esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, we explored the impacts of climate factors and human activities, especially land cover change, on the WUE dynamics in the TNR during the period of 1982 to 2015. Our results demonstrated that climate factors played dominant roles in affecting the WUE trends, contributing to approximately 84% of the WUE trends, while the land cover change played a relatively small role. Furthermore, our results also showed that the forests had the highest WUE values in the humid areas of the eastern TNR and that croplands had the highest WUE values in the arid areas of the western TNR. Of the total area, 44.34% displaying a significant increase and 5.89% displaying a significant decrease in the WUE from 1982 to 2015.  
This study quantitatively assessed the impact of afforestation on the WUE in the TNR, which is critical for gaining a better understanding of the environmental changes due to the afforestation program in China. Overall, although the climate factors had a larger impact on the WUE dynamics than human activities under the background of climate change, our results suggested that the impacts of afforestation programs on the forest carbon and water cycles should be considered because they contribute to approximately 16% of the WUE change trends.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2072-4292/11/23/2766/s1](http://www.mdpi.com/2072-4292/11/23/2766/s1)]([http://www.mdpi.com/2072-4292/11/23/2766/s1](http://www.mdpi.com/2072-4292/11/23/2766/s1)). Figure S1: The methodological workflow of the research. Figure S2: Interannual variations of the spatially averaged WUE (WUE-M) of the different regions over the TNR from 1982-2015. Figure S3: The change levels of the annual WUE in China from 1982-2015. Figure S4: Partial dependence plots (PDP) for the climate factors. Figure S5. The classification of the WUE trends (\(p<0.05\)) in the five subregions. Table S1. The criteria for the classification of the WUE trends.  
**Author Contributions:** [PERSON] and [PERSON] conceived of and designed the experiments. [PERSON] processed and analyzed the data. All authors contributed to the ideas, writing, and discussion.  
**Funding:** This study was supported by the National Key Research and Development Program of China (Nos. 2016 YFB0501404 and 2016 YFA0600103).  
**Acknowledgments:** The authors thank [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] for the helpful comments that improved this manuscript and appreciate the comments from Doctor [PERSON] from Queens College, City University of New York.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Purity of Lignin  
#### 3.2.1 Effect of Coagulation Time  
The purity of lignin is an important parameter. Elements such as sulfur, calcium, and sodium can be found in the final lignin product. The variation in the levels of several elements present in the lignin cake as a function of coagulation time is shown in Figure 4. The sodium content in the pre-hydrolysate is low since chips were not treated with white liquor as in the kraft pulping process. As seen, it is clear that the purity of the obtained lignin is influenced by the coagulation time. In fact, the percentages of calcium and sulfur decrease significantly with increasing coagulation time. Calcium, sulfur, and sugars levels of 4.5%, 3.7%, and 4.0%, respectively were measured when the coagulation time was 60 min. The increase in coagulation time to 180 min led to a decrease in the percentage of calcium (1.5%) and sulfur (1.25%). However, the sugar level at that point increased to 5.5%. As discussed above, a higher coagulation time leads to higher filtration rates, which allow soluble species to pass easily through the lignin cake and to not be partially retained. Most of the Na, S, and Ca are soluble in the acidified filtrate and are expected to pass through the filter after good coagulation. For this reason, their levels decreased as the coagulation time increased. Most of the sugars are in the oligomeric form and are bound to lignin. As the coagulation time increases, the lignin particles (together with the sugars) become larger and more sugars are precipitated with the lignin. This leads to an increase in the lignin cake's sugar content.  
Figure 4: Effect of the coagulation time on sugar, calcium, sodium, and sulfur percentages in the lignin cake at a temperature of 40 \({}^{\circ}\)C and 8.56 kg\(\cdot\)m\({}^{-3}\).  
Figure 3: Effect of coagulation time on filtration step with the following conditions: sulfuric acid concentration at 8.56 kg\(\cdot\)m\({}^{-3}\) and filtration temperature at 40 \({}^{\circ}\)C.
