esa-sceva/satcom-chunk-collection huggingface dataset:
# High Spatial and Temporal Resolution Bistatic Wind Lidar
###### Abstract
Generally, the accuracy of lidar measurements is affected by atmospheric phenomena, as the effects of absorption, refraction and dispersion become increasingly significant with long-range measurements. During conditions of haze and only light fog, the laser light is scattered from an increased number of aerosols within the measurement volume, enhancing the signal rate. Depending on the particle density, forward scattering along the optical path can cause a multitude of spurious detections, effectively lowering the measurement data rate while increasing the measurement uncertainty. Ultimately, during conditions of very heavy fog, measurement fails completely due to the intense scattering and absorption of the laser light. Contrastingly, rain affects the measurement only slightly, since raindrop signals can be easily filtered out due to the significantly different vertical  
Figure 1: A monostatic lidar generally introduces a large measurement uncertainty when operating over complex terrain. This is the result of calculating the horizontal velocity based on the difference of varying radial wind velocities measured at different locations and times within a single measurement cycle in which the measurement beam is tilted in different directions. velocity of the raindrops. The impact of atmospheric refraction is difficult to investigate in practice, but it was calculated to be negligible with respect to major factors influencing the total measurement uncertainty. For the signal bandwidths used with the PTB bistatic lidar, the impact of dispersion is expected to be practically negligible. PTB's high-resolution bistatic lidar facilitates non-invasive wind velocity measurements at a sampling rate of up to 10 Hz (depending on the weather conditions at the measurement height) and is fully traceable to the SI units based upon the set-up's geometry, laser wavelength, and the digital time base used for frequency measurements. The detailed evaluation of the measurement uncertainty is currently in progress. Preliminary results indicate that the measurement uncertainty amounts to less than 1% at sufficiently high rates of detected particles.
# Translation-Based Embeddings with Octonion for Knowledge Graph Completion
## 3 Methodology
### Preliminaries
#### 3.1.1 Background and Calculation Rules of Octonion
For any octonion \(a=\sum_{\omega=0}^{7}a_{\omega}e_{\omega}\) on octonion algebra \(\mathbb{O}\), where \(a_{\omega}\in\mathbb{R}\) and \(e_{0}=1\), \(e_{1}=i,e_{2}=j,e_{3}=k,e_{4}=l\), \(e_{5}=il,e_{6}=jl\), \(e_{7}=kl\), reference [17] proposes the following definitions and theorems:  
\[\mathbf{s}(a)=\left(a_{0},a_{1},a_{2},a_{3},a_{4},a_{5},a_{6},a_{7}\right)^{\top} \in\mathbb{R}^{8} \tag{1}\]  
\[\mathbf{m}(a)=\left[ \begin{array}{cccccccc}a_{0}&-a_{1}&-a_{2}&-a_{3}&-a_{4}&-a_ {5}&-a_{6}&-a_{7}\\ a_{1}&a_{0}&-a_{3}&a_{2}&-a_{5}&a_{4}&a_{7}&-a_{6}\\ a_{2}&a_{3}&a_{0}&-a_{1}&-a_{6}&-a_{7}&a_{4}&a_{5}\\ a_{3}&-a_{2}&a_{1}&a_{0}&-a_{7}&a_{6}&-a_{5}&a_{4}\\ a_{4}&a_{5}&a_{6}&a_{7}&a_{0}&-a_{1}&-a_{2}&-a_{3}\\ a_{5}&-a_{4}&a_{7}&-a_{6}&a_{1}&a_{0}&a_{3}&-a_{2}\\ a_{6}&-a_{7}&-a_{4}&a_{5}&a_{2}&-a_{3}&a_{0}&a_{1}\\ a_{7}&a_{6}&-a_{5}&-a_{4}&a_{3}&a_{2}&-a_{1}&a_{0}\end{array} \right]\in \mathbb{R}^{8\times 8} \tag{2}\]  
