esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Study of Coverage Hole Detection Techniques in Wireless Sensor Networks +
## 2 Methodology
### Sensing Coverage Hole Detection and Repair Algorithms for WSNs  
To find coverage holes, [PERSON] et al. [19] describe a successful chord-based method. The CBHC technique uses the fewest possible sensor nodes while providing complete detecting network coverage by limiting the detecting coverage region overlay.  
The simulation findings demonstrate that the suggested methodologies for hole detection and healing are effective at finding coverage holes and effectively overlaying the holes that were detected.
## 5 A Note on Orbit Fitting and Orbit Extrapolation Accuracies
### Accuracy of Orbit Extrapolation  
The estimated broadcast parameters can be used to extrapolate satellite trajectories with the user algorithm (Table 1b) outside of the fitting interval. Figure 1b shows the average orbit extrapolation error for an interval of up to 20 min using broadcast parameters obtained from a 10 min long fit interval (cf. Figure 1a). It can be seen that the extrapolation error hits 1 cm after 8 min (elapsed time from the end of the fitting interval), and 1 decimeter after just 14 min. This rapid error growth suggests that the period of validity of the ephemeris produced when fitting to 10 min of a dynamic orbit arc should not exceed 10 to 20 min when an orbit prediction accuracy better than dm-level is targeted. This, in turn, means that in order to employ the proposed orbit determination scheme for real applications, new orbital parameters should be produced every few minutes, e.g., by implementing sliding batch processing or a Kalman filter to continuously update the estimation of the ephemeris.
# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 3 Results  
### Validation of Downscaled FPAR and LAI  
#### 3.1.1 Cross Validation of Downscaled FPAR and LAI  
The downscaled 30 m FPAR and LAI demonstrate finer scale features with a clear identification and have a high accuracy (Figures 4 and 5). We compared the downscaled FPAR/LAI in July 2012 with corresponding retrieved high resolution (30 m) FPAR/LAI products by [54]. Specifically, the high resolution (30 m) LAI and FPAR was retrieved by using an instantaneous quantitative model based on the law of energy conservation and the concept of recollision probability [5] from Chinese HJ-IB satellite observations data. This model separated direct energy absorption in the canopy from energy absorption caused by multiple scattering between the soil and the canopy, and took the direct sunlight and diffuse skylight into consideration. Validation against field measurements indicated that FPAR and LAI retrievals had high accuracy [54]. In general, a good linear relationship existed between the downscaled FPAR and retrieved FPAR (Figure 6), R\({}^{2}\) could reach 0.82 and 0.76 of the downscaled FPARfrom GLASS data (D_FPAR_GLASS) and downscaled FPAR from MODIS data (D_FPAR_MODIS), respectively. RMSE between downscaled FPAR and retrieved FPAR were 0.10 and 0.12 respectively. Meanwhile, good consistency was shown between the downscaled LAI and retrieved LAI (Figure 7). RMSE between the downscaled LAI and retrieved LAI were 0.63 and 0.69, respectively. However, we found that most plots in Figure 6 were distributed under the 1:1 line, which demonstrated that both the downscaled LAI from GLASS data (D_LAI_GLASS) and the downscaled LAI from MODIS data (D_LAI_MODIS) were lower than the retrieved LAI from the HJ-1B data. The main reason may be that the problem of mixed pixels led to the low values of the reference data, i.e., GLASS and MODIS 1 km LAI. Specifically, some other types of land cover (such as bare land, grassland and artificial land) exist in a GLASS/MODIS 1 km pixel, which may lead to low values of LAI in the growing season. Furthermore, high resolution (30 m) FPAR/LAI products from HJ-1 were generated monthly and retrieved from the maximum value in the month, which could also have led to the overestimation of FPAR and LAI.  
Figure 4: Multi-scale maps of FPAR in July 2012: (**a**) global LAnd Surface Satellite (GLASS) FPAR (1 km); (**b**) MODerate Resolution Imaging Spectroradiometer (MODIS) FPAR (1 km); (**c**) D_FPAR_GLASS (30 m); (**d**) D_FPAR_MODIS (30 m). The red pixels are the training samples. Compared with MODIS and GLASS 1 km FPAR, the downscaled 30 m FPAR demonstrate finer scale features with a clear identification.  
Figure 5: Multi-scale maps of LAI in July 2012: (**a**) GLASS LAI (1 km); (**b**) MODIS LAI (1 km); (**c**) D_LAI_GLASS (30 m); (**d**) D_LAI_MODIS (30 m). The red pixels are the training samples. Compared with MODIS and GLASS 1 km LAI, the downscaled 30 m FPAR demonstrate finer scale features with a clear identification.
