esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Test Circuit and Experimental Results  
The following equipment and apparatus were used:  
* A repeated pulse generator, 100 W, 220 V, 50 Hz HAEFELy--Switzerland;
* A digital measuring system type TR-AS IOO-12/4, no: 1291, Dr.Straus--Germany.  
Test set-up: The low-voltage impulses (1.2/50 \(\upmu\)s) were applied between each IT terminal and earth from the repeated pulse generator. When the impulse was applied on an IT terminal, the other IT terminals and their corresponding MT and T terminals were connected to earth by 500 \(\Omega\) resistances. The neutral terminals were also connected directly to earth during the entire test. The MT and T homolog terminals of the IT terminal on which the voltage was applied were left free (see Figure 12).  
Figure 11: Overvoltage that is transmitted to the tertiary winding when the high-voltage winding is energized with lightning impulse.  
Figure 12: Measurement circuit for transmitted voltages.  
The maximum value recorded between the medium-voltage winding and ground at a 292.4 V pulse was 35.9 V. Compared to the value obtained in the simulation, 36.4323 V, this is relatively close (see Figures 10 and 13).  
The percentage error between the voltage value that we obtained by modeling and that resulting from the measurement for the medium-voltage winding and ground was 1.46%.  
The maximum value of the voltage that is transmitted to the tertiary winding of the transformer at a pulse of 292.4 V is 15.74 V. Compared with the value obtained in the simulation, 16.0302 V, it can be seen that there are small differences between the results (see Figures 11 and 13).  
The percentage error between the voltage value that we obtained by modeling and that resulting from measurement for the voltage that was transmitted to the tertiary winding was 1.81%.  
The difference between the results that we obtained with the proposed method and the experimentally obtained results may be due to the approximations introduced in the mathematical model for the determination of overvoltages, such as the following:  
* The negation of magnetic core losses and Joule losses;
* The variation in the resistance of the conductors of the transformer windings with time, generated by the variation over time in the frequency of the electric field that was produced by the lightning impulse.  
Before a pulse is applied, a period of time should be allowed to allow any residual charge to dissipate [5; 38; 39; 40]; this may influence the value of the measured overvoltages.
# Unlocking Everyday Wisdom: Enhancing Machine Comprehension with Script Knowledge Integration
## 4 Experiments
### Experimental Results
#### 4.2.2 Question-Answering Accuracy  
As the dataset we were using was from the SemEval contest, the test set did not come with ground truth labels. Therefore, we evaluated our system against the validation set. As shown in Table 1, our proposed method improved upon the TriANs with respect to the validation accuracy. The best model, which combined all of the three proposed techniques, achieved an 84.22% accuracy, which was 0.8% higher than the baseline model. Upon comparing variants 1, 3, and 4, as well as the proposed model, we notice that all of the introduced techniques contributed to the performance gain. We also studied the interaction between the LM feature and the handcrafted features by comparing the baseline, variant 1, variant 2, and variant 3. While both helped the performance, using the LM features alone seems better than combining the two.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Variant** & **Handcrafted Feat.** & **LM Feat.** & **Multi-Hop** & **PE** & **Accuracy** \\ \hline TriAN (baseline) & ✓ & & & & 83.42\% \(\pm\) 0.03\% \\ Variant 1 & & & & & 82.42\% \(\pm\) 0.04\% \\ Variant 2 & ✓ & ✓ & & & 83.49\% \(\pm\) 0.04\% \\ Variant 3 & & ✓ & & & 84.05\% \(\pm\) 0.06\% \\ Variant 4 & & ✓ & ✓ & & 84.13\% \(\pm\) 0.05\% \\ Ours & & ✓ & ✓ & ✓ & 84.22\% \(\pm\) 0.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Validation accuracy of model variants for the MCScript dataset. We ablated the effect of handcrafted features (Handcrafted Feat.), LM features (LM Feat.), multi-hop attention (Multi-hop), and positional embeddings (PE). We repeated the experiments for 3 times and reported the average and standard deviation numbers.  
