esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 2 Theory and Methodology
### Construction of the Comprehensive Evaluation Model  
The key process of TOPSIS is to construct the positive and negative ideal solutions, which, respectively, represent the optimal solution and the worst solution of each evaluation indicator [23]. By calculating the weighted Euclidean distance between the evaluation object and the positive and negative ideal solutions based on TOPSIS, the fitting degree of each evaluation object and the positive ideal solution can be obtained, and the fitting degree is used as the criterion for evaluating the quality of the object. The model construction steps are as follows:  
Step 1: Establish the weight matrix \(R\) according to the evaluation indicator data matrix \(V\) and the method discussed in the previous section. The weight matrix \(R=\big{(}Z_{ij}\big{)}_{m\times n}\) is obtained by multiplying the matrix \(V\) with the weighting coefficients \(\omega\). The weight coefficient \(\omega=(\omega_{1},\omega_{2},\omega_{3},\omega_{4})\) (\(n=1,2,3,4\)) is calculated by the EWM.  
\[Z_{ij}=v_{ij}\times\omega_{j}(i=1,\,2,\cdots,\,m;j=1,\,2,\cdots,n) \tag{11}\]  
\[R=\begin{bmatrix}v_{11}&v_{12}&\cdots&v_{1n}\\ v_{21}&v_{22}&\cdots&v_{2n}\\ \cdots&\cdots&\ddots&\ldots\\ v_{m1}&v_{m2}&\cdots&v_{mm}\end{bmatrix}\begin{bmatrix}\omega_{1}&0&\cdots&0 \\ 0&\omega_{2}&\cdots&0\\ \cdots&\cdots&\ddots&\ldots\\ 0&0&\cdots&\omega_{n}\end{bmatrix}=\begin{bmatrix}z_{11}&z_{12}&\cdots&z_{1n} \\ z_{21}&z_{22}&\cdots&z_{2n}\\ \cdots&\cdots&\ddots&\ldots\\ z_{m1}&z_{m2}&\cdots&z_{mm}\end{bmatrix} \tag{12}\]  
Step 2: Define the positive and negative ideal solutions of the evaluated object.  
\[\begin{array}{ll}Z^{+}&=\big{(}z_{1}^{+},z_{2}^{+},\cdots,z_{n}^{+}\big{)}\\ &=(\max\{z_{11},z_{21},\cdots,z_{m1}\},\max\{z_{12},z_{22},\cdots,z_{m2}\}, \cdots,\max\{z_{1n},z_{2n},\cdots,z_{mn}\})\end{array} \tag{13}\]  
\[\begin{array}{ll}Z^{-}&=\big{(}z_{1}^{-},z_{2}^{-},\cdots,z_{n}^{-}\big{)} \\ &=(\min\{z_{11},z_{21},\cdots,z_{m1}\},\min\{z_{12},z_{22},\cdots,z_{m2}\}, \cdots,\min\{z_{1n},z_{2n},\cdots,z_{nm}\})\end{array} \tag{14}\]  
Step 3: Calculate the Euclidean distance from the evaluated object to the positive and negative ideal solutions.  
\[D_{i}^{+}=\sqrt{\sum_{j=1}^{n}\left(z_{ij}-z_{j}^{+}\right)^{2}}\,\ (\ i=1,\,2,\cdots,m) \tag{15}\]  
\[D_{i}^{-}=\sqrt{\sum_{j=1}^{n}\left(z_{ij}-z_{j}^{-}\right)^{2}}\,\ (\ i=1,\,2, \cdots,m) \tag{16}\]  
Step 4: Calculate the fitting degree of the evaluated object to the positive and negative ideal solutions.  
\[C_{i}=\frac{D_{i}^{-}}{D_{i}^{+}+D_{i}^{-}} \tag{17}\]  
The larger the value \(C_{i}\), the closer the evaluated object is to the positive ideal value, and the better the performance of the evaluated object. We used the \(C_{i}\) value of each sensor as its final score to build the comprehensive evaluation model, and according to the sensor scores, the sensors with good comprehensive performance can be selected to recombine the sensor array.
