esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
AI has transformed the management of DFUs by delivering significant advancements in classification, prediction, segmentation, and detection. Traditional approaches, such as visual assessments and manual measurements of wound dimensions, vary depending on the clinician's experience. On the other hand, AI-driven tools enable highly accurate early detection of ulcers through advanced image processing techniques and machine learning models that extract features, such as color intensity and texture metrics.  
Generative AI has played a critical role in overcoming one of the most critical challenges in medical AI, which is data scarcity. The limited availability of diverse datasets has hindered the development and generalizability of robust AI models, particularly in DFU management, where privacy concerns often restrict the collection of high-quality medical data. By creating realistic synthetic datasets through advanced techniques such as GANs and diffusion models, generative AI provides a solution to this problem. These synthetic datasets allow researchers and developers to validate models while reducing dependence on large, costly real-world data collections.  
Integrating smartphone applications into DFU management helps solve key challenges in traditional healthcare delivery, such as frequent hospital visits and limited specialist access. These applications enable monitoring, measuring wound dimensions, and tracking healing progress. Clinicians can remotely review patient data and provide timely recommendations, making these tools particularly valuable for the early detection of DFUs.
# Magnetooptics in Cylindrical Structures
## 4 A Simple Cylindrically Layered Structure  
The structure consists of a core in the region \(0\leq\varrho\leq a\) characterized by \(\tilde{\epsilon}^{(1)}\) and \(\tilde{\mu}^{(1)}\), and a cladding in the region \(a\leq\varrho\) characterized by \(\tilde{\epsilon}^{(2)}\) and \(\tilde{\mu}^{(2)}\) (Figure 1). To operate as a waveguide, the general solutions, Equation (17), of Equation (16) are replaced in the core by a set of Bessel functions of first kind, i.e., \(\mathcal{Z}_{v}^{(1)}\left(\chi_{z}^{(1)}\varrho\right)=\mathcal{J}_{v}\left( \chi_{z}^{(1)}\varrho\right)\) and \(\mathcal{Z}_{v}^{(1)}\left(\eta_{z}^{(1)}\varrho\right)=\mathcal{J}_{v}\left( \eta_{z}^{(1)}\varrho\right)\). In the cladding, the acceptable solutions are given by a set of modified Bessel functions of third kind deduced from \(\mathcal{Z}_{v}^{(2)}\left(\chi_{z}^{(2)}\varrho\right)=\mathrm{j}^{-\
u-1} \frac{2}{\pi}\mathcal{K}_{v}(\xi_{z}^{(2)}\varrho)\) and \(\mathcal{Z}_{v}^{(2)}\left(\eta_{z}^{(2)}\varrho\right)=\mathrm{j}^{-\
u-1} \frac{2}{\pi}\mathcal{K}_{v}\left(\xi_{z}^{(2)}\varrho\right)\) for \(\eta_{z}^{(2)}=\mathrm{j}^{(2)}_{z}\) and \(\chi_{z}^{(2)}=\mathrm{j}^{(2)}_{z}\). It is convenient to introduce the effective index of refraction, \(N=\beta\lambda_{\mathrm{vac}}/\left(2\pi\right)\), the relative magnetic permeability, \(\kappa_{m\pm}^{(i)}=\mu_{\pm}^{(i)}/\mu_{\mathrm{vac}}\), and the relative electric permittivity, \(\kappa_{e\pm}^{(i)}=\varepsilon_{\pm}^{(i)}/\varepsilon_{\mathrm{vac}}\). The squared transverse propagation, \(\kappa_{\pm}^{2}\), and attenuation, \(\gamma_{\pm}^{2}\), constants are defined as,  
\[\kappa_{\pm}^{(1)2} = \left(\frac{2\pi}{\lambda_{\mathrm{vac}}}\right)^{2}\left(\kappa _{m\pm}^{(1)}\kappa_{e\pm}^{(1)}-N^{2}\right)\equiv\kappa_{\pm}^{2}, \tag{19a}\] \[\kappa_{\pm}^{(2)2} = \left(\frac{2\pi}{\lambda_{\mathrm{vac}}}\right)^{2}\left(\kappa _{m\pm}^{(2)}\kappa_{e\pm}^{(2)}-N^{2}\right)\equiv-\gamma_{\pm}^{2}. \tag{19b}\]  
The symbols \(\lambda_{\mathrm{vac}}\), \(\mu_{\mathrm{vac}}\), and \(\varepsilon_{\mathrm{vac}}\) denote the radiation wavelength, magnetic permeability, and electric permittivity in a vacuum, respectively. Deduced from the boundary conditions at the interface \(\varrho=a\), the eigenvalue equation takes the form,  
Figure 1: Dielectric circular cylindrical waveguide with the axial magnetization, \(\mathbf{M}\), in a circular cylinder coordinate system with the unit vectors \(\hat{\varrho}\), \(\hat{\varphi}\), and \(\hat{z}\). A field point is specified by \(\varrho=\sqrt{x^{2}+y^{2}}\), \(\varphi\), and \(z\). The core region of the radius \(a\), \(\varrho\leq a\), is characterized by the permeability and permittivity tensors, \(\tilde{\mu}^{(1)}\) and \(\tilde{\epsilon}^{(1)}\). The cladding region, \(\varrho\geq a\), is characterized by the permeability and permittivity tensors, \(\tilde{\mu}^{(2)}\) and \(\tilde{\epsilon}^{(2)}\).
