esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Machine Learning-Statistical Method for Anomaly Detection in Flight Data
## 2 Literature Review
[11] used a real-world dataset of a commercial passenger jet, and their work was focused on the descent phase of the flight. This work, based on iOrca [12], which is the scalable or indexed version of Orca [13], was developed by ([PERSON] and [PERSON], 2003). Similar to our work, Orca is also based on the k nearest neighbor algorithm. Pairwise distance is calculated in the Orca, whereas in the method proposed in this paper, distance is measured between a data point and its neighborhood to calculate the density of that data point. The performance of iOrca and MKAD was studied by [PERSON] et al. [14]. An anomaly score was given by both methods. Scoring anomalies helps to distinguish between two anomalous data points. The whole flight was labeled as normal or abnormal by MKAD, whereas iOrca was able to point out the location of the anomaly as well. MKAD performed better for a group of flights, whereas iOrca was better for individual flights  
The work conducted by [PERSON] and [PERSON] [15] used a similar algorithm as proposed in this paper. Local outlier probability (LoOP) is a method derived from the local outlier factor algorithm, which is the basis of this paper. LoOP is less dependent on parameter k of the k nearest neighbor. However, this paper discussed a method to decide the optimal value of parameter k. Moreover, the work conducted by [PERSON] and [PERSON] [15] differs in the way raw data are processed and how the flight data are input into the algorithm. Anomaly detection in aircraft operations using a self-organizing map neural network (SOM NN) was performed by [PERSON] et al. [16]. It is a neural network-based unsupervised method with two layers known as the input layer and output layer. The input layer is a vector representation of the data, and the output layer presents the input data in a self-organized manner. SOM NN transforms data points in high dimensions into two-dimensional space. This reduction in dimensionality helps in further analysis of the data as the structure of the dataset is better visualized. Thus, SOM clusters similar data points, and the anomalous data points are discovered easily. Deep Learning algorithms such as recurrent neural networks (RNNs) can handle long sequences of multivariate time series data without any dimensionality reduction and can detect anomalies in hidden features as well [17]. RNNs can have long short-term memory (LSTM) cells or gated recurrent units (GRUs). A key attribute of RNNs is their ability to persevere information for later use in the network. This makes RNN more suitable for the analysis of time series data, such as flight data that changes over time. After reviewing the existing work on flight data analysis, the following section focuses on the local outlier factor technique and its applications to flight data analysis.
## 2 MG Concept and Requirements
### MG Structure
In distributed fashion, each element has been considered to be an agent which independently decides to participate in SG or MGMS. This offers advantages such as plug-and-play patterns, no need for a dedicated communication infrastructure, fast response to system failure, and power grid consisting of independent entities [29; 30]. Moreover, due to the significant advantages of MGMS characteristics when deploying MAS, trends toward the implementation of SG in the form of multi-MG have been augmented recently. It is estimated that future SG would be multi-MG that works in the MAS environment [31; 32]. Figure 1 proposes the control level of individual MG without centralized and decentralized approach. The local controller in this figure can be considered to be located in EMS, or distributed in each element. Figure 1: MG architecture.
# Development of a Low-Cost Automated Demand Response Controller for Home Energy Management
## 2 Materials and Methods
### Demand Response Controller  
The demand response controller is implemented with an Arduino. Figure 4 shows the system architecture. It consists of four modules: a smart socket module, an infrared module, a temperature sensor, and a voltage/current module. Figure 5 shows the architecture diagram of the voltage/current module where the microcontroller (MCU) takes the voltage and current signals measured by voltage and current sensors and calculates the root mean square (RMS) values of voltage and current of the load, the real power, and the energy consumption, as per Equations (1)-(4).  
\[V_{rms}=\sqrt{\frac{\sum_{n=1}^{149}V(n)^{2}}{149}}\ Volt \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Request Payload** & **Response Payload** & **Requester** & **Responder** \\ \hline oadrQueryRegistration & oadrCreatedPartyRegistration & VEN & VTN \\ \hline oadrCreatePartyRegistration & oadrCreatedPartyRegistration & VEN & VTN \\ \hline oadrCancelPartyRegistration & oadrCancelPartyRegistration & VEN & VTN \\ \hline oadrRequestRegistration & oadrResponse & VTN & VEN \\ \hline \hline \end{tabular}
\end{table}
Table 2: EiRegisterParty data carrier.  
