esa-sceva/satcom-chunk-collection huggingface dataset:
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## Inverse Models
### Temporal Inversion
#### 4.3.2 A First Method with a Geometric a Priori: The Projected Disk Method  
The Projected Disk method relies on the mesh resulting from the static inversion. We assumed that the displacement recorded by GNSS at each time step could be explained by the pressurization of a subpart of the final mesh. This a priori assumes that the tail of the dike did not close up after the magma had intruded, which seems a reasonable assumption considering that magma is viscous, quickly solidifying and that lateral intrusions were probably emplaced above the level of neutral buoyancy implying that they remained under pressure ([PERSON] et al., 2016; [PERSON] et al., 2016). An average plane was defined from this original mesh. We inverted a circular area onto this plane, for a given location (\(x\),\(y\)), radius (\(R\)) and overpressure \(P\). The projection of this circle onto the original mesh was chosen to select a subpart of the original mesh, which defines the new pressure source. This method works well if the static geometry is simple (i.e., if it is possible to define a mean plane and a projection). However, with a more complex geometry, in particular if there are strong curvatures of the fracture, the projection may lead to an absurd geometry.
# Application of Machine Learning Algorithms in Real-Time Monitoring of Conveyor Belt Damage
## 3 Results and Discussion  
The results were analyzed in two stages. First, the graphical visualization of the registered signals in time \(t\) was studied, comparing the simultaneous indications of three strain gauges in order to assess the features related directly to the loads and damages. Then, the ML algorithms were tested, assessing their ability to classify the loads and damages.
# Monolithically Integrated THz Detectors Based on High-Electron-Mobility Transistors
## 7 Conclusions  
We demonstrate THz detector designs using an AlGaN/GaN HEMT MMIC technology with excellent optical sensitivity and noise-equivalent-power performance. The underlying theory was derived and verified by measurement. Based on this theory, various detector designs and a focal plane array were realized and presented. The THz detectors were de  
Figure 10: Measurement of the THz source focal spot from 100 GHz to 1.1 THz.  
veloped in a 150 nm GaN HEMT MMIC process. The monolithic integration of the HEMTs in various antenna structures is unique in design and enables a compact implementation in large arrays with minimal coupling between the array elements. We show experimental results for the single THz detectors and a focal plane array in the 0.1 THz-1.5 THz range. The detection mechanism presented here is not band-limited, and ultra-broadband designs with a bandwidth larger than 2 THz are possible. Only the antenna structures are indeed used to define the operating frequency and the bandwidth. The THz detectors have an optical sensitivity greater than 20 mA/W at 1 THz with an NEP < 100 pW/Hz. The lowest achieved optical NEP is less than 10 pW/Hz at 175 GHz. With a weak THz source, the resulting focal plane array demonstrates a real-time capability of 25 frames per second at frequencies up to 600 GHz.  
All measurement results are the values obtained at the connector reference plane. These values can be used when designing a system, making the devices shown here suitable for building sensitive THz systems.
###### Abstract  
Diamond-like carbon (DLC) thin films, prepared by a radio frequency plasma-enhanced chemical vapor deposition (PECVD) system, were investigated for application as an anti-reflective coating (ARC) for dye-sensitized solar cells (DSSCs) with a change in film thickness. The strength of the Raman spectrum, G-peak position, and I\({}_{D}\)/I\({}_{C}\) ratio, related to \(sp^{3}\) bonds in the DLC thin films, is directly attributed to some tribological properties including surface roughness, hardness, elastic modulus, friction coefficient, and contact angle. Some optical properties, such as transmittance, refractive index, and absorption coefficient, were examined after changing the thickness of DLC thin films. The optimal short-circuit current density (J\({}_{sc}\)), open-circuit voltage (\(V_{sc}\)), and fill factor (FF) values were obtained for the significantly improved conversion efficiency (CE) from 4.92% to 5.35% in the 60 nm thick PECVD DLC ARC for DSSCs with hard and hydrophobic surfaces.  