# Augmented Reality and the Digital Twin: State-of-the-Art and Perspectives for Cybersecurity
## 4 Perspectives for Cybersecurity
### Securing AR and DT Technologies  
Although the presented technologies in Section 3.3.4 are proven suitable for AR implementation in DTs, they might carry adverse effects when used in security. The dominant Microsoft HoloLens solution addresses several security and privacy concerns in its second version. The implemented security framework ensures, for instance, end-to-end encryption. Thus, it is highly recommended to utilize the second version instead of HoloLens 1 for any security-focused applications. In terms of HHDs, such as smartphones or tablets, a variety of security vulnerabilities are known. These depend on the type of the device, including hardware, the underlying software, and installed applications. Such vulnerabilities can be easily found in databases, such as the CVE ([[https://cve.mitre.org/](https://cve.mitre.org/)]([https://cve.mitre.org/](https://cve.mitre.org/)), accessed on 20 July 2021). Thus, the risk of attackers exploiting these exposures is relatively high. Depending on the use case, current HHDs might lack sufficient security measures and provide an easy entry point for attackers. Especially, in the context of security applications, it may not be advisable to use such unsecured devices.  
Moreover, some technologies might be added to be able to conduct proper security analyses with the AR-DT combination. This includes simulation-enabling tools, but first and foremost, the implementation of intrusion detection mechanisms with visual representations to detect system intruders. Thus, next to integrating system logs, network traffic and other environmental data of the regarded system, technologies for anomaly detection, correlation engines, or SIEM tools are to be integrated.  
To provide sufficient confidentiality, integrity, and availability for the AR-DT combination, additional tools or concepts might be implemented. These security-fosteringtechnologies might provide proper access control mechanisms, data encryption, and secure synchronization between the physical system and the AR-based DT. These security enhancements are especially required for features of _(1) Continuous data flow_ and _(2) Continuous tracking of virtual and physical objects_ (cf. Section 3.3.4): The continuous data flow requires secure synchronization capabilities and might even need data encryption technologies. The tracking might also require encryption or anonymization techniques to ensure privacy of the end user.
# Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers
## 4 Discussion
### Countermeasures
#### 4.2.4 Satellite Architecture  
The QKD satellite could be designed in external shape and materials to minimize reflection and scattering of light towards the ground. This would required the laser terminal to increase its output power to achieve the same degree of disruption, though there may come a point where the laser power has to be increased to impractical levels.  
The case where the QKD receiver is on board the satellite, satellite can be designed to shield its optics from off-axis exposure, minimize scattering surfaces such as antennae near the optic, and increase the internal baffling within the telescope.
## 2 Results and Discussion
### Analysis of the Volatiles in P. Nobby's Pacific Sunset Flowers
#### 2.2.1 Volatile Compounds of \(P\). [PERSON]'s Pacific Sunset Flowers over a 24 h Period  
Figure 2 shows that the odor was the strongest at 09:00 and gradually declined until 13:00, at which point it began to decline rapidly. [PERSON] _et al._[1] studied the differences in the concentrations of \(P\). _schilleriana_ at three stages during the day (05:00 to 13:00, 13:00 to 21:00 and 21:00 to 05:00) and found that during the first stage approximately 87% of the volatiles were emitted. They also reported that \(P\). _schilleriana_ began to emit the volatiles just after sunrise, reaching a peak of scent production 4-5 h later, and becoming nearly scentless after approximately 15:00.  
The odor of flowers changes at different times to attract day and night pollinators. The main constituents of \(P\). [PERSON]'s Pacific Sunset, \(\alpha\)-farnesene, geraniol and linalool, also change at different times (Figure 3). The results showed that linalool was the main constituent throughout the entire day, wheres \(\alpha\)-farnesene increased and linalool decreased at night (19:00 to 03:00). Many studies have reported that linalool is known as an attractant for Apidae and Colletidae bees, but seems to be unattractive to Halictidae bees and brachyceran flies [29, 30, 31]. [PERSON] [2] noted that the pollinating insects of _Phalaenopsis_ are bees, which are diurnal; therefore, it can be inferred that this is the reason linalool decreases at night. There is an advantage for the plant to have its scent output at maximal  
Figure 2: Changes in the volatile components of \(P\). [PERSON]’s Pacific Sunset over a 24 h period. Values are means \(\pm\) SD of triplicates. Values having different superscripts are significantly (\(p<0.05\)) different.  
levels only when the flower is ready for pollination and concomitantly when its potential pollinator is active [32]. _C. breweri_ flowers, despite being moth-pollinated, do not show noticeable differences in emissions between day and night. Snapdragon flowers, on the other hand, are bee-pollinated and show a marked peak of emission during the day [33].
