esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Related studies assessing 0- to 24 h volatilization following foliar urea-N application to a golf course putting green justified a copious N fertilization rate (20 kg N/ha) as necessarily supportive of resolute measurement by the described flux chambers [3,23]. Videlicet, meaningful conclusions are the more likely product of experiments generating a mean response significantly different from zero. [PERSON] et al. [23] went on to encourage additional field study of NH\({}_{3}\) volatilization from lower, more typical urea-N application rates to putting greens.  
Despite ostensibly lacking an array of fertilizer N application rates supporting simultaneous comparison of NH\({}_{3}\) loss, the described experiments constitute a response to that encouragement. Likewise, treating experiment run as a fixed effect in combined analysis affords comparative inference into purposely varied levels of experiment run, as well as significant interactions with common treatment levels.  
As a result, these findings do not unequivocally counter published conclusions arising from field studies using flux chambers to quantify NH\({}_{3}\) loss from plots receiving two or more rates of folily applied urea-N. Specifically, NH\({}_{3}\) volatilization loss as a percentage of urea-N applied remains independent of fertilization rate [29,30]. The authors propose rapid assimilation of urea by foliage, driven by concentration gradient at the cell membrane and precluding facilitated transport, as the mechanism responsible for this independence [32, 33, 34]. Thus, unlike ionic plant-available nutrient form reliance on facilitated diffusion, neutral urea molecules avoid crowded carrier protein gates and delayed assimilation [35,36].  
In summary, over the 0 to 24 h period following a 7 to 10 kg N/ha application made under comparable environmental and cultural conditions using conventional urea fertilizer, 3-8% of foliar urea-N applications can be expected to volatilize. Conversely, 0.7 to 1.1% of methylol urea liquid fertilizer (containing 60% short-chain methylene urea) or 0.7 to 2.2% of urea complimented with DCD and NBPT volatilized as NH\({}_{3}\).
# Reliable Augmentation and Precise Identification of EPG Waveforms Based on Multi-Criteria DCGAN
## 2 Materials and Methods
### Evaluating Indicators of Generated Data
#### 2.4.2 Accuracy Analysis of Generated Data  
The loss curve can validate the effectiveness of the model but cannot evaluate the quality of the generated data. Therefore, an evaluation metric is needed to assess the quality of the generated data. In this study, MMD was used as the evaluation metric to quantify the differences between the generated data and the real data. MMD determines whether two distributions are identical and is commonly utilized to assess the similarity between distributions, such as vectors, strings, sequences, and images [26]. It is widely applied in various fields, including neural network optimization, domain adaptation, intelligent fault detection, estimating class proportions in unlabeled sample sets, data adaptability assessment, image feature extraction, regression, and time series prediction tasks.  
In DCGAN, the discriminator is trained to identify the differences between real samples (training set) and generated samples, validating the effectiveness and accuracy of MMD for measuring the discrepancy between generated and real data [27]. Therefore, MMD was used to analyze the similarity between the generated data and the original data in this study. A larger MMD value indicates greater dissimilarity between the generated data and the original data, whereas a smaller MMD value indicates stronger similarity between the generated data and the original data. The calculation for MMD is as Equation (6):  
\[MMD^{2}=\frac{1}{m^{2}}\sum_{i,j=1}^{m}k\big{(}x_{i},x_{j}\big{)}-\frac{2}{mn} \sum_{i,j=1}^{m,n}k(x_{i},y_{i})+\frac{1}{n^{2}}\sum_{i,j=1}^{n}k\big{(}y_{i}, y_{j}\big{)} \tag{6}\]  
The Gaussian kernel function, which served as a similarity function between data points in the MMD calculation, was chosen as the kernel function. It is a monotonic function of the Euclidean distance between two vectors, as shown in Equation (7):  
\[k\big{(}x,x^{\prime}\big{)}=e^{\frac{\left|x-x^{\prime}\right|^{2}}{2x^{2}}} \tag{7}\]where \(\sigma\) is the bandwidth, which controls the local influence range of the Gaussian kernel function. In this study, the Gaussian kernel within MMD performed well in capturing the non-linear relationships between data points within the distributions, which was essential for evaluating the similarity between the generated and experimentally obtained data of EPG signals.
