esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Coupling Analysis and Reliability Assessment of Actuation System Based on Bond Graph Model
## 2 Bond Graph Model of Actuation System
### Energy Transfer Model of Actuator
The working expression of the hydraulic pump is as follows:  
\[P=\frac{T}{D} \tag{4}\]  
\[Q=D\times\omega \tag{5}\]  
where \(P\) is the pressure of the hydraulic pump, \(D\) is its displacement, \(Q\) represents its flow, and \(T\) is the torque of the pump shaft. The resistive element R\({}_{\text{Kilp}}\) is used to represent the internal and external hydraulic leakage of the pump. In terms of the hydraulic cylinder, the resistive element R\(\cdot\) is also used to represent its internal leakage, the resistive element R\(\cdot\) is used to represent the pressure loss of the pipeline and bypass valve port, the capacitive element CRhj is used to represent liquid compressibility, and the element TF (effective area of piston A) is used to simulate the cylinder, converting flow and pressure in the hydraulic system into force and speed in mechanical energy.
## 3 Results and Discussion
### Method Validation
#### 3.2.4 Calibration Curve and Linearity  
Linearity of the method was evaluated by plotting the ratio of AA peak area to IS peak area against the corresponding analyte concentration. Using the simple linear regression model, we determined the regression equations, the slope and the intercept of the calibration plots, and the [PERSON]'s determination coefficient (R\({}^{2}\)) for the three calibration curves, solvent-based, surrogate matrix-matched, and authentic matrix-matched in the range of 0.5-5.0 \(\upmu\)g/mL free AA. Solvent-based calibration exhibited a regression equation y = 0.766x \(-\) 0.152, slope 0.766, y intercept 0.152, R\({}^{2}\) 0.9952; surrogate matrix-matched calibration showed a regression equation y = 0.587x + 0.097, slope 0.587, y intercept 0.097, R\({}^{2}\) 0.9984, while authentic matrix-matched calibration revealed a regression equation y = 0.717x + 0.417, slope 0.717, y intercept 0.417, R\({}^{2}\) 0.9979. Good linearity was established for all calibration curves in the tested concentration range. [PERSON] et al. [40] reported similar linear range and R\({}^{2}\) in plasma samples of 32 healthy male volunteers.  
No carryover was observed in a blank following QCs with the highest AA concentration.
## 4 Results and Performance Evaluation
### Effectiveness of the Shape Extraction Algorithm  
DLFBR was designed to be flexible to different kinds of inputs. We used three different inputs for DLFBR: the raw AE signal, the envelope signal, and the extracted shape signal. To illustrate the effectiveness of preprocessing with shape extraction, the results for impulse detection from DLFBR with the same sample but different input types are compared in Figure 6. Figure 6c shows the results of impulse detection with the input that used the proposed preprocessing method. Similarly, Figure 6a,b show the results when the inputs were the raw signal and the preprocessing signal with envelope analysis.  
As seen in Figure 6a, applying DLFBR to the raw signal sometimes failed to detect the impulse, even if the impulse clearly showed a high amplitude. Furthermore, DLFBR predicted a number of boundary boxes for the same impulse. These boxes may be overlapping or separate because of the small troughs and peaks inside the impulse; this can cause DLFBR to make incorrect predictions. The DLFBR results with the envelope analysis are shown in Figure 6b. This method could detect most of the impulses and led to better predictions, but the overlapping between boundary boxes still occurred. The results of DLFBR with the SE preprocessing are shown in Figure 6c. This technique outperformed the other two methods, detecting all of the impulses and predicting only one boundary box for each impulse.
