esa-sceva/satcom-chunk-collection huggingface dataset:
# Perceived Brightness and Resolution of Holographic Augmented Reality Retinal Scan Glasses
## 2 Materials and Methods
### Perceived Resolution Study Design
We performed 15 trials each for horizontal and vertical lines for different stimulus colors (laser wavelengths red [640 nm], green [521 nm], blue [452 nm]), stimulus brightness (maximum radiant flux red [272 nW]), green [736 nW], blue [1263 nW], equiluminant radiant flux green [66 nW], blue [1145 nW]), and LED monitor backgrounds [black, red, green, blue, orange] to measure the influence of different stimulus and background conditions on the perceived resolution. The LED monitor was set to a luminance of 27 cd/m\({}^{2}\) for each color and 1 cd/m\({}^{2}\) for the black background. The right spectacle lens with the holographic optical combiner had an optical transmittance of \(\tau_{\text{C}}\) = 0.90. The optical power of the retinal scan glasses was measured by setting an optical power meter (PM160 power meter; Thorlabs, Inc.; Newton, NJ, USA) in the beam path such that all light contributing to the image was captured by the optical sensor. The wavelengths of the retinal scan glasses as well as the luminance of the LED monitor were measured with a spectroradiometer (JETI spectraval 1511; JETI Technische Instrumente GmbH; Jena, Germany). The wavelength measurement only depends on the relative magnitudes of optical power. Therefore, measuring the wavelengths of retinal scan glasses with a spectro-radiometer will provide accurate results, while measuring the luminance directly will not. Figure 4: **(a)** Chimrest in front of LED monitor displaying an even green background. **(b)** LED monitor seen through right spectacle lens with embedded HOE. **(c)** Red rectangle stimulus from glasses on green background seen through right spectacle lens. **(d)** Trial stimulus with horizontal red lines, green background. **(e)** Trial stimulus with vertical red lines, green background. **(f)** Trial stimulus with horizontal green lines, green background. **(g)** Trial stimulus with green horizontal lines, red background. **(h)** Trial stimulus with blue horizontal lines, orange background. Generally, the perceived resolution threshold not only depends on the spatial frequency but also on the contrast [10]. If the contrast between stimuli and background is too small, the perceived resolution threshold decreases across spatial frequencies [11]. Previous research showed that this influence is persistent up to contrast levels of about 0.5 [12]. Since we wanted to avoid the influence of contrast on the perceived resolution in this study, we needed to ensure that the contrast between the retinal scan stimuli and the Lambertian background stayed above 0.5. As shown in Section 2.1.1, however, this contrast can only be calculated correctly if the brightness for both display types is denoted in the same physical unit. We therefore first derive a formula for this calculation in the first part of the Results Section before presenting the results of the study in the second part of the section.
## 3 Possibilities for the Efficient Design of FEA for the Calculation of the Load-Notch-Strain Curve
### Relationship between the Load-Notch-Strain Curve for the Initial Load and the Hysteresis Branch  
Between the load-notch-strain curve for the initial load and the load-notch-strain curve for the hysteresis branch, there exists a relationship by which the load-notch-strain curve of the initial load can be inferred from the load-notch-strain curve of the hysteresis branch without simulation. By halving the ranges of the elasticity-theoretical stress \(\Delta\sigma_{\text{el}}\) and the strain ranges \(\Delta\varepsilon\) of every second class of the load-notch-strain curve of the hysteresis branch, the load-notch-strain curve of the initial load is obtained [48]; see Equations (9) and (10), where \(\text{n}_{\text{C}}\) is the number of classes. Figure 5 illustrates this procedure, following the descriptions given in Section 2.2. Due to this connection, it is only necessary to carry out the FEA with the double stress-strain curve according to Equation (6) for the hysteresis branch; the load-notch-strain curve for the initial load can be calculated from this. Thus, one-third of the load steps to be simulated are unnecessary.  
