esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Type/Position Classification of Inter-Floor Noises Generated on Unlearned Positions
### Position Classification of Inter-Floor Noises Generated from Unlearned Positions  
To address the second question in Section 4, \(\mathcal{M}_{\hat{\Lambda}_{\text{position},\text{VGG16}}}\) the optimized VGG16 with \(\mathbf{P}\in\text{Q}^{(\text{train})}\) using the position labels in Section 3.5 was evaluated on the new dataset. Because position classification with the model was evaluated using 5-fold cross validation in Section 3.5, there exists a model for each fold. \(\text{Q}^{(\text{test})}\) and \(\text{Q}^{(\text{train})}\) are mutually exclusive in the position domain, thus position classification accuracies cannot be quantified, but position classification results of \(\mathbf{P}\in\text{Q}^{(\text{test})}\) into the learned 9 positions can be visualized. Position classification was evaluated as follows: (1) The new inter-floor noise dataset with position labels was converted to \(\mathbf{P}\in\text{Q}^{(\text{test})}\). (2) \(\mathbf{P}\in\text{Q}^{(\text{test})}\) zero centered on the mean value of \(\mathbf{P}\in\text{Q}^{(\text{train})}\) was classified into \(C=9\) position categories using \(\mathcal{M}_{\hat{\Lambda}_{\text{position},\text{VGG16}}}\) for all models. (3) Average position classification results were calculated and drawn in a confusion matrix. An average can be used to check the concentration of classification results into the three learned positions on 3 F.  
Figure 7 shows the confusion matrix for position classification results. The vertical axis shows position labels of \(\mathbf{P}\in\text{Q}^{(\text{test})}\), and the horizontal axis shows position labels of \(\mathbf{P}\in\text{Q}^{(\text{train})}\). The value in each cell is an average of the corresponding classification results. Although the position classification results cannot be quantified, floor classification results can be quantified from the confusion matrix. One can see that 98.8% of the classifications are concentrated at the three learned positions near their true positions on 3F. This shows that \(\mathcal{M}_{\hat{\Lambda}_{\text{position},\text{VGG16}}}\) is robust against position changes on 3 F.  
Summarizing the results of the type/position classification, VGG16 showed the robustness to the inter-floor noises with the position errors. The tests validate the applicability of the proposed method to probable situations where almost of inter-floor noises as an input of the classifier are generated at unlearned positions.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Predicted Label**} \\ \cline{3-7}  & & **MB** & **HD** & **HH** & **CD** & **VC** \\ \hline \multirow{2}{*}{True label} & MB & **97.7** & 0.00 & 1.20 & 1.10 & 0.00 \\  & HH & 0.60 & 0.00 & **99.4** & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix for type classification results (%) against the new inter-floor noise dataset.
## 2 Theoretical Background
### Teager-Kaiser Energy Cepstral Coefficients (TECCs)
#### 2.5.2 Gammatone Filter  
The Gammatone is a linear filter that is obtained from the impulse response of a sinusoid modulated by a gamma distribution function, that is to say,  
\[g(t)=at^{n_{f}-1}e^{-2n\mathrm{i}\mathrm{ERB}(f_{c})t}\cos(2\pi f_{c}t+\phi) \tag{8}\]  
for a given centre frequency \(f_{c}\). In (8), \(a\) is the impulse response function amplitude, \(n_{f}\) is the desired filter order, \(b\) the filter bandwidth, and \(\mathrm{ERB}(f_{c})\) is the Equivalent Rectangular Bandwidth defined by [51]; \(\phi\) indicates the phase of the carrier. Therefore, the filter frequency response becomes  
\[G(\omega)=\frac{A}{2}\frac{6}{\left(2\pi b\mathrm{ERB}(f_{c})+j(\omega-\omega _{c})\right)^{4}}+\frac{A}{2}\frac{6}{\left(2\pi b\mathrm{ERB}(f_{c})+j(\omega +\omega_{c})\right)^{4}}) \tag{9}\]  
with filter gain \(A\) and non-dimensionalised \(\omega_{c}=2\pi f_{c}\). The main advantages of Gammatone filters, with respect to the symmetric triangular ones originally employed for MFCCs extraction, are that (i) they are broader and smoother and (ii) they are asymmetrical and non-constant-Q, i.e., the filter frequency response overlap is not constantly 50%. This second point allows for them to emphasise the lower frequencies content, which is of greater relevance in both speech processing and Structural Health Monitoring, while larger bandwidths and less sharp transitions improve the noise robustness [58]; moreover, a Gammatone filterbank is denser in the frequency domain than the mel-frequency-based triangular filterbank. These advantages with respect to the MFCCs and similar alternatives are documented for making the TECCs more effective in the ambit of speech and sound processing.
