esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Home Network as The Universal Center of Reference
### Why to Leave the Beaten Path  
We argue that user-centric approaches are well suited to the following requirements for secure identifiers in future home networks:  
* **R1: Autonomy of Home Network Domains**: The management of identities must remain in control of the users of a home network, which we define as a domain.
* **R2: Hierarchical Identities for Entities**: The identity must reflect the membership of the entity to a certain home network as well as the individual entity itself.  
* **R3: Provable Identities**: Entities must be able to prove their membership in the home network domain to a) other entities in their own home network and b) to entities in another home network.
* **R4: Offline Authentication**: Entities must be able to authenticate to other entities without the help of a third service that has to be online all the time for the scheme to work.
* **R5: Scalability**: An identifier scheme must scale to the huge numbers of devices in networked homes that all need to authenticate to each other.
* **R6: User-Friendliness**: The system must be as easy to use as possible.  
The most widely-used scheme to identify users is simply a password in a challenge-response protocol. This is also the scheme that is still predominant in today's home networks. While it provides good control to users, works offline and enables authentication on a per user-basis (R1, R3, R4), it suffers from lack of scalability (think of configuration) (R5) and does not fulfill R2 nor R6, either. Given the usual issues with passwords, this scheme does not seem to be a good step forward and should be discontinued in home networking.  
Every now and then, the claim is made that smart cards might be a good alternative instead. We view them as a useful complementary tool within our own concept but believe that they need a proper framework to really exploit their possibilities. They are also a rather expensive option as one card per entity is needed and must be configured. The additional hardware that has to be purchased (today) is another drawback.  
Our proposal is thus to leave this well-trodden path once and for good and design a scheme that is based around turning the home network into an authority for the devices that are part of it.
# Texture Construction Edge Detection Algorithm
## 2 Texture Construction Edge Detection Algorithm  
This section explains how the TCEDA proposed in this paper uses the block texture change to extend the edge information of the image. When there is a grayscale change between the adjacent pixels, it will be included in the algorithm to determine whether to retain it as the edge of the image of the texture information. As to how to build the edge through the effective block texture feature, this paper distinguishes it in the following three parts: image pre-processing, the optimal edge thinning process, and edge texture construction processing. First, the image pre-processing converts the input color image into a grayscale image. In addition, to reduce the noise interference caused by the imaging technology and the operation of the optical device during the acquisition process, a Gaussian smoothing filter is used to weaken the variation of the grayscale value between the adjacent pixels in the image and the Sobel operator is used to calculate the gradient values of the image. Then, in order to filter the invalid texture variation features, the redundant pixels and weaker edges of the gradient change are regarded as noise. Therefore, a fixed threshold is set in the algorithm to filter noise to reduce its interference with the contour edges of the details in the image. Second, for the optimal edge thinning process, to avoid excessive unnecessary analysis of the image pre-processing results and reduce the complexity of the subsequent object edge construction, the algorithm uses the non-maximum suppression method to find pixels with larger regional gradient values. The point is used as a candidate edge point and the thinning texture template is then used to filter out redundant pixels to achieve the optimal thinning effect. Finally, in the edge texture construction processing part, since the texture change with a long extension has more meaningful edge features, the algorithm establishes the edge texture through the connection between the central block and the four adjacent blocks. The steps in the algorithm are described in detail in the subsections below.
## 3 Results
### Enzymatic Characterization  
The effect of temperature, pH, metal ions, enzyme inhibitors, and various sugars on enzyme activity is shown in Figure 2a-d. The optimal catalytic temperature of Mg-\(\beta\)gl and Hu-\(\beta\)gl was 40 \({}^{\circ}\)C (Figure 2a). The relative activity of Mg-\(\beta\)gl and Hu-\(\beta\)gl remained at a high level (\(>\)70%) between 20 \({}^{\circ}\)C and 50 \({}^{\circ}\)C. At 60 \({}^{\circ}\)C or above, a significant decrease in enzyme activity was observed. The optimal catalytic pH of Mg-\(\beta\)gl and Hu-\(\beta\)gl are also shown in Figure 2b. Between a pH of 2.0 and 6.0, the enzyme activity of Mg-\(\beta\)gl and Hu-\(\beta\)gl was above 75%, indicating that Mg-\(\beta\)gl and Hu-\(\beta\)gl had an effective catalytic capacity in acidic medium. Furthermore, Mg-\(\beta\)gl and Hu-\(\beta\)gl had the highest activity at a buffer pH of 4.0.  