# Multi-Hypergraph Neural Networks for Emotion Recognition in Multi-Party Conversations
## 5 Results and Discussions  
### Overall Performance  
Table 3 shows the performance of different models on the MELD and EmoryNLP test sets. We can see that our model outperformed all baselines, which demonstrated the effectiveness of our proposed model. At the same time, we found that the models using CSK on MELD generally performed better, while our model achieved good results without relying on external knowledge. In this paper, we focused on modeling the two kinds of dependencies among speakers by building multiple hypergraphs, so we did not incorporate external knowledge. On the EmoryNLP dataset, we found that the models using large-scale pre-trained models to extract features had better results. For example, both DAG-ERC and TUCORE-GCN used RoBERTa as feature extractors. These models could achieve over 39% on EmoryNLP. Our model also used RoBERTa as a feature extractor and achieved relatively better results by separately modeling the two speaker dependencies. Compared with DialogXL, BERT+MTL, and ERMC-DisGCN, our model had at least a 2% improvement in the two datasets. This also showed that our model could identify the emotions of utterances better than the previous models studied on the multi-party conversation dataset.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**\#** & **Batch size** & **Dropout** & **Lr** & **Window** & **SSHG** & **NSHG** \\ \hline MELD & 32 & 0.1 & 0.001 & 1 & 1 & 1 \\ EmoryNLP & 16 & 0.4 & 0.0009 & 4 & 4 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter settings.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **CSK** & **MELD** & **EmoryNLP** \\ \hline RoBERTa & \(\times\) & 62.88 & 37.78 \\ \hline DialogueRNN & \(\times\) & 57.03 & - \\ +RoBERTa & \(\times\) & 63.61 & 37.44 \\ DialogueCRN & \(\times\) & 58.39 & - \\ VAE-ERC & \(\times\) & 65.34 & - \\ DialogXL & \(\times\) & 62.4 & 34.73 \\ BERT+MTL & \(\times\) & 61.90 & 35.92 \\ CoG-BART & \(\times\) & 64.81 & 39.04 \\ COSMIC & \(\surd\) & 65.21 & 38.11 \\ TODKAT & \(\surd\) & 65.47 & 38.69 \\ _EmoCaps_* & \(\times\) & 63.51 & - \\ \hline DialogueGCN & \(\times\) & 58.10 & - \\ +RoBERTa & \(\times\) & 63.02 & 38.10 \\ RGAT & \(\times\) & 60.91 & 34.42 \\ +RoBERTa & \(\times\) & 62.80 & 37.89 \\ TUCORE-GCN & \(\times\) & 62.47 & 36.01 \\ +RoBERTa & \(\times\) & 65.36 & 39.24 \\ DAG-ERC & \(\times\) & 63.65 & 39.02 \\ ERMC-DisGCN & \(\times\) & 64.22 & 36.38 \\ SKAIG & \(\surd\) & 65.18 & 38.88 \\ _MMGCN_* & \(\times\) & 57.72 & - \\ \hline ERMC-MHGNN & \(\times\) & **66.4** & **40.1** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall performance on the two datasets. ‘-’ signifies that no results were reported for the given dataset. ‘CSK’ stands for a model that introduced commonsense knowledge, ’\(\surd\)’ and ‘\(\times\)’ represent using ’CSK’ and not using ’CSK’ respectively. ‘*’ represents the results of the model in the text-only modality.
## 3 Lower-Level Controller
### Steering Controller  
Controller Design  
Figure 3 shows a schematic diagram of the articulated steering structure's movement. Since the steering hydraulic cylinders are fixed on the front and rear body, respectively, the articulated angular speed can be calculated from the linear speed of the piston push rod in the hydraulic steering cylinder. Based on this, the hydraulic fluid flow into the hydraulic cylinder can be controlled to determine the articulated angular speed, which is the steering speed of the vehicle.  
The lengths of the hydraulic steering cylinder on the left and right sides are  
\[L_{1}L_{2} =\sqrt{r^{2}+R^{2}-2 rR\cos(\Omega_{0}+\gamma)} \tag{17}\] \[R_{1}R_{2} =\sqrt{r^{2}+R^{2}-2 rR\cos(\Omega_{0}-\gamma)}\]  
where \(R=OR_{1}\), \(r=OR_{2}\), \(\Omega_{0}=\angle L_{0}OL_{2}=\angle R_{0}OR_{2}\).  
The force arm corresponding to the pushing force of the hydraulic cylinder is  
\[h_{\rm L1}=\frac{Rr\sin(\Omega_{0}+\gamma)}{L_{1}L_{2}},\;\;h_{\rm L2}=\frac {Rr\sin(\Omega_{0}-\gamma)}{R_{1}R_{2}} \tag{18}\]  
Figure 3: Articulated steering structure motion sketch.  
