esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Tracking Results  
In this part we introduce the different results according to the several stages of tests. Figures 1 and 2 show the capacity of V9 transmitters according to depth and distance to the receptor. The signal is received at the surface using the VH165 omnidirectional hydrophone.  
In the two cases, we see that the V9 transmitters are limited by the depth and the distance. In Figures 3 and 4 we test the capacity of the V13 tags to broadcast a signal according to two scenari. The capacity of V13 to transmit a message according to the distance to the receptor is illustrated by Figure 3 and the capacity of the transmitters V13 under extreme conditions is shown in Figure 4. In order to better anticipate the behavior of a spider crab, we tested the power of a transmitter under a rock. We were not able to capture the signal from a V9 under a submerged rock. On the other hand, Figure 4 shows that we can capture the signal from a V13 under the same conditions up to 170 m.  
Figure 1: Relation between depth and signal power for V9 tags.  
Figure 2: Relation between distance and signal power for V9 tags.  
According these tests, we decide to equip the spider crab with V13 TP VEMCO sensor (Figure 5) and to start an active tracking campaign on an individual Maja 1 during two months.  
Figure 4: Relation between distance and signal power for a V13 tag under a rock.  
Figure 5: Maja 1 equipped by a V13 TP VEMCO transmitter.  
Figure 3: Relation between distance and signal power for V13 tags.  
To perform this monitoring, two researchers went every day to the study site with a small boat. Due to the size and characteristics of the boat, and for safety reasons, it was impossible to track the spider crabs in severe weather and sea conditions. The results are illustrated by Figure 6. In Figure 6, we distinguish the different positions of the [PERSON] individual equipped with a V13 TP. The color intensity of each point determines the signal strength. In Figure 6, we have represented the trajectory of [PERSON] according to the dates of recorded data during the two months.  
Figure 6 shows an interesting result: the trajectory of [PERSON]. Indeed we can observe a displacement along isobaths (blue lines). It is very important because that the choice of V13 transmitters and active tracking allowed us to monitor a part of the crab behavior. This result confirms that the technology can be used to study a species.
## 5 Mechanisms Used by Industrial Technologies to Improve Performance Metrics
### MAC Layer Contention Mechanism and Communication Scheduling  
A MAC protocol can generally be designed to operate using two mechanisms: (i) contention-free (scheduled communication) and (ii) contention-based. Contention-free approaches, e.g., dedicated timeslot-based, are more suitable for supporting real-time communication while shared timeslots (_i.e._, contention-based mechanisms) favor soft real-time applications.  
Contention-based communication protocols, such as CSMA, are unable to provide timing guarantees when delivering messages. They are prone to packet loss by the hidden terminal problem (internal interference). Since ZigBee Pro runs on a CSMA-based MAC protocol, it is unsuitable for applications that require reliable and timely packet delivery, although WIA-PA and ISA100.11a use a CSMA-based MAC (slow hopping) for subnet discovery and retries. These latter are capable of switching to a slotted scheme where every link is scheduled to transmit at a predefined slot (TDMA) and channel offset, thereby avoiding the issue of internal interference. This mechanism is shown in Figure 2, in which the combination of slow hopping and slotted hopping is displayed. A similar form of communication scheduling is also used in TSMP, WirelessHART, and IEEE 802.15.4e.  
Both WISA, WirelessHART, and WIA-PA use TDMA-based mechanisms with exclusively dedicated timeslots which do not support any variation in traffic [13]. However, ZigBee Pro, ISA100.11a, and the 802.15.4e MAC standard allow the user or centralized system manager to configure the timeslot length. This could be advantageous for coping with variable data traffic rates on the network which could be a characteristic of factory automation applications requiring real-time operations. However, as individual nodes are unable to make autonomous decisions, existing technologies are unable to provide hard real-time guarantees, especially in the presence of variable data traffic.  