## 4 Experiments and Results
### Discussions  
We compare the proposed method with various state-of-the-art models, i.e., the CLBP [5], GLCM [5], LBP [5], LPQ [5], ORB [5], PFTAS [5, 25], VLAD-SVM [24], and CNN approaches in various architectures, i.e., [29; 64; 65; 66; 67], MIL [37], DCGAN [38], IDSNet [40], ResNet50-KWELM [39], Xception-SVM [41], and Deep semantic-GLCM [42]. It is found that the proposed DeepCA feature extraction technique combined with the FCNN classifiers work significantly better than other traditional approaches and the state-of-the-art methods for images at all magnification factors; this is especially true for proposed method 3. In this regard, the classification accuracy obtained by proposed method 3 for the 40\(\times\) magnification factor is the highest at 97.2%, followed by those of the Deep semantic-GLCM [42], DCGAN [38] and proposed method 2, which achieve accuracies of 96.75%, 96.5% and 95.8%, respectively. For the 100\(\times\) magnification factor, proposed method 3 also obtains the highest classification accuracy (97.1%) followed by the Xception-SVM [41] (96.25%) and CNN4 proposed by [67] (95.9%) whereas proposed method 2 and the DCGAN [38] achieve approximate accuracy rates of 94.1% and 94.0%, respectively. Unfortunately, in the case of the 200\(\times\) magnification factor, the proposed methods cannot achieve the best classification accuracy. Proposed method 1, proposed method 2, and proposed method 3 only produce accuracies of 88.8%, 94.9%, and 96.3%, respectively. In contrast, the highest classification accuracy of 97.1% is achieved by the CNN4 followed by Deep semantic-GLCM [42] which achieve accuracies of 96.57%. However, the classification accuracies obtained by the proposed methods are still better than those of several traditional methods. At the 400\(\times\) magnification factor, proposed method 3 and the CNN4 also achieve the highest classification accuracy (96.0%) followed by proposed method 2 (95.3%). As seen in Table 3, although the classification accuracy of the CNN4 is quite consistently better for 40\(\times\), 100\(\times\) and 200\(\times\) magnified images as compared to CNN1 [65], CNN2 [64], DeCAF-CNN [29], CNN3 [66], and the proposed method 3. However, the performance of the proposed methods is obtained by all automatic feature extraction processes, whereas CNN4 combined the CNN with several hand-crafted feature extraction methods. Moreover, Figure 13 illustrates a comparison between the average classification efficiencies of the state-of-the-art approaches and the proposed methods at all magnification factors. It can be seen that the proposed methods, especially proposed method 3, can significantly and effectively classify the input images.
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 4 TSN Standards for Automotive Communications
### TSN Support for Automated Driving
#### 4.6.2 Time Synchronization  
In Automated Driving systems, sensor fusion algorithms need sensor data with timestamps to relate data referring to the same time window. Sensors must therefore synchronize their time to a common time base. In addition, Level 4 and 5 systems also require redundancy, to allow the system to remain operational and reach a minimal risk condition even in the case of the failure of a link or of the grandmaster.  
The TSN standards that enable achieving a common notion of time for sensor fusion are IEEE 802.1 AS-2011 [33] and IEEE 802.1 AS-2020 [4], with its recent revision that improves clock synchronization reliability providing replication mechanisms to be adopted in case of a switch (bridge in the IEEE terminology) fault or frame loss.
