esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Individual Contributions of Climate Effects and the VGC Effect  
For each active season, we further quantified the individual contributions of the VGC effect, climate effects of the concurrent season, and climate effects of the preceding season on the 20-year vegetation growth (Figure 8). We combined the impacts of temperature, precipitation, and sunshine duration into the single variable of climate effects. The results showed that the greening trends in the three active growing seasons were predominantly attributed to the VGC (the contributions are 50%, 67%, and 58%, respectively) (Figure 8), confirming the positive carryover effect of vegetation seasonal growth. It is interesting to note that the VGC effect had the strongest correlation with NDVI during the LGS (Figure 3) but the VGC effect contributed the most to vegetation growth in PGS (Figure 8). We also found that PGS was the season whose inter-annual variation trend (slope) of vegetation growth was most similar to that of the growing-season mean (gray dashed line, Figure 8). Concurrent seasonal climate factors also played a positive role in the three active growing seasons of vegetation growth (the contributions are 14.24% (EGS), 19.38% (PGS), and 13.80% (LGS), Figure 8). In comparison, the climate of the preceding season played a much smaller role in the vegetation greening for the three active growing seasons (the contributions are 0.64% (EGS), 5.58% (PGS), and 10.03% (LGS), Figure 8). Residuals is the contributions of other driving factors, including human activities and natural disturbances, that could influence vegetation growth but were not analyzed in our research. However, the residuals had a positive impact on vegetation greening, especially in the EGS (Figure 8). From Figure 3, VGC- and humid-induced vegetation greening from the early growth season sustained to the late growing season (ignoring residuals), which was the primary factor dominating vegetation growth in the upper and middle reaches of the Yellow River in the 20 years period.  
Figure 7: Spatial distribution of the partial correlations between NDVI of each year and that of the lag time, after controlling for the climate variable of both years.
# Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
## 4 Discussion  
### Ablation Study  
In order to verify the effectiveness of the proposed adaptive hybrid strategy, we conducted ablation experiments on CTAFK. Models with different hybrid strategies were used as backbone networks in CTAFNet. The experimental results on three datasets are shown in Table 8. Among them, CCTT means two layers of convolution and two layers of Transformer encoder stacking. Add represents the direct element-wise addition of local and global feature maps. Cat represents concatenate of local and global feature maps along the spectral axis. Adapt means the adaptive hybrid strategy proposed in this paper. Comparing the experimental results, it can be found that the pure CNN model has achieved much better performance than the pure Transformer model, and a possible explanation is that Transformer model requires more training data, and the data size of the current HSI open-source dataset is not enough to train a pure Transformer model with good performance [33]. When using the CNN-Transformer hybrid architecture, selecting an unsuitable hybrid strategy may cause performance degradation. For example, the hybrid model with the add strategy performs worse than pure CNN model on the three datasets, the hybrid model with the CCTT strategy performs poorly on the aerial dataset and DFC2018 dataset, and the hybrid model with the cat strategy performs worse than pure CNN model on the DFC2018 dataset. The reason for this phenomenon might be that the attention mechanism has mixed the intra-class and inter-class contexts when extracting the global information [58], and the artificially specified hybrid strategies cannot distinguish between different contexts, which leads to poor generalization ability of the model. By contrast, the proposed adaptive hybrid strategy can enhance the useful context information by adjusting the weight, thus extracting more generalized and discriminative features and overcoming the limitations of artificially specified hybrid strategies. The model using adaptive hybrid strategy achieves the best classification accuracy on three datasets, which proves the effectiveness of the proposed adaptive hybrid strategy.
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 2 Model Description
### Sky Division  
#### 2.2.1 Division of RAS  
To accurately analyse the interference between an NGSO constellation and an RAS telescope, it is necessary to consider a large number of representative pointings for both systems. The ITU Rec. S.1586-1 divides the entire sky visible to the RAS telescope into a finite number of pointings to represent the entire sky, with 30 rings parallel to the horizon and each ring subdivided into smaller cells with an approximate width of 90/30 cos(elev), where elev is the elevation angle of the ring. There are 2334 cells in total.