ISA100.11a and WirelessHART use a superframe management technique to maintain real-time communication for high traffic loads. The superframe period can determine several network performance parameters, such as packet delivery latency, energy consumption, and bandwidth utilization. ISA100.11a allows tradeoffs by enabling the system manager to determine the superframe period. A shorter-period superframe results in lower packet delivery latency and higher bandwidthutilization, but results in greater energy consumption, while a longer-period superframe has the opposite effect. ZigBee Pro and WIA-PA (in the cluster/star level) also allow the transmission of superframes with different lengths in the beacon mode.
## 2 Problem Description  
### Description of FPTLIR  
HIR mainly consists of a travelling mechanism and a flying mechanism. In some HIRs, the travelling mechanism is placed above the flying mechanism, whereas in others, it is located below it. The travelling mechanism may be either two-wheeled or one-wheeled. This paper focuses on a one-wheeled HIR, specifically the FPTLIR, as the object of study.  
The FPTLIR mainly consists of two main components: the flying mechanism and the travelling mechanism, as shown in Figure 1. The flying mechanism is based on a hexacopterdrone and includes folding arms, a control chamber, motors, and rotor blades [29]. The travelling mechanism mainly consists of rolling and pressing components. The rolling components include the travelling wheel and auxiliary guide wheels, while the pressing components include the main pressing wheel and auxiliary pressing wheels.  
The FPTLIR primarily focuses its inspection along the ground wire, a kind of PTL. The inspection process can be divided into three primary phases: flight landing, walking inspection along the ground wire, and flight detachment. During the flight landing phase, the FPTLIR uses its flying mode to attach itself to the PTL. The robot can choose different points to attach, as shown in Figure 1; the red circle indicates different points, where point 1 is in the middle of the span and point 11 is closer to the power towers. It then switches to the travelling mode to perform the inspection along the PTL. Upon the completion of its multi-span inspection tasks, it returns to flying mode to detach from the PTL and descend to the ground. The groove angle of the travelling wheel is 100\({}^{\circ}\), the width is 80 mm, and the diameter is 150 mm. The diameter of the PTL is 18 mm, and the length is of the span is 200 m. Some key parameters are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Type** & **Parameter/Unit** & **Value** \\ \hline Mass/kg & 38 \\ Dimension/(mm \(\times\) mm \(\times\) mm) & 1250 \(\times\) 1250 \(\times\) 1200 \\ Moment of inertia about the x-axis/kg & 5.8 \\ FPTLIR parameters & Moment of inertia about the y-axis/kg & 5.8 \\ Moment of inertia about the z-axis/kg & 8.5 \\ Diameter of travelling wheel/mm & 150 \\ Groove angle of travelling wheel/\({}^{\circ}\) & 100 \\ Groove width of travelling wheel/mm & 80 \\ \hline PTL length/m & 200 \\ Linear density kg/m & 0.91 \\ PTL parameters & Axial stiffness/N/m & \(8.12\times 10^{7}\) \\ Bending stiffness/N\(\cdot\)m\({}^{2}\) & 6580 \\ Diameter/mm & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: FPTLIR/PTL system parameters.  
Figure 1: Schematic diagram of the FPTLIR working in an overhead PTL environment.
