esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Network  
Our architecture includes a feature extractor backbone, spatial and channel attention modules and a detection head. Inspired by HourglassNet [18] we use a top-down bottom-up network. However, different from conventional network, we aggregate both convolutional layer output feature maps within each residual block. A skip layer connection is added with this output and provided as input for the next convolution block. In our proposed method the final feature maps is generated by combining the outputs obtained by two stacked hourglass networks. To precisely emphasize the attributes of target objects in the generated feature maps, we employ a channel-wise attention module along with the spatial attention module after the feature extractor network. What makes our architecture better in detecting logos than conventional detectors is the newly added two attention modules prior to the detection head. The two branches using channel and spatial attention modules, respectively, produce category-wise keypoint heatmaps of the input images. Such feature maps are generated by their respective attention modules to emphasize the network capacity of learning longer-range dependencies and help to know what and where can be found in the image. For accurate detection of target logos in feature maps, we perform matrix element-wise addition to these two category-wise feature maps. The aggregated final feature maps is given as input to detection head. The detection head is similar to CenterNet. The overall architecture of LogoNet is shown in Figure 1.  
The detail of architecture is described as follows. Section 2.1 provides detail about feature extractor network. The spatial attention module and channel-wise attention module are explained in Sections 2.2 and 2.3, respectively. Detection head is described in Section 2.4. Lightweight-CNNs models are reported in Section 2.5. The Domain-Adaptation-based logo detection method is described in Section 2.6.
## 3 Results and Discussion
### Release Assay  
The release of curcumin was evaluated by placing rehydrated BCC film on the surface of a buffer solution in modified Franz diffusion cells. The release profiles under non-transdermal conditions are shown in terms of concentration of released curcumin (Figure 5a), and percentage of curcumin relative to the actual amount of curcumin loaded into the BC pellicles (Figure 5b). The hydrophobicity of curcumin means that it is poorly soluble in water, and its release into aqueous solutions without the addition of surfactant was very low (data not shown). The release rate was enhanced by the addition of surfactant to increase the solubility of hydrophobic molecules. On previous work, [PERSON] et al. (2015) also reported that the release of curcumin from silk hydrogel films was significantly enhanced when the release assay was performed in PBS with Tween80 and methanol supplement compared to that of PBS without supplement [38]. The pH of the solution was found to have had a minor effect on the dissolution of curcumin, but the addition of surfactants to the solution was observed to significantly increase the molecule's solubility. Burst release was observed in the first 8 h, as the packed matrix of air-dried BCC became loose when immersed in water [12]. The release rate then slowed in a controlled manner. The total amount of curcumin released from BCC films was also found to increase in line with the initial amount of curcumin that was absorbed into the films. Pure curcumin is highly unstable  
Figure 4: Tensile strength (**a**), Young’s modulus (**b**), and % elongation at break (**c**) of BC and curcumin-loaded BC films. Values were expressed as mean \(\pm\) SD (\(n=5\)).  
in alkaline aqueous solutions (pH \(\geq\) 7.0). Therefore, the concentrations of curcumin released from BCC films into the acetate buffer were higher than those in the PBS buffer. Degradation should occur very slowly at pH 1-6, whereas degradation could occur at a significantly higher rate in PBS at pH 7.4 due to the instability of curcumin at this pH [18; 39]. This degradation could cause the decrease in the concentration of curcumin in the solution that was seen in the release assay after 10 h. This result demonstrates that BC is a good candidate matrix for the controlled release of curcumin. For the use of the film as a topical patch on skin, the control release of curcumin into the skin might also be adjusted by the addition of surfactant on the target area.
## 4 Discussion
### Study Limitations  
Several limitations must be acknowledged. Firstly, the retrospective nature of this study could introduce selection bias. Secondly, the radiographic interpretations were conducted without knowledge of additional clinical characteristics such as the PTB results. This lack of information could have influenced the diagnostic accuracy. Importantly, neither the AI model nor the clinician had access to this clinical information.  
