esa-sceva/satcom-chunk-collection huggingface dataset:
# Jamming Detection and Suppression Technique for OTFS Systems in an AWGN Channel
## 4 Simulation Results
### Jamming Detection Performance
Compared to the single-tone interference scenario, the actual false alarm probability under partial-band interference deviates noticeably from the theoretical analysis results. Moreover, the behavior of the actual false alarm probability becomes more complex as the jamming factor increases. This phenomenon is caused by the combined effects of noise and partial-band jamming signal. On the one hand, the interplay of noise and jamming increases the randomness of the signal spectrum. On the other hand, the partial-band jamming signals cause the spectral amplitude of the received signal to deviate from the Rayleigh distribution assumed in theoretical analysis. These factors ultimately result in a certain degree of deviation between the theoretical analysis and the actual results. Figure 11 illustrates the detection performance of the conventional FCME and the proposed algorithm under partial-band jamming with a jamming factor of \(\xi=0.4\) in both AWGN and EVA channels, with a target false alarm probability of \(P_{fa}=0.05\). In simulations, the carrier frequency is set to 4 GHz, and the speed of communication terminal is up to 500 km/h. Simulation results show that the proposed FCME algorithm remains effective in the EVA channel. In simulations, the detection probability of the proposed algorithm  
Figure 9: The detection probability of partial-band jamming in an OTFS system. tends to converge when the JSR is around 15 dB, indicating that jamming detection remains effective in the EVA channel. Additionally, under low JSR conditions, the proposed FCME demonstrates performance comparable to that of the conventional FCME in the EVA channel. Compared to the traditional FCME algorithm, the proposed method exhibits a higher false alarm probability in the EVA channel. The reason is that frequency-selective fading caused by the multipath channel reduces the initialization threshold, leading to an increased likelihood of detecting non-jamming signals as interference.
## 5 Discussion
### Expected Effects of Reduced Model Calibration Sample Size on Prediction Performance  
It is useful to discuss the likely effects of reduced model calibration sample size on each of our study predictors. As sample size decreases, the prediction accuracy of all predictors would degrade, but not necessarily in a uniform manner across the five model fits. Here, a reduction in sample size is likely to create a problem in selecting the bandwidth for GWR and similarly the neighbourhood for KED (_i.e._, non-stationary relationships cannot be determined due to diminishing local information). As a result, the optimal bandwidth or neighbourhood will tend to a global form. Thus, GWR prediction accuracy would tend to that of the corresponding MLR model, whilst KED-LN prediction accuracy would tend to that of the corresponding KED-GN model. These effects will be more pronounced in KED-LN as its trend component can be viewed as GWR with a box-car kernel whose localness is determined using only un-weighed local data subsets; whereas GWR with a distance-decay kernel, such as the bi-square, can still model locally whist utilising all available information (see [PERSON] _et al._) [57]. In addition, KED's residual variography would also suffer from diminishing information, ultimately resulting in a pure nugget variogram. Thus, KED's prediction accuracy would also then tend to MLR in this instance. Similar remarks can be made with respect to GWRK whose prediction accuracy would also tend to that of MLR as sample size reduces. Overall, MLR would be the least affected by a reduction in model calibration size, as its non-spatial form requires the least information for reliable model parameter estimation.
# Slotted E-Shaped Meta-Material Decoupling Slab for Densely Packed MIMO Antenna Arrays
## 2 Methodology of Design and Measurement Analysis
### Antenna Array without Decoupling Slab  
Utilizing CST Microwave studio, Figure 1a shows a 2 \(\times\) 1 microstrip patch without DS that comprises one cell of the array of antenna. The impedance bandwidth of the two elements is enhanced by truncating the ground plane as shown in Figure 1b. The dimensions of the two same patch antennas are: L = 17 mm, W = 20.5 mm, and the gap between the two radiators antenna is 18.31 mm. The design will be implemented on FR-4 lossy substrate with thickness (h) equal to 1.6 mm, dielectric constant (\(\epsilon_{r}\)) equal to 4.5, and tangent loss (tan \(\delta\)) equal to 0.025.  
It was found that the current density distributed over the antenna array surface is higher without DS. Figure 2 shows high surface current density.  
Figure 1: Structure of \(2\times 1\) microstrip patch array of antenna without decoupling slab (**a**) front view (**b**) back view.  
