esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 5 Conclusions  
In this paper, we proposed an adaptive multipath traffic loading based on RPL to meet the needs of energy consumption and end-to-end delay. First, we designed an energy-balanced routing algorithm to improve the network lifetime. By establishing a practical model and proposing a standard of energy dispersion degree measurement, it can effectively determine the degree of energy balance of nodes. Based on this metric, we present a fast algorithm to obtain the optimal distribution of the data. The algorithm maintains the stability of the parent set, thus improving the stability of the network.  
Furthermore, in order to decrease end-to-end delay, we converted the data transmission model to a M/M/1/S/FCFS model based on cache utilization, calculated the average waiting time of data, and then obtained the waiting time of the bottleneck node in the multi-path. Using the calculated time value, we proposed a traffic allocation algorithm based on greedy thought, which can optimize the end-to-end delay.  
Finally, taking into account the application needs of different scenarios, we integrated the two above algorithms. If the network prioritizes the survival time, priority is given to the energy balance, and then the end-to-end delay. If end-to-end delay is the main factor of the network, we consider that the cache utilization decreases the loss of packets. The results indicate that the three schemes all improve the reliability of routing, increase the network lifetime, decrease the end-to-end delay and decrease the time for DAG resetting.  
It can be seen from the experiment that the nodes around the sink still consume more energy due to their bearing more traffic. They are still the bottlenecks of the entire network. In future work, the nodes around the sink will be optimized to maximize their survival time. Such measures include deploying more nodes around the sink, increasing their lifetime by turning off the radio, increasing the transmission distance of a single hop, and even using mobile sink nodes to make the energy consumption of the entire network balance, extending the survival time of the network.  
This work was supported in part by Natural Science Research and Development Projects of Jiangsu Province (No. BY2016066-05), the National Science Foundation of China (No. 61379064, 61572260, 61572261, 61772448), the National Science Foundation of Jiangsu Province of China (No. BK20140462).  
**Author Contributions:** [PERSON] designed all the algorithms and wrote the paper. [PERSON] made a careful revision of the article and proposed amendments. [PERSON] performed the experiments and analyzed the data.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: PDR (Packet Deliver Rate) in different network size.
# Surface Plasmon Nanolaser: Principle, Structure, Characteristics and Applications  
[PERSON] Key Laboratory of Optical Information and Pattern Recognition, School of Optoelectronics and Energy, Wuhan Institute of Technology, Wuhan 430205, China; [EMAIL_ADDRESS] (L.X.); [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (F.Y.); [EMAIL_ADDRESS]  
nonlinear enhancement of optical processes, nanoscale optical interactions, etc. [20; 21; 22]. The SP-based nanolaser is called the world's smallest laser and is the core component for achieving nanoscale optoelectronic integration. This ultra-small nanolaser has the potential for very broad application in a series of fields. It can be used in biosensors, microscopy, and laser surgery in chemical and biomedical engineering, and it is also possible to use nanolasers to identify chemicals. In the field of material processing, it can be used to fabricate and analyze metamaterials that are smaller than the wavelength of light emitted by current lasers. Supermaterials are used in super lenses that see a single virus or DNA molecule. At the same time, nanolasers are also widely used in optical computing, information storage and nano-analysis. Nanolasers can be used in circuits that can automatically regulate switching. Some lasers have been able to switch faster than 20 billion times per second. If the laser is integrated and mounted on the chip, the amount of computer disk information storage and the information storage capacity of future photonic computers can be improved, and the integrated development of information technology can be accelerated [23; 24; 25].  
However, because surface plasmons need to be excited with metal, the presence of metal means that the laser has a high loss characteristic, resulting in a relatively short transmission distance of light [26; 27; 28; 29; 30]. In order to achieve a lower threshold and lower loss transmission, a variety of nanolaser structures have been designed, which can be generally classified into metal-dielectric-metal structure nanolasers, metal nanoparticles plasmonic nanolasers [31; 32; 33; 34], nanowire surface plasmonic nanolasers, array nanowires plasmonic nanolasers, W-G mode plasmonic nanolasers, etc [35; 36]. In addition, many research groups have applied excellent optical gain materials such as perovskite, MoS\({}_{2}\), graphene, and monolayer transition-metal dichalcogenides (TMDCs) to the research of nanolasers and made breakthroughs, effectively making up for the loss in the laser system [37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47]. This paper introduces plasmonic nanolasers that have been experimentally proven and have made breakthroughs in recent years. In particular, the plasmonic nanolaser based on metal halide perovskite nanowires that replace conventional semiconductor nanowires is highlighted.
