esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The paper proposes a generalized algorithm for dynamic modeling of the wind system class of type: single-rotor, 1-DOF planetary transmission, and counter-rotating generator.  
The counter-rotating generator requires a dual-output speed increase, leading to a branched power flow configuration where the input power is distributed to the two parallel-connected planetary units. The analytical equation of motion is derived by combining the dynamic equations of the shafts, the mechanical characteristics of the wind rotor and electric generator, and the kinematic and static equations of the planetary gear transmission, which together describe the complex interactions within this system. This system of equations allows the analytical establishment of the wind system equation of motion and implicitly its operating point by numerical solution in transient mode and in steady-state.  
The analytical study and the results of the numerical simulations carried out on a case study of a wind turbine with a rated power of 100 kW allowed us to draw the following conclusions:  
* The proposed generalized modeling algorithm allows obtaining analytically the equation of motion of the wind system, formulated as a nonhomogeneous differential equation of the second order in a single independent variable, describing the wind rotor motion;
* By numerically solving the equation of motion, using the MATLAB-Simulink R2014a software, the dynamic response of the wind system in transient mode and the operating point in steady-state are obtained;
* The analysis of the dynamic response in transient mode, when starting from rest at constant wind speed, allowed the identification of the starting time of the wind system,as well as the stresses induced by the inertial load alone and by its combination with the generator load;
* Unlike the case of traditional wind turbines, equipped with a conventional generator with a fixed stator, the counter-rotating generator allows an additional input of power brought by the mobile stator \(GS\); in the analyzed case, the additional power supply by \(GS\) in steady-state is ~6.4%.  
The proposed generalized algorithm can be applied, with rigorous adaptations, to other types of wind systems, regardless of their complexity: with one or more wind rotors, with conventional or counter-rotating electric generators, with fixed-axis or planetary speed increase. Likewise, the developed MATLAB-Simulink R2014a model can also be applied iteratively for the purpose of constructive-functional optimization of this particular type of wind system, as well as in the simulation scenarios of variable operational conditions determined by the change in wind speed.  
The authors intend to address in the future the dynamic optimization of such wind systems and the validation of theoretical results through the experimental research of some functional models on specialized testing rigs.
# Development of an Autonomous Underwater Helicopter with High Maneuverability
## 4 Mechatronics Design of AUH Prototype
### Control System  
The control system is used to adjust attitude and move along paths, using the positioning information received from iUSBL in real time. The control system is designed around a STM32F103 VCT6 microprocessor based on an ARM-M3 kernel, which integrates the depth meter, the altimeter and the IMU (Inertial measurement unit) sensor and power output unit interface, buoyancy adjustment unit interface, positioning system interface and energy unit interface.  
The control system block diagram is shown in Figure 9. The AUH needs to complete the translation and yaw steering functions in the XOY plane, the lifting and pitch control in the XOZ plane. The yaw angle is controlled by the horizontal propellers' differential output, and the pitch angle is controlled by the vertical propellers' differential output. The translation in the horizontal direction is controlled by the open loop of the horizontal propellers. The heave motion is controlled by the buoyancy adjustment system. Considering that the interference of the yaw angle and pitch angle is relatively small when  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **12 V** & **16 V** \\ \hline Max thrust (Forward) & 34.8 N & 50.0 N \\ Max thrust (Backward) & 29.4 N & 40.2 N \\ Min thrust & 0.098 N & \\ speed & 300–3800 _rev_/min & \\ Max current & 25 Amps & \\ Max power & 350 Watt & \\ \hline \hline \end{tabular}
\end{table}
Table 1: T200 propeller characteristics.  
Figure 8: (**a**) Schematic diagram of the buoyancy adjustment system; (**b**) picture of the buoyancy adjustment system.  
working normally, only simple coupling treatments can be carried out for yaw and pitch. When the velocity component is superimposed on the angle control component, the angle control PID (proportional-integral-derivative) parameters should be associated with the velocity component, and different angle PID parameters should be taken at different conditions. Because of the heavy workload of establishing continuous function correspondence, a piecewise PID algorithm is adopted to ensure the control accuracy.  
