esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, we showed a tuneable hybrid signal processing system to increase the maximum transmission reach for a DD system. Considering only the optical part of the hybrid module (n-stage SD/SC), we demonstrated that adjusting only the delay in the middle of the optical structure was sufficient to mitigate the CD. The more transmission reach was desired, the more optical complex structure is required. Using a four-stage SD/SC, a transmission of more than 574 ps/nm of accumulated CD (35 km of equivalent SSMF) was demonstrated with less than 1 dB of SNR penalty at KP4, compared to a back-to-back transmission.  
Using only electronic processing with an NN equalizer or MLSE, we showed a transmission with 164 ps/nm (10 km of equivalent SSMF) of accumulated CD, in the same scenario. Adding the NN equalizer as post-processing in the hybrid module, we transferred part of the complexity from the optical to the electronic domain. For a one-stage hybrid module (one-stage SD/SC + NN (L = 16)), we showed a CD mitigation of \(\approx\)200 ps/nm (12 km of equivalent standard single-mode fiber length). The tolerance was roughly doubled for a two-stage hybrid module (two-stage SD/SC + NN (L = 16)). Removing the SC from the hybrid structure (two-stage SD + NN (L = 16)) reduced the optical complexity module without losing the capacity of mitigating the CD. However, there was a \(\approx\)1.5 dB penalty compared to a complete two-stage hybrid module.  
A key component to mitigate the CD in the optical modules is the time delay. We showed that it is possible to keep this delay constant for a specific range of transmission reach and simplify the optical structure. For the two-stage hybrid module and considering 0 dB SNR penalty at KP4 FEC compared to a back-to-back transmission, we showed a transmission with an accumulated CD of \(\approx\)370 ps/nm (23 km of equivalent SSMF) using the time delay for a 12 km transmission. For the hybrid two-stage SD, the proposal module, there was no penalty by fixing the time delay. At the same scenario, we showed a transmission with accumulated CD of \(\approx\)300 ps/nm (18 km of equivalent SSMF). Finally, we analyzed the required number of neurons in the hidden layer of the NN. For both structures, the ideal number calculated was between 8 and 16 neurons.  
Conceptualization, S.M.R., F.D.R., H.B. and D.Z.; Investigation, S.M.R.; Supervision, F.D.R., H.B. and D.Z.; Validation, S.M.R.  
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 766115. It is also supported by the European Research Council through the ERC-CoG FRECOM project (grant agreement no. 771878).  
The authors declare no conflict of interest.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 4 Results
### Confidence Intervals for Individual Stand Predictions  
The probabilities of the predicted category in the validation datasets were assessed as given in Equation (12). The 95% confidence intervals of the predicted probabilities were calculated using Equation (14) and the Sentinel-1 datasets from the period 16 January-20 August, that is, in total 10 scenes (see Section 4.3). Basic statistics of the widths of the confidence intervals by the windstorm category are shown in Table 8. The median and mean of the intervals vary from 11% to 13%.  
Figure 11 shows statistics of the widths of predictions intervals by the damage categories for the stands in the validation dataset together with the outliers. The statistics are the median, 25 th and 75 th percentiles and 1.5 times the interquartile range.  
Figure 10: The predicted forest damages in a sub-area of the study area (EPGS:3067).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Damage Category** & **Mean of Predictions** & **Std of Predictions** & **Min of Intervals** & **Median of Intervals** & **Mean of Intervals** & **Max of Intervals** \\ \hline
1 & 0.787 & 0.077 & 0.052 & 0.105 & 0.109 & 0.686 & 0.036 \\
2 & 0.678 & 0.110 & 0.055 & 0.113 & 0.120 & 0.429 & 0.041 \\
3 & 0.708 & 0.114 & 0.061 & 0.131 & 0.132 & 0.223 & 0.033 \\ \hline \end{tabular}
\end{table}
Table 8: Examples of the statistics of the 95% confidence intervals of the predictions of the probability of the category with the largest probability when ten Sentinel-1 scenes from the period 16 January–20 August 2017 were used. The statistics are shown by the threshold of the minimum of the stand area.  
