esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Phenology  
The NDVI values were calculated from MODIS MOD9 GA v6.1 imagery [70]. This dataset represents the ground-level surface spectral reflectance. To mask low-quality data, pixels were removed using a bitmask filter based on the MODIS QA values to remove the influence of cloud, cloud shadow, snow, and ice. In addition, water pixels were removed using the MOD4W.005 MODIS/Terra Land Water Mask derived from MODIS and SRTM L3 [91]. As such, the NDVI values were produced with a low amount of influence from atmospheric conditions such as cloud cover. The MODIS Surface Reflectance (MOD09 GA) data were processed in Google Earth Engine (GEE) to estimate the NDVI for each trail for the 24-year period from 2001 to 2024. Based on the temporal resolution of the satellite sensors and missing observation dates, the MODIS reflectance values have some temporal gaps [92]. A variety of smoothing methods can be used to convert the raw NDVI and GPP values into a daily time series to allow for the interpretation of seasonal patterns, such as the Savitzky-Golay (SG), spline smoothing, and Gaussian approaches; however, there is no agreement on the optimal method in all locations [93]. The spline smoothing method was used here based on successful implementation in phenological studies and broad application across habitats [94; 95; 96]. Temporal smoothing and gap-filling using splines were conducted in R with the greenbrown phenology package ([[https://greenbrown.r-forge.r-project.org](https://greenbrown.r-forge.r-project.org)]([https://greenbrown.r-forge.r-project.org](https://greenbrown.r-forge.r-project.org)) (accessed on 20 January 2025)) [97]. The phenological metrics were generated based on [PERSON] et al. [98] to calculate the annual phenology metrics: start of season (SOS), end of season (EOS), length of season (LOS), mean spring value (MSP), mean autumn value (MAU), rate of spring green-up (RSP), rate of autumn senescence (RAU), position of peak (POP), position of trough (POT), and mean growing season (MGS). To examine the seasonal shifts in terms of the snow cover, the MOD10A1.061 Terra Snow Cover Daily Global 500 m data product [71] was analyzed to identify the first day of no snow on each trail for the years 2001-2023.
# BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network
## 4 Backup Path Design Method Based on BPDM-GCN
### Data Transmission Path Optimization Algorithm Based on BPDM-GCN
Lines 11-16 indicate how the algorithm utilizes data from the experience replay pool to conduct training for both the Actor and Critic networks. In formula \(H_{1}=\text{RrLU}\big{[}\hat{A}(\xi E)W_{0}\big{]}\), \(\xi\) is a scaling parameter used to adjust the scale of the link state feature vector matrix \(E\), each row of \(E\in R^{I*C}\) represents the eigenvector of the link \(e_{i,j}\), \(W_{0}\) is the weight matrix of the GCN layer, \(\hat{A}(\xi E)\) is the matrix multiplication of the normalized adjacency matrix \(\hat{A}\) and the scaled link state feature matrix \(\xi E\), and \(H_{1}\) is obtained by nonlinear transformation by modifying the linear unit activation function R rLU.In formula \(Z=\text{soft}\max\big{(}\hat{A}\text{Re LU}\big{[}\hat{A}(\xi e)W_{0}\big{]}W_ {1}\big{)}\), the result of \(\hat{A}(\xi E)W_{0}\) is applied to the modified linear unit Re \(LU\) activation function, the Re \(LU\) function introduces nonlinear factors, multiplies with \(\hat{A}\), and performs matrix multiplication with another weight matrix \(W_{1}\). Finally, \(Z\) is normalized by the soft max function. The backup path design in BPDM-GCN is divided into two cooperative stages:  
(1) Route establishment stage: Through iterative training of DDPG-GCN, the link weight matrix is dynamically optimized to reflect the real-time status of the network topology (such as latency and bandwidth utilization). (2) Route selection stage: Based on the link weight matrix, the incremental shortest path tree algorithm (Algorithm 2) is used to calculate the load balanced and shortest backup path. ```
Input: Network topology \(G=(V,E)\), link \(e_{i,j}\) weight \(w_{e_{i,j}}\) in the topology Output: The maximum disjoint backup path \(backup\_path_{s,d}\) from the starting node \(s\) to the destination node \(d\) (1)  Initialize \(w_{e_{i,j}}\in T\), \(G=(V,E)\) (2)  Function build shortest path tree \(T_{1}\) (3)  dist = {}, parent= {},visited = {} (4)  For each neighbor \(v\) of \(s\) (5)  End for (6)  return (dist, parent) (7)  End function (8)  Function build incremental shortest path tree \(T_{2}\) (9)  For each neighbor \(v\) of \(s\) (10)  remove link \(e_{s,v}\) (11)  End for (12)  Output backup path \(backup\_path_{s,d}\) (13)  End function
```  
**Algorithm 2**Algorithm flow of backup path based on incremental shortest path tree  
Two-stage dynamic correlation by link weight matrix: When the network topology changes, DDPG-GCN retrains and updates the link weight matrix, triggering real-time adjustment of the incremental shortest path tree to ensure that the backup paths always adapt to the latest network conditions.
