esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Pre- and Post-Equalizer  
According to the literature, a segregated design of the pre- and post-equalizer is mainly practiced. The computation of the pre-equalizer coefficients is based on the zero-forcing principle. In contrast, this work is concentrated on jointly designing and optimizing the pre- and post-equalizer. Moreover, the multi-level signals, which are produced by the pre-equalizer, target to partially compensate the channel interferences, especially the cross-talk from the adjacent channel. The concept of the joint-PPE scheme is to improve the system BER by shifting a few equalization efforts to the transmitter, while considering the power constraint and minimal surge in the transmitter complexity.  
The pre-equalizer is characterized as a linear FIR filter with a given length of \(L_{\text{P}}\), and it is defined as  
\[b_{\mu}(k)=\sum_{i=1}^{L_{\text{P}}}p_{i,\mu}\cdot s[k-(i-1)],\qquad\mu=1, \ldots,n_{\text{T}}\enspace, \tag{7}\]  
with \(p_{i,\mu}\) representing the pre-equalizer coefficient at \(i\)-th instance at the \(\mu\)-th input. As the input signal \(s(k)\) consists of concatenated frames from both the MIMO inputs, the pre-equalizer is applied to \(s(k)\) instead of executing the pre-equalization separately to the individual MIMO inputs. When the identical pre-equalizer coefficients are implemented on both layers, the marginal increase in the transmitter's complexity is advantageous. Moreover, the elimination of cross-talk is primarily targeted. The graphical representation of the constellation diagram with multiple levels is demonstrated in Figure 2. Prior to the transmission, the PAM-2 modulated signal undergoes the pre-equalization with \(L_{\text{P}}=2\) in (a) and \(L_{\text{P}}=3\) in (b), resulting in generation of the multi-level signaling. Additionally, the power constraint for the first layer is also depicted in Figure 2.  
The joint-PPE filter is calculated using an interior-point optimization algorithm, which is subjected to constraints [16]. The optimization problem is tailored as follows  
\[\begin{split}\underset{p(k),f_{\text{P}}\
u(k)}{\text{minimize}} &\theta_{\mu}(h_{\
u\mu}(k))\qquad\qquad\qquad\
u=1,\ldots,n_{ \text{R}}\\ \text{subject to}& f_{\mu\
u}(k)*h_{\
u\mu}(k)*p(k)\approx z_{\
u}(k), \quad\forall\
u\\ \text{and}& s_{\mu,M-1}=b_{\mu,N-1}=\text{q}\cdot P _{s,\mu}^{(\max)}\enspace,\end{split} \tag{8}\]  
where \(*\) refers to the convolution operator, and \(z_{\
u}(k)\) is the approximation of the PPE filter with the Nyquist filter. The prime goal of (8) is to encounter the ISI first and consequently, deal with the ICI. The suggested length of the pre-equalizer is restricted to two or four taps because a minimal increase in the transmitter complexity is essential.  
From (8), it is clear that an accurate channel estimate is required in order to compute the joint-PPE coefficients. Additionally, the amplitude of the joint-PPE coefficients is constrained by the maximum output power of the laser source.
## 4 Performance Evaluation of the CEEMDAN-WP  
In order to evaluate the performance of the CEEMDAN-WP, we consider the nonlinear signal  
\[r(t)=\underbrace{-t\sin(2\pi f_{1}t)-t^{2}\cos(2\pi f_{2}t)}_{r_{1}(t)}+r_{2 }(t) \tag{19}\]  
where \(f_{1}=20\) Hz, \(f_{2}=50\) Hz, and \(r_{2}(t)\) are the noise signals with a SNR of 5 dB. The sampling rate is set to 400 Hz and the sampling time is 1 s. Moreover, it is assumed that the unit of signal \(r(t)\) is centimeter (cm). Figure 4 shows the vibration amplitudes of signals \(r(t)\) and \(r_{1}(t)\), as well as their PSD functions. Following this, the signal \(r(t)\) is processed by employing CEEMDAN, CEEMDAN-WP, and WP, separately.  
