esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning
## 4 Proposed Method
### Data Preprocessing  
In general, a set of network flows from the datasets can be treated as graph data, as each of the network host IP addresses can be represented as a graph node, and network communication flows between each host can be represented as edges. Since the original dataset forms an unattributed graph where all nodes have the same label of 1, it is challenging to make predictions based solely on node features in the feature learning process. To comprehensively determine whether a node belongs to a botnet, it is necessary to consider the labels of the edges connecting the nodes.  
As hackers can instruct all their controlled bot hosts in the botnet to simultaneously initiate continuous access to specific network targets at specific times [48], there is a significant difference in communication volume between benign nodes, malicious nodes, and benign nodes under attack. Hence, the labels of the edges connecting the nodes also exhibit substantial variations.  
To address this, we preprocess the data to incorporate the edge labels as a new feature of each node. We calculate the sum of all edge labels connected to a node and divide it by the node's degree. This normalized value serves as the new feature for the node. It is important to note that we do not want this new feature to be a learnable parameter, so we perform this preprocessing step to obtain the node's updated feature.
## 4 Conclusions  
In our simulation results, we found that a smaller ROF could provide a better BER performance, and to optimize the PS performance, we needed to consider both the signal entropy and the error correction capability of an LDPC code. In practical usage, there will be many potential limits on the ROF and FEC overhead, since in most cases, we can only use an appropriate ROF for a certain FEC overhead. We have shown that the optimization was not highly sensitive to either the ROF or FEC overhead, and we could do the optimization on any limit of the ROF and FEC overhead. The improvement can be achieved without any equipment upgrade, which means that the proposed LDPC-coded PS-NPS-8-PAM scheme can be widely applied in data center communications and other short-reach applications. We have shown that the Nyquist pulse shaping could provide a 0.43 dB improvement, and the probabilistic shaping provided a 1.1 dB gain. By the joint use of probabilistic shaping and Nyquist pulse shaping, we obtained a 1.27 dB performance gain.  
This research was conducted by X.H. M.Y. offered experimental support. [PERSON], [PERSON], [PERSON] and [PERSON] supervised the NPS and PS idea and simulation during [PERSON]'s internship at Juniper Networks. Both the simulation and the experimental demonstration were supervised by [PERSON]. This research was funded by Juniper Networks and NSF.  
The authors declare no conflict of interest.
## 5 Experimental Results and Analysis
### Anti-Spoofing Effect of the Multi-Correlator Structure
In subfigure (b), the positioning of the target receiver remains stable and is not obviously affected by the spoofing, which consolidates the effectiveness of the proposed anti-spoofing method. Figure 13: All correlators’ outputs of I branches of the counterfeit signals with long delays.
## 1 Introduction
This capability allows for the proactive identification of potential spatial conflicts between construction elements. Early detection of such clashes is crucial for managing complex projects where coordinating concurrent activities and resources is paramount. However, while Synchro is effective at spatial conflict detection, it has limitations in fully resolving these conflicts. The tool does not possess advanced functionalities for automatically adjusting the construction schedule or resource allocation to eliminate the identified conflicts. Consequently, project managers may need to manually manipulate the schedule or resource plans within Primavera P6 to sequence activities differently and find a feasible, conflict-free solution. This paper presents Optimizio [8; 9; 10], a heuristic scheduling tool enhanced with functionalities that address the limitations associated with conventional resource leveling and conflict resolution processes. Optimizio automates the entire workflow, offering significant advantages for project management, including the ability to tackle complex scheduling problems, flexibility, and ease of use, which eliminates the need for additional training. The proposed approach offers seamless integration from Primavera P6's project planning data to spatial conflict reports from Synchro 4D. This facilitates the streamlined execution of resource leveling and clash resolution while adhering to all predefined project constraints. The resulting feasible schedule is generated in a format that is directly compatible with Primavera P6, enabling efficient workflows for industry professionals. Through rigorous validation by domain experts, the solution demonstrates its promise for addressing real-world challenges in industrial applications. The proposed Optimizio approach and its implementation details are thoroughly discussed in this paper. The organization of the paper is as follows: Section 2 presents the materials and methods, providing a detailed description of the problem, a benchmark instance utilized for evaluation purposes, and the overall methodology employed by Optimizio. Section 3 showcases the results of the proposed algorithms. Finally, the major findings, implications, and future research directions are explored in Section 4.
