esa-sceva/satcom-chunk-collection huggingface dataset:
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
## 3 Related Work
This is an innovative approach, mainly due to the current lack of real implementations of INC in industrial environments using state of the art DPP. Simulations and mathematical analysis are well-known methods to test and validate proposals, but our scope is beyond theoretical investigation. In fact, the aforementioned simulations and mathematical analysis are an abstraction of reality, so taking all factors into account becomes virtually impossible. Therefore, our testbed environment is built with physical equipment, which allows us to test network processing concepts and the decision making in the data plane, aiming for the enhancement of performance in communications. Additionally, we propose an efficient solution implemented entirely on the data plane that establishes a well-balanced task delegation between the network and data center servers.
# Tailored Quantum Alternating Operator Ansatzes for Circuit Fault Diagnostics
## 3 Circuit Fault Diagnostics  
Diagnosing errors and faults for gate-based digital circuits is an area of intense research, as large-scale integrated circuits and specialized circuit designs have become abundant in many scientific and engineering disciplines. Increasingly sophisticated automation techniques used to check and correct errors in the circuit design and fabrication are increasingly relied upon in practice. Because of the underlying combinatorial nature of this problem as well as the local design of many modern circuits, this is a class of problems that is well suited to typical NISQ [2, 11] architectures.  
We employ a simple stuck-at-fault model for analyzing circuits with a given string of inputs and empirically found string of outputs [25]. Let \(n\) be the number of wires in the circuit and \(n_{o}\) be the number of output wires. Each wire in the circuit is either healthy or stuck at either zero (SA0) or one (SA1). Under the assumption that faults are equally likely to occur on every wire, the task of circuit fault diagnostics (CFD) is an optimization problem for finding the minimum number of faults needed to explain the input-output pair [2].  
For the purpose of our discussion in this paper, we consider gates that are one-input/one-output, two-input/one-output, or one-input/two-output. One-input/one-output gates are an identity gate ID or an inverter gate INV. Two-input/one-output gates are OR, AND, XOR, NOR, NAND, or XNR. One-input/two-output gates are a fan-out gate FAN, a fan-out gate with an inverter on the first output F10, a fan-out gate with an inverter on the second output F01, or a fan-out gate with an inverter on both F11.  
A configuration for the problem is a \(2n\)-bit string, where over bits \(1,\ldots,n\) are the wire bits and over bits \(n+1,\ldots,2n\) are the fault bits. A valid configuration \((w_{1},\ldots,w_{n},f_{1},\ldots,f_{n})\) has an erroneous value of a wire \(w_{i}\) if the corresponding \(f_{i}\) is nonzero.  
The inputs to a circuit are considered to also be potentially faulty such that the input wires themselves could be SA0 or SA1, but the output wires have to correspond to the empirical value they have (otherwise, this diagnosis would be invalid), but that value may come from error propagation in the circuit or from the output value itself being SA0 or SA1.  
For whatever faults are present in the circuit, the output values can always be made to match the empirical value by taking the nonconforming output values and flipping their values (as well as their associated fault bit). As such, the size of the valid configuration space is \(2^{n-n_{o}}\)[3], since we have precisely \(n-n_{o}\) wire locations where the fault flag can be 0 or 1. (The fault flag on the \(n_{o}\) output wires is then forced to be such that the configuration is indeed valid.) Figure 1 shows all possible valid fault explanations for a small circuit. Note that there are 3 non-output wires and \(2^{3}\) valid fault configurations.
## 2 Basic Theoretical Parameters of the FE-SEA Method
### Internal Loss Factor  
#### 2.2.1 Internal Loss Factor of the Structure  
The internal loss factor of the structure is used to represent the ratio of the energy loss per unit time of each subsystem to the average energy within the unit frequency interval.  
