esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Calibration of Material Coefficients Within the Digital Models Based on Measured Results  
The results from the measurement survey were compared with those obtained by applying absorption coefficients to the surfaces of both digital models. This comparison, known as the tuning or calibration process, involves minimizing the differences between measured and simulated values by adjusting the absorption coefficients in the digital model [34]. The scattering coefficients were assumed to be equal to 0.01 at all frequencies since the surfaces of the halls are smooth and geometrically constructed in the digital models. The scattering coefficients would play a role in case of irregular surfaces that are digitally simplified with a representation of a flat 3D-face.  
The primary acoustic parameter considered in this process is the reverberation time (T20), as it is more reliable than other parameters such as clarity. This is because T20 is less sensitive to the precise position of the receiver. In contrast, calibrating C50 is very challenging, as this parameter varies significantly depending on the source and receiver positions.  
The tuning was conducted to ensure that the discrepancy between simulated and measured T20 values remained within 5% across all frequency bands. However, minor variations between the measured data and the calibrated model are attributed to physical  
Figure 5: Measured results related to reverberation time T20 (**a**) and speech clarity index C50 (**b**).  
factors that influenced the survey results. These small discrepancies may be due to environmental elements such as temperature variations [35], which mainly impacts the high frequencies as they are more sensitive to being absorbed when the temperature is low.  
Figure 6 shows the comparison between the measured and simulated T20 values for both Sisto V Hall and Aula Magna. The calibration process was iterated until the adjusted values fell within the just noticeable difference (JND), corresponding to 5% of the value.  
The values of the absorption coefficients of the final calibration used in both digital models are summarized in Table 2.
## 4 Reconfigurability Judgment Analysis and Autonomous Control Method
### Alignment Autonomous Control Method  
The flow chart of the alignment autonomous control can be seen in Figure 3.  
Combining Equations (25)-(27), and (30), the alignment autonomous control method of HPDME-ASVIS can be designed as:  
Step 1: the device displacement, rotational speed, air spring pressure, and other sensor signals are collected, and the initial alignment state is calculated;  
Step 2: the alignment FRD is calculated by analyzing the reconfigurability.  
Step 3: if \(\gamma=1\) go to Step 4; if \(\gamma=0.9\) go to Step 5; if \(\gamma=0.1\) go to Step 6; otherwise, HPDME-ASVIS must be shut down immediately.  
Step 4: the alignment control method under hard constraint is executed until the alignment state reaches the given control precision range, end;  
Step 5: by solving Equation (28), the smallest adjustment \(\left[\varepsilon^{*}_{\min},\varepsilon^{*}_{\max},\varepsilon^{*}_{x_{c}}, \varepsilon^{*}_{\beta},\varepsilon^{*}_{z}\right]\) of each soft constraint boundary is obtained, and \(\left[\varepsilon^{\max}_{P_{\min}},\varepsilon^{\max}_{p_{\max}},\varepsilon^ {\max}_{x_{c}},\varepsilon^{\max}_{\beta},\varepsilon^{\max}_{z}\right]\) in Equation (26) is replaced to modify the hard constraint boundary. The alignment control method based on a new constraint boundary is used to control the alignment state. Then go to Step 4 after modifying the hard constraint boundary;  
Step 6: the emergency protection device is activated, end.  
Comparing Figure 3a,b, the advantages of the autonomous control method can be seen intuitively. When the alignment system under hard constraints is judged as uncontrollable, the feasible solution to the problem can be broadened by modifying the control algorithm and expanding the hard constraint boundary automatically.
# Current-Assisted Single Photon Avalanche Diode (CASPAD) Fabricated in 350 nm Conventional CMOS
## 3 Results and Discussion  
### Electric Field Simulation  
Device physics simulations were done in Silvaco ATLAS to verify the CASPAD functionality and to determine the breakdown region. The simulated plot with electric field contours at the central \"SPAD\" (single-photon avalanche diode) junction can be seen in Figure 3. The electric field in the junction between the N-well and the p substrate is high enough to cause an avalanche breakdown when there is an electron. It should be noted that the vertical junction (shown in Figure 3) has lower field than the lateral junction. This is expected to not have a major effect in photon detection probability because most of the photo-electrons generated in the detection volume are guided toward the p\({}^{+}\) anode region by the drift field and these photo-electrons are more likely to go to the lateral junction. The capacitance of the p-n junction is estimated to be around 1 fF from device physics simulations.  