## 5 IETF ROLL Working Group
### Research Challenges
#### 5.4.2 Asymmetric Links  
The current RPL standard assumes the use of symmetrical links. For the selection of an optimal parent, the current RPL standard uses an approach based on the receipt of DIO messages from neighboring nodes. The node will select a parent based on the information in this message. However a node will use this link in the opposite direction of the receival of the DIO message. In case ofasymmetric links or unidirectional links the node will not be able to reach his preferred parent, which results in the reselection of another parent after some time. As such, this mechanism can be extended to also cope with asymmetric links.
## 4 Methodology
### Performance Metrics (PM)  
Choosing one or another performance metric to evaluate and compare NIDSs can be erroneously interpreted especially by practitioners. For instance, the accuracy metric is not recommended when considering unbalanced data which is, in fact, a common characteristic of network traffic data for attack classification. Because of that, the authors recommend using \(Recall(R)\), \(Precision(P)\), \(F1\) or Area Under the Curve (AUC) to evaluate ML-based NIDSs when dealing with this kind of data. Next, a brief description is provided for each of the performance metrics considered.  
* Recall (R). It is also known as sensitivity or TPR (True Positive Rate) and represents the ability of the classifier to detect all the positive cases, as depicted by Equation (8), \[Recall(R)=\frac{TP}{TP+FN}\] (8) where TP (True Positive) is the number of times a positive sample is correctly classified. Instead, FN (False Negative) counts how many times a negative sample is miss-classified.
* Precision (P). It evaluates the ability of the classifier to avoid positive samples miss-classification and it is defined in Equation (9), \[Precision(P)=\frac{TP}{TP+FP}\] (9)where FP (False Positive) counts how many times negative samples are classified as positive.
* F1 score. It is the harmonic mean of the previous two values, as depicted in Equation (10). A high F1 score value (close to 1) means an excellent performance of the classifier. \[F1=\frac{2\times R\times P}{R+P}\] (10)
* AUC. The AUC is a quantitative measurement of the area under the Receiver Operating Characteristic (ROC) curve which is widely used as a performance metric in NIDSs in particular and IDSs in general [10; 42]. The ROC curve compares the evolution of the TP rate versus the FP rate for different values of the classifying threshold. Consequently, the AUC is a performance indicator such that classifiers with AUC = 1 behave perfectly, i.e., it is able to correctly classify all the observations, while a random classifier would get an AUC value around 0.5.
* Weighted average. For each class \(i=1,\ldots,C\), the \(weighted\_avg(PM_{i})\) computes the weighted average of each metric \(PM_{i}\) previously introduced times by the corresponding support \(q_{i}\) (the number of true observations of each class), being \(Q\) the total number of observations. These weighted metrics are defined as shown in Equation (11). \[weighted\_avg(PM_{i})=\frac{\sum_{i=1}^{C}PM_{i}\times q_{i}}{Q}\] (11)
## 4 Simulation and Analysis
### Low Friction (\(\mu\) = 0.3)  
Figure 7 shows the CarSim model phantom on a low friction road by the four control systems above. The simulation results are shown in Figure 8, in which Figure 8a-h are vehicle trajectory, speed, yaw rate, sideslip angle, motor output torque of front left, front right, rear left, and rear right wheels, respectively.  
