esa-sceva/satcom-chunk-collection huggingface dataset:
# STOD: Towards Scalable Task-Oriented Dialogue System on MultiWOZ-API
## 4 Methodology
### Scalable Task-Oriented Dialogue Modeling Framework
#### 4.1.1 API Query Generation  
Traditional task-oriented dialogue systems always follow a two-stage method to query the database: first building a dialogue state tracker (DST) to model dialogue states, and then using the states to query the database with predefined handcrafted rules. These methods have two disadvantages: (1) Dialogue states always be domain specific. (2) The current dialogue system only supports predefined handcrafted query rules, which are also domain specific. These problems make it difficult to effectively transfer to other domains.  
Considering the above problems, we focus on domain-independent API call generation instead of domain-specific dialogue state tracking. The goal of this task is to build an API query generator, which takes the dialogue context as input and predicts an API query. The API query is used to interact with the database and consists of the API name and the corresponding input parameters.
## 3 Results  
The SOT results are shown in Figures 1-3. ES analysis (Figure 1) shows statistical significance (\(p<0.05\)) for the CES, with higher values for footballers. Of the six SOT conditions, COG displacement differed significantly between the groups only for conditions ES5 and ES6 which provided conflicting information to the sense organs, carried out on a moving platform with eyes closed (ES5) or a moving visual surround (ES6). These results reflect significant differences in the use of vestibular stimuli (VES) between people training regularly and those not performing any regular sport.  
The usefulness of the signals from the other sensory organs involved in the control of body balance (VIS, SOM, and PREF) did not highlight any significant differences between the two groups (Figure 2).  
There was no impact on the selection of an appropriate motor strategy (MS1-MS6) by people playing football (Figure 3).  
During MCT, statistically significant differences between the two groups were found in the symmetry of loading of the lower limbs during all the trials (Figure 4). Football players were characterized by disproportionate distribution of body weight with a predominance of left leg.  
Figure 4: Motor Control Test—weight symmetry (WS) results for the lower limbs for small, medium and large platform transitions in backward (SB, MB, LB) and forward (SF, MF, LF) direction for footballers and control group. The symbol * refers to a significant difference at \(p<0.05\).  
Figure 3: Sensory Organization Test—motor strategy (MS) for particular SOT conditions for footballers and control group.  
Postural response latencies (Figure 5) and their amplitudes (Figure 6) did not reveal any statistically significant differences between the two groups, which means that neither the reaction time nor the angular velocity of COG during the trials depends on the physical activity.  
In the ADT there were no statistically significant differences between the measured parameters for both groups, which means that the adaptive postural response system is independent of physical activity. The results are shown in Figure 7.  
Figure 5: Motor Control Test—postural latencies for left (LL) and right (LR) lower limb for small, medium and large platform transitions in backward (SB, MB, LB) and forward (SF, MF, LF) direction for footballers and control group.  
Figure 6: Motor Control Test—amplitudes of the postural responses for left (AL) and right (AR) lower limb for small, medium and large platform transitions in backward (SB, MB, LB) and forward (SF, MF, LF) direction for footballers and control group.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.21 Anchor-Based Connectivity-Aware Routing (ACAR)  
Position-based routing protocol ACAR for vehicular ad hoc networks was proposed in [45]. This protocol uses the greedy-forwarding approach and store-carry-forwarding to reduce loss of information. ACAR proposes to make routing more efficient in a fully connected scenario based on position and direction; in a network sparse scenario, selection is based on connectivity (density).  
The proposal considers two scenarios: if the network is (1) dense or (2) sparse. In the first case, each vehicle knows the position and direction of itself and its neighbors (assuming the use of GPS). The data packet is then transferred from source to destination using this closest path. In the second case, if there is no neighboring vehicle in the range of the node that ensures connectivity, the store-carry-forward mechanism is started. This method introduces a delay, but it is more admissible than dropping the packet; the workflow is described in Figure 13.  
Figure 12: Mechanism procedure of greedy perimeter stateless routing (GPRS)-modified (GPSR-M) [44].
