esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Deep Learning for Data Caching in Edge Network
###### Abstract  
The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network, as well as reducing latency to access popular content. In that respect, end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e., at close proximity to the users. In addition to model-based caching schemes, learning-based edge caching optimizations have recently attracted significant attention, and the aim hereafter is to capture these recent advances for both model-based and data-driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, many key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning, as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for caching.  
deep learning; content caching; network optimization; edge network Article  
A Survey of Deep Learning for Data Caching in Edge Network
# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 2 Methods  
The geographical areas, i.e., the island of Sardinia and the rice-growing districts, for which the SMARTerra decision support system is currently employed, and the meteorological data are described in Section 2.1. The back-end and front-end infrastructures of SMARTerra, summarized in Figure 1, are presented in Sections 2.2 and 2.3, respectively.  
The back-end of SMARTerra is responsible for the activities and functionalities that are not directly visible to end-users but are crucial to the operation of DSS as a whole. In particular, the back-end undertakes daily the following tasks, all performed in a full automatic way:  
* Downloading and pre-processing the weather measurements and forecasts (Section 2.2.1);
* Efficiently storing the large volume of meteorological data (Section 2.2.2);
* Spatially interpolating the temperature, humidity and rain accumulation measurements with the Kriging with External Drift technique (Section 2.2.3);
* Spatially interpolating the temperature, humidity and rain accumulation forecasts from the BOLAM model (Section 2.2.4);
* Forecasting the risk indices and alert levels associated to the rice blast disease (Section 2.2.5);  
Figure 1: A schematic description of the back-end and front-end of the SMARTerra decision support system.  
* Forecasting the dates of the hatching of locust eggs (Section 2.2.6);
* Producing the raster files for the measured weather data and for the agricultural pests (Section 2.2.7) and generating a PDF report summarizing the whole output in a few pages. On the other hand, the WebGIS portal (Section 2.3) serves as an interface with the user, who can access maps and data of the following:
* The hourly meteorological measurements over the entire territory of Sardinia, spatialized at 100 m resolution;
* The risk indices and level alerts for _Pyricularia oryzae_ at 10 m resolution over the area of the rice-growing districts of Oristano and San Gavino;
* The cumulative rainfall and cumulative growing degree days, and the dates predicted for the hatching of locust eggs over the entire regional territory at 100 m resolution.
# A Phase Angle-Modulated Bat Algorithm with Application to Antenna Topology Optimization
## 1 Introduction
The traditional antenna optimization by meta-heuristic algorithms is to firstly determine the shape of the antenna based on empirical research and then to only optimize the dimensions of the given geometric parameters. This method not only highly depends on antenna designers' experiences but is also not capable of designing more unexpected antenna geometries. In this paper, the topology optimization method was used in small antenna designs, which are necessary for wearable or portable devices [29; 30]. Antenna topology optimization first appeared in [31] for creating more unexpected resonant structures based on GA. The antenna topology optimization method works by discretizing the design space into several pixels, and each pixel is filled with air or conductors. This feature enables pixels to make full use of the space in a small volume, which is also a key reason why it can be used in antenna miniaturization design [32; 33]. In designing radio frequency identification (RFID) tag antennas [34], microstrip patch antennas [8], and Multi-input Multi-output (MIMO) antennas [35], researchers have shown great interest in this method. However, antenna topology optimization often faces the problem of the \"dimensionality curse\", i.e., the design space exponentially expands with the number of design variables (i.e., the number of pixels). Inspired by AMBA, this paper proposes a new binary bat algorithm, named phase angle-modulated bat algorithm (P-AMBA), to solve high-dimensional binary optimization problems. By applying a six-dimensional trigonometric function, the proposed algorithm can greatly reduce the dimension of optimization variables and achieve a better convergence speed and global search capability than the basic BBA and AMBA. Herein, numerical tests of the knapsack problem and a design example of a compact dual-band planar monopole antenna topology optimization verify the superiority of P-AMBA in convergence speed and optimization ability. The rest of this paper is arranged as follows. The standard BA and BBA are elaborated in Section 2. In Section 3, the AMBA and P-AMBA are explained in detail. Section 4 uses 12 0-1 knapsack problems to carry out numerical verification experiments and a design example of a compact dual-band planar monopole antenna to demonstrate the effectiveness of the proposed P-AMBA. Finally, Section 5 concludes the paper and provides suggestions for future studies.