As discussed in Section 2, it is important to have four propellers and a buoyancy adjustment system for the disc-shaped AUH to be highly maneuverable. Therefore, in this section, an AUH prototype with four propellers and a buoyancy adjustment system was developed. In addition, it will be tested in the next section to demonstrate its maneuverability.
# Organic Crystals for THz Photonics
## 5 Applications of Organic THz Sources
### Pharmaceutical and Chemical Industry
With spectroscopic THz imaging, a spatial distribution map was obtained, indicating regions with a various concentration of CaCO\({}_{3}\) in a polymer matrix. The results of this study demonstrated that the THz system based on DSTMS crystals has great potential as a rapid analytical tool for qualitative and quantitative analysis of powdered materials.
# An Overview of Shared Mobility Operational Models in Europe
## 3 Carsharing
### Operational Challenges of Carsharing in Europe  
Carsharing is promoted as a sustainable urban mobility option, with evidence that it can reduce private car ownership and use, thereby lowering greenhouse gas emissions. However, realizing these sustainability benefits is not assured, as some carsharing programs carry the danger of increasing rather than replacing private vehicle use, which might counteract environmental benefits [23]. Carsharing must be seamlessly integrated with public transportation, where careful design guarantees that it enhances rather than subtracts from it. According to [24], if carsharing services are not properly integrated, they may potentially decrease the demand for public transportation and increase traffic, especially when customers choose shared cars over public transportation because they are more convenient to use. Carsharing must be used in combination with links to transportation and more comprehensive emission-reduction plans in order to successfully accomplish sustainability goals [24].  
Carsharing still serves only a small fraction of mobility needs in Europe and remains far from being mainstream in many cities [23]. According to [25], only approximately one-third of Europeans are willing to completely adopt shared mobility services because they are concerned about the availability, safety, and cleanliness of vehicles. Another problem is accessibility: carsharing fleets are typically concentrated in crowded urban areas, providing little coverage in suburban and rural areas. Because carsharing programs are less beneficial in lower-income and outlying areas, this uneven access raises concerns about fairness.  
According to [25], carsharing companies face high costs and infrastructure needs from an operational and economic perspective. This is mainly due to the increased wear and tear of vehicles, where maintaining a fleet of shared vehicles (fuel, cleaning, and maintenance) can be up to 30% more expensive than maintaining a private vehicle. Fleet operations also depend on adequate infrastructure (dedicated parking spots and charging stations for electric carsare vehicles), although only about 40% of urban areas in the EU have the facilities that could support large-scale carsharing. The financial success of services is put to the test by these limitations, which increase complexity and running expenses. Traditional car rentals, ride-hailing, and other shared modes of transportation present a serious threat to carsharing businesses that operate on slim margins. Profitability is challenging to attain due to the large investment in capital needed and the requirement to maintain low prices for competition. In order to expand their financially sustainable user base, many carsharing providers are still experimenting with business models and depending on outside funding or partnerships [25].
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 7 Limitations and Challenges
### Multi-Modal Detection  
Detecting fake news solely through fake image detection has limitations. Sometimes, images are unrelated to the text, leading to misinformation, and manipulations may remain undetected [126]. Additionally, mismatched images from different timelines or locations can blend seamlessly with text, making them difficult to spot. Fake news creators adapt to varying text styles and social contexts, making visual and text combinations unreliable. To address these challenges, a comprehensive approach is needed. Alongside fake image analysis, investigating diffusion network patterns is crucial. Analyzing the propagation of fake content across networks can help uncover subtle cases. Multi-modal research, encompassing image and network analysis, can bridge gaps in detection. This holistic strategy, utilizing cues from image and network patterns, enhances our ability to identify complex instances of fake news dissemination.
