esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Governing Equations  
Consider the flow from the source or sink at the intersection of two plane walls. \(2\alpha\) is taken as the angle between the walls, as depicted in Figure 1. The walls are assumed to be stretching/shrinking at a rate \(s\) in such a way that the velocity at the wall can be written as \(\overset{\wedge}{u_{r}}=U_{w}=\frac{s}{r}\). Furthermore, \(\alpha>0\) represents the case of a divergent channel, while for \(\alpha<0\), the same channel is convergent. The channel is assumed to be saturated by the nanofluid consisting of water (the base fluid) and the copper (Cu) or silver (Ag) nanoparticles. A thermal equilibrium between the base fluid and the nanoparticles is assumed. A uniform transverse magnetic field of strength \(B_{\circ}\) is applied across the channel to analyze the flow under the influence of an external body force. For the said flow, a polar coordinates system \(\left(r,\theta\right)\) is used to formulate the problem. Under the said conventions, the velocity field takes the form \(V=\left(u_{r},\,0,\,0\right)\), where \(u_{r}\) is a function of both \(r\) and \(\theta\). The equations of continuity, momentum and energy in polar coordinates under the imposed assumptions become [38]:  
\[\frac{1}{r}\frac{\partial}{\partial r}(r\overset{\wedge}{u_{r}})=0 \tag{1}\]  
\[\rho_{\sigma}\left(\overset{\wedge}{u_{r}}\overset{\partial}{\overset{ \wedge}{\partial r}}\right)=-\frac{\partial p}{\partial r}+\mu_{\sigma}\left[ \frac{\partial^{2}\overset{\wedge}{u_{r}}}{\partial r^{2}}+\frac{1}{r}\frac{ \partial\overset{\wedge}{u_{r}}}{\partial r}+\frac{1}{r^{2}}\frac{\partial^{2 }\overset{\wedge}{u_{r}}}{\partial\theta^{2}}-\frac{u_{r}}{r^{2}}\right]- \frac{\sigma_{\sigma^{\prime}}B_{0}^{2}}{\rho_{\sigma^{\prime}}r^{2}}\overset{ \wedge}{u_{r}} \tag{2}\]  
\[-\frac{1}{\rho_{\sigma^{\prime\prime}}}\frac{\partial p}{\partial r}+\frac{2} {r^{2}}\frac{\mu_{\sigma^{\prime\prime}}}{\rho_{\sigma^{\prime\prime}}}\frac{ \partial\overset{\wedge}{u_{r}}}{\partial\theta}=0 \tag{3}\]  
Figure 1: Schematic diagram of the flow problem. \[\hat{u_{r}}\frac{\partial\hat{T}}{\partial r}=\frac{k_{uf}}{\left(\rho C_{{}_{P}} \right)_{uf}}\left[\frac{\partial^{2}\hat{T}}{\partial r^{2}}+\frac{1}{r}\frac {\partial\hat{T}}{\partial r}+\frac{1}{r^{2}}\frac{\partial^{2}\hat{T}}{ \partial\theta^{2}}\right]+\frac{\mu_{uf}}{\left(\rho C_{{}_{P}}\right)_{uf}} \left[4\left(\frac{\partial\hat{u_{r}}}{\partial r}\right)^{2}+\frac{1}{r^{2}} \left(\frac{\partial\hat{u_{r}}}{\partial\theta}\right)^{2}\right] \tag{4}\]  
Boundary conditions for the problem are:  
\[\hat{u_{r}}=\frac{U_{c}}{r},\hskip 28.452756 pt\frac{\partial\hat{u_{r}}}{ \partial\theta}=0,\hskip 28.452756 pt\frac{\partial\hat{T}}{\partial\theta}=0 \hskip 5.690551 pt\text{at}\hskip 5.690551 pt\theta=0 \tag{5}\] \[\hat{u_{r}}=U_{w}=\frac{s}{r},\hskip 42.679134 pt\hat{T}=\frac{ \hat{T_{w}}}{r^{2}}\hskip 5.690551 pt\text{at}\hskip 5.690551 pt\theta=\pm\alpha\]  
where \(U_{c}\) is the velocity at the centerline of the channel and \(U_{w}\), \(\hat{T_{w}}\), respectively, represent the velocity and temperature at the wall of the channel.
