esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion on Clinical Relationship Extraction
### Supervised Learning Methods
#### 6.3.2 Language Model-Based Methods  
Language model-based approaches have shown improved performance in many NLP tasks as these language models use contextual information into account to represent the features. Then, a classifier is added on top of the language model output to perform the classification of relationships in the end. It is also a supervised learning model as the inputs are well defined for each instance. The language models popularly used in NLP tasks are ULMFit, ELMO, BERT, etc. Out of them, BERT ([PERSON] et al., 2014), introduced by Google in 2019, has become extremely popular for various NLP tasks including RE. Its breakthrough has resulted in improved performance in many NLP tasks because of its strong ability to pretrain deep bidirectional representations of any unlabelled text by conditioning on its context on both sides in all the 12 transformer layers. For biomedical clinical texts, two BERT-based models were later introduced such as BioBERT ([PERSON] et al., 2014), trained on biomedical PubMed corpus, and Clinical BERT ([PERSON] et al., 2014), trained on a biomedical corpus, clinical notes, and only discharge summaries. These models have the same model architecture as that of BERT, but they were trained on a medical corpus.  
BERT and the biological and clinical versions of BERT gained high popularity for RE tasks on clinical texts. Since these are language models, there is no need to generate and represent the features. The entire text, i.e., the complete sentence or the complete paragraph of each instance, is taken as input to the model. [PERSON] et al. (2019) ([PERSON] et al., 2019) established state-of-the-art results in temporal RE in clinical domain using pretrained domain-specific as well as fine-tuned BERT: 0.684F for in-domain texts and 0.565F for cross-domain texts. [PERSON] et al. (2020) ([PERSON] et al., 2020) used BERT-based models, including BioBERT and Clinical BERT. The BERT models used there performed really well for some of the classes, but for other classes, the Random Forest Classifier using different input features performed better. [PERSON] et al. (2020) ([PERSON] et al., 2020) established that the Fine-Tuned BERT eclipsed the performance of other models for RE on clinical narratives. Overall, the language models have shown superior performances than other models on clinical RE tasks. BERT (cased and uncased), BioBERT and Clinical BERT were the language models used by [PERSON] et al. (2021) ([PERSON] et al., 2021). All of the BERT models, with an impressive macro-averaged F1-score of 0.93, outshone the performance of all of the other rule-based or deep learning methods on this dataset. Therefore, in most cases, language models such as BERT have outshone other ML and deep learning methods for clinical RE due to their capability to learn from the context. Some clinical RE tasks using traditional language model-based methods are tabulated in Table A5.
## 2 Background  
The electromagnetic field emitted by a distant point source of light such as a star reaches the pupil of the telescope in the form of a plane wave. However, aberrations from either the propagation medium or the telescope itself make the wavefront depart from this ideal view. For simplicity, it is taken into consideration only the region of the wavefront reflected in three adjacent segments on a three ring segmented hexagonal mirror. There is piston misalignment in between the segments that introduces discontinuities in the phase of the wavefront. Figure 1 shows an intensity image recorded at the detector plane produced by the wavefront just described.  
The intensity of the field is recorded at a distance \(d=9\) m, away from the focus with four different wavelengths. This distance is such that the full peak width of the diffraction pattern is twice larger than the image blur due to the atmospheric turbulence, as explained here [14]. The focal length of the telescope is set to be \(f=170\) m.  
Four different wavelengths are considered to give the network the ability to distinguish piston errors that surpass the ambiguity range [15]. The largest \(\lambda_{0}=700\) nm is taken as the reference wavelength and three shorter ones to disambiguate \(\lambda_{1}=0.930\lambda_{0}\), \(\lambda_{2}=0.860\lambda_{0}\) and \(\lambda_{3}=0.790\lambda_{0}\). All piston values throughout the paper are measured at the wavefront. If a single wavelength would be used instead, diffraction patterns would be periodic with respect to the piston step values and the algorithm would not be able to predict which one gave rise to those images.  
The wavefront at the pupil propagates to the observation plane by means of Fresnel equation. The observation plane is located at a distance \(z\) from the pupil. The distance \(z\) can be related to the focal length \(f\), and the defocus distance \(d\) through Newton lens formula \(z=\frac{(f-d)f}{d}\).  
The intensity distribution at the observation plane is the squared magnitude of the complex field after propagation. Eventually, an image like the one in Figure 2 is produced.  
Figure 1: Diagram of one step Markov Decision Process.  