Furthermore, the AI model (PLR = 0.97 [0.90-1.05]) and the clinician (PLR = 1.45 [1.11-1.83]) exhibited low positive likelihood ratios. These findings suggest that while the model performs well in high-prevalence settings, further validation is required to determine its applicability in primary care settings, where DFO prevalence is lower.  
In addition, the low-level specificity observed in our model (4.4%) reflects its prioritization of sensitivity in order to minimize missed diagnoses in a high-risk population. While this trade-off may be acceptable in specialized diabetic foot units--where the goal is early detection and timely referral--it may limit the model's usefulness in settings with lower DFO prevalence. Future iterations should aim to improve specificity by integrating complementary clinical variables (e.g., probe-to-bone test, ulcer duration, lesion site, and inflammatory markers) and optimizing the classification thresholds, particularly to adapt the model to broader clinical environments.  
Additionally, while the model was trained and validated specifically for diabetic foot ulcers (DFUs), its applicability to other types of chronic ulcers or osteomyelitisitis of non-diabetic origin remains unknown. Future research is required to evaluate the generalizability of the model across different ulcer etiologies and clinical contexts.
# Analysis of Load Transfer and the Law of Deformation within a Pile-Supported Reinforced Embankment
###### Abstract  
In this paper, the load-transfer mechanism and settlement behaviors of the pile-supported reinforced embankment are reviewed by laboratory model tests, and a series of finite element method (FEM) modellings are conducted to analyze the soil-arching geometry and embankment deformation patterns of the pile-supported reinforced embankment. The results show that: the embankment load distribution is significantly impacted by the filling cohesion because of the effect of cohesion on the interaction between particles. The soil pressure difference between the center and corner of the pile caps decreases with the increase of filling cohesion. The pile-subsoil stress ratio decreases with the increase of filling cohesion. The embankment deformation behavior and soil-arching geometry are less affected by the change in filling cohesion compared with the influence of pile spacing. That may because of the fact that although the cohesion of the embankment filling has been increased, the granular material's properties have not been fundamentally changed. Pile-subsoil different settlement decreases with the increase of embankment filling cohesion, and the different settlement at the mid-span between four piles decreases by 4.09% and 6.34%, respectively, as filling cohesion increases from 0 kPa to 11 kPa and 25 kPa. The height of the soil-arching crown decreases with the increase of filling cohesion, and the height of the soil-arching crown between horizontal adjacent piles decreases by 3.85%, 7.69%, and 9.62%, as filling cohesion increases from 5 kPa to 15 kPa, 25 kPa and 45 kPa. The rate of decrease in soil-arching height gradually decreases with increasing cohesion. The height of the soil-arching between the horizontal adjacent piles is about 1.0 \((s-a)\). The height of soil-arching between the diagonal adjacent piles is about 1.0\(\sqrt{2}\)\((s-a)\). The differential settlement at the same height inside the embankment decreases with the increase of filling cohesion, and the height of the equal settlement plane is basically the same as the height of soil arching.  
pile-supported reinforced embankment; model test; soil arching effect; cohesion; equal settlement plane +
Footnote †: journal: Applied Sciences
# Analysis and Design of an X-Band Reflectarray Antenna for Remote Sensing Satellite System
###### Abstract
The unit cell is simulated as a periodic structure using Floquet modes [33; 34]. Varying the dimension of the unit cell changes the corresponding equivalent surface impedance boundary and therefore the reflection phase shift. The advantage of using this stacked configuration is that it allows the acquisition of a wide range of nearly linear phase-shift changes of more than 360\({}^{\circ}\). This property is important for the implementation of any required phase distribution along the designed reflectarray. In this study, GA was chosen to optimize the phases of the reflecting elements to obtain a flat-top radiation pattern with a beam width of around 29\({}^{\circ}\), side lobe level (SLL) of less than \(-\)20 dB, and allowable ripple level (ARL) of around \(-\)3 dB. The contribution of this work is the optimization of these phases to achieve the desired performance of an optimized flat-top pattern for an MEO system. This paper is organized as follows. Section 2 introduces the complete reflectarray antenna design, specifications, and modeling procedure. Section 3 presents the analysis of the feeding antenna. In Section 4, the incident field distribution on the plane of the reflectarray, and the corresponding reflection phase distribution for both pencil beam and tilted beam radiation patterns, are obtained. Section 5 introduces the procedures for obtaining the reflection phase distribution for a reflectarray with a flat-top radiation pattern using GA. Section 6 discusses the analysis of the unit cell of the reflectarray. Section 7 presents the results and discussions of three reflectarray designs: broadside pencil beam, tilted pencil beam, and flat-top radiation pattern. The reflectarray of the flat-top radiation pattern was fabricated and measured to show the experimental verification. Finally, Section 8 presents the conclusion.
