esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Experiment of Magnetic Antenna Vibration and Noise Reduction System
## 4 Test Verification and Analysis
### Dynamic Simulation Test  
In order to verify the vibration isolation performance of the magnetic antenna damping system, the project team conducted a simulation test on an electric shaking table. The test method is as follows:  
1. Install the magnetic antenna damping system on the shaking table, and use the pressure plate to compact the four supports;
2. Set the test conditions according to the vibration level of the magnetic antenna installation platform, the range of vibration frequency is from 5 Hz to 200 Hz, the acceleration is 1 g, and the vibration mode is random vibration;
3. Arrange an acceleration sensor on the antenna end, and obtain the vibration response parameters on the antenna in real time through a signal analyzer.  
The installation diagram of the shaking table simulation test is shown in Figure 9.  
In this vibration test, two positions were selected during the layout of the acceleration sensor: the antenna and the shell. The vibration test was carried out in the \(Z\) direction, and we obtained the vibration response curve of the antenna end through the dynamic signal analyzer, and calculated the attenuation rate of magnetic antenna damping device under a given vibration stress. The test results are shown in Figure 10.  
Through the test data, we can easily find that, in the \(Z\) horizontal direction, under the condition of 1 g, the vibration attenuation of the antenna at 5-200 Hz is 0.01 times, the vibration reduction effect is excellent, and the attenuation exceeds 40 dB at the interested frequency 60 Hz, which well satisfies the vibration reduction need. It can be seen that the vibration transmissibility spiked up to near 0.1 (\(-\)20 dB) when the frequency was near 100 Hz. This is due to the natural mode of the connection structures between the silicone rubber strip and the electric shaking table, mainly attributed to flexibility of radial clearance and axial rubber gasket between the annular polyoxymethylene structures and the tubular fiberglass shell. This can be improved in final application by filling glue into the clearance between the annular polyoxymethylene structures and the tubular fiberglass shell.  
Figure 9: Installation diagram of the magnetic antenna on the vibrating table.
## 5 Security Analysis of the Repaired Protocol
### Intruding on the FN Attacks  
In the repaired protocol, the _FN_ no longer stores the key between an _SN_ and an _HN_. Therefore, compromising an _FN_ would only leak the keys between the _SN_s and the _FN_. The compromised _FN_ would not be able to impersonate an _SN_ to communicate with the _HN_. It is true that the compromised _FN_ will still be able to access the _SN_ in an _SN_-_FN_ communication, but no extra access, e.g., data exclusive for the _HN_, will be given to the _FN_. This protocol also assures a secure _SN_-_FN_ communication, and vice versa if all secrets stored in the _HN_ are compromised.
# Exploration of Swarm Dynamics Emerging from Asymmetry
## 2 Basic Algorithm of Swarm Chemistry
Lines 17-20: After the above calculation is complete for all the agents, the velocities and then the locations are updated by time-integration. ```
1:for all\(i\in\) agents do
2:\(\mathcal{N}\leftarrow\{j\
eq i\}\) that satisfies \(\left|\vec{x}_{j}-\vec{x}_{i}\right|<R^{i}\)
3:if\(\left|\mathcal{N}\right|=0\)then
4:\(\vec{a}\leftarrow\left(r_{\pm 0.5},\ r_{\pm 0.5}\right)\) // Random steering
5:else
6:\(\left\langle\vec{x}\right\rangle\leftarrow\sum_{j\in\mathcal{N}}\vec{x}_{j}/ \left|\mathcal{N}\right|\)
7:\(\left\langle\vec{v}\right\rangle\leftarrow\sum_{j\in\mathcal{N}}\vec{v}_{j}/ \left|\mathcal{N}\right|\)