where \(\mathbf{s}(a)\) is the coordinate vector of \(a\), and \(\mathbf{m}(a)\) is the component matrix. According to the reference [17], the coordinate vector and component matrix of octonion satisfy the following Theorems 1-3:  
**Theorem 1**. : _For any \(u,v\in\mathbb{R}\) and \(a,b\in\mathbb{O}\),_  
\[\mathbf{s}(ua+vb)=u\mathbf{s}(a)+v\mathbf{s}(b)\]  
\[\mathbf{m}(ua+vb)=u\mathbf{m}(a)+v\mathbf{m}(b)\]  
**Theorem 2**. : _For any \(a\in\mathbb{O}\),_  
\[a=0\Leftrightarrow\mathbf{s}(a)=\mathbf{0}\Leftrightarrow|\mathbf{m}(a)|=0\Leftrightarrow\mathbf{ m}(a)=\mathbf{0}\]  
Figure 4: Multiplication rules of the unit octonion in tabular form. \[a\text{ is invertible} \Leftrightarrow \mathbf{m}(a)\text{ is invertible}\]  
**Theorem 3**. : _For any \(a,b\in\mathbb{O}\),_  
\[\mathbf{s}(ab) = \mathbf{m}(a)\mathbf{s}(b)\] \[\mathbf{m}(ab) = \mathbf{m}(a)\mathbf{m}(b)\]  
According to the above theorems, the multiplication between two octonions can be replaced by that between the component matrix and coordinate vector. For two octonions \(a,b\in\mathbb{O}\), their coordinate vectors are \(\mathbf{s}(a)\) and \(\mathbf{s}(b)\), respectively, and their product can also be represented by coordinate vector \(\mathbf{s}(ab)\). Since \(\mathbf{s}(ab)=\mathbf{m}(a)\mathbf{s}(b)\), only one of the coordinate vectors needs to be represented in the form of a component matrix, i.e., \(\mathbf{s}(a)\Rightarrow\mathbf{m}(a)\), and then through the multiplication between matrix \(\mathbf{m}(a)\) and vector \(\mathbf{s}(b)\), the coordinate vector of the product of two octonions \(a\) and \(b\) can be easily obtained, which can be converted into the traditional octonion form with imaginary parts at any time.
# A Survey from Real-Time to Near Real-Time Applications in Fog Computing Environments
## 2 Material and Methods
### Material
#### 2.1.1 Edge Computing  
In the last few years, the scientific community needed to define new concepts to embrace the new computation models that increased IoT worldwide scenery. Edge concepts are in this set of new computational paradigms. As they are the basis for several new applications, it is worth noticing to understand them clearly.  
Edge computing terminology does not have a single established definition. Currently, it is possible to find two biases that conceptualize edge and differentiate it, or not, from fog computing. (1) Edge computing is identified with users' devices and fog computing is an intermediate layer between edge and cloud computing; (2) Edge computing is considered a resource for communication, computing, control and storage, in close proximity to devices and end users. In this concept, there is no need to include the fog layer, since the edge and cloud layer perform all communication and processing [5].  
From the point of view of this article, the edge refers to the devices, sensors, or other data sources at the edge of the network. In the center of the network, there is a data center, a cloud, which processes all data that arrives there. The management layer that arranges data between cloud and edge is the fog substrate [6].  
Therefore, edge computing cannot be conceptualized as a data center or as a simple sensor that converts from analog to digital and collects/sends data [7]. It is possible to conceptualize edge computing as a layer composed of data providers (sensors) and mobile devices next to sensors/actuators with some processing capacity, such as smartphones, tablets, and PDAs [8].  
