esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluating Satellite-Retrieved Far-Red SiF with Data on Vegetation Activity  
### Comparison with NDVI  
To date, no suitable independent measurements of plant fluorescence exist for proper validation of SiF retrieved from satellite sensors. However, local fluorescence measurement techniques are rapidly advancing and show their value in studying the ecophysiology of photosynthesis at the leaf level and in situ ([52] and references therein). During the last decade, SiF has been successfully measured from tower [26; 64; 65; 66], aircraft [27; 67; 68] and satellite platforms [32; 34; 35; 41]. For evaluation purposes, vegetation indices from satellite measurements (such as the NDVI) can be used as first proxies for SiF from spaceborne measurements due to their similar spatio-temporal patterns; they can therefore be used as a qualitative indicator of vegetation activity. Another evaluation method is to compare the spatial SiF patterns derived from different satellites based on different methodologies [21; 34; 53].  
Figure 3: Example of the spatial pattern of SiF retrieved over North Africa and the Middle East for April 2013 (lower panel). A true colour image for the same geographical region is shown for comparison (upper panel). The white colour indicates grid boxes without data points. The regular pattern of missing data points is caused by a specific sequence of GOME-2 instrument modes around that time.  
We note that the absolute fluorescence values will depend on the assumed spectral shape of the emissions [21; 52] and on other algorithm assumptions.  
Figure 4 illustrates the good qualitative agreement between the spatial patterns of NDVI and SiF over North America for July 2013 suggesting that the results of our retrieval setup indeed indicate the presence of green vegetation. Over the eastern US, the northwestern US coastal area and the southern part of Mexico high NDVI values are measured, while over the western US and northern Mexico low NDVI values are observed in line with the general patterns of SiF values.  
However, there are also clear differences between the spatial patterns of NDVI and SiF, especially in areas covered with tundra, taiga and rainforest vegetation. While in September SiF is already small over the tundra/taiga, NDVI is still high. Rainforest areas show slightly lower SiF radiance values compared to other areas with similar NDVI values (not shown). SiF values of evergreen forests also tend to show some minor seasonality, which in turn is hardly observed from NDVI data. It is expected that SiF measurements provide additional information that is not covered by vegetation indices due to signal saturation, bidirectional reflectance distribution function (BRDF) effects [69], and because materials such as soil, wood, and dead biomass also absorb PAR but do not contribute to photosynthesis [70]. The correlation between NDVI and SiF at the global scale is moderately strong (\(R=0.78\), a value similar to correlations given by [PERSON] et al. [53]), but large NDVI values can be observed at low SiF levels. In the next paragraph, we therefore compare SiF with GPP values derived from flux towers.
## 2 Materials and Methods
### Agrobacteriumium-Mediated Transformation in Cotton  
The cotton cultivar MNH-786 selected for expression of Cry1 Ac is a high yielding genotype approved for routine cultivation in the field, and has been shown to have high regeneration potential [34]. The _A. tumefaciens_ GV1301 harboring CLCuV-Ac (CLCuKoV-BuC1-Cry1 Ac-NOS) was used to transform cotton _G. hirsutum_ 'MNH-786\({}^{\prime}\) using the shootapex method [35]. This transformation procedure has been previously optimized at the CEMB Laboratory. Briefly, cotton seeds were acid de-linted, surface sterilized in Tween-20  