esa-sceva/satcom-chunk-collection huggingface dataset:
# Methane Detection Based on Improved Chicken Algorithm Optimization Support Vector Machine
## 6 Conclusions  
In order to detect the concentration of methane accurately, the support vector machine optimized by improved chicken swarm optimization (ICSO-SVM) was used in this paper. First, the data were obtained by the methane detecting system. Next, in order to verify the validity of the ICSO-SVM model for predicting methane, CSO-SVM, GA-SVM, and PSO-SVM were used for comparison.  
This study draws the following conclusions:  
1. The mean squared error was adopted as the fitness function of the models. The experimental results show that the ICSO algorithm more easily finds a global optimum, and can converge more stably than the other three algorithms. The results also show that the ICSO algorithm has satisfactory convergence, and that it is effective for the improvement of the CSO algorithm.
2. The samples were randomly selected from the whole dataset. The train-test procedure was repeated five times with four models. Compared with the other three optimization algorithms, the prediction values and predicted average relative error percentage of the ICSO-SVM model are obviously superior.
3. From the 50 train-test repeats experiment, we can see that the recovery rate of ICSO-SVM model shows better stability than other three models. The average recovery rates of ICSO-SVM, CSO-SVM, GA-SVM, and PSO-SVM were 101.23, 103.15, 113.58, and 125.61, respectively. The average mean squared errors of the four models were \(1.12\times 10^{-5}\), \(1.23\times 10^{-5}\), \(3.56\times 10^{-5}\), and \(3.22\times 10^{-5}\), respectively. These experimental results verify the feasibility and validity of ICSO-SVM for predicting the concentration of methane.  
These are initial steps. Further research should focus on integrating the detection system and algorithm into methane detection equipment, meaning it would be possible to detect methane concentration and obtain a concentration value quickly. Finally, the equipment should be tested at in civil, ambient, and industrial spaces.  
[PERSON] and [PERSON] conceived, designed, and performed the experiment; [PERSON] and [PERSON] proposed the methodology; [PERSON] wrote the original draft preparation, [PERSON] and [PERSON] reviewed and edited the paper; [PERSON] and [PERSON] supervised the whole work and acquired funding. This research was funded by National Natural Science Foundation of China (Nos. 61771419, 61501394), and Natural Science Foundation of Hebei Province of China (No. F2017203220). The authors declare no conflict of interest.
# A Novel Evolution Strategy of Level Set Method for the Segmentation of Overlapping Cervical Cells
## 2 Methodology
### Cellular Component Segmentation  
The goal of cellular component segmentation is to achieve contour initialization by nuclei detection and cellular clump segmentation. In this paper, we used the contour initialization method [33], because it provides an efficient architecture for cellular clumps segmentation, which can be easily achieved by using the high grayscale contrast between the cellular components and the background. As illustrated in Figure 2, the contour initialization method can be divided into the following steps. Firstly, a super-pixel map (see Figure 2b, which is supposed as the preparation for the generation of a clean edge map (see Figure 2c) can be acquired after a quick shift algorithm [65]. Then, a convex hull (see Figure 2d) is generated after a connected components analysis [66] based on the clean edge map. In addition, a Gaussian mixture model (GMM) is fitted iteratively to cluster pixels in the region of the convex hull into the segmentation of cellular clumps (see Figure 2e). Afterwards, the nuclei detection can be completed (see Figure 2f) by using the MSER algorithm [67] based on the low grayscale values and homogeneous texture of the nuclei within the clump region. At last, cytoplasm contour is initialized as a shape prior by the extrapolation of the allocation that clusters each pixel of the clump boundary to its owner nucleus based on the Euclidean distance, illustrated as Figure 2g.  
Figure 1: The architecture of the proposed method takes the contour initialization as the input to perform touching cell splitting (pink arrow) and overlapping cell segmentation (purple arrow). MSTF: morphological scaling-based topology filter and DCTLSM: 2D codimension two-object level set method. The “Fig.” and “Eq.” denote “Figure” and “Equation” respectively.  
