esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Evaluation Results of Other Methods on BQ  
**Table A1.** The evaluation results of other methods on BQ. 1L-MLP stands for a one-layer MLP, LR stands for logistic regression [51], and DCNN stands for a dynamic convolutional neural network with k-max pooling [47]. When SVM, MLP, and LR are used for evaluation, the sentence representation is obtained by implementing average pooling transformation on the variable-length word (character) embedding sequences.  
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Emb** & **P** & **R** & **F** & **Acc.** \\ \hline \(RNN\) & c & 66.23 & 64.14 & 65.17 & 64.53 \\ \(RNN\) & w & 65.78 & 61.86 & 63.76 & 63.09 \\ \(RNN\)+st.+LASER & w & 65.53 & 64.38 & 64.95 & 64.49 \\ \(RNN\)+dy.+LASER & w & 63.81 & 65.45 & 64.62 & 63.86 \\ \(RNN\)+st.+SB-BK & w & 65.22 & 64.33 & 64.77 & 64.25 \\ \(RNN\)+dy.+SB-BK & w & 66.36 & 63.73 & 65.02 & 64.01 \\ \(RNN\)+st.+SEMTS & w & 65.69 & 64.58 & 65.13 & 64.61 \\ \(RNN\)+dy.+SEMTS & w & 64.75 & 64.89 & 64.82 & 64.17 \\ \hline \(LSTM\)[42] & c & 68.62 & 70.57 & 69.58 & 70.56 \\ \(LSTM\)[42] & w & 67.15 & 67.61 & 67.38 & 68.02 \\ \(LSTM\)[42]+st.+LASER & w & 70.61 & 71.52 & 71.06 & 70.10 \\ \(LSTM\)[42]+dy.+LASER & w & 68.76 & 70.93 & 69.83 & 69.28 \\ \(LSTM\)[42]+st.+SB-BK & w & 70.23 & 70.33 & 70.28 & 70.27 \\ \(LSTM\)[42]+dy.+SB-BK & w & 69.54 & 71.73 & 70.62 & 69.34 \\ \(LSTM\)[42]+st.+SEMTS & w & 68.56 & 73.38 & 70.89 & 71.33 \\ \(LSTM\)[42]+dy.+SEMTS & w & 68.27 & 71.27 & 69.74 & 70.16 \\ \hline \(SVM\)[52] & c & 62.87 & 63.43 & 63.15 & 63.39 \\ \(SVM\)[52] & w & 63.98 & 63.90 & 63.94 & 63.71 \\ \(SVM\)[52]+st.+LASER & w & 64.52 & 63.08 & 63.79 & 64.56 \\ \(SVM\)[52]+dy.+LASER & w & 65.85 & 62.44 & 64.10 & 64.17 \\ \(SVM\)[52]+st.+SB-BK & w & 64.76 & 63.77 & 64.26 & 63.73 \\ \(SVM\)[52]+dy.+SB-BK & w & 63.94 & 63.78 & 63.86 & 63.45 \\ \(SVM\)[52]+st.+SEMTS & w & 64.92 & 62.31 & 63.59 & 64.11 \\ \(SVM\)[52]+dy.+SEMTS & w & 65.27 & 63.42 & 64.33 & 63.78 \\ \hline \(1L\)-\(MLP\) & c & 65.83 & 62.71 & 64.23 & 63.89 \\ \(1L\)-\(MLP\) & w & 64.96 & 62.64 & 63.78 & 63.26 \\ \(1L\)-\(MLP\)+st.+LASER & w & 65.34 & 63.60 & 64.46 & 63.73 \\ \(1L\)-\(MLP\)+dy.+LASER & w & 64.91 & 63.86 & 64.38 & 63.69 \\ \(1L\)-\(MLP\)+st.+SB-BK & w & 64.37 & 63.61 & 63.99 & 63.88 \\ \(1L\)-\(MLP\)+dy.+SB-BK & w & 63.95 & 64.47 & 64.21 & 63.57 \\ \(1L\)-\(MLP\)+st.+SEMTS & w & 64.54 & 65.04 & 64.79 & 63.95 \\ \(1L\)-\(MLP\)+dy.+SEMTS & w & 64.89 & 62.59 & 63.72 & 63.82 \\ \hline \hline \end{tabular}
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 6 Evaluation
### Results and Discussion
#### 6.2.5 Impact of the PQC Algorithm on Performance in the Video-Streaming Use Case
Conversely, BIKE-L1-F512 (0.22%), HQC-192-F512 (0.25%), K768-F512 (0.27%), and K1024-F512 (0.26%) achieved higher retransmission rates than AES-256. Figure 11: Video streaming total throughputs: (a) PQC–AES-256 (with F512). (b) PQC–AES-256 (with F1024). As shown in Figure 12b, in comparison to the algorithms paired with F512, the KEM algorithms paired with F1024 exhibited an opposite trend, with the exception of HQC-192. According to the results, HQC-192-F1024 achieved a retransmission rate of 0.28%, which was 0.03% higher than that of HQC-192-F512. Similarly, a downward trend was recorded for CRYSTALS-Kyber as the security level increased, which differs from the retransmission rates observed when the KEM algorithm was paired with F512. K512-F1024 achieved the highest retransmission rates of 0.32%, which was higher than that of K512-F512 by 0.17% while K768-F1024 and K1024-F1024 achieved lower retransmission rates of 0.09% and 0.12% than K768-F512 and K1024-F512, respectively. It was further confirmed that BIKE-L5-F1024 (0.22%), HQC-128-F1024 (0.25%), and K512-F1024 (0.32%) all achieved higher retransmission rates than conventional AES-256. This suggests that further optimization may be necessary in order to use these algorithms effectively in real-time applications.
