esa-sceva/satcom-chunk-collection huggingface dataset:
# Punching Shear Strength of Reinforced Concrete Flat Plates with GFRP Vertical Grids
## 4 Test Results
### Amount of Shear Reinforcement  
Figure 8 shows the effects of the GFRP shear reinforcement on the shear strength of the concrete flat plate based on the load displacement relationships of the G-184B, G-246B, G-307B and G-369B specimens. As shown in Figure 8, although all specimens showed similar behavior until they reached the maximum load, the displacement increased with the applied load. As expected, the maximum load increased with the amount of shear reinforcement. Considering the G-184B specimen as a reference, it was determined that the maximum load increased by 2% for the G-246B specimen, 11% for the G-307B specimen and 21% for the G-369B specimen. In the specimen with a high shear reinforcement ratio, the radial compressive force near the column acted horizontally. A substantial portion of this force was transmitted into the region around and under the lower part of the shear reinforcement. The eccentricity of the axial compressive force due to the high shear reinforcement ratio caused horizontal splitting cracks on the top surface of the slab. Consequently, the specimens with a high shear reinforcement ratio showed a rapid load reduction after reaching the maximum load.  
Figure 7: Cont.  
Figure 7: Crack patterns in the cut section: (**a**) Control; (**b**) G-184B; (**c**) G-184C; (**d**) G-246A; (**e**) G-246B; (**f**) G-246C; (**g**) G-307A; (**h**) G-307B; (**i**) G-369B.
# Insider Threat Detection Using Machine Learning Approach
## 4 Experimental Results
### Discussion  
There were four distinct classification models that we utilised. Compared to the anomaly detection methods, the ML classification models produced the best overall results. The SVM model achieved the best results by successfully identifying all threat users. The primary step in the SVM experiment is finding the best combination of C and gamma parameters. A high value of C attempts to minimise the misclassification of the training data, while a low value smooths the model. Conversely, if the gamma value is too large, it will lead to an overfitting problem.  
As the findings show, relying on SMOTE to achieve an equal balance with the majority class may not always the best option, depending on the desired outcome. Despite the fact that SMOTE did not raise the FI \(-\) score in the NN model, it boosted recall at the expense of accuracy and precision, an entirely desirable outcome when it comes to detecting insider threats, as a few false alarms are more desirable than undetected attacks. We would also like to emphasise that the original SMOTE paper [52] achieved excellent results by combining SMOTE and random under-sampling. Under-sampling was not used in this study because all data instances were necessary. Random forest and AdaBoost are machine learning algorithms designed explicitly for imbalanced datasets. We note that SMOTE is not required to achieve excellent results with these two methods. The overall performance of AdaBoost is superior to that of the random forest in all cases.  
A comparison of other previous similar methodologies is presented in Table 8. The main similarity is that they handle collecting the data the same way as this research, where each data instance represents a user feature vector. The SVM model obtained the highest overall performance compared to the other algorithms. It achieved 100% accuracy. Furthermore, it has a higher recall (100%) and fewer false alarms. Additionally, the CNN  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1 \(-\) Score** & **Accuracy** \\ \hline NN & 0.98 & 0.95 & 0.96 & 0.95 \\ SVM & 1.00 & 1.00 & 1.00 & 1.00 \\ AdaBoost & 0.98 & 0.94 & 0.95 & 0.94 \\ RandomForest & 0.95 & 0.72 & 0.81 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of classification methods with SMOTE.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1 \(-\) Score** & **Accuracy** \\ \hline OCSVM & 0.94 & 0.86 & 0.89 & 0.86 \\ iForest & 0.92 & 0.91 & 0.88 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the anomaly detection methods.  
model [34] has achieved a high accuracy (100%). However, since no values are provided for the other evaluation metrics, it is difficult to ascertain how well they are doing.
