esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Honeydew and Its Effects on Parasitism  
BPH produced less honeydew when feeding on IR62 compared to IR64 and honeydew produced on IR62 was mainly derived from xylem feeding (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Honeydew Production**} & \multicolumn{2}{c}{**Variety and Nitrogen**} & \multicolumn{2}{c}{\(\chi^{2}\)**-Values 1**} \\ \hline  & **IR62** & \multicolumn{2}{c}{**IR64**} & \multicolumn{2}{c}{**Variety**} & \multicolumn{1}{c}{**Nitrogen**} \\ \hline  & **0 Added N** & **150 kg N ha-1** & **0 Added N** & **150 kg N ha-1** & & \\ \hline Total (mm\({}^{2}\)) & 0.31 \(\pm\) 0.08 & 0.44 \(\pm\) 0.09 & 1.20 \(\pm\) 0.07 & 0.99 \(\pm\) 0.15 & 51.173 *** & 0.186 ns \\ Xylem Derived (proportion) & 0.85 \(\pm\) 0.10 & 0.65 \(\pm\) 0.10 & 0.10 \(\pm\) 0.03 & 0.20 \(\pm\) 0.08 & 38.908 *** & 0.284 ns \\ \hline \hline \end{tabular}  
* \({}^{1}\): ns = \(p\) \(>\) 0.05; *** = \(p\)\(\leq\) 0.001; variety–nitrogen interactions were non-significant.  
\end{table}
Table 1: Honeydew production by BPH on IR64 (susceptible) and IR62 (resistant) rice plants. Numbers are means \(\pm\) SEM (N = 5).  
Figure 3: Effects of nitrogen (0 or 150 kg ha-1 of nitrogen) and variety (IR62: resistant and IR64: susceptible) on parasitism of WBPH and BPH eggs by _Anagrus_ spp. and _Oligosita_ spp. in field plots. Experiments were conducted during the dry (**A,C**) and wet (**B,D**) seasons of 2013. Standard errors are indicated (N = 6); results of GLMs are indicated as * = \(p\)\(\leq\) 0.05 and *** = \(p\)\(\leq\) 0.001. BPH = brown planthopper and WBPH = whitebacked planthopper.  
Parasitism of BPH eggs by _Anagrus_ spp. was relatively low during the dry season exposure but higher during the wet season exposure (\(\chi^{2}=24.833\), \(p<0.001\)). There was a significant nitrogen-honeydew interaction (\(\chi^{2}=8.558\), \(p=0.014\)) because of higher parasitism by _Anagrus_ spp. of BPH eggs on plants with IR62-derived honeybee than on plants without honeybee in high-nitrogen plots (irrespective of variety), but similar parasitism levels on plants with different honeyde treatments in low-nitrogen plots (Figure 4A,B).  
Parasitism of BPH eggs by _Oligosita_ spp. was similar during the dry and wet season exposures (\(\chi^{2}=2.085\), \(p=0.149\)). A higher proportion of eggs were parasitized in IR64 plants (\(\chi^{2}=5.986\), \(p=0.014\)) and under high nitrogen (\(\chi^{2}=8.522\), \(p=0.004\)). Honeydew treatments had no significant effect on parasitism by _Oligosita_ spp. (\(\chi^{2}=5.010\), \(p=0.082\)) (Figure 4C,D). The covariate 'egg density' significantly, positively influenced parasitism rates (\(\chi^{2}=77.133\), \(p<0.001\)).
# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
## 2 Model Description  
### CNN-BiConvLSTM Model  
In terms of environment configuration, Tensorflow1.14 was employed to construct and train the DNN models. The CNN-BiConvLSTM model used in this study consisted of 3 components: A CNN encoder, a bi-directional recurrent neural networks (BiRNNs) encoder, and a CNN decoder. As shown in Figure 1a, a CNN encoder was used to extract high-level features of input radar images. It consisted of 3 CNN layers with 8, 16, and 32 filters and a kernel size of 5 \(\times\) 5, where each CNN layer was followed by an average pooling layer with a stride of 2. The spatial sizes of the feature were simultaneously reduced, which served the CNN-BiConvLSTM model well since it required less memory usage. Furthermore, a larger receptive field was obtained for the BiRNN encoder compared to the original inputs. Thus, it would be helpful in dealing with the rapid spatial movements of radar echoes. The BiRNN encoder (Figure 1a,b) was used to capture spatial-temporal variations from high-level features. It was constructed by stacking 4 BiConvLSTM layers with 32 filters and a kernel size of 5 \(\times\) 5. The CNN decoder (Figure 1a) was finally used to transform the high-level features to radar images. It consisted of 3 CNN transpose (Deconv) layers with 32, 16, and 8 filters and a kernel size of 5 \(\times\) 5, corresponding to the CNN encoder. It is worth noting that the CNN encoder and decoder share parameters for all input and output radar images to ensure unique relationships between high-level features and radar images. The leaky rectified linear unit (ReLU) activity layer was used for all CNNs and CNN transpose layers to avoid the vanishing gradient problem. The formula is as follows:  
\[f(x)=\left\{\begin{array}{ll}\alpha x&\quad\text{for }x<0\\ x&\quad\text{for }x\geq 0\end{array}\right.\qquad\alpha=0.1 \tag{1}\]
## 3 Results
### Physical Performance Data
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Playing Positions**} & **T-Half Test** & **Yo-Yo IR 1** & **Medicine Ball** \\  & **[s]** & **Test [m]** & **Throw (3 kg [m])** \\ \cline{2-4}  & \multicolumn{3}{c}{**Mean \(\pm\) SD (95\% CI)**} \\ \hline Pivots & 5.97 \(\pm\) 0.40 & 1391 \(\pm\) 288 & **11.6 \(\pm\) 1.09** \\ (n = 14) & (5.78–6.17) & (1250–1533) & (11.1–12.2) \\ Wings & 5.80 \(\pm\) 0.36 & **1591 \(\pm\) 282** & 10.2 \(\pm\) 1.22 \\ (n = 14) & (5.61–6.00) & (1449–1732) & (9.62–10.8) \\ Backs & **5.62 \(\pm\) 0.32** & 1533 \(\pm\) 294 & 10.8 \(\pm\) 0.80 \\ (n = 12) & (5.41–5.83) & (1381–1686) & (10.2–11.5) \\ Goalkeepers & 6.45 \(\pm\) 0.35 & 1104 \(\pm\) 117 & 11.2 \(\pm\) 1.03 \\ (GK; n = 10) & (6.22–6.68) & (937–1271) & (10.5–11.9) \\ \hline \hline \(p\) & **<0.001** & **<0.001** & **0.007** \\ \(\eta_{\text{p}}\)\({}^{2}\) & **0.41** & **0.33** & **0.23** \\ \hline \hline \multirow{2}{*}{Partial effects (\(p\))} & GK vs. Wings: \textless{}0.001 & GK vs. Wings: \textless{}0.001 & Pivots vs. Wings: 0.005 \\ \cline{1-1}  & GK vs. Backs: \textless{}0.001 & GK vs. Backs: 0.002 & \\ \cline{1-1}  & GK vs. Pivots: 0.016 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Agility, endurance and throwing performance depending on playing positions. CI = confidence interval. Relevant mean differences (dependent variable: positions) and performance maxima are marked in bold.
## 1 Introduction
However, very little research on this aspect has been carried out in real concert halls due to both technical and economic issues. Therefore, the present study attempts to clarify the influence of diffusive surface location on the objective and subjective aspects by means of both in-situ measurements and perceptual listening tests. Since both technical and economic issues would limit the research, a flexible environment--the hall Espace de Projection at IRCAM (Paris)--has been involved. Six configurations have been created by varying the location of the diffusive surfaces over the front, mid and rear part of the lateral walls, while the other surfaces have been maintained absorptive or reflective. Moreover, two reference conditions, that is, fully absorptive and reflective boundaries of the hall have been tested. The ISO 3382 objective acoustic parameters, such as reverberation time (\(T_{30}\)), early decay time (\(EDT\)), clarity (\(C_{80}\)), definition (\(D_{50}\)), center time (\(T_{\rm s}\)), and interaural cross-correlation (\(IACC\)) have been estimated from the measured impulse responses. Furthermore, subjective investigations have been performed in order to identify the detectable differences between different locations of the diffusive surfaces.