# A Two-Step High-Order Compact Corrected WENO Scheme
## 2 Numerical Methods
### The 5 th-Order WENO Scheme  
The core principle of the WENO scheme lies in constructing a high-order numerical flux approximation through a nonlinear weighted combination of several lower-order schemes on substencils. These weights are dynamically adjusted based on the smoothness properties of the function within each stencil. To achieve a fifth-order WENO scheme, three second-order flux approximations for \(\hat{f_{j+\frac{1}{2}}}\) are generated from function values at the grids on substencils, as illustrated in Figure 1  
\[E_{0}=\big{\{}\ f_{j-2},\ f_{j-1},\ f_{j}\big{\}},\ E_{1}=\big{\{}\ f_{j-1},\ f_{j},\ f_{j+1}\big{\}},\ E_{2}=\big{\{}\ f_{j},\ f_{j+1},\ f_{j+2}\big{\}}.\]  
The three second-order flux approximations for \(\hat{f_{j+\frac{1}{2}}}\) are  
\[\begin{array}{l}E_{0}:\ \hat{f_{j+\frac{1}{2}}}=\frac{1}{3}\hat{f_{j-2}}- \frac{7}{6}\hat{f_{j-1}}+\frac{11}{6}\hat{f_{j}}\\ E_{1}:\ \hat{f_{j+\frac{1}{2}}}=-\frac{1}{6}\hat{f_{j-1}}+\frac{5}{6}\hat{f_{j}}+ \frac{1}{3}\hat{f_{j+1}}\\ E\[\hat{f}_{j+\frac{1}{2}}=\omega_{0}\hat{f}_{j+\frac{1}{2}}^{(E_{0})}+\omega_{1} \hat{f}_{j+\frac{1}{2}}^{(E_{1})}+\omega_{2}\hat{f}_{j+\frac{1}{2}}^{(E_{2})}, \tag{8}\]  
where  
\[\omega_{i}=\frac{\gamma_{i}}{\gamma_{0}+\gamma_{1}+\gamma_{2}}\text{and}\ \ \gamma_{i}=\frac{c_{i}}{(\varepsilon+IS_{i})^{2}}, \tag{9}\]  
and \(c_{0}=\frac{1}{10}\), \(c_{1}=\frac{3}{5}\), and \(c_{2}=\frac{3}{10}\). \(IS_{i}\) represents the smoothness indicators and can be found in Reference [8].
## 4 Initial Trajectory Generation
### Polynomial Trajectory Generation as QP  
In minimum-snap trajectory generation, total trust force, i.e., attitude acceleration, is proportional to the fourth derivative (snap) of the trajectory [53]. The gracefulness of such behavior helps to avoid generating excessive control commands. Subsequently, a slight variation of minimum-snap trajectory generation was proposed in [35], where segment times or keyframes were fixed initially. Once start and goal positions were provided, RRT* [49] was utilized for finding an obstacle-free path between the start and the goal poses as a sequence of optimal waypoints. Initial segment times (\(m_{d}\)), which were estimated using optimal waypoints, were calculated according to the maximum velocities that the quadrotor is allowed to fly due to set technical limits. Let \(p_{i}(t)\) be the \(d\)th-order polynomial in the \(i\)th segment that describes as follows:  
\[p_{i}(t)=\lambda_{0}t^{0}+\lambda_{1}t^{1}+\lambda_{2}t^{2}+\lambda_{3}t^{3}+ \cdots+\lambda_{d}t^{d}. \tag{16}\]Each \(p_{i}(t)\) provides a flat output for a given time index \(t\). \(\lambda_{j},j=0,\ldots,d\) denotes the polynomial coefficients. The objective or cost function \(J(\Gamma_{i})\) can be fully determined by penalizing the derivatives of squares [35]:  
\[J(\Gamma_{i})=\int_{t_{i}}^{t_{i+1}}\xi_{0}p_{i}(t)^{2}+\xi_{1}\dot{p}_{i}(t)^{ 2}+\xi_{2}\ddot{p}_{i}(t)^{2}+\cdots+\xi_{k_{r}}p^{(k_{r}^{i})}(t)^{2}=P_{i}^{T }Q(T_{i})P_{i}, \tag{17}\]  
where \(P_{i}\) is a vector whose elements contain polynomial coefficients: \(\xi_{0},\xi_{1},\ldots,\xi_{k_{r}}^{i}\), where \(k_{r}^{i}\) is the highest order of derivative and \(Q(T_{i})\) is Hassin matrix, which contains the \(i\)th segment squares of derivatives. Since there are \(m_{d}\) number of segments, total cost \(J(\Gamma)\) can be expressed by:  
\[J(\Gamma)=\begin{bmatrix}P_{1}\\ \vdots\\ P_{m_{d}}\end{bmatrix}^{T}\begin{bmatrix}Q(T_{1})\\ &\ddots\\ &&Q(T_{m_{d}})\end{bmatrix}\begin{bmatrix}P_{1}\\ \vdots\\ P_{m_{d}}\end{bmatrix}. \tag{18}\]  
For a smooth flight experience, ensuring the continuity of derivatives between segments is necessary. Hence, imposing constraints between segments, e.g., velocity, acceleration, jerk, and snap is needed, which can be formulated as follows:  
