esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The large-scale utilization of hydrogen energy is currently hindered by challenges in low-cost production, storage, and transportation. This study focused on investigating the impact of the diaphragm cavity profile on the movement behavior and stress distribution of a dual-stage diaphragm compressor. Firstly, an experimental platform was established to test the gas mass flowrate and fluid pressures under various preset conditions. Secondly, a simulation path integrating the finite element method simulation, theoretical stress model, and movement model was developed and experimentally validated to analyze the diaphragm stress distribution and deformation characteristics. Finally, comparative optimization analyses were conducted on different types of diaphragm cavity profiles. The results indicated that the driving pressure differences at the top dead center position reached 85.58 kPa for the first-stage diaphragm and 75.49 kPa for the second-stage diaphragm. Under experimental conditions of 1.6 MPa suction pressure, 8 MPa second-stage discharge pressure, and 200 rpm rotational speed, the first-stage and second-stage diaphragms reached the maximum center deflections of 4.14 mm and 2.53 mm, respectively, at the bottom dead center position. Moreover, the cavity profile optimization analysis indicated that the double-arc profile (DAP) achieved better cavity volume and diaphragm stress characteristics. The first-stage diaphragm within the optimized DAP-type cavity exhibited 173.95 MPa maximum principal stress with a swept volume of 0.001129 m\({}^{3}\), whereas the second-stage optimized configuration reached 172.57 MPa stress with a swept volume of 0.0003835 m\({}^{3}\). This research offers valuable insights for enhancing the reliability and performance of diaphragm compressors.  
diphrag compressor; theoretical stress analysis; finite element method; double arc profile; cavity profile optimization Article
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 4 Proposed Approach  
In this section, we will introduce the DTPCH-C algorithm in detail considering four aspects: (1) Section 4.1 introduces the method used to calculate the optimal cluster head number; (2) in Section 4.2, the cluster head election strategy is proposed; (3) in Sections 4.3-4.6, the principle of the scheme with the dynamic transmission power of CHs is introduced in detail; and (4) Section 4.7 describes the cluster maintenance process.
## 2 Materials and Methods
### Procedure  
The decorating process: the acidified grapheme (0.1 g) was placed into pure analytical alcohol (50 mL) and subjected to ultrasonic treatment to form a suspension. Then, a small amount of Ce(NO\({}_{3}\))\({}_{3}\) was put into this suspension and continued to sonicate for 12 h, where the molar ratio of Ce(NO\({}_{3}\))\({}_{3}\) to graphene was 0.08:1. After that, slowly drip an appropriate amount of ammonia into the mixture until the PH of the solution is 8. The mixed solution was transferred to an autoclave and heated in a muffle furnace for 8 h and then cooled to room temperature with the furnace. The reaction temperature was set to 190 \({}^{\circ}\)C. Subsequently, the powder precursor in the solution was obtained by a centrifugal process and dried at 90 \({}^{\circ}\)C for 24 h. The resulting powder precursor was heated in a roasting furnace for 1 h to obtain GNPs decorated rare earth oxide CeO\({}_{2}\) material (CeO\({}_{2}\)-GNPs).  
Preparation of composites: Different types of GNPs together with the as-received Al powders were mixed through via high energy ball milling (HEBM) process under Ar atmosphere, as schematically shown in Figure 2. 0.5 wt.% GNPs, 99.2 wt.% Al powders and 0.3 wt.% stearic acids were placed in stainless steel jars containing quartz milling balls of 5 mm diameter. The ratio of balls to powder ratio was 10:1. The milling process was completed using a planetary ball mill at 400 rpm for varying milling times up to 2 h. Then, the starting powders were sintered by a hot press sintering system at a temperature of 595 \({}^{\circ}\)C for 2 h in a vacuum of 10\({}^{-3}\) torr under a pressure of 23 MPa. The heating rate was maintained at 100 \({}^{\circ}\)C/min. In this study, the matrix 2024 Al alloy was also prepared by the same procedure for comparison.  
