esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Anterior Single-Leg Jump Landing  
The result of the ANOVA indicated a significant shoe effect on dorsiflexion ROM (F\({}_{2,28}\) = 3.829, \(p\) = 0.035, [PERSON]'s \(\Lambda\) = 0.675, \(\eta\)p\({}^{2}\) = 0.639), total ROM in the sagittal plane (F\({}_{2,28}\) = 7.554, \(p\) = 0.006, [PERSON]'s \(\Lambda\) = 0.590, \(\eta\)p\({}^{2}\) = 0.854), ankle joint stiffness (F\({}_{2,28}\) = 7.431, \(p\) = 0.009, [PERSON]'s \(\Lambda\) = 0.445, \(\eta\)p\({}^{2}\) = 0.810), and MLSI (F\({}_{2,28}\) = 7.418, \(p\) = 0.004, [PERSON]'s \(\Lambda\) = 0.382, \(\eta\)p\({}^{2}\) = 0.884). Post hoc pairwise tests indicated that the high collar resulted in a significantly smaller dorsiflexion ROM, compared to the elastic collar (\(p\) = 0.031, dz = 0.511) and low collar (\(p\) = 0.043, dz = 0.446) (Table 1), while a significantly smaller total ROM was observed for the high collar, compared to the low collar (\(p\) = 0.023, dz = 0.756) in the sagittal plane (Figure 3). The ankle joint stiffness was significantly larger for the high collar, compared to the low collar (\(p\) = 0.030, dz = 1.040) and elastic collar (\(p\) = 0.003, dz = 0.629) (Figure 4). MLSI was significantly smaller for the shoe with the high collar, compared to the low collar (\(p\) = 0.004, dz = 1.232) (Table 2). No other main effects of shoe conditions were detected (Tables 1 and 2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{3}{c}{**Shoe Collar Condition**} & \multicolumn{3}{c}{**Pairwise Post Hoc**} \\ \cline{2-7}  & **High** & **Elastic** & **Low** & **H vs. E** & **H vs. L** & **E vs. L** \\ \hline \multicolumn{7}{c}{Anterior single-leg jump landing} \\ \hline APSI & 0.17 (0.014) & 0.17 (0.022) & 0.17 (0.024) & 1.000 (0.101) & 1.000 (0.131) & 0.714 (0.194) \\ MLSI \({}^{\star}\) & 0.039 (0.004) & 0.040 (0.007) & 0.045 (0.006) & 1.000 (0.204) & 0.001 (1.232) & 0.051 (0.116) \\ VSI & 0.42 (0.049) & 0.42 (0.050) & 0.43 (0.059) & 1.000 (0.035) & 0.387 (0.234) & 0.312 (0.263) \\ DPSI & 0.45 (0.045) & 0.45 (0.050) & 0.46 (0.058) & 1.000 (0.016) & 0.569 (0.220) & 0.526 (0.225) \\ \hline \multicolumn{7}{c}{Lateral single-leg jump landing} \\ \hline APSI & 0.064 (0.007) & 0.063 (0.007) & 0.063 (0.010) & 1.000 (0.116) & 1.000 (0.087) & 1.000 (0.007) \\ MLSI \({}^{\star}\) & 0.14 (0.009) & 0.14 (0.009) & 0.14 (0.012) & 0.982 (0.203) & 0.359 (0.411) & 0.060 (0.588) \\ VSI & 0.39 (0.038) & 0.38 (0.049) & 0.38 (0.048) & 1.000 (0.078) & 1.000 (0.159) & 1.000 (0.071) \\ DPSI & 0.42 (0.038) & 0.41 (0.047) & 0.40 (0.060) & 1.000 (0.058) & 0.547 (0.340) & 0.614 (0.270) \\ \hline \multicolumn{7}{c}{Note. \({}^{\star}\) represents a significant difference within a subject factor. High (H), Elastic (E), and Low (L) represent three football shoe conditions: high collar, elastic collar, and low collar, respectively.} \\ \end{tabular}
\end{table}
Table 2: Mean (standard deviation) of dynamic postural stability index and pairwise post hoc \(p\)-value (Cohen’s dz) during tasks in the high-, elastic-, and low collar shoe conditions.
