esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Results
#### 4.4.5 Use-Case #2  
For the second used case, we consider images from the work of [PERSON] et al. [58] (Figure 14), which is a chronological evolution of an ulcer in a diabetic male patient who is 54 years old with type 2 diabetes, with image A showing the initial appearance of the ulcer, Image B showing the evolution of the DFU after 11 days, and Image C after 12 days.  
From Figures 15 and 16, we can see that semantic similarity is closest to Infection on day 1 for image A. According to the process applied by the doctor after debridement of the lesion on day 11 [58], and the treatment protocol consisting of application of topical administration of AgNPs solution, we have the image after 12 days, which shows a drastic improvement as observed on both the radar charts, implying the ulcer is responsive to the treatment. However, based on the conclusion we studied in the work of [PERSON] et al. [58], where they seem to be more satisfied with the result on day 13, our model tends to show a regression from day 12 to day 13 in both the radar plots. They tend to again point towards a higher similarity to infection.  
We shall now analyze what the numbers show in terms of similarity from Tables 5 and 6. When the cosine distance is used on arrival on day 1, the similarity is clearly set to infection. However, once treatment is started, we can see that on day 12 there is a higher similarity to none and healthy, which is the same for Euclidean distance. For day 13, there is also the highest similarity to none, but instead of an increased similarity to both none and healthy, it shows a slight reduction. There may be an influence on the way the images are taken, which opens up the need to have a clearly defined protocol for the DFU images.  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **None** & **Infection** & **Ischemia** & **Both** & **Healthy** \\ \hline
**Image A** & 0.10 & **0.43** & 0.06 & 0.00 & 0.09 \\ \hline
**Image B** & 0.14 & 0.04 & 0.00 & **0.17** & 0.12 \\ \hline
**Image C** & **0.46** & 0.00 & 0.06 & 0.20 & 0.36 \\ \hline \end{tabular}
\end{table}
Table 4: Results by applying Euclidean distance for Use-Case #1.  
Figure 14: DFU images for Use-Case #2 [58].  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **None** & **Infection** & **Ischemia** & **Both** & **Healthy** \\ \hline
**Image A** & 0.45 & **0.81** & 0.41 & 0.39 & 0.47 \\ \hline
**Image B** & 0.54 & 0.49 & 0.36 & **0.62** & 0.54 \\ \hline
**Image C** & **0.77** & 0.27 & 0.30 & 0.53 & 0.71 \\ \hline \end{tabular}
\end{table}
Table 3: Results by applying cosine distance for Use-Case #1.  
Figure 16: Longitudinal similarity for Use-Case #2: Euclidean distance.  
Figure 15: Longitudinal similarity for Use-Case #2: cosine similarity.
###### Abstract  
In this paper, a Spidron fractal dipole antenna with a ferrite-loaded artificial magnetic conductor (AMC) is presented. By applying ferrite composed of nickel-zinc with a high permeability value, a compact AMC that operates in the broadband frequency range within the high-frequency/very-high-frequency/ultra-high-frequency (HF/VHF/UHF) bands was designed. A Spidron fractal-shaped dipole antenna with a quasi-self-complementary structure was designed and combined with a miniaturized ferrite-loaded AMC. This allowed the designed AMC-integrated dipole antenna to operate in a wide frequency band, covering the HF/VHF/UHF bands, with low-profile characteristics. A prototype of the proposed Spidron fractal dipole antenna with the AMC was manufactured and measured and found to meet low VSWR (voltage standing wave radios) specifications of <3.5 within the 20-500 MHz bandwidth range. The simulated and measured results are in good agreement. The size of the Spidron fractal dipole antenna with the AMC is \(0.03\times 0.026\times 0.001\,\lambda^{3}\) relative to the wavelength of the lowest operating frequency. The received power of the Spidron fractal dipole antenna with the AMC was also measured when it was applied to relatively small applications, such as a mancpack in this case.  
