esa-sceva/satcom-chunk-collection huggingface dataset:
# Recycling Red Ceramic Waste as a Raw Material for Lightweight Aggregates
## 3 Results and Discussion
### Characterisation of Lightweight Aggregates
#### 3.1.3 Particle Density (\(\rho d\))  
The compliance of the samples with the requirements established by Standard EN 13055-1 [60] for lightweight aggregates, which defines a maximum particle density limit (\(\rho d\)) of 2.0 g/cm\({}^{3}\), was widely observed. All the formulations investigated met this criterion in two of the sintering conditions assessed, totalling 12 specimens in compliance with the normative parameter. The particle density values are shown in Table 5. The data show a wide variation in densities, which ranged from 1.42 to 2.46 g/cm\({}^{3}\). This dispersion can be attributed to differences in the proportions of constituent materials and the different heating rates adopted. The analysis confirms the direct influence of composition and thermal conditions on the behaviour of the samples.  
The data from the prepared samples showed significant \(\rho d\) results when sintered at 1200 and 1250 \({}^{\circ}\)C, demonstrating a strong correlation with the bloating index results obtained in this grouping. For this temperature range, the results from R100 to R50 varied from 1.42 to 1.78 g/cm\({}^{3}\), with R100 at 1250 \({}^{\circ}\)C and R90 at 1200 \({}^{\circ}\)C. According to [PERSON] et al. [61], these results indicate that these specimens have potential for making lightweight structural aggregates. It has been demonstrated that sintering these mixtures at temperatures of at least 1200 \({}^{\circ}\)C is more advantageous for producing LWAs.  
According to Table 5, an increase in the density of samples R100, R90, R80 and R70 could be seen during the temperature variation from 1100 to 1150 \({}^{\circ}\)C, while a decreasing trend was noted at 1200 and 1250 \({}^{\circ}\)C. This variation in the density of the aforementioned samples can be attributed to their respective bloating indices, as shown in Figure 6. In general, it can be assumed that the lack of a vitrified layer resulted in an increase in \(\rho d\) at 1150 \({}^{\circ}\)C. Meanwhile, vitreous formation and mineralogical decomposition, resulting from the increase in temperature, led to an increase in porosity and, consequently, a progressive decrease in density [8; 55].  
A substantial decrease in \(\rho d\) was observed at temperatures of 1200 and 1250 \({}^{\circ}\)C, in comparison to the data obtained at 1100 and 1150 \({}^{\circ}\)C. Regardless of the RCW content added, sintering at the higher temperatures resulted in granules with a particle density of less than 2.0 g/cm\({}^{3}\), meeting the criteria required of LWAs [60]. Furthermore, there was a certain similarity between the results obtained for RCW contents of up to 80%. Thereafter, when the RCW content was increased, there was greater dispersion in the results. According to Table 4, sample R100, composed of 100% RCW, showed progressive gains in volume at temperatures of 1200 and 1250 \({}^{\circ}\)C. These results are therefore justified by the \(BI\) data.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 6 Analysis and Validation of Statistical Model for AMQP IoT Attack Detection
### Detecting the Brute Force Attack
#### 6.4.1 Detection Results for Normal and Malicious Datasets in Brute Force Attacks  
Figure 18 illustrates the classification results of network packets into normal, malicious, and AMQP RST/ACK packets associated with brute force attacks. A custom algorithm was developed to identify the unique characteristics of brute force attack signatures, with all other packets categorized as normal.  
The statistical model produced the following results:  
* A total of 95.77% of the packets were classified as normal traffic.
* A total of 3.83% of the packets were identified as malicious traffic.
* A total of 0.40% of the packets were classified as AMQP RST/ACK brute force attack packets.  
These results demonstrate the script's accuracy in detecting and distinguishing brute force attack packets within the dataset.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 4 Dimensioning Tools: Interference and Fading Characterization
### Multiuser with Multiantenna Receiver  
In order to cancel the multiuser interference, a Zero-Forcing decoder is used by projecting the \(n_{r}\times 1\) received signal on the kernel of the space formed by the \(n_{r}\times 1\) fading vectors of the \((n_{u}-1)\) other scheduled users. As stated by [25], the orthogonality constraints consume \((n_{r}-n_{u}+1)\) degrees of freedom. The receiver diversity for each sensor device is limited then to \((n_{r}-n_{u}+1)\).  