## 3 Results and Discussion
### Lipid Production and Fatty Acids Profiles  
The _Chromochloris zofingiensis_ biomass produced from three concentrations of whey wastewater (10%, 20%, and 50%) in comparison to BBM medium was tested for total lipid content and fatty acid profiles (Tables 3 and 4). _C. zofingiensis_ accumulated lipid contents of 30.49% \(\pm\) 1.65%, 24.72% \(\pm\) 2.08%, and 19.19% \(\pm\) 1.43% when cultivated in 10%, 20%, and 50% whey wastewater, respectively, compared to the BBM medium experiment (34.59% \(\pm\) 4.10% total lipids). The lipid content achieved in 10% whey wastewater was higher than that obtained in 20% and 50% whey wastewater. There was no significant difference between the total lipids contents achieved in BBM medium and that in 10% whey wastewater. The lipid content of _C. zofingiensis_ differs across various types of wastewater and even using the same type of wastewater [16; 24; 25; 26; 27; 28; 29; 30; 59]. In this study, using 10% whey wastewater diluted in BBM medium, _C. zofingiensis_ accumulated almost the same amount of lipids as reported in previous studies using dairy wastewater [24] and piggery wastewater [25]. However, [PERSON] et al. [59] reported that _C. zofingiensis_ achieved a higher lipid content of 44.12% in anaerobically digested swine wastewater diluted with fishery wastewater (20:80). Recently, [PERSON] et al. [30] found a total lipid content of 38% using _C. zofingiensis_ cultivated in molasses wastewater (2 g L\({}^{-1}\)). On the other hand, [PERSON] et al. [60] and [PERSON] et al. [28] reported lower total lipid contents of approximately 20% when using synthetic wastewater and dairy wastewater, respectively. [PERSON] et al. [16] compared raw dairy wastewater (DWW) with blue-green medium (BG11 medium) for biofuel production using three microalgal species (_Chlorella_ sp., _Scenedesmus_ sp., and _Chromochloris zofingiensis_). They demonstrated that, after eight days of cultivation, _Chromochloris zofingiensis_ and _Scenedesmus_ sp. exhibited the best lipid content using raw dairy wastewater as the culture medium compared to BG11 medium, with lipid contents of around 27.7% and 15.3%, respectively. Conversely, _Chlorella_ sp. grown on BG11 medium exhibited a higher total lipid content (28.2%) [16]. Differences in the lipid content of _Chromochloris zofingiensis_ cultivated in various types of wastewater can be attributed to factors such as nutrient content, light intensity, photoperiod, culture conditions, and CO\({}_{2}\) concentration [61]. Among these, the two most important parameters influencing lipid content in microalgae cultivated in wastewater are nutrient availability and light intensity [62]. The fatty acid profile of lipids is a critical factor for evaluating the potential of microalgae for biofuel and other bioproduct applications [63].
# A 0.18 um CMOS Millimeter Wave Antenna-on-Chip with Artificial Magnetic Conductor Design
## 3 Results  
To verify the design results, the reflection coefficient of the proposed antenna was measured. Figure 15 shows the micrograph of the chip antenna under measurement. The instrument used for measurement was the Keysight N5291A vector network analyzer, shown in Figure 16 with SOLT calibration and a measuring range of 10 MHz-120 GHz. The measurement results were obtained by feeding the GSG probe, as shown in Figure 17. Under the standard of \(-10\) dB return loss, the measurement operating frequency could reach 33.8 GHz-110 GHz, which achieved up to 103.4% fractional bandwidth. The trend of simulation and measurement results was similar, but the measured results had a better reflection coefficient, which may have been caused by the impedance-matching effect of the lower needle measurement.  
In order to understand the peak gain and field pattern, Figure 18 shows the E and H planes of 42 GHz and 72 GHz. As shown in Figure 18a, when the frequency was 42 GHz, it can be seen from the E-plane that the maximum gain was \(-6.3\) dBi at a Theta angle of 30 degrees, and the maximum gain was \(-23.7\) dBi at a Phi angle of 70 degrees. From the H plane, we can see that the maximum gain of the Theta plane was \(-26.9\) dBi at 80 degrees, and the maximum gain of the Phi plane was \(-9.2\) dBi at 0 degrees. Figure 18b shows the field pattern at 72 GHz. From the E plane, it can be seen that the maximum gain was \(-6.4\) dBi at a Theta angle of 50 degrees and \(-19.6\) dBi at a Phi angle of 70 degrees. From the H plane, we can see that there was a maximum gain of \(-15.4\) dBi at a Theta angle of 80 degrees and a maximum gain of \(-8.8\) dBi at a Phi angle of 0 degrees.  
