esa-sceva/satcom-chunk-collection huggingface dataset:
# An Energy-Efficient Routing Protocol for Reliable Data Transmission in Wireless Body Area Networks
## 4 Energy-Efficient Routing Protocol for Reliable Data Transmission in WBAN
### Routing Process  
The routing process of the proposed protocol is divided into three stages: initialization stage, the best next hop node selection stage and data forwarding stage. The overall routing process is shown in Figure 3.  
Figure 3: Flow chart of the proposed protocol.
## 1 Introduction
### Magnetic Nanotechnology
In cancer therapy MNPs, especially iron oxide nanoparticles, are used in cancer treatment by catalysing the Fenton reaction to produce toxic ROS, causing tumour cell death [44; 45]. In drug and gene delivery, MNPs improve specificity by guiding therapeutic agents directly to disease sites. For instance, poly-L-lysine-coated Fe\({}_{3}\)O\({}_{4}\)@FePt nanoparticles target glioblastoma cells, enhancing chemotherapy efficiency [46]. In gene delivery, magnetofection directs viral vectors to lung tissue, improving gene transfection and therapeutic outcomes [47; 48; 49]. Moreover, the utilisation of MNPs as carriers has expanded into the realm of in vivo corneal regeneration, with studies evaluating the feasibility of directly delivering nanoparticle-loaded CEC into the anterior chamber and maintaining cellular viability under various loading conditions [35; 50].
# Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk
## 3 Methodology  
In our research, we used Matlab Simulink 2023b to create the simulation environment for a DSRC-based V2X system. We created the network's physical layer based on the IEEE 802.11p standard; however, we emphasize that the introduced general concept can be flexibly adapted to cellular technology-based systems. The following subsections will present the proposed control concept, the simulation environment, and the performed scenarios.
# Enhanced Fully Generalized Spatial Modulation for the Internet of Underwater Things
## 1 Introduction
We analyze and evaluate the receiver's computational complexity of the suggested EFGSM by defining the total number of real operations (TNRO) needed at their maximum likelihood (ML) decoders. Furthermore, it is compared with the computational complexity of the other current SMTs. The rest of the paper is structured as follows: Section 2 represents the relevant literature. In Section 3, we explain the proposed EFGSM system model. The performance analysis of the EFGSM is introduced in Section 4. Section 5 presents the simulation results, and finally the paper is concluded in Section 6.
## 3 Results and Discussion
### Volatile Odor Active Ester Compounds during the Simulated Fermentation of Blueberry Wine  
The odor activity value (OAV) is a common index used to evaluate the contribution of volatile compounds of wine to the actual flavor, which can be calculated by the ratio of the content of the volatile compound to its odor threshold [46]. It is generally believed that an OAV greater than 1 indicates that it contributes to the odor, and a larger OAV indicates a greater individual contribution of the compound [47]. The ester compounds with OAV greater than 1 are shown in Table 2. In the S sample, the OAVs of phenethyl acetate and isoamyl acetate were lower than 1; however, the OAVs of phenethyl acetate and isoamyl acetate were greater than 1 in the CS1, CS5, CS10, PSS, PS10, WS5, and WS10 samples, indicating that co-fermentation directly promoted the contribution of phenethyl acetate and isoamyl acetate to the fruity and floral flavors. Although the content of isoamyl acetate was lower than that of phenethyl acetate, its threshold was lower, leading to a higher OAV than phenethyl acetate. The OAV of phenethyl acetate and isoamyl acetate were both the highest in the sample of CS1, which was 9.68 and 32.33 times that of the S sample, suggesting that they could contribute to more floral and fruity flavors in the