esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact FEM Implementation for Parabolic Integro-Differential Equations in 2D
## 3 Galerkin Discretizations  
Let \(\mathcal{T}\) denote a regular partition of the domain \(\Omega\in\mathbb{R}^{2}\) into disjoint triangles \(K\) of diameter \(h_{K}\) such that:  
* \(\cup_{K_{i}\in\mathcal{T}}\overline{K_{i}}=\overline{\Omega}\), and any pair of triangles intersect along a complete edge, at a vertex, or not at all;
* No vertex of any triangle lies on the interior of a side of another triangle.  
Let \(\mathbb{V}\) be the finite element space defined by  
\[\mathbb{V}:=H^{1}_{0}(\Omega)\cap\{\phi\in C(\overline{\Omega}):\ \phi|_{K}\in\mathbb{P}_{1}\ \forall K\in\mathcal{T}_{h}\},\]  
where \(\mathbb{P}_{1}\) is the space of polynomials in \(d\) variables of degree at most \(1\).  
With \(\mathcal{N}=\{z_{1},\cdots,z_{M}\}\) as the set of nodes of \(\mathcal{T}\), we consider the nodal basis \(\mathcal{B}=\{\phi_{1},\cdots,\phi_{M}\}\), where the hat function \(\phi_{l}\in\mathbb{P}_{1}\) is characterized by \(\phi_{l}(z_{k})=\delta_{kl}\), where \(\delta_{kl}\) is the Kronecker delta.  
Let \(0=t_{0}<t_{1}<\ldots<t_{N}=T\) be a partition of \([0,T]\) with \(\tau_{n}:=t_{n}-t_{n-1}\). Set \(f^{n}(\cdot)=f(\cdot,t_{n})\) for \(t=t_{n},\ n\in[0,1,\ldots,N]\). Now, for \(n=1,2,\cdots,N\), we define  
\[\partial v^{n}:=\frac{v^{n}-v^{n-1}}{\tau_{n}},\ \ \ t_{n-1/2}:=\frac{t_{n}+t_{n-1}}{2}\ \ \ \text{and}\ \ \ v^{n-1/2}:=\frac{v^{n}+v^{n-1}}{2}.\]  
In this article, we consider uniform time steps, i.e., \(\tau=\tau_{1}=\ldots=
## 3 Results and Discussion
### Microencapsulation Process and Evaluation of Process Efficiency  
_V. vinifera_ leave extract represents a rich source of active compounds, but in its concentrated dried form, shows up as a sticky, poor-handling, unstable material negatively affecting its use as a functional ingredient, as well as, in its lyophilized form, if not properly stored, can quickly interact with the humidity of the surrounding environment, and give rise to serious instability phenomena. Thus, based on [PERSON] et al.'s 2011 study [15], maltodext (M) as loading carrier and pectin (P) as coating polymer have been selected to process as extract model the 'Aglianico' variety leaves aqueous extract (named AGL-28, obtained from leaves harvested in November using a microwave power of 300 W for 2 min, see Table 3) by spray-drying technique. Previously, a combination of maltodext (M) and pectins (P), was developed as a tandem-polymeric matrix by spray-drying to encapsulate dried herbal extracts [15]. The excellent obtained results led us to apply the same approach to AGL-28 with the aim to enhance both the usability and stability also verifying  
Figure 4: HPLC profile of Aglianico of November leaf extract at 280 nm. (**A**) MeOHH\({}_{2}\)O (70:30) (red line) and H\({}_{2}\)O (black line). (**B**) MetOH:H\({}_{2}\)O (70:30) (red line) and EtOH (black line).  
the versatility of the microencapsulation process. To apply the MP spray drying matrix to the new 'Aglianico' extract (A), a preliminary study was carried out on the suitable concentration of polymeric matrix (M/P/A ratio and total liquid feed concentration) to be used to obtain satisfactory production yields and encapsulation efficiencies. Several preliminary tests were carried out using M in a range between 9.5% and 5%: percentages lower than 5% determined low process yields due to the dispersion of the material on the walls of the drying chamber, while a concentration higher than 10% determined a reduction of the process yield as well as a bad interaction with the extract in water [46; 47]. Pectin was used in a range between 0.4% and 0.2% because a concentration higher than 0.5% causes an increase in liquid feed viscosity with consequent difficulties to be processed by spray-drying.  
The most significant results were summarized in Table 7 and compared to each other. A process yields greater than 75% was obtained for the blank powders MP1-MP3 with the best result for the 96/4 M/P ratio. This selected matrix was able to load the 0.5% w/w of AGL-28 raw extract with a very satisfactory process yield (83.26%, Table 5). Furthermore, the actual extract content (AEC), extremely close to the theoretical (TEC), leads to an encapsulation efficiency of 93.96%, confirming an almost complete loading of the extract.