\[\sigma_{\text{el,i}}=\frac{\Delta\sigma_{\text{el,2i}}}{2}\] with \[\text{i}=1,\;\ldots,\;\text{n}_{\text{C}} \tag{9}\] \[\varepsilon_{\text{i}}=\frac{\Delta\varepsilon_{\text{i}}}{2}\] with \[\text{i}=1,\;\ldots,\;\text{n}_{\text{C}} \tag{10}\]
## 4 Results
### Fluid Contacts Determination Using Repeat Formation Test (RFT) Data  
A total of nineteen RFT pressure tests were carried out within the E-BK1 reservoir (2942 m to 2983 m), and thirty-one in E-AJ1 (2677 to 2735 m) and E-CB1 (2509 to 2544 m), respectively, to determine formation pressures, fluid contacts, fluid gradients, and densities. The pressure versus depth plot for E-BK1 (Figure 9) shows that this reservoir is gas-filled with a calculated gradient of 0.45 psi/m and a density of 0.32 g/cc. A gas pressure of less than 3850 psi was recorded. Fluid contact determination was impossible in this reservoir due to the missing RFT data. In E-AJ1 (Figure 10), two main fluid contacts, oil-water and gas-oil contacts (OWC and GOC), were observed at 2699 m and 2723 m where the pressure gradient lines intersect. The gradients of the gas line, oil line, and water line were 0.33 psi/m, 0.98 psi/m, and 1.44 psi/m, respectively. Pressures below 3928 psi, 3906 psi, and 3920 psi were observed for the oil, gas, and water lines, respectively. The data for E-CB1 (Figure 11) shows that a single oil-water contact (OWC) was encountered at 2528.5 m, with water and oil gradients of 1.37 psi/m and 0.85 psi/m, respectively. Additionally, the densities of water and oil were determined to be 0.97 g/cc and 1.00 g/cc. Within the reservoir interval (2509 m to 2544 m), water and oil pressure lines below 3720 psi and 3690 psi were measured. Since E-CB1's supercharged points are unreliable, they were ignored in the pressure-depth plot.  
Table 3 below summarizes the water, oil, and gas gradients determined from the pressure data of each well. The results show that the water gradient ranges between 1.37 and 1.44 psi/m (0.97 and 1.01 g/cc equivalent). The oil gradient ranges between 0.85 and 0.98 psi/m (1 and 0.69 g/cc), and the gas gradient ranges between 0.33 and 0.45 psi/m (0.23 and 0.32 g/cc).  
Figure 11: Well E-CB1 RFT pressure vs depth plot displaying oil-water contact (GWC) and identification of fluid densities. Blue squares indicates data points.  
Figure 10: Well E-AJ1 RFT pressure vs depth plot displaying gas–water contact (GWC) and identification of fluid densities. Blue squares indicates data points.
## 6 Performance Evaluation
### Simulation Setting  
The simulation scenario involves two high-load RSUs (RSU1 and RSU2), each associated with 10 vehicles, and one low-load RSU (RSU3) associated with 5 vehicles, reflecting the uneven geographical distribution of the vehicles. To ensure that the DQN algorithm converges more stably and efficiently to an optimal strategy, we set the discount factor \(\gamma\) to 0.9, the replay memory buffer \(\phi\) to 3000, and the update frequency \(\kappa\) of the evaluation network parameters to 100 [34]. According to [16; 28], the key simulation parameters are listed in Table 1. Additionally, the parameters \(t_{un}^{tol}\), \(C_{n}\), \(B_{n}\), \(C_{un}\), \(d_{un}\), \(\forall u\in\mathcal{U}_{n},\forall n\in\mathcal{N}\) are randomly selected from the specified ranges in the table, and the simulation results for each parameter are averaged over 30 simulations to ensure their validity and reliability.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Transmission power of the vehicle \(p_{un}\) & 20 dBm \\ \hline Computation density \(f_{un}\) & 100 CPU cycle/bit \\ \hline Computation intensity of coordinate & 40 CPU cycle/bit \\ transformation \(X_{tra}\) & \\ \hline Noise power \(\sigma^{2}\) & \(-\)100 dBm \\ \hline Processing delay tolerance \(t_{un}^{tol}\) & 2–4 s \\ \hline Task arrival rate \(\lambda_{un}\) & 1 task/s \\ \hline Computing resource of the RSU \(C_{n}\) & 100–180 G CPU cycle/s \\ \hline Available bandwidth of the RSU \(B_{n}\) & 60–140 MHz \\ \hline Computing resource of the vehicle \(C_{un}\) & 4–8 G CPU cycle/s \\ \hline Task data size \(d_{un}\) & 10–18 MB \\ \hline Convergence threshold \(\rho\) & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The simulation parameters.  
To evaluate the performance of our scheme, we compare it with the following three schemes.  
1. Average bandwidth allocation (ABA): In this approach, the RSU allocates the same bandwidth to each vehicle that needs to offload tasks [35].
2. Without collaborative computing (WCC): Tasks can be processed either locally on the vehicle or by the associated RSU [36].
