esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization and Experimental Study of Penetration Depth of Pocket Penetrometer
## 3 Test Results and Analysis  
### Indoor Test Results and Analysis  
The above data processing method was applied to extract the penetration resistance from the penetration test curves of the two types of disturbed red clay samples, while normalizing the water content to the liquidity index. The results of fitting the penetration resistance to the liquidity index for the two types of red clay soils are shown in Figure 13. Both types of red clays show the phenomenon that the value of soil penetration resistance decreases as the liquidity index increases.  
Function fitting was performed to obtain the fitting equation:  
Red clay 1:  
\[I_{L}=1.16*\exp(-0.13F) \tag{1}\]  
Red clay 21:  
\[I_{L}=1.03*\exp(-0.14F) \tag{2}\]  
\(I_{L}\)--Liquidity index  
\(F\)--Penetration resistance  
According to the fitting equations, the penetration resistance at the liquid limit was back-calculated as follows: red clay 1: 3.3 N; red clay 21: 2.2 N. The penetration resistance at the plastic limit was calculated as follows: red clay 1: 11.7 N; red clay 11: 10 N. Therefore, it is feasible to use the empirical equations to predict the consistency and state of the soil, but there is a certain degree of error in the empirical formula. This error is due to the small size of the probe, which leads to a small difference in the penetration resistance values of the soil in different states of consistency, thus affecting the accuracy of the penetration resistance. In addition, the traditional liquid-plastic limit test has errors.  
As shown in Figure 14, the shear strength corresponding to the same water content (positive stress 50 kPa) was compared with the end resistance (penetration resistance/probe area). The fitting equations were obtained:  
Red Clay 1:  
\[\tau=0.036*P-0.3 \tag{3}\]  
Red Clay 21:  
\[\tau=0.034*P-0.8 \tag{4}\]  
\(\tau\)--Undrained shear strength at 50 kP normal stress  
\(P\)--Tip resistance  
Figure 13: Fitting of penetration resistance and liquidity index of different red clays.  
Therefore, it can be seen that there is a very strong linear relationship between end resistance and shear strength. In addition, the parameters of the equation of the relationship between end resistance and shear strength of different red clays are basically the same. It can be concluded that the relationship between end resistance and shear strength is less affected by the parameters of the liquid-plastic limit of the soil body. The current commonly used expression for the cone tip resistance of saturated clay is:  
\[q_{c}=c_{u}N_{c}+\sigma_{v0} \tag{5}\]  
\(c_{u}\)--Undrained shear strength  
\(q_{c}\)--Tip resistance  
\(N_{c}\)--Tip resistance coefficient  
\(\sigma_{v0}\)--Vertical stress  
Equations (3) and (4) are formally identical to Equation (5), and the cone tip resistance coefficient \(N_{c}\) = 27.7, 29.4 can be obtained.  
In summary, Table 4 can be obtained. All four equations showed high correlations and relatively low prediction errors for their respective soil samples, indicating that these equations fit very well for specific sample types.
## 3 Experimental Setup and Result Analysis  
### Experimental Setup  
The experimental system is shown in Figure 7, including two parts: the transmitter and the receiver. The transmitter contains direct current (DC) voltage sources, an LED, an STM32 (STMicroelectronics32) development board, and a drive circuit board. The receiver contains an industry camera, a ROS (Robot Operating System) robot, and a computer (Dell Inspiron 5557, Windows 10, 4G RAM, Intel (R) Core (TM) i5-6200U CPU @ 2.4 GHz). DC voltage sources supply power to the LED and circuit boards. The LED emits a modulated optical signal to the receiver. The STM32 development board generates modulation information and the drive circuit board drives the whole transmitter circuit system. The industry camera is installed on the ROS robot, receiving modulated information from the transmitter and the ROS robot simulates the movement of a vehicle. The computer processes the received images from the industry camera with the proposed tracking algorithm to track the LED signal source in real time. The open source computer vision library (OpenCV, which was founded by Intel in 1999 and is now supported by Willow Garage. And version OpenCV 3.4.0 is used in this paper which was released on December 23, 2017) is used to complete processing process with C++  
Figure 6: Detailed process of the proposed tracking algorithm. language. Specifications of the industry camera, LED, ROS robot, STM32 development board, and the drive circuit board are shown in Table 1.  
