esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Sensorless FOC with Luenberger-Type Back-EMF Observer  
The back-EMF observer can be used in conjunction with the vector control strategy, as shown in Figure 3.  
Figure 3: Block diagram of the sensorless FOC with the Luenberger-type back-EMF observer and tracking observer.  
Figure 2: Block diagram of the PMSM in rotational reference frame \(dq\).  
Such a scheme can provide an alternative or redundant solution to the sensored control drive. Thus, the drive is considered sensorless by removing the mechanical sensor from the rotor shaft and estimating rotor speed or position. However, this kind of observer does not provide direct feedback on rotor speed and position. To solve this issue, a tracking observer can be used to process the output of the back-EMF observer. However, this sensorless strategy is based on the machine model, and it is very complex, leading to higher computational requirements for MCU. Furthermore, the implementation and correct tuning of the several PI controllers, filters, etc., may be time-consuming. Table 1 shows the main functions employed in this sensorless structure.  
However, one should notice that the sensorless components in Table 1 are not matching with the block diagram in Figure 3 because the other components are part of the back-EMF observer and tracking observer blocks. These are illustrated within the next sections.
## 5 Conclusions  
In this paper, based on the flow characteristics of the submerged inlet, the distributed submerged inlet configuration applied to the large maneuvering state is proposed. Firstly, the accuracy of the numerical simulation method is verified. Then, the flow characteristics of the conventional submerged inlet and the distributed submerged inlet are analyzed respectively in the steady and transient large maneuvering states, and the following conclusions can be obtained:  
1. The trend of transient and equivalent steady state calculation results of the two configurations of inlet during pitching maneuver is consistent, but the value of \(\sigma\) calculated in steady state is slightly higher than that calculated in transient state. When \(\alpha=-30^{\circ}\), the difference between the steady-state and transient results is the largest, which decreases with the increase of \(\alpha\). When \(\alpha=30^{\circ}\), there is almost no difference between the steady and transient results. In the course of the yaw maneuver, the trend of transient and equivalent steady state calculation results of \(\sigma\) is consistent, but the value of \(\sigma\) calculated in steady state is slightly lower than that calculated in transient state. When the absolute value of \(\beta\) is less than \(20^{\circ}\), the transient and steady calculation results are almost consistent. When \(\beta\) is close to \(\pm 30^{\circ}\), the transient calculation result is slightly higher than the steady calculation result.
2. When flying at a high angle of attack, the \(\sigma\) and \(D_{1}\) of the distributed submerged inlet and conventional submerged inlet have little difference. Compared with the conventional submerged inlet configuration, \(\sigma\) at the outlet of the distributed submerged inlet configuration is increased by 44.2% and \(D_{1}\) is reduced by 66.3% in the case of high sideslip flight, which greatly improves the performance of the inlet in the case of high sideslip maneuver flight, and significantly improves the sideslip limit of the submerged inlet.
3. In the transient calculation results of the large maneuver flight state, the two submerged inlet configurations show an obvious hysteresis effect, which is more obvious with the increase of angular velocity.  
Conceptualization, J.Z.; methodology, J.Z.; software, J.Z.; validation, J.Z.; formal analysis, J.Z.; investigation, J.Z.; resources, J.Z.; data curation, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, B.M.; visualization, J.Z.; supervision, B.M.; project administration, B.M.; funding acquisition, B.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [the National Natural Science Foundation of China] grant number [12202363] and [the Natural Science Basic Research Program of Shaanxi] grant number [2021]Q-084].  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Microbiological Analyses  
The microbiological counts of GPP did not reveal the presence of any of the microbial groups object of investigation. [PERSON] et al. [32] assessed that the absence of microorganisms in the GPP is due to the oven-drying treatment performed on grape pomace. The ewes' milk before pasteurization was characterized by a concentration of TMM of 6.32 Log CFU/mL that is higher than the limit of <500,000 CFU/mL reported by the Commission Regulation (EC) No 853/2004 for raw ewes' milk. High levels of TMM in raw ewe's milk before processing into cheese are often detected [33; 34]; LAB cocci were found at the same level (\(10^{6}\) CFU/mL) of TMM, while LAB rods were one Log unit lower. Similar results were previously reported by [PERSON] et al. [35] in raw ewes' milk used for PDO Pecorino Siciliano cheese production. These results indicated that milking month exerts a limited influence on the microbial load of bulk milk. After pasteurization, TMM and coccus LAB were found at \(10^{3}\) CFU/mL while LAB rods were not detected. These results confirmed what previously reported by [PERSON] et al. [36] and [PERSON] et al. [37] that thermoduric indigenous milk LAB are able to survive the pasteurization process. No statistical significant differences (\(p>0.05\)) were found for the levels of microorganisms object of investigation during all steps of cheese making. After inoculation with NMSC, all milks showed approximately 7.0 Log cycles of TMM and almost the same levels of mesophilic coccus LAB, confirming that _L. lactis_ inoculum occurred at \(10^{7}\) CFU/mL. After coagulation, all control and experimental curds reached values of TMM and LAB cocci of about \(10^{8}\) CFU/g showing an increase of microbial counts as a consequence of whey draining [34].  