Figure 2c shows the effect of metal ions and enzyme inhibitors on enzyme activity. The concentrations of Zn\({}^{2+}\), Mn\({}^{2+}\), Fe\({}^{2+}\), and Fe\({}^{3+}\) at 5 mM had a significant activating effect on the enzyme activity of the two enzymes Mg-\(\beta\)gl and Hu-\(\beta\)gl, contributing to an increase of 23.72-70.72%, while Cu\({}^{2+}\) had a significant inhibiting effect on the enzyme activity of Mg-\(\beta\)gl and Hu-\(\beta\)gl, with a decrease of 14.87-32.89%. The four enzyme inhibitors selected in this experiment were inconspicuous for Mg-\(\beta\)gl and Hu-\(\beta\)gl, and the enzyme activity only decreased by 2.71-7.66%. The effect of various sugars on enzyme activity is presented in Figure 2d. The nine selected various sugars showed activating effects on Mg-\(\beta\)gl and Hu-\(\beta\)gl, among which cellobiose was particularly effective in activating the enzyme activity of Mg-\(\beta\)gl and Hu-\(\beta\)gl, increasing the enzyme activity by 25.67-40.80%. Besides cellobiose, other sugars did not show a significant activation effect on Mg-\(\beta\)gl and Hu-\(\beta\)gl.  
Regarding the effect of ethanol on enzyme activity, the activity of \(\beta\)-glucosidase was inhibited in the presence of ethanol (Figure 3). Mg-\(\beta\)gl maintained more than 65% activity at 15% ethanol concentration, while Hu-\(\beta\)gl maintained more than 75% activity at 15% ethanol concentration.  
Figure 2: Temperature (**a**), pH (**b**), metal ions and enzyme inhibitors (**c**), and various sugars (**d**) profiles of the activity of the purified enzymes Mg-\(\beta\)gl and Hu-\(\beta\)gl. The values represent the mean of three replicate determinations. The error bars represent SD.
# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
## 7 Potential Real-World Applications  
### The Military and Secure Communication  
Modulation classification or recognition is the most essential part of secure communication. The proposed modulation classification model differentiates modulation types, which helps to identify legitimate signals and detect potential threats. This leads to operational security in defense communication [3; 49]. Recent advancements in AMR technologies are vital for military applications, as highlighted by [PERSON] et al., in [63], who emphasize the necessity of modulation recognition in obstructing enemy communication. Additionally, [PERSON] et al., in [64], discuss the widespread use of modulation recognition technology in modern radio communications, including military electronic warfare, where determining the modulation type of intercepted enemy signals is crucial for successful demodulation and information retrieval. This technology not only aids in electronic warfare support but also plays a significant role in electronic protection against enemies' jamming signals.
## 5 Numerical Results and Performance Analysis
### Simulation Setup
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & \multicolumn{2}{c}{**Values (Units)**} \\ \hline Number of nodes in substrate & Access layer network & 10 nodes \\ networks & Aggregation layer network & 5 nodes \\  & Core layer network & 3 nodes \\ \hline Network capacity in substrate & Access layer network & 40 Gbps \\ networks, \(C_{(u,v)}^{P}\) & Aggregation layer network & 80 Gbps \\  & Core layer network & 80 Gbps \\ \hline Maximum length of links & Access layer network & 20 km \\ in substrate networks, \(l_{(r,d),ij}^{P}\) & Aggregation layer network & 50 km \\  & Core layer network & 100 km \\ \hline Node connectivity in substrate & \multicolumn{2}{c}{(0.3, 0.4)} \\ networks & \multicolumn{2}{c}{(0.3, 0.4)} \\ \hline Bandwidth of each & \multicolumn{2}{c}{} \\ substrate link, \(B_{ij}^{P}\) & \(B_{ij}^{P}=3\times 10^{8}\) m/s & \\ \hline \multirow{3}{*}{VNU capacity of substrate network nodes} & Uniform condition & 5 VNUs per substrate node \\  & & Access layer network & 3 VNUs \\  & Non-uniform & Aggregation layer network & 5 VNUs \\  & condition & Core layer network & 10 VNUs \\ \hline NS types & \multicolumn{2}{c}{3 types, s-VCC, eRTC and eMBB} \\ \hline Types of VNUs & \multicolumn{2}{c}{} \\ \hline Maximum total traffic demands & s-VCC & 500 MB \\ of NSs, \(f_{(u,v),ij}^{(s,k),(s,l)}\) & eRTC & 6 GB \\  & eMBB & 9 GB \\ \hline Latency threshold & s-VCC & 30 ms \\ (LT), \(T_{th}^{s}\) & eRTC & 80 ms \\  & eMBB & 150 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters.
