esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Viscous Damping Model and Hysteretic Damping Model
###### Abstract  
A damping model is one of the key factors in dynamic analysis. Viscous damping and hysteretic damping models are commonly used in structural damping models. In this study, transient and steady responses are analyzed for a single degree of freedom system based on the two damping models. The attenuation coefficient and damped natural frequency are important parameters of the transient response. In addition, the vibration amplitude is an important parameter of the steady response. When the relative errors of the parameters for the two damping models are less than 10%, the threshold of the damping ratio is selected as 0.1736 and the threshold of the loss factor is 0.3472. The numerical examples show that the dynamic responses based on the viscous damping model are approximately equal to those based on the hysteretic damping model in small damping cases. With the increase in the damping ratio, the difference between the dynamic responses calculated by the two damping models gradually increases. In large damping cases, the two damping models must be distinguished, and the choice of the damping model depends on the characteristic of dissipate energy.  
viscous damping model; hysteretic damping model; damping ratio; transient response; steady response +
Footnote †: journal:  
0000-0002-4000-0000-0000-0000-0000-0000-0000-0000-0000-  
applied in the frequency-domain analysis, and the corresponding time-domain response is divergent [19; 20]. On the basis of the complex damping model, [PERSON] and [PERSON] [21] constructed a hysteretic damping model by using Hilbert transform relation to overcome the time domain divergence problem of the complex damping model. Based on the assumption of the external excitation relation, [PERSON] et al. [22] further proposed the corresponding time-domain calculation method for the single degree of freedom (SDOF) system and the complex mode superposition method for the multi degree of freedom (MDOF) system. However, the time-domain numerical method based on the hysteretic damping model involves Hilbert transform of displacement, and the traditional time-domain integral method cannot be applied directly. Moreover, the frequency response function of the hysteretic damping model is non-causal, which needs to be further improved [23; 24].  
Elastic analysis is the basic research of dynamic analysis, and the non-linearly elastoplastic analysis can be equivalent to elastic analysis [25]. In this paper, the comparison of the viscous damping model and hysteretic damping model is analyzed in the elastic stage. The dynamic response consists of transient and steady responses. In Section 2, the difference between transient responses based on the two damping models is discussed. The attenuation coefficient and damped natural frequency are compared. In Section 3, the difference between steady responses based on the two damping model is discussed. The vibration amplitude ratio is compared. The numerical examples with different damping ratio are discussed based on the two damping models in Section 4.
# An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing
## 3 Artificial Neural Network based Resource Allocation Methodology
 ^{T}(j,c,i,k) \tag{29}\]  