In general, the internal loss factor of the structure consists of three basic components, as follows:  
\[\eta_{i}=\eta_{is}+\eta_{ir}+\eta_{ib} \tag{17}\]  
where \(\eta_{is}\) is the loss factor of the structure, which is generated by the internal friction of the structure; \(\eta_{ir}\) is the loss factor generated by the \"acoustic-vibration radiation\" of the structure; and \(\eta_{ib}\) is the loss factor generated by the damping at the boundary junction. \(\eta_{is}\) follows the change function of the material characteristics of the structure. \(\eta_{ir}\) is generated by the outward radiation of the vibration of the structural subsystem. The acoustic-radiation loss factor in the light-weight plate contributes more to the internal loss factor of the plate. The acoustic-radiation loss factor \(\eta_{ir}\) is a function of frequency f, and the relationship is as follows:  
\[\eta_{ir}(f)=\frac{\rho_{0}c\sigma_{sa}}{2\pi f\rho_{s}} \tag{18}\]  
where \(\rho_{0}\) is the air density, \(c\) is the sound velocity, \(\sigma_{sa}\) is the radiation ratio of the subsystem, and \(\rho_{s}\) is the area mass density of the subsystem. The calculation formula is as follows:  
\[\frac{\partial^{2}u(x,t)}{\partial t^{2}}+R^{2}C_{I}^{2}\frac{\partial^{4}u( x,t)}{\partial t^{4}}=0 \tag{19}\]
## 9 Conclusions and Future Work  
We extend our previous work [7] and provide legalized data exchange and protection services in the semantic cloud. We propose a solution to overcome the privacy and legal obstacles when Cloud Service Providers (CSPs) intend to deploy their cloud resources and services for their potential customers. A pandemic investigation scenario is demonstrated to explain why the LaaS is applicable for making a dataset disclosure decision either within a single jurisdiction or across jurisdictions.  
Semantic Web technologies are applied to the semantic legal policy representation for data exchange and protection. The semantic legal policies are represented as a combination of ontologies and stratified Datalog rules with negation (or _Datalog_ -). More specifically, we use cq-programs with default logic reasoning over description logic for policy exceptions handling.  
In the semantic cloud infrastructure, semantic legal policies are enforced in the super-peer to enable Law-as-a-Service (LaaS) and subsequent queries for CSPs and their customers. The agent at the law-aware super-peer is a unique guardian that provides data integration and protection services for its peers within a super-peer domain. Each agent at the super-peer also offers data exchange and protection services across super-peer domains.  
Future work includes further exploiting the non-monotonic reasoning of policy exceptions handling and the expressive power of semantic legal policy under a hybrid integration of ontologies and non-monotonic rules.
## Abbreviations  
3 GPP 3 rd Generation Partnership Project  
ABC  Ambient Backscatter Communication  
AGPS  Assisted Global Positioning System  
AI  Artificial Intelligence  
ANN  Artificial Neural Networks  
AR  Augmented Reality  
BS  Base Stations  
BLE  Bluetooth Low Energy  
CapEX  Capital Expenditure  
CDMA  Code-Division Multiple Access  
CRC  Cyclic Redundancy Check  
D2D  Device-to-Device  
EE  Energy efficient  
eMBB  Enhanced Mobile Broadband  
FBMC  Filter-bank Multicarrier  
GFDM  Generalized Frequency-Division Multiplexing  
GPS  Global Positioning System  
GSM  Global System for Mobile Communication  
H2M  Human-to-Machine  
HCS  Human-Centric Service  
HDNN  High Dimension Neural Networks  
HetNet  Heterogeneous Network  
ICS  Industrial Control System  
IERC  IoT European Research Cluster  
IIoT  Industrial Internet of Things  
IoE  Internet of Everything  
IoNT  Internet of NanoThings  
IoT  Internet of SpaceThings  
IoT  Internet of Things  
IoUT  Internet of UnderwaterThings  
IRS  Reflective Surface  
ITU  International Telecom Union  
KPI  Key Performance Indicator  
LoRaWAN  Long Range Wide Area Network  
LPWA  Low-Power Wide-Area  
LPWAN  Low-Power Wide-Area Networks  
LSTM  Long Short-Term Memory  
LTE  Long Term Evolution  
M2M  Machine-to-Machine  
MAC  Message Authentication Code  
mbRLLC  Mobile broadband RLLC  
MEC  Mobile Edge Computing  
MIMO  Multiple-Input-Multiple-Output  
MIT  Massachusetts Institute of Technology  
ML  Machine Learning  
mMTC  Massive Machine Type Communication  