## 3 Battery Mathematical Models
### Battery Degradation Model
#### 3.3.1 Charging/Discharging Rate  
A battery degradation model which comprises the temperature, the number of cycles and the charging/discharging rate is firstly developed in [21]. The capacity loss (\(Q_{loss}\)) is given by the Arrhenius Equation (12). Cycle's exponent \(z\) is set to be constantly 0.74. Furthermore, the Pre-exponential factor \(A\) and the activation energy \(E_{a}\) are calculated respectively from Equations (13) and (14) which are obtained as a result of the data fitting process [21]:  
\[Q_{loss}\ =\ A_{Cnt_{e}}e^{\frac{-E_{a}(Cnt_{e})}{RT}}n^{0.74} \tag{12}\]  
\[A_{Cnt_{e}}\ =\ e^{\xi_{4}Cnt_{e}^{2}-\xi_{5}Cnt_{e}+\xi_{6}} \tag{13}\]  
\[E_{a}(Cnt_{e})\ =\ \xi_{1}e^{\xi_{2}Cnt_{e}}+\xi_{3} \tag{14}\]  
where \(\xi_{1}\), \(\xi_{2}\), \(\xi_{3}\), \(\xi_{4}\), \(\xi_{5}\) and \(\xi_{6}\) symbolize constant parametric coefficients.
## 4 Experiment and Results
### Performance Measures
#### 4.3.6 Recall  
It represents the ratio of predicted correct labels to the total number of predicted labels averaged over all the instances. Equation (46) computes the _recall_ score.  
\[Recall=\frac{1}{N}\sum_{i=1}^{N}\biggl{(}\frac{MLC(x_{i})\cap Y_{i}}{MLC(Y_{i} )}\biggr{)}. \tag{46}\]
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 6 Conclusions  
The methods to localize the forest damages caused by windstorms using space-borne SAR data were developed and possibilities to an operative system investigated. Multitemporal Sentinel-1 time series were used.  
Support vector machine (SVM) gave the largest overall accuracies among the three methods tested, improved k-NN (ik-NN), multiple logistic regression (MLR) and SVM. The proportion of correctly classified stands (OA) in a separate validation data was 79%, and 75% if only one Sentinel-1 scene after the damage was used. The user's accuracy (UA) for severe damages was 62%, and 75% for slight damages. The producer's accuracies (PAs) were somewhat lower. The accuracy of 75% was achieved using only one Sentinel-1 scene after the damage, here two days after the damage, in addition to the data before the damage.  
Using segmentation-based calculation units only slightly increased the OA, implying that this approach may presume further work. Most likely, not only SAR data, but also inventory and other auxiliary data should be used in the segmentation methodology.  
The study indicates that the damages could be localized using only one Sentinel-1 scene after the damage implying a time-lag of potential satellite SAR-based assessment method would be just a few days after the damage. This gives promises that a SAR-based near-real-time semi-automatic operative system to monitor windstorm damages is feasible.  
**Author Contributions:** Conceptualization, E.T., J.P., O.A., H.H. and G.R.; methodology, E.T.; validation, E.T. and H.H.; formal analysis, E.T.; investigation, E.T., O.A. and J.P.; data curation, G.R., H.H. and E.T.; writing--original draft preparation, E.T.; writing--review and editing, E.T., O.A. and J.P.; visualization, E.T., G.R and O.A.; and project administration, E.T. and J.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The study was funded by Business Finland as a part of the project conducted by the Finnish Forest Centre, grant number 4480/31/2018. O.A. was supported by Business Finland MULTICO project under grant 6501/31/2019. All authors were also supported by their Institutes.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Sentinel-1 satellite data are available from ESA via Copernicus Open Access Hub at no cost. Derived data supporting the findings of this study are available from the corresponding author [PERSON] on request.  
