esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 2 Related Works
### Aodvm  
AODVM [9] is another modification of AODV that discovers multiple node-disjoint paths between a source and a destination in a wireless network. As with AOMDV, intermediate nodes under AODVM receive duplicates of RREQs, record the information contained in these copies in the RREQ table, and re-transmit the received RREQ copies instead of discarding them. Information stored in the RREQ table includes the source that generated the RREQ, the intended destination of the RREQ, the neighbor who transmitted the RREQ, and some additional information. However, unlike the original AODV and AOMDV, intermediate nodes do not send an RREP back to the source even if they have routes to the destination. Instead, the destination sends an RREP to the source for each received RREQ copy though the path traversed by the RREQ copy.  
When the destination receives the first RREQ copy from one of its neighbors, it updates its sequence number, generates an RREP, and sends the RREP back to the source. When the destination receives duplicates of the RREQ from other neighbors, it updates the sequence number and sends RREPs for each of them. The RREP contains a _last_hop_ID_ field to indicate the neighbor from which the corresponding RREQ was received. When an intermediate node receives an RREP from one of its neighbors, it removes the corresponding entry to this neighbor from its RREQ table and adds a new entry into its routing table to indicate the discovered route to the destination. The intermediate node then identifies the neighbor in the RREQ table via which the path to the source is the shortest and forwards the RREP to that neighbor. Then the entry corresponding to this neighbor is deleted from the RREQ table. To ensure that an intermediate node does not participate in multiple paths, whenever a node overhears one of its neighbors broadcasting RREP, it removes that neighbor from its RREQ table. If an intermediate node cannot forward the RREP (either because there is no route to the source or because its RREQ table is empty), the node sends a route discovery error (RDER) message back to the neighbor that forwarded the RREP to this node. When the neighbor receives an RREP, it searches its RREQ table for the next shortest path to the source and attempts to forward the RREP to the source through the path. To confirm how many paths were discovered and established, the source sends a route confirmation (RRCM) message to the destination for each received RREP. The RRCM also contains information about the hop count of the route and the first and last hops.  
Figure 1a shows the process of route discovery of AODVM. The route discovery is a three-way handshake process. Since many control messages, including RREQs, RREPs, RDERs, and RRCMs, are used to discover and establish routes, and because duplicates of RREQs are not discarded at intermediate nodes, AODVM generates too many duplicates of the control messages in wireless networks, resulting in high control overhead for discovering and establishing routes in the networks.
# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 3 Methodology
### Rumor Detection Classifier  
In this paper, we explore a wide range of supervised learning classification models for rumor detection and subject them to extensive study using different active learning strategies. These classifiers LR, SVM, DTC, NB, RFC, KNN, the Gaussian Process (GP) classifier, Multi-Layer Perceptron (MLP), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and AdaBoost (Ada). Unless otherwise specified, all model parameters were set to their default values.  
We employed two LR models: one trained with the standard approach and the other utilizing Stochastic Gradient Descent (SGD). For simplicity, we refer to these as LR and LR(SGD), respectively. Additionally, we employed three SVM classifier models: one with a linear kernel, denoted as SVM(Linear), another with a Radial Basis Function (RBF) kernel, denoted as SVM(RBF), and the third with an RBF kernel trained using SGD, which we denote as SVM(SGD). It is important to note that, unlike LR, SVM models with a linear kernel or trained with SGD lack the ability to predict probabilities for object classes. This limitation confines their usage to representative-based query strategies. Furthermore, we assessed the performance of DTC using both the Gini and Entropy criteria, which are denoted as DTC(Gini) and DTC(Entropy), respectively.  
Feature extraction is one of the most crucial phases of supervised machine learning, and has a significant influence on classification accuracy. Researchers seeking to achieve better rumor classification performance have experimented with combinations of various features and supervised machine learning classifiers. Several common features, including content-based, user-based, propagation-based and behavior-based features, were selected for experimentation in our study. The diverse features extracted from online social media posts play a vital role in rumor detection using machine learning models.
