esa-sceva/satcom-chunk-collection huggingface dataset:
## Impact Sources, Methods, and Performance Rating Standards in Republic of Korea
### Rating Method of Heavy Weight Impact Sound  
In the pre-certification system for multi-family housing floor structures, the method for calculating the single number quantity of heavy-weight impact sound performance (\(L^{\prime}{}_{i,Fmax,AW}\)) is outlined in KS F 2863-2. As shown in Figure 2, the \(1/1\) octave band range from 63 Hz to 500 Hz is used. Once the maximum floor impact sound level for each frequency band is measured, the reverse A-weighting correction curve is applied, and it is adjusted in 1 dB intervals upwards or downwards. The reverse A-weighting correction curve is lowered within a range where the total sum of the maximum floor impact sound levels (\(L^{\prime}{}_{i,Fmax}\)) exceeding the reverse A-weighting correction curve does not exceed 8.0 dB. The value at the 500 Hz band of the adjusted reference curve is then designated as \(L^{\prime}{}_{i,Fmax,AW}\).  
In the post-certification performance verification system, the method for calculating the single number quantity of heavy-weight impact sound insulation performance for floor structures is specified in KS F ISO 717-2. The evaluation method in KS F ISO 717-2 allows for the calculation of \(L^{\prime}{}_{i,Fmax}\) using both the \(1/1\) octave band and \(1/3\) octave band. The values are then adjusted using the A-weighting correction factors for each frequency band, as referenced in KS F ISO 717-2, Table 2. By summing the corrected results across the frequency bands, the A-weighted maximum impact sound pressure level, \(L^{\prime}{}_{iA,Fmax}\), can be derived.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Octave Band Center** & **Impact Force Exposure Level,** & **Impact Force Exposure Level,** \\
**Frequency** & \(L_{\text{FE}}\) & \(L_{\text{FE}}\) \\
**Hz** & **dB re 1 N (Bang Machine)** & **dB re 1 N (Rubber Ball)** \\ \hline
31.5 & 47.0 \(\pm\) 1.0 & 39.0 \(\pm\) 1.0 \\ \hline
63 & 40.0 \(\pm\) 1.5 & 31.0 \(\pm\) 1.5 \\ \hline
125 & 22.0 \(\pm\) 1.5 & 23.0 \(\pm\) 1.5 \\ \hline
250 & 11.5 \(\pm\) 2.0 & 17.0 \(\pm\) 2.0 \\ \hline
500 & 5.5 \(\pm\) 2.0 & 12.5 \(\pm\) 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Impact force exposure level in each octave band of the bang machine and rubber ball.  
Figure 1: Standard heavy-impact sources. (**a**) Bang machine; (**b**) rubber ball.
## 3 Results and Discussion
### Characterization of CNCs Properties
#### 3.1.4 Charges Density by Conductometric Titration  
From the conductometric titration (Table 2), the concentration of weak acid (-COOH) derived was 0.52 mmol/kg for CNC\({}_{\text{COOH}}\), while the concentration of strong acid (-OSO3H) was 0.3 mmol/kg for CNC\({}_{\text{SO3H}}\). As for CNC\({}_{\text{COOR}}\), the concentrations of weak and strong acids (-COOH and -OSO3H) were 0.63 and 0.25 mmol/kg, respectively. It can be confirmed that CNC\({}_{\text{COOR}}\) have the highest charges density attributed to total charges, which was the result of residuals of carboxylic groups and sulfate half ester groups which remained after the esterification.  
Figure 2: Z potential values versus pH of CNCs with carboxylic (CN\({}_{\text{COOH}}\)), sulfate half ester (CN\({}_{\text{SO3H}}\)) and ester groups (CN\({}_{\text{COOR}}\)).
## 3 Results and Discussion
### Antioxidant Capacity of Extracts
#### 3.4.2 Extracts Obtained with Ultrasound-Assisted Extraction (UAE)  
The two-way analysis of variance showed that the water-methanol and water-ethanol extracts did not differ significantly in antioxidant capacity (average 123 umol of Trolox/1 g of DW); however, all water-alcoholic extracts were characterised by higher antioxidant activity than water extracts. Among the water extracts obtained using the UAE method, the extract from apple leaves of the Ligol cultivar had the significantly highest antioxidant capacity (106.3 umol of Trolox/1 g of DW). The water extract from apple leaves of the Gloster cultivar had a significantly higher antioxidant capacity than the extract from the  
Figure 10: Antioxidant capacity in apple leaf extracts prepared using the ASE method.  
