esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Proton and Neutron Irradiations
#### 3.1.2 Radiation Induced Wavelength Shift  
The primary measurement scheme of the OGA is to automatically detect the characteristic wavelength of the connected FBG and track its wavelength. The OGA conducted these wavelength measurements with an uncertainty of \(\pm\)0.005 nm [17]. To evaluate Radiation Induced Wavelength Shift (RIS), the OGA was used to record the characteristic wavelength of the FBG immediately prior to any irradiation, actively during the irradiation and immediately after each dose step. The results of this  
Figure 4: Collected spectra of specimen RAD03 from test Set 3 illustrating the reflection power of the FBGs vs. dose. The “characteristic FBG wavelength” denotes the central wavelength of the narrow passband feature at 1550 nm that is always present in the spectra of \(\pi\)-FBGs. The resolution of the Optical Gauge Amplifier (OGA) is 0.01 pm.  
evaluation conducted during the proton irradiations are illustrated in Figure 5. The locus of results indicate no significant statistical correlation between RIS and dose when the following conditions are considered: 0.010 nm of RIS could be reasonably justified by a mere 1.0 \({}^{\circ}\)C increase in the temperature of the test placard as the test placard absorbs the dose and warms and the \(\pm\)0.005 nm measurement uncertainty of the OGA. The observed RIS results are in agreement with those of [PERSON] et al. as the RIS across their three relevant test cases was less than \(\pm\)0.040 nm with dose rates 50 times higher and an accumulated dose \(\sim\)8.5 times higher [18]. The reported range of wavelength deviations in Figure 5 was less than \(\pm\)0.010 nm throughout the test. Based on previous experience, wavelength fluctuations of less than \(\pm\)0.010 nm over the course of a multi-hour test are typical and therefore it was concluded that any RIS is insignificant.  
One test placard accommodating one of each FBG category was also exposed to the neutron irradiation. Similar to the proton irradiation experiments, no radiation effect on the FBGs' performance was observed due to neutrons. All wavelength shifts were attributed to normal temperature fluctuations in the experimental environment.
## 1 Introduction
However, due to the complexity of the mixed fermentation system in PM, research on the functional microorganisms in PM still relies on the study of cultivable microorganisms. So far, eight novel species have been isolated from PM [14; 15; 16; 17; 18; 19]. In addition, it was reported that the main acid-producing functional microbial groups in PM are _Clostridium_[20; 21; 22; 23; 24; 25], _Oscillihacter_[26], _Caproicibacterium_, and _Caproiciproducens_[27; 28]. It is estimated that the proportion of currently known culturable microorganisms isolated from PM is 3-33% of the total number of microbial taxa presumably harbored in PM [13; 29]. Enrichment culture is an effective method for the isolation of difficult-to-culture microorganisms from environmental samples. [PERSON] et al. isolated several bacteria with the ability to degrade environmental pollutants (including lindane and endosulfan) from their enrichment culture [30]. Moreover, [PERSON] et al. added activated sludge to the enrichment medium (MSM), which was rapidly detected by ATR-FTIR, and _Bacillus_ with polyhydroxyalkanoate (PHA)-producing ability was also isolated [31]. In addition, it was shown that species within the genus _Clostridium_ were the dominant microorganisms in the PM. Moreover, _Clostridium_ species have a strong ability to produce short-chain fatty acids (SCFAs), such as butyric acid, acetic acid, and caproic acid, as well as the ability to produce spores and thermoresistance [32]. Therefore, applying heat-shock pretreatment to PM enables selectivity for enriching certain microbial groups, such as _Clostridium_ and _Bacillus_[33]. Clostridial growth medium (CGM) is commonly used for isolating Clostridia and studying anaerobic fermentation characteristics [34]. [PERSON] et al. enriched _Clostridium_ _butyricum_ YD-4 from PM using CGM, and revealed that the butyric-acid yield was 3.44 g/L [25]. In addition, [PERSON] et al. isolated 54 microbial strains from PM using CGM [35]. In other studies, the mixed bacteria system in PM was enriched using glucose-based CGM and heat treatment, which resulted in a high yield of caproic acid (6.68 g/L) and butyric acid (2.46 g/L) [36]. In the present study, PM was enriched using a clostridial growth medium and submitted to heat shock. The fermentation products and microbial-community composition of enriched fermented broth (EFB) in different rounds of the enrichment were studied to determine the effects of the enrichment medium, enrichment rounds, and heat-shock treatment on the metabolites and microbial succession patterns in the PM. The obtained findings are of great significance for the development of the functional fermentation broth of PM and the isolation of difficult-to-culture microorganisms.