The decision condition for \(\mathbb{T}_{j}^{(c)}\) allocated to \(\mathbb{R}_{i}^{(k)}\) consists of intrinsic attributes of \(\mathbb{T}_{j}^{(c)}\) (\(p_{j}^{(c)}\), \(v_{j}^{(c)}\), \(\underline{d}_{j}^{(c)}\), \(\overline{d}_{j}^{(c)}\), \(L_{c,i}^{(k)}\)) and dynamic features of \(\mathbb{R}_{i}^{(k)}\) defined in Equation (30). \[ \begin{bmatrix}g_{j0}^{(c_{0})}&p_{j0}^{(c_{0})}&l_{j0}^{(c_{0})}&v_{j0}^{(c_{ 0})}&\underline{d}_{j0}^{(c_{0})}&\overline{d}_{j0}^{(c_{0})}\end{bmatrix} ^{T} \cdots\quad\mathbb{T}_{j0}^{(c_{0})}\\ \begin{bmatrix}g_{j1}^{(c_{1})}&p_{j1}^{(c_{1})}&l_{j1}^{(c_{1})}&v_{j1}^{(c_{ 1})}&\underline{d}_{j1}^{(c_{1})}&\overline{d}_{j1}^{(c_{1})}\end{bmatrix} ^{T} \cdots\quad\mathbb{T}_{j1}^{(c_{1})}\\ \begin{bmatrix}g_{j2}^{(c_{2})}&p_{j2}^{(c_{2})}&l_{j2}^{(c_{2})}&v_{j2}^{(c_{ 2})}&\underline{d}_{j2}^{(c_{2})}&\overline{d}_{j2}^{(c_{2})}\end{bmatrix} ^{T} \cdots\quad\mathbb{T}_{j2}^{(c_{2})}\\ &\vdots&\hskip 142.26378 pt,n=\left|\mathcal{Q}_{i}^{(k)}\right|\\ \begin{bmatrix}g_{j_{n}}^{(c_{n})}&p_{j_{n}}^{(c_{n})}&l_{j_{n}}^{(c_{n})}&v_{ j_{n}}^{(c_{n})}&\underline{d}_{j_{n}}^{(c_{n})}&\overline{d}_{j_{n}}^{(c_{n})} \end{bmatrix} ^{T}\cdots\quad\mathbb{T}_{j_{n}}^{(c_{n})}\\ \hline \begin{bmatrix}\mu_{i,k}^{(k)}&\mu_{i,p}^{(k)}&\mu_{i,l}^{(k)}&\mu_{i, l}^{(k)}&\mu_{i,d}^{(k)}&\mu_{i,d}^{(k)}\end{bmatrix} ^{T}\cdots\quad\mathbf{\mu}_{i}^{(k)} (t)\\ \begin{bmatrix}\sigma_{i,g}^{(k)}&\sigma_{i,p}^{(k)}&\sigma_{i,l}^{(k)}&\sigma_ {i,v}^{(k)}&\sigma_{i,d}^{(k)}&\sigma_{i,d}^{(k)}\end{bmatrix} ^{T}\cdots\quad \mathbf{\sigma}_{i}^{(k)}(t)\end{bmatrix}\end{bmatrix} \tag{30}\]  
where \(\mathbf{\mu}_{i}^{(k)}(t)\) and \(\mathbf{\sigma}_{i}^{(k)}(t)\) are the mean value and standard deviation value of the processing pending queue \(\mathcal{Q}_{i}^{(k)}\) at time \(t\), respectively. Figure 8: Artificial Neural Network model for the MT completion status prediction. The outputs of the ANN are formulated in Output (31), which means the possibilities of completion status as Equations (24)-(26). \[\mathbf{O}(j,c,i,k)\triangleq \begin{bmatrix}O_{0}&O_{1}&O_{2}\end{bmatrix} ^{T}(j, c,i,k) \tag{31}\]  
Since the real completion status obtained by sampling is a discrete value as Equation (32), we convert the outputs into the style of one-hot encoding, which is in the same style of Equation (28). \[\hat{\mathbf{y}}(j,c,i,k)\triangleq \begin{cases}\begin{bmatrix}1&0&0\end{bmatrix} ^{T}&\text{Completion status }2,\ \left(d_{j}^{(c)}\leq\underline{d}_{j}^{(c)} \right)\\ \begin{bmatrix}0&1&0\end{bmatrix}^{T}&\text{Completion status }1,\ \left( \underline{d}_{j}^{(c)}<d_{j}^{(c)}\leq\overline{d}_{j}^{(c)}\right)\\ \begin{bmatrix}0&0&1\end{bmatrix}^{T}&\text{Completion status }0,\ \left( \overline{d}_{j}^{(c)}\leq d_{j}^{(c)}\right)\end{cases}  \tag{32}\]
## 1 Introduction  
The frequent occurrences of oil pollution issues have become more and more alarming. It is very important to find effective strategies to solve the aforementioned issues [1; 2; 3; 4; 5]. Oil-water separation materials with a porous structure and a superhydrophobic surface, such as sponges [6; 7; 8; 9], foams [10; 11; 12; 13; 14], carbon aerogels [15; 16; 17], and meshes [18; 19; 20], have been widely studied due to their excellent water repellency and good oil-water separation capacity. The preparation of these superhydrophobic materials is based on changing the chemical composition or the microscopic structure [21; 22; 23; 24]. In nature, biomass materials possess many exceptional biological properties by virtue of their unique structures, and these materials are gaining significant attention [25; 26]. Cotton, a typical environmentally friendly biomass material, contains abundant cellulose fibers with hydrophilic surface hydroxyl groups and a three-dimensional (3D) network structure [27; 28; 29]. The modification of cotton to produce superhydrophobic materials for the separation of water and oil has been widely investigated in recent decades [30; 31]. Nevertheless, most of the preparation methods have some limitations such as a cumbersome preparation process, expensive reagents, or low oil sorption capacity. Hence, it is highly desirable to develop a facile and green method for synthesizing highly hydrophobic composites derived from cotton with high separation capacity and low material costs, for dealing with substandard oil-water mixtures and offshore crude oil leaks.  
