esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
## 3 Image Acquisition, Preprocessing, and Postprocessing
In the blurring of an image, the aim is to remove drastic changes in pixel values which usually occur on border pixels so the border becomes fuzzy. [PERSON] and [PERSON] [61] tested 16 different filters for noise removal and concluded that the best one is an adaptive median filter. Some of the other filters used in papers of wound analysis:
* Median filter [49; 60; 62; 63]. * Gaussian filter (blur) [64]. * Ordered statistics filter [8]. * Image cropping In [PERSON] et al. [44] authors resized images to fit the input of their CNN architecture, while [PERSON] et al. [65] segmented the target wound region with an annotation app. This was because if original images were fed into the network, it would learn most of the features of the background and therefore would not accurately learn wound features. * Superpixel segmentation Authors like [PERSON] et al. [5] and [PERSON] [66] divide the input image into pixel groups that contain similar features, also called superpixels. Superpixel segmentation is often performed using simple linear iterative clustering (SLIC) algorithm. * Color correction Color correction is often performed by using a combination of gray world estimator and retinex theory as in [8; 52; 63; 67]. * Color homogenization An anisotropic diffusion filter is used for the homogenization of colors along with the preservation of edges, thus only smoothing between edges as in [8; 52; 63; 67]. * Contrast equalization The aim is to enhance contrast in the image to bring out subtle differences, usually done with contrast limited adaptive histogram equalization (CLAHE) as in [44]. Most papers, after the proposed wound image analysis, finally perform some type of postprocessing, such as noise removal, edge softening, or conduct some morphological operations to make the segmented wound region look natural. To refine the output image, [PERSON] et al. [58] perform morphological operations like hole filling or area base filtering. [PERSON] et al. [37] apply morphological closing and filling to remove noise and soften the edges, while [PERSON] et al. [52] smooth the object by sequential dilation and erosion of the image with a diamond structuring element. [PERSON] et al. [8] also use morphological operation of hole filling, area opening and closing, bridging, thickening, and thinning.
# Manipulation Planning for Large Objects through Pivoting, Tumbling, and Regrasping
## 2 Related Works  
### Non-Prehensile Manipulation  
The non-prehensile manipulation allows a robot to manipulate an object without firmly grasping it by taking advantage of contact with environment [5]. Examples of non-prehensile manipulation include pushing [6], tumbling [7], scooping [8], tilting [9], throwing [10], catching [11], batting [12], sliding [13] and pivoting [14].  
Pivoting is efficient for manipulating heavy objects because the weight of the object is mostly supported by the table. [PERSON] et al. [15] first proposed the manipulation of a heavy object by pivoting. [PERSON] et al. [16] and [PERSON] et al. [17] proposed motion planners to reorient an object by pivoting. [PERSON] et al. [18; 19] proposed a motion planner planning an object path during the pivoting gait. [PERSON] et al. [20] proposed a tumbling motion planner. In addition, there have been some research on motion planning combining the pivoting gait with other manipulation styles. [PERSON] et al. [21] proposed the manipulation planner combining the pivoting gait with tumbling. [PERSON] et al. [22; 23] combined pivoting, pushing and tumbling. In all the above mentioned works, the pivoting gait was planned for a simple box shaped objects. On the other hand, this is a first trial on planning the motion of a general shaped object by combining pivoting, tumbling and regrasping. In addition, our planner considers both SP and USP to efficiently manipulate an object.
# Micro-Satellite Systems Design, Integration, and Flight
## 2 Systems Modeling of the CubeSat
### Flight Assembly  
To take a systems perspective of the CubeSat's hardware and assembly, the Engineering Development Unit (EDU) satellite was constructed. The EDU was built to perform verification and validation testing, as shown on the right side of the Vee diagram. A complete version (EDU) of the satellite was made. The EDU version was the same as the flight version. However, it would not go to space; it only serves as the standard for the flight satellite. See Figure 9 below.  
Some of the components used include the following:  
* Teensy 3.5 flight computer microcontroller.
* RockBLOCK radio transceiver (Iridium network).
* Adafruit LSM9 DS1 IMU.
* Two SparkFun TB6612 FNG dual Motor Drivers.
* INA169 Analog DC Current Sensor Breakout Board.
* 5 V regulator.
* 5 V Reg cam.  
The documentation that was made while putting together the EDU version helped to make the Flight Unit CubeSat. Also, the work instruction that was created was used as a reference for other modular CubeSat missions.
