esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Structure of U-Net  
Appendix A shows the structure and characteristics of U-Net. Figure 11 shows the U-Net structure. In the structure of the encoder and decoder, this model has 23 convolutional layers. U-Net's encoder is called a contracting path, and it converts a high-dimensional image into a low-dimensional image and extracts features. After that, it is composed of a decoder structure called an expanding path that changes the encoded image back to its original form. Unlike other semantic segmentation models, U-Net adapts to the skip connection mechanism to connect compressed data from the contracting path to the expanding path directly.
# Bacteriotherapy in Breast Cancer
## 3 Anticancer Substances Released by Bacteria
### Bacterial Peptides in Cancer Therapy
#### 3.2.5 Proximicins  
Proximicins is a family of three novel aminofuran antibiotics produced by _Verrucosispora_ strain. Proximicins B, with a molecular mass of approximately 413 kDa, is able to inhibit the growth of gram-positive bacteria, whereas proximicins C, with a molecular mass of approximately 436 kDa, has the inhibitory growth effect only on Brevibaccillus brevis. Surprisingly, gram-negative bacteria such as _Escherichia coli_ K12, _Pseudomonas fluorescens_, _Proteus mirabilis_, and yeasts such as _Saccharomyces cerevisiae_are resistant to all of the proximicins. The unique features in the chemical structures of proximicins include 4-amino-furan-2-carboxylic acid, a hitherto unknown \(\gamma\)-amino acid. They are weak bactericidal peptides, but they show the powerful cytostatic activity on human breast carcinoma (MCF 7). An in vivo experiment of proximicin C on gastric adenocarcinoma (AGS) cells demonstrated that this peptide can provide cell cycle arrest at the G0/G1 phase after 24 h and increase the number of apoptotic cells after 40 h. It can also upregulate intracellular levels of p53 and the cyclin kinase inhibitor p21 in the AGS cells [57].
## 6 Conclusions  
While further studies must focus on establishing a reliable benchmark to be used as an operational SMC retrieval method from GNSS-R data, as a consequence of our findings and investigations, the results achieved in this work are very encouraging. The conclusions derived from this study can be summarized as follows:  
* A new method to retrieve SMC from GNSS-R is presented and validated, purely based on a bistatic radar physical modeling of the dielectric permittivity using Fresnel reflection coefficients and accounting the effects of BSR and VOD.
* This new approach is applied and tested with one month of CYGNSS GNSS-R data (April 2019 at the eastern region of China), in combination withICESat-2 and/or SMAP BSR and VOD products. The tests carried out with ICESat-2 BSR data have shown the high sensitivity in SMC retrieval to high BSR values, due to the high sensitivity of ICESat-2 to land surface microrelief.
* This CYGNSS SMC approach is validated with SMAP SMC products, and the statistical assessment provides an R-square of 0.6 (RMSE of 0.05 and zero p-value) for 4568 test points evaluated during April 2019 at the eastern region of China.  
Future work will be conducted, but not restricted, to study sensitivity on SMC retrieval to different incidence angles, BSR, and VOD parameters, and assessing the reflectivity models. Additional SMC sources for validation should also be explored, focusing the effort to establish a reliable benchmark for SMC retrieval in terms of the above variables. Finally, CYGNSS calibration procedures and incorporation of incoherent scattering in the bistatic radar model must also be investigated.  
A.C. motivated the initial idea and carried out the computational programming, data analysis, experimental results, and draft preparation; I.M. materialized the initial concept though investigation of previous research, formulation of new theory and methodology, draft preparation, supervision of achieved results, funding support, and provided international academic exchange; S.J. provided supervision, mentorship, funding support and computational resources as well as undertook revision tasks. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Strategic Priority Research Program Project of the Chinese Academy of Sciences, grant number XDA23040100; the Jiangsu Province Distinguished Professor Project, grant number R2018T20, the Talent Start-Up Funding project of NUIST, grant number 141104190100, the Startup Foundation for Introducing Talent of NUIST, grant number 2243141801036, and the R&D+I Program of the Universidad Politecnica de Madrid (ProgramAPropio UPM 2019).  
Great appreciation is extended to ESA and NASA for providing the data access. Special thanks are given to the three reviewers for their constructive comments and helpful suggestions.  
The authors declare no conflict of interest.
