esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Experimental Results  
In order to be more convincing, two groups of actual engineering signals are used to further verify PTSR method in the underdamped state, which can further improve the effect of feature extraction on the original method.  
Figure 4: The output signal (**a**) time domain curve of the PTSR system (**b**) time domain curve of PTSR method in the underdamped state (**c**) frequency domain curve of PTSR method (**d**) frequency domain curve of PTSR method in the underdamped state (\(m=3,p=0.5,q=1.2,\gamma=1\)).  
algorithm (PSO) is used to search for the best parameter [32]. In the PSO algorithm, both learning factors are set to 2, population size is set to 60, inertia weight is set to 0.5 and the adaptive function is the SNR mentioned in Equation (6). Meanwhile, the search range of parameters \(m\), \(p\) and \(q\) are set to \(m\in(0.0001,4),\ p\in(0.0001,1.5)\) and \(q\in(0.0001.2)\), respectively. Finally, the search results are \(m=3.0340,\ p=0.4869\) and \(q=1.0326\), and the output signal is shown in Figure 7.  
Figure 7 shows that both methods restore the periodicity of time-domain signals to a certain extent, but the waveform in Figure 7b has a higher clarity and larger amplitude. In Figure 7c,d, the amplitude of the characteristic frequency is obviously prominent from other frequencies, but the value is higher in Figure 7d. In addition, the SNR is increased from \(-28.0015\) dB to \(-19.1526\) dB and \(-17.8624\) dB by the two methods, respectively. Through these, it can be concluded that the PTSR method in the underdamped state has stronger noise filtering effect and can obtain better output effects.  
Figure 7: The output signal of SKF 6205-2 RSjEM (**a**) time domain curve of the PTSR system (**b**) time domain curve of PTSR method in the underdamped state (**c**) frequency domain curve of PTSR method (**d**) frequency domain curve of PTSR method in the underdamped state.
## 2 Design of the Solar-Powered ECG Device
### Software Design  
The ECG signal obtained after passing the analog to digital converter is usually a collection of many signals, including ECG and noise signals [24]. There are many noisesources, but mainly from power line noise (50 Hz or 60 Hz) [25], roaming the base, creating motion, and contact noise hiding a lot of necessary information. For ECG devices with a low sampling frequency, \(<\)400 Hz, we have not seen the effect of these disturbances, but when sampling is at a higher frequency, the effect of this noise on ECG signals is quite obvious. In our design, ECG devices can operate at the sampling frequency 2133 Hz, and so, eliminating this noise effect is particularly important. The most important goal is to separate the ECG signal from noises. The high sampling frequency makes the ECG signal more accurate and gives the information needed to detect various cardiovascular diseases. Therefore, before processing the noise-canceling ECG signal, it is important to obtain important features of the ECG signal.  
Using the FFT transformation method, we determined the noise of the ECG signal obtained within the frequency range from 0 Hz to 10 Hz. Therefore, we designed a filter in this range to remove noise from the ECG signal. Noise signals from 60 Hz power lines also affect large ECG signals. In order to eliminate these two types of noise in two different frequency bands, we had to design two filters, a low pass filter at 60 Hz frequency and a high pass filter with a frequency of 0 Hz to 10 Hz [21,26].
# Measuring the Effect of Mental Health on Type 2 Diabetes
## 4 Methods  
### DoWhy and Causal Inference  
Causal inference refers to the process of inferring the effects of interference. For example, inferring the effects of drugs on diseases demonstrates causal inference. DoWhy is one of the methodologies of causal inference. DoWhy is an open-source python library for causal inference, and it provides the most powerful frameworks known for causal inference, such as potential outcomes and graphical models that are based on modeling assumptions and identifying the causal effect. Many people have made key contributions to improving the usability and functionality of the library, such as the integrated Pandas interface for DoWhy's four steps.  
The DoWhy library makes three contributions [7]. First, the DoWhy library provides a principled way to model a given problem as a causal graph, so all assumptions are explicit. Second, the DoWhy library combines graphical models and potential results to provide a unified interface for many popular causal inference methods. Last, but not least, the DoWhy library automatically tests the validity of the assumptions and evaluates the robustness of the assumptions against violations.  
