esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Description
### Discrete-Time Controller
The discrete-time duty cycles are given by  
\[d_{1}[n] = \kappa_{i}(i_{\text{ref}}-i_{L}[n])-\kappa_{1}(\frac{1}{3}v_{g}-v_{ 1}[n]) \tag{3}\] \[d_{2}[n] = \kappa_{i}(i_{\text{ref}}-i_{L}[n])\] (4) \[d_{3}[n] = \kappa_{i}(i_{\text{ref}}-i_{L}[n])+\kappa_{2}(\frac{2}{3}v_{g}-v_ {2}[n]) \tag{5}\]  
where \(\kappa_{i}\), \(\kappa_{1}\) and \(\kappa_{2}\) are feedback coefficients and \(\mathbf{x}[n]\) stands for the sampled state variable \(\mathbf{x}(t)\) at time instant \(nT\), \(n=1,2,...\). The duty cycles \(d_{i}[n]\) are decided periodically based on the samples \(\mathbf{x}[n]\) of the state variables at each start of switching period. For that, one sample of the state variables per period is used. It is worth to note that the duty cycles \(d_{i}[n]\) (\(i=1,2,3\)) are defined as the ratio between the fraction of the switching period \(T\) during which the switch S\({}_{i}\) is closed (on state) and this period and therefore they must be constrained between 0 and 1. This means that they can be saturated to 0 or to 1 if the expressions in (3)-(5) give a value outside the interval \((0,1)\). Therefore, a saturation function must be used to limit the value of \(d_{i}[n]\) in this interval. The final expressions for the duty cycles are  
\[ \begin{array}{l}d_{1}[n]=\text{sat}\left(\kappa_{i}(i_{\text{ref}}-i_{L})- \kappa_{1}(\frac{1}{3}v_{g}-v_{1})\right)\\ d_{2}[n]=\text{sat}(\kappa_{i}(i_{\text{ref}}-i_{L}))\\ d_{3}[n]=\text{sat}(\kappa_{i}(i_{\text{ref}}-i_{L})+\kappa_{2}(\frac{2}{3}v_ {g}-v_{2}))\end{array}  \tag{6}\]  
where \(\text{sat}(\cdot)\) is the saturation function defined as follows  
\[\text{sat}(d_{i})= \begin{cases}d&\text{if }0\leq d_{i}\leq 1\\ 0&\text{if }d_{i}\leq 0\\ 1&\text{if }d_{i}\geq 1\end{cases}  \tag{7}\]  
The driving signals \(u_{i}\) are appropriately phase-shifted in such a way that the signal \(u_{1}\) is high and the switch S\({}_{1}\) is closed (on) during the time interval \((nT,(n+d_{1}[n])T)\), the signal \(u_{2}\) is high and the switch S\({}_{2}\) is closed (on) during the time interval \((n+d_{2}[n]+\frac{1}{3})T\) while the signal switch S\({}_{3}\) is closed (on) during the time interval \((n+d_{3}[n]+\frac{2}{3})T\), where \(T\) is the constant switching and sampling period. Three of the switching instants will be given in a fixed pattern according to the values of \(d_{1}[n]\), \(d_{2}[n]\) and \(d_{3}[n]\). These are \(nT\), \(\tau_{2}=(n+d_{1}[n]+\frac{1}{3})T\) and \(\tau_{4}=(n+d_{2}[n]+\frac{2}{3})T\). This fixed pattern switching is due the phase shift between the driving signals \(u_{i}\) (\(i=1,2,3\)). The other remaining switching instants are \(\tau_{1}=(n+d_{3}[n])T\), \(\tau_{3}=(n+d_{1}[n])T\) and \(\tau_{5}=(n+d_{2}[n])T\). For stability analysis the operation during only one period is needed. During one switching period, These are \(nT=0\), \(\tau_{2}=t_{2}=(DT+\frac{T}{3})\) and \(\tau_{4}=t_{4}=(DT+\frac{2T}{3})\), \(\tau_{1}=t_{1}=DT\), \(\tau_{3}=t_{3}=DT\) and \(t_{5}=DT\).
