esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Driven Waste Management in Innovating Space Exploration
## 6 International Collaboration and Policy
### Future Research and Recommendations  
This document highlights critical areas for advancing smart waste management technologies in space exploration, particularly for long-duration missions to Mars and lunar bases. It emphasises the need for methods to process more materials with higher recovery rates. Research should prioritise converting waste into usable resources like water, oxygen, and building materials through advanced chemical and biological processes. Energy efficiency is vital, necessitating the exploration of low-energy processing technologies and energy recovery systems that transform waste into energy. Automating waste sorting and processing is essential to reduce human involvement, allowing crew members to focus on other critical tasks. Developing AI-driven robotics for autonomous waste identification, sorting, and processing is a priority. Given space and weight constraints in spacecraft, research into innovative materials and designs that minimise the size and weight of waste management systems without sacrificing efficiency is necessary. Enhancing the durability and reliability of these technologies is crucial for long-term operations in harsh space conditions. Integrating waste management into habitat design is also essential, ensuring that these systems work seamlessly with life support functions like food production and air purification. Establishing international standards for waste management in space is essential for global practice standardisation and technology sharing.
# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
## 3 Experiment Results
### Classification
Notably, the improvement was more evident on the FFDM datasets (VinDr-Mammo, CMMD, CDD-CESM, BMCD) than that on the digitized film mammography datasets (MiniDDSM), which implies that the proposed method could leverage the fine-grained features of FFDM images for cancer classification more efficiently. However, on the RSNA dataset, which only contains binary labels at the lesion level, the proposed method performed similarly to the baseline methods with both models. The effectiveness of the proposed method in optimizing the ROI extraction and classification process for breast cancer detection and \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Metric** & **Size** & **Precision** & **Recall** & **F1 Score** \\ \hline Negative & 12,256 & 0.92 & 0.93 & 0.97 \\ Positive & 6750 & 0.91 & 0.92 & 0.85 \\ Weighted Average & 17,514 & 0.92 & 0.92 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Metrics for predicted test set data, 92% accuracy. Figure 6: Classification performance of method across various experiments as shown in Table 4.  
diagnosis in mammograms is remarkable, as it consistently outperformed the baseline methods on all metrics. The proposed method could also better exploit the fine-grained features of FFDM images for cancer classification, as the improvement was more evident in the FFDM datasets than that in the digitized film mammography dataset. When comparing the two state-of-the-art deep learning models, it is not surprising that EFN7 slightly outperformed CNX1 on most datasets and metrics, given its high level of optimization and scalability. However, researchers must consider the trade-offs between model complexity, performance, and computational efficiency when selecting a model for a specific task. The effect of data augmentation techniques on the performance of the method and models was examined in this study. Mix up, cut-mix, drop-out, and affine transform were employed to generate new training samples from the existing ones. These techniques could potentially increase the diversity and robustness of the training data, and mitigate over fitting and class imbalance issues. Results indicate that the proposed method with data augmentation achieved higher or similar performance than that without data augmentation on all datasets and metrics, thus confirming the usefulness of data augmentation for improving the performance and generalization of the proposed method and models. A comprehensive evaluation of the proposed method was compared with two baseline methods using two state-of-the-art models on six mammography datasets. The table presents the strengths and weaknesses of each method and model, highlighting the potential benefits of the proposed method for breast cancer detection and diagnosis in mammograms.
## 3 Concept
### Design Goals  
As mentioned in Section 1, the overarching goal is to combine the sustainability and throughput of track-based mass transportation systems with the speed of aircraft, as well as the individual flexibility and comfort of road traffic.  
For this, the team composed the necessary design goals for the DDT of two components. First, the minimum requirements of the system were set, to enable the basic functionalities:  
* Velocity: average trip speed of more than \(500\,\mathrm{km}/\mathrm{h}\), to compete with regional aircraft.
* Throughput: at least 10,000 passengers per hour-direction, to be comparable to modern railway systems.
