esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Perspectives  
The suitability of a new online, noninvasive, biasless detector for relative dose monitoring in patients undergoing charged particle radiotherapy treatments was investigated. The device, a floating electrode connected on the patient's skin, collects the charge delivered by the beam and returns a current signal proportional to the total charge deposited by the beam. The system was firstly characterized in vitro, irradiated inside a dosimetric water-tank with a clinical monochromatic 60 MeV proton beam. Short-, mid-, and long-term reproducibilities were studied and their values resulted better than 4.3%. The system resulted was independent from the temperature (5% maximum difference in the 20-40 \({}^{\circ}\)C range) and its response was proportional to the irradiation field area with a good confidence degree (R\({}^{2}\) = 0.9978). The characterization evidenced a very good linearity with the dose absorbed in the water tank, in the 2-20 Gy range, with R\({}^{2}\) = 0.9999 and a deviation from linearity of less than 2%.  
The device's response was also independent from the proton beam dose rate in the 10-40 Gy/min range, where a maximum percentage difference of 3% was found.  
Preliminary in vivo tests highlighted the capability of the device in acting as beam monitoring during a patient proton-therapy treatment and its ability in the verification of the correct dose delivered among the various fractions of the treatment. The set of performed tests, even if preliminary, show the capability of the system in operating as a beam monitoring device. The acquired results appear statistically robust, where no standard deviation bigger of 4.3% was observed in the sets of the repeated measures. On the other way, the system characterization must be completed, including additional tests. For example, the system response with respect to different beams' energies must be studied in order to exclude (or determine) potential dependencies with the proton linear energy  
Figure 7: Proton beam signal (orange dots) measured by secondary electron monitoring (SEM) and the corresponding signal acquired by the electrode (black dots) positioned on the patient’s skin (see Figure 3). It is interesting to note that the sudden and unexpected beam stop during irradiation (at around second 85) and its next recovery (at around second 90) is perfectly followed by the electrode signal.  
transfer (or LET). Additionally, materials other then water but similar to biological tissue composition must be investigated. The in vivo characterization, that obviously suffers from a lack of sufficient data, must be repeated with several more measures in order to reach a good statistical significance.  
Improvement of the _Electrode_ read-out circuits are ongoing to minimize the current leakage during in vivo irradiations. Its characterization under electrons and carbon ion beams is under way as well.
## 3 Case Study
### Proposal and Testing of Improving Solutions  
According to the procedure shown in Figure 1, this section aims at proposing workstation layout and equipment changes in order to reduce the values of risk indexes.  
As stated in the previous Section 3.3.2, NIOSH lifting index can be reduced by:  
* reducing the vertical distance for the operations OP10 and OP20 of Figure 3, so the height of the shelf where the two components are located at the beginning of the working cycle;
* reducing the maximum horizontal distance between the assembly and the body for the operation OP60, which depends on the height of the support surface of the cart, where the assembly is placed.  
For this purposes, the shelf has been modified: its height from the ground has been reduced from 1400 mm, as in the previous configuration, 1170 mm. Since the workbench is 900 mm above the ground, the vertical dislocation has been significantly reduced.  
The new cart has been designed in order to significantly increase the height of the support surface from the ground: from 500 mm to 1030 mm. In addition, a break has been made to facilitate the positioning of the assembly. In this way the horizontal distance is reduced and the worker does not assume a posture with a large trunk flexion.  
Figure 10 shows the new workstation layout.  
Concerning, the counter-reaction force due to the screwing operations, the gun screwdriver has been replaced with an angle screwdriver (Figure 11) with 300 mm lever arm (size \"a\"), very commonly found on the market.  
Figure 11: Angle screwdriver; size of lever arm equal to “a”.  
Figure 10: Workstation layout after equipment changes.  
Figure 12 shows the working tasks after the workstation layout and equipment changes, simulated in Tecnomatix Process Simulate software environment.  
A simulation has been run in order to numerically evaluate the 4 selected risk indexes. Table 5 shows the risk indexes values related to the new workstation configuration and the comparison with the risk index values evaluated in the previous configuration.  
Results show that by modifying the workstation layout and equipment, the risk indexes drastically reduce.  
Concerning the critical issues that emerged in the previous analysis, the NIOSH index has been reduced by 38.5% and now it falls within the low risk area. Concerning the exerted forces, despite a reduction of 66%, counter-reaction forces still exceed the upper limit. A further solution could be to change the characteristics of the bolted joints in such a way that a lower tightening torque is required.  