\[C_{i}p_{i}=\mathbf{d}_{i},\quad C_{i}=\begin{bmatrix}\xi_{0}\\ \xi_{k_{r}}\end{bmatrix}_{i},\quad\mathbf{d}_{i}=\begin{bmatrix}d_{0}\\ d_{k_{r}}\end{bmatrix}_{i}, \tag{19}\]  
where \(C_{i}\) contains a mapping matrix whose entries contain the start and end coefficients of \(i\)th segment, whereas \(d_{i}\) contains derivative values, i.e., start and end of \(i\)th segment. Taking all constraints of \(m_{n}\) segments:  
\[C\begin{bmatrix}p_{1}\\ \vdots\\ p_{m_{d}}\end{bmatrix}=\begin{bmatrix}\mathbf{d}_{1}\\ \vdots\\ \mathbf{d}_{m_{d}}\end{bmatrix}. \tag{20}\]  
Now this can be solved as a constrained quadratic programming (QP) problem.
## 2 Materials and Methods
### Characterization Methods
#### 2.3.2 Near-Infrared Spectroscopy (NIR)  
Near-Infrared (NIR) Spectroscopy was employed as a rapid and easy method to estimate _A. platensis_ biomass growth during its cultivation, following the protocol proposed by [PERSON] et al. [48]. Measurements were performed with a SPECTRUM Two N\({}^{\text{TM}}\) NIR spectrometer (Perkin Elmer, Waltham, MA, USA) using a diffused silica Near-Infrared Reflectance Module (NIRM), at 1000-2500 nm, with 8 cm\({}^{-1}\) resolution and 64 scans. Measurements were performed daily without any sample preparation; 10 mL of cultivation were directly placed in a glass Petri dish (\(\mathcal{O}\) 6 cm) without pretreatment, and they were returned to the culture after measurement, maintaining sterile conditions to avoid contamination. The diffuse reflectance NIR response at 1062.6 nm was used to depict the biomass content of _A. platensis_ cultures. In order to minimize the impact of slight biomass variations among the 19 cultures under study, data were normalized to the ones collected on Day 0 of cultivation.
## 4 Computational Complexity  
We investigate the efficiency of the proposed algorithm. Table 4 shows the time complexity of the kernel SVM and the proposed algorithm. The training algorithm is DBSCAN; hence, the worst-case complexity is essentially the same as DBSCAN which takes \(O(l^{2})\) time for computing distances among \(l\) training data points. This is better than the standard kernel SVM, which takes the worst-case time of \(O(l^{3})\) for kernel matrix inversion (empirical scaling is \(O(l^{2+\delta})\) for some \(1>\delta>0\)) [22; 23]. The training algorithm has also the feature of Deutsch-Jozsa; hence, it can be quantum-enhanced by using an \(n\)-Toffoli gate to speed it up. The speed-up does not change the worst-case scaling of the training algorithm's complexity. The cost of the prediction part is \(O((n_{sv}+n_{t})n_{t}d)\) for computing the \(J_{t}\) and \(J_{int}\) matrices. This scaling seems to be worse than that of the kernel SVM, but, in practice, this is not an issue. We could divide the test data into \(n_{b}\) batches and run the prediction algorithm one time for each batch. The scaling becomes \(O((n_{sv}+\frac{n_{t}}{n_{b}})n_{t}d)\) in this case. For example, when \(n_{b}=n_{t}\) (i.e., we predict one datum at the time, as in the kernel SVM), we recover the same scaling as the kernel SVM. The cost of the prediction part depends on finding the ground state of the Ising model, which is equivalent to solving a QUBO problem. This, in general, could take an exponentially large amount of time, depending on the solver algorithm. However, to make a reasonable prediction, it is not necessary to find the optimal solution. The Ising prediction part can be solved by classical simulated annealing, or other QUBO solver and can be potentially accelerated by using adiabatic quantum computation or quantum annealing [34]. One might question about the possibility to combine the DBSCAN-DJ training algorithm with the kernel SVM prediction. However, since our training algorithm does not produce \(\vec{w}\) and \(\vec{b}\) vectors, it cannot be used for kernel SVM prediction.
