esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The present research was designed to analyze the chemical profile, AA, and antifungal properties of three types of commercial EOs including CCEO, CVBEO, and CVLEO against _Penicillium_ spp. Our results revealed a variable chemical composition of the investigated EO samples with (\(E\))-cinnamaldehyde (CCEO and CVBEO), and eugenol (CVLEO) being major substances of their chemical profile. Among the EOs, the strongest AA was detected for the CVLEO (488.0 \(\pm\) 1.2 TEAC; 84.0 \(\pm\) 0.3%) which was significantly (\(p\)! 0.05) different. The strongest antifungal efficacies (_in vitro_ experiments) were displayed by all analyzed EOs in the 500 \(\upmu\)L/L concentration against _P. crustosum_, which inhibition zones ranged from 13.00 \(\pm\) 1.73 mm (CVBEO) to 14.67 \(\pm\) 1.15 mm (CCEO). The findings from the evaluation of MC, a\({}_{\text{w}}\), and microbial characterization of the wheat bread showed excellent growth possibilities for fungi. Realized vapor contact method displayed dose-dependent antifungal actions of all EOs with the highest MGI in 250 \(\upmu\)L/L of the CVBEO against _P. citrinum_ (95.23 \(\pm\) 9.17%). Finally, our findings suggest that the CCEO, CVBEO, and CVLEO can be used as novel natural substances for the shelf-life prolongation of wheat bread. Moreover, the data complement our previous reports, thereby contributing to the creation of a more complete report dealing with the biological characteristics of various commercial EOs obtained from the same company (Hanus Ltd., Nitra, Slovakia).  
Conceptualization, V.V., H.D. and M.K.; methodology, V.V., H.D., L.G., N.L.V., M.V. and M.K.; software, V.V.; validation, V.V, P.L.K. and M.K.; formal analysis, V.V., H.D. and M.K.; investigation, V.V., H.D., L.G., N.L.V., M.V., P.L.K. and M.K.; resources, M.K.; data curation, M.K.; writing--original draft preparation, V.V, H.D. and M.K.; writing--review and editing, V.V., H.D. and M.K.; visualization, V.V., H.D. and M.K.; supervision, M.K.; project administration, M.K.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the grant APVV-20-0058 \"The potential of the essential oils from aromatic plants for medical use and food preservation\".  
Not applicable.  
This work was supported by the grants of the VEGA no. 1/0180/20.  
The authors declare no conflict of interest.
## 2 Method
### Multi-Head Loss Block
the model converges and the final model is obtained. Since this method only changes the calculation method of loss in the model training but does not change the structure of the model or its inference process, there is no extra cost in the model inference process. This method is easy to implement and can effectively improve the performance of the model without increasing its complexity or overhead. The Multi-head Loss Block structure used in GLFFNet is shown in Figure 5, which mainly includes semantic attention layer and lightweight decoder of the segmentation task, FCNHead. For the feature map of each input scale, Multi-head Loss Block first extracts the semantic information of the feature map with semantic attention layer and then inputs it into FCNHead for decoding and output segmentation results. After calculating the CE loss of five decoders, the weight of loss calculated by main decoder is artificially set to 1.0, a full connection layer is used to learn the weights of the other four auxiliary losses in the weighted fusion, and the weight range is specified as 0.1 to 0.4. Finally, the weighted loss is obtained by weight fusion. The formula for calculating weighted loss in Multi-head Loss Block is as follows:  
\[L_{Multi-head}=\alpha_{0}L_{CE\_0}+\sum_{s=1}^{S}\alpha_{s}L_{CE\_s} \tag{9}\]  