## 1 Introduction
### Motivations and Our Contributions  
In contrast to [27; 28], Nakagami-\(m\) fading channels are deployed for an IRS-aided system in a recent work [29]. The performance is decided by two phase configuration designs including random and coherent phase shifting. As the main performance metrics, the authors in [29] derived formulas of the outage behavior and the bit error rate when binary modulation schemes are applied. The closed-form approximations for the ergodic capacity are extra performance evaluations. However, we first need to answer how many IRSs are sufficient to obtain the expected achievable rate. Secondly, Rayeligh and Rician channels could be applied for several practical situations of UAV. Therefore, this study prefers to examine the achievable rate in practical scenarios where hardware impairment is a key factor in degraded performance. We can summarize our contributions as follows.  
* We consider an IRS-NOMA-UAV system without direct links, which consists of a source and several IRSs. We focus on the performance analysis of a group of two users and further determine the impact of hardware impairment.
* We derive closed-form expressions for the achievable rates for two NOMA users under the channel models of Rayleigh and Rician. Compared with recent work [30], our result could be combined with their result to provide complete ergodic performance analysis in a more practical circumstance.
* We employ Monte Carlo simulations to validate the analytical outage probabilities. The achievable rate of each user mainly depends on power allocation factors rather than other main parameters such as the number of IRSs, the number of meta-surface elements and the IRS reflecting coefficient.  
The remainder of this article is organized as follows. In Section 2, a double IRS-NOMA-UAV system model is described, and the respective received signal with hardware impairment is formulated. We provide the derivation of closed-form ergodic achievable rates for a group of IoT users in Section 3. We aim to verify the results of computations in Section 4. Finally, we summarize concluding remarks and future research in Section 5.
## 4 Discussion
### Endurance and Strength  
The analysis of both LT\({}_{1}\) and LT\({}_{2}\) thresholds reveals significant relationships with strength parameters. Practically, this shows how strength influences endurance and aerobic capacity, which is essential for volleyball players who need to sustain high performance throughout matches. The median speed at LT\({}_{1}\) of 1.80 m/s aligns with the findings published by [PERSON] et al. [33] who suggested that well-trained athletes participate in various forms of physical exercise and have an increased aerobic capacity. Furthermore, LT\({}_{2}\) was determined at a mean velocity of 2.56 m/s. These results will be further analyzed alongside strength, which, however, were correlated with and confirmed by the findings of [PERSON] et al. [34]. In their paper, the strength and power level are positively correlated with the anaerobic capacity. This is particularly relevant for training strategies, where athletes can benefit from strength exercises that enhance both aerobic and anaerobic capacities. Further on, strength performance was correlated to LT\({}_{1}\), specifically through mean RF during the 1 RM squat (\(p=0.0060\), r = 0.3401). These results suggest that lower body strength plays an important role in enhancing aerobic performance, supporting the conclusions of [PERSON] et al. [35], who stated that strength will affect aerobic performance by improving muscle recruitment. However, the lack of correlation between LT\({}_{1}\) and maximum F during SQs suggests that absolute strength may not directly impact submaximal physical exercise, as stated by [PERSON] et al. [36] in endurance sports. For coaches, this suggests that aerobic conditioning should be combined with strength training to optimize endurance. Yet a comprehensive approach that combines strength development, endurance training, active recovery, and adequate rest is important for avoiding long-term injuries. Structured recovery days, along with cross-training methods, can help maintain optimal muscle health and ensure that strength training does not negatively affect performance or increase susceptibility to injuries. However, we furthermore describe the significance of upper body strength, highlighted by the correlations of BP performance with LT\({}_{1}\), while the relationships between LT\({}_{2}\) and strength measures during the SQs and BPs indicate that peak strength may be important for maintaining high-performance levels, as noted [PERSON] et al. [37] in elite sport climbers. Moreover, the correlation of mean and maximum power output during the DL and LT\({}_{2}\) emphasizes the role of dynamic strength in sustaining physical performance under fatigue.
