esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compendium of Parameterized Problems at Higher Levels of the Polynomial Hierarchy
## 3 Propositional Logic Problems
### Quantified Boolean Satisfiability with Bounded Treewidth  
Let \(\psi=\delta_{1}\vee\cdots\vee\delta_{u}\) be a DNF formula. For any subset \(Z\subseteq\mathrm{Var}(\psi)\) of variables, we define the incidence graph \(\mathrm{IG}(Z,\psi)\) of \(\psi\) with respect to \(Z\) to be the graph \(\mathrm{IG}(Z,\psi)=(V,E)\), where \(V=Z\cup\{\delta_{1},\ldots,\delta_{u}\}\) and \(E=\{\{\delta_{j},z\}:1\leq j\leq u\text{ and }z\in Z\text{ and }z\text{ occurs in the clause }\delta_{j}\}\). If \(\psi\) is a DNF formula, \(Z\subseteq\mathrm{Var}(\psi)\) is a subset of variables, and \((\mathcal{T},(B_{t})_{t\in T})\) is a tree decomposition of \(\mathrm{IG}(Z,\psi)\), we let \(\mathrm{Var}(t)\) denote \(B_{t}\cap Z\), for any \(t\in T\).  
The following parameterized decision problems are variants of \(\mathrm{QSat}_{2}\), where the treewidth of the incidence graph graph for certain subsets of variables is bounded.  
\(\mathrm{QSAT}_{2}(\mathrm{ittw})\)  
_Instance:_ A quantified Boolean formula \(\varphi=\exists X.\forall Y.\psi\), with \(\psi\) in DNF.  
_Parameter:_ The treewidth of the incidence graph \(\mathrm{IG}(X\cup Y,\psi)\) of \(\psi\) with respect to \(X\cup Y\).  
_Question:_ Is \(\varphi\) satisfiable?  
**Complexity:** fixed-parameter tractable [4; 5].  
\(\mathrm{QSAT}_{2}(\exists\)-\(\mathrm{ittw})\)  
_Instance:_ A quantified Boolean formula \(\varphi=\exists X.\forall Y.\psi\), with \(\psi\) in DNF.  
_Parameter:_ The treewidth of the incidence graph \(\mathrm{IG}(X,\psi)\) of \(\psi\) with respect to \(X\).  
_Question:_ Is \(\varphi\) satisfiable?  
**Complexity:** para-\(\Sigma^{\mathrm{P}}_{2}\)-complete [6; 7].  
\(\mathrm{QSAT}_{2}(\forall\)-\(\mathrm{ittw})\)  
_Instance:_ A quantified Boolean formula \(\varphi=\exists X.\forall Y.\psi\), with \(\psi\) in DNF.  
_Parameter:_ The treewidth of the incidence graph \(\mathrm{IG}(Y,\psi)\) of \(\psi\) with respect to \(Y\).  
_Question:_ Is \(\varphi\) satisfiable?  
**Complexity:** para-\(\Sigma^{\mathrm{P}}_{2}\)-complete [6; 7].  
The above problems are parameterized by the treewidth of the incidence graph of the formula \(\psi\) (with respect to different subsets of variables). Since computing the treewidth of a given graph is NP-hard, it is unlikely that the parameter value can be computed in polynomial time for these problems. Alternatively, one could consider a variant of the problem where a tree decomposition of width \(k\) is given as part of the input.
# Application of the Segmented Correlation Technology in Seismic Communication with Morse Code
## 4 Data Analysis  
The energy of the seismic signal decreases as the distance increases, and the target signal at 60 m is certainly the weakest [38; 39; 40]. Through reading the collected signal, whose SNR is 3.83 dB at 60 m, the target signal is completely drowned in noise as shown in the Figure 6a.  
The signal in Figure 6a is too different from that in Figure 4a to distinguish the target signal, so the existence of the target signal needs to be identified. Spectrum analysis is first adopted to process the collected data, which can detect the frequency of the target signal [41; 42]. According to results of the spectral analysis in Figure 6b, the frequencies of collected signals are mainly concentrated below 100 Hz while the 200 Hz frequency of the target signal is not detected. Moreover, even if a 180-22 Hz band-pass filter is employed to filter the collected data, the target signal is still not found, while a lot of other vibration signals are obtained, such as footstep signals and elevator signals in Figure 6c. That is to say, the target signal is submerged by other vibration signals so that it is impossible to realize seismic communication within 60 m with a band-pass filter at the site under these conditions.  