## 4 Experiments  
### Fault Diagnosis of Rolling Bearings  
#### 4.1.1 Dataset Description  
The analyzed experimental dataset was gathered from the Case Western Reserve University [42]. Figure 5 shows a test rig that generates the experimental data. The experimental platform comprises a torque transducer, a 2 hp motor (left), and a dynamometer (right).The type of bearings that support the motor shaft was 6205-2 RS JEM SKF. Electro-discharge machining is added to cause a single point fault on the inner race of the bearing, ball elements, and outer race. Accelerometers were installed at the dead end to sample vibration signals at 12 kHz for the faults localized on the inner race, rolling elements, and outer race. The data samples obtained from the different health conditions, including four conditions, are employed for analysis. Table 1 shows the two sets that have been analyzed. The defect size of the first dataset was 0.007 with no loading, and the second dataset was obtained with a defect size of 0.014 under 1 hp loading.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Dataset** & **Health** & **Loading** & **Defect Size** & **Training** & **Testing** & **Target** \\  & **Condition** & **(hp)** & **(Inches)** & **Samples** & **Samples** & **Value** \\ \hline \multirow{3}{*}{Bearing Dataset I} & Health & 0 & - & 50 & 50 & 1 \\  & Inner race fault & 0 & 0.007 & 50 & 50 & 2 \\  & Ball fault & 0 & 0.007 & 50 & 50 & 3 \\  & Outer race fault & 0 & 0.007 & 50 & 50 & 4 \\ \hline \multirow{3}{*}{Bearing Dataset II} & Health & 1 & - & 50 & 50 & 1 \\  & Inner race fault & 1 & 0.014 & 50 & 50 & 2 \\  & Ball fault & 1 & 0.014 & 50 & 50 & 3 \\ \cline{1-1}  & Outer race fault & 1 & 0.014 & 50 & 50 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the bearing fault dataset.
# Research on the Face Forgery Detection Model Based on Adversarial Training and Disentanglement
## 2 Related Works
### Adversarial Learning and Feature Disentanglement  
Adversarial learning has been adeptly applied in the field of image synthesis, which incorporates both a generator and a discriminator to realistic images. Meanwhile, adversarial training is acknowledged as a strategy method to improve the generalizations. SLADD [23] uses a data augmentation closely resembling face forgery and employs adversarial learning to generate rich forgery types, effectively improving the generalization. [PERSON] et al. [24] introduce a special identity discriminator, leveraging adversarial learning to eliminate the negative impact of forgery methods and identities to help the classifier learn essential, common discriminant features.  
Feature disentanglement serves to break down complex features into more interpretable, simple features. [PERSON] et al. [13] first explore the influence of content information on the generalization performance of face forgery detection. They devise an easily embed-dable disentanglement framework, and further propose a Content Consistency Constraint and a Global Representation Contrastive Constraint to enhance the independence of disentangled features and remove the impact of content information. UCF [8], from the perspectives of style and content, adopts a disentanglement framework and proposes a contrastive regularization to decompose face forgery images features into common and specific forgery features and only relies solely on common features for detection.  
The primary limitation of the aforementioned methods lies in their reliance on existing datasets, which may hinder the models in adapting effectively to new or varied counterfeiting scenarios, leading to poor performance in real-world applications. The reliance on existing datasets not only burdens the model with extraneous information but also obscures critical signs of forgery, potentially causing a decline in detection accuracy when confronted with novel forgery methods.
# Review of the Double-Row Pile Supporting Structure and Its Force and Deformation Characteristics
###### Abstract  
The conventional support forms of foundation pit retaining piles include single-row piles, double-row piles, anchor-row piles, and so on. The double-row pile supporting structure is widely used in the deep foundation pit supporting the engineering of wharves, bridges, subways, tunnels, and high-rise and super-high-rise buildings. This study on double-row pile supporting structures mainly focuses on four aspects: (1) The influence of dimension parameters, such as pile diameter and pile length, and engineering parameters, such as pile spacing and row spacing, on the deformation control of a double-row pile structure and the stability control of foundation pits; (2) Influence of the soil arch effect on the stress and deformation of the double-row pile supporting structure; (3) Study on the deformation characteristics and rules of the components and the whole structure of the double-row pile supporting structure; (4) Study on the calculation model of pile-soil interactions. Based on the above four aspects, this paper summarizes the latest research status of the existing double-row pile supporting structure and its stress and deformation characteristics. The deformation characteristics and calculation model of the pile-soil interaction of double-row piles are reviewed and evaluated. Finally, the problems and deficiencies in the research on double-row pile support are summarized. These results provide a reference for future research on the double-row pile supporting structure of the foundation pit and the numerical analysis and calculation model and lay a solid foundation for further development of the theory.  