The expression for the relative linear velocity between the piston push rod in the cylinder and the piston cylinder is  
\[\frac{\mathrm{d}X_{L}}{\mathrm{d}t}=h_{\mathrm{L1}}\dot{\gamma},\ \ \frac{\mathrm{d}X_{R}}{ \mathrm{d}t}=h_{\mathrm{L2}}\dot{\gamma} \tag{19}\]  
where \(X_{\mathrm{L}}\) and \(X_{\mathrm{R}}\) are the corresponding displacements of the piston rods in the left and right hydraulic cylinders, respectively.  
The flow rates in the hydraulic cylinders on the left and right sides are  
\[Q_{\mathrm{L}}=A_{\mathrm{r}}\frac{\mathrm{d}X_{\mathrm{L}}}{\mathrm{d}t},\ \ Q_{ \mathrm{R}}=A_{\mathrm{p}}\frac{\mathrm{d}X_{\mathrm{R}}}{\mathrm{d}t} \tag{20}\]  
where \(A_{\mathrm{p}}\) and \(A_{\mathrm{r}}\) are the practical cross-sectional areas of the rodless and rod chambers of the steering cylinder, respectively. As a result, the total flow provided by the steering valve should be  
\[Q=Q_{\mathrm{L}}+Q_{\mathrm{R}} \tag{21}\]  
From this, the frequency required for the steering motor controller can be calculated by  
\[pulse=\frac{360*Q*s}{1.8*\eta*d} \tag{22}\]  
where \(s\) is the motor subdivision (20 in this paper), \(\eta\) is the system efficiency (0.765, after experimental determination), and \(d\) is the steering valve displacement (500 mL/r). We set 2000 Hz as the maximum control command to obtain the normalized steering control command.  
\[cmd=pulse/2000 \tag{23}\]  
Based on this, the angular speeds at different articulated angles and control commands are shown in Figure 4, with the left turn on the left figure and the right turn on the right figure.
## 2 Protocol Design and Equipment
### Equipment  
The equipment listed in Table 2 is designed to facilitate both the audio-visual recording of the location and the collection of objective environmental factors, as given in Table 3. What equipment is brought on site should be adjusted depending on availability, needs of the researchers, and whether only one of the protocol stages will be carried out, or both. The equipment selected should be neutral and not noticeable. In general, this means dark or neutral colors as opposed to high-visibility colors and selecting compact equipment.  
The use of class 1 or 2 sound level meters has been stipulated to maintain verifiable consistency and quality of data across all soundscape studies which make use of this protocol, as well as with data collected under various other environmental acoustics purposes. As the accuracy of acoustic information gathered at the site is the most vital in the discussion of soundscape indices, specific requirements have only been set out for the acoustic equipment. Class 1 is highly preferred, but consideration is made for cost and availability of equipment. It should be noted what standard of SLM was used in the data collection and appropriate consideration of the precision and tolerances of the equipment should be taken during the data analysis.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**Equipment** & **Requirements** \\ \hline Tripod stand & With add-on hooks/holders for AMB microphone, SLM, environmental meter(s) and 360\({}^{\circ}\) camera with suitable suspension for microphones \\ \hline
360\({}^{\circ}\) camera & 4 K, 5.1 K or better resolution video, with suitable battery life and optional remote control \\ \hline Spatial audio/Ambisonics microphone system & (AMB) & Min. quality should be First-order Ambisonics (FOA) capability, however systems which achieve higher-order ambisonics would be preferred where available. \\ \hline Multi-channel field recorder & Min. inputs to accommodate output from AMB microphone \\ \hline Windshield(s) for AMB and SLM microphones & This can be a single large windshield which can accomodate both microphones or separate windscreens for each microphone \\ \hline Sound Level Meter (SLM) & class 1 (preferred) or class 2 with omnidirectional pattern measurement microphone \\ \hline Binaural recording system & Portable, worn by the researcher or with a mounted binaural head \\ \hline Sound calibrator for SLM, AMB microphones and binaural system & According to IEC 60942: 2017 Electroacoustics—Sound calibrators [42] \\ \hline Environmental meter(s) & See Table 3 for the recommended metrics \\ \hline Tablets and/or printed questionnaires & Internet connectivity or offline app to submit the questionnaires on site \\ \hline \end{tabular}
\end{table}
Table 2: Recommended equipment for implementing the SSID protocol. SLM: Sound Level Meter; AMB: Ambisonics; BIN: Binaural; QUE: Questionnaires.
