esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**BCs.**} & \multicolumn{8}{c}{**Effect of Strain Energy on the First Natural Frequency [Hz]**} \\ \cline{2-10}  & & _L/h = 10_ & _L/h = 20_ & _L/h = 30_ & _L/h = 40_ & _L/h = 50_ & _L/h = 60_ & _L/h = 70_ \\ \hline C-F & TMM & 1310.3 & 345.99 & 156.09 & 88.332 & 56.700 & 39.441 & 29.007 \\  & \(C_{f,a}\) & 0.2977 & 0.4212 & 0.4596 & 0.4757 & 0.4839 & 0.4886 & 0.4915 \\  & \(C_{f,b}\) & 0.5954 & 0.5317 & 0.5152 & 0.5088 & 0.5057 & 0.5040 & 0.5029 \\  & \(C_{c}\) & 0.1068 & 0.0471 & 0.0252 & 0.0155 & 0.0104 & 0.0074 & 0.0056 \\  & Equation (36) & 1310.3 & 345.99 & 156.09 & 88.332 & 56.700 & 39.441 & 29.007 \\ \hline C-P & TMM & 5071.0 & 1526.7 & 764.63 & 462.35 & 309.75 & 221.79 & 166.50 \\  & \(C_{f,a}\) & 0.0331 & 0.1636 & 0.2877 & 0.3709 & 0.4230 & 0.4557 & 0.4768 \\  & \(C_{f,b}\) & 0.7606 & 0.5332 & 0.4388 & 0.4041 & 0.3935 & 0.3927 & 0.3960 \\  & \(C_{c}\) & 0.2063 & 0.3032 & 0.2736 & 0.2250 & 0.1835 & 0.1515 & 0.1273 \\  & Equation (36) & 5071.1 & 1526.7 & 764.65 & 462.36 & 309.76 & 221.80 & 166.50 \\ \hline C-C & TMM & 6897.4 & 1911.3 & 905.78 & 528.24 & 345.30 & 242.96 & 180.05 \\  & \(C_{f,a}\) & 0.0231 & 0.1288 & 0.2386 & 0.3155 & 0.3658 & 0.3991 & 0.4218 \\  & \(C_{f,b}\) & 0.8646 & 0.7048 & 0.6206 & 0.5773 & 0.5532 & 0.5386 & 0.5292 \\  & \(C_{c}\) & 0.1123 & 0.1664 & 0.1409 & 0.1072 & 0.0810 & 0.0623 & 0.0490 \\  & Equation (36) & 6897.5 & 1911.3 & 905.8 & 528.2 & 345.3 & 243.0 & 180.1 \\ \hline P-P & TMM & 3665.7 & 1230.8 & 652.27 & 407.94 & 279.50 & 203.34 & 154.46 \\  & \(C_{f,a}\) & 0.0493 & 0.2104 & 0.3449 & 0.4300 & 0.4813 & 0.5121 & 0.5308 \\  & \(C_{f,b}\) & 0.5957 & 0.3350 & 0.2548 & 0.2370 & 0.2419 & 0.2547 & 0.2698 \\  & \(C_{c}\) & 0.3550 & 0.4545 & 0.4004 & 0.3330 & 0.2769 & 0.2332 & 0.1994 \\  & Equation (36) & 3665.8 & 1230.8 & 652.28 & 407.95 & 279.51 & 203.35 & 154.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect of strain energies on the first natural frequency of three-layered beams with different end conditions depending on the variation in _L/h_.
## 4 Conductive Polymers
### Immobilization by Enzymatic Polymerization of CP Monomers  
Another method to encapsulate enzymes within CPs is through enzyme-catalyzed polymerization. Enzymatic polymerization of CP monomers typically involves using a redox enzyme to catalyze the generation of a redox substance (such as H\({}_{2}\)O\({}_{2}\)), triggering a polymerization reaction [144]. This results in the formation of CPs, with the redox enzyme simultaneously being immobilized within the CPs.  
