esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Space Used by Passengers Boarding and Alighting
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Runs**} & \multirow{2}{*}{\begin{tabular}{c} **Occupied Space** \\ **(m\({}^{2}\)/pass)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Rear Distance** \\ **(cm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Lateral Right** \\ **Distance (cm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Lateral Left** \\ **Distance (cm)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Front Distance** \\ **(cm)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Distance \(d\) to the** \\ **Passenger** \\ **without Reduced** \\ **Mobility (cm)** \\ \end{tabular} } \\ \hline
1 & 2.63 & 103.77 & 83.53 & 72.39 & 36.05 & 102.74 \\
2 & 3.67 & 98.95 & 78.17 & 87.29 & 55.84 & 97.22 \\
3 & 3.64 & 102.55 & 70.56 & 97.20 & 58.82 & 125.53 \\
4 & 4.06 & 106.38 & 76.20 & 82.59 & 60.92 & 118.45 \\
5 & 3.65 & 125.99 & 85.74 & 84.51 & 42.23 & 104.22 \\
6 & 4.60 & 125.06 & 100.34 & 69.43 & 60.88 & 109.80 \\
7 & 4.17 & 108.96 & 101.21 & 60.18 & 62.79 & 122.18 \\
8 & 3.76 & 104.91 & 105.82 & 68.31 & 55.48 & 105.78 \\
9 & 3.49 & 117.09 & 91.05 & 65.24 & 53.17 & 112.20 \\
10 & 2.91 & 100.40 & 89.71 & 54.75 & 48.57 & 115.21 \\
11 & 4.52 & 114.16 & 125.03 & 64.19 & 47.16 & 119.81 \\ Average & 3.74 & 109.84 & 91.58 & 73.28 & 52.90 & 112.10 \\ Standard & 1.09 & 24.23 & 28.29 & 25.46 & 20.66 & 18.51 \\ Deviation & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average space occupied by passengers without reduced mobility alighting the train. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Runs**} & \multirow{2}{*}{\begin{tabular}{c} **Occupied Space** \\ **(m\({}^{2}\)/pass)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Rear Distance** \\ **(cm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Lateral Right** \\ **Distance (cm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Lateral Left** \\ **Distance (cm)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Front Distance** \\ **(cm)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Distance \(d\) to the** \\ **Passenger** \\ **without Reduced** \\ **Mobility (cm)** \\ \end{tabular} } \\ \hline
1 & 1.57 & 57.22 & 46.17 & 50.91 & 37.39 & 76.86 \\
2 & 1.76 & 70.45 & 62.51 & 41.78 & 41.58 & 83.37 \\
3 & 1.65 & 71.16 & 59.53 & 45.50 & 39.58 & 73.12 \\
4 & 2.30 & 81.03 & 59.60 & 58.49 & 37.64 & 79.23 \\
5 & 1.93 & 61.77 & 68.93 & 53.35 & 45.57 & 76.26 \\
6 & 1.91 & 60.54 & 67.92 & 54.44 & 40.89 & 80.39 \\
7 & 1.72 & 51.06 & 67.20 & 48.28 & 42.95 & 78.93 \\
8 & 1.37 & 47.58 & 49.27 & 50.01 & 50.97 & 69.51 \\
9 & 1.35 & 50.21 & 51.70 & 49.67 & 44.22 & 68.42 \\
10 & 1.56 & 50.08 & 67.95 & 67.09 & 45.27 & 79.01 \\
11 & 1.65 & 55.63 & 67.45 & 50.17 & 41.19 & 79.55 \\ Average & 1.71 & 59.70 & 60.75 & 51.79 & 42.48 & 76.79 \\ Standard & & & & & & \\ Deviation & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average space occupied by passengers without reduced mobility boarding the train.