## 3 Results and Discussion
### Effect of a Single Factor on the AS and Suspension of EFLPs  
#### 3.2.1 EFLP Diameter Size  
The data from the tests on the effects of diameter size on the AS and suspension of EFLPs (Figure 1) showed that regardless of their particle diameter size, their \(\upeta\) values clearly varied in the initial stage (about 6 h) of the test process and then gently reached a stable balance over 36 h in all the studied systems. This meant that although a large portion of the EFLPs had already precipitated, there was still a portion being dispersed and suspended and then becoming stable in the solutions. So, these EFLPs had the potential to be suspended and transported in the groundwater along with the groundwater flow, which is consistent with the results obtained above by analyzing the \(\zeta\) values.  
The \(\upeta\) values were also different for the three size particles and increased rapidly with increasing sizes, with their maximum values at 25, 34 and 39 h for EFLPs with 3.4, 1.9 and 1.1 \(\upmu\)m D\({}_{50}\) (Figure 1), respectively. Thus, the smaller the particle size, the more difficult the AS of the particle and the stronger its dispersion and suspension in the groundwater. In any case, regardless of the particle size, the \(\upeta\) value reached a stable balance over 36 h. This was consistent with the results from the column tests [16], implying that diameter size is an important factor in the stability of EFLPs dispersed and suspended, and then a certain amount of these EFLPs could be kept suspended in the studied systems. Considering the low velocity of groundwater flow, these EFLPs could achieve sus  
Figure 1: Effects of particle diameter size on the AS rate of EFLPs.  
short transport distance in porous media, benefiting their suspension and transport along the groundwater flow.
## 3 Methodology
### Study Area  
As a preliminary step, an analysis of the current traffic patterns and characteristics was conducted considering the Lombok Strait, Indonesia as the study area depicted in Figure 3.  
As an archipelagic country, Indonesia established three major international passages to incorporate worldwide shipping: archipelagic sea lanes (ASLs) I, II, and III. The Lombok Strait is a strait adjoining the Makassar Strait, located at ASL II. This strait is located between the well-known islands of Bali and Lombok, which connects the Indian Ocean with the Bali Sea and Makassar Strait as an adjoining of archipelagic sea lanes and serves as one of the chokepoints in the maritime supply chain [35]. This area has VTS and TSS, which were established in 2020 and currently also act as associated protective measures for the newly approved and established particularly sensitive sea areas (PSSAs) of Nusa Penida Island and Gili Island. The width of the TSS is 5.40 nautical miles (nm) with a 0.5 nm separation zone, and the total length of the TSS is 37.79 nm. The TSS design reflected the recommended track that had already been established for ASL as guidance for all transiting ships, as depicted in Figure 3a.  
Studies in the Lombok Strait have pointed to this area as one of the most important alternative routes for seaborne trade flow through the Malacca Strait, and the closure of the Malacca Strait and South China Sea will increase the trade volume by weight (157-270%) and by value (1500%) in the Lombok and Makassar Straits [36; 37]. In addition, the Lombok Strait is one of the major routes in the Asia-Pacific region alongside the Alas Strait, which connects Australia to Asia. For example, 82% of the iron ore exported to China comes from Australia [38], and most ports are located in the western part of Australia. Port Hedland is the major port for exporting natural resources such as iron ore to the world, exported 474 million tons of iron ore in 2716 ships to China in 2023 alone [39].