# The Emerging Energy Internet: Architecture, Benefits, Challenges, and Future Prospects
## 1 Introduction
### FREEDM Architecture with Multiple Levels  
The FREEDM architecture is a vision that integrates renewable energy resources into the grid, and the residential users manage their energy needs through a standardized plug-and-play interface [19]. It combines a wide scale of DRERs and DESDs into the smart grid powered with the Internet, having real-time communication between the power devices [28]. It can aid in developing dynamic scheduling and management facilities for an efficient and pollution-free green industry and society [29]. The FREEDM structure has interfaces for both industrial and domestic consumers, and has a wide scope of flexibility for constantly the changing behavior of DRERs, enabling a decentralized energy market with a two-way energy flow of active end users. This allows customers to sell the excess power back to the grid at a real-time price, which is obtained through an intelligent energy management software that analyses the pricing information based on the real-time demand and generation.  
The FREEDM system architecture coexists with the legacy of the AC system power infrastructure, with only minimal issues related to the raising and lowering of circuit voltages. A utility company or  
Figure 1: Vision of the energy Internet.  
subdivision association will be the owner of the local FREEDM structure, and customers will have their own DRERs and DESDs in addition to their loads [30]. This will maintain a two-way energy exchange.  
In order to provide an uninterrupted power supply in the fault conditions, an effective isolation and reconfiguration ability is provided to the power line by an intelligent fault isolation management (IFM) device in the FREEDM structure [31]. The fault isolation device (FIDs) will communicate with IFMs, as well as other FIDs, through reliable and secure communication. The functions of FID include isolating the fault line and reclosing the section when the fault is cleared. Hence, the functions of the sectionalizer and recloser are achieved by IFM [32].  
The operation of the FREEDM structure is distributed into five levels. The levels of operation in the FREEDM design are shown in Figure 2. The description of the different levels of operation is as follows.
# LDPC Coded Massive MIMO Systems
###### Abstract  
We design a coded massive multiple-input multiple-output (MIMO) system using low-density parity-check (LDPC) codes and iterative joint detection and decoding (JDD) algorithm employing a low complexity detection. We introduce the factor graph representation of the LDPC coded massive MIMO system, based on which the message updating rule in the JDD is defined. We devise a tool for analyzing extrinsic information transfer (EXIT) characteristics of messages flowing in the JDD and the three-dimensional (3-D) EXIT chart provides a visualization of the JDD behavior. Based on the proposed 3-D EXIT analysis, we design jointly the degree distribution of irregular LDPC codes and the JDD strategy for the coded massive MIMO system. The JDD strategy was determined to achieve a higher error correction capability with a given amount of computational complexity. It was observed that the coded massive MIMO system equipped with the proposed LDPC codes and the proposed JDD strategy has lower bit error rate than conventional LDPC coded massive MIMO systems.  
massive MIMO; LDPC codes; joint detection and decoding; low complexity; density evolution Article
# XGBoost-Based Detection of DDoS Attacks in Named Data Networking
## 3 The Attack Model and Detection Method  
In the context of increasingly intelligent complex network attacks, hybrid attacks targeting NDN networks, specifically I-CIFPA attacks, exhibit cross-layer concealment characteristics. This section conducts an in-depth modeling analysis of the I-CIFA and CPA hybrid attacks to uncover their core attack mechanisms. Based on this analysis, an improved XGBoost-enhanced detection framework is proposed to enhance the model's generalization ability while ensuring real-time performance.
## 2 Materials and Methods
For this purpose, the open source QGIS 3.16 software is used in this work [31]. The images were loaded into QGIS, georeferencing was performed, and the area of each flood polygon was calculated. QGIS allows you to calculate the areas of polygons using the built-in Field Calculator. The area of the polygon is calculated using Formula (1):  
\[S=\sum_{i=1}^{n}A_{i} \tag{1}\]  
where \(S\) is the total area of the polygon (km\({}^{2}\) or m\({}^{2}\)), and \(A_{i}\) is the area of individual polygons included in the flood zone. Each polygon is on average 0.5 km\({}^{2}\). In total, there are 74 polygons in the actual flooded area. Based on these data, the total area of the actual flooded zone for spring 2019 is as follows:  
\[S_{real}=\sum_{i=1}^{74}0.5\ \approx 37\ \text{km}^{2} \tag{2}\]  
It is important to take into account that in real conditions the area of each polygon may vary, which is taken into account in automatic calculations of QGIS using the Field Calculator tool. This approach allows for a flexible analysis of flood distribution. Using the same method, we calculated the area of the simulated flooded territory for 68 flood polygons for spring 2019:  
\[S_{flood}=\sum_{i=1}^{68}0.5\ \approx 34\ \text{km}^{2} \tag{3}\]  
To assess the accuracy of the model, the percentage of coincidence of the modeled territory with the real flood zone was calculated using the following formula:  
\[Match\ percentage\ (\%)=\frac{S_{flood}}{S_{real}}\times 100\%\approx 91.89\% \tag{4}\]  
The obtained percentage of coincidence of 91.89% for spring 2019 indicates sufficient accuracy of the hydrodynamic model. The calculation error is 8.1%. Following the methodology described earlier, the corresponding percentage of coincidence was calculated for the 2020 and 2021 flood events, where for 2020, the observed flood area was 26.6 km\({}^{2}\), and the modeled flood area was 23.7 km\({}^{2}\), with a degree of coincidence of 89.09%, and for 2021, the observed flood area was 18.8 km\({}^{2}\), and the modeled flood area was 19.6 km\({}^{2}\), with a degree of coincidence of 95.91%.
