esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Enhancement of Brillouin Stokes Signal (II): Influence of Core Diameter and Fiber Length
### Effects of Small Core Diameters  
Figure 6 shows the measured BGS of a 5-\(\upmu\)m PFGI-POF with 62.5-\(\upmu\)m core at pump power \(P_{\text{in}}\) of 5, 10, 15, and 20 dBm. The polarization state optimized for \(P_{\text{in}}\) of 20 dBm was employed for all the measurements. The center frequency of the BGS, _i.e._, the BFS, was approximately 2.81 GHz, which is slightly lower than previously-reported value of 2.83 GHz [27] due to the difference in room temperature [30]. Even when \(P_{\text{in}}\) was as low as 5 dBm, small but clear BGS was observed.  
Figure 7 shows the \(P_{\text{in}}\) dependences of the relative Stokes power, when 5-m PFGI-POFs with core diameters of 62.5 \(\upmu\)m and 120 \(\upmu\)m (Asahi Glass Co., Ltd., 2009) were employed. The reference power was set to about \(-\)63 dBm, which is the Stokes power when \(P_{\text{in}}\) is sufficiently low. The dependence  
Figure 6: BGS dependence on pump power \(P_{\text{in}}\).  
curve of the PFGI-POF with 62.5-\(\upmu\)m core was about 10 dB lower in pump power than that with 120-\(\upmu\)m core, which indicates that, even at the same pump power, we can largely enhance the Stokes signal by using a PFGI-POF with a smaller core diameter.  
One of the reasons for the 10-dB curve shift is the difference in Brillouin threshold power \(P_{\text{th}}\). By substituting into Equations (1) and (2) the values of \(b=2\)[33], \(K=0.667\)[7], \(g_{\text{B}}=3.09\)\(\times 10^{-11}\) m/W [27], \(a=0.056\) /m (\(=250\) dB/km), and \(L=5\) m, \(P_{\text{th}}\) of the PFGI-POF with 120-\(\upmu\)m core (\(A_{\text{eff}}=209\)\(\upmu\)m\({}^{2}\)[40]) was calculated to be 97.7 W. On the other hand, \(P_{\text{th}}\) of the PFGI-POF with 62.5-\(\upmu\)m core (\(A_{\text{eff}}=108.9\)\(\upmu\)m\({}^{2}\)) was calculated to be 53.3 W, which is lower than 97.7 W by 2.63 dB. Thus, the curve shift observed in Figure 7 can be partially explained by the difference in \(P_{\text{th}}\), but its amount of 10 dB is much larger than the calculated value.  
Another reason for the curve shift is the improvement of optical coupling efficiency at the butt coupling when the Stokes light generated in the PFGI-POF propagates back and is injected into the SMF. Although, due to the unstable core alignment and the rough surface of the PFGI-POF, it is difficult to measure the coupling loss accurately, we confirmed that the loss with the PFGI-POF with 62.5-\(\upmu\)m core was several dB lower than that with 120-\(\upmu\)m core. This fact, along with the difference in internal structure designed by different manufacturers, moderately explains the 10-dB curve shift.
## 1 Introduction
designed an integrated AMB controller, which consists of parallel co-frequency ANF and cascaded multi-frequency improved DTNFs [15]. And they proposed a bandwidth factor rectification method for the ANF based on displacement stiffness perturbations. The robustness of the method at different rotational speeds is verified by simulation and experiment respectively, and the vibration displacement of the rotor is significantly reduced. [PERSON] et al. used a digital twin to simulate the performance of a magnetic bearing system in real time [16]. The comparison between simulation data and experimental data verified the effectiveness of this method. This technology can be used to identify problems in the magnetic bearing system before a failure occurs. [PERSON] et al. proposed an optimized active disturbance rejection control (ADRC) algorithm to enhance the speed stability of permanent magnet synchronous motors (PMSMs) [17]. The method makes up for the deficiency of typical ADRC through the fusion of multiple control quantities. Simulations and experiments verified the robustness of the method. [PERSON] et al. provided a method to resolve the problem of improper counterweight angles [18]. A rotary device can be installed to enable the relative orientation of the counterweight plate against the rotor to be adjusted. In conclusion, researchers have proposed many solutions to the vibration problem of AMB rotors. But most of the methods are from the perspective of vibration suppression, ignoring the characteristics of the rotor itself. When the rotor imbalance is large, it is difficult to make the rotor run smoothly through external suppression. With the help of the system's own control module and monitoring module, a dynamic balance correction method for the AMB rotor based on ANF and ICM is proposed in this paper, which aims to reduce the vibration in the system by reducing the mass imbalance of the rotor itself. The remaining content of this paper is arranged as follows. The second part introduces the AMB system and establishes the dynamic model of the system. The third part introduces the realization process of the dynamic balance correction method in this paper, including the tracking and calculation of the displacement signal by using the improved ANF, the solution of the dynamic balance counterweight by using the ICM and the treatment of the residual imbalance. In the fourth part, the dynamic balance correction method is verified by simulation. The fifth part further verifies the dynamic balance effect of the method in this paper through experiments. The conclusion is in the last part.