Gala cultivar, which was characterised by the significantly lowest antioxidant activity among all extracts prepared using the ultrasound-assisted extraction method (74.6 \(\upmu\)mol of Trolox/1 g of DW).  
The one-way analysis of variance confirmed that both the cultivars of apple leaves and the type of solvent used had a significant impact on the antioxidant capacity of the extracts. Significant differences among the average antioxidant activity were observed for extracts from different cultivars of apple leaves, respectively, Ligol > Gloster > Gala, as well as for different types of extracts, water-methanol > water-ethanol > water. In the 70% ethanol extract from apple leaves of the Ligol cultivar, obtained using UAE, [PERSON] et al. [46] obtained a similar antioxidant capacity, amounting to approximately 130 \(\upmu\)mol of Trolox/1 g of DW. However, in the study by [PERSON] and [PERSON] [8], the antioxidant activity apple leaf extracts ranged, depending on the cultivar, from 105.7 to 200.2 \(\upmu\)mol Trolox/1 g of DW. These authors investigated the antioxidant activity of leaf extracts from different fruit trees and bushes, including apple, quince, chokeberry, cranberry, etc. Their results showed that apple leaf extract exhibited the third-highest content of total polyphenols, whereas it had one of the lowest antioxidant activities, which was explained by the differences in the polyphenol profiles of different plant species.
## 2 First Principle Model of System
### First Principle Modeling
Considering that the system controls the gas temperature, by fixing the air flow rate and adjusting the fuel flow rate, it is necessary to establish a mathematical model from fuel flow rate to gas temperature. Because the air mass enters the combustor, the inlet air temperature, the initial temperature of cooling water, and the cooling environment temperature of the combustor wall are basically unchanged and can be regarded as constant under certain working conditions, so the second, third, and fourth terms on the right of Equation (8) can be regarded as constant terms. Therefore, the Laplace transform of the Equation (8) can be obtained. \[V\rho_{p}c_{p}sT(s)+(\rho_{air-in}w_{air-in}c_{p}+K_{1}A\alpha+\rho_{water}w_{ water}c_{water}\beta)T(s)=H\rho_{fuel}w_{s}(s) \tag{9}\]  
In addition, considering that there is always a certain pure delay in the actual thermal system, the transfer function from fuel flow rate to gas temperature can be obtained as follows  
\[G(s)=\frac{T(s)}{w_{s}(s)}=\frac{K_{p}}{T_{p}s+a}e^{-\mathrm{r}s} \tag{10}\]  
where \(K_{p}=H\rho_{fuel}\), \(T_{p}=V\rho_{p}c_{p}\), \(a=\rho_{air-in}w_{air-in}c_{p}+KA_{1}\alpha+\rho_{water}w_{water}c_{water}\beta\). The transfer function described by Equation (10) can be numerical by substituting the data in Table 1 into Equation (10) and converting the unit of fuel flow rate to L/min. \[G_{1}(s)=\frac{5650}{3.91s+12.08}e^{-(3-5)s} \tag{11}\]  
In Table 1, the values of parameters, such as calorific value, density, specific heat capacity, and heat transfer coefficient, were obtained according to relevant manuals; the values of parameters, such as flow rate, volume, and area were obtained according to structure and working conditions of the actual system. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Parameters** & **Unit** & **Value** \\ \hline \(H\) & calorific value of fuel & J/kg & \(4.29\times 10^{7}\) \\ \(\rho_{air-in}\) & density of input air & kg/m\({}^{3}\) & 1.293 \\ \(w_{air-in}\) & flow-rate of input air & m\({}^{3}\)/s & 0.267 \\ \(\rho_{water}\) & density of cooling water & kg/m\({}^{3}\) & 1000 \\ \(w_{water}\) & flow-rate of cooling water & m\({}^{3}\)/s & 0.0125 \\ \(c_{water}\) & specific heat capacity of cooling water & J/(kg \(\cdot\)\(\circ\) C) & 4178 \\ \(\rho_{fuel}\) & fuel density & kg/m\({}^{3}\) & 780 \\ \(\rho_{p}\) & density of gas & kg/m\({}^{3}\) & 1.4 \\ \(c_{p}\) & specific heat capacity of gas & J/(kg \(\cdot\)\(\circ\) C) & 1117 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model parameters of gas temperature.
# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 4 Experiment Analysis
### Convergence Analysis  
A predictive model's efficiency is a critical factor in practical engineering scenarios. APSO-CNN-Bi-LSTM model demonstrates superior prediction accuracy and enhanced convergence properties--evidenced by needing fewer iterations to reach optimal performance, as shown in Figure 10--a significant advantage. This quicker convergence implies that the model training phase can be completed in a shorter timeframe, reducing computational resources and energy consumption. This leads to cost savings, a crucial aspect for businesses and organizations implementing such systems at scale. Figure 11 further reinforces this advantage by illustrating that our proposed model requires less time to cease training across all four datasets than alternative models. This rapid training cessation accelerates the initial deployment process and facilitates more efficient iterative upgrades in response to new data or changing environmental conditions. In essence, the ability of the APSO-CNN-Bi-LSTM model to expedite the initial training and future updates is a testament to its practical viability. It underscores how advancements in algorithm design--incorporating adaptive optimization strategies with sophisticated neural network architectures--can translate into tangible benefits beyond mere accuracy improvements, addressing real-world constraints such as time-to-market and operational expenses. This efficiency is particularly valuable in industries like environmental monitoring, where timely insights and adaptive responses to air quality changes can have immediate public health and economic implications. The prediction time step for this part is set to 1 h.  
Figure 10: Train Loss. Subfigures (**a–d**) are the Loss values of different models on four datasets.
# Biometrics for Internet-of-Things Security: A Review
## 6 Present Challenges
### Limitations of Applying Biometric-Cryptographic Techniques to IoT Data Encryption/Decryption  
In Section 5, several biometric-cryptography-based schemes, such as fuzzy commitment, fuzzy vault and fuzzy extractor, are introduced and discussed. In the design of biometric authentication systems for the IoT, biometric-cryptographic techniques are not a common option for two reasons. The first reason is that cryptographic key binding or generation involves a large amount of computation, thus imposing heavy computing costs on resource-constrained IoT devices [99]. The second reason is that often the currently available biometric-cryptographic techniques (e.g., fuzzy commitment and fuzzy vault) cannot be directly applied to the feature sets formed in the unencrypted domain. This is because these techniques require fixed-length feature representations [100] in order to measure feature disparity using metrics such as hamming distance or set difference, but converting raw biometric data collected by IoT devices to the required formats not only creates extra computational load, but also likely decreases authentication accuracy.
# Seismic Response Study of L-Shaped Frame Structure with Magnetorheological Dampers
## 4 Model Description and Verification  
### Model Description  
The calculation model of the L-shaped frame structure with MRDs is presented in Figure 7. The L-shaped frame structure has ten floors with a total height of 34.5 m. The  
Figure 6: The schematic diagram of damping force distribution.  
heights between the first and the third floors are 4.5 m, and the heights of the other floors are 3.0 m. The column spacing in the X direction is 6.3, 4.5, and 4.5 m, and column spacing in the Y direction is all 6.3 m. The cross-sectional sizes of X directional beams are 0.25 \(\times\) 0.65, 0.25 \(\times\) 0.6, and 0.25 \(\times\) 0.6 m, and those of Y directional beams are all 0.3 \(\times\) 0.7 m. The cross-sectional sizes of columns of 1 to 3 floors are 0.7 \(\times\) 0.7 m, and those of other floors are 0.6 \(\times\) 0.6 m. The thickness of all floors is 0.12 m; Poisson's ratio \(\mu\) = 0.2, elastic modulus \(E\) = 3.25 \(\times\) 10\({}^{4}\) N/mm\({}^{2}\), and density \(\rho\) = 2500 kg/m\({}^{3}\). Assuming the damping ratios of the first- to second-order vibration modes of the structure are set to 5%, the bottom columns are assumed to be consolidated. As the deformation of frame structure under seismic load is mainly concentrated in the bottom floors, the MRDs are, therefore, set on the first to sixth floors, as seen in Figure 7. Numbers in brackets of Figure 7 indicate the node number.
## 3 Semantic Educational Recommender Systems
### The SERS Approach  
As introduced in the previous section, in our research we focus on deploying semantic recommender systems in the educational domain. From the literature, semantic recommender systems are those whose performance are based on a knowledge base usually defined as a concept diagram (like a taxonomy or thesaurus), or an ontology [30]. In our approach, the idea is to extend the functionality of standards-based LMS with adaptive navigation support in order to provide learners with personalized and inclusive scenarios. To this end, the approach considers standards and specifications to support the semantic modeling of the knowledge required for the recommendation process [31]. Thus, we coined the concept of Semantic Educational Recommender Systems (SERS) [32]. This concept entitles the following terms:  
* **Recommender system**, which as previously introduced, is a tool to help users in decision making processes in environments with information overload by offering recommendations as links, thus providing adaptive navigation support. Recommenders apply recommendation algorithms, which facilitate the automation of the recommendation process.