DoWhy focuses on explicitly modeling and validating causal assumptions and provides a four-step causal inference method for causal reasoning. A key feature of DoWhy is a state-of-the-art refutation API that can automatically test causal assumptions for all estimation methods. Through integration with the EconML library, DoWhy supports average causal effect estimation and Conditional Average Treatment Effects (CATE) estimation for backdoors, frontdoors, instrumental variables, and other identification methods.  
Causal inference follows four steps: modeling, identification, estimation, and refutation. First, the modeling step encodes prior knowledge as a formal causal graph for each problem. This serves to make each causal assumption explicit. Next, identification uses graph-based methods to identify the causal effect (estimand), and estimation uses statistical methods for estimating the identified estimand. Finally, refutation tries to refute the obtained estimate by testing the robustness of the initial model's assumptions.  
Causal inference based on the DoWhy library can analyze the influence of factors such as stress intervention on diabetes. First, DoWhy is used to develop causal assumptions and graphically represent structural parts based on diabetes health data. Subsequently, the correct estimand is formulated based on the causal model, and a suitable method is used to estimate the effect. Finally, the robustness of estimates to assumption violations is checked using three different methods. Figure 2 shows the research process of the study, and Appendix A shows the code used for causal inference.
## 3 Methodology
### Data Stream Simulation  
The study at hand focuses on testing the existing drift detection methods on multi-class balanced and imbalanced data streams with sudden, gradual, and incremental drifts. Therefore, to know where exactly the drift is occurring and what is the actual number of samples in each class, i.e., the class imbalance ratio, we simulated different data streams in a way that each stream had two versions, the balanced data stream and the imbalanced data stream. The reason for creating two versions of the same stream is to identify the performance difference between drift detection methods on balanced and imbalanced data streams when the drift type is the same. It is because we are uninformed of the drift location and drift type in real data streams that evaluating the performance of drift detection methods on such data streams is difficult [48]. As a result, for our investigations, synthetically produced data streams are employed in which we know the drift locations, drift type, and class imbalance ratio at the time of drift occurrence. We used a massive online analysis (MOA) [49] framework to generate the streams. The RandomRBFGenerator, RandomTreeGenerator, and LEDGenerator classes were used to generate different multi-class data streams. Every stream contains 100K samples, after every 25K samples, a new concept replaces the old one. The'sudden' and 'gradual' drifts are easy to generate using the width (w) property. For sudden, we set w=1, and for gradual w = 5K, which means during these 5K instances, the concept will change gradually. The position (p) is set as 25K in all cases, which means it is the center of each drift (i.e., concept drift occurs after every 25K). Table 2 shows the summary of the generated streams.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stream** & **Generator** & **Drift** & **CIR** & **C** & **A** & **I** \\ \hline \multirow{3}{*}{Balance\_SSS} & RRBF, RT & S,S,S & 2/2/2/2/2, & & & \\  & & & 2/2/2/2/2, & & & \\  & & & 5/4/3/2/1, & & & \\ Imbalance\_SSS & RRBF, RT & S,S,S & 5/4/3/2/1, & 5 & 21 & 100K \\  & & & 5/4/3/2/1 & & & \\  & & & 2/2/2/2/2, & & & \\ Balance\_GGG & RRBF, RT & G,G,G & 2/2/2/2/2, & 5 & 21 & 100K \\  & & & 2/2/2/2/2 & & & \\  & & & 5/4/3/2/1, & & & \\ Imbalance\_GGG & RRBF, RT & G,G,G & 5/4/3/2/1, & 5 & 21 & 100K \\  & & & 5/4/3/2/1 & & & \\  & & & 2/2/2/2/2, & & & \\ Balance\_III & RRBF, RT & I,I,I & 2/2/2/2/2, & 5 & 21 & 100K \\  & & & 2/2/2/2/2 & & & \\  & & & 5/4/3/2/1, & & & \\ Imbalance\_III & RRBF, RT & I,I,I & 5/4/3/2/1, & 5 & 21 & 100K \\  & & & 5/4/3/2/1 & & & \\  & & & 5/4/3/2/1 & & & \\ \hline \hline \end{tabular} CIR = Class Imbalance Ratio; C = Classes; A = Attributes; I = Instances.  
\end{table}
Table 2: Summary of generated data streams.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 3 Proposed Approach
### Implementation
#### 3.2.2 Rsu  
The RSU equipment used in this work was the Siemens ESCoS RSU. RSUs are present in road infrastructure, connecting it to the G5 network.  