# Investigation of the Opposite-Electrode Effect on the Planar Solid-State Pulse-Forming Line
## 7 Conclusions  
Our results show that the proposed opposite-electrode PFL outperforms the full-electrode PFL in terms of withstand voltage and thermal characteristics, and the PFL with an opposite-electrode structure has a higher energy density. A ground electrode covering the entire lower surface can provide additional edge capacitance for a planar solid-state PFL, which makes the characteristic impedance of the full-electrode PFL lower. However, through theoretical and simulation analyses, we found that when the ground electrode and the high-voltage electrode have unequal widths, the edge electric field of the electrode will be enhanced and may cause local overheating after continuous operation. Therefore, we designed the opposite-electrode PFL with a ground electrode structurally identical to the high-voltage electrode and positioned them directly opposite each other, and the simulation results show that the edge electric field of the opposite-electrode PFL's high-voltage electrode is lower. Although the experimental results show that the opposite-electrode PFL has a slightly higher characteristic impedance compared to the FPFL, its withstand voltage increased from 23 kV to 33 kV and it has better thermal characteristics. Consequently, the opposite-electrode structure of the planar solid-state PFL may enhance both the compactness and energy utilization efficiency of the pulse source and electroporation system.  
Conceptualization, F.Z. and S.L.; Methodology, Z.F., F.Z. and S.L.; Software, Z.F., Y.L. and C.J.; Validation, Z.F. and C.J.; Formal analysis, Y.L.; Investigation, F.Z.; Resources, S.L.; Data curation, Z.F. and C.J.; Writing--original draft, Z.F.; Writing--review & editing, F.Z., Y.L. and S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
This study aims to assess the dynamic stall of the wind turbine blade undergoing pitch oscillation (PO) and oscillating freestream (OF), respectively. Firstly, a thin-airfoil theoretical analysis was performed to differentiate between these two dynamic effects. During upstroke, PO results in a positive effective airfoil camber, while OF has an additional negative effective airfoil camber, and yet in contrast during downstroke, PO decreases the effective camber, while OF increases the effective camber. Secondly, the equivalence relation between PO and OF is investigated by numerically solving the unsteady Reynolds-averaged Navier-Stokes equations. The difference between PO and OF mainly exists in the linear part of the aerodynamic loads. Because the difference is great at high reduced frequencies or angle of attack (AOA) amplitudes, PO and OF should be treated separately for dynamic stall from different aerodynamic sources. Thirdly, the Beddoes-Leishman dynamic model coupled with [PERSON]'s rotational stall delay model was used to predict the yawed responses of the blade section. The obtained results show different aerodynamic responses between PO and OF, although consideration of rotational augmentation can greatly improve the accuracy of the lift and drag coefficients. To improve the understanding and coupling modeling of rotational augmentation and dynamic stall, an extended analysis of the coupled effect was performed as well.  
dynamic stall; pitch oscillation; oscillating freestream; rotational augmentation; wind turbine
# An Enhanced Lightweight IoT-based Authentication Scheme in Cloud Computing Circumstances
## 4 Security Analysis and Performance Evaluation
### Security of Session Key
#### 4.2.1 Known-key Security  
In our scheme, \(CS\) computes new session key every time the authentication is correct. This means that, \(CS\) uses the new random nonce \(\left(n_{U}^{new}\right)\), \(\left(n_{S}^{new}\right)\) and \(\left(n_{CS}^{new}\right)\), and its current timestamp \(\left(T_{CS}^{new}\right)\) to compute a fresh session key, avoiding the compromise of previous session keys. If the attacker knows \(M_{7}=\left\{D_{6},D_{7},D_{10},D_{8},D_{9},D_{11}\right\}\), he/she cannot compute a valid session key \((SK_{U-S})\) without random nonce and \(CS^{\prime}\)s timestamp. Even though the attacker knows past session key \(\left(SK_{U-S}^{old}\right)\), he/she cannot compute the new session key by means of any type of combination.
# Cleaning of Phototrophic Biofilms in a Show Cave: The Case of Tesoro Cave, Spain
## 3 Results and Discussion
### Recommended Cleaning Protocols  
The recommended procedure is first to carry out mechanical cleaning in those cases where the biofilm is thick, such as, for example, in cases of bryophytes and dense algae biofilms. Subsequently, hydrogen peroxide must be used to remove the adhered residues, and not be mechanically removed. This allows the use of less of the chemical and optimization of the results.  
