esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
Utilizing a low-latency video streaming server provided by the host, we enabled the teleoperating client to visually observe the robot's activities from afar. The client connected to the video server on the host and rendered the received video feed to the screen during our tests. The server and client scripts used the Python OpenCV library [32]. Compared to other robotic platforms, the tested robot control infrastructure has limited outside factors for interruption to the QoS. This is due to the isolation of LAN connections between the server and the Baxter robot, thus resulting in a relatively stable robot processing latency. In contrast, we would expect a larger latency variance in networks with higher traffic volume. However, the client connections are varied in each test and receive no higher priority than other traffic on the VPN network. Due to this, we consider the latency measured in client-to-host and total request duration to be applicable to different-sized operations. Additionally, it demonstrates the relatively small impact that traffic volume itself has on latency fluctuations.
# Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
###### Abstract  
As wetlands are one of the world's most important ecosystems, their vulnerability necessitates the constant monitoring and mapping of their changes. Satellite-based remote sensing has become an essential data source for mapping and monitoring wetlands. As wetlands are dynamic ecosystems, their classification depends on many different parameters. However, considering their complex structure; wetlands tend to be challenging land cover for classification, which sometimes requires the use of multi-sensor remote sensing techniques. The objectives of this study were: (i) to investigate the monthly dynamics of several wetland classes using multi-sensor parameters; (ii) to find correlations between the investigated parameters. Thus, we extracted the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from Landsat 8, and extracted dual polarization backscatter values (VH-VV) from the Sentinel-1 satellite at a monthly period over a year. The results showed strong correlation between the LST and the NDVI values of 0.94, and strong correlation between the microwave (VH) and both thermal and optical parameters with a 0.81 correlation coefficient, while there was weak or no correlation between the VV and the other investigated parameters. We strongly recommend that future studies clarify the Sentinel-1 backscatter values in wetland areas, by taking multiple field measurements close to the image acquisition time.  
re mode sensing; wetlands; UAV; Sentinel-1; land surface temperature +
Footnote †: journal: Geo-Information
## Data, Methodology and Results
### Methodology
#### 2.2.2 Ocean Tidal Correction
\tag{10}\]  
The improvement of the positioning relative to the reference ellipsoid by the OTCP method is the spatial distance \(C_{\rm T}\) between the specular reflection point on the ocean tidal surface and that on the ellipsoid, expressed as follows:  
\[C_{\rm T}=\sqrt{\omega_{\rm X}^{2}+\omega_{\rm Y}^{2}+\omega_{\rm Z}{}^{2}}. \tag{11}\]  
Similarly, for the static-state elevation correction, after \(g\) times iterative correction, the cut-off threshold is satisfied, and the total static-state elevation correction components \(\delta_{\rm X}\), \(\delta_{\rm Y}\), \(\delta_{\rm Z}\) are as follows:  
\[\left[ \begin{array}{c}\delta_{\rm X}\\ \delta_{\rm Y}\\ \delta_{\rm Z}\end{array} \right]=\left[ \begin{array}{c}\sum\limits_{q=1}^{g} \varepsilon_{\rm X}\\ \sum\limits_{q=1}^{g}\varepsilon_{\rm Y}\\ \sum\limits_{q=1}^{g}\varepsilon_{\rm Z}\end{array} \right]. \tag{12}\]The improvement of the positioning accuracy by the static-state elevation correction to the ellipsoid positioning is the spatial distance \(C_{\text{G}}\) between the specular reflection point on geoid and that on the ellipsoid, expressed as follows:  
\[C_{\text{G}}=\sqrt{\delta\chi^{2}+\delta\chi^{2}+\delta_{Z}{}^{2}}. \tag{13}\]  
The positioning accuracy improvement \(C\) by the OTCP method to the geoid positioning is the vector difference between \(C_{\text{T}}\) and \(C_{\text{G}}\):  
\[C=\sqrt{\left(\omega_{\text{X}}-\delta_{\text{X}}\right)^{2}+\left(\omega_{ \text{Y}}-\delta_{\text{Y}}\right)^{2}+\left(\omega_{Z}-\delta_{Z}\right)^{2}}. \tag{14}\]  
The results of \(C\) are discussed in detail in Section 2.3. The average value \(R\) of the tidal time-varying elevation correction gradient modulo of a track (or segment) containing \(u\) specular reflection points can be expressed as:  
\[R=\frac{\sum\limits_{\text{s}=1}^{u-1}\left|C_{\text{s}+1}-C_{\text{s}}\right|} {u-1}. \tag{15}\]  
