esa-sceva/satcom-chunk-collection huggingface dataset:
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 4 Proposed Framework
### Parson
#### [PERSON]  
The algorithm proposed by [PERSON] called \"AppliSense\" is another approach that does not use Markov models [47]. The philosophy is that some appliances like lamps and kettles are purely resistive, where others like washing machines and air conditions are more inductive and some appliances like laptops are more capacitive. By measuring not only the power consumption, but also the amount of reactive power it is revealed if there is inductive or capacitive appliances.  
The algorithm is supervised since it requires a signature database to be created prior to usage. The signature database consists of how the changes will be in reactive and real power for a specific device when it is turned ON and OFF. In their paper, the authors show a method where this database can be created of a user, by using a smart-phone application and turning ON and OFF appliances in the house, and inputting the information in the mobile application [47]. In this paper the database is created by data collected from the sub-meters measuring only the devices.  
The algorithm looks for significant power changes in the data. If a change is found it is assumed to be an ON or OFF event. The change is now compared to all the values in the database. If a close match is found it is assumed to be this device, if not it is an unknown device. This limits the approach to Type-1 appliances, and the algorithm requires the reactive power to be measured.
## 2 Related Work
### Discussion of the State-of-the-Art and Research Question  
As it has been highlighted, robust design activities in product development change dramatically when part and product geometry is defined. Thus, the objective of a holistic system improvement in early robust design turns into a minimization of the sensitivity of KCs in later design stages. Despite the enormous importance of a robust concept, robust design is primarily applied in late product development stages in practice [20] because of missing quantitative information in the concept stage, leading to difficulties in the specific implementation of design principles and the verification of their effectiveness [63]. Motivated by the present discrepancy, this paper aims at bridging the gap between early and late robust design to attain a common understanding as well as in-depth knowledge of the respective effects. This intended enhanced awareness of the high importance of both early and late robust design is regarded as a prerequisite for future research and application of early robust design in the context of a comprehensive robust design methodology. In this respect, the following research question is to be answered: How does early robust design, represented by the _Independence and Information Axiom_, affect the optimization of parameter and tolerance values in subsequent robust design stages?
# Experimental and Theoretical Study of the Influence of Saline Soils on Frozen Wall Formation
###### Abstract  
This paper examines the impact of salinity on the thermophysical properties of soils during artificial freezing. It focuses on analyzing heat and mass transfer in saline soils for constructing a frozen wall around a mineshaft at a potash salt deposit. The presence of salts in the groundwater near the contact point with water-protective strata is common in these deposits. Experimental studies were conducted on clay, chalk, and sand to understand the effect of salinity on the freezing temperature, unfrozen water content, specific heat capacity, and thermal conductivity of wet soil. These findings were used to simulate heat and mass transfer in saline soils using a one-dimensional model. The technique of circumferential averaging was introduced to account for the thermal impact of freeze pipes. The results indicate that higher soil salinity leads to a faster decrease in soil temperature under freezing conditions, although this dependence is weak for clay. This study also revealed that an increase in initial salinity results in a reduction in the thickness of the frozen wall. It was found that, for chalk and sand, there exists a range of initial salinity during which the frozen wall's thickness is almost independent of the initial salinity.  
artificial ground freezing; salinity of soil; mathematical modeling; thermophysical properties of soils +
Footnote †: journal: Applied sciences
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 3 The Proposed Scheme
### Image Preprocessing
#### 3.1.1 Image Segmentation and Bit Plane Separation Cross-Reorganization  
To better protect the important part of the ROS, the original ROS is first segmented, and the best threshold is selected by the OTSU algorithm. The part over the threshold is the ROI, and the part under the threshold is the RONI. After segmentation, the two regions start to undergo bit plane separation reorganization. Firstly, the high five bits of the ROI and the low three bits of the RONI are combined and paired into a new 8-bit image called ShareA. Then, the low three bits of the ROI and the high five bits of the RONI are combined and paired into a new 8-bit image called ShareB. This results in ShareB containing less important pixel information and a more important ShareA. ShareB is prioritized over ShareA in the subsequent information embedding process, protecting the ROI of the medical image.  
