esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Measurements of the Input-Referred Intermodulation Effect in a Solid-State Amplifier
The conversion losses (CL) calculated for each of the cases are defined as:  
\[\mathrm{CL[dB]} =\mathrm{P_{IN}[dBm]}-\mathrm{P_{IMD}[dBm]} \tag{1}\]  
Figure 8: Example of measurement results of (**a**) Avantek SC82-2342M amplifier, (**b**) Crystek CRBAMP-100-6000 amplifier, (**c**) Mini-Circuits ERA-3 amplifier and (**d**) R820T DVB-T USB dongle. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Power at Input of DUT at f\({}_{1}\) and f\({}_{2}\) (dBm)** & **Power of 2f\({}_{1}-\) f\({}_{2}\) IMD** & **Analytical Characteristic of Power of 2f\({}_{1}-\) f\({}_{2}\) IMD** & **Conversion Loss for 2f\({}_{1}-\) f\({}_{2}\) IMD** \\ \(-\)23.9 & \(-\)76.4 & \(-\)116 & 92.6 \\ \(-\)20.8 & \(-\)79.4 & \(-\)98.1 & 77.4 \\ \(-\)17.9 & \(-\)76.9 & \(-\)81.0 & 63.1 \\ \(-\)14.8 & \(-\)63.1 & \(-\)63.1 & 48.3 \\ \(-\)11.8 & \(-\)47.4 & \(-\)47.4 & 35.5 \\ \(-\)8.86 & \(-\)40.1 & \(-\)40.1 & 31.3 \\ \(-\)5.88 & \(-\)36.8 & \(-\)36.8 & 30.9 \\ \(-\)2.90 & \(-\)30.7 & \(-\)30.7 & 27.8 \\ \(0.14\) & \(-\)23.0 & \(-\)23.0 & 23.1 \\ \(2.99\) & \(-\)17.4 & \(-\)17.4 & 20.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measurement results for Mini-Circuits ERA-3 amplifier. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Power at Input of DUT at f\({}_{1}\) and f\({}_{2}\) (dBm)** & **Power of 2f\({}_{1}-\) f\({}_{2}\) IMD** & **Analytical Characteristic of Power of 2f\({}_{1}-\) f\({}_{2}\) IMD** & **Conversion Loss for 2f\({}_{1}-\) f\({}_{2}\) IMD** \\ \(-\)23.9 & \(-\)80.4 & \(-\)116 & 91.7 \\ \(-\)20.8 & \(-\)77.9 & \(-\)102 & 81.6 \\ \(-\)17.9 & \(-\)75.7 & \(-\)90.0 & 72.1 \\ \(-\)14.8 & \(-\)75.8 & \(-\)76.9 & 62.1 \\ \(-\)11.8
# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
## 4 Routing Algorithms Based on Node Path Prediction and Cache Management
### Scheduled Cache Management Mechanism  
When more time passes throughout the network's message dispersion process, there will be a lot more copies available. Nodes should swiftly tell other nodes to delete the copies of the message they receive in order to decrease cache occupation and erase unnecessary message copies.  
Each node maintains a two-column collection of delivered messages with each element stored in the collection as a key-value pair. Concurrent hash mapping table storage is utilized to enable the simultaneous updating of delivered messages in many connections since a node may generate connections to multiple nodes at once. The delivered collection is constructed as {message ID1: delivery time; message ID2: delivery time;...}. When two nodes connect and send a message, if the receiving node is the message's destination node and the information contained in the message does not already exist in the current delivered collection, the message's ID and delivery time are added to the collection, and the message that was previously stored in the cache is deleted. Figure 2 displays the set of nodes' delivered messages that are stored in a structure consisting of an array, a chain table, and a red-black tree. Each element of the array is a joint, and each writes operation locks the joint for this operation, and the data is stored by hashing the elements into the chain table or red-black tree of which node. A red-black tree develops from a message chain table when there are more elements in the chain table, while a chain table develops from a red-black tree when there are fewer elements.  