In this study, carbon aerogels were prepared by anchoring zeolitic imidazolate frameworks (ZIFs), nanoparticles, and carbon nanotubes (CNTs) on the surface of cotton balls (CBs), followed by carbonization. The carbonized ZIF-8 and CNTs on carbonized CBs (CZIF-8/CNTs/CCBs) were examined by field emission scanning electron microscopy (FE-SEM), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), energy-dispersive X-ray spectrometry (EDS), and X-ray diffraction (XRD). Furthermore, the oil-water separation performance and the reusability of the samples were also examined. The results show that the material prepared in this study has good prospects for application in the field of oil-water separation.
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 2 Sweep Frequency Technique
The strongest linear relationship was identified between the 2.8 GHz and 3 GHz frequency range, with R\({}^{2}\) varying between 0.90 and 0.94. Both studies have demonstrated the potential use of microwaves to monitor excess moisture in concrete structures as well as to detect crack propagation in reinforced concrete structures, offering a low-cost option for structural health monitoring (SHM). Further investigation is required to validate the results prior to the implementation of the proposed embedded sensors into actual structures.
## 2 Methodology
### Saliency Detection in Frequency Domain
#### 2.2.1 Initial Saliency Map  
Gamma correction (i.e., power operation on each pixel) is performed to reduce the impacts of uneven illumination, while Gaussian high-pass filtering is to suppress the ripples and reflections to some extent. The transfer function of Gaussian high-pass filter is:  
\[H(u,v)=1-\exp(\frac{-D^{2}(u,v)}{2{D_{0}}^{2}}), \tag{2}\]  
where \(H(u,v)\) is the transfer function of the filter, \(D(u,v)\) is the distance from point \((u,v)\) to the origin of the Fourier transform center, and \(D_{0}\) is the cut-off frequency of the filter.  
Amplitude spectrum of the image mainly contains the information of brightness, and phase spectrum mainly contains the information of texture structure [30]. Therefore, a phase spectrum reconstruction is performed to reduce the impacts caused by ripples and uneven illumination. Then, an initial saliency map is obtained.
# A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature
## 4 Experiment  
The Time-Frequency and Delay-Doppler characteristics provide the ability to comprehensively represent the information of the target. In this paper, the two signal features were composed of joint features to recognize the target, and the CNN approach was used for recognizing the underwater targets. A multi-input feature target recognition network structure was designed, and the combined Time-Frequency and Delay-Doppler properties of signals were used to identify the targets. To assess the efficacy of the proposed combined features and recognition network model for target recognition, several mature CNNs were also constructed: the VGG16, GoogleNet, and ResNet models were used to validate the new recognition model by assessing the classification accuracy of several recognition models.
## 5 Conclusions  
Using Southwest China as the study area, this study assessed five ESs in 2000 and 2018 using remote sensing and statistical yearbook data; their distributions were then discussed. Based on this analysis, the trade-off/synergistic relationships of the five changing ES values were analyzed using spatial statistics and analysis methods. The generalized additive model (GAM) was then used to analyze the nonlinear relationship between the change values of ESs and ESTD in order to understand the patterns of change in various types of ESs that affect the trade-off/synergistic relationships among ESs. The main conclusions are as follows. The mean values of five ESs--water yield, food production, carbon sequestration, soil conservation, and habitat quality--all increased in four provinces of Southwest China from 2000 to 2018, increasing by 365.8 m\({}^{3}\)/ha, 13.92 t/hm\({}^{2}\), 497.09 TgC/yr\({}^{2}\), 138.48 t/km\({}^{2}\), and 0.002, respectively. Calculation of ESTD for the five ESs revealed that soil conservation is traded off with all ESs except habitat quality, and this trade-off increases slightly; water yield is in synergy with all ESs except soil conservation, with synergy decreasing; and habitat quality is in synergy with all ESs except food production, wherein synergy tends to increase. Finally, the nonlinear relationship between the value of the change in ESs and ESTD was analyzed using GAMs. Changes in water yield had the greatest impact on ESTD compared to other ESs, except food production, wherein changes in all three ESs did not have a large impact on ESTD. Food production dominates the trade-offs/synergies relationship between it and soil conservation, and in carbon sequestration and soil conservation;carbon sequestration is the dominant player in their trade-offs/synergistic relationship. Habitat quality occupies a secondary position of influence, except in trade-offs/synergies involving food production. Due to the huge internal variation in different study areas and high substratum heterogeneity, the trade-offs/synergies of ESs present a complex nonlinear relationship. The flexibility of GAMs enables the mapping of the response curve between the ESs and trade-offs/synergies, which is applicable to the exploration of the driving factors.  