Figure 2: Dimensions of the CASPAD device.
## 4 Methodology
### Clustering  
In this phase, we use the embedding matrix \(Z\) that we obtained in the embedding-vector creation phase. We apply the fuzzy c-means algorithm on \(Z\) to obtain the membership matrix \(W\), where \(W\in[0,1]^{n\times k}\), \(k\) is the number of microservices or clusters and \(\sum_{j=1}^{k}W_{ij}=1\) for every \(i\). The fuzzy c-means algorithm determines \(W\) and a centroid matrix \(\Gamma\), where \(\Gamma\in\mathbb{R}^{k\times p}\)) by finding the local minimum of the following:  
\[\sum_{i=1}^{k}\sum_{j=1}^{n}W_{ji}||Z_{j}-\Gamma_{i}||_{2}^{2} \tag{8}\]  
Here, the membership score (\(W_{ji}\)) is a metric that represents the degree of association between a class \(j\) and a microservice \(i\). The value of \(W_{ji}\) ranges from 0 to 1, inclusive, and it is computed for every class and microservice. After computing the membership score, we assign each class to a microservice if its score is higher than the maintainability threshold. The maintainability threshold is a predefined value that determines the minimum membership score a class must have to belong to a microservice. It is worth noting that if a class has membership scores higher than the threshold for two or more microservices, it can be assigned to multiple microservices. The ability to assign a class to multiple microservices is an important feature because it allows us to minimize communication between microservices. By duplicating a class in multiple microservices, the microservice does not have to make an API call to another microservice. However, the maintainability threshold must be carefully chosen to avoid too much redundancy, which can negatively impact performance and scalability.
## 3 Results
### Trypsin Digestion  
Using Peptide Cutter [35], in silico trypsin digestion was conducted on the Mut2 amino acid sequence. Proteins were digested using 1 mg/mL trypsin at a 15:1 sample:enzyme ratio. Trypsin cleaves the carboxyl side of lysine and arginine, except when either of the amino acids is followed by a proline [14]. The in silico analysis, using Peptide Cutter software, of the digestion of the mutant 8S globulin was done to determine the digestion sites and the peptides that were produced upon digestion. The lactostatin peptide (IIAEK) was released at position 187 near the C-terminal of the K residue at position 186.  
Figures 7 and 8 show the SDS-PAGE of the undigested and digested Wt and Mut2 8S\(\alpha\) globulin, respectively. Based on the intensity of the bands corresponding to the 49 kDa protein of interest from the undigested, there is a decreasing concentration of the 8S\(\alpha\) globulin as digestion progresses. Comparing the Wt and Mut2 digests, it is also notable to mention that the Mut2 digests had more intense bands compared to the Wt digests, considering both had the same concentration (0.50 mg/mL) among all digests.  
Figure 6: Elution profile of the Wt and Mut2 8S\(\alpha\) globulin after hydrophobic interaction chromatography (HIC) using Biorad butyl toyoperal. Red—Wt 8S\(\alpha\) globulin; blue—Mut2 8S\(\alpha\) globulin.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Sample** & **Retention Time (min)** & **Elution Volume (mL)** \\ \hline
**Wt** & 92 & 23 \\
**Mut2** & 88 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Retention time and elution volume of Wt and Mut2 8S\(\alpha\) globulin after HIC using Biorad butyl toyoperal.  
Figure 7: SDS-PAGE of undigested and digested (**A**) Wt 8S\(\alpha\) globulin and (**B**) Mut2 8S\(\alpha\) globulin. Lanes—1: protein marker, 2: undigested sample, 3: 2-h digest, 4: 6-h digest, 5: 12-h digest, and 6: 24-h digest.  
Densitometric analysis was conducted on both undigested and digested Wt and Mut2 8S\(\alpha\) globulin proteins to further analyze the effect of digestion on the intensity of the bands corresponding to 49 kDa. In both analyses, the peak corresponding to the 49 kDa band is decreasing as digestion hour lengthens. Consistent with the SDS-PAGE results mentioned previously, the peaks in Mut2 digests were higher than those of the Wt digests.
