esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of SG Operations for Smart Manufacturing
### Smart Manufacturing Using Distribution and Demand Side Management through SGs  
Based on the distribution and demand side management of SGs, as well as the classification of SG technologies (i.e., communication technology, information provision, computing intelligence, and cybersecurity in Figure 1), the related works on the applications of SGs using various techniques are classified in Table 1. Neural networks [10; 11; 21], smart meters [12; 13; 14; 22], and artificial intelligence [15] were adopted for power control; cyber-physical systems [18; 23; 24], big data [16; 19; 25], machine learning [17; 18; 26], AI [20; 27] were adopted for demand side management, and network security [28; 29; 30; 31; 32] was used for communication and information transmission. All of them were implemented in smart manufacturing applications.  
In the realm of smart factories, which integrate machinery, personnel, and equipment in the Industrial Internet of Things (IIoT), along with interconnected communication and computing networks, the importance of network security technology is heightened. This technology serves the crucial roles of identifying and safeguarding against potential information risks within the operational networks, as well as facilitating network restoration when required. Key applications in this domain involve the implementation of secure and dependable Advanced Metering Infrastructure (SRAMI) [28] and Information and Communication Technology (ICT) [32]. These applications are instrumental in addressing concerns related to data transmission reliability and security.  
Two-way authentication mechanisms play a vital role in ensuring the security of information exchanges between the SG and users [29]. Additionally, assessing system risks within the physical infrastructure of SG networks [30; 31] is an integral part of maintaining their robustness and security. Note that smart manufacturing and smart factories are central to the technological shift towards Industry 4.0. Utilizing AI technology for data analysis and enhancing the automation of network entities is an essential and inherent aspect of this transformation. Further details regarding these concepts will be expounded upon in subsequent sections.
# Low-Complexity QRD-\(M\) with Path Eliminations in MIMO-OFDM Systems
## 3 Conventional QRD-\(M\)
The proposed QRD-\(M\) eliminates unnecessary survival paths by comparing the ASEDs and adaptively calculated threshold at each layer. In path eliminations, paths larger than the threshold are regarded as unnecessary survival paths and are eliminated.
## 1 Introduction
These control strategies were conducted under the assumption that the post-yield damping characteristics of the MREA were linear or post-yield damping proportional to velocity. However, research has shown that when the MREA operates at high-impact speeds, the MREA fluid flow in the orifice can transition from laminar to turbulent flow, and as a result, the post-yield damping is more accurately represented by quadratic damping, that is, post-yield damping proportional to the square of velocity [29; 30; 31]. Thus, we developed a generalized Bingham number (GBN) control for an MR shock mitigation system that was developed considering such quadratic damping [32], and these results showed that the GBN control can achieve a soft landing. The theoretical underpinnings of the GBN control algorithm in the context of a linear stroke MREA exhibiting quadratic damping was presented [32]. In this study, the original contribution focuses on an experimental validation of this control algorithm. Thus, in this study, we seek to verify and validate that the impact mitigation performance of the quadratic GBN control can outperform the linear BN control when a linear stroke MREA is used at high-impact speeds when quadratic damping can be substantial. A flow-mode (linear stroke) MREA is designed, analyzed, fabricated, and tested so that the MREA damping force under shock load can be characterized in detail. Then, the governing equation of a single-degree-of-freedom MR shock mitigation system with quadratic damping is analyzed, and the GBN control is developed, with the control objective being a soft landing. The deceleration, velocity, and displacement formulas of the payload are analyzed using the GBN control. The dynamic response of the MR shock mitigation system under different GBNs is analyzed. Finally, the shock mitigation performance of the GBN control (using quadratic damping) to BN control (using linear damping) is conducted via both simulation and an experiment.
