esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Ferment Processing Parameters on the Quality of White Pan Bread
## 3 Results and Discussion
### Ferment Storage Time
#### 3.2.2 Effect of Ferment Storage Time on Bread Processing
The dough  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fement Storage** & **Mixing** & **Proof** & **Specific** & **Crumb** & & **Crumb Colour** \\
**and** & **Time** & **Time** & **Volume** & **Firmness** & & **L\({}^{*}\)** & **a\({}^{*}\)** & **b\({}^{*}\)** \\
**Inclusion Level** & **(min)** & **(min)** & **(cm\({}^{2}\)/g)** & **Force (g)** & **L\({}^{*}\)** & **a\({}^{*}\)** & **b\({}^{*}\)** \\ \hline
**Control** & & & & & & & \\
**0 h** & & & & & & \\
30\% & & & & & & \\
50\% & & & & & & \\
70\% & & & & & & \\
**24 h** & & & & & & \\
30\% & & & & & & \\
prepared with 30% fresh ferment (0 h stored) demonstrated high stability after proofing. Ferment stored for 24 h and used at 30% and 50% inclusion levels resulted in a dough that had slightly softer properties and minor stickiness compared to the control. However, when used at a 70% inclusion level, the dough was softer and had increased stickiness compared to the control. Doughs were more balanced and had increased extensibility when the ferment was used at 50% and 70% levels of inclusion. Moulding performance was good for the doughs containing 30% and 50% ferment inclusion, while the dough containing 70% ferment showed average moulding. After proofing, regardless of the level of inclusion, all doughs exhibited high dough stability. Storage of the ferment for 48 h and 72 h had an impact on dough softness and stickiness in the same manner as the fresh ferment. Ferment, stored for 48 h and used at 30% and 50% levels of inclusion, enhanced the balance between extensibility and resistance of the dough and resulted in dough that had medium to high stability after proofing. However, when used at a 70% level of inclusion, the dough exhibited greater extensibility which was undesirable and medium stability after proofing. Ferment, when stored for 72 h, resulted in a dough with excessive extensibility and medium stability after proofing when used at 50% and 70% levels of inclusion. Based on these observations, it was determined that ferment stored for 24 h, to a maximum of 48 h, should be used for bread making at inclusion levels not exceeding 50% to produce dough with optimal properties.
## 2 Principle of TDOA Method and Beamforming Method  
### TDOA Method for Four Sensors  
Four sensors linear arrays are used for locating here. The sensor arrangement is shown in Figure 1. Four sensors (1, 2, 3, and 4 in Figure 1) arranged adjacent to each other at equal intervals and P is the location of AE source. Suppose the distance between two adjacent sensors is d and the speed of AE wave is v. The time that AE signal generated by AE source P (x,y) reaches the sensor 1 is \(t_{1}\). The time differences of arrival between signal arrival times at sensor 2, 3, 4, and sensor 1 are \(\Delta t_{2}\), \(\Delta t_{3}\), and \(\Delta t_{4}\), respectively. AE source coordinates satisfy the following relationships  
\[\left\{\begin{array}{l}x^{2}+y^{2}=v^{2}t_{1}^{2}\\ \left(x-d\right)^{2}+y^{2}=v^{2}\left(\Delta t_{2}+t_{1}\right)^{2}\\ \left(x-2d\right)^{2}+y^{2}=v^{2}\left(\Delta t_{3}+t_{1}\right)^{2}\\ \left(x-3d\right)^{2}+y^{2}=v^{2}\left(\Delta t_{4}+t_{1}\right)^{2}\end{array}\right. \tag{1}\]By subtracting formula 1 from formula 2, 3, and 4 in Equation (1), we can get that  