Figure 2: Surface current density of \(2\times 1\) microstrip patch antenna array without decoupling slab.  
A computer aid design tool, which is CST Microwave studio, was used to calculate the simulation results. Figure 3a shows the maximum isolation is \(-23.7\) dB and the operating frequency is from \(9.22\):\(10.5\) GHz with a bandwidth equal to \(1.28\) GHz. Figure 3b shows a real photo for the fabricated antenna array.  
It was assumed in the simulation that the external conditions are absent, that the boundary absorber material is perfectly matched and supporting the concept of putting the array in open space.
# Beam Dynamics Design of a Multi-Ion RFQ for Medical Application
## 4 Error Analysis
### Mechanical Errors Analysis  
In addition to the input beam errors, there are fabrication and assembly errors that can also adversely affect the beam stable transmission and the quality of output beam. After referencing the fabrication experience of similar RFQs [18; 21; 31; 55] and consulting with the manufacturer, the mechanical error specifications for this RFQ were determined. The mechanical error terms and their corresponding values are presented in Table 6. Mechanical error analysis is carried out using the Monte Carlo simulation method within the  
Figure 10: Transmission efficiency of the RFQ versus input beam parameters (**a**) The beam current. (**b**) The same voltage multiplier. (**c**) The input emittance. (**d**) The energy spread. (**e**) The beam spatial displacement for centroid. (**f**) The beam divergence-coordinate displacement for centroid.  
TRACEWIN program, which assesses the impact of errors by simulating numerous randomized cases. In each Monte Carlo trial, the values of the error items remain constant along the full length of the structure but vary randomly between different trials. After setting the error items and their corresponding ranges, TRACEWIN generates a set of random numbers uniformly distributed within the specified intervals. These values are then assigned to the RFQ electrode structure for simulation. This process is repeated 1000 times, and each simulation result is recorded and analyzed. The cumulative probability distribution graph of the results from the mechanical error analysis is shown in Figure 11.  
In Figure 11a, the maximum additional beam loss is 9% when considering mechanical errors, with a 70% probability of maintaining a transmission efficiency above 95%. In Figure 11b,c, longitudinal emittance increases by 2.74%, and the transverse emittance grows by 3.2%. The emittance growth limit for the multi-ion RFQ is set below 5%, and the extra beam loss is 10%. Both the beam loss and emittance growth meet the operational requirements. In practice, modern five-axis CNC machining routinely achieves vane-tip geometric accuracies of 20 \(\upmu\)m or better, and on-site alignment using laser trackers holds positional tolerances within 50 \(\upmu\)m over meter-scale compen  
Figure 11: Cumulative probability distribution graph of the results for the mechanical error. (**a**) Extra beam loss. (**b**) Longitudinal emittance growth. (**c**) Transverse emittance growth.  
manufactured and assembled within the simulated error envelope with a sufficient margin, ensuring a comfortable safety factor against mechanical deviations.
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 3 Simulation and Experimental Results
### Simulation Results of SMC and PDSMC
#### 3.1.1 Step Output  
Chattering was observed in the output of SMC whereas the PDSMC output had less chattering which can be seen in Figure 6. The solid red line shows the output of SMC which has chattering effects and the dotted blue line shows the output of PDSMC which has a small amount of chattering. As mentioned earlier, the switching gain K of PDSMC was smaller than SMC.  
\begin{table}
\begin{tabular}{c c c} \hline
**Serial. NO** & **SMC** & **PDSMC** \\ \hline
1 & K = 550 & K = 275 \\
2 & C = 16 & \(k_{p}=8\), \(k_{d}=0.5\) \\
3 & D = 500 & D = 500 \\
4 & Step input & Step input \\ \hline \end{tabular}
\end{table}
Table 2: SMC and PDSMC paramters.  
Figure 6: Sliding mode control (SMC) and proportional derivative sliding mode control (PDSMC) output response against step input.
