esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 5 Conclusions  
The integration of space and ground networks is a good solution with wide coverage and high transmission efficiency. A variety of ELM models are proposed in this paper, to further improve the data transmission quality and improve the stability of data interaction between satellite and ground base station. The blockage predicted by the extended ELM model is one of the most important factors affecting satellite data transmission. The simulation results also show that our model is effective, efficient, and accurate. With the help of the extended ELM model, the satellite can find the data transmission link with the highest transmission efficiency and the fastest transmission speed. This method can not only guarantee data transmission reliability, but also reduce data transfer time and increase data transmission efficiency. The features of the ELM model are its light weight and the ability of the model deployed to many edge devices. Edge devices can quickly and efficiently collect data and fit ELM models locally. The time performance has been greatly improved, further ensuring the stability of the interaction between satellites and ground base stations. Regarding the future development of this work, at present, the model accuracy of this work is limited by the technical limitations of weather collection equipment. If more weather features can be collected in the future, the model performance will be further improved. In addition, rainy weather is not the only influencing factor for the communication efficiency of satellite communication links. In future work, it can be challenged to comprehensively consider multiple influencing factors and propose further optimization schemes for the routing design of the SAGIN architecture.  
Conceptualization and methodology, X.Y.; writing--original draft preparation, writing--review and editing, J.L. and H.D.; software, validation and visualization, Y.Z.; data collection, data analysis and data curation, F.L. and M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results
### ANN-LM Optimisation Performance Modelling Results
In terms of MSE values, the RBFNN algorithm typically beats the GRNN algorithm. The MSE value at a protection distance of 35 km for the RBFNN algorithm was 0.168153 when no neurons (0 neurons) were used. The MSE values considerably dropped to 1.88574 \(\times\) 10\({}^{-13}\) and 3.61909 \(\times\) 10\({}^{-18}\) with 50 and 100 neurons, respectively. As the number of neurons rises, the MSE decreases, indicating increased performance and accuracy. At a protection distance of 35 km, the GRNN algorithm, on the other hand, generated an MSE value of 0.168270 when no neurons were used. The MSE values for 50 and 100 neurons were slightly higher, at 1.89594 \(\times\) 10\({}^{-13}\) and 7.44741 \(\times\) 10\({}^{-18}\), respectively.
## 3 Materials and Methods
### Dataset Description and Characterization
Many of the alert types listed in Table 2 correspond to well-known adversarial tactics observed in real-world cybersecurity incidents. For example, alert_id 2 (\"Unknown IMAP Command\") was indicative of reconnaissance or probing of mail services, while alert_id 13 (\"Invalid HTTP content\") and alert_id 14 (\"consecutive small TCP segments\") reflected evasion techniques and fragmentation attacks targeting web services. Alerts such as 0, 5, and 21 represented classic port scanning behavior, commonly used during the initial stages of an attack to identify open services. These patterns aligned with tactics described in the MITRE ATT&CK [3] framework and reinforced the realism of the dataset for studying attacker attribution. As illustrated in Figure 1, the box plot displays the distribution of alert sequence lengths across the dataset. In this graph, the Y-axis corresponds to the number of alerts (i.e., the length of each sequence), while the X-axis represents a single position (labeled \"1\"), given that only one variable was being analyzed. The box represents the interquartile range (IQR), capturing the middle 50% of the data, with the horizontal line inside the box indicating the median. The whiskers extend to the smallest and largest values not classified as outliers, and individual points beyond the whiskers denote extreme values. An analysis of the figure reveals that the majority of sequences were relatively short, as evidenced by the position of the box near the lower end of the scale. Nevertheless, several extreme outliers were observed, with the longest sequences reaching up to nearly 100,000 alerts. The most prominent outliers appeared at around 90,000 and 60,000 and between 20,000 and 40,000 alerts. This distribution was markedly skewed, indicating that while most attack sequences were brief, there existed a minority of extraordinarily long sequences that significantly deviated from the norm. Statistical summary metrics supported these observations. The dataset comprised a total of 82,092 sequences, with an average length of 67.28 alerts and a median of 18.00. The shortest sequence contained a single alert, while the longest reached 95,427. The standard deviation, at 532.83, further reflected the high dispersion in sequence lengths. Percentile analysis showed that 95% of sequences were shorter than 185 alerts and 97% were under 298 alerts. This reinforced the notion that extremely long sequences were statistical anomalies and not representative of typical behavior. To conduct more focused and interpretable analyses, this study restricted attention to sequences with lengths between 9 and 243 alerts. This range was selected to retain the diversity of attack patterns while excluding trivial or excessively long outliers. Applying this filter resulted in a refined subset of 53,830 sequences, which balanced representativeness and tractability. This selection enabled a more targeted investigation of attacker behavior, which was critical for the development of effective and explainable attribution models.
