esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance and Simulation Results
### The Modified BA Protocol, MBA  
Contrary to the original BA protocol, the modified protocol ensures that the entire number of the tags in reader interrogation zone will be recognized (identified) whatever C.E probability is. Moreover, it recognizes between the collision and the interference (affected one tag response) responses for the period of staying tags. Thus, enhanced identification efficiency is obtained by consuming a lower total number of cycles than the original BA protocol for the same value of probability of interference.  
Furthermore, a simulation of each protocol existed to treat the capture effect until the present time was conducted (without using bit-tracking technology, which is used in GQT1, GQT2, GBT, and TBT). Our proposed modified protocol, MBA, has shown better performance and efficiency (a lower number of consumed total cycles) when the number of staying tags is high (the tags move at low velocity, for blocking purposes). Finally, the impact of C.E probability, interference probability, the total number of tags existed in simulation area, and the length and distributions of tag IDs were measured to compare the modified protocol with the other existing protocols.  
Figure 4: The impact of capture effect.  
Figure 5: The impact of interference.
###### Abstract  
This study evaluated the odor mitigation potential of rice husk biochar in a simulated dairy bedded pack over 21 days. Biochar was incorporated into a dairy manure-sawdust mixture at 5% and 10% dry weight. Emissions of key odorous compounds--ammonia (NH\({}_{3}\)), sulfur compounds, volatile fatty acids, phenol, \(p\)-cresol, and indole--were evaluated. Odor units were assessed to determine perceived odor reduction. Biochar significantly reduced NH\({}_{3}\) and dimethyl sulfide (DMS) emissions: NH\({}_{3}\) by 27% and 43%, and DMS by 53% and 75%, at 5% and 10% application, respectively. The NH\({}_{3}\) reduction was attributed to ammoniacal nitrogen adsorption, while the DMS reduction likely resulted from enhanced air permeability suppressing anaerobic bacterial activity. The 5% biochar treatment, achieving 63% and 70% of the NH\({}_{3}\) and DMS reductions attained by the 10% treatment, respectively, offers a more practical and cost-effective option. Other odorous compounds were not significantly affected. A temporary reduction in odor units was observed on day 7. Rice husk biochar contains 14.5% atomic Si, primarily as silica, which supports structural stability but hinders pore development, reducing adsorption efficiency. These findings demonstrate the importance of biochar's physicochemical properties in odor mitigation. Future research should evaluate long-term field performance, microbial interactions, and silica modification strategies.  
rice husk biochar; ammonia; odor; dimethyl sulfide; dairy bedded pack
## 2 Handcrafted Representation-Based Approach
### Space-Time-Based Approaches
#### 2.1.4 Discussion  
Space-Time-based approaches have been evaluated by many researchers on different well-known datasets--including simple and complex activities as recoded in Table 1. Some merits of these approaches are as follows: (1) STVs-based approaches are suitable for recognition of gestures and simple actions. However, these approaches have also produced comparable results on complex datasets such as Human Motion database (HMDB-51), Hollywood2, and University of Central Florida (UCF-101); (2) The space-time trajectory-based approaches are especially useful for recognition of complex activities. With the introduction of dense trajectories, these approaches have become popular due to their high accuracy for challenging datasets. In recent years, trajectory-based approaches are getting lot of attention due to their reliability under noise and illumination changes; (3) Space-time feature-based approaches have achieved state-of-the art results on many challenging datasets. It has been observed that descriptors such as HOG3D, HOG/HOF, and MBH are more suitable for handling intra-class variations and motion challenges in complex datasets as compared to local descriptors such as N-jet.  
However, these approaches have some limitations as follows: (1) STVs-based approaches are not effective in recognising multiple persons in a scene; these methods use a sliding window for this purpose which is not very effective and efficient; (2) Trajectory-based approaches are good at analysing the movement of a person in view invariant manner but to correctly localize the 3D XYZ joint position of a person is still a challenging task; (3) Space-time features are more suitable for simple datasets; for effective results on complex datasets, combination of different features is required which raises the computational complexity. These limitations can cause hindrance for real-time applications.
