esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Analysis of Variance (ANOVA)  
To further investigate the strength effects of temperature, polypropylene fiber (PPF) length, and PPF content, a three-factor analysis of variance was performed, and the results are shown in Table 5. The three-factor analysis of variance was used to study the effects of PPF content, PPF length, and temperature on the strength. From Table 5, it can be seen that PPF content, PPF length, and temperature were significant (F = 47.924, \(p\) = 0.000 \(<\) 0.05; F = 73.448, \(p\) = 0.000 \(<\) 0.05; F = 24410.222, \(p\) = 0.000 \(<\) 0.05), indicating that the main effects existed and that PPF content, PPF length, and temperature had a differential effect on strength. Based on the size of the F-value, it can be determined that the order of the influencing factors was temperature > PPF length > PPF content. Under conditions where PPF content, PPF length, and temperature were significant, the significance of the two-way interaction between them was explored. The results showed that the interaction of PPF content \(\times\) PPF length, PPF content \(\times\) temperature, and PPF length \(\times\) temperature (F = 3.229, \(p\) = 0.040 \(<\) 0.05; F = 12.614, \(p\) = 0.000 \(<\) 0.05; F = 7.021, \(p\) = 0.002 \(<\) 0.05) were all significant, and the significance ranking was PPF content \(\times\) temperature > PPF length \(\times\) temperature > PPF content \(\times\) PPF length. It can be seen that the significance of the interaction related to temperature was the most obvious, and the interaction of PPF content \(\times\) temperature was the most significant. The comprehensive ranking shows that temperature > PPF length > PPF content > PPF content \(\times\) temperature > PPF length \(\times\) temperature > PPF content \(\times\) PPF length. Figures 16-18 show the mean square histograms under each influence parameter, and the values at the top of the histograms represent the mean values of compressive strength, from which it can be seen that the strength variation was not particularly obvious under the influence of PPF admixture and PPF length.  
Figure 16: PPF Dosage & PPF Length.  
Figure 17: PPF Length & Temperature.
## 3 Sensitivity Analysis and Genetic Algorithms for Design Optimization of the MRE-Based ATDVA
### Derivation of Equations for the Magnetic Field and Mass of the MRE-Based ATDVA
\[\Phi_{eq}=\frac{N\cdot I}{R_{eq}} \tag{13}\]  
Because the MRE is positioned in the middle of the side for the coil to be wound on, the magnetic flux density \(B_{1}\) is given by Equation (14). \[B_{1}=\frac{\Phi_{eq}}{A}=\frac{N\cdot I\cdot\mu bc(h-d)}{l_{1}bc(h-d)+2l_{2} \pi R_{S}^{2}(h-d)+l_{3}\pi R_{S}^{2}c} \tag{14}\]  
The mass of the MRE-based ATDVA can be expressed as the sum of masses of the core and the coil wound on it, as given by the following equation:  
\[M=\pi R_{S}^{2}\times(a-2c)\times\rho_{2}+\pi R_{C}^{2}\times L_{c}\times\rho _{C} \tag{15}\]  
Figure 14: Magnetic circuit analogy model of adaptive tuned MRE vibration absorber. where \(R_{S}\), \(L_{S}\) and \(\rho_{s}\) represent the radius, length and density, respectively, of the core on which the coil is wound, and \(R_{C}\), \(L_{C}\) and \(\rho_{c}\) are the radius, length and density, respectively, of the coil. Because the coil wound on the core is multilayered, its length can be expressed as the product of the length of the wound coil per unit length and the number of layers. Figure 15 shows a cross-section of the core on which the coil is wound. So \(X_{k}\) corresponding to layer \(k\) can be expressed as Equation (16). \[X_{k}=2\Big{\{}R_{S}+\big{(}1+k\sqrt{3}\big{)}R_{C}\Big{\}} \tag{16}\]  
The number of coil windings per unit length of the core is defined as a value divided by the diameter of the coil with a unit length of 1. The length \(l_{k}\) of the coil wound on layer \(k\) can be expressed as Equation (17). Figure 12:0  
Because the coil is wound over a length of \(l_{k}\) from the first layer to layer \(k\), the length of the wound coil per unit length is given by Equation (18). \[ \begin{array}{l}l_{c}=\sum\limits_{n=1}^{k}\frac{1}{2R_{C}}\times 2\pi \Big{\{}R_{S}+R_{C}+(k-1)\sqrt{3}R_{C}\Big{\}}\\ =k\pi\times\frac{1}{2R_{C}}\times\Big{\{}2R_{S}+2R_{C}+(k-1)\sqrt{3}R_{C}\Big{\}} \end{array}  \tag{18}\]  
Consequently, the length of the coil wound on the core equals the product of \(l_{c}\) and the coil winding length (\(a\)-\(2c\)), and substituting the values into Equation (15) yields Equation (19) for the mass of the MRE-based ATDVA. \[M=\pi R_{S}^{2}\times L_{S}\times\rho_{S}+l_{c}\times\pi R_{S}^{2}\times(a-2c )\times\rho_{S} \tag{19}\]  
Therefore, optimization should be performed for the MRE-based ATDVA to have the mass determined by Equation (19) and maximize the magnetic field strength in Equation (14). In doing so, a genetic algorithm was used.