To extract this diversity, we assume that the ZF decoder is followed by a MRC that combines the \((n_{r}-n_{u}+1)\) received observation. The fading coefficient \(A_{f}^{(j)}\) observed by each sensor in Figure 2 is then chi-squared distributed with \(2(n_{r}-n_{u}+1)\) degrees of freedom.  
**Proposition 8** (MU case with ZF-MRC decoder).: _For the MU-MIMO case with ZF decoder followed by a MRC, the equivalent fading distribution averaged on the random noise and interference,_  
\[\mathbb{E}_{l,P_{n}}\ \mathbb{E}_{A_{f}}\big{[}\mathds{1}_{\{A_{f,k}\leq A_{f }<A_{f,k-1}\}}\big{]}=\sum_{p=0}^{n_{r}-n_{u}}\frac{1}{p!}\Big{(}\Xi_{p}(s_{k}) -\Xi_{p}(s_{k-1})\Big{)}. \tag{41}\]  
_The interference Laplace transform is approximated by,_  
\[\mathcal{L}_{l}^{\text{MU}}(s)\approx\mathcal{L}_{l}(n_{u}s). \tag{42}\]  
Proof.: For the multiuser case, \(n_{u}\) users in each cell transmit in the same RR. The interference becomes higher than the SISO case. As all neighboring users are multiplexed on the same RR, we approximate the sum of interference coming from the group of \(n_{u}\) users by an interfering signal generated by a random interferer transmitting with \(n_{u}P_{\text{RR}}\). We have validated this approximation using numerical results as it will be shown in the next section.  
The rest of the proof is similar to Proposition 7 considering \(2(n_{r}-n_{u}+1)\) degrees of freedom.
## 3 Results and Discussion
### Theoretical Assay  
DFT theory using B3 LYP/6-31 HG** is used for computational study of five of the fatty acids which are extracted from AO and HERA. Figure 10 shows the optimized HOMO, LUMO, and MEPs for the tested compounds. The chemical parameters were calculated using equations as reported by [54], and are listed in Table 7.  
7.1 Stability Inter- and Intermolecular Interaction against Fe Surface \"FMOs\" Frontier Molecular Orbitals Analysis  
The \"HOMO; High occupied molecular orbital\" and \"LUMO; Low unoccupied molecular orbital\" are known as \"FMOs; Frontier Molecular orbitals\", which are defined by donating/accepting-electrons, respectively, that may determine the path binding of AO and HERA with a steel surface [55]. The FMO gap is used to measure a molecule's chemical reactivity and kinetic stability. Raising the inhibitor's HOMO energy and reducing the surface's LUMO energy, improved (inhibitor-surface) stabilization [56].  
**EHOMO** in aqueous phase is higher than gas phase and the components are arranged in decreasing order as follows: **Palmitic acid > Palmitoleic > Mytistic acid > Generic > Linoleic acid** (Table 7). The greater the \(\mathbf{E_{HOMO}}\) value is than \(\mathbf{E_{LUMO}}\), the greater the likelihood of losing valance electrons and the greater the tendency to donate electrons toward the surface of iron, and greater inhibition potency than the gas phase [56; 57]. The **HOMOs** were localized over all carboxy groups in all tested fatty acids while **LUMOs** were tagged  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**EC\({}_{50}\) (\(\mu\)g/mL)**} \\ \hline  & DPPH* assay & FRAP assay \\ \hline
**AO** & 3559.08 \(\pm\) 161.75 & 1288.58 \(\pm\) 169.21 \\ \hline
**HERA** & 3621.43 \(\pm\) 316.05 & 1655.86 \(\pm\) 240.18 \\ \hline
**Quercetin** & 38.62 \(\pm\) 2.77 & 20.18 \(\pm\) 2.23 \\ \hline
**Gallic Acid** & 17.41 \(\pm\) 2.15 & 17.01 \(\pm\) 1.35 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The DPPH and FRAP EC\({}_{50}\) values of AO, HERA, quercetin, and gallic acid.  
acid**. ESP surfaces' color variation showed the variation for their values. Highest negative region related to the highest efficiency for their penetration effect over the Fe surface.