[PERSON] [33] used this initial contour to construct the LSF and a distance map as the velocity terms for contour evolution. In this way, the coarse-to-fine segmentation was achieved after the minimization of the multiple energy functionals. However, this nucleus-based allocation caused the misallocation of the initial contour points, as illustrated in Figure 3a.
## 2 Proposed Enhancements to Prevent Local Bulging Failure
### Outward-Force-Resisting Capacity of Added Inner Restrainer  
A mathematical expression for the outward-force-resisting capacity is a prerequisite for designing an inner restrainer. In this study, the outward-force-resisting capacity of the inner restrainer is derived using yield line theory [18]. The three-dimensional shape and deformation after the application of the outward force on the right inner restrainer illustrated in Figure 2a are shown in Figure 3a,b. It is assumed that plastic bending deformations occur at line segments EG, EH, DG, GH, and HF of the flange. The ABED and BCFE segments in the upper vertical web are assumed to undergo only in-plane deformation owing to the constraints provided by the mortar filler. In addition, shear governs the deformation of those segments because the lengths of the deformed segments are not long compared  
Figure 2: Enhancements to prevent local bulging failure: (**a**) Inner restrainer; (**b**) Thickened unbonding material.  
to their depth, as discussed in Section 4.2. Based on these assumptions, the internal and external work owing to plastic deformation can be calculated as follows, respectively:  
\[W_{I2}=2\bigg{[}2\big{(}0.6F_{ys}A_{ws}\big{)}d_{s}+8d_{s}\bigg{(}\frac{t_{s}^{2}}{ 4}\bigg{)}F_{ys}\bigg{]} \tag{4}\]  
\[W_{E2}=2\bigg{(}\frac{P_{d}}{2}\bigg{)}d_{s} \tag{5}\]  
where \(W_{I2}\) and \(W_{E2}\) are the internal and external work, respectively; \(P_{c,w2}\) is the outward-force-resisting capacity of the inner restrainer; \(F_{ys}\) is the yield strength of the inner restrainer; and \(B_{c}\), \(B_{r}\), and \(t_{r}\) are the widths of the steel core and outer restrainer and thickness of the outer restrainer, respectively. Additionally, \(A_{ws}\) is the cross-sectional area of the upper or lower web of the inner restrainer and \(t_{s}\) is the thickness of the inner restrainer. By equating the internal and external work expressed by Equations (4) and (5), respectively, \(P_{c,w2}\) can be calculated as follows:  
\[P_{c,w2}=4\big{(}0.6F_{ys}A_{ws}\big{)}+4t_{s}^{2}F_{ys} \tag{6}\]
# Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy
## 2 Data
### MRI Data  
MRI images were collected from the same volunteers from whom the US volumes were acquired. The volunteers' knees were imaged using a 3T MRI system (Siemens Magnetom 3T Prisma, Erlangen, Germany). The MRI scans were acquired with knee positioned at 0\({}^{\circ}\), 30\({}^{\circ}\), 60\({}^{\circ}\) and 90\({}^{\circ}\) knee flexion for each volunteer. For the 0\({}^{\circ}\) knee flexion, the scan is acquired with the volunteer in the supine position. For the other knee flexion angles, the volunteer lay in the left/right lateral incumbent position for the corresponding leg to be scanned, using the cushion specifically designed to keep the leg at the desired angle. A total of 32 3D MRI volumes were obtained each of 256 \(\times\) 256 \(\times\) 192 voxels, where each voxel is of size 0.66 mm \(\times\) 0.66 mm \(\times\) 0.69 mm.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 7 Technological Integration and Evaluation
### Consumer Technology Integration
Incorporating individualised HRTFs into speech signal processing can significantly enhance the intelligibility and spatial localisation of spoken audio within immersive environments. This advancement is particularly beneficial for applications such as virtual meetings, telepresence, and assistive listening devices, where precise auditory cues are essential. For instance, research has demonstrated that personalised HRTFs improve speech intelligibility by providing accurate spatial cues, thereby reducing cognitive load and enhancing user experience in complex auditory scenes [218].
