esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 3 Experimental Setup and Results
### Results
#### 3.3.3 Application of the Learned Representation  
An extrinsic way to assess the quality of a representation is to impose a task: in this case, classifying candidate light curves as true exoplanets (_Confirmed_) or another variability (_False Positive_).  
Table 4 presents the results of this binary classification. Here, we compare different representations, including the auto-encoders and supervised methods from the global-folded light curve shape (with \(T=300\) bins). The \"1D CNN\" is a supervised method that extracts features based on convolutions, while \"RNN\({}_{t}\)\" does it based on recurrence, both with the classification layer mentioned in Section 3.2.3 on top. The 1D CNN network architecture is inspired by [6], until the third convolutional block using only the global view on this KOI dataset (a subset of the TCE). While the RNN\({}_{t}\) architecture is the recurrent encoder of RAE\({}_{t}\)/VRAE\({}_{t}\) without the decoder phase.  
In Table 4, it can be seen that the representation of the variational extension (VRAE\({}_{t}\)) gives an improvement on all the classification metrics with respect to the RAE\({}_{t}\) representation, as well as being better than the other unsupervised methods. This can be explained due to the generalization capacity of the implemented latent variables within the variational model. Furthermore, the results of the S-VRAE\({}_{t}\) suggest that the method succeeds in using the Flux standard deviation of the light curve, as it is able to codify better patterns in the representation to distinguish in the classification task. Indeed, the S-VRAE\({}_{t}\) representation has the best classification performance among unsupervised techniques on all the metrics, being quite close to the supervised ones. This indicates that, without access to labels, we are obtaining a representation almost as good as the supervised method on that task and at the same time, useful in other factors.
## 3 Overview of OCC
### Performance Evaluation Metrics  
In an OCC based Li-Fi system, the number of frames required to represent a bit is the crucial factor in determining the modulation techniques' efficiency. In a typical OCC based Li-Fi system using OOK modulation, the encoded bits are observed as stripes in the image at the receiver side. Thus, the more the stripes in the image, the more bits can be encoded. However, the width of these stripes is proportional to the LED transmitter's symbol rate. Thus, for a higher symbol rate, the corresponding bit rate will be higher, which results in an increase in number of bits per second. Each bit is represented with the smaller duration of on-off cycles causing the stripe width to become thinner. However, due to the pixel bleeding effect, the image becomes blurred and the width of the stripes becomes difficult to decode. On the other hand, if the stripe width is wider, the number of bits recovered could be less. Hence, there must be a balance between bit representation, modulation technique, image processing, and size of transmitter.  
In addition to Signal-to-Noise Ratio(SNR) and Bit Error Ratio (BER), frames-per-bit [7] metric will also be used to evaluate the performance of the proposed modulation technique. SNR under various ambient lighting conditions and distances will be recorded. To measure SNR the ratio of overall pixel intensity of the image before transmission and during transmission will be estimated.
## 2 Methodology
### Orbital Configuration and Sensor Type
#### 2.2.2 Synthetic Aperture RADAR  
The second hypothetical sensor is a Ku/Ka dual-band SAR similar to the Terrestrial Snow Mass Mission (TSMM) that is currently under consideration by the Canadian Space Agency (CSA) [20]. A Ku/Ka dual-band SAR is expected to have a better response to snow mass than other existing spaceborne SARs. The third evaluated sensor is a Sentinel-1-like C-band SAR [46]. It represents an existing SAR instrument that is currently used for snow mass detection even though the scattering characteristics of C-band radiation in dry or shallow snow can be of limited value [26]. However, given that Sentinel-1 is currently operational now and into the future, it is considered here as a viable snow information source that should be included in this current study. Two C-band SAR instruments are included here to mimic the Sentinel-1 A/B constellation. The two C-band SARs share the same orbital plane but with a 180\({}^{\circ}\) phase difference.
