esa-sceva/satcom-chunk-collection huggingface dataset:
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 3 Methodology
### Preprocessing the Wi-Fi Data  
#### 3.2.1 Simplification of the Original Data Fields  
The original Wi-Fi data are sent by character stream. The raw data collected by Wi-Fi contain the following fields: probe device MAC, source MAC, destination MAC, frame main type, frame subtype, scan channel, signal strength RSS, and timestamp. Examples of raw Wi-Fi data are shown in Figure 4. The specific meanings of each field in the data are as follows: Probe MAC, which is the MAC address of the Wi-Fi module of the probe itself, mainly used for distinguishing different probes; source MAC, which is the MAC address of the transmitting end of the Wi-Fi signal collected by the probe, mainly for mobile devices; destination MAC, which is the MAC address of the receiving end of the Wi-Fi signal collected by the probe, possibly for mobile devices or fixed devices; frame type, which is one of the values \"00\", \"01\", and \"02\", corresponding to management frames, control frames, and data frames, respectively; frame sub-type, which is the sub-type of the Wi-Fi signal frame collected by the probe under different main types; scan channel, which is the specific communication frequency band where the Wi-Fi signal is located; signal strength, which is the strength of the Wi-Fi signal collected by the probe, with the minimum value being \(-100\); the larger this value is larger, the stronger the signal strength is, and the closer the Wi-Fi signal transmitting device is to the probe; timestamp, which is the moment when the Wi-Fi data are collected.  
In order to take into account the data necessary for the subsequent algorithm, the method only retains the source MAC address, detected timestamp, and signal strength.
## 3 Results and Discussions
### Angular Stability Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Resonant Frequency (GHz)**} & \multicolumn{3}{c}{**Attenuation (dB)**} & \multicolumn{3}{c}{**Fractional Bandwidth (\%)**} \\ \cline{2-13}
**Angle (\(\theta\))** & \multicolumn{2}{c}{**Band I**} & \multicolumn{2}{c}{**Band II**} & \multicolumn{2}{c}{**Band I**} & \multicolumn{2}{c}{**Band II**} & \multicolumn{2}{c}{**Band I**} & \multicolumn{2}{c}{**Band II**} \\ \cline{2-13}  & **TE** & **TM** & **TE** & **TM** & **TE** & **TM** & **TE** & **TM** & **TE** & **TM** & **TE** & **TM** \\ \hline
0\({}^{\circ}\) & 7.90 & 7.70 & 11.94 & 12.08 & 48.72 & 45.02 & 49.21 & 43.82 & 35.27 & 34.76 & 21.62 & 20.37 \\
30\({}^{\circ}\) & 7.91 & 7.76 & 11.92 & 12.01 & 48.99 & 45.22 & 49.65 & 44.39 & 39.10 & 40.45 & 22.45 & 21.63 \\
45\({}^{\circ}\) & 7.95 & 7.79 & 11.90 & 12.00 & 50.09 & 46.62 & 50.21 & 45.65 & 45.01 & 46.25 & 23.77 & 23.44 \\
60\({}^{\circ}\) & 8.02 & 7.84 & 11.84 & 11.94 & 52.30 & 48.63 & 52.32 & 47.89 & 58.56 & 60.75 & 28.22 & 27.90 \\
75\({}^{\circ}\) & 8.09 & 7.84 & 11.70 & 11.94 & 57.47 & 55.9 & 56.63 & 55.45 & 92.91 & 98.12 & 39.29 & 36.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fractional bandwidth as a function of incidence angle. Figure 4: (**a**) Shielding performance of the FSS unit cell at normal incidence for TE and TM polarization states, (**b**) SE of the FSS for TE polarization under oblique angles of incidence ensuring the angular stability up to 75\({}^{\circ}\), (**c**) SE of unit cell structure for TM wave mode, (**d**) FSS response under various polarization angles at normal incidence, confirming polarization insensitivity up to 90\({}^{\circ}\) for TE mode, and (**e**) SE plot of the FSS to the polarization angle variations at \((\theta=0^{\circ})\) for the TM mode.
