esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions
Therefore, the measurements and results obtained here are useful to test the flux line mobility under the influence of small AC fields during cooling from T \(>\) Tc to T \(<\) Tc. Conservatively, one could say that the AC field has some function in homogenizing the trapped flux, which could indicate some frequency influence. This could be shown when the sample was 'treated'. In summary, it can be stated that no evidence was found that magnetic flux trapping can be suppressed with small alternating magnetic fields during the cooling phase, especially when T\(\sim\)T\({}_{c}\), which shall drive out flux lines of the sample. For the values of cool-down speed and AC frequency tested here it seems that it only forces an homogeneous field distribution, which was best observed when the Nb sample was annealed and buffered chemically polished. The parameter space explored in this initial study was somewhat limited and further experiments are required to finally assess if superimposing an AC field during transition can enhance the performance of SRF cavities. A potential experiment could involve keeping a sample below but close to its transition temperature for an extended time while applying an AC field. Furthermore, one could investigate so-called temperature cycles as were performed with Pb (without AC fields) to reduce the trapped flux [26]. In order to get deeper information about the time scale of arising flux trapping, polarized neutrons imaging can be a great help. The short transit time of a neutron and thus the interaction time with arising trapped magnetic field is less than 10 \(\upmu\)s for a path length of 10 mm, so it could be used to probe time dependent flux trapping in thinner samples. For superconductivity, position-resolved information about processes in the bulk of samples, being in the Meissner state or Shubnikov state, with and without flux lines, can probably be best achieved with polarized neutron imaging. Conceptualization: W.T., O.K., T.J. Methodology: W.T., O.K., T.J. Software: W.T. Validation: W.T., O.K., T.J. Writing--original draft preparation: W.T., O.K., T.J. Funding acquisition: W.T. All authors have read and agreed to the published version of the manuscript. This research was funded by the Federal Ministry of Education and Research-BMBF, project 05 K10 FK1. Not applicable. **Acknowledgments:** We are very grateful to [PERSON], [PERSON], [PERSON] and [PERSON] for their help with the measurements at the Berlin research reactor BER II, to the Helmholtz-Zentrum Berlin Wannsee for the support with its infrastructure and of course to the BMBF (Federal Ministry of Education and Research) Germany. This work was part of the BMBF project 05 K10 FK1. **Conflicts of Interest:** The authors declare no conflict of interest. **Remark:** The PONTO II instrument operated by the UAS Berlin had to be finally dismantled due to the shutdown of the BER II (Berlin Experimental Reactor II) in 2019.
## 2 Materials and Methods
### Robustness of Data Collection Moment by Design  
The INSESS-COVID10 instrument introduces an innovative structure in the questionnaire, intended to allow a long period of data collection while preserving the comparability of the data collected. This is a very relevant characteristic of the questionnaire that allows extensions of the data collection period in such a way that keeps the property of considering data together for the analysis. This provides an important advantage in front of small samples, as providing longer period for data collection valid sample can increase without limit in the validity of previously collected data.  
The proposal made in our work is that all questions from the questionnaire are divided in two categories:  
* Static: Characteristic that keeps static along the entire study period (age, sex, place of birth, etc.)
* Dynamic: Characteristic that might change value along the study period  
The proposal is to require answers in some fixed moments along time for all Dynamic questions in the questionnaire (Figure 2). The methodology is general, but for the case of INSESS-COVID19, it was decided to fix three moments of inquire: Pre-pandemics (January 2020), post-pandemics (July 2020) and expectations for the future (January 2021).  
Figure 1: Structure of the INSSSS-COVID19 questionnaire.  
Introducing this design in the questionnaire has the property that the study gains robustness with respect to the specific date in which the citizen is participating into the project. The questionnaire asks about situations/perceptions in this three fixed time points, so the data collection process can be as long as required and data still permits the analysis of the dynamics of the phenomenon. Answers of persons participating in July, or August, or October, still provide information about the situation of the person in January 2020, July 2020 and January 2021, so they can be analyzed together. Considering the critical situation of the BASS during the 1 sr wave of pandemics, this solution is overcoming the limitation that most of the territory could not dedicate time to the study in June-July, and without introducing this kind of design the viability of the project would not survive.  
The impact of the 1 st wave of the pandemics becomes measurable through the differences between July and January 2020.  
The consequence is that this design introduces packs of variables in the questionnaire, which are not anymore independent, and specific procedures to analyze them in the correct way will be required. These are introduced later in the paper.