Figure 14: Simulation results of current antenna distribution: (**a**) 42 GHz; (**b**) 72 GHz.  
Figure 15: Fabricated antenna photograph.  
Figure 16: Keysight N5291A vector network analyzer.  
Figure 17: The simulated and measured result of the proposed antenna.  
Figure 18: _Cont._
## 2 Materials and Methods
### Data Preprocessing  
Before training, the images were preprocessed with the following two methods.  
**The U-Net within the TensorFlow framework requires standardized image sizes:** To meet the internal pooling operations of the U-Net, [PERSON] et al. [30] resized the training and validation images to 512 \(\times\) 256 pixels (Resize Method). This 2:1 proportion was chosen because it approximates the glottis dimensions. However, this often yielded an undesired deformation of the images resulting in unnatural glottis geometry (Figure 5). Hence, we now suggest a different method called the Region of Interest (ROI) method.  
**Region of Interest Method (ROI):** For resizing the images to the desired 2:1 scale (based on the glottis geometry), the following new approach was performed. Within each video, bounding boxes were generated and combined to \(\text{B}_{\text{Ref}}\), enclosing all included segmentation masks (Figure 6a). Afterwards, the smallest (\(\text{B}_{2:1}\geq\text{B}_{\text{Ref}}\)) and largest possible bounding boxes in the desired 2:1 scale were determined, defining the boundaries of available ROIs (Figure 6b,c). The region of interest (ROI) for each image may now be an automatically and randomly chosen box \(\text{B}_{\text{Var}}\) within the defined area, yielding more variety in training data regarding the position of the glottis in the image as well as surrounding information (Figure 6d,e).  
Figure 5: Potential induced deformations using the previously suggested resizing method by ref. [30].  
Figure 6: Process of the suggested ROI method in preprocessing for the training data. A randomly chosen ROI is presented in (**e**).
## 1 Introduction
Based on a comprehensive literature survey, research on chloride penetration and transport mechanisms of unsaturated cementitious systems incorporating ultrafine slag powders are far from adequate. This is particularly true when the unsaturated specimens were exposed to the corrosive tidal zone of marine environments. All of this calls for the need for careful investigations to explore the fundamental aspects of slag addition on changes to microstructure-based transport properties under drying-wetting exposure. The aim of the present work was to probe the hydration kinetics of ultrafine slag powders and to understand the microstructure characteristics of the hardened slag-blended cement paste specimens. The chloride transport properties of the corresponding mortar specimens under cyclic drying-wetting conditions were subsequently explored. The slag replacement level (0, 25, 65, and 85%) and degree of saturation (60, 80, and 100%) were considered in the mortar specimen preparation. The reactivity of slag with the hydration period was determined using the ethylene diamine tetraacetic acid (EDTAA) dissolution method. The pore solution chemistry, including the concentrations of ions Na\({}^{+}\), K\({}^{+}\), and OH\({}^{-}\) as the hydration time elapsed, was figured out. Changes in the hydration products and pore structure with the inclusion of slag were characterized by means of thermogravimetric analysis TGA/DTG and mercury porosimetry, respectively. The chloride transport properties, with or without binding considerations, were measured. The effects of slag content, as well as the degree of saturation, on microstructure-based transport properties were emphasized and discussed in detail. The aim of this research is to help contractors and engineers make more reliable and sensible decisions for mixture designs using ultrafine blast furnace slag in marine concrete structures, in addition to more economic and sustainable concrete production.
# Stability and Control of Surrounding Rock of a Trapezoidal Roadway Retained with Hard Roof Cutting
###### Abstract  
Hard roof top-cutting and gob-side roadway retention is an effective way to improve the panel recovery ratio and reduce ground pressure. Based on the condition of Pingmei No.2 Mine, this paper establishes a stability mechanics model for the roof in a trapezoidal top-cutting roadway with inclined coal seam, in order to analyze the factors influencing the stability of the roof. This paper studies the deformation characteristics and control mechanism of the surrounding rock in a trapezoidal top-cutting roadway, and proposes targeted stability control technologies for the surrounding rock. The results showed that: (1) in a trapezoidal top-cutting roadway in the hard roof with inclined coal seam, the tensile stress of the uncut roof was inversely proportional to the coal seam dip angle, roof thickness and top-cutting height, while it was proportional to the top-cutting angle. According to actual engineering conditions, the top-cutting angle and height of the roof of the 21,100-panel were determined to be 10\({}^{\circ}\) and 5.0 m, respectively; (2) the special structure of the trapezoidal roadway led to asymmetric stress distribution in the surrounding rock, especially in the roof and rib. Using top-cutting, the pressure relief reduced the roof stress from 6.73 MPa to 2.04 MPa, the high stress zone moved to the inside of the solid coal, and the roof slid and deformed along the top line, showing characteristics of a \"large deformation on the top side\"; and (3) high-strength long anchor cables were used to reinforce the roof on the cut top side. Telescope U-shaped steel and windshield cloth were used to block gangue and prevent wind leakage in the roadway. The on-site industrial test measured the maximum subsidence of the roof at 120 mm, and the maximum layer separation was 29 mm. Relative to non-top-cutting methods, the roof and sides showed significantly reduced deformation throughout the mining operations, which verified the reliability of the control technology.  
