esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Computational Study
### Experimental Results
The selected buffer size of 10 samples in each case covers around 95% of the injected bursts, but several bursts of longer duration were generated. In these situations, a loss of information inevitably occurs. However, the PC method--with a smaller ratio of ISE increase--was better able to maintain the nominal loop performance than the RC method. The difference in these deterioration ratios is a factor of over 2. Since the controllers were tuned for almost identical nominal loop performance, it would seem that this reduced susceptibility seems due to the actions of the predictive packet loss compensator in the path between the sensor and actuator. Even in situations in which dust durations exceed 10 samples--causing information loss in the RC scheme--the model of the PC continues to serve an output predicted from the last good sample, allowing appropriate open-loop controls to be applied. Comparing the obtained execution times, one may observe that the CPU overhead from implementing and designing the predictive controllers is significantly different between the methods, with the RC scheme taking nearly 229 times longer to design and apply the controls. This is not surprising as the method required the solution of a difficult non-linear search problem. The computation time can of course be reduced with a corresponding reduction in \(S\), but this leads to further performance  
Figure 6: Evolution of identified process parameters for the model used in the experiments. The two dominant parameters (\(b_{2}\) and \(a_{1}\)) are shown bolded. degradation (in fact, the method fails completely with \(S<30\) as insufficient points are then employed to parameterize the frequency response and correctly achieve the phase margin). In summary, the proposed controller design technique seems to be a simple and low-overhead means to design a digital PID-like controller for use with the proposed adaptive/predictive packet loss compensator. This compensator has good resilience to packet losses between the sensor-controller. The suggested packet buffering scheme employed in the controller-actuator link seems to be a weaker link, but still performs as well as a regular playback buffer without inducing delays in the link. However performance deterioration can still occur in situations in which a burst length exceeds the buffer size. The use of more reliable compensation methods in this link will be a focus of our future work.
# Ultrathin and Electrically Tunable Metamaterial with Nearly Perfect Absorption in Mid-Infrared
## 4 Conclusions  
In summary, we studied an ultrathin (\(<\lambda_{0}/15\)) and electrically tunable graphene metamaterial perfect absorber working at the mid-infrared region. The Q-values of the absorber can be tuned through rather independent parameters, namely, radiation loss (\(\mathrm{Q_{r}}\)), mainly modulated by W, and intrinsic loss (\(\mathrm{Q_{a}}\)) by the graphene with tunable absorption through back-gating. Our work suggests that near-independent Q-tuning could be realized by integrating graphene into metamaterial absorbing systems, which may facilitate the devising and experimental process of finding critical damping. Our work also provides new thinking for designing and realizing tunable infrared devices, and may be valuable for wider applications of perfect absorbers in mid-infrared sensors, absorbers, and detectors.  
[PERSON] conceived and performed the experiments, analyzed the data, and wrote the paper. [PERSON] provided the experimental materials and laboratory equipment and directed the research. [PERSON], [PERSON], and [PERSON] contributed to the discussion and interpretation of the results. This work was supported by National Key Research Program of China under Grant No. 2016 YFA0302000, National Natural Science Foundation of China (Grant No. 11674070/11634012/11427807), and Shanghai Science and Technology Committee under Grant No. 18 JC1420402 and 18 JC1410300.  
Part of the experimental work was carried out at the Fudan Nanofabrication Laboratory. The authors declare no conflicts of interest.
## 2 Methods of Encapsulation
### Liposomes  
Liposomes consist of a single or multiple bilayer membranes composed of phospholipids that have a hydrophilic head and hydrophobic tail groups. The hydrophilic heads are in contact with the internal aqueous layer and the external environment, while the tails form a bilayer. Thanks to this, it is possible to encapsulate both water- and fat-soluble compounds in liposomes. However, the entrapment of compounds soluble in both media is not possible. A particular advantage of this technology is the possibility of the simultaneous  
Figure 3: Freeze-drying encapsulation (created with BioRender.com (accessed on 26 November 2023)).  
encapsulation of lipophilic compounds (in the layer between the tails) and hydrophilic compounds (inside the liposome). This allows for the use of compounds with different structures in one capsule, which may have a synergistic effect [38,39]. A diagram of the liposomal encapsulation method is shown in Figure 4.  
