esa-sceva/satcom-chunk-collection huggingface dataset:
# Zinc Plating on Inkjet-Printed Ti3C2 Tx MXene: Effect of Electrolyte and PEG Additive
## 1 Introduction
Moreover, their hydrophilic functional groups improve electrode wettability [30; 31; 32; 33] with electrolytes, resulting in lower interface impedance and more uniform ion flux [34; 35]. In particular, the HCP crystal structure of Ti\({}_{3}\)C\({}_{2}\) MXene was recently demonstrated to be particularly \"zincophilic\" [24; 27; 36; 37; 38; 39] because of the synergistic effect of a matching lattice degree between Ti\({}_{3}\)C\({}_{2}\)T\({}_{\mathrm{x}}\) and Zn and because the presence of halogenated terminations on the Ti\({}_{3}\)C\({}_{2}\)T\({}_{\mathrm{x}}\) surface favors the production of a coherent heterogeneous MX/Zn interface, which influences and regulates the subsequent homogeneous Zn growth [35; 40; 41]. In this work, we investigated the effect on the Zn nucleation and growth of both a thin, zincophilic Ti\({}_{3}\)C\({}_{2}\)T\({}_{\mathrm{x}}\) substrate and a PEG400 additive in Zn aqueous electrolytes. Inkjet printing was employed as a suitable technique to quickly achieve Ti\({}_{3}\)C\({}_{2}\)T\({}_{\mathrm{x}}\) uniform coatings on SS316 substrates. The mechanism of Zn plating in ZnCl\({}_{2}\) and ZnSO\({}_{4}\) solutions with and without PEG400 was investigated by electrochemical potentiodynamic techniques as well as microstructural and morphological analyses in order to assess the best condition for optimal Zn growth. It was found that the quality of the Zn layer grown on the Ti\({}_{3}\)C\({}_{2}\)T\({}_{\mathrm{x}}\) layer, in terms of microstructure, strictly depends on the kind of electrolyte used, and PEG400 was demonstrated to effectively improve the homogeneity of Zn plating only in ZnCl\({}_{2}\) electrolytes. In this work, inkjet printing was employed as a non-conventional technique, rarely reported in the literature, for the surface modification of battery electrodes. A printable Ti\({}_{3}\)C\({}_{2}\)T\({}_{\mathrm{x}}\) MXene ink was formulated and jetted onto SS316 substrates, providing uniform Ti\({}_{3}\)C\({}_{2}\)T\({}_{\mathrm{x}}\) coatings. We focused our investigations on the effect of the thin Ti\({}_{3}\)C\({}_{2}\)T\({}_{\mathrm{x}}\) coating and the electrolyte composition with and without PEG400 additive on the Zn nucleation and growth behavior. Electrochemical potentiodynamic techniques as well as microstructural and morphological analyses assessed the best conditions for optimal Zn growth. It was found that, in terms of microstructure, the quality of Zn grown on the printed Ti\({}_{3}\)C\({}_{2}\)T\({}_{\mathrm{x}}\) strictly depends on the kind of electrolyte used, and PEG400 was demonstrated to effectively improve the homogeneity of Zn plating only in ZnCl\({}_{2}\) electrolytes.
## 2 Materials and Methods
### Classification Methods
#### 2.5.4 Decision Tree  
Decision trees are logical models used to predict the value of a dependent variable based on the values of independent variables [47; 48; 49]. A decision tree starts at the root and branches out into leaves. Each feature (attribute) in the data represents the nodes of the tree, while the leaves represent the class labels. The paths between nodes create the classification rules [50]. Decision trees are frequently preferred due to their easy-to-interpret structure  
In this study, Bayesian Optimization was applied for hyperparameter optimization of the machine learning methods used. Bayesian Optimization is an effective approach for increasing model accuracy and minimizing the trial-and-error process during hyperparameter selection.
## 2 Methods
### Filter Design  
In this section, an optimal filter design is proposed to fit the directivity pattern of the array, whose design was specified in Section 2.4, to the desired directivity pattern specified in Section 2.3. The following filter design is based on numerical convex optimization and has the advantage that only one global minimum exists. In general, this end-fire design can also be used with different desired directivity patterns and array designs. In Section 3, we indicate the ideal values of the constants for the desired directivity pattern and array design proposed in this study.  
