esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed WSN Routing Scheme  
A WSN routing task which consists of stable or limited mobile nodes and a base station is considered as the problem. To achieve an efficient and robust routing operation, major features of typical WSNs are taken into consideration. First, failures in communication nodes are more probable in WSNs than classical networks, as nodes are often located in unattended places and they use a limited power supply. Therefore the network should not be affected by a node's failure and should be in an adaptive structure to maintain the routing operation. This is performed by sustaining different paths alive in a routing task. A node transferring data to the base sends it in divided parts (as data packages) using different paths. When a failure occurs in a path, the associated data package cannot arrive at the base. To achieve guaranteed delivery, acknowledgement signals are used. In the case of an absent acknowledgement for a data package, the source node resends that package to a different path. By performing acknowledgement-associated data transfers and sustaining different paths alive, routing becomes more robust. It is obvious that some paths in this type of network would be shorter, allowing for lower energy costs. Transmission on these paths should be more frequent to reduce the total cost of energy consumed using these paths. In other words, more data packages should be transferred along shorter paths to achieve a lower energy consumption.  
Second, nodes in WSNs present stringent energy constraints. They consume much more energy when they are in communication. In our proposed approach, the energy levels of the nodes should also be considered as well as the lengths of the paths. This is performed by choosing nodes having more energy in a routing task. Thus, the average network lifetime would be increased.  
Third, the bandwidth of wireless links in WSNs is limited. It is important not to involve too much information about overhead of the routing task in the communications. This is also a means of preserving more energy. We propose a new communication technique using ant agents in Section 2.1.  
Fourth, some node mobility should be allowed in some specific WSN applications. In our approach, nodes are considered to be normally stable. However, probable changes in node locations do not preclude network operation safety. Instead, it causes some setup stage to organize paths well. However, transfer of data packages is still performed in this stage as quality grows over time by exploring new paths.  
To summarize the operation of the routing scheme, a node having information for the base station initializes the routing task by transferring data in packages to different neighbor nodes. Each node then chooses other neighbor nodes and so on. Thus, paths towards the base are formed and each routing operation supplies some information about optimum paths for the consequent routing tasks. Whileperforming this operation, some agents (artificial ants) are used to achieve efficient routing. This operation is explained in the following section.
## 3 Control Layer Architecture  
This proposal uses the controller concept from the SDN architecture. We have designed a control layer running on top of the convergence layer of the nodes as a context-aware system. Some of these nodes, the ones selected to be controllers, run the controller module of the control layer. Similar to the SDN controller, the proposed controllers keep an overview of the network by gathering network measurements from the data plane. On the other hand, in OppNets, the control and data planes are coupled in the node. Thereby,the controller-driven OppNet nodes perform the GAFA functionality introduced in Section 1 for gathering, aggregating, and disseminating network measurements.  
Any node could potentially be a controller. Whether a node functions as a controller depends on the nature of the network. In a vehicular network (VANET), the controllers could be the roadside units; in an information-centric network, they could be well-connected nodes. For this particular work, we consider a generic OppNet. Thus, the generic criteria of selecting the more central nodes, i.e., the ones with more contacts, has been applied.  
Figure 1 shows a summary of how the control layer implements the node's GAFA and the controller functionalities. Firstly, the nodes in the OppNet sense local context measurements (Figure 1a). Detailed information can be found in Section 3.1. The nodes disseminate an aggregation of context measurements upon a contact (Figure 1b). Section 3.3 develops the aggregation methodology and Section 3.2 shows how the dissemination of context information is performed. Finally, Figure 1c shows how controllers process the received context information to obtain a prediction of a network indicator at a future time (Section 3.5). From this prediction, the controller determines an action to be performed by the nodes consisting of the modification of a forwarding algorithm parameter (Section 3.6). The controller disseminates this action upon a contact with another node (Section 3.7).  
A list of abbreviations is provided in Abbreviations section to help read the rest of the document.