# Repellency of Veratraldehyde (3,4-Dimethoxy Benzaldehyde) against Mosquito Females and Tick Nymphs
## 1 Introduction
Veratraldehyde (3,4-dimethoxybenzaldehyde), found among the constituents of the essential oils of _Cymbopogon javanensis_, _Erngium potterium_, _Rubus ideas_ (raspberry), _Zingiber officinal_ (ginger) and _Mentha piperita_ (peppermint) [22], is an organic compound that is widely used as a flavor and odorant. Veratraldehyde is used in the preparation of 4-chloromethyll-2- (dimethoxyphenyl)-1,3-dioxolane, which is used in the synthesis of (+)-lithospermic acid and has anti-HIV activity [23]. Veratraldehyde is related to benzaldehyde and it is commercially popular because of its pleasant woody fragrance. In a high-throughput bioassay using the yeast _Saccharomyces cerevisiae_ to identify phenolic agents for the control of the aflatoxinic fungus _Aspergillus flavus_, veratraldehyde showed significant antifungal activity [24]. Veratraldehyde is used for H\({}_{2}\)O\({}_{2}\) production by _Pleurotus evrggii_, and it is reduced by aryl-alcohol dehydrogenase to its corresponding alcohols, which are oxidized by aryl-alcohol oxidase, producing H\({}_{2}\)O\({}_{2}\)[25]. Veratraldehyde (CAS no. 120-14-9) is also listed as a food additive in the Korea Food Additives Code [26]. However, the repellent activity of veratraldehyde against mosquitoes and ticks is not known. In this study, to develop new natural repellents comparable to conventional arthropod repellents, such as the synthetic compound DEET, the repellent activity of veratraldehyde and pump spray formulations containing 10% veratraldehyde as active ingredients against _A. albopictus_ females, _C. pipiens pallens_ females and _H. longicornis_ nymphs as well as the absorption of veratraldehyde via skin was evaluated under laboratory and indoor conditions.
## Materials and Methods
### Analysis Methods
#### 2.3.1 Selection of Candidate Spectral Features
The indices and their groupings are as follows: indices used for the identification of pigment variation: Modified Chlorophylll Absorption Reflectance Index (MCARI), The Transformed Chlorophylll Absorption and Reflectance Index(TCARI), Chlorophyll Absorption Ratio Index (CARI), Triangular Vegetation Index (TVI), Anthocyanin Reflectance Index (ARI), Normalized Total Pigment to Chlorophyll Ratio Index (NPCI), Structure Insensitive Pigment Index (SIPI); for biophysical parameters: Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Narrow-Band Normalized Difference Vegetation Index (NBNDVI), Modified Simple Ratio (MSR), Plant Senescence Reflectance Index (PSRI); for water and nitrogen content: Nitrogen Reflectance Index (NRI); for photosynthetic activity: Photochemical Reflectance Index (PRI), The Physiological Reflectance Index (PHRI); for greenness: Greenness Index (GI). The calculation formulas of the 16 vegetation indices are shown in Table 4. \begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Features** & **Definition and Description** \\ \hline \multirow{8}{*}{Spectral index feature} & SDr/SDb & The ratio of SDr to SDb \\  & SDr/SDy & The ratio of SDr to SDy \\  & SDr/SDg & The ratio of SDr to SDg \\  & SDy/SDb & The ratio of SDy to SDb \\  & SDg/SDb & The ratio of SDg to SDb \\  & SDr \(-\) SDb)/(SDr + SDb) & Normalized value of SDr and SDb \\  & (SDr \(-\) SDy)/(SDr + SDy) & Normalized value of SDr and SDy \\  & (SDr \(-\) SDg)/(SDr + SDg) & Normalized value of SDr and SDg \\  & (SDy \(-\) SDb)/(SDy + SDb) & Normalized value of SDy and SDb \\  & (SDg \(-\) SDb)/(SDg + SDb) & Normalized value of SDg and SDb \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c} \hline
**Features** & **Definition and Description** \\ \hline W1 & Band width to the left of the absorption feature \\ W2 & Band width to the right of the absorption feature \\ A & Absorption area of the absorption feature \\ \(\lambda\) & The band position of the absorbing feature \\ H & Absorption peak depth relative to the envelope in the 550–750 nm band \\ \hline \end{tabular}
\end{table} 
Table 3: Spectral absorption features based on continuum removal.
