esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Designing the \(4\times 1\) Microstrip Square Patch Array Antenna
### Effects from the Tilting Angle when the Metallic Plate Length Is 10 mm  
To increase gain in the 4 \(\times\) 1 patch array antenna using tapered power distribution, two metallic plates were appended along the array axis (\(x\)-axis), as shown in Figure 7a. The width of the metallic plates, \(w_{\rm m}\), is the same as the ground plane width of the 4 \(\times\) 1 patch array antenna. The effects on performance from varying tilting angle \(\theta\) of the metallic plates were investigated. In this case, the tilting angle was varied from 0\({}^{\circ}\) to 90\({}^{\circ}\) at increments of 10\({}^{\circ}\) when the length of the metallic plates was fixed at \(l_{\rm m}\) = 10 mm. Figure 7b,c shows the input reflection coefficients, and the frequencies and magnitudes at the minimum of the input reflection coefficients. The minimum frequency of the input reflection coefficients decreased from 24.106 GHz to 24.02 GHz as \(\theta\) increased from 0\({}^{\circ}\) to 60\({}^{\circ}\). When \(\theta\) increased to 70\({}^{\circ}\) and 80\({}^{\circ}\), the minimum frequency increased to 24.072 GHz and 24.164 GHz, respectively. The minimum frequency decreased to 23.918 GHz when \(\theta\) increased to 90\({}^{\circ}\). The magnitude at the minimum frequency ranged from \(-\)24.59 dB to \(-\)33.52 dB except for \(\theta\) = 60\({}^{\circ}\) and 90\({}^{\circ}\). For \(\theta\) = 60\({}^{\circ}\), the value decreased to \(-\)51.08 dB, whereas it increased to \(-\)16.66 dB for \(\theta\) = 90\({}^{\circ}\). Figure 7d,e shows the realized gain characteristics in the +z-axis direction, and the frequencies and values of maximum gain. The maximum gain frequency ranged from 24.1 GHz to 24.2 GHz when \(\theta\) ranged from 0\({}^{\circ}\) to 80\({}^{\circ}\), whereas it decreased to 23.9 GHz at \(\theta\) = 90\({}^{\circ}\).  
When \(\theta\) increased from 0\({}^{\circ}\) to 30\({}^{\circ}\), the maximum gain value decreased from 10.12 dBi to 4.05 dBi owing to the null on the +z-axis direction. As \(\theta\) increased from 40\({}^{\circ}\) to 70\({}^{\circ}\), the value increased from 8.57 dBi to 13.12 dBi. However, it decreased in the range 11.24 dBi to 11.52 dBi, when \(\theta\) increased to 80\({}^{\circ}\) and 90\({}^{\circ}\). Figure 7f,g compares the radiation patterns on the y-z and z-x planes in the +z-axis direction, whereas Figure 7h,i compares 3D radiation patterns when \(\theta\) = 30\({}^{\circ}\) and \(\theta\) = 70\({}^{\circ}\), respectively. We observed a null on the +z-axis direction when \(\theta\) = 30\({}^{\circ}\), and maximum gain was highest when the tilting angle was \(\theta\) = 70\({}^{\circ}\). Therefore, \(\theta\) = 70\({}^{\circ}\) was chosen for maximum gain enhancement. Note that this tilting angle is similar to that of a standard pyramidal horn antenna.
# A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images
## 3 Results and Discussion
### Classification in Skeletal Muscle  
Based on the reviews shown in Table 1 for the classification of muscles using deep learning from 2018 to 2020, three network architectures were useful for classifying skeletal muscle. The use of augmentation, rotation, and transferred learning is needed to solve problems in skeletal muscle area data. Besides, cropping strategies need to be considered to classify skeletal muscles, especially in the calf muscle area.
# Review of Automatic Estimation of Emotions in Speech
## 3 Acoustic Analysis of Speech  
Like many applications for speech, classical SER methods start with suitable processing of the input speech signal to: (1) eliminate less useful aspects of signals, (2) reduce data size, (3) focus on relevant acoustics, and (4) transform audio into pertinent features. For almost all speech uses, to extract pertinent information usually needs analysis or processing of the input (i.e., the time waveform of speech, as from a microphone with variations in air pressure that make up the sound of speech). Analysis is especially helpful for speech, as compared to other types of data such as video, because speech is highly encoded: concepts in the brain transform into many dynamic neural commands to VT organs (lips, tongue, velum), which then move synchronously as air from the lungs passes through the tubes of the VT. The resulting speech waveform is thus a signal with highly indirect information about its relevant linguistic information.  
