esa-sceva/satcom-chunk-collection huggingface dataset:
# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 2 Methodology
### Augmentation Techniques
#### 2.2.2 Dgan  
GAN (Generative Adversarial Networks) are powerful augmentation tools [116]. Its key components are two deep neural networks: a generator and a discriminator [117]. The generator is a network that produces new data samples that are indistinguishable from real data. It starts with random noise as input and continuously refines it through its internal layers, ultimately aiming to generate highly realistic data. Discriminator acts as a deep neural network classifier. It receives data, typically in the same format the generator produces (like images for image generation). This data undergoes a two-stage analysis. First, convolutional layers, like those in a Convolutional Neural Network (CNN), meticulously dissect the data, extracting characteristic features. Then, fully connected layers analyze these features and make a final judgment. The verdict is delivered as a probability score between 0 and 1. A value closer to 1 signifies a high likelihood of the data being real, while a value closer to 0 suggests the generator likely generates it. The continuous refinement of GAN, between generator and discriminator is called adversarial training, expressed mathematically through an objective function \(V\left(D,G\right)\) as follows [118]:  
\[min_{G}\ max_{D}V(D,G)=\mathrm{E}_{x\sim pdata(x)}\big{[}\log\,D(x)\big{]}+ \mathrm{E}_{z\sim p_{z}(z)}[\log(1-D(G(z))] \tag{1}\]  
where \(P\) denotes the data distribution, \(p_{z}(z)\) signifies the prior distribution of the input noise, and \(G(z)\) refers to the mapping into the data space.  
In this research, the core component is a neural network architecture where a sequence of layers progressively transforms random noise vectors into the desired data. The optimized hyperparameter for the DGAN model is shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Turned Value** \\ \hline \multicolumn{2}{c}{**Generator**} \\ \hline Layers & 6 \\ Units per layer & (256, 256, 256, 256, 256, 256) \\ Learning rate & 0.0002 \\ Batch size & 150 \\ Training epochs & 8000 \\ Embedding vector size & 26 \\ \hline \multicolumn{2}{c}{**Discriminator**} \\ \hline Layers & 5 \\ Units per layer & (256, 256, 256, 256, 256) \\ Learning rate & 0.0002 \\ Training epochs & 8000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimized hyperparameters for the DGAN model.
# Dynamic Risk Assessment in Cybersecurity: A Systematic Literature Review
## 4 Results
### Mathematical Model Methods
In the proposed framework, the actions of the defender and the attacker are mathematically formulated to determine their optimal strategies. The dynamic game theory is employed to show the interactions between the two aforementioned parties, where the attacker's goal is to maximise their impact on the system, and the defender tries to minimise the potential damage caused by attacks. The authors conducted case studies using a modified IEEE 14-node AC/DC hybrid test system, demonstrating how defence and attack strategies affect the system's functionality under coordinated attacks. In addition their findings revealed that false data injection (FDI) attacks pose a great threat to AC/DC hybrid systems. [PERSON] et al. [52] proposed a DRA model for CPSs. The model assesses the physical consequences that can be caused in a SCADA system by cyber attacks quantitatively. The CVSS scoring is used to estimate the severity and exploiting probability of software vulnerabilities (SVs). The model takes into account various attributes such as time, the attacker's characteristics, network security defence mechanisms, and propagation, utilising mathematical formulas in order to assess the probability of exploiting different possible attack paths. The effectiveness of the model was validated through simulations on modified IEEE 14-bus and 118-bus systems. Additionally, the model identifies high-risk substations within the CPS network.
# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 3 Networked Microgrids' Control
### Control
#### 3.2.3 Control Schemes
By considering factors such as operation cost [109; 110; 112; 113; 137; 138; 139; 140; 141; 142], energy loss [186], restoring loads [192], load of congested line [112], and energy not supplied [140], environmental sustainability, and grid constraints, optimization methods allow NMGs to dynamically adapt to changing conditions in real time. These advanced control strategies enable NMGs to operate with increased efficiency, optimize the utilization of renewable resources, and respond dynamically to fluctuations in energy demand and supply. Additionally, optimization-based control contributes to the resilience and adaptability of NMGs, making them well-suited for addressing the challenges associated with distributed energy generation and variable loads in a networked environment. Artificial intelligence-based techniques are increasingly recognized for their role in advancing microgrid control and management. These AI-driven methods can dynamically adjust to changing conditions, predict system behavior, optimize resource allocation, and enhance fault detection and response capabilities. In pursuit of these advancements, certain studies concentrate on developing AI-based controllers, such as the artificial neural network-based controller [124; 196], DNQ-based controller [193; 194; 195; 196] and the data-driven distributed secondary frequency controller. The objective functions of these methods vary, encompassing optimizing energy sharing among MGs [124; 131; 196], optimizing the pricing policy [193; 194], as well as minimizing operation cost [195].
