esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study investigates the role of abscisicic acid (ABA) in bolstering drought resistance in plants, employing \"Panjiang _Sophora vicifolia_\" as the subject. A simulated drought scenario was created using polyethylene glycol (PEG-6000) to examine the impact of varying drought intensities (0%, 5%, 20% PEG) and ABA concentrations (0, 10, 50, 100, 200 mg\(\cdot\)L\({}^{-1}\)) on the germination and physiological parameters of _Sophora vicifolia_. The results showed that in the absence of ABA, the germination rate (GR), germination potential (GP), and germination index (GI) of _S. vicifolia_ seeds initially increased and then decreased with escalating PEG-induced drought stress. At PEG-induced drought stress levels of 5% and 20%, the activities of peroxidase (POD) and catalase (CAT), along with the malondialdehydehyde (MDA) content, were significantly higher than in the control (CK) (\(p<0.05\)). In response to drought stress, _S. vicifolia_ seeds adapted by modulating germination behavior, augmenting the content of someoregulatory substances, and boosting the activity of protective enzymes. The addition of ABA markedly enhanced GR, GE, GI, activities of POD, superoxide dismutase (SOD), and CAT, as well as the levels of MDA and proline (Pro) under drought conditions (\(p<0.05\)). Relative to CK, low ABA concentrations (10-100 mg\(\cdot\)L\({}^{-1}\)) resulted in increased GR, GP, GI, POD, SOD, CAT, MDA, and Pro levels; whereas, at a higher concentration (200 mg\(\cdot\)L\({}^{-1}\)), although GR, GP, and GI decreased, POD, SOD, CAT, MDA, and Pro levels increased. Through principal component analysis and membership function comprehensive evaluation, it was determined that administering 50 mg\(\cdot\)L\({}^{-1}\) ABA was most effective in enhancing drought resistance in _S. vicifolia_ seedlings.  
Sophora vicifolia; exogenous ABA; drought stress; seed germination; physiological traits Article
## 3 Methodology  
### Test of Transmitted Wave in the Pile  
The side-hole transmitted wave method [20] places a detector in the measuring hole near the pile to be tested, then excites vibration at the top or side of the pile foundation, and then uses the detector to collect the transmitted wave of the pile body. Finally, the pile length and pile body quality are analyzed according to the collected wave characteristics, as shown in Figure 2.  
However, due to the limitation of the test site and the large depth of the pile, it is difficult to drill the inclination hole on the side of the pile, so the test method is improved, and a more economical and convenient method of transmitting waves in the pile is proposed. One side is used to provide transmitted waves and the other side is used to receive transmitted waves, as shown in Figure 3. The transmission wave method in the pile can effectively solve the problem of difficulty in taking vertical super long side holes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Layer** & **Formation** & **H/m** & **\(\gamma\)/(kN\(\cdot\)m\({}^{-3}\))** & **\(E_{S}\)/(MPa)** & **\(c\)/(kPa)** & **\(\varphi\)/(\({}^{\circ}\))** & **\(f_{a}\)/(kPa)** \\ \hline
1 & Fill & 2.1 & & & & & \\
2 & Silt & 19.4 & 16.5 & 2.16 & 8.9 & 2.8 & 55 \\
3 & Clay & 23.8 & 17.6 & 3.06 & 20.3 & 5.4 & 80 \\
4 & Silty clay & 26.1 & 18.1 & 4.09 & 31.3 & 7.6 & 120 \\
5 & Gravel & 2.9 & & & & & 200 \\
6 & Silty clay & 10.9 & 18.9 & 4.45 & 41.1 & 9.4 & 180 \\
7 & Gravel & 18.1 & & & & & 200 \\ \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical indexes of soil in the test site.  
Figure 2: Schematic diagram of the side-hole transmitted wave method.  
During the test, the transmitted wave is generated by hammering with a heavy hammer, and the three-component detector is placed at different depths to receive the transmitted wave. According to the arrival time of the first wave, it can be used to determine the length of the pile and the depth of reinforcement, as shown in Figure 4.  
The test of the transmitted wave method in the pile was carried out on the No. 1 pile and No. 2 pile. The main test steps are as follows:  
Figure 4: Detection principle of the transmitted wave method in the pile.  