# The Optimization of UAV-Assisted Downlink Transmission Based on RSMA
## 5 Simulation Analysis
### Simulation Analysis of MD-AFSA Algorithm Performance
On the F4 function, the solution error of MD-AFSA is reduced by 99.71% compared with AFSA; on the F5 function, the solution error of MD-AFSA is reduced by 83.70% compared with AFSA; on the F6 function, the solution error of MD-AFSA is 93.67% lower than that of AFSA. The optimization test of single extremum function and multi-extremum function shows that the MD-AFSA algorithm proposed in this paper has better convergence speed and optimization ability than the original algorithm, and can quickly find the optimal solution point of the objective function and solve the optimization problem. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **F4** & **F5** & **F6** \\ \hline AFSA & \(2.94\times 10^{-1}\) & \(6.64\times 10^{-2}\) & \(7.61\times 10^{-4}\) \\ M-AFSA & \(8.36\times 10^{-4}\) & \(1.91\times 10^{-2}\) & \(2.29\times 10^{-4}\) \\ D-AFSA & \(7.50\times 10^{-1}\) & \(7.97\times 10^{-2}\) & \(1.02\times 10^{-2}\) \\ MD-AFSA & \(8.44\times 10^{-4}\) & \(1.08\times 10^{-2}\) & \(4.81\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Error after 100 iterations of a multi extremum function. Figure 4: Multi-extremum function test. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **F1** & **F2** & **F3** \\ \hline AFSA & \(1.71\times 10^{-4}\) & \(7.51\times 10^{-3}\) & \(1.82\times 10^{2}\) \\ M-AFSA & \(1.69\times 10^{-5}\) & \(7.52\times 10^{-4}\) & \(8.14\times 10^{1}\) \\ D-AFSA & \(8.46\times 10^{-1}\) & \(1.15\times 10^{-1}\) & \(2.53\times 10^{2}\) \\ MD-AFSA & \(6.40\times 10^{-7}\) & \(9.91\times 10^{-5}\) & \(1.50\times 10^{1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Error after 100 iterations of a single extremum function. Under the same experimental conditions (the hardware platform parameters and algorithm parameters are consistent), the time characteristics of different algorithms are tested, and the end condition of the algorithm is that the maximum number of iterations is reached or the difference between the optimization function value and the actual optimal value is less than 0.01. The test results are shown in Table 4.  
According to Table 5, when the optimization error is 0.01, D-AFSA can't reduce the time consumption of the algorithm, while M-AFSA and MD-AFSA can effectively reduce the running time of the algorithm and improve the efficiency and practicability of the algorithm.
## 3 Results and Discussion
### Physical Characteristics of the Prepared Formulations  
Prepared insulin niosome emulgel, insulin niosome gel, insulin emulgel and insulin gel were homogenous in nature. Higher encapsulation efficiency (\(42.5\%\pm 2\%\)), good drug loading (\(13.5\%\pm 3\%\)) with nanometric particle size (\(885\pm 45\) nm) were observed in prepared insulin niosomes. Interestingly, insulin is soluble in phosphate buffer (pH7.4) and also in the lipid mixture. Hence, the high entrapment efficiency and drug loading of insulin is due to its solubility in surfactant/cholesterol lipid phase and also the internal aqueous compartment of niosome vesicles. In addition, the higher encapsulation efficiency observed in insulin niosomes could be attributed to the high cholesterol concentrations included in niosomes. Increase in cholesterol concentration can presumably reorganize the chain order of the liquid-state bilayer and thereby enhances the strength of the nonpolar tail of nonionic surfactant. It was reported previously that, increasing the cholesterol percentage to 50% with the surfactant resulted in increasing the niosome particle size and subsequently increasing its encapsulation efficiency [23]. A representative size distribution curve of insulin niosome is shown in Figure 2. The figure demonstrates narrow distribution (PDI \(<0.3\)) of niosome particles in the formulation while the data in Table 3 signifies average vesicles size of 885 nm (insulin niosome). The measurement of the formulation viscosities are illustrated in Table 3. It is clear that, the viscosity increased from 6550 cP into 12,250 cP for insulin gel and insulin niosome emulgel, respectively. The increasing of the viscosity could be explained according to the formulation ingredients. Simple gel formulation showed low viscosity, while insulin emulgel and insulin niosome emulgel showed higher viscosity.  