Video sequences were recorded with the industry camera introduced above to better reflect the results of the proposed algorithm under different circumstances in the applications of a CMOS-based VLC system. In the experiment, white, green, blue, and red LEDs were used to record three different videos, corresponding to the three channel models which have been introduced in 2.1. Because the proposed algorithm does not involve background modeling, such as background subtraction or a frame difference method, the three channel models are actually the same when applying our algorithm. The transmitter simulates the traffic light and the receiver, which is an ROS robot with a computer with
# Cyber Resilience Limitations in Space Systems Design Process: Insights from Space Designers
###### Abstract  
Space technology is integral to modern critical systems, including navigation, communication, weather, financial services, and defence. Despite its significance, space infrastructure faces unique cyber resilience challenges exacerbated by the size, isolation, cost, persistence of legacy systems, and lack of comprehensive cyber resilience engineering standards. This paper examines the engineering challenges associated with incorporating cyber resilience into space design, drawing on insights and experiences from industry experts. Through qualitative interviews with engineers, cybersecurity specialists, project managers, and testers, we identified key themes in engineering methodologies, cybersecurity awareness, and the challenges of integrating cyber resilience into space projects. Participants emphasised the importance of incorporating cybersecurity considerations from the earliest stages of design, advocating for principles such as zero-trust architecture and security by design. Our findings reveal that experts favour Model-Based Systems Engineering (MBSE) and Agile methodologies, highlighting their synergy in developing flexible and resilient systems. The study also underscores the tension between principles-based standards, which offer flexibility but can lead to inconsistent implementation, and compliance-based approaches, which provide clear measures but may struggle to adapt to evolving threats. Additionally, the research recognises significant barriers to achieving cyber resilience, including insider threats, the complexity of testing and validation, and budget constraints. Effective stakeholder engagement and innovative funding models are crucial for fostering a culture of cybersecurity awareness and investment in necessary technologies. This study highlights the need for a comprehensive cyber resilience framework that integrates diverse engineering methodologies and proactive security measures, ensuring the resilience of space infrastructure against emerging cyber threats.  
cyber resilience challenges; space designers interview; cyber resilience framework; space systems design; space systems security; model-based systems engineering (MBSE); anti-satellite (ASAT) threats +
_Article_  
Cyber Resilience Limitations in Space Systems Design Process: Insights from Space Designers
# Deep Learning for Neuromuscular Control of Vocal Source for Voice Production
## 2 Materials and Methods
### LeTalker
#### 2.2.3 Glottal Flow  
The three-mass vocal fold model was connected to the subglottal and supraglottal airways. The glottal flow equation, \(u_{g}\) which is the primary vocal source can then be derived using the incident (P\({}^{+}\)) and reflected (P\({}^{-}\)) pressures at the glottis from the vocal tract. The computation of the glottal flow equation was described in [37]. Here, only the final equation was given:  
\[u_{g}=\left(\frac{a_{g}c}{k_{t}}\right)\left\{-\frac{a_{g}}{A^{*}}\pm\left[ \left(\frac{a_{g}}{A^{*}}\right)^{2}+\frac{4k_{t}}{\rho c^{2}}\big{(}P_{s}^{ +}-P_{e}^{-}\big{)}\right]^{1/2}\right\} \tag{1}\]  
where \(\rho=0.00114\) g/cm\({}^{3}\) is the air density, \(c\) is the speed of sound in air, \(a_{g}\) is the glottal area, \(P_{s}^{+}\) is the forward traveling subglottal incident pressure on the glottis, and \(P_{e}^{-}\) is the backward traveling supraglottal incident pressure on the glottis (see Figure 2B). If \(A_{s}\) is the subglottal area, and \(A_{e}\) is the supraglottal area, then the effective vocal tract area, \(A^{*}\) was given as  
\[A^{*}=\frac{A_{s}A_{e}}{A_{s}+A_{e}} \tag{2}\]  
and \(k_{t}\) as  
\[k_{t}=1-2\big{(}a_{g}/A_{e}\big{)}\big{(}1-a_{g}/A_{e}\big{)} \tag{3}\]
## 4 Simulation Results
### Comparative Analysis for PI, PI-SMC and SMC  
The effect of load increase on SBBC performance with PI, PI-SMC and the proposed SMC controllers is analyzed. In this simulation, the DC power of the load was increased from 375 to 500 W for PI and PI-SMC controllers. In addition, to better show the performance of the SMC controller, the load was increased from 250 to 500 W. Figure 9 compares the AC current stabilization time for the three controllers. The AC current with PI controller exhibits a stabilization time of 0.4 s. This control system presents a delay between measurement and switching signal significantly degrading the waveform near zero crossing and presenting a \(THD_{i}\) of 17.23% (Figure 9a). For PI-SMC, the hysteresis modulation strategy improves the waveform near zero crossing of current, reducing \(THD_{i}\) to 3.85% (Figure 9b); nevertheless, the current stabilization (amplitude) is reached after 24 cycles, the same time as the PI controller. Finally, The proposed SMC allows regulating the AC current, rapidly responding to operation changes in only 0.04 ms. In this case, it can be observed that the current response is the fastest (Figure 9c). It can be concluded that SMC and the use of an ABH allow decreasing the \(THD_{i}\) to 3.7% and SMC presents the fastest response under perturbations.  