## 4 Simulation and Discussion
### Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & **GA** & **MGA** & **PSO** & **MPSO** & **GSA** & **MGSA** & **TLBO** & **MTLBO** & **GWO** & **MGWO** \\ \hline \multirow{3}{*}{F18} & P.I. & 0.0018 & 0.0025 & 0.0004 & 0.0006 & 0.0035 & 0.0037 & 0.0029 & 0.0030 & 0.0010 & 0.0015 \\  & P.C.R. & 1.8757 & 2.5166 & 0.4211 & 0.6029 & 3.4917 & 3.7297 & 2.9081 & 3.0689 & 1.0903 & 1.5060 \\  & O.T.S. & **0.0979** & **0.0576** & **0.1963** & **0.1722** & **0.2609** \\ \hline \multirow{3}{*}{F19} & P.I. & 0.0025 & 0.0037 & 0.0008 & 0.0014 & 0.0037 & 0.0048 & 0.0035 & 0.0041 & 0.0016 & 0.0026 \\  & P.C.R. & 2.5233 & 3.7541 & 0.8540 & 1.4255 & 3.7397 & 4.8479 & 3.5075 & 4.1393 & 1.6122 & 2.6632 \\  & O.T.S. & **0.4878** & & **0.0811** & & **1.4218** & & **0.0983** & & **0.4146** \\ \hline \multirow{3}{*}{F20} & P.I. & 0.0028 & 0.0051 & 0.0009 & 0.0021 & 0.0045 & 0.0056 & 0.0036 & 0.0083 & 0.0017 & 0.0042 \\  & P.C.R. & 2.8411 & 5.1640 & 0.9246 & 2.1106 & 4.5773 & 5.6775 & 3.6700 & 4.8358 & 1.7797 & 4.2165 \\  & O.T.S. & **0** & **0.0846** & **0** & & **0.1673** & & **0.1526** \\ \hline \multirow{3}{*}{F21} & P.I. & 0.0027 & 0.0056 & 0.0010 & 0.0023 & 0.0044 & 0.0057 & 0.0041 & 0.0054 & 0.0020 & 0.0045 \\  & P.C.R. & 2.7466 & 5.6009 & 1.0173 & 2.3777 & 4.4041 & 5.7909 & 4.1728 & 5.4967 & 2.0670 & 4.5858 \\  & O.T.S. & **0.1158** & & **0.0913** & & **0** & **2.1696** & & **0.5408** \\ \hline \multirow{3}{*}{F22} & P.I. & 0.0030 & 0.0071 & 0.0010 & 0.0030 & 0.0041 & 0.0064 & 0.0048 & 0.0065 & 0.0022 & 0.0050 \\  & P.C.R. & 3.0287 & 7.1416 & 1.0454 & 3.0463 & 4.1996 & 6.4820 & 4.8034 & 6.5908 & 2.2729 & 5.0265 \\  & O.T.S. & **0.1123** & & **0.3930** & & **1.2738** & & **0.4395** & & **0.6946** \\ \hline \multirow{3}{*}{F23} & P.I. & 0.0035 & 0.0089 & 0.0012 & 0.0039 & 0.0045 & 0.0075 & 0.0051 & 0.0080 & 0.0026 & 0.0065 \\  & P.C.R. & 3.5326 & 8.9343 & 1.2184 & 3.9863 & 4.5094 & 7.5026 & 5.1003 & 8.0755 & 2.6291 & 6.5630 \\ \cline{1-1}  & O.T.S. & **2.0295** & & **0.0757** & & **1.2981** & & **0.2980** & & **0.7469** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Cont.
# Design and Multi-Objective Optimization of Fiber-Reinforced Polymer Composite Flywheel Rotors
## Manufacturing Process
### Turning Process and Interference Fits  
To prepare the rims for interference fit assembly, the outer diameter and possibly the surface on the inside of each rim need to be machined. The machining step is necessary to ensure the correct dimensions and tolerances and smooth surfaces required for interference fit assembly. Further improvement of the surface finish is possible by sanding. Machining is typically employed utilizing a lathe.  
