esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing User Experience with Visual Controls for Local Differential Privacy
## 3 Differential Privacy Preliminaries  
Differential privacy is a rigorous mathematical framework that quantifies the privacy guarantees of an algorithm. It ensures that the output of an algorithm only reveals a little about any individual in the dataset [2]. The fundamental equation of DP is \(Pr[A(D)\in S]\leq exp(\varepsilon)*Pr[A(D^{\prime})\in S]+\delta\), where \(A\) is the algorithm, \(D\) and \(D^{\prime}\) are two datasets that differ by one entry, \(S\) is any subset of possible outputs, \(\varepsilon\) (epsilon) is the privacy parameter, and \(\delta\) (delta) is a small probability of failure. The privacy budget, denoted by \(\varepsilon\), quantifies the trade-off between privacy protection and data utility.  
There are two key properties for DP: (1) _Sequential Composition_: For \(k\)\(\varepsilon\)-differentially private mechanisms applied sequentially as \(M(D)=(M_{1}(D),M_{2}(D),\ldots,M_{k}(D))\), where \(M(D)\) represents the output after noise is added, the result satisfies \((\varepsilon_{1}+\varepsilon_{2}+...+\varepsilon_{k})\)-DP. (2) _Parallel Composition_: If these mechanisms are applied to disjoint subsets of the data, the result satisfies \(\max(\varepsilon_{1},\varepsilon_{2},...,\varepsilon_{k})\)-DP [11].  
**Laplace Mechanism.** The Laplace mechanism is one of the foundational techniques in DP, designed to achieve _pure differential privacy_. It works by adding noise drawn from a Laplace distribution to the output of a function. The Laplace mechanism is defined as \(M(D)=f(D)+Lap(\Delta f/\varepsilon)\), where \(M(D)\) represents the output after noise is added, \(f(D)\) is the actual function result, \(\Delta f\) is the sensitivity of the function, \(\varepsilon\) is the privacy parameter,and \(Lap(b)\) is the Laplace distribution with scale parameter \(b\). The probability density function of the Laplace distribution is \(Lap(x|b)=\frac{1}{2b}\exp(-\frac{|x|}{b})\), where \(x\) is the location parameter (the center of the distribution), and \(b>0\) is the scale parameter.  
**Gaussian Mechanism.** The Gaussian mechanism provides more flexibility by introducing the concept of _approximate differential privacy_ through the inclusion of a failure probability parameter \(\delta\). The Gaussian mechanism is defined as \(M(D)=f(D)+\mathcal{N}(0,\sigma^{2})\), where \(\mathcal{N}(0,\sigma^{2})\) is the Gaussian distribution with mean 0 and variance \(\sigma^{2}\), and \(\sigma\) is calibrated based on the sensitivity of the function and the desired privacy parameters. The standard deviation \(\sigma\) is calculated as \(\sigma=\frac{\sqrt{2\ln(1.25/\delta)}\Delta f}{\varepsilon}\), where \(\Delta f\) is the \(L2\) sensitivity of the function. Gaussian mechanism's ability to maintain data utility while preserving privacy makes it especially valuable for iterative processes and large-scale statistical analyses.
## 4 Performance  
### Experimental Setup  
In order to evaluate the performance of the proposed ACT, we simulated CODA, CODA with compression at the source node (CODA-Comp), CRRT and the ACT together by using TOSSIM, which is a simulator for TinyOS. TinyOS is an operating system developed for event-based sensor  
Figure 13: Data Compression and Energy Consumption.  
networks at UC Berkeley. TOSSIM provides a simulation environment that simulates a real sensor network with TinyOS. An application in TinyOS consists of components from each network layer and hardware. The application running on TOSSIM can be run on real sensor nodes such as a micaz or a telos. Thus, we can say that our implemented simulation reflects the real world. Table 1 lists the specifications of simulation environment and Table 2 lists the specifications of the simulated sensor mode. Hundred sensor nodes sensed data at a random sensing interval between 100 ms to 1,000 ms and transferred the data to the sink node with a packet containing 10 data samples. We selected three performance matrixes: efficiency, fairness, QoD (quality of data), and energy [7, 13].
