots,m-1;k=1,2,3,4)\end{cases} \tag{24}\]  
Since the polynomial optimized trajectory has the possibility of re-collision with obstacles, this paper adds a safety corridor to the overall optimized path by establishing inequality constraints. The corridor width is set to half of the grid to ensure sufficient safety distance, and the formula is shown below.  
\[P_{i}-r\leq F_{i}(T_{i-1})\leq P_{i}+r\quad(i=2,3,\ldots,m) \tag{25}\]  
where \(r\) denotes the corridor width.
## 2 Materials and Methods  
### hUC-MSC Isolation and Identification  
Umbilical cord tissue was obtained from individual mothers that had signed informed written consent, which was approved by the Institutional Review Board of National Cheng Kung University Hospital (IRB No. A-BR-104-045) and conducted in accordance with the Helsinki Declaration. Human umbilical cord mesenchymal stem cells were isolated and identified from umbilical cord tissue as previously described [16; 17]. Briefly, umbilical cord tissue was cut to small pieces ranging 1 to 2 mm\({}^{3}\), then, 2-mg/mL Collagen NB6 (SERVA) was used for digestion at 37 \({}^{\circ}\)C for 120 min while shaking. Collected tissue samples were centrifuged, and the cell pellet was resuspended in minimal essential medium \(\alpha\) (MEM\(\alpha\), Thermo Fisher Scientific, Waltham, MA, USA) containing 18% fetal bovine serum (FBS; Thermo Fisher Scientific), 4-ng/mL fibroblast growth factor basic (bFGF; Thermo Fisher Scientific) and 50-mg/mL gentamicin (Thermo Fisher Scientific) at 37 \({}^{\circ}\)C, 5% CO\({}_{2}\) with saturated humidity. To remove nonadherent cells from plastic-adherent colonies, after seven days, cultures were washed with PBS three to five times and then further cultured up to eleven days, with medium changes every 3 days. According to the cell growth state, e.g., large colony formation, cells were passaged. Finally, hUC-MSCs were maintained in MEM\(\alpha\) supplemented with 10% FBS (Thermo Fisher Scientific) and 4-ng/mL bFGF (Thermo Fisher Scientific), 5% CO\({}_{2}\) with saturated humidity, passaged every time it reached approximately 70-90% confluence. hUC-MSCs were characterized by flow cytometry to analyze the CD surface marker expressions (CD44, CD73, CD90, CD105, CD11b, CD19, CD34, CD45 and HLA-DR; BD Stemflow hMSC Analysis Kit; BD Biosciences, San Jose, CA, USA). Additionally, hUC-MSCs were also examined for their trilineage differentiation capability (osteocytes, chondrocytes and adipocytes), yielding positive results.
###### Abstract  
Owing to the massive expansion and intermittent nature of renewable power, green hydrogen production, storage, and transportation technologies with improved economic returns need to be developed. Moreover, the slowness of the dehydrogenation reaction is a primary barrier to the commercialization of liquid organic hydrogen carrier (LOHC) technology. The present study focused on increasing the speed of dehydrogenation, resulting in the proposal of a triple-loop dehydrogenation system comprising reaction, heating, and chilling loops. The reactor has a rotating cage containing a packed bed of catalyst pellets, which is designed to enhance both heat and mass transfer by helping to detach precipitated hydrogen bubbles from the catalyst surface. In addition, the centrifugal force aids in isolating the gas phase from the LOHC liquid. A dehydrogenation experiment was conducted using the reaction and chilling loops, which revealed that the average hydrogen production rate during the first hour was 52.6 LPM (liter per minute) from 26.3 L of perhydro-dibenzyl-toluene with 1.5 kg of 0.5 wt% Pt/Al2O3 catalyst. This was approximately 48% more than the value predicted with the reaction kinetics measured with a small-scale plug flow dehydrogenation reactor with less than 1.0 g of 5.0 wt% Pt/Al2O3 catalyst. The concept, construction methods, and results of the preliminary gas infiltration, flow visualization, and reactor pumping experiments are also described in this paper.  
hydrogen storage; liquid organic hydrogen carrier (LOHC); dehydrogenation; triple loop system; rotating packed bed reactor
# Automatic Modulation Recognition Using Compressive Cyclic Features
## 3 CS-based AMR  
For a CC-based AMR, the sampling rate for signal acquisition is always several times larger than the Nyquist rate to ensure the absence of the aliasing effect in the area of cyclic frequencies. For instance, to recognize a more extensive number of modulations, many studies appeal to 8 th-order CCs [8; 9]. According to the result presented before, the oversampling factor reaches up to 11 with 0.35 roll-off factor. Besides, a large amount of symbols (a large observation interval) is also necessary for better recognition performance in most cases [3]. All of the above yields a heavy computational and storage burden. Fortunately, the development of CS enlightens a way to relieve this burden. By employing the sparsity of CMs, [PERSON] and [PERSON] [11] estimated CMs from compressive measurements, then derived CC through cyclic moment to cumulant formula [9]. Because of the requirement of all the necessary \(n\)th- and lower-order CMs, it seems cumbersome.