# Fault Studies and Distance Protection of Transmission Lines Connected to DFIG-Based Wind Farms
## 2 Fault Analysis of DFIG-Based Wind Farm and Its Verification
### Case Studies
The error calculation formula is shown as:  
\[\sigma=\frac{1}{N}\sqrt{\sum_{i=1}^{N}\left(\frac{x_{i}-x_{t}}{x_{t}}\right)^ {2}}, \tag{16}\]  
where \(x_{i}\) and \(x_{t}\) are separately the measurement value and true value of fault distance. The errors of fault distance measurement results are separately shown in Tables 1 and 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Fault Type**} & \multicolumn{3}{c}{**The Error of Fault Distance Measurement Result**} \\ \cline{2-4}  & **5 km** & **10 km** & **15 km** \\ \hline Single-phase ground & 0.17\% & 0.20\% & 0.22\% \\ Two-phase ground & 0.23\% & 0.16\% & 0.30\% \\ Phase-to-phase short circuit & 0.04\% & 0.63\% & 0.03\% \\ Three-phase short circuit & 0.07\% & 0.18\% & 0.03\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of the time-domain method when faults occur. Figure 10: Comparison of two kinds of distance protection methods. According to the two tables, the accuracy of the calculation results using the time-domain method is significantly high, and no error is more than 1%. The errors of the calculation results using the Fourier method are significantly bigger than that of the time-domain method. The time-domain distance protection method can accurately and quickly identify fault distances when faults occur on the transmission line connected to a DFIG-based wind farm.
# IoT Helper: A Lightweight and Extensible Framework for Fast-Prototyping IoT Architectures
## 5 Experiments
### Pull-Mode Experiments  
We performed six different experiments related to four different scenarios, as shown in Table 2. Each scenario uses a different number of sensors and actuators:  
* Scenario 1 contains only one sensor;
* Scenario 2 contains two sensors and two actuators;
* Scenario 3 contains three sensors and three actuators;
* Scenario 4 contains five sensors and five actuators;  
We subscribed a Java application to receive data from IoT Helper for each scenario. Each experiment tests all four scenarios for a total of 10 min with different acquisition frequencies ranging from 0.1 data points collected per seconds (one data point every 10 s; the slowest frequency) to 8 data points collected per second (one data point collected every 125 ms; the fastest frequency). Table 2 reports the total expected number of messages and those actually received by the client app in the four scenarios. Note that for the purpose of this experiment the number of clients is irrelevant since we are essentially measuring the capacity of the embedded system to collect data and push messages in real time to the data channel.  
The results demonstrated that when messages are sent with low frequency, for example, one message every 10 or 5 s (the first two rows in Table 2), all the messages are completely handled, even with five sensors and five actuators. With higher frequencies, a delay appears. All messages are sent and received eventually, but some of them occur after the 10 min time frame of the experiment. This is due essentially to the limited processing power of the Arduino Mega CPU, which was not able to dispatch data in real time.  
Figure 8 shows the percentage of messages that were dispatched after the deadline. As discussed, with low frequencies (0.1 and 0.2), there were no errors in any scenario. With frequencies of one and two messages per second, the amount of errors is very limited (less  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Freq.** & **Expected** & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Scenario 4** \\ \hline
0.1 msgs/s & 60 & 60 & 60 & 60 & 60 \\ \hline
0.2 msgs/s & 120 & 120 & 120 & 120 & 120 \\ \hline
1 msgs/s & 600 & 598 & 597 & 596 & 595 \\ \hline
2 msgs/s & 1200 & 1195 & 1193 & 1189 & 1190 \\ \hline
4 msgs/s & 2400 & 2312 & 2313 & 2301 & 2292 \\ \hline
8 msgs/s & 4800 & 4632 & 4623 & 4580 & 4534 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Messages received for each scenario with different frequencies.  
than 1%) even in Scenario 4, with five sensors and five actuators; with higher frequencies, the number of missed messages increases to 5%, especially in Scenario 4.  