Liposomes are created by mixing phospholipids with other compounds in an aqueous solution. The most commonly used phospholipid is lecithin obtained from soy or egg yolk, but fatty acids can also form liposomes. One method of creating liposomes is to incorporate hydrophilic components into the interior of the liposome, and hydrophobic components into the lipid bilayer during liposome formation. Another approach is to encapsulate compounds into existing liposomes. This involves an additional driving force, often obtained by using ammonium sulfate and calcium acetate. This method is used when there is a higher compound-to-lipids ratio. The isolation of liposomes involves the use of various techniques including microfluidization. Often, organic solvents such as ethanol, methanol, or hexane are also used to dissolve hydrophilic or hydrophobic substances. The size of liposomes ranges from several nanometers to micrometers. The size and the structure are influenced by the composition, production method, and environmental conditions. To obtain smaller liposomes with a uniform, typical spherical structure, it is necessary to use energy; for example, through ultrasonication or high-pressure homogenization [40, 41, 42, 43].  
In the food sector, the liposome encapsulation method is used for flavorings, amino acids, essential oils, vitamins, minerals, antioxidant compounds, preservatives, enzymes, and probiotics [43,44].  
The main disadvantages of this technique include its low efficiency, a highly acidic pH, the use of organic solvents, and the costs associated with the use of sonification, homogenization, or pressure forces [43].
# Disguised Full-Duplex Covert Communications
## 5 Numerical Results
This explains the reason why \"0.1% \(P_{S}\)\" outperforms those with higher \(\alpha\)% in Figure 3 in the high \(P_{S}\) region. Figure 4 compares the average covert rate \(r_{C,R}\) as the destination transmit power budget \(\bar{P}_{D}\) changes. It can be seen that \"5% \(P_{S}\)\" and random \(P_{D}\) schemes perform close to the optimal scheme when \(\bar{P}_{D}\) is low. The reasons are that \(P_{D}^{*}\) is dominated by \(\bar{P}_{D}\) in this region and that fixed or randomly chosen \(P_{D}\) in the compared schemes is reduced to \(\bar{P}_{D}\) if \(P_{D}>\bar{P}_{D}\). For the rest of the \(\bar{P}_{D}\) regions, our proposed solutions achieve the highest covert rate which once more highlights the necessity of optimizing the \(r_{P}\) and \(P_{D}\). Figure 3: The average covert rate \(r_{C,R}\) versus the source node power \(P_{S}\): Applying more (less) \(P_{D}\) is preferred when \(P_{S}\) is low (high). Figure 2: Node placements  
Figure 5 demonstrates the average covert rate \(r_{C,R}\) for different noise uncertainty bounds \(\zeta\) at the warden node. Quantitatively, a larger \(\zeta\) widens the upper bound for \(P_{D}^{*}\) in (20) so that the covert rate, which is proportional to \(P_{D}\), has more chance to be enhanced on average. Also, from the qualitative aspect, the larger \(\zeta\) there is, the more confusion the warden node undergoes in deciding the existence of covert communications. Figures 6 and 7 illustrate the average covert rate \(r_{C,R}\) and DEP, respectively, when the minimum DEP threshold \(\epsilon\) changes. The covert rates decline in a monotonic manner as \(\epsilon\) increases and eventually become zero when a perfect DEP of 0.5 is imposed on. Figure 7 also verifies that the proposed optimal solutions provide just enough DEP above the threshold \(\epsilon\)  
Figure 4: The average covert rate \(r_{C,R}\) versus the destination node power budget \(\bar{P}_{D}\): Close performance among the presented schemes for low \(\bar{P}_{D}\) since the optimal \(P_{D}\) is dominantly limited by \(\bar{P}_{D}\). Figure 5: The average covert rate \(r_{C,R}\) versus the noise uncertainty bound \(\zeta\) [dB]: The more unsettled \(\zeta\) is, the easier it is to perform covert transmissions. in general, while the other baseline schemes achieve unnecessarily high DEP by sacrificing the covert rate.
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 6 Implementation Results and Discussion
### Results  
In order to analyse the performance of the proposed decoder, we consider three parameters: BER performance, hardware utilisation, and processing throughput.