In the equipment documentation [46], the interface XFER is described. It is an RSU interface based on WebSocket Secure (WSS). It provides bi-directional data exchange and device management functions [44]. It also enables the issuance of commands to the RSUso that it has certain behaviours; for instance, echoing a received message. Therefore, this interface was chosen to help implement the RSU application. However, this approach requires the presence of a client. An intermediary component, Middleware RSU (M_RSU), was developed to address this, acting as an XFER client. For this reason, the Siemens RSU will be referred to as Physical RSU (P_RSU). The combination of these two components is referred to as the RSU, as both combined have the expected behaviour of an RSU in the proposed approach. The M_RSU application was developed in Python and used the standard library modules for cryptographic operations.  
As seen before, the RSU handles three different message exchange scenarios. Thus, M_RSU and P_RSU act together to perform them. Each one of them is described below:  
**Cellular Network (Smarthone) \(\rightarrow\) RSU**: First, the M_RSU receives the secure message via the broker. Then, it validates the message (deprotection), and if it is valid, sends it to the P_RSU's XFER interface, which will forward the message to the G5 network. Before sending it to the P_RSU, the M_RSU first converts the message from the protocol format to the format that the OBU requires.
**G5 Network (OBU) \(\rightarrow\) RSU**: As an XFER client, the M_RSU uses the \"subscribe\" command to instruct the P_RSU to forward upstream and downstream messages. Therefore, when the P_RSU receives a message from the OBU, it forwards it to the M_RSU, where it undergoes validation (deprotection). If the message passes validation, it is published for smartphones to receive.
**RSU \(\rightarrow\) G5 and Cellular network**: In this scenario, the messages are being generated by the RSU. M_RSU will protect them and send them to the cellular network (via the broker) and the G5 network (via the P_RSU).
## 3 Improved SIFT Algorithm
### Scale Space Increases the Stability Factor  
The traditional scale space construction formula is  
\[\delta(o,s)=\delta_{0}2^{o+\frac{s}{6}},oe[0,\ldots,O-1],se[0,\ldots,S+2] \tag{9}\]  
where \(\delta\) is the scale space coordinate; \(O\) is the number of octaves; \(S\) is the number of layers in the group; \(\delta_{0}\) is the scale of the reference layer; \(o\) is the index of the octave of the group; and \(s\) is the index of the layer in the group.  
When building the Gaussian pyramid at the beginning, the input image should be pre-blurred as the image of the 0 th layer of the 0 th group, which is equivalent to discarding the highest sampling rate of the spatial domain. Therefore, the usual practice is to double the scale of the image to generate the \(-1\) group. Apply a Gaussian blur of \(\delta_{-1}=0.5\) to it, if the size of the input image is doubled with bilinear interpolation, then it is equivalent \(\delta_{-1}=1\).  
When constructing a Gaussian pyramid, the scale coordinates of each layer in the group are calculated as follows:  
\[\delta(s)=\sqrt{(k^{s}\delta_{0})^{2}-(k^{s-1}\delta_{0})^{2}} \tag{10}\]  
The value of \(k\) is shown in Formula (4).  
To reduce error and matching time, a stabilizer \(P\) is added, and then a new Gaussian pyramid is constructed. The scale coordinates of each layer in the group are calculated as follows:  
\[\delta(s)=\sqrt{(w^{s}\delta_{0})^{2}-(w^{s-1}\delta_{0})^{2}} \tag{11}\]  
**Definition 1**.: _Stabilizer factor \(P\)_  
_To reduce error and matching time, the stabilizer \(P\) is added, where \(P=\frac{w}{k}\). The stabilizer factor \(P\) is shown in Equation (12):_  
\[P=0.4^{5}+0.9 \tag{12}\]  
Proof.: Because the number under the square root of formula (11) is greater than or equal to 0:  
\[(w^{s}\delta_{0})^{2}-(w^{s-1}\delta_{0})^{2}\geq 0. \tag{13}\]  
According to [36], \(\delta_{0}=1.6\), so  
\[(w^{s})^{2}-(w^{s-1})^{2}\geq 0. \tag{14}\]  
According to \(w=pk\), so  
\[(p^{s}k^{s})^{2}-(p^{s-1}k^{s-1})^{2}\geq 0. \tag{15}\]  
Since the value of \(k>0\)_and_\(p>0\),  
\[(p^{2}k^{2})\geq 1. \tag{16}\]  
According to [36], usually \(3\leq S\leq 5\), so  
\[P>\frac{1}{2^{\frac{1}{5}}}\geq 0.87, \tag{17}\]and because we want to remove redundant feature points, if \(P<1\), then \(0.87\leq P<1\).  
