esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The fundamental restriction in data transmission through communication channels has two crucial aspects. These are transmitting the data within spectral bandwidth limitations and making this transmission robust to noise with the highest possible data bit rate. As proposed by the Shannon Limit Theorem [1], the channel capacity is directly proportional to the spectral bandwidth and Signal to Noise Ratio (SNR) and in order to maximize the channel capacity both parameters should be optimized.  
The ratio of Energy per bit to Noise Power Spectral Density Ratio (\(E_{b}\)/\(N_{0}\)) expresses the relation between SNR and Link Spectral Efficiency (LSE). The following equation shows this function. The most important aim of PBCS is to maximize the LSE value at the possible lowest SNR level. This situation directly points the minimization of the area under the graph for \(E_{b}\)/\(N_{0}\)_vs_. Bit Error Rate (BER) space:  
\[\frac{E_{b}}{N_{0}}=\ \frac{S\ /C}{N\ /W}=\ \frac{S\ /\ N}{C\ /\ W}=\ \frac{10\ ^{SNR}\ /\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!  
these corrected patterns are fed into the Artificial Neural Network (ANN). Having been trained for all possible sets of glossaries, the ANN recovers the incoming noise added signal pattern into the associated binary code. The information is reconstructed by concatenating these binary codes.  
PBCS can provide some advantages for military communications where robustness against noise generated by jammers is an important issue. It can be used to transmit the signal with minimal error, even within improved SNR characteristics, by adaptively switching to an appropriate pattern glossary set. Moreover, since the glossaries are formed by the user, an external party cannot decode the signal pattern without knowledge of the glossary. This also allows the system to be used as part of an encryption system. High link spectral efficiency capability allows an increased data rate in low frequency ranges, which improves the data transfer performance of submarine communication systems.  
The remaining sections of the paper are organized as follows: in Section 2, the PBCS model is described. In Section 3, the transmitter and the receiver structures of PBCS are introduced in detail. In Section 4, simulation results are presented. The conclusions of the paper are drawn in Section 5.
# Joint Communication-Motion Planning in Networked Robotic Systems
## 3 Cognitive-Relay-Assisted Robot System with Interference Constraints
### Joint Communication-Motion Planning: Decide
#### 3.3.2 JCMP Solutions  
Equation (11) involves the joint optimization of the motion (position of the relay robot \(x_{r,k}\)) and communication (transmit powers of the relay robot \(P_{r,k}\) and of the sensing robot \(P_{s,k}\)). Firstly, we formulate the same communication planning problem as in Equation (7) and the optimal transmit power is given by Equation (8). By substituting Equation (8) into Equation (11), we may transform the original problem into a motion planning problem, which is only relative to the positions \(x_{r}=\{x_{r,1  
Figure 5: _Cont_.  
In Figure 5b, the PU is located near the end point of the sensing robot' trajectory, while the energy saving ratio is 1.45%. Either non-JCMP or JCMP is adopted, the relay robot adopting has to move close the sensing robot as the sensing robot approaches the PU, which significantly limits the transmit power of the sensing robot. Additionally, the stringent interference imposed by the PU limits the feasible trajectories of the relay robot. Hence, the achievable energy saving of JCMP is stringent.  
The same situation was observed for the cases, where the PU is located in the adversarial positions near the base station or along the trajectory of the sensing robot as illustrated in the Figure 5c,d. In these scenarios, the feasible solution space is tightly constrained, hence JCMP has very limited potential to find a better trajectory than non-JCMP. As can be observed in Figure 5d, in order to pose low interference on the PU, the relay robot moves too far away from the base station and even exceeds the sensing robot, and hence the PU may prohibit the relay robot from providing effective relaying.  
Some insights may already be inferred from the results of the simple system of a single PU and three SUs. Firstly, it may be found that the location of the PU has a noticeable impact on the optimal trajectories of both non-JCMP and JCMP, as well as on the achievable energy consumption. It may also be anticipated that as the maximum tolerable interference-to-noise ratio decreases or the PU is located further away from the operating area of the secondary relay system, the feasible solution space may be relaxed, and the trajectories and the energy-saving ratio may be closer to those in the cases without PU as shown in Figure 3.  
As was also illustrated by the results, sophisticated interactions have emerged in a simple four-node CR system; hence, many more configurations may need to be analyzed, leading to a series of open problems and new findings. Due to the limited space of this introductory treatise, these open problems will be investigated in our future works.