artificial magnetic conductor (AMC); dipole antenna; ferrite; HF/VHF/UHF; low-profile; Spidron fractal +
Footnote †: journal: Applied Physics Letters
# Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
## 2 Materials and Methods
### Methods  
Firstly, to define the chemical composition (i.e., possible differences) of the three different samples of sand particles, micro X-ray fluorescence (XRF) analyses were carried out. XRF elemental analyses were performed by using the M4 TORNADO (Bruker Corporation; Billerica, MA, USA), an energy-dispersive X-ray spectrometer. In more detail, 8 micro-XRF spectra were collected for each sample, and the average values were discussed. Hyperspectral (HS) images were acquired using a NIR Spectral Camera equipped with an ImSpector N17E (SPECIM Ltd., Oulu, Finland) (Figure 4). The system operates in the NIR wavelength range (1000-1700 nm), with a spectral resolution of 5 nm. The imaging spectrometer is coupled with an InGaAs photodiode array positioned above a light source. The light source comprises a cylindrical architecture with diffuse light, housing five halogen bulbs. This setup is mounted on a conveyor belt, capable of variable-speed movement up to 50 mm/s (DV S.R.L., Padova, Italy). The HS images were then analyzed using PLS_Toolbox (version 9.1; Eigenvector Research, Inc., Wenatchee, WA, USA) and  
Figure 3: Reference polymer samples (i.e., laboratory standards) were selected for training the classification model: PP, PE, PS, PVC, and PET.  
Finally, to validate the polymer identification results obtained through the proposed HSI-based classification approach, Fourier Transform Infrared (FT-IR) spectroscopy was employed. Specifically, the Attenuated Total Reflection (ATR) FT-IR device, namely the IRAffinity-IS spectrophotometer (Shimadzu Corp., Kyoto, Japan), operating in the 4000-400 cm\({}^{-1}\) range, was utilized.
# FEM Analysis of Superplastic-Forming Process to Manufacture a Hemispherical Shell
###### Abstract  
Superplastic materials are characterised by extreme lightness and remarkable ductility. Instead of a punch, a gas is used to push the sheet into the die cavity, and it is precisely regulated to control the material's strain rate. Forming a superplastic material while maintaining a high strain rate sensitivity index requires the forming gas to follow a precise pressure-time loading curve. This can be excellently predicted with the aid of the finite element method (FEM). Therefore, for the superplastic material to exhibit its best formability throughout the entire process, it is necessary to control the strain rate step by step to keep the maximum strain rate within the material's optimal superplastic range. In this work, the results of a superplastic-forming process used to create a hemispherical shell are presented. This was carried out using both a circular blank of uniform thickness and a blank with a conical cross-section. The analysis was performed using finite element modelling. Specifically, the results obtained using 3D analysis were compared with those obtained using axisymmetric analysis for conditions of axial symmetry. Using the conical cross-section blank helped achieve a more uniform thickness distribution in the produced hemispherical shell. The success of the numerical activity was validated through results from appropriate experimental work conducted on the magnesium alloy AZ31. The results show that, by employing a blank characterised by a conical section profile, the thickness distribution appears more uniform than that of a constant-thickness blank.  
superplastic forming; finite element analysis; hemispherical shell; magnesium alloy 2025  
5 June 2025  
10 July 2025  
19 July 2025
# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 4 Retinal Vessels Segmentation Techniques
### Model-Based Techniques  
The concept of deformable model is used to describe a set of computer vision techniques and algorithms that abstractly model the variability of a certain class of objects in an image (vessels in retina image). The most basic versions of these algorithms concern shape variations modeling, where the shape is represented as flexible curve or surface, and is then deformed to match specific instance of the object class [108]. The deformation is not an arbitrary process, rather it is performed based on two powerful theories, _Energy Minimization_ and _Curve Evolution_, which have roots in physics, geometry and approximation theories [109]. Deformable models can be divided into two main categories: parametric and geometric ones [110].
## 3 Description of the Proposed VLC Prototype
### Hardware Design and Implementation
#### 3.1.1 Discussions on the Design of the VLC Emitter  
In order to demonstrate that any LED-based traffic light can be updated in order to become an intelligent data broadcasting device, the VLC emitter is developed based on a standard 200 mm LED-based traffic light. The commercial traffic is in agreement with the SR EN 12368 [68] and the SR EN 60598-1 [69] standards and its emitted light has an irradiance of 190 \(\upmu\)/cm\({}^{2}\) (i.e., measured at 1 m).  