Figure 2: Diffraction image of three segments with piston errors between them. Distinctive intensity ripples at the borders between segments are caused by wavefront discontinuities. These discontinuities are, in turn, due to piston errors. Movement orders are commanded to segments \(A\) and \(B\) to minimize the effects of the errors between the three.  
The study takes also into consideration the atmospheric turbulence effects. The simulated intensity image at the defocus plane is filtered with the long exposure transfer function of the atmosphere [16]. A value for Fried parameter of \(r_{0}=0.2\,\mathrm{m}\) was chosen.  
Table 1 displays a summary of the parameters used in the simulation.
## 4 Results and Discussion
### Unsteady Simulation Results
#### 4.2.2 Unsteady Behavior of Diffuser Stall  
The unsteady behavior of TVD and SVD is presented based on unsteady numerical simulation results to examine the evolution of vortices in the diffuser passages. The unsteady pressure fluctuations at different spans of the TVD entrance (point 1 in Figure 14) are shown in Figure 15. Compared with the unsteady pressure fluctuation at the other  
Figure 14: Spectrum results at different locations (\(\varphi=0.7\ Q_{d}\)): (a) TVD; (b) SVD.  
Figure 13: Detailed vortex structures at TVD vane leading-edge (\(\varphi=0.7\ Q_{d}\)).  
spans, the pressure fluctuation near the shroud side has a higher amplitude and more complex modal shape.  
To investigate the evolution of vortices near the diffuser entrance as the stall cell propagates, the instantaneous vortex structures and limiting streamlines were examined, as shown in Figure 16. In these figures, vortex structure behaviors are visualized with \(Q\)-definition and colored by nondimensional helicity. At time \(t=t_{0}\), near the suction surface of the right diffuser vane, only the clockwise-rotating transverse vortex I (Figure 13) is found. Besides, two centers of vortices appear near the suction surface of the vane leading-edge. The counterclockwise-rotating transverse vortex II (in Figure 13) appears at time \(t=t_{1}\). Meanwhile, two centers of vortices appearing at time \(t=t_{0}\) are merged, making the shroud-side flow more disordered. When the impeller runs to \(t=t_{2}\), the transverse vortex II generated from the right diffuser vane increases in size due to the high adverse pressure gradient. The transverse vortex II then moves toward the leading edge of the adjacent left vane and blocks the throat of the diffuser. Note that the flow near the shroud side and leading edge of the right diffuser vane is more disordered, and the flow separation region occupies one-third of the suction surface of the whole vane at time \(t=t_{2}\).  
The pressure fluctuations at 0.95 span of the leading edge of TVD and SVD (point 1 in Figure 14) are compared in Figure 17. Compared with TVD, the amplitude of pressure fluctuation of SVD is decreased sharply. The instantaneous vortex structures of SVD at two moments are shown in Figure 18. Under the action of pressure difference and longitudinal vortex, the high-momentum fluid rotating clockwise through the vane root flows to the shroud side of the suction surface, which prevents the development of the separated flow. It is worth noting that the longitudinal vortex does not propagate into the diffuser passage at the two moments. As can be seen from the limiting streamline figure, in SVD, the vortex scale and its point of origin did not change significantly in two moments. Therefore, the flow field in the SVD passage is relatively stable compared to that of TVD.  
Figure 16: Unsteady behaviour of vortices (TVD, \(\varphi=0.7\ Q_{\mathrm{d}}\)): (a) \(t=t_{0}\); (b) \(t=t_{1}\); (c) \(t=t_{2}\).  
Figure 15: Unsteady pressure fluctuations at the leading edge of TVD (\(\varphi=0.7\ Q_{\mathrm{d}}\)).
## 5 Results and Discussion
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reduction Ratio (r)** & **Accuracy (\%)** & **FLOPs (M)** & **Latency (ms)** & \(\Delta\)**Accuracy (vs. r = 2)** \\ \hline r = 2 (default) & 94.7 \(\pm\) 0.3 & 180 & 198 & — \\ \hline r = 4 & 94.6 \(\pm\) 0.3 & 169 & 186 & –0.1 pp \\ \hline r = 8 & 94.3 \(\pm\) 0.3 & 163 & 179 & –0.4 pp \\ \hline r = 16 & 93.8 \(\pm\) 0.4 & 159 & 174 & –0.9 pp \\ \hline \hline \end{tabular}
\end{table} 
Table 7: ASA reduction ratio study. Figure 8: Within-class and between-class embedding distance variation with occlusion levels. This figure illustrates the comparison of intra-class embedding distances and inter-class pairs under different levels of occlusion. Increasing the level of occlusion results in both an increase in intra-class distance and a decrease in inter-class distance, which indicates a loss in separability. However, it is observed that HUE-Net still maintains a sufficient margin separation, which shows robustness in the embedding space. We applied temperature scaling to ElasticFace and ArcFace to test whether HUE-Net's superior ECE arose purely from postprocessing. As shown in Table 8, while TS improved calibration for the baselines, HUE-Net with PMB-VM remained the most calibrated even without tuning, confirming that learned uncertainty outperforms static scaling methods.