However, a reduction in cycle time of about 4 s was also observed. Therefore, considering the unchanged production volume (900 pieces per shift), the recovery time for each cycle would increase. This could balance, at least in part, the biomechanical load due to the exerted forces, which exceed the maximum limit.  
Finally, although it already fell within the low risk area, the OWAS index was also reduced by 10%.
## 4 Discussion
The results obtained from the WoS country map and the Scopus country map are highly similar. Summing up, the most frequently occurring countries are the following: China, Italy, Spain, India, and Taiwan. The recently contributing countries are as follows: Tunisia, Saudi Arabia, Egypt, Morocco, Malaysia, and Pakistan. The most co-occurring countries are the following: Italy and Spain, Taiwan and United Kingdom,United Kingdom and China, and Finland and Spain. From these findings, we can conclude that though the leading positions in the ontology- and fuzzy-based IS field belong to China, Spain, Italy, India, and Taiwan, the research is expanding to new countries. We also observed that the leading countries in the ontology- and fuzzy-based IS topic have strong international collaboration (through co-occurrence matrix). Thus, we can conclude that such international collaboration among countries allows to develop the topic of interest and increase international visibility. Summing up, ontology and fuzzy logic contribute to IS topic by expanding traditional IS (MRQ) to be _intelligent IS_, which are applicable for solving complex, vague (fuzzy), and semantically rich (ontological) information collection, saving, processing, and sharing tasks in different application domains of various countries. Another significant advantage and result of this SMS is the usage of two scientific databases WoS and Scopus. It allows us to observe larger effects with sufficient precision and make more general conclusions. Moreover, two separate country maps for WoS and Scopus allow us to determine that found visible trends for the countries participating in the study of the ontology- and fuzzy-based IS field is similar. Based on this, we can summarize that if we want to get a rough view of the field of interest (i.e., the ontology- and fuzzy-based IS field) in a short time, it may be sufficient to use one database, i.e., either WoS or Scopus. However, if we want to explore the field of interest in depth, it is better to use more data sources, e.g., both WoS and Scopus. Based on the found answers to the defined research questions, we have proposed a better understanding of the ontology- and fuzzy-based IS field. It encourages further research directions for more effective and efficient IS development. However, some in-depth research methods, such as root cause analysis, co-citation, co-authorship, etc., will be necessary to apply in future research. Moreover, it would be useful to extend the current study to other scientific databases.
## 2 Configuration of FSSs  
With the proposed design of square-loop and circular-loop FSSs, periodic structures are comprised of an array of square loops and circular loops printed on both top and bottom sides of the supporting substrate. Figure 1a shows the layout of the square-loop FSS and the structure of the circular-loop FSS is shown in Figure 1b. The Roger 4003 substrate with loss tangent of 0.0027 and relative permittivity of 3.38 is used for the design of the proposed FSSs. The thickness of the dielectric substrate and metal thickness are chosen as 12 mil and 0.7 mil, respectively. Top and bottom sides of square-loop and circular-loop structures are symmetric as shown in Figure 1a,b.  
The FSS is interpreted as capacitive or inductive structures or sometimes a combination of both. Therefore, the FSS can be modelled as an equivalent circuit of \(L\)-\(C\) networks. For both square-loop and circular-loop array structures, the equivalent circuit model (ECM) can be identical and is shown in Figure 1c. The ECM is a combination of two series \(L\)-\(C\) networks and mutual coupling components for the top and bottom sides of the substrate. The substrate is modelled as a short transmission line, which has a thickness \(h\). When a perpendicularly polarized plane wave is impinged at the FSS, the capacitance is formed between gaps of the adjacent loops of the conducting material due to electric charge accumulation and the inductance occurs due to a flow of current around the adjacent loops. These electric charge densities are accumulated at the horizontal (\(x\)-direction) strips of the loops, creating the capacitive part \"C.\" Whereas, the magnetic field is induced around the vertical (\(y\)-direction) strips of the loops due to the flow of electric current that forms of an inductive part \"\(L\)\" in the ECM. Further, an electromagnetic interaction occurs between the top and bottom FSSs separated by the thin substrate, creating the mutual induction \"\(M\)\" and series capacitance \"\(C_{s}\)\" in the ECM ([PERSON] et al., 2016).