These data also confirmed the dominance of lactococci among the microbial community of the curds, reaching values of about 9 Log CFU/g in all control and experimental primosale cheeses. These results highlighted that the addition of 1% (\(w\)/\(w\)) of GPP did not influence the fermentation process, carried out by the four strains of _L. lactis_ (Mise36, Mise94 Mise169 and Mise190) used individually.
# Research on a Multi-Type Barcode Defect Detection Model Based on Machine Vision
## 4 Experiment and Results Analysis
### Defect Detection Experiment
Table 5 presents the resulting Precision, Recall, and F1 score. Y8-LiBAR Net achieved an F1 score of 0.922, exceeding ViT-B/16 by 0.012 (1.2 percentage points) and the classical SVM-RBF by 0.046 (4.6 percentage points) while preserving balanced Precision and Recall. This improvement indicates that the proposed dual-branch feature-fusion strategy effectively captures both global semantic structure and local texture cues essential for reliable defect recognition. To further explain the model's discriminative ability for each category, a row-normalized confusion matrix for the validation set was plotted, as shown in Figure 12. From the figure, we can observe the following: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Variant** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & \(Recall_{D}\) \\ \hline Y8-LiBAR Net & 0.9250 & 0.9185 & 0.9250 & 0.9187 & 0.7667 \\ \(w/o\) ViT branch & 0.8821 & 0.8673 & 0.8795 & 0.8721 & 0.5912 \\ \(w/o\) ResNet branch & 0.8634 & 0.8419 & 0.8562 & 0.8473 & 0.5238 \\ \(w/o\) Shared Encoder & 0.9073 & 0.8962 & 0.9028 & 0.8987 & 0.6984 \\ \(w/o\) Orthogonal & 0.9167 & 0.9081 & 0.9142 & 0.9103 & 0.7246 \\ Regularization & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation Experiment Results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Detection Method** & **Precision** & **Recall** & **F1 score** \\ \hline Y8-LiBAR Net & 0.9185 & 0.925 & 0.9217 \\ SVM-RBF & 0.872 & 0.88 & 0.876 \\ DenseNet-121 & 0.895 & 0.905 & 0.9 \\ ViT-B/16 & 0.905 & 0.915 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative Results of Barcode Defect Detection. 1. Non-barcode samples were all correctly rejected, demonstrating strong robustness against background interference. 2. The recall rate for defective barcodes significantly increased to 76.7%, with the remaining approximately 23% of missed samples mainly concentrated in low-contrast defect scenarios, such as light scratches, minor oil stains, and edge damage. This indicates that future work can focus on hard example mining and cross-scale attention mechanisms for further optimization. The Y8-LiBAR Net dual-branch fusion network significantly enhanced the detection ability for key defects while maintaining an overall accuracy above 92%, proving the complementarity of convolutional local features and Transformer global dependencies. Future work will consider incorporating lightweight Transformers and knowledge distillation techniques to reduce inference latency and perform imbalanced learning and data augmentation for extreme defect samples, further improving the reliability and real-time performance of industrial barcode defect detection systems.