# Response Prediction for Linear and Nonlinear Structures Based on Data-Driven Deep Learning
###### Abstract  
Dynamic analysis of structures is very important for structural design and health monitoring. Conventional numerical or experimental methods often suffer from the great challenges of analyzing the responses of linear and nonlinear structures, such as high cost, poor accuracy, and low efficiency. In this study, the recurrent neural network (RNN) and long short-term memory (LSTM) models were used to predict the responses of structures with or without nonlinear components. The time series \(k\)-means (TSkmeans) algorithm was used to divide label data into different clusters to enhance the generalization of the models. The models were trained with different cluster acceleration records and the corresponding structural responses obtained by numerical methods, and then predicted the responses of nonlinear and linear structures under different seismic waves. The results showed that the two deep learning models had a good ability to predict the time history response of a linear system. The RNN and LSTM models could roughly predict the response trend of nonlinear structures, but the RNN model could not reproduce the response details of nonlinear structures (high-frequency characteristics and peak values).  
Keywords:linear and nonlinear structures; RNN; LSTM; structural response analysis; TSkmeans algorithm +
Footnote †: journal:  
processing speed. As an effective means to resolve complex engineering problems, it has been widely used in various disciplines [5; 6; 7]. Therefore, DL can be used as a powerful forecasting tool to predict the response of complex nonlinear structural systems, thus replacing or simplifying complex numerical analysis processes, to a certain extent. It provides a new way to solve the nonlinear vibration problem in seismic engineering.
# Vehicle Interior Noise Prediction Based on Elman Neural Network
## 5 Prediction and Optimization of Noise in the Vehicle
### Prediction of Noise Value in the Vehicle  
#### 5.3.1 Experimental Design  
The thickness of the panels as determined in the side impact and NVH simulation analyses was treated as the input variable, and the maximum sound pressure level of the target point in a vehicle, the body mass, the first-order global modal of the body-in-white, and the maximum intrusion of the B-pillar P4 and P5 points were treated as the output variables. These variables were submitted to Optistruct and LS-DYNA for the simulation calculations, and the generated datasets were used for training and testing the Elman neural network algorithm. To ensure that the input variables took points uniformly in the design interval, the Hammersley method was used for sampling. A total of 200 sets of sample data were obtained and equally divided between the training and test sets.
## 1 Introduction
Reference [12] proposed a novel research scheme using density-based clustering and a backtracking strategy. In order to isolate the targets from noises, CFSFDP is employed to cluster targets by the spatial intensive distribution. Compared with several state-of-arts methods, this algorithm is more effective for dim targets with lower signal-to clutter ratio (SCR). Since this clustering algorithm has not been applied in WSNs so far, we adopt in this work in a WSN to choose cluster heads and form clusters. Our main contributions can be listed as follows:  
* We apply the CFSFDP algorithm to a WSN to choose cluster heads and form clusters. * We take nodes' residual energy into consideration in CFSFDP-E algorithm to balance the energy consumption. The original CFSFDP uses the information of local density and distance to group data points in the dataset. However, all the nodes have limited energy in a WSN. Considering this unique attribute, we should take residual energy as a third key factor in the clustering process. * Different forms of energy to get a better performance are discussed and we use the value of local density, distance and residual energy after normalization as three main variables in the clustering scheme. * Since the clustering process needs to be repeated in every round, we set a dynamic threshold for cluster head selection and it can determine an appropriate number of CHs and make every CM be clustered reasonably. * With the same simulation environment and parameters, it is proved that our proposed CFSFDP-E algorithm in WSN outperforms the classical protocols (LEACH [13], LEACH-C [14], PEGASIS [15] and SEP [16]), some improved protocols based on LEACH (ALEACH [17], C-LEACH [18] and K-LEACH [19]) and some new protocols which were proposed in the last three years (KM-LEACH [3], DBCH [20] and EESCA [21]). The rest of this paper is organized as follows: Section 2 summarizes some related clustering protocols in WSN. The theory of the CFSFDP algorithm is described in Section 3. Based on CFSFDP, Section 4 proposes the implementation of our improved CFSFDP-E algorithm in WSNs. In Section 5,we compare the performance of CFSFDP-E with those of several other protocols. Finally, we make a conclusions and suggest potential future work in Section 6.