# Smart Attacks Learning Machine Advisor System for Protecting Smart Cities from Smart Threats
## 7 Comparative Analysis and Discussion
results. In [30], a genetic algorithm selected 10 features before applying the SVM model that achieved relevant results. In [31], Filter-based grouping linear correlation coefficient (FGLCC) and a combinatory approach of FGLCC with cuttlefish (FGLCC-CFA) were used and achieved good results. In [32], the \"Pigeon Inspired Optimizer (PIO)\" was proposed as a new method, where the \"cosine\" and \"sigmoid\" transfer functions were used to find the fitness function of the optimization process, and achieved good results. In [23], the authors used a multi-level classification approach where SVM and ELM were used according to their approach, and achieved high accuracy. In [26], DPC-GS-MND achieved the best results after SALMA, but it used all the features. In [27], RNN was used, but the techniques did not achieve high results as RNN is best suited for sequence models. Figure 12 compares the accuracy between the proposed system and other state-of-the-art methods. Figure 11: Accuracy comparison between the proposed system and previous approaches on the NSL-KDD dataset [5; 13; 14; 15; 16; 17; 18; 19; 20; 22; 23]. Figure 12: Accuracy comparison between the SALMA and previous approaches on the KDDCUP99 dataset [21; 24; 25; 26; 27; 28; 29; 30]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy (\%)** & **No. of Features** \\ \hline Simplified Swarm Optimization [28] & 93.3 & 6 \\ \hline Least squares SVM [29] & 92.8 & 6 \\ \hline SVM [30] & 94.8 & 10 \\ \hline FGLCC-CFA [31] & 95.05 & 10 \\ \hline FGLCC [31] & 92.59 & 16 \\ \hline Sigmoid \_PIO [32] & 94.7 & 10 \\ \hline Cosine\_PIO [32] & 96 & 7 \\ \hline SVM and ELM [23] & 95.75 & 6 \\ \hline DPC-GS-MND [26] & 96.83 & 41 \\ \hline LSTM-RNN [27] & 91 & 41 \\ \hline Proposed Approach (**SALMA**) & **99.24** & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison between the proposed system and previous approaches on the KDD-CUP99 dataset.
## 5 Conclusions  
Our device was successfully tested in the lab and field in a wide range of temperature settings. Quantitative assessment can be efficiently performed via methods based on statistical analysis of agreement that are implemented in open-source software. The results revealed a fixed bias of 0.331 and a proportional bias of 0.982 between the core and the surface temperature.  
The field experiment that followed was designed and conducted for five consecutive days. The device confirmed its power efficiency during the five consecutive days of its function. Measurements were successfully logged, with the device having no failure in any of the 15- or 1-min intervals along the course of this study.  
The device can be used in similar applications, e.g., other hosts, and in a wide variety of environmental conditions. Moreover, it promotes open-source, custom made solutions for conducting experiments with small cost allowing for relatively large sample sizes that can achieve the anticipated by design statistical power in formal hypothesis testing.  
Last, it can be easily modified by researchers depending on their experimental needs. Any part can be replaced in case of malfunction, without affecting its performance.  
the methodCompare package. Only the modified lines are presented. The enumeration of the lines follows the source code website's format [46], S3: The raw data of the laboratory experiment.  
Conceptualization, E.P.B., C.T.N. and N.T.P.; methodology, E.P.B., V.G.R., K.D.Z., N.T.P. and C.T.N.; software, E.P.B.; validation, E.P.B. and V.G.R.; formal analysis, E.P.B.; resources, E.P.B., V.G.R., N.T.P. and C.T.N.; data curation, E.P.B.; writing--original draft preparation, E.P.B., V.G.R., N.T.P. and C.T.N.; writing--review and editing, E.P.B., V.G.R., K.D.Z., N.T.P. and C.T.N.; visualization, E.P.B.; supervision, N.T.P. and C.T.N.; project administration, E.P.B., N.T.P. and C.T.N.; funding acquisition, N.T.P. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the European Union's Horizon 2020 Research and Innovation Program FF-IPM (grant agreement No 818184).  
Not applicable.  