8:\(\vec{a}\leftarrow c_{1}^{i}\left(\left\langle\vec{x}\right\rangle-\vec{x}_{i} \right)+c_{2}^{i}\left(\left\langle\vec{v}\right\rangle-\vec{v}_{i}\right)+c_ {3}^{i}\sum_{j\in\mathcal{N}}\left(\vec{x}_{i}-\vec{x}_{j}\right)/\left|\vec{x }_{i}-\vec{x}_{j}\right|^{2}\) // Cohesion, alignment and separation
9:if\(r<c_{4}^{i}\)then
10:\(\vec{a}\leftarrow\vec{a}+\left(r_{\pm 5},\ r_{\pm 5}\right)\) // Random perturbation
11:endif
12:endif
13:\(\vec{v}_{i}^{\prime}\leftarrow\vec{v}_{i}+\vec{a}\)
14:\(\vec{v}_{i}^{\prime}\leftarrow\min(V_{n}^{i}/\left|\vec{v}_{i}\right|,\ 1)\cdot\vec{v}_{i}^{\prime}\) // Limiting overspeed
15:\(\vec{v}_{i}^{\prime}\gets c_{5}^{i}(V_{n}^{i}/\left|\vec{v}_{i}\right| \cdot\vec{v}_{i})+(1-c_{5}^{i})\vec{v}_{i}^{\prime}\) // Self-propulsion
16:endfor
17:for all\(i\in\) agents do
18:\(\vec{v}_{i}\leftarrow\vec{v}_{i}^{\prime}\) // Update velocity
19:\(\vec{x}_{i}\leftarrow\vec{x}_{i}+\vec{v}_{i}\) // Update position
20:endfor
```  
**Algorithm 2** Kinetic Rules in Swarm Chemistry [9]. Line 2: The set of neighboring agents (i.e., other agents within the local perception range), \(\mathcal{N}\) of agent \(i\) is found. Lines 3-4: The number of neighboring agents, \(\left|\mathcal{N}\right|\), is then calculated. If the set \(\mathcal{N}\) is empty, i.e., if there is no agent within its perception range, a random acceleration is set, which means a random straying by the agent. Lines 6-7: Otherwise, the average positions and velocities of nearby particles are calculated, respectively. Line 8: A tentative acceleration is calculated according to the three Boids rules (cohesion, alignment and separation). Lines 9-11: At a given probability \(c_{4}\), a random perturbation is applied to the tentative acceleration. Line 13: A tentative velocity is calculated by time-integrating the acceleration. Line 14: The tentative velocity is limited to prevent overspeed. Line 15: The speed is approximated to its own normal speed (self-propulsion). Lines 17-20: After the above calculation is complete for all the agents, the velocities and then the locations are updated by time-integration.
# Mixed Method for Isogeometric Analysis of Coupled Flow and Deformation in Poroelastic Media
## 2 Governing Equations
### Temporal Discretization  
The generalized trapezoidal rule (GTR) is applied for the temporal discretization of the coupled system of matrix equations in (32). Representing the vector of unknowns by \(\mathbf{X}=\{\mathbf{u}^{c},\mathbf{p}^{c}\}^{\intercal}\), we have the GTR approximation  
\[\begin{split}\frac{\partial\mathbf{X}}{\partial t}\bigg{|}_{n+\theta} &=\frac{\mathbf{X}_{n+1}-\mathbf{X}_{n}}{\Delta t}\\ \mathbf{X}_{n+\theta}&=(1-\theta)\mathbf{X}_{n}+\theta\mathbf{X}_{n+1} \end{split} \tag{33}\]  
where \(\theta\) is a time integration parameter which has limits \(0\leq\theta\leq 1\) and \(n\) is a time step identifier. Adopting backward Euler time stepping (\(\theta=1\)) with time step \(\Delta t\) and applying (32) and (33) we obtain the system of equations  
\[\begin{bmatrix}-\mathbf{K}&\mathbf{Q}\\ \mathbf{Q}^{\intercal}&\mathbf{S}+\Delta t\mathbf{P}\end{bmatrix}\begin{vmatrix}\mathbf{u} ^{c}\\ \mathbf{p}^{c}\end{vmatrix}_{n+1}=\begin{bmatrix}-\mathbf{K}&\mathbf{Q}\\ \mathbf{Q}^{\intercal}&\mathbf{S}\end{bmatrix}\begin{vmatrix}\mathbf{u}^{c}\\ \mathbf{p}^{c}\end{vmatrix}_{n}+\Delta t\begin{vmatrix}-\dot{\mathbf{f}}_{u}\\ f_{p}\end{vmatrix}_{n+1} \tag{34}\]  
which is a linear system in this case, for poroelasticity, as the coefficient matrices are independent of the unknowns.
## 4 Experimental Results
### Experimental Setup  
This study proposes an approach to protect biometric data based on the use of a neural fuzzy extractor built using two types of trigonometric neurons: cosine and cotangent. The main goal of the model is to ensure reliable and secure transformation of voice data into cryptographic keys or passwords, minimizing the risk of compromising biometric templates.  
Figure 8: Schematic of neuron formation from three sectors and three types of neurons.  