## 2 Materials and Methods
### Isolation of Filamentous Fungi from American Cranberry Plants  
Seven whole plants with ripe fruits were randomly sampled from the plantation after the first, second, and third year after planting and transported to the laboratory at a temperature of +8 \({}^{\circ}\)C. Roots, leaves, and fruits were used to isolate the filamentous fungi that inhabit them and are capable of growing under laboratory conditions. Universal peptone-glucose agar medium with rose bengal, which enables the growth of a large group of molds and yeast, and selective solid media for fungi of the genera _Fusarium_, _Botrytis_, _Rhizoctonia_, and _Penicillium_ were used. To do this, the plants were washed with tap water, disinfected for 15 min in 2.0% sodium hypochlorite, and then rinsed five times in sterile distilled water. Ten 1 cm long fragments of roots, leaves, and fruits were placed on agar media in Petri dishes with a diameter of 9.0 cm. The following media were used for the isolation of fungi from root fragments: universal Martin's Rose Bengal agar (MRB) containing glucose 10 g, pepton tryptone 5.0 g (BTL, Lodz, Poland), KH\({}_{2}\)PO\({}_{4}\) 1.0 g, MgSO\({}_{4}\)\(\times\) 7 H\({}_{2}\)O 0.5 g, rose bengal dye 0.05 g, streptomycin 0.03 g, and agar 16 g in 1 L of water; Ko and Hora medium (K&H) for the species _Rhizoctonia solani_ contained gallic acid 0.4 g, K\({}_{2}\)HPO\({}_{4}\) 1 g, MgSO\({}_{4}\)\(\times\) 7 H\({}_{2}\)O 0.5 g, KCl 0.5 g, FeO\({}_{4}\)\(\times\) 7 H\({}_{2}\)O 0.01 g, NaNO\({}_{2}\) 0.2 g, chloramphenicol0.05 g, streptomycin 0.05 g, metalaxyl M 0.07 g, mankozeb 1.18 g (Ridomil Gold\({}^{\text{\textregistered}}\) MZ 67.8 WG, Syngenta Crop Protection AG, Basel, Switzerland), prochloraz 0.005 g (Mirage 450 EC\({}^{\text{\textregistered}}\) fungicide, Makhteshim Chemical Works Ltd., Beer Sheva, Israel), and agar 16 g in 1 L of water [18; 19]; Nash and Snyder medium (N&S) for the genus _Fusarium_ contained peptokab (BTL) 15 g, KH\({}_{2}\)PO\({}_{4}\) 1 g, MgSO\({}_{4}\)\(\times\) 7 H\({}_{2}\)O 0.5 g, chloramphenicol0.1 g, pentachlorinotbenzene (PCNB) 0.1 g, streptomycin 0.05 g, malachite green dye 0.0025 g, and agar 16 g in 1 L of water [20]. For the isolation of fungi from leaves and fruits, there were MRB, N&S. _B. cinerea_ (BSM) medium included glucose 2 g, tannic acid 5 g, NaNO\({}_{3}\) 0.1 g, K\({}_{2}\)HPO\({}_{4}\) 0.1 g, MgSO\({}_{4}\)\(\times\) 7 H\({}_{2}\)O 0.2 g, KCl 0.1 g, chloramphenicol0.2 g, PCNB 0.02 g, mancozek 0.016 g (Pennoczeb 80 WP, Cerexagri SAS, Plaisir, France), rose bengal dye 0.05 g, and agar 16 g in 1 L of water [21], and medium for the genus _Penicillium_ (RYES) contained peptone 5 g, yeast extract 3 g, sucrose 15 g, chloramphenicol0.05 g, tetracycline 0.05 g, PCNB 1 g, Bengal rose 0.025 g, and agar 16 g in 1 L of water [22]. The incubation was carried out for 2 weeks at 26 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C. After this time, the isolates were repicked in the appropriate agar medium and incubated under the same conditions as the pure cultures. The fungi were identified by DNA sequence analysis.