sample of CS1. As for ethyl esters, ethyl octanoate, with a high content and low threshold (5 ug/L), had the greatest influence on flavor of fruit wine and could give wine flavors of pineapple, pear, and floral [48]. The OAV of ethyl hexanoate with the same low threshold (5 ug/L) was second only to ethyl octanoate and could give wines with green apple and purple apple flavors. The OAVs of ethyl octanoate and ethyl hexanoate were the highest in the CS1 sample, which were 1350.05 and 116.43. In addition, ethyl decanoate also had significant effects on the overall flavor and could directly promote the fruity and floral flavors of samples [49]. Anyway, the OAV of ethyl laurate was greater than 1 in only the samples of CS1, CS5, PS1, and PS5, thus directly giving these samples sweet, waxiness, and floral flavors.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 5 Studies toward Clinical Applications of TD-NIRS
### Measuring Optical Properties
#### 5.2.1 Homogenous Semi-Infinite Media or Infinite Slab
evaluated the performance of the six-wavelength TD-NIRS system using phantoms varying the contents of water, lipid, and an absorber [68]. The performance was confirmed with the measurements using a magnetic resonance imaging system. [PERSON] et al. [140] recovered the spectra of \(\mu_{a}(\lambda)\) and \({\mu_{s}}^{\prime}(\lambda)\) of irregularly-shaped homogeneous media from TD-NIRS measurements. The analytical solutions of the TD-DE are usually not applicable to irregularly shaped media, and the finite element method (FEM) was employed to solve the TD-DE. The FEM solutions for non-absorbing medium were multiplied by exp(-\(\mu_{a}\)\(ct\)) to incorporate the attenuation by absorption.
# Energy Harvesting over Rician Fading Channel:
## 3 System Performance
### SER Analysis  
In this section, we derive the formula for symbol error rate (SER) at the destination. In fact, the theoretical SER at the receiving node can be computed in terms of the end-to-end SNR at that node [34]. By taking into account the randomness of the SNR in our model, the SER at node \(S_{i}\) can be derived by taking the expectation with respect to the random variable SNR:  
\[SER_{i}=E\left[\omega Q\left(\sqrt{2\theta\gamma_{i}}\right)\right],i\in 1,2, \tag{17}\]  
where \(Q(t)=\frac{1}{\sqrt{2\pi}}\int\limits_{t}^{\infty}e^{-x^{2}/2}dx\) is the Gaussian Q-function; \(\omega\) and \(\theta\) are constants that are specific for each modulation type--in particular, \(\omega=1,\theta=1\) for Binary Phase Shift keying (BPSK) and \(\omega=1,\theta=2\) for Quadrature Phase Shift Keying (QPSK). Hence, before obtaining the SER formula, the CDF of \(\gamma_{i}\) is needed. Let \(F_{\gamma_{i}}(x)\) be the CDF of \(\gamma_{i}\). By exchanging the order of integration and changing variable, we come up with the following formula:  
\[SER_{i}=\frac{\omega\sqrt{\theta}}{2\sqrt{\pi}}\int\limits_{0}^{\infty}\frac{ e^{-\theta x}}{\sqrt{x}}F_{\gamma_{i}}(x)dx. \tag{18}\]  
Now, we can claim the following theorem on SER.  
**Theorem 2** (Sen--Exact form).: _The exact symbol error rate for node \(S_{1}\) of the proposed half-duplex bidirectional wireless sensor network using time-switching energy harvesting strategy over a Rician fading channel can be expressed as:_  
\[SER_{1}=\frac{\omega}{2}-\zeta_{1}e^{-2K}\omega\sqrt{\theta}\sum \limits_{l=0}^{\infty}\sum\limits_{k=0}^{\infty}\sum\limits_{m=0}^{l}\sum \limits_{n=0}^{m}\sum\limits_{p=0}^{n}\frac{K^{l+k}\zeta_{1}^{k}a^{m-n}b^{p}c^ {\frac{m-p+k+1}{2}}}{l!p!(n-p)!(m-n)!(l!)^{2}}\times\] \[\times\int\limits_{0}^{\infty}\frac{e^{-\theta x-b\zeta_{2}\tau_ {th}(x)}}{\sqrt{\tau x}}K_{m-2n+p+k+1}\left(2\sqrt{[\zeta_{1}+a\xi_{2}\tilde{ \gamma}_{th}(x)]\xi_{2}\tilde{\gamma}_{th}(x)c}\right)\frac{[\zeta_{2}\tilde{ \gamma}_{th}(x)]^{\frac{3m-2n+p+k+1}{2}}}{[\zeta_{1}+a\xi_{2}\tilde{\gamma}_{ th}(x)]^{\frac{m-2n+p+k+1}{2}}}dx, \tag{19}\]  
_where \(\tilde{\gamma}_{th}(x)=\frac{x}{1-ax}\)._  
Proof of Theorem 2.: See Appendix B.