## Appendix A Evaluation Metrics and Validation  
Performance was systematically evaluated using precision, recall, F1 score, and ROC-AUC on an independent validation dataset (20% of total samples), consistent with the standard practices for binary text classification. Additionally, confusion matrices and error analyses were conducted to understand false positives and false negatives, informing further model refinements.
# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 4 Failure Prediction and Container Migration Strategies  
### Problem Formulation  
Fault-prediction models are very important for the fault prediction of edge devices. High-precision fault prediction can reduce unnecessary migration costs caused by incorrect judgments, which is significant for subsequent migration strategies. There are several technical challenges in designing failure prediction models for edge devices. First, edge-device failures can be caused by software or hardware issues. According to estimates by Microsoft domain experts, more than a hundred root causes of edge-device failures exist. Examples include operating system crashes, application errors, disk failures, misconfigurations, memory leaks, software incompatibilities, overheating, and service exceptions. In such complicated scenarios, simple rule- or threshold-based models cannot achieve accurate predictions. Second, there is a high imbalance in the percentage of failure data. In general, the equipment failure data is considerably less than normal data. This imbalance makes it difficult to train the prediction model. Hence, data must be preprocessed to balance normal and failure data. To address these two challenges, we introduce a machine learning-based approach for edge-device failure prediction. Data preprocessing is described in Section 5.  
Through long-term monitoring and analysis of various performance indicators of the nodes, changes in the node performance indicators under normal operation were found to be regular and stable. When a node fails, its performance index can fluctuate abnormally either suddenly or gradually, which is a symptom of node failure [29]. Therefore, it can be concluded that the node indicators are correlated in the time dimension. In addition, although the failure types are different, the nodes often experience a period of state evolution between failure states. In other words, when the performance parameters of a certain time node are compared with those of others, a significant difference indicates that a failure is likely to occur. The specificity, variation, and correlation of such node performance parameters in the time dimension may be key to early symptom identification of node failures. Through these comprehensive considerations, this study considers establishing the fault prediction of edge devices as a time-series model and can use some typical machine-learning algorithms related to time-series prediction to predict the fault situation at a certain moment in the future using the data from the previous period. Specifically, this study used two well-known time-series machine-learning algorithms: long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms.
## 2 Materials and Methods
### Sample Analysis
#### 2.3.2 High Performance Hydrophobic Interaction Chromatography  
The pDNA(t) concentration was determined by HPLC-HIC as described previously [26]. Briefly, a 4.6 \(\times\) 100 mm HIC Source 15 PHE PE column (phenyl ligand, polystyrene/divinylbenzene matrix) was connected to an Akta Purifier 10 UPC system (GE Healthcare, Chicago, IL, USA) and equilibrated with 1.5 M ammonium sulfate in 10 mM Tris-HCl, pH 8.0. Diluted samples 1:5 in an equilibration buffer of 30 \(\mu\)L were injected to the column. The column was then washed for 1.4 min with an equilibration buffer at 1 mL/min. At the end of this period, an isocratic elution was performed with 10 mM Tris-HCl buffer, pH 8.0, at 1 mL/min. This condition was maintained over the next 0.7 min to elute the bound species. After this period, the column was re-equilibrated with 1.5 M ammonium sulfate in 10 mM Tris-HCl, pH 8.0 for 5.5 min. The pDNA(t) was quantified using a calibration curve constructed with standards of the plasmid purified prepared in the 10-50 \(\mu\)g/mL concentration range. To calculate the concentration of pDNA(t) in each sample, the area of the corresponding blanks was subtracted from the total area of all peaks on the chromatogram. The HPLC purity degree of pDNA(t) was defined as the percentage of the plasmid to total area.
# UVSQ-SAT, a Pathfinder CubeSat Mission for Observing Essential Climate Variables
## 2 Scientific Rationale of the UVSQ-SAT Mission
### Scientific Requirements
Figure 1: UV solar spectral irradiance at 215 nm over the past 40 years from the SATIRE-S model.
## 2 Materials and Methods
### Optimization of the Pretreatment Process  
Sturgeon head is a complex mixture of soft tissue (skin and muscle), semi-soft tissue (small cartilage pieces), and hard tissue (bone, gill, and scale). Gelatin was to be extracted firstly from the soft-tissue, and then, from the semi-soft tissue and the hard tissue. For these purposes, the head skin, which was the most abundant soft tissue of the head, was used as a representative sample of the soft tissue to optimize the pretreatment and extraction conditions. Then, residues after the first extraction, which were mainly composed of the semi-soft tissue and the hard tissue, were used to optimize the decalcification process for re-extraction.  