The aim of this algorithm is to minimize the quadratic error \(\mathbf{error}_{m}\) between the directivity pattern obtained by a microphone array \(\mathbf{b}_{m}(f_{p})\) and a desired frequency independent directivity pattern \(\mathbf{\hat{b}}_{m}\)[7]:  
\[\begin{split}\mathbf{error}_{m}&=\mathbf{G}_{mn}(f_ {p})\mathbf{w}_{n}(f_{p})-\mathbf{\hat{b}}_{m}=\mathbf{b}_{m}(f_{p})-\mathbf{ \hat{b}}_{m}\\ \min_{\mathbf{w}_{n}(f_{p})}\|\mathbf{error}_{m}\|_{2}^{2}\end{split} \tag{14}\]  
This minimization task will be subjected to additional constraints, and therefore, the beamformer will be termed the Constrained Least-Squares Beamformer (CLSB).  
In the following subsections, the main minimization task and the used constraints will be explained paying particularly attention to the WNG and different spatial areas. These areas are shown in Figure 4.  
Additionally, this optimization process is placed within an optimization loop in order to optimize several important constants. This optimization procedure will be explained in the last subsection of this section.
## 2 Materials and Methods
### Physical and Mechanical Characterization of MBCs  
#### 2.3.1 Apparent Density  
The apparent density of materials is considered one of the main parameters, allowing for the prediction of other significant characteristics (porosity, water absorption, strength, thermal conductivity, etc.) and is a good indicator of mechanical properties [36; 43; 46]. The density, and especially the porosity, of the MBCs depends on many parameters, among which are the chemical composition, particle size, and distribution of the used substrate together with the mycelium growth and interaction between the components. The apparent density was determined according to EN ISO 29470:2020 [47] in a dry stage on samples with a parallelogram shape, consisting of measurements of the sample's mass (precision of 0.01 g) and size (by a caliper with a precision of 0.01 mm). The apparent density of each sample was calculated by the formula:  
\[\rho_{a}=\frac{m_{d}}{a\cdot b\cdot h} \tag{1}\]  
where \(\rho_{a}\) is the apparent dry density, in kg/m\({}^{3}\); \(m_{d}\) is the mass in dry condition, in kg; and \(a\), \(b\), \(h\) are the dimensions (length, width, and height) of the samples in dry condition, in m.
## 4 Simulation Results
### Small-Signal Characterization  
As plotted in Figure 9, S\({}_{21}\) varies smoothly from 19.2 to 20.2 dB across 24-31 GHz at room temperature, with a maximum at 25.3 GHz and rapid out-of-band attenuation. Within the same band, S\({}_{11}\) and S\({}_{22}\) are below \(-10\) dB and \(-16.7\) dB, respectively, each displaying two distinct notches, demonstrating broadband input and output matching characteristics. The fact that the S\({}_{22}\) curve exhibits a much deeper notch at 31.4 GHz than that at 25.4 GHz is a direct result of the OMN design strategy, which emphasizes improved high-frequency matching while intentionally introducing some mismatch at lower frequencies to flatten the in-band response. Moreover, S\({}_{21}\) declines by no more than 1.2 dB as the temperature rises, with negligible in-band ripple, indicating that the PA maintains a stable small-signal gain across varying temperatures. Although S\({}_{22}\) is slightly more susceptible to temperature changes compared to S\({}_{11}\), both remain within acceptable tolerance levels.  
Figure 8: The complete layout of the GaN MMIC PA.
# A Message Passing-Assisted Iterative Noise Cancellation Method for Clipped OTFS-BFDM Systems
## 3 Proposed MP-AIC Scheme of Clipping Noise
### Statistical Properties of Clipping Noise
Since each path on the time-varying multipath channel as described in (10) is a Rayleigh channel, the mean over all paths is 0 and the sum of the variances is 1. Combining (20), we get that \(\mathbf{\psi}\sim\mathcal{CN}(0,\beta^{-2}\sigma_{\delta}^{2}\mathbf{1})\). Compared with (12) and (13), the ECM in (23) is unchanged and remains sparse. The overall noise variance in the clipped OTFS-BFDM system is:  
\[\sigma_{All}^{2}=\beta^{-2}(\sigma_{\delta}^{2}+\sigma_{n}^{2}), \tag{24}\]  
which serves as the noise input parameter of MP decoding algorithm.