# Crossover Rate Sorting in Adaptive Differential Evolution
## 3 Results
### Numerical Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & \(\mathbf{10*D}\) & \(\mathbf{30*D}\) & \(\mathbf{50*D}\) & \(\mathbf{100*D}\) \\ \hline LSHADE-RSP\_s vs. & \(8/15/7\) & \(8/13/9\) & \(6/10/14\) & \(6/7/17\) \\ LSHADE-SPACMA & \((-0.72)\) & \((-16.83)\) & \((-62.19)\) & \((-83.76)\) \\ \hline LSHADE-RSP\_s vs. & \(0/28/2\) & \(5/20/5\) & \(12/14/4\) & \(18/9/3\) \\ jSO & \((-9.76)\) & \((6.64)\) & \((42.59)\) & \((101.81)\) \\ \hline LSHADE-RSP\_s vs. & \(3/15/12\) & \(10/9/11\) & \(10/8/12\) & \(11/15/4\) \\ EBOwithCMAR & \((-53.67)\) & \((-24.75)\) & \((6.79)\) & \((47.58)\) \\ \hline NL-SHADE-RSP\_s vs. & \(11/7/12\) & \(3/4/23\) & \(0/2/28\) & \(0/0/30\) \\ LSHADE-SPACMA & \((-5.52)\) & \((-163.45)\) & \((-241.37)\) & \((-257.55)\) \\ \hline NL-SHADE-RSP\_s vs. & \(9/8/13\) & \(3/4/23\) & \(0/1/29\) & \(1/0/29\) \\ jSO & \((-22.72)\) & \((-160.24)\) & \((-237.64)\) & \(-239.21) \\ \hline NL-SHADE-RSP\_s vs. & \(7/11/12\) & \(3/4/23\) & \(0/0/30\) & \(1/1/28\) \\ EBOwithCMAR & \((-41.34)\) & \((-155.81)\) & \((-240.52)\) & \(-231.45) \\ \hline NL-SHADE-LBC\_s vs. & \(8/18/4\) & \(4/9/17\) & \(2/3/25\) & \(2/1/27\) \\ LSHADE-SPACMA & \((10.80)\) & \((-106.52)\) & \((-185.40)\) & \((-202.37)\) \\ \hline NL-SHADE-LBC\_s vs. & \(4/22/4\) & \(1/8/21\) & \(2/2/26\) & \(3/2/25\) \\ jSO & \((-3.88)\) & \((-143.58)\) & \((-178.59)\) & \((-174.86)\) \\ \hline NL-SHADE-LBC\_s vs. & \(2/19/9\) & \(1/10/19\) & \(2/3/25\) & \(2/2/26\) \\ EBOwithCMAR & \((-56.85)\) & \((-127.87)\) & \((-164.10)\) & \((-176.55)\) \\ \hline L-NTADE\_s vs. & \(8/12/10\) & \(13/8/9\) & \(12/3/15\) & \(11/3/16\) \\ LSHADE-SPACMA & \((-26.36)\) & \((18.21)\) & \((-14.62)\) & \((-45.39)\) \\ \hline L-NTADE\_s vs. & \(7/12/11\) & \(11/12/7\) & \(16/10/4\) & \(18/4/8\) \\ jSO & \((-37.93)\) & \((30.81)\) & \((83.28)\) & \((73.70)\) \\ \hline L-NTADE\_s vs. & \(3/16/11\) & \(11/13/6\) & \(17/5/8\) & \(13/5/12\) \\ EBOwithCMAR & \((-54.66)\) & \((25.78)\) & \((49.77)\) & \((31.42)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Algorithms with crossover rate sorting vs. alternative approaches, CEC 2017, Mann–Whitney tests and total standard score.