Following previous articles [6; 10], the effects of pretreatment with HCl (0.05 M), NaOH (0.1 M), or their combination (NaOH \(\rightarrow\) HCl) with modifications of treatment time (1, 3, and 6 h) on the intensities of \(\alpha\)-, \(\beta\)-, and \(\gamma\)-bands of gelatin in sodium dodecyl sulfate-polyacrylamideamide gel electrophoresis (SDS-PAGE) and yield were tested.  
After separating the tissues of the head, the skin was cut into small pieces (10-12 mm \(\times\) 10-12 mm), weighed, and pretreated with 0.05 M HCl (_w/v_ 1:10) or with 0.1 M Na OH at 4 \({}^{\circ}\)C while stirring at 150 rpm for 1, 3, and 6 h. For the combined pretreatment (NaOH \(\rightarrow\) HCl), the sample was first treated with 0.1 M NaOH for 1 h and subsequently with 0.05 M HCl for 1 h.  
After the pretreatment, the supernatants were separated by centrifugation (10,000\(\times\)\(g\) for 15 min at 4 \({}^{\circ}\)C) and stored for further analysis. The precipitates were cut into smaller pieces (5-6 mm \(\times\) 5-6 mm) and used to extract gelatin with DW (_w/v_ 1:5) at 65 \({}^{\circ}\)C and pH 7 for 1 h when stirring at 200 rpm. As a control, the mixed tissue without pretreatment was used. The extracted solution was centrifuged (10,000\(\times\)\(g\) for 15 min at 21 \({}^{\circ}\)C), the supernatant was collected, and vacuum filtered (Advantec No. 6 filter paper, with a pore size of approximately 3 \(\upmu\)m, Advantec, Tokyo, Japan). The extracted and pretreated solutions were directly used to observe the intensities of \(\alpha\)-, \(\beta\)-, and \(\gamma\)-bands of gelatin in SDS-PAGE, as described in Section 2.7. Finally, the yield (%, dry gelatin weight/wet sample weight) was calculated after freeze-drying the extract. Here, the wet weight of the sample before the pretreatment was used as the wet sample weight.
# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
## 2 Related Work
Studies have been conducted on the application of reinforcement learning (RL) to queueing systems. This research field can be categorized further based on the RL techniques used; for example, in model-based RL, a specific queueing network model is considered to optimize the network's performance, and the focus is on the use of RL techniques and managing these limitations by exploring the effectiveness of this approach in other network architectures [17]. The authors propose a DRL-based AQM, Learning Fair Queue (LFQ), that dynamically adapts per-flow buffer sizes using a neural network. It addresses the challenge of managing real-time queues for diverse traffic conditions. Results display enhanced throughput fairness and lower latency compared to traditional AQMs [18]. The author presented a Deep Q-Network (DQN) algorithm employed for smart queue management. The procedure includes simulating traffic scenarios with two types of protocols, UDP and TCP, using a neural network. Specifically, the DQN agent can select to increase, decrease, or preserve weights for diverse classes of traffic: Gold, Silver, and Bronze. The outcomes show that the DQN agent can effectively manage the weights for the traffic flows. The average reward stabilizes positively, showing convergence after approximately 1500 training episodes [19]. The authors of [20] outlined the use of a Deep Deterministic Policy Gradient (DDPG) algorithm to reduce the service rate and provide a probabilistic latency guarantee, finding that the proposed method offered probabilistic upper bounds on the system's end-to-end delay. This approach is more challenging than standard practice control methods, which only consider average performance metrics such as the mean delay. In the study in [21], a WFQ system achieved a reduction in the packet loss rate by utilizing a SAC technique, although this scheme did not account for latency. The solution in [22] employed deep RL (DRL) techniques for dynamic bandwidth allocation in WFQ routers. Specifically, it emphasized the application of algorithms such as DDPG and SAC to handle bandwidth allocation across multiple queues, with the goal of minimizing packet loss, reducing the average queue length, and decreasing the average delay.
