esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Kriging-Based Probability Density Estimation for FCTP
### Kriging Model  
The Kriging surrogate model is an unbiased estimation model [33] that combines global approximation with local random errors to fit highly nonlinear models well. The Kriging surrogate model represents the response value as a combination of a parameterized polynomial model and a non-parameterized stochastic process [32], which is generally expressed as follows:  
\[y(\mathbf{x})\approx y_{K}(\mathbf{x})=G^{T}(\mathbf{x})\mathbf{\beta}+z(\mathbf{x}) \tag{8}\]  
where \(y(\mathbf{x})\) is true model, \(y_{K}(\mathbf{x})\) is the pending Kriging model, \(\mathbf{G}(\mathbf{x})=[G_{1}(\mathbf{x}),G_{2}(\mathbf{x}),\ldots,G_{k}(\mathbf{x})]^{T}\) is the matrix of basis functions, \(k\) is the number of basis functions, \(\mathbf{\beta}=[\beta_{1},\beta_{2},\ldots,\beta_{k}]^{T}\) is matrix of unknown weight coefficients, which can be estimated from the training samples, \(\mathbf{G}^{T}(\mathbf{x})\mathbf{\beta}\) denotes parameterized polynomial model, which gives trend of the model, and \(z(\mathbf{x})\) denotes a non-parametrizable stochastic process, which provides information about deviation of the model, the probabilistic properties are as follows:  
\[\left\{ \begin{aligned} E(z(\mathbf{x}))&=0\\ Var(z(\mathbf{x}))&=\sigma_{z}^{2}\\ Cov[z(\mathbf{x}_{i}),z(\mathbf{x}_{j})]&=\sigma_{z}^{2} \big{[}R\big{(}\theta,\mathbf{x}_{i},\mathbf{x}_{j}\big{)}\big{]}\end{aligned} \right. \tag{9}\]  
where \(\sigma_{z}^{2}\) is the random process variance, and \(\theta\) is the correlation parameter to be sought. Assuming that there are \(p\) training samples \(\{\mathbf{x}_{i}:i=1,2,\ldots,p\}\), the correlation matrix between the samples is denoted as follows:  
\[\mathbf{R}= \begin{bmatrix}R(\mathbf{x}_{1},\mathbf{x}_{1})&R(\mathbf{x}_{1},\mathbf{x}_{2})& \ldots&R\big{(}\mathbf{x}_{1},\mathbf{x}_{p}\big{)}\\ R(\mathbf{x}_{2},\mathbf{x}_{1})&R(\mathbf{x}_{2},\mathbf{x}_{2})&\ldots&R\big{(}\mathbf{x}_{2},\mathbf{x}_{p} \big{)}\\ \vdots&\vdots&\ddots&\vdots\\ R\big{(}\mathbf{x}_{p},\mathbf{x}_{1})&R\big{(}\mathbf{x}_{p},\mathbf{x}_{1})&\ldots&R\big{(}\mathbf{x}_{p},\mathbf{x}_{p} \big{)}\end{bmatrix}  \tag{10}\]  
For prediction point \(\mathbf{x}\), the correlation vector is  
\[r(\mathbf{x})=\big{[}R(\mathbf{x}_{1},\mathbf{x}),R(\mathbf{x}_{2},\mathbf{x}),\ldots,R\big{(}\mathbf{ x}_{p},\mathbf{x}\big{)}\big{]} \tag{11}\]  
After selecting the correlation function, according to the Kriging theory [34], the optimal unbiased prediction response of model can be expressed as follows:  
\[y_{K}(\mathbf{x})=G^{T}(\mathbf{x})\hat{\mathbf{\beta}}+\mathbf{r}^{T}(\mathbf{x})\mathbf{R}^{-1} \Big{(}Y-g\hat{\mathbf{\beta}}\Big{)} \tag{12}\]  
\[\hat{\mathbf{\beta}}=\Big{(}g^{T}\mathbf{R}^{-1}g\Big{)}^{-1}\Big{(}g^{T}\mathbf{R}^{-1} \mathbf{Y}\Big{)} \tag{13}\]where \(\hat{\beta}\) is estimate of \(\beta\), \(Y\) is output value of the training samples, and \(g\) is a matrix consisting of the values of \(k\) basis functions at \(p\) training samples.