## 2 Materials and Methods
### Experimental Work  
#### 2.2.1 Experimental Setup  
Various combinations of laser power, scanning speeds, track overlapping, hatching distances and layer thicknesses were used to determine the melting behaviour and the resulting product density. In addition to the machine runs, the resulting densities were also measured and the next machine runs were followed. While no official design of experiments (DOE), like the Box-Behnken Design (BBD) or Central Composite Design (CCD), was utilised because of limitations in machine time and material resources, the experimental strategy adhered to an organised grid-based method. To ensure adequate coverage and independence within the parameter space, the grid-based experimental design was analysed for parameter distribution and correlations. The configuration varied in laser power (60-200 W), scanning speed (700-3000 mm/s) and layer thickness (0.025-0.05 mm) over 42 trials, ensuring comprehensive coverage with 17 power variations, 19 scanning speeds and 3 layer thicknesses. The correlation matrix showed weak relationships for layer thickness (r = \(-\)0.1509 with laser power, r = \(-\)0.1134 with scanning speed), but a strong  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Min Value** & **Max Value** & **Status** \\ \hline Laser power [W] & 40 & 150 & Variable \\ \hline Scanning speed [mm/s] & 200 & 1500 & Variable \\ \hline Track overlapping [\%] & / & / & Fixed value at 30 \% \\ \hline Hatch spacing [mm] & / & / & Fixed value at 0.035 \\ \hline Layer thickness [mm] & 0.025 & 0.035 & Variable \\ \hline \hline \end{tabular}
\end{table}
Table 1: Process parameters and their intervals.  
relationship between laser power and scanning speed (r = 0.8814). This combination, which is typical in additive manufacturing to balance the energy input [30], represents a pairing of parameters controlled by the process. The different parameter levels guaranteed a thorough sampling of the parameter space. This guaranteed diverse sampling throughout the established parameter space while preventing unnecessary configurations. Future research will apply formal DOE methods.
# Terahertz Single-Pixel Imaging Improved by Using Silicon Wafer with SiO\({}_{2}\) Passivation
## 4 THz Single-Pixel Imaging Experiments  
We now study the improvement of the THz single-pixel imaging quality by using the as-fabricated THz photo-controlled modulators based on passivated silicon wafers. Three targets were prepared: three-arm, four-arm and six-arm cartherels.  
To quantify the image quality, we adopt signal-to-noise ratio (SNR) as the figure of merit following refs [13; 27; 28]. It is defined as the average value of intensity in the bright region, \(\mu(\text{signal})\), divided by the standard deviation of the noise in the signal-free area of the image, \(\sigma(\text{background})\), that is [13; 28]  
\[\text{SNR}\equiv\frac{\mu(\text{signal})}{\sigma(\text{background})}\,. \tag{7}\]  
Figure 4 compares the reconstructed terahertz images by using two photomodulators made of the passivated silicon wafer with the highest MF and the conventional bare silicon wafer, i.e., samples S5 and S1. These terahertz images were obtained under the same experimental conditions: the laser power is 330 mW, and the sampling ratio is 100%. Results show that for the bare silicon wafer, the reconstructed images have very poor quality due to the small MF of 0.3, so that the SNR is not available; however, for the passivated silicon wafer with the highest MF of 0.9, the reconstructed image is very clean and clear, and the corresponding SNRs are about 1.0. In other words, the terahertz single-pixel imaging is greatly improved by replacing the conventional photomodulator based on the bare silicon wafer with the new one based on the passivated silicon wafer.  
