esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental
### Catalyst Characterization  
Powder X-ray diffraction (XRD) patterns were recorded on a Bruker AXS D8 Focus diffractometer instrument (Beijing Persee Instrument Co., Ltd., Beijing, China) operating at 40 kV and 40 mA with Cu target K\(\alpha\)-ray (\(\lambda\) = 0.154056 nm) irradiation. Diffraction data (20) was collected between 2\({}^{\circ}\) and 80\({}^{\circ}\) with a scanning rate of 2\({}^{\circ}\)/min. For calculation of the interlayer space of ABen by the Bragg equation we used 2 dsin\(\theta\) = n\(\lambda\), where \(\lambda\) = 0.154 nm is the X-ray wavelength, \(\theta\) is the Bragg diffraction angle, and n is the series of diffraction [57].  
The Brunauer-Emmet-Teller method (BET) were performed at 77 K on Micromeritics ASAP2020 equipment (Micromeritics Instrument (Shanghai) Ltd., Shanghai, China). The samples were degassed in vacuum (\(\approx\)1 \(\upmu\)m Hg) at 200 \({}^{\circ}\)C for 300 min. The specific surface area was calculated using the BET model and the pore size distribution was obtained from the adsorption branch of the N2 isotherm by the Barret-Joyner-Halenda (BJH) model.  
Transmission and high resolution transmission electron microscopic (TEM) images were obtained on a JEOL JSM-2010 electron microscope (Japan Electronics Co., Ltd., Tokyo, Japan). The powders were suspended in ethanol for 10 min under ultrasonic treatment before they were deposited onto the carbon-film-coated copper grids. The average gold particle diameter and its distribution were determined by counting at least 50 particles.  
Inductively Coupled Plasma-Atomic Emission Spectrometry (ICP-AES) (optima 5300 DV, Perkin-Elmer, PE, Waltham, MA, USA) quantitative analyses (Au line 267.595 A, plasma power 1200W, plasma flow 1.5 L/min).  
X-ray photoelectron spectra (XPS) were carried out on a PerkinElmer PHI1600 system (Thermo Fisher Scientific, Waltham, MA, USA) using a single Mg-K-X-ray source operating at 300 W and 15 kV. The spectra were obtained at ambient temperature with an ultrahigh vacuum. The binding energies were calibrated using the C 1s peak of graphite at 284.5 eV as a reference.  
The morphologies of the samples were investigated by scanning electron microscopy (SEM, FEI Quanta 200 F, American FEI Company, Hillsboro, Missouri, MO, USA).  
Thermogravimetrc analysis (TGA) and differential scanning calorimetry (DSC) were simultaneously performed on a CTR-4P instrument (Shanghai precision & scientific instrument Co., Ltd., Shanghai, China) at a linear heating rate of 10 \({}^{\circ}\)C/min in air atmosphere.
# Design of Adits for People Passing Spacing in High Altitude Highway Tunnels in Cold Regions
## 5 Conclusions  
A series of numerical simulations were conducted considering altitude and longitudinal ventilation velocity to investigate the characteristics of temperature, CO concentration, and human eye visibility at characteristic heights. Adits for people passing spacing in highway tunnels at high altitude were designed. Following are the major conclusions.  
The increase in altitude leads to an acceleration of smoke diffusion. Under the same longitudinal ventilation wind velocity, the upstream smoke diffusion inhibition is minimally affected by altitude. However, the downstream smoke diffusion significantly increases with altitude, including diffusion velocity and diffusion length. The rise in temperature at a characteristic height and the decrease in longitudinal visibility at a characteristic height are attributed to reduced air buoyancy at higher altitudes, resulting in a reduction in available safety egress time.  
Longitudinal ventilation in tunnels effectively suppresses upstream smoke diffusion from the fire source and enhances downstream smoke dispersion. At the same altitude, an increase in wind velocity results in elevated rates and an extended hazardous range at a characteristic tunnel height downstream of the fire source.  