## 5 Application of the MVD Algorithm and Discussion
Thus, the approach of achieving uniform strain distribution across multiple curves, as discussed, can be similarly applied to temperature-dependent materials to ensure consistency and reliability in simulations across different temperature ranges. To illustrate the aforementioned situation of generating multiple multilinear curves with varying strain-hardening exponents, the following regression equation between the strain-hardening exponent (\(n^{\prime}\)) and the ratio of yield stress to ultimate strength (\(R_{\text{e}}/R_{\text{m}}\)), valid for most steels, serves as an example [38]:  
\[n^{\prime}=-0.33\bigg{(}\frac{R_{\text{e}}}{R_{\text{m}}}\bigg{)}+0.40. \tag{12}\]  
As the regression equation does not directly associate the cyclic strain-hardening exponent with hardness, regression equations correlating yield and ultimate strength to Brinell hardness are used, which are valid for 42 CrMo4 low-alloy steel [32]:  
\[ \begin{array}{l}R_{\text{e}}=\frac{354.6-0.517 HB}{1-\left(\frac{HB}{27} \right)+\left(\frac{HB}{80}\right)^{2}}\\ R_{\text{m}}=\frac{HB^{2}}{400}+1.6 HB+270.\end{array}  \tag{13}\]  
Using Equations (11)-(13), multilinear curves are generated, as illustrated in Figure 11a. It is evident that each curve is optimized independently, resulting in varying strain intervals between curves and differing numbers of points. However, despite these variations, all curves remain within the prescribed 1% stress deviation limit. This demonstrates the MVD algorithm's ability to optimize each individual curve separately while maintaining overall adherence to the specified stress deviation threshold. If requiring all curves to share identical discrete strain values, one approach could be to first identify the curve with the highest number of individual discretized points. If multiple curves have the same number of points, the curve with the lowest stress deviation \(SD_{\text{fin}}\) is chosen. Finally, the discretized strain values from the selected curve should be applied to discretize all others. In this specific case, the curve with a hardness value of 550 HB had the greatest number of points, which was 5. The discretized strain values obtained from this curve were, therefore, used to discretize all other curves. Consequently, the multilinear curves illustrated in Figure 11b were obtained. It is evident that all multilinear curves remained within the initially defined stress deviation limit of 1%. This process ensures uniformity in strain distribution across all multilinear curves, as well as the consistency and accuracy of modeling the material at different hardness levels. Figure 10: Generation of multiple multilinear curves based on hardness value for modeling surface-hardened components in FEA, SD\({}_{max}\) = 1%.
## 4 Privacy-Preserving Smart Metering with Authentication Capability
### Primitive Functions
#### 4.1.2 Identity-Based Sequential Aggregate Signature  
The aggregate signature scheme consists of the following four algorithms: \(Setup\), \(KeyDer\), \(Sign\), and \(Vf\).  
1. **Setup.** The trusted key generation center (KGC) initially runs the \(Setup\) algorithm to generate the master public key \(mpk\) and master secret key \(msk\).
2. **Key Derivation.** The KGC runs the \(KeyDer\) on the input of \(msk\) and user identity \(ID\in\{0,1\}^{*}\), and outputs the private key \(sk_{ID}\) for the user's \(ID\).
3. **Sign.** A signer with \(ID\) runs the \(Sign\) algorithm on the input of \(sk_{ID}\), message \(m\in\{0,1\}^{*}\), aggregate-so-far \(\sigma\), and list of identity-message pairs \(L_{i-1}=((ID_{1},m_{1}),\ldots,(ID_{i-1},m_{i-1}))\), and returns new aggregate signature \(\sigma^{\prime}\); or \(\bot\) if any of the inputs are invalid.
4. **Verification.** The verifier runs the \(Vf\) algorithm on the input of \(mpk\), list of identity-message pairs \(L_{n}=((ID_{1},m_{1}),\ldots,(ID_{n},m_{n}))\), and aggregate signature \(\sigma\), and returns 1 or 0.  
For consistency, we require that \(Vf(L_{n},\sigma_{n})=1\) for all \(n\in\mathbb{N}\) and all \(\{(ID_{i},m_{i})|1\leq i\leq n,ID_{i}\in\{0,1\}^{*},m_{i}\in\{0,1\}^{*}\}\) where the probability is over the experiment  
\[(mpk,msk)\stackrel{{\$}}{{\leftarrow}}Setup\] \[\text{For all }i=1,\ldots,n\text{ do}\] \[sk_{ID_{i}}\stackrel{{\$}}{{\leftarrow}}KeyDer(msk,ID_{i})\] \[\sigma_{0},L_{0}\leftarrow\varepsilon\] \[\text{For }i=1,\ldots,n\text{ do}\] \[\sigma_{i}\stackrel{{\$}}{{\leftarrow}}Sign(sk_{ID_ {i}},m_{i},L_{i-1},\sigma_{i-1})\] \[L_{i}\leftarrow((ID_{1},m_{1}),\ldots,(ID_{i},m_{i})).\]  
The proposed scheme is constructed based on the primitive identity-based aggregate signature protocol in [29]. The following theorem show that the signing and verification algorithms in the proposed scheme are secure in the random oracle model if the IBSAS problem is difficult relative to its associated bilinear-group generator \(\mathcal{G}\).  