## 5 Conclusions  
Eddy covariance flux measurements taken in the 2015-2020 period show that the southern taiga old memorose spruce forest acts as an atmospheric CO\({}_{2}\) source or sink in both the annual and growing season time scales depending on the environmental conditions of the particular year. The changes in the NEE of the forest are dependent on the GPP/TER ratio variability. The anomalously dry, warm, and sunny conditions observed in the growing season (2018) led to an increase in CO\({}_{2}\) uptake from the atmosphere due to the increased GPP and decreased TER observed at the site. Anomalously warm winter conditions provided an early CO\({}_{2}\) uptake in spring in the memorose spruce forest; however,  
Figure 9: The relationship between the mean winter (1 November-31 March) GPP/TER ratio and the winter temperature anomaly in the memorose spruce forest (NF, this study) and the paludified spruce forest (PF) [51] for the 2015–2020 period. Dash bars correspond the winters of the period (e.g., the label 15/16 means the winter 2015/2016).  
it did not increase the annual GPP/TER ratio. The TER in the cool and wet growing season (2017) was more sensitive to the temperature variations than in the dry and warm (2018). The comparison of the flux data obtained from the memorose spruce forest on well-drained mineral soils with the estimates obtained from the paludified spruce forest [51] located within the same landscape showed that the NEE, GPP, TER, and GPP/TER ratio of the ecosystems differently responds to the changes in the environmental conditions. The opposite dynamics of TER at the sites between the cool and wet (2017) and the warm and dry (2018) growing seasons were detected. Therefore, the difference in the local soil moisture regimes of the southern taiga spruce forests may lead to alternative responses of the CO\({}_{2}\) ecosystem-atmosphere exchange to climate anomalies. Additional data containing long-term flux measurements from southern taiga forest ecosystems are essential to test this hypothesis and to estimate the range of potential variability in the ecosystem-atmosphere CO\({}_{2}\) exchange due to the local differences in the soil moisture regimes.  
Writing--original draft preparation, V.M.; data curation, project administration, A.V.; visualization I.Y.; supervision, J.K. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the grant of the Russian Science Foundation (21-14-00209).  
Not applicable.  
The meteorological and flux data used in the study are available upon request at European fluxes database cluster ([[http://www.europe-fluxdata.eu](http://www.europe-fluxdata.eu)]([http://www.europe-fluxdata.eu](http://www.europe-fluxdata.eu)); access date: 20 May 2022).  
We thank [PERSON] from A.N. Severtsov Institute of Ecology and Evolution of RAS (Moscow, Russia) for the photos of the flux towers in the memorose spruce forest and in the paludified spruce forest (Ru-Fy2 and Ru-Fyo FLUXNET stations).  
The authors declare no conflict of interest.
## 3 Preliminaries  
Definition 1: Parking lot network: We define a parking lot network as an undirected graph \(G=(V,E,A)\), where \(V\) is a set of nodes \(N\), with each node corresponding to a parking lot. \(E\) represents a set of \(M\) edges, and \(A\in\bar{R}^{N\times N}\) denotes the adjacency matrix (constructed based on distance or semantic similarity, for example).  
Definition 2: Parking signal matrix: The observed values of parking lot network \(G\) at time slot \(t\) are represented by the parking signal matrix \(X_{t}=(x_{t,1},x_{t,2},\ldots,x_{t,N})^{T}\in R^{N\times C}\). Here, \(x_{t,v}\in R^{C}\) represents the feature vector of node \(V\) at time \(t\) (such as the number of available parking spaces), and \(C\) denotes the number of features.  
