esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Load Transfer Mechanism of Pile-Supported Embankment Based on Response Surface Method
## 3 Analysis of the Numerical Simulation Results
### Single-Factor Test and Results Analysis
#### 3.1.2 Internal Friction Angle  
As shown in Figure 7a, when the internal friction angle was 20-40\({}^{\circ}\), the pile-soil stress ratio decreased rapidly with the increase in the internal friction angle of the embankment filler. When the internal friction angle was greater than 40\({}^{\circ}\), the change rate of the pile-soil stress ratio slowed down. The variation law of the pile-soil differential settlement was basically the same as that of the pile-soil stress ratio.  
As shown in Figure 7b, when the internal friction angle was 20-40\({}^{\circ}\), with the increase in the internal friction angle, the exertion of the soil arching effect increased rapidly. When the internal friction angle was greater than 40\({}^{\circ}\), the soil arching effect tended to be stable. The  
Figure 7: Influence of internal friction angle on load transfer. (**a**) variation of pile-soil stress ratio and pile-soil differential settlement; (**b**) variation law of load sharing ratio.  
main reason for this is that the soil arching effect is essentially a load transfer mechanism caused by the shear stress on the relative sliding surface between embankment fillers. The greater the internal friction angle of the embankment filler, the greater the shear stress on the shear plane; shear stress will transfer more load to the pile, so the load-sharing ratio of the soil arching effect will be greater.
## 4 Materials and Methods
### PTMs In Silico Prediction  
We investigated PTMs such as phosphorylation, glycosylation, ubiquitination, and PEST sequences using prediction tools tailored for each modification. To estimate the phosphorylation of serine, threonine, and tyrosine residues, we used the NetPhos 3.1 Server ([[https://services.healthtech.dtu.dk/service.php?NetPhos-3.1](https://services.healthtech.dtu.dk/service.php?NetPhos-3.1)]([https://services.healthtech.dtu.dk/service.php?NetPhos-3.1](https://services.healthtech.dtu.dk/service.php?NetPhos-3.1))) (accessed on 1 March 2023), considering scores above 0.5, and consulted PhosphositePlus [29] as well. To predict N- and O-linked glycosylation at asparagine (N), serine (S), and threonine (T) residues, we employed the GPP Prediction Server ([[https://comp.chem.nottingham.ac.uk/glyco/](https://comp.chem.nottingham.ac.uk/glyco/)]([https://comp.chem.nottingham.ac.uk/glyco/](https://comp.chem.nottingham.ac.uk/glyco/))) (accessed on 1 March 2023). To identify lysine (K) ubiquitination, we utilized the PRmePRed tool ([[http://www.ubpred.org](http://www.ubpred.org)]([http://www.ubpred.org](http://www.ubpred.org))) (accessed on 1 March 2023). Lastly, to locate PEST sequences, we applied the ePEST algorithm ([[https://emboss.bioinformatics.nl/cgi-bin/emboss/epestfind](https://emboss.bioinformatics.nl/cgi-bin/emboss/epestfind)]([https://emboss.bioinformatics.nl/cgi-bin/emboss/epestfind](https://emboss.bioinformatics.nl/cgi-bin/emboss/epestfind))) (accessed on 1 March 2023).
## 2 Background and Related Work
### Retention Error  
A retention error is a data error caused by the loss of retained electric charge. When data is stored on a flash memory page and read after a certain period, the data value changes because of the loss of electric charge retained in the data. In terms of retention error, the error rate rises as retention time increases. The retention error tends to increase rapidly until about 11 days after data is written, after which the error increase rate tends to decrease [5]. Furthermore, because of the nature of the error, retention error occurs more frequently for cold data, where data update is difficult once it is stored on a page than for hot data, where data update occurs frequently. Consequently, by storing hot data in an area prone to retention errors, the error rate can be effectively reduced.  
