esa-sceva/satcom-chunk-collection huggingface dataset:
# PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems
## 3 Framework of the Proposed Scheduling Method
### Assumptions  
1. Dispatching-related information is known or can be obtained from the manufacturing execution system, including workpiece processing time, maximum queue size (max. work in process, WIP) in front of a workstation, and the time each workstation is available;
2. Each station can process one workpiece lot at a time;
3. The transportation time of workpieces is not considered;
4. The processing of a workpiece cannot be interrupted.  
In consideration of the complexity of semiconductor manufacturing systems, a PSO-SVM-based scheduling method with a performance-driving approach and feedback mechanism is proposed. This system can adjust dispatching parameters according to varying manufacturing environments (operating status), such as changes in the number of WIPs, available time of equipment, the workload of downstream equipment, and intervals between two successive workpieces. Figure 1 illustrates the framework of the proposed scheduling method.  
The proposed scheduling method mainly contains four research contents: (1) composite dispatching rules (CDR), (2) feature selection, (3) performance prediction model, and (4) parameter optimization model. The \"simulation model\" is a virtual semiconductor manufacturing system, which is built according to an actual semiconductor manufacturing system in our previous work.  
CDR is a dispatching policy that is linked with considerations for process restrictions, equipment maintenance, and other factors. We may access a large number of samples that are kept in the \"database\" based on the simulation model. Feature selection (offline work, shown by broken lines in Figure 1) is performed before creating performance prediction models and parameter optimization models to decrease redundant features and computation time. The performance prediction model is a relational model that generates anticipant performances according to the operational condition of the manufacturing system. The parameter optimization model is also a relational model that outputs optimized parameters based on the operating status and estimated performances. Subsequently, the weight parameters of CDR are correlated with the selected features and six performances of the manufacturing system. Thus, the scheduling system can adaptively update dispatching parameters, form the most suitable dispatching rules, and implement the adaptive scheduling course.  
Figure 1: Research methodology of the proposed PSO-SVM method.
# On-Demand Scheduling of Command and Responses for Low-Power Multihop Wireless Networks +
## 5 Evaluation
### Parameter Selection for Legacy Schemes  
First, we conduct simulations while varying each parameter, \(T_{C}\), \(T_{R}\), and \(K\) to find appropriate values for each algorithm in our setup. Figure 6 plots the influences of each parameter. In the simulations, we set \(M\) to 3 to cope with unexpected losses (e.g., inter-node interference/collision or queue losses). Figures 6a,b show that PRR is little influenced by dissemination random jitter \(T_{C}\), but affected significantly by the response  
Figure 5: Response and dissemination time update.  
jitter \(T_{R}\). Most packet losses are from responses because, for dissemination, each node can receive from not only its parent but also its neighbors. The results show how critical the response synchronization effect is. Finally, Figure 6c shows that [PERSON]'s suppression threshold \(K\) does have notable effect on performance, and any value below 5 has insufficient dissemination PRR. Therefore, for the remainder of our evaluation, we use fixed \(K=5\) and \(T_{C}=200\) ms, with varying \(T_{R}\).
## 5 Performance Analysis and Discussion
### Performance Metric  
The goal of scheduling algorithm considered here is to find a schedule map in such a way that the cost is optimized under user-defined deadline constraint. The performance metrics used to evaluate the proposed DSB algorithm are given below.
## 3 Parameter Analysis and Optimization Design
### CLNVD's Optimum Design  
#### 3.2.1 Optimum Strategies for the CLNVD  
In order to ensure that the CLNVD has a large coefficient \(f_{d}\) and coefficient \(f_{E}\) under the circumstance of frequent earthquakes (or fortification earthquakes), on the basis of the analysis in Section 3.1, the following steps are adopted to optimize the CLNVD:  
1. The position of the intermediate column \(\beta=0.5\) is selected, as well as the beam section and lever amplification factor \(\chi\); the line stiffness ratio \(\rho\) of beam to column is calculated. According to Equations (9) and (10), the geometric amplification coefficient is calculated. \(1/(1+1/3^{1+\alpha})^{1-0.5\alpha}\gamma\) is judged to estimate whether it is higher in comparison with the pre-set target of \(f_{d}\). If it is not higher, the following methods are applied: to increase \(\chi\), or to decrease \(\rho\).