According to Equations (8)-(15), the tidal elevation positioning correction gradient modulo is affected by both the tidal height and the geoid undulation. The former differs according to time and geographical location like offshore/deep sea, the latter is determined by gravity anomaly at the location of the specular reflection point. This issue is discussed in Section 3. The above tidal time-varying elevation correction positioning is based on the assumption that the normal and the radial directions of the specular reflection point are identical. Actually, there is a difference, which would result in positioning error. Therefore, it is necessary to further correct the specular reflection point to the plane perpendicular to its normal projection. The normal projection correction method is applied by directly solving spatial geometric relationship between the normal projection and the reflection path without approximation. The positioning errors of normal-radial difference and approximate substitution are reduced [13].
# Practical Application of Mesh Opportunistic Networks
## 2 Theoretical Background  
### Opportunistic Networks or OppNets  
Opportunistic networks or OppNets, and specifically local mesh networks, have been seen to be very useful in emergency situations [5]. The term OppNets was coined by [PERSON] in 2005, thinking of mobile seed networks that can expand locally and be deployed, for example, for communications after an earthquake [6; 7]. An opportunistic mesh network allows for direct communication between mobile devices that are within reach, which can be called nodes, even if there is no cellular or Wi-Fi coverage. The OppNet mesh network uses intermediate nodes to send messages from their sender (or source) to their receiver in an opportunistic way [8]. To do this, low-energy connections can be used to establish personal wireless networks that connect mobiles and their data screens to portable mesh radio devices, for example, using Bluetooth protocols. These networks were originally conceived and designed for emergency situations where no alternative stable telephone communication is available. However, the concept transcends its use for emergencies, and facilitates the use of a free public radio spectra for social, interpersonal, and community communications outside of data networks, Wi-Fi networks or other means to access the Internet [9]. The mesh network can grow and be strengthened by adding new nodes, represented by mobile users and their OppNet devices, and is likely one day to become integrated into the mobile device itself. Obviously, no manufacturer or operator is currently interested in offering inexpensive equipment that bypasses data tariffs and the control of large portals, permitting private communications independent of the data giants and with no need to pay an operator fee.  
In a typical mesh OppNet scenario, the individual uses their mobile device (without network connection) as a screen, linked to an OppNet radio device that sends the desired information and that simultaneously acts as a node to receive information from others (see Figure 1). Thus, mesh OppNets are networks in which the nodes may be individual users, vehicles, fixed devices, etc. [10]. The more users in the mesh OppNet, the more nodes, and the more nodes, the greater the scope for communication and coverage. This means that the coverage provided by mesh OppNets varies in time and space depending on the density and mobility of the users with devices.  
As a society, we now assume that mobile media can be characterized not by its technological convergence but rather, by its network organization [11]. However, this network articulation tends to refer to the content and its distribution. By contrast, mesh OppNet communication between people and small communities can also occur according to this model of network organization. Accordingly, mobile media, communication studies, and electronic commerce must consider these emerging technologies, in parallel to the extension of smartphones, which will have important consequences for community and social communications, and in territorial management [12].
## 3 Proposed Methodology  
The traditional PSO algorithm applies the velocities on the particles to explore new solutions. We propose an enhanced PSO by incorporating a bilayer local search approach with it. The first layer of local search is applied on the whole population, i.e., on each particle in the population in every iteration. Thus, it improves the quality of the solutions obtained by traditional PSO by searching their neighborhoods. The second layer of local search is implemented on the pool of the best particles found in different iterations. This layer is to give additional chances to the best particles to look for better solutions in their neighborhoods. Such local searches allow the particles to have a better exploration around the current position as otherwise a particle may move from a position missing out a nearby potential solution.  