Table 4 presents the main evaluation indexes under the given scenario, including the vehicle entry speed \(V_{c}\), final speed \(V_{f}\), the peak value of vehicle sideslip angle \(\mid\!\beta_{\max}\!\mid\), and the peak value of yaw rate \(\mid\!r\!\mid\). The simulation results show that, compared with System 1, the maximum safe speed by System 2 is increased by 15.6 km/h, reaching up to 40.3 km/h. The maximum safe speeds of System 3 and System 4 are very close, both reaching 42.9 km/h. Compared with System 2, the \(\mid\!\beta_{\max}\!\mid\) of System 3 and System 4 are reduced by 1.75\({}^{\circ}\) and 2.95\({}^{\circ}\), respectively, indicating the vehicle stability of System 4 is the best.  
Figure 6: Co-simulation framework.  
Figure 7: CarSim model phantom on a low friction road.  
Figure 8: Comparison of results on a low friction road.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Index** & **System \(\updownarrow\)** & **System \(\updownarrow\)** & **System \(\updownarrow\)** & **System \(\updownarrow\)** \\ \hline \(V_{c}\) (km/h) & 24.7 & 40.3 & 42.9 & 42.9 \\ \(V_{f}\) (km/h) & 24.7 & 24.7 & 24.7 & 24.7 \\ \(|\beta_{\max}\,|\) (deg) & 5.09 & 5.13 & 3.38 & 2.18 \\ \(|r|\) (deg/s) & 22.02 & 22.48 & 33.46 & 29.65 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of evaluation indicators on a low friction road.
# Pre-Training Multi-Concept Question Embeddings for Knowledge Tracing
## 5 Experiments
### Ablation Study  
To validate the effectiveness of each module in our model, we designed the following ablation experiments:  
* PMQE: The initial model.
* no-text-emd: Removed the text embedding of concepts, using one-hot encoding for concept representation as in most methods.
* no-c2q-weight: Removed the question-to-concept weight matrix, decoupling question embeddings from concepts and training with independent weights.
* no-side-info: Removed additional auxiliary information used for difficulty analysis, relying solely on question embeddings to compute difficulty, i.e., \[\hat{D}=\text{MLP}_{Q}(\mathbf{q})\] (9)
* no-difficulty: Removed the entire difficulty analysis module, effectively removing the difficulty constraint, and used only the question-concept bipartite graph to guide model optimization. Without the difficulty analysis module, the model's loss degenerates to: \[\mathcal{L}(\mathbf{Q},\mathbf{C})=\mathcal{L}_{G}\] (10)  
For the ablation study conducted using the Assist09 dataset with the DKT model, the results are presented in Figure 4. By systematically removing key components of the PMQE model, we observed that each module significantly impacts knowledge tracing performance. Specifically, removing the concept text embedding (no-text-emb) resulted in the greatest performance decline (a decrease of 1.06% in ACC and 0.68% in AUC), underscoring the importance of semantic representations provided by pre-trained language models for modeling concepts. The absence of the question-to-concept weight matrix (no-c2q-weight) led to reductions of 0.71% in ACC and 0.21% in AUC, indicating that explicitly modeling the association weights between questions and concepts effectively captures complex relational structures. When auxiliary information for questions was removed (no-side-info), the performance drop was relatively minor (0.32% in ACC and 0.15% in AUC), suggesting that the fundamental embeddings already capture core features, though auxiliary information still provides supplementary discriminative features for difficulty evaluation. Notably, eliminating the difficulty analysis module (no-difficulty) resulted in ACC and AUC decreases of 0.79% and 0.70%, respectively, confirming the critical role of a dynamic, multi-source difficulty assessment mechanism in accurately estimating student abilities. Overall, the experimental results demonstrate that the model integrates semantic embeddings, structural constraints, and cognitive diagnostic theories to achieve multidimensional optimization in the knowledge tracing task.
# Design of an Open Remote Electrocardiogram (ECG) Service
## 3 An Open Service for Remote ECG
### Relay Server in the Cloud  
We consider that the browser and the ECG sensor share a Web server in the cloud that has the role of relaying the ECG data from the patient to the doctor. The Web server provides two kinds of endpoints: one for the patient, another for the doctor. Both of them upgrade to WebSockets.  
The cloud server that implements the relay point requires a limited computational capacity and may exist only for the time needed by the doctor to read the ECG. Therefore, it is the ideal candidate for a microservice instance [23] since a Platform as a Service (PaaS) provider can create a new instance of a microservice in seconds, make it accessible on the Internet, and dispose of it after use.
