esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation and Experimental Results
### Experimental Result 1: Anti-Collision via V2V
Vehicles are assumed to decelerate with 5 m/s\({}^{2}\)[76]. Figure 13b,c shows the locations of objects without and with the proposed system only after 1.2 s, respectively. In Figure 13b, we can find that an accident occurs between V1 and pedestrian. On the other hand, V1 can slow down its speed by receiving data from V2, and avoids the predicted collision as shown in Figure 13c. From experimental results, we show that the proposed inter-vehicle data fusion takes less than 30 ms from object detection to decision-making. In addition, the existence of the pedestrian in blind spot can be identified by the proposed data fusion scheme. These results demonstrate that we can prevent from the predicted accident in real-time.
# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
###### Abstract  
In the domain of remote sensing research, the extraction of roads from high-resolution imagery remains a formidable challenge. In this paper, we introduce an advanced architecture called PCCAU-Net, which integrates Pyramid Pathway Input, CoordConv convolution, and Dual-Inut Cross Attention (DCA) modules for optimized performance. Initially, the Pyramid Pathway Input equips the model to identify features at multiple scales, markedly enhancing its ability to discriminate between roads and other background elements. Secondly, by adopting CoordConv convolutional layers, the model achieves heightened accuracy in road recognition and extraction against complex backdrops. Moreover, the DCA module serves dual purposes: it is employed at the encoder stage to efficiently consolidate feature maps across scales, thereby fortifying the model's road detection capabilities while mitigating false positives. In the skip connection stages, the DCA module further refines the continuity and accuracy of the features. Extensive empirical evaluation substantiates that PCCAU-Net significantly outperforms existing state-of-the-art techniques on multiple benchmarks, including precision, recall, and Intersection-over-Union(IoU). Consequently, PCCAU-Net not only represents a considerable advancement in road extraction research, but also demonstrates vast potential for broader applications, such as urban planning and traffic analytics.  
road extraction; pyramid pathway input; CoordConv; Dual-Input Cross Attention (DCA) Modules +
Footnote †: journal: Applied Sciences
# The Impact of Psychological Health on Childhood Obesity: A Cross-Developmental Stage Analysis
## 2 Materials and Methods
### Psychological Measures and BMI in Early Childhood (8-11 Years Old)
#### 2.1.3 Methodological Approach  
In the present study, we analyzed the data collected from questionnaires filled out by children aged 8-11. These questionnaires encompassed a range of psychological measures, including Affect Dysregulation score, Affect Suppression score, and Adaptive Reflection. Alongside these psychological metrics, data on the children's Body Mass Index (BMI) categories, specific BMI values, and BMI percentiles were collected. The BMI categories were encoded into numerical values for analytical purposes: Healthy (0), Overweight (1), and Obese (2). The calculation of Body Mass Index (BMI) adhered to established formulas, which take into account the child's weight and height. Subsequently, the derived BMI was contextualized using a percentile growth chart, which benchmarks an individual's BMI against population standards adjusted for age and sex. This comparison enabled the categorization of children and adolescents into distinct weight status groups based on percentile ranges: a 'Healthy weight' classification corresponds to a BMI between the 5 th and less than the 85 th percentile, 'Overweight' is assigned for BMIs from the 85 th to less than the 95 th percentile, and 'Obesity' is designated for a BMI at or above the 95 th percentile, as defined by clinical growth charts [20]. A Spearman correlation analysis was conducted to explore the relationship between children's psychological measures and their BMI categories, aiming to uncover any significant associations that could illuminate the interplay between psychological well-being and physical health in this age group.  