# Pseudorange Measurement Method Based on AIS Signals
## 2 Principle of Pseudorange Measurement in AIS-R  
### The TOA Measurement Method  
The range measurement system based on radio signals is essentially a transmission delay measurement system [22]. At the same time benchmark, the transmitter sends out an electromagnetic wave with easily monitored features at the appointed time \(t_{T}\), then the receiver detects the signal feature and records the reception time \(t_{R}\). The difference between the two data is the transmission delay \(\Delta t\). Transmission delay multiplied by the speed of an electromagnetic wave \(c\) is the distance between the transmitter and receiver \(d\).  
\[d=c\times\Delta t=c\times(t_{R}-t_{T}) \tag{1}\]  
The method for calculating the distance \(d\) by calculating the signal transmission delay \(\Delta t\) is known as the time of arrival method. In practical application, as there is no guarantee that the receiver and transmitter adopt the same time benchmark, the result of Equation (1) contains a system clock error, thus distance \(d\) is called the pseudorange.
# Systematic Literature Review of Challenges in Blockchain Scalability
## 7 RQ2: What Vital Root Factors Are Causing Scalability Issue in Blockchain?
### Transaction Cost  
Transaction fee plays a vital role in public blockchains, i.e., Bitcoin. Miner tends to make selection of the transactions to be verified based on the fee associated with it. This directly affects the confirmation time of a transaction, thus influencing the throughput and latency. The transaction with a small fee may suffer massive confirmation delays, and this logically happens because the consensus mechanism promises to reward miners based on the associated fee in a transaction.
###### Abstract  
In wireless sensor network-based water pipeline monitoring (WWPM) systems, a vital requirement emerges: the achievement of low energy consumption. This primary goal arises from the fundamental necessity to ensure the sustained operability of sensor nodes over extended durations, all without the need for frequent battery replacement. Given that sensor nodes in such applications are typically battery-powered and often physically inaccessible, maximizing energy efficiency by minimizing unnecessary energy consumption is of vital importance. This paper presents an experimental study that investigates the impact of a hybrid technique, incorporating distributed computing, hierarchical sensing, and duty cycling, on the energy consumption of a sensor node in prolonging the lifespan of a WWPM system. A custom sensor node is designed using the ESP32 MCU and nRF24L01+ transceiver. Hierarchical sensing is implemented through the use of LSM9 DS1 and ADXL344 accelerometers, distributed computing through the implementation of a distributed Kalman filter, and duty cycling through the implementation of interrupt-enabled sleep/wakeup functionality. The experimental results reveal that combining distributed computing, hierarchical sensing and duty cycling reduces energy consumption by a factor of eight compared to the lone implementation of distributed computing.  
wireless sensor network-based water pipeline monitoring; green wireless sensor networks; energy conservation techniques; distributed computing; hierarchical sensing; duty cycling
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR
### Discussion and Limitations
#### 3.3.2 Optimization-Based Real-Time Super-Resolution Methods  
Optimization-based real-time SR methods are one of the popular subclasses of real-time SR techniques. These approaches utilize the SR problem as an optimization task, intending to locate the HR images from LR inputs by minimizing a cost function that balances fidelity to the observed data with smoothness or regularization terms. There are five types of optimization techniques utilized in real-time SR tasks, such as gradient descent, iterative backprojection, Bayesian inference, sparse coding, and convex optimization.  
