esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Correlation-Based Feature Selection Approach  
In this study, we extracted 94 attributes from each region of interest (ROI) in a dataset of 5252 breast ultrasound images. These attributes were designed to capture various shape, texture, and intensity features relevant to distinguishing between benign and malignant lesions. To refine the feature set, a correlation-based feature selection (CFS) approach was applied in this study. This method is designed to select subsets of features that are highly correlated with the class label (indicating benign or malignant lesions) while maintaining low inter-correlation among the features themselves.  
During the feature selection process, we used a best-first search strategy in conjunction with the CFS approach to explore different subsets of features. This strategy repetitively added features that maximized a defined evaluation criterion based on the correlation between features and the class labels while minimizing redundancy. For example, from the initial features, several key features from different groups were selected for their high relevance and low correlation with other features. For instance, spatial gray-level dependence matrix (SGLD) features were extensively evaluated, given their ability to capture texture details crucial for distinguishing lesions. Similarly, Fourier features with shape context and centroid distance were carefully assessed for their shape descriptors, which provide critical insights into the lesion's contour and structure [38].  
Through this selection process, we refined the original set of 94 features down to the most relevant 43 features which are listed in Table 2. These selected features were those that demonstrated the highest predictive power for classifying the lesions as benign or malignant.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature No.** & **Feature Name** \\ \hline
1–8 & Spatial gray-level dependence matrix (SGLD) \\
9–16 & Fourier with shape context \\
17–20 & Fourier with centroid distance (magnitude) \\
21–24 & Fourier with centroid distance (phase) \\
25 & Intensity in the mass area \\
26 & Gradient magnitude in the mass area \\
27 & Orientation \\
28 & Depth–width ratio \\
29–30 & Distance between mass shape and best fit ellipse \\
31 & The average gray changes between tissue area and mass area \\
32 & The average gray changes between posterior and mass area \\
33–34 & The histogram changes between tissue and mass \\
35 & Comparison of the gray value of left, post, and right under lesion \\
36 & The number of lobulate areas \\
37 & The number of protuberances \\
38 & The number of depressions \\
39 & Lobulation index \\
42–43 & Elliptic-normalized circumference \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of sonographic features considered in this study.
## 4 Security Analysis of the Proposed Protocol
### Heuristic Analysis
#### 4.2.6 Body Area Sensor Node Impersonation Attack  
This attack [25] gives the adversary, i.e., the legitimate inside user, an opportunity: The user could obtain the body area sensor node's secret key \(x_{j}\) and create a faulty session key for the new physician. However, this attack makes no sense in our scheme. Factually, this adversary cannot extract this secret \(x_{j}\) from \(B_{7},B_{8}\), and \(B_{9}\) because they do not possess the secret value \(r_{g}\) of \(GWN\). Therefore, the proposed protocol resists sensor node impersonation attacks.
# Frequency-Domain and Spatial-Domain MLMVN-Based Convolutional Neural Networks
## 3 CNNMVN Learning Algorithm and Error Backpropagation
### Pooling Layers for CNNMVN  
The general purpose of the pooling layer is to reduce the size of a feature map resulting from the processing in a convolutional layer. There are two types of pooling operations for CNNMVN presented in [62]--the \"max\" pool and the \"average\" pool.  
The \"max\" pool operation performs downsampling by choosing the maximum value in the corresponding window. Since CNNMVN is a complex-valued neural network, it was considered to utilize the \"max\" pool operation by comparing the phases of the complex numbers or comparing their magnitudes.  
The \"average\" pool is a downsampling technique that averages the values in the corresponding window. Unlike the \"max\" pool, this approach is more suitable for CNNMVN since we should not subjectively choose the component of the complex number on which the downsampling is based.  
If the pooling layer is applied after the convolutional one, it affects the error-backpropagation process. In the case of \"max\" pooling, we should calculate only the errors of the pooled pixels, as the other values were dropped and did not participate in further processing. In such a case, Equation (28) should contain only those input vectors whose convolved pixels were pooled. In the case of \"average\" pooling, we should distribute the error among all the pixels in the corresponding pool window.