We represent a particle in PSO in an encoded vector form to apply PSO operations easily on them. Hence, we also propose a decoding method to build a real comprehensive solution out of a particle. The overall workflow of proposed bilayer local search-enhanced particle swarm optimization (BLS-PSO) is sketched in Figure 2.  
The proposed BLS-PSO technique to solve a capacitated vehicle routing problem (CVRP) can broadly be thought as having three major parts: the application of the particle swarm optimization (PSO), the decoding step to obtain the detail routes encoded in a solution and the bilayer local search approach to search for a better solution in the neighborhood. We discuss these steps in detail in the following subsections.
# An Improved Opportunistic Routing Protocol Based on Context Information of Mobile Nodes
## 4 Conclusions and Future Work  
In this paper, we proposed an improved opportunistic routing protocol, where the context information of average distance travelled and average time elapsed from the reception of a message to delivery of the message to the destination node was used. In the proposed protocol, average distance and average time were updated whenever a message is delivered to a destination node. Then, both the proposed average distance and average time as well as delivery predictability of PRoPHET protocol were used to decide message forwarding. The performance of the proposed protocol was compared with PRoPHET and RPC protocol. Simulation results show that the proposed protocol had better performance than PRoPHET and RPC from the aspect of delivery ratio, overhead ratio, and delivery latency for varying buffer size, message generation interval, and the number of nodes. In our future work, we will develop an analytical model of the proposed protocol in detail and verify the numerical results in this paper. After that, the optimization model of the proposed protocol will be developed  
Figure 12: Overhead ratio for different number of nodes.  
Figure 13: Delivery latency for different number of nodes.  
and solved using particle swarm optimization model, which is one of the widely used bio-inspired optimization techniques.  
[PERSON], [PERSON] and [PERSON] conceived and designed the proposed scheme. [PERSON] and [PERSON] conceived and designed the simulations; [PERSON] and [PERSON] performed the simulations; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper. Authors have read and approved the final manuscript.  
The research of [PERSON] and [PERSON] was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2016R1D1A1B03930299).  
The authors declare no conflicts of interest.
# Differential Signalling in Free-Space Optical Communication Systems
## 1 Introduction
Note that, to avoid an adaptive setting of the DTL, high pass filtering (e.g., AC-coupled circuitry) could be a solution, but this will necessitate an increase in the transmit power to compensate for the filter loss and also induces a baseline wander effect [21]. In summary, the main advantages of the DSS technique are: (i) no requirement for CSI or high complexity blind signal detection at the Rx; (ii) no need for a feedback signal to adjust the threshold level; (iii) no pilot overhead and hence on loss in the system throughput; (iv) reducing the adverse effect of background noise at the Rx [22]; (v) mitigation of the effects of the atmospheric channel such as fog and smoke [9], turbulence [3], and PE [4]; and (vii) the use of a single aperture for both FSO links (that ensures high correlation between two FSO channels). A practically-feasible implementation of DSS with a highly correlated turbulence influenced channel was also proposed in [3]. In [4], we adopted the same concept to mitigate PE induced fading. It was practically shown that DSS was effectively reducing the fluctuations in the DTL of the signal. In addition, a closed-form expression for bit-error-rate (BER) of FSO DSS link with PE was derived and confirmed by simulations. The Monte Carlo technique was used to simulate each case scenario. This paper presents a considerable extension of our previous results, presenting the closed-form expressions of DTL and BER for an FSO link with both effects of turbulence and PE derived, which have not been reported before. Note that, compared with previous works, in particular [3; 4], the original contributions made are: (i) considering a differential signaling technique in correlated channels; (ii) in [3], we only considered weak turbulence, whereas, in this paper, moderate to strong turbulence is investigated; and (iii) in [3; 4], turbulence and PE effects were investigated separately, whereas, in this paper, we consider joint effects of both in a FSO link. The work presented here is also different from works of other teams [23; 24; 25; 26], where the joint model of turbulence and pointing errors were presented. In [23; 24; 25; 26], no differential signalling was considered and the analysis reported cannot be applied to the FSO link with differential signalling in correlated channels. The paper is organised as follows. In Section 2, the concept and theory behind the DSS technique are explained. Section 3 considers the case of the lossy channel as well as the background noise, whereas the influences of weak and moderate to strong turbulence regimes are discussed in Section 4. In Section 5, the channel with PE effect is investigated. The joint influence of turbulence and PE is introduced in Section 6. Section 7 presents an example of numerical results illustrating the performance improvement offered by DSS, and, lastly, Section 8 concludes the paper.