Figure 1: Manufacturing process of fiber composite FESS rotors.  
As described above, multi-rim rotors increase the SED, among others, due to interference fits. Interference fits may be created by temperature fits or press fits or a combination of the two. The basic idea of temperature fits is that materials expand (or contract) when temperature changes. Composite materials often have a low coefficient of thermal expansion (CTE). In fact, unidirectional carbon and aramid fiber composites may even have a negative CTE in fiber direction. Hence, deformation must be sufficient to achieve adequate radial compression. However, a temperature fit is a good option to fit a metallic rim or hub into a composite rim. Alternatively, a press fit may be employed. In [15] two different methods for press fitting are presented. To facilitate press fit assembly, certain rim geometries may be incorporated, see Figure 2. The first method uses tapered surfaces of the rims. The second method employs a tapered guiding tool in front of the inner rim to expand the outer rim during the process. By using tapered shapes the vertical forces, required to fit the inner rim into the outer rim, can be reduced.
Although the basis function expansion method resolves the problem of large-scale inverse problems, the critical problem of selecting the type and number of basis functions remains. The technique using the Levenberg-Marquardt method also involves the intricate problem of selecting the correct regularization parameter. A probabilistic method using Kalman filtering [21] was also proposed to stabilize the solution. Although the instability problem was mostly resolved, the reconstructed force quality appeared to degrade with the increase in system noise. In addition to regularization and probabilistic methods, [PERSON] et al. [22] proposed a method to use the experimentally measured response of a composite structure to calibrate the transfer function between the acquired response and the impact load. However, the proposed method requires the calibration of the transfer function from an exceptionally large amount of training data for a single structure. Recently, neural networks were employed to predict the impact loads acting on a simple nonlinear system [23; 24; 25]. Although studies using neural networks succeeded in predicting the impact force applied on simple nonlinear structures such as composite plates, the results showed variation in responses according to the material properties and geometry. Large amounts of training data must be accumulated for effective practical applications of neural networks. Thus, the reconstruction of the impact load applied to complex production equipment (which is generally assembled with various parts made of different types of material) continues to be a challenging task. This paper presents a method for identifying an unknown impact load through strain analysis with a newly designed transducer. We designed the transducer with strain gauges and reconstructed the impact load by developing a conversion model based on strain analysis. The reconstructed impact load was employed as an input load curve in the finite element analysis. Furthermore, it was validated by comparing the strain measured on an experimental specimen and that calculated by the simulations. Finally, the developed method was validated by reconstructing the impact load by a vehicle chassis on the engineering jig structure in the vehicle production line of Hyundai Motors, for the design of lightweight production equipment.