By using the photomodulator made of sample S5, Figure 5 shows that the quality of reconstructed images increases with the sampling ratio in general. The change trend of the SNR with the sampling ratio also confirms this phenomenon, i.e., as the sampling ratio increases, the sharpness of the image gradually increases. The image can be well reconstructed only for sampling ratios of more than 25%. For a sampling ratio of 25%, there still exists pronounced background noise. The noise is greatly suppressed as the sampling ratio increases.  
Figure 4: Top panel: optical images of three targets. Middle and bottom panels: reconstructed \(32\times 32\) terahertz images (\(N=32\)) based on photomodulators made of passivated and bare silicon wafers, i.e., samples S5 and S0, respectively. The images were reconstructed under full sampling. The scalar bars denote 500 \(\upmu\)m.  
Figure 5: Reconstructed \(32\times 32\) terahertz images under different sampling ratios: 25%, 50%, 75% and 100%. Scalar bars denote 500 \(\upmu\)m.
# Matching Quality Detection System of Synchronizer Ring and Cone
## 2 Theoretical Model of the Friction Cone in the Synchronization Process  
The synchronizer is used for shifting of the transmission. During the shifting process, the synchronizer relies on the friction cone between synchronizer ring and cone to synchronization. When there is a difference between rotational speeds of the friction cone surfaces, a friction torque is generated on the friction cone  
Figure 3: Schematic diagram of the force of the synchronizer.  
due to the axial force. Under this action, the gear speed will rapidly decrease or increase until it is equal to the synchronizer ring speed. At the same time, there will be an inertia torque opposite to the direction of rotation on the friction cone [15]. Among gear speed, ring speed, and axial force, the friction torque plays an important role in the performance of the synchronizer [16].  
According to the friction principle, the structural diagram of a single friction cone can be as shown in Figure 4.  
The meaning of each symbol in the figure is as follows:  
\(\omega_{t}\): Angular velocity of the input part of the synchronizer;  
\(J_{r}\): The moment of inertia of the input part of the synchronizer;  
\(\alpha\): Half cone angle of the friction cone;  
\(R\): Average radius of the friction cone;  
\(F_{a}\): Axial force;  
\(T_{f}\): Friction torque of the synchronizer;  
\(\omega_{c}\): Angular velocity of the output part of the synchronizer;  
\(J_{c}\): The moment of inertia of the output part of the synchronizer;  
Since the friction surface of the synchronizer is tapered, the average radius of the friction cone can be equivalent to:  
\[R=\frac{2\Big{(}R_{m1}^{3}-R_{m2}^{3}\Big{)}}{3\Big{(}R_{m1}^{2}-R_{m2}^{2} \Big{)}} \tag{1}\]  
where \(R_{m1}\) is the radius of the large end of the cone and \(R_{m2}\) is the radius of the small end. Next, with axial force \(F_{a}\), the friction torque of friction cone can be obtained as follows [17]:  
\[T_{f}=\frac{\mu F_{a}R}{2\sin\alpha} \tag{2}\]  
where \(\mu\) is the friction coefficient of the friction cone of the synchronizer. It can be seen from the formula that during the synchronization process, as time changes, the applied force changes, and the friction torque changes [18].  
The output of the transmission is connected to the entire vehicle, so its inertia moment is considerable, which means that the speed at the output of the transmission remains constant in the moment of shifting. The input is synchronized with the output by friction, and thereby the following formula can be obtained:  
\[J_{r}\frac{d\omega}{dt}=T_{f} \tag{3}\]  
IAccording to the calculated average radius of the friction cone and the friction torque of friction cone, synchronization time \(t_{T}\) can be solved as [19]:  
Figure 4: Structural schematic diagram of a single friction cone.  