Many physical signals other than speech (e.g., video and general audio) display direct relationships to their desired content (e.g., shapes of image objects; sounds from physical events). For speech, on the other hand, the sequence of individual time samples has little direct relationship to useful information, including emotions, ideas, and the language used. The spectral envelope of the transfer function of the VT is of main concern in most applications for speech, because it relates to VT shape, which speakers control intentionally. For SER, one must include intonational analysis as well, as VT variations appear to be a lesser component of emotion.
# The eIDAS Regulation: A Survey of Technological Trends for European Electronic Identity Schemes
## 2 Methodology
### eID Schemes Selection  
In the eIDAS regulation [1], an eID scheme is defined as \"a system for electronic identification under which electronic identification means are issued to natural or legal persons, or natural persons representing legal persons\", where an eID means is \"a material and/or immaterial unit containing person identification data and which is used for authentication for an online service\". Each MS can notify one or more eID schemes based on one or multiple eID means.  
As this work aims to highlight technological trends for eIDAS eID schemes, we focus our analysis on the list of notified eIDAS schemes available on the European Commission's official website (September 2022) [6]. We performed a preliminary analysis in November 2021, and the results are available on our companion website ([[https://st.fbk.eu/complementary/FARES2022_2](https://st.fbk.eu/complementary/FARES2022_2)]([https://st.fbk.eu/complementary/FARES2022_2](https://st.fbk.eu/complementary/FARES2022_2)), accessed on 1 November 2021). In the following, we report the results of our latest technological trends analysis (September 2022). We aim to check if there are any recently notified eID schemes that we did not consider during the first phase. Figure 2 displays the complete list under analysis, which consists of the eID schemes of 18 (among 27) MSs (There are MSs that, at the time of the analysis (September 2022), still do not have pre-notified, peer-reviewed, or notified scheme. However, based on Article 7 of eIDAS 2.0 [5] regulation, the notification in eIDAS 2.0 becomes mandatory, and each MS MUST have at least one notified scheme) together with the eID scheme of the United Kingdom (Even if, due to Brexit (1 February 2020), the United Kingdom's solution is no longer listed on the European Commission's official website, it was taken into consideration within this analysis since it was formerly notified).  
Note that, since some MSs offer multiple eIDAS schemes and/or means (e.g., the Belgian national itsme mobile app and eID Card), we consider a total of 24 eIDAS eID schemes with 40 eIDAS eID means within our analysis. Furthermore, we also consider a solution used in Baltic countries called \"SMART-ID\". This eID scheme is more recent and may reflect current technological trends. As a final remark, our updated analysis shows an improvement in the status of notified eIDAS eID schemes. While during our first analysis (November 2021), there were 19 notified eID schemes with 31 eID means, in our latest analysis, these numbers changed to 24 notified eID schemes with 40 eID means.
## 5 Proposed Solution
### Federated Deep Deterministic Policy Gradient (Fed-DDPG)  
The fed-DDPG algorithm comprising LSTM in the actor-critic framework aims to enhance the accuracy of individual DDPG algorithms at each agent. This involves exchanging the model parameters among the DDPG agents, allowing the simultaneous optimization of energy consumption for each vehicle and the UAV. The DDPG algorithm is executed on each vehicle, and federated averaging is applied to the parameters obtained from the DDPG algorithm of multiple vehicles to implement the fed-DDPG algorithm. The proposed fed-DDPG algorithm optimizes the agents' actions together to reduce the entire cost function of the system. An episode is the sequence of events from when the UAV starts flying and either returns to the initial position or the TTI ends. The objective of the UAV is to maximize the average vehicle data traffic and the number of vehicles served. The state at time \(t\) is the current location of UAV, the current coordinates and the remaining UAV battery power (\(P_{t}\)). In Figure 4, the UAV at coordinates \((x_{t},y_{t})\) serves vehicles and moves either to the east \((x_{t+1},y_{t})\), south \((x_{t},y_{t-1})\), west \((x_{t-1},y_{t})\), north \((x_{t},y_{t+1})\) or back to the initial point.