# Blockchain Applications in Education: A Systematic Literature Review
## 4 Discussion
### Research Question 2: What Are the Features of Blockchain That Could Benefit Education?
#### 4.2.8 Immutability  
Transactions stored in a blockchain are almost impossible to modify, since, depending on the type of platform and the number of network participants, the computational effort required is very high. As a consequence, they are considered to be immutable in practice.  
In the educational field, it is a key feature to have tamper-proof, available, authenticated and protected against repudiation data on certificates, grades, competences, etc. The immutability characteristic warrants that data will remain unchanged forever and, if something is modified by someone, the alteration(s) will also be immutably accounted.  
For example, [PERSON] et al. [41] propose an intelligent agent that monitors autonomously educational information and protects it from unauthorized modifications.
# Fuzzy Multivariate Regression Models for Seismic Assessment of Rocking Structures
## 2 Methods
### Formal Analysis  
Fuzzy logic is a widely recognized concept in the field of soft computing, encompassing methods that create practical algorithms by incorporating structured human expertise. It represents a logical framework tailored to encapsulate modes of human understanding that lean towards approximations rather than strict precision [57]. Fuzzy logic involves managing fuzzy sets, where the elements consist of varying degrees of membership functions. The degrees of membership within fuzzy sets are between the interval of [0, 1]. When an axiom is absolutely accurate, the membership value within fuzzy sets assumes a value of 1. Conversely, when it is entirely untrue, the membership value is 0. The outcome yielded by a membership function is referred to as an antecedent. While the input parameters are crisp, they undergo transformation into fuzzy variables through the membership functions. The use of fuzzy rule-based modeling has many applications in predicting the seismic response of a structure [58].  
A fuzzy linear regression model may be suitable for predicting the maximum seismic demand for systems where damage continuously evolves during the response. However, this approach is not applicable when dealing with a rocking response. As long as rocking overturning occurs, the damage index tends to infinity, introducing a discontinuity in the response. As a result, the finite rocking response should be examined using fuzzy linear regression models, while rocking overturning can be addressed using logistic regression combined with fuzzy logic.
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 4 Theoretical Analysis
### Impact of \(d\) on BECPB  
According to Equation (19), it can be shown that its first part increases with \(d\), while the second part is a constant. Hence, \(E_{\text{bat,I}}\) is an increasing function of \(d\). Similarly, \(E_{\text{bat,M}}\) also increases as \(d\) increases.  
To sum up, the BECPBs for both types of batteries increase with \(d\). The reason behind this phenomenon is that the transmission energy depends on the transmission distance \(d\) and it increases with \(d\).  
Specifically, for the ideal battery, it can be shown by Equation (23) that its BECPB increase with \(\gamma\).
# A Light-Weighted Machine Learning Approach to Channel Estimation for New-Radio Systems
## 4 Proposed ML Method
### Network Architecture
#### 4.1.3 Complexity Analysis  
In this paper, we compare computational complexity through the number of multiplications for the existing methods and the proposed method. The computational complexity of the existing ML and the proposed ML add the LS method for the input data \(\mathbf{\hat{G}_{\mathrm{LS}}}\) and the \(N_{\mathrm{fft}}\) point FFT to transform from CIR to CFR, respectively. The results are shown in Table 2, where \(Q=2^{\{\log_{2}(N)\}}\) is the largest power of 2, while it is less than \(N\). Here, \(N_{\mathrm{fft}}\) is the FFT size. In addition, \(N\) and \(L\) are the pilot signal size and the length of the CIR, respectively. These results show that the existing ML is lower than the MMSE method and slightly higher than the LS method, but the proposed ML is lower than the LS method as well as the MMSE method.