Definition 3: Graph convolution: According to [40], the graph convolution operation can be well approximated by a first-order Chebyshev polynomial. This can be expressed as:  
\[Z=(I_{N}+D^{-\frac{1}{2}}AD^{-\frac{1}{2}})X\Theta+b \tag{1}\]  
In the equation, \(A\in R^{N\times N}\) represents the graph structure as a positive semidefinite matrix, and \(D\) is the degree matrix of \(A\). \(X\in R^{N\times C}\) and \(Z\in R^{N\times F}\) refer to the input and output of the GCN, respectively. \(\Theta\in R^{C\times F}\) and \(b\in R^{F}\) represent the weights and biases, respectively. The vacant parking space prediction problem can be described as follows: Given a parking lot network \(G=(V,E,A)\) and a \(P\)th-order parking signal matrix \(X_{(t-P):t}=(X_{t-p},X_{t-p+1},\ldots,X_{t-1})\in R^{p\times N\times C}\), the mapping function \(f(\cdot)\) maps \(X_{(t-P):t}\) to the next \(Q\)th-order parking signal matrix \(X_{t:(t+Q)}=(X_{t},X_{t+1},\ldots,X_{t+Q-1})\in R^{Q\times N\times C}\). The representation is as follows:  
\[X_{t:(t+Q)}=f(X_{(t-P):t};G) \tag{2}\]
## 4 Conclusions  
High-precision strain measurement systems are crucial in industrial production and scientific research. However, baseline drift caused by factors such as ambient temperature can significantly affect signal acquisition accuracy. Traditional methods like high-pass filtering, discrete wavelet transform, and empirical mode decomposition fail to effectively address this issue. However, with the continuous development of artificial intelligence, various models have become powerful tools for processing time series data. In this paper, a Transformer-based model is designed to remove baseline drift from the signal. The encoder of this model uses a self-attentive mechanism to learn the interrelationships within the input signal and to capture its key features. The decoder then generates a corrected signal. The network can be trained to capture drift signals hidden within time- and frequency-domain features. The ability of the RNN, GRU, AGRU, and Transformer models to eliminate drift was tested on training sets of simulated voltage signals, both with and without Gaussian noise. The experimental results show that the Transformer model has a superior ability to eliminate signal baseline drift compared to the other three models. To further verify the model's validity, an electronic universal tensile machine and a high-frequency induction heating device were used to test the model under different temperatures and forces. The experimental results in Figures 7 and 8 demonstrate that the proposed Transformer model can effectively eliminate drift in dynamic signals and has significant potential for practical applications.  
In the future, to improve the speed and accuracy of data processing in the acquisition system and enhance the system's reliability and real-time performance, we will deploy the algorithm designed in this paper on embedded hardware platforms, such as the NVIDIA Jetson, to verify the feasibility of the proposed method.  
Figure 8: Drift-elimination results when subjected to force.  
**Author Contributions:** Conceptualization, Y.W.; methodology, Y.W.; software, Y.W.; resources, L.Z.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W., X.Q. and X.Y.; funding acquisition, Q.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Key Research and Development Plan of Shanxi Province (No. 202102030201005), the National Natural Science Foundation of China (Grant No. 52105594), and the Fundamental Research Program of Shanxi Province (Grant No. 20210302124274).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors have no conflicts of interest to disclose.
## 4 Statistical Analyses of the Collected Data  
The whole dataset is presented in Appendix A. The minimum, maximum, mean, standard deviation, and range of the parameters included in the database are shown in Table 2.  
The correlation coefficients between all possible variables are shown in Table 3. High positive or negative correlation coefficient values between input variables can result in low efficiency and difficulty in determining the impact of these variables on the response. There are no significant correlations between the independent input variables, which was to be expected. As expected, it is clear that there is a strong correlation between the mortar \(f_{h}\) and input parameters, such as the amount of cement and granite powder, water-to-cement ratio (W/C), and curing method. In addition, Figure 6 shows the frequency histogram of the six input parameters. These figures are extremely useful because they can help identify the range of parameter values where the data are insufficient and where more data are required.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Descriptive** & **Cement** & **Dry Quartz** & **Water** & **Granite** & **Curing Time** & **\(f_{c}\) [MPa]** & \(f_{h}\) [MPa]** \\
**Statistics** & **[kg/m\({}^{3}\)]** & **Sand** & **[kg/m\({}^{3}\)]** & **[kg/m\({}^{3}\)]** & **[kg/m\({}^{3}\)]** & **[kg]** & **[days]** & **[days]** & **[days]** \\ \hline Min & 358.40 & 1536.00 & 256.00 & 0.00 & 56.00 & 14.00 & 0.70 \\ Max & 512.00 & 1536.00 & 256.00 & 153.60 & 90.00 & 38.00 & 3.08 \\ Mean & 435.20 & 1536.00 & 256.00 & 76.80 & 73.00 & 26.69 & 1.33 \\ Standard deviation & 57.39 & 0.00 & 0.00 & 57.39 & 17.04 & 4.49 & 0.44 \\ Spread & 153.60 & 0.00 & 0.00 & 153.60 & 34.00 & 24.00 & 2.38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Range of input and output variables.  
Figure 5: Average pull-off strength \(f_{h}\) of the near-surface layer \(f_{c}\).  
