esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Electrical Resistivity Tomography for Landslide Assessment
### Pole-Dipole  
The pole-dipole configuration is a technique widely used in electrical tomography to investigate landslides, using one current electrode and two potential electrodes to measure the resistivity of the subsoil. This technique stands out for its good vertical and horizontal resolution, deep penetration, and sensitivity to both vertical and horizontal structures, according to studies by [45; 46; 47; 48; 49; 50; 51].  
Figure 11: Resistivity values of rocks and minerals.  
Figure 10: Number, order, activity, and use of electrodes in the arrangement used.  
In the field of landslides, the pole-dipole method has proven effective in identifying the sliding surface, detecting areas with water saturation, and distinguishing between stable and unstable materials, as evidenced by the research of [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45], yet it has not been diversified in this field.  
Two-dimensional electrical tomography has great advantages, allowing for high horizontal and vertical resolution, especially with the pole-dipole arrangement, great depth, and flexibility in the field.  
ERT was chosen due to the pole-dipole configuration, which provides high sensitivity both horizontally and vertically, enabling the identification of anomalies. This configuration was selected for the following advantages:  
\(\circ\)   High resolution in depth and length;  
\(\circ\)   It allows the identification and/or contrasting of different geological contacts;  
\(\circ\)   It allows the correlation of resistivity values with highly saturated materials;  
\(\circ\)   It allows the obtainment of profiles with the topography of the terrain.
# Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning
## 2 Background
### Drone Attack Models
#### 2.1.1 Denial of Service  
In [10], a denial of service (DoS) attack was tested against the Parrot ANAFI Drone. The drone was connected to a Wi-Fi access point, that emulates a ground controller. The identified threat was the compromise of the Wi-Fi password, which can foster adversarial attempts to send falsified flight commands. With no knowledge of the password, the adversary may still be able to perform a deauthentication attack and attempt to crack the same. A third option could simply be an attempt to disconnect communication between the drone and the access point (ground controller).  
A DoS attack can also be perpetrated through an adversary who is able to disrupt a drone-to-ground controller or ground controller-to-cloud communication channels by flooding the communication channel with a large volume of fictitious network traffic, consequently overwhelming the computational assets, and preventing them from continuing with routine operations. Such an attack can be carried out using a technique such as 'Low Orbit Ion Cannon' with TCP flooding attack enabled on port 80 if the communication between the ground controller and the cloud services adopts the TCP protocol. The Hping3 is command-line software that can also be deployed to carry out a TCP SYN flood attack (DoS) against a drone system asset [10].  
A ground controller station can also be compromised by the adversary, which will then send a suspicious signal to mislead a drone or even cause a crash. Through an analysis of the receiver signal strength indicator (RSSI) values and by triangulating the numbers with neighboring drones and their data, the scheme proposed in [11] can prove to be resilient to DoS attacks.
# Opportunities for Early Detection and Prediction of
## 4 Research Direction and Future Work
### Research Direction: Situational Awareness Ransomware Prediction Framework
#### 4.1.3 Projection Phase  
Projection is the third phase of the situational awareness framework, in which the risk assessment constructed during the comprehension will be used to predict ransomware attacks on the SCADA system. The outcome of the situational risk assessment will be used to create several scenarios for ransomware attacks. When the situation changes, current scenarios are updated, or new scenarios are added. The purpose of creating these scenarios is to get the SCADA system ready for potential ransomware attacks in light of the current operational situation. For each scenario, a ransomware incident response will be prepared. According to the situation, the incident responses are updated and adapted when the list of scenarios is updated.  
The projection is essential for preventing potential future ransomware attacks. Typically, such projection is made based on data gathered from various sources. Therefore, the data should represent the operational environment in which ransomware is executed. This includes the runtime data gathered during the execution of the ransomware running process and the situational data that reflect the operational environment of the process. By coupling both data types, the projection will be situational-aware, and does not only rely on the ransomware behavior but also the system parameters.