Since \(k\) is a decreasing function, so must \(w\) be; \(k\) is also an exponential function. To facilitate the derivation, \(P\) was designed as an exponential function as follows:  
\[P=A^{S}+b \tag{18}\]  
To ensure that \(P\) is a decreasing function, let \(0<A<1\). When \(S\) is infinite, \(AS=0\)_and_\(P=B\). To ensure that the value of \(P\) can remove redundant points, let \(B=0.9\) because \(0.87\leq P<1\), \(A^{S}\) needs to meet \(0<A^{S}<1\). Usually \(3\leq S\leq 5\), so  
\[0<A^{S}<0.1^{\frac{1}{3}}<0.47. \tag{19}\]  
Let \(A=0.4\) to ensure that the filtering window and feature points are not too small.  
Therefore, \(P=0.4^{S}+0.9\).
# CoFi: Coding-Assisted File Distribution over a Wireless LAN
## 1 Introduction
With the coding based batching and caching schemes, CoFi is able to significantly reduce the control overhead and redundant transmissions in traditional 802.11 like protocols, thus improving the throughput performance, while ensures full MAC layer reliability. In designing the coding based batch transmission and MAC caching protocols, we have tried to improve not just the throughput and reliability, but also the per-packet delay. Note that the throughput-delay relation with network coding is not straightforward, since for each coded chunk of data, the receiver must wait until the entire batch can be decoded. In general, the delay performance of network coding depends on both the end-host computation time and the batch transmission time [14]. The end-host delay is usually negligible on a powerful mobile device, especially when using fast coding implementations [15]. Therefore, the per-packet delay is usually constrained by the batch transmission time, which depends on the channel condition, as well as the batch size. With extensive trace based simulation, we demonstrate that the per-packet delay of CoFi can be close to 802.11, especially in a lossy wireless network, while the effective throughput can be more than twice higher. Such a performance advantage renders it particularly useful for applications such as bulk file transfer and streaming, which can tolerate an initial buffering delay. The surprisingly large performance gap between CoFi and 802.11 may seem counter-intuitive, given that network coding does not fundamentally improve the network capacity [16]. Towards a more rigorous justification, we perform reverse-engineering of both protocols using theoretical analysis. With a simple analytical model, we prove that CoFi and 802.11 essentially approach different fairness measures in an asymptotic manner, thus leading to disparate throughput efficiency. The remainder of this paper is organized as follows. In Section 2, we review existing work on network coding and MAC caching protocols that improve the performance of wireless networks. We continue to discuss the major design issues of CoFi in Section 3, and then describe the implementation CoFi in Section 4. Section 5 evaluates the performance of CoFi, with respect to throughput and delay, as well as the computation cost of network coding. Section 6 presents an in-depth analysis of the asymptotic properties of CoFi, aiming at a theoretical justification of its performance. Finally, Section 7 summarizes the paper and presents our future work.
# Experimental Demonstration of Beam Scanning of Dual-Metasurface Antenna
## 2 Materials and Methods
### Implementation  
A center-band frequency of 10.65 GHz was considered. A Rogers RT5880 (\(\epsilon_{r}=2.2\), \(\tan\delta=0.0009\)) 3.175 mm-thick substrate was placed between the upper radiating layer and the lower variable-impedance plane. The unit cell of the modulated metasurface was chosen to be about 1/10 of the wavelength, i.e., 3 mm. The air gap between the reconfigurable plane and the ground was set to 1.5 mm in order to allow enough space for the varactors.  
The modulation period \(p\) of the top impedance surface was computed from (2), setting a radiation angle of 20\({}^{\circ}\) for a 0 V bias voltage of the varactors, and this resulted in \(p=27\) mm. Since the unit cell of the modulated metasurface was 3 mm wide, the cosi  
Figure 3: Surface reactance of the whole structure vs gap width in the array of strips, for two different values of the varactors’ bias voltage.  
nusoidal variation of the reactance in one period was sampled in nine points, i.e., with sufficient sampling. For the radiating part, eight modulation periods were chosen for a total length of 216 mm; this was a good compromise between beam width and (leaky-wave) tapering and, at any rate, yielding low \(S_{21}\) towards the matched load.  
