esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modelling and Methodology  
### Wind Turbine and Wind Farm Models  
The real wind farm model studied in this paper is an offshore wind farm situated in Northern Europe. According to the reference [20], the Bonus \(1\) MW wind turbine was installed in the wind farm and the wind turbine properties are shown in Table 1 (\(P_{i}\) is the individual wind turbine power output and \(v_{i}\) the incoming wind speed).  
According to the reference [20], the target wind farm has an irregular boundary shape as indicated in Figure 1. According to the wind farm boundary features, it generally consists of two portions: the left portion of a curved boundary line and the right portion of a polyline boundary. In order to increase the accuracy of mathematical representation of the left portion boundary line, it was further divided into two interpolated curves of \(y_{1}(x)\) (blue color in the figure) and \(y_{2}(x)\) (green color in the figure) with polynomial approximation. The right portion polyline \(p\) ((X1, Y1), (X2, Y2),..., (X7, Y7)) was defined by vertex points, which have been marked by the red points in Figure 1. Assuming a given wind turbine has an \(X\)-axis coordinate of \(x_{i}\), the feasible \(Y\)-axis coordinate interval that is located inside the wind farm boundary can be represented by:  
\[C(x_{i})=\{z|y_{L}(x_{i})\leq z\leq y_{U}(x_{i})\} \tag{1}\]  
where \(y_{L}(x_{i})\) and \(y_{U}(x_{i})\) are the lower and upper bounds at coordinate \(x_{i}\), and they are determined by the mathematical expression of the wind farm boundary. The feasible \(y\) coordinates are the union of all feasible \(Y\)-axis coordinate intervals as given by:  
\[Y(X,i)=\cup C(x_{i})\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline Rated power & 1000 kW \\ Rotor diameter & 54 m \\ Hub height & 60 m \\ Cut-in wind speed & 3 m/s \\ Rated wind speed & 15 m/s \\ Cut-out wind speed & 25 m/s \\ Cubic power curve & \(P=0.2963v_{i}^{3}\) kW \\ \hline \end{tabular}
\end{table}
Table 1: Properties of the wind turbine model for the real offshore wind farm.  
where \(X=[x_{1}y_{1}\ x_{2}y_{2}\ldots x_{N}y_{N}]\) consists of \(x\) and \(y\) coordinates of \(N\) number of wind turbines and has a total of 2\(N\) variables. The viable wind farm layout must guarantee all wind turbines are positioned among the feasible intervals, which meets:  
\[\sum_{i=0}^{N}\phi(X,i)=0 \tag{3}\]  
The term is further defined by:  
\[\phi(X,i)=\left\{\begin{array}{l}1\ y_{i}\
otin Y(X,i)\\ 0\ y_{i}\in Y(X,i)\end{array}\right. \tag{4}\]  
where \(y_{i}\) is the wind turbine \(Y\)-axis coordinate, \(Y(X,i)\) is the feasible \(Y\)-axis coordinate intervals defined in Equation (2).
# Formulation of Alkali-Activated Slag Binder Destined for Use in Developing Countries
## 3 Results
### Thermogravimetric Analysis (TGA/DTG)  
To simplify the microstructural analysis, only a specific Na\({}_{2}\)O percentage was chosen. The chosen pastes were SH 4, SC 2 and SHC 1/1.5. They all had a Na\({}_{2}\)O weight percentage of 4.96% in relation to the BFS. Paste SHC 1/1.5 was chosen because it had the highest mechanical strength of its series. The DTG curves at the different curing ages are represented in Figure 7. On the DTG curves, three mass loss intervals were selected: (a) the first interval was below 300 \({}^{\circ}\)C and showed two peaks. Peak 1 can be attributed to the loss of the combined water of CSH gel and peak 2 to the loss of the combined water of the CASH gel [19; 33; 34]. The main peak was the CSH gel; (b) the second was 300-500 \({}^{\circ}\)C, in which peak 3 developed (attributed to the decomposition of the hydrotalcite phase [19; 33]); (c) the last interval was between 500 and 1000 \({}^{\circ}\)C (it contained peaks 4 and 5) and was attributed to carbonate phases like calcium and sodium carbonates or carboaluminates [19].  