## 3 Results and Discussion
### Isotherms Study
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Adsorbents**} & \multirow{2}{*}{**Conditions**} & \multirow{2}{*}{**Metals**} & \multicolumn{3}{c}{**Parameters**} \\ \cline{4-6}  & & & \(q_{\text{m}}\) **(mg/g)** & \(b\) **(L/mg)** & \(\mathbf{R^{2}}\) \\ \hline \multirow{4}{*}{PAN–oxime fiber} & Single & Pd(II) & 244.06 \(\pm\) 10.68 & 0.435 \(\pm\) 0.138 & 0.928 \\  & & Pt(IV) & 175.64 \(\pm\) 6.62 & 0.052 \(\pm\) 0.012 & 0.960 \\ \cline{2-6}  & \multirow{2}{*}{Binary} & Pd(II) & 234.54 \(\pm\) 11.53 & 0.589 \(\pm\) 0.214 & 0.905 \\  & & Pt(IV) & - & - & - \\ \hline \multirow{4}{*}{Amberjet(tm 4200} & Single & Pd(II) & 310.05 \(\pm\) 13.25 & 0.107 \(\pm\) 0.026 & 0.964 \\  & & Pt(IV) & 422.65 \(\pm\) 21.33 & 0.239 \(\pm\) 0.053 & 0.955 \\ \cline{1-1} \cline{2-6}  & \multirow{2}{*}{Binary} & Pd(II) & 163.35 \(\pm\) 6.09 & 0.340 \(\pm\) 0.093 & 0.955 \\ \cline{1-1}  & & Pt(IV) & 198.61 \(\pm\) 6.64 & 0.473 \(\pm\) 0.116 & 0.966 \\ \hline \hline \end{tabular} “-” represents that the data cannot be estimated using the Langmuir model.  
\end{table} 
Table 1: Langmuir parameters in single and binary metal systems.
## 3 Methodology
### Benchmark Profiling Using LOF
DBSCAN is often compared with LOF as an outlier model, even with large scale analysis [46]. However, DBSCAN is more applicable to cluster analysis data applications rather than anomaly detection. Clusters with varying densities cannot be easily identified, only high and low densities. Due to the lack of a weighted score, DBSCAN does not allow a patient privacy officer to prioriti their investigation into potentially inappropriate behaviour. Once the officer has investigated the noise points, there is the 'needle-in-a-haystack' problem of investigating border points. This is insufficient, and a weighted anomaly score is required to enable more nuanced investigation. However, a comparison of LOF with the DBSCAN approach is presented in the implementation as justification of the use of LOF for the detection methodology. As an example of active profiles within the dataset (as defined by highest frequency value) is displayed in Table 4. Here, standard time frequency considerations are employed to show that User 1016 accessed data 32,557 times for a mean average of 49.50 s during the time period in which the data were collected. Further clarification on EPR data can be found in [46]. However, for clarity, User ID refers to the unique ID number assigned to the user of the EPR data. Frequency refers to the number of occurrences or actions performed by the user when accessing the EPR records. The Mean, Standard Deviation (STD), Max and 5 th Percentile are time-series-based measures of central tendency and measures of dispersion used to construct the features for the LOF and DBSCAN investigation (as discussed in Section 3.3). However, as the aim is to perform this type of detection using LOF. The dataset, whilst comprehensive, is unbalanced in terms of the distribution of the data across the four data types. Figure 5 displays the distribution of the data after the feature extraction and normalisation process. The graph displays that the majority of the dataset is comprised of patient ID access. This would make sense as, logically, the most likely data accessed would be related to patients within a hospital setting. The lowest portion of the dataset is comprised of the User ID information; again, this is logical, as the hospital will have fewer staff than patients.