where \(L_{Multi-head}\) is the weighted loss ultimately used for gradient descent in the whole model training process, \(\alpha_{0}\) is loss weight of the output result of main decoder and is set as 1.0, \(L_{CE\_0}\) is loss calculated by the output of main decoder, \(\alpha_{s}\) is the loss weight of the output result of the \(s\)-th decoder and obtained by learning, and its range is from 0.1 to 0.4, and \(L_{CE\_s}\) is loss calculated by the output of the \(s\)-th decoder.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**MPA**} & \multirow{2}{*}{**Size**} & \multirow{2}{*}{**Substrate**} & \multirow{2}{*}{**Frequency**} & \multicolumn{2}{c}{**Resonant**} & \multirow{2}{*}{**Absorption \%**} & \multirow{2}{*}{**Polarization**} & \multirow{2}{*}{**Application**} \\  & & & **Band** & & **Simulated** & **Measured** & & **Simulated** & **Measured** \\ \hline
[13] & CSRR & \(10\times 10\times 1.6\) & FR-4 & K & 21.6 & 21.55 & 99.9\% & 99.68\% & Yes & Absorber and sensor \\ \hline
[14] & \
\begin{tabular}{c} Metap programmable \\ analog differentiation \\ \end{tabular} & – & – & C & 5.05-5.4 & 5.05-5.4 & \(\approx\)100\% & \(\approx\)100\% & N/A &
\begin{tabular}{c} Absorber and \\ analog differentiation \\ \end{tabular} \\ \hline
[24] &
\begin{tabular}{c} Wrenched \\ square shape \\ \end{tabular} & \(10.4\times 10.4\times 1\) & FR-4 & S, X, Ku & 3.2 & 3.43 & 95.75\% & 94.56\% & Yes & Absorber \\  & & 1 & 11.15 & 11.11 & 97.69\% & 97.98\% & Yes & Absorber \\  & & & 16.73 & 16.69 & 95.64\% & 96.67\% & \\ \hline
[45] & SSRR & \(10\times 10\times 1\) & FR-4 & S, X, Ku & 3.4, & \(\approx\)3.3 & 99.6, & 99.5\% & Yes & Absorber \\  & & & 13 & \(\approx\)12.9 & 99.1 & 99\% & Yes & Absorber \\ \hline
[46] &
\begin{tabular}{c} Eight \\ identical \\ 7-shapes \\ \end{tabular} & \(8\times 8\times 0.4\) & polyimide & X, Ku & 8.5, & 8.7 & 99.9\% & 96\% & & & \\  & & 13.5, & 14.1 & 99.5\% & 97\% & & Absorber \\  & & & 17 & 17.6 & 99.9\% & 94\% & & \\ \hline
[47] & V-shaped & \(8\times 8\times 1.6\) & FR-4 & Ku, K & 15.52, & 15.6 & 98.38\% & \(\approx\)96\% & No & Absorber \\  & & & 27.24 & N/A & 90.7\% & N/A & & \\ \hline
[48] & T-shaped &
\begin{tabular}{c} \(8.5\times 8.5\times \\ 0.2403\) \\ \end{tabular} & polyimide & Ku, K & 16.77 & 16.85 & 98.7\% & 98.6\% & No & Absorber \\  & & & 30.92 & 30.79 & 99.3\% & 96.2\% & No & Absorber \\ \hline
[49] &
\begin{tabular}{c} Diagonal slot \\ patch \\ \end{tabular} & \(16\times 16\times 1.6\) & FR-4 & Ku & 12.45 & 12.31 & 99.73\% & 99\% & No & Absorber \\  & & & 14.18 & 13.97 & 99.87\% & 99\% & \\ \hline
[27] &
\begin{tabular}{c} Fourfold \\ resonator \\ \end{tabular} & \(9\times 9\times 1.6\) & FR-4 & Ku & 13.62 & 13.6 & 99.99\% & 99.9\% & Yes & Absorber and sensor \\  & & & 16.30 & 16.5 & 99.99\% & 99\% & Yes & Absorber and sensor \\ \hline Proposed & SSRR & \(10\times 10\times 1.6\) & FR-4 & Ku, K & 14.12, & 14.11 & 99.51\% & 97.6\% & Yes & Absorber and sensor \\  & & & 17.53 & 17.56 & 99\% & 93\% & & \\  & & & 19.91 & 20.1 & 99.5\% & 96.6 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison table.
## 2 Transmitarray Antenna Design  
In the design of thin planar array antenna with separated feed, there are always five steps [37]. The first step is to find a suitable feed structure. The feed at the optimized position is an important factor related to the gain enhancement and the SLL reduction. The following step is the analysis of unit cell. Periodical boundary condition with plane wave incidence are used to get series of transmission coefficients by sweeping the related dimension. The third step will be the design of TA according to the transmission coefficients at 28 GHz, which are extracted from the previous step. In addition, the fourth step will be the joint simulation of TA and feed structure to achieve the desired performance, furthermore, the final step is to optimize the transmission coefficients of elements at different positions on the array to reduce the SLL, which will be discussed in the next section.
## 6 Results and Discussion
### Validation by Using HFSS  
The CST result of S\({}_{21}\) for the unit cell was also validated using the FEM based software HFSS. The proposed metamaterial unit cell exhibited 3 resonance peaks with almost similar magnitude at 2.40 GHz, 4.50 GHz and 9.40 GHz, whereas the CST yielded resonances at 2.38 GHz, 4.55 GHz and 9.42 GHz. Therefore, the CST and HFSS results were in excellent harmony (Figure 20). Table 5 represents the EMR of some previously reported metamaterials based on dimension, resonance frequency and frequency band.  
