esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Sorghum Yield  
The CNTR parcel returned a yield of 5818 kg ha\({}^{-1}\). The two NZ returned 6032 kg ha\({}^{-1}\) each, while 7 CZ parcel gave 6627 kg ha\({}^{-1}\). Compared with the CNTR, the three zeolitite-treated parcels gained from 3.7 to 13.9%, although the yield results are not statistically significant.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**TN \(\boldsymbol{\delta^{15}\)N vs air**}} & \multicolumn{2}{c}{**Fix \(\boldsymbol{\delta^{15}\)N vs air**}} \\ \hline
**Time** & **Treatment** & **\%\({}_{\infty}\)** & **\%\({}_{\infty}\)** \\ \hline Nov-2012 & CNTR & 3.86 (0.15) A & 6.00 (0.17) B \\  & 5 NZ & 3.83 (0.22) A & 5.28 (0.45) AB \\  & 15 NZ & 3.38 (0.19) A & 4.55 (0.31) A \\  & 7 CZ & 7.46 (0.53) B & 8.27 (0.36) C \\ \hline May-2013 & CNTR & 3.75 (0.20) AB & 3.34 (0.15) A \\  & 5 NZ & 3.34 (0.06) A & 3.38 (0.30) A \\  & 15 NZ & 3.31 (0.62) A & 3.84 (0.04) A \\  & 7 CZ & 4.87 (0.17) B & 3.11 (0.12) A \\ \hline Jun-2013 & CNTR & 3.11 (0.28) A & 3.65 (0.43) A \\  & 5 NZ & 3.37 (0.10) A & 3.38 (0.17) A \\  & 15 NZ & 3.32 (0.05) A & 3.46 (0.13) A \\  & 7 CZ & 3.27 (0.17) A & 3.73 (0.19) A \\ \hline Sep-2013 Bulk & CNTR & 2.68 (0.11) A & 1.37 (0.11) A \\  & 5 NZ & 2.39 (0.09) A & 1.83 (0.23) AB \\  & 15 NZ & 2.73 (0.14) A & 1.79 (0.03) AB \\  & 7 CZ & 2.55 (0.29) A & 2.09 (0.16) B \\ \hline Sep-2013 Rhizo & CNTR & 4.22 (0.38) A & 2.47 (0.26) A \\  & 5 NZ & 4.17 (0.31) A & 2.81 (0.75) A \\  & 15 NZ & 4.55 (1.11) A & 2.40 (0.46) A \\  & 7 CZ & 4.45 (0.19) A & 2.52 (0.31) A \\ \hline NEZ & & 18.4 (0.5) & 17.3 (0.4) \\ \hline Urea & & \(-\)1.18 (0.06) & - \\ \hline Diammonium & & \(-\)0.89 (0.09) & - \\ Phosphate & & \(-\)0.89 (0.09) & - \\ \hline Treatment & \(\ast\) & \(\ast\) \\ Time & \(\ast\) & \(\ast\) \\ Treatment\({}^{\ast}\)Time & \(\ast\) & \(\ast\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average \({}^{15}\)N natural abundance in soil N pools (TN and Fix N-NH\({}_{4}\)\({}^{+}\)). Standard deviation within brackets. Significant (\(p<0.05\)) or non-significant (\(p>0.05\)) differences between treatments at each sampling time are indicated by capital letters. \({}^{\ast\ast\ast}\) and “\(\chi\)” symbols indicates the presence or the absence, respectively, of a significant difference among the variables for Treatments and Time in the experiment period and correlating treatments with time (Treatment+Time).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{\(\boldsymbol{\delta^{15}\)N vs Air**}} \\ \hline
**Plant Organ** & **Treatment** & **\%\({}_{\infty}\)** \\ \hline Roots & CNTR & 10.2 (1.4) AB \\  & 5 NZ & 6.33 (1.69) A \\  & 15 NZ & 10.3 (2.8) AB \\  & 7 CZ & 11.6 (2.7) B \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average \({}^{15}\)N natural abundance in sorghum organs at the harvest. SD within brackets. Significant (\(p<0.05\)) or non-significant (\(p>0.05\)) differences between treatments for each organ are indicated by capital letters.