* Energy type: 100% electric operation, to enable sustainable operation.
* Automation: pilot-free operation. This is required technically but also improves reliability and operational costs.
* Safety: less than 0.04 fatalities per 100 million passenger kilometers, to be comparable to civil aircraft [18].  
Secondly, a set of competing optimization criteria was defined, to guide the design decision process:  
* **Low cost infrastructure*
* Minimal ground acquisition.
* Simple construction process.
* Simple transportation of infrastructure elements to the construction site.
* Flexible route planning through versatile routing options (banking).
* **Economic operation*
* Low maintenance (contact-free operation).
* Low waiting times in hub (recharging).
* **Superior passenger experience*
* On demand (small pod size).
* High reliability.
* Low noise.
* Low vibration.
* Seamless connection to existing modes of transportation.  
Design decisions have to strike a balance between each of the three optimization criteria while sticking to the minimum requirements.  
The criteria were chosen from an economic perspective, highlighting the capital expenditures (CAPEX) dominated by the infrastructure, the operational expenditures(OPEX) and the revenue created through passenger tickets and therefore the experience connected with the product.  
In general, the design goals impact the design decisions in a way that they point toward a design with simple infrastructure, gathering most of the complexity in the pods. This simplifies maintenance and enables incremental improvements over time.  
Based on the design goals, the DDT framework will be applied in the following sections.
###### Abstract  
This study explores the integration of advanced artificial intelligence (AI) techniques with infrared thermography for diagnosing diabetic peripheral neuropathy (DPN) and peripheral arterial disease (PAD). Diabetes-related foot complications, including DPN and PAD, are leading causes of morbidity and disability worldwide. Traditional diagnostic methods, such as the monofilament test for DPN and ankle-brachial pressure index for PAD, have limitations in sensitivity, highlighting the need for improved solutions. Thermographic imaging, a non-invasive, cost-effective, and reliable tool, captures temperature distributions of the patient plantar surface, enabling the detection of physiological changes linked to these conditions. This study collected thermographic data from diabetic patients and employed convolutional neural networks (CNNs) and vision transformers (ViTs) to classify individuals as healthy or affected by DPN or PAD (not healthy). These neural networks demonstrated superior diagnostic performance, compared to traditional methods (an accuracy of 95.00%, a sensitivity of 100.00%, and a specificity of 90% in the case of the ResNet-50 network). The results underscored the potential of combining thermography with AI to provide scalable, accurate, and patient-friendly diagnostics for diabetic foot care. Future work should focus on expanding datasets and integrating explainability techniques to enhance clinical trust and adoption.  
artificial intelligence; visual transformers; CNN; diabetic foot; diabetic peripheral neuropathy; peripheral arterial disease; thermography +
Footnote †: journal: Applied Sciences  
Article  
Advanced AI-Driven Thermographic Analysis for Diagnosing Diabetic Peripheral Neuropathy and Peripheral Arterial Disease
# A Compact Size Antenna for Extended UWB with WLAN Notch Band Stub
## 3 Results and Discussions
### Surface Current Distribution  
To further understand the resonating behavior of the proposed antenna, Figure 7 illustrates surface current distribution at various resonating and notch band frequencies. It can be observed that at the lower resonance of 4 GHz, the current distribution is lower at the upper part of the radiator, which results in the generation of that frequency. Contrary to that, at the notch band, the current show maximum value around the upper stub and across it, while at the lower side, almost no current distribution is available. This causes the mitigation of the 5.5 GHz band. Furthermore, it can also be observed from Figure 7 that current is distributed across the radiator, which causes the generation of a higher resonance. Hence, it is also verified that the upper stub (rectangular stub) is responsible for mitigating the WLAN band from the UWB spectrum.
# Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves
## 2 Methods  
### Experimental Setup  
We used a Swissto12 material characterisation kit (MCK) operating in the THz frequency range to obtain the scattering parameters of the plant leaves. The MCK was attached to a Keysight Technologies N5224A microwave network analyser (NA), the frequency range of which was shifted in the THz frequency range via a Virginia Diode vector NA extender module WM-250 (WR 1.0), enabling operation in the frequency range of 0.75 to 1.1 THz with a resolution of 2 GHz. The MCK comprised of two conical waveguide horn transitions with further two sections of the low-loss corrugated waveguide. A small aperture between the two low-loss corrugated waveguides allows the material samples to be inserted into the system during the measurement. Moreover, each half of the MCK comprises a waveguide which transitions from a rectangular waveguide at one end to a corrugated circular waveguide at the other. Furthermore, one half of the MCK, is fixed, while the other half is movable (to easily accommodate the insertion of the sample to be measured). To avoid any structural damage while the leaf specimen was clamped in the MCK for observation, we used two PTFE caps that enabled a uniform compression of the samples as shown in Figure 2. Prior to the measurement, the setup was configured using the two-port short-open-load-thru (SOLT) calibration technique.
###### Abstract  
The classical pre-stack seismic inversion technique uses the Zoeppritz equation and its simplified versions to calculate the PP and S-waves in multilayered media at arbitrary incidence angles, aiding in inverting the subsurface velocity and density parameters. Despite its widespread application, the amplitude variation with angle (AVA) inversion based on the Zoeppritz equation has limitations regarding the accuracy. The AVA neglects transmission losses and the effects of multiple reflections during seismic wave propagation, resulting in reduced resolution. In contrast, the propagation matrix theory offers a comprehensive range of reflection coefficients for P- and S-waves in multilayered media at arbitrary incidence angles, thereby theoretically enhancing the inversion accuracy. However, the seismic responses obtained using this method exist in the slowness-frequency domain and require constant slowness for consistency along a profile. This assumption is violated when variations in the P-wave velocity occur within the subsurface, affecting the incidence angle of propagating seismic waves. This study modifies the propagation matrix theory to compute AVA seismic responses and applies it to pre-stack multiparameter inversion. The effectiveness of the modified method was validated by deriving theoretical AVA seismic responses and comparing them to solutions from a typical layered media model. The modified theory was also employed for seismic pre-stack inversion. Numerical simulations and field data tests demonstrated that the new propagation matrix method offers a high accuracy and stability.  
AVA seismic response; pre-stack seismic inversion technique; propagation matrix theory; reflection coefficients; Zoeppritz equations +
Footnote †: journal: Applied sciences  
Article
