esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Program  
### Prototype Building and Scaled Sub-Structure  
The prototype building is assumed to be located in a high seismic zone with a 8-degree seismic design intensity in China. The corresponding design peak ground acceleration (PGA) value of the design earthquake with a 10% probability of exceedance in 50 years equals to 0.20 g. The site condition is site-class 2. According to the China seismic design code [16], the structure should satisfy the  
Figure 1: Sketch of supporting structure for air-cooled condenser (ACC) system with steel diagonal braces.  
seismic demands under three seismic hazard levels, namely, minor earthquake with 63.3% probability of exceedance in 50 years (Level 1), moderate earthquake with 10% probability of exceedance in 50 years (Level 2) and major earthquake with 2% probability of exceedance in 50 years (Level 3). The structure should keep elastic under the minor earthquake (Level 1), should not collapse under the major earthquake (Level 3), and should be in use after repairment under the moderate earthquake (Level 2). The corresponding peak ground acceleration (PGA) values of the minor earthquake and major earthquake are 0.10 g and 0.40 g, respectively. The main dimensions of the building are shown in Table 1.  
A sub-structure was selected from the prototype building to fabricate the scaled specimen, as shown in Figure 2. Due to restraint of the laboratory space, the scale factor for length was determined as 1/8. Table 2 shows the typical scale factors for the specimen, which were obtained by the principle of dimensional analysis [17]. Additional masses were added on the A-type brackets by 12 pieces of concrete slabs to accommodate the vertical loads which were generated by the large industrial facilities.  
\begin{table}
\begin{tabular}{c c c} \hline
**Component** & **Quantity** & **Value** \\ \hline \multirow{4}{*}{RC pipe column} & Column grid & 22.78 m \(\times\) 22.36 m \\  & Number & 25 \\  & Height & 50 m \\  & External diameter & 4 m \\  & Wall thickness & 0.4 m \\ \hline \multirow{4}{*}{Steel truss} & Length & 11.39 m \\  & Width & 11.18 m \\  & Height & 5 m \\ \hline A-shaped bracket & Height & 11 m \\ \hline \end{tabular}
\end{table}
Table 1: Main dimensions of components.  
\begin{table}
\begin{tabular}{c c c} \hline
**Variable** & **Equation** & **Scale Factor** \\ \hline Length & \(S_{L}\) & 1/8 \\ Mass & (\(S_{L}\))2 & 1/64 \\ Time & (\(S_{L}\))1/2 & 1/\(\sqrt{8}\) \\ Force & (\(S_{L}\))2 & 1/64 \\ Displacement & \(S_{L}\) & 1/8 \\ Acceleration & \(S_{F}\)/\(S_{M}\) & 1 \\ Stiffness & \(S_{L}\) & 1/8 \\ \hline \end{tabular}
\end{table}
Table 2: Typical scale factors of the model structure.  
Figure 2: Plan layouts of the prototype structure and selected sub-structure.
## 4 Experimental Evaluation and Performance Analysis
### Concerning Both Users' Demands and Cloud Manufacturing Platform Benefits  
This section considers the interests of both cloud manufacturing platforms and users, meeting the expectations of both parties for completing maintenance tasks. The influences of different dimension platform benefit and user satisfaction weights on the calculation results were studied using the experimental parameters shown in Table 4.  
Figure 9: Fitness curves of the genetic algorithm after operation. (**a**) Experimental parameters of No. 6. (**b**) Experimental parameters of No. 7. (**c**) Experimental parameters of No. 8. (**d**) Experimental parameters of No. 9. (**e**) Experimental parameters of No. 10.  
The changes in platform and user satisfaction under the different weights are shown in Figure 10.  
It can be seen from Figure 10 that, with an increase in user weights, the user satisfaction degree increased. Obviously, the proportion of the goal values can be adjusted by assigning different weight coefficients, reflecting the results envisaged by the scheme decision maker when assigning the weight coefficient, such that the results of the simulation experiment are in accordance with the expectations.  