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Genes/Constructs** & **Primer Name** & **Sequence** & **Product Size** & **Restriction** \\  & & (5\({}^{\prime}\)-\(\cdot\)\(\cdot\)3\({}^{\prime}\)) & **(bp)** & **Site** \\ \hline \multirow{2}{*}{Cry1 Ac-NOS} & Cry-NOSF & CGGGATACCATGGACAACAACCCAAACATCAA & \multirow{2}{*}{2160} & _Kpn1_ \\ \cline{2-4}  & Cry-NOSR & CCGGATCCGATTCCGATTAGATACATAG & & _BamHI_ \\ \hline \multirow{2}{*}{CLCuKoV-Bu-C1} & CLCuVC1-F & CGCGATACCTTTGCTAATTAGAGACAAC & \multirow{2}{*}{455} & _Kpn1_ \\ \cline{2-4}  & CLCuVC1-R & CGGGATACCTAATTCCTAGCCCTTATTACCAG & & _Kpn1_ \\ \hline \multirow{2}{*}{CLCuKoV-Bu-V1} & CLCuVV1-F & CGCGATACCTGATTGGGTCAATTAGAGACAACAC & \multirow{2}{*}{455} & _Kpn1_ \\ \cline{2-4}  & CLCuVV1-R & CGCGATACCTAATTCCTAGCCCTTATTACCAG & & _Kpn1_ \\ \hline \multirow{2}{*}{LIR} & IR-F & TGACTTTGGTCAATTAGAGACAACAC & \multirow{2}{*}{455} & \multirow{2}{*}{--} \\ \cline{2-4}  & IR-R & TAATTCCTAGCCCTTATTACCAG & & \\ \hline \multirow{2}{*}{Cry1 Acinternal} & Cry-F & TCCCTCTCTCTGTCCGTCTA & & \\ \cline{2-4}  & Cry-R & CGTTTTCCCATAGTTCCATA & & \\ \hline \multirow{2}{*}{CLCuKoV-BuC1-Cry1 Ac-NOS} & C1 Cry-F & GGATCGGAAAATGGTCCT & & \\ \cline{2-4}  & Cry1-R & CGAACTCTTCGATCCTCGG & & \\ \hline \multirow{2}{*}{CLCuKoV-V1-Cry1 Ac-NOS} & V1 Cry-F & CCGTTCACGGTTTTAGGTAT & & \\ \cline{2-4}  & CryV1-R & GTGTAACCGGTTTTCAATGGCTT & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Oligonucleotide primers, restriction enzymes, and amplicons.  
for 3 min, and transformed to a solution containing 0.1% HgCl\({}_{2}\) and Sodium dodecyl sulphate (SDS). The sterilized seeds were held at 30 \({}^{\circ}\)C for 1-2 days in the dark. The shoot-tips of the germinating embryos were excised using a sharp sterilized blade and immediately transferred to the _A. tumefaciens_ inoculum suspension carrying CLCuV-Ac and PK2-Ac plasmids. These embryos were transferred to Murashige Skoog MS-medium (kinin-1 mg/mL) antibiotic-free media for 3 days at 25 \({}^{\circ}\)C. After three days of co-cultivation, the cotton plantlets were transferred to a sterile test tube containing MS with hygromycin (50 \(\upmu\)g/mL) and cefotaxime (250 \(\upmu\)g/mL). When desirable, higher hygromycin concentrations were used to reduce the rate of false-positive transformants (data not shown). A total of 5000 cotton embryos were isolated during the plant transformation experiment (Table 3).
# Evaluating the Feasibility of Intelligent Blind Road Junction V2I Deployments
## 2 Intelligent Perception System (IPS) Overview  
The following section presents an overview of the system architecture necessary to enable the blind road junction V2I use case.  
As previously mentioned, the safety of blind road junctions can likely be improved by the introduction of so-called FSNs. An FSN is a system, mounted at an appropriate vantage point, composed of one or more sensing modalities, e.g., RGB cameras, LiDAR, radar, and thermal cameras, that allow an object detection algorithm to perceive regions of interest. As such, one or more FSNs combined with V2X communications capabilities can be referred to as an Intelligent Perception System (IPS), illustrated in Figure 3. At these blind or occluded locations, one or more FSNs can be installed in positions where sensors can perceive the region of interest and any roads that may connect to it to form an IPS. Using sensor fusion technology [40], information about the region(s) of interest can collected into some form of Local Dynamic Map (LDM) [41] and transmitted to nearby CIVs in a publisher-subscriber groupcast type information dissemination model.  
Figure 3: The Intelligent Perception System (IPS) with a CIV, in red, approaching a blind T-junction that is served by a Fixed Sensor Node (FSN), in navy, with an appropriate vantage of the occluded region. Two configurations: (1) FSN computing, (2) car computing.  