## 3 Results and Discussion
### Aroma Reconstitution Test
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compounds**} & \multicolumn{2}{c}{**Concentrations (\(\mu\)g/L)**} & \multirow{2}{*}{**Thresholds**} & \multicolumn{2}{c}{**OAVs**} \\ \cline{3-6}  & & **DS** & & **PS** & **(\(\mu\)g/L)** & **DS** & **PS** \\ \hline \multicolumn{6}{c}{**Esters**} \\
62 & Citronellol \({}^{\text{a}}\) & 372.06 \(\pm\) 9.17 & 12.32 \(\pm\) 0.04 & 18\({}^{\text{j}}\) & 21 & \textless{}1 \\
63 & (E)-\(\beta\)-damascenone \({}^{\text{a}}\) & 626.89 \(\pm\) 22.22 & 35.18 \(\pm\) 0.01 & 0.4\({}^{\text{e}}\) & 1622 & 88 \\
65 & trans-Nerololidol \({}^{\text{a}}\) & 63.36 \(\pm\) 1.16 & - & 400\({}^{\text{j}}\) & \textless{}1 & - \\
66 & trans-\(\beta\)-ionone \({}^{\text{a}}\) & 2.34 \(\pm\) 0.07 & - & 4.5\({}^{\text{j}}\) & \textless{}1 & - \\
67 & Eugenol \({}^{\text{a}}\) & - & 77.72 \(\pm\) 0.19 & 21\({}^{\text{e}}\) & - & 4 \\
68 & (E,E)-Farnesol \({}^{\text{a}}\) & 11.09 \(\pm\) 1.70 & - & 1000\({}^{\text{k}}\) & \textless{}1 & - \\
**Acids** & & & & & & \\
69 & Acetic acid \({}^{\text{a}}\) & 179.9 \(\pm\) 0.52 & 6827.92 \(\pm\) 1.95 & 160,000\({}^{\text{e}}\) & \textless{}1 & \textless{}1 \\
70 & 2-Methylpropanoic & 292.58 \(\pm\) 23.08 & 12,938.45 \(\pm\) 481.50 & 1580\({}^{\text{e}}\) & \textless{}1 & 8 \\
71 & Butyric acid \({}^{\text{a}}\) & - & 386.28 \(\pm\) 0.39 & 964\({}^{\text{d}}\) & - & \textless{}1 \\
72 & 3-Methylbutanoic & 387.43 \(\pm\) 7.79 & 4033.45 \(\pm\) 309.44 & 1050\({}^{\text{e}}\) & \textless{}1 & 4 \\
73 & Hexanoic acid \({}^{\text{a}}\) & 610.95 \(\pm\) 24.63 & 1307.33 \(\pm\) 1.17 & 2520\({}^{\text{d}}\) & \textless{}1 & \textless{}1 \\
75 & Octanoic acid \({}^{\text{a}}\) & 272.16 \(\pm\) 23.36 & 1040.49 \(\pm\) 0.78 & 2700\({}^{\text{d}}\) & \textless{}1 & \textless{}1 \\
77 & Decanoic acid \({}^{\text{a}}\) & 219.86 \(\pm\) 17.54 & - & 13,737\({}^{\text{d}}\) & \textless{}1 & - \\
**Sulfides** & & & & & & \\
3- & & & & & & \\
79 & Methylthiopropanol & 29.51 \(\pm\) 3.23 & 406.02 \(\pm\) 0.39 & 2110\({}^{\text{d}}\) & \textless{}1 & \textless{}1 \\ \multicolumn{6}{c}{\({}^{\text{a}}\)} \\ \hline \hline \end{tabular}  
* Quantification by LLE-GC-MS. \({}^{\text{b}}\) Quantification by SPME-GC-MS. \({}^{\text{c}}\) Quantification by DI-GC-MS. \({}^{\text{d}}\) [PERSON] and Xu et al. [47]: 46% ethanol/water (\(v/v\)). \({}^{\text{e}}\) [PERSON] and [PERSON] et al. [48]: [PERSON]. \({}^{\text{f}}\) [PERSON] et al. [49]: 58% ethanol/water (\(v/v\)). \({}^{\text{g}}\) [PERSON] [50]: water. \({}^{\text{h}}\) [PERSON] [36]: water. \({}^{\text{j}}\) [PERSON] et al. [51]: 18% ethanol/water (\(v/v\)). \({}^{\text{l}}\) [PERSON] et al. [35]: water. \({}^{\text{k}}\) [PERSON] et al. [52]: wine. \({}^{\text{l}}\) [PERSON] et al. [53]: 40% ethanol/water (\(v/v\)). \({}^{\text{m}}\) [PERSON] et al. [54]: 10% (\(v/v\)) ethanol/water (\(v/v\)) adjusted to pH 3.5 with tartaric acid.  
\end{table} 
Table 2: _Cont._
## 4 Numerical Simulations of a 3D-Printed Brace  
In order to evaluate the efficiency of 3D-printed braces made of PLA material, the numerical simulations were repeated, adopting modulus of elasticity values from Table 6 and Poison's ratio [v] and shear modulus [G] values from the literature [40], as presented in Table 7.  