# NAS-CRE: Neural Architecture Search for Context-Based Relation Extraction
## 3 Method
### Overall Framework  
Figure 2 shows the overall framework of our proposed NAS-CRE. When dealing with a sentence, we use SentencePiece, which splits the sentence into many tokens. SentencePiece does not divide words according to spaces; instead, it divides English words into smaller semantic units and reduces the vocabulary. Each token in the sentence  
generates a 768-dimensional embedding vector \(X\) through the XLNet model pretrained on 13 GB of plain text [15]. In addition, each token has an encoded position \(P\) to mark the entity. For each word vector, we concatenate \(X\) and \(P\).  
When we process the encoded position of entities in a sentence, we mark each word of the sentence. If the word is a head entity, we mark it as 2; if the word is a tail entity, we mark it as 3; and the rest of the words are marked as 1. To have a one-to-one correspondence between the encoded position and tokens, we mark the encoded position on the tokens generated by SentencePiece. Here, we give an example. Suppose we have the sentence \"[PERSON] lives in the USA\". We want to extract the relationship of entity pair {_[PERSON], USA_}. The tokens generated by SentencePiece are [\"Will','is','lives','in','the', 'USA']. When performing position marking, 'Will' and 'is' are marked as 2, 'USA' is marked as 3, and the rest of the words are marked as 1. Therefore, the position is marked as [2, 2, 1, 1, 1, 3].  
The token embedding and the encoded position are connected and fed to the NAS-RNN network to obtain the relationship \(o_{s}\). For other entities in the sentence, we use the same method to obtain the relationship, \(o_{1}\), \(o_{2}\),..., \(o_{k}\) between entities, and then calculate their attention score \(o_{s}^{\prime}\), i.e.,  
\[o_{s}^{\prime}=\sum_{i=0}^{k}\theta_{i}o_{i} \tag{1}\]  
\[\theta_{i}=\frac{exp(score(o_{i},o_{s}))}{\sum_{j=0}^{k}exp(score(o_{j},o_{s}))} \tag{2}\]  
where \(o_{i}\) represents each relationship, and \(score(*,*)\) represents the score between entities calculated by the attention mechanism. We connect the obtained \(o_{s}\) with \(o_{s}^{\prime}\), **o** = [\(o_{s}\),\(o_{s}^{\prime}\)]. Then, to predict their relation, we feed the encoded relation **o** to the softmax layer, i.e.,  
\[p(\textbf{r}\mid\{e_{1},e_{2}\},s)=\frac{exp(a_{i}\cdot\textbf{o}+b_{i})}{ \sum_{i=1}^{a_{i}}exp(a_{i}\cdot\textbf{o}+b_{i})} \tag{3}\]  
where \(a_{i}\) is a weight, and \(b_{i}\) is a bias.
## 2 Materials and Methods
### Prediction Accuracy Evaluation  
In order to evaluate the prediction accuracy of the model, the data are divided into training sets and prediction sets. The data from 2006 to 2018 are used for model training and test, and the data from 2019 are used for the model forecast to evaluate the model effect.  