# Challenges for Teaching Wireless Communications Standards at the Graduate Level
## 4 Laboratory Experiences for Telecommunications Standards' Education
### Experiences of Collaboration with the Telecom Operator  
The experience of collaboration with telecom providers is very important to teach the standards to students in a practical way. According to the proposed methodology, in the first phase, the teacher describes with the help of slides, e.g., the downlink and uplink logical, transport, and physical channels of the LTE system and their functionalities; then the students learn and memorize the names and their related use (often only for the examination). In the next phases, in a telecom laboratory, a telecom technician monitors the LTE network with probes installed at the eNodeB and uses traffic tools to show the radio access requirements, the flow of data, which channels are involved, and how they are used to manage access and to establish a connection from UE to the eNodeB/core network and vice versa. As a consequence, the use of practical instruments and tools can improve the learning of LTE channels. For example, the teacher can explain the Radio Resource Control (RRC) states and the impact on UE latency tied to the change of the connection states [19], as shown in Figure 2.  
Instead, a traffic analyzer tool used by the telecom operator can highlight which connection or idle states are activated in succession, and from case-to-case, the latency times can be calculated directly as in Figure 3. The student is therefore more motivated to understand and learn about the problem with practice.  
Another work project allows the students to use the tools to monitor the traffic in the cells and analyze the different throughput in the downlink available according to the link adaptation concept, as in Figure 4. During the first phase, the students understand that the adaptation of modulation and coding to the link conditions is limited by the signal-to-noise plus the interference of other cells. In the second phase, the students can understand what good or bad channel conditions mean by analyzing the Signal-to-Interference-Noise Ratio (SINR) parameters shown in the tool.  
Moreover, they can also learn the new concepts of Self-Organizing Networks (SON) introduced in 3 GPP Release 9 for self-configuration, self-optimization, and self-healing of the network and analyze machine learning applications to optimize the management of specific Key Performance Indicators (KPIs) with respect to the users' satisfaction, i.e., the quality of experience [20; 21; 22].  
Figure 3: UE latency analysis shown by the telecom tool.  
Figure 2: Radio Resource Control (RRC) states in the Universal Mobile Telecommunications System (UMTS) system.
# Study on Temperature Characteristics of Lubrication Film of Valve Plate Pair in Axial Piston Pumps
###### Abstract  
The film temperature distribution of the valve plate pair in axial piston pumps affects its lubrication, leakage, and friction. In order to investigate the film temperature distribution of the valve plate pair in axial piston pumps, a test platform was constructed including three displacement sensors for the oil film thickness and eleven thermocouples for the film temperature distribution of the valve plate pair. An accurate film shape model of the valve plate pair was built according to the three-point film thickness test data. Based on the film shape model, the film temperature model of the valve plate pair was developed considering the viscous oil temperature characteristics, the energy loss caused by leakage and viscous friction in the film, and the heat conduction among the oil, cylinder block, and valve plate. The influence of different swash plate tilt angles and operating pressures on the valve plate film temperature was studied. The test results indicate that the film temperature of the valve plate pair increases as the working pressure and swash plate tilt angle increase. The theoretical and experimental absolute errors of the film temperature in the circumferential range [\(-60^{\circ}\), \(60^{\circ}\)] of the valve plate high-pressure side are less than \(3.5^{\circ}\)C. As the swash plate tilt angle varies from \(12^{\circ}\) to \(16^{\circ}\) and working pressure from \(3\) MPa to \(7\) MPa, the minimum film thickness position and the maximum temperature point move accordingly in the circumferential range [\(-15^{\circ}\), \(5^{\circ}\)] of the valve plate pair.  
Keywords:axial piston pump; valve plate; wedge angle; azimuth angle; temperature characteristics +
Footnote †: journal:
# A High Gain Modified Quadratic Boost DC-DC Converter with Voltage Stress Half of Output Voltage
## 3 Design Considerations
### Selection of the Capacitor  
The capacitor was designed based on the ripple voltage \(\Delta V_{\text{CX}}\), the charging current following through it, the duty cycle, and the switching frequency \(f_{sw}\), respectively. The corresponding differential equation of each capacitor was formulated and is given below.  
