esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup  
The schematic diagram of the output performance test for the valveless piezoelectric pump with airfoil baffles is depicted in Figure 3. A signal generator (AFG 3022B, Tektronix Beaverton, OR, USA) and power amplifier (HVP-300D, NJFN, Nanjing, China) provide a driving voltage of 100 V to excite the piezoelectric vibrator. The parameters of the piezoelectric vibrators are shown in Table 2[26]. Starting from 0\({}^{\circ}\) angle of attack, inlet and outlet pipes with airfoil baffles of the same angle are selected to assemble a valveless piezoelectric pump for multiple experiments. Subsequently, the driving frequency is adjusted to obtain different working frequency intervals between different experimental groups. Experiments are conducted within respective working ranges, each with a variation of 1 Hz. Utilizing an analytical balance with a precision of 0.001 g [27], the mass of liquid output by the valveless pump per unit time is measured to obtain the relationship curve between the output flow rate and driving frequency at zero back pressure for the valveless piezoelectric pump with airfoil baffles. During the flow rate test, a laser displacement sensor (LK-G35, Keyence, Osaka, Japan) is employed to measure the displacement of the center point of the circular vibrator. The test results are used to set the boundary conditions for the flow field simulation. A straight pipe is connected at the outlet of the piezoelectric pump, and the height difference in the liquid level at the inlet and outlet is recorded under different driving frequencies to obtain the relationship curve between the output back pressure at zero flow rate and the frequency for the valveless piezoelectric pump.  
The types of fluids suitable for piezoelectric micropumps mainly include pure water, alcohols, ketones, and non-strong acids and bases with low viscosity values. In addition, considering electrolyte interference, experimental cleaning efficiency, environmental protection, and economy, deionized water was used as the working medium in all tests.  
Figure 3: Experimental schematic diagram.  
\begin{table}
\begin{tabular}{c|c c c c c|c c c c|c c c c} \hline \hline \multicolumn{1}{c}{**Material**} & \multicolumn{3}{c}{**Young’s Modulus (GPa)**} & \multicolumn{3}{c}{**Pieroelectric Constant (C m\({}^{-1}\))**} & \multicolumn{3}{c}{**Permiticity (f/m)**} & \multicolumn{3}{c}{**Poisson’s**} & \multicolumn{3}{c}{**Density**} & \multicolumn{3}{c}{**Resonant Frequency**} \\  & & & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{Beryllium bounce} & \multicolumn{3}{c}{128} & \multicolumn{3}{c}{/} & \multicolumn{3}{c}{/} & \multicolumn{3}{c}{0.35} & \multicolumn{3}{c}{860} & \multicolumn{3}{c}{/} \\  & [121 & 75.4 & 75.2 & 0.0 & 0.0 & [ 0 & 0 & \(-\)5.4] & & & & \\  & [75.4 & 121 & 75.2 & 0.0 & 0.0 & [ 0 & 0 & \(-\)5.4] & & & \\  & [75.2 & 75.2 & 111 & 0.0 & 0 & 0.0 & 0.0 & 15.8] & & & & \\  & [0 & 0 & 0 & 21.1 & 0 & 0 & 0.12.3 & 0 & 0 & 0 & 7.346] & & \\  & [0 & 0 & 0 & 0 & 21.1 & 0 & 12.3 & 0 & 0 & & \\  & [0 & 0 & 0 & 0 & 22.6] & 0 & 0 & 0 & 0 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material parameters of the PZT vibrator.
# Swin-APT: An Enhancing Swin-Transformer Adaptor for Intelligent Transportation
## 3 Methods
### Light-Weight Backbone Swin-L  
The model is designed based on Swin-Transformer [8], which is a lightweight backbone network used for initial feature extraction of road images. Features are hierarchically extracted from input images using the Swin-Transformer. The backbone structure includes image patch partitioning, image embedding, and encoding layers, which are capable of capturing image features at different levels. Swin-Transformer increases the receptive field of image patches by using W-MSA, making it particularly suitable for fine-grained classification tasks such as image classification and image segmentation.  
However, issues such as a large number of model parameters, high computational resource requirements, and long inference time arise when directly applying the vanilla Swin-Transformer to road segmentation. It is not suitable for real-time predictions in intelligent transportation and driving environments. As a result, the Swin-Tiny is modified by changing the intermediate feature dimension and reducing the number of blocks, resulting in a lightweight network called Swin-Light (Swin-L).  
Specifically, a 4-layer structure of the Swin-Transformer is applied, where each layer consists of an image patch merging module and consecutive Swin-Transformer blocks. The numbers of Swin-Transformer blocks for each layer are set as 1, 1, 3, and 2, and the hidden dimensions of each layer are set as 48, 96, 192, and 384.  