Based on the CEEMDAN and FFT methods, nine IMF components and their corresponding PSD functions were obtained, as shown in Figure 5. It can be seen that the IMF2-IMF4 components contain the dominate frequency information of the signal. Hence, they were retained, and the other components were removed. This is equivalent to forming a band-pass filter. Figure 6a shows the vibration amplitude of the first-time restructured signal (Signal X1). Following this, a three-level WP technique was applied to denoise the signal X1, and the second-time restructured signal (Signal X2) was obtained, as shown in Figure 6b. Additionally, a single WP technique was employed to denoise the  
Figure 4: The amplitudes of \(r(t)\) and \(r_{1}(t)\) along with their PSD functions.  
signal \(r(t)\), and the result (Signal X3) is depicted in Figure 6c. In general, SNR and root mean square error (RMSE) play important roles in evaluating the effect of noise reduction. They take the forms  
\[\text{SNR}=10\log_{10}\Biggl{\{}\frac{\sum_{i=1}^{n}S_{i}^{2}}{\sum_{i=1}^{n} \left(S_{i}-S^{\prime}_{i}\right)^{2}}\Biggr{\}} \tag{20}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}\left(S_{i}-S^{\prime}_{i} \right)^{2}} \tag{21}\]  
where \(S_{i}\) and \(S^{\prime}_{i}\) represent the real signal and the denoised signal, respectively, and \(n\) is the length of the signal. The maximum SNR and minimum RMSE are better with regard to the effect of noise reduction. Table 1 shows the statistical results of SNR and RMSE under the three different denoising methods. In Table 1, it can be seen that the SNR obtained via the CEEMDAN-WP method is higher than that obtained using the other two methods. The RMSE obtained via the CEEMDAN-WP method is lower than that obtained using the other two methods. That is to say, the CEEMDAN-WP outperforms the single CEEMDAN and WP methods. Hence, based on the GNSS-RTK measurement, the vibration signals of a long-span bridge may be denoised by employing the CEEMDAN-WP method.  
Figure 5: The intrinsic mode function (IMF) components of signal \(r(t)\) and the corresponding PSD functions.
# Improving Vulnerability Management Through Process Mining
## 4 An Exemplary Use Case
### The Business Perspective
#### 4.4.2 Business Goals  
To create a basis of evaluation of the project, we defined business goals in collaboration with the process owner. The overall goal was refined to three distinct sub goals.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Source** & **Mode** & **Point of View** & **Description** \\ \hline DefectDojo & Manual & Internal & Documentation of findings \\  & & & from internal manual tests \\  & & & Bug Bounty program for external \\  & & & domains \\ Qualys & Automatic & Internal & Regular scans of servers and \\  & & & network devices \\ Offensity & Automatic & External & Regular scans of web apps and \\  & & & external domains \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sources of vulnerabilities.  
As already explained in Section 2, it is vital for the vulnerability management process to be as efficient as possible because every unpatched vulnerability is a risk to the organization. One goal is therefore to use process mining to make the process faster. This should be achieved by figuring out where and why time is lost by priority, source, asset, and assignment group. If especially long throughput times in a certain category are detected, it should be possible to perform a root cause analysis to find why the process is lagging.  
However, the vulnerability management process should not only be fast but the vulnerabilities should also be properly remediated. This is to ensure that they no longer provide an attack surface or create further vulnerabilities in the system. Ensuring proper remediation was defined as the second goal of this project. A proper remediation is assumed if the real process follows the defined process since it makes sure that all steps are covered. Therefore, for goal two, it is first necessary to find out if the process is following the target process as presented in Figure 5. If not, the next sub goal is to figure out where and why it deviates.  
Finally, the process should not be analyzed and improved just one time but the project should enable a continuous monitoring and improvement of the process. For the third goal, an overview of all vulnerabilities and their positions in the process each day should be given. Hence, the process owner can react if vulnerabilities are stuck in one step for too long. Furthermore, it should be possible to flexibly analyze the process if new questions arise.  
Consequently, the project aims to (i) increase the process speed, (ii) ensure proper remediation, and (iii) enable continuous management of the process.
## 7 Conclusions  
We have provided background on the IRL problem, reviewed the main algorithmic approaches with their formal descriptions, and discussed the applicability of IRL concepts as the algorithmic basis of a ToM in AI. The main goal in the IRL problem is to retrieve the reward function that best explains an agent's behaviour--the agent's desires in the ToM context. The foremost challenge in IRL comes from it being an ill-posed problem: a policy may be optimal under any number of reward functions, including degenerate ones; therefore, algorithms must incorporate heuristics to discriminate between solutions. Another important consideration is how the reward function is characterised, usually as a function of features of the environment. Different approaches have been taken to define the features and structure that characterise this function.  