## 3 Results and Analysis  
### Temporal and Spatial Evolution of Temperature Field  
Figure 3 illustrates the relationship curve between the dimensionless temperature and dimensionless radius of the roadway-surrounding rock under the specified conditions: air temperature of 25 \({}^{\circ}\)C, VRT of 50 \({}^{\circ}\)C, and thermal conductivity of 2 \(W/(\text{m}\cdot\text{K})\). It is observed that, as the surrounding rock extends radially to the deep, the dimensionless temperature gradually increases, and the rate of temperature rise gradually decreases until it reaches the initial virgin rock temperature. Additionally, the figure indicates that, in the early stages of ventilation, the roadway's surrounding rock is minimally affected by disturbances. However, as the ventilation time extends, the cooling range of the surrounding rock continues to expand. For ventilation periods of 1 day, 31 days, 181 days, and 361 days, the cooling radius of the surrounding rock is \(<\)1 \(R\), 3 \(R\), 6 \(R\), and 7 \(R\), respectively.  
Furthermore, as ventilation time extends, the temperature of the roadway wall gradually decreases, but the rate of temperature drop also decreases. As ventilation time progresses from 1 day to 31 days, 181 days, and 361 days, the wall temperature of the roadway is reduced by 45%, 82%, 90%, and 92%, respectively, compared to the virgin temperature. This occurs because, after the ventilation of the roadway, a temperature difference arises between the surrounding rock and the airflow, leading to convective heat transfer. This disrupts the virgin thermal equilibrium state, and the airflow carries away the heat from the surrounding rock, resulting in a decrease in its temperature. With the continuation of ventilation, the temperature difference between the surrounding rock and the roadway wall gradually increases, and the cooling range of the surrounding rock extends to its interior. However, the extension rate gradually decreases until a new equilibrium state is reached.  
Figure 2: The relationship curve between \(R\) and parameters E, G, and H.  
Figure 4 illustrates the relationship curve between dimensionless temperature and dimensionless time of the roadway-surrounding rock under specified conditions: air temperature of 25 \({}^{\circ}\)C, VRT of 50 \({}^{\circ}\)C, and thermal conductivity of 2 W/(m\(\cdot\)K). It is evident that, with an increase in ventilation time, the dimensionless temperature of the surrounding rock gradually decreases, and the cooling rate also gradually decreases, eventually stabilizing at a fixed value. Additionally, the figure shows that the degree of disturbance in the dimensionless temperature of the surrounding rock gradually decreases with an increase in dimensionless radius. After 361 days of ventilation, as the dimensionless radius increased from \(R=0\) to \(R=3\), the temperature of the surrounding rock decreased by 92%, 54%, 31%, and 18%, respectively, compared to the virgin temperature.
# Satellite Image Categorization Using Scalable Deep Learning
## 4 Results  
Satellite Image Classification Dataset-RSI-CB256 is used for this research. The batch size for experiments is 32. The input size of the proposed network is 600 \(\times\) 600 px. The image samples are resized to fit in the model. Our approach involves utilizing the RMSProp optimizer, which has a decay rate of 0.9 and a momentum of 0.9. Additionally, we incorporate batch normalization after each convolution layer with a momentum of 0.99, and we apply a weight decay of 1 \(\times\) 10\({}^{-5}\). To prevent overfitting, we implement a dropout rate of 0.2 on the final layer. As suggested by [49], we gradually increase the learning rate from 0 to 0.256 during the first 5 epochs, and we subsequently decrease it by a factor of 0.97 every 2.4 epochs. Splits of 80%, 10%, and 10% are used for training, validation, and testing. The sample selection for training and validation remains the same for all experiments. A validation set is utilized for an early stopping process.