hard roof; trapezoidal roadway; cutting top to retain roadway; stability of surrounding rock; control technology 2025 15 348
## 5 Performance Evaluation
### Experimental Results
#### 5.4.1 Experiment 1  
In this experiment, since shallow (or traditional) anomaly detectors (e.g., OCSVM, Isolation Forest) generally achieve lower classification performance compared to deep anomaly detectors, we focused on deep anomaly detection. Therefore, we first trained global deep anomaly detectors using DAE and DAE+OCSVM. Then, we evaluated their performance using \(ACC\), \(DR\), \(FA\), \(PR\), \(F1\), and \(HD\). Table 2 compares the performance of three global anomaly detectors: DAE (With-malicious), where the threshold is determined using a malicious electricity consumption dataset; DAE (No-malicious), where the threshold is calculated without using a malicious electricity consumption dataset; and our proposed detector DAE+OCSVM (No-malicious), which does not require a threshold because OCSVM can automatically determine it. From all evaluation metrics in this table, it is evident that the performance of DAE (No-malicious) is limited due to the absence of a malicious electricity consumption dataset, which leads to incorrect threshold selection. Conversely, DAE (With-malicious) achieves good performance under the impractical assumption of ideal threshold selection based on the availability of a real malicious electricity consumption dataset, which is not available. Moreover, DAE+OCSVM (No-malicious) achieves superior performance (e.g., \(ACC\) is 93.61% and \(DR\) is 96.74%) without the need for the calculation of a threshold or knowledge on malicious electricity consumption data because OCSVM can determine the threshold automatically during the training phase. Therefore, the existing DAE-based methods are only effective when there are known malicious samples, as shown in the DAE (With-malicious). However, in more practical situations where malicious samples are unknown, these methods perform poorly, as shown in the DAE (No-malicious). In contrast, our proposed detector DAE+OCSVM (No-malicious) achieves high performance without needing malicious samples.
# Substrate-Driven Atomic Layer Deposition of High-\(\kappa\) Dielectrics on 2D Materials
## 5 Conclusions  
The integration of uniform and ultra-thin high-\(\kappa\) dielectrics on 2D materials is a challenge for future electronic and sensing applications in these systems. In fact, the seed layer and pre-functionalization approaches typically employed to promote ALD nucleation on 2D materials presents both advantages and disadvantages, and direct ALD growth remains highly desirable. This paper provided a detailed review of the literature reports on the direct ALD of uniform high-\(\kappa\) dielectric films on monolayer graphene and MoS2, driven by the interaction with the underlying substrates. In the case of CVD 1 L graphene on copper, the enhancement of ALD nucleation was correlated with the electrostatic effect of polar traps (at the graphene/metal interface) on the adsorption of the water precursor. For 1 L epitaxial graphene on SiC(0001), a similar electrostatic effect happens due to the positive charges at the interface between the buffer layer and the substrate, which cause n-type doping of the graphene and its enhanced reactivity toward ALD precursors. Finally, in the case of 1 L MoS2 exfoliated on gold substrate, the improvement in ALD growth is explained by the combined effect of electrostatic charges at the interface and of the tensile strain induced on the 2D material by the gold. Hence, opportunely choosing the substrate, excellent ALD coverage, and optimal structural and insulating properties of the high-\(\kappa\) layers can be obtained on 2D materials. For all these systems, the electrical insulating properties of the grown high-\(\kappa\) dielectrics have been examined. Finally, the open issues and challenges for applications of these high-\(\kappa\)/2D material systems have been thoroughly discussed.  
Writing--original draft preparation, E.S.; writing--review and editing, F.G., R.L.N. and F.R.; supervision, F.G.; funding acquisition, F.G. and F.R. All authors have read and agreed to the published version of the manuscript.  