## 3 Optimization Problem and Method for TMD Design  
Taking the numerical nonlinear constrained optimization problems in engineering into account, there are many methods that are available in the literature. Basically, these optimization methods can be divided into two types. The first are gradient-based optimization algorithms that use the first and second derivatives. The second are the direct search algorithms, which do not need derivatives. Although direct search algorithms reach the solution slower than gradient-based algorithms, they are more robust against the noise in the objective function and its constraints [23].  
The optimization criterion is identified by the design purpose. For example, displacement minimization improves safety and reduces deformations in the structure, while acceleration minimization prevents damage to non-structural elements. Acceleration minimization also reduces the disturbing vibration, which affects the level of comfort in the structure and reduces the base shear force.  
To determine the optimal TMD parameters for the response reduction of a cantilever beam, the closed form of the statistical objective functions (\(f_{1}\)), which denotes the mean square of acceleration of the \(n\)th freedom of the beam and the mean square of the displacement (\(f_{2}\)) of the \(n\)th freedom of the beam, can be expressed as follows:  
\[f_{1}(k_{d},m_{d}\,,c_{d}\,,)=\sigma^{2}_{AAn} \tag{12}\]  
\[f_{2}(k_{d},m_{d}\,,c_{d}\,,)=\sigma^{2}_{Dn} \tag{13}\]  
The following can be used to determine the optimization constraints:  
\[0\leq k_{d}\leq\overline{k}_{d} \tag{14}\]  
\[0\leq m_{d}\leq\overline{m}_{d} \tag{15}\]  
\[0\,\leq c_{d}\leq\overline{c}_{d} \tag{16}\]  
The upper limits of the coefficients of TMD stiffness, mass, and damping are represented by \(\overline{k}_{d}\), \(\overline{m}_{d}\), and \(\overline{c}_{d}\) in Equations (14)-(16), respectively.
## 5 Testing Plans
### Thermal Vacuum Test  
The purpose of the thermal vacuum (T-VAC) test is to simulate a space environment to test whether the CubeSat can withstand thermal loading during a series of hot and cold cycles. A thermal bake-out test is performed before shipping the CubeSat to the launcher to remove any volatilities present on the CubeSat. The thermal vacuum test can identify and expose design issues before components are assembled into larger systems. An HVC-3500 thermal vacuum system will be used to perform the T-VAC testing. Usually, the thermal vacuum system operates at high temperatures ranging from \(-40\) to 100 \({}^{\circ}\)C, where the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency (Hz)** & **Amplitude (G)** & **Sweep Rate (Oct/min)** \\ \hline
10–2000 & 0.5 & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 16: Low-level sine sweep test profile [3].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**HTV** & \multicolumn{4}{c}{**Dragon**} & \multicolumn{2}{c}{**Cygnus**} \\ \hline
**Frequency (Hz)** & **PSD (g\({}^{2}\)/Hz)** & **Frequency (Hz)** & **PSD (g\({}^{2}\)/Hz)** & **Frequency (Hz)** & **PSD (g\({}^{2}\)/Hz)** \\ \hline
20 & 0.005 & 20 & 0.02 & 20 & 0.004 \\ \hline
50 & 0.02 & 200 & 0.02 & 30 & 0.004 \\ \hline
120 & 0.031 & 2000 & 0.001 & 70 & 0.015 \\ \hline
230 & 0.031 & & & 150 & 0.015 \\ \hline
1000 & 0.0045 & & & 2000 & 0.006 \\ \hline
2000 & 0.0013 & - & - & - & - \\ \hline Overall (grms) & 4.05 & Overall (grms) & 3.2 & Overall (grms) & 2.44 \\ \hline Duration (s) & 60 & Duration (s) & 60 & Durations (s) & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Possible random vibration testing profiles [3].  
Figure 20: Vibration test sequence.  
system is prepared with a chiller system (LABFREEZ HRHC-635N) to keep the refrigerant cooled during the cold cycles.  
The different ranges in temperature shown in Table 18 shall be considered to perform the T-VAC testing for different launch vehicles and conditions. These include the thermal environment of the three possible JAXA launch vehicles [3] inside the ISS and outside the ISS while still in the pod. A preliminary test profile from QB 50 [14] will be used to test the mass model. An arbitrary soak time is assumed, which will be verified after the mass model testing for the actual CubeSat test. In addition, the pressure input for the actual CubeSat test will also be confirmed post the mass model testing.
