esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Estimation Results  
In this study, we focused on media companies that publish news content on the search engine Naver. We collected data from 63 media companies that provide news content with the external link method and 60 media companies that provide news content with the internal link method. The source of the data is KISLINE, a corporate information service operated by Nice Rating Information. KISLINE is Korea's leading corporate information service that allows users to view corporate financial data. To compare two groups of production efficiency, financial information from 2013 to 2020 of a total of 123 companies was used. Among various financial information, we select several input variables for comparing production efficiency. The input variables are total assets, cost of sales, and labor costs, and the output variable is sales. Table 1 below shows sample statistics on input and output variables of the companies used in this study. As can be seen from Table 1 above, the total assets, cost of sales, and labor costs of media companies that provided news through internal links are generally higher than those that use external links. As a result, the sales volume of the internal link method is also larger. Table 2 below shows the estimation results of the frontier production function of the external links group and the internal links group estimated using Frontier 4.1. In addition, it shows the \(\beta^{\star}\) value of the meta-frontier production function calculated using the LP and QP methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**External Links**} & \multicolumn{2}{c}{**Internal Links**} \\ \hline Revenue & 42,816,719,009,009 & 69,907,377,822.2997 \\ (Standard Deviation) & (154,272,331,365.156) & (163,570,830,182.209) \\ Total Asset & 44,685,966,418.9189 & 100,480,211,045.993 \\ (Standard Deviation) & (305,725,470,939.046) & (197,841,629,900.742) \\ Total Cost of Sales & 14,363,353,306.3063 & 31,625,542,355.4007 \\ (Standard Deviation) & (62,303,023,103.1901) & (107,567,506,640.0230) \\ Total Wage & 3,526,651,310.8108 & 7,133,729,564.4599 \\ (Standard Deviation) & (10,605,526,263.9267) & (15,679,361,371.4579) \\ Number of firms & 63 & 60 \\ Numbers & 222 & 287 \\ of Observations & & & \\ \hline \hline \end{tabular} Unit: KRW (From ‘Revenue’ to ‘Total Wage’). Note: 1 USD = 1399 KRW (4 September 2022, Ministry of Economy and Finance, Republic of Korea).  
\end{table} 
Table 1: Sample statistics.
# Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments
## 6 Multi-Operator Game for Channel Selection
### Existence of a Nash Equilibrium  
We aim to prove that a pure Nash equilibrium does indeed exist for our game proposal. We do this by showing that this game possesses a weighted potential function [15]. For this proof, we assume that \(n=1\), i.e., each operator is only choosing one channel out of the channels available for transmission. We denote \(w^{i}=\sum_{\mathrm{s}}(\lambda^{i}\cdot N_{co}^{i}\cdot p_{\mathrm{s}}^{i} \cdot T_{\mathrm{s}})\). In order to prove that a Nash Equilibrium exists for this game, we consider \(j\in\mathcal{N}\) and define the function \(\phi\), such that:  
\[\phi(x^{i},x^{-i})=\frac{1}{2}\sum_{c}\left[(\sum_{j}w^{j}x_{c}^{j})\times(2 \lambda_{c}^{e}+\sum_{j}w^{j}x_{c}^{j})\right]\]\[=\frac{1}{2}(\sum_{j\
eq i}w^{j}x_{c}^{i}+w^{j}x_{c}^{i})\times(2 \lambda_{c}^{e}+\sum_{j\
eq i}w^{j}x_{c}^{j}+w^{j}x_{c}^{i})\] \[\qquad\qquad\qquad+\frac{1}{2}(\sum_{j\
eq i}w^{j}x_{c^{\prime}}^{i }+w^{j}x_{c^{\prime}}^{i})\times(2\lambda_{c^{\prime}}^{e}+\sum_{j\
eq i}w^{j} x_{c^{\prime}}^{j}+w^{j}x_{c^{\prime}}^{i}) \tag{10}\]  
Now, suppose that player \(i\) changes its choice from channel \(c\) to channel \(c^{\prime}\), then  