Figure 3: Schematic diagram of the transmitted wave method in the pile.  
Before the test, the acoustic tubes were processed in the steel bar processing shed on the construction site. Each acoustic tube was 6 m long and 12 cm in diameter. The acoustic tubes were connected by flanges. The acoustic tube is bound onto the steel cage and it goes down into the pile hole together with the steel cage. Finally, the redundant part of the acoustic tube above the ground is cut off. After 28 days of grouting at the bottom of the pile, we drilled holes at the bottom of the acoustic tube and buried the PVC pipe, which is used to place the three-component detector and conduct on-site testing. This part of the test photos is shown in Figure 5.
## 5 Use Case  
The summary EHR archetype defined by the Spanish Ministry of Health (_CENEN13606-COMPOSITION.HistoriaClinicaResumida.v1_) for the communication of health data among regional health institutions [23] is an appropriate initial EHR archetype to build a large use case of an augmented EHR. It encompasses elements concerning the patients' EHRs, which can be EOIs to enrich the archetype with several EHR augmentations. This section shows the design process of the augmented summary EHR archetype (_CENEN13606-COMPOSITION.HistoriaClinicaResumidaAugmented.v1_) from scratch, as a result of enriching the summary EHR archetype with EHR augmentations related to drug-drug interactions and disease-related treatments. The EOIs selected from the summary EHR archetype are patients' active medication and active episodes of patients' diseases, located at paths  
_/content[archetype_id=\"at0043\"]/members[archetype_id=\"at0024\"]/members[archetype_id=\"at0053\"]/members[archetype_id=\"at0054\"]/items[archetype_id=\"at0172\"]/parts[archetype_id=\"at0181\"]/value_  
and  
_/content[archetype_id=\"at0043\"]/members[archetype_id=\"at0042\"]/members[archetype_id=\"at0046\"]/items[archetype_id=\"at0158\"]/value_  
respectively. These EOIs are intended to hold coded values from SNOMED-CT terminology, which can be augmented with potentially interesting additional information from public Semantic Web datasets. Figure 10 shows the initial EHR archetype and the initial EHR extract showing the selected EOIs. Drug information to enrich a patient's active medication with drug-drug interactions can be found in the DrugBank database. Information on treatments for specific diseases to enrich the active episodes of patients' diseases with existing treatments and their effects is available on the NDFRT ontology. Equivalencies between coded terms from source (SNOMED-CT) and target (DrugBank, NDFRT) terminologies are identified using the BioPortal terminology mapping service. The EOI binding triples to establish conceptual mappings between source and target terminologies are built by requesting such a service for mappings between source and target terminologies for the EOI coded terms. A LOD representation of the target terminologies is required to obtain the augmentation content through SPARQL queries. The LOD cloud contains a LOD representation of the DrugBank database (Bio2 RDF DrugBank), whereas the NDFRT terminology expressed as LOD is located at the BioPortal ontology repository. The automatic procedure described in section Materials and Methods (B) is used to build the EOI binding triples taking several arguments as inputs, where only the initial EHR extract must be provided at runtime. The other arguments are defined as part of the EHR augmentation at design time. Tables 1 and 2 summarize the required arguments for both augmentations. In Table 1, note that the URL prefix of data items in the target dataset differs from the identifier URL of the target terminology returned by the mapping service. In this case, the URL prefix of data items in the target dataset can be identified by querying VoID descriptors as explained in section Materials and Methods (A).
## 5 Simulations
### Situation II: Nonzero Initial Velocity Case A: planar RR underactuated robot  
Considering the nonzero beginning velocity, we selected the same parameters as those in [49] and applied a nonzero beginning velocity \(\dot{q}_{10}\) on the underactuated link, given by the following:  
\[\dot{q}_{10}=0.02\;\mathrm{rad/s} \tag{36}\]  
The parameters of trajectory were obtained as follows:  
\[\left\{\begin{array}{l}A=5.6474\;\;\mathrm{rad}\\ w=0.7219\;\;\mathrm{rad/s}\\ t_{2}=4.5666\;\;\mathrm{s}\end{array}\right. \tag{37}\]  
On the basis of Case A in Situation I, the initial velocity of the passive joint was set to 0.02 rad/s. In Figure 6, when \(t=5.5666\;\mathrm{s}\), the states of all links converge to their target values, the torque converges to zero, and the system is stabilized at the target states. The system is stabilized at the given position. These results prove that this method can also ensure the system converges to the target position quickly, even if the passive joint has a nonzero initial velocity.  
