esa-sceva/satcom-chunk-collection huggingface dataset:
# An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector
## 4 Results and Discussions
### Validation  
After setting the various parameters within the MiniSom Function, it is necessary to initialize the weights and start training the network. The weights associated with each output node are initialized randomly to small values close to zero, and the number of iterations is set to 10,000. Before visually studying the output map, it is necessary to validate the network according to the quantization and topographic errors. The quantization error allows us to assess the quality of network learning and shows how well the map fits the source data. This parameter is calculated by determining the average distance of the sample vectors to the BMU. The closer the error is to zero, the higher the quality of the map and the more correctly arranged the output data. Topographic error defines the percentage of vectors for which the first and second BMUs are not adjacent. A sample for which these two nodes are not adjacent counts as an error. If the topographic error is 0, no error has occurred. If it is 1, the topology was not preserved for any of the samples, and relationships from the input dataset are lost in the output map. As shown in Figure 8, both parameters tend to zero so the well-trained network preserves in the output map the topological relationships present in the source dataset.  
Before studying the results obtained from projecting the input data into the output map, it is essential to analyse the U-Matrix (Unified Distance Matrix). This matrix contains the various Euclidean distances between the input data and its associated weight. Through the study of the U-Matrix, an initial analysis of the potential clusters in the map is possible. In Figure 9a, the U-Matrix is represented using a heat map. If the node's colour is lighter, that node has similar properties to neighbouring nodes. On the other hand, if the colour is dark blue, that node has different characteristics from the neighbouring node. Eight clusters are visible while doing an initial qualitative study of the map's colour tones, which the artificial intelligence programme projects the input data into.  
Figure 8: Parameters for network validation: (**a**) quantization error; (**b**) topographic error.
# Meander Structure Analysis Techniques Using Artificial Neural Networks
## 2 Materials and Methods
### Structure of the Time Delay Neural Network  
Due to the generalization and model flexibility features, the time delay neural network (TDNN) is capable of generalizing from historical data, allowing it to make predictions even when the exact input-output relationships are complex and nonlinear. The buffer size, which is the size of the input sequence window, can have an impact on the accuracy of the TDNN's predictions. The size of the buffer determines the amount of historical data that the TDNN can take into account when making predictions. It is especially important when characteristics vary dynamically.  
The general TDNN equation is presented in Equation (3):  
\[Y(t)=f(x(t),x(t-\tau),\ldots,x(t-N_{d}\times\tau),w), \tag{3}\]  
where \(x=[x_{1},x_{2},\ldots x_{m}]\) represents the vector of the input signals, \(Y=[Y_{1}Y_{2}\ldots Y_{n}]\) represents the output vector of the output signals, \(N_{d}\) represents the total number of the delay steps, \(\tau\) represents the time delay parameter, and \(w\) represents the internal weight of the neural network. Here, \(m\) is the number of input signals, and \(n\) is the number of output signals.  
The base of the TDNN network consists of the previously discussed FFNN network. The same suitable FFNN structures were used in the TDNN network. The (6-9-11-1) structure was used for the prediction of \(t_{d}\). The \(S_{11}\) and \(S_{21}\) characteristics were predicted with the (6-9-16-1) structure. The presented TDNN predicted the \(t_{d}\) characteristic (Figure 4), where \(x\) represents the vector of input parameters \(x=[L_{c},N,l,2a,c,f]\) and \(Y\) represents the vector of \(Y=[t_{d}]\) in the output. In this particular case, \(m=6\) and \(n=1\).  
The only difference in this case is that the TDNN had time buffers, and the input vector with the parameters \(x=[\begin{array}{cc}L_{c}&N&l&2a&c&f\end{array}]\) would be provided to the network repeatedly in different time steps. The size of the time delay buffer \(N_{d}\) would vary during the investigation in order to obtain more accurate predictions of \(S_{21}\), \(S_{11}\), and \(t_{d}\). The same dataset would be used for the FFNN was used here.  
During training, the MSE would be calculated in order to analyze the influence of the time delay buffers. Verification would be performed with the test dataset. The range of the test data was within the range of the training data. The predictions of \(S_{21}\), \(S_{11}\), and \(t_{d}\) using the TDNN and FFNN would be compared.  