2. CLNVD's parameters (such as intermediate column section, lever section, negative stiffness, damping coefficient, and damping index) are selected so that the values of \(k_{d}^{\prime\prime}/k\) and \(k_{N}/k\) are within the target area (See Figure 11d). The inter-story displacement target value and the iterative method in Section 3.1 can be used to calculate \(k_{d}^{\prime\prime}\); the \(\eta_{d}\) is calculated through Equation (18).
3. \(\gamma\) and \(\eta_{d}\) are substituted into Equation (3) to calculate the displacement amplification coefficient \(f_{d}\), and Equation (4) is utilized to calculate the energy dissipation coefficient \(f_{E}\).  
Figure 12: Impact of inter-story displacement on \(f_{d}\) and \(f_{E}\): (**a**) impact of \(U_{1}\) on \(f_{d}\) and \(f_{E}\); (**b**) impact of \(\chi\) and \(U_{1}\) on \(f_{E}\); (**c**) impact of \(i_{b}\) and \(U_{1}\) on \(f_{E}\); (**d**) impact of \(c\) and \(U_{1}\) on \(f_{E}\).  
4. \(f_{E}\) is judged if the predetermined goals are met. If not, the following methods are applied for optimization: to enhance the stiffness \(i_{b}\) and damping coefficient \(c\); to enhance the stiffness \(i_{b}\) and the lever's coefficient \(\chi\); to increase the lever's coefficient \(\chi\) and reduce the damping coefficient \(c\); or to reduce the NSD's negative stiffness \(k_{N}\). It is necessary to verify again if \(k_{N}/k\) and \(k_{d}^{\prime\prime}/k\) are within the region proposed in Section 3.1.5, and Step (3) and Step (4) should be repeated. If \(f_{E}\) cannot meet the set target when \(k_{d}^{\prime\prime}/k\) is on the upper limit of the target region in Figure 11d, we should increase the beam section and return to Step (3).
###### Abstract
In a previous paper, we discriminated, with excellent accuracy, healthy colon cells (FHC line) from cancerous ones (CaCo-2 line) according to FTIR spectra measured in transmission mode [16]. These cells were grown on glass coverslips and the discrimination was limited to absorption values measured in the 2700-3700 cm\({}^{-1}\) spectral range, because glass slides are transparent to IR radiation only in such a range. In this work, we extended the investigation to a wider spectral range, including both the 1000-1760 cm\({}^{-1}\) (LWR) and the 2700-3700 cm\({}^{-1}\) (HWR) regions. Such measurements were allowed (i) by using a slide reflecting the IR radiation as a substrate on which the cells were grown and (ii) by using the transfection measurement method. A few machine learning algorithms were used to develop classification models in order to assign unknown spectra to the proper class. The aim of this work was to investigate which algorithm and which of the two spectral ranges allowed for a better classification of unknown cells. The obtained results point out that the employed classification models were able to discriminate the spectra from different types of cells with high accuracy, sensitivity, and specificity, particularly as far as the NN model is concerned. The performance of the classification models resulted to be excellent even when applied independently to the LWR and HWR spectra. This result is interesting because it suggests that it is possible to perform FTIR analysis of cell samples on glass slides (which are commonly used in medical practice) with excellent classification performance. Thus, this study represents a further investigation supporting the use of the FTIR spectroscopy and machine learning algorithms as complementary diagnostic tools in cytology.
## 3 Experimental Results
### Steel Beam  
#### 3.2.1 Failure Process  
The six steel frame beam specimens experienced a similar failure process, namely, the yielding stage, the elastoplastic stage, and the plastic failure stage under low cyclic repeated loading. The specimens were in the elastic stage at the initial stage of loading. With the increase of the displacement amplitude and cycle number, the specimens were in the elastoplastic stage. The local buckling was discovered at the bottom flange of the specimens. The local buckling deformation occurred with the web. With the further increase of the displacement amplitude and cycle number, the plastic hinge formed at the root of the beam, and the bearing capacity was reduced until specimen failure. However, the entire failure process of the specimens, which belongs to the ductile failure, is relatively slow and exhibits a good energy dissipation performance. Compared with uncorroded specimens, with the increase of the corrosion degree, the declines amplitude of the maximum flexural capacity of corroded specimens increases.  