# Residual Level of Chlorpyrifos in Lettuces Grown on Chlorpyrifos-Treated Soils
###### Abstract  
This study was conducted to investigate the residual loss of chlorpyrifos and the amount transferred to lettuce from soil. Field trials on lettuce were conducted in two different greenhouses located in Yongin (field 1) and Gwangju (field 2) in Korea. Soil and lettuce samples were collected on different days after the treatment of chlorpyrifos at two different levels. The initial residue of chlorpyrifos (0.86 and 2.31 mg/kg) in soils decreased to 0.06 and 0.18 mg/kg, respectively, at 36 days after treatment (DAT) in field 1, and the initial residue with values of 11.10 and 22.59 mg/kg decreased to 1.20 and 3.04 mg/kg, respectively, at 43 DAT in field 2. In field 1, the half-lives of chlorpyrifos were approximately 8.4 and 9.0 days for soils treated with 0.86 and 2.31 mg/kg, respectively, while in field 2, the half-lives of chlorpyrifos were approximately 18.7 and 13.9 days for soils treated with 11.10 and 22.59 mg/kg, respectively. Residue levels of chlorpyrifos on lettuce were 0.66-5.98% and 2.71-13.26% compared to the initial concentration in the soil. Therefore, the management guideline of chlorpyrifos for lettuce-cultivating soils could be suggested to be 0.75 mg/kg with regards to the Positive List System level of chlorpyrifos on lettuce of 0.01 mg/kg.  
chlorpyrifos; lettuce; pesticide residue; translocation; soil +
Footnote †: journal: Applied Soft. 2018, 8, 2343; doi:10.3390/app8122343
## 4 Conclusions  
In conclusion, we have successfully prepared surface modified samples with plasma nitriding and PVD methods. The results indicate both methods significantly increase the surface hardness of the sample. Namely, the highest value for surface hardness was obtained when only PVD TiN/AlTiN coating was employed, 2938 HV0.01, followed by 2679 HV0.01 when both plasma nitridation and PVD coating was applied. In the case of the elastic modulus, the reverse trend can be observed with 166 GPa for the plasma nitridted and PVD-coated sample and 202 GPa for the PVD-coated sample. Surface roughness was lower, Ra = 0.1895 \(\upmu\)m, for the duplex coating in contrast to the PVD coating alone, Ra = 0.23335 \(\upmu\)m. The lowest wear coefficients, thus higher wear resistance, were measured when PVD coating was applied. Wear coefficients of K = 1.47 \(\times\) 10\({}^{-10}\) mm\({}^{3}\)/Nm and K = 8.46 \(\times\) 10\({}^{-10}\) mm\({}^{3}\)/Nm were measured for PVD/plasma nitridted and PVD coatings, respectively. These values are an order of magnitude lower, than that of plasma nitridted sample. The duplex system shows better wear resistance, which in our case correlates well with the elastic modulus of the tested surfaces, whereas the hardness values and the surface roughness for both PVD-coated samples were similar. Thus, the wear properties of a layer are dependent on the structural and mechanical properties of the previously applied layer. Hardening, plasma nitriding and PVD surface modification in sequence provide surfaces with lower elastic modulus and high toughness resulting in superior wear resistance.  
Conceptualization, E.B. and T.A.K.; methodology, L.T.; software, Z.N.; validation, L.T., and T.A.K.; formal analysis, A.L.G.; investigation, L.T.; resources, L.T.; data curation, Z.N.; writing--original draft preparation, E.B.; writing--review and editing, A.L.G.; visualization, T.A.K.; supervision, E.B.; project administration, E.B.; funding acquisition, E.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by THE HUNGARIAN STATE, NATIONAL RESEARCH, DEVELOPMENT AND INNOVATION OFFICE, grant number 2019-2.1.11-TET-2019-00093. The APC was funded by Transylvan Museum Society.  
Not applicable.  
\begin{table}
\begin{tabular}{c c} \hline
**Test Samples** & **K (mm\({}^{3}\)/Nm)** \\ \hline N-H & 6.32 \(\times\) 10\({}^{-9}\) \\ N-H-PN & 1.95 \(\times\) 10\({}^{-9}\) \\ N-H-PVD & 8.46 \(\times\) 10\({}^{-10}\) \\ N-H-PN-PVD & 1.47 \(\times\) 10\({}^{-10}\) \\ \hline \end{tabular}
\end{table}
Table 6: The wear coefficient of the tested samples.  