Data Availability Statement: Data is contained within the article or supplementary material. The data presented in this study are available in supplementary material S3.  
The authors thank four (4) anonymous reviewers for their feedback that significantly improved the manuscript.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Context-Based Energy Disaggregation in Smart Homes
###### Abstract  
In this paper, we address the problem of energy conservation and optimization in residential environments by providing users with useful information to solicit a change in consumption behavior. Taking care to highly limit the costs of installation and management, our work proposes a Non-Intrusive Load Monitoring (NILM) approach, which consists of disaggregating the whole-house power consumption into the individual portions associated to each device. State of the art NILM algorithms need monitoring data sampled at high frequency, thus requiring high costs for data collection and management. In this paper, we propose an NILM approach that relaxes the requirements on monitoring data since it uses total active power measurements gathered at low frequency (about 1 Hz). The proposed approach is based on the use of Factorial Hidden Markov Models (FHMM) in conjunction with context information related to the user presence in the house and the hourly utilization of appliances. Through a set of tests, we investigated how the use of these additional context-awareness features could improve disaggregation results with respect to the basic FHMM algorithm. The tests have been performed by using Tracebase, an open dataset made of data gathered from real home environments.  
energy; smart grid; smart home; metering; energy efficiency; Gaussian mixture models; Factorial Hidden Markov Models; energy disaggregation; context awareness; non intrusive load monitoring
# Three-Dimensional Broadband and Isotropic Double-Mesh Twin-Wire Media for Meta-Lenses
## 4 Conclusions  
We propose a 3D double-mesh twin-wire metamaterial for meta-lenses by introducing additional symmetries in the conventional wire medium. The proposed structure exhibits excellent isotropy and low dispersion over a wide bandwidth without a cut-off frequency. In addition, it provides a wide range of the effective refractive index designable by geometry. These distinctive features are well corroborated by a Luneburg lens that is engineered based on such structure.  
Conceptualization, O.Q.-T.; formal analysis, H.W. and Q.C.; writing--original draft preparation, H.W., Q.C. and O.Z.; writing--review and editing, Q.C., O.Z. and O.Q.-T.; visualization, H.W.; supervision, Q.C., O.Z. and O.Q.-T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data that support the findings of this study are available from the corresponding author, [PERSON], upon reasonable request.  
The authors would like to thank [PERSON] for helpful discussions.  
Figure 6: Full-wave simulated absolute value of \(E\)-field in the meta-lens whose background is filled with the same unit cell as in the lens exterior at: (**a**) 12 GHz; (**b**) 18 GHz; (**c**) 24 GHz. The lens circumference is outlined by a dashed line.
# Code Similarity and Location-Awareness Automatic Program Repair
## 5 Discussion  
Genetic programming can find the correct patch in the potential search space. Like Genprog [2], its main problem is that the large search space only contains a small number of correct patches. This can lead to consuming a lot of time looking for candidate patches or failing to search. In order to reduce the search space, we combine the search information with code similarity to guide the repair process. Experiments show that code similarity guides the right direction for genetic programming and significantly improves the repair effect. Secondly, we further optimize the search space through our location-awareness strategy. Experiments have shown that the strategy significantly improves repair efficiency.  
In research question 1, it can be seen that SLARepair outperforms Genprog in repair success rate and repair efficiency from Tables 2 and 3, Figure 7. The main reasons are threefold. The first point is that SLARepair combines code similarity to guide the repair process, while Genprog uses only genetic programming. Genetic programming randomly selects repair ingredients, which, when combined with code similarity, allows for a more efficient selection of suitable repair ingredients. The second point is that the location-awareness strategy makes it easier for SLARepair to find the correct patch. The improvement of the repair ability is verified after the location-awareness strategy subdivides the search space from Figure 8. The third point is that SLARepair uses the test case prioritization method combined with test filtering to further reduce the time spent on patch verification.  
In research question 2, we compare SLARepair with other methods. Although CapGen [15] and SimFix [10] also involve code similarity analysis, it can be seen that code similarity analysis combined with location-awareness strategies can lead to better repair results from Table 4 and Figure 9. At the same time, we can also see the complexity of fixing bugs in Figure 9. Furthermore, we should also note that despite the test case prioritization method and test filtering techniques, the patch verification process is still time-consuming.
