esa-sceva/satcom-chunk-collection huggingface dataset:
# New Miniature Narrow Band Microstrip Diplexer for Recent Wireless Communications
## 1 Introduction  
Over the past few years, numerous ground-breaking advancements have been made in wireless communication systems. These include introducing ultra-wideband systems, the rapid development of wireless internet technologies like Wi-Fi and WiMAX, and mobile wireless communication systems that employ 3G and 4G innovations. More robust radio frequency components are required to keep up with the pace of development. Satellites have shifted their focus from strictly stationary communications to dynamic fields, including remote sensing, maritime applications, and mobile [1].  
Various communication systems rely heavily on multiplexers. Broadband wireless communications, radio transmission, satellite communication, and so on are all examples. The most elementary multiplexer design is called a diplexer. It enables the use of a single antenna by two transmitters operating on separate channels. For various cellular radio system standards, diplexer technology is employed in both the base station and the radio handset. The diplexer features a power divider and two filters with defined frequency responses, physical dimensions, and bandwidths, as seen in Figure 1. The transmit filter must withstand the rather powerful signals generated by the transmitter. However, the receiver must be sensitive enough to notice weak signals. In addition, diplexers are commonly employed in the transceivers of broadband wireless access communication systems. Increased demand for these broadband access systems has increased the need for tiny, low-cost diplexers. These diplexers separate incoming and outgoing signals in the front end [2].  
The negative group delay (NGO) has recently garnered a lot of attention, particularly in the realm of RF and microwave devices. The NGD idea is a relatively new field of electrical and electronic engineering study. It is predicated on an uncommon function: producing an output signal that appears to be generated in advance of the input signal. Due to the long delays and signal distortion that arise with positive group delay (PGD), achieving a certain quality of signal transmissions needs attention to signal phase characteristics in addition to signal amplitude characteristics. The NGD function has been used to create innovativedesign features of RF and microwave electronic devices, such as power dividers, filters, antennas, and amplifiers, as well as to produce experimental high-performance electronic devices [3;4].  
This work shows a new small microstrip diplexer built on a Kappa substrate. This diplexer uses a variety of step impedance resonators and uniform impedance resonators along with input and output ports. As can be seen in its frequency responses, the projected diplexer exhibits effective band isolation of greater than 35 dB. It also exhibits very narrow bands at each channel, with negative group delays at several frequency values.
## 3 Results
### Hatching Rate of Zebrafish and African Clawed Frog Embryos  
The detailed results of the hatching rate in the individual embryonic tests on zebrafish are highlighted in Figures 1-3. The first hatching was recorded at 72 hpf; 16.7% of the embryos in the control group were hatched within this observation time. In most of the experimental groups, a higher hatching rate compared to the control group was recorded at this time. At 72 hpf, statistically significant earlier hatching was found in the experimental groups exposed to fluoxetine hydrochloride at 1 ug/L (52.8% of hatched embryos, \(p<0.01\)), citalopram hydrochloride at 1 ug/L (58.3% of hatched embryos, \(p<0.01\)), 10 ug/L (54.3% of hatched embryos, \(p<0.01\)), and 100,000 ug/L (55.9% of hatched embryos, \(p<0.01\)). Similarly, significant differences in the hatching rate compared to the control group were also observed in the embryonic test with citalopram hydrochloride and fluoxetine hydrochloride as a mixture at 72 hpf. Statistically significant (\(p<0.01\)) earlier hatching was recorded in all the tested concentrations at 72 hpf. A total of 62.9, 69.7 and 65.7% of hatched embryos was found in the experimental groups exposed to the mixture of antidepressants at low, medium, and high concentrations, respectively. No significant differences in the hatching rate between the control and experimental groups were found at 96 hpf; almost all the embryos were hatched at this time.  
Figure 1: The hatching rate (%) of zebrafish exposed to fluoxetine hydrochloride (hpf—hours post-fertilization). The asterisk indicates a significant difference (**\(p<0.01\)) between the control and the experimental groups at the same time of exposure.  