* **Semantic**, as it allows recommendation designers to provide high-level descriptions of the recommendations, which are also interpretable by the recommender system, both in the process of selecting and delivering the most appropriate recommendations and in the process of automatically generating new recommendations. Moreover, these descriptions allow explaining users their rationale, thus supporting trust in the system. And no less important, they facilitate the information exchange among the software components involved in the recommendation process that is orchestrated in the context of a service-oriented architecture.
* **Educational**, since it is applied in the educational domain, which has specific particularities (e.g., recommendations should be guided by educational criteria and not just by the learners' preferences) and potentially, they can cover any action (e.g., read or contribute) to any object in the LMS, so the involvement of the education is needed to identify the recommendations that are appropriate for their formal e-learning scenarios.  
Hence, based on educational criteria, SERS are characterized by guiding learners in their interaction within e-learning platforms. To this end, they provide personalized and inclusive recommendations that could target any possible action within an LMS. These recommendations are semantically described and can be obtained thanks to the information exchange among the different components involved in the process of generating and delivering the recommendations. In order to clarify the differences between ERS and SERS, a comparison regarding eight key features is provided in Table 2.
# Approaches to the Functionalization of Organosilicon Dendrones Based on Limonene
## 3 Results and Discussion  
Limonene is a natural monoterpene that is widespread in nature. The technology of its isolation from the peel of citrus fruits as a waste product of the food industry is a well-studied and streamlined process [46; 47]. It is interesting to note that citrus essential oils contain only d-limonene, while its isomer, l-limonene, is found in conifers. The commercial availability of limonene and its low toxicity predetermined its use as an alternative solvent to hexane or cyclohexane. In addition to this utilitarian use, attempts to use limonene for the synthesis of polymers as an alternative to crude oil and gas were reported. These reasons shaped our interest in limonene regarding the synthesis of dendrimers and JDs. JDs are often obtained using naturally occurring compounds with varying functionality as the parent branching site. Our interest in limonene as a branching point was guided by the possibility to selectively implement the hydrosilylation reaction, one of the main reactions of organosilicon chemistry that is used, in particular, for the synthesis of carbosilane and siloxane dendrimers. As we noted in the Introduction, limonene is involved in hydrosilylation selectively at the isoprenyl double bond, thus retaining the possibility of further functionalization of the cyclohexene double bond. This approach is efficient in the syntheses of amphiphilic dendrons, JDs, or dendronized polymers.  
We have shown in a previous study that limonene-based dendrons with various peripheral groups can be obtained. This work, which is a logical continuation of that study, explores the ways to further functionalize the cyclohexene double bond and functionalize the peripheral substituents. These approaches would make it possible, in the future, to obtain dendrons with various natures and use the peripheral function for further modifications or to build more complex dendronized structures.
## 3 Results and Discussion
### Atomizer Geometry Simplification  
The atomizer geometry can be simplified in several steps; see Table 3. The most complex approach uses the full-scale atomizer model including the inlet ports. This model allows for the air-core to divert from the swirl chamber centreline. Since the atomizer used here can be divided into three identical parts, the periodic model can be easily created. This simplification ensures that the air-core axis is equal to the swirl chamber centreline, which may subdue some large-scale flow instabilities. This model can be further simplified, as the inlet port geometry can be substituted by an imprint of the inlet port on the wall of the swirl chamber to form the inlet boundary condition. However, the inlet velocity profile on the boundary of the swirl chamber is not trivial and it must be pre-calculated to yield correct results. This pre-calculation can be performed using a simple tetrahedron mesh with inlet port, a single-phase model and a steady solution. This velocity profile must be interpolated on the no-port inlet boundary. The main advantage of the no-port mesh is the absence of highly skewed cells near the swirl chamber; therefore, the mesh creation process is much faster and the mesh quality is better.  
The last possible simplification is to create a 2D axisymmetric model with the swirl velocity component. This model requires setting the inlet radial and swirling velocity as two independent components to keep the momentums in both directions. However, the results show that conserving the angular momentum can be inaccurate due to distortion of the inlet velocity profile, which virtually increases the inlet angular momentum. This phenomenon is also well observed on the no-port version, which underestimated the air-core dimensions as well as the inlet pressure.  