## 5 The Cryptographic Suite
### Confidentiality  
Confidentiality of messages is achieved by encrypting them with a block cipher. Encryption is achieved by splitting cleartext in blocks of fixed, predefined bit-length and then encrypting every block. In the most general case, cleartext length is not multiple of the block length. Thus padding is necessary. However, padding has the negative effect that the ciphertext may result up to one block longer than the corresponding cleartext. This effect is an instance of the ciphertext expansion phenomenon.  
While ciphertext expansion is negligible in a traditional network, it becomes relevant in wireless sensor networks and, in particular, underwater acoustic networks. In these networks, communication and energy limitations require to keep a message size small and ciphertext expansion may introduce an overhead that is not negligible anymore. For instance, in the AES encryption algorithm the block size is 128 bits. As the message size in the UAN project is, on average, around 720 bits, then the message expansion due to padding would be around 18%.  
In order to completely avoid the ciphertext expansion problem, we use the CipherText Stealing (CTS) technique that alters the processing of the last two blocks of plaintext, resulting in a reordered transmission of the last two blocks of ciphertext and no ciphertext expansion [27].
## 4 Experiments and Results
### The Impact of Different Mobility Units  
In this experiment, we first discuss the impact of different mobility units and average execution time for SMRU to adjust the position of chargers after sensor mobility. After the SMRU selects the charger position, this experiment randomly moves sensors from 2 to 10 units to simulate the movement  
Figure 10: Comparison of the execution time of charger deployment and modified deployment using SMRU.  
Figure 9: The comparison results of different charging angles.  
of sensors. Due to SMRU does not recalculate the positions of all the chargers to find the global optimal solution after sensor mobility; it can reduce many execution times. The experimental results show that the execution time will increase with the number of sensors, and the increase in the mobility units of the sensor will increase the average execution time, as shown in Figure 11.  
Next, we discuss the number of chargers used for SMRU to adjust the position of chargers after sensor mobility. Although SMRU does not recalculate the positions of all the chargers to find the global optimal solution after sensor mobility. However, the experimental results found that the number of chargers used does not increase too much after the sensor mobility. In addition, the number of chargers will increase with the number of sensors, and there is no obvious difference in the number of chargers used between different mobility units, as shown in Figure 12.  
Figure 11: The impact of different mobility units and average execution time for search most and remove useless (SMRU) strategy to adjust the position of chargers after sensor mobility.  
Figure 12: The impact of different mobility units and the number of chargers used for SMRU to adjust the position of chargers after sensor mobility.
# Nitrogen Budget of Short Rotation Forests Amended with Digestate in Highly Permeable Soils
## 3 Results and Discussion  
### Components of the Soil N Budget  
#### 3.1.1 N Leaching  
Figure 2 shows that the leaching of N-NO\({}_{3}\) in the soil profile in SRF is rather fast. The N-NO\({}_{3}\) concentration tends to disappear in the first 2-3 months after each digestate distribution. Moreover, the highly dynamic hydrology reduces significantly the soil buffering capacity as suggested by the N-NO\({}_{3}\) concentration from the surface down to 90 cm. The higher N-NO\({}_{3}\) concentrations recorded sometimes  
at 90 cm were due to the sampling period: as the residence time is strongly influenced by meteorological conditions, the high values of N-NO\({}_{3}\) recorded at the end of May 2010 were coupled with the heavy rainfall immediately after the first digestate slurry spreading. It is important to underline the unusual high baseline value in A0 (control plots) observed during the first three months of the experimental activities (Figure 2), which was probably due to the N residual from the previous agricultural activities (sweet corn cultivation). Data on N-NH\({}_{4}\) and N-NO\({}_{2}\) are not reported because they were negligible in concentration with respect to N-NO\({}_{3}\).  
The only exceptions were some peaks of N-NH\({}_{4}\) (10-15 mg L\({}^{-1}\)) observed at 30 cm in P1 and P2 plots, during 12 May 2010 and 26 October 2010 samplings, possibly associated with an incomplete nitrification of the N-NH\({}_{4}\) in the digestate distributed during the previous days. In terms of mass balance, the total amount of N leached from each plot is showed in Table 3.  