## 5 Conclusions  
This study found that human activities based on WiFi CSI signals are time-series data with spatiotemporal characteristics. To automatically identify human activities from WiFi CSI signals, we propose the SM-TCNNET model, which is capable of extracting spatial-temporal features simultaneously. The model consists of a spatial module, which is used to extract spatial features, and a TCN module, which captures temporal dependencies using residual blocks of dilated convolution. The final classification output is obtained by a linear layer. We verified the effect of the TX and RX spacing distance on recognition accuracy and found that the spacing distance is optimal at 2 m. We also found that a distance of about 20 cm from the human body to the two connections is the best case for a certain TX and RX interval. We achieved 99.88% and 99.80% activity recognition accuracy in the self-selected dataset and StanWiFi dataset, respectively, and compared it with other methods to show that the method has high recognition accuracy and robustness.  
Conceptualization, Y.Z.; data curation, Z.Z.; formal analysis, Z.G.; investigation, S.G.; methodology, T.L.; project administration, Y.C.; resources, Y.Z.; supervision, S.G. and Y.C.; validation, T.L. and Z.Z.; visualization, T.X.; writing--original draft, T.L.; writing--review & editing, Y.Z. and T.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Hebei Provincial Education Department under grant CXY2023005. This study was supported by the Hebei Provincial Science and Technology Program under grant 21350701D.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 14: Confusion matrix for StanWiFi dataset.
# A Survey on Surface Defect Inspection Based on Generative Models in Manufacturing
## 2 Overview of Generative Models
### Variational Auto-Encoder  
VAEs are generative models that intend to learn both an encoder and a decoder to map input data to values in a continuous latent space [28]. Consider defect generation: the encoder learns to map a defect sample into a continuous embedding that can be interpreted as a latent variable in a probabilistic model, while the decoder learns to reconstruct the defect sample from its learned embedding. The loss function of a VAE consists of two terms. One is the reconstruction loss term \(L_{rec}\) that forces the decoder to recover the original image \(x\) from its embedding \(\hat{\lambda}\). The other is the regularization term \(L_{reg}\) that aims to make the distribution of learned embedding \(N(\mu,\sigma)\) conform to the standard normal distribution as much as possible, which measures the Kullback-Leibler (KL) divergence between these two distributions. The loss function is defined as in Equation (1).  
\[L_{VAE}=L_{rec}+\lambda L_{reg}=||x-\hat{\lambda}||^{2}+KL(N(\mu,\sigma),N(0,1)) \tag{1}\]  
where \(\lambda\) is the weight of the regularization term, which is used to balance the importance between the reconstruction loss and the regularization term. In the context of sample generation, VAEs can be used for signal generation [36; 37], image generation [38; 39], and text generation [40; 41].
## 3 Concept of Driver-Aid System  
Vehicles (e.g., cars) with Ackermann-based steering systems due to their geometry and characteristics (positive caster angle), have the ability to stabilize vehicle direction. In contrast, the geometry of the steering system in articulated vehicles, makes the driver responsible for stabilizing the steering system. The driver's perception limits the accuracy of vehicle heading, the directional stability of movement, and the top permissible speed. To improve the directional stability of the articulated tractors, the limited sensitivity of the human brain should be supported by the driver-aid system. Since the driver sensitivity of yaw deviation is limited to about 0.5\({}^{\circ}\) and this is the main cause of poor directional stability of articulated vehicles, the driver-aid system should straighten the frame and eliminate small frame twist that lead to growing deviations.  
For this reason, the proposed system consists of a steering wheel rotation, a frame articulation sensor, and an additional hydraulic circuit for straightening the frame (Figure 6a). For the driver-aid system, we assumed that such a system should act only when the main steering system controlled by the driver was not operating (maneuver was not conducted), and that its flow should be as small  
Figure 5: View of the modified loader with driver before front axle and direct hydraulic steering system during straight-line test (**a**), and driver reactions during modified loader straight-line test at a speed 28 km/h (**b**).  
as possible and not disturb the main steering system operations, in case of driver-aid system failure. Moreover, its energy consumption should be low and jerk accelerations should not be generated. Hence, the driver-aid system switched on an additional pump to straighten the frame when the steering system wheel sensor indicated that the frame twist was below 1\({}^{\circ}\). It was assumed that such a situation corresponded to straight-line riding. The system should stop when the frame articulation sensor indicated frame straightening. Due to safety reasons, the flow in the driver-aid system was assumed to be 10% of the main steering system flow, considering the maximal steering wheel angular speed was 100 rpm, according to [25; 26]. In the analyzed case, it was 10 dm3/min.  
