esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
1. The container transportation cost and vehicle waiting time cost are incorporated into the container backwards short cost, forming an optimisation model aimed at minimising both container transportation time and backwards short cost.
2. To address the slow convergence speed of the GA, a hybrid GA-GWO algorithm has been developed by integrating the GWO algorithm into the GA search process. This hybrid algorithm not only comprehensively searches the solution space, balancing global and local optimisation capabilities, but also significantly enhances convergence and stability.
3. The GA-GWO hybrid algorithm has been applied to optimise the scheduling of container transport vehicles in coal mines. This application has improved the algorithm's convergence accuracy and speed while also reducing total costs, container reversal time, and completion time of the container schedule, outperforming both the SA, GA, and GWO algorithms.  
Although the hybrid GA-GWO algorithm provides new ideas for the solution of the inverted-short scheduling problem for coal mine containers, there is still room for improvement and depth in the scheduling strategy. Due to the carbon emissions from traditional fuel trucks in the container transport process, transportation costs are high, and with the release and implementation of the \"double carbon\" policy, as well as the initial application of pure electric trucks in the surface coal mine transport, low-carbon transportation will be the hot direction of the surface coal mine transport. Compared with traditional fuel trucks, pure electric trucks improve transportation efficiency, reduce vehicle queuing time, and greatly reduce carbon emissions and transportation costs. However, it is necessary to consider the choice of charging stations for pure electric trucks, battery charging time, and re-planning the vehicle transportation path.  
Conceptualization, B.H. and Z.X.; methodology, B.H.; software, B.H.; validation, B.H., A.S. and Z.X.; formal analysis, B.H.; survey, B.H.; resources, B.H.; data management, B.H.; first draft, B.H.; writing--comments and editing, B.H.; visualisation, B.H. and A.S.; supervision, Z.X. and Y.Y.; project management, Y.Y.; financing acquisition, B.H., Z.X., A.S. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC), grant number 72361032.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to laboratory policy requirements or restrictions such as confidentiality agreements.  
The authors declare no conflicts of interest.
## 3 Results
### Relationship between the PSI and JRF during Inclined and Declined Walking  
As shown in Figure 12, the PSI in the anterior-posterior direction (APSI) was not significantly correlated with the JRF at the ankle, knee, or hip during inclined walking (r = 0.021, 0.282, and 0.238). Similarly, a non-significant correlation was found between the APSI and JRF of the ankle, knee, and hip (r = 0.076, \(-\)0.427, and \(-\)0.562, respectively) during declined walking.  
Meanwhile, as can be seen in Figure 13, the PSI in the medial-lateral direction (MLSI) was found to positively correlate with the JRF at the knee (r = 0.380, \(p\) < 0.05) but was not significantly correlated with the JRF at the ankle or hip (r = 0.186 and 0.248, respectively) during inclined walking. During declined walking, the MLSI was found to be negatively correlated with the JRF at the hip (r = \(-\)0.365, \(p\) < 0.05) but was not significantly correlated with the JRF at the ankle or knee (r = \(-\)0.073 and 0.018, respectively).  
Furthermore, the findings showed that the PSI in the proximal-distal direction (VSI) was positively correlated with the JRF at the ankle, knee, and hip (r = 0.734, 0.769, and 0.720, respectively; \(p\) < 0.05) during inclined walking. Similarly, there was a positive correlation between the VSI and JRF at the ankle, knee, and hip (r = 0.931, 0.926, and 0.936, respectively; \(p\) < 0.05) during declined walking (Figure 14).  
Figure 11: Relationship between the RCOF (peak GRF) and the JRF of the lower limb during slope walking.  
Figure 12: Relationship between the APSI and JRF of the lower limb during slope walking. * Significant different at \(p<0.05\).  
Figure 13: Relationship between the MLSI and JRF of the lower limb during slope walking. * Significant different at \(p<0.05\).
## 3 Results  
### Chemistry  
For the purpose of this research, we synthesized 15 new hydrazide-hydrazones of 4-methyl-1,2,3-thiadiazole-5-carboxylic acid (**2**-**16**) (Scheme 1). Novel compounds (**2**-**16**) were obtained in good yields (57-98%). Synthesized substances are stable solids and can be dissolved in DMSO at room temperature. The successful synthesis of novel hydrazide-hydrazones was confirmed by elemental analysis and the FT-IR, \({}^{1}\)H NMR, and \({}^{13}\)C NMR spectra.  