Figure 5: planar R\(\ddot{\mathrm{R}}\) underactuated robot with the states (34).  
**Case B: planar \(\mathrm{R\bar{R}}\) underactuated robot**  
Considering the nonzero initial velocity, we selected the same parameters as those in [20] and applied a nonzero beginning velocity \(\dot{q}_{20}\) on the underactuated link, given by the following:  
\[\dot{q}_{20}=0.001\ \mathrm{rad/s} \tag{38}\]  
The parameters of trajectory were obtained as follows:  
\[\left\{\begin{array}{l}A=1.0462\ \ \mathrm{rad}\\ w=1.0486\ \ \mathrm{rad/s}\\ t_{2}=5.7831\ \mathrm{s}\end{array}\right. \tag{39}\]  
The initial velocity of the passive joint was \(\dot{q}_{20}=0.001\ \mathrm{rad/s}\) in this situation. In Figure 7, when \(t=6.7831\ \mathrm{s}\), the angle of each link moves to the target angle, the angular velocity converges to zero, and the torque converges to zero. The planar \(\mathrm{R\bar{R}}\) underactuated robot is stabilized at the given states. These simulation results prove that the proposed method can solve the stable control problem of this system with a nonzero initial velocity.  
Figure 6: Planar RR underactuated robot with the states (36).
## 2 Model  
### Pairing Hamiltonians in Superconductivity  
The BCS pairing Hamiltonian has been widely used in different fields of physics--condensed matter physics, nuclear physics, etc. The Hamiltonian is typically expressed in terms of bosonic or fermionic creation (annihilation) operators, \(c^{\dagger}_{\pm m}\) and \(c_{\pm 1}\), and \(n^{F}_{\pm 1}=c^{\dagger}_{\pm m}c_{\pm m}\) are fermionic number operators [52]  
\[H_{BCS}=\sum_{m=1}^{N}\frac{\epsilon_{m}}{2}\left(n^{F}_{m}+n^{F}_{-m}\right)+ \sum_{l=1}^{N}\sum_{m=1}^{N}V_{ml}c^{\dagger}_{m}c^{\dagger}_{-m}c_{-l}c_{l} \tag{1}\]  
where the matrix elements are real and can be estimated or calculated, e.g., for superconductors, in terms of Coulomb force and the electron phonon interaction. Pairs of fermions are labeled by the quantum numbers \(m\) and \(-m\), according to the Cooper pair situation where paired electrons have equal energies but opposite momenta and spins: \(m=(\mathrm{p},\uparrow)\) and \(-m=(-\mathrm{p},\downarrow)\). This is shown in Figure 1. These are time-reversed and degenerate partnerswhose energies are considered to be phenomenological parameters. In order to realize the quantum simulation, the BCS Hamiltonian is transformed into a Hamiltonian with Pauli operators which can be mapped on a system of qubits with the algebraic morphism between spins and fermions.  
Then, the fermionic pair operators can be mapped onto a system of qubits which can be represented by the Pauli operators of \(\sigma_{m}^{x}\), \(\sigma_{m}^{y}\), and \(\sigma_{m}^{z}\) through the transformation \(\{\sigma_{m}^{x}\), \(\sigma_{m}^{y}\), \(\sigma_{m}^{z}\}=[c_{m}^{\dagger}c_{-m}^{\dagger}+c_{-m}c_{m}\), \(ic_{-m}c_{m}+ic_{m}^{\dagger}c_{-m}^{\dagger}\), \(n_{m}^{F}+n_{-m}^{F}-1]\).  
The transformed Hamiltonian from Equation (1) can be re-written in the qubit form as [52]:  
\[H_{\text{BCS}}=\sum\
olimits_{m=1}^{N}\frac{\epsilon_{m}}{2}\sigma_{m}^{z}+ \sum\
olimits_{m<l}\frac{V_{m}}{2}\left(\sigma_{m}^{x}\sigma_{l}^{x}+\sigma_{ m}^{y}\sigma_{l}^{y}\right) \tag{2}\]  
where \(\epsilon_{m}=\epsilon_{m}+V_{mm}\).
