esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects
## 1 Introduction
Indeed, the current state-of-the-art lacks methods that can derive an appropriate probability distribution function from detailed subjective expert knowledge. An additional difficulty experienced when conducting risk assessment modeling in wind farm construction is the consideration of risk factors affecting schedule and cost as independent. Delays in project schedule will often result in increased project costs, where impacts on cost are generally accompanied by project delays [12]. Treating these cost and schedule impacts as dependent during risk assessment modeling will help generate more realistic results. Methods for modeling the dependence of a risk factor's impact on cost and schedule, however, remain relatively unexplored. To address these limitations, this paper proposes a methodology that is designed to enhance risk assessment outcomes by (1) assisting risk analysts in fitting appropriate distributions for detailed subjective knowledge of the cost and schedule impacts of risk factors (see dark grey area, Figure 1) and (2) adapting existing methods to model the dependence between the cost and schedule impact of a risk factor in a Monte Carlo simulation (MCS)-based risk assessment model. Fuzzy logic is used to process and quantify subjective knowledge, which is then fit to a probabilistic distribution function that represents the marginal distribution for the impact on either cost or schedule. Then, copula-based modeling of multivariate distributions [13] is used to model the dependence between the cost and schedule impact of a risk factor. The remaining sections of this paper are organized as follows: MCS in risk assessment, input modeling for MCS, and correlation and dependence between input distributions are first discussed in a literature review section. Then, the research methodology is explained. Next, an illustrative case study is presented to demonstrate the functionality of the proposed method, and a sensitivity analysis is performed to establish its validity. The final section summarizes conclusions and future research directions.
## 2 Radio Planning Aspects
### Link Budget  
Table 3 gives the link budget for the TETRA and LTE systems and for the downlink (DL) and the uplink (UL). The link budget was calculated for a mobile receiver, in which the antennas are mounted on a vehicle, with a gain of 2 dBi for the TETRA system and 3.5 dBi for the LTE system. Also, in the base station, the antennas have an omnidirectional pattern with a gain of 7 dBi for the TETRA system and a directional pattern with a gain of 15 dBi for the LTE system.  
Radioelectric coverage was calculated for a rural environment, so a noise-limited scenario was assumed.  
For the LTE system, the sensitivity is calculated by:  
\[S\left(\mathrm{dBm}\right)=\text{-}174+F+10\log\left(N\mathrm{rb}\times RB \right)+\text{SN}\it{IR} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Route Number** & **Mean Error (dB)** & **Standard Deviation (dB)** \\ \hline I & 1.04 & 8.34 \\ II & 8.96 & 9.20 \\ II & 3.63 & 8.45 \\ IV & \(-0.44\) & 7.58 \\ V & 10.65 & 12.19 \\ \hline \end{tabular}
\end{table}
Table 2: Mean error and standard deviation of the difference between the propagation model and measurements for each route.  
Figure 4: Comparison between measurements and the propagation model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & \multicolumn{3}{c}{**TETRA**} & **LTE** \\ \hline
**Parameter** & **Units** & & **DL** & **UL** & **DL** & **UL** \\ \hline Power Tx & dBm & \(P_{\mathrm{tx}}\) & 44 & 40 & 35 & 28 \\ Gain Tx & dBi & \(G_{\mathrm{tx}}\) & 7 & 2 & 15 & 3.5 \\ Losses Tx & dB & \(L_{\mathrm{tx}}\) & 2 & 0.5 & 2 & 0.5 \\ PIRE & dBm & \(P_{\mathrm{tx}}+G_{\mathrm{tx}}-L_{\mathrm{tx}}\) & 50 & 41.5 & 48 & 31 \\ Fade Margin & dB & \(FM\) (90\%) & 7.7 & 7.7 & 7.7 & 7.7 \\ Gain Rx & dBi & \(G_{\mathrm{rx}}\) & 2 & 7 & 3.5 & 15 \\ Losses Rx & dB & \(L_{\mathrm{rx}}\) & 0.5 & 2 & 0.5 & 2 \\ Bandwidth & & \(BW\) & 25 kHz & & 5 MHz \\ Sensitivity & dBm & \(S\) & \(-103\) & \(-106\) & \(-95\) & \(-100\) \\ Lmax & dB & \(PIRE-FM+G_{\mathrm{rx}}-L_{\mathrm{rx}}-S\) & 146.3 & 144.8 & 138.3 & 136.3 \\ \hline \end{tabular}
\end{table}
Table 3: Link budget for the TETRA and LTE systems.  