The maximum flexural capacity of B-2-B-6 specimens were obtained from Figure 8, as 128.35 kN, 123.81 kN, 110.68 kN, 102.07 kN, and 95 kN, respectively. By comparison with the B-1 specimen (136.9 kN) the decreased rates are calculated as 6.22%, 9.56%, 19.15%, 25.44% and 30.31%, respectively. At the same time, due to the buckling of the specimen bottom end flange, the web and the plastic hinge occurred, the lateral resisting stiffness of beam specimens was reduced. The typical failure model of the specimen is shown in Figure 9.  
Figure 8: _Cont._  
Figure 7: Regression fitting results of mechanical properties and weight loss ratio. (**a**) Yield strength; (**b**) ultimate strength; (**c**) elongation; (**d**) elasticity modulus.
## 6 Conclusions  
In this work, we have investigated the performance of the FL method including a group of heterogeneous nodes. In particular, a hardware-software integrated FL model is developed by using a set of Raspberry PI devices and virtual machines acting as an FL client with their datasets. Performance is analyzed for the case of an image classification problem with a widely known CIFAR10 dataset. Given the importance of distributed intelligence with heterogeneous wireless nodes in the upcoming 6G technology, a set of experiments are performed to analyze the FL performance in different cases. Main issues such as differing computation resources, uneven distributions of datasets, and heating issues of wireless devices were considered while performing the experiments. In addition, novel technologies such as the users of pre-trained networks for knowledge transfer, were also considered. A more pro-analysis and concluding remarks are also presented during the discussion of simulation results. This study can be highly useful when considering the deployments of FL methods over heterogeneous 6G environments to enable large-scale, connected, cost-efficient, and reliable distributed intelligence.  
In conclusion, our study's multifaceted approach, spanning cooling mechanisms, heterogeneous compensation strategies, overfitting mitigation, scalability analyses, and the integration of pretraining and Transfer Learning, provides a holistic understanding of FL's dynamics across heterogeneous nodes. These nuanced findings not only contribute significantly to the academic discourse but also hold practical implications for real-world 6G deployments. By illuminating the complexities and offering viable solutions, our research empowers the seamless integration of FL in diverse, large-scale, connected, cost-efficient, and reliable distributed intelligence systems, laying the foundation for the future of intelligent wireless networks.  
Conceptualization, D.T. and S.S.S.; methodology, S.S.S.; software, L.R. and D.N.; validation, L.R., S.S.S. and D.N.; formal analysis, S.S.S. and D.N.; investigation, L.R.; resources, D.T.; data curation, L.R.; writing--original draft preparation, D.N.; writing--review and editing, D.T., D.N, and S.S.S.; visualization, L.R.; supervision, D.T.; project administration, D.T.; funding acquisition, D.T. All authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by the ECOSISTER project funded under the National Recovery and Resilience Plan (NRRP), Mission 04 Component 2 Investment 1.5--NextGenerationEU, Call for tender n. 3277 dated 30 December 2021, Award Number: 0001052 dated 23 June 2022 and by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on \"Telecommunications of the Future\" (PE00000001--program \"RESTART\").  
Data Availability Statement:Data available on request due to restrictions e.g., privacy or ethical.  
The authors declare no conflict of interest.
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 1 Introduction
Furthermore, to achieve the aim of the study, two objectives were determined: to develop independent 2-D and 3-D yield estimation methodologies and to analyse and compare the success of the two PRS techniques for yield estimation.
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 2 Development and Application of Multimodal Interaction Technology in Remote Education  
Before discussing the technical development of multimodal interaction, it is helpful to illustrate how such systems are typically structured in remote education settings. Figure 1 presents a general flow of information in a multimodal learning system. It begins with learner input, followed by data acquisition using sensor devices, and then proceeds to data processing and analysis. The system responds through adaptive feedback delivered to learners and instructors.