## 4 Experiment I--DSM Quality Evaluation
### Comparison of DSMs to the Top Height H100 at Forest Inventory Plots
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Data Pairs** & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Max**} & \multicolumn{2}{c}{**68.3\% Quantile**} & \multicolumn{2}{c}{**95\% Quantile**} \\ \hline _Inventory Plot_ & **Corr** & **RRMSE** & **Corr** & **RRMSE** & **Corr** & **RRMSE** & **Corr** & **RRMSE** \\ \hline LiDAR2001 & 0.509 & 1.833 & 0.429 & 0.992 & 0.547 & 1.383 & 0.485 & 0.994 \\ LiDAR2010 & 0.575 & 1.762 & 0.794 & 0.630 & 0.600 & 1.429 & 0.802 & 0.708 \\ HRSC2003 & 0.596 & **1.345** & 0.648 & 0.854 & 0.627 & **1.173** & 0.640 & 0.940 \\ ALOS2008 & 0.437 & 1.982 & 0.502 & 1.354 & 0.459 & 1.793 & 0.516 & 1.449 \\ Cartosat2008 & 0.576 & 1.662 & 0.643 & 1.211 & 0.595 & 1.548 & 0.632 & 1.307 \\ RapidEye2009 & **0.373** & **2.012** & **0.370** & **1.785** & **0.376** & **1.949** & **0.373** & **1.829** \\ WorldView2012 & 0.610 & 1.540 & 0.766 & 0.770 & **0.675** & 1.287 & 0.743 & 0.898 \\ Aerial2012 & 0.585 & 1.628 & **0.830** & **0.586** & 0.630 & 1.355 & **0.823** & **0.696** \\ TanDEM-X2013 & **0.612** & 1.668 & 0.645 & 1.330 & 0.627 & 1.569 & 0.642 & 1.390 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Correlation coefficients between DSM metrics and with the H100 height of forest inventory plots and RRMSE by taking H100 from 2008 and from 2013 as reference (the best and worst results are marked in bold). Figure 6: Comparison of canopy height metrics from LiDAR2001 **(a)**, LiDAR2010 **(b)**, HRSC2003 **(c)** ALOS2008 **(d)**, Cartosat2008 **(e)**, RapidEye2009 **(f)**, WorldView2012 **(g)**, Aerial2012 **(h)** and TanDEM-X2013 **(i)**with the H100 heights derived from forest inventory plots (\(n=184\)). By using the maximum heights of each inventory plot (as shown graphically in Figure 6), Aerial2012 has the highest correlation with the inventory H100 heights and the lowest RMSE, followed by the LiDAR2010 and WorldView2012 DSMs, while Cartosat2008 yields slightly worse results than WorldView2012. Correlation coefficients based on the 95% quantile are similar to the results derived from the maximum height value.
## 5 Integrated System Testing  
Next, we performed three test scenarios (see Table 1) in the setting shown in Figure 11. In Scenario 1, we set Sensors 1 and 2 to transmit monitoring data every 30 min for a full day, to test that data were transmitted via the Ethernet. Scenario 2 was a replication of Scenario 1, except that we changed the path to the GSM/SMS module. In Scenario 3, we tested the smart gateway to determine whether it was capable of automatically switching between Ethernet and GSM/SMS communication. In addition, in order to ensure the correctness and reliability of wireless sensing network data transmission, this paper uses a reliable data collection scheme with efficiency and management considerations [15].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case Number** & **Transmission Pathway** & **Intervals** & **Total Test Time** & **Node Numbers** \\ \hline
1 & Ethernet & 30 min & 1 day & 1, 2 \\
2 & GSM/SMS & 30 min & 1 day & 1, 2 \\
3 & Automatic switching between & 10 min & 1 h & 1 \\  & Ethernet and GSM/SMS & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three test case scenarios.  