**Theorem 2**.: _Let \(\mathcal{G}\) be a bilinear-group generation algorithm and \(\mathsf{AS}\) be the associated identity-based sequential aggregate signature scheme given by Sections 4.2.3 and 4.2.4. Suppose there exists a for \(F\) against \(\mathsf{AS}\) in the random oracle model that makes at most \(q_{h_{1}}\) queries to its hash oracles, at most \(q_{k}\) queries to its key derivation oracle, at most \(q_{s}\) queries to its signing oracle, and outputs lists of length at most \(n_{\text{max}}\). There is then an algorithm \(B\) for the IBSAS problem relative to \(\mathcal{G}\) such that__Appl. Sci._**2015**, \(5\)  
\[Adv^{IBSAS}_{\mathcal{A}\mathcal{S}}(F)\leq e(n_{max}(q_{s}+1)+q_{ k})\cdot Adv^{IBSAS}_{\mathcal{G}}(B)\] \[+q_{h}^{2}/l_{min}(\mathcal{G}),\]  
_where \(l_{min}(\mathcal{G})\) is the minimum bit-length of the order \(p\) of a bilinear group output by \(\mathcal{G}\)._  
**Proof.** The proof is given in [29].
## 2 Routing Protocols Overview
### Convergence Process of EIGRP  
The process begins with the use of Hello packets, which are sent to establish neighbor relationships and share crucial information. Once routers recognize each other, they exchange Update packets containing information about reachable destinations and their associated metrics. These data are essential for constructing and updating topology tables. EIGRP routers then utilize the DUAL algorithm to calculate feasible distances and determine the optimal paths. The continuous exchange of routing information through Update packets ensures that network updates occur in real time, allowing routers to quickly adapt to changes in topology. This combined process enhances EIGRP's responsiveness and reliability, especially in dynamic network environments where conditions are constantly changing. The convergence process of EIGRP is illustrated in Figure 1.  
Figure 1 shows the step-by-step process of how EIGRP routers establish neighbor relationships, exchange routing information, calculate optimal paths using DUAL, and update the network topology to ensure efficient routing.
# SINR-Based MCS Level Adaptation in CSMA/CA Wireless Networks to Embrace IoT Devices
## 5 Performance Evaluation  
To evaluate the performance of the proposed algorithm, we conducted simulations with various combinations of the numbers of APs and users using different MCS levels and the proposed algorithm. In all the simulations, APs and users (i.e., wireless devices) are randomly distributed using two independent homogeneous PPPs in a given area. All participating APs start sending CBR traffic of 30 Mbps to each wireless node associated with them when the simulations start. All simulations run for 2 s of simulation time and all simulation results are averaged values from 10 repetitions with different seed values for randomness unless stated otherwise.
# TASE: Task-Aware Speech Enhancement for Wake-Up Word Detection in Voice Assistants +
## 3 Materials and Methods
### Data Augmentation  
For the purpose of training the system with representative noise samples of realistic scenarios of the device use case, several Room Impulse Responses (RIR) are created based on the Image Source Method [44], for a room of dimensions \((L_{x},L_{y},L_{z})\), where \(2\leq L_{x}\leq 4.5,2\leq L_{y}\leq 5.5,2.5\leq L_{z}\leq 4\) m, with microphone and source randomly located at any \((x,y)\) point within a height of \(0.5\leq z\leq 2\) m. Every TV and music original recordings are convolved with different RIRs to simulate the noise signal picked up by the microphone of the device in a given room.  
Although we have tested several data augmentation techniques, we have found that background noise addition is the most significant with respect to performance. Thus, we keep it as the main data augmentation technique in this work. Clean speech samples are combined with different noise recordings (TV, music, conversations, and office and living room noise) within a wide range of SNRs \(([5,30]\) or \([-10,50]\) dB SNR). This aims at improving the performance of the models against noisy environments. In each epoch, we create different noisy samples by randomly selecting a sample of background noise for  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Noise Type** & **Dataset** \\ \hline Living Room & QUT-NOISE (HOME-LIVINGB) [40] \\ TV & IberSpeech-RTVE Challenge [41] \\ Music & Free Music Archive \\ Conversations & Podcasts in Spanish \\ Office & In-house OK Aura WUW Dataset \\ \hline \hline \end{tabular}
\end{table}
Table 3: Background noise datasets.  
each speech event and combining them with a randomly chosen SNR in a specified range. Other data augmentation techniques, such as time stretching, pitch shifting, cropping, clipping distortion, and fading with different probabilities, are discarded since no significant improvements are found in initial tests for very noisy scenarios.