# Automatic Bridge Crack Detection Using a Convolutional Neural Network
## 3 Experimental Results and Discussion
### Experimental Results
#### 3.1.3 Model Comparison With or Without ASPP Module  
In Section 2.2.3., we explained the theory of the ASPP module capturing object and image context information [39] on multiple scales, thereby improving the crack detection accuracy. To further demonstrate this, we conducted experiments to verify the effectiveness of the ASPP module. The experimental results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Epochs** & **Test Accuracy** \\ \hline Proposed Model & 300 & 96.37\% \\ Resnet18 & 300 & 91.59\% \\ Resnet34 & 300 & 92.00\% \\ Resnet50 & 300 & 89.45\% \\ Vgg16 & 300 & 84.67\% \\ Vgg19 & 300 & 85.41\% \\ \hline \hline \end{tabular}  
* Compared with other models, the proposed model achieved a higher crack detection accuracy of 96.37\% without pre-training, which proved that the proposed model could make more effective use of the dataset.  
\end{table}
Table 1: Model comparison.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Epochs** & **Test Accuracy** \\ \hline Proposed Model (with atrous convolution) & 300 & 96.37\% \\ With maxpooling & 300 & 92.33\% \\ With standard convolution & 300 & 90.35\% \\ \hline \hline \end{tabular}  
* Since the atrous convolution effectively maintains the image resolution while expanding the receptive field, the model performance of using the atrous convolution is better than using the maxpooling layer and standard convolution with a stride of 2.  
\end{table}
Table 2: Model comparison with or without the atrous convolution.  
Compared with the model without the ASPP module, the crack recognition rate of the model with the ASPP module is 5.77% higher. This proves that the application of ASPP in crack detection can better fuse multi-scale image context information, and thus effectively utilize the image feature information to obtain a higher network prediction accuracy.
# An Overview of Cooperative Driving in the European Union: Policies and Practices
## 4 Current State of the Art of C-ITS Systems
### C-Roads Pilot Sites
#### 4.1.10. Czech Republic  
Czech Republic activities, within the C-Roods programme, are outlined in six different pilot sites, from deployment and test (DT)1 to DT6. The services tested cover almost all Day-1 applications, excluding only shockwave damping and GLOSA tasks, and focus particularly on public transport safety. Both ITS-G5 and cellular-based technologies are being investigated with the aim of developing a hybrid framework. Several implementing bodies among transport operators, research organisations, and equipment supplier companies are involved, namely Road and Motorway Directorate (RSD), Brenske komunikace company, Sprava Zeleznicni Dopravni Cesty (SZDC) railway operator, public transport companies of Ostrava and Plzen, INTENS Corporation, AZD Praha, O2, T-Mobile, Skoda Auto, and Czech Technical University.  
According to schedule, pilot sites from DT1 to DT5 were completed in 2018, while DT6 has just been activated. Therefore, all DT will be operational throughout 2019, and 2020 will be dedicated to ex-post evaluation.