N leaching was significantly higher in the SRF plots than in the permanent meadow plots. This could be explained both considering a greater N uptake of permanent grass vegetation with respect to new tree plantations, and, by considering uneven spreading of N in the SRF soil, in fact the distribution area represents only 36% of the total field given that digestate was applied only in the inter-rows.  
Figure 2: Contour plot of the N-NO\({}_{3}\) concentration in the 0–90 cm soil horizon obtained via Kriging interpolation method during the monitored period in SRF plots (**A0–A2**). The dashed red lines are the digestate spreading dates.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Rainfall (mm)** & **Leaching at 90 cm (mm)** & **N-Tot Leaching (kg ha\({}^{-1}\))** \\ \hline
**A0** & & & 35.2 \(\pm\) 2.8 \\
**A1** & 1603 & 564 \(\pm\) 113 & 81.4 \(\pm\) 4.0 \\
**A2** & & & 111.2 \(\pm\) 7.6 \\ \hline
**P0** & & & 14.8 \(\pm\) 0.6 \\
**P1** & 1603 & 536 \(\pm\) 107 & 34.9 \(\pm\) 4.2 \\
**P2** & & & 82.9 \(\pm\) 7.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Percolation rate at 90 cm depth and leaching of N for each plot.
## 2 Results  
### Instrumental Measurement of Skin Parameters  
The corneometry test demonstrated a statistically significant enhancement of skin hydration in the group receiving the cream with sheep colostrum (the colostrum cream group): mean skin hydration increased substantially from 35.9 to 51.1 units (Figure 1, Table 1). In contrast, in the placebo group receiving the cream without colostrum (placebo group), only a slight, insignificant change in hydration was noted (from 42.2 to 46.2 units). In order to accurately describe this skin parameter, the median and the interquartile range (median (25%; 75%)) of the percentage of changes between the first and second measurements for both groups were calculated (Table 2). The results indicate a significantly higher chance of improving skin hydration in the colostrum cream group (45.5%), compared to the placebo group (9.5%). Figure 1: Skin hydration before and after treatment in the colostrum cream and placebo groups; \(\mathbf{\ast\ast}\)\(p<0.0001\). The two groups demonstrated similar mean TEWL values before the application of the creams: _viz._, colostrum 21.1 g/h/m\({}^{2}\) and placebo 20.8 g/h/m\({}^{2}\). After eight weeks of treatment, the TEWL values in both groups improved by about five units, i.e., from a normal level (15-25 g/h/m\({}^{2}\)) to a healthy condition (10-15 g/h/m\({}^{2}\)) [12]. These changes were statistically significant in both groups, \(p<0.05\) (Figure 2, Table 1). Skin firmness, assessed by the R0 and F0, improved in both groups over the course of the experiment. R0 represents the passive response of the skin to force, and F0 represents the firmness of the skin at the time of suction [13,14]. Mean R0 values decreased from 0.201 to 0.177 units in the colostrum cream group and from 0.207 to 0.177 units in the placebo group (Figure 3, Table 1). Similarly, mean F0 values decreased from 32.6 to 27.1 units in the colostrum cream group and from 31.6 to 26.7 units in the placebo group (\(p<0.001\)) (Figure 4, Table 1). No significant changes in erythema, melanin content, or sebum were observed at the end of this study in either the colostrum cream or placebo groups. Detailed data are presented in Tables 1 and 2.