## 4 Numerical Example
### Results of Dataset 2
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Model** & **MSE** & **PRR** & **PP** & **MAE** & **R\({}^{2}\)** & **adj\_R\({}^{2}\)** & **AIC** & **BIC** & **PRV** & **RMSPE** & **TS** & **PC** & **PIC** \\ \hline GO & 12.908 & 0.378 & 0.203 & 3.405 & 0.986 & 0.984 & 91.959 & 93.951 & 3.496 & 3.497 & 4.540 & 24.183 & 234.453 \\ \hline DS & 28.063 & 19.580 & 1.081 & 3.520 & 0.969 & 0.965 & 111.228 & 113.220 & 4.946 & 5.146 & 6.694 & 31.173 & 507.239 \\ \hline IS & 10.564 & 0.868 & 0.305 & 2.659 & 0.989 & 0.987 & 90.266 & 93.253 & 3.042 & 3.073 & 3.991 & 22.010 & 182.937 \\ \hline YID1 & 13.688 & 0.378 & 0.203 & 3.609 & 0.986 & 0.983 & 93.984 & 96.971 & 3.498 & 3.500 & 4.544 & 24.212 & 236.054 \\ \hline YID2 & 20.264 & 0.250 & 0.188 & 4.483 & 0.979 & 0.975 & 99.356 & 102.343 & 4.257 & 4.258 & 5.528 & 27.547 & 347.844 \\ \hline YR & 49.421 & 57.178 & 1.497 & 5.519 & 0.951 & 0.938 & 128.759 & 132.742 & 6.110 & 6.435 & 8.375 & 34.168 & 795.482 \\ \hline YE & 14.539 & 0.378 & 0.203 & 3.834 & 0.986 & 0.982 & 95.976 & 99.959 & 3.498 & 3.499 & 4.543 & 24.379 & 237.366 \\ \hline PZ & 11.972 & 0.868 & 0.305 & 3.014 & 0.989 & 0.985 & 94.266 & 99.245 & 3.042 & 3.073 & 3.991 & 22.795 & 185.918 \\ \hline PNZ & 11.225 & 0.867 & 0.305 & 2.826 & 0.989 & 0.986 & 92.269 & 96.252 & 3.042 & 3.073 & 3.992 & 22.310 & 184.354 \\ \hline IFD & 95.295 & 3.369 & 0.851 & 9.411 & 0.900 & 0.882 & 124.499 & 127.487 & 8.663 & 9.206 & 11.988 & 40.706 & 1623.369 \\ \hline TC & 14.494 & 0.830 & 0.300 & 3.600 & 0.987 & 0.982 & 97.871 & 102.849 & 3.367 & 3.382 & 4.392 & 24.229 & 223.750 \\ \hline TP & 11.944 & 0.863 & 0.301 & 2.955 & 0.990 & 0.985 & 95.356 & 102.326 & 2.819 & 2.857 & 3.711 & 23.552 & 165.504 \\ \hline
3P & 11.972 & 0.868 & 0.305 & 3.014 & 0.989 & 0.985 & 94.266 & 99.245 & 3.042 & 3.073 & 3.991 & 22.795 & 185.918 \\ \hline Vub & 6.869 & 0.239 & 0.137 & 2.346 & 0.994 & 0.991 & 87.500 & 92.479 & 2.291 & 2.327 & 3.023 & 18.628 & 109.369 \\ \hline NEW & 5.650 & 0.040 & 0.038 & 2.075 & 0.994 & 0.993 & 88.732 & 92.715 & 2.181 & 2.181 & 2.832 & 16.818 & 95.153 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of criteria from Dataset 2. Figure 4: Prediction of all models for Dataset 2. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Multi-Citeria** & **GO** & **DS** & **IS** & **YID1** & **YID2** & **YR** & **YE** & **PZ** & **PNZ** & **IFD** & **TC** & **TP** & **3P** & **Vub** & **NEW** \\ \hline _MCDM_ & 0.6335 & 1.3213 & 0.5780 & 0.6459 & 0.7963 & 2.1961 & 0.6593 & 0.6027 & 0.5896 & 2.2936 & 0.6670 & 0.5865 & 0.6027 & 0.4370 & 0.3906 \\ \hline _MCDMR_ & 0.0403 & 0.1479 & 0.0198 & 0.0494 & 0.0787 & 0.2721 & 0.0555 & 0.0305 & 0.0279 & 0.2889 & 0.0523 & 0.0294 & 0.0306 & 0.0064 & 0.0043 \\ \hline _MCDMM_ & 0.2450 & 0.6751 & 0.2084 & 0.2541 & 0.3522 & 1.7409 & 0.2643 & 0.2263 & 0.2167 & 2.1920
# Utilization of ICT as a Digital Infrastructure Concerning Disaster Countermeasures in Japan
## 6 Utilization Potential of Social Media Mapping as a Digital Infrastructure
### Utilization of Twitter during the Heavy-Rain Disaster in Western Japan in 2018
Accordingly, if text formats for tweets according to the purpose are prepared beforehand, the convenience of information senders, rescuers, and supporters will be improved. Second, the unification of disaster-related hashtags can be raised. There is a possibility of confusion caused by information overload, as multiple types of hashtags indicating the same content are created during a disaster. Therefore, in the early stages of a disaster, the generation of various hashtags can be prevented by preparing unified hashtags and its utilization method that can be easily used by the