As shown in Figure 7, peaks 1, 2 and 3 appeared at practically all the curing time and for all the activators, but with different heights and width peaks. Peaks 4 and 5 were more visible for series SC and SHC that contained Na\({}_{2}\)CO\({}_{3}\) as an activator. The pastes with CO\({}_{3}\)-2 ions produce compounds of type carboaluminate (C\({}_{3}\)A.CaCO\({}_{3}\).12H\({}_{2}\)O) [20]. The partial and total mass losses for all the studied pastes are summarized in Table 3 for the different curing ages. The mass loss in the interval 50-300 \({}^{\circ}\)C presented a great evolution between 7 and 28 curing days; these data confirm that the most developed products within this time interval are CSH and CASH gel.  
The study of mass loss within different temperature intervals corroborated the more marked presence of the carbonated phases in pastes SC and SHC. The mass loss derived from the hydrotalcite phase was greater in paste SH. The total mass loss increased with curing time and demonstrated the hydration reaction evolution.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Paste**} & \multirow{2}{*}{**Age (Days)**} & \% Mass Loss & \% Mass Loss & \% Mass Loss & \% Mass Loss \\  & & **50–300 \({}^{\circ}\)C** & **300–500 \({}^{\circ}\)C** & **50–1000 \({}^{\circ}\)C** & **50–1000 \({}^{\circ}\)C** \\ \hline \multirow{3}{*}{SH 4} & 7 & 9.88 & 1.89 & 1.34 & 13.11 \\  & 28 & 12.19 & 2.60 & 2.03 & 16.82 \\  & 90 & 12.25 & 3.12 & 2.10 & 17.47 \\ \hline \multirow{3}{*}{SC 2} & 7 & 8.72 & 2.06 & 3.81 & 14.59 \\  & 28 & 12.94 & 2.29 & 3.73 & 19.10 \\  & 90 & 12.04 & 2.73 & 4.59 & 19.36 \\ \hline \multirow{3}{*}{SHC 1/1.5} & 7 & 7.83 & 2.31 & 3.42 & 13.56 \\  & 28 & 13.98 & 2.46 & 3.32 & 19.80 \\ \cline{1-1}  & 90 & 12.15 & 3.02 & 4.46 & 19.64 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mass loss as a percentage for the thermogravimetry (TG) curves for the pastes cured for 7, 28 and 90 curing days.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 2 Overview of Smart Grid
### Smart Grid Infrastructure
#### 2.1.1 Smart Grid Domains
The transmission domain typically consists of transmission lines, substations, energy storage systems, and measurement and control systems. The transmission system is typically monitored and controlled through a Supervisory Control And Data Acquisition (SCADA) system which communicates with field and control devices throughout the transmission grid [12; 13]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Domain** & **Communication Interface** & **Electrical Interface** \\ \hline Market & Service provider, Operations, Generation, Transmission, Distribution, Customer & None \\ \hline Operations & Markets, Service provider, Transmission, Distribution, Customer, Generation & None \\ \hline Service provider & Markets, Operations, Customer, Distribution, Generation & None \\ \hline Transmission & Markets, Operations Generation, Distribution & Generation, Distribution \\ \hline Distribution & Operations, Transmission, Customer, Service Provider & Transmission, Customer \\ \hline Customer & Markets, Operations, Service provider, Distribution & Distribution, Generation \\ \hline Generation incl. DER & Markets, Operations, Transmission, Customer & Transmission, Customer \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Smart Grid domains, electrical and communication interface [23]. * **Distribution incl. DER Domain:** This domain is the connection between the transmission and the customer domain. The distribution domain may include DERs located at customer or at grid operator. In a SG environment, the distribution domain communicates with the market domain due to the market domains potential to affect local power consumption and generation [12; 13; 14]. * **Customer Domain:** The customer or end-user could be private, commercial or industrial. In addition to consume the energy, the customer could also generate and feed the grid with excess energy or stored energy. In cases where the customer generate and deliver energy consumer is referred to as a prosumer [14; 26]. Reliable communication is required for information exchange between the different domains to ensure reliable operations of the power grid and its applications. Similar to NIST in the US, in Europe, the Smart Grid Coordination Group defined its Smart Grid Architecture Model [11; 27; 28]. There are similarities between the two models, the domains are the same. In addition to domains this model is also divided in layers and zones. This is a three dimensional model consisting of five interoperability layers (Business, Function, Information, Communication, and Components). The two dimensions are divided in domains (Generation, Transmission, Distribution, DER, and Customer Premises), and zones (Process, Field, Station, Operation, Enterprise, and Market). Table 3 shows the different layers, dimensions and zones of SGs.