## 3 Results and Discussion
### The pH Effect on Adsorption Isotherms of CPs on MPs  
The Freundlich and Langmuir models were employed to describe the experimental data from the adsorption mechanism studies. The obtained parameter values of the isotherm models are presented in Table S6, while Figure 4 shows the fitting results of both the Freundlich and Langmuir isotherms in the experiment regarding the adsorption of CPs on PEp, PE_PCPs_1, PE_PCPs_2 PEg, PEg, PET, PP and PLA in water with different pH values.  
Figure 3: Plots for the sorption kinetics, based on the pseudo-second order model of (**a**) 4-CP, (**b**) 2,4-DCP, (**c**) 2,4,6-TCP and (**d**) PCP on PEp, PE_PCPs_1, PE_PCPs_2, PEg, PEg, PET, PP and PLA in water at different pH values (n = 2).  
The presented results (Table S6 and Figure 4) indicate that the sorption of CPs can be described well with both adsorption isotherm models, based on the high R\({}^{2}\) values obtained. The values of parameter n (<1) obtained by the Freundlich model imply a chemical adsorption process between the investigated CPs and MPs [17; 24; 33; 40; 65]. However, the better correlations with the Langmuir model indicate a monolayer coverage of the sorbate over a homogenous sorbent surface [66]. The obtained q\({}_{\rm max}\) values calculated using the Langmuir adsorption isotherms imply that, at pH 7, the highest adsorption capacity toward the selected MPs has 4-CP, lowered selected MPs and it ranged between 100 and 1000. The results of the \(\alpha\)-CP and \(\alpha\)-CP are shown in Figure 4.  
\(\mathrm{q_{max}}\) = 33.4-205.7 \(\mathrm{\SIUnitSymbolMicro g}\)/g. On the other hand, for the remaining CPs, 2,4-DCP, 2,4,6-TCP and PCP, the highest adsorption capacities were obtained at pH 4, and they were in the ranges \(\mathrm{q_{max}}\) = 15.5-198.8 \(\mathrm{\SIUnitSymbolMicro g}\)/g \(\mathrm{q_{max}}\) = 30.4-259.9 \(\mathrm{\SIUnitSymbolMicro g}\)/g \(\mathrm{q_{max}}\) = 25-190.6 \(\mathrm{\SIUnitSymbolMicro g}\)/g, respectively. The results for the calculated \(\mathrm{q_{max}}\) values were consistent with the obtained kinetic results (Figure 3 and Table S5). Additionally, the calculated \(\mathrm{K_{L}}\) values for the Langmuir adsorption isotherms were between 0 and 1 for all results, implying that the adsorption process of the selected CPs on the MPs was favorable regardless of the changes in the synthetic water matrix pH [12], and with values near zero (0.003-0.300 \(\mathrm{\SIUnitSymbolMicro g}\)/g), it can be postulated that the process would be almost irreversible.
# Deep Learning-Based Imagery Style Evaluation for Cross-Category Industrial Product Forms
## 4 Imagery Style Evaluation (ISE) Method for Cross-Category Industrial Product Forms
### Product Form Design Decision Process Based on ISE Method  
In traditional industrial product design workflows, the development cycle is often prolonged, typically taking several weeks to months to complete stages such as market research, needs analysis, concept design, and review. Generally, the strategic department first conducts user demand research and analysis, then communicates the results to the design department. Designers, based on this input, generate multiple (typically 3 to 5) initial design proposals, which are then evaluated and screened through cross-departmental collaboration. However, this coordination is prone to delays at individual stages, significantly extending the overall development timeline.  