[PERSON] and colleagues utilized H\({}_{2}\)O\({}_{2}\) oxidation to initiate the one-step oxidative polymerization of pyrrole-modified glucose oxidase (GOx), pyrrole-containing monomers (Py and 1-amino pyrrole (Py-NH\({}_{2}\))), and a cross-linker (Py-Py). This process led to the formation of a self-encapsulated nanoenzyme (n(GOx-PPy)). Compared to free GOx, n(GOx-PPy) exhibited excellent temperature stability and pH stability [149].  
[PERSON] and colleagues achieved the one-step polymerization of corresponding monomers (aniline, pyrrole, and thiophene) through enzyme-catalyzed polymerization, forming nanoparticles PANI/GOx, PPy/GOx, and PTh/GOx [150] (Figure 7). Additionally, [PERSON] and collaborators immobilized GOx within PPy, enhancing the stability of GOx [132].  
Hybrid materials containing inorganic nanoparticles (such as gold or silver nanoparticles) and CPs exhibit unique properties. The presence of oxygen or nitrogen elements in the polymer enhances its adsorption capability for inorganic nanoparticles, allowing the inorganic nanoparticles to form bonds with the conjugated diene segments of the CPs chains. [PERSON] and colleagues reported the synthesis of PANI/AuNPs&GOx and Ppy/AuNPs&GOx nanocomposites through the introduction of AuNPs or chloroquine acid (HAuCl\({}_{4}\)) to assist in the enzymatic catalysis of monomers aniline and pyrrole [151].  
Figure 6: A schematic of polydopamine/polypyrrole/glucose oxidase (PDA/PPy/GOx) as a highly sensitive and stable amperometric glucose sensor. (**a**), One-pot chronopotentiometric co-deposition of GOx and PDA/PPy on electrodes. (**b**), The sensing mechanism of the PDA/PPy/GOx amperometric glucose sensor. [138].
## 4 Results and Discussion
### Dispersion Curve Inversion and Vs Models  
The dispersion curves obtained using the three techniques, F-K, SPAC, and ESAC, and presented in Figure 4, are plotted within the theoretical limits of the wavenumber (Kmin, Kmax). The dispersion curves are valid between 7 and 12 Hz in El-Attaf and between 5 and 12 Hz in Ain-Defla. In the El-Abadia city, the curves are valid between 5.5 and 11 Hz. This difference is due to local site conditions. The dispersion curves are well correlated at most sites (Figure 4). At sites ATF1 and AIN1, the dispersion curves obtained with the ESAC technique tend to diverge from the other curves at high frequencies. We note that at these sites, the array was deployed on slightly sloping terrain, which might indicate that the ESAC technique could be more sensitive to slopes than the other techniques.  
An average curve was calculated at each site. In this way, a better constrained dispersion curve is used for the inversion process to obtain a better consistency of the resulting Vs profiles. The results of the inversion are shown in Figure 5. In the El-Attaf city, the engineering bedrock corresponds to the Miocene marls and sandstones, with a Vs value varying between 970 and 1280 m/s. Soft and stiff Quaternary alluvium occupies the first 30 m of the soil column. In the Ain-Defla city, the bedrock Vs value varies between 1390 and 1450 m/s. The thickness of the Quaternary deposits varies between 16 and 43 m. At El-Abadia city, the bedrock Vs value is divided into two different ranges, between 870 and 900 m/s to the east (ABD1, ABD3), and between 1150 and 1200 m/s to the west (ABD2, ABD4). This difference is related to the change in bedrock composition from Late Pliocene sandstones in the east to Miocene marls and sandstones in the west. The thickness of the Quaternary layers varies between 11 and 29 m.