# Uncertainty Analysis and Improvement of Propellant Gauging System Applied in Space
## 3 Measuring Uncertainty Analysis and the Modified Model
### Measuring Uncertainty Analysis
#### 3.1.1 Monte Carlo Method  
This section mainly introduces the detailed application of the Monte Carlo Method to the error analysis. All the effect factors are well considered in the measurement uncertainty of the propellant volume.  
The total volume of the tank is assumed to be \(V_{0}\), and \(10^{6}\) values of \(V_{0}\) are obtained by a random normal distribution (\(V_{01},V_{02},V_{03},\cdots,V_{0n};\ n=10^{6}\)), the mean value of which is \(V_{0}\), and the standard error is set according to the error. The uncertainty of other effect factors is set to be 0 when the uncertainty of the tank total volume is discussed. The \(V_{Ln}\) can be calculated according to Equation (7) when the uncertainty of all the effect factors equals 0.  
The accuracy of each independent factor should first be settled when calculating the propellant volume. A normal distribution is applied to each factor, and the mean value of the uncertainty is 0. Then, the Monte Carlo Method can be used to calculate the uncertainty after each independent factor has its own uncertainty model.
## 2 Molecular Mechanisms of Antimicrobial Agents Recalcitrance in Clinically Important Biofilms
### Nutritional Limitation and Stress Responses
#### 2.2.3 The Stringent Response  
The stringent response is an adaptive response allowing bacteria to survive under nutrient-deprived conditions. It was first observed under amino acid starvation conditions. Briefly, in response to amino acid starvation, bacteria adjust metabolic activities by rapidly initiating a metabolic switch from protein synthesis to amino acid synthesis. Ribosomal protein genes are downregulated, while amino acid biosynthetic genes are upregulated. The signaling pathway is initiated and controlled by the second messenger (p)ppGpp pair of guanosine penta and tetra phosphate, PPPGpp and ppGpp, respectively. The intracellular concentration of the (p)ppGpp alarmone is regulated by RelA/SpoT Homologue (RSH) enzymes, which synthesise (p)ppGpp from ATP (Adenosine triphosphate) and GTP (Guanosine-5\({}^{\prime}\)-triphosphate)/GDP (Guanosine diphosphate) and hydrolyze (p)ppGpp to GTP/GDP and pyrophosphate, respectively. Unlike RelA, which has only synthetic activity stimulated by amino acid deprivation, SpoT is a bifunctional enzyme with both (p)ppGpp synthetic and hydrolytic activities acting as a signaling hub protein that senses and modulates responses to multiple nutritional stresses other than amino acid starvation such as fatty acid, carbon, iron, and phosphate-depletedenvironments [59; 60; 61]. Amino acid starvation leads to increased levels of deacylated tRNAs in the cytosol. The binding of these uncharged tRNAs to the ribosomal A-site is the signal that recruits RelA synthetase, which upon binding to the 50S ribosomal subunit is being activated and proceeds to (p)ppGpp alarmone synthesis. Further, the alarmone binds to RNA polymerase and modulates gene transcription [59; 62], directing the cellular metabolic resources to amino acid synthesis and restoring the normal levels of aminoacylated tRNAs.  
Since biofilm grown under nutrient starvation conditions is much less susceptible to a variety of differently acting antibiotics, the logical hypothesis that (p)ppGpp alarmone disrupts a killing mechanism common to diverse agents such as oxidative damage was launched and tested by [PERSON] et al. [56]. In support of the above hypothesis and molecular mechanism underlying the stringent response [59] are the findings that (p)ppGpp alarmone-deficient biofilms formed by a _P. aeruginosa_\(\Delta\)RelA \(\Delta\)SpoT double-knockout strain were more susceptible than wild-type biofilms towards antibiotics from several different classes including ofloxacin, meropenem, colistin, and gentamicin [56].  
The second messenger (p)ppGpp is not only a transcriptional regulator but is also involved in the emergence of bacterial persisters [63], as discussed below.