## 5 Results and Discussion
### Performance Comparison between the Proposed Attn-MAPPO Algorithm and the Benchmark  
In this subsection, we compared the proposed Attn-MAPPO approach with the MAPPO benchmarks [13]. The architecture was the same for both, but the Attn-MAPPO used an attention mechanism to extract the most valuable information as the input for the network. To validate the performance of the proposed Attn-MAPPO algorithm, two traffic scenarios were set up with (1) two CAVs and three HDVs, and (2) four CAVs and five HDVs. The first setting was simple due to the sparse traffic density, while the second was complex. The comparison results of the episode reward in the training is illustrated in Figure 5. The results showed that in the simple setting, namely with the two CAVs, three HDVs, both the algorithms performed comparably but moderately better than the benchmark. Yet, as the number of vehicles increased, the benchmark performance declined significantly and the Attn-MAPPO algorithm showed a stronger performance. In the 30 tests, one collision occurred using the proposed method and four collisions occurred using the benchmark in the complex setting. Table 1 shows that for the proposed algorithm, the collision rate could be considerably diminished in the complex traffic setting. This implies that the added attention module was able to extract the critical information for decision-making, whereas the benchmark did not have this capability.  
Figure 5: Comparison of the episode reward in the training between the benchmark and the proposed Attn-MAPPO (proposed) algorithm.
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 5 Future Challenges
### Handling Channel Uncertainty  
In general, due to the large number of passive reflective elements in RISs, imperfect CSI is inevitable. Considering channel uncertainty, the resource optimization problem would be a stochastic counterpart of the problems discussed earlier. In particular, the CSI random error would make the constraints appear in a probabilistic form, and the objective function takes an extra expectation.  
If the distribution of the channel uncertainty is known, this statistical information can be used to transform the probabilistic constraints into deterministic ones and compute the expectation of the objective function explicitly [48, 49, 50]. However, due to the cascaded channel created by the RIS, the statistical information of the CSI might be complicated, making the transformation from stochastic problems into deterministic ones suffer performance loss, and/or intractable expectation computation. In those cases, the Monte Carlo simulation-based method could be used to handle the channel uncertainty [51].  
Figure 10: Performance comparison of different learning-based methods in the secrecy rate optimization problem under \(N=10\), \(M=10\). All the simulations are implemented on Colab with TensorFlow 2 and backend GPU. For training, 800,000 independent channels are generated (with the corresponding optimized phase obtained by GD methods for supervised learning). For testing, 200,000 independent channels are used. The adopted neural networks consist of three fully-connected hidden layers, containing 500, 250, and 200 neurons. The rectified linear unit (ReLu) is used as the activation function for the hidden layers and the linear activation function is applied to the output layer. (**a**) Continuous phase case. (**b**) Discrete phase case.  
On the other hand, learning-based methods can be modified to tackle uncertain CSI, even when the distribution of the channel uncertainty cannot be described in closed-form. In particular, when preparing the training data, we generate both the true CSI and the CSI added with uncertainty. During the training, we input the observed CSI (which contains errors) to the DNN but compute the loss function or reward function using error-free CSI. In this way, the learning system can automatically learn to \"denoise\" the CSI, while learning the mapping of the RIS phase shifts.
# Multiple-Parallel Morphological Anti-Aliasing Algorithm Implemented in FPGA
## 2 Multiple-Parallel Morphological Anti-Aliasing Algorithm
### Calculation of the Anti-Aliased Color  
After obtaining the blend weight coefficient of each pixel in the image, we start to calculate the color of the anti-aliased image. In our algorithm, the color of the anti-aliased image is also calculated in a single channel, so we also need to carry out the calculation of the anti-aliased color in the R, G, and B channels of the image.  