Figure 2: Representative size distribution curve of insulin niosome.  
A representative scanning electron microscopy image of prepared insulin niosome emulgel is shown in Figure 3. Figure indicated small spherical vesicles distributed through the network of macromolecular polymer used. No marked crystals of insulin observed in the formulation indicating a very good solubility of the drug.
## 1 Introduction
### Effects of Plants on Formaldehyde Removal  
It has been well established that potted plants can help to phytoremediate a diverse range of indoor air pollutants. In particular, a substantial body of literature has demonstrated the ability of the potted plant system to remove VOCs from the indoor air. These findings have largely originated from laboratory-scale chamber experiments, with several studies drawing different conclusions regarding the primary VOC removal mechanism and removal efficacies [20; 21; 22; 23]. The process of VOC depletion found in most studies is through the microbial activity in the substrate and rhizosphere, where bacteria absorb the VOCs and metabolize them as a nutrient source [22; 23; 24; 25]. It is also important to consider that plant selection has a significant impact on the depletion of gaseous pollutants from the air [21; 22; 23; 26; 27]; therefore, one of the main aims of this study is to execute controlled laboratory experiments to select the best components to build up an active plant-based system that will aim to improve indoor air quality.  
In 2011, [PERSON] and [PERSON] tested the formaldehyde removal efficiency of the root area and aerial parts independently and found that while the aerial parts of plants were capable of VOC removal, removal by the root area occurred at a substantially faster rate [25]. Other research has identified the potential for the microorganisms existing on and in leaves to remove VOCs [28; 29]. However, most recent research has acknowledged that the mechanisms of removal are mainly located in the substrate, rather than the plant itself [30; 31; 32].  
Based on the studies mentioned, it is valid to assume that a plant together with its substrate can have a positive removal effect on the concentration of formaldehyde in indoor environments. However, the extent to which different plants remove formaldehyde is not well known yet. This paper presents the results of a study on the uptake of formaldehyde and CO\({}_{2}\) from selected potted plants and substrates, with the objective of using the outcome of these experiments to select the best-performing plant and substrate for the construction of an indoor plant-based system (biowall).
## 4 Results and Analysis
### Robustness Results on Synthetic Images
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{Type (mAP50) (\%)} & YOLOv56u & YOLOv5m6u & YOLOv5l6u & YOLOv8s & YOLOv8m & YOLOv8l & YOLOv8x \\ \hline \multirow{4}{*}{**YOLOv8**} & Clean & 71.6 & 75.8 & 78.4 & 66.2 & 72.0 & 74.9 & 79.3 \\ \cline{2-9}  & Rain & 49.1 & 52.0 & 58.9 & 44.8 & 48.7 & 53.1 & 62.4 \\  & Fog & 46.4 & 50.3 & 57.2 & 41.0 & 46.6 & 51.6 & 61.3 \\  & Frost & 43.4 & 48.4 & 57.4 & 41.3 & 45.4 & 52.3 & 61.7 \\  & Snow & 47.5 & 51.4 & 57.7 & 42.4 & 47.2 & 52.4 & 62.0 \\ \hline \multirow{4}{*}{**YOLOv8**} & Brightness & 54.2 & 61.3 & 66.4 & 48.2 & 55.2 & 60.6 & 68.1 \\  & Dark & 53.7 & 59.7 & 65.9 & 46.8 & 54.4 & 60.0 & 67.1 \\  & Saturate & 55.1 & 60.7 & 65.7 & 47.8 & 55.9 & 62.7 & 70.8 \\ \hline \multirow{4}{*}{**YOLOv8**} & GaussianNoise & 47.7 & 50.1 & 56.5 & 40.1 & 45.4 & 52.4 & 60.0 \\  & SpeckleNoise & 46.0 & 48.5 & 55.6 & 39.4 & 44.4 & 51.7 & 60.4 \\  & ZoomBlur & 14.1 & 16.7 & 20.9 & 11.8 & 15.0 & 17.9 & 22.4 \\  & MotionBlur & 46.2 & 47.4 & 55.1 & 39.8 & 45.4 & 51.5 & 61.7 \\ \hline \multirow{4}{*}{**YOLOv8**} & ElasticTransform & 48.7 & 51.2 & 57.1 & 44.3 & 47.4 & 52.5 & 62.0 \\  & JpegCompression & 49.7 & 51.7 & 58.3 & 46.5 & 49.4 & 53.9 & 63.0 \\  & Occlusion & 5.1 & 7.5 & 10.4 & 4.0 & 6.9 & 7.7 & 11.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The benchmark results of YOLOv5 and YOLOv8. We show robustness metric (mAP50) under each corruption based on the car, truck, and bus classes at severe level. Figure 13: Bar chart of benchmark results of YOLOv5 and YOLOv8. We show change in robustness metric (mAP50) under different corruption types at middle level.