Figure 3: OpenADR Event signal timeline versions [18].  
\[I_{rms}=\sqrt{\frac{\sum_{n=1}^{149}{I(n)}^{2}}{149}}\ A \tag{2}\]  
\[P_{nxe,i}=\frac{\sum_{n=1}^{149}{V(n)}\times I(n)}{149}\ W \tag{3}\]  
\[E=\left(\sum_{i=1}^{60}{P_{nxe,i}}\right)\times\frac{1}{60}\ W.s \tag{4}\]  
where \(V(n)\) and \(I(n)\) are the sampled voltage and current of load at time \(n\), respectively; \(V_{rms}\) and \(I_{rms}\) are the root mean square (RMS) of voltage and rms of current of load, respectively; 149 is the sampling rate of the voltage/current module (149 points per cycle, 16.7 ms per cycle for 60 Hz of voltage/current); 60 is the frequency of the voltage/current; \(P_{nxe,i}\) is the average power of cycle \(i\); \(E\) is the consumed energy of load in terms of \(W.s\), which represents the electricity bill. For a steady power system, the \(V_{rms}\) should be kept constant, not fluctuating, while the value of \(I_{rms}\) reflects the load level (heavy or light).  
The PLC used in this system complies with HomePlug AV, IEEE802.3, and IEEE802.3u. It transmits and receives Internet data via power lines in the home. Figure 6 shows the architecture diagram of the smart socket, where the MCU can receive commands from the VEN through PLC and control the on/off switch of the AC power socket through the relay. Therefore, the load connected to the smart socket can be shed per the VEN's request. Figure 7 depicts the architecture diagram of the infrared module, where the MCU can receive the command from the VEN and initiate the infrared remote control. The SD card in this module stores infrared control codes through the infrared learning process. According to the demand control command, the MCU emits the corresponding infrared control code from the SD card.  
Figure 4: Demand response controller system architecture.  
Figure 5: Architecture diagram of the voltage/current module.
# Pruning Optimization over Threshold-Based Historical Continuous Query
###### Abstract  
With the increase in mobile location service applications, spatiotemporal queries over the trajectory data of moving objects have become a research hotspot, and continuous query is one of the key types of various spatiotemporal queries. In this paper, we study the sub-domain of the continuous query of moving objects, namely the pruning optimization over historical continuous query based on threshold. Firstly, for the problem that the processing cost of the Mindist-based pruning strategy is too large, a pruning strategy based on extended Minimum Bounding Rectangle overlap is proposed to optimize the processing overhead. Secondly, a best-first traversal algorithm based on E3 DR-tree is proposed to ensure that an accurate pruning candidate set can be obtained with accessing as few index nodes as possible. Finally, experiments on real data sets prove that our method significantly outperforms other similar methods.  
moving object; historical continuous query; pruning optimization; spatiotemporal index
## 5 Conclusions and Future Work  
Firstly, the weighting matrix is extracted from the data themselves of each sensor, and the weighted data are obtained by multiplying the raw data with the weighting matrix. The weak features in raw data are strengthened, and the effect of interference information caused by strong noise and variable working conditions on data features is weakened; the weighted data of all sensors are used as the input of DWSC to realize the decoupling of the channel and spatial correlation. In the proposed method, the data of multiple sensors are weighted and fused, taking into account the spatial and temporal information in the data. Through the comparative experiments between a different number of sensor inputs, the weighted and unweighted data, different fault diagnosis methods, and multiple datasets, the method based on MSDWF and DWSC shows superior performance, but it is worth pointing out that the training speed of the method needs further optimization and improvement.  
Next, we will first consider optimizing this method to reduce the time it takes for each round of training. Secondly, the method will be tested in more complex and harsh environments and the performance in load adaptability so as to optimize and improve the method in a targeted manner. In addition, we will also test whether the accuracy can be further improved when using other types of sensors and different measuring points.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Average (\%)** & **Standard Deviation** \\ \hline LightGBM & 64.70 & 0.106 \\ \hline ShuffleNet & 92.45 & 0.030 \\ \hline ResNet & 88.15 & 0.075 \\ \hline MCCNN & 95.95 & 0.015 \\ \hline DCNN & 58.45 & 0.037 \\ \hline Proposed Method & 100 & — \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average accuracy and SD of the different methods.  
Figure 16: Comparisons between the different fault diagnosis methods.  
**Author Contributions:** Conceptualization, T.W. and X.X.; Data curation, X.X.; Formal analysis, T.Y. and H.X.; Methodology, T.W. and H.P.; Software, T.W. and X.Z.; Supervision, X.C.; Validation, H.P. and X.C.; Visualization, T.W.; Writing--original draft, T.W.; Writing--review & editing, T.W. and X.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
###### Abstract
Moreover, our work relies only on user-generated metadata and is evaluated both qualitatively and quantitatively. A visual overview of the proposed approach is illustrated in Figure 1.  
