esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence
## 1 Literature Review
### Deep Neural Network
#### 1.1.2 Recurrent Neural Network (RNN)  
An RNN, short for recurrent neural network, is a specialized neural network explicitly designed for sequence modeling. A directed graph is a representation that can be used to characterize the connections that exist between neurons in an RNN [36]. The RNN is particularly suitable for Natural Language Processing (NLP) applications because it can analyze input sequences using its internal state. The outputs of the RNN are generated by iteratively applying the identical function for every occurrence using the identical approach. Calculations that have been used before are employed to determine the performance. Within the RNN design, the input length is utilized to ascertain the size of each separate time step. The data given to the architecture at time step \(t\) are denoted as \(x_{t}\), whereas the hidden state at time step \(t\) is denoted as \(s_{t}\):  
\[s_{t}=f(Ux^{t}+Ws^{t-1}+b) \tag{1}\]  
Utilizing the hidden state from the previous time phase and the current input, as referenced in the study [37], it is possible to compute the current hidden state \(s_{t}\) using Equation (1) given in the previous sentence. The input and recurrent of the cell at time \(t\) are represented by the symbols \(x^{t}\) and \(s^{t-1}\), respectively. The weights that are shared over time are denoted by the symbols \(U\) and \(W\), while the bias is represented by \(b\). The pointwise non-linear activation function is represented by the function \(f\). Regrettably, the  
Figure 1: Architecture of Conv. RNN, Conv. LSTM, Conv. GRU, Conv. BRNN, Conv. BLSTM, and Conv. BGRU.  
RNN cannot identify the relationship between pertinent information when the time interval is extensive. Long Short-Term Memory is suggested by the study [38] as a solution to the \"long-term dependency\" issue.
## 3 Resilience
The comprehensive nature of the FEMA P-58 code extends beyond assessing structural damages to encompass non-structural components, casualties, repair costs, and repair time. By estimating the total potential impacts--including downtime, loss of functionality, and economic implications-- this methodology equips engineers with the necessary tools to design structures that are not only resistant to seismic events but also capable of a faster recovery. Implementing FEMA P-58 code is thus crucial in ensuring the resilience of critical infrastructures, as it allows for a holistic understanding of how these infrastructures would fare and recover in the event of a seismic disturbance. In addition, a smart city is resilient if it is supplied with safeguards to effectively absorb potential shocks and stresses to its social and technical networks and infrastructures. This resiliency enables it to maintain the requisite levels of function, structure, and identification despite stress.
# A Survey on Recent Advanced Research of CPS Security
## 4 Discussion
### Architecture Layers  
#### 4.1.1 Perception Layer  
In the perception layer, the primary mission is to identify and measure objects and then collect and process the state information. Therefore, sensor attack and defense technology are the key research directions.  
Accurate sensor data are the premise of CPS safety, without which a controller can be deceived and make incorrect decisions. Therefore, most sensor nodes need effective protection measures. To detect abnormal control behavior, researchers have proposed event-aware finite-state automaton (eFSA) [18] or context extraction [28] techniques. Furthermore, devices should be protected against the leakage of sensitive information to prevent sensor information abuse [98; 99]. As some sensors can gather acoustic signals that humans cannot hear, an attacker can inject malicious commands with hidden voice commands [33; 34]. To defend against such attacks, vendors should filter unnecessary audio channels in advance.
## 2 Materials and Methods
### Data
#### 2.1.3 Land-cover Data  
Given the lack of land-cover data before 2000, we used land-cover data in 2001 for the entire period simulations. The MODIS land-cover data at 500-m spatial resolution (MCD12Q1) for 2001 was resampled to 0.0727\({}^{\circ}\) to drive the TL-LUE model during 1981 and 2012. The International Geosphere-Biosphere Programme classification system was used in this study, including categories of evergreen needle-leaf forest (ENF), deciduous coniferous forest (DNF), evergreen broad-leaved forest (EBF), deciduous broad-leaved forest (DBF), evergreen broad-leaved forest (ENF), deciduous coniferous forest (DNF), evergreen broad-leaved forest (EBF), mixed forest (MF), sparse shrubland (OS), dense shrubland (CS), grassland (GRASS), farmland (CROP), and other surface cover types (NOV).