## 3 Sample  
The single crystal copper sample under investigation was grown vertically upward in a unidirectional solidification experiment. The solidification reaction occurred in a cylindrical graphite crucible of 10 mm inner diameter and 180 mm long, which was positioned insidea high-purity Al\({}_{2}\)O\({}_{3}\) tube of 16 mm inner diameter. This graphite crucible-alumina tube assembly was inserted through the cylindrical cavity of a vertical electric tube furnace. The lower end of the alumina tube was closed, and the upper end was O-ring sealed to a head, which allowed vacuum/atmosphere control so that the solidification reaction occurred in an inert atmosphere, filled with either argon or nitrogen. The solidification experiment was conducted with the electric furnace set at 1300\({}^{\circ}\)C. A hollow water-cooled cold finger, which was vertically attached to the bottom of the furnace, served to have a positive temperature gradient established upward. The furnace-cooling assembly was supported by a carriage, which moved upward on three ball bushing guided rods driven from below by two symmetrically placed ball screw rods, which were turned by a synchronous stepper motor using a belt drive. The rate of motion was computer controlled.  
In an effort to have control over nucleation events, the cavity of the graphite crucible was made to have a cone-shaped bottom with the opening angle of 30\({}^{\circ}\). The initial position of the furnace-cooling assembly relative to the graphite crucible-alumina tube assembly was determined such that the copper in the crucible was all in the liquid state. The molten copper in the graphite crucible was then solidified vertically in keeping with the motion of the furnace-cooling assembly driven upward at a controlled speed, 10\(\upmu\)m/s for the sample in question. Upon completion of the experiment, specimens were taken from the directionally grown sample for metallographic examination. For the present sample, the cross section normal to the growth direction was examined by optical microscope using specimens taken at both ends of the cylindrical part. No grain boundaries were visible in them. The sample was then selected as a single crystal sample to be analysed by using monochromatic neutron imaging methods. Its size was 9 mm diameter, 97 mm axial length.
# Impacts of Heat and Drought on Gross Primary Productivity in China
## 1 Introduction
[PERSON] et al. [15] studied the combination of heat and drought and found that compared with any single-factor extreme, combined heat and drought events led to the strongest observed carbon sink reduction. At present, researchers often use two kinds of methods to study the impacts of extreme climate events on GPP. One method is to compare and analyze GPP values obtained before and after the occurrence of extreme climate events [32; 40; 41], and the other method is to first extract the extreme climate events and extreme GPPs and then study the relationship between them through correlation analysis [2; 14; 15]. Very few studies have utilized copula functions to analyze the impacts of extreme climate events on vegetation GPP. The copula is a probability model that represents a multivariate uniform distribution, and it can be used to examine the association or dependence between many variables [42; 43]. It is widely used in statistics, finance, risk management, and other fields [44; 45; 46]. Therefore, based on climate (temperature and precipitation) data, GPP data from 1982 to 2017, and China's vegetation zoning vector boundary data, this study analyzed the trends of GPP, temperature, and the drought index (standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI)) in China over the past 36 years and estimated the probabilities of extreme negative GPP anomalies under different heat and drought conditions by using multidimensional copula functions. This study contributes to the understanding of the possible distribution of GPP under specific climatic conditions, to making appropriate agricultural production decisions, and to understanding the biochemical dynamics of the global ecosystem carbon cycle.