# Preconception Health Attitudes and Behaviours of Women: A Qualitative Investigation
###### Abstract  
The preconception period is a critical window in which maternal health can profoundly affect both individual and intergenerational health. Despite its importance, little information about women's preconception health attitudes, behaviours and information preferences exists, yet these details are vital to inform targeted health communication. Semi-structured interviews were conducted to explore women's attitudes to preconception health (areas of importance, support sources, enablers and barriers), behaviours (information seeking and health actions taken) and information preferences. Interviews were transcribed, coded and thematically analysed. Fifteen women participated (\(n=7\) preconception, \(n=7\) pregnant and \(n=1\) postpartum). Women perceived optimising lifestyle behaviours including a healthy diet, regular physical activity, reducing alcohol intake and pre-pregnancy vitamin supplementation as important preconception health actions to adopt. Few women acknowledged the importance of formal preconception health checks and screening with health professionals. Barriers to achieving health behaviour change included anxiety, stress and challenges obtaining reputable information. Participants reported a lack of preconception information about supplementation requirements, safe foods and exercise recommendations. Information preferences included the internet or their general practitioner. Whilst women predominantly prioritised optimising diet and physical activity prior to pregnancy, there appeared to be limited awareness of preconception health checks and screening, highlighting a need for broader awareness of overall preconception health and wellbeing.  
preconception; pre-pregnancy; pre-natal; attitudes; health behaviour; pregnancy planning Article
# SPN-Based Dynamic Risk Modeling of Fire Incidents in a Smart City
## 2 Materials and Methods
### Methodologies for Analyzing the Performance of SPN  
After establishing the SPN model, it is necessary to verify the feasibility of the model from three aspects: safety and boundedness, liveness and reachability, and fairness. After confirming that the model is feasible, performance analysis on the SPN is carried out [17]:  
1. Construct the SPN model of the system.
2. Establish a Markov chain [19] that is isomorphic to this model.
3. Based on this Markov chain, calculate the steady-state probabilities of various states of the system, enabling an in-depth analysis and evaluation of relevant performance indicators in the fire rescue process, such as discrete event points.  
After obtaining the isomorphic Markov chain, according to the relevant theorems of the Chapman-Kolmogorov equation and the stationary distribution of MC, the matrix Equation (2) is obtained:  
\[\left\{\begin{array}{l}\mathrm{Y}Q=0\\ \mathrm{\sum\limits_{i=1}^{n}Y_{i}=1}\end{array}\right. \tag{2}\]  
Here, Y represents the steady-state probability vector, Q is the infinitesimal generator matrix of the Markov chain, and \(Y_{i}\) denotes the steady-state probability of the i-th state. The equation ensures that the sum of all steady-state probabilities equals 1, reflecting the normalization condition of the system.  
The delay of each transition follows a continuous random variable with an exponential distribution. Through the calculated steady-state probabilities of the Markov chain, further analyses are conducted on the busy rate of places, the utilization rate of transitions, and the average delay time, etc.
## 3 Rational for Optimization in WSNs
### Domains of Optimizations in Wireless Sensor Networks
#### 3.2.3 Energy Efficiency _vs_. Security  
As wireless sensor networks are rapidly growing, the need for effective security approaches are also becoming important. Many sensor networks have mission-critical tasks and may interact with sensitive data such as military applications, so it is clear that security needs to be taken into account at the time of design. While WSNs derive from wireless _ad hoc_ networks, due to inherent resource and computing constraints, security in sensor networks poses different challenges than the traditional network security.  
All security approaches require some amount of resources for implementation, including data memory, storage and energy to run the sensors. However, these resources are very limited in wireless sensor nodes and they are non-replenishable, so in order to build an effective security mechanism, it is necessary to limit the code size of the security algorithm.  
For example, a common sensor which is relatively cheap and widely used in the research area is the TelosB. It has 16-bit, 8 MHz RISC CPU with only 10 KB RAM, 48 KB program memory, and 1,024 KB flash storage [32]. With this limitation, the operating system (OS) built for the sensors must also be quite small. The total code space of TinyOS, the standard OS for wireless sensors, is approximately 4 KB and the core scheduler occupies only 178 B, so, the code size for any security- related code must also be small.  