###### Abstract  
One of the critical challenges facing the mining sector is related to the prevention and mitigation of catastrophic incidents associated with its tailing dams. As mining tailings are very heterogeneous and field characterization is expensive and complex, geotechnical properties of these materials are largely unknown. The seismic cone penetration test (SCPTu) provides a field approach to estimate a large array of geotechnical information, including the liquefaction potential of tailing dams. Yet, the exploration of strain softening behaviors in geomaterials under undrained loading, utilizing the state parameter (\(\psi\)) inferred from SCPtu tests initially applied to soft soils, has been often used for mining tailings. This study is concerned with the implementation of a tailing classification system which uses the ratio between the small strain shear modulus and the cone tip resistance (\(G_{0}/q_{t}\)). A series of laboratory tests was executed, and three different methodologies were adopted to assess the effects of (partial) drainage conditions based on 531.26 m of SCPtu measurements conducted at three different upstream iron ore tailing dams in Brazil. Furthermore, the \(G_{0}/q_{t}\) ratio is integrated with \(\psi\) to assess the liquefaction tendencies of the investigated materials. The findings reveal the heterogeneous nature of the tailings, wherein indications of partial drainage are discernible across numerous records. Liquefaction analyses demonstrate that the tailings exhibit a contractive behavior in over 94% of the SCPtu data, confirming their susceptibility to flow liquefaction. Our findings are relevant for site characterization within iron ore tailing dams and other mining sites with similar geotechnical attributes.  
Keywords:tailing dams; SCPtu; liquefaction; drainage conditions +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Characterization of the MWCNTs
#### 3.1.4 XRD Measurement Analysis  
X-ray diffraction was applied in order to investigate the changes in the crystalline structure of the MWCNTs. Figure 4 shows the XRD patterns of the MWCNTs. It is clear from the figure that there are strong bands at 20 at 28.08\({}^{\circ}\), 31.4\({}^{\circ}\), 34\({}^{\circ}\), and 44.66\({}^{\circ}\) for the oxidized MWCNTs, corresponding to an interplanar space of 3.41 A and 2.05 A, respectively. This could be due to the (002) and (100)/(101) planes of the MWCNTs (graphite structure) providing improved crystallinity and a decline in the amorphous structure due to acid treatment. Similar observations have been described for McNTs prepared via the CVD method [46; 47].  
Figure 2: BET specific surface area (**A**) and adsorption–desorption isotherm examination (**B**) of the MCNTs.  
Figure 4: XRD diffraction of the MWCNTs.  
Figure 3: SEM examination at 2 \(\upmu\)m and magnification (8000\(\times\)) for the prepared (**A**) and purified (**B**) MWCNTs.
# BeiDou Augmented Navigation from Low Earth Orbit Satellites
## 4 Results and Discussion  
In this section, the performance of LEO constellation used to argument the BeiDou-3 is assessed with several metrics including satellite visibility, PDOP, accuracy and convergence of positioning.
## 3 RFI Detection and Mitigation Algorithm in Synthetic Aperture Microwave Radiometry Imagery
### Full-Polarimetric Mode Algorithm
Actually, this interpolation technique could also be used for the ocean pixels as well, but in many cases (_i.e._, Figure 18 below) the number of corrupted pixels in the whole image is so large, that the algorithm does not function. Therefore, we have resorted to use the mean values over the corrupted ocean pixels, and then interpolate only the contaminated land pixels. Figures 18, 19 and 20 show three different examples of the application of the RFI detection and mitigation algorithm described above for the real SMOS data in full-polarimetric case. Figure 18 corresponds to a SMOS image over the Aegean Sea acquired on September 3, 2010 (a few snap-shots earlier than that in Figure 15). Figure 18a shows the original SMOS image at X-polarization (\(\mathrm{T}_{\mathrm{XX}}\)). Figure 18b shows the RFI-cleaned image. Figure 18c shows the RFI map that was subtracted from Figure 18a to obtain Figure 18b. Finally Figure 18d shows the confidence map. As can be appreciated, the lowest confidence values happen in the central parts of the largest regions detected as contaminated by RFI, while for small RFI sources, the interpolated values have a large confidence value (\(>\)0.8-0.9). Figure 19: RFI in SMOS: Scenario 7. SMOS image over the Iberian Peninsula and North of Africa acquired on September 3, 2010 ascending pass: **(a)** Original \(\mathrm{T_{XX}}\) image; **(b)** RFI-cleaned \(\mathrm{T_{XX}}\) image. Figure 18: RFI in SMOS: Scenario 6. SMOS image over the Aegean Sea on September 3, 2010 ascending pass. **(a)** Original \(\mathrm{T_{XX}}\) image; **(b)** RFI-cleaned \(\mathrm{T_{XX}}\) image; **(c)** estimated RFI map; and **(d)** confidence level map. One of the features of this algorithm is that the coastline appears to be better defined. Actually, the coastline information is added in the interpolation process to separate the ocean and land parts, which are put together afterwards. However, no additional information is included in the image reconstruction process and the instrument's resolution is therefore not improved, and consequently, on the land side, brightness temperature decreases when approaching the coastlines from the land, as in the original RFI-corrupted image, when the coastline mask was not applied.
# Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach
## 5 Results and Discussion
### 4 th Experiment--Statistical Analysis of Amplitude's Correlation Coefficient  
The aim of this experiment is to implement a global characterization of the evolution classes of the detected PSs from the perspective of amplitude's mean correlation coefficient, and to see if any further common behavior can be noticed. This analysis has been conducted on the SL Sentinel-1 dataset. To facilitate the analysis process, the map containing five classes of evolution was considered. Figure 17 shows the spatial distribution of the dataset's global Pearson index \(\varrho\) - Equation (12)  
Statistical mean values of this parameter across whole scene, across the evolution classes, and across the PS classification output points stack are synthesized in Table 5. As expected, the absolute mean correlation index is much higher in the case of detected PS, relative to the other points of the scene (0.49 compared to 0.24). The former value is also very high compared to scene's mean correlation coefficient--0.26. From the five LDA classes, one presents a mean coefficient value (0.42) which is visible greater compared to the other four. This aspect is consistent with the fact that most of the stable targets belong to a single LDA evolution class (93.3% in the case of the analyzed situation). As previously mentioned, PS are prone to detection on stable structures, which, in the analyzed urban area, mainly exist on concrete construction areas. Therefore, it is expected that all the detected PS have a similar temporal evolution, this fact was confirmed by the LDA classification and current analysis. Therefore, it can be observed that LDA class 5 corresponds to the build-up areas.  
The points which were not classified as PS have been supplementary divided into four evolution classes by the LDA method. In the case of three of those classes, the mean value of the correlation index is comparable to its mean value from across the scene--0.26. The remaining class contains more emphasized temporal variations compared to the others, its mean correlation coefficient being significantly lower--0.19. This class (LDA class 1) is the one corresponding to water areas.  
Figure 17: Spatial distribution of dataset’s correlation coefficient—Constanta region.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Points Set** & \(\rho\) **Mean Value** \\ \hline whole scene & 0.26 \\ non-PS & 0.24 \\ PS & 0.49 \\ LDA class 1 & 0.19 \\ LDA class 2 & 0.26 \\ LDA class 3 & 0.26 \\ LDA class 4 & 0.28 \\ LDA class 5 & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics of the global correlation coefficient.
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 3 System Models and Problem Analysis  
Maintaining the security of time synchronization in centralized IWSNs with low overhead is not a trivial task. In addition to various attack types, the unique network structure with multi-hop and time source has also increased the pressure of security defense on time synchronization. In this section, we introduce the network model, clock model, and attack model and further analyze the problem.
# Line Clipping in 3D: Overview, Techniques and Algorithms
## 4 Mathematical Background  
We consider a line \(L\) that passes through two points \(P_{0}(x_{0},y_{0},z_{0})\) and \(P_{1}(x_{1},y_{1},z_{1})\) in three-dimensional space (see Figure 12).  
For an arbitrary point \(P(x,y,z)\) on the line, the parametric equation of the line is  
\[x =x_{0}+(x_{1}-x_{0})\cdot t\] \[y =y_{0}+(y_{1}-y_{0})\cdot t\] \[z =z_{0}+(z_{1}-z_{0})\cdot t.\]  
The equation of the line may also be written symmetrically as  
\[\frac{x-x_{0}}{x_{1}-x_{0}}=\frac{y-y_{0}}{y_{1}-y_{0}}=\frac{z-z_{0}}{z_{1}- z_{0}} \tag{1}\]  
and, if we assume that  
\[a =x_{1}-x_{0} \tag{2}\] \[b =y_{1}-y_{0}\] (3) \[c =z_{1}-z_{0}, \tag{4}\]  
then the equation of the line can also be written as  
\[\frac{x-x_{0}}{a}=\frac{y-y_{0}}{b}=\frac{z-z_{0}}{c}. \tag{5}\]
## 1 Introduction  
Recent progress in technical control has offered possibilities for studying the interactions between pure quantum mechanical systems and their coupling to reservoirs, which can be extended as unique tools for quantum sensing [1]. Alkali metal atoms are convenient, in terms of the technical requirements needed for sensors. Neutral atoms in chip-scale atomic vapor have been utilized as a device for many applications, including atomic clocks [2], atomic magnetometers cell [3], and for radio-frequency (RF) fields sensors [4]. Atom-based electromagnetic field sensor technologies hold great promise for realizing such applications. The most advanced experimental demonstrations include high dynamic field ranges (exceeding 120 dB), high-intensity RF fields (up to 10 kV/m) [5], broadband RF detection (on the order of THz) [6], and high sensitivity (\(\upmu\)V\(\cdot\) cm\({}^{-1}\)/Hz\({}^{1/2}\)) [4;7]. Recent advances have experimentally achieved sensitivities of 55 nV \(\cdot\) cm\({}^{-1}\)/ Hz\({}^{1/2}\), which approaches the quantum projection noise limit [8]; however, going beyond this, it is possible by using squeezed states or Schrodinger-cat states of light field [9].  
