esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Results
### Influence of Wing Load on Deformation  
In the wind tunnel experiment, the change of wind speed will bring the difference in Reynolds number, which will slightly affect the pressure distribution along the chord, and also lead to the difference of wing load, which will greatly influence the membrane deformation behavior. Therefore, the wing load is the primary index to be considered in the design of experimental wind speed. Taking solar UAV as an example, the wing load \(W/S\) is generally between 2.4 kg/m\({}^{2}\) and 6.3 kg/m\({}^{2}\)[4]. The wind speeds of the experiment are  
Figure 8: Typical airfoil curve and shape comparison before and after deformation: (**a**) Typical airfoil of near space UAV; (**b**) The airfoil shape before and after deformation.  
Figure 9: Deformation of 25% and 50% span section.  
set as 6.4 m/s, 9.0 m/s, and 11.0 m/s, respectively, to simulate the membrane skin wing under different flight parameters. The experimental conditions are portrayed in Table 3.  
When the \(\alpha=4^{\circ}\), the membrane deformation under different experimental conditions is shown in Figures 10 and 11. It can be seen that the wing loads does not affect the overall deformation trend of the membrane, whether on the upper surface or the lower surface. Firstly, the front area of the upper surface is slightly concave, the rear area is greatly convex, and the rear edge is slightly upturned. The maximum convex amount all appears near 70% chord length. Secondly, the lower surface shows a concave trend as a whole, and there is a small bending at the trailing edge consistent with the upper surface. The maximum concave amount appears near 45% chord length. As shown in Figure 10, for the upper surface, the bulge amplitude at the rear area is approximately proportional to the wing load. When \(W/S=7.8\) kg/m\({}^{2}\), the maximum deformation is 2.336 mm. The front concave area reflects the superposition effect of prestress and aerodynamic force. When the wing load is small, the aerodynamic suction is not enough to offset the concave deformation caused by prestress, so Figure 10a shows an obvious depression in the front area. As the wing load increases, the aerodynamic force gradually occupies the dominant role, and the concave appears a mitigation trend. As shown in Figure 10b,c, when the \(W/S=7.8\) kg/m\({}^{2}\), most areas of the front area are convex. As shown in Figure 11, for the lower surface, although the concave amount of the membrane is positively correlated with the wing load, they are not strictly proportional. When the wing load is increased by 3 times, the maximum deformation is only doubled.
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 2 Materials and Methods
### Segmentation of Time Series  
#### 2.2.1 Multiresolution Segmentation of SITS  
For the segmentation process, we created a time series of the 10 m spatial resolution bands (blue, green, red, and near-infrared) to take advantage of the best spatial resolution possible supported by Sentinel-2. We used an approach based on the multiresolution segmentation (MRS) [54], a region-growing algorithm that starts from the pixel level and aggregates pixels based on homogeneity conditions set by the user. The most important is the scale parameter (SP), which dictates the size of the object and, therefore, the internal homogeneity of the object. In order to objectively select the SP, we used the ESP2 tool (Estimation of Scale Parameter 2) [55], a method that automatically selects the appropriate SPs based on the local variance of objects across multiple scales [56; 57]. We used a bottom-up hierarchical segmentation and selected the Level 2 of the results, which visually fitted best with the target crop parcels.  
Segmenting multiple images from time series can be memory and time-consuming. Since the boundaries of the crop parcels can change their geometry over the timeframe analyzed, we selected all cloud free images available for each test area: 26 images for TA1 and 18 images for TA2. This resulted in 104 bands for TA1 and 72 bands for TA2 to be used in the segmentation process.
## 3 Geometrically Exact 3D Beam Model and Variational Form for Assigned Strains
### The Beam Model in Case of Measured Strains  
The effect of all external loads acting on the pipe, such as the soil pressure and the thermal loads, is taken into account in the proposed formulation through the value of the measured strain \(\bar{\rho}(s)\), whose distribution over the beam is obtained by means of an interpolation of the measured data at a certain number of cross-sections. This information represents the actual effect of arbitrary distributions of loads.  