Figure 19: **(a)**\(2\times 2\) array of diagonal elements for 90\({}^{\circ}\) rotation; **(b)** Comparison of S\({}_{21}\) results. A, B, C and D are the four positions of unit cell in the 2 \(\times\) 2 array.  
Figure 20: Comparison of transmission coefficient (S\({}_{21}\)) by CST and HFSS results.
## 3 Results and Discussion
### Vegetation Indices
#### 3.3.2 VITO Tool Results  
The olive tree canopies of five selected VIs were extracted using the VITO tool. The models are classified into the class \"olive tree canopies\" and the class \"other\" (Figure 13). Differences in the size and spatial distribution of olive canopy class surfaces vary between VIs, which is expected given the different formulas and spectral bands used when generating the indices.  
Figure 12: VIs models: (**a**) NDVI; (**b**) NDRE; (**c**) GNDVI; (**d**) MCARI2; (**e**) RDVI2.
## 3 The Proposed Model
### Application of CSRO Algorithm for Data Transmission  
The main function of the CSRO algorithm is maximizing the lifespan of networks and minimizing energy utilization of all sensor nodes. Assume that \(h_{1}\) is the most objective function, such that \(CH\) is selected as the next hop, \(CH\), with a superior RE, to route the data; such that, for maximizing the network lifespan, for instance, \(h_{1}\) is maximization. Assume that \(h_{2}\) is another main function that has a minimal distance among the \(CH\)s to the next hop \(CH\), and the next hop \(CH\), to the base station \((BS)\). This procedure occurs under the instruction to decrease the energy utilization of the network as required for minimizing \(h_{2}\). Assume that \(h_{3}\) is the third main function; thus, \(CH\)s is selected as the next hop among the \(CH\)s with a lesser node degree. In order to improve the lifespan of the network, \(h_{3}\) must be minimized. Assume that \(b_{ij}\) is a Boolean variable, determined as follows:  
\[b_{ij}=\begin{cases}1&if\text{ }next-hop(CH_{i})=CH_{j},\text{ }\forall_{i,j}1\leq i,\text{ }j\leq m\\ 0&Otherwise\end{cases} \tag{21}\]  
\[\text{Minimize }F=\frac{1}{h_{1}}\times\beta_{1}+h_{2}\times\beta_{2}+h_{3} \times\beta_{3} \tag{22}\]  
which is subject to the following:  
\[dis\big{(}CH_{i},CH_{j}\big{)}\leq d_{max}CH_{j}(C+BS) \tag{23}\]  
\[\sum_{j=1}^{m}b_{ij}=1\text{ }and\text{ }1\
eq j \tag{24}\]  
The constraint in (23) means that the next hop node of \(CH_{i}\) will be in the range of \(CH_{i}\), and that the next hop node is \(CH_{j}\). \(\beta_{1}\), \(\beta_{2},\beta_{3}\) indicate the anchor nodes with the target distance. The constraint in (25) declares that the next hop node of \(CH_{i}\) is unique, for instance, \(CH_{j}\), and the constraint makes sure that there could not be 0 or 100% weight on either of the objectives.
# Design and Modelling of a Novel Integrated Photonic Device for Nano-Scale Magnetic Memory Reading
###### Abstract  
Design and simulations of an integrated photonic device that can optically detect the magnetization direction of its ultra-thin (\(\sim\)12 nm) metal cladding, thus'reading' the stored magnetic memory, are presented. The device is an unbalanced Mach Zehnder Interferometer (MZI) based on InP Membrane on Silicon (IMOS) platform. The MZI consists of a ferromagnetic thin-film cladding and a delay line in one branch, and a polarization converter in the other. It quantitatively measures the non-reciprocal phase shift caused by the Magneto-Optic Kerr Effect in the guided mode which depends on the memory bit's magnetization direction. The current design is an analytical tool for research exploration of all-optical magnetic memory reading. It has been shown that the device is able to read a nanoscale memory bit (400 \(\times\) 50 \(\times\) 12 nm) by using a Kerr rotation as small as 0.2\({}^{\circ}\), in the presence of a noise \(\sim\)10 dB in terms of signal-to-noise ratio. The device is shown to tolerate performance reductions that can arise during the fabrication.  