# Dense Pure Tungsten Fabricated by Selective Laser Melting
## 3 Results
### Microstructure Characterization  
Dense pure tungsten blocks were successfully prepared with the following SLM parameters: P = 400 W, ET = 200 \(\upmu\)s, pd = 75 \(\upmu\)m, and hd = 100 \(\upmu\)m. In Figure 8a, a SEM image of the surface of this sample shows it to be smooth with wheat-like ripples. From the top face inverse pole figure (IPF) map (Figure 8b) revealed by electron backscatter diffraction (EBSD) mapping, on the whole, and equiaxed grain morphologies, which can be ascribed to the rotated laser scanning between layers, were observed. The white dashed lines outline part of the molten track whose width is decided by the scanning hatch distance (hd). Inside the molten track, directional solidification occurs, resulting in a slender gain shape. In addition, random crystallographic orientation is observed, which may be related to the layer-by-layer rotating scanning strategy [12]. Heterogeneous grain size distribution can be observed in the IPF map of the top face. The D\({}_{10}\), D\({}_{50}\), and D\({}_{90}\) data of the grain size distribution are detected as 1.81, 3.61, and 17.5 \(\upmu\)m, respectively. The average grain size (taken as 5.12 \(\upmu\)m), is much smaller than the laser spot diameter of 75 \(\upmu\)m. Here, the fine grains result from the fast cooling conditions in the selective laser melting of tungsten. However, as marked by the white \"x\", there are some extra-large grains (>100 \(\upmu\)m) which may be related with local overheating around cracks.  
Microstructural anisotropy is found in the top and side face of sample, as shown in Figure 8c,d. In the top face, an equiaxed grain structure is observed, while in the side face, the grains tend to grow along the building direction. During the SLM process, the maximum temperature gradient is basically  
Figure 8: **(a)** SEM image of surface morphology; **(b)** surface inverse pole figure (IPF Z) map detected by EBSD (electron backscatter diffraction) in which misorientation above 15\({}^{\circ}\) is adopted to identify grain boundaries. Grain orientation of ( 001), (101), and (111) are presented by red, green and blue color, respectively; **(c)** SEM image of the surface microstructure; and **(d)** SEM image of the side face microstructure.  
along the building direction and, thus, the grains prefer to grow along the BD (building direction). Some micro-cracks are pointed out by the white arrows and need to be considered in future studies.
## 2 System Design
### Hardware Design  
#### 2.2.1 Adjustable Automatic Oscillator Circuit Design  
The circuit of this study comprises a buck circuit, timer circuit, and amplifier circuit. The system produces continuous pulse outputs through energized coil and pig iron that generates electromagnetic effects and produces magnetic forces. Adjusting the frequency of the electric field produces a fixed force that activates oscillating impact-echo device to regularly generate impacts on a concrete object.  
1. First-level buck circuit: For future applications, our lab designed the system based on simplicity, portability, and convenience. Thus, resistor-capacitor buck circuits were adopted to reduce volume and cost, and to replace cumbersome transformers. For rectification, a bridge rectifier that was composed of four 1N4007 diodes was adopted. Subsequently, nine 1K5W (1K Ohm can withstand 5 watts power) cement resistors that limit capacitance were connected to the charging current. Regarding filters, four 220V470 uf (470 Farad can withstand 220 voltage) were used in the AC circuit; reactance caused by the capacitors resulted in the reduction of voltage to meet the voltage required for the electrical load. As for the voltage regulation, three 1K5W cement resistors were designed to be voltage dividers, and Zener diodes were used to maintain a specific voltage.
2. Second-level IC555 timer circuit: The output signal from the buck circuit of the previous level was 5 V, which could activate IC555 to produce modulation at periods ranging from microseconds to hours. Next, R1 and R2 (precision variable resistors) were used to adjust the frequency to generate a complete and continuous square wave signal.
3. Third level amplifier circuit: Because the output signal from the previous level had a low current value (5 V) and because the current amplification ratio of a single transistor was limited and could not drive the load of a large power system, two 2 SC3457 transistors were used to construct an amplifier to improve high-frequency characteristics and prevent abnormal power loading from damaging the elements. Therefore, high power was acquired to drive enameled wire for charging or discharging. In addition, the on and off positions of two light-emitting diodes was used by our lab to indicate whether the amplifying circuit was connect. The overall amplifier circuit is illustrated in Figure 2.  