# Airborne Electromagnetics to Improve Landslide Knowledge in Tropical Volcanic Environments
## 4 Results
### Identification of Involved Materials and Geological Models
#### 4.1.1 La Medaille  
Figure 6a,b shows two resistivity grids at two different depths, between 4 and 7 m and between 48 and 47 m deep. For each, the geological formations and the landslide were reported. The dacitic formations (breccias and bedrock) show high resistivity values (>65 \(\Omega\).m, Figure 6a). It is possible to observe them in the western part of the landslide. At the level of the landslide, the resistivity values are between 20 \(\Omega\).m and 35 \(\Omega\).m, corresponding to the dacitic screes observed in the boreholes and described in [58,59] and [61]. Deeper (Figure 6b), high resistivity values (>50 \(\Omega\).m) are imaged to the west and northwest of the landslide and correspond to the dacitic formations, whereas the landslide area is characterized by a low resistivity, below 15 \(\Omega\).m. A resistivity profile is shown in Figure 7a  
Figure 7: Interpreted resistivity profiles. (**a**) La Medaille landslide (hillshade map is produced with Helimap DTM, 2013); (**b**) Morne-Figure area (hillshade map is produced with Litto3D DTM, IGN, 2010).  
and is completed in Figure S1. Nearby boreholes are also projected on this profile to identify the different materials (Figure 7a). At the surface, a thin conductive layer (C1) is visible with a resistivity of approximately 15 \(\Omega\).m. It would represent the superior clayey layer described in the different boreholes. Under it, a more resistant layer (R2 \(\sim\)50 \(\Omega\).m) can be observed. It is assimilated by the dacitic formations of the old debris avalanche described in [54]. Below R2, a thick layer (R3) with a lower resistivity (from 15 \(\Omega\).m to 30 \(\Omega\).m) is imaged. This layer is the one observed in Figure 6a and was interpreted as breccias or fossilized screes in [54; 55]. The deep conductive thick layer (C2) would correspond to weathered andesite. This conductive layer, present along the landslide (Figure 6b, Figure 7a and Figure S1) and not revealed with the older boreholes, would be 30 m thick and would overcome the bedrock composed of andesite (R4). Table 3 details each formation.
## 2 Difference Pattern-Sequence-Based Forecasting (DPSF) Method  
The PSF algorithm was particularly developed to forecast data for a time series which contains pattern or is seasonal, thus the prediction error is very small for such time series. However, if the time series follows some trends or is not seasonal, then the error increases. This can be observed in the illustrative examples provided in the later sections. The \"nottem\" dataset is very seasonal, thus the predictions of PSF are observed to be better than that of ARIMA. However, in the \"CO\({}_{2}\)\" dataset, the result of PSF is not as good as that of ARIMA. This is because the \"CO\({}_{2}\)\" dataset follows an upward trend. The forecasting results with the PSF method are degraded with positive or negative trends. To tackle this problem the DPSF model was proposed [3].  
The DPSF method is a modification of the PSF algorithm. The time series is differenced once. These differenced data are then used for prediction using the PSF algorithm. The predicted values are then appended to the differenced time series, which was used for prediction using PSF. Finally, the original time series is attempted to be regenerated using the reverse method of the first-order differencing process.  
The DPSF method gives better results for data where positive or negative trends can be observed in the data. However, the PSF method does not work well with such datasets and prefers seasonal datasets. This can also be observed in examples shown in Sections 4.1 and 4.2. An example in Section 4.1 uses a seasonal dataset (_nottem_), where the  
Figure 2: Prediction with PSF algorithm (Source: [27]).  
PSF results are better than the DPSF results. In Section 4.2, the \(CO_{2}\) dataset is used, which shows a positive trend. Here the results of DPSF are significantly better than those of PSF.
# Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm
## 3 Impedance-Based Stability Analysis of SSO  
The impedance-based stability analysis was widely used and developed in recent years. It was firstly proposed to analyze the stability of inverter-grid system in Reference [19]. Under the assumption that the inverter is controlled as a current source parallel with an output impedance, it will remain stable with a nonideal grid if the ratio of the grid impedance to the inverter output impedance satisfies the Nyquist criterion. It was developed by proposing a simplified method based on aggregated RLC circuit model to intuitively explain and quantitatively evaluate this type of SSO [5]. This method has a clear physical meaning and is easy to carry out as follows: at the resonant frequency \(f_{r}\) decided by negative \(X_{PMSG}\) and positive \(X_{Grid}\), if the total resistance \(R_{PMSG}+R_{Grid}\) is negative, the grid-connected D-PMSG system will be unstable. This paper chose this method to analyze grid-connected D-PMSG stability, and simulation is conducted to verify the correctness.
