esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Comparison of Different Experiments
#### 4.3.2 Comparative Experiment of Different Loss Functions  
To confirm the validity of the loss functions, we designed the comparative experiment using the CUB-200-2001 dataset and compared the changes of the loss values of the functions when the number of iterations increased. The experimental results are shown in Figure 10, where the behavior of the different loss function is reported. The green curve refers to only the first term \(L_{P}\) of the mixed loss function proposed in this paper. The blue curve represents the addition of the auxiliary function \(L_{PA}\) to the upper-layer network. It can adjust the upper-layer network to make it more focused on the regional feature information while enabling the model to converge faster and lower the overall loss value. The pink curve indicates the addition of the auxiliary classifier term \(L_{PB}\) in the lower-layer network, which allows the lower-layer network to adjust its extracted global features. Since the global features extracted by the lower-layer network have more abundant characteristic information than the regional features, the loss value is decreased more, and the classification accuracy is increased. The remaining curve, the orange one, is the variation curve of the mixed loss function proposed by this paper. The loss value of the model in the training process is close to 0. At the same time, in this curve, we can see a significant downward trend in the loss values and a significant increase in the rate of convergence. In addition to adding two auxiliary classifiers, the central loss function is added to reduce the intra-class variance and increase the inter-class variance. Additionally, the results in Table 1 show that the accuracy of the identification can be effectively improved.  
Figure 10: Loss values of the different loss function.  
In the test partition of the CUB200-2011 dataset, there were approximately 20 images for each category. We compared obtained accuracies of the model using different loss functions, and chose to set \(\alpha=0.3\) and \(\beta=0.6\). The results are shown in Table 1. The accuracy of the model proposed in this paper reached 84.12% on public datasets. This result is better than some strong supervised learning methods and some weakly supervised learning methods mentioned in the related works in the first chapter.
## 2 Related Work
### Depth Estimation in Dynamic Scenes  
Handling dynamic scenes in MDE remains a significant challenge due to the violation of photometric consistency when objects move between frames. One promising approach is to separate the depth estimation process for static and dynamic regions. The authors proposed a model that jointly estimates depth, camera motion, and object motion, enabling better depth prediction for moving objects [15]. However, this method still suffers from scale ambiguity in dynamic regions, where depth predictions for moving objects are not aligned with the static background. The authors introduced a self-supervised framework that decouples depth estimation for static and dynamic regions by using pseudo-depth labels for moving objects [6]. This decoupling allows for better depth predictions in dynamic regions but still faces the challenge of aligning depth scales between static and dynamic regions. The scale inconsistency between these regions remains an open problem, as depth for dynamic objects tends to be less accurate [16]. Our proposed DI-MDE method builds on this line of work by integrating a scale-alignment module that resolves the scale ambiguity between dynamic and static regions, ensuring consistent depth predictions across the entire scene.
## 1 Introduction  
Primary reconstruction of ablative mandibular defects may not always be possible due to various reasons. Medical morphologies precluding longer and complex surgeries; fracture of previous load bearing titanium plates, unavailability of microsurgical team, and resection following osteoradinecrosis are some of the common scenarios where secondary mandibular reconstruction is undertaken [1]. The use of vascularised bone in secondary mandibular reconstruction allows for superior restoration of articulation, deglutition, and mastication; improvement of facial appearance; and patient's overall quality of life [2].  
The use of computer assisted design (CAD) or computer aided manufacturing (CAM) is well established in primary reconstruction of craniofacial defects involving both maxilla and mandible. Their usage has been applied to post traumatic facial deformity, defect reconstruction after tumor ablation, congenital craniofacial deformity, facial contouring,and orthognathic surgeries (to name a few). Comparison of the outcome of free fibular flap for mandibular reconstruction with and without the application of virtual surgical planning has been carried out and has concluded that the use of VSP is beneficial for optimizing the accuracy, consolidation of bony segments, and operating time, while increase the predictability of results for the surgeon [3]. However, most of them are limited to defects requiring no or limited changes during surgery [4; 5; 6; 7; 8; 9].  
