esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Load Balancing for Blockchain-Based Healthcare System in Smart Cities
## 2 Related Work
### Load Balancing in Blockchain Networks  
Load balancing in blockchain networks has been considered in several works where researchers have proposed a fair method of executing blockchain-related tasks. In Table 1, we present a summary of these techniques along with our proposed idea at the end.  
Authors in [39] considered a vehicular network scenario, where vehicles offload their mining tasks to the roadside units. The idea of the work is to divide the road area into clusters and assign each mining node to a cluster. By using an auction-based game theoretic algorithm, the work increases the transmission rate of mining tasks and also ensures the fairness of tasks at the mining nodes.  
In [40], a genetic algorithm is proposed to evaluate the feasible resource allocation solution for IoT networks. After the first step, simple additive weighting and multicriteria decision making are used to find the optimal allocation of resources such that the load is balanced, and the requirements of task delay and node energy consumption are achieved.  
The work in [41] considered a blockchain-based electric vehicle scenario, where energy cooperation is desired. A contract incentive mechanism is proposed to promote energy cooperation among the vehicles and achieve energy load balancing. Similarly, the work in [42] introduced a peer-to-peer energy trading mechanism for blockchain-based smart grids. Smart contracts were developed for energy trading such that electric load balancing is achieved.  
In [43], a financial transaction system is considered, and an efficient data-sharing scheme is proposed so that the data load is balanced. The Kademlia-based distributed hash table (DHT) is proposed for broadcasting blockchain-related data. Additionally, the work in [44] proposed a round-robin mechanism for financial blockchains. The detailed performance analysis of such blockchain systems and the impact of load balancing is studied. The block creation and block validation performance are analyzed.  
\begin{table}
\begin{tabular}{c c c} \hline
**Blockchain Network Used** & **Load Balancing Technique** & **Key Idea** \\ \hline Vehicular network [39] & Auction mechanism & Clustering mechanism \\  & & Transmission rate improvement \\  & & Fairness \\ \hline \multirow{3}{*}{IoT [40]} & Genetic algorithm & \multirow{3}{*}{Find feasible resource allocation} \\  & Simple additive weighting & \\  & Multicriteria decision making & \\ \hline Electric vehicles [41] & Contract incentive mechanism & Incentive based energy cooperation \\ \hline Smart grids [42] & P2P energy trading & Smart contract for energy trading \\ \hline Financial systems [43] & Kademlia based DHT & Data sharing based on DHT \\ \hline Financial systems [44] & Round Robin & Block creation performance \\  & & Block validation performance \\ \hline IoT (Our technique) & Minimum makespan & Fair distribution of nonce tasks \\  & Prioritization & Quick execution of sensitive data \\ \hline \end{tabular}
\end{table}
Table 1: Load balancing techniques used in blockchain.
# Design of a NARX-ANN-Based SP Controller for Control of an Irrigation Main Canal Pool
## 2 ANN-Based System Identification of an Irrigation Main Canal Pool
The first and second models are nonlinear and are based on a NARX-ANN and a TD-NARX-ANN with recurrent architectures [53] due to the advantages of this type of structures, among which are the possibility of approximating any function with the desired precision, provided that it presents a limited number of discontinuities [43]. For nonlinear system identification the Matlab Neural Network-based System Identification Toolbox was used. Recurrent ANNs are characterized by having feedback (or connections) between neurons in one layer and neurons in previous layers and in the same layer, in addition to forward connections, as well as maintaining a certain dependence on events that occurred in past moments (memory effect), which is convenient in the system identification procedure [43]. The development of the best architecture for the NARX-ANN-based models requires the determination of the number of hidden layers, the activation functions, the process time delays, and the efficient training algorithm [53]. The mathematical formulation of the NARX-ANN-based model can be written by the following discrete time expression [71]:  
\[y(t)= \begin{array}{c}f(u(t-1),u(t-2),\ldots,u(t-n_{u}),\\ y(t-1),y(t-2),\ldots,y(t-n_{y})),\end{array}  \tag{1}\]  
Figure 2: Schematic diagram of the first pool in ZCAMC, where LS is a level sensor, GPS is a gate position sensor, PLC is a programmable logic controller, \(Q_{i}(t)\) are the water flows; \(u_{1}(t)\) is the gate position, \(q_{1}(t)\) is the lateral discharge, \(r_{L1}\) is the water level setpoint. where \(u(t)\) and \(y(t)\) denote respectively the input and output of the model at the discrete time step \(t\), \(n_{u}\), \(n_{y}\) are the input-memory and output-memory orders respectively, and \(f(\cdot)\) is a nonlinear function represented by the ANN. The sampling period of the input and output data was \(T=\)30 s.  