## 3 Data Description and Preprocessing  
### Data Source  
The crash data used in this research were retrieved from the Louisiana DOTD's crash database. In addition to crash, temporal, environmental, and other factors, the database also contains incident-related information. These include crash notification time, time ambulance called, time ambulance departed, time police arrived, and time lanes opened, among other incident response information. Only crashes that occurred on highways classified as rural interstates were considered for this study. A total of 13,196 crashes that occurred between 2016 to 2019 were extracted for the analysis. A brief description of each variable is described in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Data Used** & **Significant Features** & **Modeling Technique** \\ \hline [PERSON] et al., 2019 [5] & 2014–2016 & \begin{tabular}{c} Percentage of lane closures, nighttime, \\ weekends, off-peak hours, and an \\ increasing number of responders \\ \end{tabular} & Hazard-based duration models \\ \hline [PERSON] and [PERSON] 2000 [37] & 1994 and 1995 & \begin{tabular}{c} Peak hours, nighttime, Friday, Sunday, \\ rain, fog, fatality, single-occupancy \\ vehicles, and pickup trucks \\ \end{tabular} & Hazard-based duration models \\ \hline [PERSON] et al., 2016 [53] & 2016 to 2011 & \begin{tabular}{c} Higher-occupancy vehicles, snow, rain, \\ heavy vehicles, shoulder blockage, \\ fatality, injury, property damage, \\ vehicle fire, and disabled vehicles \\ \end{tabular} & Bayesian neural networks \\ \hline [PERSON] et al., 2015 [38] & 2009 & \begin{tabular}{c} Travel lanes blocked, total closure, \\ injury involved, fire involved, heavy \\ truck involved, and traffic control \\ \end{tabular} & \begin{tabular}{c} Binary probit model and \\ switching regression model \\ \end{tabular} \\ \hline [PERSON] et al., 2016 [13] & 2013–2015 & \
\begin{tabular}{c} Injury, day of week, time, roadway \\ geometry \\ \end{tabular} & Quantile regression \\ \hline [PERSON] et al., 2018 [15] & 2008 to 2010 &
\begin{tabular}{c} Incident type, number of vehicles, \\ trucks, injuries, fatalities \\ \end{tabular} & Bayesian network \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of significant factors impacting ICT. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Category** & **Description** & **Code** & **Frequency** & **Mean** & **Std. Dev** \\ \hline \multirow{4}{*}{Response characteristics} & \multirow{4}{*}{Response to crash} & Short & - & 7323 & 0.65 & 0.48 \\ \cline{3-6}  & & Medium & - & 1508 & 0.13 & 0.34 \\ \cline{1-1} \cline{3-6}  & & Intermediate & - & 1061 & 0.09 & 0.29 \\ \cline{1-1} \cline{3-6}  & & Long & - & 1437 & 0.13 & 0.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive statistics of variables in the crash dataset.
# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 3 Overview of Fault Detection and Diagnosis (FDD) in DHC Systems
### Fault Detection in Substations and Customer Facilities  
Nowadays, a current preconeceived idea considers that most of the end-use substations in district heating systems work well. This means that it is taken for granted that the facilities deliver or use exactly the right amount of energy to cover customer's needs. [PERSON] and [PERSON] (2015) [113] showed that this is not the case and almost three-quarters of the substations analyzed present faults or symptoms of faults, which could lead to higher return temperatures. This fact is unacceptable especially in 4 GDH and 5 GDHC, where it is important to achieve a very low return temperature.  
In an analysis of the most common faults in DH substations performed as part of the H2020 project TEMPO [26], a survey-based study by [PERSON] et al. (2019) [114] found that the largest fault category was leakages (33%), closely followed by faults in the customers' internal heating systems (31%). This fact reveals that it might be difficult for the energy utilities to get access to all faults present in the customers' facilities because the DH operators are only usually allowed to access to the substation and not to the internal heating system. Therefore, utilities must make important efforts to establish a good relationship with customers. Common practice to achieve this is to have maintenance contracts with the customers or to include free of charge inspections in the DH agreement. In addition, a proper fault detection system must include customer-sited substations as the main element to inspect. This can be done by analyzing the components of the substation individually, or/and looking at the customer heat load patterns. In the following sections, a literature review of FDD methods for different parts of customer-sited DHC stations is presented.
# Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response
## 4 Examples  
In this section, we demonstrate the performance of the FWNN on synthetic EMT data set with three noise variations and a pseudo-synthetic field data set using the complete data processing workflow with adaptive processing [12]. The FWNN system is applied for constructing a prediction model of the signal code using the Matlab script run on a GTX 950M GPU computer with 8 gig RAM to train and execute the demodulation appropriately.
## 4 Performance Evaluation
### Datasets  
We perform our experiments on three widely used and publicly available network intrusion detection datasets: NSL-KDD, UNSW-NB15, and CIC-IDS2017.  
