esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control
## 5 Kalman Filter Estimation and LQG Control
### Factoring in Velocity  
The previous model showed the precise representation of the pendulum cart system. Although the monowheel system is analogous to the pendulum cart system, it is not precisely the same. The pendulum cart system gives the state \(x\) (position) as the primary output. In contrast, for the monowheel system, we require the velocity to be the system output, and the reference input is a velocity that the rider provides manually. The reference point \(x\) (position state) of all other states is controllable and observable in the pendulum cart system, which is in the continuous-time domain. However, the monowheel system works in discrete time. In reality, the input is not bounded, and hence manual constraints are needed. Converting the original LQR model into a more realistic system by replacing the initial position reference input with a joystick's velocity input overcomes such challenges. A saturation block is introduced to bound the intake within practical limits, followed by a rate limiter to bound the acceleration. The velocity input is then passed through an integrator to generate a corresponding position signal, which is fed into the plant. The entire model is converted into a discrete time-domain system to enable the smooth functioning of sensors and micro-controllers while working on the hardware. Figures 13 and 14 provide real-time velocity responses. The light-blue plot shows the controlled input after the action of LQR control. The green plot represents the vehicle's final velocity output after factoring in self-balancing control action and dynamic stability. The orange plot shows the rider's velocity as an input in real time, within 30 s.  
Figure 12: Velocity responses with LQR and LQG control. (**a**) Velocity response with LQR control. (**b**) Velocity estimation during LQR control. (**c**) Velocity response with LQG control. (**d**) Velocity estimation during LQG control.  
Figure 15 demonstrates the velocity response of the system (with noise) when a step reference input is given. The system takes about 1.6 s to stabilize.  
Figure 14: Reference point.  
Figure 13: Input-regulated output speed.  
Figure 15: Step response of system with a weight of 80 kg.
# Quantum-Secure Coherent Optical Networking for Advanced Infrastructures in Industry 4.0
## 2 State of the Art
### OTU4 Framing and Protocol Weakness  
The adoption of OTU4 framing in coherent optical networks exacerbates security issues by complicating things further. Essential for high-efficiency data transport, OTU4 has an open architecture, introducing vulnerabilities when working together with legacy protocols such as Modbus. Generally, Modbus, a serial communications protocol designed for programmable logic controllers (PLCs), is added into OTU4 networks through management interfaces to support industrial control systems. This integration is accomplished by using Modbus commands encapsulated into the frame structure by means of OTU4 overhead bytes to enable remote monitoring and control capabilities. The problem with Modbus is that it lacks basic security features like authentication and encryption, and when carried within OTU4 frames, these vulnerabilities persist. Modbus, a protocol widely implemented in Supervisory Control and Data Acquisition (SCADA) systems, does not employ encryption, authentication, or access controls [19; 20]. As a result, OTU4 systems are susceptible to man-in-the-middle attacks, unauthorized access, and eavesdropping.  
Attack surfaces on OTU4 frames include control fields like the Trail Trace Identifier (TTI) and Bit Interleaved Parity 8 (BIP 8), which are particularly easy to exploit. These fields are specifically identified as key points for injecting malicious payloads and for creating covert communication channels [21]. Additionally, existing SCADA security mechanisms are insufficient primarily in safeguarding OTU4 systems in the critical infrastructure environments [22]. Furthermore, legacy protocols, which are integrated with modern optical networks, possess vulnerabilities [23].  
The detailed structure of an OTU4 frame is shown in Figure 2, and contains its critical control fields, TTI, BIP-8, and FAS. The diagram shows how Modbus protocol commands are encapsulated in these fields and identifies the points at which manipulation can take place.  
To counter both classical and quantum attack vectors, novel security protocols have been developed to cope with these protocol-level vulnerabilities in the presence of the quantum computing threat.
## 6 Simulation of Microdroplet Injection Process
### Effects of Fluid Viscosity on the Jetting Process  
Furan resin was used as the jetted fluid in this experiment, and laminar flow was selected as the flow type. However, the simulations (as shown in Figure 12) demonstrate that turbulence can be produced while maintaining all other parameters at a constant level by altering fluid viscosity.  
The turbulent flow's pressure distribution was wave-like, as seen above, and the channel's ends had erratic flows. Therefore, the fluid's turbulent fluxes were examined using transient observation and unsteady analysis.  
Turbulence was generated by the fluid flow mechanism, which included fluid interactions (such as fluid-wall and intermolecular interactions) inside the chamber and anisotropic drag in the air domain. Figure 13 depicts the droplet jetting condition corresponding to each fluid viscosity.  