integrated photonics; InP; magneto-optic; MZI; mode conversion; PMA; multi-layered thin film; magnetic memory; MOKE; Fourier transformation Article
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Conductive Metals
#### 2.2.2 Gold Nanoparticles (AuNPs)  
AuNPs are widely known for their optical and biological properties. They could be used for highly sensitive diagnostic assays, thermal ablation, radiotherapy enhancement, and drug and gene delivery. They are mainly prepared via the chemical reduction method using gold salts such as hydrogen tetrachloroaurate (HAuCl\({}_{4}\)) as a precursor and citrate as a reducing agent [51]. Au nanoparticles chemically synthesized by citrate reduction can be immobilized onto chitosan-treated soybean knitted fabric via the exhaustion method [53]. A gold coating with solution deposition on knitted fabric is highly conductive, stretchable, wearable, and washable [54].
# Deep Learning for Laryngopharyngeal Reflux Diagnosis
## 4 Discussion  
Deep learning techniques have been applied to several mainstream medical images, such as CT, MRI and ultrasound. However, only a few methods are devoted to endoscope images or laryngoscope images particularly. The hypothesis was that raw laryngoscope images can be used to diagnose LPR directly and conveniently, which has been verified by previous works [2; 16]. Following those works, MII-24 h pH monitoring was adopted as the gold standard to diagnose LPR, which was more accurate and objective than the RFS method.  
The results of Tables 2 and 3 indicate that deep neural networks are promising to capture discriminant representations for the LPR task. Although an overall classification accuracy of 73.4% may be inadequate for real settings of clinical diagnosis, a relatively small image dataset would be responsible, which included 114 subjects with only 37 pH-positive cases. As the database is enlarged in the future, deep learning techniques will achieve a better performance.  
Another contribution of this work is that the extensive experiments were conducted to test the performance of some prominent deep neural networks on this particular task. A bunch of baselines were set up for future study, to build LPR diagnostic system with deep learning techniques. Unfortunately, based on the data of this research, the popular techniques in transfer learning and few-shot learning were found to be unable to enhance the performance further. On the other hand, the deep learning technique is a kind of quick diagnostic tool for LPR, which can be applied before and after therapy to evaluate the effect more conveniently than MII-24 h pH monitoring.  
[PERSON] et al. proposed an LPR diagnostic method where predefined texture and color features were extracted on seven manually drawn regions [2]. In our work, discriminant features are learned automatically in an end-to-end fashion, which allows a clinician to sidestep the tedious work and eliminate the subjective bias due to manual drawing jobs. With the technology development of deep learning, the features of laryngoscope image could be recognized from every angle of view, not just with only two features, as color and texture. Furthermore, as the datasets were augmented by sequentially applying random rotation, color jittering, random flip and random crop during the training and testing phase, the effect of interference factors, such as the distance, angle of the camera and white balance of laryngoscope images, would be eliminated. The future work will involve testing more powerful network architectures, particularly those new models in attention mechanism, transfer learning and few-shot learning.  
When the data are enough for the deep learning system, the performance of the technique will be better. The collection of images from multi-center is a good choice.
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 3 System Model
### Energy Consumption Model  
There are two types of losses that occur with underwater acoustic signals: absorption loss and spreading loss. These losses are affected by different factors, including signal frequency, transmission distance, and bandwidth. In this article, the Thorp propagation model is employed to create the underwater acoustic channel for the energy consumption model [35]. To calculate the total loss of an underwater acoustic signal with a frequency of '_f_' in kHz at a distance of '_d_' in km, the following formula is used:  
\[A(d,f)=d^{k}\alpha(f)^{d} \tag{1}\]  
where '_k_' represents the energy spreading factor and \(\alpha(f)\) denotes the absorption coefficient. The energy spreading factor, '_k_', describes the propagation geometry, taking a value of 1 for cylindrical spreading, 1.5 for practical spreading, and 2 for spherical spreading. In this case, we set '_k_' to 1.5. The absorption coefficient \(\alpha(f)\) can be determined using [PERSON]'s formula shown in Equation (2), measured in dB/km:  
\[10\log\alpha(f)=\frac{0.11f^{2}}{1+f^{2}}+\frac{44f^{2}}{4100+f^{2}} \tag{2}\]  
Equation (2) can be used for frequencies above 400 Hz. Equation (3) can be used to calculate the absorption coefficient of a signal at frequencies below 400 Hz [36]:  
\[10\log\alpha(f)=0.002+\frac{0.11f^{2}}{1+f^{2}}+0.011f^{2} \tag{3}\]  
The expression for the energy consumed by the nodes to transmit a packet containing '_m_' bits over a distance '_d_' is given by [37]:  
\[E_{tx}(m,d)=mE_{elec}+mT_{b}CHde^{\alpha(f)d} \tag{4}\]  
where \(E_{elec}\) is the unit energy consumed by the electronics to process one bit of message, \(T_{b}\) refers to the bit duration, \(C\) is equal to \(2\pi(0.67)10^{-9.5}\), and \(H\) represents the depth of node.  