Next, we discuss the influence of different satisfaction levels set by the platform and users on the experimental results. The parameters of the genetic algorithm were set as detailed above, and the weight coefficients were set as in Parameter No. 13. The satisfaction requirements of the platform and users are shown in Table 5.  
As can be seen from Figure 11, whether for the platform or for users, increasing the satisfaction requirement reduced the satisfaction value, while reducing the satisfaction requirement increased the satisfaction value, consistent with the expected results. The experimental results indicate that the maintenance resource allocation scheme proposed in this article can simultaneously consider the interests of both cloud manufacturing platforms and users. The model is reasonable and effective.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & \(\mathbf{\tau}\) & \(\mathbf{w_{t}}\) & \(\mathbf{w_{c}}\) & \(\mathbf{k_{t}}\) & \(\mathbf{k_{p}}\) \\ \hline No. 11 & 0.1 & 0.5 & 0.5 & 0.5 & 0.5 \\ No. 12 & 0.3 & 0.5 & 0.5 & 0.5 & 0.5 \\ No. 13 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\ No. 14 & 0.7 & 0.5 & 0.5 & 0.5 & 0.5 \\ No. 15 & 0.9 & 0.5 & 0.5 & 0.5 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Combined experimental parameters.  
Figure 10: Satisfaction under different weights of the platform and users.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & \(\mathbf{a_{ft}}\) & \(\mathbf{b_{ft}}\) & \(\mathbf{a_{fc}}\) & \(\mathbf{b_{fc}}\) & \(\mathbf{a_{t}}\) & \(\mathbf{b_{t}}\) & \(\mathbf{a_{p}}\) & \(\mathbf{b_{p}}\) \\ \hline No. 16 & 2 & 7 & 2 & 7 & 2 & 7 & 2 & 7 \\ No. 17 & 4 & 9 & 4 & 9 & 2 & 7 & 2 & 7 \\ No. 18 & 1 & 5 & 1 & 5 & 2 & 7 & 2 & 7 \\ No. 19 & 2 & 7 & 2 & 7 & 4 & 9 & 4 & 9 \\ No. 20 & 2 & 7 & 2 & 7 & 1 & 5 & 1 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Satisfaction requirement parameters.
# A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining
## 6 Experimental Results  
Several experiments were carried out to compare the effectiveness and efficiency of the proposed GMPSO algorithm to the state-of-the-art single-objective cpGA2 DT [19] and NSGA-II-based [23] approaches. The algorithms used in the experiments were implemented in Java, using a PC with an Intel Core i7-6700 quad-core processor and 8 GB of RAM under the 64-bit Microsoft Windows 10 operating system. Three real-world datasets [48], called chess, mushroom, and foodmart, were used in the experiments. Because the foodmart dataset originally consisted of items and their purchase quantities, only the distinct items in the foodmart dataset were extracted and used in the experiments. A synthetic dataset, namely, T10I4D100K, was also generated using the IBM database generator [49] for the experiments. The characteristics of the datasets used in the experiments are shown in Table 5. The used parameters are defined as: \(\#|\mathbf{D}|\): the total number of transactions; \(\#|\mathbf{I}|\): the number of distinct items; **AvgLen**: the average transaction length; **MaxLen**: the maximal length transactions; and **Type**: The dataset type.  
The maximum number of iterations is set to 50 and population size is set to 50 in the experiments for all the compared algorithms. All the experiments are conducted five times and the average values are calculated. The single-point crossover and bit-wise mutation are then performed and the rates are respectively set to 0.9 and 0.01 for the cpGA2 DT algorithm. The elitism method is used in the cpGA2 DT algorithm to select populations. The results in terms of the runtime, four side effects, and the scalability are discussed and analyzed as follows.