general public. The background for this is the information gathering and rescue activities using Twitter in Nagano Prefecture during Typhoon Hagibis in October 2019. Nagano Prefecture used their official account for disaster prevention information as a tool to gather rescue request information, and encouraged users that need be rescued to submit tweets with images, location information, and the hashtag \"#TyphoonHagibisDamageinNagano\" resulting in 50 cases of tweets leading to successful rescues (Yamamoto, 2020) [36]. Based on these real cases during disasters, each local government can promote the setting of rules for utilizing Twitter according to the local situation beforehand. Lastly, the improvement of information literacy for each individual user can be raised. It is ideal to have users refrain from submitting personal tweets (opinions, thoughts, and vague personal comments) as well as tweets that may not be reliable or valid, and voluntarily delete old tweets related to disasters as the situation is constantly changing. Especially for tweets related to rescue requests and cooperation, it is essential to delete the tweet right after the situation is resolved. This will prevent the tweets of those still waiting to be rescued from being buried among other tweets as well as protect personal information such as an individual's address. Additionally, users must give serious thought to the information they are retweeting with the awareness that retweets made with good intentions may also cause confusion due to information overload. In this regard, [PERSON] et al. (2019) [37] pointed out the difficulty in searching for and extracting the important tweets related to rescue requests and cooperation during the heavy-rain disasters in North Kyusyu in 2017 and in Western Japan in 2018.
# Deep Embedment (DE) FRP Shear Strengthening of Concrete Bridge Slabs under Loads Close to Supports
## 3 Experimental Programme
### Strengthening Technique  
To simplify the drilling process and avoid intersecting the longitudinal bars, the deep embedment strengthening process was executed with a special drilling machine invented by the authors, as shown in Figure 6, which can be used in vertical and inclined embedment strengthening, as shown in Figure 6a,b. The strengthening steps are: (1) Holes = 1.5 times of the diameter of embedded bars are drilled through the depth of concrete slabs. During the drilling process, the concrete dust is aspired using a vacuum system (see Figure 1a); (2) The holes are cleaned using a helicoid-shaped steel brush capable of removing the particles from the walls of the hole, the particles are then eliminated by the vacuum system (see Figure 1b); the cleaning procedure is repeated until the dust is totally removed; (3) The epoxy resin is prepared according to the recommendations of the supplier, and is slowly poured into the holes (see Figure 1c); (4) The deep embedment bars are cut in the desired length, cleaned with acetone, and are slowly introduced into the holes, removing the excess resin (see Figure 1d). To guarantee a proper curing of the adhesive, the specimens are tested at least two weeks after the deep embedment application.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Deep Embedment** & **Diameter** & **Tensile Strength** & **Elastic Modulus** & **Ultimate** \\
**Bar/Epoxy** & **(mm)** & **(MPa)** & **(GPa)** & **Strain** \\ \hline
**Steel** & 10 & 504 & 200 & 0.0025 \\
**CFRP** & 9 & 1581 & 156 & 0.0101 \\
**BFRP** & 9 & 1011 & 83 & 0.0122 \\
**GFRP** & 9 & 835 & 480 & 0.0174 \\
**FY-Z epoxy** & N/A & 40.5 & 1.2 & 0.0017 \\ \hline \end{tabular}
\end{table}
Table 2: Mechanical property of embedment bars and adhesives.  