In the process of the calculation, we first take the original color of each pixel and then take the weight coefficient of the pixel blended by the color of its adjacent pixels in the horizontal and vertical directions. After calculating the anti-aliased color of the pixel in the horizontal and vertical directions, respectively, we take the average value to obtain the final anti-aliased color of the pixel. Formula (1) is a formula for calculating the anti-aliased color of a pixel.  
\[\begin{array}{l}C_{newX}=\left(1-W_{top}-W_{down}\right)\times C_{old}+W_{ top}\times C_{top}+W_{down}\times C_{top}\\ C_{newY}=\left(1-W_{left}-W_{right}\right)\times C_{old}+W_{left}\times C_{ left}+W_{right}\times C_{right}\\ C_{new}=\left(C_{newX}+C_{newY}\right)\div 2\end{array} \tag{1}\]  
In Formula (1), \(C_{old}\) is the original color of each pixel; \(C_{top}\), \(C_{down}\), \(C_{left}\), and \(C_{right}\) are the original colors of its adjacent pixels, respectively; \(W_{top}\), \(W_{down}\), \(W_{left}\), and \(W_{right}\) are the weight coefficients of each pixel blended by its adjacent pixels; \(C_{newX}\) and \(C_{newY}\) are  
Figure 5: Area texture.  
Figure 6: Horizontal aliasing patterns.  
the horizontal and vertical anti-aliased colors of each pixel, respectively; \(C_{new}\) is the final anti-aliased color of each pixel.
# Preparations for Galileo PRS in Poland
###### Abstract  
This article discusses the increasing security risk for the Global Navigation Satellite System (GNSS) due to both unintentional and deliberate interference (attacks), which have gotten significantly worse in 2022 due to tense the international situation. The upcoming Galileo Public Regulated Service (PRS), which is more resilient and robust than initial GNSS open services, is one of the key solutions for that problem. The technical description of this service, aspects regarding its implementation in the EU and the role of designated governmental authorities in that process are extensively covered in the first sections of the article. The next relevant issue brought up in the paper is the PRS signals' coexistence with amateur services operating within the same frequency resources, which have recently became a source of significant controversy in Europe. Finally, the article presents the Polish contribution to the Galileo PRS preparatory actions, covering the participation in two international R&D projects, the developed measurement station and initial results for the GNSS receiver's jamming and spoofing resistance tests, as well as the concept of the Galileo PRS threats detection system.  
GNSS; Galileo PRS; jamming; spoofing; GNSS threats detection system +
Footnote †: journal: Sensors  
1
Footnote 1: email: {[EMAIL_ADDRESS]}
# Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers
## 2 Methods
### Free-Space Link Loss
The turbulence profile is given by the commonly-used Hufnagel-Valley profile [11]:  
\[C_{n}^{2}(h)=0.00594(\frac{w}{27})^{2}(h\times 10^{-5})^{10}\exp(\frac{-h}{1000} )+2.7\times 10^{-16}\exp(\frac{-5}{1500})+A\exp(\frac{-h}{100}), \tag{5}\]  
where \(h\) is the height above the ground (in metres), \(A\) is the value of \(C_{n}^{2}\) at ground level, and \(w\) is the 'pseudowind' given by  
\[w=\sqrt{\frac{1}{15\times 10^{3}}\int_{5\times 10^{3}}^{20\times 10^{3}}V^{2}(h)\,dh}, \tag{6}\]  
where \(V(h)\) is given by the Bufton wind model as  
\[V(h)=w_{s}h+V_{g}+30\exp[-(\frac{h-9400}{4800})^{2}], \tag{7}\]  
where \(w_{s}\) is the slew rate of the ground station and \(V_{g}\) is the ground wind speed. This optical loss model is used in conjunction with the scattering results and orbital dynamics calculations to estimate the photon rate reaching the QKD receiver.