## 4 Experiment Result
### Answer to RQ3--Ablation Studies  
#### 4.3.1 Analysis of the Number of Iterations  
To determine the number of iterations of FLFR, we conduct related experiments. The results of the experiment are shown in Figure 2, where the abscissa in the figure is the number of iterations, ranging from 1 to 10, and the ordinate is the evaluation index EXAM score. From the results, it can be seen that the images are divided into two types, where one is in the three datasets of Print-token, Elevator, and Tcas. After many iterations, the fault localization indicator tends to be stable; the other is in the three datasets of Print-token2, Tot-info, and Commons-math. There are peaks and valleys in the iterative process. The stable regions and peaks and valleys of these images represent the optimal results in the iterative process. It can be seen that except for the Tcas dataset, the fault localization effect has stabilized after 10 iterations.  
We analyzed the results. During the iterative localization process, the fault localization result of this round affects the generation of test cases, and the newly generated test cases are used as the input for the next round of fault localization, which also affects the result of fault localization. After many iterations, we found that more test cases covered the possible faulty program lines, the fault localization effect gradually reached the best, and  
Figure 2: The number of iterations.  
the image formed by the evaluation index was close to the optimal value, forming image stable areas and peaks and valleys. From the results of the 6 programs so far, 10 iterations is a good choice.
# Chinese Event Detection without Triggers Based on Dual Attention
## 4 Experiment
### Experiment Summary  
Extensive experiments on the ACE 2005 dataset and the dam safety operation log dataset show that EDWTDA performs better than all current baselines. The experimental results from the ACE 2005 dataset show that EDWTDA improves precision, recall, and F1-score by 3.40%, 3.90%, and 3.67%, respectively, over the best baseline JMCEE. From the experimental results on the dam safety operation log dataset, it can be seen that EDWTDA improves by 4.18%, 4.58%, and 4.39% in precision, recall, and F1-score, respectively, over the best baseline JMCEE. The comparison experiments verify that EDWTDA can solve the polysemous problem and the word-trigger mismatch problem, as well as improve event detection precision and recall.  
Ablation analysis confirms that the word embedding vectors generated by ALBERT are rich in semantics. ALBERT can solve the problem of polysemy and improve the model's prediction ability. It has been demonstrated that lexical annotations and entity labels have filtering functions to improve the model's prediction ability. The dual attention mechanism can capture key semantics in sentences and documents, which can then simulate hidden event trigger words and solve the word-trigger mismatch problem. Meanwhile, the polysemous problem can be solved by using document context to improve the model's recall. It is verified that the focal loss function can fix the sample imbalance and improve the model's generalization ability. The classification number analysis confirms that the binary classification approach can solve the multi-label problem and improve classification accuracy.
# A Study on the Optimization of the Coil Defect Detection Model Based on Deep Learning
## 4 Case Study and Analysis
### Result Analysis and Discussion
#### 4.3.2 Discussion  
In comparison with structural modifications and lightweight approaches, model scaling proved to be the most effective technique for enhancing performance and achieving optimal results in model optimization. Additionally, data augmentation was employed to further boost performance. During this process, learning conditions were meticulously designed to identify the optimal model, and a detailed analysis of the results was conducted. Compared with other performance improvement techniques, applying model scaling, which varies the ratio of depth and width in the model, yields the best outcome. When analyzing the results of model scaling, increasing the depth and decreasing the width relative to the base model demonstrated the most favorable results.  
Upon analyzing the final model, the proposed model in this study exhibited improved performance compared with the basic models of YOLOv5. However, a limitation of this work is the lower AP values for the 'NG' class when analyzing each class. Moreover, while the actual inspection time was faster than that of a human inspector, there was no substantial difference compared with the basic model of YOLOv5. To address this issue, various failure data can be additionally collected and utilized for model training to rectify the class-related problems. This will enhance the performance across all classes of the proposed model.
## 4 Data Collection  
In this comparison, we use the xAPI dataset originally introduced by [PERSON] et al. [17]. It comprises records from 480 students collected via an LMS and structured according to the xAPI standard, allowing detailed tracking of student interactions and behaviors.  