A key concern for the implementation of an IPS is the most appropriate location of the computation. Processing of the various sensing modalities by the object detection algorithm and the sensor fusion technologies at the FSN(s) requires a significant degree of computational power. Thus, it would be reasonable to suggest that the computation should be deployed where the data are being generated, i.e., co-located with the FSN(s). However, the installation and maintenance costs of such a deployment may be prohibitive. To avoid the cost of the computation, the sensor data from the FSN(s) could instead be transmitted, with associated meta-data, directly to the nearby CIVs for onboard processing. However, depending on the number of sensors per FSN and the number of FSNs at the junction, the volume of data to be transmitted may be significant. Alternatively, as illustrated in Figure 4, computing paradigms such as Cloud Computing [42] or MEC [39] in conjunction with Software-Defined Networking (SDN) [43] and Network Function Virtualisation (NFV) [44] technologies as part of 5G networking slicing [45] could be used. The location of the computation for the FSN(s) can have a significant bearing on the performance capabilities and cost of the IPS.
## 4 Security Threats
### Implementation
#### 4.4.5 Common Control Channel Jamming  
Many approaches base the proposed overall spectrum sharing procedure on (one or more) common control channel usage. The consequences of jamming or flooding such channels are already described in previous sections, focusing either on the negotiation, or the rules distribution phase of such approaches. However, the list of possible problems includes further attacks, which may also affect the phase of node conformance to the DSA network rules.  
There are protocols proposed to date that base the implementation of DSA-oriented collision avoidance mechanisms on common control channels. For instance, there are methods based on the exchange of modified RTS/CTS messages for CSMA/CA networks, whose headers enclose extra spectrum information [23]. Other approaches require the use of as many common sub-channels as the available channels of a DSA network. Whenever a node occupies a data channel, he must inform any potential user about this channel's state by transmitting a special signal (or else, a busy tone) over the respective common sub-channel [37].  
In case a control channel is jammed, the network could be disrupted and some nodes could become isolated [38]. The resulting unawareness regarding the condition and the state of the available channels in a DSA network would lead them lacking in resources while the spectrum would remain underutilized. Moreover, a malicious node may transmit fake busy tone signals over selected sub-channels in order to mislead its competitors and prevent them from accessing particular spectrum portions. To make matters worse, an attacker could transmit the aforementioned signals over all the available sub-channels aiming at preventing all his competitors from transmitting any data packets and affecting their received QoS. A more rational attacker may try to also benefit from such behavior by occupying the vacant spectrum portions and, consequently, increasing his gained profits. Finally, a more far-fetched and difficult to realize technique would be for an adversary to transmit \"inverse\" signals over these sub-channels fading the currently transmitted one. The respective channels would falsely seem vacant, possibly letting more than one user occupy them, which can, in turn, increase interference.
# M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders
## 6 Conclusions  
The improved MPPSK modulation with a not strictly limited bandwidth is proposed in this paper. The stacked DSAE network demodulator was developed for the MPPSK signals. Given by the theoretical analysis and simulation, its proper structure and parameters were obtained. It was demonstrated that deep learning models can be utilized for data detection in communication systems. The proposed stacked DSAE network extracts features from the modulation signals and uses a softmax layer for detection. The results about the structure and system parameters are useful to generalize the features to much broader application scenarios, whose features cannot be directly derived from the training datasets. Furthermore, the technique developed in this paper results in a much better detection performance than existing detectors. We hope that this research will open up new frontiers in applying deep learning techniques for communication such as in detecting strictly band-limited signaling, which was designed by the Nyquist criterion to ensure ISI-free reception in Gaussian channels.  
[PERSON] conceived and wrote this paper; [PERSON] and [PERSON] designed the simulations and experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] contributed figures plotting. All authors have read and agreed to the published version of the manuscript.  
This work was jointly supported by the Open Grant of State Key Laboratory of Millimeter Waves (No. K202111) and the Scientific Research Project of Zhejiang Provincial Department of Education, China (No. Y201737971).  