# Systematic Review of Mecanum and Omni Wheel Technologies for Motor Impairments
## 3 Results
### Cost and Accessibility  
Multiple studies stress the importance of affordability as a prerequisite for real-world deployment, particularly in home environments. A widely cited strategy is the use of off-the-shelf components. [PERSON] et al. [51] demonstrated that a fully operational holonomic wheelchair could be constructed using a standard frame, hobbyist Mecanum wheels, Arduino controllers, and a Bluetooth interface, significantly reducing the production cost without compromising core functions. Modularity was also applied by [PERSON] et al. [49], who developed a semi-autonomous platform with swappable control components and customizable functionality, promoting reusability and scalable production. Another cost-saving direction involves alternative materials and manufacturing methods. [PERSON] et al. argued that 3D printing and lightweight composite plastics can substitute expensive metal frames, enabling more accessible production pipelines for custom or small-batch builds [48]. In a similar spirit, [PERSON] et al. [43] presented a wheelchair design that met standards for safety and reliability using inexpensive Arduino boards, steel tubing, and modular Mecanum drive units, proving that regulation compliance is feasible within a budget-conscious framework.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study** & **Function** & **Connection** & **Mobility Legend** & **Mechanical Innovation** & **Control Innovation** & **Motens** & **Recommendations** \\ \hline \multirow{4}{*}{[PERSON] et al. [44]} & \multirow{4}{*}{Automatic vehicle behavior} & \multirow{4}{*}{Automatic vehicle behavior} & \multirow{4}{*}{Endline independent} & \multirow{4}{*}{Monitor frame for} & \multirow{4}{*}{LEM-based} & \multirow{4}{*}{LEM-based} & \multirow{4}{*}{LEM-based} & \multirow{4}{*}{LEM-based} & \multirow{4}{*}{LEM-based} & \multirow{4}{*}{LEM-optimal} \\  & & & & & & & &  
Accessibility is also enhanced by using consumer-grade devices for interaction and control. Smartphones or tablets serve as intuitive and widely available user interfaces, eliminating the need for proprietary systems that would otherwise increase costs [51]. Open-source software and community-driven platforms enable further democratization, particularly within maker and rehabilitation engineering communities. Furthermore, researchers have emphasized that cost-efficiency must not sacrifice usability; for instance, [PERSON] et al. combined low-power motors with a BCI control system and visual navigation, delivering impressive autonomy results while keeping hardware complexity moderate [50]. The data were collected and summarized in Table 5.
## 2 Theory and Methodology  
### Theory of Traditional Particle Filter Algorithm  
As a nonparametric implementation of nonlinear Bayesian filtering, the particle filter (PF) approximates the posterior probability distribution through random sampling and importance resampling. Unlike classical methods such as the Kalman filter, which struggle with highly nonlinear systems, the PF demonstrates strong parameter identification capabilities in nonlinear and non-Gaussian systems without relying on linear dynamic models or Gaussian assumptions. This makes PF more effective and versatile for practical applications. The traditional particle filtering algorithm for estimating the seepage pressure state vector [28] proceeds as follows:  
Step 1: Importance Sampling (Initialization)  
For \(i=1,2,\ldots,N\), sampling new particles \(x_{k}^{i}\) According to the  
\[x_{k}^{i}\sim q\left(x_{k}^{i}\left|x_{k-1}^{i},x_{k}\right.\right)\]  
where \(x_{k}^{i}\) represents the particle state sequence at \(k\) time, and \(q(\cdot)\) denotes the importance sampling density function.  