## 5 Conclusions  
In this paper, a DBRC is operated for battery charging application. Taking advantage of the flexible control options associated with the converter itself, two modulation methods are analyzed, designed, simulated and tested experimentally. The major contributions include: (1) Constant-current and constant-voltage operation can be achieved through proper modulation strategies. Both of them can be realized with full soft-switching and low rms tank current as well. (2) The operating characteristics of the converter with two modulation methods are comprehensively analyzed, which is universal for related researches. (3) The design procedures are extensive and representative, which provides guidance for industrial design and operation. The measured efficiency of the phase-shift based method (Method I) is higher than that of another frequency-based method. The main loss source is the conduction loss, which may be further reduced using a new generation silicon-carbide power transistor. Moreover, the two methods can be extended by adding more modulation variables to achieve different optimization objectives.  
Conceptualization, J.W.; Data curation, S.Z.; Formal analysis, J.W.; Funding acquisition, S.H.; Investigation, H.C.; Methodology, J.W.; Project administration, X.L.; Software, S.Z.; Supervision, X.L.; Validation, S.H.; Visualization, H.C.; Writing--original draft, J.W.; Writing--review & editing, S.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Fundo para o Desenvolvimento das Ciencias e da Tecnologia of Macau under Grant 0065/2019/A2, in part by National Natural Science Foundation of China under Grant No. 62003057, in part by Natural Science Foundation of Jiangsu Province under Grant No. BK20191029, and in part by The Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant No. 19 KJB470001.  
This work was supported in part by the Fundo para o Desenvolvimento das Ciencias e da Tecnologia of Macau under Grant 0065/2019/A2, in part by National Natural Science Foundation of China under Grant No. 62003057, in part by Natural Science Foundation of Jiangsu Province under Grant No. BK20191029, and in part by The Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant No. 19 KJB470001.  
The authors declare no conflicts of interest.  
Figure 11: Measured efficiency of two proposed modulation methods.
## 4 Spectrum of Heterogenous Services for Transport Means and the Communication of Information
### The Information Flows and Modelling for the Context of Tracking
#### 4.3.1 Tracking the Road Vehicle
Apart from EPC/RFID tags, the GIAI data could also be carried in two-dimensional data carriers such as GS1-128, GS1 DataMatrix, and GS1 matrix barcode quick response (QR) code (which are as well ISO/IEC 15424 symbology identifiers used in GS1 system). However, the system using data reported from trackside reader must be able to distinguish vehicle GIAI from other identification numbers or GIAIs that might be identified when passing the tag's reader. Reading reliability is a critical aspect ultra-high-frequency radio frequency identification (RFID) system when many markers are in the reading area with complex road obstructions. The frequency of reading errors can be caused not only by the position and orientation of the random tag but also due to RF shading by other objects and tags may misalign the main sections of the label and the scanner antennas. When considering why to practically increase system coverage and overall read the output in a closed environment, a possible solution is to employ multiple readers with overlapping reading areas. However, when labels are in the reading areas of several readers, the usual label the separation protocol will experience a severe clash in reader-reader (R2R) communication, unless conflicting readers can agree on appropriate time and space sharing schemes. Indefinite multiple access to time allocation and the lack of end-time sequence can further affect the reading process, mainly when the RFID system is used for applications that require real-time access, observation, and sensing. Collaborative reader code in a harmonic RFID system provides asynchronous tag access for all readers. The tag can distinguish redirection from multiple readers and then formulate the answer accordingly [35]. Some papers analyse the multiantenna readers and single antenna tags to achieve longer read range when reader downlink the multiple tags (as specified in Table 5).
# Airglow Observation and Statistical Analysis of Plasma Bubbles over China
## 4 Statistical Analysis of the Occurrence of Plasma Bubbles  
Based on the airglow observation data over a long period, the occurrence of plasma bubbles can be analyzed and studied. The data used to do the statistical analysis in Fuke began from 1 September 2014 to 31 August 2015. The image processing method was used to process these optical data, and clear and recognizable images of plasma bubbles can be obtained. Then we made a statistical analysis of the occurrence of equatorial plasma bubbles (EPBs), and investigated the monthly and seasonal variations of EPB occurrence.