To further extract the target signal, the SCTMC is employed to process the collected data obtained at 60 m. During the seismic communication experiment, the time of the seismic signal transmitted by the source is designed so that the rough position of the target signals can be predicted in collected data at 60 m. Then the SCTMC cuts the collected data into 4 s length segments, which are cross correlated with the decoded signal. By fine-tuning the position of cutting in collected data according to the cross-correlation results, the target signal (its SNR is 7.5 dB) is extracted while other vibration signals (including footstep signals) are suppressed. When the threshold of the correlation signal is set to 8.5, the binary code 10101010100110110110010101110 of the collected signal can be obtained, as show in Figure 6d. Then, we take advantage of the source to transmit the SOS signal again in this location, and similar results are obtained 60 m away from the source. At this time, the communication distance is 120 m and the relay distance is 60 m.  
Figure 5: The distribution of 5 CDI-S2C-2 geophones, 5 GEIWSR-II seismometers and a 500 N vibroseis source.
## 3 The GAGAN Framework: Evolutionary Optimization in GAN Architectures
### Genetic Operators in GAGAN
#### 3.2.1 Selection Process  
The selection process determines which individuals from the current population will reproduce. Tournament selection is employed, where a subset of individuals is randomly chosen, and the one with the best fitness is selected to produce offspring. This method enhances robustness and promotes genetic diversity, crucial for effective search space exploration.
_Algorithms_**2015**, \(8\), 712-722; doi:10.3390/a8030712  
_algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
Gradient-Based Iterative Identification for Wiener Nonlinear Dynamic Systems with Moving Average Noises  
**[PERSON], [PERSON] *, [PERSON] and [PERSON]**  
School of Electrical and Automatic Engineering, Changshu Institute of Technology, Hushan Road  
No.99, Changshu 215500, China; E-Mails: [EMAIL_ADDRESS] (L.Z.); [EMAIL_ADDRESS] (H.X.);  
[EMAIL_ADDRESS] (P.Z.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-158-5156-2542.  
Academic Editor: [PERSON]  
_Received: 30 June 2015 / Accepted: 20 August 2015 / Published: 26 August 2015_  
**Abstract:** This paper focuses on the parameter identification problem for Wiener nonlinear dynamic systems with moving average noises. In order to improve the convergence rate, the gradient-based iterative algorithm is presented by replacing the unmeasurable variables with their corresponding iterative estimates, and to compute iteratively the noise estimates based on the obtained parameter estimates. The simulation results show that the proposed algorithm can effectively estimate the parameters of Wiener systems with moving average noises.  
**Keywords:** nonlinear dynamic system; stochastic gradient; iterative algorithm; output error moving average; parameter estimation
# Intelligent Reflective Surfaces for Wireless Networks:
## 3 IRS-Assisted Wireless Networks
### Optimization and Design of IRSs
#### 3.3.2 Multiuser SISO Scenarios  
In [55] the authors consider a multiuser SISO IRS-assisted system in a LOS scenario and define the received signal-to-interference-plus-noise ratio (SINR) as the metric to be optimized at the receiver by considering that the impinging signals upon an IRS are often mixed with interfering signals. To tackle this issue, the concept of intelligent spectrum learning (ISL) is introduced, which uses an offline trained CNN at the IRS controller to infer the interfering signals directly from the user signals. A distributed control algorithm is proposed to maximize the received SINR by configuring an active/inactive binary status of the IRS elements. Simulation results exhibit a performance improvement by employing this deep learning approach.  
In [56], the authors aim at minimizing the MSE in a multiuser SISO multi-IRS assisted federated learning system. LOS conditions are assumed and a joint optimization of the user power allocation and the IRS phase-shift matrices via an alternating optimization (AO) algorithm is performed. The IRSs are considered to have a binary status, i.e., On/Off state. Simulation results show that the proposed algorithms lead to improving the convergence and accuracy of the federated learning approach and offer gains over baselines strategies without IRSs.  