## 4 Traffic Filtering-Based Techniques
### Enhanced Port Locking (EPL)  
#### 4.4.1 EPL Concepts  
EPL [15] is an enhanced version of the PL approach. The EPL approach was proposed to improve the PL approach for unicast traffic performance in HSR connected-ring networks. The EPL works with  
Figure 7: Filtering unicast traffic under the PL approach.  
the same locking concept as the PL. However, unlike the PL that prunes unicast traffic for only DANH rings, the EPL approach filters the unicast traffic not only for DANH rings, but also for QuadBox rings.  
The EPL approach uses a parameter \(\psi\) to trigger learning stages. Parameter \(\psi\) is an integer number that represents the number of returned frames required to activate EPL effect. When a source node sends unicast frames to a destination node in an HSR network, the network will work as the standard HSR protocol at the first frame. From the second frame to the \(\psi^{\text{th}}\) frame, the network will work under the PL effect to filter traffic for DANH rings. And from the (\(\psi^{\text{th}}+1\)) frame, the network will work under the EPL effect to prune traffic for both DANH and QuadBox rings.
## 1 Introduction  
With the development of smart devices for Internet of Things (IoT), various sensor applications are becoming increasingly necessary. Among them, micro-electro-mechanical system (MEMS) sensors are receiving particular attention due to their small size, high signal-to-noise ratio (SNR), and low cost [1; 2; 3; 4]. Sensing diminutive signals with a small output current and small output voltage amplitude using MEMS sensors requires large amplification. Therefore, high gain, high input impedance, and low-noise operational amplification are essential for various sensor interfaces [5; 6; 7]. To reduce output noise, chopper stabilization or auto-zeroing techniques are typically used [8]. The auto-zeroing technique typically operates using two phases. The first phase stores flicker noise and offset in the auto-zero capacitor, and the next phase subtracts the stored flicker noise and offset from the input signal. Because of these two phases, the auto-zeroing technique performs discrete-time operation. In addition, auto-zeroing techniques cause switching noise as well as noise folding of high-frequency components due to sampling operations, thus chopper-stabilized architecture is preferred for low-power and low-noise continuous-time applications [9]. Chopper stabilization techniques modulate the offset and flicker noise with a chopper frequency (\(\ell_{\mathrm{chop}}\)), leaving only thermal noise in the base band. However, the offset and the flicker noise are up-modulated, and these up-modulated noise signals are referred to as ripple. To attenuate this ripple, the ripple reduction loop (RRL) was proposed [10]. RRL integrates the generated ripple voltage at the output node and feeds back the integrated ripple to the input stage negatively. However, there is a limit in bandwidth because the transfer function of the amplifier produces a notch at the chopper frequency.  
The bandwidth is increased by using a multi-path structure--a combination of a low-frequency path (LFP) and a high-frequency path (HFP). The transfer functions of the LFP and HFP induce a first-order frequency response in the system (a first-order system) by applying nested Miller compensation [11]. In the HFP, a class-AB output stage is implemented to improve the power efficiency. The proposed low-noise multi-path operational amplifier was fabricated using a 0.18-\(\upmu\)m 1P6M complementary metal-oxide-semiconductor (CMOS) process. The average current consumption is 96.7 \(\upmu\)A and the active area is 1.18 mm\({}^{2}\). The proposed low-noise multi-path amplifier has a unit gain bandwidth (UGBW) of 3.16 MHz, an input referred noise of 11.8 nV/\(\upmu\)Hz, and a noise efficiency factor (NEF) of 4.46.
## 1 Introduction
### Research Objective  
The main objective of this research was to design, develop and implement a system (WiSPH) in conjunction with IEEE 802.15.4 to detect and alert trained professionals about persons falling to the ground, as well as event-based monitoring to report tachycardia and bradycardia [3]. The objectives of this work include:  
1. developing a fall detection algorithm to handle data transmitted from an accelerometer;
2. generating an event-based, reliable and scalable network algorithm;
3. employing a reliable data encryption scheme (AES ) for use within the WSN;
4. creating a domestic WSN infrastructure which allows patients freedom of movement without losing communication;* programming a mobile application and Web page for easy access to live monitoring and information access;
* transmitting _push notifications_ of abnormal events to be relayed to caretakers, doctors or family members.