## 2 Materials and Methods  
### Hyperspectral Imaging Systems  
Full-resolution reflectance and fluorescence images were collected using an in-house developed visible and near-infrared (VNIR) hyperspectral imaging system [23]. The light source for the VNIR reflectance was a 150 W quartz tungsten lamp (Dolan Jenner, Boxborough, MA, USA). For fluorescence imaging, two UV narrowband light sources were used, each with four 10 W, 365 nm, LEDs (LED Engin, San Jose, CA, USA). VNIR reflectance images in 125 wavelengths within the 419-1007 nm spectral range and fluorescence images in 60 wavelengths within the 438-718 nm range were acquired using a 23 mm focal length lens, an imaging spectrograph (Hyperspec-VNIR, Headwall Photonics, Fitchburg, MA, USA), and a 14-bit electron-multiplying charge-coupled device (EMCCD) camera (Luca DL 604M, Andor Technology, South Windsor, CT, USA).  
A separate hyperspectral imaging system was used to acquire reflectance images in the SWIR region. The illumination source for this system was a custom-designed two-unit lighting system, each with four 150 W gold-coated halogen lamps with MR16 reflectors. The detection unit included a 25 mm focal length lens and a hyperspectral camera, including a 16-bit mercury cadmium telluride array detector and an imaging spectrograph (Hyperspec-SWIR, Headwall Photonics, Fitchburg, MA, USA). The SWIR reflectance images were acquired in a wavelength range of 842-2532 nm (287 wavelengths).
## 2 Materials and Methods
From the four images \(E_{1,2,3,4}\), phase shifts \(\phi\), which relied on OPLs between the reference glass and the sample surface, were simply derived as: \(\phi(x,y)=\tan^{-1}\left(\frac{E_{1}-E_{2}-E_{3}+E_{4}}{E_{1}-E_{2}+E_{3}+E_{4}}\right)\)[15], and then phase-unwrapping [16] was followed to resolve 2 pi ambiguities in the \(\phi\). Note that prior to PSI, to compensate for phase error caused by the slope between the reference glass and the sample, the reference glass was pre-aligned to be parallel to the sample stage by identifying a zero-order interference fringe with another microscope slide as a flat sample placed on the sample plate. Given the relative position of the reference glass above the sample, however, the phase shifts may induce a reversed surface shape. To mitigate this inversion issue, an absolute subtraction of the unwrapped phase map was performed with a maximum of the phase shifts. By dividing the corrected phase map by \(4\pi/\lambda_{0}\), eventually, a variation in the physical distances was obtained and re-adjusted based on its mean, which finally was used to describe a surface profile. However, the configuration of the interferometer used was similar to a Mirau interferometer, which has been used in recent years for the metrology of a patterned wafer [17], thin film [18], and micro electro mechanical systems (MEMS) [19]. Most Mirau interferometers commonly employ a commercial interferometry microscope objective involving a small reference mirror and a beam splitter in the body to generate an equal path length-induced interference for a sample in air. For submerged samples, such as a pad in slurry, however, this arrangement is not suitable for use because when the objective is immersed in the liquid, the sample and reference beam paths may not be identical due to refractive index mismatch between the surrounding media. Thus, interference is not ensured. Furthermore, a highly reflective metallic mirror in the objective may degrade signal contrast in the interference for the inspection of low reflectance materials. In contrast, the proposed interferometer has an individual immersion objective and a movable reference glass, which aides index matching and allows the reference glass to be properly positioned within the coherence length, ensuring high-contrast interference on the submerged materials.