# Wedge Design and Dynamic Characteristics Analysis of Unequal-Length Wedge Slant Support Clutch
## 6 Conclusions  
Aimed at the problems of long response time and the large impact of the output shaft of centrifugal-disengaged slant support clutches in actual working conditions, this paper proposed a design method for a slant support clutch with unequal-length wedges to achieve the goal of \"slow connection\" in the process of differential engagement.  
1. The design parameters of unequal-length wedge were provided, and the wedge angle constraint, lift constraint, PCE constraint, and other conditions were verified. A series of \"slow contact\" clutch design schemes were determined, and the engagement performance of the unequal-length wedge slant support clutch was verified through experiments.
2. Under the condition of using the same type of wedge, the instantaneous angular acceleration of the outer ring of the \"slow connect\"-type clutch demonstrated a significant trend of first decreasing and then increasing with the increase in the number of long wedges. The response and engagement times of the clutch demonstrated a decreasing trend with an increase in the number of long wedges. Under the same number of wedges, the instantaneous angular acceleration, clutch response time, and engagement time of the outer ring of the clutch decreased with an increase in the wedge difference.
3. There is a direct proportional relationship between the wedge difference and critical detachment speed. For every 0.005 mm increase in wedge difference, the critical detachment speed of the wedge increased by approximately 2%. The effectiveness of  
Figure 16: (**a**) Curve of resistance of inner and outer rings of clutch as function of speed, (**b**) low-resistance zone, and (**c**) high-resistance zone.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Wedge**} & \multicolumn{2}{c}{**Critical Disengagement Speed (rpm)**} & \multirow{2}{*}{**Relative Error**} \\ \cline{2-2} \cline{4-4}  & **Theoretical Scope** & & **Test Value** \\ \hline Baseline & [1268.32, 1340.54] & 1325 & 1.12\% \\ E & [1224.68, 1360.94] & 1430 & 5.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison between the critical detachment speed of the wedge and experimental data.  
the critical detachment mathematical model was demonstrated through experimental verification.  
Conceptualization, C.Z.; methodology, J.W. and M.L.; software, X.W.; supervision, C.Z. and H.Y.; validation, X.W. and M.L.; writing--original draft, J.W.; writing--review and editing, H.Y. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Natural Science Foundation of China (Grant no. 52075552).  
All data, models, and codes generated or used in the study are included in this article.  
The authors declare no conflict of interest.
# An Overview of GEO Satellite Communication Simulation Systems
## 4 Comparative Evaluation of Simulation Tools
### Orbit and Constellation Modeling  
Orbit and constellation modeling are critical for accurate GEO system evaluations. STK developed by Analytical Graphics, Inc. (Exton, PA, USA ) and the General Mission Analysis Tool (GMAT) developed under the leadership of NASA lead this domain, providing robust and high-fidelity orbital propagation and mission planning capabilities. STK offers advanced three-dimensional visualization and sensor modeling, essential for accurate coverage analysis [33], widely used by NASA, ESA (European Space Agency), and leading aerospace corporations such as Boeing and Airbus. GMAT specializes in precise trajectory optimization and mission scenario modeling [34], utilized extensively for NASA's mission planning and interplanetary explorations. Conversely, MATLAB requires external orbital data input, primarily through interfaces with STK. Tools like GNU Radio, Network Simulator 3 (NS-3), Objective Modular Network Testbed in C++ (OMNeT++), and Optimized Network Engineering Tools (OPNETs) do not directly support orbit modeling and depend on external integration.
# The Future of Healthcare with Industry 5.0: Preliminary Interview-Based Qualitative Analysis
## 4 Results and Discussion
### Challenges and Barriers to Implementing I5.0  
In the quest to harness the transformative potential of I5.0 in healthcare, it is imperative to understand the obstacles that stand in the way of integrating these advanced technological approaches, as pointed out by [PERSON] et al. (2018).  