## 4 Research Methodology
### Performance Measures
#### 4.2.2 The Resilience of the Demand Forecasting System in the Face of Ongoing Attack  
The resilience of PRDFS and BRDFS are measured using the following metric for resilience [44]:  
\[\textbf{{R}}=\textbf{{P}}_{\textbf{{n}}}/\textbf{{P}} \tag{8}\]  
where \(\textbf{{P}}_{\textbf{{n}}}\) and \(\textbf{{P}}\) present the out-of-sample one step ahead demand forecasting accuracy with and without attacks, respectively. The predictive accuracy is measured in both point estimates and confidence intervals. In the single point estimate, the predicted output is the most expected value of demand. We use the mean arctangent absolute percentage error (MAAPE) to measure the accuracy of single point forecasts [45]. In the MAAPE approach, the accuracy is defined as follows:  
\[\textbf{{Accuracy}}=\textbf{{100}}-\textbf{{MAAPE}}\times\textbf{{100}} \tag{9}\]  
\[\textbf{{MAAPE}}\textbf{{=}}\frac{\textbf{{1}}}{\sum\limits_{t=1}^{N}}( \textbf{{AAPE}}_{t})\]  
\[\textbf{{MAAPE}}_{t}=\textbf{{tan}}^{-1}\left(\left|\frac{\textbf{{A}}_{t}- \textbf{{F}}_{t}}{\textbf{{A}}_{t}}\right|\right) \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Label**} \\ \cline{3-4}  & & \
\begin{tabular}{c} **Positive** \\ **(Malicious)** \\ \end{tabular} &
\begin{tabular}{c} **Negative** \\ **(Normal)** \\ \end{tabular} \\ \hline Actual Status & Positive (Malicious) & True Positive (TP) & False Negative (FN) \\ \cline{2-4}  & Negative (Normal) & False Positive (FP) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance Metric.  
where \(\mathbf{A_{t}}\) and \(\mathbf{F_{t}}\) denote the actual and forecasted values at data point \(\mathbf{t}\), respectively. \(\mathbf{N}\) is the number of data points.  
In the interval estimate, the predicted outputs are the upper and lower bounds of forecasts. In this approach, if the actual demand lies between the forecasted upper and lower bounds, the forecast is correct. Interval estimates are made for the 99.7% confidence level. The 99.7% limits lie three standard deviations below and three above the mean.
# Elimination Properties for a Probabilistic Scheduling Problem
## 7 Computational Experiments
### Algorithm Stability  
Let \(F\) be the solution value determined by the tested algorithm and \(F^{*}\) the value of the reference solution. Relative error of the solution \(F\)  
\[\delta=\frac{F-F^{*}}{F^{*}}100\% \tag{19}\]  
indicates by how many percent the solution determined by the algorithm is worse or better than the reference one. Taking into consideration a set of examples, we can then assess the quality of solution designated by the algorithm solutions, and thus indirectly--the quality of the algorithm.  
Let \(\tilde{\eta}\) be an example of probabilistic data, \(\mathcal{Z}(\tilde{\eta})\) data set generated from \(\tilde{\eta}\) by disturbance of task execution times according to the assumed schedule. We have further:  
\(\mathsf{A}_{ref}\)--algorithm designating reference solutions,  
\(\mathsf{A}\)--algorithm whose resistance we are testing (in our case \(\mathsf{A}_{P}\) or \(\mathsf{A}_{\mathcal{P}+B}\)),  
\(\pi_{(\mathsf{A},x)}\)--solution designated by algorithm \(\mathsf{A}\) for data \(x\), \(F(\pi_{(\mathsf{A},x)},y)\)--value of criterion function of solution \(\pi_{(\mathsf{A},x)}\) for the example \(y\).  
Then  
\[\Delta(\mathsf{A},\tilde{\eta},\mathcal{Z}(\tilde{\eta}))=\frac{\sum_{\varphi \in\mathcal{Z}(\tilde{\eta})}F(\pi_{\mathsf{A},\tilde{\eta}},\varphi)-\sum_{ \varphi\in\mathcal{Z}(\tilde{\eta})}F(\pi_{(\mathsf{A}_{ref},\varphi)},\varphi )}{\sum_{\varphi\in\mathcal{Z}(\tilde{\eta})}F(\pi_{(\mathsf{A}_{ref},\varphi )},\varphi)},\]  
we call _resistance of solution_\(\pi_{(\mathsf{A},\tilde{\eta})}\) (designated by algorithm \(\mathsf{A}\) for the example \(\tilde{\eta}\)) on the set of disturbed data \(\mathcal{Z}(\tilde{\eta})\).  
