esa-sceva/satcom-chunk-collection huggingface dataset:
# Coupled Vibration Analysis of Multi-Span Continuous Cable Structure Considering Frictional Slip
###### Abstract
However, this method cannot calculate the slip state of a complex cable system at the support. In summary, for a multi-span continuous complex cable system, to calculate the nonlinear coupled vibration response of the suspension structure under the consideration of the friction-slip state, it is still necessary to discretize the cable structure by using the finite element method and then solve it by using the method of direct integration over the time step. This paper simulates the slip of the main cable at the top of a cable tower by drawing on the three-node slip cable element. The two-node catenary cable element simulates the nonlinearity of the main cable, coupled to form a subsystem model of the main cable structure, and is combined with the direct stiffness method to establish a system model of the moving load. By analyzing the force relationship between the moving load and the main cable, the coupled vibration dynamics of the moving load and the cable are deduced to control equations. Based on the Newmark-\(\beta\) direct integration method combined with the dynamic stiffness method, the nonlinear system of equations of motion is solved. The coupled vibration response between the moving load and the main cable can be obtained when the crane is traveling with a load. The corresponding calculation program is compiled. Combined with the dynamic load test and simulation results of engineering cases, the correctness and feasibility of the vibration equations and the program can be verified by comparing and analyzing the finite element calculation results, the dynamic load test values and the program calculation results.
## 4 Hybrid Action Space SAC-Based MLLM Task Offloading and Resource Allocation Scheme
### Complexity Analysis of AD-SAC  
#### 4.4.1 Time Complexity  
The proposed AD-SAC algorithm employs two independent agents, each utilizing four neural networks (value network, soft Q-network, policy network, and target value network). Assuming each network consists of \(L\) fully-connected layers with hidden dimension \(H\), the computational complexity for a single forward pass is \(O(LH^{2})\). During each training iteration with batch size \(B\), the algorithm performs forward and backward propagation across all eight networks, resulting in a time complexity of \(O(8 BLH^{2})\) per iteration. Additionally, the policy integration step using maximum a posteriori optimization introduces a computational overhead of \(O(Bd_{\mu}d_{\
u})\), where \(d_{\mu}\) and \(d_{\
u}\) represent the discrete  
Figure 2: The structure of the hybrid action space AD-SAC algorithm.  
and continuous action dimensions, respectively. Therefore, the overall time complexity per training epoch is \(O(BLH^{2}+Bd_{\mu}d_{\
u})\), which scales linearly with the batch size and quadratically with the hidden layer dimension.
## 2 Materials and Methods
### Encapsulation of Bacteriophage K5 by Spray Drying  
K5 bacteriophages were encapsulated by an in situ cross linking of alginate and bivalent calcium ions during spray drying. This process was invented and patented by [PERSON] et al. [48] and slightly modified in this study. An overview of the process is given in Figure 1.  
In the first step, a pre-suspension of the matrix components was prepared. This contained demineralized water, 2% (\(w/w\)) sodium alginate of low viscosity (Merck KGaA, Darmstadt, Germany), 1% (\(w/w\)) milk protein (either micellar casein powder or whey protein isolate powder; both produced by the Dairy for Research and Training, University of Hohenheim, Stuttgart, Germany), 1% (\(w/w\)) succinidic acid (Carl Roth GmbH and Co. KG, Karlsruhe, Germany), and 0.5% (\(w/w\)) calcium hydrogen phosphate dihydrate (Carl Roth GmbH and Co. KG, Karlsruhe, Germany). The pre-suspension had a pH value of \(\sim\)3.5 but was adjusted with a 25% ammonia solution (Carl Roth GmbH and Co. KG, Karlsruhe, Germany) to a pH of 5.6. For practical reasons, the calcium salt and the alginate were added after the pH adjustment and the pH value was checked again. After continuously stirring overnight at 20 \({}^{\circ}\)C, 1% purified bacteriophage K5 lysate (Section 2.1.2) was added and stirred for 1 h.  