\[\left\{\begin{array}{l}-2 xd+d^{2}=v^{2}(2t_{1}\Delta t_{2}+\Delta t_{2}^{2})\\ -4 xd+4d^{2}=v^{2}(2t_{1}\Delta t_{3}+\Delta t_{3}^{2})\\ -6 xd+9d^{2}=v^{2}(2t_{1}\Delta t_{4}+\Delta t_{4}^{2})\end{array}\right. \tag{2}\]  
Then, we can get  
\[t_{1}=\frac{3\Delta{t_{2}}^{2}-3\Delta{t_{3}}^{2}+\Delta{t_{4}}^{2}}{-6\Delta t _{2}+6\Delta t_{3}-2\Delta t_{4}} \tag{3}\]  
Substituting Equation (3) into formula 1 and 2 in Equation (2), respectively, we can get unitary quadratic equations with \(x\) and \(v^{2}\) as unknowns. Solving the equations, we can get  
\[x=\frac{d\Big{(}8t_{1}\Delta t_{2}+4\Delta{t_{2}}^{2}-2t_{1}\Delta t_{3}- \Delta{t_{3}}^{2}\Big{)}}{8t_{1}\Delta t_{2}+4\Delta t_{2}}^{2}-4t_{1}\Delta t _{3}-2\Delta{t_{3}}^{2} \tag{4}\]  
Dividing formula 1 by formula 2 in Equation (1), we can get  
\[\frac{x^{2}+y^{2}}{\left(x-d\right)^{2}+y^{2}}=\frac{{t_{1}}^{2}}{\left( \Delta t_{2}+t_{1}\right)^{2}} \tag{5}\]  
Solving Equation (5), we can get  
\[y=\sqrt{\frac{{t_{1}}^{2}d^{2}-2 xdt_{1}}{\Delta{t_{2}}^{2}+2\Delta t_{2}t_{1}} -x^{2}} \tag{6}\]
## 5 Discussion of Measurement Results: Model of Charged Defects Motion Near a Waveguide
### The Disappearance of the Waveguide Is Observed only When the Sample Is Heated  
We offer a simple model of how the pyroelectric effect can influence the waveguide refractive index. During waveguide formation, mobile H\({}^{+}\)-ions are indiffused in the crystal near-surface layer and form some new phases with different lattice parameter [12]. Dislocations need to arise during this process on the waveguide border to prevent crystal destruction because of lattice parameter mismatch. A dislocation grid formed on oxygen ion-lattice in LiNbO\({}_{3}\) and has negative charge. It attracts H\({}^{+}\)-ions and they are placed around the waveguide and can move along dislocation lines like on tubes. Thus waveguide is partially shielded from external electric field and also pyroelectric  
Figure 5: \(P_{\text{out}}\) and temperature plots versus time for sample covered by conductive paste. Black and red lines are \(P_{\text{out1}}\) and \(P_{\text{out2}}\) respectively, gray line is temperature.  
field by the surrounding ions which decrease electric field intensity inside the waveguide. In this way waveguide and bulk crystal will have a difference in refractive index changing during action of pyroelectric effect.  
This effect works only for sample heating because refractive index of bulk crystal is increased when the pyroelectric field is opposite to the spontaneous polarization direction and waveguide contrast \(\Delta n\) becomes lower. If we cool the sample waveguide contrast \(\Delta n\) becomes higher and waveguide \"moves away\" from danger condition.  
In the Study of Y-splitters, It Was Found That Only One Arm of the Splitter Can Be Affected by This Phenomenon (Or One Arm Can Be Affected Much More Than the Other)  
Also, as a system with mobile charges, the arms of a Y-splitter are a coupled system because they have a junction point. In this case, the mobile charges, which are uniformly distributed near the two arms of the Y-splitter in the absence of the pyroeffect, can be redistributed under the influence of the pyroeffect so that their number near one arm becomes larger than near the other arm. Then the effect of their impact will be different for the two arms. An approximate diagram of such a process is shown in Figure 6.  
This phenomenon leads to the fact that one arm disappears completely, and the second arm does not disappear at all or partially disappears for a short time.