Navigating the Transitional Years: Psychological and Biometric Profiles in Pre-Teens (11-12 Years Old)  
The dataset consists of 15 children, which includes 8 girls and 7 boys. Within the group, 7 children are classified as 'Healthy', 5 as 'Overweight', and 3 as 'Obese'. The mean and standard deviation (SD) of Body Mass Index (BMI) for the 'Healthy' category is 18.99 \(\pm\) 0.91, for the 'Overweight' category is 22.40 \(\pm\) 0.92, and for the 'Obese' category is 31.43 \(\pm\) 4.00. Across all categories, the overall mean BMI is 22.61 with an SD of \(\pm\)5.06.  
The dataset encompasses a variety of variables that intricately combine psychological assessments with biometric information for children aged 11 to 12 years. This comprehensive collection of data includes the following key elements:
# Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes
## 4 Discussion
This could be explained by (i) land contamination which exists in altimetry results, (ii) altimetry processing in general, (iii) tide gauge measurement errors, or (iv) the fact that \(\mathit{SLA}_{\mathit{L3}}\) data were collected at a mean distance of 7.9 km from the tide gauge station and even by the time difference between compared measurements. The reasoning above is confirmed by the comparison of \(\mathit{SLA}_{\mathit{L3}}\) with \(\mathit{SLA}_{\mathit{buoy}}\) measurements from the buoy in the Gulf of Riga (Figure 12). Even though there should be very little if any land influence on the altimeters that measured over the buoy station, a distinguishable offset is still present between the remotely sensed and modelled sea level (Figure 12a,b). This indicates possible errors in altimetry processing. However, the smaller ME, RMSE, and STD on Figure 12b compared with Figure 11b indicate that the performance of the NEMO model is better in the open sea compared with the coastal Baltic Sea. In general, validation of altimetry products with tide gauge data in the coastal area, although constantly improving, remains a challenge [34; 36]. This is because of various coastal effects as well as possible geoid connection issues. Moreover, a mismatch of characteristic (temporal and spatial) scales inevitably exists when sampling the SSH variability with different methods, such as coastal tide gauges, relatively coarse-gridded models and remote sensing products.
# Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs
## 4 Experimentation
### Data Augmentation Discussion  
To increase the diversity and size of the dataset and prevent overfitting, we experimented with data augmentation techniques that modify the data to generate additional images or create images that can improve the robustness of the training model without collecting new data. However, we did not observe any significant improvement in the performance of the convolutional neural network model after applying data augmentation. The data augmentation techniques that we experimented with include random rotation, random flipping, rescaling, and random contrast adjustment.  
* Random rotation: This technique randomly rotates the images by a certain angle within a specified range. We expected this to introduce some variations in the orientation of the images and make the model more invariant to rotation, but it did not have any noticeable effect on the accuracy or loss;
* Random flipping: This technique randomly flips the images horizontally or vertically (only horizontally in our experiment). We expected this to introduce some variations in the symmetry of the images and make the model more invariant to flipping but it did not have any noticeable effect on the accuracy or loss;
* Rescaling: This technique randomly rescales the images by a certain factor within a specified range. We expected this to introduce some variations in the size of the images and make the model more invariant to scaling but it did not have any noticeable effect on the accuracy or loss;
* Random contrast adjustment: This technique randomly adjusts the contrast of the images by a certain factor within a specified range. We expected this to introduce some variations in the brightness of the images and make the model more invariant to contrast but it did not have any noticeable effect on the accuracy or loss.  
The four aspects of data augmentation we carried out did not show a noticeable effect on improving the performance of our system as expected. One possible explanation is that these augmentation techniques did not sufficiently alternate the distinctive features of camel breeds in the augmented images. In other words, the performed augmentation operations very well reserved the distinctive features of camel breeds in the augmented images. This seems to indicate that we need to apply more aggressive augmentation techniques in order to render sufficient alternation to the distinctive features of camel breeds in the augmented training dataset, with the hope of being able to noticeably improve our system's prediction accuracy and robustness.