# Kinetics and Thermodynamics of Efficient Phosphorus Removal by a Composite Fiber
## 2 Materials and Methods
### Static Adsorption and Phosphorus Removal Experiment of ZSFB Composite Fiber
#### 2.1.3 Experiment on Isothermal Adsorption  
Seven groups of 1 g of ZSFB composite fiber were prepared and placed in 250 mL conical flasks, and 100 mL potassium dihydrogen phosphate (\(\mathrm{KH_{2}PO_{4}}\)) solution was added with a concentration of 5, 10, 30, 60, 100, 150 or 200 mg/L (calculated by phosphorus concentration) for each group, respectively. The conical flasks were placed in a water bath thermostatic oscillator for 48 h adsorption at 25 \({}^{\circ}\)C with a speed of 150 r/min. Then, the adsorption solution was extracted with a syringe, and the concentration of phosphorus in the adsorption solution was measured by the ammonium molybdate spectrophotometric method. The phosphorus adsorption capacity of the composite fiber was calculated, and the adsorption isotherm was drawn based on the results. The experiments were also conducted at 15 and 35 \({}^{\circ}\)C with the same method.
# Temporal Instability Analysis of Injury Severities for Angle and Non-Angle Crashes at Roundabouts
## 3 Data Description  
Data available for this study were roundabout crashes provided by Florida's Crash Analysis Reporting Data System, which were gathered over eight years from 1 January 2012 to 31 December 2019. For this study, every two years is regarded as a period, including the 2012-2013, 2014-2015, 2016-2017, and 2018-2019 time periods in the dataset. \begin{table}
\begin{tabular}{c c c c} \hline
**Region** & **Periods** & **Number of Roundabouts** & **Number of Accidents** & **Accident Rate** \\ \hline Wisconsin & 2009–2014 & 53 & 1467 & - \\ Korea & 2007–2010 & 93 & 137 & - \\ Erftkreis & - & 27 & 335 & - \\ Washington & 2001–2013 & - & 3067 & - \\ Oregon & 2011–2015 & - & 1006 & - \\ UK & 1999–2003 & 1162 & - & 1.87\% \\ Arizona & - & 17 & - & 2\(\sim\)6\% \\ Germany & - & - & - & 162/a million vehicles \\ \hline \end{tabular} Note: Reprinted/adapted with permission from Ref. [45]. [PERSON], [PERSON], and [PERSON], Evaluation of roundabout-related single-vehicle crashes; published by Transportation Research Record, 2017, Ref. [47]. Erftkreis District Police Authority, data from a study of 27 roundabouts in the Erftkreis, 2002, Ref. [48]. [PERSON], [PERSON], and [PERSON], Accelerating Roundabouts in the United States: Volume IV of VII-A Review of Fatal and Severe Injury Crashes at Roundabouts, 2015., Ref. [50]. [PERSON], [PERSON], and [PERSON], Review of accident research at roundabouts; published by Transportation Research Circular, 2005, Ref. [51]. [PERSON], Effect of Roundabouts on Accident Rate and Severity in Arizona, published by Arizona State University, 2016.  