The layer between the end devices and the cloud, also called the edge layer, could be implemented in different ways in terms of some variations: the devices which serve as the intermediate edge nodes, the networks, and communication protocols used by the edge layer as well as the services delivered by the edge layer [9]. Based on this context, the paper shows that three different implementations of Edge Computing can be employed: Fog Computing, Mobile Edge Computing and Cloudlet. The first one deals with leveraging M2M devices like gateways and wireless routers through Fog Computing Nodes. The second one, Mobile Edge Computing, suggests using cellular networks as base stations for intermediate nodes with storage and processing capabilities. In the last one, Cloudlet, the authors defend the use of dedicated devices with capacities similar to a data center but on a lower scale existing in consumers' surroundings.  
Finally, it is essential to highlight that Fog computing is a distributed approach that comes down from its Edge computing nature and derives from the need to deal with the Cloud computing centralized approach [10].
## 2 Experimental Details
### Characterization and Electrochemical Test  
The distribution of particle size of the Si and Si/C composite powder synthesized by HEMM and carbonization was determined by laser diffraction particle size analyzer SALD-2300 (Shimadzu Corp., Kyoto, Japan). Crystallinity of the powders was studied by X-ray diffractometer (Mini flex 600, rigaku) with Cu-K\(\alpha\) radiation (\(\lambda\) = 0.15418 nm) operated at 600 W over the 20 range of 10-70\({}^{\circ}\). The morphology and microstructures of the as-milled powders and the cycled electrodes were examined by field-emission scanning electron microscopy (FE-SEM; MIRA3, TECSA, Brno-Kohoutovice, Czech Republic) and transmission electron microscopy (TEM; JEM-ARM200F, JEOL Ltd., Tokyo, Japan).  
For the evaluation of electrochemical characteristics, the composite electrodes were prepared by coating on the copper (Cu foil, Wellcos Co., Ltd., Gunpo-si, Gyeonggi-do, Korea) substrate using a blade coater with slurry consisted of 80 wt % active material, 10 wt % of SFG6 graphite (Sigma-Aldrich, Yonggin-si, Gyeonggi-do, Korea) and 2 wt % of Ketjenblack (Sigma-Aldrich) as conductive agents, and 8 wt % of poly acrylic acid (PAA, Mw = 250,000, Sigma-Aldrich) as binder materials, which were dissolved in DI water. The screen-printed electrodes were dried at 100 \({}^{\circ}\)C for 10 min. After drying, they were cut into 16-mm diameter discs and subsequently, heat-treated at 100 \({}^{\circ}\)C for 6 h under vacuum. The electrochemical test was performed using coin cells (half-cells). The CR-2032 coin-type cells were assembled in a high-purity argon (99.99%) filled glove box. Lithium foil was used as both the counter and reference electrodes. The Celgard 2400 was used as the separator. A solution of one molar lithium hexafluorophosphate (LiPF\({}_{6}\), PANAX ETEC Co. Ltd, Busan, Korea, purity: 99.9%) dissolved in a solvent mixture of ethylene carbonate (EC)/diethyl carbonate (DEC)/fluoroethylene carbonate (FEC) (5:70:25 by volume) was used as electrolyte. Cyclic voltammetry test for the cells was performed using ZIVE MP1 potentiostat analyzer system (ZIVE MP1, WonATech Corp., Seoul, Korea) with a cut-off potential ranges of 0.01-1.5 V (at constant current density of 2.8 mA cm\({}^{-2}\)) at 0.1 and 0.2 C-rate for the first and second cycles, respectively, and 1.0 C-rate from the third cycle. All the electrochemical tests were conducted at room temperature. The EIS (electrochemical impedance spectroscopy) measurements were carried out for Si/C nanocomposite powder with an impedance analyzer (ZIVE MP1, WonATech Corp., Seoul, Korea) over a frequency range from 100 kHz to 10 mHz.
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 4 Simulation Study
### Test Results of the ERSR  
The purpose of the simulations presented in this section is to investigate the effects of different number of forwarding on the \(ERSR\) of these protocols. The experimental set-up is the same as the previous one. Figure 9 illustrates the results of the \(ERSR\) versus the number of hops. As shown in the figure, the metric of CLA is better than that of VBF and much better than that of MFLOOD. The reason is that CLA always select the path most likely to  
Figure 8: Packet delivery fraction of different hops.  