Patients scheduled for secondary mandibular reconstruction have a unique set of problems. After tumor ablation and irradiation therapy, displacement of the residual bone position by retraction of muscles and ligaments, mixed tissue layers, diminished vessel elasticity is a common finding [10; 11]. The original mandible as a template for plate bending is not present, the absence of teeth precludes occlusal guidance, and the presence of soft-tissue contractor makes surgical correction more difficult and potentially more hazardous as compared to primary reconstructive surgery performed immediately after bone cancer removal [12].  
Achieving an accurate virtual planning is the first and foremost step of this whole concept of CAD/CAM technology. Having the initial and current radiological data is a prerequisite for virtual planning process in secondary reconstruction so as to obtain a virtual defect template before 3D printing. However, previous imaging data might not be available in every cases. The aim of the study was to devise a virtual planning algorithm where the available clinico-radiologic information along with radiological data bank can be used to design a defect template facilitating the further steps of 3D printing and secondary mandibular reconstruction.
# Perceived Accessibility: Impact of Social Factors and Travel Modes in Melbourne's West
## 3 Results
### Perceived Accessibility
#### 3.1.3 Perceived Accessibility and Age  
Vulnerable populations such as older adults are often more dependent on public transport due to their reduced abilities to drive cars [27]. However, in the case of Melbourne's west, where the service is deemed inaccessible to older adults, they appeared to be increasingly isolated from public transport. As shown in Figure 2, perceived accessibility to public transport tends to decline as one ages, dropping by 68% from the mean score of 3.4 among the youngest adults (19-20) to 1.1 among the oldest adults (75-79 years). The Mann-Whitney U test confirmed that indeed older adults regarded their perceived accessibility to be significantly lower with both existing travel modes (\(p<0.05\)) and public transport only (\(p<0.001\)). Moreover, while both younger and older adults agreed that their perceived accessibility would be diminished if they restricted travels for daily activities to public transport, with the older adults feeling significantly dissatisfied with public transport use. On all four measures of perceived accessibility, older adults (60 years and above) were more likely to disagree or strongly disagree that public transport was accessible to them (Figure 3). For instance, they were twice as likely to disagree that they could easily perform their daily activities with public transport. The likelihood that individuals would not be able to live as they want, engage in their preferred activities, or have satisfactory access to their preferred activities using public transport was also higher. This lack of accessibility and satisfaction with using public transport among the older population is seriously problematic in the current ageing environment of Australian cities.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**With Existing Travel Mode**} & \multicolumn{2}{c}{**With Public Transport**} & \\  & **Mean Score** & **Mann–Whitney U** & **Mean Score** & **Mann–Whitney U** & **Difference (A–B)** \\  & **(A)** & **Test** & **(B)** & **Test** & **(\%)** \\ \hline
**Gender** & & & & & \\ Male & 3.81 & 2640.50 & 3.00 & 2489.00 & 21.3 \\ Female & 3.66 & & 2.76 & & 24.5 \\
**Age groups** & & & & & \\ Under 60 years & 3.82 & 706.50 * & 2.96 & 561.50 ** & 22.5 \\
60 years and above & 2.83 & & 2.01 & & 29.0 \\
**Travel mode** & & & & & \\ Frequent PT users & 3.75 & 2561.00 & 3.18 & 1142.00 *** & 15.2 \\ Infrequent PT users & 3.62 & & 2.20 & & 39.2 \\ \hline \hline \end{tabular}  
* * \(p<0.05\), ** \(p<0.01\)*** \(p<0.001\). Note: frequent public transport (PT) travellers use public transport at least twice per week.  
\end{table}
Table 5: Assessment of perceived accessibility based on socioeconomic and travel characteristics.  
Figure 2: Mean perceived accessibility score between age groups.
## 3 Results
### Evaluation of the Improved Algorithm  
Table 5 compares the experimental results for various versions of YOLOv5s. In YOLOv5s - Mish, leaky ReLU was replaced by the Mish activation function. YOLOv5s + CBAM is the original YOLOv5s with CBAM after the backbone network.  
