esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Very High Plastic Clays with the Combined Effect of Sand, Line, and Polypropylene Fibers
## 4 Results
### Effect of Fiber Content on UCS  
The effect of fiber content on UCS is shown in Figure 9. Four fiber contents were considered: 0.3, 0.6, 0.9, and 1.2% (by wt%). Soil specimens were compacted at their respective optimum characteristics. Samples of untreated clay (unreinforced, non-stabilized) were compared against clay-sand mixtures (80% of clay and 20% of sand). The UCS tests were performed after 28 and 90 days of curing in laboratory conditions (24 \(\pm\) 2 \({}^{\circ}\)C, 50 \(\pm\) 2% RH). The results show that the UCS can significantly be increased when using sand, lime and fibers together in the proper proportions.  
The UCS increased from 361 to 402 kPa for the clay samples treated with sand after 28 days of curing, indicating an improvement of just 11.7%. For the samples containing 20% sand, 3.34% lime and PP fibers, the strength increased to 800, 2523, 2842, 2860, and 2474 kPa for 0, 0.3, 0.6, 0.9, and 1.2% fiber contents, respectively. From the 28 to 90 days of curing, the strength continued to rise to even higher levels (see Figure 9); for example, for mixtures with a fiber content of 0.6%, the UCS was as high as 4593 kPa (12.76 times higher than that of the raw clay). There is no doubt that this remarkable increase in strength is attributed to the combined effect of the three different additives (lime, sand, and fibers) in the proper proportions.  
The PP fibers in combination with the cementitious compounds formed by lime will cause a substantially increase in strength. The existence of fibers limits the relative displacements between the different ingredients, thus, improving interlocking, density and stiffness [17; 26]. Furthermore, when a soil-fiber mass is loaded, the fibers with their tensile capacity receive part of the interparticle forces, creating a pseudo-cohesion which improves the interfacial mechanism [3].  
Figure 10 shows the failure mode of two soil samples that were stressed in the unconfined compression test apparatus, differing only in that the one is unreinforced, while the other had been reinforced with fibers. As shown, a shear plane inclined at an angle  
Figure 9: Effect of different additives on UCS.  
of approximately 45\({}^{\circ}\) is visible in the case of the unreinforced clay, whereas a shear zone exists in the case of the reinforced clay specimen. The latter is due to the bridging effect exerted by fibers, restricting the relative displacement between the two parts of the sample. In addition, deformation evolved slowly in the reinforced samples, thus declaring a more ductile behavior in comparison with plain clay.  
Figure 11 shows the stress-strain curves for the samples treated with sand, lime, and PP fibers (the latter at different percentages). For comparative purposes, the stress-strain curve for the untreated clay is also plotted on the same figure. As it can be seen, the effect of these additives is appreciable in terms of peak stress, strain at failure, and initial slope.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 6 Simulation Results
### Impact of Loss of Energy \(\ell_{e}\) Parameter
#### 6.1.2 On the Network Lifetime  
Prolonging the network lifetime can be reached by saving the nodes' residual energy. To maximize the network lifetime, we should study the impact of the loss of the energy parameter.  
For this, we consider three scenarios; in each scenario, each source node sends one packet every 0.1 s for the parameter values given by Table 2. The communication range\(R=80\) m, while \(\ell_{e}=\{2\%,4\%,\)\(6\%,8\%,10\%,12\%,14\%,16\%,18\%,20\%,22\%,24\%,26\%\}\). In the first scenario, the number of nodes is fixed such that \(\mathrm{N}=100\). In Figure 4, the first scenario shows the simulation results after the round when the first node is declared dead. The results show that, to increase the network lifetime, the value of \(\ell_{e}\) parameter should be set such that \(\ell_{e}=20\%\).  
In the second scenario, the number of nodes is fixed such that \(\mathrm{N}=120\). In Figure 4, the second scenario shows the simulation results after the round when the first node is declared dead. The results show that, to increase the network lifetime, the value of \(\ell_{e}\) parameter should be set such that \(\ell_{e}=14\%\).  
From the above three scenarios, we can conclude that there is a decreasing relationship between the network size (\(\mathrm{N}\)) and the optimal value of the loss energy parameter \(\ell_{e}\) that maximizes the network lifetime. It is concluded that the maximum lifetime is reached when \(\ell_{e}=20\%\) for the network size \(\mathrm{N}=100\), the maximum lifetime is reached when \(\ell_{e}=18\%\) for the network size \(\mathrm{N}=120\), and the maximum lifetime is reached when \(\ell_{e}=14\%\) for the network size \(\mathrm{N}=140\) for the parameters defined in Table 1 and their values given by Table 2. The communication range\(R=80\) m.  