# Emergent Search of UAV Swarm Guided by the Target Probability Map
## 3 The Local Rules of ESUSTPM  
### The Local Rules  
The missions of the UAV swarm are to achieve area coverage, target search and reduction in environmental uncertainty as much as possible before the deadline in the cooperative search. In addition, swarm must keep the flocking state with the given space for communication connectivity as far as possible in the process of task execution. Thus, the agents can communicate with other agents in the same communication net built by the agents within the given distance. Then, each agent adopts the three closest neighbors chosen by the distance and TPM to calculate the velocity vector of the monomer itself in the onboard computer. The local rules of ESUSTPM contain two parts: the local rules between UAVs and the local rules between UAV agents and the nearby environmental grids. The velocity vector of each UAV in the swarm can be calculated in the interaction with the neighbors and the nearby grids. The details of the local rules are listed as follows:  
1. Direction and distance to the first closest neighbor, \(\overrightarrow{F_{1}}\);
2. Direction and distance to the second closest neighbor, \(\overrightarrow{F_{2}}\);
3. Direction and distance to the third closest neighbor, \(\overrightarrow{F_{3}}\);
4. Direction to the nearby grid with the highest target existing probability, \(\overrightarrow{F_{4}}\).  
Figure 2 is the sketch map of the local rules in the ESUSTPM, and the black square denotes the nearby grid with the highest target existing probability around each UAV.  
Generally, eight surrounding squares are distributed around each UAV in the swarm. The local rules between UAV agents in the swarm require the velocities and positions of the three closest neighbors. The information of the target existing probability in the eight grids is calculated and updated in the emergent process; then, the grids with the highest target probability are adopted as the guide of the UAV movement.
# Application of Compact Folded-Arms Square Open-Loop Resonator to Bandpass Filter Design
## 5 Filter Experimentation  
The radio frequency PCB milling procedure is employed in the manufacture of the proposed bandpass filter. The substrate material employed for the filter circuit construction is Rogers RT/Duroid 6010 LM with a relative permittivity, _ex,_ of 10.8, a loss tangent, tan \(\delta\), of 0.0023, and a substrate thickness, h, of 1.27 mm. The images of the filter device and the measurement results are shown in Figure 10. The entire footprint of the constructed filter device is 36.08 mm by 6.74 mm, which corresponds to 0.26 \(\mathrm{\SIUnitSymbolMicro\meter}\times\) 0.05 \(\mathrm{\SIUnitSymbolMicro\meter}\), with \(\mathrm{\SIUnitSymbolMicro\meter}\) representing the guided wavelength of the 50 \(\mathrm{\SIUnitSymbolOhm}\) microstrip line with the centre frequency of 2.2 GHz. The filter measurement was conducted using a Keysight Technologies USA, NS230A Vector Network Analyzer (VNA), and two 50 \(\mathrm{\SIUnitSymbolOhm}\) sub-miniature version A (SMA) connectors from RS Pro London UK, connected to each input/output port. The measurement results indicate a minimum insertion loss of 0.9 dB and a return loss better than 19.2 dB, as shown in Figure 10.
###### Abstract  
For a sensing network comprising multiple directional sensors, maximizing the number of covered targets but minimizing sensor energy use is a challenging problem. Directional sensors that can rotate to modify their sensing directions can be used to increase coverage and decrease the number of activated sensors. Solving this target coverage problem requires creating an optimized schedule where (1) the number of covered targets is maximized and (2) the number of activated directional sensors is minimized. Herein, we used a discrete particle swarm optimization algorithm (DPSO) combined with genetic operators of the genetic algorithm (GA) to compute feasible and quasioptimal schedules for directional sensors and to determine the sensing orientations among the directional sensors. We simulated the hybrid DPSO with GA operators and compared its performance to a conventional greedy algorithm and two evolutionary algorithms, GA and DPSO. Our findings show that the hybrid scheme outperforms the greedy, GA, and DPSO algorithms up to 45%, 5%, and 9%, respectively, in terms of maximization of covered targets and minimization of active sensors under different perspectives. Finally, the simulation results revealed that the hybrid DPSO with GA produced schedules and orientations consistently superior to those produced when only DPSO was used, those produced when only GA was used, and those produced when the conventional greedy algorithm was used.  