The accuracy of the individual prediction, that is, the confidence interval of the probability of the predicted category, also depends on the area of an individual damaged stand. The confidence interval becomes generally narrower when the area of a stand increases up to about 1-2 ha (Figure 12).  
Figure 11: Distributions of the confidence intervals of the prediction probability of the category with the largest probability by the damage category, shown as boxplots, that is, median, 25 th and 75 th percentiles, 1.5 times of the interquartile ranges and individual outliers. The width of the box is proportional to the square root of the number of the observations of the damage category.  
Figure 12: The width of the confidence interval of the probability of an individual prediction as a function of the area of the damaged stand.
## 5 Hotspot Optical Fronthaul Using OFDM Multi-IF Band Transmission  
In this section, we present physical layer simulations carried out by means of VPIphotonics software suite [65], in order to verify the successful operation of the proposed 5G optical fronthaul architecture for OFDM multi-IF signals with SC modulation of 64-QAM format. Two simulated setups were employed: a) the setup of a reference single wavelength link without the use of the ROADM and b) the setup of the proposed WDM hotspot link with four ROADMs in a bus topology. By comparing the performance of these two fronthaul links, useful conclusions about the additional degradation induced by the WDM transmission and the ROADM filtering effect were derived. It is worth noting that the verified simulation models described in Section 4 were incorporated in both setups, so as to provide reliable simulation results.
# Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic
## 2 Feature Sets
### AGM Feature Vector  
The AGM (AGgregation & Mode) vector was originally proposed for the exploration and discovery of patterns in the Internet Background Radiation [16]. Defined for unidirectional traffic, this vector captures the behavior of hosts by observing the use of eight lightweight flow features, plus the total number of packets. Features are: srcIP, destIP, srcPort, dstPort, Protocol, TCPflag, TTL, and pktLength. After selecting how hosts are profiled--either as data-senders (sources) or data-receivers (destinations)--and an observation time, the _basic AGM vector_ stores the number of unique values, the mode, and the number of packets assigned to the mode of every one of the previous features (for instance, \"dstPort\" becomes \"#dstPort\", \"mode_dstPort\", and \"pkts_mode_dstPort\"). Thus, the basic AGM vector contains 22 features (\(7\times 3\) + total number of packets; see Table 1). To operate solely with numerical features, the _extended AGM vector_ removes some nominal features that show a highly spread distribution (e.g., mode_dstIP) or transforms them into a set of _dummy_ variables (aka _one-hot encoding_) based on expected values shown in frequency tables (e.g., \"mode_protocol\" transforms into the binary \"TCP\", \"UDP\", and \"ICMP\" features, and other possible protocols get 0 for these tree new features). The AGM traffic representation can be further consulted in Reference [16].
# New Flame Retardant Systems Based on Expanded Graphite for Rigid Polyurethane Foams
## 3 Results and Discussion
### Growth Kinetics of PU Systems  
Foaming behavior was determined by measuring temperature and processing time--start, growth and tack-free time.  
Figure 2: Viscosity of the polyol system modified with graphite, silica and ionic liquid.  
The reaction of the synthesis of rigid polyurethane foams is classified as highly exothermic, during which large amounts of heat are released. Based on the growth rate of the foams and the temperature during RPUF synthesis, the reactivity of the polyol-isocyanate system can be determined. Analyzing the obtained results, it can be easily seen that for all modified samples the maximum temperature was decreased during the synthesis. The effect of the addition of expanded graphite on the maximum temperature is very visible and additionally intensified by the addition of silica and ionic liquid. Thanks to its very good thermal conductivity, EG has the ability to dissipate part of the heat generated during synthesis to the outside of the foam, which is manifested by a reduction in the maximum temperature of RPUF synthesis. In turn, a further decrease in the temperature of modified systems is associated with the introduction of further fillers containing additional groups that react with isocyanate, leading to side reactions, which release less heat than in the case of polyurethane bond synthesis.  