Novel derivatives of 4-methyl-1,2,3-thiadiazole-5-carboxylic acid (**2**-**16**) on the \({}^{1}\)H NMR spectra possessed the following characteristic signals: The singlet signal for the NH group was in the range of 12.10-12.87 ppm and the singlet signal for the =CH group was at \(\delta\) 8.06-8.63 ppm, which confirmed the successful conduction of condensation reactions. Signals for other fragments of synthesized molecules (**2**-**16**) were observed at expected values of chemical shift.  
On the \({}^{13}\)C NMR spectra for compounds (**2**-**16**), signals for =CH and C=O groups were observed in the range of \(\delta\) 135.59-146.67 ppm and 163.31-164.61 ppm, respectively. Additionally, on the FT-IR spectra, we observed characteristic signals for =CH and C=O groups at expected regions.  
Synthesized substances (**2**-**16**) were subjected to in vitro antimicrobial bioassays and to lipophilicity analysis (**2**-**6**, **8**-**16**).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Compound No.** & **Log P\({}_{\text{acetone}}\)** & **Log P\({}_{\text{acetonitrile}}\)** & **Log P\({}_{\text{1,4-dioxane}}\)** & **Log P\({}_{\text{methanol}}\)** \\ \hline
**2**
# A Prediction Model for Additive Manufacturing of Inconel 718 Superalloy
## 2 Experimental Setup  
### Materials and Equipment  
All samples were fabricated by an EOS SLM 3D printer (EOS GmbH, M290, Germany) which has a build volume of 250 \(\times\) 250 \(\times\) 325 mm and an ytterbium fiber laser of 400 W. IN718 powder was also provided by the EOS company with an average particle size of 12 microns. The powder composition was reported here [24]. The samples for this study were designed and fabricated in the dimensions of 5 \(\times\) 5 \(\times\) 6 mm (length \(\times\) width \(\times\)height). For measuring the dimensions of the samples, the FAROARM(r) EDGE and Laser Scan arm (Faro, Lake Mary, FL, USA) coordinate measurement system was applied in this study. Cloud data points were obtained from the FAROARM(r) EDGE scanner and these data points were then converted into CAD models. These CAD models were measured using Polyworks Inspector software (Innovmetric, Novi, MI, USA). Additionally, a Vickers hardness tester, LECO (LECO, LM 300 AT, Saint Joseph, MI, USA), was employed to analyze the microhardness of each sample on the top surface of the sample. A load of 500 g was applied for 10 s and a minimum of four indentations were made and reported.
# Improving Data Communication of Enhanced Loran Systems Using 128-_ary_ Polar Codes
## 2 Materials and Methods
### The Proposed 128-ary Polar Coding Scheme for eLoran
#### 2.2.1 Polar Codes for 128-_ary_ PPM
 \tag{9}\]  
where \(X\) and \(X^{\prime}\) are defined as follows [19]:  
\[X=1\cdot\hat{u}_{1,o}^{2i-2}+\alpha\cdot\hat{u}_{1,e}^{2i-2}\quad,on\;GF(q), \tag{10}\]  
\[X^{\prime}=0\cdot\hat{u}_{1,o}^{2i-2}+\beta\cdot\hat{u}_{1,e}^{2i-2}\quad,on\;GF( q). \tag{11}\]  
where \(\hat{u}_{1,o}^{2i-2}\) and \(\hat{u}_{1,e}^{2i-2}\) are sub-vectors of \(\hat{u}_{1}^{2i-2}\) with odd and even indices. Equations (8) and (9) define the recursive structure of the Successive Cancellation (SC) decoder. As SC is a soft-decision decoding (SDD) algorithm, it requires soft inputs from the demodulator. To provide these inputs, we developed an SS demodulation method, detailed in Section 2.2.2. A well-known limitation of the standard SC decoder is its susceptibility to error propagation, where an incorrect decision at an early stage can corrupt all subsequent decoding steps. To mitigate this issue, we employ the Successive Cancellation List (SCL) decoder, an enhanced algorithm proposed in [26], which approaches Maximum Likelihood (ML) performance. The SCL decoder mitigates error propagation by maintaining a list of \(L\) most likely candidate paths throughout the decoding process. Following SCL decoding, the decoding results that satisfy the CRC are selected as the final recovered message.