## 2 Results and Discussion
### Results
#### 2.1.2 Functional Annotation of Predicted Proteins in Nr and Swiss-Prot Databases  
To retrieve proteins with the highest sequence similarity, distinct unigenes were aligned to Nr and Swiss-prot databases using Blastx algorithm (_E_-value \(<\) 1 \(\times\) 10\({}^{-5}\)). Out of 71,095 all-unigenes from Pg-Con and Pg-MeJA, 54,920 (77.25%) were found to have significant similarity to 12,416 protein accessions in Nr database, whereas 35,448 (49.92%) with significant identities to Swiss-prot database were matched with 22,198 protein accessions. It was reported that longer contigs were more likely to  
\begin{table}
\begin{tabular}{c|c c|c c|c} \hline \multirow{2}{*}{**Length of Nucleotides (bp)**} & \multicolumn{2}{c|}{**Contigs**} & \multicolumn{2}{c|}{**Unigenes**} & \multirow{2}{*}{**All-unigenes**} \\ \cline{2-2} \cline{5-6}  & **Pg-Con** & **Pg-MeJA** & **Pg-Con** & **Pg-MeJA** & \\ \hline
0–199 & 85,621 & 82,631 & 14,443 & 12,401 & 0 \\
200–299 & 16,761 & 17,177 & 12,719 & 13,661 & 13,923 \\
300–399 & 9576 & 9502 & 9  
show Blast matches in the protein databases [27]. Our results also showed that 94% of unigenes over 500 bp in length had Blast matches in the Nr database, while only 42% of unigenes shorter than 300 bp did (Figure 1).  
In the Nr database, most of the matched sequences (85.44%) had _E_-values between 0 and 1 \(\times\) 10\({}^{-15}\) and the rest 14.56% had _E_-values from 1 \(\times\) 10\({}^{-15}\) to 1 \(\times\) 10\({}^{-5}\) (Figure 2A). Likewise, the _E_-value distribution in the Swiss-prot database revealed that the majority of matched sequences (78.84%) had _E_-values from 0 to 1 \(\times\) 10\({}^{-15}\) and the remaining 21.16% had the _E_-values between 1 \(\times\) 10\({}^{-15}\) and 1 \(\times\) 10\({}^{-5}\) (Figure 2B). The translated amino acid sequences of unigenes showed a high similarity to the sequences from the Nr database. Nearly 94.09% of the Blastx hits were in a similarity range between 40% and 100% and only 5.91% of hits had similarity values less than 40% (Figure 2C). The similarity distribution in the Swiss-prot was also more similar to that of the Nr database. Similarities between 40% and 100% were found for 93.13% of query sequences against Swiss-prot, whereas only 6.87% had lower homologies with \(<\)40% identity (Figure 2D). Homologies among different species are illustrated in Figure 2E, where out of the matched 54,920 unigenes in Nr, 22,083 (40.21%) were matched to _V. vinifera_ followed by _Lycopersicon esculentum_ (8226; 14.98%) and _Amygdala persica_ (5161; 9.40%). Thus _P. ginseng_ was genetically more related to _V. vinifera_.
## 2 Signal and System Model
The term \(\tilde{c}_{i}(t-\tau_{i})\) represents the spreading sequence after filtering of the front-end, and here, the simplifying condition:  
\[\tilde{c}_{i}(t)\approx c_{i}(t) \tag{8}\]  
is assumed and the impact of the front-end filter is neglected. \(\eta(t)\) is the down-converted and filtered disturbing component, \(\eta[t]=j[t]+w[t]\). Considering the interference term \(j[t]\), the mean power of the sweep interference can be defined as:  
\[J=\text{Var}\{j[t]\} \tag{9}\]  
The jammer-to-noise ratio (JNR) is defined as follows:  
\[\frac{J}{N}=\frac{J}{\sigma_{IF}^{2}}=\frac{J}{N_{0}B_{IF}} \tag{10}\]  
where \(\sigma_{IF}^{2}\) is the variance of the IF noise, \(N_{0}/2\) is the power spectral density of the IF noise and \(B_{IF}\) is the front-end bandwidth. In order to avoid the cross-terms resulting from the interaction between the positive and negative frequency parts of the spectrum, the analytic representation of the received signal is adopted, provided as follows:  
\[y_{a}[t]=y[t]+j\hat{y}[t] \tag{11}\]  
where the analytic signal \(y_{a}[t]\) has a real part \(y[t]\) and an imaginary part \(\hat{y}[t]\), which contains the Hilbert transform of \(y[t]\). The imaginary part is a version of the original real part with a \(90^{\circ}\) phase shift. The use of the analytic signal has two advantages: first, interference between positive and negative frequencies can be eliminated, as the analytic signal \(y_{a}[t]\) has components belonging only to the half plane of positive frequencies; second, even though this filtering suppresses negative frequencies and the sampling rate is reduced, it does not introduce any loss of information.