# Transformation of Trigonometric Functions into Hyperbolic Functions Based on Cable Statics
## 5 Discussion of Results
This research presents the following contributions: (1) The right triangle formulas with hyperbolic functions establish a relationship between the sides of a right triangle and the hyperbolic angles, based on the principles of cable statics; (2) a right triangle function with a hyperbolic angle is introduced, which relates the three sides of the triangle, the hyperbolic angle, and Euler's number; (3) deriving the law of hyperbolic cosines and hyperbolic tangents using hyperbolic functions; (4) the derivation of [PERSON]'s formula with hyperbolic functions, which can also be referred to as the second law of hyperbolic cosines, as it relates the sides of a non-right triangle to their respective hyperbolic angles, linking the hyperbolic cosines of the triangle to each of its sides. This article is intended for both mathematics education and research in the field, since it integrates a mathematical formulation derived from physical phenomena with practical applications in engineering. By linking traditional trigonometry concepts with innovative methods based on hyperbolic functions, the findings show applications such as the following: (1) the transformation of hyperbolic functions that is used in right angles, (2) the law of hyperbolic tangents and cosines that is used to solve trigonometry problems. Calculus and electricity problems are also solved; therefore, the presented formulation is validated in different fields of engineering and mathematics, allowing for its multidisciplinary use and being a didactic tool that can be adopted in future research.
# Multicriteria Decision Making and Its Application in Geothermal Power Project
## 3 Methodology
### Weighted Aggregated Sum Product Assessment (WASPAS)  
For each potential decision-making unit (DMU), its ranking can be determined by the product between the rating of each criterion's weight [33; 34].  
1. The matrix of decision-making is normalized.  
For an advantageous decision criterion, the normalization is shown in Equation (17) as follows:  
\[\mathrm{q_{ij}}=\frac{\mathrm{x_{ij}}}{\mathrm{maxx_{ij}}},\ \mathrm{i}=1,2,\ \ldots,\ \mathrm{n;j}=1,\ 2,\ \ldots,\ \mathrm{m} \tag{17}\]  
For a disadvantage decision criterion, it is shown in Equation (18):  
\[\mathrm{q_{ij}}=\frac{\mathrm{x_{ij}}}{\mathrm{minx_{ij}}},\ \mathrm{i}=1,2,\ \ldots,\ \mathrm{n;j}=1,\ 2,\ \ldots,\ \mathrm{m} \tag{18}\]
2. The relative significance of the \(i^{\mathrm{th}}\) alternative is determined, using the Weighted Sum Model (WSM) as: \[\mathrm{S_{i}^{1}}=\sum_{j=1}^{n}\mathrm{q_{ij}}\times\mathrm{w_{j}}\] (19)
3. The performing evaluation index of the \(i^{\mathrm{th}}\) options is determined by using the Weighted Product Model (WPM) as Equation (20): \[\mathrm{S_{i}^{2}}=\prod_{j=1}^{n}\left(\mathrm{q_{ij}}\right)^{\mathrm{w_{j}}}\] (20)
4. The total relative importance is calculated by a combination of Equations (19) and (20).  
The weighted combination of the addition and multiplication approach for WASPAS model is determined as follows:  
\[\mathrm{S}=\lambda\mathrm{S_{i}^{1}}+(1-\lambda)\mathrm{S_{i}^{2}}=\lambda \sum_{j=1}^{n}\mathrm{q_{ij}}\times\mathrm{w_{j}}+(1-\lambda)\prod_{j=1}^{n} \left(\mathrm{q_{ij}}\right)^{\mathrm{w_{j}}} \tag{21}\]  
with \(\lambda\) as the coefficient where \(\lambda\in\) [0,1]. Normally, its value is defaulted to \(\lambda=0.5\).  
The options are then finally ordered based on the index of performance, and the best location would score the greatest.