This work was funded in part by MUR in the framework of the FlagERA-JTC 2019 project ETMOS. E.S. acknowledges the PON project EleGaNTe (ARS01_01007) funded by MUR for financial support.  
Figure 13: Electrical characteristics (I\({}_{\text{D}}\) -V\({}_{\text{G}}\)) of back-gated field effect transistors fabricated using (i) 1 L graphene without Al2O3 on top (no Al2O3), (ii) 1 L graphene with Al2O3 deposited after being transferred onto SiO2 (Al2O3 ALD after Gr transfer), and (iii) 1 L graphene with Al2O3 deposited before the transfer (Al2O3 ALD before Gr transfer). Image adapted with permission from [75], copyright IOP, 2017.
# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
## 2 Methods
### Swin Transformer Algorithm  
The CNN model, a classical deep learning architecture, effectively extracts informative features from images using a convolutional kernel. During model creation, the size of the convolution kernel can be adjusted to extract features at varying scales. Unlike CNNs, a transformer [31] discards the traditional method of convolutional feature extraction and extracts features via a self-attentive mechanism. The Swin transformer connects every pixel in an image to extract the overall features. The self-attention in the transformer is calculated as in Equation (1) and is schematically shown in Figure 2.  
\[\text{Attention}(Q,K,V)=\text{SoftMax}\Bigg{(}\frac{QK^{\text{T}}}{\sqrt{D}}+B \Bigg{)}V \tag{1}\]where \(Q,K,V\in\ R^{M^{2}\times d}\) are the query, key and value matrices, \(D\) is the query / key dimension, \(M^{2}\) is number of patches in a window, and \(B\) is a positional parameter.  
From the formula and the schematic, it can be seen that as long as the image size is fixed, the computational complexity of self-attention is also fixed, and the total computational complexity increases linearly with the image size, which greatly increases the computational cost. To solve this problem, the researchers proposed a vision transformer [32], a model that attempts to split the image into multiple windows and then computes the self-attention in each window. Although the vision transformer reduces the amount of computation by virtue of this operation, it also isolates the exchange of information between different windows. To address the shortcomings of the transformer, [PERSON] et al. [33] proposed the  
Figure 1: Overall framework diagram of Swin–MRDB. The input image is MS\(\uparrow\) and the PAN output image is HRHS.  
Figure 2: Self-attentive calculation process.  
Swin transformer, which is a transformer model with a layered design. This model adds a moving window interaction mechanism to the vision transformer, which splits the image into several non-overlapping windows. For example, if the input image size is \(H\times W\times C\), the Swin transformer will patch partition the image size to \(\frac{HW}{M^{2}}\times M^{2}\times C\), where \(\frac{HW}{M^{2}}\) is the number of windows and \(M^{2}\) is the window size. The Swin transformer will first calculate the self-attention in each window and then interact with the information of different windows through the operation of moving windows, solving the problem of not being able to interact with the information of different windows while reducing the amount of calculation.
###### Abstract  
Reversible data hiding in encrypted images (RDH-EI) schemes based on secret sharing have emerged as a significant area of research in privacy protection. However, existing algorithms have limitations, such as low embedding capacity and insufficient privacy protection. To address these challenges, this paper proposes an RDH-EI scheme based on adaptive median edge detection (AMED) and matrix-based secret sharing (MSS). The algorithm creatively leverages the AMED technique for precise image prediction and then integrates the (\(r\), \(n\))-threshold MSS scheme to partition the image into \(n\) encrypted images. Simultaneously, it embeds identifying information during segmentation to detect potential attacks during transmission. The algorithm allows multiple data hiders to embed secret data independently. Experimental results demonstrate that the proposed algorithm significantly enhances the embedding rate while preserving reversibility compared to current algorithms. The average maximum embedding rates achieved are up to 5.8142 bits per pixel (bpp) for the (3, 4)-threshold scheme and up to 7.2713 bpp for the (6, 6)-threshold scheme. With disaster-resilient features, the algorithm ensures \((n-r)\) storage fault tolerance, enabling secure multi-party data storage. Furthermore, the design of the identifying information effectively evaluates the security of the transmission environment, making it suitable for multi-user cloud service scenarios.  
adaptive median edge detection; matrix-based secret sharing; reversible data hiding in encrypted images; high embedding capacity +
Footnote †: journal: Applied sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))