esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Radio Resource Management with Carrier Aggregation in LTE Advanced
## 4 Results and Discussion
### Simulation Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Traffic Model** & **Algorithm** & **Average User Throughput (\%)** & **Average Cell Throughput (\%)** \\ \hline \multirow{3}{*}{Video} & LG-PF & 16.73 & 13.43 \\  & LL-PF & 28.30 & 19.86 \\  & RR & 32.51 & 6.38 \\ \hline \multirow{3}{*}{HTTP} & LG-PF & 26.82 & 14.70 \\  & LL-PF & 35.62 & 10.48 \\  & RR & 31.29 & 12.64 \\ \hline \multirow{3}{*}{VoIP} & LG-PF & 27.95 & 16.87 \\  & LL-PF & 39.40 & 31.81 \\ \cline{1-1}  & RR & 29.71 & 17.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average improvement of proposed method. Figure 9: Fairness index for different number of users. In order to estimate the spectral efficiency, mixed type of data consists of video, HTTP, and VoIP are considered. From the obtained results in Figure 10, it is clearly shown that the proposed method uses the spectrum more efficiently than the other two methods. By considering the delay threshold and probability of packet loss, the proposed method prevents the waste of the resources and increases the efficiency. On the other hand, RR is a channel unaware method which blindly allocates RBs to the user without considering the channel condition which causes the lowest spectral efficiency. However, in considering the previous data rate, the LG-PF method is marginally better than RR. Overall, the proposed method improves 5.84%, 16.65%, and 7.28% compared to LG-PF, LL-PF, and RR, respectively. Figure 11 shows the average user throughput comparison of LTE and LTE-A users with different bandwidth. This comparison reveals the effect of the implementation of CA to all types of user in the cell. LTE users have been allocated single CC with 20 MHz of bandwidth and the LTE-A users have been allocated 4 CCs with a maximum 40 MHz of bandwidth. Simulation results show that LTE-A users achieve higher throughput than the LTE users. However, LTE users obtain a significant amount of throughput as well despite having only one CC. This proves that the proposed method can allocate resources to all users fairly and the effect of CA is marginal to LTE users.
# An Efficient and Accurate Adaptive Time-Stepping Method for the Landau-Lifshitz Equation
## 3 Computational Experiments
### Periodic Forcing Term  
We consider a one-dimensional LL equation on domain \(\Omega=(0,1)\), \(N_{x}=100\) and \(h=1/100\) with a forcing term  
\[\mathbf{m}_{t}=-\mathbf{m}\times\mathbf{m}_{xx}+\mathbf{f}, \tag{10}\]  
which has an exact solution [4]  
\[\mathbf{m}^{\varepsilon}(x,t)=\begin{pmatrix}u^{\varepsilon}(x,t)\\ v^{\varepsilon}(x,t)\\ w^{\varepsilon}(x,t)\end{pmatrix}=\begin{pmatrix}\cos(x^{2}(1-x)^{2})\sin(t) \\ \sin(x^{2}(1-x)^{2})\sin(t)\\ \cos(t)\end{pmatrix}. \tag{11}\]  
Figure 3: Numerical solutions at (**a**) \(t=0.01\), (**b**) \(t=0.07\), and (**c**) \(t=0.1\). Numerical and analytic solutions of (**d**) \(u(x,t)\), (**e**) \(v(x,t)\), and (**f**) \(w(x,t)\) at \(t=0.1\). The differences between the exact solution and the computational solutions (**g**) \(u(x,t)\), (**h**) \(v(x,t)\), and (**i**) \(w(x,t)\) at \(t=0.1\). (**j**) The time evolution of discrete energy.  
Therefore, a corresponding forcing term is adopted for the governing LL equation. For brevity, we used \(X=x^{2}(1-x)^{2}\).  