Figure 6: Details of invented drilling equipment: (**a**) Vertical drilling; (**b**) Inclined drilling; (**c**) Drilling equipment.
## 2 Related Work on Bridging Between Sensors and Applications
### Middleware for Sensor Network Management Systems  
Research on integrating sensors with applications begins on the lowest level, namely with research on middleware concepts which manage the communication within sensor networks. Due to their advanced functionality and the resulting challenges, _wireless_ sensor networks (WSNs) are of particular interest. Foundational work on managing WSNs includes research areas such as routing protocols [17,18], optimization of in-network communication [19], coverage optimization of sensor networks [20,21], the optimization of data collection paths [22], and the localization of sensors within a network [23,24].  
Such basic functionality for managing sensor networks is provided by WSN middleware. A comprehensive survey on WSN middleware approaches is given by [PERSON] _et al._[25]. Examples for WSN middleware solutions are MundoCore [26], Mires [27] or MiLAN [28]. Such middleware approaches which serve sensor network management functionality do not focus on enabling easy access to sensors for applications. Hence, they can be considered as closer to the lower sensor layer and do not fully reach out to the application layer as depicted in Figure 1. However, such middleware solutions may serve as the basis for other approaches such as Sensor Web infrastructures.
## 1 Introduction
In the proposed algorithm, the received preamble signal (after Doppler compensation) is correlated with the local reference signal, and the correlation peaks occur at the positions corresponding to the multipath delay. According to the position of these correlation peaks, the sparsity and multipath delays can be determined initially, and the multipath delays can be used to simplify the dictionary matrix of the OMP algorithm. Hence, compared with the OMP algorithm, the proposed algorithm not only solves the problem of the iteration termination condition, but also has a lower computational complexity. 4. The simulated and experimental results demonstrate that using the designed preamble signal, the proposed algorithms can estimate Doppler and channel state information effectively and improves the performance of the UWA communication system. The remainder of this paper is organized as follows. In Section 2, the system model is introduced and the problem to be solved is stated. In Section 3, the designed preamble signal, and the proposed Doppler estimation and channel estimation algorithms are presented. The performance of the proposed algorithms is evaluated by simulations and experimental data analysis in Section 4. Finally, the conclusion of this paper is given in Section 5.
# Degradation of Trace Organic Contaminants by a Membrane Distillation--Enzymatic Bioreactor
## 3 Results and Discussion
### Effluent Toxicity  
The charged metabolites and highly reactive radicals produced following the oxidation of redox-mediators may improve TrOC degradation [18; 46], but these can also cause an increase in effluent toxicity [18; 47]. In this study, it was not possible to relate individual metabolites to specific parent compounds because we investigated a mixture of 30 TrOCs. Hence, the overall bacterial toxicity of the reaction mixture and permeate was evaluated at the end of each run. Of the three mediators tested, SA significantly increased the toxicity of the solution in the enzymatic bioreactor, whereas HBT and VA showed no effect on toxicity levels (Table 2). Compared to the background toxicity of the mixture of lacase and TrOCs in the enzymatic bioreactor of MD-EMBR (\(<\)1 to 1.8 rTU; \(n=2\)), toxicity in the enzymatic bioreactor due to addition of HBT, VA and SA ranged from \(<\)1 to 1.7 rTU (\(n=2\)), 3.3 to 3.9 rTU (\(n=2\)) and 109 to 116 rTU (\(n=2\)), respectively. Notably, the final effluent (i.e., membrane permeate) was not toxic to bacteria (\(<\)1 rTU) for any of the enzyme/mediator combinations, indicating that MD not only retained TrOCs and lacase but also the transformation byproducts and radicals responsible for inducing bacterial toxicity. This is an added advantage of coupling a high retention membrane to the enzymatic bioreactor.