# A QR Code-Based Approach to Differentiating the Display of Augmented Reality Content
## 4 Discussions  
The proposed system has combined confidential QR codes and AR. Hence, when users scan the marker, they are no longer limited in getting the same and fixed virtual objects. Besides, the system can present various AR effects according to different user groups or preferences.  
Past studies [11; 12; 13] focus on improving the recognition of AR without using markers. However, these approaches still require pre-registration to match the image features and virtual objects. Furthermore, these 3D objects remain fixed and cannot update promptly after the registration. The stability and accuracy of scanning are also lower than marker-based techniques. That is why we combine the QR code-based approach with the AR system to break these limitations.  
Several studies [17; 18; 19] have explored similar issues. However, their emphasis is on recognizing QR markers, especially positioning the AR on QR codes after calculating the coordinates. We build our theory upon these studies, but we do not just treat QR codes as markers. Instead, we further incorporate the authentication mechanism to propose the differentiated display of AR content, which is the significant contribution of this study.  
Finally, research [21; 22] uses multi-markers to provide different AR content in different environments. Compared to their results, our system only requires one QR code to generate various virtual objects, thus saving costs on producing extra QR markers.
# Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting
## 2 Application and Assessment of Models
### Error Assessment  
To carry out a comparative study of the performance of Artificial Intelligence models, several metrics have been computed. These have been evaluated together with its standard deviation (sd). The bias of the variable to be forecast is obtained with the normalized Mean Bias Error (nMBE), Equation (6). Positive values indicate underestimation and negative, overestimation.  
\[\text{nMBE}=\sum_{k=1}^{n}\frac{\hat{y}_{k}-y_{k}}{n}/y_{max} \tag{6}\]  
where, \(y_{max}\) is the maximum value of the objective variable.  
The measurement of how spread out are these residuals are computed with the normalized Mean Squared Error (nRMSE). It is considered an excellent error metric for numerical predictions. This gives an indication of the model's ability to forecast the overall load shape that is reflected in the data, Equation (7), which is strictly positive and the results decrease as the error approaches zero.  
\[\text{nRMSE}=\sqrt{\sum_{k=1}^{n}\frac{(\hat{y}_{k}-y_{k})^{2}}{n}}/y_{max} \tag{7}\]  
The coefficient of determination (R\({}^{2}\)) indicates how close the forecast values are to the regression line of the objective values, Equation (8). It is a common metric to evaluate numerical predictions. Its values are limited in the range \([0,1]\), where higher values mean that the forecast variable matches the objective variable and lower ones do not.  
\[\text{R}^{2}=\frac{\sum_{k=1}^{n}(y_{k}-\hat{y}_{k})^{2}}{\sum_{k=1}^{n}(y_{k} -\overline{y})^{2}} \tag{8}\]  
where, \(\overline{y}\) is the mean value of the objective variable.  
Figure 4: Configuration of a Deep Learning model.
# Structural Performance of R-Type Steel Damper Used in Reinforced Concrete Members
## 3 Phase I Test Results  
### Behavior of Specimens  
The shear force-displacement curve obtained from the Phase I test is shown in Figure 5. Specimens D1 and D2 showed stable elasticity and plastic behavior up to the target displacement of \(\pm\)15 mm. As shown in Table 2, D1 first yielded in the first cycle at a displacement  
Figure 4: View of test setup of Phase II test specimens: (**a**) M1; (**b**) M2.  
of 1.85 mm and load of 18.1 kN. By contrast, D2, with an additional lower horizontal member, first yielded in the first cycle at a displacement of 0.57 mm and a load of 36 kN. In other words, D2 had 6.4 times higher rigidity than D1 owing to the addition of the lower horizontal member. This means that a damper with higher initial rigidity can dissipate external energy more quickly when the external load is transmitted to the structure.  