Figure 8: Examples of the generative results of our LSTM language model. The sentences in bold font are the inputs; the subsequent content was completed by the model.
# Design and Development of OECT Logic Circuits for Electrical Stimulation Applications
## 2 Materials and Methods
### Testbed for Characterization of Individual OECT Devices
To overcome this, we introduced a Q1 MOSFET switch, which connected the drain terminal of the OECTs to the common ground when no pulse was generated. As the pulse durations in the applications of interest were typically short (<1 ms), the introduction of Q1 ensured that the OECTs were on the ground potential and thereby could be controlled via the voltages applied to gate terminals G1 and G2. Figure 4: Image of the developed testbed for characterization of OECTs. The Bluetooth and MCU modules are on the blue top board on the left, the DC/DC step up and pulse generator are integrated on the green top board in the middle, and the two printed OECTs tested for their switching capabilities are plugged into the FFC docks on the right. The wires on the right side connect specific points in the test circuit to the oscilloscope for analysis of voltage levels and current propagation. Four different OECT architectures were used in the experiments:  
* 1 mm channel width;
* 2 mm channel width;
* 3 mm channel width;
* 6 \(\times\) 1 mm channel width (interdigitated). In each experiment, the OECT devices in the parallel branches were always of the same architecture, and the experiments were repeated with multiple samples of the same architecture. In all tests, one of the OECTs in the parallel branch was set to its conductive state by setting the gate voltage to the logic low level (\(\sim\)0.3 V), while the other OECT was set to its non-conductive state by setting the gate voltage to the logic high level (\(\sim\)1.3 V). The signals were recorded for 30 pulses, after which the respective states of these two transistors were reversed, and the recording was repeated. This process was performed for five different current amplitudes (1, 2, 5, 10 and 15 mA) as well as five different maximal output voltages of the pulse generator (20, 30, 40, 50 and 60 V). In addition, due to the inherent humidity dependence in electrolyte-gated transistors, the testing protocol considered changes in the humidity conditions by repeating the tests in two different relative humidity (RH) levels: 25 %RH and 65 %RH. The pulse width was 400 \(\upmu\)s, and the stimulation frequency was set to 30 Hz in all tests, which were parameter values corresponding to the standard protocol in the applications of interest.
## 6 Conclusions  
This study proposed a modified design of the TSBB converter to improve power efficiency using fewer conduction components and measured the optimal output power range. The proposed TSBB converter improved power efficiency in buck and buck-boost topologies by reducing the conduction loss caused by the diode in the switch-off section power efficiency. A 100 W prototype was designed and fabricated to verify the improvement. Experiments were conducted in the CV/CC modes of three topologies, and the power efficiency was measured for 10-80 W. In the buck topology, power efficiency improved in the entire power range of 10-80 W; it increased on average by 0.75-1.36% and 0.83-2.27% in the CV and CC modes, respectively. In the buck-boost topology step-down, the power efficiency improved in the entire power range of 10-80 W; it improved on average by 0.73-0.99% and 3.33-4.75% in the CV and CC modes, respectively. In the buck-boost topology step-up, the power efficiency increased on average by 1.65-2.00% in the entire power range of 10-80 W in the CV mode, and by 2.17-2.77% in the power range of the 10-50 W in the CC mode.  
In future research, we will study how to reduce the conduction and switching losses by reducing the voltage and current stress of semiconductors for efficiency improvement. In addition, we will analyze the effect on output ripple under various conditions by using inductance, capacitance, and switching frequency as design variables, and study how these parameters affect converter efficiency.  
Conceptualization, S.K. and H.J.; methodology, S.K. and H.J.; software, S.K. and H.J.; validation, S.K. and H.J.; formal analysis, S.K. and H.J.; investigation, S.K. and H.J.; data curation, S.K. and H.J.; writing--original draft preparation, S.K. and H.J.; writing--review and editing, S.K. and H.J.; supervision, H.J. and S.-h.L.; project administration, H.J. and S.-h.L.; funding acquisition, H.J. All authors have read and agreed to the published version of the manuscript. This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2021-KA164174). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Data sharing not applicable. Conflicts of Interest: The authors declare no conflict of interest.