## 3 Results and Discussion
### Accuracy Improvement
Specifically, EMG signals corresponding to three holding motions, namely holding a glass of water, water bottle, and pen, in the public EMG dataset [28] were used in this study. The data preprocessing and data augmentation techniques used for brainwave signals were also applied to the EMG signals. Figures 11 and 12 show the accuracies obtained via GND v1 and GNDv2, respectively. Figure 11: Classification accuracy of EMG data with GNDv1; the symbols and error bars represent the average accuracy and standard error, respectively. The augmented dataset significantly increased the accuracy of EMG signals. When 800 samples were used, the maximum accuracy was approximately 71.1%, which was very high considering that the accuracy for a normal distribution was approximately 63.5%. When 80 and 160 samples were augmented with GNDv1, the accuracy increased over \(\beta\) and saturated when \(\beta\) was approximately 1. Meanwhile, when 480 or 800 samples were augmented with GNDv1, the maximum accuracy was obtained when \(\beta\) was 0.4. This difference can be attributed to an insufficient number of random noises. When the number of generated random noises is small, the actual distribution of the generated random noise can be different from the expected distribution (i.e., GNDv1). Thus, the effects of \(\beta\) on the accuracy are different for small and large samples. When artificial EMG data were created using GNDv2, a high accuracy was also obtained, as shown in Figure 12. When 800 samples were used, the maximum accuracy was approximately 72.6%, which was significantly higher than the value obtained with the normal distribution. Moreover, the accuracy tends to be high for negative \(\kappa\), suggesting that high-quality EMG signals can be artificially generated when random noise distribution is slightly skewed to the left. The value of \(\kappa\), corresponding to the maximum accuracy, is different for small (i.e., 80 and 160) and large (i.e., 480 and 800) samples. This difference can also be explained by the actual distribution of generated random noise.
# On End-to-End Intelligent Automation of 6G Networks
## 4 6G End-to-End Network Automation Challenges
### Security Architecture  
Last but not least, the seventh challenge facing 6G network automation and orchestration is having a suitable security architecture. This is particularly important given the larger potential attack surfaces (due to the presence of the extreme edge devices) and the added stakeholders involved in 6G networks as compared to 5G networks [104; 105]. One key parameter of the security of the 6G network and corresponding orchestration mechanisms is the Level of Trust (LoT) [106]. LoT has been identified as a key value indicator for 6G networks by the Hexa-X European project led by Ericsson and Nokia [107]. This indicator is important because 6G networks are expected to have a more pervasive impact on humans. Thus, humans need to trust that any system, technology, or infrastructure is secure and the corresponding decisions made by them are indeed the correct decisions.  
Based on the aforementioned, any 6G orchestration framework or system needs to have several underlying security features. Firstly, the security mechanisms deployed need to be applicable system-wide and distributed across multiple autonomous managers. This helps to ensure that the response to any potential cyber-attacks is localized and performed in real time. Secondly, these mechanisms need to support a high level of collaboration and cooperation to ensure the E2E protection of all entities and stakeholders supported by the 6G orchestration framework. Thirdly, they need to be tailored to their deployment location and the placed load at the asset to be protected. Fourthly, the security mechanisms should also be capable of dealing with quantum computing-based threats given the rapid growth in this area. Finally, these mechanisms need to also be suitable for physical layer security and not just at the upper network and architecture layers.  
In addition to these features, the impact of network slicing needs to be accounted for. This is crucial since any security compromise or anomaly in any of the resources or services can propagate to other dependent resources or services. In turn, this can compromise other network slices that may be sharing these resources and services. Accordingly, any 6G orchestration framework needs to take this into account, particularly if network slices are deployed across several domains and have differing administrative, technological, and organizational policies. Moreover, these security mechanisms need to be modular and autonomous in nature to ensure that they are scalable with the increasing number of network slices. Therefore, as illustrated above, the security aspect should not be neglected when designing and developing 6G orchestration frameworks since missing any security threat can have serious repercussions not only to the network and its entities, but also to the lives of the humans interacting with it.
## 4 Conclusions  
Based on the obtained data, the following conclusions can be drawn:  
1. The synthesis conditions for obtaining molecularly imprinted structures with lead and terbium salts as the templates are developed. The molar ratio of template/monomer/monomer/cross-linker = 1:5:5:8 is the most accurate for the successful synthesis of MIP along with NIP.