Spray drying was performed using the Mini Spray Dryer B-290 (B��CHI Labortechnik GmbH, Essen, Germany) and the settings listed in Table 1. Meanwhile, the suspension was stirred continuously. The produced bacteriophage K5 capsules (powder) were collected and stored in sealed reaction vessels (Section 2.5.1).  
Figure 1: In situ complexation of alginate, calcium ions, and milk proteins during spray drying (method modified according to [48]). (**a**) Process scheme of the encapsulation process. (**b**) Schematic drawing (not to scale) of the encapsulation process with suspension (above, blue frame), atomized droplet (center, turquoise frame), and capsule (bottom, orange frame) (figure modified according to [49]).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Setting** \\ \hline Feed flow volume & 3.6 mL min\({}^{-1}\) \\ Gas volume flow & 473–536 L h\({}^{-1}\) \\ Aspirator & 90\% (35 m\({}^{3}\) h\({}^{-1}\)) \\ Inlet temperature & 110 \({}^{\circ}\)C \\ Outlet temperature & 53–59 \({}^{\circ}\)C \\ Wet-bulb temperature & 35 \({}^{\circ}\)C \\ \hline \hline \end{tabular} \({}^{1}\) Determined acc. to Krischer and Kast [50] on the basis of a relative humidity of 89%, an absolute humidity of 4.6 g kg\({}^{-1}\), and an outside temperature of 4.9 \({}^{\circ}\)C (measured at the weather station of the University of Hohenheim, Stuttgart, Germany).  
\end{table}
Table 1: Spray drying parameters and settings used for encapsulation.
## 2 Results and Discussion
### Characterization of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\)
#### 2.1.5 FTIR Analysis  
In order to further identify the functional groups on the surface of the synthesized sample, FTIR test was used, as shown in Figure 6. The tensile vibration peak at 3420 cm\({}^{-1}\) corresponds to the -OH functional group. The absorption peak at 2852 cm\({}^{-1}\) belongs to methylene, and the peak intensity of PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) at this position is significantly weaker than that of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\), which was mainly due to the rich methylene ((-CH\({}_{2}\))\({}_{17}\)) in N, N-dimethyloctadecylamine. The absorption peak at 1244 cm\({}^{-1}\) was the epoxy group, and the peak intensity of PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) at this position is significantly stronger than that of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) because the C18 functional group was grafted by the epoxy ring opening reaction. According to the increase in the intensity of the methylene peak and the decrease in the intensity of the epoxy group peak, it was shown that the C18 functional group was successfully grafted.
## 4 AE Signal Analysis and Application
### AE Energy Evolution during the Loading Process  
Since the experimental results are similar, the following loading experiment on a reinforced concrete sewage pipe with 1500 mm pipe diameter is selected to analyze the mechanical response law of the AE signal.  
Figure 5 shows the characteristic curves of AE signal response during the concrete sewage pipe loading process. In the online elastic compression stage (OA), the AE signal is sparse, and the amplitude presents a low level overall due to the fewer damage activities occurring inside the specimen. With the increase of the load, the pipe loading enters the plastic damage stage (AB), many cracks expand and develop, irreversible plastic damage occurs, and cracks are still generated in the load retaining stage. The internal structure of the specimen continuously undergoes \"destabilization-equilibrium-destabilization\", and the load shows simultaneous fluctuation changes. When the load reaches the peak load level, the specimen cracks accumulate and penetrate the main damage--and the load decreases significantly--then the AE amplitude reaches the peak. When the sample enters the post-load peak stage (BD), the failure degree of the sample is gradually reduced, and the amplitude of the AE signal gradually decreases until it finally disappears altogether.  
Figure 5: Characteristic curve of pipeline loaded AE signal.  