If \(\hat{\mathcal{P}}\) is a set of examples of probabilistic data of the examined problem, then the expression  
\[\mathcal{S}(\mathsf{A},\hat{\mathcal{P}})=\frac{1}{|\hat{\mathcal{P}}|}\sum_{ \tilde{\eta}\in\hat{\mathcal{P}}}\Delta(\mathsf{A},\tilde{\eta},\mathcal{Z}( \tilde{\eta})) \tag{20}\]we call a _resistance coefficient_ of algorithm A on set \(\hat{\mathcal{P}}\). The smaller its value, the more stable the solutions determined by the algorithm, i.e., random disturbances of data results in less change in the cost of the solution.
# Predicting the Gap in the Day-Ahead and Real-Time Market Prices Leveraging Exogenous Weather Data
## 2 Learning Algorithms and Methodologies
### Support Vector Regression (SVR)  
The SVR method is a non-linear learning algorithm. One of the most common versions of SVR regression is \(\epsilon\)-SV regression. The goal of \(\epsilon\)-SV regression is to find a function that has the most \(\epsilon\) divergence for all the data points. The algorithm accepts errors only within the range of \(\epsilon\), as presented in (2)-(5).  
\[\min_{w,b,\xi,\xi} \frac{1}{2}w_{2}^{2}+C\sum_{i=1}^{m}(\xi_{i}+\xi_{i}^{*})\] (2) subject to \[w^{T}\phi(x_{i})+b-y_{i}\leq\epsilon+\xi_{i} \tag{3}\] \[y_{i}-w^{T}\phi(x_{i})-b\leq\epsilon+\xi_{i}^{*}\] (4) \[\xi_{i},\xi_{i}^{*}\geq 0,i=1,\ldots,m \tag{5}\]  
Here, the constant, \(C>0\), balances the flatness of a function and the amount up to which deviations larger than \(\epsilon\) are tolerated. \(\phi(x_{i})\) maps \(x_{i}\) into a higher-dimensional space, where \(w\) and \(b\) are coefficients. \(\xi\) and \(\xi^{*}\) represent the distance from the actual values to the margin of the \(\epsilon\)-tube with support vectors. Errors outside the margin are penalized linearly.  
A predictor, \(g\), of the SVR with \(m\)-training examples is presented in (6).  
\[g_{\text{SVR}}(x)=\sum_{i=1}^{m}(-\alpha_{i}+\alpha_{i}^{*})K(x_{i},x)+b. \tag{6}\]  
The SVR with an RBF kernel is a non-linear algorithm, and it enables choosing the acceptable error of the model. The hyper-parameter \(\epsilon\) controls the maximum acceptable error for the model. Thus, it is expected that the SVR with an RBF kernel predicts prices better than the LASSO algorithm.
## 2 Research Framework  
The system framework of the model proposed in this paper is shown in Figure 1 below. Its working principle is divided into three steps:  
1. Extract the feature vectors of the state of the marine fuel system and use Gaussian white noise to expand the data samples;
2. A differential evolution algorithm is used to adaptively acquire the extreme learning initialization parameters, and the fault diagnosis model for marine fuel system is constructed for different types of data, and the minimum value of the objective function is estimated;
3. Get the test model with SaDE-ELM training and diagnose the most probable state of the marine fuel system according to the new sample.  
Figure 1: Research framework.
## 4 Simulations and Performance Estimation
### Influence Mechanism of the PER on SKR under Different Transmission Distances  
In this section, a simulation design is conducted to compare the accuracy requirements of polarization extinction ratio (PER) under different transmission distances, including 15 km, 25 km, 50 km, and 100 km. Here, we set the residual ratio of the frequency modulation s = 0.01.  
As shown in Figure 4, with increasing transmission distance, to maintain a relatively high SKR, the requirement for PER increases correspondingly. Specifically, the SKRs of 15 km, 25 km, 50 km, and 100 km transmission distances reduce to zero when the PER is below 21.93 dB, 22.23 dB, 25.95 dB, and 30.9 dB, respectively. Take the 25 km transmission distance as an example; the SKR descends sharply during the PER range from 22.23 dB to 27 dB (approximately 60% of the SKR under the perfect PER case). This work provides the accuracy requirement of PER for subsequent polarization compensation algorithm design in digital signal processing methods.  