This is a very important aspect, because, in other works, custom made VLC emitters having different characteristics are used. Usually, these custom made traffic lights have powers that are significantly higher than the ones that are specified in the traffic equipment standards [32]; they are using a significantly higher number of LEDs to enable MIMO communications with camera-based VLC receivers [60] or they have a modified light radiation pattern. All of these modifications enable such systems to achieve enhanced performances in terms of communication distances, data rates, and bit error ratio (BER). Although, for technology demonstration purposes, these approaches are fully justified, as they provide a hint on the achievable performances, the usage of standard complying equipment should be investigated, as it provides a realistic view on the performances in real situations. Thus, using commercial traffic lights that are similar to the ones that are found in every intersection and that follow the requirements of the existing standards provides an important step in confirming the compatibility between the VLC technology and automotive applications. The LED  
Figure 2: Diagram of the visible light communications system. The VLC receiver encloses a logarithmic transimpedance circuit that is able to receive signals at variable power intensities, adaptive gain, adaptive filters, adaptive triggering, and adaptive decoding. These functions enable it to have an optimal configuration adapted to the changeable atmospheric conditions.  
traffic light has been enhanced with an external module consisting of a data processing unit and a digital power switch. A 180 MHz microcontroller has been used for data processing due to cost and efficiency reasons. The microcontroller is the central element of the VLC emitter, as it is responsible for message generating, coding, modulating, and frame building. Based on the data to transmit sequence, the microcontroller commands the switching of the LEDs through the digital power switch. Accordingly, an amplitude-modulated light beam is generated and transmitted through the free space optical channel, as illustrated in Figure 3.
# A Method of Accuracy Increment Using Segmented Regression
## 5 Analysis of Proposed Method Based on Statistical Simulation
Such an assumption can be proven using the chi-squared test with high confidence probability. The breakpoint estimation bias has preferable values when the general paraboloid method is used. However, the benefit is negligible and averages 0.337% compared with the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistical Characteristic** & **General Paraboloid** & **Simplified Paraboloid** \\ \hline Mathematical expectation of \(x_{\text{br1}}\) & 25.746 & 25.753 \\ \hline Standard deviation for \(x_{\text{br1}}\) & 2.218 & 2.13 \\ \hline Minimum of \(x_{\text{br1}}\) & 17.158 & 17.07 \\ \hline Maximum of \(x_{\text{br1}}\) & 36.143 & 36.306 \\ \hline Skewness for \(x_{\text{br1}}\) & –0.055 & 0.054 \\ \hline Mathematical expectation of \(x_{\text{br2}}\) & 70.113 & 70.35 \\ \hline Standard deviation for \(x_{\text{br2}}\) & 2.397 & 2.274 \\ \hline Minimum of \(x_{\text{br2}}\) & 62.138 & 63.486 \\ \hline Maximum of \(x_{\text{br2}}\) & 84.242 & 83.238 \\ \hline Skewness for \(x_{\text{br2}}\) & 0.583 & 0.536 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Numerical characteristics of breakpoint abscissas estimates. Figure 6: Histograms for breakpoint abscissas for different optimization options: (**A**)—estimates of the first breakpoint for general paraboloid; (**B**)—estimates of the second breakpoint for general paraboloid; (**C**)—estimates of the first breakpoint for simplified paraboloid; (**D**)—estimates of the second breakpoint for simplified paraboloid. simplified paraboloid method. The highest percentage of estimate bias (in relative values) is 3.012%. In the case of a long-term breakpoint, the simplified paraboloid method has, on average, a narrower range of change of breakpoint estimates. Let us analyze the proposed method in comparison with the method of simple enumeration. To obtain the approximately 3% of breakpoint abscissas estimate bias, the method of simple enumeration requires at least 33 possible values for each breakpoint. Therefore, it is necessary to repeat computations for at least 1089 iterations in the case of two breakpoints. At the same time, the proposed method requires 25 iterations and additional calculations of the paraboloid optimum. Therefore, the proposed method reduces the computing time by at least 30 times compared to the method of simple enumeration. A comparison of the simulation results for a range of initial data provides the ability to conclude approximately the same accuracy characteristics for SLR models based on general and simplified paraboloid usage. Therefore, in practical cases, the adoption of the simplified paraboloid method usage is more advantageous when creating a segmented regression model because of the reduction in computations and calculation time.
# TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
## 4 Experiment
### Evaluation of SST Prediction  
In this section, we first evaluated the predictive performance of TD-LSTM for sea surface temperatures. Taking the world's largest sea-coral sea area as an example, five observation points were randomly selected, a time-dependent parameter matrix fusion was performed on their historical observation data, and the TD-LSTM model was trained using the fused sequence to predict the temperature of the next month. We set the time-dependent parameter of TD-LSTM to \(l_{c}=6\), \(l_{p}=2\). For better comparison, other methods have the same input sequence as TD-LSTM. We also set different input sequence lengths for comparison, which were 8 (months) and 24 (months), respectively. Since 8 hasthe same sequence length as \(l_{c}=6\), \(l_{p}=2\), 24 has the same time span as \(l_{c}=6\) and \(l_{p}=2\). Table 1 shows the MSE of all methods on BOA_Argo at SST of Coral Sea. The results show that TD-LSTM performance is not the most prominent at each point, but the average MSE is the best. In other words, TD-LSTM is better than other methods in overall forecasting. Figure 5 shows the average MSE for each model at 5 locations. It can be seen that TD-LSTM has the best performance. TD-LSTM is 1.6% better than 8-LSTM, 4.3% better than 24-LSTM, 4% better than TD-SVR, 25.1% better than 8-SVR, 6.2% better than 24-SVR, and 11.7% better than TD-MLPR. It is 40.9% better than 8-MLPR and 36.1% better than 24-MLPR.
## 1 Introduction
Design considerations and calculations required for realization of a very inexpensive point-to-point (PTP) MMW communication channel, in general, will be given in this study.
## 1 Introduction
Moreover, the influence of the sulphonation reaction on the porous structure of the chemically modified polymer, as well as its sorption properties towards phenols and pharmaceuticals was evaluated. All the polymers were synthesized via a suspension method using toluene as a basic pore-forming diluent. To prepare polymers with modified porous structure, 1 mL of toluene was replaced with an appropriate amount of phenol (P), 2,4,6-trichlorophenol (T) or their mixture (M). Moreover, the chemical modification of pDVB was performed in order to introduce sulfonic groups onto the polymer surface. The prepared materials were tested in the sorption processes of phenolic compounds (a multicomponent mixture consisted of phenol, 2-chlorophenol, 2,4-dichlorophenol and 2,4,6-trichlorophenol), salicylic acid and ibuprofen using dynamic method--solid phase extraction (SPE). The elemental and ATR-FTIR analyses confirmed the chemical structure of the tested materials and the correct course of the modification process. The polymers possessed well developed porous structure with specific surface areas of 440 to 560m\({}^{2}\)/g and bimodal pore size distribution. It was shown that the type of the porogen, as well as the sulphonation reaction influenced the porosity of pDVB polymers. SPE experiments revealed that changes in the porous structure of the polymers had an impact on their sorption properties. In the case of 2,4-dichlorophenol and 2,4,6-trichlorophenol, pDVB-T and pDVB-M had higher sorption capacity than pDVB, pointing out the positive role of 2,4,6-trichlorophenol in the formation of pores suitable for the sorption of these compounds. The similar effect was observed for ibuprofen uptake. The opposite effect was obtained for salicylic acid.
## 4 Methodology
### Overview of the Deep Learning Models Used for Classification
#### 4.2.5 DenseNets  
Introduced by [PERSON] et al., Densely Connected Convolutional Networks, or DenseNets, expand upon ResNets' skip connection framework [64]. DenseNets enhance network connectivity by ensuring each layer receives input from all preceding layers, facilitating maximal information flow. The hallmark _dense block_ sees layers incorporating feature maps from all prior layers, enhancing gradient flow and fostering feature reuse, thereby improving efficiency and accuracy, while minimizing parameter counts [64].  