## 1 Introduction
### Research Methodology  
This article presents the results of selected measurement methods carried out in the zone of discontinuous deformations occurring in the right-of-way. The basic research to assess the evenness of the pavement was the measurement with a laser profilograph. This measurement makes it possible to determine the IRI describing the longitudinal evenness of the pavement. The IRI is considered to be one of the basic parameters used to describe the degradation of the technical condition of the pavement [14; 36]. The IRI is expressed as the value of the longitudinal evenness of the pavement for a 50 m-long section, characterized by linear discontinuous deformations of the pavement, often appearing as a single phenomenon over a 50 m section; of course, sometimes they are phenomena occurring in a group of several successive ones.  
Therefore, the research and analysis were directed to answer the following questions:  
* Can the measurement with a laser profilograph and the IRI determined on its basis identify a single inequality in the field?  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ride Quality**} & \multicolumn{6}{c}{**IRI Thresholds at Different Speeds (m/km)**} \\ \cline{2-7}  & **20** & **40** & **60** & **80** & **100** & **120** \\ \hline Very Good & \textless{}5.72 & \textless{}2.86 & \textless{}1.90 & \textless{}1.43 & \textless{}1.14 &  
Do other methods currently used in surveying enable precise localization of linear discontinuous surface deformations?  
The research used commonly used geodetic measurements: technical leveling and GPS satellite positioning, in order to determine the displacements of points forming the observation lines along the right-of-way. For the detailed identification of linear discontinuous deformations, low-ceiling photogrammetry was used with the use of an unmanned aerial vehicle. The graph of the measurement methods used in this research is shown in Figure 2.  
The comparison of the selected methods and their advantages and disadvantages allowed for the assessment of their usefulness in visual representation zones of linear discontinuous deformations.
# A Precise Prediction of Tunnel Deformation Caused by Circular Foundation Pit Excavation
## 4 Result Interpretation
### Tunnel Diameter Change  
Figure 6 shows the tunnel diameter (\(D\)) change varying with \(H_{\text{ec}}\)/C (defined as the unloading ratio), where \(H_{\text{ec}}\) is the current excavation depth and \(C\) is the tunnel cover depth. The positive value represents the elongation, while the negative value represents compression. The tunnel lining caused by both basement excavation types was elongated vertically and compressed horizontally. It is clear that with the increase of the basement excavation depth, the elongation (\(\Delta D_{V}\)) and compression (\(\Delta D_{H}\)) of the tunnel lining increased. The square and circular basement resulted in a maximum change in the tunnel diameter by 0.09% \(D\) and 0.11% \(D\), respectively. The maximum tunnel diameter change caused by the circular basement excavation was 22% larger than that caused by the square basement.  
To better understand the tunnel diameter change for the two different types of basement excavation, the earth pressure change around the tunnel is shown in Figure 7, where it can be seen that the maximum earth pressure change occurred in the soil around the tunnel crown, while a smaller change occurred at the springlines, and little change occurred at the invert. Because the earth pressure reduction was larger at the crown than at the springlines, the tunnel lining was elongated vertically and compressed horizontally as shown in Figure 6. By comparing the two types of basement excavation, the earth pressure change at the tunnel crown and invert was almost the same because of the same excavation amount assumed by the model. However, owing to the effect of arching, the earth pressure change for the other soil elements of the square basement was smaller than that in the circular basement excavation. Thus, the tunnel diameter change caused by the circular basement excavation was larger than that caused by the square basement, as shown in Figure 6. Based on the recommendations of the British Tunnelling Society [46], the maximum tunnel lining change ((\(\Delta D_{V}+\Delta D_{H}\))/\(D\)) should not exceed 2%. Thus, it was determined that the maximum deformation of the tunnel (i.e., 0.16% \(D\)) was smaller than the proposed allowable limit.  
Figure 6: Comparison of tunnel diameter change.