## 6 Discussion
#### Limitations and Suggestions for Further Research  
As any study, our study is not without its limitations. The first limitation is related to the limited scope of the research; only procurers working in Dutch public organisations were surveyed. We therefore have to be careful about generalizing the findings to public procurers working in other countries or to private procurers. Moreover, the Netherlands is considered to be a front-runner with regards to SPP [17], which could mean that the antecedents differ compared to countries that are lagging more behind. It would however be interesting to see if the identified antecedents of the various types of SPP follow a similar pattern for procurers working in other countries or contexts.  
Second, we only surveyed public procurers that are subscribers to the newsletters of procurement associations. Although these organisations are considered to be the sources of information for practitioners in the Netherlands, there might be public procurers that were not in their database. We thus have to be careful about generalizing the findings.  
Third, in the study we examined the role of knowledge, skills, affective commitment to change, and the organisational learning capacity on the various types of SPP, as determinants of the ability, motivation, and opportunity. However, other variables also of course affect the ability, motivation, and opportunity to procure in a sustainable manner. For example, only one type of commitment to SPP has been measured, affective commitment to SPP, while there are three different kinds of commitment to SPP [10]. As the affective commitment to SPP was only found to influence GPP and none of the other types of SPP, it would be interesting to see if commitment fueled out of something else than a personal conviction, such as peer pressure or fear for penalties, does influence the implementation of the other types of SPP.  
Fourth, in this study we only measured the perceived degree of the different variables, including the implementation of SPP. We thus only know how frequently public procurers think they implemented GPP or chose IPP, but not if this is actually how often they paid attention to this. More research using the actual procurement data is therefore necessary.
## 4 Simulation Results and Discussion
### Simulation for Two-hop Setup  
We then enlarge the map to 350 m\(\times\) 350 m to investigate the performance of our solution in two-hop networks. The same number of beacons and placement scheme are used for the beacons. Whereas the number of sensors \(Ns\) is gradually increased and sensors are randomly deployed which follows uniform distribution. Given the transmission range of beacon and sensor determined by propagation parameters listed in Table 1, the coverage of beacons is about 64.24%. In other words, about 64.24% sensors are of one-hop to some beacons, and about 35.76% sensors are of two-hop cases. The network connectivity (_i.e._, average number of neighbors) can be calculated basing on a specific network topology. After applying our localization algorithm, the percentage of unsolved sensor nodes (_i.e._, the percentage of sensors whose locations cannot be estimated using the localization technique) is computed, and the mean localization error of the location estimation is compared with that using MLE in the same network settings, as listed in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline The number of survey points & Average accuracy (m) \\ \hline
5 by 5 & 24.88 \\
10 by 10 & 22.15 \\
20 by 20 & 18.90 \\
40 by 40 & 17.73 \\
80 by 80 & 16.11 \\
160 by 160 & 15.81 \\
500 by 500 & 15.31 \\ \hline \end{tabular}
\end{table}
Table 2: The accuracy performance of the kNN approach with various number of sampling points equally spread on a 100 m by 100 m map.  
Given the same beacon coverage, it can be expected that as the number of sensors increases, the network connectivity will increase, thus more sensors' locations can be resolved using spring-relaxation, which is reflected in Table 3. It can also be observed that the average estimation error of our algorithm decreases when more neighbors present in the cooperative localization, whereas the average estimation error of MLE generally maintains at 170 m. The simulation results suggest that our algorithm outperforms MLE in terms of accuracy in the same network setup.
# Recent Progress in Fast Distributed Brillouin Optical Fiber Sensing
## 4 Developments of the Fast BOTDA Schemes  
Recently, to improve the performance of the BOTDA scheme, several novel fast schemes are proposed: optical frequency comb (OFC) technique, optical frequency-agile (OFA) technique, slope-assisted (SA) technique, and the optical chirp chain (OCC) technique.