When the full 3D mesh is compared with the periodic one in terms of velocity profiles (Figure 9) and global parameters (Table 3), only minor differences can be found. In most practical calculations, the periodic mesh is more cost-effective. The no-port version did not prove prediction reliability even if the inlet velocity profile is pre-calculated and therefore is not recommended for further use.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mesh**} & \(p_{I}\) & _SCA_ & \(d_{ac\_exit}\) & \(d_{a1 mm}\) & \(U_{I}\) & \(V_{I}\) & \(C_{D}\) \\ \cline{2-7}  & **[Pa]** & [\({}^{\circ}\)] & **[mm]** & **[mm]** & **[m/s]** & **[m/s]** & **[–]** \\ \hline Experimental data & 8800 & 78.0 & 2.99 & 1.67 & 3.1 & 2.2 & 0.41 \\ \hline
2D Momentum-based & 6900 & 56.1 & 2.88 & 1.48 & 3.4 & 1.7 & 0.46 \\ \hline
2D \(v_{p}\) & 8800 & 62.2 & 3.06 & 1.64 & 3.5 & 2.2 & 0.41 \\ \hline
3D PER No-port & 6700 & 56.7 & 2.83 & 1.47 & 3.5 & 1.9 & 0.47 \\ \hline
3D PER No-port-profile & 8550 & 63.0 & 2.98 & 1.49 & 3.6 & 2.4 & 0.42 \\ \hline
3D PER & 9200 & 65.3 & 3.07 & 1.85 & 2.9 & 2.6 & 0.40 \\ \hline
3D Full & 9350 & 61.3 & 3.10 & 1.66 & 3.7 & 2.4 & 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of model geometry. _Re_ = 2000, laminar model, transient.
## 3 Results
The total heat dissipation surface area of the vibration damper is \(A_{B}=0.105154\) m\({}^{2}\), with the end-face surfaces accounting for \(68\%\) and the cylindrical surfaces accounting for \(32\%\). The temperature rise in the oil is a result of the conversion of internal energy from the oil friction forces into heat. Figure 9 shows the dependence of the total friction torque on the relative velocity \(\omega\). For the assumed permissible operating temperature of the M30000 oil, \(T_{B,\text{lim}}=90\)\({}^{\circ}\)C, and the corresponding relative velocity \(\omega=0.7\) s\({}^{-1}\), the calculated total torque generated by the friction forces in the oil is \(M=59.02\) N\(\cdot\)m.  
Analyzing the oil films where friction is generated and denoting the friction torques produced in the cylindrical spaces by \(M_{1}\) and \(M_{2}\) and the friction torque generated in the end-face spaces by \(M_{0}\), their contributions to the total torque can be calculated. These contributions are \(M_{1}+M_{2}=0.18\cdot M\) and \(2\cdot M_{0}=0.82\cdot M\). Specifically, the friction torque generated in the internal oil film is \(M_{1}=0.08\cdot M\), and in the external oil film it is \(M_{2}=0.10\cdot M\). The obtained result clearly shows that, for the analyzed damper, the primary surfaces responsible for dissipating the energy of torsional vibrations are the frontal  
Figure 8: The housing temperature of the damper as a function of (**a**) the relative velocity \(\omega\), and (**b**) the resultant relative eccentricity \(\varepsilon\). Figure 9: The dependence of the friction torque on cylindrical and end-face surfaces on the relative velocity \(\omega\). surfaces. Performing such calculations for dampers with different geometries may lead to the optimization of the damper's design, which would involve minimizing material consumption and reducing the damper's weight while maintaining its damping performance. This is, therefore, very important information from the perspective of the designer. Figure 10 shows the behavior of the function \(P_{t}(\omega)\), which defines the power dissipated in the form of heat and transferred through the damper housing. As the relative velocity \(\omega\) increases, the amount of heat generated by friction forces in the oil films also increases. For the assumed permissible oil temperature \(T_{B,\text{lim}}=90\)\({}^{\circ}\)C, the maximum value of heat generated and dissipated into the surroundings is \(P_{t,\text{max}}=41\) W. The relationship shown in this graph is of great importance to the designer. By knowing the power of vibrations generated on the crankshaft, it is possible to determine whether the damper will be able to mitigate their presence in real time.
## 1 Introduction
### Paper Organization  
The remainder of this paper demonstrates these contributions through comprehensive technical development and validation. Section 2 establishes essential background on DShot protocols and the security threat landscape, while Section 3 presents our threat model and attack taxonomy. The SMART DShot architecture and the ML algorithm framework are detailed in Section 4, followed by extensive simulation results and hardware validation in Section 5. Security implications and deployment considerations are analyzed in Section 6, with conclusions and future research directions provided in Section 7.