The characteristic times in Table 2 indicate that the addition of fillers increases the foam start and rise times. This is most likely due to the formation of additional nucleation centers, such as active solid filler particles. This consequently leads to increased bubble formation and to an extended start and rise time. Increased viscosity of mixtures containing solid fillers also inhibits further bubble growth, which significantly determines the foam behavior and extends the synthesis time. Moreover, the rate of PU polymerization itself is slowed down due to the presence of fillers which limit the mobility of the molecules leading to phase separation and an increase in the isocyanate conversion time during the reaction. Despite the fact that the addition of graphite, silica and ionic liquid affects the processing time, the total preparation time of PU foams is still within the permissible operating conditions [17; 18]. The presented results are consistent with other materials described in the literature.
## 5 Materials and Method
### MCs Extraction and Analysis  
Three MCs variants including MC-LR, -RR and -YR with extracellular forms (EMC-LR, EMC-RR and EMC-YR) and intracellular forms (IMC-LR, IMC-RR and IMC-YR) were extracted and condensed from water samples using the method modified according to [PERSON] et al. [32]. Simply, the glass microfiber filter (GF/C, Whatman, Whatman Inc., Clifton, NJ, USA) was used to separate extra- and intra-cellular MCs. The samples of filtered water were concentrated on the SPE cartridges. After freeze-thawed three times, the filters with intra-cellular MCs were extracted using 5% acetic acid once, followed by 75% methanol twice with ultrasonic treatment prior to pre-concentration over the SPE cartridges. Before high performance liquid chromatography (HPLC) analysis, the extracts were stored at \(-20\)\({}^{\circ}\)C. The identify and quantity of three MC congeners were determined by HPLC (Agilent 1200, Agilent Technologies, Santa Clara, CA, USA) with quaternary pump (G1311A), autosampler (G1329A), thermostated column compartment (G1316A), diode-array detector (G1315D), and an Agilent Eclipse XDB-C 18 column (4.6 \(\times\) 150 mm i.d.; 5 \(\upmu\)m particle size). Gradient elution of water/0.05% trifluoracetic acid (A) and acetonitrile (B) was used by varying the volume percentage of B from 30 to 40% over 15 min, and held constant for an additional 5 min. Injection volume was 20 \(\upmu\)L, and chromatograms were analyzed and integrated at 238 nm. Microcystins in the samples were compared with MC-LR, MC-RR and MC-YR standards based on peak areas and retention times. After concentrating the samples, the detection limits for the three MC congeners in intra- and extra-cellular forms were at the 0.01 \(\upmu\)g/L level.
## 2 Experiment and Theory Calculation  
An in-house DC-sliced velocity imaging system was utilized to carry out the experiment. The system has been further detailed elsewhere [36; 37]. In brief, a biconvex lens focuses femtosecond laser pulses into the reaction chamber at a repetition rate of 1 kHz, a center wavelength of 800 nm, and a pulse duration of 70 fs. The polarization vector of the laser is perpendicular to the direction of ion flight, and the laser intensity in the focal volume is estimated to be 40 to 300 TW/cm\({}^{2}\), which was calibrated by measuring the yield ratio of Ar\({}^{2+}\)/Ar\({}^{+}\)[38]. After being seeded with helium in a 1:10 ratio, the gaseous iodine bromide sample is expelled into the reaction chamber (\(\sim\)5.0 \(\times\) 10\({}^{-9}\) mbar) using a pulsed valve (General Valve, Parker, Cleveland, OH, USA) that repeats at a rate of 100 Hz for 130 us. The supersonic molecular beam interacts at a right angle with the linearly polarized femtosecond pulse in the middle of the extraction and repeller plates. As the fragment ion clouds pass through the velocity mapping lens, they are stretched and detected using micro-channel plates (MCPs) coupled with the P47 phosphor screen. An oscilloscope (LeCroy Wave Pro) is coupled to a photomultiplier tube (Hamamatsu H7732-11), which records the related time-of-flight (TOF) mass spectra in one dimension. An intensified charge-coupled device camera (ICCD, PI-MAXII) with a time resolution of 5 ns is used to capture the two-dimensional images. Utilizing a digital delay/pulse generator (DG645, Stanford Instruments, Sunnyvale, CA, USA), timing sequencing control is performed.  