# Application of Thin-Film Microextraction to Analyze Volatile Metabolites in A549 Cancer Cells
## 2 Results and Discussion
### VOCs Released from A549 Cancer Cells
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & **Cell Culture Medium** & \multicolumn{2}{c}{**Cell-Free Medium**} \\ \cline{5-10}
**Compound Name** & **CAS** & **R\({}^{2}\)** & **LOD [ppb]** & **LOQ [ppb]** & **Mean Conc.** & **Standard** & **Standard** & **Mean Conc.** & **Standard** \\  & **Name** & & & & **[ppb]** & **Deviation** & **[ppb]** & **[ppb]** \\ \hline Carbon Disulfide & 75-15-0 & 0.9957 & 1.360 & 4.121 & 10.423 & 4.343 & 5.552 & 0.851 \\
2,3-Butanedione & 431-03-8 & 0.9880 & 2.280 & 6.909 & 6.918 & 3.184 & 2.581 * & 0.370 \\ Pyrrole & 109-97-7 & 0.9986 & 0.782 & 2.370 & 5.073 & 1.087 & 2.297 & 0.435 \\ Pyridine & 110-86-1 & 0.9986 & 0.763 & 2.313 & 1.796 * & 0.369 & 1.114 * & 0.097 \\ Methylpyrazine & 109-08-0 & 0.9948 & 1.496 & 4.533 & 5.191 & 1.916 & 2.827 * & 0.185 \\ Benzaldehyde & 100-52-7 & 0.9987 & 0.747 & 2.264 & 4.872 & 0.823 & 10.011 & 0.654 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Concentration [ppb] of target volatiles measured in the headspace samples of cell culture medium and cell-free reference medium along with calibration parameters. Concentrations higher than LOD but lower than LOQ are labeled with an asterisk. Figure 5: VOCs detected at significantly higher (respectively, lower) levels in the headspace of A549 cell culture medium (red boxes) compared to the reference sample of cell-free RPMI-1640 medium control (blue boxes). Boxes represent the 25 th, 50 th and 75 th percentile, while whiskers refer to the 5 th and 95 th percentile. Mean values are indicated with a yellow diamond. Intriguingly, similarities between the results for the A549 cells and ex vivo studies on VOC emissions from surgically excised tumor tissue were observed, whereby pyridine was significantly produced from both these cancerous sample types compared to their respective nontransformed reference controls. The interdisciplinary convergence in VOCs release from tumor tissues and A549 cells was observed, also, for 2,3-butanedione and pyrrole; however, the increase in their levels from tumorous lung tissue did not reach statistical significance compared to reference healthy lung tissue in another study [19].
## 2 RNP-WMN Problem
### Problem Formulation
#### 2.2.4 Connected Client Ratio  
The connected client ratio is defined as the ratio of the number of connected clients to the number of clients in a WMN, calculated by  
\[\Gamma(G)\ =\frac{\sum_{i=1}^{n}\beta_{c_{i}}}{n}\times 100(\%) \tag{4}\]  
where \(n\) is the number of mesh clients, \(\beta_{c_{i}}\) is a variable that indicates whether client \(c_{i}\) is a connected client or not, defined as follows  
\[\beta_{c_{i}}=\begin{cases}1&\text{if $c_{i}$ is a connected client}\\ 0&\text{otherwise}\end{cases} \tag{5}\]  
The main goal of the RNP-WMN problem is to find the set of locations for \(m\) mesh routers (set \(\{(x_{r_{i}},y_{r_{i}})|\forall r_{i}\in R\}\)) so that the network performance is the best. In this work, we focus on optimizing two important performance metrics, namely connected router ratio (\(\Psi(G)\)) and connected client ratio (\(\Gamma(G)\)), as defined in Sections 2.2.2 and 2.2.4, respectively. Since the larger \(\Psi(G)\) and \(\Gamma(G)\) metrics, the better the network performance, the RNP-WMN problem is formulated as follows:  
\[\begin{cases}Maximize\ \Psi(G)\\ Maximize\ \Gamma(G)\end{cases} \tag{6}\]  
subject to the following constraints:  
\[0<x_{r_{i}}<W \tag{7}\] \[0<y_{r_{i}}<H \tag{8}\]  
where \(W\) and \(H\) are the width and the height of the WMN area, respectively.  
The RNP-WMN problem as defined in (6) can be solved by optimization algorithms. In this work, we apply the Multi-Verse Optimizer Algorithm (MVO) to solve it, and more information is provided in the following sections.