3. Traditional offloading mechanism (TOM): Vehicles need to transmit raw data for task offloading.
# A Fully Integrated Low-Power Multi-Mode RF Receiver for BDS-3/GPS
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MDPI & Multidisciplinary Digital Publishing Institute \\ PVT & Process, voltage, and temperature \\ BDS-3 & BeiDou navigation satellite system-3 \\ GPS & global positioning system \\ PNT & positioning, navigation, and timing \\ IoT & Internet of Things \\ GLONASS & global navigation satellite system \\ IMRR & image rejection ratio \\ LNA & low-noise amplifier \\ ACTBA & active balun amplifier \\ DC & direct current \\ CBPF & complex bandpass filter \\ Gm & transconductance amplifier \\ RHCP & right-hand circular polarization \\ LO & local oscillator \\ IF & intermediate frequency \\ NF & noise figure \\ Tia & Transimpedance amplifier \\ ADC & analog-digital conversion \\ Gr & gain control range \\ \(IP_{\text{1 dB}}\) & input 1 dB compression point \\ IIP3 & input third-order intercept point \\ IBP & in-band power \\ OBIP & out-of-band interference power \\ PGA & programmable gain amplifier \\ BPF & bandpass filter \\ FS & frequency synthesizer \\ DCOC & DC offset correction \\ IQ & in-phase and quadrature \\ CM & common-mode \\ CMFB & common-mode feedback \\ MSB & most significant bit \\ LSB & least significant bit \\ SPI & serial peripheral interface \\ QFN & quad flat no-lead \\ PLL & phase-locked loop \\ \end{tabular}
## 3 Results and Discussion
### Collective Summary and Comparison
#### 3.2.1 The Low-Organic Soils  
The test results for low-organic soils are from samples taken from two sites (sample No. 1 and No. 2.). In the case of sample No. 1, apart from the clear dominance of the silt fraction and its similar content in the case of the reference method and the Helos laser diffractometer, no other significant similarities were observed. The results of the Analysette 22 laser diffractometer method were not comparable to alternative methods (Figure 12). The share of the silt fraction determined by this method was more than 1.6 and 1.5 times higher than the hydrometric and laser methods using the Helos laser diffractometer, respectively. However, the proportions of sand and clay fractions determined by the reference method were lower, with the proportion of clay almost three times higher and the proportion of sand almost four times lower compared to the Helos laser diffractometer.  
Figure 11: The particle size cumulative distribution density for high-organic soils (sample Nos. 8–12).  
Figure 12: A summary of the results of sample No. 1.  
The results of the granulometric tests of sample No. 2 confirmed the dominance of the silt fraction, the content of which was much higher than in sample No. 1 (Figure 13). Moreover, a visible similarity was observed in the results of the tests using the laser diffractometer methods (Analysette 22 and Helos), which, however, differed significantly from the hydrometric reference method. Taking both samples into account, relatively repeatable test results were only obtained with the Analysette 22 laser diffractometer.
# An Energy Efficient MAC Protocol for Wireless Passive Sensor Networks
## 4 Experiments and Analysis
### Simulation Results
#### 4.2.2 Throughput  
Throughput is another important indicator to measure network performances. Figure 6 shows curves of throughput. The vertical ordinate represents throughput and the horizontal ordinate represents time interval.  
In the S-MAC protocol, each node uses a fixed contention window to compete for the access of channel. Therefore, the probability of collision among nodes is extraordinarily high. As can be seen  
Figure 5: Energy consumption of sensor nodes under different loads.  
from the figure, all curves show a downward trend with different degrees because the throughput of the network naturally decreases with the decrease of the data flow of the network. The throughput of the IEEE 802.11 MAC protocol is the heaviest because the nodes are always in the listening state. When data is incoming or outgoing, data transmission happens immediately, so the amounts of data packets are large. When the time interval is less than 4 s, the network load is relatively heavy and the throughput of the IS-MAC protocol is higher than that of the S-MAC protocol. When the time interval is 1 s, the advantage of the IS-MAC protocol is most obvious. In this case, the transmission interval of data packets is 1 s, the network load is the heaviest, and the competition is the most intense. The value of the contention window determines the backoff time. When the network load is relatively heavy, the fixed contention window will lead to consecutive data collisions, so data retransmission will be frequent. This reduces the network throughput. The fixed contention window is adopted in the S-MAC protocol while the contention window is dynamically changed according to the network load in the IS-MAC protocol. Therefore, the data collision is more likely to happen in the S-MAC protocol than in the IS-MAC protocol under heavy network load, which means that data retransmission has less frequency and the throughput is larger in the IS-MAC protocol. However, when the time interval is more than 4 s, the network load is relatively small and the data collision becomes little. The throughput of the IS-MAC protocol is only slightly higher than that of S-MAC protocol. In summary, when the network load is small, the IS-MAC protocol has no greater advantage than the S-MAC protocol in throughput. When the network load is larger, the IS-MAC protocol has an obvious advantage over the S-MAC protocol.