## 3 Results and Discussion
### Synthesis and Characterization
#### 3.1.2 FTIR Characterizations  
The FTIR spectra presented in Figure 2 illustrate the characteristic vibrational modes of graphene oxide (GO), natural zeolite, and the synthesized GO-zeolite composites (ZGO2.5, ZGO5, and ZGO10). The GO spectrum (green) exhibits a broad peak at 3359 cm\({}^{-1}\), corresponding to hydroxyl (O-H) groups and adsorbed water molecules within the GO interlayers. The peak at 1720 cm\({}^{-1}\) is attributed to carboxyl groups, while the absorption at 1631 cm\({}^{-1}\) corresponds to aromatic C=C stretching vibrations, characteristic of sp\({}^{2}\)-hybridized carbon domains. Additionally, ether (C-O) groups are observed at 1034 cm\({}^{-1}\) and 1061 cm\({}^{-1}\), confirming the presence of oxygen-containing functional groups that enhance GO hydrophilic properties [29; 32; 41; 42]. The FTIR spectrum of natural zeolite (pink) displays a broad absorption band at 3483 cm\({}^{-1}\), associated with O-H stretching vibrations  
Figure 1: (**a**) Uv–visible spectrum of graphene oxide; (**b**) Uv–visible spectrum of natural zeolite; (**c**) spectra of natural zeolite impregnated with GO at a concentration of 10 mg mL\({}^{-1}\) (ZGO10), 5 mg mL\({}^{-1}\) (ZGO5), and 2.5 mg mL\({}^{-1}\) (ZGO2.5); all spectra were recorded in aqueous solution.  
from the hydroxyl groups. The peak at 1627 cm\({}^{-1}\) is linked to the bending vibrations of water molecules coordinated with Na\({}^{+}\) and Ca\({}^{2+}\) ions in the zeolite framework. The strong band at 1021 cm\({}^{-1}\) corresponds to Si-O-Si stretching vibrations, which are indicative of the presence of silicate tetrahedra. The peak at 788 cm\({}^{-1}\) represents the symmetric stretching of Si-O-Si bonds, confirming a well-ordered crystalline structure typical of clinoptilolite zeolites [43; 44]. The spectra of the nanocomposite materials (blue, red, and black) exhibited similarities, with variations primarily due to the interaction between GO and zeolite. O-H stretching vibrations are present at 3417 cm\({}^{-1}\), 3400 cm\({}^{-1}\), and 3395 cm\({}^{-1}\), corresponding to the contributions of the hydroxyl groups from both zeolite and GO. The characteristic C=C vibrations of GO, initially at 1631 cm\({}^{-1}\), appear shifted slightly to 1629 cm\({}^{-1}\) and 1627 cm\({}^{-1}\), indicating possible interactions between the GO functional groups and the zeolite framework. This small redshift (-4 cm\({}^{-1}\)) suggests electron density alterations in sp\({}^{2}\) carbon domains, likely due to weak interactions such as hydrogen bonding or Van der Waals forces. Additionally, the peaks at 1028 cm\({}^{-1}\) and 1040 cm\({}^{-1}\) in the composite spectra correspond to aromatic carbon ring vibrations, confirming the presence of benzene-like structures from GO. The peak at 789 cm\({}^{-1}\), initially present in the natural zeolite spectrum, experienced a redshift, indicating no significant structural disruption in the zeolite framework but rather surface-level interactions with GO.
# An Improved Contact Force Model of Polyhedral Elements for the Discrete Element Method
## 3 Calculation of Contact Force  
The contact model proposed by [PERSON] includes two parameters typically utilized for calculating contact forces between spheres, as shown in Equation (1).  
\[F_{n}=k_{n}\times\delta \tag{1}\]  
where \(\delta\) represents the contact depth and \(k_{n}\) represents the stiffness coefficient. The contact depth is calculated by the contact detection algorithm, so the normal stiffness coefficient determines the accuracy of the contact force calculation. Unlike spherical elements, there are various contact modes among polyhedral elements, which may result in Equation (1) being inapplicable in all contact modes. Therefore, a method based on impact experiments is proposed in this section to determine the stiffness correction factor, and Equation (1) is modified for different contact modes of the polyhedral elements.