MPS  Multipurpose 3 CLS and energy services  
MTC  Machine-type Communicaiton  
muRLLC  Massive uRLLC  
NFC  Near Field Communication  
NOMA  Non-Orthogonal Multiple Access  
OAM  Orbital Angular Momentum  
OMA  Orthogonal Multiple Access  
OFDM  Orthogonal Frequency-Division Multiplexing  
OpEX  Operational Expenditure  
OWC  Optical Wireless Communications
# 5G-Based Smart Healthcare and Mobile Network Security: Combating Fake Base Stations
## 5 Results and Discussion
### Discussion  
Although our detection scheme successfully filtered these cells' signals out because they did not match our whitelist or the legal MCC-MNC configurations, this does not mean that they are all malicious base stations that are intended to perform attacks. After discussing with the related authorities about this result, we suspect that there could be several reasons for it: First, some network operators will share the same cell to reduce the physical cost of maintaining the hardware equipment. Second, during the long period of scanning, there might be errors that occur when decoding the packets since our setup is lacking an external GPS clock to provide a more precise timing signal and has to rely on the internal clock on the SDR. Thirdly is that there might just be a testing/experimenting signal coming from another laboratory nearby the campus. Therefore, we cannot conclude whether or not these unknown base stations are all malicious, but we did treat them as illegal.  
The results showed that the detection scheme is feasible, without the need to install dedicated apps on the UE devices. And the data collected can be used to analyze and filter out nearby cells according to the whitelist configuration. We also discovered the caveat of our current design: for its large-scale deployment in a real-world mobile network, one may need to consider keeping track of the roaming networks or other guest networks and identifying and adding them to the detection criteria. And multiple operators can share important information, like helping maintain the whitelist together and forming an alliance, to maximize the effect of this detection scheme.  
When an unknown or suspicious base station is reported by the detector, we advise that the operators can further respond in several ways. First, they should further investigate the signal by other means, either a different detector or other detection methods, and check the detailed record, comparing it to the up-to-date information of their own network as well as notifying other operators and/or the authorities. Also, one should watch for any patterns over a period of time, preferably across multiple records, to make better decisions.
# Improving Monarch Butterfly Optimization Algorithm with Self-Adaptive Population
## 5 Simulation Results
### Mbo vs. Symbo
#### 5.1.1 \(D=30\)  
In this subsection, the dimension of thirteen benchmarks are set to 30. For MBO and SPMBO, they have the same function evaluations (FEs) at each generation. Therefore, the maximum generation (\(t_{m}\)) is considered as the stop condition, which is 50 as mentioned above. For the thirty implementations, the best, mean, and worst function values and Standard deviation (Std) values obtained by MBO and SPMBO are recorded in Table 2.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**No.** & **Name** & **lb** & **ub** & **opt** & **Separability** & **Modality** \\ \hline F01 & Alpine & \(-10\) & \(10\) & \(0\) & Separable & Multimodal \\ F02 & Brown & \(-1\) & \(4\) & \(0\) & Nonseparable & Unimodal \\ F03 & Dixon \& Price & \(-10\) & \(10\) & \(0\) & Nonseparable & Multimodal \\ F04 & Fletcher-Powell & \(-\pi\) & \(\pi\) & \(0\) & Nonseparable & Multimodal \\ F05 & Holzman 2 & \(-10\) & \(10\) & \(0\) & Separable & Multimodal \\ F06 & Levy & \(-10\) & \(10\) & \(0\) & Nonseparable & Multimodal \\ F07 & Penalty \#1 & \(-50\) & \(50\) & \(0\) & Nonseparable & Multimodal \\ F08 & Penalty \#2 & \(-50\) & \(50\) & \(0\) & Nonseparable & Multimodal \\ F09 & Perm & \(-D\) & \(D\) & \(0\) & Separable & Multimodal \\ F10 & Powell & \(-4\) & \(5\) & \(0\) & Separable & Unimodal \\ F11 & Rastrigin & \(-5.12\) & \(5.12\) & \(0\) & Nonseparable & Multimodal \\ F12 & Schwefel 2.21 & \(-100\) & \(100\) & \(0\) & Nonseparable & Unimodal \\ F13 & Zakharov & \(-5\) & \(10\) & \(0\) & Nonseparable & Unimodal \\ \hline \hline \end{tabular}
\end{table}
Table 1: Benchmark functions.  