Figure 14: (**a**) Exterior of the sensor node device. (**b**) Interior of the sensor node device.
## 5 Optimization Techniques for Routing in Wireless Sensor Networks
### Quality of Service  
The network application business and its functionalities prompt the need for ensuring a QoS (Quality of Service) in the data exchange. In particular, effective sample rate, delay bounded and temporary precision are often required. Satisfying them is not possible for all the routing protocols as the demands may be opposite to the protocol principles. For instance, a routing protocol could be designed to extend the network lifetime while an application may demand an effective sample rate which forces periodic transmissions and, in turn, periodic energy consumptions. Figure 1 shows the relation of QoS and its dependence to the routing protocol goal and to the routing protocol strategy.
## 3 Proposed Online Fault Diagnosis Model
### System Update  
In the proposed online fault detection and diagnosis model, known fault data from the DSK are combined with the optimal fault signature of unknown signals to detect new fault modes. \(k\)-means clustering and the proposed MPDFCDF cluster evaluation method are used to determine the number of fault modes (\(k_{opt}\)) in the combined data. If \(k_{opt}\) is larger than the number of known faults \(F_{dsk}\), the model automatically updates the DSK to incorporate the newly detected fault class.
# Multiple Thermal Parameter Inversion for Concrete Dams Using an Integrated Surrogate Model
## 2 Methodology
### Mathematical Model Construction for Multiple Thermal Parameters Inversion
#### 2.1.2 Mathematical Model Construction  
According to Section 2.1.1, in the concrete temperature field analysis, the primary thermal parameters of concrete are temperature conductivity \(a\), thermal conductivity \(\lambda\), adiabatic temperature rise \(\theta\), specific heat \(c\), density \(\rho\), and heat release coefficient \(\beta\). Concrete density and specific heat can be obtained accurate and stable values by testing. Moreover, there is a definite relationship:  
\[\lambda=ac\rho \tag{10}\]  
Therefore, the thermal parameters to be inverted are the temperature conductivity (or thermal conductivity), the adiabatic temperature rise, and the surface heat release coefficient.  
Figure 2: Diagram of the water pipe cooling for concrete. (**a**) describes the laying of cooling water pipes; (**b**) describes the simplified treatment of water pipe cooling.  
FEM-based thermal parameter inversion of concrete can be regarded as an optimization problem. With the optimization criterion of minimizing the error between the monitored and calculated temperatures, the concrete thermal parameters are identified by an optimization algorithm. Assuming that the dam concrete is an isotropic homogeneous material, the mathematical model for the inversion analysis can be expressed as  
\[\left\{\begin{aligned} & F(X)=\sum\limits_{i=1}^{p}\sum\limits_{j=1}^{q} \left|T_{ij}-T_{ij}^{F}\right|\\ & s.t.\ x_{k}\in[a_{k},b_{k}]\ (k=1,2,\dots,u)\end{aligned}\right. \tag{11}\]  
where \(F(\cdot)\) is the objective function; \(X=(x_{1},\ x_{2},\ \dots,\ x_{n})\) is the parameter vector to be inverted; and \(u\) is the number of parameters to be inverted. \(i\) and \(j\) are the numbers of temperature measurement point and monitoring sequence, respectively; \(p\) and \(q\) are the total number of measurement points and the monitoring times, respectively; \(T_{ij}\) and \(T_{ij}^{F}\) are the monitoring temperature and calculation temperature, respectively; \([a_{k},b_{k}]\) are the feasible domains of the parameters to be inverted.
# A Hierarchical Control System for Autonomous Driving towards Urban Challenges
## 2 Hierarchical Control System
### The Decision-Making Mechanism Based on Two-Stage FSM  
The Decision-Making Mechanism (DMM) [28] of SDCs employs FSM to handle the mission planning and behavior on-road driving. The first DMM called the Mission FSM (M-FSM), which manages the vehicle's missions. The second DMM named the Control FSM (C-FSM) mimics the vehicle's status on the road. After observing the surrounding object's trajectory from the perception and the missions data, M-FSM and C-FSM are determined as following in Figure 2. M-FSM is categorized into five classes: Ready, Stop-and-Go (SAG), Change-Lane(CL), E-stop, avoid obstacle mode. In particular, the M-FSM consists of a C-FSM that manages the control states of the vehicle. A human needs to control the vehicle starting or stopping on-road driving by reducing or increasing the acceleration, respectively, to mitigate damage to the engine. Likewise, the SAG mode is designed to mimic all human behaviors mentioned beforehand. When urgent situations appear, the E-stop mode is activated. The CL mode consists of two control states lane-keeping and lane-changing, which actuates when lane changing state is demanded. The obstacle avoiding mode activates when the obstacles have been detected lying on the path. The transition condition is a directed graph in the M-FSM, and the descriptions of all transition conditions in Figure 2 are summarized as following in Table 1.  