## 3 Experimental Results and Discussion
### Basic Physical Parameters
#### 3.2.5 Comparison between Ordinary Concrete and Basalt Fiber-Reinforced Concrete  
According to the data of Tables 6 and 7, the curves of the compressive strength of the two kinds of concretes in the two cooling methods can be seen in Figure 17.  
In the air cooling method, the static compressive strength of basalt fiber-reinforced concrete is improved 16.1%, 16.6%, 39.9%, 12.1%, and 25.6% more than ordinary concrete at room temperature, 200, 400, 600, and 800 \({}^{\circ}\)C, respectively. While, in the water method, the static compressive strength of basalt fiber-reinforced concrete is improved 16.1%, 14.3%, 28.8%, 28.6%, and 29.6% more than ordinary concrete at room temperature, 200, 400, 600, and 800 \({}^{\circ}\)C, respectively.  
Form the comparison of the above data, the static compressive strength of basalt fiber-reinforced concrete is continuously improved compared to ordinary concrete with the increase of the temperature. Thus, it can be seen that the addition of basalt fiber to concrete can effectively reduce the effects of high temperatures on concrete and further improve the bearing capacity of concrete.  
Through the comparison of the failure pattern, at the same temperature and using the same cooling method, the failure pattern of basalt fiber-reinforced concrete is superior to ordinary concrete. In the different cooling methods, the failure pattern of the two concrete specimens in air cooling is better than in water cooling.
# Engineering Challenges Ahead for Robot Teamwork in Dynamic Environments
## 3 Teamwork Challenges
### Organizational Structure and Decision-Making
The middleware PROViDE [36] is part of a multi-agent framework called ALICA [37]; ALICA teams have a decentralized team structure. PROViDE offers a choice of replication and agreement protocols for common decision variables. If a team decision about the value of an environment variable needs to be made, all team members broadcast their opinion to their teammates. The developer may choose the level of replica consistency depending on the specific application requirements in the face of unreliable communications, temporarily disconnected robots, and diverging sensor readings by the robots. The replicated values of a decision variable can lead to a situation where a robot receives several divergent observations from its teammates in addition to its own observed value. Thus, after the replication phase, a robot needs to decide which value from the set of available opinions it will accept locally as its own value. This may lead to a situation where the individual team members accept different values of the decision variable as their own individual \"view of the world\". Now, we need a third coordination phase where the robots agree on a single joint value. Such a decision could be based on majority voting, priorities, timestamps, or other criteria. Thus, there are three distinct phases in team decision-making that resemble the typical process of decision-making in human teams (added in parentheses):  
1. Replication of individually perceived values of the decision variable to teammates (team members learn about diverse opinions in the team). 2. Team members locally commit to a value (team members determine their own opinion). 3. If needed, conflicting choices are resolved by a specified conflict resolution protocol (the team consolidates diverging opinions and arrives at a joint decision). In summary, based on the PROViDE middleware, the application developer can tune the middleware by choosing from a set of provided protocols and, thus, can adapt the quality and overhead of decision-making to diverse application requirements. However, this raises another question. One might argue that such an abundant choice of strategies shifts the complexity onto choosing the right combination of strategies. This argument cannot be ignored. One solution might be to identify reusable typical patterns of strategy combinations for specific application scenarios. This can be addressed in future work.
## 4 Discussion
### Effect on Adjacent Teeth  
Unlike fixed appliances, clear aligners encompass the entire dentition, and the points of force application are difficult to determine. Additionally, the mechanical characteristics of the polymer influence the performance of the aligners. Thus, it is unclear how applying a force on a single tooth may transmit forces and moments across the entire arch. [PERSON] et al. [24] used a multi-tooth mechanical orthodontic simulator to evaluate the forces and moments produced by clear aligners on the maxillary arch during a 0.20 mm facial displacement. The group reported that for the maxillary right central incisor, the simulated displacement generated a facial force and a moment to tip the crown facially. On adjacent teeth, clinically relevant reciprocal forces and moments were present: lingual forces on both adjacent teeth, a moment to tip the crown lingually on the adjacent central incisor, and a clinically insignificant moment on the lateral incisor.  