# An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services
## 5 Related Work
### Dummy-Based Location Selection in the Internet of Things Privacy Protection  
The main purpose of the dummy-based location selection scheme is to select a dummy location for the anonymous user. [PERSON] et al. [8] proposed a dummy-location selection (DLS) scheme achieving k-anonymity for LBS users. However, the computation cost of the scheme is very high. Based on the entropy metric [11], the DLS scheme considers the side information which may be exploited by the adversary to select the dummy location. To ensure that the chosen dummy locations can spread as far as possible, the authors also proposed an enhanced-DLS [8] scheme that extends the clocking region while maintaining the same level of privacy as the DLS scheme. [PERSON] et al. [14] considered the need for privacy zones and proposed two schemes of virtual generation: circle-based and grid-based dummy. The authors analyzed the existing problems of the DLS scheme and designed an ADLS attack scheme, but the DLS could not resist the attack of the ADLS scheme [9]. [PERSON] et al. [35] incorporated a new type of side information and presented a new metric named transition-entropy. Then, they designed an scheme called robust dummy generation (RDG) which can resist the Viterbi attack. [PERSON] et al. [6] proposed a k-anonymity based scheme which preserves user location information by generating dummy locations that can be used to hide the user's real location. At present, most of the existing schemes utilize dummy location to protect the user's location privacy. However, the computation complexity of the schemes are still very high. Therefore, how to effectively select the dummy location in the Internet of Things is still a challenge. In this paper, we introduced the Enhanced-DLP for IoT user and devices. By considering side information and computational overhead, the Enhanced-DLP can effectively select the reasonable dummy locations and protect against side information attacks.
# Event-Based Probabilistic Embedding for POI Recommendation
## 2 Topic And Region Embedding
### Preliminary  
**Definition 1** (Poi).: _A POI is a uniquely identified site (e.g., a bar, gym or restaurant). In our model, a POI has three attributes: identifier, location and category. We use \(v\) to represent a POI identifier and \(l_{v}\) to denote its corresponding geographical attribute in terms of longitude and latitude coordinates. We use the notation \(W_{v}\) to denote the set of its category._  
**Definition 2** (Check-in Activity).: _A user check-in activity is represented by a five-tuple \((u,v,l_{v},\)\(W_{v},t)\) denoting user \(u\) visits POI \(v\) at time \(t\)._  
**Definition 3** (Event).: _An event is represented by a four-tuple \((u,v,t_{q},l_{q})\) denoting user \(u\) to visit POI \(v\) at time \(t_{q}\) at location \(l_{q}\)._  
**Definition 4** (User Profile).: _For each user \(u\), we create a user profile \(D_{u}\), which consists of a set of check-in activities associated with \(u\). The dataset \(D\) used in our model consists of user profiles, i.e., \(D=\{D_{u}:u\in U\}\)._  
**Problem 1** (Event-based Embedding).: _Given a check-in activity dataset \(D\) and an event \(e=(u,v,t_{q},l_{q})\), our goal is to embed \(e\) according to the \(u\)'s profile, \(l_{q}\), \(t_{q}\), the category \(W_{v}\) and location \(l_{v}\) of POI \(v\)._
# CERP: Cooperative and Efficient Routing Protocol for Wireless Sensor Networks
## 2 RPL Routing Mechanism Overview
After a node has received a DIO, it will begin counting down from that point. As soon as the timer runs out, they broadcast DIO messages to all of their nearby neighbors. This means that every node in the DODAG knows its stable set of parents and can reliably send packets down the route to the DODAG's center, as shown in Figure 1.  
Yet research shows that the packet loss ratio for multi-hop lines may approach 20 percent or more as the number of hops grows [8]. Thus, selecting more stable and high-quality routes is a viable method to improve RPL's reliability. This is achieved by taking into account more efficient link quality [9]. In the coming sections, we will include numerous works and research that focus on improving the RPL routing mechanism for greater reliability.
## 4 Results
### Overview of the Model Training  
Figure 4 presents the training progress of the best performing LSTM model, the optimum hyperparameters of which are given in Table 4. The figure shows the change in the loss value, \(R^{2}\), and RMSE w.r.t. epoch as the model continues its training with a constant learning rate of \(10^{-3}\). The loss value, \(R^{2}\), and RMSE for training and validation datasets converge around epoch number 1000, and the model tends to overfit beyond 1000 epochs.  
Figure 5 shows the outcomes of the training (left side) and testing (right side) SM predictions for all stations. The scatter plots between measured and estimated values for the training and testing datasets show a similar pattern when compared. The main population of the points is along the 1-1 line. The model can make good predictions with MAE of less than 0.035. In the second row, violin plots show the measurement and prediction distributions. The left side of the violin corresponds to actual values, while the right side stands for the predictions. In an ideal case, we should see a mirror-like shape, which is also the case for our predictions with small differences due to the error previously mentioned in the scatter plots.  
