esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain-Based Multiple Authorities Attribute-Based Encryption for EHR Access Control Scheme
## 2 Related Work
### Outsourcing Decryption  
In existing ABE schemes, the ciphertext size and decryption cost increase with the complexity of the attribute policy. Therefore, [PERSON] et al. [19] first proposed an outsourcing ABE scheme in 2011. They outsourced the complex ABE decryption computation to a third-party cloud server for decryption, which reduces the computation of the decryption process on the user side. Since the third-party cloud server is not fully trusted, the correctness of the outsourced decryption result needs to be verified. [PERSON] et al. [20] proposed an ABE scheme that supports the verification of outsourced decryption by introducing a verification element in the ciphertext. The re-encryption key is generated based on the user key and sent to a third-party cloud server for decryption. [PERSON] et al. [21] proposed a method to convert any ABE scheme with outsourced decryption into an ABE scheme with verifiable outsourced decryption with verifiability. [PERSON] et al. [8] proposed an accountable and verifiable outsourced decryption CP-ABE scheme. This scheme achieves accountability for the user key leakage problem and the verifiable outsourced scheme by transferring the key. [PERSON] et al. [22] proposed a CP-ABE scheme for outsourced decryption in the IoT environment. Fog nodes assist in implementing verifiable outsourced decryption, which reduces the complexity of user computation. [PERSON] [23] proposed a lightweight verifiable CP-ABE scheme applied to wireless body-sensing networks, which provides users with correctness verification of outsourced decryption. [PERSON] et al. [24] proposed a blockchain-based searchable attribute-based encryption scheme BC-SABE that supports user outsourcing decryption functions. The blockchain system replaces the traditional centralized server, responsible for generating threshold parameters and key management. [PERSON] et al. [25] constructed an eHealth fine-grained access control system AC-FEH in a fog computing scenario, which uses fog nodes for data encryption and decryption operations to minimize the computational cost for data owners and users. [PERSON] et al. [26] proposed a scheme with fine-grained access control, outsourced decryption, and ciphertext verification with the help of cloud servers and blockchain in the IoMT ecosystem. The scheme is based on the chameleon hash function to construct data user private keys with conflict resistance, semantic security, and keyless exposure.
# Instantaneous Reactive Power Theory in the Geometric Algebra Framework
###### Abstract  
In this paper, a new approach for instantaneous reactive power analysis in the geometric algebra (GA) environment is presented. The different formulations of the instantaneous reactive power theory (IRPT) proposed, to date, have been developed in three-phase systems. There, an instantaneous power variable, and two/three reactive power variables, all handled independently, were introduced. Thanks to GA, it is possible to carry out a global treatment where an instantaneous power multivector is defined. Thus, in the same multidimensional entity all the power variables are included. From the instantaneous power multivector, the instantaneous power current and the instantaneous reactive current are determined. It should be noted that in this mathematical framework there is no limitation on the number of phases, and the extension of the IRPT to the analysis of multi-phase systems appears in a natural manner. In this study, a systematic approach with the most relevant definitions and theorems corresponding to the proposed methodology has been established. Two practical cases of five-phase and three-phase systems have been included to apply the new established formulation.  
Keywords: instantaneous reactive power; electric power quality; geometric algebra; harmonic compensation; active power filter +
Footnote †: journal: Applied
# Hybrid MU-MIMO Precoding Based on K-Means User Clustering
## 3 Spatial Compatibility and Channel Modeling
### Channels Separation Metric  
The spatial channel correlation is important for multiple-antenna users (Single User (SU)-MIMO communications); however, in the context of single-antenna users, it is the set of spatial correlation matrices of users that determine the network performances.  
A metric used to quantify how separated the channels of two users are was described in [15; 16]. Assuming that \(\mathbf{h}_{i}\) and \(\mathbf{h}_{k}\) are the channels of two users, the channel separation metric is defined as:  
\[\cos\left(\angle\left(\mathbf{h}_{i},\mathbf{h}_{j}\right)\right)\triangleq \mathbb{V}\left\{\frac{\mathbf{h}_{i}^{H}\mathbf{h}_{k}}{\sqrt{\mathbb{E}\left\{ \left|\left|\mathbf{h}_{i}\right|\right|^{2}\right\}\mathbb{E}\left\{\left| \left|\left|\mathbf{h}_{k}\right|\right|^{2}\right\}}\right\}, \tag{12}\]  
where \(\cos\left(\angle\left(\mathbf{h}_{i},\mathbf{h}_{k}\right)\right)\in\left[0,1 \right]\) and \(\mathbb{V}\left\{x\right\}\) is the variance of a random variable \(x\).  