As shown in Figure 5 and Table 2, D2 had a higher rate of strength increase after yielding than D1. That is, in D1, the load at target displacement of the first cycle was 1.56 times the yield load, whereas in D2, it was 2.77 times. However, there was no significant difference in the load at the target displacement for each of the specimens during five cycles. Furthermore, D1 and D2 showed little decrease in stiffness and strength during five repeated loads. In the first forward cycle, D1 showed strength of up to 23.3 kN, whereas D2 showed strength of up to 90.5 kN (i.e., 3.9 times higher load resistance capacity). In the fifth cycle, D2 showed around 3.6 and 3.4 times higher strength in the positive and negative directions than compared to D1, respectively. The shear force-displacement curve in Figure 5 and the deformation state at the target displacement in Figure 6 confirms that the deformation of the specimen is stable and no in-plane and out-of-plane buckling were found even after the experiment was completed. Therefore, the experimental results confirmed that D2 has excellent load resistance and deformation capacity even though it has a relatively simple shape.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multicolumn{3}{c}{**At First Yield**} & \multicolumn{6}{c}{\(P_{y}\)/\(P_{y}\) at Target Displacement for Each Cycle**} \\ \cline{2-10}  & \multirow{2}{*}{**Cycle**} & \multirow{2}{*}{**Load (\(P_{y}\))** (\(\mathbf{k}\)N)**} & \multirow{2}{*}{**Disp.** (mm)**} & & \multirow{2}{*}{**Directions**} & \multirow{2}{*}{**1 st**} & \multirow{2}{*}{**2 nd**} & \multirow{2}{*}{**3 rd**} & \multirow{2}{*}{**4 th**} & \multirow{2}{*}{**5 th**} \\ \cline{1-1} \cline{6-10}  & & & & & & & & & & \\ \hline D1 & +1 st & +18.1 & +18.5 & Positive & \(-\)1.56 & \(-\)1.65 & \(-\)1.66 & \(-\)1.66 & \(-\)1.66 \\ D2 & +1 st & +36.0 & +0.57 & Positive & 2.51 & 2.79 & 2.84 & 2.84 & 2.84 \\  & & & Negative & \(-\)2.77 & \(-\)2.93 & \(-\)2.91 & \(
# Fabricating Sustainable All-Cellulose Composites
## 5 Production of ACC  
### Processing Routes  
There are two main ways to produce ACCs [102; 170; 171; 172; 173]. The first method is the one-step method. It is based on the partial dissolution of cellulosic materials, where the same raw material is used to create both reinforcement and matrix (Figure 6) [6; 14]. This type of composite can also be called a self-reinforcing composite [172]. Here, the chosen cellulose solvent system will partially dissolve the surface or weaker regions of the reinforcement fibres, and the dissolved cellulose will form a matrix surrounding the initial fibre structure during its precipitation. Meanwhile, the core of the reinforcing fibres maintains its original structure and chemical crystallinity, which has a reinforcing effect on the composite [131]. One of the great benefits of this approach is that interfaceless composites can be prepared where the boundaries between reinforcement and matrix are indefinite between the phases [14]. In addition, the undissolved fibre content of the composite can easily be controlled by changing the dissolution time [102].  
Another way to manufacture ACCs is the two-step method, in which a high-strength reinforcing fibre is impregnated with a solution containing dissolved cellulose, which forms a matrix around the fibre structure after the regeneration process. This way, the composite is formed from solid cellulose and dissolved cellulose, which are chemically bound to each other at a specific temperature, pressure and curing time. The advantage of this method is that the fibre content of the composite is easily controlled by changing the mixing ratio [102], and the processing method is beneficial for composite bulk production [173].  
Figure 6: One-step method of creating all-cellulose composite with NaOH-urea solvent system.
## 2 Materials and Methods
### Datasets
#### 2.1.2 Reanalysis Datasets  
(1) ERA5 dataset: ERA5 is the fifth-generation atmospheric reanalysis dataset released by the European Center for Medium-Range Weather Forecasts (ECMWF). It provides daily and monthly estimates of atmospheric, terrestrial, and oceanic climate variables on a global scale based on information assimilation techniques. ERA5 has a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and a temporal resolution of monthly. It is accessible on the Climate Data Store ([[https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)]([https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)) [last access: 26 June 2023]). This study collected fourteen variables from 2015 to 2020. Please refer to Table S1 in the Supplementary Materials for a detailed description.  