## 5 Experiment and Analysis
### Evaluation of Position Performance
#### 5.2.3 Evaluation on the Self-Collected Sequence  
In Section 5.2.2, in order to add dynamic objects to the original Euroc dataset, we fix the camera in an indoor environment and record a sequence, including a pedestrian walking in front of the camera. In this section, we will use this sequence to evaluate the performance of Dynamic-DSO and DSO in the actual dynamic environment by comparing the point cloud maps and camera trajectories.  
The camera trajectories and point cloud maps estimated by Dynamic-DSO and DSO are shown in Figure 15. Although the camera remains stationary, there is relative motion between the pedestrian and the camera. As shown in Figure 15a, DSO is cheated by the dynamic pedestrian, which leads to the wrong judgment of the camera state and the large drift of the estimated trajectory. Compared with  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**High Dynamic** & \multicolumn{3}{c}{**DSO**} & \multicolumn{3}{c}{**Dynamic-DSO**} & \multicolumn{2}{c}{**Rmse**} \\ \cline{2-7}
**Sequence** & **Rmse** & **Mean** & **Std** & **Rmse** & **Mean** & **Std** & **Improvement** \\ \hline V101\_syn & 1.06 & 0.83 & 0.65 & 0.17 & 0.16 & 0.07 & 84\% \\ V201\_syn & 0.51 & 0.39 & 0.34 & 0.15 & 0.13 & 0.08 & 71\% \\ V202\_syn & 1.72 & 1.52 & 0.81 & 0.24 & 0.21 & 0.11 & 86\% \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of Absolute Pose Error (APE) for translation part in modified Euroc dataset. (Unit: m).  
Figure 14: Boxplot of the trajectory error between Dynamic-DSO and DSO in V101_syn, V201_syn and V202_syn.  
Figure 13: Positioning results comparison of DSO and Dynamic-DSO in three axes x, y, z. GT means the groundtruth. Comparison in V101_syn, V201_syn and V202_syn is shown from left to right.  
DSO, the camera trajectory estimated by Dynamic-DSO is almost at a point without large-scale drift after suppressing the interference of the pedestrian.  
In order to further explain the impact of dynamic object on algorithm, some keyframes selected during the algorithm running are shown in Figure 16. As shown in Figure 16a, due to the interference of dynamic objects, most of the points that DSO chooses to track fall on the dynamic pedestrian. Although the camera is fixed on the desktop, DSO mistakenly judges that the camera is moving due to the relative motion between the camera and the pedestrian. In contrast, Dynamic-DSO successfully detects the position of the pedestrian and selects points in static region to track.
## 4 Aeroelastic Stability and Response Analyses
### Test Case 2
(**a**) Root locus of the integrated system. (**b**) Focus on the rigid body and sloshing modes. The second gust response analysis is performed at \(U_{\infty}=21.1\,\mathrm{m/s}\) (a speed 1% higher than the flutter speed of the frozen fuel model and 97% of the body freedom flutter mode critical speed of the lateral sloshing model), with a gust intensity of \(w_{g_{a}}=0.2\,\mathrm{m/s}\). In this flight condition, the model with linear sloshing is unstable due to the critical bending-torsional mode. Figure 13 compares the responses of the four considered models in terms of local angle of attack and Figure 13b shows the comparison in terms of the response of the modal variable associated with the first bending mode. The frozen case results in an unstable response, whereas the case of vertical sloshing presents a damped response. The lateral sloshing model exists in an unstable condition due to the critical bending-torsional antisymmetric mode. However, the applied gust, as symmetric input, is unable to trigger this dynamics. In addition, the body freedom flutter occurs at a higher velocity and this leads to a more damped response. The addition of vertical sloshing further dampens the response of the full sloshing case. The last analysis is performed in the body freedom flutter condition at \(U_{\infty}=22.5\,\mathrm{m/s}\) (with speeds 4% and 8% higher than the body freedom flutter mode critical speeds of the frozen and lateral sloshing models, respectively) and \(w_{g_{a}}=0.2\,\mathrm{m/s}\). The local angle of attack and the first mode responses are shown in Figure 14a,b comparing the four considered cases. The linear sloshing model leads to a flutter response with divergent exponential envelope. The same occurs more slowly for the linear sloshing response. In contrast, vertical sloshing helps prevent the response from growing indefinitely, favoring the onset of limit cycle oscillations (LCOs). With regard to full sloshing, the oscillations are