The authors declare no conflict of interest.
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 4 Video and Audio Distortions
### Audio Degradations
#### 4.2.2 Clipping  
The clipping effect was generated by multiplying the audio signal by four different amplitude multipliers (11, 15, 25, 55). As a result, four different test conditions were generated for this audio distortion (Table 5). The reference values used as amplitude multiplier were taken from the TCD-VoIP dataset [30].
## 2 Validation of FLAC Modeling on Wave Transmission across Rock Joints  
In the slope model, the normally incident wave is applied under the bottom of the slope. Before the transmitted wave arrives at the slope surface, just wave reflection and wave transmission exist. When the wave transmits to the slope surface, wave transformation as well as wave reflection occur. Subsequently, obliquely incident wave transmission across a joint set also occurs. Therefore, wave transmission and attenuation are very complicated due to wave transformation, wave reflection, and wave superposition.  
In order to verify the capability of FLAC on wave transmission across rock joints, the modeling results are compared with analytical solutions with the propagator matrix method [16], adopting the displacement discontinuity model. In this section, FLAC results of normally incident P and Sv waves transmission across a single joint are compared with analytical solutions, and the effects of joint normal, shear stiffness, and incident wave types (P and Sv waves) on wave transmission in FLAC modeling are verified. Effects of joint spatial configuration and multiple wave reflections among joints on wave transmission are subsequently verified through comparison with theoretical solutions for normally incident P wave transmission across a joint set. Finally, obliquely incident P wave transmission across a single joint is studied to verify the dependence of wave transmission on the incident angle and the two-dimensional effect [15].
# Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?
## 2 Robust Statistics Principles
### Robust Estimates for Regression Problems
#### 2.2.1 Huber and [PERSON] Families of Loss Functions  
The key idea behind robust estimation is to use loss functions which appropriately penalize outliers in the measurements. Loss functions can be classified according to the shape of their score function \(\psi\) as _monotone_ or _recsedending_. Among the redescending category, estimators for which \(\psi(x_{r})=0,\ x_{r}<\infty\) are denoted as _strongly redescending_[44]. Several loss functions exist in the literature, the most common being [PERSON] and [PERSON]'s bisquare families of functions. The family of monotone Huber functions is defined as (again using an arbitrary scalar variable \(x\))  
\[\rho_{a}^{H}(x) =\left\{\begin{array}{ll}x^{2}&\text{if}\quad|x|\leq a\\ 2a|x|-a^{2}&\text{if}\quad|x|>a\end{array}\right., \tag{9}\] \[\psi_{a}^{H}(x) =\left\{\begin{array}{ll}x&\text{if}\quad|x|\leq a\\ a\ \text{sign}(x)&\text{if}\quad|x|>a\end{array}\right.,\] (10) \[W_{a}^{H}(x) =\min\left\{1,\frac{a}{|x|}\right\}, \tag{11}\]  
then \(\rho_{a}^{H}(x)\) is quadratic around 0 and increases linearly with \(x\). In the case of location estimation, the limit cases, \(a\to\infty\) and \(a\to 0\), correspond to the mean and median estimates, respectively. The Huber loss function constitutes a combination of the \(\rho_{\ell_{2}}\) and \(\rho_{\ell_{1}}\) functions, behaving as a LS for small errors and as LAV for larger ones. The parameter \(a\) is chosen based on the target asymptotic relative efficiency (ARE) at a distribution. Thus, \(a_{0.95}=1.345\) indicates that the M-estimator based on Huber's loss function poses an ARE of 0.95 at the standard normal distribution [44].  