\[2\phi(x^{i},x^{-i})-2\phi(x^{i^{\prime}i},x^{-i})=w^{j}x_{c}^{i} \times(2\lambda_{c}^{e}+\sum_{k}w^{k}x_{c}^{k})+w^{i}x_{c}^{i}\times(\sum_{k}w ^{k}x_{c}^{k})\] \[-w^{i}x_{c^{\prime}}^{i}\times(2\lambda_{c^{\prime}}^{e}+\sum_{k} w^{k}x_{c^{\prime}}^{k})-w^{i}x_{c^{\prime}}^{i}\times(\sum_{k}w^{k}x_{c}^{k})=w^{i }x_{c}^{i}(2\lambda_{c}^{e}+2\sum_{k}w^{k}x_{c}^{k})-w^{i}x_{c^{\prime}}^{i}( 2\lambda_{c^{\prime}}^{e}+2\sum_{k}w^{k}x_{c^{\prime}}^{k})\] \[\qquad\qquad\qquad=w^{j}\mathcal{U}_{i}^{e}(x^{i},x^{-i})-w^{j} \mathcal{U}_{i}^{e}(x^{i},x^{-i}), \tag{11}\]  
where \(\mathcal{U}_{i}^{e}\) is the cost function associated with \(U_{i}^{e}\), such that  
\[\mathcal{U}_{i}^{e}=-\log(\pi_{s}w_{s}^{i})+2\sum_{c}x_{c}^{i} [\sum_{k}w^{k}x_{c}^{k}+\lambda_{c}^{e}] \tag{12}\]  
Because a weighted potential function exists for our proposed game, pure Nash equilibriums exist and we prove, via simulations, their efficiency. For \(n>1\), the potential function still stands. Instead of changing the choice from one channel to another, we are changing from one strategy (two or more channels selected) to another.
## 3 Path Planning Problem
### Path Planning Objectives
#### 3.1.1 Path Length  
Based on the Euclidean distance, the segment length can be calculated between two consecutive points. By summing all the segments of this path, the length of the entire path can be obtained. \(p=[S=p_{0},p_{1},p_{2},...,p_{n},p_{n+1}=T]\) represents path \(p\). \(p_{i}=(x_{i},y_{i})\) and \(p_{i+1}=(x_{i+1},y_{i+1})\) denotes two consecutive points or rotation points (RPs). The length of a line segment is calculated using Equation (1):  
\[dis(p_{i},p_{i+1})=\sqrt{(x_{i}-x_{i+1})^{2}+(y_{i}-y_{i+1})^{2}}. \tag{1}\]  
A path consists of some consecutive segments, and the total length of path \(p\) can be calculated as follows:  
\[Length(p)=\sum_{i=0}^{n}dis(p_{i},p_{i+1}). \tag{2}\]
## 6 Conclusions  
In this paper, we discussed the GPU-accelerated multiresolution SBR algorithm to calculate the target strength of underwater targets. By means of comparing with the theoretical value and the literature, our numerical results showed good agreement. Moreover, compared with the experiment results on the CPU and GPU, the proposed approach achieves 750\(\times\) speedup and improved the runtime performance efficiently. The next stage of the work will be porting the code to a multi-GPU platform, further realizing the real-time prediction of the target strength of underwater targets.  
Conceptualization, G.Z. and X.W.; methodology, G.Z. and N.S.; software, S.S. and X.W.; validation, L.W.; formal analysis, G.Z. and X.W.; writing--original draft preparation, G.Z. and X.W.; visualization, N.S.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This work was funded in part by the China Postdoctoral Science Foundation (2013M540735); in part by the National Nature Science Foundation of China under Grant 61901388, 61301291, 61701360, 61502367, 61501346, 61571345, 91538101, 61801359, 61401337; in part by the 111 Project under Grant B08038; in part by Yangtze River Scholar Bonus Schemes; in part by the Ten Thousand Talent Program; and in part by the Fundamental Research Funds for the Central Universities.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Targets** & **CPU-Based SBR** & **GPU-Based SBR** & **GPU-Based MSBR** & **Speedup Ratio** & **Speedup Ratio** \\  & & & & **(CPU)** & **(GPU)** \\ \hline Sphere & 4336.35 s & 4.45 s & 418.65 ms & 974.68 & 10.65 \\ Cylinder & 1390.06 s & 1.46 s & 389.28 ms & 897.97 & 3.75 \\ Corner reflector & 924.41 s & 1.20 s & 499.44 ms & 768.42 & 2.41 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison of average runtime per angle of three targets at 15 KHz.