Figure 17: True stress–true strain curves for test specimens of direction 2 [90\({}^{\circ}\)].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specimen** & **Modulus of Elasticity [MPa]** & **Ultimate Tensile Stress [MPa]** & **Ultimate Strain** \\ \hline
1.1 & 3127.1 & 47.06 & 0.0351 \\
1.2 & 2857.4 & 48.49 & 0.0394 \\
1.3 & 3004.0 & 48.36 & 0.0469 \\
1.4 & 2988.4 & 48.97 & 0.0505 \\
1.5 & 3241.4 & 50.42 & 0.0223 \\
3.1 & 3121.2 & 35.76 & 0.0217 \\
3.2 & 2871.3 & 32.82 & 0.0131 \\
3.3 & 2887.7 & 37.46 & 0.0200 \\
3.4 & 3089.4 & 33.10 & 0.0297 \\
3.5 & 2518.7 & 34.17 & 0.0152 \\ \hline \end{tabular}
\end{table}
Table 5: Summary of tensile test results.  
Figure 19: Displacements of the brace: (**a**) in the \(X\)-axis, (**b**) in the \(Y\)-axis, (**c**) in the \(Z\)-axis (Second numerical model).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Orientation**} & \multirow{3}{*}{**Measure**} & \multirow{3}{*}{**Range**} & \multirow{3}{*}{**Min**} & \multirow{3}{*}{**Max**} & \multirow{3}{*}{**Mean**} & \multirow{3}{*}{**St. Dev**} & \multicolumn{3}{c}{**95\%**} & \multicolumn{3}{c}{**99\%**} \\ \cline{5-10}  & & & & & & & & \(\mathbf{X_{mean}\textgreater{}}\) & \(\mathbf{X_{mean}\textgreater{}}\) & \(\mathbf{X_{mean}\textgreater{}}\) & \(\mathbf{X_{mean}\textgreater{}}\) \\ \hline \multirow{2}{*}{\(0^{\circ}\)} & E [MPa] & 384.00 & 2857.40 & 3241.40 & 3043.66 & 146.10 & 2848.97 & 3238.35 & 2719.95 & 3367.37 \\  & \(\sigma_{\text{ult}}\) [MPa] & 3.35 & 47.06 & 50.42 & 48.66 & 1.21 & 46.96 & 50.36 & 458.83 & 51.49 \\ \multirow{2}{*}{\(90^{\circ}\)} & E [MPa] & 602.50 & 2518.70 & 3121.20 & 2897.66 & 240.38 & 2592.19 & 3203.13 & 2389.75 & 3405.57 \\  & \(\sigma_{\text{ult}}\) [MPa] & 4.63 & 32.82 & 37.46 & 34.66 & 1.94 & 32.31 & 37.01 & 30.76 & 38.57 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Descriptive statistics of 3D-printed PLA specimens.  
Figure 18: Results of primary stresses on the spinal brace: (**a**) S11; (**b**) S22.  
In primary stress S11 results (Figure 18a) a local stress concentration of 48.9 MPa is observed, slightly exceeding the tensile strength of the material in this direction, which was calculated equal to 48.66 MPa. However, almost the whole surface of the brace is stressed with values below 30 MPa, well below the tensile strength of the material. Similarly, in the S22 primary stress results (Figure 18b), stresses below 25 MPa are observed over the entire surface of the brace, which is smaller than the tensile strength in direction 2 which was found to be equal to 34.66 MPa. Comparing the contact pressure results of Figure 20 with those of Figure 7, it is observed that in the simulation with PLA material, higher pressures are developed due to the higher modulus of elasticity and resulting higher stiffness of the brace.