As illustrated in Figure 14, the ISE method proposed in this study provides a streamlined approach to facilitate decision-making during the early concept design phase and improve overall efficiency. In this workflow, enterprises can adopt the image-style-based database constructed in this study or build an internal database using historical market feedback and product form data. Upon receiving a design request from the marketing department (e.g., developing a mouse with specific functions and stylistic tendencies), designers can collect and upload representative product images that reflect their interpretation of the intended style into the ISE system. This step helps verify whether their style understanding aligns with the preferences of the target user group. Based on the ISE system's feedback regarding user preference fit, designers can further refine design goals and tasks and develop multiple concept proposals accordingly. These initial proposals do not need to be immediately subjected to interdepartmental review. Instead, designers can first reassess them using the ISE method to analyze how well they match user preferences. If the model's output indicates alignment with both user expectations and company design objectives, the screened concepts can proceed to the next stage of evaluation. Otherwise, the design direction can be redefined and alternative concepts generated and validated.  
Combined with the ISE method, the design process gains the capability to identify and correct directional biases early on, thereby reducing the risk of delays caused by initial misjudgments or excessive cross-functional communication. This not only shortens the concept design phase but also allows more time for detailed development and technical validation, ultimately improving both design quality and interdepartmental collaboration.  
Figure 14: Examples of product form design decision process based on ISE method.
# Influence of the Polarity of the Electric Field on Electrorheometry
## 3 Results and Discussion
### Corstarch/Oil Suspension  
#### 3.2.1 Shear Electrorheometry  
Figure 7a shows the viscosity curves of the working fluids measured under steady-shear flow, following the experimental protocol detailed in Section 2.2.1. It is worth highlighting that the curve of the ERF without the influence of the electric field is parallel to the one of the carrying fluid. This result means that the ERF exhibited a Newtonian-like behavior under shear flow without the presence of the electric field, but with a viscosity slightly larger than that of the carrying fluid due to the presence of the suspended particles. Figure 7a also shows that the viscosity of the ERF increased by increasing the intensity of the imposed electrical field. The ERFs can be modeled reasonably well by the Bingham plastic model (Equation (2)), from which the dynamic yield stress value, \(\tau_{yd}\), can be obtained by extrapolating the experimental curves until \(\dot{\gamma}=0\):  
\[\tau=\eta_{p}\dot{\gamma}+\tau_{yd} \tag{2}\]  
where \(\tau\) is the shear stress, \(\eta_{p}\) is the plastic viscosity, and \(\dot{\gamma}\) is the shear rate. The dependence of the dynamic yield stress with the applied electric field (\(E_{0}\)) is given by a power law function (\(\tau_{yd}=A\cdot E_{0}^{B}\)), whose fitting parameters are shown in Table 3 for the ERF considered in this study. It has been widely accepted in the literature [8; 27] that \(\tau_{yd}\propto E_{0}^{2}\); however, the electric field exponents \(B^{\prime}s\) in our cases were significantly lower due to water content, which is consistent with the results reported in the literature by [PERSON] et al. [28] and [PERSON] and [PERSON] [25].  
Figure 6: (**a**) Time evolution of the minimum radius for the Newtonian fluid at \(h_{0}\); no step strain was applied, just the electric field. (**b**) First (\(t=0\) s) and last image (\(t=8.5\) s) of the liquid bridging the initial height for the different polarity: top row for inverse (Supplementary Material, Video S1) and bottom row for direct (Supplementary Material, Video S2).
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 5 Design and Development of Satellite Ground Stations
### Equipment of Ground Station
#### 5.2.2 Terrestrial and LEO Satellite Antenna System  
As designed, the antenna system in this group is usable with both terrestrial and LEO satellites, covering small satellites and amateur radio satellites with a variety of frequencies. This research designed the system using a total of four antenna sets:  
* Crossed Yagi-Uda VHF antenna (Figure 6, item 4): A directional antenna for receiving radio frequencies in the 136-150 MHz range. It is a right-hand circular polarization crossed Yagi-Uda to prevent mismatch due to the unpredictable alignment of satellite antennas, which can cause a loss of up to 3 dB. The antenna is designed for maximum gain, developed as a 5 m boom-length antenna with 10 elements \(\times\) 2, as shown in Figure 8a.