Table 2 summarizes both multiuser SISO approaches [55; 56], and their assumptions in the system model, as well as the performance metrics and the main results.
# \(\alpha\)-Fluctuating Nakagami-\(m\) Fading Model for Wireless Communications
## 4 Performance Analysis of the \(\alpha\)-Fluctuating Nakagami Model
### ABER Performance of the \(\alpha\)-Fluctuating Nakagami Model
The resultant ABER expressions are derived in closed form and valid for arbitrary channel parameters, but from the practical standpoint, it is important to understand ABER high-SNR behavior, since it bounds from above the performance quality. Applying the results of Corollary 1, the high-SNR behavior of (15)-(17) can be obtained as follows. Corollary 3: _The high-SNR asymptotics of the average bit error rate expressions of the ABER (24) and (25) are given by:_  
* _for the coherent modulations:_ \[\overline{\mathrm{P}}_{\sigma r}^{c}\big{|}_{\gamma\to\infty}\approx\frac{ \delta_{1}m_{s}\Gamma\Big{(}\frac{am+1}{2}\Big{)}}{2\sqrt{\pi\Gamma}(m+1)(m_{s} +m)\zeta^{m}}\tilde{\gamma}^{-\frac{am}{2}}\sum_{j=1}^{\delta_{3}}\epsilon_{ 2,j}^{-\frac{am}{2}},\] (29)
* _for the non-coherent modulations:_ \[\overline{\mathrm{P}}_{\sigma r}^{nc}\big{|}_{\gamma\to\infty}\approx\frac{ \alpha m_{s}\Gamma\big{(}\frac{am}{2}\big{)}}{2(m_{s}+m)\Gamma(m)\zeta^{m}} \tilde{\gamma}^{-\frac{am}{2}}\sum_{j=1}^{\epsilon_{3}}\epsilon_{1,j}( \epsilon_{2,j})^{-\frac{am}{2}}.\] (30)  
In wireless communications through fading channels, it is a common practice to describe the possible performance improvements due to channel conditions in terms of the so-called diversity gain \(\mathcal{G}_{D}\) and the coding gain \(\mathcal{G}_{C}\), which are defined via the asymptotic ABER expression in the following form \(\overline{\mathrm{P}}_{\sigma r}\big{|}_{\gamma\to\infty}=(\mathcal{G}_{C} \tilde{\gamma})^{-\mathcal{G}_{D}}\). Corollary 4: _The diversity gain for the \(\alpha\)-Fluctuating Nakagami model linearly scales with the envelope transformation coefficient \(\alpha\), and does not depend on the shadowing coefficient \(m_{s}\), i.e., \(\mathcal{G}_{d}=\frac{am}{2}\) for both coherent and non-coherent modulations._
## 5 Conclusions  
This paper proposes a method for spoofing detection and direction-finding based on low-cost commercial board components. By using low-cost commercial board components, approximate DOA calculation accuracy can be obtained. The main conclusions are as follows:  
1. Low-cost commercial board components can be used for large-scale deployment and implementation due to their low-cost and low-complexity algorithms, with high feasibility and potential application value;
2. Compared with professional equipment, low-cost commercial board components must solve two problems in anti-spoofing applications, namely, power-on clock error and hardware delay. The hardware delay includes cable delay error, multipath error, and antenna phase center error. In this paper, the influence of the two problems on the carrier difference is theoretically deduced, and the corresponding error correction method is proposed. Aiming at the power-on clock error, it is proposed to use pseudo-range and instantaneous Doppler values to correct it. For the hardware delay error, this paper uses the method of depicting the time-series average sky map to compensate for it. Finally, an experiment is carried out on the error term, and the experimental results are consistent with the theoretical derivation and the actual situation;
3. In the anti-spoofing method, a binary hypothesis testing model is constructed. With a certain false alarm rate, a 100% spoofing detection rate is achieved. Additionally, all spoofing signals are identified. Furthermore, the direction information of the spoofer is obtained under the spoofing condition. The direction error is within \(5^{\circ}\) range, achieving high accuracy.  
In future work, we will pay more attention to the following research. One is to explore the relationship between the configuration of the antenna array and the accuracy of DOA at low elevation angles. The second is to apply low-cost commercial board components to more anti-spoofing scenarios. The third is to conduct test experiments in combination with more common low-cost components.  