\[\left\{\begin{array}{l}\text{C}_{1}=\frac{\Delta\text{Q}_{1}}{\Delta V_{ \text{C1}}}=\frac{\Delta I_{\text{C1}}dT}{\Delta V_{\text{C1}}}=\frac{(2-d) dV_{0}}{(1-d)^{2}\Delta V_{\text{C1}}f_{sw}R}\\ \text{C}_{2}=\frac{\Delta\text{Q}_{2}}{\Delta V_{\text{C2}}}=\frac{\Delta I_{ \text{C2}}dT}{\Delta V_{\text{C2}}}=\frac{dV_{0}}{(1-d)\Delta V_{\text{C2}} f_{sw}R}\\ \text{C}_{3}=\frac{\Delta\text{Q}_{2}}{\Delta V_{\text{C3}}}=\frac{\Delta I_{ \text{C2}}dT}{\Delta V_{\text{C3}}}=\frac{dV_{0}}{(1-d)^{2}\Delta V_{\text{C3 }}f_{sw}R}\\ \text{C}_{4}=\frac{\Delta\text{Q}_{4}}{\Delta V_{\text{C4}}}=\frac{\Delta I_{ \text{C1}}d^{2}}{\Delta V_{\text{C4}}}=\frac{dV_{0}}{(1-d)\Delta V_{\text{C4 }}f_{sw}R}\\ \text{C}_{0}=\frac{\Delta\text{Q}_{1}}{\Delta V_{\text{C1}}}=\frac{I_{\text{C 2}}dT}{\Delta V_{\text{C0}}}=\frac{dV_{0}}{R\Delta V_{\text{C0}}f_{sw}}\\ \end{array}\right. \tag{44}\]
## 4 Discussion
### Clinical Implications
#### 4.6.2 Enhanced Material Design for Clinical Applications  
The architectural and material innovations in GTR membranes, such as CS/Gel scaffolds and microcavity-rich designs, offer promising solutions for creating tissue-specific regenerative environments [56]. These biomimetic scaffolds not only mimic the extracellular matrix but also promote cell adhesion, polarization, and growth factor release, essential for successful tissue integration and repair [57]. For cancer patients undergoing radiotherapy or chemotherapy, who may face significant tissue loss and delayed healing, these materials could provide critical support for reconstruction and regeneration [4]. The ability to incorporate bioactive molecules that counteract the immunosuppressive effects of cancer treatments further enhances the applicability of GTR membranes in oncology [34].  
The development of multifunctional GTR membranes that combine regenerative, antibacterial, and anti-inflammatory properties could address multiple challenges simultaneously in clinical settings [58]. For example, diabetic patients with chronic wounds are at a heightened risk of infection [59], making antibacterial membranes a crucial innovation. Similarly, anti-inflammatory membranes could modulate chronic inflammation in cancer patients or those with poorly controlled diabetes, fostering a more conducive environment for tissue repair [60].
## 4 Discussions  
### POD Uncertainties on the Excess Phase and Bending Angle Inversion  
The requirements for COSMIC-2 POD are 10 cm. There are multiple POD solutions, sometimes as many as five, at a GPS epoch in the operational datasets. The multiple POD solutions in the operational POD generally match these requirements with a maximum difference of less than 10 cm; however, uncertainties exist from a few centimeters to meters. Using UCAR POD, the different solutions can affect the excess phase results, hence the bending angle. Figure 10 shows multiple POD operational products' C2E1 mass center position radial difference. Most of the time, the POD differences are less than 10 cm. Sometimes it can be as high as a few meters. This is probably due to the different solutions  
Figure 9: Refractivity Comparisons with (**a**) UCAR and (**b**) ERA5.  
from observation periods, POD antennas, or combinations of different signal systems (GPS/GLOAASS).  
For the excess phase calculation, the POD bias can be taken into excess phase calculation since the geometric distance between the POD antenna and the reference satellite transmitter antenna needs to be removed. Removing the single difference clock bias in the excess phase can also affect the L3 clock bias term. Using different POD solutions from operations, POD uncertainties can contribute to the excess phase difference (often as a trend, not a constant) and bending angle uncertainty. An example of the RO profiles under this condition is shown in Figure 10 using different POD results for a selected case: 18:46 UTC for G15 of C2E1. The changing satellite distance difference of 0.1 m in about 200 s (Figure 10a) is translated into excess phase (Figure 10b) about 0.25 m simultaneously. Here, the POD difference affects the calculated range between the GNSS satellite and RO antenna and the single differencing due to the direct link calculation between the LEO and referenced GNSS satellite. The bending angle difference at the upper altitude is significant in this situation, 10% different at 50 km (Figure 10c). Another scenario is when the two POD solutions are not on the same epoch but overlapped in time after the polynomial interpolation is applied to different POD solutions; the resulting antenna position difference can sometimes be even more than meter levels, affecting the excess phase and bending angle. This bending angle profile may be removed as outliers during the quality check, depending on the amplitude of the position error. This may be resolved during reprocessing using one day's observations (longer arcs) to make the dynamic orbit fitting more accurate than hourly batches.