# Enhanced Joint and Separable Reversible Data Hiding in Encrypted Images with High Payload
## 6 Conclusions  
In this work, two (joint and separable) reversible data hiding systems using an enhanced embedding pattern and a new measurement function in encrypted images which offer a high payload are proposed. In the joint system, data extraction and image restoration are done jointly. Here, the encrypted image is divided into four sets, instead of blocks, and two sets can be used for data hiding, which helps to increase the embedding capacity. Moreover, we consider the actual value of pixels, instead of an estimated value, and we exploit the absolute difference between neighboring pixels in preference to prediction error to calculate the smoothness. By avoiding block division and prediction error, we use the spatial correlation property of the natural images fully. For this reason, performance of the proposed work is superior to the performance of other works. In the separable method, data extraction and image recovery are separable and the hidden information bits are extracted without any error. Besides, as we avoid prediction error for separable method too, the proposed separable scheme outperforms other works in terms of reversibility. Furthermore, before hiding data, we can encrypt the information bits to further increase the security of the system.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF-2014R1A1A1004521, NRF-2016R1D1A1B03934653). All authors discussed the contents of the manuscript and contributed to its presentation. [PERSON] designed and implemented the proposed scheme, analyzed the simulation data, and wrote the paper under the supervision of [PERSON]. The authors declare no conflict of interest.
## 2 Data and Theory Description
### Theory and Method of Retrieving PWV
#### 2.2.4 PWV Derived from Remote Sensing Satellite  
The MERSI water vapor 5 min product is generated with in three near-infrared channels centered at 0.905, 0.940 and 0.980 \(\upmu\)m and the two window channels centered at 0.865 and 1.030 \(\upmu\)m according to the differential absorption method. The three near-infrared channels centered have different water vapor sensitivities under the same atmospheric condition. Under a given atmospheric condition, the derived water vapor values from the three channels can be different. Thus, a mean PWV value of three channels can be calculated according to the following steps.  
1. The weighted mean value of the three water vapor absorption channels is combined based on the sensitivity and the PWV is obtained according to the equation [47]: \[\text{PWV}=\sum\text{f}_{\text{i}}\omega_{\text{i}}\] (10) where \(\omega_{\text{i}}\) is the PWV values derived from each water vapor absorption channel; f1 is the normalized weighting parameters correspond to each band and subscription i refers to the channel.
2. The weighting parameter of each band is calculated based on the sensitivity of the transmission in each of the channels to the PWV. \[\text{f}_{\text{i}}=\eta_{\text{i}}/\sum\eta_{\text{i}}\] (11) \[\eta=\left|\frac{\Delta\tau}{\Delta w}\right|\] where \(\Delta\tau\) is the transmittance variation in one unit length; \(\Delta\)w is the PWV variation in one unit length. It is computed numerically from simulated curves of transmittance versus precipitable water vapor; \(\eta_{\text{i}}\) is sensitivity.  
In Sentinel-3A/OLCI, the retrieval algorithm mainly relies on the differential absorption algorithm to link the radiation ratio of the Sentinel-3A two NIR water vapor absorption channels [48]. According to [PERSON] and [PERSON] [49], neural networks trained with the Matrix Operator Model (MOMO) can measure atmospheric water vapor on the land and ocean. Thus, the total calculation in this study was performed as follows [50]:  
\[\text{PWV}=\text{k}_{0}+\text{k}_{1}\log(\text{R})+\text{k}_{2}\log^{2}(\text {R}) \tag{12}\]  
where k0,k1 and k2 indicate the regression coefficients and R is the ratio of the radiation from channel 17 of the OLCI instrument to the radiation of channel 18. In this study, the secondary terrestrial full-resolution product derived from Sentinel-3A is selected to perform the experiment.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
### Hydrogels as Sophisticated, Biomimetic Materials
#### 1.2.2.2. Polysaccharide-Based Hydrogels  
Polysaccharides can be derived from animals, plants, microbes or algae, and have applications in many fields such as pharmaceutics and matrix synthesis for cell culture [30]. Polysaccharide-based hydrogels can retain large volumes of water and the molecular interactions that make up these hydrogels are hydrophobic interactions, hydrogen bonding and ionic crosslinking [30]. Both alginate and chitosan are polysaccharides that can be fabricated into hydrogels. Alginate is derived from the cell walls of brown algae and has been widely used in tissue engineering and drug delivery applications [31]. Chitosan is a linear polysaccharide derived from chitin found in crustaceans, where its degree of deacetylation dictates its rheological properties [30]. The main advantages of chitosan hydrogels stem from its biocompatibility, low cost of production and antimicrobial properties [30].
# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 9 Challenges in the Hybrid Deep Learning Model for Brain Stroke Detection and Prediction
### Overfitting on Training Data  
Overfitting happens when a model performs extraordinarily well on training data but suffers in untested real-world scenarios. This might be caused by excessive model complexity, inadequate training samples, or noise in the dataset. Overfitting produces incorrect predictions in clinical contexts, limiting the model's utility. Regularization techniques like dropout, weight decay, and data augmentation can assist in minimizing overfitting, but they need precise parameter tweaking. Furthermore, cross-validation procedures must be used to confirm that the model generalizes effectively across diverse patient groups.  
Addressing these challenges is crucial for increasing the model's dependability, assuring accurate stroke prediction, and enabling practical use in clinical settings. Using sophisticated data-balancing approaches, optimizing model hyperparameters, and utilizing joint research efforts can dramatically improve predictive performance. A well-optimized hybrid deep learning model has the potential to enable early and precise stroke diagnosis, resulting in enhanced patient care and medical decision-making.
# Weed Detection Method Based on Lightweight and Contextual Information Fusion
## 4 Conclusions  
In response to the challenges of sugar beet and weed detection in complex backgrounds, this study proposes an enhanced algorithm, CCCS-YOLO, based on YOLOv5. Firstly, the algorithm introduces the C3-Faster structure to achieve a lightweight network with higher accuracy. Secondly, contest aggregation and CARAFE modules are incorporated to enhance the fusion capability of contextual information in feature maps, thereby improving the algorithm's performance in small target detection. Finally, the addition of the Soft-NMS-EIoU structure enhances detection accuracy. To demonstrate the performance of our model, tests were conducted on two datasets, both yielding favorable results.  
On the sugar beet and weed dataset, CCCS-YOLO outperforms YOLOv5s, showing a 5.1% increase in [EMAIL_ADDRESS]:0.95, a 3.8% increase in F1 score, and a 4.4% improvement in accuracy. The improved algorithm also surpasses other networks such as Faster-RCNN, YOLOv7, and YOLOv8. Tests on the sesame weed dataset confirm the robust performance of the improved algorithm, exhibiting improvements across various metrics compared to YOLOv5.  
The proposed algorithm is part of an application for laser weeding on wheeled vehicles, playing a crucial role in identifying and locating weeds. This contribution facilitates the advancement of intelligent and mechanized weed control. In future research, exploring lightweight network architectures will continue to further enhance algorithm performance, making it more suitable for deployment on edge devices.  
Methodology, H.L.; algorithm, C.Z., J.L. and H.C.; software, C.Z. and H.C.; validation, Z.X. and Z.O.; writing--original draft preparation, C.Z. and J.L.; writing--review and editing, C.Z. and H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (51875414) and the 2020 Wuhan City Science and Technology Program Project (2020010601012292).  
Not applicable.  
The data presented in this study are openly available in [**lincolnbeet_dataset**], reference number [32].  
The authors declare no conflict of interest.  
Figure 10: (**a**) The Precision–Recall (PR) curve for CCCS-YOLO, while (**b**) depicts the PR curve for YOLOv5s.
# Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie
###### Abstract  
Harmful Algal Blooms (HABs), predominantly driven by cyanobacteria, pose significant risks to water quality, public health, and aquatic ecosystems. Lake Erie, particularly its western basin, has been severely impacted by HABs, largely due to nutrient pollution and climatic changes. This study aims to identify key physical, chemical, and biological drivers influencing HABs using a multivariate regression analysis. Water quality data, collected from multiple monitoring stations in Lake Erie from 2013 to 2020, were analyzed to develop predictive models for chlorophyll-a (Chl-a) and total suspended solids (TSS). The correlation analysis revealed that particulate organic nitrogen, turbidity, and particulate organic carbon were the most influential variables for predicting Chl-a and TSS concentrations. Two regression models were developed, achieving high accuracy with R\({}^{2}\) values of 0.973 for Chl-a and 0.958 for TSS. This study demonstrates the robustness of multivariate regression techniques in identifying significant HAB drivers, providing a framework applicable to other aquatic systems. These findings will contribute to better HAB prediction and management strategies, ultimately helping to protect water resources and public health.  