# Simulation and Design of Three 5G Antennas
## 5 Design of an Eight-Element MIMO Array Antenna for Mobile Terminals with a Metal Frame
### Optimization of the Parameters for the Eight-Element MIMO Array Antenna  
The placement of the \"Ring-Slot\" antenna units also affects the resonance frequency and bandwidth of the array antenna. Next, through simulation optimization, including adding T-type decoupling structures to the antenna array, selecting suitable antenna unit positions, and adjusting the spacing distance, the performance of the array antenna will be improved.
## 3 Computational Model
### Computational Procedure  
For a given H-Type rotor geometry and rotational speed \(\omega\), a value of the local TSR \(\lambda\) is chosen by assuming that the interference factor \(a\) is unity. Thus, \(Re_{\rm b}\) and \(\alpha\) are evaluated as first approximations, and the airfoil \(C_{\rm L}\) and \(C_{\rm D}\) characteristics are interpolated from the test data reported by [PERSON] and [PERSON] [38]. Then, using Equation (7), the normal and tangential force coefficients of the blade section are estimated, while Equation (10) allows the upwind function to be evaluated. With the first value of \(f\), Equation (9) is used to calculate another value for the interference factor, and the iterations continue until successive sets of \(a\) are reasonably close. Convergence will be achieved, as the error is less than 0.0001. Once the final value of the upwind zone '\(a\)' has been calculated, the local relative velocity \(V_{\rm r}\) is determined from Equation (7) and the local AoA from Equation (1). A similar procedure is repeated for the downwind half-cycle. Additionally, it should be noted that here, the upwind velocity is an equivalent velocity expressed as \(V_{\rm e}=(2a-1)V_{\rm w}\).
## 3 Results and Discussion
### Correlations Between Damage Index and Impact Parameters
Collinearity between these explanatory variables can lead to instability in the solution space. Therefore, the product \(a\times\Delta v\) was used in a further analysis, which correlated quite well with the tuber damage index \(DI\). Taking these considerations into account and assuming the hypothesis that there is a relationship between the value of the tuber damage index and the peak acceleration and velocity change during the impact recorded by the IRD 400 device, the following model was formulated:  
\[DI=\lambda_{m}a\Delta v\]  
where: \(DI\) tuber damage index, %; \(\lambda_{m}\) is the experimental coefficient, %-s-m\({}^{-1}\); \(a\) is the peak impact acceleration (in times the acceleration of gravity \(g\)); and \(\Delta v\) is the change in velocity during the impact, m-s\({}^{-1}\). The unknown values of the \(\lambda_{m}\) coefficient were developed using statistical analysis methods. Table 6 presents the values of the \(\lambda_{m}\) regression coefficients together with their statistical evaluation for the selected surfaces and potato varieties. The values of the determination coefficients are varied. The lowest evaluation of the regression models (R\({}^{2}\) in the range of 49.87-55.31%) was obtained for the tuber drop and the IRD 400 device on bulk potatoes. This is logical because the impacts of tubers or devices with bulk potatoes are characterized by the greatest randomness, resulting from central and eccentric impacts. The best evaluation of the regression models (R\({}^{2}\) in the range of 92.91-93.02%) was obtained for impacts with a steel surface.
## 3 Results and Discussions
### Lubricity of PIL as an Additive in Water  
The variation of the coefficient of friction and its mean value observed using IL-DEE as an additive in water is presented in Figure 11. Lubrication with pure water resulted in very high and unstable friction. Applying one wt.% of IL-DEE reduced friction by 3.9 times. The lubrication with an additional sample resulted in much more stable friction, which had a running-in period of almost 10 min at the onset of the test.  