# A Dual Stage Low Power Converter Driving for Piezoelectric Actuator Applied in Micro Mobile Robot
## 1 Introduction
The majority of existing topologies focus on efficient piezoelectric driving for large-scale and high-power applications, which reduces the efficiency and power density of the power supply [20; 21; 22; 23; 24; 25]. Harvard's team has previously produced a small-scale power conversion interface. However, as described in References [12], the boost tapped-inductors they use have difficulty in manufacturing compared to general flyback transformers. At the same time, in the direct current to alternating current (DC-AC) stage, the detection of current becomes quite difficult in the case of low power. This article is dedicated to improving and validating the short board mentioned above. The main objectives of this study are as follows: (a) To determine the equivalent model parameters based on the impedance characteristics of piezoelectric bimorph actuators; (b) to introduce driving methods and requirements for piezoelectric actuators in MMRs; (c) to describe a circuit topology and its control strategy with low mass, high conversion ratio, high power density, and high efficiency; (d) to present a prototype that is capable of driving piezoelectric actuators; and (e) to observe and analyze the experimental results of the circuit output and displacement of material driving. This paper is organized as follows. Section 1 introduces the electric properties and driving methods of piezoelectric actuators. Section 2 presents the overall circuit structure and control scheme. Sections 3 and 4 describe the simulation, fabrication specifications, and experimental realization of the circuit topology. Finally, Section 5 provides the conclusion of the study and directions for future research.
## 5 Experiment  
### Jamming Evaluation  
In order to verify the accuracy of the jamming power estimation, several experiments are conducted with different modulation types. Sixteen UAVs are located in a circle with a radius of 5 m, the jamming power of each UAV is 50 mW, and the transmitted power of each target is 1 W. The center of the UAVs is 1000 m far from the target, while the distance within the target is 200 m. Assume the jamming beam's width is 30\({}^{\circ}\), and the target is within the beam. The bit error ratios (BERs) of different modulation types are shown in Figure 11. An improvement in BER indicates that the jamming impacts the signal quality, especially the complex modulation format, which shows a higher BER in different directions. In addition, the two jamming transmitted power estimation methods show good consistency, proving that the proposed method is applicable to different communication systems.  
Furthermore, constellation diagrams are obtained from the demodulated signals. Figure 12a illustrates the constellation diagram of the target receiver under ideal conditions without jamming, where the signals are uniformly distributed across different quadrants, demonstrating high communication quality. However, as jamming is introduced, as shown in Figure 12b, it becomes challenging to recover the original signals as they overlap and cannot be effectively separated. This further validates the significant impact of jamming signals on the communication system.  
Figure 11: The BERs of different modulation types, and the carrier frequency is 400 MHz.  
Figure 10: Running time comparison between the array-based algorithm and the waveform superposition algorithm.  
The relative errors of the transmitted power and BER are calculated to validate the proposed transmitted power estimation method's accuracy further. As shown in Figure 13, the results across all modulation formats demonstrate the high precision of the estimation method. Due to the uncertainty of the channel and the small BER, the relative error of BER is slightly larger than that of the transmitted power. The accuracy is higher in the high-order modulation than in the low-order modulation, which is expected due to the higher complexity of the high-order modulation.