## 3 Three-Dimensional Joint Inversion Algorithm  
### Cross-Gradient Function  
The cross-gradient function defined by [PERSON] and [PERSON] [10] is as follows:  
\[t(x,y,z)=\
abla m_{a}(x,y,z)\times\
abla m_{b}(x,y,z)=(t_{x},t_{y},t_{z}), \tag{6}\]where \(m_{a}\) and \(m_{b}\) are models of different physical parameters. The specific forms of \(t_{x}\), \(t_{y}\) and \(t_{z}\) are given as follows:  
\[\left\{\begin{array}{l}t_{x}(m_{a},m_{b})=\frac{\partial m_{a}(y,z)}{\partial y }\frac{\partial m_{b}(y,z)}{\partial z}-\frac{\partial m_{b}(y,z)}{\partial y} \frac{\partial m_{a}(y,z)}{\partial z}\\ t_{y}(m_{a},m_{b})=\frac{\partial m_{a}(x,z)}{\partial x}\frac{\partial m_{b}( x,z)}{\partial z}-\frac{\partial m_{b}(x,z)}{\partial x}\frac{\partial m_{a}(x,z)}{ \partial z}\\ t_{z}(m_{a},m_{b})=\frac{\partial m_{a}(x,y)}{\partial x}\frac{\partial m_{b}( x,y)}{\partial y}-\frac{\partial m_{b}(x,y)}{\partial x}\frac{\partial m_{a}(x,y)}{ \partial y}\end{array}\right., \tag{7}\]  
The expression of the cross-gradient term is the following:  
\[\Phi_{cg}=t^{T}t=\left[\begin{array}{l}t_{x}\\ t_{y}\\ t_{z}\end{array}\right]^{T}\left[\begin{array}{l}t_{x}\\ t_{y}\\ t_{z}\end{array}\right]=t_{x}^{T}t_{x}+t_{y}^{T}t_{y}+t_{z}^{T}t_{z}, \tag{8}\]  
By performing a Taylor expansion of \(t\) against model \(m\) (\(m\) represents \(m_{a}\) or \(m_{b}\)) at \(m_{0}\), and ignoring partial derivatives of more than one order, we can obtain the following:  
\[t=t_{0}+\frac{\partial t}{\partial m}(m-m_{0}), \tag{9}\]  
Under the condition of homogeneous half space, \(t_{0}=0\). [PERSON] et al. used the finite difference method to discretize grids at different positions [21]. According to the formula he derived, \(\frac{\partial t}{\partial m}\) was discretized, making the discretization matrix \(B_{x}=\frac{\partial t_{x}}{\partial m}\), \(B_{y}=\frac{\partial t_{y}}{\partial m}\), \(B_{z}=\frac{\partial t_{z}}{\partial m}\) and \(B=\left(B_{x}\;B_{y}\;B_{z}\;\right)^{T}\), and we can obtain (10):  
\[\frac{\partial\Phi_{cg}}{\partial m}=2\Big{[}(B_{x})^{T}B_{x}+\big{(}B_{y} \big{)}^{T}B_{y}+(B_{z})^{T}B_{z}\Big{]}(m-m_{0})=2B^{T}B(m-m_{0}) \tag{10}\]  
In the joint inversion, we strengthened the structural similarity between different physical models by minimizing the cross-gradient term. The joint inversion based on the cross-gradient theory has another advantage: when there is no correlation between \(m_{a}\) and \(m_{b}\), the cross-gradient term equals zero, which can avoid artifacts that may arise during the joint inversion.
## 3 Results
### Elements Correlations  
While studying the relationship of the respective elements (transformed data), a significant correlation was revealed between Al, V, Cr (June) and As, the first three representing lithophile elements [95]. Though it can be noted that the concentrations of these elements  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline
**Variable** & **Al** & **V** & **Cr** & **Mn** & **Fe** & **Ni** & **Cu** & **Zn** & **As** & **Cd** & **Sb** & **Hg** & **Pb** & **N** \\ \hline
**Trinec** & & & & & & & & & & & & & & \\
**June** & & & & & & & & & & & & & \\
**Oct.** & & & & & & & & & & & & & \\
**Czechia** & & & & & & & & & & & & & \\
**[90]** & 440 & 1.18 & 1.0 & 449 & 349 & 1.2 & 5.9 & 33.9 & 0.26 & 0.18 & 0.1 & 0.041 & 2.9 & 11,822 \\
**Czechia** & & & & & & & & & & & & & \\
**[27]** & & & & & & & & & & & & & & \\
**Zumarraga** & & & & & & & & & & & & & \\
**[37]** & & & & & & & & & & & & & & \\
**Azkoitia** & & & & & & & & & & & & & \\
**[37]** & & & & & & & & & & & & & & \\
**Eregli 1** [35] & & & & & & & & & & & & & & \\
**[35]** & & & & & & & & & & & & & & \\ \hline \end{tabular} \({}^{1}\)_Hypum cupressforme._  
\end{table}
Table 2: Median values of element contents in moss specimens (ug g\({}^{-1}\)) and (wt.%) for Al, Fe and N compared to levels reported in relevant studies in Czechia [27; 90], Spain (Zumarraga, Azkoitia) and Turkey (Eregli) [35].  