# Wi-Fi Fingerprint-Based Indoor Localization Method via Standard Particle Swarm Optimization
## 3 System Overview
### SPSO Algorithm for Localization  
To obtain the optimal predicted location of \(Fint_{test\ i}\), the optimal value of the parameter \(K\) and the coordinate \((x,y)\) must be solved. The SPSO algorithm can be used for this purpose. The target fitness function is defined by Equation (18). \[f(x,y,K)= \sqrt{\left(x-\sum_{k=1}^{K}\frac{\omega_{\text{Euc}\ k}}{\sum_{k=1}^{K} \omega_{\text{Euc}\ k}}x_{train}^{sim_{\text{Euc}\ k}}\right)^{2}+\left(y- \sum_{k=1}^{K}\frac{\omega_{\text{Euc}\ k}}{\sum_{k=1}^{K}\omega_{\text{Euc} \ k}}y_{train}^{sim_{\text{Euc}\ k}}\right)^{2}} \tag{18}\] \[+\sqrt{\left(x-\sum_{k=1}^{K}\frac{\omega_{\text{cos}\ k}}{\sum_{ k=1}^{K}\omega_{\text{cos}\ k}}x_{train}^{sim_{\text{Euc}\ k}}\right)^{2}+\left(y-\sum_{k=1}^{K} \frac{\omega_{\text{cos}\ k}}{\sum_{k=1}^{K}\omega_{\text{cos}\ k}}y_{train}^{ sim_{\text{cos}\ k}}\right)^{2}}\]  
At each iteration, the minimum fitness value of the particle and particle swarm were determined. Finally, the optimal location estimation was obtained. The specific procedure is summarized in Algorithm 1. In addition, the mathematical model of the algorithm can be found at the link: [[https://github.com/Kiron666/SPSO_2P](https://github.com/Kiron666/SPSO_2P)]([https://github.com/Kiron666/SPSO_2P](https://github.com/Kiron666/SPSO_2P)) (accessed on 30 June 2022). ```
1:
2: The offline fingerprints data \(\mathbf{Fin}_{train}\) and the coordinates data \(\mathbf{RP}_{train}\);
3: The query fingerprint \(\text{Fin}_{query}\);
4:
5: The location estimation of the query fingerprint. 6: Offline data collection, and obtain the training data \(\mathbf{Fin}_{train}\), \(\mathbf{RP}_{train}\);
7: Obtain the query data \(\text{Fin}_{query}\);
8:
9:
10:
11:
12:
13:
14:
15:
16:
```  
The algorithm procedure of localization  
At each iteration, the minimum fitness value of the particle and particle swarm were determined. Finally, the optimal location estimation was obtained. The specific procedure is summarized in Algorithm 1. In addition, the mathematical model of the algorithm can be found at the link: [[https://github.com/Kiron666/SPSO_2P](https://github.com/Kiron666/SPSO_2P)]([https://github.com/Kiron666/SPSO_2P](https://github.com/Kiron666/SPSO_2P)) (accessed on 30 June 2022).
# Research on Coal Structure Prediction Method Based on Genetic Algorithm-BP Neural Network
## 2 Geological Overview
### Analysis of Logging Curve Characteristics of Coal Structure  
As the degree of coal seam fragmentation increases, the physical properties and chemical composition of different coal structures change and their physical properties such as porosity, permeability, and fracture density vary greatly. These changes have relative response characteristics on the logging curve.  
Experimental results from the preliminary project conducted by other research teams demonstrate that, in the coal seam, the more fractured the coal body is, the looser the coal quality is, and the more likely it is to expand the diameter. The well diameter curve reflects the change in the well diameter in the drilling process. The low mechanical strength of the formation will cause the collapse of the shaft wall and an increase in the well diameter log value. Since the radioactivity of coal itself is very weak, natural gamma logging reflects the content of impurities such as clay minerals in coal by measuring the content of radioactive elements in the formation. When the degree of coal seam fragmentation increases, the cracks in the coal seam become more developed and impurities such as clay minerals fill them, resulting in an increase in the natural gamma logging value. As the degree of coal seam fragmentation increases, the degree of development of its fracture system increases, the density decreases, and the acoustic time difference increases.  
In the relatively complete logging data of the parameter wells with coal core data in the study area, the acoustic time difference (AC), density (DEN), natural gamma (GR), and deep lateral resistivity (RD) curves were selected to conduct a correlation analysis between the coal structure and the logging curves. The results are shown in Figure 5; the linear relationship between coal structure and logging response is weak, and the logging response ranges for different coal structures exhibit significant overlap. The crossplot method is ineffective in distinguishing coal structures. However, neural networks excel in handling complex nonlinear tasks and can be utilized to capture the nonlinear relationship between  
Figure 4: Coal core photos of the target layer coal structure in the study area. (**a**) Primary-structure coal; (**b**) crushed-structure coal; (**c**) crushed coal.  
coal structure and logging response, enabling the differentiation of coal structures based on logging responses.