Comprehensive consideration of the deceleration personnel evacuation velocity under the effects of altitude and panic psychology during fires and the influence of altitude on hazardous range spread as well as a recommended reduction factor are proposed to design adits for people passing spacing in highway tunnels at different altitudes. The adit for people passing spacing should be reduced by 89%, 55%, 43%, and 27% at altitudes of 2 km, 3 km, 4 km, and 5 km, respectively, in comparison to the spacing at sea level (0 m altitude).  
The speed and strategies of personnel evacuation under the comprehensive impact of high altitude and panic psychology, as well as the spread of toxic gases at a characteristic height, should be further explored in future works.  
Conceptualization, Y.C. and Z.L.; Methodology, Y.C.; Software, Y.C.; Validation, Y.C.; Formal analysis, Y.C.; Investigation, Z.L.; Data curation, Y.C.; Writing--original draft, Y.C.; Writing--review & editing, Z.L.; Visualization, Y.C.; Supervision, Z.L.; Project administration, Z.L.; Funding acquisition, Z.L. 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, grant number 41461016 and grant number 41761015.  
Not applicable.  
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.
# Depth-Adaptive Deep Neural Network Based on Learning Layer Relevance Weights
## 3 Depth-Adaptive Deep Neural Network
### Class-Dependent ANNA  
The second ANNA approach is similar to the first approach. Nevertheless, it learns layers' weights, \(w_{i\ell}^{t}\), with respect to each class, \(i\). Therefore, the proposed class-dependent objective function is designed as follows:  
\[\mathcal{J}=\sum_{i=1}^{C}\sum_{u=1}^{M^{t}}\sum_{j\in B^{u}}\sum_{k\in B^{u} }\gamma_{ij}\gamma_{ik}\sum_{\ell=1}^{\mathcal{L}^{t}}d_{jk}^{t}w_{i\ell}^{t \;q}-\lambda_{i}\left(\sum_{\ell=1}^{\mathcal{L}^{t}}w_{i\ell}^{t}-1\right) \tag{16}\]  
After deriving \(\mathcal{J}\) with respect to \(w_{i\ell}^{t}\) and proceeding with the same optimization steps introduced in the class independent approach, we obtain the updated equation of the weights \(w_{i\ell}^{t}\):  
\[w_{i\ell}^{t}= \frac{\frac{1}{\sum_{j\in B^{t}}\sum_{k\in B^{t}}\eta_{ij}\gamma_{ ik}\;d_{jk}^{t}+H_{\ell}^{t-1}}}{\sum_{\ell=1}^{C}\left(\frac{1}{\sum_{j\in B^{t}} \sum_{k\in B^{t}}\eta_{ij}\gamma_{ik}\;d_{jk}^{t}+H_{\ell}^{t-1}}\right)} \tag{17}\]  
where  
\[H_{i\ell}^{t}=\sum_{u=1}^{M^{t}}\sum_{j\in B^{u}}\sum_{k\in B^{u}}\gamma_{ij} \gamma_{ik}\;d_{jk}^{t} \tag{18}\]  
In order to relax the need to store all the mapped vectors \(x_{j}^{\ell}\) over all batches, \(H_{i\ell}^{t}\) is expressed as  
\[H_{i\ell}^{t}=\sum_{j\in B^{t}}\sum_{k\in B^{t}}\gamma_{ij}\gamma_{ik}\;d_{jk} ^{t}+H_{i\ell}^{t-1} \tag{19}\]  
The layer fuzzy relevance weight is then calculated as the maximum of the class fuzzy relevance weights, as follows:  
\[w_{\ell}^{t}=max_{i}\big{(}w_{i\ell}^{t}\big{)} \tag{20}\]  
Note that in both two proposed approaches, the fuzzy relevance weights are learned from the data and their values range from 0 to 1. Algorithm 1 depicts the steps of the Layer Fuzzy Relevance Weight Learning adopted by both ANNA approaches.  