# A Multiscale Approach to Geomagnetic Storm Morphology Analysis Based on DMA Activity Measures
## 3 Results and Discussion
### Discussion
The solar wind data time series (especially the flux speed) contain a great amount of small-scale variations that are displayed as separate short-period zones (Figure 4d,e). For this storm, we display a comparison between the activity measure based on the \"Energy\" indicator (Figure 8b) and the one based on the \"Length\" indicator (Figure 8c) for the proton density data  
Figure 7: The \(B_{z}\) IMF component **(a)** and the \(X\) magnetic field component from the HON observatory **(c)** during a geomagnetic storm of December 2006. Below each component is the plot of its activity measure **(b,d)** based on the “Energy” indicator. to demonstrate the principal difference between these indicators. At the same \(\Delta\) and \(q\) parameters, the measure peaks look more \"combined\" on the \"Length\" plot as if they were parts of one long-period disturbance in the time series, whereas on the \"Energy\" plot, the separate peaks are emphasized and the difference in their energy is more visible. For Storm 4 from our selection, the one without a SSC prior to the storm onset, as seen in Figure 9 for the \(X\) geomagnetic component analysis, generally, the same fragments of extreme dynamics (the main storm phase and local disturbances in the recovery phase) were detected by the \"Energy\" and \"Length\" activity measures as for the previous storms. Abrupt increases and decreases, like in previous cases, are clearly seen as vertical positive (red) or negative zones. Therefore, the activity measure algorithm detects similar physical processes in the magnetosphere and confirms its stable work. Finally, as a quantitative assessment of the activity for geomagnetic data, a histogram was built, depicting the amount of the measure levels within the interval \([-1,\,1]\). The histograms were built for the \"Energy\" and \"Length\" indicators applied to the \(X\) geomagnetic component at various \(\Delta\) and \(q\) levels for the geomagnetic storm on 22-29 June 2015. The results show that, both for the \"Energy\" and \"Length\" indicators, most of the measure values are concentrated within the interval \([-1,\,0]\). This agrees with the common storm-related dynamics seen on the horizontal geomagnetic field components--abrupt decreases related to the storm onset and main phase, and slow increases on the storm recovery phase. Figure 10 displays an example for such histograms for \"Energy\" and \"Length\" indicators at \(\Delta=1000\) and \(q=1\). Note that the \"Energy\" maximal occurrence (Figure 10a) corresponds to \([-1,\,-0.5]\), whereas the \"Length\" maximal occurrence (Figure 10b) corresponds to \([-0.5,0.0]\), which, in our opinion, means that there are generally fewer extreme and dynamically \"jagged\" segments than the extreme and dynamically \"energetic\" ones. Figure 8: The flux speed time series during a storm on 22–29 June 2015 (**a**) and the activity measure plots for this time series based on indicators: “Energy” (**b**), and “Length” (**c**).
## 3 Factors Influencing the Genetic Transformation of Cucumber  
_A. tumefaciens_ achieves genetic transformation by transferring the T-DNA containing the gene of interest into host cells. In this technique, _Agrobacteriumium_-plant interaction occurs when the bacteria are exposed to a large number of plant-derived chemicals, which include the routinely secreted chemicals, such as organic acids (pH 5.0-5.8) and other secondary metabolites, and the wound-releasing chemicals, such as phenolic compounds [5]. Subsequently, a series of related genes are expressed to direct the excision of T-DNA from the Ti plasmid, transfer to the host cell, integrate into the host genome, and be expressed in the host cell [5]. During _Agrobacteriumium_-mediated transformation, the transformation frequency is determined by several major factors including pre-culture and co-culture periods, _Agrobacteriumium_ strain, acetosyringone (AS) concentration, bacterial cell density, duration of exposure to _Agrobacteriumium_, and selection system used.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variety** & **Explant Type** & **Treatment** & **Regeneration Frequency (\%)** & **Reference** \\ \hline
14–111 & Colybdenum & 3.0 mg\(\cdot\)L\({}^{-1}\) 6-BA + 2.0 mg\(\cdot\)L\({}^{-1}\) ABA + 1.0 mg\(\cdot\)L\({}^{-1}\) AgNO\({}_{3}\) & 86.7\% & [38] \\