We emphasize that most typical data collection tasks generate data with frequencies that are below 2 data points per second. Therefore, we can conclude that IoT Helper can be effectively used in these cases.
## 4 Results and Discussion
### Data Collection and Preparation
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Feature Name** & **Symbol** & **Unit** & **Mean** & **STD** & **Min** & **Max** & **Skewness** \\ \hline Fly ash & FA & kg/m\({}^{3}\) & 246.72 & 159.99 & 0.00 & 620.00 & \(-\)0.3385 \\ GGBS & GGBS & kg/m\({}^{3}\) & 176.27 & 177.38 & 0.00 & 560.00 & 0.4777 \\ Sodium silicate & SS & kg/m\({}^{3}\) & 117.56 & 36.08 & 32.00 & 205.50 & 0.2317 \\ Sodium hydroxide & SH & kg/m\({}^{3}\) & 44.78 & 22.51 & 0.00 & 108.00 & 0.1219 \\ Superplasticizer & SP & kg/m\({}^{3}\) & 1.52 & 2.57 & 0.00 & 9.00 & 1.3455 \\ Fine aggregate & fag & kg/m\({}^{3}\) & 656.26 & 81.65 & 495.00 & 887.00 & 0.3134 \\ Coarse aggregate & cag & kg/m\({}^{3}\) & 1082.58 & 239.87 & 525.40 & 1381.35 & \(-\)1.3052 \\ Added water & w & kg/m\({}^{3}\) & 64.72 & 62.10 & 0.00 & 216.00 & 0.4727 \\ \hline Curing temperature & CT & \({}^{\circ}\)C & 48.16 & 28.21 & 19 & 100 & 0.4981 \\ Curing period & CP & days & 21.76 & 14.99 & 1 & 56 & 0.6225 \\ \hline Compressive strength & CS & MPa & 38.45 & 14.95 & 1.25 & 80.51 & 0.1296 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical description for the features in the dataset.
# Analysis of Explainable Goal-Driven Reinforcement Learning in a Continuous Simulated Environment
## 3 Methods and Proposed Architecture
### Introspection-Based Method
```
1:Initialize function \(Q\)
2:Initialize \(\hat{Q}\) function objective
3:Initialize memory \(D\) with long \(N\)
4:for episode 1 to M do
5: Initialize state queue \(S\) with initial state
6:repeat
7:\(\epsilon\leftarrow\) update \(\epsilon\) with \(\epsilon\)-decay
8:\(s_{t}\leftarrow\) dequeue current state from \(S\)
9: Select an \(a_{t}\) action according the \(s_{t}\) state using \(\epsilon\)-greedy policy
10: Take action \(a_{t}\), observe reward \(r_{t}\), and next \(s_{t+1}\) state
11: Enqueue next state \(s_{t+1}\) into \(S\)
12: Store the transition \((s_{t},a_{t},r_{t},s_{t+1},done_{t})\) in \(D\)
13: Take a random sample of the transitions \((s_{j},a_{j},r_{j},s_{j+1},done_{j})\) from \(D\)
14:\(y_{j}=\left\{ \begin{array}{ll}r_{j}&\text{if }s_{t+1}\text{ is final}\\ r_{j}+\gamma\max_{a^{\prime}}\hat{Q}(s_{j+1},a^{\prime})&\text{otherwise} \end{array} \right.\)
15: Update \(Q\) related to \(y_{j}\)
16: Each C steps, \(\theta_{\hat{Q}}\leftarrow\theta_{Q}\)
17:\(\hat{P_{S}}\approx\hat{P_{S}}\in\hat{P}\)
18:until\(s_{t}\) is terminal or \(r_{t}\) had been negative 25 times
19:endfor
20:Return \(Q\), \(\hat{P_{S}}\)
```  
**Algorithm 2** Explainable goal-driven reinforcement learning approach for computing the probability of success using the introspection-based method. The algorithm is mainly based on [29] and includes the probabilistic introspection-based method.