More specifically, the herein presented approach is based on a large Flickr dataset consisting of approx. 80 K geo-tagged images taken in the center of Athens, Greece. Since the boundaries of the vernacular regions are not strictly defined, we evaluate the produced results using a ground truth generated by local residents that reflects their perception of vernacular regions; in many AOI detection approaches, e.g., in [10], this approach is considered to be the most appropriate in order to define the corresponding boundaries.As we have already mentioned, the perception of AOIs between a tourist and a resident is significantly different. Thus, in the ground truth generation process, though participants were current or former residents, they were asked to identify the corresponding type of AOIs seen from a tourist's perspective. We should clarify herein that for the sake of evaluation we use a set of touristic POIs from the socially generated database of OpenStreetMap. ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/))). Figure 1: A visual overview of the proposed approach. The rest of the paper is organized as follows: In the next Section 2 we present relevant research efforts that also exploit user-generated geodata and metadata from Flickr, focusing on those that aim to extract AOIs and representative tags from them. The proposed methodology is thoroughly presented in Section 3. More specifically, we present the proposed geo-clustering algorithm in Section 3.1. Then, in Section 3.2 we present the approach we follow in order to extract meaningful tags from each extracted AOI. In Section 4 we present in detail the dataset that has been used throughout all our experiments. We also include extensive qualitative and quantitative experimental results, along with a few examples so as to facilitate understanding of the geo-clustering process. Finally, discussion, conclusions and plans for future work are drawn within Section 5.
## 3 Case Study and Parametric Analysis
### Case Introduction and Calculation Parameters  
The second Dapu Road Tunnel is a highway tunnel across the Huangpu River of Shanghai. It runs nearly parallel to the original one. The tunnel was opened in February 2010; the outer diameter of the tunnel is 11 m, the inner diameter is 10.04 m, the segment thickness is 0.48 m, and the width is 1.5 m. The tunnels constructed adopted an EPDM gasket, which has a section shape, as shown in Figure 7. Additionally, the gasket is designed to provide an effective seal under the groundwater pressure of 0.8 MPa when the offset is up to 15 mm or the gap opening is up to 8 mm.  
The EPDM gasket is approximately considered as a linear elastic material, with an elastic modulus (\(E\)) of 10 MPa and Poisson's ratio (\(v\)) of 0.5. The calculation parameters take the EPDM surface fractal dimension \(D_{f}=2.2\), the root mean square height of the EPDM gasket surface \(h_{\text{rms}}\)\(=25\) nm [37], and the dynamic viscosity coefficient of groundwater \(\mu=10^{-3}\)N\(\cdot\)s/m\({}^{2}\). The length of sealing interface \(L_{x}=22\) mm, and the length of the EPDM gasket per meter shield tunnel \(L_{y}=6\) m. For the leakage calculation, the important roughness is the part for \(q_{L}<q<q^{*}\) where \(q^{*}\) is the wavenumber where the contact area percolates when increasing the magnification, which approximately occurs when \(A(q)=0.4\). Therefore, the minimum surface fluctuation frequency \(q_{0}=q_{\text{L}x}\), and the maximum surface fluctuation frequency \(q_{1}=7.8\times 10^{9}\).  
Figure 7: The EPDM joint leakage model diagram. \(F_{N}\) represents assembly stress, which can be calculated according to the rubber strain (assume the rubber is linear elasticity). \(L_{x}\) is the width of the contact surface of the gasket, and \(L_{y}\) is the length of the circumferential joints per meter of the tunnel.  
According to the German STUVA, the tunnel already leaks 0.024 L/m\({}^{2}\)/day when no seepage marks are observed in the tunnel lining [38]. Some of the leakages evaporate without being found, so even if the tunnel is not stored for leaking by the water storage method, the leakage amount is large. The tunnel waterproof level is 2--that is, when it is mainly dry, there is already a leakage of 0.1 L/m\({}^{2}\)/day. Therefore, this paper takes 0.1 L/m\({}^{2}\)/day as the waterproof performance evaluation standard.
## 6 Design of Magnetic Signal Processor
### Acquisition Board Design  
The triaxial flux gate typically outputs three-channel analog signals. The principle of the signal acquisition circuit is illustrated in Figure 18. The input signal first passes through an input protection circuit to limit the amplitude and prevent the transient high voltage from damaging subsequent analog channels. The signal then enters the impedance matching network to reduce waveform distortion. Following this, the signal passes through the signal-preprocessing circuit, which provides high-impedance input to the external signal and performs RF filtering of the signal to mitigate aliasing and reduce RF interference. Finally, the signal passes through the analog/digital (A/D) driver and enters the A/D converter (ADC). The field-programmable gate array (FPGA) controls the ADC chip for data acquisition and temporarily stores the collected data in the system memory.  
The digital acquisition section first isolates the Larmor frequency signal from the power line. Subsequently, it performs low-pass filtering on the Larmor signal to eliminate out-of-band noise. Finally, it outputs a low-jitter digital logic signal through a high-speed hysteresis comparator. The high-performance FPGA processes the digital logic signal to calculate and output the magnetic field strength.