## 3 Results and Discussion
### In Vitro Cytotoxicity and Antiproliferative Activity of the Saponin Mixtures  
Each of the 14 variants of saponin mixtures generated by Design Expert 11 software were tested in a similar way used for the cytotoxicity evaluation of standard saponins, with determination of cell viability in both normal and tumor cells. The obtained results are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mixture Variants**} & \multicolumn{3}{c}{**Cell Viability (\%)**} \\ \cline{2-4}  & **NCTC** & **Hep-2** & **Hep-2** \\  & **(MEM + FBS)** \\ \hline
1 & \(44\pm 2.04\) & \(4\pm 1.59\) & \(49\pm 2.82\) \\ \hline
2 & \(77\pm 1.15\) & \(37\pm 1.24\) & \(77\pm 3.44\) \\ \hline
3 & \(72\pm 3.59\) & \(60\pm 3.84\) & \(66\pm 3.59\) \\ \hline
4 & \(63\pm 1.41\) & \(38\pm 3.68\) & \(69\pm 1.55\) \\ \hline
5 & \(69\pm 1.82\) & \(2\pm 0.95\) & \(61\pm 3.00\) \\ \hline
6 & \(73\pm 4.04\) & \(49\pm 3.31\) & \(64\pm 2.97\) \\ \hline
7 & \(79\pm 2.58\) & \(57\pm 3.74\) & \(73\pm 3.98\) \\ \hline
8 & \(60\pm 0.61\) & \(2\pm 1.24\) & \(63\pm 0.35\) \\ \hline
9 & \(81\pm 0.31\) & \(36\pm 1.34\) & \(77\pm 3.08\) \\ \hline
10 & \(65\pm 2.32\) & \(2\pm 0.41\) & \(67\pm 2.56\) \\ \hline
11 & \(71\pm 2.49\) & \(63\pm 3.69\) & \(69\pm 3.35\) \\ \hline
12 & \(50\pm 3.02\) & \(17\pm 0.31\) & \(44\pm 2.98\) \\ \hline
13 & \(65\pm 1.31\) & \(2\pm 0.72\) & \(70\pm 2.28\) \\ \hline
14 & \(63\pm 3.03\) & \(2\pm 2.11\) & \(62\pm 1.20\) \\ \hline Response range & \(44\) to \(81\) & \(2\) to \(64\) & \(44\) to \(77\) \\ \hline Ratio of max to min & \(1.84\) & \(39.25\) & \(1.73\) \\ \hline Transformation & none & Natural log & none \\ \hline \hline \end{tabular}  
* The values represent mean \(\pm\)SD (n = 3); \(p<0.01\)  
\end{table}
Table 4: _In vitro_ cytotoxicity results for saponin mixtures. (\(\alpha\)-hederin, hederagenin and hederacoside C) tested in fibroblasts cells (NCTC) and tumoral cells (Hep-2), determined by MTT assay.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Run**} & \multicolumn{3}{c}{**Mixture Composition, \(\mu\)g/mL**} \\ \cline{2-4}  & **A:** & **B:** & **C:** \\  & **\(\alpha\)-Hederin** & **Hederagenin** & **Hederacoside C** \\ \hline
1 & 8.000 & 381.029 & 310.971 \\
2 & 3.824 & 100.000 & 596.176 \\
3 & 2.000 & 279.100 & 418.900 \\
4 & 3.514 & 496.486 & 200.000 \\
5 & 8.000 & 139.800 & 552.200 \\
6 & 2.000 & 343.630 & 354.370 \\
7 & 2.000 & 188.104 & 509.896 \\
8 & 5.305 & 301.292 & 393.403 \\
9 & 3.824 & 100.000 & 596.176 \\
10 & 5.305 & 301.292 & 393.403 \\
11 & 2.000 & 424.557 & 273.443 \\
12 & 8.000 & 462.150 & 229.850 \\
13 & 8.000 & 230.898 & 461.102 \\
14 & 5.305 & 301.292 & 393.403 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental matrix of the mixture modeling.  
From the values obtained for cell viability at 24 h of Hep-2 response in medium without FBS, it can be seenthat for the variation limits were very large and it was used as a means of transformation: In (\(\gamma\)).