## 1 Introduction  
The excellent performance of cement-treated aggregate mixture (CTAM) in terms of, for example, workability, strength, durability and load spreading capacity [1], makes it widely used in China [2], Netherlands [3], Egypt [4], Spain [5] and Australia [6], etc. Its properties are greatly influenced by the compaction method and the corresponding level [7]. On the one hand, the cement dosage and moisture content, which influence the strength and modulus of the mixture, are highly dependent on the compaction method and level [8]. On the other hand, the in-situ compaction degree is always calculated by the maximum dry density derived from the laboratory compaction.  
The Proctor compaction method is commonly used to compact cement-treated material. In different countries, the specific criteria for this method differ significantly in terms of hammer weight, blow number and mold geometry, etc. [9; 10; 11]. The Proctor test uses impact loads to compact material in a stiff nonyielding mold [12], which increases the content of fine aggregate [13]. The vibration compaction method was developed to simulate the in-situ compaction process of CTAM in China.  
The serious inconsistencies regarding some key parameters such as static pressure, vibrating frequency and eccentric angle make it difficult to be used widely [14; 15; 16; 17]. In addition, the strong vibration energy during operation may endanger the structural safety of the laboratory, and the vibration noise can have negative effects on the hearing of test operators. With the wide applications of cement-treated materials, such as cement-treated aggregate [1; 16], cement-treated waste [2; 18], cement-treated soil [11], cement-treated recycled material [19; 20], cement-asphalt-treated material [21], and cement-treated material with reclaimed asphalt mixture [9; 17; 22; 23], it is quite necessary to develop an alternative method to design cement-treated mixtures.  
Nowadays, the contribution of cement-based materials to sustainability is a topic of study [24; 25; 26; 27] and as a result, the performance of several additions that could also be suitable for use in cement-treatment materials has been analyzed. This fact also makes it necessary to study new methods for the design of cement-treated mixtures.  
In many countries, Superpave gyratory compactors (SGC) have been widely used to design asphalt mixtures [28], as well as to compact unbound material [29] and soil [12; 30]. A compaction curve is automatically drawn in the compaction process, which can be used to evaluate the relative density of the mixture. In addition, a 150 mm-diameter cylindrical mold allows mixtures with maximum particle size over 26 mm to be compacted.
## 5 Experimental Evaluation and Analysis
### Experimental Results
#### 5.2.5 Number of Live Nodes  
When more than half of the sensor nodes are dead, the network simulation is terminated. Thus, the number of live nodes is another important feature in the mentioned network. Figure 15 shows that the number of live nodes in the SFRRP protocol is more compared to that of the Flooding protocol. The reason is a high number of redundant generated packets in the Flooding protocol. This causes a high energy consumption of the network nodes. As shown by simulation results, the number of live nodes is equal in the SFRRP protocol. However, it decreases in the Flooding protocol after the half-life network. Furthermore, all the nodes are live in the proposed protocol, because energy consumption of the nodes is low. It leads to network life being high in this protocol.
# Secure Inter-Frame Space Communications for Wireless LANs
## 2 Periodic Interference Detection Techniques
### Detection of Carrier Frequency and Bandwidth  
If the carrier frequency is to be measured to determine a periodic interference, the assumed interference's carrier frequencies must be known. If the received signal resides within a narrow frequency band centered around an assumed carrier frequency, the incoming signal may be determined as the assumed interference. This method needs to know all the interferences' frequencies and their spectral band characteristics, or this information should be adaptively given according to the places near the known radio system. Many radio systems do not reveal which frequency they use and how it is used. This method needs a vast list of radio signal frequencies and bandwidth, and tries to analyze and match the frequency of the pulses [11]. It is also difficult to measure the tone frequency since the pulse width is short. With 20-MHz sampling, a pulse width of 1 \(\upmu\)s provides only 20 samples.