When two nodes come together, they check their own caches to see if any messages from the other node's delivered collection are present, and if they do, they delete this message. Then, they update the delivered messages that are not stored in the current node according to the other node's collection, and alert other nodes in the network to delete any messages that have already reached their destinations in order to free up memory and maximize cache space.  
After the experimental test, the survival time of each message is set to one and a half cycles with a better effect. This can ensure the prompt removal of useless message copies and maintenance of a certain number of useful copies to ensure the timely delivery of messages. One cycle is the average delivery time of messages, and the average delivery time is determined using Equation (18) below. Create a timer for each node's collection, check for expired delivered messages every minute and delete them in time to release the cache. Just before the simulation is through, remove all messages from the double-column collection of delivered messages and recycle the memory in time.  
\[T=\sum\left(AT_{m}-GT_{m}\right)/N_{m} \tag{18}\]  
where \(AT_{m}\) is the delivery time of the message, \(GT_{m}\) is the message generation time and \(N_{m}\) is the number of messages.
## 3 Results and Discussion
### Inhibitory Activity of the Legume Honeys against Cholinesterases and Tyrosine  
3.5.1. Activity of the Legume Honeys against Cholinesterases  
Some neurodegenerative diseases, such as Alzheimer's disease, lead to the decay of of acetylcholine and choline concentrations. Concurrently, the AChE enzyme concentration increases markedly, and problems with the potential breakdown of cholinergic synapses and cellular nerve dysfunction can happen [12]. Such a situation, which decreases cholinergic or neurotransmitter activity in the central nervous system, can cause a successive injury to brain memory and cognitive function. Thus, inhibiting the enzyme cholinesterase can help prevent and treat AD. Most studies on brain function have focused on \(\gamma\)-aminobutyric acid (GABA), considered the primary inhibitory neurotransmitter of the central nervous system [85]. The investigation of the activity of AChE and BChE is of clear relevance since, in the late stages of Alzheimer's disease, levels of AChE decline by up to 85%, and BChE becomes the predominant cholinesterase in the brain [12; 13; 14; 86].  
Our study investigated the five leguminous honey's in vitro perspective inhibitory effect on ACHe and BCHe. Our results, shown in Table 1, indicate that the action of the indigo honey was more effective against BChE, with percentages of inhibition up to 27.4%. Astragalus honey caused an inhibition of 13.4%; on the other hand, sainfoin honey inhibited BChE at 9.24%. Alfafa honey and carob honey were ineffective. The action of the honeys against the acetylcholinesterase, AChE, was weaker. Sainfoin exhibited the same inhibitory power (9.1%). Indigo honey, the most effective in inhibiting BChE, was ineffective against AChE; astragalus and sainfoin honeys exhibited similar AChE inhibition, ranging between 5.04% (sainfoin) and 6.2% (astragalus). The cholinesterase-inhibitory activity exhibited by the honey has been reported in different in vitro studies. [PERSON] et al. analyzed 47 kinds of Polish honeys, finding an AChE-inhibitory activity reaching 39.06%. The results also confirmed that the divergence observed among the types of honey could be due to their different botanical origins [37]. [PERSON] et al. ascertained that fig honey showed AChE and BChE IC\({}_{50}\) values of 46.0 \(\upmu\)mL\({}^{-1}\) and 46.8 \(\upmu\)mL\({}^{-1}\), respectively [87]. In vivo studies showed that the administration of 20% of acacia honey effected a decrease in the AChE level from 533.67 to 302.45 mM/min/g of tissue/mg protein \(\times\) 10\({}^{-2}\) in the brain of rats [88].