# Path Planning of Multi-Type Robot Systems with Time Windows Based on Timed Colored Petri Nets
## 5 Path Planning for MTRSs Based on TCPNs
### Preprocessing of the Forbidden Regions  
Any forbidden region \(r_{q}\in R^{A}\) should not be visited by robots at any state (i.e., marking). This can be done by removing the places that correspond to the forbidden regions and their input and output arcs from the TCPN model. In Algorithm 2, the place \(p_{q}\) corresponding to forbidden region \(r_{q}\) was removed from the TCPN system along with its corresponding row information from \(\mathbf{Pre}\), \(\mathbf{Post}\), and \(\mathbf{M}_{0}\). In addition, all the input and output transitions of \(p_{q}\) and the column information from \(\mathbf{Pre}\) and \(\mathbf{Post}\) were removed. As a consequence, the forbidden regions were excluded from the planned trajectories. Note that \(t_{i}^{\cdot}\) and '\(t_{i}\) denote the output places and input places of transition \(t_{i}\), respectively.  
```
0: A TCPN system \(G=\langle N,\mathbf{\theta},\mathbf{M}_{0}\rangle\), where \(N=(P,T,Co,\mathbf{Pre},\mathbf{Post})\);
0: A simplified TCPN system \(G=\langle N,\mathbf{\theta},\mathbf{M}_{0}\rangle\) that does not contain forbidden regions
1:for each forbidden region \(r_{q}\in R^{A}\)do
2: remove the row corresponding to the place \(p_{q}\) from \(\mathbf{Pre}\), \(\mathbf{Post}\), and \(\mathbf{M}_{0}\), respectively, where \(p_{q}\in P\) is the place that represents region \(r_{q}\);
3:\(P:=P\backslash\{p_{q}\}\); % remove place \(p_{q}\) from the set \(P\)
4:for each\(t_{i}\in T\)
5:if\(t_{i}^{\cdot}=p_{q}\) or '\(t_{i}=p_{q}\), then
6: remove the column corresponding to transition \(t_{i}\) from \(\mathbf{Pre}\), \(\mathbf{Post}\), \(\mathbf{w}\), and \(\mathbf{\theta}\), respectively; % remove the paths related to the forbidden region \(r_{q}\)
7:\(T:=\cap\{t_{i}\}\);% remove the input transition or output transition of \(p_{q}\) from the set \(T\)
8:endif
9:endfor
10:endfor
```  
**Algorithm 2**Preprocessing of the forbidden regions
## 1 Introduction  
The \(b\)-value is a pivotal parameter in seismology, quantifying the relationship between earthquake magnitudes and their occurrence frequency in a specific area. This metric plays a crucial role in decoding earthquake patterns and assessing future seismic risks. It transcends a mere statistical measure, serving as a vital gauge of the Earth's crustal stress state. Generally, lower \(b\)-values signal higher stress levels in a region, while higher \(b\)-values often denote lower stress conditions. This inverse relationship has been substantiated through extensive studies on fault systems' physical properties, creep-slip dynamics along fault lines, and the movement of pore pressures within the crust, as evidenced by seminal works [1; 2; 3; 4; 5].  
The study of the \(b\)-value's spatial and temporal evolution in areas prone to frequent, strong earthquakes is particularly fascinating. There is a rich history of successfully predicting the location and magnitude of potential strong earthquakes by identifying regions with low \(b\)-values, known as asperities. Notable examples include the San Jacinto-Elsinore fault system in Southern California [6], the Parkfield segment of the San Andreas fault [7], and the 2014 Parkfield \(M\) 6.0 earthquake analysis [5]. A consistent pattern observed before some major earthquakes is a prolonged, steady decrease in \(b\)-values, culminating in a significant seismic event, such as the 2008 Wenchuan \(M_{w}\) 7.9 earthquake in China [8] and the 2011 East Japan \(M_{w}\) 9.0 earthquake [9]. Regular monitoring of \(b\)-values' spatial and temporal heterogeneity serves as a standard method for identifying precursors to potential large earthquakes.  