Figure 4: Impact of \(\ell_{e}\) parameter on the network lifetime varying number of nodes for the first scenario, the second scenario and the third scenario.
# Observational Analysis of Variation Characteristics of GPS-Based TEC Fluctuation over China
## 3 Results and Discussion
### Daily Occurrence Ratio of TEC Fluctuations from 2002 to 2012
Figure 9: The daily occurrence ratio (by DOY) of the different-level TEC fluctuations at the 21 selected GNSS sites from 2002 to 2012. The green line represents the daily occurrence ratio of weak TEC fluctuations that satisfy \(0.25\leq\mathrm{ROTI}<0.5\); the blue line represents the daily occurrence ratio of moderate TEC fluctuations that satisfy \(0.5\leq\mathrm{ROTI}<1\); and the red line represents the daily occurrence ratio of strong TEC fluctuations that satisfy \(\mathrm{ROTI}\geq 1\). The straight yellow lines denote the day of the summer solstice of each year. The site names of each subfigure are as follows: (**a**) YONG, QION, GUAN, KMIN, XIAM, XIAG; (**b**) LUZH, WUHN, XIAA, ZHNZ, DLHA, TAIN; (**c**) XNIN, BJFS, BJSH, JIXN, WUSH, URUM; (**d**) DXIN, SUIY, HLAR.
# Fault Controls on Hydrocarbon Migration--An Example from the Southwestern Pearl River Mouth Basin
## 5 Discussion
### Implications for Prospect Analysis in the Study Area  
Based on our understanding of the relationship between SGR values and the lateral sealing capacity of faults, combined with the model of hydrocarbon migration and accumulation in the fault array traps described above, we can try to predict potentially undiscovered accumulations (Figure 12).  
Hydrocarbons from the mature source rocks towards the basin edge continue to expell oil, which migrates vertically along permeable fault zones (F6-F10) and flows into permeable reservoir intervalls, where the presence of fault seals is crucial for the formation of hydrocarbon accumulations.  
In this context, F8 exhibits weaker lateral fault sealing, allowing hydrocarbons to directly cross it and enter traps that are bounded by the basin boundary fault. This process can lead to hydrocarbon accumulation across the entire Zhuhai Formation reservoirs (Figure 12). In contrast, F7 demonstrates robust lateral sealing in its lower segment but weaker sealing in the upper portion. Its vertical sealing capacity is robust and far fromfailure. Consequently, hydrocarbons accumulate in the hanging wall trap of the E\({}_{3}\)z\({}_{3}\) sequence (aligned with the lower fault segment), while in the E\({}_{3}\)z\({}_{1}\) and E\({}_{3}\)z\({}_{2}\) sequences (corresponding to the upper fault segment), hydrocarbon flow laterally permeates the fault zone and infiltrates the fault compartment bounded by the basin boundary fault (Figure 12).  
Regarding F9 and F10, both exhibit pronounced lateral sealing and moderate vertical sealing. Importantly, F9 is positioned predominantly in front of F10 in terms of the hydrocarbon flow path. It acts as a lateral barrier, implying that hydrocarbons must breach the lateral sealing limit to access the fault array traps in front of or even behind F10. Considering the robust lateral sealing of F9 and F10, the highest likelihood for hydrocarbon accumulation exists in the hanging wall trap within the entire Zhuhai Formation of F9 (Figure 12).  
Interestingly, hydrocarbon accumulations found on the Shenhu Uplift (see Figure 1b for locations) host oil, which is thought to have formed in the Wenchang A basin based on compositional analyses [67]. This would indicate that migration pathways through the studied fault block array, as well as through the basin boundary fault (Zhu III South Fault), exist. Note the potential limitations of this study, as the conceptual model of hydrocarbon migration and accumulation only considered the fault sealing capacity and the maturity and distribution of source rocks. Further related oil-source correlation and petroleum geochemistry research will improve this study.
## 4 Numerical Results Analysis
### Acoustic Field Results Analysis
#### 4.4.2 Fluid-Induced Radiated Noise  
Radiated noise calculations are performed on the basis of fluid-borne noise calculations after adding the air domain and establishing the infinite element boundary conditions at the radiated air surface. A cloud map and a directivity curve of the radiated noise are shown in Figure 19. Orientation and location are set as the same to the experiment, as shown in Figure 3. Unlike the experimental results, it shows full-space curves, while the experiment could only show half-space curves because of ground constraints. Figure 19a shows the sound pressure level distribution of the model pump at vertical impeller midsection (left) and parallel impeller midsection (right). Figure 19b presents the directive curve obtained by interpolating the sound pressure level values of the monitoring points. the radiated noise level of the color chart based on the value.  