double-row piles; force characteristics; deformation characteristics; pile-soil interaction; calculation model +
Footnote †: journal: Applied Sciences
# Iterative Refinement of Uniformly Focused Image Set for Accurate Depth from Focus
## 3 Experimental Results
### Error Metrics  
In order to evaluate quantitatively, we use following several measurements, which are used in previous works [41; 41; 42; 43; 44; 45; 46; 47; 48]: Root mean squared error (linear) (Equation (7)), Root mean squared error (log) (Equation (8)), Absolute relative error (Equation (9)), Squared relative error (Equation (10)), and Accuracy (% of y such that, Equation (11)):  
\[RMS=\sqrt{\frac{1}{T}\sum_{i,j}\left|y_{i,j}-y_{i,j}^{*}\right|^{2}}, \tag{7}\]  
\[\mathbf{RMS}(log):\log_{10}=\sqrt{\frac{1}{T}\sum_{i,j}\left|\log y_{i,j}-\log y _{i,j}^{*}\right|^{2}}, \tag{8}\]  
\[rel=\frac{1}{T}\sum_{i,j}\left|y_{i,j}-y_{i,j}^{*}\right|/y_{i,j}^{*}, \tag{9}\]  
\[srel=\frac{1}{T}\sum_{i,j}\left|y_{i,j}-y_{i,j}^{*}\right|^{2}/y_{i,j}^{*}, \tag{10}\]  
\[\left(\frac{y_{i}}{y_{i}^{*}},\frac{y_{i}^{*}}{y_{i}}\right)=\sigma<th, \tag{11}\]  
where \(y\) is depth map, \(y^{*}\) is ground-truth, and \(T\) is depth-pixels.  
Figure 10: (**a**) Comparison with Deep Depth from Focus (DDFF)Net on LFSD dataset [28]. All samples have 10 frames. First-row: input image, second: results from DDFFNet [22], third: our result, last-row: ground-truth. (**b**) Effect of guided filter [24] on the depth map: it helps keeping edges.
## 3 A Taxonomy for Planar FSS
### Patch Cell with Additional Elements Around  
Adding elements to a central patch is a common strategy in FSS design to either reduce the size of the unit cell or achieve multiband performance. This section introduces several examples of such designs, with a selection illustrated in Figure 7.  
One example is presented in [56], which features a two-faced planar circuit. The unit cells on the top and bottom layers consist of four metallic triangular patches and meandering lines connected to the corners of a square loop. This design operates at 4.9 GHz and 6.3 GHz with a unit cell size of 0.14 wavelengths.  
Figure 6: Some examples of patch unit cells: (**a**) interconnected square ring and patches [26]; (**b**) metal gratings using rectangular patches [52]; (**c**) full-, quarter- and half-circular patches [53]; (**d**) rectangular strips in chiral geometry [54].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Design** & **Frequency (GHz)** & **Length (\(\lambda\))** & **Reference** \\ \hline Squared patch & 77 & 0.51 & [50] \\ Squared patch with energy harvesting & 2.2 & 0.42 & [51] \\ Pairs of rectangular patches and squared loop & 1 & 0.02 & [26] \\ Cascaded layers-based design & 9.9/10.4/10.6 & 0.28 & [52] \\ Multilayer circular patches & 9.3/11.3/13.6 & 0.62 & [53] \\ Cross-polarization converter & 5.8 & 0.35 & [54] \\ Textile squared patch & 70/110 & 0.45 & [55] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of patch unit cells considered.  
Another design is an FSS with two metal layers, incorporating square loops combined with hexagonal rings and hexagons, as described in [57]. This FSS exhibits tri-band behavior, operating at 13, 24, and 25 GHz, with a compact unit cell size of 0.18 wavelengths.  
The addition of active elements like PIN diodes can significantly reduce the unit cell size while enhancing functionality. For instance, a three-layer active FSS design operates at 8.3 and 10.7 GHz with a unit cell size of 0.16 wavelengths. The top and bottom layers consist of four square patches separated by capacitive gaps, while the middle layer is composed of periodic circular apertures [58]. Table 4 summarizes these proposals.  
These examples demonstrate how adding elements to a central patch can optimize the FSS design for size and multiband operation, providing solutions for various electromagnetic shielding and filtering applications.