## 3 The Data  
The data for this evaluation is taken from the 2008-2011 update tasks from TAC. The summarization task consists of two sets of 10 documents in each of 40+ document sets. The first set of 10 is the \"base\"; the second set occurs later in time and is called the \"update\". Participants' programs create two summaries: One that summarizes the first ten documents and another that summarizes the second ten, focusing only on novel information not contained in the first ten.  
In 2010, and continuing to 2011, the notion of a \"guided summary\" was added. Five categories were established and a set of \"aspects\", specific information relevant to the category, was defined for each. Every data set was linked to one category. Inclusion of information that addressed the various aspects was a major focus of the evaluation for both the base and update summaries.  
NIST has sponsored a summarization evaluation every year since 2001 (DUC through 2007; TAC from 2008 on). Methods to evaluate the submitted summaries have been a major effort and have, of course, evolved over the years. Beginning in 2004, ROUGE [4] became the automatic method used, replacing a measure of precision and recall of the sentences in the generated summaries. Due to the improved quality of generated summaries, ROUGE scores are no longer as good a predictor of summary quality (when compared with human evaluation of summaries) as they once were. Hence the search for new automatic tools to measure summary quality.  
Human evaluation is an expensive undertaking. It, too, has evolved over the years. Various methods to judge the content of the summaries as compared to those generated by humans have been used, beginning with SEE [16, 17] and moving to a pyramid score [18], which is the main content evaluation tool at this point. Linguistic quality was first measured by using a set of questions to rate each summary. These questions were modified, extended, and shrunk over the years and are now subsumed in two scores, one which also includes content, called \"overall responsiveness\", and one for linguistic quality only.
# Ten Years of TerraSAR-X--Scientific Results
###### Abstract  
This special issue is a collection of papers addressing the scientific utilization of data acquired in the course of the TerraSAR-X mission. The articles deal with the mission itself, the accuracy of the products, with differential interferometry, and with applications in the domains cryosphere, oceans, wetlands, and urban areas. This editorial summarizes the content.  
synthetic aperture radar; TerraSAR-X; SAR interferometry; land subsidence; precise orbit determination; geometric and radiometric calibration; PSI
# Secure Video Communication Using Multi-Equation Multi-Key Hybrid Cryptography
## 4 Experimentation
### Number of Keys Generated  
The number of generated keys is proportional to the number of generated video segments. This statistic was considered for the research because the proposed solution incorporates multiple key technologies for enhanced security. Multiple keys do not affect memory usage because the transmitter and receiver only temporarily store the keys. In addition, since each key is used only once, an increase in the number of keys has no effect on the retrieval delay.
# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 5 The Latest Research on Anomaly Detection (a.k.a. Fault Detection) and Diagnosis in PV
#### 5.0.1 Detectable Faults by ML-Based Methods
Results show as optimal the Gaussian kernel for the SVM model and two or three features from the whole set of ten. An emulated (not software but by dedicated hardware simulator) GCPV system is used in [71] to test a novel RK-RFKmeans and RK-RFED. Faults emulated at the grid side are open-circuit (F1) and standalone mode protection (F3), while on the PV side are poor connection and/or erroneous reading (F2), open-circuit/short-circuit/sudden disconnection (F5) and partial shading from 10-20% (F4). This paper introduced two new RF classifiers based on RK-RF that extract nonlinear features using a reduced kernel PCA (RK-PCA) technique to decrease the computational complexity of K-PCA for large data sets. The data reduction is based on two schemes; Euclidean distance metric and K-means clustering. Comparison with ML bases methods such as SVM, DT, ND, DA, KNN and RNN show that the two proposed methods perform very well. A novel approach based on a 2D CNN is proposed in [72]; this CNN is trained with 2D scalograms from PV system data. This 2D CNN is proposed into two configurations: one derived from a pre-trained AlexNet CNN in which the last three layers are fine-tuned to provide a six-way classifier, and another where the results from a pre-trained AlexNet layer (fc7) are used with a classical classifier (RF and SVM). Faults considered detectable with the proposed approach are PS, LL, OC, arc-fault and faults (LL and OC) in PS. Good results are obtained from the fine-tuned AlexNet but also by the pre-trained AlexNet + SVM. This paper also outlines how data from MPPT (Imax and Vmax) are significant for obtaining good accuracy (performance halves without these data). In [73] is proposed a hierarchical model for anomaly detection and a multimodal classifier to recognize five common faults in PV. The anomaly detection is realized in two steps: an Auto Gaussian Mixture Model (Auto-GMM) acts as an unsupervised ML model to detect anomalies, and this is further filtered using an auto-thresholding methodology applied to a local anomaly index (LAI) that is derived for each probable anomaly. For the classification, the authors propose a multimodal feature extraction procedure based on the Fourier spectrum derived from PV strings currents. Three classifiers are compared to classify five common PV faults: SVM, bagging and XGBoost. With the extracted multimodal features, the XGBoost model has proved to perform the best. In Table 5 are reported some recent review papers dealing with ML-based models for fault/anomaly detection and diagnosis in PV.