## 3 Results
### Inter-Batch Variability
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Backet** & \multicolumn{2}{c}{**Batch 1**} & \multicolumn{2}{c}{**Batch 2**} & \multicolumn{2}{c}{**Batch 3**} \\ \cline{2-7}
**System** & **N** & **Mean (\(\pm\)SD)** & **N** & **Mean (\(\pm\)SD)** & **N** & **Mean (\(\pm\)SD)** & \(p\) \\ \hline SYNTHESIS & 10 & 5.00 (\(\pm\)0.08) & 10 & 5.04 (\(\pm\)0.08) & 10 & 5.05 (\(\pm\)0.12) & 0.492 \\ \hline NU-EDGE & 10 & 4.69 (\(\pm\)0.27) & 10 & 4.65 (\(\pm\)0.30) & 10 & 4.57 (\(\pm\)0.25) & 0.584 \\ \hline DISCOVERY & 10 & 4.06 (\(\pm\)0.15) & 10 & 4.06 (\(\pm\)0.09) & 10 & 3.98 (\(\pm\)0.10) & 0.224 \\ \hline FLI TWIN & 10 & 3.74 (\(\pm\)0.40) & 10 & 3.85 (\(\pm\)0.37) & 10 & 4.18 (\(\pm\)0.22) & 0.006 ** \\ \hline MINI SPRINT & 10 & 4.45 (\(\pm\)0.20) & 10 & 4.28 (\(\pm\)0.16) & 10 & 4.17 (\(\pm\)0.16) & 0.005 ** \\ \hline OMNIARCH+ & 10 & 4.00 (\(\pm\)0.08) & 10 & 3.91 (\(\pm\)0.20) & 10 & 4.01 (\(\pm\)0.11) & 0.266 \\ \hline SCAPE & 10 & 3.90 (\(\pm\)0.27) & 10 & 3.90 (\(\pm\)0.18) & 10 & 4.01 (\(\pm\)0.14) & 0.596 \\ \hline MINI-TWIN & 10 & 4.09 (\(\pm\)0.13) & 10 & 4.11 (\(\pm\)0.18) & 10 & 4.04 (\(\pm\)0.08) & 0.484 \\ \hline JAZZ & 10 & 5.04 (\(\pm\)0.28) & 10 & 4.48 (\(\pm\)0.25) & 10 & 4.62 (\(\pm\)0.21) & \textless{}0.001 *** \\ \hline MINI MASTER & 10 & 4.23 (\(\pm\)0.23) & 10 & 4.06 (\(\pm\)0.08) & 10 & 4.19 (\(\pm\)0.14) & 0.067 \\ \hline MINIPREVAIL & 10 & 4.19 (\(\pm\)0.26) & 10 & 4.10 (\(\pm\)0.14) & 10 & 4.00 (\(\pm\)0.06) & 0.064 \\ \hline VICTORY & 10 & 4.11 (\(\pm\)0.12) & 10 & 4.18 (\(\pm\)0.26) & 10 & 4.05 (\(\pm\)0.12) & 0.290 \\ \hline \hline \end{tabular} SD: Standard deviation. Kruskal–Wallis test was performed for SYNTHESIS®, DISCOVERY®, FLI TWIN®, and SCAPE®. A multivariate ANOVA was performed for the other systems. ** \(p\) \(<\) 0.01; ** \(p\) \(<\) 0.001.  
\end{table} 
Table 4: Actual slot angulation in the different batches for each system (inter-batch variability of slot angulation). The declared tip values were \(5^{\circ}\) for NU-EDGE® and SYNTHESIS® and \(4^{\circ}\) for the rest of the systems. Figure 7: Bracket slot angulation in the different batches (inter-batch variability), displayed as the Confidence interval (CI) in degrees. Declared values were \(5^{\circ}\) for NU-EDGE® and SYNTHESIS® and \(4^{\circ}\) for the rest of the systems.
# On Transform Domain Communication Systems under Spectrum Sensing Mismatch: A Deterministic Analysis
## 5 Conclusions  
In this work, we discussed the TDCS system with respect to the IoT, where TDCS has proved to not only be a competitive candidate of traditional CR overlay technology, but also of great prospect in the IoT. However, due to some strict requirements, especially the need of ideal spectrum sensing results between transceivers, cognitive radio has limited its theoretical analysis and practical studies. This leads us to study the performance of TDCS in the presence of SSM. By adopting an intuitive spectral bin representation and by investigating QPSK and CCSK, it is found that the influence of SSM can be analyzed deterministically through a mismatch factor by applying it to the original SNR, regardless of the modulation methods. Finally, a general mathematical analysis for TDCS performance under any proposed mismatched spectrum sensing pattern is proposed, which may trigger some inspiration for further studies.  