The generation of AE signals is closely related to the deformation and damage of materials. AE signals can effectively characterize the internal damage and damage degree of concrete materials, and AE energy, as one of the basic parameter indicators, can reflect the material damage evolution process in real-time. The cumulative AE energy reflects the superposition of the material structure damage on the time series. The more severe the material damage, the faster the cumulative AE energy increases, and the greater the slope of the curve. The response curves of AE energy and cumulative energy versus load during the pipeline loading process are shown in Figure 6. It can be found that the evolution trend of the cumulative AE energy curve corresponds to the characteristics of each loading stage of the pipeline specimen, and the AE energy is excited in a cluster in the time domain. When the specimen is loaded into the plastic damage stage, the number of AE activities triggered by internal damage activities is significantly increased, and the energy level of the AE signal changes synchronously with the loading process, showing a gradual step-up trend. In the residual strength stage, the AE energy and cumulative energy curves rise rapidly when the peak load is reached, after which the AE energy starts to decrease until it finally disappears along with the decrease in the number and degree of damage activities. After the peak load is reached, the cumulative AE energy stops rising and remains stable.
## 4 Simulation and Numerical Results  
In this section, some simulation results are presented to verify the performance of the proposed joint signal processing schemes based on sparse reconstruction in an integrated MIMO-OFDM-IM system. Some parameters required in the simulation are shown in Table 1. In the experiment, we consider an application scenario in which the integrated platform simultaneously sends integrated signals to two targets at a range of 50 m and 100 m, as well as to two communication users, and the velocity of the targets is 20 m/s and 10 m/s, respectively. The integrated platform performs matched filtering and parameter estimation after receiving the echo signal. Meanwhile, the communication users receive and demodulate the communication signals.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Bandwidth & 20 MHz \\ Carrier frequency & 5 GHz \\ Duration of pulses & 32 μs \\ Number of OFDM symbols & 8 \\ Number of subcarriers & 256 \\ Number of subblocks & 32 \\ Total number of pulses & 32 \\ Lower limit of the number of state combinations & 6 \\ Upper limit of the number of samples & 10 \\ Modulation mode of communication information & BPSK \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of simulation.
# Semi-Coprime Array with Staggered Beam-Steering of Sub-Arrays
## 1 Introduction
The different scattering response for each mode is utilized to shape the radiation pattern and suppress the side lobes. A linear array, in any case, conventionally has been treated as a single array having single beam pattern. However, a decade ago a coprime sensors array (CSA) was proposed [27], in which an NULA was split into two sub-arrays (SAs). In this SAA, the two SAs with large inter-element spacing were used to generate two different beam patterns. To unify the two beam patterns, in the pioneering work [27], their product was taken, but in subsequent works, their minimum [28] and hybrid [29; 30] were also been considered. The CSA offers a small HPBW with fewer elements compared to SULA but a PSRL poorer than SULA. To enhance the PSRL, in [18; 31; 32], more sensors were added to the CSA, thus forming an extended CSA (ECSA). Recently, a modified version of the CSA, called the semi-coprime array (SCA) was proposed [33] in which an NULA is split into three SAs. Without compromising the HPBW, this array offers a PSRL higher than the CSA but still lower than SULA. It should be noted that all the arrays discussed above offer their best performance metrics, i.e., HPBW and PSRL, when steered in the broadside direction. As the main beam is steered away from the broadside, the performance of all the arrays worsens, either in terms of HPBW, PSRL, or both. In some cases directivity also decreases when the main beam is steered away from the broadside. A few existing arrays offer the same HPBW/PSLR for different look angles but require the elements to be relocated when steered to a new angle [11; 34]. However, in most scenarios, it is not possible to relocate them at the run time. In this paper, we propose staggered beam-steering of SAs in an SAA. Simulation results show that beam-steering the SAs slightly away from the desired steering angle can help generate a narrower beam pointed towards the desired angle, if the minimum of the beam patterns is taken to unify them. In combination with this technique, Chebyshev weights have been employed to improve the PSRL. For the proof of concept, we have applied staggered steering on an SCA only with a minimum processor. However, it can be applied to any SAA. We have shown in this paper, that an SCA with staggered steering (SCASS) outperforms the existing ULAs, NULAs and SAAs in terms of HPBW, PSLR and directivity, especially when steered away from the broadside. The rest of the paper is organized as follows. Section 2 describes the basic and proposed forms of an SCA. Section 3 presents the simulation results of the proposed array and compares it with different existing arrays. Finally, Section 4 discusses the simulation results and concludes the paper.