target coverage problem; directional sensor networks; genetic algorithm; discrete particle swarm optimization 2022128503. [[https://doi.org/10.3390/app12178503](https://doi.org/10.3390/app12178503)]([https://doi.org/10.3390/app12178503](https://doi.org/10.3390/app12178503))
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 3 Problem Formulation and System Model
### Combined Channel Model  
Because of the tendency toward harsh channel statuses in mmWaves due to impairments such as obstructions, considerable research effort has been devoted to designing an accurate channel model for mmWave communications. The channel status is further degraded by the high mobility of users who frequently make handoffs between base stations. For this reason, a channel model accurately representing the network environment should be considered to capture and analyze the network scenario.  
Based on real-world statistical measurements at frequencies of 28 and 73 GHz in New York City, the authors in [38] presented a channel model for dense urban deployment. The path loss measurements adopted in this paper are as follows.  
\[\begin{split} PL_{LOS}(dB)=\alpha+\beta 10\text{log}_{n}(d)+\xi_{c}, \\ \xi_{c}\sim N(0,\theta),\end{split} \tag{3}\]  
where \(d\) is the distance, \(\alpha\) and \(\beta\) are the least-square fits of floating intercept and slope over a covered distance, respectively. \(\xi_{c}\) is the log-normal shadowing. The shadowing variance is given by \(\theta\).
_Applied_  
_sciences_  
_Article_  
**A 5G C-RAN Optical Fronthaul Architecture for**  
**Hotspot Areas Using OFDM-Based Analog**  
**IFoF Waveforms**  
**[PERSON] 1,*, [PERSON] 1(c), [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 2(c), [PERSON] 2, [PERSON] 1 and [PERSON] 1  
1 Department of Informatics, Aristotle University of Thessaloniki, 5412 Thessaloniki, Greece; [EMAIL_ADDRESS] (C.V.); [EMAIL_ADDRESS] (A.M.); [EMAIL_ADDRESS] (P.M.); [EMAIL_ADDRESS] (G.K.); [EMAIL_ADDRESS] (A.M.); [EMAIL_ADDRESS] (N.P)  
2 LioniX International B.V., 7500 AL Enschede, The Netherlands; [EMAIL_ADDRESS] (C.G.H.R.); [EMAIL_ADDRESS] (P.W.L.V.D.); [EMAIL_ADDRESS] (R.M.O.)
* Correspondence: [EMAIL_ADDRESS]; Tel.: +30-2310-990588  
Received: 1 July 2019; Accepted: 26 September 2019; Published: 28 September 2019  
**Featured Application: The proposed fronthaul architecture may be a promising solution for the future Fifth Generation (5G) Fiber-Wireless (FiWi) networks to provide broadband access services to overcrowded small-scale hotspots.**  
**Abstract:** Analog fronthauling is currently promoted as a bandwidth and energy-efficient solution that can meet the requirements of the Fifth Generation (5G) vision for low latency, high data rates and energy efficiency. In this paper, we propose an analog optical fronthaul 5G architecture, fully aligned with the emerging Centralized-Radio Access Network (C-RAN) concept. The proposed architecture exploits the wavelength division multiplexing (WDM) technique and multicarrier intermediate-frequency-over-fiber (IFoF) signal generation per wavelength in order to satisfy the demanding needs of hotspot areas. Particularly, the fronthaul link employs photonic integrated circuit (PIC)-based WDM optical transmitters (Txs) at the baseband unit (BBU), while novel reconfigurable optical add-drop multiplexers (ROADMs) cascaded in an optical bus are used at the remote radio head (RRH) site, to facilitate reconfigurable wavelength switching functionalities up to 4 wavelengths. An aggregate capacity of 96 Gb/s has been reported by exploiting two WDM links carrying multi-IF band orthogonal frequency division multiplexing (OFDM) signals at a baud rate of 0.5 Gbd with sub-carrier (SC) modulation of 64-QAM. All signals exhibited error vector magnitude (EVM) values within the acceptable 3 rd Generation Partnership Project (3 GPP) limits of 8%. The longest reach to place the BBU away from the hotspot was also investigated, revealing acceptable EVM performance for fiber lengths up to 4.8 km.  