Figure 10 shows the DC voltage response. The power converter with PI control (Figure 10a) takes 395.7 ms to regulate the DC voltage and presents an overvoltage of 12.75%. In comparison, PI-SMC allows reducing the overvoltage to 3.75%, maintaining the same stabilization time of the PI control (Figure 10b). This result evidences the delay caused by the PI control when it is used as external loop, limiting the transient response of the closed loop system. On the other hand, the DC voltage stabilizes in 30 ms, when the SMC strategy is used and the response exhibits a low overvoltage of 0.1% (Figure 10c). This evidences that SMC responds rapidly, protecting sensitive DC loads against overvoltages.
## 5 The Molecular Mechanisms Behind the Resistance of Biofilms to Antimicrobial Agents
### Quorum Sensing Systems and Their Regulation of Efflux Pumps  
Many molecular processes regulate resistance associated with biofilms, notably QS and essential efflux pumps. QS is a cell-to-cell communication mechanism that employs signaling molecules, including acyl-homoserine lactones (AHLs) in Gram-negative bacteria and oligopeptides in Gram-positive bacteria, to synchronize bacterial action [145; 146; 147]. This mechanism allows biofilm bacteria to detect their population density and collectively modulate gene expression, including genes related to antimicrobial resistance and biofilm development [148].  
The QS systems Las, Rhl, and Pqs in _P. aeruginosa_ govern the expression of several efflux pumps, such as MexAB-OprM, MexEF-OprN, and MexCD-OprJ. These efflux pumps actively discharge diverse antibiotics, including \(\beta\)-lactams, fluoroquinolones, and aminoglycosoides, facilitating MDR. The Las and Rhl systems govern the synthesis of signaling molecules such AHLs, which subsequently enhance the expression of efflux pump genes, augmenting the bacterium's resilience against antimicrobial threats [149; 150]. The Pqs system, using 2-heptyl-3-hydroxy-4-quinolone (PQS) signaling, has been associated with the augmentation of efflux pump activity, further facilitating antibiotic tolerance in biofilms [149].  
In Gram-positive bacteria like _S. aureus_, the accessory gene regulator (agr) QS system regulates resistance mechanisms. The agr system regulates the NorA efflux pump, which expels fluoroquinolones and other hydrophobic substances. The QS-regulated synthesis of virulence factors and biofilm matrix constituents often correlates with heightened efflux pump activity, establishing a synergistic barrier to antimicrobial drugs [151; 152].  
Efflux pumps react to QS signals and affect QS signaling itself. In _P. aeruginosa_, efflux pumps like MexAB-OprM influence the internal levels of QS signaling molecules such as AHLs, establishing a feedback loop. Research has shown that the overexpression or chemical induction of the MexCD-OprJ efflux pump in _P. aeruginosa_ can attenuate QS signaling [153; 154]. This interaction guarantees that QS signals are maintained at appropriate quantities to coordinate collective bacterial activities successfully.
## 4 Results and Discussion
#### Discussion  
This research focuses solely on bidding techniques that combined renewable energy sources while minimizing the uncertainties and disadvantages associated with wind energy. In addition, a statistical model for calculating the market clearing price (MCP) in the presence of wind energy sources is proposed. The Weibull distribution of probability is used to tackle wind speed uncertainties, which are then converted into wind power. Furthermore, the KDM process is used to reduce the number of wind power measurements. Moreover, the variability of renewable energy is calculated in terms of overestimation and underestimation. This task is accomplished using the OGSA optimization technique. The model and method introduced in this study provide a comprehensive approach to investigating the issue of supplier profit maximization. These protocols can assist electricity market participants in successful implementation of bidding policies and maximization of their individual profits by enhancing their strategic advantage in electricity markets.