Figure 5 is the retention error distribution of 3D TLC NAND flash, which is reported by [PERSON] et al. [1]. From the figure, it is obvious that the number of errors on the middle page is higher, whereas the number of errors on the lower page is relatively low. It can be seen that the number of errors on the upper page is less than on the middle page but greater than on the lower page. The overall distribution clearly indicates that the number of error occurrences is relatively high in the lower, middle, and 0.82-0.85 layers. Additionally, the number of errors in the upper layer is relatively low. Therefore, it can be seen that the areas vulnerable to retention errors are the middle page and the upper page of the lower, middle, and upper-middle layers.  
There have been several studies [5; 6; 18; 19; 20] to reduce retention error, and the most similar technique to the proposed method is WARM [6]. WARM proposes a write-hotness aware retention management policy. It classifies hot and cold write data through a window-based algorithm to mitigate refresh overhead, which significantly increases the internal write amount of flash memory. WARM increases the lifespan of flash memory by selectively performing refresh only for write-cold. It exploits the property that write-hot data requiresa shorter retention time guarantee. Similar to WARM, our proposed hot/cold-aware data placement policy utilizes data hotness. The difference is that we exploit the error rate differentiation according to the physical location of 3D NAND to place hot- and cold-data at high and low retention error rates, respectively.
## 2 Materials and Methods
### Description of Existing GPP Models Used in this Study and the New Models  
A total of 8 common satellite-data-based GPP models and 22 global GPP products were included in this study. The 8 existing models were the eddy covariance-light use efficiency (EC-LUE) model [9], the vegetation-indices (VI) model [25], the temperature-greenness (TG) model [26], the vegetation photosynthesis model (VPM) [11], the carbon fixation model (CFIX) model [27], the greenness-radiation (GR) model [28], the alpine vegetation (AVM) model [29], and the moderate resolution imaging spectroradiometer (MODIS) model [30]. At the site scale, we reproduced the 8 common satellite-data-based GPP models and compared them with the two new models in this study.  
The two new models were adaptations of the EC-LUE model, which included corrections for cloudiness and CO\({}_{2}\) concentration, with two derivations for determining water stress: One from the evaporative fraction (i.e., the LUE-EF model) and another from the normalized difference water index (NDWI) (i.e., the LUE-NDWI model), as described below.
## 3 Finite Element Model and Experiment
### Experimental Setup  
The setup of the experiment is shown in Figure 3. The experimental procedure was as follows: firstly, the specimen was clamped by a holder, and the preload of the bolt was applied by a torque wrench. Next, the LF vibration was excited by a shaker (B&K\({}^{\circledR}\), Model type: 4809, Virum, Denmark). Meanwhile, the HF probing wave was generated by a signal generator (Tektronix\({}^{\circledR}\), AFG 31000, Beaverton, OR, USA) and amplified by an amplifier (Ritec\({}^{\circledR}\), GA-2500A, Warwick, RI, USA) before introducing on the specimen by an actuator (PI\({}^{\circledR}\), P-885.11, Karlsruhe, Germany). Lastly, the response signals of the bolted joints under the mixed excitations were captured with an accelerometer (B&K\({}^{\circledR}\), Model Type: 4393, Virum, Denmark).  
Figure 3: Experimental system.  
Figure 2: Finite element model of the bolted joints: (**a**) assembling, (**b**) geometric parameters, (**c**) load and boundary conditions, (**d**) mesh.
# GPSR: Gradient-Prior-Based Network for Image Super-Resolution
## 4 Experiments
### Results with Bicubic (BI) Degradation  
We compare the proposed GPSR with the nine state-of-the-art methods, including LapSRN [38], HAN [12], DBPN [34], RDN [11], EDSR [10], SAN [15], PA-EDSR [14], DRN [40], and NLSN [13] in terms of quantitative results and visual quality. Following [10, 29], we also propose a self-ensemble model and denote it as GPSR+.