Figure 4: Secure key rates versus polarization extinction ratio curves corresponding to different transmission distances. (The repetition frequency is 300 MHz, the pulse width is 1 ns, and the modulation variance VA = 14.94 at 15 km, VA = 8 at 25 km, VA = 4.55 at 50 km, s = 0.01, VA = 3.78 at 100 km. The reverse reconciliation efficiency \(\beta\) is 0.95, the electrical noise in shot noise unit is \(v_{de}/N_{0}\) = 0.1, and the quantum efficiency \(\eta\) is 0.56.)
## 2 Materials and Methods: Modelling the Robot Arm and the Original and Improved Trajectories
### Dynamic Analysis of RV-2 AJ Robot Arm  
The dynamic analysis of RV-2 AJ arm requires the computation of the inertia matrix for each joint. This computation is performed using SolidWorks software by drawing the robot model and applying its real geometric and mass measurements. SolidWorks offers the possibility to transform all of the robot parameters in a URDF (Unified Robotic Description Format) file; this file can be used for the simulation in MATLAB software using Robotic Toolbox [43]. Figure 1 presents the CAD model of RV-2 AJ arm in SolidWorks environment and the creating of its URDF file for MATLAB software. The URDF file allows us to import the dynamic parameters of RV-2 AJ arm into a MATLAB environment, where we can visualize the robot arm and calculate its forward and inverse kinematics using the Robotic Toolbox that provides the following:  
* RigidBodyTree (RBT) object,
* Home configuration function,
* Inverse Kinematic solver.  
Figure 1: RV-2 AJ model in SolidWorks and the created URDF (Unified Robotic Description Format) file for the CAD model of the RV-2 AJ arm.  
Figure 2 visualizes the imported structure of RV-2 AJ arm in the MATLAB environment and the script code used for it. The visualization of the robot arm uses the RV-2 AJ.URDF definition file and the \"show (robot, Qhome)\" command performs the visualization, as can be followed in the MATLAB code.  
For finding better or \"quasi-optimal\" solution and proving the effectiveness of the newly elaborated whip-lashing method, two paths were generated with the same starting and ending points but with different internal motions. During the application of the method both paths require the rotation of second-third-fourth joints, whereas the first and the fifth articulation are not used.
# Locating Two-Level Systems in a Superconducting Xmon Qubit
## 1 Introduction  
Superconducting qubits are attractive candidates for the construction of quantum computers. They are now moving into the era of 'noisy intermediate-scale quantum' (NISQ) technology [1], and preliminary error correction algorithms have been implemented [2; 3; 4; 5; 6]. In the NISQ region, qubit fluctuations directly limit the circuit depth of the algorithm. Therefore, further progress in this system requires not only instant high-fidelity single- and two-qubit gates but also longer coherence times and stable performance.  
Generally, fluctuations in decoherence time and qubit frequency have been attributed to quasiparticles or TLS defects [7; 8]. The intrinsic properties of quasiparticles have been deeply investigated in recent decades [9; 10; 11; 12; 13; 14; 15; 16]. However, the microscopic nature and locations of the TLSs still remain undetermined [17]. Although the inherent losses induced by TLSs restrict the performance of superconducting circuits made of aluminum or niobium, these circuits, including superconducting coplanar waveguide (CPW) resonators and superconducting qubits, are effective tools to investigate TLSs.  
TLS defects have been investigated using different methods based on superconducting qubits, such as direct microwave spectroscopy [18; 19; 20; 21; 22; 23; 24; 25], strain spectroscopy [26; 27; 28], long-term time-domain measurement [29; 30; 31; 32; 33; 34], dielectric loss and participation ratio [35; 36; 37]. These works partially analyze spectral or temporal data, or demonstrate with fixed-frequency qubits. Here, we both spectrally and temporally analyze the relaxation time \(T_{1}\) fluctuations and qubit frequency \(\Delta\omega/2\pi\) fluctuations of a frequency-tunable Xmon qubit and discover that two independent Lorentzians are needed to describe the fluctuations. We then focus on the original data of \(T_{1}\) fluctuations and find two unusual oscillations. From the fitting of the oscillation data, we calculate the couplings between the qubit and TLS and find that two near-resonant TLSs are possibly located near the junctions.