(2) EGG4 dataset: CAMS global greenhouse gas reanalysis (EGG4) is a reanalysis dataset focusing on CO\({}_{2}\) and methane released by ECMWF. By simulating the CO\({}_{2}\) fluxes from terrestrial vegetation, it captures the variability of CO\({}_{2}\) at various temporal scales from daily to interannual. EGG4 has a spatial resolution of 0.75\({}^{\circ}\)\(\times\) 0.75\({}^{\circ}\) and a temporal resolution of monthly. It is available on the Atmosphere Data Store ([[https://ads.atmosphere.copernicus.eu/](https://ads.atmosphere.copernicus.eu/)]([https://ads.atmosphere.copernicus.eu/](https://ads.atmosphere.copernicus.eu/)) [last access: 26 June 2023]). In this study, a total of eight variables were collected from 2015 to 2020. Among them, the TCCO2 variable characterizing the CO\({}_{2}\) column concentration data was used to validate the model's accuracy, and the remaining variables were used to construct the model. We downscaled EGG4 data to a resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) by spatial interpolation. For more detailed descriptions, please refer to Table S2 in the Supplementary Materials.
## 3 Biosynthesis of Lily Fragrance Components
### Biosynthetic Pathways of Benzenoid/Phenylpropanoids  
Benzenoid/phenylpropanol volatiles are the second-largest category of floral volatile organic compounds in plants [13]. The shikimate pathway converts the aromatic amino acidphenylalanine (Phe) into cinnic acid (CA) via phenylalanine ammonia-lyase (PAL) [36; 37; 38]. In lily fragrance, cinnamic acid can be converted into the precursor benzoic acid through the \(\beta\)-oxidation and non-\(\beta\)-oxidation pathways [39; 40], which is then catalyzed by enzymes such as BSMT and BAHD to produce methyl benzoate and ethyl benzoate [16]. In other plant volatile synthesis pathways, CA can also be hydroxylated by cinnamate-4-hydroxylase (C4H) to form p-coumarate, which is further modified to produce a series of volatile benzenoids/phenylpropanoids [41; 42; 43].  
PAL catalyzes the deamination of phenylalanine to form CA, the first step in phenylpropanoid biosynthesis [36; 37]. The transcriptome of _Lilium_ 'Siberia' contains seven _PAL_ genes, the expression of which is consistent with the release of benzenoid/phenylpropanoid compounds during flower development but remains almost unchanged during the circadian rhythm [21]. CA is further modified through the \(\beta\)-oxidation and non-\(\beta\)-oxidation pathways to form benzenoids/phenylpropanoids [13]. In the \(\beta\)-oxidation pathway of petunias, four reactions are catalyzed by CNL/AAE (cinnamate-CoA ligase/acyl-activating enzyme), CHD (cinnamoyl-CoA hydratase-dehydrogenase), and KAT (3-ketoacyl-CoA thiolase) [44; 45; 46]. Two _KAT_ genes were found in _Lilium_ 'Siberia', and their expression slightly increased after flower development [21]. Key genes in the BSMT family play a direct role in the production and release of methyl benzoate in lily fragrance [47]. Researchers have found that the BAHD family member _LoAAT1_ in _Lilium_ 'Siberia' is not only the preferred candidate gene for ethyl benzoate production but is also the first to be reported as participating in the biosynthesis of methyl benzoate [16].
## 2 Methods
### Experimental Setup
#### 2.3.2 Data Augmentation  
Although DL models rely on large datasets to capture complex patterns, they may tend to memorize the training data when the data diversity is insufficient, leading to overfitting.  
Figure 4: Stiffness degradation curves under different loads.  
Figure 3: Damage progression curves under different loads.  
The initial data in this study are derived from regularized stiffness degradation curves, which exhibit a relatively smooth degradation trend. Therefore, this paper proposes two data augmentation methods to meet the model's need for a sufficient amount of training data and to enhance the model's generalizability.  
Time series combination: In addition to being easy to implement and computationally efficient, this technique generates new samples that maintain some similarity with existing samples while introducing a degree of variability, thus enhancing the overall generalization capability of predictive models. In this study, the stiffness degradation data under five different load conditions were combined into three groups: \(E_{20\%}(n)\) and \(E_{40\%}(n)\), \(E_{40\%}(n)\) and \(E_{60\%}(n)\), and \(E_{60\%}(n)\) and \(E_{80\%}(n)\). A mean operator was applied to these combinations, generating three new datasets, \(E_{\text{a}}(n)\), \(E_{\text{b}}(n)\), and \(E_{\text{c}}(n)\), to help the model learn the mechanism of blade stiffness degradation.  