## 1 Introduction
Their analysis suggests a range from a minimum of 91.68 to a maximum of 225.33 million tons of CO\({}_{2}\) per year from the transport sector in Bangkok. [PERSON] [28] found that in the transport sector, the modal shift from typical passenger cars to mass transit modes clearly plays an important role in reducing energy demand, CO\({}_{2}\) emissions, and air pollution. [PERSON] et al. [29] proposed different (both individual and integrated) transport and land-use scenarios for Khon Kaen University (KKU) in Khon Kaen city, Thailand. They noted that the use of electric motorcycles (EMCs) had the greatest effect on combating CO\({}_{2}\) emissions, followed by land-use control instruments, restrictions on private vehicle utilization, and public transport modifications. [PERSON] et al. [30] recently adopted the KKTP area in Khon Kaen, Thailand, as a case study and found that the implementation of the light rail transit (LRT) system and other potential measures (such as park and ride (P&R) and improvement of the feeder system) could, to a certain extent, reduce CO\({}_{2}\) emissions. APERC [31] evaluated combined fuel economy and urban planning policies in Asia-Pacific Economic Cooperation (APEC) economies and assumed that efficient urban planning will lead to a 5-20% lower level of vehicle saturation compared to the business-as-usual (BAU) scenario. The compact city and sustainable urban planning contributed about one-third of the total energy savings in the improved efficiency scenario in 2040 compared to BAU. The previous studies found that TOD (avoid, A), public transport improvement (shift, S), and the use of EVs (improve, I) have the potential to reduce GHG emissions and energy consumption. However, comprehension of their explicit impacts is lacking, particularly in medium-sized cities in developing countries with high-growth urbanization. The main objectives of the research are to analyze, evaluate, and compare the CO\({}_{2}\) emissions and energy consumption reduction performances for three individual scenarios, TOD, LRT, and EVs, and two integrated scenarios in Khon Kaen city, Thailand. The remainder of this paper is organized as follows: Section 2 describes the framework employed for the potential reduction of CO\({}_{2}\) emissions and energy consumption levels for a mid-sized city. Section 3 discusses model development. Section 4 presents the results of the analysis and discussion. Finally, Section 5 draws the conclusions. As step 1, illustrated in Figure 1, was previously described in Section 1, the remainder of this section outlines the work undertaken to develop the transport and land-use scenarios for the study area (steps 2-4). The following section (Section 3) considers the model development (step 5) and Section 4 describes the modeling results and the analysis and evaluation of the policy scenarios (steps 6-8).
# Dispersion Curve Interpolation Based on Kriging Method
## 2 Data  
The raw data used in this study came from continuous observations taken from July to August 2020. Jilin University placed 361 short-period seismometers in Changbai volcano. Figure 1 shows the study area. In the ambient noise tomography, we used vertical component data for about 30 days. The data processing part of the raw data was the same as the procedure described by [PERSON] et al. (2007) in 2007:  
1. Data preprocessing including demeaning, detrending and filtering (0.08-2 Hz);
2. Cut continuous waveforms into one day's length;
3. Time domain normalization and frequency domain spectrum whitening, which eliminates the influence of seismic activity;
4. Cross-correlate the waveforms of each station pair;
5. Drop the Noise Cross-correlation Functions with signal-to-noise ratio ([PERSON] and [PERSON], 2011) less than 2;
6. Extract dispersion curves with FTAN ([PERSON] and [PERSON], 2011);
7. Inverse the dispersion curves.  
Figure 1: The study region. The blue triangles represent the locations of seismometers. The red symbols indicate the three main craters in the region.  
We then obtained a 3D model of the Changbai volcano using the direct inversion method [19]. The endpoint of the grid is 127.55/42.45. The size of each inversion grid was 0.03\({}^{\circ}\)\(\times\) 0.03\({}^{\circ}\) (in longitude and latitude). \"Weight\" is the balancing parameter between the data fitting term and smoothness, and we set 6.5 for it. The \"maxiter\" is set to be 10. Figure 2 shows the ambient noise tomography results. Due to the low ray density in the depth (15 km), the area labeled as a red star in Figure 2c lacks accurate V\({}_{5}\) information.