To verify the operation capability, the possibility of such system actions was investigated, considering a high moment of inertia of the rotated front and rear parts of the vehicle. For this reason, an additional hydraulic system powered with a 24V electric motor, 2.2 kW power, and 10 dm3/min pump flow was installed on 20-ton wheel loader, and the time domain characteristics were determined. The results (Figure 6b) showed that during 0.3 s of operation, the pump achieved almost nominal flow at 8 dm3/min, and the frame turned about 0.5\({}^{\circ}\), a value comparable to the initial frame twist. This was a favorable result because it indicated a high speed of turn corrections almost without any time lag, and with a low power consumption at a small pump flow rate, and indicated the possibility to operate such systems.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 4 Methodology
### Scenarios and Discussion
#### 4.3.2 Scenario 2: 80 Nodes  
In this scenario, the network consisted of 80 mobile nodes. The performance of the routing protocols was evaluated similarly to Scenario 1. The increased node density and the presence of fixed nodes influenced the performance metrics.
## 3 Material and Methods
### Structural Equation Modeling Framework  
For the data analysis in this research, the SEM-VALTM model was developed to analyze the influence of causal factors and to propose strategies for the administration of Thailand under the sustainability policy towards Thailand 5.0. The details of the SEM-VALTM model are as follows [17, 47, 48].  
SEM-VALTM model: The SEM-VALTM model is a framework illustrating that the strength of relationships between variables diminishes as the time interval and differences between variables increase. In the context of longitudinal data analysis, this implies that the correlation of later measurements is lower than that of initial measurements, as shown in Figure 2[16, 48].  
From Figure 2, the data analysis steps can be explained as follows [15, 49]:  
Figure 2: Relationship Characteristics of the SEM-VALTM Model.  
Model Specification: Define the model by specifying that the measurement score values must not have discrepancies. The parameter for the influence of true scores in the first measurement affects the true scores in subsequent measurements [49].
2. Identification of the Model: For any structural equation model, when analyzing to estimate parameters, the model must be identified to ensure that it is identifiable. A model is properly identified if the number of parameters to be estimated is fewer than the number of observed variable variance-covariance matrix members [15].
3. Parameter Estimation: Once identifiability is confirmed and the model is over-identified, the program estimates all model parameters. These parameters are then used to compute the variance-covariance values of the observed variables in the model.
4. Model Fit Assessment: Check if the hypothetical model aligns with the empirical data. In this research, the model fit indices considered include a non-significant chi-square statistic, the chi-square/degrees of freedom ratio \(\chi^{2}/df\) should be less than 2, or less than 5 for more complex models; the Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) should be 0.90 or higher, preferably 0.95 or higher; the Standardized Root Mean Square Residual (SRMR) should be less than 0.05 for good fit and 0.05 to 0.79 for acceptable fit; and the Root Mean Square Error of Approximation (RMSEA) should be less than 0.05 for excellent fit, 0.05 to 0.07 for good fit, and 0.08 to 0.09 for acceptable fit [46, 47].
5. Model Modification: Based on the analysis results, consider modifying the model by adding or removing influence paths that significantly reduce the chi-square value, enhancing the model's fit with empirical data.
6. Parameter Estimation in the SEM-VALTM model with Latent Growth Variables--estimate parameters including factor loadings, means and standard deviations, and correlation coefficients [50].  
For this model construction, only stationary data at the same level are used. Factors not meeting these criteria must be excluded or replaced using the Augmented Dickey-Fuller approach [51, 52].