```
Input: \(\left\{x_{j}^{t}\right\}_{j\in B^{t}}\): the mapping of the input instances that belong to the current batch \(B^{t}\) \(\left\{\gamma_{ij}\right\}_{j\in B^{t}}\): the membership coefficients according to the training set \(i=1\ldots C\) \(\left\{H_{t}^{t-1}\right\}_{\ell=1\ldots\ell^{t-1}}\): the sum of the intra-class distances at the previous batch \(B^{t-1}\) Output: \(\left\{w_{\ell}^{t}\right\}_{\ell=1\ldots\ell^{t}}\) the learned layers fuzzy relevance weights at iteration \(t\)  Compute the distance \(d_{jk}^{\ell}\) for all pairs \(\left(x_{j}^{\ell},x_{k}^{\ell}\right)\in B^{t}\) using (7).  Compute the intra-class distance \(H_{\ell}^{t}\) for each layer \(\ell\in\mathcal{L}^{t}\) using (14) for class-independent approach or (19) for class-dependent approach.  Compute the fuzzy relevance weight \(w_{\ell}^{t}\) for each layer \(\ell\in\mathcal{L}^{t}\) using (15) for class-independent approach or (20) for class-dependent approach.
```  
**Algorithm 1**Layer Fuzzy Relevance Weight Learning Algorithm
# An Improved Image Semantic Segmentation Method Based on Superpixels and Conditional Random Fields
## 4 Experimental Evaluation
### Ablation Study  
The core idea of the improved method lies in the utility of superpixels and the optimization of CRF model. First, to evaluate the importance of the utility of superpixels, we directly compared the plain FCN-8s model with the boundary optimized one. Then, the FCN-8s with CRF and our proposed method are implemented sequentially. For better understanding, the results of these comparative experiments are shown in Figure 9 and Table 1.  
From the results, we see that methods with boundary optimization by superpixels consistently perform better than the counterparts without optimization. For example, as shown in Table 1, the method with boundary optimization is 3.2% better than the plain FCN-8s on VOC dataset, while the improvement becomes 2.8% on Cityscapes dataset. It can be observed that the object boundaries in Figure 9c are closer to the ground truth than Figure 9b. Based on the above experimental results, we can conclude that the segmentation accuracy of the object boundaries can be improved by the utility of superpixels. Table 1 also shows that, for the FCN-8s with CRF applied as a post-processing step, the better performance can be observed after boundary optimization. As shown in Figure 9d,e, the results of our method are more similar with the ground truth than that without boundary optimization. The mIoU scores of the two right-most columns also corroborated this point, with an improvement of 5% on VOC dataset and 4.1% on Cityscapes dataset.  
The purpose of using CRFs is to recover the boundaries more accurately. We compared the performance of plain FCN-8s with/without CRF under the same situation. From Table 1, it is clear that CRF consistently boosts classification scores on both the VOC dataset and the Cityscapes dataset. Besides, we compared the performance of boundary optimized FCN-8s with/without CRF. In conclusion, methods optimized by CRFs outperform the counterparts by a significant margin, which shows the importance of boundary optimization and CRFs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DatasetMethod** & **Plain FCN-8s** & **With BO 1** & **With CRF** & **Our Method** \\ \hline VOC 2012 & 62.7 & 65.9 & 69.5 & 74.5 \\ Cityscapes & 56.1 & 58.9 & 61.3 & 65.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The mIoU scores of the comparative experiments.  
Figure 9: An example result of the comparative experiments. (**a**) The input image, (**b**) the result of plain FCN-8s, (**c**) the boundary optimization result by superpixels, (**d**) the result of FCN-8s with CRF post-processing, (**e**) the result of our method, and (**f**) the ground truth.