Figure 7: Test results of the Packet Latency. (**a**) Data Latency when need One hop. (**b**) Data Latency when need Two hop. (**c**) Data Latency when need Three hop.  
succeed in transmission. Smaller probability of packet loss over the entire transmission link. \(ERSR\) of CLA is much closer to 1.00 than other methods. Especially, since the MFLOOD forwards packets in a blind approach. Many unrelated network nodes are involved in forwarding packets, and most of these packets eventually fail to reach the destination node and are dropped. Thus, there are a lot of packets are sent in the network, and the \(ERSR\) of MFLOOD is the lowest of these three methods.
# Study of Basalt Fibers and Graphene Enriched Polymers on Bond Behavior of FRP Bars in Concrete
###### Abstract  
In this work is investigated the bond behavior of FRP bars considering basalt as a fiber and also cases where graphene is introduced as a polymer filler. Standard FRP bars where glass is used as fiber is tested under the same conditions in order to have a benchmark. Three different temperatures are considered in the tests, including room temperature and a temperature higher than the glass transition of the polymer. At room temperature the effect of concrete strength and the bar diameter on bond is also investigated. The study developed is experimental, through pull-out tests, as well as numerical, through simulations by the Finite Element method. Cases where basalt fibers are associated to helically-wounded surface treatment showed at least 41.7% superior bond behavior among all cases considered with this fiber at room temperature. Studies with graphene were exploratory and limited to only one percentage of addition. However, results suggest that the addition is able to improve behavior of the polyester resin of the matrix at room temperatures by 49% and at higher temperatures bond degradation was 44% smaller when compared with the literature. It was not observed a statistically significant influence of the concrete strength on bond. Diameter influence on bond was mostly linked to surface treatment.  
Keywords:concrete; FRP; basalt; bond; graphene +
Footnote †: journal: Applied Sciences
# DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images
## 5 Conclusions  
In this paper, a DBMF method was developed to improve the feature extraction process for tree species classification. The proposed method effectively combines high-resolution spatial information with spectral information. The spectral information extraction branch employs Bi-LSTM and a triple-attention mechanism; the spatial information extraction branch uses the sandglass module and a 3D-CNN based on the connection between BN and the fusion activation function. The features extracted from the two branches were activated by the fusion function to prevent overfitting. The statistical performance of the proposed method on datasets for six dominant tree species captured by the HJ-1A and Sentinel-2 satellites was found to be better than that of other state-of-the-art methods. As such, the proposed DBMF is suitable for forestry remote sensing application technology.  
This study demonstrates the potential of the proposed DBMF model as a powerful classifier for complex landscapes, such as for tree species classification. In the future, we will apply the DBMF framework to other HS datasets, develop a more efficient framework and feature selection processes, and use a wider range of training datasets to extend the model to the complete inventory process.  
[PERSON] proposed the algorithm and performed the experiment. [PERSON] supervised the study, analyzed the results, and provided insightful suggestions for the manuscript. [PERSON] drafted the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Fundamental Research Funds for the Central Universities (No. 2572017 PZ10), Basic Scientific Research Project of Heilongjiang Provincial Universities (No. 145109219). All the works were conducted at Forestry Intelligent Equipment Engineering Research Center.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to still being used in a proceeding project.  
The authors declare no conflict of interest.
## 4 Cellular Network Reconstruction and Validation  
The implemented database allows for the crowdsourced collection of a large amount of information from several sources to derive deep knowledge about the global cellular network, irrespective of the specific technology involved.  
In our scenario, where radio cellular signals are exploited for estimating the position of a device, the measurements acquired by the latter are the only source of information that can be used to extract knowledge about the network. Since information may be easily incomplete or affected by errors, we have been forced to relax some logical constraints (e.g., not null) to deal with missing data. Subsequent analyses have also been designed by taking such issues into account.  