# An Integrated Software for Computing Mechanical Properties of Crystalline Material by Means of XRD
## 3 Results and Discussion
### Thickness of Coating Layer  
To determine the thickness of the coating layer, the measurements using tilt angles \(\psi\) and \(\psi_{0}\) were used. When \"Count\" is pressed, the thickness of the coating layer is determined, as shown in Figure 11. The coating thickness \(t\) is determined from the formulas as below [10]:  
\[f(I,\psi,\psi_{0},\theta)=\frac{I_{\psi}\cos\psi_{0}[1-\cot(\theta-\psi_{0})\cot \theta}{I_{\psi}\sin(\theta+\psi)[1-\tan\psi\cot\theta]} \tag{29}\]  
\[g(\mu,\psi_{0},\theta)=\mu/\big{(}\frac{1}{\cos\psi_{0}[1-\cot(\theta-\psi_{0}) \cot\theta}-\frac{1}{\sin(\theta+\psi)[1-\tan\psi\cot\theta]}\big{)} \tag{30}\]  
\[t=\frac{\ln[f((I,\psi,\psi_{0},\theta)]}{g(\mu,\psi_{0},\theta)} \tag{31}\]  
where \(\theta\) is Bragg angle and \(\mu\) and \(\mu_{f}\) are the linear absorption coefficients of the substrate and coating layer used, respectively, and \(I_{\psi}\) is the maximum X-ray intensity in the fixed-\(\psi\) method.  
Three JIS-type SS400 samples with dimensions of 10 \(\times\) 40 \(\times\) 40 mm were polished, electroplated for 6 to 30 min to obtain the thicknesses of 2 \(\mu\)m, 2.25 \(\mu\)m, 2.75 \(\mu\)m, 3.25 \(\mu\)m  
Figure 11: Dialog box for determining the thickness of the nickel-plating layer.  
Figure 10: Dialog box for determining the hardness of quenched S50C.  
and 3.5 \(\upmu\)m, and observed directly under a microscope (MS). The thicknesses were measured by the Eddy current technique at nine positions in the central area of each sample to obtain the average values and their standard deviations.  
Table 6 lists experimental conditions for XRD thickness measurements. Figure 11 shows the diffraction lines of the 3 \(\upmu\)m plating sample. Table 7 compares the thickness of nickel-coating layers, measured using microscope techniques, Eddy current (EC), and XRD. The XRD technique allows determining 95% confidence limits of the computed values using a single measurement. The three techniques gave agreed thickness, and they are within the confidential limits.
# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 4 Types of Wireless Sensor Network
### Wireless Body Sensor Networks (WBSNs)  
Considered as one of the most enabling technologies, WBSNs are mostly used in medical and non-medical applications. The non-medical applications include environmental monitoring system, video clip and recording voice by a mobile phone, social networking and gaming software applications, iris recognition, poisonous gas or fire in the home, and fingerprints and facial patterns. Medical applications include continuous observation data such as blood pressure, body temperature, heartbeat, and blood sugar. It is a subfield of WSNs due to its continuous monitoring; the main intention is the healthcare of people when they are in critical situations. The intelligent biosensors are used as a sensor device in wireless BSNs, which are fixed outside or inside the human body to sense the interesting information from the human body; then, sensory data are transferred to the base station for further processing. Wireless BSNs are widely used in many applications such as healthcare, medical and non-medical, and telemedicine. The main challenges faced in wireless BSNs are low power, low data transfer rate, inaccessible power source, piezoelectric energy, and the trade-off between communication and processing. The routing protocol in Wireless BSNs is classified in various groups such as thermal routing protocols (energy consumption, objective, packet delivery ratio, temperature rise, and delay), QoS routing protocols (throughput, energy consumption, objective, mobility, packet delivery ratio, and delay), cluster-based protocols (metrics, energy consumption, objective, average delay, packet delivery ratio, and security), and cross-layered routing protocols (average delay, packet delivery ratio, energy consumption, objective, metrics, and mobility) [73]. WBSNs must satisfy the quality of service (QoS) demands of users/applications and the relevant network. In order to describe QoS criteria, users must first determine the type of applications that need to be used as well as their data delivery models from sensors to the base station, such as continuous/time driven, query driven, event driven, or hybrid driven. Each of these data delivery model types has its own set of requirements for quality of service. Every data delivery model has its own QoS criteria for QoS metrics except for the continuous/time data delivery model, while the other data delivery models must meet all four essential requirements: real-time delivery, reliable delivery, energy efficiency, and flexibility to network channel circumstances [74]. The continuous data-driven paradigm usually withstands minor delays and packet losses. While there is a high rate of packet loss on a communication connection, QoS structures or methods must take extra provisions, for events such as a heart attack delivering crucial data. It ensures that the important data reach the destination in a timely and accurate manner [75].