## 5 MTMD Applications on Taizhou Bridge
### Performance Evaluation of the MTMD Application  
With the parameters for the MTMD systems above, the buffeting displacement of Taizhou Bridge in the middle of the main girder is compared to the non-MTMD case to make a performance evaluation of the MTMD application. The buffeting displacements of Taizhou Bridge with and without MTMDs are shown in Figure 19.  
As shown in Figure 19, the lateral and vertical buffeting displacements of the main girder of Taizhou Bridge are both successfully suppressed. The RMS of the lateral buffeting displacement has  
Figure 19: Comparison of the buffeting displacements at the midspan with and without MTMD: (**a**) Lateral; (**b**) Vertical.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cases** & **Number of TMDs \(N_{\text{d}}\)** & **Mass Ratio \(\mu_{\text{d}}\)** & **Damping Ratio \(\zeta_{\text{d}}\) (\%)** & **Frequency Band-Width \(\delta_{\text{d}}\)** \\ \hline Lateral MTMD & 13 & 0.03 & 2 & 0.5 \\ Vertical MTMD & 11 & 0.02 & 2 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Preferred values for the design parameters of MTMD.  
been reduced to 0.083 m from 0.105 m with a 21.0% reduction, while the RMS in the vertical case is decreased to 0.255 m from 0.361 m with a 29.4% reduction. To better present the performance of the MTMD system, a spectral analysis is conducted on the buffeting displacements in Figure 19. The PSDs of the displacements with and without MTMD are presented in Figure 20. It is obvious that the energies contained in the dominant frequencies are remarkably mitigated by the MTMD, which means the damper system has fully exerted its function. In conclusion, with the preferred mechanical parameters determined by the step-updating framework for MTMD application, the buffeting responses of the long-span triple-tower suspension bridge under strong winds can be effectively mitigated.
# Bayesian Model Infers Drug Repurposing Candidates for Treatment of COVID-19
## 3 Results
### Identified Drug Signatures through Network Integration and Associated with Titer  
In order to assess possible drug signatures associated with the viral titer, an enriched titer sub-network was generated, and compared to known drug signatures and to drug-target co-regulated gene sets extracted from the Enrichr database [15]. This analysis identified kinase signatures for the protein kinase DNA-Activated catalytic subunit (PRKDC) [OR:7.9; \(p\)-value: 1.3e-11], serine/threonine protein kinase (mTOR) [OR:7.1; \(p\)-value: 2.6e-7], serine/threonine protein kinase 24 (STK24) [OR:4.9; \(p\)-value:1.7e-5], ribosomal protein S6 kinase alpha-3 (RP56 KA3) [OR:4.4; \(p\)-value: 9.4e-5], and lysine deficient  
Figure 3: **Model Nodes.** Selected nodes were highlighted to demonstrate model validation such as ACE2 (**A**), or as potential new therapeutic targets, such as CHEK1 (**B**) and MAOB (**C**). Arrowed edges indicate a positive causal connection (e.g., A \(\rightarrow\) B for A inducing B), and T-shaped arrows indicate a negative causal connection (e.g., A \(\rightarrow\) B for A repressing B).  
Figure 2: **Ensemble Model.** A probabilistic ensemble model from proteomics and viral titer was generated by bAlcis®, with nodes representing proteins and titer and edges the conditional dependence between nodes (**A**). The titer 2-degree sub-network was extracted for further evaluation (**B**).  
protein kinase 1 (WNK1) [OR:4.4; _p_-value: 9.4e-5] that were all enriched in the titer subnetwork at the indicated odds ratios/_p_-values. In addition to the kinase signatures, signatures for known drug compounds were also significantly enriched in the titer subnetwork Table 1. The prediction that results from these enrichments is that these compounds through their impact on the titer subnetwork have the potential influence of modifying the titer.  
