esa-sceva/satcom-chunk-collection huggingface dataset:
# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
## 2 Related Work  
### Masking-Based Speech Enhancement  
When training neural networks in a supervised manner, it is essential to define a proper training target in order to ensure effective learning. The training targets for speech enhancement can be mainly categorized into two groups: (i) mapping-based, and (ii) masking-based approaches. The mapping-based methods learn a regression function relating a noisy speech to the corresponding clean speech directly while the masking-based methods estimate time-frequency masks given a noisy speech. A variety of training targets have been studied. [PERSON] et al. evaluated and compared the performance of various mapping-based and masking-based targets [PERSON] et al. (2018). It may be controversial to argue which method is better, yet many cases have shown that the masking-based methods (e.g., ideal ratio masks) tend to perform better than the mapping-based methods [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018) in terms of enhancement results. In this work, we design the proposed model within a masking-based framework. We use the time-frequency masking functions as an extra layer in the neural network [PERSON] et al. (2018). This way, the model implicitly incorporates the masking functions when optimizing the network which will be detailed in Section 3.1.
## 3 Numerical Results and Discussion
There are two methods to increase the temporal gradient of the electric field amplitude. One is increasing the intensity of the driving field, meanwhile keeping the pulse duration constant. The other one is decreasing the pulse duration but fixing the intensity of the driving field. As the harmonic yield can also be enhanced by increasing the intensity of the driving laser field, it is difficult to identify the effect of the temporal gradient of the electric field amplitude from that of the pulse intensity for the former method. Consequently, we use the latter method to test the role of the temporal gradient of the electric field amplitude in controlling the high-order harmonic generation from atoms in a circularly polarized laser field. In Figure 5a, the harmonic spectra for circularly polarized laser fields of three different pulse durations are presented. The intensity of the laser field is fixed at \(1.0\times 10^{14}\) W/cm\({}^{2}\). The three different pulse durations are 10, 50, and 90 optical cycles, respectively. For a comparison, the harmonic spectra for linearly polarized laser fields are also presented in Figure 5b. As shown in Figure 5a for circularly polarized laser field, the harmonic yield increases with decreasing pulse duration. Whereas, in the case of linearly polarized laser field, the harmonic yield decreases with decreasing pulse duration. For clarity, we used Equation (7) to calculate the integrated harmonic yield near the harmonic cutoff from 27.19 eV to 37.19 eV and more pulse durations are considered. The result is shown in Figure 5c. One can clearly see that the integrated harmonic yield increases with shortening the pulse duration. Whereas in the case of a linearly polarized laser field, the integrated harmonic yield near the cutoff from 56.67 eV to 66.67 eV decreases with shortening the pulse duration, as shown in Figure 5d. This is because in the case of linearly polarized laser field, the electron return to the parent ion happens every half cycle, independent of the temporal gradient of the electric field amplitude. The longer the pulse duration is, the more electron returns there will be. The total harmonic yield is determined by adding the contributions of all the electron returns happening during the whole pulse duration. Hence, the harmonic yield increases with increasing the pulse duration, as shown in Figure 5b,d. Hence, it is confirmed that in the case of circularly polarized laser field, the larger the temporal gradient of the electric field amplitude is, the more trajectories can lead to the electron return. As a result, the harmonic yield increases.
# Image-Based Tactile Deformation Simulation and Pose Estimation for Robot Skill Learning
## 1 Introduction
### Related Works  
Different tactile sensors are applied in a wide range of robotic studies. One noteworthy sensor among these studies is GelSight [1]. GelSight uses a flat layer of gel as the skin for contact, with one side of the surface reflective. The reflective surface is illuminated with LED lights from different angles. The camera captures the shade image of the surface and reconstructs a depth image using photometric stereo [2]. Another tactile solution commonly used in Bristol Robotics Laboratory is TacTip [3]. The basic TacTip model uses ahemispherical silicone rubber skin filled with silicone gel as the contact surface, mimicking the fingertip of humans. Pins with white-colored tips are evenly distributed on the black inner surface of the hemispherical skin. The distribution of pins on deformed skin is captured by a camera, which is used to reconstruct the shape of the tip surface, or the shape of the contact object, using different methods [4,5]. Some derivatives of TacTip also explored optimization possibilities. For instance, DigiTact [6] merged the shape characteristics of GelSight-based DIGIT [7] and TacTip, making the sensor more compact and robust.  