* Crossed Yagi-Uda UHF antenna (Figure 6, item 1): A directional antenna for receiving radio frequencies in the 400-438 MHz range. It is a right-hand circular polarization crossed Yagi-Uda to prevent mismatch due to the unpredictable alignment of satellite antennas, which can cause a loss of up to 3 dB. The antenna is designed for maximum gain, developed as a 2.5 m boom length antenna with 21 elements \(\times\) 2, as shown in Figure 8a.
* Grid antenna size of 60 cm \(\times\) 90 cm with LPDA feed (Figure 6, item 9 and 10): A wideband directional antenna, as shown in Figure 8b, designed to cover a wide frequency range of 600-6500 MHz. This covers frequencies not supported by other antennas, allowing for various applications, such as ADS-B signals from airplanes, STD-C and Inmarsat aero signals, and 2.4 GHz and 5.8 GHz frequencies from UAVs. However, due to its small size, it has low gain and a pattern that can receive interference from surrounding directions, resulting in a high noise temperature. The LPDA feed covers a wide range of frequencies, leading to high interference and insufficient capability for weak signal reception.
* Discone antenna (Figure 6, item 7): An ultra-wideband omnidirectional antenna capable of receiving data from 25-3000 MHz, but without gain. It is suitable for satellites with high link margins or terrestrial signals.  
Figure 8: Terrestrial and LEO satellite antenna system. (**a**) Yagi-Uda antenna system, (**b**) Grid antenna with LPDA feed.
## 4 Discussion
### Considerations for the WIP Process Parameters in Metal ME  
The optimization of the WIP process parameters is beyond the scope of this work. However, to increase the understanding of the metal ME process, it is necessary to briefly discuss the factors to be considered in determining temperature in the WIP process.  
The determination of the WIP-process temperature strongly depends on the characteristics of the substrate material (in this case, POM). Higher temperature is correlated with higher polymer fluidity: to increase the WIP effect, the temperature must be increased. However, greater fluidity of a material implies greater possibility of deformation due to external loads (or to its own weight). Therefore, in order to increase the efficiency of the WIP process, it is important to determine the highest temperature at which deformation does not occur.  
This problem must be considered when WIP is introduced into the metal ME process. A polymer containing metal powder has a higher thermal conductivity than a pure polymer, so that the heat applied during WIP process rapidly diffuses into the interior, and deformation of the part occurs at a lower temperature due to the rapid heat diffusion into the part [43]. In other words, in terms of deformation, material that includes metal powder is more sensitive to temperature than pure polymer is. This temperature sensitivity varies depending on the shape of the part: a thin-walled part deforms at a lower temperature than a thick part. Setting the proper WIP temperature becomes more difficult when a part consists of sub-parts with varying thicknesses. In addition, the temperature sensitivity to the deformation also varies depending on the kind of metal powder used.  
The Introduction of the WIP process into metal ME obviously helps to improve the mechanical properties of the finished part. However, there is also a disadvantage in that  
Figure 16: Variations of properties with/without WIP: (**a**) area fraction in green part, (**b**) density in green part, (**c**) density in white part, (**d**) tensile strength in white part.  
more variables need to be considered to make the process more efficient. This will be further explored in future studies.
# An Optimized Load Balance Solution for Multi-homed Host in Heterogeneous Wireless Networks
## 4 Model Simulation and Analysis
### Simulation Results and Analysis
#### 4.2.3 Utilization of Multiple Connections Using Utility Connection Equation and AHP
At low system power state, the system model stops the load balance technique and perform handover. The AHP module is still in effect as it is used to rank the available connections and the proposed system use the highest ranked connection to perform handover through it. The applications are processed in general mode, based on social media application class processing for which the dependency is based on power consumption. So in case of low power, the proposed system will transmit all applications data over the less power consuming connection. Based on our calculations, the proposed system will offload all applications data over WIFI#1 as it is the less power consuming interface. The system will retain its normal unequal cost load balance based on application class as the system power is above the low power state. The proposed system assumes classification of data, which requires a classifier module and the proximity of this module may be in a miss due to heavy application load. The AHP numerical distribution is static and designed based on relative importance degree of the attributes, and changing those values may yield a different result. The mitigation for that already exists with the aid of the utility connection equation which settles the ranking priorities into an adequate ratio used as a distribution ratio of the application data. Despite the separate controller that is used to control and manage the end terminal traffic, it has some concerns. These are represented in being a single point of failure and a bottleneck for the network traffic processing, the extra propagation, and processing delay that exist between controlled MH and its controller.