where \(F\) is the noise figure (7 dB for the DL and 2 dB for the UL), \(N\)rb is the number of resource blocks (25 for a bandwidth of 5 MHz for the radio channel), \(RB\) is the bandwidth of each resource block (180 kHz), and \(SNIR\) is the average signal-to-interference-and-noise ratio (5 dB)  
In the proposed UL for LTE, a real throughput of 4.5 Mbps can be achieved with a sensitivity of \(-100\) dBm using a 16 QAM modulation, allowing the transmission of full HD (1920 \(\times\) 1080) video streaming. The maximum path loss (\(L_{\rm max}\)) is in agreement with [PERSON] et al. [22] for the TETRA system and with [PERSON] et al. [23] for the LTE system. As seen in Table 3, for both systems, the worst case is the UL, and the maximum path losses that can be compensated for are 144.8 dB for the TETRA system and 136.3 dB for the LTE system. These values were used in the calculations for both systems.
## 5 HUVEC Model Applications
### Cardiovascular and Metabolic Diseases
#### 5.1.4 Diabetes Mellitus  
Diabetes Mellitus (DM) is a heterogeneous disorder characterized by a loss in glucose homeostasis and insulin resistance [131]. DM is a CV risk factor, and hyperglycemia is associated with chronic endothelium inflammation and endothelial dysfunction [132; 133; 134; 135; 136; 137].  
HUVEC models have been used to study the molecular mechanisms involved in EC damage induced by high glucose concentrations. Hyperglycemia has been shown to decrease NO synthesis, to activate proinflammatory signaling pathways [138; 139; 140], to induce changes in the protein expression profile [141], and to increase the production of ROS [142], leading to apoptosis and endothelial dysfunction [143]. In contrast, decreasing glucose levels is not enough to turn off the intracellular pro-oxidant environment; this persistence is called metabolic memory [135].  
The use of oral hypoglycemic agents, such as teneligliptin reduces ROS production and induces overexpression of antioxidant genes. This drug can counteract the apoptotic phenotype and restore proliferative properties [135]. Other compounds with the same features, such as osthol [144] and phosphocreatinin [145] have been studied, and have shown similar effects and benefits. Moreover, high-density lipoproteins (HDL) [136] and microRNA-199a-3p [137] promote EC proliferation and migration under hypergly comditions, activating different signaling pathways and promoting repair of the damaged vascular endothelium. These findings suggest that HDL and microRNA-199a-3p may be considered as potential therapeutic targets [136; 137].
# Efficient Key-Value Data Placement for ZNS SSD
## 3 Motivation
### Limitations of ZenFS  
We found that ZenFS does not utilize host information and does not have a garbage collection logic. To check this fact, we created a ZNS SSD emulation environment [14] and evaluated the current state of ZenFS. The emulated ZNS SSD had a zone size of 16 MiB, the number of zones was 512 and the total size was 8 GiB. RocksDB's internal benchmark program, _\"db_bench\"_ was used to evaluate ZenFS. The workload used _\"flllseq + overwrite\"_ for the experiments. _\"flllseq\"_ generates the sequential keys with values and _\"overwrite\"_ randomly generates keys and values in the previous key range. The experiment was conducted with each of the 4 million (around 3.1 GB) and 5 million (around 3.9 GB) key-value pairs.  
The experiment results are shown in Figure 5. The figure shows that the inserts of the 4 million key-value pairs did not have any problems. However, it was found that 5 million key-value pairs could not execute the workload until the overwrite was finished. According to the results, this indicates that ZenFS did not efficiently utilize the space of ZNS SSD.  
Moreover, based on the inserts of the 4 million key-value pairs results in the figure, it was found that ZenFS could only reserve a small number of available zones when it began to overwrite. This is because the zones were not reclaimed efficiently due to the zone allocation method of ZenFS, which did not properly consider the lifetime of the zonefile.  
Figure 6 shows the average interval time of logical deletion and physical deletion. It classifies the data in SHORT, MEDIUM and EXTREME according to the lifetime of data. \"Logical deletion\" means data are deleted by ZenFS and marked as invalid. \"Physical deletion\" means data are physically deleted from the ZNS SSD. The figure shows the average time it takes for data to be deleted after being inserted into a zone. According to the figure, MEDIUM and EXTREME immediately physically deleted after logical deletion. In accordance with these results, it was found that the physical deletion of data with a short lifetime was interrupted by long-lifetime data sharing the same zone. Consequently, in Figure 5, there is only a small number of available zones when it began to overwrite.  