Experimentally, Swin-Light reduces 35% of parameters compared to Swin-Tiny (from 60 M to 39 M). However, performance degradation may occur due to the significant decrease in network parameters and complexity, as the scene features may not be fully captured by the model.
## 2 Related Works
The third approach, which achieved the best result so far, is integrating transformer-based embeddings and a BI-LSTM network [20]. The proposed model uses pretrained \"transformer\" models, specifically T5, in conjunction with a BI-LSTM architecture which is effective in processing sequential data by considering the past and future context. This research evaluated several pre-processing techniques and different hyperparameters to \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Model Size** \\ \hline paraphrase-albert-small-v2 (AS) & 43 MB \\ all-MiniLM-L6-v2 (M6) & 80 MB \\ bert-base-uncased (BB) & 110 MB \\ all-MiniLM-L12-v2 (M12) & 120 MB \\ multi-qa-distilbert-cos-v1 (MD) & 250 MB \\ all-distilroberta-v1 (DR) & 290 MB \\ stsb-distilbert-base (SD) & 330 MB \\ multi-qa-mpnet-base-dot-v1 (MM) & 420 MB \\ quora-distilbert-base (QD) & 500 MB \\ Sentence-T5-large (T5) & 640 MB \\ all-roberta-large (RL) & 1360 MB \\ Sentence-T5-XL (TX) & 2370 MB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SentenceTransformers model size. identify the most efficient architecture. Experiments were conducted using a standard benchmark dataset named the North Texas Dataset. This research achieved a state-of-the-art correlation value of 92.5%. A recent study published in 2024 proposed paraphrase generation and supervised learning for improving ASAG performance [21]. First, they provided a sequence-to-sequence deep learning model that targets generating plausible paraphrased reference answers conditioned on the provided reference answer. Secondly, they proposed a supervised grading model based on sentence-embedding features. The grading model enriches features to improve accuracy, considering multiple reference answers. Experiments are conducted both in Arabic and English. They show that the paraphrase generator produces accurate paraphrases. Using multiple reference answers, the proposed grading model achieves a root mean square error of 0.6955, a Pearson correlation of 88.92% for the Arabic dataset, an RMSE of 0.779, and a Pearson correlation of 73.5% for the English dataset. While fine-tuning pre-trained transformers on the English dataset provided a state-of-the-art performance (RMSE: 0.762), our approach yields comparable results. Data augmentation has become important in ASAG because more alternative answers can help accommodate the diversity of student answers. Howeve, generating these manually is difficult and needs significant effort. Some suggested methods used for augmenting data in NLPs include random deletion, synonym replacement, random swap, and back translation [13; 17]. And in recent years, paraphrase generation become one of the effective strategies in data augmentation. [PERSON] et al. in Ref. [22] used BART and GPT-2 as the paraphrasing model. With the development of GPT, we also think that GPT can be one of the good strategies to generate paraphrasing, especially with the existence of GPT-3.5 or GPT-4.
## 1 Introduction  
Pustular psoriasis comprises a group of inflammatory cutaneous diseases characterized by multiple non-follicular pustules over an erythemat base corresponding histologically to collections of neutrophils in the epidermis [1]. Pustular psoriasis is genetically and clinically separate from psoriasis vulgaris, even if previous or concomitant plaque psoriasis may be found. The identification of specific genetic mutations (i.e., IL36 RN, CARD14, and AP153) in subgroups of patients suggest the role of the genetic component and a dysregulation in different pathways in the pathogenesis [2; 3]. Historically, different entities of pustular psoriasis have been characterized with respect to their onset and morphologic pattern including acute (von Zumbusch), subacute annular, chronic (acral), and mixed [4]. The European Rare and Severe Psoriasis Expert Network (ERASPEN) recognized three main clinical phenotypes of pustular psoriasis, namely Generalized Pustular Psoriasis (GPP), Acrodermatitis Continua of Hallopeau (ACH), and Palmoplantar Pustulosis (PPP) [5]. GPP, the most severe variant, is a multisystemic inflammatory disease characterized by intermittent flares of an extensive skin erythema covered by aseptic pustules, systemic symptoms (i.e., fever and fatigue), and extracutaneous manifestations (i.e., arthritis, uveitis, acute respiratory distress syndrome, and cardiovascular shock, cholestasis). ACH is characterized by pustular lesions involving acral extremities of the hands and feet, with onychodystrophy evolving to progressive destruction of the nail apparatus (anonychia), with or without underlying osteolysis. PPP is the most common variant, presenting with sterile, erupting pustules, located symmetrically on the palms and soles, with a chronic and cyclic course, occurring on a desquamative and erythemato background. A predilection for histologic involvement of the acrosyringium is a specific feature of PPP.  