Figure 3: The (6-9-11-1) structure of the FFNN network for prediction of \(t_{d}\).
## 3 Materials and Methods
### Statistical Error Analysis  
The statistical error analysis of the frequency scan measurements at 6 GHz, 7 GHz, and 8 GHz in the horizontal and vertical polarization is presented in this section. The errors include the mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), relative absolute error (RAE), and the mean squared error (MSE) as presented in Tables 6-11. In particular, Table 6 shows the error analysis for the estimated normal distribution (Y) and the estimated fitness curve (F). The first five columns describe the error in the PDF, and the last five columns describe the errors in the fitness curve of the 6 GHz horizontal polarization. Table 7 presents the error analysis for the estimated normal distribution (Y) and the estimated fitness curve (F). The first five columns describe the error in the  
7 GHz horizontal polarization scan for all sites after denoising and applying the algorithm to all sites, including those without the potential to cause interference.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Sites** & **MAE\_Y** & **RMSE\_Y** & **MAPE\_Y** & **RAE\_Y** & **MSE\_Y\((\times 10^{4})\)** & **MAE\_F** & **RMSE\_F** & **MAPE\_F** & **MAE\_F** & **MSE\_F\((\times 10^{4})\)** \\ \hline A & 80.63 & 98.93 & 1.6353 & 0.1190 & 0.9784 & 69.27 & 91.10 &  
Additionally, Figure 44 reports the 7 GHz vertical polarization scan for all sites after denoising and applying the algorithm to all sites, including those without the potential to cause interference. Figure 45 presents the 8 GHz horizontal polarization scan for all sites after denoising and applying the algorithm to all sites, including those without the potential to cause interference. Finally, Figure 46 gives the 8 GHz vertical polarization scan for all sites after denoising and applying the algorithm to all sites, including those without the potential to cause interference.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Sites** & **MAE_Y** & **RMSE_Y** & **MAPE_Y** & **MAE_Y** & **MSE_Y\((\times 10^{4})\)** & **MAE_F** & **RMSE_F** & **MAPE_F** & **MAE_F** & **MSE_F\((\times 10^{4})\)** \\ \hline A & 109.19 & 147.68 & 1.9333 & 0.1324 & 2.1811 & 110.38 & 137.71 & 1.5699 & 0.1
## 3 Semantic Mapping via Segmentation
### Depth Segmentation  
The methods discussed until now focus on features that change along the vertical dimension of the object. For this reason, the key was to distinguish among different vertical layers, either in specific ranges (for height segmentation) or individually (for angle segmentation). We now look into objects that maintain the same structure throughout their vertical extension but that manifests a characteristic depth profile. In particular, we are interested in identifying objects like doors or cabinets.  
For the sake of simplicity, we focus here on the detection of doors, but the method may apply to other objects with similar characteristics. In this case, it is possible to identify the reference top and bottom heights of a door and points above it, or below (i.e., reflected on the floor), can be discarded. All the layers in between can then be merged, throwing away the height information and preserving only the nearest points for each horizontal angle step, as done in the height segmentation. Again, clusters of consecutive points with a distance smaller than \(D_{max}\) are computed. We visit all points ordered by a horizontal angle and calculate the Euclidean distance (on the XY plane) with the next point. If this distance is greater than \(D_{max}\), the cluster is closed and a new one is opened. In the case of a door, for instance, the value of \(D_{max}\) needs to be chosen so that it is smaller than the width of the door frame. Otherwise, all significant points would be fused into the same cluster, making the definition of the object more complex.  
An example of this segmentation method for the detection of doors is presented in Figure 8. First, the point-cloud is sectioned in clusters and the distance between each pair of clusters is computed. This distance is the Euclidean distance on an XY plane, from the last point of one cluster (red circles in Figure 8c) to the first point of the next cluster (blue circles in Figure 8c). If those distances are within the tolerance interval defined by a minimum object width (\(OW_{min}\)) and maximum object width (\(OW_{max}\)), there is a match, and the middle point between the corresponding clusters is computed (yellow circle in Figure 8c) and labeled as the position of the detected object.  