## 4 Conclusions  
In this work, we have carried out several one-dimensional numerical studies to analyze and to enhance the generation of harmonics in a bubbly liquid from a single-frequency ultrasonic field in two cavities. Our results show that higher source amplitudes (20-25 kPa), lower source frequencies (200-400 kHz), and higher void fractions (\(3.2725\times 10^{-5}-9.8175\times 10^{-5}\)) are more suitable for the enhancement of the amplitude of harmonic components amplitudes in the free-walled cavity of length \(L=\lambda/4\). Moreover, when keeping a constant void fraction in the cavity, smaller bubbles are more adequate for this purpose. This latter could especially be useful in practice. On the other hand, higher source amplitudes and lower source frequencies are more suitable for the enhancement of the amplitude of harmonic components amplitudes in the open-field cavity of length \(L=16\lambda\), whereas the modification of void fractions has no influence on this enhancement over the frequency range considered here. Moreover, when keeping a constant void fraction in this cavity, the optimal bubble size is \(f_{0}/f\approx 5\), \(f_{0}/f\approx 7\), and \(f_{0}/f\approx 9\) for  
Figure 8: Maximum amplitude of \(2f\) (**a**), \(3f\) (**b**), and \(4f\) (**c**) in the cavity of length \(L=16\lambda\) vs. \(f_{0}/f\), for three different source frequencies \(f=200\) kHz (blue line), \(f=300\) kHz (green line), and \(f=400\) kHz (red line).  
Figure 7: Maximum amplitude of \(2f\) (**a**), \(3f\) (**b**), and \(4f\) (**c**) in the cavity of length \(L=16\lambda\) vs. \(f_{0}/f\), for three different void fractions \(v_{\triangledown}\) (blue line), \(2v_{\triangledown}\) (green line), and \(3v_{\triangledown}\) (red line).  
maximizing the 2 nd, 3 rd, and 4 th harmonic, respectively. This latter could be particularly useful in practice.  
Conceptualization, M.T.T.S. and C.V.; methodology, M.T.T.S. and C.V.; software, M.T.T.S. and C.V.; validation, M.T.T.S. and C.V.; formal analysis, M.T.T.S. and C.V.; investigation, M.T.T.S. and C.V.; writing--original draft preparation, M.T.T.S. and C.V.; writing--review and editing, M.T.T.S. and C.V.; supervision, C.V.; project administration, C.V.; funding acquisition, C.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Agency for Research (Agencia Estatal de Investigacion, AEI), Ministry of Science and Innovation of Spain (Ministerio de Ciencia e Innovacion), and the European Regional Development Fund (FEDER), grant number DPI2017-84758-P.  
Not applicable.  
Data availability Statement: Data is contained within the article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Analysis of the CRB for Short Observation Intervals
### CRB when \(\Delta\rho>\rho_{chip}\)
#### 4.1.1 Phase Altimetry  
In this case, we assume the angle \(\phi_{\text{T}}\) as known. Now, in order to obtain the new expression \(\text{CRB}_{all}(h)\), we remove the row and the column of \(\mathbf{J}(\mathbf{\
abla})\) associated with \(\phi_{\text{T}}\) and then compute the matrix inversion \(\mathbf{J}^{-1}(\mathbf{\
abla})\), as is described in Appendix A. We obtain the following expression:  
Figure 4: \(\sqrt{\text{CRB}(h)}\) as a function of \(\Delta\rho\) for the different GNSS signals considered. \(B_{fe}\) is the bandwidth of the receiver’s front-end assumed for each signal, set to match the signal bandwidth, i.e., \(B_{Tx}/2\) (see Table 1). The two vertical lines represent the chip lengths: 293.25 m, of the GPS L1 CA and Galileo E1 BC signals; and 29.32 m, of the GPS L5 and Galileo E5 signals.  