Cases where the green color is not completely eliminated can be treated exceptionally well with sodium hypochlorite, first testing a small rock area to check its effectiveness. If the green color does not disappear with the hydrogen peroxide, or subsequently with sodium hypochlorite, it is likely because the cells with the chlorophyll pigment are embedded in a calcite matrix.  
Most of the rock art paintings are in a gallery not accessible to visitors (Galeria de Breuil, Figure 1), and therefore without lighting. No green biofilms were observed in this part of the cave. However, Sala del Aguila has a marked problem of colonization by phototrophic microorganisms, associated with the presence of a lighting source, which is very close to some of the rock art painting. This converts the cave into no longer a natural monument, but a site of cultural interest, and therefore the precautions that the current legislation provides for this site were adopted. The occurrence of a lighting point relatively near a painted horse protome, a typology from the Upper Paleolithic, threatened the painted area (Figure 10). The occurrence of bryophyte protonema and/or phototrophic microorganisms represents a real danger for the conservation of the painting. Considering these conditions, mechanical cleaning was proposed for the bryophytes and active phototrophic colonization, which was supervised at all times by the archaeological direction of the cave throughout the duration of the intervention.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 5 Taxonomy for Task Scheduling Algorithms in Fog Computing
### Problem Solution Techniques
#### 5.2.7 Fuzzy Logic Approach  
Fuzzy logic is a mathematical framework capable of handling uncertain and imprecise information [30]. In task scheduling, this approach can aid in managing the system's environmental unpredictability and variability, such as fluctuating workload and resource availability, typical of fog computing.
## 4 Second Step: Regional Pole Placement  
### Overview  
The aim of this section is to formulate the condition ensuring that the remaining \(2N-p\) poles belong to certain regions of the complex plane in a way that is computationally reliable, and then to explain how to properly choose \(\mathbf{k}_{r}\) that fulfils such a requirement. First, the first-order representation of the controlled system is employed:  
\[\left\{\begin{array}{l}\dot{\mathbf{x}}=\mathbf{A}\mathbf{x}+\mathbf{B}u\\ u=-\mathbf{k}^{T}\mathbf{x}\end{array}\right., \tag{11}\]  
where  
\[\mathbf{x}=\left\{\begin{array}{l}\dot{\mathbf{q}}\\ \mathbf{q}\end{array}\right\}\ \mathbf{A}=\left[\begin{array}{cc}-\mathbf{M}^{-1} \mathbf{C}&-\mathbf{M}^{-1}\mathbf{K}\\ \mathbf{I}&\mathbf{0}\end{array}\right]\ \mathbf{B}=\left\{\begin{array}{l} \mathbf{M}^{-1}\mathbf{b}\\ \mathbf{0}\end{array}\right\}\ \mathbf{k}=\left\{\begin{array}{l}\mathbf{f}\\ \mathbf{g}\end{array}\right\}. \tag{12}\]  
Then, the system is transformed into a reduced one representing the \(2N-p\) poles whose location is not exactly imposed. Such a reduction introduces the state of the reduced system, \(\hat{\mathbf{x}}\in\mathbb{R}^{2N-p}\), by exploiting the following transformation  
\[\mathbf{x}=\mathbf{V}\hat{\mathbf{x}}, \tag{13}\]  
where \(\mathbf{V}\) has been defined in Equation (7). Let \(\mathbf{U}\in\mathbb{R}^{(2N-p)\times 2N}\) be the pseudoinverse of \(\mathbf{V}.\) Since the columns of \(\mathbf{V}\) are linearly independent and \(\mathbf{U}\) is its left inverse, it follows that \(\mathbf{U}\mathbf{V}=\mathbf{I}_{2N-p}\). Hence the reduced subsystem is defined as follows:  
\[\left\{\begin{array}{l}\dot{\mathbf{x}}=\mathbf{U}\mathbf{A}\mathbf{V}\hat\mathbf{{x}}+\mathbf{U}\mathbf{B}\hat{u}\\ \hat{u}=-\mathbf{k}^{T}\mathbf{V}\hat{\mathbf{x}}\end{array}\right. \tag{14}\]  
Since it has been defined \(\mathbf{k}=\mathbf{k}_{0}+\mathbf{V}\mathbf{k}_{r}\) (see Equation (7) and (13)) can be further manipulated as follows:  
\[\left\{\begin{array}{l}\dot{\mathbf{x}}=\mathbf{U}(\mathbf{A}-\mathbf{B}\mathbf{k}_ {0})\mathbf{V}\hat{\mathbf{x}}+\mathbf{U}\mathbf{B}\hat{u}\\ \hat{u}=-\mathbf{\left(\mathbf{V}^{T}\mathbf{V}\mathbf{k}_{r}\right)}^{T}\hat{\mathbf{x}} \end{array}\right. \tag{15}\]  