## 6 Results and Discussion  
### Porosity Evolution and Self-Healing Efficiency  
The porosity evolution of the self-healing cement formulations containing Barite, Pozzolan, and Chalcedony was analyzed over time using PHREEQc geochemical simulations as shown in Figure 12 in the Appendix A. The graph presents porosity changes for multiple DOE (Design of Experiment) combinations, specifically focusing on low-porosity formulations. The logarithmic time scale captures both early-stage reactions and long-term stability trends, providing insights into the self-healing mechanisms of the cement in response to environmental conditions. At the start of the simulation, there is a noticeable drop in porosity, indicating the early-stage reaction of Pozzolan and Chalcedony. This suggests the formation of secondary cementitious compounds, particularly Calcium Silicate Hydrate (C-S-H) gels, which fill initial voids in the cement matrix. The rapid decline in porosity can be attributed to early-stage mineral hydration and precipitation, particularly due to the dissolution of reactive Pozzolan and Chalcedony. Following the initial drop, the porosity stabilizes temporarily, showing only minor fluctuations. This phase indicates a balance between mineral dissolution and precipitation. The slight increase observed around 10\({}^{-2}\) to 10\({}^{0}\) days suggests the dissolution of highly soluble phases like Calcite and Portlandite, which may temporarily increase porosity. However, the presence of Pozzolan and Chalcedony ensures the continuous formation of C-S-H phases, preventing excessive porosity growth. A gradual increase in porosity is observed in the long-term phase (10\({}^{2}\) to 10\({}^{6}\) days). This can be linked to thermal expansion, mineral conversion, or delayed dissolution of less reactive silica phases. The presence of Chalcedony, which dissolves at a slower rate, may contribute to delayed silica availability, influencing long-term strength. Additionally, the presence of Barite (BaSO\({}_{4}\)) may be influencing sulfate interactions, leading to slow reprecipitation processes.  
Despite slight variations, all low-porosity formulations show consistent behavior, indicating that the cement composition remains structurally stable over extended timeframes. The variations in different DOE combinations suggest that adjustments in Pozzolan and Chalcedony ratios impact reaction kinetics, but overall porosity remains within a narrow range (2.80-2.95%), demonstrating effective self-sealing properties.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### Cse-Cic-IDS2018 Results
#### 4.4.3 Majority-Class Performance  
Figure 3 presents the F1-score comparison for majority classes (benign, Bot, Brute Force, DDoS, and DoS) across traditional oversampling techniques and GAN-based augmentation strategies.  
Across Bot, Brute Force, DDoS, and DoS, all methods--including both traditional oversampling techniques and GAN-based models--achieve perfect or near-perfect F1-scores (99-100%), indicating that these attack categories are well captured by the classifier regardless of augmentation strategy.  
In the benign class, however, noticeable differences emerge. Traditional methods such as SMOTE, ADASYN, and ROS result in relatively lower F1-scores (93-94%), whereas all GAN-based methods--including SNNL-enhanced variants--consistently achieve higher F1-scores around 99%. This suggests that GAN-generated samples provide more representative decision boundaries even for complex majority classes such as benign, where subtle noise and variability are common.  
Overall, these results indicate that GAN-based augmentation methods offer stable and high-performing detection across majority classes. The inclusion of SNNL does not adversely affect majority-class performance, reinforcing the reliability and consistency of the proposed approach in realistic NIDS settings.