## 3 RCU-Style Non-Blocking Synchronous Algorithms
### Multi-Version Read Log Update (MV-RLU)
#### 3.3.1 Procedure of a Node Update  
Figure 2(a) shows the operation procedure of a single linked list synchronized with MV-RLU.  
1. The local clock is maintained in the thread's metadata when MV-RLU enters the MV-RLU section in the same way that RLU does.
2. Thread W1 creates a new version, \(B^{\prime}\), in the per-thread log to modify node \(B\) and modifies the value (denoted as 1 in Figure 2(a)). Similar to RLU, MV-RLU specifies global clock + 1 as the update clock (denoted as write clock in the log of W1 and commits for the update.
3. After creating the version \(B^{\prime}\), the thread R1 enters the critical section (also known as the MV-RLU section) and reads the version \(B^{\prime}\) (1).
4. Upon request, thread W2 also creates version \(B^{\prime\prime}\) in the log to modify node \(B\) and updates the value (1). The thread W2 stores the update clock in the log (assuming the write clock is 15 (1) and commits it.
5. After that, the thread \(R2\) enters the MV-RLU section and reads the latest valid version \(B^{\prime\prime}\) based on local clock (1).
6. During the above procedure, log reclamation starts when a thread's log usage exceeds a certain level. It waits until there is no thread reading a version other than the latest version and then overwrites the latest version on the original node (1). Then, areas that are no longer accessed by threads are recycled in the log by MV-RLU's garbage collector.
## 3 Results and Discussion  
### Composition Analysis of Swine and Furfurral Wastewater  
Before microalgal cells were grown in the mixed wastewater, the essential parameters of the stock wastewater were measured (Table 1). The concentrations of COD, TN and TP in SW were 4316.40, 425.41 and 14.18 mg L\({}^{-1}\), respectively. The FW had a high COD concentration, reaching 50,978.67 mg L\({}^{-1}\). This was approximately 12 times higher than the COD level in the SW. However, the concentrations of TN and TP in the furfurral wastewater were 33.47 and 1.01 mg L\({}^{-1}\), which were lower than those in the SW. The pH value of the FW was only 3.1 because it contained many organic acid substances such as acetic acid [17]. The FW was not suitable to directly culture microalgae not only because of its excessive COD concentration, but also because of its low pH value.  
However, it was possible to obtain appropriate concentrations of nutrients for microalgae growth by mixing the two types of wastewater. To achieve the appropriate TN and TP concentration of wastewater, the FW and SW were mixed together for microalgae cultivation. The physicochemical characteristics of the mixed wastewater of SW and FW at different mixing ratios are shown in Table 1. With the increase of the proportion of FW, the concentrations of TN and TP decreased, showing more characteristics of FW. The COD/TN ratio of mixed wastewater increased with the decrease in the SW:DFW ratio, which was beneficial to the microalgae growth in high-strength ammonium wastewater [25]. An increase of carbon/nitrogen can eliminate the ammonia toxicity of SW. [PERSON] et al. [16] found that the cell growth and viability of _C. vulgaris_ were significantly improved when the carbon/nitrogen ration increased from 17:20 to 5:1, 25:1 and 125:1 by addition of glycerol into manure-free piggery wastewater with an initial ammonium concentration of 220 mg L\({}^{-1}\) that was harmful to cells and inhibited microalgal growth. The possible reason was that the enhancement of carbon/nitrogen ratios promoted cell division and stimulated the absorption of ammonia, which facilitated the growth of cells and mitigated the ammonia toxicity. [PERSON] et al. [31] also observed that the addition of exogenous alpha-ketoglutate, citric acid and glucose to high strength ammonia wastewater greatly promoted ammonia assimilation and cell growth of _Chlorella_ sp. Compared to alpha-ketoglutate and citric acid, glucose was a better carbon source because it generated more energy and hydride donors that are critical to ammonia assimilation. This was an efficient way to alleviate ammonia toxicity in wastewater, accelerating ammonia assimilation by adding a carbon source for algae cultivation.  