# Generic Component-Based Mission-Centric Energy Model for Micro-Scale Unmanned Aerial Vehicles
## 3 Model
### Machine Learning Approaches for Power Functions  
A generic approach is to fit the energy model for a given quadcopter by first specifying the type of power function of each component and determine the coefficients through expert knowledge or experiments. Each type of power function has different coefficients that need to be estimated to adapt them to the component in question. To this end, three approaches are possible:  
**White-Box**: This approach uses specific system knowledge to determine the necessary coefficients based on the physics of the system. Such approaches rely on either the electrical, dynamic or fluid system theory and the known behaviour of the component. Very few data are necessary for the approach, but it generally lacks in precision, because some effects are not modelled or some coefficients are hard to measure.
**Black-Box**: This approach views the component as a black box. The coefficients of the power functions of different components are fitted to the data using machine learning techniques like regressions, gradient descent or evolutionary algorithms. These aim to minimise \(\epsilon_{C_{i}}\) in the learning process. The used models are typically neural networks or genetic programming. All these methods typically need a large amount of data from a sufficient set of environmental conditions to guarantee stable results. Additionally, hyperparameters need to be chosen manually by the user.
**Grey-Box**: This approach combines the first two approaches. It uses system knowledge to derive analytical power function with unknown coefficients from the behaviour of the component using expert knowledge. Afterwards, the coefficients are fitted to experimental data to parametrise the model. This approach needs far less experimental data than the black-box approach, but still enables adaptation to real hardware.  
The benefit of the proposed generic model is the ability to select one of these approaches for each component individually. For sufficiently complex components like the motors of quadcopters we will aim for the Grey-Box approach, as it enables a precise model fitting to the hardware in question with reasonable effort to acquire training data.
## 2 Results and Discussion
### Variation in Total Volatiles Content and Major Chemical Classes from Young and Mature Leaf  
Differences in total amount of leaf volatiles, measured by GC-MS total ion current peak area, were observed between citrus types and, to a lesser extent, between cultivars, as well as between leaf developmental stage, especially in Ponkan, Liubencheng, Yuhuanyou and Huyou (Figure 1). The lowest amount of volatiles was found in either young or mature leaves of pomelos, and there was no marked difference between the other cultivars (Figure 1), except that, in general, young leaves produced higher amounts of volatiles than mature leaves (Figure 1).  
Terpenoids constitute the main part of leaf volatiles, accounting for 71.22 to 98.47% of total volatile amount, and monoterenes, or monoterenes hydrocarbons and oxygenated monoterenes together, were the main terpenids, especially in pomelos (Figure 2). Oxygenated sesquiterpenes were present in a low amount, with a maximum of 0.63% in total volatiles of Zaoxiangyou young leaves (Figure 2). Aldehydes varied greatly among different samples, from 1.60% of total volatiles in Ponkan young leaves to 24.21% in Zaoxiangyou mature leaves (Figure 2). The percentage of each chemical class in total volatiles varied among citrus types and their cultivars. For example, pomelos were rich in monoterenes hydrocarbons (57.74% to 71.93%), but low in oxygenated monoterenes (0.80%-5.10%), while in young leaves of Ponkan and Hongshigan, the amount of monoterenes hydrocarbons was less than that of oxygenated monoterenes (Figure 2).  
Figure 1: Total volatile contents of young and mature leaves of five citrus types.  
The percentage of each chemical class also varied with leaf developmental stage. Monoterpenes were the major class in leaves regardless of stage and cultivar. Monoterpene hydrocarbons were the most represented class, and mature leaves exhibited high amounts during development, while young leaves were characterized by a higher percentage of oxygenated monoterenes in most cultivars and by a lower percentage of aldehydes (Figure 2). Sesquiterpenes were present in large numbers, but in lower amounts in almost all cultivars. These observations could indicate that the different accumulation patterns of monoterenes and sesquiterpenes identified in leaf volatiles may be due to differences in biosynthetic pathways and substrate availability in different citrus cultivars and types.