These optical tactile sensors are used in a variety of robotic tasks, from pose estimation to surface following. For instance, in [8], the denoised image of a gripper holding a tube, captured using an RGB camera, as well as the depth image of the tube surface captured with a GelSight [1] tactile sensor, are both encoded into feature values. These values are used to as a comparison with values in a database using the cosine similarity method. The object pose of the most similar feature value is output as the estimated pose. Some derivatives of GelSight, for instance, 9 DTact [9], have shown strong application potential. This sensor can reconstruct depth maps of object surfaces like GelSight, following the mechanism in DTact [10]. Furthermore, a dataset of force and sensor feedback pairs is created to train a neural network to estimate the force of contact. Another optical tactile sensor, TacTip [3], is more commonly used in dynamic object contact tasks. For instance, ref. [11] used a convolutional neural network and Gaussian density networks to estimate pose and shear. The pose and shear information are then used to enable accurate object pushing and surface following. The result demonstrated the manipulator can push the object to the goal position with error less than 5 mm.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 6 Analysis and Validation of Statistical Model for AMQP IoT Attack Detection
### Detecting the MiTM Attack
#### 6.3.3 Detection Results for Malicious Dataset in MiTM Packets  
Figure 16 presents the analysis of a malicious dataset using the AMQP MiTM detection script. The results were analyzed to evaluate the model's precision and false positive ratio. Key findings include the following:  
* A total of 63.54% of packets were identified as malicious.
* A total of 30.21% of these malicious packets were associated with MiTM attacks.
* A total of 6.25% of packets were classified as normal traffic.  
These results emphasize the model's robustness in detecting MiTM attacks within a malicious dataset while maintaining a low false positive rate for normal traffic.
## 2 Mathematical Model
### Governing Equation for the Connecting Wall  
The shell-fluid and core-fluid are separated by a connecting wall through which heat-exchange occurs. The governing equation for the connecting wall temperature, \(T_{w}^{*}\), follows the one-dimensional heat conduction equation:  
\[\frac{\partial T_{w}^{*}}{\partial t^{*}}=\frac{\partial^{2}T_{w}^{*}}{ \partial{z^{*}}^{2}}+h_{c}^{*}(T_{c}^{*}-T_{w}^{*})+h_{sf}^{*}(T_{sf}^{*}-T_{w }^{*}). \tag{7}\]
## 5 Conclusions  
This user study showed that our CDSS explained by either feature contribution or example had a significant impact on healthcare practitioners' decision-making, demonstrating great potential in clinical settings. A comparison between these XAI methods showed no significant difference in the advice-taking from a CDSS, however, both methods may lead to over-reliance issues. Although many healthcare practitioners preferred to have both explanations in a CDSS, there was a clear preference for explanation by feature contribution over explanation by example. We identified the inclination towards CDSS use as an important factor in the advice-taking from an explainable CDSS among obstetricians. We also discovered that healthcare practitioners with different expertise preferred different types of explanations; therefore, CDSS developers should carefully select XAI methods based on the target users of the CDSS.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122010323/s1](https://www.mdpi.com/article/10.3390/app122010323/s1)]([https://www.mdpi.com/article/10.3390/app122010323/s1](https://www.mdpi.com/article/10.3390/app122010323/s1)), Form S1: The Google form used to conduct this survey.  
Conceptualization, Y.D. and C.M. ([PERSON]); methodology, Y.D., A.M.A., C.M. ([PERSON]), F.M.M. and C.M. ([PERSON]); formal analysis, Y.D.; investigation, Y.D., A.M.A., C.M. ([PERSON]), F.M.M. and C.M. ([PERSON]); writing--original draft preparation, Y.D.; writing--review and editing, Y.D., A.M.A., C.M. ([PERSON]), F.M.M. and C.M. ([PERSON]); supervision, C.M. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
PEARS study was supported by University College Dublin and Nationalaternity Hospital Medical Fund.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Human Research Ethics Committee of University College Dublin (protocol code LS-E-22-02-Mooney and date of approval 6 January 2022).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
No additional data are available.  
We would like to gratefully thank people who helped disseminating the survey and all participating healthcare practitioners.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CDSS & Clinical Decision Support System \\ XAI & Explainable Artificial Intelligence \\ AI & Artificial Intelligence \\ ANN & Artificial Neural Network \\ SVM & Support Vector Machine \\ RQ & Research Question \\ GP & General Practitioner \\ PEARS & Pregnancy Exercise and Nutrition Research Study \\ GDM & Gestational Diabetes Mellitus \\ SHAP & Shapley Additive Explanations \\ BMI & Body Mass Index \\ WOA & Weight of Advice \\ HCP & Healthcare Practitioner \\ KNN & K-Nearest Neighbours \\ \end{tabular}
# Design and Verification of Large-Scaled Flapping Wings for High Altitude Environment
## 3 Results
### Test Flight at High Altitudes  
The flight test of the assembled aircraft was carried out at different altitudes. On one hand, it is to verify whether the designed wing is suitable for different altitudes; on the other hand, it is to explore the influence of altitude.  