# 3D DNA Nanostructures: The Nanoscale Architect
## 5 Applications
### Encapsulation
#### 5.1.4 Membrane-Spanning DNA Nanopores  
DNA nanostructures have been used to build transmembrane channels, sometimes referred to as _membrane-spanning DNA nanopores_, that implant into a cellular membrane. Tubular DNA nanostructures decorated with hydrophobic groups along its outside enable a DNA nanostructure to embed itself into a lipid membrane as an artificial transmembrane channel [111; 112]. We consider this an important function of encapsulation in isolating a leak-free channel for molecules to be transported across. Natural transmembrane channels span their lipid bilayer and typically help to regulate the nutrients within a cell. DNA nanopores may enable forced control over the nutrition gradient of cells by implementing commonly used locking mechanisms or could be used for biophysical studies of how materials pass through the membrane by monitoring voltage changes from the DNA nanopore that signal molecules passing through. The [PERSON] group demonstrated that designed DNA nanopores are functionally similar to their associated natural counterpart when measured for transport of K\({}^{+}\) ions [98] (Figure 6B). Proteins maybe also be passed through the channels and each event can be measured by the transduced electrical signal of the channel [113]. Gating can also be applied which blocks the channel under specifically programmed conditions to modulate the flow of molecules through the channel [114; 115].
## 4 Results and Discussions
### Verifications of Optimum Conditions  
The experiments were performed further according to optimum conditions listed in Table 8 and the results are listed in Table 9. The reported values were 100%, 19.9584 \(\times\) 10\({}^{-4}\) mol/s\(\cdot\)L, 1.1675 1/s, and 0.4148 mol-CO\({}_{2}\) /L-mol-solvent for E\({}_{\rm F}\), \(R_{A}\), \(K_{\rm C}\),\(a\) and \(\phi\), respectively. The values are listed in bracket words. Compared with Nos. 1-25, the obtained values were all maximum, i.e., the results could be confirmed. This also demonstrates the feasibility of the Taguchi experimental design in this study.
## 2 Materials and Methods
### Analytical Methods  
To measure the total biomass of planktonic cells, optical density (OD) was measured using a UV/Vis spectrophotometer (Effendorf Biospectrometer, AG., Hamburg, Germany), and then corrected to biomass for _R. sphaeroides_ to determine the amount of the dry weight. A biomass-to-OD\({}_{660}\) correlation for _R. sphaeroides_ of 0.55 g L\({}^{-1}\) cell dry weight per unit OD\({}_{600}\) was obtained (data not shown). The quantity of attached bacterial calls onto the graphite felt cathode were determined by a gravimetric method for each bioachode. The weight of dried cathode, before and after MES operation, was measured. A mass change of dried cathode after MES operation was calculated as biofilm weight.  
The zeta-potential of _R. sphaeroides_ and the graphite felt electrodes was measured using a Zetasizer (Nano-ZS92; Malvern Panalytical Ltd., Malvern, UK). All samples were analyzed in 0.1 M sodium phosphate buffer (pH 7.0). All potentials were measured against an Ag/AgCl reference electrode (3.0 M NaCl, Bioanalytical systems, West Lafayette, IN, USA). Cyclic voltammetry was performed using a potentiostat (SP-200; Biologics, Paris, France). Oxygen was removed from the electrolyte by bubbling with oxygen-free N\({}_{2}\) for 10 min before electrochemical measurements. The applied scanning rate was 20 mV s\({}^{-1}\) within the ranges of \(-\)0.3 to 0.6 V and \(-\)0.4 to 0.2 for [Fe(CN)\({}_{6}\)]\({}^{3-}\). Sodium phosphate buffer (0.1 M at pH 7.0) was used as the electrolyte. The microscopic features of the samples were investigated using a MIRA 3 LMU model scanning electron microscope (TESCAN, Czech Rep.) at an accelerating voltage of 10 kV. For liquid chromatography analysis, medium samples and standards were filtered through a syringe filter with pore size of 0.2 \(\upmu\)m (Adventec Ltd., Tokyo, Japan). The filtrate of the samples was analyzed by a high-performance liquid chromatography (Agilent 1260; Agilent Technologies, Santa Clara, CA, USA) equipped with an HPX-87 H column.