## 2 Methods
### The Proposed Hybrid Early Warning Method
#### 2.1.3 Strategies for Determining Early Warning Levels  
The conventional warning method and critical sliding warning method have different warning levels. The final early warning level should be determined by different strategies according to different needs:  
1. Conservative Strategy: choose the higher warning level of the two methods' results. This strategy is conservative and can ensure the immediacy of early warning, but there may be many false warnings;
2. Real Early Warning Strategy: choose the lower warning level of the two methods' results. This strategy has relatively fewer false warnings but poorer immediacy;
3. Single-Method Strategy: use either the conventional early warning method or the critical sliding early warning method to determine the early warning level. Comparedwith the proposed hybrid early warning method, it obtains less information, so the real early warning rate and immediacy are lower.  
The choice of these three strategies needs to be determined according to the actual situation of the specific landslide, and with consideration to the following four main aspects of analysis and decision making:  
1. Whether a genuine warning for the current landslide existed in the past. If a landslide warning has been issued previously, the conservative strategy should be taken to ensure the warning's immediacy;
2. The risk of landslide instability and the degree of disaster impact. Conservative strategy should be chosen if the risk of landslide instability is high and the impact of a disaster is severe;
3. Computing resources for monitoring and early warning systems. If system computing resources are limited, the single-method strategy should be chosen to reduce the amount of calculation for most landslides with low instability risk;
4. The quality of monitoring data of the current landslide. If the quality of monitoring data is poor, the real early warning strategy should be chosen to reduce false warnings.
## 3 OCC Performance Improvement
### Focal Length and Pixel Edge Length  
The focal length indicates how much of a scene is captured by a camera. The relationship between \(\mathrm{d}_{\alpha,\beta}\) and the distance behind the lens at which the focused image is formed \((\mathrm{d}_{\mathrm{im}})\) can be expressed according to the Newtonian form of the lens law  
\[\left(1-\frac{\mathrm{d}_{\alpha,\beta}}{\mathrm{f}_{\mathrm{o}}}\right)\left( 1-\frac{\mathrm{d}_{\mathrm{im}}}{\mathrm{f}_{\mathrm{o}}}\right)=1, \tag{8}\]  
where \(\mathrm{f}_{\mathrm{o}}\) is the focal length of the camera. The object, i.e., the LED light source, needs to be focused appropriately to avoid any kind of image distortion.  
The area of the projected image depends on the focal length and the pixel edge length of the image sensor as expressed in  
\[\mathrm{A}_{\mathrm{c}}=\frac{\mathrm{A}_{\mathrm{f}}\mathrm{f}_{\mathrm{o}} {}^{2}}{\rho^{2}\mathrm{d}_{\alpha,\beta}^{2}}, \tag{9}\]  
where \(\rho\) is the pixel edge length and \(\mathrm{A}_{\mathrm{l}}\) is the effective area of the light source exposed to illumination.  
It can be noticed that a large focal length effectively contributes to high-distance communication. Here, it is worth mentioning that \(\rho^{2}\) signifies the size of the image sensor per pixel. The focal length determines the total AOV of the camera. From Figure 3, the AOV can be measured horizontally, vertically, or diagonally. The AOV of a camera with sensor dimensions of \(\mathrm{p}\times\mathrm{q}\) can be expressed as  
\[\partial_{\mathrm{aov}}=2\cos^{-1}\frac{2\mathrm{f}_{\mathrm{o}}}{\sqrt{ \mathrm{L}^{2}+4\mathrm{f}_{\mathrm{o}}^{2}}}, \tag{10}\]  
\[\mathrm{here,\ }\partial_{\mathrm{aov}}=\left\{\begin{array}{ll}\partial_{ \mathrm{vert}},&\mathrm{L}=\mathrm{p},\\ \partial_{\mathrm{horz}},&\mathrm{L}=\mathrm{q},\\ \partial_{\mathrm{diag}},&\mathrm{L}=\sqrt{\mathrm{p}^{2}+\mathrm{q}^{2}}, \end{array}\right.\]  
where \(\partial_{\mathrm{vert}},\ \partial_{\mathrm{horz}},\ \mathrm{and}\ \partial_{\mathrm{diag}}\) represent the vertical, horizontal, and diagonal AOVs, respectively.  
Figure 3: The illustration of the camera coverage.  