By defining \(\hat{\mathbf{A}}=\mathbf{U}(\mathbf{A}-\mathbf{B}\mathbf{k}_{0})\mathbf{V},\hat{ \mathbf{B}}=\mathbf{U}\mathbf{B}\) and \(\hat{\mathbf{k}}=\mathbf{V}^{T}\mathbf{V}\mathbf{k}_{r}\), for brevity of representation, the reduced system can be finally written as  
\[\left\{\begin{array}{l}\dot{\mathbf{x}}=\hat{\mathbf{A}}\hat{\mathbf{x}}+\hat{ \mathbf{B}}\hat{u}\\ \hat{u}=-\hat{\mathbf{k}}^{T}\hat{\mathbf{x}}\end{array}\right. \tag{16}\]
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 3 Channel Estimation  
In this study, we analyze delay and Doppler-correlated Rayleigh fading channels, acknowledging that real-world channels inherently exhibit correlation [11]. The spatially correlated channel vector between the \(m\)th AP and the \(k\)th user, denoted as \(g_{mk}\in\mathbb{C}^{N\times 1}\) (where \(m=1,\ldots,M\) and \(k=1,\ldots,K\)), is described by (56), with \(\beta_{mk}\) representing the large-scale fading coefficient.  
\[g_{mk}=\beta_{mk}^{1/2}h_{mk}. \tag{56}\]  
CF-MMIMO systems exhibit significant macro-diversity and beneficial signal propagation [11], both of which are amplified by the deployment of a large number of Access Points (APs) [11]. Consequently, traditional Maximum Ratio (MR) processing [11] is commonly employed due to its optimal performance in environments with minimal inter-user interference. However, practical implementations often achieve substantial improvements in Spectral Efficiency (SE) through the use of Minimum Mean Square Error (MMSE) processing techniques, which effectively reduce inter-user interference [2,3]. The highest SE is attained through centralized processing, where the central processing unit (CPU) utilizes CSI to coordinate interference suppression among APs. Nevertheless, for practical reasons, the local execution of receiving, combining, and transmitting the precoding at each AP is preferred.  
The following linear channel estimators are compared: Maximum Ratio (MR), Minimum Mean Squared Error (MMSE), Zero Forcing (ZF), and Partial Zero Forcing (PZF) channel estimators, in both traditional and DMD/PiDMD/mpeDMD fashions.  
Figure 6 displays the norm of the eigenvalues of the Toeplitz structure of the matrix \(R\) considering the simulation scenario used in this work. In order to compute the Correlation matrix expression, (6) is used. This graph will be used in the next sections as a baseline for comparison.  
Initially, it is crucial to determine whether there exists a relatively small group of eigenvalues that are significantly larger in magnitude than the others, which is essential for a proper DMD application. To demonstrate this empirically, several simulations are conducted using the scenario described in the Simulation Scenario section, employing a Monte Carlo algorithm with 500 scenarios. Figure 7 displays a typical graph illustrating the composition of normalized eigenvalues. Clearly visible in this graph is a small subset of eigenvalues that can characterize the system. This condition ensures the efficient use of the proposed algorithm.
## 4 Neurodegenerative Diseases
### Parkinson Disease  
PD is the second most frequent NDD globally [1; 8]. There is an increasing prevalence of PD as age advances, and is predicted to increase to 12.9 million in 2040 globally. Furthermore, it is not common before the age of 50, and it is more frequent in men than women between 50 and 59 years old [16].  
In PD, there is an accumulation of misfolded \(\alpha\)-Syn proteins (known as Lewy bodies) in dopaminergic neurons of the substantia nigra, considered a biomarker in PD [8; 14; 16]. The accumulation of \(\alpha\)-Syn protein leads to motor symptoms (bradykinesia, postural instability, resting tremors, and stiffness) [14; 41] and non-motor symptoms in the GI tract, like constipation, which affects 80% of individuals [1; 7; 9; 14; 16; 35], cognitive deficits, winogenital complications, and olfactory damage (hyposmia). Hyposmia often anticipates diagnosis by years, suggesting an early accumulation of the \(\alpha\)-Syn protein in the olfactory bulb [14].  