# Liquid Crystal-Tuned Planar Optics in Terahertz Range
###### Abstract  
Recently, terahertz waves of higher frequencies compared to microwave and radio frequency have shown great potential in radar detection and high-speed wireless communication. To spatially control the wavefront of terahertz beams, various novel components, such as terahertz filters, polarization converters and lenses, have been investigated. Metamaterials and metasurfaces have become the most promising technique for the free manipulation of terahertz waves. Metadevices integrated with liquid crystals have been widely used in active terahertz devices. In this review, the birefringence of liquid crystals in the terahertz band and terahertz devices based on liquid crystals are summarized. By integrating liquid crystals with plasmonic metamaterials, the functions become dynamically adjustable and are reconstructed. Utilizing liquid crystals to change the resonance of metamaterials, tunable filters, absorbers, and programmable metasurfaces are realized. To solve the problem of low efficiency, terahertz wavefront shaping devices based on dielectric metasurfaces and liquid crystals, such as a variable deflection angle grating and zoom metalenses, are presented. Finally, we discuss and anticipate the future developments of liquid-crystal-integrated meta-devices, which will inspire broad applications in terahertz communication and imaging.  
liquid crystal; metamaterial; tunable; terahertz +
Footnote †: journal: 2023, 13, 1428. [[https://doi.org/10.3390/app13031428](https://doi.org/10.3390/app13031428)]([https://doi.org/10.3390/app13031428](https://doi.org/10.3390/app13031428))
## 4 Conclusions  
A detailed unsteady numerical study was carried out to investigate the effects of the HP rotor trailing-edge radius on the unsteady flow characteristics in the integrated AITD. The HP rotor wake was simulated by a sweeping rod to decouple its influence from other secondary flows. The radial pressure gradient in the integrated AITD passage decreased with increasing rod diameter (\(d/s\)), especially at its first bend. The wake in the lower part of the AITD channel was exhausted rapidly before reaching the LPT-GV inlet because of the stretching between its own circumferential motion and the radial upward secondary flow for all values of \(d/s\), especially when \(d/s\) was small. For the upper part of the AITD channel, the wake vortices were gathered, squeezed and dissipated because of the stronger radially upward secondary flow when \(d/s\) was small. When \(d/s\) was large, these processes were delayed because of the stronger wake that reduced the radial pressure gradient, and some residual wake vortices in the upper part of the channel could reach the LPT-GV.  
For the LPT-GV, the pressure gradient from the pressure surface to the suction surface was enhanced with increasing \(d/s\), especially in the lower region of the blade passage. In the blade tip region, the passage vortices were significantly enhanced in the small \(d/s\) cases because of the enrichment of low momentum fluid and the stronger radial pressure gradient. In the blade root region, the difference between the passage vortices was small because the wake vortices had been dissipated upstream of the LPT-GV in all cases. In the midspan of the blade, the length, thickness and existence time of the separation bubble decreased with increasing \(d/s\), resulting in a reduction in the loss of the separation bubble; however, the strong wake induced the boundary layer transition to occur earlier, increasing the wet area of the turbulent boundary, which in turn led to an increase in losses.  
The flow loss in the integrated AITD was dominated by wake dissipation, passage vortices and boundary layer separation and increased with increasing \(d/s\). In the scope of this study, as the aerodynamic gain of the wake was still not enough to compensate for its  
Figure 18: Total pressure loss of the integrated AITD and its components in all cases.  
loss increment, the authors suggested that selecting a small trailing-edge radius of HPT is conducive to improving the aerodynamic performance of the integrated AITD.  
Conceptualization, Z.L.; validation, H.L. and G.L.; formal analysis, J.G. and Y.Z.; writing-original draft preparation, Z.L.; writing-review and editing H.L. and G.L.; project administration, X.L.; supervision, G.L., G.X. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"the National Natural Science Foundation of China, grant number 51906242\" and \"the National Science and Technology Major Project, grant number J2019-II-0007-0027 and J2019-III-0020-0064\".  
The authors declare no conflict of interest.