## 6 Conclusions  
We have proposed a neural network structure based on channel-attention-Net and LSTM, which is named CAE-LSTM-Net for data decoding in an OCC system. Benefiting from the CNN's capability of extracting features, our proposed channel-attention-Net can generate a precise descriptor of input images. Transforming the decoding problem of a frame into a time-series classification problem, we proposed an LSTM-based equalizer to mitigate the performance deterioration caused by ISI. The experimental results demonstrate that our proposed CAE-LSTM-Net-based decoding scheme significantly improves the performance of data rate and BER in an OCC system. Based on the proposed decoding algorithms, a record-high data rate of 44.03 kbit/s is achieved by the RGB-LED-based OCC system, also meeting the FEC requirement.  
Conceptualization, W.G.; methodology, P.L.; software, P.L.; investigation, M.L.; writing--review and editing, P.L. and W.G.; supervision, W.G.; project administration, P.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Scalable and Consistent Method for Multi-Component Gravity-Gradient Data Processing
## 3 Synthetic Data Application
### Falcon AGG Data  
The simulated Falcon AGG components \(\mathbf{g}_{xy}\) and \(\mathbf{g}_{uv}\) are shown in Figure 5. Each component has \(N=145,\!350\) observation points, totaling \(N_{t}=290,\!700\) simulated data. After transforming the simulated data from the north-east system to the data system (step 1 of Algorithm 1), we estimated the mass distribution at 39.02 s and computed the predicted data in the data system (steps 3 and 4 of Algorithm 1). Refer to the Supplementary Material for the Falcon AGG data transformed into the data system (see Figure S1).  
Figure 6a,b show, in the north-east system, the residuals between the simulated data (Figure 5) and the predicted data for the two components of the Falcon AGG system. The insets display histograms of the residuals, with means of 0.16 and 0.18 evotos, and standard deviations of 2.04 and 1.70 evotos, for the \(\mathbf{g}_{xy}\) and \(\mathbf{g}_{uv}\) components, respectively. The bell-shaped histograms and the estimated standard deviations are close to those of the added Gaussian noise (1.94 and 1.48 evotos), which suggests an excellent fit to the data. Figure 6c shows the root mean square error (RMSE) as a function of iterations. The RMSE curve flattens just below 2 evotos, consistent with the noise levels, further supporting that our method has converged properly. Taken together, the residual distribution and the RMSE convergence indicate that the solution fits the data within the expected noise level, without evidence of overfitting.  
Figure 4: GPS altitude (WGS84) was converted to downward-positive \(z\)-coordinates over the Kauring test site, Australia.  
Figure 5: Synthetic Falcon AGG data in the north-east system. Simulated noise-corrupted components (**a**) **g**xy and (**b**) **g**xy at 145,350 observation points.  
Figure 6: Synthetic Falcon AGG data. The residuals and the histograms of the residuals are shown as insets (**a**,**b**) between the simulated (Figure 5a,**b**) and the corresponding predicted (**a**) **g**xy and (**b**) **g**uv components (in edvos). The mean \(\mu\) and standard deviations \(\sigma\) (in edvos) are shown in each histogram. (**c**) Convergence curve, showing the root mean square error (RMSE) along successive iterations.  
Figure 7 shows two transformation results predicted by the estimated equivalent layer using our method. Despite the strong presence of geologic noise, simulated by 300 small sources, both the predicted \(\mathbf{g}_{zz}\) (Figure 7a) and the predicted \(\|\
abla\mathbf{g}_{z}\|\) (Figure 7b) provide a clear definition of the boundaries of the two main geologic bodies. The geologic noise is visible as short-wavelength components in these transformation results.
# Use of Binary Classification in Non-Invasive Load Monitoring
## 6 Test Results  
### Indicators for Assessing the Quality of Identification  
Four indicators were selected to assess the quality of identification, as follows: _Accuracy_, _Precision_, _Recall_, and \(F\)1.  
The _Accuracy_ metric indicates what proportion of the data were classified correctly. It indicates the probability of correctly identifying an average device.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{6}\]  
where  
TP--the number of elements belonging to the class and correctly classified into it;  
FP--number of elements not belonging to the class and misclassified in the class;  
FN--number of elements belonging to the class and not classified in the class;  
TN--number of elements not belonging to a class and not classified in a class.  