## 3 Passivity Based Robust Switching Control Design for Posture Stabilization  
The primary purpose of control law is to cause the robot to move from any initial position \((x_{i},y_{i})\in\Re^{2}\) to the desired set of position such as  
\[E_{d}:=\{x_{d},y_{d}\in\Re\big{|}\,dist(x(t)\ \ y(t),\ x_{d}\ y_{d})\leq B\} \tag{3}\]  
where \(B\in\Re\) is the positive bounded real number which can be determined by the specific performance error bound. Additionally, the orientation of the robot should reach the desired angle (\(\theta\)) such that \(\theta\rightarrow\theta_{d}\in\Re\). In this paper, without losing generality, we assume the desired position and orientation as \((x_{d},y_{d},\theta_{d})=(0,0,0)\). Next, in order to construct the passivity-based switching control, let's consider the following total energy of the robot system as the Lyapunov function,  
\[L_{t}(t)=L_{v}(t)+L_{w}(t)=\kappa_{v}(t)+\varphi_{v}(x,y)+\kappa_{w}(t)+ \varphi_{w}(\theta) \tag{4}\]  
where \(L_{t}(t)\in\Re\) is the total energy; \(L_{v}(t)=\kappa_{v}(t)+\varphi_{v}(t)\) and \(L_{w}(t)=\kappa_{w}(t)+\varphi_{w}(t)\) where \(\kappa_{v}(t)\) and \(\kappa_{w}(t)\) are forward motion and rotational kinetic energies, and \(\varphi_{v}(t)\) and \(\varphi_{w}(t)\) are (x,y)-navigation energy and an orientation potential energy, respectively. In particular, the kinetic energies can be defined as \(\kappa_{v}(t)=1/2\ mv(t)^{2}\) and \(\kappa_{w}(t)=1/2\ I_{r}w(t)^{2}\). **Remark 1**: _(Properties of potential energy) Functions \(\varphi_{v}(x,y)\geq 0\) (\(\varphi_{v}:\Re^{2}\rightarrow\Re\) ) and \(\varphi_{w}(\theta)\geq 0\) (\(\varphi_{v}:\Re\rightarrow\Re\) ) are the (x,y)-navigation potential function and \(\theta\)-orientation potential function respectively. These functions satisfy the following properties: (1) \(\varphi_{v}(x,y)=0\Leftrightarrow(x,y)=0\) and (2) \((\frac{\partial\varphi_{v}(x,y)}{\partial x},\frac{\partial\varphi_{v}(x,y)}{ \partial y})=0\)\(\Leftrightarrow(x,y)=0\). Similarly, for the \(\theta\)-potential function \(\varphi_{w}(\theta)\), \(\varphi_{w}(0)=0\) and \(\frac{\partial\varphi_{w}(\theta)}{\partial\theta}=0\Leftrightarrow\theta=0\)[19; 25]._  
For this study, let's define the following potential energies such that \(\varphi_{v}(x,y)=\frac{1}{2}(K_{x}x^{2}+K_{y}y^{2})\) for the target position of WMR as the origin of (x,y)-plane and \(\varphi_{v}(\theta)=\frac{1}{2}K_{\theta}\theta^{2}\) for the desired orientation as 0, satisfying **Remark 1**, and where \(K_{x}\), \(K_{y}\), and \(K_{\theta}\in\Re\) are the positive coefficients. Figure 1: A robot with the center of the mass at \((x_{c},y_{c})\) and the geometric center at \((x_{o},y_{o})\) with respect to the reference frame (\(o\),\(x\),\(y\)).
# Performance Analysis of Magnetorheological Porous Fabric Composite
## 2 Preparation and Experiments
### Experiments  
Mechanical properties of the MR porous fabric composite have been tested by comparative experiments. The experimental setups are shown in Figure 3. The commercial rheometer (Physica MCR 302, Anton Paar Co. Ltd., Graz, Austria) has been used to measure the rheological properties of the tested samples. The use of parallel plate rotation tests groove and PP20 type plate rotor (diameter is 20 mm). During the test, the sensor probe was 1 mm away from the liquid level of the material (under zero-field conditions). At the same time, in order to improve the accuracy of the test, the water bath circuit was used to control the temperature of the test device, and the temperature of the test environment was controlled at a stable value. Figure 3a shows the experimental system of MR porous fabric composite. Through the control platform to set the test instructions, the magnetic field control unit will be given a specific magnetic flux density. Then the rheometer starts to test the rheological properties of the material and upload the test results to the control platform.  
Figure 3: Experimental set-ups of the MR porous fabric composite. (**a**) Experimental system: (1) control platform, (2) rheometer, (3) test platform, (4) magnetic field control unit. (**b**) main body of test.  
Figure 2: Non-woven fabric being pretreated (The left is the soaking solution with lithium stearate content of 1 wt%, the middle is 1.5 wt% of that, and the right is 2 wt% of that).  
In order to deeply analyze the performance of the proposed MR material, the steady shear and dynamic oscillatory modes of the rheometer have been carried out. Figure 3b shows the test body of the test. In the steady shear mode, the pretreated samples are placed in the measuring region between the parallel plate and electromagnet. The test temperature is fixed at 25 \({}^{\circ}\)C by the thermostatic system. The shear rate changes from 0.1 to 100 s\({}^{-1}\). The shear stress and viscosity at different shear rates were obtained by applying magnetic flux density from 0 to 0.75 T to the sample.  