Random noise injection: The use of this technique to create new training samples offers two key advantages. First, in theory, it can produce numerous new samples, thus expanding the training set. Second, when properly adjusted, random noise can introduce variance within the sequence while preserving the fundamental patterns that are crucial for prediction, thereby improving the generalization ability of the predictive model [27]. In this study, the initial stiffness degradation data are obtained through curve fitting, making the data relatively smooth. However, in real-world testing environments for wind turbine blades, stiffness degradation is influenced not only by fatigue accumulation but also by external environmental factors. By introducing random variables, the dynamic behavior and randomness during the blade testing process are described [28]. In this paper, random noise with a fixed signal-to-noise ratio of 65 dB is added to the training dataset.
## 3 System Model  
The system model used in this paper is shown in Figure 1. All vehicles are connected with RSUs by using V2I communication. These RSUs are placed on the roadside at different geographical areas to provide the coverage area of the complete highway. These RSUs are attached with servers for efficient computing to make them fog-computing nodes. These fog nodes are backwardly connected with the Internet cloud. Vehicles move with uniform velocity, and the expected time of attachment with each RSU is the same. Vehicles have one or more different types of tasks that are required to be executed. These tasks are mainly divided into three different types, such as emergency or safety tasks, traffic management tasks and infotainment tasks. The time sensitivity of execution of each type of task varies along with their task size. Vehicles want to offload these tasks for timely execution.  
In this work, we consider \(R\) RSUs, and each RSU is connected with \(V\) vehicles on the road. Each vehicle \(n_{i}\) attached with a RSU has one to three tasks for execution. These tasks are categorized as \(t_{1}\), \(t_{2}\) and \(t_{3}\) and are prioritized from low to high priority level, respectively. \(T\) is the total number of tasks that are required to be executed and are calculated as:  
\[T=\sum_{i=1}^{V}\sum_{j=1}^{k}n_{i}(t_{j}) \tag{1}\]  
The downloading data rate for a vehicle to download a task solution from a fog node \((DR_{1})\) is computed as:  
\[DR_{1}=log_{2}(1+\gamma_{v,f}) \tag{2}\]  
where \(\gamma_{v,f}\) is the signal to noise ratio between the vehicle and the fog computing node.  
The data rate for downloading executed tasks from a cloud node such as TCCC \((DR_{2})\) is calculated as:  
\[DR_{2}=log_{2}(1+\gamma_{v,c}) \tag{3}\]  
where \(\gamma_{v,c}\) is the signal to noise ratio between a vehicle and a cloud node. We assume that there are multiple channels available in each fog node to transmit all executed tasks simultaneously without considering any queuing delay. We used the path loss propagation model along with Nakagami-m multipath fading [41] to find the RSU coverage range.
# A Survey of Information Extraction Based on Deep Learning
## 2 Entity Relationship Extraction Based on Deep Learning
### Supervised Learning--Joint Extraction
#### 2.2.4 Joint Extraction Based on Graph Structure  
Compared with the pipeline method, the above joint extraction method avoids the problems of error accumulation and poorly connected sub-tasks in the traditional method, takes into account the dependency between two tasks, and improves the overall task accuracy. However, it cannot deal with the overlap problem in relationship extraction well. More and more researchers are exploring the use of graph neural networks to solve the overlap problem, of which GCN and GAT are typical.  
[PERSON] et al. proposed an end-to-end relationship extraction model based on graph convolutional networks (GCNs) that jointly learn named entities and relationships. The role between named entity recognition and relationship extraction is considered by a relation-weighted GCN. The model combines RNN and GCN to extract not only sequence features of each word but also region-dependent features using linear and dependency structures, and the relationship of each word pair is predicted using the complete word graph, considering implicit features between all word pairs in the text, solving the problem of entity overlap [57]. [PERSON] et al. addressed the problems of error propagation in relationship extraction, redundancy in prediction, and inability to solve the relationship overlap problem. They proposed a joint entity relationship extraction model BSGB (BiLSTM + SDA-GAT + BiGCN) based on graph neural networks, which extracts the sequence features of each word and the local dependency features of each word, and employs a graph convolutional network that considers the implicit features between all word pairs in the text to predict the relationship of each word pair, thus, solving the overlap problem [58]. [PERSON] et al. proposed a joint entity-relationship extraction model based on graph convolution-enhanced multichannel decoding (graph convolution-enhanced multi-channel decoding joint entity and relation extraction, GMCD-JERE); the next step can consider how to improve the extraction performance of the model in the case of insufficient samples and uneven distribution and explore solutions for long or cross-sentence entity relationship extraction [59].