This work is supported by the MOST Program of International S and T Cooperation under grant 2016 YFE0123200, in part by the National Natural Science Foundation of China under grant 61471100, grant 61101090, and grant 61571082, in part by the Science and Technology on Electronic Information Control Laboratory under grant 6142105040103, and in part by the Fundamental Research Funds for the Central Universities under grant ZYGX2015012 and grant ZYGX2014Z005.  
[PERSON] and [PERSON] contributed to the idea and the writing of this work; [PERSON] and [PERSON] designed the simulation environment; [PERSON], [PERSON] and [PERSON] helped edit the writing of this work; [PERSON] contributed the algorithm of the work.  
There is no conflicts of interests.  
Figure 11: BER comparison of the TDCS and NC-OFDM.
# A Stigmergy-Based Differential Evolution
## 4 Experimental Results
### Benchmark Used in the Experiments  
The experimental testbed consists of 27 functions. We used 14 multidimensional objective functions (Table 3) that have different search spaces and different complexity levels. For example, the functions: Step, Ackley, Griewank, Levy, Rastrigin, Schwefel and Schwefel 2.22 have many local minima near the global optimum. The functions: Sphere, Sum Powers, Sum Squares and Trid have only one global optimum and are bowl-shaped. The functions Zakharov, Dixon and Price and Rosenbrock have a valley-shaped form, and the Zakharov function has only one global optimum, whereas the other two functions have several optima. Moreover, 13 two-dimensional objective functions (Table 4) were tested. Some of these functions have an optimal value solution outside the vector 0,...,0. This is useful for testing a bias towards such a value when new metaheuristic approaches are proposed, as suggested by [57]. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Function** & \(\mathbf{f(x)}\) & **Limits \(\mathbf{x_{i}}\)** & \(\mathbf{f^{*};x^{*}}\) \\ \hline F1: Sphere & \(\sum\limits_{i=1}^{d}x_{i}^{2}\) & \([-5.12,\,5.12]\) & \(0;\,0,\,\ldots,\,0\) \\ F2: Step & \(\sum\limits_{i=1}^{d}(\lfloor x_{i}+0.5\rfloor)^{2}\) & \([-100,\,100]\) & \(0;\,[-0.5,\,0.5)\) \\ F3: Sum Powers & \(\sum\limits_{i=1}^{d}|x_{i}|^{i+1}\) & \([-1,\,1]\) & \(0;\,0,\,\ldots,\,0\) \\ F4: Sum Squares & \(\sum\limits_{i=1}^{d}ix_{i}^{2}\) & \([-10,\,10]\) & \(0;\,0,\,\ldots,\,0\) \\ F5: Dixon and Price & \((x_{i}-1)^{2}+\sum\limits_{i=2}^{d}i(2x_{i}^{2}-x_{i-1})^{2}\) & \([-10,\,10]\) & \(0;x_{i}=2^{-\frac{i^{2}-1}{2}}\) \\ F6: Rosenbrock & \(\sum\limits_{i=1}^{d-1}\Bigl{[}100\bigl{(}x_{i+1}-x_{i}^{2}\bigr{)}^{2}+(x_{i} -1)^{2}\Bigr{]}\) & \([-30,\,30]\) & \(0;\,1,\,\ldots,\,1\) \\ F7: Schwefel 2.22 & \(\sum\limits_{i=1}^{d}|x_{i}|+\sum\limits_{i=1}^{d}\bigl{[}x_{i}\bigr{]}\) & \([-10,\,10]\) & \(0;\,0,\,\ldots,\,0\) \\ F8: Trid & \(\sum\limits_{i=1}^{d}(x_{i}-1)^{2}-\sum\limits_{i=2}^{d}x_{i}x_{i-1}\) & \([-d^{2},d^{2}]\) & \(\frac{-d(d+4)(d-1)}{d};x_{i}=i(d+1-i)\) \\ F9: Zakharov & \(\sum\limits_{i=1}^{d}x_{i}^{2}+\biggl{(}\sum\limits_{i=1}^{d}0.5 ix_{i}\biggr{)}^ {2}+\biggl{(}\sum\limits_{i=1}^{d}0.5 ix_{i}\biggr{)}^{4}\) & \([-5,\,10]\) & \(0;\,0,\,\ldots,\,0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(D>2\) objective functions. The optimum value is denoted as \(f^{*}\) and \(x^{*}\) is the candidate solution. hypothesis cannot be rejected, suggesting (with 5% certainty) that the execution time of both algorithms is similar for each experimental instance.