The bias scheme of the varactors in the variable-impedance plane is depicted in Figure 4, which shows an alternate-potential scheme. The necessary single DC bias was conveyed by two 0.15 mm-wide buses placed at the opposite sides of the metasurface; the RF reactance of the very thin (high-impedance) lines was considered enough to decouple RF and DC without the need for a filter, and that was confirmed by simulations.  
The resulting complete structure of the designed antenna is shown in Figure 5. The upper modulated metasurface is depicted in Figure 5a, while Figure 5c shows the lower reconfigurable impedance plane; the vertical stackup of the antenna can be seen in Figure 5b. The overall thickness of the structure was less than \(\lambda_{0}/6\) at the working frequency of 10.65 GHz, resulting in a very small form factor. It should be noted from Figure 6 that the unit cell of the reconfigurable metasurface was twice as large as that of the upper layer, in order to reduce the total number of varactors needed in the structure, which amounted to 360.  
The excitation was provided with a 50 Ohm coaxial cable, which was matched to the antenna by a properly designed tapered section (Figure 5a).  
Figure 4: DC voltage distribution in the reconfigurable metasurface.  
Figure 5: Complete model of the designed antenna. (**a**) Upper sinusoidally modulated reactance surface. (**b**) Lateral view of the antenna. (**c**) Reconfigurable plane; the inset shows one of the two DC buses.
## 2 The FDTD Algorithm  
The FDTD algorithm uses Yee cells to discretize the target, as shown in Figure 1. This algorithm uses center-difference discretization for Maxwell's curl equation in both time and space, resulting in explicit iterations of the electric and magnetic fields [5].  
Taking the \(x\)-direction electric field \(E_{x}\) as an example, its time iteration formula is  
\[\begin{array}{ll}E_{x}^{n+1}(i+\frac{1}{2},j,k)=&CA(m)\cdot E_{x}^{n}(i+\frac {1}{2},j,k)\\ &+CB(m)\cdot[\frac{\underset{}{H_{x}^{n+\frac{1}{2}}}(i+\frac{1}{2},j+\frac{1}{ 2},k)-H_{x}^{n+\frac{1}{2}}(i+\frac{1}{2},j-\frac{1}{2},k)}{\Delta y}\\ &-\frac{H_{y}^{n+\frac{1}{2}}(i+\frac{1}{2},j,k+\frac{1}{2})-H_{y}^{n+\frac{1}{ 2}}(i+\frac{1}{2},j,k-\frac{1}{2})}{\Delta z}]\end{array} \tag{1}\]  
Among them, \((i+1/2,j,k)\), \((i+1/2,j+1/2,k)\), \((i+1/2,j-1/2,k)\), \((i+1/2,j,k+1/2)\), and \((i+1/2,j,k-1/2)\) are the spatial sampling points; \(n\), \(n+1/2\), and \(n+1\) are the temporal sampling points; \(H_{y}\) and \(H_{z}\) are the magnetic fields in the y-direction and z-direction, respectively; \(\Delta y\) and \(\Delta z\) are the spatial grid sizes in the y-direction and z-direction, respectively; and \(CA(m)\) and \(CB(m)\) are the coefficients related to the time step \(\Delta t\) and medium parameters, and their calculation formulas are as follows:  
\[CA(m)=\frac{1-\frac{\sigma(m)\Delta t}{2\varepsilon(m)}}{1+\frac{\sigma(m) \Delta t}{2\varepsilon(m)}},\;CB(m)=\frac{\frac{\Delta t}{\varepsilon(m)}}{1+ \frac{\sigma(m)\Delta t}{2\varepsilon(m)}} \tag{2}\]  
where \(\varepsilon\) and \(\sigma\) are the permittivity and conductivity, respectively. Due to the use of an explicit iterative format, the electric field iteration in the FDTD algorithm is only spatially related to the magnetic field at adjacent positions, and the same goes for the magnetic field iteration. No matrix operation is involved in the iteration process, resulting in high computational efficiency. However, due to the use of structural mesh discretization in this method, the fitting to the target is poor. When dealing with electromagnetic problems such as complex structures and large-sized targets, it is necessary to use a large number of grids to discretize the above targets, which results in high computational complexity and a long computation time. To improve the computational efficiency, GPU parallel computing has become an effective solution.