## 5 Discussion
### Effect of Step Width on Shear Characteristics  
The influence of rock step width on the failure mode and curve type of the samples is pronounced, as depicted in Figure 15a,b. With a narrow rock step width (16 mm), the rock slab exhibits lower bending and shear stiffness, leading to the formation of a multitude of fractures within the slab during the shearing process. Upon the failure of the rock slabs, they tend to incline along the fracture lines, resulting in point contacts between the fractured blocks and the step blocks, which helps to sustain shear resistance. This mechanism is reflected in the shear stress curve, which exhibits an approximately flat segment. As the stress concentration at the point contacts gradually increases, damage to the steps becomes evident, causing a drop in the stress curve before it enters the residual stage. Conversely, as the step width increases, stress concentration is less likely due to the reduced inclination of the rock slab. This results in a more uniform distribution of stress, and the interface damage tends to progress in a relatively smooth manner.  
Figure 15: Effect of step width on sample shear characteristics with geometry size 200 mm: (**a**) Three typical failure modes; (**b**) Shear–displacement curves; (**c**) Shear parameters.  
Figure 15c shows the effect of rock step width on the shear mechanical parameters. An analysis of the figure reveals that the minimum values for peak shear stress, shear elastic modulus, and residual strength, which are observed in the 16 mm sample, are 1.68 MPa, 0.159 GPa, and 0.612 MPa, respectively. When the layer thickness ranges from 18 to 28 mm, these parameters exhibit irregular fluctuations, with ranges of 2.03-2.31 MPa, 0.182-0.239 GPa, and 0.969-1.27 MPa, respectively. It can be deduced that decreasing the step width will result in a drop in the shear parameter values, which then stabilise within a given range when the step width increases.
# Cavitator Design for Straight-Running Supercavitating Torpedoes
## 3 Verification of Mathematical Models
### Forces on Control Fins  
In this study, cavity-piercing control fins with a rectangular planform and a wedge-like cross-sectional shape were adopted for the high-speed supercavitating vehicle because they are known to be favorable in a supercavitating flow [7]. A schematic of the vertical cavity-piercing fin is shown in Figure 6. For the calculation of forces on vertical fins, the well-known methods of the linear theory of supercavitating underwater hydrofoils [21,23,24] were used. Interference with the hull was not considered here; therefore, the hydrofoils are considered isolated rectilinear hydrofoils with small aspect ratios. The points of application of the forces on the vehicle were located on the axes of rotation of the fins. In the scheme, \(c\) and \(h_{f}\) represent the chord and span of the wetted part of the fin, respectively; \(\beta_{f}\) represents the wedge angle of the fin; \(\alpha_{f}\) represents the angle of attack of the fin relative to the inflow, and it is the same as the deflection angle of the fin in the case of the vertical fin; and \(F_{yf}\) represents the hydrodynamic lateral force on the vertical fin, arising at a nonzero angle of attack.  
The cavitation drag and lift coefficients for the symmetric cavitating wedge are expressed as follows:  
\[F_{xf_{\mu},l}=0.5\rho V^{2}S_{f}\Big{(}c_{fx}+2c_{fp}\Big{)},\ F_{yf}=0.5\rho V ^{2}S_{f}c_{fy} \tag{18}\] \[c_{fp}=\frac{0.075}{(\log(\text{Re})-2)^{2}},\ \text{Re}=\frac{V_{c}}{v}\] \[for\ \alpha_{f}<\frac{\beta_{f}}{2};\ c_{fx}=\frac{2\beta_{f}^{2}l }{\pi(l-c)},\ c_{fg}=0.5\pi\alpha_{f}\lambda_{f}\] \[for\ \alpha_{f}\geq\beta_{f}/2;\ c_{fx}=0.2\pi\alpha_{f}^{2} \lambda_{f},\ c_{fg}=0.2\pi\alpha_{f}\lambda_{f}\]  
where \(S_{f}\) represents the wetted area of the fin, \(l\) is the cavity length, and \(\lambda_{f}=h_{f}^{2}/S_{f}\) is the aspect ratio of the wetted part of the fin.  