Step 2: Weight Update  
Based on current observations \(z_{k}\), calculate the weights for the new set of particles \(\left\{x_{k}^{i}\right\}_{i=1}^{N}\):  
\[\omega_{k}^{i}\ =\ \omega_{k-1}^{i}p\left(z_{k}\left|x_{k}^{i}\right.\right)p \left(x_{k}^{i}\left|x_{k-1}^{i}\right.\right)/q\left(x_{k}^{i}\left|x_{k-1}^{ i},x_{k}\right.\right)\]  
where \(z_{k}\) denotes the observation sequence, \(w_{k}^{i}\) represents the particle weight at \(k\) time, and \(p(\cdot)\) stands for the probability density function.  
Normalization: \(\omega_{k}^{i}\ =\ \omega_{k}^{i}r\sum\limits_{i=1}^{N_{k}}\omega_{k}^{i}\)  
Step 3: Resampling  
Calculate the number of effective particles: \(N_{eff}\ =\ 1/\sum\limits_{i=1}^{N_{k}}\left(\omega_{k}^{i}\right)^{2}\)  
If it is less than the given threshold, \(N_{th}:N_{eff}<N_{th}\) resample to get a new set of particles \(\left\{x_{k}^{i},1/N\right\}_{i=1}^{N_{k}}\)  
Step 4: State Estimation  
The seepage pressure state is estimated by calculating the weight and state of all particles \(\hat{x}_{k}\ =\ \sum\limits_{i=1}^{N_{k}}\omega_{k}^{i}x_{k}^{i}\)  
Variance estimation: \(p_{k}\ =\ \sum\limits_{i=1}^{N_{k}}\omega_{k}^{i}\left(x_{k}^{i}-\hat{x}_{k} \right)\left(x_{k}^{i}-\hat{x}_{k}\right)^{T}\) and return to step 2.
###### Abstract  
The objective of this study is to implement a more realistic integrated analysis model for micro gas turbines by incorporating secondary flow and combustion efficiency into the existing model, which includes main engine components such as the compressor and turbine, and to validate this model by comparing it with test results. The study was based on the JetCat P300-RX, which has a maximum thrust level of 300 N. Simulations were performed using ANSYS CFX, employing the \(\kappa\)-\(\omega\) SST turbulence model and a mixing plane interface between individual components. The eddy dissipation model (EDM), with a combustion efficiency of 90%, was used as the combustion model. A user subroutine was also applied for the power matching of the compressor and turbine to calculate the fuel flow rate in each iteration. For secondary flow, it was assumed that 3% of the total air flow rate would flow through the secondary path and be applied to the compressor and turbine. Simulations were conducted over a range of 30,000 to 104,000 RPM, with ground conditions evaluated, including altitude-simulated conditions. To validate the analysis model, engine performance metrics such as pressure ratio, air flow rate, fuel flow rate, and exhaust gas temperature (EGT) were compared with test results. The results demonstrated that errors were less than 5% for most engine performance metrics, except for EGT and fuel flow. The discrepancy in EGT was attributed to differences in the sensing methods, while the variation in fuel flow was found to be due to the lubrication system and losses due to the secondary air flow. Consequently, this study confirmed that the integrated simulation model accurately predicts engine performance. The results indicate that the integrated simulation model provides a more realistic prediction of overall engine performance compared to previous studies. Therefore, it can evaluate detailed thermo-fluid properties without the need for component performance maps, enhancing performance evaluation and analysis.  