Figure 11: Relationship between nozzle radius and single-nozzle flow rate.  
Figure 12: Turbulence analysis, (**a**) turbulence intensity; (**b**) turbulence velocity distribution; (**c**) pressure vector plot; (**d**) pressure plot.  
The results of the simulation show that fluid viscosity affects jetting quality and the flight of the satellite droplets. At very low viscosities, satellite droplets that cannot merge with the main droplet are generated during the jetting process. Increasing the viscosity by a reasonable amount, e.g., up to 2.71 cp or 4.28 cp, allows the satellite droplet to catch up with the main droplet; the \"catch-up time\" was shorter at 2.71 cp than at 4.28 cp. Energy losses during the jetting process are greater at higher viscosities than at lower viscosities, leading to longer catch-up times. At excessively high viscosities (9.00 cp), the jetted droplets exhibit a long \"tail,\" which indicates that the droplet formation process (which includes column stretching, neck shrinking, droplet breaking, and stable movement) stopped at the jetting stage.  
Figure 13: Droplet jetting state at each level of fluid viscosity, (**a**) 1.00 cp; (**b**) 1.80 cp; (**c**) 2.71 cp; (**d**) 4.28 cp; (**e**) 7.25 cp; (**f**) 9.00 cp.
## 4 Performance Estimation
### Performance Estimation Function  
Let us summarize the proposed approach for performance estimation in a sequence of steps. We had completed the following steps before the call of the performance estimation function:  
1. A certain number of labels is given.
2. The labels are clustered. As a result, we have different partitions.
3. Classification is done using the clusters for different partitions.
4. A table with the performance measures of classification for all clusters is filled in (\(P^{T}\), \(R^{T}\) and \(F1^{T}\)). \(TP\), \(FP\), \(FN\) for all clusters are saved.  
The input for the performance estimation function is the following:  
* Partitions configuration (the number of different partitions, their sizes).
* Clusters configuration (\(TP\), \(FP\), \(FN\) as the result of classification, \(C\), \(N_{C}\), \(A_{C}\)).
* A table with the measures (\(P^{T}\), \(R^{T}\) and \(F1^{T}\)).  
Finally, we are able to list all steps of the performance estimation function (denoted as **function**PerfeEstimate in the Algorithm 1):  
1. For each possible further partitioning of each cluster in each partition: 1. count the number of leaves on different layers of the predicted taxonomy (\(nL_{N+i}\), \(nL_{N+i-1}\)) 2. estimate the number of positives and negatives \(TP^{*}_{cluster}\), \(FP^{*}_{cluster}\), \(FN^{*}_{cluster}\). 3. estimate relative measures \(P^{*}_{cluster}\), \(R^{*}_{cluster}\), \(F1^{*}_{cluster}\).
2. For each cluster, estimate the best further partitioning (\(F1^{*max}=\max\limits_{i\in S}F1^{i*}\)).
3. For each partition (for each \(k\)), estimate the absolute and relative parameters on the current layer: \(TP^{*result}_{k}\), \(FN^{*result}_{k}\), \(FP^{*result}_{k}\), \(P^{*result}_{k}\), \(R^{*result}_{k}\), \(F1^{*result}_{k}\).
4. Choose the best partition based on \(\max\limits_{k_{min}\subseteq k\leq k_{max}}F1^{*result}_{k}\).  
The estimation function returns the best partition and the estimated performance for it.
## 2 Materials and Methods
### Bacterial Inhibition Assays  
Two different assays were used. Strain L5B5 was cultured in nutrient agar with 2% of glycerol (NA-glycerol) to promote the expression of genes involved in the secondary metabolism [24]. Plates were incubated at 28 \({}^{\circ}\)C for 24 h. After that, 0.4 cm diameter of biomass was removed from culture with a hole puncher and stuck in TSA plates with each pathogenic bacterium, which were prepared as follows: The pathogen bacterium was cultured in 7 mL of trypticase soy broth (TSB) at 30 \({}^{\circ}\)C and 180 rpm between 18-24 h until reached a McFarland standard 2. This culture was added to a 30 mL sterilized TSA close to jellyify (45-50 \({}^{\circ}\)C). Once plates solidified, 0.4 cm biomass of the tested bacteria was stuck to the plates and cultured for 24 h at 30 \({}^{\circ}\)C. Inhibition of pathogens was successful when zone of inhibition around the L5B5 strain biomass was observed. Bioactivity was tested against both Gram-positive and Gram-negative bacteria, including _Bacillus cereus_ CECT 148, _Staphylococcus aureus_ CECT 4630, _Escherichia coli_ DSM 105182, _Pseudomonas aeruginosa_ CECT 110, and _Acinetobacter baumannii_ DSM 30007. These pathogenic bacteria were used for both first and second assays.  