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 2 Related Work
### Multi-UAV Cooperative Search Energy Consumption Problem
Therefore, the energy consumption of the UAV during flight, hovering and data collection between each target node must be considered when path planning is performed. We propose a multi-UAV cooperative search algorithm based on the greedy algorithm (MUCS-GD) and a multi-UAV cooperative search algorithm based on the binary search algorithm (MUCS-BSAE). MUCS-GD assigns paths for multi-UAV collaborative searching based on UAV energy consumption, while MUCS-BSAE assigns paths for multi-UAV collaborative search by balancing the work duration of each UAV under the constraint of energy consumption.
# Study on Noise Reduction with Paving Different Low Noise Pavement Materials
## 1 Introduction
Emphasis should be given that there are some obvious differences between these two methods [13; 14]. First, CPX uses a microphone to measure the sound pressure level, while OBSI uses a microphone probe to measure the sound intensity, and thus we can study the directionality of the sound when measuring the sound intensity. Second, the microphone of CPX and the microphone probe of OBSI are placed in different relative positions with the interaction of the tire and pavement, which makes the results quite different in the near-field measurement due to the different mechanisms of tire/road noise generation and amplification [15; 16]. Third, to reduce the influence of other interferences, CPX usually needs to use an enclosure around the tested tire and microphone, while OBSI generally does not need because it can identify the direction of the sound source [17]. The measurement results of CPX and OBSI are highly correlated with the reference tires used in the test, wherein the reference tires of CPX are specified by ISO 11819-3 [18], and the reference tires of OBSI are specified by ASTM F2493 [19]. Besides using the measurement results of the roadside noise or the tire/road noise, one can also study the noise reduction performance of the pavement by analyzing the variations of the internal noise of the driving vehicle. However, the type and operation condition of the vehicle have a great influence on the measurement results when using this method, especially when considering that there is the big attenuation at high frequencies. Therefore, we usually do not measure the internal noise of the driving vehicle to study the contribution of pavement on noise reduction performance [20; 21]. In this paper, to directly measure the sound pressure level of the tire/road noise source, we propose to use CPX, which can also reduce the influence of other interferences on the measurement results simultaneously. A CPX trailer is used to study the noise reduction performance of low noise pavements in each lane of the highway. We also study the noise reduction performance of paving different low noise pavement materials in two adjacent lanes when considering the traffic flow, the vehicle type, and the road capacity. This study can provide some guidelines on controlling traffic noise when considering the economic and environmental benefits for practical applications.
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 3 Results and Discussion
### Advantage of Proposed Method over 2D-SSA  
#### 3.5.1 Applying SP-SSA on General Images  
In the proposed approach, 2D-SSA is applied on each and every superpixel segmented region. Hence, it can be considered as a local 2D-SSA approach that can extract accurate spatial information on each single object. In the case of global 2D-SSA, features are over-smoothed, and features are not prominent for specific classes. In local 2D-SSA instead, object-specific texture information can be highlighted. In Figure 15, the popular cameraman image and an artificial test image are used to demonstrate the effectiveness of the proposed approach over the 2D-SSA approach.  
When the cameraman image is reconstructed using the 2D-SSA method, the Mean Square Error (MSE) comes out to 115.8865; however, when the same image is reconstructed using the proposed SP-SSA approach, the MSE reduces to 93.0468. A similar behavior is also observed with the test image. With the proposed SP-SSA method, the MSE reduces to 237.1038 from 287.5323. This signifies that the proposed method can reconstruct an image with minimum error and can effectively integrate local information during the reconstruction process.
# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
## 2 Materials and Methods
### Index for the Quality Evaluation of Sharpened Images
#### 2.5.3 Universal Image Quality Index (UIQI)  
The UIQI is an index of similarity between two images that correlates luminosity and intensity distortions between two images [28]. Values approaching one indicate a higher quality of spectral information. The UIQI is defined by Equation (24):  
\[\text{UIQI}(x,y)=\frac{4\sigma_{xy}\overline{xy}}{\left(\sigma_{x}^{2}+\sigma _{y}^{2}\right)\left(\overline{x}^{2}+\overline{y}^{2}\right)} \tag{24}\]  
where \(x\) and \(y\) refer to the original and sharpened images; \(\sigma_{xy}\) refers to the covariance of \(x\) and \(y\); \(\sigma_{x}\) and \(\sigma_{y}\) refer to the variances of \(x\) and \(y\), respectively; and \(\overline{x}\) and \(\overline{y}\) refer to the means of \(x\) and \(y\), respectively.