The 163 g flapping wing before weight reduction (Figure 9d) was assembled with the prototype vehicle, and flight tests were conducted at different altitudes. The lower altitude area refers to Xi'an, Shaanxi Province, where the altitude is about 500 m (Figure 17a), while the higher altitude area is Lhasa, Tibet Autonomous Region, where the altitude is about 4500 m (Figure 17b).  
The flight tests of the vehicle's load capacity and endurance were carried out in the Xi'an area. The vehicle was equipped with an inverted T-tail and the same drive method as the wind tunnel experiment mechanism. As shown in Table 8, the assembled vehicle had an empty load of \(\sim\)1150 g and a maximum takeoff weight of \(\sim\)1300 g. A total of approximately one hour of flight testing was successfully completed, where the capacity of the battery was 106 Wh. The environmental conditions at this time were a temperature of approximately 20 \({}^{\circ}\)C and a wind speed rating of 1 level.  
Subsequently, the flight tests were conducted in Lhasa, with an empty load of 1100 g and the same maximum takeoff weight. The flight time was reduced by 25% to 45 min compared to the altitude of 500 m, where the temperature was 13 \({}^{\circ}\)C and the wind speed was level 3.  
After flight tests at two altitudes, the designed flapping wing was verified to possess excellent aerodynamic performance, capable of withstanding considerable loads and endurance.
## 4 Proposed Method
### Dataset Design  
In the OTFS system considered, the symbol is transformed from the DD domain to the time domain via ISFFT and Heisenberg transform. After the wireless channel transmission, the received signal is transformed into the DD domain via the Wigner transform and SFFT. We design a data acquisition scheme to extract two distinct data flows in the time and DD domains from the above process and employ them as the input to the neural network, as shown in Figure 2. We consider \(M\) subcarriers in the delay dimension and \(N\) symbols in the Doppler dimension.  
We construct the time-domain data vector \(\Theta\) with a dimension of \(2\times MN\), using the in-phase (real) component and quadrature (imaginary) component of \(r[l]\) (\(l=0,\ldots,MN-1\)), which is a discrete sampled version of the received time-domain signal \(r(t)\):  
\[\Theta=\begin{bmatrix}[\text{Re}(r[l])]_{l\in[0,MN-1]}\\ [\text{Im}(r[l])]_{l\in[0,MN-1]}\end{bmatrix}\in\mathbb{R}^{2\times MN}. \tag{9}\]  
To generate data for the DD-domain stream, we split the DD-domain symbol grid \(y_{\text{dd}}\) into an in-phase (real) component and a quadrature (imaginary) component with the dimensions of \(N\times M\) and construct a DD-domain input data matrix \(\Gamma\) with a dimension of \(2\times N\times M\):  
Figure 3: Differentiated embedded pilot insertion scheme.  
\[\Gamma=\begin{bmatrix}[\text{Re}(y_{\text{dd}}[k,l])]_{k\in[0,N-1],l\in[0,M-1]}\\ \begin{bmatrix}[\text{Im}(y_{\text{dd}}[k,l])]_{k\in[0,N-1],l\in[0,M-1]}\end{bmatrix} \in\mathbb{R}^{2\times N\times M}.\end{bmatrix} \tag{10}\]
# Load Forecasting with Machine Learning and Deep Learning Methods
## 3 Methodology
### Performance Evaluations  
The models are evaluated by their ability to correctly predict the target variable. To adequately analyze the forecast obtained by the different models and avoid the randomness of the optimization process, a seed is used. Furthermore, to ensure the reproducibility of the obtained configurations, the cross-validation method is implemented. Thus, the performance analysis is realized in validation and test sets. To highlight different aspects that the models may present, they are evaluated by a group of metrics without interrelation. The dispersion, tendency, and capability to reproduce the results in actual and future time points are evaluated. These metrics are the normalized mean bias error (\(nMBE\)), and the normalized root mean squared Error (\(nRMSE\)). The \(nMBE\) provides a measure of the general bias of a given variable, where positive and negative values represent underestimation or overestimation of the information, respectively (17).  
\[nMBE=\sum_{k=1}^{n}\frac{\hat{y}_{k}-y_{k}}{n}/y_{max} \tag{17}\]where, \(y_{max}\) is the maximum value of the objective variable.  