The original YOLOv5s model had the fastest detection speed, but the lowest accuracy, as measured by mAP\({}_{75}\) and precision. YOLOv5s - Mish, for which only the linear activation function has been optimized, has the same parameter size and detection speed as the original YOLOv5s model, but the precision, recall, and mAP\({}_{75}\) were 1.03, 1.18, and 1.88 percentage points higher, respectively. Introducing only CBAM, the model parameters increased in size by 0.13 MB, the recall rate and detection speed decreased slightly, and the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Used for Object Detection** & **Backbone** & **mAP\({}_{75}\)/\%** & **mAP\({}_{50}\)/\%** & **Size of Parameters/MB** & **FPS** \\ \hline Faster Region-Based & & & & & \\ Convolutional Neural Network & ResNet50 & 66.90 & 89.90 & 164.48 & 26.3 \\ (R-CNN) & & & & & \\ Mask R-CNN & ResNet50 & 79.10 & 93.20 & 174.8 & 26.0 \\ RetinaNet & ResNet50 & 72.00 & 93.40 & 144.4 & 26.6 \\ Single-Shot Multibox Detector & SSDVGG16 & 78.70 & 94.10 & 97.56 & 48.8 \\ (SSD) & & & & & \\ You Only Look Once version 5 & CSPDarknet & 70.36 & 93.54 & 28.22 & 500 \\ (YOLOv5s) & CSPDarknet + CBAM & 74.03 & 94.69 & 28.35 & 476 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of different smoke detection models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Used for** & **Size of Parameters/MB** & _P/\%_ & _R/\%_ & **mAP\({}_{75}\)/\%** & **FPS** \\ \hline YOLOv5s & 28.22 & 85.95 & 93.57 & 70.36 & 500 \\ YOLOv5s – Mish & 28.22 & 86.98 & 94.75 & 72.24 & 500 \\ YOLOv5s + Convolutional Block & & & & & \\ Attention Module (CBAM) & 28.35 & 87.34 & 92.03 & 71.06 & 487 \\ Improved YOLOv5s & 28.35 & 87.66 & 93.78 & 74.03 & 476 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison between different versions of YOLOv5s in detecting smoke.  
precision rate and mAP\({}_{75}\) increased by 1.39 and 0.70 percentage points, respectively. The improved YOLOv5s method proposed in this paper combines the advantages of the two optimizations. Although the detection speed was slightly slower, the precision rate, recall rate, and mAP\({}_{75}\) were better compared to the original YOLOv5s model by 1.71, 0.21, and 3.67 percentage points, respectively. Hence, this approach can realize the high precision and rapid identification of smoke from straw burning.
# Non-Invasive Glucose Measurement Technique Based on Time-of-Flight
###### Abstract  
A novel, low-cost sensor for the measurement of physiological levels of glucose in samples has been developed using Time-of-Flight (ToF) technology. The sensor, built with off-the-shelf components, leverages the scattering of light by glucose molecules to determine concentration changes by measuring the phase shift of light signals propagating through the sample. Experimental validation demonstrates the feasibility of this approach, correlating glucose concentration with optical scattering effects at a wavelength of 850 nm. This technique offers the potential for a cost-effective, non-invasive alternative to traditional methods of glucose monitoring, with applications in continuous glucose monitoring systems to improve diabetes management by reducing patient discomfort and enhancing monitoring accuracy. Further optimization of the sensor parameters and expanded testing are still needed.  
glucose measurement; Time-of-Flight; optical scattering; infrared sensing; low-cost biosensors +
Footnote †: journal: Applied  
1
Footnote 1: email: _Applied_  
Footnote †: journal: Applied  
1
Footnote 1: email: _Applied_  
This paper presents a novel low-cost sensor based on ToF measurement of infrared light through a sample. The authors' goal was to determine the feasibility of this method, along with trying to minimize the cost and the need for specialized equipment.  