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 5 Discussion
### Comparative Performance Analysis
#### 5.2.1 Dataset-Oriented Performance  
Another issue concerning the finding of the best suited model for a problem at hand is the dataset selection for training. This is a critical issue in the field of deep learning in particular, as the data used to train a model is one of the most influential factors for the performance of the neural network. Therefore, in autonomous driving where the models work in real time, they should be trained in an earlier stage with the most suitable data. Moreover, the comparison of the semantic segmentation models should be made for the same dataset. Thus, research papers use the same dataset to compare different models. Nonetheless, in the area of real-time semantic segmentation, most of the models are being compared in the \"Cityscapes\" and \"CamVid\" datasets. Although this fact simplifies the searching for the fastest, yet most accurate, segmentation model it may not provide the most objective decision for the choice of the most suitable method. In particular, in the area of autonomous driving where every mistake may cause irreversible consequences, there is a practical necessity for training and testing the deep-learning models used in a  
Figure 26: Basic modules of efficient Semantic Segmentation.  
wide variety of situations of the external environment (day, light, traffic, etc.). For example, \"Cityscapes\" and \"CamVid\" which are one of the most popular and most used datasets for benchmark tests, lack some vital features, regarding their diversity. In detail, they contain images with good/medium weather conditions on daytime. However, an efficient and safe autonomous driving system must have the ability to function under adverse weather conditions, such as snowfall and of course at night-time, especially in the case of emergency. To this end, one could adopt transfer learning techniques as will be discussed in Section 6 to remedy this limitation raised by currently available datasets.
## 4 Results and Discussion
### Sensor Robot Design as Access Point
#### 4.1.3 Communication Unit for Sharing Information  
The communicator (230 in Figure 7a) can transmit and receive wireless data to and from the operator terminal or other robot devices using a mobile communication module on a short-range communication band.  
The near field communication module is for near field communication and is internally equipped with Bluetooth, Radio, WIFI Direct (WFD: TCP/IP), NFC, and a universal serial bus (USB). The robot device can configure an ad-hoc network with the operator terminal or at least one other robot device through the communication unit, which transmits the access information generated by the recognizing unit to the operator terminal or other robot devices.
## 2 Seismic Analysis of Liquid Storage Tanks
### Failure Modes
#### 2.3.2 Elephant's Foot Buckling  
Elephant's foot buckling (EFB), which is an elastic-plastic buckling, is one of the most prevalent failure modes observed in cylindrical steel storage tanks during seismic events. This localized failure occurs near the tank base and is characterized by a high concentration of compressive meridional stress that results in outward bulging or buckling (Figure 4). This phenomenon is primarily driven by the interaction of vertical compressive stresses, hoop tensile stresses, and significant shear stresses, often exacerbated by base uplift during seismic loading. When these stresses exceed the critical buckling capacity of the tankmaterial, EFB initiates. This localized failure is particularly dangerous as it can rapidly propagate around the tank's circumference, compromising its structural integrity.  
The critical buckling stress \(\sigma_{ep}\) for elastic-plastic behavior is expressed as follows [22; 41]:  
\[\sigma_{ep}=\frac{\sigma_{e}}{r+1}\Bigg{[}1-\left(\frac{pR}{tf_{y}}\right)^{2} \Bigg{]}\left(1-\frac{1}{1.12+r^{1.5}}\right)\left(r+\frac{f_{y}}{250}\right) \tag{14}\]  
where \(\sigma_{e}\) is the elastic critical buckling stress in Equation (10), \(r=R/\left(400\;t\right)\) is a non-dimensional parameter based on the radius-to-thickness ratio, \(p\) is the total internal pressure (hydrostatic + hydrodynamic) acting on the tank wall in Equation (4), and \(f_{y}\) is the yield strength of the tank material.  
Elephant's foot buckling occurs when the meridional stress (\(\sigma_{m}\)) in the tank wall exceeds the critical elastic-plastic buckling stress \(\sigma_{ep}\) in Equation (14). The meridional stress is calculated as follows:  
\[\sigma_{m}=\frac{OM}{\pi R^{2}t}+\frac{W}{2\pi Rt} \tag{15}\]  
where \(OM\) is the overturning moment and \(W\) is the total weight of the tank and its content.  
For tanks with pre-existing shell deformations, the likelihood of EFB increases significantly. Localized dents, uneven wall thicknesses, and residual stresses amplify stress concentrations, reducing the critical buckling capacity.