The specifics of the data cross-reorganization process are illustrated in Figure 2, and Equation (6) explains it.  
\[\begin{split} ShareA(i)&=[High5 Plane_{ROI}(i)+Low3 Plane_{RONI}(i)]\\ ShareB(i)&=[High5 Plane_{RONI}(i)+Low3 Plane_{ROI}(i)] \end{split} \tag{6}\]
## 3 Results and Analysis
### Measurement Results  
To experimentally validate the energy harvesting efficiency of this metasurface, we fabricated a \(5\times 5\) array, as shown in Figure 10a,b. A horn antenna was fed by a signal generator and illuminated the metasurface with a distance of R. Power was delivered to the central unit cell and measured by the power meter. The far field distance of R was defined according to the aperture of the horn. The experiment setup is shown in Figure 10c.  
Figure 10: A photograph of the \(5\times 5\) array from (**a**) the top and (**b**) the bottom. (**c**) The schematic diagram of the measurement setup used in the experiment.  
Figure 9: The harvesting efficiencies (**a**) without, (**b**) with square-shaped metal via rings.  
The incident power was calculated by multiplying the incident power intensity and the physical area of the central unit. The incident power intensity \(S\) at the distance of \(R\) from the horn was calculated using the following equation [16]:  
\[\overset{\rightarrow}{S}=\overset{\rightarrow}{\overset{\rightarrow}{\mathit{ \rho}}}\frac{\mathit{GP_{\text{IN}}}}{4\pi R^{2}} \tag{1}\]  
where \(P_{\text{IN}}\) is the input power and \(G\) is the gain of the horn. The reason for measuring a center unit cell is that it was the closest to the one in the infinite array. In order to achieve an EM coupling environment similar to the one in the simulation, the other unit ports of the array were all connected to a 50 \(\Omega\) load (see Figure 10b).  
To adjust the incidence angle in the experiments, we rotated the metasurface while keeping the transmitting horn fixed. The center point of the metasurface was aligned to that of the horn plane. Figure 11 shows the measured harvesting efficiency of the TE and TM waves under oblique incident angles. The maximum efficiency at the normal incidence was 88% at 5.91 GHz, which had a slight shift of 1.9% to the simulated 5.8 GHz. Compared with the original frequency of 5.91 GHz at the normal incidence, the operation frequency in the broadside direction shifted only 12 MHz (0.2%) up to a 75\({}^{\circ}\) incidence angle for both the TE and TM waves. When the incidence angle was 60\({}^{\circ}\), the maximum harvesting efficiency was 60%. The differences between the simulated and measured results come from three aspects. Firstly, the fabricated metasurface array was \(5\times 5\), while the simulated one was an infinite array. The finite array had the edge effect, which would influence the central cell's performance. Secondly, the simulation software imitated the infinite array by setting the period boundary condition around the unit cell, which might have caused a difference with the actual infinite array. Thirdly, the fabrication tolerances might have also induced errors. This work focused on the frequency stability over a wide range of incidence angles, and the measured results were in reasonable agreement with the simulated ones.
## 3 Framework
### Architecture
#### 3.1.2 Experiments and Measurements
From a control plane perspective, the data collected from the vUE probes can be gathered for several purposes, such as detecting anomalies and enhancing the channel selection process, learning about users behavior (mobility patterns, connection time, data usage, etc.) and extracting context information (number of users, number of base stations among others). Accordingly, concerning the user plane, data collected from vUE experiment campaigns could be incorporated to improve the service. In this way, supervised learning has been proposed for the QoE correlation model [15].
