esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Improved Hybrid SSA with an EDO (ISSAEDO) Algorithm
### Proposed Improved Hybrid SSA with the EDO (ISSAEDO) Algorithm
#### 3.3.2 Bound-Constraint Adjustment Approach  
During the initial iterations, it is possible that the decision variables generated by metaheuristic algorithms may not be incorporated into the search process. The bound-constraint adjustment strategy is commonly employed to address this problem and eliminate infeasible decision variables. The random method is considered a crucial process in the context of bound-constrained adjustment. The proposed methodology involves replacing the choice variables that fall outside the specified boundaries with randomly generated values that lie within the boundaries. This analytical process is illustrated by the following equation:  
\[X_{i,d}^{anned}=\begin{cases}X_{i,d},&\text{if}X_{d}^{LB}\leq X_{i,d}\leq X_{d}^ {UB}\\ X_{d}^{LB}+rand(0,1)\times(X_{d}^{UB}-X_{d}^{LB}),\\ &\text{if}X_{i,d}<X_{d}^{LB}\\ X_{d}^{LB}+rand(0,1)\times(X_{d}^{UB}-X_{d}^{LB}),\\ &\text{if}X_{d}^{UB}<X_{i,d}.\end{cases} \tag{22}\]  
The decision variable's valid value is denoted as \(X_{i,d}^{anned}\), while \(X_{i},d\) represents the value that surpasses the variable's boundaries. The lower and upper bounds of the variable are denoted as \(X_{d}^{LB}\) and \(X_{d}^{LIB}\), respectively. Additionally, the function rand \((0,1)\) generates a random number between \(0\) and \(1\) with a uniform distribution. The fundamental characteristic of the random process is its capacity to sustain diversity within a given population, rendering it an indispensable attribute.  
The ISSAEDO algorithm aims to address the challenge of local optima and avoiding falling into it encountered in parameter extraction from PV models through its hybrid nature and the integration of multiple optimization techniques. We used the DE technique and a bound constraint for this reason; in addition, these strategies are also beneficial in a population-based optimization algorithm that excels at global exploration of the solution space. It maintains a population of candidate solutions and employs mutation, crossover, and selection operations to explore the search space more extensively. It was chosen for its global search capabilities, while bound constraint handling techniques are crucial for ensuring that solutions generated by the optimization algorithm are feasible and adhere to problem-specific constraints. Combination of these techniques can enhance the robustness of optimization algorithms, making them more effective in finding high-quality solutions for a variety of problems.
## 4 Numerical Results and Discussion
Figure 13 shows the variation in numerical Von Mises stress results in the curve according to the number of loaded cylinders, highlighting a close correlation between the FEM and T-Splines results, with a minor but consistent difference. The curve shows that starting from the weight of 15 cylinders, the elastic limit of the aluminum alloy 6061-T6 (298 MPa) is reached. We conclude that the storage capacity of a single cylinder is equivalent to 15 empty cylinders. Overall, the results demonstrate that the T-Splines approach provides superior numerical robustness, efficiently capturing stress variations in the loading application area while maintaining a stable and convergent solution. The method's ability to refine specific regions without increasing computational costs makes it a powerful alternative to the conventional FEM for structural applications involving complex geometries and high stress gradients. Figure 12: The Von Mises stress of the LPG cylinder: (**a**) numerical result of Abaqus/Standard FEM, (**b**) numerical result of IGA with T-Splines. Figure 13: LPG cylinders load—Von Mises stress curves. The results obtained demonstrated how IGA allows for rapid analyses, a very important advantage for real-time simulation. This fast processing ability is crucial in the context of digital twin, where instantaneous simulations are needed for evaluating and optimizing the performance of physical systems in real time. Furthermore, IGA offers increased accuracy in calculations, which is essential for the modeling of objects and processes in IOT simulation's systems. Even if this approach offers significant added values, it is very important to note that some challenges remain regarding the management of complex non-uniform geometries and computational costs for large-scale problems. The treatment of singularities, especially in regions with high stress concentrations, is another area that requires further development. However, these challenges can be addressed through the use of extended versions of IGA (such as X-IGA), which provide greater flexibility in handling complex geometries and localized phenomena like crack growth. X-IGA, in particular, offers robust solutions for accurately capturing the initiation and propagation of cracks by using enriched basis functions that can handle singularities more effectively. Based on all of these numerical results, confidence is held by applying the IGA with T-Splines method to numerically evaluate the mechanical performance of the LPG cylinder in energy storage applications.