# Influence of Girder Flaring on Load Effect in Girders of Composite Steel Bridges
## 5 Parametric Study  
To verify the general applicability of the outlined equivalent GDF approach for flexure and shear in interior and exterior girders, a parametric study is followed. This study considers different aspects that strongly affect the behavior of splayed girder bridges. These parameters are represented in the 15 bridges (B1-B15) listed in Table 1, which include variations in girder spacing S, slab thickness t\({}_{\text{s}}\), cross-bracing spacing D, number of girders n, girder web depth d, and span length L. Table 2 shows the ranges of these parameters, each varied from the specified values of the reference bridge B1. For each parameter, two additional configurations are created--one with a lower value and one with a higher value--while keeping the rest constant. This results in a total of 15 splayed girder bridges with different parameters to be considered in this study. It can be said that each parameter is studied using three bridges, including the reference bridge.  
Since the girder spacing is the most critical parameter that greatly impacts the live load distribution, two splayedness measures are considered in the parametric study. The first one is the splayedness ratio (S\({}_{2}-S_{1}\))/L, which represents the degree of splayedness of the bridge, and the second one is the girder spacing ratio S\({}_{1}\)/S\({}_{2}\), which considers the girder spacing without the span length. In the latter case, the splayedness ratio (S\({}_{2}-S_{1}\))/L is kept constant while varying the S\({}_{1}\)/S\({}_{2}\) ratio. Hence, four bridges in total are considered for the effect of girder spacing--two for each aspect.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**Girder Spacing Parameter**} & \multirow{2}{*}{**Slab**} & \multirow{2}{*}{**Cross-Bracing**} & \multirow{2}{*}{**No. of**} & \multirow{2}{*}{**Girder**} & \multirow{2}{*}{**Span**} \\ \cline{2-2} \cline{5-6}  & **(S\({}_{2}-S_{1}\))/L** * & & & & & & & **Length** \\ \cline{2-2} \cline{5-6}  & **S\({}_{1}\) (mm)** & **S\({}_{2}\) (mm)** & **S\({}_{1}\) (mm)** & **S\({}_{2}\) (mm)** & **t\({}_{\text{s}}\) (mm)** & **D (m)** & **n** & **d (mm)** & **L (mm)** \\ \hline Reference (B1) & 2250 & 4500 & 2250 & 4500 & 220 & 40 & 5 & 1700 & 40,000 \\ \hline Lower value & 1500 & 4500 & 1500 & 3750 & 150 & 5 & 3 & 1400 & 30,000 \\ \hline Upper value & 3000 & 4500 & 3000 & 5250 & 300 & 10 & 7 & 2000 & 50,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Range of variables considered in the parametric study.  
Figure 12: Comparison of finite element results with hand calculation for reference bridge B1: (a) flexural effect; (b) shear effect.
## 4 Applications of GenAI-Driven CV in Smart Agriculture
### Livestock and Aquaculture Monitoring  
#### 4.5.1 Livestock Monitoring  
The integration of CV and artificial intelligence (AI) is revolutionising livestock monitoring by providing non-invasive, scalable, and automated alternatives to traditional labour-intensive methods. This advancement not only enhances operational efficiency but also significantly improves animal welfare, behavioural understanding, health diagnostics, and yield optimisation.  
In livestock environments, CV plays a crucial role in identifying individual animals, tracking their movements, and assessing behavioural patterns. For example, [PERSON] et al. [229] developed a deep learning-based pose estimation framework for red pandas, demonstrating its potential for tracking subtle movements and social interactions in farm animals. Similarly, [PERSON] [230] highlighted the application of restricted set classification for animal re-identification, which is crucial for livestock traceability and population management. High-resolution stereo-vision systems, as used by [PERSON] and [PERSON] [231] for extracting morphological parameters of marine species, also show promise for livestock body condition scoring.  