## 2 Material and Method
### Design of Experiment  
The drilling process was performed on Litz Hitech TV-600 Tap and Drill computer numerical control (CNC) machine (Litz Hitech Corp., Taichung City, Taiwan) with a two line system consisting of air and cryogenic gas. The spray pressure system was adjusted by a regular valve and was observed by an airflow monitor. The injection nozzle was located 45 degrees vertical to the drilling tool. The tool diameter was three times bigger than the workpiece thickness. Thus, the drilling method was programed in the form of a counter-bore drilling cycle. Based on the reference standard of the cutting tool supplier, the spindle speed was set at 6100 rpm. Three levels of the feed rate were proposed at 228 mm/min, 589 mm/min, and 1006 mm/min, respectively. The three difference lubrication modes were set by cryogenic CO\({}_{2}\), dry air, and no lubrication (dry mode) with the pressure set at 1.5 bar. The experimental system is illustrated in Figure 3.  
The cutting parameters were designed using the orthogonal array method with three different levels for each parameter. The details are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **Triple** & **Low Helix** & **High Helix** \\ \hline Tip angle (Degree) & 120 & 120 & 120 \\ Helix angle (Degree) & 0 & 5 & 40 \\ Coating & Diamond & Diamond & Diamond \\ Diameter (Inches) & 1/8 & 1/8 & 1/8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specification of the tool.  
Figure 3: The drilling process system **(a)** 1. Litz Hitech TV-600 Tap and Drill computer numerical control (CNC) machine; 2. Dynamometer; 3. Thermal camera; 4. Air nozzle; 5. Fixture; **(b)** 1. Computer; 2. Data Acquisition Device (DAQ); 3. Camera; 4–5. Cryogenic Gas; 6. Air flow meter; 7. Air regulator; 8. Gas regulator.  
Figure 2: The geometry of the tool.  
The surface roughness (R\({}_{a}\)), the uncut fiber (U\({}_{c}\)), and the delamination (D\({}_{L}\)) were used to evaluate the quality of the hole. Measuring equipment (VK-X200, Keyence, Osaka, Japan) was used to verify the surface roughness. Microscope equipment (MF-A2017D, Mitutoyo, Sakado, Japan) was used to measure the uncut fiber and the delamination of the hole [36]. The uncut fiber area and the delamination are shown in Figure 4.  
The delamination is computed from the ratio of \(D_{max}\) with \(D_{nom}\) of the hole by Equation (1).  
\[F_{d}=\frac{D_{max}}{D_{nom}}, \tag{1}\]  
where \(D_{max}\) is calculated by double distance from the center of the nominal hole to the furthest point extension, the nominal diameter (\(D_{nom}\)), and the tool diameter are similar.  
The uncut fiber area was computed from the section of the fiber that has not been cut by Equation (2).  
\[A_{UC}=A_{nom}-A_{ex}, \tag{2}\]  
where \(A_{nom}\) is the circle area with the diameter of the tool drill. \(A_{ex}\) is the area of the surface without areas of uncut fiber.
## 2 Results and Discussion
### Analytical Method Validation by Wiping Method  
To validate a proposed method based on the wiping sample collection, standard 2-nonenal placed in a petri dish was wiped with 0.1 g of dry gauze and analyzed by HS-SPME/GC-MS. Linearity was validated by triplicate analyses for 2-nonenal at 0, 1, 2, 5, 10, 20, and 50 ng. The calibration curve was linear with a correlation coefficient of 0.991, and the relative standard deviations (RSDs) of peak height counts at each point ranged from 1.8 to 9.6 % (\(n=3\)). The LOD (S/N = 3) and LOQ (S/N = 10) of 2-nonenal in gauze were 22 pg and 74 pg, respectively. Intra-day and inter-day precisions expressed as RSD (%) were found to be 9.6 and 3.6%, respectively. These results show that the method is highly sensitive and reproducible for the determination of 2-nonenal by the wiping method.