micro turbojet engine; computational fluid dynamics; performance analysis; secondary air flow; integrated analysis model +
Footnote †: journal: Medical Engineering Journal
# Real-Time Behaviour of Dredged Slurry Treated by Air-Booster Vacuum Consolidation
## 7 Test Results
### Water Yield  
By setting the starting time of the air boosting as \(t^{\star}\) = 0, the water yield during \(t^{\star}\) = \(-\)3-0 h (i.e., before air boosting) can be evaluated from the records of the electronic balance (Table 4). For comparison, the water yield during \(t^{\star}\) = 0.5-3.5 h (i.e., after air boosting) is added. It is noted that the PWP will be influenced during air boosting (as detailed in the following section), and the water yield is dependent on the PWP gradient. Thus, to make a meaningful comparison, the water yield during air boosting (i.e., \(t^{\star}\) = 10 min = 1/6 h) is not included. Instead, we included the water yield data for the time period after 0.33 h of the vacuum preloading (i.e., \(t^{\star}\) = 0.5-3.5 h; the vacuum pump was restarted after air boosting). It is noted that the PWP induced by the pressurised air soon disappeared when the air boosting was stopped; that is, the PWP of the slurry at \(t^{\star}\) = 0.5 h will be the same as that right before boosting (i.e., \(t^{\star}\) \(\rightarrow\) 0).  
It is clear from Table 4 that the rate of water yield reached a stable value (\(\sim\)1.7 g/h) before air boosting. However, after air boosting, there was an obvious increase in the water yield, i.e., the rates during \(t^{\star}\) = 0.5-1.5 and \(t^{\star}\) = 1.5-2.5 h reached 4.2 g/h and 2.8 g/h, respectively. Finally, during \(t^{\star}\) = 2.5-3.5 h, the rate of the water yield returned to that before air boosting. The increased water yield rate indicates that the drainage consolidation of the dredged slurry was promoted, even though the pore water pressure gradients were the approximately the same for the time periods before and after air boosting.
# Performance Investigation of Active, Semi-Active and Passive Suspension Using Quarter Car Model
## 4 Results
Furthermore, as observed in the sprung mass displacement, the fully active suspension returned quickly to equilibrium with less oscillations than the semi-active suspension. Figure 13: Sprung mass displacement responses of the passive, semi-active suspension, and fully active suspension for the three (3) different road profiles. Based on the sprung mass acceleration response, it can be said that the semi-active and fully active suspensions displayed improved levels of comfort over the passive suspension, with the semi-active suspension had a slightly greater performance than the fully active setup. Figure 15 compares the suspension working space of the passive, active, and semi-active suspension systems for each of the three road profiles. In this case, only the peak amplitude of the working space response was of interest since it would need to be accommodated in the vehicle design. For all three road profiles, the semi-active and fully active suspensions increased the peak suspension working space by a maximum of 17% and 9%, respectively, over the passive suspension system, which represented a decreased level of performance. Figure 16 compares the tyre deflection responses of the suspension systems for each of the three road profiles. Across all three road profiles, the semi-active and fully active suspensions had quite similar responses in terms of noticeably higher peak amplitudes of tyre deflection than the passive suspension system, but it had improvements of slightly lower settling times and less overshoot. This resulted in both the fully active and semi-active suspensions offering only a 6% reduction of rms tyre deflection compared to the passive suspension system, which represents a marginal improvement in road-holding ability. Figure 14: Sprung mass acceleration responses of the passive, semi-active suspension, and fully active suspension for the three (3) different road profiles. Figure 16: Tyre deflection responses of the passive, semi-active suspension, and fully active suspension for the three (3) different road profiles. Figure 15: Suspension working space responses of the passive, semi-active suspension, and fully active suspension for the three (3) different road profiles.
## 5 Numerical Analysis
### The Numerical Analysis Related to the Profit
#### 5.2.3 Overall Profit and Service Differentiation  
Given that \(\delta_{1}=0.7\), \(\delta_{2}=0.3\), \(\alpha=0.6\), \(\beta=0.5\), \(\Delta s=[0,\,1]\), and \(\eta=1\), the impact of service differentiation \(\Delta s\) on the overall profit of supply chain \(\pi\) under centralized and decentralized decision making is depicted in Figure 7.  