Figure 7: Angle segmentation: (**a**) real scenario with different models of chairs; (**b**) point-cloud with \(\theta_{l}\), \(\theta_{m}\) and \(\theta_{h}\) highlighted.
## 1 Introduction
### Contribution  
Conforming to the challenges mentioned above; energy constraints, network stability, traffic congestion, and link failure of the device play an essential determinant to establish an optimal route and it has to be addressed accordingly to enhance the overall network performance. The challenges can be demonstrated through the application of an effective routing decision technique. However, the other well-known existing routing approaches have been focusing on only two or three metrics factors of the devices (two or three combinations of metrics, i.e., device mobility, energy consumption, link quality, or queue length size) for route computation. As per the author's best knowledge, this  
Figure 3: Basic structure and framework of the proposed energy, mobility, queue length, and link quality-aware routing (EMBLR) routing scheme.  
study is the first to integrate all the parameter metrics in one single metric for the route computation process by using a Backpressure algorithm to evaluate the performance of the D2D communications in a large-scale IoT 5G network. Therefore, this paper proposes a multicriteria-aware optimal EMBLR routing approach to enhance the D2D communications in a large-scale IoT 5G networks. Furthermore, the proposed EMBLR routing approach does not introduce extra control overhead packets in the network, as a consequence, it decreases the complexity of the route computation algorithm. Thus, simplicity and applicability are the vital features of the proposed EMBLR routing approach for reliable data transmission as compared to the other well-known existing routing approaches. The contributions of the paper can be summed up as follows:  
1. The proposed EMBLR routing approach utilizes the functionalities (hybrid and Dijkstra algorithm multi-path concept of routing) of the well-known existing routing approaches and modifies accordingly for the full combination of device parameter metrics, i.e., mobility, energy consumption, link quality, and queue length size of the devices for the route selection procedure.
2. We evaluate the energy consumption, mobility, queue length size, and link quality of the intermediate devices between source and destination devices for the topology sensing and route discovery process.
3. We apply the MCDM multicriteria decision mechanism for the selection of an optimal route among the multiple routes, which provides weight to the intermediate devices based on the estimated value.
4. We study the QoS performance metrics of the proposed EMBLR routing approach and compare it with the well-known existing routing approaches under various device speeds for the D2D communications in large-scale IoT 5G networks.  
The rest of the paper is organized as follows. Section 2 describes the system model of the proposed framework, followed by results and discussion obtained through extensive simulation in Section 3. Finally, the conclusion and future research of the proposed work are presented in Section 4.
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 8 Conclusions  
In this paper, a secure certificateless group authentication scheme for D2D communication is presented, the user activities data from smartphone sensors are analyzed for continuous authentication as well. The proposed scheme is designed for particular D2D scenario intended for public safety or field trip application for extreme environments, where the smartphone acts as the UEs for every terminal user. Note that most of the user equipments (UEs) are out of the effective range of cellular networks. Consequently, an efficient group key distribution method is constructed, which drastically reduces the communication cost compared with conventional one-to-one key distribution. In this case, the group key updating mechanism only requires a small modification in the SN side, while the decrypting information in the UEs side remains constant as soon as the UEs are validated. Additionally, continuous authentication strategy adopting smartphone sensor behavior analysis is adopted, where the unique user behavioral data acquired by accelerometer and gyroscope sensors in the smart phone (UE) is processed and characterized by time and frequency domain features. Continuous authentication is performed with the adopted biometric parameter periodically. Security and performance analysis demonstrate that the proposed scheme can yield desired security properties towards various attacks. The proposed design is efficient compared with state-of-the-art D2D authentication schemes.  
Conceptualization, H.T. and I.C.; Methodology, H.T.; Formal analysis, H.T.; Investigation, S.X. and S.P.; Writing--Original Draft Preparation, H.T.; Writing--Review and Editing, H.T. and Y.S.; Supervision, I.C. This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean Government under Grant NRF-2017R1D1A3B03034005 and by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF-2017R1A6A1A03015496). The authors declare no conflict of interest.