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
###### Abstract  
With the rise of a new generation of low Earth orbit (LEO) satellite constellations and the advancement of the 6G network, satellite-terrestrial integrated Internet of Things (IoT) in the future will achieve global coverage through the integration of LEO constellation, and extend computing to the edge of the network through the deployment of edge computing services in LEO constellation so as to meet the demand of mass connection and low latency data processing. The LEO constellation network of the future will be an edge cloud network combining network and computing. In this paper, we propose a computation offloading strategy for the combined optimization of energy and computational load in a LEO constellation edge cloud network (hereinafter referred to as LEO-ECN). First, we establish the LEO-ECN system model, in which the user task can be offloaded to the satellite through the multi-hop path. Then, a cost model considering energy consumption and load calculation is proposed. Finally, a joint optimization problem to minimize energy consumption and balance the LEO-ECN load is established, which is a convex optimization problem. The simulation result demonstrates that, compared with the benchmark strategy, our proposed strategy has better performance and can improve the computing resource utilization of LEO-ECN.  
LEO constellation; edge computing; computation offloading; joint optimization Article
## 5 Conclusions  
In this paper, we proposed an ensemble-based transfer learning approach using two methods: simple averaging and weighted averaging. The proposed framework extracts superior sparse patterns and features from MRI images. To avoid overfitting, we used data augmentation techniques. After obtaining superior results from experiments, it can be inferred that both ensemble methods are equally competent. Furthermore, we investigated the performance of the models using uncropped and cropped MRI images. In future work, we aim to further refine and expand the proposed ensemble framework by incorporating additional transfer learning models, exploring different ensembling approaches, evaluating its performance on various AD databases, and leveraging adversarial networks for synthetic image generation. We also plan to test the results of our architecture on other AD databases to improve the generalization of our model. These advancements can potentially enhance the accuracy, robustness, and generalization of the model for Alzheimer's disease classification.  
**Author Contributions:** Conceptualization, P.G., K.C. and M.P.; methodology, P.G., K.C. and X.Z.; software, P.G. and K.C.; validation, A.S., S.P., T.J. and M.P.; formal analysis, P.G. and X.Z.; investigation, P.G., K.C. and X.Z.; resources, A.S., S.P. and T.J.; data curation, P.G. and X.Z.; writing--original draft preparation, P.G.; writing--review and editing, P.G., K.C., X.Z., A.S., S.P., T.J. and M.P.; visualization, K.C., X.Z. and A.S.; supervision, M.P.; project administration, S.P. and T.J.; funding acquisition, T.J. and M.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** We OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease [24].  
**Conflicts of Interest:** The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.  
Figure 4: Visualizations for examples of MRI images from the OASIS-3 dataset.
## 4 Limitations of Past Research and Implementation Barriers
### Public Acceptance  
Manufacturers like Google currently plan to release AVs by the year 2020 [13], which may result in the safer and more efficient use of roadways. The goal of mobility innovation and automation is to transform the travel choices of people, and as such, its implications on urban PT. Automation carries many social prospects in addition to the legal precepts of licensing and security. Future travellers may have the choice of using smartphones, consuming meals and engaging in recreational activities during their private or shared transit. Even though Google has logged over 1.1 million kilometres [90] through AVs on public roads in California, high costs limit the spread of autonomous cars on the consumer market [38; 91], a challenge that may best be addressed through autonomous PT if the perception of the public is duly considered, and initiatives to promote a general understanding of autonomous PT (e.g., buses)-mainly safety features are conveyed to the users. Adoption of closely travelling autonomous buses on dedicated lanes may increase the capacity of lanes [92], which may in return mean fuel savings, as less brake-accelerate manoeuvre will be needed. Preliminary data from the CityMobil2 automated bus project has revealed that there is currently a positive attitude from the general public, as Figure 5 [28] illustrates.