## Appendix A  
The electric dipole moments (ED) inside the unit cell can be calculated by integrating the induced current \(J\) over the whole volume:  
\[P=\frac{i}{\omega}\int Jdr, \tag{1}\]  
with  
\[J=-i\omega\varepsilon_{0}\left(n^{2}-1\right)E, \tag{2}\]  
Here, \(\omega\) denotes the angular frequency, \(E\) is the electric field, and \(\varepsilon_{0}\) is the vacuum permittivity. Then the magnetic dipolar (MD) moments of the unit cell can be expressed as:  
\[m=\frac{1}{2c}\int\left(r\times J\right)\,dr, \tag{3}\]  
where \(c\) is the speed of light in a vacuum. The electric and magnetic quadrupole moments (EQ and MQ) can be written as:  
\[\begin{array}{l}Q_{\alpha\beta}^{e}=\frac{1}{J\omega}J\left[\tau_{\alpha}J_ {\beta}+r_{\beta}J_{\alpha}-\frac{2}{3}\delta_{\alpha\beta}(r\cdot J)\right]\, dr,\\ Q_{\alpha\beta}^{m}=\frac{1}{3c}\int\left\{\left[r\times J\right]_{\alpha}r_{ \beta}+\left[r\times J\right]_{\beta}r_{\alpha}\right\}\,dr,\end{array} \tag{4}\]  
where \(\delta\) is the Dirac delta function and the subscripts \(\alpha\), \(\beta=x\), \(y\), \(z\). Then we can obtain the scattering cross-sections associated with P, MD, EQ, and MQ, which are given by:  
\[\begin{array}{l}C_{sca}^{(ed)}=\frac{k_{0}^{4}}{6\pi|E_{0}|^{2}}|p|^{2},C_{sca }^{(md)}=\frac{k_{0}^{4}\mu_{0}}{6\pi|E_{0}|^{2}}|m|^{2},\\ C_{sca}^{(aq)}=\frac{k_{0}^{6}}{720\pi|E_{0}|^{2}}\Sigma\Big{|}Q_{\alpha\beta}^ {e}\Big{|}^{2},C_{sca}^{(mq)}=\frac{k_{0}^{2}}{80\pi|E_{0}|^{2}}\Sigma\Big{|}Q _{\alpha\beta}^{m}\Big{|}^{2},\end{array} \tag{5}\]  
assuming the electric field amplitude of the incident plane wave is \(E_{0}=1\) V/m and the permeability in a vacuum is \(\mu_{0}=1\).
## Nomenclature  
\begin{tabular}{l l} Al\({}_{2}\)O\({}_{3}\) & Alumina \\ \(\Delta P\) & Differential pressure (Pa, bar) \\ \(\Delta m\) & Differential mass (mg) \\ \(\Delta d\) & Differential pore diameter (m) \\ \(d_{p}\) & Pore diameter (m) \\ \(d_{mg}\) & Mean pore diameter (m) \\ FM & Face mask \\ GLDP & Gas liquid displacement porometry \\ HgP & Mercury porometry \\ \(h\) & Distance between the liquid reservoir level and sample surface (LEP porometry) \\ IFTS & Institut de la Filtration et des Techniques Separatives \\ \(J\) & Volume flow (m\({}^{3}\)/s) \\ \(L\) & Permeability (m\({}^{3}\)/Pa s) \\ LEIP & Liquid intrusion/extrusion porometry \\ LEP & Liquid extrusion porometry \\ LLDP & Liquid-liquid displacement porometry \\ \(m\) & Drained mass (mg) \\ MF & Micro Filtration \\ MWCO & Molecular Weight Cut Off (Da) \\ PF & Particle filtration \\ PEMFC & Polymer Electrolyte Membrane Fuel Cells \\ \(P_{G}\) & Gas pressure (Pa) \\ \(P_{Atm}\) & Atmospheric Pressure (Pa) \\ \(P_{C}\) & Capillary pressure (Pa) \\ PET & Polyethylene membrane \\ PSD & Pore size distribution \\ UF & Ultrafiltration \\
**Greek Letters** & Interfacial or surface tension of the fluid pairs and membrane surface (mN/m) \\ \(p\) & Liquid density (Kg/m\({}^{3}\)) \\ \(\theta\) & Contact angle (\({}^{\circ}\)) \\ \end{tabular}