## 4 Performance Evaluation  
In this section, we conduct simulations using the NS-3 simulator to assess the performance of MJTAR. Additionally, we implement the GSR [10] and GyTAR [11] protocols previously designed for urban settings in NS-3. As the first vehicular ad-hoc routing protocol tailored for urban environments, GSR forwards packets by computing the junction sequence based on the shortest distance from the source to the destination. The choice of mobility model substantially influences protocol behavior and simulation results. Hence, we employ the SUMO simulator for the mobility model and integrate NS-3 with SUMO for the simulations. This section describes the simulation environment in Section 4.1 and details the performance analysis of packet delivery ratio, end-to-end delay, and byte overhead in Section 4.2.  
Figure 10: Concept of dynamic multiple junction selection technique applying ant colony algorithm. (**a**) 1-hop junction selection probability; (**b**) 2-hop junction selection probability; (**c**) Average probability of choosing a 2-hop junction; (X: No vehicle traffic).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Ant Metrics**} \\ \hline
**Current** & **One-Hop** & **Two-Hop** & **One-Hop** & **Sum the** & **The probabilities** & \\
**Junction ID** & **Junction ID** & **Junction ID** & **Routability** & **of Same** & \(p\_avg_{best}\) \\
**(\(J_{i}\))** & **(\(J_{j}\))** & **(\(I_{k}\))** & **(\(route\_pi_{ij}\))** & **(\(route\_pi_{jk}\))** & **Neighbors** & \\ \hline J3 & J2 & J9 & 0.1333 & 0.0851 & 0.1092 \\ J3 & J4 & J7 & 0.4000 & 0.6383 & 0.5191 \\ J3 & J8 & J9 & 0.4667 & 0.2766 & 0.3716 \\ \hline \hline \end{tabular}
\end{table}
Table 5: ACO metrics creation procedure.
# A Review on Styrene Substitutes in Thermosets and Their Composites
## 4 Properties of Styrene-Free Thermosets and Their Composites
### Properties of Thermosets and Their Composites  
The dynamic and static mechanical properties of resins are contributed by their molecular structure and crosslinking degree, which further determines the properties of the composites when same fiber reinforcements were used. The reason why styrene is commonly used as RD is that its rigid benzene structure of styrene would endow rigidness on the resulting resins. For example, two bifunctional isocyanates, i.e., IEM and 3-isopropenyldimethylbenzyl isocyanate (TMI), were used as RDs for hemp fibers reinforced AESO composites, respectively [83]. Both IEM and TMI carries one C=C bond and one isocyanate group, which could concurrently improve the crosslinking density of AESO resins and the fiber-matrix interfacial adhesion of composites. However, the TMI-AESO composites presented much higher \(T_{\text{g}}\), storage modulus, tensile strength, tensile modulus, flexural strength and flexural modulus than the IEM-AESO composites because TMI monomer contains a benzene ring [83]. Thus, the renewable monomers with rigid structure such as lignin derivatives have been generally studied to develop biobased RDs. The VE resins mixed with MG or ME had comparable \(T_{\text{g}}\) with styrene-VE resins [53]. By contrast, the thermosets using fatty acids-derived RDs showed much lower \(T_{\text{g}}\) than others due to the flexible fatty acid chains [74].  
On the other hand, crosslinking density reflects the curing degree and polymerization efficiency to some extent. For example, NVP with high reactivity would accelerate the free radical polymerization, resulting in the generation of cured resins with high crosslinking density [81]. BDDMA and TMPTMA were used to replace styrene in AESO system because they have more double bonds than styrene and hence can provide more unsaturated sites, resulting in resins with high crosslinking density and \(T_{\text{g}}\)[39]. According to the rubbery elastic theory, the calculated crosslinking density of IG-AESO and TMPTMA-AESO resins are 22921 and 46537 mol/m\({}^{3}\), respectively, which are much higher than that of pure AESO (5612 mol/m\({}^{3}\)), leading to the higher \(T_{\text{g}}\) and modulus [72]. This proves that the \(T_{\text{g}}\) and stiffness of the crosslinked polymers have a close relationship with their crosslink density, which is in agreement with other polymer resin systems [84; 85; 86; 87].