The experimental part includes the preliminary extraction of 128-dimensional feature vectors from voice images using autoencoders based on convolutional neural networks (CNNs) described in Section 4. These features are fed to the input of neurons based on trigonometric proximity measures. As shown in Section 5, the cosine measure should give better results when processing data with low correlation, while the cotangent measure, on the contrary, should demonstrate advantages when working with highly correlated features. For optimal data processing, cotangent neurons were additionally adapted to separately process positively and negatively correlated inputs. To evaluate the efficiency of the neural fuzzy extractor with the two types of trigonometric neurons, cosine and cotangent, experiments were conducted on two datasets: AIC-spkr-130 and RedDots.  
The AIC-spkr-130 dataset includes records that are divided into two groups: \"Impostors\" (class #60), used for calibration, and legitimate users, randomly selected in the amount of 30 subjects for training the neural fuzzy extractor. Here, the following voice images of the AIC-spkr-130 were used for training, which were obtained under the conditions of the \"normal\" state of the subjects. Images obtained under the altered psychophysiological state of the subjects were used as test images.  
For each converter, the FRR and FAR indicators are estimated, where FRR is determined based on \"Genuine\" images that are not involved in training, and FAR is estimated on all other classes, except for the one for which the neural fuzzy extractor is trained. A similar procedure was performed for the RedDots dataset. Here, the first class of data is used for calibration (\"Impostors\"), and 30 random images are used to train the neural fuzzy extractor.  
To better understand the effectiveness of the neural fuzzy extractor with different types of trigonometric neurons, comparative experiments are conducted using neurons of exclusively cosine and exclusively cotangent types. This will allow us to more accurately determine under what conditions each metric shows the best results and what advantages their combined use provides.
# Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty
## 5 Numerical Experiment and Discussions
### Result Validation  
We have validated our proposed result using a statistical hypothesis in this subsection.  
Figure 4: Network lifetime comparison between LEACH and the proposed approach.  
Figure 5: Total residual energy of all clusters vs. number of rounds.  
_Null Hypothesis_ (H\({}_{\rm o}\)): the average number of simulation rounds falls within the 95% confidence interval, specifically between 1800 and 2300.  
_Alternate Hypothesis_ (H\({}_{1}\)): the number of simulation rounds does not fall within the 95% confidence interval.  
We set the significance level at \(\alpha=0.05\), and \(T\) represents a random variable following the \(t\)-distribution. The 95% confidence interval is between 1800 and 2300. The simulation was executed 50 times, and the average result obtained was 2200. Using statistical calculations, we have determined that the _T-Score_ is 0.3496 and \(p\) (the probability of \(T\) being greater than 0.3496) is 0.7968.  
Since \((p>\alpha=0.05)\), we do not have sufficient evidence to reject the null hypothesis (H\({}_{\rm o}\)). Therefore, we can conclude that the average number of simulation rounds falls within the 95% confidence interval, i.e., 1800 and 2300.
## 3 Results
### Kinematic Analysis
#### 3.2.2 Non-Linear Analysis
Figure 10: Non-linear analysis of position and mean displacement of swing upper limb joints. H: healthy; S: stroke; LyE, largest Lyapunov exponent; S1: Multiscale Entropy Scale 1; S2: Multiscale Entropy Scale 2; CI, Multiscale Entropy Complexity Index; EXT, extension; FLE, flexion; AD, adduction; ABD, abduction; IR, internal rotation: ER, external rotation; UD: ulnar deviation; RD: radial deviation; * \(p\)-value \(<\) 0.05: Mann–Whitney test. Figure 11: Non-linear analysis of position and mean displacement of stance upper limb joints. H: healthy; S: stroke; LyE, largest Lyapunov exponent; S1: Multiscale Entropy Scale 1; S2: Multiscale Entropy Scale 2; CI, Multiscale Entropy Complexity Index; EXT, extension; FILE, flexion; AD, adduction; ABD, abduction; IR, internal rotation: ER, external rotation; UD: ulnar deviation; RD: radial deviation; * \(p\)-value \(<\) 0.05: Mann–Whitney test. In the frontal plane, the stroke group exhibited more negative (convergent) LyE values for shoulder DISP in both limbs, whereas the healthy group demonstrated positive (divergent) LyE values for the swing limb and negative (convergent) values for the stance limb, with significant differences observed for the stance limb (\(p\) = 0.017). MSE measures for the shoulder showed significant reductions for POS in both the swing (S2: \(p\) < 0.001; CI: \(p\) < 0.001) and stance limbs (S2: \(p\) = 0.016; CI: \(p\) = 0.028). For the elbow, MSE measures revealed significant reductions for POS in the swing limb (CI: \(p\) = 0.026). For the wrist, POS LyE values were significantly less negative in the stance limb for the stroke group (\(p\) = 0.005). Regarding DISP, LyE values in the stroke group were positive (divergent) in the swing limb and negative (convergent) in the healthy group, though these differences were not statistically significant (\(p\) > 0.05) (Figures 10 and 11). In the transverse plane, the stroke group showed reduced convergence in POS and MDISP for the shoulder, with less negative LyE values and significant differences in POS LyE for both limbs (\(p\) < 0.001; \(p\) = 0.011). For the elbow, reduced convergence was observed in POS and MDISP in both limbs, with significantly less negative LyE values for POS in the swing limb (\(p\) = 0.034). MSE measures also showed significant reductions in POS for the swing limb (S1: \(p\) = 0.017; S2: \(p\) = 0.026; CI: \(p\) = 0.019). For the wrist, reduced convergence was observed in POS, and DISP LyE values were less divergent (positive) in both limbs, though no significant differences were reported (\(p\) > 0.05) (Figures 10 and 11).