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 3 Results
### HYBRID-MAPPED Location Results  
Figure 10 illustrates Applying a mapping filter as the final filter had a significant effect on the research results, so much so that the location error was reduced by more than 20%.  
The average error in this method is 1.44 m. The error variance in this method is 0.34, and the standard deviation is 0.58.
## 3 Incremental Redundancy
### Codeword Extension  
Finally, codeword extension bits reduce the rate of the code. The probability of a successful decoding with these extension bits is highly dependent on the specific code being used. The code specifications often characterize this probability, but only under the assumption that the original  
Figure 3: SNR after updating the log-likelihood ratios (LLRs) of \(f\) bits based on a transmission of their XOR with \(\text{SNR}_{IR}=0\) dB.  
codeword and the extension bits are received with the same SNR. Unfortunately, this is generally not the case in practice.  
In order to simplify our derivations, we define the effective SNR of a codeword as  
\[\text{SNR}_{\text{eff}}=\left(E\left[\frac{1}{\text{SNR}}\right]\right)^{-1}, \tag{15}\]  
where the expectation is taken over the bits in the codeword. When all the bits in the codeword have the same energy \(E_{b}\), \(\text{SNR}_{\text{eff}}\) is equivalent to dividing \(E_{b}\) by the average noise power. Figure 4 illustrates the probability of decoding failure for different noise powers and distributions of signal strength within a codeword. Solid curves, which correspond to different distributions with the same \(\text{SNR}_{\text{eff}}\) are nearly identical, while dashed curves show the effect of a 25% variation in \(\text{SNR}_{\text{eff}}\). We therefore assume that the probability of failure mostly depends on \(\text{SNR}_{\text{eff}}\), not on the SNR variance within the codeword.
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 7 Experimental Results and Analysis
### Federated Learning Scenario 2: IID--Partitioned Dataset
#### 7.5.1 Training on Distinct MNIST Subsets  
For this experiment, we selected Model 3 ([196, 64, 196]), which demonstrated the best performance in the previous test scenario. Each device received a distinct subset of the MNIST dataset, and, after 10 local training epochs, federated averaging was performed. The results confirm that the federated model achieves convergence with improved performance compared to centralized training, highlighting the effectiveness of the federated aggregation process. In this scenario, models go through four federated rounds. Table 7 provides a detailed overview of the anomaly detection rates and F1 scores for digit '0', which is representative of the overall trend as the results for the other digits remain similar. The best-performing global model, obtained after the first federated round (**Federated 0**), achieves an F1 score of 0.8729, which surpasses the 0.8689 score obtained in the centralized setting. Interestingly, the subsequent federated rounds result in a gradual decline in the F1 score. However, a closer analysis reveals that this decline coincides with improved training loss and lower AD rates across all the test sets, including MNIST, EMNIST, and MNIST-C (excluding the \"fog\" variant). This indicates that the model's reconstruction capability is improving, even though the F1 score suggests otherwise. Although counterintuitive at first glance, the decline in F1 score reflects the broader generalization power gained through FL. By sharing diverse local representations, the aggregated model becomes better at reconstructing previously unseen variations in digit structure, particularly beneficial for EMNIST and distorted MNIST-C examples. The exception is the \"fog\" variant, where the visual characteristics diverge significantly from MNIST (e.g., grayscale textures), limiting the model's ability to generalize due to the lack of such patterns in the training data. While reconstruction quality improves, prioritizing AD performance (measured by the F1 score) introduces a trade-off: extended federated training may lead to overgeneralization and reduced discriminative power. Thus, in this scenario, a single round of federated learning is sufficient to obtain a high-performing global model, balancing reconstruction ability and AD accuracy. This observation that the final models selected by the current early stopping criterion may not always achieve the best AD performance highlights a key limitation in the training process. To address this, several practical solutions can be implemented. Specifically, our current dual-phase early stopping method relies solely on the training loss as the stopping criterion, without directly considering AD performance. While this approach simplifies the stopping decision during local training, it could be enhanced by incorporating AD performance into the stopping criteria. However, tracking such metrics locally would increase computational overhead and power consumption.