# Highly Efficient GaN Doherty Power Amplifier for N78 Sub-6 GHz Band 5G Applications
## 2 Doherty Power Amplifier  
The Doherty power amplifier (DPA), conceived by [PERSON] in 1936 [PERSON] (1936), is designed for highly efficient linear power amplification. In Figure 1, we present the schematic of our proposed DPA, emphasizing its key components. This configuration includes two amplifiers, the main class AB amplifier (carrier) and the auxiliary class C amplifier (peak). Two \(\lambda/4\) transmission lines are integrated for phase correction and impedance translation, along with active load impedance modulation.  
In practical terms, the main amplifier efficiently handles low input power, achieving peak efficiency and voltage levels. However, when it reaches the breakpoint threshold, as depicted in Figure 2, the auxiliary amplifier dynamically adjusts the load and amplifies output power while maintaining high voltage levels and efficiency. According to theoretical considerations [PERSON] (2008), the DE at the breakpoint is expected to be equivalent to the efficiency at saturation and remain consistently high across the entire Doherty region. However, the presence of parasitic components within the transistors and the knee voltage effect, which leads to a gradual and smoother activation of the auxiliary transistor, results in a decrease in the efficiency profile within the Doherty region. Consequently, the efficiency achievable in practical applications at the breakpoint is typically lower than the efficiency observed under the saturation point, as demonstrated in Figure 2.  
Figure 1: An illustration of the planned Doherty power amplifier.  
Figure 2: Efficiency plot for the Doherty amplifier against input power [PERSON] (2008).  
The DPA's performance factors include output power, gain, linearity, and efficiency. Drain efficiency (\(DE\) or \(\eta\)) is defined as:  
\[DE=\eta(\%)=\frac{P_{out}}{P_{DC}}\times 100=\frac{0.5V_{out}*I_{out}}{(V_{dc, main}*I_{dc,main})+\Big{(}V_{dc,peak}*I_{dc,peak}\Big{)}*100, \tag{1}\]  
where \(P_{out}\) and \(P_{DC}\) are output DC power provided by the PA, \(V_{out}\) and \(I_{out}\) are output voltage and current at the fundamental frequency, and \(V_{dc,main}I_{dc,main}\) and \(V_{dc,peak}I_{dc,peak}\) are the DC power of the main and peak PA, respectively.  
Power-added efficiency (\(PAE\)) is another efficiency indicator defined as:  
\[PAE(\%)=\frac{P_{out}-P_{in}}{P_{DC}}\times 100, \tag{2}\]  
Gain (\(G\)) is calculated as:  
\[G(dB)=10\text{log}\bigg{(}\frac{P_{out}}{P_{in}}\bigg{)}\;[\text{dB}], \tag{3}\]
# Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
## 3 Results and Discussion
### Input Data  
#### 3.1.1 Samson Dataset  
Samson contains 156 bands covering the wavelength range from 401 nm to 889 nm with the spectral resolution of 3.13 nm. It has no blank channels or badly noised channels. To reduce the computational cost, the Samson dataset used in our experiment is partially cropped from the original image starting with the (252,332) pixel, which has the size of 95 \(\times\) 95 and three endmembers corresponding to soil, tree, and water. The pseudo-color image and the spectral reflectance of endmembers are shown in Figure 2[33].  
Figure 2: (**a**) Pseudo-color image of Samson; (**b**) spectral signatures of endmembers in Samson.
# Fault Feature Extraction of Parallel-Axis Gearbox Based on IDBO-VMD and t-SNE
## 1 Introduction
Traditional dimensionality reduction methods such as principal component analysis (PCA) do not perform well on nonlinear structural data, and local linear embedding (LLE) can only preserve the original manifold structure of the data. On the other hand, t-distributed stochastic neighbor embedding (t-SNE) not only has excellent dimensionality reduction capabilities for nonlinear data but also helps separate and cluster fault types. With the development of machine learning, population-based algorithms have been widely applied to VMD due to their advantages in optimization algorithm effectiveness. Compared to existing algorithms, the cockroach optimization algorithm (DBO) has stronger optimization capabilities and faster convergence speed. However, its structure still needs further improvement to meet practical needs. Therefore, addressing the gearbox fault diagnosis problem in parallel-axis gearboxes, this paper proposes a diagnostic method based on the improved cockroach optimization algorithm for optimizing VMD, feature extraction, and combination with t-SNE. This algorithm optimizes VMD through IDBO to determine the number of decomposed intrinsic mode functions (IMFs) and the optimal penalty factor. It extracts features such as time-frequency domain, permutation entropy, fuzzy entropy, and sample entropy as feature vectors. As gear fault signals are nonlinear vibration signals, support vector machines (SVMs) excel in handling nonlinear problems, aiding in capturing these complex features. Therefore, SVM is chosen as the fault classifier to enhance the accuracy of fault diagnosis. Through experiments, the feasibility and practicality of the proposed method are verified.
# Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids
## 4 Problem Formulation and Problem Solution  
In order to reduce the WSN's energy consumption and extend its lifetime, the topology control of the WSN needs to be considered. The topology control generally includes two parts: sensor association and CHs selection, which only considers the data transmission phase of the WSN. Based on the existing work, in order to optimize the energy consumption of sensor nodes during the entire working phase, this paper considers the following three issues: acquisition time design, CHs selection and sensor association. First, the CHs are selected based on the CHs selection algorithm. Then, a multi-constrained mixed integer optimization problem of joint acquisition time design and sensor association is formulated. By solving this optimization problem, the current acquisition time and the sensor association under the current CHs can be determined. After that, select the CHs again and repeat this process until the CHs, acquisition time and sensor associations do not change. At this time, we think that a high-quality sub-optimal solution has been obtained. Of course, since the initial acquisition time and sensor association are unknown, at the beginning of the iteration, we randomly select \(K\) sensor nodes as CHs and the remaining nodes automatically become CMs.
# Supply-Demand Dynamic Matching in Cloud Manufacturing Based on Hypernetwork Model
## 3 Modeling of the SDM Problem Based on the Hypernetwork Model
### Hypernetwork Model
#### 3.2.2 Manufacturing Task Network Model (_T_Net_)  
The manufacturing system's task network comprises numerous task nodes interconnected by edges. The Manufacturing Task Network Model (_T_Net_) is formally defined by Equations (6)-(10).  
\[T\_Net=<T,E^{T},W^{T}>, \tag{6}\] \[T=\{t_{j}|j=1,2,...,NoT\},\] (7) \[t_{j}=<Q^{i}_{T},St^{j}_{T},Et^{j}_{T},C^{i}_{T},B^{j}_{T},\eta^ {j}_{T}>,\] (8) \[E^{T}=\{e^{T}_{ii^{\prime}}|e^{T}_{ii^{\prime}}=0,1;j,i^{\prime} =1,2,...,NoT\},\] (9) \[W^{T}=\{w^{T}_{jj^{\prime}}\in(0,1]|e^{T}_{jj^{\prime}}=1;j,j^{ \prime}=1,2,...,NoT\}. \tag{10}\]  
\(T\) denotes the set of task nodes, \(E^{T}\) represents the set of edges indicating relationships between tasks, and \(W^{T}\) signifies the functional similarity between pairs of similar tasks. The parameter \(NoT\) in \(T\) indicates the total number of task nodes. For the tasks \(j\) and \(j^{\prime}\), if the manufacturing functions of the two tasks are similar, a relationship exists between the nodes, denoted by \(e^{T}_{jj^{\prime}}=1\). The variable \(w_{jj^{\prime}}T\) represents the degree of similarity between the task \(j\) and \(j^{\prime}\). The parameters \(Q^{j}_{T^{\prime}},St^{j}_{T},Et^{j}_{T},C^{j}_{T},B^{j}_{T},\eta^{j}_{T}\) represent the quality required, the latest start time requested, the latest delivery time, the maximum cost limit, the demand quantity, and the credibility for each task, respectively.  
3.2.3. The Hyperedges in the Supply-Demand Network (\(E^{S\_T}\))  
The hyperedges of the supply-demand network represent the relationships between the service network layer (\(S\_Net\)) and the task network layer (\(T\_Net\)). The hyperedge set (\(E^{S\_T}\)) is expressed as Equations (11).  
\[E^{S\_T}=\{e_{ij}^{S\_T}|e_{ij}^{S\_T}=0,1;i=1,2,...,NoS,j=1,2,...,NoT\}. \tag{11}\]  
Within this network model, the matching relationship refers to the situation that the \(i\)th service of the service layer is capable of executing the \(j\)th task of the task layer. This indicates that \(s_{i}\) and \(t_{j}\) can be matched, thereby establishing a hyperedge connection between the service node and the task node, at this time \(e_{ij}^{S\_T}=1\).