# WGAN-Based Realization Process of Gravel Soil for Hydraulic Property Simulation
## 5 Results Analysis
### Two-Point Correlation Functions
The figure offers insights into the porosity and connectivity characteristics of the generated porous media, facilitating comparisons with the primitive soil prototypes to identify any discernible differences. Figure 9: Probability function (FF) of finding a porous space at different 3D distances within three groups of gravel soil models. The PF is calculated for each faces along the \(d_{XY}\) of the diagonal direction. The black curves represent the PF of the three groups of the training set, which is 64 \(\times\) 64 \(\times\) 64 in size and derived from primitive soil prototypes. Meanwhile, the red curves show the mean PF obtained from 14 WGAN-GP realizations of size 64 \(\times\) 64 \(\times\) 64. The light green curve is the PF of each realization. Visually, the realizations appear consistent with the primitive soil prototypes. There is a best match between the average statistics of the training sets and those of the realizations (comparing the solid red and blue curves) along the \(x\) direction, with a maximum absolute deviation of 0.05. Meanwhile, a slightly poor fit can also be observed along the \(z\) direction, with a maximum absolute deviation of 0.22. The lineal-path function, also known as the connectivity function, reveals strong agreement between the average statistics of the training sets and the realizations (solid red and blue curves) along the x, y, and z directions, with a maximum absolute deviation of approximately 0.05. Comparatively, the soil prototype exhibited slightly higher connectivity. This comparison suggests that while the WGAN model realizations may not be flawless, they appear to surpass those generated by recent algorithms such as simulated annealing (SA) and multiple-point statistics (MPS) in terms of quality. Moreover, the WGAN model demonstrates superior efficiency when generating a large number of model realizations.
# Architecting and Deploying IoT Smart Applications: A Performance-Oriented Approach
## 2 Background and Related Work
### LPWAN Technologies for IoT  
In recent years, LPWAN technologies have been developed to meet the needs of IoT. LPWAN provides long transmission ranges, low energy consumption and low bandwidth [16], which make it an attractive technology for IoT applications that send a few dozens of bytes every couple of minutes or hours, such as street lighting, pollution monitoring and irrigation for agriculture-i.e., smart cities and smart agriculture scenarios. LPWANs follow a star topology, where sensors send data directly to a data hub called gateway, which has a stable energy source and Internet connection. Three LPWAN technologies are leading the forefront of this field: Narrowband IoT (NB-IoT) [31], Sigfox [32] and LoRaWAN [33]. NB-IoT, developed by 3 GPP, uses a subset of the LTE standard that limits the bandwidth to 200 kH, thus using cellular base stations for communication with devices. Sigfox uses unlicensed ISM bands to connect end-devices to base stations based on patented technologies.  
LoRa operates in the sub-GHz ranges, such as 433 MHz and 868 MHz (Europe) and 915 MHz (USA and Brazil) [17]. The physical layer is called Long Range (LoRa), and the upper layers are called LoRaWAN. The latter is standardized by the LoRa Alliance [33] and can reach distances of some kilometers with a bandwidth up to 50 Kbps and a typical payload of fewer than 100 bytes. The LoRaWAN architecture comprises LoRa end-nodes (i.e., sensors and actuators) and LoRa gateways that have an active IP connection and forwards LoRa packets to a centralized server. The server is further divided into network server (deals with network issues) and application (App) server, dealing with different applications. LoRaWAN defines an ALOHA-based media access control protocol on top of LoRa communication, as well as providing security features such as authentication and cryptography.  
There are different ways of implementing the LoRaWAN architecture: (a) Open source software that can be installed in a cloud datacenter or fog according to the scenario, such as ChirpStack [34], previously called LoRa Server; (b) The Things Network (TTN), a crowdfunded online community that implements a LoRaWAN server and makes them available as a service (some services are not free, though) [35]; (c) Proprietary solutions, such as Loriot [36]; (d) The development of a new implementation that means in practice reinventing the wheel.