# Optimization Methods of Compressively Sensed Image Reconstruction Based on Single-Pixel Imaging
## 3 Methods
### DWT-Based Decomposition Optimization
This proposed constraint condition is added into DWT, so Equation (4) is updated as:  
\[\hat{s}=\arg\!\min_{s}\!\left\|s\right\|_{0}\text{s.t.}\left\|\Phi\Psi W^{-1}s- y\right\|_{2}^{2}\leq\varepsilon. \tag{11}\]  
The estimation \(\hat{s}\) of wavelet coefficients is obtained by solving the problem (11) with quadratically constrained linear program (QCLP), and the estimation of the reconstructed original signal is gotten by Equation (12) as follows:  
\[\hat{x}=\Psi W^{-1}\hat{s}. \tag{12}\]  
Because our imaging scenes are non-sparse optical signals, this method of proposing the constraint matrix can theoretically make our imaging scene be represented more sparsely and improve the imaging quality, and the effectiveness of this method will be demonstrated by experiments in Section 4.
# Experimental Validation of GNSS Interferometric Radio Occultation
## 4 Data Processing, Analysis and Discussion
### Interferometric RO Processing
#### 4.3.1 iRO Raw Doppler Estimation  
We have computed the auto-correlation Equation (6) of the recorded signals using \(T_{i}=1\) ms for all 1 ms intervals in the data set. In order to increase the SNR and improve the precision of the Doppler estimation, we have further coherently integrated these 1-ms observables by averaging \(N_{c}\) consecutive measurements. This is possible because the Doppler rate is small and longer integrations do not lose coherence.  
With \(N_{c}=1000\), the estimated Doppler is presented in Figure 7, where it can be seen that the Doppler observations are inconsistent with the predicted Doppler, and the Doppler observations keep constant during the last period of the data collection (after SoD51000).  
Figure 6: Doppler frequency estimated with open loop processing. (**a**) shows the complete measurement period. (**b**) zooms into the end period of the experiment when the transmitter was occulting.  
It is mentioned in [8] that the antenna beam should be narrow enough to avoid interferences from other GNSS transmitters. However, it can be seen from Figure 3 that a second satellite, i.e., SBAS PRN123 (ASTRA-5B), is also within the main beam of the antenna, and contributes significantly to the output of the auto-correlation in Equation (6). In the presence of two GNSS transmitters the auto-correlation function will be the sum of the auto-correlation functions of both transmitters. No cross-correlation term appears, because the PRNs of different transmitters are orthogonal to each other.
# Inspection System for Vehicle Headlight Defects Based on Convolutional Neural Network
## 2 System Configuration  
The traditional work environment for manufacturing a conventional vehicle headlight is shown in Figure 1a. It is an environment in which several workers assemble two or more headlight parts in one conveyor system. The worker determines the vehicle type of the headlight and selects the parts to be assembled. However, in the case of some parts, the structure of the headlights is similar, as shown in Figure 1b,c, which may result in errors in the assembly process.  
The method proposed in this paper automatically determines the defects for different types of parts, as shown in Figure 2. The alignment is calibrated by extracting the features of the vehicle headlight when the vehicle headlight is first input to the system, and whether the headlight is defective or not is determined based on the CNN after detecting the region of interest (ROI).  
The classifier system for different part types consists of an inspector module, PLC module, inspection lighting module, and camera module. The functions of each part type are as follows.  
* **Inspection lighting module**: This module controls the amount of light for image shooting when a vehicle headlight is input into the inspection region. It is controlled by the inspector module.
* **Camera module**: When a vehicle headlight is input into the inspection region for detecting defects, the part is photographed by the camera module and the captured image is transmitted to the inspector module.
* **Inspector module**: The inspector module receives vehicle headlight images from the camera module, detects defects in the headlight area of the received images, and transmits information on the presence of defects to the PLC.  
Figure 1: Traditional work environment and headlight images.  
Figure 2: CNN-based vehicle headlight defects of the detection system structure.  
