esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Deployable Scissor Lift Brace to Mitigate Earthquake Risks of Soft-Story Buildings
###### Abstract  
This article introduces a novel smart deployable scissor lift brace system designed to mitigate earthquake risks in buildings prone to the soft-story effect. The system addresses the limitations of traditional retrofitting methods, providing an efficient solution for enhancing the structural integrity of buildings while preserving the functionality of open lower floors, commonly used for car parking or retail spaces. The soft-story effect, characterized by a sudden reduction in lateral stiffness in one or more levels of a building, often leads to catastrophic collapses during large earthquakes, resulting in significant structural damage and loss of life. The proposed system is triggered by signals from the Earthquake Early Warning (EEW) system, advanced technologies capable of detecting and broadcasting earthquake alerts within seconds which are currently implemented in countries and regions such as Japan, parts of the USA, and parts of Europe. The smart deployable system functions by instantly activating upon receiving EEW signals. Unlike traditional retrofitting approaches, such as adding braces or infill walls, which compromise the open layout of lower floors, this innovative device deploys dynamically during seismic events to enhance the building's stiffness and lateral stability. The article demonstrates the system's functionality through a conceptual framework supported by proof-of-concept experiments. Historical earthquake time histories are simulated to test its effectiveness. The results reveal that the system significantly improves the stiffness of the structure, reducing displacement responses during events of seismic activity. If properly proportioned and optimized, this system has the potential for widespread commercialization as a seismic risk mitigation solution for buildings vulnerable to the soft-story effect.  
caption=, ] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [  
Earthquakes themselves do not cause casualties, it is the effects of earthquakes, such as the collapse of buildings, that cause them. According to the US geological survey, in 2019, earthquakes were responsible for an estimated 808,717 deaths in the 21 st century, with an average of 42,500 fatalities every year affecting humans between 2000 and 2019. The earthquake of 9.1 magnitude in the Indian Ocean off the coast of Sumatra resulted in a series of large stunamis which happened in the year 2004 and took 227,899 lives. The magnitude 8.9 Japan earthquake which also resulted in tsunamis in March 2011 was responsible for more than 28,000 deaths; in comparison, the smaller magnitude 7.0 earthquake which occurred in Haiti in January 2010 resulted in an estimated 222,500 fatalities. In recent history, the Pacific Rim is the most affected by seismic activity, with over 80% of the world's largest earthquakes occurring in this region [1].
# Reconfigurable Supply Chain Selection: Literature Review, Research Roadmap and New Trends
## 4 New Perspectives for Reconfigurable Supply Chain Selection
### Resilience  
The COVID-19 crisis, geopolitical uncertainty, etc., have significantly impacted supply chain management. Indeed, supply chains must be resilient to mitigate the effects of market disruptions and be able to withstand them. This concept has largely emerged in the context of supply chains after the COVID-19 pandemic. In this context, [PERSON] et al. [53] showed the important role of resilience and sustainability in supply chain to deal with market disruptions. In the context of reconfigurable supply chains, [PERSON] et al. [41] showed the role of resilience in reconfigurable supply chains in both proactive (pre-disruption decision-making) and reactive (devoted to recovery planning) domains. Several reactive and proactive approaches have proved their effectiveness in building resilient supply chains in their restructuring after the COVID-19 pandemic [54]. The authors of [55] showed the important role of reconfiguration in ensuring a resilient supply chain, as supply chain reconfigurability is characterized by structural and process variety which is beneficial for supply chain resilience [45; 56]. All these works show the high correlation between reconfigurability and resilience of supply chains. It is important in future work to answer the following questions:  
* How can resilience be a criterion for selecting the reconfigurable supply chain configuration?
* How can the resilience of the supply chain be guaranteed through the six reconfigurability characteristics?