Figure 4: Routine for zone allocation in ZenFS. L1, L2 and L3 represent the extents containing the SSTable files from level 1, 2, and 3 in RocksDB, respectively. IV represents invalid extent of the deleted SSTable file.  
Figure 5: Zone usage variation in the _“flllseq + overwrite”_ workloads.  
Therefore, it was found that ZenFS could not efficiently utilize its space. Moreover, it requires the data placement to be split according to data lifetime. Hence, this paper proposes the lifetime-based zone allocation method and the garbage collection method for the key-value store in the ZNS SSD.
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 3 Our Method
### Network Architecture
#### 3.2.3 Reconstructing Self-Expressive Matrices  
The idea of the traditional multi-view self-expressive model is to learn serval self-expression layers inserted between encoders and decoders to simulate the self-expression properties and generates consistent subspace descriptions represented by matrix \(C\in\mathbb{R}^{N\times N}\). However, the number of parameters in conventional network grows quadratically with the number of data points \(N\), which poses a limitation on its applicability to large-scale dataset, as the memory requirements for restoring an \(N\times N\) matrix might become prohibitive.  
As shown in Figure 2, we propose a model to reconstruct the self-expression matrix for each representation of the input data and integrate the view-commonness and view-inconsistency using the shared layer and specific layer. In this way, the number of parameters is independent of the number of data points and can be adjusted flexibly based on the available memory resources. This allows our network to train the representation model of self-expressive coefficients by a small batch of data, providing great scalability for arbitrary sizes of datasets.  
For each latent feature of each view in the dataset, we use Shared layer and Specific layer with 3-layer MLPs individually to approximate the solution of using embedded self-expressive layer, where the hidden units of MLPs are \([1024,1024,1024]\). The network can be represented as \(SE(Z^{(v)};\Theta_{\epsilon}^{(v)})\), where \(\Theta_{\epsilon}(v)\) donates the parameters of this module including the Shared MLP Layers and the Specific MLP layers. We can reconstruct the self-expressive coefficients in the following formulation:  
\[C^{(v)}=\alpha\mathcal{T}_{b}({Z^{(v)}_{C}}^{T}{Z^{(v)}_{S}}) \tag{2}\]  
where \(Z^{(v)}_{C}\in\mathbb{R}^{d\times N}\) represents the output of shared layer and \(Z^{(v)}_{S}\in\mathbb{R}^{d\times N}\) represents the output of a specific layer and \(\alpha\) is a constant. Through Equation (2), we can calculate self-expressive matrix \(C\in\mathbb{R}^{N\times N}\) by a constant number of parameters rather than learning it directly. \(\mathcal{T}_{b}(\cdot)\) is a learnable soft thresholding operator, which is defined as:  
\[\mathcal{T}_{b}(w)=sgn(w)\max(|w|-t,0) \tag{3}\]where \(t\) is a learnable parameter. The soft thresholding function has the property of shrinking smaller values to zero, effectively inducing sparsity by encouraging the elimination of less significant components. It helps retain important information while reducing the impact of noise.
# Graph-Driven Deep Reinforcement Learning for Vehicle Routing Problems with Pickup and Delivery
## 5 Experiments
### Generalization Study
#### 5.3.3 Generalization on Real-World Benchmarks  
We also employ real-world benchmark datasets following prevailing studies [30,36] to verify the generalization ability of the proposed approach. In particular, we apply the model trained on problems with 150 nodes to tackle these instances in the benchmark dataset. As for the conventional VRP-PD, the instances are derived from the literature [30] based on CVRPLib. As shown in Table 4, GDRL attains the best routing performance among a range of heuristic and DRL-based methods. GDRL achieves a length reduction of 39.09% and 8.33% when compared with the competitive heuristic ALNS and state-of-the-art DRL-based POMO. Regarding the improved VRP-PD with benchmark instances used in [36], the proposed approach shows excellent routing performance with high effectiveness and efficiency, as illustrated in Table 5. GDRL obtains an average tour length of 28,259.75, outperforming DRL-based AM (Greedy), AM (Sampling), and POMO by 139.90%, 115.09%, and 15.38%, respectively.