In the dynamic oscillatory mode, the shear strain sweep mode is used to measure the dynamic properties of MR porous fabric composite. The applied magnetic flux density changes from 0 T to 0.6 T with an increment of 0.3 T. The storage modulus and loss modulus can be measured at various magnetic flux densities.
###### Abstract
[26] studied the ore-rock movement in a vertical ore pass and the characteristics of the damage caused by the impact with and the cutting of ore-rock blocks to the ore pass wall when the moving ore-rock blocks collided with the ore pass wall, and they constructed a damage model of the sharp and blunt surfaces of ore-rock blocks impacting the shaft wall based on contact mechanics. However, despite these studies, issues related to the deformation and destruction of ore passes still occur frequently, significantly impacting mining operations in terms of safety and efficiency. For this reason, [PERSON] et al. [27] studied the motion law of ore rock and the impact damage law using kinematic analysis and distinct element codes. They revealed the relationship among impact position, speed, degree of dispersion, and impact kinetic energy of ores in an ore pass. After that, [PERSON] et al. [28] was able to gather the collapse information of an ore pass by using 3D visualization analysis. [PERSON] et al. [29] established a laboratory bench of similarity simulations for the investigation of the damage mechanisms within the main ore pass. Most of these studies focus on the location and nature of ore-rock collisions with the ore pass wall, and only a few studies to date have focused on the ore pass wall's damage depth and lost volume after the ore-rock collision using the numerical simulation method. Considering the Hertz contact theory [30], the elastoplastic body tangential contact theory [31; 32; 33], as well as the modified soil yield velocity formula [34], this paper, while taking into account the specific characteristics of the ore-rock blocks impacting the ore pass wall, establishes the colliding conditions of the ore-rock block colliding with the ore pass wall, proposes equations for calculating the three-dimensional coordinates of the collision point, its depression angle, and the direction angle of the ore-rock block's first impact with the ore pass wall. This study also presents a contact model between the ore-rock blocks and the ore pass wall under a range of impact forces in different directions to analyze the damage caused by the ore-rock blocks to the ore pass wall. Additionally, it investigates the calculation methods and the specific formulas for the normal restitution coefficient (NRC) and tangential restitution coefficient (TRC), as well as the effects of the impact velocity and ore-rock block sizes on NRC, TRC, and the loss area and volume of the ore pass wall.
# Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules
## 1 Introduction
To add fuzzy control rules on the current FLC, the proposed self-evolving design method uses two criteria: Criterion 1 is based on the incremental estimated control error obtained using the system's inverse function, and Criterion 2 is based on the minimal distance allowed between the center of two consecutive membership functions. To delete fuzzy control rules, two criteria to delete \"less active\" or \"less informative\" fuzzy control rules are used. To evaluate the proposed self-evolving FLC, a simulated nonlinear continuously stirred tank reactor (CSTR) plant is used. The main contributions of the proposed self-evolving design methodology for direct FLCs are: the evolving of a control structure composed of univariate control rules, which together with the defined evolving mechanisms, makes the controller better interpretable by human operators, and having a light control structure (small number of membership functions and control rules); the criteria to add control rules are defined in order to reduce the sensitivity to noise/outliers; to delete fuzzy control rules, two criteria to delete \"less active\" or \"less informative\" rules are presented; and the threshold for the criteria to add or remove control rules are intuitively defined (by the percentage of the variables range and \(\epsilon\in[0,1]\)). The paper is organized as follows. Section 2 presents the FLC controller to be online designed by the proposed methodology. Section 3 presents the proposed self-evolving FLC design method. Section 4 analyzes the performance of the proposed approach on a nonlinear continuously stirred tank reactor system. The final conclusions are presented in Section 5.