The metric indicates how efficiently the transmitter can serve user \(i\) without affecting user \(k\), and vice versa. Strongly-correlated channels exhibit higher values, whereas for Rayleigh uncorrelated fading, the variance is \(1/N_{T}\) and decreases when the number of antennas increases. In general, the variance depends on the spatial channel correlation and equals zero when the users have orthogonal correlation matrices, \(\angle\left(\mathbf{h}_{i},\mathbf{h}_{k}\right)=\frac{\pi}{2}\).  
In MU-MIMO systems, a set of users is called \(\epsilon\)-orthogonal, if \(\cos\left(\angle\left(\mathbf{h}_{i},\mathbf{h}_{k}\right)\right)<\epsilon\) for every \(i\
eq k\). The users can be grouped into disjoint sets according to a desired threshold \(\epsilon\), such that semi-orthogonal sets could be scheduled over independent resources.
## 2 Nonlinear-Trajectory SAR Deception Jamming Algorithm Based on HDE  
This section will systematically derive the HDE-based nonlinear-trajectory deceptive jamming algorithm. First, the geometric model of the nonlinear-trajectory SAR system is introduced, then the principle of SAR deceptive jamming is presented, and finally, the principle of the HDE algorithm is derived in detail.
## 4 Security Attacks
### Network Layer
#### 4.2.3 DDoS Attack  
Distributed denial of service (DDoS) attacks [18; 29; 31; 32; 42] target the availability of blockchain systems by overwhelming network resources. In these attacks, an adversary leverages a network of compromised devices (a botnet) to flood the network with an excessive number of requests. This flood exhausts processing power and bandwidth, rendering the system unable to service legitimate traffic. High-profile incidents, such as those experienced by Bitfinex in 2017 and 2020, demonstrate the disruptive potential of DDoS attacks. Effective countermeasures include traffic filtering, rate limiting, and enhancements in network resilience and scalability to manage sudden spikes in traffic.  
Figure 3: Eclipse attack isolating a victim node and feeding it false blockchain data.
## 2 Methods
### Data Analysis
#### 2.3.3 Hyperspectral Image Analysis  
For the APEX field site, we used the aerial imagery to perform a supervised classification analysis using a random forests (RF) model to classify PFTs across the landscape [77]. We designated four different classes representative of dominant site PFTs. The classes were coniferous forests, low shrub, tussock meadow, and graminoid fen (Figure S4). Before performing the analysis, we manually identified approximately one hundred training points within each class using a high-resolution aerial photograph collected in July of 2016 using an eBee senseFly unmanned aerial system (UAS) mounted with a Canon S110 camera (Figure 4, left panel). Although identifying training points in the field would have been ideal, we are confident that we were able to select training points that accurately reflected the ascribed vegetation cover class based on the image and our knowledge of the site. The UAS was flown at a height of approximately 92 m above the ground. The resulting imagery had a 3-cm ground resolution and was mosaicked together and georeferenced using a series of high-resolution GPS points that were collected via total station at the time of data collection. The UAS image was mosaicked using the Agisoft Photoscan software platform. RF analyses were completed using the randomForest R package [78] and data were processed using the rgdal R package [79]. Standard model parameters were used in the RF model. Five hundred trees were created, and the number of variables considered at each node was the square root of the number of parameters. We used a model selection approach, as implemented in the rfUtilities R package [80], to find the most parsimonious and explanatory set of predictors. We used the overall out-of-bag accuracy and Cohen's kappa coefficient to evaluate model performance [81]. Variable importance was assessed using the mean decrease in accuracy and mean decrease in impurity upon iterated variable permutation, as implemented in the randomForest R package; variables that are more important will tend to cause higher mean decreases in both accuracy and node impurity when they permuted [77]. All associated mapmaking was performed using Quantum GIS [82].  