**Acknowledgments:** The damage and non-damage stand data with stand boundaries were provided by the Finnish Forest Centre. Four anonymous reviewers provided constructive comments that improved the text. We thank all institutions and individuals who have contributed to the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results
### Application of VAR and RNN Methods to Field Dataset
The Figure 6 shows that four out of five forecasts are excellent (error of about 1-2%). One of the values \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Sequential ERT Surveys** & \multicolumn{8}{c}{**Resistance at the Various Quadrupoles (\(\Omega\))**} \\ \hline  & Q1 & Q2 & Q3 & Q4 & Q5 & Q6 & Q7 & Q8 & Q9 & Q10 \\ STEP 10 & 1.76 & 19.16 & 8.80 & 1.65 & 12.03 & 14.33 & 16.15 & 13.40 & 19.71 & 8.88 \\ STEP 11 & 1.68 & 19.21 & 8.46 & 3.59 & 9.63 & 13.15 & 15.29 & 12.93 & 19.13 & 9.16 \\ STEP 12 & 1.74 & 19.13 & 8.46 & 3.09 & 10.06 & 13.33 & 15.60 & 13.20 & 19.66 & 9.37 \\ STEP 13 & 2.04 & 18.87 & 9.33 & 1.08 & 9.64 & 12.81 & 15.66 & 13.37 & 20.80 & 10.33 \\ STEP 14 & 1.66 & 19.07 & 8.47 & 3.17 & 10.78 & 13.17 & 15.32 & 12.75 & 19.46 & 9.25 \\ STEP 15 & 1.77 & 19.02 & 8.50 & 3.24 & 10.08 & 12.94 & 15.18 & 12.81 & 19.65 & 9.11 \\ STEP 16 & 1.92 & 18.90 & 8.40 & 2.95 & 10.79 & 13.19 & 15.29 & 12.66 & 19.42 & 9.39 \\ STEP 17 & 1.83 & 19.12 & 9.69 & 1.15 & 9.24 & 12.72 & 16.72 & 14.35 & 21.21 & 11.87 \\ STEP 18 & 1.84 & 18.29 & 7.29 & 0.22 & 11.95 & 16.95 & 15.46 & 15.47 & 19.09 & 12.43 \\ \hline
**Sequential Predictions** & \multicolumn{8}{c}{**Predicted resistance at the Various Quadrupoles (\(\Omega\))**} \\ \hline  & PRED 1 & 1.85 & 18.80 & 8.47 & 1.38 & 10.95 & 14.07 & 15.80 & 14.12 & 20.05 & 11.10 \\ PRED 2 & 1.86 & 18.77 & 8.47 & 1.25 & 11.04 & 14.12 & 15.84 & 14.19 & 20.09 & 11.24 \\ PRED 3 & 1.87 & 18.75 & 8.47 & 1.12 & 11.12 & 14.18 & 15.88 & 14.27 & 20.13 & 11.37 \\ PRED 4 & 1.88 & 18.72 & 8.47 & 0.99 & 11.21 & 14.24 & 15.92 & 14.35 & 20.17 & 11.50 \\ PRED 5 & 1.89 & 18.69 & 8.47 & 0.88 & 11.29 & 14.30 & 15.96 & 14.43 & 20.22 & 11.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental measurements and corresponding predictions using Vector Autoregressive (VAR) statistical method. (at the third time step in the \"future\") shows much less prediction accuracy (outlier?). The same graph is calculated for each quadrupole. For this dataset, the prediction uncertainties range between \(\pm\)1% and \(\pm\)5%. In general, predictions retrieved by Recurrent Neural Network result are very consistent with the predictions shown in Table 2. The reason for this consistency probably arises in the fact that both the VAR and Neural Network methods are based on the same multivariate approach of data analysis. However, the principal advantage of RNN over VAR is that RNN can model a sequence of records so that each pattern can be assumed to be dependent on previous ones. In particular, LSTM allows for properly selecting the fraction of previous events to consider. This approach allows for properly managing complex historical datasets, improving the reliability of the predictive model(s) [67].
## 3 Results
### Effect of Intensity Processing Procedures  
While there was generally greater variability between extraction configurations, intensity processing procedures had varying effects between extraction configurations and other IP procedures, especially at the lower end of the DSC score distribution, as shown in Figures 2 and 3. In this regard, N3 intensity inhomogeneity correction had a markedly different impact on brain extraction than KDE-based, WhiteStripe, and Z-score intensitynormalization, which had more idiosyncratic and less significant effects. Figures 4 and 5 show the impact of the intensity inhomogeneity correction and intensity normalization techniques on the mean DSC score of each extraction configuration relative to the control pipeline. For T1-MPRAGE images, BET \(\mid\) B,f0.2,g0.3, BET \(\mid\) B,f0.1, BET \(\mid\) g\(-\)0.3, BET \(\mid\) f0.2, BET \(\mid\) f0.8, and BET \(\mid\) f0.2,g0.3 cluster around similar mean DSC values regardless of intensity processing, while other BET configurations, like BET \(\mid\) B, BET \(\mid\) R, and BET, show larger changes in mean DSC depending on the intensity processing procedure.  