## 3 Information Leakage Analysis of Merkle Tree-Based Authentication Schemes
### Previous Schemes and Their Vulnerabilities
#### 3.2.1 Generic Merkle Tree-Based Authentication  
In generic Merkle tree-based authentication [12], the original data block is used together with the tree, rather than using the tree only, which was further adopted to proof-of-ownership process in the cloud data deduplication literature [16].  
As shown in Figure 3, the proof in authentication includes the content of the challenged block along with its sibling path. In this example, the challenged data block \(B_{1}\) and partial information about the Merkle tree rooted at \(H_{1-4}\) (i.e., \(\{H_{1},H_{2},H_{1-2},H_{3-4},H_{1-4}\}=MT_{(B_{1},B_{2},B_{3},B_{4})}\backslash \{H_{3},H_{4}\}\)) can be exposed to the public after the first authentication request. Therefore, in the second authentication attempt, the challenging block might be randomly chosen in \(\{B_{5},B_{6},B_{7},B_{8}\}\) for maximal authentication coverage, and this covers only half of the entire data set (because it excludes the blocks \(\{B_{1},B_{2},B_{3},B_{4}\}\)). The next challenging block can be chosen from \(\{B_{3},B_{4}\}\) or \(\{B_{5},B_{6}\}\), covering a quarter of the data in a similar way.  
Thus, generic Merkle tree-based authentication method does not guarantee consistent authentication coverage as authentication is done repeatedly, and the maximum number of challenge-responses is limited to the number of data blocks.  
However, the adversary is still able to guess the size of the authenticated data by using Equation (1), given publicly accessible data block size \(|B|\), proof size \(|P|\), hash value size \(|\mathcal{H}|\), and the size of sibling path \(L=(|P|-|B|)/|\mathcal{H}|+1\).
## 2 Materials and Methods
The fluorescence lifetime of Er\({}^{3+}\):\({}^{4}\)I\({}_{13/2}\) energy level was obtained by exciting the samples with laser pulses of the 976 nm laser diode, recording the signal using a digital oscilloscope (Tektronix TDS350) and fitting the decay traces by single exponential. Estimated error of the measurement was \(\pm\)0.20 ms. The detector used for this measurement was a Thorlabs PDA10 CS-EC. The glass with x = 2.5 was irradiated with 3 MeV alpha particles from the U-120 Cyclotron accelerator. The beam current was 3 nA and the total fluence was 10\({}^{12}\)\(\alpha\)/cm\({}^{2}\). The UV-Vis-NIR transmittance spectra were measured using an Ocean Optics QE65000 spectrometer with operation between 200 and 900 nm. The detector is thermoelectric-cooled and has a CCD (charge coupled device) array from Hamamatsu with 1044 \(\times\) 64 elements and a dynamic range of 40,000:1 for a single integration time. The reflectance in 0.06-2 THz range spectra were obtained using TPS Spectra 3000 THz spectrometer from TeraView. The resulting photo-modified surface profile was then analyzed using Wyko NT1100 optical profiling system to assess photo-induced surface modification. The surface roughness data such as Ra (arithmetical mean deviation of the assessed profile), Rq (root mean squared), Rz (average distance between the highest peak and lowest valley) and Rt (Maximum Height of the Profile) were determined. After irradiation, the micro-photoluminescence (PL) was measured at the surface of the glasses at room temperature using the 532 nm line of a continuous-wave Nd:YAGlaser for excitation. The excitation laser beam was focused on the sample with a 40X magnifying high-numerical aperture (NA) objective. The photoluminescence signal was collected with the same objective and detected with a TE-cooled InGaAs array camera attached to a 750 mm spectrometer. The spatial resolution of the micro-PL setup was 1 \(\upmu\)m.