esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Optical and Morphological Characterizations  
In Figure 1, the optical absorptions (black curves) and the photoluminescence emissions (red lines) of the single components used for the matrix synthesis are shown. The photoinitiator (Figure 1a) shows a very high absorption for wavelengths shorter than 450 nm for a concentration that was the same used in the synthesis of the hybrid hydrogel matrix (25 mg/mL) and an emission band centered at about 510 nm. The PEGDA\({}_{700}\) (Figure 1b) shows a weak absorption band at 305 nm and a double peak emission at 485 nm and 510 nm, respectively. The absorption and the PL emission of the AgNPs-PMAA are shown in Figure 1c; the shoulder at 300 nm in the absorption spectrum is due to the presence of NO\({}_{3}\)- residual ions from the synthesis [61]. The AgNPs-PMAA aqueous solution exhibits a fluorescence peak at 660 nm. In Figure 1d, the absorption of the solid hydrogel matrix of PEGDA\({}_{700}\) and AgNPs-PMAA and its emission are shown. The fluorescence band clearly shows the two contributions originating from the AgNPs-PMAA and the solid PEGDA\({}_{700}\) hydrogel matrix.  
Figure 1: \(Cont.\)  
The HRTEM images of the AgNCs-PMAA after the centrifugation process and before the embedding in the hybrid matrix is reported in Figure 2a,b, highlighting a mean size of 1.84 \(\pm\) 0.60 nm. In Figure 2c, a picture of the solid optical device composed by the hydrogel matrix inside a plastic cuvette is reported.  
In Scheme 1, a simple drawing of the structural arrangement of the AgNCs-PMAA in the PEGDA\({}_{700}\) matrix is reported. The black lines represent the crosslinked PEGDA chains while the orange-cailed lines are the PMAA polymer molecules and the blue spheres are the AgNCs. The crosslinking reaction traps the AgNCs-PMAA nanostructures within the PEGDA chain network, producing a stabilization of the fluorescent nanoclusters in a porous environment, which allows the diffusion of the Pb(II) contaminated drops of water added on the top of the solid matrix.  
Figure 1: UV-Vis absorption (black lines) and normalized PL emission (red lines) spectra of: (**a**) photoinitiator Irgacure 819, (**b**) PEGDA\({}_{700}\), (**c**) AgNCs-PMAA in aqueous solution, (**d**) solid hydrogel matrix of PEGDA\({}_{700}\) with AgNCs-PMAA.  
Figure 2: (**a,b**) HRTEM images of AgNCs-PMAA; (**c**) photograph of the AgNCs embedded in the PEGDA matrix inside the plastic cuvette.
## 6 Space-Segment Optimization Aspects
### Applicable Optimization Methods for Space Segment  
Looking back at Table 2, we can identify the commonly used constellation optimization methods as:  
* Brute-force approaches, such as the MC method (typically used to evaluate constellation designs together with theoretical modeling approaches [141; 142]);
* Evolutionary algorithms, such as GA[21; 74; 143; 144] and PSO[20; 55];
* SA methods[21; 143; 145];
* ML-based methods[22; 146];
* Other methods: in addition to the above-mentioned methods, there are also some advanced algorithms that have evolved from the previous ones, such as MOGA[19] or HRPSO[55], that combine the optimization process with analytical methods to improve or expand the optimization performance.  
In the remainder of this subsection, we will provide overviews for the general versions of the most common methods.
## 4 Discussion
### Limitations and Future Works  
There are still several limitations in the present study. Firstly, preliminary laboratory research was implemented to investigate the effect of air route alternation and display design on an operator's SA, performance and mental workload during simulated flight tasks. The participants adopted in our study were not pilots but graduate students from an aeronautical science and engineering department with experience in operating the simulated flight platform. Considering the participants' ability and experience, we simplified the flight task and extracted the basic and essential operation (i.e., flight attitude control) as experimental tasks. The participants also received adequate training with appropriate standards to ensure they were competent and qualified for the experiment. Although the reference value of this probleme exploratory research can benefit from the above efforts, experienced pilots, as well as flight scenarios with higher fidelity and complexity (e.g., taxi route crossing, flight fault, air route alternation in severe meteorological condition, etc.) will be considered in future work to improve the reliability and richness of the research conclusion. Secondly, although we have examined the applicability of display designs to support an operator's SA and performance in the condition of air route alternation, the display interfaces tested in our study belonged to relatively traditional mainstream designs, owing to the function limitations of the current simulated flight platform. As a next step, the simulated display designs should be further developed and advanced concepts (e.g., head-up display, enhanced visual display, synthetic visual display, etc.) should be incorporated to evaluate the display utility in abnormal situations due to unplanned events [72].