The _Precision_ indicator indicates the extent to which we can trust positive identifications. It indicates the probability that, if the system identifies a device, a signal from that device has actually been given to its input, as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]_Recall_ is an indicator of the probability with which a class will be correctly recognized. This indicates the probability that if a signal from a specific device is applied to the system input, it will be correctly recognized as follows:  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
The \(F1\) measure is the harmonic mean of the _Precision_ and _Recall_ indicators. The choice of the harmonic mean instead of the arithmetic mean makes this indicator more sensitive to results close to zero. Owing to this property, it is easy to see if one of the indicators (_Precision_ or _Recall_) has a very small value.  
\[F1=\frac{2*Precision*Recall}{Precision+Recall} \tag{9}\]  
The selected indicators are the most readily used indicators to assess the quality of identification, as they capture the features relevant to classification.  
Using the data selected by the mRMR method and the classifiers described in Section 4, the identification of the electricity consumers listed in Section 3 was carried out. Performance tests of the defined classifiers were performed. The Repeated Holdout method [30] was used to obtain a correct estimation of the quantities used to assess the quality of the identification. A total of 1000 learning and testing operations were performed, each time dividing the dataset into two subsets Figure 5, as follows: learning subset (90%) and test subset (10%).
## 2 Methods
### Blinding, Randomization, and Allocation  
An independent researcher prepared the randomization using Clinstat software (University of York, York, UK) [33] and allocated the participants to groups at a 1:1 ratio. The randomization codes were generated in blocks of 4 to 8 to prevent researchers from predicting allocation. The randomization sequence generated was kept in opaque and sealed envelopes numbered sequentially. The random allocation was performed after acquiring baseline data by the principal investigator (EQS). All outcomes' assessors (RHCJ and JLV)  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Intervention Group (n = 25)** & **Control Group (n = 25)** \\  & **Mean (SD)** & **Mean (SD)** \\ \hline
**Age (years)** & 59.1 (6.4) & 56.5 (9.4) \\
**Body mass (kg)** & 74.4 (15.6) & 74.2 (14.8) \\
**Height (cm)** & 162.0 (0.1) & 164.0 (0.1) \\
**Body mass index (kg/m2)** & 23.5 (4.8) & 22.9 (3.6) \\
**Sex (Male/Female)** & (M = 6 / F = 19) & (M = 5/ F = 20) \\
**Type 2 Diabetes (number of participants, \%)** & 22 (88\%) & 19 (76\%) \\
**Time of onset of diabetes (years)** & 13.8 (10.0) & 18.2 (9.8) \\
**Education** & & \\   Elementary education incomplete & 4 (16\%) & 2 (8\%) \\   Elementary education complete & 6 (24\%) & 7 (28\%) \\   High school complete & 7(28\%) & 9 (36\%) \\   Higher education incomplete & 1 (4\%) & 2 (8\%) \\   Higher education complete & 7 (28\%) & 5 (20\%) \\
**Socioeconomic status** & & \\
1 to 3 Brazilian minimum salary/month & 13 (52\%) & 15 (60\%) \\
3 to 5 Brazilian minimum salary/month & 5 (20\%) & 4 (16\%) \\   Up to 5 Brazilian minimum salary/month & 6 (24\%) & 2 (8\%) \\
**DPN symptoms (MNSI score)** & 5.4 (1.9) & 6.9 (2.1) \\
**DPN severity (Fuzzy score)** & 4.4 (2.6) & 4.6 (2.3) \\
**Tactile sensitivity (number of areas, median (IQR))** & 0 (0–4) & 0 (0–3) \\
**Vibration Perception (number of feet, \%)** & & \\   absent—L & 10 (40\%) & 6 (24\%) \\   absent—R & 10 (40\%) & 9 (36\%) \\   reduced—L & 5 (20\%) & 9 (36\%) \\   reduced—R & 3 (12\%) & 5 (20\%) \\
**Foot Strength (\%BW)** & & \\   Hallux—L & 13.8 (6.6) & 11.7 (6.2) \\   Hallux—R & 13.8 (6.9) & 13.6 (6.8) \\   Toe—L & 9.4 (5.2) & 9.3 (5.1) \\   Toe—R & 9.7 (4.2) & 8.5 (2.9) \\ \hline \hline \end{tabular} Abbreviations: L—left, R—right, BW—bodyweight, MNSI—Michigan Neuropathy Screening Instrument.  