# Low-Altitude Sensing Model: Analysis Leveraging ISAC in Real-World Environments
## 1 Introduction
### Background
Many models remain at the theoretical stage without real-world validation, making it difficult to achieve the expected results in complex environments. Specifically, the ISAC coverage models proposed in [6; 15] lack real-world testing and cannot guarantee their applicability in low-altitude scenarios. While [19] validated communication channel models through testing, the focus was solely on communication coverage, without addressing the validation of sensing capabilities. This study proposed an innovative experimental strategy, conducting single base station sensing capability tests in a millimeter-wave (mmW) frequency far-field over-the-air (OTA) anechoic chamber, with precise parameter control to validate the single base station model. Additionally, leveraging the world's first commercial mmW ISAC network, we collaborated with leading industry suppliers to set up an outdoor testing platform to verify the effectiveness of the sensing network topology in real-world scenarios. This approach fills the gap in the practical validation of ISAC fusion networks, providing technical support for low-altitude ISAC base station network construction and opening new paths for future research and engineering practices.
# VIIRS Day/Night Band--Correcting Striping and Nonuniformity over a Very Large Dynamic Range
## 3 Results
### Histogram Matching Method Results
Detecting these features can be enhanced with advanced image processing techniques, but destriping should be done first for these techniques to work effectively. Figure 12.2 Nighttime scene of Hurricane Patricia with HMM destriping from 23 October 2015 at 07:45 UTC over the Pacific Ocean and Mexico. The radiances are plotted on a linear scale over the range of 0 to \(5\times 10^{-9}\) W\(\cdot\)cm\({}^{-2}\cdot\)sr\({}^{-1}\) from black to white. The image is 750 by 1200 pixels. Figure 12.3 (**a**) Nighttime scene of Typhoon Vongfong without destriping; and (**b**) after HMM destriping from 7 October 2014 at 17:03 UTC. The image is 656 by 940 pixels and is located near center of swath. Gray scale is linear from 2 \(\times\) 10\({}^{-9}\) to 3.6 \(\times\) 10\({}^{-8}\) W\(\cdot\)cm\({}^{-2}\cdot\)str\({}^{-1}\) from black to white. The examples of imagery here show the qualitative impact of striping, but it is also important to understand the impact quantitatively, especially when considering the impact to processes that require radiometric accuracy. Figure 23 shows the gain corrections used to correct striping Figure 22 as a percentage of the radiance signal. This shows the quantitative impact of striping as well as the quantitative benefits of correction. Note the similarity with Figure 11 that shows the gain correction for the MGS. From this we conclude that most of the striping in bright nighttime DNB images are caused by gain ratio errors that are transferred from LGS to MGS to HGS. Typically striping over 2% is visible in uniform scenes. Many of the detectors in many of the aggregation modes, especially near the edge of the swath, do not meet these criteria. The striping increases toward the edge of scan as would be expected from the imagery. Figure 12.4 Image showing (**a**) image without destriping; and (**b**) after the HMM destriping algorithm is applied. Nighttime image shows sea ice in the Atlantic Ocean off the coast of Antarctica, taken 9 September 2014 at 00:55 UTC. The radiance range is 9.5 \(\times\) 10\({}^{-9}\) to 2.3 \(\times\) 10\({}^{-8}\) W\(\cdot\)cm\({}^{-2}\cdot\)str\({}^{-1}\).
## 2 Materials and Methods
### Ligand Docking
#### 2.5.2 Molecular Dynamics Study  
Molecular dynamics simulations were prepared separately for the MMP-2, MMP-8, and MMP-9 enzymes in a solvent alone (apo form) and when a-tomatine and chlorhexidine molecules were bound to the protein structures (halo form).  
Molecular Dynamics Simulation System Setup  
The preparation of the protein systems for molecular dynamics simulations was done via immersing each protein and protein-ligand complex in a solvent box with dimensions of 10 A \(\times\) 10 A \(\times\) 10 A. The SPC [36; 37] setting was chosen for the water molecules in the solvent box. The SPC setting was preferred to ensure more realistic results. In the SPC setting, the bonds in the water molecules are fixed at 1 A, and the angle between hydrogens is fixed at 109.5 degrees. The solvent box required the addition of inorganic ions to create an exact model of the physiological environment in the mouth. An NaCl ion at a concentration of 0.15 M was added to the solvent box using the Monte Carlo method. A system setup was performed using the OPLS3e force field.  