9330 & Colybdenum & 1.5 mg\(\cdot\)L\({}^{-1}\) 6-BA + 1.0 mg\(\cdot\)L\({}^{-1}\) AgA & 96.7\% & [18] \\ Xintai mici & Colybdenum & 1.5 mg\(\cdot\)L\({}^{-1}\) 6-BA + 0.5 mg\(\cdot\)L\({}^{-1}\) ABA + 2.0 mg\(\cdot\)L\({}^{-1}\) AgNO\({}_{3}\) & 86.1\% & [21] \\ Chuanly No. 2 & Colybdenum & 1.5 mg\(\cdot\)L\({}^{-1}\) 6-BA + 0.5 mg\(\cdot\)L\({}^{-1}\) ABA & 96.0\% & [44] \\  & Colybdenum & 3.0 mg\(\cdot\)L\({}^{-1}\) 6-BA + 0.5 mg\(\cdot\)L\({}^{-1}\) IAA & 70.0\% & [11] \\ Jinyan No. 4 & Colybdenum nodes & 3.0 mg\(\cdot\)L\({}^{-1}\) 6-BA + 1.5 mg\(\cdot\)L\({}^{-1}\) ABA + 1.5 mg\(\cdot\)L\({}^{-1}\) AgNO\({}_{3}\) & 90.0\% & [22] \\ SS2 & Colybdenum nodes & 3.0 mg\(\cdot\)L\({}^{-1}\) 6-BA + 1.5 mg\(\cdot\)L\({}^{-1}\) ABA + 1.5 mg\(\cdot\)L\({}^{-1}\) AgNO\({}_{3}\) & 90.0\% & [30] \\
9330 & Colybdenum nodes & 2.0 mg\(\cdot\)L\({}^{-1}\) 6-BA + 1.0 mg\(\cdot\)L\({}^{-1}\) ABA + 2.0 mg\(\cdot\)L\({}^{-1}\) AgNO\({}_{3}\) & 90.0\% & [25] \\ Xintai mici & Colybdenum nodes & 0.5 mg\(\cdot\)L\({}^{-1}\) 6-BA + 1.0 mg\(\cdot\)L\({}^{-1}\) ABA & 82.7\% & [28] \\ Xintai mici & Colybdenum nodes & 1.5 mg\(\cdot\)L\({}^{-1}\) 6-BA & 94.3\% & [45] \\ Green Long & Colybdenum nodes & 1.5 mg\(\cdot\)L\({}^{-1}\) 6-BA + 4.5 mg\(\cdot\)L\({}^{-1}\) AgNO\({}_{3}\) & 100.0\% & [10] \\ Gergana & Hypocotyl & 1.0 mg\(\cdot\)L\({}^{-1}\) BA & 93.3\% & [14] \\ Poinsett 76 & Embryonal axis & 4.44 uM 6-BA + 1.59 uM NAA & 82.0\% & [12] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Application of plant hormones and AgNO\({}_{3}\) in the explant bud induction stage of different varieties of cucumber.
###### Abstract  
Future 6G networks are expected to utilize Massive Multiple-Input Multiple-Output (M-MIMO) and follow a user-centric cell-free (UCCF) architecture. In a UCCF M-MIMO network, the user can be potentially served by multiple surrounding Radio Units (RUs) and Distributed Units (DUs) controlled and coordinated by a single virtualized Centralized Unit (CU). Moreover, in an M-MIMO network, each transmit frontend is equipped with a Power Amplifier (PA), typically with nonlinear characteristics, that can have a significant impact on the throughput achieved by network users. This work evaluates a UCCF M-MIMO network within an advanced system-level simulator considering multicarrier transmission, using Orthogonal Frequency-Division Multiplexing (OFDM), realistic signal-processing steps, e.g., per resource block scheduling, and a nonlinear radio frontend. Moreover, both idealistic independent and identically distributed (i.i.d.) Rayleigh and 3D ray-tracing-based radio channels are evaluated. The results show that under the realistic radio channel, the novel user-centric network architecture can lead to an almost uniform distribution of user throughput and improve the rate of the users characterized by the worst radio conditions by over 3 times in comparison to a classical, network-centric design. At the same time, the nonlinear characteristics of the PA can cause significant degradation of the UCCF M-MIMO network's performance when operating close to its saturation power.  
massive MIMO; user-centric cell-free networks; OFDM; 3D ray tracing; nonlinear distortion
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 9 BC and Cyberattack Prevention
### BC in the Literature
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**BC Type** & **Ref.** & **Security Threat** & **Study Highlights** \\ \hline \multirow{8}{*}{Private} & [120] & DDOS and Linking attacks & Relies only on AES symmetric encryption for data integrity \\ \cline{2-4}  & [138] & Data Tampering & BC-based trust model and node authentication using smart contracts to reduce latency. \\ \cline{2-4}  & [106] & Data Tampering & Performance evaluation of the computational complexity associated with the ledger. \\ \cline{2-4}  & [147] & Internal attacks & Three-phase sequential detection using sensor node hash values, node signatures, and voting degree \\ \cline{2-4}  & [146] & Worm attack & Relies on IOTA Tangle \\ \cline{2-4}  & [142] & Sybil attack & Relies on IOTA Tangle \\ \hline \multirow{8}{*}{Consortium} & [136] & Internal attacks & Utilizes a smart contract in conjunction with BC \\ \cline{2-4}  & [109] & Sybil, MITM, DoS, Message Substitution, and Replay attacks & Identity management and secure authentication mechanism \\ \cline{2-4}  & [139] & Internal attacks & Employs HECC to generate public and private keys \\ \cline{2-4}  & [145] & DoS and Sybil attacks & Employs dual public and private BCs which implement a PoW and PoA consensus, respectively, for authentication of each BC. \\ \hline \multirow{8}{*}{N/A} & [140] & Greyhole and blackhole internal attacks & Test-bed experiments using a data-driven trust mechanism to reduce network transaction throughput. \\ \cline{2-4}  & [52] & Physical or logical data tampering & Limits the size of the BC and uses a time window with a circular buffer mechanism to reduce the BC’s length. \\ \cline{2-4}  & [149] & Attacks on the localization process & Uses both behaviour and data trust values to determine the reliability level. \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Existing research on BC-based WSN security (N/A means not available).