The \(nRMSE\) measures the variability in the errors between the measured and simulated values, thereby giving an indication of the model's variance, as shown in (18). Thus, with the application of this formula, the normalized standard deviation of the predicted dataset is reflected with respect to the actual observation of these data.  
\[nRMSE=\sqrt{\sum_{k=1}^{n}\frac{(y_{k}-y_{k})^{2}}{n}}/y_{max} \tag{18}\]
## Materials and Methods
### Remote Sensing Products  
We obtained surface reflectance data from the MODIS sensor corrected by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm [60], whose data were processed by [50], between January 2000 to December 2017. MAIAC provides a new method for deriving aerosol and land surface reflectance products. This algorithm simultaneously retrieves atmospheric aerosols and bidirectional reflectance from MODIS data, detects clouds and corrects for atmospheric effects on dark vegetation surfaces and bright desert targets, in a moving window of up to 16 days of MODIS.  
With the MODIS/MAIAC reflectance bands, the vegetation indices, the enhanced vegetation index (EVI) [61] used as a proxy of the canopy photosynthetic activity and the green chromatic coordinate index (Gcc) [62; 63] used as a proxy for leaf flush, were calculated by the Equations (1) and (2). Evapotranspiration data were obtained by the MOD16A2 product. For the analysis at the RJA site, we used the average weighted by the area of the pixels that intercepted the study plot (n = 2).  
\[\text{Gcc}=\text{G}/(\text{R}+\text{G}+\text{B}) \tag{1}\]  
\[\text{EVI}=2.5*((\text{NIR}-\text{R})/(\text{NIR}+6*\text{R}-7.5*\text{B}+1)) \tag{2}\]  
where R, G, B and NIR are the digital numbers (DN) in the red, green, blue and near infrared channels, respectively.  
Regression model was calculated to examine the linear relationship between variables, using the coefficient of determination (\(R^{2}\)), with 95% confidence intervals. A nonparametricKruskal-Wallis (KW) test was applied at a significance level of 5%, to test the hypothesis of equality between the evaluated samples.
## 5 Reliability Optimization Design Method Oriented to the Stability of Configuration Transformation
### Reliability Optimization Design Based on Improved Genetic Algorithm
(5) Mutation. Mutation refers to the replacement of certain gene values in the individual code string with other gene values according to the mutation probability to form a new individual. The paper uses Gaussian mutation operator, which means replacing the original gene value with a random number conforming to the normal distribution of mean \(\mu\) and variance \(\sigma 2\) during mutation operation. Assuming that there are 12 random numbers \(r_{i}(i=1,2,\cdots,12)\) uniformly distributed in the range of [0,1], a random number conforming to the normal distribution \(N\big{(}\mu,\sigma^{2}\big{)}\) can be obtained by the following equation:  
Figure 10: The tuning process of the improved genetic algorithm. \[Q=\mu+\sigma\left(\sum_{i=1}^{12}r_{i}-6\right) \tag{48}\]  
When Gaussian mutation operation is performed from \(X=x_{1}x_{2}\cdots x_{k}\cdots x_{l}\) to \(X=x_{1}x_{2}\cdots x_{k}^{\prime}\cdots x_{l}\), the value range of gene value at mutation point \(x_{k}^{\prime}\) is \(\left[U_{\text{min}}^{k},U_{\text{max}}^{k}\right]\), and we assume as follows:  
\[\left\{ \begin{array}{l}\mu=\frac{U_{\text{max}}^{k}+U_{\text{min}}^{k}}{2}\\ \sigma=\frac{U_{\text{max}}^{k}+U_{\text{min}}^{k}}{6}\end{array} \right. \tag{49}\]  
The new gene value \(x_{k}^{\prime}\) can be determined by the following equation:  
\[x_{k}^{\prime}=\frac{U_{\text{max}}^{k}+U_{\text{min}}^{k}}{2}+\frac{U_{ \text{max}}^{k}+U_{\text{min}}^{k}}{6}\left(\sum_{i=1}^{12}r_{i}-6\right) \tag{50}\]  
(6) Nonlinear programming. The MATLAB R2021b (version number: 9.11) optimization toolbox contains the fmincon function that can be directly called on to solve nonlinear programming problems. The function starts from an estimated value to search the minimum value of the nonlinear multivariate function under the constraint condition. The MATLAB program code of improved genetic algorithm for reliability optimization of configuration transformation ability of metamorphic mechanisms is shown in the Supplementary Materials.