## 5 Experimental Results and Discussion  
### Zero-Shot Cross-Modal Retrieval Results  
In the zero-shot learning experiment, we compared the performance of the proposed methodology with the baseline of the cross-modal transfer (CMT) methodology, which is one of the representative state-of-art methods using the CIFAR-10 dataset [6]. CMT selected two classes and created a zero-shot category of unseen-classes. In this process, the remaining eight labels were used for training as seen-classes, and an experiment was performed to classify the two selected unseen semantic labels. We used 38 k images in eight seen categories in the training model. In the test phase, 12 k images in two zero-shot (unseen) categories were used for zero-shot cross-modal retrieval.  
To conduct the zero-shot learning experiment, the hierarchical structure of the semantic classes was designed in the proposed methodology. The hierarchical structure was manually constructed based on B-CNN, which is one of the prior studies using hierarchical information of CIFAR-10 [40]. Figure 5a shows the CIFAR-10 hierarchical knowledge used in the experiment, which is constructed so that two child classes with high semantic similarity can share one super-class. Figure 5b shows the zero-shot category sets. Set1 is composed of a \"cat-dog\", which has very high visual similarity and faces difficulty in getting help from other labels. In contrast, set5 consists of a \"cat-truck\", which has different visual similarity, and might achieve relatively high zero-shot classification performance with the help of other semantic labels.  
Figure 6 compares the accuracy of classification of two zero-shot categories of CIFAR-10 using CMT, one of the existing state-of-arts methodologies, and the proposed methodology. The left dark red bar of each category represents the performance of CMT, and the right dark blue bar represents  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & \multicolumn{3}{c}{**Unseen Confidence Estimator**} & \multicolumn{3}{c}{**Image Embedding Networks**} & \multicolumn{3}{c}{**Test Embedding Networks**} \\ \cline{2-10}
**Layer** & **Weights Shape** & **Feature Map** & **Activation** & **Layer** & **Weights Shape** & **Feature Map** & **Activation** & **Layer** & **Weights Shape** & **Activation** \\  & **[**,  
the performance of the proposed methodology. It shows that the performance of our methodology is better than that of CMT for all zero-shot category sets. There is no large difference in sets such as \"deer-ship\" and \"cat-truck,\" which is relatively easy, but a relatively large difference in sets such as \"cat-dog\" and \"airplane-automobile.\" These experimental results confirm that the proposed HSL can make more efficient zero-shot cross-modal retrieval.
## 2 Related Work
### Degraded Image Restoration  
The early degraded image restoration was based on physics approaches such as dark channel prior [40] and color attenuation prior [41], which primarily relied on handcrafted priors derived from physical degradation like atmospheric scattering models. Whilethese methods achieved reasonable results under specific conditions, their performance typically declined significantly in complex real-world scenarios. Later, data-driven deep learning methods emerged and demonstrated superior performance by directly learning degradation patterns from paired training data. FFA-Net [42] improved multi-scale feature fusion capability through feature attention mechanisms. AOD-Net [43] jointly optimized transmission maps and atmospheric light parameters using an end-to-end framework. However, these methods still faced limitations when generalizing to unseen degradation types and real-world distribution shifts.  
Recent studies have combined transformer with CNN to capture both local and global dependencies. DehazeFormer [44] integrated CNN-based local feature extraction with transformer-based global context modeling. Restormer [45] employed channel attention and gated convolutions for efficient feature refinement. The diffusion model has gained prominence in restoration tasks due to its powerful image generation capability. Methods like WeatherDiff [46] and DiffBir [47] achieved state-of-the-art performance in multi-weather restoration through iterative optimization of degraded images. WeatherDiff enabled unified restoration across multiple degradation types using weather-conditioned embedding. DiffBir bridged the synthetic-real data gap by jointly training degradation estimation and image generation. However, few studies have specifically focused on traffic image restoration based on incorporating traffic characteristics.