# Intelligent Traffic Management in Next-Generation Networks
## 3 DL/ML Applications in Software-Defined Networking
### Traffic Prediction  
The objective of network traffic prediction is to forecast the amount of traffic expected based on historical data in order to avoid future congestion and maintain high network quality [105]. It helps to keep the over-provisioning of the resources as low as possible, avoid congestion and decrease communication latency [106; 107]. Network traffic prediction can be formulated as the prediction of the future traffic volume (\(\hat{y}_{t+l}\)) based on the historical and current traffic volumes (\(X_{t-l+1}\), \(X_{t-l+2}\),..., \(X_{t}\)). Therefore, the objective of the ML/DL models is to find the parameters that minimize the error between the predicted and observed traffic with respect to \(X_{t-l+1}\), \(X_{t-l+2}\),..., \(X_{t}\) (Equations (1) and (2)).  
\[W^{*}=\underset{W^{*}}{\text{argmin}}\;L(y_{t+l},\hat{y}_{t+l};W^{*}) \tag{1}\]  
\[\hat{y}_{t+l}=f([X_{t-l+1},X_{t-l+2},\ldots,X_{t}]) \tag{2}\]  
where \(y_{t+l}\) and \(\hat{y}_{t+l}\) are the observed and predicted value at time \(t+l\), respectively, \(f(.)\) is the activation function, \(L\) is the loss function, and \(W^{*}\) is the optimal set of parameters.
# Satellite IoT Edge Intelligent Computing: A Research on Architecture
## 2 Related Work
### Related Research on Distributed Deep Learning  
In recent years, many companies have released their own distributed machine-learning models, including distributed training models and distributed reasoning models [20; 21; 22; 23; 24; 25; 26; 27]. A distributed training system architecture called Parameter Server is proposed in the paper [21]. In this architecture, the nodes are divided into two categories: the Parameter Server node is responsible for storing and managing parameters; the worker node is responsible for performing specific calculation tasks, based on part of the training data, and obtaining the gradient of the parameters. Ring Allreduce [22] is a very mature and efficient algorithm in the field of high-performance computing (HPC), but it is rarely used in distributed machine learning. In recent work, Uber's open-source distributed deep-learning framework, Horovod [23], and Baidu's open-source deep-learning framework, Paddleaddle [24], have introduced the Ring Allreduce algorithm to accelerate the training process of deep neural networks. Atpresent, Alibaba open-source is the first lightweight deep-learning end-side inference engine [25]. The engine can directly run the deep-learning model on an embedded device that is versatile, lightweight, high-performance, and easy to use.
## 2 Related Works
### Grover's Search Algorithm  
A significant research trend has emerged in the development of cryptographic techniques leveraging GSA [15], encompassing diverse applications including quantum asymmetric key cryptography [19; 20], quantum key agreement protocols [16; 17], and quantum digital signature schemes [21]. GSA is a cornerstone of quantum computing, offering a quadratic speedup over classical search algorithms for unstructured databases. GSA operates on a quantum register of \(n\)-qubits, which represents \(N=2^{k}\) possible states. The algorithm efficiently identifies a target state \(|w\rangle\) by iteratively applying two operations: the Oracle \(O\) and the Diffusion operator \(D\).  
_Initial State Preparation:_ The algorithm begins with the initialization of the quantum state \(|\psi\rangle\), which is typically chosen as the uniform superposition state: \(|\psi\rangle=H^{\otimes k}\,|0\rangle^{\otimes k}=\frac{1}{N}\sum_{x=0}^{N-1}|x\rangle\), where \(H\) is the Hadamard gate applied to each qubit, \(\otimes\) means tensor, i.e., \(H^{\otimes k}\) is equivalent to \(H\otimes H\otimes\ldots\otimes H\) with \(k\) terms.  
_Oracle Operation:_ The Oracle \(O\) is a problem-specific unitary operator that marks the target state \(|w\rangle\) by flipping its amplitude's sign. For a target state \(|w\rangle\), the Oracle is defined as:  
\[O=|x\rangle=\left\{\begin{array}{l}-|x\rangle\text{ if }x=w\\ |x\rangle\text{ otherwise }\end{array}\right.,\text{ or }O=I-2|w\rangle \langle w|,\]  
where \(I\) is the identity operator and \(|w\rangle\) is the target state to be searched for.  