# Multicell Interference Management in Device to Device Underlay Cellular Networks
## 4 RB Allocation
### RB Problem Formulation and Solution
Problem (12) is an integer linear programming problem and even the relaxed version of it always has an integer optimal value since the constraint matrix is totally unimodular. This problem can be solved via the well known Hungarian algorithm [33] in polynomial time with respect to the number of the available RBs at each cell. Leveraging the potential game properties, Algorithm 1 converges after a finite number of iterations. Moreover, at each step, Algorithm 1 improves not only the utility function of each cell but also the potential function of the game i.e., the total interference. After a finite number of iterations, a RB assignment is selected which minimizes the total interference of the network. In order for Algorithm 1 to be practically implementable, there is a need for a communication control channel between the different cells i.e., the eNBs and the D2D cells. This control channel will carry the necessary information in order for the cells to execute Algorithm 1. For the case of eNBs, this can be done using the X2 interface [34] which has been already designed to implement this type of communication between the eNBs in LTE networks. For the D2D cells it can be assumed that there is a control channel between the D2D receivers and the respective serving eNB they reside in [19]. The complexity of Algorithm 1 belongs to the class of Polynomial local search (PLS) problems [35]. At each step, each eNB runs the Hungarian algorithm in order to update its RB assignment. The complexity of the Hungarian algorithm is \(O(K^{3})\) with respect to the number of the available RBs. For the D2D cells the complexity of the update step is \(O(K)\) since the number of servedusers is only one i.e., the respective D2D transmitter. The convergence of the above algorithm is guaranteed but not necessarily in polynomial time. However, at every step of the Algorithm 1 the total interference is being reduced. As it will be seen in Section 6.1.1 the required number of iterations for convergence was quite low with respect to the number of players involved. By applying Algorithm 1, we will obtain a RB assignment for all cells of the network which minimizes the total interference in the network.
## 3 Results
### Structural Repair and Airframe Condition Monitoring by Using RFID and CMB
#### 3.5.3 RFID as a Labeling Tool  
Our proposed solution is to attach the RAIN RFID tags to a critical part of the aircraft--on particular components, for example, the flap. The tag could be stuck on the outer surface of the flap. This tag would identify the component as a particular item with all its specifications (number of rivets), which would be monitored by the camera. Previously, the bar codes or numbered marks were used; however, they could not be read by a camera if they were on the inner surface. Therefore, we suggest using RFID tags, which would be read by a camera attached on a UAV; then, the particular component (flap) would be labeled with the image. Based on the image labeling process, the identification, denoising, and classification process follows. In the last step, after the postprocessing, we know if some element is missing.
# Ocean Surface Topography Altimetry by Large Baseline Cross-Interferometry from Satellite Formation
## 4 CFS for Baseline Decorrelation Compensation
### Design Principle of CFS
#### 4.2.3 Ocean Surface Slope  
The slope here refers to the ocean surface slope that extends beyond at least one resolution grid of 1 km. The influence of the wave slope variation within one resolution grid causes the signal decorrelation rather than frequency shift and can be decomposed into wave volume or time decorrelation, etc.  
The OST variation is normally less than a few decimeters over a spatial extension of tens or hundreds of kilometers. Suppose a sub-mesoscale eddy of 50 km diameter and 50 cm height anomaly located in the middle of the swath, then the slope introduced by the eddy is merely \(\pm 2.1\) arcsec, and its influence to frequency shift can be totally omitted. Under rough condition where surges may reside, the slope could be large enough that it causes a non-negligible frequency shift, however, the OST altimetry of LB-IRA is restricted to work below level 4 of sea state (corresponds to SWH smaller than 2 m), since OST of high sea state is considered unreliable.
## 4 Results and Discussion
### Experimental Results
#### 4.2.2 Experiments with Shield Gas Chamber  
Figure 13 shows the influence of the carrier gas volume flow to shielding gas volume flow ratio on the oxygen concentration of the three-jet nozzle and the coaxial nozzle inside the shield gas chamber.  
Figure 12: Influence of the carrier gas flow to shield gas flow ratio on the present oxygen concentration.  