If horizontal fins are required to be installed, the hydrodynamic drag and lift forces can be calculated using Equation (18), except for \(\alpha_{f}=\delta_{f}+\alpha_{B}\), where \(\delta_{f}\) represents the deflection angle of the fin.
## 2 Different ROP Models
### Shortcomings of Traditional ROP Models  
Although ROP models like the Modified Warren and drag bit models provide useful theoretical frameworks, they lack flexibility when applied to diverse formations or varying drilling conditions. Factors such as bit wear, formation heterogeneity, and variations in fluid properties significantly affect the accuracy of these models. Traditional models also rely on empirical coefficients that are specific to particular formations or bit types, limiting their generalizability across different geological contexts. Furthermore, these models often fail to adapt to real-time data, resulting in a lag between field observations and model predictions, which can impact decision-making during drilling operations.  
To address these limitations, this study leverages machine learning models to predict UCS with improved accuracy, enabling better estimates of drillability and ROP. Machine learning approaches, particularly ensemble methods, can overcome the limitations of traditional models by capturing non-linear relationships and adapting to real-time data changes. By enhancing UCS predictions, these models support more reliable drillability assessments across a wide range of geological conditions, ultimately improving drilling efficiency and cost management.
## 5 Conclusions  
In this paper, for the IGBT module solder layer bubble defect detection task, two types of IGBT module solder layer bubble defect data sets are collected and labeled, and an improved algorithm based on YOLOv5, the YOLOv5n_SEST model, is proposed. A detection layer for small target bubbles is added, which effectively detects more tiny bubbles. For the problem of unbalanced samples in the data set, the bounding box loss function is improved to improve the comprehensive detection effect of the model. For improving the detection capability capacity of bubble defects capability, the C3 STR module is added to the backbone network; Swin Transformer Blocks are used to construct multi-layer feature maps; and the self-attention is restricted to the moving window to ensure that the computational effort is within the controllable range to achieve efficient feature extraction. The experimental results show that the improved model achieves 94.5% accuracy for common bubble defects, 85.4% accuracy for all defects, 5.6% improvement in mAP, and 110 f/s speed. The algorithm can well perform the task of real-time detection of industrial IGBT solder layer defects, ensuring the detection speed of the one-stage algorithm and improving the accuracy of the YOLOv5 model. The algorithm can well perform the task of real-time detection of industrial IGBT solder layer defects, ensuring the detection speed of the one-stage algorithm and improving the accuracy of the YOLOv5 model.  
The degradation of the detection performance for irregular and dense bubbles at the edges is a limitation of the model. Therefore, we will continue to explore and optimize the performance of this model for edge bubbles, study the algorithms that are universally applicable to various industrial fields, and promote the application of artificial intelligence in unmanned quality inspection and intelligent inspection processes in the future.  
Conceptualization, Q.L. and X.L.; Data curation, Q.L. and Y.Z.; Formal analysis, K.N.; Funding acquisition, X.L.; Investigation, Q.L. and K.N.; Methodology, Q.L.; Project administration, Q.L.; Software, Q.L.; Supervision, K.N.; Validation, Y.Z.; Visualization, Q.L.; Writing--original draft, Q.L.; Writing--review & editing, Q.L., X.L. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Sichuan Science and Technology Program (No. 2017 GZ0303), Sichuan Academician (Expert) Workstation Fund Project (No. 2016 YSGZZ01), Special Funding for High-level New Talent Training (No. B12402005), and Talent Introduction Project of Sichuan University of Light Chemical Industry (No. 2021 RC16).  
**Institutional Review Board Statement:** Not applicable.  
Figure 8: Model performance analysis: (**a**) F1_curve; (**b**) P_curve; (**c**) PR_curve; (**d**) R_curve.