In the embryonic test on the African clawed frogs, the first hatching was already recorded at 48 hpf (data not shown). At this time, all the embryos in the control group were hatched. Similarly, most embryos in the experimental groups finished their hatching in this time with the exception of the experimental groups exposed to citalopram hydrochloride at 0.1 ug/L and 10,000 ug/L (only 88.9% of hatched embryos) and fluoxetine hydrochloride at 1 ug/L (only 94.4% of hatched embryos). However, the statistical analysis did not reveal any significant differences (\(p>0.05\)) in the hatching rate between the control and experimental groups at the same time of exposure.
# Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network
## 2 Rotor Fault Diagnosis Based on VMD Symmetric Polar Image and Fuzzy Neural Network  
A novel method for fault feature extraction and pattern recognition is proposed to eliminate the influence of environmental noise on the precision of extracting feature information of symmetrical mirror image of rotor vibration signals in this paper.  
First of all, VMD is applied to decompose the rotor vibration signal and comprehensive evaluation factor (CEF) method is used to reconstruct the signal. Secondly, two-dimensional feature parameters of the snowflake image of the reconstructed signal are calculated to construct multidimensional feature vectors. In the end, the fuzzy neural network is applied for rotor fault diagnosis. Flow chart of the proposed method is shown in Figure 1.  
1. Collection of rotor vibration signal. The vibration signals of various fault states of rotor are collected by the ZT-3 rotor fault-simulating experimental device.
2. Decomposition of the signal. VMD is applied to decompose rotor vibration signals into several IMF components. During VMD decomposition, the parameters K and \(\alpha\) are selected according to the principle of the maximum value of center frequency and the principle of minimum value of multi-scale fuzzy entropy (MFE).
3. Signal reconstruction. In order to eliminate environmental noise, the background signal and other disturbing components, and retain useful IMF components, the original rotor vibration signal is reconstructed by the CEF method.
4. Feature extraction. The reconstructed signal is transformed into a two-dimensional snowflake image through the symmetrical polar coordinate method. Two-dimensional feature parameters of the snowflake image are used to form a state feature vector, which can be obtained by the gray level co-occurrence matrix.
5. Fuzzy neural network recognition. The extracted state feature vectors are randomly allocated into two sets, which are the training sample set and the test sample set, respectively. The training sample set is used for training the fuzzy neural network. The testing data is imported into the well-trained fuzzy neural network. Then different working conditions of the rotor can be recognized and the recognition results can be automatically output.  
Figure 1: Flow chart of rotor fault diagnosis based on VMD symmetric polar image and fuzzy neural network.
###### Abstract  
Low-frequency noise, the frequency range from approximately 10 Hz to 200 Hz, has been recognized as a special environmental noise problem. The World Health Organization recognizes the special place of low-frequency noise as an environmental problem. Noise can damage hearing, and it affects the whole body. Low-frequency sound is heard by humans, but infrasound is not audible. Low-frequency sound is most often measured based on a weighting function of the frequency. The A-weighted level underestimates the effects of low-frequency noise. For the detrimental effects of low-frequency sound, it would be appropriate to apply measurements using Z-weighting. The aim of this paper was to propose a comprehensive method of acoustic risk assessment (CMARA) that implements the effects of low-frequency values of noise exposure. The proposed methodology has been applied in practice at four workplaces for seven work activities. A risk assessment using the proposed CMARA method for individual activities shows that the noise exposure time may pose a health risk in the occupational and environmental process due to exposure to low-frequency noise at the limit of audibility. A high risk was assessed for activities WA2 (machining) and WA3 (spot welding). This paper highlights the need to measure low-frequency noise using Z-filter weighting.  
acoustics risk assessment; low-frequency noise; prevention of acoustic risks Article
# Axial Compression Behavior of Steel Angle-Corrugated Steel Plate-Confined Concrete Columns
## 3 Experimental Results and Discussion
### Load-Axial Displacement Curves  
Figure 6 shows the forms of the load-axial displacement curves of the SA-CS columns and the CFST-1 column, which were similar. The value of the ultimate bearing capacity (\(N_{\text{u}}\)) and corresponding displacement (\(\Delta_{\text{u}}\)) of the specimens are also illustrated in Figure 6. The \(N_{\text{u}}\) and \(\Delta_{\text{u}}\) of the two types of columns were not compared in this section due to the different section sizes, and only the load-axial displacement curves of the SA-CS columns with varying section widths were compared. According to the information in the figure, it can be seen that both the bearing capacity and deformation capacity of the specimens improved with the increase in the section width. Compared with the SA-CS-230 column, the \(N_{\text{u}}\) and \(\Delta_{\text{u}}\) of the SA-CS-280 column improved by 38.24% and 5.96%, and those of the SA-CS-330 column enhanced by 85.32% and 14.51%, respectively.  