# Eight-Port Modified E-Slot MIMO Antenna Array with Enhanced Isolation for 5G Mobile Phone
## 4 Conclusions  
In this paper, an eight-element antenna array incorporating an I-shaped frame and a modified E-slot etched on the ground is presented. The proposed antenna is simple to design and easy to fabricate. An impedance bandwidth of more than 250 MHz under \(-6\) dB conditions is achieved in this work. The ECC for the entire frequency range is less than 0.01, and the level of element isolation is well below \(-21\) dB. This work achieves these better performances since we etched the modified E-slot on the ground. The antenna system channel capacity obtained is 43.5 b\(/\)s\(/\)Hz, while the efficiency is more than 76%.  
**Author Contributions:** Conceptualization, H.S.A. and Z.Z.; methodology, H.S.A., B.W. and S.H.K.; software, N.O.P. and Z.Z.; validation, B.H. and S.H.K.; formal analysis, N.O.P.; investigation, H.S.A.; resources, B.W. and B.H.; data curation, S.H.K. and N.O.P.; writing--original draft preparation, B.H. and Z.Z.; writing--review and editing, B.H. and B.W.; visualization, S.H.K. and B.W.; supervision, Z.Z.; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **BW (GHz)** & **S\({}_{\text{ij}}\) (dB)** & **ECC** & **Eff. (\%)** & **PCC (bps/Hz)** & **Elements** & **Gain (dB)** \\ \hline
[1] & 3.4–3.7 (\(-6\) dB) & \textgreater{}15 & \textless{}0.1 & 50–75 & 38.1 & \(8\times 8\) & 4 \\
[5] & 3.4–3.6 (\(-6\) dB) & \textgreater{}12 & \textless{}0.1 & 47–65 & 37.5 & \(8\times 8\) & 3.2 \\
[14] & 3.4–3.6 (\(-6\) dB) & \textgreater{}17.5 & \textless{}0.05 & 62–76 & 40.8 & \(8\times 8\) & Not Given \\
[18] & 3.4–3.6 (\(-6\) dB) & \textgreater{}15 & \textless{}0.15 & 45–60 & 35 & \(8\times 8\) & 4.8 \\
[36] & 3.4–3.6 (\(-10\) dB) & \textgreater{}19.1 & \textless{}0.012 & 59–98 & Not Given & \(8\times 8\) & Not Given \\
[37] & 3.4–3.6 (\(-10\) dB) & \textgreater{}12.7 & \textless{}0.13 & 39–50 & Not Given & \(4\times 4\) & Not Given \\ Proposed & 3.4–3.65 (\(-6\) dB) & \textgreater{}21 & \textless{}0.01 & 34–76 & 43.5 & \(8\times 8\) & 4.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison table.  
Figure 11: Diversity gain.  
**Funding:** The Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, funded this project under grant no. RG-14-135-43.  
**Data Availability Statement:** All data have been included in the study.  
**Acknowledgments:** The authors are thankful to the Deanship of Scientific Research, King Abdulaziz University for providing financial support vide grant number (RG-14-135-43).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Dynamic Sensor Scheduling Algorithms  
This section describes two scheduling management schemes for organizing the sensing tasks, the Centralized Adaptive Scheduling Algorithm (CASA) and the Distributed Adaptive Scheduling Algorithm (DASA). The main assumptions of the network are: (1) All sensors are homogeneous with the same transmission range; (2) The sensors are fixed without location information; (3) Symmetric communication channel: all links between sensors are bidirectional; (4) All sensors perform the sensing task periodically. Note that there are no base stations to coordinate or supervise activities among sensors.
# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 3 Data Preprocessing
### Feature Values  
From the I-V curves, parameters such as open-circuit voltage (\(V_{\alpha c}\)), short-circuit current (\(I_{sc}\)), maximum power point voltage (\(V_{mpp}\)), and maximum power point current (\(I_{mpp}\)) are calculated to obtain the required feature values for the model. Additional features like irradiance, module open-circuit voltage, short-circuit current, and the number of modules in a solar string are also used as model features. A total of 10 feature values used in the model are shown in Table 1. By using the specifications of the modules, such as open-circuit voltage and short-circuit current, along with the number of modules, the open-circuit voltage (\(V_{\alpha c^{\prime}}\)) and short-circuit current (\(I_{sc^{\prime}}\)) of the string under normal conditions (1000 W/m\({}^{2}\) irradiance without any faults) can be calculated. Feature 7 in Table 1 calculates the difference between the current open-circuit voltage (\(V_{\alpha c}\)) and the open-circuit voltage under normal conditions (\(V_{\alpha c^{\prime}}\)), aiding the model in identifying faults that would cause a decrease in the open-circuit voltage.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Content** \\ \hline Feature 1 & \((V_{mpp}\times I_{mpp})/(V_{\alpha c}\times I_{sc})\) \\ Feature 2 & \(I_{mpp}/I_{sc}\) \\ Feature 3 & \(V_{\alpha c}/I_{mpp}\) \\ Feature 4 & \(V_{mpp}/I_{sc}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of Feature Values.  
Figure 19: Crack fault current–voltage curve.  
Figure 18: Crack fault current–voltage curve simulation circuit diagram.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 5 Taxonomy for Task Scheduling Algorithms in Fog Computing
### Optimization Criteria
#### 5.1.2 Energy Consumption  
Different models for energy consumption have been proposed in the literature. The more relevant ones are reported here.  
Energy consumption derives from different activities executed by a fog node, such as computation and data transmission. For example, if the \(j\)-th edge device wants to transfer the \(i\)-th task at time \(t\), the total energy consumption is calculated as the sum of the energy required for computation (\(E_{comp}\)) and the energy needed for data transmission (\(E_{tran}\)), defined as follows [49]:  
\[E_{\text{comp}}(i,j,t)=\alpha_{1}c_{i}(f_{j})^{2}\]  
\[E_{\text{tran}}(i,j,t)=\alpha_{2}s_{i}/r(t)\]  
\[E_{\text{total}}=E_{\text{comp}}+E_{\text{tran}}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are the coefficient of energy consumption in computing and transmission, \(f_{j}\) is the CPU clock frequency of mobile devices, \(r(t)\) is the network transmission rate at time \(t\), and \(s_{i}\) and \(c_{i}\) are the content size and the amount of computation.  
In [41], the energy consumed to process as task \(i\) in a fog node is denoted as \(E_{f}\). Moreover, as the task is transmitted from the end-user to the fog node, the energy consumption for data transmission and reception is denoted by \(E_{l}^{i}\) and \(E_{r}^{i}\), respectively. The authors express the total cost at the fog node side as follows:\[E_{f}=E_{t}^{i}+E_{r}^{i}+\alpha C_{f}^{i}\]  
where \(\alpha\)\(C_{f}^{i}\) is the weighted transmission energy with \(\alpha\) being the relative weight and \(C_{f}^{i}\) being the relative cost for processing task \(i\) at the fog node.