Few epidemiological studies have been conducted on pustular psoriasis, mostly focused on GPP and in Japan, whereas epidemiologic data in European countries are rarer [6]. The aim of this study was to investigate the phenotypes of pustular psoriasis and the patients' characteristics observed in a real-life setting.
## 3 Theoretical Background
### Alarm Reduction Algorithm Based on Hierarchical Clustering
#### 3.1.3 Definition of the Clustering Problem  
The clustering method is now described as the following: among all triggered alarms, a group of generalized alarms is found; the number of alarms within each generalized alarm exceeds or is equal to a given threshold; and the distance between the alarms is as small as possible. This method is proved to be an NP complete problem, that is, the exact solution cannot be obtained in feasible time. [PERSON] presented an approximate algorithm [44] as shown in Algorithm 1.  
```
0: a set of events; a threshold T; a set of trees of all the attributes considerer;
0: an alarm/ /an abstract event
1: select an arbitrary alarm A, each member of which is a leaf in a tree
2:while the number of events A covers is less than T do
3: select an arbitrary member of A, and replace the member with its direct parent
4:endwhile
```  
**Algorithm 1** [PERSON]'s alarm hierarchical clustering algorithm
## 3 Numerical Studies
### Damage Detection as a Function of Noise Level in Measured Strain Data  
This section presents the results of a numerical study that explored the damage detection quality as a function of the noise level. Section 3.2 reported iFEM results using strain data contaminated with 5% noise, where the two Unitcells reported a mixed level of success. The results of Unitcell-2 for Damage Cases-1 and 3 are successful in detecting damage and provided information regarding damage position and orientation. The effect of noise is further investigated here using Unitcell-2 strain data for reconstructing the strain field of Damage Case-1. The strain data is contaminated incrementally using eight different noise levels from 2.5% to 20%. The results of the study are presented as contour plots of \(I_{D}\) with a threshold of 0.5, and the plots are shown in Figure 23.  
Figure 22: Benchmark iFEM results of \(I_{D}\) for Damage Case-3 (5% noise) using: (**a**) no threshold, and (**b**) threshold = 0.5.  
The study results showed that successful damage detection is possible up to a noise level of 10%. For noise levels below 10%, the prominent \(I_{D}\) peak is observed at the center of the plate, coinciding with the damage location. As the noise level increased further, additional peaks at far-field locations started to become more prominent. Even up to a noise level of 17.5%, it could be claimed that the prominent peak is at the center of the plate, and the use of a higher threshold could lead to successful damage detection by isolating the central peak. But that is no longer the case for noise levels of 20% or greater, as far-field \(I_{D}\) peaks are dominant, and successful damage detection was no longer possible. The results further demonstrate the robustness of Unitcell-2 results in the presence of random noise and are promising for potential practical applications.
# EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX
## 4 EdgeX over Kubernetes
### Hpa  
In this section, we explain how the autoscaling of EdgeX services works in EoK. To enable service autoscaling through the HPA feature of Kubernetes, the minimum/maximum number of running pods in the cluster must be set through _minReplicas_, _maxReplicas_, and _threshold_ in the deployment file of the service. _cAdvisor_ monitors the pod resources aggregated at _Metrics-Server_; _kube-controller-manager_ compares the collected metrics with the threshold specified in _PodSpec_ and calculates the desired number of pods every 15 s period by default.  
When the Central Processing Unit (CPU) resources of the pod exceed the given threshold, _kube-controller-manager_ calculates the desired number of pods for the overloaded service and sends them to _kube-apiserver_. After that, _kube-apiserver_ deploys the additional number of pod replicas to reach the desired number of pods in the cluster. On the contrary, if the desired number of pods is less than the current number of pods, _kube-apiserver_ terminates pod replicas for service downscaling.  
For example, in Figure 4, the _Virtual Device_ service sets _minReplicas_ as 2, _maxReplicas_ as 5, and threshold as 50% of the CPU resources. The _cAdvisor_ collects each CPU usage and sends it to _Metrics-Server_. _Metrics-Server_ averages the CPU usages and sends it to _Metrics Aggregator_ in _kube-apiserver_. The HPA of _kube-controller-manager_ compares the current CPU usage and threshold, which is 50%, to determine whether the CPU usage exceeds the threshold or not; if the current CPU usage is higher than the threshold, it deploys additional replicas of the _Virtual Device_ service through _Deployment_. At this time, these are registered additionally to an EdgeX node by following the service deployment procedure shown in Figure 3.  