Subsequently, optical microscope (Nikon eclipse, MA-200), environmental scanning electron microscope (Quanta200 FEDAX), and transmission electron microscope (TEM, Jeol JEM-2100) were used to observe the microstructure of the prepared composite samples. In addition, the prepared samples of the composites fabricated were measured for their hardness and tensile properties by using a micro-Vickers hardness tester (HVS-1000A) and a universal tensile testing machine (Sansi JXA-105). Vickers hardness test was performed on the polished sample, the loading force was 0.3 kg, and the pressure was held for 15 s. For the tensile tests, the tensile rate was set to 0.5 mm/min. In order to reduce errors,  
Figure 2: Schematic diagram of the preparation process for GNPs@CeO\({}_{2}\)/2024 Al composites.  
10 indentation inspection tests were required for each hardness pattern. The tensile test of each material was carried out for 6 repetitions to ensure the correct change trend of the properties obtained.
# Vegetation Classification in Urban Areas by Combining UAV-Based NDVI and Thermal Infrared Image
## 3 Result and Discussion
### Spectral Comparison and Vegetation Misclassification Analysis Using a Spectrometer  
As the basketball court and waterproof coated roof described above were classified as vegetation, the reflectance of each land cover was investigated using a spectrometer.  
Figure 12: Vegetation map classified by applying NDVI around trees and grass lands.  
Figure 13: Vegetation map classified by applying NDVI around basketball court and waterproof coating roof.  
Figure 14 shows the reflectance at each wavelength for each land cover. In particular, the measured results confirmed that vegetation was significantly different in the wavelength range of 700-900 nm compared to a basketball court and a waterproof coated roof. Table 7 shows the reflectance and calculated NDVI values for specific wavelengths of each land cover, with the blue wavelength at 475 nm, the green wavelength at 560 nm, the red wavelength at 668 nm, and the NIR wavelength at 840 nm. NDVI uses specific values of the red wavelength of 668 nm and NIR wavelength of 840 nm. The NDVI value is a calculation formula of R and NIR, and if the calculated result is similar to vegetation, it can be classified as vegetation. Therefore, since the spectroscopic results can confirm a wide range of wavelength bands, differences in each land cover can be identified. However, if vegetation is classified only through NDVI, only R and NIR values are used, so if the calculated results are similar, it may be misclassified as vegetation, as in this study.  
Figure 14: Reflectance by wavelength using ASD FieldSpec4 spectrometer.
## 3 Materials
### Datasets Used in the Experiments
#### 3.2.1. Group #1: Stationary Textures Acquired under Steady Imaging Conditions  
* ALOT-95-S-N: Ninety-five stationary textures from the ALOT dataset (Section 3.1.1) with images taken from the \"c113\"group. Six samples per class were obtained by subdividing the original images into non-overlapping sub-images of dimensions 256 px \(\times\) 256 px.
* CBT-99-S-N: Ninety-nine stationary textures from the CBT dataset (Section 3.1.2).
* Drexel-18-S-N: Eighteen stationary textures from the Drexel dataset (Section 3.1.4) with images taken from the \"D1_IN00_OUT00\"group. Four samples per class were obtained by subdividing the original images into non-overlapping sub-images of dimensions 64 px \(\times\) 64 px.
* Kylberg-Sintorn-25-S-N: All 25 colour textures in the Kylberg-Sintorn dataset (Section 3.1.10) with images taken from the 0\({}^{\circ}\) group. Each image was subdivided into 24 non-overlapping images of dimensions 864 px \(\times\) 864 px.
* MBT-120-S-N: One hundred and twenty stationary colour textures from MBT (Section 3.1.13). Each image was subdivided into four non-overlapping sub-images of dimensions 320 px \(\times\) 320 px, giving four samples per class.
* MondialMarmi-25-S-N: All 25 colour textures of the MondialMarmi dataset (Section 3.1.12) with images taken from the 0\({}^{\circ}\) group. Each image was subdivided into four non-overlapping sub-images of dimensions 750 px \(\times\) 750 px, giving 16 samples per class.
* Outex-192-S-N: One hundred ninety-two stationary colour textures from the Outex dataset (Section 3.1.15) with images acquired under the following conditions: scale = 100 dpi, rotation 0\({}^{\circ}\) and illuminant = \"inca\". We cropped the central part of each image and subdivided it into 20 non-overlapping sub-images of dimensions 128 px \(\times\) 128 px.  