Figure 4: Accuracy of the best-performing LSTM model according to epoch. The upper figure shows the training progress of the model w.r.t. loss value per epoch, and the lower figure shows the change in accuracy w.r.t. \(R^{2}\) and RMSE.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Case No.**} & \multicolumn{4}{c}{**Train**} & \multicolumn{4}{c}{**Test**} \\  & \(\mathbf{R^{2}}\) & **RMSE** & **ubRMSE** & **MAE** & \(\mathbf{R^{2}}\) & **RMSE** & **ubRMSE** & **MAE** \\ \hline Case-I & 0.366 & 0.101 & 0.101 & 0.082 & 0.337 & 0.105 & 0.104 & 0.085 \\ Case-II & 0.663 & 0.074 & 0.074 & 0.057 & 0.651 & 0.076 & 0.076 & 0.058 \\ Case-III & 0.875 & 0.045 & 0.045 & 0.033 & 0.843 & 0.051 & 0.051 & 0.037 \\ Case-IV & 0.908 & 0.038 & 0.038 & 0.028 & 0.860 & 0.048 & 0.046 & 0.034 \\ Case-V & 0.922 & 0.035 & 0.035 & 0.026 & 0.871 & 0.046 & 0.045 & 0.033 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy analysis of LSTM with different features set.
# Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association
## 3 Multiple Target Tracking
### Two Point Differencing Initialization  
In the paper, the two-point (current and past) differencing initialization in the CV model is extended to the CA model. The initial state vector and covariance matrix for track \(t\) are, respectively, calculated as:  
\[\mathbf{\hat{x}}_{ini}^{t}(k-1|k-1)=\left[\begin{array}{c}z^{tx}(k-1)\\ \frac{z^{tx}(k-1)-z^{tx}(k-2)}{\Delta}\\ z^{ty}(k-1)\\ \frac{z^{ty}(k-1)-z^{ty}(k-2)}{0}\end{array}\right],P_{ini}^{t}(k-1|k-1)=\left[ \begin{array}{c}\frac{r_{x}^{2}\ r_{y}^{2}\ r_{y}^{2}\ r_{y}^{2}\ 000}{\Delta\ \frac{r_{y}^{2}\ r_{y}^{2}\ 000}{ \Delta\ \frac{r_{y}^{2}\ r_{y}^{2}\ 000}}}\\ \frac{r_{x}^{2}\ r_{y}^{2}\ r_{y}^{2}\ r_{y}^{2}\ 000}{\Delta\ \frac{r_{y}^{2}\ r_{y}^{2}\ 000}{ \Delta\ \frac{r_{y}^{2}\ r_{y}^{2}\ 000}{\Delta\ \frac{r_{y}^{2}\ r_{y}^{2}\ 000}}}},\ k=3,\ldots,N_{K}+1.\end{array}\right. \tag{6}\]  
The state is confirmed as the initial state of a track with the following maximum speed gating: \(\|\mathbf{z}^{t}(k-1)-\mathbf{z}^{t}(k-2)\|\leq\Delta\cdot V_{max}\), where \(\|\cdot\|\) denotes vector norm, and \(V_{max}\) is the maximum speed of a target. Since the initial acceleration cannot be obtained with two points, it is set to zero in \(\mathbf{\hat{x}}_{ini}^{t}(k-1|k-1)\), thus the target is assumed to move at a constant velocity. Other remaining measurements become past measurements and can be associated to measurements in the next frame. In the following frame, they will vanish unless they
# Retinex Based Image Enhancement via General Dictionary Convolutional Sparse Coding
## 3 Proposed Method
### Proposed Objective Function  
In the proposed method, CSC is applied to the Retinex model to effectively decompose the image's illumination and reflectance. The main idea of the CSC Retinex model is to search the appropriate basis in advance for the reflectance function, and then decompose the illumination by identifying more detailed structures or features in the reflectance function. Therefore, the key step is to construct a dictionary for expressing the reflectance component of the input image.  
Our model is based on the following assumptions:  
* In general, since the illumination function is spatially smooth, it can be expressed as \(\frac{\alpha}{2}||\
abla l||_{2}^{2}\) as the regularization term.
* For the reflectance function, it is generated by CSC as mentioned in Section Section 3.1 by \(\frac{1}{2}||r-\sum_{k=1}^{K}d_{k}*z_{k}||_{2}^{2}+\beta\sum_{k=1}^{K}||z_{k}||_ {1}\)
* Based on the reflectivity, the constraints \(l\geq s\) and \(r\geq 0\) are added.  