# Thermostable D-Allulose 3-Epimerase for Long-Term Food-Compatible Continuous Production Systems
## 2 Materials and Methods
### TcDAEase Immobilization Procedure  
The bacteria used for enzyme immobilization were cultivated as described in Section 2.2. A three-step adsorption-crosslinking-embedding approach was employed using a chitosan-diatomaceous earth (DE) composite as the support matrix. Briefly, chitosan (10 g/L) was dissolved in 1% (\(v/v\)) acetic acid with constant stirring. Diatomaceous earth (100 g/L) and glutaraldehyde (0.5 g/L) were added, and the pH was adjusted to 4.5 using 1 M NaOH to activate the support. Wet biomass was then added at the desired concentration, followed by gentle stirring at 25 \({}^{\circ}\)C for 1 h to facilitate adsorption. Chitosan precipitation was induced by gradually adjusting the pH using either 1 M NaOH or 1.4 M Na\({}_{2}\)SO\({}_{4}\) to reach one of three target pH ranges: acidic (5.0-6.0), neutral (6.5-7.0) or alkaline (10.0-11.0). To enhance mechanical strength, 1.4 M Na\({}_{2}\)SO\({}_{4}\) was used for osmotic dehydration, after which the immobilized material was filtered, washed thoroughly with deionized water, and dried at 60 \({}^{\circ}\)C for 2 h until a constant weight was achieved. The final immobilized enzyme preparation was stored at 4 \({}^{\circ}\)C until use. To optimize immobilization efficiency and operational stability, three key parameters were systematically investigated. The effects of wet cell loading (5-30 g/L), chitosan precipitation pH (5.0-6.0, 6.5-7.0, and 10.0-11.0), and chitosan concentration (5-40 g/L) were investigated. For each condition, protein loading capacity and activity recovery were quantified. The enzyme activity of the immobilized preparations was determined under standard assay conditions. The activity recovery (%) of the immobilized TcDAEase was calculated as the ratio between the specific activity (U\(\cdot\)g\({}^{-1}\)) of the immobilized preparation and that of the corresponding amount of free wet cells. Breakpoint analysis and first-order kinetic fitting were applied to model saturation and decay trends for protein loading and catalytic activity, respectively.
## 2 Methods  
The present scoping review was conducted following the JBI methodology [30] for scoping reviews. The Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) [31] Checklist for reporting was used.  
**Search Strategy:** An initial search was conducted on MEDLINE to identify relevant keywords and index terms, which were then applied across multiple databases, including Cochrane Central, Scopus, and PEDro. Gray literature sources, such as Google Scholar and reference lists of relevant studies, were also reviewed to capture additional records.  
**Data Screening:** All identified records were imported into EndNote, and duplicates were removed. Two independent reviewers conducted a two-stage screening process. First, titles and abstracts were screened based on the inclusion criteria, followed by a full-text assessment. Any disagreements were resolved through discussion with a third reviewer to maintain consistency and reduce bias.  
**Data Analysis:** Data extraction was performed using a form based on the JBI tool, capturing key study details, outcomes, and relevant biomechanical findings. A thematic analysis approach was applied to synthesize data across studies, focusing on outcomes like plantar pressure distribution and foot stability. Quantitative metrics, where available, were analyzed to highlight the biomechanical impacts of different lacing techniques.