# On the Use of LoRaWAN for Mobile Internet of Things: The Impact of Mobility
## 3 Mobility Models and Methodology
### Mobility Models
#### 3.1.1 Random Waypoint Mobility Model  
The random waypoint mobility model (RWP) [19] is one of the three mobility models that are classified under the random mobility category. The other two models are the random walk and the random direction models. RWP has been widely used in simulating ad hoc networks; it requires three parameters: the simulation area, speed, and pause time. According to RWP, a node selects a random location within the simulated area along with a random moving speed. The node then starts moving toward the selected location at the selected speed. Once the node reaches its selected location, it waits for a time known as the pause time; it then selects another random location to move to and selects a new speed randomly. The process is repeated by each individual node until the end of the simulation period.
# Acoustic Emission Activity for Characterizing Fracture of Marble under Bending
## 2 Experimental Section
### Acoustic Emission  
AE monitoring took place by means of two piezoelectric sensors, (R15, Mistras). Their resonance comes at 150 kHz and their positions on the specimens are shown in Figure 2. The center of one sensor was placed 15 mm away from the point of the expected crack, which was secured by a notch. The second sensor was placed 40 mm away. Both sensors were positioned at the same side of the crack in order to be able to characterize the distortion of the signals as they propagate for the additional distance of 40 mm. Nevertheless, the effect of distortion will not be discussed herein and will be treated separately in another study. Acoustic coupling was improved by silicon grease between the sensors face and the specimens' surface. AE activity was captured by a two-channel PCI-2 Mistras board with sampling rate of 5 MHz. The threshold was 40 dB, as well as the pre-amplification. A schematic representation of a waveform is seen in Figure 5. Some of the main features are the maximum amplitude, AMP (usually in dB), and the duration, DUR (period between the first and the last threshold crossing). The \"rise time\" (RT) (which is the time between the first threshold crossing and the point of peak amplitude in us) is related to the fracture mode of the crack and so is the inverse of the slope of the initial part of the signal (RA value, RT/A in us/V). The energy (ENE) is defined as the area under the rectified signal envelope. Frequency content can be measured by AF (average frequency), which is the total number of threshold crossings divided by the duration, while there are other indices based on the spectrum of the FFT.
###### Abstract  
Nowadays, recommendation systems have been successfully adopted in variant online services such as e-commerce, news, and social media. The recommenders provide users a convenient and efficient way to find their exciting items and increase service providers' revenue. However, it is found that many recommenders suffered from the cold start (CS) problem where only a small number of ratings are available for some new items. To conquer the difficulties, this research proposes a two-stage neural network-based CS item recommendation system. The proposed system includes two major components, which are the denoising autoencoder (DAE)-based CS item rating (DACR) generator and the neural network-based collaborative filtering (NNCF) predictor. In the DACR generator, a textual description of an item is used as auxiliary content information to represent the item. Then, the DAE is applied to extract the content features from high-dimensional textual vectors. With the compact content features, a CS item's rating can be efficiently derived based on the ratings of similar non-CS items. Second, the NNCF predictor is developed to predict the ratings in the sparse user-item matrix. In the predictor, both spare binary user and item vectors are projected to dense latent vectors in the embedding layer. Next, latent vectors are fed into multilayer perceptron (MLP) layers for user-item matrix learning. Finally, appropriate item suggestions can be accurately obtained. The extensive experiments show that the DAE can significantly reduce the computational time for item similarity evaluations while keeping the original features' characteristics. Besides, the experiments show that the proposed NNCF predictor outperforms several popular recommendation algorithms. We also demonstrate that the proposed CS item recommender can achieve up to 8% MAE improvement compared to adding no CS item rating.  
recommendation systems; cold start problems; neural networks; collaborative filtering 2021  
3
## 4 Experiment and Analysis
### Visualization  
The above experiments explore the regions of interest in detection models using the Gradient-weighted Class Activation Mapping (Grad-CAM) [30] visual attention maps. The results are shown in Figure 9.  