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 4 Results
### Test Results and Analysis
#### 4.2.4 Pascal Voc 2012 Dataset Validation
In this paper, the selection and design of the model were mainly carried out considering actual application, where the model can be easily deployed using embedded devices, in order to achieve real-time object detection. The characteristics of light weight and fast detection make the YOLOV5s model highly competitive in a variety of embedded device deployments. In conclusion, the model proposed in this paper has the following advantages. Firstly, the model can automatically detect wildlife in the video stream. Secondly, the improved YOLOV5s model is very small in scale, which makes it easy to deploy to a variety of embedded devices. This reduces hardware costs for users, which is of great value in practical applications. Thirdly, the detection speed of the improved YOLOV5s model is very fast, easily meeting the needs of real-time detection of wild animals. However, most of the dataset in this paper is in relatively good light, with a small number of dusk and night images. Therefore, working at night may not be applicable to the model proposed in this paper. At the same time, if the UAV flight is high and the proportions of the target are small, the target will be difficult to detect, which is the disadvantage of the model proposed in this paper.
# Visual-Inertial Navigation System Based on Virtual Inertial Sensors
## 4 Analysis and Experiments of Visual-Inertial Odometry Optimization
### Localization and Mapping Experiments in Degenerated Environments
Figure 10: (**a**) Skewed image of ground conditions in Experiment 3; (**b**) real scene of the outdoor experimental environment; (**c**) comparison of the localization performance between VINS-VIMU and VINS-Fusion, using GPS data collected simultaneously as the actual route benchmark; (**d**) localization performance of VINS-VIMU; (**e**) localization performance of VINS-Fusion. Through experimental comparisons, we found that the visual-inertial SLAM aided by VIMU performed better than the VINS-Fusion method in these scenarios. Our system can solve the problem of too fast and too large camera motion during data collection and has better initialization performance. Additionally, our system can better adapt to the inaccuracy of initial pose information.
# Video Multi-Scale-Based End-to-End Rate Control in Deep Contextual Video Compression
###### Abstract  
In recent years, video data have increased in size, which results in enormous transmission pressure. Rate control plays an important role in stabilizing video stream transmissions by balancing the rate and distortion of video compression. To achieve high-quality videos through low-bandwidth transmission, video multi-scale-based end-to-end rate control is proposed. First, to reduce video data, the original video is processed using multi-scale bicubic downsampling as the input. Then, the end-to-end rate control model is implemented. By fully using the temporal coding correlation, a two-branch residual-based network and a two-branch regression-based network are designed to obtain the optimal bit rate ratio and Lagrange multiplier \(\lambda\) for rate control. For restoring high-resolution videos, a hybrid efficient distillation SISR network (HEDS-Net) is designed to build low-resolution and high-resolution feature dependencies, in which a multi-branch distillation network, a lightweight attention LCA block, and an upsampling network are used to transmit deep extracted frame features, enhance feature expression, and improve image detail restoration abilities, respectively. The experimental results show that the PSNR and SSIM BD rates of the proposed multi-scale-based end-to-end rate control are \(-1.24\%\) and \(-0.50\%\), respectively, with \(1.82\%\) rate control accuracy.  
end-to-end rate control; super resolution; convolutional neural network; Lagrange multipliers; video multi-scale +
Footnote †: journal: Applied sciences
## 3 DTSPN Intersecting Regions Algorithm  
Problem 2.2 can now be formulated as a Generalized Traveling Salesman Problem (GTSP) with intersecting node sets in the following manner. The GTSP can be described with a directed graph \(\mathcal{G}:=(\mathcal{N},\mathcal{A},\mathcal{V})\), with nodes \(\mathcal{N}\) and arcs \(\mathcal{A}\) where the nodes are members of predefined node sets \(\mathcal{V}_{i},i=1,2,\ldots,n\). Here each node represents sample vehicle pose \(S_{i},i=1,2,\ldots,m\), and the arc connecting node \(S_{i}\) to node \(S_{j}\) represents the length of the minimum length path for a Dubins vehicle \(c_{i,j}=\mathcal{L}_{\rho}(S_{i},S_{j})\) from configuration \(S_{i}\) to configuration \(S_{j}\). The node set \(\mathcal{V}_{k}\) corresponding to region \(\mathcal{R}_{k}\) contains all samples whose projection lies in \(\mathcal{R}_{k}\), \(\mathcal{V}_{k}:=\{S_{i}\;|\;\mathcal{P}(S_{i})\in\mathcal{R}_{k}\}\) for \(i\in\{1,2,\ldots,m\}\) and \(k\in\{1,2,\ldots,n\}\). The objective of the GTSP is to find a minimum cost cycle passing through each node set exactly one time. An example instance of Problem 2.2 can be seen in Figure 1.  