# Effect of Human Hair Fibers on the Performance of Concrete Incorporating High Dosage of Silica Fume
###### Abstract  
Sustainable development in structural materials is currently getting attention all around the world. Solid waste, building and demolition waste, natural resources, and their reuse are the most obvious strategies for achieving sustainability in the construction industry. Solid waste human hair fiber (HHF) with a diameter of 70 \(\upmu\)m and a length of 30-40 mm is used as a fiber, having a dosage of 0%, 1%, 1.5%, 2%, 3%, 4%, and 5%, while silica fume (SF) with a dosage of 0%, 5%, 10%, 15%, 20%, 25%, and 30% is used as a cement substitute. A drop of 50 mm to 75 mm slump was witnessed for the water-cement ratio used in the M20 mix design of concrete. The concrete's mechanical properties, such as compressive, split tensile, and flexural strength, were determined after 28 days of water curing. The concept of the response surface methodology (RSM) for optimizing human hair fiber concrete (HHFC) and SF substitution was used, which was validated by the polynomial work expectation. The model is statistically significant when the fluctuation of the analysis of variance (ANOVA) is analyzed using a \(p\)-value with a significance level of 0.05. The test results showed that the use of 2% human hair as fiber and 15% SF as a cementitious additive or cement replacement considerably improved the strength of concrete. The compressive, flexural, and split tensile strengths of HHFC improved by 14%, 8%, and 7%, respectively, which shows the significance of human hair and the partial replacement of cement with SF. Moreover, SEM analysis was carried out to study the microstructure of the concrete matrix.  
low-carbon sustainable concrete; human hair fiber; silica fume; ANOVA; RSM; SEM; solid waste +
Footnote †: journal: Applied Sciences
## 5 Simulation and Analysis  
### The Data of Simulation  
The modified IEEE33 node system is used as an arithmetic example in this paper. The IEEE33 node system is accessed to wind turbines, PVs, gas turbines, VPSSH, BES, CBs, and SVG, with the access locations and capacities as shown in Table 1, and the system structural topology as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Device** & **Position** & **Capacity (kW)** \\ \hline WT1 & 24 & 500 \\ \hline WT2 & 29 & 500 \\ \hline PV1 & 14 & 300 \\ \hline PV2 & 20 & 300 \\ \hline VPSSH1 & 17 & 500 \\ \hline VPSSH2 & 33 & 300 \\ \hline BES1 & 18 & 150 \\ \hline BES2 & 25 & 100 \\ \hline SVG1 & 22 & 300 \\ \hline SVG2 & 27 & 300 \\ \hline GT1 & 5 & 250 \\ \hline GT2 & 11 & 200 \\ \hline \end{tabular}
\end{table}
Table 1: System device configuration parameters.  
OLTC is connected to the connecting point of the system and the large power grid, with an adjustable range from 0.95 pu to 1.05 pu, and the adjustment step size is 0.01 pu. Capacitor banks access node 8 with an adjustable capacity of 400 kVar and the adjustment step size is 100 kVar. The active power to keep the VPSSH turbine idling at the phase modulation stage is 20% of the rated power, and the minimum power factor of the VPSSH converter is 0.75. The operating efficiency of VPSSH and BES is 0.8 and 0.9, respectively. The prediction M of Model predictive control is 4, meaning it predicts the system's output for the next 1 h.  
To verify the effectiveness of the proposed method and model, the optimization results were compared with those that did not consider VPSSH phase modulation in the day-ahead stage, and in the intra-day stage, the results of rolling optimization based on MPC are compared with traditional open-loop rolling optimization results, and comparisons are also made with and without considering the daily operating capacity. For multi-time scale simulation needs, Figures 4 and 5 show the day-ahead forecast values and intra-day rolling forecast data for wind, PV, and load. The intra-day rolling forecast data are obtained by adding normally distributed errors to the day-ahead forecast values [22]. In the day-ahead and intra-day dispatch stages, these forecast data of day-ahead and intra-day are brought into Equation (10) and Equations (12) and (13), respectively, to optimize the system.  
The optimal model proposed in this paper includes non-convex nonlinear constraints, which need to be transformed into a second-order cone programming problem by convex relaxation, and then solved using a commercial solver [23]. The simulation is conducted in Matlab R2022b, using the Yalmip optimization tool for modeling and Cplex for solving.  
Figure 4: Day-ahead forecast values for wind, PV and loads.  
Figure 3: Modified IEEE33 node system.
## 3 Experimental Results and Discussion  
In this work, three case studies were utilized to validate the prediction accuracy and forecasting capability of the DGM(2,1) model and its modified models. First, data from each sample was used to establish the DGM(2,1) models as Equations (1)-(5). After this, the Fourier correcting approach and the Markov chain were employed to construct the modified models, including the MDGM(2,1), the FDGM(2,1) and the FMDGM(2,1), based on the predicted series of the DGM(2,1) model, as Equations (6)-(15). Then, the predicted series of the proposed models were used to determine the actual residual. Some evaluation standards as shown in Equations (16)-(19) were considered to test the accuracy of the proposed models. In addition, the developing coefficient, \(a\), of the DGM(2,1) model was also used to assess the forecasting capability of the proposed models.  