Figure 6: The relation between input variables and tensile strength.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Input Parameters**} & \multicolumn{3}{c}{**Output**} \\ \cline{3-8}
**Variables** & **Cement** & **Granite** & **W/C Ratio** & **Curing** & **Curing** & **\(f_{c}\)** & **Full-Off** \\ \cline{3-8}  & & **Powder** & **W/C Ratio** & **Curing** & **Curing** & **\(f_{c}\)** & **Full-Off** \\ \hline \multirow{4}{*}{Input} & Cement & \(1.00\) & \(-1.00\) & \(-0.99\) & - & - & \(0.32\) & \(0.56\) \\  & Granite Powder & \(-1.00\) & \(1.00\) & \(0.99\) & - & - & \(-0.32\) & \(-0.56\) \\  & W/C ratio & \(-0.99\) & \(-0.99\) & \(1.00\) & - & - & \(-0.31\) & \(-0.55\) \\  & Curing method & - & - & - & 1.00 & - & \(0.05\) & \(0.52\) \\  & Curing time & - & - & - & - & 1.00 & - & \(0.05\) \\  & \(f_{c}\) & \(0.32\) & \(-0.32\) & \(-0.31\) & \(0.05\) & - & \(1.00\) & \(0.12\) \\ Output & Pull-off & \(0.56\) & \(-0.56\) & \(-0.55\) & \(0.52\) & \(0.05\) & \(0.12\) & \(1.00\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation matrix of the input and output variables.
## 2 Materials and Methods
### Remote Sensing Data  
To understand how robust the algorithms are to varying inputs, data from two different data sources, MODIS (products MOD09Q1, MOD13Q1, MYD13Q1) and MSI /Sentinel-2 (Level--2A) that were available in Google Earth Engine (GEE) were used. Normalized difference vegetation index (NDVI) [43] and enhanced vegetation index (EVI) [44] have been the most commonly used vegetation indices in retrieving vegetation phenology [45]. However, the estimates vary among methods [4] and the crop season [45], with very different or even conflicting results [45; 4]. Thus, to have a comprehensive evaluation of how the different indices can affect the algorithms, we used the NDVI and two-band enhanced vegetation index (EVI2) [46]. The MOD13Q1 and MYD13Q1 products offer a 16-day maximum value composition (MVC) of NDVI and EVI2 indices at a spatial resolution of 250 m. Aggregated, the MOD and MYD products can offer an 8-day temporal resolution. The MOD09Q1 product offers an 8-day MVC in the red and near-infrared regions of the electromagnetic spectrum at 250 m, while the MSI/Sentinel-2 sensors collect data every five days at a resolution of 10 m in similar spectral regions. For the reflectance data, NDVI and EVI2 for each field pixel in each image were calculated according to Equations (1) and (2) [43; 46].  
\[\text{NDVI}=\text{NIR}-\text{Red}/\text{NIR}+\text{Red} \tag{1}\]  
\[\text{EVI2}=2.5\times(\text{NIR}-\text{Red})/(\text{NIR}+(2.4\times\text{ Red})+1) \tag{2}\]  
where NIR and Red are near-infrared and red reflectances; both vegetation indices were used since most of these packages have not been tested before using different indices. Thus, the algorithms were assessed with varying temporal, spatial, radiometric, and spectral resolutions, totaling 48 different experiments (6 packages \(\times\) 4 data sources \(\times\) 2 indexes).
## 5 Error Analysis and Correction
### Reconstruction Experiment  
In order to capture the movement information of fish and meet the image clarity, a fish tank with size of 1 m by 0.5 m by 0.5 m, with a surrounding LED strip, is used and eight numbered cameras of GoPro Hero4 with an image resolution of 1980 by 1080 and frame rate of 120 fps are fixed on an aluminum alloy bracket in the peripheral area, three of which are arranged on the long side and one is arranged on the short side. The overall experimental platform is shown in Figure 10. It should be noted that the distribution of eight cameras are centrosymmetrical to enable shooting with a wider range.  
For the underwater error analysis, we fixed the calibration plate at the bottom of the fish tank and shot it from the air by the fixed camera, which set the world coordinate system with the plane of the calibration plate as Z = 0. Then we poured the water into tank, measured the water depth, and shot again. After that, the calibration plate corner can be reconstructed based on the underwater 3D reconstruction theory mentioned above. Figure 11 shows the result of underwater 3D reconstruction  
Figure 10: Experimental platform. (**a**) The test bench layout diagram, (**b**) The picture of the real test bench.  