# Modeling Polymer Microencapsulation Processes Using CFD and Population Balance Models
## 2 Materials and Methods
### CFD Simulation Strategy
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Inertial Frame (Tank)** & **Rotating Frame (Agitator)** \\ \hline
**Mass** & \(\frac{\partial\rho}{dt}+\
abla\cdot\left(\rho\overset{\rightarrow}{v}\right)=S_{m}\) & \(\frac{\partial\rho}{dt}+\
abla\cdot\left(\rho\overset{\rightarrow}{v}_{r} \overset{\rightarrow}{v}_{r}\right)=S_{m}\) \\ \hline
**Momentum conservation** & \(\frac{\partial}{\partial t}\left(\rho\overset{\rightarrow}{v}\right)+\
abla \cdot\left(\rho\overset{\rightarrow}{v}\overset{\rightarrow}{v}\right)=-\
abla p+\
abla\cdot\overset{ \rightarrow}{\tau}+\rho\overset{\rightarrow}{\delta}+\overset{\rightarrow}{ F}\) & \(\rho\left(2\overset{\rightarrow}{\omega}\overset{\rightarrow}{\omega}\overset{ \rightarrow}{v}_{r}+\overset{\rightarrow}{\omega}\overset{\rightarrow}{ \omega}\overset{\rightarrow}{\omega}\overset{\rightarrow}{\omega}\overset{ \rightarrow}{\tau}\right)=\) \\ \hline
**Turbulence** & \(\frac{\partial}{\partial t}\left(\rho k\right)+\frac{\partial}{\partial x_{i}} \left(\rho ku_{j}\right)=\frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{u _{j}}{\tau_{i}}\right)\frac{\partial k}{\partial x_{j}}\right]+G_{k}+G_{b}-\rho e -Y_{M}+S_{k}\) \\ \(\frac{\partial}{\partial t}\left(\rho e\right)+\frac{\partial}{\partial x_{j}} \left(\rho eu_{j}\right)=\frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{u _{j}}{\tau_{j}}\right)\frac{\partial e}{\partial x_{j}}\right]+\rho C_{1}S_{ \varepsilon}-\rho C_{2}\frac{\rho^{2}}{k\overset{\rightarrow}{\omega}\tau \sqrt{\alpha}}+C_{1\varepsilon}\frac{\rho}{C_{3\varepsilon}}G_{b}+S_{ \varepsilon}\) \\ \hline
**Multiphase flow** & \(\frac{\partial}{\partial t}\left(\alpha_{q}\rho_{q}\overset{\rightarrow}{v}_{q} \right)=\
abla\cdot\left(\alpha_{p}q_{q}\overset{\rightarrow}{v}_{q}\right)=\) \\  & \(-\alpha_{q}\
abla p+\
abla\cdot\overset{\rightarrow}{\tau}_{q}+\alpha_{q}\rho_{q} \overset{\rightarrow}{\delta}+\sum\limits_{p=1}^{n}\left(\overset{n}{R}_{pq}+ \dot{m}_{pq}\overset{\rightarrow}{v}_{pq}-\dot{m}_{qp}\overset{\rightarrow}{v }_{qp}\right)+\left(\overset{\rightarrow}{F}_{q}+\overset{\rightarrow}{F}_{ lift,q}+F_{vm,q}\right)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Governing equations for the simulation models used. The emulsion microencapsulation simulations were executed on a workstation equipped with an Intel(r) Xeon(r) W-265 CPU (Intel, Santa Clara, CA, USA) operating at 3.50 GHz. This processor features 12 cores with hyperthreading technology, resulting in a total of 24 logical processors available for parallel computations. The workstation also provides 128 GB of RAM to meet the memory requirements of the simulations. All cases were parallelized across 12 processors, achieving an average wall-clock time of 3.7 s per iteration with peak RAM usage reaching approximately 10.5 GB. Local bin fraction and flow velocity monitors were added to check for convergence. Simulations took up to 25,000 iterations/26 h to converge.
# An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs
## 6 Experimentation and Result Analysis
### Result Analysis under Various Vehicle-Density Ratios
#### 6.3.2 Test Case 2: Medium Density (30 Vehicles)  
In this test case, 30 vehicles are considered, where each vehicle was arbitrarily scattered over deployment area A with a distance of 1 km. Generally, an increase in the number of vehicles increases the complexity of the route establishment process. The server is at the origin (0,0) to broadcast data to all vehicles in the VANET.  