**Acknowledgments:** The authors wish to thank [PERSON] and [PERSON] for their technical support related to surface characterization. The authors acknowledge Surface Modification Technologies Pvt. Ltd (SMTPL) India, especially [PERSON], for providing the PVD-coated samples.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Deep Learning-Based Optimization for Maritime Relay Networks
## 2 System Model
### Marine Communication Environment  
In maritime environments, the highly dynamic and variable propagation characteristics of oceanic wireless channels render traditional modeling methods inadequate. To address this issue, a novel channel modeling approach is proposed, which integrates both large-scale and small-scale fading effects through the combination of statistical distribution models with time-series analysis. This approach is designed to provide a more accurate representation of wireless channel characteristics within oceanic environments. For simplicity, it is assumed that the statistical distribution of the channel follows a Rayleigh distribution, and the SNR is modeled as a random variable following an exponential distribution. Additionally, the temporal correlation of channel parameters, including the impacts of wave height and wind speed variations on channel characteristics, is taken into account. This facilitates a more accurate representation of the dynamic propagation characteristics of wireless channels in maritime environments. The channel model is formulated as follows:  
\[H(t)=g(t)\cdot\alpha(t), \tag{1}\]  
where \(g(t)=\frac{1}{L^{n}}\) represents large-scale fading, \(L\) denotes the distance, \(n\) is the path loss exponent and \(\alpha(t)\) represents small-scale fading.  
Furthermore, accurately obtaining instantaneous CSI in maritime environments is challenging. In traditional terrestrial communication systems, where complete CSI is available for each transmission link, the relay selected must satisfy the following condition:  
\[K^{*}=\arg\max_{1\leq k\leq K}R_{k}=\arg\max_{1\leq k\leq K}\frac{1}{2}\log \biggl{(}1+\frac{Pg_{k}}{\sigma^{2}}\biggr{)}, \tag{2}\]  
where \(R_{k}\) represents the end-to-end rate when the \(k\)-th relay is cooperating. \(K^{*}\) denotes the selected relay, while \(K\) is the total number of relay nodes.  
Figure 1: Maritime network foundation model.
## 3 Results and Discussion
### Confusion Matrices of NIRExpNet and Global Network  
In order to compare NIRExpNet and global network further, we list the confusion matrix of global network and NIRExpNet in Figure 5. It is seen that the NIRExpNet can achieve a higher recognition rate on every type of expression. The increases of the recognition rates are from 5% to 9%, except in the case of the recognition rate on happiness, which only increases 0.01%. This may be due to the fact that happiness already has a very high recognition rate (96.00%).  
It is observed from Figure 5 that anger is confused with disgust and sadness, disgust is confused with anger and fear, fear is confused with disgust and surprise, happiness is confused with surprise, sadness is confused with anger and fear, and surprise is confused with fear. This may indicate the movement pattern of one type of expression being somewhat similar to that of the other types of expression. However, the NIRExpNet can decrease the confusion on all types of expression, especially in the case of recognizing disgust. The global network confused disgust with anger and fear. However, the NIRExpNet only confused disgust with anger.  
One type of expression can be confused with two other types of expression. In some cases NIRExpNet selectively decreased one type of confusion more. In the case of recognizing fear, NIRExpNet mainly decreased the situation of confusing fear with disgust (from 17.50% to 8.00%). In the case of recognizing surprise, NIRExpNet mainly decreased the situation of confusing surprise with fear (from 17.56% to 9.41%). This may be because the movement pattern of fear is less similar to that of disgust, and the pattern of surprise is less similar to that of fear.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Architecture** & **Accuracy** \\ \hline U & 31.27\% \\ L & 43.87\% \\ U+L (Local) & 50.13\% \\ Global & 72.05\% \\ NIRExpNet (Global + Local) & 78.42\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons of different streams combinations.