## 2 Model Problem of the Allen-Cahn Equation and Finite Difference Approximation
### POD Reduced System
**Step 1**: We consider the representation \(\mathbf{u}\approx\mathbf{V}_{k}\tilde{\mathbf{u}}\), where \(\mathbf{V}_{k}\in\mathbb{R}^{n\times k}\), and \(\tilde{\mathbf{u}}\in\mathbb{R}^{k}\) (\(k\ll n\))  
\[\mathbf{V}_{k}\tilde{\mathbf{u}}(t_{j+1})=\mathbf{A}^{-1}(\mathbf{B}\mathbf{V}_ {k}\tilde{\mathbf{u}}(t_{j}))+\mathbf{A}^{-1}(\mathbf{F}(\mathbf{V}_{k} \tilde{\mathbf{u}}(t_{j}))). \tag{7}\]
**Step 2**: We apply the Galerkin projection along the subspace generated by the columns of \(\mathbf{V}_{k}\), which forms an orthonormal basis \((\mathbf{V}_{k}^{T}\mathbf{V}_{k}=\mathbf{I}\in\mathbb{R}^{k\times k})\). The resulting system is given by  
\[\tilde{\mathbf{u}}(t_{j+1})=(\mathbf{V}_{k}^{T}\mathbf{A}^{-1}\mathbf{B} \mathbf{V}_{k})\tilde{\mathbf{u}}(t_{j})+\mathbf{V}_{k}^{T}\mathbf{A}^{-1}( \mathbf{F}(\mathbf{V}_{k}\tilde{\mathbf{u}}(t_{j}))). \tag{8}\]  
Finally, we obtain the following POD reduced system  
\[\tilde{\mathbf{u}}(t_{j+1})=\underbrace{\mathbf{C}}_{\mathbf{precupputed}:k \times k}\tilde{\mathbf{u}}(t_{j})+\underbrace{\mathbf{V}_{k}^{T}\mathbf{A}^{- 1}}_{\mathbf{precupputed}:k\times n}\underbrace{\mathbf{F}(\mathbf{V}_{k} \tilde{\mathbf{u}}(t_{j}))}_{n\times 1}. \tag{9}\]  
where \(\mathbf{C}=\mathbf{V}_{k}^{T}\mathbf{A}^{-1}\mathbf{B}\mathbf{V}_{k}\in \mathbb{R}^{k\times k}\) can be precomputed and reused in each iteration. For the nonlinear term, although we can precompute \(\mathbf{V}_{k}^{T}\mathbf{A}^{-1}\), the computational complexity for multiplying it with \(\mathbf{F}(\mathbf{V}_{k}\tilde{\mathbf{u}}(t_{j}))\) still depends on the original dimension \(n\). This issue can be overcome by applying DEIM or GPOD approximation as explained next.
## 4 Seismic Response of CSBs Under Uniform Excitations
### Seismic Responses of Towers and Priers
#### 4.2.2 Displacements Responses  
Figure 15a,b show the absolute displacements of tower subjected to different excitations. As the towers are fixed on the shake table, the recorded movement on node T1 is approximately equal to the input excitation. It is obvious that the displacement of node T2 is similar to the node T1, while the node T3 and T4 are quite different to the base node. The similar phenomenon can be found in displacement responses subjected to the ScTCU in Figure 15b. The displacement response subjected to the ScTCU is significant larger than ScEL.  
Based on the response of the node T1, the relative displacement of tower key nodes can be calculated. Figure 16a,b shows the relative displacement response of tower subjected to different excitations. For the both NNF and PNF excitations, the relative displacements on the node T3 and node T4 are significant larger than the node T2. It can explain why the displacement of node T3 and node T4 are different from that of node T2 in the Figure 15.  
Figure 16: Relative displacement time history response of 3# tower (**a**) under ScEL; (**b**) under ScTCU.  
Figure 14: Maximum acceleration responses (**a**) 3# tower; (**b**) 4# tower.  
Figure 15: Displacement time history response of 3# tower (**a**) under ScEL; (**b**) under ScTCU.  
For comparison, the maximum displacements and relative displacements of 3# tower are extracted and compared in Figure 17a,b. Both for displacement and relative displacement, the maximum values happen on the tower top node. However, the response values subjected to ScTCU are significant larger than ScEL. For the top node, the displacement and relative displacement subjected to ScTCU are 31.8 mm and 30.8 mm, while those are 1.6 mm 1.9 mm subjected to ScEL.  
It shows the velocity pulse has great impact on displacement and relative displacement responses, while have little effect on the acceleration response.