HAB; Chlorophyll-a; total suspended solids; water quality parameters; linear regression model; Pearson's correlation coefficient; ANOVA test; relative importance 2020
###### Abstract  
Volume grating is a key optical component due to its comprehensive applications. Other than the common grating structures, volume grating is essentially a predesigned refractive index distribution recorded in materials, which raises the challenges of metrology. Although we have demonstrated the potential application of ellipsometry for volume grating characterization, it has been limited due to the absence of general forward model reflecting the refractive index distribution. Herein, we introduced a distributed dielectric constant based rigorous coupled-wave analysis (RCWA) model to interpret the interaction between the incident light and volume grating, with which the Mueller matrix can be calculated. Combining with a regression analysis with the objective to match the measured Mueller matrices with minimum mean square error (MSE), the parameters of the dielectric constant distribution function can be determined. The proposed method has been demonstrated using a series of simulations of measuring the volume gratings with different dielectric constant distribution functions. Further demonstration has been carried out by experimental measurements on volume holographic gratings recorded in the composite of polymer and zinc sulfide (ZnS) nanoparticles. By directly fitting the spatiotemporal concentration of the nanoparticles, the diffusion coefficient has been further evaluated, which is consistent to the result reported in our previous investigations.  
Keywords:ellipsometry; volume grating; nanostructure metrology; distributed dielectric constant model; holography +
Footnote †: journal: Applied Physics Letters
# A Review of Vertical Shaft Technology and Application in Soft Soil for Urban Underground Space
## 4 Technological Advancements in Mechanized Systems
### Shaft Boring Machine (SBM)  
Herrenknecht, in partnership with Rio Tinto in the \"Mine of the Future\" program, developed the SBM, designed for shaft excavations with diameters up to 12 m and depths up to 2000 m in compact rock formations. The SBM is engineered for the excavation of large-deep shaft structures characterized by mechanization, high efficiency, automation, and modularity [34]. The machine utilizes a rotating cutter wheel with disc cutters for shaft excavation via static pressure [35]. Hydraulic cylinders allow the wheel to thrust against the rock and rotate around the shaft bottom. A clam-shell device, similar to traditional shaft mucking, removes cuttings, which are lifted by buckets. The machine moves down and up the shaft against the shaft wall using a system of thrusting grippers. The grippers and their connected cylinders also help maintain the verticality and stability of the machine. The machine operates on the same principles as the TBM but in a vertical mode [36]. Although the working principle and structural composition of the SBM and TBM are similar, significant differences exist in the cutterhead structure. The SBM cutterhead features a conical structure, while the TBM cutterhead has a planar structure, primarily determined by their respective working characteristics, as illustrated in Figure 5.  
Additional shaft construction operations, such as utility installation, rock bolting, and shaft concrete lining, can occur simultaneously with shaft boring. This method, to a depth of approximately 460 m, is comparable in cost to traditional sinking, after which the SBM provides a distinct cost advantage. The SBM demonstrates considerable productivity advantages, as it excavates much faster than drill-and-blast methods [31; 38]. The significant benefit of the SBM lies in its capability to perform drilling and support operations concurrently, making it adaptable to strata with adverse geological conditions. Currently, it achieves drilling depths exceeding one thousand meters [39].  
The SBM facilitates high-speed excavation of deep vertical shafts with diameters up to 8.5 m, making it suitable for various boring operations. It is considered very safe, as it requires fewer personnel in the process. However, the SBM has some limitations; its straight-line drilling approach enhances precision but renders the method relatively inflexible. The process is restricted to specific sizes and lengths, which may become expensive on a cost-per-meter basis. Fast drilling necessitates high-capacity chip removal and is best suited for reasonably stable ground conditions.
# Improved Dab-Deformable Model for Runway Foreign Object Debris Detection in Airport Optical Images
## 3 Methodology
### Ts  
Dab-Deformable modifies the global-to-global querying mechanism in the DETR decoder by employing sparse spatial convolution, which focuses attention on local areas around key points. This approach avoids exponential operations on sequences and reduces computational complexity, enabling the model to process multi-scale feature maps efficiently. Although the design of Dab-Deformable is highly innovative, it overlooks the drawbacks introduced by the increased length of feature sequences after dimensionality reduction and concatenation of multi-scale feature maps. During the encoding stage, attention is allocated across the feature sequences, and in the decoding stage, the attention sequences are queried. While the increased length of feature sequences allows the model to capture more information, it also distributes attention more sparsely across the sequence. Consequently, the number of queries required in the decoder also increases. To address this issue, we propose a dynamic threshold selector. After the encoder allocates attention to the feature sequences, the attention sequences are scored, and elements below the threshold are filtered out. This approach retains the global attention of the encoder while significantly reducing the computational overhead of the decoder.  