# Independent Double-Boost Interleaved Converter with Three-Level Output
## 2 Converter Topology Analysis
### CCM Operation of the Proposed Converter  
Considering the CCM operation, Figure 4 shows the steady-state waveforms for the inductor voltage and current at a duty cycle D larger and smaller than 0.5. For a duty cycle D larger than 0.5, the voltage ratio of the proposed converter can easily be deduced by the classic boost converter analysis approach [34], where the average inductor voltage equation is:  
\[\mathrm{V_{in}D+(V_{in}-V_{C1})(1-D)=0} \tag{1}\]  
where the capacitors \(\mathrm{C_{1}}\) and \(\mathrm{C_{2}}\) voltages are:  
\[\mathrm{V_{C1}=V_{C2}=\frac{V_{out}}{2}} \tag{2}\]  
The deduced voltage gain is given as:  
\[\frac{V_{out}}{V_{in}}=\frac{2}{(1-D)} \tag{3}\]  
Working at a duty cycle D smaller than 0.5, the average inductor voltage equation is:  
Figure 3: Presumptive functioning waveforms and switching stage correlations.  
\[\mathrm{V_{in}D}+(\mathrm{V_{in}}-\mathrm{V_{C1}})0.5-\mathrm{V_{C1}}(0.5-\mathrm{D})=0 \tag{4}\]  
Thus, for this condition, the voltage gain can be expressed as:  
\[\frac{\mathrm{V_{out}}}{\mathrm{V_{in}}}=\frac{2\mathrm{D}+1}{(1-\mathrm{D})} \tag{5}\]  
Considering (3) and (5), Figure 5 shows the DC conversion ratio M(D), of the proposed IDBIC converter for CCM operation in conjunction with the regular boost converter gain.
## 3 Results and Discussion
### Scanning Electron Microscopy  
Figure 4 shows the surface morphology of Ga- or Y-doped perovskite materials and was obtained by scanning electron microscopy. In order to highlight the shapes and sizes of the particles, the studied materials were analyzed in the low-vacuum mode. Thus, from the presented images, one may see that in both cases the particles have a spherical shape, with dimensions of hundreds of nm, being strongly agglomerated. Furthermore, from the images presented in Figure 4, it can be seen that the nature of the dopant did not influence the shape of the particles.  
Figure 4: SEM images and EDX/element mapping of LMO:Ga and LMO:Y perovskite materials.  
Figure 3: (**a**) FT\(-\) IR spectra for undoped and Ga- or Y-doped LaMnO\({}_{3}\) synthesized by sol–gel technique, where the significant absorption bands are marked on the graph. (**b**) The zoom for the FT\(-\)IR spectra in the 400–1500 cm\({}^{-1}\) range.  
Moreover, it has to be mentioned that quantitative analysis by EDX demonstrates that the samples have the expected/determined elemental composition. The stoichiometry of the obtained samples was confirmed by the quantification of the elemental atomic ratio of La:Mn:O, i.e., 1:1:3. Thus, a homogeneous distribution of the corresponding La, Mn, O and Ga/Y elements in the synthesized samples was observed.
## 2 Materials and Methods
### Sesame Feedstock and Produced Biochar Characterization
#### 2.3.2 Cation Exchange Capacity (CEC)  
The modified ammonium-acetate compulsory displacement method ([PERSON] and [PERSON], 1996) was followed to determine the cation exchange capacity (CEC-NH\({}_{4}\)) of the biochar, as described by [18]. Before starting the CEC extraction, a pre-leaching step was applied to reduce interference from soluble salts using de-ionized water by shaking the suspension (1: 20, biochar: water) at 180 rpm for 5 min (five times). Then, the biochar sample was saturated with sodium acetate solution (1M Na-OAc, pH 8.2) by adding 15 mL to the sample, and the mixture was shaken for 10 min (repeated three times). After that, the samples were washed three times with ethanol to remove excess Na from the biochar surface. Finally, NH\({}_{4}\)-acetate (1M NH\({}_{4}\)-OAc, pH 7) was used three times to displace sodium ions, and the released Na\({}^{+}\) ion was measured by atomic absorption spectroscopy.