# Wave Polarization Control in Anisotropic Locally Resonant Materials
###### Abstract  
Elastic wave propagation in solids can be controlled and manipulated by properly designed metamaterials. In particular, polarization conversion can be obtained by using anisotropic materials. In this paper, we propose a three-component locally resonant material with non-symmetrically coated inclusions, and we study the effect of the anisotropic equivalent mass on band gap formation and the polarization conversion of elastic waves. The equivalent frequency-dependent mass tensor is obtained through the two-scale homogenization approach. The study of the eigenvalues of the mass tensor enables to predict band gaps and polarization bands, as well as identifying a priori the effect of different geometric and material parameters, thus opening the way to metamaterial optimization.  
anisotropic mass; metamaterials; locally resonant materials; homogenization; wave polarization conversion +
Footnote †: journal: Applied Sciences
## Results
### Survey Analysis  
In part II of the study, 43 patients of all patients included within the study population of part I completed the follow-up survey. As many patients were contacted years after their removal surgery, we experienced major difficulties contacting them (e.g., invalid phone numbers, incorrect addresses in the patient data). Furthermore, not all of the successfully contacted patients were willing to participate in part II of the study. Most of the patients completing the survey stated that they did not consider the removal surgery as a severe burden. A minority stated that the surgery was a burden, with two patients experiencing postoperative complications (Table 13). In one case, the complication was hypesthesia and, in the other, double vision. Both complications began after the osteosynthesis. An even larger number of patients indicated that the burden of surgery was no more severe than leaving the plates in situ. The majority of the patients undergoing removal surgery stated that they benefitted from plate removal, and even patients suffering from postoperative complications after plate removal considered the surgery as beneficial. The reasons for the beneficial feeling are presented in Table 14.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Subcategory** & **Case (n(\%))** \\ \hline Plate removal as severe burden & Yes & 7 (16.3) \\  & No & 36 (83.7) \\ \hline Burden more severe than leaving plates in situ & Yes & 2 (4.7) \\  & No & 41 (95.3) \\ \hline Plate removal as benefit & Yes & 37 (86.0) \\  & Neutral & 6 (14.0) \\  & No & 0 (0.0) \\ \hline Plate removal as benefit despite complications & Yes & 5 (71.4) \\  & No & 2 (28.6) \\ \hline \hline \end{tabular}
\end{table}
Table 13: Evaluation of the survey.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Subcategory** & \multicolumn{2}{c}{**Orbital Fracture**} \\ \cline{3-4}  & & **No (n(\%))** & **Yes (n(\%))** \\ \hline Amount of complications & No complication & 94 (81.7) & 108 (91.5) \\  & One complication & 17 (14.8) & 10 (8.5) \\  & Two complications & 4 (3.5) & 0 (0.0) \\  & \(p=0.034\)* & \\ \hline \hline \end{tabular}  
* The result of [PERSON]’s Chi-square test is \(\chi^{2}(2)=6.748\), \(p=0.034\), [PERSON]’s V = 0.170.  
\end{table}
Table 12: Statistical correlation between amount of complications after plate removal and orbital fractures.
## 3 Results and Analyses
### Time-Latitude Distributions of the Monthly and Zonal-Mean GW Ep  
Following the steps mentioned in Section 2.4, the global monthly and zonal-mean GW Ep between 20 and 35 km during 2007 to 2020 are derived, and the time-latitude distributions of the GW Ep values at the four height levels, 20 km, 25 km, 30 km, and 35 km, are derived and are presented in Figure 5 with logarithmic scale.  
As we can see, in the altitude range of 20-35 km the values of GW Ep decrease with the increase in altitude, especially in the low latitude area (30\({}^{\circ}\) S-30\({}^{\circ}\) N). Additionally, at each of the four height levels, the GW Ep shows a clear seasonal cycle. Specifically, over mid and high latitudes, especially in the latitudes higher than 40\({}^{\circ}\) N/S, the maximum values of Ep are observed during November-March in the Northern Hemisphere (NH) and May-September in the Southern Hemisphere (SH), which is consistent with previous studies [9; 57]. At each of the four height levels, the GW Ep is stronger in high latitudes of the southern winter hemisphere than those of the northern winter hemisphere. The stronger and longer-lasting eastward wind during the winter seasons of the SH compared with the winter seasons of the NH should partly contribute to this distribution pattern of GW Ep values [57; 61]. In addition, it can be seen from Figure 5 that after the year 2016, the values of monthly zonal-mean GW Ep decreased compared with previous years, especially at the high level of 35 km.  
Figure 5: Time–latitude distributions of the GW Ep values at the four height levels: 20 km, 25 km, 30 km, and 35 km. The unit of the color bar is monthly and zonal-mean GW Ep using logarithmic scale (ln(J/kg)).