To enhance the comprehension of the ultrafast dynamic processes involved in the IBr molecule, we conducted various theoretical calculations by using the Gaussian 09 software [39]. The molecular structure was optimized using the B3 LYP/aug-cc-pVTZ level [40; 41]. The potential energy curves of ionic IBr\({}^{2+}\) were calculated at the EOM-CCSD level of theory using a def2 TZVP basis set [42; 43].
## 5 Simulation Results and Analysis
### Computational Complexity Analysis  
The computational complexity analysis is used to estimate the amount of computational resources (such as time and memory) required to run an algorithm. It provides an understanding of how the algorithm scales with the input size and helps in optimizing the algorithm or selecting alternative approaches if the complexity is too high.  
To calculate the computational complexity of the proposed algorithm, each part of the code is analyzed to determine the number of operations or iterations performed in terms of the input size. In the code, the main loop iterates 'iter' number of times. Within each iteration, there are multiple calculations and operations performed, such as matrix multiplications, norm calculations, and updates of variables. The complexity of each of these operations is analyzed and sums them up to obtain an overall complexity estimate for the code. In addition, the input size-dependent variables, such as M and L, and their impact on the complexity are also considered.  
The proposed algorithm has high computational complexity as it is cubic in nature. This is because it involves a matrix inversion operation. After the matrix inversion operation, the other main time-consuming operations are matrix multiplication operations.  
For \(\mathbf{r}\) updates, the calculation of \(\Xi_{1}\) takes \(O(KM^{2}L^{2})\) and the calculation of \(\gamma\) takes \(O(M^{2}L^{2})\); therefore, the complexity of the update of \(\mathbf{r}\) using Equation (25) is \(O(KM^{2}L^{2}+M^{2}L^{2}+M^{3}L^{3})\). Overall, the computational complexity of the algorithm is \(O(2(KM^{2}L^{2}+M^{2}L^{2}+M^{3}L^{3}))\) at each iteration.
## 4 Risk Analysis for Current Protocols
### Automated Identification System (AIS)  
The Automated Identification System is a tracking system that uses transponders on a ship and is used by vessel traffic services. AIS is used to communicate and broadcast metadata (e.g., name, coordinates, etc.) via a VHF transmitter that is built into the transceiver. This data is often picked up by AIS receivers at ports and other vessels. The information is used for better navigation, collision avoidance, and maritime environment protection. The signals are received by AIS transceivers fitted on other ships or on land-based systems. The received information can be displayed on a screen or chart plotter, showing the other vessels' positions in much the same manner as a radar display. Currently, there are no common security measures used to protect AIS protocol [37]. EAIS is a closely related protocol to AIS. The main differences being that it encrypts its traffic and is not as widely used [38].
# Specific Resonant Properties of Non-Symmetrical Microwave Antennas
## 3 Results and Discussion
### Resonance Properties of Measured Classical Cylindrical Antennas
\begin{table}
\begin{tabular}{c c} \hline \hline
**The Effect of Changing the Slimness of the Antenna** & **The Effect of Changing** \\ \multicolumn{2}{c}{**(Changing Diameter \(\phi\)) at the Same Height**} & **the Slimness of the Antenna (Changing Height \(\hat{l}\)) at the \\ \multicolumn{2}{c}{\(l\), on the Change of the \(l/\lambda_{res}\)Ratio**} & **Same Diameter \(\phi\), on the Change of the \(l/\lambda_{res}\)Ratio** \\ \hline \(\Omega_{\phi\ell}l_{20}>\Omega_{\phi\ell 12}\)\(l_{20}>\Omega_{\phi\ell 25}\)\(l_{20}\) & \(\Omega_{\phi\ell 20}>\Omega_{\phi\ell 130}<\Omega_{\phi\ell 120}\) \\ \(0.196\)\(0.189\)\(0.181\) & 0.1960 0.225 0.245 \\ \(\Omega_{\phi\ell 50}>\Omega_{\ell 12}\)\(l_{50}>\Omega_{\phi 25}\)\(l_{50}\) & \(\Omega_{\phi 12}l_{20}<\Omega_{\phi 12}\)\(l_{50}<\Omega_{\phi 12}\)\(l_{70}\) \\ \(0.225\)\(0.215\)\(0.205\) & 0.189 0.215 0.233 \\ \(\Omega_{\phi\ell 20}>\Omega_{\phi 12}\)\(l_{70}>\Omega_{\phi 25}\)\(l_{70}\) & \(\Omega_{\phi 