Recent advances have provided new capabilities in RF or microwave (MW) sensing. Typical high-frequency microwave fields have a wavelength range on the order of millimeters or centimeters. In chip-scale atomic vapor cell, the signal is limited by atomic density. In contrast to the characteristics of atomic spin, Rydberg atoms with high principal quantum number have exaggerated characteristics including dipole-dipole or Van der Waals interactions [10]. Using a buffer-gas-filled cell to improve atomic coherence is not feasible, as collision will destroy the electric dipole coherence. The high atomic density can be obtained by ultraviolet light desorption effect or heating the atomic vapor cell. Under high-density atomic gas, the sensing accuracy and sensitivity are limited by the spectral broadening [11], including Doppler broadening, collision broadening, and interaction dephasing induced by Rydberg atom collisions. The dephasing observed in the experiments has been explained by averaging the effects resulting from Van den Waals interaction of Rydberg-Rydberg interaction or coupling between Rydberg atoms and the inner surface of the glass cell [12; 13; 14].  
In this paper, by measuring the Rydberg electromagnetically-induced-transparency (EIT) signal, we observed Rydberg level shift caused by Stark interaction of Rydberg atoms and the mean electric field which is generated by Rydberg atom collision induced ionization in a cesium atomic ensemble. The results are obtained by means of Doppler-free spectroscopy with high signal-to-noise ratio, which is essential for high-precision measurements. Understanding the microscopic mechanism of charged particles production in thermal Rydberg atomic vapor and its corresponding electric field distribution is an important part of future experiments and applications.
## 5 GLI-Split Implementation
### Round-Trip-Time Evaluation  
For the evaluation, all inter-domain link delays in the test network (see Figure 10) have been set to _2 ms_. In the following, we take a brief look at the round-trip-time (RTT) in all six possible combinations of source and destination nodes (see Table 1). In the first four scenarios, no mapping lookups are required and, hence, the RTT just comprises the corresponding inter-domain link delays. This base delay applies to all six combinations and comprises the eight single link delays back and forth between source and destination domain. In the fifth scenario, a classic IPv6 host in a GLI-domain communicates with a GLI-host in another GLI-domain. In this case in the return direction, the GLI-host performs a mapping lookup because the returned address from the DNS is a GLI-address. This results in an additional delay of _12 ms_ for the initial message. For a subsequent message, the mapping has already been cached and no further lookups are required. The largest RTT occurs in the last scenario, where two GLI-hosts in different domains communicate. This time, a lookup at each domain is required which adds two times _12 ms_ to the base delay. However, this again applies only to the first messages. While this result may seem contradictory, the evaluation does not consider the advanced mechanisms which can be utilized by upgraded GLI-nodes. The next section explains some of these in detail.
## 3 Results
### Imaging Performance with Limited Data
#### 3.2.1 Reconstructions with Limited Prior Information  
To investigate a scenario of limited prior information where only the boundary of the head is known, the model of Figure 2 was filled with white matter only for the NT case. Moreover, taking into account that the dielectric properties of gray and white matter are not very different (see Table 2), gray matter is replaced with white matter in Figure 2 to reduce the model complexity [41]. The resulting head model is then used in two WT cases (WT1 and WT2), \"Case II.1\" and \"Case II.2\", which differ by the presence or absence of the CSF layer with \(\epsilon_{r}\approx 68.4-44.9j\). Cross-sectional views of the 3-D models for these cases are shown in Figure 10 while the true values of \(\Re(\epsilon_{r})\) is shown in Figure 11.  