## 2 Related Works
An efficient load balancing optimization algorithm for the employment of energy effectual routing as well as a load balancing protocol were used to enhance the network lifetime [19; 20]. The authors in [21] explored the cluster-based backpressure routing algorithm for IoTs, which discusses the energy and congestion issues in IoT environment. A brief discussion is also presented related to the sustainable development through the Internet of Things in [22; 23]. A novel framework was proposed to handle environmental monitoring using wireless sensors connected with the internet, wherein two distinctive SNs were involved. Moreover, clients observed the information using the web application on the internet from anywhere. When the information of the SN surpassed the specified range, a notification was sent to theclients insisting on the environmental setup being altered accordingly [24; 25; 26]. Energy is a critical issue in WSNs and the IoT, specifically when deployed in smart city applications and this was discussed briefly in [27; 28; 29; 30]. Research Objectives  
1. To establish secure routing for confidential data of the IoT through sensor nodes with heterogeneous energy utilizing MLRP. 2. To optimize the energy and improve the network lifetime using a hybrid-based TEEN (H-TEEN) protocol that has load balancing capacity. 3. To improve the storage capacity using the ubiquitous data storage protocol (U-DSP).
# Energy Efficient Communications for Reliable IoT Multicast 5G/Satellite Services
## 6 Conclusions  
In this paper, the main challenges and requirements that lead to the integration of satellite and terrestrial networks are reviewed, where 5G could be the enabling technology towards the era of convergence. Hundreds of billions of smart devices connected to the 5G network will create a true\"Internet of Everything\" and, in this scenario, the integration of the satellite segment can provide global connectivity and multicast distribution or configuration applications transparent to the end users. Specifically, the NC schemes application to SatComm is addressed, pointing out the improvement in terms of delivery delay, network capacity saving and the reduction of packets erasures in multicast services. In addition, an energy efficient multicast satellite communications scheme is investigated in view of applications to M2M/IoT scenarios. To provide reliable content delivery to a group of remote devices the proposed approach jointly adopts: (i) a RLNC scheme; (ii) an optimized repetition of the OFDM data symbols over multiple subcarriers; and (iii) different modulations adapted on the channel status basis for minimizing the overall energy consumption needed to deliver the content to a group of devices in a reliable way. Remarkable performance in terms of energetic cost, scalability, adaptability and robustness is always pointed out with a significant delivery delay reduction, which indicates also a beneficial effect on the packet throughput.  
All authors contributed equally to the paper. [PERSON] mainly worked on the system architecture, while [PERSON] on system model and performance evaluation and [PERSON] on the state of the art.  
This research received no external funding.  
The authors declare no conflict of interest.
# Secure Transmission for Buffer-Aided Relay Networks in the Internet of Things
## 6 Conclusions  
In this paper, we propose three secure transmission schemes for buffer-aided relay networks in IoT. To take full advantage of buffer-aided relay, the max-link relay selection policy is adopted to enhance the secrecy performance by selecting the main link with the best rate. Furthermore, for each schemes, we also derive the exact expressions of the secrecy outage probability, the average secrecy throughput and the end-to-end delay in closed-form by utilizing the Markov chain theory under both the NCE and CE scenarios, respectively, which provides an effective way to evaluate the secrecy performance of each proposed scheme. Our numerical results indicate that, when the total power \(P_{Total}\) is small, the performance achieved by the SCJ-OPA scheme is similar to that of the NJ scheme. On the other hand, the SCJ-OPA scheme can also achieve almost identical performance as the SCJ scheme when \(P_{Total}\) is high. In other words, the SCJ-OPA scheme achieves better performance across the whole transmit power range of interest than the other two schemes, which is because the factor \(\alpha\) can be dynamically allocated under different total transmit power. These results could be useful in the design of buffer-aided relay IoT networks under multiple eavesdroppers scenarios.  
[PERSON] and [PERSON] conceived the model; [PERSON] performed the simulation results and wrote the paper; and [PERSON], [PERSON] provided some suggestions and revised the paper.  
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61771487 and 61371122.  
The authors declare no conflict of interest.