In the second assay, strain L5B5 was cultured in a flask with 150 mL of nutrient broth (NB) with 2% of glycerol (_v/v_) at 30 \({}^{\circ}\)C and 180 rpm until saturation was reached. The same procedure was carried out using glucose, mannose and fructose as alternative carbon source aiming to determine the differential gene expression, according to the culture conditions. After incubation, the cultures were filtered through a nucleopore polycarbonate filter with 0.22 um pore size and the filtrate was tested for antibacterial activity. Pathogenic bacterial suspensions were prepared in 0.9% (_w/v_) sterile saline solution and adjusted to 0.5 McFarland units using a densitometer BioSan DEN-1B with wavelength \(\lambda\) = 565 \(\pm\) 15 nm (BioSan, Riga, Latvia). Inhibitory analysis was carried out in duplicate in a SPECTROstarNano microplate's device (BMG Labtech, Offenburg, Germany) adding a volume of 150 uL to each well. The wells contained 20 uL pathogenic saline solution + 30 uL culture medium + 100 uL filtrate, a negative control with 100 uL filtrate + 50 uL of culture medium, a positive control (20 uL pathogenic saline solution + 130 uL culture medium), and a blank well (150 uL culture medium). The microplate was set up as follows: orbital shaking at 500 rpm, spiral reader, measurement every 30 min and 30 \({}^{\circ}\)C of temperature for 24 h. Additionally, the filtrate of NB-glycerol was lyophilized to a concentrate in 2X and 5X, which were checked with the same protocol.
## 3 Problem Formulation
### Modeling of MGO
#### 3.1.2 Constraints  
(1) Net load constraint  
Since the direction of net load energy flow is unique each time, the net load constraints of the MG can be described as:  
\[nl_{b,i}^{t}-nl_{s,i}^{t}=l_{i}^{t}+es{s}_{i}^{t}-pv_{i}^{t} \tag{6}\]  
\[nl^{\max}d_{b,i}^{t}\geq nl_{b,i}^{t}\geq 0 \tag{7}\]  
\[nl^{\max}d_{s,i}^{t}\geq nl_{s,i}^{t}\geq 0 \tag{8}\]  
\[d_{b,i}^{t}+d_{s,i}^{t}\leq 1 \tag{9}\]  
where \(l_{i}^{t}\) is the load of the \(i\)-th MG, \(pv_{i}^{t}\) is the PV power generation and \(nl^{\max}\), \(nl^{\min}\) are the maximum and minimum net load of the MG, respectively. \(d_{b,i}^{t}\), \(d_{s,i}^{t}\) is a variable of 0,1.  
(2) Energy storage constraint  
The constraint for the energy storage system [25] can be described as:  
\[ess_{i}=\left(es{s}_{i}^{ch}\eta^{ch}-es{s}_{i}^{dis}\eta^{dis}\right)\Delta t \tag{10}\]  
\[ess_{i}^{\max}\geq ess_{i,t}\geq ess_{i}^{\min} \tag{11}\]  
\[SOC_{i,t}^{\rm ess}=SOC_{i,t-1}^{\rm ess}+ess_{i,t} \tag{12}\]  
\[SOC_{i,t}^{\rm ess,max}\geq SOC_{i,t}^{\rm ess}\geq SOC_{i,t}^{\rm ess,min} \tag{13}\]  
\[SOC_{i,0}^{\rm ess}=SOC_{i,T}^{\rm ess} \tag{14}\]  
Figure 2: Structure of energy trading.  
where \(\textit{ess}_{i}^{ch},\textit{ess}_{i}^{dis}\) are the charge and discharge energy for the storage system in the \(i\)-th MG, respectively; \(\eta^{ch},\eta^{fli}\) are the charge and discharge efficiency of the energy storage system in the \(i\)-th MG, respectively; \(\textit{ess}_{i}^{\max}\), \(\textit{ess}_{i}^{\min}\) are the maximum and minimum charge and discharge energy for the storage system in the \(i\)-th MG, respectively; \(SOC_{i,l}^{\rm ess}\), \(SOC_{i,l-1}^{\rm ess}\) are the energy stored at time \(t\) and time \(t\)-1 in the \(i\)-th MG, respectively; \(SOC_{i,l}^{\rm ess,max}\), \(SOC_{i,l}^{\rm ess,min}\) are the maximum and minimum energy stored for the storage system in the \(i\)-th MG, respectively; \(SOC_{i,0}^{\rm ess}\), \(SOC_{i,T}^{\rm ess}\) are the energy stored at time 0 and time T in the \(i\)-th MG, respectively.  