In our quest to apply \(b\)-values more effectively in assessing seismic hazard in zones with a high probability of strong earthquakes, we conducted a retrospective analysis of the \(b\)-value's temporal and spatial evolution before the \(M_{s}\) 6.8 Luding earthquake on 5 September 2022 in Sichuan, China. This study also explores the potential risks of strong earthquakes in the northern section of the eastern boundary of the Sichuan-Yunnan block.
## 2 Polar Subdivision  
Catmull-Clark subdivision is a powerful technique widely used in representing smooth quadrilateral surface. But for many-sided blends and vertices of high valence, Catmull-Clark subdivision often generates ripples where many facets join or where features are extruded [17]. To improve the subdivision quality in this case, [PERSON] _et al._ developed a new scheme called _polar subdivision_[16] that generalizes bicubic spline subdivision to control nets with _polar structures_. The polar structure is a combinatorial structure of surfaces of revolution, often appearing at points of high valence in meshes. Apart from the extraordinary vertices, the polar structure is a layer of quadrilaterals adjacent to the triangles, as shown in Figure 2. The structures naturally appears in the design of surfaces of revolution. Comparing with 4-3 subdivision, bicubic polar subdivision is more preferable for polar structures because it generates some bicubic patches in the surface as bridges which connect quads to triangles smoothly. As bicubic spline subdivision, it is natural to be combined with Catmull-Clark subdivision and can augment the capabilities of existing Catmull-Clark subdivision.  
Figure 2: Polar control net near an extraordinary point. \(v\) denotes the vertex in polar net and \(e\) denotes the edge point. The control points have subscripts i which indicates the radial distance to the extraordinary point, subscripts j (modulo valence n) which indicates the circular index. \(v_{1,j}\) and \(e_{1,j}\) is written as \(v_{j}\) and \(e_{j}\) for abbreviation.  
The process of polar subdivision provided by [PERSON] _et al._ only includes subdivisions in radial layers. In order to apply the polar subdivision on the surface of arbitrary quadrilateral meshes, [PERSON] _et al._[17] combined the original polar subdivision with Catmull-Clark subdivision. In order to leverage and preserve the good results of radial subdivision and keep control net consistent, the subdivisions should be performed independently. It offers us a chance that the wavelet analysis in circular layers and radial layers can be constructed independently.
###### Abstract  
The randomness and volatility of large-scale clean energy output represented by wind power and photovoltaic lead to difficulties in grid connection. The problems of abandoned wind, light, and water become increasingly prominent. The adjustment capacity of traditional thermal power is limited and it is difficult to ensure the consumption of high proportion clean energy. On this basis, the marginal benefit hedging rule in economics is introduced into the hydropower and photovoltaic joint operation system in this paper. A two-stage spatio-temporal hedging strategy is designed to solve the spatio-temporal conflict problem in the hydropower and photovoltaic joint system. The multi-objective joint dispatching model of hydropower and photovoltaic system considering system benefits, risks, and stability is established, which can be solved by a MOEA/D-GABS algorithm with selection strategy. The joint system scheduling schemes under different schemes are analyzed by case. The results demonstrate that, compared with the traditional multi-objective decision-making scheme, the flood control risk in each period of the reservoir in the proposed method is controlled to be no more than 1.63 \(\times\) 10\({}^{-3}\) (the flood control standard corresponding to the 50-year flood control risk is 0.006); the flood limit water level of the reservoir is increased from 583.00 m to 583.70 m, which improves the benefit of the reservoir; and the water utilization rate is effectively improved. On the other hand, compared with the traditional scheme, the proposed method reduces the peak valley difference of the combined system by 50.67% and 59.68% in typical sunny and cloudy scenarios, respectively, which greatly reduces the uncertainty of photovoltaic output, and the stability of the combined system is improved. It is shown that the proposed method can be used to guide the economic dispatch of a complementary system with hydropower as the regulating energy.  