## 2 Materials and Methods  
### Study Sites  
The two eddy covariance (EC) flux towers are located in restored and natural mangrove forest (Figure 1). The restored mangrove forest (120.57\({}^{\circ}\) E, 27.58\({}^{\circ}\) N) is located at the estuary of the Aojiang River in Zhejiang province, a region dominated by a subtropical monsoon climate. This area experienced an average annual rainfall and temperature of 1588 mm and 20.4 \({}^{\circ}\)C during 2020-2022 [14]. The estuary is influenced by tidal dynamics, with semidiurnal tides that are crucial in nutrient transport and sediment deposition. The dominant plant species is _Kandelia obovata_, which was planted in two phases (2014 and 2018) as part of a coastal restoration project. The young age of the restored mangroves is reflected in their relatively low canopy height, ranging from 0.54 to 2.57 m, occupying an area of 11 ha.  
In contrast, the natural mangrove forest (114.03\({}^{\circ}\) E, 22.5\({}^{\circ}\) N) is located at Mai Po Nature Reserve in Hong Kong SAR, a Ramsar-listed wetland of international importance [15]. This area experienced a mean annual temperature and mean annual precipitation of 23.5 \({}^{\circ}\)C and 2431.2 mm, respectively, during the 30-year period from 1991 to 2020 ([[https://www.hko.gov.hk/en/cis/normal/1991_2020/normals.htm](https://www.hko.gov.hk/en/cis/normal/1991_2020/normals.htm)]([https://www.hko.gov.hk/en/cis/normal/1991_2020/normals.htm](https://www.hko.gov.hk/en/cis/normal/1991_2020/normals.htm)), accessed on 15 April 2025). The dominant species is _Kandelia obovate_, but the trees are significantly older, with an age range of 20-25 years. This mature forest has a well-developed canopy structure, with a mean height of 6.5 m, and a dense root system that enhances sediment stabilization and carbon sequestration [15]. The natural mangrove forest covers an area of 115 ha.
# Text-Guided Refinement for Referring Image Segmentation
## 4 Experiment
### Datasets  
We trained and evaluated the proposed TGRN on three widely used referring image segmentation datasets: UNC [42], UNC+ [42], and G-Ref [30]. All three datasets are built upon MS-COCO [43], containing 19,994, 19,992, and 26,711 images, respectively. They provide 142,209, 141,564, and 104,560 referring expressions, describing over 50,000 unique objects. The UNC dataset contains referring expressions that are relatively short, averaging fewer than 4 words, often relying on spatial descriptions. In contrast, the UNC+ dataset prohibits the use of spatial location terms during data collection, forcing expressions to focus exclusively on the appearance attributes of the target objects. This restriction makes the task more challenging, as the matching between language and visual regions depends entirely on appearance-based information. The G-Ref dataset further increases complexity with longer referring expressions, averaging 8.4 words, which include richer contextual details and diverse linguistic structures. Overall, the combination of these datasets provides a diverse and representative testbed for evaluating referring image segmentation. They differ in sentence complexity, spatial grounding, and linguistic structure, thereby covering a broad range of real-world challenges. Their complementary nature enables a comprehensive validation of our model's generalization capability across various scenarios.
## 4 Evaluation of the Mechanism
### Simulator Description and Simulation Parameters  
To evaluate the mechanism, simulations were performed using the ns-3 simulator [11]. The mechanism required the implementation of new components that implement the functionalities described in [8]. In addition, methods were implemented to select the weights of the links presented Section 2.  
Simulations were performed using the US-backbone topology [12]. This network consists of 39 nodes connected by 61 bidirectional links. The network has 10 nodes that act as PE switches (points of attachment of destination networks and traffic sources). Specific nodes (PEs) are chosen to function as connection points for both traffic sources and destinations (_Source-Client Network_--SCN, and _Destination-Client Network_--DCN), as illustrated in Figure 2. DCNs and SCNs are networks in which destinations and sources of traffic reside. DCNs and SCNs can be reached via PE nodes. All inter-node links were assigned 100 Mb/s of bandwidth with a delay of 1 ms. The links between the PE and the source and destination nodes were set to 1 Gb/s with a delay of 1 ms. Network traffic was generated using the internal pseudo-random number generator of the ns-3 simulator.  