###### Abstract  
In order to improve the control precision and robustness of the existing proportion integration differentiation (PID) controller of a 3-Revolute-Revolute (3-RRR) parallel robot, a variable PID parameter controller optimized by a genetic algorithm controller is proposed in this paper. Firstly, the inverse kinematics model of the 3-RRR parallel robot was established according to the vector method, and the motor conversion matrix was deduced. Then, the error square integral was chosen as the fitness function, and the genetic algorithm controller was designed. Finally, the control precision of the new controller was verified through the simulation model of the 3-RRR planar parallel robot--built in SimMechanics--and the robustness of the new controller was verified by adding interference. The results show that compared with the traditional PID controller, the new controller designed in this paper has better control precision and robustness, which provides the basis for practical application.  
kinematics model; virtual bench; genetic algorithm controller; robustness Article
# Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions
## 1 Introduction
In a wireless communication system, it is important to decide which technique to use according to the system requirements and characteristics along with cost and complexity constraints. While there are various combinations of techniques that make up the wireless communication system, it is difficult to get a clear answer as to the best scheme for a complex multiuser system under a range of typical operating conditions. This paper proposes a technique that can appropriately bring out the merits of various combinations of techniques. 3. Finally, the proposed idea can be described from a different perspective and provides insights into other system implementations. The key idea here is to use multiple dimensions appropriately. The beauty of this approach is that it can even be applied to other multiple access systems. The various system models can be implemented by using additional dimensions. This paper demonstrates that the system throughput can be increased by using multiple dimensions. This concept can be extended further. If the additional dimensions are used without degradation or with a little tradeoff, the system throughput can be increased. The idea presented in this paper provides a multitude of interesting avenues for future research. The remainder of this paper is organized as follows. Section 2 describes the problems of the existing scheme and explains the solution and the reasonability of the proposed scheme. Section 3 describes the overall system model and the proposed scheme. Also, this section presents the performance of the proposed scheme. Section 4 presents simulation results and compares with the conventional scheme under various conditions. Section 5 considers some extensions about the implementation of the proposed scheme. Finally, conclusion is provided in Section 6.
# ZnO/Ag Nanocomposites with Enhanced Antimicrobial Activity
## 3 Results and Discussion
### Characterization of ZnO/Ag Nanocomposites
#### 3.1.2 Morphological, Size, and Compositional Analysis of ZnO/Ag Nanocomites  
The SEM images of the synthesized ZnO/Ag nanocomposites are shown in Figure 2. Figure 2 displays the agglomerated particles of pure ZnO. Figure 2a-f shows the samples prepared by means of concentrations of 0, 1, 3, 5, 7, and 10 at% of silver. The effect of Ag on the size of ZnO particles is clear. In Figure 2, with 0% Ag, there are both small and big particles. After adding 1% Ag (Figure 2b), all the grains are small, which should be the best sample for antibacterial purposes, as they have a higher surface/volume ratio. Increasing the amount of Ag only decreases the number of small particles, so they almost disappear in samples with 7% (Figure 2e) and 10% (Figure 2f). In this way, an increase in the amount of Ag by more than 5% varies the particle size, causing the surface/volume ratio to decrease considerably. The size of the Ag particles was very small (<30 nm), and for this reason, transmission electron microscopy (TEM) was used to evaluate the particle size and composition of the Ag nanoparticles.  