In the present case, we also wanted to evaluate the influence of fatty acid anions on lubricity. Comparing the results from a previous study where bis(2-hydroxyethyl)ammonium oleate was used as an additive in the water; we found that PILs with different anions similarly performed (Figure 11). Only a 3.3% difference in favor of the oleic acid anion was obtained.  
Similar to the coefficient of friction, lubrication with W + IL-DEE solution resulted in effective wear reduction (Table 4). Compared to additive-free water, the wear was reduced by 1.9 times. However, the wear was 14.5% higher than that observed in the case of the oleic acid anion [33]. There could be a few reasons for that. Firstly, the concentration  
Figure 11: Variations of the coefficient of friction and its mean value observed during the W + IL-DEE tribo-test. * value of the coefficient of friction observed in our previous study [33].  
of the additive, which worked well for oleic acid anion containing protic ionic liquid, may not work for erucic acid anion with PIL. Therefore, the concentration of the bis(2-hydroxyethyl)ammonium erucate additive should be determined to obtain optimum results. Secondly, the reactivity of shorter alkyl chains containing ionic liquids is higher. Therefore, oleic acid anion with PIL has an advantage.  
The wear traces on the plate appear to have minor scratches oriented along the sliding direction (Figure 12a). However, the wear scar on the ball has no scratches (Figure 12b). Therefore, scratches on the wear trace were formed due to three-body wear when wear debris came into contact. The high-resolution SEM image shows those scratches in more detail (Figure 13). Moreover, a small amount of material is accumulated at the sides of the wear trace, which is already  
The EDS composition of the wear trace reveals the oxygen and carbon-rich layer (Table 5). In contrast, more than 7% nitrogen and very little oxygen and carbon were found on the intact plate surface. Even though IL-DEE is present in a small concentration in water, its molecules adsorb on the surface. During friction, rubbing between surfaces occurs, leading to the decomposition of this layer. It could lead to the formation of metal soap or friction polymers [34; 35].
# Potato Leaf Disease Segmentation Method Based on Improved UNet
## 2 Materials and Methods
### Optimize the Loss Function  
UNet is employed for pixel-level segmentation tasks, with the most common loss function being the cross-entropy loss. To enhance performance, a linear combination of cross-entropy loss (BCELoss) and Dice loss is utilized [32]. In segmentation tasks, the number of background pixels often far outweighs that of defect samples. Relying solely on cross-entropy loss could lead the network to converge to local minima. The Dice loss function measures the similarity between samples, focusing on the accuracy of foreground image classification while emphasizing background pixels [33]. This approach effectively mitigates the issue of foreground-background sample imbalance. A non-linear loss function approach is adopted to address extreme data imbalance by combining the Dice loss with the cross-entropy loss. This method not only considers the overall image loss, but also pays greater attention to variations in target object loss, thus counteracting the influence of feature region size on segmentation accuracy. The objective is to emphasize accurate loss computation for target regions while considering the entire image context, aiming to improve segmentation precision.  
BCELoss function's loss rate (\(L_{Bcz}\)) is calculated as follows:  
\[L_{Bce}=-\frac{1}{n}\sum_{n=1}^{N}\left(y_{n}\times\ln x_{n}+(1-y_{n})\times \ln(1-x_{n})\right) \tag{3}\]  
The Dice loss function's loss rate (\(L_{Dice}\)) can be calculated as follows:  
\[L_{Dice}=1-\frac{2|X\cap Y|}{|X|+|Y|} \tag{4}\]  
where \(|X\cap Y|\) represents the intersection between sets \(X\) and \(Y\), \(|X|\) and \(|Y|\) respectively represent the number of pixels in sets \(X\) and \(Y\), N denotes the total number of pixels in the segmented image, n stands for the number of training iterations, \(x_{n}\) corresponds to the independent variable used during loss function training, and \(y_{n}\) signifies the dependent variable of gradient changes.
# Survey on Modelling and Techniques for Friction Estimation in Automotive Brakes
## 2 Techniques for the Estimation of BLCF in Automotive Disc Brakes  
### General Remarks  
In this section, the work concentrates on research into a tool capable of predicting the actual BLCF in automotive brakes. As repeatedly stated, the requested tool is important to monitor the brake operation and its actuation for safety and environmental purposes.  
During the last decades, ever more sophisticated models have been developed.  