It can be observed that, in both centralized and decentralized strategies, the profit function of the CBEC supply chain exhibits an inflection point \(\Delta s^{*}\), representing the optimal service differentiation threshold. When \(\Delta s<\Delta s^{*}\), the overall profit of the CBEC supply chain increases with the degree of service differentiation. Conversely, when \(\Delta s>\Delta s^{*}\), the overall profit decreases as the degree of service differentiation increases, highlighting the risks of overinvestment. To operationalize this insight, \(\Delta s^{*}\) is defined as the Service Elasticity Index (SEI), which quantifies the marginal profit impact of service differentiation per unit cost. This index provides a novel metric to prioritize IT investments by identifying  
Figure 6: The relationship between overall profit and service cost coefficient.  
Figure 7: The relationship between overall profit and service differentiation.  
the inflection point where additional service enhancements yield diminishing returns (see Theorem 6 and Table 1 for derivations). In addition, given a certain level of service differentiation, a supply chain adopting a centralized strategy can achieve greater profits. When formulating service differentiation strategies, enterprises should comprehensively consider these influencing factors. Simply increasing the degree of service differentiation may not necessarily benefit the CBEC supply chain and could even lead to negative utility. IT initiatives should be aligned with SEI to avoid diminishing returns. This approach is particularly critical for SMEs with constrained budgets, enabling them to target IT solutions that deliver measurable competitive advantages.
# Trends in LPWAN Technologies for LEO Satellite Constellations in the NewSpace Context
###### Abstract  
This study applies satellite constellations in Internet of Things (IoT) communications, specifically within low-power wide-area network (LPWAN) technologies in the NewSpace context. It comprehensively categorizes and describes the functionality and typology of low Earth orbits (LEOs), examines the societal impacts of these technologies, and provides an in-depth analysis of IoT communication architectures and protocols utilizing satellites. Additionally, the study identifies and addresses the challenges faced in this domain while highlighting future trends and developments. By collating and synthesizing pertinent information, this research offers a thorough overview of the opportunities and challenges in this evolving field of study.  
satellite; communications; IoT; NewSpace; LPWAN; LEO
## 4 Discussion  
Reasonable integration of different geophysical and geological data can be utilized intensively to evaluate the physical characteristics and properties of the subsurface layering sequence and to determine the structural elements that can be utilized for optimal planning of the future land-use of the investigated site [41].  
Geologically, a field survey accomplished with q literature survey [13; 42; 43] at many geological outcrops and points of inspection reveals the investigated site represented by main geological units related to various geological formations. The dominant formation in the investigated site is the formation of Qurn, which is composed of main two geological units representing most of the investigated site, which corresponds to the clayey marl that represents the main geotechnical problem.  
Interpretation of surveyed seismic refraction profiles, as supported and confirmed by GPR results, subdivided the near-surface subsurface deposits into main two layers: the first layer, equivalent to highly weathered limestone which is matched with geological inspection points and the obtained results of the resistivity sounding data, and a second layer of hard limestone. In addition, the geoelectrical resistivity data (VES), utilizing the Schlumberger configuration, point out the investigated site represented by different geologic units as stated by the ranges of resistivity values, indicating that the subsurface stratigraphic section consists of two main distinctive layers of overburden deposits fluctuating between weathered deposits and argillaceous limestone ending by bed rock of highly compacted limestone.  
Based on the findings and the obtained results of the merged and integrated environmental geological and geophysical techniques, it has been possible to divide the study area  
Figure 14: Geoelectric resistivity cross-sections: (**a**) profile-01, (**b**) profile-02, and (**c**) profile-03.  
into two main units with distinct geophysical and geological characteristics, which can be described briefly as the following: unit (A) that covers most portions of the investigated site except the southwestern part and represents the relatively optimum area for construction purposes after removing the overburden deposits (highly weathered and argillaceous limestone deposits), which can used for heavy construction activities; and unit (B) that covers the southwestern part of the investigated site, which is highly weathered and marked by low seismic velocities and low true resistivity values, in addition to that it can be described as very steep lowland and can be used for open areas such as parking and gardens, as illustrated in Figure 15.