## 1 Introduction
By comparing and analyzing different neural network models, it was found that, due to its good memory and selection functions, LSTM can comprehensively consider the spatiotemporal effects of deformation and settlement caused by engineering construction and the influence of construction parameters, thus demonstrating reliability and applicability in the prediction process. However, it should be noted that the prediction accuracy of the LSTM network is affected to a certain extent by the fluctuation of structural parameters, and improper network parameter settings may make it difficult to achieve the expected goals. Based on the above research, this article adopts the PSO algorithm for parameter optimization, covering the number of neurons, Dropout, and Batch_size of the LSTM structure and comprehensively considering settlement data and related working condition parameters. A PSO-LSTM settlement prediction model was constructed to explore the dynamic evolution process of buried pipeline collapse. Based on the numerical analysis model of the dynamic evolution of buried pipeline collapse, the model is trained and tested using settlement data and corresponding input parameters. Three models, namely the modified Gaussian curve, LSTM, and PSO-LSTM, were used for comparative analysis of settlement prediction, which verified the reliability of the settlement prediction model based on PSO-LSTM proposed in this study for the dynamic evolution process of buried pipeline collapse. Based on unit life and death technology, this paper considers the dynamic evolution process of soil loss during the collapse process, constructs a pipe-soil nonlinear coupling model for buried pipelines passing through the collapse area, and uses the PSO algorithm to calculate the number of neurons, Dropout, and Batch- size to optimize parameters and establish a settlement prediction model for the dynamic evolution process of buried pipeline collapse based on PSO-LSTM. On the one hand, this model makes up for the inability of theoretical analysis methods to solve complex nonlinear problems, and on the other hand, it avoids the research difficulty caused by harsh experimental conditions. Compared with the traditional soil collapse simulation method of applying displacement load, this article adopts unit life and death technology. For applied research on collapse geological hazards, this technology can more realistically simulate soil loss during the gradual development of collapse. There is insufficient research content on intelligent prediction of the non-uniform settlement of pipelines. This settlement prediction model can provide a reference for safety risk management, disaster early warning, and intelligent monitoring when pipelines suffer from soil collapse disasters.
# Infrared Radiation favorably Influences the Quality Characteristics of Key Line Juice
Interestingly, in past few years, researchers have found an alternate for this problem by introducing Infrared Radiation (IR) between 0.5 to 100 \(\upmu\)m wavelengths for the preservation of juices especially citrus juice. The merits of infrared application include rapid heating system, straight and even diffusion of heat into the liquid sample. In addition to this, by this new method of processing the nutrient content of the juices is preserved [15]. Commonly, infrared is divided into three regions, namely the near-infrared (0.78-1.4 \(\upmu\)m), mid-infrared (1.4-3.0 \(\upmu\)m) and far-infrared (3.0-1000 \(\upmu\)m) [14], with far-infrared commonly employed in food processing industries [15]. During this process, the surface of the food comes in contact with the infrared and gets heated up, with the heat then gradually diffusing into the inside layers of the product. The time period required to dehydate apple slices was reduced by 50% by using near-infrared [16]. Moreover, [PERSON] et al. [17] report reflected positive results as infrared managed to reduce the color change significantly. Whereas another report by [PERSON] et al. [18] indicates that in the case of olive leaves, the total phenol content was amplified in contrast to fresh leaves. Although, there are a large number of advantages of infrared application in the food processing industry, yet it has not been used broadly to preserve the juices to retain their natural quality along with extended shelf life. Therefore, this study aims to investigate the application of infrared radiation on key lime juice by monitoring variations in its physical, chemical, enzymatic, and microbiological characteristics in comparison with the conventional thermal preservation method.
## 3 Results  
### Phytochemical Analysis of C. incana Ethanolic Extract  
The chemical compositions of the constituents of the _C. incana_ ethanolic extract, along with their percentage content, order of elution, and retention time, are listed in Table 1. Chemical analysis of the extract revealed 27 components, accounting for 91.5% of the overall composition. The HPLC/MS chromatogram of the extract is shown in Figure 1.  