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 5 Simulation Analysis  
Co-simulation was carried out by MATLAB/Simulink and CarSim, and the simulation condition is a double-shifted line condition. Because the roads driven by driverless ferry vehicles in the school are all asphalt pavements, a review of the data shows that both dry and wet asphalt pavements contain a pavement adhesion coefficient of 0.8. Therefore, the pavement adhesion coefficient was selected to be 0.8. Firstly, the longitudinal tracking effect of the vehicle with or without tolerance space is compared and analyzed via simulation under the condition of a constant speed of 35 km/h. Secondly, the lateral control effect of the vehicle under different speeds and different predictive time domains is analyzed. Finally, when the driverless ferry vehicle faces the unknown obstacles suddenly appearing in the front or rear half of the road under coordinated control, the effect of obstacle avoidance and trajectory tracking, the change of front wheel angle, the tracking effect of yaw angle, and the tracking error are compared with those of driverless ferry vehicle under uncoordinated control to analyze the coordinated control effect. Table 2 shows the main vehicle parameters, Table 3 shows the planning module parameter settings, and Table 4 shows the tracking controller parameter settings.
# C-SAR/02 Satellite Polarimetric Calibration and Validation Based on Active Radar Calibrators
## 5 Conclusions  
This study utilized multi-band active calibrators to calibrate and validate the polarization performance of China's C-SAR/02 satellite. To address the issue of polarization calibration matrix variation along the range direction, four ARCs and multiple CRs were deployed at different ranges. Before the satellite transit, the devices were adjusted and parameters were uploaded. Subsequently, images were successfully acquired and processed to obtain the polarization scattering matrix, from which the distortion matrix was derived. Among these, the four active calibrators formed different combinations for calibration calculations. We conducted an in-depth analysis of the observation matrices of these four devices, solved for the distortion matrices of different device combinations, and performed accuracy validation using CRs within the experimental area. The results indicate that incorporating active calibrators deployed at different ranges for calibration leads to higher accuracy in polarization isolation and amplitude imbalance metrics compared with using devices concentrated at one point.  
The accuracy of the experimental results was examined using CRs at different range directions, and the results showed that the corrected polarization characteristics were closer to the ideal state when the CRs for the validation were closer to the calibration equipment; therefore, the influence of this factor needs to be considered in the subsequent calibration experiments and further researched with respect to eliminating this influence.  
The experimental results demonstrate that our ARC-based method is applicable not only to the C-SAR/02 satellite but also to other SAR satellites with quad-polarization capability. The multi-band active calibrator demonstrated excellent performance, providing reliable technical support for regular performance testing and authenticity validation of satellites. Furthermore, the experimental design employing multiple sets of equipment effectively enhances the precision of the experiments.  
[PERSON] wrote the manuscript and designed the comparative experiments; [PERSON] supervised the study and revised the manuscript; [PERSON] and [PERSON] assisted [PERSON] in designing the article structure and conducting experiments; [PERSON] assisted [PERSON] in obtaining experimental data. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2022 YFC2807000/2022 YFC2807003, and the Common Application Support Platform for Land Observation Satellites of China's Civil Space Infrastructure (CASPLOS_CCSI).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 5 Simulations Results and Discussions
### Case Study 2: Home Is Equipped with DGs
#### 5.2.3 Carbon Emission  
The carbon emissions of the unscheduled and scheduled appliances using the heuristic algorithms are shown in Figure 11. The hourly peak emissions were recorded by the unscheduled case, which is 144 pounds at the 18 th time slot, and the combined 24 h carbon emissions are 2372 pounds. The GA, PSO, BFA, ACO, WDO, GPSO, GBFA, and GACO gave peak emissions of 117 pounds at the 19 th hour slot, 132 pounds at the 19 th hour slot, 14  
127 pounds at time slot 19, 100 pounds, 130 pounds at time slot 19, 104 pounds at time slot 19 and 107 pounds at time slot 19, respectively. Our proposed GWDO-based ECS gave the lowest carbon emissions peak among these techniques, which is 88 pounds at time slot 19. Comparing the combined emissions for 24 h with unscheduled, our proposed ECS reduced carbon emissions by 33.8%. The rest of the benchmarks techniques reduced carbon emissions by 18.8%, 8.2%, 13.3%, 28.7%, 26.4%, 25%, 28.4%, and 28.1% for GA, PSO, BFA, ACO, WDO, GPSO, GBFA, and GACO, respectively.