(3) Transferable load constraints  
\[l_{i}^{t}=load_{i}^{t}+\Delta l_{i}^{t} \tag{15}\]  
\[\sum_{t=0}^{t=T}\Delta l_{i}^{t}=0 \tag{16}\]  
\[\textit{c-}load_{i}^{t}\geq\Delta l_{i}^{t}\geq-\textit{c-}load_{i}^{t} \tag{17}\]  
where \(l_{i}^{t}\) is the transferable value of load in the \(i\)-th MG at time \(t\); \(load_{i}^{t}\) is the original load in the \(i\)-th MG at time \(t\).
# Improved Training of CAE-Based Defect Detectors Using Structural Noise
## 2 Contrasts between the Classic Usage of Autoencoders and Ours
### Working Principle of Autoencoders  
Autoencoders are NN models trained through unsupervised learning algorithms. A simple structure of an autoencoder is shown in Figure 1.  
A CAE takes data \(x\) as inputs, encodes \(f(x)\) them into \"compressed\" formats \(h\) (dimensionality reduction in the latent space as \"codes\"), and then decodes \(g(h)\) them into \"uncompressed\" data \(x^{\prime}\) (restored dimensionality), with as little losses as possible. The circles and lines in Figure 1 correspond to neurons and weights, which mimic the operations of biological synapses. The outputs of one layer of neurons are weighted by their respective coefficients and are transferred to the next layer of neurons. As no labeled datum is involved during its training phase, an autoencoder is said to be an unsupervised NN. More specifically, as input data are the only references to adjust its weights, it is said to be a \"self-supervised NN\". After that a datum has been processed by the autoencoder, an energy function computes the differences between its uncompressed and its original forms. Then, in order to decrease the errors, these differences are exploited to fine-tune the weights within the NN. Therefore, a CAE attempts to learn efficient data encoding from successive compressions/decompressions: \"compressions\" are kinds of feature extractions from the data, whereas \"decompressions\" are reconstructions of the data from the features extracted.
## 3 Results
### Effect of Electrical Treatment on Microalgae Growth in the Photobioreactor
In contrast, the 3 V + nutrient addition treatment resulted in a 15.78% decrease in maximum cell density. To investigate the effects of electrical stimulation on _Nitzschia closterium_ f. _minutissima_'s nutrient uptake efficiency and the environmental changes within the photobioreactor, daily samples were taken to measure total nitrogen (TN), total phosphorus (TP), and pH levels. The results showed that electrical stimulation significantly affected the nitrogen and phosphorus uptake of _N. closterium_ f. _minutissima_ (\(p\) < 0.05). As shown in Figure 10b, TN levels were lowest under 1 V stimulation over 72 h, indicating the highest nitrogen consumption. In contrast, TN levels under 3 V remained relatively stable between 25 and 30 mg/L after 24 h. The 0.5 V group exhibited a similar trend to the control but slightly lower. At 72 h, both the 0.5 V and 1 V groups significantly consumed more nitrogen than  
Figure 9: Growth curve (n = 3, \(p\) < 0.05) (**a**) and biomass dry weight (**b**) of _N. closterium f. minutissima_ under direct current (DC) stimulation at different voltages in photobioreactors. Different letters indicate significant differences among treatments. (Note: The horizontal axis of the growth curve here is in hours instead of days.). \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **K\({}_{1}\)** & **K\({}_{2}\)** & **K\({}_{3}\)** & **K\({}_{w}\)** & **K\({}_{max}\)** & **MCD (\(\times\)10\% cell/mL)** \\ \hline CK  
the control (\(p<0.05\)), with increases of 9.20% and 20.74%, respectively. In contrast, the 3 V group consumed significantly less nitrogen, 52.06% lower than the control (\(p<0.05\)). Similarly, phosphorus consumption was significantly influenced by electrical stimulation. As shown in Figure 10b, at 72 h, the 1 V group consumed 10.85% more phosphorus than the control (\(p<0.05\)), while the 0.5 V group showed a non-significant increase of 8.60% over the control. However, the 3 V group consumed 58.45% less phosphorus than the control (\(p<0.05\)), indicating a significant inhibitory effect under 3 V stimulation, while 1 V stimulation promoted phosphorus uptake. As shown in Figure 10c, voltage had a pronounced effect on pH changes in \(N\). _closterium_ f. _minutissima_ cultures (\(p<0.05\)). The pH under 3 V continuously decreased during the first 36 h, while the other groups showed a pH increase, stabilizing around 8.4, indicative of healthy growth. Between 36 and 48 h, the pH of the 3 V group began to rise, while the other groups started to decline, with the 1 V group exhibiting the smallest decrease and the most stable pH levels. From 48 to 72 h, the pH in the 1 V group stabilized, while the other groups showed a gradual increase, with all groups converging around 8.4 by the end of the experiment. Figure 10: Changes in total nitrogen (TN) (n = 3, \(p<0.05\)) (a), total phosphorus (TP) (n = 3, \(p<0.05\)) (b), and pH (n = 3, \(p<0.05\)) (c) in photobioreactors under different voltages of direct current (DC) stimulation.