The variational form of the problem equations in terms of kinematic variables only (displacement form)  
\[\Phi(u)=\frac{1}{2}\int_{0}^{\ell}\Bigl{\{}(\rho-\bar{\rho})^{T}\mathbf{C}_{ \rho}(\rho-\bar{\rho})\Bigr{\}}\;ds=\text{stationary} \tag{14}\]  
can be seen as a minimization problem, in an energy norm, of the difference between the strain \(\rho\) coming from the kinematic relationships and that obtained from the measurements \(\bar{\rho}\).  
A simple and useful way to rewrite the functional in mixed form is to introduce a new stress quantity so defined  
\[\mathbf{t}=\mathbf{C}_{\rho}(\rho-\bar{\rho}) \tag{15}\]  
that allows to transform the problem as  
\[\Phi(u)=\int_{0}^{\ell}\Bigl{\{}\mathbf{t}^{T}(\rho-\bar{\rho})-\frac{1}{2} \mathbf{t}^{T}\mathbf{F}_{\rho}\mathbf{t}\Bigr{\}}\;ds \tag{16}\]  
where \(\mathbf{F}_{\rho}=\mathbf{C}_{\rho}^{-1}\) is the cross-section compliance matrix. It is worth noting that the constitutive matrix is not strictly required to obtain the displacements from the assigned strain [13; 14], but it is here used just as a metric matrix for homogenizing the different strain components. Obviously, if again we impose a priori the constitutive law, that in this case rewrite as \(\mathbf{t}=\mathbf{C}_{\rho}(\rho-\bar{\rho})\), we get the displacement format in Equation (14).  
The new variational formulation in Equation (16), if equivalent in solution to those proposed in Equation (14), depends on the measured strains \(\bar{\rho}\) in a linear way, that is only in its first variation. Since it allows a great simplification of the code it will be adopted in the following. For the analysis, furthermore, we will adopt the MIP description of the problem proposed in [12] that allows rendering the Newton method, that we will use to solve the nonlinear problem, very robust and efficient compared to the formulation in displacement variables only.  
In particular, the variational problem in Equation (16) is replaced by the following form where a numerical integration is adopted  
\[\Phi(u)=\sum_{g}\biggl{\{}\mathbf{t}_{g}^{T}(\rho_{g}-\bar{\rho}_{g})-\frac{ 1}{2}\mathbf{t}_{g}^{T}\mathbf{F}_{\rho}\mathbf{t}_{g}\biggr{\}}w_{g} \tag{17}\]  
with \(w_{g}\) the Gauss point weight and the subscript \(g\) denotes quantities evaluated at the \(g\)th Gauss point of the discrete model.
## 3 Analysis of Experimental Results
### Relative Dynamic Elastic Modulus  
The relative dynamic modulus of elasticity of self-compacting concrete with RCA varies at various replacement rates, as shown in Figure 5. Similar variations in the relative dynamic elastic modulus of self-compacting concrete were observed with various RCA replacement rates. The relative dynamic elastic modulus exhibits an overall \"increase first, then decrease\" fluctuation pattern due to the rise in the number of dry and wet cycles and the erosion of the sulfate coupling. Throughout the wet and dry cycles, the relative dynamic elastic moduli decreased by 76.3%, 80.9%, 73.1%, 61.2%, and 59.6% for each group. RCASCC-75 and RCASCC-100, which neared destruction, experienced larger relative dynamic elastic modulus drops. For this phenomenon, RCA has a higher porosity than NCA, which affects the rate of SO4\({}^{2-}\) erosion while also increasing the contact area between SO4\({}^{2-}\) and the cement's hydration products, improving the rate at which calcite and other products are produced, filling internal pores, increasing specimen density, and enhancing relative dynamic elastic modulus [9]. Its expansion stress keeps increasing as calcium alumina and other hydration products are produced and stored, which accelerates the development of pores and microcracks [32,33]. As a result, the specimen's internal structure is destroyed, and eventually, the damage spreads to the surface of the specimen, causing the relative dynamic elastic modulus to fail [34]. RCASCC-0 saw the lowest relative dynamic elastic modulus drops, followed by RCASCC-25 and RCASCC-50. RCASCC-0, RCASCC-25, and RCASCC-50 all had relative dynamic elastic moduli that were greater than 70%, and they all maintained a high level of resistance to sulfate erosion. This result demonstrated that the relative dynamic modulus of elasticity of self-compacting concrete was significantly influenced by various RCA substitution rates. The relative dynamic modulus of elasticity of self-compacting concrete changed in a manner that was more impressive the greater the replacement rate of RCA [35].