## 2 Harmonic Analysis of DC-Link Capacitor Current for One VSI
In addition to the second carrier harmonic component, the sixth, tenth, and fourteenth carrier harmonic components also cancel each other out because they have cosine terms similar to the second carrier harmonics. Figure 3: The carrier waves (**top**) and second carrier harmonic components (**bottom**) for both VSIs. Figure 5a illustrates the frequency spectrum of the dc-link capacitor current for various modulation indices. The second carrier harmonic is dominant for all modulation indices when there is no phase difference. The second, sixth, and tenth carrier harmonics are cancelled when the proposed method is applied where there is a \(\pi/2\) rad phase difference between the carrier waves, as shown in Figure 5b. Figure 6 shows the dc-link capacitor current of the dual VSIs dual PMSMs system for various phase shifting angles under different power factors. The minimum dc-link capacitor current is observed at a phase shifting angle of \(90^{\circ}\) (\(\pi/2\) rad) or \(270^{\circ}\) (\(3\pi/2\) rad). At these angles, maximum cancellation of the second carrier harmonic component is achieved, resulting in the lowest dc-link capacitor current. In addition, the optimal phase shifting angle remains at \(90^{\circ}\) even at low power factors. As the phase shifting angle moves away from \(90^{\circ}\) and \(270^{\circ}\), the capacitor current increases because the cancellation of the second carrier harmonic decreases. Figure 4: Frequency spectrum of dc-link capacitor current for M = 0.6126 and pf = 0.966 in dual VSIs dual PMSMs system: (**a**) without phase difference; (**b**) with phase difference. Figure 5: Frequency spectrum of the dc-link capacitor current for various modulation indices in dual VSI dual PMSM system: (**a**) without phase difference; (**b**) phase difference = \(\pi/2\) rad.
# Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding
## 3 Methodology
### Refining  
In the process of initial superpixel extraction, multiple seed points may be placed in a homogeneous region in order to maintain the convex shape to keep the correct clustering, which may tend to increase over-segmentation and reduce semantics. Meanwhile if there is an edge inside an object, the object will be divided into two homogeneous regions. To reduce redundant superpixels so that the final superpixels can be adapted to differently shaped targets, a regional merger is applied to combine the over-segmented initial superpixels. The region adjacency graph (RAG) of each superpixel is constructed as shown in Figure 9.  
The RAG, which is an undirected graph, provides a spatial view of the relationships between superpixels on a graph, where each superpixel is regarded as a node \(v_{i}\). Let \(G\) = (\(V\),\(E\)) be the RAG for superpixel \(p_{i}\), where \(V=\{v_{1},v_{2},\cdots,v_{n}\}\) is a set of nodes, and \(E=\{e_{1},e_{2},\cdots,e_{n}\}\) is a set of corresponding boundaries. Each edge has a weight \(Ew_{i}\) that represents the similarity between two adjacent nodes. We use the luminance average \(\overline{I}\) and texture entropy \(en\) to achieve a reasonable representation of the regional similarity, as follows:  
\[Ew_{i}=aw_{l}+bw_{en}, \tag{17}\]  
\[w_{l}=\frac{\min(\overline{I}(c_{i}),\overline{I}(c_{j}))}{\max(\overline{I}( c_{i}),\overline{I}(c_{j}))}, \tag{18}\]  
\[w_{en}=\frac{\min(en(c_{i}),en(c_{j}))}{\max(en(c_{i}),en(c_{j}))}, \tag{19}\]  
where \(c_{i}\) and \(c_{j}\) are adjacent nodes; and \(a\) and \(b\) are two weight factors \((a+b=1)\). The entropy value of image texture is widely used to characterize image texture information [18; 36], which can be defined as:  
\[en=-\sum_{i=0}^{H}P(i)\log[P(i)], \tag{20}\]  
where \(H\) is the number of grayscale differences and \(P(i)\) is the probability of each gray difference obtained by the histogram statistic. When the texture of the IR image is simple, the luminance average \(\overline{I}\) is more important than the texture entropy \(en\), so we can set \(a\) greater than \(b\). When \(Ew_{i}\) is greater than the threshold \(Ew_{T}\), \(p_{i}\) and \(p_{j}\) are highly similar and, thus, the merging operation is performed. In the following experiments, we set \(Ew_{T}=0.8\), \(a=0.5\), and \(b=0.5\). All superpixel blocks are searched, and the merging process of each layer is iterated until convergence; then, a final superpixel map is obtained, which has the desired characteristics. The clustering algorithm is summarized in Algorithm 2. Figure 9: Example of RAG in a local region. (**a**) A local region in the initial segmented map; (**b**) the corresponding RAG; and (**c**) the next RAG after merging \(p_{1}\) and \(p_{5}\), if the weight \(w\) is greater than \(w_{T}\).