\[\mathbf{f} = \mathbf{m}_{t}+\mathbf{m}\times\mathbf{m}_{xx}\] \[= \begin{pmatrix}\cos(X)\cos(t)+\left[(X^{\prime})^{2}\sin(X)-X^{ \prime\prime}\cos(X)\right]\sin(t)\cos(t)\\ \sin(X)\cos(t)+\left[(X^{\prime})^{2}\cos(X)+X^{\prime\prime}\sin(X)\right] \sin(t)\cos(t)\\ -\sin(t)+X^{\prime\prime}\sin^{2}(t)\end{pmatrix}.\]  
The computational domain is defined as explained in Section 2.1. For final time \(T=0.1\), tolerance \(tol=10^{-7}\) and maximum time step size \(\Delta t_{\max}=0.5\mu^{2}\) are selected as appropriate parameter values. Figure 4a-c show the snapshots of the computational results of \(\mathbf{m}(x,t)\) at \(t=0.01\), \(0.07\), and \(0.1\), respectively. Figure 4d-f and g-i display the numerical solutions and differences of \(u(x,t)\), \(v(x,t)\), and \(w(x,t)\) with the matching exact solutions at \(t=0.1\), respectively. Computational results are represented by a circle, and analytic solutions are represented by solid lines. We can see that the results from the proposed method show good agreement with the exact solution. See Appendix A Table A1 for enumerated parameters.  
Figure 4: Numerical solution at (**a**) \(t=0.01\), (**b**) \(t=0.07\), and (**c**) \(t=0.1\). Numerical and analytic solutions of (**d**) \(u\), (**e**) \(v\), and (**f**) \(w\) at \(t=0.1\). The differences between the exact solution and the numerical solutions (**g**) \(u\), (**h**) \(v\), and (**i**) \(w\) at \(t=0.1\).
## 2 Business Concept with the Developed IoT-Based Gas Level Sensor
### Requirements  
The following requirements were considered, among others:  
* Low price;
* Wi-Fi communication;
* The sensor must perform two measurements per day (to improve data quality);
* Powered by non-rechargeable AA-size lithium/iron disulfide (Li/FeS\({}_{2}\)) batteries;
* Operating temperature range from \(-10\,^{\circ}\)C to \(50\,^{\circ}\)C;
* Possibility of use and installation in stationary tanks from 100 to 3400 L;
* Easy installation and configuration;
* IP65 housing;
* The location and ID of each IoT-based sensor must be known;
* It must be developed taking into account Mexican standards;
* The international standards for electronic design must be considered.  
Figure 3 shows the block diagram of the IoT-GL-Sensor. This consists of four main blocks.  
The general description of each block is listed below:  
* Power supply: provides the power needed for the operation of the electronic device;
* Sensor: measures the position of the magnetic field of the dial gauge magnet, and the analog signals are shared with the MCU;
* MCU: converts the analog signal to a digital signal and processes it to determine the percentage of LPG level in the tank. Afterward, it uploads the data to the Wi-Fi;
* Wi-Fi: used to send data to the gateway, which in turn sends it to a server over the Internet.  
The data generated by the device can be consulted remotely at any time. However, the value of the last reading sent to the server will be displayed in the app and on the gas company's web platform, as shown in Table 2. Seven percent of the LPG in the tank has  
Figure 3: Block diagram of the IoT-GL-Sensor.  
Figure 2: Business concept with the developed IoT-GL-Sensor.  
been selected as the percentage from which the alert starts, as according to the gas company, this percentage lasts on average between 4 and 7 days. This time is enough for the user and the company to schedule the refill. Nevertheless, this minimum percentage can be programmed according to the client's needs.  
When the IoT-GL-Sensor does not perform any activity, it goes into sleep mode, to reduce power consumption and increase battery lifespan.
## 3 New Pulse Duration Limits of Modelocked Thin-Disk Lasers
### Conclusions  
One remarkable common point of most recently achieved results is the large fraction of the available emission bandwidth that could be exploited in modelocked operation in the thin-disk geometry. This seems to indicate that much shorter pulses can be obtained with materials with significantly larger emission bandwidths, such as for example Yb:CALGO, by optimizing SESAM and soliton modelocking parameters. Furthermore, this opens the door to 100-W level oscillators with short  
Figure 19: **(a)** Emission spectrum of the mixed cubic sesquioxide Yb:LuSoO as a result of the combination of Yb:LuO and Yb:ScO; **(b)** Spectrum of the modelocked laser delivering 96 fs, showing a 12.5 nm-wide (FWHM) modelocked spectrum, together with the emission spectrum of Yb:LuSoO. The observed red-shift is due to the low inversion level at which the laser operated, with an output coupling rate of 2.6%; **(c)** Autocorrelation trace of the 96 fs pulses, showing clean sech\({}^{2}\) shaped soliton pulses.  
pulse durations (sub-100 fs), which would be ideally suited to drive the targeted strong-field physics experiments directly with a table-top oscillator.  