# Removing Land Subsidence Impact from GPS Horizontal Motion in Tianjin, China
## 4 Discussion
Another limitation arises from the scarcity of high-precision horizontal displacement measurement data within the study area, which limits our ability to compare these data with our findings. Historically, research on land subsidence in Tianjin has predominantly focused on vertical displacement measurements, with limited attention given to horizontal displacements. Consequently, there is a lack of comparative data against which we can validate our model's predictions. Presently, technologies such as LiDAR [28] are capable of rapidly and accurately measuring horizontal displacements. The high-precision horizontal displacement data provided by these technologies could serve both as a means of validating our results and as supplementary information to enhance our understanding of the phenomenon. In summary, while our study provides valuable insights into the effects of land subsidence on horizontal movements, the limitations described above underscore the need for further research incorporating extended temporal datasets and high-precision measurement technologies to better understand and address the challenges posed by land subsidence in the Tianjin area.
# Summertime Aerosol Radiative Effects and Their Dependence on Temperature over the Southeastern USA
## 2 Methodology
### Spaceborne Observations
#### 2.1.1 Aatsr
The AOD products were in good agreement and the AATSR product exhibited trends similar to those published in the literature (see Figures S1 and S2, and Table S1 for more information). The LST algorithm uses pixel-by-pixel TOA radiometrically and geometrically calibrated brightness temperatures from the 11 and 12 \(\upmu\)m channels. The retrieval coefficients are dependent on the biome, fractional vegetation cover, precipitable water, satellite zenith view angle, and the time of day (day or night). Both the fractional vegetation cover and precipitable water are seasonally dependent, whereas the biome is invariant. The Level 3 product exploited here has been cloud-cleared and re-gridded onto a regular equal-angle grid. For more information, see the AATSR LST Algorithm Theoretical Basis Document [39] and the Validation Report [40]. The LST data also include pixel level uncertainties that were incorporated into the analysis. In practice, we averaged the uncertainty values in a similar way as we did for the AOD values. Averaging the uncertainties provides us with a conservative uncertainty estimate, since in error propagation random components are reduced, but we do not consider this; instead we treat them as systematic errors. In the analysis, we modelled the data uncertainty as normally distributed with zero mean and standard deviation of half the given uncertainty. With this uncertainty model, the true data points were assumed to lie within the given uncertainty range with a probability of 95%.
## 1 Introduction  
Varroosis is the growing global concern in beekeeping [1; 2; 3; 4] since it shifted from the original host, _Apis cerana_, to _Apis mellifera_[5]. It is currently the most prevalent disease of honey bees that contributes to colony losses worldwide [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. Varroa mites [17] are external obligate parasites of the honey bee, which feed on fat body deposits [18; 19]. Both small wounds on the cuticle and the depletion of fat bodies make them more vulnerable to other diseases, such as viruses, fungi, or bacteria [20].  
Many efforts have been undertaken to identify the best ways to prevent and control varroosis. Beekeepers have various options to control the parasitic varrooa mite in honey bee colonies, but no empirical data are available on the methods they apply in practice. [PERSON] et al., 2022 [21], surveyed the set of 19 different varroa diagnosis and control measures in Europe taken from the annual COLOSS questionnaire on honey bee colony losses. The adoption of integrative activities that beekeepers apply for on-apiary production to attain optimal health for humans, honey bees, and the environment, even called good beekeeping practices (GBPs) [22] and disease-specific biosecurity measures in beekeeping (BMBs) [23], may prevent varroa, improving its control in a sustainable way. In this sense, it reduces the need for the use of veterinary medicines in apiculture, the risk of antimicrobial resistance (AMR), and residues in hive products. Monitoring varroa infestation levels in the bee colonies plays a pivotal role in preventing colonies' losses and properly orienting beekeepers toward the adoption of a sustainable apiculture. Nevertheless, thanks to an integrated pest management (IPM) approach, the effective use of veterinary medicines plays a vital role in the achievement of good honey bee colony management [1; 24]. Unfortunately, a one-fits-all control method for varroa control is not available, as each method has its benefits and drawbacks [25]. In addition, currently, there are no alignments among different countries concerning national laws, sanitary regulations, and administrative provisions in beekeeping [24]. However, a good understanding of honey bee biology and pathogens is also required to guarantee the proper management of honey bees.  
Online questionnaire surveys are commonly recommended for studies wishing to reach large numbers of people from distant geographical areas, allowing a quantitative approach to examine geographically distinct practices and beliefs [26]. Surveys are also useful tools to periodically reassess those practices after corrective actions have been put into place [27]. Given the importance of varroosis for the beekeeping sector worldwide, this survey, as a risk assessment tool, enabled us to figure out the knowledge of beekeepers on varroosis and its related biosecurity measures, including its prevention and control, as well as on the use of the medicines at the apiary level. This survey was distributed at the international level to monitor beekeepers' knowledge in different regions of the world on the aforementioned topics.