Deep learning advancements have enabled more robust recognition of complex behaviours. [PERSON] et al. [232] reviewed the evolution of behaviour detection in pigs and cattle, noting the transition from simple movement tracking to comprehensive behavioural understanding. [PERSON] et al. [233] successfully applied deep convolutional networks for tail tracking in cows, aiding in stress and discomfort assessment. [PERSON] et al. [234] enhanced swine behaviour detection using YOLO models integrated with efficient aggregation networks, enabling real-time monitoring in commercial farming settings.  
Vision-based systems can automatically detect and quantify contact behaviour in pigs, as shown by [PERSON] et al. [235], aiding in the early detection of aggression and health issues. [PERSON] and [PERSON] [236] introduced PrunedYOLO-Tracker for recognising and tracking basic cow behaviours in group housing conditions. [PERSON] et al. [237] proposed a self-activated CNN approach for buffalo breed identification, and [PERSON] et al. [238] developed a handheld RGB-D camera system for rapid pig weight assessment, underscoring the potential of mobile vision solutions.
## 4 Discussion
### Analysing the Integration of the Proposed Approach with LIME  
Integrating LIME with the approach based on the examples generated allowed for a more in-depth analysis of the classifiers' behaviour, providing a visual explanation of the most relevant areas for decision making. In the case of EfficientNet, trained with the control dataset, LIME revealed significant inconsistencies: in many examples, the regions highlighted as most relevant for classification did not correspond to the polyp area, even when the degree of confidence was high. As the size of the polyp increased, the degree of confidence also grew, but the model did not consistently focus on the correct area of the polyp.  
On the other hand, in ViT, which was also trained with the control dataset, a similar behaviour was observed. LIME correctly highlighted the polyp area as relevant only in the examples where the polyps were larger. When the polyp size was smaller or remained unchanged, the model focussed on regions that did not correspond to the polyp, even when the classification was correct, suggesting that ViT is less accurate at identifying relevant areas correlated to polyp features in scenarios with less size variation.  
In classifiers trained with the p2p dataset, LIME generally correctly highlighted the region where the polyp was present, especially in examples with larger polyps. This indicates a better adaptation of these classifiers to artificial variations in polyp size. However, an exception was observed in ViT, which correctly classified an image with no change in polyp size. Still, LIME did not identify the polyp as the main region of interest, highlighting inconsistencies in the model.  
Applying LIME and the proposed approach provided a more detailed view of the classifiers' limitations and capabilities. Although the classifiers showed an adequate response to variations in polyp size, this integration revealed weaknesses in identifying the most relevant regions associated with polyps' features for classification. This highlights the need for further refinement of the classifiers to guarantee that the regions identified as relevant are consistently aligned with the features of the polyps, which is essential to increase the reliability of these models, especially in clinical applications.
## 2 HEV Modelling and Control
### HEV Architecture and Model
Once the torque required at the input shaft of the transmission is known, it is crucial to split this demand between the ICE and the EM as follows:  
\[T_{del}=T_{ICE}\cdot\tau_{ICE}\cdot\gamma+T_{EM}\cdot(1-\gamma) \tag{7}\]  
where \(T_{del}\) is the torque delivered at the transmission input shaft by the two power sources, while \(\tau_{ICE}\) is the gear ratios between ICE and the transmission. \(\gamma\) is the split term, for which values of 0 and 1 relate to pure electric mode and pure thermal mode, respectively. A value of \(\gamma\) lower than 1 denotes power assist torque split mode, while battery charging is performed when \(\gamma\) is greater than 1 and, in this case, the EM performs as an alternator. Using the knowledge of torque provided by the ICE and its rotational speed, the instantaneous fuel consumption can be found through the brake specific fuel consumption (BSFC) map. Regarding the electrical energy consumption, the battery power demand is computed first in Equation (8), then the instantaneous variation in state of charge (SOC) is calculated in Equation (9) as follows:  
\[P_{butt}=(\omega_{EM}\cdot T_{EM})+\mathcal{L}_{EM}+P_{aux} \tag{8}\]  
\[\dot{SOC}=\frac{V_{oc}-\sqrt{V_{oc}^{2}-4R_{in}P_{batt}}}{2R_{in}Q_{batt}At} \tag{9}\]  
where \(\omega_{EM}\) is the rotational speed of the electric machine; \(\mathcal{L}_{EM}\) represents the electrical power loss and it is derived empirically from the characteristic tables as a function of the torque and speed of the EM. \(P_{aux}\) represents the auxiliaries power request, set to 200 W for this study. Concerning the variation in SOC, it is computed in Equation (9) using the equivalent open circuit approach, where \(V_{oc}\), \(R_{in}\), and \(Q_{batt}\) are the open circuit voltage, the internal resistance, and the battery capacity (expressed in A/s), respectively; \(\Delta t\) is the simulation timestep.