# Trustworthy Transaction Spreading Using Node Reliability Estimation in IoT Blockchain Networks
###### Abstract
It uses the number of generated/stored/duplicated transactions, battery status, network connection status, connection bandwidth, and the number of transaction anchoring as the input features, and subsequently models the next hop of the transaction traverse in its neural network structure. The next hop is determined using the Q-value from the Q-learning model. A transaction spreads hop by hop to the next nodes with a higher reward value from the Q-learning function. The Q-value is updated for each transaction hopping within a single transaction traverse from the origin to the destination. At the end of a single transaction traverse, the graph convergence network (GCN) is activated with the Q-values of network nodes as input features of the GCN. The GCN generates node embeddings to identify highly reliable network areas. The GCN calculates the node embeddings using its message-passing process and then activates node classification to identify a reliable area. The identified reliable area guides the spreading path of the next transaction traverse to the destination node. The proposed trustworthy transaction spreading methods are evaluated in a virtual simulation environment. The test simulation environment was operated over an interplanetary file system (IPFS). The IPFS testbed included various virtual machines and demonstrated the practical applicability of the proposed operational control method. A virtual machine can serve as an IoT node. Custom-developed blockchain software modules were installed on multiple virtual machines using Docker containers. The superior performance of the proposed approach was demonstrated in the test environment. The remainder of this paper is organized as follows. Relevant studies are presented in Section 2. Section 3 provides for transaction spreading and consensus. Section 4 describes Q-learning and the GCN for optimizing transaction spreading over a reliable area. The computational results and concluding remarks are presented in Sections 5 and 6, respectively.
## 3 Parameter Selection for the MSFGWO Algorithm
### Population Size and Iterations  
The performance of the GWO algorithm is closely related to the population size and the number of iterations. The population size affects the calculation accuracy, while the number of iterations affects the calculation efficiency. Over the hour-long experiment, an average of 13.68 satellites could be observed per second. Therefore, for the parameter  
Figure 4: Schematic overview of the MSFGWO algorithm.  
experiments, data from the 138 th second were used, taking 14 visible satellites as a basis for setting parameters in this magnitude range. In the traversal method, it was set to take four satellites and calculate DGDOP values \(C_{14}^{4}=1001\) times. The optimal DGDOP value obtained was 312.8203, with a time consumption of 0.0408 s.
# Development of a Self-Powered Magnetorheological Damper System for Cable Vibration Control
## 4 Experimental Results and Discussion
### The Stay Cable with a Self-Povered MR Damper System
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mode Order**} & \multicolumn{4}{c}{**Damping Ratio (\%)**} \\ \cline{2-5}  & **Uncontrolled** & **MR Damper in Passive Mode** & **Electromagnetic Damping** & **Self-Powered MR** \\  & & **Passive Mode** & **Alone (Load MR Damper)** & **Damper System** \\ \hline
1 & 0.59 & \(1.16\) & 0.68 & 0.87 \\
2 & 0.15 & \(1.10\) & 0.54 & 1.45 \\
3 & 0.11 & \(1.23\) & 0.65 & 1.69 \\
4 & 0.12 & \(1.07\) & 0.74 & 1.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons of modal damping ratios of the cable with different control devices. Figure 16: Identified damping coefficients and stiffness coefficients of the self-powered MR damper. (**a**) Identified stiffness coefficients; (**b**) Identified damping coefficients. Figure 15: Hysteretic loops of the self-powered MR damper for different modes of the cable. (**a**) First mode; (**b**) Second mode; (**c**) Third mode; and (**d**) Fourth mode. Unlike the self-powered MR damper system in this paper, other mentioned self-powered MR dampers, where the MR damper and the generator are at the same location, have not exhibited negative stiffness behavior [27]. The results imply that the negative stiffness may be attributed to the relative positions of the rotary DC generator and the damper in a self-powered MR damper system. Hence, the proposed self-powered MR damper system, where the generator and the MR damper are separated, seems to be more promising than the conventional, compact self-powered MR damper. However, the effects of the relative positions of the rotary DC generator and the damper on the induced negative stiffness of the self-powered MR damper and control performance of the cable still require further investigation.