# Coupled Dual-Loop Optoelectronic Oscillator Based on Stimulated Brillouin Scattering
## 1 Introduction
The mainly frequency stabilization techniques contain a phase-locked loop [17; 18; 19] and thermal control [20; 21]. The phase-locked method requires an external reference signal to the loop of OEO, and the reference signal affects the phase noise of the oscillation signal. The thermal control method requires that all the thermally sensitive devices must be placed in a constant temperature system, which is difficult to realize in practical applications. A stability improved SBS-based TOEO has been designed in [22], and the maximum power and frequency drift at 10.69 GHz are about 1.4 dB and 1 ppm in the case of testing for 1000 s.  
In this paper, a TOEO with coupled dual-loop (CDL) based on SBS is theoretically analyzed and experimentally demonstrated. In our scheme, the CDL is constructed by cascading two modulators, a dual-output Mach-Zehnder intensity modulator (DOMZM) and a single output Mach-Zehnder intensity modulator (MZM). One optical output of the DOMZM is directly injected to MZM, and the other optical output of the DOMZM is transformed into a microwave signal to modulate the MZM. The narrow gain spectrum of SBS is employed to choose the oscillation frequency and realize tunability. By joining the CDL, we not only increase SMSR and lower phase noise, but also enhance stability of the generated microwave signal. In the experiment, high-quality microwave signals with a widely tunable range from 2 to 18 GHz are generated. When the oscillation frequency is chosen as 5, 10, or 15 GHz, the phase noise is as low as \(-\)95 dB/Hz at 10 kHz frequency offset and the SMSR is superior to 60 dB. The frequency drift is less than 0.3 ppm and the power drift is lower than 0.2 dB during 30 min in lab condition, which confirms a relatively higher stability. To verify the advantage of CDL design, we also built a single loop SBS-based TOEO by disconnecting the CDL. Comparing with the single loop SBS-based TOEO, the proposed TOEO can improve the SMSR of 57 dB, the phase noise of 15.2 dB at 10 kHz offset, and effectively avoid frequency hopping or multi-mode oscillation.
## 3 Theoretical Analysis  
### Distribution Characteristics of Crustal Stress in Wudong Coal Mine  
To tackle the serious deformation problem of the B8 roadway in the Wudong Coal Mine, four representative measuring points are allocated in the B8 roadway. The hollow inclusion stress-relief method is used to monitor the original rock stress, which is a kind of stress-relief method. It can determine the magnitude and direction of the three-dimensional stress in the original rock by resetting the measuring probe in a single borehole. The monitoring results showed that: (1) the measured value of vertical principal stress approaches or is slightly less than the weight of the overlying strata. (2) The value of vertical principal stress is 7.43-9.13 MPa, an average of 7.90 MPa. The maximum horizontal principal stress  
\begin{table}
\begin{tabular}{c c c c} \hline
**Coal Seam Roof and Floor** & **The Name of the Rock** & **Thickness (m)** & **Lithologic Character** \\ \hline immediate roof & siltstone & 2–5 & gray, dark gray, bedding and joint development \\ \hline false roof & mudstone, carbonaceous & 0.3–2 & gray, black-gray, bedding and fissure development, argillaceous cement \\ \hline immediate floor & mudstone, carbonaceous & 0.3–1.5 & gray-black, thin layered, fissures development, argillaceous cementation \\ \hline \end{tabular}
\end{table}
Table 1: The lithology of coal seam roof and floor.  
Figure 2: Cross section of the roadway.  
is 23.75-24.85 MPa, an average of 24.3 MPa. The ratio (lateral pressure coefficient) of the maximum horizontal principal stress to the vertical principal stress is 2.72-3.19, which is above 1, suggesting that the area is strongly influenced by tectonic movement. The tectonic stress plays a controlling role in the stress field. In-situ stress is mainly horizontal stress that belongs to the type of tectonic stress field. (3) The difference between the maximum horizontal principal stress and the minimum horizontal principal stress is significant. That is, the influence of horizontal stress on the roof, floor and ribs of the roadway exhibited a clear directionality, and the in-situ stress field in the Wudong Coal Mine is a tectonic stress field dominated by horizontal stress. In particular, the direction of the in-situ stress field has a larger effect on the stability of the roadway. It is thus believed that the size of the stress is the main reason influencing the stability of the roadway, leading to serious deformation of the roadway in the high-stress area.