Figure 4: **(Left) Unmanned aerial image rendered in three visible bands, including a near-infrared band. Image has been clipped to the extent of the hyperspectral aerial image. The UAS image was used to select supervised classification training points. (Right) Extent of hyperspectral aerial dataset, rendered in true-color RGB bands at 457, 570, and 650 nm. The APEX water table manipulation areas can be seen as the white squares to the north of the images.**
## 3 Results and Discussion
### Comprehensive Analysis  
Taking the COD response coefficient field formed by pollution point sources as an example (Figure 5), it was found that reclamation activities led to changes in the pollutant response field formed by each pollution point source. With the superposed implementation of reclamation activities, the response coefficient gradually increased. This also demonstrated that the pollution in Haizhou Bay was gradually increasing due to the overlapping implementation of the activities.  
By analyzing the changes of pollutant EC caused by the implementation of each scenario, it was found that these reclamation projects had a spread impact on the EC of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Declamation Scheme** & \(R_{1}\) & \(R_{2}\) & \(R_{3}\) & \(R_{4}\) & \(R\) & **Evaluation Result** \\ \hline A & - & - & - & - & - & - \\ B & 10 & 8 & 10 & 10 & 9.5 & feasible \\ C & 10 & 10 & 10 & 4 & 8.5 & feasible \\ D & 4 & 8 & 4 & 4 & 5 & basically feasible \\ E & 0 & 0 & 0 & 0 & 0 & infeasible \\ F & 0 & 0 & 0 & 0 & 0 & infeasible \\ \hline \hline \end{tabular}
\end{table}
Table 23: Feasibility evaluation of EC (scenario 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Declamation Scheme** & **Evaluation Result** & **Area (km\({}^{2}\))** & **Appropriate Scale of** **Declamation (km\({}^{2}\))** \\ \hline A & - & 0.00 & \\ B & feasible & 18 & \\ C & feasible & 64 & \\ D & basically feasible & 83 & \\ E & infeasible & 105 & \\ F & infeasible & 114 & \\ \hline \hline \end{tabular}
\end{table}
Table 24: The maximum allowable area for CR in Haizhou Bay (scenario 3).  
the four pollutants, and the impact on NH\({}_{4}\)-N and PO\({}_{4}\)-P was greater than on COD and NO\({}_{\rm X}\)-N. In addition, the change of EC of pollutants had an extensive relationship with the location of the implementation of the reclamation schemes. For the same reclamation project, due to different scenarios, the sequence and location of the reclamation changed, leading to strong changes in EC.  
According to the comprehensive evaluation and analysis of the three scenarios (Table 25), the conclusions were as follows. For scenario 3, schemes B, C and D were all permissible, and the suitable area for reclamation was 83 km\({}^{2}\), which was the scenario with the least impact on environment capacity, among all reclamation scenarios. Therefore, scenario 3 is recommended as the best reclamation scheme. In scenario 2, schemes B and C were allowed to be implemented, and the suitable area for reclamation was 64 km\({}^{2}\), which is recommended as the second-best reclamation scenario. In scenario 1, only scheme B could be implemented and the reclamation suitable area for reclamation was the smallest (18 km\({}^{2}\)). Therefore, it was the worst reclamation scenario and is not recommended.
## 3 Results and Discussion
### DTF Experiment Results
#### 3.2.2 DTF NOx Index
Except for abnormal values showing a 25% to 37% difference in NOx concentration to that of a neighboring time slot, it is believed that the overall calculated NOx concentrations accurately represent the actual trend in NOx emissions [21]. The average absolute error containing and excluding an abnormal value were 10.45% and 6.18%, respectively. Therefore, the NOx prediction formula comprising the basic properties of coal is considered to be meaningful, unlike the previous studies, and it will be used as useful data for the preemptive response of power plants.
# Underground Mine Road Detection Using Deep Learning Technique
## 3 Experiment and Results  
### Experimental Parameter Settings  
The training process was carried out in the Windows + cuda10.1 system environment, the computer configuration is i5-10300H CPU, 16 GB memory, and the graphics card is NVIDIA GTX1650 ti 4G memory GPU. A neural network program was written using the Paddle2.2 framework. In this experiment, STDC was used as the backbone network, and mini-batch stochastic gradient descent was selected for training. In this paper, the \"poly\" strategy was used to adjust the learning rate, and the power was set to 0.9. Compared with the traditional strategy of updating the learning rate, the \"poly\" strategy has a more obvious effect on accelerating the network convergence. The experimental parameters are shown in Table 1.