* Parquet-37-S-N: All 38 classes of the Parquet dataset (Section 3.1.16). We retained six samples per class and centre-cropped the images. The final dimension ranges from 480 px \(\times\) 480 px-1300 px \(\times\) 1300 px.
* PlantLeaves-20-S-N: The entirety of the PlantLeaves dataset (Section 3.1.17).
* RawFoot-68-S-N: All 68 classes of the RawFoot dataset (Section 3.1.19) with the following imaging conditions: illuminant = \"D65\"and illumination intensity = 100%. We obtained four samples per class by subdividing the original images into four non-overlapping tiles of dimensions 400 px \(\times\) 400 px.
* STex-202-S-N: Two hundred and two stationary colour textures from STex (Section 3.1.20).
* USPTex-137-S-N: One hundred thirty-seven stationary colour textures from USPTex (Section 3.1.21).
* VisTex-89-S-N: Eighty-nine stationary colour textures from VisTex (Section 3.1.22). Each image was subdivided into four non-overlapping sub-images of dimensions 256 px \(\times\) 256 px.
* VxC_TSG-42-S-N: All 42 classes of the VxC_TSG dataset (Section 3.1.23).
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 2 Materials and Methods
### Analysis Steps  
The steps for simulating excavation support measures and earthwork excavation processes using PLAXIS 3D were as follows.  
Step 1: Setting the model's geometric profile and soil layer parameter  
The boundary of the overall model was defined based on the building site area and the depth of excavation. Specifically, the boundary extended to the construction site perimeter plus five times the depth of the excavation. Additionally, the soil layer depth was determined by the geological drilling depth. The engineering properties of the soil layers were established using site borehole surveys, an in situ test, and laboratory results, with the groundwater level set accordingly.  
Step 2: Simulation of structure  
Adjacent buildings were simulated using the plate element, and the diaphragm wall on the construction site modeled with plate elements. Each level of support was represented by node-to-node anchors, while encasement and intermediate piles were modeled with beam elements.  
Step 3: Element mesh  
Material properties were assigned to all soil layers and structural components. The geometry was divided into elements for computation purposes. The mesh density settings were adjusted to determine the average element size and the number of generated triangular elements.  
Step 4: Hydraulic condition  
PLAXIS can analyze effective stress, where the total stress is the soil's effective stress plus the total water stress. This total water stress is made up of static water pressure, seepage water pressure, and excess pore water pressure. If the project does not involve water pressure, there is no need to set a hydraulic condition model.  
Step 5: Construction step  
First, the diaphragm wall and intermediate pile beam elements within the construction site were activated, along with the plate elements for adjacent buildings. In the first construction step, the initial layer of the earth excavation was performed, the site's water level adjusted, and the first layer of support installed. Continue this process for eachsubsequent layer of excavation and support installation until the final excavation depth is reached.
## 4 Results and Discussion  
### Analysis of Characteristic Peaks of Aggregates and Mineral Powders  
Before spectral analysis, transmittance was converted into absorbance by OMNIC data analysis software 9.2. The original absorbance spectra of coarse aggregates, fine aggregates, and mineral powders were drawn by Matlab software R2019b after peak searching and peak marking, as shown in Figure 4. Obviously, characteristic peaks of aggregates and mineral powder are in the range of 2000-500 cm\({}^{-1}\). Moreover, coarse limestone aggregates display eight absorption peaks, 2513 cm\({}^{-1}\), 1797 cm\({}^{-1}\), 1462 cm\({}^{-1}\), 1443 cm\({}^{-1}\), 1428 cm\({}^{-1}\), 1414 cm\({}^{-1}\), 876 cm\({}^{-1}\), and 712 cm\({}^{-1}\), which are typical peaks of carbonate compounds according to previous research on limestone [38; 39]. And granite  
Figure 3: ATR-FTIR measurement of aggregate coated with asphalt mortar.  
Figure 2: Samples preparation: (a) cleaning; (b) drying; (c) aggregates coated with asphalt mortar.  
powders display four absorption peaks, Si-O stretching vibration at 1009 cm\({}^{-1}\), Si-Si stretching vibration at 776 cm\({}^{-1}\), Si-Al stretching vibration at 727 cm\({}^{-1}\), O-Si-O stretching vibration at 647 cm\({}^{-1}\), according to previous research on granite [38; 39; 40]. Granite powders and granite fine aggregates display similar absorption peaks with a maximum absorption peak at 1000 cm\({}^{-1}\).