The target is detected by both 2-D and 3-D algorithms, but there are significant image artifacts due to the skin, fat, and bone tissue regions which are not taken into account in the inverse model. These image artifacts are comparable in 2-D and 3-D, while the contrast in the estimated dielectric properties near the target is quite higher in the 3-D reconstructions. The RMSEs of both algorithms are similar but the 3-D has much larger \(e_{max}\) values, which suggests that the 3-D algorithm may be more sensitive to limited prior information. Comparing the results of Cases II.1 and II.2, it can be concluded that not including a thin layer with high contrast such as CSF in the inverse model's initial guess does not have a significant impact in the reconstructions.  
Figure 12: 3-D reconstructions of \(\Re(\varepsilon_{r})\) at 1.0 GHz: (**a**) y-z slice, (**b**) x-z slice, and (**c**) x-y slice for Case II.1; (**d**) y-z slice, (**e**) x-z slice, and (**f**) x-y slice for Case II.2.
## 4 Experiments  
The experiments were designed with two major goals in mind: to provide valid conclusions for current and mid-term IoT deployments, and to be easily contrasted and compared with other hardware and software implementations. Specifically, the experiments were designed with the aim of providing insightful results about three different aspects: (1) to test the feasibility of using high security communications mechanisms on the lower layers of the IoT architecture (i.e., the end-devices); (2) to determine whether the use of different ECC curves may yield energy consumption and data throughput improvements over RSA when comparing implementations that provide the same security level; and (3) to determine the impact of modifying the clock frequency of the SoC when executing the computationally-intensive algorithms required to provide the selected security levels.  
With the mentioned goals in mind, the ESP32 module was tested for the 21 scenarios that result as a combination of the seven generated certificates and the three available SoC frequencies. For each scenario, the 512-byte JSON file provided by the ESP32 HTTPS server was downloaded 100 times by the virtual machine, while obtaining different measurements of energy consumption and throughput.
## 2 Background and Problem Setting
### Problem Definition  
In our objective to employ a deep learning-based approach for eliminating the chaos introduced at the transmitter side from the received signal in a long-haul WDM optical communication system utilizing the DP-16 QAM modulation scheme, our main goal is to minimize the bit error rate (BER) as a critical performance metric in communication systems [48]. To achieve this, we aim to find a mapping function \(\mathcal{F}_{\theta}\) parameterized by \(\theta\). The mapping function \(\mathcal{F}_{\theta}\) should be capable of removing the chaos from the received signal, denoted as \(\mathcal{X}^{\dagger}\), and recovering the original signal, denoted as \(\mathcal{X}\). We define the set of original signals in the system as \(\mathcal{X}\in\{0,1\}^{(1\times d)}\), where \(d\) represents the length of the original sampled signals. The chaos adding function is denoted as \(I\), such that \(\mathcal{X}^{\dagger}=I(\mathcal{X})\)  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Learning rate for the VAE model & 0.0003 \\ Learning rate for the student model & 0.001 \\ Optimizer & Adam [47] \\ Total epochs per update & 8 \\ Update time step & 600 \\ Mini-batch size & 128 \\ Aggregation model for VAE and student models & Informer (Attention and Convolution) \\ Activation function for the VAE model & ELU \\ VAE-student coefficient \(\lambda\) & 0.6 \\ Gradient norm & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters for the DeepChaos+ framework include the VAE model, the student model, and the Informer Aggregation model.  
The mapping function \(\mathcal{F}_{\theta}:\mathcal{X}^{\dagger}\rightarrow\tilde{\mathcal{X}}\) can be optimized by finding the optimal parameters \(\theta^{*}\) for \(\mathcal{F}_{\theta}\) that minimize the expected BER, expressed as:  
\[\theta^{*}:=\arg\min_{\theta}\mathcal{B}(\tilde{\mathcal{X}},\mathcal{X}). \tag{5}\]  
Here, \(\mathcal{B}(\tilde{X},\mathcal{X})\) represents the BER between the reconstructed signal \(\tilde{\mathcal{X}}\) from the received signal with added chaos (\(\mathcal{X}^{\dagger}\)) and the original signal (\(\mathcal{X}\)). By optimizing the \(\theta\) parameters of the mapping function \(\mathcal{F}_{\theta}\), we aim to train a model that can effectively eliminate the chaos from the received signal. This deep learning-based approach offers the advantage of faster noise removal compared to traditional methods. Additionally, the trained model can generalize well to handle unseen signals with similar properties, providing robust chaos elimination in a wide range of scenarios.