## 2 Material and Methods
### Data Analysis Using Spectrograms  
An important step in the pre-processing of signals such as ECG, HR, and BR is the generation of spectrograms. Spectrograms provide a time-frequency analysis that reveals the energy distribution of the waveform across both time and frequency domains. This is achieved by segmenting the signal into shorter intervals and calculating the amplitudes of harmonic components within each segment. This method streamlines subsequent signal processing stages, eliminating many preliminary steps typically involved.  
Linear interpolation is necessary because the recording of a single heartbeat consists of discrete points. The spectrogram, created using a moving window sample interval-based Fourier transform, displays changes in frequency over time, providing a comprehensive way to analyze biomedical signals by highlighting their frequency content and temporal variations.  
For the BR signal, the spectrogram reveals regular breathing patterns and their frequency components. In the ECG signal, the spectrogram displays the periodic nature of heartbeats and their spectral characteristics. Similarly, HR spectrograms depict the dynamic changes in heart rate over time. An illustrative example of information gathered using spectrograms is given in Figure 4, which shows the spectrograms for the measured signals with the EQV sensor, including HR, BR, and ECG. These spectrograms illustrate the spectral content changes of these signals over time, providing insights into their temporal and frequency characteristics. The time plot shows the time domain signals for BR, HR, and ECG over a period of 120 min.  
The 2D spectrograms offer a different perspective, with the x-axis representing time (s) and the y-axis representing frequency (Hz). The color intensity indicates the amplitude of the frequencies. For the BR signal, the spectrogram highlights the dominant frequencies in the BR signal and their variations over time. In the case of the HR signal, a spectrogram shows the dynamic changes in heart rate, with higher frequencies corresponding to faster heart rates. For ECG signals, it provides a clearer view of the frequency components over time. This representation is useful for identifying specific frequency bands that correspond to different phases of the cardiac cycle.  
In the 3D spectrogram representation, a detailed view on how the frequency components of the signals evolve over time is shown. The x-axis represents frequency (Hz), the y-axis represents time (s), and the z-axis (color intensity) represents the amplitude of the frequency components. For the BR, the spectrogram reveals periodic changes in frequency, indicative of the regular breathing pattern. In the case of the HR, the spectrogram reveals the dynamic changes in heart rate, with higher frequencies corresponding to faster heart rates. For the ECG, the spectrogram indicates the periodic nature of the heartbeats and the frequency variations between them.
## 4 Experimental Setup
### Evaluation Metrics
#### 4.3.1 Performance Measurements  
Accuracy and F1-Score are used to measure the performance of LePB-SA4 RE compared to other models in all experiments of this study. The formulas are as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{18}\]  
\[F1\text{-}Score=2\cdot\frac{Precision\cdot Recall}{Precision+Recall} \tag{19}\]  
where the _Precision_ and _Recall_ are calculated as \(\frac{TP}{TP+FP}\) and \(\frac{TP}{TP+FN}\); the concepts of _TP_, _FP_, _TN_, and _FN_ have been widely used and will not be elaborated on in this section.
# Two-Layer-Graph Clustering for Real-Time 3D LiDAR Point Cloud Segmentation
## 3 Proposed Method
### Storage Structures
#### 3.1.2 Point Cloud Set Graph:  
Two processes complete the method proposed in this paper, the clustering of point clouds. The first step is a channel-level clustering process. So a structure for storing the point cloud set after the first segmentation is designed. In Figure 3, a graph node structure represents a point cloud set, and the lines connecting different nodes present the neighbor relationship of nodes. It has the same number of channels as a range graph, but in each channel, the number of graph nodes is not consistent.  
Table 2 shows the information of a point cloud set node. Each node represents a point cloud set, contains information about start position, end position, channel number and category label in the current channel.  
The set graph is the second-layer graph structure we proposed. In Figure 3, the set graph merges the point clouds that belong to the same category into one node.  