## 3 Simulation Results and Discussion
### Performance Analysis
#### 3.2.1 Visibility Test of GNSS Satellites
Nonetheless, if side lobe signals are used, the number of visible satellites in multi-GNSS on both sides increases dramatically and can lead to more accurate navigation solution. A summary of the simulation results of the visibility test is provided in Table 2. If each individual system or only the main lobe signal is used, a serious visibility problem may occur. Thus, GNSS satellite visibility should be sufficiently ensured to estimate PNT by configuring the multi-GNSS and the side lobe signal.
## 2 Equations of Saturated Soils  
Saturated soils are regarded as a two-phase medium consisting of a solid skeleton and the pore fluid in the voids of the solid skeleton. The pore fluid and the interaction between the two phases make the mechanical properties of saturated soils very different from one-phase soils. Therefore, two governing equations describing the mechanical properties of saturated soils are determined.
## 4 Discussion  
There are certain differences in the impact of different combinations of temperature and precipitation on NPP globally. [PERSON] et al. analyzed the trend in global land vegetation NPP changes from 1982 to 1999, and found that NPP significantly increased in regions accounting for more than 25% of the global vegetation area [42]. Due to different climatic conditions, it showed a characteristic of being mainly distributed in tropical regions and high-latitude areas in the northern hemisphere. [PERSON] et al. evaluated the spatiotemporal variation characteristics of grassland NPP in Tanzania based on a regression model of temperature, precipitation, and evapotranspiration from 2001 to 2019 [43]. The results showed a significant positive correlation between NPP and temperature. Within the dry and semi-dry areas of Northwest China, the correlation between NPP and temperature and precipitation gradually increased over time, mainly showing positive correlations [44]. In the Tibetan Plateau region, NPP showed a positive correlation with air temperature and a negative correlation with precipitation in some areas, and its vegetation changes were mainly affected by air temperature [45]. In the southern humid zone [46], such as the Yangtze River's middle and lower regions [47], the Yunnan-Guizhou Plateau [48], and the southeastern coastal area [27], vegetation grows better with more water, heat, and light, and carbon accumulates sufficiently. NPP is greatly influenced by climatic factors and exhibits different seasonal and interannual patterns of change. In the humid and semi-humid region of Northeast China, precipitation primarily influences the spatial distribution of NPP [49].  
Compared with other regions, the correlation between NPP and temperature and precipitation in the northwest region of Liaoning is more consistent. The northwest region of Liaoning belongs to the semi-arid region, and the climatic and environmental conditions are similar to those of the northwest arid and semi-arid region. Therefore, from a long-term trend perspective, both regions are positively correlated with temperature and precipitation. However, the degree of this positive correlation varies, and we speculate that this may be due to differences in land use and soil types between the two regions. Compared with the Qinghai-Tibet Plateau region and other humid areas, its NPP changes are mainly influenced by temperature and precipitation factors, and in some areas, it shows a negative correlation with climate factors. This difference is influenced by factors such as different combinations of temperature and precipitation, land use types, and soil types.  
Given that temperature and precipitation are the primary climatic factors that affect NPP, we can elucidate the overall impact of climate change on NPP, thereby increasing the total amount of NPP. Furthermore, given that ecologically fragile areas in developing countries around the world are particularly sensitive and vulnerable to climate change, an effective assessment of NPP fluctuations can mitigate the decline in NPP due to climate change and enhance the adaptability of these areas.
## 3 Results and Discussion
However, rate coding is based on the firing rate, while here, the spectral width of the individual spikes is encoded. Therefore, we refer to this type of coding as spectrum coding. This is a unique advantage of the proposed technology, which is caused by incorporating mode-locking effects in the technology. Moreover, this is the first optical system that has amplitude-dependent non-linear properties in both amplitude and spectrum width governed by a power-dependent threshold. The design encompasses non-linear processing with power-threshold and spectral re-shaping, paving the way for a novel type of optical neuron with variable bandwidth adapted to the requirements of emerging 6G networks.