_Diffusion Operation:_ After applying the Oracle, the algorithm amplifies the amplitude of the marked state using the Diffusion operator \(D\), defined as:  
\[D=2|\psi\rangle\langle\psi|-I,\]  
where \(I\) is the identity matrix and \(|\psi\rangle\) is determined in the initial state phase. This operation reflects the quantum state about the average amplitude, increasing the probability of the target state being measured.  
Measurement: After \(r=\left\lfloor\frac{\pi}{4}\sqrt{N}\right\rfloor\) iterations of applying \(O\) and \(D\), the quantum state is measured, yielding the target state \(|w\rangle\) with high probability.  
It is worth noting that a variant of GSA has been proposed [22], featuring specific modifications to the original approach.  
**Property: Bitwise XOR Oracle Composition**  
1. The initial state is fixed as \(|\,\psi\rangle\)= \(|\,++\rangle\). The corresponding diffusion operation is \(2|\,++\rangle\langle++|-I\).  
2. The targets are regarded as \(w_{i}\in\{\)00,01,10,11\(\}\), where \(0\leq i\leq 3\). The oracle operator is defined as \(O_{w_{i}}=I-2|w_{i}\rangle\langle w_{i}|\) for each target. If the target \(w_{0}\) is related to the sequence \(w_{1}\), \(w_{2}\), and \(w_{3}\) by \(w_{0}=w_{1}\oplus w_{2}\oplus w_{3}\) (bitwise XOR operation), the composition of oracle operators satisfies the following relationship: \[O_{w_{1}}O_{w_{2}}O_{w_{3}}|\psi\rangle=\pm O_{w_{0}}|\psi\rangle.\]
## 5 Experimental Results and Analysis  
In the comprehensive simulation, the results are validated using the RTunit platform (The software system version is RTunit Stido2020). The experimental validation setup is shown in Figure 13 below. The parameters used in both the experiment and the Simulink simulation are identical.  
The hardware controller for the experiment is illustrated in Figure 14. To implement the controller and feedback system, either RTU-BOX204 or RTU-BOX201 (hardware controller) (RTunit Information Technology Co., Ltd., Nanjing, China) can be utilized. The actuator can be selected from the RTM-PEH series module or the RTI series three-phase driver, with the controlled object being a permanent magnet synchronous motor. Additional components of the hardware system include RTM-RACK (an integrated module including RTM-PEH series, etc.), RTC-Encoder (an encoder adapter board), RTC-ADC (an ADC adapter board), RTC-Fiber 6/12/24 (a fiber adapter board), and RTC-Power (a power adapter board) (RTunit Information Technology Co., Ltd., Nanjing, China). Furthermore,  
Figure 11: Position and error curves of the controller.  
Figure 12: Step signal position curve amplification.  
the encoder provides U, V, and W signals, which exhibit a phase difference of 120 degrees from each other. This phase relationship aids in estimating the initial position of the rotor's d axis when the motor starts, enabling quick determination of the rotor's approximate position during the initial phase.  
It is important to note that the system employs a closed-loop feedback mechanism to keep the rotor in the designated position. Sensors, such as encoders, measure the rotor's actual position and compare it to the set position. The resulting position error signal serves as the input for the controller. Based on this error signal, the controller adjusts the current components along the d axis and the q axis to generate the required torque for correcting the position error.  
The results of the experiment are illustrated in Figures 15-18. In alignment with the simulation analysis, the experimental validation is primarily categorized into four forms, as detailed in Sections 5.1-5.4.
## 2 Materials and Methods
### Optimization Model
#### 2.3.1 Data Sets and Parameters  
The data for the optimization problem is described below:  
* \(P=\{1,\ldots,n_{p}\}\), the set of 15 min periods for one year with \(n_{p}=8760\);
* \(T=\{1,2\}\), the set of available tariffs (1--normal; 2--optional);
* \(WT=\{1,2\}\), the set of wind turbines;
* \(B=\{1,2\}\), the set of batteries that can be used;
* \(pb\), battery lifetime in years (\(pb\in\{5,10,15,20\}\));
* \(c(i,j)\), price (EUR/kWh) in period \(i\) adopting the tariff \(j,\ i,j\in P,T\);
* \(pw(i,j)\), energy production (kWh) in period \(i\) using the turbine \(j,i\in P,j\in WT\);
* \(d(i)\), energy demand (kWh) in period \(i,i\in P\).