The best route results of medium-density vehicles are presented in Figure 6(a). Figure 6(b) presents the mean route reliability scores of all algorithms. Here, JMA achieves best route reliability among all algorithms for most run ranges. The performance of JMA dominates the rest of the algorithm, whereas the rest of the compared algorithms provide poorer performance in most run ranges. Initially, PSO variants obtain the best route-discovery score, but later it fails to sustain the best route discovery over a course of iterations. Figure 7 provides the best route-discovery results of all algorithms, in which JMA achieves the best route in the fast-run range and sustains the same for the rest of the run ranges. Therefore, JMA achieved better route-discovery scores than the PSO, GA and cuckoo algorithms. Thus, the overall results for medium-density vehicles concludes that the overall performance of JMA achieves 13.45%, 10.26% and 9.38% better performance compared to the GA, PSO and CS algorithms, respectively.
# The Influence of Bit Edge Shape Parameters on Bone Drilling Force Based on Finite Element Analysis
## 2 Construction and Verification of Finite Element Model
### Influence of Bit Structure Parameters on Drilling Force  
Drill bits with excellent bit structure parameters can be used by surgeons easily and reduce mechanical damage to bones. However, because there are more parameters of the cutting edge and their influences on the drilling process are different, i.e., the size of the point angle is the main factor affecting the sharpness of the drill; the helix angle will affect  
Figure 3: Variation of the drilling force with drilling parameters.  
the magnitudes of the axial force and torque as well as chip space; edge radius is also a factor affecting the cutting process. Therefore, point angle, helix angle, and edge radius are selected as the primary research objects. According to the characteristics of the bit, it is determined that the point angle \(2\phi\), helix angle \(\beta\), and edge radius r are the three critical factors affecting the drilling force. Their numerical ranges are 95\({}^{\circ}\)-125\({}^{\circ}\)[34; 35; 36], 21\({}^{\circ}\)-35\({}^{\circ}\)[17; 18], and 0.02 mm-0.04 mm [37].  
According to the simulation results of 2.2, drilling parameters are set to drilling speed 1100 r/min, feed 0.03 mm/r. Single factor simulation is used to analyze the influence of cutting edge on the drilling force.  
Figure 4 shows the finite element simulation results of the influence of each parameter on the drilling force. As can be seen in Figure 3(a), the drilling force is proportional to the point angle and decreases with the point angle. As shown in Figure 3(b), the helix angle is negatively related to the drilling force, which decreases with the helix angle. As can be seen in Figure 3(c), there is a positive correlation between the radius of the drill's cutting edge and the drilling force. Therefore, the drilling force can be reduced, and the drilling quality can be ensured when a particular values of point angle, cutting edge radius and helix angle are used.  
Figure 4: The influences of bit edge shape parameters on drilling force.