As already mentioned, several projects have started worldwide with the objective of collecting spatio-temporal information about cellular networks in a collaborative form. Among the public ones, we considered _OpenCellID_, due to its size and popularity. In addition, we also obtained a proprietary anonymized dataset thanks to the contribution of the company _u-blox_. Both datasets are collections of measurements, i.e., recordings pertaining to the detection of a serving or neighbor cell (that, in our schema, correspond to table Observation and table Neighbour, respectively).  
In the following, after a short introduction of the two datasets (Section 4.1), we move on to the population of the database, focusing on two different aspects: the validation of the measurements (Section 4.2) and the automatic generation of network information (Section 4.3).
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 5 Simulation Performance and Analysis
### Satellite Network Traffic Forecasting  
After obtaining the IMFs and residuals of the satellite network traffic through EMD, ARIMA is utilized to forecast IMFs. The residuals are forecasted using the IGWO-BPNN model.
# A Watermarking Protocol Based on Blockchain
## 5 Watermarking Protocol
### Identification and Arbitration Protocol  
The protocol is run by \(\mathcal{CP}\) to identify the responsible distributor of a pirated copy of \(\bar{X}\), who was the legitimate copyright owner of \(\bar{X}\), with undeniable evidence [4; 5]. As shown in Table 3, the first step of the protocol consists of extracting the watermark \(W^{\prime}\) from the pirated copy of \(\bar{X}\), denoted as \(X^{\prime}\). After the extraction of \(W^{\prime}=W^{\prime}_{\mathcal{CP}}\|N^{\prime}\), \(\mathcal{CP}\) can access its databases and use \(W^{\prime}_{\mathcal{CP}}\) to search them for a match. If a possible match is found [11], \(\mathcal{CP}\) can retrieve the tokens saved during the purchase transaction of \(\bar{X}\), which are \(X_{d}\), \(T_{X}\), \(pk_{\mathcal{RA}}^{X}\), \(\mathbb{E}_{pk_{\mathcal{RA}}^{X}}(N)\), \(\mathbb{S}_{\mathcal{RA}}(pk_{\mathcal{RA}}^{X},\mathbb{E}_{pk_{\mathcal{RA} }^{X}}(N))\), and \(E_{pk_{\mathcal{RA}}}(B_{id},pk_{\mathcal{RA}}^{X},\mathbb{E}_{pk_{\mathcal{RA} }^{X}}(N))\). Then, \(\mathcal{CP}\) can send the tokens, together with \(W^{\prime}\), to \(\mathcal{J}\) in the message \(m_{1}\). \(\mathcal{J}\) receives \(m_{1}\) and verifies the signature \(\mathbb{S}_{\mathcal{RA}}(pk_{\mathcal{RA}}^{X},\mathbb{E}_{pk_{\mathcal{RA} }^{X}}(N))\). Then, it searches the blockchain \(\mathcal{BC}\) for a node using \(pk_{\mathcal{RA}}^{X}\) and \(\mathbb{E}_{pk_{\mathcal{RA}}^{X}}(N)\) as search keys. If a node is found, \(\mathcal{J}\) can access the tokens published by the node, which are reported in Table 2, and compare them with those one received by \(\mathcal{CP}\). If all the tokens match, \(\mathcal{J}\) can send \(pk_{\mathcal{RA}}^{X}\), \(\mathbb{E}_{pk_{\mathcal{RA}}^{X}}(N)\), and \(E_{pk_{\mathcal{RA}}}(B_{id},pk_{\mathcal{RA}}^{X},\mathbb{E}_{pk_{\mathcal{RA} }^{X}}(N))\) to \(\mathcal{RA}\) in the message \(m_{2}\). \(\mathcal{RA}\) decrypts \(E_{pk_{\mathcal{RA}}}(B_{id},pk_{\mathcal{RA}}^{X},\mathbb{E}_{pk_{\mathcal{RA }}^{X}}(N))\) and verifies the received tokens. If all data are correct, \(\mathcal{RA}\) decrypts \(\mathbb{E}_{pk_{\mathcal{RA}}^{X}}(N)\) and sends \(B_{id}\) and \(N\) to \(\mathcal{J}\) in the message \(m_{3}\). Upon receiving \(m_{3}\), \(\mathcal{J}\) compares \(N^{\prime}\) and \(N\). If \(N^{\prime}=N\), the identity of the buyer \(B_{id}\) is revealed, and \(\mathcal{J}\) can adjudicate him/her to be a traitor, thus closing the case. Otherwise, the protocol ends without exposing any identity.