Figure 1: Trends of the parameter \(p\), the number of butterflies in land 1 \(NP_{1}\), and the number of butterflies in land 2 \(NP_{2}\) for the tested monarch butterfly optimization (MBO) and self-adaptive population MBO (SPMBO) algorithms. (**a**) \(p\); (**b**) \(NP_{1}\); (**c**) \(NP_{2}\).  
From Table 2, it can be observed that, in terms of the mean and worst function values, SPMBO has the absolute advantage over the MBO algorithm on the thirteen benchmarks. By studying the SD values, we can see, the final functions values obtained by SPMBO are located in a smaller range than the basic MBO algorithm. This indicates that SPMBO is an effective intelligent algorithm that performs in a more stable way. Unfortunately, for best function values, SPMBO only narrowly defeated the basic MBO algorithm (7 vs. 6).  
The superiority of SPMBO on functions F01-F13 is also shown in Figure 2, which clearly reveals that SPMBO performs better than MBO throughout the entire optimization process.
## 5 Conclusions  
In this paper, a TransUNet-based semantic segmentation technique is presented with sparse matrix and coordinate attention for remote-sensing images. To minimize the background interference and highlight the small features, an improved coordinate attention module is used to focus on interest goals and the goal location. The transformer model is improved to enhance the computation efficiency and refine segmentation results. To reduce the category imbalance, the loss function is coupled with both the cross-entropy loss and Dice loss functions. The experimental results demonstrate that, when compared to alternative approaches on the three datasets, the suggested algorithm has a superior segmentation effect. The attention mechanism can be also applied to other computer vision tasks, and its influence on different network performance is worthy of further study.  
Conceptualization, C.G.; methodology, S.G.; software, Y.H.; investigation, L.D. and Y.H.; writing--original draft preparation, Y.H.; writing--review and editing, L.D. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China, grant number, 42101017, in part by the funding project of the Northeast Geological S&T Innovation Center of China Geological Survey, grant number, QCJ2022-23, QCJ2022-24, QCJ2022-26, in part by the China Postdoctoral Science Foundation, grant number, 2020M680979, and in part by the Basic Scientific Research Project of the Higher Education Institutions of Liaoning Province, grant number, JYTMS20231165.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Vaihingen**} & \multicolumn{3}{c}{**Potsdam**} & \multicolumn{3}{c}{**LoveDA**} \\ \cline{2-10}  & **mIoU** & **mF1** & **OA** & **mIoU** & **mF1** & **OA** & **mIoU** & **mF1** & **OA** \\ \hline Baseline & 67.90 & 77.20 & 90.11 & 71.15 & 81.54 & 87.13 & 48.88 & 64.14 & 67.79 \\ Proposed & 71.48 & 81.81 & 90.50 & 74.34 & 83.77 & 88.90 & 52.57 & 67.98 & 70.80 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons with the baseline method (%).  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 3 Progress of Information Fusion for Multi-Source Material Data
### Algorithms for Material Data Fusion
#### 3.4.5 Big Data Algorithms for Material Data Fusion  
Material data is regarded as a kind of big data, because it has the key properties of big data, known as the 4V (volume, variety, velocity and veracity) property. Big data techniques are applicable in material science. For example, data organization and indexing techniques in big data can be used to organize material data. [PERSON] et al. [PERSON] et al. (2016) implemented the BSI (Bit-Sliced Indices) arithmetic over Hadoop MapReduce for the top-\(k\) (preference) query in big data analytics. Their experiments showed that the BSI approach was much faster than previous methods. [PERSON] et al. [PERSON] et al. (2016) proposed a heterogeneous sorting algorithm that utilized both multi-core CPUs and many-core GPUs. The sorting performance was better than the parallel reference implementation when sorting data that exceeds the memory capacity of GPUs. [PERSON] et al. also demonstrated that the heterogeneous sorting algorithm achieved high efficiency in large datasets. [PERSON] et al. [PERSON] et al. (2016) presented the first deep learning solution toward a scientific image indexing problem, using a collection of over 300K microscopic images. The results showed that their proposal was 54% better than a dictionary lookup method, which is now popularly used in material data science. [PERSON] et al. [PERSON] et al. (2016) proposed a high-throughput infrastructure for the automated calculation of molecular properties with a focus on battery electrolytes, aiming to ultimately address all chemical components present in the electrolyte, as well as the interactions between them. Their framework was used to perform complex computation on very large data sets and is proven to be efficient and scalable for computing complex properties.