This paper is organized as follows: the operating principle of the proposed sensor along with the experimental setup are presented in Section 1, while in Section 4, the experimental results are shown along with their evaluation. Section 5 concludes the paper.
# A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network
## 5 Results and Discussion
### Network Energy Efficiency (NEE)  
Network energy efficiency is calculated as the amount of residual energy measured at the final stage of simulation. The mathematical expression to calculate the residual energy is given as follows,  
\[ARE=\frac{1}{n}\times\sum_{i=1}^{n}RE_{i} \tag{4}\]  
where \(n\) represents the total number of nodes, \(REi\) represents the residual energy of the node '\(i\)'. Figure 5 represents the graphical outcome of network energy efficiency with respect to the number of vehicles in the network. The performance of the proposed work is compared with earlier work, namely GPSR, MM-GPSR and GEO-LU. By analyzing the table values and graphs, it can be understood that our proposed MNUG-CLA protocol performed better than the earlier research methods.
## 5 Experiments and Discussions  
To test the effectiveness of the LNSSCL algorithm, experiments were conducted on real network datasets and synthetic networks, comparing the proposed algorithm with 12 representative community detection algorithms. The selected benchmark algorithms for experimentation include community detection algorithms based on random walk such as Walktrap [23] and Infomap [24]; modularity-based algorithms CNM [36], Louvain [37], and Leiden [38]; label propagation-based algorithms LPA [39], TS [27], GLLPA [29], and SSCLPA [30]; hierarchical clustering algorithm Paris [40], Markov chain-based community detection algorithm MSC [25], and the stochastic competitive learning algorithm based on the particle competition mechanism SCL [14].
## 2 Numerical Methods  
### Fluid Dynamics  
The commercial package ANSYS-CFX17.2 is used for the fluid simulation. Reynolds-averaged Navier-Stokes equations are employed to describe the physics of unsteady, compressible, viscous 3D flow [19]. Equation (1) includes the Reynolds-averaged continuity equation, momentum equation, and energy equation.  
\[\left\{\begin{array}{l}\frac{\partial p}{\partial t}+\frac{\partial}{ \partial x_{j}}\big{(}\rho U_{j}\big{)}=0\\ \frac{\partial}{\partial t}\big{(}\rho U_{i}\big{)}+\frac{\partial}{\partial x _{j}}\big{(}\rho U_{i}U_{j}\big{)}=-\frac{\partial p}{\partial x_{i}}+\frac{ \partial}{\partial x_{j}}\big{(}\tau_{ij}-\rho\overline{u_{i}u_{j}}\big{)}+S_ {M}\\ \frac{\partial}{\partial t}(\rho h_{tot})-\frac{\partial p}{\partial t}+\frac{ \partial}{\partial x_{j}}\big{(}\rho U_{j}h_{tot}\big{)}=\frac{\partial}{ \partial x_{j}}\Big{(}\lambda\frac{\partial T}{\partial x_{j}}-\rho\overline{ u_{j}u_{j}}\Big{)}+\frac{\partial}{\partial x_{j}}\Big{[}U_{j}\big{(}\tau_{ij}- \rho\overline{u_{i}u_{j}}\big{)}\Big{]}+S_{E}\end{array}\right., \tag{1}\]  
where \(t\) is time, \(\rho\) is density, \(U\) is the average component of velocity, \(x\) is the displacement, \(P\) is pressure, \(\tau\) is the molecular stress tensor (including both the normal and shear components of stress), \(u\) is the time varying component of velocity, \(\rho\overline{u_{i}u_{j}}\) is the Reynolds stresses, \(S_{M}\) is the momentum source term, \(T\) is temperature, \(S_{E}\) is the energy source term, and \(h_{tot}\) is the mean total enthalpy, which is defined by the expression below:  
\[h_{tot}=h+\frac{1}{2}U_{i}U_{i}+k, \tag{2}\]  
where \(k\) is the turbulent kinetic energy, given by:  
\[k=\frac{1}{2}\overline{u_{i}^{2}}. \tag{3}\]  
The boundary conditions of the fluid domain on the fluid-structure interface are represented by Equation (4):  
\[d_{f}=d_{s}, \tag{4}\]  
where \(d\) is the node displacement on the fluid-structure interface, the subscript \(f\) represents the fluid domain, and the subscript \(s\) represents the solid domain. The variable transferred from the solid domain to the fluid domain through the fluid-structure interface is the node displacement.  