Figure 9: Measuring position and filtered position of marker.  
with water depth of 206 mm. The green plane in the figure represents the plane of the calibration plate and the positions of the intersection points are the actual positions of the calibration plate corners. It can be seen that combinations with a large error of underwater 3D reconstruction includes camera 4 and 8.  
Figure 12 shows the positions of the reconstruction points of above partial combinations in the Y-Z plane, the reconstruction points are tilted at both ends of the Y axis, and cameras 5 and 7 are arranged in the positive direction of the Y axis, while cameras 1 and 3 are arranged in the negative direction of \(Y\)-axis, that is, the location where tilt is close to the camera.  
By analyzing the error of 3D reconstruction in water and in the air, we can find that the error in the water is significantly larger than that in the air. The reasons for this are as follows:  
1. The principle of underwater 3D reconstruction is to determine the intersection of the optical path and the water surface with the position of camera and marker and to solve the position with least squares method based on the intersection, which weakens the ability of least squares method to reduce the error.
2. Because of the accuracy of process and other issues, the plane of calibration plate and the water surface can only be approximately parallel, which introduces error simultaneously.
3. As we all know, the error increases as the distance of the point and camera increase.
# The Influence of Feature Representation of Text on the Performance of Document Classification
## 4 Results
### F1-Score Rankings
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Brown2** & **Brown4** & **Brown10** & **Brown15** & **Reuters8** & **WebKB** & **20 News** & **Avg. Rank** \\ \hline bow-TF & **1** & **2** & **2.5** & 4 & 4 & **4** & 9.5 & **3.86** \\ bow-TFIDF & 5 & 13.5 & 6.5 & 11.5 & **1** & **4** & **1** & 6.07 \\ \hline word2 vec-25 & 8.5 & 7 & 12.5 & 11.5 & 7.5 & **4** & 9.5 & 8.64 \\ word2 vec-50 & 2 & 7 & 12.5 & 4 & 4 & **4** & 9.5 & 6.14 \\ word2 vec-75 & 5 & 13.5 & 12.5 & 11.5 & 4 & **4** & 9.5 & 8.57 \\ word2 vec-100 & 5 & 7 & 12.5 & 11.5 & 4 & **4** & 9.5 & 7.64 \\ \hline doc2 vec-25 & 3 & 7 & **2.5** & 4 & 4 & **4** & 3.5 & 4.00 \\ doc2 vec-50 & 11.5 & **2** & **2.5** & 4 & 7.5 & 8 & 3.5 & 5.57 \\ doc2 vec-75 & 8.5 & **2** & **2.5** & **1** & 9 & 9.5 & 3.5 & 5.14 \\ doc2 vec-100 & 13 & 7 & 6.5 & 4 & 10 & 9.5 & 3.5 & 7.64 \\ doc2 vec-200 & 14.5 & 7 & 6.5 & 11.5 & 14 & 12 & 9.5 & 10.71 \\ doc2 vec-500 & 16 & 13.5 & 6.5 & 11.5 & 14 & 15 & 9.5 & 12.29 \\ doc2 vec-1000 & 14.5 & 13.5 & 12.5 & 11.5 & 11 & 15 & 9.5 & 12.50 \\ \hline gow-avg & 8.5 & 13.5 & 12.5 & 11.5 & 14 & 12 & 15 & 12.43 \\ gow-qrt & 11.5 & 13.5 & 12.5 & 11.5 & 14 & 15 & 15 & 13.29 \\ gow-hist & 8.5 & 7 & 12.5 & 11.5 & 14 & 12 & 15 & 11.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Multi-objective ranking of the 16 variants of the three document representation models by document classification task with respect to the F1-score. The last column reports the average rankings over the seven tasks. The top-ranked models in each column are shown in bold. Figure 5: Radar-chart comparison of the multi-objective rankings by task (**left**) and their averages (**right**) for the four top-performing variants of the document representation models in each of the four groups with respect to F1-score. In summary, according to the F1-score, doc2 vec variants with low-dimensional embedding spaces and bow-TF outperformed other document representation models. For data sets involving small-sized documents, one should select the TFIDF variant of the BOW model, while for the others, low-dimensional variants of doc2 vec are to be preferred. The precision and recall rankings, reported in Tables 12 and 13, mostly confirmed the regularities observed here, providing a single novel insight; the word2 vec model leads to high-precision classification models.