\[\text{CRB}_{alt}(h) = \frac{1}{8\gamma\,\text{SNR}_{0}\,\sin^{2}\left(\theta_{el}\right) \left(k^{2}R\left(0\right)-\frac{R^{\prime\prime}\left(0\right)}{c^{2}}\right)} \tag{41}\] \[\approx \frac{1}{32\lambda\,\text{SNR}_{0}R\left(0\right)}\left(\frac{ \lambda}{\pi\sin\left(\theta_{el}\right)}\right)^{2},\]  
by making the approximation of \(R^{\prime\prime}(0)/c^{2}\) very small (e.g., \(\approx\)0.1 for the worst case, i.e., the Galileo E5 signal with \(B_{fe}=B_{Tx}\)) compared to \(k^{2}R(0)\) (e.g., \(\approx\)623), given the signal bandwidths considered. The phase information is now effectively used and the bound multiple orders of magnitude lower. As expected, it is directly proportional to \(\lambda^{2}\), i.e., the lower the carrier wavelength, the higher the possible precision of the \(h\) estimate. In addition, we observe that when comparing Equation (41) with Equation (35), the dependence on \(R^{\prime\prime}(0)\) can be neglected now. This implies that in this phase altimetry case, the estimation performance of the phase altimetry techniques should be negligibly impacted by the bandwidth of the signal used. As a consequence, the use of the new GNSS with higher bandwidths, but with the same carrier frequency, should bring little improvement to the achievable precision of the phase altimetry techniques for short \(T_{obs}\) when \(\Delta\rho>\rho_{clip}\).
# A Chinese-Kazakh Translation Method That Combines Data Augmentation and R-Drop Regularization
## 3 Method
### Utilizing R-Drop for Model Fine-Tuning  
To overcome the problem of overfitting when using large models on small datasets, we introduced the Dropout technique for regularizing training of deep neural networks [38]. Although Dropout is very effective, it introduces randomness, leading to noticeable inconsistency between training and inference. Therefore, we adopted the R-Drop training strategy, which aims to mitigate the inconsistency between training and inference by making the output distributions of different sub-models generated by Dropout consistent with each other. Specifically, in each mini-batch, we perform two forward passes for each data sample. R-Drop constrains the different sub-models generated by Dropout by minimizing the bidirectional KL divergence to encourage their output distributions to be as consistent as possible. In this way,R-Drop is able to better balance the output differences between different sub-models and improve the consistency of the model between the training and inference stages.  
Specifically, when given training data \(D=\{x_{i},y_{i}\}_{i=1}^{n}\), for each training sample \(x_{i}\), it undergoes two forward propagations through the network, resulting in two output predictions: \(P_{1}(y_{i}|x_{i})\) and \(P_{2}(y_{i}|x_{i})\). Due to Dropout randomly dropping some neurons each time, \(P_{1}\) and \(P_{2}\) are different prediction probabilities obtained from two different subnetworks (from the same model) (as shown in Figure 2). R-Drop leverages the difference between these two prediction probabilities and applies symmetric Kullback-Leibler (KL) divergence to constrain \(P_{1}\) and \(P_{2}\) :  
\[L_{KL}^{i}=\frac{1}{2}(D_{KL}(P_{1}(y_{i}\mid x_{i})\|P_{2}(y_{i}\mid x_{i}))+ D_{KL}(P_{2}(y_{i}\mid x_{i})\|P_{1}(y_{i}\mid x_{i}))) \tag{1}\]  
Additionally, the conventional maximum likelihood loss function is included.  
\[L_{NLL}^{i}=-\log P_{1}(y_{i}\mid x_{i})-\log P_{2}(y_{i}\mid x_{i}) \tag{2}\]  
The final training loss function is:  
\[L_{i}=L_{NLL}^{i}+\alpha*L_{KL}^{i}. \tag{3}\]  
Here, \(\alpha\) is used to control the coefficient of \(L_{KL}^{i}\), making the training of the entire model straightforward. R-Drop, on the other hand, constrains the parameter space by deliberately imposing constraints on the outputs between sub-models during the training process, ensuring consistency among different outputs and reducing the inconsistency between training and testing.  
By adopting the R-Drop training strategy, we can alleviate the issue of inconsistency between training and inference and enhance the generalization ability of deep neural networks on small datasets. This approach helps optimize the performance and stability of the model, thereby improving its effectiveness in practical applications.