# A Voltage Multiplier Circuit Based Quadratic Boost Converter for Energy Storage Application
## 3 Loss and Non-Ideal Gain Analysis
### Non-Ideal Gain  
Using the principle of energy conservation, the proposed converter's non-ideal gain with parasitic inductor resistance can be derived as follows.  
\[\begin{gathered} P_{in}=P_{o}+P_{L_{total}}\\ P_{in}=\frac{V_{in}V_{O}\big{(}3-D^{2}\big{)}}{R(1-D)^{2}}\\ P_{L_{\text{fan}_{total}}}=(\frac{2V_{O}}{R(1-D)})^{2}r_{L1}+( \frac{2V_{O}}{R(1-D)^{2}})^{2}r_{L2}\\ P_{o}=\frac{{V_{O}}^{2}}{R}\\ V_{O}=\frac{V_{in}R(1-D)^{2}\big{(}3-D^{2}\big{)}}{R(1-D)^{4}+4r_{L1} (1-D)^{2}+4r_{l2}}\end{gathered} \tag{19}\]  
where \(r_{L1}\) and \(r_{L2}\) are parasitic resistances of \(L_{1}\) and \(L_{2}\).  
It can be seen that the non-ideal gain depends on load resistance (\(R\)) and parasitic resistances. The variation of this gain with duty-cycle is as shown in Figure 6. As the load resistance is decreased, the voltage gain also reduces for the same value parasitic resistances.  
Figure 5: (**a**) Bifurcation of losses in the proposed converter at 45 W, (**b**) efficiency of the proposed converter.  
Figure 6: Non-ideal gain of the proposed converter.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 6 Validation of the Proposed Protocol
### Mathematical Model of SMTrust
#### 6.2.1 Isotonicity  
The isotonicity refers to preserving the order of the weights of two paths when prefixed by the same third path [73]. Mathematically, the path weight structure of SMTrust (\(P\), \(+\), \(fn\), \(\leq\)) is (left) isotonic for paths \(x\), \(y\), \(z\) belongs to \(P\), infers that if \(fn(x)\leq fn(y)\) then \(fn(z+x)\leq fn(z+y)\).
## 2 Materials and Methods
### Study Area
An analysis of (2010-2020) of rainfall data from the study area reveals significant intra-annual and inter-annual variability, a common feature of tropical rainfall patterns. The data show a consistent seasonal cycle, with peak rainfall between June and September. July and August consistently record the highest monthly rainfall, often exceeding 200 mm. For instance, in 2020, August received 298 mm of rain, while July 2016 saw 298 mm. Conversely, the period from November to March is markedly dry, with many months recording zero rainfall. This pronounced seasonality has profound implications for the Tiga Dam's geotechnical integrity. During the rainy season, particularly in peak months like July and August, the dam and its environs are subjected to intense hydrological stress. The reservoir's water level rises substantially at the upstream section, increasing hydrostatic pressure and seepage forces within the dam body. This heightened water-soil interaction can lead to internal erosion processes such as piping or suffusion, where finer particles are progressively washed away, potentially compromising the dam's structural stability. Simultaneously, the downstream area faces a different yet equally critical challenge. The soil is directly exposed to high-intensity rainfall events. Rain splash, sheet flow, and fill formation--all erosional processes--are particularly active during these wet months. For example, in July 2018 and August 2018, both months experienced exceptionally high rainfall (294 mm each), likely resulting in significant soil detachment and transport downstream. The contrast between wet and dry seasons further complicates the geotechnical scenario. During the dry months (November to March), the dam's downstream section experiences minimal rainfall, often zero, as seen consistently in the data. This leads to desiccation, causing soil shrinkage and crack formation. When the rains return, these cracks facilitate rapid water ingress, accelerating soil saturation and potentially triggering expansion or localized slope failures. Moreover, the inter-annual rainfall variability adds another layer of complexity. Some years, like 2012 and 2018, show higher overall rainfall, suggesting more severe erosional stress. In contrast, years with lower rainfall might see reduced immediate erosion but could experience more pronounced wetting-drying cycles, exacerbating soil expansion contraction behavior. Figure 3a-c show the damage to the earth material structure adjacent to downstream areas of the Tiga dam structure. Figure 3: _Cont._