Q.H.: methodology, software, validation, formal analysis, writing--original draft. L.P.: methodology, investigation, resources, data curation, supervision. K.H.: data curation, methodology, visualization. W.D.: resources, supervision. Y.L.: investigation, data curation. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the National Natural Science Foundation of China (42071222), the Sichuan Science and Technology Program (2022 JDJQ0015), and the Tianfu Qingcheng Program (ZX20220027).  
Data Availability Statement: Data are not currently available to the public as of the publication of this paper.  
The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.
## 3 Experimental Phenomena and Their Analysis
### Experimental Results  
#### 3.3.1 Load-Displacement Curves  
The load-displacement curves for each specimen are shown in Figure 15 (the horizontal coordinates are the actuator position displacement values, and the vertical coordinates are the actuator load values), from which it can be seen that:  
Figure 14: Diagram of experimental phenomena. (**a**) J1 crack distribution; (**b**) J2 crack distribution.  
1. Compared to the hysteresis curves of J1 and J2, the other hysteresis curves are bowed, and the hysteresis curves are generally smooth and full. The area of the hysteresis curve increases with the increase in displacement load, which indicates that both J1 and J2 have a certain plastic deformation capacity and good energy dissipation capacity. At the same time, the loading force and displacement of the two new joints are approximately linear at the beginning of the loading when the joints did not crack; with the increase in force, they gradually became non-linear.
2. The hysteresis circle of J2 is significantly larger than that of J1. The ultimate displacements of J2 and J1 are 115.8 mm and 127.6 mm, respectively, and the ultimate displacement of J2 is 10.2% higher than that of J1. This indicates that the deformation of the bolt in the energy dissipation phase of J2 is greater than that of J1, enhancing the energy dissipation performance of the joints. It can be seen that the increase in bolt strength grade will increase the energy dissipation performance of the joints.
3. The hysteresis curves of both J1 and J2 are asymmetrical during reciprocating loading. This is due to the corbel limiting the rotation of the precast beam during negative loading, increasing the stiffness of the joints, decreasing the bolt deformation, and reducing the energy dissipation capacity.
# A Smart Framework for Managing Natural Disasters Based on the IoT and ML
## 3 Methodology
### Proposed Algorithm  
This paper proposed a classification algorithm applied to historical data in the cloud. The results of algorithms will be as rules. The cloud sends these rules to the fog nodes to classify any new sensed data from the first layer. Thus, fog nodes will detect and notify any potential threat in RT. In the case of a threat, an alert is sent to the citizens to take precautions, and to the civil defense to prepare for any emergency. Moreover, the results from fog nodes will be used as feedback to the proposed algorithm in the cloud for continuous enhancement over time.  
The proposed algorithm relied on periodic classification. This classification gives an early notification of the threat and then alerted people. Moreover, with the continuation of the threat indicator over time, the algorithms can estimate the level of threat. In addition, the proposed algorithm depended on the best three accuracies of the machine learning models that were tested. Where the three algorithms are adopted together, the majority is taken in the test case, which will enhance the reliability of classification.  
Figure 4 explains the main steps of proposed algorithm in the case of training and testing. The steps are:  
* Reading dataset of historical data in cloud;
* Preprocessing data by filtering a nominal value;
* Removing the missing value or replaced by average value of the feature;
* Normalizing values of each feature;
* Converting the date value to month number;
* Selecting features depending on correlation value with threat column (Y);
* Applying SMOTE function to address the unbalanced data issue;
* Dividing data to training and testing (80, 20);
* Applying ML models (we selected five models RF, SVM, DT, KNN, and LR);
* Comparing results based on the confusion matrix;
* Selecting the best three models.