2. MIPs contain large number of pores in comparison with NIPs, what is evidenced by the SEM analysis.
3. A mechanism for obtaining MIP with a terbium template is suggested. The reaction is supposed to promote the hydrogen bonding of the salt part of rare earth metals with the carboxylic groups of methacrylate acid and the nitrogen groups of 2-vinylpyridine through the further polymerization of the monomers with the help of the cross-linker and initiator.
4. The sorption effectiveness (in view of the studied sorption properties) of the developed MIP is more than seven times higher in comparison with the NIP. According to the PSO kinetic model, the NIP might bind adsorbates weakly but rapidly, while in the case with the MIP, slower but more extensive adsorption is observed.  
Figure 18: Desorption degree of Tb.  
The desorption process of terbium from MIP and NIP allows extraction of 89.73% and 52.25% of the metal, respectively.  
Based on the above-mentioned findings, it can be concluded that the developed MIPs can be considered promising new-generation sorbents for the selective sorption of target rare earth metals in industry.  
Conceptualization, L.A., R.K.; data curation, L.A., Y.M., A.B., R.K.; formal analysis, L.A., Y.M., R.K.; funding acquisition, Y.M., R.K.; investigation, L.A., Y.M., A.B., R.K.; methodology, L.A., R.K.; project administration, L.A.; resources, Y.M., R.K.; supervision, L.A., R.K.; validation, L.A., Y.M.; visualization, Y.M., R.K.; writing--original draft, L.A., Y.M., A.B., R.K.; writing--review and editing, L.A., R.K. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Ministry of Science and Higher Education of the Republic of Kazakhstan, grant numbers AP19174288, AP19674527, and AP13067631.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors thank [PERSON] (Nazarbayev University, Astana, the Republic of Kazakhstan) for conducting the SEM analysis of the imprinted and non-imprinted structures. The authors thank [PERSON] and [PERSON] (Al-Farabi Kazakh National University, Almaty, the Republic of Kazakhstan) for consulting about the calculations of the rate constants of the sorption for MIP and NIP.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MIP & Molecularly imprinted polymer \\ NIP & Non-imprinted polymer \\ SEM & Scanning electron microscopy \\ FTIR & Fourier transform infrared spectroscopy \\ IF & Imprinting factor \\ MAA & Methacrylic acid \\
2-VP & 2-vinylpyridine \\ DVB & Divinylbenzene \\ EGDMA & Ethylene glycol dimethacrylate \\ HEC & Hydroxyethyl cellulose \\ AIBN & Azobisisobutyronitrile \\ \end{tabular}
## 2 Materials and Methods
### Ground Truth Data for Validation and Model Evaluation Criteria  
Five sites on PEI were selected to collect ground truth data. Using a Real-Time Kinematic (RTK) GPS with sub-meter accuracy, a total of 200 validation points were collected at each site. These points were equally distributed in each class, meaning that 50 points were collected per class at each site. The same ground truth data were used to validate LULC maps generated with Sentinel-2A and Landsat-8 imagery.  
Several statistical indicators were used to assess the accuracy of the models. The overall accuracy of the models was used to describe the correct proportion of mapped pixels. The overall accuracy considers that 100% of all the classified reference sites are mapped accurately [47]. The overall accuracy was calculated using the following formula:  
\[\text{Ovrall~{}Accracy~{}(\%)}=\frac{\text{Number~{}of~{}correctly~{} classified~{}pixels}}{\text{Total~{}number~{}of~{}referenced~{}site~{}pixels}}\times 100 \tag{1}\]  
Similarly, each LULC class's accuracy was determined using producer/user accuracies. Producer/user accuracy determines the real feature on the ground surface correctly shown on the classified map [47]. The producer and user accuracy were calculated using the following formula:  
\[\text{Producer/User~{}Accuracy~{}(\%)}=\frac{\text{Correctly~{} classified~{}pixels~{}in~{}one~{}category}}{\text{Total~{}classified~{}pixels~{}in~{}all~{}categories}}\times 100 \tag{2}\]  
The kappa coefficient is another statistical indicator to evaluate classification accuracy. Kappa evaluates how well the classification has performed compared to the randomly assigned value. Its values range from -1 to 1, with the lowest value indicating that the classification is not better than a random classification, while a value close to a positive one indicates that the classification is significantly better than the random classification [48]. The kappa coefficient was calculated using the following formula:  
\[\text{Kappa~{}Cofficient}=\frac{(TS-TCS)-\sum(Column~{}total*Row~{}Total)}{TS^{2}- \sum(Column~{}total*Row~{}Total)}\times 100 \tag{3}\]  
where _TS_ is the total number of samples, _TCS_ is the total number of classified samples, and column sum and row sum represent the total number of classified pixels for each class in each column and row, respectively.