25}l_{20}<\Omega_{\phi 25}\)\(l_{50}<\Omega_{\phi 25}\)\(l_{70}\) \\ \(0.245\)\(0.233\)\(0.222\) & 0.181 0.205 0.222 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Values of resonance points \(l/\lambda_{res}\) of cylindrical antennas. Figure 10 represents a concentrated distribution of the position of the resonant points of the antennas when their slimness changes, in the form of a change of height or diameter. The display of the three antenna diameter values is on three parallel horizontal axes. The fourth lower horizontal axis is a measure of the \(l/\lambda\) ratio. The length of the antenna is shown in the figure as a parameter. Each of the three resonant points of the same length is connected by a dashed line. From Figure 9, it can be observed that when changing the slimness of the antenna and increasing its diameter, the resonant point of the antenna shifts to the left towards lower values of the \(l/\lambda\) ratio. This shift is proportionally the same on all lengths and all diameters of antennas. The inclination of the individual dashed lines that this shift expresses is almost parallel. Small differences correspond to measurement inaccuracies, like incorrect reading of the resonant frequency, manufacturing inaccuracy of the antenna, changes in the position of the antenna mounting, etc.
# Methods for Cognitive Diagnosis of Students' Abilities Based on Keystroke Features
## 2 Related Works
### Research Method
#### 2.2.2 Clustering Algorithm  
Clustering algorithms are a category of unsupervised learning algorithms [16], which seek to partition the samples within a given dataset into distinct clusters so that the samples belonging to the same cluster have a great deal of similarity whereas those across different clusters have marked differences. Based on their principles and characteristics, clustering algorithms can be roughly classified into several types, such as partitioning clustering algorithms, hierarchical clustering algorithms, density-based clustering algorithms, grid-based clustering algorithms, and model-based clustering algorithms.  
In this experiment, the various data were relatively independent and exhibited a certain level of dispersion. Considering the convergence speed and interpretability of the algorithm, the K-means algorithm was selected for clustering analysis in this study. The K-means algorithm is a partition-based clustering algorithm. The underlying principle of the K-means algorithm is to partition the samples in the dataset into K clusters. The goal is to minimize the sum of the distances from each sample within a cluster to the center of that cluster. Euclidean distance is commonly used as the distance metric in calculating K-means, and its calculation formula is as follows:  
\[d(x,y)=\sqrt{\sum_{i=1}^{n}(x_{i}-y_{i})^{2}} \tag{4}\]  
Here, \(x=(x_{1},x_{2},\cdots,x_{n})\) and \(y=(y_{1},y_{2},\cdots,y_{n})\) are two samples, and \(n\) is the dimension of the samples. The mathematical model of the K-means algorithm can be expressed as follows:  
\[min_{C_{1},C_{2},\ldots,C_{k}}\sum_{i=1}^{k}\sum_{x_{j}\in C_{i}}||x-\mu_{i}|| ^{2} \tag{5}\]  
Here, \(k\) is the number of clusters, \(C_{i}\) represents the \(i\)-th cluster, \(x\) is a data point in cluster \(C_{i}\), \(\mu_{i}\) is the centroid of cluster \(C_{i}\), and \(||x-\mu_{i}||\) represents the distance from data point \(x\) to centroid \(\mu_{i}\). Through the minimization of this objective function, the K-means algorithm strives to identify an optimal clustering approach that makes the data points within each cluster as closely grouped around its centroid as possible, thereby achieving the purpose of clustering.  
In the context of this study, the dimension of the samples corresponded to the number of extracted keystroke features, including the keystroke speed and modification frequency. Subsequently, each sample was assigned to the cluster with the nearest clustering center. Following this assignment, the center of each cluster was recalculated, which represented the mean of all samples within that cluster. This process was carried out iteratively until the clustering centers no longer changed or the change was negligible, or the preset maximum number of iterations was reached.