Healthcare professionals identify several significant barriers to the implementation of emerging technologies. A considerable number of respondents also highlight resistance to change, signaling a need for enhanced digital literacy among both patients and healthcare providers and emphasizing the necessity of targeted training programs [6]. Privacy and security concerns are prevalent, reflecting apprehension about the safeguarding of sensitive data within new technological systems, in line with the observations of [PERSON] et al. [36]. Financial constraints are frequently mentioned, indicating a pressing concern about the high costs and substantial investments required for technology infrastructure, as highlighted by [PERSON] et al. [31]. Also, the rapid obsolescence of technologies is mentioned by professionals and is also highlighted in the literature [31].  
Although there is widespread concern among professionals about the lack of interoperability standards in healthcare, the truth is that there are established standards, such as Health Level 7 (HL7), which appeared in 1987 and has evolved from v2 to v3 and Clinical Document Architecture (CDA). The Fast Healthcare Interoperable Resource (FHIR) is also currently in use [54; 55].  
While not as widely noted, ethical, legal, and sustainability considerations are acknowledged, suggesting an awareness of the complex, multifaceted nature of technology adoption in the healthcare sector and reflecting the viewpoint of [4; 25]. These insights collectively underscore the need for a holistic strategy that addresses financial, educational, technical, and ethical aspects to facilitate the successful integration of healthcare technologies.  
The following table (Table 6) presents the main insights drawn from the interviews.
## 2 Materials and Methods
### Myogenic Differentiation  
C2C12 were cultivated at 1 \(\times\) 10\({}^{4}\) cells per well in culture plates (VWR, Darmstadt, Germany), which were precoated with 0.1% collagen in 0.1 M acetic acid. After 48 h, the growth medium was replaced with myogenic differentiation medium (MD), which consisted of DMEM-LG, supplemented with 2% horse serum (HS, Millipore, Darmstadt, Germany). The plates were divided into three groups: static, periodic fluid shear stress (PFSS), and cyclic fluid shear stress (CFSS) conditions. They were then incubated for up to seven days with a regular medium which was changed twice per week. FSS was induced by incubating the culture plates on a horizontal rocking system previously established in the cell culture incubator with a rocking angle of 10\({}^{\circ}\). The rocking frequency was determined to be 10 cycles per min with an optimized force that kept the cells covered with the medium throughout the entire rocking period. The liquid viscosity was assumed to be 10\({}^{-3}\) Pa s, and the FSS was calculated using the following previously reported formula [52].  
\[\tau=\frac{\pi\mu\theta_{\text{max}}}{2\delta^{2}\text{T}}\]  
\(\tau=\text{FSS (Pa)}\)  
\(\mu=\text{Fluid viscosity (Pa s)}\)  
\(\theta_{\text{max}}=\text{Maximum rocking angle (rad)}\)  
\(\delta=\text{Liquid depth/length-ratio (h${}_{0}$/(2R))}\)  
\(\text{h${}_{0}$}=\text{Liquid depth (mm)}\)  
\(\text{R}=\text{Radius of the cultivation plate (mm)}\)  
\(\text{T}=\text{Rocking period, duration of one cycle (s)}\)  
Briefly, cells were exposed to two different types of fluid shear stress: (1) CFSS, which was applied in a 24-well culture plate with a base area of 1.93 cm\({}^{2}\). To achieve a liquid depth of 2.59 mm, the medium was reduced to 500 \(\mu\)L per well. Based on the formula, the FSS on the bottom of the culture plate was calculated to be 1.68 mPa; (2) PFSS, which was applied for only 1 h per day in 12-well culture plates with a base area of 3.85 cm\({}^{2}\). The volumes of reagents and media, as well as the number of cells seeded, were adjusted accordingly. A medium volume of 1205 \(\mu\)L per well was chosen to expose the cells to the FSS, corresponding to a liquid depth of 3.13 mm. The FSS on the bottom of the culture plate was calculated to be 2.3 mPa. The cells were provided with fresh medium twice a week. Cells were also cultivated in parallel under static conditions, serving as controls. The effects of CFSS and PFSS on cell viability and proliferation capacity were assessed at 3 h, 24 h, and 72 h. Meanwhile, MD capacity was evaluated at 3 h, 3 d, and 7 d post-induction. The cells were fixed in 4% paraformaldehyde(PFA, [PERSON], Karlsruhe, Germany) stored in PBS at 4 \({}^{\circ}\)C until further use.