Regarding the CFST-2 column, this section does not compare its load-displacement curves due to the different material properties. The \(N_{\text{u}}\) and \(\Delta_{\text{u}}\) of the CFST-2 column were 1813.5 kN and 1.18 mm, respectively.
## 4 Thermal Simulation  
### Thermal Resistance Calibration and Model Semplification  
As it was stated in Section 2.2, surface power loss has been imported in the thermal simulation environment from the output of the electromagnetic simulation. More specifically, power losses have been mapped onto the geometrical _xyz_ coordinates. This obtained association has been exported in a dedicated text file that has been imported in the thermal simulation. The CAD model geometry must be exactly the same as the previous electromagnetic stage in order to ensure the proper power loss mapping in both simulation environments. It is performed a steady-state simulation, by using the FEM-based COMSOL software, in order to obtain the temperature field inside the package. In the first instance, thermal resistance \(\mathrm{R_{thj,a}}\) at steady-state between junction (i.e., the chip) and ambient was calculated [24]. It was considered to place the package on a 1.6 mm-thick PCB with four copper layers, as depicted in Figure 8; a 100 \(\upmu\)m-thick solder was interleaved between the  
Figure 6: Surface current density, with a current equal to 90 A at 500 kHz as input.  
Figure 7: Volume loss density, with a current equal to 90 A at 500 kHz as input.  
PCB and the package. Convective heat flux was implemented as a boundary condition, with a convective coefficient equal to 10 W/m\({}^{2}\)K that reproduces the heat exchange with still air at 20 \({}^{\circ}\)C. Heat power (1 W) was forced on the top junction surface. Rthj-a was finally calculated as the difference between the average junction temperature and air temperature (20 \({}^{\circ}\)C) and results equal to 40 \({}^{\circ}\)C/W.  
In the following, and in the framework of the presented integrated electromagnetic-thermal approach, a simplified thermal FEM has been developed. In this last thermal model, PCB was not modeled in order to make the simulation simpler and quicker. An equivalent heat transfer coefficient has been searched for to be applied on the SiP-exposed pad in order to fit the Rthj-a found in the previous and more detailed (i.e., with PCB) simulation (40 \({}^{\circ}\)C/W). The obtained equivalent heat transfer coefficient was 750 W/(m\({}^{2}\) K), not representative of real heat exchange with the air, being only a fitting parameter. Three-dimensional tetrahedral mesh is considered. In Table 2, the material properties or the simulated objects, retrieved from suppliers, are shown.
###### Abstract  
By using the two-frequency coherence function model of a beam in a turbulent medium and the localized wave theory of the polychromatic beam, we develop the spectrum average mutual coherence function of the localized wave of Bessel-Gaussian amplitude envelope and the spectrum average coherence length of spherical wave. By the spectrum average coherence length and the spectrum average mutual coherence function, we construct a received probability of vortex modes carried by localized wave of Bessel-Gaussian amplitude envelope in anisotropic turbulent seawater. Our results show that the received probability of signal vortex modes increases with the increase of half-modulated pulse width of the input pulse, turbulent inner scale, anisotropic factor of turbulence and rate of dissipation of kinetic energy per unit mass of fluid, but it increases with the decrease of the Bessel cone angle and the dissipation rate of the mean-squared temperature. We also find that there is a maximum effective beam waist for a given receiving aperture, and the vortex mode is more sensitive to salinity fluctuations than to temperature fluctuations in turbulence. Our conclusions show that localized wave of Bessel-Gaussian amplitude envelope is a more suitable beam for the vortex mode communication than conventional vortex waves.  