* **PLC module**: The PLC module receives information regarding the presence of defective vehicle headlights from the inspector module and classifies vehicle headlights as superior or inferir based on the presence of defects.
## 4 Conclusions  
Summarized, the ANN4 FLES-based competition using reconstructed mass and PDG mass can perform comparably to the existing method. In general, using a more complex topology, it reduces background slightly better than the existing approach in the KF Particle Finder, even though the significance is decreased slightly on average. Especially, the amount of ghost particles were reduced almost over the whole range for both particles, and the background reduction around the PDG mass of \(K_{\rm s}\) was strong, even though a similar model might show other results, depending on the learned features. The reason for a good background reduction is most likely that the existing competition is only based on the mass distance to the known PDG mass of a particle, whereas the neural network can also learn patterns between reconstructed mass and PDG mass that can be used to classify particles correctly.  
The neural network approach is currently only classifying between \(\Lambda\) and \(K_{\rm s}\), whereas the existing method is furthermore cleaning the background by suppressing, for example, \(\gamma\)-decays and other cleanup methods. In the presented results, the network was not able to reject a particle entirely, hence neither classifying it as \(K_{\rm s}\) nor \(\Lambda\). The extension of the model by allowing either particle rejection or a classification between more particles that interact as background for each other, might require an increase of the underlying model complexity, but also can help to reduce the background even further.  
Figure 8: Invariant mass distributions of \(K_{\rm s}=\pi^{+}\pi^{-}\) and \(\Lambda={\rm p}\pi^{-}\), including signal–background ratio and significance for the ANN4 FLES approach.  
Figure 7: Invariant mass distributions of \(K_{\rm s}=\pi^{+}\pi^{-}\) and \(\Lambda={\rm p}\pi^{-}\) for the existing competition, including signal–background ratio and significance.  
The ANN4 FLES package itself will now be improved with respect to its runtime for the planned applications in the real-time reconstruction chain of the future CBM experiment. After these improvements, ANN4 FLES will be integrated into the physics analysis module of the FLES package.  
Conceptualization, I.K.; Methodology, A.B. and R.L.; Software, A.B. and R.L.; Validation, A.B. and R.L.; Investigation, A.B. and R.L.; Resources, A.B.; Data curation, A.B.; Writing original draft, A.B., I.K. and R.L.; Visualization, R.L.; Supervision, I.K.; Project administration, I.K.; Funding acquisition, I.K. All authors have read and agreed to the published version of the manuscript.  
This research was partly supported by the Federal Ministry of Education and Research (grant number 01 IS21092), Germany, and Helmholtz Research Academy Hesse for FAIR (project ID 2.1.4.2.5), Germany.  
The authors declare no conflict of interest.
# Haystack Fires in Australia: Causes and Considerations for Preventative Management
## 5 Monitoring of Agricultural Parameters
### Haystack Monitoring  
Currently, the method most commonly recommended to farmers to monitor the temperature of stored hay is to push a crowbar into the stack. The crowbar is then left for approximately two hours and upon removal the length of time the crowbar can be held is an indication of stack temperature (Table 5) [61; 62; 63]. An alternative method is to create a temperature probe from a length of metal pipe. One end is flattened with holes then drilled into that end and the pipe pushed into the stack. A thermometer on a length of wire is then inserted into the pipe, left for 15 min and retrieved [61; 63]. Both of these methods need to be repeated at a number of locations across the stack. They also have similar limitations: (a) the need to take multiple measurements is time-consuming and (b) they will only reach one to two metres into the stack [61].  
Other non-quantitative methods used looking for steam rising from hay, condensation of corrosion under the shed roof, mould or unusual odours and slumping of sections of the stack [63].  
More recently, sensors that record, log and transmit temperature data from haystacks or sheds have become commercially available to Australian farmers. These include Quanturi haytech (farmscan.com.au/pages/haytech), Incyt Hay Storage Monitoring (incyt.com.au/collections/microclimate-monitoring) and HayShepherd (shepherdmonitoringystems.com.au). These either measure the temperature within the bale, stack or under the shed roof and transmit the data using wireless or satellite networks.