Seen from the figure, both the cloud map and the directivity diagram of radiation noise corresponding to blade-passing frequency show dipole characteristics (\"8 shape\"), which is confirmed by the previous radiation noise test. It indirectly means that the pressure fluctuation caused by the rotor-stator interaction between the impeller and volute is the main source of the radiation noise because there is a conversion of energy taking place here. The sound pressure level of radiation noise in the air domain presents the highest near the pump shell surface and decreases along the radial direction. From the forward direction of the cloud diagram, the gradient of the sound pressure level is small. The light blue band area formed at the entrance pipe indicates that the flow instability is more pronounced at the impeller inlet area. In general, the CFD/CFA method used in this paper provides an accurate estimate of the flow-induced noise characteristics under the design operation point. However, stimulation in the form of velocity field fluctuations comes from the inner blade channels and acoustic effects at off-design (overload) point of operation would be very high, which needs more consideration in the future.  
Figure 19: Radiated noise characteristics under 6\(f_{0}\): (**a**) cloud map; (**b**) directivity and radiated noise level curve.
# Compact Dual-Band Tapered Open-Ended Slot-Loop Antenna For Energy Harvesting Systems
## 3 Experimental Results and Simulation
Moreover, it is obvious that the proposed antenna provides a reasonable high realized gain at both operating bands. The proposed antenna has a size of 32.5 \(\times\) 53.5 mm\({}^{2}\). This size is considered to be a compact structure with respect to the lower resonance frequency (920 MHz). Nevertheless, the size might further reduce by using either a metamaterial structure or developing new patterns to realize the antenna with the same performance over a much-reduced footprint. The antenna can be employed to collect the electromagnetic energy in order to feed to a rectifier for RF-DC energy conversion. This application doesn't necessitate a specific alignment, but it can achieve similar performance in either broadside or end-fire coordinates. Figure 5: Antenna normalized simulated and measured radiation patterns at lower (0.92 GHz) and higher (2.4 GHz) band frequencies.
## 2 Preliminary Description of a Multirotor UAV
### EPU Components
#### 2.3.2 BLDC Motor  
BLDC motor is a permanent magnet electric motor with an electronic commutation system. It is driven by a rectangular shape input voltage (six-step commutation) provided by an ESC. Electromagnets (armature) are located on the stator of the motor, while permanent magnets are located on the rotor. Compared to conventional DC motors that use mechanical commutation (brushes), BLDC motors have higher efficiency, increased reliability, higher torque by weight, reduced noise, and easier maintenance. There are two basic types of BLDC rotor design realization. In the first type, the rotor is positioned inside the stator (inrunner), while in the second type (commonly used in multirotors) the rotor is positioned outside the stator (outrunner). There is a large range of commercially available BLDC motors with output power from tens of W up to several kW. The recommended propeller size and operating voltage range are given by the manufacturer in the form of tabular specification of combined  
Figure 3: Electric propulsion unit (EPU) schematic.  
motor and propeller performance (i.e., voltage and current consumption at obtained thrust). Load on the \(i\)-th motor is imposed by drag torque of the \(i\)-th rotor and can be expressed as  
\[\tau_{R_{i}}=k_{\tau_{i}}{\omega_{i}}^{2}, \tag{17}\]  
where \(k_{\tau_{i}}\) is the drag torque factor of the \(i\)-th rotor (Nms\({}^{2}\)). It depends on the geometry of the propeller, the air density, and the motor power, and it is defined by the following expression  
\[k_{\tau}\ =C_{P}\rho Ar^{3}, \tag{18}\]  
where \(C_{P}\) is the rotor power coefficient.  