## 2 Related Work
### Cloud Service Composition Based on Non-Heuristic Algorithms  
Non-heuristic algorithms can find the optimal solution for a problem and are commonly used for optimization problems [4]. Two well-known non-heuristic algorithms are integer programming and linear programming. [PERSON] et al. [9] proposed a QoS evaluation method that incorporated SLA satisfaction and designed a service selection algorithm based on this method. [PERSON] et al. [10] proposed a new penalty-based optimization model in which QoS constraints can be relaxed but incur penalties accordingly. However, adjusting the penalty function parameters is computationally expensive. [PERSON] et al. [11] combined graph planning and fuzzy logic to solve the QoS-aware service composition problems, developing fuzzy rules to rank services based on user preferences and, thereby, reducing the search space. [PERSON] et al. [12] proposed an FCDC green service composition method, which minimizes energy and network resource consumption on physical servers and switches in cloud data centers to achieve green service composition optimization. However, this method is not suitable for dealing with large-scale search spaces. [PERSON] et al. [13] proposed a generic model for adjusting context information using service metadata specifications, which selects appropriate services under the user's QoS constraints and current context. [PERSON] et al. [14] proposed three intuitionistic fuzzy representations of virtual service devices. Six intuitionistic fuzzy estimations that include service device uncertainty were proposed. The proposed uncertainty estimations allow the definition of new QoS metrics and can be used to determine the quality of service composition across wide-ranging service systems. [PERSON] et al. [15] designed a multi-objective algorithm called HypE-C, which employs three correlation-based local search strategies within the frame work of the HypE (the hypervolume estimation algorithm for multiobjective optimization) to achieve better trade-offs among multiple conflicting QoS criteria. [PERSON] et al. [16] proposed a fast energy-centered and QoS-aware service composition approach (FSCA-EQ) for the IoT service composition. FSCA-EQ adopts a hierarchical optimization approach that uses the compromise ratio method (CRM) to pre-select services that meet the user's QoS requirements. Then, to reduce the energy consumption of IoT devices and extend their lifespan, the concept of relative advantage is applied to select the optimal service as the final composite service.  
Non-heuristic algorithms are typically straightforward to implement and have relatively low time complexity. However, they require an enumeration of all possible compositions, and the computational complexity increases with the size of the problem, making them unsuitable for large-scale problems. They may also encounter local optimal solutions when facing specific problems.
###### Abstract  
Underground structures can be vulnerable during strong earthquakes, and seismic mitigation systems designed for these structures are instrumental in improving multiple aspects of seismic performance. To deal with this problem, a novel isolation system is proposed for underground structures, employing the incorporation of a negative-stiffness amplification system (NSAS) and an isolator. The proposed NSAS consists of the subconfiguration of a spring with positive stiffness in parallel with a dashpot, which is then in series with a negative-stiffness device. The mechanical model and physical realization of the NSAS are presented, based on which the energy-dissipation-enhancement mechanism of NSAS is detailed. On this basis, comprehensive parameter analyses were conducted between the NSAS isolation system and a conventional isolation system. Analysis results showed that the NSAS exhibited a significant energy-dissipation-enhancement effect, in which the series connection of the negative and positive stiffnesses amplified the dashpot's deformation for enhanced energy-dissipation capacity and efficiency. Compared with a conventional isolator, the NSAS isolation system provided the underground structure with a multiperformance and multilevel mitigation effect, particularly yielding lower responses of displacement and shear forces at the same time. More vibration energy could be dissipated by NSAS, thereby alleviating the energy-dissipation burden of underground structures.  
underground structure; damping enhancement; soil-underground structure interaction; negative stiffness; isolation +
Footnote †: journal: Applied Sciences