# A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning
## 4 Conclusions  
This paper proposes a VPN-encrypted traffic identification method based on ensemble learning to achieve accurate identification of encrypted traffic. Firstly, a VPN-encrypted traffic feature selection method is proposed. We perform VPN-encrypted traffic mRMR feature selection for the redundant features that exist in Time-Related VPN-encrypted traffic to obtain the optimal Time-Related VPN-encrypted traffic feature set. Secondly, a VPN-encrypted traffic identification model based on ensemble learning is proposed to address the imbalance of data class. Finally, anoptimization method of the VPN-encrypted traffic identification model is proposed to solve the problem of the low identification rate of the previous VPN-encrypted traffic ensemble learning model. In summary, the average identification rate of the FL-XGB-VPN-encrypted traffic identification method proposed in this paper reaches more than 97% for VPN-encrypted traffic. Meanwhile, we also validate the FL-XGB-encrypted traffic identification method proposed in this paper on other encrypted traffic data. The experimental results show that the FL-XGB-encrypted traffic identification method proposed in this paper also has a high identification rate for other encrypted traffic data. This indicates that the set of methods proposed in this paper has high universality in encrypted traffic identification. Therefore, the FL-XGB-VPN-encrypted traffic identification model proposed in this paper can efficiently identify malicious network encryption. The method helps to improve the network service quality and also has certain practical value to maintain network security.  
Conceptualization, J.C. and X.-L.Y.; methodology, J.C.; software, X.-L.Y.; validation, J.C. and X.-L.Y.; formal analysis, Y.C.; investigation, Y.C. and J.-C.F.; resources, J.-C.F.; data curation, Y.C. and J.-C.F.; writing--original draft, X.-L.Y.; writing--review and editing, J.C.; visualization, J.-C.F.; supervision, C.-L.C.; project administration, C.-L.C.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
Figure 17: Comparison of different algorithms on the ISCXTor2016 dataset.  
**Funding:** This work was supported by the Science and Technology Development Plan projects of Jilin Province, No. 20210201134 GX.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** This study was based entirely on theoretical basic research and did not involve humans.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Comparison of Local Details and Overall Visual Quality Across Models  
To thoroughly assess the effectiveness of the TPOT-based fur simulation algorithm in replicating the hair characteristics of the white-lipped deer, we conducted a detailed comparative analysis of four representative regions: the face, tail, abdomen, and torso. Each region was magnified and examined in models produced using the proposed method, traditional texture mapping, and particle hair simulation approach. The comparative results are presented in Table 3.  
A total of 40 participants were invited to evaluate the simulation quality of different modeling approaches based on the comparative results presented in Table 2. This group included 30 undergraduate students from various academic disciplines, five postgraduate students specializing in Plateau Ecological Engineering, and five animal protection professionals from the Qinghai-Tibet Plateau Natural Museum. Among them, 28 undergraduates indicated that the traditional texture mapping method delivered only average performance in simulation realism, while 20 participants favored the TPOT-based approach for its improved overall simulation accuracy. Additionally, 26 participants identified the particle hair method as providing the most realistic facial rendering effect. Specifically, four postgraduate students and three animal protection workers expressed the view that the TPOT-based model outperformed the traditional method in terms of visual fidelity, although it was slightly less realistic than the particle hair approach. A detailed summary of the evaluation results is presented in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Project Name** & \
\begin{tabular}{c} **Traditional Hair** \\ **Mapping Texture** \\ \end{tabular} & **Particle Hair** &
\begin{tabular}{c} **Fur Produced by Our** \\ **Simulation Methods** \\ \end{tabular} \\ \hline Face & & & & \\ Tail & & & \\ Abdomen & & & \\ Main body torso & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of local and global visual comparisons among the three modeling approaches. The results offer strong empirical support and theoretical guidance for the continued refinement of fur simulation algorithms.
## 5 Conclusions  
After the detailed study and screening of those factors significantly affecting load bridging and the subsequent development of a Gaussian process model, the following can be concluded:  
* The number of columns in a unit load, the height of the payload, the friction coefficients of the payload's contact with the pallet deck, and the contact friction between packages were all found to be significant factors influencing the bending response of pallet analogs loaded with stacked boxes.
* The Gaussian process model can act as a surrogate model for the finite element simulation with a prediction error of five percentage points of the estimated deflection ratio.