## 1 Introduction
### Impulsive Noise  
Impulsive noise in wireless systems may arise from several different sources, such as lightning, electrical switches, motors, vehicle ignition circuits, and reflections from sea waves, and it is known that it can severely degrade the performance of communications systems [9,10].  
In [11], for instance, the performance of energy detection with selection combining and equal gain combining is investigated, and it is shown that the sensing performance can be affected by impulsive noise. Also in [11] a non-linear method based on GLRT was proposed. However, apart from this work, little effort has been put in the investigations on the influence of impulsive noise in cognitive radio receivers in the context of spectrum sensing. In particular no analysis has been previously made considering the different eigenvalue based methods, and the non-realistic conventional model has been applied in the literature.
# Influence of Source Shape on Semi-Airborne Transient Electromagnetic Surveys
## 3 Results
### 3D Model  
#### 3.2.1 Electric Fields in the Earth  
Figure 11 illustrates the electric field distribution in the surface oxy plane at various time instants for both linear and curved sources, where Figure 11a-c represent the curved source responses and Figure 11d-f depict the linear source responses. In the presence of the 3D low-resistivity anomaly, the electric field distribution undergoes significant distortion, with the current being preferentially channeled toward the conductive body. Consequently, a minimum in the electric field magnitude develops within the region occupied by the 3D anomaly, consistent with the findings reported by [PERSON] et al. [15]. A comparative analysis of Figures 4 and 11 reveals that the shallow electric field distribution in the 3D model closely resembles that of the homogeneous half-space model, except for the localized low-amplitude distortion coinciding with the 3D anomaly. This observation indicates that the source geometry predominantly influences the shallow electric field distribution during early time periods while having minimal impact on the deep electric field distribution at later times. Conversely, the 3D conductive anomaly primarily affects the electric field distribution at depth. These results suggest that the influences of the source geometry and the 3D conductive anomaly on the electric field distribution can be considered effectively independent of each other.
## 3 Measurement Techniques of the Electro-Optic Coefficient
### Nonlinear Ellipsometry
In the low birefringence approximation, with the assumption of \(\Delta|r_{s}|^{2}\approx 0\), the amplitude of the \(P_{ac}\) component at \(\Omega\) is given by:  
\[ \begin{array}{l}P_{ac}(\Omega)=\frac{k_{0}}{n^{2}}\frac{\sin^{2}(\alpha)}{ \sqrt{n^{2}-\sin^{2}\alpha}}\,P_{0}\big{|}r_{s}\big{|}^{2}\cdot V_{m}\,\cdot \\ \cdot\big{\{}\mathrm{Re}\big{(}\chi_{333}^{(2)}-\chi_{113}^{(2)}\big{)}\cdot \sin(\Psi_{ps}+\Psi_{c})\cdot\mathrm{Im}\big{(}\chi_{333}^{(2)}-\chi_{113}^{( 2)}\big{)}\big{[}\tan\Phi-\cos(\Psi_{ps}+\Psi_{c})\big{]}\big{]}\end{array}  \tag{28}\]  
with \(\chi_{333}^{(2)}=3\chi_{113}^{(2)}\) in the weak poling approximation. By varying the phase difference \(\psi_{c}\) introduced by the compensator from 0 to \(2\pi\), Equations (26) and (28) give the parametric equations of an ellipse. There are two working points (labeled 1 and 2) where the \(P_{dc}\) reach half of its maximum value: \(P_{dc}^{(1,2)}=\frac{1}{2}P_{dc\max}\). Those conditions can be found when: \(\Psi=\Psi_{ps}+\Psi_{c}=\pm\pi/2\). By using a lock-in amplifier, it is possible to measure the effective \(P_{ac}\) signal in these two working points and then it is possible to retrieve the electro-optic coefficient by the relation:  
\[r_{33}=\frac{3\lambda_{0}}{2\pi}.\sqrt{2}\cdot\frac{\sqrt{n^{2}-\sin^{2}\alpha} }{n^{2}\sin^{2}\alpha}\cdot\frac{P_{ac}^{(1,ct)}-P_{ac}^{(2,ct)}}{4P_{dc}^{(1, 2)}}\cdot\frac{1}{V_{m}}=3r_{13} \tag{29}\]  