## 3 Modeling and Analysis
### Simulation Results Analysis
#### 3.4.2 Comparison of Stator Stress  
Figure 9 illustrates the stress cloud diagrams of the stators in the motors at the moment that maximum stress occurs.  
It is shown that the maximum stress of the four motors stators are 984.9 MPa (TRUM-1), 1059.9 MPa (TRUM-2), 993.7 MPa (TRUM-3), and 1061 MPa (TRUM-4), respectively. They all exceed the stator material yield strength (404 MPa), so the stator will be plastically deformed. Comparing the data, it can be found that the maximum stress of the stator of motors with the disc spring are slightly larger than that without disc spring which means that the stator plastic deformation of motors without the disc spring will be larger than that with disc spring. However, these maximum stress values are very close, indicating that the addition of a disc spring to the structure does not have a significant effect on the stator. Meanwhile, it can be seen through the cloud diagram that during the impact process, the maximum stress of the stator occurs at the web of the intermediate base root to the stator tooth root. Therefore, improving the structure of the stator from reducing the stress concentration at the web can be considered.
# Selection of Key Frames for 3D Reconstruction in Real Time
## 3 Materials and Methods
### Quality Threshold  
Real digital images do not contain any imaginary part. However, due to the transformation, they are represented as complex numbers, consisting of a real and imaginary part, as z = x + jy, according to the image coordinates. Complex numbers have an angle in the complex plane, as well as a magnitude. We were interested in the magnitude of the decomposed discrete signals of the transformed images. The angle, corresponding to the phase, contains information about the image structure, whereas the magnitude denotes the periodic structure of cosines and sines and those intensities [12]. As proposed by [PERSON] [13], we used the magnitude of the Fourier spectrum to investigate the frame quality. The magnitude was defined as:  
\[\left|\mathrm{F(u,v)}\right|=\sqrt{\mathrm{R}^{2}(u,v)+\mathrm{I}^{2}(u,v)} \tag{1}\]  
where R is the real part and I is the imaginary part of the Fourier transformation. First, we adapted the method proposed by [PERSON] et al. [14], which distinguishes between blurred and homogeneous areas in the image. This makes the analysis more robust and omits homogeneous parts as our calibration setting contains many homogeneous areas. Those have the same magnitude spectrum as blurred areas. Instead of calculating the magnitude for the whole frame, we only needed to perform the calculation of the magnitude on smaller chunks of the frame, divided into several equally sized boxes. We found a box size that fits properly to frame calibration objects that were seen in the captured frame. The experiments showed that if the boxes were too small, areas were restricted to mostly homogeneous areas, and if the boxes were too large, most of the area was dominated by homogeneous areas. To determine homogeneous and heterogeneous boxes, we first calculated the standard deviation of the magnitude spectrum in each box as:  
\[\sigma=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}\left(x_{i}-\overline{x}^{2}\right)} \tag{2}\]  
where \(N\) denotes the number of samples, \(\overline{x}\) the mean magnitude of all samples, and \(x_{i}\) the current samples magnitude. If the standard deviation was relatively low, it indicated a homogeneous area that was not part of the area of interest. Therefore, we needed to define a threshold that separates boxes from homogeneous areas. If the standard deviation of a box exceeded the threshold, it was considered for the quality measurement. The mean of the magnitude was calculated to measure the rate of intensity changes of relevant boxes.
## 3 Operation of the European Transmission System
### The European Awareness System (EAS)  
The exchange of real-time data between Transmission System Operators is made possible by the EAS technology platform [70]. Various measurements, including frequency and cross-border exchange, are entered by all operators. Their results are then provided to show the status of each TSO on the platform across all Europe. EAS is required to:  
* Foresee the consequences of developments in the systems of neighboring TSOs that must be considered.
* Prevent the extension of the phenomenon and restrict the extension of an unsettling scenario.
* Request the use countermeasures between TSOs to reduce the effects of a disturbance.
* Plan defense, recovery, resynchronization, and reactivation procedures.
* Transfer the required data through a fast, reliable, and unambiguous means of communication.