To achieve robustness, a desirable property of \(\rho\)-functions is boundedness, which implies redescending \(\psi\)-functions that tend to 0 at infinity. A popular choice is the Tukey's bisquare or biweight family of functions,  
\[\rho_{c}^{B}(x) =\left\{\begin{array}{ll}1-\left(1-\left(\frac{x}{c}\right)^{2} \right)^{3}&\text{if}\quad|x|\leq c\\ 1&\text{if}\quad|x|>c\end{array}\right. \tag{12}\] \[\psi_{c}^{B}(x) =x\left(1-\left(\frac{x}{c}\right)^{2}\right)^{2}I(|x|\leq c),\] (13) \[W_{c}^{B}(x) =\left(1-\left(\frac{x}{c}\right)^{2}\right)^{2}I(|x|\leq c), \tag{14}\]  
with \(c>0\) a constant parameter and \(I(|x|\leq c)\) the indicator function, i.e. \(I(|x|\leq c)=1\) if \(|x|\leq c\) and 0 if \(|x|>c\). Tukey's loss function is nonconvex and bounded, which makes it robust to large outliers, whose influence is completely denied. Similar to Huber-based M-estimation, the value of the parameter \(c\) controls the degree of robustness and it is chosen to achieve certain ARE [44]. For 95% efficiency at the Gaussian distribution, Tukey parameter \(c_{0.95}=4.685\).  
Figure 1: Illustration of the loss (**left**), score (**middle**) and weighting (**right**) functions for different classical and robust estimators. Here, the families of Huber and Tukey functions are depicted with parameters \(a=1.345\) and \(c=4.685\), respectively.
## 3 Performance Analysis
\[\eta=\frac{\alpha^{2}}{\alpha^{2}+(\Delta\beta)^{2}}\Biggl{[}1+\frac{4e^{- \alpha L}sin^{2}\left(\frac{\Delta\beta L}{2}\right)}{\left(1-e^{-\alpha L} \right)^{2}}\Biggr{]} \tag{9}\]  
\(\Delta\beta\) is the phase mismatch [32]  
\[C^{km}(t)=\sum_{f=1}^{M\times w}C_{f}^{km}P(t-fT_{c}) \tag{2}\]0  
where \(d\), \(L\), \(\gamma\), \(\alpha\), \(\text{P}_{\text{P}}\), \(\text{P}_{\text{q}}\), \(\text{P}_{\text{r}}\), \(\alpha\), \(D_{c}\), and \(SD\) are the degeneracy factor; the transmission length; the nonlinearity coefficient; the attenuation of the fiber; the transmitted power per channel; and the chromatic and slope dispersion, respectively. Considering that the desired user is the first user in the \(q\)th DWDM channel, then the received optical field at the photodetector of the first user in the \(q\)th DWDM channel after the decoding process is  
\[C^{km}(t)=\sum_{f=1}^{M\times w}C_{f}^{km}P(t-fT_{c}) \tag{2}\]1  
The first, second, third, fourth, and fifth terms in Equation (11) are the electric field of the first user in the \(q\)th channel; the electric field of the MAI, and the total electric field of FWM at \(f_{q}\), respectively. The total signal current can be derived as  
\[C^{km}(t)=\sum_{f=1}^{M\times w}C_{f}^{km}P(t-fT_{c}) \tag{2}\]2  
The first term in Equation (12) is the desired signal, the MAI component is represented in the second term; while the third, fourth, and fifth terms are due to the FWM, shot, and thermal noises, respectively. Then,  
\[ \begin{array}{ll}i_{\text{FWM}}(t)&=2\Re P_{q}^{\text{FWM}}(t)\cos^{2}(\theta _{q}-\theta_{\text{FWM}})+2\Re\sqrt{\frac{P_{r,qm}}{M\times w}(t)P_{q}^{\text{ FWM}}(t)}\cos(\theta_{q})\cos(\theta_{q}-\theta_{\text{FWM}})\\ &+2\Re\sum\limits_{m=j+1}^{M}\sqrt{\frac{P_{r,qm}}{M\times w}I_{qm,j}(t_{qm,j} )P_{q}^{\text{FWM}}(t)\cos(\theta_{q})\cos(\theta_{q}-\theta_{\text{FWM}})} \\ &\text{where the first, second, and third terms represent the mean current of FWM, the signal-FWM, and MAI-FWM, respectively.}\end{array}  \tag{13}\]  