We point out that the measurements in the two working points 1 and 2 are faster than the measurement based on the sampling of the whole ellipse, but suffer from lower accuracy. The faster measurements can be usefully adopted in monitoring the time decay of the electro-optic coefficient, as we will show later. Usually, when analyzing experimental data in an ellipsometric measurement, one makes the assumption that the laser beam is passing just twice through the polymer layer. However, if the reflectance of the semiconductor electrode is not negligible at the measurement wavelength, the electrodes system can behave as a Fabry-Perot resonator and the evaluation of the electro-optic coefficient obtained via the application of the simplified single pass model is wrong [46]. As most researchers are using indium tin oxide (ITO) as the semitransparent electrode, this means that, unless a deeper theoretical approach is used [45, 47, 48, 49], all the measurements obtained for wavelengths larger than about 1\(\upmu\)m are affected by large errors due to the fact that ITO has got its plasma resonance in near-infrared and becomes reflective. Better results can be obtained in the near-infrared region by using aluminium doped zinc oxide (ZnO:Al) or, alternatively, more transparent electrodes [50, 51].
## 2 Experimental Procedures
### Electrical Conductivity Measurements  
Surface electrical conductivity was incrementally measured at the exposed mortar surface, after step grinding of 10 mm of material. DC electrical conductivity was measured using a custom-built contact electrical conductivity probe connected to an ultra-high precision digital electrometer/high resistance meter (Keithley 6517B, Tektronix Inc., Beaverton, OR, USA) capable of measuring resistances up to 1018 with a 10 \(\times\) 10\({}^{-18}\) A current resolution. The probe, presented in detail in reference [2], consists of a circular head comprised of 22 concentrically-arranged spring-loaded pin electrodes with conductive rubber ends for optimal contact with the non-planar cement surfaces. The head rested on a z-translational stage, which could be lowered and brought into contact with the specimen at constant force by means of a lever. Conductivity, assumed exclusively attributable to nanotube presence, is anticipated to vary with interrogation depth as a result of variable chloride ion concentration at the instant depth.
## 4 Proposed Scheme (Multi-BSM Approach)  
RSUs are responsible for detecting anomalies in the network. It is assumed that all the RSUs are legitimate and no malicious RSU is present. All the RSUs are connected to each other and linked with a centralized database that contains all the relevant important information. The centralized database is only accessed through RSUs and contains informationabout the connected vehicles present in the network, and the information is available to RSUs in real time.  
The multi-BSM approach is used to detect anomalies, i.e., position falsification attacks, to be specific, in the vehicular connected environment. Every vehicle that joins the network shares its basic information such as speed, direction, location, etc., to its surrounding neighbors, e.g., RSUs and other vehicles.
## 2 Related Work  
This section gives an overview of related topics and previous surveys related to IEC's challenges, characteristics, benefits, potential use cases and applications, and market drivers. Previous works and surveys have covered reviews, state of arts, software and applications, vehicular and heterogeneous networks, IoT, content awareness and IEC in 5G [17]. According to the main objective of this study, the related works included overviews [18; 19; 20] surveys of IEC [21; 22; 23; 24], characteristics and benefits [25; 26], challenges of IEC [19; 22], potential use cases and applications [5; 7; 11], and market drivers [27].  
Previous studies in intelligence edge computing in Table 1 investigated published work from 2014 to 2020 in 5G and its integration with multi-access edge computing. These works were summarized by the topic, year and main contributions. Our research demonstrates intelligence edge computing in both 5G and 6G networking. Furthermore, our work includes market drivers and open cases in 6G networks. Vertical markets include entertainment such as tele-presence, holo-portation, and multi-sense technologies. For instance, conducting teleconference video-call in daily meetings where several people carry out their presence to the same location. In addition to healthcare uses, such as tele-surgery, tele-monitoring, and haptics technologies, automotive applications and technologies which have a huge use in different industries including broadcasting, communication, computing, instrumentation, security, programming, and networks.