## 2 Comparative Evaluation of UCS and TCP Estimation Methods
### Experimental Setup for UCS and TCP Estimation Evaluation  
The four selected methods were tested using a Leica laser tracker AT 403 measuring system, which is a class of position reading several times higher in relation to industrial robots. The laboratory robotic cell consisted of two ABB IRB6640 robots with a 130 kg payload, 3.2 m workspace radius, and IRC5 robot controllers. Although ABB robots represent orientation using quaternions, all calculations in this study were performed using their equivalent 3 \(\times\) 3 rotation matrix representations to ensure compatibility with the applied transformation and estimation algorithms [17]. The collected data have been aligned with a fixed granite reference plate and gathered via Polyworks Inspector(r) 2023 probing software and a Tooling Ball Reflector 0.5\" (TBR). Each robot system has been examined separately in each performed measurement. The laboratory stand is presented in Figure 2. Robot TCP position transformations were read on the controller side together with the laser position reading. Ten positions were randomly chosen within the assumed robot workfield, varying both the tool's orientation and position. Depending on the selected method, robot readings were recorded either with or without TCP translation, without causing any actual change in the robot's physical position. Each method was applied to estimate the UCS (and TCP where applicable), and the results were compared using the positional error of estimated points in the UCS compared to ground-truth coordinates and residual transformation error--the difference between expected and reconstructed robot poses. Calculations have been performed in Mathworks MATLAB(r) software, version R2020a, using selected algorithms.
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Presented LED Driver in [18]** & **Proposed LED Driver** \\ \hline \multirow{4}{*}{Circuit topology} & Single-stage & Single-stage \\  & (integrating a interleaved boost & (integrating a interleaved buck-boost PFC \\ PFC converter with a half-bridge & converter with a half-bridge & converter with coupled inductors and \\  & LLC resonant converter) & a half-bridge-type series-resonant converter \\ \cline{1-1} Operating in Utility-Line & \multirow{2}{*}{90–130 V} & \multirow{2}{*}{210–230 V} \\ Voltage Range & & & \\ \hline Output Rated Power & 144 W (36 V/4 A) & 165 W (235 V/0.7 A) \\ \hline Power Switches & 2 (\(S_{1}\), \(S_{2}\)) & 2 (\(S_{1}\), \(S_{2}\)) \\ \hline Diodes & 8 (\(D_{1}\)–\(D_{6}\), \(D_{B1}\), \(D_{B2}\)) & 12 (\(D_{1}\)–\(D_{\text{LK}}\), \(D_{B11}\)–\(D_{\text{B22}}\), \(D_{\text{L1}}\)–\(D_{\text{L4}}\)) \\ \hline Capacitors & 6 (\(C_{f}\), \(C_{in1}\), \(C_{in2}\), \(C_{g}\), \(C_{r}\), \(C_{o}\)) & 6 (\(C_{f}\), \(C_{in1}\), \(C_{in2}\), \(C_{C}\), \(C_{r}\), \(C_{o}\)) \\ \hline Inductors & 4 (\(L_{f}\), \(L_{B1}\), \(L_{B2}\), \(L_{r}\)) & 4 (\(L_{f}\), \(L_{B11}\) and \(L_{B12}\), \(L_{B21}\) and \(L_{B22}\), \(L_{r}\)) \\ \hline Transformer & 1 (with a magnetic inductor \(L_{m}\)) & 0 \\ \hline Measured power factor & 0.99 (at 110 V) & 0.992 (at 220 V) \\ \hline Measured current THD & 10\% (at 110 V) & 6.55\% (at 220 V) \\ \hline Measured efficiency & 88\% (at 110 V) & 90.22\% (at 220 V) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons between the existing single-stage LED driver for streetlight applications in [18] and the proposed one.
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 3 Experiments
### Related Experiment Information
#### 3.1.4 Comparison Models  
We conducted comparison experiments using twelve state-of-the-art models, which can be classified into six categories, including:  
1. CNN-based lightweight semantic segmentation networks: Fast-SCNN [38] and ABC-Net [2];
2. CNN-based attention networks: DANet [43] and MANet [33];
3. Novel networks specialized in semantic segmentation of remote sensing images: A2 FPN [44], EANet [45], and VPANet [46];
4. Hybrid U-shaped network with Transformer as the encoder and CNN as the decoder: DC-Swin [18] and BANet [47];
5. Encoders and decoders are all Transformer-based U-shaped networks (pure Transformer structure): SwinUNet [48] and SegFormer [49];