* Aside from the significance of each studied factor, the interactions between them were found to be of high relevance to the study of load bridging. Load bridging on unit loads must be studied as a system, where variations in any characteristic will potentially influence its effect on the bending response of the remaining factors.
* The number of columns in a unit load affects the bending response. Increased package size reduces unit load bending in most scenarios, but columns' interactions with friction forces and payload height can cause an opposite change in deflection.
* Change in payload height translates to a change in the bending of the boards when supporting large boxes with low payload friction. The deflection of unit loads with smaller boxes tends not to be affected by payload height.  
* Pallet top deck friction can influence the bending response of certain unit loads, providing a potentially simple method to improve pallet performance.
* The frictional forces of the packages significantly influence the bending responses of the pallet segments, but the addition of containment methods could potentially restrict this slipping behavior. This shows that containment could limit the effect of package friction on unit load deflection.  
The research project also identified that due to the complexity of the factor interactions, a simple individual adjustment factor for the load bridging effect of the various unit load factors is not feasible. Therefore, to predict the load bridging effect for unit loads, a simplified model that could be applied to everyday scenarios is required, increasing the potential applications of the Gaussian process model.  
E.M. and L.H. research conceptualization. E.M. Factor screening and Gaussian process model development, data collection, experimentation and writing. E.M. and L.H. data analysis collaboration. This research project was conducted under the supervision of [PERSON]. All authors have read and agreed to the published version of the manuscript.  
The research was financially supported by the Industrial Affiliate Program of the Center for Packaging and Unit Load Design at Virginia Tech.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Problem Formulation
### Airborne Sensor Model  
The airborne sensor model is the basic element of target search, which describes the detection and discovery relationship of the UAV to the search target. In this paper, we consider the UAV airborne infrared sensor, whose field of view is shown in Figure 2. When the UAV flies horizontally at a fixed altitude, the width of the ground detection range of the on-board sensors can be estimated by the following equation, assuming that the influence of roll angle is disregarded:  
\[d_{s}=2\cdot h\cdot\frac{\tan\gamma_{s}}{\tan\alpha_{s}}\sqrt{1+(\tan\alpha_{ s})^{2}} \tag{5}\]  
where \(h\leq h_{\max}\) is the altitude of the UAV, \(\alpha_{s}\) is the mounting angle of the sensor, and \(\gamma_{s}\) is the field of view of the sensor, the value of which is related to the physical properties of the specific sensor.  
To simplify the problem, the detection range \(d_{s}\) of the airborne sensor is considered to cover a grid cell. The UAV searches for a target with the airborne sensors and makes one observation of a grid cell at one time step, and the results of each observation are independent of each other. Assume there are only two possible outputs, namely the detection of the target \(O_{t}=D_{t}\) and the failure to detect the target \(O_{t}=\overline{D}_{t}\). Consider that there is a certain detection error in the sensor. Even if the target exists at location \(x_{t}\), it is not guaranteed that the target position can be detected. Then, the probability of not detecting the target when the target is located in \(x_{t}\) is as follows:  
\[P\big{(}\overline{D}_{t}|x_{t}\,\big{)}=1-P(D_{t}|x_{t}\,) \tag{6}\]  
where \(P(D_{t}|x_{t}\,)\) indicates the probability that the sensor will detect the target at the location \(x_{t}\).
## 4 Discussion
### Analysis of Component Reliability and Maintainability
#### 4.2.2 Sorting Station Trough  
The results obtained for the sorting station chute reveal alarmingly low reliability, with a probability of failure-free operation of only 40% after 182 days. This unacceptable value highlights the need for either frequent and costly replacements that generate production interruptions, or measures to radically improve reliability. The roughness and wear analysis highlighted significant values, with a maximum average roughness Ra of 1.2 \(\upmu\)m and a total height of the wear profile Rt of 22.5 \(\upmu\)m at 400 rpm after 2000 cycles. The maintainability analysis shows a 40% probability that the chute will be replaced after 40 h, and for an 80% certainty, 45 h are required. Preliminary tribological tests with alternative materials, such as Pucest panels with metal inserts or Hardox 400 steel, have not yet yielded conclusive results, requiring practical experimentation of prototypes under real quarry conditions.