## 3 Results and Discussion
### Retrofitted Joints
#### 3.2.2 Joint T2 (Brace Overall Buckling)  
The axial load-displacement curve of the non-retrofitted T2 joint assuming large deformation is shown in Figure 8. The critical buckling load of the non-retrofitted T2 joint is about 120 kN, and the buckling happens out of the plane of the joint as shown in Figure 3b. The joint behaves linearly until the buckling point where the softening initiates and continues to the failure. Unlike the chord ovalization mode, no hardening section is observed in the post-buckling behavior under this mode of buckling.  
In order to improve the buckling behavior, various brace-retrofitted length and ply distributions of FRP wrapping were modeled. In all of the retrofitted cases, four plies with the distribution of (0/90)s were applied to the chord. The obtained axial load-displacement curves are compared in Figure 9. When the brace was retrofitted with 8 plies with various orientations (Figure 9a), the retrofitting hardly increased the critical load; it caused just about 8% improvement for (90/90)2s distribution. It means that the fibers should be in the direction of the brace longitudinal axis. This low increase is because increasing the moment of inertia by FRP wrapping is insignificant. The same buckling mode was observed for the retrofitted joint.  
Figure 6: The effect of (**a**) ply distributions, and (**b**) number of plies, on the axial load-displacement curve of the retrofitted T1 joint.  
Figure 7: Failure of the fiber-reinforced polymer (FRP) patches in the retrofitted T1 joint (the chord ovalization buckling mode). Composite failure modes: (**a**) fiber compression; (**b**) fiber tension; (**c**) matrix compression; (**d**) matrix tension.  
Investigation of the brace retrofitted length with 8 plies of (90/90)2s distribution shows that the use of FRP composite with the length of equal or less than half of the brace length may improve the post-buckling behavior by decreasing the effective buckling length. Longer lengths may partly increase the critical load by increasing the moment of inertia. In general, the FRP retrofitting cannot significantly improve the post-buckling behavior of the CHS T-joints, if the governing buckling mode is the brace overall buckling.  
The composite FPR failure under the brace overall buckling is shown in Figure 10. In this buckling mode, the matrix tension and compression failures are the most possible FRP failure because of the inherent weakness of the matrix behavior, especially under tension loads. Initiation of the matrix tension failure happens in 40% of the buckling load. This failure initiates at the Crown Point, and by increasing the displacement, this failure zone may propagate to upper parts of the brace composite patch.
# LoRa Enabled Smart Inverters for Microgrid Scenarios with Widespread Elements
## 3 LoRaWAN Specifications and Applications
### LoRaWAN MAC Protocol  
LoRa modulation, which represents the physical layer of LoRaWAN infrastructure, is a proprietary technology. Although it is patented, its theoretical and mathematical definitions have not been made available to the public in detail. By contrast, LoRaWAN, which defines the network architecture as a MAC layer protocol, is open-source [51].  
A LoRaWAN system consists of three major players. End-nodes which are the low-power transceivers, gateways, and network servers [7]. Since LoRa end-nodes are connected to gateways in a star configuration, similarly for the gateway-server connections, LoRa topology is sometimes called star-of-stars [13]. In this network arrangement, only the wireless bidirectional node-gateway links are of LoRa/LoRaWAN type and the gateway  
Figure 2: Spectrogram capture of LoRa frames with different spreading factors.  
server connections are created by utilizing other (higher bandwidth) communication solutions, Figure 3. Furthermore, in some configurations, an end node may be placed in a position that is in the range of more than one gateway. In such cases, it is the network-server's duty to detect and mitigate the problem to avoid packet duplication.  
LoRaWAN MAC protocol is based on pure ALOHA, which means the nodes start transmitting whenever they have a message ready [52].