## 1 Introduction
[13] proved that human dynamical motion models can be constructed in a low-dimensional latent space. Humanoid robot dynamical motion models were learned in the research by [PERSON] and [PERSON] [14] by using kernel PCA-GPDM. Compared with conventional neural network methods, GPDM has advantages over learning motion models with modest amounts of training data. It is efficient in motion learning and the time cost is much less. Figure 1: Estimation of joint angles for robot imitation using a particle filter. Figure 2: NAO humanoid robot comprising 25 joint angles. In this study, the main function of GPDM is to construct a compact state space and build motion models. Then, we replace the state space of particle filter with kernel PCA-GPDM learned compact latent state space, and efficient humanoid robot motion models based on GPDMs are applied to state tracking in the learned latent state space based on a particle filter. Following the architecture shown in Figure 3, we use a particle filter and GPDM learning to realize high-dimensional humanoid robot state estimation in a reduced 3D latent space at low computational cost. Figure 4 details the humanoid robot motion learning and prediction system. The high-dimensional space of humanoid robot joint angles is first reduced to a 3D state space using kernel PCA. Then, a 3D dynamical motion model is learned by GPDM. A decoder is used to reconstruct the humanoid robot joint angles to ensure that the learned 3D latent variables can be mapped onto the joint angle space, such that the learned motion models can be applied to particle-filter state estimation. A 3D latent state space is obtained for reducing the computational cost of robot state estimation. The learned 3D dynamical motion models are applied to the particle filter for particle state tracking. Specifically, a prediction system with similarity measurements based on the dynamical motion models is proposed for humanoid robot state estimation. The main contributions of this paper can be summarized as follows. * We construct a real-time humanoid robot imitation system with low computational cost based on particle filter and GPDM. The high-dimensional state space is replaced by the GPDM learned compact state space and the GPDM model defines the motion model of the particle filter. Figure 4: Motion learning and prediction using a Gaussian process dynamical model (GPDM). \(M\) is the dimension of the joint angle space, \(x_{t}\) is the state of a particle at time \(t\), and \(\theta_{t+1}\) represents the joint angles of the humanoid robot, which are reconstructed from state \(x_{t+1}\) using a GPDM decoder. Figure 3: Proposed joint angle estimation of humanoid robot using particle filter in 3D space with learned motion models for imitating human motion ( kernel principal component analysis (KPCA)). * A prediction system with similarity measurement for state tracking of the particle filter is proposed to improve state estimation accuracy. Experiments demonstrate that the proposed particle filter can estimate the joint angles of a humanoid robot efficiently using the GPDM in the 3D state space for real-time imitation of human motion.
# A Novel Approach to Enhance the Energy Efficiency of a NOMA Network
## 6 Performance Analysis  
In this section, we analyze our proposed resource allocation algorithms through extensive MATLAB simulations. Through the simulations, we assume one base station is placed in the centre and the users are classified randomly in a circular area. In the NOMA system, we assume that only two users are superpositioned on each sub-channel. In the OFDMA system, every user can be allocated to one sub-channel only. During the simulation, we compared the performances of NOMA and OFDMA systems using the same resource allocation algorithm. Table 2 illustrates the values of the simulation parameters.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 16 Enhancement Prospects
#### 2.4.5 Network Management Tool Enhancement to Improve UE Differentiation  
NB-IoT UE is expected to be able to perform differentiation according to maximal tolerable delay per service to optimize the radio resource usage. This is because, in the last release, the UE can be differentiated according to traffic model (periodic communication indicator, periodic time, scheduled communication time, traffic profile) and battery indication.  
Section 2 has presented the NB-IoT standard and the corresponding enhancements from Release 13 until today. It has highlighted the main design changes and the corresponding further enhancements, i.e., deployment flexibility, physical channels and signals, positioning, multicast, new power classes, improved data rates, multicarrier operations, mobility support, improved scheduling, NB-IoT small cell support etc.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 4 Ecosystem
### Enabling Technologies
#### 4.3.4 Hardware Technology  
Many chaotic systems can be very expensive and slow to compute, making them infeasible for real-time scenarios. By comparison, hardware-based approaches are much faster and still maintain the high level of security required [199].  
In a proposal by [PERSON] et al. [200], chaos generators were implemented in part by the Coordinate Rotational Digital Computer (CORDIC), a hardware-efficient algorithm for calculating various functions such as tangents and hyperbolics. The types of chaos generators used in hardware algorithms are much the same as standard software approaches, such as iterative maps [199], cat maps [201], and the double-humped logistic map [202].  