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stimulus** & **Template** & **Monomer and cross-linker** & **Form of MIPs** & **Reference** \\ \hline \multirow{6}{*}{Lysozyme} & NIPAAm, AA, DMAPMA, MBAAm & Particle & [24] \\ \cline{2-5}  & NIPAAm, AAA, AAm, MBAA & Core-shell & [33, 34] \\ \cline{2-5}  & NIPAAm, VBIDA, AAm, MBAA & Bulk & [35] \\ \cline{2-5}  & 2,4-D & NIPAAm, 4-VP, EGDMA & Particle & [36] \\ \cline{2-5}  & RGDS Peptide & NIPAAm, AAm, DMAPMA, MBAA & Hydrogel layers & [37] \\ \cline{2-5}  & Propranolol & NIPAAm, MPABA, EGDMA & Particle with brush & [38] \\ \cline{2-5}  & Curcuminids & NIPAAm, 4-VP, EGDMA & Core-shell & [39] \\ \cline{2-5}  & Sulfamethazine & NIPAAm, AAm, EGDMA & Core-shell & [40] \\ \hline \multirow{2}{*}{CIFX} & CIFX & NIPAAm, AAA, EGDMA & Core-shell & [41] \\ \cline{2-5}  & 2,4-5-Tichlorophenol & NIPA, AAA, EGDMA & Core-shell & [42] \\ \cline{2-5}  & 4-Amino pyridine & NIPA, AAA, EGDMA & Hydrogel & [43] \\ \cline{2-5}  & Cu(II) ion & NIPAAm, VBEDA,MBAA & Hydrogel & [44] \\ \cline{2-5}  & DBTS & Chitosan, Glutaraldehyde & hydrogel & [45] \\ \cline{2-5}  & Cisplatin & HEMA, AAA, MBAA & Hydrogels & [46] \\ \cline{2-5}  & TBA, AAm, MA, MBAA & Hydrogels & [47] \\ \cline{2-5}  & BSA & AMPS, NIPA, AAm & Hydrogel & [48] \\ \cline{2-5}  & DMAPMA, NiPAm, MBAA & Bulk & [49] \\ \cline{2-5}  & Ca\({}^{24}\) or Pb\({}^{24}\) & NIPA, MAA, MBAA & Gel & [50] \\ \hline Atrial natriuretic peptide & NIPA, MAA, MBAA & Particle & [51] \\ \hline Cyt c or Lys & NIPA, MAA, AAm, MBAA & Bulk & [52] \\ \hline Adenine{2-5}  & NIPA, MAA, EGDMA & Bulk & [53] \\ \hline \(p\)-Nitrophenyl1 phosphate & NPP, DVB & Bulk & [54] \\ \hline BHb & NIPA, MBA & Core-shell & [55] \\ \hline Sulfadiazine & NIPA, EGDMA & Core-shell & [56] \\ \hline Dopamine & MAA, AAM, MBAA & Particle & [57] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of different responsive MIPs.
## 3 Materials and Methods
### Resampling Strategies
#### 3.1.4 Tomek Link  
Tomek link (T-Link) [36] is another advanced sampling technique proposed to enhance the nearest neighbor classification method by means of removing noise and examples in the border of the classes. Although the objective of the method is not returning balanced data, it can be considered as a guided undersampling method when the training instances from the majority class are removed. The basis of the strategy is given below:  
1. Two training instances \(x\) and \(y\) from different classes are considered.
2. The distance between these two instances is denoted by \(d(x,y)\).
3. The pair of values (\(x\), \(y\)) is called T link if there is not an instance \(z\) that fulfils the condition \(d(x,z)<d(x,y)\) or \(d(y,z)<d(x,y)\).
4. If any two instances are a T-link, either one of them is as a noise or both instances are in the boundary of the classes.
## 2 Materials and Methods
### Characterization  
The procured materials were initially tested using Fourier transform infrared (FTIR) spectroscopy (Shimadzu-QATR 10, Kyoto, Japan) to identify different compounds present in the sample powders. Moreover, to analyze the characteristics and presence of several organic compounds and bonding behavior in the developed aloe vera powder, FTIR spectroscopy is a convenient technique. Field emission scanning electron microscopy (FESEM) (ZEISS, Sigma 500, Schaffhauserstrasse, Germany) was used to acquire images of the sample. In addition, the porosity developed in the samples was also observed using this technique. Along with this, microstructure characterization was also performed using FESEM assisted with energy dispersive spectroscopy (EDS) techniques (ZEISS, Sigma 500, Germany). The phase identification was achieved through the X-ray diffraction (XRD) technique (Malvern Panalytical, Malvern, UK) with a scanning range (20) from 20\({}^{\circ}\)-110\({}^{\circ}\) setting the K-alpha 2/K-alpha1 ratio at 0.5 and the step size at 0.01313. The measurements were performed through the X'Pert Highscore software (version 2.1) of PANalytical and the values for different phases were obtained using the ICSD cards available with the software.  