# Research on the Decoupling of the Parallel Vehicle Tilting and Steering Mechanism
## 4 Discussion  
The presently established mathematical model is infinitely rigid, ignoring the effects of vehicle dynamics. The low-speed experiments verified the kinematics of the proposed parallel mechanism. The experiment had the prototype drive at a low, constant speed with different tilting angles in order to dilute the influence that is caused by the nonlinear change of the tires. The dynamic properties of the proposed parallel mechanism and the dynamic interaction between the parallel mechanism and the vehicle require further research.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \(\delta^{\prime}\) & \(\delta\) & \(\hat{\varphi_{\rm v}}\) & **Trajectory** \\ \hline \(\theta_{\rm v}\) = 10 deg & 0.45\% & 0.57\% & 0.98\% & 1.04\% \\ \(\theta_{\rm v}\) = 20 deg & 0.64\% & 0.78\% & 2.49\% & 2.09\% \\ \hline \end{tabular}
\end{table}
Table 4: The influence of prototype tilting on steering.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(\theta_{\rm v}\) = 0 deg & \(\theta_{\rm v}\) = 10 deg & \(\theta_{\rm v}\) = 20 deg \\ \hline \(\delta^{\prime}\) & 0.46\% & 0.69\% & 0.91\% \\ \(\delta\) & 0.51\% & 0.77\% & 1.07\% \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of theory and the experiment.  
The experiment has verified that the proposed model is correct. The parallel mechanism that was designed by this method can reduce the influence of vehicle tilting on the steering angle of the outer and inner wheels by up to 0.64% and 0.78%, respectively. The SGCR of the prototype is between 1.198 and 0.961. This is an acceptable range [22]. The experimental results satisfied the purpose of this study.  
The parallel mechanism that was designed by the application of this method can change the steering geometry by adjusting the \(\varphi\) value and the lengths of \(DE\) and \(DC\), such that \(\delta\) and \(\delta^{\prime}\) satisfy the ASC corresponding to different wheelbases.  
The proposed method decouples the spatial parallel mechanism effectively. It also provides an ATV suspension design method. The present study's method is suitable for decoupling various spatial parallel mechanisms.  
The mathematical model includes wheel alignment parameters but it does not include the shock absorber. The shock absorber also influences the movement of the parallel mechanism. Future research should include the deformation of the shock absorber.
## 5 Factors Affecting the Anaerobic Digestion Process and Biogas Production
### Substrate Type  
Numerous biomass feedstocks can be used for biogas production, depending on their nutritional composition. Accordingly, these compositions influence biogas yield, methane content, degradation kinetics, and biomass biodegradability [59]. The critical nutritional compositions of biomass substrates suitable for biogas production are carbohydrates, protein, and fats. Theoretical estimations of biogas percentage and methane yield from these nutrients have been reported in the literature and are calculated using the Buswell formula [60]. Protein-rich substrates have a high potential for methane yield,but their degradation gives off ammonium ions, leading to an alkalinity increase in the AD process. The increase in alkalinity improves the digest value as fertilizer while preventing the activities of methanogens. This inhibition occurs during the equilibrium shift from ammonium to ammonia, typically in changing concentrations. In addition, literature remarks suggest that microorganisms can acclimatize to environments with high ammonia concentrations while efficiently producing biogas [61; 62].  
Lipid-rich substrates, such as fats, possess great methane yield potential, such as animal and plant tissue waste, biodegradable kitchen and canteen waste, grease and oil mixture, etc. [63]. These substrates release long-chain fatty acids during their degradation, which are typically toxic to the microbial environment and cause a drop in pH [64; 65; 66]. There are several other types of biomass that are used for biogas production apart from the protein and lipid-rich substrates, for example, substrates with a high degree of lignocellulosic (wheat straw, sorghum, rice straw, etc.) [67]. This type of biomass is hard to degrade due to these three reasons: (i) recalcitrant nature, (ii) heterogeneous structure, and (iii) low accessibility by enzymes, such as carbohydrate polymers [68; 69; 70]. However, pre-treatment mechanisms can help break down the heterogeneous matrix, thus, increasing the porous and surface area of the lignocellulosic biomass and enhancing biogas production.  
Characterization of the waste substrates is performed to ascertain the composition of each substrate. This is generally physical and chemical composition regarding volatile solids, total solids, C/N ratio as well as elemental analysis for carbon, nitrogen, hydrogen, and sulfur [71; 72]. During substrate characterization, the place (source) where the substrate was collected is vital, as waste chemical content is affected by many factors, such as weather conditions and the type of soil where the original substances were grown [73; 74]. Physical and chemical compositions depend on the type of substrate, for example, carbohydrates have carbon and hydrogen while proteins and lipids have nitrogen as part of their composition [75; 76]. These substrate compositions can be analyzed using different analytical techniques.