\end{table} 
Table 1: Statistics on roundabout accidents by region [45; 46; 47; 48; 49; 50; 51]. In the dataset, crashes are classified into seven categories according to the collision modes: front to rear, front to front, angle, sideswipe-same direction, sideswipe-opposite direction, rear to side, and rear to rear. This study maintains the original angle crashes while taking all other collision modes as non-angle crashes. The number of angle collisions at roundabouts totaled 1351 over the eight years. In comparison, 3288 non-angle collisions occurred during the period. The information in the dataset covers crashes, roads, crash-involved people, vehicles, and other aspects. Details include the severity of the injury, the type of collision, the influence of alcohol, weather, work zone conditions, and more. Three injury severities were identified as outcome variables: no injury, minor injury (possible and non-incapacitating injury), and severe injury (incapacitating injury and fatality) [31]. Follow-up studies only presented and discussed those variables that were statistically significant parameters in the model estimation. Figure 1 shows the percentage distribution of no injury, minor injury, and severe injury for angle and non-angle crashes during the four analysis periods (2012-2013, 2014-2015, 2016-2017, 2018-2019). Table 2 shows the descriptive statistics of the variables retained after the correlation analysis.
# Deep Learning-Based Bird's Nest Detection on Transmission Lines Using UAV Imagery
## 4 Bird's Nest Detection Network Framework
### ROI Mining Faster RCNN Structure  
In this work, we propose a method based on the 2-stage faster RCNN framework. We accomplish this by improving faster RCNN and proposing ROI mining faster RCNN. The ROI mining faster RCNN is based on ResNet-101 and is used for extracting features from images. This is done in order to ensure the accuracy of object detection in real-time applications and considering the detection problem in image samples obtained using an unmanned aerial vehicle (UAV). In order to improve the accuracy of bounding box coordinates generated by the algorithm, we use k-means clustering for extracting anchor boxes. In addition, we propose the focal loss function in RPN stage to balance the number of foreground and background samples. Moreover, we present the ROI mining module for solving the class imbalance problem during the training process. The overall flow chart is shown in Figure 6.  
We use the annotated images as the input of the model, ResNet automatically extracts the features of the images and generates feature maps. The RPN network generates multiple candidate ROIs based on the information of the feature maps. Then a classifier is used to distinguish these ROIs into foreground and background, and a regression is used to make preliminary adjustments to the position of these ROIs. The processed ROIs are combined with the image information to obtain proposals, and input them into ROI pooling to obtain the output results of the same dimension. After that, we use ROI mining to select these results, input the selected special results into the final regression and classification networks. The method automatically generates the detection boxes, and finally obtains the detection maps.  
Figure 6: ROI mining faster region-based convolutional neural networks (RCNN) flow chart.  
The main content of this work is to solve the problem of class imbalance in the model training process. It is noticeable that the main cause of this problem is the error in the classification stage. In training, it is difficult for the classifier to recognize small-sized objects and it is easy to classify them as background, which leads to the imbalance between foreground and background. In the RPN stage, pixel-wise object detection, i.e., using the regression layer to locate, may cause errors. Most of these errors are caused by incorrect classification. Therefore, after solving the problems in the classification stage, we no longer consider the localization problems.
# Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management
## 3 Methodology
### A System Using Big Data and AI for Intelligent Decision Support  
The level of success of a DSS is assessed using two parameters. The first parameter is the data input rate and output rate, which represent the information that is useful for the decision-making process. The second parameter is time. To improve the efficiency of the DSS in this study, a conceptual framework using big data and AI to enhance the above-mentioned parameters is proposed. The decision points of the standard manual for heat/cold wave and forest fire crisis management and the services provided by the decision-maker in the event of a disaster are summarized in Figure 4, along with the big data system processes and big data analysis. The proposed big data process is composed of data sources, collection, storage, analysis, and presentation order, and is built on the basis of the data lake (DL) form. The data collection is performed based on the type of natural disaster that a given DSS is built to deal with. The choice of data source for the analysis is extremely important. If the collected data are not reliable, they will be difficult to process and discard. Extreme disasters, such as heat and cold waves, need to be analyzed, forecasted, and planned for in advance. By contrast, with forest fires, the post-disaster response includes population evacuation, which is more important than forecasting. Therefore, camera data should be analyzed to predict the speed and direction of the flames and to send information to the people in the affected areas.  
To collect and store data, it is important to build a DL. A DL differs from a data warehouse (DW) in that the former contains both structured and unstructured data. Post data collection, it undergoes the process of extraction, transformation, and loading (ETL); however, this process is time-consuming and makes it difficult to arrive at a timely decision. Hence, this system splits the DL archive into two parts. The first is a platform that can store the data in the original form without going through the ETL process, and the second is the data analysis platform that undertakes the ETL process. The collected data include real-time data and batch data. Real-time data are structured data, which can be periodically collected and processed by the existing DWs. Batch data are the raw and unstructured data that need to be used for analysis.