###### Abstract
The autonomous orbit determination of navigation constellation uses only bidirectional ranging data of the inter-satellite link and lacks space-time benchmark information related to the Earth. Therefore, this technology cannot eliminate or suppress accumulation of the overall rotation errors of the constellation, making it difficult to operate autonomously for a long time [5; 6; 7; 8; 9; 10]. Therefore, the design index requirement of GPS Block IIF satellites is changed to a URE of less than 2 m in a 60-day autonomous navigation [11]. [PERSON] et al. proved the unobservability of the overall rotation of the satellite constellation with inter-satellite measurements only and proposed an algorithm to constrain the right ascension of the ascending point by analyzing the long-predicted reference ephemeris provided by the Operational Control Segment (OCS) [12]. [PERSON] et al. pointed out that the orbit determination errors in the tangential and normal directions caused by the overall constellation rotation up to kilometer level, and the influence of errors in the predicted Earth Orientation Parameter (EOP), reach to meter level [13]. [PERSON] et al. applied simulation data to study autonomous orbit determination [14]. With the development of BDS, several scholars researched the autonomous orbit determination technology of the navigation constellation by simulation, showing that the uncertainty of the right ascension of the ascending point \(\Omega\) at the orbital plane will lead to overall rotation errors in orbit determination results [15; 16; 17; 18; 19; 20; 21; 22]. In addition, other scholars also successively carried out related work, including algorithm research and simulation analysis [23; 24; 25; 26; 27; 28; 29]. The aforementioned studies aided in carrying out the research in this paper. This paper focuses on the problem of the overall constellation rotation correction in autonomous orbit determination, three correction cases are designed, and experiment results of different cases are analyzed and compared using inter-satellite observations.
# Systematic Review of Mecanum and Omni Wheel Technologies for Motor Impairments
## 5 Limitations of the Review Process  
This review focused on peer-reviewed studies describing implemented Mecanum or omni wheel-based mobility systems. As a result, conceptual or theoretical designs without working prototypes were excluded, which may limit the general scope of technological exploration.  
Additionally, most of the included studies were evaluated under controlled laboratory conditions, using predefined metrics such as vibration amplitude [31], tilt angle [28], or energy consumption [43]. While these benchmarks offer technical insights, they may not reflect real-world usage scenarios involving varied terrain, user fatigue, or caregiver interaction.  
Participant demographics were also often underreported or narrowly defined, limiting insight into how these systems perform across diverse user groups, especially those with cognitive or severe physical impairments.  
Another constraint was the absence of a universal framework for assessing study quality. Although we applied a qualitative evaluation based on implementation level and clarity of reporting, no standardized risk-of-bias tool for engineering studies was used. This may impact the reproducibility and comparability of findings.  
Finally, the heterogeneity of reporting styles, performance metrics, and testing protocols made direct cross-study comparison challenging. Developing shared evaluation standards and reporting guidelines for assistive mobility technologies would improve future review quality and facilitate benchmarking across platforms.