To increase the communication distance, the entire coverage area of the camera should be correspondingly increased. The total area of the camera coverage is illustrated in Figure 3 and can be represented as  
\[\mathrm{d_{ct}}=4\mathrm{d_{b}^{2}}\tan\left(\frac{\partial_{\mathrm{vert}}}{2} \right)\tan\left(\frac{\partial_{\mathrm{horz}}}{2}\right), \tag{11}\]  
where \(\mathrm{d_{b}}\) is the distance between the center of the image sensor and the coverage area. The coverage area is very important in OCC as it determines how much of the area can be utilized to set the LED for data transmission.
## 3 Polyaryletterketone
### Polyetherketoneketone (PEKK)  
Polyetherketoneketone (PEKK) is an advanced, high-performance polymer that has garnered widespread attention for its exceptional properties and versatility, particularly within the biomedical field [58]. Initially introduced by [PERSON] in 1962 [59], PEKK was first employed in the industrial and military fields due to its robust mechanical and thermal characteristics [58]. In recent years, however, significant advancements in material science and manufacturing technologies have shifted the focus toward its potential as a biomaterial, driving innovation in medical and dental applications [58; 60]. What sets PEKK apart is its outstanding mechanical strength, excellent chemical resistance, and high thermal stability, all of which position it as a leading material among thermoplastic composites. This methacrylate-free thermoplastic offers remarkable shock absorption and fracture resistance, making it a reliable choice for demanding environments. With an elastic modulus of 5.1 GPa, PEKK outperforms its counterpart, PEEK, offering enhanced strength and elasticity that are critical in load-bearing applications [61]. A notable feature of PEKK is its similarity to human bone in terms of density and elastic modulus, which significantly reduces the risk of bone resorption when used in implants [62]. This unique compatibility makes it particularly well suited for orthopedic and cranial applications [63]. Furthermore, the presence of an additional ketone group in its molecular structure provides an avenue for enhanced surface chemical modifications, enabling tailored interactions with biological environments and expanding its scope of application [58].  
In the fields of orthopedics and dentistry, PEKK has emerged as a transformative material, demonstrating its value in restorative, prosthetic, and implant procedures. Its biocompatibility, combined with exceptional mechanical properties, has established it as an ideal material for cranial reconstruction and mandibular implants [59; 60; 64]. The integration of AM technologies such as 3D printing has further amplified its potential [65]. These methods facilitate the creation of intricate, patient-specific implants, such as custom bone analogues designed for mandibular reconstruction, offering a level of precision and adaptability previously unattainable [64]. PEKK's adoption in spinal fusion proceduresunderscores its growing role as a reliable, implantable-grade material [59]. Its superior thermal stability, mechanical resilience, and ease of surface modification have made it the preferred choice for high-demand biomedical applications [58,65]. As the demand for advanced biomaterials continues to rise, PEKK is poised to play a pivotal role in the developing next-generation medical devices and implants [58]. Table 5 shows the mechanical properties of PEEK, PEKK, and Ti.
## 2 System Model
### Channel Model
#### 2.3.2 Channel Gain  
Channel gain \(h\) is the gain produced by the exploitation of the channel between the Tx and Rx such as the diversity gain, multipath, hardening, etc. [11]. We fitted the channel gain \(H\) into a gamma distribution, as shown in Figure 4, with PDF \(f_{H}\) given in Equation (11). The parameters for the fit gain distributions are summarized in Table 3.  
\[f_{H}(h)=\frac{1}{\Gamma(b)c^{b}}h^{b-1}e^{-h/c} \tag{11}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Frequency & 28 GHz \\ Scenario & _UMI_ \\ Tx Power & 0 dBm \\ Array type & _URA_ \\ Number of elements & 256 \\ Antenna spacing & 0.5 \(\lambda\) \\ Half-Power Beamwidth & \(10^{\circ}\) \\ Link Type & _LOS_ \\ RF Bandwidth & 800 MHz \\ User Terminal Height & 1.5 m \\ Base Station Height & 35 m \\ \hline \end{tabular}
\end{table}
Table 2: NYUSIM simulation parameters used for the channel simulations.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Description** \\ \hline \(a\) & 0.57 & Gamma distribution shape parameter \\ \(b\) & 1.45 & Gamma distribution scale parameter \\ \(c\) & 966.5 & Exponential distribution exponent \\ \hline \end{tabular}
\end{table}
Table 3: Parameters of the fitted distributions. \(a\) for the exponential distribution and \(b,c\) for the gamma distribution.  
Figure 2: Illustration of our mmWave model (**left**) and the channel model (**right**). The channel model follows the NYU Wireless Group mmWave channel model [11].