# Three Rapid Methods for Averaging GPS Segments
## 3 Three Rapid Methods
### Outlier Removal  
The data contains many noisy trajectories. We, therefore, consider detecting and removing outliers as a preprocessing step. For simplicity, we apply an existing generic outlier detection method called Local outlier factor (LOF) [14], See Figure 11. It calculates the local density for every point as the distance to its furthest \(k\) nearest neighbor. Points having a significantly lower density than their neighbors, are more likely to be outliers. The outlierness of a point is the ratio of its density relative to the density of its neighbors. We apply LOF separately to the source and destination sets and detect  
Figure 10: Examples of the three methods for handling the curvy model on four distinct samples.  
Figure 9: Examples of divide-and-conquer (RapSeg-C) approach for handling the curvy model.  
the top 10% of the outlier points. To be safe, a trajectory is excluded if either its start or end point is detected as an outlier.  
We smooth the effect of the outlier removal as follows. We calculate the descriptors (S, M, D) for the set both with (variant 1) and without (variant 2) outlier removal as illustrated in Algorithm 6. The final descriptors are then weighted averages of the two alternatives. We experimentally select the weights as \(a=0.016\), \(b=0.600\), and \(c=0.510\), which are optimized for the training dataset, see Section 4.  
```
1:a, b, c = 0.016, 0.600, 0.510;
2:IF SIZE (variant 2) > 3:
3:RETURN variant 2;
4:ELSE:
5:S\({}_{1}\), M\({}_{1}\), D\({}_{1}\)\(\leftarrow\) variant 1;
6:S\({}_{2}\), M\({}_{2}\), D\({}_{2}\)\(\leftarrow\) variant 2;
7:IF SIZE(**M\({}_{2}\)**) == 0://**M\({}_{2}\)** is empty
8:S = 2 * a * S\({}_{1}\) + (1 + a) * S\({}_{2}\);
9:D = 2 * a * D\({}_{1}\) + (1 + a) * D\({}_{2}\);
10:RETURN S, D;
11:ELSE:
12:RETURN S\({}_{2}\), (b* **M\({}_{2}\)** + (1 - b) * (c * S\({}_{2}\) + (1 - c) * D\({}_{2}\))), D\({}_{2}\);
```  
**Algorithm 6**Embedding (variant 1, variant 2)
## 2 Development of Identification Procedure
### Key Features for Debonding Damage Identification  
In this study, machine learning was applied to analyze the correlations between various damage configurations and the resulting changes in the natural frequency. Among the various machine learning techniques, the authors chose the ANN, which is advantageous for defining the relationship between data.  
Damage identification using a debonding damage map based on various combinations of damage variables requires a great deal of data. Thus, the dataset is likely to include unnecessary components that do not have a significant effect on the actual results but would adversely affect the accuracy of the identification model. To address this problem, data were preprocessed using a normalizing technique that provides data uniformity by converting the degree of change in the measured data into a percentage. First, the natural frequency change of each configuration was determined by comparing the natural frequencies obtained through modal analysis of the first to the sixth of the model with  
\begin{table}
\begin{tabular}{c} **Eng.  
debonding damage (based on the debonding damage map) with those of the model with no damage as follows:  
\[x_{i,j}=\left(\frac{N_{f,undamaged}-N_{f,ij}}{N_{f,undamaged}}\right)\times 100(i=1,2, \cdots,\ 6,j=1,2,\cdots,1104) \tag{1}\]  
where \(i\) and \(j\) are the mode of the natural frequency and the number of damage configurations, respectively. Hence, there are a total of 1105 configurations (undamaged configuration + 1104 damage configurations), and \(x_{i,j}\) is the natural frequency change rate. The symbols of \(N_{f,undamaged}\) and \(N_{f,ij}\) are the natural frequency of undamaged blades and damaged blades, respectively.  
Based on this natural frequency change rate, the authors selected the relevant key features of the natural frequencies, which exhibited a high correlation between damage variable and natural frequency as Equation (2). Here Equation (2) means the average natural frequency change rate \(X_{i}\) for each natural frequency mode.  
\[X_{i}=\frac{\sum_{j=1}^{1104}x_{i,j}}{1104}(i=1,2,\cdots,\ 6) \tag{2}\]