###### Abstract  
Recent studies have demonstrated the high potential of drones as tools to facilitate wildlife radio-tracking in rugged, difficult-to-access terrain. Without estimates of accuracy, however, data obtained from receivers attached to drones will be of limited use. We estimated transmitter location errors from a drone-borne VHF (very high frequency) receiver in a hilly and dense boreal forest in southern Quebec, Canada. Transmitters and the drone-borne receiver were part of the Motus radio-tracking system, a collaborative network designed to study animal movements at local to continental scales. We placed five transmitters at fixed locations, 1-2 m above ground, and flew a quadrotor drone over them along linear segments, at distances to transmitters ranging from 20 m to 534 m. Signal strength was highest with transmitters with antennae pointing upwards, and lowest with transmitters with horizontal antennae. Based on drone positions with maximum signal strength, mean location error was 134 m (range 44-278 m, n = 17). Estimating peak signal strength against drone GPS coordinates with quadratic, least-squares regressions led to lower location error (mean = 94 m, range 15-275 m, n = 10) but with frequent loss of data due to statistical estimation problems. We conclude that accuracy in this system was insufficient for high-precision purposes such as finding nests. However, in the absence of a dense array of fixed receivers, the use of drone-borne Motus receivers may be a cost-effective way to augment the quantity and quality of data, relative to deploying personnel in difficult-to-access terrain.  
radio-tracking; Motus; drone; boreal forest; precision; accuracy; response surface; foret Montgomery 1
## 3 Distributed Optical Fibre Sensing Applied to Geo-Hydrological Applications
### Ground Subsidence and Earth Fissure Monitoring
This limited detection capability is indeed an intrinsic limitation of the proposed approach: the full potential of DOFSs is not exploited, as the sensor cable is intrinsically a concatenation of cumulative strain sensors acting as a long-range extensometer, and cracks in between two clamping points cannot be distinguished. Eventually, [PERSON] et al. [223] reported the application of BOTDA to monitor earth fissure in a real test site in Wuxi, China. The cable was installed in a 40 cm-depth trench and anchored to the ground to follow its deformation. A certain amount of stretch was applied to the cable to have an initial strain of approx 900-1200 \(\mu\)\(\varepsilon\). An extra strain-free fibre laid in parallell to the cable was measured for temperature compensation, and a maximum strain of approximately 400 \(\mu\)\(\varepsilon\) was observed during a five-year measurement campaign.
###### Abstract  
Underwater sensor networks (UWSNs) have become a hot research topic because of their various aquatic applications. As the underwater sensor nodes are powered by built-in batteries which are difficult to replace, extending the network lifetime is a most urgent need. Due to the low and variable transmission speed of sound, the design of reliable routing algorithms for UWSNs is challenging. In this paper, we propose a Q-learning based delay-aware routing (QDAR) algorithm to extend the lifetime of underwater sensor networks. In QDAR, a data collection phase is designed to adapt to the dynamic environment. With the application of the Q-learning technique, QDAR can determine a global optimal next hop rather than a greedy one. We define an action-utility function in which residual energy and propagation delay are both considered for adequate routing decisions. Thus, the QDAR algorithm can extend the network lifetime by uniformly distributing the residual energy and provide lower end-to-end delay. The simulation results show that our protocol can yield nearly the same network lifetime, and can reduce the end-to-end delay by 20-25% compared with a classic lifetime-extended routing protocol (QELAR).  