Figure 4: HPA architecture of EoK.  
Therefore, we can conclude that EOK improves the QoS as well as throughput in an edge computing environment by applying the HPA of Kubernetes to dynamically increase the number of replicas in response to an increase in user requests or computational load.
## 3 Results and Discussion
### Comparative Experiment  
In this paper, the SDCB-YOLO model is compared with current mainstream algorithms. As presented in Table 3, the YOLOv3-tiny model significantly lags behind the proposed SDCB-YOLO model in terms of model size, number of parameters, computational complexity, and accuracy. While YOLOv4m achieves a considerable improvement in accuracy compared to YOLOv3-tiny, its excessive number of parameters and other indicators render it unsuitable for practical applications on devices. Although the SDCB-YOLO model is slightly larger in size and has a higher computational complexity than YOLOv5n, it demonstrates a notable enhancement in accuracy. Compared to the YOLOv8n, SDCB-YOLO achieves a higher accuracy with only a marginal increase in the number of parameters and computational complexity. It is evident from the comparisons that SDCB-YOLO excels in various evaluation criteria.  
To more intuitively demonstrate the advantages of SDCB-YOLO, this paper compares the detection results of YOLOv8n and SDCB-YOLO in various complex scenarios. Shown in Figure 5(a),b are the detection results of the same image using YOLOv8n and SDCB-YOLO, respectively. Figure 5(a) exhibits a detection error, while Figure 5(b) can detect correctly. Figure 5(c),d are the comparison of the results after image blurring processing. Figure 5(c) is the result of YOLOv8, and Figure 5(d) is the result of SDCB-YOLO. It is evident that SDCB-YOLO has more accurate detection results. Figure 5(e),g represent the results of YOLOv8, while Figure 5(f),h represent the results of SDCB-YOLO. It is evident that even when both models detect the correct results, SDCB-YOLO achieves a higher confidence level. This verifies the superiority of SDCB-YOLO.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **GFLOPs** & **Model Size (MB)** \\ \hline YOLOv3-tiny & 85.6 & 83.5 & 88.6 & 59.0 & 12.9 & 16.6 \\ YOLOv4m & 94.5 & 88.9 & 94.5 & 69.6 & 53.0 & 46.98 \\ YOLOv5n & 90.4 & 86.5 & 90.7 & 64.1 & 4.1 & 3.74 \\ YOLOv5s & 91.8 & 89.2 & 92.5 & 68.7 & 15.8 & 13.79 \\ YOLOv8n & 93.0 & 87.3 & 93.6 & 70.4 & 8.1 & 5.97 \\ SDCB-YOLO & 95.1 & 92.7 & 97.1 & 78.2 & 8.3 & 6.13 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison Results of Different Models.  
Figure 6: Comparison chart of detection before and after improvement.
# Pushover Analysis on Infill Effects on the Failure Pattern of Reinforced Concrete Frames
## 3 Results and Discussions
### Effective Slab Width  
Stress of the top and bottom slab reinforcement is shown in Figure 13. Figure 13 indicates that the rebar near the beams yielded at 2% story drift. Stress of the top reinforcement is slightly larger than that of the bottom reinforcement. A comparison between axis A and axis C indicates that infill affects the stress of the slab reinforcement. The participation of slab reinforcement on two sides of the beam of axis B is larger than those of axes A and C. Meanwhile, participation of slab reinforcement on two sides of the beam of axis A is larger than those of axis C. It can be concluded that role of the slab is weakened by infills and the effective slab width should be adjusted according to the infill types.  