Some IRL methods also address other core ToM attitudes: beliefs about the environmental dynamics modelled as the transition probabilities and about the states in the form of observations, and intentions, with considerations of potentially suboptimal observed behaviour with respect to the true agent goals and the modelling of multiple intentions. Further considerations have been addressed in IRL algorithms, including the size and complexity of the state space, sample efficiency and robustness to noisy or incomplete observations, the participation of the observer in the environment and its game-theoretical and recursive consequences, ToM as introspection, the incorporation of prior knowledge and structured representations, and the environments and benchmarks available for further research and applications.  
As demonstrated in this review, the IRL framework encapsulates the core elements of ToM succinctly while providing enough flexibility for many and various solution methods and extensions to be developed. As such, it holds great promise as a cradle for the algorithmic basis of a ToM in AI.  
Conceptualization, M.S.H.; investigation, J.R.-S.; writing--original draft preparation, J.R.-S.; writing--review and editing, J.R.-S. and M.S.H.; visualization, J.R.-S.; supervision, M.S.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
However, there are still many challenges in the utilization and application of spaceborne GNSS-R raw IF signals, such as the fact that only a few satellites have publicly released raw IF data, the accumulation of raw data is limited, and there is still room for improvement in algorithms and accuracy. This article focuses on summarizing the current processing status of spaceborne GNSS-R raw IF signals, introducing relevant satellites and some important cutting-edge research fields, including coherent detection and inland water monitoring, sea surface height retrieval, ice height (or ice sheet height) retrieval, ionospheric total electron content and disturbance observation, and tropospheric monitoring. Although the previous literature has explored certain aspects of this topic, such as land applications [30], these discussions are limited in scope. This article offers a more comprehensive overview, encompassing recent advancements in land, ocean, ionosphere, and troposphere applications of spaceborne GNSS-R raw IF signals. It also highlights the progress made, future prospects, and some existing challenges associated with the use of spaceborne GNSS-R raw IF signals in these domains. This review will provide unique and useful insights into the processing of spaceborne GNSS-R raw IF signals and their various application fields, and also provide some inspiration for researchers who are studying or will study the use of spaceborne GNSS-R raw IF signals for related field applications. The organizational structure of this article is as follows: the second part introduces the current status of spaceborne GNSS-R microsatellites; the third part provides a detailed introduction to coherent detection and inland water monitoring; the fourth part discusses two methods for sea surface height retrieval, including code delay and carrier-phase sea surface height measurement; the fifth part introduces ice cover height; and the sixth and seventh parts respectively discuss total electron content and disturbance observations of the ionosphere, as well as the monitoring of the troposphere. Finally, a summary of the entire article and future prospects for the raw IF signals of spaceborne GNSS-R is presented.
## 3 The Virtual-Force-Based Guidance Law
### Driven of the Virtual-Force-Based Guidance Law  
According to the analysis above, the UAV is governed by four virtual forces: a virtual centripetal force, a virtual spring force, a virtual drag force, and a virtual repulsive force. The guidance law is calculated by the combination of four virtual forces. The procedure is given below:  
* Step 1: Obtain the state \((x,y,\psi)\) of the UAV.
* Step 2: Determine the reference point \(P_{r}=(x_{r},y_{r},\psi_{r},\omega_{r})\) and calculate the reference center \(O\). The reference center \(O=(x_{o},y_{o})\) is determined by the reference point \(P_{r}\) and the reference radius \(r=|\upsilon_{r}/\omega_{r}|\) at point \(P_{r}\). \[\left\{\begin{array}{ll}x_{o}=x_{r}+r\cdot\cos(\psi_{r}+\texttt{sign}_{+}( \omega_{r})\cdot\frac{\pi}{2})\\ y_{o}=y_{r}+r\cdot\sin(\psi_{r}+\texttt{sign}_{+}(\omega_{r})\cdot\frac{\pi}{2 })\end{array}\right.\] (14) where \[\texttt{sign}_{+}(x)=\left\{\begin{array}{ll}1\,,&x\geq 0\\ -1,&x<0\end{array}\right.\] (15)  
Figure 4: Local obstacle detected in the reference path.  
* Step 3: Calculate the four virtual forces according to (6), (7), (11), and (12).
* Step 4: Obtain the resultant virtual force in the forward and lateral direction by decomposing the virtual forces in the two directions \[F_{f}=F_{vtf}+F_{vdf}+F_{vcf}\] (16) \[F_{l}=F_{vll}+F_{vdl}+F_{vcl}+F_{vr}\] (17) where the subscripts \(f\) and \(l\) are used to denote the components of forces in the UAV's forward and lateral direction.
