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Introduction RFID technology uses radio waves to transfer data, for the purposes of automatically identifying and tracking tags attached to objects.
In RFID systems data are exchanged between a local reader and a remote transponder (also known as tag) which is composed of a RFID IC chip and an antenna connected to the IC chip at the feeding point of the tag antenna [1].
The antenna plays an important role in transferring data, and its design perhaps becomes a most challenging task for RFID system [2].
In the present paper, Hilbert fractal theory and technique are employed to achieve the goal of reduction dimension of antenna.

Therefore, in this study, the automotive electronic control components recycling strategy and a technology roadmap in accordance with China’s actual conditions are provided for energy conservation and toxic environmental emissions reduction.
Their EIO model is based upon the Department of Commerce 480x480 commodity sector data and a variety of environmental impact indices, including the toxic release inventory (TRI) and energy use.
This model is comprised of national economic input-output models and publicly available resource use and emissions data.
However, this approach is not practical in view of the immense data requirements.
According to the statistics data of China automotive industry yearbook 2008[12], the automotive industry gross output value is 1724202400000 yuan in 2007, and the automotive electronics industry gross output value is about 258630360000 yuan according to the price proportion.

Data is recorded when the AC is on until the evaporator pressure is stable.
The results of the pressure data recording and the calculation of the refrigerant saturation temperature data were tabulated and also presented in the form of a graph of saturated pressure vs time, and saturation temperature vs time.
This data is recorded at the variable speed of the air passing through the evaporator at 2.00 m/s, 2.18 m/s, and 2.45 m/s.
This data record is in a period of 120 seconds.
For a good illustration of the pressure behavior at start-up, the observation data is shown in graphical form as shown in Figure 1a.

As a test of the commercial viability of FSW in this particular road transport application, the fatigue data from the FS welds were compared with those obtained from MIG welded joints.
Data are presented for a stress range of 126 MPa and 138 MPa.
The S-N data for the MIG and FS welds are shown in Fig. 4.
It is worth noting that although root flaws lead to a significant reduction in the fatigue life of the FS welded joints, these specimens still show lives within the scatter of the MIG S-N data, with a flaw area of 2.24% still giving a life equal to that observed for the ‘best’ MIG welded sample.
In addition, the lack of root flaws in the FS welds made with a gap meant that the fatigue results were more repeatable than the 0mm gap data.

We analyzed the time series data from immediately after the voltage was turned off until 40 μs elapsed.
Figs. 2(d) and 2(e) respectively show the same data as Figs. 2(a) and 2(b) but each color scale is only modified to enhance the visibility of the Fig. 3.
However, since the provided data covers only from at the depth of 0.13 eV to 0.8 eV from the edge of the conduction band (Ec), the shape near Ec and the midgap is determined by extrapolating the provided data.
As for the donor-like states, which has not been obtained from the n-type sample, the proto-type shape is defined by reference to data published in Ref. [1, 13, 14] for p-type 4H-SiC (0001) with dry oxidation.
Since the shape of the distribution does not seem to vary largely even after POA treatment in the published data [1, 13], we simply shifted the proto-type energy distribution for each mesh element such that Dit at the depth of 0.35 eV from Ec takes a measured value.

The third step involves gathering data from various sources, including texts, research materials, and historical data.
The analysis covers the timeline from the 2000s to the 2020s, and data was collected from reputable databases and industry reports.
The data gathering process was comprehensive and unbiased, ensuring credible results.
However, there is an urgency to measure the impact of the regulations against the quantifiable data expressed through the consumption and growth in fashion and textile industry.
Information on Australia | Economic Indicators, Historic Data & Forecasts | CEIC (ceicdata.com) [accessed 3 March 2023]

The test results obtained show that the Fourier analysis is a convenient data processing technique in active thermal NDT.
(5) As a simple start point for further analysis, we can assume that the presence of subsurface defects can be simulated with reduction in material thickness.
Maximum differential temperature signal vs. defect size and depth (series 1: defect diameter 20mm, series 2: defect diameter 10mm, series 3: defect diameter 5mm) Conclusions The test results obtained show that the Fourier analysis is a convenient data processing technique in active thermal NDT.
Maldague, Defect Depth Retrieval from Pulsed Phase Thermographic Data on Plexiglas and Aluminum Samples, Thermosense XXXI, SPIE 2004,5405:523-531
[13] Takahide Sakagami,Shiro Kubo,Takeshi Endo, Development of a new processing technique of sequential temperature data after pulse heating for quantitative nondestructive testing, Thermosense XXVI, SPIE, 2004, 5405: 357-365.

Crystallographic data obtained by orientation contrast microscopy (OIM) are combined with the geometrical configuration of grain boundaries at triple junctions.
After linking geometrical and structural information, the data were analyzed according to a procedure described in the literature [1].
CR % reduction Annealing T Annealing time Fe - 26 wt% Si 70% 1100°C 2h Fe - 2.8 wt% Si 75% 800°C 4h Microstructural information was obtained by optical microscopy.
As a consequence of that, insufficient data on the low-angle grain boundaries (LAGBs) are available and therefore the Read-Shockley model [2] could not be verified here.
The differences in the energy values could differ by a factor of 2 or even 3, which cannot be attributed to a statistical spread on the data.

Effective AIS perform some key function in the three stages such as data collection, data maintenance, data management, data control (including safety) and information generation.
The fact is hard to track who enter the data only the amount of increase.
The validation checks are made on the data when it is being processed.
These checks ensure that the data is processed correctly
• Data Control: Data controls are put in place to minimize the number of errors and omission that occur in the file system.

When the user presses “Load Data”, a box is shown to insert the desired date of the daily schedule.
After pressing “OK”, the data are retrieved from the database.
When the button “Load Data” is pressed, all of the data related to the extra product will be loaded from the database, as shown in the interface at the bottom of the same figure.
Both of the transactions are recorded by pressing the "Save Data" button in the interface.
The seller must “Save Data”, as shown in the interface of Figure 7 for the next day.