###### Abstract  
The accurate monitoring of greenhouse gas (GHG) concentrations is crucial in mitigating global warming. The imaging Fourier transform spectrometer (IFTS) is an effective tool for measuring GHG concentrations, offering high throughput and a wide spectral measurement range. In order to address the issue of spectral inconsistency during the detection process of the target gas, which is influenced by external environmental factors, making it difficult to achieve high-precision gas concentration inversion, this paper proposes a multi-scale feature attention (MDISE) model. The model uses a multi-scale dilated convolution (MD) module to retain both global and local shallow features of the spectra; introduces the one-dimensional Inception (1D Inception) module to further extract multi-scale deep features; and incorporates the channel attention mechanism (SE) module to enhance attention to important spectral wavelengths, suppressing redundant and interfering information. A target gas detection system was built in the laboratory, and the proposed model was tested on gas samples collected by two channels of a short and medium-wavelength infrared imaging Fourier transform spectrometer (SMWIR-IFTS). The experimental results show that the MDISE model reduces the root mean square error (_RMSE_) in both channels by 79.14%, 76.59%, and 69.80%, and 81.45%, 82.65%, and 74.01%, respectively, compared to the partial least squares regression (PLSR), support vector regression (SVR), and conventional one-dimensional convolutional neural network (1D-CNN) models. Additionally, the MDISE model achieved average coefficient of determination (\(R^{2}\)) values of 0.997 and 0.995 for the concentration intervals in both channels. The MDISE model demonstrates excellent performance and significantly improves the accuracy of GHG concentration inversion.  
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## 2 Materials and Methods
### Image Segmentation Steps  
In each image, three ROIs were delineated manually by using the open-source image analysis tool 3D Slicer (RRID: SCR_005619), version 4.11.20210226 [14]. We applied the same segmentation order as used in the clinical procedure. First, the myocardium region (ROI\({}_{\text{myoc}}\)) was delineated, then reference regions (ROI\({}_{\text{ref}}\)) were found, and finally, the exclusion regions (ROI\({}_{\text{excl}}\)) were determined. ROI\({}_{\text{myoc}}\) is defined by the epicardial and endocardial borders of the LV and thus forms a myocardial ring. Because the selection of ROI\({}_{\text{myoc}}\) is independent of the mathematical quantification method used, it was delineated only once per analysis and used for both mathematical quantification methods; that is, ROI\({}_{\text{myoc}3\sigma}=\text{ROI}_{\text{myoc}\text{FWHM}}=\text{ROI}_{\text{myoc}}\). Next, as the selection of ROI\({}_{\text{ref}}\) depends on the mathematical quantification method used [6; 10; 11], two different ROI\({}_{\text{ref}}\)s were selected: ROI\({}_{\text{ref}3\sigma}\), representing an area of healthy or dark myocardium; and ROI\({}_{\text{ref}\text{FWHM}}\), representing an area of pathological or bright myocardium (Figure 2).  
After delineating ROI\({}_{\text{myoc}}\) and ROI\({}_{\text{ref}}\), the specialists used a preliminary calculation of pathological regions in the image to delineate ROI\({}_{\text{excl}}\), which eliminates potential artifacts such as blood pools from further LGE extent calculations. Because the result of quantification differs when using different mathematical quantification methods (Figure 2), they delineated two ROI\({}_{\text{excl}}\) regions: ROI\({}_{\text{excl}3\sigma}\) and ROI\({}_{\text{excl}\text{FWHM}}\). In the calculations of LGE extent, the ROI\({}_{\text{excl}}\) regions were treated as healthy regions.