\end{table}
Table 1: DPN-related outcomes and anthropometric and demographic characteristics at baseline for the control and intervention groups.  
were blinded to block sizes and group codes. Due to the nature of the study (physical therapy intervention based on exercises), it was not possible to blind the principal investigator ([PERSON]) responsible for delivering the intervention. In addition, the participants could also be blinded to the exercise intervention. The trial statistician was also blind to treatment allocation until the main analysis was accomplished.
## 5 Graph Simulations and Results
### Results
#### 5.2.2 Shortest-Delay Mode over Non-Trivial Link Metrics  
The advantages of window jitter over \"standard\" jitter is less significant when link metrics are not uniform: the ability to identify better paths by introducing fixed minimum delays (\(\alpha J_{m}\)) per hop degrades, as depicted in Figure 11. For these non-trivial link metrics, the simulated results show that the use of the adaptive jitter presented in Section 3.3 is more adequate. This is because routers using adaptive jitter can take the actual link metric (e.g., ETX, bandwidth, _etc._) into consideration, rather than the single presence of these links in the path.  
Figure 11a depicts the fact that adaptive jitter clearly outperforms window jitter and \"standard\" jitter in terms of optimality index. As depicted in Figure 11b for random link quality values, this benefit from the adaptive jitter is compatible with a low level of packet collisions (similar to the level achieved with window jitter and significantly lower than the level achieved with \"standard\" jitter) in networks with heterogeneous link qualities (_i.e._, non-uniform metrics).  
Discrimination of RREQs based on the quality of traversed links is performed by introducing pre-forwarding delays. This entails a trade-off between RREQ path optimality and RREQ flooding delays, as depicted in Figure 12, for the three types of jitter. In general, the better the path indicated in the first RREQ received by the intended destination, the more delay between the RREQ transmission by the source and its reception in the destination. This can be observed, in particular, for networks of constant router density (Figure 12b). Results from Figure 12a indicate, in addition, that additional delay caused by adaptive jitter with respect to window jitter strongly depends on the network density: as more paths are available to reach the destination (because the network is denser), the heterogeneity of the quality of the involved links in flooding is also higher and the adaptive jitter type allows one to deliver route requests (RREQs) faster, while window jitter cannot reduce the per-hop delay beyond a minimum value \(\alpha J_{m}\).  
Figure 11: Shortest-delay RREQ flooding. (**a**) Optimality index; (**b**) RREP (route reply) packet collisions.
## 4 Results and Discussion
### Time-Frequency Analysis of Monitoring Points
#### 4.1.2 Fourier Spectrum  
Fast Fourier transform was performed on the acceleration time history of monitoring point to analyze the change in the frequency domain. Figure 13 shows the Fourier spectrum of roof midpoint at different incident angle. The variation law of the spectrum peak amplitude of the monitoring point at different incident angles is the same as the acceleration response. With the increase in incident angle, the horizontal spectrum peak amplitude decreases, and the vertical spectrum peak amplitude increases significantly. Oblique incidence makes the vertical energy increase sharply. When the incident angle changes from 15\({}^{\circ}\) to 30\({}^{\circ}\), the dominant frequency of the monitoring point decreases from 1.46 Hz to 0.78 Hz, which may be caused by the decrease in the elastic effect of soil-immersed tunnel system due to oblique incidence. The reduced dominant frequency is closer to the fundamental frequency of the soil-immersed tunnel system, which is unfavorable from the perspective of tunnel seismic resistance and deserves attention.  