Moreover, to analyze the chemical structure of the PLA coating developed over the substrate, Raman spectroscopy (Horiba, Montpellier, France) with an excitation source wavelength of 532 nm with a wavelength range of 500-3500 cm\({}^{-1}\) was utilized. Moreover, the coating thickness was determined under FESEM microscopy by observing a cross-section of the sample.
## 1 Introduction  
Our voice is the primary way of interpersonal communication. Not only for professional speakers or singers, many everyday routines rely on a functioning voice and sound production. The major part of the voice production takes place in the upper airway, beginning at the larynx. The vocal folds (VFs), located in the larynx, produce the primary sound. The air flow passes by and stimulates their oscillation [1]. The vocal tract finally forms the audible human voice.  
The voice forming process as a whole is still being researched and not fully understood yet. The biomechanical properties of the VFs play a crucial role in the whole phonation process [2]. In this oscillating system, the eigenfrequencies are not only set by the spatial dimensions, but also by the mechanical properties. Changes in the elasticity are a good marker for benign and malignant changes. In addition, the diagnosis of voice disorders profits from new methods, which are able to examine the biomechanical properties of the VFs [3; 4]. Therefore, information about the biomechanics of the VFs is not only beneficiary for research, but also for clinical assessments.  
Consequently, the biomechanical characterization of the VFs is a broad research field. Different methods have been developed to test the VFs. Roughly divided, mechanical approaches [1; 5] (e.g., traction testing, rheometry, indentation testing), optical approaches [6] (e.g., videostroboscopy, videoendoscopy), and medical imaging techniques [7] (e.g., synchrotron X-ray microtomography [8], optical coherence tomography (OCT) [9], ultrasound (US)) are used to examine the biomechanics of the VFs.  
US is already being used to examine the vocal folds, like B-mode- [10], Doppler- [11], Nakagami-imaging [12], and elastography [13; 14; 15]. These imaging techniques, except B-mode-imaging, display the elastic characteristics of the vocal folds directly or indirectly [16]. Elastography is an examination method where the biomechanical properties are displayed on medical images. Ultrasound elastography (USE) is widely used to assess changes in different organ systems, like the liver, breast, or prostate [17]. The transcutaneous US was used to examine the vocal folds and has shown usable results with elastography [13; 14; 15] or with digital image correlation [18].  
In this work, we want to show an image registration-based approach to examine the mechanical properties of the VFs. Especially, Young's modulus and the strains under compression in the VFs should be measured. The newly-implemented algorithm was tested on gelatin phantoms and showed promising results. To demonstrate the feasibility on VFs, we used a porcine ex vivo larynx and studied its biomechanical properties. We could depict strain images and estimations of the elastic properties. These findings could show a new promising approach to examine the VFs, in order to reach a better understanding of the voice forming process and open up new diagnostic methods.
## 6 Facilities for Scientific Observation Instruments
### Network Mass Storage
#### 6.3.2 Network Mass Storage Control System  
The network storage groups are used in an extreme cold region under low air pressure. Ordinary computer devices, especially hard disks, will possibly break down and fail to provide a reliable storage space. The self-developed special control system is used here to deal with all kinds of possible problems, such as making fault switches to ensure the storage space's continuous usage.  
The network storage equipment is controlled and managed by the (PC monitor) PCM and the (site master control) SMC, as is seen in Figure 1. The allocation of responsibilities is as follows. Firstly, the network storage three devices' operation state is decided by the PCM according to the real-time data acquisition about these storages. In this way, part of the data acquisition and the power switch are supported by it as well. Then, since the storage equipment needs their work temperature above 0 \({}^{\circ}\)C, a local exclusive using temperature control system is developed here inside PCM.  
The hardware of the mass storage network includes the NAS groups, the equipment controllers, the temperature sensors, the heaters, the monitor machines PC/104, and a high-speed Ethernet, as is shown in Figure 8.  
The information's physical channel has two kinds, the Ethernet channel and one-way signal channel. The Ethernet channel can perform two-way communication, while the one-way signal channel can only transfer one-way signals like sensor signal, equipment control signal, power supply control signal, heating control signals, etc.  
The mass storage system network's control software realizes the NAS's automatic scheduling and maintenance, ensuring continuously available storage space. Its main duties are collecting network storage equipment's operation conditions, monitoring possible related faults and key links of the storage system, executing the device switching on faulty equipment, and realizing the redundant control mechanism.  