# Calculations of Cross-Sections for Positron Scattering on Benzene
## 2 Theory and Computational Details  
### Molecular Structure  
The conducted simulations concern collisions between a positron and the benzene molecule. Benzene is a cyclic organic molecule containing six carbon atoms and six hydrogen atoms, displaying a hexagonal, planar structure. The program package Gaussian v16 was employed to optimize the structure of the benzene molecule using the Density Functional Theory (DFT) [31]. The Becke, 3-parameter, Lee-Yang-Parr (B3 LYP) exchange-correlation functional [32; 33] and the 6-311++G(2 df,2p) basis set [34] are used. The optimized parameters are 1.391 Angstrom for the CC bond length and 1.082 Angstrom for the CH bond length.
## 3 Methodology and Cooperative Game Theory Models
### Mathematical Forms of the Cooperative Game Models
#### 3.1.3 Models III and IV  
These models have been designed to measure countries' maximum (Model III using Equations (7) and (8)) and minimum (Model IV using Equations (7) and (9)) rational range of benefit expectations [\(x_{i\_max}\), \(x_{i\_min}\)] from the grand coalition within the core. After applying Model II, the obtained optimal \(Z\) value is utilized as a constant parameter in the equations for Models III and IV instead of as a variable. Upon successfully running a single optimization of Models I and II, Models III and IV are then operated for each player \(i\) separately and independently. Mathematically, Models III and IV have the same constraints and objective functions, with the only difference being the selection of the _maximization_ and _minimization_ of the objective function. While maximizing Model III, the objective function (Equation (8)) maximizes the benefit of individual \(i\) in order to determine the upper bound of its individual benefit, \(x_{i\_max}\). While minimizing Model IV, the objective function (Equation (9)) minimizes the benefit of individual \(i\) in order to determine the lower bound of its individual benefit, \(x_{i\_min}\).
## 3 Experimental Results and Discussions  
### Pre-Amplifier  
A regenerative amplifier has also been used as the pre-amplifier. The regenerative amplifier with Nd:YAG as gain medium could suppress the ASE effect through loss of control by the pockels cell, and achieved higher energy of about 10 mJ. However, the time signal to noise ratio after regenerative amplifier was only 23 dB due to leakage pre-pulses from the pockels cell. The time signal to noise ratio after further amplification decreased to only 10 dB, and the pre-pulse energy was amplified to 7.7 mJ, which could consume energy stored in gain medium, as shown in Figure 2. A pulse picker after regenerative amplification based on electro-optic switches can eliminate pre-pulses and improve the time signal to noise ratio to 40 dB [25], but this will increase system complexity. In order to improve time signal to noise ratio and decrease system complexity, we designed an off-axis four-pass diode-end-pumped Nd:YVO\({}_{4}\) amplification architecture as the pre-amplifier, which could provide high gain with excellent time signal to noise ratio. With an input energy of 55 \(\upmu\)J and 28.6 mJ pump energy into the pre-amplifier stage, up to 4.5 mJ pulse energy at 1 kHz was acheived, and the gain of the pre-amplifier stage could reach 81. With further amplification, the time signal to noise ratio after main amplification was still excellent, which was measured by a photodetector (DET10A, Thorlabs), as shown in Figure 3. To increase the dynamic range of the time signal to noise ratio, the photodetector was overexposed to get the pre-pulse amplitude (2 mV), as shown in Figure 3a. Then the main pulse  
Figure 1: Schematic of the laser system. PBS: polarization beam splitter; FR: Faraday rotator; HWP: half-wave plate; BE: beam expander; TFP: thin-film plate; LD: laser diode.  
was reduced to \(1/100\) with a filter. The amplitude of the main pulse was 385 mV within the linear range of the photodetector. The time signal to noise ratio was better than 42 dB.