diamond-like carbon (DLC); anti-reflective coating (ARC); dye-sensitized solar cells (DSSCs); plasma-enhanced chemical vapor deposition (PECVD) +
Footnote †: journal: Applied Sci. 2021, 11, 358. [[https://doi.org/10.3390/app11010358](https://doi.org/10.3390/app11010358)]([https://doi.org/10.3390/app11010358](https://doi.org/10.3390/app11010358))
optimal conditions for developing DLC ARCs in which the transmittance and refractive index are acceptable at the appropriate thicknesses. The tribological, optical, and structural characteristics of the DLC thin films were also analyzed at different thicknesses to improve the energy CE and long-term stability of DSSCs.
# Multimodal Chinese Sarcasm Detection Integrating Audio Attributes and Textual Features
## 2 Related Work
### Multimodal Sarcasm Detection  
In multimodal sarcasm detection, the core task is to utilize information from different modalities (such as text, audio, images, etc.) to analyze the interaction between data from each modality, thereby effectively extracting the implicit sarcastic information and better predicting sarcasm labels [21; 22]. Compared to unimodal approaches, multimodal applications are more complex, handling a greater variety of feature data [23], and modalities may either coordinate or interfere with each other.  
Previous studies have highlighted the individual roles of each modality in multimodal sarcasm detection [24]. For instance, [PERSON] et al. discussed these contributions in detail [25].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Tool** & **Strengths** & **Limitations** \\ \hline OpenSMILE & Provides a broad and detailed range of acoustic features; widely used in affective computing research. & The sheer number of features may introduce redundancy, and many are not optimized for sarcasm-specific detection tasks. \\ \hline VGGish & Utilizes a pre-trained deep model to generate abstract audio embeddings, allowing for compact representation of complex signals. & Restricted to Mel spectrogram inputs and typically requires a substantial amount of audio data for effective results. \\ \hline Proposed Approach & Emphasizes task-relevant acoustic indicators (e.g., pitch, loudness, jitter) while avoiding excess noise from irrelevant features. & May miss some nuanced audio cues due to its focus on a narrower, more targeted feature set. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of audio feature extraction tools.  
[PERSON] et al. proposed a simple hierarchical-attention-based fusion neural network [26, 27]. [PERSON] et al. studied emotional relationships within and across modalities, proposing an interactive cross-modal interaction graph [28]. [PERSON] enhanced the connection between image and text modalities [29].  
This paper focuses on audio-text multimodal sarcasm detection, which, to our knowledge, has received limited attention in the field. We propose the use of audio attributes as integral features to bridge the gap between text and audio modalities, enhancing the overall performance of sarcasm detection. By incorporating a multi-scale convolutional architecture, our model captures both local and global feature interactions, enabling more robust and accurate sarcasm detection across modalities.