## 4 Robust Learning and Outliers  
In statistics, outliers are observations that deviate dramatically from other data points. Outliers can result from measurement error, rounding error, human error, long-tailed noise distribution, etc. Outliers are a general feature of several realistic datasets; their likelihood in raw data varies between 1-10% [35; 36]. However, it is hard to identify outliers even though there are many of them in actual data, and it is not easy to detect such multiple outliers; moreover, it is sometimes impossible to tell whether a point is an outlier or not. Furthermore, defining outliers in multivariate or methodological data can be difficult or even impossible, and testing for such various deviations is fully involved and sometimes leads to extensive computations. There is usually no need to disregard these observations when implementing a data-driven model, only to reduce their impact on the design parameters.  
Training DNNs with the traditional backpropagation learning algorithm minimizes the training data's traditional performance measurement function (MSE loss function). However, this conventional backpropagation learning algorithm is sensitive to outliers that can distort the training data. It can be considered optimal only for noise-free data or for data corrupted by errors from the Gauss distribution when the mean is zero [35; 36]. When gross errors or outliers contaminate the data, it leads to an unreliable method. To overcome the problem of outliers, several robust statistical estimators that are not severely affected by deviations from observations, such as M-estimators [20; 21; 23; 24; 26; 29; 31], R-estimators, L-estimators, and LTS [30], and LMedS [37], have been used in recent research papers. In this paper, we address the robust training of DNN based on using M-estimators as robust loss functions to substitute the conventional MSE loss function, which loses robustness in the presence of outliers in the V2X dataset.
## 3 Vibration Signal Preprocessing and Fault Feature Extraction
### Method Verification Experiments
Furthermore, compare the envelope spectra in Figures 4, 8 and 10; it can be found that the reconstructed signal through GWO-VMD had the closest amplitudes to \(s(t)\) at the emulated fault feature frequency and its 2-fold and 3-fold frequencies (20 Hz, 40 Hz, and 60 Hz). This indicates that the GWO-VMD-based method can recover fault feature frequencies from noise better than basic VMD can.
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 3 Methodology
### Dataset Selection
#### 3.1.4 Dataset Format  
The dataset is composed of three Excel files. The first file contains thirteen features extracted from raw GPS signals recorded during UAV missions. Each feature column is divided into eight sub-columns representing the values of the specified feature for each channel at the same time. The second file comprises 158,174 GPS signal samples, including simulated attacks. It contains thirteen features along with a target variable (output) that identifies the type of signal. Genuine signals are labeled as 0, while the spoofed are classified as 1, 2, and 3, corresponding to simplistic, intermediate, and sophisticated attacks, respectively. Each feature in this file also follows the eight-channel structure, resulting in a three-dimensional data format. Finally, a simplified 2D version of the dataset is also available, which is a flattened conversion of the original 3D structure. In this version, each row corresponds to a GPS signal from a single channel rather than aggregating signals across all eight channels in one row. However, rows having zero values indicating unlocked channels that are not actively tracking any satellite are excluded from the dataset, resulting in a total of 510,530 GPS signal samples.