The empirical analysis of three samples is presented as follows.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Range of Developing Coefficient \(a\)** & **Forecasting Capability** \\ \hline
1 & \(-a\leqslant 0.3\) & The model can be used for medium and long-term forecasting \\
2 & \(0.3<-a\leqslant 0.5\) & The model is suitable for short-term forecasting \\
3 & \(0.5<-a\leqslant 0.8\) & The model is carefully employed in short-term forecasting \\
4 & \(0.8<-a\leqslant 1\) & The model should be modified with residual \\
5 & \(-a>1\) & The model is not suitable for forecasting \\ \hline \hline \end{tabular}
\end{table}
Table 1: Forecasting capability of the grey model.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Commonly Used Protocols
#### LoRa and LoRaWAN  
LoRa is an unlicensed band physical layer technology that transmits data signals in the subGHz ISM band. LoRa allows low-data-rate long-range, low-power wireless communication. LoRa has a range of up to 15 km in rural areas and up to 5 km in urban areas, with data rates of 0.3 kbps-50 kbps in Europe and 0.9 kbps-50 kbps in the US [37; 38]. LoRaWAN is an open standard that was developed on top of LoRa. It consists of an end device, gateway, network server, and application server. LoRaWAN is located in the data link layer and provides a complete solution by adding a network layer that includes features such as security, authentication, and data routing [21].
## 8 Technical Challenges for 6G Development
### Terahertz Frequency
#### 8.1.6 Spectrum Regulation  
The International Telecommunication Union (ITU) has designated frequencies at 0.12 THz and 0.2 THz for wireless communications, yet guidelines for using the spectrum beyond 0.3 THz are still undefined. It is crucial to engage in joint endeavors both within the ITU framework and throughout World Radiocommunication Conference (WRC) sessions to foster agreement on these matters [42].
## 2 Data and Methods
### Atmospheric RT Model
We also neglect the spectral dependence of LER in UV, which is less than 0.05 over both land and ocean in the UV, causing \(<\)2% error in \(E_{clr}\) (see Figure 6 in [PERSON] et al. [35]). Even with spectrally independent \(\tau_{c}\) and LER, our algorithm accounts for the spectral dependence of diffuse cloud transmittance, which results from interactions between the cloud and the Rayleigh atmosphere and ozone absorption. A \(C_{T}\) error analysis [41] shows that the C1 cloud model with OMI-derived effective \(\tau_{c}\) at 360 nm can also be used to describe attenuation of surface UV irradiance by non-absorbing aerosols (e.g., sulfates and oceanic) or their mixture with clouds. The surface UV radiation is more strongly attenuated by UV absorbing aerosols (e.g., smoke and dust) of the same optical depth. These transitory plumes of absorbing aerosols are detected using the OMI UV aerosol index (AI). Either \(C_{T}\) or the absorbing aerosol correction is applied for each OMI pixel. The choice between these two alternative techniques is based on the threshold values of LER and AI. Currently, the AI correction technique is applied if AI \(>\) 0.5 and LER \(<\) 0.15. The error budget is estimated for each OMI pixel assuming a horizontally homogeneous scene. The noise (random error) in OMI radiance measurement is considered to be much smaller than retrieval errors due to uncertainties in the clear sky atmospheric radiative transfer model, \(E_{clr}\), \(C_{T}\) and ETS irradiance (about 3% [23]). The \(E_{clr}\) errors are determined by errors in input geophysical parameters: total column ozone amount, surface pressure and reflectivity [35]. The strategy of the \(C_{T}\) error estimate is as follows: we model the TOA reflectance and true cloud transmittance \(C_{T}^{true}=E/E_{clr}\) for different cloud/aerosol scenarios. Next, we use this TOA reflectance to infer the effective cloud optical thickness, \(\tau_{c}\), and \(C_{T}\) specifically for the C1 model (C\({}_{T}^{\mathrm{Cl}}\)) to estimate \(C_{T}\) error, i.e., \(C_{T}^{true}-C_{T}^{\mathrm{Cl}}\), for different OMI viewing geometries and cloud/aerosol models (i.e., cloud drop size distribution, cloud height and cloud fraction, aerosol optical properties, etc.). Finally, we combine all errors to estimate the total uncertainity in the surface irradiance as \(\Delta E\sim 10\%\) in the UVB and \(\Delta E\sim 7\%\) in the UVA (see Table 5.5 in [41]).