As reported in Figure 1, the techniques in the literature range among:  
* static and dynamic models, which require an ad hoc parameter calibration against the specific plant;
* neural networks, which demand an accurate design of experiments for the learning and training process;
* state observers, which give an estimation based on information provided by the vehicle sensors without the need to accurately model the phenomena affecting the tribological contact.  
In the following, the text highlights limitations and dwells on a comparative analysis among the existing techniques. First, studies regarding the classical model-based approach are surveyed. Among these, [PERSON]'s dynamic model [17] is acknowledged as the most advanced model among the brake community. Hereunto, the paper provides an assessment of the latter, which proves to be unsuitable for on-board applications. In addition, the cellular automata method is featured as a supporting tool to the classical model-based approach for the depiction of frictional and wear behaviour in the contact zone. Thereafter, artificial neural networks are presented as an effective technique to handle nonlinear dynamic modelling in the place of phenomenological regression models, whose application would be awkward and cumbersome. In conclusion, a new methodology based on the state observation technique will be herein proposed as an innovative solution for online estimation of the BLCF. This latter is expected as a promising solution since it uses measurements coming directly from the vehicle sensors by avoiding an analytical description of the brake phenomenology and does  
Figure 1: Pie chart arising of the state of art of techniques for the estimation of the BLCF.  
not require the implementation of models that cause a high computational burden, such as temperature distribution models. The observer results are compared to a reference profile generated through the vehicle dynamics simulator IPG CarMaker.
# An EV SRM Drive and Its Interconnected Operations Integrated into Grid, Microgrid, and Vehicle
## 4 G2V/V2G Operations
### Experimental Evaluation
#### 4.3.1 G2V Operation  
The battery charging voltage is set at \(v_{b}^{*}=158\) V, and the maximum charging current is set at \(i_{b}^{*}=10\) A. The charging process can be divided into a constant current mode and a constant voltage mode. In the constant current charging mode, the battery is charged with 10 A. The measured phase voltage \(v_{an}\) and three line currents \((i_{a},\ i_{b},\ i_{c})\) of the three-phase SMR are shown in Figure 19a, the \((v_{gs},v_{ds},i_{Lp},i_{Ls})\) of the CLLC resonant converter are shown in Figure 19b, and the voltage and current of battery and DC-link \((v_{b},\ i_{b},v_{dc},i_{dc})\) are shown in Figure 19c. Furthermore, the measured results in constant voltage charging mode are shown in Figure 20. The results of the two power stages indicate that (i) \(i_{a}\) synchronized with \(v_{an}\), three-phase balanced, and lowly-distorted line currents \((i_{a},\ i_{b},\ i_{c})\) verify the satisfactory performance of the SMR; (ii) normal operation of the CLLC converter can be observed from the waveforms of Figure 19b. The zero-voltage switching (ZVS)  
Figure 18: Established schematic for G2V/V2G operations.  
behavior is assured by the relationship between \(v_{gs}\) and \(v_{ds}\); and (iii) the constant current charging with \(i_{b}=10\) A (\(v_{b}<158\) V) is achieved. As \(v_{b}\) reaches 158 V, the constant voltage charging is applied automatically, with the results depicted in Figure 20a-c. The above similar comments can also be made for the measured results.  
Figure 19: Measured results of developed inverter in G2V mode under constant current charging: (**a**) \((v_{an})\), \((i_{a}\), \(i_{b}\), \(i_{c})\) from the grid; (**b**) \((v_{gs},v_{ds},i_{Lp},i_{Ls})\) of the CLLC resonant converter; (**c**) \((v_{b}\), \(i_{b})\), \((v_{dc},i_{dc})\).
## 2 System Architecture
### Digital Fractional-N Sub-Sampling PLL (SSPLL)  
The fine loop is implemented using a digital fractional-N sub-sampling PLL structure. The high-frequency output of the DCO is sampled by the DTC output, and this signal is phase-compared using a TDC with the DTC output. The phase comparison results are fed into the digital loop filter (DLF) and DTC gain calibration block. In the DLF path, the SYNC and dithering blocks adjust the DCO output frequency, while the DTC gain calibration path calibrates the DTC gain using the quantization (QE) value from DSM.  