# Development of a Hardware-in-the-Loop Platform for a Teleoperation Flexibility Robotic System
## 2 Description of HIL Simulation System
### Kinematics of Simulator
#### 2.3.1 Slave Device
The Jacobian matrix shows the relationship between the angular velocity of the joint and linear velocity of the end-effector. \[\dot{\text{x}}\text{=}\text{J}(\Theta)\dot{\Theta} \tag{4}\]  
when \(\dot{\text{x}}\in\Re^{3\times 1}\) is the vector of linear velocity, \(\text{J}(\Theta)\in\Re^{3\times 6}\) is the Jacobian matrix, and \(\dot{\Theta}\in\Re^{6\times 1}\) is the vector of angular velocity. The pseudoinverse-method-based formula is expressed as follows:  
\[\Delta\Theta\text{=}\alpha\text{J}^{\text{T}}\left(\text{J}\text{J}^{\text{T} }\right)^{-1}\text{e} \tag{5}\]  
where \(\Delta\Theta\in\Re^{6\times 1}\) denotes the displacement of the joint angles, \(\alpha\in\Re^{6\times 6}\) is a constant, and \(\text{e}\in\Re^{3\times 1}\) is the vector error between the desired position and position of the slave robot. The angle of each joint is calculated as follows:  
\[\Theta_{\text{t}}\text{=}\Theta_{\text{t-1}}\text{+}\alpha\text{J}^{\text{T}} \left(\text{J}\text{J}^{\text{T}}\right)^{-1}\text{e} \tag{6}\]  
when \(\Theta_{\text{t}}\), \(\Theta_{\text{t-1}}\in\Re^{6\times 1}\) are vectors of the joint angle at times \(\text{t}\) and \(\text{t}-1\), respectively.
## 2 Methods
### Statistical Analysis and Research Questions  
For statistical analysis, the level of data augmentation by AAFT was set to one dependent factor and represented by AAFT(1), AAFT(2), AAFT(3), AAFT(4), AAFT(5). The representation AAFT(4) represents that the original data were augmented by quadruple. The other dependent factor was set to the level of manipulation for the recognizability of input image features which were represented by \(\mathcal{I}_{\text{easy}}\), \(\mathcal{I}_{\text{fair}}\), \(\mathcal{I}_{\text{chial}}\). Statistical analysis was performed to explain the effects of the two factors (either positive or negative) on CNN (AlexNet) performance in terms of the training loss and the test accuracy.  
A repeated measures analysis of variance (rANOVA) was performed to identify the effect of two dependent factors with the significance level of \(p<0.05\) (IBM SPSS Statistics, v25, Chicago, IL, USA). If the assumption of sphericity was violated for the main effects, the degree of freedom was corrected using Greenhouse-Geisser estimates of sphericity. The Bonferroni-adjusted pairwise comparison was used, and the result was reported in the form of \"mean difference (standard error).\" Meanwhile, statistically analyzing the CNN performance, the following research questions were addressed:  
**(Q1)** \"How did the level of data augmentation by AAFT affect CNN performance?\"  
**(Q2)** \"How did the level of manipulation for the recognizability of input image features affect CNN performance?\"  
**(Q3)** \"What was the optimal combination of the two factors for the best CNN performance?\"  
(Q1) and (Q2) could be answered by investigating the main effect of the level of data augmentation by AAFT and the level of manipulation for the recognizability of input image  
Figure 5: An example of gym-workout classification via AlexNet with an input image \(\mathcal{I}_{\text{easy}}\)[54].  
features, respectively. The answer for (Q3) could be found by observing the interaction effect between the two factors as well as pairwise comparison result with corresponding mean and standard deviation values.
# Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication
## 3 Biomarkers in Diabetics with Known HF
### Biomarkers of Inflammation and Fibrosis  
Inflammatory biomarkers and biomarkers of fibrosis are not commonly used in routine clinical practice, but their evaluation in diabetics with HFrEF and HFpEF should be considered. The most important biomarkers having a certain predictive value are growth differential factor-15 (GDF-15), high-sensitivity C-reactive protein (hs-CRP), soluble suppressor tumorigenicity-2 (sST2), galectin-3 and interleukin-6 family cytokines.
## 3 Key Technologies of UAV-Assisted SAGINs
### Security Control
#### 3.2.1 Security Attacks
Anti-jamming technologies are essential for SAGINs, and current methods and research advancements addressing frequency band jamming are discussed under physical-layer security. 2. _Fraudulent Attacks (FAs)_: Attackers disguise themselves as legitimate users or systems to gain illegal benefits or damage the system [69]. These attacks exploit system permission vulnerabilities or weaknesses in communication protocols. A common example is attackers manipulating BGP routing protocol rules by forging IP prefixes and injecting short paths to hijack traffic. Comprehensive encryption and authentication mechanisms are necessary to mitigate such threats. 3. _Zero-Day Attacks (ZDAs)_: These attacks are widespread and target undisclosed or unpatched vulnerabilities, posing high risks and being difficult to prevent. Regular vulnerability assessments and anomaly detection are essential for defense. [PERSON] et al. [71] proposed a novel predictive queuing analysis method to accurately forecast network performance metrics in the LEO management layer, detecting zero-day network attacks with over a 94% accuracy, precision, recall, and F1-score. 4. _Eavesdroping Attacks (EAs)_: Attackers intercept and listen to communication content to illegally acquire sensitive information. Eavesdropping attacks can be classified into active and passive modes based on the attack method [72]: (1) Active: attackers forge or tamper with communication data. (2) Passive: attackers secretly listen without being detected. In active eavesdropping, attackers not only steal data but may also engage in further fraudulent activities. To counter this threat, data encryption prevents direct data misuse, and intrusion detection systems can promptly identify attack behaviors. 5. _Flood Attacks and DoS_: A common internet attack method that floods target servers with large volumes of junk requests, depleting system resources. Distributed denial of service (DDoS) is a variant involving attacks launched simultaneously from multiple distributed devices [73]. DDoS is simple, swift, and highly scalable. For example, in 2023, cloud service provider Akamai suffered a DDoS attack with the peak request traffic reaching 900.1 Gbps.