# Cybersecurity Risk Assessment in Smart City Infrastructures +
###### Abstract  
The article is devoted to cybersecurity risk assessment of the dynamic device-to-device networks of a smart city. Analysis of the modern security threats at the IoT/IIoT, VANET, and WSN inter-device infrastructures demonstrates that the main concern is a set of network security threats targeted at the functional sustainability of smart urban infrastructure, the most common use case of smart networks. As a result of our study, systematization of the existing cybersecurity risk assessment methods has been provided. Expert-based risk assessment and active human participation cannot be provided for the huge, complex, and permanently changing digital environment of the smart city. The methods of scenario analysis and functional analysis are specific to industrial risk management and are hardly adaptable to solving cybersecurity tasks. The statistical risk evaluation methods force us to collect statistical data for the calculation of the security indicators for the self-organizing networks, and the accuracy of this method depends on the number of calculating iterations. In our work, we have proposed a new approach for cybersecurity risk management based on object typing, data mining, and quantitative risk assessment for the smart city infrastructure. The experimental study has shown us that the artificial neural network allows us to automatically, unambiguously, and reasonably assess the cyber risk for various object types in the dynamic digital infrastructures of the smart city.  
Keywords:cybersecurity; dynamic network; machine learning; network attack; neural network; risk assessment; smart city; quantitative risk; ANN; IoT; IIoT; VANET; WSN +
Footnote †: This is an extended version of conference paper. [PERSON]; [PERSON] The Security Risk Analysis Methodology for Smart Network Environments. In Proceedings of the 2020 International Russian Automation Conference (RussAutoCon), Scothi, Russia, 6-12 September 2020; pp. 437-442.
# Scale-Free Features in Collective Robot Foraging
## 1 Introduction
Successful collective foraging often requires a delicate combination of several extensively studied multi-agent sub-behaviors such as deployment [27; 28], exploration [29; 30], aggregation [31; 32] or information sharing [33; 34]. Hence, even though collective foraging itself can be considered to be a specific task within a large class of multi-agent problems, it rightfully receives separate attention in numerous contemporary studies [16; 35; 36; 37; 38]. Moreover, collective foraging is a promising behavior for many real-world applications such as exploration by aerial vehicles [39], underwater monitoring [40], or optimization of electrical networks [41]. Therefore, the foraging performance of artificial multi-agent systems, potentially in combination with other types of AI, is worthwhile investigating in depth. In particular, in robot swarms, various fundamental questions have already been addressed such as the influence of interference [42; 43], regulation of information flow [33] or achievement of consensus [44]. Nevertheless, other relevant questions are still open to research, including how does the distribution of individual features change in relation to the input from the environment or social interactions? Is there a connection between particular feature distributions and the performance of the swarm? To address these questions with respect to scale-free properties, we simulate the foraging behavior in a robot swarm and analyze the emergence of scale-free features. For this purpose, the complexity of the foraging task is advantageous as it offers a wide range of features that can be examined for their statistical tendency to be scale-free. Our goal can be split into the following: (i) investigating the existence of scale-free features in a robot swarm performing the foraging task, (ii) studying the correlation between these features and the swarm performance, (iii) discussing the potential role of feedback mechanisms in the emergence of such scale-free features. We begin with defining the robot (microscopic) and the swarm (macroscopic) behaviors in Sections 2.1 and 2.2, respectively. The link between these two levels of behaviors is formulated using statistical distributions and elaborated on in Section 2.4. In Section 2.5, we describe the experimental setup. Thereafter, in Section 3 we demonstrate the occurrence of scale-invariant features--such as those related to the communication degree or times spent in foraging or resting--and their correlation with swarm performance. Furthermore, we discuss the present feedback mechanisms that may support the emergence of scale-free features in a sophisticated set of scenario configurations. Lastly, the paper is concluded in Section 4.