The fine tracking loop in the proposed WBPLL employs a simple 1-bit D flip-flop (DFF) as the sampler for the DCO signal, which is remarkably straightforward in design. However, using a DFF sampler introduces two main issues: the potential for metastability between the DTC and DCO signals and the sampled DCO signal lagging behind the DTC output. This lag causes the TDC to always generate positive values, preventing the PLL from reaching a steady state. Both problems are mitigated by adding a negative offset to the TDC output, effectively resolving the metastability issue and ensuring the stable operation of the PLL. Given the wide frequency range of the wideband PLL, appropriate offsets are employed based on the frequency inferred from the AFC and FLL counter values, enhancing the third loop locking performance. The digital fractional-N sub-sampling loop manages the fine frequency tuning of the PLL and adjusts the output frequency of the DCO within an even smaller range compared to the CAPLL loop, using 5 bits out of the total 15 control bits. This loop operates using a thermometer code approach to ensure the linearity of the DCO frequency with respect to the control code, providing precise frequency adjustments.  
This architecture effectively balances high performance, low power consumption, and compact design, making it suitable for various applications, including 5G FR1 networks and IoT devices.
## 1 Introduction
To sum up, the hybrid control method combining intelligent control and the traditional control method has become the main method for vibration suppression of manipulators at present. The boundary control method is also widely used in vibration suppression, but the design control law is complicated and sometimes the vibration suppression effect is not good. Most studies do not consider the systematic impact of the motor on the whole system. Based on these, the main contributions of this paper are as follows:  
(1) A fuzzy backstepping control method based on an RBF neural network is proposed. An RBF neural network is combined with a backstepping control method, and unknown parameters are compensated for by the RBF neural network. (2) A controller is designed, and nonlinear dynamic equations are established. The simulated platform was built using Simulink, and the vibration suppression effect was analyzed using two parameters: the error of displacement and the error of velocity. (3) The dynamic equation of the flexible manipulator considering the motor is established, the specific parameters of the motor are established, and the influencing factors and dynamic characteristics of the system are considered and compared with the RBF neural network boundary control method and RBF neural network backstepping method. The rest of the paper is as follows: In the second part, with a model approach for the identification of joint and link compliance of an industrial manipulator, partial differential equations of the flexible manipulator are established based on the energy method and Hamilton principle. In the third part, an improved RBF neural network is designed, a new fuzzy backstepping control method based on an RBF neural network is proposed, and Lyapunov function and control law are designed. In the fourth part, the simulation platform is established using Simulink. The effect of vibration suppression is measured by displacement error, velocity error, stable frequency, and stable point, compared with the RBF neural network boundary control method and RBF neural network backstepping control. The fifth part summarizes the work and looks forward to the future.
# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 2 MTD Techniques
### Discussion
#### 2.3.2 Integration with Existing Security Defense Mechanisms  
MTD defends attackers by shifting the attack surface, but by its nature, this defense mechanism cannot cover the vulnerability of the system itself. For instance, software randomization [38; 39; 40] (classified as Shuffling/Software layer), a common MTD technique, does not eliminate the existence of vulnerabilities in software. Attackers are still capable of performing vulnerability attacks on specific targets through exploiting mining, buffer overflow, and other methods. We can expect that, with software randomization, different users have different binary codes and, thus, cannot perform attacks on other targets using the same approach.  
Another example is instruction set randomization [43; 44] (classified as Shuffling/Data layer). Although it can prevent attackers from inserting binary instructions into the target program to execute an attack successfully, the vulnerability of the target program is also not eliminated, and well-designed worms and viruses can still break through the defense of instruction set randomization.  
Therefore, we need to clarify the idea that in order to achieve a genuine defense winning, MTD must be integrated with existing security defense mechanisms. Existing network security defenses such as firewalls, intrusion detection systems, and anti-virus systems have been deployed in the network with network topology and a configuration that is relatively fixed, while introducing MTD into them changes the existing network configuration, thus potentially leading to increased resource consumption, reduced network availability, and possible mutual interference with existing network security defense techniques.  
We believe that MTD must be implemented appropriately without affecting existing network operations and must adapt to existing network infrastructure, network services, and network protocols. The development trend of MTD needs to integrate better with existing network security protection technology and be embedded better into the existing network.