Figure 8: Mass storage control system.
# Efficient Top-\(k\) Spatial Dataset Search Processing
## 3 Notations, System Model and Problem Description
### System Model  
In this paper, we design a system model involving two parties: the data owner and the data user. They cooperate with each other to accomplish top-\(k\) spatial dataset searches.  
* The data owner owns the spatial data repository and provides a search engine for the data user to discover the desired spatial datasets. First, comprehensive data preprocessing is performed, which includes cleaning the raw data and constructing indexes to enhance the organization and accessibility of the spatial information. Second, these indexes are leveraged to deliver efficient search services, ensuring rapid query responses and scalability.
* The data user submits an exemplar dataset to the data owner to initiate a top-\(k\) spatial dataset search. Then, the data owner performs search processing on the data repository using a search index. After finding the datasets, it returns these datasets as the search result to the data user.
## 1 Introduction
In the study mentioned in [16], several artificial neural networks (ANNs) algorithms were proposed to estimate voltage instability in power systems. These ANN models, based on different training algorithms, were developed and subjected to a comparative analysis, aiming to accurately predict the voltage collapse phenomenon. Existing power loss estimators have many weaknesses, such as complicated methods, many static assumptions, and dependency on the slack unit. If the existing configuration of any network is randomly changed from its base case, then these estimators may produce negligible and unacceptable errors [17]. A different technique was proposed in [17] that has the ability to solve the problems mentioned in a constant and direct way. A large number of power flow solutions are used to create a large dataset for training artificial neural networks. Therefore, through some numerical experiments, this technique proves to be an effective tool for accurately and cost-effectively estimating real and reactive power losses, but with a relatively high number of iterations and CPU time. In this context, this study proposes an alternative methodology to obtain the power curve versus total loss of real and reactive power, avoiding the complexities associated with the Jacobian matrix, in addition to identifying the maximum loading point or critical point (CP) in electrical power systems. The objective is to develop an alternative and effective tool that can quickly assist in obtaining the total power loss curve with a low number of iterations and CPU time in relation to the works presented in the literature. Continuation power flow methods consist of several nonlinear equations, and as mentioned, singularity in the Jacobian matrix at the CP of electrical systems. Additionally, these methods require many iterations and consume a significant amount of CPU time, in contrast to the use of artificial neural networks (ANNs), as proposed in this article. The following sections will present the materials and methods, systems, ANNs, a flowchart, and analyses (MSE, MAPE, and Kruskal-Wallis test) for evaluating the error between the obtained and desired vectors. Subsequently, the results of the network's training and validation performance will be discussed, along with comparisons of the applied analyses. Finally, a discussion of the obtained results and conclusions will be provided.
###### Abstract
established a database of the daily maintenance costs of bridges in the United States, with the logarithm of the maintenance cost as a dependent variable and the service life of the bridge as an independent variable, and they proposed a prediction model based on a backtracking search algorithm [15]. [PERSON] et al. proposed a decision-making method based on performance detection and maintenance cost records of Chinese bridges, and used an annealing algorithm to evaluate the future maintenance costs and expected losses of bridges [16]. Then, the genetic algorithm was used to find the decision-making scheme with the lowest maintenance costs in the whole life cycle of the bridge. This is a decision-making method combining bridge performance and costs but does not explain the quantitative relationship between bridge performance and maintenance costs. Most of the existing bridge maintenance cost prediction models are based on the maintenance cost prediction of a single type of input indicator with a small sample base. This affects the applicability and generalization ability of the model. Bridge maintenance cost prediction is a typical and complex nonlinear regression problem. The prediction models established based on traditional statistical analysis methods and simple data-driven intelligent algorithms have certain defects in prediction accuracy and generalization performance. It is necessary to analyze, in detail, the degree of influence of many factors that affect the costs of bridge maintenance and, on this basis, use intelligent algorithms to deeply learn and extract the characteristics of actual engineering data. Thus, a set of computing processes and methods that combine data preprocessing, influencing factor analyses, and intelligent predictions are established. An effective \"reference\" for bridge managers would involve quantitative prediction of bridge maintenance costs using conventional bridge maintenance data. Based on the above, a database of the maintenance costs in bridges was constructed in this study, based on practical engineering data, containing the maintenance cost samples of 268 bridges in Chinese inland and coastal areas. The database includes many factorsthat affect maintenance costs, for example bridge location, bridge technical condition, bridge maintenance time. The fully-connected ANN and CNN were used to establish the intelligent prediction model of maintenance costs, respectively, and the prediction accuracies were compared. We also propose a new method to optimize bridge life cycle maintenance costs and predict the time-varying law of maintenance cost.