## 4 Results
### Baseline Systems
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**AI Model** & **Acc** & **Prec** & **Rec** & **F1** & **Bacc** & **Mcc** & **AuxRoc** \\ \hline RF (XAI-IDS) & **0.84** & **0.78** & **0.42** & 0.36 & **0.68** & **0.32** & **0.62** \\ RF (K-best [35]) & 0.82 & 0.40 & 0.35 & **0.33** & 0.60 & 0.24 & 0.60 \\ \hline ADA (XAI-IDS) & **0.81** & **0.41** & **0.47** & 0.21 & **0.63** & **0.27** & 0.58 \\ ADA (K-best [35]) & **0.81** & 0.34 & 0.39 & **0.29** & 0.60 & 0.24 & **0.62** \\ \hline DNN (XAI-IDS) & **0.88** & 0.51 & **0.48** & 0.46 & **0.69** & 0.40 & **0.69** \\ DNN (K-best [35]) & 0.87 & **0.56** & 0.44 & **0.58** & 0.68 & **0.56** & 0.62 \\ \hline SVM (AAI-IDS) & 0.87 & **0.60** & 0.43 & 0.43 & 0.66 & 0.36 & **0.70** \\ SVM (K-best [35]) & **0.88** & **0.48** & **0.44** & **0.44** & **0.67** & **0.37** & **0.70** \\ \hline KNN (XAI-IDS) & **0.88** & **0.52** & **0.54** & **0.73** & **0.72** & 0.47 & **0.73** \\ KNN (K-best [35]) & **0.88** & **0.52** & 0.45 & 0.50 & 0.67 & **0.50** & 0.67 \\ \hline MLP (XAI-IDS) & **0.90** & **0.75** & **0.54** & **0.72** & 0.51 & 0.33 & 0.68 \\ MLP (K-best [35]) & 0.87 & 0.44 & 0.43 & 0.51 & **0.71** & **0.39** & **0.70** \\ \hline LightGBM (XAI-IDS) & **0.84** & **0.39**these tables, our main findings are as follows. For CICIDS-2017, our framework has the same or better precision and recall in six of the seven models. Our framework also has the same or better performance for all seven AI models for all other metrics except the Matthews correlation coefficient for LightGBM. For the RoEduNet-SIMARGL2021 dataset, our framework has better precision and recall for all models except LightGBM. The same findings were found for all metrics except the AUCROC for ADA, SVM, and MLP. For the NSL-KDD dataset, Table 18 shows that our framework has better precision for all models except the DNN, while it has better recall in all models except SVM. To summarize, we see that our framework is better in almost all models analyzed for the three datasets, except for LightGBM for the RoEduNet-SIMARGL2021 dataset and SVM for the NSL-KDD dataset. Finally, we believe that this experiment is important since depending on a lower number of features is important for the better explainability of AI models. In other words, explaining the decision-making of AI models using the top 5 features can be used in reality instead of explaining it using the top 20 features (which would be impractical and time-consuming for security analysts).
## 5 Data Offloading Solution
### Estimating the Amount of Resources  
As the amount of resources allocated to each task directly determines the efficiency of task completion, resource allocation becomes a critical issue in high-performance uploading mechanism devising. Our goal is to minimize the task execution cost. Since the duration for task performers to perform a task is critical to both the time used for finishing the task and the energy expenditure of the vehicles, the total task completion duration should be firstly optimized when assigning the CPU resources of the vehicles. Therefore, the minimization problem is formulated as follows:  
\[\begin{split} P:\text{min}\sum\limits_{u=1}^{U}\sum\limits_{t=1}^{T }\sum\limits_{m=1}^{f_{\text{max}}}\sum\limits_{u,t}^{m}\frac{s_{u,t}c_{u,t}}{f _{u,t}^{\text{max}}}\\ s.t.\text{C1}:x_{u,t}^{m}\in\{0,1\},\forall u\in U,t\in T,m\leq f_{ \text{max}}\\ \text{C2}:\sum\limits_{m=1}^{f_{\text{max}}}x_{u,t}^{m}=1, \forall u\in U,t\in T,m\leq f_{\text{max}}\\ \text{C3}:\sum\limits_{u=1}^{U}\sum\limits_{t=1}^{T}\sum\limits_{m=1 }^{f_{\text{max}}}f_{u,t}^{m}\leq R_{all}\end{split} \tag{15}\]  
where \(f_{\text{max}}\) is a pre-defined maximum allocation resource threshold for each task, and \(x_{u,tm}\) is resource choice profile. In \(P\), constraints C1 and C2 guarantee that the resource selection of each task is binary. C3 limits the tasks to be assigned from the UAVs within the resource permissible range. Let the resource set of each task be \(R_{set}=\{0.1,\,0.2,\,\ldots,f_{t},f_{max}\}\) and let the maximum weight of the owned parking resources within the monitoring area of the UAVs be \(R_{all}\), \(P\) is then changed into obtaining the minimum value of the multi-dimensional knapsack issue, which is tackled here by dynamic programming [35].