These algorithms are often tested on physical hardware to prove their claimed effectiveness. Examples of the hardware used are the Virtex-IV [203] and the Virtex-5 FPGA [202]. These tests resulted in high throughput with a small utilized area. [PERSON] et al. [201] also proposed a more generic hardware structure for an algorithm that exhibits good reusability for use in different systems.
## 2 Materials and Methods
### Polar Transformation  
We first mapped the 2D spectrograms to polar coordinates [25]. The resulting scatter plots had unfilled spaces. We used linear interpolation to fill these gaps and obtained the polar-transformed spectrogram images [34]. After interpolation, the polar-transformed spectrogram images exhibited a densely spaced, high-intensity low-frequency region. We implemented a reverse polar transformation, which resulted in wider spacing between peaks in the low-frequency region. The reverse polar transformation is advantageous over the non-reverse polar transformation. With the reverse polar transformation, the low frequency region is located in the periphery of the polar space so that it enhances the distinction between atrial fibrillation (AF) and normal sinus rhythm signals. The polar-transformed images were colored using the \"jet\" colormap, which shows a color spectrum from blue to red. Image intensity values were rescaled to the range [0, 255] as unsigned 8 bit integers, and images were saved in.png format for subsequent deep learning model development and testing.  
Figure 2 compares the rectangular and polar spectrogram layouts. Given identical image dimensions (\(2r\) x \(2r\)), the polar spectrogram (Figure 2b) covers a greater distance (i.e., \(2\pi r\) vs. \(2r\)) at the lowest frequency compared to the conventional rectangular spectrogram (Figure 2a). Note that the four corners in Figure 2b remain empty in the polar spectrogram representation.
## 5 Case Study
### Simulation Results of the Non-Cooperative Game of Customers  
To evaluate the proposed non-cooperative game between customers, Figure 8 illustrates every individual's strategy in a dynamic process at the 7 th time slot. It is obvious that every strategy will converge to a value in a few integrations, which is known as the Nash equilibrium. After this static stable strategy is used, no customer has any incentive to unilaterally change this strategy because no one can receive a larger welfare. At the bottom, there is a red line which equals 0 in the entire iteration. The red line means this customer chose not to participate in DR at the 7 th time slot because s/he cannot receive a larger welfare, while s/he needs not perform any regulation. It proves our proposed DR scheme is sufficiently voluntary. Figure 9 shows the actual rebate for every customer in DR at the 7 th time slot. It is also obvious that every one's rebate can converge into an equilibrium value. It should be noted that the summary of the rebate is a constant and is known as a red envelop published by the ISO. As a result, in the dynamic game process, some customers' rebates will increase while others' will decrease.  
The scalability of the proposed DR scheme is presented in Table 1. The proposed scheme achieves good scalability with respect to the increasing number of DR customers. It is observed that the iteration number gradually decreases with the increasing number of DR customers. This is because, the total rebate is constant but more customers try to get the rebate, and it means the total rebate ratio will increase quickly.  
Figure 8: The dynamic process of the non-cooperative process for every customers’ ideal rebate ratio.  
Figure 9: The dynamic process of the non-cooperative process for every customers’ actual rebate value.  
In the dynamic game process, it is very important for every customer to estimate the summary of all other customers' strategy, that is, to estimate \(\hat{K}_{i}(t)\). Using our proposed Gossip algorithm, every customer just needs to exchange information with a few familiar customers through the social network and then everyone can get an accurate estimation value. Figure 10 illustrates one of the customer's estimation value at the 7 th time slot with the iterations. It should be noted that in the entire process, Algorithm 1 in the previous flowchart will iterate 10 times, while once \(k_{i}^{opt}(t)\) is updated, the Gossip algorithm, i.e., Algorithm 2, should be executed to estimate the newest \(\hat{K}_{i}(t)\). The Gossip algorithm can converge into an accurate value in approximately 9 iterations.  