## 3 Experiment
### Data Processing
#### 3.2.2 Normalization Process  
To improve the performance of the model to achieve a better migration effect, the original data are linearly transformed using the maximum-minimum normalization method to normalize the data between 0 and 1. Without loss of generality, \(\chi=\{x_{1}\), \(x_{2}\), \(x_{3}\dots\dots x_{n}\}\) is used to denote the original vibration signals, and the maximum and minimum values in the original vibration signals are xmax and \(x_{\text{min}}\), respectively. \(X=\{X_{1}\), \(X_{2}\), \(X_{3}\dots\dots X_{n}\}\) is used to denote the transformed data. The maximum-minimum normalization method is shown in Equation (12).  
\[X_{i}=\frac{x_{i}-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{13}\]
## 5 Experiment and Discussion
### Experiment Result and Discussion
#### 5.3.5 Runtime Comparison  
Because the network is constantly changing, the requirement of DHIN embedding will occur at any time and frequently. In order to meet the requirements of DHIN, the run-ning time is one of the most important embedding method indicators under the premise of ensuring sufficient accuracy of downstream tasks. The lower the time cost of the algorithm, the better the comprehensive performance. In this section, we evaluate the running time of MRDE and other comparative methods. Table 4 shows the total running time of each method on the two datasets, where the underlined part is the best result, and the bold part is the MRDE result.  
As can be seen from Table 4, JUST and CTDNE spend more time on embedding, and the static method JUST obviously exceeds an order of magnitude. Whenever an embedding needs to be obtained, the static model must be run on the current full graph, and the model must be completely retrained. Therefore, static methods are difficult to adapt to the needs of real-time due to the long running time. The CTDNE will try to walk randomly many times whenever new edges arrive, but the DHINs allow parallel edges between the same node pair, this means the number of edges may be much greater than the number of nodes and the edge-based methods will take much longer running time than node-based methods on an average, especially on edge-dense networks.
# A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search
## 4 Simulation Setup and Results
### Performance Comparison
This difference is due to the fact that MOILP and MOTS prioritize maximizing the total number of hosted VMs over the specific VM types, while the other algorithms aim for a more balanced allocation of all VM types. In Figure 12, the number of accepted VMs of different types across the available VMs (\(M=15\)) is presented for a single problem instance with \(N=200\). The results for FF, FFD, and WF are excluded due to their inefficiency. The x-axis corresponds to the individual PMs (1 to 15), and each group of bars represents the performance of one algorithm (e.g., MOILP, MOTS, MOACO, or MOGA), with the bars segmented by VM type (S, M, L, and XL), as shown in the legend. The height of each bar indicates the number of VMs of a specific type hosted on the respective PM. These results corroborate earlier findings on the resource utilization efficiency of the MOILP model and the MOTS algorithm. Both demonstrate the highest number of accepted VMs, with MOACO and MOGA following closely. The bar segments also reveal how each algorithm distributes the VM types: some favor larger VMs, while others show a more balanced allocation across types. Figure 10: Average percentage of hosted VMs of type L.  
Figure 9: Average percentage of hosted VMs of type M.  
Figure 13-15 compare the average percentage of CPU, RAM, and storage usage across active PMs as \(N\), the number of VM requests, increases. Each plot includes data for MOILP, MOTS, MOACO, and MOGA. The average percentage of resource usage is calculated by dividing the total resources used by the total number of active PMs. MOILP consistently outperforms the other algorithms in terms of maximizing resource utilization (CPU, memory, and storage) across active PMs. MOTS performs well, slightly behind MOILP, while MOACO and MOGA show more variability, especially at higher VM loads, due to a higher number of active PMs. Figure 11: Average percentage of hosted VMs of type XL. Figure 12: Distribution of hosted VMs by type across PMs for \(N=200\). Figure 14: Average percentage of RAM usage among active PMs. Figure 4:0 Average percentage of CPU usage among active PMs with respect to N  
In Figure 16, the average execution time (in seconds) for different numbers of VM requests is presented. MOILP exhibits a sharp increase in execution time as \(N\) grows, due to its high computational complexity, particularly for larger problem sizes. For \(N=240\), the execution time exceeds 2500 s, making it the slowest algorithm when handling large numbers of VM requests. In contrast, MOTS, MOACO, and MOGA maintain much lower execution times, staying under 500 s, even for \(N=240\)--five times faster than MOILP. MOTS strikes the best balance between efficiency and execution time when compared to MOACO and MOGA.