## 4 Experimental Results and Discussion
### Packet Delivery Ratio (PDR)  
Table 6 compares packet delivery and packet loss ratios to the number of nodes. Figures 9 and 10 show that the PDR decreased when the number of nodes increased. The PDRs obtained using different algorithms for 100 nodes were 97% for the cross-layer-based adaptive threshold technique and 95% for the cross-layer fuzzy logic method. If the PDR value is high, all data from the base station will be received without any data loss. Therefore, the proposed approach achieves high performance efficiency when compared to other algorithms.
# Human Factors and Ergonomics in Industry 5.0--A Systematic Literature Review
## 4 Discussion
### Digital Human Modeling  
Traditional ergonomic methods are enriched by the possibilities of the digital twin, which is now emphasized with digital human modeling by the individual anthropometric values of a single human worker, but also by the data collected from their work, which not only allows for visualization but also optimization due to the possibility of behavior prediction [128]. By leveraging tools like JACK, HumanCAD, Siemens Tecnomatix, and virtual reality-based systems [17; 39; 40], organizations can perform detailed ergonomic risk assessments, optimize workspace designs, and enhance worker safety and efficiency. These technologies support the evaluation of posture-related risks, simulation of biomechanical loads, and optimization of user-centered workspaces, fostering a sustainable approach to design. Integrating advanced technologies such as motion capture systems and virtual reality further enriches the analysis, providing immersive and interactive experiences for both real-time and offline ergonomic assessments [36; 37; 41].  
Despite the significant potential of digital human modeling, several limitations highlight existing scientific gaps. There is a need for precise reproduction of external anthropometric dimensions and their linkage to internal skeletal structures. Inaccuracies can result in significant errors, reducing the validity of biomechanical analyses and potentially causing ergonomic risks. While current tools support various ergonomic standards (e.g., RULA, OWAS, NIOSH), they lack advanced capabilities for muscle force estimation and joint load analysis, restricting their application in detailed biomechanical investigations. Many such tools require high technical expertise and specialized equipment, posing challenges for widespread adoption, particularly in small and medium-sized enterprises (SMEs). Advanced systems and motion capture setups involve substantial costs, limiting their accessibility for broader industrial applications. By addressing these limitations, digital human modeling can become a more accessible and reliable tool, advancing ergonomic design and fostering safer and more efficient working environments.
## 4 Discussion
### Simulation Biases in Different Ecosystems  
At the footprint scale, the poor correlations detected in the forest areas may be explained by canopy complexity [12; 53]. The poor correlations found in the grassland areas may be the result of soil reflectance where the vegetation cover is absent.  
Regarding the underestimation of SIF encountered using VIs and meteorological data, we conclude that it is difficult to link stable and structurally driven VIs with the highly variable phenomenon of plant photosynthesis. Another reason is that the reflectance-based VIs typically encounter saturation effects for areas with higher vegetation, such as in forested areas or and in croplands that use higher fertilizers. The VIs are also easily affected by soil reflectance in the grasslands of a semi-arid region [6; 12; 54].  
Many factors lead to the different simulated accuracies for different ecosystems. For different plants in different ecosystems, photosynthesis processes and mechanisms vary. Examples include energy partitioning between photosystems I and II, canopy structure, stoichiometry, and the fluorescence properties of these two photosystems, photorespiration, and photosynthetic pathways (C3 versus C4) [14]. C4 plant photosynthesis evolved from the C3 pathway, adapting to high light intensity, high temperature, and dryness; they have more resource-use efficiencies and photosynthesis strength [22]. [PERSON] et al. [22] also argued that the effect of temperature on SIF for different plants may be affected by other biotic and abiotic factors, and that the relationships observed between SIF and GPP have significant linear relationships for C3 and C4 crops, but have different slope values.  
Vegetation canopy structural features, named the clumping index, will significantly affect land surface energy and carbon balances and also vegetation photosynthesis and SIF [7; 42; 55].  
From the analysis above, we can conclude that for different vegetation, photosynthesis processes, and mechanisms, the SIF trend is different because of the difference in canopy structure; hence, it is better to study SIF separately for different ecosystems.