In a dense block, each layer \(l\) is connected to all previous layers. Mathematically, the output of the \(l\)-th layer, denoted as \(x_{l}\), is defined as:  
\[x_{l}=H_{l}([x_{0},x_{1},x_{2},\dots,x_{l-1}]), \tag{6}\]  
where \(H_{l}\) represents the composite function of batch normalization (BN), followed by a rectified linear unit (ReLU) and a convolution (Conv) operation, and \([x_{0},x_{1},x_{2},\dots,x_{l-1}]\) denotes the concatenation of the feature maps produced by all preceding layers 0 to \(l-1\). This dense connectivity pattern ensures that each layer has direct access to the gradients from the loss function and the original input signal, improving gradient flow and promoting feature reuse.  
DenseNets are specified by their layer counts, such as DenseNet121 or DenseNet201, which indicate the depth of the network. Between dense blocks, _transition layers_ are introduced to manage feature map dimensions. A transition layer typically consists of batch normalization, a \(1\times 1\) convolution to reduce the number of feature maps, and a \(2\times 2\) average pooling operation to down-sample the feature maps. Mathematically, a transition layer can be expressed as:  
\[x_{out}=\text{AvgPool}(H_{t}(x_{in})), \tag{7}\]  
where \(H_{t}(x_{in})\) represents the transformation applied by the batch normalization and \(1\times 1\) convolution operations on the input feature maps \(x_{in}\) and AvgPool denotes the average pooling operation. Figure 7 illustrates the DenseNet121 architecture along with the proposed dense blocks and transition layers.  
Figure 6: ResNet50 architecture.  
This architecture's efficacy lies in its robust feature representation and reduced redundancy. By leveraging dense connections, DenseNets improve the parameter efficiency and enhance the learning process, resulting in models that are not only powerful, but also efficient for various deep learning tasks.
# Train-Induced Unsteady Airflow in a Metro Tunnel with a Ventilation Shaft
## 3 Results and Discussions
### The Development Law of Piston Wind Speed
#### 3.1.1 PW-WVS  
Figure 5a shows the development of the piston wind induced by the metro train in a tunnel with a ventilation shaft. \(v_{u}\) and \(v_{d}\) are the upstream and downstream piston wind speeds of the tunnel, respectively. The area enclosed by the curves in Figure 5a (marked as \(Q_{T-in}\) and \(Q_{T-out}\)) represents the total air volume quantity flowing into (from the tunnel entrance) and out (from the tunnel exit) of the tunnel, respectively. Due to the presence of the ventilation shaft, the development of the piston wind upstream and downstream of the tunnel is significantly different. Both \(v_{u}\) and \(v_{d}\) increase as the train enters the tunnel and then continue to increase until the train is completely running inside. More concretely, \(v_{u}\) increases at a faster rate and reaches its maximum value, \(v_{u-max}\) when the train arrives at the shaft position. As the train is passing by the ventilation shaft, \(v_{u}\) starts to decrease and \(v_{d}\) continues to increase. Later, when the metro train travels to the downstream of the tunnel, \(v_{u}\) continues to decrease. On the contrary, \(v_{d}\) continues to increase to its peak value, i.e., \(v_{d-max}\), and maintains this speed until the train leaves the tunnel. Both \(v_{u}\) and \(v_{d}\) begin to decrease once the train leaves and finally drop to the same value. The effect of shaft parameters on \(v_{u}\) and \(v_{d}\) will be discussed in Section 3.2.  
Figure 5: Development law for the piston wind (**a**) PW-WVS, (**b**) PW-NVS.
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## 1 Introduction
### SDN Overview
In other words, SDN will enable the dynamical changes of paths to higher-bandwidth and lower-latency paths and traffic prioritization [2]. Network services will be provisioned as easily as web services. As the operating systems of personal computers provide a programming abstraction of the underlying hardware, the future network operating systems (NOSs) will be developed to provide a centralized network abstraction of the underlying network elements with an intuitive interface and the perception of seemingly infinite network resources for the users [6].