# Optimal Design of One-Sided Exponential Adaptive EWMA Scheme Based on Median Run Length
## 5 Comparisons of the Performance
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline  & & \multicolumn{4}{c}{\(MRL_{0}=100\)} & \multicolumn{4}{c}{\(MRL_{0}=200\)} & \multicolumn{4}{c}{\(MRL_{0}=300\)} & \multicolumn{4}{c}{\(MRL_{0}=500\)} \\ \cline{2-13}  & & **AEWMA** & **EWMA** & **Shewhart** & **AEWMA** & **EWMA** & **Shewhart** & **AEWMA** & **EWMA** & **Shewhart** & **AEWMA** & **EWMA** & **Shewhart** \\  & \(\lambda^{+}\) & **0.053** & **0.053** & **-** & **0.051** & **0.051** & **-** & **0.050** & **0.050** & **-** & **0.050** & **0.050** & **-** \\  & \(k\) & **1.303** & - & - & **1.379** & - & **-** & **1.414** & - & **-** & **1.467** & **-** & **-** \\ \(c\) & \(h^{+}\) & 5.425 & **1.292** & **4.9651** & **5.925** & 1.363 & **5.6616** & **6.475** & **1.401** & 6.0681 & **7.000** & **1.454** & **6.5798** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 4: The optimal parameters and \(MRL_{1}\) for the upper-sided exponential AEWMA, EWMA, Shewhart scheme when \(c_{1}=1.1,c_{2}=5,MRL_{0}\in\{100,200,300,500\}\).
## 3 Adaptive FNN-FDS Hybrid Model
### Calculation Procedures of FNN-FDS Hybrid Model  
According to principles of the adaptive FNN-FDS hybrid model, the structure flowchart is given in Figure 5. The calculation steps are summarized as follows:  
Step 1: Convert raw training data into matrix form.  
Step 2: Apply training data matrix to FNN model and FDS model, and evaluate the prediction accuracy.  
Step 3: Evaluate the prediction accuracy of FNN model and FDS model, which is defined as P1 and P2, respectively. If the maximum value of P1 and P2 is lower than P0 where P0 is predefined as the converge criterion, detect the singular value of the raw data; otherwise, repeat the calculation of the FNN-FDS hybrid prediction.  
Step 4: If P1 is higher than P2, apply prediction data matrix to FNN model, otherwise apply prediction data matrix to FDS model, and calculate the predicted increasing oil production rate of targeted producers by N\({}_{2}\) huff-n-puff;  
Step 5: Determine the EOR ratio by N\({}_{2}\) huff-n-puff in fractured-cavity carbonate reservoir and evaluate the oil incremental effect.  
Figure 5: The structure flowchart of the FNN-FDS hybrid model.
###### Abstract  
The main distresses of asphalt pavements in seasonally frozen regions are due to the effects of water action, freeze-thaw cycles, and so on. Basalt fiber, as an eco-friendly mineral fiber with high mechanical performance, has been adopted to reinforce asphalt mixture in order to improve its mechanical properties. This study investigated the freeze-thaw damage characteristics of asphalt mixtures reinforced with eco-friendly basalt fiber by volume and mechanical properties--air voids, splitting tensile strength, and indirect tensile stiffness modulus tests. Test results indicated that asphalt mixtures reinforced with eco-friendly basalt fiber had better mechanical properties (i.e., splitting tensile strength and indirect tensile stiffness modulus) before and after freeze-thaw cycles. Furthermore, this study developed logistic damage models of asphalt mixtures in terms of the damage characteristics, and found that adding basalt fiber could significantly reduce the damage degree by about 25%, and slow down the damage grow rate by about 45% compared with control group without basalt fiber. Moreover, multi-variable grey models (GM) (1,N) were established for modelling the damage characteristics of asphalt mixtures under the effect of freeze-thaw cycles. GM (1,3) was proven as an effective prediction model to perform better in prediction accuracy compared to GM (1,2).  
Keywords: asphalt mixture; basalt fiber; freeze-thaw cycle; logistic damage model; grey model +
Footnote †: journal: Applied Sciences