Further power scaling will require thinner disks (\(<\)100 \(\upmu\)m) from improved growth runs, which should support efficient single-fundamental mode operation at high powers. The resulting low gain per single-pass will require operation in vacuum to reach the targeted 100-W level. Furthermore, future improved dispersive mirrors and SESAM designs with low IA and high-damage thresholds such as the ones suggested in the previous paragraph will be key to support these future steps. Finally, the development of novel materials with broader emission bandwidths will be an ongoing topic of investigation.  
In the meantime, although the current output power levels obtained at record-short pulse durations are still moderate, intracavity levels already reach several hundreds of watts. This already opens the door to nonlinear optics experiments driven inside a thin disk oscillator [22]. This is a promising new approach that is currently being investigated.
## 3 Results  
Within the framework of this paper, acoustic metrics (i.e., the physical dataset) are meant to inform and support the analysis of soundscape quality (i.e., the perceptual dataset). Therefore, it was decided to consider only the sound level monitoring during the time slots of the day that were covered by the soundscape assessment protocol. Thus, sound levels were analyzed for four days (from Mondays to Thursdays, since the monitoring was interrupted on Friday mornings), considering the interval from 07:00 a.m. to 07:00 p.m. Furthermore, in order to get more insights into the physical acoustic environments of the investigated living rooms, loudness was computed starting from the original signals as additional psychoacoustic parameter [17].  
This section is divided in three parts: Section 3.1 deals with statistical differences of sound levels and loudness with respect to temporal factors and between living rooms. Section 3.2 approaches the same differences from the soundscape (i.e., perceptual) data angle. Finally, Section 3.3 considers the associations between equivalent sound levels and the number of people in the living rooms.
## 1 Introduction
[17], which employed a fuzzy-logic-based PID controller for motor control, the untuned model yields a settling time of 34.5 s, whereas the tuned version achieves a significantly reduced settling time of 10 s, with a trade-off of only 5% in overshoot. This 25-s improvement is crucial for enabling the system to stabilize more rapidly during transient conditions, highlighting the importance of controller optimization. Conversely, a suboptimal controller design may exacerbate torque  
Figure 1: Cross-sectional view of various topologies of SRMs: (**a**) 6/4 and (**b**) 8/6 configurations; (**c**) 8/6 segmented rotor. ripple or limit the power delivered to the machine [18]. Therefore, optimization at the component level may not ensure the overall performance of the drive system, as the system is strongly interdependent. In these cases, a system-level optimization approach is required to simultaneously optimize the entire drive system to achieve the desired performance and efficiency. This paper presents the current state of the System-Level Design Optimization Method (SLDOM) for SRM drives, highlighting existing challenges and identifying potential solutions for its implementation. Although the study was conducted with a focus on switched reluctance machines, most of the existing literature concentrates on motor applications, with only a limited number of studies addressing generator configurations. Section 2 maps out the SLDOM, dividing it into the following two categories: single-level and multi-level. Section 3 discusses design space reduction, primarily through the use of surrogate models and parameter criticality assessment, while presenting research conducted on these topics with a focus on SRM drives. Section 4 proposes a method for control enhancement and acceleration of optimization through SLDOM. Finally, Section 5 discusses future research directions based on the SLDOM and Section 6 concludes this paper.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 4 Discussion
### Case 2: Self-Constructed Bearing Experimental Dataset
#### 4.3.4 Visualization and Analysis Under Noise Interference  
In order to show the diagnostic results of the models for different fault types with a simulated SNR of 4 dB, this experiment visualized the confusion matrices of EfficientNetV2-S, RepViT, MPNet, and the proposed model.  
The visualization results are shown in Figure 17, where the diagnostic P of the proposed model is 100% for fault labels 3 and 5 and 89% for fault labels 0, 1, and 2. Compared to EfficientNetV2-S, MPNet, and RepViT, which correctly diagnosed all samples with fault label 3, the diagnostic P for fault label 5 differed significantly from that of the proposed model, with differences of 22%, 28%, and 16%, respectively. In addition, all models performed poorly in the diagnosis of fault label 4. Despite the proposed model having eight more misclassified samples compared to the model with the fewest (EfficientNetV2-S), HFDF-EffNetV2 performed better overall. The result demonstrates that the HFDF strategy effectively mitigates the issue of the lightweight model falling into local overfitting.  