Further, de novo, we identified novel links between SARS-CoV-2 titer and host proteins expression that illuminated unique biological signature associated with COVID-19. Several of these associated proteins have therapeutic options already on the market or in clinical trials (Table 2) and serve as possible repurposing opportunities to impact the levels of the SARS-CoV-2 virus in patients.
# Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
## 4 Performance Evaluation
### Data Processing and Metrics of Interest  
To collect simulation data, we employed the method of replications [49]. For each set of input parameters, we run the simulator for 600 s of the system time one hundred times with different random number seeds. The results obtained in each run have been used to form a sample with independent identically distributed (iid) observations [50] and, thus, the results, presented in further subsections, are considered to be averaged over all the different seeds we have utilized. Given the extensive number of observations, the confidence intervals obtained are very narrow. Therefore, in the presentation of results, only point estimates are depicted.  
We consider several metrics of interest, including the fraction of satisfied Interest requests and the mean full delay. In addition, we also report the share of received compute results in time. The latter metric integrates both latency and reliability in a single bundle, providing comprehensive insights into the system behavior.
## Appendix C Maximum Backward Time Slack Heuristic Scheduling Method  
The calculation steps of the maximum backward time slack heuristic scheduling method [34; 35] are as follows:  
1. Calculate the backward time slack of each target. For a target sequence \((i,\ldots,j)\), the backward time slack of the observation of target \(i\) is the time that \(i\) can move backward without violating the observation time window constraint and the attitude maneuver constraint, which can be calculated by the back-forward iteration as \[\Delta t_{bi}^{j}=\begin{cases}t_{wei}-t_{oei}&i=j\\ \min\left(\Delta t_{bi+1}^{j}+t_{osi+1}-t_{pi+1},t_{wei}-t_{oei}\right)&i<j \end{cases}\] (23)  
The sequence's backward time slack is the minimum backward time of all selected targets, which can be defined as  
\[\Delta t_{lbS}=\min\left(\Delta t_{i}^{j_{n}}\right)\quad i=j_{1},\ldots,j_{n _{s}}, \tag{24}\]  
where sequence \(S=\{j_{1},j_{2},\ldots,j_{n_{s}}\}\). Calculate the backward time slack of target \(i\) after target \(i\) is inserted at position \(p\), which is  
\[\Delta t_{bS,j}^{p}=\begin{cases}\min\left(t_{wei}-t_{oei},\Delta t_{p+1}^{j_ {n}}-\left(t_{osp+1}^{new}-t_{osp+1}\right)\right)&p<n_{s}\\ t_{wei}-t_{oei}&p=n_{s}\end{cases}, \tag{25}\]where \(t_{o\in i}\) is the observation end time after target \(i\) is inserted into position \(p\). Insertion position \(p\) is defined as the position where an unselected target \(i\), between the selected target \(p\) and \(p+1\), is inserted. After target \(i\) is inserted into position \(p\), the start time of observation mission \(p+1\) in the original sequence becomes  
\[t_{osp+1}^{new}=t_{o\in p}+\Delta t_{Ap,i}+\Delta t_{o}+\Delta t_{Ai,p+1}\qquad p <n_{s}. \tag{11}\]  
The sequence's backward time slack after target \(i\) is inserted into position \(p\) is \(\Delta t_{lbs,i}^{p}\);  
Determine whether the observation result sequence satisfies the attitude maneuver constraint after inserting an unselected target into the maximum sequence's backward time slack position. Subsequently, select the targets that satisfy the attitude maneuver constraint after the insertion. If the number of satisfied targets is more than 0, go to step 3, otherwise the calculation is finished;  
Select target \(i\) and insertion position \(p\) that allows the sequence to obtain the maximum sequence's backward time slack after insertion. Insert target \(i\) at position \(p\), update the observation result sequence, and remove target \(i\) from the unselected target set;  
If the number of unselected targets is more than 0, return to step 1, otherwise the calculation is finished.