## 3 System Overview
### ROI Management  
In this subsection, a detailed description of ROI management operation is depicted, and the algorithm that manages the ROI displacement is presented. ROI management, consisting of ROI classification and ROI tracking, is performed after the frame prediction.
# Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification
## 4 Results
### Ablation Experiments  
In this section, we conduct ablation studies to analyze how each component affects the few-shot recognition performance. Table 4 shows the results of our ablation studies on the FewRel 2.0 development set. PT is a prompt tuning approach based on BERT [2]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Model** & **1-Shot(15\%)** & **1-Shot (50\%)** & **5-Shot(15\%)** & **5-Shot (50\%)** \\ \hline \multirow{4}{*}{FewRel 2.0 test} & Sentence-Pair & 77.67\% & 80.31\% & 84.19\% & 86.06\% \\  & Threshold & 63.41\% & 76.48\%. & 65.43 \% & 78.95\% \\  & NAV & 77.17\% & 81.47\% & 82.97\% & 87.08\% \\  & MNAV & 79.06\% & 81.69\% & 85.52\% & 87.74\% \\  & CKPT & **80.37\%** & **83.02\%** & **86.26\%** & **88.12\%** \\ \hline \multirow{4}{*}{FewRel 2.0 val} & Sentence-Pair & 70.32\% & 75.48\% & 74.27\% & 78.43\% \\  & Threshold & 63.28\% & 76.32\% & 66.89\% & 80.30\% \\ \cline{1-1}  & NAV & - & 78.54\% & - & 80.44\% \\ \cline{1-1}  & MNAV & - & 78.23\% & - & 81.25\% \\ \cline{1-1}  & CKPT & **73.28\%** & **81.25\%** & **77.92\%** & **83.62\%** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy(%) results on FewRel 2.0 dataset for the four available settings for this benchmark.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **5-Way 1-Shot** & **5-Way 5-Shot** \\ \hline Sentence-Pair & 10.19 \(\pm\) 0.81\% & - \\ Threshold & 6.87 \(\pm\) 0.48\% & 13.57 \(\pm\) 0.46\% \\ NAV & 8.38 \(\pm\) 0.80\% & 18.38 \(\pm\) 2.01\% \\ MNAV & 12.39 \(\pm\) 1.01\% & 30.04 \(\pm\) 1.92\% \\ \hline CKPT & **15.14 \(\pm\) 1.12\%** & **32.26 \(\pm\) 2.13\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Micro F1 results on Few-Shot TACRED.  
and uses the traditional cross entropy loss function. NOTA-Loss is a novel loss function approach Section 3.4 for few-shot NOTA classification. PT+NOTA-Loss is a prompt tuning approach using the novel loss function approach (Section 3.4) and does not use the method of extending the label. It indicates that the model using NOTA-Loss achieves better results compared with the model using the traditional cross-entropy loss. This is because NOTA-Loss uses coefficients to amplify the loss of two samples that are not related to the two entities, and the model learns more about these parts of the samples. In addition, the label of the masked language model in the pre-train stage is the entire vocab text. Therefore, in the prompt tuning prediction stage, the model may predict words with similar meanings to the label, causing model classification errors. As shown in Table 4, we use commonsense knowledge to expand the label, which can reduce the contingency of the masked language model for label prediction and improve the prediction performance of the model.
## 2 Materials and Methods
### Active Ankle Plantarflexion Test and Active Ankle Dorsiflexion Test  
The ROM of the ankle joint (or synovial joint) was assessed using the same methodology as in previous studies [24; 25]. For the AAPFT and AADFT, the Intraclass Correlation Coefficients (ICCs) for these tests are generally considered high, indicating good reliability. Specifically, a previous study found the reliability of the AADFT to be excellent, with ICCs as high as 0.91 and 0.97 [26]. Other studies have similarly reported high reliability for the AAPFT, with ICCs ranging from 0.82 to 0.96 [27].  