Limited power or energy is the biggest constraint for wireless sensor networks. We assume that once the sensor nodes are deployed in a network, they cannot be easily replaced or recharged. Thus, charge taken with them to the final location must be conserved to extend the lifetime of the individual sensor node along with the sensor network. When implementing a cryptographic function or security protocol within a network, the energy impact of the added security code must be also taken into account. The extra power consumed by sensor nodes due to the addition of security, is related to the processing required for security functions like encryption, decryption, data signing, _etc._, so a fine tuning of these two is essential.
## 2 Experimental Setup  
The schematic of the THz imaging system is shown in Figure 1. A dual-mode laser (DML) generates a frequency-tunable optical beating signal, which was converted to the THz radiation by a UTC-PD. The output power of the THz radiation was about 50 microwatt at 300 GHz. Details of the DML and the UTC-PD were described in our previous works [6,10,11]. We used two custom-designed, high-density polyethylene lenses (L1 and L2) and a variable iris for the spatial filtering and the collimation of the THz beam. A membrane-type 50:50 THz beam splitter (BS) [16] was inserted. The transmitted THz beam was mechanically scanned by a 7 cm diameter circular monogon mirror. The mirror surface was tilted by 45\({}^{\circ}\) from its primary rotating axis (axis 1). For two-dimensional beam steering, another rotating axis (axis 2) was inserted, and the center of the mirror surface was located at the crossing of the two axes so that the reflection point on the mirror surface was invariant during the mirror rotation. The reflected THz beam was deflected and focused on the sample surface by a custom-designed telecentric f-\(\theta\) lens (L3). The working distance of L3 was set to be 7 cm, and the distortion-free field of view (FOV) diameter was estimated to be \(\sim\)6 cm with a scanning angle of 40\({}^{\circ}\) (\(-\)20\({}^{\circ}\)\(\sim\)20\({}^{\circ}\)) for both axes. The THz beam reflected from the sample surface was directed and focused on a commercial Schottky barrier diode (SBD) through the BS and an off-axis parabolic mirror (M2) of 5 cm effective focal length. A lock-in amplifier was used, with modulation frequency and time constant of 100 kHz and 300 us, respectively.  
Another motor-actuated translator (axis 3) moved the sample mount for larger samples. Images can either be obtained by axis 1 and 3 (mode 1), or by axis 1 and 2 (mode 2). Axis 1 scanned the beam direction repeatedly in the forward and backward directions. Although the maximum speed of the motor is 300 rpm, the scan speed of axis 1 was set to be 1.4 s/line including acceleration and deceleration time for mechanical stability. Total imaging time depends on the number of lines. It requires 14 min to obtain an image of 600 lines (\(1240\times 600\) pixels). A removable mirror M3 was inserted for homodyne signal amplification, which will be described later. For single-frequency imaging, M3 should be removed to prevent the interference effect.
## 5 Results and Discussions
### Data Comparison of Different Sampling Points  
In general, it is well accepted that the more sampling points are acquired at an active sensor network, the better the signals are measured in an accurate, stable, and practicable manner. Different sampling points signals of S0 mode are extracted to calculate the magnitude/amplitude-based DIs and energy-based DIs in the time-frequency domain. We only select the representative path such as actuator-sensor path A1-S4, as shown in Figures 5 and 6.  
Figure 5: The magnitude and energy damage indexes (DIs) of different sampling points in the time domain (**a**) Normalized magnitude-based DIs; (**b**) Normalized energy-based DIs.  
Figure 6: The amplitude and energy DIs of different sampling points in the frequency domain (**a**) Normalized amplitude-based DIs; (**b**) Normalized energy-based DIs.  
A\({}_{1}\)-S\({}_{4}\) stands for the sampling points of the common length in the time domain, and A\({}_{1}\)-S\({}_{4}\)-L refers to the sampling points of the longer length from the time domain. A\({}_{1}\)-S\({}_{4}\)-f and A\({}_{1}\)-S\({}_{4}\)-L-f correspond to the common length and longer length in the frequency domain, respectively. In the time domain, the magnitude-based and energy-based DIs of two different types of sampling points are exactly identical, as shown in Figure 5. Based on the amplitude-based DIs in the frequency domain, it is found that almost no fluctuations of DIs are observed for the reason that the measured signals are prone to be contaminated with lower noise and improved reliability in practice. Moreover, ultrasonic guided waves are highly dispersive and multimodal, which causes some difficulties in the process of signal feature extraction. Nevertheless, the overall distribution of magnitude/amplitude-based and energy-based DIs are well consistent. Considering that the differences between the magnitude/amplitude-based and energy-based DIs of different sampling points are very small, and they can be ignorable, to some extent. Therefore, the sampling points of the common length are selected to analyze and process the acquired signals in the following studies.