# On Maximizing the Probability of Achieving Deadlines in Communication Networks
## 3 Statistical Model for the DAP
### Link and DAP Statistics  
Data packets are composed of application data and a deadline. For the deadline, we define the initial time to deadline (initial TTD) as a system-wide parameter. This represents the maximum allowable age for application data to arrive at the destination in order to be useful for the application. The deadline for the application data is therefore calculated by adding the initial TTD to the chronological time at which the data are passed down at their source to the network layer (NET) and the corresponding packet is formed. Subsequently, the node begins relaying the packet toward the destination. When a neighboring node receives the packet, its deadline is inspected. The packet is then routed with the aim of meeting its deadline.  
Regardless of the trajectory followed toward the destination, each packet encounters numerous delays along the way. These are caused by transmission queues, channel access delays, retransmission timeouts, propagation delays, and protocol stack processing delays [31]. These delays are random with diverse statistics. We model the joint delay from the instant a packet is delivered to the NET of node \(u\) until it is successfully delivered one hop downstream to the NET of neighboring node \(v_{i}\) by a random variable with PDF \(f_{uv_{i}}(\tau)\), with \(\tau\) denoting the delay. Much work has been carried out to capture the link statistics (see, for example, the references in [32]). Because link delays are often dominated by path loss and channel fading, we assume that link PDFs are static and that link delays are random variables independent from each other.  
As a packet moves toward its destination, realizations of the link delays add up over time, thereby reducing the remaining time to deadline (remaining TTD). The DAP of a packet _en route_ depends on its remaining TTD (the shorter the remaining TTD, the lower the DAP). The cumulative distribution function that describes this relationship (DAP-CDF), seen from the NET, is denoted by \(\bar{F}_{u}(\tau)\), where \(u\) is the node to which the DAP-CDF pertains and \(\tau\) is the remaining TTD.  
Routing tables and DAP-CDFs are closely related. Any DAP-CDF \(\bar{F}_{u}(\tau)\) can be determined by the method provided in [32]. However, it is to be noted that the corresponding algorithm in [32] requires complete and central knowledge of the link statistics and of the routing tables for the entire network. Our objective here is to find a distributed algorithm for each node to determine its DAP-CDF \(\bar{F}_{u}(\tau)\) using only locally available knowledge. For this, a key observation is that the DAP-CDFs of neighboring nodes toward the destination are interdependent. We explore this interdependence in the following subsection and use it as the basis for our proposed algorithms.
## 5 IETF ROLL Working Group  
### Group Description and Key Protocols  
#### 5.1.1 Description  
The specific properties of LLNs imply specific routing requirements for these networks. The ROLL working group [36] focuses on building routing solutions for LLNs because evaluation of existing routing protocols like OSPF, IS-IS, AODV, and OLSR indicate that they do not satisfy all of the specific routing requirements. More specific, the working group focuses on industrial (RFC 5673) [37], connected home (RFC 5826) [38], building (RFC 5867) [39] and urban sensor networks (RFC 5548) [40] for which different routing requirements were specified.  
The working group focuses only on an IPv6 routing architectural framework while also taking into account high reliability in presence of time varying loss characteristics and connectivity with low-power operated devices with limited memory and CPU in large scale networks. The main realization of this working group is the design of the IPv6 route-over Routing Protocol for LLNs, also called RPL, which covers the routing requirements of all the application domains.
# Foreground Objects Detection by U-Net with Multiple Difference Images
## 4 Experimental Results
### Multiple Difference Images vs. Multiple Original Images  
In this section, we show experimental results according to the types of input images. We compare two cases of using multiple original images and multiple difference images as the input of networks. FgSegNet-v2 [42] predicts foreground objects using only the current image as the input of the networks. We modify it to use multiple original images or multiple difference images. In both cases, subtracting with mean is used as preprocessing. Training is done using 24 scenes in Table 1. Two hundred images from each scene are used, so 4800 images in total are used in training.  