## 4 Effect of Non-Idealities Analysis  
In this section, the non-ideal circuit model of OTA will be considered. The non-ideal terminal effects of OTA are considered, including non-ideal current transfer error parameters and non-ideal finite input and output parasitic elements. Figure 4 illustrates a non-ideal OTA model with various parasitic elements [19], where \(\text{R}_{\text{P}}\), \(\text{R}_{\text{nj}}\), \(\text{R}_{\text{oj}}\), \(\text{R}_{\text{qj}}\), \(\text{C}_{\text{Pj}}\), \(\text{C}_{\text{nj}}\), \(\text{C}_{\text{oj}}\), and \(\text{C}_{\text{qj}}\) represent the finite parasitic resistances and capacitances of the input and output terminals of the jth OTA, respectively. In Figure 4, the non-ideal dual-output currents of OTA are given as \(\text{I}_{\text{O}}=\gamma\text{g}_{\text{m}}(\text{V}+-\text{V}-)\) and \(\text{I}_{\text{Q}}=\text{n}\text{g}_{\text{m}}(\text{V}+-\text{V}-)\), where \(\gamma=1-\varepsilon_{\gamma\text{j}}\) and \(\eta=1-\varepsilon_{\tau\text{j}}\). Here, \(\varepsilon_{\gamma\text{j}}\) (\(\mid\varepsilon_{\gamma\text{j}}\mid<\)1) and \(\varepsilon_{\tau\text{j}}\) (\(\mid\varepsilon_{\tau\text{j}}\mid<\)1) indicate the non-ideal dual-output currents of OTA current tracking errors. Taking the non-ideal OTA model into account, the biquad shown in Figure 2 can be modified to become the biquad presented in Figure 5. The effect of the non-ideal current transfer error parameters and the parasitic impedance elements on the characteristic parameters of \(\omega_{\text{o}}\) and Q has been investigated to indicate the performance of the proposed biquad. Accounting for the parasitic elements in Figure 5, the non-ideal characteristic equation, \(\text{D}_{1}(\text{s})\), of the equivalent circuit model in Figure 5 becomes:  
\[ \begin{array}{l}\text{D}_{1}(\text{s})=\beta_{1}\text{s}^{2}\text{C}_{1}^{ \prime}\text{C}_{2}^{\prime}\text{g}_{\text{m}1}(1+\frac{\text{P}}{\beta_{1} \text{C}_{1}^{\prime}\text{C}_{2}\text{g}_{\text{m}1}})+\alpha_{2}\alpha_{3} \text{s}\text{C}_{1}^{\prime}\text{g}_{\text{m}2}\text{g}_{\text{m}3}(1+ \frac{\text{Q}}{\alpha_{2}\alpha_{3}\text{C}_{1}^{\prime}\text{g}_{\text{m}2} \text{g}_{\text{m}3}})\\ \hskip 14.226378 pt+\alpha_{1}\alpha_{2}\alpha_{3}\text{g}_{\text{m}1}\text{g}_{ \text{m}2}\text{g}_{\text{m}3}(1+\frac{\text{R}}{\alpha_{1}\alpha_{2}\alpha_{3} \text{g}_{\text{m}1}\text{g}_{\text{m}2}\text{g}_{\text{m}3}})\end{array}
# SSTNDS: A SET-Based Non-Dominated Sorting Algorithm for Multi-Objective Optimization Problems
## 4 Proposed Non-Dominated Sorting Algorithm
### Algorithm Complexity Analysis  
The algorithm complexity of SETNDSs are analyzed in this section. \(N\) and \(M\) represent the size and number of objectives as defined above. As is analyzed in [6], the time complexity of sorting with lexicographic order is _O(MNlogN)_. The worst time complexity is _O(MN\({}^{2}\))_ since each solution needs to find \(M\) preferential sets to combine into a dominant region. From the previous description, each solution only needs to compare with at most one solution from its dominant region to determine the rank of it, so at most \(N\)-\(1\) times of comparing will be employed, and the relevant time complexity is _O(M(N\(-1\)))_. In total, the best time complexity will be _O(MNlogN)_ and the worst will be _O(MN\({}^{2}\))_. From the perspective of storage space, it will need two \(M\)*\(N\) storage spaces for the sorted objective set A and location information set \(PS\), respectively, so the space complexity is _O(MN)_.  
Table 3 gives the time and space complexity of the SETNDS algorithm we proposed together with other seven general non-dominated sorting algorithms. The time complexity in the worst case remains stuck at _O(MN\({}^{2}\))_, though the efficiency of the improved sorting algorithms is improved. Only BOS reduces the best-case time complexity to _O(MNlogN)_, while other improved algorithms are reduced to _O(MN\(\sqrt{N}\))_. The SETNDS algorithm reduces the best-case time complexity to _O(MNlogN)_ and worst-case time complexity to _O(MN\({}^{2}\))_.  
A series of experiments will be discussed in the next section to demonstrate the efficiency of the proposed algorithm.
# Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use
## 4 Results and Discussion
### PHY Performance
#### 4.1.4 Link Budget and Receiver Sensitivity
\begin{table}
\begin{tabular}{c c c c c c} \hline
**ECC** & \(R_{s}\) & **GFSK** & **QPSK** & \(\pi\)/4 Shift DQPSKK** & **D8 PSK** \\ \hline \multirow{4}{*}{No ECC} & 1.0 & \(-\)84.5 & \(-\)82.0 & \(-\)79.7 & \(-\)74.1 \\  & 1.5 & \(-\)82.7 & \(-\)80.2 & \(-\)77.9 & \(-\)72.3 \\  & 2.0 & \(-\)81.5 & \(-\)79.0 & \(-\)76.7 & \(-\)71.0 \\ \hline \multirow{4}{*}{(127, 113) BCH} & 1.0 & \(-\)86.6 & \(-\)85.3 & \(-\)82.5 & \(-\)77.2 \\  & 1.5 & \(-\)84.8 & \(-\)83.5 & \(-\)80.7 & \(-\)75.4 \\  & 2.0 & \(-\)84.6 & \(-\)82.2 & \(-\)79.4 & \(-\)74.2 \\ \hline \multirow{4}{*}{(127, 85) BCH} & 1.0 & \(-\)88.7 & \(-\)87.6 & \(-\)84.7 & \(-\)79.7 \\  & 1.5 & \(-\)87.0 & \(-\)85.8 & \(-\)82.9 & \(-\)77.9 \\ \cline{1-1}  & 2.0 & \(-\)85.7 & \(-\)84.6 & \(-\)81.7 & \(-\)76.7 \\ \hline \end{tabular}
\end{table} 
Table 10: Examples of \(S_{\text{dBm}}\) (dBm). \(\text{PER}\leq 10^{-2}\), \(L_{\text{PSDU}}=127\) bytes, \(NF_{\text{dB}}=13\) dB and \(I_{\text{dB}}=6\) dB. \begin{table}
\begin{tabular}{c c c c c c} \hline
**ECC** & \(R_{s}\) & **GFSK** & **QPSK** & \(\pi\)/4 Shift DQPSKK** & **D8 PSK** \\ \hline \multirow{4}{*}{No ECC} & 1.0 & \(-\)82.2 & \(-\)81.0 & \(-\)77.6 & \(-\)73.1 \\  & 1.5 & \(-\)80.4 & \(-\)79.2 & \(-\)75.8 & \(-\)71.3 \\  & 2.0 & \(-\)79.2 & \(-\)78.0 & \(-\)74.6 & \(-\)70.1 \\ \hline \multirow{4}{*}{(127, 113) BCH} & 1.0 & \(-\)84.3 & \(-\)84.4 & \(-\)81.7 & \(-\)76.4 \\  & 1.5 & \(-\)82.5 & \(-\)82.6 & \(-\)79.9 & \(-\)74.6 \\  & 2.0 & \(-\)81.3 & \(-\)81.3 & \(-\)78.6 & \(-\)73.4 \\ \hline \multirow{4}{*}{(127, 85) BCH} & 1.0 & \(-\)86.7 & \(-\)86.7 & \(-\)83.7 & \(-\)78.6 \\  & 1.5 & \(-\)85.0 & \(-\)84.9 & \(-\)81.9 & \(-\)76.8 \\ \cline{1-1}  & 2.0 & \(-\)83.7 & \(-\)83.7 & \(-\)80.7 & \(-\)75.6 \\ \hline \end{tabular}
\end{table} 
Table 11: Examples of \(S_{\text{dBm}}\) (dBm). \(\text{PER}\leq 10^{-3}\), \(L_{\text{PSDU}}=127\) bytes, \(NF_{\text{dB}}=13\) dB and \(I_{\text{dB}}=6\) dB. \begin{table}
\begin{tabular}{c c c c c c} \hline
**ECC** & \(R_{s}\) & **GFSK** & **QPSK** & \(\pi\)/4 Shift DQPSKK** & **D8 PSK** \\ \hline \multirow{4}{*}{No ECC} & 1.0 & \(-\)84.5 & \(-\)82.0 & \(-\)79.7 & \(-\)74.1 \\  & 1.5 & \(-\)82.7 & \(-\)80.2 & \(-\)77.9 & \(-\)72.3 \\  & 2.0 & \(-\)81.5 & \(-\)79.0 & \(-\)76.7 & \(-\)71.0 \\ \hline \multirow{4}{*}{(127, 113) BCH} & 1.0 & \(-\)86.6 & \(-\)85.3 & \(-\)82.5 & \(-\)77.2 \\  & 1.5 & \(-\)84.8 & \(-\)83.5 & \(-\)80.7 & \(-\)75.4 \\  & 2.0 & \(-\)84.6 & \(-\)82.2 & \(-\)79.4 & \(-\)74.2 \\ \hline \multirow{4}{*}{(127, 85) BCH} & 1.0 & \(-\)88.7 & \(-\)87.6 & \(-\)84.7 & \(-\)79.7 \\  & 1.5 & \(-\)87.0 & \(-\)85.8 & \(-\)82.9 & \(-\)77.9 \\ \cline{1-1}  & 2.0 & \(-\)85.7 & \(-\)84.6 & \(-\)81.7 & \(-\)76.7 \\ \hline \end{tabular}
\end{table} 
Table 10: Examples of \(S_{\text{dBm}}\) (dBm). \(\text{PER}\leq 10^{-2}\), \(L_{\text{PSDU}}=127\) bytes, \(NF_{\text{dB}}=13\) dB and \(I_{\text{dB}}=6\) dB.