1. NSL-KDD (NSL-KDD Cup'99 Dataset) is a widely used dataset in the field of network intrusion detection and security. It is an improved version of the original KDD Cup '99 datasets, designed to address some shortcomings in the latter, such as redundancy and unrealistic traffic patterns. NSL-KDD contains a large collection of network traffic data, including both normal and various types of malicious activities (i.e., DoS, Probe, R2L, and U2R attacks), making it a valuable resource for training and evaluating intrusion detection systems.
2. UNSW-NB15 (University of New South Wales Network-Based 15) consists of network traffic data captured in a controlled environment, simulating a real network. This dataset includes a diverse range of attack types (contains nine types of attacks, including Brute Force, DoS, and Web Attacks, etc.) and normal traffic, providing a realistic representation of network security challenges for developing and testing intrusion detection systems.
3. CIC-IDS2017 (Canadian Institute for Cybersecurity Intrusion Detection Evaluation Dataset 2017) is a comprehensive dataset that includes various attack scenarios, such as DoS, DDoS, and Port Scans. It offers a wide variety of network traffic scenarios, including both benign and malicious traffic, across different network protocols. This dataset is particularly valuable for researchers and practitioners working on cybersecurity, as it helps in the development and assessment of effective intrusion detection and prevention mechanisms.  
We summarize the main attack types in each dataset in Table 1 so that we can choose similar attack types from two datasets to simulate the cross-domain transfer learning task.
# An Update on Polyphosphate In Vivo Activities
## 5 Unicellular Eukariotes  
PolyP presence is evolutionary and not restricted to prokaryotes. The slime mould _Dictyostelium discoideum_ and various fungi, such as the model system Saccharomyces cerevisiae, contain polyP. Eukaryotes express a polyP-modifying enzyme not found in bacteria, endopolyphosphatase1 (PPN1). PPN1 has endo- but also exopolynophatase activities and is found in various compartments, including the cytosol, the nuclear and mitochondrial membranes, and within vacuoles [69; 70; 71]. At these sites, PPN1 colocalises with PPX1, and the activity of both polyP-modifying enzymes is significantly increased by Mg\({}^{2+}\) but not by Ca\({}^{2+}\) ions [7; 25]. The optimal pH for both enzymes is close to 7.0. Similarly to bacterial PPK, the anionic polysaccharide heparin is a competitive inhibitor for yeast endo- and exopolynophatases, most likely by competing for the polyanion-binding site in the enzymes [47; 72]. Deficient yeast strains in PPX1 and PPN1 still have endopolyphosphatase activity, indicating that other yet unknown enzymes with overlapping substrate specificity exist [69]. Indeed, a yeast strain with vacuole-targeted exopolynophatase Ppx1 and concomitantly depletion of the two endopoly phosphatases (\(ppn1\Delta ppn2\Delta\), vt-Ppx1) is deficient in polyPase activity [73].  
A specific polyP-rich organelle are acidocalcisomes that are membrane-bound vesicular structures initially described in _Tryunosoma crucii_; however, they exist in other unicellular eukaryotes, bacteria, and human cells [74]. In addition to polyP, acidocalcisomes are rich in Ca\({}^{2+}\) and play a role in intracellular calcium signalling and osmoregulation. Acidocalcisomes are acidic, and their specific milieu is maintained by two proton pumps, i.e., vacuolar H\({}^{+}\)-pyrophosphatase (V-H\({}^{+}\)-PPase) and H\({}^{+}\)-ATPase (V-H\({}^{+}\)-ATPase) and a Ca\({}^{2+}\)-ATPase, which mediates the transport of H\({}^{+}\) into the acidocalcisome lumen in exchange for Ca\({}^{2+}\). Ca\({}^{2+}\) ions are released from acidocalcisomes into the cytoplasm via an inositol 1,4,5-trisphosphate receptor (IP\({}_{3}\) receptor) bound Ca\({}^{2+}\) channel [75; 76; 77]. At the acidocalcisome membrane, the vacuolar transporter chaperone (VTC) synthesises polyP from ATP and transports the nascent polymer chain into the organelle lumen [42]. The acidocalcisome polyP is hydrolys by PPX, and the cleaved P\({}_{\text{i}}\) is exported via Na\({}^{+}\)/P\({}_{\text{i}}\) symporters [78].  