The energy consumption of a sensor node when receiving m bit \(E_{rx}\) can be obtained by:  
\[E_{rx}(m,d)=mE_{elec} \tag{5}\]  
the residual energy \(E_{rx}\) of a sensor node can calculated as:  
\[E_{resi}=E_{init}-(E_{rx}+E_{tx}) \tag{6}\]
# A Reliable Publish-Subscribe Mechanism for Internet of Things-Enabled Smart Greenhouses
## 4 Performance Evaluation  
The objective of this chapter is to assess the effectiveness of the categorized topic structure we propose for the MQTT broker. This evaluation is conducted based on several key performance indicators, including pub-sub message size, the number of messages transmitted from the greenhouse gateway to the main server, the processing time for message transactions in the main server, the coverage rate of events, and the throughput of subscriptions.  
* Pub-sub message size: About the topic matter, it is noteworthy that the SGP MQTT message arrangement necessitates the utilization of 20 bytes for transmitting each message about alterations in the greenhouse environment to a single server. In contrast, the traditional MQTT protocol consumes 100 bytes for the same purpose, as shown in Figure 4.
* The number of messages: This describes the overall capacity of a broker to effectively handle a particular number of messages during a given period. The significance of this measure is of utmost relevance within the realm of intelligent greenhouses, where the interchange and management of real-time data represent an important role in improving climate circumstances and efficiently using resources to enhance plant development [36; 37; 40].
* Message processing time: This refers to the amount of time it takes for the primary server to receive incoming messages, which has a direct influence on the system's capacity to stay synchronized with events taking place in the surrounding environment. The system's capacity to promptly and effectively address real-world occurrences is immediately impacted. By closely monitoring and strategically optimizing the processing times of messages, it is possible to guarantee that applications using the MQTT protocol can efficiently adapt to the ever-changing circumstances of the environment [34; 41; 42].
* Subscription throughput: The assessment of MQTT protocol performance is heavily reliant on the analysis of subscription throughput, as shown by previous studies conducted by various academics [16; 28; 43]. The term \"message delivery rate\" pertains to the speed at which a broker can transmit messages to its subscribers. The significance of this statistic lies in its direct influence on the responsiveness and efficiency of communication inside a system that utilizes the MQTT protocol. A high rate of subscription throughput signifies the MQTT broker's ability to effectively manage a large volume of subscribers and swiftly distribute messages to them [43; 44]. Real-time transmission is of utmost importance in greenhouse applications [16].  
Therefore, by the assertion made in this research, which pertains to the overall suitability of this methodology inside a greenhouse setting, we must substantiate and assess its efficacy. As a result, the findings of the assessment are compared to those of the standard MQTT and other current techniques.
## 3 Results
### Contingency Statistics  
The categorical statistical metrics of 3B42 V7 and IMERG V5 at different time scales are shown in Table 3.  
The precision of 3B42 V7 and IMERG V5 at 3 h, daily, monthly and annual scales were compared and analyzed. IMERG V05 demonstrated better performance than 3B42 V7 in detecting small time scale precipitation events, with high values of POD and CSI (0.13, 0.39 vs. 0.58, 0.82) and (0.08, 0.22 vs. 0.14, 0.18), respectively (Table 3), as well as reasonably high values of FAR and FBI (0.79, 0.66 vs. 0.83, 0.81) and (0.65, 1.17 vs. 3.63, 4.37), respectively.  
The performance of the categorical statistical measures at the monthly and annual levels are shown in (Table 3). 3B42 V7 and IMERG V05 produced good results for rainfall estimation, with POD values and CSI values (0.99, 1 vs. 1, 1) and (0.07, 0 vs. 1.07, 1), respectively. Similarly, for the FAR and FBI, the results obtained were close to the perfect values, (0.08, 0 vs. 0.08, 0) and (1, 1 vs. 1.09, 1), respectively.  
In general, IMERG V05 is better at detecting precipitation events, in particular at capturing precipitation traces and solid precipitation at a 3-hourly and daily scale, while 3B42 V7 can estimate precipitation on a large time scale.