## 2 Methodology
### Link Budget Analysis
#### 2.6.1 Receiver Sensitivity  
In the LoRa module selected for the system architecture, the SX1262 [35] transmitter's receiver sensitivity, S, can be calculated to attain the minimum signal level. Equation (2) calculates the minimum signal level to achieve target performance based on bandwidth [40].  
\[S=-174+10\log(BW)+NF+SNR_{\text{lim }it} \tag{2}\]  
where \(BW\) is the bandwidth in Hz, \(NF\) is the noise figure gain and \(SNR_{\text{lim }it}\) is the limiting signal-to-noise ratio.
## 4 Results and Discussion
### Simulation Experiment Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **MoM** & **ZlogZ** & **PSO-MFoM** & **PSO-ZlogZ** & **PSO-MLE** & **PSO-MLE** \\
**Estimation Methods** & **(2 nd/4 th-Order)** & **(1 st-Order)** & **(0.5 th/1 st-Order)** & **(0.25 th-Order)** & **(1D)** & **(2D)** \\ \hline Time/s & \(2.43\times 10^{-2}\) & \(8.94\times 10^{-3}\) & \(3.02\times 10^{-2}\) & \(3.66\times 10^{-2}\) & \(8.51\times 10^{-2}\) & \(9.37\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Estimate time of each parameter estimation method. Figure 8: Comparison of spatial search in different dimensions of the maximum likelihood function. the results of this experiment, where the specific calculation of the Cramer-Rao bound (CRB) can be found in the literature [38], and the values of each parameter node in the figure are obtained from the calculation of 30 independent replications of the experiment. Observing Figure 9a,b, it can be found that the parameter estimation results obtained using PSO for the one-dimensional likelihood function are unstable, exhibiting significant fluctuations with errors oscillating beyond 0.1. Conversely, the results obtained from the two-dimensional likelihood function space search show more stable parameter estimation performance, with the RRMSE smaller than 0.1, and it approaches the CRB lower bound, which is consistent with the previous analysis. The parameter estimation performance of the maximum likelihood estimation method is not limited by the range of the shape parameters, while other parameter estimation methods are to some extent limited by the range of the shape parameters, i.e., accurate estimation of the shape parameters can be achieved only within the specified range. When the shape parameter is small (corresponding to the heavy trailing phenomenon of sea clutter), the estimation of higher order moments (positive 2 nd/4 th- and positive 1 st-order logarithmic moments in Figure 9) is affected by the cumulative error of the sample, which leads to the poor performance of its parameter estimation. In addition, the estimation performance of all parameter estimation methods decreases with the increase in shape parameters.
## 4 Performance Evaluation  
In this section, the performance of R-Curve, Arrow-Curve, and the previously proposed H-Curve is applied and compared using MATLAB simulation [35] and hardware prototype using the Node MCU hardware tool. This section is organized as follows: firstly, the performance parameters used to measure the efficiency of each model in simulation and hardware are defined; in the second section, performance evaluation using MATLAB simulation is presented; in the third section, performance evaluation using Node MCU is presented; in the fourth section, the results of simulation and hardware evaluations are compared and, at last, replacing fixed anchors rather than one mobile anchor is studied.