hydropower and photovoltaic joint dispatch; hedge scheduling; multi-objective decision making; optimization algorithm +
Footnote †: journal: Applied Sciences
# Cross-Layer Methods for Ad Hoc Networks--Review and Classification
## 3 Cross-Layer Method Publications Review
### Multiple Layers
#### 3.2.1 External Entity Multiple Layers Control and Optimization
The purpose of the optimization is to select routes and distribute flows along them, to set the transmission power, transmission rate, signal-code constructions of transmitters, parameters of MIMO space-time coding, and channel access time (Table 12). [PERSON] et al. [100] propose a cross-layer optimization of the ad hoc MIMO network throughput. The simultaneous optimization of route selection, bandwidth selection for each packet transmission, and transmission power for each MIMO complex-matrix channel is performed. The optimization takes place at the network, channel, and physical layers. The optimization problem is decomposed into two sub-problems: network layer, and physical and link layer together. The two optimization problems are then solved by the plane dissection method and subgradient method. [PERSON] et al. [101] propose an optimization of ad hoc network operation parameters with cognitive radio to maximize the network throughput through dual decomposition. The transmission rate at the transport layer, the routes at the network layer, and the channel access time at the channel layer are used together to optimize network throughput. [PERSON] et al. [102] propose the optimization of network performance at all five layers through vertical decomposition of the optimization problem. The paper uses the Lagrange multiplier method and derives a partial Lagrangian formula that takes into account the network's target function and constraints from all five layers. Using dual decomposition, the complex optimization problem is decomposed into three simple problems with control parameters moved iteratively between the three problems. Solving the three optimization problems is computationally challenging, so the authors proposed a simplified heuristic to solve the three problems.
## Materials and Methods
### LC-ESI-MS/MS-MRM Analyses  
The identification and quantification of phenolic acids, flavonoid aglycones, and flavonoid glycosides was carried out using liquid chromatography coupled to electrospray ionization triple quadrupole mass spectrometry. The method for simultaneous analysis of all mentioned polyphenol groups was partly founded on the basis on previous experiments [19; 27]. The LC-MS system consisted of a 1200 Series LC system (Agilent Technologies, Santa Clara, CA, USA), which was coupled with a 3200 QTRAP mass spectrometer with Turbo V\({}^{\text{TM}}\) source and an electrospray ionization (ESI) probe (AB Sciex, Redwood City, CA, USA). The sample or standards' solutions at a volume of 3 \(\mu\)L were injected on an Eclipse XDB-C18 column (4.6 mm \(\times\) 150 mm; 5 \(\mu\)m; Agilent Technologies, Santa Clara, CA, USA), whose temperature was maintained at 25 \({}^{\circ}\)C. The mobile phases were 0.1% HCOOH in milli-Q water (solution A) and 0.1% HCOOH in acetonitrile (solution B). The gradient elution (flow rate 400 \(\mu\)L min\({}^{-1}\)) was used: 0-1.5 min 13% B; 1.5-2 min 13-20% B; 2-4.5 min 20% B; 4.5-5 min 20-25% B; 5-8 min 25% B; 8-9 min 25-33% B; 9-11 min 33% B; 11-13 min 33-60% B; 13-16 min 60% B; 16-18 min 60-80% B; 18-21 min 80% B; 23-28 min conditioning with 13% B. QTRAP worked in multiple reaction monitoring (MRM) mode. Optimal mass analyzer conditions and the selection of product ions were determined experimentally (results are given in Table S1). MS parameters were set as a capillary temperature of 500 \({}^{\circ}\)C, negative ionization mode source voltage \(-\)4500 V, curtain gas at 30 psi, nebulizer gas at 55 psi. MS data acquisition and processing was performed using Analyst 1.5 was software (AB Sciex, Redwood City, CA, USA) analytes. The calibration curves were generated using peak areas of the most intense MRM transitions. The linearity ranges, LOD (limit of detection; at a signal-to-noise ratio of 5:1) and LOQ (limit of quantification; at a signal-to-noise ratio of 10:1) values for all analytes were established (Table S2). LC-MS assay was performed in triplicate for each standard solution and sample.