Before the experiment, participants were instructed to lie supine on a fixed table. The testing leg was extended and rested on the table, while the non-testing leg was flexed. A K-move V3 sensor was securely strapped to the fifth metatarsal tuberosity of the testing leg. Each test lasted 5 s, with a 5 s rest interval between trials. Participants were informed of the sensations they might experience during the testing process and were instructed to actively stop plantarflexion or dorsiflexion when they felt \"they had reached their maximum stretch and could not continue further\".  
For the AAPFT, participants were required to start in the anatomical position, with the testing leg's ankle joint naturally resting in the sagittal plane and the subtalar joint in a neutral position, without any plantarflexion or dorsiflexion (Figure 4a). During the test, participants actively engaged their ankle muscles to perform maximal plantarflexion, stopping the movement upon reaching their maximum range, and then rested (Figure 4b). Throughout the procedure, the examiner ensured that participants did not lift or engage their hip or knee joints. Additionally, the examiner closely observed the ankle movement to prevent foot eversion, as such extraneous motion would involve midtarsal joint dorsiflexion in addition to talocrural joint dorsiflexion [28].  
For the AADFT, the starting position was identical to that of the AAPFT. Participants were instructed to perform maximal dorsiflexion when the test started (Figure 4c). During this test, the same methods as in the AAPFT were applied by the examiner.  
Figure 3: PKET protocol. (**a**): PKET starting position. (**b**): PKET ending position.
## 2 Measurement Principles and Device
### Linearity  
In the measurement of emissivity, the linearity of the measurement device response is an important factor that affects the accuracy of the measurement results [26; 27]. In order to evaluate the influence of linearity, [PERSON], [PERSON] et al. used two identical blackbodies to measure the linearity of the device, one of which was still used as a reference blackbody, and the other was used to simulate the sample under test [26]. The conventional linearity measurement usually adopts the double-aperture method [8], which is considered without systematic error and is widely used in international comparison.  
Referring to the subgraph in Figure 9, the steps for measuring the linearity of the system by the double diaphragm method are the following: 1. Block half of the light beam, record the signal S(A); 2. block the other half of the light beam, record the signal S(B); 3. when not blocking any part of light beam, record the total signal S(A + B); 4. block all of the light beam, record S(0); then, the linear correction factor (\(f\)) can be calculated as follows:  
\[f=\frac{\text{S(A+B)-S(0)}}{\text{S(A)+S(B)-2S(0)}}\]  
By this method, the linearity at half position of the full-scale signal can be obtained. In order to obtain linearity at other locations, the signal needs to be attenuated to 50% full scale signal, and then the process is repeated to obtain the signal at 25% position. Similarly, linearity at 12.5% full scale signal position can be obtained.  
As can be seen from Figure 9, the linearity of the device is nearly 1% at different percentages of the full scale.  
We also tried other methods to measure linearity, such as using a series of filters with known transmittance to measure linearity, due to the refractive index, resulting in measurement results which were not very reliable.  
Figure 8: Measurement repeatability.