## 2 Related Work
### Hybrid Approach  
The use of hybrid approaches has been introduced in recent years to mitigate the limitations of the above-mentioned approaches by integrating physics knowledge from traffic flow models with machine learning models. Using macroscopic traffic flow models with the Gaussian process (GP), [PERSON] and colleagues developed a physics-regularized Gaussian process (PRGP) for TSE [7]. As described by the general nonlinear partial differential equations, physics-informed learning (PIL) involves the training of neural networks to solve machine learning problems. [PERSON] and [PERSON] introduced PIL as a substitute for numerical schemes for solving PDEs [8]. Using a deep neural network to encode the PDE,the PIL approach approximates the unknown variables as well as assesses its consistency with the physical parameters provided. [PERSON] and [PERSON] validated the Greenshields-based LWR using SUMO simulated data for loop detector scenarios using PIL [29]. The combined micro-macro models developed by [PERSON] et al. aimed to model TSE using sensor data collected from probe vehicles [30]. Using loop detectors and probe vehicle data, [PERSON] et al. extended PIL-based TSE to the second-order ARZ model [31]. In addition, they extended their study to estimate fundamental diagrams (FDs) and determine model parameters through machine learning surrogates under the PIL framework [32]. In a recent paper, deep convolutional neural networks are employed to estimate high-resolution traffic speed dynamics with sparse probe vehicle movements [33]. However, all these studies are limited to a single stretch of a road. In complex road networks, traffic dynamics differ significantly from those of a single road segment. Additionally, the traffic on one transport network link is not independent of traffic on other networks.
## 5 Conclusions  
This study conducted numerical modelling work on investigating the reinforcement performance of full-grouting rock bolts. A number of numerical PO tests were conducted on rock bolts. Two diverse types of rock bolts were used. One was the traditional metal rock bolts and the other one was the FRP rock bolts. During the PO process, the PO force and displacement were recorded. The force-displacement results were summarised and compared. According to this study, the following conclusions were obtained.  
1. The sliding function of modified pile elements had a marginal influence on the reinforcement performance of rock bolts. This was independent of the EL of rock bolts. No matter whether the sliding function was considered, the metal rock bolts and FRP rock bolts showed consistent reinforcement performance.
2. The EL has a paramount influence on the reinforcement performance of rock bolts. When the EL was too short, the reinforcement performance of rock bolts was not reliable. The largest anchorage force of rock bolts was not stable. Therefore, when conducting PO tests of rock bolts, it is recommended that the EL should be relatively long enough.
3. Before the rock bolts reached the largest anchorage force, there was generally a linear relation between the largest anchorage force and the EL. This linear relation was applicable for both metal rock bolts and FRP rock bolts. Moreover, the linear regression analysis method was robust in predicting the critical rupture force of rock bolts.
4. The FRP rock bolts showed a paramount difference in reinforcement performance compared with metal rock bolts. For the force-displacement curve, the FRP rock bolts showed a much smaller initial stiffness. Consequently, when the FRP rock bolts reached the largest anchorage force, the corresponding PO displacement was much smaller.  
Writing: J.C., S.W., G.S. and H.Z.; conceptualisation: K.S., K.Z. and A.Z. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (51904302, 52174093).  
Not applicable.  
Data Availability Statement: Data were included in this article.  
The authors declare no conflict of interest.
# Image-Based Dedicated Methods of Night Traffic Visibility Estimation
## 2 Backgrounds
### Dark Channel Prior (DCP)  
Atmospheric scattering models have been widely used in image processing and computer vision. The model describing the foggy image represents [12]:  
\[I(x)=J(x)t(x)+A(1-t(x)) \tag{4}\]  
\(I(x)\) is an image captured by the camera (fog image), \(x\) is the 2D spatial location, \(J(x)\) is an image with no fog on a sunny day, \(A\) is the atmospheric light, and \(t(x)\) is the transmittance. Now assume that the transmittance \(t(x)\) is constant in the local \(\Omega(x)\). Taking the min operation for Equation (4), we have the following:  
\[\underset{y\in\Omega(x)}{min}(F(y))=t(x)\underset{y\in\Omega(x)}{min}(J^{c}(y ))+(1-t(x))A^{c} \tag{5}\]  