STEM allows determination of the shape and particle size distribution of the silver nanoparticles and their location on the nanocomposite. Figure 3 shows selected STEM images and the element mapping of the ZnO/Ag nanocomposites. The STEM image (Figure 3a) shows the presence of the Ag nanoparticles on the zinc oxide, thus forming the ZnO/Ag nanocomposite, which corresponds to sample Zn1 (1 at% of Ag). The Ag nanoparticles are mostly deposited in spherical form on the surface of the zinc oxide. The size of the silver nanoparticles ranged between 10 and 30 nanometers. The distribution map of Zn (Figure 3b) was collected using K\(\alpha\) X-rays. Figure 3c shows the distribution map of O using K\(\alpha\) X-rays. The Ag map in Figure 3d also consists of K\(\alpha\) X-rays. The Ag maps can be observed even for some small particles. It is clear that in the case of the Ag nanoparticles shown here, the Ag signals are distributed in different regions as Zn signals or O signals, perfectly indicating the position of the Ag nanoparticles.  
Figure 1: XRD patterns for the different silver concentrations of the nanocomposites (Zn1, Zn2, Zn3, Zn4, Zn5, and Zn6).  
Figure 3: (**a**) STEM images of ZnO/Ag nanocomposites; (**b**) Elemental mapping of Zn collected using K\(\alpha\) X-rays, (**c**) Elemental mapping of O collected using K\(\alpha\) X-rays; (**d**) Elemental mapping of Ag collected using K\(\alpha\) X-rays.  
Figure 2: SEM images of ZnO/Ag nanocomposites: (**a**) Zn1, (**b**) Zn2, (**c**) Zn3, (**d**) Zn4, (**e**) Zn5, and (**f**) Zn6.
# Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
## 4 Discussion
### Prescriptive Maintenance Actions
This usually considers proactive decisions that align operational goals with maintenance needs  
Figure 7: Characterisation of prescriptive maintenance activities within four dominant industrial sectors. Industry 4.0 technologies are added based on their presence in the industrial sector. to ensure continuity and regulatory compliance. The aerospace domain highlights the importance of synchronising operation and maintenance planning, considering that the cost of failure is exceptionally high [99]. The railway sector shows that PsM activities prioritise user experience, infrastructure safety, and service reliability. Prescriptions are primarily determined by the prioritisation of critical assets and the spatial targeting of high-risk or high-use infrastructure segments. These actions often align with real-time data usage, enabling strategic asset management and user-centric improvement. As with the aerospace domain, PsM activities in railways shift from simple performance metrics (e.g., reliability, availability) to aligning with service quality and safety.
## 4 Discussion
### Annalsysis of Hyperspectral FWC Imaging  
Although conventional spectroscopy techniques provide only one set of spectral information per individual sample, the HSI technique can produce full spectral information from every pixel. Thus, the mixture-sample images obtained using the LS-SVM model can show different spectral and spatial information for each pixel. In Figure 7, the mixture samples with different FWC levels are not easily distinguished by the naked eye, except for the 100% FWC sample. However, the LS-SVM images clearly classify the different FWC contents based on the number of yellow and red pixels, which increase with increasing FWC levels. Therefore, the obtained images present the FWC concentrations on a linear color scale with different colors being used for each pixel, facilitating improved understanding of the spatial variation in FWC content in the mixture samples.  
From Figure 7, it is particularly difficult to evaluate the FWCs added to the OF up to 30% concentration with the naked eye. However, the LS-SVM approach detects and distinguishes the increasing concentrations. This is because the FW fertilizer has a different pigment to the other OF ingredients, as shown in Figure 7, and thus, the color difference between the two groups reveals the FWC level. Note that pigment-based spectral features could strongly affect the performance of the LS-SVM model and, thus, the output images. Consequently, application of chemometric methods with various preprocessing techniques, and the HSI technique used in this study has considerable potential for quantitative evaluation of FWCs in OF.