# Dynamic Ring Exploration with \((H,s)\) View +
## 5 Upper Bound of Exploration Time for \(S\geq N-1\)
In the first part, \(H^{\prime}\lfloor(n-H^{\prime})/H^{\prime}\rfloor+1\) nodes are explored and, in the second part, the remaining \(n-H^{\prime}\lfloor(n-H^{\prime})/H^{\prime}\rfloor-1\) nodes are explored. ```
1:\(p\gets 1\)//starting the first part
2:while (\(p\leq\lfloor(n-H^{\prime})/H^{\prime}\rfloor\)) do
3: Let \(t\) be the current step and \(v_{i}\) be the current node
4:\(\textsc{ExpH}(t,v_{i})\)
5:\(p\gets p+1\)
6:\(p\gets 1\)//starting the second part
7:while (\(p\leq\lceil\log(n-H^{\prime}\lfloor(n-H^{\prime})/H^{\prime}\rfloor-1)\rceil\)) do
8: Let \(t\) be the current step and \(v_{i}\) be the current node
9:\(\textsc{ExpHalF}(t,v_{i})\)
10:\(p\gets p+1\)
```  
**Algorithm 5** Exploration algorithm for \(S\geq n-1\)  
**Theorem 3**: _For \(S\geq n-1\), the exploration time of \(1\)-interval connected rings by a single agent with the \((H,S)\) view is upper-bounded by \(O(n^{2}/H+n\log H)\)._  
It suffices to show that \(A\) completes exploration within \(O(n^{2}/H+n\log H)\) steps by Algorithm 5 when \(S\geq n-1\). It is proven that the total exploration time of the first part is \(O(n^{2}/H)\) and that of the second part is \(O(n\log H)\). We first consider the first part. Note that, since \(2H^{\prime}\leq n\), \(1\leq\lfloor(n-H^{\prime})/H^{\prime}\rfloor\) and thus the first part is always executed at least once. Let \(t_{p}\) be the step when \(A\) starts the \(p\)-th \(\textsc{ExpH}(t,v_{i})\). We can show that for \(1\leq p\leq\lfloor(n-H^{\prime})/H^{\prime}\rfloor\), \(A\) can explore \(H^{\prime}\) nodes by \(\textsc{ExpH}(t_{p},v_{i})\) by induction and the exploration time of the first part is \(O(n^{2}/H)\) as in the proof of Lemma 2. We then consider the second part. By Lemma 2, \(A\) exists at the right or left extremity of \(V^{t}\) and \(|\overline{V^{t}}|=n-H^{\prime}\lfloor(n-H^{\prime})/H^{\prime}\rfloor-1\leq 2H ^{\prime}\) at the start of the second part. Thus, since \(A\) explores a half of \(\overline{V^{t}}\) within \(n-1\) steps by Lemma 4, the exploration time of the second part is \(O(n\log H)\). As a result, the exploration time of Algorithm 3 is \(O(n^{2}/H+n\log H)\).
## 3 Experimental Results
### Electrical Resistance Testing
Accordingly, the difference in electrical resistance owing to such damage decreased as the MWCNT concentration increased. The cement paste made of cement and a MWCNT solution exhibited the lowest electrical resistance in CP-0.5-7D-X at 0.4 k\(\Omega\), which is the lowest among all the composites studied. The electrical resistance of the composite decreased as a CNT network was formed when the concentration of the electrically conductive MWCNTs increased. The increase in electrical resistance with curing time was the highest in Co-0.125-28D-O, at 24.8 k\(\Omega\), owing to the effect of the decreased moisture. The difference in electrical resistance owing to the presence of damage was 7.4 k\(\Omega\) for cement paste, 35.1 k\(\Omega\) for mortar, and 40.3 k\(\Omega\) for concrete. The damage could be detected for cement paste and mortar using not only the heat property and thermal images but also the electrical resistance property. In contrast, damage detection for concrete was not possible using the heat property and thermal images, but it was still possible using the electrical resistance property.
## 2 Background  
### Light Waves  
Light can be classified into visible and invisible wavelengths according to human vision, as shown in Figure 2. The wavelengths of visible light range from 380 to 760 nm. Violet, indigo, blue, green, yellow, orange, and red light have wavelengths from short to long. The wavelengths of invisible light are longer than 760 nm and shorter than 380 nm. Visible spectra are adopted in many optical systems, but invisible spectra are also applied in some technologies.
# Radio Channel Modelling for VHF System Operating in the Offshore Wind Farms Propagation Environment
## 2 Methods  
### Itu-R BT.1893-1 Model  
For the purposes of researching wind turbines' impact on radio communication systems and the possibility of correct modeling of the radio channel, two methods were adopted, described in recommendation ITU-R BT.1893-1 \"Assessment methods of impairment caused to digital TV reception by wind turbines\" [11].  