Molecular Dynamic Simulation Protocols  
The next step was the initiation of molecular dynamics simulations, which involved the study of the motion of atoms in the solvent environment, following rigorous molecular docking calculations. The protein-solvent systems prepared for this purpose were subjected to molecular dynamics simulations separately in halo form and apo form. Molecular dynamics simulations were performed using the Desmond [38] module in Maestro. The simulations were performed as 100 nanosecond (ns) simulations. A relaxation protocol of 2 ns was used. Pressure, temperature, and the particle number were kept constant. The temperature was kept constant at 310 K on a Nose-Hoover thermostat [39], and the pressure was kept constant at a 1.01325 bar on a Martyna-Tobias-Klein [40] barostat. These precise and detailed adjustments ensured complete control over the system and produced reliable and accurate results. A total of nine molecular dynamics simulations of the MMP-2, MMP-8, and MMP-9 enzymes, an apo form for each protein, a tomatine-protein complex, and a chlorhexidine-protein complex were run at the same settings for accurate comparison.
# The Challenges of Advanced CMOS Process from 2D to 3D
## 2 Lithography of Nano-Scaled Transistors
### Challenges in ArF lithography with Multi-Patterning [12]  
#### 2.1.1 Overlay  
Multi-patterning lithography in 10 nm and 7 nm technology nodes has driven the allowed overlay error down to extreme low values. In such cases, high order overlay correction schemes were needed to control the process variability. Meanwhile, the increase of the number of split layers results in an exponential increase of metrology complexity in the total overlay and alignment tree while the process stack includes more hard-mask steps.  
As a result, the set-up and the verification of overlay metrology recipe becomes more critical and a holistic approach that addresses total overlay optimization from process design to process set-up for volume manufacturing are required [13]. Therefore, overlay accuracy has to be improved in order to increase pattern precision. There are some factors e.g., metrology, wafer and mask which impact overlay accuracy. Table 2 presents different approaches to improve overlay accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Node 10 nm** & **Node 7 nm** \\ \hline Line Pitch & 45 nm & 32 nm \\ Hole Pitch & 65 nm & 45 nm \\ Single expose & EUV 0.33 NA & EUV 0.33 NA \\ Hybrid patterning & SADP + EUV cuts & SAQP + EUV cuts \\ ArF patterning & SADP + ArF LELE & SAQP + ArF LELELE \\ \hline \hline \end{tabular}
\end{table}
Table 1: Critical pitches in logic M1 (the first metal layer at back end of the line (BEOL), specially in copper interconnect process) and possible patterning methods (LE: litho + Etch multiple exposures).
# An Integrated Optimization Method of High-Speed Railway Rescheduling Problem at the Network Level
## 2 Literature Review
* The real-world experiments show that the proposed model and algorithm work well, which can reschedule the train timetable and the train set circulation plan integratedly during an emergency. Moreover, the proposed integrated optimization method can be applied to analyze some characteristics of the network, such as the bottleneck sections of the network, the key time of the whole day, and the importance of the depots. The remainder of this paper is organized as follows. In Section 3, we formulate an integrated optimization model of the train timetable and train set circulation plan under the condition of HSR network emergencies. In Section 4, we propose an algorithm based on NSGA-III to solve the model. In Section 5, we test our model and algorithm on a busy part of China's HSR network and obtain a series of feasible solutions for the operators to choose from. And we find out the most critical section and occurrence time and analyze the importance of the depots in the network. Conclusions and future research are discussed in Section 6.
## 3 Results and Discussion
### Performance  
The mean image size was 4571 \(\times\) 2339 pixels and the mean time to segment a single image (with morphological filtering) was 5.2 s. Consistent with WaveCluster's theoretical complexity, the runtime of our approach (without morphological filtering) increased linearly with image area, as shown in Figure 5:  
Figure 4: Example false positive WaveCluster results overlaid on original images in red, ground truth in yellow. WaveCluster does not have a spherical clustering restriction; consequently, it can follow the contours of architectural distortions and spiculated lesions. (**a**) has two “false” positives along the contours of an architectural distortion, while (**b**) has one false positive at the bottom of the image and one FP resulting from oversegmentation of an actual lesion.