## 6 Conclusions and Discussions  
To evaluate the short-term and long-term displacement of the soft soil and tunnels when constructing a metro in soft soil, this paper presented a three-dimensional numerical model based on the project of Suzhou rail transit line S1. The modified Cam-clay model was used to simulate the strain-softening effect of soft soil. Moreover, the proposed model can consider the grouting parameters and the water leakage of the assembled segment lining, which have a great impact on the settlement of soft soil and tunnel during and after construction. In addition, the simulation results of the project of Suzhou rail transit line S1 were compared with the field observations, and the sensitivity of key parameters was carried out by the established numerical model. The results showed that the grouting volume, the thicknesses of soft soil under the tunnel, and the tunnel leakage conditions have a significant impact on the stratum and tunnel settlement. Notably, the tunnel leakage conditions have the greatest impact on the long-term consolidation settlement. Moreover, when the tunnel meets the secondary waterproof requirements, the increase of the thicknesses of soft soil under the tunnel basically does not increase the long-term settlement of the stratum and the tunnel but increases the corresponding short-term settlement.  
Compared with previous studies, this paper obtained the entire history of surface and tunnel displacement by linking construction period factors and long-term settlement. The obtained results can be used to guide the construction of similar projects. However, the proposed three-dimensional model has some shortcomings, such as the inability to consider the consolidation and cementation of the grouting material, the sub-consolidation settlement of subsoil, and the attitude of the shield machine, which also have significant effects on the short-term and long-term settlement of tunnels and formations. Thus, related research needs to be further studied in the future.  
Conceptualization, H.J. and D.Y.; methodology, H.J.; software, D.Z.; validation, H.J.; formal analysis, H.J.; investigation, H.J.; resources, S.Z.; data curation, H.J.; writing--original draft preparation, H.J.; writing--review and editing, H.J. and D.Y.; visualization, H.J.; supervision, D.Y.; project administration, D.Y.; funding acquisition, D.Y. All authors have read and agreed to the published version of the manuscript.  
This research received was funded by the Natural Science Foundation \"Joint Fund Project\" (No. U1834208).  
Not applicable.  
The data presented in this study are available from the corresponding author upon request.  
The authors gratefully acknowledge financial support from the Natural Science Foundation \"Joint Fund Project\" (No. U1834208).  
Figure 13: Displacement of surface and tunnel under different seepage points. (**a**) Development of surface displacement with time; (**b**) Development of segment displacement with time.
## 3 Data and Method
### Data Source
#### 3.1.3 Sampling Method for Landslide Data  
For model construction, both training and testing samples are needed. Training samples are used for calculating the coefficients of landslide influence factors, and testing samples are applied to test the predictive capability of the model.  
Considering the accuracy of data, we only considered the landslides with an area greater than 1000 m\({}^{2}\), thus 6682 landslides (area \(>\) 1000 m\({}^{2}\)) were selected, with a total area of 29.48 km\({}^{2}\). The median value of each landslide elevation was used to roughly distinguish the landslide source area and debris deposits. The area larger than the median elevation is the landslide source area; otherwise it is the landslide debris deposits (Figure 9). Finally, the source area of these landslides is 15.79 km\({}^{2}\).  
In order to equally train the model toward positive and negative observations, we balanced the samples by randomly selecting a number of non-sliding points that are equal to the number of the real landslide observations (6682), resulting in 50% landslide points and 50% non-sliding points. Non-landslide points chosen from the outside the buffer zone of the landslide source area (buffer radius = 100 m). 6682 random points were employed as the landslide sample points which were randomly selected from the entire source area. Finally, we got 13,364 samples including 6682 sliding points and 6682 non-sliding points. Then we randomly selected 9364 samples for training modeling  
Figure 9: Landslide inventory map showing extracted landslide source areas (red).  
(including 4682 landslide points and 4682 non-sliding points), and the remaining 4000 samples were used for tests (including 2000 landslide points and 2000 non-landslide points).