Acidocalcisomes contain additional polyP-modifying enzymes such as vacuolar soluble PPase (VSP), PPX, and acid phosphatase. Upon hyperosmotic stress, _Tryanosoma crucii_ produces long-chain polyP, thus lowering P\({}_{\text{i}}\) and cation levels and consequently reducing osmotic pressure [22]. Based on these data, osmoregulation is considered an essential function of polyP in bacteria and unicellular eukaryotes. However, it is currently unknown whether this fundamental cellular function is conserved in eukaryotes, especially as the intracellular polyP levels are much lower in complex organisms.
# Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering
## 4 Results and Discussion  
This section introduces the experimental configuration, parameters, and analysis of the proposed approach. We implemented our method on Network Simulator (NS-2), a versatile simulation tool known for its ability to model various network scenarios and interfaces [41]. The NS2 Simulation parameters utilized in this study are listed in Table 2. The messages or data packets contain both header and payload information, facilitating the monitoring, tracking, and updating of the transmission process. The sample simulation outputs of cluster formation of the proposed DA-TRPED model using the Network Simulator 2 (NS-2) tool are given in Figure 3. The proposed framework was evaluated by five performance parameters, namely, the lifetime of the CHs and CMs, throughput, packet delivery ratio, count of clusters, end-to-end delay, and packet drop rate.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Ranges Used** \\ \hline Mobility type & Freeway mobility \\ \hline Propagation type & 2-way ground \\ \hline Antenna type & Omni-directional \\ \hline MAC layer protocols & IEEE 802.11p \\ \hline Beam-forming technology & mmWave \\ \hline Maximum count of packets in queue & 50 \\ \hline Population size (ants) & 100 \\ \hline Movement of nodes & Bidirectional \\ \hline Count of iterations & 100 \\ \hline Size of grid & 4 km\({}^{2}\) \\ \hline Velocity & 22–30 m/s \\ \hline Count of network nodes & 20, 40, 60,80 and 100 vehicles \\ \hline Protocols & UDP (transport layer) \\ \hline Traffic type & CBR \\ \hline Transmission range & Dynamic \\ \hline Magnitude of packets & 512 bytes \\ \hline No. of executions & 20 \\ \hline Confidence rate & 90\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters and configurations utilized in NS-2 to evaluate the performance of the proposed VANET model.
## 3 Results and Discussion
### Influence of Numerical Aperture on Focusing Situation in Glass
\(D_{\text{in}}/D_{\text{s}}\) is the ratio of spot diameter in glass and spot diameter in air, which determines the expansion ratio of laser spot in glass to that in air. As can be seen from Figure 9c, spot diameter in air \(D_{\text{s}}\) decreased with increasing numerical aperture. The laser spot in glass was expanded, compared to that in air for all numerical apertures of objective lenses, which was caused by refraction of laser beam in glass. In addition, higher numerical aperture showed larger spot expansion ratio than lower numerical aperture, which indicated that laser spot was largely expanded. This result agreed well with the conclusion of ray tracking that higher numerical aperture caused a loose focusing situation in glass. Therefore, numerical aperture has a great influence on the focusing situation of laser beam in glass, and it is important to choose a proper numerical aperture of objective lens in the fusion micro-welding of glass. Figure 8: Focusing situation for various numerical apertures in air and glass.