\[\mathrm{S}\,=\|\mathrm{F}\|_{2}=\sqrt{\sum_{j=1}^{D}\mathrm{f}_{j}^{2}},\ \mathrm{F}=[\mathrm{f}_{1},\mathrm{f}_{2},\ldots,\mathrm{f}_{n}]\in\mathrm{R} ^{D} \tag{21}\]  
\[\mathrm{F}^{{}^{\prime}}=\mathrm{S}\cdot\mathrm{F}\quad\begin{cases}\mathrm{S }_{j}=1,&\mathrm{if}\ \mathrm{S}_{j}\geq\mathrm{Adptheshold}\\ \mathrm{S}_{j}=0,&\mathrm{if}\ \mathrm{S}_{j}<\mathrm{Adptheshold}\end{cases} \tag{22}\]  
The representation \(F=[f_{1},f_{2},\ldots,f_{n}]\in R^{D}\) denotes the attention sequence output of the encoder, where \(D\) denotes the dimensionality of the features. The importance of each feature sequence is scored by calculating its \(L2\) norm \(\|F\|_{2}\). An adaptive thresholding mechanism is then employed to filter out elements with scores below the threshold.  
The importance of the attention sequence vectors output by the encoder is measured by calculating their \(L2\) norms as indicators of attention significance. A learnable threshold parameter is introduced, which is dynamically adjusted during training through backpropagation. This design enables the decoder to focus more effectively on key features, thereby mitigating the issue of attention dispersion caused by long sequences.
# Amplitude and Phase Information Interaction for Speech Enhancement Method
## 4 Experimental Setup
### Evaluation Protocol  
There are two main methods for evaluating the quality of speech enhancement: subjective evaluation and objective evaluation. Subjective evaluation provides a direct reflection of human perception of speech quality, but it is time-consuming and requires human participation. Therefore, in the experiments, objective evaluation methods are primarily chosen to simulate human perception of speech quality using various types of data.  
STOI is short-time objective intelligibility measure. It is calculated using the following formula [21]:  
\[d_{j,m}=\frac{\left(x_{j,m}-\mu_{x_{j,m}}\right)^{T}\left(\overline{y}_{j,m}- \mu_{y_{j,m}}\right)}{\|x_{j,m}-\mu_{x_{j,m}}\|\|\overline{y}_{j,m}-\mu_{y_{j,m }}\|} \tag{17}\]  
\[STOI=\frac{1}{JM}\sum_{j,m}d_{j,m} \tag{18}\]  
where \(m\) represents the m-th frame of the speech, \(j\) represents the j-th one-third octave band, \(x\) represents the clean speech, \(y\) represents the normalized and clipped enhanced speech, \(\mu\) represents the sample average of the corresponding vectors, \(d_{j,m}\) represents the intermediate intelligibility measure, \(M\) represents the total number of frames, \(J\) represents the number of one-third octave bands, and \(STOI\) is the average of the intermediate intelligibility measure. The value of \(d_{j,m}\) is between 0 and 1 due to the magnitude relationship between vector products and modulus products, and the value of \(\sum\limits_{j,m}d_{j,m}\) is between 0 and \(JM\). The value of \(STOI\) is also between 0 and 1.  
A higher value indicates a higher proportion of speech that is intelligible to human ears after speech enhancement, resulting in better perceived speech quality. When presenting evaluation results, the STOI value is typically expressed as a percentage.  
SI-SDR is scale-invariant signal-to-distortion ratio. It is calculated using the following formula:  
\[SI-SDR=10\log_{10}\frac{\|X_{C}\|^{2}}{\|X_{N}\|^{2}} \tag{19}\]  
where \(X_{C}\) represents the time-domain vector of the clean speech, \(X_{N}\) represents the time-domain vector of the noise, where \(X_{N}\) is obtained by subtracting the model's output speech from the clean speech. A higher SI-SDR value indicates less distortion in the enhanced speech compared to the clean speech.