## 4 Results
### Condition 1
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **Parameters** & **Controller** & **Settling Time** & **Peak Time (s)** & **Maximum** \\  & & **(s)** & & **Overshoot** \\ \hline \(1\) & \(\Delta\)f\({}_{1}\) & PI & 68 & 6 & 0.056 \\  & & Fuzzy PI & 45 & 7.8 & 0.051 \\  & & IT-2 Fuzzy PI & 39 & 7 & 0.01 \\ \hline  & \(\Delta\)f\({}_{2}\) & PI & 62 & 6 & 0.054 \\  & & Fuzzy PI & 40 & 8 & 0.049 \\  & & IT-2 Fuzzy PI & 38 & 7.8 & 0.009 \\ \hline  & \(\Delta\)f\({}_{3}\) & PI & 60 & 7 & 0.054 \\  & & Fuzzy PI & 38 & 7.9 & 0.05 \\  & & IT-2 Fuzzy PI & 30 & 8 & 0.009 \\ \hline  & \(\Delta\)ACE\({}_{1}\) & PI & \textgreater{}100 & 3 & 0.025 \\  & & Fuzzy PI & 50 & 3.5 & 0.02 \\  & & IT-2 Fuzzy PI & 45 & 2 & 0.01 \\ \hline  & \(\Delta\)ACE\({}_{2}\) & PI & \textgreater{}100 & 3 & 0.026 \\  & & Fuzzy PI & 55 & 3.5 & 0.021 \\  & & IT-2 Fuzzy PI & 42 & 2.5 & 0.009 \\ \hline  & \(\Delta\)ACE\({}_{3}\) & PI & 59 & 4.7 & 0.1 \\  & & Fuzzy PI & 70 & 9.8 & 0.065 \\  & & IT-2 Fuzzy PI & 60 & 8 & 0.008 \\ \hline  & \(\Delta\)P\({}_{\text{ie1}}\) & PI & \textgreater{}100 & 10.5 & 0.01 \\  & & Fuzzy PI & 60 & 20 & 0 \\  & & IT-2 Fuzzy PI & 52 & 15 & 0.008 \\ \hline  & \(\Delta\)P\({}_{\text{ie2}}\) & PI & \textgreater{}100 & 10.4 & 0.012 \\  & & Fuzzy PI & 60.5 & 19 & 0 \\  & & IT-2 Fuzzy PI & 57 & 15 & 0.009 \\ \hline  & \(\Delta\)P\({}_{\text{ie3}}\) & PI & 60 & 4.8 & 0.04 \\  & & Fuzzy PI & 55 & 10 & 0.065 \\  & & IT-2 Fuzzy PI & 52 & 7 & 0.01 \\ \hline
2 & \(\Delta\)f\({}_{1}\) & PI & 65 & 6 & 0.06 \\  & & Fuzzy PI & 48 & 7.8 & 0.075 \\  & & IT-2 Fuzzy PI & 42 & 6 & 0.008 \\ \hline  & \(\Delta\)f\({}_{2}\) & PI & 68 & 6 & 0.064 \\  & & Fuzzy PI & 47.5 & 7.8 & 0.075 \\  & & IT-2 Fuzzy PI & 39 & 6 & 0.01 \\ \hline  & \(\Delta\)f\({}_{3}\) & PI & 63 & 7 & 0.055 \\  & & Fuzzy PI & 48 & 8 & 0.074 \\  & & IT-2 Fuzzy PI & 35 & 5.6 & 0.012 \\ \hline  & \(\Delta\)ACE\({}_{1}\) & PI & \textgreater{}100 & 3 & 0.01 \\  & & Fuzzy PI & 55 & 3.6 & 0.012 \\  & & IT-2 Fuzzy PI & 43 & 2 & 0.009 \\ \hline  & \(\Delta\)ACE\({}_{2}\) & PI & \textgreater{}100 & 3 & 0.02 \\  & & Fuzzy PI & 49 & 3.5 & 0.022 \\  & & IT-2 Fuzzy PI & & & 0.01 \\ \hline  & \(\Delta\)ACE\({}_{3}\) & PI & 59 & 4.7 & 0.058 \\  & & Fuzzy PI & 68 & 9.8 & 0.1 \\  & & IT-2 Fuzzy PI & 48 & 6 & 0.013 \\ \hline  & \(\Delta\)P\({}_{\text{ie1}}\) & PI & \textgreater{}100 & 10.5 & 0.01 \\  & & Fuzzy PI & 55 & 22.2 & 0.052 \\  & & IT-2 Fuzzy PI & 46 & 18 & 0.008 \\ \hline  & \(\Delta\)P\({}_{\text{ie2}}\) & PI & \textgreater{}100 & 10.4 & 0.01 \\  & & Fuzzy PI & 66 & 20 & 0 \\  & & IT-2 Fuzzy PI & 50 & 15 & 0.007 \\ \hline  & \(\Delta\)P\({}_{\text{ie3}}\) & PI & 60 & 4.8 & 0.048 \\  & & Fuzzy PI & 70 & 10 & 0.058 \\  & & IT-2 Fuzzy PI & 58 & 7.1 & 0.007 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance analysis of various control approaches used in the test system.