## 4 Experiment and Analysis of Experimental Results
### Comparative of the Model Descriptive Capability  
To verify the correctness of the above analysis, a comparative study between the traditional models and the proposed model will be given. Referring to ref. [24], the simulation result is shown in Figure 17. By using Equation (25), the waveform error of literature [24] is 2.5189%, and that of this paper is 1.1361%, which is reduced by 1.38%. Comparing Figure 17c,d, the meshing frequencies \(f_{m}\) and \(2f_{m}\) can be observed clearly, and some sidebands occur at the frequency locations of \(f_{m}\ \pm\ f_{c}\), \(f_{m}\ \pm\ 3f_{c}\). However, the frequency amplitude of the phenomenological model constructed in this paper is 92.5% higher than that of the literature [24]. To summarize the foregoing, the proposed model is preferred for its ability to describe the actual vibration of planetary gearbox.  
As shown in Figure 18, a comparison was made between the simulated signal and the measured signals at three different rotational speeds when the sun gear had a broken tooth. The resulting waveform errors were found to be 0.61%, 1.14%, and 0.35%, respectively, all within the range of 2%. Moreover, the variance of the three error values was 0.0013, indicat  
Figure 16: Sun gear tooth with 3 mm broken tooth failure: (**a**) simulated signal; (**b**) measured signal; (**c**) frequency spectrum of simulated signal; (**d**) frequency spectrum of measured signal.  
Figure 17: Comparison of the experimental method [24] and proposed method under healthy conditions: (**a**) vibration signal of experimental method [24]; (**b**) vibration signal of proposed method; (**c**) frequency spectrum of experimental method [24]; (**d**) frequency spectrum of proposed method.  
ing that the model exhibits relatively stable error behavior during speed variations. This observation suggests that the model demonstrates robustness across different rotational speeds, yielding dependable results.
## 2 Finite Element Model
### FE Model Using the CEL Method  
In geotechnical numerical analysis, soil is defined by an elastoplastic model. In ABAQUS, the elastic model and the plastic model are defined separately. This study adopts a combined Mohr-Coulomb model and linear elastic model. The linear elastic stress-strain relationship for soil is expressed as Equation (1).  
\[\left\{\begin{array}{c}\varepsilon_{11}\\ \varepsilon_{12}\\ \varepsilon_{13}\\ \gamma_{11}\\ \gamma_{12}\\ \gamma_{13}\end{array}\right\}=\left[\begin{array}{cccccc}1/E&-\
u/E&-\
u/E &0&0&0\\ -\
u/E&1/E&-\
u/E&0&0&0\\ -\
u/E&-\
u/E&1/E&0&0&0\\ 0&0&0&1/G&0&0\\ 0&0&0&0&1/G&0\\ 0&0&0&0&0&1/G\end{array}\right]\left\{\begin{array}{c}\sigma_{11}\\ \sigma_{22}\\ \sigma_{33}\\ \sigma_{12}\\ \sigma_{23}\end{array}\right\} \tag{1}\]  
In Equation (1), \(E\) represents the elastic modulus of the soil. \(G\) represents the shear modulus of the soil. The relationship between \(G\) and \(E\) is \(G=E/[2/(1+\
u)]\), and \(\upsilon\) represents the [PERSON]'s ratio of the soil.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Description** & **Value or Normalization** \\ \hline \(D\) & Diameter of the penetrator structure & \(D=74\) mm \\ \(L_{1}\) & Height of the penetrating head & \(L_{1}=\frac{D}{2\pi\alpha}\cdot 0.25\) \\ \(L_{2}\) & Height of the penetrating tube & / \\ \(L\) & Height of the penetrator structure, which is equal to & \(L/D\) \\ \(L\) & the sum of \(L_{1}\) and \(L_{2}\) & \\ \(b\) & Width of the end face on the penetrating head & \(b/D\) \\ \(\alpha\) & Angle of the penetrating head & / \\ \(d_{\text{p}}\) & Depth of the penetration process & \(d_{\text{p}}/D\) \\ \(h_{\text{s}}\) & Height of the soil heave & \(h_{\text{s}}/D\) \\ \(l_{\text{s}}\) & Distance from the penetrator structure to the margin & \(l_{\text{s}}/D\) \\ \(F_{\text{end}}\) & Resistance of the end face of the penetrator structure & / \\ \(F_{\text{side}}\) & Resistance of the side of the penetrator structure & / \\ \(F_{\text{total}}\) & Combination of \(F_{\text{end}}\) and \(F_{\text{side}}\) & / \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the penetrator structure and penetration performance.  