The first of these methods--described in Annex 1 of the document--models the propagation for a single wind turbine, but when applied separately to individual turbines it also allows us to analyze the entire wind farms (as the entire wind farm effectively constitutes a \"network\" of interference sources, the methods for the cumulative effects calculation, such as the k-LMN, can well be utilized here). As will be shown in the subsequent parts of this paper, including the section describing the measurement campaigns, the Annex 1 model particularly allows us to address the reflection from the turbines' rotors, which makes its very useful for far-field conditions. If the distance between the turbine and the signal source is significant, it is the reflection from the rotors that is by far the most dominant effect. On the other hand, the model included in Annex 2 of the recommendations [11], while also applicable both for individual turbines and for entire wind farms, is much more suitable for the near-field condition. The reason for that is that, unlike the Annex 1 model, in Annex 2 the reflection from the turbine's tower (mast) is the major reflection scenario. If the distance between the turbine and the signal source is satisfying the near-field criterion, a much greater \"portion\" of this signal will reflect from the tower, rather than from the rotor (due to very shallow transmission angle with respect to the rotor). As a result, the Annex 2 method will be applicable to the studies of the impact of the farm on ship terminals, which may be located in close proximity to the turbines, while for the scenarios where the distance between the transmitter and the turbines is significant, the method from Annex 1 should be used (Please note that, theoretically, Annex 2 can also be applied for the far field scenarios, but for the reasons explained above, its results will be less reliable compared to those derived from the Annex 1.). Those applicability guidelines regarding the two Annexes from the ITU-R 1893-1 recommendation--proposed by the authors--is one of the important outputs of this paper and it will be discussed more thoroughly in the subsequent sections.
# New Displacement Method for Free Embedded Cantilever Walls in Sand
### The Horizontal Earth Pressure Distribution and the Displacement of a Typical 3 m-High Wall  
The horizontal earth pressure distribution obtained from one of the FE model combinations (H = 3 m, d = 2.0 m, \(\gamma_{1}\) = \(\gamma_{2}\) = \(\gamma_{3}\) = \(\gamma_{4}\) = 20 kN/m\({}^{3}\), \(\phi_{1}\) = \(\phi_{2}\) = \(\phi_{3}\) = \(\phi_{4}\) = 35\({}^{\circ}\), L = 0, \(E_{1}\) = \(E_{2}\) = \(E_{3}\) = \(E_{4}\) = 50,000 kPa, \(EI\) = 2.5 \(\times\) 10\({}^{6}\) kN-m\({}^{2}\), and 1 m-thick reinforced concrete wall) has been compared with those of the distributions offered by formulae in the literature (Figure 16). Figure 16 depicts the wall displacement obtained from the finite element (FE) model, represented by a red line. The plot of wall displacement demonstrates a predominantly linear behavior, indicating a rigid response. The maximum displacement observed for the 3 m-high wall is approximately 0.012 m. Notably, at a depth of 1.6 m, the wall solely undergoes rotation without any horizontal displacement. This specific depth serves as the rotational point for analysis purposes. In the soil retained by the wall, extending to the rotational point depth, the horizontal earth pressure distribution (\(\sigma_{\rm x}\)) obtained from the FE model exhibits a linear pattern. The \(\sigma_{\rm x}\) values determined using the active earth pressure coefficients proposed by [PERSON]'s approach ([PERSON] [31] and [PERSON] [28]) are also illustrated in Figure 16. Remarkably, both formulae yield identical pressure distributions with the FE model up to the depth of the rotational point. However, below the rotational point depth, the pressure distribution provided by the FE model undergoes an abrupt change. At this region, the active pressure is gradually replaced by the passive pressure. On the resisting side of the wall, the FE model presents a parabola-like distribution of horizontal passive pressure above the rotational point depth. [PERSON] [28] has developed a formula that calculates the weighted average of the passive earth pressure, taking into account various parameters including the wall rotation and soil modulus. The calculated passive horizontal earth pressure using [PERSON]'s formula, along with the Coulomb-passive formula, are also included in Figure 16 for comparison. It is worth noting that the Coulomb-passive formula considers the ultimate state, leading to significant deviations from the FE results. As a result, in practical applications, the Coulomb results are often divided by a safety factor. On the other hand, the distribution offered by [PERSON] [28] exhibits better accuracy due to its ability to incorporate the actual wall rotation as an input parameter. For further details on the calculations and formulae, refer to Section S5.  
Figure 16: The horizontal earth pressure distributions from different sources: FE-Model, Coulomb-active, Coulomb-passive, and Hamderi [28] active and passive earth pressures.