Gradient descent-based optimization methods improve the performance of real-time SR. There are several approaches conducted to optimize the real-time SR. In [68], the authors tackled the perception-distortion trade-off in SR as a multi-objective optimization issue. They developed a novel optimizer by combining a gradient-free evolutionary algorithm (EA) with the gradient-based Adam optimizer. The EA facilitates the exploration of alternative optimization tactics, whereas Adam improves their accuracy. This method provides a set of ideal models that cater to various balancing points between perception and distortion. The authors then created a fusion network to merge these models into one more powerful model, establishing a better balance between perception and distortion, which is well-suited for lightweight deployment. Apart from this, [69] investigated image restoration and synthesis using gradient descent algorithms. They examined at both linear and non-linear restoration challenges, as well as how to incorporate a known degradation model into a gradient-based restoration method.To investigate gradient-based image restoration, researchers employed a bicubic SR. It tested real bicubic degradation against CNN and linear approximations, utilizing MSE as the loss function. The Residual-in-Residual Dense Block (RRDB) architecture handled the restoration using five gradient descent steps to recover images from degraded inputs.
###### Abstract  
In recent years, artificial intelligence (AI) has significantly impacted agricultural operations, particularly with the development of deep learning models for animal monitoring and farming automation. This study focuses on evaluating the Depth Anything Model (DAM), a cutting-edge monocular depth estimation model, for its potential in poultry farming. DAM leverages a vast dataset of over 62 million images to predict depth using only RGB images, eliminating the need for costly depth sensors. In this study, we assess DAM's ability to monitor poultry behavior, specifically detecting drinking patterns. We also evaluate its effectiveness in managing operations, such as tracking floor eggs. Additionally, we evaluate DAM's accuracy in detecting disparity within cage-free facilities. The accuracy of the model in estimating physical depth was assessed using root mean square error (RMSE) between predicted and actual perch frame depths, yielding an RMSE of 0.11 m, demonstrating high precision. DAM demonstrated 92.3% accuracy in detecting drinking behavior and achieved an 11% reduction in motion time during egg collection by optimizing the robot's route using cluster-based planning. These findings highlight DAM's potential as a valuable tool in poultry science, reducing costs while improving the precision of behavioral analysis and farm management tasks.  
cage-free housing; poultry behavior; machine vision; deep learning Article
## 6 Simulation Results
### Performance Analysis
#### 6.2.2 Performance Comparison of Different Aspects
It can be observed that the throughput rapidly increased before 300 kb and then reached a stable state with increasing buffer size. This is because as the buffer size gradually increased, it caused a decrease in packet loss rate; therefore, the system throughput increased. When the system had no packet loss, the throughput tended to stable. Meanwhile, the proposed algorithm had the highest throughput compared with the other two schemes; hence, it can be proven that MADDQN was effective in selecting the routing with a large throughput. Figure 11 illustrates the relationship of the number of nodes and optimization goal under different weights of latency and channel throughput. The simulation results show that, with the rise in \(\omega_{1}\), the optimization goal was much larger. This is because, although  
Figure 7: Average loss rate versus the number of nodes. the optimization goal optimized both delay and throughput, when the weight of delay was high, latency was optimized more, while the optimization of throughput was relatively weak. Moreover, when the node number was between four and six, delay accounted for a large proportion of the optimization objective. Thus, when \(\omega_{1}=0.9\), the total optimization objective was the highest. However, in order to optimize both delay and throughput to a better level, we chose the case \(\omega_{1}=0.5\), \(\omega_{2}=0.5\). In the application, different parameters can also be chosen according to different needs. For example, the weight of \(\omega_{1}\) can be increased for safety information with higher requirements on latency. For systems where throughput is more important, the proportion of \(\omega_{2}\) can be increased appropriately, but the sum of \(\omega_{1}\) and \(\omega_{2}\) must be equal to 1. Figure 8: Average loss rate versus buffer size. Figure 9: Average throughput versus the number of nodes. The effect of the number of nodes and the buffer size on the optimization goal is investigated in Figures 12 and 13. The optimization goal was derived from Equation (27), which is a comprehensive indicator of channel throughput and transmission delay. In Figure 12, the delay increased and the throughput decreased as the number of nodes grew; thus, the optimization objective was gradually reduced. This shows that, as the number of nodes increased, the overall performance of the system worsened. In Figure 13, both latency and throughput tended to rise as the buffer size increased, but latency rose faster, having a greater impact on the optimization objective. Therefore, the optimization goal showed a slight decrease after combining these two indicators. Moreover, we can observe that our proposed algorithm always outperformed the existing algorithms, indicating that the MADDQN algorithm could better tradeoff the channel throughput and transmission delay under any topological condition and buffer size to achieve the optimization goal. Figure 11: Relationship of optimization goal and number of nodes under different weights. Figure 10: Average throughput versus buffer size.