The motor configuration defines the number of stator and rotor poles, for example, a 12N14P configuration consists of 12 stator poles and 14 rotor poles. Motor velocity constant (back EMF constant) \(K_{v}\) defines the number of revolutions per applied voltage in the case with no load (propeller is not mounted on the motor rotor),  
\[K_{v}=\frac{RPM_{no-load}}{u_{peak}}. \tag{19}\]  
BLDC motors of small dimensions and large velocity constants (\(K_{v}>2000\)) are mainly used to propel micro and small aircraft intended for entertainment or sports (drone racing). On the other hand, medium-sized BLDC motors with medium velocity constants (\(2000>K_{v}>200\)) are used to propel aircraft intended for photography or similar tasks where cargo masses are up to \(\approx 10\) kg. Finally, large BLDC motors with small velocity constants (\(K_{v}<200\)) are intended for heavy equipment and loads and are also considered for personal aerial vehicles (PAV) propulsion, the introduction of which is planned for the next decade [22].
## 5 Simulations and Analyses
### Algorithm Simulation  
In order to validate the effectiveness of the proposed strategy, we conducted comparative tests on a two-degrees-of-freedom robotic arm model in two aspects (robustness test and force tracking test) against four control algorithms: traditional impedance control (CIC), adaptive hybrid impedance control (AIC) [20], adaptive variable impedance control (VIC) [22], and the fractional-order PID adaptive impedance (FOAIC) control proposed in this paper. The common parameters of these four algorithms remained consistent across various simulation environments.
# State of Health Prediction for Lithium-Ion Batteries Using Transformer-LSTM Fusion Model
## 3 SOH Prediction Based on Transformer-LSTM Fusion Model
### Transformer-LSTM Fusion Model
#### 3.1.1 Transformer Module  
To better understand battery degradation trends and capture contextual insights, we utilized the transformer framework, as illustrated in Figure 8. The main components of the transformer model include the input embedding layer, positional encoding, encoder, decoder, and output layer. During training, the input embedding layer first converts the input sequence into a high-dimensional vector representation. It adds positional encoding to the embedding vector to maintain each position's relative and absolute position information in the sequence. Next, the encoder uses the self-attention mechanism to process the input features and generate context-related representations for each position, thereby capturing long-range dependencies in the sequence. The decoder combines the output of the encoder with the previous prediction results and generates the final prediction sequence through self-attention and cross-attention mechanisms. The output layer maps the decoder results to predicted values. During training, the transformer uses backpropagation to optimize the loss function and adjusts layer weights to capture complex relationships in sequence data, enhancing prediction accuracy.  
In SOH prediction, transformer can effectively capture the dependencies between time steps in historical data with its self-attention mechanism. It not only focuses on local information but also fully grasps the global context, thereby accurately capturing the trend of battery degradation. In addition, transformer can process multidimensional feature data and fuse the interactions between different features through a multi-head attention mechanism to further improve the accuracy of SOH prediction. For this reason, transformer has demonstrated excellent modeling capabilities and outstanding prediction performance in battery SOH prediction.
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 4 Simulation Results
### Impact of AP Locations
To show this result more clearly, we conduct the simulations in a square area with the dimensions of 10 m by 10 m, as shown in Figure 2. By fixing three APs at the locations (10 m, 10 m), (10 m, 0 m) and (0 m, 10 m), we calculate the error bounds with respect to the mobile location (5 m, 5 m) as a function of the fourth AP \(\in[0,10\text{ m}]\times[0,10\text{ m}]\) in Figures 8-10.
## 4 Results and Discussion
### Rectification Ratio  
The rectification ratio is an important physical factor used to verify the current sensitivity and conductivity. It can be acquired from the I-V characteristic experiment. The calculation of the rectification ratio demonstrates the ratio between the forward-bias and the reverse-bias currents at various V\({}_{\text{MSM}}\), written as  
\[RR=\frac{I_{f}\left(V_{MSM}\right)}{I_{r}(-V_{MSM})} \tag{1}\]  
where \(RR\) is the rectification ratio, \(I_{f}\) is the forward-bias diode current, and \(I_{r}\) is the reverse-bias diode current [25; 26].  
Figure 6 illustrates the rectification ratio at varied V\({}_{\text{MSM}}\). It is seen that the rectification ratio increases as V\({}_{\text{MSM}}\) increases. The highest rectification ratios of about 32.50 and 19.12  
Figure 5: Static resistance of the proposed MSM diodes with local indigo.  
are obtained in the first and the second MSM diodes, respectively. A high \(RR\) represents a high sensitivity of the current transfer and conductivity. The highest \(RR\)s of silicon-based MSM devices for photodetectors are normally between 105 and 108 at 2 V\({}_{\text{MSM}}\)[27]. Although this proposed MSM diode shows similar characteristics of \(RR\)s to those of the silicon-based MSM devices, its \(RR\), about three times lower than those of the silicon-based MSM devices, is found to be between 1 and 3 V\({}_{\text{MSM}}\).