Figure 4: The impact of intensity processing procedures on brain extraction performance as the percent difference in mean DSC for T1-MPRAGE MRIs. The bars in the subplots represent the variation in mean DSC associated with each IP procedure/extraction configuration relative to the mean DSC of the control pipeline. Subplots (**a**) through (**k**) show the effect of the IP Procedures on the accuracy of the following configurations: BET (**a**), BET \(\mid\) B (**b**), BET \(\mid\) B,f0.1 (**c**), BET \(\mid\) B,f0.2,g0.3 (**d**), BET \(\mid\) R (**e**), BET \(\mid\) f0.2 (**f**), BET \(\mid\) f0.2,g0.3 (**g**), BET \(\mid\) f0.8 (**h**), BET \(\mid\) g0.3 (**i**), BET \(\mid\) g0.3 (**j**), and optiBT (**k**). Error bars indicate the standard error. Bars deflecting upward indicate an increase in mean DSC, and bars deflecting downward indicate a reduction in DSC.
# A Review of DSP-Based Enabling Technologies for Cloud Access Networks
## 2 Cloud Access Network Concept
### CAN Architecture
To allow increased flexibility in network configuration and versatility, spectral converters have also been proposed and investigated [11], which enables signals to be converted from one spectral location  
Figure 3: Cloud Access Network (CAN) Architecture. to a new spectral location, purely in the optical domain, thus an optical signal can be relocated to occupy a different spectral location. The spectral converter can, therefore, eliminate the probability of channel blocking and allow increased channel utilisation. For example, if a signal is located in a specific spectral position and is to be added to an aggregated signal which does not have that specific spectral position unoccupied the spectral converter can be used to relocate the signal to a free spectral position before combination with the aggregate signal in the soft-ROADM. Compared to existing spectral converter technologies [12; 13] the proposed spectral converters have the following key features:  
* Low complexity and thus low cost due to no OE or EO converters or extra light sources. * Virtually no distortion to the converted signal waveform. * Strict transparency to the underlying signal characteristics. * SDN-controllable reconfigurability of parameters, such as conversion efficiency, and frequency detuning range. * The utilisation of optical bandwidth outside the operating range of the ROTs. * Conversion-induced latency is negligible. The fact that the spectral converters can reposition signals in the optical spectrum, with frequency detuning range potentially as large as 50 to 100 GHz, means that optical bandwidth outside the operating bandwidth of the ROTs can also be utilised, as signals can be spectrally up-shifted, transported through the CAN and then spectrally down-shifted. This also provides a means of cost effectively, incrementally scaling up network capacity, as spectral converters can be added as needed without the need to upgrade ROTs with higher bandwidth ROTs. The aforementioned CAN network elements, therefore, enable the realisation of a CAN with a highly dynamic architecture, as it can be reconfigured on demand by a centralised SDN controller, to meet the traffic demands of the prevailing services. This highly flexible network with dynamic bandwidth provision thus allows the operator to change from the traditional operating model of fixed bandwidth provision and offer service driven, cost-effective bandwidth provision, thus offering an agile, responsive and future-proof network that can support the emerging and constantly evolving network applications.
## 3 Results and Discussion
### Water Depth Optimization  
Optimization of culture depth was based on the comparison of trials performed under process conditions 1 and 2. An increase of 0.8 g m\({}^{-2}\) d\({}^{-1}\) in the global biomass productivity with the 0.13 m water depth compared with the 0.20 m water depth was observed. These results are in agreement with those obtained by San Pedro et al. [51] and de Vree et al. [50], who reported that large pond depths lead to light-limited cells and the creation of large dark zones wherein microalgae do not photosynthesize, reducing the net productivity of the culture. Dark respiration can comprise as much as 30% of the strain's maximum specific growth rate [55] and can explain the small decrease obtained in the global specific growth rate with the increase of the water depth.  
The quality of biomass produced in both process conditions remained the same in terms of quantity of lipids and EPA. PUFA/SFA and \(\omega\)-6/\(\omega\)-3 ratios were also identical for both process conditions. On the other hand, we verified a 54% increase in energy consumption with process condition 2, as a result of higher paddlewheel rotation speed. A power consumption increase between 0.20 m and 0.10 m water depth trials was reported by Mendoza et al. [56]. These authors showed that a 0.20 m water column is the optimum value for minimizing power consumption. Chiaramonti et al. described an increase in total head losses caused by a reduction in culture depths [57].  
Thus, a decrease in water depth induced a small improvement of global biomass productivity, however, the power consumption was the differentiator factor. The lower energy required with a pond depth of 0.20 m made it the best option for microalgal cultivation.