The TD-NARX-ANN-based model is expressed as [72; 73]:  
\[ \begin{array}{rl}y(t)=&f(u(t-d-1),u(t-d-2),\ldots,u(t-d-n_{u}),\\ &y(t-1),y(t-2),\ldots,y(t-n_{y})),\end{array}  \tag{2}\]  
where \(d\) (\(d>0\)) is the number of process time delay sampling periods. We consider \(d=10\) which implies a time delay of 300 s.  
Figures 3 and 4 illustrate the diagrams with the recurrent architectures of the NARX-ANN and TD-NARX-ANN-based models that have been designed, which have an input layer with 13 memory blocks for the input and output signals, a hidden layer with three neurons, and an output layer with one neuron. The activation functions in the hidden and output layers are hyperbolic tangent sigmoid and piecewise-linear (symmetric saturating linear) respectively. The difference between the architecture of these models lies in the TD-NARX-ANN-based model has the time delay block \(Z^{-d}\) highlighted in red line.
# UAV Dynamic Non-Terrestrial Transmission Channel Analysis Based on SSCM-RT Model
## 2 Theoretical Background  
The channel fading model parameters and the concept of spatial consistency are introduced in this section, which provides a theoretical basis for the research of this paper.
## 4 Numerical Results
### Minimizing Make-Span
Therefore, the ACO algorithm offers a better result in terms of optimizing the schedule with the objective of minimizing the scheduling time or makespan, C\({}_{max}\). To further test the performance of the developed ACO algorithm with the total number of operations, apart from this use case (defined as small case) that consists of 30 operations of all the jobs (Figure 3a), two more cases are defined including medium case (60 operations, Figure 3b) and large case (100 operations, Figure 3c). It can be observed in Figure 3 that the parameters such as number of ants in the colony \(A\), and tuning parameter \(Q\), needs to be adjusted accordingly to the number of operations in the problem. In addition, problems with more operations might require more iterative steps for the solution to converge. In the next sub-section, effects of these tuning parameters on performance of the algorithm and the number of required iterations will be discussed in details.
# Effect of Autochthonous Lactic Acid Bacteria-Enhanced Fermentation on the Quality of Suancai
## 3 Results and Discussion  
### The Physicochemical Properties in Suancai with Different Starters  
The physicochemical properties of Suancai fermented with different starters are shown in Figure 1. The pH and TTA are regarded as important evaluation indexes during the fermentation process of fermented vegetable foods, and the pH values of fermented vegetable were below 4.0 that can be considered the end of fermentation [20]. The inoculation of LABs could significantly decrease the pH and it ranged from 3.16 to 3.52 (Figure 1A). The pH of inoculated Suancai was significantly lower than the CON. LABs can produce organic acids, especially lactic acid. Then, the contents of the TTA and organic acid increased significantly in the inoculated Suancai with LABs in this study (Figure 1A,B). As a homofermatative LAB, _Lb. plantarum_ could produce many acids, especially lactic acid. Therefore, the inoculation of _Lb. plantarum_ had the lowest pH (Figure 1A) and a high content of lactate (Figure 1B). In addition, the contents of malate, citrate, and succinate in the LP were the highest (Figure 1B). _Lb. brevis_, an obligately heterofermatative LAB, produced a mixture of lactic acid, acetic acid, ethanol, and CO\({}_{2}\), and then its inoculation obtained a high content of lactate and acetate (Figure 1B). However, the inoculation of _Leu. Mesenteroides_, as also a heterofermatative LAB, had a lower content of lactate and acetate than the inoculation of _Lb. brevis_. Potentially, _Leu. Mesenteroides_ was an acid-sensitive microorganism and commonly grow well with an initial pH [21]. This is in accordance with previous studies on paocai [22].  
Figure 1: The contents of physicochemical properties (**A**) and organic acids (**B**) in Suancai fermented with different autochthonous LABs. CON, natural fermented Suancai. LP, LB, and LM represent the inoculated Suancai with _Lb. plantarum_, _Lb. brevis_, and _Leu. mesenteroides_. TTA, total titratable acidity, expressed in % lactic acid. Different letters represent significant differences (\(p<0.05\)).  