## 4 Methodology  
Geometric and atmospheric corrections were performed on the images using the Google Earth Engine. Band combinations known to depict for e.g., vegetation bands (7, 5, 3, and 7, 6, 4), and agriculture bands (5, 4, 1, and 6, 5, 2) were employed during the image classification process. Several classifiers were tested, and the random forest (RF) algorithm was selected as a result of its high performance on our dataset. To examine the accuracy (which refers to how a classified map reflects the reality on the ground), we relied on the internal confusion matrix generated by the RF algorithm. The total number of accurate pixels was further divided by the total number of pixels in the error matrix to yield the overall accuracy.  
Figure 1: Emalahleni (study area).
## 3 Novel Framework for Advanced Synchrophasor Analysis in Modern Control Centres
### Disturbance Detection  
Anomaly detection or novelty detection is an active research area in machine learning to automatically detect abnormal signal behavior or signal patterns that deviate strongly from the expected or majority behavior. This is different from the outlier detection (see Section 3.3), in which the negative examples in the training dataset are unlabeled or unknown. Different techniques are proposed in literature including classification based approaches (e.g., one-class support vector machine), clustering based approaches (e.g., density-based clustering) or subspace-based approaches (e.g., principle component analysis) and have a high resemblance with outlier detection methods. Further literature can be found in [51; 52]. Compared to other approaches, the z-score based _disturbance detection module_ computes anomaly scores instead of a binary decision. This is done by deriving features in the time- and frequency-domain and by applying a Z-transformation considering a fixed amount of historical feature values. The resulting z-scores are computed for each PMU measurement channel separately assuming a Gaussian feature distribution within the normal grid operation. The basic workflow is given in Figure 5.  
The input matrix \(X\) consists of the \(N\) last measurement samples \(\underline{x}^{n}\) and are normalized into the range \([0;1]\). Within the feature extraction step, a feature vector \(\underline{x}^{n}_{\text{F}}\) is created for each sample using signal analysis techniques in the time- and frequency-domain-see (3) and (4).  
\[\underline{x}^{n}_{\text{F}}=f_{\text{F}}\Big{(}\underline{x}^{n}_{\text{N}}, \underline{\theta}_{\text{F}}\Big{)} \tag{3}\]  
\[\underline{X}_{\text{F}}=\left[\underline{x}^{n}_{\text{F}}\right]^{n=N}_{n=1 }=\left[\underline{x}^{k}_{\text{F}}\right]^{k=K}_{k=1}\text{ with }\underline{x}^{k}_{\text{F}}\in\mathbb{R}^{N}\text{ and }\underline{x}^{n}_{\text{F}}\in \mathbb{R}^{K} \tag{4}\]  
After the feature extraction phase, z-scores \(z^{k}\) are computed for each feature using the actual feature value \(\underline{x}^{k}_{\text{F},N}\), the mean and the standard deviation of all past \(N\) features of the current measurement channel \(\underline{x}^{k}_{\text{F}}\). The z-score vector \(\underline{z}\) contains the z-scores from all feature values-see (5) and (6).  
\[z^{k}\Big{(}\underline{x}^{k}_{\text{F}}\Big{)}=\frac{\left|\underline{x}^{k} _{\text{F},N}-\mu\Big{(}\underline{x}^{k}_{\text{F}}\Big{)}\right|}{\sigma \Big{(}\underline{x}^{k}_{\text{F}}\Big{)}} \tag{5}\]  
\[\underline{z}=\left[z^{k}\right]^{k=K}_{k=1} \tag{6}\]  
Figure 5: Z-score based disturbance detection model.  
The final z-score for a given PMU measurement channel can be derived from the maximum value within the z-score vector. A short overview of the \(K\) features is given in Table 3. As a very rough approach, an anomaly level can be estimated depending on these z-score values using the overview in Table 4.