Figure 16: Comparison of experimental results of different models under different SNRs in Case 2.
# In-Orbit Results and Attitude Analysis of the SNUGLITE CubeSatellite
## 4 Discussion
The ESTCube-1 CubeSat attitude sensor suite includes COTS equipment, two magnetometers, and four gyroscopes, along with six fine sun-sensors, which are superordinate equipment to the photodiode-based sun sensor. The fine sun-sensor can provide a sun-tracking error smaller than \(1^{\circ}\) (3-\(\sigma\)), but it is not categorized as a low-cost sensor, with a price exceeding \(\xi\)12,000 if six sides of the satellite are fully equipped [26]. Among previous studies, the RAX satellites attitude sensor configuration is the most similar to the attitude sensors of the SNUGLITE CubeSat [8]. RAX-1 and RAX-2 were three-unit CubeSats equipped with two COTS three-axis magnetometers and a three-axis MEMS gyroscope, with different photodiode configurations (9 and 17 photodiodes for RAX-1 and RAX-2, respectively). An extended Kalman filter was considered for attitude estimation. However, the performance of the attitude determination was analyzed by the state error covariance of the filter. Though the RAX CubeSats attitude system performance using the covariance matrix of the filter was below \(1.5^{\circ}\) (3-\(\sigma\)), it could not represent the actual attitude estimation performance, because a Kalman filter assumes that input errors have a Gaussian distribution [21]. However, there are unexpected biases or disturbances in sensor measurements that increase the actual attitude errors. Instead, attitude analyses need to be evaluated using a reliable performance presented by independent reference sources. Further, the filtering and tuning processes of the RAX CubeSats were conducted offline after the satellite sensor measurements were downloaded. In general, the in-orbit validations of former attitude systems for the CubeSat platform have limitations, as generalized performances have not been analyzed. In this paper, an attitude estimation analysis of the LQG-based attitude system using low-cost sensors was presented. Generalized performance was achieved by comparing two sun-pointing vectors using independent reference sources with a sufficient number of samples. This methodology can be used in future analyses of attitude systems for the CubeSat platform, particularly for systems using low-cost sensors.
# A Review of Requirements for Gamma Radiation Detection in Space Using CubeSats
## 2 Cubesat Specifications and Constraints
### Electrical Power Subsystem  
The Electrical Power Subsystem provides electrical power and consists of solar cells, batteries, a power distribution unit, and a power conditioning unit. Batteries are used in tandem with solar cells to support various operation modes. Lithium-ion batteries are widely popular in nano-satellites, as they have higher energy densities [3]. Commercial vendors are providing solar cells with more than 30% efficiency and a variety of configurations including body-mounted panels and deployable panels [19]. For missions requiring more power, deployable solar panels are preferred.
# Application Study of the High-Strain Direct Dynamic Testing Method
## 3 High-Strain Direct Dynamic Test and Static Load Test
### The Static Load Test  
To avoid interference from dynamic load tests, static load tests were conducted after a 28-day resting period. The displacement of the four test piles under load over time was recorded in detail during the loading and unloading processes, resulting in the load-settlement (Q-s) curves shown in Figure 11. These curves indicate no sharp drops, suggesting no failure in the piles or bearing layers, thus meeting the expected safety and functional requirements.  
Figure 10: Pile static resistance distribution curves of the four test piles (a) #1–5, (b) #2–7, (c) #3–9, and (d) #4–17.  
The FBG sensors embedded within the piles continued to monitor strain distribution during loading, enabling the calculation of axial force distribution changes with increasing loads. Figure 12 shows the axial force distribution curves for the four test piles at load levels of 0.4, 1, and 2 times the characteristic bearing capacity.  
The axial force initially increases with depth under negative friction before reaching a turning point and then decreases as depth increases because of the transition from negative to positive friction. This turning point, where pile settlement equals foundation settlement, is known as the \"neutral point\". In Figures 11 and 12, the highest axial force in the test piles is shown to be 11,005.7 kN, 10,096.0 kN, 10,946.6 kN, and 14,196.8 kN, respectively, at the highest test load. This force is higher than the designed ultimate bearing capacity. Despite these high forces, the piles did not fail, suggesting that the actual ultimate bearing capacity is more than twice the characteristic bearing capacity, thus making this conservative estimation reliable.  