* Step 5: Obtain the input command of the VFGL by \[v_{c}=v+dt\cdot F_{f},\;\omega_{c}=\frac{F_{l}}{v}\] (18) \[v_{c}=\texttt{limit}[v_{c},v_{min},v_{max}]\] (19) \[\omega_{c}=\texttt{limit}[\omega_{c},-\omega_{max},\omega_{max}]\] (20) where \(dt\) is the control interval in practical application. \(v_{min}\) and \(v_{max}\) are the minimum and the maximum velocity of the UAV. \(\omega_{max}\) is the maximum course rate. The expression of \(x=\texttt{limit}[x,x_{min},x_{max}]\) is \[x=\left\{\begin{array}{ll}x_{max},&x\in(x_{max},+\infty)\\ x,&x\in[x_{min},x_{max}]\\ x_{min},&x\in(-\infty,x_{min})\end{array}\right.\] (21)
# Empowering a Broadband Communications Course with a Unified Module on 5G and Fixed 5G Networks +
## 4 Results
### Students' Feedback
#### 4.1.2 Topics of Interest  
In the fifth question, students were tasked with choosing the most captivating features of the 5G/F5G module presented, and their choices are depicted in Figure 8. Analysis of this figure reveals that the subjects generating the most interest, according to the students' feedback from the prior academic year, are those that the students are already well-acquainted with. These topics are the services offered by telecommunication networks, the five generations of wireless networks, and the 5G and F5G business models. For the students of the current academic year, another possible response was given: \"How the research around 6G is shaping\" to also examine the impact of the 6G sub-section on the students. As evident, one out of four students selected this option.  
Figure 6: Percentage distribution of students’ responses to the question, “How likely is it to engage further with the 5G-F5G subject (e.g., through a thesis, further reading, or choosing a relevant postgraduate course)?” for the previous and current academic years.  
Figure 7: Percentage distribution of student’s responses to the question, “How much has this particular module influenced your opinion about the 5G-F5G subject?” for the previous and the current academic years.  
Next, the students were asked to determine the most captivating subject(s) covered in the entirety of the \"Broadband Communications\" course. They were presented with a selection of ten topics and had the option to choose more than one topic. This question was addressed only to the students of the previous academic year since the questionnaire for this academic year was provided prior to the course completion. The results are shown in the following table.  
Table 2 illustrates that 80% of students found the 5G and F5G modules to be the most captivating topics, likely due to their incorporation of cutting-edge technologies and forward-looking services that ignited their curiosity. Additionally, DSL was deemed significant by nearly half of the students, which is unsurprising given that DSL is the primary method of Internet access in Greece, a technology with which the majority of students are already acquainted. Moreover, one-third of the students also expressed interest in the Internet and TCP/IP standard, Broadband Applications/services-B-Networks-ISDN, Ethernet standard, and PON standards, all of which represent crucial technologies and standards in daily use. Conversely, topics such as switching techniques, introduction, N-ISDN network, and ATM were less popular among students, likely due to their limited familiarity with these technologies.
## 4 Remote Sensing Platforms Used in Peatland Research
### Multiple Platforms
#### 4.5.3 Restoration Monitoring  
Monitoring the success of peatland restoration projects is crucial for evaluating ecosystem recovery and carbon assimilation. [PERSON] et al. (2021) modelled GPP through MODIS data and used a handheld spectrometer to obtain small-scale spectral data. They evaluated the models' reliability for estimating ecosystem photosynthesis in peatlands at different scales. The study aimed to identify the factors influencing GPP in peatland ecosystems and determine if large-scale models utilising satellite data can accurately estimate GPP at smaller scales by comparing the results with field measurements. Their findings showed that the model successfully estimated GPP from remotely sensed data at both small and large scales, and the estimates correlated well with the GPP measured using chamber and Eddy Covariance (EC) techniques.  
Additionally, [PERSON] et al. (2019) assessed the long-term monitoring capabilities of RADARSAT-2 and UAV imagery for tracking peatland restoration success over time [145]. Likewise, [PERSON] et al. (2020) studied the potential use of RADARSAT-2 satellite and high-resolution UAV imagery for monitoring the restoration of peatlands. They suggested that RADARSAT-2 data with UAV imagery could be valuable for monitoring peatland restoration success over time despite acknowledging limitations in the study design [15].