## 2 HJ-1B instrument description  
China plans to launch two small optical satellites and one small SAR satellite, called the \"2+1\" Project, in 2008. The project is also called as the Small Satellite Constellation for Disaster Prediction and Monitoring which will be participated in Disaster Monitoring Constellation (DMC). The establishment of a small satellite constellation will make China able to monitor disasters and environmental changes more efficiently. The revisit time of the satellite constellation is 48-96 hours. The satellite designed for fire detection is also called a HJ-1B satellite, and carries two CCD camerasand one thermal scanner. The HJ-1B satellite payload consists of two wide field multi-spectral cameras and one Infrared scanner, whose main specifications are shown in Table 1.  
The HJ-1B satellite has been specifically designed to support detection and quantitative characterization of various disasters, such as active fires, active volcanoes, earthquake or others. The Infrared camera possesses mid-infrared (MIR: 3.50\(\sim\)3.90 \(\upmu\)m) and thermal infrared (TIR: 10.5\(\sim\)12.5\(\upmu\)m) spectral channels with 150 m and 300 m resolutions, respectively. The Multi-spectral CCD camera possesses RED (0.63\(\sim\)0.69 \(\upmu\)m) and NIR (0.76\(\sim\)0.90 \(\upmu\)m) spectral channels with a 30 m resolution. The revisit interval of Multi-spectral CCD camera and infrared scanner is 48 hours. Compared with the MODIS MIR (3.9 \(\upmu\)m) band, the MIR channel of HJ-1B is more sensitive to fires due to the shorter wavelength (3.50-3.90 \(\upmu\)m) used, but on the other hand, it is more affected by sunlight. HJ-1B has higher spatial resolutions of 300 m and 150 m for the TIR and MIR channels, respectively. Such high resolutions are useful for the accurate estimation of the background temperature of ambient pixels, which is important for the bi-spectral method [26]. The HJ-1B sensor has a NEdT of 1 K. Its saturation levels are 500 K and 340 K for MIR and TIR channels, respectively.
# An Improved Dual-Sorting NSGA-II Method for Optimal Radiation Shielding Design
## 3 Results and Discussion
### Optimization Efficiency Analysis
#### 3.3.2 Comparison of Optimization Efficiency Across Different Methods  
Based on the population superiority metric defined in Section 3.3.1, the overall evolutionary speed of the populations obtained using the classical NSGA-II and the dual-sorting NSGA-II methods, as described in Section 3.2, was analyzed. To reduce variability, the population superiority values were averaged over five independent optimization runs. Additionally, to verify the scalability of the dual-sorting NSGA-II method, the population evolution speed was further investigated under an alternative genetic encoding scheme (Case 2) with a different optimization efficiency. The results are presented in Figure 8.  
Figure 8a illustrates the evolution of the population superiority metric \(V(C_{t})\) over generations \(t\) for both the classical NSGA-II method and the dual-sorting NSGA-II method. Additionally, Figure 8b provides a detailed comparison of the optimization efficiency between the two approaches. As shown in Figure 8, following an initial phase characterized by high randomness, the dual-sorting NSGA-II method surpasses the classical NSGA-II method in population superiority by generation 10 in both Case 1 and Case 2. By generation 15, a significant divergence in superiority becomes evident between the two approaches.  
In Case 1, aside from minor fluctuations between generations 25 and 30, the dual-sorting method consistently exhibits over a 20% improvement in optimization efficiency compared to the classical NSGA-II method. Notably, in the final stages of evolution, when \(V(C_{t})\) > 0.8 (i.e., \(t\) > 28), the efficiency advantage of the dual-sorting algorithm becomes increasingly pronounced, reaching an enhancement of over 40% at \(V(C_{t})=0.91\).  
A similar trend is observed in Case 2. Once \(V(C_{t})\) > 0.8 (i.e., \(t\) > 20), the dual-sorting method's efficiency improvement exceeds 30%. Moreover, compared to Case 1, the mutation strategy in Case 2 results in higher optimization efficiency beyond generation 10.  
These findings highlight the effectiveness of the \(O\)-based sorting mechanism and the new crowding distance metric in reducing interference from suboptimal solutions and maintaining evolutionary pressure on the Pareto-optimal front, particularly when the optimal solution set is more comprehensive.  
Figure 8: (a) Changes in the population superiority metric \(V(C_{t})\) over generations for both methods; (b) ratio of population superiority metrics between the two methods across generations. The dual-sorting method consistently outperforms the classical NSGA-II, with up to 40% efficiency gain in later generations.