Before the hardware (circuits) was connected to the software (the programs), it was required to undergo simulation and verification tests using Multisim (It is an electronic schematic capture  
Figure 2: Overall circuit diagram.  
and simulation program. Production company location in Austin, Texas, USA) After the hardware system settings were finalized, the next step was to transmit echo signals to the computer program for anomaly analysis. The overall experimental setup is presented in Figure 3.
# Coherent Chaotic Communication Using Generalized Runge-Kutta Method
## 2 Materials and Methods
### Analysis of the Gokyildirim Discrete Model  
In this study, we used the Gokyildirim et al. chaotic oscillator [48] as a mathematical model for designing the discrete chaos generator. This system is newly reported and has not been thoroughly explored yet; therefore it is of great interest for both theoretical studies and possible applications. The Gokyildirim oscillator is described by the following system of ordinary differential equations:  
\[\begin{split}\dot{x}&=y+xy-yz;\\ \dot{y}&=x+ayz;\\ \dot{z}&=by^{2}+xy;\end{split} \tag{8}\]  
where \(a=0.1\), \(b=0.7\).  
The finite-difference model of the system was obtained by applying the RK-alpha integration method (5) to system (8):  
\[\begin{split} h_{\alpha}=\alpha h;\\ x_{n+\alpha}=x_{n}+h_{\alpha}(y_{n}+x_{n}y_{n}-y_{n}z_{n});\\ y_{n+\alpha}=y_{n}+h_{\alpha}(x_{n}+ay_{n}z_{n});\\ z_{n+\alpha}=z_{n}+h_{\alpha}(by_{n}^{2}+x_{n}y_{n});\\ x_{n+1}=x_{n}+h(\alpha_{1}(y_{n}+x_{n}y_{n}-y_{n}z_{n})+\alpha_{2}( y_{n+\alpha}+x_{n+\alpha}y_{n+\alpha}-y_{n+\alpha}z_{n+\alpha}));\\ y_{n+1}=x_{n}+h(\alpha_{1}(x_{n}+ay_{n}z_{n})+\alpha_{2}(x_{n+ \alpha}+ay_{n+\alpha}z_{n+\alpha}));\\ z_{n+1}=x_{n}+h(\alpha_{1}(by_{n}^{2}+x_{n}y_{n})+\alpha_{2}(by_ {n+\alpha}^{2}+x_{n+\alpha}y_{n+\alpha})).\end{split} \tag{9}\]  
where \(\alpha\) is a value of the parameter alpha.  
Let us investigate the influence of system (8)'s parameters on its dynamics. Figure 1 shows the two-dimensional bifurcation diagram (parametric chaotic set) plotted for parameters \(a\) and \(b\). Note that the density-based spatial clustering of applications with noise (DBSCAN) clustering method for periodicity calculation was used to plot all the two-dimensional bifurcation diagrams in this study. The number of clusters is indicated by the color. The color on the two-dimensional bifurcation diagram indicates the complexity of the system dynamics: the closer the color is to red, the more chaotic behavior the system exhibits. It is reasonable to choose system parameters as close to red zones as possible for practical chaotic signal generation.  
One can observe that multistable behavior exists in the investigated system for certain parameter combinations. Figure 2 shows the basins of attraction and the phase space for two sets of parameter values of the discrete Gokyildirim et al. system obtained using the RK-alpha integration method.  
The next subsection investigates the dependence between the dynamics of the discrete system and the alpha parameter.  
Figure 1: A 2D bifurcation diagram based on parameters \(a\) and \(b\).