Experiments with only UDP traffic were performed. The generators were started at the source nodes. For each flow, packet sizes were randomly generated from a normal distribution with a mean of 1350 B and a standard deviation of 150 B. The time between the appearance of individual packets for a single flow was drawn randomly according to an exponential distribution with a mean of 10 ms. The number of packets for each flow was randomly generated from a Pareto distribution with a mean of 150 and a shape coefficient of 1.5. The time between the start of the subsequent flows was drawn according to an exponential distribution with a mean of 1 ms. The traffic parameters were chosen so that the bandwidth of the network was significantly exceeded for the scenario without the proposed mechanism. This approach allows for the evaluation of the gain from implementing multipath transmission for different weight calculation schemes.  
For all scenarios, the weights of the links versus the load of the link are shown in Figure 3. All simulations were repeated 20 times. The same values of the parameters of the pseudorandom number generators were used for each test scenario in order to obtain repeatable conditions. Then, the 95% confidence intervals were calculated according to the Student's t-distribution.
## 4 Materials and Methods
### Analysis of Nonvolatile Compounds by HPLC-MS
#### 4.5.2 Detection of VOCs  
VOC detection was performed via gas chromatography-mass spectrometry (GC-MS) using a Model 7890B GC system (Agilent, Santa Clara, CA, USA) and a PEGASUS(r) BT GC-TOFMS system (Leco, Chicago, IL, USA).  
Chromotographic condition: A 19091S-433 HP-5 MS capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) with high-purity helium (99.999%) and a linear velocity (1.0 mL/min) was used. The injection mode consisted of a nonsplit injection, and the temperatures of the injector and detector were set at 270 \({}^{\circ}\)C and 305 \({}^{\circ}\)C, respectively. The oven was initially set to 40 \({}^{\circ}\)C. The temperature of the samples was then gradually raised at a rate of 15 \({}^{\circ}\)C/min to reach 125 \({}^{\circ}\)C. The temperature was subsequently increased at a rate of 5 \({}^{\circ}\)C/min and then at a rate of 10 \({}^{\circ}\)C/min until it reached 210 \({}^{\circ}\)C. Finally, to achieve a final temperature of 305 \({}^{\circ}\)C, the oven temperature was increased at a rapid rate of 20 \({}^{\circ}\)C/min. This peak temperature was maintained for 5 min. The VOCs were also detected by Verygenome Technology Co., Ltd., Guangzhou, China.  
Mass spectrum conditions: The mass spectra were acquired in the electron impact (EI) ionization mode with an energy of 70 eV. The temperatures of the ion source, quadrupole mass detector, and transfer line were set to 240 \({}^{\circ}\)C, 160 \({}^{\circ}\)C, and 270 \({}^{\circ}\)C, respectively. The mass scanning range was 50-600 \(m/z\), with a 5 min delay to start acquisition. The scanning speed, frequency, and step size were 1562 U/s, 2.7 (number of scans/s), and 0.1 \(m/z\), respectively. The identification and quantification analysis were performed using the selected ion monitoring (SIM) mode.  
Quantitative and qualitative analyses of VOCs: Peak deconvolution was performed on the original file via Qualitative Workflows software (version B.08.00), and peaks were identified by searching NIS 17.0. The peaks were then aligned and quantified via Qualitative Workflow B.08.00 quantitative analysis software and corrected according to the internal standard response. The Qualitative Workflow B.08.00 software setup parameters were as follows: compound discovery, find by molecular features; peak filtration, peak height \(\geq\)600 counts; compound identification, asymmetry (\(m/z\)) of \(-\)0.3 Da + 0.7 Da; smallest match (\(m/z\)), 30 Da; abundance ratio contrast uncertainty, 40%; and score (rev), 60.00. VOCs were semi-quantified and analyzed based on the peak areas of the internal standard using Equation (1) as follows:  
\[\text{Relative content }(\mu\text{g/g})=\frac{\text{The content of IS }(\mu\text{g})\times\frac{\text{ Peak area of target compounds}}{\text{Feak area of IS}}}{\text{Amount of peanut }(\text{g})} \tag{1}\]