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Researchers** & **Methodology** & & **Findings** & **Limitations (for Future Works)** \\ \hline [PERSON] et al. [73] & Analytical & - & Demand-bundling is necessary to maintain the competitiveness of PT against SAVs and private AV cars in dense urban areas. - High purchase and cleaning cost of SAVs is still a deterrence. - AVs may remain in private use due to low variable costs. - AVs decrease travel time and increase average vehicle speeds. - AVs increase VMT on long roads. - AVs increase GHG impacts on construction and procurement level - AVs decrease environmental impacts at mobility level by 60\%. - Central city area benefits from lower noise emissions due to the high penetration rate of AVs. & - AVs increase the VMT on long roads. \\ [PERSON] et al. [87] & Traffic simulation modelling & - & AVs increase GHG impacts on construction and procurement level & - AVs decline modelling software should be used. \\ [PERSON] et al. [88] & Analytical & - & Technological, policy and user acceptance are barriers. & - Pilot projects to analyse the transport impacts of AVs. \\  & & - & Gasoline hybrid SAVs had better cost and travel time performance & - Carbon taxes. \\  & & - & than electric SAVs. - Vehicle occupancy levels. \\ [PERSON] and [PERSON] [89] & Simulation & - & Cost benefit of gasoline hybrid & - Trip combinations. \\  & & - & SAVs depends on electric SAV & - Trip load reduction. \\  & & - & price and gasoline cost. - Electric SAV capable of & - Comparison with PT services and automation. \\  & & - & zero-carbon transport if coupled with a renewable power grid. & - Emissions and energy use. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.
# Multi-Feature Behavior Relationship for Multi-Behavior Recommendation
## 5 Experimental Results And Evaluation
### Dataset Description and Evaluation Metrics  
We used the Taobao and MovieLens datasets to evaluate the recommendation performance of the MFBR model. Table 1 shows the statistical information of the two datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**ine Dataset** & **User** & **Item** & **Interaction** & **Interactive Behavior Type** \\ \hline ine MovieLens & 67,788 & 8704 & 9.9 \(\times\) 10\({}^{6}\) & [Dislike, Neutral, Like] \\ Taobao & 147,894 & 99,037 & 7.6 \(\times\) 10\({}^{6}\) & [Page View, Favorite, Cart, Purchase] \\ ine & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Statictics for Taobao and MovieLens.  
**Taobao Data:** This dataset is from Taobao, one of the largest e-commerce sites in China, and contains a total of 147,894 users and 99,037 items interaction records with four interaction behaviors: _page view, favorite, add-to-cart, and purchase_. In our experiments, the purchase behavior is set as the target behavior and the other behaviors are set as auxiliary behaviors.  
**MovieLens Data:** This dataset is sourced from the MovieLens website and records interaction data for a total of 67,788 users and 8,704 items. In our experiments, the users' ratings of items are discretized based on previous research work (\(rating\in[1,\ldots,5]\)) [6]: \((1)\)\(rating\leq 2:dislike,(2)\)\(2<rating<4:neutral,(3)\)\(rating\geq 4:like\). In this dataset, \"like\" is set as the target behavior and the rest of the behaviors are set as auxiliary behaviors.  
**Evaluation Metrics:** To evaluate the success of the recommendation model, we use the _Hit Ratio(HR@N)_ and the _Normalized Discounted Cumulative Gain_ (_NDCG@N_) as indicators. The higher _HR_ and _NDCG_ ratings indicate more precise recommendation outcomes.