The prediction effect was evaluated by the root mean square error (RMSE) and mean absolute error (MAE). RMSE refers to the expected value of the square root of the difference between the estimated value of the parameter and the observed value of the parameter, which can be used to evaluate the change degree of the data. MAE is the average value of the absolute error, which can better reflect the actual situation of the predicted value error. The smaller the RMSE and MAE value, the better the accuracy of the prediction model in describing the experimental data. The calculation formula is as follows:  
\[RMSE=\sqrt{\frac{1}{m}\sum_{i=1}^{m}\left(f_{i}(\text{X})-y_{i}\right)^{2}} \tag{12}\]  
\[MAE=\frac{1}{m}\sum_{i=1}^{m}\lvert f_{i}(\text{X})-y_{i}\rvert \tag{13}\]  
where \(m\) is the number of forecasting operation, \(f_{i}(\text{X})\) is the value of the \(i\)th forecast, and \(y_{i}\) is the value of \(i\)th observation. At the same time, we use MAPE and ER to indicate the percentage of different errors in the total amount, where MAPE is the mean absolute percentage error, and ER is the ratio of RMSE to average demand.  
Figure 3: The parameter optimization framework for long-term and short-term models.
## 4 The Proposed DC-CAAE Method
### CAAE Structure  
A CAAE network combines a CAE with adversarial training, and its core is still to use a generator \(G\) and a discriminator \(D\) for adversarial learning to distinguish between real data and fake data. However, unlike an ordinary adversarial training network, the data that CAAE needs to distinguish are not a natural image but a coded vector \(z\). The real data and fake data to be identified are generated by the encoder in the CAE and a predefined random probability distribution, respectively. Finally, the network used for image generation is not the former generator, but the decoder in the CAE. Its model architecture is shown in Figure 4.  
The \(\mathbf{x}\) in the CAAE represents the image data of the preprocessed network traffic. Enter \(\mathbf{x}\) into a CAE and have the encoder encode it to generate a potential vector \(\mathbf{z}\). The decoder then attempts to decode the potential vector and regenerate the image data \(\mathbf{x}^{\prime}\). The key to this model is that the encoder and the discriminator form an adversarial network. The discriminator predicts whether \(\mathbf{z}\) comes from real or fake data by learning constantly. The whole process of adversarial learning can be thought of as constantly adjusting the encoder to bring the resulting data distribution closer to a predefined \(p(\mathbf{z}^{\prime})\).
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 5 Discussion
### Solutions for Addressing SAR-Vegetation Interaction  
As mentioned in 4.2., the presence of vegetation would limit the viability of SAR-based snow cover classification approaches. The most successful algorithm specifically for forested regions was developed by [PERSON] et al. (1988), utilizing two reference images to minimize the effect of a forest. Based on that, [PERSON] et al. (1997, 1998, 1999, 2000) accomplished several improvements resulting in the Helsinki University of Technology (TKK)-developed snow cover detection method aiming at boreal forest regions. Its forest compensation algorithm makes use of a semi-empirical forest backscattering model, which utilizes the stem volume information to estimate the amount of backscattering originating from the forest and further eliminates this factor from subsequent calculations.  
However, in addition to the general problem of lower accuracy in open areas, the biggest limitation of the TKK method is that it requires prior knowledge of forest stem volumes, which is difficult to obtain. Thus, a purely spaceborne image-based approach to address the interaction between SAR imagery and vegetation needs to be developed. Hopefully, in the recent paper published by [PERSON] et al. (2014), a land cover type-dependent classification strategy which can be applied to densely vegetated forest and agricultural regions was proposed. The method was tested in five mountainous study areas around the globe and the accuracy above 0.75 is confirmed in all regions by validation with optical-based SCE product. This study provides a novel way to map SCE in all land cover types and should be utilized in the future studies.  
Additionally, the potential of utilizing PolSAR, Polarimetric Interferometric SAR (PolInSAR) as well as Tomographic SAR (TomoSAR) to estimate 3D forest structure and biomass (2012, 2013, 2014) should be further integrated into current SAR-based snow cover detection algorithms to mitigate the negative effect of vegetation. This is also proposed in ESA's 7 th Earth Explorer selected mission, BIOMASS (equipped with fully polarimetric P-band SAR, planned to launch in 2020) (2014).