6. Hybrid U-shaped network with CNN as the encoder and Transformer as the decoder: UNetFormer [17].
## 3 Methodology
### Methodology Structure  
Firstly, the BTOP model simulates runoff in the training period. The parameters are automatically optimized and input into the BTOP model to simulate runoff during the testing period. Then, the simulated runoff flow is compared with the observed flow to obtain the error series of the training period, which is used as the input value of the time series model. After normalized pre-processing of the error series, the Prophet, LSTM, and ARIMA models are used to predict the time series of the training period error sequence, and the predicted error series of the testing period is obtained. Finally, the predicted error series is used to correct the simulated flow to obtain the corrected flow. Five indicators are used to compare the corrected flow and the original simulated flow: the \(NSE\) index, Pearson correlation coefficient \(R\), mean absolute error (\(MAE\)), root mean square error (\(RMSE\)), and mean absolute percentage error (\(MAPE\)) (Figure 4).
# Acoustical Properties of Fiberglass Blankets Impregnated with Silica Aerogel
## 2 Materials and Methods
### Materials Characterization  
Microstructural observations such as particle distribution of the silica aerogels within fiberglass mats were performed using scanning electron microscopy (SEM). Images were obtained with a FEI Nova NanoSEM 230 instrument (FEI, Hillsboro, OR, USA) at an accelerating voltage of 10 kV and a minimum working distance of 5 mm. The silica aerogels were fixed on the sample holder using a carbon pad and subsequently coated with 15-20 nm of platinum for SEM analysis.  
The acoustical properties of aerogel blankets were measured in a 10 mm impedance tube that was custom made by Materiacustica [24]. This 2-microphone tube setup was developed to test small material specimens in accordance with the standard ISO 10534-2:2001 [25]. This setup enabled us to measure the normalized surface acoustic impedance, complex reflection coefficient and sound absorption coefficient of a hard-backed porous layer in the frequency range of 300-3000 Hz. The spacing between the two microphones was 30 mm, which is usual for this frequency range as recommended in the standard [25]. The thickness of the samples used in the acoustic experiments was between 7 and 11 mm, which is a typical thickness of a commercial product [23, 26]. Figure 1 illustrates a typical specimen of fiberglass blanket impregnated with aerogel that was used in the acoustic experiments. Figure 2 shows a photograph and jigsaw drawing of the vertically standing impedance tube.  
Figure 1: 10 mm diameter of fiberglass blanket samples cut for fitting into the impedance tube.
## 1 Introduction
The first scheme was proposed in 2017 by [PERSON] et al. [26]. They converted the three-dimensional coordinates into integers and chose the central vertex for additional data embedding. Based on the integer coordinates, the idea of homomorphic encryption is further used in [27], and is improved in [28]. In [29], the 3D spatial subdivision and space encoding techniques are designed. Additionally, the MSB prediction technique is also introduced in [30]. The majority voting method is then used to predict multi-MSBs of the usable vertices [31]. To further investigate more usable vertices, half of the vertices are chosen as usable in [32] to improve the embedding rate. In [33], the ring Co-XOR encryption technique is proposed, utilizing the ring's center vertex to predict the ring's edge vertices for data hiding. However, the redundancy issue of these state-of-the-art schemes has not been effectively eliminated. In this paper, two novel techniques, multi-group partition (MGP) and closest pair prediction (CPP), are proposed to further improve the performance of the reversible data hiding scheme in encrypted 3D mesh models. The MGP can better identify optimal grouping situations, while the CPP can effectively predict vertex coordinates. Using both techniques, we can efficiently compress 3D mesh models for abundant additional data embedding. Based on these two proposed techniques, the main contributions of our proposed scheme can be summarized into two aspects as follows:  
1. The MGP technique classifies more vertices as usable, and can adaptively choose different parameters for different models to find the optimal grouping strategy. 2. The CPP technique chooses the closest reference vertex to predict the current usable vertex to improve the prediction accuracy and only pay a little cost of redundancy. Finally, the embedding rates of our proposed scheme have been significantly improved compared to other state-of-the-art schemes. Section 2 illustrates the proposed reversible data hiding scheme in encrypted 3D mesh models in detail. Section 3 presents the experimental results, and Section 4 provides conclusions.
# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 4 Handwritten Chinese Text Recognition
### Page-Level Text Recognition  
In addition to recognizing line-level text, driven by the critical value of text recognition for fields such as industry and education, scholars have recently conducted several studies on recognizing page-level handwritten text. Refs. [107; 108; 109; 110] recognize traditional ancient texts read from top to bottom, and Ref. [111] recognizes texts under standard test paper formatting specifications, which are restricted to a specific layout. Faced with unconstrained text, [PERSON] et al. [112] extracted text lines from unconstrained handwritten documents using Generative Adversarial Networks (GAN). [PERSON] et al. [113] identified the failure of existing methods to correctly identify Chinese text when adding keywords and word replacement as a two-dimensional problem. Therefore, the team proposed a Structural Attention Network (SAN) model to learn irregular structures to recognize text correctly with insertions and swaps. A critical attempt and advancement of unconstrained page-level HCTR was in 2022; [PERSON] et al. [114] proposed a new end-to-end weakly supervised page-level HCTR method, PageRank. The method consists of three modules, a module for detecting and recognizing characters, a reading order module for determining intercharacter relationships and determining head and tail characters, and a graph-based decoding module for outputting the detection and recognition results, trained in a weakly supervised manner.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Year** & **Experimental Platform** & **Framework** & **Method Type** & **Without LM** & **LM** & **Data Synthesis** \\ \hline Attention- & \multirow{2}{*}{2019} & \multirow{2}{*}{n/a} & \multirow{2}{*}{Tensorflow} & segmentation- & \multirow{2}{*}{88.74} & \multirow{2}{*}{96.35} & \multirow{2}{*}{\(\times\)} \\ \cline{1-1} \cline{5-8} \cline{7-8} \cline{8  
supervised learning framework. This method achieves solving for the the HCTR page-level reading order problem under weak supervision and can handle unconstrained text. It also provides new ideas and inspirations to other researchers to solve the page-level text recognition problem.
## 2 MIMO Beamforming-Based Mathematical Model and Routing Algorithm to Navigate a Collection of AUAS
\begin{bmatrix}\begin{bmatrix} \mathbf{V}_{ki}^{(1)}\cdot\mathbf{S}_{i}^{(1)}\end{bmatrix}_{k=0,i=1}^{3,3}& 0&0\\ 0&\begin{bmatrix}\mathbf{V}_{ki}^{(2)}\cdot\mathbf{S}_{i}^{(2)}\end{bmatrix}_{ k=0,i=1}^{3,3}&0\\ 0&0&\begin{bmatrix}\mathbf{V}_{ki}^{(3)}\cdot\mathbf{S}_{i}^{(3)}\end{bmatrix}_{ k=0,i=1}^{3,3}\end{bmatrix} _{12\times 9} \begin{bmatrix}\mathbf{x}_{3\times 1}^{(1)}\\ \mathbf{x}_{3\times 1}^{(3)}\end{bmatrix} + \begin{bmatrix} \mathbf{J}_{4\times 1}^{(1)}\\ \mathbf{J}_{4\times 1}^{(2)}\\ \mathbf{J}_{4\times 1}^{(3)}\end{bmatrix} , \tag{2}\]  
where \(0\) denotes zero matrices. Following the proposed model (1), we state a simple algorithm (Algorithm 1) to present \(N\)-beamformed received signals for each drone \(u\) at time \(t\), i.e., \(\mathbf{y}^{(u)}(\omega_{i},a_{i},\theta_{i},t)\), where \(\mathbf{x}^{(u)}(t)\) denotes the source signal of drone \(u\) at time \(t\). **Input:**\(M\), \(N\), \(f_{i}\), and \(t\). **Output:**\(\mathbf{y}^{(1)},\mathbf{y}^{(2)},\cdots,\mathbf{y}^{(M)}\). 1. **for**\(i=1\) to \(M\) compute \(\theta_{i}\), \(a_{i}\), \(\omega_{i}\), and \(\psi_{i}\)
2. **for**\(u=1\) to \(M\) compute \(\mathbf{x}^{(u)}\) at \(t\)
3. **for**\(u=1\) to \(M\)**for**\(k=1\) to \(N\)**for**\(i=1\) to \(M\) construct \(\mathbf{S}_{i}^{(u)}\) and \(\mathbf{V}_{ki}^{(u)}\)  
**end for**\(i\)  
**end for**\(k\)  
\(\mathbf{z}^{(u)}\leftarrow\mathbf{S}_{i}^{(u)}\cdot\mathbf{x}^{(u)}\)  
\(\mathbf{y}^{(u)}\leftarrow\mathbf{V}_{ki}^{(u)}\cdot\mathbf{z}^{(u)}\)  
**end for**\(u\)  
**Algorithm 1** Multi-beam beamforming-based AUAS swarm routing algorithm  
For further clarification, we present a flowchart corresponding to the AUAS swarm model followed by the algorithm as shown in Figure 1.