In the Mohr-Coulomb model, the plastic failure of soil materials is governed by shear stress, as shown by the undrained shear strength, \(S_{\text{u}}\), in Equation (2).  
\[S_{\text{u}}=c+\sigma\tan\varphi \tag{2}\]  
In Equation (2), \(c\) represents the cohesion of the soil, and \(\varphi\) represents the internal friction angle of the soil. Normal stress, \(\sigma\), can be simplified to the self-weight stress of the soil at a certain depth, as \(\sigma=\gamma d_{\text{p}}\), where \(\gamma\) represents the unit weight of the soil and \(d_{\text{p}}\) represents the depth of the soil.  
\(F_{\text{side}}\) and \(F_{\text{end}}\) are the integral results of the stresses exerted on the sidewall and the end face of the penetrator structure. In Abaqus, this process is accomplished through the Explicit Stress Integration Algorithm.
# Tag-to-Tag Interference Suppression Technique Based on Time Division for RFID
## 3 Received Signal Model  
The received signal, \(r(k)\), at a sample index \(k\) can be modeled as  
\[r(k)=x_{1}(k)+x_{2}(k)+x_{3}(k)+\ldots+x_{L}(k)+n(k), \tag{1}\]  
which can be summarized as  
\[r(k)=\sum_{l=1}^{L}x_{l}(k)+n(k) \tag{2}\]  
where \(l\) represents the \(l\)th tag, \(L\) is the total number of tags transmitting the signal to the reader, \(x_{l}(k)\) is the transmitted signal of the \(l\)th tag, and \(n(k)\) is the additive white Gaussian noise (AWGN) with independent and identically distributed components with zero mean and variance \(\sigma^{2}\).  
Using [PERSON] free-space equation, the relationship between the signal strength and distance, and between the received power at the antenna of the tag, \(P_{tag}\), and the transmitted power from the reader, \(P_{r}\) is given by  
\[P_{tag}=P_{r}G_{r}G_{tag}\bigg{(}\frac{\lambda}{4\pi d}\bigg{)}^{2} \tag{3}\]  
where \(G_{r}\) and \(G_{tag}\) are the transmitter and receiver antenna gains, respectively, \(\lambda\) is the wavelength (m), and \(d\) is the distance between the transmitter and the receiver antennas [28; 29]. Equation (3) can be expressed in decibel form as  
\[P_{tag}[dB]=P_{r}[dB]+G_{r}[dB]+G_{tag}[dB]\ -10 log_{10}\bigg{(}\frac{4\pi d }{\lambda}\bigg{)}^{2} \tag{4}\]  
which describes the amount of power available to the RFID tag for operation. Note that in this paper, \([dB]\) refers to the decibel scale of the specific value. Since the power of the radio-frequency signal decreases when it propagates into space, there is a difference between the power delivered from the transmitting antenna and the power obtained at the receiving antenna, and this is known as the path loss [30; 31]. In Equation (4), \(10 log_{10}\bigg{(}\frac{4\pi d}{\lambda}\bigg{)}^{2}\) is the free-space path loss factor, and is defined as \(P_{loss}[dB]\).  
The decreasing trend of the signal in terms of the log-distance path loss is mathematically modeled as [32]  
\[P_{loss}(dB)=P_{loss}(r_{0})+\ 10 nlog\bigg{(}\frac{d}{r_{0}}\bigg{)} \tag{5}\]  
where \(r_{0}\) is a reference distance, \(P_{loss}(r_{0})\) is the free-space path loss at a distance \(r_{0}\), \(n\) is a value that depends on the surroundings and building type (a more hostile environment results in a higher value of \(n\)), and the path loss; accordingly, it is higher for the same distance when compared to an environment with a lower value of \(n\)[33; 34; 35].