# Radio Frequency Compatibility Evaluation of S Band Navigation Signals for Future BeiDou
## 4 Radio Frequency Compatibility Evaluation
In code tracking, IRNSS, Galileo candidate and Globalstar introduce less interference impacts on CPM(8), and the mean \(\Delta(C/N_{0})^{\mathrm{inter,worst}}_{\mathrm{eff,CTSSC}}\) of CPM(8) is over 0.02 dB smaller than that of MSK-BOCs(4,4), followed by BOCs(4,4) and BPSK(8). From the compatibility evaluation results above, it is concluded that although the introduction of BDS S band candidate inevitably increases intersystem interference on existing or planned signals in same frequency band, the performance degradation is very little below 0.15 dB at the maximum. The effect of each BDS candidate caused by other systems in S band is less than 0.8 dB at the maximum. It even can be ignored. On the whole, the CPM(8) results in less mutual interference with most S band signals in acquisition and code tracking, followed by MSK-BOC(4,4), BOCs(4,4) and BPSK(8). Thus, CPM(8) is more superior as a future BDS signal solution in S band.
## 3 Measurement Set-Up and Prototype
### Scenarios
#### 3.3.3 Field Railway Tests  
The objective of the field tests is to analyse the performance of the proposed system in a realistic railway line.  
This test is performed in the railway environment in a low railway density traffic line. The selected location presents a low coverage area, which provides the most restricting option to prove this proposed implementation. This location is between Guardo (Plasencia, Spain) and Mataporquera (Cantabria, Spain).  
The Guardo-Mataporquera railway section is a non-electrified single track in the north of Spain with no communication infrastructure deployed nowadays. It follows a route  
Figure 3: Software-distributed modules architecture.  
65 km long, including two main stations (Guardo and Mataporquera stations) and two secondary stations (Santibanez de la Pena and Vado-Cervera stations).
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 5 Discussion
At the case study altitude, the semi-major axis decay observed during phasing remained negligible, within several kilometers. This should have little impact on mission lifetime, considering that satellites of PocketQube or CubeSat configurations at this altitude would remain in orbit for approximately 5 years. One of the primary limitations of the approach is the extended phasing duration when the ballistic coefficient ratio is low and no deployables are present. This can be mitigated by increasing the surface area difference between drag modes through additional deployables. However, doing so introduces complexity and potential failure points. Alternatively, relaxing the cube-shaped constraint can improve performance, albeit at the cost of manufacturing and integration complexity. Maintaining both constraints may result in suboptimal phasing durations, and at least one should be reconsidered to ensure mission viability. Despite this, however, an important advantage of the many-in-one strategy is that satellites can begin operations before final constellation geometry is achieved. This is particularly useful for missions that are not highly sensitive to precise spatial configuration during early operations. The method also introduces added risk due to the multiple disengagements required. This risk scales with constellation size, as each stage adds a potential point of failure. However, failure at any stage does not necessarily compromise the entire mission, as undeployed units may still operate as part of a reduced-capability constellation. Collision risk is also elevated immediately after separation, as relative velocities are small. This risk is mitigated through careful tuning of \(\Delta v\), the separation angle, and by leveraging the natural divergence introduced through drag. Finally, the study relies heavily on simulation and on the NRLMSISE-00 atmospheric model, which has known limitations in capturing fine-scale atmospheric variations [17,20]. While real-time updates to model inputs, such as those applied in Planet Labs' operational systems in [10], can improve accuracy, uncertainties in drag modeling remain a significant challenge in differential drag control.
# Study on Components Determination and Performance Evaluation of LS Pre-Maintenance Agent
## 6 Conclusions  
The study aims at the early damage of asphalt pavement in strong UV radiation areas, develops a LS pre-maintenance agent, determines its optimum composition ratio through indoor tests, and studies its effect on road performance. The main conclusions are as follows:  
1. The asphalt pavement maintenance function is determined and the main components of the LS pre-maintenance agent are rock asphalt, reducing agent, penetrant, solvent oil, dispersant, and LS agent. The raw material types are determined by contrast tests.
2. The optimum ratio of the components is determined based on the best proportion of penetrant and solvent oil for solubility ability. The best proportion for quick-drying, long-term storage ability, and the amount of dispersant and LS agent, rock asphalt, reducing agent, penetrant and solvent oil is 30:20:20:30. The LS agent is 5% of the four components total mass. The dispersant is 0.4%.
3. Digital image technology is used in the permeability test to provide accurate measurements of the LS pre-maintenance agent penetration depth. The results showed that the pavement should be closed at least 2 h after spraying and can extended to 8 h time permitting. The dosage of 0.5 kg/m\({}^{2}\) can ensure sufficient penetration effect.