Figure 13 shows that the shielding gas chamber practically eliminates the influence of the carrier gas volume flow to shield gas volume flow ratio for both nozzles. Even for the very large ratio of 6, no significant increase in oxygen concentration is found for the three-jet nozzle. There is a tendency of improvement in oxygen concentration at the same gas ratio with increasing shielding gas volume flow for both nozzles. Furthermore, the coaxial nozzle exhibits a lower oxygen concentration at the same gas ratio for shielding gas volume flows larger than 15 L/min.
## 3 Control Strategy of Wind Storage System to Suppress Low-Frequency Oscillation  
### Evaluation Indicators for Oscillation Degree  
The rotor motion of a synchronous generator reflects the frequency dynamics of the power system, as shown in Figure 6. The blue arrow part is the maximum speed deviation \(\Delta\omega_{\text{max}}\). When low-frequency oscillations occur in the power grid, the SG rotor will continue to oscillate, causing the generator speed \(\omega\) to change and deviate from the initial speed \(\omega_{0}\). The indicators for measuring the dynamic characteristics of the rotor include the speed change rate \(\frac{d\omega}{dt}\) and the speed deviation \(\Delta\omega\), among them, \(\frac{d\omega}{dt}\) mainly affect the fatigue degree of the mechanical spindle, and \(\Delta\omega\) mainly affects the speed  
As shown in Figure 6, the longer the oscillation time of the rotor, the larger the oscillation area, and the greater the impact on the frequency quality of the power grid. The calculation equation \(S_{\omega}\) for the rotor oscillation area is as follows:  
\[S_{\omega}=\int_{t_{1}}^{t_{2}}\lvert\omega-\omega_{0}\rvert dt \tag{26}\]  
In the equation, \(t_{1}\) and \(t_{2}\), respectively, represent the starting and ending times of the rotor deviating from the synchronous speed. It can be seen that the longer the continuous oscillation time of the power grid, the greater the \(S_{\omega}\), and the greater the impact on the frequency stability of the power grid.  
In conclusion, the influence of the unbalanced power of the suppression system on the synchronous machine speed mainly considers three aspects: restraining \(\frac{d\omega}{dt}\), avoiding rotor machine fatigue and equipment accidents; suppressing \(\Delta\omega\) and avoiding unit stall or overspeed protection; suppressing \(S_{\omega}\) and avoiding excessive oscillation time and amplitude.  
However, in practical applications, most countries' power grids have regulations on the frequency offset and rate of change of frequency (RoCoF) of the power grid, rather than the changes related to rotor speed. When the frequency is 60 Hz, the lower limit of frequency protection is 59.3 Hz, that is, the frequency offset is 0.7 Hz, and the frequency change rate is constrained between 0.12 Hz/s and 1.2 Hz/s. The measurement of RoCoF is generally based on the tangent of a given point on the frequency response curve, estimated through frequency measurements of 0.1-0.5 s in a short period of time. The time window for calculating the RoCoF of wind farm integration points should be greater than 100 ms and less than 200 ms.
# Influence of Plastic Waste on the Workability and Mechanical Behaviour of Asphal Concrete
## 2 Materials and Methods
### Plastic Waste  
The different types of plastic waste used as polymers in this project were collected in a dedicated recycling facility that collects plastic waste from different sources (urban and industrial sources). After receiving the plastic waste, this facility separates the plastic types; trims the elements into a specific gradation; and washes, dries, and extrudes the material to produce pellets. The plastic waste applied in this study was collected just before the pellets' production to reduce the recycling cost and the emissions associated with energy  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Property** & **Standard** & **Units** & **Gneiss 8/20** & **Gneiss 4/12** & **Sand 0/4** & **Filler** & **Limit** \\ \hline Flakiness index (FI) & EN 933-3 [34] & \% & FI\({}_{15}\) & FI\({}_{15}\) & — & — & FI\({}_{20}\) \\ Resistance to fragmentation: & EN 1097-2 [35] & \% & LA\({}_{20}\) & LA\({}_{20}\) & — & — & LA\({}_{30}\) \\ Los Angeles (LA) & & & & & & & \\ Reistance to wear: & EN 1097-1 [36] & \% & M\({}_{\rm DE}\)10 & M\({}_{\rm DE}\)10 & — & — & M\({}_{\rm DE}\)15 \\ micro-Deval (M\({}_{\rm DE}\)) & EN 1097-8 [37] & \% & PSV\({}_{50}\) & PSV\({}_{50}\) & — & — & PSV\({}_{50}\) \\ \multicolumn{1}{c}{Water absorption (WA)} & EN 1097-6 [38] & \% & 0.5 & 0.6 & 0.6 & — & WA\({}_{24}\)1 \\ Assessment of fines: & & EN 933-9 [39] & g/kg & — & — & MB\({}_{\rm F}\)10 & MB\({}_{\rm F}\)10 & MB\({}_{\rm F}\)10 \\ methylene blue (MB\({}_{\rm F}\)) & & & & & & & \\ Voids of dry compacted & & & & & & & \\ filler (v) & EN 1097-4 [40] & \% & — & — & — & 32 & \(\gamma_{28/38}\) \\ Delta ring and ball (\({}^{\circ}\)C) & EN 13179-1 [41] & \({}^{\circ}\)C & — & — & — & 14 & \(\Delta_{\rm R\&B}\) \\ \hline \hline \end{tabular} Note: According to the European standards (EN), the requirements for each parameter are indicated through acronyms and numbers that represent one of the categories considered in EN 13043 for aggregates.  