In the present study, when a lingual force was applied to the target tooth, the maxillary left central incisor, the adjacent maxillary central incisor also experienced forces in the lingual direction. When the activation on the target tooth was increased, a greater force magnitude was generated on the adjacent central incisor with thermoformed aligners, whereas the force magnitude stayed relatively constant with TC-85. The effect on the adjacent lateral incisor was more complex and dependent on material type and activation amount. With ATMOS and Zendura FLX, a 0.25 mm activation on tooth 21 resulted in lingual force experienced by the lateral incisor (0.86 N and 0.21 N, respectively). With TC-85, the reciprocal forces on the lateral incisor were in the facial direction at 0.25 mmactivation (0.34 N). When the activation was further increased to 0.50 mm, the force felt by the lateral incisor changed to opposite directions with ATMOS and Zendura FLX (0.54 N and 0.41 N, respectively). With TC-85, the increase in activation resulted in only a minor increase in force magnitude in the same direction.  
In conventional biomechanics based on fixed appliance therapy, equilibrium dictates that a limally directed force on the central incisor be balanced by facially directed forces on the adjacent teeth. The results from the present study indicate that this is not necessarily true with clear aligners. The contact geometry between the clear aligner and the dentition is complex and dynamic, and highly dependent on factors such as material properties and planned tooth movements. In general, the unpredictable forces and moments measured on the target tooth and the adjacent teeth in the present study highlight the complexity of the biomechanics of clear aligner therapy and may help explain the ongoing challenges in achieving predictable clinical outcomes with this treatment modality.
# Evaluation of Bol Corrosion Degree Based on Non-Destructive Testing and Neural Network
## 2 Corrosion Mechanism and Experimental Study of Anchor Bolts
### Electrochemical-Accelerated Corrosion Results  
The same anchor bolt was used as the specimen in the experiment. In order to achieve the desired corrosion level promptly, the corrosion test was conducted for a total of six different durations, with a 72-h period and intervals of 12 h between each corrosion session. A visual assessment of the anchor bolts with varying corrosion rates is shown in Figure 4. The corrosion rate for each steel bar sample was calculated as follows: the initial mass of the non-corroded anchor bolt is denoted as \(m_{0}\), and the mass of the anchor bolt after corrosion is denoted as \(m_{1}\). The corrosion rate of the anchor bolt is  
\[C_{r}=\frac{m_{0}-m_{1}}{m_{0}}\times 100\% \tag{8}\]  
During the process of electrochemical corrosion, the mass loss of particles can be estimated according to Faraday's Law:  
\[\Delta m=\frac{MIt}{zF} \tag{9}\]  
In the equation, \(\Delta m\) represents the mass loss of the anchor bolt. \(M\) stands for the molar mass, \(z\) refers to the absolute value of the total number of positive or negative valence states in a compound, \(F\) represents the Faraday constant with a value of \(F=9.65\times 10^{4}\) C/mol, \(I\) represents the current intensity, and \(t\) represents the time the current flows in seconds (s).  
The corrosion products are mainly composed of \(Fe^{2+}\) and \(Fe^{3+}\)[16]. In this study, the theoretical mass losses of the anchor bolt after 12 h of accelerated corrosion were 31.25 g (for \(Fe^{3+}\)) and 20.84 g (for \(Fe^{2+}\)). In natural corrosion, the current density of the anchor bolt varies from 0.1 uA/cm\({}^{2}\) to 10 uA/cm\({}^{2}\). The value of 0.04 A/cm\({}^{2}\) used in this study was  
Figure 4: Samples with different degrees of corrosion.  
Figure 3: Electrochemical-accelerated corrosion test.  
400 times the maximum value of the range. The 12 h corrosion period in the experiment corresponds to over 200 days in real-life conditions. The corrosion time and mass loss are shown in Table 1.