## 8 Challenges and Future Directions  
The following directions will help to create more sophisticated, safe, and effective vehicle communication systems by addressing the changing opportunities and problems in the field of VANETs:  
* **Integration with AI:** The integration of the proposed VANET architecture with artificial intelligence could be investigated in subsequent studies. Incorporating artificial intelligence could improve decision-making processes and traffic management.
* **Quantum-resistant security solutions:** Recent developments in quantum computing have the potential to pose a threat to the conventional cryptographic algorithms and protocols that require computational assumptions such as discrete logarithm and factorization problems. To ensure the long-term security of VANETs, future efforts should concentrate on the development and integration of post-quantum cryptographic algorithms [90].
* **Scalability and efficiency improvements:** Especially in high-density urban contexts with a large number of vehicles and gadgets, it is important to investigate strategies to improve the scalability and efficiency of the suggested architecture. For this purpose, it may be necessary to optimize the procedures of key management and the decentralized ledger to execute activities more quickly and effectively.
* **Incentivization:** The proposed DLT-based framework could be significantly improved by incorporating an incentivization mechanism that encourages OBUs to share data. This approach is likely to result in a substantial increase in data volume, thereby ensuring the generation of accurate and reliable statistical information.
* **Real-World Implementation and Testing:** It is possible to gain useful insights into the actual challenges and performance of the proposed VANET architecture by conducting implementation studies and pilots in the real world. Taking into account data from the real world and comments from users, would help develop the model.
# Sensor Observation Service API for Providing Gridded Climate Data to Agricultural Applications
## 3 Methodology
### NARO Data Component  
The NARO data component is for managing the internal data of the NARO Agent system. It also works on retrieving data from AmGSD. The NARO data component imports AMD_Tools for retrieving data from AmGSD. Figure 3 demonstrates the data flow from AmGSD to a client and how the NARO data component processes the data. When there is a request from a client, the NARO data component calls the getData function of AMD_Tools for retrieving data, and then, the data are returned to AMD_Tools in an array format. The NARO data component converts the data array into a CSV file, which is the internal data model used for caching. Later, the data are converted into SOS format and returned to the client.  
Figure 3: Sequence diagram of retrieving data. O&M, Observation and Measurements.  
Figure 2: NARO (National Agriculture and Food Research Organization) Agent diagram. SOS (Sensor Observation Service).  
A client only issues a getObservation request to SOS API of NARO Agent; then, it can receive a response. The client does not need to know the data structure of AmGSD and the functions of AMD_Tools. Request and response are independent of programming language. A client only needs to know the specification of SOS API and the service endpoint.
# Design of a Compact Multiband Monopole Antenna with MIMO Mutual Coupling Reduction
## 5 Conclusions  
In this paper, the authors discuss the evolutionary steps of the compact multiband monopole antenna and its current distribution, which result in impressive wide bandwidth, radiation efficiency, radiation patterns, and gains. The overall performance of the compact multiband monopole antenna makes it highly suitable for compact devices such as IoT sensors. Furthermore, the authors also discuss a method to suppress the mutual coupling between two closely spaced antennas. The method is simple to configure, requires no additional components, and achieves excellent isolation. It will significantly increase the SNR and data throughput. This makes it highly suitable for MIMO antenna systems, such as WiFi-7 infrastructure. The measurement results are in good agreement with the simulations, validating the performance of the compact multiband monopole antenna and the mutual coupling suppression method for the MIMO antenna. Furthermore, these antennas are suitable for a variety of compact sensor devices and wireless infrastructure applications.  
Conceptualization, C.-K.L., D.-B.L., H.-C.L. and C.-C.L.; methodology, C.-K.L., D.-B.L. and H.-C.L.; software, C.-K.L.; validation, C.-K.L., H.-C.L. and C.-C.L.; formal analysis, C.-K.L., D.-B.L. and H.-C.L.; investigation, C.-K.L. and H.-C.L.; data curation, C.-K.L.; writing--original draft preparation, C.-K.L. and H.-C.L.; writing--review and editing, C.-K.L., D.-B.L. and H.-C.L.; supervision, D.-B.L.; project administration, D.-B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Taiwan University of Science and Technology.  
Not applicable.  
The data presented in this study are available via email upon request to the corresponding author.  
The authors declare no conflicts of interest.