LAB had the nitrite degradation ability and its inoculation could decrease the nitrite concentration in fermented food [10; 13; 23]. Compared with the CON, the inoculation of the LP, LB, and LM could significantly decrease the nitrite concentration of Suancai in this study (Figure 1A). Three groups had different nitrite depletion abilities, LB and LP had a better capability to reduce nitrite compared with LM. Nitrite depletion is important for the safety of Suancai and the nitrite concentration was below the GB standard after fermentation (Figure 1A).
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 4 Cooperative Relative Positioning
### GNSS-Based Relative Localization
#### 4.2.3 Relative Positioning with Raw GNSS Measurements  
The concepts of differential GNSS can be extended by moving from the classic base-station/vehicle setup for absolute position towards a vehicle/vehicle setup for relative position estimation. By sharing pseudorange measurements over V2V communication, each vehicle can subtract the neighbor's pseudoranges from its own and compute, in this way, a relative position, while at the same time reducing the correlated GNSS errors common to both vehicles. [PERSON] et al. introduce a relative localization approach on the basis of the exchange of GNSS pseudorange data in [91]. They present the concept, but do not present concrete simulations or measurements. In [92], [PERSON] et al. present a tight integration approach, where pseudorange observations from two vehicles are subtracted from each other, and show its theoretical potential through simulations. Later, in [93], this is tested in mainly a suburban environment yielding a standard deviation error of 3.4 m for a 12-min run. [PERSON] et al. estimate the baseline between the vehicles using a weighted least squares algorithm and weighting each pseudorange according to the received carrier-to-noise ratio in [94]. Experimental results with the static 3 m and 8 m baseline on a rooftop yield baseline length errors of up to 40 m. [PERSON] et al. demonstrate that by exchanging GNSS pseudorange's between the vehicles, an unbiased relative position estimate is obtained at the expense of increasing the noise [95]. With this method, the distance between the vehicles can be estimated with less than 0.80 m and 1.30 m accuracy in highway and urban environments, respectively. A technique in which the pseudorange biases are estimated in a cooperative way between the vehicles is presented by [PERSON] et al. in [96]. A horizontal relative positioning error of 2.46 m is achieved. However, pseudorange bias estimation is a slow process that takes above ten minutes to converge.
## 4 Conclusions  
To achieve high-quality inter-satellite laser communication and compensate for the Doppler frequency shift caused by relative motion, this paper investigates a novel Fabry-Perot etalon external cavity diode laser (FP-ECDL). The paper begins by providing an explanation of the specific implementation of the FP-ECDL laser's external cavity structure and then conducts a performance analysis through optical testing experiments. Specifically, using a delayed self-heterodyne linewidth measurement system and a fiber Michelson interferometer noise testing system, the paper obtains the beat spectra, phase noise, and frequency noise power spectral density curves of the FP-ECDL with two different F-P etalon accuracies. The Lorentzian linewidth of the FP-ECDL laser is calculated from the frequency noise spectrum. Finally, a method involving the bidirectional temperature scanning of the laser is proposed to achieve the selection of the laser's optimal operating point and wavelength no-mode-hop tuning. The experimental results demonstrate that improving the F-P etalon accuracy (coating reflectivity) enhances the laser's linewidth and phase noise characteristics, reducing its low-frequency phase noise and Lorentzian linewidth. Moreover, the bidirectional temperature-scanning method for FP-ECDL achieves optimal operating point selection and no-mode-hop tuning within a range of 160 pm.  
In the future, achieving more precise, faster, and higher-resolution wavelength tuning, along with integrating this FP-ECDL with the bidirectional temperature scanning method as part of a laser terminal, could lead to excellent applications and broader market prospects in large constellation systems, such as the U.S. \"Starlink\", China's \"Star Network\", the UK's \"OneWeb\", and others.  
Conceptualization, S.H.; methodology, S.H.; software, P.L. and X.L.; validation, S.L. and W.W.; formal analysis, H.Q.; investigation, X.C.; data curation, L.Z.; writing--original draft, S.H. and P.L.; writing--review and editing, S.H. and P.L.; visualization, Y.X.; supervision, M.W.; project administration, C.G. and M.W.; funding acquisition, C.G. and M.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hubei Province (No. 2022 CFA007) and the Science and Technology Project of Hubei Province (No. 2023 BEB016).  