## 2 Modern Physical Layer
### Commercial and Research Modems
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
** Institution** & **Modem** & **Presented** & **Platform** & \begin{tabular}{l} **Center** \\ **Frequency (kHz)** \\ \end{tabular} & \
\begin{tabular}{l} **Range** \\ **(km)** \\ \end{tabular} &
\begin{tabular}{l} **Bit Rate** \\ **(bps)** \\ \end{tabular} & \multicolumn{1}{l}{**Modulation**} \\ \hline University of Southern California & USC [30] & 2006 & ATMEGA128L MCU(Mica2) & 18 & 0.5 & 600 & FSK \\ University of California, Irvine & UCI [31] & 2006 & Tmote (MSP430 MCU) & 1.5 & 0.01 & 48 & 8-FSK \\ University of Connecticut & uConn [38] & 2007 & TMS320C6713 DSP & 12.5 & (Lab) & 6,200 & OFDM (QPSK) \\ Massachusetts Institute of Technology & rdModem [35] & 2006 & TMS320C6713 DSP & 12 & 0.016 & 550 & QPSK \\ University of California, Santa Barbara & AquaModem [36] & 2005 & TMS320C6713 DSP & 24 & 0.44 & 133 & DSSS \\ Kookmin University & Kookokmin [29] & 2009 & ATMega128L MCU & 30 & 0.03 & 5000 & OOK \\ Massachusetts Institute of Technology & AquaNode [32] & 2007 & ADBlackkin B533 DSP & 30 & 0.4 & 300 & FSK \\ University of California, San Diego & UCSD [2] & 2010 & FPGA & & & & \\ Northwestern Polytechnical University in China & NPUC [39] & 2007 & ADSP-TS101 + FPGA & N/A & (Lab) & 1000 & OFDM (BPSK) \\ North Carolina State University & NCSU [33] & 2008 & Atmega 168 MCU & 47.5 & 0.1 & 31 & 4-FSK \\ Gangueing-Wenju National University & GaWoNU [28] & 2012 & ARM Cortex M3 & 70 & 0.07 & 200 & OOK \\ ITACA Institute & ITACA [48] & 2012 & CS015P20 & 85 & 0.1 & 1000 & Coherent FSK \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of research modems. OFDM, orthogonal frequency-division multiplexing; DSSS, direct-sequence spread-spectrum; OOK, on-off keying. In order to achieve a desirable trade-off between the different parameters compared in Table 1 and decrease costs, research is still in progress on acoustic modems. In Table 2, a comparison of state-of-the-art research modems is presented. Unlike commercial modems, comparisons among research modems are significantly more complicated, as the information provided in different papers is less standardized. In this case, the center frequency, data rate and modulation are detailed. The performances of the different modulations are also compared in Table 2. Although more complex modulations (DSSS and OFDM) yield promising communication performance results, simple modulations (FSK and OOK) are also used in several reliable recent works, but with low communication performance.
# Optimization of Ultrasonic Powder Coatings on the Surface of Treated Materials
## 1 Introduction  
Currentcoating methods, such as galvanic, spraying, and chemical and physical deposition, have a number of limitations regardingthe types of materials used, which is mainly due to the incompatibility of coatings and matrix materials. For example, it is extremely difficult to apply a refractory coating onthe surface of a material having a lower melting point or on ceramic material. Other disadvantages are related to the need to maintain a certain temperature or to control the pressure and composition of the gas atmosphere when applying coatings, which requires expensive laser, are or vacuum equipment [1; 2; 3].  
The method of applying nanocoationson the surface of a product using free working bodies accelerated by the ultrasonic vibrations of the walls of the resonance chamber is an example of a close combination of physical and chemical processes, and the quality of the final product depends entirely on the optimal course used. The fact is that the strength of a coating is determined by the adhesive forces of intermolecular attraction, which are enhanced as a result of the occurrence of a chemical bond between the adhesive and the substrate, and the necessary depth of introducing nanoparticles into the substrate is dictated by the physics of the interaction of the working bodies, the coating particles and the surface of the processed product. The choice of optimal conditions for the expected implementation of each of the parties to this process is a necessary step in obtaining the maximum efficiency of the technology under consideration.  
The considered method possesses a number of attractive features that may make it useful for many practical applications. First, this method can provide not only a rapid formation of fine-grained structure in the coated surface layers similar to what shot peening treatment does [4; 5; 6; 7] but also coating and armoring. Second, this method allows the armoring of metal surfaces with particles of other metal or ceramic materials, which can be capable of significantly changing the characteristics of the metal surfaces. Third, the coating can be carried out under atmospheric pressure and room temperature by using simple and cheap ultrasonic-based devices, depending on the operation conditions. Fourth, this method may work with different kinds of surfaces that can be treated.