# Analysis of Ground Subsidence Vulnerability in Urban Areas Using Spatial Regression Analysis
###### Abstract  
The main cause of ground subsidence accidents in urban areas is cavities formed by damage to underground utilities. For this reason, the attribute information of underground utilities should be used to prepare against ground subsidence accidents. In this study, attribute information (pipe age, diameter, burial depth, and density) of six types of underground utilities (water, sewer, gas, power, heating, and communication) and history information of ground subsidence were collected. A correlation analysis was conducted using the collected data, and a prediction model of vulnerability to ground subsidence was developed through the ordinary least squares (OLS) method and spatial regression analysis (spatial lag model (SLM) and spatial error model (SEM)). To do this, the target area was divided into a grid of 100 m \(\times\) 100 m. Datasets were constructed using the attribute information of underground utilities included in the divided grid and the number of ground subsidence occurrences. To analyze the OLS of the constructed data, the variance inflation factor (VIF) of the attribute information of underground utilities was studied. An OLS analysis was conducted using the appropriate factors, and the results show that the spatial data were autocorrelated. Subsequently, SEM and SLM analyses, which were spatial regression analyses, were conducted. As a result, the model using SLM was selected as suitable for analyzing the vulnerability of ground subsidence, and the density of six types of underground utilities was found to be the highest influencing factor. In addition, a vulnerability map of ground subsidence in the target area was prepared using the model. The vulnerability map demonstrates that regions with frequent ground subsidence can be predicted to be highly vulnerable.  
O 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).  
**Key words:** OLS analysis; spatial regression; SEM; SLM; ground subsidence
# Channel Modeling for In-Body Optical Wireless Communications +
## 3 Results
### Verification
In addition, the absorption coefficients of other tissues that are rich in blood and water bare a strong resemblance to the blood absorption coefficient in the region between 400 and 600 nm, while the impact of water becomes visible after 900 nm. The higher concentration of blood and water results in increased attenuation in the wavelengths, where each of them has relatively high absorption. Figure 2c depicts the experimental data for the reduced scattering coefficient of each of the complex tissues, namely skin, bone, brain, and breast, against the analytical results extracted from (12). The experimental parameters for each of the tissues are available in Table 3, alongside their sources. This figure verifies the validity and accuracy of the extracted expressions for the reduced scattering coefficient due to the proved fact that they provide a very close fit to the experimental data. Furthermore, all of the reduced scattering  
Figure 2: Attenuation coefficients of complex tissues and their constituents as a function of \(\lambda\). (**a**) Absorption coefficient of tissue constituents. (**b**) Absorption coefficient of complex tissues. (**c**) Reduced scattering coefficient of complex tissues. coefficients not only exhibit a more linear behavior than the corresponding absorption coefficients, but also hold significantly higher values. This highlights the detrimental effect that scattering plays in the propagation of optical radiation through the human body. Moreover, it is evident that, as \(\lambda\) increases, the reduced scattering coefficient decreases, which suggests that, as we increase the transmission wavelength, the impact of scattering could prove to diminish. For example, for skin tissue, as \(\delta\) rises from 400 nm to 1000 nm, the reduced scattering coefficient decreases by 87.5%. Finally, we observed that skin caused almost half an order of magnitude higher attenuation than the rest of the tissues under investigation, while brain tissue exhibited the lowest.
# Study of 5G-NR-MIMO Links in the Presence of an Interferer
## 4 Numerical Results  
We performed simulations of the \(2\times 2\) Alamouti STC in the time-space domain with the presence of a general interferer, a smart jammer, and a PBN jammer, described in Sections 3.1, 3.2 and 3.3, respectively. The simulations were performed in both Matlab and Simulink platforms.  
The simulation general block diagram is described in Figure 4, and the Simulink block diagram is illustrated in Figure 5. In this experiment, we focused on two leading parameters that reflect the system performance: BER and SIR. The transmission chains have a data block generator. Next, the data enter a modulator block. We chose the M-PSK modulation, but other modulations can be selected. The M-PSK signals have equal energy, i.e., a Constant Envelope (CE), which is energetically efficient, and propose low PAPR pre-coding [45]. The STC encoder encodes the symbol sequence to obtain \(\vec{S}\) and \(\vec{J}\) and transmits on two antennas. The MIMO block filters the signals through an MIMO multi-path Rayleigh slow-fading channel. At the receiver, two antennas receive the signal vector \(\vec{R}\) where the reverse operations are performed: the STC combiner and the M-PSK demodulator. As we mentioned in the introduction, we assume a private case of the HetNet scenario, which included TDD network accesses and an FD mode at the receiver, the transmitter, and with un-shared scheduler assume. The presence of the interference signals is continual.  
Figure 3: Demonstration of the 50% bandwidth (BW) partial band noise (PBN) jammer.