## 3 System Model and Basic Assumptions
### A&F MIMO Signal Model with Pre-Equalization  
Pre-Equalization algorithms treat all received signals as interference, except for the desired data symbols, such that the interference signals from other receiver antennas are minimized and nullified in the detection of the desired signal. In this subsection, we propose two structures for the A&F protocol based on the ZF and MMSE Pre-Equalization algorithms, considering the channel estimation of first-hop, as shown in Figure 3.  
Before the Pre-Equalization step, channel estimation should be performed. In this sense, channel estimation is utilized to increase the capacity of Orthogonal Frequency Division Multiple Access (OFDMA) systems [42], improving the system performance in terms of throughput, Bit Error Rate (BER), and other parameters that are employed to measure the quality of the system. Downlink channel estimation in LTE is carried out through the use of Cell-specific Reference Signals (CRS). Pilot symbols are inserted during subcarrier mapping in both time and frequency. Furthermore, CRS are used both for demodulation and feedback calculation.  
The resource blocks in LTE are allocated over M layers, K subcarriers, and L time slots. In Figure 3, the received signal described by (2) is considered. The matrix \(\mathbf{Y}_{\mathrm{AF}}^{1}\) is comprised of both data symbols and pilot symbols. Taking into account this assumption, \(\mathbf{Y}_{\mathrm{AF}}^{1}\) can be divided into two parts, the first corresponding to the pilot symbols (\(\mathbf{Y}_{\mathrm{AF}_{\mathrm{P}}}^{1}=\mathbf{H}_{\mathrm{P}}^{1}\mathbf{ X}_{\mathrm{P}}+\mathbf{N}_{\mathrm{AF}_{\mathrm{P}}}^{1}\)) and the second to the remaining data symbols (\(\mathbf{Y}_{\mathrm{AF}_{\mathrm{S}}}^{1}\)). The Least Squares (LS) channel estimator [32] is used to equalize the channel frequency responses at pilot locations sent from different transmitter at all receiver antennas, which can be formulated as follows:  
\[\hat{\mathbf{H}}_{\mathrm{P}}^{1}=\mathbf{X}_{\mathrm{P}}^{1}\mathbf{Y}_{ \mathrm{AF}_{\mathrm{P}}}^{1}. \tag{6}\]  
Once the frequency response of the position of the pilot symbols has been obtained, the channel response of data symbols position can be derived by interpolation employing the adjacent pilot symbols. For this, in this paper, we consider the Bi-Cubic Interpolation (BCI) method [33]. Therefore, in this second stage, \(\hat{\mathbf{H}}^{1}\) is obtained, as performed in [39].
# Remote Control Device to Drive the Arm Gestures of an Assistant Humanoid Robot
## 1 Introduction
### New Contribution  
The exact reproduction of human motion by robots is usually a challenging task because of the different proportions, degrees of freedom and joint ranges of humans and robots. Considering the mechanical configuration of the robot joints as a limitation, the new contribution of this work is the development of a low-cost remote control device that replicates the proportions and degrees of freedom of the arms of a humanoid robot. The advantage of this proposal is twofold: it simplifies the task of mapping the gestures for robot replication, and the remote telecontrol is easier because a human operator intuitively maps user input to output when controlling bioinspired devices [29; 30].  
The device allows a remote operator to define expressive and pointing arm gestures [31], whereas the humanoid robot can replicate such gestures. The device is composed of a mechanical structure with a static T-shaped configuration with two passive articulated arms attached that replicate the shape and degrees of freedom of the telecontrolled robot. The system registers the angular position of each joint using potentiometers and this information is mapped and transmitted to the robot. Then, the robot arms mimic the angular positions of the joints defined in the remote controller.  
The paper is structured as follows. Section 2 describes some background on remote robot control. Section 3 describes the humanoid mobile robot that is remotely controlled. In Section 4, the remote controller is presented in a detailed manner. Section 5 presents the experiments performed to validate the remote controller. Final remarks are given in Section 6.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Orbital Angular Momentum Antennas
#### 3.4.3 OAM Selection  
Selecting the usage of OAM waves is a crucial adaptation for the new era of communications due to their benefits. Such improvements and updates on our existing systems can be way cheaper than facilitating other new methods (ref. [81]). In addition, the OAM method can coexist with other state-of-the-art methods, such as gMIMO and metasurfaces, which also guarantee high purity between OAM modes (ref. [88]). That said, we can easily anticipate that OAM is for sure one of the basic systems that will be used in the next generation of communication systems.