# An Early Investigation of the HHL Quantum Linear Solver for Scientific Applications
## 5 Scientific Applications and Evaluation
### Power Flow Problem in Power Grid
\begin{bmatrix}\frac{\partial\Delta\vec{P}}{\partial\vec{\theta}}&\frac{ \partial\Delta\vec{P}}{\partial|\vec{V}|}\\ \frac{\partial\Delta\vec{Q}}{\partial\vec{\theta}}&\frac{\partial\Delta\vec {Q}}{\partial|\vec{V}|}\end{bmatrix}\begin{bmatrix}\Delta\vec{\theta}\\ \Delta|\vec{V}|\end{bmatrix} =- \begin{bmatrix}\Delta\vec{P}\\ \Delta\vec{Q}\end{bmatrix}  \tag{4}\]  
where \(\Delta P_{k}\) and \(\Delta Q_{k}\) are computed using the admittance matrix, nodal power balance equation, and mismatch equations with the data from the last iteration or initial guess. Then, \(\vec{\theta}\) and \(|\vec{V}|\) are updated by \(\Delta\vec{\theta}\) and \(\Delta|\vec{V}|\), respectively. The algorithm is considered converged when \(\|\Delta\vec{P}\|\) and \(\|\Delta\vec{Q}\|\) are smaller than a convergence tolerance. It is worth noting that while HHL can solve Equation (4) for the normalized solution state \([\Delta\vec{\theta}^{T}\ \Delta|\vec{V}|^{T}]^{T}\) with limited accuracy, the un-normalized vector could have a smaller norm than the accuracy of HHL. Thus, the final accuracy of voltage magnitude and phase angles is much higher than the accuracy used in HHL. This situation is similar to iterative refinement in semi-definite optimization in [13].
## 3 System Overview
### Battery Characteristics  
Typically, an IoT device's battery is selected based upon its capacity. Often, designers forget the criterion that a battery is not a constant voltage source. Internal resistance changes over the voltage drop when the battery discharges. Thus, it is important to design and test the IoT device, including the battery characteristics, under more realistic conditions.  
The rate of battery discharge is dependent on the current drawn. Typically, this can be obtained from the battery discharge curve. When the battery discharges, battery internal resistance rises. The maximum current drawn by the DUT leads to a drop in the battery voltage. Internal resistance is equal to the current drawn. The battery model is shown in Figure 12. In a given time, battery voltage \(V_{batt}(T)\) is computed by Equation (10) where \(V_{oc}(t),\ R_{int}(t),\ i(t)\) denote battery open circuit voltage, internal resistance, and the drawn current, respectively.  
\[V_{batt}(T)=V_{oc}(t)-R_{int}(t)\ i(t). \tag{10}\]  
Figure 11: Low current measurement setup. (**a**) Low current measurement using a capacitor. (**b**) Capacitor discharge curve.  
There are battery simulating instruments that plot the curves, and those instruments are expensive. However, the device's shutdown voltage can be selected when running through the battery simulated model using such an expensive device. These devices can be used to simulate the custom low current profile in order to obtain the battery discharge curve. The battery life can be estimated based on time to discharge the battery to a selected low battery voltage threshold using such an instrument's results. It can save a lot of the time which would otherwise be needed to complete the work.  
Unfortunately, not every researcher has access to those high-end devices. In that scenario, researchers have to estimate the battery life using Equation (11). We used Equation (11) as the high-end instrumentation supports were not available.  
\[\begin{split} Battery\ Life=\frac{Specified\ Amp\ Hour\ capacity}{A\ average\ current}\ battery\ derating\ factor\.\end{split} \tag{11}\]  
The average current is illustrated in Figure 13 and is calculated as  
\[\begin{split} Average\ current\ \big{(}I_{average}\big{)}=\frac{(I_{sleep \_}T_{sleep})+(I_{active}T_{active})}{T_{sleep}+T_{active}}.\end{split} \tag{12}\]  
Figure 12: Battery model.  
Figure 13: System average current consumption.  
In our experiment, we used a 1 Ohm resistor as the shunt resistor. During the testing, the battery voltage was kept at 4.0 V and up. In the case of high current draw states, it lost a maximum voltage of 450 mV as the burden voltage. We had to make sure to operate the device without undervoltage. The dominant factors contributing to energy consumption are explored in the following results chapters in this paper.