###### Abstract  
To date, it has remained unclear whether gastrointestinal symptoms, which are frequently observed in patients with multiple sclerosis (MS), are accompanied by pathology of the enteric nervous system (ENS). Here, the neurotransmitter signature of ENS neurons and morphological alterations of interstitial cells of Cajal (ICCs) were studied in patients with MS and mice with experimental autoimmune encephalomyelitis (EAE), which is an animal model of MS. Immunohistochemistryal analysis was performed on colonic whole mounts from mice with EAE and on paraffin-embedded sections of intestinal tissue from patients with MS. Antibodies against neurotransmitters or their enzymes (including vasoactive intestinal peptide (VIP), neuronal nitric oxide synthase (nNOS), and choline acetyltransferase (ChAT)) were used in conjunction with pan-neuronal markers. In addition, the presence of anoctamin 1 (ANO1)-expressing ICCs was studied. ENS changes were observed in the myenteric plexus, but they were absent in the submucosal plexus of both EAE mice and patients with MS. There was a significant decrease in the percentage of ChAT-positive neurons in EAE mice as opposed to a trend toward an increase in patients with MS. Moreover, while ANO1 expression was decreased in EAE mice, patients with MS displayed a significant increase. Although additional studies are necessary to accomplish an in-depth characterization of ENS alterations in MS, our results imply that such alterations exist and may reveal novel insights into the pathophysiology of MS.  
ChAT; EAE; ENS; immunohistochemistry; interstitial cells of Cajal; MS; myenteric plexus; neurotransmitters +
Footnote †: journal: Applied Sciences
## 2 Related Work
### Unsupervised Learning Models for Monocular Depth Estimation Based on Stereo Images (USI Models)  
[PERSON] et al. [28] proposed an unsupervised framework for monocular depth prediction without a pre-training step or annotated depth ground truth. They used the L2 loss between the reconstructed and target images as a simple image-reconstruction loss. However, the L2 loss generated blurry images as it converged to a stable value rather than finding an exact value for each pixel. Therefore, it is not suitable for photorealistic image synthesis, which limits the accuracy improvement of the depth maps generated by their models. [PERSON] et al. [6] presented an unsupervised learning-based model training stereo image pairs of the KITTI 2015 driving dataset. They proposed a novel objective function including an image-reconstruction loss based on the L1 and SSIM [5], a disparity-smoothness loss, and specifically a left-right consistency loss. Furthermore, they extended their scope to monocular supervision by adding a network of learning consecutive video frames [1]. They also presented a minimal reprojection loss to handle the occlusion problem, a full-resolution multi-scale sampling approach to reduce visual artifacts and a simple auto-masking method to filter out pixels that did not change appearance from one frame to the next. Inspired by recent deep-learning methods for super-resolution, [PERSON] et al. [29] proposed a sub-pixel convolutional layer extension for depth super-resolution that accurately synthesized high-resolution disparities from their corresponding low-resolution features. They used a linear combination of the L1 loss andsingle-scale SSIM loss as the image-reconstruction loss. They also introduced a differentiable flip-augmentation layer to accurately fuse predictions from the image and its horizontally flipped version, which reduced the effect of left and right shadow regions generated by occlusions. [PERSON] et al. [7] used the GMSD [30], one of the high-performance conventional IQA algorithms, as the image-reconstruction loss function in a symmetric GAN [31] structure. They proved that GMSD loss effectively contributes to performance improvement. They also used a relativistic discriminator [32] to solve the problem of training instability in GANs. The advantage of these models is that the training image data can be easily acquired by binocular cameras. Therefore, it is more feasible to easily learn the models in various environments than the supervised models. On the other hand, these methods have a problem, in that artifacts are generated at occlusion boundaries due to pixels in the occlusion region that are not visible in both images [6].  
Table 1 summarizes the image-reconstruction loss used by the unsupervised learning-based studies mentioned in Sections 2.2 and 2.3, respectively. The combination of L1 loss and SSIM is the most widely used.
# Optimizing Pretrained Autonomous Driving Models Using Deep Reinforcement Learning
## 5 Experiments and Results
### Fusion Coefficient Analysis  
The evolution of the learnable fusion coefficient \(\alpha\) is a key indicator of how the policy balances prior knowledge against newly acquired representations. In our Side-Tuning setup, \(\alpha\) acts as a soft feature-selector between the frozen PPB and the EB that captures task-specific patterns. Because \(\alpha\) is optimized jointly with the actor-critic parameters, its trajectory reveals the relative importance the agent assigns to each branch as learning scales.  
To analyze the coefficient, we ran CILRL-v3 for \(10^{9}\) environment steps and observed its learning curve throughout training. Figure 9 shows the resulting curves for five independent seeds, obtained by initializing it randomly in \((0.0,1.0)\). It can be observed that all trajectories eventually stabilize and converge around 0.35 (i.e., below the 0.5 midpoint). After prolonged interaction with the environment, the policy network therefore allocates roughly two-thirds of its representational weight to the EB while retaining about one-third from the PPB. This consistent attraction around this value suggests that, for this particular fine-tuning task, the agent leans on newly learned skills without discarding the robust prior embeddings of the pretrained branch.