# Evaluating GNSS Receiver Resilience: A Study on Simulation Environment Repeatability
## 1 Introduction
This methodology enables repeatable experiments, allowing for a rigorous assessment of the receiver software's response to various interference types and power levels. Our findings reveal that, even within a controlled environment, the evaluation of interference effects presents significant challenges. We demonstrate that accurate assessments require a statistical approach, highlighting the inherent variability in receiver responses. This work aims to shift the paradigm in GNSS interference research, advocating for a more systematic and statistically robust methodology. By providing a controlled environment and repeatable measurements, this study sets a foundation for a more standardized approach to assessing GNSS receiver vulnerability [28]. The main contribution of this work lies in the proposed method for a statistically sound assessment of GNSS module response to disturbances. By applying this method to an extensive dataset acquired through statistically sound testing in a controlled environment, we clearly demonstrate the inherent unpredictability of GNSS receiver software's responses, particularly when operating within the power limits of jamming signals. The publicly available dataset further supports this finding and is anticipated to be an invaluable resource for researchers developing and validating jamming detection and mitigation techniques, ultimately fostering a more statistically robust framework for evaluating GNSS receiver vulnerability to interference. The article is structured in six sections. The Section 2 lays the groundwork by discussing the fundamentals of interference and its detection in GNSS receivers. The Section 3 details the methodology for statistically evaluating receiver responses. The Section 4 describes our laboratory setup, including its requirements and limitations. The Section 5 presents key measurement results that illustrate the challenges of interference assessment. Finally, the Section 6 discusses our findings and offers recommendations for future research in this area.
## 2 Formal Analysis
### Analytical Structure of the Cross-Section
To identify the associated frequencies, we take the Fourier transform of \(\sigma^{(1)}(\omega,\tau)\) with respect to the delay \(\tau\) between the attosecond XUV pulse and the optical dressing pulse:  
\[\tilde{\sigma}^{(1)}(\omega,\omega^{\prime})=\int\limits_{-\infty}^{\infty}d \tau\;\sigma^{(1)}(\omega,\tau)e^{i\omega^{\prime}\tau}. \tag{23}\]  
From Equation (20) it follows that \(\tilde{\sigma}^{(1)}(\omega,\omega^{\prime})\) can be written as:  
\[\tilde{\sigma}^{(1)}(\omega,\omega^{\prime})=\frac{2\pi\omega}{c}\sum_{f} \Big{(}\tilde{\mathcal{F}}_{f}(\omega,\omega^{\prime})-\tilde{\mathcal{F}}_{ f}(-\omega,\omega^{\prime})+\tilde{\mathcal{F}}_{f}^{*}(\omega,-\omega^{ \prime})-\tilde{\mathcal{F}}_{f}^{*}(-\omega,-\omega^{\prime})\Big{)}, \tag{24}\]  
where  
\[\tilde{\mathcal{F}}_{f}(\omega,\omega^{\prime})=\int\limits_{-\infty}^{\infty }d\tau\tilde{F}_{0f}(\omega,\tau)F_{f0}(\tau)e^{i(\omega^{\prime}-\omega)\tau}. \tag{25}\]  
By making use of the analytical results provided in the appendix, one can see that the function \(\tilde{\mathcal{F}}_{f}(\omega,\omega^{\prime})\) has the following structure:  
\[\tilde{\mathcal{F}}_{f}(\omega,\omega^{\prime})=\sum_{kj}\frac{A_{kj}(\omega, \omega^{\prime})}{i(\Delta_{k}-\Delta_{j}+\omega^{\prime})}, \tag{26}\]  
where the functions \(A_{kj}(\omega,\omega^{\prime})\) have no poles as a function of \(\omega^{\prime}\). Therefore, the XUV one-photon cross-section \(\tilde{\sigma}^{(1)}(\omega,\omega^{\prime})\) has poles, as a function of \(\omega^{\prime}\), at positions \(\Delta_{k}-\Delta_{j}\), in which \(\Delta\)'s are combined in a way allowed by dipole selection rules. It is remarkable that some of these poles are entirely independent of the dressing-laser photon energy \(\omega_{d}\), and some of the other poles depend only on \(\omega_{d}\). We explore this in more detail in the following section.