## 3 Processing and Extraction
### Conventional Extraction Methods
#### 3.1.1 Lipid Fraction  
The composition of fatty acids in extracted oils is mainly determined by the species of fruit as the source of seeds. However, the yield appears to be dependent on extraction conditions, i.e., solvent type, time of extraction and pomace pretreatment method. In the case of the solvent type, organic solvents used in conventional extraction methods are characterized by different polarities. They are defined by different dielectric constant values--a measure of solvent polarity which determines the solute-solvent correlation. The optimum dielectric constant values range from 6-8 and result in higher oil yields. However, the higher polarity of solvents results in limitations in the solubility of extracted lipids and can lead to their hydrolysis. What is more, TAGs are amphipathic compounds and some solvents may cause hydrogen bonding of TAG ester groups. So, in conclusion, higher polarity may be a reason of lower oil yield, despite the fact that increasing the polarity of the solvent causes the opening of cell walls and improves compound release. What is more, in the case of plant oils, they also contain more polar constituents, e.g., phospholipids and tocoperhoels, which are the source of components that may present greater affinity to more polar solvents [85; 86]. Hexane is a widely employed solvent in fat extraction processes due to its low polarity and it was used in most of the reviewed studies. However, it is a chemical substance with proven toxicity and water-polluting ability, so it should not be used in the food industry [87]. Considering berry seed oils extraction, ethanol is used as a solvent in the extraction of fat from blackberry seeds, resulting in higher yields compared to hexane--11.8% and 14.2%, respectively [88]. The higher oil yield value, when a more polar solvent was used, may be connected to the phenomena of partly polar compounds in the fat fraction. The time of the process is also a factor influencing efficiency. Comparing the extraction of oil from dried raspberry seeds using hexane, a higher yield was connected with a longer extraction time: the process lasting 8 h gave a 14.33% yield [89] and a 2 h extraction resulted in a 10.7% yield [60]. The analyzed studies also showed the significant impact of seed pretreatment methods on extraction efficiency. Considering raspberry by-products, dried pomaces [59] or seeds [60] were more susceptible to oil extraction processes than wet material [57]. In addition, replacing drying at room temperature with oven-drying resulted in higher fat yield values [89].
## 4 Proposed Predistortion Model with Coefficient Approximation  
Since the inverse method from (5) includes a root within \(g_{inv}(t)\), it does require the approximation approach within hardware modules for real-time implementation. For the VLC system, the approximation approach shown in the inverse function is impractical. We introduce a simple predistortion scheme using a simple coefficient approximation to mitigate this issue. We propose implementing the predistortion using the coefficient changed according to LED color without sampling the LED transfer function.  
This method uses the static transfer function of Equation (2). We modify coefficients \(b_{0}\), \(b_{1}\), and \(b_{2}\) in (3) to the predistortion function. As mentioned, if \(\zeta<0.5\), the convex transfer function can be obtained. Therefore, the \(1-\zeta\) value is substituted instead of \(\zeta\) of (3) and coefficients. Now, those coefficients can be expressed as follows.  
\[b_{0}=1-\zeta,\hskip 14.226378 ptb_{1}=1,\hskip 14.226378 ptb_{2}=4\zeta-2 \tag{6}\]  
When the coefficients in (6) are substituted in (2), the polynomial predistortion function \(g(t)\) is  
\[g(t)=(4\zeta-2)(I_{in}(t)-I_{DC})^{2}+I_{in}(t)-I_{DC}-\zeta+1 \tag{7}\]  
Figure 3: A block diagram of OFDM transceiver using predistorter.  
Figure 4 shows the nonlinearity of the LED, predistortion function, and linearized LED transfer function. Concave curves have a characteristic of the nonlinearity of the LED. Furthermore, convex curves are based on predistortion functions. If the predistortion function \(g(t)\) enters the input \(I_{in}(t)\) of the LED transfer function in (2), the final transfer function becomes linear. \(\zeta\) is determined according to the LED color. The proposed method can process if we know the LED color. Therefore, the sampling for making the predistortion like the conventional method is unnecessary. The proposed method has the advantage of simplicity when implementing the predistortion module.