# CFD and Experimental Comparison for Micro-Pump Performance in Space Applications: A Case Study
## 2 Materials and Methods
### Design Considerations
#### 2.1.2. Volute  
The design of an associated volume is necessary in order to have a complete pump stage. The volume cross section area was designed by means of constant angular momentum, based on previous studies and on theory [34; 35; 36; 37]. Thus, the double volute represents the best option for this type of pump as it can significantly reduce the radial forces acting on the impeller. As the bearings materials specific for space pumps (carbon or ceramics) do not have high mechanical strength, the radial loads should be as low as possible. Figure 5 presents the volute design, with an interior diameter of 41.1 mm, a channel width of 4.8 mm, and a channel height of 5.5 mm. The other dimensions, due to the proprietary nature of the design and to safeguard intellectual property rights, cannot be disclosed in this publication.  
After all the design iterations were verified with CFD simulations and the efficiency was equal or higher than the imposed one, the final impeller was verified with the volute and the resulting geometries were considered as output from the theoretical design step.
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 2 Prediction Method Based on Statistical Analysis Theory
### Specific Methods Listed  
(1) Historical average model (HA)The historical average method is a basic forecasting technique commonly employed to predict data with periodic or seasonal patterns. It forecasts future values by computing the average of the same time period from historical data, assuming that the data follow a certain cyclical pattern. The method is simple, and its parameters can be estimated online using the least squares (LS) method. It is capable of handling variations in traffic flows over different times and periods to some extent. However, its static prediction performance is limited, as it does not reflect the inherent uncertainty and nonlinear characteristics of dynamic traffic flows. In particular, it is unable to address the impact of random disturbances or respond to unforeseen events, such as accidents, within the traffic system [7].  
(2) Autoregressive Integrated Moving Average model (ARIMA)  
Unlike other time series methods that require fixed initialization for simulation, the ARIMA model is composed of three components: Autoregression (AR), Differencing (I), and Moving Average (MA). By fitting the historical data, the model generates parameters and makes predictions. It views the traffic flow at a given time as a more general non-stationary stochastic process, typically with three or six model parameters [8].  
Based on a substantial number of continuous data, the ARIMA model offers high prediction accuracy, making it particularly suitable for stable traffic flows. However, the model primarily approaches prediction from a pure time series analysis perspective and does not account for the flow relationships between upstream and downstream road sections. Therefore, it is recommended to use this model in conjunction with other models for more comprehensive predictions [9].  
(3) Kalman filter model  
Kalman filtering theory (KF), introduced by [PERSON] in 1960, has found application in various domains, including time series modeling in statistics and economics. Kalman filtering is typically employed in non-stationary stochastic environments and offers relatively low computational complexity. However, it is only applicable to linear systems and is highly sensitive to noise. For state estimation and prediction in nonlinear systems, the use of extended Kalman filtering or nonlinear filtering methods is necessary. Given that the model is based on linear estimation, its performance may deteriorate when the prediction interval is less than 5 min and when the randomness and nonlinearity of traffic flow variations are more significant [10].