# SA-GAN: Stain Aclimation Generative Adversarial Network for Histopathology Image Analysis
Thus, it is important to condense the intra-laboratory variations between train and test parts of a dataset for training efficient deep models. In this context, so far, several automated stain normalization techniques [11; 12; 13; 14] have been developed to standardize the staining inconsistencies in histopathology images. Such techniques could be used as pre-processing strategies for cancer classification [11; 12] and detection [13; 14] models to improve the accuracies. However, previously proposed stain normalization algorithms suffer from the errors induced by color channel independence assumption. Moreover, existing methods address the problem of stain normalization by mapping images of a given dataset to a single reference image selected from the target domain. This paper aims to identify whether the color matching algorithms can build an efficient mapping between the two domains by learning the color distribution of whole dataset, instead of relying on a single image. Based on this objective, we model the stain normalization task as an image-to-image translation task. In the normalization mechanism, color patterns of source domain (training data) are transferred to target domain (test data) while structural contents remain preserved. In this study, we designed a robust generative adversarial network (GAN) based stain transfer strategy, named SA-GAN, which learns the color distributions of entire domain as well as color statistics of a target image instead of just relying only on a single image. The SA-GAN, in a nutshell, contains one generator and two discriminators. The generator model generates images. Among two task-specific discriminators, one enforces the generated image to have the correct color appearance and the second discriminator enforces the texture to be maintained. The SA-GAN network is jointly trained with input training  
Figure 1: Stain variations in H&E-stained histopathology images. Images are taken from colon cancer dataset [5]. images and a novel metric called color attribute constraint metric extracted from a selected single reference image. Our proposed SA-GAN overcomes the domain adaptation issue by transferring stains across datasets (which describe a similar pathology, having different staining properties). By normalizing the new test data according to training data domain, test images will get color appearance of training images with conserved texture properties. After normalization, feature differences between two datasets become minimized. It is now expected that a classification model trained on training data would be fit for new test data. The designed scheme obtained robust performance in terms of structure preserving and color transferring of histopathology images. The remainder of this paper is organized as follows: Section 2 presents the literature review. A detailed description of the proposed methodology is given in Section 3. Section 4 contains data description, evaluation procedure, results, and discussion. Section 5 concludes the paper.
## 2 Materials and Methods  
A schematic of the Tm-doped fiber laser setup mode-locked by a NOLM is shown in Figure 1 and has been described in a previous paper [6]. The proposed oscillator consists of two loops combined by a 2 \(\times\) 2 fused polarization-maintaining (PM) fiber coupler with a splitting ratio of 90:10. A 0.14 m-long PM, highly thulium-doped fiber (Coherent, PM TSF-5/125) was used as the gain medium in the main loop. The active fiber was pumped in a co-propagation scheme through a 1560/2000 wavelength-division multiplexing (WDM) coupler by a home-built Er/Yb-co-doped all-fiber laser (EYDFL) with a continuous-wave (CW) output power of up to 1.6 W at 1564 nm. A polarization-sensitive fiber optic isolator (ISO) was used to ensure unidirectional operation. The output coupler (OC) was a 1 \(\times\) 2 PM fiber coupler that extracted 30% of the intracavity power.  
The right loop of the developed TDFL acts as a fast artificial saturable absorber (NOLM). This nonlinear fiber loop, which is based on the optical Kerr effect, causes a phase-shift difference for counter-propagating pulses in the loop [30]. The counter-propagating pulses interfere with each other during propagation through the central fiber coupler, and hence the transmission of the loop depends on the amplitude of the pulse launched into the NOLM, the loop length, and the asymmetric splitting ratio of the fiber coupler [31]. Almost the entire laser cavity was fusion-spliced using single-mode, PM, PANDA-style fibers and components. A single-mode, non-PM, ultra-high numerical-aperture fiber (UHNA4) with a core/clad diameter of 2.2/125 \(\upmu\)m, numerical aperture of NA = 0.35, and group velocity dispersion of \(\beta_{2}\) =93 ps\({}^{2}\) km\({}^{-1}\)[32] was employed as a dispersion compensation fiber. This was the only non-PM component in the entire oscillator setup. Apart from the dispersion-compensation fiber (DCF), a passive, anomalous-dispersion PM1950 fiber was used in the NOLM to ensure a sufficient phase shift difference between the counter-propagating pulses in the loop for a stable mode-locking operation.  