# Structure Preserving Convolutional Attention for Image Captioning
## 4 Experiments
### Performance and Analysis  
#### 4.3.1 Attention Structure Selection  
We explore the respective roles of cross channel attention and convolutional spatial attention, and the performance of their various combinations. We choose 2D-LSTM model as our baseline because it has 2D-latent-state that is suitable for our purposes. At the same time, it does not add an attention module so that we can observe the role of different attractions in the captioning system. As we show in Figure 3. (1) **S**: only spatial attention model. (2) **C**: only channel attention model. (3) **C**-**S**: we first compute the channel attention weights \(M_{t}^{5}\). Then based on \(M_{t}^{5}\), the image feature \(V\) and spatial attention model, we can calculate the modulated feature \(C_{t}\). (4) **S**-**C**: we first compute the spatial attention then calculate channel attention weight. (5) **C**\(||\)**S**: this model as illustrated in Figure 2. We calculate channel attention and spatial attention in parallel, as Equation (10). We use convolutional spatial attention to preserve spatial structure of attention and concatenate hidden state at each step to get more temporally contextual information.  
Based on the results listed in Table 2, we have the following observations: (1) Comparing to the performance of S, the performance of C shows that more channel information can help improve in  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Param** & **Millisecond/Image** \\ \hline Soft-Attention [5] & 248.8 M & 57.6 \(\pm\) 0.7 \\
1D-LSTM + SPCA & 264.8 M & 70.95 \(\pm\) 0.9 \\
2D-latent state [19] & 205.7 M & 55.7 \(\pm\) 0.4 \\
2D-latent-LSTM + SPCA & 223.9 M & 64.7 \(\pm\) 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters and speed results on Structure Preserving Convolutional Attention (SPCA). Speed results based on beam size 3, batch size 20 on an NVIDIA Titan X GPU.  
Figure 3: Different structures of our SPCA.  
image captioning. Furthermore, all of S-C, C-S and S\(\parallel\)C can achieve better performance than only S or C, which proves spatial attention and channel attention are complementary to each other, one for 'where', the other for 'what'. (2) For both datasets, S\(\parallel\)C gets better scores than the other sequentially combining pattern.
# Analysis of Instantaneous Energy Consumption and Recuperation Based on Measurements from SORT Runs
## 3 Results
### Linear Regression Model
Additionally, based on the Ljung-Box test, the hypothesis regarding the independence \begin{table}
\begin{tabular}{c c c c c} \hline  & **Estimate** & **Std.dev** & \(\mathbf{t}_{i}\) & \(\mathbf{p}_{i}\) \\ \hline \(\alpha_{0}^{S}\) & 0.6075259 & 0.4305105 & 1.411175 & 0.1590541 \\ \(\alpha_{1}^{A}\) & 0.1275664 & 0.0771862 & 1.652711 & 0.0992590 \\ \(\alpha_{2}^{A}\) & 0.9628874 & 0.4170395 & 2.308864 & 0.0215153 \\ \(\alpha_{3}^{A}\) & 0.3278099 & 0.0499561 & 6.561956 & 0.0000000 \\ \(\alpha_{1}^{B}\) & \(-\)0.5219556 & 0.0694659 & \(-\)7.513842 & 0.0000000 \\ \(\alpha_{3}^{B}\) & 0.0601848 & 0.0258195 & 2.330982 & 0.0203043 \\ \(\alpha_{1}^{D}\) & 0.1174327 & 0.0141410 & 8.304434 & 0.0000000 \\ \(\alpha_{3}^{D}\) & 0.3962660 & 0.0731979 & 5.413629 & 0.0000001 \\ \hline \end{tabular}
\end{table} 
Table 3: Estimates of structural parameters, standard deviation of parameters, t-statistic values and \(p\)-values (statistical significance). of residuals should be rejected (see also Figure 6, 'Autocorrelation function', which may mean that at certain points during the test additional power consumers may have been activated (e.g., a compressor to pressurise the system) that were not taken into account, or that the reading of the predictors may have occurred with a certain delay). The linear model explains the variability of instantaneous energy consumption and recuperation in over 90%; however, as the analysis of residuals shows, additional factors influencing energy consumption and recuperation should be investigated, or predictors used in the model with lags due to readings delay should be taken into account.