A node stores the start position, end position and channel number of the point cloud set, so reading the information of a node can quickly find the corresponding point cloud set. Using nodes instead of searching each point cloud is beneficial to improve the efficiency of the second clustering processing.
# Closed-Form Analytical Solutions for the Deflection of Elastic Beams in a Peridynamic Framework
## 6 Conclusions  
In this paper, straight elastic beams are analyzed within the framework of peridynamics. Closed-form analytical solutions to both peristatic and peridynamic equations are presented for various examples with different types of loadings and boundary conditions. In addition to the cases analyzed in the literature, novel solutions for the clamped edge, roller-clamped edge, free edge, concentrated force, and concentrated moment are presented. The validation of PD formulations and solutions is accomplished by comparing the deflections against the corresponding results according to the Bernoulli-Euler beam theory. A very good agreement between the non-local and the classical theories is observed for the case of the small horizon sizes, which shows the capability of the derived PD equations and proposed boundary conditions.  
The derived closed-form solutions include the horizon size in the explicit form such that the non-locality of the PD theory can be analyzed in a natural way. Furthermore, the results are useful to validate the numerical methods and numerical codes developed to solve PD equations.  
It is worth noting that, unlike the classical beam theory, many different types of constraints can be applied within the peridynamic framework. Indeed, the deflection function can be specified over the interaction domain in many different ways, in addition to the cases discussed in this paper. Future studies should be related to analyzing different types of PD constraints as they influence the deformed shape of the beam and deriving the corresponding analytical solutions.  
Figure 17: Displacement vs. time for simply supported beams under impact load. (**a**) Orbit of \(x=L/2\), (**b**) orbit of \(x=L/4\).  
**Author Contributions:** Conceptualization, Z.Y. and K.N.; methodology, Z.Y. and K.N.; writing--original draft preparation, Z.Y. and K.N.; writing--review and editing, Z.Y. visualization, Z.Y. and Y.C.; supervision, C.-C.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System
## 4 Emerging Technologies
### Intelligence  
#### 4.3.1 Artificial Intelligence (AI)  
Artificial Intelligence (AI) is the recreation of human brainpower performed by machines, specifically by a computer. Thanks to AI, humans' intelligence can adapt to machines. Machines operate based on standards and prototypes driven by sophisticated algorithms for various procedures. Thus, machines can move and manipulate things, identify different actions, or understand and solve complex problems [42].  
As the use of AI in the healthcare industry is growing, it has become a subject of great concern. Research has been conducted on the AI interface and its use by patients or doctors for decision making and its related work in the field of the healthcare sector. Recently, with the use of AI and the Internet of Medical Things (IoMT), consumer health applications have been a boon to mankind. With the advent of a voice-assisted installed base in many smart gadgets, consumers are more dependent on AI for their regular updates which also includes healthcare. Smart devices and gadgets are used to monitor patients' health. AI enables the ability to identify any complications or health deterioration, which can drastically do cost-cutting for both the hospital and the patients [43].  
Moreover, extracting, integrating, and analyzing from a huge set of unstructured and inconsistent patient data leads to an effective, accurate, and faster prediction and better clinical decision with the help of special intelligent tools. Although it has some limitations, the learning algorithms significantly improve clinical care. With all this in mind, few would argue that AI causes a paradigm shift in healthcare and that there may be value in applying AI to predicting potential disorders [44]. AI is becoming the crux of healthcare and now it is rapidly evolving to make its presence in the medical field. It is becoming extremely sophisticated at human-level intelligence and performs tasks more quickly and at a lower cost. As we are in an era of integrating physics-based models and data-driven models with AI, and continuously update a real asset in the operation of the existing models, it seems that in the near future robotic systems, through well-established AI techniques, will be capable to substitute surgeons and provide accurate surgical operations without the need of surveillance. Hence, it could be understood that improved and quicker results through AI provides appropriate timely decisions and predictive analysis that can assist in decision making and framing strategies to come out with innovative ideas for the betterment of the healthcare industries [43]. New data mining techniques such as Machine Learning and Deep Learning have been integrated to AI in the last years for improved behavior of the machines.