## 3 Results  
On the average, males were taller, heavier, had a higher BMI and were slightly older than the female participants (Table 1). There were no sex differences in resting SBP, DBP, MAP and HR. the CONT (\(p>0.838\)) or INCR (\(p>0.856\)) cuff inflation protocol (Table 3). Because there were no statistically significant differences in the AOP between the DOM and NDOM legs or between males and females, we pooled data. There were no statistically significant differences in the AOP when measured using either cuff inflation protocol in either the DOM or NDOM legs or between the DOM and NDOM legs. Finally, after combining data from both legs, there were no statistically significant differences (\(p=0.990\)) in the AOP when measured using the CONT and INCR cuff inflation protocols. We considered multiple variables as predictors of AOP. The coefficients and significance of the predictor variables were calculated from the ANOVA results. The data analysis indicated that the coefficient for thigh circumference (2.43 \(\pm\) 1.10) was the only factor that significantly contributed (\(p=0.0278\)) to AOP. Resting blood pressure (SBP, DBP, MAP), thigh skinfold thickness or thigh volume did not significantly contribute to AOP. After accounting for thigh circumference, no other factor contributed to AOP. There were no significance differences between the first and second measures of AOP using either the CONT or INCR cuff inflation protocols in either the DOM or NDOM legs in males or females (Table 4). The Pearson Correlation Coefficients between the first and second measurements of AOP using CONT or INCR cuff inflation protocol all exceeded 0.99. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Continuous** & **Incremental** \\  & **Cuff Inflation** & **Cuff Inflation** \\ \hline MALES & & \\ First Measurement (\(n=20\)) & \(215.5\pm 35.7\) & \(198.1\pm 22.2\) \\ Second Measurement (\(n=20\)) & \(214.6\pm 33.6\) & \(196.7\pm 19.7\) \\ Difference & \(0.9\pm 5.4\) & \(1.3\pm 6.2\) \\ \hline FEMALES & & \\ First Measurement (\(n=20\)) & \(207.5\pm 51.4\) & \(209.8\pm 54.3\) \\ Second Measurement (\(n=20\)) & \(207.4\pm 50.1\) & \(210.6\pm 58.2\) \\ Difference & \(-0.4\pm 7.9\) & \(-0.8\pm 11.8\) \\ \hline COMBINED MALES/FEMALES & & \\ First Measurement (\(n=40\)) & \(211.3\pm 43.7\) & \(203.9\pm 41.4\) \\ Second Measurement (\(n=40\)) & \(211.0\pm 42.3\) & \(203.6\pm 43.5\) \\ Difference & \(0.3\pm 6.7\) & \(0.3\pm 9.4\) \\ \hline \end{tabular} Values are mean \(\pm\) SD. No significant differences (\(p>0.05\)) between the first and second measures of AOP using either of the cuff inflation protocols (continuous; incremental) in males or females or when pooling male and female data.  
\end{table} 
Table 4: Repeated Measures of AOP Using Two Cuff Inflation Protocols.
# ADLBiLSTM: A Semantic Generation Algorithm for Multi-Grammar Network Access Control Policies
## 4 Experiments
### Semantic Generation for Firewalls  
Compared to ACL data, firewall data is more cumbersome and contains many definition statements, making it more difficult to extract features from firewall data. Our model had advantages in this scenario. Table 6 demonstrates the performance of four models on the task of firewall security policy semantic generation. Through comparison, we found that the method proposed in this paper achieved significant improvements in precision (\(P\)), recall (\(R\)), and \(F1\) score compared to the baseline model. \(P\) increased from 64.74% to 96.09%, \(R\) increased from 68.4% to 96.18%, and the \(F1\) score increased from 66.51% to 96.13%. This was due to our model structure, and as we can see from the data in the table, the self-attention mechanism preprocessed complex data by extracting the importance of the original data, providing convenience for subsequent networks to extract contextual features. This is crucial in complex redundancy scenarios such as firewall data, as it helps the model capture key semantic information more accurately, and double-layer BiLSTM can extract better contextual features than single-layer BiLSTM, which together improve the accuracy of semantic generation.  
The results of the above two experiments indicated that due to the advantages of the self-attention mechanism and the double-layer BiLSTM network structure, the model  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Model** & **\(P\) (\%)** & **\(R\) (\%)** & **\(F1\) (\%)** \\ \hline \multirow{4}{*}{Firewall\_test} & Baseline & 64.74 & 68.4 & 66.51 \\ \cline{2-5}  & Baseline + DL-BiLSTM & 89.76 & 91.53 & 90.63 \\ \cline{2-5}  & Baseline + Self-attention & 94.54 & 94.54 & 94.54 \\ \cline{2-5}  & Ours & 96.09 & 96.18 & 96.13 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The model’s semantic generation results for firewall.  