\end{table}
Table 1: Physical properties of aggregates and specifications requirements.  
Figure 1: Grading of the aggregate’s mixture and specification gradation limits.  
consumption. Figure 2 shows the four types of plastics used in this study, namely, ABS (acrylonitrile butadiene styrene), HDPE (high-density polyethylene), HDPE500 (a cheaper alternative to the standard HDPE for less demanding situations in terms of wear resistance and impact), and LDPE(uw) (low-density polyethylene from urban waste). Typical values for the melting point of LDPE and HDPE are approximately 120 and 140 \({}^{\circ}\)C, respectively. Because ABS is an amorphous polymer, it does not have an actual melting point, but it changes from a glassy to a rubbery state (glass-transition temperature) at around 100 \({}^{\circ}\)C.
## 1 Introduction
### Research Gap  
After presentation of the current state of the art and the methodological background, two research questions (RQ) for the plausibility check of FE simulations arise.  
RQ1: Is there a multilabel classification framework capable of predicting the specific sources of errors for different FE simulations?  
RQ2: In combination with the classification model, which CNN architecture is particularly suitable for detecting the causes of errors?  
Figure 4: Example of a residual building block and the Inception module according to [36; 37].  
These questions are intended to show whether the detection of specific faults in FE simulations is possible without creating a purely application-specific solution.
# Spam Classification Based on Signed Network Analysis
## 3 A Primer on Social Psychological Theories for Signed Networks
### Social Status Theory  
Social status theory explains that the sign of an edge is determined by the status difference between nodes. For example, the link created from node x to node y would be positive if node x considers node y to have a higher status than itself. Consider the edge sign prediction problem illustrated in Figure 2. Here, our goal is to predict the unknown sign of the edge from node A to node B (simply denoted as the A-B edge), i.e., the red dotted link, using the A-X edge and the B-X edge. Unlike the surprise measurement of structural balance theory, the surprise measurement used with social status theory is conducted separately for the edge initiator and the edge destination, since it considers directed networks, and these measurements are called the generative surprise and the receptive surprise, respectively. According to a study by [PERSON] [30], the generative surprise (denoted as \(s_{g}(T_{i})\)) is computed by the number of standard deviations by which the actual number of positive A-B edges in the data differs from the expected positive numbers created by the baseline. Here, the baseline is defined as the overall fraction of positive edges that a user expects to create. Consider type \(T_{12}\) in Figure 2. For node A, since it receives a positive sign from node X, it might think that it has a higher status than others. In this case, according to social status theory, it will be likely to give negative signs to other nodes (e.g., node B), but if the actual sign of the A-B edge turns out to be positive, then the generative surprise value will be high. Receptive surprise is  
seen, both theories could result in different views of the same network, but in this paper, we do not discuss the subtle, differing interpretations that these two theories can make about the same network, since this is beyond the scope of this paper. We do, however, note that they are both still effective as key factors for discrimination between spammers and non-spammers, which will be shown later.  
**4. Feature Extraction and Validation**  
To investigate how well our hypothesis fits to actual data, we measure the aforementioned surprise values for spammers and non-spammers in a Twitter dataset [8,32]. The basic statistics of the dataset are given in Table 1. Note that this dataset includes the information about labeled spammers, which we can use as ground-truth data. Since the two theories that we adopt in this paper are intended for signed networks, if the given network does not have sign information, then we cannot directly apply our scheme. Note that the Twitter dataset has no sign information. To handle this issue, we develop a graph-converting method, which will be explained in the following subsection.