Comparing the Fourier spectrum of the monitoring point with the input seismic wave (Figure 14), it can be seen that the spectrum peak amplitude of monitoring point is amplified, and the spectrum peak amplitude of roof midpoint is greater than that of the floor. The spectrum peak amplitude of roof midpoint is larger than that of the input seismic wave when the frequency is less than 2 Hz, but smaller than that of the input seismic wave when the frequency is greater than 2 Hz. The spectrum peak amplitude of floor midpoint is also the same, but the critical frequency appears at 1.5 Hz. This means that the seismic response of the roof and floor in the frequency domain is similar, but the frequency range at different locations is different. Therefore, seismic design should pay attention to checking the strength of the tunnel roof, as well as consider the impact of oblique incidence of seismic waves to avoid underestimating the seismic response of the tunnel, resulting in damage to the tunnel structure.  
Figure 14: Fourier spectrum comparison between monitoring points and incident seismic wave.  
Figure 13: Fourier spectrum of roof midpoint: (**a**) horizontal; (**b**) vertical.
# Effect of Demand Side Management on the Operation of PV-Integrated Distribution Systems
###### Abstract  
In this new era of high electrical energy dependency, electrical energy must be abundant and reliable, thus smart grids are conducted to deliver load demands. Hence, smart grids are implemented alongside distributed generation of renewable energies to increase the reliability and controllability of the grid, but, with the very volatile nature of the Distributed Generation (DG), Demand Side Management (DSM) helps monitor and control the load shape of the consumed power. The interaction of DSM with the grid provides a wide range of mutual benefits to the user, the utility and the market. DSM methodologies such as Conservation Voltage Reduction (CVR) and Direct Load Control (DLC) collaborate in the reduction of plant generation and reciprocally to the comprehensive cost. The aim of this paper is to investigate the effects caused by the implementation of DSM on the operation of PV-integrated distribution systems. The algorithms of CVR, DLC and the combination of CVR and DLC were implemented using OpenDSS and MATLAB. The effectiveness of the aforementioned schemes was verified on IEEE 30-Bus test system. Various possible integration scenarios between Photovoltaic (PV) and DSM schemes are illustrated. The optimal integration of such schemes constraining the reduction of energy consumed by the user and utility is presented. The results show that the implemented DSM algorithms provide a noticeable reduction in energy losses and reduction in consumed energy.  
distributed generation; distribution systems; demand side management; conservation voltage reduction; direct load control +
Footnote †: journal: Applied Sciences
# Experimental Validation of Peer-to-Peer Distributed Voltage Control System
## 6 Results of the Experiment
### Key Performance Indicators
#### 6.3.1 Convergence Time  
The voltage control algorithm is online and adjusts itself continuously. When a change in the generation profile occurs, there are two possibilities: either there is a voltage problem or not. If there is no voltage problem, the control algorithm stays idle. However, if there is a voltage problem, then the agents start to undertake action. The observer agents change the control signals until the voltage problems are resolved. If they succeed, then the control signals converge to a stable value, and the voltages converge to a value within the limits. In this paper, we define the convergence time as the time it takes from a moment when the voltage exceeds the limits until the moment when the voltage is restored within the limits. As demonstrated in Figure 11, it takes the algorithm around 3 min to regulate the voltages within the defined limits, which is an acceptable time for voltage problems.  
It is worth mentioning that the intervention time of the interface protection relay of Triphase inverter is much less than the convergence time. The intervention time of the interface protection relay of Triphase inverter is less than 1 ms. The Triphase inverter is configured to trip at 280 V. This means that there is 27 V as voltage margin, since the algorithm starts regulating the voltage when the PCC voltage is higher than 253 V. Hence, the inverter in our setup is able to correct the voltages before reaching 280 V. If an inverter trips at 253 V (maximum voltage defined by the standard EN 50160), then \(V^{\text{max}}\) of the proposed algorithm should be set to a value lower than 253 V (i.e., 240 V), in a way to make sure that the convergence time is sufficient to correct the PCC voltages before reaching the maximum voltage at which the inverter trips.