# Demodulation Technique Based on Laser Interference for Weak Photo-Acoustic Signals on Water Surface
## 3 Results
### Amplitude Measurement Results  
The spectrum of interference signals was analyzed first. The amplitude was derived directly with characteristic ratio method at high frequencies. In the case of low frequencies, high-frequency carriers were added to the reference optical path, and a signal generator was used to create 10 kHz high-frequency signals, which were fed to the reference speaker in the pool to excite carrier waves. This was followed by demodulation with improved PGC demodulation method. The test procedure covers all frequency bands between 200 Hz and 10 kHz, each corresponding to five repeated measurements. Some results are given in Table 3. For 500 Hz low-frequency signals, the modulation depth \(H\) was first estimated from the spectrum, and then the initial phase was obtained with arc tangent method, finally leading to the amplitude demodulation results. For 3-kHz high-frequency signals, after extraction of low-frequency bandwidth, the sum of spectrum amplitudes was calculated. Next a ratio of this sum to that of spectrum amplitudes after a left frequency shift by \(\omega_{s}\) was calculated, which is known as the characteristic ratio \(R\). In the last step, the water surface acoustic wave amplitude was determined with the look-up table \(A_{s}(R)\).  
Figure 8: Errors based on extraction of central frequency points in the spectrum.  
\begin{table}
\begin{tabular}{c c c c c} \hline _f_/**Hz** & \(H\) (\(A_{e}\) = 39.28 nm) & \(\theta_{e}\)/**rad** & \(A_{s}\)/**nm** & \(\sigma\)/**nm** \\ \hline \multirow{4}{*}{500} & \multirow{4}{*}{0.39} & 0.628 & 48.59 & \multirow{4}{*}{1.63} \\ \cline{3-3}  & & 1.309 & 50.03 & \\ \cline{3-3}  & & 0.211 & 51.77 & \\ \cline{3-3}  & & 0.785 & 53.42 & \\ \cline{3-3}  & & 1.553 & 51.22 & \\ \hline \multirow{4}{*}{3000} & \multirow{4}{*}{**Sum of Low/V**} & **Sum of high/V** & \(R\) & \(A_{s}\)/**nm** & \(\sigma\)/**nm** \\ \cline{3-3}  & & 3.157 & 1.044 & 3.023 & 31.64 \\ \cline{2-3}  & & 2.684 & 0.861 & 3.117 & 30.78 \\ \cline{1-1} \cline{2-3}  & & 2.551 & 0.854 & 2.986 & 32.00 \\ \cline{1-1} \cline{2-3}  & & 4.003 & 1.408 & 2.843 & 33.44 \\ \cline{1-1} \cline{2-3}  & & 3.996 & 1.453 & 2.750 & 34.44 \\ \hline \end{tabular}
\end{table}
Table 3: Some amplitude measurement results.  
The standard deviations of amplitudes measured at different frequency bands are given in Figure 9. The figure implies a lower standard deviation of the demodulated signals at high frequencies. For low-frequency signals, due to errors arising from estimation of carrier characteristic ratio and PGC demodulation, the repeatability was lower, but still within the limit of 2.5 nm. In sum, the amplitude measurements in our study provide satisfactory repeatability.
## 3 Regulatory Mechanisms Controlling AHL Synthesis in _Sinorhizobium meliloti_
### Transcriptional Mechanisms  
#### 3.1.1 Autoregulation  
The Sin system is subject to autoregulation through positive and negative feedback mechanisms which are mediated exclusively by ExpR, without the requirement of additional regulators [34]. The autoregulatory activity of ExpR depends upon the presence of AHLs which assist the strength of DNA binding [51]. AHL-activated ExpR induces _sint_ through a binding site upstream of _sint_ and inhibits _sinR_ through another binding site upstream of _sint_. Moreover, AHL-activated ExpR activates _expR_ expression weakly. Considering that _sint_ expression is dependent on levels of SinR and that activation of _sint_ expression requires lower levels of AHL than _sinR_ repression (1 nM _vs_ 40 nM), AHL and ExpR levels will determine the dominance of the positive or negative feedback. Thus, at low AHL levels a positive feedback loop activates expression of _sint_, leading to increased AHL levels. At high AHL levels, repression of _sint_ and therefore reduction of _sint_ expression takes place. As the AHL levels increase, the positive autoregulation by AHL-activated ExpR increases ExpR levels, which leads to a stronger repression of _sint_, and the corresponding decrease in _sint_ expression and AHL levels.