In Table 2, estimation values and the actual values of total rebate value in every iteration are showed. It can be found that the algorithm is accurate enough to support the customers making decisions in the non-cooperative game
## 4 Strain and Temperature Testing
### Strain Testing  
The experimental set-up for the strain testing is shown in Figure 10. The fiber sensor device under test (DUT) was held between Stages 1 and 2, using clamps C1 and C2. DUT refers to the FP and FBG sensing structure in the fiber. Stage 1 was fixed while Stage 2 can be moved precisely using a X-Y-Z axis controller. Fiber alignment was done using the controller, which later stretched the fiber in horizontal direction by 5.03 \(\upmu\)m per step. This stretching induced a strain in the fiber to be sensed by the DUT. The distance between the two stages (Stages 1 and 2) before stretching was set as 18 cm. The sensors' peak wavelength and spectrum data were captured using an FBG interrogator (si155, from Micron Optics) at 1 kHz rate. The results of strain measurement and testing are presented in the form of peak wavelength response and spectrum evolution of the sensors.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Spirit One 1040-8-SHG (Second Harmonic Generator)** \\ \hline Repetition rate & 200 KHz OR 1 MHz \\ Pulse energy & \textgreater{}39 \(\upmu\)J at 200 KHz \\ Pulse width & \textless{}400 fs (327 fs) \\ Pulse picker divider & 3230 \\ Beam Diameter at exit & 2 \(\pm\) 0.2 mm \\ Wavelength (SHG) & 520 \(\pm\) 3 nm \\ Output power (SHG) & \textgreater{}4 W at 200 KHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Laser parameters.  
Figure 9: Spectrum of the fabricated FP in adiabatic fiber taper.
# Treatment Methods for Antibiotic Mycelial Residues: A Review
## 3 Treatment Methods for AMR
### Biochemical Methods
#### 3.2.2 Anaerobic Digestion  
An anaerobic digestion (AD) is a multistep biochemical conversion process wherein facultative bacteria and obligate anaerobes metabolize organic matter into methane, carbon dioxide, and other byproducts under anaerobic conditions [85]. Long recognized as promising for AMR treatment, this technology offers dual benefits: effective elimination of residual antibiotics alongside conversion of low-value AMR into valuable biogas and nutrient-rich digestate, enabling efficient resource recovery and circularity [45; 86]. Crucially, akin to composting, AD obviates the need for AMR dewatering pretreatment, conferring significant cost advantages over conventional methods, like incineration and landfilling.  
[PERSON] et al. [64] directly utilized untreated erythromycinomycin mycelial residue as a substrate for AD, conducting methanogenic potential and kinetic experiments under varying temperatures. Their results indicate that methane production peaked at 35 \({}^{\circ}\)C, with no significant inhibitory effects observed on anaerobic microorganisms--particularly methanogens--demonstrating the feasibility of employing antibiotic-containing erythromycinomycin residue in anaerobic fermentation systems without prior antibiotic removal pretreatment. [PERSON] et al. [49] investigated the anaerobic co-digestion of AMR with corn straw, revealing that the co-digestion system harbored a more diverse and functionally enriched microbial community structure. This attribute enhanced the stability of the methanogenic process while enabling effective degradation of ARGs within AMR. [PERSON] et al. [87] examined thermo-alkaline pretreatment's impact on penicillin mycelial residue digestion. They found that it significantly increased soluble chemical oxygen demand (COD), suspended solids solubilization, and biodegradability, thereby enhancing biogas production and providing vital technical insights for process optimization.  
However, practical implementation faces challenges. The inherently slow growth of anaerobic microorganisms necessitates protracted retention times, demanding large-scale reactor configurations that increase capital and operational costs while occupying significant space [88]. Furthermore, the process is highly sensitive to parameters, including the organic loading rate, pH, ammonia nitrogen concentration, temperature, and volatile fatty acid levels [89], imposing stringent operational control requirements. Additionally, malodorous hydrogen sulfide emissions during digestion present a notable environmental concern [90].  
While technically advantageous for AMR treatment, AD's large-scale deployment is hampered by its technological complexity and high capital intensity. Future efforts should concentrate on innovations, such as targeted functional microbial consortia acclimation, optimized multiphase reactor design, and intelligent monitoring systems, to surmount barriers to industrial application and enhance market competitiveness.