## 4 Radiometric Processing Workflow
### Sensor-Related Calibration  
Radiometric sensor calibration determines the radiometric response of an individual sensor [120; 121; 122]. The calibration process includes several phases: a relative radiometric calibration, which aims for a uniform output across the pixels and time, a spectral calibration, which determines the spectral response of the bands, and an absolute radiometric calibration, which determines the transformation from pixel values to the physical unit radiance. A comprehensive review on calibration procedures for high-resolution radiance measurements can be found in [PERSON] et al., [123] and [PERSON] [124]. In the following, we will focus on sensor calibration procedures that are required to generate reflectance maps from spectral UAV data. These procedures will provide sensor-specific calibration factors that are applied to the captured spectrometric datasets. While the calibration steps are essentially the same for point, line, and 2D imagers, the complexity increases with data dimension, since every pixel needs to be characterized and calibrated. In many cases, researchers have implemented their own calibration procedures for small spectrometers or spectral imagers used in UAVs, since either the systems have been experimental setups, or the sensor manufacturers of small-format sensors have not provided calibration files or suitable calibration procedures. The examples in this section are taken from studies with the point spectrometer Ocean Optics STS-VIS [22] and USB 4000 [25], the pushbroom system Headwall Photonics Micro-Hyperspec [38; 125],  
Figure 5: The full data processing workflow to create a reflectance data product. First, sensor-related calibration procedures are carried out. Relative calibration (RC\({}_{1}\)) and spectral calibration (SC) transform the digital numbers (DN) of the sensor to normalized DN (DN\({}_{\text{n}}\)). Further, absolute radiometric calibration (RC\({}_{2}\)) can be carried out to generate at-sensor radiance (L\({}_{\text{s}}\)). Second, the data is transformed to reflectance factors (R) with the empirical line method (ELM), based on a second radiometrically calibrated reference device on the ground, the UAV, or models. Geometric processing (GP) is an estimation of the relative position and orientation of the measurements, and composes the data into a scene. Radiometric block adjustment can be used at different steps in the process to optimize the radiometry of the scene and correct for bidirectional reflectance distribution function (BRDF) effects. The geometric processing (GP) composes the data into a scene. Additional modules may then transform the reflectance factors in the scene to reflectance quantities (c.f. Section 4.4), and shadows and topography effects may be corrected. Independent radiometric reference targets are used to validate the data. The processing procedures are tracked in metadata to allow an accurate interpretation of the results.  
other custom-made pushbroom sensors [40; 126], 2D imagers Cubert Firefleye [43; 127; 128; 129], Rikola FPI [68], and Tetracam MCA and mini-MCA models [50; 130; 131].
# Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
## 4 Data Collection Scheme Based on Opportunistic Node Connections
### Opportunistic Connection Random Graph Construction
The whole working time of nodes in this data collection period is set to a fixed value to explore the impact of different status transition frequency of nodes on the link connectivity. In the figure it can be seen that, with a fixed whole working time of the node, the link connectivity between nodes decreases as the max status transition frequency of nodes increases. For two adjacent nodes with higher status transition frequency, the link connectivity between them is higher. The main reason is that the higher status transition frequency of a node increases the communication opportunities with its neighbors, and further improves the link connectivity between them. Based on the analysis above, the mobile sink can first obtain the message source set V by gathering their IDs stored in the \"Message Source\" field included in the probe messages, and then obtain the edge set E by analyzing the neighbors' IDs of each message source that stored in the \"Source Information\" field, by gathering all message sources' whole working time and status transition frequencies stored in the \"Source Information\" field it can calculate the link connectivity between any two adjacent nodes in set V according to Equation (14), thus constructing an OCRG. By constructing OCRG, the mobile sink models the opportunistic node connections in the data collection scope, based on which, it could calculate the optimal data forwarding path in advance for each node to send their sensing data to improve the reliability of data transmission. Furthermore, the source node could obtain its data forwarding path in advance therefore reduce the delay in each hop, and due to the intermittent communication mode of the nodes, it could also improve the success rate of the data transmission. Figure 6 depicts an OCRG with three nodes \(v_{i}\), \(v_{j}\) and \(v_{k}\) that adopt the asynchronous working-sleeping cycle strategy to save energy. Nodes \(v_{i}\) and \(v_{j}\) are adjacent neighbors while  
Figure 5: Link connectivity between four node pairs (\(v_{1}\) is adjacent to \(v_{2}\), \(v_{3}\) is adjacent to \(v_{4}\), \(v_{5}\) is adjacent to \(v_{6}\), \(v_{7}\) is adjacent to \(v_{8}\)) varies by the max status transition frequency of nodes, in which the whole working time of nodes are set to 80 percent of \(T_{p}\). and \(v_{k}\) are adjacent neighbors, and the link connectivity between them are \(P_{v_{i}v_{j}}\) and \(P_{v_{i}v_{k}}\), respectively, hence there exists an opportunistic connection. Since node \(v_{i}\) is not adjacent to \(v_{j}\), their link connectivity \(P_{v_{i}v_{k}}\) is 0.
## 4 Discussion
### Time Assumption  
Regarding the time assumption, [PERSON]'s equation [6] estimates the mean concentric time as one-tenth or 10% of the total trial duration because it assumes that (i) no rest between repetitions is present, i.e., the total trial time is \"active time\" and that (ii) concentric and eccentric portions of a single repetition have an equal duration (50%) and [6; 16]. On the contrary, in the current study, \"active time\", or time actually spent in concentric or eccentric actions, is in fact only 87% of the total time trial. In fact, about 13% of the time was actually occupied by a \"transition phase\" between repetitions. During this phase, participants take contact with the box and perform an oscillatory movement (backward and forward) of the torso before standing up again. Moreover, concentric and eccentric portions of a single repetitions were different from the estimated 50% (concentric portion = 47.7% \(\pm\) 4.9; eccentric portion = 52.9% \(\pm\) 5.1). The combination of the above factors may explain why the single mean concentric time (expressed as % of the total time) measured in our study was in fact was 8% and not 10%, as originally assumed by [PERSON]'s method.  
The practical consequences of the overestimation of single mean concentric phase by [PERSON]'s method is an underestimation of the movement velocity.