An appropriate nutrient environment is an essential prerequisite for microalgae growth. Excessive concentrations of nutrients in wastewater can inhibit microalgal cells growth [26], while deficient concentrations are unable to support long-term growth of microalgae, leading to a low removal efficiency of nutrients from the wastewater [7].
# Characterization of a COTS-Based RF Receiver for Cubesat Applications +
## 5 Test Results  
### Measure of the S-Parameters of the IF Low-Pass Filter  
In this subsection, an example of a component-level test exploiting the RF test points is shown. The S-parameters have been measured on a low-pass filter placed on the receiver IF section. The IF low-pass filter has been custom designed to reject the LO spur that couples on the IF, because of the low insulation of the mixer ports. The measure of the S-parameters has allowed to verify the filter performance and to validate the simulation model.  
Figure 5a shows a photograph of the test setup, which is constituted of a network analyzer [25] that has been connected to the filter through two mate-connectors. Figure 5b shows an enlarged photograph of the connection of the mate-connectors to the input and output RF test points foreseen for the filter.  
The network analyzer has been calibrated using the custom calibration kit shown in Figure 4 within the 30 kHz-3 GHz frequency range, setting the test power level equal to \(-\)30 dBm.  
Figure 5: Photograph of the test setup implemented to test the custom IF section (390 MHz) low-pass filter: (**a**) The whole test setup; (**b**) A zoom on the mate of the instrument connectors to the filter RF test points.  
Figures 6 and 7 report the measured S\({}_{21}\) and S\({}_{11}\) parameters, respectively, of the filter compared with the result of the design simulation.  
The measured and simulated S\({}_{21}\) parameters agree very well. A 4.14 dB in-band attenuation has been measured at 390 MHz frequency, which is exactly equal to the attenuation obtained by simulations at the same frequency. Likewise, the measured and simulated S\({}_{11}\) parameters agree well. At 390 MHz, a \(-19.7\) dB value has been measured on the S\({}_{11}\) parameter, which is excellent from the impedance-matching point of view. Therefore, the obtained results show that the layout implementation of the low-pass filter has been done correctly and the RF test points have been effective in performing the experimental characterization of a single component, even if already integrated in a wider system like the receiver board.
# A Low-Complexity Pilot-Based Frequency-Domain Channel Estimation for ICI Mitigation in OFDM Systems
## 3 Proposed Pilot Pattern and Frequency-Domain Coupling Matrix Estimation
However, since the energy of each diagonal may be very different, instead of applying a two-dimensional (2D) interpolation method, we propose performing a \(2Q+1\) 1D spline interpolation, i.e., a 1D interpolation for each of the main diagonals. With the proposed method and pilot pattern, we estimate the channel coupling matrix for every OFDM symbol with pilots. However, between OFDM pilot symbols, there are \(T_{\text{space}}\) data symbols. In addition, a spline interpolation in the time domain is also proposed to track the time variations of the coupling matrix.
# Performance Evaluation of Recycled Asphalt
## 2 Materials and Experiments
### Mortar Cementing Method for the Strength of Agglomerate RAP Materials  
At present, there is no suitable method to directly test the strength of agglomerate RAP materials. For this reason, this paper explored the mortar cementing method. Using cement mortar with a fixed ratio, different proportions of the agglomerate RAP Materials are cemented together, and the splitting strength of the mold is tested, and the strength is directly extrapolated according to the strength test result, and the strength when the cement mortar amount is zero can be obtained. This is used to characterize the strength of agglomerate RAP materials [26].  
In the test, the cement mortar was prepared according to the ratio of water: cement: sand = 1:2:3. Only 2.36 mm\(\sim\)16 mm particle size was used for RAP materials, and the RAP materials content in the specimens are set as 0%, 30%, 40%, and 50% [27; 28]. Making four groups of cube specimens with a size of \(150\times 150\times 150\) mm, two for each group, vibratory compaction the specimens. Some of the molded specimens were shown in Figure 3. The specimens shall be cured at room temperature for two days (demolding on the first day will have a great impact on the specimens), and shall be watered and wet cured at intervals. After demolding, it was placed in the cement concrete curing room for three days. The strength of the specimens was tested on the fifth day.  
Additionally, this paper used the same grade of cold RAP materials and new aggregate, and used different cement dosages to make specimens, and tested the splitting strength to ensure the reliability of the conclusion [29].  
Figure 3: The prepared mortar cementing recycled asphalt pavement (RAP) materials specimens. (**a**) 70% cement mortar + 30% RAP, (**b**) 50% cement mortar + 50% RAP.