Keywords:underwater sensor networks; routing protocol; lifetime-extended; delay-aware; Q-learning technique +
Footnote †: journal: Sensors
# Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos
## 3 Experiments
### Results and Discussion
#### 3.2.1 Quantitative Results
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**AP**} & \multicolumn{8}{c}{**F1**} \\ \cline{2-13}  & \(t=10\) & \(t=20\) & \(t=30\) & \(t=40\) & \(t=50\) & \(t=60\) & \(t=10\) & \(t=20\) & \(t=30\) & \(t=40\) & \(t=50\) & \(t=60\) \\ \hline Ablation1 & 0.881 & 0.921 & **0.939** & 0.944 & 0.948 & 0.947 & 0.729 & 0.782 & 0.823 & 0.848 & 0.874 & **0.897** \\ Ablation2 & 0.912 & 0.917 & 0.934 & 0.942 & 0.958 & 0.964 & 0.672 & 0.790 & **0.829** & 0.849 & 0.860 & 0.854 \\ Ablation3 & 0.905 & 0.923 & 0.933 & 0.940 & 0.942 & 0.942 & 0.671 & 0.750 & 0.795 & 0.823 & 0.842 & 0.855 \\ Ablation4 & **0.915** & **0.929** & 0.937 & 0.939 & 0.942 & 0.940 & 0.601 & 0.753 & 0.800 & 0.824 & 0.831 & 0.832 \\ DSA [58] & 0.882 & 0.880 & 0.888 & 0.929 & 0.936 & 0.932 & 0.742 & 0.777 & 0.802 & 0.818 & 0.838 & 0.838 \\ DSTA [59] & 0.912 & 0.923 & 0.929 & 0.928 & 0.939 & 0.946 & 0.674 & 0.763 & 0.815 & 0.829 & 0.844 & 0.859 \\ ViViT [60] & 0.736 & 0.744 & 0.766 & 0.770 & 0.760 & 0.755 & 0.679 & 0.682 & 0.676 & 0.679 & 0.675 & 0.682 \\ PM & 0.913 & 0.923 & 0.938 & **0.946** & **0.959** & **0.966** & **0.757** & **0.806** & 0.825 & **0.860** & **0.876** & 0.891 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of AP and F1 across varying video lengths for PM and other models. The time step \(t\) indicates the length of video clips from the beginning. The best performance for each length is shown in bold, and the second-best performance is underlined. Precision are determined at threshold \(th\) in which F1 achieved the best performance. Note that in this experiment, ViViT, which classifies the existence of events from the entire video, is excluded from the comparison method since it is not possible to make predictions at each time step. As shown in Table 2, PM achieves the highest performance in all evaluation metrics than those of the other models. The comparison between PM and Ablations1-4 indicates the effectiveness of introducing each novelty in terms of prediction accuracy. DSA and DSTA also performed as well as the Ablation studies, although not as well as PM. Furthermore, we confirm that PM correctly predicts positive samples earlier than other models. Since PM has a high mTTE while ensuring high accuracy, it may be able to support players and coaching staff in making timely decisions based on the results. In summary, it is verified that PM is a quantitatively outstanding model for shot prediction tasks in soccer.
# Improving a Rapid Alignment Method of Tomography Projections by a Parallel Approach
## 2 Computational Methods
### Joint Reconstruction-Reprojection Method  
Including many parameters directly within the optimisation pool can sometimes be detrimental, as the solution space is filled with local minima (the _curse of dimensionality_[35]), where the solution may stagnate [36].  
When only the detector shift parameters are of interest [12], a different path is possible [29]: after having reconstructed an object with a conventional algorithm, a set of synthetic projections is generated by employing the reconstructed volume; these reprojections are then registered with the actual tomography dataset (Figure 1). The procedure is iterated until the shift parameters are nullified. The actual alignment is retrieved by using the phase-correlation [37] as a meaningful similarity metric for the two sets.  
Figure 1: Illustration of the reprojection algorithm concept: a real projection (**A**) is affected by a severe misalignment in the \(x\) axis. A tomography reconstruction (not shown) will exhibit blurred details due to this defect. The synthesised projection (**B**) calculated for the same projection angle of (**A**) will be severely blurred but centred. A similarity measure is used to infer the parameters of the geometry transform which realigns (**A**) on (**B**), iteratively producing an aligned dataset.