## 3 Lateral Stability Tracking Control of Designed Rollator
### Rollator Kinematic Model  
Figure 5 exhibits the kinematic model of the rollator. \((X_{r},Y_{r})\) and \(\left(X_{f},Y_{f}\right)\) are the center coordinates of the rear axis and the front axis, respectively. \(\varphi\) is the yaw angle, \(\delta_{f}\) the steering angle of front wheels, \(v_{r}\) the velocity of the rear axle center, \(v_{f}\) the velocity of front axle center, \(L\) is the wheel track, \(R\) is the radius, \(P\) is the center of rotation, \(M\) is the center of the rear axis, and \(N\) is the center of the front axis.  
Assuming that the steering radius and the radius of road curvature are the same in the steering process, the velocity of the rear axis \(v_{r}\) can be described as:  
\[v_{r}=\dot{X}_{r}\cos\varphi+\dot{Y}_{r}\sin\varphi \tag{1}\]  
The kinematic model of the rollator is presented as:  
\[\begin{array}{l}\dot{X}_{f}\sin\left(\varphi+\delta_{f}\right)-\dot{Y}_{f} \cos\left(\varphi+\delta_{f}\right)=0\\ \dot{X}_{r}\cos\varphi-\dot{Y}_{r}\sin\varphi=0\end{array} \tag{2}\]  
According to Equation (2), we can define:  
\[\begin{array}{l}\dot{X}_{r}=v_{r}\cos\varphi\\ \dot{Y}_{r}=v_{r}\sin\varphi\end{array} \tag{3}\]  
Figure 5: Kinematic model of four-wheeled rollator.  
where \(v_{r}\) is the robot speed, \((X_{r},Y_{r})\) and \((X_{r},Y_{r})\) are the coordinate of rear-wheel and front-wheel, respectively, and \(\varphi\) and \(\delta_{f}\) are the course angle and turning angle of front-wheel, respectively. From the mathematical relationship between the front and rear wheels, the following can be obtained as:  
\[\begin{split} X_{f}&=X_{r}+L\cos\varphi\\ Y_{f}&=Y_{r}+L\sin\varphi\end{split} \tag{4}\]  
Furthermore, the angle rate \(\omega\) is obtained as:  
\[\omega=\frac{v_{r}\tan\delta_{f}}{L} \tag{5}\]  
where \(L\) is the wheel base of front and rear wheels.  
Therefore, the kinematic model of rollator could be written as:  
\[\left[\begin{array}{c}\dot{X}_{r}\\ \dot{Y}_{r}\\ \dot{\varphi}\end{array}\right]=\left[\begin{array}{c}\cos\varphi\\ \sin\varphi\\ 0\end{array}\right]v_{r}+\left[\begin{array}{c}0\\ 0\\ 1\end{array}\right]\omega \tag{6}\]  
where \(\xi_{s}=\left[X_{r},Y_{r},\varphi\right]^{T}\) is the system state and \(u_{S}=\left[v_{r},\omega\right]^{T}\) is the control value.