are increased in June samples collected from the ground (TR1-11/13/21), the same relationship was observed in both seasons. Another significant correlation presented in both samplings is between Zn and Cd (chalcophile elements) [96]. These are also negatively correlated to the previous group of Al (June), V, Cr and As. It can imply that they are deposited and/or absorb differently and, thus, originate in different sources.  
Additionally, a moderate correlation was found between As and Fe (both months), Cr and Fe (October) and Cd, Hg and Zn (June). Nitrogen was significantly correlated with Zn, Cd and Hg in June and Zn and Hg in October (also stated in [37]). This indicates that the nitrogen pollution in the area can be associated with industrial sources, influenced by combustion during winter (Cd) [97]. For a more detailed relationship assessment, see Figure 5.
## 3 Evaluation of Remote Sensing Products and GPP Simulations
Figure 4 shows the spatial distribution of the bare soil proportion from the BGCDV_NEW experiment simulations and the MEaSURES vegetation continuous field in 1982, 1992, 2002, and 2012. Compared to MEaSURES, the simulated percentage of vegetation from the BGCDV_NEW experiment was greater in the semi-arid region of the TP in 1980. Compared to 1982, the simulated proportion of bare soil from the BGCDV_NEW experiment tended to decrease in the semi-arid and arid regions of the TP. As shown in Figure 4, the simulated proportion of bare soil from MEaSURES tended to increase in the southeast region of the TP from 1982 to 2012. Overall, the simulated percentage of vegetation from the BGCDV_NEW experiment was greater than that from MEaSURES. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**PBIAS (\%)**} & \multicolumn{3}{c}{**RMSE (gC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\))**} & \multicolumn{3}{c}{**Corr**} \\ \hline
**Experiment** & **Dangx** & **Haibei** & **Maqu** & **Dangx** & **Haibei** & **Maqu** & **Dangx** & **Haibei** & **Maqu** \\ \hline NEW & 16.4 & \(-\)11.0 & 11.3 & 0.322 & 1.107 & 1.084 & 0.940 & 0.890 & 0.947 \\ GLASS & 151.4 & 45.9 & 23.1 & 1.587 & 1.723 & 1.239 & 0.740 & 0.948 & 0.968 \\ FLUXCOM & 43.6 & 44.1 & 16.8 & 0.520 & 1.582 & 1.472 & 0.779 & 0.881 & 0.923 \\ NIRv & \(-\)31.4 & 118.7 & 89.6 & 0.659 & 4.184 & 3.554 & 0.637 & 0.931 & 0.959 \\ MODIS & 66.7 & 45.7 & 5.2 & 0.569 & 1.727 & 0.905 & 0.812 & 0.947 & 0.957 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistical results of the simulated GPP, GLASS GPP, and FLUXCOM GPP against observations at a monthly scale. Figure 5 shows the climatology of the GLASS, NIRv, FLUXCOM, FLUXCOM1, and MODIS GPPs and the simulated GPP from the BGCDV_NEW experiment. As shown in Figure 5, a large area of the arid region of the TP has missing values for the NIRv, FLUXCOM, FLUXCOM, and MODIS GPP products. All remote sensing GPP products and the GPP simulated using CLMS.0-DGVM show a similar pattern, with GPP increasing from northwest to southeast and with the maximum at the southeast edge of the TP. Compared to the MODIS GPP product, the NIRv GPP product overestimated GPP in the east of the TP, and the FLUXCOM1 GPP product underestimated GPP in sub-humid regions. The simulated GPP from the BGCDV_NEW experiment was underestimated in the sub-humid regions and overestimated in the semi-arid and arid regions compared to the remote sensing GPP products. Figure 3: Model simulations from CLM5.0-DGVM and remote sensing GPP products versus observations of monthly GPP (unit: gC\(\cdot\)m\({}^{-2}\)\(\cdot\)d\({}^{-1}\)), NEE (unit: gC\(\cdot\)m\({}^{-2}\)\(\cdot\)d\({}^{-1}\)), and Re (unit: gC\(\cdot\)m\({}^{-2}\)\(\cdot\)d\({}^{-1}\)) at the Dangx, Haibei, and Maqu sites. Figure 4: Proportion of bare soil (unit: %) on the TP. Left: CLM-BGCDV; right: MEaSURES. Figure 5: Climatology and trend of GPP (gC:m\({}^{-2}\).d\({}^{-1}\)) from May to October.