Keywords:localized wave of Bessel-Gaussian amplitude envelope; spectrum average mutual coherence function; spectrum average coherence length; vortex modes; seawater turbulence +
Footnote †: journal: Marine Science and Engineering
## 4 Experimental Components and Setup
### Overall Experimental Setup  
The experimental procedure was carried out at various stages. A prototype of a thermoelectric cooling system was designed to provide cool air at a certain temperature using a TEC technology that connected to the DC power source (as required see Table 3). As cold plates are used to remove the heat from the hot side of the TEC module, the cold plate outlet is connected to the liquid cooler to cool down the liquid again. The current experimental setup serves two purposes (i) To provide cool air (5 \({}^{\circ}\)C to 22 \({}^{\circ}\)C) at various parameters; (ii) To provide hot air (22 \({}^{\circ}\)C to 28 \({}^{\circ}\)C) to the insulated room as a byproduct. The TEC module's side extended U-section, inlet, and final temperature at the heat sink is measured using K-type of thermocouples to test the performance of the developed system. Three thermocouples are attached to the insulated room (size of the room is 0.5 \(\times\) 0.5 \(\times\) 0.5 m\({}^{3}\)) at different points to measure the temperature (see Figure 2e). First, the system's performance was examined at a different applied current of 2, 3, 4, 6 and 8 A to measure the effect on the cooling mechanism. In the next experiment series, the current will remain fixed at 4A, and the speed of the inlet air flow rate will change to 2.25, 2.55 and 3.55 m/s. Lastly, the inlet water flow rate will be changed while keeping the current at 4A and the inlet air flow rate at 2.25 m/s. The reason behind choosing input current and inlet air flow to be fixed was to investigate the effect of these parameters' on the system behavior. The water flow rate varies to 3.04, 2.16 and 1.80 L/min. Figure 1 shows the overall experimental setup with details of three major modules: heat exchanger module, chiller, and radiator with fan.  
Two power supplies were used, and the supply current to the TEC was adjusted manually for supplying required power input to the system. After all the components were secured on the position, the NI DAQ data acquisition system was activated. K-type of thermocouples were used to record the temperature at the various point every 5 s. Finally, during the experiments mentioned above, the whole setup was thermally insulated to minimize losses due to conduction and convection to the surrounding. For the even comparison, the initial temperature of the thermoelectric cooling system was kept at room temperature (i.e., 20 \({}^{\circ}\)C-23 \({}^{\circ}\)C). After the equilibrium ambient conditions, power supplied to the TEC, inlet fan, and Peltier thermoelectric cooler was turned on.
## 2 Materials and Methods
### Analysis of Calcium and Magnesium and Estimation of Phosphorous Content  
Concentration of mineral cations calcium and magnesium in <3000 Da and >3000 Da fractions, was determined by ion chromatography with the equipment Metrohm 850 Professional IC (Metrohm; Villebon-sur-Yvette, France). The ion exchange resin column Metrosep C4 150 \(\times\) 4 mm (Metrohm) was used as a stationary phase while the mobile phase was 1.7 mM nitric acid and 0.7 mM dipicolinic acid (Sigma-Aldrich) in ultra-pure water. Detection was done by conductivity. All the determinations were done in triplicate. Phosphorous content was estimated by FTIR (Fourier Transform Infra-Red) spectroscopy and from calcium content; in the bone calcium is present in the form of nanocrystals of calcium phosphates, namely tri-calcium phosphate, Ca\({}_{3}\)(PO\({}_{4}\))\({}_{2}\), and, in a lower amount, di-calcium phosphate, CaHPO\({}_{4}\)[20]. Phosphates, in calcium phosphate complexes, show infra-red absorption bands in the ca. 900-1150 cm\({}^{-1}\) frequency region, where the PO\({}_{4}\)\({}^{3-}\) group appears at the higher frequencies, 1000-1150 cm\({}^{-1}\), and the HPO\({}_{4}\)\({}^{2-}\) group shows a pick at lower frequency values, ca. 900-960 cm\({}^{-1}\)[21]. The FTIR equipment _TENSOR II_ ([PERSON]; Lyon, France) and the software _OPUS n.8_ were used for spectra acquisition and treatment (noise and baseline correction, and integration).