Figure 4: Comparison of confusion matrices among four models.  
proposed in this paper performed well in automatic semantic generation. Whether it was faced with security policies with simple grammatical structure and short length, or with complex grammatical structure and cumbersome structure, the model effectively performed automated semantic generation.  
The self-attention mechanism used in the model captured long-distance dependencies in the input sequence and focus on the most relevant parts to the current task, thereby improving the model's semantic understanding ability. The double-layer BiLSTM structure fully utilized the before and after information of the sequence, encoded the input sequence bidirectionally, and further enhanced the model's context awareness.  
Therefore, this structure combining the self-attention mechanism and the double-layer BiLSTM enabled our model to perform well in automatic semantic generation tasks, cope with security policies of different complexities, and generate its semantics.
## 1 Introduction
Secondly, the approximate aggregation model of the VPP response process is constructed to consider the difference in communication delay and response speed between different DER devices. Finally, a bi-level optimization model is used to realize the EFC strategy. In the upper-level model, the decision table for EFC is made online by the dispatching center, which is solved by electromechanical transient simulation combined with the improved particle swarm optimization (PSO) algorithm. In the lower-level VPP instruction allocation, the dynamic response priority sequence of DERs is constructed based on the improved time margin and state margin of DER devices. The remainder of this paper is organized as follows. Section 2 introduces the hierarchical management and control architecture of DERs in the form of VPP and the bi-level optimization scheme of EFC. Section 3 describes the EFC strategy with multi-resource coordination in the dispatching center. Section 4 introduces the dynamic response priority formulation and control instruction allocation strategy in the VPP control center. In Section 5, the case study is carried out, and the discussions for practical implementations are given. Finally, conclusions are drawn in Section 6.
## 2 Materials and Methods
### Data Augmentation  
Given the significant computational resources required to create individual data samples, the resulting dataset was relatively small. To enhance the training foundation, data augmentation presents a viable solution. The augmentation techniques focused on the following dataset properties:  
* Mirroring;
* Scaling;
* Resolution;
* Noise level.  
These data augmentation methods were carefully chosen to reflect realistic conditions of objects buried underground. Objects can vary in size and orientation, warranting the adoption of scaling and mirroring techniques. Moreover, because the resolution and noise levels do not perfectly align with the real system, even when using a digital twin, employing augmentation methods for resolution and noise level proves practical.  
The mirroring of the data was performed both horizontally and vertically, with the data along the path axis being additionally stretched or compressed by \(\pm 10\,\%\). Both for time (vertical axis) and path (horizontal axis), the resolution was reduced by up to 50%. The added noise was normally distributed and was chosen so that the average signal-to-noise ratio across the entire dataset was at least 25 dB.
## 1 Introduction
[14] applies Riemannian manifold optimization to reflective surface coefficient optimization. The Riemannian manifold algorithm can converge within a relatively small number of iterations, resulting in low computational complexity and significantly reduced computation time. In this paper, we propose a manifold optimization data detection algorithm which is able to be applied to MIMO-OFDM systems with rapidly changing channels. Specifically, we derive the manifold optimization problem for time-varying channel scenarios. We use a GAN-based method to obtain the initial estimate of the channels, and then use the zero-forcing (ZF) detection to obtain the initial estimation of the transmitted signals. To reduce the complexity of solving the proposed optimization problem, we divide a data frame into several subframes, each contains several OFDM symbols, and then solve the optimization problem in subframe units. Pilot symbols are utilized to evaluate the estimated signals and determine the termination condition for the optimization process. The simulation results show that the proposed algorithm achieves better BER performance than the algorithm that uses an estimated channel for ZF detection while maintaining low computational complexity. The rest of the paper is organized as follows. Section 2 presents the system model of MIMO-OFDM systems. Section 3 describes the manifold optimization algorithm under time-invariant channels. Section 4 describes the proposed manifold optimization-based data detection algorithm for MIMO-OFDM systems under time-varying channels, including problem formulation and complexity reduction. The results of the simulations are presented in Section 5, and the conclusions are presented in Section 6.