**Keywords:** 5G networks; analog intermediate-frequency-over-fiber; optical fronthaul; multicarrier communication; OFDM; PIC-based transmitter; ROADM
# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
## 4 Experimental Results and Discussion
### Experimental Setup
Calculating the average of average precision (AP) under each category allows for comparison and evaluation of the performance of different algorithms. AP is a measure of the algorithm's performance on different target categories by calculating the maximum precision at different recall levels.
## 5 Conclusions  
This paper proposed an agricultural environment monitoring server system that integrates environmental and soil sensors, GPS module, CCTV _etc._ into a device to collect information for monitoring the environment at crop plantations, and provides real-time environmental monitoring and various application services based on the information. The proposed agricultural environment monitoring server system could work without supplying any external power in an agricultural environment with insufficient power infrastructure by using a solar cell module as its power supply.  
Although a prototype of the agricultural environment monitoring server system was manufactured to monitor the outdoors environment for implementation of the designed system and performance evaluation, and the initial prototype version 1 could monitor the outdoors environment, however, it could not be used as planned because the power consumed by the server was significantly larger than the power generated in the solar cell module so that use time was short. In order to solve this problem, in prototype version 2 the server was rebuilt to implement the system with low power embedded board modules, and it was found that this version operated normally through the field tests.  
If the agricultural environment monitoring server system proposed in this paper is applied to the agricultural environment, environmental information could be monitored even at a remote site, and it is expected that it would contribute to an increase in crop yields and the improvement of quality in the agricultural field by supporting the decision making of producers through the analysis of the collected information. Future study is scheduled to miniaturize the current system, and improve the current multi-server communication with socket communications to provide a structure in which communication is possible even in regions where the Internet is not connected.
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 4 Implementation of Proposed Model in Healthcare Scenario
### Attribute-Based Access Control Implementation
#### 4.4.3 Policy Decision Point (PDP)  
ABAC is central to our integrated risk-aware RBAC and ABAC model, with the PDP as the core component. Upon receiving a request from the PEP, the PDP gathers relevant attributes from the PIP and calculates a risk score. The PDP then approves or denies the request based on these inputs. Selecting an appropriate PDP engine was crucial, and after extensive research, we narrowed the options to authorforce-ce-core-pdp-engine and wso2-balana [39]. We chose authorforce-ce-core-pdp-engine for its accessibility and high customizability, making it the ideal solution for implementing custom access control policies [39; 40].  
The \"authorforce-ce-core-pdp-engine\" is a robust and stable PDP engine known for its efficient policy evaluation capabilities. Compliant with the XACML standard, it ensures compatibility with XACML-based applications and systems [41]. The engine reads the fixed PDP:xml to locate all policy sets and allows users to set a fixed root policy set, aiding in the customization of XACML access control policies. This approach enhances clarity and  
Figure 4: ABAC sequence diagram for the proposed model.  
is ideal for applications with increasing access requests, efficiently handling complex access control scenarios.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 6 Computing the Minimum Frame with a Genetic Algorithm  
TDMA slot allocation has been well studied by the research community, and several algorithms based on the vertex coloring in a graph have been proposed to address this problem. The bandwidth reservation problem in a TDMA-based scheme is equivalent to the satisfiability (SAT) problem, which is known to be NP-complete [47; 48]. Based on this, the use of meta-heuristics like genetic algorithms, may provide a good solution in less time than the required with ILP solvers. In this section, we present a genetic algorithm to compute a frame that guarantees collision-free transmissions and receptions in all nodes.  