\[t_{T}=\frac{J_{r}(\omega_{r}-\omega_{c})}{T_{f}}=\frac{2J_{r}\Delta\omega\sin\alpha} {\mu F_{a}R} \tag{4}\]
# Comparative Analysis of Producer Mobility Management Approaches in Named Data Networking
## 3 Analysis of Scheme Designs
### Map-Me
* **Map-Me Example** An illustrative example of the operation of Map-Me-IU is shown in Figure 3a. The producer moves from PoA C to PoA H. When the producer attaches to PoA H, it first increments the sequence number (from 0 to 1) and then sends an IU using its prefix (/A/B). Therefore, the IU will travel from the producer at its new location (PoA H) to PoA C, as directed by the FIBs. The reception of the IU at PoA H induces the change in the face associated with the producer's prefix (/A/B) to the incoming face of the IU (H2). The same is performed at PoA D, which changes the face to D2 and finally at C, which changes the face to C3. All these changes are performed after comparing the sequence number in the IU (1) to the sequence number in each PoA's TFIB and finding that the IU indeed holds a higher sequence number. As a result, the path between the old PoA and new PoA is now established, and new incoming consumer interests can reach the producer by following the path I-F-B-C-D-H and finally the producer. We depict an example of the scope discovery operation in Figure 2(b). Here, the producer moves from PoA C to H, passing by PoA D. The producer increments the sequence number and transmits an IN at each attachment to a new PoA. When the consumer's interest reaches C, it finds that the face associated with the required prefix (/A/B) is unavailable (face C2), which indicates that the producer has disassociated from this PoA. Therefore, this interest  
Figure 3: Example Map-Me operation. is tagged as a \"scope discovery interest\" with a sequence number 0 (as indicated by the TFIB of C) and is broadcast to one-hop neighbors B and D.  
When the scope discovery interest reaches PoA B, the sequence number of the scope interest (zero) is compared to the sequence number associated to this prefix in B's TFIB (zero). Therefore, the scope interest is dropped at B. Indeed, the producer did not attach to this PoA during its mobility. However, when the scope discovery interest reaches PoA D, the sequence number of the scope interest (zero) is compared to the sequence number associated to this prefix in D's TFIB (zero). Therefore, the scope interest sequence number is changed to one, and the scope interest further propagates and is broadcast to the one-hop neighbors. The same behavior is pursued at PoA H. The received scope interest has a sequence number of one, which is smaller than the one found in the TFIB of PoA H (two). As a result, the scope discovery is broadcast after incrementing its sequence number to two. This way, the scope interest reaches the producer at its new PoA (H).
## 1 Introduction
A promising alternative is a compound derived from a natural product, like 2\({}^{\prime}\)/\({}^{\prime}\)4-dihydroxy-6-methoxy-3,5-dimethylchalcone (chalcone) isolated from _Eugenia aquea_ leaves, demonstrating anticancer activity. This chalcone compound can reduce cell proliferation against MCF-7 human breast cancer with an IC\({}_{50}\) of 74.5 \(\upmu\)g/mL (250 \(\upmu\)M) and induce apoptosis via the activation of the poly(adenosine diphosphate-ribose) polymerase (PARP) [23]. Further, it has shown activity against T47D human breast cancer cell lines, with an IC\({}_{50}\) of 42.49 \(\upmu\)g/mL (142.58 \(\upmu\)M) [24]. Efforts to increase the activity of this chalcone compound by modifying it into pyrazoline benzenesulfonamide derivative (Modifina) have been carried out, and it has activity as an ER\(\alpha\) inhibitor [25]. However, Modifina has poor gastrointestinal absorption, necessitating structural modifications to improve its pharmacokinetic properties. In recent years, computational approaches have become invaluable tools in drug discovery, mainly for in silico studies aimed at predicting ligand-receptor interactions, structure-activity relationships, and molecular mechanisms at the atomic level. When integrated with experimental (wet labs) studies, these methods can significantly reduce both the time and cost associated with drug development [26]. Among the commonly employed computational techniques are molecular docking, molecular dynamics simulations, and pharmacophore modeling. These methods are often used in combination to provide a more comprehensive understanding of molecular interactions and potential drug activity. Pharmacophore modeling is typically used in the early stages to identify compounds that have the potential to interact with target receptors [27]. Then, molecular docking helps predict the binding mode and affinity of these compounds to the receptor [28]. Finally, molecular dynamics simulations serve to validate the docking results by offering insights into the stability and behavior of the ligand-receptor complex over time [29]. In this study, we evaluated the anti-breast cancer activities of novel pyrazoline benzenesulfonamide derivatives (PBD) targeting ER\(\alpha\) using in silico approaches. The PDB compounds were design by structurally modifying Modifina to generate new derivatives with potential anticancer activity. We first assessed the drug-likeness of the designed compounds using [PERSON]'s rule of five followed by cytotoxicity predictions against human cell lines. Pharmacokinetics and toxicity profile were then evaluated using in silico ADMET  
Figure 1: Chemical structures of estrogen receptor alpha (ER\(\alpha\)) inhibitors. (absorption, distribution, metabolism, excretion, and toxicity) analysis. Molecular docking was performed to evaluate the anti-breast cancer activities of PDB compounds, followed by molecular dynamics simulations to better understand the inhibition of PBD compounds against ER\(\alpha\). Finally, pharmacophore modelling was performed to identify the chemical features (electronic and steric) in the PBD that contribute to their binding with ER\(\alpha\).