Table 5 shows the performance of the trained network on CDnet 2014 dataset according to the input of original images and multiple difference images. The numbers of original images and difference images are varied according to the interval between frames, as shown in Table 5, where 50 frames are considered for the input of the network. We show performance in two different aspects. One is applying a trained network on scenes used in training. The other is applying a trained network on scenes that are not used in the training. Using multiple original images gives a slightly better result than using multiple difference images in scenes used in training. However, using multiple difference images shows a distinctly better performance than for scenes which are not used in the training. At 10-frame intervals, we could reach a 27.5% reduction in false detection by using five difference images compared to using six original images, and we could reach a 28.6% reduction in false detection by using 10 difference images compared to using 11 original images. We can conclude that using multiple difference images as the input of networks gives improved accuracy and generalization power.
# A Unified Point Multiplication Architecture of Weierstrass, Edward and Huff Elliptic Curves on FPGA
## 2 Mathematical Background over \(GF(2^{m})\)
We implemented a reduction algorithm using combinational logic, as it requires only shift and exclusive (OR) operations. ``` Input: Polynomial, \(B(x)\) with \(2\times m-1\)-bit length Output: Polynomial, \(B(x)\) with \(m\)-bit length
1. \(for\) (\(i\) from \(15\) down to \(8\)) do
1.1 \(Temp\longleftarrow B[i]\)
1.2 \(B[i-8]\longleftarrow B[i-8]\oplus(Temp\ll 23)\)
1.3 \(B[i-7]\longleftarrow B[i-7]\oplus(Temp\gg 9)\)
1.4 \(B[i-5]\longleftarrow B[i-5]\oplus(Temp\ll 1)\)
1.5 \(B[i-4]\longleftarrow B[i-4]\oplus(Temp\gg 31)\)
2.\(Temp\longleftarrow B[7]\gg 9\)
3.\(B[0]\longleftarrow B[0]\oplus Temp\)
4.\(B[2]\longleftarrow B[2]\oplus(Temp\ll 10)\)
5.\(B[3]\longleftarrow B[3]\oplus(Temp\gg 22)\)
6.\(B[7]\longleftarrow B[7]\) & ok1 FF
7. Return (\(B[7]\), \(B[6]\), \(B[5]\), \(B[4]\), \(B[3]\), \(B[2]\), \(B[1]\), \(B[0]\))Modular inversion.Algorithms 1 and 3 require an inversion operation during the projective to affine conversions. In state-of-the-art, several inversion algorithms exist to operate the polynomial inversion. Therefore, we bear a quad-block version of the Itoh-Tsuji algorithm [27] to implement polynomial inversion. For the \(GF(2^{233})\) field, the Itoh-Tsuji algorithm needs \(m-1\) squares followed by 10 modular multiplications, where \(m\) specifies the length of the polynomial [11; 13]. As shown in Figure 1, we connected a square block after the modular multiplier. This strategy permits us to implement the quad-block variant of an Itoh-Tsuji algorithm by executing the first square using a multiplier circuit and then the second square using the square block in the same cycle after the multiplication. This implies that the Itoh-Tsuji inversion algorithm is executed utilizing the resources of the multiplier and square circuits. It is essential to mention that the adder, square, and multiplier circuits in the ALU of our Figure 1 require one clock cycle for implementation because we implemented these circuits using combinational logic.
## 4 Discussion
### Techniques for Measuring the Changes in the Residual Lower Limb
#### 4.1.2 Anthropometric Measurement  
In the anthropometric measurement technique, the circumference of each segment of the residual limb volume is calculated using frustum and cylinder formulas and is summed up to determine the residual limb volume [31]. Normally, researchers use a normal measuring tape to measure the residual limb circumference, but there are some issues that may be related to the usage of a normal measuring tape. The first possible error that may occur during the measurement sessions depends on whether the researchers take the measurement in a consistent place and whether equal tension is applied while taking the measurement from one session to another [4]. Precautionary steps may be taken using appropriate tools such as the spring-loaded measurement tape [21; 32; 33], and only one researcher is allowed to take the circumference reading for all measurement sessions as they may apply a constant amount of tape tension. Another issue of this technique is in determining whether a 4 cm interval down to the distal of the residual limb [31; 32], a 5 cm interval from the ischium level down to the residual limb tip [28], or one-eighth of an inch [21] is enough to determine the residual limb volume using frustum and cylinder formulas. Some validity tests may be needed to determine the best amount of interval and marking locations for the anthropometric measurement technique. This technique has been shown to be a poor technique for measuring the residual limb volume since the residual limb volume is not well related to the circumference, as reported by [PERSON] et al. [21] and [PERSON] et al. [33].