# Object-Environment Fusion of Visual System for Automatic Pear Picking
## 2 Materials and Methods
### Picking Pose Estimation
#### 2.4.2 Sampler  
Some researchers have proposed to uniformly sample picking candidates on a sphere centered on the object, which covers most of the possible picking poses. However, workers usually pick pears from the front, sides, or bottom. This means that uniform sampling would bring about a waste of resources. Therefore, empirically, we sample six basic picking types that are denoted by the set \(G_{b}=\{\overset{O}{\text{\bf T}},\overset{O}{\text{\bf T}}\}\), \(\overset{O}{\text{\bf T}}\), \(\overset{O}{\text{\We represent basic picking as \(\prescript{C}{E}{\bf T}_{i}=[r_{i}|p_{i}]\), where \(p_{i}=[0,0,0]\in\mathbb{R}^{1\times 3}\) and \(r_{i}\in\mathbb{R}^{3\times 3}\) denote the position and orientation of the gripper in the object frame. To obtain the basic picking, we decouple the orientation \(r_{i}\) as approaching vector \(v_{i}\) and in-plane rotation \(\theta\). We use a three-fingered gripper with axial symmetry for a simulation experiment, which ensures that \(\theta\) has no impact on the orientation. Therefore, the calculation of basic picking is only related to the approaching vector. \(r_{i}\) follows the reference [33]:  
\[r_{i}=\left[\begin{array}{ccc}a_{i}&b_{i}&c_{i}\\ -b_{i}&a_{i}&0\\ -a_{i}c_{i}&-b_{i}c_{i}&a_{i}^{2}+b_{i}^{2}\end{array}\right] \tag{3}\]  
where approaching vector \(v_{i}=[a_{i},b_{i},c_{i}]\) satisfies \(\sqrt{a_{i}^{2}+b_{i}^{2}+c_{i}^{2}=1}\).  
The approaching vectors \(v_{i}\) of six basic picking types are defined as in Equation (4). They are combined with Equations (2) and (3) to obtain the picking pose \(\prescript{C}{E}{\bf T}_{i}\) in the camera frame, as shown in Equation (5). Here, \(\prescript{C}{\bf T}=[r_{o}|p_{o}]\) represents the object pose, and \(r_{o}\) and \(p_{o}\) are the orientation and position of the object in the camera frame. \(p_{o}\) is provided by object perception, which changes according to the different objects. When fixing the camera with the eyes-to-hand mode, we believe that \(r_{o}\) can be represented by a specific value due to the similarity between the pear and circle. \(r_{o}\) is defined as in Equation (6). Therefore, we generate six picking candidates \(G_{c}=\{\prescript{C}{E}{\bf T}_{1},\prescript{C}{E}{\bf T}_{2},\prescript{C}{E} {\bf T}_{3},\prescript{C}{E}{\bf T}_{4},\prescript{C}{E}{\bf T}_{5},\prescript{C }{E}{\bf T}_{6}\}\), as shown in Figure 9a.  
\[\begin{cases}v_{1}=[1,0,0]\\ v_{2}=[0,-1,0]\\ v_{3}=[1/\sqrt{3},-1/\sqrt{3},-1/\sqrt{3}]\\ v_{4}=[0,0,-1]\\ v_{5}=[1/\sqrt{3},1/\sqrt{3},-1/\sqrt{3}]\\ v_{6}=[0,1,0]\end{cases} \tag{4}\]  
\[\prescript{C}{E}{\bf T}_{i}=\prescript{C}{\bf T}\left[\begin{array}{cccc}a_{ i}&b_{i}&c_{i}&0\\ -b_{i}&a_{i}&0&0\\ -a_{i}c_{i}&-b_{i}c_{i}&a_{i}^{2}+b_{i}^{2}&0\\ 0&0&0&1\end{array}\right] \tag{5}\]  
\[r_{o}=\left[\begin{array}{ccc}0&-1&0\\ 0&0&-1\\ 1&0&0\end{array}\right] \tag{6}\]  
Figure 9: (**a**) Ficking candidates \(G_{c}\); (**b**) collision-free picking candidates \(G_{f}\).