Next, the GTSP can be converted to an Asymmetric TSP through a series of graph transformations due to Noon and Bean [17]. What follows is a brief summary of the Noon-Bean transformation from [17] as it is used in this work. The transformation is best described in three stages. The first stage converts the asymmetric GTSP to a GTSP with mutually exclusive node sets. The second stage converts the GTSP to the canonical form by eliminating intra-set arcs. Finally the third stage converts the canonical form to a clustered TSP and then to an Asymmetric TSP.
## 2 Materials and Methods
### Boundary layer sensitivity analysis.  
A mesh with a special refinement (three layers) near the walls and other without it were tested. Only a part of the domain was used (DOC and a section of the exhaust pipe) in order to reduce computational requirements. As can be seen in Table 5, refinement along the boundary layer adds a considerable number of extra cells and does not provide significant variations.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Engine Speed (min\({}^{-1}\))** & \multicolumn{3}{c}{**1000**} & \multicolumn{3}{c}{**1700**} & \multicolumn{3}{c}{**2400**} \\ \hline
**Engine Torque (Nm)** & **10** & **60** & **110** & **10** & **60** & **110** & **10** & **  
Boundary layer refinement results differed from the base case by less than 1% and required five times more cells. It can be stated that the model is not boundary layer-sensitive and, hence, boundary layer refinement is omitted.  
(g) Wall treatment sensitivity analysis.  
Three different wall approaches were tested to study their influence in the results of the model: standard, non-equilibrium and enhancement wall functions (Figure 8).  
The standard wall functions proposed by [PERSON] [37] have been widely used, but the assumption of logarithmic velocity distribution treatment may not be adequate for complex non-equilibrium flows. To overcome this, non-equilibrium wall functions are based on pressure-gradient sensitized Launder [PERSON]'s [37] log-law for mean velocity.  
Enhanced wall treatment is a near-wall modelling method that combines a two-layer model with enhanced wall functions. A one-equation relationship is used to evaluate the laminar sub-layer with fine mesh and transition to log-low function for the turbulent part of the boundary layer. The restriction that the near-wall mesh must be suitably fine might impose large computational requirements.  
More accurate wall functions such as non-equilibrium or enhanced showed no more than a 3% discrepancy with the standard approach, while requiring eight times more mesh elements. Consequently, the standard wall functions were selected.
# A Low-Overhead Countermeasure against Differential Power Analysis for AES Block Cipher
## 2 Preliminaries  
The fundamental working of the AES is shown in Figure 1. It takes a 128-bit key (for a variant of AES-128) and plaintext as input and results in a 128-bit ciphertext as an output. We refer interested readers to [33] for the complete mathematical descriptions and formulations.  
A variant of AES, named AES-128, encryption starts from the initial round followed with an additional ten rounds. Each round consists of the following four operations [34]:  
* **SubBytes** splits the input data into bytes and then passes the input byte by byte through the substitution box (S-box). It is a non-linear substitution.
* **ShiftRows** determines each row of the 128-bit internal state of the cipher shifted by the fixed amount.
* **MixColumns** provides diffusion to the AES. It performs linear transformation, which makes AES secure against many attacks.
* **AddRoundKey** is responsible for performing a bitwise exclusive-OR (XOR) operation in each round.  
In the last round, the MixColumns operation can be avoided. The ciphertext, at the end of the last round, presents the value of the StateWord Register [35]. It is important to note that this is the place where the DPA attack can be performed by inverting ShiftRows and the SubBytes operations on the selected byte. Despite these essential operations (SubBytes, ShiftRows, MixColumns, and AddRoundKey), a key expansion is responsible for generating a distinct key for each round during the execution of the AES protocol. AddRoundKey of AES completely transform the nature of the data, as it performs an XOR operation in each round. The attacker exploits the power consumption, as it solely depends on the processed data. The attacker records a large number of power tracks with the fact that the complexity of data processing is linear to the power consumption [36]. Moreover, the attacker already knows the working of the AES algorithm, and therefore, he/she targets the intermediate stage, e.g., AddRoundKey, which completely changes the nature of the data [37]. The attacker can also exploit the linearity property of ShiftRows and MixColumns. This vulnerability enables an attacker to build a hypothetical model to extract the secret key.