4. Use the RTFOT test to compare the LS pre-maintenance agent regeneration effect to the indexes of aged asphalt. The results will show that it has excellent restore and activation ability to the aged asphalt and greatly improves the low temperature anti-cracking ability of asphalt pavement.
5. Compare the appearance and \(MLR\) results of the LS pre-maintenance agent after the artificial UV radiation aging test. The anti-light aging time of the LS pre-maintenance agent can last 2 years in  
Figure 10: Appearance comparison after UV aging (0, 8, 12, 24, 48 h); (a) LS pre-maintenance agent; (b) Rhinophilat.  
Lhasa. Its light resistance performance is much better than the Rhinophilat agent, which can meet the requirements of pre-maintenance work in strong UV radiation areas.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
The writers wish to acknowledge the financial support of this research by the Training Project of High Level Technical Personnel in Transportation Industry (No. 213021160088), National Science Foundation of China (No. 51708044), and the fundamental Research Funds for the Central Universities (No. 310821161015, 300102218408).  
The authors declare no conflicts of interest.
## 5 Experimental Analysis
### Objective Function and Optimal Solution Set Optimization
#### 5.1.2 Dominant Solution Set Comparison  
The Feasible-Rate and Spread metrics were used to measure the feasibility and spread of the dominant solution set, respectively. Feasible-Rate assesses the proportion of solutions that satisfy all constraints and have variables within the upper and lower bounds among all generated solutions, where a higher Feasible-Rate indicates the better feasibility of the generated optimal solution set. Spread calculates the standard deviation of the distances between individuals that are farthest apart on each objective, where a larger Spread indicates a wider distribution of the algorithm's generated solution set, making it less prone to local optima.  
In the performance experiment regarding the solution set, under the same conditions as the objective function optimization test, the NSGA-III algorithm was tested first, followed by comparison with the c-DPEA, MTCMO, MSCEA, and URCMO algorithms. The Feasible-Rate and Spread values for the optimized dominant solution sets were obtained after testing, and they are shown in Figures 7 and 8.  
From Figure 7, it can be seen that the NSGA-III, MSCEA, c-DPEA, MTCMO, and URCMO algorithms converged to a Feasible-Rate of \(1\) at the \(27\)th, \(42\)nd, \(55\)th, \(49\)th, and \(72\)nd iterations, respectively. This indicates that the dominant solution sets generated with all five algorithms satisfied the constraints and had extremely high feasibility. Additionally, the NSGA-III algorithm converged faster than the other four algorithms.  
Taking the example of the last \(20\) iterations when the dominant solution sets generated with all five algorithms satisfied the constraints (Feasible-Rate \(=1\)), Figure 8 shows that the Spread values obtained with the NSGA-III, MSCEA, c-DPEA, MTCMO, and URCMO algorithms were \(1.157\), \(1.018\), \(1.036\), \(1.013\), and \(1.033\), respectively. The NSGA-III algorithm produced the largest Spread, demonstrating the excellent spread of the dominant solution set generated with the NSGA-III algorithm when optimizing LEO satellite distributed jamming.  
In summary, the feasibility, convergence, and spread of the dominant solution set generated with the NSGA-III algorithm were far superior to those of the other four algorithms. This confirms the strong overall performance of the NSGA-III algorithm in optimizing distributed jamming for LEO satellite constellations.  
Figure 8: Spread values generated with different optimization algorithms.  
Figure 7: Feasible-Rate generated with different optimization algorithms.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 3 Intersection-Based Link-Adaptive Beaconless Forwarding for City Scenarios
### Routing Protocol Design
#### 3.2.1 Redundant Packet Elimination  
One of the drawbacks of simple data dissemination techniques such as flooding [49] is that the intermediate relay nodes forward duplicate data packets, resulting in redundant data packets flowing throughout the network. This degrades the network performance, especially in dense scenarios due to the high chances of packet collision and network congestion. Flooding also leads to significant overhead and high bandwidth consumption [42]. To minimize this issue in ILBFC, each node upon reception of a new data packet, records the sequence number. This allows a node to distinguish between a new data packet and a duplicate one, thereby discarding the duplicate data packets.