# Meander Line Super-Wideband Radiator for Fifth-Generation (5G) Vehicles
## 5 Conclusions  
A comprehensive design approach for a meander line radiator tailored for super-ultra-wideband (SUWB) applications is presented. The demonstrated approach successfully designs a highly wideband radiator suitable for fifth-generation (5G) mobile frequency applications that covers both the lower (sub-6 GHz) and higher (28 GHz) frequency bands. The theoretical calculations employed for dimension determination and the simulation results yield satisfactory measured performance. The proposed design incorporates an aerial patch excited by a microstrip feedline positioned atop the substrate and complemented by a continuous ground plane. By utilizing a theoretical formula, the meander line structure efficiently covers both frequency range 1 (FR1) and frequency range 2 (FR2) of the upper 5G frequency spectrum. Operating at 28 GHz, the antenna achieves a peak gain of 7.5 dBi. The simulation demonstrates an impressive bandwidth ratio (BWR) of 9.09:1 for a \(-\)10 dB reflection coefficient and spanning a wide frequency range of 53.4 GHz (ranging from 6.6 GHz to 60 GHz). With its compact linear meander line planar structure occupying a small area of 34 mm \(\times\) 20 mm \(\times\) 1.6 mm, the proposed antenna fulfills the bandwidth requirements for diverse applications such as radar, satellite communication, 5G millimeter-wave transmission, and the sub-bands of the sixth generation.  
Methodology, S.P.; software, N.R.P.; validation, N.R.P. and S.P.; investigation, N.R.P.; resources, J.K.; data curation, J.K.; writing--original draft preparation, N.R.P.; writing--review and editing, J.K.; visualization, S.P.; supervision, J.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data are contained within the article.  
The authors declare no conflict of interest.
## 2 Methodology
### Frequency Domain Analysis  
Frequency domain analysis includes FFT and finite element modal analysis. Modal analysis can reveal the inherent characteristics of engineering entities, including their natural frequency and vibration mode, which can also predict their dynamic response in the elastic domain. The modal analysis equation is as follows: [M\({}_{0}\)][U\({}_{0}\)] + [K][U] = 0 [40]. The corresponding characteristic equation is as follows: ([M\({}_{0}\)]-\(\omega_{1}\)2[M])[U] = 0. Then, the natural frequency \(f_{1}\) is: \(f_{1}=\omega_{1}/2\pi\). {U}\({}_{i}\) is the eigenvalue, which refers to a certain vibration mode of the model. In addition, FFT is used to analyse the vibration responses of rock and soil masses, which can clearly identify different frequency components of a signal. FFT can quickly identify the main components of the signal and can be quickly filtered, and has become a common method for dealing with seismic signals. The essence of FFT is to decompose the seismic signal \(a(t)\) into a combination of several sine waves of different frequencies, \(F\)(a). The mathematical expression for the FFT is as follows: \(F(\text{a})=\int_{-\infty}^{+\infty}x(t)e^{-j2\pi at}dt\), where \(a(t)\) is the acceleration time history [41].
## 1 Introduction  
In an era of rapid technological innovation, effectively filtering relevant documents from large databases is essential for research, planning, and informed decision making. Traditional semantic searches using keywords, while useful, often return a mix of relevant and irrelevant documents [1]. This is because different terms can describe similar concepts, or identical terms can have multiple meanings across different technical fields. Patents, in particular, employ broad non-standard terminology to maximize claim scope, making exact keyword matches unreliable [2].  
The goal of this study is to develop and evaluate natural language processing (NLP) and machine learning (ML) techniques that can efficiently and accurately filter documents relevant to a specific technology domain. This study applied four sets of methods for comparison. They comprised two NLP techniques: topic modeling using latent Dirichlet allocation (LDA) and non-negative matrix factorization (NMF); an unsupervised ML (UML) method that clustered features extracted from keyword frequency and positional occurrence, dimensionally reduced by t-distributed stochastic neighbor embedding (t-SNE), supervised ML (SML) methods based on statistical models like gradient boosting, random forest, and support vector machines; and a deep learning model based on artificial neural networks (D-ANNs).  