Spatial and temporal discretization are based on a second-order upwind difference scheme and a second-order backward Euler scheme, respectively. In addition, the k-\(\varepsilon\) turbulence model and scalable wall functions are employed for the wall treatment.
###### Abstract  
Novel antimicrobial compounds can be added to foods directly or incorporated into packaging materials in order to improve food safety and shelf life. One such potential antimicrobial compound is the bioinorganic complex Co(II)-citrate (NH4)[Co(\(\xi\)\({}_{6}\)H\({}_{5}\)O)\({}_{7}\)] (Co-cit). Its antimicrobial activity against _Listeria monocytogenes_, _Staphylococcus aureus_, _Escherichia coli_, and _Pseudomonas aeruginosa_ was investigated in solution, both alone and in combination with existing preservatives. The antimicrobial activity of poly(lactic acid) films (PLA) with incorporated Co-cit (23.3% \(w/w\)) against _L monocytogenes_ was determined using culture medium and model foods (slices of turkey ham and smoked salmon). In nutrient broth, without preservatives, all four bacteria were significantly (\(p<0.05\)) inhibited by 0.5 mM Co-cit, and _L. monocytogenes_ and _P. aeruginosa_ were the most sensitive. The addition of preservatives to the broth increased the antimicrobial activity of Co-cit in many cases, but not in a way that was consistent between the different bacteria investigated and was likely due to additional physiological stress exerted on the cells rather than any effect on the activity of the Co-cit itself. PLA films with Co-cit were bacteriostatic against _L. monocytogenes_ on artificial media and on foods. However, in the latter case, the PLA film itself was the main contributor to the inhibition. Composite PLA-(Co-cit) films exhibited antimicrobial activity against foodborne bacteria and have potential application as active packaging materials to enhance food safety.  
Keywords:shelf-life extension; _Listeria monocytogenes_; active packaging; food surfaces +
Footnote †: journal: Applied Sciences
# Unique Triboelectric Nanogenerator Using Carbon Nanotube Composite Papers
## 4 Discussion
### Performance Evaluation of TENG Using CNT Composite Paper  
First, we investigated whether triboelectricity generation could be achieved using the CNT composite papers. This was confirmed. For these experiments, SG101, (6,5)-chirality, and (7,6)-chirality CNTs with semiconducting properties and NC7000 CNT with metallic properties were used. Triboelectricity generation was observed in both cases. Therefore, it is considered that CNTs do not serve to increase the output in triboelectric power generation with these CNT composite papers; rather, they act as a conductive material to assist charge transfer within the composite papers, regardless of their metallic or semiconducting nature. As discussed in Section 3.1, this is assumed to be due to the mixture of surfaces that are advantageous for triboelectricity generation (pulp area) and those that enable the collection of the generated charge (CNT network area) if viewed locally. This feature can be realized due to the CNT composite paper. In contrast, many splits and small peaks in the output peak are observed in addition to noise, as commonly observed in Figures 5, 7 and 9. We think the reason for these is the surface irregularities of the CNT composite paper. Ideally, the surfaces of the two materials should make contact and separate at completely the same timing, and when this is achieved, charge transfer occurs simultaneously and a large output peak can be obtained. However, the CNT composite paper has surface irregularities on the order of tens \(\mu\)m at the maximum in the height direction depending on the position within the surface, so the timing of contact and separation can be considered to shift due to differences in height, even within the same sample. This timing shift is considered to have led to a shift in the timing of charge transfer during triboelectricity generation, resulting in the generation of a large number of small peaks.