The red part in Figure 9 indicates the area of focus of the neural network. This paper compares the difference in class activation mapping between the proposed method and the baseline model when detecting standard and corrupted images. ResNet50 pays uneven attention and focuses on regions that sometimes exceed the extent of the falsified regions, which is especially evident when detecting images after interference. This result explains the significant degradation of the baseline's detection performance on corrupted datasets. The proposed method focuses on the critical areas of high variances, such as the nose, mouth, and eyes, which can effectively find more subtle forgery clues. Moreover, the proposed method can accurately locate the forged regions of forged images on both standard and corrupted images and has a better detection ability.  
Figure 8: The results of mainstream algorithms and this paper work on robustness test: (**a**) True positive rate (TPR) of each model on different corrupted datasets; (**b**) True negative rate (TNR) of each model on different corrupted datasets.
# Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization
## 5 Results
### Constellation Learning  
The constellation diagram represented by the I/Q signal learned by the LSTM autoencoder is one of the ways to visualize the modulation capability. The constellation diagram of the VQ-LSTM-AE is shown in Figure 6. Figure 6 shows the modulation results of the model for second-, fourth-, eighth, sixteenth, and thirty-second-order signals, respectively. The model converges within 100 epochs for all types of signals. Among them, the constellation diagrams shown in Figure 5(a),b are similar in distribution to traditional BPSK and QPSK, so the performance is also similar to the traditional method; Figure 5(c) shows a constellation distribution that is different from that of traditional 8 PSK or 8 QAM. The model sets one constellation point at the center surround by the other constellation points. This change increases the Euclidean distance between points, making it easier to distinguish them from each other. The training in Figure 5(d),e was carried out under the background of \(E_{b}/N_{0}=12\) dB. Compared with the traditional method, the distribution of the constellation diagram tends to evolve towards a concentric circle structure, which also changes the interval and optimizes the space utilization efficiency.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **VQ-LATM-AE** \\ \hline \(E_{b}/N_{0}\) (dB) & 7 (N = 2/4/8/16) \\ Batch Size & 12 (N = 32) \\ t & 256 \\ z & 4 \\ Learning rate & 0.01 \\ D & 64 \\ \(\beta\) & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training hyperparameter setting table.  
Figure 6: _Cont._
## 3 Results and Discussions
### Photovoltaic Properties  
It is possible to calculate the efficiency of the DSSC system by factoring in photovoltaic properties such as light-harvesting efficiency (LHE), electron injection efficiency (\(\Delta G^{injct}\)), electron regeneration energy (\(\Delta G^{region}\)), open-circuit voltage (\(V_{OC}\)), excited-state lifetime (\(\tau\)), and the electronic coupling constant ( \(1\,V_{RP}\) ) [31]. The following Table 3 indicates the photovoltaic properties of the studied selected dyes in both the gas and ethanol solvent phases.
## 2 Survey of Commercial Small Platforms
### Passive Optical
#### 3.2.2 Sounder: Multispectral and Hyperspectral  
A good example of hyperspectral infrared sounder capable of measuring atmospheric pressure over the sea surface on CubeSat is EON-IR [62]. This instrument is under development with spatial resolution comparable to legacy sounders such as Infrared Atmospheric Sounding Interferometer (IASI), Atmospheric Infra-Red Sounder (AIRS), and Cross-track Infrared Sounder (CrIS).  
Table 15 presents the details of the available multispectral and hyperspectral sounders instruments, in terms of spatial resolution, aperture size, swath, mass, power consumption, and data rate. For each optical sensor, it classifies (nano-, micro-, mini-, and large-satellite) according to the payload power and mass that can support the available commercial platforms summarized in Table 2.
## 3 Results and Discussion
### Short- and Long-Term Tests
According to the authors, their system was able to adequately simulate the range of pressure to which insects are normally subjected in their natural environment. Their control system was equipped with ordinary solenoid valves and an on/off algorithm. They also defined a dead band of \(\pm\)1.0 hPa in the control algorithm to improve the stability of their system. Although not reported by [24], the conventional solenoid valves used as their actuators to control the pressure should present a short lifespan due to the high number of operations required in entomology experiments, which are usually quite long. The proportional solenoid valves employed in the current design allow the use of better control algorithms and the achievement of higher performance and should also last longer in applications such as the one addressed here. The chamber described here presents a significant contribution in terms of performance since its stability and accuracy were superior to that described by [24]. The main reasons for the better performance of the current chamber are the proportional solenoid valves (which are better than ordinary solenoid valves) and the PI-controller (which is better than an on/off controller). Although the PI-controller was tuned empirically through several attempts, a proper performance was achieved for the entomology applications. The literature also reports smaller barometric chambers in which pressure was manually controlled by injecting volumes of air and checking a barometric pressure gauge [10; 17; 29]. Although such designs are technically feasible, they require the user to continuously check the pressure and, despite this, poor performance (i.e., accuracy, stability) is expected to occur while controlling the pressure. In addition, reproducing real pressure fluctuations would hardly be achieved by manual control, since the changes in barometric pressure are small and occur very slowly in nature.