## 3 Results and Discussion
### Molecular Docking Studies  
#### 3.4.1 Docking Studies with PHF Tau  
Blind docking studies were conducted to determine the binding interactions with PHF Tau, and those peptide sequences that did not bind to the PHF Tau binding sites were excluded after the first round of blind docking. The sequences that were excluded included SKAWVCDGN (TauRP1-1), AWVCDGDNDC (TauRP1-3), VCDGDNDCED (TauRP1-5), DNDCEDNSDE (TauRP1-9), NDCEDNSDEE (TauRP1-10), DCEDNSDEEN (TauRP1-11) and CEDNSDEENC (TauRP1-12). We then performed a second round of blind docking using the seven remaining peptides and decreased the size of the grid box while increasing the exhaustiveness because it has been shown that increasing the exhaustiveness results in a more comprehensive search, which decreases the probability of missing the best docking conformation [93]. With these adjusted parameters, four of the seven peptides were found to bind within the tau binding pocket. The results are shown in Table 4 and Figure 4. These sequences included KAWVCDGDNDN (TauRP1-2), WVCDGDNDCE (TauRP1-4), GDNDCEDNSD (TauRP1-8) and DNSDEENCES (TauRP1-14). Of these, TauRP1-2 had a binding affinity of \(-4.3\) kcal/mol and was almost fully folded on itself while in close proximity to amino acids R349 and K353 on the tau protein. TauRP1-4 binds within the pocket in binding modes 1 and 2 with binding affinities of \(-4.4\) kcal/mol. The first binding pose is located close to Q351 and is also folded on itself, whereas the second binding pose, although in a similar binding position, is more spread out and in proximity to the K359 of the tau binding pocket. TauRP1-8 seems to be embedded in the pocket, as it appears to interact with tau with residues ranging from Q351 to K369. This corroborates with its comparatively higher binding affinity of \(-4.9\) kcal/mol. Lastly, the TauRP1-14 peptide bound in a highly similar conformation to the corresponding 23-amino-acid peptide in LRP1 and demonstrated the highest binding affinity (\(-5.7\) kcal/mol) in binding mode 2. This peptide also binds to the tau protein and is in close proximity to residues K353 to K369 of the tau protein. The residues K353 and K369 of Tau protein are particularly significant due to their role in glycation and are known to prevent binding with microtubules [94; 95].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Sequence** & **Mode 1** & **Mode 2** & **Mode 3** & **Mode 4** & **Mode 5** & **Mode 6** & **Mode 7** & **Mode 8** & **Mode 9** \\ \hline TauRP1-2 & \(\bigstar\) & \(\bigstar\) & \(\bigstar\) & \(\  
Given these results, we then performed molecular dynamics studies of the docked PHF complexes with the above four peptide sequences to determine the stability of the complexes.
## 4 Deconvolution
### Feature Model Decomposition
#### 4.3.3 Strength and Weakness  
* Uses adaptive FIR filters
* Can resist interference and noise
* Highly dependent on signal characteristics  
The decomposition goal of FMD is to capture the characteristics of mechanical faults, taking into comprehensive consideration the impulsivity and periodicity of signals, while exhibiting robust resistance to other interference and noise. This method employs adaptive finite impulse response (FIR) filters for signal decomposition, unconstrained by filter shape, bandwidth, or center frequency, thereby achieving a more thorough decomposition effect.  
Notably, FMD can accurately decompose mechanical fault information without prior knowledge of the fault period, especially for compound faults that dominate in different frequency bands. However, the introduction of adaptive FIR filters also means that the performance of FMD is influenced to some extent by the initial parameter selection of the filter bank. Improper initial parameter settings may lead to suboptimal signal decomposition results or even the inability to effectively extract fault features. Furthermore, the performance of FMD is highly dependent on the characteristics of the signal itself.
# The Bimodal Neutron and X-ray Imaging Driven by a Single Electron Linear Accelerator
## 4 Discussion  
The industrial applications of neutron imaging have long suffered from the lack of a suitable neutron source that can deliver an intense neutron beam with a long lifespan [1]. Reactor sources, or spallation neutron sources, are reluctantly ruled out for their high construction and operating costs. Isotopic neutron sources cannot provide the necessary brilliant neutron beam, and some suffer from short half-lives [16]. Therefore, only the accelerator-driven neutron sources would be considered, in which the e-LINAC can deliver high fluxes for both neutrons and photons, and the even higher flux of X-rays fortunately compensates for the relatively low intrinsic detection efficiency of the detector for several hundred keV to MeV photons. Therefore, besides the relatively low cost and modest footprint [17], the most attractive property of the e-LINAC-driven system is that it can provide the imaging photon beam and neutron beam almost simultaneously with a negligible difference between their imaging beam geometries because both the two radiations are emitted from the same volumetric photoneutron source. The successive photon imaging and neutron imaging measurements within one e-LINAC operation facilitate the fusion of the photon image and the neutron image. This unique property makes the e-LINAC driven system be a promising bimodal imaging system.