## 3 Results and Discussion
### Effect of HPH on Protein Functional Properties
Table 2 presents the interfacial tension between 2% \(w/w\) APPC solutions and sunflower oil. In all cases the interfacial tension values are included in the range between 1 and 10 mN/m, and the APPCs are suitable for emulsion stabilizers. Additionally, the foaming capacity of all the APPC samples was relatively low (FC = 20% and FS = 50%), and no significant differences were observed between them. The low foaming capacity is an additional advantage for the application of APPCs for emulsions, as it minimizes excessive foam formation. Higher values of foaming capacity would be desirable for other applications for food products, such as bread, ice cream, and some confectionery products [43]. According to these results, two different HPH treatment conditions were chosen for nanoemulsion preparation: 800 bar for 4 passes (800/4), as the HPH condition that led to maximum protein recovery yield (26.9 g proteins/100 g dry biomass), and 600 bar for 1 pass (600/1), as the HPH condition that achieved the maximum oil-holding capacity (2.8 g oil/g APPC) and the higher APPC protein purity (58.7% \(w/w\) compared to 53.3% \(w/w\) of 800/1). Additionally, according to [PERSON] et al. (2015), the energy consumption of HPH treatment at 800 bar for 1 pass for microalgal suspensions of 1% \(w/w\) and 5% \(w/w\) was approximately 8  
Figure 3: Dependence of surface tension of APPC aqueous solutions on the solution protein concentration. The APPCs were recovered from untreated (control) and HPH-treated _A. pyrenoidosis_ biomass at 400, 600, and 800 bar for 1 pass (400/1, 600/1, and 800/1, respectively) and at 400, 600, and 800 bar for 4 passes (400/4, 600/4, and 800/4, respectively). The shaded area corresponds to the protein concentration of APPC aqueous solutions where the difference between their surface tension and their plateau surface tension is a maximum of 5%. Error bars represent the standard deviation between experimental replications. Abbreviations: APPC: _A. pyrenoidosis_ protein concentrate, HPH: high-pressure homogenization. and 2 MJ/kg dry biomass, respectively, while 600 bar for 1 pass required approximately 6 and 1 MJ/kg dry biomass, respectively [22]. Considering that a four-pass treatment requires four times the energy, the energy consumption cost of the 600/1 HPH condition is significantly lower compared to the 800/4 condition, which is an additional advantage of the 600/1 condition.
## 3 Results
### Antibacterial and Hemocompatibility Analysis  
The antibacterial behavior of the developed composite material is shown in Figure 8a-e. The degradation of Mg in the physiological medium results in the dissolution of Mg\({}^{2+}\) ions. This could work as an antibacterial function for Mg against different bacterial agents. However, excessive degradation could lead to implant failure at an early stage of implantation. Moreover, during carpal injuries, major issues during implantation are inflammation due to bacterial infection caused by the native material of the implant, despite the antitoxic properties of the material used [68; 69]. Therefore, an infusion of aloe vera solution was applied to enhance the antibacterial rate of the composite developed in this study.  
The aloe vera solution was initially tested without adding a porous substrate against Gram-positive _B. subtilis_ and Gram-negative _E. coli_. The experiment was executed by considering the different concentrations of aloe vera solution mixed with DI water, taking a  
Figure 8: (**a**) Antibacterial analysis against _E. coli_ revealing zone of inhibition of aloe vera solution with 50 mg/mL concentration, (**b**) with 100 mg/mL, and (**c**) PLA coated sample. (**d**) Antibacterial analysis against _B. subtilis_ revealing zone of inhibition of aloe vera solution with 50 mg/mL concentration and 100 mg/mL and (**e**) PLA coated sample. (**f**) Hemocompatibility analysis of the developed composite samples.  
minimum concentration of 50 mg/mL and a maximum of 100 mg/mL. The concentration was tested against both strains of bacteria. It was assessed that aloe vera solution, in pure liquid form, was impressively effective against _B. subtilis_ but did not show any inhibitory effect against _E. coli_. However, after incorporating it with the porous substrate and PLA coating, it showed a higher inhibitory effect against _E. coli_ with an inhibitory zone of 30 mm. In contrast, this value was lower, i.e., 18.2 mm, against _B. subtilis_.  
The hemocompatibility of the developed composite samples is shown in Figure 8f. A material is said to be hemocompatible if its hemolysis percentage is below 5% [70,71]. Therefore, in the current study, it can be observed that when aloe vera solution was added to the porous substrate, the hemolysis percentage was 3%, whereas after coating with PLA, it escalated to 3.7%. However, it was below the standard limit bar of 5%, making these samples hemocompatible. Of note, the native materials were hemolytic in nature, with porous Mg showing 7% and pure Mg 6% hemolysis percentage.
# A Survey on Change Detection and Time Series Analysis with Applications
## 2 Decomposition Methods into Frequency Domain  
In this section, several spectral analysis methods are briefly reviewed in short sections, and their advantages and weaknesses are discussed. The review has the purpose of focusing on the most popular and state-of-the-art spectral analysis methods applied in almost every field of science. The section starts with the Fourier transform defined for equally spaced or evenly sampled time series and continues by reviewing its modifications that ease the stringent conditions of Fourier transform.