# Use of Real-Time GNSS-RF Data to Characterize the Swing Movements of Forestry Equipment
## 3 Results
### Swing Angle Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Model** & \(n\) & \(C^{-}_{\text{\#}_{0}}\) & \(C^{+}_{\text{\#}_{0}}\) & \(I^{-}_{\text{\#}_{0}}\) & \(I^{+}_{\text{\#}_{0}}\) & \(I^{0}_{\text{\#}_{0}}\) & **Result** & \(C^{-}_{\text{\#}_{0}}\) & \(C^{+}_{\text{\#}_{0}}\) & \(I^{+}_{\text{\#}_{0}}\) & **\(I^{+}_{\text{\#}_{0}}\)** & **\(I_{1}\) Result** \\ \hline
**G.SU.2.5** & 72 & 186.29 & 194.13 & 161.88 & 197.85 & Reject & 0.988 & 1.057 & 0.9 & 1.1 & Reject \\
**G.SU.5** & 72 & 184.46 & 195.91 & 146.23 & 178.72 & Fail & 0.934 & 1.042 & 0.9 & 1.1 & Feject \\
**G.SU.10** & 65 & 188.92 & 221.63 & 141.74 & 173.23 & Fail & 0.539 & 0.876 & 0.9 & 1.1 & Fail \\
**H.SU.2.5** & 72 & 175.16 & 205.88 & 156.19 & 190.90 & Fail & 0.533 & 0.779 & 0.9 & 1.1 & Fail \\
**H.SU.5** & 72 & 177.62 & 203.34 & 157.62 & 192.64 & Fail & 0.717 & 0.972 & 0.9 & 1.1 & Fail \\
**H.SU.10** & 64 & 192.70 & 220.46 & 129.30 & 158.04 & Fail & 0.625 & 0.893 & 0.9 & 1.1 & Fail \\
**G.R.2.5** & 68 & 195.02 & 205.03 & 144.68 & 176.83 & Fail & 0.946 & 1.035 & 0.9 & 1.1 & Reject \\
**G.R.5** & 66 & 196.55 & 212.46 & 132.87 & 162.40 & Fail & 0.886 & 1.044 & 0.9 & 1.1 & Fail \\
**G.R.10** & 44 & 229.89 & 263.50 & 95.75 & 117.03 & Fail & 0.468 & 0.902 & 0.9 & 1.1 & Fail \\
**H.R.2.5** & 67 & 187.15 & 218.16 & 160.66 & 196.36 & Fail & 0.603 & 0.920 & 0.9 & 1.1 & Fail \\
**H.R.5** & 63 & 199.18 & 221.60 & 144.72 & 176.88 & Fail & 0.864 & 1.097 & 0.9 & 1.1 & Fail \\
**H.R.10** & 48 & 216.13 & 245.22 & 119.30 & 145.81 & Fail & 0.699 & 1.022 & 0.9 & 1.1 & Fail \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of equivalence-based regression results. The model represents the various combinations of transmission interval (2.5,5,10); transponder location, grapple (G) or heel (H); and cycle element, swing unload (SU), and return (R). Sample size is denoted by n, and the approximate joint two one-sided 95% confidence intervals for the slope and intercept are: (\(C^{-}_{\text{\#}_{1}}\), \(C^{+}_{\text{\#}_{1}}\)) and (\(C^{-}_{\text{\#}_{0}}\), \(C^{+}_{\text{\#}_{0}}\)), respectively. The former should be contained by the intercept interval of equivalence, (\(I^{-}_{\text{\#}_{0}}\), \(I^{+}_{\text{\#}_{0}}\)) = \(\overline{y}\pm 25\%\), and the latter by the slope interval of equivalence (\(I^{-}_{\text{\#}_{1}}\), \(I^{+}_{\text{\#}_{1}}\)) = 1 \(\pm\) 0.25. Figure 6: The GNSS-RF transponders create a movement progression track between data points through the duration of the trials which are shown for the two (2) transponder locations and three (3) transmission intervals used in the experiment in addition to the cab location.