Not applicable.
## 2 Materials and Methods
### Petrophysical Properties Computed from Micro-Tomography Images
#### 2.3.1 X-ray Tomography Data Acquisition  
All the samples have been imaged using a X-ray Computed Tomography (XRCT) scanner (EasyTom 150) at the Institute of Evolution Sciences of Montpellier (ISEM), Montpellier, France, except N04. The pixel size is 12 \(\upmu\)m. N04 has been imaged using a XRCT scanner (Bruker Skyscan 1172) at the laboratory unit of Development of EXperimental Methodologies (DMEX), Pau, France. For this sample, the pixel size is 10 \(\upmu\)m.
## 4 Results and Discussion
### Experiment and Analysis
#### 4.2.3 Ablation Experiment
The road surface is the feature that achieves the best segmentation results for all network models, achieving more than 96%, as the main element of the scene, with an adequate amount of point clouds data that is relatively evenly distributed and has very similar characteristics. In contrast, the segmentation of the lane lines is relatively poor, with only 66.2% accuracy, which is because the lane line and the road surface, as adjacent features, have the same point clouds data density, which leads to problems in the segmentation of the two features at the junction, and the segmentation results at the junction are easily confused. Some of the lane lines are easily recognized as the road surface. Figure 14: (**a**) Analytical plot of accuracy assessment of a single class of feature IOU for the ablation experiment; (**b**) the analytical plot of the overall accuracy of the ablation experiment versus the accuracy assessment of the mIOU.
# A Hybrid Tolerance Design Method for the Active Phased-Array Antenna
###### Abstract  
With the increase of the working frequency of the array antenna, tolerance design has become increasingly important. The state-of-art tolerance design methods mainly deal with the position tolerance of the discrete elements. However, the geometric errors of the whole array have resulted from two aspects: (1) the position errors of the discrete elements and (2) the form errors of the continuous reflection plate. To optimize the position tolerance and flatness simultaneously, a hybrid tolerance design method is proposed. First, the relation between the performance of the array antenna and hybrid tolerances was determined based on the second order Taylor expansion. Then the expectation and variance of the performance were derived. Finally, the hybrid tolerances were optimized and the performance of the antenna was improved. Simulation results proved the effectiveness and efficiency of the proposed hybrid tolerance design method.  
array antenna; tolerance design; Taylor expansion; hybrid geometric errors; statistical analysis +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Proposed Method
#### 3.3.1 Adversarial Training Network Framework
The adversarial mechanism between the generator and the discriminator effectively prevents the leakage of sensitive information, thus avoiding the accuracy losses that may arise from traditional noise addition methods. Mathematically, the optimization goal of the generator is to minimize the privacy loss component in the adversarial loss function, ensuring that the generated data have a low probability of being recognized as real, that is:  
\[\min_{G}\mathbb{E}_{z\sim p_{z}}[\log(1-D(G(z)))], \tag{18}\]  
while the discriminator aims to maximize its ability to distinguish between generated and real data:  
\[\max_{D}\mathbb{E}_{\mathbf{X}\sim p_{\mathrm{real}}}[\log D(\mathbf{X})]+ \mathbb{E}_{z\sim p_{z}}[\log(1-D(G(z)))], \tag{19}\]  
Through this game-theoretic mechanism, the generator and discriminator optimize each other, ensuring that the generated data maintain privacy while retaining the key features of the original data. The structure and parameter selection of both the generator \(G\) and the discriminator \(D\) are critical in this task. The generator \(G\) must have sufficient representational capacity to ensure that the data \(\mathbf{X}^{\prime}\) generated from the low-dimensional representation \(z\) closely resemble the original data structure and features. At the same time, the discriminator \(D\) needs a powerful network structure capable of effectively distinguishing between generated and real data. In practice, the structures of the generator and discriminator can be implemented through a combination of multi-layer fully connected networks, convolutional networks, and other architectures to enhance the model's representational and discriminative capabilities.
# A Systematic Review of Radio Frequency Threats in IoMT
## 3 Results and Discussion  
The purpose of this section is to discuss the results retrieved from the publications and discuss two research questions.  