We consider the following energy function for Retinex to simulate and explain how the HVS perceives color:  
\[\min_{r\geq 0,l\geq s,d,z}E(r,l,d,z)=\frac{1}{2}||r-\sum_{k=1}^{K}d_{k}*z_{k}||_{2 }^{2}+\beta\sum_{k=1}^{K}||z_{k}||_{1}+\frac{\alpha}{2}||\
abla l||_{2}^{2}+ \frac{\eta}{2}||l-s-r||_{2}^{2} \tag{9}\]  
here, \(\alpha\), \(\beta\) and \(\eta\) are positive numbers for regularization parameters.  
In our proposed model, the reflectance can be better represented by a trained dictionary than the SCR model, and the first and second terms in Equation (9) can be interpreted as the regularization terms for reflectance \(r\). In addition, by applying an iterative algorithm in our proposed model, we construct a dictionary that can derive the optimal reflectance each time the algorithm is repeated. Therefore, the following alternating minimization method [8] is used to solve Equation (9).
## 3 Results and Discussion
### Residual Levels of Organochlorine Pesticides in Grain
The average levels of DDT metabolites (DDD and DDE) in maize grain over the years of the study did not always correspond to the values noted in fertilization treatments (Figure 7a,b). The lower levels of organochlorine pesticide residues in maize grain harvested from plots fertilized with higher doses of organic matter in the form of MBM (2.0 and 3.0 t ha\({}^{-1}\)) were the result of improved physico-chemical soil properties, which enabled the plants to activate defense mechanisms against contaminants [34; 51]. MBM introduces into the soil a  
Figure 7: Residual levels of organochlorine pesticides in maize grain, \(\mu\)g kg\({}^{-1}\): interactions between years and treatments. (**a**) content of \(\gamma\)-HCH, (**b**) content of DDT, (**c**) content of DDD, (**d**) content of DDE. Values followed by the same letters do not differ significantly in Tukey’s (HSD) test (\(p<0.05\)). \(\gamma\)-HCH—\(\gamma\)-hexachlorocyclohexane, DDT—1,1,1-Trichloro-bis-2,2 [4-chlorophenylophenyl]-ethane, DDD—dichlorophenoxythalloroethane, DDE—dichlorophenoxythalloroethylene. complex of mineral nutrients available to plants [36; 62; 63], and well-nourished maize plants are better able to cope with harmful factors by deactivating them through physiological processes [64]. Despite the fact that OCPs were detected in maize grain, their concentrations did not exceed the maximum residue levels. In Commission Regulation No. 149/2008 [65], the maximum residue levels of OCPs in cereal grain were set at 10 \(\upmu\)g kg\({}^{-1}\) for \(\gamma\)-HCH and 50 \(\upmu\)g kg\({}^{-1}\) for \(\Sigma\)DDT.
# Fusion Information Multi-View Classification Method for Remote Sensing Cloud Detection
## 1 Introduction
In order to solve the above problems, a multi-layer network structure is used to extract the texture, boundary, and high-level abstract information of super pixels, and the statistical features of the basic three-channel color data are utilized. The data of different extracted views are essentially relevant because they provide complementary information for the same data in semantics. Many methods show that the combination of multiple views learning is superior to the simple method of using a connected view or learning from each view alone [20; 21]. The structural relationship between pixels and the cloud state of the pixel itself belong to a privilege information. In order to unify the use of each view, a multi-view classification method based on information fusion is proposed. In the training stage, three feature views are used. The model introducing the privileged information mechanism can accurately determine the super pixel category by using the extracted two feature views (except the privilege information features). Overall, our main contributions are as follows:  
1. A feature extraction network at the super pixel level is constructed to extract the fast features of super pixels in cloud-containing remote sensing images at multi-scales. The cloud content in super pixels and the cloud-containing marker state information of adjacent super pixels are effectively utilized. 2. A multi-view support vector machine cloud detection classifier based on fusion information is constructed and the solving algorithm based on quadratic convex optimization is given. 3. We provide a multi-view classification dataset based on remote sensing cloud super pixels. The new model is used to classify the super pixels and synthesize the cloud mask bipartite graph. Experiments are carried out in images with different cloud contents to verify the effectiveness of the new method. The rest of this article is organized as follows. Section 2 introduces the research status of multi-view learning and an advanced multi-view classification method. In Section 3, we introduce the framework of the proposed method in detail, including the multi-view feature extraction method and fusion information multi-view classification model. Section 4 shows the experimental results on two remote sensing datasets and discusses the results of the experiment. Finally, Section 5 summarizes the research work.