# Using FEM to Assess the Effect of Orthodontic Forces on Affected Periodontium
## 3 Results
### Lower Central Incison (LCI)  
1.1 Predictive Factors in the Modification of the Equivalent Tension Values in the Tooth-Periodontal Ligament-Alveolar Bone Complex (\(\sigma\) ech)  
The \(\sigma\) ech values show linear growth, based on the force applied in the case of healthy periodontitis and when the damage is up to 33%. In the case of 66% periodontal damage, the growth is exponential. The values of \(\sigma\) ech differ significantly depending on the applied force (H = 10.665, \(p\) = 0.009, 95% CI). Using multivariate analysis, we subsequently analyzed the importance of the parameters that influence \(\sigma\) ech tension values, the independent variables included were the applied force and the extent of the periodontitis damage. The analysis of the results of the multiple correlation in the case of lower central incis  
Figure 11: Linequalization of the LLI using a 3 N force: (**a**) von Mises tensions, no HBL; (**b**) tooth movement obtained, no HBL.  
Figure 12: Linequalization of the LLI using a 3 N force: (**a**) tooth movement, 33% HBL; (**b**) tooth movement, 66% HBL.  
Figure 10: Linequalization of the LLI using a 1 N force: (**a**) von Mises tensions, 66% HBL; (**b**) tooth movement, 66% HBL.  
lingualization highlights that both the applied force and the degree of damage have a major effect on \(\sigma\) ech (\(\beta\)f = 0.65, \(p\) = 0.003, \(\beta\)Af.P = 0.56, \(p\) = 0.008) (Table 5).  
1.2 Predictive Factors in the Modification of the Tension Values in the Direction of an Applied Perpendicular Force to the Tooth's Vestibular Surface (\(\sigma\) c)  
Using multivariate analysis, we determined the influence of \(\sigma\) c tension values with applied force and the extent of the periodontium damage as independent variables. As such, the applied force had a significant impact on the value of \(\sigma\) c for the lower central incisors (\(\beta\)f = 0.65, \(p\) = 0.003), with periodontal damage having a significant influence (\(\beta\)AfP = 0.56, \(p\) = 0.008) (Table 4). Correlation analysis showed that the applied force had a major impact on \(f\) (\(\beta\)f = 0.48, \(p\) = 0.044), as did the presence of periodontal disease (\(\beta\)Af.P = 0.61, \(p\) = 0.016) (Table 5).  
1.3 Correlation of \(\sigma\) ech, \(\sigma\) c, and f Based on the Value of the Applied Force  
Figure 13 shows that the equivalent tension in the tooth-periodantal ligament-alveolar bone complex (\(\sigma\) ech \(-\) r = 0.65) was most affected by the magnitude of the applied force; close behind was tension value in the direction in which the force is applied (\(\sigma\) c \(-\) r = 0.65). The recorded displacement values (f \(-\) r = 0.48) were not affected significantly by the magnitude of applied force.
## 5 Major Test Results and Findings
### Vortex-Induced Vibrations in Uniform Wind  
The test curves of the vertical and torsional root mean square (RMS) were subject to different mean wind speeds (\(U\)) for the deck response at mid-span and closure of the service state, under uniform wind with most unfavorable wind attack angle, where the responses and wind speed were transferred to the prototype values, as shown in Figure 18a,b, respectively. The VIV with lock-in range was also measured in the full bridge aeroelastic model test, which is similar to the sectional model test. Furthermore, the vertical response of 2-2 cross-section(mid-span) is larger than that of 3-3 cross-section(closure), as shown in Figure 18a, while the torsional response of the closure section is greater than that of the mid-span section, as shown in Figure 18b. It is also demonstrated that an asymmetric structure's torsional mode differs from that of a symmetric structure. In addition, it is found in Figure 18 that the sharp wind fairings can significantly mitigate the VIV, and the vertical or torsional VIV of the bridge with sharp wind fairings exceeds the deck's permitted amplitude with a very small probability.  
Figure 17: Measurement point layout.