# Estimating BDS-3 Satellite Differential Code Biases with the Single-Frequency Uncombined PPP Model
## 3 Experimental Data and Processing Strategy  
In this study, the estimation of satellite DCBs incorporates the simultaneous modeling of the ionosphere using the generalized triangular series function model. However, it is important to note that the time-varying nature of the ionosphere exhibits unique characteristics at different latitudes [27; 28]. Taking into consideration the potential influence of ionospheric on the accuracy of the estimated satellite DCBs, it becomes imperative to carry out comprehensive analyses across various time periods and latitude distributions. This approach will provide valuable insights into the temporal and spatial variations and aid in improving the overall precision of the DCB estimates.  
Considering the experiment's objectives and prerequisites, the dataset used in this study consists of observations gathered from 25 globally distributed IGS stations over the period of March 2023 (DOY 60-90). This temporal span possesses a significant representative nature due to its coverage of diverse geomagnetic and solar conditions, which are depicted in Figure 3. Within this temporal scope, the solar activity level proxy F10.7, displays a variation ranging from 129 to 190 solar flux units. Notably, the Kp index, which serves as an indicator of geomagnetic activity, surges to a value exceeding 6 on DOY 82 and 83, underscoring the occurrence of a severe geomagnetic storm during this period.  
Figure 3: F10.7 and Kp indexes over the period of March 2023 (DOY 60–90).  
Figure 2: Schematic of satellite DCBs estimation using the SF uncombined PPP model.  
Notably, only the observations on the B1 frequency of each station are utilized in the analysis. Table 1 provides an overview of the data and processing strategy employed in this study for estimating satellite DCBs.  
It is essential to emphasize that the BDS-3 satellite DCBs obtained using the SF uncombined PPP model are closely tied to the precise clock products employed in the analysis. The choice of dual-frequency IF combinations varies among different analysis centers. Most analysis centers typically utilize the B1/B3 IF combination to generate precise clock products, while some analysis centers may use the B1/B2 IF combination instead. This distinction in the selection of the IF combination can impact the specific BDS satellite DCB estimates. In this study, the precise clock products provided by Wuhan University (WUM) are employed, and the strategy for generating WUM products has shifted from B11/B2I to B11/B3I since DOY 1, 2019 [30]. As a result, when referring to satellite DCBs within the scope of this experiment, it specifically pertains to the C2I-C6I satellite DCBs.  
The estimated DCBs were subsequently compared to the DCB products provided by CAS. Notably, the DCB products from CAS are computed using the pseudorange-leveled carrier-phase approach. This approach incorporates a generalized triangular series function to model the ionosphere for each individual station while simultaneously estimating the DCBs [31].