Owing to the large difference in the mode field diameters between the small-core UHNA4 fiber and the PM1950, a thermal core expansion method [33] was adopted for fiber joint formation. After optimization of the filament fusion-scanning process across the splice joint, a laser power transmission of 89% was achieved for the light propagation from the PM1950 (7.2 \(\upmu\)m core diameter) to the UHNA4 (2.2 \(\upmu\)m core diameter) fiber. An in-line polarization controller (PC) inserted onto the DCF was employed to adjust the intracavity polarization state and start the mode-locking operation. In the experiment, the DCF was 5.58 m long. The entire length of the laser cavity was \(\sim\)13.2 m, while the total net dispersion was adjusted to \(-\)0.016 ps\({}^{2}\).
# A Compendium of Parameterized Problems at Higher Levels of the Polynomial Hierarchy
## 3 Propositional Logic Problems
### Weighted Quantified Boolean Satisfiability for the \(k\)-\(*\) Classes  
Consider the following variants of \(\Sigma_{2}^{\mathrm{P}}[k*]\)-WSat, most of which are \(\Sigma_{2}^{\mathrm{P}}[k*]\)-complete.  
\(\Sigma_{2}^{\mathrm{P}}[k*]\)-WSat(\(\mathrm{3 DNF}\))  
_Instance:_ A quantified Boolean formula \(\phi=\exists X.\forall Y.\psi\) with \(\psi\in\mathrm{3 DNF}\), and an integer \(k\).  
_Parameter:_\(k\).  
_Question:_ Does there exist a truth assignment \(\alpha\) to \(X\) with weight \(k\), such that \(\forall Y.\psi[\alpha]\) evaluates to true?  
**Complexity:**\(\Sigma_{2}^{\mathrm{P}}[k*]\)-complete [7,8].  
\(\Sigma^{\mathrm{P}}_{2}[k*]\)-\(\mathrm{WSat}^{\leq k}\)  
_Instance:_ A quantified Boolean formula \(\phi=\exists X.\forall Y.\psi\), and an integer \(k\).  
_Parameter:_\(k\).  
_Question:_ Does there exist an assignment \(\alpha\) to \(X\) with weight at most \(k\), such that \(\forall Y.\psi[\alpha]\) evaluates to true?  
**Complexity:**\(\Sigma^{\mathrm{P}}_{2}[k*]\)-complete [7; 8].  
\(\Sigma^{\mathrm{P}}_{2}[k*]\)-\(\mathrm{WSat}^{\geq k}\)  
_Instance:_ A quantified Boolean formula \(\phi=\exists X.\forall Y.\psi\), and an integer \(k\).  
_Parameter:_\(k\).  
_Question:_ Does there exist an assignment \(\alpha\) to \(X\) with weight at least \(k\), such that \(\forall Y.\psi[\alpha]\) evaluates to true?  
**Complexity:**\(\Sigma^{\mathrm{P}}_{2}[k*]\)-complete [7].
## 2 SGP System Description
### Enhancements  
Several theoretical enhancements are possible. Sunrises can be predicted if early measurements are taken. This is because the light intensity increases gradually over some time interval before one passes the threshold. If a light intensity measurement is taken that is above the night baseline value but yet below the threshold then this is a sign that a sunrise is approaching soon and the sample rate can be dynamically increased. This is illustrated by Figure 4(a) which shows an authentic intensity plot obtained using a webcam. Clearly the intensity rises for about 20 min before the sun breaks.  
However, it may be more difficult to get a pre-warning of a sunset in this way as this will suddenly drop below the threshold value. One way to overcome this is to observe additional features. If the light sensor is capable of capturing color spectrum information then additional information can be exploited to better predict sunrises and sunsets. For instance, the CCD sensors in digital cameras are capable of detecting color as well as intensity. This is because sunsets and sunrises often are characterized by large changes in hue which affect entire scenes. Such changes in hue occur prior to sunsets and thus a detection in hue change can be used to predict an upcoming sunset. This is illustrated in Figure 4(a) which presents a 24 h plot obtained using a webcam. The hue \(h(r,g,b)\) of each pixel is computed from the \(r\), \(g\) and \(b\) (red, green and blue) components as follows:  
\[h(r,g,b)=atan2\left(2r-g-b,\sqrt{3}(g-b)\right) \tag{8}\]  
The steady line shows the overall image intensity and the other line illustrates overall image hue. Clearly, the hue is changing dramatically just before the sunrise and sunset. In fact, these can be seen as the two peaks in the hue plot. However, the exploitation of such features is a topic of future research.  
Figure 4: (**a**) Gradual increase in intensity towards sunset; (**b**) Sudden hue changes predict sunrises and sunsets.