The signal current for bit '1' is  
\[ \begin{array}{ll}i(1)&=\frac{\Re wP_{r,q1}}{M\times w}+\frac{\Re}{M\times w} \sum\limits_{m=j+1}^{M}P_{r,qm}I_{qm,j}(t_{qm,j})+2\Re P_{q}^{\text{FWM}}(1) \cos^{2}(\theta_{q}-\theta_{\text{FWM}})\\ &+2\Re\sqrt{\frac{P_{r,q1}}{M\times w}P_{q}^{\text{FWM}}(1)\cos(\theta_{q}) \cos(\theta_{q}-\theta_{\text{FWM}})}\\ &+2\Re\sum\limits_{m=j+1}^{M}\sqrt{\frac{P_{r,qm}}{M\times w}I_{qm,j}(t_{qm,j} )P_{q}^{\text{FWM}}(1)\cos(\theta_{q})\cos(\theta_{q}-\theta_{\text{FWM}})+i_ {sh}(1)+i_{th}(t)}\end{array}
## 3 Results and Discussion
### Relationship between Biological and Physicochemical Parameters and Nutrient Content  
Analysis of the establishment of possible relationships of biological parameters linked to nutrients and physicochemical parameters were established. This was done in order to see dependencies or \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**Biosolids Analysed for Each Season of the Year**} \\ \cline{2-5}  & **Spring** & **Summer** & **Autumn** & **Winter** \\ \hline H\({}^{\prime}\) & 2.61 \(\pm\) 0.08 a & 2.78 \(\pm\) 0.17 a & 2.46 \(\pm\) 0.12 a & 2.60 \(\pm\) 0.10 a \\ * SEI & 293.3 \(\pm\) 63.3 b & 488.3 \(\pm\) 115.6 a & 311.7 \(\pm\) 36.9 a, b & 331.7 \(\pm\) 40.7 a, b \\ \hline \hline \end{tabular} The results are the mean \(\pm\) standard deviation (3 replicates). Rows with different letters are significantly different according to [PERSON]’s analysis with \(p\leq\) 0.05. * (mmol hydrolysed substrate).  
\end{table} 
Table 4: Indices of microbial and functional diversity of biosolids collected and evaluated in the UWWTP at different season of the year. influences of the biological parameters on those. A product-moment correlation of Pearson was made (Table 5). Significant correlations occurred for MBC, MBN, C\({}_{\text{mic}}\)/N\({}_{\text{mic}}\), DSH activity, FDA activity, URS activity, qCO\({}_{2}\), qFDA, H\({}^{\prime}\) and SEI. MBC correlated positively with TOC, C/N, Fe, B and Mn and negatively with P, Zn, Cu, qCO\({}_{2}\) and qFDA. MNB had positive correlations with B and FDA and negative correlations with K, Zn, Cu, C\({}_{\text{mic}}\)/N\({}_{\text{mic}}\) and H\({}^{\prime}\). The C\({}_{\text{mic}}\)/N\({}_{\text{mic}}\) relationship correlated positively with EC, URS and H\({}^{\prime}\) and negatively with FDA and qFDA. The DSH activity only presented negative correlation with SEI. The FDA activity presented negative correlations with Fe, URS and H\({}^{\prime}\). URS activity correlated positively with K, Cu and H\({}^{\prime}\) and negatively with B and N-MB. The qCO\({}_{2}\) showed positive correlations with Zn, Cu, and qFDA and negative correlations with TOC, B and Mn. qFDA correlated positively with TOC and Zn and negatively with C/N, Fe and Mn. With respect to H\({}^{\prime}\) index, there was a positive correlation with Fe and SEI. SEI correlated positively with TOC, Fe and Mn. Regarding the establishment of correlations between physicochemical and biological parameters (Table 5), it was observed that MBC, MBN, C\({}_{\text{mic}}\)/N\({}_{\text{mic}}\), qCO\({}_{2}\), qFDA and URS activity could be used to predict the behaviour of some physicochemical properties such as TOC or of agronomic interest in biosolids samples. For example, a high MBC content would correspond to a high TOC content in