## 2 Materials and Methods
### Data Processing and Interpretation
EKKO Project 5 reflection hyperbola curve fitting procedures, employed only in cases where the GPR transect was perpendicular to the drain line directional trend, allowed soil radar velocities to be refined at each site (from the original TDR measurements), and in turn, improve the accuracy of GPR profile depth scales. Soil radar velocities, refined via EKKO Project 5, were 0.072 m/ns on 5 April 2017 and 0.063 m/ns on 24 January 2018 at Site 1, 0.067 m/ns on 25 January 2018 at Site 2, and 0.060 m/ns on both 6 November 2017 and 5 February 2018 at Site 3. The only processing steps used to produce GPR profiles via EKKO Project 5 were (1) application of a signal saturation correction filter (i.e., Dewow) to remove slowly decaying low frequency noise and (2) utilization of a spreading and exponential calibrated compensation gain function to amplify potential GPR drainage pipe responses. An interpretation module in EKKO Project 5 allowed potential drainage pipe responses in a GPR profile to be marked (Figure 6). With GPR and RTK/GNSS data collected together, EKKO Project 5 was then employed to save a spreadsheet file of the latitudes, longitudes, and depths corresponding to the marked potential drainage pipe responses. Furthermore, because GPR and RTK/GNSS data were collected concurrently, EKKO Project 5 was then able to generate a KMZ file that stored a site map of the GPR transects along with the marked potential drainage pipe locations. Opening this KMZ file in Google Earth allowed GPR measurement transects and the possible pipe locations to be overlaid on an aerial image. Figure 5: GPR profile drainage pipe responses; (**a**) horizontally compressed upside down U-shaped feature (i.e., reflection hyperbola) within yellow line oval results from the GPR transect being oriented perpendicular to drain line directional trend (from Site 1); (**b**) the banded linear feature within the yellow line oval results from the GPR transect being oriented the over top and along the trend of a drain line (from Site 3). Figure 6: GPR profile from Site 1 with potential reflection hyperbola drainage pipe responses marked via EKKO Project 5. Because GPR and RTK/GNSS data are collected concurrently, the latitude, longitude, and depth corresponding to a potential drainage pipe response mark (red dot) could then all be saved (via EKKO Project 5) into spreadsheet and KMZ files.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Grid Monitoring and Control through Smart Metering
#### 4.2.3 Mirubee  
Grid monitoring through smart metering applications have also been examined in the \"Mirubee\" project, realised in Spain. In this project an electricity metering system has been proposed, which has been able to identify the electricity consumption of each device connected to the grid, through a so-called signature the device entailed. Once the consumption was logged, it was forwarded to a cloud server through Wi-Fi technology [80].
# Occlusion Avoidance in a Simulated Environment Using Reinforcement Learning
## 3 The RL Environment
### Observation Space  
As stated previously, we intended to create an environment that allows end-to-end learning from images. However, having depth information would presumably improve the agent's performance, so rendering RGB-D images of the scene would be desirable. Due to the wide availability of commercial RGB-D cameras, this environment is not unrealistic. Depth images can be simply acquired from the Z buffers of the renderer. It is worth noting, however, that depth images generated this way are somewhat \"too perfect\", given that real depth cameras usually have gaps at object boundaries due to the parallax effect.  
Once the new camera position is calculated, it is turned into Cartesian coordinates, and the look-at orientation is computed. Then, the camera pose is converted into a standard homogeneous format \([\mathbf{R}\ T]\), and, then, a Hard Flat Shader is used to render the RGB image. By default, the RGB and depth images are rendered at a \(512\times 512\) resolution, while other render settings are set to PyTorch3D's defaults.  
During our experiments, we noticed that all of PyTorch3D's renderers tended to incorrectly calculate the maximum amount of triangle faces to use per bin, resulting in a final render with parts of certain more detailed objects missing. We solved this issue by automatically calculating a much higher limit for this variable, based on the number of triangles in the scene. However, this resulted in considerably higher render times in scenes that contained more complex geometries.