## 2 Materials and Methods
Figure 2: Detailed views of the 3D model of the resorbed and the virtually augmented mandible with modeled implant-supported restorations, remaining dentition, periodontal ligaments, gingiva, cortical, and cancellous bone layers. (**a**) Front view of complete model of the resorbed mandible with placed implant-supported restorations (**b**) Perspective view of complete model of the resorbed mandible with placed implant-supported restorations. (**c**) Front view of complete modeled mandible with simulated bone augmentation and placed implant-supported restorations. (**d**) Perspective view of complete modeled mandible with simulated bone augmentation and placed implant-supported restorations. The failure criteria for materials are generally expressed in terms of stress or strain. The biological response of bone tissue to loads applied depends, according to [PERSON]'s \"mechananostat\" theory, on the strain recorded in the tissue [25,26]. The octahedral shear (equivalent) strain is considered to be the most relevant strain for this theory and to be far more conservative than other types of strain such as maximum compression strain. According to the mechanostat theory, the recommended range of strain is 1000-3000 \(\mu\epsilon\). Bellow 1000 \(\mu\epsilon\), bone tissue experiences stress shielding, leading to bone atrophy. Above 3000 \(\mu\epsilon\), the bone tissue is exposed to pathologic overload, which leads to bone damage and absorption [27]. In the simulations of this study, the octahedral shear (equivalent) strain was tracked. For calculation of the octahedral shear strain, strain component tensors, \(\varepsilon_{xx}\), \(\varepsilon_{yy}\), \(\varepsilon_{zz}\), \(\gamma_{yy}\), \(\gamma_{yz}\), and \(\gamma_{zx}\), were recorded near the implant site. These strain component tensors were then used to calculate the principal strains ( \(\varepsilon_{1}\), \(\varepsilon_{2}\), \(\varepsilon_{3}\)), through matrix diagonalization, using a modified version of [PERSON]'s symmetric strain tensor [28] as follows:  
\[T_{\varepsilon}=\left[ \begin{array}{ccc}\varepsilon_{xx}&\frac{1}{2}\gamma_ {xy}&\frac{1}{2}\gamma_{xz}\\ \frac{1}{2}\gamma_{yx}&\varepsilon_{yy}&\frac{1}{2}\gamma_{yz}\\ \frac{1}{2}\gamma_{zx}&\frac{1}{2}\gamma_{zy}&\varepsilon_{zz}\end{array}  \right]. \tag{1}\]  
The principal strains obtained, \(\varepsilon_{1}\), \(\varepsilon_{2}\), and \(\varepsilon_{3}\), were then used to obtain the octahedral shear (equivalent) strain as follows:  
\[\varepsilon_{oct}=\frac{2}{3}\sqrt{\left(\varepsilon_{1}-\varepsilon_{2} \right)^{2}+\left(\varepsilon_{2}-\varepsilon_{3}\right)^{2}+\left( \varepsilon_{3}-\varepsilon_{1}\right)^{2}}. \tag{2}\]  
Surface averaging of each octahedral shear strain value calculated for each point was done to avoid numerical artifacts of local peak values. Figure 3: Complete mandible assembly in the simulation environment with applied forces corresponding to pterygoid muscles (P) and the masseter muscles (M) and restrictions at the temporomandibular joint surfaces.
# FREDA: Few-Shot Relation Extraction Based on Data Augmentation
## 1 Introduction
Next, by utilizing the Euclidean distance algorithm, the interactive attention module assigns higher coefficient values to the parts where the instances in the support set and the instances in the query set, as well as the instances in the relation triple and the instances in the query set, interact intensively with each other. Finally, we directly add the support set and relation triple representations after passing through the interactive attention module. The difference between our work and other existing results is that our approach only uses information already in the instances and does not incorporate external information. Moreover, our ideas are more suitable for application to FSRE for two reasons: (1) When training models, common approaches to data enhancement are the use of unlabeled datasets [26] and the fuller use of labelled datasets [27]. Our approach is to make full use of the dataset and enhance the accuracy of various types of prototypes by adding the relation triple, which improves the results effectively and has a better domain adaptation capability. (2) To improve performance on the test set, we incorporate the interactive attention module during model training. This module calculates the similarity between  
Figure 1: Extraction of the relation triple information. the query set instances, support set instances, and relation triple instances. We prioritize the highly interactive parts between instances to utilize the inherent information of the dataset fully. We conducted experiments on FewRel1.0 [28] and FewRel2.0 (domain adaptation) [29] in four few-shot settings, and both of our methods yielded better boosts, which illustrates the effectiveness of our approach. Our contribution is as follows:  
1. For the first time, we invoke relation triple information to enhance and correlate the support set's relation and entity pair features through a data augmentation approach. 2. We address the issue of limited resources by incorporating an interactive attention module, which focuses on highly interactive portions among instances, thereby improving resource constraints. 3. Our model show improvements in both FewRel1.0 and FewRel2.0 (domain adaptation) datasets, with particularly significant results in FewRel2.0 (achieving a maximum improvement of 9.79 points). This demonstrates the strong domain adaptation capability of our model.