The expression for the effective slab width (\(b_{ef}\)) is defined in Equation (17), where the equivalent strain method is used [23]:  
\[b_{ef}=\frac{\int_{0}^{x}\sigma(x)dx}{\sigma_{\text{bm}}}+b \tag{17}\]  
The calculated results of each axis are summarized in Table 4. The effective slab width decreases significantly due to infills, and values according to the ACI code [27] and other researchers [28; 29] are on the conservative side, especially in the full-filled infill case. According to the FEM results, effective  
Figure 12: The bending moments in columns (kN.m). (**a**) Column bending moment of axis A; (**b**) column bending moment of axis B; and (**c**) column bending moment of axis C.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Column Moment/kN.m**} \\ \cline{2-13}  & \multicolumn{4}{c}{**Axis A**} & \multicolumn{4}{c}{**Axis B**} & \multicolumn{4}{c}{**Axis C**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Column 1**} & \multicolumn{2}{c}{**Column 2**} & \multicolumn{2}{c}{**Column 3**} & \multicolumn{2}{c}{**Column 4**} & \multicolumn{2}{c}{**Column 5**} & \multicolumn{2}{c}{**Column 6**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Top**} & **Bottom** & **Top** & **Bottom** & **Top** & **Bottom** & **Top** & **Bottom** & **Top** & **Bottom** & **Top** & **Bottom** \\ \hline FEM & 4.5 & 8.9 & 7.2 & 9.3 & 2.0 & 5.9 & 6.4 & 6.1 & 5.2 & 9.5 & 4.4 & 8.0 \\ \hline Chinese code [15] & 4.5 & 8.9 & 5.5 & 11.0 & 2.6 & 5.2 & 4.2 & 8.4 & 4.9 & 9.8 & 4.1 & 8  
slab width should be equal to \(b+4t\) for interior joints and \(b+2t\) for exterior joints with full-filled infills. The effective slab width should be equal to \(b+6t\) for interior joints and \(b+3t\) for exterior joints with half-filled infills (where \(t\) is the thickness of the slab). The effective slab width of axis B is larger than the ACI code and existing research results, which means it might be unsafe during the design process.
# New Construction Solutions of Gear Using in Space Vehicle Control Systems
## 3 Results
Figure 17 is a set of the results of stresses on the major axis of models deformed by the cam generator for selected short variants of flexsplines. Maximum stress levels for these constructions are significantly higher than the results obtained for standard length models (Figure 16). This is the result of a design with significantly fewer flexsplines, directly increasing the flexural stiffness of the body. The most unfavorable stress distribution occurred in variant V because areas of very high stress also occurred in the transition from the sleeve body to its outer flange. In the variant X given for the stress comparison, the values in both the rim and bottom of the flexspline were much lower than in the other constructions. All stress diagrams shown in Figures 16 and 17 have the highest stress values at the transition radius from the toothed wheel rim to the flexspline body. It is caused by the unsymmetrical pressure of the wave generator on the internal diameter of the flexspline, resulting from the rigidity of the wheel body on one bottom. On the opposite side of the toothed rim, the stresses are lower because they result only from deformations of the flexspline. The generator edge effect only occurs in compliant flexsplines with a bottom or flange, and its impact decreases with increasing length. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Range** \\ \hline d\({}_{1}\) & z\({}_{1}\)-m\({}_{1}\) \\ d\({}_{\rm H1}\) & m\({}_{1}\)\(\cdot\)(z\({}_{1}\) - 2h\({}_{\rm a}^{*}\) - 2c\({}^{*}\)) \\ d\({}_{\rm w}\) & d\({}_{\rm H1}\) - 2\(\cdot\)h\({}_{\rm H1}\) \\ d\({}_{2}\) & \(<\) 0.6d\({}_{\rm H1}\) \\ h\({}_{1}\) & (0.05\(\div\) + 0.015)\(\cdot\)d\({}_{1}\) \\ h\({}_{2}\) & (0.5 + 0.85)\(\cdot\)h\({}_{1}\) \\ h\({}_{3}\) & (2 \(\div\) 3)\(\cdot\)h\({}_{1}\) \\ \(\beta\)\({}_{\rm b}\) & (45\({}^{\circ}\) + 60\({}^{\circ}\)) \\ R\({}_{1}\) & (10 \(\div\) 20)\(\cdot\)m \\ R\({}_{2}\) & (2 \(\div\) 3)\(\cdot\)h\({}_{2}\) \\ b\({}_{\rm w}\) & (0.1 \(\div\) 0.3)\(\cdot\)d\({}_{1}\) \\ aw & (0.15 \(\div\) 0.25) \(\cdot\)b\({}_{\rm w}\) \\ L & (0.7 \(\div\) 1.2)\(\cdot\)d\({}_{1}\) \\ L\({}_{\rm c}\) & L + h\({}_{\rm h}\) \\ a & \(\rm L-(b_{\rm w}+a_{\rm w}+R_{1})\) \\ b & 0.5\(\cdot\)(d\({}_{\rm w}-d_{2}\)) \\ O\({}_{\rm a}\) & \(\rm b_{\rm w}+a_{\rm w}+R_{1}\) \\ O\({}_{\rm b}\) & 0.5\(\cdot\)(d\({}_{\rm w}-b\)) \\ \hline \end{tabular}
\end{table} 
Table 2: Main geometric parameters for the new shape of flexspline (variant X), where: z\({}_{1}\) = 198, m\({}_{1}\) = 0.76, 2h\({}_{\rm a}^{*}\)—tooth head height factor, c\({}^{*}\)—tip clearance factor.