* \(M=\sum_{i\in P}d(i)\), increased value representing the sum of the energy demand values;
* \(kb(i)\), capacity of the battery \(i\) (kW), \(i\in B\);
* \(cb(i)\), cost of the battery \(i\) in Euros, \(i\in B\);
* \(cWT(j)\), cost of wind turbine \(j,j\in WT\);
* \(rr=0.02\), inflation rate of the energy price purchased from the grid.  
In Table 3, are presented some details regarding the wind turbine set, and Table 4 presents some details regarding the batteries set.  
Figure 4: Building load profile.  
Regarding the cost of electricity from the grid, there is a price adjustment each year, also known as the energy price inflation rate. For this study, the energy price inflation rate was adopted as 2% per year, represented by \(e\), according to [24]. Equation (4) determines the energy cost for each period and year.  
\[POE_{i}=POE_{0}\cdot(1+e)^{i} \tag{4}\]
## 5 Use Case
### Security Concept  
The security concept defines the cybersecurity requirements necessary to reduce the ITEM's vulnerability to cyberattacks, forming the basis for its technical specification and lifecycle development. For threat scenarios with acceptable risks, cybersecurity claims document the justification. Each threat scenario is assigned a cybersecurity assurance level (CAL), determined by evaluating the attack impact (severity) and attack vector (attack approach). The CAL dictates the evidence required for validating ITEM security. For threat scenarios requiring risk reduction, cybersecurity goals are defined, specifying the target state to achieve acceptable risk. Each goal is supported by at least one cybersecurity requirement, detailing measures for prevention, detection, and monitoring. Theserequirements must be linked to ITEM elements and include operational environment requirements specifying conditions for fulfillment. The diagrams of the model are shown in Figures 14-15.
## 4 Simulation Results
### Adjacent Pixel Correlation  
Table 2 shows the adjacent pixels correlation of the four original images, the encrypted image and the encrypted image in other multi-image encryption method. An efficient encryption should significantly reduce the adjacent pixel correlation of the original images. It can be defined as  
\[CC(X,Y)\ =\ \frac{E[(X-\mu_{X})(Y-\mu_{Y})]}{\sigma_{X}\sigma_{Y}}, \tag{21}\]  
where \(X\) and \(Y\) are two different pixel sequences, in which each pixel is the adjacent pixel of \(X\). \(\mu\) and \(\sigma\) denote the mean value and the standard derivation, respectively. \(E\)\([\bullet]\) represents the mathematical expectation.  
Table 2 shows that the correlation between adjacent pixels of the ciphertext is much lower than that of the original images, and it's also lower than the results of other methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Correlation Coefficient** & **Horizontal** & **Vertical** & **Diagonal** \\ \hline cameraman & 0.966649 & 0.978250 & 0.978232 \\ peppers & 0.973320 & 0.976367 & 0.980875 \\ baboon & 0.879297 & 0.799324 & 0.799965 \\ coco & 0.989351 & 0.990687 & 0.990696 \\ ciphertext & 0.000046 & 0.000203 & 0.002142 \\ Ref. [36] & 0.002800 & 0.034100 & \(-\)0.054700 \\ Ref. [22] & \(-\)0.026800 & 0.013500 & 0.010300 \\ Ref. [23] & 0.025300 & 0.019700 & 0.026900 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Adjacent pixel correlation.  
Figure 7: Histogram: (**a–d**) original images, (**e**) encrypted image, (**f**) encrypted image using different original images.
## 6 Grid Impact
### Quality of Electrical Signal  
High penetration of EVs and resulting incorporation of equipment with great power electronics produces current harmonics and voltage variations that deteriorate downstream equipment [125; 127; 131].  