# Concise Robust Control of the Nonlinear Ship in Course-Keeping Control
## 5 Simulation Experiments and Result Analysis  
### Nonlinear Ship Course-Keeping Experiment Results and Analysis  
Without considering the disturbance of wind and waves, the results of ship course-keeping control by the different methods are shown in Figures 6 and 7. The target course is 30 degrees. The step curves of 0-2000 s are the results of a door-closing slope of \(-20\) dB, \(-40\) dB, \(-60\) dB under PID control. From 400 s to 2000 s in Figure 7, the overlay results of the first 400 s of the corresponding rudder angle are plotted. It can be seen that the control performance of the CROB method with a \(-20\) dB door-closing slope is close to that of PID control. However, the rudder angle is larger and the response is faster; the rudder angle decreases gradually with the increase in the door-closing slope, and the system response also decreases. If the door-closing slope is \(-60\) dB, the maximum rudder angle is less than \(20^{\circ}\). As a result, under the same conditions, increasing the door-closing slope is beneficial to decrease the maximum rudder angle. Thus, it can improve navigation safety to a certain extent. In particular, when the vessel is at high-speed navigation, instantaneous application of a large rudder angle should be avoided. In this paper, all the simulation are carried out on Matlab 2015 with the Simulink toolbox, on a 64-bit Windows 10 operating system.
## 6 Discussion
### Static vs. Dynamic Comparison  
When designing a rock bolt suitable for rock-burst prone conditions, both static and dynamic loading conditions need to be considered. Figure 26 compares the statically and dynamically loaded force vs. displacement graph of the tested bolt with a diameter of 28 mm and steel yield strength of 922 MPa.  
By comparing the static and dynamic load vs. displacement results, the failure mechanisms and the effected bolt capacity can be identified. Under static loading conditions, the bolt undergoes failure at the later stage of loading, where the bolt fails as load increases. Under dynamic loading conditions, the bolt fails in the initial loading stage, due to the impact of higher strain rates. Thus, in dynamically loaded conditions, such as rock burst prone excavations, the dynamic capacity of the bolt is critical.  
Figure 26: Static vs. dynamic load vs. displacement comparison.
## 2 Materials and Methods
### Study Design and Participants  
This pilot randomized controlled trial was conducted to evaluate the effects of algae-based supplementation on metabolic parameters, oxidative stress, inflammatory biomarkers, and physical performance in athletes over a 30-day intervention period. Participants were randomly assigned to one of two groups: a supplementation group, which consumed 6 g/day of a powdered extract derived from _Ulva_ spp., or a control group, which did not receive any supplement. Both groups followed standardized nutritional and supplementation guidelines aligned with current sports nutrition recommendations. These included individualized targets for carbohydrate (5-7 g/kg/day), protein (1.6-2.2 g/kg/day), and fat intake (approximately 1 g/kg/day), as well as ensuring adequate intake of micronutrients based on dietary reference values. Regarding supplementation, all participants were encouraged to consume isotonic drinks for hydration and protein shakes post-exercise, which are common practices among athletes, and this practice was maintained throughout the intervention to avoid introducing confounding variables.  
A total of 70 male athletes aged 18 years or older were recruited from elite soccer and handball teams through collaboration agreements with their coaching and medical staff. All participants competed at the national level and were actively engaged in structured training programs comprising at least four sessions per week, including technical, tactical, and strength-conditioning work. To be considered for the study, athletes had to have a minimum of 3 years of continuous training experience in their respective sports.  
Inclusion criteria required participants to (i) be healthy and physically active males; (ii) have no chronic illnesses (e.g., diabetes, hypertension, cardiovascular disease); (iii) be free from musculoskeletal injuries or surgeries in the last 6 months; (iv) have maintained stable body weight (\(\pm\)2 kg) for the previous 3 months; and (v) have refrained from using supplements or medications known to affect metabolism, inflammation, or oxidative stress. Exclusion criteria included the following: (i) current smoking, (ii) following special or restrictive diets (e.g., ketogenic, vegan); (iii) recent participation in other clinical studies; or (iv) known intolerance to seaweed or algae-based products.  
Participants were randomly allocated using a computer-generated simple randomization procedure, resulting in 35 athletes per group. The study did not include placebo administration or blinding. This decision was not aimed at improving perceived efficacy, but rather at preserving the natural routines of the control group. The inclusion of a placebo might have altered behavior or adherence patterns, potentially confounding biochemical results. To reduce this risk and maintain protocol consistency, both groups received the same dietary guidance and were monitored regularly throughout the study.