# Software Development Process Considerations in GNSS-Denied Navigation Project for Drones
## 3 Software Development Process Given for Consideration in This Paper
The drone-dedicated software development process and its details were addressed in the software development plan (SDP). It was prepared before the development activities began in the project planning phase [9] to reduce the overall risk associated with the software development activities. Now, we will analyse the software development process in detail. Software Development Process and Activities in the GNSS-Denied Navigation System Development Project for Drones  
After considering, many factors like requirements certainty, scope stability, ease of change, etc., that helped choose the project approach mentioned in [9], the decision was made to apply an adaptive approach over a hybrid or plan-driven (sometimes called \"predictive\") approach for software development, with some modifications that will be addressed further in this chapter. According to this paper's title, we will discuss the process for drone-dedicated software development only, not for the entire project. The approach for the whole project was the subject of a project management plan (PMP) or project plan [36] and is beyond the scope of this paper. Regarding the software development process for drones, even though the software requirements [13; 37] and scope were stable and were derived from the system level [15], the degree of innovation was high. Therefore, there needed to be a \"place\" left for changes as the developers' knowledge matured with the product development and as the software was the main and key part of the product (the drone). The development team size was also considered, and it was below 10, which was suitable for an adaptive approach [9; 30]. One of the objectives of the software development process was to reduce the risk associated with the development activities [27]. Another was to introduce resilience by providing the team's ability to adapt and respond quickly to uncertainty related to high product innovation and new process approaches. The general view of the software development process for drones implemented in the project is shown in Figure 2.  
The process was focused on three basic activities: iteration planning, software implementation and testing (coding and tests) and retrospective [17; 30] (a review and conclusion). Input data that were needed to perform the process activities were the software development plan (SDP), software requirements and design descriptions of components developed during the project as software solutions. The outcome of the process was the software release. The applied process, in general, was an adaptive one, based on three important scrum pillars: transparency, inspection and adaptation [30]. These pillars formed the basis for every activity within this process.
# An In-Line Coaxial-to-Waveguide Transition for Q-Band Single-Feed-Per-Beam Antenna Systems
## 3 Results
### Discussion  
The transition performance was evaluated in terms of RL and IL over the whole Q-band, and confirmed the suitability of such an in-line configuration. In the Table 2, a brief comparison of the similar works which constitute the state of the art is provided, taking into account both the in-line coaxial-to-waveguide (coax2 wg) and the in-line microstrip-to-waveguide (us2 wg) transitions and comparing for all the cases the bandwidth with RL higher than 20 dB: the proposed transitions with a comparable performance are all proposed at lower frequency ranges and adopt solutions which are too critical in Q-band, as a shaped pin [26; 28] or a tunable adjustable fin [25]. As explained in the first paragraph of Section 2, these techniques introduce additional elements in the geometry, which translate in additional potential errors.  
Figure 14: Return loss comparison between the tests on the groove and pin incorrect centering.  
Figure 15: Field distribution in case 4, on the cross-section where the pinis shorted to the ridge. The ridge and the pin are displayed in gray, and the EPO-TEK H20E in green. The pin misalignment and its non-direct contact with the ridge does not affect the TE\({}_{10}\) mode.  
The shorting between the coaxial pin and the ridge has been topic of several tolerance analysis to evaluate the design robustness respect to this manufacturing issue. The pin vertical offset has been analysed and shows an acceptable tolerance on the gap value, around the 20% (Figure 10). Moreover, the proposed solution to short the end-launcher to the waveguide does not affect the performance (Figure 12), and even if the pin is not perfectly centred in the ridge the RL keeps high enough (Figure 14) and the transverse field distribution keeps the TE\({}_{10}\) mode shape (Figure 15).  
Future works must dealt with the realization anc characterization of the proposed in-line coaxial-to-waveguide transition at Q-band. Given the thorough tolerance analysis, the expected range of variation of the most influential geometrical parameters can be controlled by using milling as manufacturing method. Alternative, more recent and appealing technologies, such as 3D printing [44] may not be able to satisfy the tight mechanical tolerances required at these high working frequencies. In particular, selective laser sintering can achieve a resolution of about 20-60 \(\upmu\)m [45]. Furthermore the equipment can be costly.