# Temperature Drift Compensation for High-G MEMS Accelerometer Based on RBF NN Improved Method
## 1 Introduction
[PERSON] proposed a complete analytical model based on the Hamilton's principle, which provides a new perspective for studying the relationship between temperature and nonlinearity [19]. [PERSON] proposed a modification of an radial basis function neural network (RBF ANN) based on temperature compensation models for interferometric fiber optical gyroscopes (IFOGs) [20]. [PERSON] proposed that the appropriate filter be designed according to different error models; they then combined the multi-model method with the extended Kalman filter (EKF) method to estimate the state optimally, which improves the accuracy and effectiveness of the sensor [21]. [PERSON] proposed a federated extended finite impulse response (EFIR) filter to improve the accuracy of the sensor [22]. [PERSON] proposed a new multi-input/single-output model based on genetic algorithm (GA) and Elman neural network (Elman NN) to improve the temperature drift modeling accuracy of the sensor [23]. [PERSON] proposed a temperature prediction and control model based on BP NN and Fuzzy-PID control method, which can successfully reduce the zero-rate output [24]. Software methods offer advantages of simple structure, low cost and convenient implementation, and are convenient for improving the precision of the MEMS accelerometer. RBF NN based on GA has been proposed and successfully applied to the temperature drift model of the MEMS gyroscope [25]. Unlike previous accelerometer temperature drift models, a new fusion algorithm (RBF + NN + GA + KF combined with temperature drift model) was proposed in this paper to make the accelerometer more accurate. The new algorithm considers not only temperature and temperature variation rate, but also temperature product term. The paper will be introduced as follows: The structure of the MEMS accelerometer and the temperature experiment are described in Section 2; the temperature drift model and fusion algorithm are shown in Section 3; Section 4 shows the temperature experiment; and Section 5 shows the results of data processing and the analysis results of various algorithms. The conclusions are given in Section 6.
## 4 Experiments
### Qualitative Experimental Results
In contrast, SegMine's extraction results are more consistent with the actual conditions in terms of spectral and textural features, accurately identifying all the mining areas and also accurately excluding all the unmined, vegetation-covered areas. In Scene 4, there are some mining areas in the upper part which are not yet contiguous. Between the mining areas, there are clearly distributed areas of vegetation cover. However, the samples constructed in this article labeled all the intermediate areas as mining areas. Possibly influenced by the wrong sample, SegMine's extraction results are basically consistent with the sample labeled region and also identify all the intermediate distributed vegetation cover areas as mining areas. SegMine's processing here is unreasonable, and further refinement is needed for subsequent studies. In Scene 5, there are three mining areas in the map, with a road connecting the two mining areas on the right side, and there are obviously no traces of mining on either side of the road. Compared to the original image, the sample labeled this road as a mining area as well, and the width is slightly wider than the actual road. The model may be misled by the wrong sample, but SegMine accurately completed the identification of the two mining areas, and, at the same time, identified the road connecting them as a mining area and as slightly wider than the labeled road, which is unreasonable. Fortunately, the misidentified area is relatively small. In Scene 6, it can be seen that there is an open-pit mining area in the lower-left. Compared to the original image, the extent of the mining area in the labeled image is inaccurate in local areas, and the boundary line is slightly rough, with excessively long straight-line segments, which is not consistent with the actual mining area boundary. On the other hand, from the spectral and texture features, the results extracted by SegMine are more delicate and match better with the actual boundary, which is more in line with the local reality. The original image from Scene 7 shows that there is an extensive mining area. Compared to the original image, the internal texture of this mining area is complex, the terrain has obvious undulations, and the shadow influence is obvious. At the same time, the min ing area labeled in the sample is obviously too small; the upper part especially omits a large mining area. In terms of spectral and textural features, the results extracted by SegMine are more in line with the actual local reality and are able to identify all the unlabeled mining areas in the sample image.