Figure 11: Q-s curves of the four test piles (**a**) #1–5, (**b**) #2–7, (**c**) #3–9, and (**d**) #4–17.
## 2 Theoretical Section
### Measurement of Weight Fraction of Glass Fiber (Gc) of Laminate Structure  
As shown in Figure 2b, destructive methods such as CT testing allow the accurate measurement of inner defects such as void content. However, this method is not practical for the analysis of ship structures owing to the need for specialized equipment and technicians, which are typically not available during ship building. Therefore, we used the following four practical and simple methods to measure, compare, and analyze the GFRP laminate specimens:  
1. Arithmetic calculation of Gc based on material design, as per ISO 12215 and other classification rules.
2. Calculation of the volume of laminate specimens using tools such as Vernier calipers, followed by calculation of Gc.
3. Measurement of the specific gravity of laminate specimens using Archimedes' principle, followed by calculation of Gc.
4. Burn-off method involving combustion of the matrix, as suggested by ASTM D3171-15, followed by weight measurement of the glass fiber to determine Gc.  
This section summarizes the procedures for these four methods and their application to laminate structures.
###### Abstract  
The present study focuses on the inhibitory effect of volatile metabolites released by _Bacillus velezensis_ CT32 on _Verticillium dahliae_ and _Fusarium oxysporum_, the causal agents of strawberry vascular wilt. The CT32 strain was isolated from maize straw compost tea and identified as _B. velezensis_ based on 16 SrRNA gene sequence analysis. Bioassays conducted in sealed plates revealed that the volatile organic compounds (VOCs) produced by the strain CT32 possessed broad-spectrum antifungal activity against eight phytopathogenic fungi. The volatile profile of strain CT32 was obtained by headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS). A total of 30 volatile compounds were identified, six of which have not previously been detected in bacteria or fungi: (Z)-5-undecene, decyl formate, 2,4-dimethyl-6-tert-butylphenol, dodecanenitrile, 2-methylpentadecane and 2,2',5,5'-tetramethyl-1,1'-biphenyl. Pure compounds were tested in vitro for their inhibitory effect on the mycelial growth of _V. dahliae_ and _F. oxysporum_. Decanal, benzothiazole, 3-undecanone, 2-undecanone, 2-undecanon, undecanal and 2,4-dimethyl-6-tert-butylphenol showed high antifungal activity, with benzothiazole and 2,4-dimethyl-6-tert-butylphenol being the most potent compounds. These results indicate that the VOCs produced by _B. velezensis_ CT32 have the potential to be used as a biofumigant for management of vascular wilt pathogens.  
biocontrol; _Bacillus velezensis_; volatile organic compounds; vascular wilt pathogens
# Firefly Optimized Resource Control and Routing Stability in MANET +
## 5 Simulation Results and Analysis  
The experimental evaluation of the FFROR technique is carried out with metrics such as energy consumption, bandwidth availability, routing stability, and throughput. The proposed FFROR technique is compared with the existing methods, namely, the energy-aware and error-resilient (EAER) routing [1] protocol, minimal energy consumption with optimized routing (MECOR) [4] protocol, energy-efficient and stable multipath routing (EESMR) technique [25], and optimized energy-efficient route assignment (OEERA) scheme [26]. The performance of the above method is compared and analyzed with the help of tables and graphs.
## 5 Case Study: A PHM System for Satellite Constellation  
### System Model and Parameters  
As shown in Figure 11, the satellite constellation model is expressed in a unified form, supporting a multilevel and multidomain simulation. The satellite constellation library on the left contains orbital models, satellite model system control models, link models, etc. The right side is the system model integrated by the above models, which can provide a  
Figure 10: Realization process of state prediction.  
Figure 9: Fault Diagnosis Workflow.  
multidimensional and multilevel view to show the different characteristics from satellite to link or from system to component.  
Based on the developed constellation model, users can simulate a dynamic response of a constellation system under reasonably defined constellation specifications, working conditions and operating parameters. In a constellation system with 20 satellites, when simulation duration and stepsize are set as 100 s and 0.1, system simulation actually consumes 842 s. The main parameters used in system simulation are listed in Table 2. Among them, constellation orbit parameters are from Iridium NEXT [30], and other satellite parameters are some reasonable defaults.