The ethanolic extract of _C. incana_ was characterized by high levels of linolenic acid (13.2%), myristic acid (12.1%), and p-cymene (10.5%). Fatty acids constituted the highest class of identified compounds (31.3%), followed by sesquiterpenes (23.9%), and monoterpenes (20.7%). In contrast, ketones constituted the lowest percentage class in the extract (0.2%).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **RT (min)** & **Compounds** & **Peak Area\%** \\ \hline
1 & 2.0 & Azulene & 0.2 \\
2 & 3.5 & 3-Octanol & 4.3 \\
3 & 5.5 & p-cymene & 10.5 \\
4 & 7.0 & Limonene & 2.1 \\
5 & 9.5 & 3-nonanone & 0.2 \\
6 & 10.2 & Unknown & 1.2 \\
7 & 11.5 & Camphor & 3.5 \\
8 & 12.1 & 1,8-Cineole & 0.2 \\
9 & 12.5 & Unknown & 3.7 \\
10 & 15.3 & p-Cumaric acid & 0.6 \\
11 & 16.0 & Jasmone & 2.8 \\
12 & 17.0 & Piperitone oxide & 4.3 \\
13 & 19.5 & Unknown & 2.3 \\
14 & 20.2 & Gallic acid & 1.1 \\
15 & 20.5 & Caffeic acid & 0.8 \\
16 & 21.8 & beta-Damascenone & 0.1 \\
17 & 23.0 & Calamenene & 0.7 \\
18 & 25.0 & Caryophyllene & 2.2 \\
19 & 26.8 & \(\delta\)-cadinene & 6.1 \\
20 & 29.8 & Spathulenol & 7.4 \\
21 & 30.0 & \(\alpha\)-bisabolol & 4.7 \\
22 & 30.8 & Myristic acid & 12.1 \\
23 & 32.5 & Palmitic acid & 6.0 \\
24 & 34.0 & Linolenic acid & 13.2 \\
25 & 35.5 & Catechin & 0.4 \\
26 & 38.0 & Quercetin & 2.5 \\
27 & 40.0 & Chlorogenic acid & 5.5 \\ \hline \hline \multicolumn{4}{c}{Total identified compounds \%} & \multicolumn{2}{c}{91.5\%} \\ \hline RT: Retention Time & & \\ \hline Fatty acids & & 31.3\% \\ Sesquiiterpenenes & & 23.9\% \\ Monoterpenes & & 20.7\% \\ Phenols & & 7.2\% \\ Alcohol & & 4.3\% \\ Flavonoids & & 2.9\% \\ Aromatic acid & & 0.8\% \\ Non-benzenoid aromatic hydrocarbon & & 0.2\% \\ Ketone & & 0.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of _C. incana_ ethanolic extract.  
Figure 1: HPLC/MS chromatogram of ethanolic extract of _C. incana_.
## 3 Results and Discussion  
### The Characteristics of Isolated Rhamolipid-Producing Bacterial Strain  
#### 3.1.1 Genetic Characterization of the Strains  
In the present study, we isolated rhamnpid-producing bacterial strains from soils from different hydrocarbon-contaminated sites. Among a total of 37 morphologically and biochemically distinct isolates that were able to grow on a mineral salt medium supplemented with 2% crude oil, the presence of the _Rhl_AB operon was confirmed for four isolates by PCR analysis (Figure S1). Among these four strains, one isolate-labeled BS4 was selected for further study based on its higher biosurfactant production ability. This isolate exhibited a positive reaction to the drop collapse test and had maximum oil displacement--7 mm in diameter. This strain showed a surface tension value of 20.8 mN m\({}^{-1}\) in MSM and yielded EI\({}_{24}\)(%) values of 62.4 % and 11.4 % with respect to cell and cell-free extracts, respectively. In comparison, _Pseudomonas aeruginosa_ strain MTCC 2453 provided a surface tension value of 26.7 mN m\({}^{-1}\) and EI of 62.7%. The strain BS4 was identified as _Shewanella_ sp. based on 16S rRNA gene analysis. A comparative phylogenetic analysis of this strain with other members of the _Shewanella_ genus is provided in Figure 1.  
The frequent occurrence of _Shewanella_ spp. in hydrocarbon-contaminated sites and their role in bioremediation has been reported in the literature [39; 40; 41]. For example, _Shewanella putrefaciens_ was isolated from the Prestige oil spill in Spain [42], and _Shewanella alga_ and _Shewanella upenei_, among predominant biosurfactants-producing bacteria, were isolated from the crude oil-contaminated sediments and seawater samples collected from five stations in the Persian Gulf [43]. Much has been studied with respect to the _Rhl_AB operon that harbors both _Rhl_A and _Rhl_B enzymes that are responsible for mono-rhammolipid biosynthesis in _P. aeruginosa_[10; 11; 44; 45]. The presence of the _Rhl_AB operon in _Serratia rubidaea_ strain SNAU02, responsible for rhamn synthesis, was confirmed by [PERSON] and [PERSON] [17].