## 4 Experimental Analysis and Results
### Implementation of Metaheuristic GA-Based Feature Selection
#### 4.6.1 Exploratory Analysis of Training and Test Sets
Because the distributions of text length in Figure 4 have normal-like distributions and to ensure they do not differ from each other, the ANOVA test is used on targets of the training and test sets for the three topics. The results of the ANOVA test are listed in Tables 6 and 7. From the results in Table 6, we can see that the \(p\)-value \(<\) 0.05 for the training set of three topics, and we fail to accept that the distributions of text length do not differ from each other. According to the results of the ANOVA test for the test set of the three topics in Table 7, we can notice that the \(p\)-value \(>\) 0.05 for the sentiment target, and we fail to reject that the distributions of text length do not differ from each other; whereas, the \(p\)-value \(<\) 0.05 for the sarcasm and stance targets and we fail to accept that the distributions of text length do not differ from each other. To get an intuition on how the features of the dataset are organized in higher dimensions, a t-distributed Stochastic Neighbor Embedding (t-SNE) is applied to visualize complex datasets into two dimensions for more understanding of the underlying patterns and relationships in the dataset features and labels. Figure 5 gives the t-SNE charts of the training and test sets, and Figures 6-8 show the density of the word counts and unique word counts for both the training and test sets, as well as the density training set based on its labels for each topic. Figure 6: Density distribution of the word counts and unique word counts of the sentiment data texts: (**a**,**c**) are the density distribution of the word counts and unique word counts of the training set regarding the sentiment classes; (**b**,**d**) are the density distribution of the word counts and unique word counts of the training and test sets. Figure 7: Density distribution of the word counts and unique word counts of the sarcasm data texts: (**a**,**c**) are the density distribution of the word counts and unique word counts of the training set regarding the sarcasm classes; (**b**,**d**) are the density distribution of the word counts and unique word counts of the training and test sets. The charts from Figure 5 are obtained by passing the Arabic text features extracted using TfidfVectorizer and reduced using the Singular Value Decomposition (SVD) method to the TSNE Visualizer. The SVD method is a matrix factorization that simplifies the Eigen-decomposition of an \(n\times n\) square matrix to any \(n\times m\) matrix. From Figure 5, we can see that there are significant overlaps between the classes of the sarcasm topic in both the training and test set in addition to the minority of the sarcasm class that may decrease the results of sarcasm as well as a multilabel classification task. For more analysis of dataset Arabic texts, Figures 6-8 show content quality as it is an essential measure for indicating how rich and diverse the content vocabulary is.
# Detection of Sub-Aroma Threshold Concentrations of Wine Methoxyprazines by Multidimensional GCMS
## 2 Materials and Methods
### Method Validation
#### 2.5.4 Limit of Blank, Limit of Detection, Limit of Quantitation, Accuracy, and Precision  
The limit of blank (LOB), limit of detection (LOD) and the limit of quantitation (LOQ) were determined using two techniques. A total of 19 blank samples were analyzed alongside three standard curves on three different days with the LOB, LOD and LOQ determined using Formulas (1), (2), and (3), respectively.  
\[\text{LOB }=\text{(blank mean) }+1.645\,\times\text{ (blank standard deviation)} \tag{1}\]  
\[\text{LOD }=\text{ LOB }+1.645\,\times\text{ (low concentration sample standard deviation)} \tag{2}\]  
\[\text{LOQ }=10\,\times\text{(blank standard deviation)} \tag{3}\]  
The LOQ was also determined by serially diluting a working standard (level 3) in triplicate to find the point at which the standard error of the replicates (precision) exceeded 5% and the quantitative value determined (accuracy) also exceeded 5%. A total of six dilution steps were employed with the lowest concentration step set at 0.022 ng/L for IPMP and SBMP and with 0.089 ng/L for IBMP. Once the lowest point was found it was multiplied by a factor of three, the equivalent of an undiluted wine sample, and reported as the LOQ.