1. RQ1: How well has IoT been integrated into health care?  
We completed a systematic analysis of research articles per year against research for jamming [19], replay [20], sniffing [21], spoofing [20,22], side-channel, and tampering attacks in the web of science PubMed and Scopus. The results clearly show that computer and wireless communication domains are dominant, and the research articles are distributed in sensing layer attacks of IoMT. Table 1 and Figure 4 show the search analysis of cyber spectrum attacks across journals as per the PRISMA checklist in Appendix A. The Scopus papers containing terms about \"jamming attack\" and \"health\" yielded 330 rows, and the investigation on WoS yielded 17 articles. Scopus papers containing terms about \"replay attack\" and \"health\" yielded 372 rows, PubMed yielded 23 rows, and WoS yielded 50 articles.  
0 articles. Scopus for papers containing terms about \"spoofing attack\" and \"health\" yielded 316 rows, PubMed yielded five, and WoS yielded 23 articles. Scopus papers containing terms about \"tampering attack\" and \"health\" yielded 25 rows, and articles on PubMed yielded 14 rows, and articles on WoS delivered 46 articles. This row-level analysis result reveals no significant research contribution in sniffing attacks on IoMT devices, and that trend follows with tampering attacks, etc.  
Table 3 shows SLR from Sections 3.1-3.5 in tabular form. Section 3.1 reviews the cyber impacts and attacks on three-layer IoT architecture to identify the possibility of compromising adversaries' assets. Moreover, unethical hacking competencies and the corresponding core behaviors in weaponizing the SDR and radiofrequency to take over critical information such as client and clinical data repository from IoMT will significantly damage the entire healthcare user experience. We have well-established standard models and best practices with health level Seven international (HL7) [23] and fast health care interoperability resource (FHIR) [24] to meet health insurance portability accountability Act (HIPAA) compliance [25]. However, few studies on radio attack analysis on IoMT data and radio frequency machine learning framework have good wireless physical security risk mitigation strategies.  
Section 3.2 studies attacks on physical layer protocol on the internet of things. The above review provides a roadmap for understanding RF physical attacks, including jamming, sniffing, spoofing, tampering, and replay attacks. In the upcoming Sections 3.3 and 3.4, we discuss the paper related to the testbed implementation for IoT field experimentation.  
2. RQ2: What is the current state of healthcare-RF cybersecurity research?
## 2 Dynamic Modeling of Industrial Robot  
### Motion Coordinate System and Coordinate Transformation of Robot  
To achieve dynamic modeling and optimal control of industrial robots, two orthogonal coordinate systems are established, including the geodetic coordinate system and the local coordinate system. As shown in Figure 1, in the geodetic coordinate system, with the starting point of motion as the origin, the \(Ox\) and \(Oy\) axes are in the horizontal plane, and the \(Oz\) axis is vertically downward along the vertical direction. To facilitate the description of the rigid body motion behavior of the robot, a local coordinate system for the industrial robot was established with the center of mass of the robot as the origin of the coordinate system. Among them, the \(Ox\) axis is along the longitudinal axis of the industrial robot, the \(Oy\) axis is perpendicular to the \(Ox\) axis and points towards the starboard direction, and the \(Oz\) axis is perpendicular to the plane of the \(Oxy\) axis, with its direction determined according to the right-hand rule. On the basis of establishing a coordinate system, the velocity and angular velocity transformation matrices of the industrial robot from the local coordinate system to the geodetic coordinate system can be obtained as follows:  
\[T_{v}=\begin{pmatrix}\cos\varphi\cos\theta&-\cos\phi\sin\varphi+\cos\varphi\sin \phi\sin\theta&\sin\varphi\sin\phi+\cos\varphi\cos\phi\sin\theta\\ \sin\varphi\cos\theta&\cos\varphi\cos\phi+\sin\varphi\sin\phi\sin\theta&-\cos \varphi\sin\phi+\cos\phi\sin\varphi\sin\theta\\ -\sin\theta&\cos\theta\sin\phi&\cos\theta\cos\phi\end{pmatrix}\]  
\[T_{\omega}=\begin{pmatrix}1&\tan\theta\sin\phi&\tan\theta\cos\phi\\ 0&\cos\phi&-\sin\phi\\ 0&\sin\phi/\cos\theta&\cos\phi/\cos\theta\end{pmatrix}\]\[\text{where }u^{\prime}=\begin{pmatrix}u&v&w\end{pmatrix}^{\text{T}}\text{ and }\Omega=\begin{pmatrix}p&q&r\end{pmatrix}^{\text{T}}.\]