Given the number of nodes \(N\) of an UA-WSN, let \(Nodes[]\) be an array of \(N\) elements stating the order of allocation of nodes to the frame. Let the following algorithm be the frame allocation method from a given specified array \(Nodes[]\),  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Path** & **Delay** \\ \hline \(aefg\) & 210 \\ \(abcdg\) & 275 \\ \(abefg\) & 275 \\ \(abcfg\) & 286 \\ \(abefcdg\) & 407 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Worst case end-to-end delay.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Message** & \multicolumn{6}{c}{**Nodes**} \\ \cline{2-7}  & \(a\) & \(b\) & \(c\) & \(d\) & \(e\) & \(f\) & \(g\) \\ \hline \(m_{ag}\) & 55 & 55 & 55 & 55 & 56 & 66 & \\ \(m_{bd}\) & 44 & 44 & 44 & 44 & 44 & 44 & 44 \\ \(m_{ce}\) & 44 & 44 & 44 & 44 & 44 & 44 & 44 \\ \(m_{db}\) & 44 & 44 & 44 & 44 & 44 & 44 & 44 \\ \(m_{ce}\) & 44 & 44 & 44 & 44 & 44 & 44 & 44 \\ \(m_{ga}\) & & 55 & 55 & 55 & 56 & 66 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Worst case response time per node.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Node** & **Messages** \\ \hline \(a\) & \(\{m_{ag},m_{bd},m_{ce},m_{db},m_{ec}\}\) \\ \(b\) & \(\{m_{ag},m_{bd},m_{ce},m_{ce},m_{ga}\}\) \\ \(c\) & \(\{m_{ag},m_{bd},m_{ce},m_{db},m_{ea}\}\) \\ \(d\) & \(\{m_{ag},m_{cc},m_{db},m_{ce},m_{ga}\}\) \\ \(e\) & \(\{m_{ag},m_{bd},m_{ce},m_{ga}\}\) \\ \(f\) & \(\{m_{  
**Conjecture 1**.: _The frame of minimum length can be found by Algorithm 1, given as input a suitable ordering of array Nodes\([]\)._  
```
foreach\(n\) in Nodes\([N]\)do  h:=0; // Slot number. while\(n\) not allocable to slot \(h\)do \(h:=h+1\);  end if  allocate \(n\) to slot \(h\);  end fore
```  
**Algorithm 1**Minimum length algorithm in pseudo-code  
From assumption made in Algorithm 1, the optimum frame can be obtained by evaluating all possible permutations of array Nodes\([]\). Eventually, if the network is large, a high computational cost is required to evaluate all the permutations. As presented in Section 4, we can find the minimum frame with ILP techniques. Based on the proposed model, we developed an evolutionary optimization technique that follows the genetic algorithms approach, which is explained in the next subsections.
# A Swarming Approach to Optimize the One-Hop Delay in Smart Driving Inter-Platoon Communications
## 5 Performance Results
However, as shown in Figure 5d,e, the one-hop throughput of each backbone vehicle with the optimal minimum contention window size is balanced under six backbone vehicles and the end-to-end throughputs from backbone vehicle 1 to other backbone vehicles with the optimal minimum contention window sizes are increased by a constant with the increasing of backbone vehicle index. Moreover, from Figure 8b,c, we can see that the one-hop delay and end-to-end delay of the proposed approach are decreased considerably under six backbone vehicles. Figure 8f shows the average increment ratio of transmission probability. It is seen that the transmission probabilities are increased by 50.1%, 80.9% and 83.3% compared with the DCF mechanism when the number of backbone vehicles are 6, 12 and 24, respectively. The similar phenomenon appears as in Figure 8a. That is, the increment ratios of the transmission probability under 12 and 24 backbone vehicles are almost the same. Figure 8: The average improvement ratio of the proposed approach’s performance under different numbers of backbone vehicles. (**a**) Decrement ratio of contention window size vs number of backbone vehicles; (**b**) Decrement ratio of one-hop delay vs number of backbone vehicles; (**c**) Decrement ratio of end-to-end delay vs number of backbone vehicles; (**d**) Increment ratio of one-hop throughput vs number of backbone vehicles; (**e**) Increment ratio of end-to-end throughput vs. number of backbone vehicles; (**f**) Increment ratio of transmission probability vs number of backbone vehicles.