Conceptualization, P.M., H.Y. and X.C.; methodology, P.M., H.Y. and R.L. ([PERSON]); investigation, Y.G., X.C. and R.L. ([PERSON]); resources, Y.G., S.L., G.Z., C.F. and J.X.; software, P.M. and H.Y.; writing--original draft preparation, P.M.; writing--review and editing, P.M., H.Y., X.C., R.L. ([PERSON]) and R.L ([PERSON]).; visualization, P.M., X.C. and Y.G.; supervision, S.L. and H.Y.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Defense Science and Technology Innovation Special Zone Innovation Workstation of China (Grant No.: CX2022-04-03-02). This work was supported by the Beijing Natural Science Foundation with Grant No. 1234041 and the State Key Laboratory of Satellite Navigation System and Equipment Technology with Grant No. CEPNT2022B01.  
Not applicable.  
The authors declare no conflict of interest.
## 4 PFAC Calibration Results
### Sentinel-3A
#### 4.1.2 Sentinel-3A Pass No. 278 and No. 335 Cal/Val Results  
Two sea-surface Cal/Val sites support the calibration of the descending Pass No.335 of Sentinel-3A (Figure 17). As the satellite descends, it meets first RDK1 Cal/Val where it is calibrated, and then about 6 s later it is again calibrated with the other CalVal site of Gavdos. The results obtained by these two Cal/Val sites agree at the sub-centimeter level. At this stage, the RDK1 Cal/Val results present somewhat larger dispersion compared to Gavdos Cal/Val. Larger scattering for the RDK1 Cal/Val may be caused by the employed calibrating region, covering a 35 km stretch (1000 km depth) of ocean water between Crete and Gavdos. That water stretch is shorter in length than that of the Gavdos south calibrating region (with 3000 m depth), and also fewer uncontaminated by land values are available for the satellite calibration (mountains of 2500 m height in the north of RDK1). Several updates and improvements of the marine geoid and the mean dynamic topography models in support of the RDK1 Cal/Val are currently ongoing.  
In the southwest tip of Crete, the CRS1 Cal/Val site provides support for the sea-surface calibration of the descending pass No. 278 of Sentinel-3A. Figure 18 presents the latest results for the Sentinel-3A. The estimated value for the bias in the sea-surface heights is found to be \(-3.68\) mm based on this CRS1 Cal/Val site.  
Figure 17: The latest calibration results for the descending Pass No. 335 of Sentinel-3A using the sea-surface calibration site located south of Crete site of RDK1 (upper) and the Gavdos Cal/Val sites (lower).  
In summary, the bias in sea-surface height for Sentinel-3A has been determined to be on average \(-\)8.04 mm (\(\pm\)5.5 mm, \(\pm\)32 mm FRM uncertainty) along its descending pass No. 335 based on Gavdos and RDK1 Cal/Val sites, while it is found to be \(-\)3.68 mm (\(\pm\)4 mm, \(\pm\)32 mm FRM uncertainty) along another descending Pass of No. 278 and using the other CRS1 Cal/Val site. It appears that the bias of Sentinel-3A in sea-surface height, as monitored at the PFAC:  
* Display a negative sign for all these calibrating passes;
* Shows an average bias of \(-\)6.82 mm (\(\pm\)32 mm FRM uncertainty) based on all three Cal/Val sites with descending orbits;
* Has no pass directional error in SSH bias as there is not significant trend at all between ascending and descending passes (\(-\)11.98 mm for Pass No. 14 and \(-\)8.04 mm for Pass No. 335).
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 2 Related Works
### Centroid-Based K-Means Clustering  
Most of the previously mentioned papers utilize either K-means or FCM techniques. [PERSON] and [PERSON] (2014) [23] introduce K-medoid clustering, comparing it to FCM, KMC, and Y-means clustering (YMC). Unlike K-means, which calculates the centroid as the average of all data points in a cluster, K-medoids use actual data points as centroids. This method is more robust to noise and outliers as it avoids relying on the mean, which can be heavily influenced by outliers. Using the KDD-Cup99 dataset, this study demonstrated that the K-medoids approach performed significantly better in intrusion detection than the other three methods: KMC, FCM, and YMC. Despite achieving a high detection rate and fewer false negatives, this algorithm has certain limitations. The authors suggest that future research could focus on improving efficiency to further enhance detection accuracy.  