## 5 Discussion
### Effectiveness of the Model  
According to the different results of theoretical examples in which we have varied the different parameters of production policy and maintenance/quality strategy, we can prove the novelty and effectiveness of our proposed model. Indeed, concerning the production policy, by increasing the average and the standard deviation of the demand and thereafter the index of service level, we obtained logical results, where the cadence of production, the optimal delivery, and the costs will follow these variations (Examples 2 and 3). From Examples 1, 2, and 3, the increasing of demand average and standard deviation will generate the increasing of the production and delivery planning, as well as the cost in order to satisfy the random demand. These production results impacted in a natural way on the result of the maintenance and quality strategy due to the analytical correlation proven on the model. On the other hand, the variation of the maintenance/quality strategy confirms the effectiveness of our model. The increase of the production plan obtained by the production policy impacts the degradation of the process as well as the average number of failures, and consequently the preventive maintenance planning, where the optimal number of preventive maintenance actions increase along with the number of quality inspections.  
These different examples and the sensitivity analysis show the robustness and dynamic of our proposed approach with any data values, hence its success in its application in a practical example.  
The proposed approach is appropriate for several industries considering the combined production, maintenance, and quality control strategy. This methodology deals with the relationship between production cadences and the failure rate of the process production system, while considering the statistical process control chart. The proposed study model is applicable in numerous manufacturing enterprises, such as companies of the automobile industry, textile, and cosmetic products, etc.
## 4 Experiments and Results Discussion
### Performance Evaluation under Different Parameters
Figure 16 shows that the average query delay will not change significantly as the block height increases, no matter which query transaction. Traversal and traceability retrieval are not included because they are implemented through multiple calls to query interfaces. Hardware or network factors may lead to the fluctuation of the single query delay or the average query delay over a period of time, but through a number of repeated tests, the average query delay is stable between 480 ns and 500 ns.
# A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
## 3 FPT Hardness of Approximation
### Hardness from Gap Hypotheses
#### 3.2.3 Inapproximability of \(k\)-Biclique and Densest \(k\)-Subgraph
This means that, to answer Question 8, one has to first settle the best known running time lower bound for exact algorithms, which would already be a valuable contribution to the understanding of the problem. Let us now point out an interesting consequence of Theorem 7 for the Denset \(k\)-Subgraph problem. This is the \"uncolored\" version of the Multicolored Densest \(k\)-Subgraph problem as defined above, where there are no partitions \(V_{1},\ldots,V_{k}\) and we can pick any \(k\) vertices in the input graph \(G\) with the objective of maximizing the number of induced edges. The approximability status of Denset \(k\)-Subgraph very much mirrors that of \(k\)-Biclique. Namely, in the parameterized setting, PIH is not known to imply hardness of approximation for Denset \(k\)-Subgraph. Furthermore, in the classic (non-parameterized) setting, Denset \(k\)-Subgraph is not known [72; 76; 77; 78; 79] to be NP-hard to approximate even to within a factor of say 1.01. Despite these, Gap-ETH does give a strong inapproximability for Denset \(k\)-Subgraph, as stated below:  
**Theorem 8** ([39]). : _Assuming Gap-ETH, there is no \(k^{o(1)}\)-FPT-approximation for Denset \(k\)-Subgraph._  
In fact, the above result is a simple consequence of Theorem 7. To see this, recall the following classic result in extremal graph theory commonly referred to as the Kovari-Sos-Turan (KST) Theorem [80]: any \(k\)-vertex graph that does not contain a \(g\)-biclique as a subgraph has density at most \(O(k^{-1/\delta})\). Now, the hardness for \(k\)-Biclique from Theorem 7 tells us that there is no FPT time algorithm that can distinguish between the graph containing \(k\)-biclique from one that does not contain \(g\)-biclique for any \(g=\omega(1)\). When the graph contains a \(k\)-biclique, we have a \(k\)-vertex subgraph with density (at least) \(1/2\). On the other hand, when the graph does not even contain a \(g\)-biclique, the KST Theorem ensures us that any \(k\)-vertex subgraph has density at most \(O(k^{1/\delta})\). This indeed gives a gap of \(O(k^{1/\delta})\) in terms of approximation Denset \(k\)-Subgraph and finishes the proof sketch for Theorem 8. Unfortunately, Theorem 8 does not yet resolve the FPT approximability of Denset \(k\)-Subgraph. In particular, while the hardness is only of the form \(k^{o(1)}\), the best known algorithm (which is the same as that of the multicolored version discussed above) only gives an approximation ratio of \(k\). Hence, we may ask whether this can be improved:  
**Open Question 9**. : _Is there an \(o(k)\)-FPT-approximation algorithm for Denset \(k\)-Subgraph?_  
This should be contrasted with Theorem 6, for which the FPT approximability of Multicolored Denset \(k\)-Subgraph is essentially resolved (up to lower order terms).