The dataset contains 16 input features that are grouped into three clearly defined categories:  
* Demographic features: attributes such as gender, Nationality, PlaceofBirth, StageID, Relation, and ParentAnsweringSurvey describe students' personal backgrounds and social contexts.
* Academic features: variables like GradeID, SectionID, Topic, and Semester represent academic contexts and settings in which student learning occurs.
* Behavioral features: engagement indicators, including Raisedhands, VisitedResources, AnnouncementsView, and Discussion, quantify the extent and type of student interactions within the LMS.  
The distribution and relevance of these features are illustrated in Figures 3 and 4. These visualizations highlight important patterns in the data, such as the variability in student engagement and demographic distributions, providing an initial indication of their potential predictive power regarding academic performance.  
The target variable, Class, categorizes student performance into three distinct groups: High, Middle, and Low. The distribution of the Class variable reveals an imbalance among the performance categories, with a predominance of students in the Middle class, followed by High and Low. This imbalance underscores the necessity of employing robust evaluation metrics beyond simple accuracy.  
The preprocessing and encoding of features were conducted automatically via Auto-Gluon's built-in pipeline; however, no missing values were found in the dataset, thereby eliminating the need for specific imputation methods. The behavioral features, especially Raisedhands, VisitedResources, and Discussion, demonstrate strong correlations with academic outcomes, reinforcing their significance as predictors within the model (see Figure 4).  
Figure 3: Distribution of categorical attributes in the dataset, including demographic (e.g., gender, Nationality), parental (e.g., Relation, ParentschoolSatisfaction), and academic context variables (e.g., StageID, Semester, StudentAbsenceDays). The frequency variations provide insight into the population’s heterogeneity.  
Figure 4: Distribution of key student interaction metrics, including Raisedhands, AnnouncementsView, VisitedResources, Discussion, and Class. These features exhibit multimodal patterns, asymmetries, and the presence of distinct behavioral profiles.
# Layout Optimisation of Frame Structures with Multiple Constraints and Geometric Complexity Control
## 6 Discussion
### Optimality of the Resulting Layouts  
The layouts for the three structures optimised using the proposed framework exhibit satisfactory mechanical performance and notable improvements in manufacturability. However, these designs may only represent the local optima due to the high non-linearity of the constraints, which are sensitive to material distributions and may conflict with one another, increasing the risk of converging to suboptimal solutions. While finer grid discretisations and higher member connectivity could potentially improve design optimality, they also lead to increased computational costs and more complex final layouts. To balance optimality, manufacturability, and efficiency, further investigation is needed. This could involve starting with simpler, suboptimal structures that meet basic mechanical constraints such as displacement and stress, and then progressively incorporating stability considerations while strategically refining grid discretisation and adjusting member connectivity.
## 3 IPTMS Architecture Design
### IPTMS with Energy Recovery
This IPTMS can provide up to 78.5% more power than the conventional power generation approach under sea-level static conditions. However, due to the impact of the flight altitude, the IPTMS cannot provide sufficient power for auxiliary power loads under other operation conditions and still requires supplementary power from the engine-driven generators. In [65], an IPTMS with a multi-mode Rankine cycle system was developed which was able to remove 10.7 kW from the coolant and satisfy the power requirements of the auxiliary systems (230 W at steady state). However, the response time of the heat transfer is slower than the power generation. [PERSON] et al. [66] designed an IPTMS for high-speed aircraft based on the Brayton cycle, using sCO\({}_{2}\) (supercritical carbon dioxide) as the coolant. This IPTMS can be recuperated (with the use of a recuperator) or non-recuperated. The recuperation depends on the ratio of heat transport to power required of the IPTMS. The IPTMS in [66] is recuperated and can provide power density greater than 3 kWe/L with up to 35% energy conversion efficiency. However, because of the recuperator, the weight and the volume of the IPTMS are significantly increased. The IPTMS design with energy recovery is usually applied to military aircraft because it can save aircraft system weight and volume by integrating different subsystems. Compared with the conventional air cycle machine, although this type of IPTMS still needs additional power input to drive the compressor, its turbine can drive a generator to provide power, converting thermal energy to electric energy. Therefore, the power density and the energy conversion efficiency could be the key target parameters for the IPTMS design to obtain a smaller and lighter system which provides more electric power.