Another study by [PERSON] and [PERSON] (2009) [24] claims that K-means is not a \"globally optimal solution\" due to its high computational cost when working with large datasets since it calculates the distance between the data object and the center of each cluster each time it iterates. Similar to the PSO-KM optimization in [PERSON] et al. (2006) [17], this paper proposes a modified K-medoids method by combining it with the improved triangle trilateral relations theorem, which can lead to better detection of abnormal behavior while reducing false positives. The K-medoids method uses a data point in the cluster instead of the mean, and the triangle theorem reduces the distance computation time. The results show that this proposed algorithm effectively separates attack data from normal data, all while reducing computation time and obtaining a very high fault detection rate and a low false drop rate. However, like [PERSON] et al. (2006) [17], this algorithm fails to address the dependence of centroid-based clustering on the initially chosen centroid. Simply choosing a different initial central point deeply influences the result.
# Path Optimization Using Metaheuristic Techniques for a Surveillance Robot
## 4 Optimization Methodology
### Software Architecture  
The implementation of the software architecture for the integration of the different optimization algorithms with the robot simulator and the LiDAR is shown in Figure 7.  
The real robot has a program that supports XML files that contain the parameters of the points mentioned in the previous section, i.e., \(\{x_{r},y_{r},\theta_{r}\}\). Thus, a computational code that converts these points from matrix format to this type of file was programmed.  
Each time the optimization algorithms provide a new combination of parameters, a program is called that converts it into XML. This file is read by the Python version 3.11.3. trajectory simulation program, exactly as the real robot would do. The simulator then stores the robot's position at each instant of time, which is used by another Matlab version is R2020b Update 8 program that detects whether there has been a collision with an obstacle and calculates the distances between intersections, the percentage of vision (percentage that has been surveilled), and the trajectory time. Then, these parameters are used in the cost function. In fact, the cost function of the optimization algorithm differentiates three cases: (i) there is a collision with one or more obstacles; (ii) there is no collision, but coverage of the space is not completed; and (iii) there is no collision, and coverage is completed.
# A New Resource Allocation Protocol for the Backhaul of Underwater Cellular Wireless Networks
## 4 Simulation and Performance Analysis Results
### Simulation Results and Performance Analysis  
In this paper, we performed many different SLSs for different parameter sets, but we show only some remarkable results in this section. As stated in Section 1, we considered two simulation scenarios as follows:  
* Scenario 1: varying the upper limit (or maximum) of the backhaul capacity of a UBS.
* Scenario 2: varying the lower limit of the resource utilization threshold where a UBS returns unused backhaul capacity when its current resource-utilization becomes less than this lower limit.
## 4 Discussion  
Previous studies have often used NDVI to investigate the spatio-temporal variations of regional vegetation. However, NDVI tends to become saturated during the flourishing period of vegetation growth and is affected by background soil disturbance [62]. Furthermore, CI is a significant parameter in the simulation of the interaction between the ecosystem and atmosphere. In this study, the spatial and temporal variation characteristics of CI in the Sanjiang Plain from 2001 to 2015 were analysed to provide a novel perspective for investigating variations in vegetation based on the CI and provide reliable foliage clumping effects for the retrieval of other vegetation structures and the simulation of land surface models. Furthermore, the effects of natural and anthropogenic forces on the spatial differentiation of CI were quantitatively explored to provide theoretical support for understanding the driving mechanism between CI and driving factors. However, the climatic factors, topographic factors, and anthropogenic factors that influenced the spatial distribution of CI were not fully considered. Although the time series of 15 years could indicate the relationships between CI and its driving factors to a certain extent, an even longer time series should be used to investigate the driving mechanism of the spatial differentiation of CI. Moreover, the spatio-temporal variations of CI for different vegetation types were not investigated.
## 4 SF-Partition-Based Clustering and Relaying (SFPCR) Scheme  
In this section, we explain the proposed SF-partition-based clustering and relaying (SFPCR) scheme in detail, which has three methods. First, we provide a detailed explanation of SF partitioning, followed by the density-based subspace clustering in the HSFZ and relay LoRa device selection mechanism, respectively.