\(y\) is any pixel contained in the local \(\Omega(x)\), \(c\) is an \(R\), \(G\), \(B\) color channel, and \(A^{c}\) usually takes the first 0.1% of the brightness of the \(c\) color channel. Adjust the Equation (5) as what follows:  
\[\underset{y\in\Omega(x)}{min}\left(\frac{F(y)}{A^{c}}\right)=t(x)\underset{y \in\Omega(x)}{min}\left(\frac{F(y)}{A^{c}}\right)+(1-t(x)) \tag{6}\]Then, find the minimum of the three colors channels in Equation (6) as what follows:  
\[\underset{c\in(r,g,b)}{\text{min}}\left(\underset{y\in\Omega(x)}{\text{min}} \left(\frac{\Gamma^{c}(y)}{A^{c}}\right)\right)=t(x)\underset{c\in(r,g,b)}{ \text{min}}\left(\underset{y\in\Omega(x)}{\text{min}}\left(\frac{\Gamma^{c}(y) }{A^{c}}\right)\right)+(1-t(x)) \tag{7}\]  
According to the mathematical definition of the dark channel, the fogless images \(J(x)\) always have smaller pixel values in one channel of \(RGB\). Supposing the dark channel values of \(J(x)\) approach 0:  
\[J^{dark}(x)=\underset{c\in[r,g,b]}{\text{min}}\left(\underset{y\in\Omega(x)} {\text{min}}\right)^{c}(y))\approx 0 \tag{8}\]  
The transmittance \(t(x)\) obtained by the above two formulas is the following:  
\[t(x)=1-\underset{c\in[r,g,b]}{\text{min}}\left(\underset{y\in\Omega(x)}{ \text{min}}\left(\frac{\Gamma^{c}(y)}{A^{c}}\right)\right) \tag{9}\]  
In sunny weather, there is always a small amount of fog on distant objects captured by the camera. Therefore, it is necessary to introduce a weight coefficient \(\omega\) (range \(0<\omega\leq 1\)). The correction formula is the following:  
\[t(x)=1-\omega*\underset{c\in[r,g,b]}{\text{min}}\left(\underset{y\in\Omega(x) }{\text{min}}\left(\frac{\Gamma^{c}(y)}{A^{c}}\right)\right) \tag{10}\]  
As shown in Figure 1, the dark channel of the original image can approximate the concentration of the fog. The higher the fog concentration, the brighter the corresponding position in the dark channel image. The dark channel image of the fog-free image is the darkest. Since the fog concentration directly affects visibility, the DCP theory can be used for visibility estimation.
# Adaptive Clustering via Symmetric Nonnegative Matrix Factorization of the Similarity Matrix
## 3 The Validity Indices
### The CL Index  
Both the DB index and the DB** index base their valuation of the clustering separability on the distances between centroids, according to the rule: the farther the centroids, the more separated the clusters. This rule does not appear profitable in the presence of nonconvex clusters or of clusters that nearly touch each other, or when the densities of the points differ much from cluster to cluster. This can be particularly troublesome when the points are affected by noise or when there are outliers. To check whether the \(r\)th and \(s\)th clusters nearly touch each other, one should compute their distance; that is, the minimum distance between each pair of points \(\mathbf{p}_{i}\in\pi_{r}\) and \(\mathbf{p}_{j}\in\pi_{s}\), with a not negligible increase in computational time. For example, this minimum distance between two clusters is used by the Dunn index [20].  
To reduce the burden of this control, we propose a new index called CL, which provides a measure of closeness. First, a small integer \(p\) (Section 6) is selected, and for any point \(\mathbf{p}_{i}\), the indices of its \(p\) nearest points are listed in \(\ell_{i}\). Using the same notations of the previous subsection, we assume that a sequence of clusterings \(\Pi^{(h)}\) of dimensions \(k^{(h)}\) is given, with \(h=1,\ldots,h_{\max}\). Let \(\pi_{r}^{(h)}\) be the cluster to which \(\mathbf{p}_{i}\) belongs. We consider the quantity  
\[x_{i}^{(h)}=\sum_{j\in\ell_{i},\ j\
eq\pi_{r}^{(h)}}\exp\big{(}-c\,\frac{\| \mathbf{p}_{i}-\mathbf{p}_{j}\|_{2}^{2}}{\mu}\big{)},\]  
where \(c\) is a constant (Section 6) large enough to make negligible in the sum the contribution of far-away points not belonging to \(\pi_{r}^{(h)}\). Set  
\[y_{h}=\sum_{i=1}^{n}x_{i}^{(h)},\quad h=1,\ldots,h_{\max}.\]  
The sequence \(y_{h}\) can be very oscillating. For this reason, we defined as the CL index the vector \(\psi_{h}\) obtained by computing the exponential moving average [23] with smoothing constant 0.1, of the vector \(y_{h}\)  
\[\psi_{1}=y_{1},\quad\psi_{h+1}=\psi_{h}+0.1(y_{h+1}-\psi_{h}),\quad h=1,\ldots,h_{\max}-1.\]  
This index is large when at least one point \(\mathbf{p}_{i}\) exists, having some sufficiently close points not belonging to the same cluster of \(\mathbf{p}_{i}\) in \(\Pi^{(h)}\). Typically, the CL index has the following behavior: a zero \(\psi_{h}\) means that the clusters of \(\Pi^{(h)}\) are well-separated, and this occurs only in the absence of noise. The increasing rate, which is bounded for small values of \(k\), tends towards infinity when \(k\) exceeds values for which separated clusters exist. This behavior, together with DB** values, suggests when stopping the increase of \(k\) if \(k_{\max}\) is not assigned.