## 1 Introduction
This way, a quantitative value of each fatty acid before and after the oxidation will be obtained, and consequently the amount of fatty acids that disappear because of the oxidation will be available. Other techniques are based on the determination of the volatile compounds responsible for the unpleasant odors and in some cases propanal, pentanal, hexanal, 2-hexenal, 3-hexenal, or 2,4-heptadien [17; 18] release were also monitored to confirm rancidity. A common methodology in food is the thiobarbituric acid reactive substances (TBARS) assay. This method is based on the determination of malondialdehydedehyde (MDA), which is a low-molecular-weight end product usually formed via the decomposition of certain primary and secondary lipid peroxidation products [19; 20]. TBARS technique has been used in many oxidation studies of food [21]. To have an objective evaluation of the industrial production of this industrial nanoSe antioxidant packaging, all these methodologies were tested to demonstrate the efficiency of the new nanoSe active packaging on packaged food. The main targets for oxidation in the lipids are the polyunsaturated fatty acids, which are vulnerable to the action of free radicals. This is due to the presence of double bonds that weaken the C-H bonds [22; 23]. The hydrolysis and/or autoxidation of fats provide short-chain aldehydes and ketones responsible for rancidity, and rancidity is associated with the characteristics off-flavor and odor [1]. Nuts have a high content of unsaturated fatty acids [24]. Owing to that, walnuts and hazelnuts were the first foods selected for this study. According to the literature, walnuts have a content of monounsaturated fatty acids (MUFA) of around 9 g/100 g and a content of polyunsaturated fatty acids (PUFA) of around 48 g/100 g. Hazelnuts have a MUFA value of approximately 46 g/100 g and PUFA of approximately 8 g/100 g [25]. Therefore, they are good candidates to study their lipid oxidation and rancidity [26], and consequently nuts and walnuts were the first target of this study. Then, several foods susceptible to rancidity, packaged by different companies and packaging lines, have been analyzed and the results are shown and discussed.
## 3 Material and Methods
### Feature Selection
Furthermore, to reduce the dimensionality of the dataset, to make it more manageable and at the same time reduce the computational resource requirements, variables \"StartTime\", \"Dir\", \"SrcAddr\" and \"DstAddr\" were excluded, since they contain dynamically assigned values (e.g., IP addresses). Additionally, variables \"sTos\" and \"dTos\" were also dropped since they are not expected to have a significant impact on the target variable, based also on the feature selection results depicted in Table 3.  
The final dataset, after the feature selection stage, eventually included 8 variables (7 features and the target column), as presented in Table 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Score** \\ \hline TotBytes & \(4.22387\times 10^{9}\) \\ Sport & \(8.21231\times 10^{8}\) \\ SrcBytes & \(6.60375\times 10^{8}\) \\ Dport & \(6.17780\times 10^{8}\) \\ Dur & \(3.30228\times 10^{8}\) \\ State & \(1.41850\times 10^{8}\) \\ TotPkts & \(4.97291\times 10^{7}\) \\ Proto & \(4.55909\times 10^{2}\) \\ dTos & \(3.11329\times 10^{-2}\) \\ sTos & \(2.86774\times 10^{-2}\) \\ StartTime & \(5.23553\times 10^{-4}\) \\ Dir & \(4.90686\times 10^{-4}\) \\ SrcAddr & \(2.38865\times 10^{-4}\) \\ DstAddr & \(2.19013\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Importance score for the 15 features included in the initial dataset. In order to create the feature selection graph, as presented in Figure 3, the following steps were performed: The \"Extra Tree Classifier\" feature selection method was applied to the final dataset to rank the features based on their importance scores. The predictive model was trained with 7 different feature variables, and the model's performance was evaluated using the accuracy metric for each number of the features provided. The results were plotted on a graph to visually analyze and assess the feature importance for each variable in correlation with the target one. Based on the results of this method, the most important variable in correlation with the target variable is \"Sport\". Figure 4 displays the correlation matrix along with a heatmap that presents the correlation coefficients for the selected eight variables of the final dataset, as depicted in Table 3. Upon analyzing the obtained results, it is evident that the so-called \"State\" variable/feature is the most significant among the selected features. On the other hand, the \"Sport\" feature appears to be the least relevant to the \"Label\" variable. Thus, Figure 3 is a visualization that presents the eight most crucial features in correlation with the target variable, namely \"Label\". These features were selected because of the SelectKBest and Extra Trees Classifier methods, which were previously described.