The ENS could be a way of communicating between the gut microbiota and the CNS and could contribute to the development of PD [14; 16]. GI impairment anticipates motor symptoms, suggesting that \(\alpha\)-Syn accumulation first takes place in the ENS and then advances to the CNS [14], indicating a potential connection to the GBA [2; 16]. Furthermore, in vivo studies evidenced that \(\alpha\)-Syn is disseminated from the intestine via the vagus nerve to the brain [5; 9; 14].  
In addition, PD patients present modifications of the gut microbiota as well as an increased permeability of the intestinal barrier [2; 5], permitting access to toxins that may impair the neuroendocrine system and GBA [5; 14; 16]. Dysbiosis has been suggested as being an early marker in PD because the aggregation of \(\alpha\)-Syn in the ENS anticipates the symptoms of PD [16].  
PD patients present low quantities of Prevotellaceae [4; 14], which are SCFA producers that contribute to intestinal barrier impairment, and low quantities of _Lachnospiraceae_ (SCFA producers as well), such as _Ruminococcus_, _Roseburia_, and _Blautia_. On the other hand, PD patients present increased _Enterobacteriaceae_ that increase levels of LPS, lead to neuroinflammation, and are related to postural instability [4; 16] and increased pro-inflammatory genera such as _Proteobacteria_[16].
## 2 Materials and Methods
### Data Preprocessing and Analysing
#### 2.2.1 VMS Data Preprocessing  
To reduce the amount of data and maintain the original VMS data characteristics, the speed, heading, latitude, longitude, and positioning time data of 32 fishing vessels during the TACs project were extracted by averaging three pieces of VMS data in chronological order. Meanwhile, the VMS data with speeds less than 0.2 knots were excluded to ensure that the vessels were in the state of casting, hauling, or sailing. Finally, 28,956 VMS data points were obtained. The VMS data were processed using the 2020 version of Microsoft Excel.
# Multi-Objective Optimization of Cascade Blade Profile Based on Reinforcement Learning
## 2 Parameterization of the Blade Profile  
The size of the database for training networks grows fast with the increased number of the input and output variables of the networks. For computation reduction, the number of parameters is required to be as few as possible. One approach is to parameterize the perturbation instead of complete blade profile. It is necessary to have an adequate number of variables to ensure geometric accuracy if parameterization is directly applied to complete description of the blade profile. However, the required number is less if the parameterization is applied on the perturbation of the blade profile.  
The parameterization is used to obtain a library of modified profiles with related parameters. The parameterization of blade profiles in this paper is achieved by using the Hicks-Henne bump functions to perturb the original airfoil with assigned coefficients. For situations without fitting the leading edge and trailing edge, the shape functions are defined as:  
\[b_{i}(x)=sin^{w}(\pi x^{\ln 0.5/\ln x_{i}}), \tag{1}\]  
where \(x_{i}\) represents the normalized position of the bump, and \(w\) controls the width of the bump (\(w\) equals 3 in this paper). The deformed profile curve can thus be described by:  
\[y(x)=y_{basic}(x)+\sum_{i=1}^{N}\alpha_{i}b_{i}(x), \tag{2}\]  
where \(\alpha_{i}\) presents the magnitude of bumps.  
According to previous numerical simulation of the internal flow field of the initial cascade done by the authors, the front middle of the blade suction surface is where the transition and a strong passage shock wave takes place. In order to control the transition position and the passage shock, for both suction surface and pressure surface, five bump functions are used to perturb the profile, and the \(x_{i}\) equals \(0.2,0.3,0.4,0.5,0.6\)\((i=1,2,3,4,5)\) in Equation (1). The shape functions of profile perturbation are shown in Figure 2.  
The geometric variable consists of ten coefficients of bump functions \(\alpha_{i}\). With Latin Hypercube Sampling (LHS), the geometric parameters database is generated, thereby generating the blade profiles library. Ranges of variation of each coefficient are given in Table 1, where \(\mu_{\alpha_{i}}\) and \(\sigma_{\alpha_{i}}\) represent expectation and standard deviation of the normal distribution of bump coefficient \(\alpha_{i}\). The distribution of the profiles is shown in Figure 3.