The data structures, which we infer from Figure 2, are utilized to implement the M-FSM and C-FSM. Each state in FSM requires a resource that is updated over time in the ROS nodes of perception. The use of priority and flag of each state is to prevent access to the same resources. The priority level of the E-stop mode is highest, following by the obstacle avoiding mode, the CL mode, and the SAG mode.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Condition** & **Description** \\ \hline
10, 20, 30, 40 & The perception informs the emergency circumstances \\
00 & Continuously works in an emergency mode \\
11 & All ROS nodes staying healthy, and the vehicle status is ready to go \\
01 & Non-dangerous and wake up to the ready state \\
41 & Un-complete obstacle avoiding mission, and the time for the mission is over \\
12, 22 & Standard scenario \\
32, 42 & Completely performs the lane-changing and obstacle-avoidance mission, respectively \\
33 & Operating in the lane changing mode \\
23 & Demanding to change the path \\
03 & Wake up to lane changing mode if a non-emergency case comes \\
44 & Handling on the avoid obstacle mission \\
14, 24 & Need to avoid the obstacle \\
21 & Finished SAG mode, wait to new mission \\ \hline \hline \end{tabular}
\end{table}
Table 1: The descriptions of transition conditions of Decision-Making Mechanism (DMM).  
Figure 2: Decision mechanism based on Mission-Finite State Machines (M-FSM) while the inside of M-FSM describes Control FSM (C-FSM).
# Accuracy Characterization of a MEMS Accelerometer for Vibration Monitoring in a Rotating Framework
## 2 Materials and Methods
### Reference Vibration Monitoring System  
The acceleration measurement system taken as a reference consists of (Figure 2):  
* An ICP _PCB 352A56_ accelerometer glued at the tip of the blade suction surface (SS) in order to measure the same acceleration of the MEMS accelerometer;
* A _PCB 482C_ signal conditioner that is wired to the PCB accelerometer using the slip ring;
* A data acquisition board _National Instruments NI USB-6341_ for the reference accelerometer data acquisition by means of an ad hoc developed _LabView_ routine. The accelerometer data were delivered to the DAQ by means of the _SENRING SNG025-065_ slip ring.  
The main specifications of both the vibration measurement systems are listed in Table 2.  
Figure 2: Functional scheme of the reference vibration monitoring system. The reference accelerometer mounted on the blade tip suction surface (SS) is powered by a signal conditioner via the slip ring wires. The signal conditioner is wired to the data acquisition board that is controlled by a remote PC by means of a _LabView_ routine.
# Appearance-Based Gaze Estimation as a Benchmark for Eye Image Data Generation Methods
## 2 Prior Work
### Learning-Based Data Generation Methods
#### 2.2.3 EyeGAN  
The EyeGAN study [25] improves on the SimGAN approach in a different manner. Instead of relying on the per-pixel refinement of the original image, the authors proposed using an eye segmentation mask as an input to the generator model. A ternary mask includes classes for the sclera, the pupil together with the iris, and the remaining periocular region. The labeled segmentation masks can be obtained as an additional output of the rendering tools, like UnityEyes that was used in the EyeGAN paper or Blender, as shown later in the \"Synthetic Blender data\" subsection. As it is very labor-intensive to manually label the real data, the segmentation network was initially trained only on the available synthetic data. To obtain the labeled masks for the real images, the generator facilitated the unsupervised domain transfer for the segmentation task. The segmentation network was updated as part of the GAN training process, and its training pool was periodically updated with the output of the generator network that was fed masks from the synthetic data set. The real eye images and their corresponding masks obtained from the segmentation network formed the training batch of the generator. Note that the image loss was no longer computed on the pair of images from different domains (synthetic and real) as in the case of SimGAN. This change, together with the guidance from domain-agnostic segmentation masks, resulted in a generator that much better preserves the eye gaze in the output images, as concluded by the EyeGAN authors. We picked EyeGAN as one of our baselines for realistic eye image generation, as the more recent example of purely GAN-based approaches. Our own schematic presentation of its framework is shown in Figure 1.