# A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques
## 5 Data Aggregation Overview
### Building Information Modeling  
Building information modeling (BIM) is a methodology designed to tackle issues related to information exchange, interoperability, and effective collaboration throughout a building's lifecycle (i.e., from feasibility and conceptual design through to demolition and re-cycling stages) [68]. It is a digital model of a building that includes semantic information about objects, such as geometry, spatial location, and extensive metadata about the building's properties, subsystems, devices, mechanical, electrical, and plumbing (MEP)  
Figure 5: Data fusion techniques [59].  
equipment, etc. BIM can be categorized into various dimensions that represent the types of data being modeled [69,70]. These dimensions include:  
* Three-dimensional BIM (geometry): A 3D model of a building or infrastructure asset. This is the most basic level of BIM, and it provides a visual representation of the asset.
* Four-dimensional BIM (time): A 3D model with time-related information, such as the construction schedule to see how the construction process will unfold over time.
* Five-dimensional BIM (cost): A 3D model extended by adding cost information to track and identify cost-saving opportunities.
* Six-dimensional BIM (sustainability): A 3D model that includes lifecycle information, such as the maintenance and operation requirements for the asset. It helps to plan long-term use of the asset and to optimize its performance.
* Seven-dimensional BIM (management): A 3D model that includes sustainability information. This enables users to evaluate the environmental impact of the asset and make design choices that enhance sustainability.
* Eight-dimensional BIM (safety): A 3D model that includes safety information. This enables users to detect and address safety risks during both the construction and operation of the asset.  
The synergy between BIM, IoT devices, and the BACS enables a more intelligent, data-driven approach to building management by helping to handle large, complex, and dynamic sensor data. While time series data from IoT sensors offer numerical values and patterns, BIM data provide context and semantic connections between the building's systems [71].
## 3 Results and Discussion
### Comparison of Machine Learning and RSM Models  
The precision and resilience of both the machine learning and standard RSM predictive models were assessed utilizing three distinct error metrics: MSE, MAPE, and R\({}^{2}\). A comparison of these metrics is presented in Figure 7. It was noted that the machine learning predictive model (RFR) demonstrated superior error metrics in comparison to the standard RSM model. Thus, one can deduce that the RFR model is the most resilient and appropriate predictive model for the forecasting of the temperature during bone drilling.  
The spindle speed was deemed crucial for all outcomes. It was noted that the rise in the drill bit diameter led to an increase in the temperature caused by drilling as the helicoid formation deepened and expanded. In contrast, the temperature declined as the feed rate rose. [PERSON] et al. (2016) documented similar discoveries while examining the drilling procedure on bone. The presence of non-uniform structures in bone samples presents obstacles in attaining uniform surface smoothness and a consistent temperature rise along the entire length of a bored hole. The cutting operation had a notable impact on the adjacent tissue of the bone samples. As per the error metrics, the random forest (RF) model demonstrated superior performance compared to the support vector machine (SVM) regression model. The prediction of the response variables by RF and SVM is plotted and compared with the experimental values in Figure 8, showing that the RF predictions were closer to the experimental values, thereby reducing the error and making RF a better-performing model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Bone Drilling Temperature Predictor Model Errors**} \\ \hline  & \multicolumn{2}{c}{**Test Errors**} & \multicolumn{2}{c}{**Training Errors**} \\ \hline  & SVR model & RFR model & SVR model & RFR model \\ \hline R\({}^{2}\) & 0.249 & 0.942 & 0.257 & 0.973 \\ MAPE & 0.125 & 0.033 & 0.115 & 0.026 \\ MSE & 33.496 & 2.568 & 32.349 & 2.148 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance metrics for different machine learning algorithms.  
Figure 7: Error metrics in the context of machine learning models compared to the Response Surface Methodology (RSM) model.