This study applied the methods to five technology domains spanning innovations in transportation systems: solid-state batteries (SSBs), electric vehicle chargers (EVCs), connected vehicles (CVs), electric vertical takeoff and landing (eVTOL) aircraft, and light detecting and ranging (LiDAR) sensors enabling autonomous vehicles. The contribution of this research is to benchmark the performance of the four sets of methods proposed to optimize document relevance filtering in these complex and dynamic information domains, enabling ongoing research and technology forecasting.  
The rest of this paper proceeds as follows: Section 2 reviews the literature on methods of document relevance filtering. Section 3 explains the data source, visualizes relevant and irrelevant topics retrieved from each technology domain using a semantic search, and describes the four sets of methods proposed for document relevance filtering. Section 4 benchmarks the performance of the four sets of methods. Section 5 discusses the results, implications, and limitations of the study. Section 6 concludes the research and suggests future work.
## 2 Materials and Methods
### Participants
#### 2.1.1 Inclusion Criteria  
* HFpEF (diagnosis according to ESC 2021 criteria):
* Signs and symptoms of HF.
* Left ventricular ejection fraction (LVEF) \(\geq\) 50%.
* Objective evidence of cardiac structural and/or functional abnormalities consistent with the presence of left ventricular diastolic dysfunction/elevated left ventricular filling pressures, including elevated natriuretic peptide.
* Sedentary men and women (structured exercise < 2 \(\times\) 30 min/week).
* Age \(\geq\) 40 years.
* Written informed consent.
* Clinically stable for \(\geq\)6 weeks.
* Optimal medical treatment for \(\geq\)6 weeks.
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 3 Proposed SMTrust Model
### Proposed Parent Selection Algorithm
#### 3.3.3 Rank and Blackhole Attacks Detection and Isolation of Attacker Nodes  
In a Rank attack, the actual rank value is changed by the malicious node to advertise a better route for attracting traffic. However, the nodes which use this malicious node for routing their traffic end up losing the packets one way or the other. In the RPL network, a rank change occurs when a child node selects a new parent with a better rank. In a Rank attack, malicious nodes advertise themselves with fake ranks and optimal routes to their neighbours, which tend to select it as a new parent. The victim nodes select the new parent with less rank to obey the RPL rank rules for a loop-free topology. This results in the separation of these nodes from effective communication in the network.  
SMTrust employs the overhearing and monitoring of neighboring nodes to detect a Rank attack. Rank attack has several variants, including decreased rank attack, increased rank attack, and worst parent attack. This research work considers decreased rank attack. When DIO message is received by a node from a neighboring node, it checks if the rank changes without DIO_seq, that is, new DIO_seq is less than or equal to current DIO_seq, it means that it is a suspicious or fake DIO [52]; therefore, a Rank attack is identified. Moreover, checking the inconsistency of the rank of the potential parent against the neighboring nodes indicates a fake DIO and Rank value [14; 52]. The sequence diagram for Rank attack detection is depicted in Figure 4.  
Similarly, a Blackhole attack is one kind of the packet dropping attacks. SMTrust accomplishes this by overhearing and monitoring the neighboring nodes whether the packets are being forwarded successfully or dropped by the trustee node. Blackhole attack is detected by the trust index of the preferred parent node. Moreover, an additional check is of success rate, to know that the packets are dropped by the node, and to confirm a packet dropping attack. Figure 5 depicts the sequence diagram of Blackhole attack detection.  
Figure 4: Detection of Rank Attack.  
According to the definition and detection of Rank and Blackhole attack, SMTrust adopts the concept of attack detection and isolation from [14; 52].