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
## 4 Results  
In this section, we present the results of the tuning process of the MLP model (see Table 2) and the comparison against other machine learning models such as SVM, RF, KNN, and GB. Experiments on the proposed dataset demonstrated satisfactory results. In order to find the best hyper-parameters, the performance of the model was evaluated by searching LR values, i.e., (a) LR = 3 \(\times\) 10\({}^{-8}\), (b) LR = 1.65 \(\times\) 10\({}^{-7}\), (c) LR = 3 \(\times\) 10\({}^{-7}\), (d) LR = 1.65 \(\times\) 10\({}^{-6}\), (e) LR = 3 \(\times\) 10\({}^{-6}\), and (f) LR = 1.65 \(\times\) 10\({}^{-5}\), as shown in Figure 9. We can see that the MLP model showed strong performance for a value of LR = 1.65 \(\times\) 10\({}^{-5}\), achieving an accuracy of 0.996 for the training dataset.  
Figure 10: Accuracy score for training dataset with MLP model during second learning rate search.  
Figure 9: Accuracy score for training dataset with MLP model during first learning rate search.
# Aromatic Amines in Organic Synthesis Part III; \(p\)-Aminocinnamic Acids and Their Methyl Esters
## 3 Materials and Methods  
All starting reagents and solvents were purchased from Aldrich Chemical Co. (Milwaukee, MI, USA). The melting points were determined with the Buchi melting point apparatus MP-1. The \({}^{1}\)H (400 MHz) and \({}^{13}\)C (100 MHz) NMR spectra were recorded on a Bruker AscentTM 400 NMR spectrometer (Bruker, Billerica, MA, USA). Dimethylsulfoxide (DMSO-\(d_{6}\)) was used as the solvent and tetramethylsilane as the internal standard. Elemental analysis was carried out by an Elementar Vario MACRO apparatus (Elementar Americas Inc., Ronkonkoma, NY, USA). The IR spectra were recorded on a Bruker spectrophotometer Vector 22 in the range of 400-4500 cm\({}^{-1}\) by the KBr pellet technique. The UV-Vis absorption and emission spectra were recorded on a Shimadzu UV-Vis Multispec-1501 spectrophotometer (Spectralab Scientific Inc., Markham, ON, Canada) and a Hitachi F-7100 spectrofluorometer (Hitachi High-Tech Corporation, Tokyo, Japan), respectively. The fluorescence quantum yield (FQY; \(\phi\)) was calculated according to Equation (1) using coumarin I in ethanol as a reference [55].  
\[\phi_{\text{s}}=\phi_{\text{ref}}\frac{I_{s}A_{ref}}{I_{ref}A_{s}}\cdot\frac{n _{s}^{2}}{n_{ref}^{2}} \tag{1}\]  
where \(I\) is the integrated intensity (area) (in units of photons) and \(n\) is the refractive index. The absorbances (A) of both the sample (s) and reference (_ref_) solution at an excitation wavelength (350 nm) was ca. 0.1.  
The fluorescence lifetimes were measured using an Edinburgh Instruments single-photon counting system (FLS920P Spectrometers (StellarNet Inc., Keystone, FL, USA)). The apparatus utilizes a picosecond diode laser for the excitation-generating pulses of about 55 ps at 375 nm. The dyes were studied at dilute solution (-0.2 in a 10 mm cell). The fluorescence decays were fitted to double-exponential functions. The average lifetime, \(\tau_{av}\), is calculated as  
\[\tau_{\text{av}}=\frac{\Sigma\tau_{\text{i}}\alpha_{\text{i}}}{\Sigma\alpha_{ \text{i}}} \tag{2}\]  
where \(\alpha_{i}\) and \(\tau_{i}\) are the amplitudes and lifetimes.  
An Electrochemical Analyzer EA9C MTM Cracow (Wieliczka, Poland) was used to determine the oxidation potential (E\({}_{\text{ox}}\)) of the synthesized compounds. Measurements were made by cyclic voltammetry using anhydrous acetonitrile with 0.1 M of tetrabutylammonium perchlorate as a supporting electrolyte. The scan rate was 300 mV/s. A typical three-electrode setup, containing a platinum 1 mm electrode as the working electrode and platinum and Ag/AgCl as the auxiliary and reference electrodes, respectively, was applied for the measurements.  
DFT calculations were carried out using the B3 LYP/6-311+G(2d,2p) method. Calculations were carried out with the Gaussian 03 program [56].