The effect of harmonic production, peak loading, and power quality degradation, among others, is more important when multiple EV chargers are present in the same location, that is, when installation of chargers is not planned, especially if they are fast chargers [104; 137; 145].  
Power quality degradation can originate from voltage deviations, harmonic currents, phase imbalance, or direct current (DC) offset. In fact, one of the reasons for ending the transfer of energy between a charger and an EV is the effect of harmonics [143]. Standards such as EN 61000 and IEEE 519 establish upper limits of harmonic currents and voltages, with power factor being the quality parameter of networks without distortion; transformers and feeders typically suffer decreased value of the power factor [130; 146].  
It is necessary to coordinate the charging and discharging of all EVs to maintain the network quality while individualizing the monitoring of each vehicle and establishing an appropriate regulation and different charging modes that do not disturb the system [134; 147].  
The origin of harmonics in EV chargers is the use of converter switching, which several authors have sought to solve by incorporating specific filtering device strategies. When properly controlled, EV chargers can work as active filters and provide variable impedance for each harmonic frequency that needs to be limited [142].  
Supra-harmonics are derived from fast chargers, and may have an impact on deterioration of the grid behavior. This is even more the case if the \(\mathsf{X}/\mathsf{R}\) ratio of distribution line is small or if there is a low short-circuit ratio and high impedance. Supra-harmonic distortion is addressed by means of an AC-DC front-end rectifier and proper input filter design. Voltage fluctuations are another type of quality problem arising as a consequence of EVs fast charging. Some authors have indicated that this problem increases if the power of the buses is increased. Similarly, voltage fluctuation is associated with installation of a fast charging station on a weak network bus [122; 141].
To analyze traffic flow changes under disruptions, this study integrates critical-link attacks into the model and uses the CCDF to assess the network's response to each attack. The findings reveal that disruptions to certain road segments cause significantly greater reductions in traffic volume than other critical-link attacks. 3. The proposed method was applied to a road network in Sri Lanka's CMC area, serving as a case study to demonstrate its effectiveness in assessing network vulnerability and identifying critical links that influence traffic flow changes and explore pre- and post-disaster transportation planning. This analysis provides valuable insights, particularly within an Asian context. This study also highlights key findings and policy implications relevant to other Asian cities with similar characteristics, extending its applicability beyond Colombo to urban transportation planning in comparable settings. The remainder of this paper is organized as follows: Section 2 presents the analytical framework of this study. The methodology, characteristics of the case study area, and data descriptions are presented in Section 3. Section 4 presents the analysis and results. The discussion and conclusion are summarized in Section 5, along with the limitations and future work.
# Analysis and Comparison of Electromagnetic Microrobotic Platforms for Biomedical Applications
## 2 Theoretical Background
### Manipulation Analysis  
From the mathematical analysis, the rank of force equation is 3 and the rank of torque equation is 2, the microrobot can maximally achieve three degrees-of-freedom (DOFs) in translation and two DOFs in rotation. Next, to achieve the five DOFs control of the microrobot, the minimum number of electromagnets is mathematical estimated. The three electromagnets can be used for three DOFs force control at a point, but normally five electromagnets are required when the orientation of the microrobot is dynamic changed. The number of electromagnets can be reduced to four, but either a nonmagnetic restoring torque or a nonmagnetic restoring force is required to stabilize the system. For two DOFs torque control, only three electromagnets are required because the three coils can generate a 3D field in a workspace. Thus, combined torque and force control requires a minimum of \(n=7\) stationary electromagnets. Similarly, the seven electromagnets also need some additional external conditions. To stabilize the five DOFs control of the microrobot, the eight electromagnets are suggested for the fixed configuration system [83]. Reconfigurable EMA system can achieve similar control authority to stationary system with fewer electromagnets. Only \(n=5\) electromagnets are required for torque and force control. Therefore, the mobile electromagnets are more particularly considered for the biomedical applications. Indeed, the field shape in the workspace can be modified by changing the location or orientation of the electromagnets during the magnetic actuation of the microrobot [81].